diff --git a/.github/workflows/style.yml b/.github/workflows/style.yml
index d681dc6627..719e6cfe90 100644
--- a/.github/workflows/style.yml
+++ b/.github/workflows/style.yml
@@ -20,7 +20,7 @@ jobs:
 
       - name: Check changed files
         id: changed-files
-        uses: tj-actions/changed-files@v44
+        uses: tj-actions/changed-files@v45
         with:
           files: api/**
 
@@ -66,7 +66,7 @@ jobs:
 
       - name: Check changed files
         id: changed-files
-        uses: tj-actions/changed-files@v44
+        uses: tj-actions/changed-files@v45
         with:
           files: web/**
 
@@ -97,7 +97,7 @@ jobs:
 
       - name: Check changed files
         id: changed-files
-        uses: tj-actions/changed-files@v44
+        uses: tj-actions/changed-files@v45
         with:
           files: |
             **.sh
@@ -107,7 +107,7 @@ jobs:
             dev/**
 
       - name: Super-linter
-        uses: super-linter/super-linter/slim@v6
+        uses: super-linter/super-linter/slim@v7
         if: steps.changed-files.outputs.any_changed == 'true'
         env:
           BASH_SEVERITY: warning
diff --git a/.gitignore b/.gitignore
index 22aba65364..29374d13dc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -153,6 +153,9 @@ docker-legacy/volumes/etcd/*
 docker-legacy/volumes/minio/*
 docker-legacy/volumes/milvus/*
 docker-legacy/volumes/chroma/*
+docker-legacy/volumes/opensearch/data/*
+docker-legacy/volumes/pgvectors/data/*
+docker-legacy/volumes/pgvector/data/*
 
 docker/volumes/app/storage/*
 docker/volumes/certbot/*
@@ -164,6 +167,12 @@ docker/volumes/etcd/*
 docker/volumes/minio/*
 docker/volumes/milvus/*
 docker/volumes/chroma/*
+docker/volumes/opensearch/data/*
+docker/volumes/myscale/data/*
+docker/volumes/myscale/log/*
+docker/volumes/unstructured/*
+docker/volumes/pgvector/data/*
+docker/volumes/pgvecto_rs/data/*
 
 docker/nginx/conf.d/default.conf
 docker/middleware.env
diff --git a/LICENSE b/LICENSE
index 071ef42bda..06b0fa1d12 100644
--- a/LICENSE
+++ b/LICENSE
@@ -4,7 +4,7 @@ Dify is licensed under the Apache License 2.0, with the following additional con
 
 1. Dify may be utilized commercially, including as a backend service for other applications or as an application development platform for enterprises. Should the conditions below be met, a commercial license must be obtained from the producer:
 
-a. Multi-tenant SaaS service: Unless explicitly authorized by Dify in writing, you may not use the Dify source code to operate a multi-tenant environment. 
+a. Multi-tenant service: Unless explicitly authorized by Dify in writing, you may not use the Dify source code to operate a multi-tenant environment. 
     - Tenant Definition: Within the context of Dify, one tenant corresponds to one workspace. The workspace provides a separated area for each tenant's data and configurations.
 
 b. LOGO and copyright information: In the process of using Dify's frontend components, you may not remove or modify the LOGO or copyright information in the Dify console or applications. This restriction is inapplicable to uses of Dify that do not involve its frontend components.
diff --git a/api/.env.example b/api/.env.example
index e41e2271d5..a3db406aea 100644
--- a/api/.env.example
+++ b/api/.env.example
@@ -39,7 +39,7 @@ DB_DATABASE=dify
 
 # Storage configuration
 # use for store upload files, private keys...
-# storage type: local, s3, azure-blob, google-storage
+# storage type: local, s3, azure-blob, google-storage, tencent-cos, huawei-obs, volcengine-tos
 STORAGE_TYPE=local
 STORAGE_LOCAL_PATH=storage
 S3_USE_AWS_MANAGED_IAM=false
@@ -73,6 +73,12 @@ TENCENT_COS_SECRET_ID=your-secret-id
 TENCENT_COS_REGION=your-region
 TENCENT_COS_SCHEME=your-scheme
 
+# Huawei OBS Storage Configuration
+HUAWEI_OBS_BUCKET_NAME=your-bucket-name
+HUAWEI_OBS_SECRET_KEY=your-secret-key
+HUAWEI_OBS_ACCESS_KEY=your-access-key
+HUAWEI_OBS_SERVER=your-server-url
+
 # OCI Storage configuration
 OCI_ENDPOINT=your-endpoint
 OCI_BUCKET_NAME=your-bucket-name
@@ -80,6 +86,13 @@ OCI_ACCESS_KEY=your-access-key
 OCI_SECRET_KEY=your-secret-key
 OCI_REGION=your-region
 
+# Volcengine tos Storage configuration
+VOLCENGINE_TOS_ENDPOINT=your-endpoint
+VOLCENGINE_TOS_BUCKET_NAME=your-bucket-name
+VOLCENGINE_TOS_ACCESS_KEY=your-access-key
+VOLCENGINE_TOS_SECRET_KEY=your-secret-key
+VOLCENGINE_TOS_REGION=your-region
+
 # CORS configuration
 WEB_API_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*
 CONSOLE_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*
@@ -101,11 +114,10 @@ QDRANT_GRPC_ENABLED=false
 QDRANT_GRPC_PORT=6334
 
 # Milvus configuration
-MILVUS_HOST=127.0.0.1
-MILVUS_PORT=19530
+MILVUS_URI=http://127.0.0.1:19530
+MILVUS_TOKEN=
 MILVUS_USER=root
 MILVUS_PASSWORD=Milvus
-MILVUS_SECURE=false
 
 # MyScale configuration
 MYSCALE_HOST=127.0.0.1
diff --git a/api/Dockerfile b/api/Dockerfile
index ccdec017ab..6483f8281b 100644
--- a/api/Dockerfile
+++ b/api/Dockerfile
@@ -55,7 +55,7 @@ RUN apt-get update \
     && echo "deb http://deb.debian.org/debian testing main" > /etc/apt/sources.list \
     && apt-get update \
     # For Security
-    && apt-get install -y --no-install-recommends zlib1g=1:1.3.dfsg+really1.3.1-1 expat=2.6.2-2 libldap-2.5-0=2.5.18+dfsg-3 perl=5.38.2-5 libsqlite3-0=3.46.0-1 \
+    && apt-get install -y --no-install-recommends zlib1g=1:1.3.dfsg+really1.3.1-1 expat=2.6.3-1 libldap-2.5-0=2.5.18+dfsg-3 perl=5.38.2-5 libsqlite3-0=3.46.0-1 \
     && apt-get autoremove -y \
     && rm -rf /var/lib/apt/lists/*
 
diff --git a/api/app.py b/api/app.py
index ad219ca0d6..91a49337fc 100644
--- a/api/app.py
+++ b/api/app.py
@@ -164,7 +164,7 @@ def initialize_extensions(app):
 @login_manager.request_loader
 def load_user_from_request(request_from_flask_login):
     """Load user based on the request."""
-    if request.blueprint not in ["console", "inner_api"]:
+    if request.blueprint not in {"console", "inner_api"}:
         return None
     # Check if the user_id contains a dot, indicating the old format
     auth_header = request.headers.get("Authorization", "")
diff --git a/api/commands.py b/api/commands.py
index 3bf8bc0ecc..3a6b4963cf 100644
--- a/api/commands.py
+++ b/api/commands.py
@@ -104,7 +104,7 @@ def reset_email(email, new_email, email_confirm):
 )
 @click.confirmation_option(
     prompt=click.style(
-        "Are you sure you want to reset encrypt key pair?" " this operation cannot be rolled back!", fg="red"
+        "Are you sure you want to reset encrypt key pair? this operation cannot be rolled back!", fg="red"
     )
 )
 def reset_encrypt_key_pair():
@@ -131,7 +131,7 @@ def reset_encrypt_key_pair():
 
         click.echo(
             click.style(
-                "Congratulations! " "the asymmetric key pair of workspace {} has been reset.".format(tenant.id),
+                "Congratulations! The asymmetric key pair of workspace {} has been reset.".format(tenant.id),
                 fg="green",
             )
         )
@@ -140,9 +140,9 @@ def reset_encrypt_key_pair():
 @click.command("vdb-migrate", help="migrate vector db.")
 @click.option("--scope", default="all", prompt=False, help="The scope of vector database to migrate, Default is All.")
 def vdb_migrate(scope: str):
-    if scope in ["knowledge", "all"]:
+    if scope in {"knowledge", "all"}:
         migrate_knowledge_vector_database()
-    if scope in ["annotation", "all"]:
+    if scope in {"annotation", "all"}:
         migrate_annotation_vector_database()
 
 
@@ -275,8 +275,7 @@ def migrate_knowledge_vector_database():
         for dataset in datasets:
             total_count = total_count + 1
             click.echo(
-                f"Processing the {total_count} dataset {dataset.id}. "
-                + f"{create_count} created, {skipped_count} skipped."
+                f"Processing the {total_count} dataset {dataset.id}. {create_count} created, {skipped_count} skipped."
             )
             try:
                 click.echo("Create dataset vdb index: {}".format(dataset.id))
@@ -411,7 +410,8 @@ def migrate_knowledge_vector_database():
                     try:
                         click.echo(
                             click.style(
-                                f"Start to created vector index with {len(documents)} documents of {segments_count} segments for dataset {dataset.id}.",
+                                f"Start to created vector index with {len(documents)} documents of {segments_count}"
+                                f" segments for dataset {dataset.id}.",
                                 fg="green",
                             )
                         )
@@ -593,7 +593,7 @@ def create_tenant(email: str, language: Optional[str] = None, name: Optional[str
 
     click.echo(
         click.style(
-            "Congratulations! Account and tenant created.\n" "Account: {}\nPassword: {}".format(email, new_password),
+            "Congratulations! Account and tenant created.\nAccount: {}\nPassword: {}".format(email, new_password),
             fg="green",
         )
     )
diff --git a/api/configs/feature/__init__.py b/api/configs/feature/__init__.py
index 303bce2aa5..f794552c36 100644
--- a/api/configs/feature/__init__.py
+++ b/api/configs/feature/__init__.py
@@ -46,7 +46,7 @@ class CodeExecutionSandboxConfig(BaseSettings):
     """
 
     CODE_EXECUTION_ENDPOINT: HttpUrl = Field(
-        description="endpoint URL of code execution servcie",
+        description="endpoint URL of code execution service",
         default="http://sandbox:8194",
     )
 
@@ -129,12 +129,12 @@ class EndpointConfig(BaseSettings):
     )
 
     SERVICE_API_URL: str = Field(
-        description="Service API Url prefix." "used to display Service API Base Url to the front-end.",
+        description="Service API Url prefix. used to display Service API Base Url to the front-end.",
         default="",
     )
 
     APP_WEB_URL: str = Field(
-        description="WebApp Url prefix." "used to display WebAPP API Base Url to the front-end.",
+        description="WebApp Url prefix. used to display WebAPP API Base Url to the front-end.",
         default="",
     )
 
@@ -272,7 +272,7 @@ class LoggingConfig(BaseSettings):
     """
 
     LOG_LEVEL: str = Field(
-        description="Log output level, default to INFO." "It is recommended to set it to ERROR for production.",
+        description="Log output level, default to INFO. It is recommended to set it to ERROR for production.",
         default="INFO",
     )
 
@@ -415,7 +415,7 @@ class MailConfig(BaseSettings):
     """
 
     MAIL_TYPE: Optional[str] = Field(
-        description="Mail provider type name, default to None, availabile values are `smtp` and `resend`.",
+        description="Mail provider type name, default to None, available values are `smtp` and `resend`.",
         default=None,
     )
 
diff --git a/api/configs/middleware/__init__.py b/api/configs/middleware/__init__.py
index f25979e5d8..e017c2c5b8 100644
--- a/api/configs/middleware/__init__.py
+++ b/api/configs/middleware/__init__.py
@@ -1,7 +1,7 @@
 from typing import Any, Optional
 from urllib.parse import quote_plus
 
-from pydantic import Field, NonNegativeInt, PositiveInt, computed_field
+from pydantic import Field, NonNegativeInt, PositiveFloat, PositiveInt, computed_field
 from pydantic_settings import BaseSettings
 
 from configs.middleware.cache.redis_config import RedisConfig
@@ -9,8 +9,10 @@ from configs.middleware.storage.aliyun_oss_storage_config import AliyunOSSStorag
 from configs.middleware.storage.amazon_s3_storage_config import S3StorageConfig
 from configs.middleware.storage.azure_blob_storage_config import AzureBlobStorageConfig
 from configs.middleware.storage.google_cloud_storage_config import GoogleCloudStorageConfig
+from configs.middleware.storage.huawei_obs_storage_config import HuaweiCloudOBSStorageConfig
 from configs.middleware.storage.oci_storage_config import OCIStorageConfig
 from configs.middleware.storage.tencent_cos_storage_config import TencentCloudCOSStorageConfig
+from configs.middleware.storage.volcengine_tos_storage_config import VolcengineTOSStorageConfig
 from configs.middleware.vdb.analyticdb_config import AnalyticdbConfig
 from configs.middleware.vdb.chroma_config import ChromaConfig
 from configs.middleware.vdb.elasticsearch_config import ElasticsearchConfig
@@ -157,6 +159,21 @@ class CeleryConfig(DatabaseConfig):
         default=None,
     )
 
+    CELERY_USE_SENTINEL: Optional[bool] = Field(
+        description="Whether to use Redis Sentinel mode",
+        default=False,
+    )
+
+    CELERY_SENTINEL_MASTER_NAME: Optional[str] = Field(
+        description="Redis Sentinel master name",
+        default=None,
+    )
+
+    CELERY_SENTINEL_SOCKET_TIMEOUT: Optional[PositiveFloat] = Field(
+        description="Redis Sentinel socket timeout",
+        default=0.1,
+    )
+
     @computed_field
     @property
     def CELERY_RESULT_BACKEND(self) -> str | None:
@@ -184,6 +201,8 @@ class MiddlewareConfig(
     AzureBlobStorageConfig,
     GoogleCloudStorageConfig,
     TencentCloudCOSStorageConfig,
+    HuaweiCloudOBSStorageConfig,
+    VolcengineTOSStorageConfig,
     S3StorageConfig,
     OCIStorageConfig,
     # configs of vdb and vdb providers
diff --git a/api/configs/middleware/cache/redis_config.py b/api/configs/middleware/cache/redis_config.py
index cacdaf6fb6..4fcd52ddc9 100644
--- a/api/configs/middleware/cache/redis_config.py
+++ b/api/configs/middleware/cache/redis_config.py
@@ -1,6 +1,6 @@
 from typing import Optional
 
-from pydantic import Field, NonNegativeInt, PositiveInt
+from pydantic import Field, NonNegativeInt, PositiveFloat, PositiveInt
 from pydantic_settings import BaseSettings
 
 
@@ -38,3 +38,33 @@ class RedisConfig(BaseSettings):
         description="whether to use SSL for Redis connection",
         default=False,
     )
+
+    REDIS_USE_SENTINEL: Optional[bool] = Field(
+        description="Whether to use Redis Sentinel mode",
+        default=False,
+    )
+
+    REDIS_SENTINELS: Optional[str] = Field(
+        description="Redis Sentinel nodes",
+        default=None,
+    )
+
+    REDIS_SENTINEL_SERVICE_NAME: Optional[str] = Field(
+        description="Redis Sentinel service name",
+        default=None,
+    )
+
+    REDIS_SENTINEL_USERNAME: Optional[str] = Field(
+        description="Redis Sentinel username",
+        default=None,
+    )
+
+    REDIS_SENTINEL_PASSWORD: Optional[str] = Field(
+        description="Redis Sentinel password",
+        default=None,
+    )
+
+    REDIS_SENTINEL_SOCKET_TIMEOUT: Optional[PositiveFloat] = Field(
+        description="Redis Sentinel socket timeout",
+        default=0.1,
+    )
diff --git a/api/configs/middleware/storage/huawei_obs_storage_config.py b/api/configs/middleware/storage/huawei_obs_storage_config.py
new file mode 100644
index 0000000000..c5cb379cae
--- /dev/null
+++ b/api/configs/middleware/storage/huawei_obs_storage_config.py
@@ -0,0 +1,29 @@
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class HuaweiCloudOBSStorageConfig(BaseModel):
+    """
+    Huawei Cloud OBS storage configs
+    """
+
+    HUAWEI_OBS_BUCKET_NAME: Optional[str] = Field(
+        description="Huawei Cloud OBS bucket name",
+        default=None,
+    )
+
+    HUAWEI_OBS_ACCESS_KEY: Optional[str] = Field(
+        description="Huawei Cloud OBS Access key",
+        default=None,
+    )
+
+    HUAWEI_OBS_SECRET_KEY: Optional[str] = Field(
+        description="Huawei Cloud OBS Secret key",
+        default=None,
+    )
+
+    HUAWEI_OBS_SERVER: Optional[str] = Field(
+        description="Huawei Cloud OBS server URL",
+        default=None,
+    )
diff --git a/api/configs/middleware/storage/volcengine_tos_storage_config.py b/api/configs/middleware/storage/volcengine_tos_storage_config.py
new file mode 100644
index 0000000000..a0e09a3cc7
--- /dev/null
+++ b/api/configs/middleware/storage/volcengine_tos_storage_config.py
@@ -0,0 +1,34 @@
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class VolcengineTOSStorageConfig(BaseModel):
+    """
+    Volcengine tos storage configs
+    """
+
+    VOLCENGINE_TOS_BUCKET_NAME: Optional[str] = Field(
+        description="Volcengine TOS Bucket Name",
+        default=None,
+    )
+
+    VOLCENGINE_TOS_ACCESS_KEY: Optional[str] = Field(
+        description="Volcengine TOS Access Key",
+        default=None,
+    )
+
+    VOLCENGINE_TOS_SECRET_KEY: Optional[str] = Field(
+        description="Volcengine TOS Secret Key",
+        default=None,
+    )
+
+    VOLCENGINE_TOS_ENDPOINT: Optional[str] = Field(
+        description="Volcengine TOS Endpoint URL",
+        default=None,
+    )
+
+    VOLCENGINE_TOS_REGION: Optional[str] = Field(
+        description="Volcengine TOS Region",
+        default=None,
+    )
diff --git a/api/configs/middleware/vdb/milvus_config.py b/api/configs/middleware/vdb/milvus_config.py
index 85466cd5cc..98d375966a 100644
--- a/api/configs/middleware/vdb/milvus_config.py
+++ b/api/configs/middleware/vdb/milvus_config.py
@@ -1,6 +1,6 @@
 from typing import Optional
 
-from pydantic import Field, PositiveInt
+from pydantic import Field
 from pydantic_settings import BaseSettings
 
 
@@ -9,14 +9,14 @@ class MilvusConfig(BaseSettings):
     Milvus configs
     """
 
-    MILVUS_HOST: Optional[str] = Field(
-        description="Milvus host",
-        default=None,
+    MILVUS_URI: Optional[str] = Field(
+        description="Milvus uri",
+        default="http://127.0.0.1:19530",
     )
 
-    MILVUS_PORT: PositiveInt = Field(
-        description="Milvus RestFul API port",
-        default=9091,
+    MILVUS_TOKEN: Optional[str] = Field(
+        description="Milvus token",
+        default=None,
     )
 
     MILVUS_USER: Optional[str] = Field(
@@ -29,11 +29,6 @@ class MilvusConfig(BaseSettings):
         default=None,
     )
 
-    MILVUS_SECURE: bool = Field(
-        description="whether to use SSL connection for Milvus",
-        default=False,
-    )
-
     MILVUS_DATABASE: str = Field(
         description="Milvus database, default to `default`",
         default="default",
diff --git a/api/configs/packaging/__init__.py b/api/configs/packaging/__init__.py
index 2d540ca584..3815a6fca2 100644
--- a/api/configs/packaging/__init__.py
+++ b/api/configs/packaging/__init__.py
@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.7.3",
+        default="0.8.2",
     )
 
     COMMIT_SHA: str = Field(
diff --git a/api/constants/recommended_apps.json b/api/constants/recommended_apps.json
index df4adc4a1f..3779fb0180 100644
--- a/api/constants/recommended_apps.json
+++ b/api/constants/recommended_apps.json
@@ -320,7 +320,7 @@
                         "icon_background": "#FFEAD5",
                         "id": "e9870913-dd01-4710-9f06-15d4180ca1ce",
                         "mode": "advanced-chat",
-                        "name": "Knowledge Retreival + Chatbot "
+                        "name": "Knowledge Retrieval + Chatbot "
                     },
                     "app_id": "e9870913-dd01-4710-9f06-15d4180ca1ce",
                     "category": "Workflow",
@@ -423,7 +423,7 @@
             "name": "Website Generator"
         },
         "a23b57fa-85da-49c0-a571-3aff375976c1": {
-            "export_data": "app:\n  icon: \"\\U0001F911\"\n  icon_background: '#E4FBCC'\n  mode: agent-chat\n  name: Investment Analysis Report Copilot\nmodel_config:\n  agent_mode:\n    enabled: true\n    max_iteration: 5\n    strategy: function_call\n    tools:\n    - enabled: true\n      isDeleted: false\n      notAuthor: false\n      provider_id: yahoo\n      provider_name: yahoo\n      provider_type: builtin\n      tool_label: Analytics\n      tool_name: yahoo_finance_analytics\n      tool_parameters:\n        end_date: ''\n        start_date: ''\n        symbol: ''\n    - enabled: true\n      isDeleted: false\n      notAuthor: false\n      provider_id: yahoo\n      provider_name: yahoo\n      provider_type: builtin\n      tool_label: News\n      tool_name: yahoo_finance_news\n      tool_parameters:\n        symbol: ''\n    - enabled: true\n      isDeleted: false\n      notAuthor: false\n      provider_id: yahoo\n      provider_name: yahoo\n      provider_type: builtin\n      tool_label: Ticker\n      tool_name: yahoo_finance_ticker\n      tool_parameters:\n        symbol: ''\n  annotation_reply:\n    enabled: false\n  chat_prompt_config: {}\n  completion_prompt_config: {}\n  dataset_configs:\n    datasets:\n      datasets: []\n    retrieval_model: single\n  dataset_query_variable: ''\n  external_data_tools: []\n  file_upload:\n    image:\n      detail: high\n      enabled: false\n      number_limits: 3\n      transfer_methods:\n      - remote_url\n      - local_file\n  model:\n    completion_params:\n      frequency_penalty: 0.5\n      max_tokens: 4096\n      presence_penalty: 0.5\n      stop: []\n      temperature: 0.2\n      top_p: 0.75\n    mode: chat\n    name: gpt-4-1106-preview\n    provider: openai\n  more_like_this:\n    enabled: false\n  opening_statement: 'Welcome to your personalized Investment Analysis Copilot service,\n    where we delve into the depths of stock analysis to provide you with comprehensive\n    insights. To begin our journey into the financial world, try to ask:\n\n    '\n  pre_prompt: \"# Job Description: Data Analysis Copilot\\n## Character\\nMy primary\\\n    \\ goal is to provide user with expert data analysis advice. Using extensive and\\\n    \\ detailed data. Tell me the stock (with ticket symbol) you want to analyze. I\\\n    \\ will do all fundemental, technical, market sentiment, and Marcoeconomical analysis\\\n    \\ for the stock as an expert. \\n\\n## Skills \\n### Skill 1: Search for stock information\\\n    \\ using 'Ticker' from Yahoo Finance \\n### Skill 2: Search for recent news using\\\n    \\ 'News' for the target company. \\n### Skill 3: Search for financial figures and\\\n    \\ analytics using 'Analytics' for the target company\\n\\n## Workflow\\nAsks the\\\n    \\ user which stocks with ticker name need to be analyzed and then performs the\\\n    \\ following analysis in sequence. \\n**Part I: Fundamental analysis: financial\\\n    \\ reporting analysis\\n*Objective 1: In-depth analysis of the financial situation\\\n    \\ of the target company.\\n*Steps:\\n1. Identify the object of analysis:\\n<Record\\\n    \\ 1.1: Introduce the basic information of {{company}}>\\n\\n\\n2. Access to financial\\\n    \\ reports \\n<Use tool: 'Ticker', 'News', and 'Analytics'>\\n- Obtain the key data\\\n    \\ of the latest financial report of the target company {{company}} organized by\\\n    \\ Yahoo Finance. \\n\\n\\n<Record 1.2: Record the analysis results acquisition date\\\n    \\ and source link >\\n3. Vertical Analysis:\\n- Get the insight of the company's\\\n    \\ balance sheet Income Statement and cash flow. \\n- Analyze Income Statement:\\\n    \\ Analyze the proportion of each type of income and expense to total income. /Analyze\\\n    \\ Balance Sheet: Analyze the proportion of each asset and liability to total assets\\\n    \\ or total liabilities./ Analyze Cash Flow \\n-<Record 1.3: Record the result of\\\n    \\ the analysis of Balance sheet cash flow and Income Statement>\\n4. Ratio Analysis:\\n\\\n    - analyze the Profitability Ratios Solvency Ratios Operational Efficiency Ratios\\\n    \\ and Market Performance Ratios of the company. \\n(Profitability Ratios: Such\\\n    \\ as net profit margin gross profit margin operating profit margin to assess the\\\n    \\ company's profitability.)\\n(Solvency Ratios: Such as debt-to-asset ratio interest\\\n    \\ coverage ratio to assess the company's ability to pay its debts.)\\n(Operational\\\n    \\ Efficiency Ratios: Such as inventory turnover accounts receivable turnover to\\\n    \\ assess the company's operational efficiency.)\\n(Market Performance Ratios: Such\\\n    \\ as price-to-earnings ratio price-to-book ratio to assess the company's market\\\n    \\ performance.)>\\n-<Record 1.4: Record the conclusions and results of the analysis.\\\n    \\ >\\n5. Comprehensive Analysis and Conclusion:\\n- Combine the above analyses to\\\n    \\ evaluate the company's financial health profitability solvency and operational\\\n    \\ efficiency comprehensively. Identify the main financial risks and potential\\\n    \\ opportunities facing the company.\\n-<Record 1.5: Record the overall conclusion\\\n    \\ risks and opportunities. >\\nOrganize and output [Record 1.1] [Record 1.2] [Record\\\n    \\ 1.3] [Record 1.4] [Record 1.5] \\nPart II: Foundamental Analysis: Industry\\n\\\n    *Objective 2: To analyze the position and competitiveness of the target company\\\n    \\ {{company}} in the industry. \\n\\n\\n* Steps:\\n1. Determine the industry classification:\\n\\\n    - Define the industry to which the target company belongs.\\n- Search for company\\\n    \\ information to determine its main business and industry.\\n-<Record 2.1: the\\\n    \\ company's industry classification >\\n2. Market Positioning and Segmentation\\\n    \\ analysis:\\n- To assess the company's market positioning and segmentation. \\n\\\n    - Understand the company's market share growth rate and competitors in the industry\\\n    \\ to analyze them. \\n-<Record 2.2: the company's market share ranking major competitors\\\n    \\ the analysis result and insight etc.>\\n3. Analysis \\n- Analyze the development\\\n    \\ trend of the industry. \\n- <Record 2.3: the development trend of the industry.\\\n    \\ > \\n4. Competitors\\n- Analyze the competition around the target company \\n-\\\n    \\ <Record 2.4: a analysis on the competition of the target company > \\nOrganize\\\n    \\ and output [Record 2.1] [Record 2.2] [Record 2.3] [Record 2.4]\\nCombine the\\\n    \\ above Record and output all the analysis in the form of a investment analysis\\\n    \\ report. Use markdown syntax for a structured output. \\n\\n## Constraints\\n- Your\\\n    \\ responses should be strictly on analysis tasks. Use a structured language and\\\n    \\ think step by step. \\n- The language you use should be identical to the user's\\\n    \\ language.\\n- Avoid addressing questions regarding work tools and regulations.\\n\\\n    - Give a structured response using bullet points and markdown syntax. Give an\\\n    \\ introduction to the situation first then analyse the main trend in the graph.\\\n    \\ \\n\"\n  prompt_type: simple\n  retriever_resource:\n    enabled: true\n  sensitive_word_avoidance:\n    configs: []\n    enabled: false\n    type: ''\n  speech_to_text:\n    enabled: false\n  suggested_questions:\n  - 'Analyze the stock of Tesla. '\n  - What are some recent development on Nvidia?\n  - 'Do a fundamental analysis for Amazon. '\n  suggested_questions_after_answer:\n    enabled: true\n  text_to_speech:\n    enabled: false\n  user_input_form:\n  - text-input:\n      default: ''\n      label: company\n      required: false\n      variable: company\n",
+            "export_data": "app:\n  icon: \"\\U0001F911\"\n  icon_background: '#E4FBCC'\n  mode: agent-chat\n  name: Investment Analysis Report Copilot\nmodel_config:\n  agent_mode:\n    enabled: true\n    max_iteration: 5\n    strategy: function_call\n    tools:\n    - enabled: true\n      isDeleted: false\n      notAuthor: false\n      provider_id: yahoo\n      provider_name: yahoo\n      provider_type: builtin\n      tool_label: Analytics\n      tool_name: yahoo_finance_analytics\n      tool_parameters:\n        end_date: ''\n        start_date: ''\n        symbol: ''\n    - enabled: true\n      isDeleted: false\n      notAuthor: false\n      provider_id: yahoo\n      provider_name: yahoo\n      provider_type: builtin\n      tool_label: News\n      tool_name: yahoo_finance_news\n      tool_parameters:\n        symbol: ''\n    - enabled: true\n      isDeleted: false\n      notAuthor: false\n      provider_id: yahoo\n      provider_name: yahoo\n      provider_type: builtin\n      tool_label: Ticker\n      tool_name: yahoo_finance_ticker\n      tool_parameters:\n        symbol: ''\n  annotation_reply:\n    enabled: false\n  chat_prompt_config: {}\n  completion_prompt_config: {}\n  dataset_configs:\n    datasets:\n      datasets: []\n    retrieval_model: single\n  dataset_query_variable: ''\n  external_data_tools: []\n  file_upload:\n    image:\n      detail: high\n      enabled: false\n      number_limits: 3\n      transfer_methods:\n      - remote_url\n      - local_file\n  model:\n    completion_params:\n      frequency_penalty: 0.5\n      max_tokens: 4096\n      presence_penalty: 0.5\n      stop: []\n      temperature: 0.2\n      top_p: 0.75\n    mode: chat\n    name: gpt-4-1106-preview\n    provider: openai\n  more_like_this:\n    enabled: false\n  opening_statement: 'Welcome to your personalized Investment Analysis Copilot service,\n    where we delve into the depths of stock analysis to provide you with comprehensive\n    insights. To begin our journey into the financial world, try to ask:\n\n    '\n  pre_prompt: \"# Job Description: Data Analysis Copilot\\n## Character\\nMy primary\\\n    \\ goal is to provide user with expert data analysis advice. Using extensive and\\\n    \\ detailed data. Tell me the stock (with ticket symbol) you want to analyze. I\\\n    \\ will do all fundamental, technical, market sentiment, and Marco economical analysis\\\n    \\ for the stock as an expert. \\n\\n## Skills \\n### Skill 1: Search for stock information\\\n    \\ using 'Ticker' from Yahoo Finance \\n### Skill 2: Search for recent news using\\\n    \\ 'News' for the target company. \\n### Skill 3: Search for financial figures and\\\n    \\ analytics using 'Analytics' for the target company\\n\\n## Workflow\\nAsks the\\\n    \\ user which stocks with ticker name need to be analyzed and then performs the\\\n    \\ following analysis in sequence. \\n**Part I: Fundamental analysis: financial\\\n    \\ reporting analysis\\n*Objective 1: In-depth analysis of the financial situation\\\n    \\ of the target company.\\n*Steps:\\n1. Identify the object of analysis:\\n<Record\\\n    \\ 1.1: Introduce the basic information of {{company}}>\\n\\n\\n2. Access to financial\\\n    \\ reports \\n<Use tool: 'Ticker', 'News', and 'Analytics'>\\n- Obtain the key data\\\n    \\ of the latest financial report of the target company {{company}} organized by\\\n    \\ Yahoo Finance. \\n\\n\\n<Record 1.2: Record the analysis results acquisition date\\\n    \\ and source link >\\n3. Vertical Analysis:\\n- Get the insight of the company's\\\n    \\ balance sheet Income Statement and cash flow. \\n- Analyze Income Statement:\\\n    \\ Analyze the proportion of each type of income and expense to total income. /Analyze\\\n    \\ Balance Sheet: Analyze the proportion of each asset and liability to total assets\\\n    \\ or total liabilities./ Analyze Cash Flow \\n-<Record 1.3: Record the result of\\\n    \\ the analysis of Balance sheet cash flow and Income Statement>\\n4. Ratio Analysis:\\n\\\n    - analyze the Profitability Ratios Solvency Ratios Operational Efficiency Ratios\\\n    \\ and Market Performance Ratios of the company. \\n(Profitability Ratios: Such\\\n    \\ as net profit margin gross profit margin operating profit margin to assess the\\\n    \\ company's profitability.)\\n(Solvency Ratios: Such as debt-to-asset ratio interest\\\n    \\ coverage ratio to assess the company's ability to pay its debts.)\\n(Operational\\\n    \\ Efficiency Ratios: Such as inventory turnover accounts receivable turnover to\\\n    \\ assess the company's operational efficiency.)\\n(Market Performance Ratios: Such\\\n    \\ as price-to-earnings ratio price-to-book ratio to assess the company's market\\\n    \\ performance.)>\\n-<Record 1.4: Record the conclusions and results of the analysis.\\\n    \\ >\\n5. Comprehensive Analysis and Conclusion:\\n- Combine the above analyses to\\\n    \\ evaluate the company's financial health profitability solvency and operational\\\n    \\ efficiency comprehensively. Identify the main financial risks and potential\\\n    \\ opportunities facing the company.\\n-<Record 1.5: Record the overall conclusion\\\n    \\ risks and opportunities. >\\nOrganize and output [Record 1.1] [Record 1.2] [Record\\\n    \\ 1.3] [Record 1.4] [Record 1.5] \\nPart II: Fundamental Analysis: Industry\\n\\\n    *Objective 2: To analyze the position and competitiveness of the target company\\\n    \\ {{company}} in the industry. \\n\\n\\n* Steps:\\n1. Determine the industry classification:\\n\\\n    - Define the industry to which the target company belongs.\\n- Search for company\\\n    \\ information to determine its main business and industry.\\n-<Record 2.1: the\\\n    \\ company's industry classification >\\n2. Market Positioning and Segmentation\\\n    \\ analysis:\\n- To assess the company's market positioning and segmentation. \\n\\\n    - Understand the company's market share growth rate and competitors in the industry\\\n    \\ to analyze them. \\n-<Record 2.2: the company's market share ranking major competitors\\\n    \\ the analysis result and insight etc.>\\n3. Analysis \\n- Analyze the development\\\n    \\ trend of the industry. \\n- <Record 2.3: the development trend of the industry.\\\n    \\ > \\n4. Competitors\\n- Analyze the competition around the target company \\n-\\\n    \\ <Record 2.4: a analysis on the competition of the target company > \\nOrganize\\\n    \\ and output [Record 2.1] [Record 2.2] [Record 2.3] [Record 2.4]\\nCombine the\\\n    \\ above Record and output all the analysis in the form of a investment analysis\\\n    \\ report. Use markdown syntax for a structured output. \\n\\n## Constraints\\n- Your\\\n    \\ responses should be strictly on analysis tasks. Use a structured language and\\\n    \\ think step by step. \\n- The language you use should be identical to the user's\\\n    \\ language.\\n- Avoid addressing questions regarding work tools and regulations.\\n\\\n    - Give a structured response using bullet points and markdown syntax. Give an\\\n    \\ introduction to the situation first then analyse the main trend in the graph.\\\n    \\ \\n\"\n  prompt_type: simple\n  retriever_resource:\n    enabled: true\n  sensitive_word_avoidance:\n    configs: []\n    enabled: false\n    type: ''\n  speech_to_text:\n    enabled: false\n  suggested_questions:\n  - 'Analyze the stock of Tesla. '\n  - What are some recent development on Nvidia?\n  - 'Do a fundamental analysis for Amazon. '\n  suggested_questions_after_answer:\n    enabled: true\n  text_to_speech:\n    enabled: false\n  user_input_form:\n  - text-input:\n      default: ''\n      label: company\n      required: false\n      variable: company\n",
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@@ -438,8 +438,8 @@
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data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: variable-aggregator\n        targetType: template-transform\n      id: 1716960791399-source-1716951236700-target\n      source: '1716960791399'\n      sourceHandle: source\n      target: '1716951236700'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: question-classifier\n        targetType: template-transform\n      id: 1716960721611-1716960736883-1716960834468-target\n      source: '1716960721611'\n      sourceHandle: '1716960736883'\n      target: '1716960834468'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: llm\n        targetType: variable-aggregator\n      id: 1716960728136-source-1716960791399-target\n      source: '1716960728136'\n      sourceHandle: source\n      target: '1716960791399'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: template-transform\n        targetType: variable-aggregator\n      id: 1716960834468-source-1716960791399-target\n      source: '1716960834468'\n      sourceHandle: source\n      target: '1716960791399'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    nodes:\n    - data:\n        desc: ''\n        selected: false\n        title: Start\n        type: start\n        variables:\n        - label: Your Email\n          max_length: 256\n          options: []\n          required: true\n          type: text-input\n          variable: email\n        - label: Maximum Number of Email you want to retrieve\n          max_length: 256\n          options: []\n          required: true\n          type: number\n          variable: maxResults\n      height: 115\n      id: '1716800588219'\n      position:\n        x: 30\n        y: 445\n      positionAbsolute:\n        x: 30\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        answer: '{{#1716913435742.output#}}'\n        desc: ''\n        selected: false\n        title: Direct Reply\n        type: answer\n        variables: []\n      height: 106\n      id: '1716806267180'\n      position:\n        x: 4700\n        y: 445\n      positionAbsolute:\n        x: 4700\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        code: \"def main(message: str) -> dict:\\n    import json\\n    \\n    # Parse\\\n          \\ the JSON string\\n    parsed_data = json.loads(message)\\n    \\n    # Extract\\\n          \\ all the \\\"id\\\" values\\n    ids = [msg['id'] for msg in parsed_data['messages']]\\n\\\n          \\    \\n    return {\\n        \\\"result\\\": ids\\n    }\"\n        code_language: python3\n        desc: ''\n        outputs:\n          result:\n            children: null\n            type: array[string]\n        selected: false\n        title: 'Code: Extract Email ID'\n        type: code\n        variables:\n        - value_selector:\n          - '1716946869294'\n          - text\n          variable: message\n      height: 53\n      id: '1716909112104'\n      position:\n        x: 638\n        y: 445\n      positionAbsolute:\n        x: 638\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        desc: ''\n        height: 490\n        iterator_selector:\n        - '1716909112104'\n        - result\n        output_selector:\n        - '1716909125498'\n        - text\n        output_type: array[string]\n        selected: false\n        startNodeType: tool\n        start_node_id: '1716946889408'\n        title: 'Iteraction '\n        type: iteration\n        width: 3393.7520359289056\n      height: 490\n      id: '1716909114582'\n      position:\n        x: 942\n        y: 445\n      positionAbsolute:\n        x: 942\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 3394\n      zIndex: 1\n    - 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data:\n        code: \"\\ndef main(email_json: dict) -> dict:\\n    import json \\n    email_dict\\\n          \\ = json.loads(email_json)\\n    base64_data = email_dict['payload']['parts'][0]['body']['data']\\n\\\n          \\n    return {\\n        \\\"result\\\": base64_data, \\n    }\\n\"\n        code_language: python3\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        outputs:\n          result:\n            children: null\n            type: string\n        selected: false\n        title: 'Code: Extract Email Body'\n        type: code\n        variables:\n        - value_selector:\n          - '1716946889408'\n          - text\n          variable: email_json\n      extent: parent\n      height: 53\n      id: '1716909122343'\n      parentId: '1716909114582'\n      position:\n        x: 421\n        y: 85\n      positionAbsolute:\n        x: 1363\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - 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'\n        isInIteration: true\n        iteration_id: '1716909114582'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-4o\n          provider: openai\n        prompt_template:\n        - id: 982014aa-702b-4d7c-ae1f-08dbceb6e930\n          role: system\n          text: \"<Task> \\nRespond to the emails. \\n<Emails>\\n{{#1716913272656.text#}}\\n\\\n            <Your response>\"\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          configs:\n            detail: high\n          enabled: true\n      extent: parent\n      height: 127\n      id: '1716909125498'\n      parentId: '1716909114582'\n      position:\n        x: 1625\n        y: 85\n      positionAbsolute:\n        x: 2567\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-4o\n          provider: openai\n        prompt_template:\n        - id: fd8de569-c099-4320-955b-61aa4b054789\n          role: system\n          text: \"<Task>\\nYou need to transform the input data (in base64 encoding)\\\n            \\ to text. Input base64. Output text. \\n<input>\\n{{#1716909122343.result#}}\\n\\\n            <output> \"\n        selected: false\n        title: 'Base64 Decoder '\n        type: llm\n        variables: []\n        vision:\n          configs:\n            detail: high\n          enabled: false\n      extent: parent\n      height: 97\n      id: '1716913272656'\n      parentId: '1716909114582'\n      position:\n        x: 1025\n        y: 85\n      positionAbsolute:\n        x: 1967\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 | join(\"\\n\\n -------------------------\\n\\n\") }}'\n        title: 'Template '\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1716909114582'\n          - output\n          variable: arg1\n      height: 53\n      id: '1716913435742'\n      position:\n        x: 4396\n        y: 445\n      positionAbsolute:\n        x: 4396\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        desc: ''\n        provider_id: e64b4c7f-2795-499c-8d11-a971a7d57fc9\n        provider_name: List and Get Gmail\n        provider_type: api\n        selected: false\n        title: listMessages\n        tool_configurations: {}\n        tool_label: listMessages\n        tool_name: listMessages\n        tool_parameters:\n          maxResults:\n            type: variable\n            value:\n            - '1716800588219'\n            - maxResults\n          userId:\n            type: mixed\n            value: '{{#1716800588219.email#}}'\n        type: tool\n      height: 53\n      id: '1716946869294'\n      position:\n        x: 334\n        y: 445\n      positionAbsolute:\n        x: 334\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-4o\n          provider: openai\n        prompt_template:\n        - id: b7fd0ec5-864a-42c6-9d04-a1958bd4fc0d\n          role: system\n          text: \"<Task>\\nYou need to encode the input data from text to base64. Input\\\n            \\ text. Output base64 encoding. Output nothing other than base64 encoding.\\\n            \\ \\n<input>\\n{{#1716951236700.output#}}\\n<output> \"\n        selected: false\n        title: Base64 Encoder\n        type: llm\n        variables: []\n        vision:\n          configs:\n            detail: high\n          enabled: true\n      extent: parent\n      height: 97\n      id: '1716951159073'\n      parentId: '1716909114582'\n      position:\n        x: 2525.7520359289056\n        y: 85\n      positionAbsolute:\n        x: 3467.7520359289056\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: Generaate MIME email template\n        isInIteration: true\n        iteration_id: '1716909114582'\n        selected: false\n        template: \"Content-Type: text/plain; charset=\\\"utf-8\\\"\\r\\nContent-Transfer-Encoding:\\\n          \\ 7bit\\r\\nMIME-Version: 1.0\\r\\nTo: {{ emailMetadata.recipientEmail }} #\\\n          \\ xiaoyi@dify.ai\\r\\nFrom:  {{ emailMetadata.senderEmail }} # sxy.hj156@gmail.com\\r\\\n          \\nSubject: Re: {{ emailMetadata.subject }} \\r\\n\\r\\n{{ text }}\\r\\n\"\n        title: 'Template: Reply Email'\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1716951357236'\n          - result\n          variable: emailMetadata\n        - value_selector:\n          - '1716960791399'\n          - output\n          variable: text\n      extent: parent\n      height: 83\n      id: '1716951236700'\n      parentId: '1716909114582'\n      position:\n        x: 2231.269960149744\n        y: 85\n      positionAbsolute:\n        x: 3173.269960149744\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        code: \"def main(email_json: dict) -> dict:\\n    import json\\n    if isinstance(email_json,\\\n          \\ str): \\n        email_json = json.loads(email_json)\\n\\n    subject = None\\n\\\n          \\    recipient_email = None \\n    sender_email = None\\n    \\n    headers\\\n          \\ = email_json['payload']['headers']\\n    for header in headers:\\n     \\\n          \\   if header['name'] == 'Subject':\\n            subject = header['value']\\n\\\n          \\        elif header['name'] == 'To':\\n            recipient_email = header['value']\\n\\\n          \\        elif header['name'] == 'From':\\n            sender_email = header['value']\\n\\\n          \\n    return {\\n        \\\"result\\\": [subject, recipient_email, sender_email]\\n\\\n          \\    }\\n\"\n        code_language: python3\n        desc: \"Recipient, Sender, Subject\\uFF0COutput Array[String]\"\n        isInIteration: true\n        iteration_id: '1716909114582'\n        outputs:\n          result:\n            children: null\n            type: array[string]\n        selected: false\n        title: Extract Email Metadata\n        type: code\n        variables:\n        - value_selector:\n          - '1716946889408'\n          - text\n          variable: email_json\n      extent: parent\n      height: 101\n      id: '1716951357236'\n      parentId: '1716909114582'\n      position:\n        x: 725\n        y: 85\n      positionAbsolute:\n        x: 1667\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        selected: false\n        template: '{\"raw\": \"{{ encoded_message }}\"}'\n        title: \"Template\\uFF1AEmail Request Body\"\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1716951159073'\n          - text\n          variable: encoded_message\n      extent: parent\n      height: 53\n      id: '1716952228079'\n      parentId: '1716909114582'\n      position:\n        x: 2828.4325280181324\n        y: 86.31950791077293\n      positionAbsolute:\n        x: 3770.4325280181324\n        y: 531.3195079107729\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        provider_id: 038963aa-43c8-47fc-be4b-0255c19959c1\n        provider_name: Draft Gmail\n        provider_type: api\n        selected: false\n        title: createDraft\n        tool_configurations: {}\n        tool_label: createDraft\n        tool_name: createDraft\n        tool_parameters:\n          message:\n            type: mixed\n            value: '{{#1716952228079.output#}}'\n          userId:\n            type: mixed\n            value: '{{#1716800588219.email#}}'\n        type: tool\n      extent: parent\n      height: 53\n      id: '1716952912103'\n      parentId: '1716909114582'\n      position:\n        x: 3133.7520359289056\n        y: 85\n      positionAbsolute:\n        x: 4075.7520359289056\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        classes:\n        - id: '1'\n          name: 'Technical questions, related to product '\n        - id: '2'\n          name: Unrelated to technicals, non technical\n        - id: '1716960736883'\n          name: Other questions\n        desc: ''\n        instructions: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        query_variable_selector:\n        - '1716800588219'\n        - sys.query\n        selected: false\n        title: Question Classifier\n        topics: []\n        type: question-classifier\n      extent: parent\n      height: 255\n      id: '1716960721611'\n      parentId: '1716909114582'\n      position:\n        x: 1325\n        y: 85\n      positionAbsolute:\n        x: 2267\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - id: a639bbf8-bc58-42a2-b477-6748e80ecda2\n          role: system\n          text: \"<Task> \\nRespond to the emails. \\n<Emails>\\n{{#1716913272656.text#}}\\n\\\n            <Your response>\"\n        selected: false\n        title: 'LLM - Non technical '\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      extent: parent\n      height: 97\n      id: '1716960728136'\n      parentId: '1716909114582'\n      position:\n        x: 1625\n        y: 251\n      positionAbsolute:\n        x: 2567\n        y: 696\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        output_type: string\n        selected: false\n        title: Variable Aggregator\n        type: variable-aggregator\n        variables:\n        - - '1716909125498'\n          - text\n        - - '1716960728136'\n          - text\n        - - '1716960834468'\n          - output\n      extent: parent\n      height: 164\n      id: '1716960791399'\n      parentId: '1716909114582'\n      position:\n        x: 1931.2699601497438\n        y: 85\n      positionAbsolute:\n        x: 2873.269960149744\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: Other questions\n        isInIteration: true\n        iteration_id: '1716909114582'\n        selected: false\n        template: 'Sorry, I cannot answer that. This is outside my capabilities. '\n        title: 'Direct Reply '\n        type: template-transform\n        variables: []\n      extent: parent\n      height: 83\n      id: '1716960834468'\n      parentId: '1716909114582'\n      position:\n        x: 1625\n        y: 385.57142857142856\n      positionAbsolute:\n        x: 2567\n        y: 830.5714285714286\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        author: Dify\n        desc: ''\n        height: 153\n        selected: false\n        showAuthor: true\n        text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":3,\"mode\":\"normal\",\"style\":\"font-size:\n          14px;\",\"text\":\"OpenAPI-Swagger for all custom tools: \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":3},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"openapi:\n          3.0.0\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"info:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"  title:\n          Gmail API\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"  description:\n          OpenAPI schema for Gmail API methods `users.messages.get`, `users.messages.list`,\n          and `users.drafts.create`.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"  version:\n          1.0.0\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"servers:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"  -\n          url: https://gmail.googleapis.com\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"    description:\n          Gmail API Server\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"paths:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"  /gmail/v1/users/{userId}/messages/{id}:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"    get:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      summary:\n          Get a message.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      description:\n          Retrieves a specific message by ID.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      operationId:\n          getMessage\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      parameters:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        -\n          name: userId\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          in:\n          path\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          required:\n          true\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          schema:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            type:\n          string\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          description:\n          The user''s email address. The special value `me` can be used to indicate\n          the authenticated user.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        -\n          name: id\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          in:\n          path\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          required:\n          true\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          schema:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            type:\n          string\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          description:\n          The ID of the message to retrieve.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        -\n          name: format\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          in:\n          query\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          required:\n          false\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          schema:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            type:\n          string\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            enum:\n          [full, metadata, minimal, raw]\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            default:\n          full\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          description:\n          The format to return the message in.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      responses:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        ''200'':\",\"type\":\"text\",\"version\":1}],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          description:\n          Successful response\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          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string.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      responses:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        ''200'':\",\"type\":\"text\",\"version\":1}],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          description:\n          Successful response with the created draft.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          content:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            application/json:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"              schema:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                type:\n          object\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                properties:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                  id:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                    type:\n          string\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                    description:\n          The immutable ID of the draft.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                  message:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                    type:\n          object\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                    properties:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                      id:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                        type:\n          string\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                        description:\n          The immutable ID of the message.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                      threadId:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                        type:\n          string\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                        description:\n          The ID of the thread the message belongs to.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                      labelIds:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                        type:\n          array\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                        items:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                          type:\n          string\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                      snippet:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                        type:\n          string\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                        description:\n          A short part of the message text.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                      historyId:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                        type:\n          string\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"                        description:\n          The ID of the last history record that modified this message.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        ''400'':\",\"type\":\"text\",\"version\":1}],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          description:\n          Bad Request - The request is invalid.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        ''401'':\",\"type\":\"text\",\"version\":1}],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          description:\n          Unauthorized - Authentication is required.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        ''403'':\",\"type\":\"text\",\"version\":1}],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          description:\n          Forbidden - The user does not have permission to create drafts.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        ''404'':\",\"type\":\"text\",\"version\":1}],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          description:\n          Not Found - The specified user does not exist.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        ''500'':\",\"type\":\"text\",\"version\":1}],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          description:\n          Internal Server Error - An error occurred on the server.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"components:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"  securitySchemes:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"    OAuth2:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      type:\n          oauth2\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      flows:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"        authorizationCode:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          authorizationUrl:\n          https://accounts.google.com/o/oauth2/auth\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          tokenUrl:\n          https://oauth2.googleapis.com/token\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          scopes:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            https://mail.google.com/:\n          All access to Gmail.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            https://www.googleapis.com/auth/gmail.compose:\n          Send email on your behalf.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            https://www.googleapis.com/auth/gmail.modify:\n          Modify your email.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"security:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"  -\n          OAuth2:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      -\n          https://mail.google.com/\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      -\n          https://www.googleapis.com/auth/gmail.compose\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      -\n          https://www.googleapis.com/auth/gmail.modify\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: yellow\n        title: ''\n        type: ''\n        width: 367\n      height: 153\n      id: '1718992681576'\n      position:\n        x: 321.9646831030669\n        y: 538.1642616264143\n      positionAbsolute:\n        x: 321.9646831030669\n        y: 538.1642616264143\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 367\n    - data:\n        author: Dify\n        desc: ''\n        height: 158\n        selected: false\n        showAuthor: true\n        text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Replace\n          custom tools after added this template to your own workspace. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Fill\n          in \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"your\n          email \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"and\n          the \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"maximum\n          number of results you want to retrieve from your inbox \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"to\n          get started. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: blue\n        title: ''\n        type: ''\n        width: 287\n      height: 158\n      id: '1718992805687'\n      position:\n        x: 18.571428571428356\n        y: 237.80887395992687\n      positionAbsolute:\n        x: 18.571428571428356\n        y: 237.80887395992687\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 287\n    - data:\n        author: Dify\n        desc: ''\n        height: 375\n        selected: true\n        showAuthor: true\n        text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"font-size:\n          16px;\",\"text\":\"Steps within Iteraction node: \",\"type\":\"text\",\"version\":1},{\"type\":\"linebreak\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"1.\n          getMessage: This step retrieves the incoming email message.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"2.\n          Code: Extract Email Body: Custom code is executed to extract the body of\n          the email from the retrieved message.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"3.\n          Extract Email Metadata: Extracts metadata from the email, such as the recipient,\n          sender, subject, and other relevant information.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"4.\n          Base64 Decoder: Decodes the email content from Base64 encoding.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"5.\n          Question Classifier (gpt-3.5-turbo): Uses a GPT-3.5-turbo model to classify\n          the email content into different categories. For each classified question,\n          the workflow uses a GPT-4.0 model to generate an appropriate reply:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"6.\n          Template: Reply Email: Uses a template to generate a MIME email format for\n          the reply.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"6.\n          Base64 Encoder: Encodes the generated reply email content back to Base64.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"7.\n          Template: Email Request: Prepares the email request using a template.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"8.\n          createDraft: Creates a draft of the email reply.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"This\n          workflow automates the process of reading, classifying, responding to, and\n          drafting replies to incoming emails, leveraging advanced language models\n          to generate contextually appropriate responses.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: blue\n        title: ''\n        type: ''\n        width: 640\n      height: 375\n      id: '1718993366836'\n      position:\n        x: 966.7525290975368\n        y: 971.80362905854\n      positionAbsolute:\n        x: 966.7525290975368\n        y: 971.80362905854\n      selected: true\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 640\n    - data:\n        author: Dify\n        desc: ''\n        height: 400\n        selected: false\n        showAuthor: true\n        text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":3,\"mode\":\"normal\",\"style\":\"font-size:\n          16px;\",\"text\":\"Preparation\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":3},{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Enable\n          Gmail API in Google Cloud Console\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"listitem\",\"version\":1,\"value\":1},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Configure\n          OAuth Client ID, OAuth Client Secrets, and OAuth Consent Screen for the\n          Web Application in Google Cloud Console\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"listitem\",\"version\":1,\"value\":2},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Use\n          Postman to authorize and obtain the OAuth Access Token (Google''s Access\n          Token will expire after 1 hour and cannot be used for a long time)\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"listitem\",\"version\":1,\"value\":3}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"list\",\"version\":1,\"listType\":\"bullet\",\"start\":1,\"tag\":\"ul\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Users\n          who want to try building an AI auto-reply email can refer to this document\n          to use Postman (Postman.com) to obtain all the above keys: https://blog.postman.com/how-to-access-google-apis-using-oauth-in-postman/.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Developers\n          who want to use Google OAuth to call the Gmail API to develop corresponding\n          plugins can refer to this official document: \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"https://developers.google.com/identity/protocols/oauth2/web-server.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"At\n          this stage, it is still a bit difficult to reproduce this example within\n          the Dify platform. If you have development capabilities, developing the\n          corresponding plugin externally and using an external database to automatically\n          read and write the user''s Access Token and write the Refresh Token would\n          be a better choice.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: blue\n        title: ''\n        type: ''\n        width: 608\n      height: 400\n      id: '1718993557447'\n      position:\n        x: 354.0157230378119\n        y: -1.2732157979666\n      positionAbsolute:\n        x: 354.0157230378119\n        y: -1.2732157979666\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 608\n    viewport:\n      x: 147.09446825757777\n      y: 101.03530130020579\n      zoom: 0.9548416039104178\n","icon":"\ud83e\udd16","icon_background":"#FFEAD5","id":"e9d92058-7d20-4904-892f-75d90bef7587","mode":"advanced-chat","name":"Automated Email Reply  "},
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data:\n        isInIteration: false\n        sourceType: iteration\n        targetType: template-transform\n      id: 1717916955547-source-1717917057450-target\n      source: '1717916955547'\n      sourceHandle: source\n      target: '1717917057450'\n      targetHandle: target\n      type: custom\n      zIndex: 0\n    - data:\n        isInIteration: false\n        sourceType: template-transform\n        targetType: end\n      id: 1717917057450-source-1711068257370-target\n      source: '1717917057450'\n      sourceHandle: source\n      target: '1711068257370'\n      targetHandle: target\n      type: custom\n      zIndex: 0\n    nodes:\n    - data:\n        desc: ''\n        selected: false\n        title: Start\n        type: start\n        variables:\n        - label: Input Text\n          max_length: null\n          options: []\n          required: true\n          type: paragraph\n          variable: input_text\n      dragging: false\n      height: 89\n      id: '1711067409646'\n      position:\n        x: 30\n        y: 301.5\n      positionAbsolute:\n        x: 30\n        y: 301.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1717917057450'\n          - output\n          variable: final\n        selected: false\n        title: End\n        type: end\n      height: 89\n      id: '1711068257370'\n      position:\n        x: 2291\n        y: 301.5\n      positionAbsolute:\n        x: 2291\n        y: 301.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        code: \"\\ndef main(input_text: str) -> str:\\n    token_limit = 1000\\n    overlap\\\n          \\ = 100\\n    chunk_size = int(token_limit * 6 * (4/3))\\n\\n        # Initialize\\\n          \\ variables\\n    chunks = []\\n    start_index = 0\\n    text_length = len(input_text)\\n\\\n          \\n    # Loop until the end of the text is reached\\n    while start_index\\\n          \\ < text_length:\\n        # If we are not at the beginning, adjust the start_index\\\n          \\ to ensure overlap\\n        if start_index > 0:\\n            start_index\\\n          \\ -= overlap\\n\\n        # Calculate end index for the current chunk\\n  \\\n          \\      end_index = start_index + chunk_size\\n        if end_index > text_length:\\n\\\n          \\            end_index = text_length\\n\\n        # Add the current chunk\\\n          \\ to the list\\n        chunks.append(input_text[start_index:end_index])\\n\\\n          \\n        # Update the start_index for the next chunk\\n        start_index\\\n          \\ += chunk_size\\n\\n    return {\\n        \\\"chunks\\\": chunks,\\n    }\\n\"\n        code_language: python3\n        dependencies: []\n        desc: 'token_limit = 1000\n\n          overlap = 100'\n        outputs:\n          chunks:\n            children: null\n            type: array[string]\n        selected: false\n        title: Code\n        type: code\n        variables:\n        - 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'\n        height: 203\n        iterator_selector:\n        - '1717916867969'\n        - chunks\n        output_selector:\n        - '1717916991709'\n        - text\n        output_type: array[string]\n        selected: false\n        startNodeType: llm\n        start_node_id: '1717916961837'\n        title: Iteration\n        type: iteration\n        width: 1289\n      height: 203\n      id: '1717916955547'\n      position:\n        x: 638\n        y: 301.5\n      positionAbsolute:\n        x: 638\n        y: 301.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 1289\n      zIndex: 1\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: ''\n        isInIteration: true\n        isIterationStart: true\n        iteration_id: '1717916955547'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-4o\n          provider: openai\n        prompt_template:\n        - id: 7261280b-cb27-4f84-8363-b93e09246d16\n          role: system\n          text: \"<Task> Identify the technical terms in the users input. Use the following\\\n            \\ format {XXX} -> {XXX} to show the corresponding technical terms before\\\n            \\ and after translation. \\n\\n<Input Text> \\n{{#1717916955547.item#}}\\n\\\n            \\n<Example>\\n| \\u82F1\\u6587 | \\u4E2D\\u6587 |\\n| --- | --- |\\n| Prompt\\\n            \\ Engineering | \\u63D0\\u793A\\u8BCD\\u5DE5\\u7A0B |\\n| Text Generation \\_\\\n            | \\u6587\\u672C\\u751F\\u6210 |\\n| Token \\_| Token |\\n| Prompt \\_| \\u63D0\\\n            \\u793A\\u8BCD |\\n| Meta Prompting \\_| \\u5143\\u63D0\\u793A |\\n| diffusion\\\n            \\ models \\_| \\u6269\\u6563\\u6A21\\u578B |\\n| Agent \\_| \\u667A\\u80FD\\u4F53\\\n            \\ |\\n| Transformer \\_| Transformer |\\n| Zero Shot \\_| \\u96F6\\u6837\\u672C\\\n            \\ |\\n| Few Shot \\_| \\u5C11\\u6837\\u672C |\\n| chat window \\_| \\u804A\\u5929\\\n            \\ |\\n| context | \\u4E0A\\u4E0B\\u6587 |\\n| stock photo \\_| \\u56FE\\u5E93\\u7167\\\n            \\u7247 |\\n\\n\\n<Technical Terms> \"\n        selected: false\n        title: 'Identify Terms '\n        type: llm\n        variables: []\n        vision:\n          configs:\n            detail: high\n          enabled: true\n      extent: parent\n      height: 97\n      id: '1717916961837'\n      parentId: '1717916955547'\n      position:\n        x: 117\n        y: 85\n      positionAbsolute:\n        x: 755\n        y: 386.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1001\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: ''\n        isInIteration: true\n        iteration_id: '1717916955547'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-4o\n          provider: openai\n        prompt_template:\n        - id: 05e03f0d-c1a9-43ab-b4c0-44b55049434d\n          role: system\n          text: \"<Task> You are a professional translator proficient in Simplified\\\n            \\ Chinese especially skilled in translating professional academic papers\\\n            \\ into easy-to-understand popular science articles. 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data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: code\n        targetType: code\n      id: 1716909122343-source-1716951357236-target\n      source: '1716909122343'\n      sourceHandle: source\n      target: '1716951357236'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: code\n        targetType: llm\n      id: 1716951357236-source-1716913272656-target\n      source: '1716951357236'\n      sourceHandle: source\n      target: '1716913272656'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: template-transform\n        targetType: llm\n      id: 1716951236700-source-1716951159073-target\n      source: '1716951236700'\n      sourceHandle: source\n      target: '1716951159073'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: llm\n        targetType: template-transform\n      id: 1716951159073-source-1716952228079-target\n      source: '1716951159073'\n      sourceHandle: source\n      target: '1716952228079'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: template-transform\n        targetType: tool\n      id: 1716952228079-source-1716952912103-target\n      source: '1716952228079'\n      sourceHandle: source\n      target: '1716952912103'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: llm\n        targetType: question-classifier\n      id: 1716913272656-source-1716960721611-target\n      source: '1716913272656'\n      sourceHandle: source\n      target: '1716960721611'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: question-classifier\n        targetType: llm\n      id: 1716960721611-1-1716909125498-target\n      source: '1716960721611'\n      sourceHandle: '1'\n      target: '1716909125498'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: question-classifier\n        targetType: llm\n      id: 1716960721611-2-1716960728136-target\n      source: '1716960721611'\n      sourceHandle: '2'\n      target: '1716960728136'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: llm\n        targetType: variable-aggregator\n      id: 1716909125498-source-1716960791399-target\n      source: '1716909125498'\n      sourceHandle: source\n      target: '1716960791399'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: variable-aggregator\n        targetType: template-transform\n      id: 1716960791399-source-1716951236700-target\n      source: '1716960791399'\n      sourceHandle: source\n      target: '1716951236700'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: question-classifier\n        targetType: template-transform\n      id: 1716960721611-1716960736883-1716960834468-target\n      source: '1716960721611'\n      sourceHandle: '1716960736883'\n      target: '1716960834468'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: llm\n        targetType: variable-aggregator\n      id: 1716960728136-source-1716960791399-target\n      source: '1716960728136'\n      sourceHandle: source\n      target: '1716960791399'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    - data:\n        isInIteration: true\n        iteration_id: '1716909114582'\n        sourceType: template-transform\n        targetType: variable-aggregator\n      id: 1716960834468-source-1716960791399-target\n      source: '1716960834468'\n      sourceHandle: source\n      target: '1716960791399'\n      targetHandle: target\n      type: custom\n      zIndex: 1002\n    nodes:\n    - data:\n        desc: ''\n        selected: false\n        title: Start\n        type: start\n        variables:\n        - label: Your Email\n          max_length: 256\n          options: []\n          required: true\n          type: text-input\n          variable: email\n        - label: Maximum Number of Email you want to retrieve\n          max_length: 256\n          options: []\n          required: true\n          type: number\n          variable: maxResults\n      height: 115\n      id: '1716800588219'\n      position:\n        x: 30\n        y: 445\n      positionAbsolute:\n        x: 30\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        answer: '{{#1716913435742.output#}}'\n        desc: ''\n        selected: false\n        title: Direct Reply\n        type: answer\n        variables: []\n      height: 106\n      id: '1716806267180'\n      position:\n        x: 4700\n        y: 445\n      positionAbsolute:\n        x: 4700\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        code: \"def main(message: str) -> dict:\\n    import json\\n    \\n    # Parse\\\n          \\ the JSON string\\n    parsed_data = json.loads(message)\\n    \\n    # Extract\\\n          \\ all the \\\"id\\\" values\\n    ids = [msg['id'] for msg in parsed_data['messages']]\\n\\\n          \\    \\n    return {\\n        \\\"result\\\": ids\\n    }\"\n        code_language: python3\n        desc: ''\n        outputs:\n          result:\n            children: null\n            type: array[string]\n        selected: false\n        title: 'Code: Extract Email ID'\n        type: code\n        variables:\n        - value_selector:\n          - '1716946869294'\n          - text\n          variable: message\n      height: 53\n      id: '1716909112104'\n      position:\n        x: 638\n        y: 445\n      positionAbsolute:\n        x: 638\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        desc: ''\n        height: 490\n        iterator_selector:\n        - '1716909112104'\n        - result\n        output_selector:\n        - '1716909125498'\n        - text\n        output_type: array[string]\n        selected: false\n        startNodeType: tool\n        start_node_id: '1716946889408'\n        title: 'Iteraction '\n        type: iteration\n        width: 3393.7520359289056\n      height: 490\n      id: '1716909114582'\n      position:\n        x: 942\n        y: 445\n      positionAbsolute:\n        x: 942\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 3394\n      zIndex: 1\n    - data:\n        desc: ''\n        isInIteration: true\n        isIterationStart: true\n        iteration_id: '1716909114582'\n        provider_id: e64b4c7f-2795-499c-8d11-a971a7d57fc9\n        provider_name: List and Get Gmail\n        provider_type: api\n        selected: false\n        title: getMessage\n        tool_configurations: {}\n        tool_label: getMessage\n        tool_name: getMessage\n        tool_parameters:\n          format:\n            type: mixed\n            value: full\n          id:\n            type: mixed\n            value: '{{#1716909114582.item#}}'\n          userId:\n            type: mixed\n            value: '{{#1716800588219.email#}}'\n        type: tool\n      extent: parent\n      height: 53\n      id: '1716946889408'\n      parentId: '1716909114582'\n      position:\n        x: 117\n        y: 85\n      positionAbsolute:\n        x: 1059\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1001\n    - data:\n        code: \"\\ndef main(email_json: dict) -> dict:\\n    import json \\n    email_dict\\\n          \\ = json.loads(email_json)\\n    base64_data = email_dict['payload']['parts'][0]['body']['data']\\n\\\n          \\n    return {\\n        \\\"result\\\": base64_data, \\n    }\\n\"\n        code_language: python3\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        outputs:\n          result:\n            children: null\n            type: string\n        selected: false\n        title: 'Code: Extract Email Body'\n        type: code\n        variables:\n        - value_selector:\n          - '1716946889408'\n          - text\n          variable: email_json\n      extent: parent\n      height: 53\n      id: '1716909122343'\n      parentId: '1716909114582'\n      position:\n        x: 421\n        y: 85\n      positionAbsolute:\n        x: 1363\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: 'Generate reply. '\n        isInIteration: true\n        iteration_id: '1716909114582'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-4o\n          provider: openai\n        prompt_template:\n        - id: 982014aa-702b-4d7c-ae1f-08dbceb6e930\n          role: system\n          text: \"<Task> \\nRespond to the emails. \\n<Emails>\\n{{#1716913272656.text#}}\\n\\\n            <Your response>\"\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          configs:\n            detail: high\n          enabled: true\n      extent: parent\n      height: 127\n      id: '1716909125498'\n      parentId: '1716909114582'\n      position:\n        x: 1625\n        y: 85\n      positionAbsolute:\n        x: 2567\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-4o\n          provider: openai\n        prompt_template:\n        - id: fd8de569-c099-4320-955b-61aa4b054789\n          role: system\n          text: \"<Task>\\nYou need to transform the input data (in base64 encoding)\\\n            \\ to text. Input base64. Output text. \\n<input>\\n{{#1716909122343.result#}}\\n\\\n            <output> \"\n        selected: false\n        title: 'Base64 Decoder '\n        type: llm\n        variables: []\n        vision:\n          configs:\n            detail: high\n          enabled: false\n      extent: parent\n      height: 97\n      id: '1716913272656'\n      parentId: '1716909114582'\n      position:\n        x: 1025\n        y: 85\n      positionAbsolute:\n        x: 1967\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 | join(\"\\n\\n -------------------------\\n\\n\") }}'\n        title: 'Template '\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1716909114582'\n          - output\n          variable: arg1\n      height: 53\n      id: '1716913435742'\n      position:\n        x: 4396\n        y: 445\n      positionAbsolute:\n        x: 4396\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        desc: ''\n        provider_id: e64b4c7f-2795-499c-8d11-a971a7d57fc9\n        provider_name: List and Get Gmail\n        provider_type: api\n        selected: false\n        title: listMessages\n        tool_configurations: {}\n        tool_label: listMessages\n        tool_name: listMessages\n        tool_parameters:\n          maxResults:\n            type: variable\n            value:\n            - '1716800588219'\n            - maxResults\n          userId:\n            type: mixed\n            value: '{{#1716800588219.email#}}'\n        type: tool\n      height: 53\n      id: '1716946869294'\n      position:\n        x: 334\n        y: 445\n      positionAbsolute:\n        x: 334\n        y: 445\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-4o\n          provider: openai\n        prompt_template:\n        - id: b7fd0ec5-864a-42c6-9d04-a1958bd4fc0d\n          role: system\n          text: \"<Task>\\nYou need to encode the input data from text to base64. Input\\\n            \\ text. Output base64 encoding. Output nothing other than base64 encoding.\\\n            \\ \\n<input>\\n{{#1716951236700.output#}}\\n<output> \"\n        selected: false\n        title: Base64 Encoder\n        type: llm\n        variables: []\n        vision:\n          configs:\n            detail: high\n          enabled: true\n      extent: parent\n      height: 97\n      id: '1716951159073'\n      parentId: '1716909114582'\n      position:\n        x: 2525.7520359289056\n        y: 85\n      positionAbsolute:\n        x: 3467.7520359289056\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: Generate MIME email template\n        isInIteration: true\n        iteration_id: '1716909114582'\n        selected: false\n        template: \"Content-Type: text/plain; charset=\\\"utf-8\\\"\\r\\nContent-Transfer-Encoding:\\\n          \\ 7bit\\r\\nMIME-Version: 1.0\\r\\nTo: {{ emailMetadata.recipientEmail }} #\\\n          \\ xiaoyi@dify.ai\\r\\nFrom:  {{ emailMetadata.senderEmail }} # sxy.hj156@gmail.com\\r\\\n          \\nSubject: Re: {{ emailMetadata.subject }} \\r\\n\\r\\n{{ text }}\\r\\n\"\n        title: 'Template: Reply Email'\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1716951357236'\n          - result\n          variable: emailMetadata\n        - value_selector:\n          - '1716960791399'\n          - output\n          variable: text\n      extent: parent\n      height: 83\n      id: '1716951236700'\n      parentId: '1716909114582'\n      position:\n        x: 2231.269960149744\n        y: 85\n      positionAbsolute:\n        x: 3173.269960149744\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        code: \"def main(email_json: dict) -> dict:\\n    import json\\n    if isinstance(email_json,\\\n          \\ str): \\n        email_json = json.loads(email_json)\\n\\n    subject = None\\n\\\n          \\    recipient_email = None \\n    sender_email = None\\n    \\n    headers\\\n          \\ = email_json['payload']['headers']\\n    for header in headers:\\n     \\\n          \\   if header['name'] == 'Subject':\\n            subject = header['value']\\n\\\n          \\        elif header['name'] == 'To':\\n            recipient_email = header['value']\\n\\\n          \\        elif header['name'] == 'From':\\n            sender_email = header['value']\\n\\\n          \\n    return {\\n        \\\"result\\\": [subject, recipient_email, sender_email]\\n\\\n          \\    }\\n\"\n        code_language: python3\n        desc: \"Recipient, Sender, Subject\\uFF0COutput Array[String]\"\n        isInIteration: true\n        iteration_id: '1716909114582'\n        outputs:\n          result:\n            children: null\n            type: array[string]\n        selected: false\n        title: Extract Email Metadata\n        type: code\n        variables:\n        - value_selector:\n          - '1716946889408'\n          - text\n          variable: email_json\n      extent: parent\n      height: 101\n      id: '1716951357236'\n      parentId: '1716909114582'\n      position:\n        x: 725\n        y: 85\n      positionAbsolute:\n        x: 1667\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        selected: false\n        template: '{\"raw\": \"{{ encoded_message }}\"}'\n        title: \"Template\\uFF1AEmail Request Body\"\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1716951159073'\n          - text\n          variable: encoded_message\n      extent: parent\n      height: 53\n      id: '1716952228079'\n      parentId: '1716909114582'\n      position:\n        x: 2828.4325280181324\n        y: 86.31950791077293\n      positionAbsolute:\n        x: 3770.4325280181324\n        y: 531.3195079107729\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        provider_id: 038963aa-43c8-47fc-be4b-0255c19959c1\n        provider_name: Draft Gmail\n        provider_type: api\n        selected: false\n        title: createDraft\n        tool_configurations: {}\n        tool_label: createDraft\n        tool_name: createDraft\n        tool_parameters:\n          message:\n            type: mixed\n            value: '{{#1716952228079.output#}}'\n          userId:\n            type: mixed\n            value: '{{#1716800588219.email#}}'\n        type: tool\n      extent: parent\n      height: 53\n      id: '1716952912103'\n      parentId: '1716909114582'\n      position:\n        x: 3133.7520359289056\n        y: 85\n      positionAbsolute:\n        x: 4075.7520359289056\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        classes:\n        - id: '1'\n          name: 'Technical questions, related to product '\n        - id: '2'\n          name: Unrelated to technicals, non technical\n        - id: '1716960736883'\n          name: Other questions\n        desc: ''\n        instructions: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        query_variable_selector:\n        - '1716800588219'\n        - sys.query\n        selected: false\n        title: Question Classifier\n        topics: []\n        type: question-classifier\n      extent: parent\n      height: 255\n      id: '1716960721611'\n      parentId: '1716909114582'\n      position:\n        x: 1325\n        y: 85\n      positionAbsolute:\n        x: 2267\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - id: a639bbf8-bc58-42a2-b477-6748e80ecda2\n          role: system\n          text: \"<Task> \\nRespond to the emails. \\n<Emails>\\n{{#1716913272656.text#}}\\n\\\n            <Your response>\"\n        selected: false\n        title: 'LLM - Non technical '\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      extent: parent\n      height: 97\n      id: '1716960728136'\n      parentId: '1716909114582'\n      position:\n        x: 1625\n        y: 251\n      positionAbsolute:\n        x: 2567\n        y: 696\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: ''\n        isInIteration: true\n        iteration_id: '1716909114582'\n        output_type: string\n        selected: false\n        title: Variable Aggregator\n        type: variable-aggregator\n        variables:\n        - - '1716909125498'\n          - text\n        - - '1716960728136'\n          - text\n        - - '1716960834468'\n          - output\n      extent: parent\n      height: 164\n      id: '1716960791399'\n      parentId: '1716909114582'\n      position:\n        x: 1931.2699601497438\n        y: 85\n      positionAbsolute:\n        x: 2873.269960149744\n        y: 530\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        desc: Other questions\n        isInIteration: true\n        iteration_id: '1716909114582'\n        selected: false\n        template: 'Sorry, I cannot answer that. This is outside my capabilities. '\n        title: 'Direct Reply '\n        type: template-transform\n        variables: []\n      extent: parent\n      height: 83\n      id: '1716960834468'\n      parentId: '1716909114582'\n      position:\n        x: 1625\n        y: 385.57142857142856\n      positionAbsolute:\n        x: 2567\n        y: 830.5714285714286\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1002\n    - data:\n        author: Dify\n        desc: ''\n        height: 153\n        selected: false\n        showAuthor: true\n        text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":3,\"mode\":\"normal\",\"style\":\"font-size:\n          14px;\",\"text\":\"OpenAPI-Swagger for all custom tools: 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       authorizationUrl:\n          https://accounts.google.com/o/oauth2/auth\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          tokenUrl:\n          https://oauth2.googleapis.com/token\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"          scopes:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            https://mail.google.com/:\n          All access to Gmail.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            https://www.googleapis.com/auth/gmail.compose:\n          Send email on your behalf.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"            https://www.googleapis.com/auth/gmail.modify:\n          Modify your email.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"security:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"  -\n          OAuth2:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      -\n          https://mail.google.com/\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      -\n          https://www.googleapis.com/auth/gmail.compose\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"      -\n          https://www.googleapis.com/auth/gmail.modify\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: yellow\n        title: ''\n        type: ''\n        width: 367\n      height: 153\n      id: '1718992681576'\n      position:\n        x: 321.9646831030669\n        y: 538.1642616264143\n      positionAbsolute:\n        x: 321.9646831030669\n        y: 538.1642616264143\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 367\n    - data:\n        author: Dify\n        desc: ''\n        height: 158\n        selected: false\n        showAuthor: true\n        text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Replace\n          custom tools after added this template to your own workspace. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Fill\n          in \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"your\n          email \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"and\n          the \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"\",\"text\":\"maximum\n          number of results you want to retrieve from your inbox \",\"type\":\"text\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"to\n          get started. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: blue\n        title: ''\n        type: ''\n        width: 287\n      height: 158\n      id: '1718992805687'\n      position:\n        x: 18.571428571428356\n        y: 237.80887395992687\n      positionAbsolute:\n        x: 18.571428571428356\n        y: 237.80887395992687\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 287\n    - data:\n        author: Dify\n        desc: ''\n        height: 375\n        selected: true\n        showAuthor: true\n        text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":1,\"mode\":\"normal\",\"style\":\"font-size:\n          16px;\",\"text\":\"Steps within Iteraction node: \",\"type\":\"text\",\"version\":1},{\"type\":\"linebreak\",\"version\":1},{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"1.\n          getMessage: This step retrieves the incoming email message.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":1},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"2.\n          Code: Extract Email Body: Custom code is executed to extract the body of\n          the email from the retrieved message.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"3.\n          Extract Email Metadata: Extracts metadata from the email, such as the recipient,\n          sender, subject, and other relevant information.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"4.\n          Base64 Decoder: Decodes the email content from Base64 encoding.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"5.\n          Question Classifier (gpt-3.5-turbo): Uses a GPT-3.5-turbo model to classify\n          the email content into different categories. For each classified question,\n          the workflow uses a GPT-4.0 model to generate an appropriate reply:\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"6.\n          Template: Reply Email: Uses a template to generate a MIME email format for\n          the reply.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"6.\n          Base64 Encoder: Encodes the generated reply email content back to Base64.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"7.\n          Template: Email Request: Prepares the email request using a template.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"8.\n          createDraft: Creates a draft of the email reply.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"This\n          workflow automates the process of reading, classifying, responding to, and\n          drafting replies to incoming emails, leveraging advanced language models\n          to generate contextually appropriate responses.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: blue\n        title: ''\n        type: ''\n        width: 640\n      height: 375\n      id: '1718993366836'\n      position:\n        x: 966.7525290975368\n        y: 971.80362905854\n      positionAbsolute:\n        x: 966.7525290975368\n        y: 971.80362905854\n      selected: true\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 640\n    - data:\n        author: Dify\n        desc: ''\n        height: 400\n        selected: false\n        showAuthor: true\n        text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":3,\"mode\":\"normal\",\"style\":\"font-size:\n          16px;\",\"text\":\"Preparation\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":3},{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Enable\n          Gmail API in Google Cloud Console\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"listitem\",\"version\":1,\"value\":1},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Configure\n          OAuth Client ID, OAuth Client Secrets, and OAuth Consent Screen for the\n          Web Application in Google Cloud Console\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"listitem\",\"version\":1,\"value\":2},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Use\n          Postman to authorize and obtain the OAuth Access Token (Google''s Access\n          Token will expire after 1 hour and cannot be used for a long time)\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"listitem\",\"version\":1,\"value\":3}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"list\",\"version\":1,\"listType\":\"bullet\",\"start\":1,\"tag\":\"ul\"},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Users\n          who want to try building an AI auto-reply email can refer to this document\n          to use Postman (Postman.com) to obtain all the above keys: https://blog.postman.com/how-to-access-google-apis-using-oauth-in-postman/.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Developers\n          who want to use Google OAuth to call the Gmail API to develop corresponding\n          plugins can refer to this official document: \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"https://developers.google.com/identity/protocols/oauth2/web-server.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"At\n          this stage, it is still a bit difficult to reproduce this example within\n          the Dify platform. If you have development capabilities, developing the\n          corresponding plugin externally and using an external database to automatically\n          read and write the user''s Access Token and write the Refresh Token would\n          be a better choice.\",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: blue\n        title: ''\n        type: ''\n        width: 608\n      height: 400\n      id: '1718993557447'\n      position:\n        x: 354.0157230378119\n        y: -1.2732157979666\n      positionAbsolute:\n        x: 354.0157230378119\n        y: -1.2732157979666\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 608\n    viewport:\n      x: 147.09446825757777\n      y: 101.03530130020579\n      zoom: 0.9548416039104178\n","icon":"\ud83e\udd16","icon_background":"#FFEAD5","id":"e9d92058-7d20-4904-892f-75d90bef7587","mode":"advanced-chat","name":"Automated Email Reply  "},
+        "98b87f88-bd22-4d86-8b74-86beba5e0ed4":{"export_data":"app:\n  icon: \"\\U0001F916\"\n  icon_background: '#FFEAD5'\n  mode: workflow\n  name: 'Book Translation '\nworkflow:\n  features:\n    file_upload:\n      image:\n        enabled: false\n        number_limits: 3\n        transfer_methods:\n        - local_file\n        - remote_url\n    opening_statement: ''\n    retriever_resource:\n      enabled: false\n    sensitive_word_avoidance:\n      enabled: false\n    speech_to_text:\n      enabled: false\n    suggested_questions: []\n    suggested_questions_after_answer:\n      enabled: false\n    text_to_speech:\n      enabled: false\n      language: ''\n      voice: ''\n  graph:\n    edges:\n    - data:\n        isInIteration: false\n        sourceType: start\n        targetType: code\n      id: 1711067409646-source-1717916867969-target\n      source: '1711067409646'\n      sourceHandle: source\n      target: '1717916867969'\n      targetHandle: target\n      type: custom\n      zIndex: 0\n    - 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data:\n        isInIteration: false\n        sourceType: iteration\n        targetType: template-transform\n      id: 1717916955547-source-1717917057450-target\n      source: '1717916955547'\n      sourceHandle: source\n      target: '1717917057450'\n      targetHandle: target\n      type: custom\n      zIndex: 0\n    - data:\n        isInIteration: false\n        sourceType: template-transform\n        targetType: end\n      id: 1717917057450-source-1711068257370-target\n      source: '1717917057450'\n      sourceHandle: source\n      target: '1711068257370'\n      targetHandle: target\n      type: custom\n      zIndex: 0\n    nodes:\n    - data:\n        desc: ''\n        selected: false\n        title: Start\n        type: start\n        variables:\n        - label: Input Text\n          max_length: null\n          options: []\n          required: true\n          type: paragraph\n          variable: input_text\n      dragging: false\n      height: 89\n      id: '1711067409646'\n      position:\n        x: 30\n        y: 301.5\n      positionAbsolute:\n        x: 30\n        y: 301.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1717917057450'\n          - output\n          variable: final\n        selected: false\n        title: End\n        type: end\n      height: 89\n      id: '1711068257370'\n      position:\n        x: 2291\n        y: 301.5\n      positionAbsolute:\n        x: 2291\n        y: 301.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        code: \"\\ndef main(input_text: str) -> str:\\n    token_limit = 1000\\n    overlap\\\n          \\ = 100\\n    chunk_size = int(token_limit * 6 * (4/3))\\n\\n        # Initialize\\\n          \\ variables\\n    chunks = []\\n    start_index = 0\\n    text_length = len(input_text)\\n\\\n          \\n    # Loop until the end of the text is reached\\n    while start_index\\\n          \\ < text_length:\\n        # If we are not at the beginning, adjust the start_index\\\n          \\ to ensure overlap\\n        if start_index > 0:\\n            start_index\\\n          \\ -= overlap\\n\\n        # Calculate end index for the current chunk\\n  \\\n          \\      end_index = start_index + chunk_size\\n        if end_index > text_length:\\n\\\n          \\            end_index = text_length\\n\\n        # Add the current chunk\\\n          \\ to the list\\n        chunks.append(input_text[start_index:end_index])\\n\\\n          \\n        # Update the start_index for the next chunk\\n        start_index\\\n          \\ += chunk_size\\n\\n    return {\\n        \\\"chunks\\\": chunks,\\n    }\\n\"\n        code_language: python3\n        dependencies: []\n        desc: 'token_limit = 1000\n\n          overlap = 100'\n        outputs:\n          chunks:\n            children: null\n            type: array[string]\n        selected: false\n        title: Code\n        type: code\n        variables:\n        - value_selector:\n          - '1711067409646'\n          - input_text\n          variable: input_text\n      height: 101\n      id: '1717916867969'\n      position:\n        x: 336\n        y: 301.5\n      positionAbsolute:\n        x: 336\n        y: 301.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n    - data:\n        desc: 'Take good care on maximum number of iterations.  '\n        height: 203\n        iterator_selector:\n        - '1717916867969'\n        - chunks\n        output_selector:\n        - '1717916991709'\n        - text\n        output_type: array[string]\n        selected: false\n        startNodeType: llm\n        start_node_id: '1717916961837'\n        title: Iteration\n        type: iteration\n        width: 1289\n      height: 203\n      id: '1717916955547'\n      position:\n        x: 638\n        y: 301.5\n      positionAbsolute:\n        x: 638\n        y: 301.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 1289\n      zIndex: 1\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: ''\n        isInIteration: true\n        isIterationStart: true\n        iteration_id: '1717916955547'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-4o\n          provider: openai\n        prompt_template:\n        - id: 7261280b-cb27-4f84-8363-b93e09246d16\n          role: system\n          text: \"<Task> Identify the technical terms in the users input. Use the following\\\n            \\ format {XXX} -> {XXX} to show the corresponding technical terms before\\\n            \\ and after translation. \\n\\n<Input Text> \\n{{#1717916955547.item#}}\\n\\\n            \\n<Example>\\n| \\u82F1\\u6587 | \\u4E2D\\u6587 |\\n| --- | --- |\\n| Prompt\\\n            \\ Engineering | \\u63D0\\u793A\\u8BCD\\u5DE5\\u7A0B |\\n| Text Generation \\_\\\n            | \\u6587\\u672C\\u751F\\u6210 |\\n| Token \\_| Token |\\n| Prompt \\_| \\u63D0\\\n            \\u793A\\u8BCD |\\n| Meta Prompting \\_| \\u5143\\u63D0\\u793A |\\n| diffusion\\\n            \\ models \\_| \\u6269\\u6563\\u6A21\\u578B |\\n| Agent \\_| \\u667A\\u80FD\\u4F53\\\n            \\ |\\n| Transformer \\_| Transformer |\\n| Zero Shot \\_| \\u96F6\\u6837\\u672C\\\n            \\ |\\n| Few Shot \\_| \\u5C11\\u6837\\u672C |\\n| chat window \\_| \\u804A\\u5929\\\n            \\ |\\n| context | \\u4E0A\\u4E0B\\u6587 |\\n| stock photo \\_| \\u56FE\\u5E93\\u7167\\\n            \\u7247 |\\n\\n\\n<Technical Terms> \"\n        selected: false\n        title: 'Identify Terms '\n        type: llm\n        variables: []\n        vision:\n          configs:\n            detail: high\n          enabled: true\n      extent: parent\n      height: 97\n      id: '1717916961837'\n      parentId: '1717916955547'\n      position:\n        x: 117\n        y: 85\n      positionAbsolute:\n        x: 755\n        y: 386.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 244\n      zIndex: 1001\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: ''\n        isInIteration: true\n        iteration_id: '1717916955547'\n        model:\n          completion_params:\n            temperature: 0.7\n          mode: chat\n          name: gpt-4o\n          provider: openai\n        prompt_template:\n        - id: 05e03f0d-c1a9-43ab-b4c0-44b55049434d\n          role: system\n          text: \"<Task> You are a professional translator proficient in Simplified\\\n            \\ Chinese especially skilled in translating professional academic papers\\\n            \\ into easy-to-understand popular science articles. 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Each\n          chunk overlap with each other to make sure the texts are consistent.  \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"The\n          code node outputs an array of segmented texts of input_texts. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: blue\n        title: ''\n        type: ''\n        width: 340\n      height: 186\n      id: '1718990593686'\n      position:\n        x: 259.3026056936437\n        y: 451.6924912936374\n      positionAbsolute:\n        x: 259.3026056936437\n        y: 451.6924912936374\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 340\n    - data:\n        author: Dify\n        desc: ''\n        height: 128\n        selected: false\n        showAuthor: true\n        text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Iterate\n          through all the elements in output of the code node and translate each chunk\n          using a three steps translation workflow. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: blue\n        title: ''\n        type: ''\n        width: 355\n      height: 128\n      id: '1718991836605'\n      position:\n        x: 764.3891977435923\n        y: 530.8917807505335\n      positionAbsolute:\n        x: 764.3891977435923\n        y: 530.8917807505335\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 355\n    - data:\n        author: Dify\n        desc: ''\n        height: 126\n        selected: false\n        showAuthor: true\n        text: '{\"root\":{\"children\":[{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Avoid\n          using a high token_limit, LLM''s performance decreases with longer context\n          length for gpt-4o. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[],\"direction\":null,\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0},{\"children\":[{\"detail\":0,\"format\":0,\"mode\":\"normal\",\"style\":\"\",\"text\":\"Recommend\n          to use less than or equal to 1000 tokens. \",\"type\":\"text\",\"version\":1}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"paragraph\",\"version\":1,\"textFormat\":0}],\"direction\":\"ltr\",\"format\":\"\",\"indent\":0,\"type\":\"root\",\"version\":1}}'\n        theme: yellow\n        title: ''\n        type: ''\n        width: 351\n      height: 126\n      id: '1718991882984'\n      position:\n        x: 304.49115824454367\n        y: 148.4042994607805\n      positionAbsolute:\n        x: 304.49115824454367\n        y: 148.4042994607805\n      selected: true\n      sourcePosition: right\n      targetPosition: left\n      type: custom-note\n      width: 351\n    viewport:\n      x: 335.92505067152274\n      y: 18.806553508850584\n      zoom: 0.8705505632961259\n","icon":"\ud83e\udd16","icon_background":"#FFEAD5","id":"98b87f88-bd22-4d86-8b74-86beba5e0ed4","mode":"workflow","name":"Book Translation "},
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@@ -553,15 +553,15 @@
             "name": "AI Front-end interviewer"
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data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512911454-1711513015189\n      source: '1711512911454'\n      sourceHandle: source\n      target: '1711513015189'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512914870-1711513017096\n      source: '1711512914870'\n      sourceHandle: source\n      target: '1711513017096'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512916516-1711513018759\n      source: '1711512916516'\n      sourceHandle: source\n      target: '1711513018759'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512924231-1711513020857\n      source: '1711512924231'\n      sourceHandle: source\n      target: '1711513020857'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512926020-1711513022516\n      source: '1711512926020'\n      sourceHandle: source\n      target: '1711513022516'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512927569-1711513024315\n      source: '1711512927569'\n      sourceHandle: source\n      target: '1711513024315'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512929190-1711513025732\n      source: '1711512929190'\n      sourceHandle: source\n      target: '1711513025732'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512930700-1711513027347\n      source: '1711512930700'\n      sourceHandle: source\n      target: '1711513027347'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513015189-1711513029058\n      source: '1711513015189'\n      sourceHandle: source\n      target: '1711513029058'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513017096-1711513030924\n      source: '1711513017096'\n      sourceHandle: source\n      target: '1711513030924'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513018759-1711513032459\n      source: '1711513018759'\n      sourceHandle: source\n      target: '1711513032459'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513020857-1711513034850\n      source: '1711513020857'\n      sourceHandle: source\n      target: '1711513034850'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513022516-1711513036356\n      source: '1711513022516'\n      sourceHandle: source\n      target: '1711513036356'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513024315-1711513037973\n      source: '1711513024315'\n      sourceHandle: source\n      target: '1711513037973'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513025732-1711513039350\n      source: '1711513025732'\n      sourceHandle: source\n      target: '1711513039350'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513027347-1711513041219\n      source: '1711513027347'\n      sourceHandle: source\n      target: '1711513041219'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: llm\n      id: 1711512802873-1711513940609\n      source: '1711512802873'\n      sourceHandle: '1711513927279'\n      target: '1711513940609'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711513940609-1711513967853\n      source: '1711513940609'\n      sourceHandle: source\n      target: '1711513967853'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513967853-1711513974643\n      source: '1711513967853'\n      sourceHandle: source\n      target: '1711513974643'\n      targetHandle: target\n      type: custom\n    nodes:\n    - data:\n        desc: ''\n        selected: true\n        title: Start\n        type: start\n        variables:\n        - label: Email\n          max_length: null\n          options: []\n          required: true\n          type: paragraph\n          variable: Input_Text\n        - label: What do you need to do? (Summarize / Reply / Write / Improve)\n          max_length: 48\n          options:\n          - Summarize\n          - 'Reply '\n          - Write a email\n          - 'Improve writings '\n          required: true\n          type: select\n          variable: user_request\n        - label: 'How do you want it to be polished? (Optional) '\n          max_length: 48\n          options:\n          - 'Imporve writing and clarity '\n          - Shorten\n          - 'Lengthen '\n          - 'Simplify '\n          - Rewrite in my voice\n          required: false\n          type: select\n          variable: how_polish\n      dragging: false\n      height: 141\n      id: '1711511281652'\n      position:\n        x: 79.5\n        y: 409.5\n      positionAbsolute:\n        x: 79.5\n        y: 409.5\n      selected: true\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        classes:\n        - id: '1711512811520'\n          name: Summarize\n        - id: '1711512812031'\n          name: Reply to emails\n        - id: '1711512812510'\n          name: Help me write the email\n        - id: '1711512813038'\n          name: Improve writings or polish\n        - id: '1711513927279'\n          name: Grammer check\n        desc: 'Classify users'' demands. '\n        instructions: ''\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        query_variable_selector:\n        - '1711511281652'\n        - user_request\n        selected: false\n        title: 'Question Classifier '\n        topics: []\n        type: question-classifier\n      dragging: false\n      height: 333\n      id: '1711512802873'\n      position:\n        x: 362.5\n        y: 409.5\n      positionAbsolute:\n        x: 362.5\n        y: 409.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        classes:\n        - id: '1711512846439'\n          name: 'Improve writing and clarity '\n        - id: '1711512847112'\n          name: 'Shorten '\n        - id: '1711512847641'\n          name: 'Lengthen '\n        - id: '1711512848120'\n          name: 'Simplify '\n        - id: '1711512848616'\n          name: Rewrite in my voice\n        desc: 'Improve writings. '\n        instructions: ''\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        query_variable_selector:\n        - '1711511281652'\n        - how_polish\n        selected: false\n        title: 'Question Classifier '\n        topics: []\n        type: question-classifier\n      dragging: false\n      height: 333\n      id: '1711512837494'\n      position:\n        x: 645.5\n        y: 409.5\n      positionAbsolute:\n        x: 645.5\n        y: 409.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Summary\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Summary the email for me. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512911454'\n      position:\n        x: 645.5\n        y: 1327.5\n      positionAbsolute:\n        x: 645.5\n        y: 1327.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Reply\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Rely the emails for me, in my own voice. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512914870'\n      position:\n        x: 645.5\n        y: 1518.5\n      positionAbsolute:\n        x: 645.5\n        y: 1518.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Turn idea into email\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Turn my idea into email. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512916516'\n      position:\n        x: 645.5\n        y: 1709.5\n      positionAbsolute:\n        x: 645.5\n        y: 1709.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: 'Improve the clarity. '\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: \"<Task> Imporve the clarity of the email for me. \\n<Email>{{#1711511281652.Input_Text#}}\\n\\\n            <Result>\"\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512924231'\n      position:\n        x: 928.5\n        y: 409.5\n      positionAbsolute:\n        x: 928.5\n        y: 409.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: 'Shorten. '\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Shorten the email for me. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512926020'\n      position:\n        x: 928.5\n        y: 600.5\n      positionAbsolute:\n        x: 928.5\n        y: 600.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: 'Lengthen '\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Lengthen the email for me. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512927569'\n      position:\n        x: 928.5\n        y: 791.5\n      positionAbsolute:\n        x: 928.5\n        y: 791.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Simplify\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Simplify the email for me. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512929190'\n      position:\n        x: 928.5\n        y: 982.5\n      positionAbsolute:\n        x: 928.5\n        y: 982.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Rewrite in my voice\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Rewrite the email for me. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512930700'\n      position:\n        x: 928.5\n        y: 1173.5\n      positionAbsolute:\n        x: 928.5\n        y: 1173.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512911454'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513015189'\n      position:\n        x: 928.5\n        y: 1327.5\n      positionAbsolute:\n        x: 928.5\n        y: 1327.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 2\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512914870'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513017096'\n      position:\n        x: 928.5\n        y: 1518.5\n      positionAbsolute:\n        x: 928.5\n        y: 1518.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 3\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512916516'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513018759'\n      position:\n        x: 928.5\n        y: 1709.5\n      positionAbsolute:\n        x: 928.5\n        y: 1709.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 4\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512924231'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513020857'\n      position:\n        x: 1211.5\n        y: 409.5\n      positionAbsolute:\n        x: 1211.5\n        y: 409.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 5\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512926020'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513022516'\n      position:\n        x: 1211.5\n        y: 600.5\n      positionAbsolute:\n        x: 1211.5\n        y: 600.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 6\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512927569'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513024315'\n      position:\n        x: 1211.5\n        y: 791.5\n      positionAbsolute:\n        x: 1211.5\n        y: 791.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 7\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512929190'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513025732'\n      position:\n        x: 1211.5\n        y: 982.5\n      positionAbsolute:\n        x: 1211.5\n        y: 982.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 8\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512930700'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513027347'\n      position:\n        x: 1211.5\n        y: 1173.5\n      positionAbsolute:\n        x: 1211.5\n        y: 1173.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512911454'\n          - text\n          variable: text\n        selected: false\n        title: End\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513029058'\n      position:\n        x: 1211.5\n        y: 1327.5\n      positionAbsolute:\n        x: 1211.5\n        y: 1327.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512914870'\n          - text\n          variable: text\n        selected: false\n        title: End 2\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513030924'\n      position:\n        x: 1211.5\n        y: 1518.5\n      positionAbsolute:\n        x: 1211.5\n        y: 1518.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512916516'\n          - text\n          variable: text\n        selected: false\n        title: End 3\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513032459'\n      position:\n        x: 1211.5\n        y: 1709.5\n      positionAbsolute:\n        x: 1211.5\n        y: 1709.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512924231'\n          - text\n          variable: text\n        selected: false\n        title: End 4\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513034850'\n      position:\n        x: 1494.5\n        y: 409.5\n      positionAbsolute:\n        x: 1494.5\n        y: 409.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512926020'\n          - text\n          variable: text\n        selected: false\n        title: End 5\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513036356'\n      position:\n        x: 1494.5\n        y: 600.5\n      positionAbsolute:\n        x: 1494.5\n        y: 600.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512927569'\n          - text\n          variable: text\n        selected: false\n        title: End 6\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513037973'\n      position:\n        x: 1494.5\n        y: 791.5\n      positionAbsolute:\n        x: 1494.5\n        y: 791.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512929190'\n          - text\n          variable: text\n        selected: false\n        title: End 7\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513039350'\n      position:\n        x: 1494.5\n        y: 982.5\n      positionAbsolute:\n        x: 1494.5\n        y: 982.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512930700'\n          - text\n          variable: text\n        selected: false\n        title: End 8\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513041219'\n      position:\n        x: 1494.5\n        y: 1173.5\n      positionAbsolute:\n        x: 1494.5\n        y: 1173.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Grammer Check\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: 'Please check grammer of my email and comment on the grammer. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711513940609'\n      position:\n        x: 645.5\n        y: 1900.5\n      positionAbsolute:\n        x: 645.5\n        y: 1900.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 9\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711513940609'\n          - text\n          variable: arg1\n      height: 53\n      id: '1711513967853'\n      position:\n        x: 928.5\n        y: 1900.5\n      positionAbsolute:\n        x: 928.5\n        y: 1900.5\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711513940609'\n          - text\n          variable: text\n        selected: false\n        title: End 9\n        type: end\n      height: 89\n      id: '1711513974643'\n      position:\n        x: 1211.5\n        y: 1900.5\n      positionAbsolute:\n        x: 1211.5\n        y: 1900.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    viewport:\n      x: 0\n      y: 0\n      zoom: 0.7\n",
+            "export_data": "app:\n  icon: \"\\U0001F916\"\n  icon_background: '#FFEAD5'\n  mode: workflow\n  name: 'Email Assistant Workflow '\nworkflow:\n  features:\n    file_upload:\n      image:\n        enabled: false\n        number_limits: 3\n        transfer_methods:\n        - local_file\n        - remote_url\n    opening_statement: ''\n    retriever_resource:\n      enabled: false\n    sensitive_word_avoidance:\n      enabled: false\n    speech_to_text:\n      enabled: false\n    suggested_questions: []\n    suggested_questions_after_answer:\n      enabled: false\n    text_to_speech:\n      enabled: false\n      language: ''\n      voice: ''\n  graph:\n    edges:\n    - data:\n        sourceType: start\n        targetType: question-classifier\n      id: 1711511281652-1711512802873\n      source: '1711511281652'\n      sourceHandle: source\n      target: '1711512802873'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: question-classifier\n      id: 1711512802873-1711512837494\n      source: '1711512802873'\n      sourceHandle: '1711512813038'\n      target: '1711512837494'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: llm\n      id: 1711512802873-1711512911454\n      source: '1711512802873'\n      sourceHandle: '1711512811520'\n      target: '1711512911454'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: llm\n      id: 1711512802873-1711512914870\n      source: '1711512802873'\n      sourceHandle: '1711512812031'\n      target: '1711512914870'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: llm\n      id: 1711512802873-1711512916516\n      source: '1711512802873'\n      sourceHandle: '1711512812510'\n      target: '1711512916516'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: llm\n      id: 1711512837494-1711512924231\n      source: '1711512837494'\n      sourceHandle: '1711512846439'\n      target: '1711512924231'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: llm\n      id: 1711512837494-1711512926020\n      source: '1711512837494'\n      sourceHandle: '1711512847112'\n      target: '1711512926020'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: llm\n      id: 1711512837494-1711512927569\n      source: '1711512837494'\n      sourceHandle: '1711512847641'\n      target: '1711512927569'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: llm\n      id: 1711512837494-1711512929190\n      source: '1711512837494'\n      sourceHandle: '1711512848120'\n      target: '1711512929190'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: llm\n      id: 1711512837494-1711512930700\n      source: '1711512837494'\n      sourceHandle: '1711512848616'\n      target: '1711512930700'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512911454-1711513015189\n      source: '1711512911454'\n      sourceHandle: source\n      target: '1711513015189'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512914870-1711513017096\n      source: '1711512914870'\n      sourceHandle: source\n      target: '1711513017096'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512916516-1711513018759\n      source: '1711512916516'\n      sourceHandle: source\n      target: '1711513018759'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512924231-1711513020857\n      source: '1711512924231'\n      sourceHandle: source\n      target: '1711513020857'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512926020-1711513022516\n      source: '1711512926020'\n      sourceHandle: source\n      target: '1711513022516'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512927569-1711513024315\n      source: '1711512927569'\n      sourceHandle: source\n      target: '1711513024315'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512929190-1711513025732\n      source: '1711512929190'\n      sourceHandle: source\n      target: '1711513025732'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711512930700-1711513027347\n      source: '1711512930700'\n      sourceHandle: source\n      target: '1711513027347'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513015189-1711513029058\n      source: '1711513015189'\n      sourceHandle: source\n      target: '1711513029058'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513017096-1711513030924\n      source: '1711513017096'\n      sourceHandle: source\n      target: '1711513030924'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513018759-1711513032459\n      source: '1711513018759'\n      sourceHandle: source\n      target: '1711513032459'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513020857-1711513034850\n      source: '1711513020857'\n      sourceHandle: source\n      target: '1711513034850'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513022516-1711513036356\n      source: '1711513022516'\n      sourceHandle: source\n      target: '1711513036356'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513024315-1711513037973\n      source: '1711513024315'\n      sourceHandle: source\n      target: '1711513037973'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513025732-1711513039350\n      source: '1711513025732'\n      sourceHandle: source\n      target: '1711513039350'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513027347-1711513041219\n      source: '1711513027347'\n      sourceHandle: source\n      target: '1711513041219'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: question-classifier\n        targetType: llm\n      id: 1711512802873-1711513940609\n      source: '1711512802873'\n      sourceHandle: '1711513927279'\n      target: '1711513940609'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: llm\n        targetType: template-transform\n      id: 1711513940609-1711513967853\n      source: '1711513940609'\n      sourceHandle: source\n      target: '1711513967853'\n      targetHandle: target\n      type: custom\n    - data:\n        sourceType: template-transform\n        targetType: end\n      id: 1711513967853-1711513974643\n      source: '1711513967853'\n      sourceHandle: source\n      target: '1711513974643'\n      targetHandle: target\n      type: custom\n    nodes:\n    - data:\n        desc: ''\n        selected: true\n        title: Start\n        type: start\n        variables:\n        - label: Email\n          max_length: null\n          options: []\n          required: true\n          type: paragraph\n          variable: Input_Text\n        - label: What do you need to do? (Summarize / Reply / Write / Improve)\n          max_length: 48\n          options:\n          - Summarize\n          - 'Reply '\n          - Write a email\n          - 'Improve writings '\n          required: true\n          type: select\n          variable: user_request\n        - label: 'How do you want it to be polished? (Optional) '\n          max_length: 48\n          options:\n          - 'Imporve writing and clarity '\n          - Shorten\n          - 'Lengthen '\n          - 'Simplify '\n          - Rewrite in my voice\n          required: false\n          type: select\n          variable: how_polish\n      dragging: false\n      height: 141\n      id: '1711511281652'\n      position:\n        x: 79.5\n        y: 409.5\n      positionAbsolute:\n        x: 79.5\n        y: 409.5\n      selected: true\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        classes:\n        - id: '1711512811520'\n          name: Summarize\n        - id: '1711512812031'\n          name: Reply to emails\n        - id: '1711512812510'\n          name: Help me write the email\n        - id: '1711512813038'\n          name: Improve writings or polish\n        - id: '1711513927279'\n          name: Grammar check\n        desc: 'Classify users'' demands. '\n        instructions: ''\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        query_variable_selector:\n        - '1711511281652'\n        - user_request\n        selected: false\n        title: 'Question Classifier '\n        topics: []\n        type: question-classifier\n      dragging: false\n      height: 333\n      id: '1711512802873'\n      position:\n        x: 362.5\n        y: 409.5\n      positionAbsolute:\n        x: 362.5\n        y: 409.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        classes:\n        - id: '1711512846439'\n          name: 'Improve writing and clarity '\n        - id: '1711512847112'\n          name: 'Shorten '\n        - id: '1711512847641'\n          name: 'Lengthen '\n        - id: '1711512848120'\n          name: 'Simplify '\n        - id: '1711512848616'\n          name: Rewrite in my voice\n        desc: 'Improve writings. '\n        instructions: ''\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        query_variable_selector:\n        - '1711511281652'\n        - how_polish\n        selected: false\n        title: 'Question Classifier '\n        topics: []\n        type: question-classifier\n      dragging: false\n      height: 333\n      id: '1711512837494'\n      position:\n        x: 645.5\n        y: 409.5\n      positionAbsolute:\n        x: 645.5\n        y: 409.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Summary\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Summary the email for me. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512911454'\n      position:\n        x: 645.5\n        y: 1327.5\n      positionAbsolute:\n        x: 645.5\n        y: 1327.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Reply\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Rely the emails for me, in my own voice. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512914870'\n      position:\n        x: 645.5\n        y: 1518.5\n      positionAbsolute:\n        x: 645.5\n        y: 1518.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Turn idea into email\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Turn my idea into email. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512916516'\n      position:\n        x: 645.5\n        y: 1709.5\n      positionAbsolute:\n        x: 645.5\n        y: 1709.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: 'Improve the clarity. '\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: \"<Task> Imporve the clarity of the email for me. \\n<Email>{{#1711511281652.Input_Text#}}\\n\\\n            <Result>\"\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512924231'\n      position:\n        x: 928.5\n        y: 409.5\n      positionAbsolute:\n        x: 928.5\n        y: 409.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: 'Shorten. '\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Shorten the email for me. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512926020'\n      position:\n        x: 928.5\n        y: 600.5\n      positionAbsolute:\n        x: 928.5\n        y: 600.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: 'Lengthen '\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Lengthen the email for me. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512927569'\n      position:\n        x: 928.5\n        y: 791.5\n      positionAbsolute:\n        x: 928.5\n        y: 791.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Simplify\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Simplify the email for me. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512929190'\n      position:\n        x: 928.5\n        y: 982.5\n      positionAbsolute:\n        x: 928.5\n        y: 982.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Rewrite in my voice\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: '<Task> Rewrite the email for me. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711512930700'\n      position:\n        x: 928.5\n        y: 1173.5\n      positionAbsolute:\n        x: 928.5\n        y: 1173.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512911454'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513015189'\n      position:\n        x: 928.5\n        y: 1327.5\n      positionAbsolute:\n        x: 928.5\n        y: 1327.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 2\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512914870'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513017096'\n      position:\n        x: 928.5\n        y: 1518.5\n      positionAbsolute:\n        x: 928.5\n        y: 1518.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 3\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512916516'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513018759'\n      position:\n        x: 928.5\n        y: 1709.5\n      positionAbsolute:\n        x: 928.5\n        y: 1709.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 4\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512924231'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513020857'\n      position:\n        x: 1211.5\n        y: 409.5\n      positionAbsolute:\n        x: 1211.5\n        y: 409.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 5\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512926020'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513022516'\n      position:\n        x: 1211.5\n        y: 600.5\n      positionAbsolute:\n        x: 1211.5\n        y: 600.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 6\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512927569'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513024315'\n      position:\n        x: 1211.5\n        y: 791.5\n      positionAbsolute:\n        x: 1211.5\n        y: 791.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 7\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512929190'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513025732'\n      position:\n        x: 1211.5\n        y: 982.5\n      positionAbsolute:\n        x: 1211.5\n        y: 982.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 8\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711512930700'\n          - text\n          variable: arg1\n      dragging: false\n      height: 53\n      id: '1711513027347'\n      position:\n        x: 1211.5\n        y: 1173.5\n      positionAbsolute:\n        x: 1211.5\n        y: 1173.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512911454'\n          - text\n          variable: text\n        selected: false\n        title: End\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513029058'\n      position:\n        x: 1211.5\n        y: 1327.5\n      positionAbsolute:\n        x: 1211.5\n        y: 1327.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512914870'\n          - text\n          variable: text\n        selected: false\n        title: End 2\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513030924'\n      position:\n        x: 1211.5\n        y: 1518.5\n      positionAbsolute:\n        x: 1211.5\n        y: 1518.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512916516'\n          - text\n          variable: text\n        selected: false\n        title: End 3\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513032459'\n      position:\n        x: 1211.5\n        y: 1709.5\n      positionAbsolute:\n        x: 1211.5\n        y: 1709.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512924231'\n          - text\n          variable: text\n        selected: false\n        title: End 4\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513034850'\n      position:\n        x: 1494.5\n        y: 409.5\n      positionAbsolute:\n        x: 1494.5\n        y: 409.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512926020'\n          - text\n          variable: text\n        selected: false\n        title: End 5\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513036356'\n      position:\n        x: 1494.5\n        y: 600.5\n      positionAbsolute:\n        x: 1494.5\n        y: 600.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512927569'\n          - text\n          variable: text\n        selected: false\n        title: End 6\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513037973'\n      position:\n        x: 1494.5\n        y: 791.5\n      positionAbsolute:\n        x: 1494.5\n        y: 791.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512929190'\n          - text\n          variable: text\n        selected: false\n        title: End 7\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513039350'\n      position:\n        x: 1494.5\n        y: 982.5\n      positionAbsolute:\n        x: 1494.5\n        y: 982.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711512930700'\n          - text\n          variable: text\n        selected: false\n        title: End 8\n        type: end\n      dragging: false\n      height: 89\n      id: '1711513041219'\n      position:\n        x: 1494.5\n        y: 1173.5\n      positionAbsolute:\n        x: 1494.5\n        y: 1173.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        context:\n          enabled: false\n          variable_selector: []\n        desc: Grammar Check\n        model:\n          completion_params:\n            frequency_penalty: 0\n            max_tokens: 512\n            presence_penalty: 0\n            temperature: 0.7\n            top_p: 1\n          mode: chat\n          name: gpt-3.5-turbo\n          provider: openai\n        prompt_template:\n        - role: system\n          text: 'Please check grammar of my email and comment on the grammar. <Email>{{#1711511281652.Input_Text#}}\n\n            <Result>'\n        selected: false\n        title: LLM\n        type: llm\n        variables: []\n        vision:\n          enabled: false\n      dragging: false\n      height: 127\n      id: '1711513940609'\n      position:\n        x: 645.5\n        y: 1900.5\n      positionAbsolute:\n        x: 645.5\n        y: 1900.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        selected: false\n        template: '{{ arg1 }}'\n        title: Template 9\n        type: template-transform\n        variables:\n        - value_selector:\n          - '1711513940609'\n          - text\n          variable: arg1\n      height: 53\n      id: '1711513967853'\n      position:\n        x: 928.5\n        y: 1900.5\n      positionAbsolute:\n        x: 928.5\n        y: 1900.5\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    - data:\n        desc: ''\n        outputs:\n        - value_selector:\n          - '1711513940609'\n          - text\n          variable: text\n        selected: false\n        title: End 9\n        type: end\n      height: 89\n      id: '1711513974643'\n      position:\n        x: 1211.5\n        y: 1900.5\n      positionAbsolute:\n        x: 1211.5\n        y: 1900.5\n      selected: false\n      sourcePosition: right\n      targetPosition: left\n      type: custom\n      width: 243\n    viewport:\n      x: 0\n      y: 0\n      zoom: 0.7\n",
             "icon": "🤖",
             "icon_background": "#FFEAD5",
             "id": "dd5b6353-ae9b-4bce-be6a-a681a12cf709",
diff --git a/api/controllers/console/admin.py b/api/controllers/console/admin.py
index a4ceec2662..f78ea9b288 100644
--- a/api/controllers/console/admin.py
+++ b/api/controllers/console/admin.py
@@ -60,23 +60,15 @@ class InsertExploreAppListApi(Resource):
 
         site = app.site
         if not site:
-            desc = args["desc"] if args["desc"] else ""
-            copy_right = args["copyright"] if args["copyright"] else ""
-            privacy_policy = args["privacy_policy"] if args["privacy_policy"] else ""
-            custom_disclaimer = args["custom_disclaimer"] if args["custom_disclaimer"] else ""
+            desc = args["desc"] or ""
+            copy_right = args["copyright"] or ""
+            privacy_policy = args["privacy_policy"] or ""
+            custom_disclaimer = args["custom_disclaimer"] or ""
         else:
-            desc = site.description if site.description else args["desc"] if args["desc"] else ""
-            copy_right = site.copyright if site.copyright else args["copyright"] if args["copyright"] else ""
-            privacy_policy = (
-                site.privacy_policy if site.privacy_policy else args["privacy_policy"] if args["privacy_policy"] else ""
-            )
-            custom_disclaimer = (
-                site.custom_disclaimer
-                if site.custom_disclaimer
-                else args["custom_disclaimer"]
-                if args["custom_disclaimer"]
-                else ""
-            )
+            desc = site.description or args["desc"] or ""
+            copy_right = site.copyright or args["copyright"] or ""
+            privacy_policy = site.privacy_policy or args["privacy_policy"] or ""
+            custom_disclaimer = site.custom_disclaimer or args["custom_disclaimer"] or ""
 
         recommended_app = RecommendedApp.query.filter(RecommendedApp.app_id == args["app_id"]).first()
 
diff --git a/api/controllers/console/apikey.py b/api/controllers/console/apikey.py
index 3f5e1adca2..35ac42a14c 100644
--- a/api/controllers/console/apikey.py
+++ b/api/controllers/console/apikey.py
@@ -57,7 +57,7 @@ class BaseApiKeyListResource(Resource):
     def post(self, resource_id):
         resource_id = str(resource_id)
         _get_resource(resource_id, current_user.current_tenant_id, self.resource_model)
-        if not current_user.is_admin_or_owner:
+        if not current_user.is_editor:
             raise Forbidden()
 
         current_key_count = (
diff --git a/api/controllers/console/app/audio.py b/api/controllers/console/app/audio.py
index 437a6a7b38..c1ef05a488 100644
--- a/api/controllers/console/app/audio.py
+++ b/api/controllers/console/app/audio.py
@@ -94,19 +94,15 @@ class ChatMessageTextApi(Resource):
             message_id = args.get("message_id", None)
             text = args.get("text", None)
             if (
-                app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]
+                app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}
                 and app_model.workflow
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
-                voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
+                voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
-                    voice = (
-                        args.get("voice")
-                        if args.get("voice")
-                        else app_model.app_model_config.text_to_speech_dict.get("voice")
-                    )
+                    voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None
             response = AudioService.transcript_tts(app_model=app_model, text=text, message_id=message_id, voice=voice)
diff --git a/api/controllers/console/app/conversation.py b/api/controllers/console/app/conversation.py
index 753a6be20c..df7bd352af 100644
--- a/api/controllers/console/app/conversation.py
+++ b/api/controllers/console/app/conversation.py
@@ -20,7 +20,7 @@ from fields.conversation_fields import (
     conversation_pagination_fields,
     conversation_with_summary_pagination_fields,
 )
-from libs.helper import datetime_string
+from libs.helper import DatetimeString
 from libs.login import login_required
 from models.model import AppMode, Conversation, EndUser, Message, MessageAnnotation
 
@@ -36,8 +36,8 @@ class CompletionConversationApi(Resource):
             raise Forbidden()
         parser = reqparse.RequestParser()
         parser.add_argument("keyword", type=str, location="args")
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         parser.add_argument(
             "annotation_status", type=str, choices=["annotated", "not_annotated", "all"], default="all", location="args"
         )
@@ -143,8 +143,8 @@ class ChatConversationApi(Resource):
             raise Forbidden()
         parser = reqparse.RequestParser()
         parser.add_argument("keyword", type=str, location="args")
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         parser.add_argument(
             "annotation_status", type=str, choices=["annotated", "not_annotated", "all"], default="all", location="args"
         )
@@ -201,7 +201,11 @@ class ChatConversationApi(Resource):
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            query = query.where(Conversation.created_at >= start_datetime_utc)
+            match args["sort_by"]:
+                case "updated_at" | "-updated_at":
+                    query = query.where(Conversation.updated_at >= start_datetime_utc)
+                case "created_at" | "-created_at" | _:
+                    query = query.where(Conversation.created_at >= start_datetime_utc)
 
         if args["end"]:
             end_datetime = datetime.strptime(args["end"], "%Y-%m-%d %H:%M")
@@ -210,7 +214,11 @@ class ChatConversationApi(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            query = query.where(Conversation.created_at < end_datetime_utc)
+            match args["sort_by"]:
+                case "updated_at" | "-updated_at":
+                    query = query.where(Conversation.updated_at <= end_datetime_utc)
+                case "created_at" | "-created_at" | _:
+                    query = query.where(Conversation.created_at <= end_datetime_utc)
 
         if args["annotation_status"] == "annotated":
             query = query.options(joinedload(Conversation.message_annotations)).join(
diff --git a/api/controllers/console/app/statistic.py b/api/controllers/console/app/statistic.py
index 81826a20d0..3ef442812d 100644
--- a/api/controllers/console/app/statistic.py
+++ b/api/controllers/console/app/statistic.py
@@ -11,7 +11,7 @@ from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from extensions.ext_database import db
-from libs.helper import datetime_string
+from libs.helper import DatetimeString
 from libs.login import login_required
 from models.model import AppMode
 
@@ -25,14 +25,17 @@ class DailyMessageStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """
-        SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(*) AS message_count
-            FROM messages where app_id = :app_id 
-        """
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    COUNT(*) AS message_count
+FROM
+    messages
+WHERE
+    app_id = :app_id"""
         arg_dict = {"tz": account.timezone, "app_id": app_model.id}
 
         timezone = pytz.timezone(account.timezone)
@@ -45,7 +48,7 @@ class DailyMessageStatistic(Resource):
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at >= :start"
+            sql_query += " AND created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -55,10 +58,10 @@ class DailyMessageStatistic(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at < :end"
+            sql_query += " AND created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
-        sql_query += " GROUP BY date order by date"
+        sql_query += " GROUP BY date ORDER BY date"
 
         response_data = []
 
@@ -79,14 +82,17 @@ class DailyConversationStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """
-        SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(distinct messages.conversation_id) AS conversation_count
-            FROM messages where app_id = :app_id 
-        """
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    COUNT(DISTINCT messages.conversation_id) AS conversation_count
+FROM
+    messages
+WHERE
+    app_id = :app_id"""
         arg_dict = {"tz": account.timezone, "app_id": app_model.id}
 
         timezone = pytz.timezone(account.timezone)
@@ -99,7 +105,7 @@ class DailyConversationStatistic(Resource):
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at >= :start"
+            sql_query += " AND created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -109,10 +115,10 @@ class DailyConversationStatistic(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at < :end"
+            sql_query += " AND created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
-        sql_query += " GROUP BY date order by date"
+        sql_query += " GROUP BY date ORDER BY date"
 
         response_data = []
 
@@ -133,14 +139,17 @@ class DailyTerminalsStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """
-                SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(distinct messages.from_end_user_id) AS terminal_count
-                    FROM messages where app_id = :app_id 
-                """
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    COUNT(DISTINCT messages.from_end_user_id) AS terminal_count
+FROM
+    messages
+WHERE
+    app_id = :app_id"""
         arg_dict = {"tz": account.timezone, "app_id": app_model.id}
 
         timezone = pytz.timezone(account.timezone)
@@ -153,7 +162,7 @@ class DailyTerminalsStatistic(Resource):
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at >= :start"
+            sql_query += " AND created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -163,10 +172,10 @@ class DailyTerminalsStatistic(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at < :end"
+            sql_query += " AND created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
-        sql_query += " GROUP BY date order by date"
+        sql_query += " GROUP BY date ORDER BY date"
 
         response_data = []
 
@@ -187,16 +196,18 @@ class DailyTokenCostStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """
-                SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
-                    (sum(messages.message_tokens) + sum(messages.answer_tokens)) as token_count,
-                    sum(total_price) as total_price
-                    FROM messages where app_id = :app_id 
-                """
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    (SUM(messages.message_tokens) + SUM(messages.answer_tokens)) AS token_count,
+    SUM(total_price) AS total_price
+FROM
+    messages
+WHERE
+    app_id = :app_id"""
         arg_dict = {"tz": account.timezone, "app_id": app_model.id}
 
         timezone = pytz.timezone(account.timezone)
@@ -209,7 +220,7 @@ class DailyTokenCostStatistic(Resource):
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at >= :start"
+            sql_query += " AND created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -219,10 +230,10 @@ class DailyTokenCostStatistic(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at < :end"
+            sql_query += " AND created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
-        sql_query += " GROUP BY date order by date"
+        sql_query += " GROUP BY date ORDER BY date"
 
         response_data = []
 
@@ -245,16 +256,26 @@ class AverageSessionInteractionStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """SELECT date(DATE_TRUNC('day', c.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
-AVG(subquery.message_count) AS interactions
-FROM (SELECT m.conversation_id, COUNT(m.id) AS message_count
-    FROM conversations c
-    JOIN messages m ON c.id = m.conversation_id
-    WHERE c.override_model_configs IS NULL AND c.app_id = :app_id"""
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', c.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    AVG(subquery.message_count) AS interactions
+FROM
+    (
+        SELECT
+            m.conversation_id,
+            COUNT(m.id) AS message_count
+        FROM
+            conversations c
+        JOIN
+            messages m
+            ON c.id = m.conversation_id
+        WHERE
+            c.override_model_configs IS NULL
+            AND c.app_id = :app_id"""
         arg_dict = {"tz": account.timezone, "app_id": app_model.id}
 
         timezone = pytz.timezone(account.timezone)
@@ -267,7 +288,7 @@ FROM (SELECT m.conversation_id, COUNT(m.id) AS message_count
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and c.created_at >= :start"
+            sql_query += " AND c.created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -277,14 +298,19 @@ FROM (SELECT m.conversation_id, COUNT(m.id) AS message_count
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and c.created_at < :end"
+            sql_query += " AND c.created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
         sql_query += """
-        GROUP BY m.conversation_id) subquery
-LEFT JOIN conversations c on c.id=subquery.conversation_id
-GROUP BY date
-ORDER BY date"""
+        GROUP BY m.conversation_id
+    ) subquery
+LEFT JOIN
+    conversations c
+    ON c.id = subquery.conversation_id
+GROUP BY
+    date
+ORDER BY
+    date"""
 
         response_data = []
 
@@ -307,17 +333,21 @@ class UserSatisfactionRateStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """
-                        SELECT date(DATE_TRUNC('day', m.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
-                            COUNT(m.id) as message_count, COUNT(mf.id) as feedback_count 
-                            FROM messages m
-                            LEFT JOIN message_feedbacks mf on mf.message_id=m.id and mf.rating='like'
-                            WHERE m.app_id = :app_id 
-                        """
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', m.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    COUNT(m.id) AS message_count,
+    COUNT(mf.id) AS feedback_count
+FROM
+    messages m
+LEFT JOIN
+    message_feedbacks mf
+    ON mf.message_id=m.id AND mf.rating='like'
+WHERE
+    m.app_id = :app_id"""
         arg_dict = {"tz": account.timezone, "app_id": app_model.id}
 
         timezone = pytz.timezone(account.timezone)
@@ -330,7 +360,7 @@ class UserSatisfactionRateStatistic(Resource):
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and m.created_at >= :start"
+            sql_query += " AND m.created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -340,10 +370,10 @@ class UserSatisfactionRateStatistic(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and m.created_at < :end"
+            sql_query += " AND m.created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
-        sql_query += " GROUP BY date order by date"
+        sql_query += " GROUP BY date ORDER BY date"
 
         response_data = []
 
@@ -369,16 +399,17 @@ class AverageResponseTimeStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """
-                SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
-                    AVG(provider_response_latency) as latency
-                    FROM messages
-                    WHERE app_id = :app_id
-                """
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    AVG(provider_response_latency) AS latency
+FROM
+    messages
+WHERE
+    app_id = :app_id"""
         arg_dict = {"tz": account.timezone, "app_id": app_model.id}
 
         timezone = pytz.timezone(account.timezone)
@@ -391,7 +422,7 @@ class AverageResponseTimeStatistic(Resource):
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at >= :start"
+            sql_query += " AND created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -401,10 +432,10 @@ class AverageResponseTimeStatistic(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at < :end"
+            sql_query += " AND created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
-        sql_query += " GROUP BY date order by date"
+        sql_query += " GROUP BY date ORDER BY date"
 
         response_data = []
 
@@ -425,17 +456,20 @@ class TokensPerSecondStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
-    CASE 
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    CASE
         WHEN SUM(provider_response_latency) = 0 THEN 0
         ELSE (SUM(answer_tokens) / SUM(provider_response_latency))
     END as tokens_per_second
-FROM messages
-WHERE app_id = :app_id"""
+FROM
+    messages
+WHERE
+    app_id = :app_id"""
         arg_dict = {"tz": account.timezone, "app_id": app_model.id}
 
         timezone = pytz.timezone(account.timezone)
@@ -448,7 +482,7 @@ WHERE app_id = :app_id"""
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at >= :start"
+            sql_query += " AND created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -458,10 +492,10 @@ WHERE app_id = :app_id"""
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at < :end"
+            sql_query += " AND created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
-        sql_query += " GROUP BY date order by date"
+        sql_query += " GROUP BY date ORDER BY date"
 
         response_data = []
 
diff --git a/api/controllers/console/app/workflow.py b/api/controllers/console/app/workflow.py
index e44820f634..b488deb89d 100644
--- a/api/controllers/console/app/workflow.py
+++ b/api/controllers/console/app/workflow.py
@@ -465,6 +465,6 @@ api.add_resource(
 api.add_resource(PublishedWorkflowApi, "/apps/<uuid:app_id>/workflows/publish")
 api.add_resource(DefaultBlockConfigsApi, "/apps/<uuid:app_id>/workflows/default-workflow-block-configs")
 api.add_resource(
-    DefaultBlockConfigApi, "/apps/<uuid:app_id>/workflows/default-workflow-block-configs" "/<string:block_type>"
+    DefaultBlockConfigApi, "/apps/<uuid:app_id>/workflows/default-workflow-block-configs/<string:block_type>"
 )
 api.add_resource(ConvertToWorkflowApi, "/apps/<uuid:app_id>/convert-to-workflow")
diff --git a/api/controllers/console/app/workflow_statistic.py b/api/controllers/console/app/workflow_statistic.py
index db2f683589..c7e54f2be0 100644
--- a/api/controllers/console/app/workflow_statistic.py
+++ b/api/controllers/console/app/workflow_statistic.py
@@ -11,7 +11,7 @@ from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from extensions.ext_database import db
-from libs.helper import datetime_string
+from libs.helper import DatetimeString
 from libs.login import login_required
 from models.model import AppMode
 from models.workflow import WorkflowRunTriggeredFrom
@@ -26,16 +26,18 @@ class WorkflowDailyRunsStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """
-        SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(id) AS runs
-            FROM workflow_runs 
-            WHERE app_id = :app_id 
-                AND triggered_from = :triggered_from
-        """
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    COUNT(id) AS runs
+FROM
+    workflow_runs
+WHERE
+    app_id = :app_id
+    AND triggered_from = :triggered_from"""
         arg_dict = {
             "tz": account.timezone,
             "app_id": app_model.id,
@@ -52,7 +54,7 @@ class WorkflowDailyRunsStatistic(Resource):
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at >= :start"
+            sql_query += " AND created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -62,10 +64,10 @@ class WorkflowDailyRunsStatistic(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at < :end"
+            sql_query += " AND created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
-        sql_query += " GROUP BY date order by date"
+        sql_query += " GROUP BY date ORDER BY date"
 
         response_data = []
 
@@ -86,16 +88,18 @@ class WorkflowDailyTerminalsStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """
-                SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(distinct workflow_runs.created_by) AS terminal_count
-                    FROM workflow_runs 
-                    WHERE app_id = :app_id 
-                        AND triggered_from = :triggered_from
-                """
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    COUNT(DISTINCT workflow_runs.created_by) AS terminal_count
+FROM
+    workflow_runs
+WHERE
+    app_id = :app_id
+    AND triggered_from = :triggered_from"""
         arg_dict = {
             "tz": account.timezone,
             "app_id": app_model.id,
@@ -112,7 +116,7 @@ class WorkflowDailyTerminalsStatistic(Resource):
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at >= :start"
+            sql_query += " AND created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -122,10 +126,10 @@ class WorkflowDailyTerminalsStatistic(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at < :end"
+            sql_query += " AND created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
-        sql_query += " GROUP BY date order by date"
+        sql_query += " GROUP BY date ORDER BY date"
 
         response_data = []
 
@@ -146,18 +150,18 @@ class WorkflowDailyTokenCostStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """
-                SELECT 
-                    date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
-                    SUM(workflow_runs.total_tokens) as token_count
-                FROM workflow_runs 
-                WHERE app_id = :app_id 
-                    AND triggered_from = :triggered_from
-                """
+        sql_query = """SELECT
+    DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+    SUM(workflow_runs.total_tokens) AS token_count
+FROM
+    workflow_runs
+WHERE
+    app_id = :app_id
+    AND triggered_from = :triggered_from"""
         arg_dict = {
             "tz": account.timezone,
             "app_id": app_model.id,
@@ -174,7 +178,7 @@ class WorkflowDailyTokenCostStatistic(Resource):
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at >= :start"
+            sql_query += " AND created_at >= :start"
             arg_dict["start"] = start_datetime_utc
 
         if args["end"]:
@@ -184,10 +188,10 @@ class WorkflowDailyTokenCostStatistic(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query += " and created_at < :end"
+            sql_query += " AND created_at < :end"
             arg_dict["end"] = end_datetime_utc
 
-        sql_query += " GROUP BY date order by date"
+        sql_query += " GROUP BY date ORDER BY date"
 
         response_data = []
 
@@ -213,27 +217,31 @@ class WorkflowAverageAppInteractionStatistic(Resource):
         account = current_user
 
         parser = reqparse.RequestParser()
-        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
-        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("start", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """
-            SELECT 
-                AVG(sub.interactions) as interactions,
-                sub.date
-            FROM
-                (SELECT 
-                    date(DATE_TRUNC('day', c.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
-                    c.created_by,
-                    COUNT(c.id) AS interactions
-                FROM workflow_runs c
-                WHERE c.app_id = :app_id
-                    AND c.triggered_from = :triggered_from
-                    {{start}}
-                    {{end}}
-                GROUP BY date, c.created_by) sub
-            GROUP BY sub.date
-            """
+        sql_query = """SELECT
+    AVG(sub.interactions) AS interactions,
+    sub.date
+FROM
+    (
+        SELECT
+            DATE(DATE_TRUNC('day', c.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
+            c.created_by,
+            COUNT(c.id) AS interactions
+        FROM
+            workflow_runs c
+        WHERE
+            c.app_id = :app_id
+            AND c.triggered_from = :triggered_from
+            {{start}}
+            {{end}}
+        GROUP BY
+            date, c.created_by
+    ) sub
+GROUP BY
+    sub.date"""
         arg_dict = {
             "tz": account.timezone,
             "app_id": app_model.id,
@@ -262,7 +270,7 @@ class WorkflowAverageAppInteractionStatistic(Resource):
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
 
-            sql_query = sql_query.replace("{{end}}", " and c.created_at < :end")
+            sql_query = sql_query.replace("{{end}}", " AND c.created_at < :end")
             arg_dict["end"] = end_datetime_utc
         else:
             sql_query = sql_query.replace("{{end}}", "")
diff --git a/api/controllers/console/auth/activate.py b/api/controllers/console/auth/activate.py
index 8ba6b53e7e..f3198dfc1d 100644
--- a/api/controllers/console/auth/activate.py
+++ b/api/controllers/console/auth/activate.py
@@ -8,7 +8,7 @@ from constants.languages import supported_language
 from controllers.console import api
 from controllers.console.error import AlreadyActivateError
 from extensions.ext_database import db
-from libs.helper import email, str_len, timezone
+from libs.helper import StrLen, email, timezone
 from libs.password import hash_password, valid_password
 from models.account import AccountStatus
 from services.account_service import RegisterService
@@ -37,7 +37,7 @@ class ActivateApi(Resource):
         parser.add_argument("workspace_id", type=str, required=False, nullable=True, location="json")
         parser.add_argument("email", type=email, required=False, nullable=True, location="json")
         parser.add_argument("token", type=str, required=True, nullable=False, location="json")
-        parser.add_argument("name", type=str_len(30), required=True, nullable=False, location="json")
+        parser.add_argument("name", type=StrLen(30), required=True, nullable=False, location="json")
         parser.add_argument("password", type=valid_password, required=True, nullable=False, location="json")
         parser.add_argument(
             "interface_language", type=supported_language, required=True, nullable=False, location="json"
diff --git a/api/controllers/console/auth/oauth.py b/api/controllers/console/auth/oauth.py
index ae1b49f3ec..ad0c0580ae 100644
--- a/api/controllers/console/auth/oauth.py
+++ b/api/controllers/console/auth/oauth.py
@@ -71,7 +71,7 @@ class OAuthCallback(Resource):
 
         account = _generate_account(provider, user_info)
         # Check account status
-        if account.status == AccountStatus.BANNED.value or account.status == AccountStatus.CLOSED.value:
+        if account.status in {AccountStatus.BANNED.value, AccountStatus.CLOSED.value}:
             return {"error": "Account is banned or closed."}, 403
 
         if account.status == AccountStatus.PENDING.value:
@@ -101,7 +101,7 @@ def _generate_account(provider: str, user_info: OAuthUserInfo):
 
     if not account:
         # Create account
-        account_name = user_info.name if user_info.name else "Dify"
+        account_name = user_info.name or "Dify"
         account = RegisterService.register(
             email=user_info.email, name=account_name, password=None, open_id=user_info.id, provider=provider
         )
diff --git a/api/controllers/console/datasets/datasets.py b/api/controllers/console/datasets/datasets.py
index a2d7dc89cd..b601014554 100644
--- a/api/controllers/console/datasets/datasets.py
+++ b/api/controllers/console/datasets/datasets.py
@@ -18,7 +18,7 @@ from core.model_runtime.entities.model_entities import ModelType
 from core.provider_manager import ProviderManager
 from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.extractor.entity.extract_setting import ExtractSetting
-from core.rag.retrieval.retrival_methods import RetrievalMethod
+from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from extensions.ext_database import db
 from fields.app_fields import related_app_list
 from fields.dataset_fields import dataset_detail_fields, dataset_query_detail_fields
@@ -422,7 +422,7 @@ class DatasetIndexingEstimateApi(Resource):
             )
         except LLMBadRequestError:
             raise ProviderNotInitializeError(
-                "No Embedding Model available. Please configure a valid provider " "in the Settings -> Model Provider."
+                "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
@@ -573,12 +573,7 @@ class DatasetApiBaseUrlApi(Resource):
     @login_required
     @account_initialization_required
     def get(self):
-        return {
-            "api_base_url": (
-                dify_config.SERVICE_API_URL if dify_config.SERVICE_API_URL else request.host_url.rstrip("/")
-            )
-            + "/v1"
-        }
+        return {"api_base_url": (dify_config.SERVICE_API_URL or request.host_url.rstrip("/")) + "/v1"}
 
 
 class DatasetRetrievalSettingApi(Resource):
diff --git a/api/controllers/console/datasets/datasets_document.py b/api/controllers/console/datasets/datasets_document.py
index 6bc29a8643..829ef11e52 100644
--- a/api/controllers/console/datasets/datasets_document.py
+++ b/api/controllers/console/datasets/datasets_document.py
@@ -302,6 +302,8 @@ class DatasetInitApi(Resource):
             "doc_language", type=str, default="English", required=False, nullable=False, location="json"
         )
         parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
+        parser.add_argument("embedding_model", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("embedding_model_provider", type=str, required=False, nullable=True, location="json")
         args = parser.parse_args()
 
         # The role of the current user in the ta table must be admin, owner, or editor, or dataset_operator
@@ -309,6 +311,8 @@ class DatasetInitApi(Resource):
             raise Forbidden()
 
         if args["indexing_technique"] == "high_quality":
+            if args["embedding_model"] is None or args["embedding_model_provider"] is None:
+                raise ValueError("embedding model and embedding model provider are required for high quality indexing.")
             try:
                 model_manager = ModelManager()
                 model_manager.get_default_model_instance(
@@ -350,7 +354,7 @@ class DocumentIndexingEstimateApi(DocumentResource):
         document_id = str(document_id)
         document = self.get_document(dataset_id, document_id)
 
-        if document.indexing_status in ["completed", "error"]:
+        if document.indexing_status in {"completed", "error"}:
             raise DocumentAlreadyFinishedError()
 
         data_process_rule = document.dataset_process_rule
@@ -417,7 +421,7 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
         info_list = []
         extract_settings = []
         for document in documents:
-            if document.indexing_status in ["completed", "error"]:
+            if document.indexing_status in {"completed", "error"}:
                 raise DocumentAlreadyFinishedError()
             data_source_info = document.data_source_info_dict
             # format document files info
@@ -661,7 +665,7 @@ class DocumentProcessingApi(DocumentResource):
             db.session.commit()
 
         elif action == "resume":
-            if document.indexing_status not in ["paused", "error"]:
+            if document.indexing_status not in {"paused", "error"}:
                 raise InvalidActionError("Document not in paused or error state.")
 
             document.paused_by = None
diff --git a/api/controllers/console/error.py b/api/controllers/console/error.py
index 1c70ea6c59..870e547728 100644
--- a/api/controllers/console/error.py
+++ b/api/controllers/console/error.py
@@ -18,9 +18,7 @@ class NotSetupError(BaseHTTPException):
 
 class NotInitValidateError(BaseHTTPException):
     error_code = "not_init_validated"
-    description = (
-        "Init validation has not been completed yet. " "Please proceed with the init validation process first."
-    )
+    description = "Init validation has not been completed yet. Please proceed with the init validation process first."
     code = 401
 
 
diff --git a/api/controllers/console/explore/audio.py b/api/controllers/console/explore/audio.py
index 71cb060ecc..9690677f61 100644
--- a/api/controllers/console/explore/audio.py
+++ b/api/controllers/console/explore/audio.py
@@ -81,19 +81,15 @@ class ChatTextApi(InstalledAppResource):
             message_id = args.get("message_id", None)
             text = args.get("text", None)
             if (
-                app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]
+                app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}
                 and app_model.workflow
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
-                voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
+                voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
-                    voice = (
-                        args.get("voice")
-                        if args.get("voice")
-                        else app_model.app_model_config.text_to_speech_dict.get("voice")
-                    )
+                    voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None
             response = AudioService.transcript_tts(app_model=app_model, message_id=message_id, voice=voice, text=text)
diff --git a/api/controllers/console/explore/completion.py b/api/controllers/console/explore/completion.py
index c039e8bca5..f464692098 100644
--- a/api/controllers/console/explore/completion.py
+++ b/api/controllers/console/explore/completion.py
@@ -92,7 +92,7 @@ class ChatApi(InstalledAppResource):
     def post(self, installed_app):
         app_model = installed_app.app
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -140,7 +140,7 @@ class ChatStopApi(InstalledAppResource):
     def post(self, installed_app, task_id):
         app_model = installed_app.app
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         AppQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id)
diff --git a/api/controllers/console/explore/conversation.py b/api/controllers/console/explore/conversation.py
index 2918024b64..6f9d7769b9 100644
--- a/api/controllers/console/explore/conversation.py
+++ b/api/controllers/console/explore/conversation.py
@@ -20,7 +20,7 @@ class ConversationListApi(InstalledAppResource):
     def get(self, installed_app):
         app_model = installed_app.app
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -50,7 +50,7 @@ class ConversationApi(InstalledAppResource):
     def delete(self, installed_app, c_id):
         app_model = installed_app.app
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -68,7 +68,7 @@ class ConversationRenameApi(InstalledAppResource):
     def post(self, installed_app, c_id):
         app_model = installed_app.app
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -90,7 +90,7 @@ class ConversationPinApi(InstalledAppResource):
     def patch(self, installed_app, c_id):
         app_model = installed_app.app
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -107,7 +107,7 @@ class ConversationUnPinApi(InstalledAppResource):
     def patch(self, installed_app, c_id):
         app_model = installed_app.app
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
diff --git a/api/controllers/console/explore/installed_app.py b/api/controllers/console/explore/installed_app.py
index 3f1e64a247..408afc33a0 100644
--- a/api/controllers/console/explore/installed_app.py
+++ b/api/controllers/console/explore/installed_app.py
@@ -31,7 +31,7 @@ class InstalledAppsListApi(Resource):
                 "app_owner_tenant_id": installed_app.app_owner_tenant_id,
                 "is_pinned": installed_app.is_pinned,
                 "last_used_at": installed_app.last_used_at,
-                "editable": current_user.role in ["owner", "admin"],
+                "editable": current_user.role in {"owner", "admin"},
                 "uninstallable": current_tenant_id == installed_app.app_owner_tenant_id,
             }
             for installed_app in installed_apps
diff --git a/api/controllers/console/explore/message.py b/api/controllers/console/explore/message.py
index f5eb185172..0e0238556c 100644
--- a/api/controllers/console/explore/message.py
+++ b/api/controllers/console/explore/message.py
@@ -40,7 +40,7 @@ class MessageListApi(InstalledAppResource):
         app_model = installed_app.app
 
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -125,7 +125,7 @@ class MessageSuggestedQuestionApi(InstalledAppResource):
     def get(self, installed_app, message_id):
         app_model = installed_app.app
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         message_id = str(message_id)
diff --git a/api/controllers/console/explore/parameter.py b/api/controllers/console/explore/parameter.py
index ad55b04043..aab7dd7888 100644
--- a/api/controllers/console/explore/parameter.py
+++ b/api/controllers/console/explore/parameter.py
@@ -43,7 +43,7 @@ class AppParameterApi(InstalledAppResource):
         """Retrieve app parameters."""
         app_model = installed_app.app
 
-        if app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]:
+        if app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}:
             workflow = app_model.workflow
             if workflow is None:
                 raise AppUnavailableError()
diff --git a/api/controllers/console/init_validate.py b/api/controllers/console/init_validate.py
index 7d3ae677ee..ae759bb752 100644
--- a/api/controllers/console/init_validate.py
+++ b/api/controllers/console/init_validate.py
@@ -4,7 +4,7 @@ from flask import session
 from flask_restful import Resource, reqparse
 
 from configs import dify_config
-from libs.helper import str_len
+from libs.helper import StrLen
 from models.model import DifySetup
 from services.account_service import TenantService
 
@@ -28,7 +28,7 @@ class InitValidateAPI(Resource):
             raise AlreadySetupError()
 
         parser = reqparse.RequestParser()
-        parser.add_argument("password", type=str_len(30), required=True, location="json")
+        parser.add_argument("password", type=StrLen(30), required=True, location="json")
         input_password = parser.parse_args()["password"]
 
         if input_password != os.environ.get("INIT_PASSWORD"):
diff --git a/api/controllers/console/setup.py b/api/controllers/console/setup.py
index 827695e00f..46b4ef5d87 100644
--- a/api/controllers/console/setup.py
+++ b/api/controllers/console/setup.py
@@ -4,7 +4,7 @@ from flask import request
 from flask_restful import Resource, reqparse
 
 from configs import dify_config
-from libs.helper import email, get_remote_ip, str_len
+from libs.helper import StrLen, email, get_remote_ip
 from libs.password import valid_password
 from models.model import DifySetup
 from services.account_service import RegisterService, TenantService
@@ -40,7 +40,7 @@ class SetupApi(Resource):
 
         parser = reqparse.RequestParser()
         parser.add_argument("email", type=email, required=True, location="json")
-        parser.add_argument("name", type=str_len(30), required=True, location="json")
+        parser.add_argument("name", type=StrLen(30), required=True, location="json")
         parser.add_argument("password", type=valid_password, required=True, location="json")
         args = parser.parse_args()
 
diff --git a/api/controllers/console/tag/tags.py b/api/controllers/console/tag/tags.py
index 7293aeeb34..de30547e93 100644
--- a/api/controllers/console/tag/tags.py
+++ b/api/controllers/console/tag/tags.py
@@ -13,7 +13,7 @@ from services.tag_service import TagService
 
 
 def _validate_name(name):
-    if not name or len(name) < 1 or len(name) > 40:
+    if not name or len(name) < 1 or len(name) > 50:
         raise ValueError("Name must be between 1 to 50 characters.")
     return name
 
diff --git a/api/controllers/console/workspace/model_providers.py b/api/controllers/console/workspace/model_providers.py
index 8c38420226..fe0bcf7338 100644
--- a/api/controllers/console/workspace/model_providers.py
+++ b/api/controllers/console/workspace/model_providers.py
@@ -218,7 +218,7 @@ api.add_resource(ModelProviderCredentialApi, "/workspaces/current/model-provider
 api.add_resource(ModelProviderValidateApi, "/workspaces/current/model-providers/<string:provider>/credentials/validate")
 api.add_resource(ModelProviderApi, "/workspaces/current/model-providers/<string:provider>")
 api.add_resource(
-    ModelProviderIconApi, "/workspaces/current/model-providers/<string:provider>/" "<string:icon_type>/<string:lang>"
+    ModelProviderIconApi, "/workspaces/current/model-providers/<string:provider>/<string:icon_type>/<string:lang>"
 )
 
 api.add_resource(
diff --git a/api/controllers/console/workspace/tool_providers.py b/api/controllers/console/workspace/tool_providers.py
index c41a898fdc..d2a17b133b 100644
--- a/api/controllers/console/workspace/tool_providers.py
+++ b/api/controllers/console/workspace/tool_providers.py
@@ -327,7 +327,7 @@ class ToolApiProviderPreviousTestApi(Resource):
 
         return ApiToolManageService.test_api_tool_preview(
             current_user.current_tenant_id,
-            args["provider_name"] if args["provider_name"] else "",
+            args["provider_name"] or "",
             args["tool_name"],
             args["credentials"],
             args["parameters"],
diff --git a/api/controllers/console/workspace/workspace.py b/api/controllers/console/workspace/workspace.py
index 623f0b8b74..af3ebc099b 100644
--- a/api/controllers/console/workspace/workspace.py
+++ b/api/controllers/console/workspace/workspace.py
@@ -194,7 +194,7 @@ class WebappLogoWorkspaceApi(Resource):
             raise TooManyFilesError()
 
         extension = file.filename.split(".")[-1]
-        if extension.lower() not in ["svg", "png"]:
+        if extension.lower() not in {"svg", "png"}:
             raise UnsupportedFileTypeError()
 
         try:
diff --git a/api/controllers/console/wraps.py b/api/controllers/console/wraps.py
index 7667b30e34..46223d104f 100644
--- a/api/controllers/console/wraps.py
+++ b/api/controllers/console/wraps.py
@@ -64,7 +64,8 @@ def cloud_edition_billing_resource_check(resource: str):
                 elif resource == "vector_space" and 0 < vector_space.limit <= vector_space.size:
                     abort(403, "The capacity of the vector space has reached the limit of your subscription.")
                 elif resource == "documents" and 0 < documents_upload_quota.limit <= documents_upload_quota.size:
-                    # The api of file upload is used in the multiple places, so we need to check the source of the request from datasets
+                    # The api of file upload is used in the multiple places,
+                    # so we need to check the source of the request from datasets
                     source = request.args.get("source")
                     if source == "datasets":
                         abort(403, "The number of documents has reached the limit of your subscription.")
diff --git a/api/controllers/service_api/app/app.py b/api/controllers/service_api/app/app.py
index ecc2d73deb..f7c091217b 100644
--- a/api/controllers/service_api/app/app.py
+++ b/api/controllers/service_api/app/app.py
@@ -42,7 +42,7 @@ class AppParameterApi(Resource):
     @marshal_with(parameters_fields)
     def get(self, app_model: App):
         """Retrieve app parameters."""
-        if app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]:
+        if app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}:
             workflow = app_model.workflow
             if workflow is None:
                 raise AppUnavailableError()
diff --git a/api/controllers/service_api/app/audio.py b/api/controllers/service_api/app/audio.py
index 85aab047a7..5db4163647 100644
--- a/api/controllers/service_api/app/audio.py
+++ b/api/controllers/service_api/app/audio.py
@@ -79,19 +79,15 @@ class TextApi(Resource):
             message_id = args.get("message_id", None)
             text = args.get("text", None)
             if (
-                app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]
+                app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}
                 and app_model.workflow
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
-                voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
+                voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
-                    voice = (
-                        args.get("voice")
-                        if args.get("voice")
-                        else app_model.app_model_config.text_to_speech_dict.get("voice")
-                    )
+                    voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None
             response = AudioService.transcript_tts(
diff --git a/api/controllers/service_api/app/completion.py b/api/controllers/service_api/app/completion.py
index f1771baf31..8d8e356c4c 100644
--- a/api/controllers/service_api/app/completion.py
+++ b/api/controllers/service_api/app/completion.py
@@ -96,7 +96,7 @@ class ChatApi(Resource):
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
     def post(self, app_model: App, end_user: EndUser):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -144,7 +144,7 @@ class ChatStopApi(Resource):
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
     def post(self, app_model: App, end_user: EndUser, task_id):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         AppQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
diff --git a/api/controllers/service_api/app/conversation.py b/api/controllers/service_api/app/conversation.py
index 734027a1c5..527ef4ecd3 100644
--- a/api/controllers/service_api/app/conversation.py
+++ b/api/controllers/service_api/app/conversation.py
@@ -18,7 +18,7 @@ class ConversationApi(Resource):
     @marshal_with(conversation_infinite_scroll_pagination_fields)
     def get(self, app_model: App, end_user: EndUser):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -52,7 +52,7 @@ class ConversationDetailApi(Resource):
     @marshal_with(simple_conversation_fields)
     def delete(self, app_model: App, end_user: EndUser, c_id):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -69,7 +69,7 @@ class ConversationRenameApi(Resource):
     @marshal_with(simple_conversation_fields)
     def post(self, app_model: App, end_user: EndUser, c_id):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
diff --git a/api/controllers/service_api/app/message.py b/api/controllers/service_api/app/message.py
index b39aaf7dd8..e54e6f4903 100644
--- a/api/controllers/service_api/app/message.py
+++ b/api/controllers/service_api/app/message.py
@@ -76,7 +76,7 @@ class MessageListApi(Resource):
     @marshal_with(message_infinite_scroll_pagination_fields)
     def get(self, app_model: App, end_user: EndUser):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -117,7 +117,7 @@ class MessageSuggestedApi(Resource):
     def get(self, app_model: App, end_user: EndUser, message_id):
         message_id = str(message_id)
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         try:
diff --git a/api/controllers/service_api/app/workflow.py b/api/controllers/service_api/app/workflow.py
index 5822e0921b..96d1337632 100644
--- a/api/controllers/service_api/app/workflow.py
+++ b/api/controllers/service_api/app/workflow.py
@@ -1,6 +1,7 @@
 import logging
 
 from flask_restful import Resource, fields, marshal_with, reqparse
+from flask_restful.inputs import int_range
 from werkzeug.exceptions import InternalServerError
 
 from controllers.service_api import api
@@ -22,10 +23,12 @@ from core.errors.error import (
 )
 from core.model_runtime.errors.invoke import InvokeError
 from extensions.ext_database import db
+from fields.workflow_app_log_fields import workflow_app_log_pagination_fields
 from libs import helper
 from models.model import App, AppMode, EndUser
 from models.workflow import WorkflowRun
 from services.app_generate_service import AppGenerateService
+from services.workflow_app_service import WorkflowAppService
 
 logger = logging.getLogger(__name__)
 
@@ -113,6 +116,30 @@ class WorkflowTaskStopApi(Resource):
         return {"result": "success"}
 
 
+class WorkflowAppLogApi(Resource):
+    @validate_app_token
+    @marshal_with(workflow_app_log_pagination_fields)
+    def get(self, app_model: App):
+        """
+        Get workflow app logs
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument("keyword", type=str, location="args")
+        parser.add_argument("status", type=str, choices=["succeeded", "failed", "stopped"], location="args")
+        parser.add_argument("page", type=int_range(1, 99999), default=1, location="args")
+        parser.add_argument("limit", type=int_range(1, 100), default=20, location="args")
+        args = parser.parse_args()
+
+        # get paginate workflow app logs
+        workflow_app_service = WorkflowAppService()
+        workflow_app_log_pagination = workflow_app_service.get_paginate_workflow_app_logs(
+            app_model=app_model, args=args
+        )
+
+        return workflow_app_log_pagination
+
+
 api.add_resource(WorkflowRunApi, "/workflows/run")
 api.add_resource(WorkflowRunDetailApi, "/workflows/run/<string:workflow_id>")
 api.add_resource(WorkflowTaskStopApi, "/workflows/tasks/<string:task_id>/stop")
+api.add_resource(WorkflowAppLogApi, "/workflows/logs")
diff --git a/api/controllers/service_api/dataset/segment.py b/api/controllers/service_api/dataset/segment.py
index e69db29fdc..e68f6b4dc4 100644
--- a/api/controllers/service_api/dataset/segment.py
+++ b/api/controllers/service_api/dataset/segment.py
@@ -67,7 +67,7 @@ class SegmentApi(DatasetApiResource):
             segments = SegmentService.multi_create_segment(args["segments"], document, dataset)
             return {"data": marshal(segments, segment_fields), "doc_form": document.doc_form}, 200
         else:
-            return {"error": "Segemtns is required"}, 400
+            return {"error": "Segments is required"}, 400
 
     def get(self, tenant_id, dataset_id, document_id):
         """Create single segment."""
diff --git a/api/controllers/web/app.py b/api/controllers/web/app.py
index aabca93338..20b4e4674c 100644
--- a/api/controllers/web/app.py
+++ b/api/controllers/web/app.py
@@ -41,7 +41,7 @@ class AppParameterApi(WebApiResource):
     @marshal_with(parameters_fields)
     def get(self, app_model: App, end_user):
         """Retrieve app parameters."""
-        if app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]:
+        if app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}:
             workflow = app_model.workflow
             if workflow is None:
                 raise AppUnavailableError()
diff --git a/api/controllers/web/audio.py b/api/controllers/web/audio.py
index d062d2893b..23550efe2e 100644
--- a/api/controllers/web/audio.py
+++ b/api/controllers/web/audio.py
@@ -78,19 +78,15 @@ class TextApi(WebApiResource):
             message_id = args.get("message_id", None)
             text = args.get("text", None)
             if (
-                app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]
+                app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}
                 and app_model.workflow
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
-                voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
+                voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
-                    voice = (
-                        args.get("voice")
-                        if args.get("voice")
-                        else app_model.app_model_config.text_to_speech_dict.get("voice")
-                    )
+                    voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None
 
diff --git a/api/controllers/web/completion.py b/api/controllers/web/completion.py
index 0837eedfb0..115492b796 100644
--- a/api/controllers/web/completion.py
+++ b/api/controllers/web/completion.py
@@ -87,7 +87,7 @@ class CompletionStopApi(WebApiResource):
 class ChatApi(WebApiResource):
     def post(self, app_model, end_user):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -136,7 +136,7 @@ class ChatApi(WebApiResource):
 class ChatStopApi(WebApiResource):
     def post(self, app_model, end_user, task_id):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         AppQueueManager.set_stop_flag(task_id, InvokeFrom.WEB_APP, end_user.id)
diff --git a/api/controllers/web/conversation.py b/api/controllers/web/conversation.py
index 6bbfa94c27..c3b0cd4f44 100644
--- a/api/controllers/web/conversation.py
+++ b/api/controllers/web/conversation.py
@@ -18,7 +18,7 @@ class ConversationListApi(WebApiResource):
     @marshal_with(conversation_infinite_scroll_pagination_fields)
     def get(self, app_model, end_user):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -56,7 +56,7 @@ class ConversationListApi(WebApiResource):
 class ConversationApi(WebApiResource):
     def delete(self, app_model, end_user, c_id):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -73,7 +73,7 @@ class ConversationRenameApi(WebApiResource):
     @marshal_with(simple_conversation_fields)
     def post(self, app_model, end_user, c_id):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -92,7 +92,7 @@ class ConversationRenameApi(WebApiResource):
 class ConversationPinApi(WebApiResource):
     def patch(self, app_model, end_user, c_id):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -108,7 +108,7 @@ class ConversationPinApi(WebApiResource):
 class ConversationUnPinApi(WebApiResource):
     def patch(self, app_model, end_user, c_id):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
diff --git a/api/controllers/web/message.py b/api/controllers/web/message.py
index 56aaaa930a..0d4047f4ef 100644
--- a/api/controllers/web/message.py
+++ b/api/controllers/web/message.py
@@ -78,7 +78,7 @@ class MessageListApi(WebApiResource):
     @marshal_with(message_infinite_scroll_pagination_fields)
     def get(self, app_model, end_user):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -160,7 +160,7 @@ class MessageMoreLikeThisApi(WebApiResource):
 class MessageSuggestedQuestionApi(WebApiResource):
     def get(self, app_model, end_user, message_id):
         app_mode = AppMode.value_of(app_model.mode)
-        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
             raise NotCompletionAppError()
 
         message_id = str(message_id)
diff --git a/api/controllers/web/wraps.py b/api/controllers/web/wraps.py
index 93dc691d62..c327c3df18 100644
--- a/api/controllers/web/wraps.py
+++ b/api/controllers/web/wraps.py
@@ -80,7 +80,8 @@ def _validate_web_sso_token(decoded, system_features, app_code):
             if not source or source != "sso":
                 raise WebSSOAuthRequiredError()
 
-    # Check if SSO is not enforced for web, and if the token source is SSO, raise an error and redirect to normal passport login
+    # Check if SSO is not enforced for web, and if the token source is SSO,
+    # raise an error and redirect to normal passport login
     if not system_features.sso_enforced_for_web or not app_web_sso_enabled:
         source = decoded.get("token_source")
         if source and source == "sso":
diff --git a/api/core/__init__.py b/api/core/__init__.py
index 8c986fc8bd..6eaea7b1c8 100644
--- a/api/core/__init__.py
+++ b/api/core/__init__.py
@@ -1 +1 @@
-import core.moderation.base
\ No newline at end of file
+import core.moderation.base
diff --git a/api/core/agent/base_agent_runner.py b/api/core/agent/base_agent_runner.py
index d8290ca608..d09a9956a4 100644
--- a/api/core/agent/base_agent_runner.py
+++ b/api/core/agent/base_agent_runner.py
@@ -1,6 +1,7 @@
 import json
 import logging
 import uuid
+from collections.abc import Mapping, Sequence
 from datetime import datetime, timezone
 from typing import Optional, Union, cast
 
@@ -45,22 +46,25 @@ from models.tools import ToolConversationVariables
 
 logger = logging.getLogger(__name__)
 
+
 class BaseAgentRunner(AppRunner):
-    def __init__(self, tenant_id: str,
-                 application_generate_entity: AgentChatAppGenerateEntity,
-                 conversation: Conversation,
-                 app_config: AgentChatAppConfig,
-                 model_config: ModelConfigWithCredentialsEntity,
-                 config: AgentEntity,
-                 queue_manager: AppQueueManager,
-                 message: Message,
-                 user_id: str,
-                 memory: Optional[TokenBufferMemory] = None,
-                 prompt_messages: Optional[list[PromptMessage]] = None,
-                 variables_pool: Optional[ToolRuntimeVariablePool] = None,
-                 db_variables: Optional[ToolConversationVariables] = None,
-                 model_instance: ModelInstance = None
-                 ) -> None:
+    def __init__(
+        self,
+        tenant_id: str,
+        application_generate_entity: AgentChatAppGenerateEntity,
+        conversation: Conversation,
+        app_config: AgentChatAppConfig,
+        model_config: ModelConfigWithCredentialsEntity,
+        config: AgentEntity,
+        queue_manager: AppQueueManager,
+        message: Message,
+        user_id: str,
+        memory: Optional[TokenBufferMemory] = None,
+        prompt_messages: Optional[list[PromptMessage]] = None,
+        variables_pool: Optional[ToolRuntimeVariablePool] = None,
+        db_variables: Optional[ToolConversationVariables] = None,
+        model_instance: ModelInstance = None,
+    ) -> None:
         """
         Agent runner
         :param tenant_id: tenant id
@@ -88,9 +92,7 @@ class BaseAgentRunner(AppRunner):
         self.message = message
         self.user_id = user_id
         self.memory = memory
-        self.history_prompt_messages = self.organize_agent_history(
-            prompt_messages=prompt_messages or []
-        )
+        self.history_prompt_messages = self.organize_agent_history(prompt_messages=prompt_messages or [])
         self.variables_pool = variables_pool
         self.db_variables_pool = db_variables
         self.model_instance = model_instance
@@ -111,12 +113,16 @@ class BaseAgentRunner(AppRunner):
             retrieve_config=app_config.dataset.retrieve_config if app_config.dataset else None,
             return_resource=app_config.additional_features.show_retrieve_source,
             invoke_from=application_generate_entity.invoke_from,
-            hit_callback=hit_callback
+            hit_callback=hit_callback,
         )
         # get how many agent thoughts have been created
-        self.agent_thought_count = db.session.query(MessageAgentThought).filter(
-            MessageAgentThought.message_id == self.message.id,
-        ).count()
+        self.agent_thought_count = (
+            db.session.query(MessageAgentThought)
+            .filter(
+                MessageAgentThought.message_id == self.message.id,
+            )
+            .count()
+        )
         db.session.close()
 
         # check if model supports stream tool call
@@ -135,25 +141,26 @@ class BaseAgentRunner(AppRunner):
         self.query = None
         self._current_thoughts: list[PromptMessage] = []
 
-    def _repack_app_generate_entity(self, app_generate_entity: AgentChatAppGenerateEntity) \
-            -> AgentChatAppGenerateEntity:
+    def _repack_app_generate_entity(
+        self, app_generate_entity: AgentChatAppGenerateEntity
+    ) -> AgentChatAppGenerateEntity:
         """
         Repack app generate entity
         """
         if app_generate_entity.app_config.prompt_template.simple_prompt_template is None:
-            app_generate_entity.app_config.prompt_template.simple_prompt_template = ''
+            app_generate_entity.app_config.prompt_template.simple_prompt_template = ""
 
         return app_generate_entity
-    
+
     def _convert_tool_to_prompt_message_tool(self, tool: AgentToolEntity) -> tuple[PromptMessageTool, Tool]:
         """
-            convert tool to prompt message tool
+        convert tool to prompt message tool
         """
         tool_entity = ToolManager.get_agent_tool_runtime(
             tenant_id=self.tenant_id,
             app_id=self.app_config.app_id,
             agent_tool=tool,
-            invoke_from=self.application_generate_entity.invoke_from
+            invoke_from=self.application_generate_entity.invoke_from,
         )
         tool_entity.load_variables(self.variables_pool)
 
@@ -164,7 +171,7 @@ class BaseAgentRunner(AppRunner):
                 "type": "object",
                 "properties": {},
                 "required": [],
-            }
+            },
         )
 
         parameters = tool_entity.get_all_runtime_parameters()
@@ -177,19 +184,19 @@ class BaseAgentRunner(AppRunner):
             if parameter.type == ToolParameter.ToolParameterType.SELECT:
                 enum = [option.value for option in parameter.options]
 
-            message_tool.parameters['properties'][parameter.name] = {
+            message_tool.parameters["properties"][parameter.name] = {
                 "type": parameter_type,
-                "description": parameter.llm_description or '',
+                "description": parameter.llm_description or "",
             }
 
             if len(enum) > 0:
-                message_tool.parameters['properties'][parameter.name]['enum'] = enum
+                message_tool.parameters["properties"][parameter.name]["enum"] = enum
 
             if parameter.required:
-                message_tool.parameters['required'].append(parameter.name)
+                message_tool.parameters["required"].append(parameter.name)
 
         return message_tool, tool_entity
-    
+
     def _convert_dataset_retriever_tool_to_prompt_message_tool(self, tool: DatasetRetrieverTool) -> PromptMessageTool:
         """
         convert dataset retriever tool to prompt message tool
@@ -201,24 +208,24 @@ class BaseAgentRunner(AppRunner):
                 "type": "object",
                 "properties": {},
                 "required": [],
-            }
+            },
         )
 
         for parameter in tool.get_runtime_parameters():
-            parameter_type = 'string'
-        
-            prompt_tool.parameters['properties'][parameter.name] = {
+            parameter_type = "string"
+
+            prompt_tool.parameters["properties"][parameter.name] = {
                 "type": parameter_type,
-                "description": parameter.llm_description or '',
+                "description": parameter.llm_description or "",
             }
 
             if parameter.required:
-                if parameter.name not in prompt_tool.parameters['required']:
-                    prompt_tool.parameters['required'].append(parameter.name)
+                if parameter.name not in prompt_tool.parameters["required"]:
+                    prompt_tool.parameters["required"].append(parameter.name)
 
         return prompt_tool
-    
-    def _init_prompt_tools(self) -> tuple[dict[str, Tool], list[PromptMessageTool]]:
+
+    def _init_prompt_tools(self) -> tuple[Mapping[str, Tool], Sequence[PromptMessageTool]]:
         """
         Init tools
         """
@@ -261,51 +268,51 @@ class BaseAgentRunner(AppRunner):
             enum = []
             if parameter.type == ToolParameter.ToolParameterType.SELECT:
                 enum = [option.value for option in parameter.options]
-        
-            prompt_tool.parameters['properties'][parameter.name] = {
+
+            prompt_tool.parameters["properties"][parameter.name] = {
                 "type": parameter_type,
-                "description": parameter.llm_description or '',
+                "description": parameter.llm_description or "",
             }
 
             if len(enum) > 0:
-                prompt_tool.parameters['properties'][parameter.name]['enum'] = enum
+                prompt_tool.parameters["properties"][parameter.name]["enum"] = enum
 
             if parameter.required:
-                if parameter.name not in prompt_tool.parameters['required']:
-                    prompt_tool.parameters['required'].append(parameter.name)
+                if parameter.name not in prompt_tool.parameters["required"]:
+                    prompt_tool.parameters["required"].append(parameter.name)
 
         return prompt_tool
-        
-    def create_agent_thought(self, message_id: str, message: str, 
-                             tool_name: str, tool_input: str, messages_ids: list[str]
-                             ) -> MessageAgentThought:
+
+    def create_agent_thought(
+        self, message_id: str, message: str, tool_name: str, tool_input: str, messages_ids: list[str]
+    ) -> MessageAgentThought:
         """
         Create agent thought
         """
         thought = MessageAgentThought(
             message_id=message_id,
             message_chain_id=None,
-            thought='',
+            thought="",
             tool=tool_name,
-            tool_labels_str='{}',
-            tool_meta_str='{}',
+            tool_labels_str="{}",
+            tool_meta_str="{}",
             tool_input=tool_input,
             message=message,
             message_token=0,
             message_unit_price=0,
             message_price_unit=0,
-            message_files=json.dumps(messages_ids) if messages_ids else '',
-            answer='',
-            observation='',
+            message_files=json.dumps(messages_ids) if messages_ids else "",
+            answer="",
+            observation="",
             answer_token=0,
             answer_unit_price=0,
             answer_price_unit=0,
             tokens=0,
             total_price=0,
             position=self.agent_thought_count + 1,
-            currency='USD',
+            currency="USD",
             latency=0,
-            created_by_role='account',
+            created_by_role="account",
             created_by=self.user_id,
         )
 
@@ -318,22 +325,22 @@ class BaseAgentRunner(AppRunner):
 
         return thought
 
-    def save_agent_thought(self, 
-                           agent_thought: MessageAgentThought, 
-                           tool_name: str,
-                           tool_input: Union[str, dict],
-                           thought: str, 
-                           observation: Union[str, dict], 
-                           tool_invoke_meta: Union[str, dict],
-                           answer: str,
-                           messages_ids: list[str],
-                           llm_usage: LLMUsage = None) -> MessageAgentThought:
+    def save_agent_thought(
+        self,
+        agent_thought: MessageAgentThought,
+        tool_name: str,
+        tool_input: Union[str, dict],
+        thought: str,
+        observation: Union[str, dict],
+        tool_invoke_meta: Union[str, dict],
+        answer: str,
+        messages_ids: list[str],
+        llm_usage: LLMUsage = None,
+    ) -> MessageAgentThought:
         """
         Save agent thought
         """
-        agent_thought = db.session.query(MessageAgentThought).filter(
-            MessageAgentThought.id == agent_thought.id
-        ).first()
+        agent_thought = db.session.query(MessageAgentThought).filter(MessageAgentThought.id == agent_thought.id).first()
 
         if thought is not None:
             agent_thought.thought = thought
@@ -356,7 +363,7 @@ class BaseAgentRunner(AppRunner):
                     observation = json.dumps(observation, ensure_ascii=False)
                 except Exception as e:
                     observation = json.dumps(observation)
-                    
+
             agent_thought.observation = observation
 
         if answer is not None:
@@ -364,7 +371,7 @@ class BaseAgentRunner(AppRunner):
 
         if messages_ids is not None and len(messages_ids) > 0:
             agent_thought.message_files = json.dumps(messages_ids)
-        
+
         if llm_usage:
             agent_thought.message_token = llm_usage.prompt_tokens
             agent_thought.message_price_unit = llm_usage.prompt_price_unit
@@ -377,7 +384,7 @@ class BaseAgentRunner(AppRunner):
 
         # check if tool labels is not empty
         labels = agent_thought.tool_labels or {}
-        tools = agent_thought.tool.split(';') if agent_thought.tool else []
+        tools = agent_thought.tool.split(";") if agent_thought.tool else []
         for tool in tools:
             if not tool:
                 continue
@@ -386,7 +393,7 @@ class BaseAgentRunner(AppRunner):
                 if tool_label:
                     labels[tool] = tool_label.to_dict()
                 else:
-                    labels[tool] = {'en_US': tool, 'zh_Hans': tool}
+                    labels[tool] = {"en_US": tool, "zh_Hans": tool}
 
         agent_thought.tool_labels_str = json.dumps(labels)
 
@@ -401,14 +408,18 @@ class BaseAgentRunner(AppRunner):
 
         db.session.commit()
         db.session.close()
-    
+
     def update_db_variables(self, tool_variables: ToolRuntimeVariablePool, db_variables: ToolConversationVariables):
         """
         convert tool variables to db variables
         """
-        db_variables = db.session.query(ToolConversationVariables).filter(
-            ToolConversationVariables.conversation_id == self.message.conversation_id,
-        ).first()
+        db_variables = (
+            db.session.query(ToolConversationVariables)
+            .filter(
+                ToolConversationVariables.conversation_id == self.message.conversation_id,
+            )
+            .first()
+        )
 
         db_variables.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
         db_variables.variables_str = json.dumps(jsonable_encoder(tool_variables.pool))
@@ -425,9 +436,14 @@ class BaseAgentRunner(AppRunner):
             if isinstance(prompt_message, SystemPromptMessage):
                 result.append(prompt_message)
 
-        messages: list[Message] = db.session.query(Message).filter(
-            Message.conversation_id == self.message.conversation_id,
-        ).order_by(Message.created_at.asc()).all()
+        messages: list[Message] = (
+            db.session.query(Message)
+            .filter(
+                Message.conversation_id == self.message.conversation_id,
+            )
+            .order_by(Message.created_at.asc())
+            .all()
+        )
 
         for message in messages:
             if message.id == self.message.id:
@@ -439,13 +455,13 @@ class BaseAgentRunner(AppRunner):
                 for agent_thought in agent_thoughts:
                     tools = agent_thought.tool
                     if tools:
-                        tools = tools.split(';')
+                        tools = tools.split(";")
                         tool_calls: list[AssistantPromptMessage.ToolCall] = []
                         tool_call_response: list[ToolPromptMessage] = []
                         try:
                             tool_inputs = json.loads(agent_thought.tool_input)
                         except Exception as e:
-                            tool_inputs = { tool: {} for tool in tools }
+                            tool_inputs = {tool: {} for tool in tools}
                         try:
                             tool_responses = json.loads(agent_thought.observation)
                         except Exception as e:
@@ -454,27 +470,33 @@ class BaseAgentRunner(AppRunner):
                         for tool in tools:
                             # generate a uuid for tool call
                             tool_call_id = str(uuid.uuid4())
-                            tool_calls.append(AssistantPromptMessage.ToolCall(
-                                id=tool_call_id,
-                                type='function',
-                                function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                                    name=tool,
-                                    arguments=json.dumps(tool_inputs.get(tool, {})),
+                            tool_calls.append(
+                                AssistantPromptMessage.ToolCall(
+                                    id=tool_call_id,
+                                    type="function",
+                                    function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+                                        name=tool,
+                                        arguments=json.dumps(tool_inputs.get(tool, {})),
+                                    ),
                                 )
-                            ))
-                            tool_call_response.append(ToolPromptMessage(
-                                content=tool_responses.get(tool, agent_thought.observation),
-                                name=tool,
-                                tool_call_id=tool_call_id,
-                            ))
+                            )
+                            tool_call_response.append(
+                                ToolPromptMessage(
+                                    content=tool_responses.get(tool, agent_thought.observation),
+                                    name=tool,
+                                    tool_call_id=tool_call_id,
+                                )
+                            )
 
-                        result.extend([
-                            AssistantPromptMessage(
-                                content=agent_thought.thought,
-                                tool_calls=tool_calls,
-                            ),
-                            *tool_call_response
-                        ])
+                        result.extend(
+                            [
+                                AssistantPromptMessage(
+                                    content=agent_thought.thought,
+                                    tool_calls=tool_calls,
+                                ),
+                                *tool_call_response,
+                            ]
+                        )
                     if not tools:
                         result.append(AssistantPromptMessage(content=agent_thought.thought))
             else:
@@ -496,10 +518,7 @@ class BaseAgentRunner(AppRunner):
             file_extra_config = FileUploadConfigManager.convert(message.app_model_config.to_dict())
 
             if file_extra_config:
-                file_objs = message_file_parser.transform_message_files(
-                    files,
-                    file_extra_config
-                )
+                file_objs = message_file_parser.transform_message_files(files, file_extra_config)
             else:
                 file_objs = []
 
diff --git a/api/core/agent/cot_agent_runner.py b/api/core/agent/cot_agent_runner.py
index 89c948d2e2..ebe04bf260 100644
--- a/api/core/agent/cot_agent_runner.py
+++ b/api/core/agent/cot_agent_runner.py
@@ -25,17 +25,19 @@ from models.model import Message
 
 class CotAgentRunner(BaseAgentRunner, ABC):
     _is_first_iteration = True
-    _ignore_observation_providers = ['wenxin']
+    _ignore_observation_providers = ["wenxin"]
     _historic_prompt_messages: list[PromptMessage] = None
     _agent_scratchpad: list[AgentScratchpadUnit] = None
     _instruction: str = None
     _query: str = None
     _prompt_messages_tools: list[PromptMessage] = None
 
-    def run(self, message: Message,
-            query: str,
-            inputs: dict[str, str],
-            ) -> Union[Generator, LLMResult]:
+    def run(
+        self,
+        message: Message,
+        query: str,
+        inputs: dict[str, str],
+    ) -> Union[Generator, LLMResult]:
         """
         Run Cot agent application
         """
@@ -46,17 +48,16 @@ class CotAgentRunner(BaseAgentRunner, ABC):
         trace_manager = app_generate_entity.trace_manager
 
         # check model mode
-        if 'Observation' not in app_generate_entity.model_conf.stop:
+        if "Observation" not in app_generate_entity.model_conf.stop:
             if app_generate_entity.model_conf.provider not in self._ignore_observation_providers:
-                app_generate_entity.model_conf.stop.append('Observation')
+                app_generate_entity.model_conf.stop.append("Observation")
 
         app_config = self.app_config
 
         # init instruction
         inputs = inputs or {}
         instruction = app_config.prompt_template.simple_prompt_template
-        self._instruction = self._fill_in_inputs_from_external_data_tools(
-            instruction, inputs)
+        self._instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
 
         iteration_step = 1
         max_iteration_steps = min(app_config.agent.max_iteration, 5) + 1
@@ -65,16 +66,14 @@ class CotAgentRunner(BaseAgentRunner, ABC):
         tool_instances, self._prompt_messages_tools = self._init_prompt_tools()
 
         function_call_state = True
-        llm_usage = {
-            'usage': None
-        }
-        final_answer = ''
+        llm_usage = {"usage": None}
+        final_answer = ""
 
         def increase_usage(final_llm_usage_dict: dict[str, LLMUsage], usage: LLMUsage):
-            if not final_llm_usage_dict['usage']:
-                final_llm_usage_dict['usage'] = usage
+            if not final_llm_usage_dict["usage"]:
+                final_llm_usage_dict["usage"] = usage
             else:
-                llm_usage = final_llm_usage_dict['usage']
+                llm_usage = final_llm_usage_dict["usage"]
                 llm_usage.prompt_tokens += usage.prompt_tokens
                 llm_usage.completion_tokens += usage.completion_tokens
                 llm_usage.prompt_price += usage.prompt_price
@@ -94,17 +93,13 @@ class CotAgentRunner(BaseAgentRunner, ABC):
             message_file_ids = []
 
             agent_thought = self.create_agent_thought(
-                message_id=message.id,
-                message='',
-                tool_name='',
-                tool_input='',
-                messages_ids=message_file_ids
+                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
             )
 
             if iteration_step > 1:
-                self.queue_manager.publish(QueueAgentThoughtEvent(
-                    agent_thought_id=agent_thought.id
-                ), PublishFrom.APPLICATION_MANAGER)
+                self.queue_manager.publish(
+                    QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
+                )
 
             # recalc llm max tokens
             prompt_messages = self._organize_prompt_messages()
@@ -125,21 +120,20 @@ class CotAgentRunner(BaseAgentRunner, ABC):
                 raise ValueError("failed to invoke llm")
 
             usage_dict = {}
-            react_chunks = CotAgentOutputParser.handle_react_stream_output(
-                chunks, usage_dict)
+            react_chunks = CotAgentOutputParser.handle_react_stream_output(chunks, usage_dict)
             scratchpad = AgentScratchpadUnit(
-                agent_response='',
-                thought='',
-                action_str='',
-                observation='',
+                agent_response="",
+                thought="",
+                action_str="",
+                observation="",
                 action=None,
             )
 
             # publish agent thought if it's first iteration
             if iteration_step == 1:
-                self.queue_manager.publish(QueueAgentThoughtEvent(
-                    agent_thought_id=agent_thought.id
-                ), PublishFrom.APPLICATION_MANAGER)
+                self.queue_manager.publish(
+                    QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
+                )
 
             for chunk in react_chunks:
                 if isinstance(chunk, AgentScratchpadUnit.Action):
@@ -154,61 +148,51 @@ class CotAgentRunner(BaseAgentRunner, ABC):
                     yield LLMResultChunk(
                         model=self.model_config.model,
                         prompt_messages=prompt_messages,
-                        system_fingerprint='',
-                        delta=LLMResultChunkDelta(
-                            index=0,
-                            message=AssistantPromptMessage(
-                                content=chunk
-                            ),
-                            usage=None
-                        )
+                        system_fingerprint="",
+                        delta=LLMResultChunkDelta(index=0, message=AssistantPromptMessage(content=chunk), usage=None),
                     )
 
-            scratchpad.thought = scratchpad.thought.strip(
-            ) or 'I am thinking about how to help you'
+            scratchpad.thought = scratchpad.thought.strip() or "I am thinking about how to help you"
             self._agent_scratchpad.append(scratchpad)
 
             # get llm usage
-            if 'usage' in usage_dict:
-                increase_usage(llm_usage, usage_dict['usage'])
+            if "usage" in usage_dict:
+                increase_usage(llm_usage, usage_dict["usage"])
             else:
-                usage_dict['usage'] = LLMUsage.empty_usage()
+                usage_dict["usage"] = LLMUsage.empty_usage()
 
             self.save_agent_thought(
                 agent_thought=agent_thought,
-                tool_name=scratchpad.action.action_name if scratchpad.action else '',
-                tool_input={
-                    scratchpad.action.action_name: scratchpad.action.action_input
-                } if scratchpad.action else {},
+                tool_name=scratchpad.action.action_name if scratchpad.action else "",
+                tool_input={scratchpad.action.action_name: scratchpad.action.action_input} if scratchpad.action else {},
                 tool_invoke_meta={},
                 thought=scratchpad.thought,
-                observation='',
+                observation="",
                 answer=scratchpad.agent_response,
                 messages_ids=[],
-                llm_usage=usage_dict['usage']
+                llm_usage=usage_dict["usage"],
             )
 
             if not scratchpad.is_final():
-                self.queue_manager.publish(QueueAgentThoughtEvent(
-                    agent_thought_id=agent_thought.id
-                ), PublishFrom.APPLICATION_MANAGER)
+                self.queue_manager.publish(
+                    QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
+                )
 
             if not scratchpad.action:
                 # failed to extract action, return final answer directly
-                final_answer = ''
+                final_answer = ""
             else:
                 if scratchpad.action.action_name.lower() == "final answer":
                     # action is final answer, return final answer directly
                     try:
                         if isinstance(scratchpad.action.action_input, dict):
-                            final_answer = json.dumps(
-                                scratchpad.action.action_input)
+                            final_answer = json.dumps(scratchpad.action.action_input)
                         elif isinstance(scratchpad.action.action_input, str):
                             final_answer = scratchpad.action.action_input
                         else:
-                            final_answer = f'{scratchpad.action.action_input}'
+                            final_answer = f"{scratchpad.action.action_input}"
                     except json.JSONDecodeError:
-                        final_answer = f'{scratchpad.action.action_input}'
+                        final_answer = f"{scratchpad.action.action_input}"
                 else:
                     function_call_state = True
                     # action is tool call, invoke tool
@@ -224,21 +208,18 @@ class CotAgentRunner(BaseAgentRunner, ABC):
                     self.save_agent_thought(
                         agent_thought=agent_thought,
                         tool_name=scratchpad.action.action_name,
-                        tool_input={
-                            scratchpad.action.action_name: scratchpad.action.action_input},
+                        tool_input={scratchpad.action.action_name: scratchpad.action.action_input},
                         thought=scratchpad.thought,
-                        observation={
-                            scratchpad.action.action_name: tool_invoke_response},
-                        tool_invoke_meta={
-                            scratchpad.action.action_name: tool_invoke_meta.to_dict()},
+                        observation={scratchpad.action.action_name: tool_invoke_response},
+                        tool_invoke_meta={scratchpad.action.action_name: tool_invoke_meta.to_dict()},
                         answer=scratchpad.agent_response,
                         messages_ids=message_file_ids,
-                        llm_usage=usage_dict['usage']
+                        llm_usage=usage_dict["usage"],
                     )
 
-                    self.queue_manager.publish(QueueAgentThoughtEvent(
-                        agent_thought_id=agent_thought.id
-                    ), PublishFrom.APPLICATION_MANAGER)
+                    self.queue_manager.publish(
+                        QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
+                    )
 
                 # update prompt tool message
                 for prompt_tool in self._prompt_messages_tools:
@@ -250,44 +231,45 @@ class CotAgentRunner(BaseAgentRunner, ABC):
             model=model_instance.model,
             prompt_messages=prompt_messages,
             delta=LLMResultChunkDelta(
-                index=0,
-                message=AssistantPromptMessage(
-                    content=final_answer
-                ),
-                usage=llm_usage['usage']
+                index=0, message=AssistantPromptMessage(content=final_answer), usage=llm_usage["usage"]
             ),
-            system_fingerprint=''
+            system_fingerprint="",
         )
 
         # save agent thought
         self.save_agent_thought(
             agent_thought=agent_thought,
-            tool_name='',
+            tool_name="",
             tool_input={},
             tool_invoke_meta={},
             thought=final_answer,
             observation={},
             answer=final_answer,
-            messages_ids=[]
+            messages_ids=[],
         )
 
         self.update_db_variables(self.variables_pool, self.db_variables_pool)
         # publish end event
-        self.queue_manager.publish(QueueMessageEndEvent(llm_result=LLMResult(
-            model=model_instance.model,
-            prompt_messages=prompt_messages,
-            message=AssistantPromptMessage(
-                content=final_answer
+        self.queue_manager.publish(
+            QueueMessageEndEvent(
+                llm_result=LLMResult(
+                    model=model_instance.model,
+                    prompt_messages=prompt_messages,
+                    message=AssistantPromptMessage(content=final_answer),
+                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
+                    system_fingerprint="",
+                )
             ),
-            usage=llm_usage['usage'] if llm_usage['usage'] else LLMUsage.empty_usage(),
-            system_fingerprint=''
-        )), PublishFrom.APPLICATION_MANAGER)
+            PublishFrom.APPLICATION_MANAGER,
+        )
 
-    def _handle_invoke_action(self, action: AgentScratchpadUnit.Action,
-                              tool_instances: dict[str, Tool],
-                              message_file_ids: list[str],
-                              trace_manager: Optional[TraceQueueManager] = None
-                              ) -> tuple[str, ToolInvokeMeta]:
+    def _handle_invoke_action(
+        self,
+        action: AgentScratchpadUnit.Action,
+        tool_instances: dict[str, Tool],
+        message_file_ids: list[str],
+        trace_manager: Optional[TraceQueueManager] = None,
+    ) -> tuple[str, ToolInvokeMeta]:
         """
         handle invoke action
         :param action: action
@@ -326,13 +308,12 @@ class CotAgentRunner(BaseAgentRunner, ABC):
         # publish files
         for message_file_id, save_as in message_files:
             if save_as:
-                self.variables_pool.set_file(
-                    tool_name=tool_call_name, value=message_file_id, name=save_as)
+                self.variables_pool.set_file(tool_name=tool_call_name, value=message_file_id, name=save_as)
 
             # publish message file
-            self.queue_manager.publish(QueueMessageFileEvent(
-                message_file_id=message_file_id
-            ), PublishFrom.APPLICATION_MANAGER)
+            self.queue_manager.publish(
+                QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
+            )
             # add message file ids
             message_file_ids.append(message_file_id)
 
@@ -342,10 +323,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
         """
         convert dict to action
         """
-        return AgentScratchpadUnit.Action(
-            action_name=action['action'],
-            action_input=action['action_input']
-        )
+        return AgentScratchpadUnit.Action(action_name=action["action"], action_input=action["action_input"])
 
     def _fill_in_inputs_from_external_data_tools(self, instruction: str, inputs: dict) -> str:
         """
@@ -353,7 +331,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
         """
         for key, value in inputs.items():
             try:
-                instruction = instruction.replace(f'{{{{{key}}}}}', str(value))
+                instruction = instruction.replace(f"{{{{{key}}}}}", str(value))
             except Exception as e:
                 continue
 
@@ -370,14 +348,14 @@ class CotAgentRunner(BaseAgentRunner, ABC):
     @abstractmethod
     def _organize_prompt_messages(self) -> list[PromptMessage]:
         """
-            organize prompt messages
+        organize prompt messages
         """
 
     def _format_assistant_message(self, agent_scratchpad: list[AgentScratchpadUnit]) -> str:
         """
-            format assistant message
+        format assistant message
         """
-        message = ''
+        message = ""
         for scratchpad in agent_scratchpad:
             if scratchpad.is_final():
                 message += f"Final Answer: {scratchpad.agent_response}"
@@ -390,9 +368,11 @@ class CotAgentRunner(BaseAgentRunner, ABC):
 
         return message
 
-    def _organize_historic_prompt_messages(self, current_session_messages: list[PromptMessage] = None) -> list[PromptMessage]:
+    def _organize_historic_prompt_messages(
+        self, current_session_messages: list[PromptMessage] = None
+    ) -> list[PromptMessage]:
         """
-            organize historic prompt messages
+        organize historic prompt messages
         """
         result: list[PromptMessage] = []
         scratchpads: list[AgentScratchpadUnit] = []
@@ -403,8 +383,8 @@ class CotAgentRunner(BaseAgentRunner, ABC):
                 if not current_scratchpad:
                     current_scratchpad = AgentScratchpadUnit(
                         agent_response=message.content,
-                        thought=message.content or 'I am thinking about how to help you',
-                        action_str='',
+                        thought=message.content or "I am thinking about how to help you",
+                        action_str="",
                         action=None,
                         observation=None,
                     )
@@ -413,12 +393,9 @@ class CotAgentRunner(BaseAgentRunner, ABC):
                     try:
                         current_scratchpad.action = AgentScratchpadUnit.Action(
                             action_name=message.tool_calls[0].function.name,
-                            action_input=json.loads(
-                                message.tool_calls[0].function.arguments)
-                        )
-                        current_scratchpad.action_str = json.dumps(
-                            current_scratchpad.action.to_dict()
+                            action_input=json.loads(message.tool_calls[0].function.arguments),
                         )
+                        current_scratchpad.action_str = json.dumps(current_scratchpad.action.to_dict())
                     except:
                         pass
             elif isinstance(message, ToolPromptMessage):
@@ -426,23 +403,19 @@ class CotAgentRunner(BaseAgentRunner, ABC):
                     current_scratchpad.observation = message.content
             elif isinstance(message, UserPromptMessage):
                 if scratchpads:
-                    result.append(AssistantPromptMessage(
-                        content=self._format_assistant_message(scratchpads)
-                    ))
+                    result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
                     scratchpads = []
                     current_scratchpad = None
 
                 result.append(message)
 
         if scratchpads:
-            result.append(AssistantPromptMessage(
-                content=self._format_assistant_message(scratchpads)
-            ))
+            result.append(AssistantPromptMessage(content=self._format_assistant_message(scratchpads)))
 
         historic_prompts = AgentHistoryPromptTransform(
             model_config=self.model_config,
             prompt_messages=current_session_messages or [],
             history_messages=result,
-            memory=self.memory
+            memory=self.memory,
         ).get_prompt()
         return historic_prompts
diff --git a/api/core/agent/cot_chat_agent_runner.py b/api/core/agent/cot_chat_agent_runner.py
index 8debbe5c5d..bdec6b7ed1 100644
--- a/api/core/agent/cot_chat_agent_runner.py
+++ b/api/core/agent/cot_chat_agent_runner.py
@@ -19,14 +19,15 @@ class CotChatAgentRunner(CotAgentRunner):
         prompt_entity = self.app_config.agent.prompt
         first_prompt = prompt_entity.first_prompt
 
-        system_prompt = first_prompt \
-            .replace("{{instruction}}", self._instruction) \
-            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools))) \
-            .replace("{{tool_names}}", ', '.join([tool.name for tool in self._prompt_messages_tools]))
+        system_prompt = (
+            first_prompt.replace("{{instruction}}", self._instruction)
+            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
+            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
+        )
 
         return SystemPromptMessage(content=system_prompt)
 
-    def _organize_user_query(self, query,  prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
+    def _organize_user_query(self, query, prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
         """
         Organize user query
         """
@@ -43,7 +44,7 @@ class CotChatAgentRunner(CotAgentRunner):
 
     def _organize_prompt_messages(self) -> list[PromptMessage]:
         """
-        Organize 
+        Organize
         """
         # organize system prompt
         system_message = self._organize_system_prompt()
@@ -53,7 +54,7 @@ class CotChatAgentRunner(CotAgentRunner):
         if not agent_scratchpad:
             assistant_messages = []
         else:
-            assistant_message = AssistantPromptMessage(content='')
+            assistant_message = AssistantPromptMessage(content="")
             for unit in agent_scratchpad:
                 if unit.is_final():
                     assistant_message.content += f"Final Answer: {unit.agent_response}"
@@ -71,18 +72,15 @@ class CotChatAgentRunner(CotAgentRunner):
 
         if assistant_messages:
             # organize historic prompt messages
-            historic_messages = self._organize_historic_prompt_messages([
-                system_message,
-                *query_messages,
-                *assistant_messages,
-                UserPromptMessage(content='continue')
-            ])
+            historic_messages = self._organize_historic_prompt_messages(
+                [system_message, *query_messages, *assistant_messages, UserPromptMessage(content="continue")]
+            )
             messages = [
                 system_message,
                 *historic_messages,
                 *query_messages,
                 *assistant_messages,
-                UserPromptMessage(content='continue')
+                UserPromptMessage(content="continue"),
             ]
         else:
             # organize historic prompt messages
diff --git a/api/core/agent/cot_completion_agent_runner.py b/api/core/agent/cot_completion_agent_runner.py
index 9e6eb54f4f..9dab956f9a 100644
--- a/api/core/agent/cot_completion_agent_runner.py
+++ b/api/core/agent/cot_completion_agent_runner.py
@@ -13,10 +13,12 @@ class CotCompletionAgentRunner(CotAgentRunner):
         prompt_entity = self.app_config.agent.prompt
         first_prompt = prompt_entity.first_prompt
 
-        system_prompt = first_prompt.replace("{{instruction}}", self._instruction) \
-            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools))) \
-            .replace("{{tool_names}}", ', '.join([tool.name for tool in self._prompt_messages_tools]))
-        
+        system_prompt = (
+            first_prompt.replace("{{instruction}}", self._instruction)
+            .replace("{{tools}}", json.dumps(jsonable_encoder(self._prompt_messages_tools)))
+            .replace("{{tool_names}}", ", ".join([tool.name for tool in self._prompt_messages_tools]))
+        )
+
         return system_prompt
 
     def _organize_historic_prompt(self, current_session_messages: list[PromptMessage] = None) -> str:
@@ -46,7 +48,7 @@ class CotCompletionAgentRunner(CotAgentRunner):
 
         # organize current assistant messages
         agent_scratchpad = self._agent_scratchpad
-        assistant_prompt = ''
+        assistant_prompt = ""
         for unit in agent_scratchpad:
             if unit.is_final():
                 assistant_prompt += f"Final Answer: {unit.agent_response}"
@@ -61,9 +63,10 @@ class CotCompletionAgentRunner(CotAgentRunner):
         query_prompt = f"Question: {self._query}"
 
         # join all messages
-        prompt = system_prompt \
-            .replace("{{historic_messages}}", historic_prompt) \
-            .replace("{{agent_scratchpad}}", assistant_prompt) \
+        prompt = (
+            system_prompt.replace("{{historic_messages}}", historic_prompt)
+            .replace("{{agent_scratchpad}}", assistant_prompt)
             .replace("{{query}}", query_prompt)
+        )
 
-        return [UserPromptMessage(content=prompt)]
\ No newline at end of file
+        return [UserPromptMessage(content=prompt)]
diff --git a/api/core/agent/entities.py b/api/core/agent/entities.py
index 5274224de5..119a88fc7b 100644
--- a/api/core/agent/entities.py
+++ b/api/core/agent/entities.py
@@ -8,6 +8,7 @@ class AgentToolEntity(BaseModel):
     """
     Agent Tool Entity.
     """
+
     provider_type: Literal["builtin", "api", "workflow"]
     provider_id: str
     tool_name: str
@@ -18,6 +19,7 @@ class AgentPromptEntity(BaseModel):
     """
     Agent Prompt Entity.
     """
+
     first_prompt: str
     next_iteration: str
 
@@ -31,6 +33,7 @@ class AgentScratchpadUnit(BaseModel):
         """
         Action Entity.
         """
+
         action_name: str
         action_input: Union[dict, str]
 
@@ -39,8 +42,8 @@ class AgentScratchpadUnit(BaseModel):
             Convert to dictionary.
             """
             return {
-                'action': self.action_name,
-                'action_input': self.action_input,
+                "action": self.action_name,
+                "action_input": self.action_input,
             }
 
     agent_response: Optional[str] = None
@@ -54,10 +57,10 @@ class AgentScratchpadUnit(BaseModel):
         Check if the scratchpad unit is final.
         """
         return self.action is None or (
-            'final' in self.action.action_name.lower() and 
-            'answer' in self.action.action_name.lower()
+            "final" in self.action.action_name.lower() and "answer" in self.action.action_name.lower()
         )
 
+
 class AgentEntity(BaseModel):
     """
     Agent Entity.
@@ -67,8 +70,9 @@ class AgentEntity(BaseModel):
         """
         Agent Strategy.
         """
-        CHAIN_OF_THOUGHT = 'chain-of-thought'
-        FUNCTION_CALLING = 'function-calling'
+
+        CHAIN_OF_THOUGHT = "chain-of-thought"
+        FUNCTION_CALLING = "function-calling"
 
     provider: str
     model: str
diff --git a/api/core/agent/fc_agent_runner.py b/api/core/agent/fc_agent_runner.py
index 3ee6e47742..13164e0bfc 100644
--- a/api/core/agent/fc_agent_runner.py
+++ b/api/core/agent/fc_agent_runner.py
@@ -24,11 +24,9 @@ from models.model import Message
 
 logger = logging.getLogger(__name__)
 
-class FunctionCallAgentRunner(BaseAgentRunner):
 
-    def run(self, 
-            message: Message, query: str, **kwargs: Any
-    ) -> Generator[LLMResultChunk, None, None]:
+class FunctionCallAgentRunner(BaseAgentRunner):
+    def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
         """
         Run FunctionCall agent application
         """
@@ -45,19 +43,17 @@ class FunctionCallAgentRunner(BaseAgentRunner):
 
         # continue to run until there is not any tool call
         function_call_state = True
-        llm_usage = {
-            'usage': None
-        }
-        final_answer = ''
+        llm_usage = {"usage": None}
+        final_answer = ""
 
         # get tracing instance
         trace_manager = app_generate_entity.trace_manager
-        
+
         def increase_usage(final_llm_usage_dict: dict[str, LLMUsage], usage: LLMUsage):
-            if not final_llm_usage_dict['usage']:
-                final_llm_usage_dict['usage'] = usage
+            if not final_llm_usage_dict["usage"]:
+                final_llm_usage_dict["usage"] = usage
             else:
-                llm_usage = final_llm_usage_dict['usage']
+                llm_usage = final_llm_usage_dict["usage"]
                 llm_usage.prompt_tokens += usage.prompt_tokens
                 llm_usage.completion_tokens += usage.completion_tokens
                 llm_usage.prompt_price += usage.prompt_price
@@ -75,11 +71,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
 
             message_file_ids = []
             agent_thought = self.create_agent_thought(
-                message_id=message.id,
-                message='',
-                tool_name='',
-                tool_input='',
-                messages_ids=message_file_ids
+                message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
             )
 
             # recalc llm max tokens
@@ -99,11 +91,11 @@ class FunctionCallAgentRunner(BaseAgentRunner):
             tool_calls: list[tuple[str, str, dict[str, Any]]] = []
 
             # save full response
-            response = ''
+            response = ""
 
             # save tool call names and inputs
-            tool_call_names = ''
-            tool_call_inputs = ''
+            tool_call_names = ""
+            tool_call_inputs = ""
 
             current_llm_usage = None
 
@@ -111,24 +103,22 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                 is_first_chunk = True
                 for chunk in chunks:
                     if is_first_chunk:
-                        self.queue_manager.publish(QueueAgentThoughtEvent(
-                            agent_thought_id=agent_thought.id
-                        ), PublishFrom.APPLICATION_MANAGER)
+                        self.queue_manager.publish(
+                            QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
+                        )
                         is_first_chunk = False
                     # check if there is any tool call
                     if self.check_tool_calls(chunk):
                         function_call_state = True
                         tool_calls.extend(self.extract_tool_calls(chunk))
-                        tool_call_names = ';'.join([tool_call[1] for tool_call in tool_calls])
+                        tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
                         try:
-                            tool_call_inputs = json.dumps({
-                                tool_call[1]: tool_call[2] for tool_call in tool_calls
-                            }, ensure_ascii=False)
+                            tool_call_inputs = json.dumps(
+                                {tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
+                            )
                         except json.JSONDecodeError as e:
                             # ensure ascii to avoid encoding error
-                            tool_call_inputs = json.dumps({
-                                tool_call[1]: tool_call[2] for tool_call in tool_calls
-                            })
+                            tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
 
                     if chunk.delta.message and chunk.delta.message.content:
                         if isinstance(chunk.delta.message.content, list):
@@ -148,16 +138,14 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                 if self.check_blocking_tool_calls(result):
                     function_call_state = True
                     tool_calls.extend(self.extract_blocking_tool_calls(result))
-                    tool_call_names = ';'.join([tool_call[1] for tool_call in tool_calls])
+                    tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
                     try:
-                        tool_call_inputs = json.dumps({
-                            tool_call[1]: tool_call[2] for tool_call in tool_calls
-                        }, ensure_ascii=False)
+                        tool_call_inputs = json.dumps(
+                            {tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
+                        )
                     except json.JSONDecodeError as e:
                         # ensure ascii to avoid encoding error
-                        tool_call_inputs = json.dumps({
-                            tool_call[1]: tool_call[2] for tool_call in tool_calls
-                        })
+                        tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
 
                 if result.usage:
                     increase_usage(llm_usage, result.usage)
@@ -171,12 +159,12 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                         response += result.message.content
 
                 if not result.message.content:
-                    result.message.content = ''
+                    result.message.content = ""
+
+                self.queue_manager.publish(
+                    QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
+                )
 
-                self.queue_manager.publish(QueueAgentThoughtEvent(
-                    agent_thought_id=agent_thought.id
-                ), PublishFrom.APPLICATION_MANAGER)
-                
                 yield LLMResultChunk(
                     model=model_instance.model,
                     prompt_messages=result.prompt_messages,
@@ -185,32 +173,29 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                         index=0,
                         message=result.message,
                         usage=result.usage,
-                    )
+                    ),
                 )
 
-            assistant_message = AssistantPromptMessage(
-                content='',
-                tool_calls=[]
-            )
+            assistant_message = AssistantPromptMessage(content="", tool_calls=[])
             if tool_calls:
-                assistant_message.tool_calls=[
+                assistant_message.tool_calls = [
                     AssistantPromptMessage.ToolCall(
                         id=tool_call[0],
-                        type='function',
+                        type="function",
                         function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                            name=tool_call[1],
-                            arguments=json.dumps(tool_call[2], ensure_ascii=False)
-                        )
-                    ) for tool_call in tool_calls
+                            name=tool_call[1], arguments=json.dumps(tool_call[2], ensure_ascii=False)
+                        ),
+                    )
+                    for tool_call in tool_calls
                 ]
             else:
                 assistant_message.content = response
-            
+
             self._current_thoughts.append(assistant_message)
 
             # save thought
             self.save_agent_thought(
-                agent_thought=agent_thought, 
+                agent_thought=agent_thought,
                 tool_name=tool_call_names,
                 tool_input=tool_call_inputs,
                 thought=response,
@@ -218,13 +203,13 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                 observation=None,
                 answer=response,
                 messages_ids=[],
-                llm_usage=current_llm_usage
+                llm_usage=current_llm_usage,
             )
-            self.queue_manager.publish(QueueAgentThoughtEvent(
-                agent_thought_id=agent_thought.id
-            ), PublishFrom.APPLICATION_MANAGER)
-            
-            final_answer += response + '\n'
+            self.queue_manager.publish(
+                QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
+            )
+
+            final_answer += response + "\n"
 
             # call tools
             tool_responses = []
@@ -235,7 +220,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                         "tool_call_id": tool_call_id,
                         "tool_call_name": tool_call_name,
                         "tool_response": f"there is not a tool named {tool_call_name}",
-                        "meta": ToolInvokeMeta.error_instance(f"there is not a tool named {tool_call_name}").to_dict()
+                        "meta": ToolInvokeMeta.error_instance(f"there is not a tool named {tool_call_name}").to_dict(),
                     }
                 else:
                     # invoke tool
@@ -255,50 +240,49 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                             self.variables_pool.set_file(tool_name=tool_call_name, value=message_file_id, name=save_as)
 
                         # publish message file
-                        self.queue_manager.publish(QueueMessageFileEvent(
-                            message_file_id=message_file_id
-                        ), PublishFrom.APPLICATION_MANAGER)
+                        self.queue_manager.publish(
+                            QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
+                        )
                         # add message file ids
                         message_file_ids.append(message_file_id)
-                    
+
                     tool_response = {
                         "tool_call_id": tool_call_id,
                         "tool_call_name": tool_call_name,
                         "tool_response": tool_invoke_response,
-                        "meta": tool_invoke_meta.to_dict()
+                        "meta": tool_invoke_meta.to_dict(),
                     }
-                
+
                 tool_responses.append(tool_response)
-                if tool_response['tool_response'] is not None:
+                if tool_response["tool_response"] is not None:
                     self._current_thoughts.append(
                         ToolPromptMessage(
-                            content=tool_response['tool_response'],
+                            content=tool_response["tool_response"],
                             tool_call_id=tool_call_id,
                             name=tool_call_name,
                         )
-                    ) 
+                    )
 
             if len(tool_responses) > 0:
                 # save agent thought
                 self.save_agent_thought(
-                    agent_thought=agent_thought, 
+                    agent_thought=agent_thought,
                     tool_name=None,
                     tool_input=None,
-                    thought=None, 
+                    thought=None,
                     tool_invoke_meta={
-                        tool_response['tool_call_name']: tool_response['meta'] 
-                        for tool_response in tool_responses
+                        tool_response["tool_call_name"]: tool_response["meta"] for tool_response in tool_responses
                     },
                     observation={
-                        tool_response['tool_call_name']: tool_response['tool_response'] 
+                        tool_response["tool_call_name"]: tool_response["tool_response"]
                         for tool_response in tool_responses
                     },
                     answer=None,
-                    messages_ids=message_file_ids
+                    messages_ids=message_file_ids,
+                )
+                self.queue_manager.publish(
+                    QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
                 )
-                self.queue_manager.publish(QueueAgentThoughtEvent(
-                    agent_thought_id=agent_thought.id
-                ), PublishFrom.APPLICATION_MANAGER)
 
             # update prompt tool
             for prompt_tool in prompt_messages_tools:
@@ -308,15 +292,18 @@ class FunctionCallAgentRunner(BaseAgentRunner):
 
         self.update_db_variables(self.variables_pool, self.db_variables_pool)
         # publish end event
-        self.queue_manager.publish(QueueMessageEndEvent(llm_result=LLMResult(
-            model=model_instance.model,
-            prompt_messages=prompt_messages,
-            message=AssistantPromptMessage(
-                content=final_answer
+        self.queue_manager.publish(
+            QueueMessageEndEvent(
+                llm_result=LLMResult(
+                    model=model_instance.model,
+                    prompt_messages=prompt_messages,
+                    message=AssistantPromptMessage(content=final_answer),
+                    usage=llm_usage["usage"] or LLMUsage.empty_usage(),
+                    system_fingerprint="",
+                )
             ),
-            usage=llm_usage['usage'] if llm_usage['usage'] else LLMUsage.empty_usage(),
-            system_fingerprint=''
-        )), PublishFrom.APPLICATION_MANAGER)
+            PublishFrom.APPLICATION_MANAGER,
+        )
 
     def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
         """
@@ -325,7 +312,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
         if llm_result_chunk.delta.message.tool_calls:
             return True
         return False
-    
+
     def check_blocking_tool_calls(self, llm_result: LLMResult) -> bool:
         """
         Check if there is any blocking tool call in llm result
@@ -334,7 +321,9 @@ class FunctionCallAgentRunner(BaseAgentRunner):
             return True
         return False
 
-    def extract_tool_calls(self, llm_result_chunk: LLMResultChunk) -> Union[None, list[tuple[str, str, dict[str, Any]]]]:
+    def extract_tool_calls(
+        self, llm_result_chunk: LLMResultChunk
+    ) -> Union[None, list[tuple[str, str, dict[str, Any]]]]:
         """
         Extract tool calls from llm result chunk
 
@@ -344,17 +333,19 @@ class FunctionCallAgentRunner(BaseAgentRunner):
         tool_calls = []
         for prompt_message in llm_result_chunk.delta.message.tool_calls:
             args = {}
-            if prompt_message.function.arguments != '':
+            if prompt_message.function.arguments != "":
                 args = json.loads(prompt_message.function.arguments)
 
-            tool_calls.append((
-                prompt_message.id,
-                prompt_message.function.name,
-                args,
-            ))
+            tool_calls.append(
+                (
+                    prompt_message.id,
+                    prompt_message.function.name,
+                    args,
+                )
+            )
 
         return tool_calls
-    
+
     def extract_blocking_tool_calls(self, llm_result: LLMResult) -> Union[None, list[tuple[str, str, dict[str, Any]]]]:
         """
         Extract blocking tool calls from llm result
@@ -365,18 +356,22 @@ class FunctionCallAgentRunner(BaseAgentRunner):
         tool_calls = []
         for prompt_message in llm_result.message.tool_calls:
             args = {}
-            if prompt_message.function.arguments != '':
+            if prompt_message.function.arguments != "":
                 args = json.loads(prompt_message.function.arguments)
 
-            tool_calls.append((
-                prompt_message.id,
-                prompt_message.function.name,
-                args,
-            ))
+            tool_calls.append(
+                (
+                    prompt_message.id,
+                    prompt_message.function.name,
+                    args,
+                )
+            )
 
         return tool_calls
 
-    def _init_system_message(self, prompt_template: str, prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
+    def _init_system_message(
+        self, prompt_template: str, prompt_messages: list[PromptMessage] = None
+    ) -> list[PromptMessage]:
         """
         Initialize system message
         """
@@ -384,13 +379,13 @@ class FunctionCallAgentRunner(BaseAgentRunner):
             return [
                 SystemPromptMessage(content=prompt_template),
             ]
-        
+
         if prompt_messages and not isinstance(prompt_messages[0], SystemPromptMessage) and prompt_template:
             prompt_messages.insert(0, SystemPromptMessage(content=prompt_template))
 
         return prompt_messages
 
-    def _organize_user_query(self, query,  prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
+    def _organize_user_query(self, query, prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
         """
         Organize user query
         """
@@ -404,7 +399,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
             prompt_messages.append(UserPromptMessage(content=query))
 
         return prompt_messages
-    
+
     def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
         """
         As for now, gpt supports both fc and vision at the first iteration.
@@ -415,17 +410,21 @@ class FunctionCallAgentRunner(BaseAgentRunner):
         for prompt_message in prompt_messages:
             if isinstance(prompt_message, UserPromptMessage):
                 if isinstance(prompt_message.content, list):
-                    prompt_message.content = '\n'.join([
-                        content.data if content.type == PromptMessageContentType.TEXT else 
-                        '[image]' if content.type == PromptMessageContentType.IMAGE else
-                        '[file]' 
-                        for content in prompt_message.content 
-                    ])
+                    prompt_message.content = "\n".join(
+                        [
+                            content.data
+                            if content.type == PromptMessageContentType.TEXT
+                            else "[image]"
+                            if content.type == PromptMessageContentType.IMAGE
+                            else "[file]"
+                            for content in prompt_message.content
+                        ]
+                    )
 
         return prompt_messages
 
     def _organize_prompt_messages(self):
-        prompt_template = self.app_config.prompt_template.simple_prompt_template or ''
+        prompt_template = self.app_config.prompt_template.simple_prompt_template or ""
         self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
         query_prompt_messages = self._organize_user_query(self.query, [])
 
@@ -433,14 +432,10 @@ class FunctionCallAgentRunner(BaseAgentRunner):
             model_config=self.model_config,
             prompt_messages=[*query_prompt_messages, *self._current_thoughts],
             history_messages=self.history_prompt_messages,
-            memory=self.memory
+            memory=self.memory,
         ).get_prompt()
 
-        prompt_messages = [
-            *self.history_prompt_messages,
-            *query_prompt_messages,
-            *self._current_thoughts
-        ]
+        prompt_messages = [*self.history_prompt_messages, *query_prompt_messages, *self._current_thoughts]
         if len(self._current_thoughts) != 0:
             # clear messages after the first iteration
             prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
diff --git a/api/core/agent/output_parser/cot_output_parser.py b/api/core/agent/output_parser/cot_output_parser.py
index c53fa5000e..d04e38777a 100644
--- a/api/core/agent/output_parser/cot_output_parser.py
+++ b/api/core/agent/output_parser/cot_output_parser.py
@@ -9,8 +9,9 @@ from core.model_runtime.entities.llm_entities import LLMResultChunk
 
 class CotAgentOutputParser:
     @classmethod
-    def handle_react_stream_output(cls, llm_response: Generator[LLMResultChunk, None, None], usage_dict: dict) -> \
-        Generator[Union[str, AgentScratchpadUnit.Action], None, None]:
+    def handle_react_stream_output(
+        cls, llm_response: Generator[LLMResultChunk, None, None], usage_dict: dict
+    ) -> Generator[Union[str, AgentScratchpadUnit.Action], None, None]:
         def parse_action(json_str):
             try:
                 action = json.loads(json_str)
@@ -22,7 +23,7 @@ class CotAgentOutputParser:
                     action = action[0]
 
                 for key, value in action.items():
-                    if 'input' in key.lower():
+                    if "input" in key.lower():
                         action_input = value
                     else:
                         action_name = value
@@ -33,37 +34,37 @@ class CotAgentOutputParser:
                         action_input=action_input,
                     )
                 else:
-                    return json_str or ''
+                    return json_str or ""
             except:
-                return json_str or ''
-            
+                return json_str or ""
+
         def extra_json_from_code_block(code_block) -> Generator[Union[dict, str], None, None]:
-            code_blocks = re.findall(r'```(.*?)```', code_block, re.DOTALL)
+            code_blocks = re.findall(r"```(.*?)```", code_block, re.DOTALL)
             if not code_blocks:
                 return
             for block in code_blocks:
-                json_text = re.sub(r'^[a-zA-Z]+\n', '', block.strip(), flags=re.MULTILINE)
+                json_text = re.sub(r"^[a-zA-Z]+\n", "", block.strip(), flags=re.MULTILINE)
                 yield parse_action(json_text)
-            
-        code_block_cache = ''
+
+        code_block_cache = ""
         code_block_delimiter_count = 0
         in_code_block = False
-        json_cache = ''
+        json_cache = ""
         json_quote_count = 0
         in_json = False
         got_json = False
 
-        action_cache = ''
-        action_str = 'action:'
+        action_cache = ""
+        action_str = "action:"
         action_idx = 0
 
-        thought_cache = ''
-        thought_str = 'thought:'
+        thought_cache = ""
+        thought_str = "thought:"
         thought_idx = 0
 
         for response in llm_response:
             if response.delta.usage:
-                usage_dict['usage'] = response.delta.usage
+                usage_dict["usage"] = response.delta.usage
             response = response.delta.message.content
             if not isinstance(response, str):
                 continue
@@ -72,24 +73,24 @@ class CotAgentOutputParser:
             index = 0
             while index < len(response):
                 steps = 1
-                delta = response[index:index+steps]
-                last_character = response[index-1] if index > 0 else ''
+                delta = response[index : index + steps]
+                last_character = response[index - 1] if index > 0 else ""
 
-                if delta == '`':
+                if delta == "`":
                     code_block_cache += delta
                     code_block_delimiter_count += 1
                 else:
                     if not in_code_block:
                         if code_block_delimiter_count > 0:
                             yield code_block_cache
-                        code_block_cache = ''
+                        code_block_cache = ""
                     else:
                         code_block_cache += delta
                     code_block_delimiter_count = 0
 
                 if not in_code_block and not in_json:
                     if delta.lower() == action_str[action_idx] and action_idx == 0:
-                        if last_character not in ['\n', ' ', '']:
+                        if last_character not in {"\n", " ", ""}:
                             index += steps
                             yield delta
                             continue
@@ -97,7 +98,7 @@ class CotAgentOutputParser:
                         action_cache += delta
                         action_idx += 1
                         if action_idx == len(action_str):
-                            action_cache = ''
+                            action_cache = ""
                             action_idx = 0
                         index += steps
                         continue
@@ -105,18 +106,18 @@ class CotAgentOutputParser:
                         action_cache += delta
                         action_idx += 1
                         if action_idx == len(action_str):
-                            action_cache = ''
+                            action_cache = ""
                             action_idx = 0
                         index += steps
                         continue
                     else:
                         if action_cache:
                             yield action_cache
-                            action_cache = ''
+                            action_cache = ""
                             action_idx = 0
-                    
+
                     if delta.lower() == thought_str[thought_idx] and thought_idx == 0:
-                        if last_character not in ['\n', ' ', '']:
+                        if last_character not in {"\n", " ", ""}:
                             index += steps
                             yield delta
                             continue
@@ -124,7 +125,7 @@ class CotAgentOutputParser:
                         thought_cache += delta
                         thought_idx += 1
                         if thought_idx == len(thought_str):
-                            thought_cache = ''
+                            thought_cache = ""
                             thought_idx = 0
                         index += steps
                         continue
@@ -132,31 +133,31 @@ class CotAgentOutputParser:
                         thought_cache += delta
                         thought_idx += 1
                         if thought_idx == len(thought_str):
-                            thought_cache = ''
+                            thought_cache = ""
                             thought_idx = 0
                         index += steps
                         continue
                     else:
                         if thought_cache:
                             yield thought_cache
-                            thought_cache = ''
+                            thought_cache = ""
                             thought_idx = 0
 
                 if code_block_delimiter_count == 3:
                     if in_code_block:
                         yield from extra_json_from_code_block(code_block_cache)
-                        code_block_cache = ''
-                        
+                        code_block_cache = ""
+
                     in_code_block = not in_code_block
                     code_block_delimiter_count = 0
 
                 if not in_code_block:
                     # handle single json
-                    if delta == '{':
+                    if delta == "{":
                         json_quote_count += 1
                         in_json = True
                         json_cache += delta
-                    elif delta == '}':
+                    elif delta == "}":
                         json_cache += delta
                         if json_quote_count > 0:
                             json_quote_count -= 1
@@ -172,12 +173,12 @@ class CotAgentOutputParser:
                     if got_json:
                         got_json = False
                         yield parse_action(json_cache)
-                        json_cache = ''
+                        json_cache = ""
                         json_quote_count = 0
                         in_json = False
-                    
+
                 if not in_code_block and not in_json:
-                    yield delta.replace('`', '')
+                    yield delta.replace("`", "")
 
                 index += steps
 
@@ -186,4 +187,3 @@ class CotAgentOutputParser:
 
         if json_cache:
             yield parse_action(json_cache)
-
diff --git a/api/core/agent/prompt/template.py b/api/core/agent/prompt/template.py
index b0cf1a77fb..ef64fd29fc 100644
--- a/api/core/agent/prompt/template.py
+++ b/api/core/agent/prompt/template.py
@@ -41,7 +41,8 @@ Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use
 {{historic_messages}}
 Question: {{query}}
 {{agent_scratchpad}}
-Thought:"""
+Thought:"""  # noqa: E501
+
 
 ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES = """Observation: {{observation}}
 Thought:"""
@@ -86,19 +87,20 @@ Action:
 ```
 
 Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
-"""
+"""  # noqa: E501
+
 
 ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES = ""
 
 REACT_PROMPT_TEMPLATES = {
-    'english': {
-        'chat': {
-            'prompt': ENGLISH_REACT_CHAT_PROMPT_TEMPLATES,
-            'agent_scratchpad': ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES
+    "english": {
+        "chat": {
+            "prompt": ENGLISH_REACT_CHAT_PROMPT_TEMPLATES,
+            "agent_scratchpad": ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES,
+        },
+        "completion": {
+            "prompt": ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES,
+            "agent_scratchpad": ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES,
         },
-        'completion': {
-            'prompt': ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES,
-            'agent_scratchpad': ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES
-        }
     }
-}
\ No newline at end of file
+}
diff --git a/api/core/app/app_config/base_app_config_manager.py b/api/core/app/app_config/base_app_config_manager.py
index 3dea305e98..24d80f9cdd 100644
--- a/api/core/app/app_config/base_app_config_manager.py
+++ b/api/core/app/app_config/base_app_config_manager.py
@@ -26,34 +26,24 @@ class BaseAppConfigManager:
         config_dict = dict(config_dict.items())
 
         additional_features = AppAdditionalFeatures()
-        additional_features.show_retrieve_source = RetrievalResourceConfigManager.convert(
-            config=config_dict
-        )
+        additional_features.show_retrieve_source = RetrievalResourceConfigManager.convert(config=config_dict)
 
         additional_features.file_upload = FileUploadConfigManager.convert(
-            config=config_dict,
-            is_vision=app_mode in [AppMode.CHAT, AppMode.COMPLETION, AppMode.AGENT_CHAT]
+            config=config_dict, is_vision=app_mode in {AppMode.CHAT, AppMode.COMPLETION, AppMode.AGENT_CHAT}
         )
 
-        additional_features.opening_statement, additional_features.suggested_questions = \
-            OpeningStatementConfigManager.convert(
-                config=config_dict
-            )
+        additional_features.opening_statement, additional_features.suggested_questions = (
+            OpeningStatementConfigManager.convert(config=config_dict)
+        )
 
         additional_features.suggested_questions_after_answer = SuggestedQuestionsAfterAnswerConfigManager.convert(
             config=config_dict
         )
 
-        additional_features.more_like_this = MoreLikeThisConfigManager.convert(
-            config=config_dict
-        )
+        additional_features.more_like_this = MoreLikeThisConfigManager.convert(config=config_dict)
 
-        additional_features.speech_to_text = SpeechToTextConfigManager.convert(
-            config=config_dict
-        )
+        additional_features.speech_to_text = SpeechToTextConfigManager.convert(config=config_dict)
 
-        additional_features.text_to_speech = TextToSpeechConfigManager.convert(
-            config=config_dict
-        )
+        additional_features.text_to_speech = TextToSpeechConfigManager.convert(config=config_dict)
 
         return additional_features
diff --git a/api/core/app/app_config/common/sensitive_word_avoidance/manager.py b/api/core/app/app_config/common/sensitive_word_avoidance/manager.py
index 1ca8b1e3b8..037037e6ca 100644
--- a/api/core/app/app_config/common/sensitive_word_avoidance/manager.py
+++ b/api/core/app/app_config/common/sensitive_word_avoidance/manager.py
@@ -7,25 +7,24 @@ from core.moderation.factory import ModerationFactory
 class SensitiveWordAvoidanceConfigManager:
     @classmethod
     def convert(cls, config: dict) -> Optional[SensitiveWordAvoidanceEntity]:
-        sensitive_word_avoidance_dict = config.get('sensitive_word_avoidance')
+        sensitive_word_avoidance_dict = config.get("sensitive_word_avoidance")
         if not sensitive_word_avoidance_dict:
             return None
 
-        if sensitive_word_avoidance_dict.get('enabled'):
+        if sensitive_word_avoidance_dict.get("enabled"):
             return SensitiveWordAvoidanceEntity(
-                type=sensitive_word_avoidance_dict.get('type'),
-                config=sensitive_word_avoidance_dict.get('config'),
+                type=sensitive_word_avoidance_dict.get("type"),
+                config=sensitive_word_avoidance_dict.get("config"),
             )
         else:
             return None
 
     @classmethod
-    def validate_and_set_defaults(cls, tenant_id, config: dict, only_structure_validate: bool = False) \
-            -> tuple[dict, list[str]]:
+    def validate_and_set_defaults(
+        cls, tenant_id, config: dict, only_structure_validate: bool = False
+    ) -> tuple[dict, list[str]]:
         if not config.get("sensitive_word_avoidance"):
-            config["sensitive_word_avoidance"] = {
-                "enabled": False
-            }
+            config["sensitive_word_avoidance"] = {"enabled": False}
 
         if not isinstance(config["sensitive_word_avoidance"], dict):
             raise ValueError("sensitive_word_avoidance must be of dict type")
@@ -41,10 +40,6 @@ class SensitiveWordAvoidanceConfigManager:
                 typ = config["sensitive_word_avoidance"]["type"]
                 sensitive_word_avoidance_config = config["sensitive_word_avoidance"]["config"]
 
-                ModerationFactory.validate_config(
-                    name=typ,
-                    tenant_id=tenant_id,
-                    config=sensitive_word_avoidance_config
-                )
+                ModerationFactory.validate_config(name=typ, tenant_id=tenant_id, config=sensitive_word_avoidance_config)
 
         return config, ["sensitive_word_avoidance"]
diff --git a/api/core/app/app_config/easy_ui_based_app/agent/manager.py b/api/core/app/app_config/easy_ui_based_app/agent/manager.py
index dc65d4439b..f503543d7b 100644
--- a/api/core/app/app_config/easy_ui_based_app/agent/manager.py
+++ b/api/core/app/app_config/easy_ui_based_app/agent/manager.py
@@ -12,67 +12,70 @@ class AgentConfigManager:
 
         :param config: model config args
         """
-        if 'agent_mode' in config and config['agent_mode'] \
-                and 'enabled' in config['agent_mode']:
+        if "agent_mode" in config and config["agent_mode"] and "enabled" in config["agent_mode"]:
+            agent_dict = config.get("agent_mode", {})
+            agent_strategy = agent_dict.get("strategy", "cot")
 
-            agent_dict = config.get('agent_mode', {})
-            agent_strategy = agent_dict.get('strategy', 'cot')
-
-            if agent_strategy == 'function_call':
+            if agent_strategy == "function_call":
                 strategy = AgentEntity.Strategy.FUNCTION_CALLING
-            elif agent_strategy == 'cot' or agent_strategy == 'react':
+            elif agent_strategy in {"cot", "react"}:
                 strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
             else:
                 # old configs, try to detect default strategy
-                if config['model']['provider'] == 'openai':
+                if config["model"]["provider"] == "openai":
                     strategy = AgentEntity.Strategy.FUNCTION_CALLING
                 else:
                     strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
 
             agent_tools = []
-            for tool in agent_dict.get('tools', []):
+            for tool in agent_dict.get("tools", []):
                 keys = tool.keys()
                 if len(keys) >= 4:
                     if "enabled" not in tool or not tool["enabled"]:
                         continue
 
                     agent_tool_properties = {
-                        'provider_type': tool['provider_type'],
-                        'provider_id': tool['provider_id'],
-                        'tool_name': tool['tool_name'],
-                        'tool_parameters': tool.get('tool_parameters', {})
+                        "provider_type": tool["provider_type"],
+                        "provider_id": tool["provider_id"],
+                        "tool_name": tool["tool_name"],
+                        "tool_parameters": tool.get("tool_parameters", {}),
                     }
 
                     agent_tools.append(AgentToolEntity(**agent_tool_properties))
 
-            if 'strategy' in config['agent_mode'] and \
-                    config['agent_mode']['strategy'] not in ['react_router', 'router']:
-                agent_prompt = agent_dict.get('prompt', None) or {}
+            if "strategy" in config["agent_mode"] and config["agent_mode"]["strategy"] not in {
+                "react_router",
+                "router",
+            }:
+                agent_prompt = agent_dict.get("prompt", None) or {}
                 # check model mode
-                model_mode = config.get('model', {}).get('mode', 'completion')
-                if model_mode == 'completion':
+                model_mode = config.get("model", {}).get("mode", "completion")
+                if model_mode == "completion":
                     agent_prompt_entity = AgentPromptEntity(
-                        first_prompt=agent_prompt.get('first_prompt',
-                                                      REACT_PROMPT_TEMPLATES['english']['completion']['prompt']),
-                        next_iteration=agent_prompt.get('next_iteration',
-                                                        REACT_PROMPT_TEMPLATES['english']['completion'][
-                                                            'agent_scratchpad']),
+                        first_prompt=agent_prompt.get(
+                            "first_prompt", REACT_PROMPT_TEMPLATES["english"]["completion"]["prompt"]
+                        ),
+                        next_iteration=agent_prompt.get(
+                            "next_iteration", REACT_PROMPT_TEMPLATES["english"]["completion"]["agent_scratchpad"]
+                        ),
                     )
                 else:
                     agent_prompt_entity = AgentPromptEntity(
-                        first_prompt=agent_prompt.get('first_prompt',
-                                                      REACT_PROMPT_TEMPLATES['english']['chat']['prompt']),
-                        next_iteration=agent_prompt.get('next_iteration',
-                                                        REACT_PROMPT_TEMPLATES['english']['chat']['agent_scratchpad']),
+                        first_prompt=agent_prompt.get(
+                            "first_prompt", REACT_PROMPT_TEMPLATES["english"]["chat"]["prompt"]
+                        ),
+                        next_iteration=agent_prompt.get(
+                            "next_iteration", REACT_PROMPT_TEMPLATES["english"]["chat"]["agent_scratchpad"]
+                        ),
                     )
 
                 return AgentEntity(
-                    provider=config['model']['provider'],
-                    model=config['model']['name'],
+                    provider=config["model"]["provider"],
+                    model=config["model"]["name"],
                     strategy=strategy,
                     prompt=agent_prompt_entity,
                     tools=agent_tools,
-                    max_iteration=agent_dict.get('max_iteration', 5)
+                    max_iteration=agent_dict.get("max_iteration", 5),
                 )
 
         return None
diff --git a/api/core/app/app_config/easy_ui_based_app/dataset/manager.py b/api/core/app/app_config/easy_ui_based_app/dataset/manager.py
index 1a621d2090..a22395b8e3 100644
--- a/api/core/app/app_config/easy_ui_based_app/dataset/manager.py
+++ b/api/core/app/app_config/easy_ui_based_app/dataset/manager.py
@@ -15,39 +15,38 @@ class DatasetConfigManager:
         :param config: model config args
         """
         dataset_ids = []
-        if 'datasets' in config.get('dataset_configs', {}):
-            datasets = config.get('dataset_configs', {}).get('datasets', {
-                'strategy': 'router',
-                'datasets': []
-            })
+        if "datasets" in config.get("dataset_configs", {}):
+            datasets = config.get("dataset_configs", {}).get("datasets", {"strategy": "router", "datasets": []})
 
-            for dataset in datasets.get('datasets', []):
+            for dataset in datasets.get("datasets", []):
                 keys = list(dataset.keys())
-                if len(keys) == 0 or keys[0] != 'dataset':
+                if len(keys) == 0 or keys[0] != "dataset":
                     continue
 
-                dataset = dataset['dataset']
+                dataset = dataset["dataset"]
 
-                if 'enabled' not in dataset or not dataset['enabled']:
+                if "enabled" not in dataset or not dataset["enabled"]:
                     continue
 
-                dataset_id = dataset.get('id', None)
+                dataset_id = dataset.get("id", None)
                 if dataset_id:
                     dataset_ids.append(dataset_id)
 
-        if 'agent_mode' in config and config['agent_mode'] \
-                and 'enabled' in config['agent_mode'] \
-                and config['agent_mode']['enabled']:
+        if (
+            "agent_mode" in config
+            and config["agent_mode"]
+            and "enabled" in config["agent_mode"]
+            and config["agent_mode"]["enabled"]
+        ):
+            agent_dict = config.get("agent_mode", {})
 
-            agent_dict = config.get('agent_mode', {})
-
-            for tool in agent_dict.get('tools', []):
+            for tool in agent_dict.get("tools", []):
                 keys = tool.keys()
                 if len(keys) == 1:
                     # old standard
                     key = list(tool.keys())[0]
 
-                    if key != 'dataset':
+                    if key != "dataset":
                         continue
 
                     tool_item = tool[key]
@@ -55,30 +54,28 @@ class DatasetConfigManager:
                     if "enabled" not in tool_item or not tool_item["enabled"]:
                         continue
 
-                    dataset_id = tool_item['id']
+                    dataset_id = tool_item["id"]
                     dataset_ids.append(dataset_id)
 
         if len(dataset_ids) == 0:
             return None
 
         # dataset configs
-        if 'dataset_configs' in config and config.get('dataset_configs'):
-            dataset_configs = config.get('dataset_configs')
+        if "dataset_configs" in config and config.get("dataset_configs"):
+            dataset_configs = config.get("dataset_configs")
         else:
-            dataset_configs = {
-                'retrieval_model': 'multiple'
-            }
-        query_variable = config.get('dataset_query_variable')
+            dataset_configs = {"retrieval_model": "multiple"}
+        query_variable = config.get("dataset_query_variable")
 
-        if dataset_configs['retrieval_model'] == 'single':
+        if dataset_configs["retrieval_model"] == "single":
             return DatasetEntity(
                 dataset_ids=dataset_ids,
                 retrieve_config=DatasetRetrieveConfigEntity(
                     query_variable=query_variable,
                     retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
-                        dataset_configs['retrieval_model']
-                    )
-                )
+                        dataset_configs["retrieval_model"]
+                    ),
+                ),
             )
         else:
             return DatasetEntity(
@@ -86,15 +83,15 @@ class DatasetConfigManager:
                 retrieve_config=DatasetRetrieveConfigEntity(
                     query_variable=query_variable,
                     retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
-                        dataset_configs['retrieval_model']
+                        dataset_configs["retrieval_model"]
                     ),
-                    top_k=dataset_configs.get('top_k', 4),
-                    score_threshold=dataset_configs.get('score_threshold'),
-                    reranking_model=dataset_configs.get('reranking_model'),
-                    weights=dataset_configs.get('weights'),
-                    reranking_enabled=dataset_configs.get('reranking_enabled', True),
-                    rerank_mode=dataset_configs.get('reranking_mode', 'reranking_model'),
-                )
+                    top_k=dataset_configs.get("top_k", 4),
+                    score_threshold=dataset_configs.get("score_threshold"),
+                    reranking_model=dataset_configs.get("reranking_model"),
+                    weights=dataset_configs.get("weights"),
+                    reranking_enabled=dataset_configs.get("reranking_enabled", True),
+                    rerank_mode=dataset_configs.get("reranking_mode", "reranking_model"),
+                ),
             )
 
     @classmethod
@@ -111,13 +108,10 @@ class DatasetConfigManager:
 
         # dataset_configs
         if not config.get("dataset_configs"):
-            config["dataset_configs"] = {'retrieval_model': 'single'}
+            config["dataset_configs"] = {"retrieval_model": "single"}
 
         if not config["dataset_configs"].get("datasets"):
-            config["dataset_configs"]["datasets"] = {
-                "strategy": "router",
-                "datasets": []
-            }
+            config["dataset_configs"]["datasets"] = {"strategy": "router", "datasets": []}
 
         if not isinstance(config["dataset_configs"], dict):
             raise ValueError("dataset_configs must be of object type")
@@ -125,8 +119,9 @@ class DatasetConfigManager:
         if not isinstance(config["dataset_configs"], dict):
             raise ValueError("dataset_configs must be of object type")
 
-        need_manual_query_datasets = (config.get("dataset_configs")
-                                      and config["dataset_configs"].get("datasets", {}).get("datasets"))
+        need_manual_query_datasets = config.get("dataset_configs") and config["dataset_configs"].get(
+            "datasets", {}
+        ).get("datasets")
 
         if need_manual_query_datasets and app_mode == AppMode.COMPLETION:
             # Only check when mode is completion
@@ -148,10 +143,7 @@ class DatasetConfigManager:
         """
         # Extract dataset config for legacy compatibility
         if not config.get("agent_mode"):
-            config["agent_mode"] = {
-                "enabled": False,
-                "tools": []
-            }
+            config["agent_mode"] = {"enabled": False, "tools": []}
 
         if not isinstance(config["agent_mode"], dict):
             raise ValueError("agent_mode must be of object type")
@@ -175,7 +167,7 @@ class DatasetConfigManager:
             config["agent_mode"]["strategy"] = PlanningStrategy.ROUTER.value
 
         has_datasets = False
-        if config["agent_mode"]["strategy"] in [PlanningStrategy.ROUTER.value, PlanningStrategy.REACT_ROUTER.value]:
+        if config["agent_mode"]["strategy"] in {PlanningStrategy.ROUTER.value, PlanningStrategy.REACT_ROUTER.value}:
             for tool in config["agent_mode"]["tools"]:
                 key = list(tool.keys())[0]
                 if key == "dataset":
@@ -188,7 +180,7 @@ class DatasetConfigManager:
                     if not isinstance(tool_item["enabled"], bool):
                         raise ValueError("enabled in agent_mode.tools must be of boolean type")
 
-                    if 'id' not in tool_item:
+                    if "id" not in tool_item:
                         raise ValueError("id is required in dataset")
 
                     try:
diff --git a/api/core/app/app_config/easy_ui_based_app/model_config/converter.py b/api/core/app/app_config/easy_ui_based_app/model_config/converter.py
index 5c9b2cfec7..a91b9f0f02 100644
--- a/api/core/app/app_config/easy_ui_based_app/model_config/converter.py
+++ b/api/core/app/app_config/easy_ui_based_app/model_config/converter.py
@@ -11,9 +11,7 @@ from core.provider_manager import ProviderManager
 
 class ModelConfigConverter:
     @classmethod
-    def convert(cls, app_config: EasyUIBasedAppConfig,
-                skip_check: bool = False) \
-            -> ModelConfigWithCredentialsEntity:
+    def convert(cls, app_config: EasyUIBasedAppConfig, skip_check: bool = False) -> ModelConfigWithCredentialsEntity:
         """
         Convert app model config dict to entity.
         :param app_config: app config
@@ -25,9 +23,7 @@ class ModelConfigConverter:
 
         provider_manager = ProviderManager()
         provider_model_bundle = provider_manager.get_provider_model_bundle(
-            tenant_id=app_config.tenant_id,
-            provider=model_config.provider,
-            model_type=ModelType.LLM
+            tenant_id=app_config.tenant_id, provider=model_config.provider, model_type=ModelType.LLM
         )
 
         provider_name = provider_model_bundle.configuration.provider.provider
@@ -38,8 +34,7 @@ class ModelConfigConverter:
 
         # check model credentials
         model_credentials = provider_model_bundle.configuration.get_current_credentials(
-            model_type=ModelType.LLM,
-            model=model_config.model
+            model_type=ModelType.LLM, model=model_config.model
         )
 
         if model_credentials is None:
@@ -51,8 +46,7 @@ class ModelConfigConverter:
         if not skip_check:
             # check model
             provider_model = provider_model_bundle.configuration.get_provider_model(
-                model=model_config.model,
-                model_type=ModelType.LLM
+                model=model_config.model, model_type=ModelType.LLM
             )
 
             if provider_model is None:
@@ -69,24 +63,18 @@ class ModelConfigConverter:
         # model config
         completion_params = model_config.parameters
         stop = []
-        if 'stop' in completion_params:
-            stop = completion_params['stop']
-            del completion_params['stop']
+        if "stop" in completion_params:
+            stop = completion_params["stop"]
+            del completion_params["stop"]
 
         # get model mode
         model_mode = model_config.mode
         if not model_mode:
-            mode_enum = model_type_instance.get_model_mode(
-                model=model_config.model,
-                credentials=model_credentials
-            )
+            mode_enum = model_type_instance.get_model_mode(model=model_config.model, credentials=model_credentials)
 
             model_mode = mode_enum.value
 
-        model_schema = model_type_instance.get_model_schema(
-            model_config.model,
-            model_credentials
-        )
+        model_schema = model_type_instance.get_model_schema(model_config.model, model_credentials)
 
         if not skip_check and not model_schema:
             raise ValueError(f"Model {model_name} not exist.")
diff --git a/api/core/app/app_config/easy_ui_based_app/model_config/manager.py b/api/core/app/app_config/easy_ui_based_app/model_config/manager.py
index 730a9527cf..b5e4554181 100644
--- a/api/core/app/app_config/easy_ui_based_app/model_config/manager.py
+++ b/api/core/app/app_config/easy_ui_based_app/model_config/manager.py
@@ -13,23 +13,23 @@ class ModelConfigManager:
         :param config: model config args
         """
         # model config
-        model_config = config.get('model')
+        model_config = config.get("model")
 
         if not model_config:
             raise ValueError("model is required")
 
-        completion_params = model_config.get('completion_params')
+        completion_params = model_config.get("completion_params")
         stop = []
-        if 'stop' in completion_params:
-            stop = completion_params['stop']
-            del completion_params['stop']
+        if "stop" in completion_params:
+            stop = completion_params["stop"]
+            del completion_params["stop"]
 
         # get model mode
-        model_mode = model_config.get('mode')
+        model_mode = model_config.get("mode")
 
         return ModelConfigEntity(
-            provider=config['model']['provider'],
-            model=config['model']['name'],
+            provider=config["model"]["provider"],
+            model=config["model"]["name"],
             mode=model_mode,
             parameters=completion_params,
             stop=stop,
@@ -43,7 +43,7 @@ class ModelConfigManager:
         :param tenant_id: tenant id
         :param config: app model config args
         """
-        if 'model' not in config:
+        if "model" not in config:
             raise ValueError("model is required")
 
         if not isinstance(config["model"], dict):
@@ -52,17 +52,16 @@ class ModelConfigManager:
         # model.provider
         provider_entities = model_provider_factory.get_providers()
         model_provider_names = [provider.provider for provider in provider_entities]
-        if 'provider' not in config["model"] or config["model"]["provider"] not in model_provider_names:
+        if "provider" not in config["model"] or config["model"]["provider"] not in model_provider_names:
             raise ValueError(f"model.provider is required and must be in {str(model_provider_names)}")
 
         # model.name
-        if 'name' not in config["model"]:
+        if "name" not in config["model"]:
             raise ValueError("model.name is required")
 
         provider_manager = ProviderManager()
         models = provider_manager.get_configurations(tenant_id).get_models(
-            provider=config["model"]["provider"],
-            model_type=ModelType.LLM
+            provider=config["model"]["provider"], model_type=ModelType.LLM
         )
 
         if not models:
@@ -80,12 +79,12 @@ class ModelConfigManager:
 
         # model.mode
         if model_mode:
-            config['model']["mode"] = model_mode
+            config["model"]["mode"] = model_mode
         else:
-            config['model']["mode"] = "completion"
+            config["model"]["mode"] = "completion"
 
         # model.completion_params
-        if 'completion_params' not in config["model"]:
+        if "completion_params" not in config["model"]:
             raise ValueError("model.completion_params is required")
 
         config["model"]["completion_params"] = cls.validate_model_completion_params(
@@ -101,7 +100,7 @@ class ModelConfigManager:
             raise ValueError("model.completion_params must be of object type")
 
         # stop
-        if 'stop' not in cp:
+        if "stop" not in cp:
             cp["stop"] = []
         elif not isinstance(cp["stop"], list):
             raise ValueError("stop in model.completion_params must be of list type")
diff --git a/api/core/app/app_config/easy_ui_based_app/prompt_template/manager.py b/api/core/app/app_config/easy_ui_based_app/prompt_template/manager.py
index 1f410758aa..82a0e56ce8 100644
--- a/api/core/app/app_config/easy_ui_based_app/prompt_template/manager.py
+++ b/api/core/app/app_config/easy_ui_based_app/prompt_template/manager.py
@@ -14,39 +14,33 @@ class PromptTemplateConfigManager:
         if not config.get("prompt_type"):
             raise ValueError("prompt_type is required")
 
-        prompt_type = PromptTemplateEntity.PromptType.value_of(config['prompt_type'])
+        prompt_type = PromptTemplateEntity.PromptType.value_of(config["prompt_type"])
         if prompt_type == PromptTemplateEntity.PromptType.SIMPLE:
             simple_prompt_template = config.get("pre_prompt", "")
-            return PromptTemplateEntity(
-                prompt_type=prompt_type,
-                simple_prompt_template=simple_prompt_template
-            )
+            return PromptTemplateEntity(prompt_type=prompt_type, simple_prompt_template=simple_prompt_template)
         else:
             advanced_chat_prompt_template = None
             chat_prompt_config = config.get("chat_prompt_config", {})
             if chat_prompt_config:
                 chat_prompt_messages = []
                 for message in chat_prompt_config.get("prompt", []):
-                    chat_prompt_messages.append({
-                        "text": message["text"],
-                        "role": PromptMessageRole.value_of(message["role"])
-                    })
+                    chat_prompt_messages.append(
+                        {"text": message["text"], "role": PromptMessageRole.value_of(message["role"])}
+                    )
 
-                advanced_chat_prompt_template = AdvancedChatPromptTemplateEntity(
-                    messages=chat_prompt_messages
-                )
+                advanced_chat_prompt_template = AdvancedChatPromptTemplateEntity(messages=chat_prompt_messages)
 
             advanced_completion_prompt_template = None
             completion_prompt_config = config.get("completion_prompt_config", {})
             if completion_prompt_config:
                 completion_prompt_template_params = {
-                    'prompt': completion_prompt_config['prompt']['text'],
+                    "prompt": completion_prompt_config["prompt"]["text"],
                 }
 
-                if 'conversation_histories_role' in completion_prompt_config:
-                    completion_prompt_template_params['role_prefix'] = {
-                        'user': completion_prompt_config['conversation_histories_role']['user_prefix'],
-                        'assistant': completion_prompt_config['conversation_histories_role']['assistant_prefix']
+                if "conversation_histories_role" in completion_prompt_config:
+                    completion_prompt_template_params["role_prefix"] = {
+                        "user": completion_prompt_config["conversation_histories_role"]["user_prefix"],
+                        "assistant": completion_prompt_config["conversation_histories_role"]["assistant_prefix"],
                     }
 
                 advanced_completion_prompt_template = AdvancedCompletionPromptTemplateEntity(
@@ -56,7 +50,7 @@ class PromptTemplateConfigManager:
             return PromptTemplateEntity(
                 prompt_type=prompt_type,
                 advanced_chat_prompt_template=advanced_chat_prompt_template,
-                advanced_completion_prompt_template=advanced_completion_prompt_template
+                advanced_completion_prompt_template=advanced_completion_prompt_template,
             )
 
     @classmethod
@@ -72,7 +66,7 @@ class PromptTemplateConfigManager:
             config["prompt_type"] = PromptTemplateEntity.PromptType.SIMPLE.value
 
         prompt_type_vals = [typ.value for typ in PromptTemplateEntity.PromptType]
-        if config['prompt_type'] not in prompt_type_vals:
+        if config["prompt_type"] not in prompt_type_vals:
             raise ValueError(f"prompt_type must be in {prompt_type_vals}")
 
         # chat_prompt_config
@@ -89,27 +83,28 @@ class PromptTemplateConfigManager:
         if not isinstance(config["completion_prompt_config"], dict):
             raise ValueError("completion_prompt_config must be of object type")
 
-        if config['prompt_type'] == PromptTemplateEntity.PromptType.ADVANCED.value:
-            if not config['chat_prompt_config'] and not config['completion_prompt_config']:
-                raise ValueError("chat_prompt_config or completion_prompt_config is required "
-                                 "when prompt_type is advanced")
+        if config["prompt_type"] == PromptTemplateEntity.PromptType.ADVANCED.value:
+            if not config["chat_prompt_config"] and not config["completion_prompt_config"]:
+                raise ValueError(
+                    "chat_prompt_config or completion_prompt_config is required when prompt_type is advanced"
+                )
 
             model_mode_vals = [mode.value for mode in ModelMode]
-            if config['model']["mode"] not in model_mode_vals:
+            if config["model"]["mode"] not in model_mode_vals:
                 raise ValueError(f"model.mode must be in {model_mode_vals} when prompt_type is advanced")
 
-            if app_mode == AppMode.CHAT and config['model']["mode"] == ModelMode.COMPLETION.value:
-                user_prefix = config['completion_prompt_config']['conversation_histories_role']['user_prefix']
-                assistant_prefix = config['completion_prompt_config']['conversation_histories_role']['assistant_prefix']
+            if app_mode == AppMode.CHAT and config["model"]["mode"] == ModelMode.COMPLETION.value:
+                user_prefix = config["completion_prompt_config"]["conversation_histories_role"]["user_prefix"]
+                assistant_prefix = config["completion_prompt_config"]["conversation_histories_role"]["assistant_prefix"]
 
                 if not user_prefix:
-                    config['completion_prompt_config']['conversation_histories_role']['user_prefix'] = 'Human'
+                    config["completion_prompt_config"]["conversation_histories_role"]["user_prefix"] = "Human"
 
                 if not assistant_prefix:
-                    config['completion_prompt_config']['conversation_histories_role']['assistant_prefix'] = 'Assistant'
+                    config["completion_prompt_config"]["conversation_histories_role"]["assistant_prefix"] = "Assistant"
 
-            if config['model']["mode"] == ModelMode.CHAT.value:
-                prompt_list = config['chat_prompt_config']['prompt']
+            if config["model"]["mode"] == ModelMode.CHAT.value:
+                prompt_list = config["chat_prompt_config"]["prompt"]
 
                 if len(prompt_list) > 10:
                     raise ValueError("prompt messages must be less than 10")
diff --git a/api/core/app/app_config/easy_ui_based_app/variables/manager.py b/api/core/app/app_config/easy_ui_based_app/variables/manager.py
index 15fa4d99fd..a1bfde3208 100644
--- a/api/core/app/app_config/easy_ui_based_app/variables/manager.py
+++ b/api/core/app/app_config/easy_ui_based_app/variables/manager.py
@@ -16,51 +16,49 @@ class BasicVariablesConfigManager:
         variable_entities = []
 
         # old external_data_tools
-        external_data_tools = config.get('external_data_tools', [])
+        external_data_tools = config.get("external_data_tools", [])
         for external_data_tool in external_data_tools:
-            if 'enabled' not in external_data_tool or not external_data_tool['enabled']:
+            if "enabled" not in external_data_tool or not external_data_tool["enabled"]:
                 continue
 
             external_data_variables.append(
                 ExternalDataVariableEntity(
-                    variable=external_data_tool['variable'],
-                    type=external_data_tool['type'],
-                    config=external_data_tool['config']
+                    variable=external_data_tool["variable"],
+                    type=external_data_tool["type"],
+                    config=external_data_tool["config"],
                 )
             )
 
         # variables and external_data_tools
-        for variables in config.get('user_input_form', []):
+        for variables in config.get("user_input_form", []):
             variable_type = list(variables.keys())[0]
             if variable_type == VariableEntityType.EXTERNAL_DATA_TOOL:
                 variable = variables[variable_type]
-                if 'config' not in variable:
+                if "config" not in variable:
                     continue
 
                 external_data_variables.append(
                     ExternalDataVariableEntity(
-                        variable=variable['variable'],
-                        type=variable['type'],
-                        config=variable['config']
+                        variable=variable["variable"], type=variable["type"], config=variable["config"]
                     )
                 )
-            elif variable_type in [
+            elif variable_type in {
                 VariableEntityType.TEXT_INPUT,
                 VariableEntityType.PARAGRAPH,
                 VariableEntityType.NUMBER,
                 VariableEntityType.SELECT,
-            ]:
+            }:
                 variable = variables[variable_type]
                 variable_entities.append(
                     VariableEntity(
                         type=variable_type,
-                        variable=variable.get('variable'),
-                        description=variable.get('description'),
-                        label=variable.get('label'),
-                        required=variable.get('required', False),
-                        max_length=variable.get('max_length'),
-                        options=variable.get('options'),
-                        default=variable.get('default'),
+                        variable=variable.get("variable"),
+                        description=variable.get("description"),
+                        label=variable.get("label"),
+                        required=variable.get("required", False),
+                        max_length=variable.get("max_length"),
+                        options=variable.get("options"),
+                        default=variable.get("default"),
                     )
                 )
 
@@ -99,17 +97,17 @@ class BasicVariablesConfigManager:
         variables = []
         for item in config["user_input_form"]:
             key = list(item.keys())[0]
-            if key not in ["text-input", "select", "paragraph", "number", "external_data_tool"]:
+            if key not in {"text-input", "select", "paragraph", "number", "external_data_tool"}:
                 raise ValueError("Keys in user_input_form list can only be 'text-input', 'paragraph'  or 'select'")
 
             form_item = item[key]
-            if 'label' not in form_item:
+            if "label" not in form_item:
                 raise ValueError("label is required in user_input_form")
 
             if not isinstance(form_item["label"], str):
                 raise ValueError("label in user_input_form must be of string type")
 
-            if 'variable' not in form_item:
+            if "variable" not in form_item:
                 raise ValueError("variable is required in user_input_form")
 
             if not isinstance(form_item["variable"], str):
@@ -117,26 +115,24 @@ class BasicVariablesConfigManager:
 
             pattern = re.compile(r"^(?!\d)[\u4e00-\u9fa5A-Za-z0-9_\U0001F300-\U0001F64F\U0001F680-\U0001F6FF]{1,100}$")
             if pattern.match(form_item["variable"]) is None:
-                raise ValueError("variable in user_input_form must be a string, "
-                                 "and cannot start with a number")
+                raise ValueError("variable in user_input_form must be a string, and cannot start with a number")
 
             variables.append(form_item["variable"])
 
-            if 'required' not in form_item or not form_item["required"]:
+            if "required" not in form_item or not form_item["required"]:
                 form_item["required"] = False
 
             if not isinstance(form_item["required"], bool):
                 raise ValueError("required in user_input_form must be of boolean type")
 
             if key == "select":
-                if 'options' not in form_item or not form_item["options"]:
+                if "options" not in form_item or not form_item["options"]:
                     form_item["options"] = []
 
                 if not isinstance(form_item["options"], list):
                     raise ValueError("options in user_input_form must be a list of strings")
 
-                if "default" in form_item and form_item['default'] \
-                        and form_item["default"] not in form_item["options"]:
+                if "default" in form_item and form_item["default"] and form_item["default"] not in form_item["options"]:
                     raise ValueError("default value in user_input_form must be in the options list")
 
         return config, ["user_input_form"]
@@ -168,10 +164,6 @@ class BasicVariablesConfigManager:
             typ = tool["type"]
             config = tool["config"]
 
-            ExternalDataToolFactory.validate_config(
-                name=typ,
-                tenant_id=tenant_id,
-                config=config
-            )
+            ExternalDataToolFactory.validate_config(name=typ, tenant_id=tenant_id, config=config)
 
         return config, ["external_data_tools"]
diff --git a/api/core/app/app_config/entities.py b/api/core/app/app_config/entities.py
index bbb10d3d76..7e5899bafa 100644
--- a/api/core/app/app_config/entities.py
+++ b/api/core/app/app_config/entities.py
@@ -12,6 +12,7 @@ class ModelConfigEntity(BaseModel):
     """
     Model Config Entity.
     """
+
     provider: str
     model: str
     mode: Optional[str] = None
@@ -23,6 +24,7 @@ class AdvancedChatMessageEntity(BaseModel):
     """
     Advanced Chat Message Entity.
     """
+
     text: str
     role: PromptMessageRole
 
@@ -31,6 +33,7 @@ class AdvancedChatPromptTemplateEntity(BaseModel):
     """
     Advanced Chat Prompt Template Entity.
     """
+
     messages: list[AdvancedChatMessageEntity]
 
 
@@ -43,6 +46,7 @@ class AdvancedCompletionPromptTemplateEntity(BaseModel):
         """
         Role Prefix Entity.
         """
+
         user: str
         assistant: str
 
@@ -60,11 +64,12 @@ class PromptTemplateEntity(BaseModel):
         Prompt Type.
         'simple', 'advanced'
         """
-        SIMPLE = 'simple'
-        ADVANCED = 'advanced'
+
+        SIMPLE = "simple"
+        ADVANCED = "advanced"
 
         @classmethod
-        def value_of(cls, value: str) -> 'PromptType':
+        def value_of(cls, value: str) -> "PromptType":
             """
             Get value of given mode.
 
@@ -74,7 +79,7 @@ class PromptTemplateEntity(BaseModel):
             for mode in cls:
                 if mode.value == value:
                     return mode
-            raise ValueError(f'invalid prompt type value {value}')
+            raise ValueError(f"invalid prompt type value {value}")
 
     prompt_type: PromptType
     simple_prompt_template: Optional[str] = None
@@ -87,7 +92,7 @@ class VariableEntityType(str, Enum):
     SELECT = "select"
     PARAGRAPH = "paragraph"
     NUMBER = "number"
-    EXTERNAL_DATA_TOOL = "external-data-tool"
+    EXTERNAL_DATA_TOOL = "external_data_tool"
 
 
 class VariableEntity(BaseModel):
@@ -110,6 +115,7 @@ class ExternalDataVariableEntity(BaseModel):
     """
     External Data Variable Entity.
     """
+
     variable: str
     type: str
     config: dict[str, Any] = {}
@@ -125,11 +131,12 @@ class DatasetRetrieveConfigEntity(BaseModel):
         Dataset Retrieve Strategy.
         'single' or 'multiple'
         """
-        SINGLE = 'single'
-        MULTIPLE = 'multiple'
+
+        SINGLE = "single"
+        MULTIPLE = "multiple"
 
         @classmethod
-        def value_of(cls, value: str) -> 'RetrieveStrategy':
+        def value_of(cls, value: str) -> "RetrieveStrategy":
             """
             Get value of given mode.
 
@@ -139,25 +146,24 @@ class DatasetRetrieveConfigEntity(BaseModel):
             for mode in cls:
                 if mode.value == value:
                     return mode
-            raise ValueError(f'invalid retrieve strategy value {value}')
+            raise ValueError(f"invalid retrieve strategy value {value}")
 
     query_variable: Optional[str] = None  # Only when app mode is completion
 
     retrieve_strategy: RetrieveStrategy
     top_k: Optional[int] = None
-    score_threshold: Optional[float] = .0
-    rerank_mode: Optional[str] = 'reranking_model'
+    score_threshold: Optional[float] = 0.0
+    rerank_mode: Optional[str] = "reranking_model"
     reranking_model: Optional[dict] = None
     weights: Optional[dict] = None
     reranking_enabled: Optional[bool] = True
 
 
-
-
 class DatasetEntity(BaseModel):
     """
     Dataset Config Entity.
     """
+
     dataset_ids: list[str]
     retrieve_config: DatasetRetrieveConfigEntity
 
@@ -166,6 +172,7 @@ class SensitiveWordAvoidanceEntity(BaseModel):
     """
     Sensitive Word Avoidance Entity.
     """
+
     type: str
     config: dict[str, Any] = {}
 
@@ -174,6 +181,7 @@ class TextToSpeechEntity(BaseModel):
     """
     Sensitive Word Avoidance Entity.
     """
+
     enabled: bool
     voice: Optional[str] = None
     language: Optional[str] = None
@@ -183,12 +191,11 @@ class TracingConfigEntity(BaseModel):
     """
     Tracing Config Entity.
     """
+
     enabled: bool
     tracing_provider: str
 
 
-
-
 class AppAdditionalFeatures(BaseModel):
     file_upload: Optional[FileExtraConfig] = None
     opening_statement: Optional[str] = None
@@ -200,10 +207,12 @@ class AppAdditionalFeatures(BaseModel):
     text_to_speech: Optional[TextToSpeechEntity] = None
     trace_config: Optional[TracingConfigEntity] = None
 
+
 class AppConfig(BaseModel):
     """
     Application Config Entity.
     """
+
     tenant_id: str
     app_id: str
     app_mode: AppMode
@@ -216,15 +225,17 @@ class EasyUIBasedAppModelConfigFrom(Enum):
     """
     App Model Config From.
     """
-    ARGS = 'args'
-    APP_LATEST_CONFIG = 'app-latest-config'
-    CONVERSATION_SPECIFIC_CONFIG = 'conversation-specific-config'
+
+    ARGS = "args"
+    APP_LATEST_CONFIG = "app-latest-config"
+    CONVERSATION_SPECIFIC_CONFIG = "conversation-specific-config"
 
 
 class EasyUIBasedAppConfig(AppConfig):
     """
     Easy UI Based App Config Entity.
     """
+
     app_model_config_from: EasyUIBasedAppModelConfigFrom
     app_model_config_id: str
     app_model_config_dict: dict
@@ -238,4 +249,5 @@ class WorkflowUIBasedAppConfig(AppConfig):
     """
     Workflow UI Based App Config Entity.
     """
+
     workflow_id: str
diff --git a/api/core/app/app_config/features/file_upload/manager.py b/api/core/app/app_config/features/file_upload/manager.py
index 3da3c2eddb..7a275cb532 100644
--- a/api/core/app/app_config/features/file_upload/manager.py
+++ b/api/core/app/app_config/features/file_upload/manager.py
@@ -13,21 +13,19 @@ class FileUploadConfigManager:
         :param config: model config args
         :param is_vision: if True, the feature is vision feature
         """
-        file_upload_dict = config.get('file_upload')
+        file_upload_dict = config.get("file_upload")
         if file_upload_dict:
-            if file_upload_dict.get('image'):
-                if 'enabled' in file_upload_dict['image'] and file_upload_dict['image']['enabled']:
+            if file_upload_dict.get("image"):
+                if "enabled" in file_upload_dict["image"] and file_upload_dict["image"]["enabled"]:
                     image_config = {
-                        'number_limits': file_upload_dict['image']['number_limits'],
-                        'transfer_methods': file_upload_dict['image']['transfer_methods']
+                        "number_limits": file_upload_dict["image"]["number_limits"],
+                        "transfer_methods": file_upload_dict["image"]["transfer_methods"],
                     }
 
                     if is_vision:
-                        image_config['detail'] = file_upload_dict['image']['detail']
+                        image_config["detail"] = file_upload_dict["image"]["detail"]
 
-                    return FileExtraConfig(
-                        image_config=image_config
-                    )
+                    return FileExtraConfig(image_config=image_config)
 
         return None
 
@@ -49,21 +47,21 @@ class FileUploadConfigManager:
         if not config["file_upload"].get("image"):
             config["file_upload"]["image"] = {"enabled": False}
 
-        if config['file_upload']['image']['enabled']:
-            number_limits = config['file_upload']['image']['number_limits']
+        if config["file_upload"]["image"]["enabled"]:
+            number_limits = config["file_upload"]["image"]["number_limits"]
             if number_limits < 1 or number_limits > 6:
                 raise ValueError("number_limits must be in [1, 6]")
 
             if is_vision:
-                detail = config['file_upload']['image']['detail']
-                if detail not in ['high', 'low']:
+                detail = config["file_upload"]["image"]["detail"]
+                if detail not in {"high", "low"}:
                     raise ValueError("detail must be in ['high', 'low']")
 
-            transfer_methods = config['file_upload']['image']['transfer_methods']
+            transfer_methods = config["file_upload"]["image"]["transfer_methods"]
             if not isinstance(transfer_methods, list):
                 raise ValueError("transfer_methods must be of list type")
             for method in transfer_methods:
-                if method not in ['remote_url', 'local_file']:
+                if method not in {"remote_url", "local_file"}:
                     raise ValueError("transfer_methods must be in ['remote_url', 'local_file']")
 
         return config, ["file_upload"]
diff --git a/api/core/app/app_config/features/more_like_this/manager.py b/api/core/app/app_config/features/more_like_this/manager.py
index 2ba99a5c40..496e1beeec 100644
--- a/api/core/app/app_config/features/more_like_this/manager.py
+++ b/api/core/app/app_config/features/more_like_this/manager.py
@@ -7,9 +7,9 @@ class MoreLikeThisConfigManager:
         :param config: model config args
         """
         more_like_this = False
-        more_like_this_dict = config.get('more_like_this')
+        more_like_this_dict = config.get("more_like_this")
         if more_like_this_dict:
-            if more_like_this_dict.get('enabled'):
+            if more_like_this_dict.get("enabled"):
                 more_like_this = True
 
         return more_like_this
@@ -22,9 +22,7 @@ class MoreLikeThisConfigManager:
         :param config: app model config args
         """
         if not config.get("more_like_this"):
-            config["more_like_this"] = {
-                "enabled": False
-            }
+            config["more_like_this"] = {"enabled": False}
 
         if not isinstance(config["more_like_this"], dict):
             raise ValueError("more_like_this must be of dict type")
diff --git a/api/core/app/app_config/features/opening_statement/manager.py b/api/core/app/app_config/features/opening_statement/manager.py
index 0d8a71bfcf..b4dacbc409 100644
--- a/api/core/app/app_config/features/opening_statement/manager.py
+++ b/api/core/app/app_config/features/opening_statement/manager.py
@@ -1,5 +1,3 @@
-
-
 class OpeningStatementConfigManager:
     @classmethod
     def convert(cls, config: dict) -> tuple[str, list]:
@@ -9,10 +7,10 @@ class OpeningStatementConfigManager:
         :param config: model config args
         """
         # opening statement
-        opening_statement = config.get('opening_statement')
+        opening_statement = config.get("opening_statement")
 
         # suggested questions
-        suggested_questions_list = config.get('suggested_questions')
+        suggested_questions_list = config.get("suggested_questions")
 
         return opening_statement, suggested_questions_list
 
diff --git a/api/core/app/app_config/features/retrieval_resource/manager.py b/api/core/app/app_config/features/retrieval_resource/manager.py
index fca58e12e8..d098abac2f 100644
--- a/api/core/app/app_config/features/retrieval_resource/manager.py
+++ b/api/core/app/app_config/features/retrieval_resource/manager.py
@@ -2,9 +2,9 @@ class RetrievalResourceConfigManager:
     @classmethod
     def convert(cls, config: dict) -> bool:
         show_retrieve_source = False
-        retriever_resource_dict = config.get('retriever_resource')
+        retriever_resource_dict = config.get("retriever_resource")
         if retriever_resource_dict:
-            if retriever_resource_dict.get('enabled'):
+            if retriever_resource_dict.get("enabled"):
                 show_retrieve_source = True
 
         return show_retrieve_source
@@ -17,9 +17,7 @@ class RetrievalResourceConfigManager:
         :param config: app model config args
         """
         if not config.get("retriever_resource"):
-            config["retriever_resource"] = {
-                "enabled": False
-            }
+            config["retriever_resource"] = {"enabled": False}
 
         if not isinstance(config["retriever_resource"], dict):
             raise ValueError("retriever_resource must be of dict type")
diff --git a/api/core/app/app_config/features/speech_to_text/manager.py b/api/core/app/app_config/features/speech_to_text/manager.py
index 88b4be25d3..e10ae03e04 100644
--- a/api/core/app/app_config/features/speech_to_text/manager.py
+++ b/api/core/app/app_config/features/speech_to_text/manager.py
@@ -7,9 +7,9 @@ class SpeechToTextConfigManager:
         :param config: model config args
         """
         speech_to_text = False
-        speech_to_text_dict = config.get('speech_to_text')
+        speech_to_text_dict = config.get("speech_to_text")
         if speech_to_text_dict:
-            if speech_to_text_dict.get('enabled'):
+            if speech_to_text_dict.get("enabled"):
                 speech_to_text = True
 
         return speech_to_text
@@ -22,9 +22,7 @@ class SpeechToTextConfigManager:
         :param config: app model config args
         """
         if not config.get("speech_to_text"):
-            config["speech_to_text"] = {
-                "enabled": False
-            }
+            config["speech_to_text"] = {"enabled": False}
 
         if not isinstance(config["speech_to_text"], dict):
             raise ValueError("speech_to_text must be of dict type")
diff --git a/api/core/app/app_config/features/suggested_questions_after_answer/manager.py b/api/core/app/app_config/features/suggested_questions_after_answer/manager.py
index c6cab01220..9ac5114d12 100644
--- a/api/core/app/app_config/features/suggested_questions_after_answer/manager.py
+++ b/api/core/app/app_config/features/suggested_questions_after_answer/manager.py
@@ -7,9 +7,9 @@ class SuggestedQuestionsAfterAnswerConfigManager:
         :param config: model config args
         """
         suggested_questions_after_answer = False
-        suggested_questions_after_answer_dict = config.get('suggested_questions_after_answer')
+        suggested_questions_after_answer_dict = config.get("suggested_questions_after_answer")
         if suggested_questions_after_answer_dict:
-            if suggested_questions_after_answer_dict.get('enabled'):
+            if suggested_questions_after_answer_dict.get("enabled"):
                 suggested_questions_after_answer = True
 
         return suggested_questions_after_answer
@@ -22,15 +22,15 @@ class SuggestedQuestionsAfterAnswerConfigManager:
         :param config: app model config args
         """
         if not config.get("suggested_questions_after_answer"):
-            config["suggested_questions_after_answer"] = {
-                "enabled": False
-            }
+            config["suggested_questions_after_answer"] = {"enabled": False}
 
         if not isinstance(config["suggested_questions_after_answer"], dict):
             raise ValueError("suggested_questions_after_answer must be of dict type")
 
-        if "enabled" not in config["suggested_questions_after_answer"] or not \
-        config["suggested_questions_after_answer"]["enabled"]:
+        if (
+            "enabled" not in config["suggested_questions_after_answer"]
+            or not config["suggested_questions_after_answer"]["enabled"]
+        ):
             config["suggested_questions_after_answer"]["enabled"] = False
 
         if not isinstance(config["suggested_questions_after_answer"]["enabled"], bool):
diff --git a/api/core/app/app_config/features/text_to_speech/manager.py b/api/core/app/app_config/features/text_to_speech/manager.py
index f11e268e73..1c75981785 100644
--- a/api/core/app/app_config/features/text_to_speech/manager.py
+++ b/api/core/app/app_config/features/text_to_speech/manager.py
@@ -10,13 +10,13 @@ class TextToSpeechConfigManager:
         :param config: model config args
         """
         text_to_speech = None
-        text_to_speech_dict = config.get('text_to_speech')
+        text_to_speech_dict = config.get("text_to_speech")
         if text_to_speech_dict:
-            if text_to_speech_dict.get('enabled'):
+            if text_to_speech_dict.get("enabled"):
                 text_to_speech = TextToSpeechEntity(
-                    enabled=text_to_speech_dict.get('enabled'),
-                    voice=text_to_speech_dict.get('voice'),
-                    language=text_to_speech_dict.get('language'),
+                    enabled=text_to_speech_dict.get("enabled"),
+                    voice=text_to_speech_dict.get("voice"),
+                    language=text_to_speech_dict.get("language"),
                 )
 
         return text_to_speech
@@ -29,11 +29,7 @@ class TextToSpeechConfigManager:
         :param config: app model config args
         """
         if not config.get("text_to_speech"):
-            config["text_to_speech"] = {
-                "enabled": False,
-                "voice": "",
-                "language": ""
-            }
+            config["text_to_speech"] = {"enabled": False, "voice": "", "language": ""}
 
         if not isinstance(config["text_to_speech"], dict):
             raise ValueError("text_to_speech must be of dict type")
diff --git a/api/core/app/apps/advanced_chat/app_config_manager.py b/api/core/app/apps/advanced_chat/app_config_manager.py
index c3d0e8ba03..b52f235849 100644
--- a/api/core/app/apps/advanced_chat/app_config_manager.py
+++ b/api/core/app/apps/advanced_chat/app_config_manager.py
@@ -1,4 +1,3 @@
-
 from core.app.app_config.base_app_config_manager import BaseAppConfigManager
 from core.app.app_config.common.sensitive_word_avoidance.manager import SensitiveWordAvoidanceConfigManager
 from core.app.app_config.entities import WorkflowUIBasedAppConfig
@@ -19,13 +18,13 @@ class AdvancedChatAppConfig(WorkflowUIBasedAppConfig):
     """
     Advanced Chatbot App Config Entity.
     """
+
     pass
 
 
 class AdvancedChatAppConfigManager(BaseAppConfigManager):
     @classmethod
-    def get_app_config(cls, app_model: App,
-                       workflow: Workflow) -> AdvancedChatAppConfig:
+    def get_app_config(cls, app_model: App, workflow: Workflow) -> AdvancedChatAppConfig:
         features_dict = workflow.features_dict
 
         app_mode = AppMode.value_of(app_model.mode)
@@ -34,13 +33,9 @@ class AdvancedChatAppConfigManager(BaseAppConfigManager):
             app_id=app_model.id,
             app_mode=app_mode,
             workflow_id=workflow.id,
-            sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(
-                config=features_dict
-            ),
-            variables=WorkflowVariablesConfigManager.convert(
-                workflow=workflow
-            ),
-            additional_features=cls.convert_features(features_dict, app_mode)
+            sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(config=features_dict),
+            variables=WorkflowVariablesConfigManager.convert(workflow=workflow),
+            additional_features=cls.convert_features(features_dict, app_mode),
         )
 
         return app_config
@@ -58,8 +53,7 @@ class AdvancedChatAppConfigManager(BaseAppConfigManager):
 
         # file upload validation
         config, current_related_config_keys = FileUploadConfigManager.validate_and_set_defaults(
-            config=config,
-            is_vision=False
+            config=config, is_vision=False
         )
         related_config_keys.extend(current_related_config_keys)
 
@@ -69,7 +63,8 @@ class AdvancedChatAppConfigManager(BaseAppConfigManager):
 
         # suggested_questions_after_answer
         config, current_related_config_keys = SuggestedQuestionsAfterAnswerConfigManager.validate_and_set_defaults(
-            config)
+            config
+        )
         related_config_keys.extend(current_related_config_keys)
 
         # speech_to_text
@@ -86,9 +81,7 @@ class AdvancedChatAppConfigManager(BaseAppConfigManager):
 
         # moderation validation
         config, current_related_config_keys = SensitiveWordAvoidanceConfigManager.validate_and_set_defaults(
-            tenant_id=tenant_id,
-            config=config,
-            only_structure_validate=only_structure_validate
+            tenant_id=tenant_id, config=config, only_structure_validate=only_structure_validate
         )
         related_config_keys.extend(current_related_config_keys)
 
@@ -98,4 +91,3 @@ class AdvancedChatAppConfigManager(BaseAppConfigManager):
         filtered_config = {key: config.get(key) for key in related_config_keys}
 
         return filtered_config
-
diff --git a/api/core/app/apps/advanced_chat/app_generator.py b/api/core/app/apps/advanced_chat/app_generator.py
index e7c9ebe097..88e1256ed5 100644
--- a/api/core/app/apps/advanced_chat/app_generator.py
+++ b/api/core/app/apps/advanced_chat/app_generator.py
@@ -4,12 +4,10 @@ import os
 import threading
 import uuid
 from collections.abc import Generator
-from typing import Literal, Union, overload
+from typing import Any, Literal, Optional, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
-from sqlalchemy import select
-from sqlalchemy.orm import Session
 
 import contexts
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
@@ -17,23 +15,18 @@ from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfig
 from core.app.apps.advanced_chat.app_runner import AdvancedChatAppRunner
 from core.app.apps.advanced_chat.generate_response_converter import AdvancedChatAppGenerateResponseConverter
 from core.app.apps.advanced_chat.generate_task_pipeline import AdvancedChatAppGenerateTaskPipeline
-from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException, PublishFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedError, PublishFrom
 from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
-from core.app.entities.app_invoke_entities import (
-    AdvancedChatAppGenerateEntity,
-    InvokeFrom,
-)
+from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom
 from core.app.entities.task_entities import ChatbotAppBlockingResponse, ChatbotAppStreamResponse
 from core.file.message_file_parser import MessageFileParser
 from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.enums import SystemVariableKey
 from extensions.ext_database import db
 from models.account import Account
 from models.model import App, Conversation, EndUser, Message
-from models.workflow import ConversationVariable, Workflow
+from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
@@ -41,7 +34,8 @@ logger = logging.getLogger(__name__)
 class AdvancedChatAppGenerator(MessageBasedAppGenerator):
     @overload
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
         args: dict,
@@ -51,7 +45,8 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
 
     @overload
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
         args: dict,
@@ -60,13 +55,14 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
     ) -> dict: ...
 
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
         args: dict,
         invoke_from: InvokeFrom,
         stream: bool = True,
-    ):
+    ) -> dict[str, Any] | Generator[str, Any, None]:
         """
         Generate App response.
 
@@ -77,44 +73,37 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         :param invoke_from: invoke from source
         :param stream: is stream
         """
-        if not args.get('query'):
-            raise ValueError('query is required')
+        if not args.get("query"):
+            raise ValueError("query is required")
 
-        query = args['query']
+        query = args["query"]
         if not isinstance(query, str):
-            raise ValueError('query must be a string')
+            raise ValueError("query must be a string")
 
-        query = query.replace('\x00', '')
-        inputs = args['inputs']
+        query = query.replace("\x00", "")
+        inputs = args["inputs"]
 
-        extras = {
-            "auto_generate_conversation_name": args.get('auto_generate_name', False)
-        }
+        extras = {"auto_generate_conversation_name": args.get("auto_generate_name", False)}
 
         # get conversation
         conversation = None
-        conversation_id = args.get('conversation_id')
+        conversation_id = args.get("conversation_id")
         if conversation_id:
-            conversation = self._get_conversation_by_user(app_model=app_model, conversation_id=conversation_id, user=user)
+            conversation = self._get_conversation_by_user(
+                app_model=app_model, conversation_id=conversation_id, user=user
+            )
 
         # parse files
-        files = args['files'] if args.get('files') else []
+        files = args["files"] if args.get("files") else []
         message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
         file_extra_config = FileUploadConfigManager.convert(workflow.features_dict, is_vision=False)
         if file_extra_config:
-            file_objs = message_file_parser.validate_and_transform_files_arg(
-                files,
-                file_extra_config,
-                user
-            )
+            file_objs = message_file_parser.validate_and_transform_files_arg(files, file_extra_config, user)
         else:
             file_objs = []
 
         # convert to app config
-        app_config = AdvancedChatAppConfigManager.get_app_config(
-            app_model=app_model,
-            workflow=workflow
-        )
+        app_config = AdvancedChatAppConfigManager.get_app_config(app_model=app_model, workflow=workflow)
 
         # get tracing instance
         user_id = user.id if isinstance(user, Account) else user.session_id
@@ -136,7 +125,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             stream=stream,
             invoke_from=invoke_from,
             extras=extras,
-            trace_manager=trace_manager
+            trace_manager=trace_manager,
         )
         contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
@@ -146,15 +135,12 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             invoke_from=invoke_from,
             application_generate_entity=application_generate_entity,
             conversation=conversation,
-            stream=stream
+            stream=stream,
         )
 
-    def single_iteration_generate(self, app_model: App,
-                                  workflow: Workflow,
-                                  node_id: str,
-                                  user: Account,
-                                  args: dict,
-                                  stream: bool = True):
+    def single_iteration_generate(
+        self, app_model: App, workflow: Workflow, node_id: str, user: Account, args: dict, stream: bool = True
+    ) -> dict[str, Any] | Generator[str, Any, None]:
         """
         Generate App response.
 
@@ -166,43 +152,29 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         :param stream: is stream
         """
         if not node_id:
-            raise ValueError('node_id is required')
+            raise ValueError("node_id is required")
 
-        if args.get('inputs') is None:
-            raise ValueError('inputs is required')
-
-        extras = {
-            "auto_generate_conversation_name": False
-        }
-
-        # get conversation
-        conversation = None
-        conversation_id = args.get('conversation_id')
-        if conversation_id:
-            conversation = self._get_conversation_by_user(app_model=app_model, conversation_id=conversation_id, user=user)
+        if args.get("inputs") is None:
+            raise ValueError("inputs is required")
 
         # convert to app config
-        app_config = AdvancedChatAppConfigManager.get_app_config(
-            app_model=app_model,
-            workflow=workflow
-        )
+        app_config = AdvancedChatAppConfigManager.get_app_config(app_model=app_model, workflow=workflow)
 
         # init application generate entity
         application_generate_entity = AdvancedChatAppGenerateEntity(
             task_id=str(uuid.uuid4()),
             app_config=app_config,
-            conversation_id=conversation.id if conversation else None,
+            conversation_id=None,
             inputs={},
-            query='',
+            query="",
             files=[],
             user_id=user.id,
             stream=stream,
             invoke_from=InvokeFrom.DEBUGGER,
-            extras=extras,
+            extras={"auto_generate_conversation_name": False},
             single_iteration_run=AdvancedChatAppGenerateEntity.SingleIterationRunEntity(
-                node_id=node_id,
-                inputs=args['inputs']
-            )
+                node_id=node_id, inputs=args["inputs"]
+            ),
         )
         contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
@@ -211,32 +183,42 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             user=user,
             invoke_from=InvokeFrom.DEBUGGER,
             application_generate_entity=application_generate_entity,
-            conversation=conversation,
-            stream=stream
+            conversation=None,
+            stream=stream,
         )
 
-    def _generate(self, *,
-                 workflow: Workflow,
-                 user: Union[Account, EndUser],
-                 invoke_from: InvokeFrom,
-                 application_generate_entity: AdvancedChatAppGenerateEntity,
-                 conversation: Conversation | None = None,
-                 stream: bool = True):
+    def _generate(
+        self,
+        *,
+        workflow: Workflow,
+        user: Union[Account, EndUser],
+        invoke_from: InvokeFrom,
+        application_generate_entity: AdvancedChatAppGenerateEntity,
+        conversation: Optional[Conversation] = None,
+        stream: bool = True,
+    ) -> dict[str, Any] | Generator[str, Any, None]:
+        """
+        Generate App response.
+
+        :param workflow: Workflow
+        :param user: account or end user
+        :param invoke_from: invoke from source
+        :param application_generate_entity: application generate entity
+        :param conversation: conversation
+        :param stream: is stream
+        """
         is_first_conversation = False
         if not conversation:
             is_first_conversation = True
 
         # init generate records
-        (
-            conversation,
-            message
-        ) = self._init_generate_records(application_generate_entity, conversation)
+        (conversation, message) = self._init_generate_records(application_generate_entity, conversation)
 
         if is_first_conversation:
             # update conversation features
             conversation.override_model_configs = workflow.features
             db.session.commit()
-            # db.session.refresh(conversation)
+            db.session.refresh(conversation)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
@@ -245,73 +227,21 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             invoke_from=application_generate_entity.invoke_from,
             conversation_id=conversation.id,
             app_mode=conversation.mode,
-            message_id=message.id
+            message_id=message.id,
         )
 
-        # Init conversation variables
-        stmt = select(ConversationVariable).where(
-            ConversationVariable.app_id == conversation.app_id, ConversationVariable.conversation_id == conversation.id
-        )
-        with Session(db.engine) as session:
-            conversation_variables = session.scalars(stmt).all()
-            if not conversation_variables:
-                # Create conversation variables if they don't exist.
-                conversation_variables = [
-                    ConversationVariable.from_variable(
-                        app_id=conversation.app_id, conversation_id=conversation.id, variable=variable
-                    )
-                    for variable in workflow.conversation_variables
-                ]
-                session.add_all(conversation_variables)
-            # Convert database entities to variables.
-            conversation_variables = [item.to_variable() for item in conversation_variables]
-
-            session.commit()
-
-            # Increment dialogue count.
-            conversation.dialogue_count += 1
-
-            conversation_id = conversation.id
-            conversation_dialogue_count = conversation.dialogue_count
-            db.session.commit()
-            db.session.refresh(conversation)
-
-        inputs = application_generate_entity.inputs
-        query = application_generate_entity.query
-        files = application_generate_entity.files
-
-        user_id = None
-        if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
-            end_user = db.session.query(EndUser).filter(EndUser.id == application_generate_entity.user_id).first()
-            if end_user:
-                user_id = end_user.session_id
-        else:
-            user_id = application_generate_entity.user_id
-
-        # Create a variable pool.
-        system_inputs = {
-            SystemVariableKey.QUERY: query,
-            SystemVariableKey.FILES: files,
-            SystemVariableKey.CONVERSATION_ID: conversation_id,
-            SystemVariableKey.USER_ID: user_id,
-            SystemVariableKey.DIALOGUE_COUNT: conversation_dialogue_count,
-        }
-        variable_pool = VariablePool(
-            system_variables=system_inputs,
-            user_inputs=inputs,
-            environment_variables=workflow.environment_variables,
-            conversation_variables=conversation_variables,
-        )
-        contexts.workflow_variable_pool.set(variable_pool)
-
         # new thread
-        worker_thread = threading.Thread(target=self._generate_worker, kwargs={
-            'flask_app': current_app._get_current_object(),
-            'application_generate_entity': application_generate_entity,
-            'queue_manager': queue_manager,
-            'message_id': message.id,
-            'context': contextvars.copy_context(),
-        })
+        worker_thread = threading.Thread(
+            target=self._generate_worker,
+            kwargs={
+                "flask_app": current_app._get_current_object(),  # type: ignore
+                "application_generate_entity": application_generate_entity,
+                "queue_manager": queue_manager,
+                "conversation_id": conversation.id,
+                "message_id": message.id,
+                "context": contextvars.copy_context(),
+            },
+        )
 
         worker_thread.start()
 
@@ -326,16 +256,17 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             stream=stream,
         )
 
-        return AdvancedChatAppGenerateResponseConverter.convert(
-            response=response,
-            invoke_from=invoke_from
-        )
+        return AdvancedChatAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)
 
-    def _generate_worker(self, flask_app: Flask,
-                         application_generate_entity: AdvancedChatAppGenerateEntity,
-                         queue_manager: AppQueueManager,
-                         message_id: str,
-                         context: contextvars.Context) -> None:
+    def _generate_worker(
+        self,
+        flask_app: Flask,
+        application_generate_entity: AdvancedChatAppGenerateEntity,
+        queue_manager: AppQueueManager,
+        conversation_id: str,
+        message_id: str,
+        context: contextvars.Context,
+    ) -> None:
         """
         Generate worker in a new thread.
         :param flask_app: Flask app
@@ -349,40 +280,30 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             var.set(val)
         with flask_app.app_context():
             try:
-                runner = AdvancedChatAppRunner()
-                if application_generate_entity.single_iteration_run:
-                    single_iteration_run = application_generate_entity.single_iteration_run
-                    runner.single_iteration_run(
-                        app_id=application_generate_entity.app_config.app_id,
-                        workflow_id=application_generate_entity.app_config.workflow_id,
-                        queue_manager=queue_manager,
-                        inputs=single_iteration_run.inputs,
-                        node_id=single_iteration_run.node_id,
-                        user_id=application_generate_entity.user_id
-                    )
-                else:
-                    # get message
-                    message = self._get_message(message_id)
+                # get conversation and message
+                conversation = self._get_conversation(conversation_id)
+                message = self._get_message(message_id)
 
-                    # chatbot app
-                    runner = AdvancedChatAppRunner()
-                    runner.run(
-                        application_generate_entity=application_generate_entity,
-                        queue_manager=queue_manager,
-                        message=message
-                    )
-            except GenerateTaskStoppedException:
+                # chatbot app
+                runner = AdvancedChatAppRunner(
+                    application_generate_entity=application_generate_entity,
+                    queue_manager=queue_manager,
+                    conversation=conversation,
+                    message=message,
+                )
+
+                runner.run()
+            except GenerateTaskStoppedError:
                 pass
             except InvokeAuthorizationError:
                 queue_manager.publish_error(
-                    InvokeAuthorizationError('Incorrect API key provided'),
-                    PublishFrom.APPLICATION_MANAGER
+                    InvokeAuthorizationError("Incorrect API key provided"), PublishFrom.APPLICATION_MANAGER
                 )
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except (ValueError, InvokeError) as e:
-                if os.environ.get("DEBUG", "false").lower() == 'true':
+                if os.environ.get("DEBUG", "false").lower() == "true":
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except Exception as e:
@@ -428,7 +349,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             return generate_task_pipeline.process()
         except ValueError as e:
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
-                raise GenerateTaskStoppedException()
+                raise GenerateTaskStoppedError()
             else:
                 logger.exception(e)
                 raise e
diff --git a/api/core/app/apps/advanced_chat/app_generator_tts_publisher.py b/api/core/app/apps/advanced_chat/app_generator_tts_publisher.py
index 0caff4a2e3..18b115dfe4 100644
--- a/api/core/app/apps/advanced_chat/app_generator_tts_publisher.py
+++ b/api/core/app/apps/advanced_chat/app_generator_tts_publisher.py
@@ -21,14 +21,11 @@ class AudioTrunk:
         self.status = status
 
 
-def _invoiceTTS(text_content: str, model_instance, tenant_id: str, voice: str):
+def _invoice_tts(text_content: str, model_instance, tenant_id: str, voice: str):
     if not text_content or text_content.isspace():
         return
     return model_instance.invoke_tts(
-        content_text=text_content.strip(),
-        user="responding_tts",
-        tenant_id=tenant_id,
-        voice=voice
+        content_text=text_content.strip(), user="responding_tts", tenant_id=tenant_id, voice=voice
     )
 
 
@@ -44,28 +41,26 @@ def _process_future(future_queue, audio_queue):
         except Exception as e:
             logging.getLogger(__name__).warning(e)
             break
-    audio_queue.put(AudioTrunk("finish", b''))
+    audio_queue.put(AudioTrunk("finish", b""))
 
 
 class AppGeneratorTTSPublisher:
-
     def __init__(self, tenant_id: str, voice: str):
         self.logger = logging.getLogger(__name__)
         self.tenant_id = tenant_id
-        self.msg_text = ''
+        self.msg_text = ""
         self._audio_queue = queue.Queue()
         self._msg_queue = queue.Queue()
-        self.match = re.compile(r'[。.!?]')
+        self.match = re.compile(r"[。.!?]")
         self.model_manager = ModelManager()
         self.model_instance = self.model_manager.get_default_model_instance(
-            tenant_id=self.tenant_id,
-            model_type=ModelType.TTS
+            tenant_id=self.tenant_id, model_type=ModelType.TTS
         )
         self.voices = self.model_instance.get_tts_voices()
-        values = [voice.get('value') for voice in self.voices]
+        values = [voice.get("value") for voice in self.voices]
         self.voice = voice
         if not voice or voice not in values:
-            self.voice = self.voices[0].get('value')
+            self.voice = self.voices[0].get("value")
         self.MAX_SENTENCE = 2
         self._last_audio_event = None
         self._runtime_thread = threading.Thread(target=self._runtime).start()
@@ -85,8 +80,9 @@ class AppGeneratorTTSPublisher:
                 message = self._msg_queue.get()
                 if message is None:
                     if self.msg_text and len(self.msg_text.strip()) > 0:
-                        futures_result = self.executor.submit(_invoiceTTS, self.msg_text,
-                                                              self.model_instance, self.tenant_id, self.voice)
+                        futures_result = self.executor.submit(
+                            _invoice_tts, self.msg_text, self.model_instance, self.tenant_id, self.voice
+                        )
                         future_queue.put(futures_result)
                     break
                 elif isinstance(message.event, QueueAgentMessageEvent | QueueLLMChunkEvent):
@@ -94,28 +90,27 @@ class AppGeneratorTTSPublisher:
                 elif isinstance(message.event, QueueTextChunkEvent):
                     self.msg_text += message.event.text
                 elif isinstance(message.event, QueueNodeSucceededEvent):
-                    self.msg_text += message.event.outputs.get('output', '')
+                    self.msg_text += message.event.outputs.get("output", "")
                 self.last_message = message
                 sentence_arr, text_tmp = self._extract_sentence(self.msg_text)
                 if len(sentence_arr) >= min(self.MAX_SENTENCE, 7):
                     self.MAX_SENTENCE += 1
-                    text_content = ''.join(sentence_arr)
-                    futures_result = self.executor.submit(_invoiceTTS, text_content,
-                                                          self.model_instance,
-                                                          self.tenant_id,
-                                                          self.voice)
+                    text_content = "".join(sentence_arr)
+                    futures_result = self.executor.submit(
+                        _invoice_tts, text_content, self.model_instance, self.tenant_id, self.voice
+                    )
                     future_queue.put(futures_result)
                     if text_tmp:
                         self.msg_text = text_tmp
                     else:
-                        self.msg_text = ''
+                        self.msg_text = ""
 
             except Exception as e:
                 self.logger.warning(e)
                 break
         future_queue.put(None)
 
-    def checkAndGetAudio(self) -> AudioTrunk | None:
+    def check_and_get_audio(self) -> AudioTrunk | None:
         try:
             if self._last_audio_event and self._last_audio_event.status == "finish":
                 if self.executor:
diff --git a/api/core/app/apps/advanced_chat/app_runner.py b/api/core/app/apps/advanced_chat/app_runner.py
index 5dc03979cf..1bca1e1b71 100644
--- a/api/core/app/apps/advanced_chat/app_runner.py
+++ b/api/core/app/apps/advanced_chat/app_runner.py
@@ -1,145 +1,197 @@
 import logging
 import os
-import time
 from collections.abc import Mapping
-from typing import Any, Optional, cast
+from typing import Any, cast
+
+from sqlalchemy import select
+from sqlalchemy.orm import Session
 
 from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfig
-from core.app.apps.advanced_chat.workflow_event_trigger_callback import WorkflowEventTriggerCallback
-from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
-from core.app.apps.base_app_runner import AppRunner
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.apps.workflow_app_runner import WorkflowBasedAppRunner
 from core.app.apps.workflow_logging_callback import WorkflowLoggingCallback
 from core.app.entities.app_invoke_entities import (
     AdvancedChatAppGenerateEntity,
     InvokeFrom,
 )
-from core.app.entities.queue_entities import QueueAnnotationReplyEvent, QueueStopEvent, QueueTextChunkEvent
-from core.moderation.base import ModerationException
+from core.app.entities.queue_entities import (
+    QueueAnnotationReplyEvent,
+    QueueStopEvent,
+    QueueTextChunkEvent,
+)
+from core.moderation.base import ModerationError
 from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
-from core.workflow.nodes.base_node import UserFrom
-from core.workflow.workflow_engine_manager import WorkflowEngineManager
+from core.workflow.entities.node_entities import UserFrom
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariableKey
+from core.workflow.workflow_entry import WorkflowEntry
 from extensions.ext_database import db
-from models import App, Message, Workflow
+from models.model import App, Conversation, EndUser, Message
+from models.workflow import ConversationVariable, WorkflowType
 
 logger = logging.getLogger(__name__)
 
 
-class AdvancedChatAppRunner(AppRunner):
+class AdvancedChatAppRunner(WorkflowBasedAppRunner):
     """
     AdvancedChat Application Runner
     """
 
-    def run(
+    def __init__(
         self,
         application_generate_entity: AdvancedChatAppGenerateEntity,
         queue_manager: AppQueueManager,
+        conversation: Conversation,
         message: Message,
     ) -> None:
         """
-        Run application
         :param application_generate_entity: application generate entity
         :param queue_manager: application queue manager
         :param conversation: conversation
         :param message: message
+        """
+        super().__init__(queue_manager)
+
+        self.application_generate_entity = application_generate_entity
+        self.conversation = conversation
+        self.message = message
+
+    def run(self) -> None:
+        """
+        Run application
         :return:
         """
-        app_config = application_generate_entity.app_config
+        app_config = self.application_generate_entity.app_config
         app_config = cast(AdvancedChatAppConfig, app_config)
 
         app_record = db.session.query(App).filter(App.id == app_config.app_id).first()
         if not app_record:
-            raise ValueError('App not found')
+            raise ValueError("App not found")
 
         workflow = self.get_workflow(app_model=app_record, workflow_id=app_config.workflow_id)
         if not workflow:
-            raise ValueError('Workflow not initialized')
+            raise ValueError("Workflow not initialized")
 
-        inputs = application_generate_entity.inputs
-        query = application_generate_entity.query
+        user_id = None
+        if self.application_generate_entity.invoke_from in {InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API}:
+            end_user = db.session.query(EndUser).filter(EndUser.id == self.application_generate_entity.user_id).first()
+            if end_user:
+                user_id = end_user.session_id
+        else:
+            user_id = self.application_generate_entity.user_id
 
-        # moderation
-        if self.handle_input_moderation(
-            queue_manager=queue_manager,
-            app_record=app_record,
-            app_generate_entity=application_generate_entity,
-            inputs=inputs,
-            query=query,
-            message_id=message.id,
-        ):
-            return
+        workflow_callbacks: list[WorkflowCallback] = []
+        if bool(os.environ.get("DEBUG", "False").lower() == "true"):
+            workflow_callbacks.append(WorkflowLoggingCallback())
 
-        # annotation reply
-        if self.handle_annotation_reply(
-            app_record=app_record,
-            message=message,
-            query=query,
-            queue_manager=queue_manager,
-            app_generate_entity=application_generate_entity,
-        ):
-            return
+        if self.application_generate_entity.single_iteration_run:
+            # if only single iteration run is requested
+            graph, variable_pool = self._get_graph_and_variable_pool_of_single_iteration(
+                workflow=workflow,
+                node_id=self.application_generate_entity.single_iteration_run.node_id,
+                user_inputs=self.application_generate_entity.single_iteration_run.inputs,
+            )
+        else:
+            inputs = self.application_generate_entity.inputs
+            query = self.application_generate_entity.query
+            files = self.application_generate_entity.files
+
+            # moderation
+            if self.handle_input_moderation(
+                app_record=app_record,
+                app_generate_entity=self.application_generate_entity,
+                inputs=inputs,
+                query=query,
+                message_id=self.message.id,
+            ):
+                return
+
+            # annotation reply
+            if self.handle_annotation_reply(
+                app_record=app_record,
+                message=self.message,
+                query=query,
+                app_generate_entity=self.application_generate_entity,
+            ):
+                return
+
+            # Init conversation variables
+            stmt = select(ConversationVariable).where(
+                ConversationVariable.app_id == self.conversation.app_id,
+                ConversationVariable.conversation_id == self.conversation.id,
+            )
+            with Session(db.engine) as session:
+                conversation_variables = session.scalars(stmt).all()
+                if not conversation_variables:
+                    # Create conversation variables if they don't exist.
+                    conversation_variables = [
+                        ConversationVariable.from_variable(
+                            app_id=self.conversation.app_id, conversation_id=self.conversation.id, variable=variable
+                        )
+                        for variable in workflow.conversation_variables
+                    ]
+                    session.add_all(conversation_variables)
+                # Convert database entities to variables.
+                conversation_variables = [item.to_variable() for item in conversation_variables]
+
+                session.commit()
+
+            # Increment dialogue count.
+            self.conversation.dialogue_count += 1
+
+            conversation_dialogue_count = self.conversation.dialogue_count
+            db.session.commit()
+
+            # Create a variable pool.
+            system_inputs = {
+                SystemVariableKey.QUERY: query,
+                SystemVariableKey.FILES: files,
+                SystemVariableKey.CONVERSATION_ID: self.conversation.id,
+                SystemVariableKey.USER_ID: user_id,
+                SystemVariableKey.DIALOGUE_COUNT: conversation_dialogue_count,
+            }
+
+            # init variable pool
+            variable_pool = VariablePool(
+                system_variables=system_inputs,
+                user_inputs=inputs,
+                environment_variables=workflow.environment_variables,
+                conversation_variables=conversation_variables,
+            )
+
+            # init graph
+            graph = self._init_graph(graph_config=workflow.graph_dict)
 
         db.session.close()
 
-        workflow_callbacks: list[WorkflowCallback] = [
-            WorkflowEventTriggerCallback(queue_manager=queue_manager, workflow=workflow)
-        ]
-
-        if bool(os.environ.get('DEBUG', 'False').lower() == 'true'):
-            workflow_callbacks.append(WorkflowLoggingCallback())
-
         # RUN WORKFLOW
-        workflow_engine_manager = WorkflowEngineManager()
-        workflow_engine_manager.run_workflow(
-            workflow=workflow,
-            user_id=application_generate_entity.user_id,
-            user_from=UserFrom.ACCOUNT
-            if application_generate_entity.invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER]
-            else UserFrom.END_USER,
-            invoke_from=application_generate_entity.invoke_from,
+        workflow_entry = WorkflowEntry(
+            tenant_id=workflow.tenant_id,
+            app_id=workflow.app_id,
+            workflow_id=workflow.id,
+            workflow_type=WorkflowType.value_of(workflow.type),
+            graph=graph,
+            graph_config=workflow.graph_dict,
+            user_id=self.application_generate_entity.user_id,
+            user_from=(
+                UserFrom.ACCOUNT
+                if self.application_generate_entity.invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER}
+                else UserFrom.END_USER
+            ),
+            invoke_from=self.application_generate_entity.invoke_from,
+            call_depth=self.application_generate_entity.call_depth,
+            variable_pool=variable_pool,
+        )
+
+        generator = workflow_entry.run(
             callbacks=workflow_callbacks,
-            call_depth=application_generate_entity.call_depth,
         )
 
-    def single_iteration_run(
-        self, app_id: str, workflow_id: str, queue_manager: AppQueueManager, inputs: dict, node_id: str, user_id: str
-    ) -> None:
-        """
-        Single iteration run
-        """
-        app_record = db.session.query(App).filter(App.id == app_id).first()
-        if not app_record:
-            raise ValueError('App not found')
-
-        workflow = self.get_workflow(app_model=app_record, workflow_id=workflow_id)
-        if not workflow:
-            raise ValueError('Workflow not initialized')
-
-        workflow_callbacks = [WorkflowEventTriggerCallback(queue_manager=queue_manager, workflow=workflow)]
-
-        workflow_engine_manager = WorkflowEngineManager()
-        workflow_engine_manager.single_step_run_iteration_workflow_node(
-            workflow=workflow, node_id=node_id, user_id=user_id, user_inputs=inputs, callbacks=workflow_callbacks
-        )
-
-    def get_workflow(self, app_model: App, workflow_id: str) -> Optional[Workflow]:
-        """
-        Get workflow
-        """
-        # fetch workflow by workflow_id
-        workflow = (
-            db.session.query(Workflow)
-            .filter(
-                Workflow.tenant_id == app_model.tenant_id, Workflow.app_id == app_model.id, Workflow.id == workflow_id
-            )
-            .first()
-        )
-
-        # return workflow
-        return workflow
+        for event in generator:
+            self._handle_event(workflow_entry, event)
 
     def handle_input_moderation(
         self,
-        queue_manager: AppQueueManager,
         app_record: App,
         app_generate_entity: AdvancedChatAppGenerateEntity,
         inputs: Mapping[str, Any],
@@ -148,7 +200,6 @@ class AdvancedChatAppRunner(AppRunner):
     ) -> bool:
         """
         Handle input moderation
-        :param queue_manager: application queue manager
         :param app_record: app record
         :param app_generate_entity: application generate entity
         :param inputs: inputs
@@ -166,31 +217,20 @@ class AdvancedChatAppRunner(AppRunner):
                 query=query,
                 message_id=message_id,
             )
-        except ModerationException as e:
-            self._stream_output(
-                queue_manager=queue_manager,
-                text=str(e),
-                stream=app_generate_entity.stream,
-                stopped_by=QueueStopEvent.StopBy.INPUT_MODERATION,
-            )
+        except ModerationError as e:
+            self._complete_with_stream_output(text=str(e), stopped_by=QueueStopEvent.StopBy.INPUT_MODERATION)
             return True
 
         return False
 
     def handle_annotation_reply(
-        self,
-        app_record: App,
-        message: Message,
-        query: str,
-        queue_manager: AppQueueManager,
-        app_generate_entity: AdvancedChatAppGenerateEntity,
+        self, app_record: App, message: Message, query: str, app_generate_entity: AdvancedChatAppGenerateEntity
     ) -> bool:
         """
         Handle annotation reply
         :param app_record: app record
         :param message: message
         :param query: query
-        :param queue_manager: application queue manager
         :param app_generate_entity: application generate entity
         """
         # annotation reply
@@ -203,37 +243,21 @@ class AdvancedChatAppRunner(AppRunner):
         )
 
         if annotation_reply:
-            queue_manager.publish(
-                QueueAnnotationReplyEvent(message_annotation_id=annotation_reply.id), PublishFrom.APPLICATION_MANAGER
-            )
+            self._publish_event(QueueAnnotationReplyEvent(message_annotation_id=annotation_reply.id))
 
-            self._stream_output(
-                queue_manager=queue_manager,
-                text=annotation_reply.content,
-                stream=app_generate_entity.stream,
-                stopped_by=QueueStopEvent.StopBy.ANNOTATION_REPLY,
+            self._complete_with_stream_output(
+                text=annotation_reply.content, stopped_by=QueueStopEvent.StopBy.ANNOTATION_REPLY
             )
             return True
 
         return False
 
-    def _stream_output(
-        self, queue_manager: AppQueueManager, text: str, stream: bool, stopped_by: QueueStopEvent.StopBy
-    ) -> None:
+    def _complete_with_stream_output(self, text: str, stopped_by: QueueStopEvent.StopBy) -> None:
         """
         Direct output
-        :param queue_manager: application queue manager
         :param text: text
-        :param stream: stream
         :return:
         """
-        if stream:
-            index = 0
-            for token in text:
-                queue_manager.publish(QueueTextChunkEvent(text=token), PublishFrom.APPLICATION_MANAGER)
-                index += 1
-                time.sleep(0.01)
-        else:
-            queue_manager.publish(QueueTextChunkEvent(text=text), PublishFrom.APPLICATION_MANAGER)
+        self._publish_event(QueueTextChunkEvent(text=text))
 
-        queue_manager.publish(QueueStopEvent(stopped_by=stopped_by), PublishFrom.APPLICATION_MANAGER)
+        self._publish_event(QueueStopEvent(stopped_by=stopped_by))
diff --git a/api/core/app/apps/advanced_chat/generate_response_converter.py b/api/core/app/apps/advanced_chat/generate_response_converter.py
index ef579827b4..5fbd3e9a94 100644
--- a/api/core/app/apps/advanced_chat/generate_response_converter.py
+++ b/api/core/app/apps/advanced_chat/generate_response_converter.py
@@ -28,15 +28,15 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
         """
         blocking_response = cast(ChatbotAppBlockingResponse, blocking_response)
         response = {
-            'event': 'message',
-            'task_id': blocking_response.task_id,
-            'id': blocking_response.data.id,
-            'message_id': blocking_response.data.message_id,
-            'conversation_id': blocking_response.data.conversation_id,
-            'mode': blocking_response.data.mode,
-            'answer': blocking_response.data.answer,
-            'metadata': blocking_response.data.metadata,
-            'created_at': blocking_response.data.created_at
+            "event": "message",
+            "task_id": blocking_response.task_id,
+            "id": blocking_response.data.id,
+            "message_id": blocking_response.data.message_id,
+            "conversation_id": blocking_response.data.conversation_id,
+            "mode": blocking_response.data.mode,
+            "answer": blocking_response.data.answer,
+            "metadata": blocking_response.data.metadata,
+            "created_at": blocking_response.data.created_at,
         }
 
         return response
@@ -50,13 +50,15 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
         """
         response = cls.convert_blocking_full_response(blocking_response)
 
-        metadata = response.get('metadata', {})
-        response['metadata'] = cls._get_simple_metadata(metadata)
+        metadata = response.get("metadata", {})
+        response["metadata"] = cls._get_simple_metadata(metadata)
 
         return response
 
     @classmethod
-    def convert_stream_full_response(cls, stream_response: Generator[AppStreamResponse, None, None]) -> Generator[str, Any, None]:
+    def convert_stream_full_response(
+        cls, stream_response: Generator[AppStreamResponse, None, None]
+    ) -> Generator[str, Any, None]:
         """
         Convert stream full response.
         :param stream_response: stream response
@@ -67,14 +69,14 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             sub_stream_response = chunk.stream_response
 
             if isinstance(sub_stream_response, PingStreamResponse):
-                yield 'ping'
+                yield "ping"
                 continue
 
             response_chunk = {
-                'event': sub_stream_response.event.value,
-                'conversation_id': chunk.conversation_id,
-                'message_id': chunk.message_id,
-                'created_at': chunk.created_at
+                "event": sub_stream_response.event.value,
+                "conversation_id": chunk.conversation_id,
+                "message_id": chunk.message_id,
+                "created_at": chunk.created_at,
             }
 
             if isinstance(sub_stream_response, ErrorStreamResponse):
@@ -85,7 +87,9 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             yield json.dumps(response_chunk)
 
     @classmethod
-    def convert_stream_simple_response(cls, stream_response: Generator[AppStreamResponse, None, None]) -> Generator[str, Any, None]:
+    def convert_stream_simple_response(
+        cls, stream_response: Generator[AppStreamResponse, None, None]
+    ) -> Generator[str, Any, None]:
         """
         Convert stream simple response.
         :param stream_response: stream response
@@ -96,20 +100,20 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             sub_stream_response = chunk.stream_response
 
             if isinstance(sub_stream_response, PingStreamResponse):
-                yield 'ping'
+                yield "ping"
                 continue
 
             response_chunk = {
-                'event': sub_stream_response.event.value,
-                'conversation_id': chunk.conversation_id,
-                'message_id': chunk.message_id,
-                'created_at': chunk.created_at
+                "event": sub_stream_response.event.value,
+                "conversation_id": chunk.conversation_id,
+                "message_id": chunk.message_id,
+                "created_at": chunk.created_at,
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
                 sub_stream_response_dict = sub_stream_response.to_dict()
-                metadata = sub_stream_response_dict.get('metadata', {})
-                sub_stream_response_dict['metadata'] = cls._get_simple_metadata(metadata)
+                metadata = sub_stream_response_dict.get("metadata", {})
+                sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
             if isinstance(sub_stream_response, ErrorStreamResponse):
                 data = cls._error_to_stream_response(sub_stream_response.err)
diff --git a/api/core/app/apps/advanced_chat/generate_task_pipeline.py b/api/core/app/apps/advanced_chat/generate_task_pipeline.py
index 2b3596ded2..94206a1b1c 100644
--- a/api/core/app/apps/advanced_chat/generate_task_pipeline.py
+++ b/api/core/app/apps/advanced_chat/generate_task_pipeline.py
@@ -2,9 +2,8 @@ import json
 import logging
 import time
 from collections.abc import Generator
-from typing import Any, Optional, Union, cast
+from typing import Any, Optional, Union
 
-import contexts
 from constants.tts_auto_play_timeout import TTS_AUTO_PLAY_TIMEOUT, TTS_AUTO_PLAY_YIELD_CPU_TIME
 from core.app.apps.advanced_chat.app_generator_tts_publisher import AppGeneratorTTSPublisher, AudioTrunk
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
@@ -22,6 +21,9 @@ from core.app.entities.queue_entities import (
     QueueNodeFailedEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
+    QueueParallelBranchRunFailedEvent,
+    QueueParallelBranchRunStartedEvent,
+    QueueParallelBranchRunSucceededEvent,
     QueuePingEvent,
     QueueRetrieverResourcesEvent,
     QueueStopEvent,
@@ -31,34 +33,28 @@ from core.app.entities.queue_entities import (
     QueueWorkflowSucceededEvent,
 )
 from core.app.entities.task_entities import (
-    AdvancedChatTaskState,
     ChatbotAppBlockingResponse,
     ChatbotAppStreamResponse,
-    ChatflowStreamGenerateRoute,
     ErrorStreamResponse,
     MessageAudioEndStreamResponse,
     MessageAudioStreamResponse,
     MessageEndStreamResponse,
     StreamResponse,
+    WorkflowTaskState,
 )
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
 from core.app.task_pipeline.message_cycle_manage import MessageCycleManage
 from core.app.task_pipeline.workflow_cycle_manage import WorkflowCycleManage
-from core.file.file_obj import FileVar
-from core.model_runtime.entities.llm_entities import LLMUsage
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.node_entities import NodeType
 from core.workflow.enums import SystemVariableKey
-from core.workflow.nodes.answer.answer_node import AnswerNode
-from core.workflow.nodes.answer.entities import TextGenerateRouteChunk, VarGenerateRouteChunk
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from events.message_event import message_was_created
 from extensions.ext_database import db
 from models.account import Account
 from models.model import Conversation, EndUser, Message
 from models.workflow import (
     Workflow,
-    WorkflowNodeExecution,
     WorkflowRunStatus,
 )
 
@@ -69,22 +65,22 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
     """
     AdvancedChatAppGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
-    _task_state: AdvancedChatTaskState
+
+    _task_state: WorkflowTaskState
     _application_generate_entity: AdvancedChatAppGenerateEntity
     _workflow: Workflow
     _user: Union[Account, EndUser]
-    # Deprecated
     _workflow_system_variables: dict[SystemVariableKey, Any]
-    _iteration_nested_relations: dict[str, list[str]]
 
     def __init__(
-            self, application_generate_entity: AdvancedChatAppGenerateEntity,
-            workflow: Workflow,
-            queue_manager: AppQueueManager,
-            conversation: Conversation,
-            message: Message,
-            user: Union[Account, EndUser],
-            stream: bool,
+        self,
+        application_generate_entity: AdvancedChatAppGenerateEntity,
+        workflow: Workflow,
+        queue_manager: AppQueueManager,
+        conversation: Conversation,
+        message: Message,
+        user: Union[Account, EndUser],
+        stream: bool,
     ) -> None:
         """
         Initialize AdvancedChatAppGenerateTaskPipeline.
@@ -106,7 +102,6 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         self._workflow = workflow
         self._conversation = conversation
         self._message = message
-        # Deprecated
         self._workflow_system_variables = {
             SystemVariableKey.QUERY: message.query,
             SystemVariableKey.FILES: application_generate_entity.files,
@@ -114,12 +109,8 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             SystemVariableKey.USER_ID: user_id,
         }
 
-        self._task_state = AdvancedChatTaskState(
-            usage=LLMUsage.empty_usage()
-        )
+        self._task_state = WorkflowTaskState()
 
-        self._iteration_nested_relations = self._get_iteration_nested_relations(self._workflow.graph_dict)
-        self._stream_generate_routes = self._get_stream_generate_routes()
         self._conversation_name_generate_thread = None
 
     def process(self):
@@ -133,13 +124,11 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
         # start generate conversation name thread
         self._conversation_name_generate_thread = self._generate_conversation_name(
-            self._conversation,
-            self._application_generate_entity.query
+            self._conversation, self._application_generate_entity.query
         )
 
-        generator = self._wrapper_process_stream_response(
-            trace_manager=self._application_generate_entity.trace_manager
-        )
+        generator = self._wrapper_process_stream_response(trace_manager=self._application_generate_entity.trace_manager)
+
         if self._stream:
             return self._to_stream_response(generator)
         else:
@@ -156,7 +145,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             elif isinstance(stream_response, MessageEndStreamResponse):
                 extras = {}
                 if stream_response.metadata:
-                    extras['metadata'] = stream_response.metadata
+                    extras["metadata"] = stream_response.metadata
 
                 return ChatbotAppBlockingResponse(
                     task_id=stream_response.task_id,
@@ -167,15 +156,17 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                         message_id=self._message.id,
                         answer=self._task_state.answer,
                         created_at=int(self._message.created_at.timestamp()),
-                        **extras
-                    )
+                        **extras,
+                    ),
                 )
             else:
                 continue
 
-        raise Exception('Queue listening stopped unexpectedly.')
+        raise Exception("Queue listening stopped unexpectedly.")
 
-    def _to_stream_response(self, generator: Generator[StreamResponse, None, None]) -> Generator[ChatbotAppStreamResponse, Any, None]:
+    def _to_stream_response(
+        self, generator: Generator[StreamResponse, None, None]
+    ) -> Generator[ChatbotAppStreamResponse, Any, None]:
         """
         To stream response.
         :return:
@@ -185,31 +176,35 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 conversation_id=self._conversation.id,
                 message_id=self._message.id,
                 created_at=int(self._message.created_at.timestamp()),
-                stream_response=stream_response
+                stream_response=stream_response,
             )
 
-    def _listenAudioMsg(self, publisher, task_id: str):
+    def _listen_audio_msg(self, publisher, task_id: str):
         if not publisher:
             return None
-        audio_msg: AudioTrunk = publisher.checkAndGetAudio()
+        audio_msg: AudioTrunk = publisher.check_and_get_audio()
         if audio_msg and audio_msg.status != "finish":
             return MessageAudioStreamResponse(audio=audio_msg.audio, task_id=task_id)
         return None
 
-    def _wrapper_process_stream_response(self, trace_manager: Optional[TraceQueueManager] = None) -> \
-            Generator[StreamResponse, None, None]:
-
-        publisher = None
+    def _wrapper_process_stream_response(
+        self, trace_manager: Optional[TraceQueueManager] = None
+    ) -> Generator[StreamResponse, None, None]:
+        tts_publisher = None
         task_id = self._application_generate_entity.task_id
         tenant_id = self._application_generate_entity.app_config.tenant_id
         features_dict = self._workflow.features_dict
 
-        if features_dict.get('text_to_speech') and features_dict['text_to_speech'].get('enabled') and features_dict[
-                'text_to_speech'].get('autoPlay') == 'enabled':
-            publisher = AppGeneratorTTSPublisher(tenant_id, features_dict['text_to_speech'].get('voice'))
-        for response in self._process_stream_response(publisher=publisher, trace_manager=trace_manager):
+        if (
+            features_dict.get("text_to_speech")
+            and features_dict["text_to_speech"].get("enabled")
+            and features_dict["text_to_speech"].get("autoPlay") == "enabled"
+        ):
+            tts_publisher = AppGeneratorTTSPublisher(tenant_id, features_dict["text_to_speech"].get("voice"))
+
+        for response in self._process_stream_response(tts_publisher=tts_publisher, trace_manager=trace_manager):
             while True:
-                audio_response = self._listenAudioMsg(publisher, task_id=task_id)
+                audio_response = self._listen_audio_msg(tts_publisher, task_id=task_id)
                 if audio_response:
                     yield audio_response
                 else:
@@ -220,9 +215,9 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         # timeout
         while (time.time() - start_listener_time) < TTS_AUTO_PLAY_TIMEOUT:
             try:
-                if not publisher:
+                if not tts_publisher:
                     break
-                audio_trunk = publisher.checkAndGetAudio()
+                audio_trunk = tts_publisher.check_and_get_audio()
                 if audio_trunk is None:
                     # release cpu
                     # sleep 20 ms ( 40ms => 1280 byte audio file,20ms => 640 byte audio file)
@@ -236,38 +231,38 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             except Exception as e:
                 logger.error(e)
                 break
-        yield MessageAudioEndStreamResponse(audio='', task_id=task_id)
+        yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
 
     def _process_stream_response(
-            self,
-            publisher: AppGeneratorTTSPublisher,
-            trace_manager: Optional[TraceQueueManager] = None
+        self,
+        tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
     ) -> Generator[StreamResponse, None, None]:
         """
         Process stream response.
         :return:
         """
-        for message in self._queue_manager.listen():
-            if (message.event
-                    and getattr(message.event, 'metadata', None)
-                    and message.event.metadata.get('is_answer_previous_node', False)
-                    and publisher):
-                publisher.publish(message=message)
-            elif (hasattr(message.event, 'execution_metadata')
-                  and message.event.execution_metadata
-                  and message.event.execution_metadata.get('is_answer_previous_node', False)
-                  and publisher):
-                publisher.publish(message=message)
-            event = message.event
+        # init fake graph runtime state
+        graph_runtime_state = None
+        workflow_run = None
 
-            if isinstance(event, QueueErrorEvent):
+        for queue_message in self._queue_manager.listen():
+            event = queue_message.event
+
+            if isinstance(event, QueuePingEvent):
+                yield self._ping_stream_response()
+            elif isinstance(event, QueueErrorEvent):
                 err = self._handle_error(event, self._message)
                 yield self._error_to_stream_response(err)
                 break
             elif isinstance(event, QueueWorkflowStartedEvent):
-                workflow_run = self._handle_workflow_start()
+                # override graph runtime state
+                graph_runtime_state = event.graph_runtime_state
 
-                self._message = db.session.query(Message).filter(Message.id == self._message.id).first()
+                # init workflow run
+                workflow_run = self._handle_workflow_run_start()
+
+                self._refetch_message()
                 self._message.workflow_run_id = workflow_run.id
 
                 db.session.commit()
@@ -275,137 +270,231 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 db.session.close()
 
                 yield self._workflow_start_to_stream_response(
-                    task_id=self._application_generate_entity.task_id,
-                    workflow_run=workflow_run
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                 )
             elif isinstance(event, QueueNodeStartedEvent):
-                workflow_node_execution = self._handle_node_start(event)
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
 
-                # search stream_generate_routes if node id is answer start at node
-                if not self._task_state.current_stream_generate_state and event.node_id in self._stream_generate_routes:
-                    self._task_state.current_stream_generate_state = self._stream_generate_routes[event.node_id]
-                    # reset current route position to 0
-                    self._task_state.current_stream_generate_state.current_route_position = 0
+                workflow_node_execution = self._handle_node_execution_start(workflow_run=workflow_run, event=event)
 
-                    # generate stream outputs when node started
-                    yield from self._generate_stream_outputs_when_node_started()
-
-                yield self._workflow_node_start_to_stream_response(
+                response = self._workflow_node_start_to_stream_response(
                     event=event,
                     task_id=self._application_generate_entity.task_id,
-                    workflow_node_execution=workflow_node_execution
+                    workflow_node_execution=workflow_node_execution,
                 )
-            elif isinstance(event, QueueNodeSucceededEvent | QueueNodeFailedEvent):
-                workflow_node_execution = self._handle_node_finished(event)
 
-                # stream outputs when node finished
-                generator = self._generate_stream_outputs_when_node_finished()
-                if generator:
-                    yield from generator
+                if response:
+                    yield response
+            elif isinstance(event, QueueNodeSucceededEvent):
+                workflow_node_execution = self._handle_workflow_node_execution_success(event)
 
-                yield self._workflow_node_finish_to_stream_response(
+                response = self._workflow_node_finish_to_stream_response(
+                    event=event,
                     task_id=self._application_generate_entity.task_id,
-                    workflow_node_execution=workflow_node_execution
+                    workflow_node_execution=workflow_node_execution,
                 )
 
-                if isinstance(event, QueueNodeFailedEvent):
-                    yield from self._handle_iteration_exception(
-                        task_id=self._application_generate_entity.task_id,
-                        error=f'Child node failed: {event.error}'
+                if response:
+                    yield response
+            elif isinstance(event, QueueNodeFailedEvent):
+                workflow_node_execution = self._handle_workflow_node_execution_failed(event)
+
+                response = self._workflow_node_finish_to_stream_response(
+                    event=event,
+                    task_id=self._application_generate_entity.task_id,
+                    workflow_node_execution=workflow_node_execution,
+                )
+
+                if response:
+                    yield response
+            elif isinstance(event, QueueParallelBranchRunStartedEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                yield self._workflow_parallel_branch_start_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
+                )
+            elif isinstance(event, QueueParallelBranchRunSucceededEvent | QueueParallelBranchRunFailedEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                yield self._workflow_parallel_branch_finished_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
+                )
+            elif isinstance(event, QueueIterationStartEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                yield self._workflow_iteration_start_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
+                )
+            elif isinstance(event, QueueIterationNextEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                yield self._workflow_iteration_next_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
+                )
+            elif isinstance(event, QueueIterationCompletedEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                yield self._workflow_iteration_completed_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
+                )
+            elif isinstance(event, QueueWorkflowSucceededEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                if not graph_runtime_state:
+                    raise Exception("Graph runtime state not initialized.")
+
+                workflow_run = self._handle_workflow_run_success(
+                    workflow_run=workflow_run,
+                    start_at=graph_runtime_state.start_at,
+                    total_tokens=graph_runtime_state.total_tokens,
+                    total_steps=graph_runtime_state.node_run_steps,
+                    outputs=json.dumps(event.outputs) if event.outputs else None,
+                    conversation_id=self._conversation.id,
+                    trace_manager=trace_manager,
+                )
+
+                yield self._workflow_finish_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
+                )
+
+                self._queue_manager.publish(QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE)
+            elif isinstance(event, QueueWorkflowFailedEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                if not graph_runtime_state:
+                    raise Exception("Graph runtime state not initialized.")
+
+                workflow_run = self._handle_workflow_run_failed(
+                    workflow_run=workflow_run,
+                    start_at=graph_runtime_state.start_at,
+                    total_tokens=graph_runtime_state.total_tokens,
+                    total_steps=graph_runtime_state.node_run_steps,
+                    status=WorkflowRunStatus.FAILED,
+                    error=event.error,
+                    conversation_id=self._conversation.id,
+                    trace_manager=trace_manager,
+                )
+
+                yield self._workflow_finish_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
+                )
+
+                err_event = QueueErrorEvent(error=ValueError(f"Run failed: {workflow_run.error}"))
+                yield self._error_to_stream_response(self._handle_error(err_event, self._message))
+                break
+            elif isinstance(event, QueueStopEvent):
+                if workflow_run and graph_runtime_state:
+                    workflow_run = self._handle_workflow_run_failed(
+                        workflow_run=workflow_run,
+                        start_at=graph_runtime_state.start_at,
+                        total_tokens=graph_runtime_state.total_tokens,
+                        total_steps=graph_runtime_state.node_run_steps,
+                        status=WorkflowRunStatus.STOPPED,
+                        error=event.get_stop_reason(),
+                        conversation_id=self._conversation.id,
+                        trace_manager=trace_manager,
                     )
-            elif isinstance(event, QueueIterationStartEvent | QueueIterationNextEvent | QueueIterationCompletedEvent):
-                if isinstance(event, QueueIterationNextEvent):
-                    # clear ran node execution infos of current iteration
-                    iteration_relations = self._iteration_nested_relations.get(event.node_id)
-                    if iteration_relations:
-                        for node_id in iteration_relations:
-                            self._task_state.ran_node_execution_infos.pop(node_id, None)
 
-                yield self._handle_iteration_to_stream_response(self._application_generate_entity.task_id, event)
-                self._handle_iteration_operation(event)
-            elif isinstance(event, QueueStopEvent | QueueWorkflowSucceededEvent | QueueWorkflowFailedEvent):
-                workflow_run = self._handle_workflow_finished(
-                    event, conversation_id=self._conversation.id, trace_manager=trace_manager
-                )
-                if workflow_run:
                     yield self._workflow_finish_to_stream_response(
-                        task_id=self._application_generate_entity.task_id,
-                        workflow_run=workflow_run
+                        task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
 
-                    if workflow_run.status == WorkflowRunStatus.FAILED.value:
-                        err_event = QueueErrorEvent(error=ValueError(f'Run failed: {workflow_run.error}'))
-                        yield self._error_to_stream_response(self._handle_error(err_event, self._message))
-                        break
-
-                if isinstance(event, QueueStopEvent):
-                    # Save message
-                    self._save_message()
-
-                    yield self._message_end_to_stream_response()
-                    break
-                else:
-                    self._queue_manager.publish(
-                        QueueAdvancedChatMessageEndEvent(),
-                        PublishFrom.TASK_PIPELINE
-                    )
-            elif isinstance(event, QueueAdvancedChatMessageEndEvent):
-                output_moderation_answer = self._handle_output_moderation_when_task_finished(self._task_state.answer)
-                if output_moderation_answer:
-                    self._task_state.answer = output_moderation_answer
-                    yield self._message_replace_to_stream_response(answer=output_moderation_answer)
-
                 # Save message
-                self._save_message()
+                self._save_message(graph_runtime_state=graph_runtime_state)
 
                 yield self._message_end_to_stream_response()
+                break
             elif isinstance(event, QueueRetrieverResourcesEvent):
                 self._handle_retriever_resources(event)
+
+                self._refetch_message()
+
+                self._message.message_metadata = (
+                    json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
+                )
+
+                db.session.commit()
+                db.session.refresh(self._message)
+                db.session.close()
             elif isinstance(event, QueueAnnotationReplyEvent):
                 self._handle_annotation_reply(event)
+
+                self._refetch_message()
+
+                self._message.message_metadata = (
+                    json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
+                )
+
+                db.session.commit()
+                db.session.refresh(self._message)
+                db.session.close()
             elif isinstance(event, QueueTextChunkEvent):
                 delta_text = event.text
                 if delta_text is None:
                     continue
 
-                if not self._is_stream_out_support(
-                        event=event
-                ):
-                    continue
-
                 # handle output moderation chunk
                 should_direct_answer = self._handle_output_moderation_chunk(delta_text)
                 if should_direct_answer:
                     continue
 
+                # only publish tts message at text chunk streaming
+                if tts_publisher:
+                    tts_publisher.publish(message=queue_message)
+
                 self._task_state.answer += delta_text
-                yield self._message_to_stream_response(delta_text, self._message.id)
+                yield self._message_to_stream_response(
+                    answer=delta_text, message_id=self._message.id, from_variable_selector=event.from_variable_selector
+                )
             elif isinstance(event, QueueMessageReplaceEvent):
+                # published by moderation
                 yield self._message_replace_to_stream_response(answer=event.text)
-            elif isinstance(event, QueuePingEvent):
-                yield self._ping_stream_response()
+            elif isinstance(event, QueueAdvancedChatMessageEndEvent):
+                if not graph_runtime_state:
+                    raise Exception("Graph runtime state not initialized.")
+
+                output_moderation_answer = self._handle_output_moderation_when_task_finished(self._task_state.answer)
+                if output_moderation_answer:
+                    self._task_state.answer = output_moderation_answer
+                    yield self._message_replace_to_stream_response(answer=output_moderation_answer)
+
+                # Save message
+                self._save_message(graph_runtime_state=graph_runtime_state)
+
+                yield self._message_end_to_stream_response()
             else:
                 continue
-        if publisher:
-            publisher.publish(None)
+
+        # publish None when task finished
+        if tts_publisher:
+            tts_publisher.publish(None)
+
         if self._conversation_name_generate_thread:
             self._conversation_name_generate_thread.join()
 
-    def _save_message(self) -> None:
+    def _save_message(self, graph_runtime_state: Optional[GraphRuntimeState] = None) -> None:
         """
         Save message.
         :return:
         """
-        self._message = db.session.query(Message).filter(Message.id == self._message.id).first()
+        self._refetch_message()
 
         self._message.answer = self._task_state.answer
         self._message.provider_response_latency = time.perf_counter() - self._start_at
-        self._message.message_metadata = json.dumps(jsonable_encoder(self._task_state.metadata)) \
-            if self._task_state.metadata else None
-
-        if self._task_state.metadata and self._task_state.metadata.get('usage'):
-            usage = LLMUsage(**self._task_state.metadata['usage'])
+        self._message.message_metadata = (
+            json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
+        )
 
+        if graph_runtime_state and graph_runtime_state.llm_usage:
+            usage = graph_runtime_state.llm_usage
             self._message.message_tokens = usage.prompt_tokens
             self._message.message_unit_price = usage.prompt_unit_price
             self._message.message_price_unit = usage.prompt_price_unit
@@ -422,7 +511,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             application_generate_entity=self._application_generate_entity,
             conversation=self._conversation,
             is_first_message=self._application_generate_entity.conversation_id is None,
-            extras=self._application_generate_entity.extras
+            extras=self._application_generate_entity.extras,
         )
 
     def _message_end_to_stream_response(self) -> MessageEndStreamResponse:
@@ -432,331 +521,15 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         """
         extras = {}
         if self._task_state.metadata:
-            extras['metadata'] = self._task_state.metadata
+            extras["metadata"] = self._task_state.metadata.copy()
+
+            if "annotation_reply" in extras["metadata"]:
+                del extras["metadata"]["annotation_reply"]
 
         return MessageEndStreamResponse(
-            task_id=self._application_generate_entity.task_id,
-            id=self._message.id,
-            **extras
+            task_id=self._application_generate_entity.task_id, id=self._message.id, **extras
         )
 
-    def _get_stream_generate_routes(self) -> dict[str, ChatflowStreamGenerateRoute]:
-        """
-        Get stream generate routes.
-        :return:
-        """
-        # find all answer nodes
-        graph = self._workflow.graph_dict
-        answer_node_configs = [
-            node for node in graph['nodes']
-            if node.get('data', {}).get('type') == NodeType.ANSWER.value
-        ]
-
-        # parse stream output node value selectors of answer nodes
-        stream_generate_routes = {}
-        for node_config in answer_node_configs:
-            # get generate route for stream output
-            answer_node_id = node_config['id']
-            generate_route = AnswerNode.extract_generate_route_selectors(node_config)
-            start_node_ids = self._get_answer_start_at_node_ids(graph, answer_node_id)
-            if not start_node_ids:
-                continue
-
-            for start_node_id in start_node_ids:
-                stream_generate_routes[start_node_id] = ChatflowStreamGenerateRoute(
-                    answer_node_id=answer_node_id,
-                    generate_route=generate_route
-                )
-
-        return stream_generate_routes
-
-    def _get_answer_start_at_node_ids(self, graph: dict, target_node_id: str) \
-            -> list[str]:
-        """
-        Get answer start at node id.
-        :param graph: graph
-        :param target_node_id: target node ID
-        :return:
-        """
-        nodes = graph.get('nodes')
-        edges = graph.get('edges')
-
-        # fetch all ingoing edges from source node
-        ingoing_edges = []
-        for edge in edges:
-            if edge.get('target') == target_node_id:
-                ingoing_edges.append(edge)
-
-        if not ingoing_edges:
-            # check if it's the first node in the iteration
-            target_node = next((node for node in nodes if node.get('id') == target_node_id), None)
-            if not target_node:
-                return []
-
-            node_iteration_id = target_node.get('data', {}).get('iteration_id')
-            # get iteration start node id
-            for node in nodes:
-                if node.get('id') == node_iteration_id:
-                    if node.get('data', {}).get('start_node_id') == target_node_id:
-                        return [target_node_id]
-
-            return []
-
-        start_node_ids = []
-        for ingoing_edge in ingoing_edges:
-            source_node_id = ingoing_edge.get('source')
-            source_node = next((node for node in nodes if node.get('id') == source_node_id), None)
-            if not source_node:
-                continue
-
-            node_type = source_node.get('data', {}).get('type')
-            node_iteration_id = source_node.get('data', {}).get('iteration_id')
-            iteration_start_node_id = None
-            if node_iteration_id:
-                iteration_node = next((node for node in nodes if node.get('id') == node_iteration_id), None)
-                iteration_start_node_id = iteration_node.get('data', {}).get('start_node_id')
-
-            if node_type in [
-                NodeType.ANSWER.value,
-                NodeType.IF_ELSE.value,
-                NodeType.QUESTION_CLASSIFIER.value,
-                NodeType.ITERATION.value,
-                NodeType.LOOP.value
-            ]:
-                start_node_id = target_node_id
-                start_node_ids.append(start_node_id)
-            elif node_type == NodeType.START.value or \
-                    node_iteration_id is not None and iteration_start_node_id == source_node.get('id'):
-                start_node_id = source_node_id
-                start_node_ids.append(start_node_id)
-            else:
-                sub_start_node_ids = self._get_answer_start_at_node_ids(graph, source_node_id)
-                if sub_start_node_ids:
-                    start_node_ids.extend(sub_start_node_ids)
-
-        return start_node_ids
-
-    def _get_iteration_nested_relations(self, graph: dict) -> dict[str, list[str]]:
-        """
-        Get iteration nested relations.
-        :param graph: graph
-        :return:
-        """
-        nodes = graph.get('nodes')
-
-        iteration_ids = [node.get('id') for node in nodes
-                         if node.get('data', {}).get('type') in [
-                             NodeType.ITERATION.value,
-                             NodeType.LOOP.value,
-                         ]]
-
-        return {
-            iteration_id: [
-                node.get('id') for node in nodes if node.get('data', {}).get('iteration_id') == iteration_id
-            ] for iteration_id in iteration_ids
-        }
-
-    def _generate_stream_outputs_when_node_started(self) -> Generator:
-        """
-        Generate stream outputs.
-        :return:
-        """
-        if self._task_state.current_stream_generate_state:
-            route_chunks = self._task_state.current_stream_generate_state.generate_route[
-                           self._task_state.current_stream_generate_state.current_route_position:
-                           ]
-
-            for route_chunk in route_chunks:
-                if route_chunk.type == 'text':
-                    route_chunk = cast(TextGenerateRouteChunk, route_chunk)
-
-                    # handle output moderation chunk
-                    should_direct_answer = self._handle_output_moderation_chunk(route_chunk.text)
-                    if should_direct_answer:
-                        continue
-
-                    self._task_state.answer += route_chunk.text
-                    yield self._message_to_stream_response(route_chunk.text, self._message.id)
-                else:
-                    break
-
-                self._task_state.current_stream_generate_state.current_route_position += 1
-
-            # all route chunks are generated
-            if self._task_state.current_stream_generate_state.current_route_position == len(
-                    self._task_state.current_stream_generate_state.generate_route
-            ):
-                self._task_state.current_stream_generate_state = None
-
-    def _generate_stream_outputs_when_node_finished(self) -> Optional[Generator]:
-        """
-        Generate stream outputs.
-        :return:
-        """
-        if not self._task_state.current_stream_generate_state:
-            return
-
-        route_chunks = self._task_state.current_stream_generate_state.generate_route[
-                       self._task_state.current_stream_generate_state.current_route_position:]
-
-        for route_chunk in route_chunks:
-            if route_chunk.type == 'text':
-                route_chunk = cast(TextGenerateRouteChunk, route_chunk)
-                self._task_state.answer += route_chunk.text
-                yield self._message_to_stream_response(route_chunk.text, self._message.id)
-            else:
-                value = None
-                route_chunk = cast(VarGenerateRouteChunk, route_chunk)
-                value_selector = route_chunk.value_selector
-                if not value_selector:
-                    self._task_state.current_stream_generate_state.current_route_position += 1
-                    continue
-
-                route_chunk_node_id = value_selector[0]
-
-                if route_chunk_node_id == 'sys':
-                    # system variable
-                    value = contexts.workflow_variable_pool.get().get(value_selector)
-                    if value:
-                        value = value.text
-                elif route_chunk_node_id in self._iteration_nested_relations:
-                    # it's a iteration variable
-                    if not self._iteration_state or route_chunk_node_id not in self._iteration_state.current_iterations:
-                        continue
-                    iteration_state = self._iteration_state.current_iterations[route_chunk_node_id]
-                    iterator = iteration_state.inputs
-                    if not iterator:
-                        continue
-                    iterator_selector = iterator.get('iterator_selector', [])
-                    if value_selector[1] == 'index':
-                        value = iteration_state.current_index
-                    elif value_selector[1] == 'item':
-                        value = iterator_selector[iteration_state.current_index] if iteration_state.current_index < len(
-                            iterator_selector
-                        ) else None
-                else:
-                    # check chunk node id is before current node id or equal to current node id
-                    if route_chunk_node_id not in self._task_state.ran_node_execution_infos:
-                        break
-
-                    latest_node_execution_info = self._task_state.latest_node_execution_info
-
-                    # get route chunk node execution info
-                    route_chunk_node_execution_info = self._task_state.ran_node_execution_infos[route_chunk_node_id]
-                    if (route_chunk_node_execution_info.node_type == NodeType.LLM
-                            and latest_node_execution_info.node_type == NodeType.LLM):
-                        # only LLM support chunk stream output
-                        self._task_state.current_stream_generate_state.current_route_position += 1
-                        continue
-
-                    # get route chunk node execution
-                    route_chunk_node_execution = db.session.query(WorkflowNodeExecution).filter(
-                        WorkflowNodeExecution.id == route_chunk_node_execution_info.workflow_node_execution_id
-                    ).first()
-
-                    outputs = route_chunk_node_execution.outputs_dict
-
-                    # get value from outputs
-                    value = None
-                    for key in value_selector[1:]:
-                        if not value:
-                            value = outputs.get(key) if outputs else None
-                        else:
-                            value = value.get(key)
-
-                if value is not None:
-                    text = ''
-                    if isinstance(value, str | int | float):
-                        text = str(value)
-                    elif isinstance(value, FileVar):
-                        # convert file to markdown
-                        text = value.to_markdown()
-                    elif isinstance(value, dict):
-                        # handle files
-                        file_vars = self._fetch_files_from_variable_value(value)
-                        if file_vars:
-                            file_var = file_vars[0]
-                            try:
-                                file_var_obj = FileVar(**file_var)
-
-                                # convert file to markdown
-                                text = file_var_obj.to_markdown()
-                            except Exception as e:
-                                logger.error(f'Error creating file var: {e}')
-
-                        if not text:
-                            # other types
-                            text = json.dumps(value, ensure_ascii=False)
-                    elif isinstance(value, list):
-                        # handle files
-                        file_vars = self._fetch_files_from_variable_value(value)
-                        for file_var in file_vars:
-                            try:
-                                file_var_obj = FileVar(**file_var)
-                            except Exception as e:
-                                logger.error(f'Error creating file var: {e}')
-                                continue
-
-                            # convert file to markdown
-                            text = file_var_obj.to_markdown() + ' '
-
-                        text = text.strip()
-
-                        if not text and value:
-                            # other types
-                            text = json.dumps(value, ensure_ascii=False)
-
-                    if text:
-                        self._task_state.answer += text
-                        yield self._message_to_stream_response(text, self._message.id)
-
-            self._task_state.current_stream_generate_state.current_route_position += 1
-
-        # all route chunks are generated
-        if self._task_state.current_stream_generate_state.current_route_position == len(
-                self._task_state.current_stream_generate_state.generate_route
-        ):
-            self._task_state.current_stream_generate_state = None
-
-    def _is_stream_out_support(self, event: QueueTextChunkEvent) -> bool:
-        """
-        Is stream out support
-        :param event: queue text chunk event
-        :return:
-        """
-        if not event.metadata:
-            return True
-
-        if 'node_id' not in event.metadata:
-            return True
-
-        node_type = event.metadata.get('node_type')
-        stream_output_value_selector = event.metadata.get('value_selector')
-        if not stream_output_value_selector:
-            return False
-
-        if not self._task_state.current_stream_generate_state:
-            return False
-
-        route_chunk = self._task_state.current_stream_generate_state.generate_route[
-            self._task_state.current_stream_generate_state.current_route_position]
-
-        if route_chunk.type != 'var':
-            return False
-
-        if node_type != NodeType.LLM:
-            # only LLM support chunk stream output
-            return False
-
-        route_chunk = cast(VarGenerateRouteChunk, route_chunk)
-        value_selector = route_chunk.value_selector
-
-        # check chunk node id is before current node id or equal to current node id
-        if value_selector != stream_output_value_selector:
-            return False
-
-        return True
-
     def _handle_output_moderation_chunk(self, text: str) -> bool:
         """
         Handle output moderation chunk.
@@ -768,17 +541,23 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 # stop subscribe new token when output moderation should direct output
                 self._task_state.answer = self._output_moderation_handler.get_final_output()
                 self._queue_manager.publish(
-                    QueueTextChunkEvent(
-                        text=self._task_state.answer
-                    ), PublishFrom.TASK_PIPELINE
+                    QueueTextChunkEvent(text=self._task_state.answer), PublishFrom.TASK_PIPELINE
                 )
 
                 self._queue_manager.publish(
-                    QueueStopEvent(stopped_by=QueueStopEvent.StopBy.OUTPUT_MODERATION),
-                    PublishFrom.TASK_PIPELINE
+                    QueueStopEvent(stopped_by=QueueStopEvent.StopBy.OUTPUT_MODERATION), PublishFrom.TASK_PIPELINE
                 )
                 return True
             else:
                 self._output_moderation_handler.append_new_token(text)
 
         return False
+
+    def _refetch_message(self) -> None:
+        """
+        Refetch message.
+        :return:
+        """
+        message = db.session.query(Message).filter(Message.id == self._message.id).first()
+        if message:
+            self._message = message
diff --git a/api/core/app/apps/advanced_chat/workflow_event_trigger_callback.py b/api/core/app/apps/advanced_chat/workflow_event_trigger_callback.py
deleted file mode 100644
index 8d43155a08..0000000000
--- a/api/core/app/apps/advanced_chat/workflow_event_trigger_callback.py
+++ /dev/null
@@ -1,203 +0,0 @@
-from typing import Any, Optional
-
-from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
-from core.app.entities.queue_entities import (
-    AppQueueEvent,
-    QueueIterationCompletedEvent,
-    QueueIterationNextEvent,
-    QueueIterationStartEvent,
-    QueueNodeFailedEvent,
-    QueueNodeStartedEvent,
-    QueueNodeSucceededEvent,
-    QueueTextChunkEvent,
-    QueueWorkflowFailedEvent,
-    QueueWorkflowStartedEvent,
-    QueueWorkflowSucceededEvent,
-)
-from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
-from core.workflow.entities.base_node_data_entities import BaseNodeData
-from core.workflow.entities.node_entities import NodeType
-from models.workflow import Workflow
-
-
-class WorkflowEventTriggerCallback(WorkflowCallback):
-
-    def __init__(self, queue_manager: AppQueueManager, workflow: Workflow):
-        self._queue_manager = queue_manager
-
-    def on_workflow_run_started(self) -> None:
-        """
-        Workflow run started
-        """
-        self._queue_manager.publish(
-            QueueWorkflowStartedEvent(),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_run_succeeded(self) -> None:
-        """
-        Workflow run succeeded
-        """
-        self._queue_manager.publish(
-            QueueWorkflowSucceededEvent(),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_run_failed(self, error: str) -> None:
-        """
-        Workflow run failed
-        """
-        self._queue_manager.publish(
-            QueueWorkflowFailedEvent(
-                error=error
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_node_execute_started(self, node_id: str,
-                                         node_type: NodeType,
-                                         node_data: BaseNodeData,
-                                         node_run_index: int = 1,
-                                         predecessor_node_id: Optional[str] = None) -> None:
-        """
-        Workflow node execute started
-        """
-        self._queue_manager.publish(
-            QueueNodeStartedEvent(
-                node_id=node_id,
-                node_type=node_type,
-                node_data=node_data,
-                node_run_index=node_run_index,
-                predecessor_node_id=predecessor_node_id
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_node_execute_succeeded(self, node_id: str,
-                                           node_type: NodeType,
-                                           node_data: BaseNodeData,
-                                           inputs: Optional[dict] = None,
-                                           process_data: Optional[dict] = None,
-                                           outputs: Optional[dict] = None,
-                                           execution_metadata: Optional[dict] = None) -> None:
-        """
-        Workflow node execute succeeded
-        """
-        self._queue_manager.publish(
-            QueueNodeSucceededEvent(
-                node_id=node_id,
-                node_type=node_type,
-                node_data=node_data,
-                inputs=inputs,
-                process_data=process_data,
-                outputs=outputs,
-                execution_metadata=execution_metadata
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_node_execute_failed(self, node_id: str,
-                                        node_type: NodeType,
-                                        node_data: BaseNodeData,
-                                        error: str,
-                                        inputs: Optional[dict] = None,
-                                        outputs: Optional[dict] = None,
-                                        process_data: Optional[dict] = None) -> None:
-        """
-        Workflow node execute failed
-        """
-        self._queue_manager.publish(
-            QueueNodeFailedEvent(
-                node_id=node_id,
-                node_type=node_type,
-                node_data=node_data,
-                inputs=inputs,
-                outputs=outputs,
-                process_data=process_data,
-                error=error
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_node_text_chunk(self, node_id: str, text: str, metadata: Optional[dict] = None) -> None:
-        """
-        Publish text chunk
-        """
-        self._queue_manager.publish(
-            QueueTextChunkEvent(
-                text=text,
-                metadata={
-                    "node_id": node_id,
-                    **metadata
-                }
-            ), PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_iteration_started(self, 
-                                      node_id: str,
-                                      node_type: NodeType,
-                                      node_run_index: int = 1,
-                                      node_data: Optional[BaseNodeData] = None,
-                                      inputs: dict = None,
-                                      predecessor_node_id: Optional[str] = None,
-                                      metadata: Optional[dict] = None) -> None:
-        """
-        Publish iteration started
-        """
-        self._queue_manager.publish(
-            QueueIterationStartEvent(
-                node_id=node_id,
-                node_type=node_type,
-                node_run_index=node_run_index,
-                node_data=node_data,
-                inputs=inputs,
-                predecessor_node_id=predecessor_node_id,
-                metadata=metadata
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_iteration_next(self, node_id: str, 
-                                   node_type: NodeType,
-                                   index: int, 
-                                   node_run_index: int,
-                                   output: Optional[Any]) -> None:
-        """
-        Publish iteration next
-        """
-        self._queue_manager._publish(
-            QueueIterationNextEvent(
-                node_id=node_id,
-                node_type=node_type,
-                index=index,
-                node_run_index=node_run_index,
-                output=output
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_iteration_completed(self, node_id: str, 
-                                        node_type: NodeType,
-                                        node_run_index: int,
-                                        outputs: dict) -> None:
-        """
-        Publish iteration completed
-        """
-        self._queue_manager._publish(
-            QueueIterationCompletedEvent(
-                node_id=node_id,
-                node_type=node_type,
-                node_run_index=node_run_index,
-                outputs=outputs
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_event(self, event: AppQueueEvent) -> None:
-        """
-        Publish event
-        """
-        self._queue_manager.publish(
-            event,
-            PublishFrom.APPLICATION_MANAGER
-        )
diff --git a/api/core/app/apps/agent_chat/app_config_manager.py b/api/core/app/apps/agent_chat/app_config_manager.py
index f495ebbf35..9040f18bfd 100644
--- a/api/core/app/apps/agent_chat/app_config_manager.py
+++ b/api/core/app/apps/agent_chat/app_config_manager.py
@@ -28,15 +28,19 @@ class AgentChatAppConfig(EasyUIBasedAppConfig):
     """
     Agent Chatbot App Config Entity.
     """
+
     agent: Optional[AgentEntity] = None
 
 
 class AgentChatAppConfigManager(BaseAppConfigManager):
     @classmethod
-    def get_app_config(cls, app_model: App,
-                       app_model_config: AppModelConfig,
-                       conversation: Optional[Conversation] = None,
-                       override_config_dict: Optional[dict] = None) -> AgentChatAppConfig:
+    def get_app_config(
+        cls,
+        app_model: App,
+        app_model_config: AppModelConfig,
+        conversation: Optional[Conversation] = None,
+        override_config_dict: Optional[dict] = None,
+    ) -> AgentChatAppConfig:
         """
         Convert app model config to agent chat app config
         :param app_model: app model
@@ -66,22 +70,12 @@ class AgentChatAppConfigManager(BaseAppConfigManager):
             app_model_config_from=config_from,
             app_model_config_id=app_model_config.id,
             app_model_config_dict=config_dict,
-            model=ModelConfigManager.convert(
-                config=config_dict
-            ),
-            prompt_template=PromptTemplateConfigManager.convert(
-                config=config_dict
-            ),
-            sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(
-                config=config_dict
-            ),
-            dataset=DatasetConfigManager.convert(
-                config=config_dict
-            ),
-            agent=AgentConfigManager.convert(
-                config=config_dict
-            ),
-            additional_features=cls.convert_features(config_dict, app_mode)
+            model=ModelConfigManager.convert(config=config_dict),
+            prompt_template=PromptTemplateConfigManager.convert(config=config_dict),
+            sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(config=config_dict),
+            dataset=DatasetConfigManager.convert(config=config_dict),
+            agent=AgentConfigManager.convert(config=config_dict),
+            additional_features=cls.convert_features(config_dict, app_mode),
         )
 
         app_config.variables, app_config.external_data_variables = BasicVariablesConfigManager.convert(
@@ -128,7 +122,8 @@ class AgentChatAppConfigManager(BaseAppConfigManager):
 
         # suggested_questions_after_answer
         config, current_related_config_keys = SuggestedQuestionsAfterAnswerConfigManager.validate_and_set_defaults(
-            config)
+            config
+        )
         related_config_keys.extend(current_related_config_keys)
 
         # speech_to_text
@@ -145,13 +140,15 @@ class AgentChatAppConfigManager(BaseAppConfigManager):
 
         # dataset configs
         # dataset_query_variable
-        config, current_related_config_keys = DatasetConfigManager.validate_and_set_defaults(tenant_id, app_mode,
-                                                                                             config)
+        config, current_related_config_keys = DatasetConfigManager.validate_and_set_defaults(
+            tenant_id, app_mode, config
+        )
         related_config_keys.extend(current_related_config_keys)
 
         # moderation validation
-        config, current_related_config_keys = SensitiveWordAvoidanceConfigManager.validate_and_set_defaults(tenant_id,
-                                                                                                            config)
+        config, current_related_config_keys = SensitiveWordAvoidanceConfigManager.validate_and_set_defaults(
+            tenant_id, config
+        )
         related_config_keys.extend(current_related_config_keys)
 
         related_config_keys = list(set(related_config_keys))
@@ -170,10 +167,7 @@ class AgentChatAppConfigManager(BaseAppConfigManager):
         :param config: app model config args
         """
         if not config.get("agent_mode"):
-            config["agent_mode"] = {
-                "enabled": False,
-                "tools": []
-            }
+            config["agent_mode"] = {"enabled": False, "tools": []}
 
         if not isinstance(config["agent_mode"], dict):
             raise ValueError("agent_mode must be of object type")
@@ -187,8 +181,9 @@ class AgentChatAppConfigManager(BaseAppConfigManager):
         if not config["agent_mode"].get("strategy"):
             config["agent_mode"]["strategy"] = PlanningStrategy.ROUTER.value
 
-        if config["agent_mode"]["strategy"] not in [member.value for member in
-                                                    list(PlanningStrategy.__members__.values())]:
+        if config["agent_mode"]["strategy"] not in [
+            member.value for member in list(PlanningStrategy.__members__.values())
+        ]:
             raise ValueError("strategy in agent_mode must be in the specified strategy list")
 
         if not config["agent_mode"].get("tools"):
@@ -210,7 +205,7 @@ class AgentChatAppConfigManager(BaseAppConfigManager):
                     raise ValueError("enabled in agent_mode.tools must be of boolean type")
 
                 if key == "dataset":
-                    if 'id' not in tool_item:
+                    if "id" not in tool_item:
                         raise ValueError("id is required in dataset")
 
                     try:
diff --git a/api/core/app/apps/agent_chat/app_generator.py b/api/core/app/apps/agent_chat/app_generator.py
index daf37f4a50..abf8a332ab 100644
--- a/api/core/app/apps/agent_chat/app_generator.py
+++ b/api/core/app/apps/agent_chat/app_generator.py
@@ -13,7 +13,7 @@ from core.app.app_config.features.file_upload.manager import FileUploadConfigMan
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfigManager
 from core.app.apps.agent_chat.app_runner import AgentChatAppRunner
 from core.app.apps.agent_chat.generate_response_converter import AgentChatAppGenerateResponseConverter
-from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException, PublishFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedError, PublishFrom
 from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
 from core.app.entities.app_invoke_entities import AgentChatAppGenerateEntity, InvokeFrom
@@ -30,7 +30,8 @@ logger = logging.getLogger(__name__)
 class AgentChatAppGenerator(MessageBasedAppGenerator):
     @overload
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         user: Union[Account, EndUser],
         args: dict,
         invoke_from: InvokeFrom,
@@ -39,19 +40,17 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
 
     @overload
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         user: Union[Account, EndUser],
         args: dict,
         invoke_from: InvokeFrom,
         stream: Literal[False] = False,
     ) -> dict: ...
 
-    def generate(self, app_model: App,
-                 user: Union[Account, EndUser],
-                 args: Any,
-                 invoke_from: InvokeFrom,
-                 stream: bool = True) \
-            -> Union[dict, Generator[dict, None, None]]:
+    def generate(
+        self, app_model: App, user: Union[Account, EndUser], args: Any, invoke_from: InvokeFrom, stream: bool = True
+    ) -> Union[dict, Generator[dict, None, None]]:
         """
         Generate App response.
 
@@ -62,60 +61,48 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
         :param stream: is stream
         """
         if not stream:
-            raise ValueError('Agent Chat App does not support blocking mode')
+            raise ValueError("Agent Chat App does not support blocking mode")
 
-        if not args.get('query'):
-            raise ValueError('query is required')
+        if not args.get("query"):
+            raise ValueError("query is required")
 
-        query = args['query']
+        query = args["query"]
         if not isinstance(query, str):
-            raise ValueError('query must be a string')
+            raise ValueError("query must be a string")
 
-        query = query.replace('\x00', '')
-        inputs = args['inputs']
+        query = query.replace("\x00", "")
+        inputs = args["inputs"]
 
-        extras = {
-            "auto_generate_conversation_name": args.get('auto_generate_name', True)
-        }
+        extras = {"auto_generate_conversation_name": args.get("auto_generate_name", True)}
 
         # get conversation
         conversation = None
-        if args.get('conversation_id'):
-            conversation = self._get_conversation_by_user(app_model, args.get('conversation_id'), user)
+        if args.get("conversation_id"):
+            conversation = self._get_conversation_by_user(app_model, args.get("conversation_id"), user)
 
         # get app model config
-        app_model_config = self._get_app_model_config(
-            app_model=app_model,
-            conversation=conversation
-        )
+        app_model_config = self._get_app_model_config(app_model=app_model, conversation=conversation)
 
         # validate override model config
         override_model_config_dict = None
-        if args.get('model_config'):
+        if args.get("model_config"):
             if invoke_from != InvokeFrom.DEBUGGER:
-                raise ValueError('Only in App debug mode can override model config')
+                raise ValueError("Only in App debug mode can override model config")
 
             # validate config
             override_model_config_dict = AgentChatAppConfigManager.config_validate(
-                tenant_id=app_model.tenant_id,
-                config=args.get('model_config')
+                tenant_id=app_model.tenant_id, config=args.get("model_config")
             )
 
             # always enable retriever resource in debugger mode
-            override_model_config_dict["retriever_resource"] = {
-                "enabled": True
-            }
+            override_model_config_dict["retriever_resource"] = {"enabled": True}
 
         # parse files
-        files = args['files'] if args.get('files') else []
+        files = args["files"] if args.get("files") else []
         message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
         file_extra_config = FileUploadConfigManager.convert(override_model_config_dict or app_model_config.to_dict())
         if file_extra_config:
-            file_objs = message_file_parser.validate_and_transform_files_arg(
-                files,
-                file_extra_config,
-                user
-            )
+            file_objs = message_file_parser.validate_and_transform_files_arg(files, file_extra_config, user)
         else:
             file_objs = []
 
@@ -124,7 +111,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             app_model=app_model,
             app_model_config=app_model_config,
             conversation=conversation,
-            override_config_dict=override_model_config_dict
+            override_config_dict=override_model_config_dict,
         )
 
         # get tracing instance
@@ -145,14 +132,11 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             invoke_from=invoke_from,
             extras=extras,
             call_depth=0,
-            trace_manager=trace_manager
+            trace_manager=trace_manager,
         )
 
         # init generate records
-        (
-            conversation,
-            message
-        ) = self._init_generate_records(application_generate_entity, conversation)
+        (conversation, message) = self._init_generate_records(application_generate_entity, conversation)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
@@ -161,17 +145,20 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             invoke_from=application_generate_entity.invoke_from,
             conversation_id=conversation.id,
             app_mode=conversation.mode,
-            message_id=message.id
+            message_id=message.id,
         )
 
         # new thread
-        worker_thread = threading.Thread(target=self._generate_worker, kwargs={
-            'flask_app': current_app._get_current_object(),
-            'application_generate_entity': application_generate_entity,
-            'queue_manager': queue_manager,
-            'conversation_id': conversation.id,
-            'message_id': message.id,
-        })
+        worker_thread = threading.Thread(
+            target=self._generate_worker,
+            kwargs={
+                "flask_app": current_app._get_current_object(),
+                "application_generate_entity": application_generate_entity,
+                "queue_manager": queue_manager,
+                "conversation_id": conversation.id,
+                "message_id": message.id,
+            },
+        )
 
         worker_thread.start()
 
@@ -185,13 +172,11 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             stream=stream,
         )
 
-        return AgentChatAppGenerateResponseConverter.convert(
-            response=response,
-            invoke_from=invoke_from
-        )
+        return AgentChatAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)
 
     def _generate_worker(
-        self, flask_app: Flask,
+        self,
+        flask_app: Flask,
         application_generate_entity: AgentChatAppGenerateEntity,
         queue_manager: AppQueueManager,
         conversation_id: str,
@@ -220,18 +205,17 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
                     conversation=conversation,
                     message=message,
                 )
-            except GenerateTaskStoppedException:
+            except GenerateTaskStoppedError:
                 pass
             except InvokeAuthorizationError:
                 queue_manager.publish_error(
-                    InvokeAuthorizationError('Incorrect API key provided'),
-                    PublishFrom.APPLICATION_MANAGER
+                    InvokeAuthorizationError("Incorrect API key provided"), PublishFrom.APPLICATION_MANAGER
                 )
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except (ValueError, InvokeError) as e:
-                if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == 'true':
+                if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == "true":
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except Exception as e:
diff --git a/api/core/app/apps/agent_chat/app_runner.py b/api/core/app/apps/agent_chat/app_runner.py
index d1bbf679c5..45b1bf0093 100644
--- a/api/core/app/apps/agent_chat/app_runner.py
+++ b/api/core/app/apps/agent_chat/app_runner.py
@@ -15,7 +15,7 @@ from core.model_manager import ModelInstance
 from core.model_runtime.entities.llm_entities import LLMMode, LLMUsage
 from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.moderation.base import ModerationException
+from core.moderation.base import ModerationError
 from core.tools.entities.tool_entities import ToolRuntimeVariablePool
 from extensions.ext_database import db
 from models.model import App, Conversation, Message, MessageAgentThought
@@ -30,7 +30,8 @@ class AgentChatAppRunner(AppRunner):
     """
 
     def run(
-        self, application_generate_entity: AgentChatAppGenerateEntity,
+        self,
+        application_generate_entity: AgentChatAppGenerateEntity,
         queue_manager: AppQueueManager,
         conversation: Conversation,
         message: Message,
@@ -65,7 +66,7 @@ class AgentChatAppRunner(AppRunner):
             prompt_template_entity=app_config.prompt_template,
             inputs=inputs,
             files=files,
-            query=query
+            query=query,
         )
 
         memory = None
@@ -73,13 +74,10 @@ class AgentChatAppRunner(AppRunner):
             # get memory of conversation (read-only)
             model_instance = ModelInstance(
                 provider_model_bundle=application_generate_entity.model_conf.provider_model_bundle,
-                model=application_generate_entity.model_conf.model
+                model=application_generate_entity.model_conf.model,
             )
 
-            memory = TokenBufferMemory(
-                conversation=conversation,
-                model_instance=model_instance
-            )
+            memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
 
         # organize all inputs and template to prompt messages
         # Include: prompt template, inputs, query(optional), files(optional)
@@ -91,7 +89,7 @@ class AgentChatAppRunner(AppRunner):
             inputs=inputs,
             files=files,
             query=query,
-            memory=memory
+            memory=memory,
         )
 
         # moderation
@@ -103,15 +101,15 @@ class AgentChatAppRunner(AppRunner):
                 app_generate_entity=application_generate_entity,
                 inputs=inputs,
                 query=query,
-                message_id=message.id
+                message_id=message.id,
             )
-        except ModerationException as e:
+        except ModerationError as e:
             self.direct_output(
                 queue_manager=queue_manager,
                 app_generate_entity=application_generate_entity,
                 prompt_messages=prompt_messages,
                 text=str(e),
-                stream=application_generate_entity.stream
+                stream=application_generate_entity.stream,
             )
             return
 
@@ -122,13 +120,13 @@ class AgentChatAppRunner(AppRunner):
                 message=message,
                 query=query,
                 user_id=application_generate_entity.user_id,
-                invoke_from=application_generate_entity.invoke_from
+                invoke_from=application_generate_entity.invoke_from,
             )
 
             if annotation_reply:
                 queue_manager.publish(
                     QueueAnnotationReplyEvent(message_annotation_id=annotation_reply.id),
-                    PublishFrom.APPLICATION_MANAGER
+                    PublishFrom.APPLICATION_MANAGER,
                 )
 
                 self.direct_output(
@@ -136,7 +134,7 @@ class AgentChatAppRunner(AppRunner):
                     app_generate_entity=application_generate_entity,
                     prompt_messages=prompt_messages,
                     text=annotation_reply.content,
-                    stream=application_generate_entity.stream
+                    stream=application_generate_entity.stream,
                 )
                 return
 
@@ -148,7 +146,7 @@ class AgentChatAppRunner(AppRunner):
                 app_id=app_record.id,
                 external_data_tools=external_data_tools,
                 inputs=inputs,
-                query=query
+                query=query,
             )
 
         # reorganize all inputs and template to prompt messages
@@ -161,14 +159,14 @@ class AgentChatAppRunner(AppRunner):
             inputs=inputs,
             files=files,
             query=query,
-            memory=memory
+            memory=memory,
         )
 
         # check hosting moderation
         hosting_moderation_result = self.check_hosting_moderation(
             application_generate_entity=application_generate_entity,
             queue_manager=queue_manager,
-            prompt_messages=prompt_messages
+            prompt_messages=prompt_messages,
         )
 
         if hosting_moderation_result:
@@ -177,9 +175,9 @@ class AgentChatAppRunner(AppRunner):
         agent_entity = app_config.agent
 
         # load tool variables
-        tool_conversation_variables = self._load_tool_variables(conversation_id=conversation.id,
-                                                   user_id=application_generate_entity.user_id,
-                                                   tenant_id=app_config.tenant_id)
+        tool_conversation_variables = self._load_tool_variables(
+            conversation_id=conversation.id, user_id=application_generate_entity.user_id, tenant_id=app_config.tenant_id
+        )
 
         # convert db variables to tool variables
         tool_variables = self._convert_db_variables_to_tool_variables(tool_conversation_variables)
@@ -187,7 +185,7 @@ class AgentChatAppRunner(AppRunner):
         # init model instance
         model_instance = ModelInstance(
             provider_model_bundle=application_generate_entity.model_conf.provider_model_bundle,
-            model=application_generate_entity.model_conf.model
+            model=application_generate_entity.model_conf.model,
         )
         prompt_message, _ = self.organize_prompt_messages(
             app_record=app_record,
@@ -238,7 +236,7 @@ class AgentChatAppRunner(AppRunner):
             prompt_messages=prompt_message,
             variables_pool=tool_variables,
             db_variables=tool_conversation_variables,
-            model_instance=model_instance
+            model_instance=model_instance,
         )
 
         invoke_result = runner.run(
@@ -252,17 +250,21 @@ class AgentChatAppRunner(AppRunner):
             invoke_result=invoke_result,
             queue_manager=queue_manager,
             stream=application_generate_entity.stream,
-            agent=True
+            agent=True,
         )
 
     def _load_tool_variables(self, conversation_id: str, user_id: str, tenant_id: str) -> ToolConversationVariables:
         """
         load tool variables from database
         """
-        tool_variables: ToolConversationVariables = db.session.query(ToolConversationVariables).filter(
-            ToolConversationVariables.conversation_id == conversation_id,
-            ToolConversationVariables.tenant_id == tenant_id
-        ).first()
+        tool_variables: ToolConversationVariables = (
+            db.session.query(ToolConversationVariables)
+            .filter(
+                ToolConversationVariables.conversation_id == conversation_id,
+                ToolConversationVariables.tenant_id == tenant_id,
+            )
+            .first()
+        )
 
         if tool_variables:
             # save tool variables to session, so that we can update it later
@@ -273,34 +275,40 @@ class AgentChatAppRunner(AppRunner):
                 conversation_id=conversation_id,
                 user_id=user_id,
                 tenant_id=tenant_id,
-                variables_str='[]',
+                variables_str="[]",
             )
             db.session.add(tool_variables)
             db.session.commit()
 
         return tool_variables
-    
-    def _convert_db_variables_to_tool_variables(self, db_variables: ToolConversationVariables) -> ToolRuntimeVariablePool:
+
+    def _convert_db_variables_to_tool_variables(
+        self, db_variables: ToolConversationVariables
+    ) -> ToolRuntimeVariablePool:
         """
         convert db variables to tool variables
         """
-        return ToolRuntimeVariablePool(**{
-            'conversation_id': db_variables.conversation_id,
-            'user_id': db_variables.user_id,
-            'tenant_id': db_variables.tenant_id,
-            'pool': db_variables.variables
-        })
+        return ToolRuntimeVariablePool(
+            **{
+                "conversation_id": db_variables.conversation_id,
+                "user_id": db_variables.user_id,
+                "tenant_id": db_variables.tenant_id,
+                "pool": db_variables.variables,
+            }
+        )
 
-    def _get_usage_of_all_agent_thoughts(self, model_config: ModelConfigWithCredentialsEntity,
-                                         message: Message) -> LLMUsage:
+    def _get_usage_of_all_agent_thoughts(
+        self, model_config: ModelConfigWithCredentialsEntity, message: Message
+    ) -> LLMUsage:
         """
         Get usage of all agent thoughts
         :param model_config: model config
         :param message: message
         :return:
         """
-        agent_thoughts = (db.session.query(MessageAgentThought)
-                          .filter(MessageAgentThought.message_id == message.id).all())
+        agent_thoughts = (
+            db.session.query(MessageAgentThought).filter(MessageAgentThought.message_id == message.id).all()
+        )
 
         all_message_tokens = 0
         all_answer_tokens = 0
@@ -312,8 +320,5 @@ class AgentChatAppRunner(AppRunner):
         model_type_instance = cast(LargeLanguageModel, model_type_instance)
 
         return model_type_instance._calc_response_usage(
-            model_config.model,
-            model_config.credentials,
-            all_message_tokens,
-            all_answer_tokens
+            model_config.model, model_config.credentials, all_message_tokens, all_answer_tokens
         )
diff --git a/api/core/app/apps/agent_chat/generate_response_converter.py b/api/core/app/apps/agent_chat/generate_response_converter.py
index 118d82c495..629c309c06 100644
--- a/api/core/app/apps/agent_chat/generate_response_converter.py
+++ b/api/core/app/apps/agent_chat/generate_response_converter.py
@@ -23,15 +23,15 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
         :return:
         """
         response = {
-            'event': 'message',
-            'task_id': blocking_response.task_id,
-            'id': blocking_response.data.id,
-            'message_id': blocking_response.data.message_id,
-            'conversation_id': blocking_response.data.conversation_id,
-            'mode': blocking_response.data.mode,
-            'answer': blocking_response.data.answer,
-            'metadata': blocking_response.data.metadata,
-            'created_at': blocking_response.data.created_at
+            "event": "message",
+            "task_id": blocking_response.task_id,
+            "id": blocking_response.data.id,
+            "message_id": blocking_response.data.message_id,
+            "conversation_id": blocking_response.data.conversation_id,
+            "mode": blocking_response.data.mode,
+            "answer": blocking_response.data.answer,
+            "metadata": blocking_response.data.metadata,
+            "created_at": blocking_response.data.created_at,
         }
 
         return response
@@ -45,14 +45,15 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
         """
         response = cls.convert_blocking_full_response(blocking_response)
 
-        metadata = response.get('metadata', {})
-        response['metadata'] = cls._get_simple_metadata(metadata)
+        metadata = response.get("metadata", {})
+        response["metadata"] = cls._get_simple_metadata(metadata)
 
         return response
 
     @classmethod
-    def convert_stream_full_response(cls, stream_response: Generator[ChatbotAppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_full_response(
+        cls, stream_response: Generator[ChatbotAppStreamResponse, None, None]
+    ) -> Generator[str, None, None]:
         """
         Convert stream full response.
         :param stream_response: stream response
@@ -63,14 +64,14 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             sub_stream_response = chunk.stream_response
 
             if isinstance(sub_stream_response, PingStreamResponse):
-                yield 'ping'
+                yield "ping"
                 continue
 
             response_chunk = {
-                'event': sub_stream_response.event.value,
-                'conversation_id': chunk.conversation_id,
-                'message_id': chunk.message_id,
-                'created_at': chunk.created_at
+                "event": sub_stream_response.event.value,
+                "conversation_id": chunk.conversation_id,
+                "message_id": chunk.message_id,
+                "created_at": chunk.created_at,
             }
 
             if isinstance(sub_stream_response, ErrorStreamResponse):
@@ -81,8 +82,9 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             yield json.dumps(response_chunk)
 
     @classmethod
-    def convert_stream_simple_response(cls, stream_response: Generator[ChatbotAppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_simple_response(
+        cls, stream_response: Generator[ChatbotAppStreamResponse, None, None]
+    ) -> Generator[str, None, None]:
         """
         Convert stream simple response.
         :param stream_response: stream response
@@ -93,20 +95,20 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             sub_stream_response = chunk.stream_response
 
             if isinstance(sub_stream_response, PingStreamResponse):
-                yield 'ping'
+                yield "ping"
                 continue
 
             response_chunk = {
-                'event': sub_stream_response.event.value,
-                'conversation_id': chunk.conversation_id,
-                'message_id': chunk.message_id,
-                'created_at': chunk.created_at
+                "event": sub_stream_response.event.value,
+                "conversation_id": chunk.conversation_id,
+                "message_id": chunk.message_id,
+                "created_at": chunk.created_at,
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
                 sub_stream_response_dict = sub_stream_response.to_dict()
-                metadata = sub_stream_response_dict.get('metadata', {})
-                sub_stream_response_dict['metadata'] = cls._get_simple_metadata(metadata)
+                metadata = sub_stream_response_dict.get("metadata", {})
+                sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
             if isinstance(sub_stream_response, ErrorStreamResponse):
                 data = cls._error_to_stream_response(sub_stream_response.err)
diff --git a/api/core/app/apps/base_app_generate_response_converter.py b/api/core/app/apps/base_app_generate_response_converter.py
index 1165314a7f..c6855ac854 100644
--- a/api/core/app/apps/base_app_generate_response_converter.py
+++ b/api/core/app/apps/base_app_generate_response_converter.py
@@ -13,32 +13,33 @@ class AppGenerateResponseConverter(ABC):
     _blocking_response_type: type[AppBlockingResponse]
 
     @classmethod
-    def convert(cls, response: Union[
-        AppBlockingResponse,
-        Generator[AppStreamResponse, Any, None]
-    ], invoke_from: InvokeFrom):
-        if invoke_from in [InvokeFrom.DEBUGGER, InvokeFrom.SERVICE_API]:
+    def convert(
+        cls, response: Union[AppBlockingResponse, Generator[AppStreamResponse, Any, None]], invoke_from: InvokeFrom
+    ) -> dict[str, Any] | Generator[str, Any, None]:
+        if invoke_from in {InvokeFrom.DEBUGGER, InvokeFrom.SERVICE_API}:
             if isinstance(response, AppBlockingResponse):
                 return cls.convert_blocking_full_response(response)
             else:
+
                 def _generate_full_response() -> Generator[str, Any, None]:
                     for chunk in cls.convert_stream_full_response(response):
-                        if chunk == 'ping':
-                            yield f'event: {chunk}\n\n'
+                        if chunk == "ping":
+                            yield f"event: {chunk}\n\n"
                         else:
-                            yield f'data: {chunk}\n\n'
+                            yield f"data: {chunk}\n\n"
 
                 return _generate_full_response()
         else:
             if isinstance(response, AppBlockingResponse):
                 return cls.convert_blocking_simple_response(response)
             else:
+
                 def _generate_simple_response() -> Generator[str, Any, None]:
                     for chunk in cls.convert_stream_simple_response(response):
-                        if chunk == 'ping':
-                            yield f'event: {chunk}\n\n'
+                        if chunk == "ping":
+                            yield f"event: {chunk}\n\n"
                         else:
-                            yield f'data: {chunk}\n\n'
+                            yield f"data: {chunk}\n\n"
 
                 return _generate_simple_response()
 
@@ -54,14 +55,16 @@ class AppGenerateResponseConverter(ABC):
 
     @classmethod
     @abstractmethod
-    def convert_stream_full_response(cls, stream_response: Generator[AppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_full_response(
+        cls, stream_response: Generator[AppStreamResponse, None, None]
+    ) -> Generator[str, None, None]:
         raise NotImplementedError
 
     @classmethod
     @abstractmethod
-    def convert_stream_simple_response(cls, stream_response: Generator[AppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_simple_response(
+        cls, stream_response: Generator[AppStreamResponse, None, None]
+    ) -> Generator[str, None, None]:
         raise NotImplementedError
 
     @classmethod
@@ -72,24 +75,26 @@ class AppGenerateResponseConverter(ABC):
         :return:
         """
         # show_retrieve_source
-        if 'retriever_resources' in metadata:
-            metadata['retriever_resources'] = []
-            for resource in metadata['retriever_resources']:
-                metadata['retriever_resources'].append({
-                    'segment_id': resource['segment_id'],
-                    'position': resource['position'],
-                    'document_name': resource['document_name'],
-                    'score': resource['score'],
-                    'content': resource['content'],
-                })
+        if "retriever_resources" in metadata:
+            metadata["retriever_resources"] = []
+            for resource in metadata["retriever_resources"]:
+                metadata["retriever_resources"].append(
+                    {
+                        "segment_id": resource["segment_id"],
+                        "position": resource["position"],
+                        "document_name": resource["document_name"],
+                        "score": resource["score"],
+                        "content": resource["content"],
+                    }
+                )
 
         # show annotation reply
-        if 'annotation_reply' in metadata:
-            del metadata['annotation_reply']
+        if "annotation_reply" in metadata:
+            del metadata["annotation_reply"]
 
         # show usage
-        if 'usage' in metadata:
-            del metadata['usage']
+        if "usage" in metadata:
+            del metadata["usage"]
 
         return metadata
 
@@ -101,16 +106,16 @@ class AppGenerateResponseConverter(ABC):
         :return:
         """
         error_responses = {
-            ValueError: {'code': 'invalid_param', 'status': 400},
-            ProviderTokenNotInitError: {'code': 'provider_not_initialize', 'status': 400},
+            ValueError: {"code": "invalid_param", "status": 400},
+            ProviderTokenNotInitError: {"code": "provider_not_initialize", "status": 400},
             QuotaExceededError: {
-                'code': 'provider_quota_exceeded',
-                'message': "Your quota for Dify Hosted Model Provider has been exhausted. "
-                           "Please go to Settings -> Model Provider to complete your own provider credentials.",
-                'status': 400
+                "code": "provider_quota_exceeded",
+                "message": "Your quota for Dify Hosted Model Provider has been exhausted. "
+                "Please go to Settings -> Model Provider to complete your own provider credentials.",
+                "status": 400,
             },
-            ModelCurrentlyNotSupportError: {'code': 'model_currently_not_support', 'status': 400},
-            InvokeError: {'code': 'completion_request_error', 'status': 400}
+            ModelCurrentlyNotSupportError: {"code": "model_currently_not_support", "status": 400},
+            InvokeError: {"code": "completion_request_error", "status": 400},
         }
 
         # Determine the response based on the type of exception
@@ -120,13 +125,13 @@ class AppGenerateResponseConverter(ABC):
                 data = v
 
         if data:
-            data.setdefault('message', getattr(e, 'description', str(e)))
+            data.setdefault("message", getattr(e, "description", str(e)))
         else:
             logging.error(e)
             data = {
-                'code': 'internal_server_error',
-                'message': 'Internal Server Error, please contact support.',
-                'status': 500
+                "code": "internal_server_error",
+                "message": "Internal Server Error, please contact support.",
+                "status": 500,
             }
 
         return data
diff --git a/api/core/app/apps/base_app_generator.py b/api/core/app/apps/base_app_generator.py
index 9e331dff4d..15be7000fc 100644
--- a/api/core/app/apps/base_app_generator.py
+++ b/api/core/app/apps/base_app_generator.py
@@ -16,17 +16,17 @@ class BaseAppGenerator:
     def _validate_input(self, *, inputs: Mapping[str, Any], var: VariableEntity):
         user_input_value = inputs.get(var.variable)
         if var.required and not user_input_value:
-            raise ValueError(f'{var.variable} is required in input form')
+            raise ValueError(f"{var.variable} is required in input form")
         if not var.required and not user_input_value:
             # TODO: should we return None here if the default value is None?
-            return var.default or ''
+            return var.default or ""
         if (
             var.type
-            in (
+            in {
                 VariableEntityType.TEXT_INPUT,
                 VariableEntityType.SELECT,
                 VariableEntityType.PARAGRAPH,
-            )
+            }
             and user_input_value
             and not isinstance(user_input_value, str)
         ):
@@ -34,7 +34,7 @@ class BaseAppGenerator:
         if var.type == VariableEntityType.NUMBER and isinstance(user_input_value, str):
             # may raise ValueError if user_input_value is not a valid number
             try:
-                if '.' in user_input_value:
+                if "." in user_input_value:
                     return float(user_input_value)
                 else:
                     return int(user_input_value)
@@ -43,14 +43,14 @@ class BaseAppGenerator:
         if var.type == VariableEntityType.SELECT:
             options = var.options or []
             if user_input_value not in options:
-                raise ValueError(f'{var.variable} in input form must be one of the following: {options}')
-        elif var.type in (VariableEntityType.TEXT_INPUT, VariableEntityType.PARAGRAPH):
+                raise ValueError(f"{var.variable} in input form must be one of the following: {options}")
+        elif var.type in {VariableEntityType.TEXT_INPUT, VariableEntityType.PARAGRAPH}:
             if var.max_length and user_input_value and len(user_input_value) > var.max_length:
-                raise ValueError(f'{var.variable} in input form must be less than {var.max_length} characters')
+                raise ValueError(f"{var.variable} in input form must be less than {var.max_length} characters")
 
         return user_input_value
 
     def _sanitize_value(self, value: Any) -> Any:
         if isinstance(value, str):
-            return value.replace('\x00', '')
+            return value.replace("\x00", "")
         return value
diff --git a/api/core/app/apps/base_app_queue_manager.py b/api/core/app/apps/base_app_queue_manager.py
index f929a979f1..4c4d282e99 100644
--- a/api/core/app/apps/base_app_queue_manager.py
+++ b/api/core/app/apps/base_app_queue_manager.py
@@ -24,9 +24,7 @@ class PublishFrom(Enum):
 
 
 class AppQueueManager:
-    def __init__(self, task_id: str,
-                 user_id: str,
-                 invoke_from: InvokeFrom) -> None:
+    def __init__(self, task_id: str, user_id: str, invoke_from: InvokeFrom) -> None:
         if not user_id:
             raise ValueError("user is required")
 
@@ -34,9 +32,10 @@ class AppQueueManager:
         self._user_id = user_id
         self._invoke_from = invoke_from
 
-        user_prefix = 'account' if self._invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end-user'
-        redis_client.setex(AppQueueManager._generate_task_belong_cache_key(self._task_id), 1800,
-                           f"{user_prefix}-{self._user_id}")
+        user_prefix = "account" if self._invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER} else "end-user"
+        redis_client.setex(
+            AppQueueManager._generate_task_belong_cache_key(self._task_id), 1800, f"{user_prefix}-{self._user_id}"
+        )
 
         q = queue.Queue()
 
@@ -66,8 +65,7 @@ class AppQueueManager:
                     # publish two messages to make sure the client can receive the stop signal
                     # and stop listening after the stop signal processed
                     self.publish(
-                        QueueStopEvent(stopped_by=QueueStopEvent.StopBy.USER_MANUAL),
-                        PublishFrom.TASK_PIPELINE
+                        QueueStopEvent(stopped_by=QueueStopEvent.StopBy.USER_MANUAL), PublishFrom.TASK_PIPELINE
                     )
 
                 if elapsed_time // 10 > last_ping_time:
@@ -88,9 +86,7 @@ class AppQueueManager:
         :param pub_from: publish from
         :return:
         """
-        self.publish(QueueErrorEvent(
-            error=e
-        ), pub_from)
+        self.publish(QueueErrorEvent(error=e), pub_from)
 
     def publish(self, event: AppQueueEvent, pub_from: PublishFrom) -> None:
         """
@@ -122,8 +118,8 @@ class AppQueueManager:
         if result is None:
             return
 
-        user_prefix = 'account' if invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end-user'
-        if result.decode('utf-8') != f"{user_prefix}-{user_id}":
+        user_prefix = "account" if invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER} else "end-user"
+        if result.decode("utf-8") != f"{user_prefix}-{user_id}":
             return
 
         stopped_cache_key = cls._generate_stopped_cache_key(task_id)
@@ -168,10 +164,12 @@ class AppQueueManager:
             for item in data:
                 self._check_for_sqlalchemy_models(item)
         else:
-            if isinstance(data, DeclarativeMeta) or hasattr(data, '_sa_instance_state'):
-                raise TypeError("Critical Error: Passing SQLAlchemy Model instances "
-                                "that cause thread safety issues is not allowed.")
+            if isinstance(data, DeclarativeMeta) or hasattr(data, "_sa_instance_state"):
+                raise TypeError(
+                    "Critical Error: Passing SQLAlchemy Model instances "
+                    "that cause thread safety issues is not allowed."
+                )
 
 
-class GenerateTaskStoppedException(Exception):
+class GenerateTaskStoppedError(Exception):
     pass
diff --git a/api/core/app/apps/base_app_runner.py b/api/core/app/apps/base_app_runner.py
index 2c5feaaaaf..1b412b8639 100644
--- a/api/core/app/apps/base_app_runner.py
+++ b/api/core/app/apps/base_app_runner.py
@@ -1,6 +1,6 @@
 import time
-from collections.abc import Generator
-from typing import TYPE_CHECKING, Optional, Union
+from collections.abc import Generator, Mapping
+from typing import TYPE_CHECKING, Any, Optional, Union
 
 from core.app.app_config.entities import ExternalDataVariableEntity, PromptTemplateEntity
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
@@ -31,12 +31,15 @@ if TYPE_CHECKING:
 
 
 class AppRunner:
-    def get_pre_calculate_rest_tokens(self, app_record: App,
-                                      model_config: ModelConfigWithCredentialsEntity,
-                                      prompt_template_entity: PromptTemplateEntity,
-                                      inputs: dict[str, str],
-                                      files: list["FileVar"],
-                                      query: Optional[str] = None) -> int:
+    def get_pre_calculate_rest_tokens(
+        self,
+        app_record: App,
+        model_config: ModelConfigWithCredentialsEntity,
+        prompt_template_entity: PromptTemplateEntity,
+        inputs: dict[str, str],
+        files: list["FileVar"],
+        query: Optional[str] = None,
+    ) -> int:
         """
         Get pre calculate rest tokens
         :param app_record: app record
@@ -49,18 +52,20 @@ class AppRunner:
         """
         # Invoke model
         model_instance = ModelInstance(
-            provider_model_bundle=model_config.provider_model_bundle,
-            model=model_config.model
+            provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
         )
 
         model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
 
         max_tokens = 0
         for parameter_rule in model_config.model_schema.parameter_rules:
-            if (parameter_rule.name == 'max_tokens'
-                    or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
-                max_tokens = (model_config.parameters.get(parameter_rule.name)
-                              or model_config.parameters.get(parameter_rule.use_template)) or 0
+            if parameter_rule.name == "max_tokens" or (
+                parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+            ):
+                max_tokens = (
+                    model_config.parameters.get(parameter_rule.name)
+                    or model_config.parameters.get(parameter_rule.use_template)
+                ) or 0
 
         if model_context_tokens is None:
             return -1
@@ -75,36 +80,39 @@ class AppRunner:
             prompt_template_entity=prompt_template_entity,
             inputs=inputs,
             files=files,
-            query=query
+            query=query,
         )
 
-        prompt_tokens = model_instance.get_llm_num_tokens(
-            prompt_messages
-        )
+        prompt_tokens = model_instance.get_llm_num_tokens(prompt_messages)
 
         rest_tokens = model_context_tokens - max_tokens - prompt_tokens
         if rest_tokens < 0:
-            raise InvokeBadRequestError("Query or prefix prompt is too long, you can reduce the prefix prompt, "
-                                        "or shrink the max token, or switch to a llm with a larger token limit size.")
+            raise InvokeBadRequestError(
+                "Query or prefix prompt is too long, you can reduce the prefix prompt, "
+                "or shrink the max token, or switch to a llm with a larger token limit size."
+            )
 
         return rest_tokens
 
-    def recalc_llm_max_tokens(self, model_config: ModelConfigWithCredentialsEntity,
-                              prompt_messages: list[PromptMessage]):
+    def recalc_llm_max_tokens(
+        self, model_config: ModelConfigWithCredentialsEntity, prompt_messages: list[PromptMessage]
+    ):
         # recalc max_tokens if sum(prompt_token +  max_tokens) over model token limit
         model_instance = ModelInstance(
-            provider_model_bundle=model_config.provider_model_bundle,
-            model=model_config.model
+            provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
         )
 
         model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
 
         max_tokens = 0
         for parameter_rule in model_config.model_schema.parameter_rules:
-            if (parameter_rule.name == 'max_tokens'
-                    or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
-                max_tokens = (model_config.parameters.get(parameter_rule.name)
-                              or model_config.parameters.get(parameter_rule.use_template)) or 0
+            if parameter_rule.name == "max_tokens" or (
+                parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+            ):
+                max_tokens = (
+                    model_config.parameters.get(parameter_rule.name)
+                    or model_config.parameters.get(parameter_rule.use_template)
+                ) or 0
 
         if model_context_tokens is None:
             return -1
@@ -112,27 +120,28 @@ class AppRunner:
         if max_tokens is None:
             max_tokens = 0
 
-        prompt_tokens = model_instance.get_llm_num_tokens(
-            prompt_messages
-        )
+        prompt_tokens = model_instance.get_llm_num_tokens(prompt_messages)
 
         if prompt_tokens + max_tokens > model_context_tokens:
             max_tokens = max(model_context_tokens - prompt_tokens, 16)
 
             for parameter_rule in model_config.model_schema.parameter_rules:
-                if (parameter_rule.name == 'max_tokens'
-                        or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
+                if parameter_rule.name == "max_tokens" or (
+                    parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+                ):
                     model_config.parameters[parameter_rule.name] = max_tokens
 
-    def organize_prompt_messages(self, app_record: App,
-                                 model_config: ModelConfigWithCredentialsEntity,
-                                 prompt_template_entity: PromptTemplateEntity,
-                                 inputs: dict[str, str],
-                                 files: list["FileVar"],
-                                 query: Optional[str] = None,
-                                 context: Optional[str] = None,
-                                 memory: Optional[TokenBufferMemory] = None) \
-            -> tuple[list[PromptMessage], Optional[list[str]]]:
+    def organize_prompt_messages(
+        self,
+        app_record: App,
+        model_config: ModelConfigWithCredentialsEntity,
+        prompt_template_entity: PromptTemplateEntity,
+        inputs: dict[str, str],
+        files: list["FileVar"],
+        query: Optional[str] = None,
+        context: Optional[str] = None,
+        memory: Optional[TokenBufferMemory] = None,
+    ) -> tuple[list[PromptMessage], Optional[list[str]]]:
         """
         Organize prompt messages
         :param context:
@@ -152,60 +161,54 @@ class AppRunner:
                 app_mode=AppMode.value_of(app_record.mode),
                 prompt_template_entity=prompt_template_entity,
                 inputs=inputs,
-                query=query if query else '',
+                query=query or "",
                 files=files,
                 context=context,
                 memory=memory,
-                model_config=model_config
+                model_config=model_config,
             )
         else:
-            memory_config = MemoryConfig(
-                window=MemoryConfig.WindowConfig(
-                    enabled=False
-                )
-            )
+            memory_config = MemoryConfig(window=MemoryConfig.WindowConfig(enabled=False))
 
             model_mode = ModelMode.value_of(model_config.mode)
             if model_mode == ModelMode.COMPLETION:
                 advanced_completion_prompt_template = prompt_template_entity.advanced_completion_prompt_template
-                prompt_template = CompletionModelPromptTemplate(
-                    text=advanced_completion_prompt_template.prompt
-                )
+                prompt_template = CompletionModelPromptTemplate(text=advanced_completion_prompt_template.prompt)
 
                 if advanced_completion_prompt_template.role_prefix:
                     memory_config.role_prefix = MemoryConfig.RolePrefix(
                         user=advanced_completion_prompt_template.role_prefix.user,
-                        assistant=advanced_completion_prompt_template.role_prefix.assistant
+                        assistant=advanced_completion_prompt_template.role_prefix.assistant,
                     )
             else:
                 prompt_template = []
                 for message in prompt_template_entity.advanced_chat_prompt_template.messages:
-                    prompt_template.append(ChatModelMessage(
-                        text=message.text,
-                        role=message.role
-                    ))
+                    prompt_template.append(ChatModelMessage(text=message.text, role=message.role))
 
             prompt_transform = AdvancedPromptTransform()
             prompt_messages = prompt_transform.get_prompt(
                 prompt_template=prompt_template,
                 inputs=inputs,
-                query=query if query else '',
+                query=query or "",
                 files=files,
                 context=context,
                 memory_config=memory_config,
                 memory=memory,
-                model_config=model_config
+                model_config=model_config,
             )
             stop = model_config.stop
 
         return prompt_messages, stop
 
-    def direct_output(self, queue_manager: AppQueueManager,
-                      app_generate_entity: EasyUIBasedAppGenerateEntity,
-                      prompt_messages: list,
-                      text: str,
-                      stream: bool,
-                      usage: Optional[LLMUsage] = None) -> None:
+    def direct_output(
+        self,
+        queue_manager: AppQueueManager,
+        app_generate_entity: EasyUIBasedAppGenerateEntity,
+        prompt_messages: list,
+        text: str,
+        stream: bool,
+        usage: Optional[LLMUsage] = None,
+    ) -> None:
         """
         Direct output
         :param queue_manager: application queue manager
@@ -222,17 +225,10 @@ class AppRunner:
                 chunk = LLMResultChunk(
                     model=app_generate_entity.model_conf.model,
                     prompt_messages=prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=index,
-                        message=AssistantPromptMessage(content=token)
-                    )
+                    delta=LLMResultChunkDelta(index=index, message=AssistantPromptMessage(content=token)),
                 )
 
-                queue_manager.publish(
-                    QueueLLMChunkEvent(
-                        chunk=chunk
-                    ), PublishFrom.APPLICATION_MANAGER
-                )
+                queue_manager.publish(QueueLLMChunkEvent(chunk=chunk), PublishFrom.APPLICATION_MANAGER)
                 index += 1
                 time.sleep(0.01)
 
@@ -242,15 +238,19 @@ class AppRunner:
                     model=app_generate_entity.model_conf.model,
                     prompt_messages=prompt_messages,
                     message=AssistantPromptMessage(content=text),
-                    usage=usage if usage else LLMUsage.empty_usage()
+                    usage=usage or LLMUsage.empty_usage(),
                 ),
-            ), PublishFrom.APPLICATION_MANAGER
+            ),
+            PublishFrom.APPLICATION_MANAGER,
         )
 
-    def _handle_invoke_result(self, invoke_result: Union[LLMResult, Generator],
-                              queue_manager: AppQueueManager,
-                              stream: bool,
-                              agent: bool = False) -> None:
+    def _handle_invoke_result(
+        self,
+        invoke_result: Union[LLMResult, Generator],
+        queue_manager: AppQueueManager,
+        stream: bool,
+        agent: bool = False,
+    ) -> None:
         """
         Handle invoke result
         :param invoke_result: invoke result
@@ -260,21 +260,13 @@ class AppRunner:
         :return:
         """
         if not stream:
-            self._handle_invoke_result_direct(
-                invoke_result=invoke_result,
-                queue_manager=queue_manager,
-                agent=agent
-            )
+            self._handle_invoke_result_direct(invoke_result=invoke_result, queue_manager=queue_manager, agent=agent)
         else:
-            self._handle_invoke_result_stream(
-                invoke_result=invoke_result,
-                queue_manager=queue_manager,
-                agent=agent
-            )
+            self._handle_invoke_result_stream(invoke_result=invoke_result, queue_manager=queue_manager, agent=agent)
 
-    def _handle_invoke_result_direct(self, invoke_result: LLMResult,
-                                     queue_manager: AppQueueManager,
-                                     agent: bool) -> None:
+    def _handle_invoke_result_direct(
+        self, invoke_result: LLMResult, queue_manager: AppQueueManager, agent: bool
+    ) -> None:
         """
         Handle invoke result direct
         :param invoke_result: invoke result
@@ -285,12 +277,13 @@ class AppRunner:
         queue_manager.publish(
             QueueMessageEndEvent(
                 llm_result=invoke_result,
-            ), PublishFrom.APPLICATION_MANAGER
+            ),
+            PublishFrom.APPLICATION_MANAGER,
         )
 
-    def _handle_invoke_result_stream(self, invoke_result: Generator,
-                                     queue_manager: AppQueueManager,
-                                     agent: bool) -> None:
+    def _handle_invoke_result_stream(
+        self, invoke_result: Generator, queue_manager: AppQueueManager, agent: bool
+    ) -> None:
         """
         Handle invoke result
         :param invoke_result: invoke result
@@ -300,21 +293,13 @@ class AppRunner:
         """
         model = None
         prompt_messages = []
-        text = ''
+        text = ""
         usage = None
         for result in invoke_result:
             if not agent:
-                queue_manager.publish(
-                    QueueLLMChunkEvent(
-                        chunk=result
-                    ), PublishFrom.APPLICATION_MANAGER
-                )
+                queue_manager.publish(QueueLLMChunkEvent(chunk=result), PublishFrom.APPLICATION_MANAGER)
             else:
-                queue_manager.publish(
-                    QueueAgentMessageEvent(
-                        chunk=result
-                    ), PublishFrom.APPLICATION_MANAGER
-                )
+                queue_manager.publish(QueueAgentMessageEvent(chunk=result), PublishFrom.APPLICATION_MANAGER)
 
             text += result.delta.message.content
 
@@ -331,25 +316,24 @@ class AppRunner:
             usage = LLMUsage.empty_usage()
 
         llm_result = LLMResult(
-            model=model,
-            prompt_messages=prompt_messages,
-            message=AssistantPromptMessage(content=text),
-            usage=usage
+            model=model, prompt_messages=prompt_messages, message=AssistantPromptMessage(content=text), usage=usage
         )
 
         queue_manager.publish(
             QueueMessageEndEvent(
                 llm_result=llm_result,
-            ), PublishFrom.APPLICATION_MANAGER
+            ),
+            PublishFrom.APPLICATION_MANAGER,
         )
 
     def moderation_for_inputs(
-            self, app_id: str,
-            tenant_id: str,
-            app_generate_entity: AppGenerateEntity,
-            inputs: dict,
-            query: str,
-            message_id: str,
+        self,
+        app_id: str,
+        tenant_id: str,
+        app_generate_entity: AppGenerateEntity,
+        inputs: Mapping[str, Any],
+        query: str,
+        message_id: str,
     ) -> tuple[bool, dict, str]:
         """
         Process sensitive_word_avoidance.
@@ -367,14 +351,17 @@ class AppRunner:
             tenant_id=tenant_id,
             app_config=app_generate_entity.app_config,
             inputs=inputs,
-            query=query if query else '',
+            query=query or "",
             message_id=message_id,
-            trace_manager=app_generate_entity.trace_manager
+            trace_manager=app_generate_entity.trace_manager,
         )
 
-    def check_hosting_moderation(self, application_generate_entity: EasyUIBasedAppGenerateEntity,
-                                 queue_manager: AppQueueManager,
-                                 prompt_messages: list[PromptMessage]) -> bool:
+    def check_hosting_moderation(
+        self,
+        application_generate_entity: EasyUIBasedAppGenerateEntity,
+        queue_manager: AppQueueManager,
+        prompt_messages: list[PromptMessage],
+    ) -> bool:
         """
         Check hosting moderation
         :param application_generate_entity: application generate entity
@@ -384,8 +371,7 @@ class AppRunner:
         """
         hosting_moderation_feature = HostingModerationFeature()
         moderation_result = hosting_moderation_feature.check(
-            application_generate_entity=application_generate_entity,
-            prompt_messages=prompt_messages
+            application_generate_entity=application_generate_entity, prompt_messages=prompt_messages
         )
 
         if moderation_result:
@@ -393,18 +379,20 @@ class AppRunner:
                 queue_manager=queue_manager,
                 app_generate_entity=application_generate_entity,
                 prompt_messages=prompt_messages,
-                text="I apologize for any confusion, " \
-                     "but I'm an AI assistant to be helpful, harmless, and honest.",
-                stream=application_generate_entity.stream
+                text="I apologize for any confusion, but I'm an AI assistant to be helpful, harmless, and honest.",
+                stream=application_generate_entity.stream,
             )
 
         return moderation_result
 
-    def fill_in_inputs_from_external_data_tools(self, tenant_id: str,
-                                                app_id: str,
-                                                external_data_tools: list[ExternalDataVariableEntity],
-                                                inputs: dict,
-                                                query: str) -> dict:
+    def fill_in_inputs_from_external_data_tools(
+        self,
+        tenant_id: str,
+        app_id: str,
+        external_data_tools: list[ExternalDataVariableEntity],
+        inputs: dict,
+        query: str,
+    ) -> dict:
         """
         Fill in variable inputs from external data tools if exists.
 
@@ -417,18 +405,12 @@ class AppRunner:
         """
         external_data_fetch_feature = ExternalDataFetch()
         return external_data_fetch_feature.fetch(
-            tenant_id=tenant_id,
-            app_id=app_id,
-            external_data_tools=external_data_tools,
-            inputs=inputs,
-            query=query
+            tenant_id=tenant_id, app_id=app_id, external_data_tools=external_data_tools, inputs=inputs, query=query
         )
 
-    def query_app_annotations_to_reply(self, app_record: App,
-                                       message: Message,
-                                       query: str,
-                                       user_id: str,
-                                       invoke_from: InvokeFrom) -> Optional[MessageAnnotation]:
+    def query_app_annotations_to_reply(
+        self, app_record: App, message: Message, query: str, user_id: str, invoke_from: InvokeFrom
+    ) -> Optional[MessageAnnotation]:
         """
         Query app annotations to reply
         :param app_record: app record
@@ -440,9 +422,5 @@ class AppRunner:
         """
         annotation_reply_feature = AnnotationReplyFeature()
         return annotation_reply_feature.query(
-            app_record=app_record,
-            message=message,
-            query=query,
-            user_id=user_id,
-            invoke_from=invoke_from
+            app_record=app_record, message=message, query=query, user_id=user_id, invoke_from=invoke_from
         )
diff --git a/api/core/app/apps/chat/app_config_manager.py b/api/core/app/apps/chat/app_config_manager.py
index a286c349b2..96dc7dda79 100644
--- a/api/core/app/apps/chat/app_config_manager.py
+++ b/api/core/app/apps/chat/app_config_manager.py
@@ -22,15 +22,19 @@ class ChatAppConfig(EasyUIBasedAppConfig):
     """
     Chatbot App Config Entity.
     """
+
     pass
 
 
 class ChatAppConfigManager(BaseAppConfigManager):
     @classmethod
-    def get_app_config(cls, app_model: App,
-                       app_model_config: AppModelConfig,
-                       conversation: Optional[Conversation] = None,
-                       override_config_dict: Optional[dict] = None) -> ChatAppConfig:
+    def get_app_config(
+        cls,
+        app_model: App,
+        app_model_config: AppModelConfig,
+        conversation: Optional[Conversation] = None,
+        override_config_dict: Optional[dict] = None,
+    ) -> ChatAppConfig:
         """
         Convert app model config to chat app config
         :param app_model: app model
@@ -51,7 +55,7 @@ class ChatAppConfigManager(BaseAppConfigManager):
             config_dict = app_model_config_dict.copy()
         else:
             if not override_config_dict:
-                raise Exception('override_config_dict is required when config_from is ARGS')
+                raise Exception("override_config_dict is required when config_from is ARGS")
 
             config_dict = override_config_dict
 
@@ -63,19 +67,11 @@ class ChatAppConfigManager(BaseAppConfigManager):
             app_model_config_from=config_from,
             app_model_config_id=app_model_config.id,
             app_model_config_dict=config_dict,
-            model=ModelConfigManager.convert(
-                config=config_dict
-            ),
-            prompt_template=PromptTemplateConfigManager.convert(
-                config=config_dict
-            ),
-            sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(
-                config=config_dict
-            ),
-            dataset=DatasetConfigManager.convert(
-                config=config_dict
-            ),
-            additional_features=cls.convert_features(config_dict, app_mode)
+            model=ModelConfigManager.convert(config=config_dict),
+            prompt_template=PromptTemplateConfigManager.convert(config=config_dict),
+            sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(config=config_dict),
+            dataset=DatasetConfigManager.convert(config=config_dict),
+            additional_features=cls.convert_features(config_dict, app_mode),
         )
 
         app_config.variables, app_config.external_data_variables = BasicVariablesConfigManager.convert(
@@ -113,8 +109,9 @@ class ChatAppConfigManager(BaseAppConfigManager):
         related_config_keys.extend(current_related_config_keys)
 
         # dataset_query_variable
-        config, current_related_config_keys = DatasetConfigManager.validate_and_set_defaults(tenant_id, app_mode,
-                                                                                             config)
+        config, current_related_config_keys = DatasetConfigManager.validate_and_set_defaults(
+            tenant_id, app_mode, config
+        )
         related_config_keys.extend(current_related_config_keys)
 
         # opening_statement
@@ -123,7 +120,8 @@ class ChatAppConfigManager(BaseAppConfigManager):
 
         # suggested_questions_after_answer
         config, current_related_config_keys = SuggestedQuestionsAfterAnswerConfigManager.validate_and_set_defaults(
-            config)
+            config
+        )
         related_config_keys.extend(current_related_config_keys)
 
         # speech_to_text
@@ -139,8 +137,9 @@ class ChatAppConfigManager(BaseAppConfigManager):
         related_config_keys.extend(current_related_config_keys)
 
         # moderation validation
-        config, current_related_config_keys = SensitiveWordAvoidanceConfigManager.validate_and_set_defaults(tenant_id,
-                                                                                                            config)
+        config, current_related_config_keys = SensitiveWordAvoidanceConfigManager.validate_and_set_defaults(
+            tenant_id, config
+        )
         related_config_keys.extend(current_related_config_keys)
 
         related_config_keys = list(set(related_config_keys))
diff --git a/api/core/app/apps/chat/app_generator.py b/api/core/app/apps/chat/app_generator.py
index ab15928b74..032556ec4c 100644
--- a/api/core/app/apps/chat/app_generator.py
+++ b/api/core/app/apps/chat/app_generator.py
@@ -10,7 +10,7 @@ from pydantic import ValidationError
 
 from core.app.app_config.easy_ui_based_app.model_config.converter import ModelConfigConverter
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
-from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException, PublishFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedError, PublishFrom
 from core.app.apps.chat.app_config_manager import ChatAppConfigManager
 from core.app.apps.chat.app_runner import ChatAppRunner
 from core.app.apps.chat.generate_response_converter import ChatAppGenerateResponseConverter
@@ -30,7 +30,8 @@ logger = logging.getLogger(__name__)
 class ChatAppGenerator(MessageBasedAppGenerator):
     @overload
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         user: Union[Account, EndUser],
         args: Any,
         invoke_from: InvokeFrom,
@@ -39,7 +40,8 @@ class ChatAppGenerator(MessageBasedAppGenerator):
 
     @overload
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         user: Union[Account, EndUser],
         args: Any,
         invoke_from: InvokeFrom,
@@ -47,7 +49,8 @@ class ChatAppGenerator(MessageBasedAppGenerator):
     ) -> dict: ...
 
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         user: Union[Account, EndUser],
         args: Any,
         invoke_from: InvokeFrom,
@@ -62,58 +65,46 @@ class ChatAppGenerator(MessageBasedAppGenerator):
         :param invoke_from: invoke from source
         :param stream: is stream
         """
-        if not args.get('query'):
-            raise ValueError('query is required')
+        if not args.get("query"):
+            raise ValueError("query is required")
 
-        query = args['query']
+        query = args["query"]
         if not isinstance(query, str):
-            raise ValueError('query must be a string')
+            raise ValueError("query must be a string")
 
-        query = query.replace('\x00', '')
-        inputs = args['inputs']
+        query = query.replace("\x00", "")
+        inputs = args["inputs"]
 
-        extras = {
-            "auto_generate_conversation_name": args.get('auto_generate_name', True)
-        }
+        extras = {"auto_generate_conversation_name": args.get("auto_generate_name", True)}
 
         # get conversation
         conversation = None
-        if args.get('conversation_id'):
-            conversation = self._get_conversation_by_user(app_model, args.get('conversation_id'), user)
+        if args.get("conversation_id"):
+            conversation = self._get_conversation_by_user(app_model, args.get("conversation_id"), user)
 
         # get app model config
-        app_model_config = self._get_app_model_config(
-            app_model=app_model,
-            conversation=conversation
-        )
+        app_model_config = self._get_app_model_config(app_model=app_model, conversation=conversation)
 
         # validate override model config
         override_model_config_dict = None
-        if args.get('model_config'):
+        if args.get("model_config"):
             if invoke_from != InvokeFrom.DEBUGGER:
-                raise ValueError('Only in App debug mode can override model config')
+                raise ValueError("Only in App debug mode can override model config")
 
             # validate config
             override_model_config_dict = ChatAppConfigManager.config_validate(
-                tenant_id=app_model.tenant_id,
-                config=args.get('model_config')
+                tenant_id=app_model.tenant_id, config=args.get("model_config")
             )
 
             # always enable retriever resource in debugger mode
-            override_model_config_dict["retriever_resource"] = {
-                "enabled": True
-            }
+            override_model_config_dict["retriever_resource"] = {"enabled": True}
 
         # parse files
-        files = args['files'] if args.get('files') else []
+        files = args["files"] if args.get("files") else []
         message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
         file_extra_config = FileUploadConfigManager.convert(override_model_config_dict or app_model_config.to_dict())
         if file_extra_config:
-            file_objs = message_file_parser.validate_and_transform_files_arg(
-                files,
-                file_extra_config,
-                user
-            )
+            file_objs = message_file_parser.validate_and_transform_files_arg(files, file_extra_config, user)
         else:
             file_objs = []
 
@@ -122,7 +113,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
             app_model=app_model,
             app_model_config=app_model_config,
             conversation=conversation,
-            override_config_dict=override_model_config_dict
+            override_config_dict=override_model_config_dict,
         )
 
         # get tracing instance
@@ -141,14 +132,11 @@ class ChatAppGenerator(MessageBasedAppGenerator):
             stream=stream,
             invoke_from=invoke_from,
             extras=extras,
-            trace_manager=trace_manager
+            trace_manager=trace_manager,
         )
 
         # init generate records
-        (
-            conversation,
-            message
-        ) = self._init_generate_records(application_generate_entity, conversation)
+        (conversation, message) = self._init_generate_records(application_generate_entity, conversation)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
@@ -157,17 +145,20 @@ class ChatAppGenerator(MessageBasedAppGenerator):
             invoke_from=application_generate_entity.invoke_from,
             conversation_id=conversation.id,
             app_mode=conversation.mode,
-            message_id=message.id
+            message_id=message.id,
         )
 
         # new thread
-        worker_thread = threading.Thread(target=self._generate_worker, kwargs={
-            'flask_app': current_app._get_current_object(),
-            'application_generate_entity': application_generate_entity,
-            'queue_manager': queue_manager,
-            'conversation_id': conversation.id,
-            'message_id': message.id,
-        })
+        worker_thread = threading.Thread(
+            target=self._generate_worker,
+            kwargs={
+                "flask_app": current_app._get_current_object(),
+                "application_generate_entity": application_generate_entity,
+                "queue_manager": queue_manager,
+                "conversation_id": conversation.id,
+                "message_id": message.id,
+            },
+        )
 
         worker_thread.start()
 
@@ -181,16 +172,16 @@ class ChatAppGenerator(MessageBasedAppGenerator):
             stream=stream,
         )
 
-        return ChatAppGenerateResponseConverter.convert(
-            response=response,
-            invoke_from=invoke_from
-        )
+        return ChatAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)
 
-    def _generate_worker(self, flask_app: Flask,
-                         application_generate_entity: ChatAppGenerateEntity,
-                         queue_manager: AppQueueManager,
-                         conversation_id: str,
-                         message_id: str) -> None:
+    def _generate_worker(
+        self,
+        flask_app: Flask,
+        application_generate_entity: ChatAppGenerateEntity,
+        queue_manager: AppQueueManager,
+        conversation_id: str,
+        message_id: str,
+    ) -> None:
         """
         Generate worker in a new thread.
         :param flask_app: Flask app
@@ -212,20 +203,19 @@ class ChatAppGenerator(MessageBasedAppGenerator):
                     application_generate_entity=application_generate_entity,
                     queue_manager=queue_manager,
                     conversation=conversation,
-                    message=message
+                    message=message,
                 )
-            except GenerateTaskStoppedException:
+            except GenerateTaskStoppedError:
                 pass
             except InvokeAuthorizationError:
                 queue_manager.publish_error(
-                    InvokeAuthorizationError('Incorrect API key provided'),
-                    PublishFrom.APPLICATION_MANAGER
+                    InvokeAuthorizationError("Incorrect API key provided"), PublishFrom.APPLICATION_MANAGER
                 )
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except (ValueError, InvokeError) as e:
-                if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == 'true':
+                if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == "true":
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except Exception as e:
diff --git a/api/core/app/apps/chat/app_runner.py b/api/core/app/apps/chat/app_runner.py
index 89a498eb36..425f1ab7ef 100644
--- a/api/core/app/apps/chat/app_runner.py
+++ b/api/core/app/apps/chat/app_runner.py
@@ -11,7 +11,7 @@ from core.app.entities.queue_entities import QueueAnnotationReplyEvent
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
-from core.moderation.base import ModerationException
+from core.moderation.base import ModerationError
 from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
 from extensions.ext_database import db
 from models.model import App, Conversation, Message
@@ -24,10 +24,13 @@ class ChatAppRunner(AppRunner):
     Chat Application Runner
     """
 
-    def run(self, application_generate_entity: ChatAppGenerateEntity,
-            queue_manager: AppQueueManager,
-            conversation: Conversation,
-            message: Message) -> None:
+    def run(
+        self,
+        application_generate_entity: ChatAppGenerateEntity,
+        queue_manager: AppQueueManager,
+        conversation: Conversation,
+        message: Message,
+    ) -> None:
         """
         Run application
         :param application_generate_entity: application generate entity
@@ -58,7 +61,7 @@ class ChatAppRunner(AppRunner):
             prompt_template_entity=app_config.prompt_template,
             inputs=inputs,
             files=files,
-            query=query
+            query=query,
         )
 
         memory = None
@@ -66,13 +69,10 @@ class ChatAppRunner(AppRunner):
             # get memory of conversation (read-only)
             model_instance = ModelInstance(
                 provider_model_bundle=application_generate_entity.model_conf.provider_model_bundle,
-                model=application_generate_entity.model_conf.model
+                model=application_generate_entity.model_conf.model,
             )
 
-            memory = TokenBufferMemory(
-                conversation=conversation,
-                model_instance=model_instance
-            )
+            memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
 
         # organize all inputs and template to prompt messages
         # Include: prompt template, inputs, query(optional), files(optional)
@@ -84,7 +84,7 @@ class ChatAppRunner(AppRunner):
             inputs=inputs,
             files=files,
             query=query,
-            memory=memory
+            memory=memory,
         )
 
         # moderation
@@ -96,15 +96,15 @@ class ChatAppRunner(AppRunner):
                 app_generate_entity=application_generate_entity,
                 inputs=inputs,
                 query=query,
-                message_id=message.id
+                message_id=message.id,
             )
-        except ModerationException as e:
+        except ModerationError as e:
             self.direct_output(
                 queue_manager=queue_manager,
                 app_generate_entity=application_generate_entity,
                 prompt_messages=prompt_messages,
                 text=str(e),
-                stream=application_generate_entity.stream
+                stream=application_generate_entity.stream,
             )
             return
 
@@ -115,13 +115,13 @@ class ChatAppRunner(AppRunner):
                 message=message,
                 query=query,
                 user_id=application_generate_entity.user_id,
-                invoke_from=application_generate_entity.invoke_from
+                invoke_from=application_generate_entity.invoke_from,
             )
 
             if annotation_reply:
                 queue_manager.publish(
                     QueueAnnotationReplyEvent(message_annotation_id=annotation_reply.id),
-                    PublishFrom.APPLICATION_MANAGER
+                    PublishFrom.APPLICATION_MANAGER,
                 )
 
                 self.direct_output(
@@ -129,7 +129,7 @@ class ChatAppRunner(AppRunner):
                     app_generate_entity=application_generate_entity,
                     prompt_messages=prompt_messages,
                     text=annotation_reply.content,
-                    stream=application_generate_entity.stream
+                    stream=application_generate_entity.stream,
                 )
                 return
 
@@ -141,7 +141,7 @@ class ChatAppRunner(AppRunner):
                 app_id=app_record.id,
                 external_data_tools=external_data_tools,
                 inputs=inputs,
-                query=query
+                query=query,
             )
 
         # get context from datasets
@@ -152,7 +152,7 @@ class ChatAppRunner(AppRunner):
                 app_record.id,
                 message.id,
                 application_generate_entity.user_id,
-                application_generate_entity.invoke_from
+                application_generate_entity.invoke_from,
             )
 
             dataset_retrieval = DatasetRetrieval(application_generate_entity)
@@ -181,29 +181,26 @@ class ChatAppRunner(AppRunner):
             files=files,
             query=query,
             context=context,
-            memory=memory
+            memory=memory,
         )
 
         # check hosting moderation
         hosting_moderation_result = self.check_hosting_moderation(
             application_generate_entity=application_generate_entity,
             queue_manager=queue_manager,
-            prompt_messages=prompt_messages
+            prompt_messages=prompt_messages,
         )
 
         if hosting_moderation_result:
             return
 
         # Re-calculate the max tokens if sum(prompt_token +  max_tokens) over model token limit
-        self.recalc_llm_max_tokens(
-            model_config=application_generate_entity.model_conf,
-            prompt_messages=prompt_messages
-        )
+        self.recalc_llm_max_tokens(model_config=application_generate_entity.model_conf, prompt_messages=prompt_messages)
 
         # Invoke model
         model_instance = ModelInstance(
             provider_model_bundle=application_generate_entity.model_conf.provider_model_bundle,
-            model=application_generate_entity.model_conf.model
+            model=application_generate_entity.model_conf.model,
         )
 
         db.session.close()
@@ -218,7 +215,5 @@ class ChatAppRunner(AppRunner):
 
         # handle invoke result
         self._handle_invoke_result(
-            invoke_result=invoke_result,
-            queue_manager=queue_manager,
-            stream=application_generate_entity.stream
+            invoke_result=invoke_result, queue_manager=queue_manager, stream=application_generate_entity.stream
         )
diff --git a/api/core/app/apps/chat/generate_response_converter.py b/api/core/app/apps/chat/generate_response_converter.py
index 625e14c9c3..0fa7af0a7f 100644
--- a/api/core/app/apps/chat/generate_response_converter.py
+++ b/api/core/app/apps/chat/generate_response_converter.py
@@ -23,15 +23,15 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
         :return:
         """
         response = {
-            'event': 'message',
-            'task_id': blocking_response.task_id,
-            'id': blocking_response.data.id,
-            'message_id': blocking_response.data.message_id,
-            'conversation_id': blocking_response.data.conversation_id,
-            'mode': blocking_response.data.mode,
-            'answer': blocking_response.data.answer,
-            'metadata': blocking_response.data.metadata,
-            'created_at': blocking_response.data.created_at
+            "event": "message",
+            "task_id": blocking_response.task_id,
+            "id": blocking_response.data.id,
+            "message_id": blocking_response.data.message_id,
+            "conversation_id": blocking_response.data.conversation_id,
+            "mode": blocking_response.data.mode,
+            "answer": blocking_response.data.answer,
+            "metadata": blocking_response.data.metadata,
+            "created_at": blocking_response.data.created_at,
         }
 
         return response
@@ -45,14 +45,15 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
         """
         response = cls.convert_blocking_full_response(blocking_response)
 
-        metadata = response.get('metadata', {})
-        response['metadata'] = cls._get_simple_metadata(metadata)
+        metadata = response.get("metadata", {})
+        response["metadata"] = cls._get_simple_metadata(metadata)
 
         return response
 
     @classmethod
-    def convert_stream_full_response(cls, stream_response: Generator[ChatbotAppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_full_response(
+        cls, stream_response: Generator[ChatbotAppStreamResponse, None, None]
+    ) -> Generator[str, None, None]:
         """
         Convert stream full response.
         :param stream_response: stream response
@@ -63,14 +64,14 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             sub_stream_response = chunk.stream_response
 
             if isinstance(sub_stream_response, PingStreamResponse):
-                yield 'ping'
+                yield "ping"
                 continue
 
             response_chunk = {
-                'event': sub_stream_response.event.value,
-                'conversation_id': chunk.conversation_id,
-                'message_id': chunk.message_id,
-                'created_at': chunk.created_at
+                "event": sub_stream_response.event.value,
+                "conversation_id": chunk.conversation_id,
+                "message_id": chunk.message_id,
+                "created_at": chunk.created_at,
             }
 
             if isinstance(sub_stream_response, ErrorStreamResponse):
@@ -81,8 +82,9 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             yield json.dumps(response_chunk)
 
     @classmethod
-    def convert_stream_simple_response(cls, stream_response: Generator[ChatbotAppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_simple_response(
+        cls, stream_response: Generator[ChatbotAppStreamResponse, None, None]
+    ) -> Generator[str, None, None]:
         """
         Convert stream simple response.
         :param stream_response: stream response
@@ -93,20 +95,20 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             sub_stream_response = chunk.stream_response
 
             if isinstance(sub_stream_response, PingStreamResponse):
-                yield 'ping'
+                yield "ping"
                 continue
 
             response_chunk = {
-                'event': sub_stream_response.event.value,
-                'conversation_id': chunk.conversation_id,
-                'message_id': chunk.message_id,
-                'created_at': chunk.created_at
+                "event": sub_stream_response.event.value,
+                "conversation_id": chunk.conversation_id,
+                "message_id": chunk.message_id,
+                "created_at": chunk.created_at,
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
                 sub_stream_response_dict = sub_stream_response.to_dict()
-                metadata = sub_stream_response_dict.get('metadata', {})
-                sub_stream_response_dict['metadata'] = cls._get_simple_metadata(metadata)
+                metadata = sub_stream_response_dict.get("metadata", {})
+                sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
             if isinstance(sub_stream_response, ErrorStreamResponse):
                 data = cls._error_to_stream_response(sub_stream_response.err)
diff --git a/api/core/app/apps/completion/app_config_manager.py b/api/core/app/apps/completion/app_config_manager.py
index a771198324..1193c4b7a4 100644
--- a/api/core/app/apps/completion/app_config_manager.py
+++ b/api/core/app/apps/completion/app_config_manager.py
@@ -17,14 +17,15 @@ class CompletionAppConfig(EasyUIBasedAppConfig):
     """
     Completion App Config Entity.
     """
+
     pass
 
 
 class CompletionAppConfigManager(BaseAppConfigManager):
     @classmethod
-    def get_app_config(cls, app_model: App,
-                       app_model_config: AppModelConfig,
-                       override_config_dict: Optional[dict] = None) -> CompletionAppConfig:
+    def get_app_config(
+        cls, app_model: App, app_model_config: AppModelConfig, override_config_dict: Optional[dict] = None
+    ) -> CompletionAppConfig:
         """
         Convert app model config to completion app config
         :param app_model: app model
@@ -51,19 +52,11 @@ class CompletionAppConfigManager(BaseAppConfigManager):
             app_model_config_from=config_from,
             app_model_config_id=app_model_config.id,
             app_model_config_dict=config_dict,
-            model=ModelConfigManager.convert(
-                config=config_dict
-            ),
-            prompt_template=PromptTemplateConfigManager.convert(
-                config=config_dict
-            ),
-            sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(
-                config=config_dict
-            ),
-            dataset=DatasetConfigManager.convert(
-                config=config_dict
-            ),
-            additional_features=cls.convert_features(config_dict, app_mode)
+            model=ModelConfigManager.convert(config=config_dict),
+            prompt_template=PromptTemplateConfigManager.convert(config=config_dict),
+            sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(config=config_dict),
+            dataset=DatasetConfigManager.convert(config=config_dict),
+            additional_features=cls.convert_features(config_dict, app_mode),
         )
 
         app_config.variables, app_config.external_data_variables = BasicVariablesConfigManager.convert(
@@ -101,8 +94,9 @@ class CompletionAppConfigManager(BaseAppConfigManager):
         related_config_keys.extend(current_related_config_keys)
 
         # dataset_query_variable
-        config, current_related_config_keys = DatasetConfigManager.validate_and_set_defaults(tenant_id, app_mode,
-                                                                                             config)
+        config, current_related_config_keys = DatasetConfigManager.validate_and_set_defaults(
+            tenant_id, app_mode, config
+        )
         related_config_keys.extend(current_related_config_keys)
 
         # text_to_speech
@@ -114,8 +108,9 @@ class CompletionAppConfigManager(BaseAppConfigManager):
         related_config_keys.extend(current_related_config_keys)
 
         # moderation validation
-        config, current_related_config_keys = SensitiveWordAvoidanceConfigManager.validate_and_set_defaults(tenant_id,
-                                                                                                            config)
+        config, current_related_config_keys = SensitiveWordAvoidanceConfigManager.validate_and_set_defaults(
+            tenant_id, config
+        )
         related_config_keys.extend(current_related_config_keys)
 
         related_config_keys = list(set(related_config_keys))
diff --git a/api/core/app/apps/completion/app_generator.py b/api/core/app/apps/completion/app_generator.py
index c0b13b40fd..7fce296f2b 100644
--- a/api/core/app/apps/completion/app_generator.py
+++ b/api/core/app/apps/completion/app_generator.py
@@ -10,7 +10,7 @@ from pydantic import ValidationError
 
 from core.app.app_config.easy_ui_based_app.model_config.converter import ModelConfigConverter
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
-from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException, PublishFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedError, PublishFrom
 from core.app.apps.completion.app_config_manager import CompletionAppConfigManager
 from core.app.apps.completion.app_runner import CompletionAppRunner
 from core.app.apps.completion.generate_response_converter import CompletionAppGenerateResponseConverter
@@ -32,7 +32,8 @@ logger = logging.getLogger(__name__)
 class CompletionAppGenerator(MessageBasedAppGenerator):
     @overload
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         user: Union[Account, EndUser],
         args: dict,
         invoke_from: InvokeFrom,
@@ -41,19 +42,17 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
 
     @overload
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         user: Union[Account, EndUser],
         args: dict,
         invoke_from: InvokeFrom,
         stream: Literal[False] = False,
     ) -> dict: ...
 
-    def generate(self, app_model: App,
-                 user: Union[Account, EndUser],
-                 args: Any,
-                 invoke_from: InvokeFrom,
-                 stream: bool = True) \
-            -> Union[dict, Generator[str, None, None]]:
+    def generate(
+        self, app_model: App, user: Union[Account, EndUser], args: Any, invoke_from: InvokeFrom, stream: bool = True
+    ) -> Union[dict, Generator[str, None, None]]:
         """
         Generate App response.
 
@@ -63,12 +62,12 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
         :param invoke_from: invoke from source
         :param stream: is stream
         """
-        query = args['query']
+        query = args["query"]
         if not isinstance(query, str):
-            raise ValueError('query must be a string')
+            raise ValueError("query must be a string")
 
-        query = query.replace('\x00', '')
-        inputs = args['inputs']
+        query = query.replace("\x00", "")
+        inputs = args["inputs"]
 
         extras = {}
 
@@ -76,41 +75,31 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
         conversation = None
 
         # get app model config
-        app_model_config = self._get_app_model_config(
-            app_model=app_model,
-            conversation=conversation
-        )
+        app_model_config = self._get_app_model_config(app_model=app_model, conversation=conversation)
 
         # validate override model config
         override_model_config_dict = None
-        if args.get('model_config'):
+        if args.get("model_config"):
             if invoke_from != InvokeFrom.DEBUGGER:
-                raise ValueError('Only in App debug mode can override model config')
+                raise ValueError("Only in App debug mode can override model config")
 
             # validate config
             override_model_config_dict = CompletionAppConfigManager.config_validate(
-                tenant_id=app_model.tenant_id,
-                config=args.get('model_config')
+                tenant_id=app_model.tenant_id, config=args.get("model_config")
             )
 
         # parse files
-        files = args['files'] if args.get('files') else []
+        files = args["files"] if args.get("files") else []
         message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
         file_extra_config = FileUploadConfigManager.convert(override_model_config_dict or app_model_config.to_dict())
         if file_extra_config:
-            file_objs = message_file_parser.validate_and_transform_files_arg(
-                files,
-                file_extra_config,
-                user
-            )
+            file_objs = message_file_parser.validate_and_transform_files_arg(files, file_extra_config, user)
         else:
             file_objs = []
 
         # convert to app config
         app_config = CompletionAppConfigManager.get_app_config(
-            app_model=app_model,
-            app_model_config=app_model_config,
-            override_config_dict=override_model_config_dict
+            app_model=app_model, app_model_config=app_model_config, override_config_dict=override_model_config_dict
         )
 
         # get tracing instance
@@ -128,14 +117,11 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             stream=stream,
             invoke_from=invoke_from,
             extras=extras,
-            trace_manager=trace_manager
+            trace_manager=trace_manager,
         )
 
         # init generate records
-        (
-            conversation,
-            message
-        ) = self._init_generate_records(application_generate_entity)
+        (conversation, message) = self._init_generate_records(application_generate_entity)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
@@ -144,16 +130,19 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             invoke_from=application_generate_entity.invoke_from,
             conversation_id=conversation.id,
             app_mode=conversation.mode,
-            message_id=message.id
+            message_id=message.id,
         )
 
         # new thread
-        worker_thread = threading.Thread(target=self._generate_worker, kwargs={
-            'flask_app': current_app._get_current_object(),
-            'application_generate_entity': application_generate_entity,
-            'queue_manager': queue_manager,
-            'message_id': message.id,
-        })
+        worker_thread = threading.Thread(
+            target=self._generate_worker,
+            kwargs={
+                "flask_app": current_app._get_current_object(),
+                "application_generate_entity": application_generate_entity,
+                "queue_manager": queue_manager,
+                "message_id": message.id,
+            },
+        )
 
         worker_thread.start()
 
@@ -167,15 +156,15 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             stream=stream,
         )
 
-        return CompletionAppGenerateResponseConverter.convert(
-            response=response,
-            invoke_from=invoke_from
-        )
+        return CompletionAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)
 
-    def _generate_worker(self, flask_app: Flask,
-                         application_generate_entity: CompletionAppGenerateEntity,
-                         queue_manager: AppQueueManager,
-                         message_id: str) -> None:
+    def _generate_worker(
+        self,
+        flask_app: Flask,
+        application_generate_entity: CompletionAppGenerateEntity,
+        queue_manager: AppQueueManager,
+        message_id: str,
+    ) -> None:
         """
         Generate worker in a new thread.
         :param flask_app: Flask app
@@ -194,20 +183,19 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
                 runner.run(
                     application_generate_entity=application_generate_entity,
                     queue_manager=queue_manager,
-                    message=message
+                    message=message,
                 )
-            except GenerateTaskStoppedException:
+            except GenerateTaskStoppedError:
                 pass
             except InvokeAuthorizationError:
                 queue_manager.publish_error(
-                    InvokeAuthorizationError('Incorrect API key provided'),
-                    PublishFrom.APPLICATION_MANAGER
+                    InvokeAuthorizationError("Incorrect API key provided"), PublishFrom.APPLICATION_MANAGER
                 )
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except (ValueError, InvokeError) as e:
-                if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == 'true':
+                if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == "true":
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except Exception as e:
@@ -216,12 +204,14 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             finally:
                 db.session.close()
 
-    def generate_more_like_this(self, app_model: App,
-                                message_id: str,
-                                user: Union[Account, EndUser],
-                                invoke_from: InvokeFrom,
-                                stream: bool = True) \
-            -> Union[dict, Generator[str, None, None]]:
+    def generate_more_like_this(
+        self,
+        app_model: App,
+        message_id: str,
+        user: Union[Account, EndUser],
+        invoke_from: InvokeFrom,
+        stream: bool = True,
+    ) -> Union[dict, Generator[str, None, None]]:
         """
         Generate App response.
 
@@ -231,13 +221,17 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
         :param invoke_from: invoke from source
         :param stream: is stream
         """
-        message = db.session.query(Message).filter(
-            Message.id == message_id,
-            Message.app_id == app_model.id,
-            Message.from_source == ('api' if isinstance(user, EndUser) else 'console'),
-            Message.from_end_user_id == (user.id if isinstance(user, EndUser) else None),
-            Message.from_account_id == (user.id if isinstance(user, Account) else None),
-        ).first()
+        message = (
+            db.session.query(Message)
+            .filter(
+                Message.id == message_id,
+                Message.app_id == app_model.id,
+                Message.from_source == ("api" if isinstance(user, EndUser) else "console"),
+                Message.from_end_user_id == (user.id if isinstance(user, EndUser) else None),
+                Message.from_account_id == (user.id if isinstance(user, Account) else None),
+            )
+            .first()
+        )
 
         if not message:
             raise MessageNotExistsError()
@@ -250,29 +244,23 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
 
         app_model_config = message.app_model_config
         override_model_config_dict = app_model_config.to_dict()
-        model_dict = override_model_config_dict['model']
-        completion_params = model_dict.get('completion_params')
-        completion_params['temperature'] = 0.9
-        model_dict['completion_params'] = completion_params
-        override_model_config_dict['model'] = model_dict
+        model_dict = override_model_config_dict["model"]
+        completion_params = model_dict.get("completion_params")
+        completion_params["temperature"] = 0.9
+        model_dict["completion_params"] = completion_params
+        override_model_config_dict["model"] = model_dict
 
         # parse files
         message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
         file_extra_config = FileUploadConfigManager.convert(override_model_config_dict or app_model_config.to_dict())
         if file_extra_config:
-            file_objs = message_file_parser.validate_and_transform_files_arg(
-                message.files,
-                file_extra_config,
-                user
-            )
+            file_objs = message_file_parser.validate_and_transform_files_arg(message.files, file_extra_config, user)
         else:
             file_objs = []
 
         # convert to app config
         app_config = CompletionAppConfigManager.get_app_config(
-            app_model=app_model,
-            app_model_config=app_model_config,
-            override_config_dict=override_model_config_dict
+            app_model=app_model, app_model_config=app_model_config, override_config_dict=override_model_config_dict
         )
 
         # init application generate entity
@@ -286,14 +274,11 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             user_id=user.id,
             stream=stream,
             invoke_from=invoke_from,
-            extras={}
+            extras={},
         )
 
         # init generate records
-        (
-            conversation,
-            message
-        ) = self._init_generate_records(application_generate_entity)
+        (conversation, message) = self._init_generate_records(application_generate_entity)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
@@ -302,16 +287,19 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             invoke_from=application_generate_entity.invoke_from,
             conversation_id=conversation.id,
             app_mode=conversation.mode,
-            message_id=message.id
+            message_id=message.id,
         )
 
         # new thread
-        worker_thread = threading.Thread(target=self._generate_worker, kwargs={
-            'flask_app': current_app._get_current_object(),
-            'application_generate_entity': application_generate_entity,
-            'queue_manager': queue_manager,
-            'message_id': message.id,
-        })
+        worker_thread = threading.Thread(
+            target=self._generate_worker,
+            kwargs={
+                "flask_app": current_app._get_current_object(),
+                "application_generate_entity": application_generate_entity,
+                "queue_manager": queue_manager,
+                "message_id": message.id,
+            },
+        )
 
         worker_thread.start()
 
@@ -325,7 +313,4 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             stream=stream,
         )
 
-        return CompletionAppGenerateResponseConverter.convert(
-            response=response,
-            invoke_from=invoke_from
-        )
+        return CompletionAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)
diff --git a/api/core/app/apps/completion/app_runner.py b/api/core/app/apps/completion/app_runner.py
index f0e5f9ae17..908d74ff53 100644
--- a/api/core/app/apps/completion/app_runner.py
+++ b/api/core/app/apps/completion/app_runner.py
@@ -9,7 +9,7 @@ from core.app.entities.app_invoke_entities import (
 )
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.model_manager import ModelInstance
-from core.moderation.base import ModerationException
+from core.moderation.base import ModerationError
 from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
 from extensions.ext_database import db
 from models.model import App, Message
@@ -22,9 +22,9 @@ class CompletionAppRunner(AppRunner):
     Completion Application Runner
     """
 
-    def run(self, application_generate_entity: CompletionAppGenerateEntity,
-            queue_manager: AppQueueManager,
-            message: Message) -> None:
+    def run(
+        self, application_generate_entity: CompletionAppGenerateEntity, queue_manager: AppQueueManager, message: Message
+    ) -> None:
         """
         Run application
         :param application_generate_entity: application generate entity
@@ -54,7 +54,7 @@ class CompletionAppRunner(AppRunner):
             prompt_template_entity=app_config.prompt_template,
             inputs=inputs,
             files=files,
-            query=query
+            query=query,
         )
 
         # organize all inputs and template to prompt messages
@@ -65,7 +65,7 @@ class CompletionAppRunner(AppRunner):
             prompt_template_entity=app_config.prompt_template,
             inputs=inputs,
             files=files,
-            query=query
+            query=query,
         )
 
         # moderation
@@ -77,15 +77,15 @@ class CompletionAppRunner(AppRunner):
                 app_generate_entity=application_generate_entity,
                 inputs=inputs,
                 query=query,
-                message_id=message.id
+                message_id=message.id,
             )
-        except ModerationException as e:
+        except ModerationError as e:
             self.direct_output(
                 queue_manager=queue_manager,
                 app_generate_entity=application_generate_entity,
                 prompt_messages=prompt_messages,
                 text=str(e),
-                stream=application_generate_entity.stream
+                stream=application_generate_entity.stream,
             )
             return
 
@@ -97,7 +97,7 @@ class CompletionAppRunner(AppRunner):
                 app_id=app_record.id,
                 external_data_tools=external_data_tools,
                 inputs=inputs,
-                query=query
+                query=query,
             )
 
         # get context from datasets
@@ -108,7 +108,7 @@ class CompletionAppRunner(AppRunner):
                 app_record.id,
                 message.id,
                 application_generate_entity.user_id,
-                application_generate_entity.invoke_from
+                application_generate_entity.invoke_from,
             )
 
             dataset_config = app_config.dataset
@@ -126,7 +126,7 @@ class CompletionAppRunner(AppRunner):
                 invoke_from=application_generate_entity.invoke_from,
                 show_retrieve_source=app_config.additional_features.show_retrieve_source,
                 hit_callback=hit_callback,
-                message_id=message.id
+                message_id=message.id,
             )
 
         # reorganize all inputs and template to prompt messages
@@ -139,29 +139,26 @@ class CompletionAppRunner(AppRunner):
             inputs=inputs,
             files=files,
             query=query,
-            context=context
+            context=context,
         )
 
         # check hosting moderation
         hosting_moderation_result = self.check_hosting_moderation(
             application_generate_entity=application_generate_entity,
             queue_manager=queue_manager,
-            prompt_messages=prompt_messages
+            prompt_messages=prompt_messages,
         )
 
         if hosting_moderation_result:
             return
 
         # Re-calculate the max tokens if sum(prompt_token +  max_tokens) over model token limit
-        self.recalc_llm_max_tokens(
-            model_config=application_generate_entity.model_conf,
-            prompt_messages=prompt_messages
-        )
+        self.recalc_llm_max_tokens(model_config=application_generate_entity.model_conf, prompt_messages=prompt_messages)
 
         # Invoke model
         model_instance = ModelInstance(
             provider_model_bundle=application_generate_entity.model_conf.provider_model_bundle,
-            model=application_generate_entity.model_conf.model
+            model=application_generate_entity.model_conf.model,
         )
 
         db.session.close()
@@ -176,8 +173,5 @@ class CompletionAppRunner(AppRunner):
 
         # handle invoke result
         self._handle_invoke_result(
-            invoke_result=invoke_result,
-            queue_manager=queue_manager,
-            stream=application_generate_entity.stream
+            invoke_result=invoke_result, queue_manager=queue_manager, stream=application_generate_entity.stream
         )
-    
\ No newline at end of file
diff --git a/api/core/app/apps/completion/generate_response_converter.py b/api/core/app/apps/completion/generate_response_converter.py
index 14db74dbd0..697f0273a5 100644
--- a/api/core/app/apps/completion/generate_response_converter.py
+++ b/api/core/app/apps/completion/generate_response_converter.py
@@ -23,14 +23,14 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
         :return:
         """
         response = {
-            'event': 'message',
-            'task_id': blocking_response.task_id,
-            'id': blocking_response.data.id,
-            'message_id': blocking_response.data.message_id,
-            'mode': blocking_response.data.mode,
-            'answer': blocking_response.data.answer,
-            'metadata': blocking_response.data.metadata,
-            'created_at': blocking_response.data.created_at
+            "event": "message",
+            "task_id": blocking_response.task_id,
+            "id": blocking_response.data.id,
+            "message_id": blocking_response.data.message_id,
+            "mode": blocking_response.data.mode,
+            "answer": blocking_response.data.answer,
+            "metadata": blocking_response.data.metadata,
+            "created_at": blocking_response.data.created_at,
         }
 
         return response
@@ -44,14 +44,15 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
         """
         response = cls.convert_blocking_full_response(blocking_response)
 
-        metadata = response.get('metadata', {})
-        response['metadata'] = cls._get_simple_metadata(metadata)
+        metadata = response.get("metadata", {})
+        response["metadata"] = cls._get_simple_metadata(metadata)
 
         return response
 
     @classmethod
-    def convert_stream_full_response(cls, stream_response: Generator[CompletionAppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_full_response(
+        cls, stream_response: Generator[CompletionAppStreamResponse, None, None]
+    ) -> Generator[str, None, None]:
         """
         Convert stream full response.
         :param stream_response: stream response
@@ -62,13 +63,13 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
             sub_stream_response = chunk.stream_response
 
             if isinstance(sub_stream_response, PingStreamResponse):
-                yield 'ping'
+                yield "ping"
                 continue
 
             response_chunk = {
-                'event': sub_stream_response.event.value,
-                'message_id': chunk.message_id,
-                'created_at': chunk.created_at
+                "event": sub_stream_response.event.value,
+                "message_id": chunk.message_id,
+                "created_at": chunk.created_at,
             }
 
             if isinstance(sub_stream_response, ErrorStreamResponse):
@@ -79,8 +80,9 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
             yield json.dumps(response_chunk)
 
     @classmethod
-    def convert_stream_simple_response(cls, stream_response: Generator[CompletionAppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_simple_response(
+        cls, stream_response: Generator[CompletionAppStreamResponse, None, None]
+    ) -> Generator[str, None, None]:
         """
         Convert stream simple response.
         :param stream_response: stream response
@@ -91,19 +93,19 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
             sub_stream_response = chunk.stream_response
 
             if isinstance(sub_stream_response, PingStreamResponse):
-                yield 'ping'
+                yield "ping"
                 continue
 
             response_chunk = {
-                'event': sub_stream_response.event.value,
-                'message_id': chunk.message_id,
-                'created_at': chunk.created_at
+                "event": sub_stream_response.event.value,
+                "message_id": chunk.message_id,
+                "created_at": chunk.created_at,
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
                 sub_stream_response_dict = sub_stream_response.to_dict()
-                metadata = sub_stream_response_dict.get('metadata', {})
-                sub_stream_response_dict['metadata'] = cls._get_simple_metadata(metadata)
+                metadata = sub_stream_response_dict.get("metadata", {})
+                sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
             if isinstance(sub_stream_response, ErrorStreamResponse):
                 data = cls._error_to_stream_response(sub_stream_response.err)
diff --git a/api/core/app/apps/message_based_app_generator.py b/api/core/app/apps/message_based_app_generator.py
index fceed95b91..c4db95cbd0 100644
--- a/api/core/app/apps/message_based_app_generator.py
+++ b/api/core/app/apps/message_based_app_generator.py
@@ -8,7 +8,7 @@ from sqlalchemy import and_
 
 from core.app.app_config.entities import EasyUIBasedAppModelConfigFrom
 from core.app.apps.base_app_generator import BaseAppGenerator
-from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException
+from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedError
 from core.app.entities.app_invoke_entities import (
     AdvancedChatAppGenerateEntity,
     AgentChatAppGenerateEntity,
@@ -35,23 +35,23 @@ logger = logging.getLogger(__name__)
 
 
 class MessageBasedAppGenerator(BaseAppGenerator):
-
     def _handle_response(
-            self, application_generate_entity: Union[
-                ChatAppGenerateEntity,
-                CompletionAppGenerateEntity,
-                AgentChatAppGenerateEntity,
-                AdvancedChatAppGenerateEntity
-            ],
-            queue_manager: AppQueueManager,
-            conversation: Conversation,
-            message: Message,
-            user: Union[Account, EndUser],
-            stream: bool = False,
+        self,
+        application_generate_entity: Union[
+            ChatAppGenerateEntity,
+            CompletionAppGenerateEntity,
+            AgentChatAppGenerateEntity,
+            AdvancedChatAppGenerateEntity,
+        ],
+        queue_manager: AppQueueManager,
+        conversation: Conversation,
+        message: Message,
+        user: Union[Account, EndUser],
+        stream: bool = False,
     ) -> Union[
         ChatbotAppBlockingResponse,
         CompletionAppBlockingResponse,
-        Generator[Union[ChatbotAppStreamResponse, CompletionAppStreamResponse], None, None]
+        Generator[Union[ChatbotAppStreamResponse, CompletionAppStreamResponse], None, None],
     ]:
         """
         Handle response.
@@ -70,24 +70,25 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             conversation=conversation,
             message=message,
             user=user,
-            stream=stream
+            stream=stream,
         )
 
         try:
             return generate_task_pipeline.process()
         except ValueError as e:
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
-                raise GenerateTaskStoppedException()
+                raise GenerateTaskStoppedError()
             else:
                 logger.exception(e)
                 raise e
 
-    def _get_conversation_by_user(self, app_model: App, conversation_id: str,
-                                  user: Union[Account, EndUser]) -> Conversation:
+    def _get_conversation_by_user(
+        self, app_model: App, conversation_id: str, user: Union[Account, EndUser]
+    ) -> Conversation:
         conversation_filter = [
             Conversation.id == conversation_id,
             Conversation.app_id == app_model.id,
-            Conversation.status == 'normal'
+            Conversation.status == "normal",
         ]
 
         if isinstance(user, Account):
@@ -100,19 +101,18 @@ class MessageBasedAppGenerator(BaseAppGenerator):
         if not conversation:
             raise ConversationNotExistsError()
 
-        if conversation.status != 'normal':
+        if conversation.status != "normal":
             raise ConversationCompletedError()
 
         return conversation
 
-    def _get_app_model_config(self, app_model: App,
-                              conversation: Optional[Conversation] = None) \
-            -> AppModelConfig:
+    def _get_app_model_config(self, app_model: App, conversation: Optional[Conversation] = None) -> AppModelConfig:
         if conversation:
-            app_model_config = db.session.query(AppModelConfig).filter(
-                AppModelConfig.id == conversation.app_model_config_id,
-                AppModelConfig.app_id == app_model.id
-            ).first()
+            app_model_config = (
+                db.session.query(AppModelConfig)
+                .filter(AppModelConfig.id == conversation.app_model_config_id, AppModelConfig.app_id == app_model.id)
+                .first()
+            )
 
             if not app_model_config:
                 raise AppModelConfigBrokenError()
@@ -127,15 +127,16 @@ class MessageBasedAppGenerator(BaseAppGenerator):
 
         return app_model_config
 
-    def _init_generate_records(self,
-                               application_generate_entity: Union[
-                                   ChatAppGenerateEntity,
-                                   CompletionAppGenerateEntity,
-                                   AgentChatAppGenerateEntity,
-                                   AdvancedChatAppGenerateEntity
-                               ],
-                               conversation: Optional[Conversation] = None) \
-            -> tuple[Conversation, Message]:
+    def _init_generate_records(
+        self,
+        application_generate_entity: Union[
+            ChatAppGenerateEntity,
+            CompletionAppGenerateEntity,
+            AgentChatAppGenerateEntity,
+            AdvancedChatAppGenerateEntity,
+        ],
+        conversation: Optional[Conversation] = None,
+    ) -> tuple[Conversation, Message]:
         """
         Initialize generate records
         :param application_generate_entity: application generate entity
@@ -147,11 +148,11 @@ class MessageBasedAppGenerator(BaseAppGenerator):
         # get from source
         end_user_id = None
         account_id = None
-        if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
-            from_source = 'api'
+        if application_generate_entity.invoke_from in {InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API}:
+            from_source = "api"
             end_user_id = application_generate_entity.user_id
         else:
-            from_source = 'console'
+            from_source = "console"
             account_id = application_generate_entity.user_id
 
         if isinstance(application_generate_entity, AdvancedChatAppGenerateEntity):
@@ -164,8 +165,11 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             model_provider = application_generate_entity.model_conf.provider
             model_id = application_generate_entity.model_conf.model
             override_model_configs = None
-            if app_config.app_model_config_from == EasyUIBasedAppModelConfigFrom.ARGS \
-                    and app_config.app_mode in [AppMode.AGENT_CHAT, AppMode.CHAT, AppMode.COMPLETION]:
+            if app_config.app_model_config_from == EasyUIBasedAppModelConfigFrom.ARGS and app_config.app_mode in {
+                AppMode.AGENT_CHAT,
+                AppMode.CHAT,
+                AppMode.COMPLETION,
+            }:
                 override_model_configs = app_config.app_model_config_dict
 
         # get conversation introduction
@@ -179,12 +183,12 @@ class MessageBasedAppGenerator(BaseAppGenerator):
                 model_id=model_id,
                 override_model_configs=json.dumps(override_model_configs) if override_model_configs else None,
                 mode=app_config.app_mode.value,
-                name='New conversation',
+                name="New conversation",
                 inputs=application_generate_entity.inputs,
                 introduction=introduction,
                 system_instruction="",
                 system_instruction_tokens=0,
-                status='normal',
+                status="normal",
                 invoke_from=application_generate_entity.invoke_from.value,
                 from_source=from_source,
                 from_end_user_id=end_user_id,
@@ -216,11 +220,11 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             answer_price_unit=0,
             provider_response_latency=0,
             total_price=0,
-            currency='USD',
+            currency="USD",
             invoke_from=application_generate_entity.invoke_from.value,
             from_source=from_source,
             from_end_user_id=end_user_id,
-            from_account_id=account_id
+            from_account_id=account_id,
         )
 
         db.session.add(message)
@@ -232,10 +236,10 @@ class MessageBasedAppGenerator(BaseAppGenerator):
                 message_id=message.id,
                 type=file.type.value,
                 transfer_method=file.transfer_method.value,
-                belongs_to='user',
+                belongs_to="user",
                 url=file.url,
                 upload_file_id=file.related_id,
-                created_by_role=('account' if account_id else 'end_user'),
+                created_by_role=("account" if account_id else "end_user"),
                 created_by=account_id or end_user_id,
             )
             db.session.add(message_file)
@@ -269,11 +273,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
         :param conversation_id: conversation id
         :return: conversation
         """
-        conversation = (
-            db.session.query(Conversation)
-            .filter(Conversation.id == conversation_id)
-            .first()
-        )
+        conversation = db.session.query(Conversation).filter(Conversation.id == conversation_id).first()
 
         if not conversation:
             raise ConversationNotExistsError()
@@ -286,10 +286,6 @@ class MessageBasedAppGenerator(BaseAppGenerator):
         :param message_id: message id
         :return: message
         """
-        message = (
-            db.session.query(Message)
-            .filter(Message.id == message_id)
-            .first()
-        )
+        message = db.session.query(Message).filter(Message.id == message_id).first()
 
         return message
diff --git a/api/core/app/apps/message_based_app_queue_manager.py b/api/core/app/apps/message_based_app_queue_manager.py
index f4ff44ddda..363c3c82bb 100644
--- a/api/core/app/apps/message_based_app_queue_manager.py
+++ b/api/core/app/apps/message_based_app_queue_manager.py
@@ -1,4 +1,4 @@
-from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException, PublishFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedError, PublishFrom
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.queue_entities import (
     AppQueueEvent,
@@ -12,12 +12,9 @@ from core.app.entities.queue_entities import (
 
 
 class MessageBasedAppQueueManager(AppQueueManager):
-    def __init__(self, task_id: str,
-                 user_id: str,
-                 invoke_from: InvokeFrom,
-                 conversation_id: str,
-                 app_mode: str,
-                 message_id: str) -> None:
+    def __init__(
+        self, task_id: str, user_id: str, invoke_from: InvokeFrom, conversation_id: str, app_mode: str, message_id: str
+    ) -> None:
         super().__init__(task_id, user_id, invoke_from)
 
         self._conversation_id = str(conversation_id)
@@ -30,7 +27,7 @@ class MessageBasedAppQueueManager(AppQueueManager):
             message_id=self._message_id,
             conversation_id=self._conversation_id,
             app_mode=self._app_mode,
-            event=event
+            event=event,
         )
 
     def _publish(self, event: AppQueueEvent, pub_from: PublishFrom) -> None:
@@ -45,17 +42,15 @@ class MessageBasedAppQueueManager(AppQueueManager):
             message_id=self._message_id,
             conversation_id=self._conversation_id,
             app_mode=self._app_mode,
-            event=event
+            event=event,
         )
 
         self._q.put(message)
 
-        if isinstance(event, QueueStopEvent
-                             | QueueErrorEvent
-                             | QueueMessageEndEvent
-                             | QueueAdvancedChatMessageEndEvent):
+        if isinstance(
+            event, QueueStopEvent | QueueErrorEvent | QueueMessageEndEvent | QueueAdvancedChatMessageEndEvent
+        ):
             self.stop_listen()
 
         if pub_from == PublishFrom.APPLICATION_MANAGER and self._is_stopped():
-            raise GenerateTaskStoppedException()
-
+            raise GenerateTaskStoppedError()
diff --git a/api/core/app/apps/workflow/app_config_manager.py b/api/core/app/apps/workflow/app_config_manager.py
index 36d3696d60..8b98e74b85 100644
--- a/api/core/app/apps/workflow/app_config_manager.py
+++ b/api/core/app/apps/workflow/app_config_manager.py
@@ -12,6 +12,7 @@ class WorkflowAppConfig(WorkflowUIBasedAppConfig):
     """
     Workflow App Config Entity.
     """
+
     pass
 
 
@@ -26,13 +27,9 @@ class WorkflowAppConfigManager(BaseAppConfigManager):
             app_id=app_model.id,
             app_mode=app_mode,
             workflow_id=workflow.id,
-            sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(
-                config=features_dict
-            ),
-            variables=WorkflowVariablesConfigManager.convert(
-                workflow=workflow
-            ),
-            additional_features=cls.convert_features(features_dict, app_mode)
+            sensitive_word_avoidance=SensitiveWordAvoidanceConfigManager.convert(config=features_dict),
+            variables=WorkflowVariablesConfigManager.convert(workflow=workflow),
+            additional_features=cls.convert_features(features_dict, app_mode),
         )
 
         return app_config
@@ -50,8 +47,7 @@ class WorkflowAppConfigManager(BaseAppConfigManager):
 
         # file upload validation
         config, current_related_config_keys = FileUploadConfigManager.validate_and_set_defaults(
-            config=config,
-            is_vision=False
+            config=config, is_vision=False
         )
         related_config_keys.extend(current_related_config_keys)
 
@@ -61,9 +57,7 @@ class WorkflowAppConfigManager(BaseAppConfigManager):
 
         # moderation validation
         config, current_related_config_keys = SensitiveWordAvoidanceConfigManager.validate_and_set_defaults(
-            tenant_id=tenant_id,
-            config=config,
-            only_structure_validate=only_structure_validate
+            tenant_id=tenant_id, config=config, only_structure_validate=only_structure_validate
         )
         related_config_keys.extend(current_related_config_keys)
 
diff --git a/api/core/app/apps/workflow/app_generator.py b/api/core/app/apps/workflow/app_generator.py
index 26bb6c0f4f..57a77591a0 100644
--- a/api/core/app/apps/workflow/app_generator.py
+++ b/api/core/app/apps/workflow/app_generator.py
@@ -4,7 +4,7 @@ import os
 import threading
 import uuid
 from collections.abc import Generator
-from typing import Literal, Union, overload
+from typing import Any, Literal, Optional, Union, overload
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -12,7 +12,7 @@ from pydantic import ValidationError
 import contexts
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.base_app_generator import BaseAppGenerator
-from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException, PublishFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedError, PublishFrom
 from core.app.apps.workflow.app_config_manager import WorkflowAppConfigManager
 from core.app.apps.workflow.app_queue_manager import WorkflowAppQueueManager
 from core.app.apps.workflow.app_runner import WorkflowAppRunner
@@ -34,32 +34,40 @@ logger = logging.getLogger(__name__)
 class WorkflowAppGenerator(BaseAppGenerator):
     @overload
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
         args: dict,
         invoke_from: InvokeFrom,
         stream: Literal[True] = True,
+        call_depth: int = 0,
+        workflow_thread_pool_id: Optional[str] = None,
     ) -> Generator[str, None, None]: ...
 
     @overload
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
         args: dict,
         invoke_from: InvokeFrom,
         stream: Literal[False] = False,
+        call_depth: int = 0,
+        workflow_thread_pool_id: Optional[str] = None,
     ) -> dict: ...
 
     def generate(
-        self, app_model: App,
+        self,
+        app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
         args: dict,
         invoke_from: InvokeFrom,
         stream: bool = True,
         call_depth: int = 0,
+        workflow_thread_pool_id: Optional[str] = None,
     ):
         """
         Generate App response.
@@ -71,27 +79,21 @@ class WorkflowAppGenerator(BaseAppGenerator):
         :param invoke_from: invoke from source
         :param stream: is stream
         :param call_depth: call depth
+        :param workflow_thread_pool_id: workflow thread pool id
         """
-        inputs = args['inputs']
+        inputs = args["inputs"]
 
         # parse files
-        files = args['files'] if args.get('files') else []
+        files = args["files"] if args.get("files") else []
         message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=app_model.id)
         file_extra_config = FileUploadConfigManager.convert(workflow.features_dict, is_vision=False)
         if file_extra_config:
-            file_objs = message_file_parser.validate_and_transform_files_arg(
-                files,
-                file_extra_config,
-                user
-            )
+            file_objs = message_file_parser.validate_and_transform_files_arg(files, file_extra_config, user)
         else:
             file_objs = []
 
         # convert to app config
-        app_config = WorkflowAppConfigManager.get_app_config(
-            app_model=app_model,
-            workflow=workflow
-        )
+        app_config = WorkflowAppConfigManager.get_app_config(app_model=app_model, workflow=workflow)
 
         # get tracing instance
         user_id = user.id if isinstance(user, Account) else user.session_id
@@ -107,7 +109,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             stream=stream,
             invoke_from=invoke_from,
             call_depth=call_depth,
-            trace_manager=trace_manager
+            trace_manager=trace_manager,
         )
         contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
@@ -118,16 +120,20 @@ class WorkflowAppGenerator(BaseAppGenerator):
             application_generate_entity=application_generate_entity,
             invoke_from=invoke_from,
             stream=stream,
+            workflow_thread_pool_id=workflow_thread_pool_id,
         )
 
     def _generate(
-        self, app_model: App,
+        self,
+        *,
+        app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
         application_generate_entity: WorkflowAppGenerateEntity,
         invoke_from: InvokeFrom,
         stream: bool = True,
-    ) -> Union[dict, Generator[str, None, None]]:
+        workflow_thread_pool_id: Optional[str] = None,
+    ) -> dict[str, Any] | Generator[str, None, None]:
         """
         Generate App response.
 
@@ -137,22 +143,27 @@ class WorkflowAppGenerator(BaseAppGenerator):
         :param application_generate_entity: application generate entity
         :param invoke_from: invoke from source
         :param stream: is stream
+        :param workflow_thread_pool_id: workflow thread pool id
         """
         # init queue manager
         queue_manager = WorkflowAppQueueManager(
             task_id=application_generate_entity.task_id,
             user_id=application_generate_entity.user_id,
             invoke_from=application_generate_entity.invoke_from,
-            app_mode=app_model.mode
+            app_mode=app_model.mode,
         )
 
         # new thread
-        worker_thread = threading.Thread(target=self._generate_worker, kwargs={
-            'flask_app': current_app._get_current_object(),
-            'application_generate_entity': application_generate_entity,
-            'queue_manager': queue_manager,
-            'context': contextvars.copy_context()
-        })
+        worker_thread = threading.Thread(
+            target=self._generate_worker,
+            kwargs={
+                "flask_app": current_app._get_current_object(),  # type: ignore
+                "application_generate_entity": application_generate_entity,
+                "queue_manager": queue_manager,
+                "context": contextvars.copy_context(),
+                "workflow_thread_pool_id": workflow_thread_pool_id,
+            },
+        )
 
         worker_thread.start()
 
@@ -165,17 +176,11 @@ class WorkflowAppGenerator(BaseAppGenerator):
             stream=stream,
         )
 
-        return WorkflowAppGenerateResponseConverter.convert(
-            response=response,
-            invoke_from=invoke_from
-        )
+        return WorkflowAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)
 
-    def single_iteration_generate(self, app_model: App,
-                                  workflow: Workflow,
-                                  node_id: str,
-                                  user: Account,
-                                  args: dict,
-                                  stream: bool = True):
+    def single_iteration_generate(
+        self, app_model: App, workflow: Workflow, node_id: str, user: Account, args: dict, stream: bool = True
+    ) -> dict[str, Any] | Generator[str, Any, None]:
         """
         Generate App response.
 
@@ -187,20 +192,13 @@ class WorkflowAppGenerator(BaseAppGenerator):
         :param stream: is stream
         """
         if not node_id:
-            raise ValueError('node_id is required')
+            raise ValueError("node_id is required")
 
-        if args.get('inputs') is None:
-            raise ValueError('inputs is required')
-
-        extras = {
-            "auto_generate_conversation_name": False
-        }
+        if args.get("inputs") is None:
+            raise ValueError("inputs is required")
 
         # convert to app config
-        app_config = WorkflowAppConfigManager.get_app_config(
-            app_model=app_model,
-            workflow=workflow
-        )
+        app_config = WorkflowAppConfigManager.get_app_config(app_model=app_model, workflow=workflow)
 
         # init application generate entity
         application_generate_entity = WorkflowAppGenerateEntity(
@@ -211,11 +209,10 @@ class WorkflowAppGenerator(BaseAppGenerator):
             user_id=user.id,
             stream=stream,
             invoke_from=InvokeFrom.DEBUGGER,
-            extras=extras,
+            extras={"auto_generate_conversation_name": False},
             single_iteration_run=WorkflowAppGenerateEntity.SingleIterationRunEntity(
-                node_id=node_id,
-                inputs=args['inputs']
-            )
+                node_id=node_id, inputs=args["inputs"]
+            ),
         )
         contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
@@ -225,18 +222,23 @@ class WorkflowAppGenerator(BaseAppGenerator):
             user=user,
             invoke_from=InvokeFrom.DEBUGGER,
             application_generate_entity=application_generate_entity,
-            stream=stream
+            stream=stream,
         )
 
-    def _generate_worker(self, flask_app: Flask,
-                         application_generate_entity: WorkflowAppGenerateEntity,
-                         queue_manager: AppQueueManager,
-                         context: contextvars.Context) -> None:
+    def _generate_worker(
+        self,
+        flask_app: Flask,
+        application_generate_entity: WorkflowAppGenerateEntity,
+        queue_manager: AppQueueManager,
+        context: contextvars.Context,
+        workflow_thread_pool_id: Optional[str] = None,
+    ) -> None:
         """
         Generate worker in a new thread.
         :param flask_app: Flask app
         :param application_generate_entity: application generate entity
         :param queue_manager: queue manager
+        :param workflow_thread_pool_id: workflow thread pool id
         :return:
         """
         for var, val in context.items():
@@ -244,50 +246,40 @@ class WorkflowAppGenerator(BaseAppGenerator):
         with flask_app.app_context():
             try:
                 # workflow app
-                runner = WorkflowAppRunner()
-                if application_generate_entity.single_iteration_run:
-                    single_iteration_run = application_generate_entity.single_iteration_run
-                    runner.single_iteration_run(
-                        app_id=application_generate_entity.app_config.app_id,
-                        workflow_id=application_generate_entity.app_config.workflow_id,
-                        queue_manager=queue_manager,
-                        inputs=single_iteration_run.inputs,
-                        node_id=single_iteration_run.node_id,
-                        user_id=application_generate_entity.user_id
-                    )
-                else:
-                    runner.run(
-                        application_generate_entity=application_generate_entity,
-                        queue_manager=queue_manager
-                    )
-            except GenerateTaskStoppedException:
+                runner = WorkflowAppRunner(
+                    application_generate_entity=application_generate_entity,
+                    queue_manager=queue_manager,
+                    workflow_thread_pool_id=workflow_thread_pool_id,
+                )
+
+                runner.run()
+            except GenerateTaskStoppedError:
                 pass
             except InvokeAuthorizationError:
                 queue_manager.publish_error(
-                    InvokeAuthorizationError('Incorrect API key provided'),
-                    PublishFrom.APPLICATION_MANAGER
+                    InvokeAuthorizationError("Incorrect API key provided"), PublishFrom.APPLICATION_MANAGER
                 )
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except (ValueError, InvokeError) as e:
-                if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == 'true':
+                if os.environ.get("DEBUG") and os.environ.get("DEBUG", "false").lower() == "true":
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except Exception as e:
                 logger.exception("Unknown Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             finally:
-                db.session.remove()
+                db.session.close()
 
-    def _handle_response(self, application_generate_entity: WorkflowAppGenerateEntity,
-                         workflow: Workflow,
-                         queue_manager: AppQueueManager,
-                         user: Union[Account, EndUser],
-                         stream: bool = False) -> Union[
-        WorkflowAppBlockingResponse,
-        Generator[WorkflowAppStreamResponse, None, None]
-    ]:
+    def _handle_response(
+        self,
+        application_generate_entity: WorkflowAppGenerateEntity,
+        workflow: Workflow,
+        queue_manager: AppQueueManager,
+        user: Union[Account, EndUser],
+        stream: bool = False,
+    ) -> Union[WorkflowAppBlockingResponse, Generator[WorkflowAppStreamResponse, None, None]]:
         """
         Handle response.
         :param application_generate_entity: application generate entity
@@ -303,14 +295,14 @@ class WorkflowAppGenerator(BaseAppGenerator):
             workflow=workflow,
             queue_manager=queue_manager,
             user=user,
-            stream=stream
+            stream=stream,
         )
 
         try:
             return generate_task_pipeline.process()
         except ValueError as e:
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
-                raise GenerateTaskStoppedException()
+                raise GenerateTaskStoppedError()
             else:
                 logger.exception(e)
                 raise e
diff --git a/api/core/app/apps/workflow/app_queue_manager.py b/api/core/app/apps/workflow/app_queue_manager.py
index f448138b53..76371f800b 100644
--- a/api/core/app/apps/workflow/app_queue_manager.py
+++ b/api/core/app/apps/workflow/app_queue_manager.py
@@ -1,4 +1,4 @@
-from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException, PublishFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedError, PublishFrom
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.queue_entities import (
     AppQueueEvent,
@@ -12,10 +12,7 @@ from core.app.entities.queue_entities import (
 
 
 class WorkflowAppQueueManager(AppQueueManager):
-    def __init__(self, task_id: str,
-                 user_id: str,
-                 invoke_from: InvokeFrom,
-                 app_mode: str) -> None:
+    def __init__(self, task_id: str, user_id: str, invoke_from: InvokeFrom, app_mode: str) -> None:
         super().__init__(task_id, user_id, invoke_from)
 
         self._app_mode = app_mode
@@ -27,20 +24,19 @@ class WorkflowAppQueueManager(AppQueueManager):
         :param pub_from:
         :return:
         """
-        message = WorkflowQueueMessage(
-            task_id=self._task_id,
-            app_mode=self._app_mode,
-            event=event
-        )
+        message = WorkflowQueueMessage(task_id=self._task_id, app_mode=self._app_mode, event=event)
 
         self._q.put(message)
 
-        if isinstance(event, QueueStopEvent
-                             | QueueErrorEvent
-                             | QueueMessageEndEvent
-                             | QueueWorkflowSucceededEvent
-                             | QueueWorkflowFailedEvent):
+        if isinstance(
+            event,
+            QueueStopEvent
+            | QueueErrorEvent
+            | QueueMessageEndEvent
+            | QueueWorkflowSucceededEvent
+            | QueueWorkflowFailedEvent,
+        ):
             self.stop_listen()
 
         if pub_from == PublishFrom.APPLICATION_MANAGER and self._is_stopped():
-            raise GenerateTaskStoppedException()
+            raise GenerateTaskStoppedError()
diff --git a/api/core/app/apps/workflow/app_runner.py b/api/core/app/apps/workflow/app_runner.py
index e388d0184b..22ec228fa7 100644
--- a/api/core/app/apps/workflow/app_runner.py
+++ b/api/core/app/apps/workflow/app_runner.py
@@ -4,129 +4,125 @@ from typing import Optional, cast
 
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.apps.workflow.app_config_manager import WorkflowAppConfig
-from core.app.apps.workflow.workflow_event_trigger_callback import WorkflowEventTriggerCallback
+from core.app.apps.workflow_app_runner import WorkflowBasedAppRunner
 from core.app.apps.workflow_logging_callback import WorkflowLoggingCallback
 from core.app.entities.app_invoke_entities import (
     InvokeFrom,
     WorkflowAppGenerateEntity,
 )
 from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
+from core.workflow.entities.node_entities import UserFrom
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
-from core.workflow.nodes.base_node import UserFrom
-from core.workflow.workflow_engine_manager import WorkflowEngineManager
+from core.workflow.workflow_entry import WorkflowEntry
 from extensions.ext_database import db
 from models.model import App, EndUser
-from models.workflow import Workflow
+from models.workflow import WorkflowType
 
 logger = logging.getLogger(__name__)
 
 
-class WorkflowAppRunner:
+class WorkflowAppRunner(WorkflowBasedAppRunner):
     """
     Workflow Application Runner
     """
 
-    def run(self, application_generate_entity: WorkflowAppGenerateEntity, queue_manager: AppQueueManager) -> None:
+    def __init__(
+        self,
+        application_generate_entity: WorkflowAppGenerateEntity,
+        queue_manager: AppQueueManager,
+        workflow_thread_pool_id: Optional[str] = None,
+    ) -> None:
+        """
+        :param application_generate_entity: application generate entity
+        :param queue_manager: application queue manager
+        :param workflow_thread_pool_id: workflow thread pool id
+        """
+        self.application_generate_entity = application_generate_entity
+        self.queue_manager = queue_manager
+        self.workflow_thread_pool_id = workflow_thread_pool_id
+
+    def run(self) -> None:
         """
         Run application
         :param application_generate_entity: application generate entity
         :param queue_manager: application queue manager
         :return:
         """
-        app_config = application_generate_entity.app_config
+        app_config = self.application_generate_entity.app_config
         app_config = cast(WorkflowAppConfig, app_config)
 
         user_id = None
-        if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
-            end_user = db.session.query(EndUser).filter(EndUser.id == application_generate_entity.user_id).first()
+        if self.application_generate_entity.invoke_from in {InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API}:
+            end_user = db.session.query(EndUser).filter(EndUser.id == self.application_generate_entity.user_id).first()
             if end_user:
                 user_id = end_user.session_id
         else:
-            user_id = application_generate_entity.user_id
+            user_id = self.application_generate_entity.user_id
 
         app_record = db.session.query(App).filter(App.id == app_config.app_id).first()
         if not app_record:
-            raise ValueError('App not found')
+            raise ValueError("App not found")
 
         workflow = self.get_workflow(app_model=app_record, workflow_id=app_config.workflow_id)
         if not workflow:
-            raise ValueError('Workflow not initialized')
-
-        inputs = application_generate_entity.inputs
-        files = application_generate_entity.files
+            raise ValueError("Workflow not initialized")
 
         db.session.close()
 
-        workflow_callbacks: list[WorkflowCallback] = [
-            WorkflowEventTriggerCallback(queue_manager=queue_manager, workflow=workflow)
-        ]
-
-        if bool(os.environ.get('DEBUG', 'False').lower() == 'true'):
+        workflow_callbacks: list[WorkflowCallback] = []
+        if bool(os.environ.get("DEBUG", "False").lower() == "true"):
             workflow_callbacks.append(WorkflowLoggingCallback())
 
-        # Create a variable pool.
-        system_inputs = {
-            SystemVariableKey.FILES: files,
-            SystemVariableKey.USER_ID: user_id,
-        }
-        variable_pool = VariablePool(
-            system_variables=system_inputs,
-            user_inputs=inputs,
-            environment_variables=workflow.environment_variables,
-            conversation_variables=[],
-        )
+        # if only single iteration run is requested
+        if self.application_generate_entity.single_iteration_run:
+            # if only single iteration run is requested
+            graph, variable_pool = self._get_graph_and_variable_pool_of_single_iteration(
+                workflow=workflow,
+                node_id=self.application_generate_entity.single_iteration_run.node_id,
+                user_inputs=self.application_generate_entity.single_iteration_run.inputs,
+            )
+        else:
+            inputs = self.application_generate_entity.inputs
+            files = self.application_generate_entity.files
+
+            # Create a variable pool.
+            system_inputs = {
+                SystemVariableKey.FILES: files,
+                SystemVariableKey.USER_ID: user_id,
+            }
+
+            variable_pool = VariablePool(
+                system_variables=system_inputs,
+                user_inputs=inputs,
+                environment_variables=workflow.environment_variables,
+                conversation_variables=[],
+            )
+
+            # init graph
+            graph = self._init_graph(graph_config=workflow.graph_dict)
 
         # RUN WORKFLOW
-        workflow_engine_manager = WorkflowEngineManager()
-        workflow_engine_manager.run_workflow(
-            workflow=workflow,
-            user_id=application_generate_entity.user_id,
-            user_from=UserFrom.ACCOUNT
-            if application_generate_entity.invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER]
-            else UserFrom.END_USER,
-            invoke_from=application_generate_entity.invoke_from,
-            callbacks=workflow_callbacks,
-            call_depth=application_generate_entity.call_depth,
+        workflow_entry = WorkflowEntry(
+            tenant_id=workflow.tenant_id,
+            app_id=workflow.app_id,
+            workflow_id=workflow.id,
+            workflow_type=WorkflowType.value_of(workflow.type),
+            graph=graph,
+            graph_config=workflow.graph_dict,
+            user_id=self.application_generate_entity.user_id,
+            user_from=(
+                UserFrom.ACCOUNT
+                if self.application_generate_entity.invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER}
+                else UserFrom.END_USER
+            ),
+            invoke_from=self.application_generate_entity.invoke_from,
+            call_depth=self.application_generate_entity.call_depth,
             variable_pool=variable_pool,
+            thread_pool_id=self.workflow_thread_pool_id,
         )
 
-    def single_iteration_run(
-        self, app_id: str, workflow_id: str, queue_manager: AppQueueManager, inputs: dict, node_id: str, user_id: str
-    ) -> None:
-        """
-        Single iteration run
-        """
-        app_record = db.session.query(App).filter(App.id == app_id).first()
-        if not app_record:
-            raise ValueError('App not found')
+        generator = workflow_entry.run(callbacks=workflow_callbacks)
 
-        if not app_record.workflow_id:
-            raise ValueError('Workflow not initialized')
-
-        workflow = self.get_workflow(app_model=app_record, workflow_id=workflow_id)
-        if not workflow:
-            raise ValueError('Workflow not initialized')
-
-        workflow_callbacks = [WorkflowEventTriggerCallback(queue_manager=queue_manager, workflow=workflow)]
-
-        workflow_engine_manager = WorkflowEngineManager()
-        workflow_engine_manager.single_step_run_iteration_workflow_node(
-            workflow=workflow, node_id=node_id, user_id=user_id, user_inputs=inputs, callbacks=workflow_callbacks
-        )
-
-    def get_workflow(self, app_model: App, workflow_id: str) -> Optional[Workflow]:
-        """
-        Get workflow
-        """
-        # fetch workflow by workflow_id
-        workflow = (
-            db.session.query(Workflow)
-            .filter(
-                Workflow.tenant_id == app_model.tenant_id, Workflow.app_id == app_model.id, Workflow.id == workflow_id
-            )
-            .first()
-        )
-
-        # return workflow
-        return workflow
+        for event in generator:
+            self._handle_event(workflow_entry, event)
diff --git a/api/core/app/apps/workflow/generate_response_converter.py b/api/core/app/apps/workflow/generate_response_converter.py
index 88bde58ba0..08d00ee180 100644
--- a/api/core/app/apps/workflow/generate_response_converter.py
+++ b/api/core/app/apps/workflow/generate_response_converter.py
@@ -35,8 +35,9 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
         return cls.convert_blocking_full_response(blocking_response)
 
     @classmethod
-    def convert_stream_full_response(cls, stream_response: Generator[WorkflowAppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_full_response(
+        cls, stream_response: Generator[WorkflowAppStreamResponse, None, None]
+    ) -> Generator[str, None, None]:
         """
         Convert stream full response.
         :param stream_response: stream response
@@ -47,12 +48,12 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             sub_stream_response = chunk.stream_response
 
             if isinstance(sub_stream_response, PingStreamResponse):
-                yield 'ping'
+                yield "ping"
                 continue
 
             response_chunk = {
-                'event': sub_stream_response.event.value,
-                'workflow_run_id': chunk.workflow_run_id,
+                "event": sub_stream_response.event.value,
+                "workflow_run_id": chunk.workflow_run_id,
             }
 
             if isinstance(sub_stream_response, ErrorStreamResponse):
@@ -63,8 +64,9 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             yield json.dumps(response_chunk)
 
     @classmethod
-    def convert_stream_simple_response(cls, stream_response: Generator[WorkflowAppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_simple_response(
+        cls, stream_response: Generator[WorkflowAppStreamResponse, None, None]
+    ) -> Generator[str, None, None]:
         """
         Convert stream simple response.
         :param stream_response: stream response
@@ -75,12 +77,12 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             sub_stream_response = chunk.stream_response
 
             if isinstance(sub_stream_response, PingStreamResponse):
-                yield 'ping'
+                yield "ping"
                 continue
 
             response_chunk = {
-                'event': sub_stream_response.event.value,
-                'workflow_run_id': chunk.workflow_run_id,
+                "event": sub_stream_response.event.value,
+                "workflow_run_id": chunk.workflow_run_id,
             }
 
             if isinstance(sub_stream_response, ErrorStreamResponse):
diff --git a/api/core/app/apps/workflow/generate_task_pipeline.py b/api/core/app/apps/workflow/generate_task_pipeline.py
index de8542d7b9..93edf8e0e8 100644
--- a/api/core/app/apps/workflow/generate_task_pipeline.py
+++ b/api/core/app/apps/workflow/generate_task_pipeline.py
@@ -1,3 +1,4 @@
+import json
 import logging
 import time
 from collections.abc import Generator
@@ -15,10 +16,12 @@ from core.app.entities.queue_entities import (
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
-    QueueMessageReplaceEvent,
     QueueNodeFailedEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
+    QueueParallelBranchRunFailedEvent,
+    QueueParallelBranchRunStartedEvent,
+    QueueParallelBranchRunSucceededEvent,
     QueuePingEvent,
     QueueStopEvent,
     QueueTextChunkEvent,
@@ -32,19 +35,16 @@ from core.app.entities.task_entities import (
     MessageAudioStreamResponse,
     StreamResponse,
     TextChunkStreamResponse,
-    TextReplaceStreamResponse,
     WorkflowAppBlockingResponse,
     WorkflowAppStreamResponse,
     WorkflowFinishStreamResponse,
-    WorkflowStreamGenerateNodes,
+    WorkflowStartStreamResponse,
     WorkflowTaskState,
 )
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
 from core.app.task_pipeline.workflow_cycle_manage import WorkflowCycleManage
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.node_entities import NodeType
 from core.workflow.enums import SystemVariableKey
-from core.workflow.nodes.end.end_node import EndNode
 from extensions.ext_database import db
 from models.account import Account
 from models.model import EndUser
@@ -52,8 +52,8 @@ from models.workflow import (
     Workflow,
     WorkflowAppLog,
     WorkflowAppLogCreatedFrom,
-    WorkflowNodeExecution,
     WorkflowRun,
+    WorkflowRunStatus,
 )
 
 logger = logging.getLogger(__name__)
@@ -63,18 +63,21 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
     """
     WorkflowAppGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
+
     _workflow: Workflow
     _user: Union[Account, EndUser]
     _task_state: WorkflowTaskState
     _application_generate_entity: WorkflowAppGenerateEntity
     _workflow_system_variables: dict[SystemVariableKey, Any]
-    _iteration_nested_relations: dict[str, list[str]]
 
-    def __init__(self, application_generate_entity: WorkflowAppGenerateEntity,
-                 workflow: Workflow,
-                 queue_manager: AppQueueManager,
-                 user: Union[Account, EndUser],
-                 stream: bool) -> None:
+    def __init__(
+        self,
+        application_generate_entity: WorkflowAppGenerateEntity,
+        workflow: Workflow,
+        queue_manager: AppQueueManager,
+        user: Union[Account, EndUser],
+        stream: bool,
+    ) -> None:
         """
         Initialize GenerateTaskPipeline.
         :param application_generate_entity: application generate entity
@@ -93,14 +96,10 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
         self._workflow = workflow
         self._workflow_system_variables = {
             SystemVariableKey.FILES: application_generate_entity.files,
-            SystemVariableKey.USER_ID: user_id
+            SystemVariableKey.USER_ID: user_id,
         }
 
-        self._task_state = WorkflowTaskState(
-            iteration_nested_node_ids=[]
-        )
-        self._stream_generate_nodes = self._get_stream_generate_nodes()
-        self._iteration_nested_relations = self._get_iteration_nested_relations(self._workflow.graph_dict)
+        self._task_state = WorkflowTaskState()
 
     def process(self) -> Union[WorkflowAppBlockingResponse, Generator[WorkflowAppStreamResponse, None, None]]:
         """
@@ -111,16 +110,13 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
         db.session.refresh(self._user)
         db.session.close()
 
-        generator = self._wrapper_process_stream_response(
-            trace_manager=self._application_generate_entity.trace_manager
-        )
+        generator = self._wrapper_process_stream_response(trace_manager=self._application_generate_entity.trace_manager)
         if self._stream:
             return self._to_stream_response(generator)
         else:
             return self._to_blocking_response(generator)
 
-    def _to_blocking_response(self, generator: Generator[StreamResponse, None, None]) \
-            -> WorkflowAppBlockingResponse:
+    def _to_blocking_response(self, generator: Generator[StreamResponse, None, None]) -> WorkflowAppBlockingResponse:
         """
         To blocking response.
         :return:
@@ -129,66 +125,69 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
             if isinstance(stream_response, ErrorStreamResponse):
                 raise stream_response.err
             elif isinstance(stream_response, WorkflowFinishStreamResponse):
-                workflow_run = db.session.query(WorkflowRun).filter(
-                    WorkflowRun.id == self._task_state.workflow_run_id).first()
-
                 response = WorkflowAppBlockingResponse(
                     task_id=self._application_generate_entity.task_id,
-                    workflow_run_id=workflow_run.id,
+                    workflow_run_id=stream_response.data.id,
                     data=WorkflowAppBlockingResponse.Data(
-                        id=workflow_run.id,
-                        workflow_id=workflow_run.workflow_id,
-                        status=workflow_run.status,
-                        outputs=workflow_run.outputs_dict,
-                        error=workflow_run.error,
-                        elapsed_time=workflow_run.elapsed_time,
-                        total_tokens=workflow_run.total_tokens,
-                        total_steps=workflow_run.total_steps,
-                        created_at=int(workflow_run.created_at.timestamp()),
-                        finished_at=int(workflow_run.finished_at.timestamp())
-                    )
+                        id=stream_response.data.id,
+                        workflow_id=stream_response.data.workflow_id,
+                        status=stream_response.data.status,
+                        outputs=stream_response.data.outputs,
+                        error=stream_response.data.error,
+                        elapsed_time=stream_response.data.elapsed_time,
+                        total_tokens=stream_response.data.total_tokens,
+                        total_steps=stream_response.data.total_steps,
+                        created_at=int(stream_response.data.created_at),
+                        finished_at=int(stream_response.data.finished_at),
+                    ),
                 )
 
                 return response
             else:
                 continue
 
-        raise Exception('Queue listening stopped unexpectedly.')
+        raise Exception("Queue listening stopped unexpectedly.")
 
-    def _to_stream_response(self, generator: Generator[StreamResponse, None, None]) \
-            -> Generator[WorkflowAppStreamResponse, None, None]:
+    def _to_stream_response(
+        self, generator: Generator[StreamResponse, None, None]
+    ) -> Generator[WorkflowAppStreamResponse, None, None]:
         """
         To stream response.
         :return:
         """
+        workflow_run_id = None
         for stream_response in generator:
-            yield WorkflowAppStreamResponse(
-                workflow_run_id=self._task_state.workflow_run_id,
-                stream_response=stream_response
-            )
+            if isinstance(stream_response, WorkflowStartStreamResponse):
+                workflow_run_id = stream_response.workflow_run_id
 
-    def _listenAudioMsg(self, publisher, task_id: str):
+            yield WorkflowAppStreamResponse(workflow_run_id=workflow_run_id, stream_response=stream_response)
+
+    def _listen_audio_msg(self, publisher, task_id: str):
         if not publisher:
             return None
-        audio_msg: AudioTrunk = publisher.checkAndGetAudio()
+        audio_msg: AudioTrunk = publisher.check_and_get_audio()
         if audio_msg and audio_msg.status != "finish":
             return MessageAudioStreamResponse(audio=audio_msg.audio, task_id=task_id)
         return None
 
-    def _wrapper_process_stream_response(self, trace_manager: Optional[TraceQueueManager] = None) -> \
-            Generator[StreamResponse, None, None]:
-
-        publisher = None
+    def _wrapper_process_stream_response(
+        self, trace_manager: Optional[TraceQueueManager] = None
+    ) -> Generator[StreamResponse, None, None]:
+        tts_publisher = None
         task_id = self._application_generate_entity.task_id
         tenant_id = self._application_generate_entity.app_config.tenant_id
         features_dict = self._workflow.features_dict
 
-        if features_dict.get('text_to_speech') and features_dict['text_to_speech'].get('enabled') and features_dict[
-                'text_to_speech'].get('autoPlay') == 'enabled':
-            publisher = AppGeneratorTTSPublisher(tenant_id, features_dict['text_to_speech'].get('voice'))
-        for response in self._process_stream_response(publisher=publisher, trace_manager=trace_manager):
+        if (
+            features_dict.get("text_to_speech")
+            and features_dict["text_to_speech"].get("enabled")
+            and features_dict["text_to_speech"].get("autoPlay") == "enabled"
+        ):
+            tts_publisher = AppGeneratorTTSPublisher(tenant_id, features_dict["text_to_speech"].get("voice"))
+
+        for response in self._process_stream_response(tts_publisher=tts_publisher, trace_manager=trace_manager):
             while True:
-                audio_response = self._listenAudioMsg(publisher, task_id=task_id)
+                audio_response = self._listen_audio_msg(tts_publisher, task_id=task_id)
                 if audio_response:
                     yield audio_response
                 else:
@@ -198,9 +197,9 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
         start_listener_time = time.time()
         while (time.time() - start_listener_time) < TTS_AUTO_PLAY_TIMEOUT:
             try:
-                if not publisher:
+                if not tts_publisher:
                     break
-                audio_trunk = publisher.checkAndGetAudio()
+                audio_trunk = tts_publisher.check_and_get_audio()
                 if audio_trunk is None:
                     # release cpu
                     # sleep 20 ms ( 40ms => 1280 byte audio file,20ms => 640 byte audio file)
@@ -213,105 +212,178 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
             except Exception as e:
                 logger.error(e)
                 break
-        yield MessageAudioEndStreamResponse(audio='', task_id=task_id)
-
+        yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
 
     def _process_stream_response(
         self,
-        publisher: AppGeneratorTTSPublisher,
-        trace_manager: Optional[TraceQueueManager] = None
+        tts_publisher: Optional[AppGeneratorTTSPublisher] = None,
+        trace_manager: Optional[TraceQueueManager] = None,
     ) -> Generator[StreamResponse, None, None]:
         """
         Process stream response.
         :return:
         """
-        for message in self._queue_manager.listen():
-            if publisher:
-                publisher.publish(message=message)
-            event = message.event
+        graph_runtime_state = None
+        workflow_run = None
 
-            if isinstance(event, QueueErrorEvent):
+        for queue_message in self._queue_manager.listen():
+            event = queue_message.event
+
+            if isinstance(event, QueuePingEvent):
+                yield self._ping_stream_response()
+            elif isinstance(event, QueueErrorEvent):
                 err = self._handle_error(event)
                 yield self._error_to_stream_response(err)
                 break
             elif isinstance(event, QueueWorkflowStartedEvent):
-                workflow_run = self._handle_workflow_start()
+                # override graph runtime state
+                graph_runtime_state = event.graph_runtime_state
+
+                # init workflow run
+                workflow_run = self._handle_workflow_run_start()
                 yield self._workflow_start_to_stream_response(
-                    task_id=self._application_generate_entity.task_id,
-                    workflow_run=workflow_run
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                 )
             elif isinstance(event, QueueNodeStartedEvent):
-                workflow_node_execution = self._handle_node_start(event)
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
 
-                # search stream_generate_routes if node id is answer start at node
-                if not self._task_state.current_stream_generate_state and event.node_id in self._stream_generate_nodes:
-                    self._task_state.current_stream_generate_state = self._stream_generate_nodes[event.node_id]
+                workflow_node_execution = self._handle_node_execution_start(workflow_run=workflow_run, event=event)
 
-                    # generate stream outputs when node started
-                    yield from self._generate_stream_outputs_when_node_started()
-
-                yield self._workflow_node_start_to_stream_response(
+                response = self._workflow_node_start_to_stream_response(
                     event=event,
                     task_id=self._application_generate_entity.task_id,
-                    workflow_node_execution=workflow_node_execution
+                    workflow_node_execution=workflow_node_execution,
                 )
-            elif isinstance(event, QueueNodeSucceededEvent | QueueNodeFailedEvent):
-                workflow_node_execution = self._handle_node_finished(event)
 
-                yield self._workflow_node_finish_to_stream_response(
+                if response:
+                    yield response
+            elif isinstance(event, QueueNodeSucceededEvent):
+                workflow_node_execution = self._handle_workflow_node_execution_success(event)
+
+                response = self._workflow_node_finish_to_stream_response(
+                    event=event,
                     task_id=self._application_generate_entity.task_id,
-                    workflow_node_execution=workflow_node_execution
+                    workflow_node_execution=workflow_node_execution,
                 )
 
-                if isinstance(event, QueueNodeFailedEvent):
-                    yield from self._handle_iteration_exception(
-                        task_id=self._application_generate_entity.task_id,
-                        error=f'Child node failed: {event.error}'
-                    )
-            elif isinstance(event, QueueIterationStartEvent | QueueIterationNextEvent | QueueIterationCompletedEvent):
-                if isinstance(event, QueueIterationNextEvent):
-                    # clear ran node execution infos of current iteration
-                    iteration_relations = self._iteration_nested_relations.get(event.node_id)
-                    if iteration_relations:
-                        for node_id in iteration_relations:
-                            self._task_state.ran_node_execution_infos.pop(node_id, None)
+                if response:
+                    yield response
+            elif isinstance(event, QueueNodeFailedEvent):
+                workflow_node_execution = self._handle_workflow_node_execution_failed(event)
 
-                yield self._handle_iteration_to_stream_response(self._application_generate_entity.task_id, event)
-                self._handle_iteration_operation(event)
-            elif isinstance(event, QueueStopEvent | QueueWorkflowSucceededEvent | QueueWorkflowFailedEvent):
-                workflow_run = self._handle_workflow_finished(
-                    event, trace_manager=trace_manager
+                response = self._workflow_node_finish_to_stream_response(
+                    event=event,
+                    task_id=self._application_generate_entity.task_id,
+                    workflow_node_execution=workflow_node_execution,
+                )
+
+                if response:
+                    yield response
+            elif isinstance(event, QueueParallelBranchRunStartedEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                yield self._workflow_parallel_branch_start_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
+                )
+            elif isinstance(event, QueueParallelBranchRunSucceededEvent | QueueParallelBranchRunFailedEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                yield self._workflow_parallel_branch_finished_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
+                )
+            elif isinstance(event, QueueIterationStartEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                yield self._workflow_iteration_start_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
+                )
+            elif isinstance(event, QueueIterationNextEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                yield self._workflow_iteration_next_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
+                )
+            elif isinstance(event, QueueIterationCompletedEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                yield self._workflow_iteration_completed_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
+                )
+            elif isinstance(event, QueueWorkflowSucceededEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                if not graph_runtime_state:
+                    raise Exception("Graph runtime state not initialized.")
+
+                workflow_run = self._handle_workflow_run_success(
+                    workflow_run=workflow_run,
+                    start_at=graph_runtime_state.start_at,
+                    total_tokens=graph_runtime_state.total_tokens,
+                    total_steps=graph_runtime_state.node_run_steps,
+                    outputs=json.dumps(event.outputs)
+                    if isinstance(event, QueueWorkflowSucceededEvent) and event.outputs
+                    else None,
+                    conversation_id=None,
+                    trace_manager=trace_manager,
                 )
 
                 # save workflow app log
                 self._save_workflow_app_log(workflow_run)
 
                 yield self._workflow_finish_to_stream_response(
-                    task_id=self._application_generate_entity.task_id,
-                    workflow_run=workflow_run
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
+                )
+            elif isinstance(event, QueueWorkflowFailedEvent | QueueStopEvent):
+                if not workflow_run:
+                    raise Exception("Workflow run not initialized.")
+
+                if not graph_runtime_state:
+                    raise Exception("Graph runtime state not initialized.")
+
+                workflow_run = self._handle_workflow_run_failed(
+                    workflow_run=workflow_run,
+                    start_at=graph_runtime_state.start_at,
+                    total_tokens=graph_runtime_state.total_tokens,
+                    total_steps=graph_runtime_state.node_run_steps,
+                    status=WorkflowRunStatus.FAILED
+                    if isinstance(event, QueueWorkflowFailedEvent)
+                    else WorkflowRunStatus.STOPPED,
+                    error=event.error if isinstance(event, QueueWorkflowFailedEvent) else event.get_stop_reason(),
+                    conversation_id=None,
+                    trace_manager=trace_manager,
+                )
+
+                # save workflow app log
+                self._save_workflow_app_log(workflow_run)
+
+                yield self._workflow_finish_to_stream_response(
+                    task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                 )
             elif isinstance(event, QueueTextChunkEvent):
                 delta_text = event.text
                 if delta_text is None:
                     continue
 
-                if not self._is_stream_out_support(
-                        event=event
-                ):
-                    continue
+                # only publish tts message at text chunk streaming
+                if tts_publisher:
+                    tts_publisher.publish(message=queue_message)
 
                 self._task_state.answer += delta_text
-                yield self._text_chunk_to_stream_response(delta_text)
-            elif isinstance(event, QueueMessageReplaceEvent):
-                yield self._text_replace_to_stream_response(event.text)
-            elif isinstance(event, QueuePingEvent):
-                yield self._ping_stream_response()
+                yield self._text_chunk_to_stream_response(
+                    delta_text, from_variable_selector=event.from_variable_selector
+                )
             else:
                 continue
 
-        if publisher:
-            publisher.publish(None)
-
+        if tts_publisher:
+            tts_publisher.publish(None)
 
     def _save_workflow_app_log(self, workflow_run: WorkflowRun) -> None:
         """
@@ -329,20 +401,22 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
             # not save log for debugging
             return
 
-        workflow_app_log = WorkflowAppLog(
-            tenant_id=workflow_run.tenant_id,
-            app_id=workflow_run.app_id,
-            workflow_id=workflow_run.workflow_id,
-            workflow_run_id=workflow_run.id,
-            created_from=created_from.value,
-            created_by_role=('account' if isinstance(self._user, Account) else 'end_user'),
-            created_by=self._user.id,
-        )
+        workflow_app_log = WorkflowAppLog()
+        workflow_app_log.tenant_id = workflow_run.tenant_id
+        workflow_app_log.app_id = workflow_run.app_id
+        workflow_app_log.workflow_id = workflow_run.workflow_id
+        workflow_app_log.workflow_run_id = workflow_run.id
+        workflow_app_log.created_from = created_from.value
+        workflow_app_log.created_by_role = "account" if isinstance(self._user, Account) else "end_user"
+        workflow_app_log.created_by = self._user.id
+
         db.session.add(workflow_app_log)
         db.session.commit()
         db.session.close()
 
-    def _text_chunk_to_stream_response(self, text: str) -> TextChunkStreamResponse:
+    def _text_chunk_to_stream_response(
+        self, text: str, from_variable_selector: Optional[list[str]] = None
+    ) -> TextChunkStreamResponse:
         """
         Handle completed event.
         :param text: text
@@ -350,184 +424,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
         """
         response = TextChunkStreamResponse(
             task_id=self._application_generate_entity.task_id,
-            data=TextChunkStreamResponse.Data(text=text)
+            data=TextChunkStreamResponse.Data(text=text, from_variable_selector=from_variable_selector),
         )
 
         return response
-
-    def _text_replace_to_stream_response(self, text: str) -> TextReplaceStreamResponse:
-        """
-        Text replace to stream response.
-        :param text: text
-        :return:
-        """
-        return TextReplaceStreamResponse(
-            task_id=self._application_generate_entity.task_id,
-            text=TextReplaceStreamResponse.Data(text=text)
-        )
-
-    def _get_stream_generate_nodes(self) -> dict[str, WorkflowStreamGenerateNodes]:
-        """
-        Get stream generate nodes.
-        :return:
-        """
-        # find all answer nodes
-        graph = self._workflow.graph_dict
-        end_node_configs = [
-            node for node in graph['nodes']
-            if node.get('data', {}).get('type') == NodeType.END.value
-        ]
-
-        # parse stream output node value selectors of end nodes
-        stream_generate_routes = {}
-        for node_config in end_node_configs:
-            # get generate route for stream output
-            end_node_id = node_config['id']
-            generate_nodes = EndNode.extract_generate_nodes(graph, node_config)
-            start_node_ids = self._get_end_start_at_node_ids(graph, end_node_id)
-            if not start_node_ids:
-                continue
-
-            for start_node_id in start_node_ids:
-                stream_generate_routes[start_node_id] = WorkflowStreamGenerateNodes(
-                    end_node_id=end_node_id,
-                    stream_node_ids=generate_nodes
-                )
-
-        return stream_generate_routes
-
-    def _get_end_start_at_node_ids(self, graph: dict, target_node_id: str) \
-            -> list[str]:
-        """
-        Get end start at node id.
-        :param graph: graph
-        :param target_node_id: target node ID
-        :return:
-        """
-        nodes = graph.get('nodes')
-        edges = graph.get('edges')
-
-        # fetch all ingoing edges from source node
-        ingoing_edges = []
-        for edge in edges:
-            if edge.get('target') == target_node_id:
-                ingoing_edges.append(edge)
-
-        if not ingoing_edges:
-            return []
-
-        start_node_ids = []
-        for ingoing_edge in ingoing_edges:
-            source_node_id = ingoing_edge.get('source')
-            source_node = next((node for node in nodes if node.get('id') == source_node_id), None)
-            if not source_node:
-                continue
-
-            node_type = source_node.get('data', {}).get('type')
-            node_iteration_id = source_node.get('data', {}).get('iteration_id')
-            iteration_start_node_id = None
-            if node_iteration_id:
-                iteration_node = next((node for node in nodes if node.get('id') == node_iteration_id), None)
-                iteration_start_node_id = iteration_node.get('data', {}).get('start_node_id')
-
-            if node_type in [
-                NodeType.IF_ELSE.value,
-                NodeType.QUESTION_CLASSIFIER.value
-            ]:
-                start_node_id = target_node_id
-                start_node_ids.append(start_node_id)
-            elif node_type == NodeType.START.value or \
-                node_iteration_id is not None and iteration_start_node_id == source_node.get('id'):
-                start_node_id = source_node_id
-                start_node_ids.append(start_node_id)
-            else:
-                sub_start_node_ids = self._get_end_start_at_node_ids(graph, source_node_id)
-                if sub_start_node_ids:
-                    start_node_ids.extend(sub_start_node_ids)
-
-        return start_node_ids
-
-    def _generate_stream_outputs_when_node_started(self) -> Generator:
-        """
-        Generate stream outputs.
-        :return:
-        """
-        if self._task_state.current_stream_generate_state:
-            stream_node_ids = self._task_state.current_stream_generate_state.stream_node_ids
-
-            for node_id, node_execution_info in self._task_state.ran_node_execution_infos.items():
-                if node_id not in stream_node_ids:
-                    continue
-
-                node_execution_info = self._task_state.ran_node_execution_infos[node_id]
-
-                # get chunk node execution
-                route_chunk_node_execution = db.session.query(WorkflowNodeExecution).filter(
-                    WorkflowNodeExecution.id == node_execution_info.workflow_node_execution_id).first()
-
-                if not route_chunk_node_execution:
-                    continue
-
-                outputs = route_chunk_node_execution.outputs_dict
-
-                if not outputs:
-                    continue
-
-                # get value from outputs
-                text = outputs.get('text')
-
-                if text:
-                    self._task_state.answer += text
-                    yield self._text_chunk_to_stream_response(text)
-
-            db.session.close()
-
-    def _is_stream_out_support(self, event: QueueTextChunkEvent) -> bool:
-        """
-        Is stream out support
-        :param event: queue text chunk event
-        :return:
-        """
-        if not event.metadata:
-            return False
-
-        if 'node_id' not in event.metadata:
-            return False
-
-        node_id = event.metadata.get('node_id')
-        node_type = event.metadata.get('node_type')
-        stream_output_value_selector = event.metadata.get('value_selector')
-        if not stream_output_value_selector:
-            return False
-
-        if not self._task_state.current_stream_generate_state:
-            return False
-
-        if node_id not in self._task_state.current_stream_generate_state.stream_node_ids:
-            return False
-
-        if node_type != NodeType.LLM:
-            # only LLM support chunk stream output
-            return False
-
-        return True
-
-    def _get_iteration_nested_relations(self, graph: dict) -> dict[str, list[str]]:
-        """
-        Get iteration nested relations.
-        :param graph: graph
-        :return:
-        """
-        nodes = graph.get('nodes')
-
-        iteration_ids = [node.get('id') for node in nodes
-                         if node.get('data', {}).get('type') in [
-                             NodeType.ITERATION.value,
-                             NodeType.LOOP.value,
-                        ]]
-
-        return {
-            iteration_id: [
-                node.get('id') for node in nodes if node.get('data', {}).get('iteration_id') == iteration_id
-            ] for iteration_id in iteration_ids
-        }
diff --git a/api/core/app/apps/workflow/workflow_event_trigger_callback.py b/api/core/app/apps/workflow/workflow_event_trigger_callback.py
deleted file mode 100644
index 4472a7e9b5..0000000000
--- a/api/core/app/apps/workflow/workflow_event_trigger_callback.py
+++ /dev/null
@@ -1,200 +0,0 @@
-from typing import Any, Optional
-
-from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
-from core.app.entities.queue_entities import (
-    AppQueueEvent,
-    QueueIterationCompletedEvent,
-    QueueIterationNextEvent,
-    QueueIterationStartEvent,
-    QueueNodeFailedEvent,
-    QueueNodeStartedEvent,
-    QueueNodeSucceededEvent,
-    QueueTextChunkEvent,
-    QueueWorkflowFailedEvent,
-    QueueWorkflowStartedEvent,
-    QueueWorkflowSucceededEvent,
-)
-from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
-from core.workflow.entities.base_node_data_entities import BaseNodeData
-from core.workflow.entities.node_entities import NodeType
-from models.workflow import Workflow
-
-
-class WorkflowEventTriggerCallback(WorkflowCallback):
-
-    def __init__(self, queue_manager: AppQueueManager, workflow: Workflow):
-        self._queue_manager = queue_manager
-
-    def on_workflow_run_started(self) -> None:
-        """
-        Workflow run started
-        """
-        self._queue_manager.publish(
-            QueueWorkflowStartedEvent(),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_run_succeeded(self) -> None:
-        """
-        Workflow run succeeded
-        """
-        self._queue_manager.publish(
-            QueueWorkflowSucceededEvent(),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_run_failed(self, error: str) -> None:
-        """
-        Workflow run failed
-        """
-        self._queue_manager.publish(
-            QueueWorkflowFailedEvent(
-                error=error
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_node_execute_started(self, node_id: str,
-                                         node_type: NodeType,
-                                         node_data: BaseNodeData,
-                                         node_run_index: int = 1,
-                                         predecessor_node_id: Optional[str] = None) -> None:
-        """
-        Workflow node execute started
-        """
-        self._queue_manager.publish(
-            QueueNodeStartedEvent(
-                node_id=node_id,
-                node_type=node_type,
-                node_data=node_data,
-                node_run_index=node_run_index,
-                predecessor_node_id=predecessor_node_id
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_node_execute_succeeded(self, node_id: str,
-                                           node_type: NodeType,
-                                           node_data: BaseNodeData,
-                                           inputs: Optional[dict] = None,
-                                           process_data: Optional[dict] = None,
-                                           outputs: Optional[dict] = None,
-                                           execution_metadata: Optional[dict] = None) -> None:
-        """
-        Workflow node execute succeeded
-        """
-        self._queue_manager.publish(
-            QueueNodeSucceededEvent(
-                node_id=node_id,
-                node_type=node_type,
-                node_data=node_data,
-                inputs=inputs,
-                process_data=process_data,
-                outputs=outputs,
-                execution_metadata=execution_metadata
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_node_execute_failed(self, node_id: str,
-                                        node_type: NodeType,
-                                        node_data: BaseNodeData,
-                                        error: str,
-                                        inputs: Optional[dict] = None,
-                                        outputs: Optional[dict] = None,
-                                        process_data: Optional[dict] = None) -> None:
-        """
-        Workflow node execute failed
-        """
-        self._queue_manager.publish(
-            QueueNodeFailedEvent(
-                node_id=node_id,
-                node_type=node_type,
-                node_data=node_data,
-                inputs=inputs,
-                outputs=outputs,
-                process_data=process_data,
-                error=error
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_node_text_chunk(self, node_id: str, text: str, metadata: Optional[dict] = None) -> None:
-        """
-        Publish text chunk
-        """
-        self._queue_manager.publish(
-            QueueTextChunkEvent(
-                text=text,
-                metadata={
-                    "node_id": node_id,
-                    **metadata
-                }
-            ), PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_iteration_started(self, 
-                                      node_id: str,
-                                      node_type: NodeType,
-                                      node_run_index: int = 1,
-                                      node_data: Optional[BaseNodeData] = None,
-                                      inputs: dict = None,
-                                      predecessor_node_id: Optional[str] = None,
-                                      metadata: Optional[dict] = None) -> None:
-        """
-        Publish iteration started
-        """
-        self._queue_manager.publish(
-            QueueIterationStartEvent(
-                node_id=node_id,
-                node_type=node_type,
-                node_run_index=node_run_index,
-                node_data=node_data,
-                inputs=inputs,
-                predecessor_node_id=predecessor_node_id,
-                metadata=metadata
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_iteration_next(self, node_id: str, 
-                                   node_type: NodeType,
-                                   index: int, 
-                                   node_run_index: int,
-                                   output: Optional[Any]) -> None:
-        """
-        Publish iteration next
-        """
-        self._queue_manager.publish(
-            QueueIterationNextEvent(
-                node_id=node_id,
-                node_type=node_type,
-                index=index,
-                node_run_index=node_run_index,
-                output=output
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-
-    def on_workflow_iteration_completed(self, node_id: str, 
-                                        node_type: NodeType,
-                                        node_run_index: int,
-                                        outputs: dict) -> None:
-        """
-        Publish iteration completed
-        """
-        self._queue_manager.publish(
-            QueueIterationCompletedEvent(
-                node_id=node_id,
-                node_type=node_type,
-                node_run_index=node_run_index,
-                outputs=outputs
-            ),
-            PublishFrom.APPLICATION_MANAGER
-        )
-        
-    def on_event(self, event: AppQueueEvent) -> None:
-        """
-        Publish event
-        """
-        pass
diff --git a/api/core/app/apps/workflow_app_runner.py b/api/core/app/apps/workflow_app_runner.py
new file mode 100644
index 0000000000..ce266116a7
--- /dev/null
+++ b/api/core/app/apps/workflow_app_runner.py
@@ -0,0 +1,371 @@
+from collections.abc import Mapping
+from typing import Any, Optional, cast
+
+from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
+from core.app.apps.base_app_runner import AppRunner
+from core.app.entities.queue_entities import (
+    AppQueueEvent,
+    QueueIterationCompletedEvent,
+    QueueIterationNextEvent,
+    QueueIterationStartEvent,
+    QueueNodeFailedEvent,
+    QueueNodeStartedEvent,
+    QueueNodeSucceededEvent,
+    QueueParallelBranchRunFailedEvent,
+    QueueParallelBranchRunStartedEvent,
+    QueueParallelBranchRunSucceededEvent,
+    QueueRetrieverResourcesEvent,
+    QueueTextChunkEvent,
+    QueueWorkflowFailedEvent,
+    QueueWorkflowStartedEvent,
+    QueueWorkflowSucceededEvent,
+)
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.graph_engine.entities.event import (
+    GraphEngineEvent,
+    GraphRunFailedEvent,
+    GraphRunStartedEvent,
+    GraphRunSucceededEvent,
+    IterationRunFailedEvent,
+    IterationRunNextEvent,
+    IterationRunStartedEvent,
+    IterationRunSucceededEvent,
+    NodeRunFailedEvent,
+    NodeRunRetrieverResourceEvent,
+    NodeRunStartedEvent,
+    NodeRunStreamChunkEvent,
+    NodeRunSucceededEvent,
+    ParallelBranchRunFailedEvent,
+    ParallelBranchRunStartedEvent,
+    ParallelBranchRunSucceededEvent,
+)
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.nodes.base_node import BaseNode
+from core.workflow.nodes.iteration.entities import IterationNodeData
+from core.workflow.nodes.node_mapping import node_classes
+from core.workflow.workflow_entry import WorkflowEntry
+from extensions.ext_database import db
+from models.model import App
+from models.workflow import Workflow
+
+
+class WorkflowBasedAppRunner(AppRunner):
+    def __init__(self, queue_manager: AppQueueManager):
+        self.queue_manager = queue_manager
+
+    def _init_graph(self, graph_config: Mapping[str, Any]) -> Graph:
+        """
+        Init graph
+        """
+        if "nodes" not in graph_config or "edges" not in graph_config:
+            raise ValueError("nodes or edges not found in workflow graph")
+
+        if not isinstance(graph_config.get("nodes"), list):
+            raise ValueError("nodes in workflow graph must be a list")
+
+        if not isinstance(graph_config.get("edges"), list):
+            raise ValueError("edges in workflow graph must be a list")
+        # init graph
+        graph = Graph.init(graph_config=graph_config)
+
+        if not graph:
+            raise ValueError("graph not found in workflow")
+
+        return graph
+
+    def _get_graph_and_variable_pool_of_single_iteration(
+        self,
+        workflow: Workflow,
+        node_id: str,
+        user_inputs: dict,
+    ) -> tuple[Graph, VariablePool]:
+        """
+        Get variable pool of single iteration
+        """
+        # fetch workflow graph
+        graph_config = workflow.graph_dict
+        if not graph_config:
+            raise ValueError("workflow graph not found")
+
+        graph_config = cast(dict[str, Any], graph_config)
+
+        if "nodes" not in graph_config or "edges" not in graph_config:
+            raise ValueError("nodes or edges not found in workflow graph")
+
+        if not isinstance(graph_config.get("nodes"), list):
+            raise ValueError("nodes in workflow graph must be a list")
+
+        if not isinstance(graph_config.get("edges"), list):
+            raise ValueError("edges in workflow graph must be a list")
+
+        # filter nodes only in iteration
+        node_configs = [
+            node
+            for node in graph_config.get("nodes", [])
+            if node.get("id") == node_id or node.get("data", {}).get("iteration_id", "") == node_id
+        ]
+
+        graph_config["nodes"] = node_configs
+
+        node_ids = [node.get("id") for node in node_configs]
+
+        # filter edges only in iteration
+        edge_configs = [
+            edge
+            for edge in graph_config.get("edges", [])
+            if (edge.get("source") is None or edge.get("source") in node_ids)
+            and (edge.get("target") is None or edge.get("target") in node_ids)
+        ]
+
+        graph_config["edges"] = edge_configs
+
+        # init graph
+        graph = Graph.init(graph_config=graph_config, root_node_id=node_id)
+
+        if not graph:
+            raise ValueError("graph not found in workflow")
+
+        # fetch node config from node id
+        iteration_node_config = None
+        for node in node_configs:
+            if node.get("id") == node_id:
+                iteration_node_config = node
+                break
+
+        if not iteration_node_config:
+            raise ValueError("iteration node id not found in workflow graph")
+
+        # Get node class
+        node_type = NodeType.value_of(iteration_node_config.get("data", {}).get("type"))
+        node_cls = node_classes.get(node_type)
+        node_cls = cast(type[BaseNode], node_cls)
+
+        # init variable pool
+        variable_pool = VariablePool(
+            system_variables={},
+            user_inputs={},
+            environment_variables=workflow.environment_variables,
+        )
+
+        try:
+            variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
+                graph_config=workflow.graph_dict, config=iteration_node_config
+            )
+        except NotImplementedError:
+            variable_mapping = {}
+
+        WorkflowEntry.mapping_user_inputs_to_variable_pool(
+            variable_mapping=variable_mapping,
+            user_inputs=user_inputs,
+            variable_pool=variable_pool,
+            tenant_id=workflow.tenant_id,
+            node_type=node_type,
+            node_data=IterationNodeData(**iteration_node_config.get("data", {})),
+        )
+
+        return graph, variable_pool
+
+    def _handle_event(self, workflow_entry: WorkflowEntry, event: GraphEngineEvent) -> None:
+        """
+        Handle event
+        :param workflow_entry: workflow entry
+        :param event: event
+        """
+        if isinstance(event, GraphRunStartedEvent):
+            self._publish_event(
+                QueueWorkflowStartedEvent(graph_runtime_state=workflow_entry.graph_engine.graph_runtime_state)
+            )
+        elif isinstance(event, GraphRunSucceededEvent):
+            self._publish_event(QueueWorkflowSucceededEvent(outputs=event.outputs))
+        elif isinstance(event, GraphRunFailedEvent):
+            self._publish_event(QueueWorkflowFailedEvent(error=event.error))
+        elif isinstance(event, NodeRunStartedEvent):
+            self._publish_event(
+                QueueNodeStartedEvent(
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_data=event.node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    start_at=event.route_node_state.start_at,
+                    node_run_index=event.route_node_state.index,
+                    predecessor_node_id=event.predecessor_node_id,
+                    in_iteration_id=event.in_iteration_id,
+                )
+            )
+        elif isinstance(event, NodeRunSucceededEvent):
+            self._publish_event(
+                QueueNodeSucceededEvent(
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_data=event.node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    start_at=event.route_node_state.start_at,
+                    inputs=event.route_node_state.node_run_result.inputs
+                    if event.route_node_state.node_run_result
+                    else {},
+                    process_data=event.route_node_state.node_run_result.process_data
+                    if event.route_node_state.node_run_result
+                    else {},
+                    outputs=event.route_node_state.node_run_result.outputs
+                    if event.route_node_state.node_run_result
+                    else {},
+                    execution_metadata=event.route_node_state.node_run_result.metadata
+                    if event.route_node_state.node_run_result
+                    else {},
+                    in_iteration_id=event.in_iteration_id,
+                )
+            )
+        elif isinstance(event, NodeRunFailedEvent):
+            self._publish_event(
+                QueueNodeFailedEvent(
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_data=event.node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    start_at=event.route_node_state.start_at,
+                    inputs=event.route_node_state.node_run_result.inputs
+                    if event.route_node_state.node_run_result
+                    else {},
+                    process_data=event.route_node_state.node_run_result.process_data
+                    if event.route_node_state.node_run_result
+                    else {},
+                    outputs=event.route_node_state.node_run_result.outputs
+                    if event.route_node_state.node_run_result
+                    else {},
+                    error=event.route_node_state.node_run_result.error
+                    if event.route_node_state.node_run_result and event.route_node_state.node_run_result.error
+                    else "Unknown error",
+                    in_iteration_id=event.in_iteration_id,
+                )
+            )
+        elif isinstance(event, NodeRunStreamChunkEvent):
+            self._publish_event(
+                QueueTextChunkEvent(
+                    text=event.chunk_content,
+                    from_variable_selector=event.from_variable_selector,
+                    in_iteration_id=event.in_iteration_id,
+                )
+            )
+        elif isinstance(event, NodeRunRetrieverResourceEvent):
+            self._publish_event(
+                QueueRetrieverResourcesEvent(
+                    retriever_resources=event.retriever_resources, in_iteration_id=event.in_iteration_id
+                )
+            )
+        elif isinstance(event, ParallelBranchRunStartedEvent):
+            self._publish_event(
+                QueueParallelBranchRunStartedEvent(
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    in_iteration_id=event.in_iteration_id,
+                )
+            )
+        elif isinstance(event, ParallelBranchRunSucceededEvent):
+            self._publish_event(
+                QueueParallelBranchRunSucceededEvent(
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    in_iteration_id=event.in_iteration_id,
+                )
+            )
+        elif isinstance(event, ParallelBranchRunFailedEvent):
+            self._publish_event(
+                QueueParallelBranchRunFailedEvent(
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    in_iteration_id=event.in_iteration_id,
+                    error=event.error,
+                )
+            )
+        elif isinstance(event, IterationRunStartedEvent):
+            self._publish_event(
+                QueueIterationStartEvent(
+                    node_execution_id=event.iteration_id,
+                    node_id=event.iteration_node_id,
+                    node_type=event.iteration_node_type,
+                    node_data=event.iteration_node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    start_at=event.start_at,
+                    node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
+                    inputs=event.inputs,
+                    predecessor_node_id=event.predecessor_node_id,
+                    metadata=event.metadata,
+                )
+            )
+        elif isinstance(event, IterationRunNextEvent):
+            self._publish_event(
+                QueueIterationNextEvent(
+                    node_execution_id=event.iteration_id,
+                    node_id=event.iteration_node_id,
+                    node_type=event.iteration_node_type,
+                    node_data=event.iteration_node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    index=event.index,
+                    node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
+                    output=event.pre_iteration_output,
+                )
+            )
+        elif isinstance(event, (IterationRunSucceededEvent | IterationRunFailedEvent)):
+            self._publish_event(
+                QueueIterationCompletedEvent(
+                    node_execution_id=event.iteration_id,
+                    node_id=event.iteration_node_id,
+                    node_type=event.iteration_node_type,
+                    node_data=event.iteration_node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    start_at=event.start_at,
+                    node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
+                    inputs=event.inputs,
+                    outputs=event.outputs,
+                    metadata=event.metadata,
+                    steps=event.steps,
+                    error=event.error if isinstance(event, IterationRunFailedEvent) else None,
+                )
+            )
+
+    def get_workflow(self, app_model: App, workflow_id: str) -> Optional[Workflow]:
+        """
+        Get workflow
+        """
+        # fetch workflow by workflow_id
+        workflow = (
+            db.session.query(Workflow)
+            .filter(
+                Workflow.tenant_id == app_model.tenant_id, Workflow.app_id == app_model.id, Workflow.id == workflow_id
+            )
+            .first()
+        )
+
+        # return workflow
+        return workflow
+
+    def _publish_event(self, event: AppQueueEvent) -> None:
+        self.queue_manager.publish(event, PublishFrom.APPLICATION_MANAGER)
diff --git a/api/core/app/apps/workflow_logging_callback.py b/api/core/app/apps/workflow_logging_callback.py
index 2e6431d6d0..60683b0f21 100644
--- a/api/core/app/apps/workflow_logging_callback.py
+++ b/api/core/app/apps/workflow_logging_callback.py
@@ -1,10 +1,24 @@
 from typing import Optional
 
-from core.app.entities.queue_entities import AppQueueEvent
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
-from core.workflow.entities.base_node_data_entities import BaseNodeData
-from core.workflow.entities.node_entities import NodeType
+from core.workflow.graph_engine.entities.event import (
+    GraphEngineEvent,
+    GraphRunFailedEvent,
+    GraphRunStartedEvent,
+    GraphRunSucceededEvent,
+    IterationRunFailedEvent,
+    IterationRunNextEvent,
+    IterationRunStartedEvent,
+    IterationRunSucceededEvent,
+    NodeRunFailedEvent,
+    NodeRunStartedEvent,
+    NodeRunStreamChunkEvent,
+    NodeRunSucceededEvent,
+    ParallelBranchRunFailedEvent,
+    ParallelBranchRunStartedEvent,
+    ParallelBranchRunSucceededEvent,
+)
 
 _TEXT_COLOR_MAPPING = {
     "blue": "36;1",
@@ -16,138 +30,189 @@ _TEXT_COLOR_MAPPING = {
 
 
 class WorkflowLoggingCallback(WorkflowCallback):
-
     def __init__(self) -> None:
         self.current_node_id = None
 
-    def on_workflow_run_started(self) -> None:
-        """
-        Workflow run started
-        """
-        self.print_text("\n[on_workflow_run_started]", color='pink')
+    def on_event(self, event: GraphEngineEvent) -> None:
+        if isinstance(event, GraphRunStartedEvent):
+            self.print_text("\n[GraphRunStartedEvent]", color="pink")
+        elif isinstance(event, GraphRunSucceededEvent):
+            self.print_text("\n[GraphRunSucceededEvent]", color="green")
+        elif isinstance(event, GraphRunFailedEvent):
+            self.print_text(f"\n[GraphRunFailedEvent] reason: {event.error}", color="red")
+        elif isinstance(event, NodeRunStartedEvent):
+            self.on_workflow_node_execute_started(event=event)
+        elif isinstance(event, NodeRunSucceededEvent):
+            self.on_workflow_node_execute_succeeded(event=event)
+        elif isinstance(event, NodeRunFailedEvent):
+            self.on_workflow_node_execute_failed(event=event)
+        elif isinstance(event, NodeRunStreamChunkEvent):
+            self.on_node_text_chunk(event=event)
+        elif isinstance(event, ParallelBranchRunStartedEvent):
+            self.on_workflow_parallel_started(event=event)
+        elif isinstance(event, ParallelBranchRunSucceededEvent | ParallelBranchRunFailedEvent):
+            self.on_workflow_parallel_completed(event=event)
+        elif isinstance(event, IterationRunStartedEvent):
+            self.on_workflow_iteration_started(event=event)
+        elif isinstance(event, IterationRunNextEvent):
+            self.on_workflow_iteration_next(event=event)
+        elif isinstance(event, IterationRunSucceededEvent | IterationRunFailedEvent):
+            self.on_workflow_iteration_completed(event=event)
+        else:
+            self.print_text(f"\n[{event.__class__.__name__}]", color="blue")
 
-    def on_workflow_run_succeeded(self) -> None:
-        """
-        Workflow run succeeded
-        """
-        self.print_text("\n[on_workflow_run_succeeded]", color='green')
-
-    def on_workflow_run_failed(self, error: str) -> None:
-        """
-        Workflow run failed
-        """
-        self.print_text("\n[on_workflow_run_failed]", color='red')
-
-    def on_workflow_node_execute_started(self, node_id: str,
-                                         node_type: NodeType,
-                                         node_data: BaseNodeData,
-                                         node_run_index: int = 1,
-                                         predecessor_node_id: Optional[str] = None) -> None:
+    def on_workflow_node_execute_started(self, event: NodeRunStartedEvent) -> None:
         """
         Workflow node execute started
         """
-        self.print_text("\n[on_workflow_node_execute_started]", color='yellow')
-        self.print_text(f"Node ID: {node_id}", color='yellow')
-        self.print_text(f"Type: {node_type.value}", color='yellow')
-        self.print_text(f"Index: {node_run_index}", color='yellow')
-        if predecessor_node_id:
-            self.print_text(f"Predecessor Node ID: {predecessor_node_id}", color='yellow')
+        self.print_text("\n[NodeRunStartedEvent]", color="yellow")
+        self.print_text(f"Node ID: {event.node_id}", color="yellow")
+        self.print_text(f"Node Title: {event.node_data.title}", color="yellow")
+        self.print_text(f"Type: {event.node_type.value}", color="yellow")
 
-    def on_workflow_node_execute_succeeded(self, node_id: str,
-                                           node_type: NodeType,
-                                           node_data: BaseNodeData,
-                                           inputs: Optional[dict] = None,
-                                           process_data: Optional[dict] = None,
-                                           outputs: Optional[dict] = None,
-                                           execution_metadata: Optional[dict] = None) -> None:
+    def on_workflow_node_execute_succeeded(self, event: NodeRunSucceededEvent) -> None:
         """
         Workflow node execute succeeded
         """
-        self.print_text("\n[on_workflow_node_execute_succeeded]", color='green')
-        self.print_text(f"Node ID: {node_id}", color='green')
-        self.print_text(f"Type: {node_type.value}", color='green')
-        self.print_text(f"Inputs: {jsonable_encoder(inputs) if inputs else ''}", color='green')
-        self.print_text(f"Process Data: {jsonable_encoder(process_data) if process_data else ''}", color='green')
-        self.print_text(f"Outputs: {jsonable_encoder(outputs) if outputs else ''}", color='green')
-        self.print_text(f"Metadata: {jsonable_encoder(execution_metadata) if execution_metadata else ''}",
-                        color='green')
+        route_node_state = event.route_node_state
 
-    def on_workflow_node_execute_failed(self, node_id: str,
-                                        node_type: NodeType,
-                                        node_data: BaseNodeData,
-                                        error: str,
-                                        inputs: Optional[dict] = None,
-                                        outputs: Optional[dict] = None,
-                                        process_data: Optional[dict] = None) -> None:
+        self.print_text("\n[NodeRunSucceededEvent]", color="green")
+        self.print_text(f"Node ID: {event.node_id}", color="green")
+        self.print_text(f"Node Title: {event.node_data.title}", color="green")
+        self.print_text(f"Type: {event.node_type.value}", color="green")
+
+        if route_node_state.node_run_result:
+            node_run_result = route_node_state.node_run_result
+            self.print_text(
+                f"Inputs: {jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}",
+                color="green",
+            )
+            self.print_text(
+                f"Process Data: "
+                f"{jsonable_encoder(node_run_result.process_data) if node_run_result.process_data else ''}",
+                color="green",
+            )
+            self.print_text(
+                f"Outputs: {jsonable_encoder(node_run_result.outputs) if node_run_result.outputs else ''}",
+                color="green",
+            )
+            self.print_text(
+                f"Metadata: {jsonable_encoder(node_run_result.metadata) if node_run_result.metadata else ''}",
+                color="green",
+            )
+
+    def on_workflow_node_execute_failed(self, event: NodeRunFailedEvent) -> None:
         """
         Workflow node execute failed
         """
-        self.print_text("\n[on_workflow_node_execute_failed]", color='red')
-        self.print_text(f"Node ID: {node_id}", color='red')
-        self.print_text(f"Type: {node_type.value}", color='red')
-        self.print_text(f"Error: {error}", color='red')
-        self.print_text(f"Inputs: {jsonable_encoder(inputs) if inputs else ''}", color='red')
-        self.print_text(f"Process Data: {jsonable_encoder(process_data) if process_data else ''}", color='red')
-        self.print_text(f"Outputs: {jsonable_encoder(outputs) if outputs else ''}", color='red')
+        route_node_state = event.route_node_state
 
-    def on_node_text_chunk(self, node_id: str, text: str, metadata: Optional[dict] = None) -> None:
+        self.print_text("\n[NodeRunFailedEvent]", color="red")
+        self.print_text(f"Node ID: {event.node_id}", color="red")
+        self.print_text(f"Node Title: {event.node_data.title}", color="red")
+        self.print_text(f"Type: {event.node_type.value}", color="red")
+
+        if route_node_state.node_run_result:
+            node_run_result = route_node_state.node_run_result
+            self.print_text(f"Error: {node_run_result.error}", color="red")
+            self.print_text(
+                f"Inputs: {jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}",
+                color="red",
+            )
+            self.print_text(
+                f"Process Data: "
+                f"{jsonable_encoder(node_run_result.process_data) if node_run_result.process_data else ''}",
+                color="red",
+            )
+            self.print_text(
+                f"Outputs: {jsonable_encoder(node_run_result.outputs) if node_run_result.outputs else ''}",
+                color="red",
+            )
+
+    def on_node_text_chunk(self, event: NodeRunStreamChunkEvent) -> None:
         """
         Publish text chunk
         """
-        if not self.current_node_id or self.current_node_id != node_id:
-            self.current_node_id = node_id
-            self.print_text('\n[on_node_text_chunk]')
-            self.print_text(f"Node ID: {node_id}")
-            self.print_text(f"Metadata: {jsonable_encoder(metadata) if metadata else ''}")
+        route_node_state = event.route_node_state
+        if not self.current_node_id or self.current_node_id != route_node_state.node_id:
+            self.current_node_id = route_node_state.node_id
+            self.print_text("\n[NodeRunStreamChunkEvent]")
+            self.print_text(f"Node ID: {route_node_state.node_id}")
 
-        self.print_text(text, color="pink", end="")
+            node_run_result = route_node_state.node_run_result
+            if node_run_result:
+                self.print_text(
+                    f"Metadata: {jsonable_encoder(node_run_result.metadata) if node_run_result.metadata else ''}"
+                )
 
-    def on_workflow_iteration_started(self, 
-                                      node_id: str,
-                                      node_type: NodeType,
-                                      node_run_index: int = 1,
-                                      node_data: Optional[BaseNodeData] = None,
-                                      inputs: dict = None,
-                                      predecessor_node_id: Optional[str] = None,
-                                      metadata: Optional[dict] = None) -> None:
+        self.print_text(event.chunk_content, color="pink", end="")
+
+    def on_workflow_parallel_started(self, event: ParallelBranchRunStartedEvent) -> None:
+        """
+        Publish parallel started
+        """
+        self.print_text("\n[ParallelBranchRunStartedEvent]", color="blue")
+        self.print_text(f"Parallel ID: {event.parallel_id}", color="blue")
+        self.print_text(f"Branch ID: {event.parallel_start_node_id}", color="blue")
+        if event.in_iteration_id:
+            self.print_text(f"Iteration ID: {event.in_iteration_id}", color="blue")
+
+    def on_workflow_parallel_completed(
+        self, event: ParallelBranchRunSucceededEvent | ParallelBranchRunFailedEvent
+    ) -> None:
+        """
+        Publish parallel completed
+        """
+        if isinstance(event, ParallelBranchRunSucceededEvent):
+            color = "blue"
+        elif isinstance(event, ParallelBranchRunFailedEvent):
+            color = "red"
+
+        self.print_text(
+            "\n[ParallelBranchRunSucceededEvent]"
+            if isinstance(event, ParallelBranchRunSucceededEvent)
+            else "\n[ParallelBranchRunFailedEvent]",
+            color=color,
+        )
+        self.print_text(f"Parallel ID: {event.parallel_id}", color=color)
+        self.print_text(f"Branch ID: {event.parallel_start_node_id}", color=color)
+        if event.in_iteration_id:
+            self.print_text(f"Iteration ID: {event.in_iteration_id}", color=color)
+
+        if isinstance(event, ParallelBranchRunFailedEvent):
+            self.print_text(f"Error: {event.error}", color=color)
+
+    def on_workflow_iteration_started(self, event: IterationRunStartedEvent) -> None:
         """
         Publish iteration started
         """
-        self.print_text("\n[on_workflow_iteration_started]", color='blue')
-        self.print_text(f"Node ID: {node_id}", color='blue')
+        self.print_text("\n[IterationRunStartedEvent]", color="blue")
+        self.print_text(f"Iteration Node ID: {event.iteration_id}", color="blue")
 
-    def on_workflow_iteration_next(self, node_id: str, 
-                                   node_type: NodeType,
-                                   index: int, 
-                                   node_run_index: int,
-                                   output: Optional[dict]) -> None:
+    def on_workflow_iteration_next(self, event: IterationRunNextEvent) -> None:
         """
         Publish iteration next
         """
-        self.print_text("\n[on_workflow_iteration_next]", color='blue')
+        self.print_text("\n[IterationRunNextEvent]", color="blue")
+        self.print_text(f"Iteration Node ID: {event.iteration_id}", color="blue")
+        self.print_text(f"Iteration Index: {event.index}", color="blue")
 
-    def on_workflow_iteration_completed(self, node_id: str, 
-                                        node_type: NodeType,
-                                        node_run_index: int,
-                                        outputs: dict) -> None:
+    def on_workflow_iteration_completed(self, event: IterationRunSucceededEvent | IterationRunFailedEvent) -> None:
         """
         Publish iteration completed
         """
-        self.print_text("\n[on_workflow_iteration_completed]", color='blue')
+        self.print_text(
+            "\n[IterationRunSucceededEvent]"
+            if isinstance(event, IterationRunSucceededEvent)
+            else "\n[IterationRunFailedEvent]",
+            color="blue",
+        )
+        self.print_text(f"Node ID: {event.iteration_id}", color="blue")
 
-    def on_event(self, event: AppQueueEvent) -> None:
-        """
-        Publish event
-        """
-        self.print_text("\n[on_workflow_event]", color='blue')
-        self.print_text(f"Event: {jsonable_encoder(event)}", color='blue')
-
-    def print_text(
-            self, text: str, color: Optional[str] = None, end: str = "\n"
-    ) -> None:
+    def print_text(self, text: str, color: Optional[str] = None, end: str = "\n") -> None:
         """Print text with highlighting and no end characters."""
         text_to_print = self._get_colored_text(text, color) if color else text
-        print(f'{text_to_print}', end=end)
+        print(f"{text_to_print}", end=end)
 
     def _get_colored_text(self, text: str, color: str) -> str:
         """Get colored text."""
diff --git a/api/core/app/entities/app_invoke_entities.py b/api/core/app/entities/app_invoke_entities.py
index 6a1ab23041..ab8d4e374e 100644
--- a/api/core/app/entities/app_invoke_entities.py
+++ b/api/core/app/entities/app_invoke_entities.py
@@ -15,13 +15,14 @@ class InvokeFrom(Enum):
     """
     Invoke From.
     """
-    SERVICE_API = 'service-api'
-    WEB_APP = 'web-app'
-    EXPLORE = 'explore'
-    DEBUGGER = 'debugger'
+
+    SERVICE_API = "service-api"
+    WEB_APP = "web-app"
+    EXPLORE = "explore"
+    DEBUGGER = "debugger"
 
     @classmethod
-    def value_of(cls, value: str) -> 'InvokeFrom':
+    def value_of(cls, value: str) -> "InvokeFrom":
         """
         Get value of given mode.
 
@@ -31,7 +32,7 @@ class InvokeFrom(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid invoke from value {value}')
+        raise ValueError(f"invalid invoke from value {value}")
 
     def to_source(self) -> str:
         """
@@ -40,21 +41,22 @@ class InvokeFrom(Enum):
         :return: source
         """
         if self == InvokeFrom.WEB_APP:
-            return 'web_app'
+            return "web_app"
         elif self == InvokeFrom.DEBUGGER:
-            return 'dev'
+            return "dev"
         elif self == InvokeFrom.EXPLORE:
-            return 'explore_app'
+            return "explore_app"
         elif self == InvokeFrom.SERVICE_API:
-            return 'api'
+            return "api"
 
-        return 'dev'
+        return "dev"
 
 
 class ModelConfigWithCredentialsEntity(BaseModel):
     """
     Model Config With Credentials Entity.
     """
+
     provider: str
     model: str
     model_schema: AIModelEntity
@@ -72,6 +74,7 @@ class AppGenerateEntity(BaseModel):
     """
     App Generate Entity.
     """
+
     task_id: str
 
     # app config
@@ -102,6 +105,7 @@ class EasyUIBasedAppGenerateEntity(AppGenerateEntity):
     """
     Chat Application Generate Entity.
     """
+
     # app config
     app_config: EasyUIBasedAppConfig
     model_conf: ModelConfigWithCredentialsEntity
@@ -116,6 +120,7 @@ class ChatAppGenerateEntity(EasyUIBasedAppGenerateEntity):
     """
     Chat Application Generate Entity.
     """
+
     conversation_id: Optional[str] = None
 
 
@@ -123,6 +128,7 @@ class CompletionAppGenerateEntity(EasyUIBasedAppGenerateEntity):
     """
     Completion Application Generate Entity.
     """
+
     pass
 
 
@@ -130,6 +136,7 @@ class AgentChatAppGenerateEntity(EasyUIBasedAppGenerateEntity):
     """
     Agent Chat Application Generate Entity.
     """
+
     conversation_id: Optional[str] = None
 
 
@@ -137,6 +144,7 @@ class AdvancedChatAppGenerateEntity(AppGenerateEntity):
     """
     Advanced Chat Application Generate Entity.
     """
+
     # app config
     app_config: WorkflowUIBasedAppConfig
 
@@ -147,15 +155,18 @@ class AdvancedChatAppGenerateEntity(AppGenerateEntity):
         """
         Single Iteration Run Entity.
         """
+
         node_id: str
         inputs: dict
 
     single_iteration_run: Optional[SingleIterationRunEntity] = None
 
+
 class WorkflowAppGenerateEntity(AppGenerateEntity):
     """
     Workflow Application Generate Entity.
     """
+
     # app config
     app_config: WorkflowUIBasedAppConfig
 
@@ -163,6 +174,7 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):
         """
         Single Iteration Run Entity.
         """
+
         node_id: str
         inputs: dict
 
diff --git a/api/core/app/entities/queue_entities.py b/api/core/app/entities/queue_entities.py
index 15348251f2..4577e28535 100644
--- a/api/core/app/entities/queue_entities.py
+++ b/api/core/app/entities/queue_entities.py
@@ -1,3 +1,4 @@
+from datetime import datetime
 from enum import Enum
 from typing import Any, Optional
 
@@ -5,13 +6,15 @@ from pydantic import BaseModel, field_validator
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.workflow.entities.base_node_data_entities import BaseNodeData
-from core.workflow.entities.node_entities import NodeType
+from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeType
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 
 
 class QueueEvent(str, Enum):
     """
     QueueEvent enum
     """
+
     LLM_CHUNK = "llm_chunk"
     TEXT_CHUNK = "text_chunk"
     AGENT_MESSAGE = "agent_message"
@@ -31,6 +34,9 @@ class QueueEvent(str, Enum):
     ANNOTATION_REPLY = "annotation_reply"
     AGENT_THOUGHT = "agent_thought"
     MESSAGE_FILE = "message_file"
+    PARALLEL_BRANCH_RUN_STARTED = "parallel_branch_run_started"
+    PARALLEL_BRANCH_RUN_SUCCEEDED = "parallel_branch_run_succeeded"
+    PARALLEL_BRANCH_RUN_FAILED = "parallel_branch_run_failed"
     ERROR = "error"
     PING = "ping"
     STOP = "stop"
@@ -38,46 +44,73 @@ class QueueEvent(str, Enum):
 
 class AppQueueEvent(BaseModel):
     """
-    QueueEvent entity
+    QueueEvent abstract entity
     """
+
     event: QueueEvent
 
 
 class QueueLLMChunkEvent(AppQueueEvent):
     """
     QueueLLMChunkEvent entity
+    Only for basic mode apps
     """
+
     event: QueueEvent = QueueEvent.LLM_CHUNK
     chunk: LLMResultChunk
 
+
 class QueueIterationStartEvent(AppQueueEvent):
     """
     QueueIterationStartEvent entity
     """
+
     event: QueueEvent = QueueEvent.ITERATION_START
+    node_execution_id: str
     node_id: str
     node_type: NodeType
     node_data: BaseNodeData
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    start_at: datetime
 
     node_run_index: int
-    inputs: dict = None
+    inputs: Optional[dict[str, Any]] = None
     predecessor_node_id: Optional[str] = None
-    metadata: Optional[dict] = None
+    metadata: Optional[dict[str, Any]] = None
+
 
 class QueueIterationNextEvent(AppQueueEvent):
     """
     QueueIterationNextEvent entity
     """
+
     event: QueueEvent = QueueEvent.ITERATION_NEXT
 
     index: int
+    node_execution_id: str
     node_id: str
     node_type: NodeType
+    node_data: BaseNodeData
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
 
     node_run_index: int
-    output: Optional[Any] = None # output for the current iteration
+    output: Optional[Any] = None  # output for the current iteration
 
-    @field_validator('output', mode='before')
+    @field_validator("output", mode="before")
     @classmethod
     def set_output(cls, v):
         """
@@ -87,41 +120,66 @@ class QueueIterationNextEvent(AppQueueEvent):
             return None
         if isinstance(v, int | float | str | bool | dict | list):
             return v
-        raise ValueError('output must be a valid type')
+        raise ValueError("output must be a valid type")
+
 
 class QueueIterationCompletedEvent(AppQueueEvent):
     """
     QueueIterationCompletedEvent entity
     """
-    event:QueueEvent = QueueEvent.ITERATION_COMPLETED
 
+    event: QueueEvent = QueueEvent.ITERATION_COMPLETED
+
+    node_execution_id: str
     node_id: str
     node_type: NodeType
-    
+    node_data: BaseNodeData
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    start_at: datetime
+
     node_run_index: int
-    outputs: dict
+    inputs: Optional[dict[str, Any]] = None
+    outputs: Optional[dict[str, Any]] = None
+    metadata: Optional[dict[str, Any]] = None
+    steps: int = 0
+
+    error: Optional[str] = None
+
 
 class QueueTextChunkEvent(AppQueueEvent):
     """
     QueueTextChunkEvent entity
     """
+
     event: QueueEvent = QueueEvent.TEXT_CHUNK
     text: str
-    metadata: Optional[dict] = None
+    from_variable_selector: Optional[list[str]] = None
+    """from variable selector"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
 
 
 class QueueAgentMessageEvent(AppQueueEvent):
     """
     QueueMessageEvent entity
     """
+
     event: QueueEvent = QueueEvent.AGENT_MESSAGE
     chunk: LLMResultChunk
 
-    
+
 class QueueMessageReplaceEvent(AppQueueEvent):
     """
     QueueMessageReplaceEvent entity
     """
+
     event: QueueEvent = QueueEvent.MESSAGE_REPLACE
     text: str
 
@@ -130,14 +188,18 @@ class QueueRetrieverResourcesEvent(AppQueueEvent):
     """
     QueueRetrieverResourcesEvent entity
     """
+
     event: QueueEvent = QueueEvent.RETRIEVER_RESOURCES
     retriever_resources: list[dict]
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
 
 
 class QueueAnnotationReplyEvent(AppQueueEvent):
     """
     QueueAnnotationReplyEvent entity
     """
+
     event: QueueEvent = QueueEvent.ANNOTATION_REPLY
     message_annotation_id: str
 
@@ -146,6 +208,7 @@ class QueueMessageEndEvent(AppQueueEvent):
     """
     QueueMessageEndEvent entity
     """
+
     event: QueueEvent = QueueEvent.MESSAGE_END
     llm_result: Optional[LLMResult] = None
 
@@ -154,6 +217,7 @@ class QueueAdvancedChatMessageEndEvent(AppQueueEvent):
     """
     QueueAdvancedChatMessageEndEvent entity
     """
+
     event: QueueEvent = QueueEvent.ADVANCED_CHAT_MESSAGE_END
 
 
@@ -161,20 +225,25 @@ class QueueWorkflowStartedEvent(AppQueueEvent):
     """
     QueueWorkflowStartedEvent entity
     """
+
     event: QueueEvent = QueueEvent.WORKFLOW_STARTED
+    graph_runtime_state: GraphRuntimeState
 
 
 class QueueWorkflowSucceededEvent(AppQueueEvent):
     """
     QueueWorkflowSucceededEvent entity
     """
+
     event: QueueEvent = QueueEvent.WORKFLOW_SUCCEEDED
+    outputs: Optional[dict[str, Any]] = None
 
 
 class QueueWorkflowFailedEvent(AppQueueEvent):
     """
     QueueWorkflowFailedEvent entity
     """
+
     event: QueueEvent = QueueEvent.WORKFLOW_FAILED
     error: str
 
@@ -183,29 +252,55 @@ class QueueNodeStartedEvent(AppQueueEvent):
     """
     QueueNodeStartedEvent entity
     """
+
     event: QueueEvent = QueueEvent.NODE_STARTED
 
+    node_execution_id: str
     node_id: str
     node_type: NodeType
     node_data: BaseNodeData
     node_run_index: int = 1
     predecessor_node_id: Optional[str] = None
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
+    start_at: datetime
 
 
 class QueueNodeSucceededEvent(AppQueueEvent):
     """
     QueueNodeSucceededEvent entity
     """
+
     event: QueueEvent = QueueEvent.NODE_SUCCEEDED
 
+    node_execution_id: str
     node_id: str
     node_type: NodeType
     node_data: BaseNodeData
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
+    start_at: datetime
 
-    inputs: Optional[dict] = None
-    process_data: Optional[dict] = None
-    outputs: Optional[dict] = None
-    execution_metadata: Optional[dict] = None
+    inputs: Optional[dict[str, Any]] = None
+    process_data: Optional[dict[str, Any]] = None
+    outputs: Optional[dict[str, Any]] = None
+    execution_metadata: Optional[dict[NodeRunMetadataKey, Any]] = None
 
     error: Optional[str] = None
 
@@ -214,15 +309,28 @@ class QueueNodeFailedEvent(AppQueueEvent):
     """
     QueueNodeFailedEvent entity
     """
+
     event: QueueEvent = QueueEvent.NODE_FAILED
 
+    node_execution_id: str
     node_id: str
     node_type: NodeType
     node_data: BaseNodeData
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
+    start_at: datetime
 
-    inputs: Optional[dict] = None
-    outputs: Optional[dict] = None
-    process_data: Optional[dict] = None
+    inputs: Optional[dict[str, Any]] = None
+    process_data: Optional[dict[str, Any]] = None
+    outputs: Optional[dict[str, Any]] = None
 
     error: str
 
@@ -231,6 +339,7 @@ class QueueAgentThoughtEvent(AppQueueEvent):
     """
     QueueAgentThoughtEvent entity
     """
+
     event: QueueEvent = QueueEvent.AGENT_THOUGHT
     agent_thought_id: str
 
@@ -239,6 +348,7 @@ class QueueMessageFileEvent(AppQueueEvent):
     """
     QueueAgentThoughtEvent entity
     """
+
     event: QueueEvent = QueueEvent.MESSAGE_FILE
     message_file_id: str
 
@@ -247,6 +357,7 @@ class QueueErrorEvent(AppQueueEvent):
     """
     QueueErrorEvent entity
     """
+
     event: QueueEvent = QueueEvent.ERROR
     error: Any = None
 
@@ -255,6 +366,7 @@ class QueuePingEvent(AppQueueEvent):
     """
     QueuePingEvent entity
     """
+
     event: QueueEvent = QueueEvent.PING
 
 
@@ -262,10 +374,12 @@ class QueueStopEvent(AppQueueEvent):
     """
     QueueStopEvent entity
     """
+
     class StopBy(Enum):
         """
         Stop by enum
         """
+
         USER_MANUAL = "user-manual"
         ANNOTATION_REPLY = "annotation-reply"
         OUTPUT_MODERATION = "output-moderation"
@@ -274,11 +388,25 @@ class QueueStopEvent(AppQueueEvent):
     event: QueueEvent = QueueEvent.STOP
     stopped_by: StopBy
 
+    def get_stop_reason(self) -> str:
+        """
+        To stop reason
+        """
+        reason_mapping = {
+            QueueStopEvent.StopBy.USER_MANUAL: "Stopped by user.",
+            QueueStopEvent.StopBy.ANNOTATION_REPLY: "Stopped by annotation reply.",
+            QueueStopEvent.StopBy.OUTPUT_MODERATION: "Stopped by output moderation.",
+            QueueStopEvent.StopBy.INPUT_MODERATION: "Stopped by input moderation.",
+        }
+
+        return reason_mapping.get(self.stopped_by, "Stopped by unknown reason.")
+
 
 class QueueMessage(BaseModel):
     """
-    QueueMessage entity
+    QueueMessage abstract entity
     """
+
     task_id: str
     app_mode: str
     event: AppQueueEvent
@@ -288,6 +416,7 @@ class MessageQueueMessage(QueueMessage):
     """
     MessageQueueMessage entity
     """
+
     message_id: str
     conversation_id: str
 
@@ -296,4 +425,57 @@ class WorkflowQueueMessage(QueueMessage):
     """
     WorkflowQueueMessage entity
     """
+
     pass
+
+
+class QueueParallelBranchRunStartedEvent(AppQueueEvent):
+    """
+    QueueParallelBranchRunStartedEvent entity
+    """
+
+    event: QueueEvent = QueueEvent.PARALLEL_BRANCH_RUN_STARTED
+
+    parallel_id: str
+    parallel_start_node_id: str
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
+
+
+class QueueParallelBranchRunSucceededEvent(AppQueueEvent):
+    """
+    QueueParallelBranchRunSucceededEvent entity
+    """
+
+    event: QueueEvent = QueueEvent.PARALLEL_BRANCH_RUN_SUCCEEDED
+
+    parallel_id: str
+    parallel_start_node_id: str
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
+
+
+class QueueParallelBranchRunFailedEvent(AppQueueEvent):
+    """
+    QueueParallelBranchRunFailedEvent entity
+    """
+
+    event: QueueEvent = QueueEvent.PARALLEL_BRANCH_RUN_FAILED
+
+    parallel_id: str
+    parallel_start_node_id: str
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
+    error: str
diff --git a/api/core/app/entities/task_entities.py b/api/core/app/entities/task_entities.py
index 7bc5598984..49e5f55ebc 100644
--- a/api/core/app/entities/task_entities.py
+++ b/api/core/app/entities/task_entities.py
@@ -3,44 +3,16 @@ from typing import Any, Optional
 
 from pydantic import BaseModel, ConfigDict
 
-from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
+from core.model_runtime.entities.llm_entities import LLMResult
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.workflow.entities.base_node_data_entities import BaseNodeData
-from core.workflow.entities.node_entities import NodeType
-from core.workflow.nodes.answer.entities import GenerateRouteChunk
 from models.workflow import WorkflowNodeExecutionStatus
 
 
-class WorkflowStreamGenerateNodes(BaseModel):
-    """
-    WorkflowStreamGenerateNodes entity
-    """
-    end_node_id: str
-    stream_node_ids: list[str]
-
-
-class ChatflowStreamGenerateRoute(BaseModel):
-    """
-    ChatflowStreamGenerateRoute entity
-    """
-    answer_node_id: str
-    generate_route: list[GenerateRouteChunk]
-    current_route_position: int = 0
-
-
-class NodeExecutionInfo(BaseModel):
-    """
-    NodeExecutionInfo entity
-    """
-    workflow_node_execution_id: str
-    node_type: NodeType
-    start_at: float
-
-
 class TaskState(BaseModel):
     """
     TaskState entity
     """
+
     metadata: dict = {}
 
 
@@ -48,6 +20,7 @@ class EasyUITaskState(TaskState):
     """
     EasyUITaskState entity
     """
+
     llm_result: LLMResult
 
 
@@ -55,34 +28,15 @@ class WorkflowTaskState(TaskState):
     """
     WorkflowTaskState entity
     """
+
     answer: str = ""
 
-    workflow_run_id: Optional[str] = None
-    start_at: Optional[float] = None
-    total_tokens: int = 0
-    total_steps: int = 0
-
-    ran_node_execution_infos: dict[str, NodeExecutionInfo] = {}
-    latest_node_execution_info: Optional[NodeExecutionInfo] = None
-
-    current_stream_generate_state: Optional[WorkflowStreamGenerateNodes] = None
-
-    iteration_nested_node_ids: list[str] = None
-
-
-class AdvancedChatTaskState(WorkflowTaskState):
-    """
-    AdvancedChatTaskState entity
-    """
-    usage: LLMUsage
-
-    current_stream_generate_state: Optional[ChatflowStreamGenerateRoute] = None
-
 
 class StreamEvent(Enum):
     """
     Stream event
     """
+
     PING = "ping"
     ERROR = "error"
     MESSAGE = "message"
@@ -97,6 +51,8 @@ class StreamEvent(Enum):
     WORKFLOW_FINISHED = "workflow_finished"
     NODE_STARTED = "node_started"
     NODE_FINISHED = "node_finished"
+    PARALLEL_BRANCH_STARTED = "parallel_branch_started"
+    PARALLEL_BRANCH_FINISHED = "parallel_branch_finished"
     ITERATION_STARTED = "iteration_started"
     ITERATION_NEXT = "iteration_next"
     ITERATION_COMPLETED = "iteration_completed"
@@ -108,6 +64,7 @@ class StreamResponse(BaseModel):
     """
     StreamResponse entity
     """
+
     event: StreamEvent
     task_id: str
 
@@ -119,6 +76,7 @@ class ErrorStreamResponse(StreamResponse):
     """
     ErrorStreamResponse entity
     """
+
     event: StreamEvent = StreamEvent.ERROR
     err: Exception
     model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -128,15 +86,18 @@ class MessageStreamResponse(StreamResponse):
     """
     MessageStreamResponse entity
     """
+
     event: StreamEvent = StreamEvent.MESSAGE
     id: str
     answer: str
+    from_variable_selector: Optional[list[str]] = None
 
 
 class MessageAudioStreamResponse(StreamResponse):
     """
     MessageStreamResponse entity
     """
+
     event: StreamEvent = StreamEvent.TTS_MESSAGE
     audio: str
 
@@ -145,6 +106,7 @@ class MessageAudioEndStreamResponse(StreamResponse):
     """
     MessageStreamResponse entity
     """
+
     event: StreamEvent = StreamEvent.TTS_MESSAGE_END
     audio: str
 
@@ -153,6 +115,7 @@ class MessageEndStreamResponse(StreamResponse):
     """
     MessageEndStreamResponse entity
     """
+
     event: StreamEvent = StreamEvent.MESSAGE_END
     id: str
     metadata: dict = {}
@@ -162,6 +125,7 @@ class MessageFileStreamResponse(StreamResponse):
     """
     MessageFileStreamResponse entity
     """
+
     event: StreamEvent = StreamEvent.MESSAGE_FILE
     id: str
     type: str
@@ -173,6 +137,7 @@ class MessageReplaceStreamResponse(StreamResponse):
     """
     MessageReplaceStreamResponse entity
     """
+
     event: StreamEvent = StreamEvent.MESSAGE_REPLACE
     answer: str
 
@@ -181,6 +146,7 @@ class AgentThoughtStreamResponse(StreamResponse):
     """
     AgentThoughtStreamResponse entity
     """
+
     event: StreamEvent = StreamEvent.AGENT_THOUGHT
     id: str
     position: int
@@ -196,6 +162,7 @@ class AgentMessageStreamResponse(StreamResponse):
     """
     AgentMessageStreamResponse entity
     """
+
     event: StreamEvent = StreamEvent.AGENT_MESSAGE
     id: str
     answer: str
@@ -210,6 +177,7 @@ class WorkflowStartStreamResponse(StreamResponse):
         """
         Data entity
         """
+
         id: str
         workflow_id: str
         sequence_number: int
@@ -230,6 +198,7 @@ class WorkflowFinishStreamResponse(StreamResponse):
         """
         Data entity
         """
+
         id: str
         workflow_id: str
         sequence_number: int
@@ -258,6 +227,7 @@ class NodeStartStreamResponse(StreamResponse):
         """
         Data entity
         """
+
         id: str
         node_id: str
         node_type: str
@@ -267,6 +237,11 @@ class NodeStartStreamResponse(StreamResponse):
         inputs: Optional[dict] = None
         created_at: int
         extras: dict = {}
+        parallel_id: Optional[str] = None
+        parallel_start_node_id: Optional[str] = None
+        parent_parallel_id: Optional[str] = None
+        parent_parallel_start_node_id: Optional[str] = None
+        iteration_id: Optional[str] = None
 
     event: StreamEvent = StreamEvent.NODE_STARTED
     workflow_run_id: str
@@ -286,8 +261,13 @@ class NodeStartStreamResponse(StreamResponse):
                 "predecessor_node_id": self.data.predecessor_node_id,
                 "inputs": None,
                 "created_at": self.data.created_at,
-                "extras": {}
-            }
+                "extras": {},
+                "parallel_id": self.data.parallel_id,
+                "parallel_start_node_id": self.data.parallel_start_node_id,
+                "parent_parallel_id": self.data.parent_parallel_id,
+                "parent_parallel_start_node_id": self.data.parent_parallel_start_node_id,
+                "iteration_id": self.data.iteration_id,
+            },
         }
 
 
@@ -300,6 +280,7 @@ class NodeFinishStreamResponse(StreamResponse):
         """
         Data entity
         """
+
         id: str
         node_id: str
         node_type: str
@@ -316,6 +297,11 @@ class NodeFinishStreamResponse(StreamResponse):
         created_at: int
         finished_at: int
         files: Optional[list[dict]] = []
+        parallel_id: Optional[str] = None
+        parallel_start_node_id: Optional[str] = None
+        parent_parallel_id: Optional[str] = None
+        parent_parallel_start_node_id: Optional[str] = None
+        iteration_id: Optional[str] = None
 
     event: StreamEvent = StreamEvent.NODE_FINISHED
     workflow_run_id: str
@@ -342,11 +328,62 @@ class NodeFinishStreamResponse(StreamResponse):
                 "execution_metadata": None,
                 "created_at": self.data.created_at,
                 "finished_at": self.data.finished_at,
-                "files": []
-            }
+                "files": [],
+                "parallel_id": self.data.parallel_id,
+                "parallel_start_node_id": self.data.parallel_start_node_id,
+                "parent_parallel_id": self.data.parent_parallel_id,
+                "parent_parallel_start_node_id": self.data.parent_parallel_start_node_id,
+                "iteration_id": self.data.iteration_id,
+            },
         }
 
 
+class ParallelBranchStartStreamResponse(StreamResponse):
+    """
+    ParallelBranchStartStreamResponse entity
+    """
+
+    class Data(BaseModel):
+        """
+        Data entity
+        """
+
+        parallel_id: str
+        parallel_branch_id: str
+        parent_parallel_id: Optional[str] = None
+        parent_parallel_start_node_id: Optional[str] = None
+        iteration_id: Optional[str] = None
+        created_at: int
+
+    event: StreamEvent = StreamEvent.PARALLEL_BRANCH_STARTED
+    workflow_run_id: str
+    data: Data
+
+
+class ParallelBranchFinishedStreamResponse(StreamResponse):
+    """
+    ParallelBranchFinishedStreamResponse entity
+    """
+
+    class Data(BaseModel):
+        """
+        Data entity
+        """
+
+        parallel_id: str
+        parallel_branch_id: str
+        parent_parallel_id: Optional[str] = None
+        parent_parallel_start_node_id: Optional[str] = None
+        iteration_id: Optional[str] = None
+        status: str
+        error: Optional[str] = None
+        created_at: int
+
+    event: StreamEvent = StreamEvent.PARALLEL_BRANCH_FINISHED
+    workflow_run_id: str
+    data: Data
+
+
 class IterationNodeStartStreamResponse(StreamResponse):
     """
     NodeStartStreamResponse entity
@@ -356,6 +393,7 @@ class IterationNodeStartStreamResponse(StreamResponse):
         """
         Data entity
         """
+
         id: str
         node_id: str
         node_type: str
@@ -364,6 +402,8 @@ class IterationNodeStartStreamResponse(StreamResponse):
         extras: dict = {}
         metadata: dict = {}
         inputs: dict = {}
+        parallel_id: Optional[str] = None
+        parallel_start_node_id: Optional[str] = None
 
     event: StreamEvent = StreamEvent.ITERATION_STARTED
     workflow_run_id: str
@@ -379,6 +419,7 @@ class IterationNodeNextStreamResponse(StreamResponse):
         """
         Data entity
         """
+
         id: str
         node_id: str
         node_type: str
@@ -387,6 +428,8 @@ class IterationNodeNextStreamResponse(StreamResponse):
         created_at: int
         pre_iteration_output: Optional[Any] = None
         extras: dict = {}
+        parallel_id: Optional[str] = None
+        parallel_start_node_id: Optional[str] = None
 
     event: StreamEvent = StreamEvent.ITERATION_NEXT
     workflow_run_id: str
@@ -402,14 +445,15 @@ class IterationNodeCompletedStreamResponse(StreamResponse):
         """
         Data entity
         """
+
         id: str
         node_id: str
         node_type: str
         title: str
         outputs: Optional[dict] = None
         created_at: int
-        extras: dict = None
-        inputs: dict = None
+        extras: Optional[dict] = None
+        inputs: Optional[dict] = None
         status: WorkflowNodeExecutionStatus
         error: Optional[str] = None
         elapsed_time: float
@@ -417,6 +461,8 @@ class IterationNodeCompletedStreamResponse(StreamResponse):
         execution_metadata: Optional[dict] = None
         finished_at: int
         steps: int
+        parallel_id: Optional[str] = None
+        parallel_start_node_id: Optional[str] = None
 
     event: StreamEvent = StreamEvent.ITERATION_COMPLETED
     workflow_run_id: str
@@ -432,7 +478,9 @@ class TextChunkStreamResponse(StreamResponse):
         """
         Data entity
         """
+
         text: str
+        from_variable_selector: Optional[list[str]] = None
 
     event: StreamEvent = StreamEvent.TEXT_CHUNK
     data: Data
@@ -447,6 +495,7 @@ class TextReplaceStreamResponse(StreamResponse):
         """
         Data entity
         """
+
         text: str
 
     event: StreamEvent = StreamEvent.TEXT_REPLACE
@@ -457,6 +506,7 @@ class PingStreamResponse(StreamResponse):
     """
     PingStreamResponse entity
     """
+
     event: StreamEvent = StreamEvent.PING
 
 
@@ -464,6 +514,7 @@ class AppStreamResponse(BaseModel):
     """
     AppStreamResponse entity
     """
+
     stream_response: StreamResponse
 
 
@@ -471,6 +522,7 @@ class ChatbotAppStreamResponse(AppStreamResponse):
     """
     ChatbotAppStreamResponse entity
     """
+
     conversation_id: str
     message_id: str
     created_at: int
@@ -480,6 +532,7 @@ class CompletionAppStreamResponse(AppStreamResponse):
     """
     CompletionAppStreamResponse entity
     """
+
     message_id: str
     created_at: int
 
@@ -488,13 +541,15 @@ class WorkflowAppStreamResponse(AppStreamResponse):
     """
     WorkflowAppStreamResponse entity
     """
-    workflow_run_id: str
+
+    workflow_run_id: Optional[str] = None
 
 
 class AppBlockingResponse(BaseModel):
     """
     AppBlockingResponse entity
     """
+
     task_id: str
 
     def to_dict(self) -> dict:
@@ -510,6 +565,7 @@ class ChatbotAppBlockingResponse(AppBlockingResponse):
         """
         Data entity
         """
+
         id: str
         mode: str
         conversation_id: str
@@ -530,6 +586,7 @@ class CompletionAppBlockingResponse(AppBlockingResponse):
         """
         Data entity
         """
+
         id: str
         mode: str
         message_id: str
@@ -549,6 +606,7 @@ class WorkflowAppBlockingResponse(AppBlockingResponse):
         """
         Data entity
         """
+
         id: str
         workflow_id: str
         status: str
@@ -562,25 +620,3 @@ class WorkflowAppBlockingResponse(AppBlockingResponse):
 
     workflow_run_id: str
     data: Data
-
-
-class WorkflowIterationState(BaseModel):
-    """
-    WorkflowIterationState entity
-    """
-
-    class Data(BaseModel):
-        """
-        Data entity
-        """
-        parent_iteration_id: Optional[str] = None
-        iteration_id: str
-        current_index: int
-        iteration_steps_boundary: list[int] = None
-        node_execution_id: str
-        started_at: float
-        inputs: dict = None
-        total_tokens: int = 0
-        node_data: BaseNodeData
-
-    current_iterations: dict[str, Data] = None
diff --git a/api/core/app/features/annotation_reply/annotation_reply.py b/api/core/app/features/annotation_reply/annotation_reply.py
index 19ff94de5e..77b6bb554c 100644
--- a/api/core/app/features/annotation_reply/annotation_reply.py
+++ b/api/core/app/features/annotation_reply/annotation_reply.py
@@ -13,11 +13,9 @@ logger = logging.getLogger(__name__)
 
 
 class AnnotationReplyFeature:
-    def query(self, app_record: App,
-              message: Message,
-              query: str,
-              user_id: str,
-              invoke_from: InvokeFrom) -> Optional[MessageAnnotation]:
+    def query(
+        self, app_record: App, message: Message, query: str, user_id: str, invoke_from: InvokeFrom
+    ) -> Optional[MessageAnnotation]:
         """
         Query app annotations to reply
         :param app_record: app record
@@ -27,8 +25,9 @@ class AnnotationReplyFeature:
         :param invoke_from: invoke from
         :return:
         """
-        annotation_setting = db.session.query(AppAnnotationSetting).filter(
-            AppAnnotationSetting.app_id == app_record.id).first()
+        annotation_setting = (
+            db.session.query(AppAnnotationSetting).filter(AppAnnotationSetting.app_id == app_record.id).first()
+        )
 
         if not annotation_setting:
             return None
@@ -41,55 +40,50 @@ class AnnotationReplyFeature:
             embedding_model_name = collection_binding_detail.model_name
 
             dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
-                embedding_provider_name,
-                embedding_model_name,
-                'annotation'
+                embedding_provider_name, embedding_model_name, "annotation"
             )
 
             dataset = Dataset(
                 id=app_record.id,
                 tenant_id=app_record.tenant_id,
-                indexing_technique='high_quality',
+                indexing_technique="high_quality",
                 embedding_model_provider=embedding_provider_name,
                 embedding_model=embedding_model_name,
-                collection_binding_id=dataset_collection_binding.id
+                collection_binding_id=dataset_collection_binding.id,
             )
 
-            vector = Vector(dataset, attributes=['doc_id', 'annotation_id', 'app_id'])
+            vector = Vector(dataset, attributes=["doc_id", "annotation_id", "app_id"])
 
             documents = vector.search_by_vector(
-                query=query,
-                top_k=1,
-                score_threshold=score_threshold,
-                filter={
-                    'group_id': [dataset.id]
-                }
+                query=query, top_k=1, score_threshold=score_threshold, filter={"group_id": [dataset.id]}
             )
 
             if documents:
-                annotation_id = documents[0].metadata['annotation_id']
-                score = documents[0].metadata['score']
+                annotation_id = documents[0].metadata["annotation_id"]
+                score = documents[0].metadata["score"]
                 annotation = AppAnnotationService.get_annotation_by_id(annotation_id)
                 if annotation:
-                    if invoke_from in [InvokeFrom.SERVICE_API, InvokeFrom.WEB_APP]:
-                        from_source = 'api'
+                    if invoke_from in {InvokeFrom.SERVICE_API, InvokeFrom.WEB_APP}:
+                        from_source = "api"
                     else:
-                        from_source = 'console'
+                        from_source = "console"
 
                     # insert annotation history
-                    AppAnnotationService.add_annotation_history(annotation.id,
-                                                                app_record.id,
-                                                                annotation.question,
-                                                                annotation.content,
-                                                                query,
-                                                                user_id,
-                                                                message.id,
-                                                                from_source,
-                                                                score)
+                    AppAnnotationService.add_annotation_history(
+                        annotation.id,
+                        app_record.id,
+                        annotation.question,
+                        annotation.content,
+                        query,
+                        user_id,
+                        message.id,
+                        from_source,
+                        score,
+                    )
 
                     return annotation
         except Exception as e:
-            logger.warning(f'Query annotation failed, exception: {str(e)}.')
+            logger.warning(f"Query annotation failed, exception: {str(e)}.")
             return None
 
         return None
diff --git a/api/core/app/features/hosting_moderation/hosting_moderation.py b/api/core/app/features/hosting_moderation/hosting_moderation.py
index b8f3e0e1f6..ba14b61201 100644
--- a/api/core/app/features/hosting_moderation/hosting_moderation.py
+++ b/api/core/app/features/hosting_moderation/hosting_moderation.py
@@ -8,8 +8,9 @@ logger = logging.getLogger(__name__)
 
 
 class HostingModerationFeature:
-    def check(self, application_generate_entity: EasyUIBasedAppGenerateEntity,
-              prompt_messages: list[PromptMessage]) -> bool:
+    def check(
+        self, application_generate_entity: EasyUIBasedAppGenerateEntity, prompt_messages: list[PromptMessage]
+    ) -> bool:
         """
         Check hosting moderation
         :param application_generate_entity: application generate entity
@@ -23,9 +24,6 @@ class HostingModerationFeature:
             if isinstance(prompt_message.content, str):
                 text += prompt_message.content + "\n"
 
-        moderation_result = moderation.check_moderation(
-            model_config,
-            text
-        )
+        moderation_result = moderation.check_moderation(model_config, text)
 
         return moderation_result
diff --git a/api/core/app/features/rate_limiting/rate_limit.py b/api/core/app/features/rate_limiting/rate_limit.py
index f11e8021f0..227182f5ab 100644
--- a/api/core/app/features/rate_limiting/rate_limit.py
+++ b/api/core/app/features/rate_limiting/rate_limit.py
@@ -19,7 +19,7 @@ class RateLimit:
     _ACTIVE_REQUESTS_COUNT_FLUSH_INTERVAL = 5 * 60  # recalculate request_count from request_detail every 5 minutes
     _instance_dict = {}
 
-    def __new__(cls: type['RateLimit'], client_id: str, max_active_requests: int):
+    def __new__(cls: type["RateLimit"], client_id: str, max_active_requests: int):
         if client_id not in cls._instance_dict:
             instance = super().__new__(cls)
             cls._instance_dict[client_id] = instance
@@ -27,13 +27,13 @@ class RateLimit:
 
     def __init__(self, client_id: str, max_active_requests: int):
         self.max_active_requests = max_active_requests
-        if hasattr(self, 'initialized'):
+        if hasattr(self, "initialized"):
             return
         self.initialized = True
         self.client_id = client_id
         self.active_requests_key = self._ACTIVE_REQUESTS_KEY.format(client_id)
         self.max_active_requests_key = self._MAX_ACTIVE_REQUESTS_KEY.format(client_id)
-        self.last_recalculate_time = float('-inf')
+        self.last_recalculate_time = float("-inf")
         self.flush_cache(use_local_value=True)
 
     def flush_cache(self, use_local_value=False):
@@ -46,7 +46,7 @@ class RateLimit:
                 pipe.execute()
         else:
             with redis_client.pipeline() as pipe:
-                self.max_active_requests = int(redis_client.get(self.max_active_requests_key).decode('utf-8'))
+                self.max_active_requests = int(redis_client.get(self.max_active_requests_key).decode("utf-8"))
                 redis_client.expire(self.max_active_requests_key, timedelta(days=1))
 
         # flush max active requests (in-transit request list)
@@ -54,8 +54,11 @@ class RateLimit:
             return
         request_details = redis_client.hgetall(self.active_requests_key)
         redis_client.expire(self.active_requests_key, timedelta(days=1))
-        timeout_requests = [k for k, v in request_details.items() if
-                            time.time() - float(v.decode('utf-8')) > RateLimit._REQUEST_MAX_ALIVE_TIME]
+        timeout_requests = [
+            k
+            for k, v in request_details.items()
+            if time.time() - float(v.decode("utf-8")) > RateLimit._REQUEST_MAX_ALIVE_TIME
+        ]
         if timeout_requests:
             redis_client.hdel(self.active_requests_key, *timeout_requests)
 
@@ -69,8 +72,10 @@ class RateLimit:
 
         active_requests_count = redis_client.hlen(self.active_requests_key)
         if active_requests_count >= self.max_active_requests:
-            raise AppInvokeQuotaExceededError("Too many requests. Please try again later. The current maximum "
-                                              "concurrent requests allowed is {}.".format(self.max_active_requests))
+            raise AppInvokeQuotaExceededError(
+                "Too many requests. Please try again later. The current maximum "
+                "concurrent requests allowed is {}.".format(self.max_active_requests)
+            )
         redis_client.hset(self.active_requests_key, request_id, str(time.time()))
         return request_id
 
@@ -116,5 +121,5 @@ class RateLimitGenerator:
         if not self.closed:
             self.closed = True
             self.rate_limit.exit(self.request_id)
-            if self.generator is not None and hasattr(self.generator, 'close'):
+            if self.generator is not None and hasattr(self.generator, "close"):
                 self.generator.close()
diff --git a/api/core/app/segments/__init__.py b/api/core/app/segments/__init__.py
index 7de06dfb96..652ef243b4 100644
--- a/api/core/app/segments/__init__.py
+++ b/api/core/app/segments/__init__.py
@@ -25,25 +25,25 @@ from .variables import (
 )
 
 __all__ = [
-    'IntegerVariable',
-    'FloatVariable',
-    'ObjectVariable',
-    'SecretVariable',
-    'StringVariable',
-    'ArrayAnyVariable',
-    'Variable',
-    'SegmentType',
-    'SegmentGroup',
-    'Segment',
-    'NoneSegment',
-    'NoneVariable',
-    'IntegerSegment',
-    'FloatSegment',
-    'ObjectSegment',
-    'ArrayAnySegment',
-    'StringSegment',
-    'ArrayStringVariable',
-    'ArrayNumberVariable',
-    'ArrayObjectVariable',
-    'ArraySegment',
+    "IntegerVariable",
+    "FloatVariable",
+    "ObjectVariable",
+    "SecretVariable",
+    "StringVariable",
+    "ArrayAnyVariable",
+    "Variable",
+    "SegmentType",
+    "SegmentGroup",
+    "Segment",
+    "NoneSegment",
+    "NoneVariable",
+    "IntegerSegment",
+    "FloatSegment",
+    "ObjectSegment",
+    "ArrayAnySegment",
+    "StringSegment",
+    "ArrayStringVariable",
+    "ArrayNumberVariable",
+    "ArrayObjectVariable",
+    "ArraySegment",
 ]
diff --git a/api/core/app/segments/factory.py b/api/core/app/segments/factory.py
index e6e9ce9774..40a69ed4eb 100644
--- a/api/core/app/segments/factory.py
+++ b/api/core/app/segments/factory.py
@@ -28,12 +28,12 @@ from .variables import (
 
 
 def build_variable_from_mapping(mapping: Mapping[str, Any], /) -> Variable:
-    if (value_type := mapping.get('value_type')) is None:
-        raise VariableError('missing value type')
-    if not mapping.get('name'):
-        raise VariableError('missing name')
-    if (value := mapping.get('value')) is None:
-        raise VariableError('missing value')
+    if (value_type := mapping.get("value_type")) is None:
+        raise VariableError("missing value type")
+    if not mapping.get("name"):
+        raise VariableError("missing name")
+    if (value := mapping.get("value")) is None:
+        raise VariableError("missing value")
     match value_type:
         case SegmentType.STRING:
             result = StringVariable.model_validate(mapping)
@@ -44,7 +44,7 @@ def build_variable_from_mapping(mapping: Mapping[str, Any], /) -> Variable:
         case SegmentType.NUMBER if isinstance(value, float):
             result = FloatVariable.model_validate(mapping)
         case SegmentType.NUMBER if not isinstance(value, float | int):
-            raise VariableError(f'invalid number value {value}')
+            raise VariableError(f"invalid number value {value}")
         case SegmentType.OBJECT if isinstance(value, dict):
             result = ObjectVariable.model_validate(mapping)
         case SegmentType.ARRAY_STRING if isinstance(value, list):
@@ -54,9 +54,9 @@ def build_variable_from_mapping(mapping: Mapping[str, Any], /) -> Variable:
         case SegmentType.ARRAY_OBJECT if isinstance(value, list):
             result = ArrayObjectVariable.model_validate(mapping)
         case _:
-            raise VariableError(f'not supported value type {value_type}')
+            raise VariableError(f"not supported value type {value_type}")
     if result.size > dify_config.MAX_VARIABLE_SIZE:
-        raise VariableError(f'variable size {result.size} exceeds limit {dify_config.MAX_VARIABLE_SIZE}')
+        raise VariableError(f"variable size {result.size} exceeds limit {dify_config.MAX_VARIABLE_SIZE}")
     return result
 
 
@@ -73,4 +73,4 @@ def build_segment(value: Any, /) -> Segment:
         return ObjectSegment(value=value)
     if isinstance(value, list):
         return ArrayAnySegment(value=value)
-    raise ValueError(f'not supported value {value}')
+    raise ValueError(f"not supported value {value}")
diff --git a/api/core/app/segments/parser.py b/api/core/app/segments/parser.py
index de6c796652..3c4d7046f4 100644
--- a/api/core/app/segments/parser.py
+++ b/api/core/app/segments/parser.py
@@ -4,14 +4,14 @@ from core.workflow.entities.variable_pool import VariablePool
 
 from . import SegmentGroup, factory
 
-VARIABLE_PATTERN = re.compile(r'\{\{#([a-zA-Z0-9_]{1,50}(?:\.[a-zA-Z_][a-zA-Z0-9_]{0,29}){1,10})#\}\}')
+VARIABLE_PATTERN = re.compile(r"\{\{#([a-zA-Z0-9_]{1,50}(?:\.[a-zA-Z_][a-zA-Z0-9_]{0,29}){1,10})#\}\}")
 
 
 def convert_template(*, template: str, variable_pool: VariablePool):
     parts = re.split(VARIABLE_PATTERN, template)
     segments = []
     for part in filter(lambda x: x, parts):
-        if '.' in part and (value := variable_pool.get(part.split('.'))):
+        if "." in part and (value := variable_pool.get(part.split("."))):
             segments.append(value)
         else:
             segments.append(factory.build_segment(part))
diff --git a/api/core/app/segments/segment_group.py b/api/core/app/segments/segment_group.py
index b4ff09b6d3..b363255b2c 100644
--- a/api/core/app/segments/segment_group.py
+++ b/api/core/app/segments/segment_group.py
@@ -8,15 +8,15 @@ class SegmentGroup(Segment):
 
     @property
     def text(self):
-        return ''.join([segment.text for segment in self.value])
+        return "".join([segment.text for segment in self.value])
 
     @property
     def log(self):
-        return ''.join([segment.log for segment in self.value])
+        return "".join([segment.log for segment in self.value])
 
     @property
     def markdown(self):
-        return ''.join([segment.markdown for segment in self.value])
+        return "".join([segment.markdown for segment in self.value])
 
     def to_object(self):
         return [segment.to_object() for segment in self.value]
diff --git a/api/core/app/segments/segments.py b/api/core/app/segments/segments.py
index 5c713cac67..b26b3c8291 100644
--- a/api/core/app/segments/segments.py
+++ b/api/core/app/segments/segments.py
@@ -14,13 +14,14 @@ class Segment(BaseModel):
     value_type: SegmentType
     value: Any
 
-    @field_validator('value_type')
+    @field_validator("value_type")
+    @classmethod
     def validate_value_type(cls, value):
         """
         This validator checks if the provided value is equal to the default value of the 'value_type' field.
         If the value is different, a ValueError is raised.
         """
-        if value != cls.model_fields['value_type'].default:
+        if value != cls.model_fields["value_type"].default:
             raise ValueError("Cannot modify 'value_type'")
         return value
 
@@ -50,15 +51,15 @@ class NoneSegment(Segment):
 
     @property
     def text(self) -> str:
-        return 'null'
+        return "null"
 
     @property
     def log(self) -> str:
-        return 'null'
+        return "null"
 
     @property
     def markdown(self) -> str:
-        return 'null'
+        return "null"
 
 
 class StringSegment(Segment):
@@ -76,24 +77,21 @@ class IntegerSegment(Segment):
     value: int
 
 
-
-
-
 class ObjectSegment(Segment):
     value_type: SegmentType = SegmentType.OBJECT
     value: Mapping[str, Any]
 
     @property
     def text(self) -> str:
-        return json.dumps(self.model_dump()['value'], ensure_ascii=False)
+        return json.dumps(self.model_dump()["value"], ensure_ascii=False)
 
     @property
     def log(self) -> str:
-        return json.dumps(self.model_dump()['value'], ensure_ascii=False, indent=2)
+        return json.dumps(self.model_dump()["value"], ensure_ascii=False, indent=2)
 
     @property
     def markdown(self) -> str:
-        return json.dumps(self.model_dump()['value'], ensure_ascii=False, indent=2)
+        return json.dumps(self.model_dump()["value"], ensure_ascii=False, indent=2)
 
 
 class ArraySegment(Segment):
@@ -101,11 +99,11 @@ class ArraySegment(Segment):
     def markdown(self) -> str:
         items = []
         for item in self.value:
-            if hasattr(item, 'to_markdown'):
+            if hasattr(item, "to_markdown"):
                 items.append(item.to_markdown())
             else:
                 items.append(str(item))
-        return '\n'.join(items)
+        return "\n".join(items)
 
 
 class ArrayAnySegment(ArraySegment):
@@ -126,4 +124,3 @@ class ArrayNumberSegment(ArraySegment):
 class ArrayObjectSegment(ArraySegment):
     value_type: SegmentType = SegmentType.ARRAY_OBJECT
     value: Sequence[Mapping[str, Any]]
-
diff --git a/api/core/app/segments/types.py b/api/core/app/segments/types.py
index cdd2b0b4b0..9cf0856df5 100644
--- a/api/core/app/segments/types.py
+++ b/api/core/app/segments/types.py
@@ -2,14 +2,14 @@ from enum import Enum
 
 
 class SegmentType(str, Enum):
-    NONE = 'none'
-    NUMBER = 'number'
-    STRING = 'string'
-    SECRET = 'secret'
-    ARRAY_ANY = 'array[any]'
-    ARRAY_STRING = 'array[string]'
-    ARRAY_NUMBER = 'array[number]'
-    ARRAY_OBJECT = 'array[object]'
-    OBJECT = 'object'
+    NONE = "none"
+    NUMBER = "number"
+    STRING = "string"
+    SECRET = "secret"
+    ARRAY_ANY = "array[any]"
+    ARRAY_STRING = "array[string]"
+    ARRAY_NUMBER = "array[number]"
+    ARRAY_OBJECT = "array[object]"
+    OBJECT = "object"
 
-    GROUP = 'group'
+    GROUP = "group"
diff --git a/api/core/app/segments/variables.py b/api/core/app/segments/variables.py
index 8fef707fcf..f0e403ab8d 100644
--- a/api/core/app/segments/variables.py
+++ b/api/core/app/segments/variables.py
@@ -23,11 +23,11 @@ class Variable(Segment):
     """
 
     id: str = Field(
-        default='',
+        default="",
         description="Unique identity for variable. It's only used by environment variables now.",
     )
     name: str
-    description: str = Field(default='', description='Description of the variable.')
+    description: str = Field(default="", description="Description of the variable.")
 
 
 class StringVariable(StringSegment, Variable):
@@ -62,7 +62,6 @@ class ArrayObjectVariable(ArrayObjectSegment, Variable):
     pass
 
 
-
 class SecretVariable(StringVariable):
     value_type: SegmentType = SegmentType.SECRET
 
diff --git a/api/core/app/task_pipeline/based_generate_task_pipeline.py b/api/core/app/task_pipeline/based_generate_task_pipeline.py
index a3c1fb5824..a43be5fdf2 100644
--- a/api/core/app/task_pipeline/based_generate_task_pipeline.py
+++ b/api/core/app/task_pipeline/based_generate_task_pipeline.py
@@ -32,10 +32,13 @@ class BasedGenerateTaskPipeline:
     _task_state: TaskState
     _application_generate_entity: AppGenerateEntity
 
-    def __init__(self, application_generate_entity: AppGenerateEntity,
-                 queue_manager: AppQueueManager,
-                 user: Union[Account, EndUser],
-                 stream: bool) -> None:
+    def __init__(
+        self,
+        application_generate_entity: AppGenerateEntity,
+        queue_manager: AppQueueManager,
+        user: Union[Account, EndUser],
+        stream: bool,
+    ) -> None:
         """
         Initialize GenerateTaskPipeline.
         :param application_generate_entity: application generate entity
@@ -61,35 +64,39 @@ class BasedGenerateTaskPipeline:
         e = event.error
 
         if isinstance(e, InvokeAuthorizationError):
-            err = InvokeAuthorizationError('Incorrect API key provided')
-        elif isinstance(e, InvokeError) or isinstance(e, ValueError):
+            err = InvokeAuthorizationError("Incorrect API key provided")
+        elif isinstance(e, InvokeError | ValueError):
             err = e
         else:
-            err = Exception(e.description if getattr(e, 'description', None) is not None else str(e))
+            err = Exception(e.description if getattr(e, "description", None) is not None else str(e))
 
         if message:
-            message = db.session.query(Message).filter(Message.id == message.id).first()
-            err_desc = self._error_to_desc(err)
-            message.status = 'error'
-            message.error = err_desc
+            refetch_message = db.session.query(Message).filter(Message.id == message.id).first()
 
-            db.session.commit()
+            if refetch_message:
+                err_desc = self._error_to_desc(err)
+                refetch_message.status = "error"
+                refetch_message.error = err_desc
+
+                db.session.commit()
 
         return err
 
-    def _error_to_desc(cls, e: Exception) -> str:
+    def _error_to_desc(self, e: Exception) -> str:
         """
         Error to desc.
         :param e: exception
         :return:
         """
         if isinstance(e, QuotaExceededError):
-            return ("Your quota for Dify Hosted Model Provider has been exhausted. "
-                    "Please go to Settings -> Model Provider to complete your own provider credentials.")
+            return (
+                "Your quota for Dify Hosted Model Provider has been exhausted. "
+                "Please go to Settings -> Model Provider to complete your own provider credentials."
+            )
 
-        message = getattr(e, 'description', str(e))
+        message = getattr(e, "description", str(e))
         if not message:
-            message = 'Internal Server Error, please contact support.'
+            message = "Internal Server Error, please contact support."
 
         return message
 
@@ -99,10 +106,7 @@ class BasedGenerateTaskPipeline:
         :param e: exception
         :return:
         """
-        return ErrorStreamResponse(
-            task_id=self._application_generate_entity.task_id,
-            err=e
-        )
+        return ErrorStreamResponse(task_id=self._application_generate_entity.task_id, err=e)
 
     def _ping_stream_response(self) -> PingStreamResponse:
         """
@@ -123,11 +127,8 @@ class BasedGenerateTaskPipeline:
             return OutputModeration(
                 tenant_id=app_config.tenant_id,
                 app_id=app_config.app_id,
-                rule=ModerationRule(
-                    type=sensitive_word_avoidance.type,
-                    config=sensitive_word_avoidance.config
-                ),
-                queue_manager=self._queue_manager
+                rule=ModerationRule(type=sensitive_word_avoidance.type, config=sensitive_word_avoidance.config),
+                queue_manager=self._queue_manager,
             )
 
     def _handle_output_moderation_when_task_finished(self, completion: str) -> Optional[str]:
@@ -141,8 +142,7 @@ class BasedGenerateTaskPipeline:
             self._output_moderation_handler.stop_thread()
 
             completion = self._output_moderation_handler.moderation_completion(
-                completion=completion,
-                public_event=False
+                completion=completion, public_event=False
             )
 
             self._output_moderation_handler = None
diff --git a/api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py b/api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py
index 8d91a507a9..8f834b6458 100644
--- a/api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py
+++ b/api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py
@@ -64,23 +64,21 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
     """
     EasyUIBasedGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
-    _task_state: EasyUITaskState
-    _application_generate_entity: Union[
-        ChatAppGenerateEntity,
-        CompletionAppGenerateEntity,
-        AgentChatAppGenerateEntity
-    ]
 
-    def __init__(self, application_generate_entity: Union[
-        ChatAppGenerateEntity,
-        CompletionAppGenerateEntity,
-        AgentChatAppGenerateEntity
-    ],
-                 queue_manager: AppQueueManager,
-                 conversation: Conversation,
-                 message: Message,
-                 user: Union[Account, EndUser],
-                 stream: bool) -> None:
+    _task_state: EasyUITaskState
+    _application_generate_entity: Union[ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity]
+
+    def __init__(
+        self,
+        application_generate_entity: Union[
+            ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity
+        ],
+        queue_manager: AppQueueManager,
+        conversation: Conversation,
+        message: Message,
+        user: Union[Account, EndUser],
+        stream: bool,
+    ) -> None:
         """
         Initialize GenerateTaskPipeline.
         :param application_generate_entity: application generate entity
@@ -101,18 +99,18 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                 model=self._model_config.model,
                 prompt_messages=[],
                 message=AssistantPromptMessage(content=""),
-                usage=LLMUsage.empty_usage()
+                usage=LLMUsage.empty_usage(),
             )
         )
 
         self._conversation_name_generate_thread = None
 
     def process(
-            self,
+        self,
     ) -> Union[
         ChatbotAppBlockingResponse,
         CompletionAppBlockingResponse,
-        Generator[Union[ChatbotAppStreamResponse, CompletionAppStreamResponse], None, None]
+        Generator[Union[ChatbotAppStreamResponse, CompletionAppStreamResponse], None, None],
     ]:
         """
         Process generate task pipeline.
@@ -125,22 +123,18 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         if self._application_generate_entity.app_config.app_mode != AppMode.COMPLETION:
             # start generate conversation name thread
             self._conversation_name_generate_thread = self._generate_conversation_name(
-                self._conversation,
-                self._application_generate_entity.query
+                self._conversation, self._application_generate_entity.query
             )
 
-        generator = self._wrapper_process_stream_response(
-            trace_manager=self._application_generate_entity.trace_manager
-        )
+        generator = self._wrapper_process_stream_response(trace_manager=self._application_generate_entity.trace_manager)
         if self._stream:
             return self._to_stream_response(generator)
         else:
             return self._to_blocking_response(generator)
 
-    def _to_blocking_response(self, generator: Generator[StreamResponse, None, None]) -> Union[
-        ChatbotAppBlockingResponse,
-        CompletionAppBlockingResponse
-    ]:
+    def _to_blocking_response(
+        self, generator: Generator[StreamResponse, None, None]
+    ) -> Union[ChatbotAppBlockingResponse, CompletionAppBlockingResponse]:
         """
         Process blocking response.
         :return:
@@ -149,11 +143,9 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
             if isinstance(stream_response, ErrorStreamResponse):
                 raise stream_response.err
             elif isinstance(stream_response, MessageEndStreamResponse):
-                extras = {
-                    'usage': jsonable_encoder(self._task_state.llm_result.usage)
-                }
+                extras = {"usage": jsonable_encoder(self._task_state.llm_result.usage)}
                 if self._task_state.metadata:
-                    extras['metadata'] = self._task_state.metadata
+                    extras["metadata"] = self._task_state.metadata
 
                 if self._conversation.mode == AppMode.COMPLETION.value:
                     response = CompletionAppBlockingResponse(
@@ -164,8 +156,8 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                             message_id=self._message.id,
                             answer=self._task_state.llm_result.message.content,
                             created_at=int(self._message.created_at.timestamp()),
-                            **extras
-                        )
+                            **extras,
+                        ),
                     )
                 else:
                     response = ChatbotAppBlockingResponse(
@@ -177,18 +169,19 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                             message_id=self._message.id,
                             answer=self._task_state.llm_result.message.content,
                             created_at=int(self._message.created_at.timestamp()),
-                            **extras
-                        )
+                            **extras,
+                        ),
                     )
 
                 return response
             else:
                 continue
 
-        raise Exception('Queue listening stopped unexpectedly.')
+        raise Exception("Queue listening stopped unexpectedly.")
 
-    def _to_stream_response(self, generator: Generator[StreamResponse, None, None]) \
-            -> Generator[Union[ChatbotAppStreamResponse, CompletionAppStreamResponse], None, None]:
+    def _to_stream_response(
+        self, generator: Generator[StreamResponse, None, None]
+    ) -> Generator[Union[ChatbotAppStreamResponse, CompletionAppStreamResponse], None, None]:
         """
         To stream response.
         :return:
@@ -198,37 +191,41 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                 yield CompletionAppStreamResponse(
                     message_id=self._message.id,
                     created_at=int(self._message.created_at.timestamp()),
-                    stream_response=stream_response
+                    stream_response=stream_response,
                 )
             else:
                 yield ChatbotAppStreamResponse(
                     conversation_id=self._conversation.id,
                     message_id=self._message.id,
                     created_at=int(self._message.created_at.timestamp()),
-                    stream_response=stream_response
+                    stream_response=stream_response,
                 )
 
-    def _listenAudioMsg(self, publisher, task_id: str):
+    def _listen_audio_msg(self, publisher, task_id: str):
         if publisher is None:
             return None
-        audio_msg: AudioTrunk = publisher.checkAndGetAudio()
+        audio_msg: AudioTrunk = publisher.check_and_get_audio()
         if audio_msg and audio_msg.status != "finish":
             # audio_str = audio_msg.audio.decode('utf-8', errors='ignore')
             return MessageAudioStreamResponse(audio=audio_msg.audio, task_id=task_id)
         return None
 
-    def _wrapper_process_stream_response(self, trace_manager: Optional[TraceQueueManager] = None) -> \
-            Generator[StreamResponse, None, None]:
-
+    def _wrapper_process_stream_response(
+        self, trace_manager: Optional[TraceQueueManager] = None
+    ) -> Generator[StreamResponse, None, None]:
         tenant_id = self._application_generate_entity.app_config.tenant_id
         task_id = self._application_generate_entity.task_id
         publisher = None
-        text_to_speech_dict = self._app_config.app_model_config_dict.get('text_to_speech')
-        if text_to_speech_dict and text_to_speech_dict.get('autoPlay') == 'enabled' and text_to_speech_dict.get('enabled'):
-            publisher = AppGeneratorTTSPublisher(tenant_id, text_to_speech_dict.get('voice', None))
+        text_to_speech_dict = self._app_config.app_model_config_dict.get("text_to_speech")
+        if (
+            text_to_speech_dict
+            and text_to_speech_dict.get("autoPlay") == "enabled"
+            and text_to_speech_dict.get("enabled")
+        ):
+            publisher = AppGeneratorTTSPublisher(tenant_id, text_to_speech_dict.get("voice", None))
         for response in self._process_stream_response(publisher=publisher, trace_manager=trace_manager):
             while True:
-                audio_response = self._listenAudioMsg(publisher, task_id)
+                audio_response = self._listen_audio_msg(publisher, task_id)
                 if audio_response:
                     yield audio_response
                 else:
@@ -240,7 +237,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         while (time.time() - start_listener_time) < TTS_AUTO_PLAY_TIMEOUT:
             if publisher is None:
                 break
-            audio = publisher.checkAndGetAudio()
+            audio = publisher.check_and_get_audio()
             if audio is None:
                 # release cpu
                 # sleep 20 ms ( 40ms => 1280 byte audio file,20ms => 640 byte audio file)
@@ -250,14 +247,11 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                 break
             else:
                 start_listener_time = time.time()
-                yield MessageAudioStreamResponse(audio=audio.audio,
-                                                 task_id=task_id)
-        yield MessageAudioEndStreamResponse(audio='', task_id=task_id)
+                yield MessageAudioStreamResponse(audio=audio.audio, task_id=task_id)
+        yield MessageAudioEndStreamResponse(audio="", task_id=task_id)
 
     def _process_stream_response(
-            self,
-            publisher: AppGeneratorTTSPublisher,
-            trace_manager: Optional[TraceQueueManager] = None
+        self, publisher: AppGeneratorTTSPublisher, trace_manager: Optional[TraceQueueManager] = None
     ) -> Generator[StreamResponse, None, None]:
         """
         Process stream response.
@@ -333,9 +327,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         if self._conversation_name_generate_thread:
             self._conversation_name_generate_thread.join()
 
-    def _save_message(
-            self, trace_manager: Optional[TraceQueueManager] = None
-    ) -> None:
+    def _save_message(self, trace_manager: Optional[TraceQueueManager] = None) -> None:
         """
         Save message.
         :return:
@@ -347,31 +339,32 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         self._conversation = db.session.query(Conversation).filter(Conversation.id == self._conversation.id).first()
 
         self._message.message = PromptMessageUtil.prompt_messages_to_prompt_for_saving(
-            self._model_config.mode,
-            self._task_state.llm_result.prompt_messages
+            self._model_config.mode, self._task_state.llm_result.prompt_messages
         )
         self._message.message_tokens = usage.prompt_tokens
         self._message.message_unit_price = usage.prompt_unit_price
         self._message.message_price_unit = usage.prompt_price_unit
-        self._message.answer = PromptTemplateParser.remove_template_variables(llm_result.message.content.strip()) \
-            if llm_result.message.content else ''
+        self._message.answer = (
+            PromptTemplateParser.remove_template_variables(llm_result.message.content.strip())
+            if llm_result.message.content
+            else ""
+        )
         self._message.answer_tokens = usage.completion_tokens
         self._message.answer_unit_price = usage.completion_unit_price
         self._message.answer_price_unit = usage.completion_price_unit
         self._message.provider_response_latency = time.perf_counter() - self._start_at
         self._message.total_price = usage.total_price
         self._message.currency = usage.currency
-        self._message.message_metadata = json.dumps(jsonable_encoder(self._task_state.metadata)) \
-            if self._task_state.metadata else None
+        self._message.message_metadata = (
+            json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
+        )
 
         db.session.commit()
 
         if trace_manager:
             trace_manager.add_trace_task(
                 TraceTask(
-                    TraceTaskName.MESSAGE_TRACE,
-                    conversation_id=self._conversation.id,
-                    message_id=self._message.id
+                    TraceTaskName.MESSAGE_TRACE, conversation_id=self._conversation.id, message_id=self._message.id
                 )
             )
 
@@ -379,11 +372,9 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
             self._message,
             application_generate_entity=self._application_generate_entity,
             conversation=self._conversation,
-            is_first_message=self._application_generate_entity.app_config.app_mode in [
-                AppMode.AGENT_CHAT,
-                AppMode.CHAT
-            ] and self._application_generate_entity.conversation_id is None,
-            extras=self._application_generate_entity.extras
+            is_first_message=self._application_generate_entity.app_config.app_mode in {AppMode.AGENT_CHAT, AppMode.CHAT}
+            and self._application_generate_entity.conversation_id is None,
+            extras=self._application_generate_entity.extras,
         )
 
     def _handle_stop(self, event: QueueStopEvent) -> None:
@@ -395,22 +386,17 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         model = model_config.model
 
         model_instance = ModelInstance(
-            provider_model_bundle=model_config.provider_model_bundle,
-            model=model_config.model
+            provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
         )
 
         # calculate num tokens
         prompt_tokens = 0
         if event.stopped_by != QueueStopEvent.StopBy.ANNOTATION_REPLY:
-            prompt_tokens = model_instance.get_llm_num_tokens(
-                self._task_state.llm_result.prompt_messages
-            )
+            prompt_tokens = model_instance.get_llm_num_tokens(self._task_state.llm_result.prompt_messages)
 
         completion_tokens = 0
         if event.stopped_by == QueueStopEvent.StopBy.USER_MANUAL:
-            completion_tokens = model_instance.get_llm_num_tokens(
-                [self._task_state.llm_result.message]
-            )
+            completion_tokens = model_instance.get_llm_num_tokens([self._task_state.llm_result.message])
 
         credentials = model_config.credentials
 
@@ -418,10 +404,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         model_type_instance = model_config.provider_model_bundle.model_type_instance
         model_type_instance = cast(LargeLanguageModel, model_type_instance)
         self._task_state.llm_result.usage = model_type_instance._calc_response_usage(
-            model,
-            credentials,
-            prompt_tokens,
-            completion_tokens
+            model, credentials, prompt_tokens, completion_tokens
         )
 
     def _message_end_to_stream_response(self) -> MessageEndStreamResponse:
@@ -429,16 +412,14 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         Message end to stream response.
         :return:
         """
-        self._task_state.metadata['usage'] = jsonable_encoder(self._task_state.llm_result.usage)
+        self._task_state.metadata["usage"] = jsonable_encoder(self._task_state.llm_result.usage)
 
         extras = {}
         if self._task_state.metadata:
-            extras['metadata'] = self._task_state.metadata
+            extras["metadata"] = self._task_state.metadata
 
         return MessageEndStreamResponse(
-            task_id=self._application_generate_entity.task_id,
-            id=self._message.id,
-            **extras
+            task_id=self._application_generate_entity.task_id, id=self._message.id, **extras
         )
 
     def _agent_message_to_stream_response(self, answer: str, message_id: str) -> AgentMessageStreamResponse:
@@ -449,9 +430,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         :return:
         """
         return AgentMessageStreamResponse(
-            task_id=self._application_generate_entity.task_id,
-            id=message_id,
-            answer=answer
+            task_id=self._application_generate_entity.task_id, id=message_id, answer=answer
         )
 
     def _agent_thought_to_stream_response(self, event: QueueAgentThoughtEvent) -> Optional[AgentThoughtStreamResponse]:
@@ -461,9 +440,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         :return:
         """
         agent_thought: MessageAgentThought = (
-            db.session.query(MessageAgentThought)
-            .filter(MessageAgentThought.id == event.agent_thought_id)
-            .first()
+            db.session.query(MessageAgentThought).filter(MessageAgentThought.id == event.agent_thought_id).first()
         )
         db.session.refresh(agent_thought)
         db.session.close()
@@ -478,7 +455,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                 tool=agent_thought.tool,
                 tool_labels=agent_thought.tool_labels,
                 tool_input=agent_thought.tool_input,
-                message_files=agent_thought.files
+                message_files=agent_thought.files,
             )
 
         return None
@@ -500,15 +477,15 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                             prompt_messages=self._task_state.llm_result.prompt_messages,
                             delta=LLMResultChunkDelta(
                                 index=0,
-                                message=AssistantPromptMessage(content=self._task_state.llm_result.message.content)
-                            )
+                                message=AssistantPromptMessage(content=self._task_state.llm_result.message.content),
+                            ),
                         )
-                    ), PublishFrom.TASK_PIPELINE
+                    ),
+                    PublishFrom.TASK_PIPELINE,
                 )
 
                 self._queue_manager.publish(
-                    QueueStopEvent(stopped_by=QueueStopEvent.StopBy.OUTPUT_MODERATION),
-                    PublishFrom.TASK_PIPELINE
+                    QueueStopEvent(stopped_by=QueueStopEvent.StopBy.OUTPUT_MODERATION), PublishFrom.TASK_PIPELINE
                 )
                 return True
             else:
diff --git a/api/core/app/task_pipeline/message_cycle_manage.py b/api/core/app/task_pipeline/message_cycle_manage.py
index 76c50809cf..5872e00740 100644
--- a/api/core/app/task_pipeline/message_cycle_manage.py
+++ b/api/core/app/task_pipeline/message_cycle_manage.py
@@ -8,7 +8,6 @@ from core.app.entities.app_invoke_entities import (
     AgentChatAppGenerateEntity,
     ChatAppGenerateEntity,
     CompletionAppGenerateEntity,
-    InvokeFrom,
 )
 from core.app.entities.queue_entities import (
     QueueAnnotationReplyEvent,
@@ -16,11 +15,11 @@ from core.app.entities.queue_entities import (
     QueueRetrieverResourcesEvent,
 )
 from core.app.entities.task_entities import (
-    AdvancedChatTaskState,
     EasyUITaskState,
     MessageFileStreamResponse,
     MessageReplaceStreamResponse,
     MessageStreamResponse,
+    WorkflowTaskState,
 )
 from core.llm_generator.llm_generator import LLMGenerator
 from core.tools.tool_file_manager import ToolFileManager
@@ -31,12 +30,9 @@ from services.annotation_service import AppAnnotationService
 
 class MessageCycleManage:
     _application_generate_entity: Union[
-        ChatAppGenerateEntity,
-        CompletionAppGenerateEntity,
-        AgentChatAppGenerateEntity,
-        AdvancedChatAppGenerateEntity
+        ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity, AdvancedChatAppGenerateEntity
     ]
-    _task_state: Union[EasyUITaskState, AdvancedChatTaskState]
+    _task_state: Union[EasyUITaskState, WorkflowTaskState]
 
     def _generate_conversation_name(self, conversation: Conversation, query: str) -> Optional[Thread]:
         """
@@ -45,17 +41,23 @@ class MessageCycleManage:
         :param query: query
         :return: thread
         """
+        if isinstance(self._application_generate_entity, CompletionAppGenerateEntity):
+            return None
+
         is_first_message = self._application_generate_entity.conversation_id is None
         extras = self._application_generate_entity.extras
-        auto_generate_conversation_name = extras.get('auto_generate_conversation_name', True)
+        auto_generate_conversation_name = extras.get("auto_generate_conversation_name", True)
 
         if auto_generate_conversation_name and is_first_message:
             # start generate thread
-            thread = Thread(target=self._generate_conversation_name_worker, kwargs={
-                'flask_app': current_app._get_current_object(),
-                'conversation_id': conversation.id,
-                'query': query
-            })
+            thread = Thread(
+                target=self._generate_conversation_name_worker,
+                kwargs={
+                    "flask_app": current_app._get_current_object(),  # type: ignore
+                    "conversation_id": conversation.id,
+                    "query": query,
+                },
+            )
 
             thread.start()
 
@@ -63,17 +65,13 @@ class MessageCycleManage:
 
         return None
 
-    def _generate_conversation_name_worker(self,
-                                           flask_app: Flask,
-                                           conversation_id: str,
-                                           query: str):
+    def _generate_conversation_name_worker(self, flask_app: Flask, conversation_id: str, query: str):
         with flask_app.app_context():
             # get conversation and message
-            conversation = (
-                db.session.query(Conversation)
-                .filter(Conversation.id == conversation_id)
-                .first()
-            )
+            conversation = db.session.query(Conversation).filter(Conversation.id == conversation_id).first()
+
+            if not conversation:
+                return
 
             if conversation.mode != AppMode.COMPLETION.value:
                 app_model = conversation.app
@@ -100,12 +98,9 @@ class MessageCycleManage:
         annotation = AppAnnotationService.get_annotation_by_id(event.message_annotation_id)
         if annotation:
             account = annotation.account
-            self._task_state.metadata['annotation_reply'] = {
-                'id': annotation.id,
-                'account': {
-                    'id': annotation.account_id,
-                    'name': account.name if account else 'Dify user'
-                }
+            self._task_state.metadata["annotation_reply"] = {
+                "id": annotation.id,
+                "account": {"id": annotation.account_id, "name": account.name if account else "Dify user"},
             }
 
             return annotation
@@ -119,28 +114,7 @@ class MessageCycleManage:
         :return:
         """
         if self._application_generate_entity.app_config.additional_features.show_retrieve_source:
-            self._task_state.metadata['retriever_resources'] = event.retriever_resources
-
-    def _get_response_metadata(self) -> dict:
-        """
-        Get response metadata by invoke from.
-        :return:
-        """
-        metadata = {}
-
-        # show_retrieve_source
-        if 'retriever_resources' in self._task_state.metadata:
-            metadata['retriever_resources'] = self._task_state.metadata['retriever_resources']
-
-        # show annotation reply
-        if 'annotation_reply' in self._task_state.metadata:
-            metadata['annotation_reply'] = self._task_state.metadata['annotation_reply']
-
-        # show usage
-        if self._application_generate_entity.invoke_from in [InvokeFrom.DEBUGGER, InvokeFrom.SERVICE_API]:
-            metadata['usage'] = self._task_state.metadata['usage']
-
-        return metadata
+            self._task_state.metadata["retriever_resources"] = event.retriever_resources
 
     def _message_file_to_stream_response(self, event: QueueMessageFileEvent) -> Optional[MessageFileStreamResponse]:
         """
@@ -148,27 +122,23 @@ class MessageCycleManage:
         :param event: event
         :return:
         """
-        message_file: MessageFile = (
-            db.session.query(MessageFile)
-            .filter(MessageFile.id == event.message_file_id)
-            .first()
-        )
+        message_file = db.session.query(MessageFile).filter(MessageFile.id == event.message_file_id).first()
 
         if message_file:
             # get tool file id
-            tool_file_id = message_file.url.split('/')[-1]
+            tool_file_id = message_file.url.split("/")[-1]
             # trim extension
-            tool_file_id = tool_file_id.split('.')[0]
+            tool_file_id = tool_file_id.split(".")[0]
 
             # get extension
-            if '.' in message_file.url:
+            if "." in message_file.url:
                 extension = f'.{message_file.url.split(".")[-1]}'
                 if len(extension) > 10:
-                    extension = '.bin'
+                    extension = ".bin"
             else:
-                extension = '.bin'
+                extension = ".bin"
             # add sign url to local file
-            if message_file.url.startswith('http'):
+            if message_file.url.startswith("http"):
                 url = message_file.url
             else:
                 url = ToolFileManager.sign_file(tool_file_id=tool_file_id, extension=extension)
@@ -177,13 +147,15 @@ class MessageCycleManage:
                 task_id=self._application_generate_entity.task_id,
                 id=message_file.id,
                 type=message_file.type,
-                belongs_to=message_file.belongs_to or 'user',
-                url=url
+                belongs_to=message_file.belongs_to or "user",
+                url=url,
             )
 
         return None
 
-    def _message_to_stream_response(self, answer: str, message_id: str) -> MessageStreamResponse:
+    def _message_to_stream_response(
+        self, answer: str, message_id: str, from_variable_selector: Optional[list[str]] = None
+    ) -> MessageStreamResponse:
         """
         Message to stream response.
         :param answer: answer
@@ -193,7 +165,8 @@ class MessageCycleManage:
         return MessageStreamResponse(
             task_id=self._application_generate_entity.task_id,
             id=message_id,
-            answer=answer
+            answer=answer,
+            from_variable_selector=from_variable_selector,
         )
 
     def _message_replace_to_stream_response(self, answer: str) -> MessageReplaceStreamResponse:
@@ -202,7 +175,4 @@ class MessageCycleManage:
         :param answer: answer
         :return:
         """
-        return MessageReplaceStreamResponse(
-            task_id=self._application_generate_entity.task_id,
-            answer=answer
-        )
+        return MessageReplaceStreamResponse(task_id=self._application_generate_entity.task_id, answer=answer)
diff --git a/api/core/app/task_pipeline/workflow_cycle_manage.py b/api/core/app/task_pipeline/workflow_cycle_manage.py
index 4935c43ac4..f10189798f 100644
--- a/api/core/app/task_pipeline/workflow_cycle_manage.py
+++ b/api/core/app/task_pipeline/workflow_cycle_manage.py
@@ -1,33 +1,41 @@
 import json
 import time
 from datetime import datetime, timezone
-from typing import Optional, Union, cast
+from typing import Any, Optional, Union, cast
 
-from core.app.entities.app_invoke_entities import InvokeFrom
+from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.queue_entities import (
+    QueueIterationCompletedEvent,
+    QueueIterationNextEvent,
+    QueueIterationStartEvent,
     QueueNodeFailedEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
-    QueueStopEvent,
-    QueueWorkflowFailedEvent,
-    QueueWorkflowSucceededEvent,
+    QueueParallelBranchRunFailedEvent,
+    QueueParallelBranchRunStartedEvent,
+    QueueParallelBranchRunSucceededEvent,
 )
 from core.app.entities.task_entities import (
-    NodeExecutionInfo,
+    IterationNodeCompletedStreamResponse,
+    IterationNodeNextStreamResponse,
+    IterationNodeStartStreamResponse,
     NodeFinishStreamResponse,
     NodeStartStreamResponse,
+    ParallelBranchFinishedStreamResponse,
+    ParallelBranchStartStreamResponse,
     WorkflowFinishStreamResponse,
     WorkflowStartStreamResponse,
+    WorkflowTaskState,
 )
-from core.app.task_pipeline.workflow_iteration_cycle_manage import WorkflowIterationCycleManage
 from core.file.file_obj import FileVar
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.ops.entities.trace_entity import TraceTaskName
 from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
 from core.tools.tool_manager import ToolManager
-from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeType
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.enums import SystemVariableKey
 from core.workflow.nodes.tool.entities import ToolNodeData
-from core.workflow.workflow_engine_manager import WorkflowEngineManager
+from core.workflow.workflow_entry import WorkflowEntry
 from extensions.ext_database import db
 from models.account import Account
 from models.model import EndUser
@@ -41,54 +49,56 @@ from models.workflow import (
     WorkflowRunStatus,
     WorkflowRunTriggeredFrom,
 )
-from services.workflow_service import WorkflowService
 
 
-class WorkflowCycleManage(WorkflowIterationCycleManage):
-    def _init_workflow_run(self, workflow: Workflow,
-                           triggered_from: WorkflowRunTriggeredFrom,
-                           user: Union[Account, EndUser],
-                           user_inputs: dict,
-                           system_inputs: Optional[dict] = None) -> WorkflowRun:
-        """
-        Init workflow run
-        :param workflow: Workflow instance
-        :param triggered_from: triggered from
-        :param user: account or end user
-        :param user_inputs: user variables inputs
-        :param system_inputs: system inputs, like: query, files
-        :return:
-        """
-        max_sequence = db.session.query(db.func.max(WorkflowRun.sequence_number)) \
-                           .filter(WorkflowRun.tenant_id == workflow.tenant_id) \
-                           .filter(WorkflowRun.app_id == workflow.app_id) \
-                           .scalar() or 0
+class WorkflowCycleManage:
+    _application_generate_entity: Union[AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity]
+    _workflow: Workflow
+    _user: Union[Account, EndUser]
+    _task_state: WorkflowTaskState
+    _workflow_system_variables: dict[SystemVariableKey, Any]
+
+    def _handle_workflow_run_start(self) -> WorkflowRun:
+        max_sequence = (
+            db.session.query(db.func.max(WorkflowRun.sequence_number))
+            .filter(WorkflowRun.tenant_id == self._workflow.tenant_id)
+            .filter(WorkflowRun.app_id == self._workflow.app_id)
+            .scalar()
+            or 0
+        )
         new_sequence_number = max_sequence + 1
 
-        inputs = {**user_inputs}
-        for key, value in (system_inputs or {}).items():
-            if key.value == 'conversation':
+        inputs = {**self._application_generate_entity.inputs}
+        for key, value in (self._workflow_system_variables or {}).items():
+            if key.value == "conversation":
                 continue
 
-            inputs[f'sys.{key.value}'] = value
-        inputs = WorkflowEngineManager.handle_special_values(inputs)
+            inputs[f"sys.{key.value}"] = value
+
+        inputs = WorkflowEntry.handle_special_values(inputs)
+
+        triggered_from = (
+            WorkflowRunTriggeredFrom.DEBUGGING
+            if self._application_generate_entity.invoke_from == InvokeFrom.DEBUGGER
+            else WorkflowRunTriggeredFrom.APP_RUN
+        )
 
         # init workflow run
-        workflow_run = WorkflowRun(
-            tenant_id=workflow.tenant_id,
-            app_id=workflow.app_id,
-            sequence_number=new_sequence_number,
-            workflow_id=workflow.id,
-            type=workflow.type,
-            triggered_from=triggered_from.value,
-            version=workflow.version,
-            graph=workflow.graph,
-            inputs=json.dumps(inputs),
-            status=WorkflowRunStatus.RUNNING.value,
-            created_by_role=(CreatedByRole.ACCOUNT.value
-                             if isinstance(user, Account) else CreatedByRole.END_USER.value),
-            created_by=user.id
+        workflow_run = WorkflowRun()
+        workflow_run.tenant_id = self._workflow.tenant_id
+        workflow_run.app_id = self._workflow.app_id
+        workflow_run.sequence_number = new_sequence_number
+        workflow_run.workflow_id = self._workflow.id
+        workflow_run.type = self._workflow.type
+        workflow_run.triggered_from = triggered_from.value
+        workflow_run.version = self._workflow.version
+        workflow_run.graph = self._workflow.graph
+        workflow_run.inputs = json.dumps(inputs)
+        workflow_run.status = WorkflowRunStatus.RUNNING.value
+        workflow_run.created_by_role = (
+            CreatedByRole.ACCOUNT.value if isinstance(self._user, Account) else CreatedByRole.END_USER.value
         )
+        workflow_run.created_by = self._user.id
 
         db.session.add(workflow_run)
         db.session.commit()
@@ -97,33 +107,37 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
 
         return workflow_run
 
-    def _workflow_run_success(
-        self, workflow_run: WorkflowRun,
+    def _handle_workflow_run_success(
+        self,
+        workflow_run: WorkflowRun,
+        start_at: float,
         total_tokens: int,
         total_steps: int,
         outputs: Optional[str] = None,
         conversation_id: Optional[str] = None,
-        trace_manager: Optional[TraceQueueManager] = None
+        trace_manager: Optional[TraceQueueManager] = None,
     ) -> WorkflowRun:
         """
         Workflow run success
         :param workflow_run: workflow run
+        :param start_at: start time
         :param total_tokens: total tokens
         :param total_steps: total steps
         :param outputs: outputs
         :param conversation_id: conversation id
         :return:
         """
+        workflow_run = self._refetch_workflow_run(workflow_run.id)
+
         workflow_run.status = WorkflowRunStatus.SUCCEEDED.value
         workflow_run.outputs = outputs
-        workflow_run.elapsed_time = WorkflowService.get_elapsed_time(workflow_run_id=workflow_run.id)
+        workflow_run.elapsed_time = time.perf_counter() - start_at
         workflow_run.total_tokens = total_tokens
         workflow_run.total_steps = total_steps
         workflow_run.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
 
         db.session.commit()
         db.session.refresh(workflow_run)
-        db.session.close()
 
         if trace_manager:
             trace_manager.add_trace_task(
@@ -135,34 +149,64 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
                 )
             )
 
+        db.session.close()
+
         return workflow_run
 
-    def _workflow_run_failed(
-        self, workflow_run: WorkflowRun,
+    def _handle_workflow_run_failed(
+        self,
+        workflow_run: WorkflowRun,
+        start_at: float,
         total_tokens: int,
         total_steps: int,
         status: WorkflowRunStatus,
         error: str,
         conversation_id: Optional[str] = None,
-        trace_manager: Optional[TraceQueueManager] = None
+        trace_manager: Optional[TraceQueueManager] = None,
     ) -> WorkflowRun:
         """
         Workflow run failed
         :param workflow_run: workflow run
+        :param start_at: start time
         :param total_tokens: total tokens
         :param total_steps: total steps
         :param status: status
         :param error: error message
         :return:
         """
+        workflow_run = self._refetch_workflow_run(workflow_run.id)
+
         workflow_run.status = status.value
         workflow_run.error = error
-        workflow_run.elapsed_time = WorkflowService.get_elapsed_time(workflow_run_id=workflow_run.id)
+        workflow_run.elapsed_time = time.perf_counter() - start_at
         workflow_run.total_tokens = total_tokens
         workflow_run.total_steps = total_steps
         workflow_run.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
 
         db.session.commit()
+
+        running_workflow_node_executions = (
+            db.session.query(WorkflowNodeExecution)
+            .filter(
+                WorkflowNodeExecution.tenant_id == workflow_run.tenant_id,
+                WorkflowNodeExecution.app_id == workflow_run.app_id,
+                WorkflowNodeExecution.workflow_id == workflow_run.workflow_id,
+                WorkflowNodeExecution.triggered_from == WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value,
+                WorkflowNodeExecution.workflow_run_id == workflow_run.id,
+                WorkflowNodeExecution.status == WorkflowNodeExecutionStatus.RUNNING.value,
+            )
+            .all()
+        )
+
+        for workflow_node_execution in running_workflow_node_executions:
+            workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
+            workflow_node_execution.error = error
+            workflow_node_execution.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
+            workflow_node_execution.elapsed_time = (
+                workflow_node_execution.finished_at - workflow_node_execution.created_at
+            ).total_seconds()
+            db.session.commit()
+
         db.session.refresh(workflow_run)
         db.session.close()
 
@@ -178,39 +222,26 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
 
         return workflow_run
 
-    def _init_node_execution_from_workflow_run(self, workflow_run: WorkflowRun,
-                                               node_id: str,
-                                               node_type: NodeType,
-                                               node_title: str,
-                                               node_run_index: int = 1,
-                                               predecessor_node_id: Optional[str] = None) -> WorkflowNodeExecution:
-        """
-        Init workflow node execution from workflow run
-        :param workflow_run: workflow run
-        :param node_id: node id
-        :param node_type: node type
-        :param node_title: node title
-        :param node_run_index: run index
-        :param predecessor_node_id: predecessor node id if exists
-        :return:
-        """
+    def _handle_node_execution_start(
+        self, workflow_run: WorkflowRun, event: QueueNodeStartedEvent
+    ) -> WorkflowNodeExecution:
         # init workflow node execution
-        workflow_node_execution = WorkflowNodeExecution(
-            tenant_id=workflow_run.tenant_id,
-            app_id=workflow_run.app_id,
-            workflow_id=workflow_run.workflow_id,
-            triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value,
-            workflow_run_id=workflow_run.id,
-            predecessor_node_id=predecessor_node_id,
-            index=node_run_index,
-            node_id=node_id,
-            node_type=node_type.value,
-            title=node_title,
-            status=WorkflowNodeExecutionStatus.RUNNING.value,
-            created_by_role=workflow_run.created_by_role,
-            created_by=workflow_run.created_by,
-            created_at=datetime.now(timezone.utc).replace(tzinfo=None)
-        )
+        workflow_node_execution = WorkflowNodeExecution()
+        workflow_node_execution.tenant_id = workflow_run.tenant_id
+        workflow_node_execution.app_id = workflow_run.app_id
+        workflow_node_execution.workflow_id = workflow_run.workflow_id
+        workflow_node_execution.triggered_from = WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value
+        workflow_node_execution.workflow_run_id = workflow_run.id
+        workflow_node_execution.predecessor_node_id = event.predecessor_node_id
+        workflow_node_execution.index = event.node_run_index
+        workflow_node_execution.node_execution_id = event.node_execution_id
+        workflow_node_execution.node_id = event.node_id
+        workflow_node_execution.node_type = event.node_type.value
+        workflow_node_execution.title = event.node_data.title
+        workflow_node_execution.status = WorkflowNodeExecutionStatus.RUNNING.value
+        workflow_node_execution.created_by_role = workflow_run.created_by_role
+        workflow_node_execution.created_by = workflow_run.created_by
+        workflow_node_execution.created_at = datetime.now(timezone.utc).replace(tzinfo=None)
 
         db.session.add(workflow_node_execution)
         db.session.commit()
@@ -219,33 +250,26 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
 
         return workflow_node_execution
 
-    def _workflow_node_execution_success(self, workflow_node_execution: WorkflowNodeExecution,
-                                         start_at: float,
-                                         inputs: Optional[dict] = None,
-                                         process_data: Optional[dict] = None,
-                                         outputs: Optional[dict] = None,
-                                         execution_metadata: Optional[dict] = None) -> WorkflowNodeExecution:
+    def _handle_workflow_node_execution_success(self, event: QueueNodeSucceededEvent) -> WorkflowNodeExecution:
         """
         Workflow node execution success
-        :param workflow_node_execution: workflow node execution
-        :param start_at: start time
-        :param inputs: inputs
-        :param process_data: process data
-        :param outputs: outputs
-        :param execution_metadata: execution metadata
+        :param event: queue node succeeded event
         :return:
         """
-        inputs = WorkflowEngineManager.handle_special_values(inputs)
-        outputs = WorkflowEngineManager.handle_special_values(outputs)
+        workflow_node_execution = self._refetch_workflow_node_execution(event.node_execution_id)
+
+        inputs = WorkflowEntry.handle_special_values(event.inputs)
+        outputs = WorkflowEntry.handle_special_values(event.outputs)
 
         workflow_node_execution.status = WorkflowNodeExecutionStatus.SUCCEEDED.value
-        workflow_node_execution.elapsed_time = time.perf_counter() - start_at
         workflow_node_execution.inputs = json.dumps(inputs) if inputs else None
-        workflow_node_execution.process_data = json.dumps(process_data) if process_data else None
+        workflow_node_execution.process_data = json.dumps(event.process_data) if event.process_data else None
         workflow_node_execution.outputs = json.dumps(outputs) if outputs else None
-        workflow_node_execution.execution_metadata = json.dumps(jsonable_encoder(execution_metadata)) \
-            if execution_metadata else None
+        workflow_node_execution.execution_metadata = (
+            json.dumps(jsonable_encoder(event.execution_metadata)) if event.execution_metadata else None
+        )
         workflow_node_execution.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
+        workflow_node_execution.elapsed_time = (workflow_node_execution.finished_at - event.start_at).total_seconds()
 
         db.session.commit()
         db.session.refresh(workflow_node_execution)
@@ -253,33 +277,24 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
 
         return workflow_node_execution
 
-    def _workflow_node_execution_failed(self, workflow_node_execution: WorkflowNodeExecution,
-                                        start_at: float,
-                                        error: str,
-                                        inputs: Optional[dict] = None,
-                                        process_data: Optional[dict] = None,
-                                        outputs: Optional[dict] = None,
-                                        execution_metadata: Optional[dict] = None
-                                        ) -> WorkflowNodeExecution:
+    def _handle_workflow_node_execution_failed(self, event: QueueNodeFailedEvent) -> WorkflowNodeExecution:
         """
         Workflow node execution failed
-        :param workflow_node_execution: workflow node execution
-        :param start_at: start time
-        :param error: error message
+        :param event: queue node failed event
         :return:
         """
-        inputs = WorkflowEngineManager.handle_special_values(inputs)
-        outputs = WorkflowEngineManager.handle_special_values(outputs)
+        workflow_node_execution = self._refetch_workflow_node_execution(event.node_execution_id)
+
+        inputs = WorkflowEntry.handle_special_values(event.inputs)
+        outputs = WorkflowEntry.handle_special_values(event.outputs)
 
         workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
-        workflow_node_execution.error = error
-        workflow_node_execution.elapsed_time = time.perf_counter() - start_at
+        workflow_node_execution.error = event.error
         workflow_node_execution.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
         workflow_node_execution.inputs = json.dumps(inputs) if inputs else None
-        workflow_node_execution.process_data = json.dumps(process_data) if process_data else None
+        workflow_node_execution.process_data = json.dumps(event.process_data) if event.process_data else None
         workflow_node_execution.outputs = json.dumps(outputs) if outputs else None
-        workflow_node_execution.execution_metadata = json.dumps(jsonable_encoder(execution_metadata)) \
-            if execution_metadata else None
+        workflow_node_execution.elapsed_time = (workflow_node_execution.finished_at - event.start_at).total_seconds()
 
         db.session.commit()
         db.session.refresh(workflow_node_execution)
@@ -287,8 +302,13 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
 
         return workflow_node_execution
 
-    def _workflow_start_to_stream_response(self, task_id: str,
-                                           workflow_run: WorkflowRun) -> WorkflowStartStreamResponse:
+    #################################################
+    #             to stream responses               #
+    #################################################
+
+    def _workflow_start_to_stream_response(
+        self, task_id: str, workflow_run: WorkflowRun
+    ) -> WorkflowStartStreamResponse:
         """
         Workflow start to stream response.
         :param task_id: task id
@@ -302,13 +322,14 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
                 id=workflow_run.id,
                 workflow_id=workflow_run.workflow_id,
                 sequence_number=workflow_run.sequence_number,
-                inputs=workflow_run.inputs_dict,
-                created_at=int(workflow_run.created_at.timestamp())
-            )
+                inputs=workflow_run.inputs_dict or {},
+                created_at=int(workflow_run.created_at.timestamp()),
+            ),
         )
 
-    def _workflow_finish_to_stream_response(self, task_id: str,
-                                            workflow_run: WorkflowRun) -> WorkflowFinishStreamResponse:
+    def _workflow_finish_to_stream_response(
+        self, task_id: str, workflow_run: WorkflowRun
+    ) -> WorkflowFinishStreamResponse:
         """
         Workflow finish to stream response.
         :param task_id: task id
@@ -348,14 +369,13 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
                 created_by=created_by,
                 created_at=int(workflow_run.created_at.timestamp()),
                 finished_at=int(workflow_run.finished_at.timestamp()),
-                files=self._fetch_files_from_node_outputs(workflow_run.outputs_dict)
-            )
+                files=self._fetch_files_from_node_outputs(workflow_run.outputs_dict or {}),
+            ),
         )
 
-    def _workflow_node_start_to_stream_response(self, event: QueueNodeStartedEvent,
-                                                task_id: str,
-                                                workflow_node_execution: WorkflowNodeExecution) \
-            -> NodeStartStreamResponse:
+    def _workflow_node_start_to_stream_response(
+        self, event: QueueNodeStartedEvent, task_id: str, workflow_node_execution: WorkflowNodeExecution
+    ) -> Optional[NodeStartStreamResponse]:
         """
         Workflow node start to stream response.
         :param event: queue node started event
@@ -363,6 +383,9 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
         :param workflow_node_execution: workflow node execution
         :return:
         """
+        if workflow_node_execution.node_type in {NodeType.ITERATION.value, NodeType.LOOP.value}:
+            return None
+
         response = NodeStartStreamResponse(
             task_id=task_id,
             workflow_run_id=workflow_node_execution.workflow_run_id,
@@ -374,29 +397,42 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
                 index=workflow_node_execution.index,
                 predecessor_node_id=workflow_node_execution.predecessor_node_id,
                 inputs=workflow_node_execution.inputs_dict,
-                created_at=int(workflow_node_execution.created_at.timestamp())
-            )
+                created_at=int(workflow_node_execution.created_at.timestamp()),
+                parallel_id=event.parallel_id,
+                parallel_start_node_id=event.parallel_start_node_id,
+                parent_parallel_id=event.parent_parallel_id,
+                parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                iteration_id=event.in_iteration_id,
+            ),
         )
 
         # extras logic
         if event.node_type == NodeType.TOOL:
             node_data = cast(ToolNodeData, event.node_data)
-            response.data.extras['icon'] = ToolManager.get_tool_icon(
+            response.data.extras["icon"] = ToolManager.get_tool_icon(
                 tenant_id=self._application_generate_entity.app_config.tenant_id,
                 provider_type=node_data.provider_type,
-                provider_id=node_data.provider_id
+                provider_id=node_data.provider_id,
             )
 
         return response
 
-    def _workflow_node_finish_to_stream_response(self, task_id: str, workflow_node_execution: WorkflowNodeExecution) \
-            -> NodeFinishStreamResponse:
+    def _workflow_node_finish_to_stream_response(
+        self,
+        event: QueueNodeSucceededEvent | QueueNodeFailedEvent,
+        task_id: str,
+        workflow_node_execution: WorkflowNodeExecution,
+    ) -> Optional[NodeFinishStreamResponse]:
         """
         Workflow node finish to stream response.
+        :param event: queue node succeeded or failed event
         :param task_id: task id
         :param workflow_node_execution: workflow node execution
         :return:
         """
+        if workflow_node_execution.node_type in {NodeType.ITERATION.value, NodeType.LOOP.value}:
+            return None
+
         return NodeFinishStreamResponse(
             task_id=task_id,
             workflow_run_id=workflow_node_execution.workflow_run_id,
@@ -416,181 +452,153 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
                 execution_metadata=workflow_node_execution.execution_metadata_dict,
                 created_at=int(workflow_node_execution.created_at.timestamp()),
                 finished_at=int(workflow_node_execution.finished_at.timestamp()),
-                files=self._fetch_files_from_node_outputs(workflow_node_execution.outputs_dict)
-            )
+                files=self._fetch_files_from_node_outputs(workflow_node_execution.outputs_dict or {}),
+                parallel_id=event.parallel_id,
+                parallel_start_node_id=event.parallel_start_node_id,
+                parent_parallel_id=event.parent_parallel_id,
+                parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                iteration_id=event.in_iteration_id,
+            ),
         )
 
-    def _handle_workflow_start(self) -> WorkflowRun:
-        self._task_state.start_at = time.perf_counter()
-
-        workflow_run = self._init_workflow_run(
-            workflow=self._workflow,
-            triggered_from=WorkflowRunTriggeredFrom.DEBUGGING
-            if self._application_generate_entity.invoke_from == InvokeFrom.DEBUGGER
-            else WorkflowRunTriggeredFrom.APP_RUN,
-            user=self._user,
-            user_inputs=self._application_generate_entity.inputs,
-            system_inputs=self._workflow_system_variables
+    def _workflow_parallel_branch_start_to_stream_response(
+        self, task_id: str, workflow_run: WorkflowRun, event: QueueParallelBranchRunStartedEvent
+    ) -> ParallelBranchStartStreamResponse:
+        """
+        Workflow parallel branch start to stream response
+        :param task_id: task id
+        :param workflow_run: workflow run
+        :param event: parallel branch run started event
+        :return:
+        """
+        return ParallelBranchStartStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_run.id,
+            data=ParallelBranchStartStreamResponse.Data(
+                parallel_id=event.parallel_id,
+                parallel_branch_id=event.parallel_start_node_id,
+                parent_parallel_id=event.parent_parallel_id,
+                parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                iteration_id=event.in_iteration_id,
+                created_at=int(time.time()),
+            ),
         )
 
-        self._task_state.workflow_run_id = workflow_run.id
-
-        db.session.close()
-
-        return workflow_run
-
-    def _handle_node_start(self, event: QueueNodeStartedEvent) -> WorkflowNodeExecution:
-        workflow_run = db.session.query(WorkflowRun).filter(WorkflowRun.id == self._task_state.workflow_run_id).first()
-        workflow_node_execution = self._init_node_execution_from_workflow_run(
-            workflow_run=workflow_run,
-            node_id=event.node_id,
-            node_type=event.node_type,
-            node_title=event.node_data.title,
-            node_run_index=event.node_run_index,
-            predecessor_node_id=event.predecessor_node_id
+    def _workflow_parallel_branch_finished_to_stream_response(
+        self,
+        task_id: str,
+        workflow_run: WorkflowRun,
+        event: QueueParallelBranchRunSucceededEvent | QueueParallelBranchRunFailedEvent,
+    ) -> ParallelBranchFinishedStreamResponse:
+        """
+        Workflow parallel branch finished to stream response
+        :param task_id: task id
+        :param workflow_run: workflow run
+        :param event: parallel branch run succeeded or failed event
+        :return:
+        """
+        return ParallelBranchFinishedStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_run.id,
+            data=ParallelBranchFinishedStreamResponse.Data(
+                parallel_id=event.parallel_id,
+                parallel_branch_id=event.parallel_start_node_id,
+                parent_parallel_id=event.parent_parallel_id,
+                parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                iteration_id=event.in_iteration_id,
+                status="succeeded" if isinstance(event, QueueParallelBranchRunSucceededEvent) else "failed",
+                error=event.error if isinstance(event, QueueParallelBranchRunFailedEvent) else None,
+                created_at=int(time.time()),
+            ),
         )
 
-        latest_node_execution_info = NodeExecutionInfo(
-            workflow_node_execution_id=workflow_node_execution.id,
-            node_type=event.node_type,
-            start_at=time.perf_counter()
+    def _workflow_iteration_start_to_stream_response(
+        self, task_id: str, workflow_run: WorkflowRun, event: QueueIterationStartEvent
+    ) -> IterationNodeStartStreamResponse:
+        """
+        Workflow iteration start to stream response
+        :param task_id: task id
+        :param workflow_run: workflow run
+        :param event: iteration start event
+        :return:
+        """
+        return IterationNodeStartStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_run.id,
+            data=IterationNodeStartStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_data.title,
+                created_at=int(time.time()),
+                extras={},
+                inputs=event.inputs or {},
+                metadata=event.metadata or {},
+                parallel_id=event.parallel_id,
+                parallel_start_node_id=event.parallel_start_node_id,
+            ),
         )
 
-        self._task_state.ran_node_execution_infos[event.node_id] = latest_node_execution_info
-        self._task_state.latest_node_execution_info = latest_node_execution_info
+    def _workflow_iteration_next_to_stream_response(
+        self, task_id: str, workflow_run: WorkflowRun, event: QueueIterationNextEvent
+    ) -> IterationNodeNextStreamResponse:
+        """
+        Workflow iteration next to stream response
+        :param task_id: task id
+        :param workflow_run: workflow run
+        :param event: iteration next event
+        :return:
+        """
+        return IterationNodeNextStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_run.id,
+            data=IterationNodeNextStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_data.title,
+                index=event.index,
+                pre_iteration_output=event.output,
+                created_at=int(time.time()),
+                extras={},
+                parallel_id=event.parallel_id,
+                parallel_start_node_id=event.parallel_start_node_id,
+            ),
+        )
 
-        self._task_state.total_steps += 1
-
-        db.session.close()
-
-        return workflow_node_execution
-
-    def _handle_node_finished(self, event: QueueNodeSucceededEvent | QueueNodeFailedEvent) -> WorkflowNodeExecution:
-        current_node_execution = self._task_state.ran_node_execution_infos[event.node_id]
-        workflow_node_execution = db.session.query(WorkflowNodeExecution).filter(
-            WorkflowNodeExecution.id == current_node_execution.workflow_node_execution_id).first()
-
-        execution_metadata = event.execution_metadata if isinstance(event, QueueNodeSucceededEvent) else None
-
-        if self._iteration_state and self._iteration_state.current_iterations:
-            if not execution_metadata:
-                execution_metadata = {}
-            current_iteration_data = None
-            for iteration_node_id in self._iteration_state.current_iterations:
-                data = self._iteration_state.current_iterations[iteration_node_id]
-                if data.parent_iteration_id == None:
-                    current_iteration_data = data
-                    break
-
-            if current_iteration_data:
-                execution_metadata[NodeRunMetadataKey.ITERATION_ID] = current_iteration_data.iteration_id
-                execution_metadata[NodeRunMetadataKey.ITERATION_INDEX] = current_iteration_data.current_index
-
-        if isinstance(event, QueueNodeSucceededEvent):
-            workflow_node_execution = self._workflow_node_execution_success(
-                workflow_node_execution=workflow_node_execution,
-                start_at=current_node_execution.start_at,
-                inputs=event.inputs,
-                process_data=event.process_data,
+    def _workflow_iteration_completed_to_stream_response(
+        self, task_id: str, workflow_run: WorkflowRun, event: QueueIterationCompletedEvent
+    ) -> IterationNodeCompletedStreamResponse:
+        """
+        Workflow iteration completed to stream response
+        :param task_id: task id
+        :param workflow_run: workflow run
+        :param event: iteration completed event
+        :return:
+        """
+        return IterationNodeCompletedStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_run.id,
+            data=IterationNodeCompletedStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_data.title,
                 outputs=event.outputs,
-                execution_metadata=execution_metadata
-            )
-
-            if execution_metadata and execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS):
-                self._task_state.total_tokens += (
-                    int(execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS)))
-
-                if self._iteration_state:
-                    for iteration_node_id in self._iteration_state.current_iterations:
-                        data = self._iteration_state.current_iterations[iteration_node_id]
-                        if execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS):
-                            data.total_tokens += int(execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS))
-
-            if workflow_node_execution.node_type == NodeType.LLM.value:
-                outputs = workflow_node_execution.outputs_dict
-                usage_dict = outputs.get('usage', {})
-                self._task_state.metadata['usage'] = usage_dict
-        else:
-            workflow_node_execution = self._workflow_node_execution_failed(
-                workflow_node_execution=workflow_node_execution,
-                start_at=current_node_execution.start_at,
-                error=event.error,
-                inputs=event.inputs,
-                process_data=event.process_data,
-                outputs=event.outputs,
-                execution_metadata=execution_metadata
-            )
-
-        db.session.close()
-
-        return workflow_node_execution
-
-    def _handle_workflow_finished(
-        self, event: QueueStopEvent | QueueWorkflowSucceededEvent | QueueWorkflowFailedEvent,
-        conversation_id: Optional[str] = None,
-        trace_manager: Optional[TraceQueueManager] = None
-    ) -> Optional[WorkflowRun]:
-        workflow_run = db.session.query(WorkflowRun).filter(
-            WorkflowRun.id == self._task_state.workflow_run_id).first()
-        if not workflow_run:
-            return None
-
-        if conversation_id is None:
-            conversation_id = self._application_generate_entity.inputs.get('sys.conversation_id')
-        if isinstance(event, QueueStopEvent):
-            workflow_run = self._workflow_run_failed(
-                workflow_run=workflow_run,
-                total_tokens=self._task_state.total_tokens,
-                total_steps=self._task_state.total_steps,
-                status=WorkflowRunStatus.STOPPED,
-                error='Workflow stopped.',
-                conversation_id=conversation_id,
-                trace_manager=trace_manager
-            )
-
-            latest_node_execution_info = self._task_state.latest_node_execution_info
-            if latest_node_execution_info:
-                workflow_node_execution = db.session.query(WorkflowNodeExecution).filter(
-                    WorkflowNodeExecution.id == latest_node_execution_info.workflow_node_execution_id).first()
-                if (workflow_node_execution
-                        and workflow_node_execution.status == WorkflowNodeExecutionStatus.RUNNING.value):
-                    self._workflow_node_execution_failed(
-                        workflow_node_execution=workflow_node_execution,
-                        start_at=latest_node_execution_info.start_at,
-                        error='Workflow stopped.'
-                    )
-        elif isinstance(event, QueueWorkflowFailedEvent):
-            workflow_run = self._workflow_run_failed(
-                workflow_run=workflow_run,
-                total_tokens=self._task_state.total_tokens,
-                total_steps=self._task_state.total_steps,
-                status=WorkflowRunStatus.FAILED,
-                error=event.error,
-                conversation_id=conversation_id,
-                trace_manager=trace_manager
-            )
-        else:
-            if self._task_state.latest_node_execution_info:
-                workflow_node_execution = db.session.query(WorkflowNodeExecution).filter(
-                    WorkflowNodeExecution.id == self._task_state.latest_node_execution_info.workflow_node_execution_id).first()
-                outputs = workflow_node_execution.outputs
-            else:
-                outputs = None
-
-            workflow_run = self._workflow_run_success(
-                workflow_run=workflow_run,
-                total_tokens=self._task_state.total_tokens,
-                total_steps=self._task_state.total_steps,
-                outputs=outputs,
-                conversation_id=conversation_id,
-                trace_manager=trace_manager
-            )
-
-        self._task_state.workflow_run_id = workflow_run.id
-
-        db.session.close()
-
-        return workflow_run
+                created_at=int(time.time()),
+                extras={},
+                inputs=event.inputs or {},
+                status=WorkflowNodeExecutionStatus.SUCCEEDED,
+                error=None,
+                elapsed_time=(datetime.now(timezone.utc).replace(tzinfo=None) - event.start_at).total_seconds(),
+                total_tokens=event.metadata.get("total_tokens", 0) if event.metadata else 0,
+                execution_metadata=event.metadata,
+                finished_at=int(time.time()),
+                steps=event.steps,
+                parallel_id=event.parallel_id,
+                parallel_start_node_id=event.parallel_start_node_id,
+            ),
+        )
 
     def _fetch_files_from_node_outputs(self, outputs_dict: dict) -> list[dict]:
         """
@@ -641,9 +649,45 @@ class WorkflowCycleManage(WorkflowIterationCycleManage):
             return None
 
         if isinstance(value, dict):
-            if '__variant' in value and value['__variant'] == FileVar.__name__:
+            if "__variant" in value and value["__variant"] == FileVar.__name__:
                 return value
         elif isinstance(value, FileVar):
             return value.to_dict()
 
         return None
+
+    def _refetch_workflow_run(self, workflow_run_id: str) -> WorkflowRun:
+        """
+        Refetch workflow run
+        :param workflow_run_id: workflow run id
+        :return:
+        """
+        workflow_run = db.session.query(WorkflowRun).filter(WorkflowRun.id == workflow_run_id).first()
+
+        if not workflow_run:
+            raise Exception(f"Workflow run not found: {workflow_run_id}")
+
+        return workflow_run
+
+    def _refetch_workflow_node_execution(self, node_execution_id: str) -> WorkflowNodeExecution:
+        """
+        Refetch workflow node execution
+        :param node_execution_id: workflow node execution id
+        :return:
+        """
+        workflow_node_execution = (
+            db.session.query(WorkflowNodeExecution)
+            .filter(
+                WorkflowNodeExecution.tenant_id == self._application_generate_entity.app_config.tenant_id,
+                WorkflowNodeExecution.app_id == self._application_generate_entity.app_config.app_id,
+                WorkflowNodeExecution.workflow_id == self._workflow.id,
+                WorkflowNodeExecution.triggered_from == WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value,
+                WorkflowNodeExecution.node_execution_id == node_execution_id,
+            )
+            .first()
+        )
+
+        if not workflow_node_execution:
+            raise Exception(f"Workflow node execution not found: {node_execution_id}")
+
+        return workflow_node_execution
diff --git a/api/core/app/task_pipeline/workflow_cycle_state_manager.py b/api/core/app/task_pipeline/workflow_cycle_state_manager.py
index bd98c82720..e69de29bb2 100644
--- a/api/core/app/task_pipeline/workflow_cycle_state_manager.py
+++ b/api/core/app/task_pipeline/workflow_cycle_state_manager.py
@@ -1,16 +0,0 @@
-from typing import Any, Union
-
-from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity
-from core.app.entities.task_entities import AdvancedChatTaskState, WorkflowTaskState
-from core.workflow.enums import SystemVariableKey
-from models.account import Account
-from models.model import EndUser
-from models.workflow import Workflow
-
-
-class WorkflowCycleStateManager:
-    _application_generate_entity: Union[AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity]
-    _workflow: Workflow
-    _user: Union[Account, EndUser]
-    _task_state: Union[AdvancedChatTaskState, WorkflowTaskState]
-    _workflow_system_variables: dict[SystemVariableKey, Any]
diff --git a/api/core/app/task_pipeline/workflow_iteration_cycle_manage.py b/api/core/app/task_pipeline/workflow_iteration_cycle_manage.py
deleted file mode 100644
index aff1870714..0000000000
--- a/api/core/app/task_pipeline/workflow_iteration_cycle_manage.py
+++ /dev/null
@@ -1,290 +0,0 @@
-import json
-import time
-from collections.abc import Generator
-from datetime import datetime, timezone
-from typing import Optional, Union
-
-from core.app.entities.queue_entities import (
-    QueueIterationCompletedEvent,
-    QueueIterationNextEvent,
-    QueueIterationStartEvent,
-)
-from core.app.entities.task_entities import (
-    IterationNodeCompletedStreamResponse,
-    IterationNodeNextStreamResponse,
-    IterationNodeStartStreamResponse,
-    NodeExecutionInfo,
-    WorkflowIterationState,
-)
-from core.app.task_pipeline.workflow_cycle_state_manager import WorkflowCycleStateManager
-from core.workflow.entities.node_entities import NodeType
-from core.workflow.workflow_engine_manager import WorkflowEngineManager
-from extensions.ext_database import db
-from models.workflow import (
-    WorkflowNodeExecution,
-    WorkflowNodeExecutionStatus,
-    WorkflowNodeExecutionTriggeredFrom,
-    WorkflowRun,
-)
-
-
-class WorkflowIterationCycleManage(WorkflowCycleStateManager):
-    _iteration_state: WorkflowIterationState = None
-
-    def _init_iteration_state(self) -> WorkflowIterationState:
-        if not self._iteration_state:
-            self._iteration_state = WorkflowIterationState(
-                current_iterations={}
-            )
-
-    def _handle_iteration_to_stream_response(self, task_id: str, event: QueueIterationStartEvent | QueueIterationNextEvent | QueueIterationCompletedEvent) \
-    -> Union[IterationNodeStartStreamResponse, IterationNodeNextStreamResponse, IterationNodeCompletedStreamResponse]:
-        """
-        Handle iteration to stream response
-        :param task_id: task id
-        :param event: iteration event
-        :return:
-        """
-        if isinstance(event, QueueIterationStartEvent):
-            return IterationNodeStartStreamResponse(
-                task_id=task_id,
-                workflow_run_id=self._task_state.workflow_run_id,
-                data=IterationNodeStartStreamResponse.Data(
-                    id=event.node_id,
-                    node_id=event.node_id,
-                    node_type=event.node_type.value,
-                    title=event.node_data.title,
-                    created_at=int(time.time()),
-                    extras={},
-                    inputs=event.inputs,
-                    metadata=event.metadata
-                )
-            )
-        elif isinstance(event, QueueIterationNextEvent):
-            current_iteration = self._iteration_state.current_iterations[event.node_id]
-
-            return IterationNodeNextStreamResponse(
-                task_id=task_id,
-                workflow_run_id=self._task_state.workflow_run_id,
-                data=IterationNodeNextStreamResponse.Data(
-                    id=event.node_id,
-                    node_id=event.node_id,
-                    node_type=event.node_type.value,
-                    title=current_iteration.node_data.title,
-                    index=event.index,
-                    pre_iteration_output=event.output,
-                    created_at=int(time.time()),
-                    extras={}
-                )
-            )
-        elif isinstance(event, QueueIterationCompletedEvent):
-            current_iteration = self._iteration_state.current_iterations[event.node_id]
-
-            return IterationNodeCompletedStreamResponse(
-                task_id=task_id,
-                workflow_run_id=self._task_state.workflow_run_id,
-                data=IterationNodeCompletedStreamResponse.Data(
-                    id=event.node_id,
-                    node_id=event.node_id,
-                    node_type=event.node_type.value,
-                    title=current_iteration.node_data.title,
-                    outputs=event.outputs,
-                    created_at=int(time.time()),
-                    extras={},
-                    inputs=current_iteration.inputs,
-                    status=WorkflowNodeExecutionStatus.SUCCEEDED,
-                    error=None,
-                    elapsed_time=time.perf_counter() - current_iteration.started_at,
-                    total_tokens=current_iteration.total_tokens,
-                    execution_metadata={
-                        'total_tokens': current_iteration.total_tokens,
-                    },
-                    finished_at=int(time.time()),
-                    steps=current_iteration.current_index
-                )
-            )
-        
-    def _init_iteration_execution_from_workflow_run(self, 
-        workflow_run: WorkflowRun,
-        node_id: str,
-        node_type: NodeType,
-        node_title: str,
-        node_run_index: int = 1,
-        inputs: Optional[dict] = None,
-        predecessor_node_id: Optional[str] = None
-    ) -> WorkflowNodeExecution:
-        workflow_node_execution = WorkflowNodeExecution(
-            tenant_id=workflow_run.tenant_id,
-            app_id=workflow_run.app_id,
-            workflow_id=workflow_run.workflow_id,
-            triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value,
-            workflow_run_id=workflow_run.id,
-            predecessor_node_id=predecessor_node_id,
-            index=node_run_index,
-            node_id=node_id,
-            node_type=node_type.value,
-            inputs=json.dumps(inputs) if inputs else None,
-            title=node_title,
-            status=WorkflowNodeExecutionStatus.RUNNING.value,
-            created_by_role=workflow_run.created_by_role,
-            created_by=workflow_run.created_by,
-            execution_metadata=json.dumps({
-                'started_run_index': node_run_index + 1,
-                'current_index': 0,
-                'steps_boundary': [],
-            }),
-            created_at=datetime.now(timezone.utc).replace(tzinfo=None)
-        )
-
-        db.session.add(workflow_node_execution)
-        db.session.commit()
-        db.session.refresh(workflow_node_execution)
-        db.session.close()
-
-        return workflow_node_execution
-    
-    def _handle_iteration_operation(self, event: QueueIterationStartEvent | QueueIterationNextEvent | QueueIterationCompletedEvent) -> WorkflowNodeExecution:
-        if isinstance(event, QueueIterationStartEvent):
-            return self._handle_iteration_started(event)
-        elif isinstance(event, QueueIterationNextEvent):
-            return self._handle_iteration_next(event)
-        elif isinstance(event, QueueIterationCompletedEvent):
-            return self._handle_iteration_completed(event)
-    
-    def _handle_iteration_started(self, event: QueueIterationStartEvent) -> WorkflowNodeExecution:
-        self._init_iteration_state()
-
-        workflow_run = db.session.query(WorkflowRun).filter(WorkflowRun.id == self._task_state.workflow_run_id).first()
-        workflow_node_execution = self._init_iteration_execution_from_workflow_run(
-            workflow_run=workflow_run,
-            node_id=event.node_id,
-            node_type=NodeType.ITERATION,
-            node_title=event.node_data.title,
-            node_run_index=event.node_run_index,
-            inputs=event.inputs,
-            predecessor_node_id=event.predecessor_node_id
-        )
-
-        latest_node_execution_info = NodeExecutionInfo(
-            workflow_node_execution_id=workflow_node_execution.id,
-            node_type=NodeType.ITERATION,
-            start_at=time.perf_counter()
-        )
-
-        self._task_state.ran_node_execution_infos[event.node_id] = latest_node_execution_info
-        self._task_state.latest_node_execution_info = latest_node_execution_info
-
-        self._iteration_state.current_iterations[event.node_id] = WorkflowIterationState.Data(
-            parent_iteration_id=None,
-            iteration_id=event.node_id,
-            current_index=0,
-            iteration_steps_boundary=[],
-            node_execution_id=workflow_node_execution.id,
-            started_at=time.perf_counter(),
-            inputs=event.inputs,
-            total_tokens=0,
-            node_data=event.node_data
-        )
-
-        db.session.close()
-
-        return workflow_node_execution
-    
-    def _handle_iteration_next(self, event: QueueIterationNextEvent) -> WorkflowNodeExecution:
-        if event.node_id not in self._iteration_state.current_iterations:
-            return
-        current_iteration = self._iteration_state.current_iterations[event.node_id]
-        current_iteration.current_index = event.index
-        current_iteration.iteration_steps_boundary.append(event.node_run_index)
-        workflow_node_execution: WorkflowNodeExecution = db.session.query(WorkflowNodeExecution).filter(
-            WorkflowNodeExecution.id == current_iteration.node_execution_id
-        ).first()
-
-        original_node_execution_metadata = workflow_node_execution.execution_metadata_dict
-        if original_node_execution_metadata:
-            original_node_execution_metadata['current_index'] = event.index
-            original_node_execution_metadata['steps_boundary'] = current_iteration.iteration_steps_boundary
-            original_node_execution_metadata['total_tokens'] = current_iteration.total_tokens
-            workflow_node_execution.execution_metadata = json.dumps(original_node_execution_metadata)
-
-            db.session.commit()
-
-        db.session.close()
-
-    def _handle_iteration_completed(self, event: QueueIterationCompletedEvent):
-        if event.node_id not in self._iteration_state.current_iterations:
-            return
-        
-        current_iteration = self._iteration_state.current_iterations[event.node_id]
-        workflow_node_execution: WorkflowNodeExecution = db.session.query(WorkflowNodeExecution).filter(
-            WorkflowNodeExecution.id == current_iteration.node_execution_id
-        ).first()
-
-        workflow_node_execution.status = WorkflowNodeExecutionStatus.SUCCEEDED.value
-        workflow_node_execution.outputs = json.dumps(WorkflowEngineManager.handle_special_values(event.outputs)) if event.outputs else None
-        workflow_node_execution.elapsed_time = time.perf_counter() - current_iteration.started_at
-
-        original_node_execution_metadata = workflow_node_execution.execution_metadata_dict
-        if original_node_execution_metadata:
-            original_node_execution_metadata['steps_boundary'] = current_iteration.iteration_steps_boundary
-            original_node_execution_metadata['total_tokens'] = current_iteration.total_tokens
-            workflow_node_execution.execution_metadata = json.dumps(original_node_execution_metadata)
-
-        db.session.commit()
-
-        # remove current iteration
-        self._iteration_state.current_iterations.pop(event.node_id, None)
-
-        # set latest node execution info
-        latest_node_execution_info = NodeExecutionInfo(
-            workflow_node_execution_id=workflow_node_execution.id,
-            node_type=NodeType.ITERATION,
-            start_at=time.perf_counter()
-        )
-
-        self._task_state.latest_node_execution_info = latest_node_execution_info
-        
-        db.session.close()
-
-    def _handle_iteration_exception(self, task_id: str, error: str) -> Generator[IterationNodeCompletedStreamResponse, None, None]:
-        """
-        Handle iteration exception
-        """
-        if not self._iteration_state or not self._iteration_state.current_iterations:
-            return
-        
-        for node_id, current_iteration in self._iteration_state.current_iterations.items():
-            workflow_node_execution: WorkflowNodeExecution = db.session.query(WorkflowNodeExecution).filter(
-                WorkflowNodeExecution.id == current_iteration.node_execution_id
-            ).first()
-
-            workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
-            workflow_node_execution.error = error
-            workflow_node_execution.elapsed_time = time.perf_counter() - current_iteration.started_at
-
-            db.session.commit()
-            db.session.close()
-
-            yield IterationNodeCompletedStreamResponse(
-                task_id=task_id,
-                workflow_run_id=self._task_state.workflow_run_id,
-                data=IterationNodeCompletedStreamResponse.Data(
-                    id=node_id,
-                    node_id=node_id,
-                    node_type=NodeType.ITERATION.value,
-                    title=current_iteration.node_data.title,
-                    outputs={},
-                    created_at=int(time.time()),
-                    extras={},
-                    inputs=current_iteration.inputs,
-                    status=WorkflowNodeExecutionStatus.FAILED,
-                    error=error,
-                    elapsed_time=time.perf_counter() - current_iteration.started_at,
-                    total_tokens=current_iteration.total_tokens,
-                    execution_metadata={
-                        'total_tokens': current_iteration.total_tokens,
-                    },
-                    finished_at=int(time.time()),
-                    steps=current_iteration.current_index
-                )
-            )
diff --git a/api/core/callback_handler/agent_tool_callback_handler.py b/api/core/callback_handler/agent_tool_callback_handler.py
index 5789965747..99e992fd89 100644
--- a/api/core/callback_handler/agent_tool_callback_handler.py
+++ b/api/core/callback_handler/agent_tool_callback_handler.py
@@ -16,31 +16,32 @@ _TEXT_COLOR_MAPPING = {
     "red": "31;1",
 }
 
+
 def get_colored_text(text: str, color: str) -> str:
     """Get colored text."""
     color_str = _TEXT_COLOR_MAPPING[color]
     return f"\u001b[{color_str}m\033[1;3m{text}\u001b[0m"
 
 
-def print_text(
-    text: str, color: Optional[str] = None, end: str = "", file: Optional[TextIO] = None
-) -> None:
+def print_text(text: str, color: Optional[str] = None, end: str = "", file: Optional[TextIO] = None) -> None:
     """Print text with highlighting and no end characters."""
     text_to_print = get_colored_text(text, color) if color else text
     print(text_to_print, end=end, file=file)
     if file:
         file.flush()  # ensure all printed content are written to file
 
+
 class DifyAgentCallbackHandler(BaseModel):
     """Callback Handler that prints to std out."""
-    color: Optional[str] = ''
+
+    color: Optional[str] = ""
     current_loop: int = 1
 
     def __init__(self, color: Optional[str] = None) -> None:
         super().__init__()
         """Initialize callback handler."""
         # use a specific color is not specified
-        self.color = color or 'green'
+        self.color = color or "green"
         self.current_loop = 1
 
     def on_tool_start(
@@ -58,7 +59,7 @@ class DifyAgentCallbackHandler(BaseModel):
         tool_outputs: Sequence[ToolInvokeMessage],
         message_id: Optional[str] = None,
         timer: Optional[Any] = None,
-        trace_manager: Optional[TraceQueueManager] = None
+        trace_manager: Optional[TraceQueueManager] = None,
     ) -> None:
         """If not the final action, print out observation."""
         print_text("\n[on_tool_end]\n", color=self.color)
@@ -79,26 +80,21 @@ class DifyAgentCallbackHandler(BaseModel):
                 )
             )
 
-    def on_tool_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
+    def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:
         """Do nothing."""
-        print_text("\n[on_tool_error] Error: " + str(error) + "\n", color='red')
+        print_text("\n[on_tool_error] Error: " + str(error) + "\n", color="red")
 
-    def on_agent_start(
-        self, thought: str
-    ) -> None:
+    def on_agent_start(self, thought: str) -> None:
         """Run on agent start."""
         if thought:
-            print_text("\n[on_agent_start] \nCurrent Loop: " + \
-                        str(self.current_loop) + \
-                        "\nThought: " + thought + "\n", color=self.color)
+            print_text(
+                "\n[on_agent_start] \nCurrent Loop: " + str(self.current_loop) + "\nThought: " + thought + "\n",
+                color=self.color,
+            )
         else:
             print_text("\n[on_agent_start] \nCurrent Loop: " + str(self.current_loop) + "\n", color=self.color)
 
-    def on_agent_finish(
-        self, color: Optional[str] = None, **kwargs: Any
-    ) -> None:
+    def on_agent_finish(self, color: Optional[str] = None, **kwargs: Any) -> None:
         """Run on agent end."""
         print_text("\n[on_agent_finish]\n Loop: " + str(self.current_loop) + "\n", color=self.color)
 
@@ -107,9 +103,9 @@ class DifyAgentCallbackHandler(BaseModel):
     @property
     def ignore_agent(self) -> bool:
         """Whether to ignore agent callbacks."""
-        return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
+        return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != "true"
 
     @property
     def ignore_chat_model(self) -> bool:
         """Whether to ignore chat model callbacks."""
-        return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
+        return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != "true"
diff --git a/api/core/callback_handler/index_tool_callback_handler.py b/api/core/callback_handler/index_tool_callback_handler.py
index 8e1f496b22..7cf472d984 100644
--- a/api/core/callback_handler/index_tool_callback_handler.py
+++ b/api/core/callback_handler/index_tool_callback_handler.py
@@ -1,4 +1,3 @@
-
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
@@ -11,11 +10,9 @@ from models.model import DatasetRetrieverResource
 class DatasetIndexToolCallbackHandler:
     """Callback handler for dataset tool."""
 
-    def __init__(self, queue_manager: AppQueueManager,
-                 app_id: str,
-                 message_id: str,
-                 user_id: str,
-                 invoke_from: InvokeFrom) -> None:
+    def __init__(
+        self, queue_manager: AppQueueManager, app_id: str, message_id: str, user_id: str, invoke_from: InvokeFrom
+    ) -> None:
         self._queue_manager = queue_manager
         self._app_id = app_id
         self._message_id = message_id
@@ -29,11 +26,12 @@ class DatasetIndexToolCallbackHandler:
         dataset_query = DatasetQuery(
             dataset_id=dataset_id,
             content=query,
-            source='app',
+            source="app",
             source_app_id=self._app_id,
-            created_by_role=('account'
-                             if self._invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end_user'),
-            created_by=self._user_id
+            created_by_role=(
+                "account" if self._invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER} else "end_user"
+            ),
+            created_by=self._user_id,
         )
 
         db.session.add(dataset_query)
@@ -43,18 +41,15 @@ class DatasetIndexToolCallbackHandler:
         """Handle tool end."""
         for document in documents:
             query = db.session.query(DocumentSegment).filter(
-                DocumentSegment.index_node_id == document.metadata['doc_id']
+                DocumentSegment.index_node_id == document.metadata["doc_id"]
             )
 
             # if 'dataset_id' in document.metadata:
-            if 'dataset_id' in document.metadata:
-                query = query.filter(DocumentSegment.dataset_id == document.metadata['dataset_id'])
+            if "dataset_id" in document.metadata:
+                query = query.filter(DocumentSegment.dataset_id == document.metadata["dataset_id"])
 
             # add hit count to document segment
-            query.update(
-                {DocumentSegment.hit_count: DocumentSegment.hit_count + 1},
-                synchronize_session=False
-            )
+            query.update({DocumentSegment.hit_count: DocumentSegment.hit_count + 1}, synchronize_session=False)
 
             db.session.commit()
 
@@ -64,26 +59,25 @@ class DatasetIndexToolCallbackHandler:
             for item in resource:
                 dataset_retriever_resource = DatasetRetrieverResource(
                     message_id=self._message_id,
-                    position=item.get('position'),
-                    dataset_id=item.get('dataset_id'),
-                    dataset_name=item.get('dataset_name'),
-                    document_id=item.get('document_id'),
-                    document_name=item.get('document_name'),
-                    data_source_type=item.get('data_source_type'),
-                    segment_id=item.get('segment_id'),
-                    score=item.get('score') if 'score' in item else None,
-                    hit_count=item.get('hit_count') if 'hit_count' else None,
-                    word_count=item.get('word_count') if 'word_count' in item else None,
-                    segment_position=item.get('segment_position') if 'segment_position' in item else None,
-                    index_node_hash=item.get('index_node_hash') if 'index_node_hash' in item else None,
-                    content=item.get('content'),
-                    retriever_from=item.get('retriever_from'),
-                    created_by=self._user_id
+                    position=item.get("position"),
+                    dataset_id=item.get("dataset_id"),
+                    dataset_name=item.get("dataset_name"),
+                    document_id=item.get("document_id"),
+                    document_name=item.get("document_name"),
+                    data_source_type=item.get("data_source_type"),
+                    segment_id=item.get("segment_id"),
+                    score=item.get("score") if "score" in item else None,
+                    hit_count=item.get("hit_count") if "hit_count" in item else None,
+                    word_count=item.get("word_count") if "word_count" in item else None,
+                    segment_position=item.get("segment_position") if "segment_position" in item else None,
+                    index_node_hash=item.get("index_node_hash") if "index_node_hash" in item else None,
+                    content=item.get("content"),
+                    retriever_from=item.get("retriever_from"),
+                    created_by=self._user_id,
                 )
                 db.session.add(dataset_retriever_resource)
                 db.session.commit()
 
         self._queue_manager.publish(
-            QueueRetrieverResourcesEvent(retriever_resources=resource),
-            PublishFrom.APPLICATION_MANAGER
+            QueueRetrieverResourcesEvent(retriever_resources=resource), PublishFrom.APPLICATION_MANAGER
         )
diff --git a/api/core/callback_handler/workflow_tool_callback_handler.py b/api/core/callback_handler/workflow_tool_callback_handler.py
index 84bab7e1a3..8ac12f72f2 100644
--- a/api/core/callback_handler/workflow_tool_callback_handler.py
+++ b/api/core/callback_handler/workflow_tool_callback_handler.py
@@ -2,4 +2,4 @@ from core.callback_handler.agent_tool_callback_handler import DifyAgentCallbackH
 
 
 class DifyWorkflowCallbackHandler(DifyAgentCallbackHandler):
-    """Callback Handler that prints to std out."""
\ No newline at end of file
+    """Callback Handler that prints to std out."""
diff --git a/api/core/embedding/cached_embedding.py b/api/core/embedding/cached_embedding.py
index b7e0cc0c2b..8ce12fd59f 100644
--- a/api/core/embedding/cached_embedding.py
+++ b/api/core/embedding/cached_embedding.py
@@ -29,9 +29,13 @@ class CacheEmbedding(Embeddings):
         embedding_queue_indices = []
         for i, text in enumerate(texts):
             hash = helper.generate_text_hash(text)
-            embedding = db.session.query(Embedding).filter_by(model_name=self._model_instance.model,
-                                                              hash=hash,
-                                                              provider_name=self._model_instance.provider).first()
+            embedding = (
+                db.session.query(Embedding)
+                .filter_by(
+                    model_name=self._model_instance.model, hash=hash, provider_name=self._model_instance.provider
+                )
+                .first()
+            )
             if embedding:
                 text_embeddings[i] = embedding.get_embedding()
             else:
@@ -41,17 +45,18 @@ class CacheEmbedding(Embeddings):
             embedding_queue_embeddings = []
             try:
                 model_type_instance = cast(TextEmbeddingModel, self._model_instance.model_type_instance)
-                model_schema = model_type_instance.get_model_schema(self._model_instance.model,
-                                                                    self._model_instance.credentials)
-                max_chunks = model_schema.model_properties[ModelPropertyKey.MAX_CHUNKS] \
-                    if model_schema and ModelPropertyKey.MAX_CHUNKS in model_schema.model_properties else 1
+                model_schema = model_type_instance.get_model_schema(
+                    self._model_instance.model, self._model_instance.credentials
+                )
+                max_chunks = (
+                    model_schema.model_properties[ModelPropertyKey.MAX_CHUNKS]
+                    if model_schema and ModelPropertyKey.MAX_CHUNKS in model_schema.model_properties
+                    else 1
+                )
                 for i in range(0, len(embedding_queue_texts), max_chunks):
-                    batch_texts = embedding_queue_texts[i:i + max_chunks]
+                    batch_texts = embedding_queue_texts[i : i + max_chunks]
 
-                    embedding_result = self._model_instance.invoke_text_embedding(
-                        texts=batch_texts,
-                        user=self._user
-                    )
+                    embedding_result = self._model_instance.invoke_text_embedding(texts=batch_texts, user=self._user)
 
                     for vector in embedding_result.embeddings:
                         try:
@@ -60,16 +65,18 @@ class CacheEmbedding(Embeddings):
                         except IntegrityError:
                             db.session.rollback()
                         except Exception as e:
-                            logging.exception('Failed transform embedding: ', e)
+                            logging.exception("Failed transform embedding: %s", e)
                 cache_embeddings = []
                 try:
                     for i, embedding in zip(embedding_queue_indices, embedding_queue_embeddings):
                         text_embeddings[i] = embedding
                         hash = helper.generate_text_hash(texts[i])
                         if hash not in cache_embeddings:
-                            embedding_cache = Embedding(model_name=self._model_instance.model,
-                                                        hash=hash,
-                                                        provider_name=self._model_instance.provider)
+                            embedding_cache = Embedding(
+                                model_name=self._model_instance.model,
+                                hash=hash,
+                                provider_name=self._model_instance.provider,
+                            )
                             embedding_cache.set_embedding(embedding)
                             db.session.add(embedding_cache)
                             cache_embeddings.append(hash)
@@ -78,7 +85,7 @@ class CacheEmbedding(Embeddings):
                     db.session.rollback()
             except Exception as ex:
                 db.session.rollback()
-                logger.error('Failed to embed documents: ', ex)
+                logger.error("Failed to embed documents: %s", ex)
                 raise ex
 
         return text_embeddings
@@ -87,16 +94,13 @@ class CacheEmbedding(Embeddings):
         """Embed query text."""
         # use doc embedding cache or store if not exists
         hash = helper.generate_text_hash(text)
-        embedding_cache_key = f'{self._model_instance.provider}_{self._model_instance.model}_{hash}'
+        embedding_cache_key = f"{self._model_instance.provider}_{self._model_instance.model}_{hash}"
         embedding = redis_client.get(embedding_cache_key)
         if embedding:
             redis_client.expire(embedding_cache_key, 600)
             return list(np.frombuffer(base64.b64decode(embedding), dtype="float"))
         try:
-            embedding_result = self._model_instance.invoke_text_embedding(
-                texts=[text],
-                user=self._user
-            )
+            embedding_result = self._model_instance.invoke_text_embedding(texts=[text], user=self._user)
 
             embedding_results = embedding_result.embeddings[0]
             embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist()
@@ -112,10 +116,7 @@ class CacheEmbedding(Embeddings):
             # Transform to string
             encoded_str = encoded_vector.decode("utf-8")
             redis_client.setex(embedding_cache_key, 600, encoded_str)
-
-        except IntegrityError:
-            db.session.rollback()
-        except:
-            logging.exception('Failed to add embedding to redis')
+        except Exception as ex:
+            logging.exception("Failed to add embedding to redis %s", ex)
 
         return embedding_results
diff --git a/api/core/entities/agent_entities.py b/api/core/entities/agent_entities.py
index 0cdf8670c4..656bf4aa72 100644
--- a/api/core/entities/agent_entities.py
+++ b/api/core/entities/agent_entities.py
@@ -2,7 +2,7 @@ from enum import Enum
 
 
 class PlanningStrategy(Enum):
-    ROUTER = 'router'
-    REACT_ROUTER = 'react_router'
-    REACT = 'react'
-    FUNCTION_CALL = 'function_call'
+    ROUTER = "router"
+    REACT_ROUTER = "react_router"
+    REACT = "react"
+    FUNCTION_CALL = "function_call"
diff --git a/api/core/entities/message_entities.py b/api/core/entities/message_entities.py
index 370aeee463..10bc9f6ed7 100644
--- a/api/core/entities/message_entities.py
+++ b/api/core/entities/message_entities.py
@@ -5,7 +5,7 @@ from pydantic import BaseModel
 
 
 class PromptMessageFileType(enum.Enum):
-    IMAGE = 'image'
+    IMAGE = "image"
 
     @staticmethod
     def value_of(value):
@@ -22,8 +22,8 @@ class PromptMessageFile(BaseModel):
 
 class ImagePromptMessageFile(PromptMessageFile):
     class DETAIL(enum.Enum):
-        LOW = 'low'
-        HIGH = 'high'
+        LOW = "low"
+        HIGH = "high"
 
     type: PromptMessageFileType = PromptMessageFileType.IMAGE
     detail: DETAIL = DETAIL.LOW
diff --git a/api/core/entities/model_entities.py b/api/core/entities/model_entities.py
index 22a21ecf93..9ed5528e43 100644
--- a/api/core/entities/model_entities.py
+++ b/api/core/entities/model_entities.py
@@ -12,6 +12,7 @@ class ModelStatus(Enum):
     """
     Enum class for model status.
     """
+
     ACTIVE = "active"
     NO_CONFIGURE = "no-configure"
     QUOTA_EXCEEDED = "quota-exceeded"
@@ -23,6 +24,7 @@ class SimpleModelProviderEntity(BaseModel):
     """
     Simple provider.
     """
+
     provider: str
     label: I18nObject
     icon_small: Optional[I18nObject] = None
@@ -40,7 +42,7 @@ class SimpleModelProviderEntity(BaseModel):
             label=provider_entity.label,
             icon_small=provider_entity.icon_small,
             icon_large=provider_entity.icon_large,
-            supported_model_types=provider_entity.supported_model_types
+            supported_model_types=provider_entity.supported_model_types,
         )
 
 
@@ -48,6 +50,7 @@ class ProviderModelWithStatusEntity(ProviderModel):
     """
     Model class for model response.
     """
+
     status: ModelStatus
     load_balancing_enabled: bool = False
 
@@ -56,6 +59,7 @@ class ModelWithProviderEntity(ProviderModelWithStatusEntity):
     """
     Model with provider entity.
     """
+
     provider: SimpleModelProviderEntity
 
 
@@ -63,6 +67,7 @@ class DefaultModelProviderEntity(BaseModel):
     """
     Default model provider entity.
     """
+
     provider: str
     label: I18nObject
     icon_small: Optional[I18nObject] = None
@@ -74,6 +79,7 @@ class DefaultModelEntity(BaseModel):
     """
     Default model entity.
     """
+
     model: str
     model_type: ModelType
     provider: DefaultModelProviderEntity
diff --git a/api/core/entities/provider_configuration.py b/api/core/entities/provider_configuration.py
index 778ef2e1ac..4797b69b85 100644
--- a/api/core/entities/provider_configuration.py
+++ b/api/core/entities/provider_configuration.py
@@ -47,6 +47,7 @@ class ProviderConfiguration(BaseModel):
     """
     Model class for provider configuration.
     """
+
     tenant_id: str
     provider: ProviderEntity
     preferred_provider_type: ProviderType
@@ -67,9 +68,13 @@ class ProviderConfiguration(BaseModel):
                 original_provider_configurate_methods[self.provider.provider].append(configurate_method)
 
         if original_provider_configurate_methods[self.provider.provider] == [ConfigurateMethod.CUSTOMIZABLE_MODEL]:
-            if (any(len(quota_configuration.restrict_models) > 0
-                     for quota_configuration in self.system_configuration.quota_configurations)
-                    and ConfigurateMethod.PREDEFINED_MODEL not in self.provider.configurate_methods):
+            if (
+                any(
+                    len(quota_configuration.restrict_models) > 0
+                    for quota_configuration in self.system_configuration.quota_configurations
+                )
+                and ConfigurateMethod.PREDEFINED_MODEL not in self.provider.configurate_methods
+            ):
                 self.provider.configurate_methods.append(ConfigurateMethod.PREDEFINED_MODEL)
 
     def get_current_credentials(self, model_type: ModelType, model: str) -> Optional[dict]:
@@ -83,10 +88,9 @@ class ProviderConfiguration(BaseModel):
         if self.model_settings:
             # check if model is disabled by admin
             for model_setting in self.model_settings:
-                if (model_setting.model_type == model_type
-                        and model_setting.model == model):
+                if model_setting.model_type == model_type and model_setting.model == model:
                     if not model_setting.enabled:
-                        raise ValueError(f'Model {model} is disabled.')
+                        raise ValueError(f"Model {model} is disabled.")
 
         if self.using_provider_type == ProviderType.SYSTEM:
             restrict_models = []
@@ -99,10 +103,12 @@ class ProviderConfiguration(BaseModel):
             copy_credentials = self.system_configuration.credentials.copy()
             if restrict_models:
                 for restrict_model in restrict_models:
-                    if (restrict_model.model_type == model_type
-                            and restrict_model.model == model
-                            and restrict_model.base_model_name):
-                        copy_credentials['base_model_name'] = restrict_model.base_model_name
+                    if (
+                        restrict_model.model_type == model_type
+                        and restrict_model.model == model
+                        and restrict_model.base_model_name
+                    ):
+                        copy_credentials["base_model_name"] = restrict_model.base_model_name
 
             return copy_credentials
         else:
@@ -128,20 +134,21 @@ class ProviderConfiguration(BaseModel):
 
         current_quota_type = self.system_configuration.current_quota_type
         current_quota_configuration = next(
-            (q for q in self.system_configuration.quota_configurations if q.quota_type == current_quota_type),
-            None
+            (q for q in self.system_configuration.quota_configurations if q.quota_type == current_quota_type), None
         )
 
-        return SystemConfigurationStatus.ACTIVE if current_quota_configuration.is_valid else \
-            SystemConfigurationStatus.QUOTA_EXCEEDED
+        return (
+            SystemConfigurationStatus.ACTIVE
+            if current_quota_configuration.is_valid
+            else SystemConfigurationStatus.QUOTA_EXCEEDED
+        )
 
     def is_custom_configuration_available(self) -> bool:
         """
         Check custom configuration available.
         :return:
         """
-        return (self.custom_configuration.provider is not None
-                or len(self.custom_configuration.models) > 0)
+        return self.custom_configuration.provider is not None or len(self.custom_configuration.models) > 0
 
     def get_custom_credentials(self, obfuscated: bool = False) -> Optional[dict]:
         """
@@ -161,7 +168,8 @@ class ProviderConfiguration(BaseModel):
         return self.obfuscated_credentials(
             credentials=credentials,
             credential_form_schemas=self.provider.provider_credential_schema.credential_form_schemas
-            if self.provider.provider_credential_schema else []
+            if self.provider.provider_credential_schema
+            else [],
         )
 
     def custom_credentials_validate(self, credentials: dict) -> tuple[Provider, dict]:
@@ -171,17 +179,21 @@ class ProviderConfiguration(BaseModel):
         :return:
         """
         # get provider
-        provider_record = db.session.query(Provider) \
+        provider_record = (
+            db.session.query(Provider)
             .filter(
-            Provider.tenant_id == self.tenant_id,
-            Provider.provider_name == self.provider.provider,
-            Provider.provider_type == ProviderType.CUSTOM.value
-        ).first()
+                Provider.tenant_id == self.tenant_id,
+                Provider.provider_name == self.provider.provider,
+                Provider.provider_type == ProviderType.CUSTOM.value,
+            )
+            .first()
+        )
 
         # Get provider credential secret variables
         provider_credential_secret_variables = self.extract_secret_variables(
             self.provider.provider_credential_schema.credential_form_schemas
-            if self.provider.provider_credential_schema else []
+            if self.provider.provider_credential_schema
+            else []
         )
 
         if provider_record:
@@ -189,9 +201,7 @@ class ProviderConfiguration(BaseModel):
                 # fix origin data
                 if provider_record.encrypted_config:
                     if not provider_record.encrypted_config.startswith("{"):
-                        original_credentials = {
-                            "openai_api_key": provider_record.encrypted_config
-                        }
+                        original_credentials = {"openai_api_key": provider_record.encrypted_config}
                     else:
                         original_credentials = json.loads(provider_record.encrypted_config)
                 else:
@@ -207,8 +217,7 @@ class ProviderConfiguration(BaseModel):
                         credentials[key] = encrypter.decrypt_token(self.tenant_id, original_credentials[key])
 
         credentials = model_provider_factory.provider_credentials_validate(
-            provider=self.provider.provider,
-            credentials=credentials
+            provider=self.provider.provider, credentials=credentials
         )
 
         for key, value in credentials.items():
@@ -239,15 +248,13 @@ class ProviderConfiguration(BaseModel):
                 provider_name=self.provider.provider,
                 provider_type=ProviderType.CUSTOM.value,
                 encrypted_config=json.dumps(credentials),
-                is_valid=True
+                is_valid=True,
             )
             db.session.add(provider_record)
             db.session.commit()
 
         provider_model_credentials_cache = ProviderCredentialsCache(
-            tenant_id=self.tenant_id,
-            identity_id=provider_record.id,
-            cache_type=ProviderCredentialsCacheType.PROVIDER
+            tenant_id=self.tenant_id, identity_id=provider_record.id, cache_type=ProviderCredentialsCacheType.PROVIDER
         )
 
         provider_model_credentials_cache.delete()
@@ -260,12 +267,15 @@ class ProviderConfiguration(BaseModel):
         :return:
         """
         # get provider
-        provider_record = db.session.query(Provider) \
+        provider_record = (
+            db.session.query(Provider)
             .filter(
-            Provider.tenant_id == self.tenant_id,
-            Provider.provider_name == self.provider.provider,
-            Provider.provider_type == ProviderType.CUSTOM.value
-        ).first()
+                Provider.tenant_id == self.tenant_id,
+                Provider.provider_name == self.provider.provider,
+                Provider.provider_type == ProviderType.CUSTOM.value,
+            )
+            .first()
+        )
 
         # delete provider
         if provider_record:
@@ -277,13 +287,14 @@ class ProviderConfiguration(BaseModel):
             provider_model_credentials_cache = ProviderCredentialsCache(
                 tenant_id=self.tenant_id,
                 identity_id=provider_record.id,
-                cache_type=ProviderCredentialsCacheType.PROVIDER
+                cache_type=ProviderCredentialsCacheType.PROVIDER,
             )
 
             provider_model_credentials_cache.delete()
 
-    def get_custom_model_credentials(self, model_type: ModelType, model: str, obfuscated: bool = False) \
-            -> Optional[dict]:
+    def get_custom_model_credentials(
+        self, model_type: ModelType, model: str, obfuscated: bool = False
+    ) -> Optional[dict]:
         """
         Get custom model credentials.
 
@@ -305,13 +316,15 @@ class ProviderConfiguration(BaseModel):
                 return self.obfuscated_credentials(
                     credentials=credentials,
                     credential_form_schemas=self.provider.model_credential_schema.credential_form_schemas
-                    if self.provider.model_credential_schema else []
+                    if self.provider.model_credential_schema
+                    else [],
                 )
 
         return None
 
-    def custom_model_credentials_validate(self, model_type: ModelType, model: str, credentials: dict) \
-            -> tuple[ProviderModel, dict]:
+    def custom_model_credentials_validate(
+        self, model_type: ModelType, model: str, credentials: dict
+    ) -> tuple[ProviderModel, dict]:
         """
         Validate custom model credentials.
 
@@ -321,24 +334,29 @@ class ProviderConfiguration(BaseModel):
         :return:
         """
         # get provider model
-        provider_model_record = db.session.query(ProviderModel) \
+        provider_model_record = (
+            db.session.query(ProviderModel)
             .filter(
-            ProviderModel.tenant_id == self.tenant_id,
-            ProviderModel.provider_name == self.provider.provider,
-            ProviderModel.model_name == model,
-            ProviderModel.model_type == model_type.to_origin_model_type()
-        ).first()
+                ProviderModel.tenant_id == self.tenant_id,
+                ProviderModel.provider_name == self.provider.provider,
+                ProviderModel.model_name == model,
+                ProviderModel.model_type == model_type.to_origin_model_type(),
+            )
+            .first()
+        )
 
         # Get provider credential secret variables
         provider_credential_secret_variables = self.extract_secret_variables(
             self.provider.model_credential_schema.credential_form_schemas
-            if self.provider.model_credential_schema else []
+            if self.provider.model_credential_schema
+            else []
         )
 
         if provider_model_record:
             try:
-                original_credentials = json.loads(
-                    provider_model_record.encrypted_config) if provider_model_record.encrypted_config else {}
+                original_credentials = (
+                    json.loads(provider_model_record.encrypted_config) if provider_model_record.encrypted_config else {}
+                )
             except JSONDecodeError:
                 original_credentials = {}
 
@@ -350,10 +368,7 @@ class ProviderConfiguration(BaseModel):
                         credentials[key] = encrypter.decrypt_token(self.tenant_id, original_credentials[key])
 
         credentials = model_provider_factory.model_credentials_validate(
-            provider=self.provider.provider,
-            model_type=model_type,
-            model=model,
-            credentials=credentials
+            provider=self.provider.provider, model_type=model_type, model=model, credentials=credentials
         )
 
         for key, value in credentials.items():
@@ -388,7 +403,7 @@ class ProviderConfiguration(BaseModel):
                 model_name=model,
                 model_type=model_type.to_origin_model_type(),
                 encrypted_config=json.dumps(credentials),
-                is_valid=True
+                is_valid=True,
             )
             db.session.add(provider_model_record)
             db.session.commit()
@@ -396,7 +411,7 @@ class ProviderConfiguration(BaseModel):
         provider_model_credentials_cache = ProviderCredentialsCache(
             tenant_id=self.tenant_id,
             identity_id=provider_model_record.id,
-            cache_type=ProviderCredentialsCacheType.MODEL
+            cache_type=ProviderCredentialsCacheType.MODEL,
         )
 
         provider_model_credentials_cache.delete()
@@ -409,13 +424,16 @@ class ProviderConfiguration(BaseModel):
         :return:
         """
         # get provider model
-        provider_model_record = db.session.query(ProviderModel) \
+        provider_model_record = (
+            db.session.query(ProviderModel)
             .filter(
-            ProviderModel.tenant_id == self.tenant_id,
-            ProviderModel.provider_name == self.provider.provider,
-            ProviderModel.model_name == model,
-            ProviderModel.model_type == model_type.to_origin_model_type()
-        ).first()
+                ProviderModel.tenant_id == self.tenant_id,
+                ProviderModel.provider_name == self.provider.provider,
+                ProviderModel.model_name == model,
+                ProviderModel.model_type == model_type.to_origin_model_type(),
+            )
+            .first()
+        )
 
         # delete provider model
         if provider_model_record:
@@ -425,7 +443,7 @@ class ProviderConfiguration(BaseModel):
             provider_model_credentials_cache = ProviderCredentialsCache(
                 tenant_id=self.tenant_id,
                 identity_id=provider_model_record.id,
-                cache_type=ProviderCredentialsCacheType.MODEL
+                cache_type=ProviderCredentialsCacheType.MODEL,
             )
 
             provider_model_credentials_cache.delete()
@@ -437,13 +455,16 @@ class ProviderConfiguration(BaseModel):
         :param model: model name
         :return:
         """
-        model_setting = db.session.query(ProviderModelSetting) \
+        model_setting = (
+            db.session.query(ProviderModelSetting)
             .filter(
-            ProviderModelSetting.tenant_id == self.tenant_id,
-            ProviderModelSetting.provider_name == self.provider.provider,
-            ProviderModelSetting.model_type == model_type.to_origin_model_type(),
-            ProviderModelSetting.model_name == model
-        ).first()
+                ProviderModelSetting.tenant_id == self.tenant_id,
+                ProviderModelSetting.provider_name == self.provider.provider,
+                ProviderModelSetting.model_type == model_type.to_origin_model_type(),
+                ProviderModelSetting.model_name == model,
+            )
+            .first()
+        )
 
         if model_setting:
             model_setting.enabled = True
@@ -455,7 +476,7 @@ class ProviderConfiguration(BaseModel):
                 provider_name=self.provider.provider,
                 model_type=model_type.to_origin_model_type(),
                 model_name=model,
-                enabled=True
+                enabled=True,
             )
             db.session.add(model_setting)
             db.session.commit()
@@ -469,13 +490,16 @@ class ProviderConfiguration(BaseModel):
         :param model: model name
         :return:
         """
-        model_setting = db.session.query(ProviderModelSetting) \
+        model_setting = (
+            db.session.query(ProviderModelSetting)
             .filter(
-            ProviderModelSetting.tenant_id == self.tenant_id,
-            ProviderModelSetting.provider_name == self.provider.provider,
-            ProviderModelSetting.model_type == model_type.to_origin_model_type(),
-            ProviderModelSetting.model_name == model
-        ).first()
+                ProviderModelSetting.tenant_id == self.tenant_id,
+                ProviderModelSetting.provider_name == self.provider.provider,
+                ProviderModelSetting.model_type == model_type.to_origin_model_type(),
+                ProviderModelSetting.model_name == model,
+            )
+            .first()
+        )
 
         if model_setting:
             model_setting.enabled = False
@@ -487,7 +511,7 @@ class ProviderConfiguration(BaseModel):
                 provider_name=self.provider.provider,
                 model_type=model_type.to_origin_model_type(),
                 model_name=model,
-                enabled=False
+                enabled=False,
             )
             db.session.add(model_setting)
             db.session.commit()
@@ -501,13 +525,16 @@ class ProviderConfiguration(BaseModel):
         :param model: model name
         :return:
         """
-        return db.session.query(ProviderModelSetting) \
+        return (
+            db.session.query(ProviderModelSetting)
             .filter(
-            ProviderModelSetting.tenant_id == self.tenant_id,
-            ProviderModelSetting.provider_name == self.provider.provider,
-            ProviderModelSetting.model_type == model_type.to_origin_model_type(),
-            ProviderModelSetting.model_name == model
-        ).first()
+                ProviderModelSetting.tenant_id == self.tenant_id,
+                ProviderModelSetting.provider_name == self.provider.provider,
+                ProviderModelSetting.model_type == model_type.to_origin_model_type(),
+                ProviderModelSetting.model_name == model,
+            )
+            .first()
+        )
 
     def enable_model_load_balancing(self, model_type: ModelType, model: str) -> ProviderModelSetting:
         """
@@ -516,24 +543,30 @@ class ProviderConfiguration(BaseModel):
         :param model: model name
         :return:
         """
-        load_balancing_config_count = db.session.query(LoadBalancingModelConfig) \
+        load_balancing_config_count = (
+            db.session.query(LoadBalancingModelConfig)
             .filter(
-            LoadBalancingModelConfig.tenant_id == self.tenant_id,
-            LoadBalancingModelConfig.provider_name == self.provider.provider,
-            LoadBalancingModelConfig.model_type == model_type.to_origin_model_type(),
-            LoadBalancingModelConfig.model_name == model
-        ).count()
+                LoadBalancingModelConfig.tenant_id == self.tenant_id,
+                LoadBalancingModelConfig.provider_name == self.provider.provider,
+                LoadBalancingModelConfig.model_type == model_type.to_origin_model_type(),
+                LoadBalancingModelConfig.model_name == model,
+            )
+            .count()
+        )
 
         if load_balancing_config_count <= 1:
-            raise ValueError('Model load balancing configuration must be more than 1.')
+            raise ValueError("Model load balancing configuration must be more than 1.")
 
-        model_setting = db.session.query(ProviderModelSetting) \
+        model_setting = (
+            db.session.query(ProviderModelSetting)
             .filter(
-            ProviderModelSetting.tenant_id == self.tenant_id,
-            ProviderModelSetting.provider_name == self.provider.provider,
-            ProviderModelSetting.model_type == model_type.to_origin_model_type(),
-            ProviderModelSetting.model_name == model
-        ).first()
+                ProviderModelSetting.tenant_id == self.tenant_id,
+                ProviderModelSetting.provider_name == self.provider.provider,
+                ProviderModelSetting.model_type == model_type.to_origin_model_type(),
+                ProviderModelSetting.model_name == model,
+            )
+            .first()
+        )
 
         if model_setting:
             model_setting.load_balancing_enabled = True
@@ -545,7 +578,7 @@ class ProviderConfiguration(BaseModel):
                 provider_name=self.provider.provider,
                 model_type=model_type.to_origin_model_type(),
                 model_name=model,
-                load_balancing_enabled=True
+                load_balancing_enabled=True,
             )
             db.session.add(model_setting)
             db.session.commit()
@@ -559,13 +592,16 @@ class ProviderConfiguration(BaseModel):
         :param model: model name
         :return:
         """
-        model_setting = db.session.query(ProviderModelSetting) \
+        model_setting = (
+            db.session.query(ProviderModelSetting)
             .filter(
-            ProviderModelSetting.tenant_id == self.tenant_id,
-            ProviderModelSetting.provider_name == self.provider.provider,
-            ProviderModelSetting.model_type == model_type.to_origin_model_type(),
-            ProviderModelSetting.model_name == model
-        ).first()
+                ProviderModelSetting.tenant_id == self.tenant_id,
+                ProviderModelSetting.provider_name == self.provider.provider,
+                ProviderModelSetting.model_type == model_type.to_origin_model_type(),
+                ProviderModelSetting.model_name == model,
+            )
+            .first()
+        )
 
         if model_setting:
             model_setting.load_balancing_enabled = False
@@ -577,7 +613,7 @@ class ProviderConfiguration(BaseModel):
                 provider_name=self.provider.provider,
                 model_type=model_type.to_origin_model_type(),
                 model_name=model,
-                load_balancing_enabled=False
+                load_balancing_enabled=False,
             )
             db.session.add(model_setting)
             db.session.commit()
@@ -617,11 +653,14 @@ class ProviderConfiguration(BaseModel):
             return
 
         # get preferred provider
-        preferred_model_provider = db.session.query(TenantPreferredModelProvider) \
+        preferred_model_provider = (
+            db.session.query(TenantPreferredModelProvider)
             .filter(
-            TenantPreferredModelProvider.tenant_id == self.tenant_id,
-            TenantPreferredModelProvider.provider_name == self.provider.provider
-        ).first()
+                TenantPreferredModelProvider.tenant_id == self.tenant_id,
+                TenantPreferredModelProvider.provider_name == self.provider.provider,
+            )
+            .first()
+        )
 
         if preferred_model_provider:
             preferred_model_provider.preferred_provider_type = provider_type.value
@@ -629,7 +668,7 @@ class ProviderConfiguration(BaseModel):
             preferred_model_provider = TenantPreferredModelProvider(
                 tenant_id=self.tenant_id,
                 provider_name=self.provider.provider,
-                preferred_provider_type=provider_type.value
+                preferred_provider_type=provider_type.value,
             )
             db.session.add(preferred_model_provider)
 
@@ -658,9 +697,7 @@ class ProviderConfiguration(BaseModel):
         :return:
         """
         # Get provider credential secret variables
-        credential_secret_variables = self.extract_secret_variables(
-            credential_form_schemas
-        )
+        credential_secret_variables = self.extract_secret_variables(credential_form_schemas)
 
         # Obfuscate provider credentials
         copy_credentials = credentials.copy()
@@ -670,9 +707,9 @@ class ProviderConfiguration(BaseModel):
 
         return copy_credentials
 
-    def get_provider_model(self, model_type: ModelType,
-                           model: str,
-                           only_active: bool = False) -> Optional[ModelWithProviderEntity]:
+    def get_provider_model(
+        self, model_type: ModelType, model: str, only_active: bool = False
+    ) -> Optional[ModelWithProviderEntity]:
         """
         Get provider model.
         :param model_type: model type
@@ -688,8 +725,9 @@ class ProviderConfiguration(BaseModel):
 
         return None
 
-    def get_provider_models(self, model_type: Optional[ModelType] = None,
-                            only_active: bool = False) -> list[ModelWithProviderEntity]:
+    def get_provider_models(
+        self, model_type: Optional[ModelType] = None, only_active: bool = False
+    ) -> list[ModelWithProviderEntity]:
         """
         Get provider models.
         :param model_type: model type
@@ -711,15 +749,11 @@ class ProviderConfiguration(BaseModel):
 
         if self.using_provider_type == ProviderType.SYSTEM:
             provider_models = self._get_system_provider_models(
-                model_types=model_types,
-                provider_instance=provider_instance,
-                model_setting_map=model_setting_map
+                model_types=model_types, provider_instance=provider_instance, model_setting_map=model_setting_map
             )
         else:
             provider_models = self._get_custom_provider_models(
-                model_types=model_types,
-                provider_instance=provider_instance,
-                model_setting_map=model_setting_map
+                model_types=model_types, provider_instance=provider_instance, model_setting_map=model_setting_map
             )
 
         if only_active:
@@ -728,11 +762,12 @@ class ProviderConfiguration(BaseModel):
         # resort provider_models
         return sorted(provider_models, key=lambda x: x.model_type.value)
 
-    def _get_system_provider_models(self,
-                                    model_types: list[ModelType],
-                                    provider_instance: ModelProvider,
-                                    model_setting_map: dict[ModelType, dict[str, ModelSettings]]) \
-            -> list[ModelWithProviderEntity]:
+    def _get_system_provider_models(
+        self,
+        model_types: list[ModelType],
+        provider_instance: ModelProvider,
+        model_setting_map: dict[ModelType, dict[str, ModelSettings]],
+    ) -> list[ModelWithProviderEntity]:
         """
         Get system provider models.
 
@@ -760,7 +795,7 @@ class ProviderConfiguration(BaseModel):
                         model_properties=m.model_properties,
                         deprecated=m.deprecated,
                         provider=SimpleModelProviderEntity(self.provider),
-                        status=status
+                        status=status,
                     )
                 )
 
@@ -783,23 +818,20 @@ class ProviderConfiguration(BaseModel):
 
             if should_use_custom_model:
                 if original_provider_configurate_methods[self.provider.provider] == [
-                    ConfigurateMethod.CUSTOMIZABLE_MODEL]:
+                    ConfigurateMethod.CUSTOMIZABLE_MODEL
+                ]:
                     # only customizable model
                     for restrict_model in restrict_models:
                         copy_credentials = self.system_configuration.credentials.copy()
                         if restrict_model.base_model_name:
-                            copy_credentials['base_model_name'] = restrict_model.base_model_name
+                            copy_credentials["base_model_name"] = restrict_model.base_model_name
 
                         try:
-                            custom_model_schema = (
-                                provider_instance.get_model_instance(restrict_model.model_type)
-                                .get_customizable_model_schema_from_credentials(
-                                    restrict_model.model,
-                                    copy_credentials
-                                )
-                            )
+                            custom_model_schema = provider_instance.get_model_instance(
+                                restrict_model.model_type
+                            ).get_customizable_model_schema_from_credentials(restrict_model.model, copy_credentials)
                         except Exception as ex:
-                            logger.warning(f'get custom model schema failed, {ex}')
+                            logger.warning(f"get custom model schema failed, {ex}")
                             continue
 
                         if not custom_model_schema:
@@ -809,8 +841,10 @@ class ProviderConfiguration(BaseModel):
                             continue
 
                         status = ModelStatus.ACTIVE
-                        if (custom_model_schema.model_type in model_setting_map
-                                and custom_model_schema.model in model_setting_map[custom_model_schema.model_type]):
+                        if (
+                            custom_model_schema.model_type in model_setting_map
+                            and custom_model_schema.model in model_setting_map[custom_model_schema.model_type]
+                        ):
                             model_setting = model_setting_map[custom_model_schema.model_type][custom_model_schema.model]
                             if model_setting.enabled is False:
                                 status = ModelStatus.DISABLED
@@ -825,7 +859,7 @@ class ProviderConfiguration(BaseModel):
                                 model_properties=custom_model_schema.model_properties,
                                 deprecated=custom_model_schema.deprecated,
                                 provider=SimpleModelProviderEntity(self.provider),
-                                status=status
+                                status=status,
                             )
                         )
 
@@ -839,11 +873,12 @@ class ProviderConfiguration(BaseModel):
 
         return provider_models
 
-    def _get_custom_provider_models(self,
-                                    model_types: list[ModelType],
-                                    provider_instance: ModelProvider,
-                                    model_setting_map: dict[ModelType, dict[str, ModelSettings]]) \
-            -> list[ModelWithProviderEntity]:
+    def _get_custom_provider_models(
+        self,
+        model_types: list[ModelType],
+        provider_instance: ModelProvider,
+        model_setting_map: dict[ModelType, dict[str, ModelSettings]],
+    ) -> list[ModelWithProviderEntity]:
         """
         Get custom provider models.
 
@@ -885,7 +920,7 @@ class ProviderConfiguration(BaseModel):
                         deprecated=m.deprecated,
                         provider=SimpleModelProviderEntity(self.provider),
                         status=status,
-                        load_balancing_enabled=load_balancing_enabled
+                        load_balancing_enabled=load_balancing_enabled,
                     )
                 )
 
@@ -895,15 +930,13 @@ class ProviderConfiguration(BaseModel):
                 continue
 
             try:
-                custom_model_schema = (
-                    provider_instance.get_model_instance(model_configuration.model_type)
-                    .get_customizable_model_schema_from_credentials(
-                        model_configuration.model,
-                        model_configuration.credentials
-                    )
+                custom_model_schema = provider_instance.get_model_instance(
+                    model_configuration.model_type
+                ).get_customizable_model_schema_from_credentials(
+                    model_configuration.model, model_configuration.credentials
                 )
             except Exception as ex:
-                logger.warning(f'get custom model schema failed, {ex}')
+                logger.warning(f"get custom model schema failed, {ex}")
                 continue
 
             if not custom_model_schema:
@@ -911,8 +944,10 @@ class ProviderConfiguration(BaseModel):
 
             status = ModelStatus.ACTIVE
             load_balancing_enabled = False
-            if (custom_model_schema.model_type in model_setting_map
-                    and custom_model_schema.model in model_setting_map[custom_model_schema.model_type]):
+            if (
+                custom_model_schema.model_type in model_setting_map
+                and custom_model_schema.model in model_setting_map[custom_model_schema.model_type]
+            ):
                 model_setting = model_setting_map[custom_model_schema.model_type][custom_model_schema.model]
                 if model_setting.enabled is False:
                     status = ModelStatus.DISABLED
@@ -931,7 +966,7 @@ class ProviderConfiguration(BaseModel):
                     deprecated=custom_model_schema.deprecated,
                     provider=SimpleModelProviderEntity(self.provider),
                     status=status,
-                    load_balancing_enabled=load_balancing_enabled
+                    load_balancing_enabled=load_balancing_enabled,
                 )
             )
 
@@ -942,17 +977,16 @@ class ProviderConfigurations(BaseModel):
     """
     Model class for provider configuration dict.
     """
+
     tenant_id: str
     configurations: dict[str, ProviderConfiguration] = {}
 
     def __init__(self, tenant_id: str):
         super().__init__(tenant_id=tenant_id)
 
-    def get_models(self,
-                   provider: Optional[str] = None,
-                   model_type: Optional[ModelType] = None,
-                   only_active: bool = False) \
-            -> list[ModelWithProviderEntity]:
+    def get_models(
+        self, provider: Optional[str] = None, model_type: Optional[ModelType] = None, only_active: bool = False
+    ) -> list[ModelWithProviderEntity]:
         """
         Get available models.
 
@@ -1019,10 +1053,10 @@ class ProviderModelBundle(BaseModel):
     """
     Provider model bundle.
     """
+
     configuration: ProviderConfiguration
     provider_instance: ModelProvider
     model_type_instance: AIModel
 
     # pydantic configs
-    model_config = ConfigDict(arbitrary_types_allowed=True,
-                              protected_namespaces=())
+    model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())
diff --git a/api/core/entities/provider_entities.py b/api/core/entities/provider_entities.py
index 0d5b0a1b2c..44725623dc 100644
--- a/api/core/entities/provider_entities.py
+++ b/api/core/entities/provider_entities.py
@@ -8,18 +8,19 @@ from models.provider import ProviderQuotaType
 
 
 class QuotaUnit(Enum):
-    TIMES = 'times'
-    TOKENS = 'tokens'
-    CREDITS = 'credits'
+    TIMES = "times"
+    TOKENS = "tokens"
+    CREDITS = "credits"
 
 
 class SystemConfigurationStatus(Enum):
     """
     Enum class for system configuration status.
     """
-    ACTIVE = 'active'
-    QUOTA_EXCEEDED = 'quota-exceeded'
-    UNSUPPORTED = 'unsupported'
+
+    ACTIVE = "active"
+    QUOTA_EXCEEDED = "quota-exceeded"
+    UNSUPPORTED = "unsupported"
 
 
 class RestrictModel(BaseModel):
@@ -35,6 +36,7 @@ class QuotaConfiguration(BaseModel):
     """
     Model class for provider quota configuration.
     """
+
     quota_type: ProviderQuotaType
     quota_unit: QuotaUnit
     quota_limit: int
@@ -47,6 +49,7 @@ class SystemConfiguration(BaseModel):
     """
     Model class for provider system configuration.
     """
+
     enabled: bool
     current_quota_type: Optional[ProviderQuotaType] = None
     quota_configurations: list[QuotaConfiguration] = []
@@ -57,6 +60,7 @@ class CustomProviderConfiguration(BaseModel):
     """
     Model class for provider custom configuration.
     """
+
     credentials: dict
 
 
@@ -64,6 +68,7 @@ class CustomModelConfiguration(BaseModel):
     """
     Model class for provider custom model configuration.
     """
+
     model: str
     model_type: ModelType
     credentials: dict
@@ -76,6 +81,7 @@ class CustomConfiguration(BaseModel):
     """
     Model class for provider custom configuration.
     """
+
     provider: Optional[CustomProviderConfiguration] = None
     models: list[CustomModelConfiguration] = []
 
@@ -84,6 +90,7 @@ class ModelLoadBalancingConfiguration(BaseModel):
     """
     Class for model load balancing configuration.
     """
+
     id: str
     name: str
     credentials: dict
@@ -93,6 +100,7 @@ class ModelSettings(BaseModel):
     """
     Model class for model settings.
     """
+
     model: str
     model_type: ModelType
     enabled: bool = True
diff --git a/api/core/errors/error.py b/api/core/errors/error.py
index 53323a2eeb..3b186476eb 100644
--- a/api/core/errors/error.py
+++ b/api/core/errors/error.py
@@ -3,6 +3,7 @@ from typing import Optional
 
 class LLMError(Exception):
     """Base class for all LLM exceptions."""
+
     description: Optional[str] = None
 
     def __init__(self, description: Optional[str] = None) -> None:
@@ -11,6 +12,7 @@ class LLMError(Exception):
 
 class LLMBadRequestError(LLMError):
     """Raised when the LLM returns bad request."""
+
     description = "Bad Request"
 
 
@@ -18,6 +20,7 @@ class ProviderTokenNotInitError(Exception):
     """
     Custom exception raised when the provider token is not initialized.
     """
+
     description = "Provider Token Not Init"
 
     def __init__(self, *args, **kwargs):
@@ -28,6 +31,7 @@ class QuotaExceededError(Exception):
     """
     Custom exception raised when the quota for a provider has been exceeded.
     """
+
     description = "Quota Exceeded"
 
 
@@ -35,6 +39,7 @@ class AppInvokeQuotaExceededError(Exception):
     """
     Custom exception raised when the quota for an app has been exceeded.
     """
+
     description = "App Invoke Quota Exceeded"
 
 
@@ -42,9 +47,11 @@ class ModelCurrentlyNotSupportError(Exception):
     """
     Custom exception raised when the model not support
     """
+
     description = "Model Currently Not Support"
 
 
 class InvokeRateLimitError(Exception):
     """Raised when the Invoke returns rate limit error."""
+
     description = "Rate Limit Error"
diff --git a/api/core/extension/api_based_extension_requestor.py b/api/core/extension/api_based_extension_requestor.py
index 4db7a99973..38cebb6b6b 100644
--- a/api/core/extension/api_based_extension_requestor.py
+++ b/api/core/extension/api_based_extension_requestor.py
@@ -20,10 +20,7 @@ class APIBasedExtensionRequestor:
         :param params: the request params
         :return: the response json
         """
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": "Bearer {}".format(self.api_key)
-        }
+        headers = {"Content-Type": "application/json", "Authorization": "Bearer {}".format(self.api_key)}
 
         url = self.api_endpoint
 
@@ -32,20 +29,17 @@ class APIBasedExtensionRequestor:
             proxies = None
             if dify_config.SSRF_PROXY_HTTP_URL and dify_config.SSRF_PROXY_HTTPS_URL:
                 proxies = {
-                    'http': dify_config.SSRF_PROXY_HTTP_URL,
-                    'https': dify_config.SSRF_PROXY_HTTPS_URL,
+                    "http": dify_config.SSRF_PROXY_HTTP_URL,
+                    "https": dify_config.SSRF_PROXY_HTTPS_URL,
                 }
 
             response = requests.request(
-                method='POST',
+                method="POST",
                 url=url,
-                json={
-                    'point': point.value,
-                    'params': params
-                },
+                json={"point": point.value, "params": params},
                 headers=headers,
                 timeout=self.timeout,
-                proxies=proxies
+                proxies=proxies,
             )
         except requests.exceptions.Timeout:
             raise ValueError("request timeout")
@@ -53,9 +47,8 @@ class APIBasedExtensionRequestor:
             raise ValueError("request connection error")
 
         if response.status_code != 200:
-            raise ValueError("request error, status_code: {}, content: {}".format(
-                response.status_code,
-                response.text[:100]
-            ))
+            raise ValueError(
+                "request error, status_code: {}, content: {}".format(response.status_code, response.text[:100])
+            )
 
         return response.json()
diff --git a/api/core/extension/extensible.py b/api/core/extension/extensible.py
index 8d73aa2b8b..97dbaf2026 100644
--- a/api/core/extension/extensible.py
+++ b/api/core/extension/extensible.py
@@ -3,6 +3,7 @@ import importlib.util
 import json
 import logging
 import os
+from pathlib import Path
 from typing import Any, Optional
 
 from pydantic import BaseModel
@@ -11,8 +12,8 @@ from core.helper.position_helper import sort_to_dict_by_position_map
 
 
 class ExtensionModule(enum.Enum):
-    MODERATION = 'moderation'
-    EXTERNAL_DATA_TOOL = 'external_data_tool'
+    MODERATION = "moderation"
+    EXTERNAL_DATA_TOOL = "external_data_tool"
 
 
 class ModuleExtension(BaseModel):
@@ -41,12 +42,12 @@ class Extensible:
         position_map = {}
 
         # get the path of the current class
-        current_path = os.path.abspath(cls.__module__.replace(".", os.path.sep) + '.py')
+        current_path = os.path.abspath(cls.__module__.replace(".", os.path.sep) + ".py")
         current_dir_path = os.path.dirname(current_path)
 
         # traverse subdirectories
         for subdir_name in os.listdir(current_dir_path):
-            if subdir_name.startswith('__'):
+            if subdir_name.startswith("__"):
                 continue
 
             subdir_path = os.path.join(current_dir_path, subdir_name)
@@ -58,21 +59,20 @@ class Extensible:
                 # in the front-end page and business logic, there are special treatments.
                 builtin = False
                 position = None
-                if '__builtin__' in file_names:
+                if "__builtin__" in file_names:
                     builtin = True
 
-                    builtin_file_path = os.path.join(subdir_path, '__builtin__')
+                    builtin_file_path = os.path.join(subdir_path, "__builtin__")
                     if os.path.exists(builtin_file_path):
-                        with open(builtin_file_path, encoding='utf-8') as f:
-                            position = int(f.read().strip())
+                        position = int(Path(builtin_file_path).read_text(encoding="utf-8").strip())
                     position_map[extension_name] = position
 
-                if (extension_name + '.py') not in file_names:
+                if (extension_name + ".py") not in file_names:
                     logging.warning(f"Missing {extension_name}.py file in {subdir_path}, Skip.")
                     continue
 
                 # Dynamic loading {subdir_name}.py file and find the subclass of Extensible
-                py_path = os.path.join(subdir_path, extension_name + '.py')
+                py_path = os.path.join(subdir_path, extension_name + ".py")
                 spec = importlib.util.spec_from_file_location(extension_name, py_path)
                 if not spec or not spec.loader:
                     raise Exception(f"Failed to load module {extension_name} from {py_path}")
@@ -91,25 +91,29 @@ class Extensible:
 
                 json_data = {}
                 if not builtin:
-                    if 'schema.json' not in file_names:
+                    if "schema.json" not in file_names:
                         logging.warning(f"Missing schema.json file in {subdir_path}, Skip.")
                         continue
 
-                    json_path = os.path.join(subdir_path, 'schema.json')
+                    json_path = os.path.join(subdir_path, "schema.json")
                     json_data = {}
                     if os.path.exists(json_path):
-                        with open(json_path, encoding='utf-8') as f:
+                        with open(json_path, encoding="utf-8") as f:
                             json_data = json.load(f)
 
-                extensions.append(ModuleExtension(
-                    extension_class=extension_class,
-                    name=extension_name,
-                    label=json_data.get('label'),
-                    form_schema=json_data.get('form_schema'),
-                    builtin=builtin,
-                    position=position
-                ))
+                extensions.append(
+                    ModuleExtension(
+                        extension_class=extension_class,
+                        name=extension_name,
+                        label=json_data.get("label"),
+                        form_schema=json_data.get("form_schema"),
+                        builtin=builtin,
+                        position=position,
+                    )
+                )
 
-        sorted_extensions = sort_to_dict_by_position_map(position_map=position_map, data=extensions, name_func=lambda x: x.name)
+        sorted_extensions = sort_to_dict_by_position_map(
+            position_map=position_map, data=extensions, name_func=lambda x: x.name
+        )
 
         return sorted_extensions
diff --git a/api/core/extension/extension.py b/api/core/extension/extension.py
index 29e892c58a..3da170455e 100644
--- a/api/core/extension/extension.py
+++ b/api/core/extension/extension.py
@@ -6,10 +6,7 @@ from core.moderation.base import Moderation
 class Extension:
     __module_extensions: dict[str, dict[str, ModuleExtension]] = {}
 
-    module_classes = {
-        ExtensionModule.MODERATION: Moderation,
-        ExtensionModule.EXTERNAL_DATA_TOOL: ExternalDataTool
-    }
+    module_classes = {ExtensionModule.MODERATION: Moderation, ExtensionModule.EXTERNAL_DATA_TOOL: ExternalDataTool}
 
     def init(self):
         for module, module_class in self.module_classes.items():
diff --git a/api/core/external_data_tool/api/api.py b/api/core/external_data_tool/api/api.py
index 58c82502ea..54ec97a493 100644
--- a/api/core/external_data_tool/api/api.py
+++ b/api/core/external_data_tool/api/api.py
@@ -30,10 +30,11 @@ class ApiExternalDataTool(ExternalDataTool):
             raise ValueError("api_based_extension_id is required")
 
         # get api_based_extension
-        api_based_extension = db.session.query(APIBasedExtension).filter(
-            APIBasedExtension.tenant_id == tenant_id,
-            APIBasedExtension.id == api_based_extension_id
-        ).first()
+        api_based_extension = (
+            db.session.query(APIBasedExtension)
+            .filter(APIBasedExtension.tenant_id == tenant_id, APIBasedExtension.id == api_based_extension_id)
+            .first()
+        )
 
         if not api_based_extension:
             raise ValueError("api_based_extension_id is invalid")
@@ -50,47 +51,42 @@ class ApiExternalDataTool(ExternalDataTool):
         api_based_extension_id = self.config.get("api_based_extension_id")
 
         # get api_based_extension
-        api_based_extension = db.session.query(APIBasedExtension).filter(
-            APIBasedExtension.tenant_id == self.tenant_id,
-            APIBasedExtension.id == api_based_extension_id
-        ).first()
+        api_based_extension = (
+            db.session.query(APIBasedExtension)
+            .filter(APIBasedExtension.tenant_id == self.tenant_id, APIBasedExtension.id == api_based_extension_id)
+            .first()
+        )
 
         if not api_based_extension:
-            raise ValueError("[External data tool] API query failed, variable: {}, "
-                             "error: api_based_extension_id is invalid"
-                             .format(self.variable))
+            raise ValueError(
+                "[External data tool] API query failed, variable: {}, "
+                "error: api_based_extension_id is invalid".format(self.variable)
+            )
 
         # decrypt api_key
-        api_key = encrypter.decrypt_token(
-            tenant_id=self.tenant_id,
-            token=api_based_extension.api_key
-        )
+        api_key = encrypter.decrypt_token(tenant_id=self.tenant_id, token=api_based_extension.api_key)
 
         try:
             # request api
-            requestor = APIBasedExtensionRequestor(
-                api_endpoint=api_based_extension.api_endpoint,
-                api_key=api_key
-            )
+            requestor = APIBasedExtensionRequestor(api_endpoint=api_based_extension.api_endpoint, api_key=api_key)
         except Exception as e:
-            raise ValueError("[External data tool] API query failed, variable: {}, error: {}".format(
-                self.variable,
-                e
-            ))
+            raise ValueError("[External data tool] API query failed, variable: {}, error: {}".format(self.variable, e))
 
-        response_json = requestor.request(point=APIBasedExtensionPoint.APP_EXTERNAL_DATA_TOOL_QUERY, params={
-            'app_id': self.app_id,
-            'tool_variable': self.variable,
-            'inputs': inputs,
-            'query': query
-        })
+        response_json = requestor.request(
+            point=APIBasedExtensionPoint.APP_EXTERNAL_DATA_TOOL_QUERY,
+            params={"app_id": self.app_id, "tool_variable": self.variable, "inputs": inputs, "query": query},
+        )
 
-        if 'result' not in response_json:
-            raise ValueError("[External data tool] API query failed, variable: {}, error: result not found in response"
-                             .format(self.variable))
+        if "result" not in response_json:
+            raise ValueError(
+                "[External data tool] API query failed, variable: {}, error: result not found in response".format(
+                    self.variable
+                )
+            )
 
-        if not isinstance(response_json['result'], str):
-            raise ValueError("[External data tool] API query failed, variable: {}, error: result is not string"
-                             .format(self.variable))
+        if not isinstance(response_json["result"], str):
+            raise ValueError(
+                "[External data tool] API query failed, variable: {}, error: result is not string".format(self.variable)
+            )
 
-        return response_json['result']
+        return response_json["result"]
diff --git a/api/core/external_data_tool/external_data_fetch.py b/api/core/external_data_tool/external_data_fetch.py
index 8601cb34e7..84b94e117f 100644
--- a/api/core/external_data_tool/external_data_fetch.py
+++ b/api/core/external_data_tool/external_data_fetch.py
@@ -12,11 +12,14 @@ logger = logging.getLogger(__name__)
 
 
 class ExternalDataFetch:
-    def fetch(self, tenant_id: str,
-              app_id: str,
-              external_data_tools: list[ExternalDataVariableEntity],
-              inputs: dict,
-              query: str) -> dict:
+    def fetch(
+        self,
+        tenant_id: str,
+        app_id: str,
+        external_data_tools: list[ExternalDataVariableEntity],
+        inputs: dict,
+        query: str,
+    ) -> dict:
         """
         Fill in variable inputs from external data tools if exists.
 
@@ -38,7 +41,7 @@ class ExternalDataFetch:
                     app_id,
                     tool,
                     inputs,
-                    query
+                    query,
                 )
 
                 futures[future] = tool
@@ -50,12 +53,15 @@ class ExternalDataFetch:
         inputs.update(results)
         return inputs
 
-    def _query_external_data_tool(self, flask_app: Flask,
-                                  tenant_id: str,
-                                  app_id: str,
-                                  external_data_tool: ExternalDataVariableEntity,
-                                  inputs: dict,
-                                  query: str) -> tuple[Optional[str], Optional[str]]:
+    def _query_external_data_tool(
+        self,
+        flask_app: Flask,
+        tenant_id: str,
+        app_id: str,
+        external_data_tool: ExternalDataVariableEntity,
+        inputs: dict,
+        query: str,
+    ) -> tuple[Optional[str], Optional[str]]:
         """
         Query external data tool.
         :param flask_app: flask app
@@ -72,17 +78,10 @@ class ExternalDataFetch:
             tool_config = external_data_tool.config
 
             external_data_tool_factory = ExternalDataToolFactory(
-                name=tool_type,
-                tenant_id=tenant_id,
-                app_id=app_id,
-                variable=tool_variable,
-                config=tool_config
+                name=tool_type, tenant_id=tenant_id, app_id=app_id, variable=tool_variable, config=tool_config
             )
 
             # query external data tool
-            result = external_data_tool_factory.query(
-                inputs=inputs,
-                query=query
-            )
+            result = external_data_tool_factory.query(inputs=inputs, query=query)
 
             return tool_variable, result
diff --git a/api/core/external_data_tool/factory.py b/api/core/external_data_tool/factory.py
index 979f243af6..2872109859 100644
--- a/api/core/external_data_tool/factory.py
+++ b/api/core/external_data_tool/factory.py
@@ -5,14 +5,10 @@ from extensions.ext_code_based_extension import code_based_extension
 
 
 class ExternalDataToolFactory:
-
     def __init__(self, name: str, tenant_id: str, app_id: str, variable: str, config: dict) -> None:
         extension_class = code_based_extension.extension_class(ExtensionModule.EXTERNAL_DATA_TOOL, name)
         self.__extension_instance = extension_class(
-            tenant_id=tenant_id,
-            app_id=app_id,
-            variable=variable,
-            config=config
+            tenant_id=tenant_id, app_id=app_id, variable=variable, config=config
         )
 
     @classmethod
diff --git a/api/core/file/file_obj.py b/api/core/file/file_obj.py
index 3959f4b4a0..5c4e694025 100644
--- a/api/core/file/file_obj.py
+++ b/api/core/file/file_obj.py
@@ -13,11 +13,12 @@ class FileExtraConfig(BaseModel):
     """
     File Upload Entity.
     """
+
     image_config: Optional[dict[str, Any]] = None
 
 
 class FileType(enum.Enum):
-    IMAGE = 'image'
+    IMAGE = "image"
 
     @staticmethod
     def value_of(value):
@@ -28,9 +29,9 @@ class FileType(enum.Enum):
 
 
 class FileTransferMethod(enum.Enum):
-    REMOTE_URL = 'remote_url'
-    LOCAL_FILE = 'local_file'
-    TOOL_FILE = 'tool_file'
+    REMOTE_URL = "remote_url"
+    LOCAL_FILE = "local_file"
+    TOOL_FILE = "tool_file"
 
     @staticmethod
     def value_of(value):
@@ -39,9 +40,10 @@ class FileTransferMethod(enum.Enum):
                 return member
         raise ValueError(f"No matching enum found for value '{value}'")
 
+
 class FileBelongsTo(enum.Enum):
-    USER = 'user'
-    ASSISTANT = 'assistant'
+    USER = "user"
+    ASSISTANT = "assistant"
 
     @staticmethod
     def value_of(value):
@@ -65,16 +67,16 @@ class FileVar(BaseModel):
 
     def to_dict(self) -> dict:
         return {
-            '__variant': self.__class__.__name__,
-            'tenant_id': self.tenant_id,
-            'type': self.type.value,
-            'transfer_method': self.transfer_method.value,
-            'url': self.preview_url,
-            'remote_url': self.url,
-            'related_id': self.related_id,
-            'filename': self.filename,
-            'extension': self.extension,
-            'mime_type': self.mime_type,
+            "__variant": self.__class__.__name__,
+            "tenant_id": self.tenant_id,
+            "type": self.type.value,
+            "transfer_method": self.transfer_method.value,
+            "url": self.preview_url,
+            "remote_url": self.url,
+            "related_id": self.related_id,
+            "filename": self.filename,
+            "extension": self.extension,
+            "mime_type": self.mime_type,
         }
 
     def to_markdown(self) -> str:
@@ -86,7 +88,7 @@ class FileVar(BaseModel):
         if self.type == FileType.IMAGE:
             text = f'![{self.filename or ""}]({preview_url})'
         else:
-            text = f'[{self.filename or preview_url}]({preview_url})'
+            text = f"[{self.filename or preview_url}]({preview_url})"
 
         return text
 
@@ -115,28 +117,29 @@ class FileVar(BaseModel):
             return ImagePromptMessageContent(
                 data=self.data,
                 detail=ImagePromptMessageContent.DETAIL.HIGH
-                if image_config.get("detail") == "high" else ImagePromptMessageContent.DETAIL.LOW
+                if image_config.get("detail") == "high"
+                else ImagePromptMessageContent.DETAIL.LOW,
             )
 
     def _get_data(self, force_url: bool = False) -> Optional[str]:
         from models.model import UploadFile
+
         if self.type == FileType.IMAGE:
             if self.transfer_method == FileTransferMethod.REMOTE_URL:
                 return self.url
             elif self.transfer_method == FileTransferMethod.LOCAL_FILE:
-                upload_file = (db.session.query(UploadFile)
-                               .filter(
-                    UploadFile.id == self.related_id,
-                    UploadFile.tenant_id == self.tenant_id
-                ).first())
-
-                return UploadFileParser.get_image_data(
-                    upload_file=upload_file,
-                    force_url=force_url
+                upload_file = (
+                    db.session.query(UploadFile)
+                    .filter(UploadFile.id == self.related_id, UploadFile.tenant_id == self.tenant_id)
+                    .first()
                 )
+
+                return UploadFileParser.get_image_data(upload_file=upload_file, force_url=force_url)
             elif self.transfer_method == FileTransferMethod.TOOL_FILE:
                 extension = self.extension
                 # add sign url
-                return ToolFileParser.get_tool_file_manager().sign_file(tool_file_id=self.related_id, extension=extension)
+                return ToolFileParser.get_tool_file_manager().sign_file(
+                    tool_file_id=self.related_id, extension=extension
+                )
 
         return None
diff --git a/api/core/file/message_file_parser.py b/api/core/file/message_file_parser.py
index 085ff07cfd..83059b216e 100644
--- a/api/core/file/message_file_parser.py
+++ b/api/core/file/message_file_parser.py
@@ -13,13 +13,13 @@ from services.file_service import IMAGE_EXTENSIONS
 
 
 class MessageFileParser:
-
     def __init__(self, tenant_id: str, app_id: str) -> None:
         self.tenant_id = tenant_id
         self.app_id = app_id
 
-    def validate_and_transform_files_arg(self, files: Sequence[Mapping[str, Any]], file_extra_config: FileExtraConfig,
-                                         user: Union[Account, EndUser]) -> list[FileVar]:
+    def validate_and_transform_files_arg(
+        self, files: Sequence[Mapping[str, Any]], file_extra_config: FileExtraConfig, user: Union[Account, EndUser]
+    ) -> list[FileVar]:
         """
         validate and transform files arg
 
@@ -30,22 +30,22 @@ class MessageFileParser:
         """
         for file in files:
             if not isinstance(file, dict):
-                raise ValueError('Invalid file format, must be dict')
-            if not file.get('type'):
-                raise ValueError('Missing file type')
-            FileType.value_of(file.get('type'))
-            if not file.get('transfer_method'):
-                raise ValueError('Missing file transfer method')
-            FileTransferMethod.value_of(file.get('transfer_method'))
-            if file.get('transfer_method') == FileTransferMethod.REMOTE_URL.value:
-                if not file.get('url'):
-                    raise ValueError('Missing file url')
-                if not file.get('url').startswith('http'):
-                    raise ValueError('Invalid file url')
-            if file.get('transfer_method') == FileTransferMethod.LOCAL_FILE.value and not file.get('upload_file_id'):
-                raise ValueError('Missing file upload_file_id')
-            if file.get('transform_method') == FileTransferMethod.TOOL_FILE.value and not file.get('tool_file_id'):
-                raise ValueError('Missing file tool_file_id')
+                raise ValueError("Invalid file format, must be dict")
+            if not file.get("type"):
+                raise ValueError("Missing file type")
+            FileType.value_of(file.get("type"))
+            if not file.get("transfer_method"):
+                raise ValueError("Missing file transfer method")
+            FileTransferMethod.value_of(file.get("transfer_method"))
+            if file.get("transfer_method") == FileTransferMethod.REMOTE_URL.value:
+                if not file.get("url"):
+                    raise ValueError("Missing file url")
+                if not file.get("url").startswith("http"):
+                    raise ValueError("Invalid file url")
+            if file.get("transfer_method") == FileTransferMethod.LOCAL_FILE.value and not file.get("upload_file_id"):
+                raise ValueError("Missing file upload_file_id")
+            if file.get("transform_method") == FileTransferMethod.TOOL_FILE.value and not file.get("tool_file_id"):
+                raise ValueError("Missing file tool_file_id")
 
         # transform files to file objs
         type_file_objs = self._to_file_objs(files, file_extra_config)
@@ -62,17 +62,17 @@ class MessageFileParser:
                     continue
 
                 # Validate number of files
-                if len(files) > image_config['number_limits']:
+                if len(files) > image_config["number_limits"]:
                     raise ValueError(f"Number of image files exceeds the maximum limit {image_config['number_limits']}")
 
                 for file_obj in file_objs:
                     # Validate transfer method
-                    if file_obj.transfer_method.value not in image_config['transfer_methods']:
-                        raise ValueError(f'Invalid transfer method: {file_obj.transfer_method.value}')
+                    if file_obj.transfer_method.value not in image_config["transfer_methods"]:
+                        raise ValueError(f"Invalid transfer method: {file_obj.transfer_method.value}")
 
                     # Validate file type
                     if file_obj.type != FileType.IMAGE:
-                        raise ValueError(f'Invalid file type: {file_obj.type}')
+                        raise ValueError(f"Invalid file type: {file_obj.type}")
 
                     if file_obj.transfer_method == FileTransferMethod.REMOTE_URL:
                         # check remote url valid and is image
@@ -81,18 +81,21 @@ class MessageFileParser:
                             raise ValueError(error)
                     elif file_obj.transfer_method == FileTransferMethod.LOCAL_FILE:
                         # get upload file from upload_file_id
-                        upload_file = (db.session.query(UploadFile)
-                                       .filter(
-                            UploadFile.id == file_obj.related_id,
-                            UploadFile.tenant_id == self.tenant_id,
-                            UploadFile.created_by == user.id,
-                            UploadFile.created_by_role == ('account' if isinstance(user, Account) else 'end_user'),
-                            UploadFile.extension.in_(IMAGE_EXTENSIONS)
-                        ).first())
+                        upload_file = (
+                            db.session.query(UploadFile)
+                            .filter(
+                                UploadFile.id == file_obj.related_id,
+                                UploadFile.tenant_id == self.tenant_id,
+                                UploadFile.created_by == user.id,
+                                UploadFile.created_by_role == ("account" if isinstance(user, Account) else "end_user"),
+                                UploadFile.extension.in_(IMAGE_EXTENSIONS),
+                            )
+                            .first()
+                        )
 
                         # check upload file is belong to tenant and user
                         if not upload_file:
-                            raise ValueError('Invalid upload file')
+                            raise ValueError("Invalid upload file")
 
                     new_files.append(file_obj)
 
@@ -113,8 +116,9 @@ class MessageFileParser:
         # return all file objs
         return [file_obj for file_objs in type_file_objs.values() for file_obj in file_objs]
 
-    def _to_file_objs(self, files: list[Union[dict, MessageFile]],
-                      file_extra_config: FileExtraConfig) -> dict[FileType, list[FileVar]]:
+    def _to_file_objs(
+        self, files: list[Union[dict, MessageFile]], file_extra_config: FileExtraConfig
+    ) -> dict[FileType, list[FileVar]]:
         """
         transform files to file objs
 
@@ -152,23 +156,23 @@ class MessageFileParser:
         :return:
         """
         if isinstance(file, dict):
-            transfer_method = FileTransferMethod.value_of(file.get('transfer_method'))
+            transfer_method = FileTransferMethod.value_of(file.get("transfer_method"))
             if transfer_method != FileTransferMethod.TOOL_FILE:
                 return FileVar(
                     tenant_id=self.tenant_id,
-                    type=FileType.value_of(file.get('type')),
+                    type=FileType.value_of(file.get("type")),
                     transfer_method=transfer_method,
-                    url=file.get('url') if transfer_method == FileTransferMethod.REMOTE_URL else None,
-                    related_id=file.get('upload_file_id') if transfer_method == FileTransferMethod.LOCAL_FILE else None,
-                    extra_config=file_extra_config
+                    url=file.get("url") if transfer_method == FileTransferMethod.REMOTE_URL else None,
+                    related_id=file.get("upload_file_id") if transfer_method == FileTransferMethod.LOCAL_FILE else None,
+                    extra_config=file_extra_config,
                 )
             return FileVar(
                 tenant_id=self.tenant_id,
-                type=FileType.value_of(file.get('type')),
+                type=FileType.value_of(file.get("type")),
                 transfer_method=transfer_method,
                 url=None,
-                related_id=file.get('tool_file_id'),
-                extra_config=file_extra_config
+                related_id=file.get("tool_file_id"),
+                extra_config=file_extra_config,
             )
         else:
             return FileVar(
@@ -178,29 +182,30 @@ class MessageFileParser:
                 transfer_method=FileTransferMethod.value_of(file.transfer_method),
                 url=file.url,
                 related_id=file.upload_file_id or None,
-                extra_config=file_extra_config
+                extra_config=file_extra_config,
             )
 
     def _check_image_remote_url(self, url):
         try:
             headers = {
-                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)"
+                " Chrome/91.0.4472.124 Safari/537.36"
             }
 
             def is_s3_presigned_url(url):
                 try:
                     parsed_url = urlparse(url)
-                    if 'amazonaws.com' not in parsed_url.netloc:
+                    if "amazonaws.com" not in parsed_url.netloc:
                         return False
                     query_params = parse_qs(parsed_url.query)
-                    required_params = ['Signature', 'Expires']
+                    required_params = ["Signature", "Expires"]
                     for param in required_params:
                         if param not in query_params:
                             return False
-                    if not query_params['Expires'][0].isdigit():
+                    if not query_params["Expires"][0].isdigit():
                         return False
-                    signature = query_params['Signature'][0]
-                    if not re.match(r'^[A-Za-z0-9+/]+={0,2}$', signature):
+                    signature = query_params["Signature"][0]
+                    if not re.match(r"^[A-Za-z0-9+/]+={0,2}$", signature):
                         return False
                     return True
                 except Exception:
diff --git a/api/core/file/tool_file_parser.py b/api/core/file/tool_file_parser.py
index ea8605ac57..1efaf5529d 100644
--- a/api/core/file/tool_file_parser.py
+++ b/api/core/file/tool_file_parser.py
@@ -1,8 +1,7 @@
-tool_file_manager = {
-    'manager': None
-}
+tool_file_manager = {"manager": None}
+
 
 class ToolFileParser:
     @staticmethod
-    def get_tool_file_manager() -> 'ToolFileManager':
-        return tool_file_manager['manager']
\ No newline at end of file
+    def get_tool_file_manager() -> "ToolFileManager":
+        return tool_file_manager["manager"]
diff --git a/api/core/file/upload_file_parser.py b/api/core/file/upload_file_parser.py
index 737a11e426..a8c1fd4d02 100644
--- a/api/core/file/upload_file_parser.py
+++ b/api/core/file/upload_file_parser.py
@@ -9,7 +9,7 @@ from typing import Optional
 from configs import dify_config
 from extensions.ext_storage import storage
 
-IMAGE_EXTENSIONS = ['jpg', 'jpeg', 'png', 'webp', 'gif', 'svg']
+IMAGE_EXTENSIONS = ["jpg", "jpeg", "png", "webp", "gif", "svg"]
 IMAGE_EXTENSIONS.extend([ext.upper() for ext in IMAGE_EXTENSIONS])
 
 
@@ -22,18 +22,18 @@ class UploadFileParser:
         if upload_file.extension not in IMAGE_EXTENSIONS:
             return None
 
-        if dify_config.MULTIMODAL_SEND_IMAGE_FORMAT == 'url' or force_url:
+        if dify_config.MULTIMODAL_SEND_IMAGE_FORMAT == "url" or force_url:
             return cls.get_signed_temp_image_url(upload_file.id)
         else:
             # get image file base64
             try:
                 data = storage.load(upload_file.key)
             except FileNotFoundError:
-                logging.error(f'File not found: {upload_file.key}')
+                logging.error(f"File not found: {upload_file.key}")
                 return None
 
-            encoded_string = base64.b64encode(data).decode('utf-8')
-            return f'data:{upload_file.mime_type};base64,{encoded_string}'
+            encoded_string = base64.b64encode(data).decode("utf-8")
+            return f"data:{upload_file.mime_type};base64,{encoded_string}"
 
     @classmethod
     def get_signed_temp_image_url(cls, upload_file_id) -> str:
@@ -44,7 +44,7 @@ class UploadFileParser:
         :return:
         """
         base_url = dify_config.FILES_URL
-        image_preview_url = f'{base_url}/files/{upload_file_id}/image-preview'
+        image_preview_url = f"{base_url}/files/{upload_file_id}/image-preview"
 
         timestamp = str(int(time.time()))
         nonce = os.urandom(16).hex()
diff --git a/api/core/helper/code_executor/code_executor.py b/api/core/helper/code_executor/code_executor.py
index 4662ebb47a..4932284540 100644
--- a/api/core/helper/code_executor/code_executor.py
+++ b/api/core/helper/code_executor/code_executor.py
@@ -15,9 +15,11 @@ from core.helper.code_executor.template_transformer import TemplateTransformer
 
 logger = logging.getLogger(__name__)
 
-class CodeExecutionException(Exception):
+
+class CodeExecutionError(Exception):
     pass
 
+
 class CodeExecutionResponse(BaseModel):
     class Data(BaseModel):
         stdout: Optional[str] = None
@@ -29,9 +31,9 @@ class CodeExecutionResponse(BaseModel):
 
 
 class CodeLanguage(str, Enum):
-    PYTHON3 = 'python3'
-    JINJA2 = 'jinja2'
-    JAVASCRIPT = 'javascript'
+    PYTHON3 = "python3"
+    JINJA2 = "jinja2"
+    JAVASCRIPT = "javascript"
 
 
 class CodeExecutor:
@@ -45,71 +47,74 @@ class CodeExecutor:
     }
 
     code_language_to_running_language = {
-        CodeLanguage.JAVASCRIPT: 'nodejs',
+        CodeLanguage.JAVASCRIPT: "nodejs",
         CodeLanguage.JINJA2: CodeLanguage.PYTHON3,
         CodeLanguage.PYTHON3: CodeLanguage.PYTHON3,
     }
 
-    supported_dependencies_languages: set[CodeLanguage] = {
-        CodeLanguage.PYTHON3
-    }
+    supported_dependencies_languages: set[CodeLanguage] = {CodeLanguage.PYTHON3}
 
     @classmethod
-    def execute_code(cls, 
-                     language: CodeLanguage, 
-                     preload: str, 
-                     code: str) -> str:
+    def execute_code(cls, language: CodeLanguage, preload: str, code: str) -> str:
         """
         Execute code
         :param language: code language
         :param code: code
         :return:
         """
-        url = URL(str(dify_config.CODE_EXECUTION_ENDPOINT)) / 'v1' / 'sandbox' / 'run'
+        url = URL(str(dify_config.CODE_EXECUTION_ENDPOINT)) / "v1" / "sandbox" / "run"
 
-        headers = {
-            'X-Api-Key': dify_config.CODE_EXECUTION_API_KEY
-        }
+        headers = {"X-Api-Key": dify_config.CODE_EXECUTION_API_KEY}
 
         data = {
-            'language': cls.code_language_to_running_language.get(language),
-            'code': code,
-            'preload': preload,
-            'enable_network': True
+            "language": cls.code_language_to_running_language.get(language),
+            "code": code,
+            "preload": preload,
+            "enable_network": True,
         }
 
         try:
-            response = post(str(url), json=data, headers=headers,
-                            timeout=Timeout(
-                                connect=dify_config.CODE_EXECUTION_CONNECT_TIMEOUT,
-                                read=dify_config.CODE_EXECUTION_READ_TIMEOUT,
-                                write=dify_config.CODE_EXECUTION_WRITE_TIMEOUT,
-                                pool=None))
+            response = post(
+                str(url),
+                json=data,
+                headers=headers,
+                timeout=Timeout(
+                    connect=dify_config.CODE_EXECUTION_CONNECT_TIMEOUT,
+                    read=dify_config.CODE_EXECUTION_READ_TIMEOUT,
+                    write=dify_config.CODE_EXECUTION_WRITE_TIMEOUT,
+                    pool=None,
+                ),
+            )
             if response.status_code == 503:
-                raise CodeExecutionException('Code execution service is unavailable')
+                raise CodeExecutionError("Code execution service is unavailable")
             elif response.status_code != 200:
-                raise Exception(f'Failed to execute code, got status code {response.status_code}, please check if the sandbox service is running')
-        except CodeExecutionException as e:
+                raise Exception(
+                    f"Failed to execute code, got status code {response.status_code},"
+                    f" please check if the sandbox service is running"
+                )
+        except CodeExecutionError as e:
             raise e
         except Exception as e:
-            raise CodeExecutionException('Failed to execute code, which is likely a network issue,'
-                                         ' please check if the sandbox service is running.'
-                                         f' ( Error: {str(e)} )')
+            raise CodeExecutionError(
+                "Failed to execute code, which is likely a network issue,"
+                " please check if the sandbox service is running."
+                f" ( Error: {str(e)} )"
+            )
 
         try:
             response = response.json()
         except:
-            raise CodeExecutionException('Failed to parse response')
+            raise CodeExecutionError("Failed to parse response")
 
-        if (code := response.get('code')) != 0:
-            raise CodeExecutionException(f"Got error code: {code}. Got error msg: {response.get('message')}")
+        if (code := response.get("code")) != 0:
+            raise CodeExecutionError(f"Got error code: {code}. Got error msg: {response.get('message')}")
 
         response = CodeExecutionResponse(**response)
 
         if response.data.error:
-            raise CodeExecutionException(response.data.error)
+            raise CodeExecutionError(response.data.error)
 
-        return response.data.stdout or ''
+        return response.data.stdout or ""
 
     @classmethod
     def execute_workflow_code_template(cls, language: CodeLanguage, code: str, inputs: dict) -> dict:
@@ -122,13 +127,13 @@ class CodeExecutor:
         """
         template_transformer = cls.code_template_transformers.get(language)
         if not template_transformer:
-            raise CodeExecutionException(f'Unsupported language {language}')
+            raise CodeExecutionError(f"Unsupported language {language}")
 
         runner, preload = template_transformer.transform_caller(code, inputs)
 
         try:
             response = cls.execute_code(language, preload, runner)
-        except CodeExecutionException as e:
+        except CodeExecutionError as e:
             raise e
 
         return template_transformer.transform_response(response)
diff --git a/api/core/helper/code_executor/code_node_provider.py b/api/core/helper/code_executor/code_node_provider.py
index 3f099b7ac5..e233a596b9 100644
--- a/api/core/helper/code_executor/code_node_provider.py
+++ b/api/core/helper/code_executor/code_node_provider.py
@@ -26,23 +26,9 @@ class CodeNodeProvider(BaseModel):
         return {
             "type": "code",
             "config": {
-                "variables": [
-                    {
-                        "variable": "arg1",
-                        "value_selector": []
-                    },
-                    {
-                        "variable": "arg2",
-                        "value_selector": []
-                    }
-                ],
+                "variables": [{"variable": "arg1", "value_selector": []}, {"variable": "arg2", "value_selector": []}],
                 "code_language": cls.get_language(),
                 "code": cls.get_default_code(),
-                "outputs": {
-                    "result": {
-                        "type": "string",
-                        "children": None
-                    }
-                }
-            }
+                "outputs": {"result": {"type": "string", "children": None}},
+            },
         }
diff --git a/api/core/helper/code_executor/javascript/javascript_code_provider.py b/api/core/helper/code_executor/javascript/javascript_code_provider.py
index a157fcc6d1..ae324b83a9 100644
--- a/api/core/helper/code_executor/javascript/javascript_code_provider.py
+++ b/api/core/helper/code_executor/javascript/javascript_code_provider.py
@@ -18,4 +18,5 @@ class JavascriptCodeProvider(CodeNodeProvider):
                     result: arg1 + arg2
                 }
             }
-            """)
+            """
+        )
diff --git a/api/core/helper/code_executor/javascript/javascript_transformer.py b/api/core/helper/code_executor/javascript/javascript_transformer.py
index a4d2551972..d67a0903aa 100644
--- a/api/core/helper/code_executor/javascript/javascript_transformer.py
+++ b/api/core/helper/code_executor/javascript/javascript_transformer.py
@@ -21,5 +21,6 @@ class NodeJsTemplateTransformer(TemplateTransformer):
             var output_json = JSON.stringify(output_obj)
             var result = `<<RESULT>>${{output_json}}<<RESULT>>`
             console.log(result)
-            """)
+            """
+        )
         return runner_script
diff --git a/api/core/helper/code_executor/jinja2/jinja2_formatter.py b/api/core/helper/code_executor/jinja2/jinja2_formatter.py
index f1e5da584c..db2eb5ebb6 100644
--- a/api/core/helper/code_executor/jinja2/jinja2_formatter.py
+++ b/api/core/helper/code_executor/jinja2/jinja2_formatter.py
@@ -10,8 +10,6 @@ class Jinja2Formatter:
         :param inputs: inputs
         :return:
         """
-        result = CodeExecutor.execute_workflow_code_template(
-            language=CodeLanguage.JINJA2, code=template, inputs=inputs
-        )
+        result = CodeExecutor.execute_workflow_code_template(language=CodeLanguage.JINJA2, code=template, inputs=inputs)
 
-        return result['result']
+        return result["result"]
diff --git a/api/core/helper/code_executor/jinja2/jinja2_transformer.py b/api/core/helper/code_executor/jinja2/jinja2_transformer.py
index b8cb29600e..63d58edbc7 100644
--- a/api/core/helper/code_executor/jinja2/jinja2_transformer.py
+++ b/api/core/helper/code_executor/jinja2/jinja2_transformer.py
@@ -11,9 +11,7 @@ class Jinja2TemplateTransformer(TemplateTransformer):
         :param response: response
         :return:
         """
-        return {
-            'result': cls.extract_result_str_from_response(response)
-        }
+        return {"result": cls.extract_result_str_from_response(response)}
 
     @classmethod
     def get_runner_script(cls) -> str:
diff --git a/api/core/helper/code_executor/python3/python3_code_provider.py b/api/core/helper/code_executor/python3/python3_code_provider.py
index 923724b49d..9cca8af7c6 100644
--- a/api/core/helper/code_executor/python3/python3_code_provider.py
+++ b/api/core/helper/code_executor/python3/python3_code_provider.py
@@ -17,4 +17,5 @@ class Python3CodeProvider(CodeNodeProvider):
                 return {
                     "result": arg1 + arg2,
                 }
-            """)
+            """
+        )
diff --git a/api/core/helper/code_executor/template_transformer.py b/api/core/helper/code_executor/template_transformer.py
index cf66558b65..6f016f27bc 100644
--- a/api/core/helper/code_executor/template_transformer.py
+++ b/api/core/helper/code_executor/template_transformer.py
@@ -5,9 +5,9 @@ from base64 import b64encode
 
 
 class TemplateTransformer(ABC):
-    _code_placeholder: str = '{{code}}'
-    _inputs_placeholder: str = '{{inputs}}'
-    _result_tag: str = '<<RESULT>>'
+    _code_placeholder: str = "{{code}}"
+    _inputs_placeholder: str = "{{inputs}}"
+    _result_tag: str = "<<RESULT>>"
 
     @classmethod
     def transform_caller(cls, code: str, inputs: dict) -> tuple[str, str]:
@@ -24,9 +24,9 @@ class TemplateTransformer(ABC):
 
     @classmethod
     def extract_result_str_from_response(cls, response: str) -> str:
-        result = re.search(rf'{cls._result_tag}(.*){cls._result_tag}', response, re.DOTALL)
+        result = re.search(rf"{cls._result_tag}(.*){cls._result_tag}", response, re.DOTALL)
         if not result:
-            raise ValueError('Failed to parse result')
+            raise ValueError("Failed to parse result")
         result = result.group(1)
         return result
 
@@ -50,7 +50,7 @@ class TemplateTransformer(ABC):
     @classmethod
     def serialize_inputs(cls, inputs: dict) -> str:
         inputs_json_str = json.dumps(inputs, ensure_ascii=False).encode()
-        input_base64_encoded = b64encode(inputs_json_str).decode('utf-8')
+        input_base64_encoded = b64encode(inputs_json_str).decode("utf-8")
         return input_base64_encoded
 
     @classmethod
@@ -67,4 +67,4 @@ class TemplateTransformer(ABC):
         """
         Get preload script
         """
-        return ''
+        return ""
diff --git a/api/core/helper/encrypter.py b/api/core/helper/encrypter.py
index 5e5deb86b4..96341a1b78 100644
--- a/api/core/helper/encrypter.py
+++ b/api/core/helper/encrypter.py
@@ -8,14 +8,15 @@ def obfuscated_token(token: str):
     if not token:
         return token
     if len(token) <= 8:
-        return '*' * 20
-    return token[:6] + '*' * 12 + token[-2:]
+        return "*" * 20
+    return token[:6] + "*" * 12 + token[-2:]
 
 
 def encrypt_token(tenant_id: str, token: str):
     from models.account import Tenant
+
     if not (tenant := db.session.query(Tenant).filter(Tenant.id == tenant_id).first()):
-        raise ValueError(f'Tenant with id {tenant_id} not found')
+        raise ValueError(f"Tenant with id {tenant_id} not found")
     encrypted_token = rsa.encrypt(token, tenant.encrypt_public_key)
     return base64.b64encode(encrypted_token).decode()
 
diff --git a/api/core/helper/model_provider_cache.py b/api/core/helper/model_provider_cache.py
index 29cb4acc7d..5e274f8916 100644
--- a/api/core/helper/model_provider_cache.py
+++ b/api/core/helper/model_provider_cache.py
@@ -25,7 +25,7 @@ class ProviderCredentialsCache:
         cached_provider_credentials = redis_client.get(self.cache_key)
         if cached_provider_credentials:
             try:
-                cached_provider_credentials = cached_provider_credentials.decode('utf-8')
+                cached_provider_credentials = cached_provider_credentials.decode("utf-8")
                 cached_provider_credentials = json.loads(cached_provider_credentials)
             except JSONDecodeError:
                 return None
diff --git a/api/core/helper/moderation.py b/api/core/helper/moderation.py
index 20feae8554..b880590de2 100644
--- a/api/core/helper/moderation.py
+++ b/api/core/helper/moderation.py
@@ -12,19 +12,20 @@ logger = logging.getLogger(__name__)
 
 def check_moderation(model_config: ModelConfigWithCredentialsEntity, text: str) -> bool:
     moderation_config = hosting_configuration.moderation_config
-    if (moderation_config and moderation_config.enabled is True
-            and 'openai' in hosting_configuration.provider_map
-            and hosting_configuration.provider_map['openai'].enabled is True
+    if (
+        moderation_config
+        and moderation_config.enabled is True
+        and "openai" in hosting_configuration.provider_map
+        and hosting_configuration.provider_map["openai"].enabled is True
     ):
         using_provider_type = model_config.provider_model_bundle.configuration.using_provider_type
         provider_name = model_config.provider
-        if using_provider_type == ProviderType.SYSTEM \
-                and provider_name in moderation_config.providers:
-            hosting_openai_config = hosting_configuration.provider_map['openai']
+        if using_provider_type == ProviderType.SYSTEM and provider_name in moderation_config.providers:
+            hosting_openai_config = hosting_configuration.provider_map["openai"]
 
             # 2000 text per chunk
             length = 2000
-            text_chunks = [text[i:i + length] for i in range(0, len(text), length)]
+            text_chunks = [text[i : i + length] for i in range(0, len(text), length)]
 
             if len(text_chunks) == 0:
                 return True
@@ -34,15 +35,13 @@ def check_moderation(model_config: ModelConfigWithCredentialsEntity, text: str)
             try:
                 model_type_instance = OpenAIModerationModel()
                 moderation_result = model_type_instance.invoke(
-                    model='text-moderation-stable',
-                    credentials=hosting_openai_config.credentials,
-                    text=text_chunk
+                    model="text-moderation-stable", credentials=hosting_openai_config.credentials, text=text_chunk
                 )
 
                 if moderation_result is True:
                     return True
             except Exception as ex:
                 logger.exception(ex)
-                raise InvokeBadRequestError('Rate limit exceeded, please try again later.')
+                raise InvokeBadRequestError("Rate limit exceeded, please try again later.")
 
     return False
diff --git a/api/core/helper/module_import_helper.py b/api/core/helper/module_import_helper.py
index 2000577a40..e6e1491548 100644
--- a/api/core/helper/module_import_helper.py
+++ b/api/core/helper/module_import_helper.py
@@ -37,8 +37,9 @@ def get_subclasses_from_module(mod: ModuleType, parent_type: type) -> list[type]
     """
     Get all the subclasses of the parent type from the module
     """
-    classes = [x for _, x in vars(mod).items()
-               if isinstance(x, type) and x != parent_type and issubclass(x, parent_type)]
+    classes = [
+        x for _, x in vars(mod).items() if isinstance(x, type) and x != parent_type and issubclass(x, parent_type)
+    ]
     return classes
 
 
@@ -56,6 +57,6 @@ def load_single_subclass_from_source(
         case 1:
             return subclasses[0]
         case 0:
-            raise Exception(f'Missing subclass of {parent_type.__name__} in {script_path}')
+            raise Exception(f"Missing subclass of {parent_type.__name__} in {script_path}")
         case _:
-            raise Exception(f'Multiple subclasses of {parent_type.__name__} in {script_path}')
\ No newline at end of file
+            raise Exception(f"Multiple subclasses of {parent_type.__name__} in {script_path}")
diff --git a/api/core/helper/position_helper.py b/api/core/helper/position_helper.py
index 8cf184ac44..3efdc8aa47 100644
--- a/api/core/helper/position_helper.py
+++ b/api/core/helper/position_helper.py
@@ -73,13 +73,13 @@ def pin_position_map(original_position_map: dict[str, int], pin_list: list[str])
 
 
 def is_filtered(
-        include_set: set[str],
-        exclude_set: set[str],
-        data: Any,
-        name_func: Callable[[Any], str],
+    include_set: set[str],
+    exclude_set: set[str],
+    data: Any,
+    name_func: Callable[[Any], str],
 ) -> bool:
     """
-    Chcek if the object should be filtered out.
+    Check if the object should be filtered out.
     Overall logic: exclude > include > pin
     :param include_set: the set of names to be included
     :param exclude_set: the set of names to be excluded
@@ -102,9 +102,9 @@ def is_filtered(
 
 
 def sort_by_position_map(
-        position_map: dict[str, int],
-        data: list[Any],
-        name_func: Callable[[Any], str],
+    position_map: dict[str, int],
+    data: list[Any],
+    name_func: Callable[[Any], str],
 ) -> list[Any]:
     """
     Sort the objects by the position map.
@@ -117,13 +117,13 @@ def sort_by_position_map(
     if not position_map or not data:
         return data
 
-    return sorted(data, key=lambda x: position_map.get(name_func(x), float('inf')))
+    return sorted(data, key=lambda x: position_map.get(name_func(x), float("inf")))
 
 
 def sort_to_dict_by_position_map(
-        position_map: dict[str, int],
-        data: list[Any],
-        name_func: Callable[[Any], str],
+    position_map: dict[str, int],
+    data: list[Any],
+    name_func: Callable[[Any], str],
 ) -> OrderedDict[str, Any]:
     """
     Sort the objects into a ordered dict by the position map.
diff --git a/api/core/helper/ssrf_proxy.py b/api/core/helper/ssrf_proxy.py
index 14ca8e943c..4e6d58904e 100644
--- a/api/core/helper/ssrf_proxy.py
+++ b/api/core/helper/ssrf_proxy.py
@@ -1,31 +1,34 @@
 """
 Proxy requests to avoid SSRF
 """
+
 import logging
 import os
 import time
 
 import httpx
 
-SSRF_PROXY_ALL_URL = os.getenv('SSRF_PROXY_ALL_URL', '')
-SSRF_PROXY_HTTP_URL = os.getenv('SSRF_PROXY_HTTP_URL', '')
-SSRF_PROXY_HTTPS_URL = os.getenv('SSRF_PROXY_HTTPS_URL', '')
-SSRF_DEFAULT_MAX_RETRIES = int(os.getenv('SSRF_DEFAULT_MAX_RETRIES', '3'))
+SSRF_PROXY_ALL_URL = os.getenv("SSRF_PROXY_ALL_URL", "")
+SSRF_PROXY_HTTP_URL = os.getenv("SSRF_PROXY_HTTP_URL", "")
+SSRF_PROXY_HTTPS_URL = os.getenv("SSRF_PROXY_HTTPS_URL", "")
+SSRF_DEFAULT_MAX_RETRIES = int(os.getenv("SSRF_DEFAULT_MAX_RETRIES", "3"))
 
-proxies = {
-    'http://': SSRF_PROXY_HTTP_URL,
-    'https://': SSRF_PROXY_HTTPS_URL
-} if SSRF_PROXY_HTTP_URL and SSRF_PROXY_HTTPS_URL else None
+proxies = (
+    {"http://": SSRF_PROXY_HTTP_URL, "https://": SSRF_PROXY_HTTPS_URL}
+    if SSRF_PROXY_HTTP_URL and SSRF_PROXY_HTTPS_URL
+    else None
+)
 
 BACKOFF_FACTOR = 0.5
 STATUS_FORCELIST = [429, 500, 502, 503, 504]
 
+
 def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
     if "allow_redirects" in kwargs:
         allow_redirects = kwargs.pop("allow_redirects")
         if "follow_redirects" not in kwargs:
             kwargs["follow_redirects"] = allow_redirects
-    
+
     retries = 0
     while retries <= max_retries:
         try:
@@ -52,24 +55,24 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
 
 
 def get(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
-    return make_request('GET', url, max_retries=max_retries, **kwargs)
+    return make_request("GET", url, max_retries=max_retries, **kwargs)
 
 
 def post(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
-    return make_request('POST', url, max_retries=max_retries, **kwargs)
+    return make_request("POST", url, max_retries=max_retries, **kwargs)
 
 
 def put(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
-    return make_request('PUT', url, max_retries=max_retries, **kwargs)
+    return make_request("PUT", url, max_retries=max_retries, **kwargs)
 
 
 def patch(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
-    return make_request('PATCH', url, max_retries=max_retries, **kwargs)
+    return make_request("PATCH", url, max_retries=max_retries, **kwargs)
 
 
 def delete(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
-    return make_request('DELETE', url, max_retries=max_retries, **kwargs)
+    return make_request("DELETE", url, max_retries=max_retries, **kwargs)
 
 
 def head(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
-    return make_request('HEAD', url, max_retries=max_retries, **kwargs)
+    return make_request("HEAD", url, max_retries=max_retries, **kwargs)
diff --git a/api/core/helper/tool_parameter_cache.py b/api/core/helper/tool_parameter_cache.py
index a6f486e81d..e848b46c56 100644
--- a/api/core/helper/tool_parameter_cache.py
+++ b/api/core/helper/tool_parameter_cache.py
@@ -9,15 +9,15 @@ from extensions.ext_redis import redis_client
 class ToolParameterCacheType(Enum):
     PARAMETER = "tool_parameter"
 
+
 class ToolParameterCache:
-    def __init__(self, 
-            tenant_id: str, 
-            provider: str, 
-            tool_name: str, 
-            cache_type: ToolParameterCacheType,
-            identity_id: str
-        ):
-        self.cache_key = f"{cache_type.value}_secret:tenant_id:{tenant_id}:provider:{provider}:tool_name:{tool_name}:identity_id:{identity_id}"
+    def __init__(
+        self, tenant_id: str, provider: str, tool_name: str, cache_type: ToolParameterCacheType, identity_id: str
+    ):
+        self.cache_key = (
+            f"{cache_type.value}_secret:tenant_id:{tenant_id}:provider:{provider}:tool_name:{tool_name}"
+            f":identity_id:{identity_id}"
+        )
 
     def get(self) -> Optional[dict]:
         """
@@ -28,7 +28,7 @@ class ToolParameterCache:
         cached_tool_parameter = redis_client.get(self.cache_key)
         if cached_tool_parameter:
             try:
-                cached_tool_parameter = cached_tool_parameter.decode('utf-8')
+                cached_tool_parameter = cached_tool_parameter.decode("utf-8")
                 cached_tool_parameter = json.loads(cached_tool_parameter)
             except JSONDecodeError:
                 return None
@@ -52,4 +52,4 @@ class ToolParameterCache:
 
         :return:
         """
-        redis_client.delete(self.cache_key)
\ No newline at end of file
+        redis_client.delete(self.cache_key)
diff --git a/api/core/helper/tool_provider_cache.py b/api/core/helper/tool_provider_cache.py
index 6c5d3b8fb6..94b02cf985 100644
--- a/api/core/helper/tool_provider_cache.py
+++ b/api/core/helper/tool_provider_cache.py
@@ -9,6 +9,7 @@ from extensions.ext_redis import redis_client
 class ToolProviderCredentialsCacheType(Enum):
     PROVIDER = "tool_provider"
 
+
 class ToolProviderCredentialsCache:
     def __init__(self, tenant_id: str, identity_id: str, cache_type: ToolProviderCredentialsCacheType):
         self.cache_key = f"{cache_type.value}_credentials:tenant_id:{tenant_id}:id:{identity_id}"
@@ -22,7 +23,7 @@ class ToolProviderCredentialsCache:
         cached_provider_credentials = redis_client.get(self.cache_key)
         if cached_provider_credentials:
             try:
-                cached_provider_credentials = cached_provider_credentials.decode('utf-8')
+                cached_provider_credentials = cached_provider_credentials.decode("utf-8")
                 cached_provider_credentials = json.loads(cached_provider_credentials)
             except JSONDecodeError:
                 return None
@@ -46,4 +47,4 @@ class ToolProviderCredentialsCache:
 
         :return:
         """
-        redis_client.delete(self.cache_key)
\ No newline at end of file
+        redis_client.delete(self.cache_key)
diff --git a/api/core/hosting_configuration.py b/api/core/hosting_configuration.py
index ddcd751286..eeeccc2349 100644
--- a/api/core/hosting_configuration.py
+++ b/api/core/hosting_configuration.py
@@ -46,7 +46,7 @@ class HostingConfiguration:
     def init_app(self, app: Flask) -> None:
         config = app.config
 
-        if config.get('EDITION') != 'CLOUD':
+        if config.get("EDITION") != "CLOUD":
             return
 
         self.provider_map["azure_openai"] = self.init_azure_openai(config)
@@ -65,7 +65,7 @@ class HostingConfiguration:
             credentials = {
                 "openai_api_key": app_config.get("HOSTED_AZURE_OPENAI_API_KEY"),
                 "openai_api_base": app_config.get("HOSTED_AZURE_OPENAI_API_BASE"),
-                "base_model_name": "gpt-35-turbo"
+                "base_model_name": "gpt-35-turbo",
             }
 
             quotas = []
@@ -77,26 +77,45 @@ class HostingConfiguration:
                     RestrictModel(model="gpt-4o", base_model_name="gpt-4o", model_type=ModelType.LLM),
                     RestrictModel(model="gpt-4o-mini", base_model_name="gpt-4o-mini", model_type=ModelType.LLM),
                     RestrictModel(model="gpt-4-32k", base_model_name="gpt-4-32k", model_type=ModelType.LLM),
-                    RestrictModel(model="gpt-4-1106-preview", base_model_name="gpt-4-1106-preview", model_type=ModelType.LLM),
-                    RestrictModel(model="gpt-4-vision-preview", base_model_name="gpt-4-vision-preview", model_type=ModelType.LLM),
+                    RestrictModel(
+                        model="gpt-4-1106-preview", base_model_name="gpt-4-1106-preview", model_type=ModelType.LLM
+                    ),
+                    RestrictModel(
+                        model="gpt-4-vision-preview", base_model_name="gpt-4-vision-preview", model_type=ModelType.LLM
+                    ),
                     RestrictModel(model="gpt-35-turbo", base_model_name="gpt-35-turbo", model_type=ModelType.LLM),
-                    RestrictModel(model="gpt-35-turbo-1106", base_model_name="gpt-35-turbo-1106", model_type=ModelType.LLM),
-                    RestrictModel(model="gpt-35-turbo-instruct", base_model_name="gpt-35-turbo-instruct", model_type=ModelType.LLM),
-                    RestrictModel(model="gpt-35-turbo-16k", base_model_name="gpt-35-turbo-16k", model_type=ModelType.LLM),
-                    RestrictModel(model="text-davinci-003", base_model_name="text-davinci-003", model_type=ModelType.LLM),
-                    RestrictModel(model="text-embedding-ada-002", base_model_name="text-embedding-ada-002", model_type=ModelType.TEXT_EMBEDDING),
-                    RestrictModel(model="text-embedding-3-small", base_model_name="text-embedding-3-small", model_type=ModelType.TEXT_EMBEDDING),
-                    RestrictModel(model="text-embedding-3-large", base_model_name="text-embedding-3-large", model_type=ModelType.TEXT_EMBEDDING),
-                ]
+                    RestrictModel(
+                        model="gpt-35-turbo-1106", base_model_name="gpt-35-turbo-1106", model_type=ModelType.LLM
+                    ),
+                    RestrictModel(
+                        model="gpt-35-turbo-instruct", base_model_name="gpt-35-turbo-instruct", model_type=ModelType.LLM
+                    ),
+                    RestrictModel(
+                        model="gpt-35-turbo-16k", base_model_name="gpt-35-turbo-16k", model_type=ModelType.LLM
+                    ),
+                    RestrictModel(
+                        model="text-davinci-003", base_model_name="text-davinci-003", model_type=ModelType.LLM
+                    ),
+                    RestrictModel(
+                        model="text-embedding-ada-002",
+                        base_model_name="text-embedding-ada-002",
+                        model_type=ModelType.TEXT_EMBEDDING,
+                    ),
+                    RestrictModel(
+                        model="text-embedding-3-small",
+                        base_model_name="text-embedding-3-small",
+                        model_type=ModelType.TEXT_EMBEDDING,
+                    ),
+                    RestrictModel(
+                        model="text-embedding-3-large",
+                        base_model_name="text-embedding-3-large",
+                        model_type=ModelType.TEXT_EMBEDDING,
+                    ),
+                ],
             )
             quotas.append(trial_quota)
 
-            return HostingProvider(
-                enabled=True,
-                credentials=credentials,
-                quota_unit=quota_unit,
-                quotas=quotas
-            )
+            return HostingProvider(enabled=True, credentials=credentials, quota_unit=quota_unit, quotas=quotas)
 
         return HostingProvider(
             enabled=False,
@@ -110,17 +129,12 @@ class HostingConfiguration:
         if app_config.get("HOSTED_OPENAI_TRIAL_ENABLED"):
             hosted_quota_limit = int(app_config.get("HOSTED_OPENAI_QUOTA_LIMIT", "200"))
             trial_models = self.parse_restrict_models_from_env(app_config, "HOSTED_OPENAI_TRIAL_MODELS")
-            trial_quota = TrialHostingQuota(
-                quota_limit=hosted_quota_limit,
-                restrict_models=trial_models
-            )
+            trial_quota = TrialHostingQuota(quota_limit=hosted_quota_limit, restrict_models=trial_models)
             quotas.append(trial_quota)
 
         if app_config.get("HOSTED_OPENAI_PAID_ENABLED"):
             paid_models = self.parse_restrict_models_from_env(app_config, "HOSTED_OPENAI_PAID_MODELS")
-            paid_quota = PaidHostingQuota(
-                restrict_models=paid_models
-            )
+            paid_quota = PaidHostingQuota(restrict_models=paid_models)
             quotas.append(paid_quota)
 
         if len(quotas) > 0:
@@ -134,12 +148,7 @@ class HostingConfiguration:
             if app_config.get("HOSTED_OPENAI_API_ORGANIZATION"):
                 credentials["openai_organization"] = app_config.get("HOSTED_OPENAI_API_ORGANIZATION")
 
-            return HostingProvider(
-                enabled=True,
-                credentials=credentials,
-                quota_unit=quota_unit,
-                quotas=quotas
-            )
+            return HostingProvider(enabled=True, credentials=credentials, quota_unit=quota_unit, quotas=quotas)
 
         return HostingProvider(
             enabled=False,
@@ -153,9 +162,7 @@ class HostingConfiguration:
 
         if app_config.get("HOSTED_ANTHROPIC_TRIAL_ENABLED"):
             hosted_quota_limit = int(app_config.get("HOSTED_ANTHROPIC_QUOTA_LIMIT", "0"))
-            trial_quota = TrialHostingQuota(
-                quota_limit=hosted_quota_limit
-            )
+            trial_quota = TrialHostingQuota(quota_limit=hosted_quota_limit)
             quotas.append(trial_quota)
 
         if app_config.get("HOSTED_ANTHROPIC_PAID_ENABLED"):
@@ -170,12 +177,7 @@ class HostingConfiguration:
             if app_config.get("HOSTED_ANTHROPIC_API_BASE"):
                 credentials["anthropic_api_url"] = app_config.get("HOSTED_ANTHROPIC_API_BASE")
 
-            return HostingProvider(
-                enabled=True,
-                credentials=credentials,
-                quota_unit=quota_unit,
-                quotas=quotas
-            )
+            return HostingProvider(enabled=True, credentials=credentials, quota_unit=quota_unit, quotas=quotas)
 
         return HostingProvider(
             enabled=False,
@@ -192,7 +194,7 @@ class HostingConfiguration:
                 enabled=True,
                 credentials=None,  # use credentials from the provider
                 quota_unit=quota_unit,
-                quotas=quotas
+                quotas=quotas,
             )
 
         return HostingProvider(
@@ -210,7 +212,7 @@ class HostingConfiguration:
                 enabled=True,
                 credentials=None,  # use credentials from the provider
                 quota_unit=quota_unit,
-                quotas=quotas
+                quotas=quotas,
             )
 
         return HostingProvider(
@@ -228,7 +230,7 @@ class HostingConfiguration:
                 enabled=True,
                 credentials=None,  # use credentials from the provider
                 quota_unit=quota_unit,
-                quotas=quotas
+                quotas=quotas,
             )
 
         return HostingProvider(
@@ -238,21 +240,19 @@ class HostingConfiguration:
 
     @staticmethod
     def init_moderation_config(app_config: Config) -> HostedModerationConfig:
-        if app_config.get("HOSTED_MODERATION_ENABLED") \
-                and app_config.get("HOSTED_MODERATION_PROVIDERS"):
+        if app_config.get("HOSTED_MODERATION_ENABLED") and app_config.get("HOSTED_MODERATION_PROVIDERS"):
             return HostedModerationConfig(
-                enabled=True,
-                providers=app_config.get("HOSTED_MODERATION_PROVIDERS").split(',')
+                enabled=True, providers=app_config.get("HOSTED_MODERATION_PROVIDERS").split(",")
             )
 
-        return HostedModerationConfig(
-            enabled=False
-        )
+        return HostedModerationConfig(enabled=False)
 
     @staticmethod
     def parse_restrict_models_from_env(app_config: Config, env_var: str) -> list[RestrictModel]:
         models_str = app_config.get(env_var)
         models_list = models_str.split(",") if models_str else []
-        return [RestrictModel(model=model_name.strip(), model_type=ModelType.LLM) for model_name in models_list if
-                model_name.strip()]
-
+        return [
+            RestrictModel(model=model_name.strip(), model_type=ModelType.LLM)
+            for model_name in models_list
+            if model_name.strip()
+        ]
diff --git a/api/core/indexing_runner.py b/api/core/indexing_runner.py
index 7a1c5e760b..af20df41b1 100644
--- a/api/core/indexing_runner.py
+++ b/api/core/indexing_runner.py
@@ -39,7 +39,6 @@ from services.feature_service import FeatureService
 
 
 class IndexingRunner:
-
     def __init__(self):
         self.storage = storage
         self.model_manager = ModelManager()
@@ -49,25 +48,26 @@ class IndexingRunner:
         for dataset_document in dataset_documents:
             try:
                 # get dataset
-                dataset = Dataset.query.filter_by(
-                    id=dataset_document.dataset_id
-                ).first()
+                dataset = Dataset.query.filter_by(id=dataset_document.dataset_id).first()
 
                 if not dataset:
                     raise ValueError("no dataset found")
 
                 # get the process rule
-                processing_rule = db.session.query(DatasetProcessRule). \
-                    filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id). \
-                    first()
+                processing_rule = (
+                    db.session.query(DatasetProcessRule)
+                    .filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id)
+                    .first()
+                )
                 index_type = dataset_document.doc_form
                 index_processor = IndexProcessorFactory(index_type).init_index_processor()
                 # extract
                 text_docs = self._extract(index_processor, dataset_document, processing_rule.to_dict())
 
                 # transform
-                documents = self._transform(index_processor, dataset, text_docs, dataset_document.doc_language,
-                                            processing_rule.to_dict())
+                documents = self._transform(
+                    index_processor, dataset, text_docs, dataset_document.doc_language, processing_rule.to_dict()
+                )
                 # save segment
                 self._load_segments(dataset, dataset_document, documents)
 
@@ -76,20 +76,20 @@ class IndexingRunner:
                     index_processor=index_processor,
                     dataset=dataset,
                     dataset_document=dataset_document,
-                    documents=documents
+                    documents=documents,
                 )
-            except DocumentIsPausedException:
-                raise DocumentIsPausedException('Document paused, document id: {}'.format(dataset_document.id))
+            except DocumentIsPausedError:
+                raise DocumentIsPausedError("Document paused, document id: {}".format(dataset_document.id))
             except ProviderTokenNotInitError as e:
-                dataset_document.indexing_status = 'error'
+                dataset_document.indexing_status = "error"
                 dataset_document.error = str(e.description)
                 dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
                 db.session.commit()
             except ObjectDeletedError:
-                logging.warning('Document deleted, document id: {}'.format(dataset_document.id))
+                logging.warning("Document deleted, document id: {}".format(dataset_document.id))
             except Exception as e:
                 logging.exception("consume document failed")
-                dataset_document.indexing_status = 'error'
+                dataset_document.indexing_status = "error"
                 dataset_document.error = str(e)
                 dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
                 db.session.commit()
@@ -98,26 +98,25 @@ class IndexingRunner:
         """Run the indexing process when the index_status is splitting."""
         try:
             # get dataset
-            dataset = Dataset.query.filter_by(
-                id=dataset_document.dataset_id
-            ).first()
+            dataset = Dataset.query.filter_by(id=dataset_document.dataset_id).first()
 
             if not dataset:
                 raise ValueError("no dataset found")
 
             # get exist document_segment list and delete
             document_segments = DocumentSegment.query.filter_by(
-                dataset_id=dataset.id,
-                document_id=dataset_document.id
+                dataset_id=dataset.id, document_id=dataset_document.id
             ).all()
 
             for document_segment in document_segments:
                 db.session.delete(document_segment)
             db.session.commit()
             # get the process rule
-            processing_rule = db.session.query(DatasetProcessRule). \
-                filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id). \
-                first()
+            processing_rule = (
+                db.session.query(DatasetProcessRule)
+                .filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id)
+                .first()
+            )
 
             index_type = dataset_document.doc_form
             index_processor = IndexProcessorFactory(index_type).init_index_processor()
@@ -125,28 +124,26 @@ class IndexingRunner:
             text_docs = self._extract(index_processor, dataset_document, processing_rule.to_dict())
 
             # transform
-            documents = self._transform(index_processor, dataset, text_docs, dataset_document.doc_language,
-                                        processing_rule.to_dict())
+            documents = self._transform(
+                index_processor, dataset, text_docs, dataset_document.doc_language, processing_rule.to_dict()
+            )
             # save segment
             self._load_segments(dataset, dataset_document, documents)
 
             # load
             self._load(
-                index_processor=index_processor,
-                dataset=dataset,
-                dataset_document=dataset_document,
-                documents=documents
+                index_processor=index_processor, dataset=dataset, dataset_document=dataset_document, documents=documents
             )
-        except DocumentIsPausedException:
-            raise DocumentIsPausedException('Document paused, document id: {}'.format(dataset_document.id))
+        except DocumentIsPausedError:
+            raise DocumentIsPausedError("Document paused, document id: {}".format(dataset_document.id))
         except ProviderTokenNotInitError as e:
-            dataset_document.indexing_status = 'error'
+            dataset_document.indexing_status = "error"
             dataset_document.error = str(e.description)
             dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
             db.session.commit()
         except Exception as e:
             logging.exception("consume document failed")
-            dataset_document.indexing_status = 'error'
+            dataset_document.indexing_status = "error"
             dataset_document.error = str(e)
             dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
             db.session.commit()
@@ -155,17 +152,14 @@ class IndexingRunner:
         """Run the indexing process when the index_status is indexing."""
         try:
             # get dataset
-            dataset = Dataset.query.filter_by(
-                id=dataset_document.dataset_id
-            ).first()
+            dataset = Dataset.query.filter_by(id=dataset_document.dataset_id).first()
 
             if not dataset:
                 raise ValueError("no dataset found")
 
             # get exist document_segment list and delete
             document_segments = DocumentSegment.query.filter_by(
-                dataset_id=dataset.id,
-                document_id=dataset_document.id
+                dataset_id=dataset.id, document_id=dataset_document.id
             ).all()
 
             documents = []
@@ -180,42 +174,48 @@ class IndexingRunner:
                                 "doc_hash": document_segment.index_node_hash,
                                 "document_id": document_segment.document_id,
                                 "dataset_id": document_segment.dataset_id,
-                            }
+                            },
                         )
 
                         documents.append(document)
 
             # build index
             # get the process rule
-            processing_rule = db.session.query(DatasetProcessRule). \
-                filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id). \
-                first()
+            processing_rule = (
+                db.session.query(DatasetProcessRule)
+                .filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id)
+                .first()
+            )
 
             index_type = dataset_document.doc_form
             index_processor = IndexProcessorFactory(index_type).init_index_processor()
             self._load(
-                index_processor=index_processor,
-                dataset=dataset,
-                dataset_document=dataset_document,
-                documents=documents
+                index_processor=index_processor, dataset=dataset, dataset_document=dataset_document, documents=documents
             )
-        except DocumentIsPausedException:
-            raise DocumentIsPausedException('Document paused, document id: {}'.format(dataset_document.id))
+        except DocumentIsPausedError:
+            raise DocumentIsPausedError("Document paused, document id: {}".format(dataset_document.id))
         except ProviderTokenNotInitError as e:
-            dataset_document.indexing_status = 'error'
+            dataset_document.indexing_status = "error"
             dataset_document.error = str(e.description)
             dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
             db.session.commit()
         except Exception as e:
             logging.exception("consume document failed")
-            dataset_document.indexing_status = 'error'
+            dataset_document.indexing_status = "error"
             dataset_document.error = str(e)
             dataset_document.stopped_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
             db.session.commit()
 
-    def indexing_estimate(self, tenant_id: str, extract_settings: list[ExtractSetting], tmp_processing_rule: dict,
-                          doc_form: str = None, doc_language: str = 'English', dataset_id: str = None,
-                          indexing_technique: str = 'economy') -> dict:
+    def indexing_estimate(
+        self,
+        tenant_id: str,
+        extract_settings: list[ExtractSetting],
+        tmp_processing_rule: dict,
+        doc_form: str = None,
+        doc_language: str = "English",
+        dataset_id: str = None,
+        indexing_technique: str = "economy",
+    ) -> dict:
         """
         Estimate the indexing for the document.
         """
@@ -229,18 +229,16 @@ class IndexingRunner:
 
         embedding_model_instance = None
         if dataset_id:
-            dataset = Dataset.query.filter_by(
-                id=dataset_id
-            ).first()
+            dataset = Dataset.query.filter_by(id=dataset_id).first()
             if not dataset:
-                raise ValueError('Dataset not found.')
-            if dataset.indexing_technique == 'high_quality' or indexing_technique == 'high_quality':
+                raise ValueError("Dataset not found.")
+            if dataset.indexing_technique == "high_quality" or indexing_technique == "high_quality":
                 if dataset.embedding_model_provider:
                     embedding_model_instance = self.model_manager.get_model_instance(
                         tenant_id=tenant_id,
                         provider=dataset.embedding_model_provider,
                         model_type=ModelType.TEXT_EMBEDDING,
-                        model=dataset.embedding_model
+                        model=dataset.embedding_model,
                     )
                 else:
                     embedding_model_instance = self.model_manager.get_default_model_instance(
@@ -248,7 +246,7 @@ class IndexingRunner:
                         model_type=ModelType.TEXT_EMBEDDING,
                     )
         else:
-            if indexing_technique == 'high_quality':
+            if indexing_technique == "high_quality":
                 embedding_model_instance = self.model_manager.get_default_model_instance(
                     tenant_id=tenant_id,
                     model_type=ModelType.TEXT_EMBEDDING,
@@ -263,8 +261,7 @@ class IndexingRunner:
             text_docs = index_processor.extract(extract_setting, process_rule_mode=tmp_processing_rule["mode"])
             all_text_docs.extend(text_docs)
             processing_rule = DatasetProcessRule(
-                mode=tmp_processing_rule["mode"],
-                rules=json.dumps(tmp_processing_rule["rules"])
+                mode=tmp_processing_rule["mode"], rules=json.dumps(tmp_processing_rule["rules"])
             )
 
             # get splitter
@@ -272,9 +269,7 @@ class IndexingRunner:
 
             # split to documents
             documents = self._split_to_documents_for_estimate(
-                text_docs=text_docs,
-                splitter=splitter,
-                processing_rule=processing_rule
+                text_docs=text_docs, splitter=splitter, processing_rule=processing_rule
             )
 
             total_segments += len(documents)
@@ -282,110 +277,110 @@ class IndexingRunner:
                 if len(preview_texts) < 5:
                     preview_texts.append(document.page_content)
 
-        if doc_form and doc_form == 'qa_model':
-
+        if doc_form and doc_form == "qa_model":
             if len(preview_texts) > 0:
                 # qa model document
-                response = LLMGenerator.generate_qa_document(current_user.current_tenant_id, preview_texts[0],
-                                                             doc_language)
+                response = LLMGenerator.generate_qa_document(
+                    current_user.current_tenant_id, preview_texts[0], doc_language
+                )
                 document_qa_list = self.format_split_text(response)
 
-                return {
-                    "total_segments": total_segments * 20,
-                    "qa_preview": document_qa_list,
-                    "preview": preview_texts
-                }
-        return {
-            "total_segments": total_segments,
-            "preview": preview_texts
-        }
+                return {"total_segments": total_segments * 20, "qa_preview": document_qa_list, "preview": preview_texts}
+        return {"total_segments": total_segments, "preview": preview_texts}
 
-    def _extract(self, index_processor: BaseIndexProcessor, dataset_document: DatasetDocument, process_rule: dict) \
-            -> list[Document]:
+    def _extract(
+        self, index_processor: BaseIndexProcessor, dataset_document: DatasetDocument, process_rule: dict
+    ) -> list[Document]:
         # load file
-        if dataset_document.data_source_type not in ["upload_file", "notion_import", "website_crawl"]:
+        if dataset_document.data_source_type not in {"upload_file", "notion_import", "website_crawl"}:
             return []
 
         data_source_info = dataset_document.data_source_info_dict
         text_docs = []
-        if dataset_document.data_source_type == 'upload_file':
-            if not data_source_info or 'upload_file_id' not in data_source_info:
+        if dataset_document.data_source_type == "upload_file":
+            if not data_source_info or "upload_file_id" not in data_source_info:
                 raise ValueError("no upload file found")
 
-            file_detail = db.session.query(UploadFile). \
-                filter(UploadFile.id == data_source_info['upload_file_id']). \
-                one_or_none()
+            file_detail = (
+                db.session.query(UploadFile).filter(UploadFile.id == data_source_info["upload_file_id"]).one_or_none()
+            )
 
             if file_detail:
                 extract_setting = ExtractSetting(
-                    datasource_type="upload_file",
-                    upload_file=file_detail,
-                    document_model=dataset_document.doc_form
+                    datasource_type="upload_file", upload_file=file_detail, document_model=dataset_document.doc_form
                 )
-                text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule['mode'])
-        elif dataset_document.data_source_type == 'notion_import':
-            if (not data_source_info or 'notion_workspace_id' not in data_source_info
-                    or 'notion_page_id' not in data_source_info):
+                text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
+        elif dataset_document.data_source_type == "notion_import":
+            if (
+                not data_source_info
+                or "notion_workspace_id" not in data_source_info
+                or "notion_page_id" not in data_source_info
+            ):
                 raise ValueError("no notion import info found")
             extract_setting = ExtractSetting(
                 datasource_type="notion_import",
                 notion_info={
-                    "notion_workspace_id": data_source_info['notion_workspace_id'],
-                    "notion_obj_id": data_source_info['notion_page_id'],
-                    "notion_page_type": data_source_info['type'],
+                    "notion_workspace_id": data_source_info["notion_workspace_id"],
+                    "notion_obj_id": data_source_info["notion_page_id"],
+                    "notion_page_type": data_source_info["type"],
                     "document": dataset_document,
-                    "tenant_id": dataset_document.tenant_id
+                    "tenant_id": dataset_document.tenant_id,
                 },
-                document_model=dataset_document.doc_form
+                document_model=dataset_document.doc_form,
             )
-            text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule['mode'])
-        elif dataset_document.data_source_type == 'website_crawl':
-            if (not data_source_info or 'provider' not in data_source_info
-                    or 'url' not in data_source_info or 'job_id' not in data_source_info):
+            text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
+        elif dataset_document.data_source_type == "website_crawl":
+            if (
+                not data_source_info
+                or "provider" not in data_source_info
+                or "url" not in data_source_info
+                or "job_id" not in data_source_info
+            ):
                 raise ValueError("no website import info found")
             extract_setting = ExtractSetting(
                 datasource_type="website_crawl",
                 website_info={
-                    "provider": data_source_info['provider'],
-                    "job_id": data_source_info['job_id'],
+                    "provider": data_source_info["provider"],
+                    "job_id": data_source_info["job_id"],
                     "tenant_id": dataset_document.tenant_id,
-                    "url": data_source_info['url'],
-                    "mode": data_source_info['mode'],
-                    "only_main_content": data_source_info['only_main_content']
+                    "url": data_source_info["url"],
+                    "mode": data_source_info["mode"],
+                    "only_main_content": data_source_info["only_main_content"],
                 },
-                document_model=dataset_document.doc_form
+                document_model=dataset_document.doc_form,
             )
-            text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule['mode'])
+            text_docs = index_processor.extract(extract_setting, process_rule_mode=process_rule["mode"])
         # update document status to splitting
         self._update_document_index_status(
             document_id=dataset_document.id,
             after_indexing_status="splitting",
             extra_update_params={
                 DatasetDocument.word_count: sum(len(text_doc.page_content) for text_doc in text_docs),
-                DatasetDocument.parsing_completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
-            }
+                DatasetDocument.parsing_completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
+            },
         )
 
         # replace doc id to document model id
         text_docs = cast(list[Document], text_docs)
         for text_doc in text_docs:
-            text_doc.metadata['document_id'] = dataset_document.id
-            text_doc.metadata['dataset_id'] = dataset_document.dataset_id
+            text_doc.metadata["document_id"] = dataset_document.id
+            text_doc.metadata["dataset_id"] = dataset_document.dataset_id
 
         return text_docs
 
     @staticmethod
     def filter_string(text):
-        text = re.sub(r'<\|', '<', text)
-        text = re.sub(r'\|>', '>', text)
-        text = re.sub(r'[\x00-\x08\x0B\x0C\x0E-\x1F\x7F\xEF\xBF\xBE]', '', text)
+        text = re.sub(r"<\|", "<", text)
+        text = re.sub(r"\|>", ">", text)
+        text = re.sub(r"[\x00-\x08\x0B\x0C\x0E-\x1F\x7F\xEF\xBF\xBE]", "", text)
         # Unicode  U+FFFE
-        text = re.sub('\uFFFE', '', text)
+        text = re.sub("\ufffe", "", text)
         return text
 
     @staticmethod
-    def _get_splitter(processing_rule: DatasetProcessRule,
-                      embedding_model_instance: Optional[ModelInstance]) -> TextSplitter:
+    def _get_splitter(
+        processing_rule: DatasetProcessRule, embedding_model_instance: Optional[ModelInstance]
+    ) -> TextSplitter:
         """
         Get the NodeParser object according to the processing rule.
         """
@@ -399,10 +394,10 @@ class IndexingRunner:
 
             separator = segmentation["separator"]
             if separator:
-                separator = separator.replace('\\n', '\n')
+                separator = separator.replace("\\n", "\n")
 
-            if segmentation.get('chunk_overlap'):
-                chunk_overlap = segmentation['chunk_overlap']
+            if segmentation.get("chunk_overlap"):
+                chunk_overlap = segmentation["chunk_overlap"]
             else:
                 chunk_overlap = 0
 
@@ -411,22 +406,27 @@ class IndexingRunner:
                 chunk_overlap=chunk_overlap,
                 fixed_separator=separator,
                 separators=["\n\n", "。", ". ", " ", ""],
-                embedding_model_instance=embedding_model_instance
+                embedding_model_instance=embedding_model_instance,
             )
         else:
             # Automatic segmentation
             character_splitter = EnhanceRecursiveCharacterTextSplitter.from_encoder(
-                chunk_size=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['max_tokens'],
-                chunk_overlap=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['chunk_overlap'],
+                chunk_size=DatasetProcessRule.AUTOMATIC_RULES["segmentation"]["max_tokens"],
+                chunk_overlap=DatasetProcessRule.AUTOMATIC_RULES["segmentation"]["chunk_overlap"],
                 separators=["\n\n", "。", ". ", " ", ""],
-                embedding_model_instance=embedding_model_instance
+                embedding_model_instance=embedding_model_instance,
             )
 
         return character_splitter
 
-    def _step_split(self, text_docs: list[Document], splitter: TextSplitter,
-                    dataset: Dataset, dataset_document: DatasetDocument, processing_rule: DatasetProcessRule) \
-            -> list[Document]:
+    def _step_split(
+        self,
+        text_docs: list[Document],
+        splitter: TextSplitter,
+        dataset: Dataset,
+        dataset_document: DatasetDocument,
+        processing_rule: DatasetProcessRule,
+    ) -> list[Document]:
         """
         Split the text documents into documents and save them to the document segment.
         """
@@ -436,14 +436,12 @@ class IndexingRunner:
             processing_rule=processing_rule,
             tenant_id=dataset.tenant_id,
             document_form=dataset_document.doc_form,
-            document_language=dataset_document.doc_language
+            document_language=dataset_document.doc_language,
         )
 
         # save node to document segment
         doc_store = DatasetDocumentStore(
-            dataset=dataset,
-            user_id=dataset_document.created_by,
-            document_id=dataset_document.id
+            dataset=dataset, user_id=dataset_document.created_by, document_id=dataset_document.id
         )
 
         # add document segments
@@ -457,7 +455,7 @@ class IndexingRunner:
             extra_update_params={
                 DatasetDocument.cleaning_completed_at: cur_time,
                 DatasetDocument.splitting_completed_at: cur_time,
-            }
+            },
         )
 
         # update segment status to indexing
@@ -465,15 +463,21 @@ class IndexingRunner:
             dataset_document_id=dataset_document.id,
             update_params={
                 DocumentSegment.status: "indexing",
-                DocumentSegment.indexing_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
-            }
+                DocumentSegment.indexing_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
+            },
         )
 
         return documents
 
-    def _split_to_documents(self, text_docs: list[Document], splitter: TextSplitter,
-                            processing_rule: DatasetProcessRule, tenant_id: str,
-                            document_form: str, document_language: str) -> list[Document]:
+    def _split_to_documents(
+        self,
+        text_docs: list[Document],
+        splitter: TextSplitter,
+        processing_rule: DatasetProcessRule,
+        tenant_id: str,
+        document_form: str,
+        document_language: str,
+    ) -> list[Document]:
         """
         Split the text documents into nodes.
         """
@@ -488,13 +492,12 @@ class IndexingRunner:
             documents = splitter.split_documents([text_doc])
             split_documents = []
             for document_node in documents:
-
                 if document_node.page_content.strip():
                     doc_id = str(uuid.uuid4())
                     hash = helper.generate_text_hash(document_node.page_content)
-                    document_node.metadata['doc_id'] = doc_id
-                    document_node.metadata['doc_hash'] = hash
-                    # delete Spliter character
+                    document_node.metadata["doc_id"] = doc_id
+                    document_node.metadata["doc_hash"] = hash
+                    # delete Splitter character
                     page_content = document_node.page_content
                     if page_content.startswith(".") or page_content.startswith("。"):
                         page_content = page_content[1:]
@@ -506,15 +509,21 @@ class IndexingRunner:
                         split_documents.append(document_node)
             all_documents.extend(split_documents)
         # processing qa document
-        if document_form == 'qa_model':
+        if document_form == "qa_model":
             for i in range(0, len(all_documents), 10):
                 threads = []
-                sub_documents = all_documents[i:i + 10]
+                sub_documents = all_documents[i : i + 10]
                 for doc in sub_documents:
-                    document_format_thread = threading.Thread(target=self.format_qa_document, kwargs={
-                        'flask_app': current_app._get_current_object(),
-                        'tenant_id': tenant_id, 'document_node': doc, 'all_qa_documents': all_qa_documents,
-                        'document_language': document_language})
+                    document_format_thread = threading.Thread(
+                        target=self.format_qa_document,
+                        kwargs={
+                            "flask_app": current_app._get_current_object(),
+                            "tenant_id": tenant_id,
+                            "document_node": doc,
+                            "all_qa_documents": all_qa_documents,
+                            "document_language": document_language,
+                        },
+                    )
                     threads.append(document_format_thread)
                     document_format_thread.start()
                 for thread in threads:
@@ -533,12 +542,14 @@ class IndexingRunner:
                 document_qa_list = self.format_split_text(response)
                 qa_documents = []
                 for result in document_qa_list:
-                    qa_document = Document(page_content=result['question'], metadata=document_node.metadata.model_copy())
+                    qa_document = Document(
+                        page_content=result["question"], metadata=document_node.metadata.model_copy()
+                    )
                     doc_id = str(uuid.uuid4())
-                    hash = helper.generate_text_hash(result['question'])
-                    qa_document.metadata['answer'] = result['answer']
-                    qa_document.metadata['doc_id'] = doc_id
-                    qa_document.metadata['doc_hash'] = hash
+                    hash = helper.generate_text_hash(result["question"])
+                    qa_document.metadata["answer"] = result["answer"]
+                    qa_document.metadata["doc_id"] = doc_id
+                    qa_document.metadata["doc_hash"] = hash
                     qa_documents.append(qa_document)
                 format_documents.extend(qa_documents)
             except Exception as e:
@@ -546,8 +557,9 @@ class IndexingRunner:
 
             all_qa_documents.extend(format_documents)
 
-    def _split_to_documents_for_estimate(self, text_docs: list[Document], splitter: TextSplitter,
-                                         processing_rule: DatasetProcessRule) -> list[Document]:
+    def _split_to_documents_for_estimate(
+        self, text_docs: list[Document], splitter: TextSplitter, processing_rule: DatasetProcessRule
+    ) -> list[Document]:
         """
         Split the text documents into nodes.
         """
@@ -567,8 +579,8 @@ class IndexingRunner:
                 doc_id = str(uuid.uuid4())
                 hash = helper.generate_text_hash(document.page_content)
 
-                document.metadata['doc_id'] = doc_id
-                document.metadata['doc_hash'] = hash
+                document.metadata["doc_id"] = doc_id
+                document.metadata["doc_hash"] = hash
 
                 split_documents.append(document)
 
@@ -586,23 +598,23 @@ class IndexingRunner:
         else:
             rules = json.loads(processing_rule.rules) if processing_rule.rules else {}
 
-        if 'pre_processing_rules' in rules:
+        if "pre_processing_rules" in rules:
             pre_processing_rules = rules["pre_processing_rules"]
             for pre_processing_rule in pre_processing_rules:
                 if pre_processing_rule["id"] == "remove_extra_spaces" and pre_processing_rule["enabled"] is True:
                     # Remove extra spaces
-                    pattern = r'\n{3,}'
-                    text = re.sub(pattern, '\n\n', text)
-                    pattern = r'[\t\f\r\x20\u00a0\u1680\u180e\u2000-\u200a\u202f\u205f\u3000]{2,}'
-                    text = re.sub(pattern, ' ', text)
+                    pattern = r"\n{3,}"
+                    text = re.sub(pattern, "\n\n", text)
+                    pattern = r"[\t\f\r\x20\u00a0\u1680\u180e\u2000-\u200a\u202f\u205f\u3000]{2,}"
+                    text = re.sub(pattern, " ", text)
                 elif pre_processing_rule["id"] == "remove_urls_emails" and pre_processing_rule["enabled"] is True:
                     # Remove email
-                    pattern = r'([a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+)'
-                    text = re.sub(pattern, '', text)
+                    pattern = r"([a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+)"
+                    text = re.sub(pattern, "", text)
 
                     # Remove URL
-                    pattern = r'https?://[^\s]+'
-                    text = re.sub(pattern, '', text)
+                    pattern = r"https?://[^\s]+"
+                    text = re.sub(pattern, "", text)
 
         return text
 
@@ -611,27 +623,26 @@ class IndexingRunner:
         regex = r"Q\d+:\s*(.*?)\s*A\d+:\s*([\s\S]*?)(?=Q\d+:|$)"
         matches = re.findall(regex, text, re.UNICODE)
 
-        return [
-            {
-                "question": q,
-                "answer": re.sub(r"\n\s*", "\n", a.strip())
-            }
-            for q, a in matches if q and a
-        ]
+        return [{"question": q, "answer": re.sub(r"\n\s*", "\n", a.strip())} for q, a in matches if q and a]
 
-    def _load(self, index_processor: BaseIndexProcessor, dataset: Dataset,
-              dataset_document: DatasetDocument, documents: list[Document]) -> None:
+    def _load(
+        self,
+        index_processor: BaseIndexProcessor,
+        dataset: Dataset,
+        dataset_document: DatasetDocument,
+        documents: list[Document],
+    ) -> None:
         """
         insert index and update document/segment status to completed
         """
 
         embedding_model_instance = None
-        if dataset.indexing_technique == 'high_quality':
+        if dataset.indexing_technique == "high_quality":
             embedding_model_instance = self.model_manager.get_model_instance(
                 tenant_id=dataset.tenant_id,
                 provider=dataset.embedding_model_provider,
                 model_type=ModelType.TEXT_EMBEDDING,
-                model=dataset.embedding_model
+                model=dataset.embedding_model,
             )
 
         # chunk nodes by chunk size
@@ -640,18 +651,27 @@ class IndexingRunner:
         chunk_size = 10
 
         # create keyword index
-        create_keyword_thread = threading.Thread(target=self._process_keyword_index,
-                                                 args=(current_app._get_current_object(),
-                                                       dataset.id, dataset_document.id, documents))
+        create_keyword_thread = threading.Thread(
+            target=self._process_keyword_index,
+            args=(current_app._get_current_object(), dataset.id, dataset_document.id, documents),
+        )
         create_keyword_thread.start()
-        if dataset.indexing_technique == 'high_quality':
+        if dataset.indexing_technique == "high_quality":
             with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
                 futures = []
                 for i in range(0, len(documents), chunk_size):
-                    chunk_documents = documents[i:i + chunk_size]
-                    futures.append(executor.submit(self._process_chunk, current_app._get_current_object(), index_processor,
-                                                   chunk_documents, dataset,
-                                                   dataset_document, embedding_model_instance))
+                    chunk_documents = documents[i : i + chunk_size]
+                    futures.append(
+                        executor.submit(
+                            self._process_chunk,
+                            current_app._get_current_object(),
+                            index_processor,
+                            chunk_documents,
+                            dataset,
+                            dataset_document,
+                            embedding_model_instance,
+                        )
+                    )
 
                 for future in futures:
                     tokens += future.result()
@@ -668,7 +688,7 @@ class IndexingRunner:
                 DatasetDocument.completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
                 DatasetDocument.indexing_latency: indexing_end_at - indexing_start_at,
                 DatasetDocument.error: None,
-            }
+            },
         )
 
     @staticmethod
@@ -679,23 +699,26 @@ class IndexingRunner:
                 raise ValueError("no dataset found")
             keyword = Keyword(dataset)
             keyword.create(documents)
-            if dataset.indexing_technique != 'high_quality':
-                document_ids = [document.metadata['doc_id'] for document in documents]
+            if dataset.indexing_technique != "high_quality":
+                document_ids = [document.metadata["doc_id"] for document in documents]
                 db.session.query(DocumentSegment).filter(
                     DocumentSegment.document_id == document_id,
                     DocumentSegment.dataset_id == dataset_id,
                     DocumentSegment.index_node_id.in_(document_ids),
-                    DocumentSegment.status == "indexing"
-                ).update({
-                    DocumentSegment.status: "completed",
-                    DocumentSegment.enabled: True,
-                    DocumentSegment.completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
-                })
+                    DocumentSegment.status == "indexing",
+                ).update(
+                    {
+                        DocumentSegment.status: "completed",
+                        DocumentSegment.enabled: True,
+                        DocumentSegment.completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
+                    }
+                )
 
                 db.session.commit()
 
-    def _process_chunk(self, flask_app, index_processor, chunk_documents, dataset, dataset_document,
-                       embedding_model_instance):
+    def _process_chunk(
+        self, flask_app, index_processor, chunk_documents, dataset, dataset_document, embedding_model_instance
+    ):
         with flask_app.app_context():
             # check document is paused
             self._check_document_paused_status(dataset_document.id)
@@ -703,26 +726,26 @@ class IndexingRunner:
             tokens = 0
             if embedding_model_instance:
                 tokens += sum(
-                    embedding_model_instance.get_text_embedding_num_tokens(
-                        [document.page_content]
-                    )
+                    embedding_model_instance.get_text_embedding_num_tokens([document.page_content])
                     for document in chunk_documents
                 )
 
             # load index
             index_processor.load(dataset, chunk_documents, with_keywords=False)
 
-            document_ids = [document.metadata['doc_id'] for document in chunk_documents]
+            document_ids = [document.metadata["doc_id"] for document in chunk_documents]
             db.session.query(DocumentSegment).filter(
                 DocumentSegment.document_id == dataset_document.id,
                 DocumentSegment.dataset_id == dataset.id,
                 DocumentSegment.index_node_id.in_(document_ids),
-                DocumentSegment.status == "indexing"
-            ).update({
-                DocumentSegment.status: "completed",
-                DocumentSegment.enabled: True,
-                DocumentSegment.completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
-            })
+                DocumentSegment.status == "indexing",
+            ).update(
+                {
+                    DocumentSegment.status: "completed",
+                    DocumentSegment.enabled: True,
+                    DocumentSegment.completed_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
+                }
+            )
 
             db.session.commit()
 
@@ -730,27 +753,26 @@ class IndexingRunner:
 
     @staticmethod
     def _check_document_paused_status(document_id: str):
-        indexing_cache_key = 'document_{}_is_paused'.format(document_id)
+        indexing_cache_key = "document_{}_is_paused".format(document_id)
         result = redis_client.get(indexing_cache_key)
         if result:
-            raise DocumentIsPausedException()
+            raise DocumentIsPausedError()
 
     @staticmethod
-    def _update_document_index_status(document_id: str, after_indexing_status: str,
-                                      extra_update_params: Optional[dict] = None) -> None:
+    def _update_document_index_status(
+        document_id: str, after_indexing_status: str, extra_update_params: Optional[dict] = None
+    ) -> None:
         """
         Update the document indexing status.
         """
         count = DatasetDocument.query.filter_by(id=document_id, is_paused=True).count()
         if count > 0:
-            raise DocumentIsPausedException()
+            raise DocumentIsPausedError()
         document = DatasetDocument.query.filter_by(id=document_id).first()
         if not document:
-            raise DocumentIsDeletedPausedException()
+            raise DocumentIsDeletedPausedError()
 
-        update_params = {
-            DatasetDocument.indexing_status: after_indexing_status
-        }
+        update_params = {DatasetDocument.indexing_status: after_indexing_status}
 
         if extra_update_params:
             update_params.update(extra_update_params)
@@ -780,7 +802,7 @@ class IndexingRunner:
                     "doc_hash": segment.index_node_hash,
                     "document_id": segment.document_id,
                     "dataset_id": segment.dataset_id,
-                }
+                },
             )
             documents.append(document)
         # save vector index
@@ -788,17 +810,23 @@ class IndexingRunner:
         index_processor = IndexProcessorFactory(index_type).init_index_processor()
         index_processor.load(dataset, documents)
 
-    def _transform(self, index_processor: BaseIndexProcessor, dataset: Dataset,
-                   text_docs: list[Document], doc_language: str, process_rule: dict) -> list[Document]:
+    def _transform(
+        self,
+        index_processor: BaseIndexProcessor,
+        dataset: Dataset,
+        text_docs: list[Document],
+        doc_language: str,
+        process_rule: dict,
+    ) -> list[Document]:
         # get embedding model instance
         embedding_model_instance = None
-        if dataset.indexing_technique == 'high_quality':
+        if dataset.indexing_technique == "high_quality":
             if dataset.embedding_model_provider:
                 embedding_model_instance = self.model_manager.get_model_instance(
                     tenant_id=dataset.tenant_id,
                     provider=dataset.embedding_model_provider,
                     model_type=ModelType.TEXT_EMBEDDING,
-                    model=dataset.embedding_model
+                    model=dataset.embedding_model,
                 )
             else:
                 embedding_model_instance = self.model_manager.get_default_model_instance(
@@ -806,18 +834,20 @@ class IndexingRunner:
                     model_type=ModelType.TEXT_EMBEDDING,
                 )
 
-        documents = index_processor.transform(text_docs, embedding_model_instance=embedding_model_instance,
-                                              process_rule=process_rule, tenant_id=dataset.tenant_id,
-                                              doc_language=doc_language)
+        documents = index_processor.transform(
+            text_docs,
+            embedding_model_instance=embedding_model_instance,
+            process_rule=process_rule,
+            tenant_id=dataset.tenant_id,
+            doc_language=doc_language,
+        )
 
         return documents
 
     def _load_segments(self, dataset, dataset_document, documents):
         # save node to document segment
         doc_store = DatasetDocumentStore(
-            dataset=dataset,
-            user_id=dataset_document.created_by,
-            document_id=dataset_document.id
+            dataset=dataset, user_id=dataset_document.created_by, document_id=dataset_document.id
         )
 
         # add document segments
@@ -831,7 +861,7 @@ class IndexingRunner:
             extra_update_params={
                 DatasetDocument.cleaning_completed_at: cur_time,
                 DatasetDocument.splitting_completed_at: cur_time,
-            }
+            },
         )
 
         # update segment status to indexing
@@ -839,15 +869,15 @@ class IndexingRunner:
             dataset_document_id=dataset_document.id,
             update_params={
                 DocumentSegment.status: "indexing",
-                DocumentSegment.indexing_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
-            }
+                DocumentSegment.indexing_at: datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
+            },
         )
         pass
 
 
-class DocumentIsPausedException(Exception):
+class DocumentIsPausedError(Exception):
     pass
 
 
-class DocumentIsDeletedPausedException(Exception):
+class DocumentIsDeletedPausedError(Exception):
     pass
diff --git a/api/core/llm_generator/llm_generator.py b/api/core/llm_generator/llm_generator.py
index 8c13b4a45c..78a6d6e683 100644
--- a/api/core/llm_generator/llm_generator.py
+++ b/api/core/llm_generator/llm_generator.py
@@ -43,21 +43,16 @@ class LLMGenerator:
 
         with measure_time() as timer:
             response = model_instance.invoke_llm(
-                prompt_messages=prompts,
-                model_parameters={
-                    "max_tokens": 100,
-                    "temperature": 1
-                },
-                stream=False
+                prompt_messages=prompts, model_parameters={"max_tokens": 100, "temperature": 1}, stream=False
             )
         answer = response.message.content
-        cleaned_answer = re.sub(r'^.*(\{.*\}).*$', r'\1', answer, flags=re.DOTALL)
+        cleaned_answer = re.sub(r"^.*(\{.*\}).*$", r"\1", answer, flags=re.DOTALL)
         result_dict = json.loads(cleaned_answer)
-        answer = result_dict['Your Output']
+        answer = result_dict["Your Output"]
         name = answer.strip()
 
         if len(name) > 75:
-            name = name[:75] + '...'
+            name = name[:75] + "..."
 
         # get tracing instance
         trace_manager = TraceQueueManager(app_id=app_id)
@@ -79,14 +74,9 @@ class LLMGenerator:
         output_parser = SuggestedQuestionsAfterAnswerOutputParser()
         format_instructions = output_parser.get_format_instructions()
 
-        prompt_template = PromptTemplateParser(
-            template="{{histories}}\n{{format_instructions}}\nquestions:\n"
-        )
+        prompt_template = PromptTemplateParser(template="{{histories}}\n{{format_instructions}}\nquestions:\n")
 
-        prompt = prompt_template.format({
-            "histories": histories,
-            "format_instructions": format_instructions
-        })
+        prompt = prompt_template.format({"histories": histories, "format_instructions": format_instructions})
 
         try:
             model_manager = ModelManager()
@@ -101,12 +91,7 @@ class LLMGenerator:
 
         try:
             response = model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                model_parameters={
-                    "max_tokens": 256,
-                    "temperature": 0
-                },
-                stream=False
+                prompt_messages=prompt_messages, model_parameters={"max_tokens": 256, "temperature": 0}, stream=False
             )
 
             questions = output_parser.parse(response.message.content)
@@ -119,32 +104,24 @@ class LLMGenerator:
         return questions
 
     @classmethod
-    def generate_rule_config(cls, tenant_id: str, instruction: str, model_config: dict, no_variable: bool, rule_config_max_tokens: int = 512) -> dict:
+    def generate_rule_config(
+        cls, tenant_id: str, instruction: str, model_config: dict, no_variable: bool, rule_config_max_tokens: int = 512
+    ) -> dict:
         output_parser = RuleConfigGeneratorOutputParser()
 
         error = ""
         error_step = ""
-        rule_config = {
-            "prompt": "",
-            "variables": [],
-            "opening_statement": "",
-            "error": ""
-        }
-        model_parameters = {
-            "max_tokens": rule_config_max_tokens,
-            "temperature": 0.01
-        }
+        rule_config = {"prompt": "", "variables": [], "opening_statement": "", "error": ""}
+        model_parameters = {"max_tokens": rule_config_max_tokens, "temperature": 0.01}
 
         if no_variable:
-            prompt_template = PromptTemplateParser(
-                WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE
-            )
+            prompt_template = PromptTemplateParser(WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE)
 
             prompt_generate = prompt_template.format(
                 inputs={
                     "TASK_DESCRIPTION": instruction,
                 },
-                remove_template_variables=False
+                remove_template_variables=False,
             )
 
             prompt_messages = [UserPromptMessage(content=prompt_generate)]
@@ -158,13 +135,11 @@ class LLMGenerator:
 
             try:
                 response = model_instance.invoke_llm(
-                    prompt_messages=prompt_messages,
-                    model_parameters=model_parameters,
-                    stream=False
+                    prompt_messages=prompt_messages, model_parameters=model_parameters, stream=False
                 )
 
                 rule_config["prompt"] = response.message.content
-                
+
             except InvokeError as e:
                 error = str(e)
                 error_step = "generate rule config"
@@ -179,24 +154,18 @@ class LLMGenerator:
         # get rule config prompt, parameter and statement
         prompt_generate, parameter_generate, statement_generate = output_parser.get_format_instructions()
 
-        prompt_template = PromptTemplateParser(
-            prompt_generate
-        )
+        prompt_template = PromptTemplateParser(prompt_generate)
 
-        parameter_template = PromptTemplateParser(
-            parameter_generate
-        )
+        parameter_template = PromptTemplateParser(parameter_generate)
 
-        statement_template = PromptTemplateParser(
-            statement_generate
-        )
+        statement_template = PromptTemplateParser(statement_generate)
 
         # format the prompt_generate_prompt
         prompt_generate_prompt = prompt_template.format(
             inputs={
                 "TASK_DESCRIPTION": instruction,
             },
-            remove_template_variables=False
+            remove_template_variables=False,
         )
         prompt_messages = [UserPromptMessage(content=prompt_generate_prompt)]
 
@@ -213,9 +182,7 @@ class LLMGenerator:
             try:
                 # the first step to generate the task prompt
                 prompt_content = model_instance.invoke_llm(
-                    prompt_messages=prompt_messages,
-                    model_parameters=model_parameters,
-                    stream=False
+                    prompt_messages=prompt_messages, model_parameters=model_parameters, stream=False
                 )
             except InvokeError as e:
                 error = str(e)
@@ -230,7 +197,7 @@ class LLMGenerator:
                 inputs={
                     "INPUT_TEXT": prompt_content.message.content,
                 },
-                remove_template_variables=False
+                remove_template_variables=False,
             )
             parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
 
@@ -240,15 +207,13 @@ class LLMGenerator:
                     "TASK_DESCRIPTION": instruction,
                     "INPUT_TEXT": prompt_content.message.content,
                 },
-                remove_template_variables=False
+                remove_template_variables=False,
             )
             statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
 
             try:
                 parameter_content = model_instance.invoke_llm(
-                    prompt_messages=parameter_messages,
-                    model_parameters=model_parameters,
-                    stream=False
+                    prompt_messages=parameter_messages, model_parameters=model_parameters, stream=False
                 )
                 rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', parameter_content.message.content)
             except InvokeError as e:
@@ -257,9 +222,7 @@ class LLMGenerator:
 
             try:
                 statement_content = model_instance.invoke_llm(
-                    prompt_messages=statement_messages,
-                    model_parameters=model_parameters,
-                    stream=False
+                    prompt_messages=statement_messages, model_parameters=model_parameters, stream=False
                 )
                 rule_config["opening_statement"] = statement_content.message.content
             except InvokeError as e:
@@ -284,18 +247,10 @@ class LLMGenerator:
             model_type=ModelType.LLM,
         )
 
-        prompt_messages = [
-            SystemPromptMessage(content=prompt),
-            UserPromptMessage(content=query)
-        ]
+        prompt_messages = [SystemPromptMessage(content=prompt), UserPromptMessage(content=query)]
 
         response = model_instance.invoke_llm(
-            prompt_messages=prompt_messages,
-            model_parameters={
-                'temperature': 0.01,
-                "max_tokens": 2000
-            },
-            stream=False
+            prompt_messages=prompt_messages, model_parameters={"temperature": 0.01, "max_tokens": 2000}, stream=False
         )
 
         answer = response.message.content
diff --git a/api/core/llm_generator/output_parser/errors.py b/api/core/llm_generator/output_parser/errors.py
index 6a60f8de80..1e743f1757 100644
--- a/api/core/llm_generator/output_parser/errors.py
+++ b/api/core/llm_generator/output_parser/errors.py
@@ -1,2 +1,2 @@
-class OutputParserException(Exception):
+class OutputParserError(Exception):
     pass
diff --git a/api/core/llm_generator/output_parser/rule_config_generator.py b/api/core/llm_generator/output_parser/rule_config_generator.py
index 8856f0c685..0c7683b16d 100644
--- a/api/core/llm_generator/output_parser/rule_config_generator.py
+++ b/api/core/llm_generator/output_parser/rule_config_generator.py
@@ -1,6 +1,6 @@
 from typing import Any
 
-from core.llm_generator.output_parser.errors import OutputParserException
+from core.llm_generator.output_parser.errors import OutputParserError
 from core.llm_generator.prompts import (
     RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE,
     RULE_CONFIG_PROMPT_GENERATE_TEMPLATE,
@@ -10,9 +10,12 @@ from libs.json_in_md_parser import parse_and_check_json_markdown
 
 
 class RuleConfigGeneratorOutputParser:
-
     def get_format_instructions(self) -> tuple[str, str, str]:
-        return RULE_CONFIG_PROMPT_GENERATE_TEMPLATE, RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE, RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE
+        return (
+            RULE_CONFIG_PROMPT_GENERATE_TEMPLATE,
+            RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE,
+            RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE,
+        )
 
     def parse(self, text: str) -> Any:
         try:
@@ -21,16 +24,9 @@ class RuleConfigGeneratorOutputParser:
             if not isinstance(parsed["prompt"], str):
                 raise ValueError("Expected 'prompt' to be a string.")
             if not isinstance(parsed["variables"], list):
-                raise ValueError(
-                    "Expected 'variables' to be a list."
-                )
+                raise ValueError("Expected 'variables' to be a list.")
             if not isinstance(parsed["opening_statement"], str):
-                raise ValueError(
-                    "Expected 'opening_statement' to be a str."
-                )
+                raise ValueError("Expected 'opening_statement' to be a str.")
             return parsed
         except Exception as e:
-            raise OutputParserException(
-                f"Parsing text\n{text}\n of rule config generator raised following error:\n{e}"
-            )
-
+            raise OutputParserError(f"Parsing text\n{text}\n of rule config generator raised following error:\n{e}")
diff --git a/api/core/llm_generator/output_parser/suggested_questions_after_answer.py b/api/core/llm_generator/output_parser/suggested_questions_after_answer.py
index 3f046c68fc..182aeed98f 100644
--- a/api/core/llm_generator/output_parser/suggested_questions_after_answer.py
+++ b/api/core/llm_generator/output_parser/suggested_questions_after_answer.py
@@ -6,7 +6,6 @@ from core.llm_generator.prompts import SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCT
 
 
 class SuggestedQuestionsAfterAnswerOutputParser:
-
     def get_format_instructions(self) -> str:
         return SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT
 
@@ -15,7 +14,7 @@ class SuggestedQuestionsAfterAnswerOutputParser:
         if action_match is not None:
             json_obj = json.loads(action_match.group(0).strip())
         else:
-            json_obj= []
+            json_obj = []
             print(f"Could not parse LLM output: {text}")
 
         return json_obj
diff --git a/api/core/llm_generator/prompts.py b/api/core/llm_generator/prompts.py
index 87361b385a..c40b6d1808 100644
--- a/api/core/llm_generator/prompts.py
+++ b/api/core/llm_generator/prompts.py
@@ -59,26 +59,29 @@ User Input: yo, 你今天咋样？
 }
 
 User Input: 
-"""
+"""  # noqa: E501
 
 SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
     "Please help me predict the three most likely questions that human would ask, "
     "and keeping each question under 20 characters.\n"
-    "MAKE SURE your output is the SAME language as the Assistant's latest response(if the main response is written in Chinese, then the language of your output must be using Chinese.)!\n"
+    "MAKE SURE your output is the SAME language as the Assistant's latest response"
+    "(if the main response is written in Chinese, then the language of your output must be using Chinese.)!\n"
     "The output must be an array in JSON format following the specified schema:\n"
-    "[\"question1\",\"question2\",\"question3\"]\n"
+    '["question1","question2","question3"]\n'
 )
 
 GENERATOR_QA_PROMPT = (
-    '<Task> The user will send a long text. Generate a Question and Answer pairs only using the knowledge in the long text. Please think step by step.'
-    'Step 1: Understand and summarize the main content of this text.\n'
-    'Step 2: What key information or concepts are mentioned in this text?\n'
-    'Step 3: Decompose or combine multiple pieces of information and concepts.\n'
-    'Step 4: Generate questions and answers based on these key information and concepts.\n'
-    '<Constraints> The questions should be clear and detailed, and the answers should be detailed and complete. '
-    'You must answer in {language}, in a style that is clear and detailed in {language}. No language other than {language} should be used. \n'
-    '<Format> Use the following format: Q1:\nA1:\nQ2:\nA2:...\n'
-    '<QA Pairs>'
+    "<Task> The user will send a long text. Generate a Question and Answer pairs only using the knowledge"
+    " in the long text. Please think step by step."
+    "Step 1: Understand and summarize the main content of this text.\n"
+    "Step 2: What key information or concepts are mentioned in this text?\n"
+    "Step 3: Decompose or combine multiple pieces of information and concepts.\n"
+    "Step 4: Generate questions and answers based on these key information and concepts.\n"
+    "<Constraints> The questions should be clear and detailed, and the answers should be detailed and complete. "
+    "You must answer in {language}, in a style that is clear and detailed in {language}."
+    " No language other than {language} should be used. \n"
+    "<Format> Use the following format: Q1:\nA1:\nQ2:\nA2:...\n"
+    "<QA Pairs>"
 )
 
 WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE = """
@@ -87,14 +90,14 @@ Here is a task description for which I would like you to create a high-quality p
 {{TASK_DESCRIPTION}}
 </task_description>
 Based on task description, please create a well-structured prompt template that another AI could use to consistently complete the task. The prompt template should include:
-- Do not inlcude <input> or <output> section and variables in the prompt, assume user will add them at their own will. 
+- Do not include <input> or <output> section and variables in the prompt, assume user will add them at their own will. 
 - Clear instructions for the AI that will be using this prompt, demarcated with <instructions> tags. The instructions should provide step-by-step directions on how to complete the task using the input variables. Also Specifies in the instructions that the output should not contain any xml tag. 
 - Relevant examples if needed to clarify the task further, demarcated with <example> tags. Do not include variables in the prompt. Give three pairs of input and output examples.   
 - Include other relevant sections demarcated with appropriate XML tags like <examples>, <instructions>.
 - Use the same language as task description. 
 - Output in ``` xml ``` and start with <instruction>
 Please generate the full prompt template with at least 300 words and output only the prompt template.
-"""
+"""  # noqa: E501
 
 RULE_CONFIG_PROMPT_GENERATE_TEMPLATE = """
 Here is a task description for which I would like you to create a high-quality prompt template for:
@@ -109,7 +112,7 @@ Based on task description, please create a well-structured prompt template that
 - Use the same language as task description. 
 - Output in ``` xml ``` and start with <instruction>
 Please generate the full prompt template and output only the prompt template.
-"""
+"""  # noqa: E501
 
 RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE = """
 I need to extract the following information from the input text. The <information to be extracted> tag specifies the 'type', 'description' and 'required' of the information to be extracted. 
@@ -134,7 +137,7 @@ Inside <text></text> XML tags, there is a text that I should extract parameters
 
 ### Answer
 I should always output a valid list. Output nothing other than the list of variable_name. Output an empty list if there is no variable name in input text.
-"""
+"""  # noqa: E501
 
 RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE = """
 <instruction>
@@ -150,4 +153,4 @@ Welcome! I'm here to assist you with any questions or issues you might have with
 Here is the task description: {{INPUT_TEXT}}
 
 You just need to generate the output
-"""
+"""  # noqa: E501
diff --git a/api/core/memory/token_buffer_memory.py b/api/core/memory/token_buffer_memory.py
index b33d4dd7cb..d3185c3b11 100644
--- a/api/core/memory/token_buffer_memory.py
+++ b/api/core/memory/token_buffer_memory.py
@@ -21,8 +21,9 @@ class TokenBufferMemory:
         self.conversation = conversation
         self.model_instance = model_instance
 
-    def get_history_prompt_messages(self, max_token_limit: int = 2000,
-                                    message_limit: Optional[int] = None) -> list[PromptMessage]:
+    def get_history_prompt_messages(
+        self, max_token_limit: int = 2000, message_limit: Optional[int] = None
+    ) -> list[PromptMessage]:
         """
         Get history prompt messages.
         :param max_token_limit: max token limit
@@ -31,52 +32,41 @@ class TokenBufferMemory:
         app_record = self.conversation.app
 
         # fetch limited messages, and return reversed
-        query = db.session.query(
-            Message.id,
-            Message.query,
-            Message.answer,
-            Message.created_at,
-            Message.workflow_run_id
-        ).filter(
-            Message.conversation_id == self.conversation.id,
-            Message.answer != ''
-        ).order_by(Message.created_at.desc())
+        query = (
+            db.session.query(Message.id, Message.query, Message.answer, Message.created_at, Message.workflow_run_id)
+            .filter(Message.conversation_id == self.conversation.id, Message.answer != "")
+            .order_by(Message.created_at.desc())
+        )
 
         if message_limit and message_limit > 0:
-            message_limit = message_limit if message_limit <= 500 else 500
+            message_limit = min(message_limit, 500)
         else:
             message_limit = 500
 
         messages = query.limit(message_limit).all()
 
         messages = list(reversed(messages))
-        message_file_parser = MessageFileParser(
-            tenant_id=app_record.tenant_id,
-            app_id=app_record.id
-        )
+        message_file_parser = MessageFileParser(tenant_id=app_record.tenant_id, app_id=app_record.id)
         prompt_messages = []
         for message in messages:
             files = db.session.query(MessageFile).filter(MessageFile.message_id == message.id).all()
             if files:
                 file_extra_config = None
-                if self.conversation.mode not in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]:
+                if self.conversation.mode not in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}:
                     file_extra_config = FileUploadConfigManager.convert(self.conversation.model_config)
                 else:
                     if message.workflow_run_id:
-                        workflow_run = (db.session.query(WorkflowRun)
-                                        .filter(WorkflowRun.id == message.workflow_run_id).first())
+                        workflow_run = (
+                            db.session.query(WorkflowRun).filter(WorkflowRun.id == message.workflow_run_id).first()
+                        )
 
                         if workflow_run:
                             file_extra_config = FileUploadConfigManager.convert(
-                                workflow_run.workflow.features_dict,
-                                is_vision=False
+                                workflow_run.workflow.features_dict, is_vision=False
                             )
 
                 if file_extra_config:
-                    file_objs = message_file_parser.transform_message_files(
-                        files,
-                        file_extra_config
-                    )
+                    file_objs = message_file_parser.transform_message_files(files, file_extra_config)
                 else:
                     file_objs = []
 
@@ -97,24 +87,23 @@ class TokenBufferMemory:
             return []
 
         # prune the chat message if it exceeds the max token limit
-        curr_message_tokens = self.model_instance.get_llm_num_tokens(
-            prompt_messages
-        )
+        curr_message_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
 
         if curr_message_tokens > max_token_limit:
             pruned_memory = []
-            while curr_message_tokens > max_token_limit and len(prompt_messages)>1:
+            while curr_message_tokens > max_token_limit and len(prompt_messages) > 1:
                 pruned_memory.append(prompt_messages.pop(0))
-                curr_message_tokens = self.model_instance.get_llm_num_tokens(
-                    prompt_messages
-                )
+                curr_message_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
 
         return prompt_messages
 
-    def get_history_prompt_text(self, human_prefix: str = "Human",
-                                ai_prefix: str = "Assistant",
-                                max_token_limit: int = 2000,
-                                message_limit: Optional[int] = None) -> str:
+    def get_history_prompt_text(
+        self,
+        human_prefix: str = "Human",
+        ai_prefix: str = "Assistant",
+        max_token_limit: int = 2000,
+        message_limit: Optional[int] = None,
+    ) -> str:
         """
         Get history prompt text.
         :param human_prefix: human prefix
@@ -123,10 +112,7 @@ class TokenBufferMemory:
         :param message_limit: message limit
         :return:
         """
-        prompt_messages = self.get_history_prompt_messages(
-            max_token_limit=max_token_limit,
-            message_limit=message_limit
-        )
+        prompt_messages = self.get_history_prompt_messages(max_token_limit=max_token_limit, message_limit=message_limit)
 
         string_messages = []
         for m in prompt_messages:
diff --git a/api/core/model_manager.py b/api/core/model_manager.py
index 7b1a7ada5b..990efd36c6 100644
--- a/api/core/model_manager.py
+++ b/api/core/model_manager.py
@@ -1,6 +1,6 @@
 import logging
 import os
-from collections.abc import Callable, Generator
+from collections.abc import Callable, Generator, Sequence
 from typing import IO, Optional, Union, cast
 
 from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
@@ -41,7 +41,7 @@ class ModelInstance:
             configuration=provider_model_bundle.configuration,
             model_type=provider_model_bundle.model_type_instance.model_type,
             model=model,
-            credentials=self.credentials
+            credentials=self.credentials,
         )
 
     @staticmethod
@@ -54,10 +54,7 @@ class ModelInstance:
         """
         configuration = provider_model_bundle.configuration
         model_type = provider_model_bundle.model_type_instance.model_type
-        credentials = configuration.get_current_credentials(
-            model_type=model_type,
-            model=model
-        )
+        credentials = configuration.get_current_credentials(model_type=model_type, model=model)
 
         if credentials is None:
             raise ProviderTokenNotInitError(f"Model {model} credentials is not initialized.")
@@ -65,10 +62,9 @@ class ModelInstance:
         return credentials
 
     @staticmethod
-    def _get_load_balancing_manager(configuration: ProviderConfiguration,
-                                    model_type: ModelType,
-                                    model: str,
-                                    credentials: dict) -> Optional["LBModelManager"]:
+    def _get_load_balancing_manager(
+        configuration: ProviderConfiguration, model_type: ModelType, model: str, credentials: dict
+    ) -> Optional["LBModelManager"]:
         """
         Get load balancing model credentials
         :param configuration: provider configuration
@@ -81,8 +77,7 @@ class ModelInstance:
             current_model_setting = None
             # check if model is disabled by admin
             for model_setting in configuration.model_settings:
-                if (model_setting.model_type == model_type
-                        and model_setting.model == model):
+                if model_setting.model_type == model_type and model_setting.model == model:
                     current_model_setting = model_setting
                     break
 
@@ -95,17 +90,23 @@ class ModelInstance:
                     model_type=model_type,
                     model=model,
                     load_balancing_configs=current_model_setting.load_balancing_configs,
-                    managed_credentials=credentials if configuration.custom_configuration.provider else None
+                    managed_credentials=credentials if configuration.custom_configuration.provider else None,
                 )
 
                 return lb_model_manager
 
         return None
 
-    def invoke_llm(self, prompt_messages: list[PromptMessage], model_parameters: Optional[dict] = None,
-                   tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                   stream: bool = True, user: Optional[str] = None, callbacks: Optional[list[Callback]] = None) \
-            -> Union[LLMResult, Generator]:
+    def invoke_llm(
+        self,
+        prompt_messages: list[PromptMessage],
+        model_parameters: Optional[dict] = None,
+        tools: Sequence[PromptMessageTool] | None = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -132,11 +133,12 @@ class ModelInstance:
             stop=stop,
             stream=stream,
             user=user,
-            callbacks=callbacks
+            callbacks=callbacks,
         )
 
-    def get_llm_num_tokens(self, prompt_messages: list[PromptMessage],
-                           tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_llm_num_tokens(
+        self, prompt_messages: list[PromptMessage], tools: Optional[list[PromptMessageTool]] = None
+    ) -> int:
         """
         Get number of tokens for llm
 
@@ -153,11 +155,10 @@ class ModelInstance:
             model=self.model,
             credentials=self.credentials,
             prompt_messages=prompt_messages,
-            tools=tools
+            tools=tools,
         )
 
-    def invoke_text_embedding(self, texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def invoke_text_embedding(self, texts: list[str], user: Optional[str] = None) -> TextEmbeddingResult:
         """
         Invoke large language model
 
@@ -174,7 +175,7 @@ class ModelInstance:
             model=self.model,
             credentials=self.credentials,
             texts=texts,
-            user=user
+            user=user,
         )
 
     def get_text_embedding_num_tokens(self, texts: list[str]) -> int:
@@ -192,13 +193,17 @@ class ModelInstance:
             function=self.model_type_instance.get_num_tokens,
             model=self.model,
             credentials=self.credentials,
-            texts=texts
+            texts=texts,
         )
 
-    def invoke_rerank(self, query: str, docs: list[str], score_threshold: Optional[float] = None,
-                      top_n: Optional[int] = None,
-                      user: Optional[str] = None) \
-            -> RerankResult:
+    def invoke_rerank(
+        self,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
         """
         Invoke rerank model
 
@@ -221,11 +226,10 @@ class ModelInstance:
             docs=docs,
             score_threshold=score_threshold,
             top_n=top_n,
-            user=user
+            user=user,
         )
 
-    def invoke_moderation(self, text: str, user: Optional[str] = None) \
-            -> bool:
+    def invoke_moderation(self, text: str, user: Optional[str] = None) -> bool:
         """
         Invoke moderation model
 
@@ -242,11 +246,10 @@ class ModelInstance:
             model=self.model,
             credentials=self.credentials,
             text=text,
-            user=user
+            user=user,
         )
 
-    def invoke_speech2text(self, file: IO[bytes], user: Optional[str] = None) \
-            -> str:
+    def invoke_speech2text(self, file: IO[bytes], user: Optional[str] = None) -> str:
         """
         Invoke large language model
 
@@ -263,11 +266,10 @@ class ModelInstance:
             model=self.model,
             credentials=self.credentials,
             file=file,
-            user=user
+            user=user,
         )
 
-    def invoke_tts(self, content_text: str, tenant_id: str, voice: str, user: Optional[str] = None) \
-            -> str:
+    def invoke_tts(self, content_text: str, tenant_id: str, voice: str, user: Optional[str] = None) -> str:
         """
         Invoke large language tts model
 
@@ -288,7 +290,7 @@ class ModelInstance:
             content_text=content_text,
             user=user,
             tenant_id=tenant_id,
-            voice=voice
+            voice=voice,
         )
 
     def _round_robin_invoke(self, function: Callable, *args, **kwargs):
@@ -312,8 +314,8 @@ class ModelInstance:
                     raise last_exception
 
             try:
-                if 'credentials' in kwargs:
-                    del kwargs['credentials']
+                if "credentials" in kwargs:
+                    del kwargs["credentials"]
                 return function(*args, **kwargs, credentials=lb_config.credentials)
             except InvokeRateLimitError as e:
                 # expire in 60 seconds
@@ -340,9 +342,7 @@ class ModelInstance:
 
         self.model_type_instance = cast(TTSModel, self.model_type_instance)
         return self.model_type_instance.get_tts_model_voices(
-            model=self.model,
-            credentials=self.credentials,
-            language=language
+            model=self.model, credentials=self.credentials, language=language
         )
 
 
@@ -363,9 +363,7 @@ class ModelManager:
             return self.get_default_model_instance(tenant_id, model_type)
 
         provider_model_bundle = self._provider_manager.get_provider_model_bundle(
-            tenant_id=tenant_id,
-            provider=provider,
-            model_type=model_type
+            tenant_id=tenant_id, provider=provider, model_type=model_type
         )
 
         return ModelInstance(provider_model_bundle, model)
@@ -386,10 +384,7 @@ class ModelManager:
         :param model_type: model type
         :return:
         """
-        default_model_entity = self._provider_manager.get_default_model(
-            tenant_id=tenant_id,
-            model_type=model_type
-        )
+        default_model_entity = self._provider_manager.get_default_model(tenant_id=tenant_id, model_type=model_type)
 
         if not default_model_entity:
             raise ProviderTokenNotInitError(f"Default model not found for {model_type}")
@@ -398,17 +393,20 @@ class ModelManager:
             tenant_id=tenant_id,
             provider=default_model_entity.provider.provider,
             model_type=model_type,
-            model=default_model_entity.model
+            model=default_model_entity.model,
         )
 
 
 class LBModelManager:
-    def __init__(self, tenant_id: str,
-                 provider: str,
-                 model_type: ModelType,
-                 model: str,
-                 load_balancing_configs: list[ModelLoadBalancingConfiguration],
-                 managed_credentials: Optional[dict] = None) -> None:
+    def __init__(
+        self,
+        tenant_id: str,
+        provider: str,
+        model_type: ModelType,
+        model: str,
+        load_balancing_configs: list[ModelLoadBalancingConfiguration],
+        managed_credentials: Optional[dict] = None,
+    ) -> None:
         """
         Load balancing model manager
         :param tenant_id: tenant_id
@@ -439,10 +437,7 @@ class LBModelManager:
         :return:
         """
         cache_key = "model_lb_index:{}:{}:{}:{}".format(
-            self._tenant_id,
-            self._provider,
-            self._model_type.value,
-            self._model
+            self._tenant_id, self._provider, self._model_type.value, self._model
         )
 
         cooldown_load_balancing_configs = []
@@ -473,10 +468,12 @@ class LBModelManager:
 
                 continue
 
-            if bool(os.environ.get("DEBUG", 'False').lower() == 'true'):
-                logger.info(f"Model LB\nid: {config.id}\nname:{config.name}\n"
-                            f"tenant_id: {self._tenant_id}\nprovider: {self._provider}\n"
-                            f"model_type: {self._model_type.value}\nmodel: {self._model}")
+            if bool(os.environ.get("DEBUG", "False").lower() == "true"):
+                logger.info(
+                    f"Model LB\nid: {config.id}\nname:{config.name}\n"
+                    f"tenant_id: {self._tenant_id}\nprovider: {self._provider}\n"
+                    f"model_type: {self._model_type.value}\nmodel: {self._model}"
+                )
 
             return config
 
@@ -490,14 +487,10 @@ class LBModelManager:
         :return:
         """
         cooldown_cache_key = "model_lb_index:cooldown:{}:{}:{}:{}:{}".format(
-            self._tenant_id,
-            self._provider,
-            self._model_type.value,
-            self._model,
-            config.id
+            self._tenant_id, self._provider, self._model_type.value, self._model, config.id
         )
 
-        redis_client.setex(cooldown_cache_key, expire, 'true')
+        redis_client.setex(cooldown_cache_key, expire, "true")
 
     def in_cooldown(self, config: ModelLoadBalancingConfiguration) -> bool:
         """
@@ -506,11 +499,7 @@ class LBModelManager:
         :return:
         """
         cooldown_cache_key = "model_lb_index:cooldown:{}:{}:{}:{}:{}".format(
-            self._tenant_id,
-            self._provider,
-            self._model_type.value,
-            self._model,
-            config.id
+            self._tenant_id, self._provider, self._model_type.value, self._model, config.id
         )
 
         res = redis_client.exists(cooldown_cache_key)
@@ -518,11 +507,9 @@ class LBModelManager:
         return res
 
     @staticmethod
-    def get_config_in_cooldown_and_ttl(tenant_id: str,
-                                       provider: str,
-                                       model_type: ModelType,
-                                       model: str,
-                                       config_id: str) -> tuple[bool, int]:
+    def get_config_in_cooldown_and_ttl(
+        tenant_id: str, provider: str, model_type: ModelType, model: str, config_id: str
+    ) -> tuple[bool, int]:
         """
         Get model load balancing config is in cooldown and ttl
         :param tenant_id: workspace id
@@ -533,11 +520,7 @@ class LBModelManager:
         :return:
         """
         cooldown_cache_key = "model_lb_index:cooldown:{}:{}:{}:{}:{}".format(
-            tenant_id,
-            provider,
-            model_type.value,
-            model,
-            config_id
+            tenant_id, provider, model_type.value, model, config_id
         )
 
         ttl = redis_client.ttl(cooldown_cache_key)
diff --git a/api/core/model_runtime/callbacks/base_callback.py b/api/core/model_runtime/callbacks/base_callback.py
index bba004a32a..92da53c9a4 100644
--- a/api/core/model_runtime/callbacks/base_callback.py
+++ b/api/core/model_runtime/callbacks/base_callback.py
@@ -18,12 +18,21 @@ class Callback:
     Base class for callbacks.
     Only for LLM.
     """
+
     raise_error: bool = False
 
-    def on_before_invoke(self, llm_instance: AIModel, model: str, credentials: dict,
-                         prompt_messages: list[PromptMessage], model_parameters: dict,
-                         tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                         stream: bool = True, user: Optional[str] = None) -> None:
+    def on_before_invoke(
+        self,
+        llm_instance: AIModel,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> None:
         """
         Before invoke callback
 
@@ -39,10 +48,19 @@ class Callback:
         """
         raise NotImplementedError()
 
-    def on_new_chunk(self, llm_instance: AIModel, chunk: LLMResultChunk, model: str, credentials: dict,
-                     prompt_messages: list[PromptMessage], model_parameters: dict,
-                     tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                     stream: bool = True, user: Optional[str] = None):
+    def on_new_chunk(
+        self,
+        llm_instance: AIModel,
+        chunk: LLMResultChunk,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ):
         """
         On new chunk callback
 
@@ -59,10 +77,19 @@ class Callback:
         """
         raise NotImplementedError()
 
-    def on_after_invoke(self, llm_instance: AIModel, result: LLMResult, model: str, credentials: dict,
-                        prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                        stream: bool = True, user: Optional[str] = None) -> None:
+    def on_after_invoke(
+        self,
+        llm_instance: AIModel,
+        result: LLMResult,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> None:
         """
         After invoke callback
 
@@ -79,10 +106,19 @@ class Callback:
         """
         raise NotImplementedError()
 
-    def on_invoke_error(self, llm_instance: AIModel, ex: Exception, model: str, credentials: dict,
-                        prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                        stream: bool = True, user: Optional[str] = None) -> None:
+    def on_invoke_error(
+        self,
+        llm_instance: AIModel,
+        ex: Exception,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> None:
         """
         Invoke error callback
 
@@ -99,9 +135,7 @@ class Callback:
         """
         raise NotImplementedError()
 
-    def print_text(
-            self, text: str, color: Optional[str] = None, end: str = ""
-    ) -> None:
+    def print_text(self, text: str, color: Optional[str] = None, end: str = "") -> None:
         """Print text with highlighting and no end characters."""
         text_to_print = self._get_colored_text(text, color) if color else text
         print(text_to_print, end=end)
diff --git a/api/core/model_runtime/callbacks/logging_callback.py b/api/core/model_runtime/callbacks/logging_callback.py
index 0406853b88..3b6b825244 100644
--- a/api/core/model_runtime/callbacks/logging_callback.py
+++ b/api/core/model_runtime/callbacks/logging_callback.py
@@ -10,11 +10,20 @@ from core.model_runtime.model_providers.__base.ai_model import AIModel
 
 logger = logging.getLogger(__name__)
 
+
 class LoggingCallback(Callback):
-    def on_before_invoke(self, llm_instance: AIModel, model: str, credentials: dict,
-                         prompt_messages: list[PromptMessage], model_parameters: dict,
-                         tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                         stream: bool = True, user: Optional[str] = None) -> None:
+    def on_before_invoke(
+        self,
+        llm_instance: AIModel,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> None:
         """
         Before invoke callback
 
@@ -28,40 +37,49 @@ class LoggingCallback(Callback):
         :param stream: is stream response
         :param user: unique user id
         """
-        self.print_text("\n[on_llm_before_invoke]\n", color='blue')
-        self.print_text(f"Model: {model}\n", color='blue')
-        self.print_text("Parameters:\n", color='blue')
+        self.print_text("\n[on_llm_before_invoke]\n", color="blue")
+        self.print_text(f"Model: {model}\n", color="blue")
+        self.print_text("Parameters:\n", color="blue")
         for key, value in model_parameters.items():
-            self.print_text(f"\t{key}: {value}\n", color='blue')
+            self.print_text(f"\t{key}: {value}\n", color="blue")
 
         if stop:
-            self.print_text(f"\tstop: {stop}\n", color='blue')
+            self.print_text(f"\tstop: {stop}\n", color="blue")
 
         if tools:
-            self.print_text("\tTools:\n", color='blue')
+            self.print_text("\tTools:\n", color="blue")
             for tool in tools:
-                self.print_text(f"\t\t{tool.name}\n", color='blue')
+                self.print_text(f"\t\t{tool.name}\n", color="blue")
 
-        self.print_text(f"Stream: {stream}\n", color='blue')
+        self.print_text(f"Stream: {stream}\n", color="blue")
 
         if user:
-            self.print_text(f"User: {user}\n", color='blue')
+            self.print_text(f"User: {user}\n", color="blue")
 
-        self.print_text("Prompt messages:\n", color='blue')
+        self.print_text("Prompt messages:\n", color="blue")
         for prompt_message in prompt_messages:
             if prompt_message.name:
-                self.print_text(f"\tname: {prompt_message.name}\n", color='blue')
+                self.print_text(f"\tname: {prompt_message.name}\n", color="blue")
 
-            self.print_text(f"\trole: {prompt_message.role.value}\n", color='blue')
-            self.print_text(f"\tcontent: {prompt_message.content}\n", color='blue')
+            self.print_text(f"\trole: {prompt_message.role.value}\n", color="blue")
+            self.print_text(f"\tcontent: {prompt_message.content}\n", color="blue")
 
         if stream:
             self.print_text("\n[on_llm_new_chunk]")
 
-    def on_new_chunk(self, llm_instance: AIModel, chunk: LLMResultChunk, model: str, credentials: dict,
-                     prompt_messages: list[PromptMessage], model_parameters: dict,
-                     tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                     stream: bool = True, user: Optional[str] = None):
+    def on_new_chunk(
+        self,
+        llm_instance: AIModel,
+        chunk: LLMResultChunk,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ):
         """
         On new chunk callback
 
@@ -79,10 +97,19 @@ class LoggingCallback(Callback):
         sys.stdout.write(chunk.delta.message.content)
         sys.stdout.flush()
 
-    def on_after_invoke(self, llm_instance: AIModel, result: LLMResult, model: str, credentials: dict,
-                        prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                        stream: bool = True, user: Optional[str] = None) -> None:
+    def on_after_invoke(
+        self,
+        llm_instance: AIModel,
+        result: LLMResult,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> None:
         """
         After invoke callback
 
@@ -97,24 +124,33 @@ class LoggingCallback(Callback):
         :param stream: is stream response
         :param user: unique user id
         """
-        self.print_text("\n[on_llm_after_invoke]\n", color='yellow')
-        self.print_text(f"Content: {result.message.content}\n", color='yellow')
+        self.print_text("\n[on_llm_after_invoke]\n", color="yellow")
+        self.print_text(f"Content: {result.message.content}\n", color="yellow")
 
         if result.message.tool_calls:
-            self.print_text("Tool calls:\n", color='yellow')
+            self.print_text("Tool calls:\n", color="yellow")
             for tool_call in result.message.tool_calls:
-                self.print_text(f"\t{tool_call.id}\n", color='yellow')
-                self.print_text(f"\t{tool_call.function.name}\n", color='yellow')
-                self.print_text(f"\t{json.dumps(tool_call.function.arguments)}\n", color='yellow')
+                self.print_text(f"\t{tool_call.id}\n", color="yellow")
+                self.print_text(f"\t{tool_call.function.name}\n", color="yellow")
+                self.print_text(f"\t{json.dumps(tool_call.function.arguments)}\n", color="yellow")
 
-        self.print_text(f"Model: {result.model}\n", color='yellow')
-        self.print_text(f"Usage: {result.usage}\n", color='yellow')
-        self.print_text(f"System Fingerprint: {result.system_fingerprint}\n", color='yellow')
+        self.print_text(f"Model: {result.model}\n", color="yellow")
+        self.print_text(f"Usage: {result.usage}\n", color="yellow")
+        self.print_text(f"System Fingerprint: {result.system_fingerprint}\n", color="yellow")
 
-    def on_invoke_error(self, llm_instance: AIModel, ex: Exception, model: str, credentials: dict,
-                        prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                        stream: bool = True, user: Optional[str] = None) -> None:
+    def on_invoke_error(
+        self,
+        llm_instance: AIModel,
+        ex: Exception,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> None:
         """
         Invoke error callback
 
@@ -129,5 +165,5 @@ class LoggingCallback(Callback):
         :param stream: is stream response
         :param user: unique user id
         """
-        self.print_text("\n[on_llm_invoke_error]\n", color='red')
+        self.print_text("\n[on_llm_invoke_error]\n", color="red")
         logger.exception(ex)
diff --git a/api/core/model_runtime/docs/en_US/schema.md b/api/core/model_runtime/docs/en_US/schema.md
index 67f4e0879d..f819a4dbdc 100644
--- a/api/core/model_runtime/docs/en_US/schema.md
+++ b/api/core/model_runtime/docs/en_US/schema.md
@@ -52,7 +52,7 @@
     - `mode` (string)  voice model.（available for model type `tts`）
     - `name` (string)  voice model display name.（available for model type `tts`）
     - `language` (string)  the voice model supports languages.（available for model type `tts`）
-  - `word_limit` (int)  Single conversion word limit, paragraphwise by default（available for model type `tts`）
+  - `word_limit` (int)  Single conversion word limit, paragraph-wise by default（available for model type `tts`）
   - `audio_type` (string)  Support audio file extension format, e.g.：mp3,wav（available for model type `tts`）
   - `max_workers` (int)  Number of concurrent workers supporting text and audio conversion（available for model type`tts`）
   - `max_characters_per_chunk` (int) Maximum characters per chunk (available for model type `moderation`)
@@ -150,7 +150,7 @@
 
 - `input` (float) Input price, i.e., Prompt price
 - `output` (float) Output price, i.e., returned content price
-- `unit` (float) Pricing unit, e.g., if the price is meausred in 1M tokens, the corresponding token amount for the unit price is `0.000001`.
+- `unit` (float) Pricing unit, e.g., if the price is measured in 1M tokens, the corresponding token amount for the unit price is `0.000001`.
 - `currency` (string) Currency unit
 
 ### ProviderCredentialSchema
diff --git a/api/core/model_runtime/entities/common_entities.py b/api/core/model_runtime/entities/common_entities.py
index 175c13cfdc..659ad59bd6 100644
--- a/api/core/model_runtime/entities/common_entities.py
+++ b/api/core/model_runtime/entities/common_entities.py
@@ -7,6 +7,7 @@ class I18nObject(BaseModel):
     """
     Model class for i18n object.
     """
+
     zh_Hans: Optional[str] = None
     en_US: str
 
diff --git a/api/core/model_runtime/entities/defaults.py b/api/core/model_runtime/entities/defaults.py
index d2076bf74a..4d0c9aa08f 100644
--- a/api/core/model_runtime/entities/defaults.py
+++ b/api/core/model_runtime/entities/defaults.py
@@ -2,107 +2,129 @@ from core.model_runtime.entities.model_entities import DefaultParameterName
 
 PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
     DefaultParameterName.TEMPERATURE: {
-        'label': {
-            'en_US': 'Temperature',
-            'zh_Hans': '温度',
+        "label": {
+            "en_US": "Temperature",
+            "zh_Hans": "温度",
         },
-        'type': 'float',
-        'help': {
-            'en_US': 'Controls randomness. Lower temperature results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive. Higher temperature results in more random completions.',
-            'zh_Hans': '温度控制随机性。较低的温度会导致较少的随机完成。随着温度接近零，模型将变得确定性和重复性。较高的温度会导致更多的随机完成。',
+        "type": "float",
+        "help": {
+            "en_US": "Controls randomness. Lower temperature results in less random completions."
+            " As the temperature approaches zero, the model will become deterministic and repetitive."
+            " Higher temperature results in more random completions.",
+            "zh_Hans": "温度控制随机性。较低的温度会导致较少的随机完成。随着温度接近零，模型将变得确定性和重复性。"
+            "较高的温度会导致更多的随机完成。",
         },
-        'required': False,
-        'default': 0.0,
-        'min': 0.0,
-        'max': 1.0,
-        'precision': 2,
+        "required": False,
+        "default": 0.0,
+        "min": 0.0,
+        "max": 1.0,
+        "precision": 2,
     },
     DefaultParameterName.TOP_P: {
-        'label': {
-            'en_US': 'Top P',
-            'zh_Hans': 'Top P',
+        "label": {
+            "en_US": "Top P",
+            "zh_Hans": "Top P",
         },
-        'type': 'float',
-        'help': {
-            'en_US': 'Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options are considered.',
-            'zh_Hans': '通过核心采样控制多样性：0.5表示考虑了一半的所有可能性加权选项。',
+        "type": "float",
+        "help": {
+            "en_US": "Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options"
+            " are considered.",
+            "zh_Hans": "通过核心采样控制多样性：0.5表示考虑了一半的所有可能性加权选项。",
         },
-        'required': False,
-        'default': 1.0,
-        'min': 0.0,
-        'max': 1.0,
-        'precision': 2,
+        "required": False,
+        "default": 1.0,
+        "min": 0.0,
+        "max": 1.0,
+        "precision": 2,
+    },
+    DefaultParameterName.TOP_K: {
+        "label": {
+            "en_US": "Top K",
+            "zh_Hans": "Top K",
+        },
+        "type": "int",
+        "help": {
+            "en_US": "Limits the number of tokens to consider for each step by keeping only the k most likely tokens.",
+            "zh_Hans": "通过只保留每一步中最可能的 k 个标记来限制要考虑的标记数量。",
+        },
+        "required": False,
+        "default": 50,
+        "min": 1,
+        "max": 100,
+        "precision": 0,
     },
     DefaultParameterName.PRESENCE_PENALTY: {
-        'label': {
-            'en_US': 'Presence Penalty',
-            'zh_Hans': '存在惩罚',
+        "label": {
+            "en_US": "Presence Penalty",
+            "zh_Hans": "存在惩罚",
         },
-        'type': 'float',
-        'help': {
-            'en_US': 'Applies a penalty to the log-probability of tokens already in the text.',
-            'zh_Hans': '对文本中已有的标记的对数概率施加惩罚。',
+        "type": "float",
+        "help": {
+            "en_US": "Applies a penalty to the log-probability of tokens already in the text.",
+            "zh_Hans": "对文本中已有的标记的对数概率施加惩罚。",
         },
-        'required': False,
-        'default': 0.0,
-        'min': 0.0,
-        'max': 1.0,
-        'precision': 2,
+        "required": False,
+        "default": 0.0,
+        "min": 0.0,
+        "max": 1.0,
+        "precision": 2,
     },
     DefaultParameterName.FREQUENCY_PENALTY: {
-        'label': {
-            'en_US': 'Frequency Penalty',
-            'zh_Hans': '频率惩罚',
+        "label": {
+            "en_US": "Frequency Penalty",
+            "zh_Hans": "频率惩罚",
         },
-        'type': 'float',
-        'help': {
-            'en_US': 'Applies a penalty to the log-probability of tokens that appear in the text.',
-            'zh_Hans': '对文本中出现的标记的对数概率施加惩罚。',
+        "type": "float",
+        "help": {
+            "en_US": "Applies a penalty to the log-probability of tokens that appear in the text.",
+            "zh_Hans": "对文本中出现的标记的对数概率施加惩罚。",
         },
-        'required': False,
-        'default': 0.0,
-        'min': 0.0,
-        'max': 1.0,
-        'precision': 2,
+        "required": False,
+        "default": 0.0,
+        "min": 0.0,
+        "max": 1.0,
+        "precision": 2,
     },
     DefaultParameterName.MAX_TOKENS: {
-        'label': {
-            'en_US': 'Max Tokens',
-            'zh_Hans': '最大标记',
+        "label": {
+            "en_US": "Max Tokens",
+            "zh_Hans": "最大标记",
         },
-        'type': 'int',
-        'help': {
-            'en_US': 'Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.',
-            'zh_Hans': '指定生成结果长度的上限。如果生成结果截断，可以调大该参数。',
+        "type": "int",
+        "help": {
+            "en_US": "Specifies the upper limit on the length of generated results."
+            " If the generated results are truncated, you can increase this parameter.",
+            "zh_Hans": "指定生成结果长度的上限。如果生成结果截断，可以调大该参数。",
         },
-        'required': False,
-        'default': 64,
-        'min': 1,
-        'max': 2048,
-        'precision': 0,
+        "required": False,
+        "default": 64,
+        "min": 1,
+        "max": 2048,
+        "precision": 0,
     },
     DefaultParameterName.RESPONSE_FORMAT: {
-        'label': {
-            'en_US': 'Response Format',
-            'zh_Hans': '回复格式',
+        "label": {
+            "en_US": "Response Format",
+            "zh_Hans": "回复格式",
         },
-        'type': 'string',
-        'help': {
-            'en_US': 'Set a response format, ensure the output from llm is a valid code block as possible, such as JSON, XML, etc.',
-            'zh_Hans': '设置一个返回格式，确保llm的输出尽可能是有效的代码块，如JSON、XML等',
+        "type": "string",
+        "help": {
+            "en_US": "Set a response format, ensure the output from llm is a valid code block as possible,"
+            " such as JSON, XML, etc.",
+            "zh_Hans": "设置一个返回格式，确保llm的输出尽可能是有效的代码块，如JSON、XML等",
         },
-        'required': False,
-        'options': ['JSON', 'XML'],
+        "required": False,
+        "options": ["JSON", "XML"],
     },
     DefaultParameterName.JSON_SCHEMA: {
-        'label': {
-            'en_US': 'JSON Schema',
+        "label": {
+            "en_US": "JSON Schema",
         },
-        'type': 'text',
-        'help': {
-            'en_US': 'Set a response json schema will ensure LLM to adhere it.',
-            'zh_Hans': '设置返回的json schema，llm将按照它返回',
+        "type": "text",
+        "help": {
+            "en_US": "Set a response json schema will ensure LLM to adhere it.",
+            "zh_Hans": "设置返回的json schema，llm将按照它返回",
         },
-        'required': False,
+        "required": False,
     },
 }
diff --git a/api/core/model_runtime/entities/llm_entities.py b/api/core/model_runtime/entities/llm_entities.py
index b5bd9e267a..52b590f66a 100644
--- a/api/core/model_runtime/entities/llm_entities.py
+++ b/api/core/model_runtime/entities/llm_entities.py
@@ -12,11 +12,12 @@ class LLMMode(Enum):
     """
     Enum class for large language model mode.
     """
+
     COMPLETION = "completion"
     CHAT = "chat"
 
     @classmethod
-    def value_of(cls, value: str) -> 'LLMMode':
+    def value_of(cls, value: str) -> "LLMMode":
         """
         Get value of given mode.
 
@@ -26,13 +27,14 @@ class LLMMode(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid mode value {value}')
+        raise ValueError(f"invalid mode value {value}")
 
 
 class LLMUsage(ModelUsage):
     """
     Model class for llm usage.
     """
+
     prompt_tokens: int
     prompt_unit_price: Decimal
     prompt_price_unit: Decimal
@@ -50,24 +52,59 @@ class LLMUsage(ModelUsage):
     def empty_usage(cls):
         return cls(
             prompt_tokens=0,
-            prompt_unit_price=Decimal('0.0'),
-            prompt_price_unit=Decimal('0.0'),
-            prompt_price=Decimal('0.0'),
+            prompt_unit_price=Decimal("0.0"),
+            prompt_price_unit=Decimal("0.0"),
+            prompt_price=Decimal("0.0"),
             completion_tokens=0,
-            completion_unit_price=Decimal('0.0'),
-            completion_price_unit=Decimal('0.0'),
-            completion_price=Decimal('0.0'),
+            completion_unit_price=Decimal("0.0"),
+            completion_price_unit=Decimal("0.0"),
+            completion_price=Decimal("0.0"),
             total_tokens=0,
-            total_price=Decimal('0.0'),
-            currency='USD',
-            latency=0.0
+            total_price=Decimal("0.0"),
+            currency="USD",
+            latency=0.0,
         )
 
+    def plus(self, other: "LLMUsage") -> "LLMUsage":
+        """
+        Add two LLMUsage instances together.
+
+        :param other: Another LLMUsage instance to add
+        :return: A new LLMUsage instance with summed values
+        """
+        if self.total_tokens == 0:
+            return other
+        else:
+            return LLMUsage(
+                prompt_tokens=self.prompt_tokens + other.prompt_tokens,
+                prompt_unit_price=other.prompt_unit_price,
+                prompt_price_unit=other.prompt_price_unit,
+                prompt_price=self.prompt_price + other.prompt_price,
+                completion_tokens=self.completion_tokens + other.completion_tokens,
+                completion_unit_price=other.completion_unit_price,
+                completion_price_unit=other.completion_price_unit,
+                completion_price=self.completion_price + other.completion_price,
+                total_tokens=self.total_tokens + other.total_tokens,
+                total_price=self.total_price + other.total_price,
+                currency=other.currency,
+                latency=self.latency + other.latency,
+            )
+
+    def __add__(self, other: "LLMUsage") -> "LLMUsage":
+        """
+        Overload the + operator to add two LLMUsage instances.
+
+        :param other: Another LLMUsage instance to add
+        :return: A new LLMUsage instance with summed values
+        """
+        return self.plus(other)
+
 
 class LLMResult(BaseModel):
     """
     Model class for llm result.
     """
+
     model: str
     prompt_messages: list[PromptMessage]
     message: AssistantPromptMessage
@@ -79,6 +116,7 @@ class LLMResultChunkDelta(BaseModel):
     """
     Model class for llm result chunk delta.
     """
+
     index: int
     message: AssistantPromptMessage
     usage: Optional[LLMUsage] = None
@@ -89,6 +127,7 @@ class LLMResultChunk(BaseModel):
     """
     Model class for llm result chunk.
     """
+
     model: str
     prompt_messages: list[PromptMessage]
     system_fingerprint: Optional[str] = None
@@ -99,4 +138,5 @@ class NumTokensResult(PriceInfo):
     """
     Model class for number of tokens result.
     """
+
     tokens: int
diff --git a/api/core/model_runtime/entities/message_entities.py b/api/core/model_runtime/entities/message_entities.py
index e8e6963b56..e51bb18deb 100644
--- a/api/core/model_runtime/entities/message_entities.py
+++ b/api/core/model_runtime/entities/message_entities.py
@@ -9,13 +9,14 @@ class PromptMessageRole(Enum):
     """
     Enum class for prompt message.
     """
+
     SYSTEM = "system"
     USER = "user"
     ASSISTANT = "assistant"
     TOOL = "tool"
 
     @classmethod
-    def value_of(cls, value: str) -> 'PromptMessageRole':
+    def value_of(cls, value: str) -> "PromptMessageRole":
         """
         Get value of given mode.
 
@@ -25,13 +26,14 @@ class PromptMessageRole(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid prompt message type value {value}')
+        raise ValueError(f"invalid prompt message type value {value}")
 
 
 class PromptMessageTool(BaseModel):
     """
     Model class for prompt message tool.
     """
+
     name: str
     description: str
     parameters: dict
@@ -41,7 +43,8 @@ class PromptMessageFunction(BaseModel):
     """
     Model class for prompt message function.
     """
-    type: str = 'function'
+
+    type: str = "function"
     function: PromptMessageTool
 
 
@@ -49,14 +52,16 @@ class PromptMessageContentType(Enum):
     """
     Enum class for prompt message content type.
     """
-    TEXT = 'text'
-    IMAGE = 'image'
+
+    TEXT = "text"
+    IMAGE = "image"
 
 
 class PromptMessageContent(BaseModel):
     """
     Model class for prompt message content.
     """
+
     type: PromptMessageContentType
     data: str
 
@@ -65,6 +70,7 @@ class TextPromptMessageContent(PromptMessageContent):
     """
     Model class for text prompt message content.
     """
+
     type: PromptMessageContentType = PromptMessageContentType.TEXT
 
 
@@ -72,9 +78,10 @@ class ImagePromptMessageContent(PromptMessageContent):
     """
     Model class for image prompt message content.
     """
+
     class DETAIL(Enum):
-        LOW = 'low'
-        HIGH = 'high'
+        LOW = "low"
+        HIGH = "high"
 
     type: PromptMessageContentType = PromptMessageContentType.IMAGE
     detail: DETAIL = DETAIL.LOW
@@ -84,6 +91,7 @@ class PromptMessage(ABC, BaseModel):
     """
     Model class for prompt message.
     """
+
     role: PromptMessageRole
     content: Optional[str | list[PromptMessageContent]] = None
     name: Optional[str] = None
@@ -101,6 +109,7 @@ class UserPromptMessage(PromptMessage):
     """
     Model class for user prompt message.
     """
+
     role: PromptMessageRole = PromptMessageRole.USER
 
 
@@ -108,14 +117,17 @@ class AssistantPromptMessage(PromptMessage):
     """
     Model class for assistant prompt message.
     """
+
     class ToolCall(BaseModel):
         """
         Model class for assistant prompt message tool call.
         """
+
         class ToolCallFunction(BaseModel):
             """
             Model class for assistant prompt message tool call function.
             """
+
             name: str
             arguments: str
 
@@ -123,7 +135,7 @@ class AssistantPromptMessage(PromptMessage):
         type: str
         function: ToolCallFunction
 
-        @field_validator('id', mode='before')
+        @field_validator("id", mode="before")
         @classmethod
         def transform_id_to_str(cls, value) -> str:
             if not isinstance(value, str):
@@ -145,10 +157,12 @@ class AssistantPromptMessage(PromptMessage):
 
         return True
 
+
 class SystemPromptMessage(PromptMessage):
     """
     Model class for system prompt message.
     """
+
     role: PromptMessageRole = PromptMessageRole.SYSTEM
 
 
@@ -156,6 +170,7 @@ class ToolPromptMessage(PromptMessage):
     """
     Model class for tool prompt message.
     """
+
     role: PromptMessageRole = PromptMessageRole.TOOL
     tool_call_id: str
 
diff --git a/api/core/model_runtime/entities/model_entities.py b/api/core/model_runtime/entities/model_entities.py
index c257ce63d2..52ea787c3a 100644
--- a/api/core/model_runtime/entities/model_entities.py
+++ b/api/core/model_runtime/entities/model_entities.py
@@ -11,6 +11,7 @@ class ModelType(Enum):
     """
     Enum class for model type.
     """
+
     LLM = "llm"
     TEXT_EMBEDDING = "text-embedding"
     RERANK = "rerank"
@@ -26,22 +27,22 @@ class ModelType(Enum):
 
         :return: model type
         """
-        if origin_model_type == 'text-generation' or origin_model_type == cls.LLM.value:
+        if origin_model_type in {"text-generation", cls.LLM.value}:
             return cls.LLM
-        elif origin_model_type == 'embeddings' or origin_model_type == cls.TEXT_EMBEDDING.value:
+        elif origin_model_type in {"embeddings", cls.TEXT_EMBEDDING.value}:
             return cls.TEXT_EMBEDDING
-        elif origin_model_type == 'reranking' or origin_model_type == cls.RERANK.value:
+        elif origin_model_type in {"reranking", cls.RERANK.value}:
             return cls.RERANK
-        elif origin_model_type == 'speech2text' or origin_model_type == cls.SPEECH2TEXT.value:
+        elif origin_model_type in {"speech2text", cls.SPEECH2TEXT.value}:
             return cls.SPEECH2TEXT
-        elif origin_model_type == 'tts' or origin_model_type == cls.TTS.value:
+        elif origin_model_type in {"tts", cls.TTS.value}:
             return cls.TTS
-        elif origin_model_type == 'text2img' or origin_model_type == cls.TEXT2IMG.value:
+        elif origin_model_type in {"text2img", cls.TEXT2IMG.value}:
             return cls.TEXT2IMG
         elif origin_model_type == cls.MODERATION.value:
             return cls.MODERATION
         else:
-            raise ValueError(f'invalid origin model type {origin_model_type}')
+            raise ValueError(f"invalid origin model type {origin_model_type}")
 
     def to_origin_model_type(self) -> str:
         """
@@ -50,26 +51,28 @@ class ModelType(Enum):
         :return: origin model type
         """
         if self == self.LLM:
-            return 'text-generation'
+            return "text-generation"
         elif self == self.TEXT_EMBEDDING:
-            return 'embeddings'
+            return "embeddings"
         elif self == self.RERANK:
-            return 'reranking'
+            return "reranking"
         elif self == self.SPEECH2TEXT:
-            return 'speech2text'
+            return "speech2text"
         elif self == self.TTS:
-            return 'tts'
+            return "tts"
         elif self == self.MODERATION:
-            return 'moderation'
+            return "moderation"
         elif self == self.TEXT2IMG:
-            return 'text2img'
+            return "text2img"
         else:
-            raise ValueError(f'invalid model type {self}')
+            raise ValueError(f"invalid model type {self}")
+
 
 class FetchFrom(Enum):
     """
     Enum class for fetch from.
     """
+
     PREDEFINED_MODEL = "predefined-model"
     CUSTOMIZABLE_MODEL = "customizable-model"
 
@@ -78,6 +81,7 @@ class ModelFeature(Enum):
     """
     Enum class for llm feature.
     """
+
     TOOL_CALL = "tool-call"
     MULTI_TOOL_CALL = "multi-tool-call"
     AGENT_THOUGHT = "agent-thought"
@@ -85,12 +89,14 @@ class ModelFeature(Enum):
     STREAM_TOOL_CALL = "stream-tool-call"
 
 
-class DefaultParameterName(Enum):
+class DefaultParameterName(str, Enum):
     """
     Enum class for parameter template variable.
     """
+
     TEMPERATURE = "temperature"
     TOP_P = "top_p"
+    TOP_K = "top_k"
     PRESENCE_PENALTY = "presence_penalty"
     FREQUENCY_PENALTY = "frequency_penalty"
     MAX_TOKENS = "max_tokens"
@@ -98,7 +104,7 @@ class DefaultParameterName(Enum):
     JSON_SCHEMA = "json_schema"
 
     @classmethod
-    def value_of(cls, value: Any) -> 'DefaultParameterName':
+    def value_of(cls, value: Any) -> "DefaultParameterName":
         """
         Get parameter name from value.
 
@@ -108,13 +114,14 @@ class DefaultParameterName(Enum):
         for name in cls:
             if name.value == value:
                 return name
-        raise ValueError(f'invalid parameter name {value}')
+        raise ValueError(f"invalid parameter name {value}")
 
 
 class ParameterType(Enum):
     """
     Enum class for parameter type.
     """
+
     FLOAT = "float"
     INT = "int"
     STRING = "string"
@@ -126,6 +133,7 @@ class ModelPropertyKey(Enum):
     """
     Enum class for model property key.
     """
+
     MODE = "mode"
     CONTEXT_SIZE = "context_size"
     MAX_CHUNKS = "max_chunks"
@@ -143,6 +151,7 @@ class ProviderModel(BaseModel):
     """
     Model class for provider model.
     """
+
     model: str
     label: I18nObject
     model_type: ModelType
@@ -157,6 +166,7 @@ class ParameterRule(BaseModel):
     """
     Model class for parameter rule.
     """
+
     name: str
     use_template: Optional[str] = None
     label: I18nObject
@@ -174,6 +184,7 @@ class PriceConfig(BaseModel):
     """
     Model class for pricing info.
     """
+
     input: Decimal
     output: Optional[Decimal] = None
     unit: Decimal
@@ -184,6 +195,7 @@ class AIModelEntity(ProviderModel):
     """
     Model class for AI model.
     """
+
     parameter_rules: list[ParameterRule] = []
     pricing: Optional[PriceConfig] = None
 
@@ -196,6 +208,7 @@ class PriceType(Enum):
     """
     Enum class for price type.
     """
+
     INPUT = "input"
     OUTPUT = "output"
 
@@ -204,6 +217,7 @@ class PriceInfo(BaseModel):
     """
     Model class for price info.
     """
+
     unit_price: Decimal
     unit: Decimal
     total_amount: Decimal
diff --git a/api/core/model_runtime/entities/provider_entities.py b/api/core/model_runtime/entities/provider_entities.py
index f88f89d588..bfe861a97f 100644
--- a/api/core/model_runtime/entities/provider_entities.py
+++ b/api/core/model_runtime/entities/provider_entities.py
@@ -12,6 +12,7 @@ class ConfigurateMethod(Enum):
     """
     Enum class for configurate method of provider model.
     """
+
     PREDEFINED_MODEL = "predefined-model"
     CUSTOMIZABLE_MODEL = "customizable-model"
 
@@ -20,6 +21,7 @@ class FormType(Enum):
     """
     Enum class for form type.
     """
+
     TEXT_INPUT = "text-input"
     SECRET_INPUT = "secret-input"
     SELECT = "select"
@@ -31,6 +33,7 @@ class FormShowOnObject(BaseModel):
     """
     Model class for form show on.
     """
+
     variable: str
     value: str
 
@@ -39,6 +42,7 @@ class FormOption(BaseModel):
     """
     Model class for form option.
     """
+
     label: I18nObject
     value: str
     show_on: list[FormShowOnObject] = []
@@ -46,15 +50,14 @@ class FormOption(BaseModel):
     def __init__(self, **data):
         super().__init__(**data)
         if not self.label:
-            self.label = I18nObject(
-                en_US=self.value
-            )
+            self.label = I18nObject(en_US=self.value)
 
 
 class CredentialFormSchema(BaseModel):
     """
     Model class for credential form schema.
     """
+
     variable: str
     label: I18nObject
     type: FormType
@@ -70,6 +73,7 @@ class ProviderCredentialSchema(BaseModel):
     """
     Model class for provider credential schema.
     """
+
     credential_form_schemas: list[CredentialFormSchema]
 
 
@@ -82,6 +86,7 @@ class ModelCredentialSchema(BaseModel):
     """
     Model class for model credential schema.
     """
+
     model: FieldModelSchema
     credential_form_schemas: list[CredentialFormSchema]
 
@@ -90,6 +95,7 @@ class SimpleProviderEntity(BaseModel):
     """
     Simple model class for provider.
     """
+
     provider: str
     label: I18nObject
     icon_small: Optional[I18nObject] = None
@@ -102,6 +108,7 @@ class ProviderHelpEntity(BaseModel):
     """
     Model class for provider help.
     """
+
     title: I18nObject
     url: I18nObject
 
@@ -110,6 +117,7 @@ class ProviderEntity(BaseModel):
     """
     Model class for provider.
     """
+
     provider: str
     label: I18nObject
     description: Optional[I18nObject] = None
@@ -138,7 +146,7 @@ class ProviderEntity(BaseModel):
             icon_small=self.icon_small,
             icon_large=self.icon_large,
             supported_model_types=self.supported_model_types,
-            models=self.models
+            models=self.models,
         )
 
 
@@ -146,5 +154,6 @@ class ProviderConfig(BaseModel):
     """
     Model class for provider config.
     """
+
     provider: str
     credentials: dict
diff --git a/api/core/model_runtime/entities/rerank_entities.py b/api/core/model_runtime/entities/rerank_entities.py
index d51efd2b3b..99709e1bcd 100644
--- a/api/core/model_runtime/entities/rerank_entities.py
+++ b/api/core/model_runtime/entities/rerank_entities.py
@@ -5,6 +5,7 @@ class RerankDocument(BaseModel):
     """
     Model class for rerank document.
     """
+
     index: int
     text: str
     score: float
@@ -14,5 +15,6 @@ class RerankResult(BaseModel):
     """
     Model class for rerank result.
     """
+
     model: str
     docs: list[RerankDocument]
diff --git a/api/core/model_runtime/entities/text_embedding_entities.py b/api/core/model_runtime/entities/text_embedding_entities.py
index 7be3def379..846b89d658 100644
--- a/api/core/model_runtime/entities/text_embedding_entities.py
+++ b/api/core/model_runtime/entities/text_embedding_entities.py
@@ -9,6 +9,7 @@ class EmbeddingUsage(ModelUsage):
     """
     Model class for embedding usage.
     """
+
     tokens: int
     total_tokens: int
     unit_price: Decimal
@@ -22,7 +23,7 @@ class TextEmbeddingResult(BaseModel):
     """
     Model class for text embedding result.
     """
+
     model: str
     embeddings: list[list[float]]
     usage: EmbeddingUsage
-
diff --git a/api/core/model_runtime/errors/invoke.py b/api/core/model_runtime/errors/invoke.py
index 0513cfaf67..edfb19c7d0 100644
--- a/api/core/model_runtime/errors/invoke.py
+++ b/api/core/model_runtime/errors/invoke.py
@@ -3,6 +3,7 @@ from typing import Optional
 
 class InvokeError(Exception):
     """Base class for all LLM exceptions."""
+
     description: Optional[str] = None
 
     def __init__(self, description: Optional[str] = None) -> None:
@@ -14,24 +15,29 @@ class InvokeError(Exception):
 
 class InvokeConnectionError(InvokeError):
     """Raised when the Invoke returns connection error."""
+
     description = "Connection Error"
 
 
 class InvokeServerUnavailableError(InvokeError):
     """Raised when the Invoke returns server unavailable error."""
+
     description = "Server Unavailable Error"
 
 
 class InvokeRateLimitError(InvokeError):
     """Raised when the Invoke returns rate limit error."""
+
     description = "Rate Limit Error"
 
 
 class InvokeAuthorizationError(InvokeError):
     """Raised when the Invoke returns authorization error."""
+
     description = "Incorrect model credentials provided, please check and try again. "
 
 
 class InvokeBadRequestError(InvokeError):
     """Raised when the Invoke returns bad request."""
+
     description = "Bad Request Error"
diff --git a/api/core/model_runtime/errors/validate.py b/api/core/model_runtime/errors/validate.py
index 8db79a52bb..7fcd2133f9 100644
--- a/api/core/model_runtime/errors/validate.py
+++ b/api/core/model_runtime/errors/validate.py
@@ -2,4 +2,5 @@ class CredentialsValidateFailedError(Exception):
     """
     Credentials validate failed error
     """
+
     pass
diff --git a/api/core/model_runtime/model_providers/__base/ai_model.py b/api/core/model_runtime/model_providers/__base/ai_model.py
index 716bb63566..79a1d28ebe 100644
--- a/api/core/model_runtime/model_providers/__base/ai_model.py
+++ b/api/core/model_runtime/model_providers/__base/ai_model.py
@@ -66,12 +66,16 @@ class AIModel(ABC):
         :param error: model invoke error
         :return: unified error
         """
-        provider_name = self.__class__.__module__.split('.')[-3]
+        provider_name = self.__class__.__module__.split(".")[-3]
 
         for invoke_error, model_errors in self._invoke_error_mapping.items():
             if isinstance(error, tuple(model_errors)):
                 if invoke_error == InvokeAuthorizationError:
-                    return invoke_error(description=f"[{provider_name}] Incorrect model credentials provided, please check and try again. ")
+                    return invoke_error(
+                        description=(
+                            f"[{provider_name}] Incorrect model credentials provided, please check and try again."
+                        )
+                    )
 
                 return invoke_error(description=f"[{provider_name}] {invoke_error.description}, {str(error)}")
 
@@ -115,7 +119,7 @@ class AIModel(ABC):
         if not price_config:
             raise ValueError(f"Price config not found for model {model}")
         total_amount = tokens * unit_price * price_config.unit
-        total_amount = total_amount.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
+        total_amount = total_amount.quantize(decimal.Decimal("0.0000001"), rounding=decimal.ROUND_HALF_UP)
 
         return PriceInfo(
             unit_price=unit_price,
@@ -136,24 +140,26 @@ class AIModel(ABC):
         model_schemas = []
 
         # get module name
-        model_type = self.__class__.__module__.split('.')[-1]
+        model_type = self.__class__.__module__.split(".")[-1]
 
         # get provider name
-        provider_name = self.__class__.__module__.split('.')[-3]
+        provider_name = self.__class__.__module__.split(".")[-3]
 
         # get the path of current classes
         current_path = os.path.abspath(__file__)
         # get parent path of the current path
-        provider_model_type_path = os.path.join(os.path.dirname(os.path.dirname(current_path)), provider_name, model_type)
+        provider_model_type_path = os.path.join(
+            os.path.dirname(os.path.dirname(current_path)), provider_name, model_type
+        )
 
         # get all yaml files path under provider_model_type_path that do not start with __
         model_schema_yaml_paths = [
             os.path.join(provider_model_type_path, model_schema_yaml)
             for model_schema_yaml in os.listdir(provider_model_type_path)
-            if not model_schema_yaml.startswith('__')
-            and not model_schema_yaml.startswith('_')
+            if not model_schema_yaml.startswith("__")
+            and not model_schema_yaml.startswith("_")
             and os.path.isfile(os.path.join(provider_model_type_path, model_schema_yaml))
-            and model_schema_yaml.endswith('.yaml')
+            and model_schema_yaml.endswith(".yaml")
         ]
 
         # get _position.yaml file path
@@ -165,10 +171,10 @@ class AIModel(ABC):
             yaml_data = load_yaml_file(model_schema_yaml_path)
 
             new_parameter_rules = []
-            for parameter_rule in yaml_data.get('parameter_rules', []):
-                if 'use_template' in parameter_rule:
+            for parameter_rule in yaml_data.get("parameter_rules", []):
+                if "use_template" in parameter_rule:
                     try:
-                        default_parameter_name = DefaultParameterName.value_of(parameter_rule['use_template'])
+                        default_parameter_name = DefaultParameterName.value_of(parameter_rule["use_template"])
                         default_parameter_rule = self._get_default_parameter_rule_variable_map(default_parameter_name)
                         copy_default_parameter_rule = default_parameter_rule.copy()
                         copy_default_parameter_rule.update(parameter_rule)
@@ -176,31 +182,26 @@ class AIModel(ABC):
                     except ValueError:
                         pass
 
-                if 'label' not in parameter_rule:
-                    parameter_rule['label'] = {
-                        'zh_Hans': parameter_rule['name'],
-                        'en_US': parameter_rule['name']
-                    }
+                if "label" not in parameter_rule:
+                    parameter_rule["label"] = {"zh_Hans": parameter_rule["name"], "en_US": parameter_rule["name"]}
 
                 new_parameter_rules.append(parameter_rule)
 
-            yaml_data['parameter_rules'] = new_parameter_rules
+            yaml_data["parameter_rules"] = new_parameter_rules
 
-            if 'label' not in yaml_data:
-                yaml_data['label'] = {
-                    'zh_Hans': yaml_data['model'],
-                    'en_US': yaml_data['model']
-                }
+            if "label" not in yaml_data:
+                yaml_data["label"] = {"zh_Hans": yaml_data["model"], "en_US": yaml_data["model"]}
 
-            yaml_data['fetch_from'] = FetchFrom.PREDEFINED_MODEL.value
+            yaml_data["fetch_from"] = FetchFrom.PREDEFINED_MODEL.value
 
             try:
                 # yaml_data to entity
                 model_schema = AIModelEntity(**yaml_data)
             except Exception as e:
                 model_schema_yaml_file_name = os.path.basename(model_schema_yaml_path).rstrip(".yaml")
-                raise Exception(f'Invalid model schema for {provider_name}.{model_type}.{model_schema_yaml_file_name}:'
-                                f' {str(e)}')
+                raise Exception(
+                    f"Invalid model schema for {provider_name}.{model_type}.{model_schema_yaml_file_name}: {str(e)}"
+                )
 
             # cache model schema
             model_schemas.append(model_schema)
@@ -235,7 +236,9 @@ class AIModel(ABC):
 
         return None
 
-    def get_customizable_model_schema_from_credentials(self, model: str, credentials: Mapping) -> Optional[AIModelEntity]:
+    def get_customizable_model_schema_from_credentials(
+        self, model: str, credentials: Mapping
+    ) -> Optional[AIModelEntity]:
         """
         Get customizable model schema from credentials
 
@@ -261,19 +264,19 @@ class AIModel(ABC):
                 try:
                     default_parameter_name = DefaultParameterName.value_of(parameter_rule.use_template)
                     default_parameter_rule = self._get_default_parameter_rule_variable_map(default_parameter_name)
-                    if not parameter_rule.max and 'max' in default_parameter_rule:
-                        parameter_rule.max = default_parameter_rule['max']
-                    if not parameter_rule.min and 'min' in default_parameter_rule:
-                        parameter_rule.min = default_parameter_rule['min']
-                    if not parameter_rule.default and 'default' in default_parameter_rule:
-                        parameter_rule.default = default_parameter_rule['default']
-                    if not parameter_rule.precision and 'precision' in default_parameter_rule:
-                        parameter_rule.precision = default_parameter_rule['precision']
-                    if not parameter_rule.required and 'required' in default_parameter_rule:
-                        parameter_rule.required = default_parameter_rule['required']
-                    if not parameter_rule.help and 'help' in default_parameter_rule:
+                    if not parameter_rule.max and "max" in default_parameter_rule:
+                        parameter_rule.max = default_parameter_rule["max"]
+                    if not parameter_rule.min and "min" in default_parameter_rule:
+                        parameter_rule.min = default_parameter_rule["min"]
+                    if not parameter_rule.default and "default" in default_parameter_rule:
+                        parameter_rule.default = default_parameter_rule["default"]
+                    if not parameter_rule.precision and "precision" in default_parameter_rule:
+                        parameter_rule.precision = default_parameter_rule["precision"]
+                    if not parameter_rule.required and "required" in default_parameter_rule:
+                        parameter_rule.required = default_parameter_rule["required"]
+                    if not parameter_rule.help and "help" in default_parameter_rule:
                         parameter_rule.help = I18nObject(
-                            en_US=default_parameter_rule['help']['en_US'],
+                            en_US=default_parameter_rule["help"]["en_US"],
                         )
                     if (
                         parameter_rule.help
diff --git a/api/core/model_runtime/model_providers/__base/large_language_model.py b/api/core/model_runtime/model_providers/__base/large_language_model.py
index cfc8942c79..ba88cc1f38 100644
--- a/api/core/model_runtime/model_providers/__base/large_language_model.py
+++ b/api/core/model_runtime/model_providers/__base/large_language_model.py
@@ -35,16 +35,24 @@ class LargeLanguageModel(AIModel):
     """
     Model class for large language model.
     """
+
     model_type: ModelType = ModelType.LLM
 
     # pydantic configs
     model_config = ConfigDict(protected_namespaces=())
 
-    def invoke(self, model: str, credentials: dict,
-               prompt_messages: list[PromptMessage], model_parameters: Optional[dict] = None,
-               tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-               stream: bool = True, user: Optional[str] = None, callbacks: Optional[list[Callback]] = None) \
-            -> Union[LLMResult, Generator]:
+    def invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: Optional[dict] = None,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -69,7 +77,7 @@ class LargeLanguageModel(AIModel):
 
         callbacks = callbacks or []
 
-        if bool(os.environ.get("DEBUG", 'False').lower() == 'true'):
+        if bool(os.environ.get("DEBUG", "False").lower() == "true"):
             callbacks.append(LoggingCallback())
 
         # trigger before invoke callbacks
@@ -82,7 +90,7 @@ class LargeLanguageModel(AIModel):
             stop=stop,
             stream=stream,
             user=user,
-            callbacks=callbacks
+            callbacks=callbacks,
         )
 
         try:
@@ -96,7 +104,7 @@ class LargeLanguageModel(AIModel):
                     stop=stop,
                     stream=stream,
                     user=user,
-                    callbacks=callbacks
+                    callbacks=callbacks,
                 )
             else:
                 result = self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
@@ -111,7 +119,7 @@ class LargeLanguageModel(AIModel):
                 stop=stop,
                 stream=stream,
                 user=user,
-                callbacks=callbacks
+                callbacks=callbacks,
             )
 
             raise self._transform_invoke_error(e)
@@ -127,7 +135,7 @@ class LargeLanguageModel(AIModel):
                 stop=stop,
                 stream=stream,
                 user=user,
-                callbacks=callbacks
+                callbacks=callbacks,
             )
         elif isinstance(result, LLMResult):
             self._trigger_after_invoke_callbacks(
@@ -140,15 +148,23 @@ class LargeLanguageModel(AIModel):
                 stop=stop,
                 stream=stream,
                 user=user,
-                callbacks=callbacks
+                callbacks=callbacks,
             )
 
         return result
 
-    def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                           model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
-                           stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,
-                           callbacks: Optional[list[Callback]] = None) -> Union[LLMResult, Generator]:
+    def _code_block_mode_wrapper(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Code block mode wrapper, ensure the response is a code block with output markdown quote
 
@@ -171,7 +187,7 @@ if you are not sure about the structure.
 <instructions>
 {{instructions}}
 </instructions>
-"""
+"""  # noqa: E501
 
         code_block = model_parameters.get("response_format", "")
         if not code_block:
@@ -183,7 +199,7 @@ if you are not sure about the structure.
                 tools=tools,
                 stop=stop,
                 stream=stream,
-                user=user
+                user=user,
             )
 
         model_parameters.pop("response_format")
@@ -195,15 +211,16 @@ if you are not sure about the structure.
         if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
             # override the system message
             prompt_messages[0] = SystemPromptMessage(
-                content=block_prompts
-                    .replace("{{instructions}}", str(prompt_messages[0].content))
+                content=block_prompts.replace("{{instructions}}", str(prompt_messages[0].content))
             )
         else:
             # insert the system message
-            prompt_messages.insert(0, SystemPromptMessage(
-                content=block_prompts
-                    .replace("{{instructions}}", f"Please output a valid {code_block} object.")
-            ))
+            prompt_messages.insert(
+                0,
+                SystemPromptMessage(
+                    content=block_prompts.replace("{{instructions}}", f"Please output a valid {code_block} object.")
+                ),
+            )
 
         if len(prompt_messages) > 0 and isinstance(prompt_messages[-1], UserPromptMessage):
             # add ```JSON\n to the last text message
@@ -216,9 +233,7 @@ if you are not sure about the structure.
                         break
         else:
             # append a user message
-            prompt_messages.append(UserPromptMessage(
-                content=f"```{code_block}\n"
-            ))
+            prompt_messages.append(UserPromptMessage(content=f"```{code_block}\n"))
 
         response = self._invoke(
             model=model,
@@ -228,33 +243,30 @@ if you are not sure about the structure.
             tools=tools,
             stop=stop,
             stream=stream,
-            user=user
+            user=user,
         )
 
         if isinstance(response, Generator):
             first_chunk = next(response)
+
             def new_generator():
                 yield first_chunk
                 yield from response
 
             if first_chunk.delta.message.content and first_chunk.delta.message.content.startswith("`"):
                 return self._code_block_mode_stream_processor_with_backtick(
-                    model=model,
-                    prompt_messages=prompt_messages,
-                    input_generator=new_generator()
+                    model=model, prompt_messages=prompt_messages, input_generator=new_generator()
                 )
             else:
                 return self._code_block_mode_stream_processor(
-                    model=model,
-                    prompt_messages=prompt_messages,
-                    input_generator=new_generator()
+                    model=model, prompt_messages=prompt_messages, input_generator=new_generator()
                 )
 
         return response
 
-    def _code_block_mode_stream_processor(self, model: str, prompt_messages: list[PromptMessage],
-                                          input_generator: Generator[LLMResultChunk, None, None]
-                                        ) -> Generator[LLMResultChunk, None, None]:
+    def _code_block_mode_stream_processor(
+        self, model: str, prompt_messages: list[PromptMessage], input_generator: Generator[LLMResultChunk, None, None]
+    ) -> Generator[LLMResultChunk, None, None]:
         """
         Code block mode stream processor, ensure the response is a code block with output markdown quote
 
@@ -303,16 +315,13 @@ if you are not sure about the structure.
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=0,
-                        message=AssistantPromptMessage(
-                            content=new_piece,
-                            tool_calls=[]
-                        ),
-                    )
+                        message=AssistantPromptMessage(content=new_piece, tool_calls=[]),
+                    ),
                 )
 
-    def _code_block_mode_stream_processor_with_backtick(self, model: str, prompt_messages: list,
-                                        input_generator:  Generator[LLMResultChunk, None, None]) \
-                                    ->  Generator[LLMResultChunk, None, None]:
+    def _code_block_mode_stream_processor_with_backtick(
+        self, model: str, prompt_messages: list, input_generator: Generator[LLMResultChunk, None, None]
+    ) -> Generator[LLMResultChunk, None, None]:
         """
         Code block mode stream processor, ensure the response is a code block with output markdown quote.
         This version skips the language identifier that follows the opening triple backticks.
@@ -378,18 +387,23 @@ if you are not sure about the structure.
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=0,
-                        message=AssistantPromptMessage(
-                            content=new_piece,
-                            tool_calls=[]
-                        ),
-                    )
+                        message=AssistantPromptMessage(content=new_piece, tool_calls=[]),
+                    ),
                 )
 
-    def _invoke_result_generator(self, model: str, result: Generator, credentials: dict,
-                                 prompt_messages: list[PromptMessage], model_parameters: dict,
-                                 tools: Optional[list[PromptMessageTool]] = None,
-                                 stop: Optional[list[str]] = None, stream: bool = True,
-                                 user: Optional[str] = None, callbacks: Optional[list[Callback]] = None) -> Generator:
+    def _invoke_result_generator(
+        self,
+        model: str,
+        result: Generator,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> Generator:
         """
         Invoke result generator
 
@@ -397,9 +411,7 @@ if you are not sure about the structure.
         :return: result generator
         """
         callbacks = callbacks or []
-        prompt_message = AssistantPromptMessage(
-            content=""
-        )
+        prompt_message = AssistantPromptMessage(content="")
         usage = None
         system_fingerprint = None
         real_model = model
@@ -418,7 +430,7 @@ if you are not sure about the structure.
                     stop=stop,
                     stream=stream,
                     user=user,
-                    callbacks=callbacks
+                    callbacks=callbacks,
                 )
 
                 prompt_message.content += chunk.delta.message.content
@@ -437,8 +449,8 @@ if you are not sure about the structure.
                 model=real_model,
                 prompt_messages=prompt_messages,
                 message=prompt_message,
-                usage=usage if usage else LLMUsage.empty_usage(),
-                system_fingerprint=system_fingerprint
+                usage=usage or LLMUsage.empty_usage(),
+                system_fingerprint=system_fingerprint,
             ),
             credentials=credentials,
             prompt_messages=prompt_messages,
@@ -447,15 +459,21 @@ if you are not sure about the structure.
             stop=stop,
             stream=stream,
             user=user,
-            callbacks=callbacks
+            callbacks=callbacks,
         )
 
     @abstractmethod
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -472,8 +490,13 @@ if you are not sure about the structure.
         raise NotImplementedError
 
     @abstractmethod
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -519,7 +542,9 @@ if you are not sure about the structure.
 
         return mode
 
-    def _calc_response_usage(self, model: str, credentials: dict, prompt_tokens: int, completion_tokens: int) -> LLMUsage:
+    def _calc_response_usage(
+        self, model: str, credentials: dict, prompt_tokens: int, completion_tokens: int
+    ) -> LLMUsage:
         """
         Calculate response usage
 
@@ -539,10 +564,7 @@ if you are not sure about the structure.
 
         # get completion price info
         completion_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.OUTPUT,
-            tokens=completion_tokens
+            model=model, credentials=credentials, price_type=PriceType.OUTPUT, tokens=completion_tokens
         )
 
         # transform usage
@@ -558,16 +580,23 @@ if you are not sure about the structure.
             total_tokens=prompt_tokens + completion_tokens,
             total_price=prompt_price_info.total_amount + completion_price_info.total_amount,
             currency=prompt_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
 
-    def _trigger_before_invoke_callbacks(self, model: str, credentials: dict,
-                                         prompt_messages: list[PromptMessage], model_parameters: dict,
-                                         tools: Optional[list[PromptMessageTool]] = None,
-                                         stop: Optional[list[str]] = None, stream: bool = True,
-                                         user: Optional[str] = None, callbacks: Optional[list[Callback]] = None) -> None:
+    def _trigger_before_invoke_callbacks(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> None:
         """
         Trigger before invoke callbacks
 
@@ -593,7 +622,7 @@ if you are not sure about the structure.
                         tools=tools,
                         stop=stop,
                         stream=stream,
-                        user=user
+                        user=user,
                     )
                 except Exception as e:
                     if callback.raise_error:
@@ -601,11 +630,19 @@ if you are not sure about the structure.
                     else:
                         logger.warning(f"Callback {callback.__class__.__name__} on_before_invoke failed with error {e}")
 
-    def _trigger_new_chunk_callbacks(self, chunk: LLMResultChunk, model: str, credentials: dict,
-                                     prompt_messages: list[PromptMessage], model_parameters: dict,
-                                     tools: Optional[list[PromptMessageTool]] = None,
-                                     stop: Optional[list[str]] = None, stream: bool = True,
-                                     user: Optional[str] = None, callbacks: Optional[list[Callback]] = None) -> None:
+    def _trigger_new_chunk_callbacks(
+        self,
+        chunk: LLMResultChunk,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> None:
         """
         Trigger new chunk callbacks
 
@@ -632,7 +669,7 @@ if you are not sure about the structure.
                         tools=tools,
                         stop=stop,
                         stream=stream,
-                        user=user
+                        user=user,
                     )
                 except Exception as e:
                     if callback.raise_error:
@@ -640,11 +677,19 @@ if you are not sure about the structure.
                     else:
                         logger.warning(f"Callback {callback.__class__.__name__} on_new_chunk failed with error {e}")
 
-    def _trigger_after_invoke_callbacks(self, model: str, result: LLMResult, credentials: dict,
-                                        prompt_messages: list[PromptMessage], model_parameters: dict,
-                                        tools: Optional[list[PromptMessageTool]] = None,
-                                        stop: Optional[list[str]] = None, stream: bool = True,
-                                        user: Optional[str] = None, callbacks: Optional[list[Callback]] = None) -> None:
+    def _trigger_after_invoke_callbacks(
+        self,
+        model: str,
+        result: LLMResult,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> None:
         """
         Trigger after invoke callbacks
 
@@ -672,7 +717,7 @@ if you are not sure about the structure.
                         tools=tools,
                         stop=stop,
                         stream=stream,
-                        user=user
+                        user=user,
                     )
                 except Exception as e:
                     if callback.raise_error:
@@ -680,11 +725,19 @@ if you are not sure about the structure.
                     else:
                         logger.warning(f"Callback {callback.__class__.__name__} on_after_invoke failed with error {e}")
 
-    def _trigger_invoke_error_callbacks(self, model: str, ex: Exception, credentials: dict,
-                                        prompt_messages: list[PromptMessage], model_parameters: dict,
-                                        tools: Optional[list[PromptMessageTool]] = None,
-                                        stop: Optional[list[str]] = None, stream: bool = True,
-                                        user: Optional[str] = None, callbacks: Optional[list[Callback]] = None) -> None:
+    def _trigger_invoke_error_callbacks(
+        self,
+        model: str,
+        ex: Exception,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> None:
         """
         Trigger invoke error callbacks
 
@@ -712,7 +765,7 @@ if you are not sure about the structure.
                         tools=tools,
                         stop=stop,
                         stream=stream,
-                        user=user
+                        user=user,
                     )
                 except Exception as e:
                     if callback.raise_error:
@@ -758,11 +811,13 @@ if you are not sure about the structure.
                 # validate parameter value range
                 if parameter_rule.min is not None and parameter_value < parameter_rule.min:
                     raise ValueError(
-                        f"Model Parameter {parameter_name} should be greater than or equal to {parameter_rule.min}.")
+                        f"Model Parameter {parameter_name} should be greater than or equal to {parameter_rule.min}."
+                    )
 
                 if parameter_rule.max is not None and parameter_value > parameter_rule.max:
                     raise ValueError(
-                        f"Model Parameter {parameter_name} should be less than or equal to {parameter_rule.max}.")
+                        f"Model Parameter {parameter_name} should be less than or equal to {parameter_rule.max}."
+                    )
             elif parameter_rule.type == ParameterType.FLOAT:
                 if not isinstance(parameter_value, float | int):
                     raise ValueError(f"Model Parameter {parameter_name} should be float.")
@@ -775,16 +830,20 @@ if you are not sure about the structure.
                     else:
                         if parameter_value != round(parameter_value, parameter_rule.precision):
                             raise ValueError(
-                                f"Model Parameter {parameter_name} should be round to {parameter_rule.precision} decimal places.")
+                                f"Model Parameter {parameter_name} should be round to {parameter_rule.precision}"
+                                f" decimal places."
+                            )
 
                 # validate parameter value range
                 if parameter_rule.min is not None and parameter_value < parameter_rule.min:
                     raise ValueError(
-                        f"Model Parameter {parameter_name} should be greater than or equal to {parameter_rule.min}.")
+                        f"Model Parameter {parameter_name} should be greater than or equal to {parameter_rule.min}."
+                    )
 
                 if parameter_rule.max is not None and parameter_value > parameter_rule.max:
                     raise ValueError(
-                        f"Model Parameter {parameter_name} should be less than or equal to {parameter_rule.max}.")
+                        f"Model Parameter {parameter_name} should be less than or equal to {parameter_rule.max}."
+                    )
             elif parameter_rule.type == ParameterType.BOOLEAN:
                 if not isinstance(parameter_value, bool):
                     raise ValueError(f"Model Parameter {parameter_name} should be bool.")
diff --git a/api/core/model_runtime/model_providers/__base/model_provider.py b/api/core/model_runtime/model_providers/__base/model_provider.py
index 780460a3f7..4374093de4 100644
--- a/api/core/model_runtime/model_providers/__base/model_provider.py
+++ b/api/core/model_runtime/model_providers/__base/model_provider.py
@@ -29,32 +29,32 @@ class ModelProvider(ABC):
     def get_provider_schema(self) -> ProviderEntity:
         """
         Get provider schema
-    
+
         :return: provider schema
         """
         if self.provider_schema:
             return self.provider_schema
-    
+
         # get dirname of the current path
-        provider_name = self.__class__.__module__.split('.')[-1]
+        provider_name = self.__class__.__module__.split(".")[-1]
 
         # get the path of the model_provider classes
         base_path = os.path.abspath(__file__)
         current_path = os.path.join(os.path.dirname(os.path.dirname(base_path)), provider_name)
-    
+
         # read provider schema from yaml file
-        yaml_path = os.path.join(current_path, f'{provider_name}.yaml')
+        yaml_path = os.path.join(current_path, f"{provider_name}.yaml")
         yaml_data = load_yaml_file(yaml_path)
-    
+
         try:
             # yaml_data to entity
             provider_schema = ProviderEntity(**yaml_data)
         except Exception as e:
-            raise Exception(f'Invalid provider schema for {provider_name}: {str(e)}')
+            raise Exception(f"Invalid provider schema for {provider_name}: {str(e)}")
 
         # cache schema
         self.provider_schema = provider_schema
-    
+
         return provider_schema
 
     def models(self, model_type: ModelType) -> list[AIModelEntity]:
@@ -92,15 +92,15 @@ class ModelProvider(ABC):
 
         # get the path of the model type classes
         base_path = os.path.abspath(__file__)
-        model_type_name = model_type.value.replace('-', '_')
+        model_type_name = model_type.value.replace("-", "_")
         model_type_path = os.path.join(os.path.dirname(os.path.dirname(base_path)), provider_name, model_type_name)
-        model_type_py_path = os.path.join(model_type_path, f'{model_type_name}.py')
+        model_type_py_path = os.path.join(model_type_path, f"{model_type_name}.py")
 
         if not os.path.isdir(model_type_path) or not os.path.exists(model_type_py_path):
-            raise Exception(f'Invalid model type {model_type} for provider {provider_name}')
+            raise Exception(f"Invalid model type {model_type} for provider {provider_name}")
 
         # Dynamic loading {model_type_name}.py file and find the subclass of AIModel
-        parent_module = '.'.join(self.__class__.__module__.split('.')[:-1])
+        parent_module = ".".join(self.__class__.__module__.split(".")[:-1])
         mod = import_module_from_source(
             module_name=f"{parent_module}.{model_type_name}.{model_type_name}", py_file_path=model_type_py_path
         )
diff --git a/api/core/model_runtime/model_providers/__base/moderation_model.py b/api/core/model_runtime/model_providers/__base/moderation_model.py
index 2b17f292c5..d04414ccb8 100644
--- a/api/core/model_runtime/model_providers/__base/moderation_model.py
+++ b/api/core/model_runtime/model_providers/__base/moderation_model.py
@@ -12,14 +12,13 @@ class ModerationModel(AIModel):
     """
     Model class for moderation model.
     """
+
     model_type: ModelType = ModelType.MODERATION
 
     # pydantic configs
     model_config = ConfigDict(protected_namespaces=())
 
-    def invoke(self, model: str, credentials: dict,
-               text: str, user: Optional[str] = None) \
-            -> bool:
+    def invoke(self, model: str, credentials: dict, text: str, user: Optional[str] = None) -> bool:
         """
         Invoke moderation model
 
@@ -37,9 +36,7 @@ class ModerationModel(AIModel):
             raise self._transform_invoke_error(e)
 
     @abstractmethod
-    def _invoke(self, model: str, credentials: dict,
-                text: str, user: Optional[str] = None) \
-            -> bool:
+    def _invoke(self, model: str, credentials: dict, text: str, user: Optional[str] = None) -> bool:
         """
         Invoke large language model
 
@@ -50,4 +47,3 @@ class ModerationModel(AIModel):
         :return: false if text is safe, true otherwise
         """
         raise NotImplementedError
-
diff --git a/api/core/model_runtime/model_providers/__base/rerank_model.py b/api/core/model_runtime/model_providers/__base/rerank_model.py
index 2c86f25180..5fb9604742 100644
--- a/api/core/model_runtime/model_providers/__base/rerank_model.py
+++ b/api/core/model_runtime/model_providers/__base/rerank_model.py
@@ -11,12 +11,19 @@ class RerankModel(AIModel):
     """
     Base Model class for rerank model.
     """
+
     model_type: ModelType = ModelType.RERANK
 
-    def invoke(self, model: str, credentials: dict,
-               query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
-               user: Optional[str] = None) \
-            -> RerankResult:
+    def invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
         """
         Invoke rerank model
 
@@ -37,10 +44,16 @@ class RerankModel(AIModel):
             raise self._transform_invoke_error(e)
 
     @abstractmethod
-    def _invoke(self, model: str, credentials: dict,
-                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
-                user: Optional[str] = None) \
-            -> RerankResult:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
         """
         Invoke rerank model
 
diff --git a/api/core/model_runtime/model_providers/__base/speech2text_model.py b/api/core/model_runtime/model_providers/__base/speech2text_model.py
index 4fb11025fe..b6b0b73743 100644
--- a/api/core/model_runtime/model_providers/__base/speech2text_model.py
+++ b/api/core/model_runtime/model_providers/__base/speech2text_model.py
@@ -12,14 +12,13 @@ class Speech2TextModel(AIModel):
     """
     Model class for speech2text model.
     """
+
     model_type: ModelType = ModelType.SPEECH2TEXT
 
     # pydantic configs
     model_config = ConfigDict(protected_namespaces=())
 
-    def invoke(self, model: str, credentials: dict,
-               file: IO[bytes], user: Optional[str] = None) \
-            -> str:
+    def invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
         """
         Invoke large language model
 
@@ -35,9 +34,7 @@ class Speech2TextModel(AIModel):
             raise self._transform_invoke_error(e)
 
     @abstractmethod
-    def _invoke(self, model: str, credentials: dict,
-                file: IO[bytes], user: Optional[str] = None) \
-            -> str:
+    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
         """
         Invoke large language model
 
@@ -59,4 +56,4 @@ class Speech2TextModel(AIModel):
         current_dir = os.path.dirname(os.path.abspath(__file__))
 
         # Construct the path to the audio file
-        return os.path.join(current_dir, 'audio.mp3')
+        return os.path.join(current_dir, "audio.mp3")
diff --git a/api/core/model_runtime/model_providers/__base/text2img_model.py b/api/core/model_runtime/model_providers/__base/text2img_model.py
index e0f1adb1c4..a5810e2f0e 100644
--- a/api/core/model_runtime/model_providers/__base/text2img_model.py
+++ b/api/core/model_runtime/model_providers/__base/text2img_model.py
@@ -11,14 +11,15 @@ class Text2ImageModel(AIModel):
     """
     Model class for text2img model.
     """
+
     model_type: ModelType = ModelType.TEXT2IMG
 
     # pydantic configs
     model_config = ConfigDict(protected_namespaces=())
 
-    def invoke(self, model: str, credentials: dict, prompt: str, 
-               model_parameters: dict, user: Optional[str] = None) \
-            -> list[IO[bytes]]:
+    def invoke(
+        self, model: str, credentials: dict, prompt: str, model_parameters: dict, user: Optional[str] = None
+    ) -> list[IO[bytes]]:
         """
         Invoke Text2Image model
 
@@ -36,9 +37,9 @@ class Text2ImageModel(AIModel):
             raise self._transform_invoke_error(e)
 
     @abstractmethod
-    def _invoke(self, model: str, credentials: dict, prompt: str, 
-                model_parameters: dict, user: Optional[str] = None) \
-            -> list[IO[bytes]]:
+    def _invoke(
+        self, model: str, credentials: dict, prompt: str, model_parameters: dict, user: Optional[str] = None
+    ) -> list[IO[bytes]]:
         """
         Invoke Text2Image model
 
diff --git a/api/core/model_runtime/model_providers/__base/text_embedding_model.py b/api/core/model_runtime/model_providers/__base/text_embedding_model.py
index 381d2f6cd1..54a4486023 100644
--- a/api/core/model_runtime/model_providers/__base/text_embedding_model.py
+++ b/api/core/model_runtime/model_providers/__base/text_embedding_model.py
@@ -13,14 +13,15 @@ class TextEmbeddingModel(AIModel):
     """
     Model class for text embedding model.
     """
+
     model_type: ModelType = ModelType.TEXT_EMBEDDING
 
     # pydantic configs
     model_config = ConfigDict(protected_namespaces=())
 
-    def invoke(self, model: str, credentials: dict,
-               texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke large language model
 
@@ -38,9 +39,9 @@ class TextEmbeddingModel(AIModel):
             raise self._transform_invoke_error(e)
 
     @abstractmethod
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke large language model
 
diff --git a/api/core/model_runtime/model_providers/__base/tokenizers/gpt2_tokenzier.py b/api/core/model_runtime/model_providers/__base/tokenizers/gpt2_tokenzier.py
index 6059b3f561..5fe6dda6ad 100644
--- a/api/core/model_runtime/model_providers/__base/tokenizers/gpt2_tokenzier.py
+++ b/api/core/model_runtime/model_providers/__base/tokenizers/gpt2_tokenzier.py
@@ -7,27 +7,28 @@ from transformers import GPT2Tokenizer as TransformerGPT2Tokenizer
 _tokenizer = None
 _lock = Lock()
 
+
 class GPT2Tokenizer:
     @staticmethod
     def _get_num_tokens_by_gpt2(text: str) -> int:
         """
-            use gpt2 tokenizer to get num tokens
+        use gpt2 tokenizer to get num tokens
         """
         _tokenizer = GPT2Tokenizer.get_encoder()
         tokens = _tokenizer.encode(text, verbose=False)
         return len(tokens)
-    
+
     @staticmethod
     def get_num_tokens(text: str) -> int:
         return GPT2Tokenizer._get_num_tokens_by_gpt2(text)
-    
+
     @staticmethod
     def get_encoder() -> Any:
         global _tokenizer, _lock
         with _lock:
             if _tokenizer is None:
                 base_path = abspath(__file__)
-                gpt2_tokenizer_path = join(dirname(base_path), 'gpt2')
+                gpt2_tokenizer_path = join(dirname(base_path), "gpt2")
                 _tokenizer = TransformerGPT2Tokenizer.from_pretrained(gpt2_tokenizer_path)
 
-            return _tokenizer
\ No newline at end of file
+            return _tokenizer
diff --git a/api/core/model_runtime/model_providers/__base/tts_model.py b/api/core/model_runtime/model_providers/__base/tts_model.py
index 64e85d2c11..70be9322a7 100644
--- a/api/core/model_runtime/model_providers/__base/tts_model.py
+++ b/api/core/model_runtime/model_providers/__base/tts_model.py
@@ -13,15 +13,17 @@ logger = logging.getLogger(__name__)
 
 class TTSModel(AIModel):
     """
-    Model class for ttstext model.
+    Model class for TTS model.
     """
+
     model_type: ModelType = ModelType.TTS
 
     # pydantic configs
     model_config = ConfigDict(protected_namespaces=())
 
-    def invoke(self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str,
-               user: Optional[str] = None):
+    def invoke(
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ):
         """
         Invoke large language model
 
@@ -35,14 +37,21 @@ class TTSModel(AIModel):
         :return: translated audio file
         """
         try:
-            return self._invoke(model=model, credentials=credentials, user=user,
-                                content_text=content_text, voice=voice, tenant_id=tenant_id)
+            return self._invoke(
+                model=model,
+                credentials=credentials,
+                user=user,
+                content_text=content_text,
+                voice=voice,
+                tenant_id=tenant_id,
+            )
         except Exception as e:
             raise self._transform_invoke_error(e)
 
     @abstractmethod
-    def _invoke(self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str,
-                user: Optional[str] = None):
+    def _invoke(
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ):
         """
         Invoke large language model
 
@@ -71,10 +80,13 @@ class TTSModel(AIModel):
         if model_schema and ModelPropertyKey.VOICES in model_schema.model_properties:
             voices = model_schema.model_properties[ModelPropertyKey.VOICES]
             if language:
-                return [{'name': d['name'], 'value': d['mode']} for d in voices if
-                        language and language in d.get('language')]
+                return [
+                    {"name": d["name"], "value": d["mode"]}
+                    for d in voices
+                    if language and language in d.get("language")
+                ]
             else:
-                return [{'name': d['name'], 'value': d['mode']} for d in voices]
+                return [{"name": d["name"], "value": d["mode"]} for d in voices]
 
     def _get_model_default_voice(self, model: str, credentials: dict) -> any:
         """
@@ -123,23 +135,23 @@ class TTSModel(AIModel):
             return model_schema.model_properties[ModelPropertyKey.MAX_WORKERS]
 
     @staticmethod
-    def _split_text_into_sentences(org_text, max_length=2000, pattern=r'[。.!?]'):
+    def _split_text_into_sentences(org_text, max_length=2000, pattern=r"[。.!?]"):
         match = re.compile(pattern)
         tx = match.finditer(org_text)
         start = 0
         result = []
-        one_sentence = ''
+        one_sentence = ""
         for i in tx:
             end = i.regs[0][1]
             tmp = org_text[start:end]
             if len(one_sentence + tmp) > max_length:
                 result.append(one_sentence)
-                one_sentence = ''
+                one_sentence = ""
             one_sentence += tmp
             start = end
         last_sens = org_text[start:]
         if last_sens:
             one_sentence += last_sens
-        if one_sentence != '':
+        if one_sentence != "":
             result.append(one_sentence)
         return result
diff --git a/api/core/model_runtime/model_providers/anthropic/anthropic.py b/api/core/model_runtime/model_providers/anthropic/anthropic.py
index 00a6bbce3b..5b12f04a3e 100644
--- a/api/core/model_runtime/model_providers/anthropic/anthropic.py
+++ b/api/core/model_runtime/model_providers/anthropic/anthropic.py
@@ -19,13 +19,10 @@ class AnthropicProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            # Use `claude-instant-1` model for validate,
-            model_instance.validate_credentials(
-                model='claude-instant-1.2',
-                credentials=credentials
-            )
+            # Use `claude-3-opus-20240229` model for validate,
+            model_instance.validate_credentials(model="claude-3-opus-20240229", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/anthropic/llm/claude-instant-1.2.yaml b/api/core/model_runtime/model_providers/anthropic/llm/claude-instant-1.2.yaml
index 929a7f8725..ac69bbf4d2 100644
--- a/api/core/model_runtime/model_providers/anthropic/llm/claude-instant-1.2.yaml
+++ b/api/core/model_runtime/model_providers/anthropic/llm/claude-instant-1.2.yaml
@@ -33,3 +33,4 @@ pricing:
   output: '5.51'
   unit: '0.000001'
   currency: USD
+deprecated: true
diff --git a/api/core/model_runtime/model_providers/anthropic/llm/llm.py b/api/core/model_runtime/model_providers/anthropic/llm/llm.py
index 81be1a06a7..46e1b415b8 100644
--- a/api/core/model_runtime/model_providers/anthropic/llm/llm.py
+++ b/api/core/model_runtime/model_providers/anthropic/llm/llm.py
@@ -51,15 +51,21 @@ if you are not sure about the structure.
 <instructions>
 {{instructions}}
 </instructions>
-"""
+"""  # noqa: E501
 
 
 class AnthropicLargeLanguageModel(LargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -76,10 +82,17 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         # invoke model
         return self._chat_generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
-    def _chat_generate(self, model: str, credentials: dict,
-                       prompt_messages: list[PromptMessage], model_parameters: dict, 
-                       tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                       stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _chat_generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke llm chat model
 
@@ -96,41 +109,39 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         credentials_kwargs = self._to_credential_kwargs(credentials)
 
         # transform model parameters from completion api of anthropic to chat api
-        if 'max_tokens_to_sample' in model_parameters:
-            model_parameters['max_tokens'] = model_parameters.pop('max_tokens_to_sample')
+        if "max_tokens_to_sample" in model_parameters:
+            model_parameters["max_tokens"] = model_parameters.pop("max_tokens_to_sample")
 
         # init model client
         client = Anthropic(**credentials_kwargs)
 
         extra_model_kwargs = {}
         if stop:
-            extra_model_kwargs['stop_sequences'] = stop
+            extra_model_kwargs["stop_sequences"] = stop
 
         if user:
-            extra_model_kwargs['metadata'] = completion_create_params.Metadata(user_id=user)
+            extra_model_kwargs["metadata"] = completion_create_params.Metadata(user_id=user)
 
         system, prompt_message_dicts = self._convert_prompt_messages(prompt_messages)
 
         if system:
-            extra_model_kwargs['system'] = system
+            extra_model_kwargs["system"] = system
 
         # Add the new header for claude-3-5-sonnet-20240620 model
         extra_headers = {}
         if model == "claude-3-5-sonnet-20240620":
-            if model_parameters.get('max_tokens') > 4096:
+            if model_parameters.get("max_tokens") > 4096:
                 extra_headers["anthropic-beta"] = "max-tokens-3-5-sonnet-2024-07-15"
 
         if tools:
-            extra_model_kwargs['tools'] = [
-                self._transform_tool_prompt(tool) for tool in tools
-            ]
+            extra_model_kwargs["tools"] = [self._transform_tool_prompt(tool) for tool in tools]
             response = client.beta.tools.messages.create(
                 model=model,
                 messages=prompt_message_dicts,
                 stream=stream,
                 extra_headers=extra_headers,
                 **model_parameters,
-                **extra_model_kwargs
+                **extra_model_kwargs,
             )
         else:
             # chat model
@@ -140,22 +151,30 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 stream=stream,
                 extra_headers=extra_headers,
                 **model_parameters,
-                **extra_model_kwargs
+                **extra_model_kwargs,
             )
 
         if stream:
             return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages)
 
         return self._handle_chat_generate_response(model, credentials, response, prompt_messages)
-    
-    def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                                 model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
-                                 stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,
-                                 callbacks: list[Callback] = None) -> Union[LLMResult, Generator]:
+
+    def _code_block_mode_wrapper(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: list[Callback] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Code block mode wrapper for invoking large language model
         """
-        if model_parameters.get('response_format'):
+        if model_parameters.get("response_format"):
             stop = stop or []
             # chat model
             self._transform_chat_json_prompts(
@@ -167,24 +186,27 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 stop=stop,
                 stream=stream,
                 user=user,
-                response_format=model_parameters['response_format']
+                response_format=model_parameters["response_format"],
             )
-            model_parameters.pop('response_format')
+            model_parameters.pop("response_format")
 
         return self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
     def _transform_tool_prompt(self, tool: PromptMessageTool) -> dict:
-        return {
-            'name': tool.name,
-            'description': tool.description,
-            'input_schema': tool.parameters
-        }
+        return {"name": tool.name, "description": tool.description, "input_schema": tool.parameters}
 
-    def _transform_chat_json_prompts(self, model: str, credentials: dict,
-                                     prompt_messages: list[PromptMessage], model_parameters: dict,
-                                     tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
-                                     stream: bool = True, user: str | None = None, response_format: str = 'JSON') \
-            -> None:
+    def _transform_chat_json_prompts(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+        response_format: str = "JSON",
+    ) -> None:
         """
         Transform json prompts
         """
@@ -197,22 +219,30 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
             # override the system message
             prompt_messages[0] = SystemPromptMessage(
-                content=ANTHROPIC_BLOCK_MODE_PROMPT
-                .replace("{{instructions}}", prompt_messages[0].content)
-                .replace("{{block}}", response_format)
+                content=ANTHROPIC_BLOCK_MODE_PROMPT.replace("{{instructions}}", prompt_messages[0].content).replace(
+                    "{{block}}", response_format
+                )
             )
             prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
         else:
             # insert the system message
-            prompt_messages.insert(0, SystemPromptMessage(
-                content=ANTHROPIC_BLOCK_MODE_PROMPT
-                .replace("{{instructions}}", f"Please output a valid {response_format} object.")
-                .replace("{{block}}", response_format)
-            ))
+            prompt_messages.insert(
+                0,
+                SystemPromptMessage(
+                    content=ANTHROPIC_BLOCK_MODE_PROMPT.replace(
+                        "{{instructions}}", f"Please output a valid {response_format} object."
+                    ).replace("{{block}}", response_format)
+                ),
+            )
             prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -228,9 +258,9 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         tokens = client.count_tokens(prompt)
 
         tool_call_inner_prompts_tokens_map = {
-            'claude-3-opus-20240229': 395,
-            'claude-3-haiku-20240307': 264,
-            'claude-3-sonnet-20240229': 159
+            "claude-3-opus-20240229": 395,
+            "claude-3-haiku-20240307": 264,
+            "claude-3-sonnet-20240229": 159,
         }
 
         if model in tool_call_inner_prompts_tokens_map and tools:
@@ -257,13 +287,18 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                     "temperature": 0,
                     "max_tokens": 20,
                 },
-                stream=False
+                stream=False,
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _handle_chat_generate_response(self, model: str, credentials: dict, response: Union[Message, ToolsBetaMessage],
-                                       prompt_messages: list[PromptMessage]) -> LLMResult:
+    def _handle_chat_generate_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: Union[Message, ToolsBetaMessage],
+        prompt_messages: list[PromptMessage],
+    ) -> LLMResult:
         """
         Handle llm chat response
 
@@ -274,22 +309,18 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :return: llm response
         """
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content='',
-            tool_calls=[]
-        )
+        assistant_prompt_message = AssistantPromptMessage(content="", tool_calls=[])
 
         for content in response.content:
-            if content.type == 'text':
+            if content.type == "text":
                 assistant_prompt_message.content += content.text
-            elif content.type == 'tool_use':
+            elif content.type == "tool_use":
                 tool_call = AssistantPromptMessage.ToolCall(
                     id=content.id,
-                    type='function',
+                    type="function",
                     function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                        name=content.name,
-                        arguments=json.dumps(content.input)
-                    )
+                        name=content.name, arguments=json.dumps(content.input)
+                    ),
                 )
                 assistant_prompt_message.tool_calls.append(tool_call)
 
@@ -308,17 +339,14 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
 
         # transform response
         response = LLMResult(
-            model=response.model,
-            prompt_messages=prompt_messages,
-            message=assistant_prompt_message,
-            usage=usage
+            model=response.model, prompt_messages=prompt_messages, message=assistant_prompt_message, usage=usage
         )
 
         return response
 
-    def _handle_chat_generate_stream_response(self, model: str, credentials: dict,
-                                              response: Stream[MessageStreamEvent],
-                                              prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_chat_generate_stream_response(
+        self, model: str, credentials: dict, response: Stream[MessageStreamEvent], prompt_messages: list[PromptMessage]
+    ) -> Generator:
         """
         Handle llm chat stream response
 
@@ -327,7 +355,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: llm response chunk generator
         """
-        full_assistant_content = ''
+        full_assistant_content = ""
         return_model = None
         input_tokens = 0
         output_tokens = 0
@@ -338,24 +366,23 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
 
         for chunk in response:
             if isinstance(chunk, MessageStartEvent):
-                if hasattr(chunk, 'content_block'):
+                if hasattr(chunk, "content_block"):
                     content_block = chunk.content_block
                     if isinstance(content_block, dict):
-                        if content_block.get('type') == 'tool_use':
+                        if content_block.get("type") == "tool_use":
                             tool_call = AssistantPromptMessage.ToolCall(
-                                id=content_block.get('id'),
-                                type='function',
+                                id=content_block.get("id"),
+                                type="function",
                                 function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                                    name=content_block.get('name'),
-                                    arguments=''
-                                )
+                                    name=content_block.get("name"), arguments=""
+                                ),
                             )
                             tool_calls.append(tool_call)
-                elif hasattr(chunk, 'delta'):
+                elif hasattr(chunk, "delta"):
                     delta = chunk.delta
                     if isinstance(delta, dict) and len(tool_calls) > 0:
-                        if delta.get('type') == 'input_json_delta':
-                            tool_calls[-1].function.arguments += delta.get('partial_json', '')
+                        if delta.get("type") == "input_json_delta":
+                            tool_calls[-1].function.arguments += delta.get("partial_json", "")
                 elif chunk.message:
                     return_model = chunk.message.model
                     input_tokens = chunk.message.usage.input_tokens
@@ -369,29 +396,24 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 # transform empty tool call arguments to {}
                 for tool_call in tool_calls:
                     if not tool_call.function.arguments:
-                        tool_call.function.arguments = '{}'
+                        tool_call.function.arguments = "{}"
 
                 yield LLMResultChunk(
                     model=return_model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=index + 1,
-                        message=AssistantPromptMessage(
-                            content='',
-                            tool_calls=tool_calls
-                        ),
+                        message=AssistantPromptMessage(content="", tool_calls=tool_calls),
                         finish_reason=finish_reason,
-                        usage=usage
-                    )
+                        usage=usage,
+                    ),
                 )
             elif isinstance(chunk, ContentBlockDeltaEvent):
-                chunk_text = chunk.delta.text if chunk.delta.text else ''
+                chunk_text = chunk.delta.text or ""
                 full_assistant_content += chunk_text
 
                 # transform assistant message to prompt message
-                assistant_prompt_message = AssistantPromptMessage(
-                    content=chunk_text
-                )
+                assistant_prompt_message = AssistantPromptMessage(content=chunk_text)
 
                 index = chunk.index
 
@@ -401,7 +423,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=chunk.index,
                         message=assistant_prompt_message,
-                    )
+                    ),
                 )
 
     def _to_credential_kwargs(self, credentials: dict) -> dict:
@@ -412,14 +434,14 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :return:
         """
         credentials_kwargs = {
-            "api_key": credentials['anthropic_api_key'],
+            "api_key": credentials["anthropic_api_key"],
             "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
             "max_retries": 1,
         }
 
-        if credentials.get('anthropic_api_url'):
-            credentials['anthropic_api_url'] = credentials['anthropic_api_url'].rstrip('/')
-            credentials_kwargs['base_url'] = credentials['anthropic_api_url']
+        if credentials.get("anthropic_api_url"):
+            credentials["anthropic_api_url"] = credentials["anthropic_api_url"].rstrip("/")
+            credentials_kwargs["base_url"] = credentials["anthropic_api_url"]
 
         return credentials_kwargs
 
@@ -452,10 +474,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                         for message_content in message.content:
                             if message_content.type == PromptMessageContentType.TEXT:
                                 message_content = cast(TextPromptMessageContent, message_content)
-                                sub_message_dict = {
-                                    "type": "text",
-                                    "text": message_content.data
-                                }
+                                sub_message_dict = {"type": "text", "text": message_content.data}
                                 sub_messages.append(sub_message_dict)
                             elif message_content.type == PromptMessageContentType.IMAGE:
                                 message_content = cast(ImagePromptMessageContent, message_content)
@@ -465,25 +484,25 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                                         image_content = requests.get(message_content.data).content
                                         with Image.open(io.BytesIO(image_content)) as img:
                                             mime_type = f"image/{img.format.lower()}"
-                                        base64_data = base64.b64encode(image_content).decode('utf-8')
+                                        base64_data = base64.b64encode(image_content).decode("utf-8")
                                     except Exception as ex:
-                                        raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")
+                                        raise ValueError(
+                                            f"Failed to fetch image data from url {message_content.data}, {ex}"
+                                        )
                                 else:
                                     data_split = message_content.data.split(";base64,")
                                     mime_type = data_split[0].replace("data:", "")
                                     base64_data = data_split[1]
 
-                                if mime_type not in ["image/jpeg", "image/png", "image/gif", "image/webp"]:
-                                    raise ValueError(f"Unsupported image type {mime_type}, "
-                                                    f"only support image/jpeg, image/png, image/gif, and image/webp")
+                                if mime_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}:
+                                    raise ValueError(
+                                        f"Unsupported image type {mime_type}, "
+                                        f"only support image/jpeg, image/png, image/gif, and image/webp"
+                                    )
 
                                 sub_message_dict = {
                                     "type": "image",
-                                    "source": {
-                                        "type": "base64",
-                                        "media_type": mime_type,
-                                        "data": base64_data
-                                    }
+                                    "source": {"type": "base64", "media_type": mime_type, "data": base64_data},
                                 }
                                 sub_messages.append(sub_message_dict)
                         prompt_message_dicts.append({"role": "user", "content": sub_messages})
@@ -492,34 +511,28 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                     content = []
                     if message.tool_calls:
                         for tool_call in message.tool_calls:
-                            content.append({
-                                "type": "tool_use",
-                                "id": tool_call.id,
-                                "name": tool_call.function.name,
-                                "input": json.loads(tool_call.function.arguments)
-                            })
+                            content.append(
+                                {
+                                    "type": "tool_use",
+                                    "id": tool_call.id,
+                                    "name": tool_call.function.name,
+                                    "input": json.loads(tool_call.function.arguments),
+                                }
+                            )
                     if message.content:
-                        content.append({
-                            "type": "text",
-                            "text": message.content
-                        })
-                    
+                        content.append({"type": "text", "text": message.content})
+
                     if prompt_message_dicts[-1]["role"] == "assistant":
                         prompt_message_dicts[-1]["content"].extend(content)
                     else:
-                        prompt_message_dicts.append({
-                            "role": "assistant",
-                            "content": content
-                        })
+                        prompt_message_dicts.append({"role": "assistant", "content": content})
                 elif isinstance(message, ToolPromptMessage):
                     message = cast(ToolPromptMessage, message)
                     message_dict = {
                         "role": "user",
-                        "content": [{
-                            "type": "tool_result",
-                            "tool_use_id": message.tool_call_id,
-                            "content": message.content
-                        }]
+                        "content": [
+                            {"type": "tool_result", "tool_use_id": message.tool_call_id, "content": message.content}
+                        ],
                     }
                     prompt_message_dicts.append(message_dict)
                 else:
@@ -576,16 +589,13 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :return: Combined string with necessary human_prompt and ai_prompt tags.
         """
         if not messages:
-            return ''
+            return ""
 
         messages = messages.copy()  # don't mutate the original list
         if not isinstance(messages[-1], AssistantPromptMessage):
             messages.append(AssistantPromptMessage(content=""))
 
-        text = "".join(
-            self._convert_one_message_to_text(message)
-            for message in messages
-        )
+        text = "".join(self._convert_one_message_to_text(message) for message in messages)
 
         # trim off the trailing ' ' that might come from the "Assistant: "
         return text.rstrip()
@@ -601,24 +611,14 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                anthropic.APIConnectionError,
-                anthropic.APITimeoutError
-            ],
-            InvokeServerUnavailableError: [
-                anthropic.InternalServerError
-            ],
-            InvokeRateLimitError: [
-                anthropic.RateLimitError
-            ],
-            InvokeAuthorizationError: [
-                anthropic.AuthenticationError,
-                anthropic.PermissionDeniedError
-            ],
+            InvokeConnectionError: [anthropic.APIConnectionError, anthropic.APITimeoutError],
+            InvokeServerUnavailableError: [anthropic.InternalServerError],
+            InvokeRateLimitError: [anthropic.RateLimitError],
+            InvokeAuthorizationError: [anthropic.AuthenticationError, anthropic.PermissionDeniedError],
             InvokeBadRequestError: [
                 anthropic.BadRequestError,
                 anthropic.NotFoundError,
                 anthropic.UnprocessableEntityError,
-                anthropic.APIError
-            ]
+                anthropic.APIError,
+            ],
         }
diff --git a/api/core/model_runtime/model_providers/azure_ai_studio/llm/llm.py b/api/core/model_runtime/model_providers/azure_ai_studio/llm/llm.py
index 42eae6c1e5..516ef8b295 100644
--- a/api/core/model_runtime/model_providers/azure_ai_studio/llm/llm.py
+++ b/api/core/model_runtime/model_providers/azure_ai_studio/llm/llm.py
@@ -213,7 +213,7 @@ class AzureAIStudioLargeLanguageModel(LargeLanguageModel):
                 model=real_model,
                 prompt_messages=prompt_messages,
                 message=prompt_message,
-                usage=usage if usage else LLMUsage.empty_usage(),
+                usage=usage or LLMUsage.empty_usage(),
                 system_fingerprint=system_fingerprint,
             ),
             credentials=credentials,
diff --git a/api/core/model_runtime/model_providers/azure_openai/_common.py b/api/core/model_runtime/model_providers/azure_openai/_common.py
index 31c788d226..32a0269af4 100644
--- a/api/core/model_runtime/model_providers/azure_openai/_common.py
+++ b/api/core/model_runtime/model_providers/azure_openai/_common.py
@@ -15,10 +15,10 @@ from core.model_runtime.model_providers.azure_openai._constant import AZURE_OPEN
 class _CommonAzureOpenAI:
     @staticmethod
     def _to_credential_kwargs(credentials: dict) -> dict:
-        api_version = credentials.get('openai_api_version', AZURE_OPENAI_API_VERSION)
+        api_version = credentials.get("openai_api_version", AZURE_OPENAI_API_VERSION)
         credentials_kwargs = {
-            "api_key": credentials['openai_api_key'],
-            "azure_endpoint": credentials['openai_api_base'],
+            "api_key": credentials["openai_api_key"],
+            "azure_endpoint": credentials["openai_api_base"],
             "api_version": api_version,
             "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
             "max_retries": 1,
@@ -29,24 +29,14 @@ class _CommonAzureOpenAI:
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         return {
-            InvokeConnectionError: [
-                openai.APIConnectionError,
-                openai.APITimeoutError
-            ],
-            InvokeServerUnavailableError: [
-                openai.InternalServerError
-            ],
-            InvokeRateLimitError: [
-                openai.RateLimitError
-            ],
-            InvokeAuthorizationError: [
-                openai.AuthenticationError,
-                openai.PermissionDeniedError
-            ],
+            InvokeConnectionError: [openai.APIConnectionError, openai.APITimeoutError],
+            InvokeServerUnavailableError: [openai.InternalServerError],
+            InvokeRateLimitError: [openai.RateLimitError],
+            InvokeAuthorizationError: [openai.AuthenticationError, openai.PermissionDeniedError],
             InvokeBadRequestError: [
                 openai.BadRequestError,
                 openai.NotFoundError,
                 openai.UnprocessableEntityError,
-                openai.APIError
-            ]
+                openai.APIError,
+            ],
         }
diff --git a/api/core/model_runtime/model_providers/azure_openai/_constant.py b/api/core/model_runtime/model_providers/azure_openai/_constant.py
index 984cca3744..0dada70cc5 100644
--- a/api/core/model_runtime/model_providers/azure_openai/_constant.py
+++ b/api/core/model_runtime/model_providers/azure_openai/_constant.py
@@ -14,11 +14,21 @@ from core.model_runtime.entities.model_entities import (
     PriceConfig,
 )
 
-AZURE_OPENAI_API_VERSION = '2024-02-15-preview'
+AZURE_OPENAI_API_VERSION = "2024-02-15-preview"
+
+AZURE_DEFAULT_PARAM_SEED_HELP = I18nObject(
+    zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，"
+    "您应该参考 system_fingerprint 响应参数来监视变化。",
+    en_US="If specified, model will make a best effort to sample deterministically,"
+    " such that repeated requests with the same seed and parameters should return the same result."
+    " Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter"
+    " to monitor changes in the backend.",
+)
+
 
 def _get_max_tokens(default: int, min_val: int, max_val: int) -> ParameterRule:
     rule = ParameterRule(
-        name='max_tokens',
+        name="max_tokens",
         **PARAMETER_RULE_TEMPLATE[DefaultParameterName.MAX_TOKENS],
     )
     rule.default = default
@@ -34,11 +44,11 @@ class AzureBaseModel(BaseModel):
 
 LLM_BASE_MODELS = [
     AzureBaseModel(
-        base_model_name='gpt-35-turbo',
+        base_model_name="gpt-35-turbo",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -53,51 +63,47 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=0.0005,
                 output=0.0015,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-35-turbo-16k',
+        base_model_name="gpt-35-turbo-16k",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -112,37 +118,37 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
-                _get_max_tokens(default=512, min_val=1, max_val=16385)
+                _get_max_tokens(default=512, min_val=1, max_val=16385),
             ],
             pricing=PriceConfig(
                 input=0.003,
                 output=0.004,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-35-turbo-0125',
+        base_model_name="gpt-35-turbo-0125",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -157,51 +163,47 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=0.0005,
                 output=0.0015,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4',
+        base_model_name="gpt-4",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -216,67 +218,57 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=8192),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=0.03,
                 output=0.06,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4-32k',
+        base_model_name="gpt-4-32k",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -291,67 +283,57 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=32768),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=0.06,
                 output=0.12,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4-0125-preview',
+        base_model_name="gpt-4-0125-preview",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -366,67 +348,57 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=0.01,
                 output=0.03,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4-1106-preview',
+        base_model_name="gpt-4-1106-preview",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -441,67 +413,57 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=0.01,
                 output=0.03,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4o-mini',
+        base_model_name="gpt-4o-mini",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -517,67 +479,57 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=16384),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=0.150,
                 output=0.600,
                 unit=0.000001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4o-mini-2024-07-18',
+        base_model_name="gpt-4o-mini-2024-07-18",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -593,67 +545,67 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=16384),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
+                    ),
+                    required=False,
+                    options=["text", "json_object", "json_schema"],
+                ),
+                ParameterRule(
+                    name="json_schema",
+                    label=I18nObject(en_US="JSON Schema"),
+                    type="text",
+                    help=I18nObject(
+                        zh_Hans="设置返回的json schema，llm将按照它返回",
+                        en_US="Set a response json schema will ensure LLM to adhere it.",
                     ),
                     required=False,
-                    options=['text', 'json_object']
                 ),
             ],
             pricing=PriceConfig(
                 input=0.150,
                 output=0.600,
                 unit=0.000001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4o',
+        base_model_name="gpt-4o",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -669,67 +621,57 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=5.00,
                 output=15.00,
                 unit=0.000001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4o-2024-05-13',
+        base_model_name="gpt-4o-2024-05-13",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -745,67 +687,57 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=5.00,
                 output=15.00,
                 unit=0.000001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4-turbo',
+        base_model_name="gpt-4o-2024-08-06",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -821,67 +753,67 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
+                    ),
+                    required=False,
+                    options=["text", "json_object", "json_schema"],
+                ),
+                ParameterRule(
+                    name="json_schema",
+                    label=I18nObject(en_US="JSON Schema"),
+                    type="text",
+                    help=I18nObject(
+                        zh_Hans="设置返回的json schema，llm将按照它返回",
+                        en_US="Set a response json schema will ensure LLM to adhere it.",
                     ),
                     required=False,
-                    options=['text', 'json_object']
                 ),
             ],
             pricing=PriceConfig(
-                input=0.01,
-                output=0.03,
-                unit=0.001,
-                currency='USD',
-            )
-        )
+                input=5.00,
+                output=15.00,
+                unit=0.000001,
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4-turbo-2024-04-09',
+        base_model_name="gpt-4-turbo",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
@@ -897,71 +829,64 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=0.01,
                 output=0.03,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-4-vision-preview',
+        base_model_name="gpt-4-turbo-2024-04-09",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             features=[
-                ModelFeature.VISION
+                ModelFeature.AGENT_THOUGHT,
+                ModelFeature.VISION,
+                ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
@@ -970,67 +895,118 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
-                    name='seed',
-                    label=I18nObject(
-                        zh_Hans='种子',
-                        en_US='Seed'
-                    ),
-                    type='int',
-                    help=I18nObject(
-                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
-                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
-                    ),
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
                     required=False,
                     precision=2,
                     min=0,
                     max=1,
                 ),
                 ParameterRule(
-                    name='response_format',
-                    label=I18nObject(
-                        zh_Hans='回复格式',
-                        en_US='response_format'
-                    ),
-                    type='string',
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
                     help=I18nObject(
-                        zh_Hans='指定模型必须输出的格式',
-                        en_US='specifying the format that the model must output'
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
                     ),
                     required=False,
-                    options=['text', 'json_object']
+                    options=["text", "json_object"],
                 ),
             ],
             pricing=PriceConfig(
                 input=0.01,
                 output=0.03,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='gpt-35-turbo-instruct',
+        base_model_name="gpt-4-vision-preview",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
+            ),
+            model_type=ModelType.LLM,
+            features=[ModelFeature.VISION],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 128000,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name="temperature",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
+                ),
+                ParameterRule(
+                    name="top_p",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
+                ),
+                ParameterRule(
+                    name="presence_penalty",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
+                ),
+                ParameterRule(
+                    name="frequency_penalty",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
+                ),
+                _get_max_tokens(default=512, min_val=1, max_val=4096),
+                ParameterRule(
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
+                    required=False,
+                    precision=2,
+                    min=0,
+                    max=1,
+                ),
+                ParameterRule(
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
+                    help=I18nObject(
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
+                    ),
+                    required=False,
+                    options=["text", "json_object"],
+                ),
+            ],
+            pricing=PriceConfig(
+                input=0.01,
+                output=0.03,
+                unit=0.001,
+                currency="USD",
+            ),
+        ),
+    ),
+    AzureBaseModel(
+        base_model_name="gpt-35-turbo-instruct",
+        entity=AIModelEntity(
+            model="fake-deployment-name",
+            label=I18nObject(
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
@@ -1040,19 +1016,19 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
@@ -1061,16 +1037,16 @@ LLM_BASE_MODELS = [
                 input=0.0015,
                 output=0.002,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='text-davinci-003',
+        base_model_name="text-davinci-003",
         entity=AIModelEntity(
-            model='fake-deployment-name',
+            model="fake-deployment-name",
             label=I18nObject(
-                en_US='fake-deployment-name-label',
+                en_US="fake-deployment-name-label",
             ),
             model_type=ModelType.LLM,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
@@ -1080,19 +1056,19 @@ LLM_BASE_MODELS = [
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
+                    name="temperature",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
                 ),
                 ParameterRule(
-                    name='top_p',
+                    name="top_p",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
                 ),
                 ParameterRule(
-                    name='presence_penalty',
+                    name="presence_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
                 ),
                 ParameterRule(
-                    name='frequency_penalty',
+                    name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
                 _get_max_tokens(default=512, min_val=1, max_val=4096),
@@ -1101,20 +1077,18 @@ LLM_BASE_MODELS = [
                 input=0.02,
                 output=0.02,
                 unit=0.001,
-                currency='USD',
-            )
-        )
-    )
+                currency="USD",
+            ),
+        ),
+    ),
 ]
 
 EMBEDDING_BASE_MODELS = [
     AzureBaseModel(
-        base_model_name='text-embedding-ada-002',
+        base_model_name="text-embedding-ada-002",
         entity=AIModelEntity(
-            model='fake-deployment-name',
-            label=I18nObject(
-                en_US='fake-deployment-name-label'
-            ),
+            model="fake-deployment-name",
+            label=I18nObject(en_US="fake-deployment-name-label"),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TEXT_EMBEDDING,
             model_properties={
@@ -1124,17 +1098,15 @@ EMBEDDING_BASE_MODELS = [
             pricing=PriceConfig(
                 input=0.0001,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='text-embedding-3-small',
+        base_model_name="text-embedding-3-small",
         entity=AIModelEntity(
-            model='fake-deployment-name',
-            label=I18nObject(
-                en_US='fake-deployment-name-label'
-            ),
+            model="fake-deployment-name",
+            label=I18nObject(en_US="fake-deployment-name-label"),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TEXT_EMBEDDING,
             model_properties={
@@ -1144,17 +1116,15 @@ EMBEDDING_BASE_MODELS = [
             pricing=PriceConfig(
                 input=0.00002,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='text-embedding-3-large',
+        base_model_name="text-embedding-3-large",
         entity=AIModelEntity(
-            model='fake-deployment-name',
-            label=I18nObject(
-                en_US='fake-deployment-name-label'
-            ),
+            model="fake-deployment-name",
+            label=I18nObject(en_US="fake-deployment-name-label"),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TEXT_EMBEDDING,
             model_properties={
@@ -1164,135 +1134,129 @@ EMBEDDING_BASE_MODELS = [
             pricing=PriceConfig(
                 input=0.00013,
                 unit=0.001,
-                currency='USD',
-            )
-        )
-    )
+                currency="USD",
+            ),
+        ),
+    ),
 ]
 SPEECH2TEXT_BASE_MODELS = [
     AzureBaseModel(
-        base_model_name='whisper-1',
+        base_model_name="whisper-1",
         entity=AIModelEntity(
-            model='fake-deployment-name',
-            label=I18nObject(
-                en_US='fake-deployment-name-label'
-            ),
+            model="fake-deployment-name",
+            label=I18nObject(en_US="fake-deployment-name-label"),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.SPEECH2TEXT,
             model_properties={
                 ModelPropertyKey.FILE_UPLOAD_LIMIT: 25,
-                ModelPropertyKey.SUPPORTED_FILE_EXTENSIONS: 'flac,mp3,mp4,mpeg,mpga,m4a,ogg,wav,webm'
-            }
-        )
+                ModelPropertyKey.SUPPORTED_FILE_EXTENSIONS: "flac,mp3,mp4,mpeg,mpga,m4a,ogg,wav,webm",
+            },
+        ),
     )
 ]
 TTS_BASE_MODELS = [
     AzureBaseModel(
-        base_model_name='tts-1',
+        base_model_name="tts-1",
         entity=AIModelEntity(
-            model='fake-deployment-name',
-            label=I18nObject(
-                en_US='fake-deployment-name-label'
-            ),
+            model="fake-deployment-name",
+            label=I18nObject(en_US="fake-deployment-name-label"),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TTS,
             model_properties={
-                ModelPropertyKey.DEFAULT_VOICE: 'alloy',
+                ModelPropertyKey.DEFAULT_VOICE: "alloy",
                 ModelPropertyKey.VOICES: [
                     {
-                        'mode': 'alloy',
-                        'name': 'Alloy',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "alloy",
+                        "name": "Alloy",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                     {
-                        'mode': 'echo',
-                        'name': 'Echo',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "echo",
+                        "name": "Echo",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                     {
-                        'mode': 'fable',
-                        'name': 'Fable',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "fable",
+                        "name": "Fable",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                     {
-                        'mode': 'onyx',
-                        'name': 'Onyx',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "onyx",
+                        "name": "Onyx",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                     {
-                        'mode': 'nova',
-                        'name': 'Nova',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "nova",
+                        "name": "Nova",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                     {
-                        'mode': 'shimmer',
-                        'name': 'Shimmer',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "shimmer",
+                        "name": "Shimmer",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                 ],
                 ModelPropertyKey.WORD_LIMIT: 120,
-                ModelPropertyKey.AUDIO_TYPE: 'mp3',
-                ModelPropertyKey.MAX_WORKERS: 5
+                ModelPropertyKey.AUDIO_TYPE: "mp3",
+                ModelPropertyKey.MAX_WORKERS: 5,
             },
             pricing=PriceConfig(
                 input=0.015,
                 unit=0.001,
-                currency='USD',
-            )
-        )
+                currency="USD",
+            ),
+        ),
     ),
     AzureBaseModel(
-        base_model_name='tts-1-hd',
+        base_model_name="tts-1-hd",
         entity=AIModelEntity(
-            model='fake-deployment-name',
-            label=I18nObject(
-                en_US='fake-deployment-name-label'
-            ),
+            model="fake-deployment-name",
+            label=I18nObject(en_US="fake-deployment-name-label"),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TTS,
             model_properties={
-                ModelPropertyKey.DEFAULT_VOICE: 'alloy',
+                ModelPropertyKey.DEFAULT_VOICE: "alloy",
                 ModelPropertyKey.VOICES: [
                     {
-                        'mode': 'alloy',
-                        'name': 'Alloy',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "alloy",
+                        "name": "Alloy",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                     {
-                        'mode': 'echo',
-                        'name': 'Echo',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "echo",
+                        "name": "Echo",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                     {
-                        'mode': 'fable',
-                        'name': 'Fable',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "fable",
+                        "name": "Fable",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                     {
-                        'mode': 'onyx',
-                        'name': 'Onyx',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "onyx",
+                        "name": "Onyx",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                     {
-                        'mode': 'nova',
-                        'name': 'Nova',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "nova",
+                        "name": "Nova",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                     {
-                        'mode': 'shimmer',
-                        'name': 'Shimmer',
-                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                        "mode": "shimmer",
+                        "name": "Shimmer",
+                        "language": ["zh-Hans", "en-US", "de-DE", "fr-FR", "es-ES", "it-IT", "th-TH", "id-ID"],
                     },
                 ],
                 ModelPropertyKey.WORD_LIMIT: 120,
-                ModelPropertyKey.AUDIO_TYPE: 'mp3',
-                ModelPropertyKey.MAX_WORKERS: 5
+                ModelPropertyKey.AUDIO_TYPE: "mp3",
+                ModelPropertyKey.MAX_WORKERS: 5,
             },
             pricing=PriceConfig(
                 input=0.03,
                 unit=0.001,
-                currency='USD',
-            )
-        )
-    )
+                currency="USD",
+            ),
+        ),
+    ),
 ]
diff --git a/api/core/model_runtime/model_providers/azure_openai/azure_openai.py b/api/core/model_runtime/model_providers/azure_openai/azure_openai.py
index 68977b2266..2e3c6aab05 100644
--- a/api/core/model_runtime/model_providers/azure_openai/azure_openai.py
+++ b/api/core/model_runtime/model_providers/azure_openai/azure_openai.py
@@ -6,6 +6,5 @@ logger = logging.getLogger(__name__)
 
 
 class AzureOpenAIProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         pass
diff --git a/api/core/model_runtime/model_providers/azure_openai/azure_openai.yaml b/api/core/model_runtime/model_providers/azure_openai/azure_openai.yaml
index be4d4651d7..867f9fec42 100644
--- a/api/core/model_runtime/model_providers/azure_openai/azure_openai.yaml
+++ b/api/core/model_runtime/model_providers/azure_openai/azure_openai.yaml
@@ -53,6 +53,12 @@ model_credential_schema:
       type: select
       required: true
       options:
+        - label:
+            en_US: 2024-08-01-preview
+          value: 2024-08-01-preview
+        - label:
+            en_US: 2024-07-01-preview
+          value: 2024-07-01-preview
         - label:
             en_US: 2024-05-01-preview
           value: 2024-05-01-preview
@@ -138,6 +144,12 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: llm
+        - label:
+            en_US: gpt-4o-2024-08-06
+          value: gpt-4o-2024-08-06
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: gpt-4-turbo
           value: gpt-4-turbo
diff --git a/api/core/model_runtime/model_providers/azure_openai/llm/llm.py b/api/core/model_runtime/model_providers/azure_openai/llm/llm.py
index 1911caa952..f0033ea051 100644
--- a/api/core/model_runtime/model_providers/azure_openai/llm/llm.py
+++ b/api/core/model_runtime/model_providers/azure_openai/llm/llm.py
@@ -1,4 +1,5 @@
 import copy
+import json
 import logging
 from collections.abc import Generator, Sequence
 from typing import Optional, Union, cast
@@ -33,16 +34,20 @@ logger = logging.getLogger(__name__)
 
 
 class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
-
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
-
-        base_model_name = credentials.get('base_model_name')
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
+        base_model_name = credentials.get("base_model_name")
         if not base_model_name:
-            raise ValueError('Base Model Name is required')
+            raise ValueError("Base Model Name is required")
         ai_model_entity = self._get_ai_model_entity(base_model_name=base_model_name, model=model)
 
         if ai_model_entity and ai_model_entity.entity.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT.value:
@@ -55,7 +60,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                 tools=tools,
                 stop=stop,
                 stream=stream,
-                user=user
+                user=user,
             )
         else:
             # text completion model
@@ -66,7 +71,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                 model_parameters=model_parameters,
                 stop=stop,
                 stream=stream,
-                user=user
+                user=user,
             )
 
     def get_num_tokens(
@@ -74,14 +79,14 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         model: str,
         credentials: dict,
         prompt_messages: list[PromptMessage],
-        tools: Optional[list[PromptMessageTool]] = None
+        tools: Optional[list[PromptMessageTool]] = None,
     ) -> int:
-        base_model_name = credentials.get('base_model_name')
+        base_model_name = credentials.get("base_model_name")
         if not base_model_name:
-            raise ValueError('Base Model Name is required')
+            raise ValueError("Base Model Name is required")
         model_entity = self._get_ai_model_entity(base_model_name=base_model_name, model=model)
         if not model_entity:
-            raise ValueError(f'Base Model Name {base_model_name} is invalid')
+            raise ValueError(f"Base Model Name {base_model_name} is invalid")
         model_mode = model_entity.entity.model_properties.get(ModelPropertyKey.MODE)
 
         if model_mode == LLMMode.CHAT.value:
@@ -91,21 +96,21 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
             # text completion model, do not support tool calling
             content = prompt_messages[0].content
             assert isinstance(content, str)
-            return self._num_tokens_from_string(credentials,content)
+            return self._num_tokens_from_string(credentials, content)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        if 'openai_api_base' not in credentials:
-            raise CredentialsValidateFailedError('Azure OpenAI API Base Endpoint is required')
+        if "openai_api_base" not in credentials:
+            raise CredentialsValidateFailedError("Azure OpenAI API Base Endpoint is required")
 
-        if 'openai_api_key' not in credentials:
-            raise CredentialsValidateFailedError('Azure OpenAI API key is required')
+        if "openai_api_key" not in credentials:
+            raise CredentialsValidateFailedError("Azure OpenAI API key is required")
 
-        if 'base_model_name' not in credentials:
-            raise CredentialsValidateFailedError('Base Model Name is required')
+        if "base_model_name" not in credentials:
+            raise CredentialsValidateFailedError("Base Model Name is required")
 
-        base_model_name = credentials.get('base_model_name')
+        base_model_name = credentials.get("base_model_name")
         if not base_model_name:
-            raise CredentialsValidateFailedError('Base Model Name is required')
+            raise CredentialsValidateFailedError("Base Model Name is required")
         ai_model_entity = self._get_ai_model_entity(base_model_name=base_model_name, model=model)
 
         if not ai_model_entity:
@@ -117,7 +122,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
             if ai_model_entity.entity.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT.value:
                 # chat model
                 client.chat.completions.create(
-                    messages=[{"role": "user", "content": 'ping'}],
+                    messages=[{"role": "user", "content": "ping"}],
                     model=model,
                     temperature=0,
                     max_tokens=20,
@@ -126,7 +131,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
             else:
                 # text completion model
                 client.completions.create(
-                    prompt='ping',
+                    prompt="ping",
                     model=model,
                     temperature=0,
                     max_tokens=20,
@@ -136,33 +141,35 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
             raise CredentialsValidateFailedError(str(ex))
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
-        base_model_name = credentials.get('base_model_name')
+        base_model_name = credentials.get("base_model_name")
         if not base_model_name:
-            raise ValueError('Base Model Name is required')
+            raise ValueError("Base Model Name is required")
         ai_model_entity = self._get_ai_model_entity(base_model_name=base_model_name, model=model)
         return ai_model_entity.entity if ai_model_entity else None
 
-    def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[list[str]] = None,
-                  stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
-
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         client = AzureOpenAI(**self._to_credential_kwargs(credentials))
 
         extra_model_kwargs = {}
 
         if stop:
-            extra_model_kwargs['stop'] = stop
+            extra_model_kwargs["stop"] = stop
 
         if user:
-            extra_model_kwargs['user'] = user
+            extra_model_kwargs["user"] = user
 
         # text completion model
         response = client.completions.create(
-            prompt=prompt_messages[0].content,
-            model=model,
-            stream=stream,
-            **model_parameters,
-            **extra_model_kwargs
+            prompt=prompt_messages[0].content, model=model, stream=stream, **model_parameters, **extra_model_kwargs
         )
 
         if stream:
@@ -171,15 +178,12 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         return self._handle_generate_response(model, credentials, response, prompt_messages)
 
     def _handle_generate_response(
-        self, model: str, credentials: dict, response: Completion,
-        prompt_messages: list[PromptMessage]
+        self, model: str, credentials: dict, response: Completion, prompt_messages: list[PromptMessage]
     ):
         assistant_text = response.choices[0].text
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_text
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_text)
 
         # calculate num tokens
         if response.usage:
@@ -208,24 +212,21 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         return result
 
     def _handle_generate_stream_response(
-        self, model: str, credentials: dict, response: Stream[Completion],
-        prompt_messages: list[PromptMessage]
+        self, model: str, credentials: dict, response: Stream[Completion], prompt_messages: list[PromptMessage]
     ) -> Generator:
-        full_text = ''
+        full_text = ""
         for chunk in response:
             if len(chunk.choices) == 0:
                 continue
 
             delta = chunk.choices[0]
 
-            if delta.finish_reason is None and (delta.text is None or delta.text == ''):
+            if delta.finish_reason is None and (delta.text is None or delta.text == ""):
                 continue
 
             # transform assistant message to prompt message
-            text = delta.text if delta.text else ''
-            assistant_prompt_message = AssistantPromptMessage(
-                content=text
-            )
+            text = delta.text or ""
+            assistant_prompt_message = AssistantPromptMessage(content=text)
 
             full_text += text
 
@@ -253,8 +254,8 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                         index=delta.index,
                         message=assistant_prompt_message,
                         finish_reason=delta.finish_reason,
-                        usage=usage
-                    )
+                        usage=usage,
+                    ),
                 )
             else:
                 yield LLMResultChunk(
@@ -264,29 +265,41 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=delta.index,
                         message=assistant_prompt_message,
-                    )
+                    ),
                 )
 
-    def _chat_generate(self, model: str, credentials: dict,
-                       prompt_messages: list[PromptMessage], model_parameters: dict,
-                       tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                       stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
-
+    def _chat_generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         client = AzureOpenAI(**self._to_credential_kwargs(credentials))
 
         response_format = model_parameters.get("response_format")
         if response_format:
-            if response_format == "json_object":
-                response_format = {"type": "json_object"}
+            if response_format == "json_schema":
+                json_schema = model_parameters.get("json_schema")
+                if not json_schema:
+                    raise ValueError("Must define JSON Schema when the response format is json_schema")
+                try:
+                    schema = json.loads(json_schema)
+                except:
+                    raise ValueError(f"not correct json_schema format: {json_schema}")
+                model_parameters.pop("json_schema")
+                model_parameters["response_format"] = {"type": "json_schema", "json_schema": schema}
             else:
-                response_format = {"type": "text"}
-
-            model_parameters["response_format"] = response_format
+                model_parameters["response_format"] = {"type": response_format}
 
         extra_model_kwargs = {}
 
         if tools:
-            extra_model_kwargs['tools'] = [helper.dump_model(PromptMessageFunction(function=tool)) for tool in tools]
+            extra_model_kwargs["tools"] = [helper.dump_model(PromptMessageFunction(function=tool)) for tool in tools]
             # extra_model_kwargs['functions'] = [{
             #     "name": tool.name,
             #     "description": tool.description,
@@ -294,10 +307,10 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
             # } for tool in tools]
 
         if stop:
-            extra_model_kwargs['stop'] = stop
+            extra_model_kwargs["stop"] = stop
 
         if user:
-            extra_model_kwargs['user'] = user
+            extra_model_kwargs["user"] = user
 
         # chat model
         messages = [self._convert_prompt_message_to_dict(m) for m in prompt_messages]
@@ -315,9 +328,12 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         return self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
 
     def _handle_chat_generate_response(
-        self, model: str, credentials: dict, response: ChatCompletion,
+        self,
+        model: str,
+        credentials: dict,
+        response: ChatCompletion,
         prompt_messages: list[PromptMessage],
-        tools: Optional[list[PromptMessageTool]] = None
+        tools: Optional[list[PromptMessageTool]] = None,
     ):
         assistant_message = response.choices[0].message
         assistant_message_tool_calls = assistant_message.tool_calls
@@ -327,10 +343,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         self._update_tool_calls(tool_calls=tool_calls, tool_calls_response=assistant_message_tool_calls)
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_message.content,
-            tool_calls=tool_calls
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_message.content, tool_calls=tool_calls)
 
         # calculate num tokens
         if response.usage:
@@ -362,13 +375,13 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         credentials: dict,
         response: Stream[ChatCompletionChunk],
         prompt_messages: list[PromptMessage],
-        tools: Optional[list[PromptMessageTool]] = None
+        tools: Optional[list[PromptMessageTool]] = None,
     ):
         index = 0
-        full_assistant_content = ''
+        full_assistant_content = ""
         real_model = model
         system_fingerprint = None
-        completion = ''
+        completion = ""
         tool_calls = []
         for chunk in response:
             if len(chunk.choices) == 0:
@@ -379,7 +392,6 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
             if delta.delta is None:
                 continue
 
-
             # extract tool calls from response
             self._update_tool_calls(tool_calls=tool_calls, tool_calls_response=delta.delta.tool_calls)
 
@@ -388,16 +400,13 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                 continue
 
             # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else '',
-                tool_calls=tool_calls
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
 
-            full_assistant_content += delta.delta.content if delta.delta.content else ''
+            full_assistant_content += delta.delta.content or ""
 
             real_model = chunk.model
             system_fingerprint = chunk.system_fingerprint
-            completion += delta.delta.content if delta.delta.content else ''
+            completion += delta.delta.content or ""
 
             yield LLMResultChunk(
                 model=real_model,
@@ -406,17 +415,15 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                 delta=LLMResultChunkDelta(
                     index=index,
                     message=assistant_prompt_message,
-                )
+                ),
             )
 
-            index += 0
+            index += 1
 
         # calculate num tokens
         prompt_tokens = self._num_tokens_from_messages(credentials, prompt_messages, tools)
 
-        full_assistant_prompt_message = AssistantPromptMessage(
-            content=completion
-        )
+        full_assistant_prompt_message = AssistantPromptMessage(content=completion)
         completion_tokens = self._num_tokens_from_messages(credentials, [full_assistant_prompt_message])
 
         # transform usage
@@ -427,27 +434,24 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
             prompt_messages=prompt_messages,
             system_fingerprint=system_fingerprint,
             delta=LLMResultChunkDelta(
-                index=index,
-                message=AssistantPromptMessage(content=''),
-                finish_reason='stop',
-                usage=usage
-            )
+                index=index, message=AssistantPromptMessage(content=""), finish_reason="stop", usage=usage
+            ),
         )
 
     @staticmethod
-    def _update_tool_calls(tool_calls: list[AssistantPromptMessage.ToolCall], tool_calls_response: Optional[Sequence[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]]) -> None:
+    def _update_tool_calls(
+        tool_calls: list[AssistantPromptMessage.ToolCall],
+        tool_calls_response: Optional[Sequence[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]],
+    ) -> None:
         if tool_calls_response:
             for response_tool_call in tool_calls_response:
                 if isinstance(response_tool_call, ChatCompletionMessageToolCall):
                     function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                        name=response_tool_call.function.name,
-                        arguments=response_tool_call.function.arguments
+                        name=response_tool_call.function.name, arguments=response_tool_call.function.arguments
                     )
 
                     tool_call = AssistantPromptMessage.ToolCall(
-                        id=response_tool_call.id,
-                        type=response_tool_call.type,
-                        function=function
+                        id=response_tool_call.id, type=response_tool_call.type, function=function
                     )
                     tool_calls.append(tool_call)
                 elif isinstance(response_tool_call, ChoiceDeltaToolCall):
@@ -456,8 +460,10 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                         tool_calls[index].id = response_tool_call.id or tool_calls[index].id
                         tool_calls[index].type = response_tool_call.type or tool_calls[index].type
                         if response_tool_call.function:
-                            tool_calls[index].function.name = response_tool_call.function.name or tool_calls[index].function.name
-                            tool_calls[index].function.arguments += response_tool_call.function.arguments or ''
+                            tool_calls[index].function.name = (
+                                response_tool_call.function.name or tool_calls[index].function.name
+                            )
+                            tool_calls[index].function.arguments += response_tool_call.function.arguments or ""
                     else:
                         assert response_tool_call.id is not None
                         assert response_tool_call.type is not None
@@ -466,13 +472,10 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                         assert response_tool_call.function.arguments is not None
 
                         function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                            name=response_tool_call.function.name,
-                            arguments=response_tool_call.function.arguments
+                            name=response_tool_call.function.name, arguments=response_tool_call.function.arguments
                         )
                         tool_call = AssistantPromptMessage.ToolCall(
-                            id=response_tool_call.id,
-                            type=response_tool_call.type,
-                            function=function
+                            id=response_tool_call.id, type=response_tool_call.type, function=function
                         )
                         tool_calls.append(tool_call)
 
@@ -488,19 +491,13 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
                         message_content = cast(TextPromptMessageContent, message_content)
-                        sub_message_dict = {
-                            "type": "text",
-                            "text": message_content.data
-                        }
+                        sub_message_dict = {"type": "text", "text": message_content.data}
                         sub_messages.append(sub_message_dict)
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
                         sub_message_dict = {
                             "type": "image_url",
-                            "image_url": {
-                                "url": message_content.data,
-                                "detail": message_content.detail.value
-                            }
+                            "image_url": {"url": message_content.data, "detail": message_content.detail.value},
                         }
                         sub_messages.append(sub_message_dict)
                 message_dict = {"role": "user", "content": sub_messages}
@@ -518,7 +515,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                 "role": "tool",
                 "name": message.name,
                 "content": message.content,
-                "tool_call_id": message.tool_call_id
+                "tool_call_id": message.tool_call_id,
             }
         else:
             raise ValueError(f"Got unknown type {message}")
@@ -528,10 +525,11 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
 
         return message_dict
 
-    def _num_tokens_from_string(self, credentials: dict, text: str,
-                                tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_string(
+        self, credentials: dict, text: str, tools: Optional[list[PromptMessageTool]] = None
+    ) -> int:
         try:
-            encoding = tiktoken.encoding_for_model(credentials['base_model_name'])
+            encoding = tiktoken.encoding_for_model(credentials["base_model_name"])
         except KeyError:
             encoding = tiktoken.get_encoding("cl100k_base")
 
@@ -543,14 +541,13 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         return num_tokens
 
     def _num_tokens_from_messages(
-        self, credentials: dict, messages: list[PromptMessage],
-        tools: Optional[list[PromptMessageTool]] = None
+        self, credentials: dict, messages: list[PromptMessage], tools: Optional[list[PromptMessageTool]] = None
     ) -> int:
         """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
 
         Official documentation: https://github.com/openai/openai-cookbook/blob/
         main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
-        model = credentials['base_model_name']
+        model = credentials["base_model_name"]
         try:
             encoding = tiktoken.encoding_for_model(model)
         except KeyError:
@@ -584,10 +581,10 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                 #  which need to download the image and then get the resolution for calculation,
                 #  and will increase the request delay
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
 
                     value = text
 
@@ -619,40 +616,39 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
 
     @staticmethod
     def _num_tokens_for_tools(encoding: tiktoken.Encoding, tools: list[PromptMessageTool]) -> int:
-
         num_tokens = 0
         for tool in tools:
-            num_tokens += len(encoding.encode('type'))
-            num_tokens += len(encoding.encode('function'))
+            num_tokens += len(encoding.encode("type"))
+            num_tokens += len(encoding.encode("function"))
 
             # calculate num tokens for function object
-            num_tokens += len(encoding.encode('name'))
+            num_tokens += len(encoding.encode("name"))
             num_tokens += len(encoding.encode(tool.name))
-            num_tokens += len(encoding.encode('description'))
+            num_tokens += len(encoding.encode("description"))
             num_tokens += len(encoding.encode(tool.description))
             parameters = tool.parameters
-            num_tokens += len(encoding.encode('parameters'))
-            if 'title' in parameters:
-                num_tokens += len(encoding.encode('title'))
-                num_tokens += len(encoding.encode(parameters['title']))
-            num_tokens += len(encoding.encode('type'))
-            num_tokens += len(encoding.encode(parameters['type']))
-            if 'properties' in parameters:
-                num_tokens += len(encoding.encode('properties'))
-                for key, value in parameters['properties'].items():
+            num_tokens += len(encoding.encode("parameters"))
+            if "title" in parameters:
+                num_tokens += len(encoding.encode("title"))
+                num_tokens += len(encoding.encode(parameters["title"]))
+            num_tokens += len(encoding.encode("type"))
+            num_tokens += len(encoding.encode(parameters["type"]))
+            if "properties" in parameters:
+                num_tokens += len(encoding.encode("properties"))
+                for key, value in parameters["properties"].items():
                     num_tokens += len(encoding.encode(key))
                     for field_key, field_value in value.items():
                         num_tokens += len(encoding.encode(field_key))
-                        if field_key == 'enum':
+                        if field_key == "enum":
                             for enum_field in field_value:
                                 num_tokens += 3
                                 num_tokens += len(encoding.encode(enum_field))
                         else:
                             num_tokens += len(encoding.encode(field_key))
                             num_tokens += len(encoding.encode(str(field_value)))
-            if 'required' in parameters:
-                num_tokens += len(encoding.encode('required'))
-                for required_field in parameters['required']:
+            if "required" in parameters:
+                num_tokens += len(encoding.encode("required"))
+                for required_field in parameters["required"]:
                     num_tokens += 3
                     num_tokens += len(encoding.encode(required_field))
 
diff --git a/api/core/model_runtime/model_providers/azure_openai/speech2text/speech2text.py b/api/core/model_runtime/model_providers/azure_openai/speech2text/speech2text.py
index 8aebcb90e4..a2b14cf3db 100644
--- a/api/core/model_runtime/model_providers/azure_openai/speech2text/speech2text.py
+++ b/api/core/model_runtime/model_providers/azure_openai/speech2text/speech2text.py
@@ -15,9 +15,7 @@ class AzureOpenAISpeech2TextModel(_CommonAzureOpenAI, Speech2TextModel):
     Model class for OpenAI Speech to text model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                file: IO[bytes], user: Optional[str] = None) \
-            -> str:
+    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
         """
         Invoke speech2text model
 
@@ -40,7 +38,7 @@ class AzureOpenAISpeech2TextModel(_CommonAzureOpenAI, Speech2TextModel):
         try:
             audio_file_path = self._get_demo_file_path()
 
-            with open(audio_file_path, 'rb') as audio_file:
+            with open(audio_file_path, "rb") as audio_file:
                 self._speech2text_invoke(model, credentials, audio_file)
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -65,10 +63,9 @@ class AzureOpenAISpeech2TextModel(_CommonAzureOpenAI, Speech2TextModel):
         return response.text
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
-        ai_model_entity = self._get_ai_model_entity(credentials['base_model_name'], model)
+        ai_model_entity = self._get_ai_model_entity(credentials["base_model_name"], model)
         return ai_model_entity.entity
 
-
     @staticmethod
     def _get_ai_model_entity(base_model_name: str, model: str) -> AzureBaseModel:
         for ai_model_entity in SPEECH2TEXT_BASE_MODELS:
diff --git a/api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py
index e073bef014..d9cff8ecbb 100644
--- a/api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py
@@ -16,19 +16,18 @@ from core.model_runtime.model_providers.azure_openai._constant import EMBEDDING_
 
 
 class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
-
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
-        base_model_name = credentials['base_model_name']
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
+        base_model_name = credentials["base_model_name"]
         credentials_kwargs = self._to_credential_kwargs(credentials)
         client = AzureOpenAI(**credentials_kwargs)
 
         extra_model_kwargs = {}
         if user:
-            extra_model_kwargs['user'] = user
+            extra_model_kwargs["user"] = user
 
-        extra_model_kwargs['encoding_format'] = 'base64'
+        extra_model_kwargs["encoding_format"] = "base64"
 
         context_size = self._get_context_size(model, credentials)
         max_chunks = self._get_max_chunks(model, credentials)
@@ -44,11 +43,9 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
             enc = tiktoken.get_encoding("cl100k_base")
 
         for i, text in enumerate(texts):
-            token = enc.encode(
-                text
-            )
+            token = enc.encode(text)
             for j in range(0, len(token), context_size):
-                tokens += [token[j: j + context_size]]
+                tokens += [token[j : j + context_size]]
                 indices += [i]
 
         batched_embeddings = []
@@ -56,10 +53,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
 
         for i in _iter:
             embeddings_batch, embedding_used_tokens = self._embedding_invoke(
-                model=model,
-                client=client,
-                texts=tokens[i: i + max_chunks],
-                extra_model_kwargs=extra_model_kwargs
+                model=model, client=client, texts=tokens[i : i + max_chunks], extra_model_kwargs=extra_model_kwargs
             )
 
             used_tokens += embedding_used_tokens
@@ -75,10 +69,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
             _result = results[i]
             if len(_result) == 0:
                 embeddings_batch, embedding_used_tokens = self._embedding_invoke(
-                    model=model,
-                    client=client,
-                    texts="",
-                    extra_model_kwargs=extra_model_kwargs
+                    model=model, client=client, texts="", extra_model_kwargs=extra_model_kwargs
                 )
 
                 used_tokens += embedding_used_tokens
@@ -88,24 +79,16 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
             embeddings[i] = (average / np.linalg.norm(average)).tolist()
 
         # calc usage
-        usage = self._calc_response_usage(
-            model=model,
-            credentials=credentials,
-            tokens=used_tokens
-        )
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
 
-        return TextEmbeddingResult(
-            embeddings=embeddings,
-            usage=usage,
-            model=base_model_name
-        )
+        return TextEmbeddingResult(embeddings=embeddings, usage=usage, model=base_model_name)
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         if len(texts) == 0:
             return 0
 
         try:
-            enc = tiktoken.encoding_for_model(credentials['base_model_name'])
+            enc = tiktoken.encoding_for_model(credentials["base_model_name"])
         except KeyError:
             enc = tiktoken.get_encoding("cl100k_base")
 
@@ -118,57 +101,52 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
         return total_num_tokens
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        if 'openai_api_base' not in credentials:
-            raise CredentialsValidateFailedError('Azure OpenAI API Base Endpoint is required')
+        if "openai_api_base" not in credentials:
+            raise CredentialsValidateFailedError("Azure OpenAI API Base Endpoint is required")
 
-        if 'openai_api_key' not in credentials:
-            raise CredentialsValidateFailedError('Azure OpenAI API key is required')
+        if "openai_api_key" not in credentials:
+            raise CredentialsValidateFailedError("Azure OpenAI API key is required")
 
-        if 'base_model_name' not in credentials:
-            raise CredentialsValidateFailedError('Base Model Name is required')
+        if "base_model_name" not in credentials:
+            raise CredentialsValidateFailedError("Base Model Name is required")
 
-        if not self._get_ai_model_entity(credentials['base_model_name'], model):
+        if not self._get_ai_model_entity(credentials["base_model_name"], model):
             raise CredentialsValidateFailedError(f'Base Model Name {credentials["base_model_name"]} is invalid')
 
         try:
             credentials_kwargs = self._to_credential_kwargs(credentials)
             client = AzureOpenAI(**credentials_kwargs)
 
-            self._embedding_invoke(
-                model=model,
-                client=client,
-                texts=['ping'],
-                extra_model_kwargs={}
-            )
+            self._embedding_invoke(model=model, client=client, texts=["ping"], extra_model_kwargs={})
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
-        ai_model_entity = self._get_ai_model_entity(credentials['base_model_name'], model)
+        ai_model_entity = self._get_ai_model_entity(credentials["base_model_name"], model)
         return ai_model_entity.entity
 
     @staticmethod
-    def _embedding_invoke(model: str, client: AzureOpenAI, texts: Union[list[str], str],
-                          extra_model_kwargs: dict) -> tuple[list[list[float]], int]:
+    def _embedding_invoke(
+        model: str, client: AzureOpenAI, texts: Union[list[str], str], extra_model_kwargs: dict
+    ) -> tuple[list[list[float]], int]:
         response = client.embeddings.create(
             input=texts,
             model=model,
             **extra_model_kwargs,
         )
 
-        if 'encoding_format' in extra_model_kwargs and extra_model_kwargs['encoding_format'] == 'base64':
+        if "encoding_format" in extra_model_kwargs and extra_model_kwargs["encoding_format"] == "base64":
             # decode base64 embedding
-            return ([list(np.frombuffer(base64.b64decode(data.embedding), dtype="float32")) for data in response.data],
-                    response.usage.total_tokens)
+            return (
+                [list(np.frombuffer(base64.b64decode(data.embedding), dtype="float32")) for data in response.data],
+                response.usage.total_tokens,
+            )
 
         return [data.embedding for data in response.data], response.usage.total_tokens
 
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -179,7 +157,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/azure_openai/tts/tts.py b/api/core/model_runtime/model_providers/azure_openai/tts/tts.py
index f9ddd86f68..af178703a0 100644
--- a/api/core/model_runtime/model_providers/azure_openai/tts/tts.py
+++ b/api/core/model_runtime/model_providers/azure_openai/tts/tts.py
@@ -17,8 +17,9 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
     Model class for OpenAI Speech to text model.
     """
 
-    def _invoke(self, model: str, tenant_id: str, credentials: dict,
-                content_text: str, voice: str, user: Optional[str] = None) -> any:
+    def _invoke(
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ) -> any:
         """
         _invoke text2speech model
 
@@ -30,13 +31,12 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
         :param user: unique user id
         :return: text translated to audio file
         """
-        if not voice or voice not in [d['value'] for d in self.get_tts_model_voices(model=model, credentials=credentials)]:
+        if not voice or voice not in [
+            d["value"] for d in self.get_tts_model_voices(model=model, credentials=credentials)
+        ]:
             voice = self._get_model_default_voice(model, credentials)
 
-        return self._tts_invoke_streaming(model=model,
-                                          credentials=credentials,
-                                          content_text=content_text,
-                                          voice=voice)
+        return self._tts_invoke_streaming(model=model, credentials=credentials, content_text=content_text, voice=voice)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
@@ -50,14 +50,13 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
             self._tts_invoke_streaming(
                 model=model,
                 credentials=credentials,
-                content_text='Hello Dify!',
+                content_text="Hello Dify!",
                 voice=self._get_model_default_voice(model, credentials),
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _tts_invoke_streaming(self, model: str,  credentials: dict, content_text: str,
-                              voice: str) -> any:
+    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
         """
         _tts_invoke_streaming text2speech model
         :param model: model name
@@ -75,23 +74,29 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
             if len(content_text) > max_length:
                 sentences = self._split_text_into_sentences(content_text, max_length=max_length)
                 executor = concurrent.futures.ThreadPoolExecutor(max_workers=min(3, len(sentences)))
-                futures = [executor.submit(client.audio.speech.with_streaming_response.create, model=model,
-                                           response_format="mp3",
-                                           input=sentences[i], voice=voice) for i in range(len(sentences))]
-                for index, future in enumerate(futures):
-                    yield from future.result().__enter__().iter_bytes(1024)
+                futures = [
+                    executor.submit(
+                        client.audio.speech.with_streaming_response.create,
+                        model=model,
+                        response_format="mp3",
+                        input=sentences[i],
+                        voice=voice,
+                    )
+                    for i in range(len(sentences))
+                ]
+                for future in futures:
+                    yield from future.result().__enter__().iter_bytes(1024)  # noqa:PLC2801
 
             else:
-                response = client.audio.speech.with_streaming_response.create(model=model, voice=voice,
-                                                                              response_format="mp3",
-                                                                              input=content_text.strip())
+                response = client.audio.speech.with_streaming_response.create(
+                    model=model, voice=voice, response_format="mp3", input=content_text.strip()
+                )
 
-                yield from response.__enter__().iter_bytes(1024)
+                yield from response.__enter__().iter_bytes(1024)  # noqa:PLC2801
         except Exception as ex:
             raise InvokeBadRequestError(str(ex))
 
-    def _process_sentence(self, sentence: str, model: str,
-                          voice, credentials: dict):
+    def _process_sentence(self, sentence: str, model: str, voice, credentials: dict):
         """
         _tts_invoke openai text2speech model api
 
@@ -108,10 +113,9 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
             return response.read()
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
-        ai_model_entity = self._get_ai_model_entity(credentials['base_model_name'], model)
+        ai_model_entity = self._get_ai_model_entity(credentials["base_model_name"], model)
         return ai_model_entity.entity
 
-
     @staticmethod
     def _get_ai_model_entity(base_model_name: str, model: str) -> AzureBaseModel | None:
         for ai_model_entity in TTS_BASE_MODELS:
diff --git a/api/core/model_runtime/model_providers/baichuan/baichuan.py b/api/core/model_runtime/model_providers/baichuan/baichuan.py
index 71bd6b5d92..626fc811cf 100644
--- a/api/core/model_runtime/model_providers/baichuan/baichuan.py
+++ b/api/core/model_runtime/model_providers/baichuan/baichuan.py
@@ -6,6 +6,7 @@ from core.model_runtime.model_providers.__base.model_provider import ModelProvid
 
 logger = logging.getLogger(__name__)
 
+
 class BaichuanProvider(ModelProvider):
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
@@ -19,12 +20,9 @@ class BaichuanProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.LLM)
 
             # Use `baichuan2-turbo` model for validate,
-            model_instance.validate_credentials(
-                model='baichuan2-turbo',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="baichuan2-turbo", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/baichuan/llm/baichuan3-turbo-128k.yaml b/api/core/model_runtime/model_providers/baichuan/llm/baichuan3-turbo-128k.yaml
index c6c6c7e9e9..d9cd086e82 100644
--- a/api/core/model_runtime/model_providers/baichuan/llm/baichuan3-turbo-128k.yaml
+++ b/api/core/model_runtime/model_providers/baichuan/llm/baichuan3-turbo-128k.yaml
@@ -33,7 +33,7 @@ parameter_rules:
   - name: res_format
     label:
       zh_Hans: 回复格式
-      en_US: response format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/baichuan/llm/baichuan3-turbo.yaml b/api/core/model_runtime/model_providers/baichuan/llm/baichuan3-turbo.yaml
index ee8a9ff0d5..58f9b39a43 100644
--- a/api/core/model_runtime/model_providers/baichuan/llm/baichuan3-turbo.yaml
+++ b/api/core/model_runtime/model_providers/baichuan/llm/baichuan3-turbo.yaml
@@ -33,7 +33,7 @@ parameter_rules:
   - name: res_format
     label:
       zh_Hans: 回复格式
-      en_US: response format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/baichuan/llm/baichuan4.yaml b/api/core/model_runtime/model_providers/baichuan/llm/baichuan4.yaml
index e5e6aeb491..6a1135e165 100644
--- a/api/core/model_runtime/model_providers/baichuan/llm/baichuan4.yaml
+++ b/api/core/model_runtime/model_providers/baichuan/llm/baichuan4.yaml
@@ -33,7 +33,7 @@ parameter_rules:
   - name: res_format
     label:
       zh_Hans: 回复格式
-      en_US: response format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/baichuan/llm/baichuan_tokenizer.py b/api/core/model_runtime/model_providers/baichuan/llm/baichuan_tokenizer.py
index 7549b2fb60..a7ca28d49d 100644
--- a/api/core/model_runtime/model_providers/baichuan/llm/baichuan_tokenizer.py
+++ b/api/core/model_runtime/model_providers/baichuan/llm/baichuan_tokenizer.py
@@ -4,17 +4,18 @@ import re
 class BaichuanTokenizer:
     @classmethod
     def count_chinese_characters(cls, text: str) -> int:
-        return len(re.findall(r'[\u4e00-\u9fa5]', text))
+        return len(re.findall(r"[\u4e00-\u9fa5]", text))
 
     @classmethod
     def count_english_vocabularies(cls, text: str) -> int:
         # remove all non-alphanumeric characters but keep spaces and other symbols like !, ., etc.
-        text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
+        text = re.sub(r"[^a-zA-Z0-9\s]", "", text)
         # count the number of words not characters
         return len(text.split())
-    
+
     @classmethod
     def _get_num_tokens(cls, text: str) -> int:
-        # tokens = number of Chinese characters + number of English words * 1.3 (for estimation only, subject to actual return)
+        # tokens = number of Chinese characters + number of English words * 1.3
+        # (for estimation only, subject to actual return)
         # https://platform.baichuan-ai.com/docs/text-Embedding
-        return int(cls.count_chinese_characters(text) + cls.count_english_vocabularies(text) * 1.3)
\ No newline at end of file
+        return int(cls.count_chinese_characters(text) + cls.count_english_vocabularies(text) * 1.3)
diff --git a/api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo.py b/api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo.py
index a8fd9dce91..d5fda73009 100644
--- a/api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo.py
+++ b/api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo.py
@@ -7,7 +7,7 @@ from requests import post
 from core.model_runtime.entities.message_entities import PromptMessageTool
 from core.model_runtime.model_providers.baichuan.llm.baichuan_turbo_errors import (
     BadRequestError,
-    InsufficientAccountBalance,
+    InsufficientAccountBalanceError,
     InternalServerError,
     InvalidAPIKeyError,
     InvalidAuthenticationError,
@@ -45,7 +45,7 @@ class BaichuanModel:
         parameters: dict[str, Any],
         tools: Optional[list[PromptMessageTool]] = None,
     ) -> dict[str, Any]:
-        if model in self._model_mapping.keys():
+        if model in self._model_mapping:
             # the LargeLanguageModel._code_block_mode_wrapper() method will remove the response_format of parameters.
             # we need to rename it to res_format to get its value
             if parameters.get("res_format") == "json_object":
@@ -94,8 +94,7 @@ class BaichuanModel:
         timeout: int,
         tools: Optional[list[PromptMessageTool]] = None,
     ) -> Union[Iterator, dict]:
-
-        if model in self._model_mapping.keys():
+        if model in self._model_mapping:
             api_base = "https://api.baichuan-ai.com/v1/chat/completions"
         else:
             raise BadRequestError(f"Unknown model: {model}")
@@ -120,14 +119,12 @@ class BaichuanModel:
                 err = resp["error"]["type"]
                 msg = resp["error"]["message"]
             except Exception as e:
-                raise InternalServerError(
-                    f"Failed to convert response to json: {e} with text: {response.text}"
-                )
+                raise InternalServerError(f"Failed to convert response to json: {e} with text: {response.text}")
 
             if err == "invalid_api_key":
                 raise InvalidAPIKeyError(msg)
             elif err == "insufficient_quota":
-                raise InsufficientAccountBalance(msg)
+                raise InsufficientAccountBalanceError(msg)
             elif err == "invalid_authentication":
                 raise InvalidAuthenticationError(msg)
             elif err == "invalid_request_error":
diff --git a/api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo_errors.py b/api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo_errors.py
index 67d76b4a29..309b5cf413 100644
--- a/api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo_errors.py
+++ b/api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo_errors.py
@@ -1,17 +1,22 @@
 class InvalidAuthenticationError(Exception):
     pass
 
+
 class InvalidAPIKeyError(Exception):
     pass
 
+
 class RateLimitReachedError(Exception):
     pass
 
-class InsufficientAccountBalance(Exception):
+
+class InsufficientAccountBalanceError(Exception):
     pass
 
+
 class InternalServerError(Exception):
     pass
 
+
 class BadRequestError(Exception):
-    pass
\ No newline at end of file
+    pass
diff --git a/api/core/model_runtime/model_providers/baichuan/llm/llm.py b/api/core/model_runtime/model_providers/baichuan/llm/llm.py
index 4f44682e9f..91a14bf100 100644
--- a/api/core/model_runtime/model_providers/baichuan/llm/llm.py
+++ b/api/core/model_runtime/model_providers/baichuan/llm/llm.py
@@ -29,7 +29,7 @@ from core.model_runtime.model_providers.baichuan.llm.baichuan_tokenizer import B
 from core.model_runtime.model_providers.baichuan.llm.baichuan_turbo import BaichuanModel
 from core.model_runtime.model_providers.baichuan.llm.baichuan_turbo_errors import (
     BadRequestError,
-    InsufficientAccountBalance,
+    InsufficientAccountBalanceError,
     InternalServerError,
     InvalidAPIKeyError,
     InvalidAuthenticationError,
@@ -37,18 +37,17 @@ from core.model_runtime.model_providers.baichuan.llm.baichuan_turbo_errors impor
 )
 
 
-class BaichuanLarguageModel(LargeLanguageModel):
-
+class BaichuanLanguageModel(LargeLanguageModel):
     def _invoke(
-            self,
-            model: str,
-            credentials: dict,
-            prompt_messages: list[PromptMessage],
-            model_parameters: dict,
-            tools: list[PromptMessageTool] | None = None,
-            stop: list[str] | None = None,
-            stream: bool = True,
-            user: str | None = None,
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
     ) -> LLMResult | Generator:
         return self._generate(
             model=model,
@@ -60,17 +59,17 @@ class BaichuanLarguageModel(LargeLanguageModel):
         )
 
     def get_num_tokens(
-            self,
-            model: str,
-            credentials: dict,
-            prompt_messages: list[PromptMessage],
-            tools: list[PromptMessageTool] | None = None,
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool] | None = None,
     ) -> int:
         return self._num_tokens_from_messages(prompt_messages)
 
     def _num_tokens_from_messages(
-            self,
-            messages: list[PromptMessage],
+        self,
+        messages: list[PromptMessage],
     ) -> int:
         """Calculate num tokens for baichuan model"""
 
@@ -111,18 +110,13 @@ class BaichuanLarguageModel(LargeLanguageModel):
             message = cast(AssistantPromptMessage, message)
             message_dict = {"role": "assistant", "content": message.content}
             if message.tool_calls:
-                message_dict["tool_calls"] = [tool_call.dict() for tool_call in
-                                              message.tool_calls]
+                message_dict["tool_calls"] = [tool_call.dict() for tool_call in message.tool_calls]
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
             message_dict = {"role": "system", "content": message.content}
         elif isinstance(message, ToolPromptMessage):
             message = cast(ToolPromptMessage, message)
-            message_dict = {
-                "role": "tool",
-                "content": message.content,
-                "tool_call_id": message.tool_call_id
-            }
+            message_dict = {"role": "tool", "content": message.content, "tool_call_id": message.tool_call_id}
         else:
             raise ValueError(f"Unknown message type {type(message)}")
 
@@ -146,15 +140,14 @@ class BaichuanLarguageModel(LargeLanguageModel):
             raise CredentialsValidateFailedError(f"Invalid API key: {e}")
 
     def _generate(
-            self,
-            model: str,
-            credentials: dict,
-            prompt_messages: list[PromptMessage],
-            model_parameters: dict,
-            tools: list[PromptMessageTool] | None = None,
-            stream: bool = True,
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stream: bool = True,
     ) -> LLMResult | Generator:
-
         instance = BaichuanModel(api_key=credentials["api_key"])
         messages = [self._convert_prompt_message_to_dict(m) for m in prompt_messages]
 
@@ -169,23 +162,19 @@ class BaichuanLarguageModel(LargeLanguageModel):
         )
 
         if stream:
-            return self._handle_chat_generate_stream_response(
-                model, prompt_messages, credentials, response
-            )
+            return self._handle_chat_generate_stream_response(model, prompt_messages, credentials, response)
 
-        return self._handle_chat_generate_response(
-            model, prompt_messages, credentials, response
-        )
+        return self._handle_chat_generate_response(model, prompt_messages, credentials, response)
 
     def _handle_chat_generate_response(
-            self,
-            model: str,
-            prompt_messages: list[PromptMessage],
-            credentials: dict,
-            response: dict,
+        self,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        credentials: dict,
+        response: dict,
     ) -> LLMResult:
         choices = response.get("choices", [])
-        assistant_message = AssistantPromptMessage(content='', tool_calls=[])
+        assistant_message = AssistantPromptMessage(content="", tool_calls=[])
         if choices and choices[0]["finish_reason"] == "tool_calls":
             for choice in choices:
                 for tool_call in choice["message"]["tool_calls"]:
@@ -194,7 +183,7 @@ class BaichuanLarguageModel(LargeLanguageModel):
                         type=tool_call.get("type", ""),
                         function=AssistantPromptMessage.ToolCall.ToolCallFunction(
                             name=tool_call.get("function", {}).get("name", ""),
-                            arguments=tool_call.get("function", {}).get("arguments", "")
+                            arguments=tool_call.get("function", {}).get("arguments", ""),
                         ),
                     )
                     assistant_message.tool_calls.append(tool)
@@ -228,11 +217,11 @@ class BaichuanLarguageModel(LargeLanguageModel):
         )
 
     def _handle_chat_generate_stream_response(
-            self,
-            model: str,
-            prompt_messages: list[PromptMessage],
-            credentials: dict,
-            response: Iterator,
+        self,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        credentials: dict,
+        response: Iterator,
     ) -> Generator:
         for line in response:
             if not line:
@@ -260,9 +249,7 @@ class BaichuanLarguageModel(LargeLanguageModel):
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=0,
-                        message=AssistantPromptMessage(
-                            content=choice["delta"]["content"], tool_calls=[]
-                        ),
+                        message=AssistantPromptMessage(content=choice["delta"]["content"], tool_calls=[]),
                         finish_reason=stop_reason,
                     ),
                 )
@@ -302,7 +289,7 @@ class BaichuanLarguageModel(LargeLanguageModel):
             InvokeRateLimitError: [RateLimitReachedError],
             InvokeAuthorizationError: [
                 InvalidAuthenticationError,
-                InsufficientAccountBalance,
+                InsufficientAccountBalanceError,
                 InvalidAPIKeyError,
             ],
             InvokeBadRequestError: [BadRequestError, KeyError],
diff --git a/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py
index 5ae90d54b5..779dfbb608 100644
--- a/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py
@@ -19,7 +19,7 @@ from core.model_runtime.model_providers.__base.text_embedding_model import TextE
 from core.model_runtime.model_providers.baichuan.llm.baichuan_tokenizer import BaichuanTokenizer
 from core.model_runtime.model_providers.baichuan.llm.baichuan_turbo_errors import (
     BadRequestError,
-    InsufficientAccountBalance,
+    InsufficientAccountBalanceError,
     InternalServerError,
     InvalidAPIKeyError,
     InvalidAuthenticationError,
@@ -31,11 +31,12 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
     """
     Model class for BaiChuan text embedding model.
     """
-    api_base: str = 'http://api.baichuan-ai.com/v1/embeddings'
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    api_base: str = "http://api.baichuan-ai.com/v1/embeddings"
+
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -45,28 +46,23 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
-        api_key = credentials['api_key']
-        if model != 'baichuan-text-embedding':
-            raise ValueError('Invalid model name')
+        api_key = credentials["api_key"]
+        if model != "baichuan-text-embedding":
+            raise ValueError("Invalid model name")
         if not api_key:
-            raise CredentialsValidateFailedError('api_key is required')
-        
+            raise CredentialsValidateFailedError("api_key is required")
+
         # split into chunks of batch size 16
         chunks = []
         for i in range(0, len(texts), 16):
-            chunks.append(texts[i:i + 16])
+            chunks.append(texts[i : i + 16])
 
         embeddings = []
         token_usage = 0
 
         for chunk in chunks:
-            # embeding chunk
-            chunk_embeddings, chunk_usage = self.embedding(
-                model=model,
-                api_key=api_key,
-                texts=chunk,
-                user=user
-            )
+            # embedding chunk
+            chunk_embeddings, chunk_usage = self.embedding(model=model, api_key=api_key, texts=chunk, user=user)
 
             embeddings.extend(chunk_embeddings)
             token_usage += chunk_usage
@@ -74,17 +70,14 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
         result = TextEmbeddingResult(
             model=model,
             embeddings=embeddings,
-            usage=self._calc_response_usage(
-                model=model,
-                credentials=credentials,
-                tokens=token_usage
-            )
+            usage=self._calc_response_usage(model=model, credentials=credentials, tokens=token_usage),
         )
 
         return result
-    
-    def embedding(self, model: str, api_key, texts: list[str], user: Optional[str] = None) \
-            -> tuple[list[list[float]], int]:
+
+    def embedding(
+        self, model: str, api_key, texts: list[str], user: Optional[str] = None
+    ) -> tuple[list[list[float]], int]:
         """
         Embed given texts
 
@@ -95,56 +88,47 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
         :return: embeddings result
         """
         url = self.api_base
-        headers = {
-            'Authorization': 'Bearer ' + api_key,
-            'Content-Type': 'application/json'
-        }
+        headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
 
-        data = {
-            'model': 'Baichuan-Text-Embedding',
-            'input': texts
-        }
+        data = {"model": "Baichuan-Text-Embedding", "input": texts}
 
         try:
             response = post(url, headers=headers, data=dumps(data))
         except Exception as e:
             raise InvokeConnectionError(str(e))
-        
+
         if response.status_code != 200:
             try:
                 resp = response.json()
                 # try to parse error message
-                err = resp['error']['code']
-                msg = resp['error']['message']
+                err = resp["error"]["code"]
+                msg = resp["error"]["message"]
             except Exception as e:
                 raise InternalServerError(f"Failed to convert response to json: {e} with text: {response.text}")
 
-            if err == 'invalid_api_key':
+            if err == "invalid_api_key":
                 raise InvalidAPIKeyError(msg)
-            elif err == 'insufficient_quota':
-                raise InsufficientAccountBalance(msg)
-            elif err == 'invalid_authentication':
-                raise InvalidAuthenticationError(msg) 
-            elif err and 'rate' in err:
+            elif err == "insufficient_quota":
+                raise InsufficientAccountBalanceError(msg)
+            elif err == "invalid_authentication":
+                raise InvalidAuthenticationError(msg)
+            elif err and "rate" in err:
                 raise RateLimitReachedError(msg)
-            elif err and 'internal' in err:
+            elif err and "internal" in err:
                 raise InternalServerError(msg)
-            elif err == 'api_key_empty':
+            elif err == "api_key_empty":
                 raise InvalidAPIKeyError(msg)
             else:
                 raise InternalServerError(f"Unknown error: {err} with message: {msg}")
-            
+
         try:
             resp = response.json()
-            embeddings = resp['data']
-            usage = resp['usage']
+            embeddings = resp["data"]
+            usage = resp["usage"]
         except Exception as e:
             raise InternalServerError(f"Failed to convert response to json: {e} with text: {response.text}")
 
-        return [
-            data['embedding'] for data in embeddings
-        ], usage['total_tokens']
-
+        return [data["embedding"] for data in embeddings], usage["total_tokens"]
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
@@ -170,32 +154,24 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         try:
-            self._invoke(model=model, credentials=credentials, texts=['ping'])
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
         except InvalidAPIKeyError:
-            raise CredentialsValidateFailedError('Invalid api key')
+            raise CredentialsValidateFailedError("Invalid api key")
 
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         return {
-            InvokeConnectionError: [
-            ],
-            InvokeServerUnavailableError: [
-                InternalServerError
-            ],
-            InvokeRateLimitError: [
-                RateLimitReachedError
-            ],
+            InvokeConnectionError: [],
+            InvokeServerUnavailableError: [InternalServerError],
+            InvokeRateLimitError: [RateLimitReachedError],
             InvokeAuthorizationError: [
                 InvalidAuthenticationError,
-                InsufficientAccountBalance,
+                InsufficientAccountBalanceError,
                 InvalidAPIKeyError,
             ],
-            InvokeBadRequestError: [
-                BadRequestError,
-                KeyError
-            ]
+            InvokeBadRequestError: [BadRequestError, KeyError],
         }
-    
+
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -207,10 +183,7 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -221,7 +194,7 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/bedrock/bedrock.py b/api/core/model_runtime/model_providers/bedrock/bedrock.py
index e99bc52ff8..1cfc1d199c 100644
--- a/api/core/model_runtime/model_providers/bedrock/bedrock.py
+++ b/api/core/model_runtime/model_providers/bedrock/bedrock.py
@@ -6,6 +6,7 @@ from core.model_runtime.model_providers.__base.model_provider import ModelProvid
 
 logger = logging.getLogger(__name__)
 
+
 class BedrockProvider(ModelProvider):
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
@@ -19,13 +20,10 @@ class BedrockProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.LLM)
 
             # Use `amazon.titan-text-lite-v1` model by default for validating credentials
-            model_for_validation = credentials.get('model_for_validation', 'amazon.titan-text-lite-v1')
-            model_instance.validate_credentials(
-                model=model_for_validation,
-                credentials=credentials
-            )
+            model_for_validation = credentials.get("model_for_validation", "amazon.titan-text-lite-v1")
+            model_instance.validate_credentials(model=model_for_validation, credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-haiku-v1.yaml b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-haiku-v1.yaml
index 53657c08a9..c2d5eb6471 100644
--- a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-haiku-v1.yaml
+++ b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-haiku-v1.yaml
@@ -52,6 +52,8 @@ parameter_rules:
     help:
       zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
       en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.00025'
   output: '0.00125'
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-opus-v1.yaml b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-opus-v1.yaml
index d083d31e30..f90fa04266 100644
--- a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-opus-v1.yaml
+++ b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-opus-v1.yaml
@@ -52,6 +52,8 @@ parameter_rules:
     help:
       zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
       en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.015'
   output: '0.075'
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-sonnet-v1.5.yaml b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-sonnet-v1.5.yaml
index 5302231086..dad0d6b6b6 100644
--- a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-sonnet-v1.5.yaml
+++ b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-sonnet-v1.5.yaml
@@ -51,6 +51,8 @@ parameter_rules:
     help:
       zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
       en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.003'
   output: '0.015'
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-sonnet-v1.yaml b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-sonnet-v1.yaml
index 6995d2bf56..962def8011 100644
--- a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-sonnet-v1.yaml
+++ b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-sonnet-v1.yaml
@@ -51,6 +51,8 @@ parameter_rules:
     help:
       zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
       en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.003'
   output: '0.015'
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v2.1.yaml b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v2.1.yaml
index 1a3239c85e..70294e4ad3 100644
--- a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v2.1.yaml
+++ b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v2.1.yaml
@@ -45,6 +45,8 @@ parameter_rules:
     help:
       zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
       en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.008'
   output: '0.024'
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v2.yaml b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v2.yaml
index 0343e3bbec..0a8ea61b6d 100644
--- a/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v2.yaml
+++ b/api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v2.yaml
@@ -45,6 +45,8 @@ parameter_rules:
     help:
       zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
       en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.008'
   output: '0.024'
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/eu.anthropic.claude-3-haiku-v1.yaml b/api/core/model_runtime/model_providers/bedrock/llm/eu.anthropic.claude-3-haiku-v1.yaml
new file mode 100644
index 0000000000..24a65ef1bb
--- /dev/null
+++ b/api/core/model_runtime/model_providers/bedrock/llm/eu.anthropic.claude-3-haiku-v1.yaml
@@ -0,0 +1,59 @@
+model: eu.anthropic.claude-3-haiku-20240307-v1:0
+label:
+  en_US: Claude 3 Haiku(EU.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 200000
+# docs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    type: int
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
+      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
+  # docs: https://docs.anthropic.com/claude/docs/system-prompts
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.00025'
+  output: '0.00125'
+  unit: '0.001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/eu.anthropic.claude-3-sonnet-v1.5.yaml b/api/core/model_runtime/model_providers/bedrock/llm/eu.anthropic.claude-3-sonnet-v1.5.yaml
new file mode 100644
index 0000000000..e3d25c7d8f
--- /dev/null
+++ b/api/core/model_runtime/model_providers/bedrock/llm/eu.anthropic.claude-3-sonnet-v1.5.yaml
@@ -0,0 +1,58 @@
+model: eu.anthropic.claude-3-5-sonnet-20240620-v1:0
+label:
+  en_US: Claude 3.5 Sonnet(EU.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 200000
+# docs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    type: int
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
+      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.003'
+  output: '0.015'
+  unit: '0.001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/eu.anthropic.claude-3-sonnet-v1.yaml b/api/core/model_runtime/model_providers/bedrock/llm/eu.anthropic.claude-3-sonnet-v1.yaml
new file mode 100644
index 0000000000..9a06a4ad6d
--- /dev/null
+++ b/api/core/model_runtime/model_providers/bedrock/llm/eu.anthropic.claude-3-sonnet-v1.yaml
@@ -0,0 +1,58 @@
+model: eu.anthropic.claude-3-sonnet-20240229-v1:0
+label:
+  en_US: Claude 3 Sonnet(EU.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 200000
+# docs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    type: int
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
+      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.003'
+  output: '0.015'
+  unit: '0.001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/llm.py b/api/core/model_runtime/model_providers/bedrock/llm/llm.py
index 3f7266f600..77bab0c294 100644
--- a/api/core/model_runtime/model_providers/bedrock/llm/llm.py
+++ b/api/core/model_runtime/model_providers/bedrock/llm/llm.py
@@ -1,8 +1,8 @@
 # standard import
 import base64
-import io
 import json
 import logging
+import mimetypes
 from collections.abc import Generator
 from typing import Optional, Union, cast
 
@@ -17,9 +17,9 @@ from botocore.exceptions import (
     ServiceNotInRegionError,
     UnknownServiceError,
 )
-from PIL.Image import Image
 
 # local import
+from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -44,37 +44,87 @@ from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 
 logger = logging.getLogger(__name__)
+ANTHROPIC_BLOCK_MODE_PROMPT = """You should always follow the instructions and output a valid {{block}} object.
+The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
+if you are not sure about the structure.
+
+<instructions>
+{{instructions}}
+</instructions>
+"""  # noqa: E501
+
 
 class BedrockLargeLanguageModel(LargeLanguageModel):
-
     # please refer to the documentation: https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html
     # TODO There is invoke issue: context limit on Cohere Model, will add them after fixed.
-    CONVERSE_API_ENABLED_MODEL_INFO=[
-        {'prefix': 'anthropic.claude-v2', 'support_system_prompts': True, 'support_tool_use': False},
-        {'prefix': 'anthropic.claude-v1', 'support_system_prompts': True, 'support_tool_use': False},
-        {'prefix': 'anthropic.claude-3', 'support_system_prompts': True, 'support_tool_use': True},
-        {'prefix': 'meta.llama', 'support_system_prompts': True, 'support_tool_use': False},
-        {'prefix': 'mistral.mistral-7b-instruct', 'support_system_prompts': False, 'support_tool_use': False},
-        {'prefix': 'mistral.mixtral-8x7b-instruct', 'support_system_prompts': False, 'support_tool_use': False},
-        {'prefix': 'mistral.mistral-large', 'support_system_prompts': True, 'support_tool_use': True},
-        {'prefix': 'mistral.mistral-small', 'support_system_prompts': True, 'support_tool_use': True},
-        {'prefix': 'cohere.command-r', 'support_system_prompts': True, 'support_tool_use': True},
-        {'prefix': 'amazon.titan', 'support_system_prompts': False, 'support_tool_use': False}
+    CONVERSE_API_ENABLED_MODEL_INFO = [
+        {"prefix": "anthropic.claude-v2", "support_system_prompts": True, "support_tool_use": False},
+        {"prefix": "anthropic.claude-v1", "support_system_prompts": True, "support_tool_use": False},
+        {"prefix": "us.anthropic.claude-3", "support_system_prompts": True, "support_tool_use": True},
+        {"prefix": "eu.anthropic.claude-3", "support_system_prompts": True, "support_tool_use": True},
+        {"prefix": "anthropic.claude-3", "support_system_prompts": True, "support_tool_use": True},
+        {"prefix": "meta.llama", "support_system_prompts": True, "support_tool_use": False},
+        {"prefix": "mistral.mistral-7b-instruct", "support_system_prompts": False, "support_tool_use": False},
+        {"prefix": "mistral.mixtral-8x7b-instruct", "support_system_prompts": False, "support_tool_use": False},
+        {"prefix": "mistral.mistral-large", "support_system_prompts": True, "support_tool_use": True},
+        {"prefix": "mistral.mistral-small", "support_system_prompts": True, "support_tool_use": True},
+        {"prefix": "cohere.command-r", "support_system_prompts": True, "support_tool_use": True},
+        {"prefix": "amazon.titan", "support_system_prompts": False, "support_tool_use": False},
     ]
 
     @staticmethod
     def _find_model_info(model_id):
         for model in BedrockLargeLanguageModel.CONVERSE_API_ENABLED_MODEL_INFO:
-            if model_id.startswith(model['prefix']):
+            if model_id.startswith(model["prefix"]):
                 return model
         logger.info(f"current model id: {model_id} did not support by Converse API")
         return None
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _code_block_mode_wrapper(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: list[Callback] = None,
+    ) -> Union[LLMResult, Generator]:
+        """
+        Code block mode wrapper for invoking large language model
+        """
+        if model_parameters.get("response_format"):
+            stop = stop or []
+            if "```\n" not in stop:
+                stop.append("```\n")
+            if "\n```" not in stop:
+                stop.append("\n```")
+            response_format = model_parameters.pop("response_format")
+            format_prompt = SystemPromptMessage(
+                content=ANTHROPIC_BLOCK_MODE_PROMPT.replace("{{instructions}}", prompt_messages[0].content).replace(
+                    "{{block}}", response_format
+                )
+            )
+            if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
+                prompt_messages[0] = format_prompt
+            else:
+                prompt_messages.insert(0, format_prompt)
+            prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
+        return self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -88,17 +138,28 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :param user: unique user id
         :return: full response or stream response chunk generator result
         """
-        
-        model_info= BedrockLargeLanguageModel._find_model_info(model)
+
+        model_info = BedrockLargeLanguageModel._find_model_info(model)
         if model_info:
-            model_info['model'] = model
+            model_info["model"] = model
             # invoke models via boto3 converse API
-            return self._generate_with_converse(model_info, credentials, prompt_messages, model_parameters, stop, stream, user, tools)
+            return self._generate_with_converse(
+                model_info, credentials, prompt_messages, model_parameters, stop, stream, user, tools
+            )
         # invoke other models via boto3 client
         return self._generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
 
-    def _generate_with_converse(self, model_info: dict, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
-                stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None, tools: Optional[list[PromptMessageTool]] = None,) -> Union[LLMResult, Generator]:
+    def _generate_with_converse(
+        self,
+        model_info: dict,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model with converse API
 
@@ -110,35 +171,39 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :param stream: is stream response
         :return: full response or stream response chunk generator result
         """
-        bedrock_client = boto3.client(service_name='bedrock-runtime',
-                                      aws_access_key_id=credentials.get("aws_access_key_id"),
-                                      aws_secret_access_key=credentials.get("aws_secret_access_key"),
-                                      region_name=credentials["aws_region"])
+        bedrock_client = boto3.client(
+            service_name="bedrock-runtime",
+            aws_access_key_id=credentials.get("aws_access_key_id"),
+            aws_secret_access_key=credentials.get("aws_secret_access_key"),
+            region_name=credentials["aws_region"],
+        )
 
         system, prompt_message_dicts = self._convert_converse_prompt_messages(prompt_messages)
         inference_config, additional_model_fields = self._convert_converse_api_model_parameters(model_parameters, stop)
 
         parameters = {
-            'modelId': model_info['model'],
-            'messages': prompt_message_dicts,
-            'inferenceConfig': inference_config,
-            'additionalModelRequestFields': additional_model_fields,
+            "modelId": model_info["model"],
+            "messages": prompt_message_dicts,
+            "inferenceConfig": inference_config,
+            "additionalModelRequestFields": additional_model_fields,
         }
 
-        if model_info['support_system_prompts'] and system and len(system) > 0:
-            parameters['system'] = system
+        if model_info["support_system_prompts"] and system and len(system) > 0:
+            parameters["system"] = system
 
-        if model_info['support_tool_use'] and tools:
-            parameters['toolConfig'] = self._convert_converse_tool_config(tools=tools)
+        if model_info["support_tool_use"] and tools:
+            parameters["toolConfig"] = self._convert_converse_tool_config(tools=tools)
         try:
             if stream:
                 response = bedrock_client.converse_stream(**parameters)
-                return self._handle_converse_stream_response(model_info['model'], credentials, response, prompt_messages)
+                return self._handle_converse_stream_response(
+                    model_info["model"], credentials, response, prompt_messages
+                )
             else:
                 response = bedrock_client.converse(**parameters)
-                return self._handle_converse_response(model_info['model'], credentials, response, prompt_messages)
+                return self._handle_converse_response(model_info["model"], credentials, response, prompt_messages)
         except ClientError as ex:
-            error_code = ex.response['Error']['Code']
+            error_code = ex.response["Error"]["Code"]
             full_error_msg = f"{error_code}: {ex.response['Error']['Message']}"
             raise self._map_client_to_invoke_error(error_code, full_error_msg)
         except (EndpointConnectionError, NoRegionError, ServiceNotInRegionError) as ex:
@@ -149,8 +214,10 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
 
         except Exception as ex:
             raise InvokeError(str(ex))
-    def _handle_converse_response(self, model: str, credentials: dict, response: dict,
-                                prompt_messages: list[PromptMessage]) -> LLMResult:
+
+    def _handle_converse_response(
+        self, model: str, credentials: dict, response: dict, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
         """
         Handle llm chat response
 
@@ -160,36 +227,30 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: full response chunk generator result
         """
-        response_content = response['output']['message']['content']
+        response_content = response["output"]["message"]["content"]
         # transform assistant message to prompt message
-        if response['stopReason'] == 'tool_use':
+        if response["stopReason"] == "tool_use":
             tool_calls = []
             text, tool_use = self._extract_tool_use(response_content)
 
             tool_call = AssistantPromptMessage.ToolCall(
-                id=tool_use['toolUseId'],
-                type='function',
+                id=tool_use["toolUseId"],
+                type="function",
                 function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                    name=tool_use['name'],
-                    arguments=json.dumps(tool_use['input'])
-                )
+                    name=tool_use["name"], arguments=json.dumps(tool_use["input"])
+                ),
             )
             tool_calls.append(tool_call)
 
-            assistant_prompt_message = AssistantPromptMessage(
-                content=text,
-                tool_calls=tool_calls
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=text, tool_calls=tool_calls)
         else:
-            assistant_prompt_message = AssistantPromptMessage(
-                content=response_content[0]['text']
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=response_content[0]["text"])
 
         # calculate num tokens
-        if response['usage']:
+        if response["usage"]:
             # transform usage
-            prompt_tokens = response['usage']['inputTokens']
-            completion_tokens = response['usage']['outputTokens']
+            prompt_tokens = response["usage"]["inputTokens"]
+            completion_tokens = response["usage"]["outputTokens"]
         else:
             # calculate num tokens
             prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
@@ -206,20 +267,25 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         )
         return result
 
-    def _extract_tool_use(self, content:dict)-> tuple[str, dict]:
+    def _extract_tool_use(self, content: dict) -> tuple[str, dict]:
         tool_use = {}
-        text = ''
+        text = ""
         for item in content:
-            if 'toolUse' in item:
-                tool_use = item['toolUse']
-            elif 'text' in item:
-                text = item['text']
+            if "toolUse" in item:
+                tool_use = item["toolUse"]
+            elif "text" in item:
+                text = item["text"]
             else:
                 raise ValueError(f"Got unknown item: {item}")
         return text, tool_use
 
-    def _handle_converse_stream_response(self, model: str, credentials: dict, response: dict,
-                                        prompt_messages: list[PromptMessage], ) -> Generator:
+    def _handle_converse_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: dict,
+        prompt_messages: list[PromptMessage],
+    ) -> Generator:
         """
         Handle llm chat stream response
 
@@ -231,7 +297,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         """
 
         try:
-            full_assistant_content = ''
+            full_assistant_content = ""
             return_model = None
             input_tokens = 0
             output_tokens = 0
@@ -240,87 +306,85 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
             tool_calls: list[AssistantPromptMessage.ToolCall] = []
             tool_use = {}
 
-            for chunk in response['stream']:
-                if 'messageStart' in chunk:
+            for chunk in response["stream"]:
+                if "messageStart" in chunk:
                     return_model = model
-                elif 'messageStop' in chunk:
-                    finish_reason = chunk['messageStop']['stopReason']
-                elif 'contentBlockStart' in chunk:
-                    tool = chunk['contentBlockStart']['start']['toolUse']
-                    tool_use['toolUseId'] = tool['toolUseId']
-                    tool_use['name'] = tool['name']
-                elif 'metadata' in chunk:
-                    input_tokens = chunk['metadata']['usage']['inputTokens']
-                    output_tokens = chunk['metadata']['usage']['outputTokens']
+                elif "messageStop" in chunk:
+                    finish_reason = chunk["messageStop"]["stopReason"]
+                elif "contentBlockStart" in chunk:
+                    tool = chunk["contentBlockStart"]["start"]["toolUse"]
+                    tool_use["toolUseId"] = tool["toolUseId"]
+                    tool_use["name"] = tool["name"]
+                elif "metadata" in chunk:
+                    input_tokens = chunk["metadata"]["usage"]["inputTokens"]
+                    output_tokens = chunk["metadata"]["usage"]["outputTokens"]
                     usage = self._calc_response_usage(model, credentials, input_tokens, output_tokens)
                     yield LLMResultChunk(
                         model=return_model,
                         prompt_messages=prompt_messages,
                         delta=LLMResultChunkDelta(
                             index=index,
-                            message=AssistantPromptMessage(
-                                content='',
-                                tool_calls=tool_calls
-                            ),
+                            message=AssistantPromptMessage(content="", tool_calls=tool_calls),
                             finish_reason=finish_reason,
-                            usage=usage
-                        )
+                            usage=usage,
+                        ),
                     )
-                elif 'contentBlockDelta' in chunk:
-                    delta = chunk['contentBlockDelta']['delta']
-                    if 'text' in delta:
-                        chunk_text = delta['text'] if delta['text'] else ''
+                elif "contentBlockDelta" in chunk:
+                    delta = chunk["contentBlockDelta"]["delta"]
+                    if "text" in delta:
+                        chunk_text = delta["text"] or ""
                         full_assistant_content += chunk_text
                         assistant_prompt_message = AssistantPromptMessage(
-                            content=chunk_text if chunk_text else '',
+                            content=chunk_text or "",
                         )
-                        index = chunk['contentBlockDelta']['contentBlockIndex']
+                        index = chunk["contentBlockDelta"]["contentBlockIndex"]
                         yield LLMResultChunk(
                             model=model,
                             prompt_messages=prompt_messages,
                             delta=LLMResultChunkDelta(
-                                index=index+1,
+                                index=index + 1,
                                 message=assistant_prompt_message,
-                            )
+                            ),
                         )
-                    elif 'toolUse' in delta:
-                        if 'input' not in tool_use:
-                            tool_use['input'] = ''
-                        tool_use['input'] += delta['toolUse']['input']
-                elif 'contentBlockStop' in chunk:
-                    if 'input' in tool_use:
+                    elif "toolUse" in delta:
+                        if "input" not in tool_use:
+                            tool_use["input"] = ""
+                        tool_use["input"] += delta["toolUse"]["input"]
+                elif "contentBlockStop" in chunk:
+                    if "input" in tool_use:
                         tool_call = AssistantPromptMessage.ToolCall(
-                            id=tool_use['toolUseId'],
-                            type='function',
+                            id=tool_use["toolUseId"],
+                            type="function",
                             function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                                name=tool_use['name'],
-                                arguments=tool_use['input']
-                            )
+                                name=tool_use["name"], arguments=tool_use["input"]
+                            ),
                         )
                         tool_calls.append(tool_call)
                         tool_use = {}
 
         except Exception as ex:
             raise InvokeError(str(ex))
-    
-    def _convert_converse_api_model_parameters(self, model_parameters: dict, stop: Optional[list[str]] = None) -> tuple[dict, dict]:
+
+    def _convert_converse_api_model_parameters(
+        self, model_parameters: dict, stop: Optional[list[str]] = None
+    ) -> tuple[dict, dict]:
         inference_config = {}
         additional_model_fields = {}
-        if 'max_tokens' in model_parameters:
-            inference_config['maxTokens'] = model_parameters['max_tokens']
+        if "max_tokens" in model_parameters:
+            inference_config["maxTokens"] = model_parameters["max_tokens"]
 
-        if 'temperature' in model_parameters:
-            inference_config['temperature'] = model_parameters['temperature']
-        
-        if 'top_p' in model_parameters:
-            inference_config['topP'] = model_parameters['temperature']
+        if "temperature" in model_parameters:
+            inference_config["temperature"] = model_parameters["temperature"]
+
+        if "top_p" in model_parameters:
+            inference_config["topP"] = model_parameters["temperature"]
 
         if stop:
-            inference_config['stopSequences'] = stop
-        
-        if 'top_k' in model_parameters:
-            additional_model_fields['top_k'] = model_parameters['top_k']
-        
+            inference_config["stopSequences"] = stop
+
+        if "top_k" in model_parameters:
+            additional_model_fields["top_k"] = model_parameters["top_k"]
+
         return inference_config, additional_model_fields
 
     def _convert_converse_prompt_messages(self, prompt_messages: list[PromptMessage]) -> tuple[str, list[dict]]:
@@ -332,7 +396,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         prompt_message_dicts = []
         for message in prompt_messages:
             if isinstance(message, SystemPromptMessage):
-                message.content=message.content.strip()
+                message.content = message.content.strip()
                 system.append({"text": message.content})
             else:
                 prompt_message_dicts.append(self._convert_prompt_message_to_dict(message))
@@ -349,15 +413,13 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                         "toolSpec": {
                             "name": tool.name,
                             "description": tool.description,
-                            "inputSchema": {
-                                "json": tool.parameters
-                            }
+                            "inputSchema": {"json": tool.parameters},
                         }
                     }
                 )
             tool_config["tools"] = configs
             return tool_config
-    
+
     def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict:
         """
         Convert PromptMessage to dict
@@ -365,15 +427,13 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         if isinstance(message, UserPromptMessage):
             message = cast(UserPromptMessage, message)
             if isinstance(message.content, str):
-                message_dict = {"role": "user", "content": [{'text': message.content}]}
+                message_dict = {"role": "user", "content": [{"text": message.content}]}
             else:
                 sub_messages = []
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
                         message_content = cast(TextPromptMessageContent, message_content)
-                        sub_message_dict = {
-                            "text": message_content.data
-                        }
+                        sub_message_dict = {"text": message_content.data}
                         sub_messages.append(sub_message_dict)
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
@@ -382,9 +442,10 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                             try:
                                 url = message_content.data
                                 image_content = requests.get(url).content
-                                with Image.open(io.BytesIO(image_content)) as img:
-                                    mime_type = f"image/{img.format.lower()}"
-                                base64_data = base64.b64encode(image_content).decode('utf-8')
+                                if "?" in url:
+                                    url = url.split("?")[0]
+                                mime_type, _ = mimetypes.guess_type(url)
+                                base64_data = base64.b64encode(image_content).decode("utf-8")
                             except Exception as ex:
                                 raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")
                         else:
@@ -393,17 +454,14 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                             base64_data = data_split[1]
                             image_content = base64.b64decode(base64_data)
 
-                        if mime_type not in ["image/jpeg", "image/png", "image/gif", "image/webp"]:
-                            raise ValueError(f"Unsupported image type {mime_type}, "
-                                             f"only support image/jpeg, image/png, image/gif, and image/webp")
+                        if mime_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}:
+                            raise ValueError(
+                                f"Unsupported image type {mime_type}, "
+                                f"only support image/jpeg, image/png, image/gif, and image/webp"
+                            )
 
                         sub_message_dict = {
-                            "image": {
-                                "format": mime_type.replace('image/', ''),
-                                "source": {
-                                    "bytes": image_content
-                                }
-                            }
+                            "image": {"format": mime_type.replace("image/", ""), "source": {"bytes": image_content}}
                         }
                         sub_messages.append(sub_message_dict)
 
@@ -412,36 +470,46 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
             message = cast(AssistantPromptMessage, message)
             if message.tool_calls:
                 message_dict = {
-                    "role": "assistant", "content":[{
-                        "toolUse": {
-                            "toolUseId": message.tool_calls[0].id,
-                            "name": message.tool_calls[0].function.name,
-                            "input": json.loads(message.tool_calls[0].function.arguments)
+                    "role": "assistant",
+                    "content": [
+                        {
+                            "toolUse": {
+                                "toolUseId": message.tool_calls[0].id,
+                                "name": message.tool_calls[0].function.name,
+                                "input": json.loads(message.tool_calls[0].function.arguments),
+                            }
                         }
-                    }]
+                    ],
                 }
             else:
-                message_dict = {"role": "assistant", "content": [{'text': message.content}]}
+                message_dict = {"role": "assistant", "content": [{"text": message.content}]}
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
-            message_dict = [{'text': message.content}]
+            message_dict = [{"text": message.content}]
         elif isinstance(message, ToolPromptMessage):
             message = cast(ToolPromptMessage, message)
             message_dict = {
                 "role": "user",
-                "content": [{
-                    "toolResult": {
-                        "toolUseId": message.tool_call_id,
-                        "content": [{"json": {"text": message.content}}]
-                    }                   
-                }]
+                "content": [
+                    {
+                        "toolResult": {
+                            "toolUseId": message.tool_call_id,
+                            "content": [{"json": {"text": message.content}}],
+                        }
+                    }
+                ],
             }
         else:
             raise ValueError(f"Got unknown type {message}")
         return message_dict
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage] | str,
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage] | str,
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -451,15 +519,14 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :param tools: tools for tool calling
         :return:md = genai.GenerativeModel(model)
         """
-        prefix = model.split('.')[0]
-        model_name = model.split('.')[1]
-        
+        prefix = model.split(".")[0]
+        model_name = model.split(".")[1]
+
         if isinstance(prompt_messages, str):
             prompt = prompt_messages
         else:
             prompt = self._convert_messages_to_prompt(prompt_messages, prefix, model_name)
 
-
         return self._get_num_tokens_by_gpt2(prompt)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
@@ -476,30 +543,36 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                 "max_tokens": 32,
             }
         elif "ai21" in model:
-            # ValidationException: Malformed input request: #/temperature: expected type: Number, found: Null#/maxTokens: expected type: Integer, found: Null#/topP: expected type: Number, found: Null, please reformat your input and try again.
+            # ValidationException: Malformed input request: #/temperature: expected type: Number,
+            # found: Null#/maxTokens: expected type: Integer, found: Null#/topP: expected type: Number, found: Null,
+            # please reformat your input and try again.
             required_params = {
                 "temperature": 0.7,
                 "topP": 0.9,
                 "maxTokens": 32,
             }
-            
+
         try:
             ping_message = UserPromptMessage(content="ping")
-            self._invoke(model=model,
-                           credentials=credentials,
-                           prompt_messages=[ping_message],
-                           model_parameters=required_params,
-                           stream=False)
-        
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                prompt_messages=[ping_message],
+                model_parameters=required_params,
+                stream=False,
+            )
+
         except ClientError as ex:
-            error_code = ex.response['Error']['Code']
+            error_code = ex.response["Error"]["Code"]
             full_error_msg = f"{error_code}: {ex.response['Error']['Message']}"
             raise CredentialsValidateFailedError(str(self._map_client_to_invoke_error(error_code, full_error_msg)))
 
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _convert_one_message_to_text(self, message: PromptMessage, model_prefix: str, model_name: Optional[str] = None) -> str:
+    def _convert_one_message_to_text(
+        self, message: PromptMessage, model_prefix: str, model_name: Optional[str] = None
+    ) -> str:
         """
         Convert a single message to a string.
 
@@ -514,7 +587,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
 
         if isinstance(message, UserPromptMessage):
             body = content
-            if (isinstance(content, list)):
+            if isinstance(content, list):
                 body = "".join([c.data for c in content if c.type == PromptMessageContentType.TEXT])
             message_text = f"{human_prompt_prefix} {body} {human_prompt_postfix}"
         elif isinstance(message, AssistantPromptMessage):
@@ -528,7 +601,9 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
 
         return message_text
 
-    def _convert_messages_to_prompt(self, messages: list[PromptMessage], model_prefix: str, model_name: Optional[str] = None) -> str:
+    def _convert_messages_to_prompt(
+        self, messages: list[PromptMessage], model_prefix: str, model_name: Optional[str] = None
+    ) -> str:
         """
         Format a list of messages into a full prompt for the Anthropic, Amazon and Llama models
 
@@ -537,27 +612,31 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :return: Combined string with necessary human_prompt and ai_prompt tags.
         """
         if not messages:
-            return ''
+            return ""
 
         messages = messages.copy()  # don't mutate the original list
         if not isinstance(messages[-1], AssistantPromptMessage):
             messages.append(AssistantPromptMessage(content=""))
 
-        text = "".join(
-            self._convert_one_message_to_text(message, model_prefix, model_name)
-            for message in messages
-        )
+        text = "".join(self._convert_one_message_to_text(message, model_prefix, model_name) for message in messages)
 
         # trim off the trailing ' ' that might come from the "Assistant: "
         return text.rstrip()
 
-    def _create_payload(self, model: str, prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[list[str]] = None, stream: bool = True):
+    def _create_payload(
+        self,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+    ):
         """
         Create payload for bedrock api call depending on model provider
         """
         payload = {}
-        model_prefix = model.split('.')[0]
-        model_name = model.split('.')[1]
+        model_prefix = model.split(".")[0]
+        model_name = model.split(".")[1]
 
         if model_prefix == "ai21":
             payload["temperature"] = model_parameters.get("temperature")
@@ -571,21 +650,27 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                 payload["frequencyPenalty"] = {model_parameters.get("frequencyPenalty")}
             if model_parameters.get("countPenalty"):
                 payload["countPenalty"] = {model_parameters.get("countPenalty")}
-                    
+
         elif model_prefix == "cohere":
-            payload = { **model_parameters }
+            payload = {**model_parameters}
             payload["prompt"] = prompt_messages[0].content
             payload["stream"] = stream
-        
+
         else:
             raise ValueError(f"Got unknown model prefix {model_prefix}")
-        
+
         return payload
 
-    def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict,
-                  stop: Optional[list[str]] = None, stream: bool = True,
-                  user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -598,18 +683,16 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :param user: unique user id
         :return: full response or stream response chunk generator result
         """
-        client_config = Config(
-            region_name=credentials["aws_region"]
-        )
+        client_config = Config(region_name=credentials["aws_region"])
 
         runtime_client = boto3.client(
-            service_name='bedrock-runtime',
+            service_name="bedrock-runtime",
             config=client_config,
             aws_access_key_id=credentials.get("aws_access_key_id"),
-            aws_secret_access_key=credentials.get("aws_secret_access_key")
+            aws_secret_access_key=credentials.get("aws_secret_access_key"),
         )
 
-        model_prefix = model.split('.')[0]
+        model_prefix = model.split(".")[0]
         payload = self._create_payload(model, prompt_messages, model_parameters, stop, stream)
 
         # need workaround for ai21 models which doesn't support streaming
@@ -619,18 +702,13 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
             invoke = runtime_client.invoke_model
 
         try:
-            body_jsonstr=json.dumps(payload)
-            response = invoke(
-                modelId=model,
-                contentType="application/json",
-                accept= "*/*",
-                body=body_jsonstr
-            )
+            body_jsonstr = json.dumps(payload)
+            response = invoke(modelId=model, contentType="application/json", accept="*/*", body=body_jsonstr)
         except ClientError as ex:
-            error_code = ex.response['Error']['Code']
+            error_code = ex.response["Error"]["Code"]
             full_error_msg = f"{error_code}: {ex.response['Error']['Message']}"
             raise self._map_client_to_invoke_error(error_code, full_error_msg)
-        
+
         except (EndpointConnectionError, NoRegionError, ServiceNotInRegionError) as ex:
             raise InvokeConnectionError(str(ex))
 
@@ -639,15 +717,15 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
 
         except Exception as ex:
             raise InvokeError(str(ex))
-        
 
         if stream:
             return self._handle_generate_stream_response(model, credentials, response, prompt_messages)
 
         return self._handle_generate_response(model, credentials, response, prompt_messages)
 
-    def _handle_generate_response(self, model: str, credentials: dict, response: dict,
-                                  prompt_messages: list[PromptMessage]) -> LLMResult:
+    def _handle_generate_response(
+        self, model: str, credentials: dict, response: dict, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
         """
         Handle llm response
 
@@ -657,7 +735,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: llm response
         """
-        response_body = json.loads(response.get('body').read().decode('utf-8'))
+        response_body = json.loads(response.get("body").read().decode("utf-8"))
 
         finish_reason = response_body.get("error")
 
@@ -665,25 +743,23 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
             raise InvokeError(finish_reason)
 
         # get output text and calculate num tokens based on model / provider
-        model_prefix = model.split('.')[0]
+        model_prefix = model.split(".")[0]
 
         if model_prefix == "ai21":
-            output = response_body.get('completions')[0].get('data').get('text')
+            output = response_body.get("completions")[0].get("data").get("text")
             prompt_tokens = len(response_body.get("prompt").get("tokens"))
-            completion_tokens = len(response_body.get('completions')[0].get('data').get('tokens'))
-            
+            completion_tokens = len(response_body.get("completions")[0].get("data").get("tokens"))
+
         elif model_prefix == "cohere":
             output = response_body.get("generations")[0].get("text")
             prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-            completion_tokens = self.get_num_tokens(model, credentials, output if output else '')
-                        
+            completion_tokens = self.get_num_tokens(model, credentials, output or "")
+
         else:
             raise ValueError(f"Got unknown model prefix {model_prefix} when handling block response")
 
         # construct assistant message from output
-        assistant_prompt_message = AssistantPromptMessage(
-            content=output
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=output)
 
         # calculate usage
         usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
@@ -698,8 +774,9 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
 
         return result
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: dict,
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, response: dict, prompt_messages: list[PromptMessage]
+    ) -> Generator:
         """
         Handle llm stream response
 
@@ -709,65 +786,59 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: llm response chunk generator result
         """
-        model_prefix = model.split('.')[0]
+        model_prefix = model.split(".")[0]
         if model_prefix == "ai21":
-            response_body = json.loads(response.get('body').read().decode('utf-8'))
+            response_body = json.loads(response.get("body").read().decode("utf-8"))
 
-            content = response_body.get('completions')[0].get('data').get('text')
-            finish_reason = response_body.get('completions')[0].get('finish_reason')
+            content = response_body.get("completions")[0].get("data").get("text")
+            finish_reason = response_body.get("completions")[0].get("finish_reason")
 
             prompt_tokens = len(response_body.get("prompt").get("tokens"))
-            completion_tokens = len(response_body.get('completions')[0].get('data').get('tokens'))
+            completion_tokens = len(response_body.get("completions")[0].get("data").get("tokens"))
             usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
             yield LLMResultChunk(
-                    model=model,
-                    prompt_messages=prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=0,
-                        message=AssistantPromptMessage(content=content),
-                        finish_reason=finish_reason,
-                        usage=usage
-                    )
-                )
+                model=model,
+                prompt_messages=prompt_messages,
+                delta=LLMResultChunkDelta(
+                    index=0, message=AssistantPromptMessage(content=content), finish_reason=finish_reason, usage=usage
+                ),
+            )
             return
-        
-        stream = response.get('body')
+
+        stream = response.get("body")
         if not stream:
-            raise InvokeError('No response body')
-        
+            raise InvokeError("No response body")
+
         index = -1
         for event in stream:
-            chunk = event.get('chunk')
-            
+            chunk = event.get("chunk")
+
             if not chunk:
                 exception_name = next(iter(event))
                 full_ex_msg = f"{exception_name}: {event[exception_name]['message']}"
                 raise self._map_client_to_invoke_error(exception_name, full_ex_msg)
 
-            payload = json.loads(chunk.get('bytes').decode())
+            payload = json.loads(chunk.get("bytes").decode())
 
-            model_prefix = model.split('.')[0]
+            model_prefix = model.split(".")[0]
             if model_prefix == "cohere":
                 content_delta = payload.get("text")
                 finish_reason = payload.get("finish_reason")
-            
+
             else:
                 raise ValueError(f"Got unknown model prefix {model_prefix} when handling stream response")
 
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content = content_delta if content_delta else '',
+                content=content_delta or "",
             )
             index += 1
-           
+
             if not finish_reason:
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=index,
-                        message=assistant_prompt_message
-                    )
+                    delta=LLMResultChunkDelta(index=index, message=assistant_prompt_message),
                 )
 
             else:
@@ -777,36 +848,33 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
 
                 # transform usage
                 usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
-                
+
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
-                        index=index,
-                        message=assistant_prompt_message,
-                        finish_reason=finish_reason,
-                        usage=usage
-                    )
+                        index=index, message=assistant_prompt_message, finish_reason=finish_reason, usage=usage
+                    ),
                 )
-    
+
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         """
         Map model invoke error to unified error
-        The key is the ermd = genai.GenerativeModel(model)ror type thrown to the caller
-        The value is the md = genai.GenerativeModel(model)error type thrown by the model,
+        The key is the ermd = genai.GenerativeModel(model) error type thrown to the caller
+        The value is the md = genai.GenerativeModel(model) error type thrown by the model,
         which needs to be converted into a unified error type for the caller.
 
-        :return: Invoke emd = genai.GenerativeModel(model)rror mapping
+        :return: Invoke emd = genai.GenerativeModel(model) error mapping
         """
         return {
             InvokeConnectionError: [],
             InvokeServerUnavailableError: [],
             InvokeRateLimitError: [],
             InvokeAuthorizationError: [],
-            InvokeBadRequestError: []
+            InvokeBadRequestError: [],
         }
-    
+
     def _map_client_to_invoke_error(self, error_code: str, error_msg: str) -> type[InvokeError]:
         """
         Map client error to invoke error
@@ -818,11 +886,16 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
 
         if error_code == "AccessDeniedException":
             return InvokeAuthorizationError(error_msg)
-        elif error_code in ["ResourceNotFoundException", "ValidationException"]:
+        elif error_code in {"ResourceNotFoundException", "ValidationException"}:
             return InvokeBadRequestError(error_msg)
-        elif error_code in ["ThrottlingException", "ServiceQuotaExceededException"]:
+        elif error_code in {"ThrottlingException", "ServiceQuotaExceededException"}:
             return InvokeRateLimitError(error_msg)
-        elif error_code in ["ModelTimeoutException", "ModelErrorException", "InternalServerException", "ModelNotReadyException"]:
+        elif error_code in {
+            "ModelTimeoutException",
+            "ModelErrorException",
+            "InternalServerException",
+            "ModelNotReadyException",
+        }:
             return InvokeServerUnavailableError(error_msg)
         elif error_code == "ModelStreamErrorException":
             return InvokeConnectionError(error_msg)
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-haiku-v1.yaml b/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-haiku-v1.yaml
new file mode 100644
index 0000000000..9247f46974
--- /dev/null
+++ b/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-haiku-v1.yaml
@@ -0,0 +1,59 @@
+model: us.anthropic.claude-3-haiku-20240307-v1:0
+label:
+  en_US: Claude 3 Haiku(US.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 200000
+# docs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    type: int
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
+      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
+  # docs: https://docs.anthropic.com/claude/docs/system-prompts
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.00025'
+  output: '0.00125'
+  unit: '0.001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-opus-v1.yaml b/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-opus-v1.yaml
new file mode 100644
index 0000000000..f9854d51f0
--- /dev/null
+++ b/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-opus-v1.yaml
@@ -0,0 +1,59 @@
+model: us.anthropic.claude-3-opus-20240229-v1:0
+label:
+  en_US: Claude 3 Opus(US.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 200000
+# docs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    type: int
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
+      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
+  # docs: https://docs.anthropic.com/claude/docs/system-prompts
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.015'
+  output: '0.075'
+  unit: '0.001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-sonnet-v1.5.yaml b/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-sonnet-v1.5.yaml
new file mode 100644
index 0000000000..fbcab2d5f3
--- /dev/null
+++ b/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-sonnet-v1.5.yaml
@@ -0,0 +1,58 @@
+model: us.anthropic.claude-3-5-sonnet-20240620-v1:0
+label:
+  en_US: Claude 3.5 Sonnet(US.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 200000
+# docs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    type: int
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
+      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.003'
+  output: '0.015'
+  unit: '0.001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-sonnet-v1.yaml b/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-sonnet-v1.yaml
new file mode 100644
index 0000000000..9f5a1501f0
--- /dev/null
+++ b/api/core/model_runtime/model_providers/bedrock/llm/us.anthropic.claude-3-sonnet-v1.yaml
@@ -0,0 +1,58 @@
+model: us.anthropic.claude-3-sonnet-20240229-v1:0
+label:
+  en_US: Claude 3 Sonnet(US.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 200000
+# docs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    type: int
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
+      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.003'
+  output: '0.015'
+  unit: '0.001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py
index 993416cdc8..251170d1ae 100644
--- a/api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py
@@ -27,12 +27,11 @@ from core.model_runtime.model_providers.__base.text_embedding_model import TextE
 
 logger = logging.getLogger(__name__)
 
+
 class BedrockTextEmbeddingModel(TextEmbeddingModel):
-
-
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -42,67 +41,56 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
-        client_config = Config(
-            region_name=credentials["aws_region"]
-        )
+        client_config = Config(region_name=credentials["aws_region"])
 
         bedrock_runtime = boto3.client(
-            service_name='bedrock-runtime',
+            service_name="bedrock-runtime",
             config=client_config,
             aws_access_key_id=credentials.get("aws_access_key_id"),
-            aws_secret_access_key=credentials.get("aws_secret_access_key")
+            aws_secret_access_key=credentials.get("aws_secret_access_key"),
         )
 
         embeddings = []
         token_usage = 0
-        
-        model_prefix = model.split('.')[0]
-         
-        if model_prefix == "amazon" :
+
+        model_prefix = model.split(".")[0]
+
+        if model_prefix == "amazon":
             for text in texts:
                 body = {
-                 "inputText": text,
+                    "inputText": text,
                 }
                 response_body = self._invoke_bedrock_embedding(model, bedrock_runtime, body)
-                embeddings.extend([response_body.get('embedding')])
-                token_usage += response_body.get('inputTextTokenCount')
-            logger.warning(f'Total Tokens: {token_usage}')
+                embeddings.extend([response_body.get("embedding")])
+                token_usage += response_body.get("inputTextTokenCount")
+            logger.warning(f"Total Tokens: {token_usage}")
             result = TextEmbeddingResult(
                 model=model,
                 embeddings=embeddings,
-                usage=self._calc_response_usage(
-                    model=model,
-                    credentials=credentials,
-                    tokens=token_usage
-                )
+                usage=self._calc_response_usage(model=model, credentials=credentials, tokens=token_usage),
             )
             return result
 
-        if model_prefix == "cohere" :
-            input_type = 'search_document' if len(texts) > 1 else 'search_query'
+        if model_prefix == "cohere":
+            input_type = "search_document" if len(texts) > 1 else "search_query"
             for text in texts:
                 body = {
-                 "texts": [text],
-                 "input_type": input_type,
+                    "texts": [text],
+                    "input_type": input_type,
                 }
                 response_body = self._invoke_bedrock_embedding(model, bedrock_runtime, body)
-                embeddings.extend(response_body.get('embeddings'))
+                embeddings.extend(response_body.get("embeddings"))
                 token_usage += len(text)
             result = TextEmbeddingResult(
                 model=model,
                 embeddings=embeddings,
-                usage=self._calc_response_usage(
-                    model=model,
-                    credentials=credentials,
-                    tokens=token_usage
-                )
+                usage=self._calc_response_usage(model=model, credentials=credentials, tokens=token_usage),
             )
             return result
 
-        #others
+        # others
         raise ValueError(f"Got unknown model prefix {model_prefix} when handling block response")
 
-
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
         Get number of tokens for given prompt messages
@@ -125,35 +113,41 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :return:
         """
-    
+
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         """
         Map model invoke error to unified error
-        The key is the ermd = genai.GenerativeModel(model)ror type thrown to the caller
-        The value is the md = genai.GenerativeModel(model)error type thrown by the model,
+        The key is the ermd = genai.GenerativeModel(model) error type thrown to the caller
+        The value is the md = genai.GenerativeModel(model) error type thrown by the model,
         which needs to be converted into a unified error type for the caller.
 
-        :return: Invoke emd = genai.GenerativeModel(model)rror mapping
+        :return: Invoke emd = genai.GenerativeModel(model) error mapping
         """
         return {
             InvokeConnectionError: [],
             InvokeServerUnavailableError: [],
             InvokeRateLimitError: [],
             InvokeAuthorizationError: [],
-            InvokeBadRequestError: []
+            InvokeBadRequestError: [],
         }
-    
-    def _create_payload(self, model_prefix: str, texts: list[str], model_parameters: dict, stop: Optional[list[str]] = None, stream: bool = True):
+
+    def _create_payload(
+        self,
+        model_prefix: str,
+        texts: list[str],
+        model_parameters: dict,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+    ):
         """
         Create payload for bedrock api call depending on model provider
         """
         payload = {}
 
         if model_prefix == "amazon":
-            payload['inputText'] = texts
+            payload["inputText"] = texts
 
-    
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -165,10 +159,7 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -179,7 +170,7 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
@@ -195,35 +186,41 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
 
         if error_code == "AccessDeniedException":
             return InvokeAuthorizationError(error_msg)
-        elif error_code in ["ResourceNotFoundException", "ValidationException"]:
+        elif error_code in {"ResourceNotFoundException", "ValidationException"}:
             return InvokeBadRequestError(error_msg)
-        elif error_code in ["ThrottlingException", "ServiceQuotaExceededException"]:
+        elif error_code in {"ThrottlingException", "ServiceQuotaExceededException"}:
             return InvokeRateLimitError(error_msg)
-        elif error_code in ["ModelTimeoutException", "ModelErrorException", "InternalServerException", "ModelNotReadyException"]:
+        elif error_code in {
+            "ModelTimeoutException",
+            "ModelErrorException",
+            "InternalServerException",
+            "ModelNotReadyException",
+        }:
             return InvokeServerUnavailableError(error_msg)
         elif error_code == "ModelStreamErrorException":
             return InvokeConnectionError(error_msg)
 
         return InvokeError(error_msg)
-    
 
-    def _invoke_bedrock_embedding(self, model: str, bedrock_runtime, body: dict, ):
-        accept = 'application/json' 
-        content_type = 'application/json'
+    def _invoke_bedrock_embedding(
+        self,
+        model: str,
+        bedrock_runtime,
+        body: dict,
+    ):
+        accept = "application/json"
+        content_type = "application/json"
         try:
             response = bedrock_runtime.invoke_model(
-                body=json.dumps(body),
-                modelId=model,
-                accept=accept,
-                contentType=content_type
+                body=json.dumps(body), modelId=model, accept=accept, contentType=content_type
             )
-            response_body = json.loads(response.get('body').read().decode('utf-8'))
+            response_body = json.loads(response.get("body").read().decode("utf-8"))
             return response_body
         except ClientError as ex:
-            error_code = ex.response['Error']['Code']
+            error_code = ex.response["Error"]["Code"]
             full_error_msg = f"{error_code}: {ex.response['Error']['Message']}"
             raise self._map_client_to_invoke_error(error_code, full_error_msg)
-        
+
         except (EndpointConnectionError, NoRegionError, ServiceNotInRegionError) as ex:
             raise InvokeConnectionError(str(ex))
 
diff --git a/api/core/model_runtime/model_providers/chatglm/chatglm.py b/api/core/model_runtime/model_providers/chatglm/chatglm.py
index e9dd5794f3..71d9a15322 100644
--- a/api/core/model_runtime/model_providers/chatglm/chatglm.py
+++ b/api/core/model_runtime/model_providers/chatglm/chatglm.py
@@ -20,12 +20,9 @@ class ChatGLMProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.LLM)
 
             # Use `chatglm3-6b` model for validate,
-            model_instance.validate_credentials(
-                model='chatglm3-6b',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="chatglm3-6b", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/chatglm/llm/llm.py b/api/core/model_runtime/model_providers/chatglm/llm/llm.py
index e83d08af71..b3eeb48e22 100644
--- a/api/core/model_runtime/model_providers/chatglm/llm/llm.py
+++ b/api/core/model_runtime/model_providers/chatglm/llm/llm.py
@@ -43,12 +43,19 @@ from core.model_runtime.utils import helper
 
 logger = logging.getLogger(__name__)
 
+
 class ChatGLMLargeLanguageModel(LargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict, 
-                prompt_messages: list[PromptMessage], model_parameters: dict, 
-                tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
-                stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         """
         Invoke large language model
 
@@ -71,11 +78,16 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
             tools=tools,
             stop=stop,
             stream=stream,
-            user=user
+            user=user,
         )
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: list[PromptMessageTool] | None = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool] | None = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -96,11 +108,16 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
         :return:
         """
         try:
-            self._invoke(model=model, credentials=credentials, prompt_messages=[
-                UserPromptMessage(content="ping"),
-            ], model_parameters={
-                "max_tokens": 16,
-            })
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                prompt_messages=[
+                    UserPromptMessage(content="ping"),
+                ],
+                model_parameters={
+                    "max_tokens": 16,
+                },
+            )
         except Exception as e:
             raise CredentialsValidateFailedError(str(e))
 
@@ -124,24 +141,24 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
                 ConflictError,
                 NotFoundError,
                 UnprocessableEntityError,
-                PermissionDeniedError
+                PermissionDeniedError,
             ],
-            InvokeRateLimitError: [
-                RateLimitError
-            ],
-            InvokeAuthorizationError: [
-                AuthenticationError
-            ],
-            InvokeBadRequestError: [
-                ValueError
-            ]
+            InvokeRateLimitError: [RateLimitError],
+            InvokeAuthorizationError: [AuthenticationError],
+            InvokeBadRequestError: [ValueError],
         }
 
-    def _generate(self, model: str, credentials: dict, 
-                prompt_messages: list[PromptMessage], model_parameters: dict, 
-                tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
-                stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         """
         Invoke large language model
 
@@ -163,35 +180,31 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
 
         extra_model_kwargs = {}
         if stop:
-            extra_model_kwargs['stop'] = stop
+            extra_model_kwargs["stop"] = stop
 
         if user:
-            extra_model_kwargs['user'] = user
+            extra_model_kwargs["user"] = user
 
         if tools and len(tools) > 0:
-            extra_model_kwargs['functions'] = [
-                helper.dump_model(tool) for tool in tools
-            ]
+            extra_model_kwargs["functions"] = [helper.dump_model(tool) for tool in tools]
 
         result = client.chat.completions.create(
             messages=[self._convert_prompt_message_to_dict(m) for m in prompt_messages],
             model=model,
             stream=stream,
             **model_parameters,
-            **extra_model_kwargs
+            **extra_model_kwargs,
         )
 
         if stream:
             return self._handle_chat_generate_stream_response(
-                model=model, credentials=credentials, response=result, tools=tools, 
-                prompt_messages=prompt_messages
+                model=model, credentials=credentials, response=result, tools=tools, prompt_messages=prompt_messages
             )
-        
+
         return self._handle_chat_generate_response(
-            model=model, credentials=credentials, response=result, tools=tools, 
-            prompt_messages=prompt_messages
+            model=model, credentials=credentials, response=result, tools=tools, prompt_messages=prompt_messages
         )
-        
+
     def _check_chatglm_parameters(self, model: str, model_parameters: dict, tools: list[PromptMessageTool]) -> None:
         if model.find("chatglm2") != -1 and tools is not None and len(tools) > 0:
             raise InvokeBadRequestError("ChatGLM2 does not support function calling")
@@ -212,7 +225,7 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
             if message.tool_calls and len(message.tool_calls) > 0:
                 message_dict["function_call"] = {
                     "name": message.tool_calls[0].function.name,
-                    "arguments": message.tool_calls[0].function.arguments
+                    "arguments": message.tool_calls[0].function.arguments,
                 }
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
@@ -223,12 +236,12 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
             message_dict = {"role": "function", "content": message.content}
         else:
             raise ValueError(f"Unknown message type {type(message)}")
-        
+
         return message_dict
-    
-    def _extract_response_tool_calls(self,
-                                     response_function_calls: list[FunctionCall]) \
-            -> list[AssistantPromptMessage.ToolCall]:
+
+    def _extract_response_tool_calls(
+        self, response_function_calls: list[FunctionCall]
+    ) -> list[AssistantPromptMessage.ToolCall]:
         """
         Extract tool calls from response
 
@@ -239,19 +252,14 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
         if response_function_calls:
             for response_tool_call in response_function_calls:
                 function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                    name=response_tool_call.name,
-                    arguments=response_tool_call.arguments
+                    name=response_tool_call.name, arguments=response_tool_call.arguments
                 )
 
-                tool_call = AssistantPromptMessage.ToolCall(
-                    id=0,
-                    type='function',
-                    function=function
-                )
+                tool_call = AssistantPromptMessage.ToolCall(id=0, type="function", function=function)
                 tool_calls.append(tool_call)
 
         return tool_calls
-        
+
     def _to_client_kwargs(self, credentials: dict) -> dict:
         """
         Convert invoke kwargs to client kwargs
@@ -265,17 +273,20 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
         client_kwargs = {
             "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
             "api_key": "1",
-            "base_url": str(URL(credentials['api_base']) / 'v1')
+            "base_url": str(URL(credentials["api_base"]) / "v1"),
         }
 
         return client_kwargs
-    
-    def _handle_chat_generate_stream_response(self, model: str, credentials: dict, response: Stream[ChatCompletionChunk],
-                                              prompt_messages: list[PromptMessage],
-                                              tools: Optional[list[PromptMessageTool]] = None) \
-            -> Generator:
-        
-        full_response = ''
+
+    def _handle_chat_generate_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: Stream[ChatCompletionChunk],
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> Generator:
+        full_response = ""
 
         for chunk in response:
             if len(chunk.choices) == 0:
@@ -283,35 +294,37 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
 
             delta = chunk.choices[0]
 
-            if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ''):
+            if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ""):
                 continue
-            
+
             # check if there is a tool call in the response
             function_calls = None
             if delta.delta.function_call:
                 function_calls = [delta.delta.function_call]
 
-            assistant_message_tool_calls = self._extract_response_tool_calls(function_calls if function_calls else [])
+            assistant_message_tool_calls = self._extract_response_tool_calls(function_calls or [])
 
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else '',
-                tool_calls=assistant_message_tool_calls
+                content=delta.delta.content or "", tool_calls=assistant_message_tool_calls
             )
 
             if delta.finish_reason is not None:
                 # temp_assistant_prompt_message is used to calculate usage
                 temp_assistant_prompt_message = AssistantPromptMessage(
-                    content=full_response,
-                    tool_calls=assistant_message_tool_calls
+                    content=full_response, tool_calls=assistant_message_tool_calls
                 )
 
                 prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
                 completion_tokens = self._num_tokens_from_messages(messages=[temp_assistant_prompt_message], tools=[])
 
-                usage = self._calc_response_usage(model=model, credentials=credentials, 
-                                                  prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
-                
+                usage = self._calc_response_usage(
+                    model=model,
+                    credentials=credentials,
+                    prompt_tokens=prompt_tokens,
+                    completion_tokens=completion_tokens,
+                )
+
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
@@ -320,7 +333,7 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
                         index=delta.index,
                         message=assistant_prompt_message,
                         finish_reason=delta.finish_reason,
-                        usage=usage
+                        usage=usage,
                     ),
                 )
             else:
@@ -335,11 +348,15 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
                 )
 
                 full_response += delta.delta.content
-        
-    def _handle_chat_generate_response(self, model: str, credentials: dict, response: ChatCompletion,
-                                       prompt_messages: list[PromptMessage],
-                                       tools: Optional[list[PromptMessageTool]] = None) \
-            -> LLMResult:
+
+    def _handle_chat_generate_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: ChatCompletion,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> LLMResult:
         """
         Handle llm chat response
 
@@ -359,15 +376,14 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
         tool_calls = self._extract_response_tool_calls([function_calls] if function_calls else [])
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_message.content,
-            tool_calls=tool_calls
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_message.content, tool_calls=tool_calls)
 
         prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
         completion_tokens = self._num_tokens_from_messages(messages=[assistant_prompt_message], tools=tools)
 
-        usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
+        usage = self._calc_response_usage(
+            model=model, credentials=credentials, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens
+        )
 
         response = LLMResult(
             model=model,
@@ -378,7 +394,7 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
         )
 
         return response
-    
+
     def _num_tokens_from_string(self, text: str, tools: Optional[list[PromptMessageTool]] = None) -> int:
         """
         Calculate num tokens for text completion model with tiktoken package.
@@ -395,17 +411,19 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
 
         return num_tokens
 
-    def _num_tokens_from_messages(self, messages: list[PromptMessage],
-                                  tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_messages(
+        self, messages: list[PromptMessage], tools: Optional[list[PromptMessageTool]] = None
+    ) -> int:
         """Calculate num tokens for chatglm2 and chatglm3 with GPT2 tokenizer.
 
         it's too complex to calculate num tokens for chatglm2 and chatglm3 with ChatGLM tokenizer,
         As a temporary solution we use GPT2 tokenizer instead.
 
         """
+
         def tokens(text: str):
             return self._get_num_tokens_by_gpt2(text)
-        
+
         tokens_per_message = 3
         tokens_per_name = 1
         num_tokens = 0
@@ -414,10 +432,10 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
             num_tokens += tokens_per_message
             for key, value in message.items():
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
                     value = text
 
                 if key == "function_call":
@@ -452,36 +470,37 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
         :param tools: tools for tool calling
         :return: number of tokens
         """
+
         def tokens(text: str):
             return self._get_num_tokens_by_gpt2(text)
 
         num_tokens = 0
         for tool in tools:
             # calculate num tokens for function object
-            num_tokens += tokens('name')
+            num_tokens += tokens("name")
             num_tokens += tokens(tool.name)
-            num_tokens += tokens('description')
+            num_tokens += tokens("description")
             num_tokens += tokens(tool.description)
             parameters = tool.parameters
-            num_tokens += tokens('parameters')
-            num_tokens += tokens('type')
+            num_tokens += tokens("parameters")
+            num_tokens += tokens("type")
             num_tokens += tokens(parameters.get("type"))
-            if 'properties' in parameters:
-                num_tokens += tokens('properties')
-                for key, value in parameters.get('properties').items():
+            if "properties" in parameters:
+                num_tokens += tokens("properties")
+                for key, value in parameters.get("properties").items():
                     num_tokens += tokens(key)
                     for field_key, field_value in value.items():
                         num_tokens += tokens(field_key)
-                        if field_key == 'enum':
+                        if field_key == "enum":
                             for enum_field in field_value:
                                 num_tokens += 3
                                 num_tokens += tokens(enum_field)
                         else:
                             num_tokens += tokens(field_key)
                             num_tokens += tokens(str(field_value))
-            if 'required' in parameters:
-                num_tokens += tokens('required')
-                for required_field in parameters['required']:
+            if "required" in parameters:
+                num_tokens += tokens("required")
+                for required_field in parameters["required"]:
                     num_tokens += 3
                     num_tokens += tokens(required_field)
 
diff --git a/api/core/model_runtime/model_providers/cohere/cohere.py b/api/core/model_runtime/model_providers/cohere/cohere.py
index cfbcb94d26..8394a45fcf 100644
--- a/api/core/model_runtime/model_providers/cohere/cohere.py
+++ b/api/core/model_runtime/model_providers/cohere/cohere.py
@@ -20,12 +20,9 @@ class CohereProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.RERANK)
 
             # Use `rerank-english-v2.0` model for validate,
-            model_instance.validate_credentials(
-                model='rerank-english-v2.0',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="rerank-english-v2.0", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/cohere/llm/llm.py b/api/core/model_runtime/model_providers/cohere/llm/llm.py
index 89b04c0279..3863ad3308 100644
--- a/api/core/model_runtime/model_providers/cohere/llm/llm.py
+++ b/api/core/model_runtime/model_providers/cohere/llm/llm.py
@@ -55,11 +55,17 @@ class CohereLargeLanguageModel(LargeLanguageModel):
     Model class for Cohere large language model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -85,7 +91,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 tools=tools,
                 stop=stop,
                 stream=stream,
-                user=user
+                user=user,
             )
         else:
             return self._generate(
@@ -95,11 +101,16 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 model_parameters=model_parameters,
                 stop=stop,
                 stream=stream,
-                user=user
+                user=user,
             )
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -136,30 +147,37 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 self._chat_generate(
                     model=model,
                     credentials=credentials,
-                    prompt_messages=[UserPromptMessage(content='ping')],
+                    prompt_messages=[UserPromptMessage(content="ping")],
                     model_parameters={
-                        'max_tokens': 20,
-                        'temperature': 0,
+                        "max_tokens": 20,
+                        "temperature": 0,
                     },
-                    stream=False
+                    stream=False,
                 )
             else:
                 self._generate(
                     model=model,
                     credentials=credentials,
-                    prompt_messages=[UserPromptMessage(content='ping')],
+                    prompt_messages=[UserPromptMessage(content="ping")],
                     model_parameters={
-                        'max_tokens': 20,
-                        'temperature': 0,
+                        "max_tokens": 20,
+                        "temperature": 0,
                     },
-                    stream=False
+                    stream=False,
                 )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[list[str]] = None,
-                  stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke llm model
 
@@ -173,17 +191,17 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         # initialize client
-        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
+        client = cohere.Client(credentials.get("api_key"), base_url=credentials.get("base_url"))
 
         if stop:
-            model_parameters['end_sequences'] = stop
+            model_parameters["end_sequences"] = stop
 
         if stream:
             response = client.generate_stream(
                 prompt=prompt_messages[0].content,
                 model=model,
                 **model_parameters,
-                request_options=RequestOptions(max_retries=0)
+                request_options=RequestOptions(max_retries=0),
             )
 
             return self._handle_generate_stream_response(model, credentials, response, prompt_messages)
@@ -192,14 +210,14 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 prompt=prompt_messages[0].content,
                 model=model,
                 **model_parameters,
-                request_options=RequestOptions(max_retries=0)
+                request_options=RequestOptions(max_retries=0),
             )
 
             return self._handle_generate_response(model, credentials, response, prompt_messages)
 
-    def _handle_generate_response(self, model: str, credentials: dict, response: Generation,
-                                  prompt_messages: list[PromptMessage]) \
-            -> LLMResult:
+    def _handle_generate_response(
+        self, model: str, credentials: dict, response: Generation, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
         """
         Handle llm response
 
@@ -212,9 +230,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         assistant_text = response.generations[0].text
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_text
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_text)
 
         # calculate num tokens
         prompt_tokens = int(response.meta.billed_units.input_tokens)
@@ -225,17 +241,18 @@ class CohereLargeLanguageModel(LargeLanguageModel):
 
         # transform response
         response = LLMResult(
-            model=model,
-            prompt_messages=prompt_messages,
-            message=assistant_prompt_message,
-            usage=usage
+            model=model, prompt_messages=prompt_messages, message=assistant_prompt_message, usage=usage
         )
 
         return response
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict,
-                                         response: Iterator[GenerateStreamedResponse],
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: Iterator[GenerateStreamedResponse],
+        prompt_messages: list[PromptMessage],
+    ) -> Generator:
         """
         Handle llm stream response
 
@@ -245,7 +262,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         :return: llm response chunk generator
         """
         index = 1
-        full_assistant_content = ''
+        full_assistant_content = ""
         for chunk in response:
             if isinstance(chunk, GenerateStreamedResponse_TextGeneration):
                 chunk = cast(GenerateStreamedResponse_TextGeneration, chunk)
@@ -255,9 +272,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                     continue
 
                 # transform assistant message to prompt message
-                assistant_prompt_message = AssistantPromptMessage(
-                    content=text
-                )
+                assistant_prompt_message = AssistantPromptMessage(content=text)
 
                 full_assistant_content += text
 
@@ -267,7 +282,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=index,
                         message=assistant_prompt_message,
-                    )
+                    ),
                 )
 
                 index += 1
@@ -277,9 +292,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 # calculate num tokens
                 prompt_tokens = self._num_tokens_from_messages(model, credentials, prompt_messages)
                 completion_tokens = self._num_tokens_from_messages(
-                    model,
-                    credentials,
-                    [AssistantPromptMessage(content=full_assistant_content)]
+                    model, credentials, [AssistantPromptMessage(content=full_assistant_content)]
                 )
 
                 # transform usage
@@ -290,20 +303,27 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=index,
-                        message=AssistantPromptMessage(content=''),
+                        message=AssistantPromptMessage(content=""),
                         finish_reason=chunk.finish_reason,
-                        usage=usage
-                    )
+                        usage=usage,
+                    ),
                 )
                 break
             elif isinstance(chunk, GenerateStreamedResponse_StreamError):
                 chunk = cast(GenerateStreamedResponse_StreamError, chunk)
                 raise InvokeBadRequestError(chunk.err)
 
-    def _chat_generate(self, model: str, credentials: dict,
-                       prompt_messages: list[PromptMessage], model_parameters: dict,
-                       tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                       stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _chat_generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke llm chat model
 
@@ -318,27 +338,28 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         # initialize client
-        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
+        client = cohere.Client(credentials.get("api_key"), base_url=credentials.get("base_url"))
 
         if stop:
-            model_parameters['stop_sequences'] = stop
+            model_parameters["stop_sequences"] = stop
 
         if tools:
             if len(tools) == 1:
                 raise ValueError("Cohere tool call requires at least two tools to be specified.")
 
-            model_parameters['tools'] = self._convert_tools(tools)
+            model_parameters["tools"] = self._convert_tools(tools)
 
-        message, chat_histories, tool_results \
-            = self._convert_prompt_messages_to_message_and_chat_histories(prompt_messages)
+        message, chat_histories, tool_results = self._convert_prompt_messages_to_message_and_chat_histories(
+            prompt_messages
+        )
 
         if tool_results:
-            model_parameters['tool_results'] = tool_results
+            model_parameters["tool_results"] = tool_results
 
         # chat model
         real_model = model
         if self.get_model_schema(model, credentials).fetch_from == FetchFrom.PREDEFINED_MODEL:
-            real_model = model.removesuffix('-chat')
+            real_model = model.removesuffix("-chat")
 
         if stream:
             response = client.chat_stream(
@@ -346,7 +367,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 chat_history=chat_histories,
                 model=real_model,
                 **model_parameters,
-                request_options=RequestOptions(max_retries=0)
+                request_options=RequestOptions(max_retries=0),
             )
 
             return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages)
@@ -356,14 +377,14 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 chat_history=chat_histories,
                 model=real_model,
                 **model_parameters,
-                request_options=RequestOptions(max_retries=0)
+                request_options=RequestOptions(max_retries=0),
             )
 
             return self._handle_chat_generate_response(model, credentials, response, prompt_messages)
 
-    def _handle_chat_generate_response(self, model: str, credentials: dict, response: NonStreamedChatResponse,
-                                       prompt_messages: list[PromptMessage]) \
-            -> LLMResult:
+    def _handle_chat_generate_response(
+        self, model: str, credentials: dict, response: NonStreamedChatResponse, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
         """
         Handle llm chat response
 
@@ -380,19 +401,15 @@ class CohereLargeLanguageModel(LargeLanguageModel):
             for cohere_tool_call in response.tool_calls:
                 tool_call = AssistantPromptMessage.ToolCall(
                     id=cohere_tool_call.name,
-                    type='function',
+                    type="function",
                     function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                        name=cohere_tool_call.name,
-                        arguments=json.dumps(cohere_tool_call.parameters)
-                    )
+                        name=cohere_tool_call.name, arguments=json.dumps(cohere_tool_call.parameters)
+                    ),
                 )
                 tool_calls.append(tool_call)
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_text,
-            tool_calls=tool_calls
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_text, tool_calls=tool_calls)
 
         # calculate num tokens
         prompt_tokens = self._num_tokens_from_messages(model, credentials, prompt_messages)
@@ -403,17 +420,18 @@ class CohereLargeLanguageModel(LargeLanguageModel):
 
         # transform response
         response = LLMResult(
-            model=model,
-            prompt_messages=prompt_messages,
-            message=assistant_prompt_message,
-            usage=usage
+            model=model, prompt_messages=prompt_messages, message=assistant_prompt_message, usage=usage
         )
 
         return response
 
-    def _handle_chat_generate_stream_response(self, model: str, credentials: dict,
-                                              response: Iterator[StreamedChatResponse],
-                                              prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_chat_generate_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: Iterator[StreamedChatResponse],
+        prompt_messages: list[PromptMessage],
+    ) -> Generator:
         """
         Handle llm chat stream response
 
@@ -423,17 +441,16 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         :return: llm response chunk generator
         """
 
-        def final_response(full_text: str,
-                           tool_calls: list[AssistantPromptMessage.ToolCall],
-                           index: int,
-                           finish_reason: Optional[str] = None) -> LLMResultChunk:
+        def final_response(
+            full_text: str,
+            tool_calls: list[AssistantPromptMessage.ToolCall],
+            index: int,
+            finish_reason: Optional[str] = None,
+        ) -> LLMResultChunk:
             # calculate num tokens
             prompt_tokens = self._num_tokens_from_messages(model, credentials, prompt_messages)
 
-            full_assistant_prompt_message = AssistantPromptMessage(
-                content=full_text,
-                tool_calls=tool_calls
-            )
+            full_assistant_prompt_message = AssistantPromptMessage(content=full_text, tool_calls=tool_calls)
             completion_tokens = self._num_tokens_from_messages(model, credentials, [full_assistant_prompt_message])
 
             # transform usage
@@ -444,14 +461,14 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 prompt_messages=prompt_messages,
                 delta=LLMResultChunkDelta(
                     index=index,
-                    message=AssistantPromptMessage(content='', tool_calls=tool_calls),
+                    message=AssistantPromptMessage(content="", tool_calls=tool_calls),
                     finish_reason=finish_reason,
-                    usage=usage
-                )
+                    usage=usage,
+                ),
             )
 
         index = 1
-        full_assistant_content = ''
+        full_assistant_content = ""
         tool_calls = []
         for chunk in response:
             if isinstance(chunk, StreamedChatResponse_TextGeneration):
@@ -462,9 +479,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                     continue
 
                 # transform assistant message to prompt message
-                assistant_prompt_message = AssistantPromptMessage(
-                    content=text
-                )
+                assistant_prompt_message = AssistantPromptMessage(content=text)
 
                 full_assistant_content += text
 
@@ -474,7 +489,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=index,
                         message=assistant_prompt_message,
-                    )
+                    ),
                 )
 
                 index += 1
@@ -484,11 +499,10 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                     for cohere_tool_call in chunk.tool_calls:
                         tool_call = AssistantPromptMessage.ToolCall(
                             id=cohere_tool_call.name,
-                            type='function',
+                            type="function",
                             function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                                name=cohere_tool_call.name,
-                                arguments=json.dumps(cohere_tool_call.parameters)
-                            )
+                                name=cohere_tool_call.name, arguments=json.dumps(cohere_tool_call.parameters)
+                            ),
                         )
                         tool_calls.append(tool_call)
             elif isinstance(chunk, StreamedChatResponse_StreamEnd):
@@ -496,8 +510,9 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 yield final_response(full_assistant_content, tool_calls, index, chunk.finish_reason)
                 index += 1
 
-    def _convert_prompt_messages_to_message_and_chat_histories(self, prompt_messages: list[PromptMessage]) \
-            -> tuple[str, list[ChatMessage], list[ChatStreamRequestToolResultsItem]]:
+    def _convert_prompt_messages_to_message_and_chat_histories(
+        self, prompt_messages: list[PromptMessage]
+    ) -> tuple[str, list[ChatMessage], list[ChatStreamRequestToolResultsItem]]:
         """
         Convert prompt messages to message and chat histories
         :param prompt_messages: prompt messages
@@ -510,13 +525,14 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 prompt_message = cast(AssistantPromptMessage, prompt_message)
                 if prompt_message.tool_calls:
                     for tool_call in prompt_message.tool_calls:
-                        latest_tool_call_n_outputs.append(ChatStreamRequestToolResultsItem(
-                            call=ToolCall(
-                                name=tool_call.function.name,
-                                parameters=json.loads(tool_call.function.arguments)
-                            ),
-                            outputs=[]
-                        ))
+                        latest_tool_call_n_outputs.append(
+                            ChatStreamRequestToolResultsItem(
+                                call=ToolCall(
+                                    name=tool_call.function.name, parameters=json.loads(tool_call.function.arguments)
+                                ),
+                                outputs=[],
+                            )
+                        )
                 else:
                     cohere_prompt_message = self._convert_prompt_message_to_dict(prompt_message)
                     if cohere_prompt_message:
@@ -529,12 +545,9 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                         if tool_call_n_outputs.call.name == prompt_message.tool_call_id:
                             latest_tool_call_n_outputs[i] = ChatStreamRequestToolResultsItem(
                                 call=ToolCall(
-                                    name=tool_call_n_outputs.call.name,
-                                    parameters=tool_call_n_outputs.call.parameters
+                                    name=tool_call_n_outputs.call.name, parameters=tool_call_n_outputs.call.parameters
                                 ),
-                                outputs=[{
-                                    "result": prompt_message.content
-                                }]
+                                outputs=[{"result": prompt_message.content}],
                             )
                             break
                         i += 1
@@ -556,7 +569,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
             latest_message = chat_histories.pop()
             message = latest_message.message
         else:
-            raise ValueError('Prompt messages is empty')
+            raise ValueError("Prompt messages is empty")
 
         return message, chat_histories, latest_tool_call_n_outputs
 
@@ -569,7 +582,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
             if isinstance(message.content, str):
                 chat_message = ChatMessage(role="USER", message=message.content)
             else:
-                sub_message_text = ''
+                sub_message_text = ""
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
                         message_content = cast(TextPromptMessageContent, message_content)
@@ -597,8 +610,8 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         """
         cohere_tools = []
         for tool in tools:
-            properties = tool.parameters['properties']
-            required_properties = tool.parameters['required']
+            properties = tool.parameters["properties"]
+            required_properties = tool.parameters["required"]
 
             parameter_definitions = {}
             for p_key, p_val in properties.items():
@@ -606,21 +619,16 @@ class CohereLargeLanguageModel(LargeLanguageModel):
                 if p_key in required_properties:
                     required = True
 
-                desc = p_val['description']
-                if 'enum' in p_val:
-                    desc += (f"; Only accepts one of the following predefined options: "
-                             f"[{', '.join(p_val['enum'])}]")
+                desc = p_val["description"]
+                if "enum" in p_val:
+                    desc += f"; Only accepts one of the following predefined options: [{', '.join(p_val['enum'])}]"
 
                 parameter_definitions[p_key] = ToolParameterDefinitionsValue(
-                    description=desc,
-                    type=p_val['type'],
-                    required=required
+                    description=desc, type=p_val["type"], required=required
                 )
 
             cohere_tool = Tool(
-                name=tool.name,
-                description=tool.description,
-                parameter_definitions=parameter_definitions
+                name=tool.name, description=tool.description, parameter_definitions=parameter_definitions
             )
 
             cohere_tools.append(cohere_tool)
@@ -637,12 +645,9 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         :return: number of tokens
         """
         # initialize client
-        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
+        client = cohere.Client(credentials.get("api_key"), base_url=credentials.get("base_url"))
 
-        response = client.tokenize(
-            text=text,
-            model=model
-        )
+        response = client.tokenize(text=text, model=model)
 
         return len(response.tokens)
 
@@ -658,30 +663,30 @@ class CohereLargeLanguageModel(LargeLanguageModel):
 
         real_model = model
         if self.get_model_schema(model, credentials).fetch_from == FetchFrom.PREDEFINED_MODEL:
-            real_model = model.removesuffix('-chat')
+            real_model = model.removesuffix("-chat")
 
         return self._num_tokens_from_string(real_model, credentials, message_str)
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            Cohere supports fine-tuning of their models. This method returns the schema of the base model
-            but renamed to the fine-tuned model name.
+        Cohere supports fine-tuning of their models. This method returns the schema of the base model
+        but renamed to the fine-tuned model name.
 
-            :param model: model name
-            :param credentials: credentials
+        :param model: model name
+        :param credentials: credentials
 
-            :return: model schema
+        :return: model schema
         """
         # get model schema
         models = self.predefined_models()
         model_map = {model.model: model for model in models}
 
-        mode = credentials.get('mode')
+        mode = credentials.get("mode")
 
-        if mode == 'chat':
-            base_model_schema = model_map['command-light-chat']
+        if mode == "chat":
+            base_model_schema = model_map["command-light-chat"]
         else:
-            base_model_schema = model_map['command-light']
+            base_model_schema = model_map["command-light"]
 
         base_model_schema = cast(AIModelEntity, base_model_schema)
 
@@ -691,16 +696,13 @@ class CohereLargeLanguageModel(LargeLanguageModel):
 
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                zh_Hans=model,
-                en_US=model
-            ),
+            label=I18nObject(zh_Hans=model, en_US=model),
             model_type=ModelType.LLM,
             features=list(base_model_schema_features),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties=dict(base_model_schema_model_properties.items()),
             parameter_rules=list(base_model_schema_parameters_rules),
-            pricing=base_model_schema.pricing
+            pricing=base_model_schema.pricing,
         )
 
         return entity
@@ -716,22 +718,16 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                cohere.errors.service_unavailable_error.ServiceUnavailableError
-            ],
-            InvokeServerUnavailableError: [
-                cohere.errors.internal_server_error.InternalServerError
-            ],
-            InvokeRateLimitError: [
-                cohere.errors.too_many_requests_error.TooManyRequestsError
-            ],
+            InvokeConnectionError: [cohere.errors.service_unavailable_error.ServiceUnavailableError],
+            InvokeServerUnavailableError: [cohere.errors.internal_server_error.InternalServerError],
+            InvokeRateLimitError: [cohere.errors.too_many_requests_error.TooManyRequestsError],
             InvokeAuthorizationError: [
                 cohere.errors.unauthorized_error.UnauthorizedError,
-                cohere.errors.forbidden_error.ForbiddenError
+                cohere.errors.forbidden_error.ForbiddenError,
             ],
             InvokeBadRequestError: [
                 cohere.core.api_error.ApiError,
                 cohere.errors.bad_request_error.BadRequestError,
                 cohere.errors.not_found_error.NotFoundError,
-            ]
+            ],
         }
diff --git a/api/core/model_runtime/model_providers/cohere/rerank/rerank.py b/api/core/model_runtime/model_providers/cohere/rerank/rerank.py
index d2fdb30c6f..aba8fedbc0 100644
--- a/api/core/model_runtime/model_providers/cohere/rerank/rerank.py
+++ b/api/core/model_runtime/model_providers/cohere/rerank/rerank.py
@@ -21,10 +21,16 @@ class CohereRerankModel(RerankModel):
     Model class for Cohere rerank model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
-                user: Optional[str] = None) \
-            -> RerankResult:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
         """
         Invoke rerank model
 
@@ -38,20 +44,17 @@ class CohereRerankModel(RerankModel):
         :return: rerank result
         """
         if len(docs) == 0:
-            return RerankResult(
-                model=model,
-                docs=docs
-            )
+            return RerankResult(model=model, docs=docs)
 
         # initialize client
-        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
+        client = cohere.Client(credentials.get("api_key"), base_url=credentials.get("base_url"))
         response = client.rerank(
             query=query,
             documents=docs,
             model=model,
             top_n=top_n,
             return_documents=True,
-            request_options=RequestOptions(max_retries=0)
+            request_options=RequestOptions(max_retries=0),
         )
 
         rerank_documents = []
@@ -70,10 +73,7 @@ class CohereRerankModel(RerankModel):
             else:
                 rerank_documents.append(rerank_document)
 
-        return RerankResult(
-            model=model,
-            docs=rerank_documents
-        )
+        return RerankResult(model=model, docs=rerank_documents)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
@@ -94,7 +94,7 @@ class CohereRerankModel(RerankModel):
                     "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
                     "are a political division controlled by the United States. Its capital is Saipan.",
                 ],
-                score_threshold=0.8
+                score_threshold=0.8,
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -110,22 +110,16 @@ class CohereRerankModel(RerankModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                cohere.errors.service_unavailable_error.ServiceUnavailableError
-            ],
-            InvokeServerUnavailableError: [
-                cohere.errors.internal_server_error.InternalServerError
-            ],
-            InvokeRateLimitError: [
-                cohere.errors.too_many_requests_error.TooManyRequestsError
-            ],
+            InvokeConnectionError: [cohere.errors.service_unavailable_error.ServiceUnavailableError],
+            InvokeServerUnavailableError: [cohere.errors.internal_server_error.InternalServerError],
+            InvokeRateLimitError: [cohere.errors.too_many_requests_error.TooManyRequestsError],
             InvokeAuthorizationError: [
                 cohere.errors.unauthorized_error.UnauthorizedError,
-                cohere.errors.forbidden_error.ForbiddenError
+                cohere.errors.forbidden_error.ForbiddenError,
             ],
             InvokeBadRequestError: [
                 cohere.core.api_error.ApiError,
                 cohere.errors.bad_request_error.BadRequestError,
                 cohere.errors.not_found_error.NotFoundError,
-            ]
+            ],
         }
diff --git a/api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py
index 0540fb740f..a1c5e98118 100644
--- a/api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py
@@ -24,9 +24,9 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
     Model class for Cohere text embedding model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -46,14 +46,10 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
         used_tokens = 0
 
         for i, text in enumerate(texts):
-            tokenize_response = self._tokenize(
-                model=model,
-                credentials=credentials,
-                text=text
-            )
+            tokenize_response = self._tokenize(model=model, credentials=credentials, text=text)
 
             for j in range(0, len(tokenize_response), context_size):
-                tokens += [tokenize_response[j: j + context_size]]
+                tokens += [tokenize_response[j : j + context_size]]
                 indices += [i]
 
         batched_embeddings = []
@@ -62,9 +58,7 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
         for i in _iter:
             # call embedding model
             embeddings_batch, embedding_used_tokens = self._embedding_invoke(
-                model=model,
-                credentials=credentials,
-                texts=["".join(token) for token in tokens[i: i + max_chunks]]
+                model=model, credentials=credentials, texts=["".join(token) for token in tokens[i : i + max_chunks]]
             )
 
             used_tokens += embedding_used_tokens
@@ -80,9 +74,7 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
             _result = results[i]
             if len(_result) == 0:
                 embeddings_batch, embedding_used_tokens = self._embedding_invoke(
-                    model=model,
-                    credentials=credentials,
-                    texts=[" "]
+                    model=model, credentials=credentials, texts=[" "]
                 )
 
                 used_tokens += embedding_used_tokens
@@ -92,17 +84,9 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
             embeddings[i] = (average / np.linalg.norm(average)).tolist()
 
         # calc usage
-        usage = self._calc_response_usage(
-            model=model,
-            credentials=credentials,
-            tokens=used_tokens
-        )
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
 
-        return TextEmbeddingResult(
-            embeddings=embeddings,
-            usage=usage,
-            model=model
-        )
+        return TextEmbeddingResult(embeddings=embeddings, usage=usage, model=model)
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
@@ -116,14 +100,10 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
         if len(texts) == 0:
             return 0
 
-        full_text = ' '.join(texts)
+        full_text = " ".join(texts)
 
         try:
-            response = self._tokenize(
-                model=model,
-                credentials=credentials,
-                text=full_text
-            )
+            response = self._tokenize(model=model, credentials=credentials, text=full_text)
         except Exception as e:
             raise self._transform_invoke_error(e)
 
@@ -141,14 +121,9 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
             return []
 
         # initialize client
-        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
+        client = cohere.Client(credentials.get("api_key"), base_url=credentials.get("base_url"))
 
-        response = client.tokenize(
-            text=text,
-            model=model,
-            offline=False,
-            request_options=RequestOptions(max_retries=0)
-        )
+        response = client.tokenize(text=text, model=model, offline=False, request_options=RequestOptions(max_retries=0))
 
         return response.token_strings
 
@@ -162,11 +137,7 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
         """
         try:
             # call embedding model
-            self._embedding_invoke(
-                model=model,
-                credentials=credentials,
-                texts=['ping']
-            )
+            self._embedding_invoke(model=model, credentials=credentials, texts=["ping"])
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
@@ -180,14 +151,14 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
         :return: embeddings and used tokens
         """
         # initialize client
-        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
+        client = cohere.Client(credentials.get("api_key"), base_url=credentials.get("base_url"))
 
         # call embedding model
         response = client.embed(
             texts=texts,
             model=model,
-            input_type='search_document' if len(texts) > 1 else 'search_query',
-            request_options=RequestOptions(max_retries=1)
+            input_type="search_document" if len(texts) > 1 else "search_query",
+            request_options=RequestOptions(max_retries=1),
         )
 
         return response.embeddings, int(response.meta.billed_units.input_tokens)
@@ -203,10 +174,7 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -217,7 +185,7 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
@@ -233,22 +201,16 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                cohere.errors.service_unavailable_error.ServiceUnavailableError
-            ],
-            InvokeServerUnavailableError: [
-                cohere.errors.internal_server_error.InternalServerError
-            ],
-            InvokeRateLimitError: [
-                cohere.errors.too_many_requests_error.TooManyRequestsError
-            ],
+            InvokeConnectionError: [cohere.errors.service_unavailable_error.ServiceUnavailableError],
+            InvokeServerUnavailableError: [cohere.errors.internal_server_error.InternalServerError],
+            InvokeRateLimitError: [cohere.errors.too_many_requests_error.TooManyRequestsError],
             InvokeAuthorizationError: [
                 cohere.errors.unauthorized_error.UnauthorizedError,
-                cohere.errors.forbidden_error.ForbiddenError
+                cohere.errors.forbidden_error.ForbiddenError,
             ],
             InvokeBadRequestError: [
                 cohere.core.api_error.ApiError,
                 cohere.errors.bad_request_error.BadRequestError,
                 cohere.errors.not_found_error.NotFoundError,
-            ]
+            ],
         }
diff --git a/api/core/model_runtime/model_providers/deepseek/deepseek.py b/api/core/model_runtime/model_providers/deepseek/deepseek.py
index d61fd4ddc8..10feef8972 100644
--- a/api/core/model_runtime/model_providers/deepseek/deepseek.py
+++ b/api/core/model_runtime/model_providers/deepseek/deepseek.py
@@ -7,9 +7,7 @@ from core.model_runtime.model_providers.__base.model_provider import ModelProvid
 logger = logging.getLogger(__name__)
 
 
-
 class DeepSeekProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -22,12 +20,9 @@ class DeepSeekProvider(ModelProvider):
 
             # Use `deepseek-chat` model for validate,
             # no matter what model you pass in, text completion model or chat model
-            model_instance.validate_credentials(
-                model='deepseek-chat',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="deepseek-chat", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/deepseek/llm/deepseek-chat.yaml b/api/core/model_runtime/model_providers/deepseek/llm/deepseek-chat.yaml
index 6588a4b5e0..4973ac8ad6 100644
--- a/api/core/model_runtime/model_providers/deepseek/llm/deepseek-chat.yaml
+++ b/api/core/model_runtime/model_providers/deepseek/llm/deepseek-chat.yaml
@@ -62,7 +62,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/deepseek/llm/llm.py b/api/core/model_runtime/model_providers/deepseek/llm/llm.py
index bdb3823b60..6d0a3ee262 100644
--- a/api/core/model_runtime/model_providers/deepseek/llm/llm.py
+++ b/api/core/model_runtime/model_providers/deepseek/llm/llm.py
@@ -13,12 +13,17 @@ from core.model_runtime.model_providers.openai.llm.llm import OpenAILargeLanguag
 
 
 class DeepSeekLargeLanguageModel(OpenAILargeLanguageModel):
-
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
 
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
@@ -27,10 +32,8 @@ class DeepSeekLargeLanguageModel(OpenAILargeLanguageModel):
         self._add_custom_parameters(credentials)
         super().validate_credentials(model, credentials)
 
-
     # refactored from openai model runtime, use cl100k_base for calculate token number
-    def _num_tokens_from_string(self, model: str, text: str,
-                                tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_string(self, model: str, text: str, tools: Optional[list[PromptMessageTool]] = None) -> int:
         """
         Calculate num tokens for text completion model with tiktoken package.
 
@@ -48,8 +51,9 @@ class DeepSeekLargeLanguageModel(OpenAILargeLanguageModel):
         return num_tokens
 
     # refactored from openai model runtime, use cl100k_base for calculate token number
-    def _num_tokens_from_messages(self, model: str, messages: list[PromptMessage],
-                                  tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_messages(
+        self, model: str, messages: list[PromptMessage], tools: Optional[list[PromptMessageTool]] = None
+    ) -> int:
         """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
 
         Official documentation: https://github.com/openai/openai-cookbook/blob/
@@ -69,10 +73,10 @@ class DeepSeekLargeLanguageModel(OpenAILargeLanguageModel):
                 #  which need to download the image and then get the resolution for calculation,
                 #  and will increase the request delay
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
 
                     value = text
 
@@ -103,11 +107,10 @@ class DeepSeekLargeLanguageModel(OpenAILargeLanguageModel):
 
     @staticmethod
     def _add_custom_parameters(credentials: dict) -> None:
-        credentials['mode'] = 'chat'
-        credentials['openai_api_key']=credentials['api_key']
-        if 'endpoint_url' not in credentials or credentials['endpoint_url'] == "":
-            credentials['openai_api_base']='https://api.deepseek.com'
+        credentials["mode"] = "chat"
+        credentials["openai_api_key"] = credentials["api_key"]
+        if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
+            credentials["openai_api_base"] = "https://api.deepseek.com"
         else:
-            parsed_url = urlparse(credentials['endpoint_url'])
-            credentials['openai_api_base']=f"{parsed_url.scheme}://{parsed_url.netloc}"
-
+            parsed_url = urlparse(credentials["endpoint_url"])
+            credentials["openai_api_base"] = f"{parsed_url.scheme}://{parsed_url.netloc}"
diff --git a/api/core/model_runtime/model_providers/fishaudio/__init__.py b/api/core/model_runtime/model_providers/fishaudio/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/model_runtime/model_providers/fishaudio/_assets/fishaudio_l_en.svg b/api/core/model_runtime/model_providers/fishaudio/_assets/fishaudio_l_en.svg
new file mode 100644
index 0000000000..d6f7723bd5
--- /dev/null
+++ b/api/core/model_runtime/model_providers/fishaudio/_assets/fishaudio_l_en.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="61.1 180.15 377.8 139.718"><path d="M431.911 245.181c3.842 0 6.989 1.952 6.989 4.337v14.776c0 2.385-3.147 4.337-6.989 4.337-3.846 0-6.99-1.952-6.99-4.337v-14.776c0-2.385 3.144-4.337 6.99-4.337ZM404.135 250.955c3.846 0 6.989 1.952 6.989 4.337v32.528c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.952-6.989-4.337v-32.528c0-2.385 3.147-4.337 6.989-4.337ZM376.363 257.688c3.842 0 6.989 1.952 6.989 4.337v36.562c0 2.385-3.147 4.337-6.989 4.337-3.846 0-6.993-1.952-6.993-4.337v-36.562c0-2.386 3.147-4.337 6.993-4.337ZM348.587 263.26c3.846 0 6.989 1.952 6.989 4.337v36.159c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.952-6.989-4.337v-36.159c0-2.385 3.147-4.337 6.989-4.337ZM320.811 268.177c3.846 0 6.989 1.952 6.989 4.337v31.318c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.952-6.989-4.337v-31.318c0-2.385 3.147-4.337 6.989-4.337ZM293.179 288.148c3.846 0 6.989 1.952 6.989 4.337v9.935c0 2.384-3.147 4.336-6.989 4.336s-6.99-1.951-6.99-4.336v-9.935c0-2.386 3.144-4.337 6.99-4.337Z" style="fill:#b1b3b4;fill-rule:evenodd"></path><path d="M431.911 205.441c3.842 0 6.989 1.952 6.989 4.337v24.459c0 2.385-3.147 4.337-6.989 4.337-3.846 0-6.99-1.952-6.99-4.337v-24.459c0-2.385 3.144-4.337 6.99-4.337ZM404.135 189.026c3.846 0 6.989 1.952 6.989 4.337v43.622c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.951-6.989-4.337v-43.622c0-2.385 3.147-4.337 6.989-4.337ZM376.363 182.848c3.842 0 6.989 1.953 6.989 4.337v56.937c0 2.384-3.147 4.337-6.989 4.337-3.846 0-6.993-1.952-6.993-4.337v-56.937c0-2.385 3.147-4.337 6.993-4.337ZM348.587 180.15c3.846 0 6.989 1.952 6.989 4.337v66.619c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.952-6.989-4.337v-66.619c0-2.385 3.147-4.337 6.989-4.337ZM320.811 181.84c3.846 0 6.989 1.952 6.989 4.337v67.627c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.951-6.989-4.337v-67.627c0-2.386 3.147-4.337 6.989-4.337ZM293.179 186.076c3.846 0 6.989 1.952 6.989 4.337v84.37c0 2.385-3.147 4.337-6.989 4.337s-6.99-1.951-6.99-4.337v-84.37c0-2.386 3.144-4.337 6.99-4.337ZM264.829 193.262c3.846 0 6.989 1.953 6.989 4.337v95.667c0 2.385-3.143 4.337-6.989 4.337-3.843 0-6.99-1.951-6.99-4.337v-95.667c0-2.385 3.147-4.337 6.99-4.337ZM237.057 205.441c3.842 0 6.989 1.953 6.989 4.337v92.036c0 2.385-3.147 4.337-6.989 4.337-3.846 0-6.99-1.951-6.99-4.337v-92.036c0-2.385 3.144-4.337 6.99-4.337ZM209.281 221.302c3.846 0 6.989 1.952 6.989 4.337v80.134c0 2.385-3.147 4.337-6.989 4.337s-6.99-1.952-6.99-4.337v-80.134c0-2.386 3.144-4.337 6.99-4.337ZM181.505 232.271c3.846 0 6.993 1.952 6.993 4.336v78.924c0 2.385-3.147 4.337-6.993 4.337-3.842 0-6.989-1.951-6.989-4.337v-78.924c0-2.385 3.147-4.336 6.989-4.336ZM153.873 241.348c3.846 0 6.989 1.953 6.989 4.337v42.009c0 2.384-3.147 4.337-6.989 4.337-3.843 0-6.99-1.952-6.99-4.337v-42.009c0-2.385 3.147-4.337 6.99-4.337ZM125.266 200.398c3.842 0 6.989 1.953 6.989 4.337v58.55c0 2.384-3.147 4.337-6.989 4.337-3.843 0-6.99-1.951-6.99-4.337v-58.55c0-2.385 3.144-4.337 6.99-4.337ZM96.7 204.231c3.842 0 6.989 1.953 6.989 4.337v18.004c0 2.384-3.147 4.337-6.989 4.337s-6.989-1.952-6.989-4.337v-18.004c0-2.385 3.143-4.337 6.989-4.337ZM68.089 201.81c3.846 0 6.99 1.953 6.99 4.337v8.12c0 2.384-3.147 4.336-6.99 4.336-3.842 0-6.989-1.951-6.989-4.336v-8.12c0-2.385 3.143-4.337 6.989-4.337ZM153.873 194.94c3.846 0 6.989 1.953 6.989 4.337v6.102c0 2.384-3.147 4.337-6.989 4.337-3.843 0-6.99-1.952-6.99-4.337v-6.102c0-2.385 3.147-4.337 6.99-4.337Z" style="fill:#000;fill-rule:evenodd"></path></svg>
\ No newline at end of file
diff --git a/api/core/model_runtime/model_providers/fishaudio/_assets/fishaudio_s_en.svg b/api/core/model_runtime/model_providers/fishaudio/_assets/fishaudio_s_en.svg
new file mode 100644
index 0000000000..d6f7723bd5
--- /dev/null
+++ b/api/core/model_runtime/model_providers/fishaudio/_assets/fishaudio_s_en.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="61.1 180.15 377.8 139.718"><path d="M431.911 245.181c3.842 0 6.989 1.952 6.989 4.337v14.776c0 2.385-3.147 4.337-6.989 4.337-3.846 0-6.99-1.952-6.99-4.337v-14.776c0-2.385 3.144-4.337 6.99-4.337ZM404.135 250.955c3.846 0 6.989 1.952 6.989 4.337v32.528c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.952-6.989-4.337v-32.528c0-2.385 3.147-4.337 6.989-4.337ZM376.363 257.688c3.842 0 6.989 1.952 6.989 4.337v36.562c0 2.385-3.147 4.337-6.989 4.337-3.846 0-6.993-1.952-6.993-4.337v-36.562c0-2.386 3.147-4.337 6.993-4.337ZM348.587 263.26c3.846 0 6.989 1.952 6.989 4.337v36.159c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.952-6.989-4.337v-36.159c0-2.385 3.147-4.337 6.989-4.337ZM320.811 268.177c3.846 0 6.989 1.952 6.989 4.337v31.318c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.952-6.989-4.337v-31.318c0-2.385 3.147-4.337 6.989-4.337ZM293.179 288.148c3.846 0 6.989 1.952 6.989 4.337v9.935c0 2.384-3.147 4.336-6.989 4.336s-6.99-1.951-6.99-4.336v-9.935c0-2.386 3.144-4.337 6.99-4.337Z" style="fill:#b1b3b4;fill-rule:evenodd"></path><path d="M431.911 205.441c3.842 0 6.989 1.952 6.989 4.337v24.459c0 2.385-3.147 4.337-6.989 4.337-3.846 0-6.99-1.952-6.99-4.337v-24.459c0-2.385 3.144-4.337 6.99-4.337ZM404.135 189.026c3.846 0 6.989 1.952 6.989 4.337v43.622c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.951-6.989-4.337v-43.622c0-2.385 3.147-4.337 6.989-4.337ZM376.363 182.848c3.842 0 6.989 1.953 6.989 4.337v56.937c0 2.384-3.147 4.337-6.989 4.337-3.846 0-6.993-1.952-6.993-4.337v-56.937c0-2.385 3.147-4.337 6.993-4.337ZM348.587 180.15c3.846 0 6.989 1.952 6.989 4.337v66.619c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.952-6.989-4.337v-66.619c0-2.385 3.147-4.337 6.989-4.337ZM320.811 181.84c3.846 0 6.989 1.952 6.989 4.337v67.627c0 2.385-3.143 4.337-6.989 4.337-3.842 0-6.989-1.951-6.989-4.337v-67.627c0-2.386 3.147-4.337 6.989-4.337ZM293.179 186.076c3.846 0 6.989 1.952 6.989 4.337v84.37c0 2.385-3.147 4.337-6.989 4.337s-6.99-1.951-6.99-4.337v-84.37c0-2.386 3.144-4.337 6.99-4.337ZM264.829 193.262c3.846 0 6.989 1.953 6.989 4.337v95.667c0 2.385-3.143 4.337-6.989 4.337-3.843 0-6.99-1.951-6.99-4.337v-95.667c0-2.385 3.147-4.337 6.99-4.337ZM237.057 205.441c3.842 0 6.989 1.953 6.989 4.337v92.036c0 2.385-3.147 4.337-6.989 4.337-3.846 0-6.99-1.951-6.99-4.337v-92.036c0-2.385 3.144-4.337 6.99-4.337ZM209.281 221.302c3.846 0 6.989 1.952 6.989 4.337v80.134c0 2.385-3.147 4.337-6.989 4.337s-6.99-1.952-6.99-4.337v-80.134c0-2.386 3.144-4.337 6.99-4.337ZM181.505 232.271c3.846 0 6.993 1.952 6.993 4.336v78.924c0 2.385-3.147 4.337-6.993 4.337-3.842 0-6.989-1.951-6.989-4.337v-78.924c0-2.385 3.147-4.336 6.989-4.336ZM153.873 241.348c3.846 0 6.989 1.953 6.989 4.337v42.009c0 2.384-3.147 4.337-6.989 4.337-3.843 0-6.99-1.952-6.99-4.337v-42.009c0-2.385 3.147-4.337 6.99-4.337ZM125.266 200.398c3.842 0 6.989 1.953 6.989 4.337v58.55c0 2.384-3.147 4.337-6.989 4.337-3.843 0-6.99-1.951-6.99-4.337v-58.55c0-2.385 3.144-4.337 6.99-4.337ZM96.7 204.231c3.842 0 6.989 1.953 6.989 4.337v18.004c0 2.384-3.147 4.337-6.989 4.337s-6.989-1.952-6.989-4.337v-18.004c0-2.385 3.143-4.337 6.989-4.337ZM68.089 201.81c3.846 0 6.99 1.953 6.99 4.337v8.12c0 2.384-3.147 4.336-6.99 4.336-3.842 0-6.989-1.951-6.989-4.336v-8.12c0-2.385 3.143-4.337 6.989-4.337ZM153.873 194.94c3.846 0 6.989 1.953 6.989 4.337v6.102c0 2.384-3.147 4.337-6.989 4.337-3.843 0-6.99-1.952-6.99-4.337v-6.102c0-2.385 3.147-4.337 6.99-4.337Z" style="fill:#000;fill-rule:evenodd"></path></svg>
\ No newline at end of file
diff --git a/api/core/model_runtime/model_providers/fishaudio/fishaudio.py b/api/core/model_runtime/model_providers/fishaudio/fishaudio.py
new file mode 100644
index 0000000000..3bc4b533e0
--- /dev/null
+++ b/api/core/model_runtime/model_providers/fishaudio/fishaudio.py
@@ -0,0 +1,26 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class FishAudioProvider(ModelProvider):
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+
+        For debugging purposes, this method now always passes validation.
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.TTS)
+            model_instance.validate_credentials(credentials=credentials)
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
+            raise ex
diff --git a/api/core/model_runtime/model_providers/fishaudio/fishaudio.yaml b/api/core/model_runtime/model_providers/fishaudio/fishaudio.yaml
new file mode 100644
index 0000000000..479eb7fb85
--- /dev/null
+++ b/api/core/model_runtime/model_providers/fishaudio/fishaudio.yaml
@@ -0,0 +1,76 @@
+provider: fishaudio
+label:
+  en_US: Fish Audio
+description:
+  en_US: Models provided by Fish Audio, currently only support TTS.
+  zh_Hans: Fish Audio 提供的模型，目前仅支持 TTS。
+icon_small:
+  en_US: fishaudio_s_en.svg
+icon_large:
+  en_US: fishaudio_l_en.svg
+background: "#E5E7EB"
+help:
+  title:
+    en_US: Get your API key from Fish Audio
+    zh_Hans: 从 Fish Audio 获取你的 API Key
+  url:
+    en_US: https://fish.audio/go-api/
+supported_model_types:
+  - tts
+configurate_methods:
+  - predefined-model
+provider_credential_schema:
+  credential_form_schemas:
+    - variable: api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key
+    - variable: api_base
+      label:
+        en_US: API URL
+      type: text-input
+      required: false
+      default: https://api.fish.audio
+      placeholder:
+        en_US: Enter your API URL
+        zh_Hans: 在此输入您的 API URL
+    - variable: use_public_models
+      label:
+        en_US: Use Public Models
+      type: select
+      required: false
+      default: "false"
+      placeholder:
+        en_US: Toggle to use public models
+        zh_Hans: 切换以使用公共模型
+      options:
+        - value: "true"
+          label:
+            en_US: Allow Public Models
+            zh_Hans: 使用公共模型
+        - value: "false"
+          label:
+            en_US: Private Models Only
+            zh_Hans: 仅使用私有模型
+    - variable: latency
+      label:
+        en_US: Latency
+      type: select
+      required: false
+      default: "normal"
+      placeholder:
+        en_US: Toggle to choice latency
+        zh_Hans: 切换以调整延迟
+      options:
+        - value: "balanced"
+          label:
+            en_US: Low (may affect quality)
+            zh_Hans: 低延迟 (可能降低质量)
+        - value: "normal"
+          label:
+            en_US: Normal
+            zh_Hans: 标准
diff --git a/api/core/model_runtime/model_providers/fishaudio/tts/__init__.py b/api/core/model_runtime/model_providers/fishaudio/tts/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/model_runtime/model_providers/fishaudio/tts/tts.py b/api/core/model_runtime/model_providers/fishaudio/tts/tts.py
new file mode 100644
index 0000000000..895a7a914c
--- /dev/null
+++ b/api/core/model_runtime/model_providers/fishaudio/tts/tts.py
@@ -0,0 +1,158 @@
+from typing import Optional
+
+import httpx
+
+from core.model_runtime.errors.invoke import InvokeBadRequestError, InvokeError
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.tts_model import TTSModel
+
+
+class FishAudioText2SpeechModel(TTSModel):
+    """
+    Model class for Fish.audio Text to Speech model.
+    """
+
+    def get_tts_model_voices(self, model: str, credentials: dict, language: Optional[str] = None) -> list:
+        api_base = credentials.get("api_base", "https://api.fish.audio")
+        api_key = credentials.get("api_key")
+        use_public_models = credentials.get("use_public_models", "false") == "true"
+
+        params = {
+            "self": str(not use_public_models).lower(),
+            "page_size": "100",
+        }
+
+        if language is not None:
+            if "-" in language:
+                language = language.split("-")[0]
+            params["language"] = language
+
+        results = httpx.get(
+            f"{api_base}/model",
+            headers={"Authorization": f"Bearer {api_key}"},
+            params=params,
+        )
+
+        results.raise_for_status()
+        data = results.json()
+
+        return [{"name": i["title"], "value": i["_id"]} for i in data["items"]]
+
+    def _invoke(
+        self,
+        model: str,
+        tenant_id: str,
+        credentials: dict,
+        content_text: str,
+        voice: str,
+        user: Optional[str] = None,
+    ) -> any:
+        """
+        Invoke text2speech model
+
+        :param model: model name
+        :param tenant_id: user tenant id
+        :param credentials: model credentials
+        :param voice: model timbre
+        :param content_text: text content to be translated
+        :param user: unique user id
+        :return: generator yielding audio chunks
+        """
+
+        return self._tts_invoke_streaming(
+            model=model,
+            credentials=credentials,
+            content_text=content_text,
+            voice=voice,
+        )
+
+    def validate_credentials(self, credentials: dict, user: Optional[str] = None) -> None:
+        """
+        Validate credentials for text2speech model
+
+        :param credentials: model credentials
+        :param user: unique user id
+        """
+
+        try:
+            self.get_tts_model_voices(
+                None,
+                credentials={
+                    "api_key": credentials["api_key"],
+                    "api_base": credentials["api_base"],
+                    # Disable public models will trigger a 403 error if user is not logged in
+                    "use_public_models": "false",
+                },
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
+        """
+        Invoke streaming text2speech model
+        :param model: model name
+        :param credentials: model credentials
+        :param content_text: text content to be translated
+        :param voice: ID of the reference audio (if any)
+        :return: generator yielding audio chunks
+        """
+
+        try:
+            word_limit = self._get_model_word_limit(model, credentials)
+            if len(content_text) > word_limit:
+                sentences = self._split_text_into_sentences(content_text, max_length=word_limit)
+            else:
+                sentences = [content_text.strip()]
+
+            for i in range(len(sentences)):
+                yield from self._tts_invoke_streaming_sentence(
+                    credentials=credentials, content_text=sentences[i], voice=voice
+                )
+
+        except Exception as ex:
+            raise InvokeBadRequestError(str(ex))
+
+    def _tts_invoke_streaming_sentence(self, credentials: dict, content_text: str, voice: Optional[str] = None) -> any:
+        """
+        Invoke streaming text2speech model
+
+        :param credentials: model credentials
+        :param content_text: text content to be translated
+        :param voice: ID of the reference audio (if any)
+        :return: generator yielding audio chunks
+        """
+        api_key = credentials.get("api_key")
+        api_url = credentials.get("api_base", "https://api.fish.audio")
+        latency = credentials.get("latency")
+
+        if not api_key:
+            raise InvokeBadRequestError("API key is required")
+
+        with httpx.stream(
+            "POST",
+            api_url + "/v1/tts",
+            json={"text": content_text, "reference_id": voice, "latency": latency},
+            headers={
+                "Authorization": f"Bearer {api_key}",
+            },
+            timeout=None,
+        ) as response:
+            if response.status_code != 200:
+                raise InvokeBadRequestError(f"Error: {response.status_code} - {response.text}")
+            yield from response.iter_bytes()
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeBadRequestError: [
+                httpx.HTTPStatusError,
+            ],
+        }
diff --git a/api/core/model_runtime/model_providers/fishaudio/tts/tts.yaml b/api/core/model_runtime/model_providers/fishaudio/tts/tts.yaml
new file mode 100644
index 0000000000..b4a446a957
--- /dev/null
+++ b/api/core/model_runtime/model_providers/fishaudio/tts/tts.yaml
@@ -0,0 +1,5 @@
+model: tts-default
+model_type: tts
+model_properties:
+  word_limit: 1000
+  audio_type: 'mp3'
diff --git a/api/core/model_runtime/model_providers/google/google.py b/api/core/model_runtime/model_providers/google/google.py
index ba25c74e71..70f56a8337 100644
--- a/api/core/model_runtime/model_providers/google/google.py
+++ b/api/core/model_runtime/model_providers/google/google.py
@@ -20,12 +20,9 @@ class GoogleProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.LLM)
 
             # Use `gemini-pro` model for validate,
-            model_instance.validate_credentials(
-                model='gemini-pro',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="gemini-pro", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/google/llm/llm.py b/api/core/model_runtime/model_providers/google/llm/llm.py
index 84241fb6c8..3fc6787a44 100644
--- a/api/core/model_runtime/model_providers/google/llm/llm.py
+++ b/api/core/model_runtime/model_providers/google/llm/llm.py
@@ -6,10 +6,10 @@ from collections.abc import Generator
 from typing import Optional, Union, cast
 
 import google.ai.generativelanguage as glm
-import google.api_core.exceptions as exceptions
 import google.generativeai as genai
-import google.generativeai.client as client
 import requests
+from google.api_core import exceptions
+from google.generativeai import client
 from google.generativeai.types import ContentType, GenerateContentResponse, HarmBlockThreshold, HarmCategory
 from google.generativeai.types.content_types import to_part
 from PIL import Image
@@ -45,16 +45,21 @@ if you are not sure about the structure.
 <instructions>
 {{instructions}}
 </instructions>
-"""
+"""  # noqa: E501
 
 
 class GoogleLargeLanguageModel(LargeLanguageModel):
-
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -70,9 +75,14 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         """
         # invoke model
         return self._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
-    
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -85,7 +95,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         prompt = self._convert_messages_to_prompt(prompt_messages)
 
         return self._get_num_tokens_by_gpt2(prompt)
-    
+
     def _convert_messages_to_prompt(self, messages: list[PromptMessage]) -> str:
         """
         Format a list of messages into a full prompt for the Google model
@@ -95,13 +105,10 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         """
         messages = messages.copy()  # don't mutate the original list
 
-        text = "".join(
-            self._convert_one_message_to_text(message)
-            for message in messages
-        )
+        text = "".join(self._convert_one_message_to_text(message) for message in messages)
 
         return text.rstrip()
-    
+
     def _convert_tools_to_glm_tool(self, tools: list[PromptMessageTool]) -> glm.Tool:
         """
         Convert tool messages to glm tools
@@ -117,14 +124,16 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                         type=glm.Type.OBJECT,
                         properties={
                             key: {
-                                'type_': value.get('type', 'string').upper(),
-                                'description': value.get('description', ''),
-                                'enum': value.get('enum', [])
-                            } for key, value in tool.parameters.get('properties', {}).items()
+                                "type_": value.get("type", "string").upper(),
+                                "description": value.get("description", ""),
+                                "enum": value.get("enum", []),
+                            }
+                            for key, value in tool.parameters.get("properties", {}).items()
                         },
-                        required=tool.parameters.get('required', [])
+                        required=tool.parameters.get("required", []),
                     ),
-                ) for tool in tools
+                )
+                for tool in tools
             ]
         )
 
@@ -136,20 +145,25 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         :param credentials: model credentials
         :return:
         """
-        
+
         try:
             ping_message = SystemPromptMessage(content="ping")
             self._generate(model, credentials, [ping_message], {"max_tokens_to_sample": 5})
-            
+
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
-            
 
-    def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict,
-                  tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None, 
-                  stream: bool = True, user: Optional[str] = None
-        ) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -163,14 +177,12 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         config_kwargs = model_parameters.copy()
-        config_kwargs['max_output_tokens'] = config_kwargs.pop('max_tokens_to_sample', None)
+        config_kwargs["max_output_tokens"] = config_kwargs.pop("max_tokens_to_sample", None)
 
         if stop:
             config_kwargs["stop_sequences"] = stop
 
-        google_model = genai.GenerativeModel(
-            model_name=model
-        )
+        google_model = genai.GenerativeModel(model_name=model)
 
         history = []
 
@@ -180,7 +192,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
             content = self._format_message_to_glm_content(last_msg)
             history.append(content)
         else:
-            for msg in prompt_messages:     # makes message roles strictly alternating
+            for msg in prompt_messages:  # makes message roles strictly alternating
                 content = self._format_message_to_glm_content(msg)
                 if history and history[-1]["role"] == content["role"]:
                     history[-1]["parts"].extend(content["parts"])
@@ -194,7 +206,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
 
         google_model._client = new_custom_client
 
-        safety_settings={
+        safety_settings = {
             HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
             HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
             HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
@@ -203,13 +215,11 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
 
         response = google_model.generate_content(
             contents=history,
-            generation_config=genai.types.GenerationConfig(
-                **config_kwargs
-            ),
+            generation_config=genai.types.GenerationConfig(**config_kwargs),
             stream=stream,
             safety_settings=safety_settings,
             tools=self._convert_tools_to_glm_tool(tools) if tools else None,
-            request_options={"timeout": 600}
+            request_options={"timeout": 600},
         )
 
         if stream:
@@ -217,8 +227,9 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
 
         return self._handle_generate_response(model, credentials, response, prompt_messages)
 
-    def _handle_generate_response(self, model: str, credentials: dict, response: GenerateContentResponse,
-                                  prompt_messages: list[PromptMessage]) -> LLMResult:
+    def _handle_generate_response(
+        self, model: str, credentials: dict, response: GenerateContentResponse, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
         """
         Handle llm response
 
@@ -229,9 +240,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         :return: llm response
         """
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=response.text
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=response.text)
 
         # calculate num tokens
         prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
@@ -250,8 +259,9 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
 
         return result
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: GenerateContentResponse,
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, response: GenerateContentResponse, prompt_messages: list[PromptMessage]
+    ) -> Generator:
         """
         Handle llm stream response
 
@@ -264,9 +274,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         index = -1
         for chunk in response:
             for part in chunk.parts:
-                assistant_prompt_message = AssistantPromptMessage(
-                    content=''
-                )
+                assistant_prompt_message = AssistantPromptMessage(content="")
 
                 if part.text:
                     assistant_prompt_message.content += part.text
@@ -275,36 +283,31 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                     assistant_prompt_message.tool_calls = [
                         AssistantPromptMessage.ToolCall(
                             id=part.function_call.name,
-                            type='function',
+                            type="function",
                             function=AssistantPromptMessage.ToolCall.ToolCallFunction(
                                 name=part.function_call.name,
-                                arguments=json.dumps(dict(part.function_call.args.items()))
-                            )
+                                arguments=json.dumps(dict(part.function_call.args.items())),
+                            ),
                         )
                     ]
 
                 index += 1
-    
+
                 if not response._done:
-                    
                     # transform assistant message to prompt message
                     yield LLMResultChunk(
                         model=model,
                         prompt_messages=prompt_messages,
-                        delta=LLMResultChunkDelta(
-                            index=index,
-                            message=assistant_prompt_message
-                        )
+                        delta=LLMResultChunkDelta(index=index, message=assistant_prompt_message),
                     )
                 else:
-                    
                     # calculate num tokens
                     prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
                     completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
 
                     # transform usage
                     usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
-                    
+
                     yield LLMResultChunk(
                         model=model,
                         prompt_messages=prompt_messages,
@@ -312,8 +315,8 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                             index=index,
                             message=assistant_prompt_message,
                             finish_reason=str(chunk.candidates[0].finish_reason),
-                            usage=usage
-                        )
+                            usage=usage,
+                        ),
                     )
 
     def _convert_one_message_to_text(self, message: PromptMessage) -> str:
@@ -328,17 +331,13 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
 
         content = message.content
         if isinstance(content, list):
-            content = "".join(
-                c.data for c in content if c.type != PromptMessageContentType.IMAGE
-            )
+            content = "".join(c.data for c in content if c.type != PromptMessageContentType.IMAGE)
 
         if isinstance(message, UserPromptMessage):
             message_text = f"{human_prompt} {content}"
         elif isinstance(message, AssistantPromptMessage):
             message_text = f"{ai_prompt} {content}"
-        elif isinstance(message, SystemPromptMessage):
-            message_text = f"{human_prompt} {content}"
-        elif isinstance(message, ToolPromptMessage):
+        elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
             message_text = f"{human_prompt} {content}"
         else:
             raise ValueError(f"Got unknown type {message}")
@@ -353,95 +352,86 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         :return: glm Content representation of message
         """
         if isinstance(message, UserPromptMessage):
-            glm_content = {
-                "role": "user",
-                "parts": []
-            }
-            if (isinstance(message.content, str)):
-                glm_content['parts'].append(to_part(message.content))
+            glm_content = {"role": "user", "parts": []}
+            if isinstance(message.content, str):
+                glm_content["parts"].append(to_part(message.content))
             else:
                 for c in message.content:
                     if c.type == PromptMessageContentType.TEXT:
-                        glm_content['parts'].append(to_part(c.data))
+                        glm_content["parts"].append(to_part(c.data))
                     elif c.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, c)
                         if message_content.data.startswith("data:"):
-                            metadata, base64_data = c.data.split(',', 1)
-                            mime_type = metadata.split(';', 1)[0].split(':')[1]
+                            metadata, base64_data = c.data.split(",", 1)
+                            mime_type = metadata.split(";", 1)[0].split(":")[1]
                         else:
                             # fetch image data from url
                             try:
                                 image_content = requests.get(message_content.data).content
                                 with Image.open(io.BytesIO(image_content)) as img:
                                     mime_type = f"image/{img.format.lower()}"
-                                base64_data = base64.b64encode(image_content).decode('utf-8')
+                                base64_data = base64.b64encode(image_content).decode("utf-8")
                             except Exception as ex:
                                 raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")
-                        blob = {"inline_data":{"mime_type":mime_type,"data":base64_data}}
-                        glm_content['parts'].append(blob)
+                        blob = {"inline_data": {"mime_type": mime_type, "data": base64_data}}
+                        glm_content["parts"].append(blob)
 
             return glm_content
         elif isinstance(message, AssistantPromptMessage):
-            glm_content = {
-                "role": "model",
-                "parts": []
-            }
+            glm_content = {"role": "model", "parts": []}
             if message.content:
-                glm_content['parts'].append(to_part(message.content))
+                glm_content["parts"].append(to_part(message.content))
             if message.tool_calls:
-                glm_content["parts"].append(to_part(glm.FunctionCall(
-                    name=message.tool_calls[0].function.name,
-                    args=json.loads(message.tool_calls[0].function.arguments),
-                )))
+                glm_content["parts"].append(
+                    to_part(
+                        glm.FunctionCall(
+                            name=message.tool_calls[0].function.name,
+                            args=json.loads(message.tool_calls[0].function.arguments),
+                        )
+                    )
+                )
             return glm_content
         elif isinstance(message, SystemPromptMessage):
-            return {
-                "role": "user",
-                "parts": [to_part(message.content)]
-            }
+            return {"role": "user", "parts": [to_part(message.content)]}
         elif isinstance(message, ToolPromptMessage):
             return {
                 "role": "function",
-                "parts": [glm.Part(function_response=glm.FunctionResponse(
-                    name=message.name,
-                    response={
-                        "response": message.content
-                    }
-                ))]
+                "parts": [
+                    glm.Part(
+                        function_response=glm.FunctionResponse(
+                            name=message.name, response={"response": message.content}
+                        )
+                    )
+                ],
             }
         else:
             raise ValueError(f"Got unknown type {message}")
-    
+
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         """
         Map model invoke error to unified error
-        The key is the ermd = genai.GenerativeModel(model)ror type thrown to the caller
-        The value is the md = genai.GenerativeModel(model)error type thrown by the model,
+        The key is the ermd = genai.GenerativeModel(model) error type thrown to the caller
+        The value is the md = genai.GenerativeModel(model) error type thrown by the model,
         which needs to be converted into a unified error type for the caller.
 
-        :return: Invoke emd = genai.GenerativeModel(model)rror mapping
+        :return: Invoke emd = genai.GenerativeModel(model) error mapping
         """
         return {
-            InvokeConnectionError: [
-                exceptions.RetryError
-            ],
+            InvokeConnectionError: [exceptions.RetryError],
             InvokeServerUnavailableError: [
                 exceptions.ServiceUnavailable,
                 exceptions.InternalServerError,
                 exceptions.BadGateway,
                 exceptions.GatewayTimeout,
-                exceptions.DeadlineExceeded
-            ],
-            InvokeRateLimitError: [
-                exceptions.ResourceExhausted,
-                exceptions.TooManyRequests
+                exceptions.DeadlineExceeded,
             ],
+            InvokeRateLimitError: [exceptions.ResourceExhausted, exceptions.TooManyRequests],
             InvokeAuthorizationError: [
                 exceptions.Unauthenticated,
                 exceptions.PermissionDenied,
                 exceptions.Unauthenticated,
-                exceptions.Forbidden
+                exceptions.Forbidden,
             ],
             InvokeBadRequestError: [
                 exceptions.BadRequest,
@@ -457,5 +447,5 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                 exceptions.PreconditionFailed,
                 exceptions.RequestRangeNotSatisfiable,
                 exceptions.Cancelled,
-            ]
+            ],
         }
diff --git a/api/core/model_runtime/model_providers/groq/groq.py b/api/core/model_runtime/model_providers/groq/groq.py
index b3f37b3967..d0d5ff68f8 100644
--- a/api/core/model_runtime/model_providers/groq/groq.py
+++ b/api/core/model_runtime/model_providers/groq/groq.py
@@ -6,8 +6,8 @@ from core.model_runtime.model_providers.__base.model_provider import ModelProvid
 
 logger = logging.getLogger(__name__)
 
-class GroqProvider(ModelProvider):
 
+class GroqProvider(ModelProvider):
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -18,12 +18,9 @@ class GroqProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(
-                model='llama3-8b-8192',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="llama3-8b-8192", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/groq/llm/llm.py b/api/core/model_runtime/model_providers/groq/llm/llm.py
index 915f7a4e1a..352a7b519e 100644
--- a/api/core/model_runtime/model_providers/groq/llm/llm.py
+++ b/api/core/model_runtime/model_providers/groq/llm/llm.py
@@ -7,11 +7,17 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
 
 
 class GroqLargeLanguageModel(OAIAPICompatLargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
@@ -21,6 +27,5 @@ class GroqLargeLanguageModel(OAIAPICompatLargeLanguageModel):
 
     @staticmethod
     def _add_custom_parameters(credentials: dict) -> None:
-        credentials['mode'] = 'chat'
-        credentials['endpoint_url'] = 'https://api.groq.com/openai/v1'
-    
+        credentials["mode"] = "chat"
+        credentials["endpoint_url"] = "https://api.groq.com/openai/v1"
diff --git a/api/core/model_runtime/model_providers/huggingface_hub/_common.py b/api/core/model_runtime/model_providers/huggingface_hub/_common.py
index dd8ae526e6..3c4020b6ee 100644
--- a/api/core/model_runtime/model_providers/huggingface_hub/_common.py
+++ b/api/core/model_runtime/model_providers/huggingface_hub/_common.py
@@ -4,12 +4,6 @@ from core.model_runtime.errors.invoke import InvokeBadRequestError, InvokeError
 
 
 class _CommonHuggingfaceHub:
-
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
-        return {
-            InvokeBadRequestError: [
-                HfHubHTTPError,
-                BadRequestError
-            ]
-        }
+        return {InvokeBadRequestError: [HfHubHTTPError, BadRequestError]}
diff --git a/api/core/model_runtime/model_providers/huggingface_hub/huggingface_hub.py b/api/core/model_runtime/model_providers/huggingface_hub/huggingface_hub.py
index 15e2a4fed4..54d2a2bf39 100644
--- a/api/core/model_runtime/model_providers/huggingface_hub/huggingface_hub.py
+++ b/api/core/model_runtime/model_providers/huggingface_hub/huggingface_hub.py
@@ -6,6 +6,5 @@ logger = logging.getLogger(__name__)
 
 
 class HuggingfaceHubProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         pass
diff --git a/api/core/model_runtime/model_providers/huggingface_hub/llm/llm.py b/api/core/model_runtime/model_providers/huggingface_hub/llm/llm.py
index f43a8aedaf..9d29237fdd 100644
--- a/api/core/model_runtime/model_providers/huggingface_hub/llm/llm.py
+++ b/api/core/model_runtime/model_providers/huggingface_hub/llm/llm.py
@@ -29,16 +29,23 @@ from core.model_runtime.model_providers.huggingface_hub._common import _CommonHu
 
 
 class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None, stream: bool = True,
-                user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
+        client = InferenceClient(token=credentials["huggingfacehub_api_token"])
 
-        client = InferenceClient(token=credentials['huggingfacehub_api_token'])
+        if credentials["huggingfacehub_api_type"] == "inference_endpoints":
+            model = credentials["huggingfacehub_endpoint_url"]
 
-        if credentials['huggingfacehub_api_type'] == 'inference_endpoints':
-            model = credentials['huggingfacehub_endpoint_url']
-
-        if 'baichuan' in model.lower():
+        if "baichuan" in model.lower():
             stream = False
 
         response = client.text_generation(
@@ -47,98 +54,100 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
             stream=stream,
             model=model,
             stop_sequences=stop,
-            **model_parameters)
+            **model_parameters,
+        )
 
         if stream:
             return self._handle_generate_stream_response(model, credentials, prompt_messages, response)
 
         return self._handle_generate_response(model, credentials, prompt_messages, response)
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         prompt = self._convert_messages_to_prompt(prompt_messages)
         return self._get_num_tokens_by_gpt2(prompt)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         try:
-            if 'huggingfacehub_api_type' not in credentials:
-                raise CredentialsValidateFailedError('Huggingface Hub Endpoint Type must be provided.')
+            if "huggingfacehub_api_type" not in credentials:
+                raise CredentialsValidateFailedError("Huggingface Hub Endpoint Type must be provided.")
 
-            if credentials['huggingfacehub_api_type'] not in ('inference_endpoints', 'hosted_inference_api'):
-                raise CredentialsValidateFailedError('Huggingface Hub Endpoint Type is invalid.')
+            if credentials["huggingfacehub_api_type"] not in {"inference_endpoints", "hosted_inference_api"}:
+                raise CredentialsValidateFailedError("Huggingface Hub Endpoint Type is invalid.")
 
-            if 'huggingfacehub_api_token' not in credentials:
-                raise CredentialsValidateFailedError('Huggingface Hub Access Token must be provided.')
+            if "huggingfacehub_api_token" not in credentials:
+                raise CredentialsValidateFailedError("Huggingface Hub Access Token must be provided.")
 
-            if credentials['huggingfacehub_api_type'] == 'inference_endpoints':
-                if 'huggingfacehub_endpoint_url' not in credentials:
-                    raise CredentialsValidateFailedError('Huggingface Hub Endpoint URL must be provided.')
+            if credentials["huggingfacehub_api_type"] == "inference_endpoints":
+                if "huggingfacehub_endpoint_url" not in credentials:
+                    raise CredentialsValidateFailedError("Huggingface Hub Endpoint URL must be provided.")
 
-                if 'task_type' not in credentials:
-                    raise CredentialsValidateFailedError('Huggingface Hub Task Type must be provided.')
-            elif credentials['huggingfacehub_api_type'] == 'hosted_inference_api':
-                credentials['task_type'] = self._get_hosted_model_task_type(credentials['huggingfacehub_api_token'],
-                                                                            model)
+                if "task_type" not in credentials:
+                    raise CredentialsValidateFailedError("Huggingface Hub Task Type must be provided.")
+            elif credentials["huggingfacehub_api_type"] == "hosted_inference_api":
+                credentials["task_type"] = self._get_hosted_model_task_type(
+                    credentials["huggingfacehub_api_token"], model
+                )
 
-            if credentials['task_type'] not in ("text2text-generation", "text-generation"):
-                raise CredentialsValidateFailedError('Huggingface Hub Task Type must be one of text2text-generation, '
-                                                     'text-generation.')
+            if credentials["task_type"] not in {"text2text-generation", "text-generation"}:
+                raise CredentialsValidateFailedError(
+                    "Huggingface Hub Task Type must be one of text2text-generation, text-generation."
+                )
 
-            client = InferenceClient(token=credentials['huggingfacehub_api_token'])
+            client = InferenceClient(token=credentials["huggingfacehub_api_token"])
 
-            if credentials['huggingfacehub_api_type'] == 'inference_endpoints':
-                model = credentials['huggingfacehub_endpoint_url']
+            if credentials["huggingfacehub_api_type"] == "inference_endpoints":
+                model = credentials["huggingfacehub_endpoint_url"]
 
             try:
-                client.text_generation(
-                    prompt='Who are you?',
-                    stream=True,
-                    model=model)
+                client.text_generation(prompt="Who are you?", stream=True, model=model)
             except BadRequestError as e:
-                raise CredentialsValidateFailedError('Only available for models running on with the `text-generation-inference`. '
-                                                     'To learn more about the TGI project, please refer to https://github.com/huggingface/text-generation-inference.')
+                raise CredentialsValidateFailedError(
+                    "Only available for models running on with the `text-generation-inference`. "
+                    "To learn more about the TGI project, please refer to https://github.com/huggingface/text-generation-inference."
+                )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
-            model_properties={
-                ModelPropertyKey.MODE: LLMMode.COMPLETION.value
-            },
-            parameter_rules=self._get_customizable_model_parameter_rules()
+            model_properties={ModelPropertyKey.MODE: LLMMode.COMPLETION.value},
+            parameter_rules=self._get_customizable_model_parameter_rules(),
         )
 
         return entity
 
     @staticmethod
     def _get_customizable_model_parameter_rules() -> list[ParameterRule]:
-        temperature_rule_dict = PARAMETER_RULE_TEMPLATE.get(
-            DefaultParameterName.TEMPERATURE).copy()
-        temperature_rule_dict['name'] = 'temperature'
+        temperature_rule_dict = PARAMETER_RULE_TEMPLATE.get(DefaultParameterName.TEMPERATURE).copy()
+        temperature_rule_dict["name"] = "temperature"
         temperature_rule = ParameterRule(**temperature_rule_dict)
         temperature_rule.default = 0.5
 
         top_p_rule_dict = PARAMETER_RULE_TEMPLATE.get(DefaultParameterName.TOP_P).copy()
-        top_p_rule_dict['name'] = 'top_p'
+        top_p_rule_dict["name"] = "top_p"
         top_p_rule = ParameterRule(**top_p_rule_dict)
         top_p_rule.default = 0.5
 
         top_k_rule = ParameterRule(
-            name='top_k',
+            name="top_k",
             label={
-                'en_US': 'Top K',
-                'zh_Hans': 'Top K',
+                "en_US": "Top K",
+                "zh_Hans": "Top K",
             },
-            type='int',
+            type="int",
             help={
-                'en_US': 'The number of highest probability vocabulary tokens to keep for top-k-filtering.',
-                'zh_Hans': '保留的最高概率词汇标记的数量。',
+                "en_US": "The number of highest probability vocabulary tokens to keep for top-k-filtering.",
+                "zh_Hans": "保留的最高概率词汇标记的数量。",
             },
             required=False,
             default=2,
@@ -148,15 +157,15 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
         )
 
         max_new_tokens = ParameterRule(
-            name='max_new_tokens',
+            name="max_new_tokens",
             label={
-                'en_US': 'Max New Tokens',
-                'zh_Hans': '最大新标记',
+                "en_US": "Max New Tokens",
+                "zh_Hans": "最大新标记",
             },
-            type='int',
+            type="int",
             help={
-                'en_US': 'Maximum number of generated tokens.',
-                'zh_Hans': '生成的标记的最大数量。',
+                "en_US": "Maximum number of generated tokens.",
+                "zh_Hans": "生成的标记的最大数量。",
             },
             required=False,
             default=20,
@@ -166,30 +175,30 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
         )
 
         seed = ParameterRule(
-            name='seed',
+            name="seed",
             label={
-                'en_US': 'Random sampling seed',
-                'zh_Hans': '随机采样种子',
+                "en_US": "Random sampling seed",
+                "zh_Hans": "随机采样种子",
             },
-            type='int',
+            type="int",
             help={
-                'en_US': 'Random sampling seed.',
-                'zh_Hans': '随机采样种子。',
+                "en_US": "Random sampling seed.",
+                "zh_Hans": "随机采样种子。",
             },
             required=False,
             precision=0,
         )
 
         repetition_penalty = ParameterRule(
-            name='repetition_penalty',
+            name="repetition_penalty",
             label={
-                'en_US': 'Repetition Penalty',
-                'zh_Hans': '重复惩罚',
+                "en_US": "Repetition Penalty",
+                "zh_Hans": "重复惩罚",
             },
-            type='float',
+            type="float",
             help={
-                'en_US': 'The parameter for repetition penalty. 1.0 means no penalty.',
-                'zh_Hans': '重复惩罚的参数。1.0 表示没有惩罚。',
+                "en_US": "The parameter for repetition penalty. 1.0 means no penalty.",
+                "zh_Hans": "重复惩罚的参数。1.0 表示没有惩罚。",
             },
             required=False,
             precision=1,
@@ -197,11 +206,9 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
 
         return [temperature_rule, top_k_rule, top_p_rule, max_new_tokens, seed, repetition_penalty]
 
-    def _handle_generate_stream_response(self,
-                                         model: str,
-                                         credentials: dict,
-                                         prompt_messages: list[PromptMessage],
-                                         response: Generator) -> Generator:
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, prompt_messages: list[PromptMessage], response: Generator
+    ) -> Generator:
         index = -1
         for chunk in response:
             # skip special tokens
@@ -210,9 +217,7 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
 
             index += 1
 
-            assistant_prompt_message = AssistantPromptMessage(
-                content=chunk.token.text
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=chunk.token.text)
 
             if chunk.details:
                 prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
@@ -240,15 +245,15 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
                     ),
                 )
 
-    def _handle_generate_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], response: any) -> LLMResult:
+    def _handle_generate_response(
+        self, model: str, credentials: dict, prompt_messages: list[PromptMessage], response: any
+    ) -> LLMResult:
         if isinstance(response, str):
             content = response
         else:
             content = response.generated_text
 
-        assistant_prompt_message = AssistantPromptMessage(
-            content=content
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=content)
 
         prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
         completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
@@ -270,15 +275,14 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
 
         try:
             if not model_info:
-                raise ValueError(f'Model {model_name} not found.')
+                raise ValueError(f"Model {model_name} not found.")
 
-            if 'inference' in model_info.cardData and not model_info.cardData['inference']:
-                raise ValueError(f'Inference API has been turned off for this model {model_name}.')
+            if "inference" in model_info.cardData and not model_info.cardData["inference"]:
+                raise ValueError(f"Inference API has been turned off for this model {model_name}.")
 
             valid_tasks = ("text2text-generation", "text-generation")
             if model_info.pipeline_tag not in valid_tasks:
-                raise ValueError(f"Model {model_name} is not a valid task, "
-                                 f"must be one of {valid_tasks}.")
+                raise ValueError(f"Model {model_name} is not a valid task, must be one of {valid_tasks}.")
         except Exception as e:
             raise CredentialsValidateFailedError(f"{str(e)}")
 
@@ -287,10 +291,7 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
     def _convert_messages_to_prompt(self, messages: list[PromptMessage]) -> str:
         messages = messages.copy()  # don't mutate the original list
 
-        text = "".join(
-            self._convert_one_message_to_text(message)
-            for message in messages
-        )
+        text = "".join(self._convert_one_message_to_text(message) for message in messages)
 
         return text.rstrip()
 
diff --git a/api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py
index 0f0c166f3e..4ad96c4233 100644
--- a/api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py
@@ -13,40 +13,30 @@ from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
 from core.model_runtime.model_providers.huggingface_hub._common import _CommonHuggingfaceHub
 
-HUGGINGFACE_ENDPOINT_API = 'https://api.endpoints.huggingface.cloud/v2/endpoint/'
+HUGGINGFACE_ENDPOINT_API = "https://api.endpoints.huggingface.cloud/v2/endpoint/"
 
 
 class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel):
-
-    def _invoke(self, model: str, credentials: dict, texts: list[str],
-                user: Optional[str] = None) -> TextEmbeddingResult:
-        client = InferenceClient(token=credentials['huggingfacehub_api_token'])
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
+        client = InferenceClient(token=credentials["huggingfacehub_api_token"])
 
         execute_model = model
 
-        if credentials['huggingfacehub_api_type'] == 'inference_endpoints':
-            execute_model = credentials['huggingfacehub_endpoint_url']
+        if credentials["huggingfacehub_api_type"] == "inference_endpoints":
+            execute_model = credentials["huggingfacehub_endpoint_url"]
 
         output = client.post(
-            json={
-                "inputs": texts,
-                "options": {
-                    "wait_for_model": False,
-                    "use_cache": False
-                }
-            },
-            model=execute_model)
+            json={"inputs": texts, "options": {"wait_for_model": False, "use_cache": False}}, model=execute_model
+        )
 
         embeddings = json.loads(output.decode())
 
         tokens = self.get_num_tokens(model, credentials, texts)
         usage = self._calc_response_usage(model, credentials, tokens)
 
-        return TextEmbeddingResult(
-            embeddings=self._mean_pooling(embeddings),
-            usage=usage,
-            model=model
-        )
+        return TextEmbeddingResult(embeddings=self._mean_pooling(embeddings), usage=usage, model=model)
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         num_tokens = 0
@@ -56,52 +46,48 @@ class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         try:
-            if 'huggingfacehub_api_type' not in credentials:
-                raise CredentialsValidateFailedError('Huggingface Hub Endpoint Type must be provided.')
+            if "huggingfacehub_api_type" not in credentials:
+                raise CredentialsValidateFailedError("Huggingface Hub Endpoint Type must be provided.")
 
-            if 'huggingfacehub_api_token' not in credentials:
-                raise CredentialsValidateFailedError('Huggingface Hub API Token must be provided.')
+            if "huggingfacehub_api_token" not in credentials:
+                raise CredentialsValidateFailedError("Huggingface Hub API Token must be provided.")
 
-            if credentials['huggingfacehub_api_type'] == 'inference_endpoints':
-                if 'huggingface_namespace' not in credentials:
-                    raise CredentialsValidateFailedError('Huggingface Hub User Name / Organization Name must be provided.')
+            if credentials["huggingfacehub_api_type"] == "inference_endpoints":
+                if "huggingface_namespace" not in credentials:
+                    raise CredentialsValidateFailedError(
+                        "Huggingface Hub User Name / Organization Name must be provided."
+                    )
 
-                if 'huggingfacehub_endpoint_url' not in credentials:
-                    raise CredentialsValidateFailedError('Huggingface Hub Endpoint URL must be provided.')
+                if "huggingfacehub_endpoint_url" not in credentials:
+                    raise CredentialsValidateFailedError("Huggingface Hub Endpoint URL must be provided.")
 
-                if 'task_type' not in credentials:
-                    raise CredentialsValidateFailedError('Huggingface Hub Task Type must be provided.')
+                if "task_type" not in credentials:
+                    raise CredentialsValidateFailedError("Huggingface Hub Task Type must be provided.")
 
-                if credentials['task_type'] != 'feature-extraction':
-                    raise CredentialsValidateFailedError('Huggingface Hub Task Type is invalid.')
+                if credentials["task_type"] != "feature-extraction":
+                    raise CredentialsValidateFailedError("Huggingface Hub Task Type is invalid.")
 
                 self._check_endpoint_url_model_repository_name(credentials, model)
 
-                model = credentials['huggingfacehub_endpoint_url']
+                model = credentials["huggingfacehub_endpoint_url"]
 
-            elif credentials['huggingfacehub_api_type'] == 'hosted_inference_api':
-                self._check_hosted_model_task_type(credentials['huggingfacehub_api_token'],
-                                                   model)
+            elif credentials["huggingfacehub_api_type"] == "hosted_inference_api":
+                self._check_hosted_model_task_type(credentials["huggingfacehub_api_token"], model)
             else:
-                raise CredentialsValidateFailedError('Huggingface Hub Endpoint Type is invalid.')
+                raise CredentialsValidateFailedError("Huggingface Hub Endpoint Type is invalid.")
 
-            client = InferenceClient(token=credentials['huggingfacehub_api_token'])
-            client.feature_extraction(text='hello world', model=model)
+            client = InferenceClient(token=credentials["huggingfacehub_api_token"])
+            client.feature_extraction(text="hello world", model=model)
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TEXT_EMBEDDING,
-            model_properties={
-                'context_size': 10000,
-                'max_chunks': 1
-            }
+            model_properties={"context_size": 10000, "max_chunks": 1},
         )
         return entity
 
@@ -128,24 +114,20 @@ class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel
 
         try:
             if not model_info:
-                raise ValueError(f'Model {model_name} not found.')
+                raise ValueError(f"Model {model_name} not found.")
 
-            if 'inference' in model_info.cardData and not model_info.cardData['inference']:
-                raise ValueError(f'Inference API has been turned off for this model {model_name}.')
+            if "inference" in model_info.cardData and not model_info.cardData["inference"]:
+                raise ValueError(f"Inference API has been turned off for this model {model_name}.")
 
             valid_tasks = "feature-extraction"
             if model_info.pipeline_tag not in valid_tasks:
-                raise ValueError(f"Model {model_name} is not a valid task, "
-                                 f"must be one of {valid_tasks}.")
+                raise ValueError(f"Model {model_name} is not a valid task, must be one of {valid_tasks}.")
         except Exception as e:
             raise CredentialsValidateFailedError(f"{str(e)}")
 
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -156,7 +138,7 @@ class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
@@ -166,25 +148,26 @@ class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel
         try:
             url = f'{HUGGINGFACE_ENDPOINT_API}{credentials["huggingface_namespace"]}'
             headers = {
-                'Authorization': f'Bearer {credentials["huggingfacehub_api_token"]}',
-                'Content-Type': 'application/json'
+                "Authorization": f'Bearer {credentials["huggingfacehub_api_token"]}',
+                "Content-Type": "application/json",
             }
 
             response = requests.get(url=url, headers=headers)
 
             if response.status_code != 200:
-                raise ValueError('User Name or Organization Name is invalid.')
+                raise ValueError("User Name or Organization Name is invalid.")
 
-            model_repository_name = ''
+            model_repository_name = ""
 
             for item in response.json().get("items", []):
-                if item.get("status", {}).get("url") == credentials['huggingfacehub_endpoint_url']:
+                if item.get("status", {}).get("url") == credentials["huggingfacehub_endpoint_url"]:
                     model_repository_name = item.get("model", {}).get("repository")
                     break
 
             if model_repository_name != model_name:
                 raise ValueError(
-                    f'Model Name {model_name} is invalid. Please check it on the inference endpoints console.')
+                    f"Model Name {model_name} is invalid. Please check it on the inference endpoints console."
+                )
 
         except Exception as e:
             raise ValueError(str(e))
diff --git a/api/core/model_runtime/model_providers/huggingface_tei/huggingface_tei.py b/api/core/model_runtime/model_providers/huggingface_tei/huggingface_tei.py
index 9454466250..97d7e28dc6 100644
--- a/api/core/model_runtime/model_providers/huggingface_tei/huggingface_tei.py
+++ b/api/core/model_runtime/model_providers/huggingface_tei/huggingface_tei.py
@@ -6,6 +6,5 @@ logger = logging.getLogger(__name__)
 
 
 class HuggingfaceTeiProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         pass
diff --git a/api/core/model_runtime/model_providers/huggingface_tei/rerank/rerank.py b/api/core/model_runtime/model_providers/huggingface_tei/rerank/rerank.py
index 34013426de..74a1dfc3ff 100644
--- a/api/core/model_runtime/model_providers/huggingface_tei/rerank/rerank.py
+++ b/api/core/model_runtime/model_providers/huggingface_tei/rerank/rerank.py
@@ -47,29 +47,28 @@ class HuggingfaceTeiRerankModel(RerankModel):
         """
         if len(docs) == 0:
             return RerankResult(model=model, docs=[])
-        server_url = credentials['server_url']
+        server_url = credentials["server_url"]
 
-        if server_url.endswith('/'):
-            server_url = server_url[:-1]
+        server_url = server_url.removesuffix("/")
 
         try:
             results = TeiHelper.invoke_rerank(server_url, query, docs)
 
             rerank_documents = []
-            for result in results:  
+            for result in results:
                 rerank_document = RerankDocument(
-                    index=result['index'],
-                    text=result['text'],
-                    score=result['score'],
+                    index=result["index"],
+                    text=result["text"],
+                    score=result["score"],
                 )
-                if score_threshold is None or result['score'] >= score_threshold:
+                if score_threshold is None or result["score"] >= score_threshold:
                     rerank_documents.append(rerank_document)
                 if top_n is not None and len(rerank_documents) >= top_n:
                     break
 
             return RerankResult(model=model, docs=rerank_documents)
         except httpx.HTTPStatusError as e:
-            raise InvokeServerUnavailableError(str(e))  
+            raise InvokeServerUnavailableError(str(e))
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
@@ -80,21 +79,21 @@ class HuggingfaceTeiRerankModel(RerankModel):
         :return:
         """
         try:
-            server_url = credentials['server_url']
+            server_url = credentials["server_url"]
             extra_args = TeiHelper.get_tei_extra_parameter(server_url, model)
-            if extra_args.model_type != 'reranker':
-                raise CredentialsValidateFailedError('Current model is not a rerank model')
+            if extra_args.model_type != "reranker":
+                raise CredentialsValidateFailedError("Current model is not a rerank model")
 
-            credentials['context_size'] = extra_args.max_input_length
+            credentials["context_size"] = extra_args.max_input_length
 
             self.invoke(
                 model=model,
                 credentials=credentials,
-                query='Whose kasumi',
+                query="Whose kasumi",
                 docs=[
                     'Kasumi is a girl\'s name of Japanese origin meaning "mist".',
-                    'Her music is a kawaii bass, a mix of future bass, pop, and kawaii music ',
-                    'and she leads a team named PopiParty.',
+                    "Her music is a kawaii bass, a mix of future bass, pop, and kawaii music ",
+                    "and she leads a team named PopiParty.",
                 ],
                 score_threshold=0.8,
             )
@@ -129,7 +128,7 @@ class HuggingfaceTeiRerankModel(RerankModel):
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.RERANK,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', 512)),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
             },
             parameter_rules=[],
         )
diff --git a/api/core/model_runtime/model_providers/huggingface_tei/tei_helper.py b/api/core/model_runtime/model_providers/huggingface_tei/tei_helper.py
index 2aa785c89d..81ab249214 100644
--- a/api/core/model_runtime/model_providers/huggingface_tei/tei_helper.py
+++ b/api/core/model_runtime/model_providers/huggingface_tei/tei_helper.py
@@ -31,16 +31,16 @@ class TeiHelper:
         with cache_lock:
             if model_name not in cache:
                 cache[model_name] = {
-                    'expires': time() + 300,
-                    'value': TeiHelper._get_tei_extra_parameter(server_url),
+                    "expires": time() + 300,
+                    "value": TeiHelper._get_tei_extra_parameter(server_url),
                 }
-            return cache[model_name]['value']
+            return cache[model_name]["value"]
 
     @staticmethod
     def _clean_cache() -> None:
         try:
             with cache_lock:
-                expired_keys = [model_uid for model_uid, model in cache.items() if model['expires'] < time()]
+                expired_keys = [model_uid for model_uid, model in cache.items() if model["expires"] < time()]
                 for model_uid in expired_keys:
                     del cache[model_uid]
         except RuntimeError as e:
@@ -52,40 +52,39 @@ class TeiHelper:
         get tei model extra parameter like model_type, max_input_length, max_batch_requests
         """
 
-        url = str(URL(server_url) / 'info')
+        url = str(URL(server_url) / "info")
 
-        # this method is surrounded by a lock, and default requests may hang forever, so we just set a Adapter with max_retries=3
+        # this method is surrounded by a lock, and default requests may hang forever,
+        # so we just set a Adapter with max_retries=3
         session = Session()
-        session.mount('http://', HTTPAdapter(max_retries=3))
-        session.mount('https://', HTTPAdapter(max_retries=3))
+        session.mount("http://", HTTPAdapter(max_retries=3))
+        session.mount("https://", HTTPAdapter(max_retries=3))
 
         try:
             response = session.get(url, timeout=10)
         except (MissingSchema, ConnectionError, Timeout) as e:
-            raise RuntimeError(f'get tei model extra parameter failed, url: {url}, error: {e}')
+            raise RuntimeError(f"get tei model extra parameter failed, url: {url}, error: {e}")
         if response.status_code != 200:
             raise RuntimeError(
-                f'get tei model extra parameter failed, status code: {response.status_code}, response: {response.text}'
+                f"get tei model extra parameter failed, status code: {response.status_code}, response: {response.text}"
             )
 
         response_json = response.json()
 
-        model_type = response_json.get('model_type', {})
+        model_type = response_json.get("model_type", {})
         if len(model_type.keys()) < 1:
-            raise RuntimeError('model_type is empty')
+            raise RuntimeError("model_type is empty")
         model_type = list(model_type.keys())[0]
-        if model_type not in ['embedding', 'reranker']:
-            raise RuntimeError(f'invalid model_type: {model_type}')
-        
-        max_input_length = response_json.get('max_input_length', 512)
-        max_client_batch_size = response_json.get('max_client_batch_size', 1)
+        if model_type not in {"embedding", "reranker"}:
+            raise RuntimeError(f"invalid model_type: {model_type}")
+
+        max_input_length = response_json.get("max_input_length", 512)
+        max_client_batch_size = response_json.get("max_client_batch_size", 1)
 
         return TeiModelExtraParameter(
-            model_type=model_type,
-            max_input_length=max_input_length,
-            max_client_batch_size=max_client_batch_size
+            model_type=model_type, max_input_length=max_input_length, max_client_batch_size=max_client_batch_size
         )
-    
+
     @staticmethod
     def invoke_tokenize(server_url: str, texts: list[str]) -> list[list[dict]]:
         """
@@ -116,12 +115,12 @@ class TeiHelper:
         :param texts: texts to tokenize
         """
         resp = httpx.post(
-            f'{server_url}/tokenize',
-            json={'inputs': texts},
+            f"{server_url}/tokenize",
+            json={"inputs": texts},
         )
         resp.raise_for_status()
         return resp.json()
-    
+
     @staticmethod
     def invoke_embeddings(server_url: str, texts: list[str]) -> dict:
         """
@@ -149,8 +148,8 @@ class TeiHelper:
         """
         # Use OpenAI compatible API here, which has usage tracking
         resp = httpx.post(
-            f'{server_url}/v1/embeddings',
-            json={'input': texts},
+            f"{server_url}/v1/embeddings",
+            json={"input": texts},
         )
         resp.raise_for_status()
         return resp.json()
@@ -173,11 +172,11 @@ class TeiHelper:
         :param texts: texts to rerank
         :param candidates: candidates to rerank
         """
-        params = {'query': query, 'texts': docs, 'return_text': True}
+        params = {"query": query, "texts": docs, "return_text": True}
 
         response = httpx.post(
-            server_url + '/rerank',
+            server_url + "/rerank",
             json=params,
         )
-        response.raise_for_status() 
+        response.raise_for_status()
         return response.json()
diff --git a/api/core/model_runtime/model_providers/huggingface_tei/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/huggingface_tei/text_embedding/text_embedding.py
index 6897b87f6d..55f3c25804 100644
--- a/api/core/model_runtime/model_providers/huggingface_tei/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/huggingface_tei/text_embedding/text_embedding.py
@@ -40,11 +40,9 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
-        server_url = credentials['server_url']
-
-        if server_url.endswith('/'):
-            server_url = server_url[:-1]
+        server_url = credentials["server_url"]
 
+        server_url = server_url.removesuffix("/")
 
         # get model properties
         context_size = self._get_context_size(model, credentials)
@@ -58,7 +56,6 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
         batched_tokenize_result = TeiHelper.invoke_tokenize(server_url, texts)
 
         for i, (text, tokenize_result) in enumerate(zip(texts, batched_tokenize_result)):
-
             # Check if the number of tokens is larger than the context size
             num_tokens = len(tokenize_result)
 
@@ -66,20 +63,22 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
                 # Find the best cutoff point
                 pre_special_token_count = 0
                 for token in tokenize_result:
-                    if token['special']:
+                    if token["special"]:
                         pre_special_token_count += 1
                     else:
                         break
-                rest_special_token_count = len([token for token in tokenize_result if token['special']]) - pre_special_token_count
+                rest_special_token_count = (
+                    len([token for token in tokenize_result if token["special"]]) - pre_special_token_count
+                )
 
                 # Calculate the cutoff point, leave 20 extra space to avoid exceeding the limit
                 token_cutoff = context_size - rest_special_token_count - 20
 
                 # Find the cutoff index
                 cutpoint_token = tokenize_result[token_cutoff]
-                cutoff = cutpoint_token['start']
+                cutoff = cutpoint_token["start"]
 
-                inputs.append(text[0: cutoff])
+                inputs.append(text[0:cutoff])
             else:
                 inputs.append(text)
             indices += [i]
@@ -92,12 +91,12 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
             for i in _iter:
                 iter_texts = inputs[i : i + max_chunks]
                 results = TeiHelper.invoke_embeddings(server_url, iter_texts)
-                embeddings = results['data']
-                embeddings = [embedding['embedding'] for embedding in embeddings]
+                embeddings = results["data"]
+                embeddings = [embedding["embedding"] for embedding in embeddings]
                 batched_embeddings.extend(embeddings)
 
-                usage = results['usage']
-                used_tokens += usage['total_tokens']
+                usage = results["usage"]
+                used_tokens += usage["total_tokens"]
         except RuntimeError as e:
             raise InvokeServerUnavailableError(str(e))
 
@@ -117,10 +116,9 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         num_tokens = 0
-        server_url = credentials['server_url']
+        server_url = credentials["server_url"]
 
-        if server_url.endswith('/'):
-            server_url = server_url[:-1]
+        server_url = server_url.removesuffix("/")
 
         batch_tokens = TeiHelper.invoke_tokenize(server_url, texts)
         num_tokens = sum(len(tokens) for tokens in batch_tokens)
@@ -135,15 +133,15 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         try:
-            server_url = credentials['server_url']
+            server_url = credentials["server_url"]
             extra_args = TeiHelper.get_tei_extra_parameter(server_url, model)
             print(extra_args)
-            if extra_args.model_type != 'embedding':
-                raise CredentialsValidateFailedError('Current model is not a embedding model')
+            if extra_args.model_type != "embedding":
+                raise CredentialsValidateFailedError("Current model is not a embedding model")
 
-            credentials['context_size'] = extra_args.max_input_length
-            credentials['max_chunks'] = extra_args.max_client_batch_size
-            self._invoke(model=model, credentials=credentials, texts=['ping'])
+            credentials["context_size"] = extra_args.max_input_length
+            credentials["max_chunks"] = extra_args.max_client_batch_size
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
@@ -195,8 +193,8 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TEXT_EMBEDDING,
             model_properties={
-                ModelPropertyKey.MAX_CHUNKS: int(credentials.get('max_chunks', 1)),
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', 512)),
+                ModelPropertyKey.MAX_CHUNKS: int(credentials.get("max_chunks", 1)),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
             },
             parameter_rules=[],
         )
diff --git a/api/core/model_runtime/model_providers/hunyuan/hunyuan.py b/api/core/model_runtime/model_providers/hunyuan/hunyuan.py
index 5a298d33ac..e65772e7dd 100644
--- a/api/core/model_runtime/model_providers/hunyuan/hunyuan.py
+++ b/api/core/model_runtime/model_providers/hunyuan/hunyuan.py
@@ -6,8 +6,8 @@ from core.model_runtime.model_providers.__base.model_provider import ModelProvid
 
 logger = logging.getLogger(__name__)
 
-class HunyuanProvider(ModelProvider):
 
+class HunyuanProvider(ModelProvider):
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -19,12 +19,9 @@ class HunyuanProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.LLM)
 
             # Use `hunyuan-standard` model for validate,
-            model_instance.validate_credentials(
-                model='hunyuan-standard',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="hunyuan-standard", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/hunyuan/llm/_position.yaml b/api/core/model_runtime/model_providers/hunyuan/llm/_position.yaml
index 2c1b981f85..ca8600a534 100644
--- a/api/core/model_runtime/model_providers/hunyuan/llm/_position.yaml
+++ b/api/core/model_runtime/model_providers/hunyuan/llm/_position.yaml
@@ -2,3 +2,4 @@
 - hunyuan-standard
 - hunyuan-standard-256k
 - hunyuan-pro
+- hunyuan-turbo
diff --git a/api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-turbo.yaml b/api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-turbo.yaml
new file mode 100644
index 0000000000..4837fed4ba
--- /dev/null
+++ b/api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-turbo.yaml
@@ -0,0 +1,38 @@
+model: hunyuan-turbo
+label:
+  zh_Hans: hunyuan-turbo
+  en_US: hunyuan-turbo
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 32000
+  - name: enable_enhance
+    label:
+      zh_Hans: 功能增强
+      en_US: Enable Enhancement
+    type: boolean
+    help:
+      zh_Hans: 功能增强（如搜索）开关，关闭时将直接由主模型生成回复内容，可以降低响应时延（对于流式输出时的首字时延尤为明显）。但在少数场景里，回复效果可能会下降。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false
+    default: true
+pricing:
+  input: '0.015'
+  output: '0.05'
+  unit: '0.001'
+  currency: RMB
diff --git a/api/core/model_runtime/model_providers/hunyuan/llm/llm.py b/api/core/model_runtime/model_providers/hunyuan/llm/llm.py
index 0bdf6ec005..b57e5e1c2b 100644
--- a/api/core/model_runtime/model_providers/hunyuan/llm/llm.py
+++ b/api/core/model_runtime/model_providers/hunyuan/llm/llm.py
@@ -23,21 +23,27 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 
 logger = logging.getLogger(__name__)
 
+
 class HunyuanLargeLanguageModel(LargeLanguageModel):
-
-    def _invoke(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                model_parameters: dict, tools: list[PromptMessageTool] | None = None,
-                stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
-
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         client = self._setup_hunyuan_client(credentials)
         request = models.ChatCompletionsRequest()
         messages_dict = self._convert_prompt_messages_to_dicts(prompt_messages)
 
         custom_parameters = {
-            'Temperature': model_parameters.get('temperature', 0.0),
-            'TopP': model_parameters.get('top_p', 1.0),
-            'EnableEnhancement': model_parameters.get('enable_enhance', True)
+            "Temperature": model_parameters.get("temperature", 0.0),
+            "TopP": model_parameters.get("top_p", 1.0),
+            "EnableEnhancement": model_parameters.get("enable_enhance", True),
         }
 
         params = {
@@ -47,16 +53,19 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
             **custom_parameters,
         }
         # add Tools and ToolChoice
-        if (tools and len(tools) > 0):
-            params['ToolChoice'] = "auto"
-            params['Tools'] = [{
-                "Type": "function", 
-                "Function": {
-                    "Name": tool.name, 
-                    "Description": tool.description,
-                    "Parameters": json.dumps(tool.parameters)
+        if tools and len(tools) > 0:
+            params["ToolChoice"] = "auto"
+            params["Tools"] = [
+                {
+                    "Type": "function",
+                    "Function": {
+                        "Name": tool.name,
+                        "Description": tool.description,
+                        "Parameters": json.dumps(tool.parameters),
+                    },
                 }
-            } for tool in tools]
+                for tool in tools
+            ]
 
         request.from_json_string(json.dumps(params))
         response = client.ChatCompletions(request)
@@ -76,22 +85,19 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
             req = models.ChatCompletionsRequest()
             params = {
                 "Model": model,
-                "Messages": [{
-                    "Role": "user",
-                    "Content": "hello"
-                }],
+                "Messages": [{"Role": "user", "Content": "hello"}],
                 "TopP": 1,
                 "Temperature": 0,
-                "Stream": False
+                "Stream": False,
             }
             req.from_json_string(json.dumps(params))
             client.ChatCompletions(req)
         except Exception as e:
-            raise CredentialsValidateFailedError(f'Credentials validation failed: {e}')
+            raise CredentialsValidateFailedError(f"Credentials validation failed: {e}")
 
     def _setup_hunyuan_client(self, credentials):
-        secret_id = credentials['secret_id']
-        secret_key = credentials['secret_key']
+        secret_id = credentials["secret_id"]
+        secret_key = credentials["secret_key"]
         cred = credential.Credential(secret_id, secret_key)
         httpProfile = HttpProfile()
         httpProfile.endpoint = "hunyuan.tencentcloudapi.com"
@@ -106,92 +112,97 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
         for message in prompt_messages:
             if isinstance(message, AssistantPromptMessage):
                 tool_calls = message.tool_calls
-                if (tool_calls and len(tool_calls) > 0):
+                if tool_calls and len(tool_calls) > 0:
                     dict_tool_calls = [
                         {
                             "Id": tool_call.id,
                             "Type": tool_call.type,
                             "Function": {
                                 "Name": tool_call.function.name,
-                                "Arguments": tool_call.function.arguments if (tool_call.function.arguments == "") else "{}"
-                            }
-                        } for tool_call in tool_calls]
-                    
-                    dict_list.append({ 
-                        "Role": message.role.value,
-                        # fix set content = "" while tool_call request
-                        # fix [hunyuan] None, [TencentCloudSDKException] code:InvalidParameter message:Messages Content and Contents not allowed empty at the same time.
-                        "Content": " ", # message.content if (message.content is not None) else "", 
-                        "ToolCalls":  dict_tool_calls
-                    })
+                                "Arguments": tool_call.function.arguments
+                                if (tool_call.function.arguments == "")
+                                else "{}",
+                            },
+                        }
+                        for tool_call in tool_calls
+                    ]
+
+                    dict_list.append(
+                        {
+                            "Role": message.role.value,
+                            # fix set content = "" while tool_call request
+                            # fix [hunyuan] None, [TencentCloudSDKException] code:InvalidParameter
+                            # message:Messages Content and Contents not allowed empty at the same time.
+                            "Content": " ",  # message.content if (message.content is not None) else "",
+                            "ToolCalls": dict_tool_calls,
+                        }
+                    )
                 else:
-                    dict_list.append({ "Role": message.role.value, "Content": message.content })
+                    dict_list.append({"Role": message.role.value, "Content": message.content})
             elif isinstance(message, ToolPromptMessage):
-                tool_execute_result = { "result": message.content }
-                content =json.dumps(tool_execute_result, ensure_ascii=False)
-                dict_list.append({ "Role": message.role.value, "Content": content, "ToolCallId": message.tool_call_id  })
+                tool_execute_result = {"result": message.content}
+                content = json.dumps(tool_execute_result, ensure_ascii=False)
+                dict_list.append({"Role": message.role.value, "Content": content, "ToolCallId": message.tool_call_id})
             else:
-                dict_list.append({ "Role": message.role.value, "Content": message.content })
+                dict_list.append({"Role": message.role.value, "Content": message.content})
         return dict_list
 
     def _handle_stream_chat_response(self, model, credentials, prompt_messages, resp):
-
         tool_call = None
         tool_calls = []
 
         for index, event in enumerate(resp):
             logging.debug("_handle_stream_chat_response, event: %s", event)
 
-            data_str = event['data']
+            data_str = event["data"]
             data = json.loads(data_str)
 
-            choices = data.get('Choices', [])
+            choices = data.get("Choices", [])
             if not choices:
                 continue
             choice = choices[0]
-            delta = choice.get('Delta', {})
-            message_content = delta.get('Content', '')
-            finish_reason = choice.get('FinishReason', '')
+            delta = choice.get("Delta", {})
+            message_content = delta.get("Content", "")
+            finish_reason = choice.get("FinishReason", "")
 
-            usage = data.get('Usage', {})
-            prompt_tokens = usage.get('PromptTokens', 0)
-            completion_tokens = usage.get('CompletionTokens', 0)
+            usage = data.get("Usage", {})
+            prompt_tokens = usage.get("PromptTokens", 0)
+            completion_tokens = usage.get("CompletionTokens", 0)
 
-            response_tool_calls = delta.get('ToolCalls')
-            if (response_tool_calls is not None):
+            response_tool_calls = delta.get("ToolCalls")
+            if response_tool_calls is not None:
                 new_tool_calls = self._extract_response_tool_calls(response_tool_calls)
-                if (len(new_tool_calls) > 0):
+                if len(new_tool_calls) > 0:
                     new_tool_call = new_tool_calls[0]
-                    if (tool_call is None): tool_call = new_tool_call
-                    elif (tool_call.id != new_tool_call.id):
+                    if tool_call is None:
+                        tool_call = new_tool_call
+                    elif tool_call.id != new_tool_call.id:
                         tool_calls.append(tool_call)
                         tool_call = new_tool_call
                     else:
                         tool_call.function.name += new_tool_call.function.name
                         tool_call.function.arguments += new_tool_call.function.arguments
-                if (tool_call is not None and len(tool_call.function.name) > 0 and len(tool_call.function.arguments) > 0):
+                if tool_call is not None and len(tool_call.function.name) > 0 and len(tool_call.function.arguments) > 0:
                     tool_calls.append(tool_call)
                     tool_call = None
 
-            assistant_prompt_message = AssistantPromptMessage(
-                content=message_content,
-                tool_calls=[]
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=message_content, tool_calls=[])
             # rewrite content = "" while tool_call to avoid show content on web page
-            if (len(tool_calls) > 0): assistant_prompt_message.content = ""
-            
+            if len(tool_calls) > 0:
+                assistant_prompt_message.content = ""
+
             # add tool_calls to assistant_prompt_message
-            if (finish_reason == 'tool_calls'):
+            if finish_reason == "tool_calls":
                 assistant_prompt_message.tool_calls = tool_calls
                 tool_call = None
                 tool_calls = []
 
-            if (len(finish_reason) > 0):
+            if len(finish_reason) > 0:
                 usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
 
                 delta_chunk = LLMResultChunkDelta(
                     index=index,
-                    role=delta.get('Role', 'assistant'),
+                    role=delta.get("Role", "assistant"),
                     message=assistant_prompt_message,
                     usage=usage,
                     finish_reason=finish_reason,
@@ -212,8 +223,9 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
             )
 
     def _handle_chat_response(self, credentials, model, prompt_messages, response):
-        usage = self._calc_response_usage(model, credentials, response.Usage.PromptTokens,
-                                          response.Usage.CompletionTokens)
+        usage = self._calc_response_usage(
+            model, credentials, response.Usage.PromptTokens, response.Usage.CompletionTokens
+        )
         assistant_prompt_message = AssistantPromptMessage()
         assistant_prompt_message.content = response.Choices[0].Message.Content
         result = LLMResult(
@@ -225,8 +237,13 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
 
         return result
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: list[PromptMessageTool] | None = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool] | None = None,
+    ) -> int:
         if len(prompt_messages) == 0:
             return 0
         prompt = self._convert_messages_to_prompt(prompt_messages)
@@ -241,10 +258,7 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
         """
         messages = messages.copy()  # don't mutate the original list
 
-        text = "".join(
-            self._convert_one_message_to_text(message)
-            for message in messages
-        )
+        text = "".join(self._convert_one_message_to_text(message) for message in messages)
 
         # trim off the trailing ' ' that might come from the "Assistant: "
         return text.rstrip()
@@ -287,10 +301,8 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
         return {
             InvokeError: [TencentCloudSDKException],
         }
-    
-    def _extract_response_tool_calls(self,
-                                     response_tool_calls: list[dict]) \
-            -> list[AssistantPromptMessage.ToolCall]:
+
+    def _extract_response_tool_calls(self, response_tool_calls: list[dict]) -> list[AssistantPromptMessage.ToolCall]:
         """
         Extract tool calls from response
 
@@ -300,17 +312,14 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
         tool_calls = []
         if response_tool_calls:
             for response_tool_call in response_tool_calls:
-                response_function = response_tool_call.get('Function', {})
+                response_function = response_tool_call.get("Function", {})
                 function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                    name=response_function.get('Name', ''),
-                    arguments=response_function.get('Arguments', '')
+                    name=response_function.get("Name", ""), arguments=response_function.get("Arguments", "")
                 )
 
                 tool_call = AssistantPromptMessage.ToolCall(
-                    id=response_tool_call.get('Id', 0),
-                    type='function',
-                    function=function
+                    id=response_tool_call.get("Id", 0), type="function", function=function
                 )
                 tool_calls.append(tool_call)
 
-        return tool_calls
\ No newline at end of file
+        return tool_calls
diff --git a/api/core/model_runtime/model_providers/hunyuan/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/hunyuan/text_embedding/text_embedding.py
index 64d8dcf795..1396e59e18 100644
--- a/api/core/model_runtime/model_providers/hunyuan/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/hunyuan/text_embedding/text_embedding.py
@@ -19,14 +19,15 @@ from core.model_runtime.model_providers.__base.text_embedding_model import TextE
 
 logger = logging.getLogger(__name__)
 
+
 class HunyuanTextEmbeddingModel(TextEmbeddingModel):
     """
     Model class for Hunyuan text embedding model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -37,9 +38,9 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
         :return: embeddings result
         """
 
-        if model != 'hunyuan-embedding':
-            raise ValueError('Invalid model name')
-        
+        if model != "hunyuan-embedding":
+            raise ValueError("Invalid model name")
+
         client = self._setup_hunyuan_client(credentials)
 
         embeddings = []
@@ -47,9 +48,7 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
 
         for input in texts:
             request = models.GetEmbeddingRequest()
-            params = {
-                "Input": input
-            }
+            params = {"Input": input}
             request.from_json_string(json.dumps(params))
             response = client.GetEmbedding(request)
             usage = response.Usage.TotalTokens
@@ -60,11 +59,7 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
         result = TextEmbeddingResult(
             model=model,
             embeddings=embeddings,
-            usage=self._calc_response_usage(
-                model=model,
-                credentials=credentials,
-                tokens=token_usage
-            )
+            usage=self._calc_response_usage(model=model, credentials=credentials, tokens=token_usage),
         )
 
         return result
@@ -79,22 +74,19 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
             req = models.ChatCompletionsRequest()
             params = {
                 "Model": model,
-                "Messages": [{
-                    "Role": "user",
-                    "Content": "hello"
-                }],
+                "Messages": [{"Role": "user", "Content": "hello"}],
                 "TopP": 1,
                 "Temperature": 0,
-                "Stream": False
+                "Stream": False,
             }
             req.from_json_string(json.dumps(params))
             client.ChatCompletions(req)
         except Exception as e:
-            raise CredentialsValidateFailedError(f'Credentials validation failed: {e}')
+            raise CredentialsValidateFailedError(f"Credentials validation failed: {e}")
 
     def _setup_hunyuan_client(self, credentials):
-        secret_id = credentials['secret_id']
-        secret_key = credentials['secret_key']
+        secret_id = credentials["secret_id"]
+        secret_key = credentials["secret_key"]
         cred = credential.Credential(secret_id, secret_key)
         httpProfile = HttpProfile()
         httpProfile.endpoint = "hunyuan.tencentcloudapi.com"
@@ -102,7 +94,7 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
         clientProfile.httpProfile = httpProfile
         client = hunyuan_client.HunyuanClient(cred, "", clientProfile)
         return client
-    
+
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -114,10 +106,7 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -128,11 +117,11 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
-    
+
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         """
@@ -146,7 +135,7 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
         return {
             InvokeError: [TencentCloudSDKException],
         }
-    
+
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
         Get number of tokens for given prompt messages
@@ -170,4 +159,4 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
             # response = client.GetTokenCount(request)
             # num_tokens += response.TokenCount
 
-        return num_tokens
\ No newline at end of file
+        return num_tokens
diff --git a/api/core/model_runtime/model_providers/jina/jina.py b/api/core/model_runtime/model_providers/jina/jina.py
index cde4313495..186a0a0fa7 100644
--- a/api/core/model_runtime/model_providers/jina/jina.py
+++ b/api/core/model_runtime/model_providers/jina/jina.py
@@ -8,7 +8,6 @@ logger = logging.getLogger(__name__)
 
 
 class JinaProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -19,14 +18,11 @@ class JinaProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.TEXT_EMBEDDING)
 
-            # Use `jina-embeddings-v2-base-en` model for validate,
+            # Use `jina-embeddings-v3` model for validate,
             # no matter what model you pass in, text completion model or chat model
-            model_instance.validate_credentials(
-                model='jina-embeddings-v2-base-en',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="jina-embeddings-v3", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/jina/rerank/rerank.py b/api/core/model_runtime/model_providers/jina/rerank/rerank.py
index de7e038b9f..79ca68914f 100644
--- a/api/core/model_runtime/model_providers/jina/rerank/rerank.py
+++ b/api/core/model_runtime/model_providers/jina/rerank/rerank.py
@@ -22,9 +22,16 @@ class JinaRerankModel(RerankModel):
     Model class for Jina rerank model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
-                user: Optional[str] = None) -> RerankResult:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
         """
         Invoke rerank model
 
@@ -40,37 +47,31 @@ class JinaRerankModel(RerankModel):
         if len(docs) == 0:
             return RerankResult(model=model, docs=[])
 
-        base_url = credentials.get('base_url', 'https://api.jina.ai/v1')
-        if base_url.endswith('/'):
-            base_url = base_url[:-1]
+        base_url = credentials.get("base_url", "https://api.jina.ai/v1")
+        base_url = base_url.removesuffix("/")
 
         try:
             response = httpx.post(
-                base_url + '/rerank',
-                json={
-                    "model": model,
-                    "query": query,
-                    "documents": docs,
-                    "top_n": top_n
-                },
-                headers={"Authorization": f"Bearer {credentials.get('api_key')}"}  
+                base_url + "/rerank",
+                json={"model": model, "query": query, "documents": docs, "top_n": top_n},
+                headers={"Authorization": f"Bearer {credentials.get('api_key')}"},
             )
-            response.raise_for_status() 
+            response.raise_for_status()
             results = response.json()
 
             rerank_documents = []
-            for result in results['results']:  
+            for result in results["results"]:
                 rerank_document = RerankDocument(
-                    index=result['index'],
-                    text=result['document']['text'],
-                    score=result['relevance_score'],
+                    index=result["index"],
+                    text=result["document"]["text"],
+                    score=result["relevance_score"],
                 )
-                if score_threshold is None or result['relevance_score'] >= score_threshold:
+                if score_threshold is None or result["relevance_score"] >= score_threshold:
                     rerank_documents.append(rerank_document)
 
             return RerankResult(model=model, docs=rerank_documents)
         except httpx.HTTPStatusError as e:
-            raise InvokeServerUnavailableError(str(e))  
+            raise InvokeServerUnavailableError(str(e))
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
@@ -81,7 +82,6 @@ class JinaRerankModel(RerankModel):
         :return:
         """
         try:
-            
             self._invoke(
                 model=model,
                 credentials=credentials,
@@ -92,7 +92,7 @@ class JinaRerankModel(RerankModel):
                     "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
                     "are a political division controlled by the United States. Its capital is Saipan.",
                 ],
-                score_threshold=0.8
+                score_threshold=0.8,
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -105,23 +105,21 @@ class JinaRerankModel(RerankModel):
         return {
             InvokeConnectionError: [httpx.ConnectError],
             InvokeServerUnavailableError: [httpx.RemoteProtocolError],
-            InvokeRateLimitError: [], 
-            InvokeAuthorizationError: [httpx.HTTPStatusError],  
-            InvokeBadRequestError: [httpx.RequestError]
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [httpx.HTTPStatusError],
+            InvokeBadRequestError: [httpx.RequestError],
         }
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            generate custom model entities from credentials
+        generate custom model entities from credentials
         """
         entity = AIModelEntity(
             model=model,
             label=I18nObject(en_US=model),
             model_type=ModelType.RERANK,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size'))
-            }
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
         )
 
-        return entity
\ No newline at end of file
+        return entity
diff --git a/api/core/model_runtime/model_providers/jina/text_embedding/jina-embeddings-v3.yaml b/api/core/model_runtime/model_providers/jina/text_embedding/jina-embeddings-v3.yaml
new file mode 100644
index 0000000000..4e5374dc9d
--- /dev/null
+++ b/api/core/model_runtime/model_providers/jina/text_embedding/jina-embeddings-v3.yaml
@@ -0,0 +1,9 @@
+model: jina-embeddings-v3
+model_type: text-embedding
+model_properties:
+  context_size: 8192
+  max_chunks: 2048
+pricing:
+  input: '0.001'
+  unit: '0.001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/jina/text_embedding/jina_tokenizer.py b/api/core/model_runtime/model_providers/jina/text_embedding/jina_tokenizer.py
index 50f8c73ed9..d80cbfa83d 100644
--- a/api/core/model_runtime/model_providers/jina/text_embedding/jina_tokenizer.py
+++ b/api/core/model_runtime/model_providers/jina/text_embedding/jina_tokenizer.py
@@ -14,19 +14,19 @@ class JinaTokenizer:
             with cls._lock:
                 if cls._tokenizer is None:
                     base_path = abspath(__file__)
-                    gpt2_tokenizer_path = join(dirname(base_path), 'tokenizer')
+                    gpt2_tokenizer_path = join(dirname(base_path), "tokenizer")
                     cls._tokenizer = AutoTokenizer.from_pretrained(gpt2_tokenizer_path)
         return cls._tokenizer
 
     @classmethod
     def _get_num_tokens_by_jina_base(cls, text: str) -> int:
         """
-            use jina tokenizer to get num tokens
+        use jina tokenizer to get num tokens
         """
         tokenizer = cls._get_tokenizer()
         tokens = tokenizer.encode(text)
         return len(tokens)
-    
+
     @classmethod
     def get_num_tokens(cls, text: str) -> int:
-        return cls._get_num_tokens_by_jina_base(text)
\ No newline at end of file
+        return cls._get_num_tokens_by_jina_base(text)
diff --git a/api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py
index 23203491e6..5033f0f748 100644
--- a/api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py
@@ -24,11 +24,12 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
     """
     Model class for Jina text embedding model.
     """
-    api_base: str = 'https://api.jina.ai/v1'
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    api_base: str = "https://api.jina.ai/v1"
+
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -38,29 +39,25 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
-        api_key = credentials['api_key']
+        api_key = credentials["api_key"]
         if not api_key:
-            raise CredentialsValidateFailedError('api_key is required')
+            raise CredentialsValidateFailedError("api_key is required")
 
-        base_url = credentials.get('base_url', self.api_base)
-        if base_url.endswith('/'):
-            base_url = base_url[:-1]
+        base_url = credentials.get("base_url", self.api_base)
+        base_url = base_url.removesuffix("/")
 
-        url = base_url + '/embeddings'
-        headers = {
-            'Authorization': 'Bearer ' + api_key,
-            'Content-Type': 'application/json'
-        }
+        url = base_url + "/embeddings"
+        headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
 
         def transform_jina_input_text(model, text):
-            if model == 'jina-clip-v1':
+            if model == "jina-clip-v1":
                 return {"text": text}
             return text
 
-        data = {
-            'model': model,
-            'input': [transform_jina_input_text(model, text) for text in texts]
-        }
+        data = {"model": model, "input": [transform_jina_input_text(model, text) for text in texts]}
+
+        if model == "jina-embeddings-v3":
+            data["task_type"] = "retrieval.passage"
 
         try:
             response = post(url, headers=headers, data=dumps(data))
@@ -70,7 +67,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
         if response.status_code != 200:
             try:
                 resp = response.json()
-                msg = resp['detail']
+                msg = resp["detail"]
                 if response.status_code == 401:
                     raise InvokeAuthorizationError(msg)
                 elif response.status_code == 429:
@@ -81,25 +78,20 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
                     raise InvokeBadRequestError(msg)
             except JSONDecodeError as e:
                 raise InvokeServerUnavailableError(
-                    f"Failed to convert response to json: {e} with text: {response.text}")
+                    f"Failed to convert response to json: {e} with text: {response.text}"
+                )
 
         try:
             resp = response.json()
-            embeddings = resp['data']
-            usage = resp['usage']
+            embeddings = resp["data"]
+            usage = resp["usage"]
         except Exception as e:
-            raise InvokeServerUnavailableError(
-                f"Failed to convert response to json: {e} with text: {response.text}")
+            raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
 
-        usage = self._calc_response_usage(
-            model=model, credentials=credentials, tokens=usage['total_tokens'])
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage["total_tokens"])
 
         result = TextEmbeddingResult(
-            model=model,
-            embeddings=[[
-                float(data) for data in x['embedding']
-            ] for x in embeddings],
-            usage=usage
+            model=model, embeddings=[[float(data) for data in x["embedding"]] for x in embeddings], usage=usage
         )
 
         return result
@@ -128,30 +120,18 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         try:
-            self._invoke(model=model, credentials=credentials, texts=['ping'])
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
         except Exception as e:
-            raise CredentialsValidateFailedError(
-                f'Credentials validation failed: {e}')
+            raise CredentialsValidateFailedError(f"Credentials validation failed: {e}")
 
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                KeyError,
-                InvokeBadRequestError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [KeyError, InvokeBadRequestError],
         }
 
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
@@ -165,10 +145,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -179,24 +156,21 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            generate custom model entities from credentials
+        generate custom model entities from credentials
         """
         entity = AIModelEntity(
             model=model,
             label=I18nObject(en_US=model),
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(
-                    credentials.get('context_size'))
-            }
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
         )
 
         return entity
diff --git a/api/core/model_runtime/model_providers/leptonai/leptonai.py b/api/core/model_runtime/model_providers/leptonai/leptonai.py
index b035c31ac5..34a55ff192 100644
--- a/api/core/model_runtime/model_providers/leptonai/leptonai.py
+++ b/api/core/model_runtime/model_providers/leptonai/leptonai.py
@@ -6,8 +6,8 @@ from core.model_runtime.model_providers.__base.model_provider import ModelProvid
 
 logger = logging.getLogger(__name__)
 
-class LeptonAIProvider(ModelProvider):
 
+class LeptonAIProvider(ModelProvider):
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -18,12 +18,9 @@ class LeptonAIProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(
-                model='llama2-7b',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="llama2-7b", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/leptonai/llm/llm.py b/api/core/model_runtime/model_providers/leptonai/llm/llm.py
index 523309bac5..3d69417e45 100644
--- a/api/core/model_runtime/model_providers/leptonai/llm/llm.py
+++ b/api/core/model_runtime/model_providers/leptonai/llm/llm.py
@@ -8,18 +8,25 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
 
 class LeptonAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
     MODEL_PREFIX_MAP = {
-        'llama2-7b': 'llama2-7b',
-        'gemma-7b': 'gemma-7b',
-        'mistral-7b': 'mistral-7b',
-        'mixtral-8x7b': 'mixtral-8x7b',
-        'llama3-70b': 'llama3-70b',
-        'llama2-13b': 'llama2-13b',
-    } 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+        "llama2-7b": "llama2-7b",
+        "gemma-7b": "gemma-7b",
+        "mistral-7b": "mistral-7b",
+        "mixtral-8x7b": "mixtral-8x7b",
+        "llama3-70b": "llama3-70b",
+        "llama2-13b": "llama2-13b",
+    }
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials, model)
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
@@ -29,6 +36,5 @@ class LeptonAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
 
     @classmethod
     def _add_custom_parameters(cls, credentials: dict, model: str) -> None:
-        credentials['mode'] = 'chat'
-        credentials['endpoint_url'] = f'https://{cls.MODEL_PREFIX_MAP[model]}.lepton.run/api/v1'
-        
\ No newline at end of file
+        credentials["mode"] = "chat"
+        credentials["endpoint_url"] = f"https://{cls.MODEL_PREFIX_MAP[model]}.lepton.run/api/v1"
diff --git a/api/core/model_runtime/model_providers/localai/llm/llm.py b/api/core/model_runtime/model_providers/localai/llm/llm.py
index 1009995c58..e7295355f6 100644
--- a/api/core/model_runtime/model_providers/localai/llm/llm.py
+++ b/api/core/model_runtime/model_providers/localai/llm/llm.py
@@ -52,29 +52,48 @@ from core.model_runtime.utils import helper
 
 
 class LocalAILanguageModel(LargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
-                stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
-        return self._generate(model=model, credentials=credentials, prompt_messages=prompt_messages,
-                              model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
+        return self._generate(
+            model=model,
+            credentials=credentials,
+            prompt_messages=prompt_messages,
+            model_parameters=model_parameters,
+            tools=tools,
+            stop=stop,
+            stream=stream,
+            user=user,
+        )
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: list[PromptMessageTool] | None = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool] | None = None,
+    ) -> int:
         # tools is not supported yet
         return self._num_tokens_from_messages(prompt_messages, tools=tools)
 
     def _num_tokens_from_messages(self, messages: list[PromptMessage], tools: list[PromptMessageTool]) -> int:
         """
-            Calculate num tokens for baichuan model
-            LocalAI does not supports
+        Calculate num tokens for baichuan model
+        LocalAI does not supports
         """
 
         def tokens(text: str):
             """
-                We could not determine which tokenizer to use, cause the model is customized.
-                So we use gpt2 tokenizer to calculate the num tokens for convenience.
+            We could not determine which tokenizer to use, cause the model is customized.
+            So we use gpt2 tokenizer to calculate the num tokens for convenience.
             """
             return self._get_num_tokens_by_gpt2(text)
 
@@ -87,10 +106,10 @@ class LocalAILanguageModel(LargeLanguageModel):
             num_tokens += tokens_per_message
             for key, value in message.items():
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
 
                     value = text
 
@@ -142,30 +161,30 @@ class LocalAILanguageModel(LargeLanguageModel):
         num_tokens = 0
         for tool in tools:
             # calculate num tokens for function object
-            num_tokens += tokens('name')
+            num_tokens += tokens("name")
             num_tokens += tokens(tool.name)
-            num_tokens += tokens('description')
+            num_tokens += tokens("description")
             num_tokens += tokens(tool.description)
             parameters = tool.parameters
-            num_tokens += tokens('parameters')
-            num_tokens += tokens('type')
+            num_tokens += tokens("parameters")
+            num_tokens += tokens("type")
             num_tokens += tokens(parameters.get("type"))
-            if 'properties' in parameters:
-                num_tokens += tokens('properties')
-                for key, value in parameters.get('properties').items():
+            if "properties" in parameters:
+                num_tokens += tokens("properties")
+                for key, value in parameters.get("properties").items():
                     num_tokens += tokens(key)
                     for field_key, field_value in value.items():
                         num_tokens += tokens(field_key)
-                        if field_key == 'enum':
+                        if field_key == "enum":
                             for enum_field in field_value:
                                 num_tokens += 3
                                 num_tokens += tokens(enum_field)
                         else:
                             num_tokens += tokens(field_key)
                             num_tokens += tokens(str(field_value))
-            if 'required' in parameters:
-                num_tokens += tokens('required')
-                for required_field in parameters['required']:
+            if "required" in parameters:
+                num_tokens += tokens("required")
+                for required_field in parameters["required"]:
                     num_tokens += 3
                     num_tokens += tokens(required_field)
 
@@ -180,102 +199,104 @@ class LocalAILanguageModel(LargeLanguageModel):
         :return:
         """
         try:
-            self._invoke(model=model, credentials=credentials, prompt_messages=[
-                UserPromptMessage(content='ping')
-            ], model_parameters={
-                'max_tokens': 10,
-            }, stop=[], stream=False)
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                prompt_messages=[UserPromptMessage(content="ping")],
+                model_parameters={
+                    "max_tokens": 10,
+                },
+                stop=[],
+                stream=False,
+            )
         except Exception as ex:
-            raise CredentialsValidateFailedError(f'Invalid credentials {str(ex)}')
+            raise CredentialsValidateFailedError(f"Invalid credentials {str(ex)}")
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         completion_model = None
-        if credentials['completion_type'] == 'chat_completion':
+        if credentials["completion_type"] == "chat_completion":
             completion_model = LLMMode.CHAT.value
-        elif credentials['completion_type'] == 'completion':
+        elif credentials["completion_type"] == "completion":
             completion_model = LLMMode.COMPLETION.value
         else:
             raise ValueError(f"Unknown completion type {credentials['completion_type']}")
 
         rules = [
             ParameterRule(
-                name='temperature',
+                name="temperature",
                 type=ParameterType.FLOAT,
-                use_template='temperature',
-                label=I18nObject(
-                    zh_Hans='温度',
-                    en_US='Temperature'
-                )
+                use_template="temperature",
+                label=I18nObject(zh_Hans="温度", en_US="Temperature"),
             ),
             ParameterRule(
-                name='top_p',
+                name="top_p",
                 type=ParameterType.FLOAT,
-                use_template='top_p',
-                label=I18nObject(
-                    zh_Hans='Top P',
-                    en_US='Top P'
-                )
+                use_template="top_p",
+                label=I18nObject(zh_Hans="Top P", en_US="Top P"),
             ),
             ParameterRule(
-                name='max_tokens',
+                name="max_tokens",
                 type=ParameterType.INT,
-                use_template='max_tokens',
+                use_template="max_tokens",
                 min=1,
                 max=2048,
                 default=512,
-                label=I18nObject(
-                    zh_Hans='最大生成长度',
-                    en_US='Max Tokens'
-                )
-            )
+                label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"),
+            ),
         ]
 
-        model_properties = {
-            ModelPropertyKey.MODE: completion_model,
-        } if completion_model else {}
+        model_properties = (
+            {
+                ModelPropertyKey.MODE: completion_model,
+            }
+            if completion_model
+            else {}
+        )
 
-        model_properties[ModelPropertyKey.CONTEXT_SIZE] = int(credentials.get('context_size', '2048'))
+        model_properties[ModelPropertyKey.CONTEXT_SIZE] = int(credentials.get("context_size", "2048"))
 
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
             model_properties=model_properties,
-            parameter_rules=rules
+            parameter_rules=rules,
         )
 
         return entity
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                  model_parameters: dict, tools: list[PromptMessageTool] | None = None,
-                  stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
-
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         kwargs = self._to_client_kwargs(credentials)
         # init model client
         client = OpenAI(**kwargs)
 
         model_name = model
-        completion_type = credentials['completion_type']
+        completion_type = credentials["completion_type"]
 
         extra_model_kwargs = {
             "timeout": 60,
         }
         if stop:
-            extra_model_kwargs['stop'] = stop
+            extra_model_kwargs["stop"] = stop
 
         if user:
-            extra_model_kwargs['user'] = user
+            extra_model_kwargs["user"] = user
 
         if tools and len(tools) > 0:
-            extra_model_kwargs['functions'] = [
-                helper.dump_model(tool) for tool in tools
-            ]
+            extra_model_kwargs["functions"] = [helper.dump_model(tool) for tool in tools]
 
-        if completion_type == 'chat_completion':
+        if completion_type == "chat_completion":
             result = client.chat.completions.create(
                 messages=[self._convert_prompt_message_to_dict(m) for m in prompt_messages],
                 model=model_name,
@@ -283,36 +304,32 @@ class LocalAILanguageModel(LargeLanguageModel):
                 **model_parameters,
                 **extra_model_kwargs,
             )
-        elif completion_type == 'completion':
+        elif completion_type == "completion":
             result = client.completions.create(
                 prompt=self._convert_prompt_message_to_completion_prompts(prompt_messages),
                 model=model,
                 stream=stream,
                 **model_parameters,
-                **extra_model_kwargs
+                **extra_model_kwargs,
             )
         else:
             raise ValueError(f"Unknown completion type {completion_type}")
 
         if stream:
-            if completion_type == 'completion':
+            if completion_type == "completion":
                 return self._handle_completion_generate_stream_response(
-                    model=model, credentials=credentials, response=result, tools=tools,
-                    prompt_messages=prompt_messages
+                    model=model, credentials=credentials, response=result, tools=tools, prompt_messages=prompt_messages
                 )
             return self._handle_chat_generate_stream_response(
-                model=model, credentials=credentials, response=result, tools=tools,
-                prompt_messages=prompt_messages
+                model=model, credentials=credentials, response=result, tools=tools, prompt_messages=prompt_messages
             )
 
-        if completion_type == 'completion':
+        if completion_type == "completion":
             return self._handle_completion_generate_response(
-                model=model, credentials=credentials, response=result,
-                prompt_messages=prompt_messages
+                model=model, credentials=credentials, response=result, prompt_messages=prompt_messages
             )
         return self._handle_chat_generate_response(
-            model=model, credentials=credentials, response=result, tools=tools,
-            prompt_messages=prompt_messages
+            model=model, credentials=credentials, response=result, tools=tools, prompt_messages=prompt_messages
         )
 
     def _to_client_kwargs(self, credentials: dict) -> dict:
@@ -322,13 +339,13 @@ class LocalAILanguageModel(LargeLanguageModel):
         :param credentials: credentials dict
         :return: client kwargs
         """
-        if not credentials['server_url'].endswith('/'):
-            credentials['server_url'] += '/'
+        if not credentials["server_url"].endswith("/"):
+            credentials["server_url"] += "/"
 
         client_kwargs = {
             "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
             "api_key": "1",
-            "base_url": str(URL(credentials['server_url']) / 'v1'),
+            "base_url": str(URL(credentials["server_url"]) / "v1"),
         }
 
         return client_kwargs
@@ -349,7 +366,7 @@ class LocalAILanguageModel(LargeLanguageModel):
             if message.tool_calls and len(message.tool_calls) > 0:
                 message_dict["function_call"] = {
                     "name": message.tool_calls[0].function.name,
-                    "arguments": message.tool_calls[0].function.arguments
+                    "arguments": message.tool_calls[0].function.arguments,
                 }
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
@@ -359,11 +376,7 @@ class LocalAILanguageModel(LargeLanguageModel):
             message = cast(ToolPromptMessage, message)
             message_dict = {
                 "role": "user",
-                "content": [{
-                    "type": "tool_result",
-                    "tool_use_id": message.tool_call_id,
-                    "content": message.content
-                }]
+                "content": [{"type": "tool_result", "tool_use_id": message.tool_call_id, "content": message.content}],
             }
         else:
             raise ValueError(f"Unknown message type {type(message)}")
@@ -374,27 +387,29 @@ class LocalAILanguageModel(LargeLanguageModel):
         """
         Convert PromptMessage to completion prompts
         """
-        prompts = ''
+        prompts = ""
         for message in messages:
             if isinstance(message, UserPromptMessage):
                 message = cast(UserPromptMessage, message)
-                prompts += f'{message.content}\n'
+                prompts += f"{message.content}\n"
             elif isinstance(message, AssistantPromptMessage):
                 message = cast(AssistantPromptMessage, message)
-                prompts += f'{message.content}\n'
+                prompts += f"{message.content}\n"
             elif isinstance(message, SystemPromptMessage):
                 message = cast(SystemPromptMessage, message)
-                prompts += f'{message.content}\n'
+                prompts += f"{message.content}\n"
             else:
                 raise ValueError(f"Unknown message type {type(message)}")
 
         return prompts
 
-    def _handle_completion_generate_response(self, model: str,
-                                             prompt_messages: list[PromptMessage],
-                                             credentials: dict,
-                                             response: Completion,
-                                             ) -> LLMResult:
+    def _handle_completion_generate_response(
+        self,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        credentials: dict,
+        response: Completion,
+    ) -> LLMResult:
         """
         Handle llm chat response
 
@@ -411,18 +426,16 @@ class LocalAILanguageModel(LargeLanguageModel):
         assistant_message = response.choices[0].text
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_message,
-            tool_calls=[]
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_message, tool_calls=[])
 
         prompt_tokens = self._get_num_tokens_by_gpt2(
             self._convert_prompt_message_to_completion_prompts(prompt_messages)
         )
         completion_tokens = self._num_tokens_from_messages(messages=[assistant_prompt_message], tools=[])
 
-        usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=prompt_tokens,
-                                          completion_tokens=completion_tokens)
+        usage = self._calc_response_usage(
+            model=model, credentials=credentials, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens
+        )
 
         response = LLMResult(
             model=model,
@@ -434,11 +447,14 @@ class LocalAILanguageModel(LargeLanguageModel):
 
         return response
 
-    def _handle_chat_generate_response(self, model: str,
-                                       prompt_messages: list[PromptMessage],
-                                       credentials: dict,
-                                       response: ChatCompletion,
-                                       tools: list[PromptMessageTool]) -> LLMResult:
+    def _handle_chat_generate_response(
+        self,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        credentials: dict,
+        response: ChatCompletion,
+        tools: list[PromptMessageTool],
+    ) -> LLMResult:
         """
         Handle llm chat response
 
@@ -459,16 +475,14 @@ class LocalAILanguageModel(LargeLanguageModel):
         tool_calls = self._extract_response_tool_calls([function_calls] if function_calls else [])
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_message.content,
-            tool_calls=tool_calls
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_message.content, tool_calls=tool_calls)
 
         prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
         completion_tokens = self._num_tokens_from_messages(messages=[assistant_prompt_message], tools=tools)
 
-        usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=prompt_tokens,
-                                          completion_tokens=completion_tokens)
+        usage = self._calc_response_usage(
+            model=model, credentials=credentials, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens
+        )
 
         response = LLMResult(
             model=model,
@@ -480,12 +494,15 @@ class LocalAILanguageModel(LargeLanguageModel):
 
         return response
 
-    def _handle_completion_generate_stream_response(self, model: str,
-                                                    prompt_messages: list[PromptMessage],
-                                                    credentials: dict,
-                                                    response: Stream[Completion],
-                                                    tools: list[PromptMessageTool]) -> Generator:
-        full_response = ''
+    def _handle_completion_generate_stream_response(
+        self,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        credentials: dict,
+        response: Stream[Completion],
+        tools: list[PromptMessageTool],
+    ) -> Generator:
+        full_response = ""
 
         for chunk in response:
             if len(chunk.choices) == 0:
@@ -494,17 +511,11 @@ class LocalAILanguageModel(LargeLanguageModel):
             delta = chunk.choices[0]
 
             # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(
-                content=delta.text if delta.text else '',
-                tool_calls=[]
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=delta.text or "", tool_calls=[])
 
             if delta.finish_reason is not None:
                 # temp_assistant_prompt_message is used to calculate usage
-                temp_assistant_prompt_message = AssistantPromptMessage(
-                    content=full_response,
-                    tool_calls=[]
-                )
+                temp_assistant_prompt_message = AssistantPromptMessage(content=full_response, tool_calls=[])
 
                 prompt_tokens = self._get_num_tokens_by_gpt2(
                     self._convert_prompt_message_to_completion_prompts(prompt_messages)
@@ -512,8 +523,12 @@ class LocalAILanguageModel(LargeLanguageModel):
 
                 completion_tokens = self._num_tokens_from_messages(messages=[temp_assistant_prompt_message], tools=[])
 
-                usage = self._calc_response_usage(model=model, credentials=credentials,
-                                                  prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
+                usage = self._calc_response_usage(
+                    model=model,
+                    credentials=credentials,
+                    prompt_tokens=prompt_tokens,
+                    completion_tokens=completion_tokens,
+                )
 
                 yield LLMResultChunk(
                     model=model,
@@ -523,7 +538,7 @@ class LocalAILanguageModel(LargeLanguageModel):
                         index=delta.index,
                         message=assistant_prompt_message,
                         finish_reason=delta.finish_reason,
-                        usage=usage
+                        usage=usage,
                     ),
                 )
             else:
@@ -539,12 +554,15 @@ class LocalAILanguageModel(LargeLanguageModel):
 
                 full_response += delta.text
 
-    def _handle_chat_generate_stream_response(self, model: str,
-                                              prompt_messages: list[PromptMessage],
-                                              credentials: dict,
-                                              response: Stream[ChatCompletionChunk],
-                                              tools: list[PromptMessageTool]) -> Generator:
-        full_response = ''
+    def _handle_chat_generate_stream_response(
+        self,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        credentials: dict,
+        response: Stream[ChatCompletionChunk],
+        tools: list[PromptMessageTool],
+    ) -> Generator:
+        full_response = ""
 
         for chunk in response:
             if len(chunk.choices) == 0:
@@ -552,7 +570,7 @@ class LocalAILanguageModel(LargeLanguageModel):
 
             delta = chunk.choices[0]
 
-            if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ''):
+            if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ""):
                 continue
 
             # check if there is a tool call in the response
@@ -560,26 +578,28 @@ class LocalAILanguageModel(LargeLanguageModel):
             if delta.delta.function_call:
                 function_calls = [delta.delta.function_call]
 
-            assistant_message_tool_calls = self._extract_response_tool_calls(function_calls if function_calls else [])
+            assistant_message_tool_calls = self._extract_response_tool_calls(function_calls or [])
 
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else '',
-                tool_calls=assistant_message_tool_calls
+                content=delta.delta.content or "", tool_calls=assistant_message_tool_calls
             )
 
             if delta.finish_reason is not None:
                 # temp_assistant_prompt_message is used to calculate usage
                 temp_assistant_prompt_message = AssistantPromptMessage(
-                    content=full_response,
-                    tool_calls=assistant_message_tool_calls
+                    content=full_response, tool_calls=assistant_message_tool_calls
                 )
 
                 prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
                 completion_tokens = self._num_tokens_from_messages(messages=[temp_assistant_prompt_message], tools=[])
 
-                usage = self._calc_response_usage(model=model, credentials=credentials,
-                                                  prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
+                usage = self._calc_response_usage(
+                    model=model,
+                    credentials=credentials,
+                    prompt_tokens=prompt_tokens,
+                    completion_tokens=completion_tokens,
+                )
 
                 yield LLMResultChunk(
                     model=model,
@@ -589,7 +609,7 @@ class LocalAILanguageModel(LargeLanguageModel):
                         index=delta.index,
                         message=assistant_prompt_message,
                         finish_reason=delta.finish_reason,
-                        usage=usage
+                        usage=usage,
                     ),
                 )
             else:
@@ -605,9 +625,9 @@ class LocalAILanguageModel(LargeLanguageModel):
 
                 full_response += delta.delta.content
 
-    def _extract_response_tool_calls(self,
-                                     response_function_calls: list[FunctionCall]) \
-            -> list[AssistantPromptMessage.ToolCall]:
+    def _extract_response_tool_calls(
+        self, response_function_calls: list[FunctionCall]
+    ) -> list[AssistantPromptMessage.ToolCall]:
         """
         Extract tool calls from response
 
@@ -618,15 +638,10 @@ class LocalAILanguageModel(LargeLanguageModel):
         if response_function_calls:
             for response_tool_call in response_function_calls:
                 function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                    name=response_tool_call.name,
-                    arguments=response_tool_call.arguments
+                    name=response_tool_call.name, arguments=response_tool_call.arguments
                 )
 
-                tool_call = AssistantPromptMessage.ToolCall(
-                    id=0,
-                    type='function',
-                    function=function
-                )
+                tool_call = AssistantPromptMessage.ToolCall(id=0, type="function", function=function)
                 tool_calls.append(tool_call)
 
         return tool_calls
@@ -651,15 +666,9 @@ class LocalAILanguageModel(LargeLanguageModel):
                 ConflictError,
                 NotFoundError,
                 UnprocessableEntityError,
-                PermissionDeniedError
+                PermissionDeniedError,
             ],
-            InvokeRateLimitError: [
-                RateLimitError
-            ],
-            InvokeAuthorizationError: [
-                AuthenticationError
-            ],
-            InvokeBadRequestError: [
-                ValueError
-            ]
+            InvokeRateLimitError: [RateLimitError],
+            InvokeAuthorizationError: [AuthenticationError],
+            InvokeBadRequestError: [ValueError],
         }
diff --git a/api/core/model_runtime/model_providers/localai/localai.py b/api/core/model_runtime/model_providers/localai/localai.py
index 6d2278fd54..4ff898052b 100644
--- a/api/core/model_runtime/model_providers/localai/localai.py
+++ b/api/core/model_runtime/model_providers/localai/localai.py
@@ -6,6 +6,5 @@ logger = logging.getLogger(__name__)
 
 
 class LocalAIProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
-        pass
\ No newline at end of file
+        pass
diff --git a/api/core/model_runtime/model_providers/localai/rerank/rerank.py b/api/core/model_runtime/model_providers/localai/rerank/rerank.py
index c8ba9a6c7c..2b0f53bc19 100644
--- a/api/core/model_runtime/model_providers/localai/rerank/rerank.py
+++ b/api/core/model_runtime/model_providers/localai/rerank/rerank.py
@@ -25,9 +25,16 @@ class LocalaiRerankModel(RerankModel):
     LocalAI rerank model API is compatible with Jina rerank model API. So just copy the JinaRerankModel class code here.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
-                user: Optional[str] = None) -> RerankResult:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
         """
         Invoke rerank model
 
@@ -43,45 +50,37 @@ class LocalaiRerankModel(RerankModel):
         if len(docs) == 0:
             return RerankResult(model=model, docs=[])
 
-        server_url = credentials['server_url']
+        server_url = credentials["server_url"]
         model_name = model
-        
-        if not server_url:
-            raise CredentialsValidateFailedError('server_url is required')
-        if not model_name:
-            raise CredentialsValidateFailedError('model_name is required')
-        
-        url = server_url
-        headers = {
-            'Authorization': f"Bearer {credentials.get('api_key')}",
-            'Content-Type': 'application/json'
-        }
 
-        data = {
-            "model": model_name,
-            "query": query,
-            "documents": docs,
-            "top_n": top_n
-        }
+        if not server_url:
+            raise CredentialsValidateFailedError("server_url is required")
+        if not model_name:
+            raise CredentialsValidateFailedError("model_name is required")
+
+        url = server_url
+        headers = {"Authorization": f"Bearer {credentials.get('api_key')}", "Content-Type": "application/json"}
+
+        data = {"model": model_name, "query": query, "documents": docs, "top_n": top_n}
 
         try:
-            response = post(str(URL(url) / 'rerank'), headers=headers, data=dumps(data), timeout=10)
-            response.raise_for_status() 
+            response = post(str(URL(url) / "rerank"), headers=headers, data=dumps(data), timeout=10)
+            response.raise_for_status()
             results = response.json()
 
             rerank_documents = []
-            for result in results['results']:  
+            for result in results["results"]:
                 rerank_document = RerankDocument(
-                    index=result['index'],
-                    text=result['document']['text'],
-                    score=result['relevance_score'],
+                    index=result["index"],
+                    text=result["document"]["text"],
+                    score=result["relevance_score"],
                 )
-                if score_threshold is None or result['relevance_score'] >= score_threshold:
+                if score_threshold is None or result["relevance_score"] >= score_threshold:
                     rerank_documents.append(rerank_document)
 
             return RerankResult(model=model, docs=rerank_documents)
         except httpx.HTTPStatusError as e:
-            raise InvokeServerUnavailableError(str(e))  
+            raise InvokeServerUnavailableError(str(e))
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
@@ -92,7 +91,6 @@ class LocalaiRerankModel(RerankModel):
         :return:
         """
         try:
-            
             self._invoke(
                 model=model,
                 credentials=credentials,
@@ -103,7 +101,7 @@ class LocalaiRerankModel(RerankModel):
                     "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
                     "are a political division controlled by the United States. Its capital is Saipan.",
                 ],
-                score_threshold=0.8
+                score_threshold=0.8,
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -116,21 +114,21 @@ class LocalaiRerankModel(RerankModel):
         return {
             InvokeConnectionError: [httpx.ConnectError],
             InvokeServerUnavailableError: [httpx.RemoteProtocolError],
-            InvokeRateLimitError: [], 
-            InvokeAuthorizationError: [httpx.HTTPStatusError],  
-            InvokeBadRequestError: [httpx.RequestError]
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [httpx.HTTPStatusError],
+            InvokeBadRequestError: [httpx.RequestError],
         }
-    
+
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            generate custom model entities from credentials
+        generate custom model entities from credentials
         """
         entity = AIModelEntity(
             model=model,
             label=I18nObject(en_US=model),
             model_type=ModelType.RERANK,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={}
+            model_properties={},
         )
 
         return entity
diff --git a/api/core/model_runtime/model_providers/localai/speech2text/speech2text.py b/api/core/model_runtime/model_providers/localai/speech2text/speech2text.py
index d7403aff4f..4b9d0f5bfe 100644
--- a/api/core/model_runtime/model_providers/localai/speech2text/speech2text.py
+++ b/api/core/model_runtime/model_providers/localai/speech2text/speech2text.py
@@ -32,8 +32,8 @@ class LocalAISpeech2text(Speech2TextModel):
         :param user: unique user id
         :return: text for given audio file
         """
-        
-        url = str(URL(credentials['server_url']) / "v1/audio/transcriptions")
+
+        url = str(URL(credentials["server_url"]) / "v1/audio/transcriptions")
         data = {"model": model}
         files = {"file": file}
 
@@ -42,7 +42,7 @@ class LocalAISpeech2text(Speech2TextModel):
         prepared_request = session.prepare_request(request)
         response = session.send(prepared_request)
 
-        if 'error' in response.json():
+        if "error" in response.json():
             raise InvokeServerUnavailableError("Empty response")
 
         return response.json()["text"]
@@ -58,7 +58,7 @@ class LocalAISpeech2text(Speech2TextModel):
         try:
             audio_file_path = self._get_demo_file_path()
 
-            with open(audio_file_path, 'rb') as audio_file:
+            with open(audio_file_path, "rb") as audio_file:
                 self._invoke(model, credentials, audio_file)
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -66,36 +66,24 @@ class LocalAISpeech2text(Speech2TextModel):
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                InvokeBadRequestError
-            ],
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [InvokeBadRequestError],
         }
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.SPEECH2TEXT,
             model_properties={},
-            parameter_rules=[]
+            parameter_rules=[],
         )
 
-        return entity
\ No newline at end of file
+        return entity
diff --git a/api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py
index 954c9d10f2..7d258be81e 100644
--- a/api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py
@@ -24,9 +24,10 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
     """
     Model class for Jina text embedding model.
     """
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -37,39 +38,33 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
         :return: embeddings result
         """
         if len(texts) != 1:
-            raise InvokeBadRequestError('Only one text is supported')
+            raise InvokeBadRequestError("Only one text is supported")
 
-        server_url = credentials['server_url']
+        server_url = credentials["server_url"]
         model_name = model
         if not server_url:
-            raise CredentialsValidateFailedError('server_url is required')
+            raise CredentialsValidateFailedError("server_url is required")
         if not model_name:
-            raise CredentialsValidateFailedError('model_name is required')
-        
-        url = server_url
-        headers = {
-            'Authorization': 'Bearer 123',
-            'Content-Type': 'application/json'
-        }
+            raise CredentialsValidateFailedError("model_name is required")
 
-        data = {
-            'model': model_name,
-            'input': texts[0]
-        }
+        url = server_url
+        headers = {"Authorization": "Bearer 123", "Content-Type": "application/json"}
+
+        data = {"model": model_name, "input": texts[0]}
 
         try:
-            response = post(str(URL(url) / 'embeddings'), headers=headers, data=dumps(data), timeout=10)
+            response = post(str(URL(url) / "embeddings"), headers=headers, data=dumps(data), timeout=10)
         except Exception as e:
             raise InvokeConnectionError(str(e))
-        
+
         if response.status_code != 200:
             try:
                 resp = response.json()
-                code = resp['error']['code']
-                msg = resp['error']['message']
+                code = resp["error"]["code"]
+                msg = resp["error"]["message"]
                 if code == 500:
                     raise InvokeServerUnavailableError(msg)
-                
+
                 if response.status_code == 401:
                     raise InvokeAuthorizationError(msg)
                 elif response.status_code == 429:
@@ -79,23 +74,21 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
                 else:
                     raise InvokeError(msg)
             except JSONDecodeError as e:
-                raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
+                raise InvokeServerUnavailableError(
+                    f"Failed to convert response to json: {e} with text: {response.text}"
+                )
 
         try:
             resp = response.json()
-            embeddings = resp['data']
-            usage = resp['usage']
+            embeddings = resp["data"]
+            usage = resp["usage"]
         except Exception as e:
             raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
 
-        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage['total_tokens'])
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage["total_tokens"])
 
         result = TextEmbeddingResult(
-            model=model,
-            embeddings=[[
-                float(data) for data in x['embedding']
-            ] for x in embeddings],
-            usage=usage
+            model=model, embeddings=[[float(data) for data in x["embedding"]] for x in embeddings], usage=usage
         )
 
         return result
@@ -114,7 +107,7 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
             # use GPT2Tokenizer to get num tokens
             num_tokens += self._get_num_tokens_by_gpt2(text)
         return num_tokens
-    
+
     def _get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
         Get customizable model schema
@@ -130,10 +123,10 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
             features=[],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', '512')),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", "512")),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
-            parameter_rules=[]
+            parameter_rules=[],
         )
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
@@ -145,32 +138,22 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         try:
-            self._invoke(model=model, credentials=credentials, texts=['ping'])
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
         except InvokeAuthorizationError:
-            raise CredentialsValidateFailedError('Invalid credentials')
+            raise CredentialsValidateFailedError("Invalid credentials")
         except InvokeConnectionError as e:
-            raise CredentialsValidateFailedError(f'Invalid credentials: {e}')
+            raise CredentialsValidateFailedError(f"Invalid credentials: {e}")
 
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                KeyError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [KeyError],
         }
-    
+
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -182,10 +165,7 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -196,7 +176,7 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/minimax/llm/chat_completion.py b/api/core/model_runtime/model_providers/minimax/llm/chat_completion.py
index 6c41e0d2a5..88cc0e8e0f 100644
--- a/api/core/model_runtime/model_providers/minimax/llm/chat_completion.py
+++ b/api/core/model_runtime/model_providers/minimax/llm/chat_completion.py
@@ -17,42 +17,48 @@ from core.model_runtime.model_providers.minimax.llm.types import MinimaxMessage
 
 class MinimaxChatCompletion:
     """
-        Minimax Chat Completion API
+    Minimax Chat Completion API
     """
-    def generate(self, model: str, api_key: str, group_id: str, 
-                 prompt_messages: list[MinimaxMessage], model_parameters: dict,
-                 tools: list[dict[str, Any]], stop: list[str] | None, stream: bool, user: str) \
-        -> Union[MinimaxMessage, Generator[MinimaxMessage, None, None]]:
+
+    def generate(
+        self,
+        model: str,
+        api_key: str,
+        group_id: str,
+        prompt_messages: list[MinimaxMessage],
+        model_parameters: dict,
+        tools: list[dict[str, Any]],
+        stop: list[str] | None,
+        stream: bool,
+        user: str,
+    ) -> Union[MinimaxMessage, Generator[MinimaxMessage, None, None]]:
         """
-            generate chat completion
+        generate chat completion
         """
         if not api_key or not group_id:
-            raise InvalidAPIKeyError('Invalid API key or group ID')
-        
-        url = f'https://api.minimax.chat/v1/text/chatcompletion?GroupId={group_id}'
+            raise InvalidAPIKeyError("Invalid API key or group ID")
+
+        url = f"https://api.minimax.chat/v1/text/chatcompletion?GroupId={group_id}"
 
         extra_kwargs = {}
 
-        if 'max_tokens' in model_parameters and type(model_parameters['max_tokens']) == int:
-            extra_kwargs['tokens_to_generate'] = model_parameters['max_tokens']
+        if "max_tokens" in model_parameters and type(model_parameters["max_tokens"]) == int:
+            extra_kwargs["tokens_to_generate"] = model_parameters["max_tokens"]
 
-        if 'temperature' in model_parameters and type(model_parameters['temperature']) == float:
-            extra_kwargs['temperature'] = model_parameters['temperature']
+        if "temperature" in model_parameters and type(model_parameters["temperature"]) == float:
+            extra_kwargs["temperature"] = model_parameters["temperature"]
 
-        if 'top_p' in model_parameters and type(model_parameters['top_p']) == float:
-            extra_kwargs['top_p'] = model_parameters['top_p']
+        if "top_p" in model_parameters and type(model_parameters["top_p"]) == float:
+            extra_kwargs["top_p"] = model_parameters["top_p"]
 
-        prompt = '你是一个什么都懂的专家'
+        prompt = "你是一个什么都懂的专家"
 
-        role_meta = {
-            'user_name': '我',
-            'bot_name': '专家'
-        }
+        role_meta = {"user_name": "我", "bot_name": "专家"}
 
         # check if there is a system message
         if len(prompt_messages) == 0:
-            raise BadRequestError('At least one message is required')
-        
+            raise BadRequestError("At least one message is required")
+
         if prompt_messages[0].role == MinimaxMessage.Role.SYSTEM.value:
             if prompt_messages[0].content:
                 prompt = prompt_messages[0].content
@@ -60,44 +66,43 @@ class MinimaxChatCompletion:
 
         # check if there is a user message
         if len(prompt_messages) == 0:
-            raise BadRequestError('At least one user message is required')
-        
-        messages = [{
-            'sender_type': message.role,
-            'text': message.content,
-        } for message in prompt_messages]
+            raise BadRequestError("At least one user message is required")
 
-        headers = {
-            'Authorization': 'Bearer ' + api_key,
-            'Content-Type': 'application/json'
-        }
+        messages = [
+            {
+                "sender_type": message.role,
+                "text": message.content,
+            }
+            for message in prompt_messages
+        ]
+
+        headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
 
         body = {
-            'model': model,
-            'messages': messages,
-            'prompt': prompt,
-            'role_meta': role_meta,
-            'stream': stream,
-            **extra_kwargs
+            "model": model,
+            "messages": messages,
+            "prompt": prompt,
+            "role_meta": role_meta,
+            "stream": stream,
+            **extra_kwargs,
         }
 
         try:
-            response = post(
-                url=url, data=dumps(body), headers=headers, stream=stream, timeout=(10, 300))
+            response = post(url=url, data=dumps(body), headers=headers, stream=stream, timeout=(10, 300))
         except Exception as e:
             raise InternalServerError(e)
-        
+
         if response.status_code != 200:
             raise InternalServerError(response.text)
-        
+
         if stream:
             return self._handle_stream_chat_generate_response(response)
         return self._handle_chat_generate_response(response)
-    
+
     def _handle_error(self, code: int, msg: str):
-        if code == 1000 or code == 1001 or code == 1013 or code == 1027:
+        if code in {1000, 1001, 1013, 1027}:
             raise InternalServerError(msg)
-        elif code == 1002 or code == 1039:
+        elif code in {1002, 1039}:
             raise RateLimitReachedError(msg)
         elif code == 1004:
             raise InvalidAuthenticationError(msg)
@@ -110,65 +115,52 @@ class MinimaxChatCompletion:
 
     def _handle_chat_generate_response(self, response: Response) -> MinimaxMessage:
         """
-            handle chat generate response
+        handle chat generate response
         """
         response = response.json()
-        if 'base_resp' in response and response['base_resp']['status_code'] != 0:
-            code = response['base_resp']['status_code']
-            msg = response['base_resp']['status_msg']
+        if "base_resp" in response and response["base_resp"]["status_code"] != 0:
+            code = response["base_resp"]["status_code"]
+            msg = response["base_resp"]["status_msg"]
             self._handle_error(code, msg)
-        
-        message = MinimaxMessage(
-            content=response['reply'],
-            role=MinimaxMessage.Role.ASSISTANT.value
-        )
+
+        message = MinimaxMessage(content=response["reply"], role=MinimaxMessage.Role.ASSISTANT.value)
         message.usage = {
-            'prompt_tokens': 0,
-            'completion_tokens': response['usage']['total_tokens'],
-            'total_tokens': response['usage']['total_tokens']
+            "prompt_tokens": 0,
+            "completion_tokens": response["usage"]["total_tokens"],
+            "total_tokens": response["usage"]["total_tokens"],
         }
-        message.stop_reason = response['choices'][0]['finish_reason']
+        message.stop_reason = response["choices"][0]["finish_reason"]
         return message
 
     def _handle_stream_chat_generate_response(self, response: Response) -> Generator[MinimaxMessage, None, None]:
         """
-            handle stream chat generate response
+        handle stream chat generate response
         """
         for line in response.iter_lines():
             if not line:
                 continue
-            line: str = line.decode('utf-8')
-            if line.startswith('data: '):
+            line: str = line.decode("utf-8")
+            if line.startswith("data: "):
                 line = line[6:].strip()
             data = loads(line)
 
-            if 'base_resp' in data and data['base_resp']['status_code'] != 0:
-                code = data['base_resp']['status_code']
-                msg = data['base_resp']['status_msg']
+            if "base_resp" in data and data["base_resp"]["status_code"] != 0:
+                code = data["base_resp"]["status_code"]
+                msg = data["base_resp"]["status_msg"]
                 self._handle_error(code, msg)
 
-            if data['reply']:
-                total_tokens = data['usage']['total_tokens']
-                message =  MinimaxMessage(
-                    role=MinimaxMessage.Role.ASSISTANT.value,
-                    content=''
-                )
-                message.usage = {
-                    'prompt_tokens': 0,
-                    'completion_tokens': total_tokens,
-                    'total_tokens': total_tokens
-                }
-                message.stop_reason = data['choices'][0]['finish_reason']
+            if data["reply"]:
+                total_tokens = data["usage"]["total_tokens"]
+                message = MinimaxMessage(role=MinimaxMessage.Role.ASSISTANT.value, content="")
+                message.usage = {"prompt_tokens": 0, "completion_tokens": total_tokens, "total_tokens": total_tokens}
+                message.stop_reason = data["choices"][0]["finish_reason"]
                 yield message
                 return
 
-            choices = data.get('choices', [])
+            choices = data.get("choices", [])
             if len(choices) == 0:
                 continue
 
             for choice in choices:
-                message = choice['delta']
-                yield MinimaxMessage(
-                    content=message,
-                    role=MinimaxMessage.Role.ASSISTANT.value
-                )
\ No newline at end of file
+                message = choice["delta"]
+                yield MinimaxMessage(content=message, role=MinimaxMessage.Role.ASSISTANT.value)
diff --git a/api/core/model_runtime/model_providers/minimax/llm/chat_completion_pro.py b/api/core/model_runtime/model_providers/minimax/llm/chat_completion_pro.py
index 55747057c9..8b8fdbb6bd 100644
--- a/api/core/model_runtime/model_providers/minimax/llm/chat_completion_pro.py
+++ b/api/core/model_runtime/model_providers/minimax/llm/chat_completion_pro.py
@@ -17,86 +17,83 @@ from core.model_runtime.model_providers.minimax.llm.types import MinimaxMessage
 
 class MinimaxChatCompletionPro:
     """
-        Minimax Chat Completion Pro API, supports function calling
-        however, we do not have enough time and energy to implement it, but the parameters are reserved
+    Minimax Chat Completion Pro API, supports function calling
+    however, we do not have enough time and energy to implement it, but the parameters are reserved
     """
-    def generate(self, model: str, api_key: str, group_id: str,
-                 prompt_messages: list[MinimaxMessage], model_parameters: dict,
-                 tools: list[dict[str, Any]], stop: list[str] | None, stream: bool, user: str) \
-            -> Union[MinimaxMessage, Generator[MinimaxMessage, None, None]]:
+
+    def generate(
+        self,
+        model: str,
+        api_key: str,
+        group_id: str,
+        prompt_messages: list[MinimaxMessage],
+        model_parameters: dict,
+        tools: list[dict[str, Any]],
+        stop: list[str] | None,
+        stream: bool,
+        user: str,
+    ) -> Union[MinimaxMessage, Generator[MinimaxMessage, None, None]]:
         """
-            generate chat completion
+        generate chat completion
         """
         if not api_key or not group_id:
-            raise InvalidAPIKeyError('Invalid API key or group ID')
+            raise InvalidAPIKeyError("Invalid API key or group ID")
 
-        url = f'https://api.minimax.chat/v1/text/chatcompletion_pro?GroupId={group_id}'
+        url = f"https://api.minimax.chat/v1/text/chatcompletion_pro?GroupId={group_id}"
 
         extra_kwargs = {}
 
-        if 'max_tokens' in model_parameters and type(model_parameters['max_tokens']) == int:
-            extra_kwargs['tokens_to_generate'] = model_parameters['max_tokens']
+        if "max_tokens" in model_parameters and type(model_parameters["max_tokens"]) == int:
+            extra_kwargs["tokens_to_generate"] = model_parameters["max_tokens"]
 
-        if 'temperature' in model_parameters and type(model_parameters['temperature']) == float:
-            extra_kwargs['temperature'] = model_parameters['temperature']
+        if "temperature" in model_parameters and type(model_parameters["temperature"]) == float:
+            extra_kwargs["temperature"] = model_parameters["temperature"]
 
-        if 'top_p' in model_parameters and type(model_parameters['top_p']) == float:
-            extra_kwargs['top_p'] = model_parameters['top_p']
+        if "top_p" in model_parameters and type(model_parameters["top_p"]) == float:
+            extra_kwargs["top_p"] = model_parameters["top_p"]
 
-        if 'mask_sensitive_info' in model_parameters and type(model_parameters['mask_sensitive_info']) == bool:
-            extra_kwargs['mask_sensitive_info'] = model_parameters['mask_sensitive_info']
-        
-        if model_parameters.get('plugin_web_search'):
-            extra_kwargs['plugins'] = [
-                'plugin_web_search'
-            ]
+        if "mask_sensitive_info" in model_parameters and type(model_parameters["mask_sensitive_info"]) == bool:
+            extra_kwargs["mask_sensitive_info"] = model_parameters["mask_sensitive_info"]
 
-        bot_setting = {
-            'bot_name': '专家',
-            'content': '你是一个什么都懂的专家'
-        }
+        if model_parameters.get("plugin_web_search"):
+            extra_kwargs["plugins"] = ["plugin_web_search"]
 
-        reply_constraints = {
-            'sender_type': 'BOT',
-            'sender_name': '专家'
-        }
+        bot_setting = {"bot_name": "专家", "content": "你是一个什么都懂的专家"}
+
+        reply_constraints = {"sender_type": "BOT", "sender_name": "专家"}
 
         # check if there is a system message
         if len(prompt_messages) == 0:
-            raise BadRequestError('At least one message is required')
+            raise BadRequestError("At least one message is required")
 
         if prompt_messages[0].role == MinimaxMessage.Role.SYSTEM.value:
             if prompt_messages[0].content:
-                bot_setting['content'] = prompt_messages[0].content
+                bot_setting["content"] = prompt_messages[0].content
             prompt_messages = prompt_messages[1:]
 
         # check if there is a user message
         if len(prompt_messages) == 0:
-            raise BadRequestError('At least one user message is required')
+            raise BadRequestError("At least one user message is required")
 
         messages = [message.to_dict() for message in prompt_messages]
 
-        headers = {
-            'Authorization': 'Bearer ' + api_key,
-            'Content-Type': 'application/json'
-        }
+        headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
 
         body = {
-            'model': model,
-            'messages': messages,
-            'bot_setting': [bot_setting],
-            'reply_constraints': reply_constraints,
-            'stream': stream,
-            **extra_kwargs
+            "model": model,
+            "messages": messages,
+            "bot_setting": [bot_setting],
+            "reply_constraints": reply_constraints,
+            "stream": stream,
+            **extra_kwargs,
         }
 
         if tools:
-            body['functions'] = tools
-            body['function_call'] = {'type': 'auto'}
+            body["functions"] = tools
+            body["function_call"] = {"type": "auto"}
 
         try:
-            response = post(
-                url=url, data=dumps(body), headers=headers, stream=stream, timeout=(10, 300))
+            response = post(url=url, data=dumps(body), headers=headers, stream=stream, timeout=(10, 300))
         except Exception as e:
             raise InternalServerError(e)
 
@@ -108,9 +105,9 @@ class MinimaxChatCompletionPro:
         return self._handle_chat_generate_response(response)
 
     def _handle_error(self, code: int, msg: str):
-        if code == 1000 or code == 1001 or code == 1013 or code == 1027:
+        if code in {1000, 1001, 1013, 1027}:
             raise InternalServerError(msg)
-        elif code == 1002 or code == 1039:
+        elif code in {1002, 1039}:
             raise RateLimitReachedError(msg)
         elif code == 1004:
             raise InvalidAuthenticationError(msg)
@@ -123,78 +120,72 @@ class MinimaxChatCompletionPro:
 
     def _handle_chat_generate_response(self, response: Response) -> MinimaxMessage:
         """
-            handle chat generate response
+        handle chat generate response
         """
         response = response.json()
-        if 'base_resp' in response and response['base_resp']['status_code'] != 0:
-            code = response['base_resp']['status_code']
-            msg = response['base_resp']['status_msg']
+        if "base_resp" in response and response["base_resp"]["status_code"] != 0:
+            code = response["base_resp"]["status_code"]
+            msg = response["base_resp"]["status_msg"]
             self._handle_error(code, msg)
 
-        message = MinimaxMessage(
-            content=response['reply'],
-            role=MinimaxMessage.Role.ASSISTANT.value
-        )
+        message = MinimaxMessage(content=response["reply"], role=MinimaxMessage.Role.ASSISTANT.value)
         message.usage = {
-            'prompt_tokens': 0,
-            'completion_tokens': response['usage']['total_tokens'],
-            'total_tokens': response['usage']['total_tokens']
+            "prompt_tokens": 0,
+            "completion_tokens": response["usage"]["total_tokens"],
+            "total_tokens": response["usage"]["total_tokens"],
         }
-        message.stop_reason = response['choices'][0]['finish_reason']
+        message.stop_reason = response["choices"][0]["finish_reason"]
         return message
 
     def _handle_stream_chat_generate_response(self, response: Response) -> Generator[MinimaxMessage, None, None]:
         """
-            handle stream chat generate response
+        handle stream chat generate response
         """
         for line in response.iter_lines():
             if not line:
                 continue
-            line: str = line.decode('utf-8')
-            if line.startswith('data: '):
+            line: str = line.decode("utf-8")
+            if line.startswith("data: "):
                 line = line[6:].strip()
             data = loads(line)
 
-            if 'base_resp' in data and data['base_resp']['status_code'] != 0:
-                code = data['base_resp']['status_code']
-                msg = data['base_resp']['status_msg']
+            if "base_resp" in data and data["base_resp"]["status_code"] != 0:
+                code = data["base_resp"]["status_code"]
+                msg = data["base_resp"]["status_msg"]
                 self._handle_error(code, msg)
 
             # final chunk
-            if data['reply'] or data.get('usage'):
-                total_tokens = data['usage']['total_tokens']
-                minimax_message = MinimaxMessage(
-                    role=MinimaxMessage.Role.ASSISTANT.value,
-                    content=''
-                )
+            if data["reply"] or data.get("usage"):
+                total_tokens = data["usage"]["total_tokens"]
+                minimax_message = MinimaxMessage(role=MinimaxMessage.Role.ASSISTANT.value, content="")
                 minimax_message.usage = {
-                    'prompt_tokens': 0,
-                    'completion_tokens': total_tokens,
-                    'total_tokens': total_tokens
+                    "prompt_tokens": 0,
+                    "completion_tokens": total_tokens,
+                    "total_tokens": total_tokens,
                 }
-                minimax_message.stop_reason = data['choices'][0]['finish_reason']
+                minimax_message.stop_reason = data["choices"][0]["finish_reason"]
 
-                choices = data.get('choices', [])
+                choices = data.get("choices", [])
                 if len(choices) > 0:
                     for choice in choices:
-                        message = choice['messages'][0]
+                        message = choice["messages"][0]
                         # append function_call message
-                        if 'function_call' in message:
-                            function_call_message = MinimaxMessage(content='', role=MinimaxMessage.Role.ASSISTANT.value)
-                            function_call_message.function_call = message['function_call']
+                        if "function_call" in message:
+                            function_call_message = MinimaxMessage(content="", role=MinimaxMessage.Role.ASSISTANT.value)
+                            function_call_message.function_call = message["function_call"]
                             yield function_call_message
 
                 yield minimax_message
                 return
 
             # partial chunk
-            choices = data.get('choices', [])
+            choices = data.get("choices", [])
             if len(choices) == 0:
                 continue
 
             for choice in choices:
-                message = choice['messages'][0]
+                message = choice["messages"][0]
                 # append text message
-                if 'text' in message:
-                    minimax_message = MinimaxMessage(content=message['text'], role=MinimaxMessage.Role.ASSISTANT.value)
+                if "text" in message:
+                    minimax_message = MinimaxMessage(content=message["text"], role=MinimaxMessage.Role.ASSISTANT.value)
                     yield minimax_message
diff --git a/api/core/model_runtime/model_providers/minimax/llm/errors.py b/api/core/model_runtime/model_providers/minimax/llm/errors.py
index d9d279e6ca..309b5cf413 100644
--- a/api/core/model_runtime/model_providers/minimax/llm/errors.py
+++ b/api/core/model_runtime/model_providers/minimax/llm/errors.py
@@ -1,17 +1,22 @@
 class InvalidAuthenticationError(Exception):
     pass
 
+
 class InvalidAPIKeyError(Exception):
     pass
 
+
 class RateLimitReachedError(Exception):
     pass
 
+
 class InsufficientAccountBalanceError(Exception):
     pass
 
+
 class InternalServerError(Exception):
     pass
 
+
 class BadRequestError(Exception):
-    pass
\ No newline at end of file
+    pass
diff --git a/api/core/model_runtime/model_providers/minimax/llm/llm.py b/api/core/model_runtime/model_providers/minimax/llm/llm.py
index 1fab20ebbc..4250c40cfb 100644
--- a/api/core/model_runtime/model_providers/minimax/llm/llm.py
+++ b/api/core/model_runtime/model_providers/minimax/llm/llm.py
@@ -34,18 +34,25 @@ from core.model_runtime.model_providers.minimax.llm.types import MinimaxMessage
 
 class MinimaxLargeLanguageModel(LargeLanguageModel):
     model_apis = {
-        'abab6.5s-chat': MinimaxChatCompletionPro,
-        'abab6.5-chat': MinimaxChatCompletionPro,
-        'abab6-chat': MinimaxChatCompletionPro,
-        'abab5.5s-chat': MinimaxChatCompletionPro,
-        'abab5.5-chat': MinimaxChatCompletionPro,
-        'abab5-chat': MinimaxChatCompletion
+        "abab6.5s-chat": MinimaxChatCompletionPro,
+        "abab6.5-chat": MinimaxChatCompletionPro,
+        "abab6-chat": MinimaxChatCompletionPro,
+        "abab5.5s-chat": MinimaxChatCompletionPro,
+        "abab5.5-chat": MinimaxChatCompletionPro,
+        "abab5-chat": MinimaxChatCompletion,
     }
 
-    def _invoke(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], 
-                model_parameters: dict, tools: list[PromptMessageTool] | None = None, 
-                stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-        -> LLMResult | Generator:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         return self._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
@@ -53,82 +60,97 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
         Validate credentials for Baichuan model
         """
         if model not in self.model_apis:
-            raise CredentialsValidateFailedError(f'Invalid model: {model}')
+            raise CredentialsValidateFailedError(f"Invalid model: {model}")
 
-        if not credentials.get('minimax_api_key'):
-            raise CredentialsValidateFailedError('Invalid API key')
+        if not credentials.get("minimax_api_key"):
+            raise CredentialsValidateFailedError("Invalid API key")
+
+        if not credentials.get("minimax_group_id"):
+            raise CredentialsValidateFailedError("Invalid group ID")
 
-        if not credentials.get('minimax_group_id'):
-            raise CredentialsValidateFailedError('Invalid group ID')
-        
         # ping
         instance = MinimaxChatCompletionPro()
         try:
             instance.generate(
-                model=model, api_key=credentials['minimax_api_key'], group_id=credentials['minimax_group_id'],
-                prompt_messages=[
-                    MinimaxMessage(content='ping', role='USER')
-                ],
+                model=model,
+                api_key=credentials["minimax_api_key"],
+                group_id=credentials["minimax_group_id"],
+                prompt_messages=[MinimaxMessage(content="ping", role="USER")],
                 model_parameters={},
-                tools=[], stop=[],
+                tools=[],
+                stop=[],
                 stream=False,
-                user=''
+                user="",
             )
         except (InvalidAuthenticationError, InsufficientAccountBalanceError) as e:
             raise CredentialsValidateFailedError(f"Invalid API key: {e}")
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: list[PromptMessageTool] | None = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool] | None = None,
+    ) -> int:
         return self._num_tokens_from_messages(prompt_messages, tools)
 
     def _num_tokens_from_messages(self, messages: list[PromptMessage], tools: list[PromptMessageTool]) -> int:
         """
-            Calculate num tokens for minimax model
+        Calculate num tokens for minimax model
 
-            not like ChatGLM, Minimax has a special prompt structure, we could not find a proper way
-            to caculate the num tokens, so we use str() to convert the prompt to string
+        not like ChatGLM, Minimax has a special prompt structure, we could not find a proper way
+        to calculate the num tokens, so we use str() to convert the prompt to string
 
-            Minimax does not provide their own tokenizer of adab5.5 and abab5 model
-            therefore, we use gpt2 tokenizer instead
+        Minimax does not provide their own tokenizer of adab5.5 and abab5 model
+        therefore, we use gpt2 tokenizer instead
         """
         messages_dict = [self._convert_prompt_message_to_minimax_message(m).to_dict() for m in messages]
         return self._get_num_tokens_by_gpt2(str(messages_dict))
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], 
-                model_parameters: dict, tools: list[PromptMessageTool] | None = None, 
-                stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-        -> LLMResult | Generator:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         """
-            use MinimaxChatCompletionPro as the type of client, anyway,  MinimaxChatCompletion has the same interface
+        use MinimaxChatCompletionPro as the type of client, anyway,  MinimaxChatCompletion has the same interface
         """
         client: MinimaxChatCompletionPro = self.model_apis[model]()
 
         if tools:
-            tools = [{
-                "name": tool.name,
-                "description": tool.description,
-                "parameters": tool.parameters
-            } for tool in tools]
+            tools = [
+                {"name": tool.name, "description": tool.description, "parameters": tool.parameters} for tool in tools
+            ]
 
         response = client.generate(
             model=model,
-            api_key=credentials['minimax_api_key'],
-            group_id=credentials['minimax_group_id'],
+            api_key=credentials["minimax_api_key"],
+            group_id=credentials["minimax_group_id"],
             prompt_messages=[self._convert_prompt_message_to_minimax_message(message) for message in prompt_messages],
             model_parameters=model_parameters,
             tools=tools,
             stop=stop,
             stream=stream,
-            user=user
+            user=user,
         )
 
         if stream:
-            return self._handle_chat_generate_stream_response(model=model, prompt_messages=prompt_messages, credentials=credentials, response=response)
-        return self._handle_chat_generate_response(model=model, prompt_messages=prompt_messages, credentials=credentials, response=response)
+            return self._handle_chat_generate_stream_response(
+                model=model, prompt_messages=prompt_messages, credentials=credentials, response=response
+            )
+        return self._handle_chat_generate_response(
+            model=model, prompt_messages=prompt_messages, credentials=credentials, response=response
+        )
 
     def _convert_prompt_message_to_minimax_message(self, prompt_message: PromptMessage) -> MinimaxMessage:
         """
-            convert PromptMessage to MinimaxMessage so that we can use MinimaxChatCompletionPro interface
+        convert PromptMessage to MinimaxMessage so that we can use MinimaxChatCompletionPro interface
         """
         if isinstance(prompt_message, SystemPromptMessage):
             return MinimaxMessage(role=MinimaxMessage.Role.SYSTEM.value, content=prompt_message.content)
@@ -136,26 +158,27 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
             return MinimaxMessage(role=MinimaxMessage.Role.USER.value, content=prompt_message.content)
         elif isinstance(prompt_message, AssistantPromptMessage):
             if prompt_message.tool_calls:
-                message = MinimaxMessage(
-                    role=MinimaxMessage.Role.ASSISTANT.value,
-                    content=''
-                )
-                message.function_call={
-                    'name': prompt_message.tool_calls[0].function.name,
-                    'arguments': prompt_message.tool_calls[0].function.arguments
+                message = MinimaxMessage(role=MinimaxMessage.Role.ASSISTANT.value, content="")
+                message.function_call = {
+                    "name": prompt_message.tool_calls[0].function.name,
+                    "arguments": prompt_message.tool_calls[0].function.arguments,
                 }
                 return message
             return MinimaxMessage(role=MinimaxMessage.Role.ASSISTANT.value, content=prompt_message.content)
         elif isinstance(prompt_message, ToolPromptMessage):
             return MinimaxMessage(role=MinimaxMessage.Role.FUNCTION.value, content=prompt_message.content)
         else:
-            raise NotImplementedError(f'Prompt message type {type(prompt_message)} is not supported')
+            raise NotImplementedError(f"Prompt message type {type(prompt_message)} is not supported")
 
-    def _handle_chat_generate_response(self, model: str, prompt_messages: list[PromptMessage], credentials: dict, response: MinimaxMessage) -> LLMResult:
-        usage = self._calc_response_usage(model=model, credentials=credentials, 
-                                          prompt_tokens=response.usage['prompt_tokens'], 
-                                          completion_tokens=response.usage['completion_tokens']
-                                        )
+    def _handle_chat_generate_response(
+        self, model: str, prompt_messages: list[PromptMessage], credentials: dict, response: MinimaxMessage
+    ) -> LLMResult:
+        usage = self._calc_response_usage(
+            model=model,
+            credentials=credentials,
+            prompt_tokens=response.usage["prompt_tokens"],
+            completion_tokens=response.usage["completion_tokens"],
+        )
         return LLMResult(
             model=model,
             prompt_messages=prompt_messages,
@@ -166,31 +189,33 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
             usage=usage,
         )
 
-    def _handle_chat_generate_stream_response(self, model: str, prompt_messages: list[PromptMessage], 
-                                              credentials: dict, response: Generator[MinimaxMessage, None, None]) \
-        -> Generator[LLMResultChunk, None, None]:
+    def _handle_chat_generate_stream_response(
+        self,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        credentials: dict,
+        response: Generator[MinimaxMessage, None, None],
+    ) -> Generator[LLMResultChunk, None, None]:
         for message in response:
             if message.usage:
                 usage = self._calc_response_usage(
-                    model=model, credentials=credentials, 
-                    prompt_tokens=message.usage['prompt_tokens'], 
-                    completion_tokens=message.usage['completion_tokens']
+                    model=model,
+                    credentials=credentials,
+                    prompt_tokens=message.usage["prompt_tokens"],
+                    completion_tokens=message.usage["completion_tokens"],
                 )
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=0,
-                        message=AssistantPromptMessage(
-                            content=message.content,
-                            tool_calls=[]
-                        ),
+                        message=AssistantPromptMessage(content=message.content, tool_calls=[]),
                         usage=usage,
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
             elif message.function_call:
-                if 'name' not in message.function_call or 'arguments' not in message.function_call:
+                if "name" not in message.function_call or "arguments" not in message.function_call:
                     continue
 
                 yield LLMResultChunk(
@@ -199,15 +224,16 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=0,
                         message=AssistantPromptMessage(
-                            content='',
-                            tool_calls=[AssistantPromptMessage.ToolCall(
-                                id='',
-                                type='function',
-                                function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                                    name=message.function_call['name'],
-                                    arguments=message.function_call['arguments']
+                            content="",
+                            tool_calls=[
+                                AssistantPromptMessage.ToolCall(
+                                    id="",
+                                    type="function",
+                                    function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+                                        name=message.function_call["name"], arguments=message.function_call["arguments"]
+                                    ),
                                 )
-                            )]
+                            ],
                         ),
                     ),
                 )
@@ -217,11 +243,8 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=0,
-                        message=AssistantPromptMessage(
-                            content=message.content,
-                            tool_calls=[]
-                        ),
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        message=AssistantPromptMessage(content=message.content, tool_calls=[]),
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
 
@@ -236,22 +259,13 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-            ],
-            InvokeServerUnavailableError: [
-                InternalServerError
-            ],
-            InvokeRateLimitError: [
-                RateLimitReachedError
-            ],
+            InvokeConnectionError: [],
+            InvokeServerUnavailableError: [InternalServerError],
+            InvokeRateLimitError: [RateLimitReachedError],
             InvokeAuthorizationError: [
                 InvalidAuthenticationError,
                 InsufficientAccountBalanceError,
                 InvalidAPIKeyError,
             ],
-            InvokeBadRequestError: [
-                BadRequestError,
-                KeyError
-            ]
+            InvokeBadRequestError: [BadRequestError, KeyError],
         }
-
diff --git a/api/core/model_runtime/model_providers/minimax/llm/types.py b/api/core/model_runtime/model_providers/minimax/llm/types.py
index b33a7ca9ac..88ebe5e2e0 100644
--- a/api/core/model_runtime/model_providers/minimax/llm/types.py
+++ b/api/core/model_runtime/model_providers/minimax/llm/types.py
@@ -4,32 +4,27 @@ from typing import Any
 
 class MinimaxMessage:
     class Role(Enum):
-        USER = 'USER'
-        ASSISTANT = 'BOT'
-        SYSTEM = 'SYSTEM'
-        FUNCTION = 'FUNCTION'
+        USER = "USER"
+        ASSISTANT = "BOT"
+        SYSTEM = "SYSTEM"
+        FUNCTION = "FUNCTION"
 
     role: str = Role.USER.value
     content: str
     usage: dict[str, int] = None
-    stop_reason: str = ''
+    stop_reason: str = ""
     function_call: dict[str, Any] = None
 
     def to_dict(self) -> dict[str, Any]:
         if self.function_call and self.role == MinimaxMessage.Role.ASSISTANT.value:
-            return {
-                'sender_type': 'BOT',
-                'sender_name': '专家',
-                'text': '',
-                'function_call': self.function_call
-            }
-        
+            return {"sender_type": "BOT", "sender_name": "专家", "text": "", "function_call": self.function_call}
+
         return {
-            'sender_type': self.role,
-            'sender_name': '我' if self.role == 'USER' else '专家',
-            'text': self.content,
+            "sender_type": self.role,
+            "sender_name": "我" if self.role == "USER" else "专家",
+            "text": self.content,
         }
-    
-    def __init__(self, content: str, role: str = 'USER') -> None:
+
+    def __init__(self, content: str, role: str = "USER") -> None:
         self.content = content
-        self.role = role
\ No newline at end of file
+        self.role = role
diff --git a/api/core/model_runtime/model_providers/minimax/minimax.py b/api/core/model_runtime/model_providers/minimax/minimax.py
index 52f6c2f1d3..5a761903a1 100644
--- a/api/core/model_runtime/model_providers/minimax/minimax.py
+++ b/api/core/model_runtime/model_providers/minimax/minimax.py
@@ -6,6 +6,7 @@ from core.model_runtime.model_providers.__base.model_provider import ModelProvid
 
 logger = logging.getLogger(__name__)
 
+
 class MinimaxProvider(ModelProvider):
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
@@ -19,12 +20,9 @@ class MinimaxProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.LLM)
 
             # Use `abab5.5-chat` model for validate,
-            model_instance.validate_credentials(
-                model='abab5.5-chat',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="abab5.5-chat", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
-            raise CredentialsValidateFailedError(f'{ex}')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
+            raise CredentialsValidateFailedError(f"{ex}")
diff --git a/api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py
index 85dc6ef51d..76fd1342bd 100644
--- a/api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py
@@ -30,11 +30,12 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
     """
     Model class for Minimax text embedding model.
     """
-    api_base: str = 'https://api.minimax.chat/v1/embeddings'
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    api_base: str = "https://api.minimax.chat/v1/embeddings"
+
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -44,54 +45,43 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
-        api_key = credentials['minimax_api_key']
-        group_id = credentials['minimax_group_id']
-        if model != 'embo-01':
-            raise ValueError('Invalid model name')
+        api_key = credentials["minimax_api_key"]
+        group_id = credentials["minimax_group_id"]
+        if model != "embo-01":
+            raise ValueError("Invalid model name")
         if not api_key:
-            raise CredentialsValidateFailedError('api_key is required')
-        url = f'{self.api_base}?GroupId={group_id}'
-        headers = {
-            'Authorization': 'Bearer ' + api_key,
-            'Content-Type': 'application/json'
-        }
+            raise CredentialsValidateFailedError("api_key is required")
+        url = f"{self.api_base}?GroupId={group_id}"
+        headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
 
-        data = {
-            'model': 'embo-01',
-            'texts': texts,
-            'type': 'db'
-        }
+        data = {"model": "embo-01", "texts": texts, "type": "db"}
 
         try:
             response = post(url, headers=headers, data=dumps(data))
         except Exception as e:
             raise InvokeConnectionError(str(e))
-        
+
         if response.status_code != 200:
             raise InvokeServerUnavailableError(response.text)
-        
+
         try:
             resp = response.json()
             # check if there is an error
-            if resp['base_resp']['status_code'] != 0:
-                code = resp['base_resp']['status_code']
-                msg = resp['base_resp']['status_msg']
+            if resp["base_resp"]["status_code"] != 0:
+                code = resp["base_resp"]["status_code"]
+                msg = resp["base_resp"]["status_msg"]
                 self._handle_error(code, msg)
 
-            embeddings = resp['vectors']
-            total_tokens = resp['total_tokens']
+            embeddings = resp["vectors"]
+            total_tokens = resp["total_tokens"]
         except InvalidAuthenticationError:
-            raise InvalidAPIKeyError('Invalid api key')
+            raise InvalidAPIKeyError("Invalid api key")
         except KeyError as e:
             raise InternalServerError(f"Failed to convert response to json: {e} with text: {response.text}")
 
         usage = self._calc_response_usage(model=model, credentials=credentials, tokens=total_tokens)
 
-        result = TextEmbeddingResult(
-            model=model,
-            embeddings=embeddings,
-            usage=usage
-        )
+        result = TextEmbeddingResult(model=model, embeddings=embeddings, usage=usage)
 
         return result
 
@@ -119,12 +109,12 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         try:
-            self._invoke(model=model, credentials=credentials, texts=['ping'])
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
         except InvalidAPIKeyError:
-            raise CredentialsValidateFailedError('Invalid api key')
+            raise CredentialsValidateFailedError("Invalid api key")
 
     def _handle_error(self, code: int, msg: str):
-        if code == 1000 or code == 1001:
+        if code in {1000, 1001}:
             raise InternalServerError(msg)
         elif code == 1002:
             raise RateLimitReachedError(msg)
@@ -148,25 +138,17 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-            ],
-            InvokeServerUnavailableError: [
-                InternalServerError
-            ],
-            InvokeRateLimitError: [
-                RateLimitReachedError
-            ],
+            InvokeConnectionError: [],
+            InvokeServerUnavailableError: [InternalServerError],
+            InvokeRateLimitError: [RateLimitReachedError],
             InvokeAuthorizationError: [
                 InvalidAuthenticationError,
                 InsufficientAccountBalanceError,
                 InvalidAPIKeyError,
             ],
-            InvokeBadRequestError: [
-                BadRequestError,
-                KeyError
-            ]
+            InvokeBadRequestError: [BadRequestError, KeyError],
         }
-    
+
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -178,10 +160,7 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -192,7 +171,7 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/mistralai/llm/llm.py b/api/core/model_runtime/model_providers/mistralai/llm/llm.py
index 01ed8010de..da60bd7661 100644
--- a/api/core/model_runtime/model_providers/mistralai/llm/llm.py
+++ b/api/core/model_runtime/model_providers/mistralai/llm/llm.py
@@ -7,14 +7,19 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
 
 
 class MistralAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
-        
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
-        
+
         # mistral dose not support user/stop arguments
         stop = []
         user = None
@@ -27,5 +32,5 @@ class MistralAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
 
     @staticmethod
     def _add_custom_parameters(credentials: dict) -> None:
-        credentials['mode'] = 'chat'
-        credentials['endpoint_url'] = 'https://api.mistral.ai/v1'
+        credentials["mode"] = "chat"
+        credentials["endpoint_url"] = "https://api.mistral.ai/v1"
diff --git a/api/core/model_runtime/model_providers/mistralai/mistralai.py b/api/core/model_runtime/model_providers/mistralai/mistralai.py
index f1d825f6c6..7f9db8da1c 100644
--- a/api/core/model_runtime/model_providers/mistralai/mistralai.py
+++ b/api/core/model_runtime/model_providers/mistralai/mistralai.py
@@ -8,7 +8,6 @@ logger = logging.getLogger(__name__)
 
 
 class MistralAIProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -19,12 +18,9 @@ class MistralAIProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(
-                model='open-mistral-7b',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="open-mistral-7b", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/moonshot/llm/llm.py b/api/core/model_runtime/model_providers/moonshot/llm/llm.py
index c233596637..3ea46c2967 100644
--- a/api/core/model_runtime/model_providers/moonshot/llm/llm.py
+++ b/api/core/model_runtime/model_providers/moonshot/llm/llm.py
@@ -30,11 +30,17 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
 
 
 class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
         self._add_function_call(model, credentials)
         user = user[:32] if user else None
@@ -49,50 +55,50 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
             model=model,
             label=I18nObject(en_US=model, zh_Hans=model),
             model_type=ModelType.LLM,
-            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL] 
-                if credentials.get('function_calling_type') == 'tool_call' 
-                else [],
+            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL]
+            if credentials.get("function_calling_type") == "tool_call"
+            else [],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', 4096)),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 4096)),
                 ModelPropertyKey.MODE: LLMMode.CHAT.value,
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
-                    use_template='temperature',
-                    label=I18nObject(en_US='Temperature', zh_Hans='温度'),
+                    name="temperature",
+                    use_template="temperature",
+                    label=I18nObject(en_US="Temperature", zh_Hans="温度"),
                     type=ParameterType.FLOAT,
                 ),
                 ParameterRule(
-                    name='max_tokens',
-                    use_template='max_tokens',
+                    name="max_tokens",
+                    use_template="max_tokens",
                     default=512,
                     min=1,
-                    max=int(credentials.get('max_tokens', 4096)),
-                    label=I18nObject(en_US='Max Tokens', zh_Hans='最大标记'),
+                    max=int(credentials.get("max_tokens", 4096)),
+                    label=I18nObject(en_US="Max Tokens", zh_Hans="最大标记"),
                     type=ParameterType.INT,
                 ),
                 ParameterRule(
-                    name='top_p',
-                    use_template='top_p',
-                    label=I18nObject(en_US='Top P', zh_Hans='Top P'),
+                    name="top_p",
+                    use_template="top_p",
+                    label=I18nObject(en_US="Top P", zh_Hans="Top P"),
                     type=ParameterType.FLOAT,
                 ),
-            ]
+            ],
         )
 
     def _add_custom_parameters(self, credentials: dict) -> None:
-        credentials['mode'] = 'chat'
-        if 'endpoint_url' not in credentials or credentials['endpoint_url'] == "":
-            credentials['endpoint_url'] = 'https://api.moonshot.cn/v1'
+        credentials["mode"] = "chat"
+        if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
+            credentials["endpoint_url"] = "https://api.moonshot.cn/v1"
 
     def _add_function_call(self, model: str, credentials: dict) -> None:
         model_schema = self.get_model_schema(model, credentials)
-        if model_schema and {
-            ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL
-        }.intersection(model_schema.features or []):
-            credentials['function_calling_type'] = 'tool_call'
+        if model_schema and {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL}.intersection(
+            model_schema.features or []
+        ):
+            credentials["function_calling_type"] = "tool_call"
 
     def _convert_prompt_message_to_dict(self, message: PromptMessage, credentials: Optional[dict] = None) -> dict:
         """
@@ -107,19 +113,13 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
                         message_content = cast(PromptMessageContent, message_content)
-                        sub_message_dict = {
-                            "type": "text",
-                            "text": message_content.data
-                        }
+                        sub_message_dict = {"type": "text", "text": message_content.data}
                         sub_messages.append(sub_message_dict)
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
                         sub_message_dict = {
                             "type": "image_url",
-                            "image_url": {
-                                "url": message_content.data,
-                                "detail": message_content.detail.value
-                            }
+                            "image_url": {"url": message_content.data, "detail": message_content.detail.value},
                         }
                         sub_messages.append(sub_message_dict)
                 message_dict = {"role": "user", "content": sub_messages}
@@ -129,14 +129,16 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
             if message.tool_calls:
                 message_dict["tool_calls"] = []
                 for function_call in message.tool_calls:
-                    message_dict["tool_calls"].append({
-                        "id": function_call.id,
-                        "type": function_call.type,
-                        "function": {
-                            "name": function_call.function.name,
-                            "arguments": function_call.function.arguments
+                    message_dict["tool_calls"].append(
+                        {
+                            "id": function_call.id,
+                            "type": function_call.type,
+                            "function": {
+                                "name": function_call.function.name,
+                                "arguments": function_call.function.arguments,
+                            },
                         }
-                    })
+                    )
         elif isinstance(message, ToolPromptMessage):
             message = cast(ToolPromptMessage, message)
             message_dict = {"role": "tool", "content": message.content, "tool_call_id": message.tool_call_id}
@@ -162,21 +164,26 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         if response_tool_calls:
             for response_tool_call in response_tool_calls:
                 function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                    name=response_tool_call["function"]["name"] if response_tool_call.get("function", {}).get("name") else "",
-                    arguments=response_tool_call["function"]["arguments"] if response_tool_call.get("function", {}).get("arguments") else ""
+                    name=response_tool_call["function"]["name"]
+                    if response_tool_call.get("function", {}).get("name")
+                    else "",
+                    arguments=response_tool_call["function"]["arguments"]
+                    if response_tool_call.get("function", {}).get("arguments")
+                    else "",
                 )
 
                 tool_call = AssistantPromptMessage.ToolCall(
                     id=response_tool_call["id"] if response_tool_call.get("id") else "",
                     type=response_tool_call["type"] if response_tool_call.get("type") else "",
-                    function=function
+                    function=function,
                 )
                 tool_calls.append(tool_call)
 
         return tool_calls
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: requests.Response,
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
+    ) -> Generator:
         """
         Handle llm stream response
 
@@ -186,11 +193,12 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: llm response chunk generator
         """
-        full_assistant_content = ''
+        full_assistant_content = ""
         chunk_index = 0
 
-        def create_final_llm_result_chunk(index: int, message: AssistantPromptMessage, finish_reason: str) \
-                -> LLMResultChunk:
+        def create_final_llm_result_chunk(
+            index: int, message: AssistantPromptMessage, finish_reason: str
+        ) -> LLMResultChunk:
             # calculate num tokens
             prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
             completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
@@ -201,12 +209,7 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
             return LLMResultChunk(
                 model=model,
                 prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=index,
-                    message=message,
-                    finish_reason=finish_reason,
-                    usage=usage
-                )
+                delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
             )
 
         tools_calls: list[AssistantPromptMessage.ToolCall] = []
@@ -220,9 +223,9 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                 tool_call = next((tool_call for tool_call in tools_calls if tool_call.function.name == tool_name), None)
                 if tool_call is None:
                     tool_call = AssistantPromptMessage.ToolCall(
-                        id='',
-                        type='',
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tool_name, arguments="")
+                        id="",
+                        type="",
+                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tool_name, arguments=""),
                     )
                     tools_calls.append(tool_call)
 
@@ -244,9 +247,9 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         for chunk in response.iter_lines(decode_unicode=True, delimiter="\n\n"):
             if chunk:
                 # ignore sse comments
-                if chunk.startswith(':'):
+                if chunk.startswith(":"):
                     continue
-                decoded_chunk = chunk.strip().lstrip('data: ').lstrip()
+                decoded_chunk = chunk.strip().lstrip("data: ").lstrip()
                 chunk_json = None
                 try:
                     chunk_json = json.loads(decoded_chunk)
@@ -255,21 +258,21 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                     yield create_final_llm_result_chunk(
                         index=chunk_index + 1,
                         message=AssistantPromptMessage(content=""),
-                        finish_reason="Non-JSON encountered."
+                        finish_reason="Non-JSON encountered.",
                     )
                     break
-                if not chunk_json or len(chunk_json['choices']) == 0:
+                if not chunk_json or len(chunk_json["choices"]) == 0:
                     continue
 
-                choice = chunk_json['choices'][0]
-                finish_reason = chunk_json['choices'][0].get('finish_reason')
+                choice = chunk_json["choices"][0]
+                finish_reason = chunk_json["choices"][0].get("finish_reason")
                 chunk_index += 1
 
-                if 'delta' in choice:
-                    delta = choice['delta']
-                    delta_content = delta.get('content')
+                if "delta" in choice:
+                    delta = choice["delta"]
+                    delta_content = delta.get("content")
 
-                    assistant_message_tool_calls = delta.get('tool_calls', None)
+                    assistant_message_tool_calls = delta.get("tool_calls", None)
                     # assistant_message_function_call = delta.delta.function_call
 
                     # extract tool calls from response
@@ -277,19 +280,18 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                         tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
                         increase_tool_call(tool_calls)
 
-                    if delta_content is None or delta_content == '':
+                    if delta_content is None or delta_content == "":
                         continue
 
                     # transform assistant message to prompt message
                     assistant_prompt_message = AssistantPromptMessage(
-                        content=delta_content,
-                        tool_calls=tool_calls if assistant_message_tool_calls else []
+                        content=delta_content, tool_calls=tool_calls if assistant_message_tool_calls else []
                     )
 
                     full_assistant_content += delta_content
-                elif 'text' in choice:
-                    choice_text = choice.get('text', '')
-                    if choice_text == '':
+                elif "text" in choice:
+                    choice_text = choice.get("text", "")
+                    if choice_text == "":
                         continue
 
                     # transform assistant message to prompt message
@@ -305,26 +307,21 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=chunk_index,
                         message=assistant_prompt_message,
-                    )
+                    ),
                 )
 
             chunk_index += 1
-        
+
         if tools_calls:
             yield LLMResultChunk(
                 model=model,
                 prompt_messages=prompt_messages,
                 delta=LLMResultChunkDelta(
                     index=chunk_index,
-                    message=AssistantPromptMessage(
-                        tool_calls=tools_calls,
-                        content=""
-                    ),
-                )
+                    message=AssistantPromptMessage(tool_calls=tools_calls, content=""),
+                ),
             )
 
         yield create_final_llm_result_chunk(
-            index=chunk_index,
-            message=AssistantPromptMessage(content=""),
-            finish_reason=finish_reason
-        )
\ No newline at end of file
+            index=chunk_index, message=AssistantPromptMessage(content=""), finish_reason=finish_reason
+        )
diff --git a/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-128k.yaml b/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-128k.yaml
index 1078e84c59..59c0915ee9 100644
--- a/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-128k.yaml
+++ b/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-128k.yaml
@@ -24,7 +24,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-32k.yaml b/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-32k.yaml
index 9c739d0501..724f2aa5a2 100644
--- a/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-32k.yaml
+++ b/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-32k.yaml
@@ -24,7 +24,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-8k.yaml b/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-8k.yaml
index 187a86999e..5872295bfa 100644
--- a/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-8k.yaml
+++ b/api/core/model_runtime/model_providers/moonshot/llm/moonshot-v1-8k.yaml
@@ -24,7 +24,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/moonshot/moonshot.py b/api/core/model_runtime/model_providers/moonshot/moonshot.py
index 5654ae1459..4995e235f5 100644
--- a/api/core/model_runtime/model_providers/moonshot/moonshot.py
+++ b/api/core/model_runtime/model_providers/moonshot/moonshot.py
@@ -8,7 +8,6 @@ logger = logging.getLogger(__name__)
 
 
 class MoonshotProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -19,12 +18,9 @@ class MoonshotProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(
-                model='moonshot-v1-8k',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="moonshot-v1-8k", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/novita/llm/llm.py b/api/core/model_runtime/model_providers/novita/llm/llm.py
index c7b223d1b7..23367ed1b4 100644
--- a/api/core/model_runtime/model_providers/novita/llm/llm.py
+++ b/api/core/model_runtime/model_providers/novita/llm/llm.py
@@ -8,19 +8,25 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
 
 
 class NovitaLargeLanguageModel(OAIAPICompatLargeLanguageModel):
-
     def _update_endpoint_url(self, credentials: dict):
-        credentials['endpoint_url'] = "https://api.novita.ai/v3/openai"
-        credentials['extra_headers'] = { 'X-Novita-Source': 'dify.ai' }
+        credentials["endpoint_url"] = "https://api.novita.ai/v3/openai"
+        credentials["extra_headers"] = {"X-Novita-Source": "dify.ai"}
         return credentials
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         cred_with_endpoint = self._update_endpoint_url(credentials=credentials)
         return super()._invoke(model, cred_with_endpoint, prompt_messages, model_parameters, tools, stop, stream, user)
+
     def validate_credentials(self, model: str, credentials: dict) -> None:
         cred_with_endpoint = self._update_endpoint_url(credentials=credentials)
         self._add_custom_parameters(credentials, model)
@@ -28,21 +34,36 @@ class NovitaLargeLanguageModel(OAIAPICompatLargeLanguageModel):
 
     @classmethod
     def _add_custom_parameters(cls, credentials: dict, model: str) -> None:
-        credentials['mode'] = 'chat'
+        credentials["mode"] = "chat"
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
-                  tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                  stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         cred_with_endpoint = self._update_endpoint_url(credentials=credentials)
-        return super()._generate(model, cred_with_endpoint, prompt_messages, model_parameters, tools, stop, stream, user)
+        return super()._generate(
+            model, cred_with_endpoint, prompt_messages, model_parameters, tools, stop, stream, user
+        )
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         cred_with_endpoint = self._update_endpoint_url(credentials=credentials)
 
         return super().get_customizable_model_schema(model, cred_with_endpoint)
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         cred_with_endpoint = self._update_endpoint_url(credentials=credentials)
 
         return super().get_num_tokens(model, cred_with_endpoint, prompt_messages, tools)
diff --git a/api/core/model_runtime/model_providers/novita/novita.py b/api/core/model_runtime/model_providers/novita/novita.py
index f1b7224605..76a75b01e2 100644
--- a/api/core/model_runtime/model_providers/novita/novita.py
+++ b/api/core/model_runtime/model_providers/novita/novita.py
@@ -20,12 +20,9 @@ class NovitaProvider(ModelProvider):
 
             # Use `meta-llama/llama-3-8b-instruct` model for validate,
             # no matter what model you pass in, text completion model or chat model
-            model_instance.validate_credentials(
-                model='meta-llama/llama-3-8b-instruct',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="meta-llama/llama-3-8b-instruct", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/nvidia/llm/llm.py b/api/core/model_runtime/model_providers/nvidia/llm/llm.py
index bc42eaca65..1c98c6be6c 100644
--- a/api/core/model_runtime/model_providers/nvidia/llm/llm.py
+++ b/api/core/model_runtime/model_providers/nvidia/llm/llm.py
@@ -21,31 +21,36 @@ from core.model_runtime.utils import helper
 
 class NVIDIALargeLanguageModel(OAIAPICompatLargeLanguageModel):
     MODEL_SUFFIX_MAP = {
-        'fuyu-8b': 'vlm/adept/fuyu-8b',
-        'mistralai/mistral-large': '',
-        'mistralai/mixtral-8x7b-instruct-v0.1': '',
-        'mistralai/mixtral-8x22b-instruct-v0.1': '',
-        'google/gemma-7b': '',
-        'google/codegemma-7b': '',
-        'snowflake/arctic':'',
-        'meta/llama2-70b': '',
-        'meta/llama3-8b-instruct': '',
-        'meta/llama3-70b-instruct': '',
-        'meta/llama-3.1-8b-instruct': '',
-        'meta/llama-3.1-70b-instruct': '',
-        'meta/llama-3.1-405b-instruct': '',
-        'google/recurrentgemma-2b': '',
-        'nvidia/nemotron-4-340b-instruct': '',
-        'microsoft/phi-3-medium-128k-instruct':'',
-        'microsoft/phi-3-mini-128k-instruct':''
+        "fuyu-8b": "vlm/adept/fuyu-8b",
+        "mistralai/mistral-large": "",
+        "mistralai/mixtral-8x7b-instruct-v0.1": "",
+        "mistralai/mixtral-8x22b-instruct-v0.1": "",
+        "google/gemma-7b": "",
+        "google/codegemma-7b": "",
+        "snowflake/arctic": "",
+        "meta/llama2-70b": "",
+        "meta/llama3-8b-instruct": "",
+        "meta/llama3-70b-instruct": "",
+        "meta/llama-3.1-8b-instruct": "",
+        "meta/llama-3.1-70b-instruct": "",
+        "meta/llama-3.1-405b-instruct": "",
+        "google/recurrentgemma-2b": "",
+        "nvidia/nemotron-4-340b-instruct": "",
+        "microsoft/phi-3-medium-128k-instruct": "",
+        "microsoft/phi-3-mini-128k-instruct": "",
     }
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
-        
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials, model)
         prompt_messages = self._transform_prompt_messages(prompt_messages)
         stop = []
@@ -60,16 +65,14 @@ class NVIDIALargeLanguageModel(OAIAPICompatLargeLanguageModel):
         for i, p in enumerate(prompt_messages):
             if isinstance(p, UserPromptMessage) and isinstance(p.content, list):
                 content = p.content
-                content_text = ''
+                content_text = ""
                 for prompt_content in content:
                     if prompt_content.type == PromptMessageContentType.TEXT:
                         content_text += prompt_content.data
                     else:
                         content_text += f' <img src="{prompt_content.data}" />'
 
-                prompt_message = UserPromptMessage(
-                    content=content_text
-                )
+                prompt_message = UserPromptMessage(content=content_text)
                 prompt_messages[i] = prompt_message
         return prompt_messages
 
@@ -78,91 +81,87 @@ class NVIDIALargeLanguageModel(OAIAPICompatLargeLanguageModel):
         self._validate_credentials(model, credentials)
 
     def _add_custom_parameters(self, credentials: dict, model: str) -> None:
-        credentials['mode'] = 'chat'
-        
-        if self.MODEL_SUFFIX_MAP[model]:
-            credentials['server_url'] = f'https://ai.api.nvidia.com/v1/{self.MODEL_SUFFIX_MAP[model]}'
-            credentials.pop('endpoint_url')
-        else:
-            credentials['endpoint_url'] = 'https://integrate.api.nvidia.com/v1'
+        credentials["mode"] = "chat"
 
-        credentials['stream_mode_delimiter'] = '\n'
+        if self.MODEL_SUFFIX_MAP[model]:
+            credentials["server_url"] = f"https://ai.api.nvidia.com/v1/{self.MODEL_SUFFIX_MAP[model]}"
+            credentials.pop("endpoint_url")
+        else:
+            credentials["endpoint_url"] = "https://integrate.api.nvidia.com/v1"
+
+        credentials["stream_mode_delimiter"] = "\n"
 
     def _validate_credentials(self, model: str, credentials: dict) -> None:
         """
-        Validate model credentials using requests to ensure compatibility with all providers following OpenAI's API standard.
+        Validate model credentials using requests to ensure compatibility with all providers following
+        OpenAI's API standard.
 
         :param model: model name
         :param credentials: model credentials
         :return:
         """
         try:
-            headers = {
-                'Content-Type': 'application/json'
-            }
+            headers = {"Content-Type": "application/json"}
 
-            api_key = credentials.get('api_key')
+            api_key = credentials.get("api_key")
             if api_key:
                 headers["Authorization"] = f"Bearer {api_key}"
 
-            endpoint_url = credentials.get('endpoint_url')
-            if endpoint_url and not endpoint_url.endswith('/'):
-                endpoint_url += '/'
-            server_url = credentials.get('server_url')
+            endpoint_url = credentials.get("endpoint_url")
+            if endpoint_url and not endpoint_url.endswith("/"):
+                endpoint_url += "/"
+            server_url = credentials.get("server_url")
 
             # prepare the payload for a simple ping to the model
-            data = {
-                'model': model,
-                'max_tokens': 5
-            }
+            data = {"model": model, "max_tokens": 5}
 
-            completion_type = LLMMode.value_of(credentials['mode'])
+            completion_type = LLMMode.value_of(credentials["mode"])
 
             if completion_type is LLMMode.CHAT:
-                data['messages'] = [
-                    {
-                        "role": "user",
-                        "content": "ping"
-                    },
+                data["messages"] = [
+                    {"role": "user", "content": "ping"},
                 ]
-                if 'endpoint_url' in credentials:
-                    endpoint_url = str(URL(endpoint_url) / 'chat' / 'completions')
-                elif 'server_url' in credentials:
+                if "endpoint_url" in credentials:
+                    endpoint_url = str(URL(endpoint_url) / "chat" / "completions")
+                elif "server_url" in credentials:
                     endpoint_url = server_url
             elif completion_type is LLMMode.COMPLETION:
-                data['prompt'] = 'ping'
-                if 'endpoint_url' in credentials:
-                    endpoint_url = str(URL(endpoint_url) / 'completions')
-                elif 'server_url' in credentials:
+                data["prompt"] = "ping"
+                if "endpoint_url" in credentials:
+                    endpoint_url = str(URL(endpoint_url) / "completions")
+                elif "server_url" in credentials:
                     endpoint_url = server_url
             else:
                 raise ValueError("Unsupported completion type for model configuration.")
 
             # send a post request to validate the credentials
-            response = requests.post(
-                endpoint_url,
-                headers=headers,
-                json=data,
-                timeout=(10, 300)
-            )
+            response = requests.post(endpoint_url, headers=headers, json=data, timeout=(10, 300))
 
             if response.status_code != 200:
                 raise CredentialsValidateFailedError(
-                    f'Credentials validation failed with status code {response.status_code}')
+                    f"Credentials validation failed with status code {response.status_code}"
+                )
 
             try:
                 json_result = response.json()
             except json.JSONDecodeError as e:
-                raise CredentialsValidateFailedError('Credentials validation failed: JSON decode error')
+                raise CredentialsValidateFailedError("Credentials validation failed: JSON decode error")
         except CredentialsValidateFailedError:
             raise
         except Exception as ex:
-            raise CredentialsValidateFailedError(f'An error occurred during credentials validation: {str(ex)}')
+            raise CredentialsValidateFailedError(f"An error occurred during credentials validation: {str(ex)}")
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
-                  tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                  stream: bool = True, \
-                  user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke llm completion model
 
@@ -176,57 +175,51 @@ class NVIDIALargeLanguageModel(OAIAPICompatLargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         headers = {
-            'Content-Type': 'application/json',
-            'Accept-Charset': 'utf-8',
+            "Content-Type": "application/json",
+            "Accept-Charset": "utf-8",
         }
 
-        api_key = credentials.get('api_key')
+        api_key = credentials.get("api_key")
         if api_key:
-            headers['Authorization'] = f'Bearer {api_key}'
+            headers["Authorization"] = f"Bearer {api_key}"
 
         if stream:
-            headers['Accept'] = 'text/event-stream'
+            headers["Accept"] = "text/event-stream"
 
-        endpoint_url = credentials.get('endpoint_url')
-        if endpoint_url and not endpoint_url.endswith('/'):
-            endpoint_url += '/'
-        server_url = credentials.get('server_url')
+        endpoint_url = credentials.get("endpoint_url")
+        if endpoint_url and not endpoint_url.endswith("/"):
+            endpoint_url += "/"
+        server_url = credentials.get("server_url")
 
-        data = {
-            "model": model,
-            "stream": stream,
-            **model_parameters
-        }
+        data = {"model": model, "stream": stream, **model_parameters}
 
-        completion_type = LLMMode.value_of(credentials['mode'])
+        completion_type = LLMMode.value_of(credentials["mode"])
 
         if completion_type is LLMMode.CHAT:
-            if 'endpoint_url' in credentials:
-                endpoint_url = str(URL(endpoint_url) / 'chat' / 'completions')
-            elif 'server_url' in credentials:
+            if "endpoint_url" in credentials:
+                endpoint_url = str(URL(endpoint_url) / "chat" / "completions")
+            elif "server_url" in credentials:
                 endpoint_url = server_url
-            data['messages'] = [self._convert_prompt_message_to_dict(m, credentials) for m in prompt_messages]
+            data["messages"] = [self._convert_prompt_message_to_dict(m, credentials) for m in prompt_messages]
         elif completion_type is LLMMode.COMPLETION:
-            data['prompt'] = 'ping'
-            if 'endpoint_url' in credentials:
-                endpoint_url = str(URL(endpoint_url) / 'completions')
-            elif 'server_url' in credentials:
+            data["prompt"] = "ping"
+            if "endpoint_url" in credentials:
+                endpoint_url = str(URL(endpoint_url) / "completions")
+            elif "server_url" in credentials:
                 endpoint_url = server_url
         else:
             raise ValueError("Unsupported completion type for model configuration.")
 
-
         # annotate tools with names, descriptions, etc.
-        function_calling_type = credentials.get('function_calling_type', 'no_call')
+        function_calling_type = credentials.get("function_calling_type", "no_call")
         formatted_tools = []
         if tools:
-            if function_calling_type == 'function_call':
-                data['functions'] = [{
-                    "name": tool.name,
-                    "description": tool.description,
-                    "parameters": tool.parameters
-                } for tool in tools]
-            elif function_calling_type == 'tool_call':
+            if function_calling_type == "function_call":
+                data["functions"] = [
+                    {"name": tool.name, "description": tool.description, "parameters": tool.parameters}
+                    for tool in tools
+                ]
+            elif function_calling_type == "tool_call":
                 data["tool_choice"] = "auto"
 
                 for tool in tools:
@@ -240,16 +233,10 @@ class NVIDIALargeLanguageModel(OAIAPICompatLargeLanguageModel):
         if user:
             data["user"] = user
 
-        response = requests.post(
-            endpoint_url,
-            headers=headers,
-            json=data,
-            timeout=(10, 300),
-            stream=stream
-        )
+        response = requests.post(endpoint_url, headers=headers, json=data, timeout=(10, 300), stream=stream)
 
-        if response.encoding is None or response.encoding == 'ISO-8859-1':
-            response.encoding = 'utf-8'
+        if response.encoding is None or response.encoding == "ISO-8859-1":
+            response.encoding = "utf-8"
 
         if not response.ok:
             raise InvokeError(f"API request failed with status code {response.status_code}: {response.text}")
diff --git a/api/core/model_runtime/model_providers/nvidia/nvidia.py b/api/core/model_runtime/model_providers/nvidia/nvidia.py
index e83f8badb5..058fa00346 100644
--- a/api/core/model_runtime/model_providers/nvidia/nvidia.py
+++ b/api/core/model_runtime/model_providers/nvidia/nvidia.py
@@ -8,7 +8,6 @@ logger = logging.getLogger(__name__)
 
 
 class MistralAIProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -19,12 +18,9 @@ class MistralAIProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(
-                model='mistralai/mixtral-8x7b-instruct-v0.1',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="mistralai/mixtral-8x7b-instruct-v0.1", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/nvidia/rerank/rerank.py b/api/core/model_runtime/model_providers/nvidia/rerank/rerank.py
index 9d33f55bc2..fabebc67ab 100644
--- a/api/core/model_runtime/model_providers/nvidia/rerank/rerank.py
+++ b/api/core/model_runtime/model_providers/nvidia/rerank/rerank.py
@@ -22,11 +22,18 @@ class NvidiaRerankModel(RerankModel):
     """
 
     def _sigmoid(self, logit: float) -> float:
-        return 1/(1+exp(-logit))
+        return 1 / (1 + exp(-logit))
 
-    def _invoke(self, model: str, credentials: dict,
-                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
-                user: Optional[str] = None) -> RerankResult:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
         """
         Invoke rerank model
 
@@ -54,16 +61,15 @@ class NvidiaRerankModel(RerankModel):
                 "query": {"text": query},
                 "passages": [{"text": doc} for doc in docs],
             }
-
             session = requests.Session()
             response = session.post(invoke_url, headers=headers, json=payload)
             response.raise_for_status()
             results = response.json()
 
             rerank_documents = []
-            for result in results['rankings']:
-                index = result['index']
-                logit = result['logit']
+            for result in results["rankings"]:
+                index = result["index"]
+                logit = result["logit"]
                 rerank_document = RerankDocument(
                     index=index,
                     text=docs[index],
@@ -71,7 +77,10 @@ class NvidiaRerankModel(RerankModel):
                 )
 
                 rerank_documents.append(rerank_document)
-
+            if rerank_documents:
+                rerank_documents = sorted(rerank_documents, key=lambda x: x.score, reverse=True)
+                if top_n:
+                    rerank_documents = rerank_documents[:top_n]
             return RerankResult(model=model, docs=rerank_documents)
         except requests.HTTPError as e:
             raise InvokeServerUnavailableError(str(e))
@@ -108,5 +117,5 @@ class NvidiaRerankModel(RerankModel):
             InvokeServerUnavailableError: [requests.HTTPError],
             InvokeRateLimitError: [],
             InvokeAuthorizationError: [requests.HTTPError],
-            InvokeBadRequestError: [requests.RequestException]
+            InvokeBadRequestError: [requests.RequestException],
         }
diff --git a/api/core/model_runtime/model_providers/nvidia/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/nvidia/text_embedding/text_embedding.py
index a2adef400d..00cec265d5 100644
--- a/api/core/model_runtime/model_providers/nvidia/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/nvidia/text_embedding/text_embedding.py
@@ -22,12 +22,13 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel):
     """
     Model class for Nvidia text embedding model.
     """
-    api_base: str = 'https://ai.api.nvidia.com/v1/retrieval/nvidia/embeddings'
-    models: list[str] = ['NV-Embed-QA']
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    api_base: str = "https://ai.api.nvidia.com/v1/retrieval/nvidia/embeddings"
+    models: list[str] = ["NV-Embed-QA"]
+
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -37,32 +38,25 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
-        api_key = credentials['api_key']
+        api_key = credentials["api_key"]
         if model not in self.models:
-            raise InvokeBadRequestError('Invalid model name')
+            raise InvokeBadRequestError("Invalid model name")
         if not api_key:
-            raise CredentialsValidateFailedError('api_key is required')
+            raise CredentialsValidateFailedError("api_key is required")
         url = self.api_base
-        headers = {
-            'Authorization': 'Bearer ' + api_key,
-            'Content-Type': 'application/json'
-        }
+        headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
 
-        data = {
-            'model': model,
-            'input': texts[0], 
-            'input_type': 'query'
-        }
+        data = {"model": model, "input": texts[0], "input_type": "query"}
 
         try:
             response = post(url, headers=headers, data=dumps(data))
         except Exception as e:
             raise InvokeConnectionError(str(e))
-        
+
         if response.status_code != 200:
             try:
                 resp = response.json()
-                msg = resp['detail']
+                msg = resp["detail"]
                 if response.status_code == 401:
                     raise InvokeAuthorizationError(msg)
                 elif response.status_code == 429:
@@ -72,23 +66,21 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel):
                 else:
                     raise InvokeError(msg)
             except JSONDecodeError as e:
-                raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
+                raise InvokeServerUnavailableError(
+                    f"Failed to convert response to json: {e} with text: {response.text}"
+                )
 
         try:
             resp = response.json()
-            embeddings = resp['data']
-            usage = resp['usage']
+            embeddings = resp["data"]
+            usage = resp["usage"]
         except Exception as e:
             raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
 
-        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage['total_tokens'])
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage["total_tokens"])
 
         result = TextEmbeddingResult(
-            model=model,
-            embeddings=[[
-                float(data) for data in x['embedding']
-            ] for x in embeddings],
-            usage=usage
+            model=model, embeddings=[[float(data) for data in x["embedding"]] for x in embeddings], usage=usage
         )
 
         return result
@@ -117,30 +109,20 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         try:
-            self._invoke(model=model, credentials=credentials, texts=['ping'])
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
         except InvokeAuthorizationError:
-            raise CredentialsValidateFailedError('Invalid api key')
+            raise CredentialsValidateFailedError("Invalid api key")
 
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                KeyError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [KeyError],
         }
-    
+
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -152,10 +134,7 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -166,7 +145,7 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/nvidia_nim/llm/llm.py b/api/core/model_runtime/model_providers/nvidia_nim/llm/llm.py
index f7b849fbe2..6ff380bdd9 100644
--- a/api/core/model_runtime/model_providers/nvidia_nim/llm/llm.py
+++ b/api/core/model_runtime/model_providers/nvidia_nim/llm/llm.py
@@ -9,4 +9,5 @@ class NVIDIANIMProvider(OAIAPICompatLargeLanguageModel):
     """
     Model class for NVIDIA NIM large language model.
     """
+
     pass
diff --git a/api/core/model_runtime/model_providers/nvidia_nim/nvidia_nim.py b/api/core/model_runtime/model_providers/nvidia_nim/nvidia_nim.py
index 25ab3e8e20..ad890ada22 100644
--- a/api/core/model_runtime/model_providers/nvidia_nim/nvidia_nim.py
+++ b/api/core/model_runtime/model_providers/nvidia_nim/nvidia_nim.py
@@ -6,6 +6,5 @@ logger = logging.getLogger(__name__)
 
 
 class NVIDIANIMProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         pass
diff --git a/api/core/model_runtime/model_providers/oci/__init__.py b/api/core/model_runtime/model_providers/oci/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/model_runtime/model_providers/oci/_assets/icon_l_en.svg b/api/core/model_runtime/model_providers/oci/_assets/icon_l_en.svg
new file mode 100644
index 0000000000..0981dfcff2
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/_assets/icon_l_en.svg
@@ -0,0 +1 @@
+<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 231 30' preserveAspectRatio='xMinYMid'><path d='M99.61,19.52h15.24l-8.05-13L92,30H85.27l18-28.17a4.29,4.29,0,0,1,7-.05L128.32,30h-6.73l-3.17-5.25H103l-3.36-5.23m69.93,5.23V0.28h-5.72V27.16a2.76,2.76,0,0,0,.85,2,2.89,2.89,0,0,0,2.08.87h26l3.39-5.25H169.54M75,20.38A10,10,0,0,0,75,.28H50V30h5.71V5.54H74.65a4.81,4.81,0,0,1,0,9.62H58.54L75.6,30h8.29L72.43,20.38H75M14.88,30H32.15a14.86,14.86,0,0,0,0-29.71H14.88a14.86,14.86,0,1,0,0,29.71m16.88-5.23H15.26a9.62,9.62,0,0,1,0-19.23h16.5a9.62,9.62,0,1,1,0,19.23M140.25,30h17.63l3.34-5.23H140.64a9.62,9.62,0,1,1,0-19.23h16.75l3.38-5.25H140.25a14.86,14.86,0,1,0,0,29.71m69.87-5.23a9.62,9.62,0,0,1-9.26-7h24.42l3.36-5.24H200.86a9.61,9.61,0,0,1,9.26-7h16.76l3.35-5.25h-20.5a14.86,14.86,0,0,0,0,29.71h17.63l3.35-5.23h-20.6' transform='translate(-0.02 0)' style='fill:#C74634'/></svg>
\ No newline at end of file
diff --git a/api/core/model_runtime/model_providers/oci/_assets/icon_s_en.svg b/api/core/model_runtime/model_providers/oci/_assets/icon_s_en.svg
new file mode 100644
index 0000000000..0981dfcff2
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/_assets/icon_s_en.svg
@@ -0,0 +1 @@
+<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 231 30' preserveAspectRatio='xMinYMid'><path d='M99.61,19.52h15.24l-8.05-13L92,30H85.27l18-28.17a4.29,4.29,0,0,1,7-.05L128.32,30h-6.73l-3.17-5.25H103l-3.36-5.23m69.93,5.23V0.28h-5.72V27.16a2.76,2.76,0,0,0,.85,2,2.89,2.89,0,0,0,2.08.87h26l3.39-5.25H169.54M75,20.38A10,10,0,0,0,75,.28H50V30h5.71V5.54H74.65a4.81,4.81,0,0,1,0,9.62H58.54L75.6,30h8.29L72.43,20.38H75M14.88,30H32.15a14.86,14.86,0,0,0,0-29.71H14.88a14.86,14.86,0,1,0,0,29.71m16.88-5.23H15.26a9.62,9.62,0,0,1,0-19.23h16.5a9.62,9.62,0,1,1,0,19.23M140.25,30h17.63l3.34-5.23H140.64a9.62,9.62,0,1,1,0-19.23h16.75l3.38-5.25H140.25a14.86,14.86,0,1,0,0,29.71m69.87-5.23a9.62,9.62,0,0,1-9.26-7h24.42l3.36-5.24H200.86a9.61,9.61,0,0,1,9.26-7h16.76l3.35-5.25h-20.5a14.86,14.86,0,0,0,0,29.71h17.63l3.35-5.23h-20.6' transform='translate(-0.02 0)' style='fill:#C74634'/></svg>
\ No newline at end of file
diff --git a/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-16k.yaml b/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-16k.yaml
new file mode 100644
index 0000000000..eb60cbcd90
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-16k.yaml
@@ -0,0 +1,52 @@
+model: cohere.command-r-16k
+label:
+  en_US: cohere.command-r-16k v1.2
+model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 1
+    max: 1.0
+  - name: topP
+    use_template: top_p
+    default: 0.75
+    min: 0
+    max: 1
+  - name: topK
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+    default: 0
+    min: 0
+    max: 500
+  - name: presencePenalty
+    use_template: presence_penalty
+    min: 0
+    max: 1
+    default: 0
+  - name: frequencyPenalty
+    use_template: frequency_penalty
+    min: 0
+    max: 1
+    default: 0
+  - name: maxTokens
+    use_template: max_tokens
+    default: 600
+    max: 4000
+pricing:
+  input: '0.004'
+  output: '0.004'
+  unit: '0.0001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-plus.yaml b/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-plus.yaml
new file mode 100644
index 0000000000..df31b0d0df
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-plus.yaml
@@ -0,0 +1,52 @@
+model: cohere.command-r-plus
+label:
+  en_US: cohere.command-r-plus v1.2
+model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 1
+    max: 1.0
+  - name: topP
+    use_template: top_p
+    default: 0.75
+    min: 0
+    max: 1
+  - name: topK
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+    default: 0
+    min: 0
+    max: 500
+  - name: presencePenalty
+    use_template: presence_penalty
+    min: 0
+    max: 1
+    default: 0
+  - name: frequencyPenalty
+    use_template: frequency_penalty
+    min: 0
+    max: 1
+    default: 0
+  - name: maxTokens
+    use_template: max_tokens
+    default: 600
+    max: 4000
+pricing:
+  input: '0.0219'
+  output: '0.0219'
+  unit: '0.0001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/llm/llm.py b/api/core/model_runtime/model_providers/oci/llm/llm.py
new file mode 100644
index 0000000000..1e1fc5b3ea
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/llm/llm.py
@@ -0,0 +1,469 @@
+import base64
+import copy
+import json
+import logging
+from collections.abc import Generator
+from typing import Optional, Union
+
+import oci
+from oci.generative_ai_inference.models.base_chat_response import BaseChatResponse
+
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    PromptMessageContentType,
+    PromptMessageTool,
+    SystemPromptMessage,
+    ToolPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
+
+logger = logging.getLogger(__name__)
+
+request_template = {
+    "compartmentId": "",
+    "servingMode": {"modelId": "cohere.command-r-plus", "servingType": "ON_DEMAND"},
+    "chatRequest": {
+        "apiFormat": "COHERE",
+        # "preambleOverride": "You are a helpful assistant.",
+        # "message": "Hello!",
+        # "chatHistory": [],
+        "maxTokens": 600,
+        "isStream": False,
+        "frequencyPenalty": 0,
+        "presencePenalty": 0,
+        "temperature": 1,
+        "topP": 0.75,
+    },
+}
+oci_config_template = {
+    "user": "",
+    "fingerprint": "",
+    "tenancy": "",
+    "region": "",
+    "compartment_id": "",
+    "key_content": "",
+}
+
+
+class OCILargeLanguageModel(LargeLanguageModel):
+    # https://docs.oracle.com/en-us/iaas/Content/generative-ai/pretrained-models.htm
+    _supported_models = {
+        "meta.llama-3-70b-instruct": {
+            "system": True,
+            "multimodal": False,
+            "tool_call": False,
+            "stream_tool_call": False,
+        },
+        "cohere.command-r-16k": {
+            "system": True,
+            "multimodal": False,
+            "tool_call": True,
+            "stream_tool_call": False,
+        },
+        "cohere.command-r-plus": {
+            "system": True,
+            "multimodal": False,
+            "tool_call": True,
+            "stream_tool_call": False,
+        },
+    }
+
+    def _is_tool_call_supported(self, model_id: str, stream: bool = False) -> bool:
+        feature = self._supported_models.get(model_id)
+        if not feature:
+            return False
+        return feature["stream_tool_call"] if stream else feature["tool_call"]
+
+    def _is_multimodal_supported(self, model_id: str) -> bool:
+        feature = self._supported_models.get(model_id)
+        if not feature:
+            return False
+        return feature["multimodal"]
+
+    def _is_system_prompt_supported(self, model_id: str) -> bool:
+        feature = self._supported_models.get(model_id)
+        if not feature:
+            return False
+        return feature["system"]
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
+        """
+        Invoke large language model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_messages: prompt messages
+        :param model_parameters: model parameters
+        :param tools: tools for tool calling
+        :param stop: stop words
+        :param stream: is stream response
+        :param user: unique user id
+        :return: full response or stream response chunk generator result
+        """
+        # print("model"+"*"*20)
+        # print(model)
+        # print("credentials"+"*"*20)
+        # print(credentials)
+        # print("model_parameters"+"*"*20)
+        # print(model_parameters)
+        # print("prompt_messages"+"*"*200)
+        # print(prompt_messages)
+        # print("tools"+"*"*20)
+        # print(tools)
+
+        # invoke model
+        return self._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_messages: prompt messages
+        :param tools: tools for tool calling
+        :return:md = genai.GenerativeModel(model)
+        """
+        prompt = self._convert_messages_to_prompt(prompt_messages)
+
+        return self._get_num_tokens_by_gpt2(prompt)
+
+    def get_num_characters(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_messages: prompt messages
+        :param tools: tools for tool calling
+        :return:md = genai.GenerativeModel(model)
+        """
+        prompt = self._convert_messages_to_prompt(prompt_messages)
+
+        return len(prompt)
+
+    def _convert_messages_to_prompt(self, messages: list[PromptMessage]) -> str:
+        """
+        :param messages: List of PromptMessage to combine.
+        :return: Combined string with necessary human_prompt and ai_prompt tags.
+        """
+        messages = messages.copy()  # don't mutate the original list
+
+        text = "".join(self._convert_one_message_to_text(message) for message in messages)
+
+        return text.rstrip()
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        # Setup basic variables
+        # Auth Config
+        try:
+            ping_message = SystemPromptMessage(content="ping")
+            self._generate(model, credentials, [ping_message], {"maxTokens": 5})
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
+        """
+        Invoke large language model
+
+        :param model: model name
+        :param credentials: credentials kwargs
+        :param prompt_messages: prompt messages
+        :param model_parameters: model parameters
+        :param stop: stop words
+        :param stream: is stream response
+        :param user: unique user id
+        :return: full response or stream response chunk generator result
+        """
+        # config_kwargs = model_parameters.copy()
+        # config_kwargs['max_output_tokens'] = config_kwargs.pop('max_tokens_to_sample', None)
+        # if stop:
+        #    config_kwargs["stop_sequences"] = stop
+
+        # initialize client
+        # ref: https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai-inference/20231130/ChatResult/Chat
+        oci_config = copy.deepcopy(oci_config_template)
+        if "oci_config_content" in credentials:
+            oci_config_content = base64.b64decode(credentials.get("oci_config_content")).decode("utf-8")
+            config_items = oci_config_content.split("/")
+            if len(config_items) != 5:
+                raise CredentialsValidateFailedError(
+                    "oci_config_content should be base64.b64encode("
+                    "'user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid'.encode('utf-8'))"
+                )
+            oci_config["user"] = config_items[0]
+            oci_config["fingerprint"] = config_items[1]
+            oci_config["tenancy"] = config_items[2]
+            oci_config["region"] = config_items[3]
+            oci_config["compartment_id"] = config_items[4]
+        else:
+            raise CredentialsValidateFailedError("need to set oci_config_content in credentials ")
+        if "oci_key_content" in credentials:
+            oci_key_content = base64.b64decode(credentials.get("oci_key_content")).decode("utf-8")
+            oci_config["key_content"] = oci_key_content.encode(encoding="utf-8")
+        else:
+            raise CredentialsValidateFailedError("need to set oci_config_content in credentials ")
+
+        # oci_config = oci.config.from_file('~/.oci/config', credentials.get('oci_api_profile'))
+        compartment_id = oci_config["compartment_id"]
+        client = oci.generative_ai_inference.GenerativeAiInferenceClient(config=oci_config)
+        # call embedding model
+        request_args = copy.deepcopy(request_template)
+        request_args["compartmentId"] = compartment_id
+        request_args["servingMode"]["modelId"] = model
+
+        chat_history = []
+        system_prompts = []
+        # if "meta.llama" in model:
+        #    request_args["chatRequest"]["apiFormat"] = "GENERIC"
+        request_args["chatRequest"]["maxTokens"] = model_parameters.pop("maxTokens", 600)
+        request_args["chatRequest"].update(model_parameters)
+        frequency_penalty = model_parameters.get("frequencyPenalty", 0)
+        presence_penalty = model_parameters.get("presencePenalty", 0)
+        if frequency_penalty > 0 and presence_penalty > 0:
+            raise InvokeBadRequestError("Cannot set both frequency penalty and presence penalty")
+
+        # for msg in prompt_messages:  # makes message roles strictly alternating
+        #    content = self._format_message_to_glm_content(msg)
+        #    if history and history[-1]["role"] == content["role"]:
+        #        history[-1]["parts"].extend(content["parts"])
+        #    else:
+        #        history.append(content)
+
+        # temporary not implement the tool call function
+        valid_value = self._is_tool_call_supported(model, stream)
+        if tools is not None and len(tools) > 0:
+            if not valid_value:
+                raise InvokeBadRequestError("Does not support function calling")
+        if model.startswith("cohere"):
+            # print("run cohere " * 10)
+            for message in prompt_messages[:-1]:
+                text = ""
+                if isinstance(message.content, str):
+                    text = message.content
+                if isinstance(message, UserPromptMessage):
+                    chat_history.append({"role": "USER", "message": text})
+                else:
+                    chat_history.append({"role": "CHATBOT", "message": text})
+                if isinstance(message, SystemPromptMessage):
+                    if isinstance(message.content, str):
+                        system_prompts.append(message.content)
+            args = {
+                "apiFormat": "COHERE",
+                "preambleOverride": " ".join(system_prompts),
+                "message": prompt_messages[-1].content,
+                "chatHistory": chat_history,
+            }
+            request_args["chatRequest"].update(args)
+        elif model.startswith("meta"):
+            # print("run meta " * 10)
+            meta_messages = []
+            for message in prompt_messages:
+                text = message.content
+                meta_messages.append({"role": message.role.name, "content": [{"type": "TEXT", "text": text}]})
+            args = {"apiFormat": "GENERIC", "messages": meta_messages, "numGenerations": 1, "topK": -1}
+            request_args["chatRequest"].update(args)
+
+        if stream:
+            request_args["chatRequest"]["isStream"] = True
+        # print("final request" + "|" * 20)
+        # print(request_args)
+        response = client.chat(request_args)
+        # print(vars(response))
+
+        if stream:
+            return self._handle_generate_stream_response(model, credentials, response, prompt_messages)
+
+        return self._handle_generate_response(model, credentials, response, prompt_messages)
+
+    def _handle_generate_response(
+        self, model: str, credentials: dict, response: BaseChatResponse, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
+        """
+        Handle llm response
+
+        :param model: model name
+        :param credentials: credentials
+        :param response: response
+        :param prompt_messages: prompt messages
+        :return: llm response
+        """
+        # transform assistant message to prompt message
+        assistant_prompt_message = AssistantPromptMessage(content=response.data.chat_response.text)
+
+        # calculate num tokens
+        prompt_tokens = self.get_num_characters(model, credentials, prompt_messages)
+        completion_tokens = self.get_num_characters(model, credentials, [assistant_prompt_message])
+
+        # transform usage
+        usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+
+        # transform response
+        result = LLMResult(
+            model=model,
+            prompt_messages=prompt_messages,
+            message=assistant_prompt_message,
+            usage=usage,
+        )
+
+        return result
+
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, response: BaseChatResponse, prompt_messages: list[PromptMessage]
+    ) -> Generator:
+        """
+        Handle llm stream response
+
+        :param model: model name
+        :param credentials: credentials
+        :param response: response
+        :param prompt_messages: prompt messages
+        :return: llm response chunk generator result
+        """
+        index = -1
+        events = response.data.events()
+        for stream in events:
+            chunk = json.loads(stream.data)
+            # print(chunk)
+            # chunk: {'apiFormat': 'COHERE', 'text': 'Hello'}
+
+            # for chunk in response:
+            # for part in chunk.parts:
+            # if part.function_call:
+            #    assistant_prompt_message.tool_calls = [
+            #        AssistantPromptMessage.ToolCall(
+            #            id=part.function_call.name,
+            #            type='function',
+            #            function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+            #                name=part.function_call.name,
+            #                arguments=json.dumps(dict(part.function_call.args.items()))
+            #            )
+            #        )
+            #    ]
+
+            if "finishReason" not in chunk:
+                assistant_prompt_message = AssistantPromptMessage(content="")
+                if model.startswith("cohere"):
+                    if chunk["text"]:
+                        assistant_prompt_message.content += chunk["text"]
+                elif model.startswith("meta"):
+                    assistant_prompt_message.content += chunk["message"]["content"][0]["text"]
+                index += 1
+                # transform assistant message to prompt message
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(index=index, message=assistant_prompt_message),
+                )
+            else:
+                # calculate num tokens
+                prompt_tokens = self.get_num_characters(model, credentials, prompt_messages)
+                completion_tokens = self.get_num_characters(model, credentials, [assistant_prompt_message])
+
+                # transform usage
+                usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=index,
+                        message=assistant_prompt_message,
+                        finish_reason=str(chunk["finishReason"]),
+                        usage=usage,
+                    ),
+                )
+
+    def _convert_one_message_to_text(self, message: PromptMessage) -> str:
+        """
+        Convert a single message to a string.
+
+        :param message: PromptMessage to convert.
+        :return: String representation of the message.
+        """
+        human_prompt = "\n\nuser:"
+        ai_prompt = "\n\nmodel:"
+
+        content = message.content
+        if isinstance(content, list):
+            content = "".join(c.data for c in content if c.type != PromptMessageContentType.IMAGE)
+
+        if isinstance(message, UserPromptMessage):
+            message_text = f"{human_prompt} {content}"
+        elif isinstance(message, AssistantPromptMessage):
+            message_text = f"{ai_prompt} {content}"
+        elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
+            message_text = f"{human_prompt} {content}"
+        else:
+            raise ValueError(f"Got unknown type {message}")
+
+        return message_text
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [],
+            InvokeServerUnavailableError: [],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [],
+            InvokeBadRequestError: [],
+        }
diff --git a/api/core/model_runtime/model_providers/oci/llm/meta.llama-3-70b-instruct.yaml b/api/core/model_runtime/model_providers/oci/llm/meta.llama-3-70b-instruct.yaml
new file mode 100644
index 0000000000..dd5be107c0
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/llm/meta.llama-3-70b-instruct.yaml
@@ -0,0 +1,51 @@
+model: meta.llama-3-70b-instruct
+label:
+  zh_Hans: meta.llama-3-70b-instruct
+  en_US: meta.llama-3-70b-instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 1
+    max: 2.0
+  - name: topP
+    use_template: top_p
+    default: 0.75
+    min: 0
+    max: 1
+  - name: topK
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+    default: 0
+    min: 0
+    max: 500
+  - name: presencePenalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: frequencyPenalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: maxTokens
+    use_template: max_tokens
+    default: 600
+    max: 8000
+pricing:
+  input: '0.015'
+  output: '0.015'
+  unit: '0.0001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/oci.py b/api/core/model_runtime/model_providers/oci/oci.py
new file mode 100644
index 0000000000..e182d2d043
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/oci.py
@@ -0,0 +1,28 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class OCIGENAIProvider(ModelProvider):
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.LLM)
+
+            # Use `cohere.command-r-plus` model for validate,
+            model_instance.validate_credentials(model="cohere.command-r-plus", credentials=credentials)
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
+            raise ex
diff --git a/api/core/model_runtime/model_providers/oci/oci.yaml b/api/core/model_runtime/model_providers/oci/oci.yaml
new file mode 100644
index 0000000000..f2f23e18f1
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/oci.yaml
@@ -0,0 +1,42 @@
+provider: oci
+label:
+  en_US: OCIGenerativeAI
+description:
+  en_US: Models provided by OCI, such as Cohere Command R and Cohere Command R+.
+  zh_Hans: OCI 提供的模型，例如 Cohere Command R 和 Cohere Command R+。
+icon_small:
+  en_US: icon_s_en.svg
+icon_large:
+  en_US: icon_l_en.svg
+background: "#FFFFFF"
+help:
+  title:
+    en_US: Get your API Key from OCI
+    zh_Hans: 从 OCI 获取 API Key
+  url:
+    en_US: https://docs.cloud.oracle.com/Content/API/Concepts/sdkconfig.htm
+supported_model_types:
+  - llm
+  - text-embedding
+  #- rerank
+configurate_methods:
+  - predefined-model
+  #- customizable-model
+provider_credential_schema:
+  credential_form_schemas:
+    - variable: oci_config_content
+      label:
+        en_US: oci api key config file's content
+      type: text-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 oci api key config 文件的内容(base64.b64encode("user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid".encode('utf-8')) )
+        en_US: Enter your oci api key config file's content(base64.b64encode("user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid".encode('utf-8')) )
+    - variable: oci_key_content
+      label:
+        en_US: oci api key file's content
+      type: text-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 oci api key 文件的内容(base64.b64encode("pem file content".encode('utf-8')))
+        en_US: Enter your oci api key file's content(base64.b64encode("pem file content".encode('utf-8')))
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/__init__.py b/api/core/model_runtime/model_providers/oci/text_embedding/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/_position.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/_position.yaml
new file mode 100644
index 0000000000..149f1e3797
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/_position.yaml
@@ -0,0 +1,5 @@
+- cohere.embed-english-light-v2.0
+- cohere.embed-english-light-v3.0
+- cohere.embed-english-v3.0
+- cohere.embed-multilingual-light-v3.0
+- cohere.embed-multilingual-v3.0
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v2.0.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v2.0.yaml
new file mode 100644
index 0000000000..259d5b45b7
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v2.0.yaml
@@ -0,0 +1,9 @@
+model: cohere.embed-english-light-v2.0
+model_type: text-embedding
+model_properties:
+  context_size: 1024
+  max_chunks: 48
+pricing:
+  input: '0.001'
+  unit: '0.0001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v3.0.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v3.0.yaml
new file mode 100644
index 0000000000..065e7474c0
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v3.0.yaml
@@ -0,0 +1,9 @@
+model: cohere.embed-english-light-v3.0
+model_type: text-embedding
+model_properties:
+  context_size: 384
+  max_chunks: 48
+pricing:
+  input: '0.001'
+  unit: '0.0001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-v3.0.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-v3.0.yaml
new file mode 100644
index 0000000000..3e2deea16a
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-v3.0.yaml
@@ -0,0 +1,9 @@
+model: cohere.embed-english-v3.0
+model_type: text-embedding
+model_properties:
+  context_size: 1024
+  max_chunks: 48
+pricing:
+  input: '0.001'
+  unit: '0.0001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-light-v3.0.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-light-v3.0.yaml
new file mode 100644
index 0000000000..0d2b892c64
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-light-v3.0.yaml
@@ -0,0 +1,9 @@
+model: cohere.embed-multilingual-light-v3.0
+model_type: text-embedding
+model_properties:
+  context_size: 384
+  max_chunks: 48
+pricing:
+  input: '0.001'
+  unit: '0.0001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-v3.0.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-v3.0.yaml
new file mode 100644
index 0000000000..9ebe260b32
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-v3.0.yaml
@@ -0,0 +1,9 @@
+model: cohere.embed-multilingual-v3.0
+model_type: text-embedding
+model_properties:
+  context_size: 1024
+  max_chunks: 48
+pricing:
+  input: '0.001'
+  unit: '0.0001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py
new file mode 100644
index 0000000000..80ad2be9f5
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py
@@ -0,0 +1,217 @@
+import base64
+import copy
+import time
+from typing import Optional
+
+import numpy as np
+import oci
+
+from core.model_runtime.entities.model_entities import PriceType
+from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
+
+request_template = {
+    "compartmentId": "",
+    "servingMode": {"modelId": "cohere.embed-english-light-v3.0", "servingType": "ON_DEMAND"},
+    "truncate": "NONE",
+    "inputs": [""],
+}
+oci_config_template = {
+    "user": "",
+    "fingerprint": "",
+    "tenancy": "",
+    "region": "",
+    "compartment_id": "",
+    "key_content": "",
+}
+
+
+class OCITextEmbeddingModel(TextEmbeddingModel):
+    """
+    Model class for Cohere text embedding model.
+    """
+
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :return: embeddings result
+        """
+        # get model properties
+        context_size = self._get_context_size(model, credentials)
+        max_chunks = self._get_max_chunks(model, credentials)
+
+        inputs = []
+        indices = []
+        used_tokens = 0
+
+        for i, text in enumerate(texts):
+            # Here token count is only an approximation based on the GPT2 tokenizer
+            num_tokens = self._get_num_tokens_by_gpt2(text)
+
+            if num_tokens >= context_size:
+                cutoff = int(len(text) * (np.floor(context_size / num_tokens)))
+                # if num tokens is larger than context length, only use the start
+                inputs.append(text[0:cutoff])
+            else:
+                inputs.append(text)
+            indices += [i]
+
+        batched_embeddings = []
+        _iter = range(0, len(inputs), max_chunks)
+
+        for i in _iter:
+            # call embedding model
+            embeddings_batch, embedding_used_tokens = self._embedding_invoke(
+                model=model, credentials=credentials, texts=inputs[i : i + max_chunks]
+            )
+
+            used_tokens += embedding_used_tokens
+            batched_embeddings += embeddings_batch
+
+        # calc usage
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
+
+        return TextEmbeddingResult(embeddings=batched_embeddings, usage=usage, model=model)
+
+    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return:
+        """
+        return sum(self._get_num_tokens_by_gpt2(text) for text in texts)
+
+    def get_num_characters(self, model: str, credentials: dict, texts: list[str]) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return:
+        """
+        characters = 0
+        for text in texts:
+            characters += len(text)
+        return characters
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            # call embedding model
+            self._embedding_invoke(model=model, credentials=credentials, texts=["ping"])
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    def _embedding_invoke(self, model: str, credentials: dict, texts: list[str]) -> tuple[list[list[float]], int]:
+        """
+        Invoke embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return: embeddings and used tokens
+        """
+
+        # oci
+        # initialize client
+        oci_config = copy.deepcopy(oci_config_template)
+        if "oci_config_content" in credentials:
+            oci_config_content = base64.b64decode(credentials.get("oci_config_content")).decode("utf-8")
+            config_items = oci_config_content.split("/")
+            if len(config_items) != 5:
+                raise CredentialsValidateFailedError(
+                    "oci_config_content should be base64.b64encode("
+                    "'user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid'.encode('utf-8'))"
+                )
+            oci_config["user"] = config_items[0]
+            oci_config["fingerprint"] = config_items[1]
+            oci_config["tenancy"] = config_items[2]
+            oci_config["region"] = config_items[3]
+            oci_config["compartment_id"] = config_items[4]
+        else:
+            raise CredentialsValidateFailedError("need to set oci_config_content in credentials ")
+        if "oci_key_content" in credentials:
+            oci_key_content = base64.b64decode(credentials.get("oci_key_content")).decode("utf-8")
+            oci_config["key_content"] = oci_key_content.encode(encoding="utf-8")
+        else:
+            raise CredentialsValidateFailedError("need to set oci_config_content in credentials ")
+        # oci_config = oci.config.from_file('~/.oci/config', credentials.get('oci_api_profile'))
+        compartment_id = oci_config["compartment_id"]
+        client = oci.generative_ai_inference.GenerativeAiInferenceClient(config=oci_config)
+        # call embedding model
+        request_args = copy.deepcopy(request_template)
+        request_args["compartmentId"] = compartment_id
+        request_args["servingMode"]["modelId"] = model
+        request_args["inputs"] = texts
+        response = client.embed_text(request_args)
+        return response.data.embeddings, self.get_num_characters(model=model, credentials=credentials, texts=texts)
+
+    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
+        """
+        Calculate response usage
+
+        :param model: model name
+        :param credentials: model credentials
+        :param tokens: input tokens
+        :return: usage
+        """
+        # get input price info
+        input_price_info = self.get_price(
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
+        )
+
+        # transform usage
+        usage = EmbeddingUsage(
+            tokens=tokens,
+            total_tokens=tokens,
+            unit_price=input_price_info.unit_price,
+            price_unit=input_price_info.unit,
+            total_price=input_price_info.total_amount,
+            currency=input_price_info.currency,
+            latency=time.perf_counter() - self.started_at,
+        )
+
+        return usage
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [KeyError],
+        }
diff --git a/api/core/model_runtime/model_providers/ollama/llm/llm.py b/api/core/model_runtime/model_providers/ollama/llm/llm.py
index 42a588e3dd..1ed77a2ee8 100644
--- a/api/core/model_runtime/model_providers/ollama/llm/llm.py
+++ b/api/core/model_runtime/model_providers/ollama/llm/llm.py
@@ -121,9 +121,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                 text = ""
                 for message_content in first_prompt_message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
-                        message_content = cast(
-                            TextPromptMessageContent, message_content
-                        )
+                        message_content = cast(TextPromptMessageContent, message_content)
                         text = message_content.data
                         break
             return self._get_num_tokens_by_gpt2(text)
@@ -145,13 +143,9 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                 stream=False,
             )
         except InvokeError as ex:
-            raise CredentialsValidateFailedError(
-                f"An error occurred during credentials validation: {ex.description}"
-            )
+            raise CredentialsValidateFailedError(f"An error occurred during credentials validation: {ex.description}")
         except Exception as ex:
-            raise CredentialsValidateFailedError(
-                f"An error occurred during credentials validation: {str(ex)}"
-            )
+            raise CredentialsValidateFailedError(f"An error occurred during credentials validation: {str(ex)}")
 
     def _generate(
         self,
@@ -201,9 +195,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
 
         if completion_type is LLMMode.CHAT:
             endpoint_url = urljoin(endpoint_url, "api/chat")
-            data["messages"] = [
-                self._convert_prompt_message_to_dict(m) for m in prompt_messages
-            ]
+            data["messages"] = [self._convert_prompt_message_to_dict(m) for m in prompt_messages]
         else:
             endpoint_url = urljoin(endpoint_url, "api/generate")
             first_prompt_message = prompt_messages[0]
@@ -216,14 +208,10 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     images = []
                     for message_content in first_prompt_message.content:
                         if message_content.type == PromptMessageContentType.TEXT:
-                            message_content = cast(
-                                TextPromptMessageContent, message_content
-                            )
+                            message_content = cast(TextPromptMessageContent, message_content)
                             text = message_content.data
                         elif message_content.type == PromptMessageContentType.IMAGE:
-                            message_content = cast(
-                                ImagePromptMessageContent, message_content
-                            )
+                            message_content = cast(ImagePromptMessageContent, message_content)
                             image_data = re.sub(
                                 r"^data:image\/[a-zA-Z]+;base64,",
                                 "",
@@ -235,24 +223,16 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     data["images"] = images
 
         # send a post request to validate the credentials
-        response = requests.post(
-            endpoint_url, headers=headers, json=data, timeout=(10, 300), stream=stream
-        )
+        response = requests.post(endpoint_url, headers=headers, json=data, timeout=(10, 300), stream=stream)
 
         response.encoding = "utf-8"
         if response.status_code != 200:
-            raise InvokeError(
-                f"API request failed with status code {response.status_code}: {response.text}"
-            )
+            raise InvokeError(f"API request failed with status code {response.status_code}: {response.text}")
 
         if stream:
-            return self._handle_generate_stream_response(
-                model, credentials, completion_type, response, prompt_messages
-            )
+            return self._handle_generate_stream_response(model, credentials, completion_type, response, prompt_messages)
 
-        return self._handle_generate_response(
-            model, credentials, completion_type, response, prompt_messages
-        )
+        return self._handle_generate_response(model, credentials, completion_type, response, prompt_messages)
 
     def _handle_generate_response(
         self,
@@ -292,9 +272,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
             completion_tokens = self._get_num_tokens_by_gpt2(assistant_message.content)
 
         # transform usage
-        usage = self._calc_response_usage(
-            model, credentials, prompt_tokens, completion_tokens
-        )
+        usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
 
         # transform response
         result = LLMResult(
@@ -335,9 +313,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
             completion_tokens = self._get_num_tokens_by_gpt2(full_text)
 
             # transform usage
-            usage = self._calc_response_usage(
-                model, credentials, prompt_tokens, completion_tokens
-            )
+            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
 
             return LLMResultChunk(
                 model=model,
@@ -394,15 +370,11 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     completion_tokens = chunk_json["eval_count"]
                 else:
                     # calculate num tokens
-                    prompt_tokens = self._get_num_tokens_by_gpt2(
-                        prompt_messages[0].content
-                    )
+                    prompt_tokens = self._get_num_tokens_by_gpt2(prompt_messages[0].content)
                     completion_tokens = self._get_num_tokens_by_gpt2(full_text)
 
                 # transform usage
-                usage = self._calc_response_usage(
-                    model, credentials, prompt_tokens, completion_tokens
-                )
+                usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
 
                 yield LLMResultChunk(
                     model=chunk_json["model"],
@@ -439,17 +411,11 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                 images = []
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
-                        message_content = cast(
-                            TextPromptMessageContent, message_content
-                        )
+                        message_content = cast(TextPromptMessageContent, message_content)
                         text = message_content.data
                     elif message_content.type == PromptMessageContentType.IMAGE:
-                        message_content = cast(
-                            ImagePromptMessageContent, message_content
-                        )
-                        image_data = re.sub(
-                            r"^data:image\/[a-zA-Z]+;base64,", "", message_content.data
-                        )
+                        message_content = cast(ImagePromptMessageContent, message_content)
+                        image_data = re.sub(r"^data:image\/[a-zA-Z]+;base64,", "", message_content.data)
                         images.append(image_data)
 
                 message_dict = {"role": "user", "content": text, "images": images}
@@ -479,9 +445,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
 
         return num_tokens
 
-    def get_customizable_model_schema(
-        self, model: str, credentials: dict
-    ) -> AIModelEntity:
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
         Get customizable model schema.
 
@@ -502,9 +466,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
                 ModelPropertyKey.MODE: credentials.get("mode"),
-                ModelPropertyKey.CONTEXT_SIZE: int(
-                    credentials.get("context_size", 4096)
-                ),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 4096)),
             },
             parameter_rules=[
                 ParameterRule(
@@ -568,9 +530,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                         en_US="Maximum number of tokens to predict when generating text. "
                         "(Default: 128, -1 = infinite generation, -2 = fill context)"
                     ),
-                    default=(
-                        512 if int(credentials.get("max_tokens", 4096)) >= 768 else 128
-                    ),
+                    default=(512 if int(credentials.get("max_tokens", 4096)) >= 768 else 128),
                     min=-2,
                     max=int(credentials.get("max_tokens", 4096)),
                 ),
@@ -612,22 +572,23 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     label=I18nObject(en_US="Size of context window"),
                     type=ParameterType.INT,
                     help=I18nObject(
-                        en_US="Sets the size of the context window used to generate the next token. "
-                        "(Default: 2048)"
+                        en_US="Sets the size of the context window used to generate the next token. (Default: 2048)"
                     ),
                     default=2048,
                     min=1,
                 ),
                 ParameterRule(
-                    name='num_gpu',
+                    name="num_gpu",
                     label=I18nObject(en_US="GPU Layers"),
                     type=ParameterType.INT,
-                    help=I18nObject(en_US="The number of layers to offload to the GPU(s). "
-                                          "On macOS it defaults to 1 to enable metal support, 0 to disable."
-                                          "As long as a model fits into one gpu it stays in one. "
-                                          "It does not set the number of GPU(s). "),
+                    help=I18nObject(
+                        en_US="The number of layers to offload to the GPU(s). "
+                        "On macOS it defaults to 1 to enable metal support, 0 to disable."
+                        "As long as a model fits into one gpu it stays in one. "
+                        "It does not set the number of GPU(s). "
+                    ),
                     min=-1,
-                    default=1
+                    default=1,
                 ),
                 ParameterRule(
                     name="num_thread",
@@ -678,9 +639,10 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     type=ParameterType.STRING,
                     help=I18nObject(
                         en_US="Sets how long the model is kept in memory after generating a response. "
-                        "This must be a duration string with a unit (e.g., '10m' for 10 minutes or '24h' for 24 hours). "
-                        "A negative number keeps the model loaded indefinitely, and '0' unloads the model immediately after generating a response. "
-                        "Valid time units are 's','m','h'. (Default: 5m)"
+                        "This must be a duration string with a unit (e.g., '10m' for 10 minutes or '24h' for 24 hours)."
+                        " A negative number keeps the model loaded indefinitely, and '0' unloads the model"
+                        " immediately after generating a response."
+                        " Valid time units are 's','m','h'. (Default: 5m)"
                     ),
                 ),
                 ParameterRule(
@@ -688,8 +650,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     label=I18nObject(en_US="Format"),
                     type=ParameterType.STRING,
                     help=I18nObject(
-                        en_US="the format to return a response in."
-                        " Currently the only accepted value is json."
+                        en_US="the format to return a response in. Currently the only accepted value is json."
                     ),
                     options=["json"],
                 ),
diff --git a/api/core/model_runtime/model_providers/ollama/ollama.py b/api/core/model_runtime/model_providers/ollama/ollama.py
index f8a17b98a0..115280193a 100644
--- a/api/core/model_runtime/model_providers/ollama/ollama.py
+++ b/api/core/model_runtime/model_providers/ollama/ollama.py
@@ -6,7 +6,6 @@ logger = logging.getLogger(__name__)
 
 
 class OpenAIProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
diff --git a/api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py
index 9e26d35afc..b4c61d8a6d 100644
--- a/api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py
@@ -37,9 +37,9 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
     Model class for an Ollama text embedding model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -51,15 +51,13 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
         """
 
         # Prepare headers and payload for the request
-        headers = {
-            'Content-Type': 'application/json'
-        }
+        headers = {"Content-Type": "application/json"}
 
-        endpoint_url = credentials.get('base_url')
-        if not endpoint_url.endswith('/'):
-            endpoint_url += '/'
+        endpoint_url = credentials.get("base_url")
+        if not endpoint_url.endswith("/"):
+            endpoint_url += "/"
 
-        endpoint_url = urljoin(endpoint_url, 'api/embed')
+        endpoint_url = urljoin(endpoint_url, "api/embed")
 
         # get model properties
         context_size = self._get_context_size(model, credentials)
@@ -67,52 +65,36 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
         inputs = []
         used_tokens = 0
 
-        for i, text in enumerate(texts):
+        for text in texts:
             # Here token count is only an approximation based on the GPT2 tokenizer
             num_tokens = self._get_num_tokens_by_gpt2(text)
 
             if num_tokens >= context_size:
                 cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
                 # if num tokens is larger than context length, only use the start
-                inputs.append(text[0: cutoff])
+                inputs.append(text[0:cutoff])
             else:
                 inputs.append(text)
 
         # Prepare the payload for the request
-        payload = {
-            'input': inputs,
-            'model': model,
-        }
+        payload = {"input": inputs, "model": model, "options": {"use_mmap": True}}
 
-        # Make the request to the OpenAI API
-        response = requests.post(
-            endpoint_url,
-            headers=headers,
-            data=json.dumps(payload),
-            timeout=(10, 300)
-        )
+        # Make the request to the Ollama API
+        response = requests.post(endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300))
 
         response.raise_for_status()  # Raise an exception for HTTP errors
         response_data = response.json()
 
         # Extract embeddings and used tokens from the response
-        embeddings = response_data['embeddings']
+        embeddings = response_data["embeddings"]
         embedding_used_tokens = self.get_num_tokens(model, credentials, inputs)
 
         used_tokens += embedding_used_tokens
 
         # calc usage
-        usage = self._calc_response_usage(
-            model=model,
-            credentials=credentials,
-            tokens=used_tokens
-        )
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
 
-        return TextEmbeddingResult(
-            embeddings=embeddings,
-            usage=usage,
-            model=model
-        )
+        return TextEmbeddingResult(embeddings=embeddings, usage=usage, model=model)
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
@@ -134,19 +116,15 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         try:
-            self._invoke(
-                model=model,
-                credentials=credentials,
-                texts=['ping']
-            )
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
         except InvokeError as ex:
-            raise CredentialsValidateFailedError(f'An error occurred during credentials validation: {ex.description}')
+            raise CredentialsValidateFailedError(f"An error occurred during credentials validation: {ex.description}")
         except Exception as ex:
-            raise CredentialsValidateFailedError(f'An error occurred during credentials validation: {str(ex)}')
+            raise CredentialsValidateFailedError(f"An error occurred during credentials validation: {str(ex)}")
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            generate custom model entities from credentials
+        generate custom model entities from credentials
         """
         entity = AIModelEntity(
             model=model,
@@ -154,15 +132,15 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size')),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],
             pricing=PriceConfig(
-                input=Decimal(credentials.get('input_price', 0)),
-                unit=Decimal(credentials.get('unit', 0)),
-                currency=credentials.get('currency', "USD")
-            )
+                input=Decimal(credentials.get("input_price", 0)),
+                unit=Decimal(credentials.get("unit", 0)),
+                currency=credentials.get("currency", "USD"),
+            ),
         )
 
         return entity
@@ -178,10 +156,7 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -192,7 +167,7 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
@@ -220,10 +195,10 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
             ],
             InvokeServerUnavailableError: [
                 requests.exceptions.ConnectionError,  # Engine Overloaded
-                requests.exceptions.HTTPError  # Server Error
+                requests.exceptions.HTTPError,  # Server Error
             ],
             InvokeConnectionError: [
                 requests.exceptions.ConnectTimeout,  # Timeout
-                requests.exceptions.ReadTimeout  # Timeout
-            ]
+                requests.exceptions.ReadTimeout,  # Timeout
+            ],
         }
diff --git a/api/core/model_runtime/model_providers/openai/_common.py b/api/core/model_runtime/model_providers/openai/_common.py
index 467a51daf2..2181bb4f08 100644
--- a/api/core/model_runtime/model_providers/openai/_common.py
+++ b/api/core/model_runtime/model_providers/openai/_common.py
@@ -22,7 +22,7 @@ class _CommonOpenAI:
         :return:
         """
         credentials_kwargs = {
-            "api_key": credentials['openai_api_key'],
+            "api_key": credentials["openai_api_key"],
             "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
             "max_retries": 1,
         }
@@ -31,8 +31,8 @@ class _CommonOpenAI:
             openai_api_base = credentials["openai_api_base"].rstrip("/")
             credentials_kwargs["base_url"] = openai_api_base + "/v1"
 
-        if 'openai_organization' in credentials:
-            credentials_kwargs['organization'] = credentials['openai_organization']
+        if "openai_organization" in credentials:
+            credentials_kwargs["organization"] = credentials["openai_organization"]
 
         return credentials_kwargs
 
diff --git a/api/core/model_runtime/model_providers/openai/llm/_position.yaml b/api/core/model_runtime/model_providers/openai/llm/_position.yaml
index ac7313aaa1..7501bc1164 100644
--- a/api/core/model_runtime/model_providers/openai/llm/_position.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/_position.yaml
@@ -5,6 +5,10 @@
 - chatgpt-4o-latest
 - gpt-4o-mini
 - gpt-4o-mini-2024-07-18
+- o1-preview
+- o1-preview-2024-09-12
+- o1-mini
+- o1-mini-2024-09-12
 - gpt-4-turbo
 - gpt-4-turbo-2024-04-09
 - gpt-4-turbo-preview
diff --git a/api/core/model_runtime/model_providers/openai/llm/chatgpt-4o-latest.yaml b/api/core/model_runtime/model_providers/openai/llm/chatgpt-4o-latest.yaml
index 98e236650c..b47449a49a 100644
--- a/api/core/model_runtime/model_providers/openai/llm/chatgpt-4o-latest.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/chatgpt-4o-latest.yaml
@@ -28,7 +28,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-0125.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-0125.yaml
index c1602b2efc..ffa725ec40 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-0125.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-0125.yaml
@@ -27,7 +27,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-1106.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-1106.yaml
index 56ab965c39..21150fc3a6 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-1106.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-1106.yaml
@@ -27,7 +27,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo.yaml
index 6eb15e6c0d..d3a8ee535a 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo.yaml
@@ -27,7 +27,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4-0125-preview.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4-0125-preview.yaml
index 007cfed0f3..ac4ec5840b 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4-0125-preview.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4-0125-preview.yaml
@@ -40,7 +40,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4-1106-preview.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4-1106-preview.yaml
index f4fa6317af..d775239770 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4-1106-preview.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4-1106-preview.yaml
@@ -40,7 +40,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4-32k.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4-32k.yaml
index f92173ccfd..8358425e6d 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4-32k.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4-32k.yaml
@@ -40,7 +40,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo-2024-04-09.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo-2024-04-09.yaml
index 6b36361efe..0234499164 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo-2024-04-09.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo-2024-04-09.yaml
@@ -41,7 +41,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo-preview.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo-preview.yaml
index c0350ae2c6..8d29cf0c04 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo-preview.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo-preview.yaml
@@ -40,7 +40,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo.yaml
index 575acb7fa2..b25ff6a812 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo.yaml
@@ -41,7 +41,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4-vision-preview.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4-vision-preview.yaml
index a63b608423..07037c6643 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4-vision-preview.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4-vision-preview.yaml
@@ -38,7 +38,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4.yaml
index a7a5bf3c86..f7b5138b7d 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4.yaml
@@ -40,7 +40,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-05-13.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-05-13.yaml
index f0d835cba2..b630d6f630 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-05-13.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-05-13.yaml
@@ -28,7 +28,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-08-06.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-08-06.yaml
index 7e430c51a7..73b7f69700 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-08-06.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-08-06.yaml
@@ -28,7 +28,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini-2024-07-18.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini-2024-07-18.yaml
index 03e28772e6..df38270f79 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini-2024-07-18.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini-2024-07-18.yaml
@@ -28,7 +28,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini.yaml
index 23dcf85085..5e3c94fbe2 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini.yaml
@@ -28,7 +28,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/gpt-4o.yaml b/api/core/model_runtime/model_providers/openai/llm/gpt-4o.yaml
index 4f141f772f..3090a9e090 100644
--- a/api/core/model_runtime/model_providers/openai/llm/gpt-4o.yaml
+++ b/api/core/model_runtime/model_providers/openai/llm/gpt-4o.yaml
@@ -28,7 +28,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openai/llm/llm.py b/api/core/model_runtime/model_providers/openai/llm/llm.py
index 06135c9584..d42fce528a 100644
--- a/api/core/model_runtime/model_providers/openai/llm/llm.py
+++ b/api/core/model_runtime/model_providers/openai/llm/llm.py
@@ -37,18 +37,25 @@ if you are not sure about the structure.
 <instructions>
 {{instructions}}
 </instructions>
-"""
+"""  # noqa: E501
+
 
 class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
     """
     Model class for OpenAI large language model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -64,8 +71,8 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         """
         # handle fine tune remote models
         base_model = model
-        if model.startswith('ft:'):
-            base_model = model.split(':')[1]
+        if model.startswith("ft:"):
+            base_model = model.split(":")[1]
 
         # get model mode
         model_mode = self.get_model_mode(base_model, credentials)
@@ -80,7 +87,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 tools=tools,
                 stop=stop,
                 stream=stream,
-                user=user
+                user=user,
             )
         else:
             # text completion model
@@ -91,26 +98,34 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 model_parameters=model_parameters,
                 stop=stop,
                 stream=stream,
-                user=user
+                user=user,
             )
 
-    def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                           model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
-                           stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,
-                           callbacks: list[Callback] = None) -> Union[LLMResult, Generator]:
+    def _code_block_mode_wrapper(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: list[Callback] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Code block mode wrapper for invoking large language model
         """
         # handle fine tune remote models
         base_model = model
-        if model.startswith('ft:'):
-            base_model = model.split(':')[1]
+        if model.startswith("ft:"):
+            base_model = model.split(":")[1]
 
         # get model mode
         model_mode = self.get_model_mode(base_model, credentials)
 
         # transform response format
-        if 'response_format' in model_parameters and model_parameters['response_format'] in ['JSON', 'XML']:
+        if "response_format" in model_parameters and model_parameters["response_format"] in {"JSON", "XML"}:
             stop = stop or []
             if model_mode == LLMMode.CHAT:
                 # chat model
@@ -123,7 +138,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                     stop=stop,
                     stream=stream,
                     user=user,
-                    response_format=model_parameters['response_format']
+                    response_format=model_parameters["response_format"],
                 )
             else:
                 self._transform_completion_json_prompts(
@@ -135,9 +150,9 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                     stop=stop,
                     stream=stream,
                     user=user,
-                    response_format=model_parameters['response_format']
+                    response_format=model_parameters["response_format"],
                 )
-            model_parameters.pop('response_format')
+            model_parameters.pop("response_format")
 
         return self._invoke(
             model=model,
@@ -147,14 +162,21 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             tools=tools,
             stop=stop,
             stream=stream,
-            user=user
+            user=user,
         )
 
-    def _transform_chat_json_prompts(self, model: str, credentials: dict, 
-                               prompt_messages: list[PromptMessage], model_parameters: dict, 
-                               tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
-                               stream: bool = True, user: str | None = None, response_format: str = 'JSON') \
-                            -> None:
+    def _transform_chat_json_prompts(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+        response_format: str = "JSON",
+    ) -> None:
         """
         Transform json prompts
         """
@@ -167,25 +189,35 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
             # override the system message
             prompt_messages[0] = SystemPromptMessage(
-                content=OPENAI_BLOCK_MODE_PROMPT
-                    .replace("{{instructions}}", prompt_messages[0].content)
-                    .replace("{{block}}", response_format)
+                content=OPENAI_BLOCK_MODE_PROMPT.replace("{{instructions}}", prompt_messages[0].content).replace(
+                    "{{block}}", response_format
+                )
             )
             prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}\n"))
         else:
             # insert the system message
-            prompt_messages.insert(0, SystemPromptMessage(
-                content=OPENAI_BLOCK_MODE_PROMPT
-                    .replace("{{instructions}}", f"Please output a valid {response_format} object.")
-                    .replace("{{block}}", response_format)
-            ))
+            prompt_messages.insert(
+                0,
+                SystemPromptMessage(
+                    content=OPENAI_BLOCK_MODE_PROMPT.replace(
+                        "{{instructions}}", f"Please output a valid {response_format} object."
+                    ).replace("{{block}}", response_format)
+                ),
+            )
             prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
-    
-    def _transform_completion_json_prompts(self, model: str, credentials: dict,
-                                            prompt_messages: list[PromptMessage], model_parameters: dict,
-                                            tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
-                                            stream: bool = True, user: str | None = None, response_format: str = 'JSON') \
-            -> None:
+
+    def _transform_completion_json_prompts(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+        response_format: str = "JSON",
+    ) -> None:
         """
         Transform json prompts
         """
@@ -202,25 +234,30 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 break
 
         if user_message:
-            if prompt_messages[i].content[-11:] == 'Assistant: ':
+            if prompt_messages[i].content[-11:] == "Assistant: ":
                 # now we are in the chat app, remove the last assistant message
                 prompt_messages[i].content = prompt_messages[i].content[:-11]
                 prompt_messages[i] = UserPromptMessage(
-                    content=OPENAI_BLOCK_MODE_PROMPT
-                        .replace("{{instructions}}", user_message.content)
-                        .replace("{{block}}", response_format)
+                    content=OPENAI_BLOCK_MODE_PROMPT.replace("{{instructions}}", user_message.content).replace(
+                        "{{block}}", response_format
+                    )
                 )
                 prompt_messages[i].content += f"Assistant:\n```{response_format}\n"
             else:
                 prompt_messages[i] = UserPromptMessage(
-                    content=OPENAI_BLOCK_MODE_PROMPT
-                        .replace("{{instructions}}", user_message.content)
-                        .replace("{{block}}", response_format)
+                    content=OPENAI_BLOCK_MODE_PROMPT.replace("{{instructions}}", user_message.content).replace(
+                        "{{block}}", response_format
+                    )
                 )
                 prompt_messages[i].content += f"\n```{response_format}\n"
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -231,8 +268,8 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         :return:
         """
         # handle fine tune remote models
-        if model.startswith('ft:'):
-            base_model = model.split(':')[1]
+        if model.startswith("ft:"):
+            base_model = model.split(":")[1]
         else:
             base_model = model
 
@@ -262,14 +299,14 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             # handle fine tune remote models
             base_model = model
             # fine-tuned model name likes ft:gpt-3.5-turbo-0613:personal::xxxxx
-            if model.startswith('ft:'):
-                base_model = model.split(':')[1]
+            if model.startswith("ft:"):
+                base_model = model.split(":")[1]
 
                 # check if model exists
                 remote_models = self.remote_models(credentials)
                 remote_model_map = {model.model: model for model in remote_models}
                 if model not in remote_model_map:
-                    raise CredentialsValidateFailedError(f'Fine-tuned model {model} not found')
+                    raise CredentialsValidateFailedError(f"Fine-tuned model {model} not found")
 
             # get model mode
             model_mode = self.get_model_mode(base_model, credentials)
@@ -277,7 +314,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             if model_mode == LLMMode.CHAT:
                 # chat model
                 client.chat.completions.create(
-                    messages=[{"role": "user", "content": 'ping'}],
+                    messages=[{"role": "user", "content": "ping"}],
                     model=model,
                     temperature=0,
                     max_tokens=20,
@@ -286,7 +323,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             else:
                 # text completion model
                 client.completions.create(
-                    prompt='ping',
+                    prompt="ping",
                     model=model,
                     temperature=0,
                     max_tokens=20,
@@ -313,11 +350,11 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         # get all remote models
         remote_models = client.models.list()
 
-        fine_tune_models = [model for model in remote_models if model.id.startswith('ft:')]
+        fine_tune_models = [model for model in remote_models if model.id.startswith("ft:")]
 
         ai_model_entities = []
         for model in fine_tune_models:
-            base_model = model.id.split(':')[1]
+            base_model = model.id.split(":")[1]
 
             base_model_schema = None
             for predefined_model_name, predefined_model in predefined_models_map.items():
@@ -329,30 +366,29 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
             ai_model_entity = AIModelEntity(
                 model=model.id,
-                label=I18nObject(
-                    zh_Hans=model.id,
-                    en_US=model.id
-                ),
+                label=I18nObject(zh_Hans=model.id, en_US=model.id),
                 model_type=ModelType.LLM,
                 features=base_model_schema.features,
                 fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
                 model_properties=base_model_schema.model_properties,
                 parameter_rules=base_model_schema.parameter_rules,
-                pricing=PriceConfig(
-                    input=0.003,
-                    output=0.006,
-                    unit=0.001,
-                    currency='USD'
-                )
+                pricing=PriceConfig(input=0.003, output=0.006, unit=0.001, currency="USD"),
             )
 
             ai_model_entities.append(ai_model_entity)
 
         return ai_model_entities
 
-    def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[list[str]] = None,
-                  stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke llm completion model
 
@@ -374,23 +410,17 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         extra_model_kwargs = {}
 
         if stop:
-            extra_model_kwargs['stop'] = stop
+            extra_model_kwargs["stop"] = stop
 
         if user:
-            extra_model_kwargs['user'] = user
+            extra_model_kwargs["user"] = user
 
         if stream:
-            extra_model_kwargs['stream_options'] = {
-                "include_usage": True
-            }
-        
+            extra_model_kwargs["stream_options"] = {"include_usage": True}
+
         # text completion model
         response = client.completions.create(
-            prompt=prompt_messages[0].content,
-            model=model,
-            stream=stream,
-            **model_parameters,
-            **extra_model_kwargs
+            prompt=prompt_messages[0].content, model=model, stream=stream, **model_parameters, **extra_model_kwargs
         )
 
         if stream:
@@ -398,8 +428,9 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         return self._handle_generate_response(model, credentials, response, prompt_messages)
 
-    def _handle_generate_response(self, model: str, credentials: dict, response: Completion,
-                                  prompt_messages: list[PromptMessage]) -> LLMResult:
+    def _handle_generate_response(
+        self, model: str, credentials: dict, response: Completion, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
         """
         Handle llm completion response
 
@@ -412,9 +443,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         assistant_text = response.choices[0].text
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_text
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_text)
 
         # calculate num tokens
         if response.usage:
@@ -440,8 +469,9 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         return result
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: Stream[Completion],
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, response: Stream[Completion], prompt_messages: list[PromptMessage]
+    ) -> Generator:
         """
         Handle llm completion stream response
 
@@ -451,7 +481,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: llm response chunk generator result
         """
-        full_text = ''
+        full_text = ""
         prompt_tokens = 0
         completion_tokens = 0
 
@@ -460,8 +490,8 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             prompt_messages=prompt_messages,
             delta=LLMResultChunkDelta(
                 index=0,
-                message=AssistantPromptMessage(content=''),
-            )
+                message=AssistantPromptMessage(content=""),
+            ),
         )
 
         for chunk in response:
@@ -474,14 +504,12 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
             delta = chunk.choices[0]
 
-            if delta.finish_reason is None and (delta.text is None or delta.text == ''):
+            if delta.finish_reason is None and (delta.text is None or delta.text == ""):
                 continue
 
             # transform assistant message to prompt message
-            text = delta.text if delta.text else ''
-            assistant_prompt_message = AssistantPromptMessage(
-                content=text
-            )
+            text = delta.text or ""
+            assistant_prompt_message = AssistantPromptMessage(content=text)
 
             full_text += text
 
@@ -494,7 +522,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                         index=delta.index,
                         message=assistant_prompt_message,
                         finish_reason=delta.finish_reason,
-                    )
+                    ),
                 )
             else:
                 yield LLMResultChunk(
@@ -504,7 +532,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=delta.index,
                         message=assistant_prompt_message,
-                    )
+                    ),
                 )
 
         if not prompt_tokens:
@@ -520,10 +548,17 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         yield final_chunk
 
-    def _chat_generate(self, model: str, credentials: dict,
-                       prompt_messages: list[PromptMessage], model_parameters: dict,
-                       tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                       stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _chat_generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke llm chat model
 
@@ -552,7 +587,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 try:
                     schema = json.loads(json_schema)
                 except:
-                    raise ValueError(f"not currect json_schema format: {json_schema}")
+                    raise ValueError(f"not correct json_schema format: {json_schema}")
                 model_parameters.pop("json_schema")
                 model_parameters["response_format"] = {"type": "json_schema", "json_schema": schema}
             else:
@@ -562,26 +597,34 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         if tools:
             # extra_model_kwargs['tools'] = [helper.dump_model(PromptMessageFunction(function=tool)) for tool in tools]
-            extra_model_kwargs['functions'] = [{
-                "name": tool.name,
-                "description": tool.description,
-                "parameters": tool.parameters
-            } for tool in tools]
+            extra_model_kwargs["functions"] = [
+                {"name": tool.name, "description": tool.description, "parameters": tool.parameters} for tool in tools
+            ]
 
         if stop:
-            extra_model_kwargs['stop'] = stop
+            extra_model_kwargs["stop"] = stop
 
         if user:
-            extra_model_kwargs['user'] = user
+            extra_model_kwargs["user"] = user
 
         if stream:
-            extra_model_kwargs['stream_options'] = {
-                'include_usage': True
-            }
+            extra_model_kwargs["stream_options"] = {"include_usage": True}
 
         # clear illegal prompt messages
         prompt_messages = self._clear_illegal_prompt_messages(model, prompt_messages)
 
+        block_as_stream = False
+        if model.startswith("o1"):
+            if stream:
+                block_as_stream = True
+                stream = False
+
+                if "stream_options" in extra_model_kwargs:
+                    del extra_model_kwargs["stream_options"]
+
+            if "stop" in extra_model_kwargs:
+                del extra_model_kwargs["stop"]
+
         # chat model
         response = client.chat.completions.create(
             messages=[self._convert_prompt_message_to_dict(m) for m in prompt_messages],
@@ -594,11 +637,56 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         if stream:
             return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages, tools)
 
-        return self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
+        block_result = self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
 
-    def _handle_chat_generate_response(self, model: str, credentials: dict, response: ChatCompletion,
-                                       prompt_messages: list[PromptMessage],
-                                       tools: Optional[list[PromptMessageTool]] = None) -> LLMResult:
+        if block_as_stream:
+            return self._handle_chat_block_as_stream_response(block_result, prompt_messages, stop)
+
+        return block_result
+
+    def _handle_chat_block_as_stream_response(
+        self,
+        block_result: LLMResult,
+        prompt_messages: list[PromptMessage],
+        stop: Optional[list[str]] = None,
+    ) -> Generator[LLMResultChunk, None, None]:
+        """
+        Handle llm chat response
+
+        :param model: model name
+        :param credentials: credentials
+        :param response: response
+        :param prompt_messages: prompt messages
+        :param tools: tools for tool calling
+        :param stop: stop words
+        :return: llm response chunk generator
+        """
+        text = block_result.message.content
+        text = cast(str, text)
+
+        if stop:
+            text = self.enforce_stop_tokens(text, stop)
+
+        yield LLMResultChunk(
+            model=block_result.model,
+            prompt_messages=prompt_messages,
+            system_fingerprint=block_result.system_fingerprint,
+            delta=LLMResultChunkDelta(
+                index=0,
+                message=AssistantPromptMessage(content=text),
+                finish_reason="stop",
+                usage=block_result.usage,
+            ),
+        )
+
+    def _handle_chat_generate_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: ChatCompletion,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> LLMResult:
         """
         Handle llm chat response
 
@@ -619,10 +707,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         tool_calls = [function_call] if function_call else []
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_message.content,
-            tool_calls=tool_calls
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_message.content, tool_calls=tool_calls)
 
         # calculate num tokens
         if response.usage:
@@ -648,9 +733,14 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         return response
 
-    def _handle_chat_generate_stream_response(self, model: str, credentials: dict, response: Stream[ChatCompletionChunk],
-                                              prompt_messages: list[PromptMessage],
-                                              tools: Optional[list[PromptMessageTool]] = None) -> Generator:
+    def _handle_chat_generate_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: Stream[ChatCompletionChunk],
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> Generator:
         """
         Handle llm chat stream response
 
@@ -660,7 +750,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         :param tools: tools for tool calling
         :return: llm response chunk generator
         """
-        full_assistant_content = ''
+        full_assistant_content = ""
         delta_assistant_message_function_call_storage: ChoiceDeltaFunctionCall = None
         prompt_tokens = 0
         completion_tokens = 0
@@ -670,8 +760,8 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             prompt_messages=prompt_messages,
             delta=LLMResultChunkDelta(
                 index=0,
-                message=AssistantPromptMessage(content=''),
-            )
+                message=AssistantPromptMessage(content=""),
+            ),
         )
 
         for chunk in response:
@@ -685,8 +775,11 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             delta = chunk.choices[0]
             has_finish_reason = delta.finish_reason is not None
 
-            if not has_finish_reason and (delta.delta.content is None or delta.delta.content == '') and \
-                delta.delta.function_call is None:
+            if (
+                not has_finish_reason
+                and (delta.delta.content is None or delta.delta.content == "")
+                and delta.delta.function_call is None
+            ):
                 continue
 
             # assistant_message_tool_calls = delta.delta.tool_calls
@@ -708,7 +801,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                     # start of stream function call
                     delta_assistant_message_function_call_storage = assistant_message_function_call
                     if delta_assistant_message_function_call_storage.arguments is None:
-                        delta_assistant_message_function_call_storage.arguments = ''
+                        delta_assistant_message_function_call_storage.arguments = ""
                     if not has_finish_reason:
                         continue
 
@@ -719,12 +812,9 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 final_tool_calls.extend(tool_calls)
 
             # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else '',
-                tool_calls=tool_calls
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
 
-            full_assistant_content += delta.delta.content if delta.delta.content else ''
+            full_assistant_content += delta.delta.content or ""
 
             if has_finish_reason:
                 final_chunk = LLMResultChunk(
@@ -735,7 +825,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                         index=delta.index,
                         message=assistant_prompt_message,
                         finish_reason=delta.finish_reason,
-                    )
+                    ),
                 )
             else:
                 yield LLMResultChunk(
@@ -745,7 +835,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=delta.index,
                         message=assistant_prompt_message,
-                    )
+                    ),
                 )
 
         if not prompt_tokens:
@@ -753,8 +843,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         if not completion_tokens:
             full_assistant_prompt_message = AssistantPromptMessage(
-                content=full_assistant_content,
-                tool_calls=final_tool_calls
+                content=full_assistant_content, tool_calls=final_tool_calls
             )
             completion_tokens = self._num_tokens_from_messages(model, [full_assistant_prompt_message])
 
@@ -764,9 +853,9 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         yield final_chunk
 
-    def _extract_response_tool_calls(self,
-                                     response_tool_calls: list[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]) \
-            -> list[AssistantPromptMessage.ToolCall]:
+    def _extract_response_tool_calls(
+        self, response_tool_calls: list[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]
+    ) -> list[AssistantPromptMessage.ToolCall]:
         """
         Extract tool calls from response
 
@@ -777,21 +866,19 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         if response_tool_calls:
             for response_tool_call in response_tool_calls:
                 function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                    name=response_tool_call.function.name,
-                    arguments=response_tool_call.function.arguments
+                    name=response_tool_call.function.name, arguments=response_tool_call.function.arguments
                 )
 
                 tool_call = AssistantPromptMessage.ToolCall(
-                    id=response_tool_call.id,
-                    type=response_tool_call.type,
-                    function=function
+                    id=response_tool_call.id, type=response_tool_call.type, function=function
                 )
                 tool_calls.append(tool_call)
 
         return tool_calls
 
-    def _extract_response_function_call(self, response_function_call: FunctionCall | ChoiceDeltaFunctionCall) \
-            -> AssistantPromptMessage.ToolCall:
+    def _extract_response_function_call(
+        self, response_function_call: FunctionCall | ChoiceDeltaFunctionCall
+    ) -> AssistantPromptMessage.ToolCall:
         """
         Extract function call from response
 
@@ -801,14 +888,11 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         tool_call = None
         if response_function_call:
             function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                name=response_function_call.name,
-                arguments=response_function_call.arguments
+                name=response_function_call.name, arguments=response_function_call.arguments
             )
 
             tool_call = AssistantPromptMessage.ToolCall(
-                id=response_function_call.name,
-                type="function",
-                function=function
+                id=response_function_call.name, type="function", function=function
             )
 
         return tool_call
@@ -821,7 +905,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: cleaned prompt messages
         """
-        checklist = ['gpt-4-turbo', 'gpt-4-turbo-2024-04-09']
+        checklist = ["gpt-4-turbo", "gpt-4-turbo-2024-04-09"]
 
         if model in checklist:
             # count how many user messages are there
@@ -830,11 +914,30 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 for prompt_message in prompt_messages:
                     if isinstance(prompt_message, UserPromptMessage):
                         if isinstance(prompt_message.content, list):
-                            prompt_message.content = '\n'.join([
-                                item.data if item.type == PromptMessageContentType.TEXT else
-                                '[IMAGE]' if item.type == PromptMessageContentType.IMAGE else ''
-                                for item in prompt_message.content
-                            ])
+                            prompt_message.content = "\n".join(
+                                [
+                                    item.data
+                                    if item.type == PromptMessageContentType.TEXT
+                                    else "[IMAGE]"
+                                    if item.type == PromptMessageContentType.IMAGE
+                                    else ""
+                                    for item in prompt_message.content
+                                ]
+                            )
+
+        if model.startswith("o1"):
+            system_message_count = len([m for m in prompt_messages if isinstance(m, SystemPromptMessage)])
+            if system_message_count > 0:
+                new_prompt_messages = []
+                for prompt_message in prompt_messages:
+                    if isinstance(prompt_message, SystemPromptMessage):
+                        prompt_message = UserPromptMessage(
+                            content=prompt_message.content,
+                            name=prompt_message.name,
+                        )
+
+                    new_prompt_messages.append(prompt_message)
+                prompt_messages = new_prompt_messages
 
         return prompt_messages
 
@@ -851,19 +954,13 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
                         message_content = cast(TextPromptMessageContent, message_content)
-                        sub_message_dict = {
-                            "type": "text",
-                            "text": message_content.data
-                        }
+                        sub_message_dict = {"type": "text", "text": message_content.data}
                         sub_messages.append(sub_message_dict)
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
                         sub_message_dict = {
                             "type": "image_url",
-                            "image_url": {
-                                "url": message_content.data,
-                                "detail": message_content.detail.value
-                            }
+                            "image_url": {"url": message_content.data, "detail": message_content.detail.value},
                         }
                         sub_messages.append(sub_message_dict)
 
@@ -889,11 +986,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             #     "content": message.content,
             #     "tool_call_id": message.tool_call_id
             # }
-            message_dict = {
-                "role": "function",
-                "content": message.content,
-                "name": message.tool_call_id
-            }
+            message_dict = {"role": "function", "content": message.content, "name": message.tool_call_id}
         else:
             raise ValueError(f"Got unknown type {message}")
 
@@ -902,8 +995,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         return message_dict
 
-    def _num_tokens_from_string(self, model: str, text: str,
-                                tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_string(self, model: str, text: str, tools: Optional[list[PromptMessageTool]] = None) -> int:
         """
         Calculate num tokens for text completion model with tiktoken package.
 
@@ -924,16 +1016,17 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         return num_tokens
 
-    def _num_tokens_from_messages(self, model: str, messages: list[PromptMessage],
-                                  tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_messages(
+        self, model: str, messages: list[PromptMessage], tools: Optional[list[PromptMessageTool]] = None
+    ) -> int:
         """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
 
         Official documentation: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
-        if model.startswith('ft:'):
-            model = model.split(':')[1]
+        if model.startswith("ft:"):
+            model = model.split(":")[1]
 
         # Currently, we can use gpt4o to calculate chatgpt-4o-latest's token.
-        if model == "chatgpt-4o-latest":
+        if model == "chatgpt-4o-latest" or model.startswith("o1"):
             model = "gpt-4o"
 
         try:
@@ -948,7 +1041,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             tokens_per_message = 4
             # if there's a name, the role is omitted
             tokens_per_name = -1
-        elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4"):
+        elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4") or model.startswith("o1"):
             tokens_per_message = 3
             tokens_per_name = 1
         else:
@@ -969,10 +1062,10 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 #  which need to download the image and then get the resolution for calculation,
                 #  and will increase the request delay
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
 
                     value = text
 
@@ -1011,37 +1104,37 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         """
         num_tokens = 0
         for tool in tools:
-            num_tokens += len(encoding.encode('type'))
-            num_tokens += len(encoding.encode('function'))
+            num_tokens += len(encoding.encode("type"))
+            num_tokens += len(encoding.encode("function"))
 
             # calculate num tokens for function object
-            num_tokens += len(encoding.encode('name'))
+            num_tokens += len(encoding.encode("name"))
             num_tokens += len(encoding.encode(tool.name))
-            num_tokens += len(encoding.encode('description'))
+            num_tokens += len(encoding.encode("description"))
             num_tokens += len(encoding.encode(tool.description))
             parameters = tool.parameters
-            num_tokens += len(encoding.encode('parameters'))
-            if 'title' in parameters:
-                num_tokens += len(encoding.encode('title'))
+            num_tokens += len(encoding.encode("parameters"))
+            if "title" in parameters:
+                num_tokens += len(encoding.encode("title"))
                 num_tokens += len(encoding.encode(parameters.get("title")))
-            num_tokens += len(encoding.encode('type'))
+            num_tokens += len(encoding.encode("type"))
             num_tokens += len(encoding.encode(parameters.get("type")))
-            if 'properties' in parameters:
-                num_tokens += len(encoding.encode('properties'))
-                for key, value in parameters.get('properties').items():
+            if "properties" in parameters:
+                num_tokens += len(encoding.encode("properties"))
+                for key, value in parameters.get("properties").items():
                     num_tokens += len(encoding.encode(key))
                     for field_key, field_value in value.items():
                         num_tokens += len(encoding.encode(field_key))
-                        if field_key == 'enum':
+                        if field_key == "enum":
                             for enum_field in field_value:
                                 num_tokens += 3
                                 num_tokens += len(encoding.encode(enum_field))
                         else:
                             num_tokens += len(encoding.encode(field_key))
                             num_tokens += len(encoding.encode(str(field_value)))
-            if 'required' in parameters:
-                num_tokens += len(encoding.encode('required'))
-                for required_field in parameters['required']:
+            if "required" in parameters:
+                num_tokens += len(encoding.encode("required"))
+                for required_field in parameters["required"]:
                     num_tokens += 3
                     num_tokens += len(encoding.encode(required_field))
 
@@ -1049,26 +1142,26 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            OpenAI supports fine-tuning of their models. This method returns the schema of the base model
-            but renamed to the fine-tuned model name.
+        OpenAI supports fine-tuning of their models. This method returns the schema of the base model
+        but renamed to the fine-tuned model name.
 
-            :param model: model name
-            :param credentials: credentials
+        :param model: model name
+        :param credentials: credentials
 
-            :return: model schema
+        :return: model schema
         """
-        if not model.startswith('ft:'):
+        if not model.startswith("ft:"):
             base_model = model
         else:
             # get base_model
-            base_model = model.split(':')[1]
+            base_model = model.split(":")[1]
 
         # get model schema
         models = self.predefined_models()
         model_map = {model.model: model for model in models}
         if base_model not in model_map:
-            raise ValueError(f'Base model {base_model} not found')
-        
+            raise ValueError(f"Base model {base_model} not found")
+
         base_model_schema = model_map[base_model]
 
         base_model_schema_features = base_model_schema.features or []
@@ -1077,16 +1170,13 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                zh_Hans=model,
-                en_US=model
-            ),
+            label=I18nObject(zh_Hans=model, en_US=model),
             model_type=ModelType.LLM,
             features=list(base_model_schema_features),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties=dict(base_model_schema_model_properties.items()),
             parameter_rules=list(base_model_schema_parameters_rules),
-            pricing=base_model_schema.pricing    
+            pricing=base_model_schema.pricing,
         )
 
-        return entity
\ No newline at end of file
+        return entity
diff --git a/api/core/model_runtime/model_providers/openai/llm/o1-mini-2024-09-12.yaml b/api/core/model_runtime/model_providers/openai/llm/o1-mini-2024-09-12.yaml
new file mode 100644
index 0000000000..0ade7f8ded
--- /dev/null
+++ b/api/core/model_runtime/model_providers/openai/llm/o1-mini-2024-09-12.yaml
@@ -0,0 +1,33 @@
+model: o1-mini-2024-09-12
+label:
+  zh_Hans: o1-mini-2024-09-12
+  en_US: o1-mini-2024-09-12
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    default: 65536
+    min: 1
+    max: 65536
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '3.00'
+  output: '12.00'
+  unit: '0.000001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/openai/llm/o1-mini.yaml b/api/core/model_runtime/model_providers/openai/llm/o1-mini.yaml
new file mode 100644
index 0000000000..60816c5d1e
--- /dev/null
+++ b/api/core/model_runtime/model_providers/openai/llm/o1-mini.yaml
@@ -0,0 +1,33 @@
+model: o1-mini
+label:
+  zh_Hans: o1-mini
+  en_US: o1-mini
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    default: 65536
+    min: 1
+    max: 65536
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '3.00'
+  output: '12.00'
+  unit: '0.000001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/openai/llm/o1-preview-2024-09-12.yaml b/api/core/model_runtime/model_providers/openai/llm/o1-preview-2024-09-12.yaml
new file mode 100644
index 0000000000..c9da96f611
--- /dev/null
+++ b/api/core/model_runtime/model_providers/openai/llm/o1-preview-2024-09-12.yaml
@@ -0,0 +1,33 @@
+model: o1-preview-2024-09-12
+label:
+  zh_Hans: o1-preview-2024-09-12
+  en_US: o1-preview-2024-09-12
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    default: 32768
+    min: 1
+    max: 32768
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '15.00'
+  output: '60.00'
+  unit: '0.000001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/openai/llm/o1-preview.yaml b/api/core/model_runtime/model_providers/openai/llm/o1-preview.yaml
new file mode 100644
index 0000000000..c83874b765
--- /dev/null
+++ b/api/core/model_runtime/model_providers/openai/llm/o1-preview.yaml
@@ -0,0 +1,33 @@
+model: o1-preview
+label:
+  zh_Hans: o1-preview
+  en_US: o1-preview
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    default: 32768
+    min: 1
+    max: 32768
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '15.00'
+  output: '60.00'
+  unit: '0.000001'
+  currency: USD
diff --git a/api/core/model_runtime/model_providers/openai/moderation/moderation.py b/api/core/model_runtime/model_providers/openai/moderation/moderation.py
index b1d0e57ad2..619044d808 100644
--- a/api/core/model_runtime/model_providers/openai/moderation/moderation.py
+++ b/api/core/model_runtime/model_providers/openai/moderation/moderation.py
@@ -14,9 +14,7 @@ class OpenAIModerationModel(_CommonOpenAI, ModerationModel):
     Model class for OpenAI text moderation model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                text: str, user: Optional[str] = None) \
-            -> bool:
+    def _invoke(self, model: str, credentials: dict, text: str, user: Optional[str] = None) -> bool:
         """
         Invoke moderation model
 
@@ -34,10 +32,10 @@ class OpenAIModerationModel(_CommonOpenAI, ModerationModel):
 
         # chars per chunk
         length = self._get_max_characters_per_chunk(model, credentials)
-        text_chunks = [text[i:i + length] for i in range(0, len(text), length)]
+        text_chunks = [text[i : i + length] for i in range(0, len(text), length)]
 
         max_text_chunks = self._get_max_chunks(model, credentials)
-        chunks = [text_chunks[i:i + max_text_chunks] for i in range(0, len(text_chunks), max_text_chunks)]
+        chunks = [text_chunks[i : i + max_text_chunks] for i in range(0, len(text_chunks), max_text_chunks)]
 
         for text_chunk in chunks:
             moderation_result = self._moderation_invoke(model=model, client=client, texts=text_chunk)
@@ -65,7 +63,7 @@ class OpenAIModerationModel(_CommonOpenAI, ModerationModel):
             self._moderation_invoke(
                 model=model,
                 client=client,
-                texts=['ping'],
+                texts=["ping"],
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
diff --git a/api/core/model_runtime/model_providers/openai/openai.py b/api/core/model_runtime/model_providers/openai/openai.py
index 66efd4797f..175d7db73c 100644
--- a/api/core/model_runtime/model_providers/openai/openai.py
+++ b/api/core/model_runtime/model_providers/openai/openai.py
@@ -9,7 +9,6 @@ logger = logging.getLogger(__name__)
 
 
 class OpenAIProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: Mapping) -> None:
         """
         Validate provider credentials
@@ -22,12 +21,9 @@ class OpenAIProvider(ModelProvider):
 
             # Use `gpt-3.5-turbo` model for validate,
             # no matter what model you pass in, text completion model or chat model
-            model_instance.validate_credentials(
-                model='gpt-3.5-turbo',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="gpt-3.5-turbo", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/openai/speech2text/speech2text.py b/api/core/model_runtime/model_providers/openai/speech2text/speech2text.py
index efbdd054f9..18f97e45f3 100644
--- a/api/core/model_runtime/model_providers/openai/speech2text/speech2text.py
+++ b/api/core/model_runtime/model_providers/openai/speech2text/speech2text.py
@@ -12,9 +12,7 @@ class OpenAISpeech2TextModel(_CommonOpenAI, Speech2TextModel):
     Model class for OpenAI Speech to text model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                file: IO[bytes], user: Optional[str] = None) \
-            -> str:
+    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
         """
         Invoke speech2text model
 
@@ -37,7 +35,7 @@ class OpenAISpeech2TextModel(_CommonOpenAI, Speech2TextModel):
         try:
             audio_file_path = self._get_demo_file_path()
 
-            with open(audio_file_path, 'rb') as audio_file:
+            with open(audio_file_path, "rb") as audio_file:
                 self._speech2text_invoke(model, credentials, audio_file)
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
diff --git a/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py
index e23a2edf87..535d8388bc 100644
--- a/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py
@@ -18,9 +18,9 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
     Model class for OpenAI text embedding model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -37,9 +37,9 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
 
         extra_model_kwargs = {}
         if user:
-            extra_model_kwargs['user'] = user
+            extra_model_kwargs["user"] = user
 
-        extra_model_kwargs['encoding_format'] = 'base64'
+        extra_model_kwargs["encoding_format"] = "base64"
 
         # get model properties
         context_size = self._get_context_size(model, credentials)
@@ -56,11 +56,9 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
             enc = tiktoken.get_encoding("cl100k_base")
 
         for i, text in enumerate(texts):
-            token = enc.encode(
-                text
-            )
+            token = enc.encode(text)
             for j in range(0, len(token), context_size):
-                tokens += [token[j: j + context_size]]
+                tokens += [token[j : j + context_size]]
                 indices += [i]
 
         batched_embeddings = []
@@ -69,10 +67,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
         for i in _iter:
             # call embedding model
             embeddings_batch, embedding_used_tokens = self._embedding_invoke(
-                model=model,
-                client=client,
-                texts=tokens[i: i + max_chunks],
-                extra_model_kwargs=extra_model_kwargs
+                model=model, client=client, texts=tokens[i : i + max_chunks], extra_model_kwargs=extra_model_kwargs
             )
 
             used_tokens += embedding_used_tokens
@@ -88,10 +83,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
             _result = results[i]
             if len(_result) == 0:
                 embeddings_batch, embedding_used_tokens = self._embedding_invoke(
-                    model=model,
-                    client=client,
-                    texts="",
-                    extra_model_kwargs=extra_model_kwargs
+                    model=model, client=client, texts="", extra_model_kwargs=extra_model_kwargs
                 )
 
                 used_tokens += embedding_used_tokens
@@ -101,17 +93,9 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
             embeddings[i] = (average / np.linalg.norm(average)).tolist()
 
         # calc usage
-        usage = self._calc_response_usage(
-            model=model,
-            credentials=credentials,
-            tokens=used_tokens
-        )
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
 
-        return TextEmbeddingResult(
-            embeddings=embeddings,
-            usage=usage,
-            model=model
-        )
+        return TextEmbeddingResult(embeddings=embeddings, usage=usage, model=model)
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
@@ -152,17 +136,13 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
             client = OpenAI(**credentials_kwargs)
 
             # call embedding model
-            self._embedding_invoke(
-                model=model,
-                client=client,
-                texts=['ping'],
-                extra_model_kwargs={}
-            )
+            self._embedding_invoke(model=model, client=client, texts=["ping"], extra_model_kwargs={})
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _embedding_invoke(self, model: str, client: OpenAI, texts: Union[list[str], str],
-                          extra_model_kwargs: dict) -> tuple[list[list[float]], int]:
+    def _embedding_invoke(
+        self, model: str, client: OpenAI, texts: Union[list[str], str], extra_model_kwargs: dict
+    ) -> tuple[list[list[float]], int]:
         """
         Invoke embedding model
 
@@ -179,10 +159,12 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
             **extra_model_kwargs,
         )
 
-        if 'encoding_format' in extra_model_kwargs and extra_model_kwargs['encoding_format'] == 'base64':
+        if "encoding_format" in extra_model_kwargs and extra_model_kwargs["encoding_format"] == "base64":
             # decode base64 embedding
-            return ([list(np.frombuffer(base64.b64decode(data.embedding), dtype="float32")) for data in response.data],
-                    response.usage.total_tokens)
+            return (
+                [list(np.frombuffer(base64.b64decode(data.embedding), dtype="float32")) for data in response.data],
+                response.usage.total_tokens,
+            )
 
         return [data.embedding for data in response.data], response.usage.total_tokens
 
@@ -197,10 +179,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -211,7 +190,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/openai/tts/tts.py b/api/core/model_runtime/model_providers/openai/tts/tts.py
index afa5d4b88a..a14c91639b 100644
--- a/api/core/model_runtime/model_providers/openai/tts/tts.py
+++ b/api/core/model_runtime/model_providers/openai/tts/tts.py
@@ -14,8 +14,9 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
     Model class for OpenAI Speech to text model.
     """
 
-    def _invoke(self, model: str, tenant_id: str, credentials: dict,
-                content_text: str, voice: str, user: Optional[str] = None) -> any:
+    def _invoke(
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ) -> any:
         """
         _invoke text2speech model
 
@@ -28,14 +29,12 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
         :return: text translated to audio file
         """
 
-        if not voice or voice not in [d['value'] for d in
-                                      self.get_tts_model_voices(model=model, credentials=credentials)]:
+        if not voice or voice not in [
+            d["value"] for d in self.get_tts_model_voices(model=model, credentials=credentials)
+        ]:
             voice = self._get_model_default_voice(model, credentials)
         # if streaming:
-        return self._tts_invoke_streaming(model=model,
-                                          credentials=credentials,
-                                          content_text=content_text,
-                                          voice=voice)
+        return self._tts_invoke_streaming(model=model, credentials=credentials, content_text=content_text, voice=voice)
 
     def validate_credentials(self, model: str, credentials: dict, user: Optional[str] = None) -> None:
         """
@@ -50,14 +49,13 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
             self._tts_invoke_streaming(
                 model=model,
                 credentials=credentials,
-                content_text='Hello Dify!',
+                content_text="Hello Dify!",
                 voice=self._get_model_default_voice(model, credentials),
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str,
-                              voice: str) -> any:
+    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
         """
         _tts_invoke_streaming text2speech model
 
@@ -71,31 +69,38 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
             # doc: https://platform.openai.com/docs/guides/text-to-speech
             credentials_kwargs = self._to_credential_kwargs(credentials)
             client = OpenAI(**credentials_kwargs)
-            model_support_voice = [x.get("value") for x in
-                                   self.get_tts_model_voices(model=model, credentials=credentials)]
+            model_support_voice = [
+                x.get("value") for x in self.get_tts_model_voices(model=model, credentials=credentials)
+            ]
             if not voice or voice not in model_support_voice:
                 voice = self._get_model_default_voice(model, credentials)
             word_limit = self._get_model_word_limit(model, credentials)
             if len(content_text) > word_limit:
                 sentences = self._split_text_into_sentences(content_text, max_length=word_limit)
                 executor = concurrent.futures.ThreadPoolExecutor(max_workers=min(3, len(sentences)))
-                futures = [executor.submit(client.audio.speech.with_streaming_response.create, model=model,
-                                           response_format="mp3",
-                                           input=sentences[i], voice=voice) for i in range(len(sentences))]
-                for index, future in enumerate(futures):
-                    yield from future.result().__enter__().iter_bytes(1024)
+                futures = [
+                    executor.submit(
+                        client.audio.speech.with_streaming_response.create,
+                        model=model,
+                        response_format="mp3",
+                        input=sentences[i],
+                        voice=voice,
+                    )
+                    for i in range(len(sentences))
+                ]
+                for future in futures:
+                    yield from future.result().__enter__().iter_bytes(1024)  # noqa:PLC2801
 
             else:
-                response = client.audio.speech.with_streaming_response.create(model=model, voice=voice,
-                                                                              response_format="mp3",
-                                                                              input=content_text.strip())
+                response = client.audio.speech.with_streaming_response.create(
+                    model=model, voice=voice, response_format="mp3", input=content_text.strip()
+                )
 
-                yield from response.__enter__().iter_bytes(1024)
+                yield from response.__enter__().iter_bytes(1024)  # noqa:PLC2801
         except Exception as ex:
             raise InvokeBadRequestError(str(ex))
 
-    def _process_sentence(self, sentence: str, model: str,
-                          voice, credentials: dict):
+    def _process_sentence(self, sentence: str, model: str, voice, credentials: dict):
         """
         _tts_invoke openai text2speech model api
 
diff --git a/api/core/model_runtime/model_providers/openai_api_compatible/_common.py b/api/core/model_runtime/model_providers/openai_api_compatible/_common.py
index 51950ca377..1234e44f80 100644
--- a/api/core/model_runtime/model_providers/openai_api_compatible/_common.py
+++ b/api/core/model_runtime/model_providers/openai_api_compatible/_common.py
@@ -1,4 +1,3 @@
-
 import requests
 
 from core.model_runtime.errors.invoke import (
@@ -11,7 +10,7 @@ from core.model_runtime.errors.invoke import (
 )
 
 
-class _CommonOAI_API_Compat:
+class _CommonOaiApiCompat:
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         """
@@ -35,10 +34,10 @@ class _CommonOAI_API_Compat:
             ],
             InvokeServerUnavailableError: [
                 requests.exceptions.ConnectionError,  # Engine Overloaded
-                requests.exceptions.HTTPError  # Server Error
+                requests.exceptions.HTTPError,  # Server Error
             ],
             InvokeConnectionError: [
                 requests.exceptions.ConnectTimeout,  # Timeout
-                requests.exceptions.ReadTimeout  # Timeout
-            ]
-        }
\ No newline at end of file
+                requests.exceptions.ReadTimeout,  # Timeout
+            ],
+        }
diff --git a/api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py b/api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py
index 6279125f46..5a8a754f72 100644
--- a/api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py
+++ b/api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py
@@ -35,22 +35,28 @@ from core.model_runtime.entities.model_entities import (
 from core.model_runtime.errors.invoke import InvokeError
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOAI_API_Compat
+from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOaiApiCompat
 from core.model_runtime.utils import helper
 
 logger = logging.getLogger(__name__)
 
 
-class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
+class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
     """
     Model class for OpenAI large language model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -77,8 +83,13 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             user=user,
         )
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -92,100 +103,93 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
-        Validate model credentials using requests to ensure compatibility with all providers following OpenAI's API standard.
+        Validate model credentials using requests to ensure compatibility with all providers following
+         OpenAI's API standard.
 
         :param model: model name
         :param credentials: model credentials
         :return:
         """
         try:
-            headers = {
-                'Content-Type': 'application/json'
-            }
+            headers = {"Content-Type": "application/json"}
 
-            api_key = credentials.get('api_key')
+            api_key = credentials.get("api_key")
             if api_key:
                 headers["Authorization"] = f"Bearer {api_key}"
 
-            endpoint_url = credentials['endpoint_url']
-            if not endpoint_url.endswith('/'):
-                endpoint_url += '/'
+            endpoint_url = credentials["endpoint_url"]
+            if not endpoint_url.endswith("/"):
+                endpoint_url += "/"
 
             # prepare the payload for a simple ping to the model
-            data = {
-                'model': model,
-                'max_tokens': 5
-            }
+            data = {"model": model, "max_tokens": 5}
 
-            completion_type = LLMMode.value_of(credentials['mode'])
+            completion_type = LLMMode.value_of(credentials["mode"])
 
             if completion_type is LLMMode.CHAT:
-                data['messages'] = [
-                    {
-                        "role": "user",
-                        "content": "ping"
-                    },
+                data["messages"] = [
+                    {"role": "user", "content": "ping"},
                 ]
-                endpoint_url = urljoin(endpoint_url, 'chat/completions')
+                endpoint_url = urljoin(endpoint_url, "chat/completions")
             elif completion_type is LLMMode.COMPLETION:
-                data['prompt'] = 'ping'
-                endpoint_url = urljoin(endpoint_url, 'completions')
+                data["prompt"] = "ping"
+                endpoint_url = urljoin(endpoint_url, "completions")
             else:
                 raise ValueError("Unsupported completion type for model configuration.")
 
             # send a post request to validate the credentials
-            response = requests.post(
-                endpoint_url,
-                headers=headers,
-                json=data,
-                timeout=(10, 300)
-            )
+            response = requests.post(endpoint_url, headers=headers, json=data, timeout=(10, 300))
 
             if response.status_code != 200:
                 raise CredentialsValidateFailedError(
-                    f'Credentials validation failed with status code {response.status_code}')
+                    f"Credentials validation failed with status code {response.status_code}"
+                )
 
             try:
                 json_result = response.json()
             except json.JSONDecodeError as e:
-                raise CredentialsValidateFailedError('Credentials validation failed: JSON decode error')
+                raise CredentialsValidateFailedError("Credentials validation failed: JSON decode error")
 
-            if (completion_type is LLMMode.CHAT and json_result.get('object','') == ''):
-                json_result['object'] = 'chat.completion'
-            elif (completion_type is LLMMode.COMPLETION and json_result.get('object','') == ''):
-                json_result['object'] = 'text_completion'
+            if completion_type is LLMMode.CHAT and json_result.get("object", "") == "":
+                json_result["object"] = "chat.completion"
+            elif completion_type is LLMMode.COMPLETION and json_result.get("object", "") == "":
+                json_result["object"] = "text_completion"
 
-            if (completion_type is LLMMode.CHAT
-                    and ('object' not in json_result or json_result['object'] != 'chat.completion')):
+            if completion_type is LLMMode.CHAT and (
+                "object" not in json_result or json_result["object"] != "chat.completion"
+            ):
                 raise CredentialsValidateFailedError(
-                    'Credentials validation failed: invalid response object, must be \'chat.completion\'')
-            elif (completion_type is LLMMode.COMPLETION
-                  and ('object' not in json_result or json_result['object'] != 'text_completion')):
+                    "Credentials validation failed: invalid response object, must be 'chat.completion'"
+                )
+            elif completion_type is LLMMode.COMPLETION and (
+                "object" not in json_result or json_result["object"] != "text_completion"
+            ):
                 raise CredentialsValidateFailedError(
-                    'Credentials validation failed: invalid response object, must be \'text_completion\'')
+                    "Credentials validation failed: invalid response object, must be 'text_completion'"
+                )
         except CredentialsValidateFailedError:
             raise
         except Exception as ex:
-            raise CredentialsValidateFailedError(f'An error occurred during credentials validation: {str(ex)}')
+            raise CredentialsValidateFailedError(f"An error occurred during credentials validation: {str(ex)}")
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            generate custom model entities from credentials
+        generate custom model entities from credentials
         """
         features = []
 
-        function_calling_type = credentials.get('function_calling_type', 'no_call')
-        if function_calling_type in ['function_call']:
+        function_calling_type = credentials.get("function_calling_type", "no_call")
+        if function_calling_type == "function_call":
             features.append(ModelFeature.TOOL_CALL)
-        elif function_calling_type in ['tool_call']:
+        elif function_calling_type == "tool_call":
             features.append(ModelFeature.MULTI_TOOL_CALL)
 
-        stream_function_calling = credentials.get('stream_function_calling', 'supported')
-        if stream_function_calling == 'supported':
+        stream_function_calling = credentials.get("stream_function_calling", "supported")
+        if stream_function_calling == "supported":
             features.append(ModelFeature.STREAM_TOOL_CALL)
 
-        vision_support = credentials.get('vision_support', 'not_support')
-        if vision_support == 'support':
+        vision_support = credentials.get("vision_support", "not_support")
+        if vision_support == "support":
             features.append(ModelFeature.VISION)
 
         entity = AIModelEntity(
@@ -195,43 +199,43 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             features=features,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', "4096")),
-                ModelPropertyKey.MODE: credentials.get('mode'),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", "4096")),
+                ModelPropertyKey.MODE: credentials.get("mode"),
             },
             parameter_rules=[
                 ParameterRule(
                     name=DefaultParameterName.TEMPERATURE.value,
                     label=I18nObject(en_US="Temperature"),
                     type=ParameterType.FLOAT,
-                    default=float(credentials.get('temperature', 0.7)),
+                    default=float(credentials.get("temperature", 0.7)),
                     min=0,
                     max=2,
-                    precision=2
+                    precision=2,
                 ),
                 ParameterRule(
                     name=DefaultParameterName.TOP_P.value,
                     label=I18nObject(en_US="Top P"),
                     type=ParameterType.FLOAT,
-                    default=float(credentials.get('top_p', 1)),
+                    default=float(credentials.get("top_p", 1)),
                     min=0,
                     max=1,
-                    precision=2
+                    precision=2,
                 ),
                 ParameterRule(
                     name=DefaultParameterName.FREQUENCY_PENALTY.value,
                     label=I18nObject(en_US="Frequency Penalty"),
                     type=ParameterType.FLOAT,
-                    default=float(credentials.get('frequency_penalty', 0)),
+                    default=float(credentials.get("frequency_penalty", 0)),
                     min=-2,
-                    max=2
+                    max=2,
                 ),
                 ParameterRule(
                     name=DefaultParameterName.PRESENCE_PENALTY.value,
                     label=I18nObject(en_US="Presence Penalty"),
                     type=ParameterType.FLOAT,
-                    default=float(credentials.get('presence_penalty', 0)),
+                    default=float(credentials.get("presence_penalty", 0)),
                     min=-2,
-                    max=2
+                    max=2,
                 ),
                 ParameterRule(
                     name=DefaultParameterName.MAX_TOKENS.value,
@@ -239,31 +243,39 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                     type=ParameterType.INT,
                     default=512,
                     min=1,
-                    max=int(credentials.get('max_tokens_to_sample', 4096)),
-                )
+                    max=int(credentials.get("max_tokens_to_sample", 4096)),
+                ),
             ],
             pricing=PriceConfig(
-                input=Decimal(credentials.get('input_price', 0)),
-                output=Decimal(credentials.get('output_price', 0)),
-                unit=Decimal(credentials.get('unit', 0)),
-                currency=credentials.get('currency', "USD")
+                input=Decimal(credentials.get("input_price", 0)),
+                output=Decimal(credentials.get("output_price", 0)),
+                unit=Decimal(credentials.get("unit", 0)),
+                currency=credentials.get("currency", "USD"),
             ),
         )
 
-        if credentials['mode'] == 'chat':
+        if credentials["mode"] == "chat":
             entity.model_properties[ModelPropertyKey.MODE] = LLMMode.CHAT.value
-        elif credentials['mode'] == 'completion':
+        elif credentials["mode"] == "completion":
             entity.model_properties[ModelPropertyKey.MODE] = LLMMode.COMPLETION.value
         else:
             raise ValueError(f"Unknown completion type {credentials['completion_type']}")
 
         return entity
 
-    # validate_credentials method has been rewritten to use the requests library for compatibility with all providers following OpenAI's API standard.
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
-                  tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                  stream: bool = True, \
-                  user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    # validate_credentials method has been rewritten to use the requests library for compatibility with all providers
+    # following OpenAI's API standard.
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke llm completion model
 
@@ -277,52 +289,47 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         headers = {
-            'Content-Type': 'application/json',
-            'Accept-Charset': 'utf-8',
+            "Content-Type": "application/json",
+            "Accept-Charset": "utf-8",
         }
-        extra_headers = credentials.get('extra_headers')
+        extra_headers = credentials.get("extra_headers")
         if extra_headers is not None:
             headers = {
-              **headers,
-              **extra_headers,
+                **headers,
+                **extra_headers,
             }
 
-        api_key = credentials.get('api_key')
+        api_key = credentials.get("api_key")
         if api_key:
             headers["Authorization"] = f"Bearer {api_key}"
 
         endpoint_url = credentials["endpoint_url"]
-        if not endpoint_url.endswith('/'):
-            endpoint_url += '/'
+        if not endpoint_url.endswith("/"):
+            endpoint_url += "/"
 
-        data = {
-            "model": model,
-            "stream": stream,
-            **model_parameters
-        }
+        data = {"model": model, "stream": stream, **model_parameters}
 
-        completion_type = LLMMode.value_of(credentials['mode'])
+        completion_type = LLMMode.value_of(credentials["mode"])
 
         if completion_type is LLMMode.CHAT:
-            endpoint_url = urljoin(endpoint_url, 'chat/completions')
-            data['messages'] = [self._convert_prompt_message_to_dict(m, credentials) for m in prompt_messages]
+            endpoint_url = urljoin(endpoint_url, "chat/completions")
+            data["messages"] = [self._convert_prompt_message_to_dict(m, credentials) for m in prompt_messages]
         elif completion_type is LLMMode.COMPLETION:
-            endpoint_url = urljoin(endpoint_url, 'completions')
-            data['prompt'] = prompt_messages[0].content
+            endpoint_url = urljoin(endpoint_url, "completions")
+            data["prompt"] = prompt_messages[0].content
         else:
             raise ValueError("Unsupported completion type for model configuration.")
 
         # annotate tools with names, descriptions, etc.
-        function_calling_type = credentials.get('function_calling_type', 'no_call')
+        function_calling_type = credentials.get("function_calling_type", "no_call")
         formatted_tools = []
         if tools:
-            if function_calling_type == 'function_call':
-                data['functions'] = [{
-                    "name": tool.name,
-                    "description": tool.description,
-                    "parameters": tool.parameters
-                } for tool in tools]
-            elif function_calling_type == 'tool_call':
+            if function_calling_type == "function_call":
+                data["functions"] = [
+                    {"name": tool.name, "description": tool.description, "parameters": tool.parameters}
+                    for tool in tools
+                ]
+            elif function_calling_type == "tool_call":
                 data["tool_choice"] = "auto"
 
                 for tool in tools:
@@ -336,16 +343,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         if user:
             data["user"] = user
 
-        response = requests.post(
-            endpoint_url,
-            headers=headers,
-            json=data,
-            timeout=(10, 300),
-            stream=stream
-        )
+        response = requests.post(endpoint_url, headers=headers, json=data, timeout=(10, 300), stream=stream)
 
-        if response.encoding is None or response.encoding == 'ISO-8859-1':
-            response.encoding = 'utf-8'
+        if response.encoding is None or response.encoding == "ISO-8859-1":
+            response.encoding = "utf-8"
 
         if response.status_code != 200:
             raise InvokeError(f"API request failed with status code {response.status_code}: {response.text}")
@@ -355,8 +356,9 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
         return self._handle_generate_response(model, credentials, response, prompt_messages)
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: requests.Response,
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
+    ) -> Generator:
         """
         Handle llm stream response
 
@@ -366,11 +368,12 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: llm response chunk generator
         """
-        full_assistant_content = ''
+        full_assistant_content = ""
         chunk_index = 0
 
-        def create_final_llm_result_chunk(index: int, message: AssistantPromptMessage, finish_reason: str) \
-                -> LLMResultChunk:
+        def create_final_llm_result_chunk(
+            index: int, message: AssistantPromptMessage, finish_reason: str
+        ) -> LLMResultChunk:
             # calculate num tokens
             prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
             completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
@@ -381,16 +384,12 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             return LLMResultChunk(
                 model=model,
                 prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=index,
-                    message=message,
-                    finish_reason=finish_reason,
-                    usage=usage
-                )
+                delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
             )
 
         # delimiter for stream response, need unicode_escape
         import codecs
+
         delimiter = credentials.get("stream_mode_delimiter", "\n\n")
         delimiter = codecs.decode(delimiter, "unicode_escape")
 
@@ -406,10 +405,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                     tool_call = AssistantPromptMessage.ToolCall(
                         id=tool_call_id,
                         type="function",
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                            name="",
-                            arguments=""
-                        )
+                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
                     )
                     tools_calls.append(tool_call)
 
@@ -434,10 +430,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             chunk = chunk.strip()
             if chunk:
                 # ignore sse comments
-                if chunk.startswith(':'):
+                if chunk.startswith(":"):
                     continue
-                decoded_chunk = chunk.strip().lstrip('data: ').lstrip()
-                if decoded_chunk == '[DONE]':  # Some provider returns "data: [DONE]"
+                decoded_chunk = chunk.strip().lstrip("data: ").lstrip()
+                if decoded_chunk == "[DONE]":  # Some provider returns "data: [DONE]"
                     continue
 
                 try:
@@ -447,30 +443,31 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                     yield create_final_llm_result_chunk(
                         index=chunk_index + 1,
                         message=AssistantPromptMessage(content=""),
-                        finish_reason="Non-JSON encountered."
+                        finish_reason="Non-JSON encountered.",
                     )
                     break
-                if not chunk_json or len(chunk_json['choices']) == 0:
+                if not chunk_json or len(chunk_json["choices"]) == 0:
                     continue
 
-                choice = chunk_json['choices'][0]
-                finish_reason = chunk_json['choices'][0].get('finish_reason')
+                choice = chunk_json["choices"][0]
+                finish_reason = chunk_json["choices"][0].get("finish_reason")
                 chunk_index += 1
 
-                if 'delta' in choice:
-                    delta = choice['delta']
-                    delta_content = delta.get('content')
+                if "delta" in choice:
+                    delta = choice["delta"]
+                    delta_content = delta.get("content")
 
                     assistant_message_tool_calls = None
 
-                    if 'tool_calls' in delta and credentials.get('function_calling_type', 'no_call') == 'tool_call':
-                        assistant_message_tool_calls = delta.get('tool_calls', None)
-                    elif 'function_call' in delta and credentials.get('function_calling_type', 'no_call') == 'function_call':
-                        assistant_message_tool_calls = [{
-                            'id': 'tool_call_id',
-                            'type': 'function',
-                            'function': delta.get('function_call', {})
-                        }]
+                    if "tool_calls" in delta and credentials.get("function_calling_type", "no_call") == "tool_call":
+                        assistant_message_tool_calls = delta.get("tool_calls", None)
+                    elif (
+                        "function_call" in delta
+                        and credentials.get("function_calling_type", "no_call") == "function_call"
+                    ):
+                        assistant_message_tool_calls = [
+                            {"id": "tool_call_id", "type": "function", "function": delta.get("function_call", {})}
+                        ]
 
                     # assistant_message_function_call = delta.delta.function_call
 
@@ -479,7 +476,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                         tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
                         increase_tool_call(tool_calls)
 
-                    if delta_content is None or delta_content == '':
+                    if delta_content is None or delta_content == "":
                         continue
 
                     # transform assistant message to prompt message
@@ -490,9 +487,9 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                     # reset tool calls
                     tool_calls = []
                     full_assistant_content += delta_content
-                elif 'text' in choice:
-                    choice_text = choice.get('text', '')
-                    if choice_text == '':
+                elif "text" in choice:
+                    choice_text = choice.get("text", "")
+                    if choice_text == "":
                         continue
 
                     # transform assistant message to prompt message
@@ -507,7 +504,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=chunk_index,
                         message=assistant_prompt_message,
-                    )
+                    ),
                 )
 
             chunk_index += 1
@@ -518,47 +515,42 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                 prompt_messages=prompt_messages,
                 delta=LLMResultChunkDelta(
                     index=chunk_index,
-                    message=AssistantPromptMessage(
-                        tool_calls=tools_calls,
-                        content=""
-                    ),
-                )
+                    message=AssistantPromptMessage(tool_calls=tools_calls, content=""),
+                ),
             )
 
         yield create_final_llm_result_chunk(
-            index=chunk_index,
-            message=AssistantPromptMessage(content=""),
-            finish_reason=finish_reason
+            index=chunk_index, message=AssistantPromptMessage(content=""), finish_reason=finish_reason
         )
 
-    def _handle_generate_response(self, model: str, credentials: dict, response: requests.Response,
-                                  prompt_messages: list[PromptMessage]) -> LLMResult:
-
+    def _handle_generate_response(
+        self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
         response_json = response.json()
 
-        completion_type = LLMMode.value_of(credentials['mode'])
+        completion_type = LLMMode.value_of(credentials["mode"])
 
-        output = response_json['choices'][0]
+        output = response_json["choices"][0]
 
-        response_content = ''
+        response_content = ""
         tool_calls = None
-        function_calling_type = credentials.get('function_calling_type', 'no_call')
+        function_calling_type = credentials.get("function_calling_type", "no_call")
         if completion_type is LLMMode.CHAT:
-            response_content = output.get('message', {})['content']
-            if function_calling_type == 'tool_call':
-                tool_calls = output.get('message', {}).get('tool_calls')
-            elif function_calling_type == 'function_call':
-                tool_calls = output.get('message', {}).get('function_call')
+            response_content = output.get("message", {})["content"]
+            if function_calling_type == "tool_call":
+                tool_calls = output.get("message", {}).get("tool_calls")
+            elif function_calling_type == "function_call":
+                tool_calls = output.get("message", {}).get("function_call")
 
         elif completion_type is LLMMode.COMPLETION:
-            response_content = output['text']
+            response_content = output["text"]
 
         assistant_message = AssistantPromptMessage(content=response_content, tool_calls=[])
 
         if tool_calls:
-            if function_calling_type == 'tool_call':
+            if function_calling_type == "tool_call":
                 assistant_message.tool_calls = self._extract_response_tool_calls(tool_calls)
-            elif function_calling_type == 'function_call':
+            elif function_calling_type == "function_call":
                 assistant_message.tool_calls = [self._extract_response_function_call(tool_calls)]
 
         usage = response_json.get("usage")
@@ -597,19 +589,13 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
                         message_content = cast(PromptMessageContent, message_content)
-                        sub_message_dict = {
-                            "type": "text",
-                            "text": message_content.data
-                        }
+                        sub_message_dict = {"type": "text", "text": message_content.data}
                         sub_messages.append(sub_message_dict)
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
                         sub_message_dict = {
                             "type": "image_url",
-                            "image_url": {
-                                "url": message_content.data,
-                                "detail": message_content.detail.value
-                            }
+                            "image_url": {"url": message_content.data, "detail": message_content.detail.value},
                         }
                         sub_messages.append(sub_message_dict)
 
@@ -618,11 +604,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             message = cast(AssistantPromptMessage, message)
             message_dict = {"role": "assistant", "content": message.content}
             if message.tool_calls:
-                function_calling_type = credentials.get('function_calling_type', 'no_call')
-                if function_calling_type == 'tool_call':
-                    message_dict["tool_calls"] = [tool_call.dict() for tool_call in
-                                                message.tool_calls]
-                elif function_calling_type == 'function_call':
+                function_calling_type = credentials.get("function_calling_type", "no_call")
+                if function_calling_type == "tool_call":
+                    message_dict["tool_calls"] = [tool_call.dict() for tool_call in message.tool_calls]
+                elif function_calling_type == "function_call":
                     function_call = message.tool_calls[0]
                     message_dict["function_call"] = {
                         "name": function_call.function.name,
@@ -633,19 +618,11 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             message_dict = {"role": "system", "content": message.content}
         elif isinstance(message, ToolPromptMessage):
             message = cast(ToolPromptMessage, message)
-            function_calling_type = credentials.get('function_calling_type', 'no_call')
-            if function_calling_type == 'tool_call':
-                message_dict = {
-                    "role": "tool",
-                    "content": message.content,
-                    "tool_call_id": message.tool_call_id
-                }
-            elif function_calling_type == 'function_call':
-                message_dict = {
-                    "role": "function",
-                    "content": message.content,
-                    "name": message.tool_call_id
-                }
+            function_calling_type = credentials.get("function_calling_type", "no_call")
+            if function_calling_type == "tool_call":
+                message_dict = {"role": "tool", "content": message.content, "tool_call_id": message.tool_call_id}
+            elif function_calling_type == "function_call":
+                message_dict = {"role": "function", "content": message.content, "name": message.tool_call_id}
         else:
             raise ValueError(f"Got unknown type {message}")
 
@@ -654,8 +631,9 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
         return message_dict
 
-    def _num_tokens_from_string(self, model: str, text: Union[str, list[PromptMessageContent]],
-                                tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_string(
+        self, model: str, text: Union[str, list[PromptMessageContent]], tools: Optional[list[PromptMessageTool]] = None
+    ) -> int:
         """
         Approximate num tokens for model with gpt2 tokenizer.
 
@@ -667,7 +645,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         if isinstance(text, str):
             full_text = text
         else:
-            full_text = ''
+            full_text = ""
             for message_content in text:
                 if message_content.type == PromptMessageContentType.TEXT:
                     message_content = cast(PromptMessageContent, message_content)
@@ -680,8 +658,13 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
         return num_tokens
 
-    def _num_tokens_from_messages(self, model: str, messages: list[PromptMessage],
-                                  tools: Optional[list[PromptMessageTool]] = None, credentials: dict = None) -> int:
+    def _num_tokens_from_messages(
+        self,
+        model: str,
+        messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+        credentials: dict = None,
+    ) -> int:
         """
         Approximate num tokens with GPT2 tokenizer.
         """
@@ -700,10 +683,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                 #  which need to download the image and then get the resolution for calculation,
                 #  and will increase the request delay
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
 
                     value = text
 
@@ -741,46 +724,44 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         """
         num_tokens = 0
         for tool in tools:
-            num_tokens += self._get_num_tokens_by_gpt2('type')
-            num_tokens += self._get_num_tokens_by_gpt2('function')
-            num_tokens += self._get_num_tokens_by_gpt2('function')
+            num_tokens += self._get_num_tokens_by_gpt2("type")
+            num_tokens += self._get_num_tokens_by_gpt2("function")
+            num_tokens += self._get_num_tokens_by_gpt2("function")
 
             # calculate num tokens for function object
-            num_tokens += self._get_num_tokens_by_gpt2('name')
+            num_tokens += self._get_num_tokens_by_gpt2("name")
             num_tokens += self._get_num_tokens_by_gpt2(tool.name)
-            num_tokens += self._get_num_tokens_by_gpt2('description')
+            num_tokens += self._get_num_tokens_by_gpt2("description")
             num_tokens += self._get_num_tokens_by_gpt2(tool.description)
             parameters = tool.parameters
-            num_tokens += self._get_num_tokens_by_gpt2('parameters')
-            if 'title' in parameters:
-                num_tokens += self._get_num_tokens_by_gpt2('title')
+            num_tokens += self._get_num_tokens_by_gpt2("parameters")
+            if "title" in parameters:
+                num_tokens += self._get_num_tokens_by_gpt2("title")
                 num_tokens += self._get_num_tokens_by_gpt2(parameters.get("title"))
-            num_tokens += self._get_num_tokens_by_gpt2('type')
+            num_tokens += self._get_num_tokens_by_gpt2("type")
             num_tokens += self._get_num_tokens_by_gpt2(parameters.get("type"))
-            if 'properties' in parameters:
-                num_tokens += self._get_num_tokens_by_gpt2('properties')
-                for key, value in parameters.get('properties').items():
+            if "properties" in parameters:
+                num_tokens += self._get_num_tokens_by_gpt2("properties")
+                for key, value in parameters.get("properties").items():
                     num_tokens += self._get_num_tokens_by_gpt2(key)
                     for field_key, field_value in value.items():
                         num_tokens += self._get_num_tokens_by_gpt2(field_key)
-                        if field_key == 'enum':
+                        if field_key == "enum":
                             for enum_field in field_value:
                                 num_tokens += 3
                                 num_tokens += self._get_num_tokens_by_gpt2(enum_field)
                         else:
                             num_tokens += self._get_num_tokens_by_gpt2(field_key)
                             num_tokens += self._get_num_tokens_by_gpt2(str(field_value))
-            if 'required' in parameters:
-                num_tokens += self._get_num_tokens_by_gpt2('required')
-                for required_field in parameters['required']:
+            if "required" in parameters:
+                num_tokens += self._get_num_tokens_by_gpt2("required")
+                for required_field in parameters["required"]:
                     num_tokens += 3
                     num_tokens += self._get_num_tokens_by_gpt2(required_field)
 
         return num_tokens
 
-    def _extract_response_tool_calls(self,
-                                     response_tool_calls: list[dict]) \
-            -> list[AssistantPromptMessage.ToolCall]:
+    def _extract_response_tool_calls(self, response_tool_calls: list[dict]) -> list[AssistantPromptMessage.ToolCall]:
         """
         Extract tool calls from response
 
@@ -792,20 +773,17 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             for response_tool_call in response_tool_calls:
                 function = AssistantPromptMessage.ToolCall.ToolCallFunction(
                     name=response_tool_call.get("function", {}).get("name", ""),
-                    arguments=response_tool_call.get("function", {}).get("arguments", "")
+                    arguments=response_tool_call.get("function", {}).get("arguments", ""),
                 )
 
                 tool_call = AssistantPromptMessage.ToolCall(
-                    id=response_tool_call.get("id", ""),
-                    type=response_tool_call.get("type", ""),
-                    function=function
+                    id=response_tool_call.get("id", ""), type=response_tool_call.get("type", ""), function=function
                 )
                 tool_calls.append(tool_call)
 
         return tool_calls
 
-    def _extract_response_function_call(self, response_function_call) \
-            -> AssistantPromptMessage.ToolCall:
+    def _extract_response_function_call(self, response_function_call) -> AssistantPromptMessage.ToolCall:
         """
         Extract function call from response
 
@@ -815,14 +793,11 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         tool_call = None
         if response_function_call:
             function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                name=response_function_call.get('name', ''),
-                arguments=response_function_call.get('arguments', '')
+                name=response_function_call.get("name", ""), arguments=response_function_call.get("arguments", "")
             )
 
             tool_call = AssistantPromptMessage.ToolCall(
-                id=response_function_call.get('id', ''),
-                type="function",
-                function=function
+                id=response_function_call.get("id", ""), type="function", function=function
             )
 
         return tool_call
diff --git a/api/core/model_runtime/model_providers/openai_api_compatible/openai_api_compatible.py b/api/core/model_runtime/model_providers/openai_api_compatible/openai_api_compatible.py
index 3445ebbaf7..ca6f185287 100644
--- a/api/core/model_runtime/model_providers/openai_api_compatible/openai_api_compatible.py
+++ b/api/core/model_runtime/model_providers/openai_api_compatible/openai_api_compatible.py
@@ -6,6 +6,5 @@ logger = logging.getLogger(__name__)
 
 
 class OAICompatProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         pass
diff --git a/api/core/model_runtime/model_providers/openai_api_compatible/speech2text/speech2text.py b/api/core/model_runtime/model_providers/openai_api_compatible/speech2text/speech2text.py
index 00702ba936..405096578c 100644
--- a/api/core/model_runtime/model_providers/openai_api_compatible/speech2text/speech2text.py
+++ b/api/core/model_runtime/model_providers/openai_api_compatible/speech2text/speech2text.py
@@ -6,17 +6,15 @@ import requests
 from core.model_runtime.errors.invoke import InvokeBadRequestError
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
-from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOAI_API_Compat
+from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOaiApiCompat
 
 
-class OAICompatSpeech2TextModel(_CommonOAI_API_Compat, Speech2TextModel):
+class OAICompatSpeech2TextModel(_CommonOaiApiCompat, Speech2TextModel):
     """
     Model class for OpenAI Compatible Speech to text model.
     """
 
-    def _invoke(
-            self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None
-    ) -> str:
+    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
         """
         Invoke speech2text model
 
diff --git a/api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py
index 363054b084..e83cfdf873 100644
--- a/api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py
@@ -19,17 +19,17 @@ from core.model_runtime.entities.model_entities import (
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
-from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOAI_API_Compat
+from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOaiApiCompat
 
 
-class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
+class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
     """
     Model class for an OpenAI API-compatible text embedding model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -39,27 +39,25 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
-       
-        # Prepare headers and payload for the request
-        headers = {
-            'Content-Type': 'application/json'
-        }
 
-        api_key = credentials.get('api_key')
+        # Prepare headers and payload for the request
+        headers = {"Content-Type": "application/json"}
+
+        api_key = credentials.get("api_key")
         if api_key:
             headers["Authorization"] = f"Bearer {api_key}"
 
-        endpoint_url = credentials.get('endpoint_url')
-        if not endpoint_url.endswith('/'):
-            endpoint_url += '/'
+        endpoint_url = credentials.get("endpoint_url")
+        if not endpoint_url.endswith("/"):
+            endpoint_url += "/"
 
-        endpoint_url = urljoin(endpoint_url, 'embeddings')
+        endpoint_url = urljoin(endpoint_url, "embeddings")
 
         extra_model_kwargs = {}
         if user:
-            extra_model_kwargs['user'] = user
+            extra_model_kwargs["user"] = user
 
-        extra_model_kwargs['encoding_format'] = 'float'
+        extra_model_kwargs["encoding_format"] = "float"
 
         # get model properties
         context_size = self._get_context_size(model, credentials)
@@ -70,7 +68,6 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
         used_tokens = 0
 
         for i, text in enumerate(texts):
-
             # Here token count is only an approximation based on the GPT2 tokenizer
             # TODO: Optimize for better token estimation and chunking
             num_tokens = self._get_num_tokens_by_gpt2(text)
@@ -78,7 +75,7 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
             if num_tokens >= context_size:
                 cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
                 # if num tokens is larger than context length, only use the start
-                inputs.append(text[0: cutoff])
+                inputs.append(text[0:cutoff])
             else:
                 inputs.append(text)
             indices += [i]
@@ -88,42 +85,25 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
 
         for i in _iter:
             # Prepare the payload for the request
-            payload = {
-                'input': inputs[i: i + max_chunks],
-                'model': model,
-                **extra_model_kwargs
-            }
+            payload = {"input": inputs[i : i + max_chunks], "model": model, **extra_model_kwargs}
 
             # Make the request to the OpenAI API
-            response = requests.post(
-                endpoint_url,
-                headers=headers,
-                data=json.dumps(payload),
-                timeout=(10, 300)
-            )
+            response = requests.post(endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300))
 
             response.raise_for_status()  # Raise an exception for HTTP errors
             response_data = response.json()
 
             # Extract embeddings and used tokens from the response
-            embeddings_batch = [data['embedding'] for data in response_data['data']]
-            embedding_used_tokens = response_data['usage']['total_tokens']
+            embeddings_batch = [data["embedding"] for data in response_data["data"]]
+            embedding_used_tokens = response_data["usage"]["total_tokens"]
 
             used_tokens += embedding_used_tokens
             batched_embeddings += embeddings_batch
 
         # calc usage
-        usage = self._calc_response_usage(
-            model=model,
-            credentials=credentials,
-            tokens=used_tokens
-        )
-        
-        return TextEmbeddingResult(
-            embeddings=batched_embeddings,
-            usage=usage,
-            model=model
-        )
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
+
+        return TextEmbeddingResult(embeddings=batched_embeddings, usage=usage, model=model)
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
@@ -145,45 +125,35 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
         :return:
         """
         try:
-            headers = {
-                'Content-Type': 'application/json'
-            }
+            headers = {"Content-Type": "application/json"}
 
-            api_key = credentials.get('api_key')
+            api_key = credentials.get("api_key")
 
             if api_key:
                 headers["Authorization"] = f"Bearer {api_key}"
 
-            endpoint_url = credentials.get('endpoint_url')
-            if not endpoint_url.endswith('/'):
-                endpoint_url += '/'
+            endpoint_url = credentials.get("endpoint_url")
+            if not endpoint_url.endswith("/"):
+                endpoint_url += "/"
 
-            endpoint_url = urljoin(endpoint_url, 'embeddings')
+            endpoint_url = urljoin(endpoint_url, "embeddings")
 
-            payload = {
-                'input': 'ping',
-                'model': model
-            }
+            payload = {"input": "ping", "model": model}
 
-            response = requests.post(
-                url=endpoint_url,
-                headers=headers,
-                data=json.dumps(payload),
-                timeout=(10, 300)
-            )
+            response = requests.post(url=endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300))
 
             if response.status_code != 200:
                 raise CredentialsValidateFailedError(
-                    f'Credentials validation failed with status code {response.status_code}')
+                    f"Credentials validation failed with status code {response.status_code}"
+                )
 
             try:
                 json_result = response.json()
             except json.JSONDecodeError as e:
-                raise CredentialsValidateFailedError('Credentials validation failed: JSON decode error')
+                raise CredentialsValidateFailedError("Credentials validation failed: JSON decode error")
 
-            if 'model' not in json_result:
-                raise CredentialsValidateFailedError(
-                    'Credentials validation failed: invalid response')
+            if "model" not in json_result:
+                raise CredentialsValidateFailedError("Credentials validation failed: invalid response")
         except CredentialsValidateFailedError:
             raise
         except Exception as ex:
@@ -191,7 +161,7 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            generate custom model entities from credentials
+        generate custom model entities from credentials
         """
         entity = AIModelEntity(
             model=model,
@@ -199,20 +169,19 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size')),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],
             pricing=PriceConfig(
-                input=Decimal(credentials.get('input_price', 0)),
-                unit=Decimal(credentials.get('unit', 0)),
-                currency=credentials.get('currency', "USD")
-            )
+                input=Decimal(credentials.get("input_price", 0)),
+                unit=Decimal(credentials.get("unit", 0)),
+                currency=credentials.get("currency", "USD"),
+            ),
         )
 
         return entity
 
-
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -224,10 +193,7 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -238,7 +204,7 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/openllm/llm/llm.py b/api/core/model_runtime/model_providers/openllm/llm/llm.py
index 8ea5819bde..34b4de7962 100644
--- a/api/core/model_runtime/model_providers/openllm/llm/llm.py
+++ b/api/core/model_runtime/model_providers/openllm/llm/llm.py
@@ -38,88 +38,115 @@ from core.model_runtime.model_providers.openllm.llm.openllm_generate_errors impo
 
 
 class OpenLLMLargeLanguageModel(LargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], 
-                model_parameters: dict, tools: list[PromptMessageTool] | None = None, 
-                stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-        -> LLMResult | Generator:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         return self._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
         Validate credentials for Baichuan model
         """
-        if not credentials.get('server_url'):
-            raise CredentialsValidateFailedError('Invalid server URL')
+        if not credentials.get("server_url"):
+            raise CredentialsValidateFailedError("Invalid server URL")
 
         # ping
         instance = OpenLLMGenerate()
         try:
             instance.generate(
-                server_url=credentials['server_url'],
-                model_name=model, 
-                prompt_messages=[
-                    OpenLLMGenerateMessage(content='ping\nAnswer: ', role='user')
-                ],
+                server_url=credentials["server_url"],
+                model_name=model,
+                prompt_messages=[OpenLLMGenerateMessage(content="ping\nAnswer: ", role="user")],
                 model_parameters={
-                    'max_tokens': 64,
-                    'temperature': 0.8,
-                    'top_p': 0.9,
-                    'top_k': 15,
+                    "max_tokens": 64,
+                    "temperature": 0.8,
+                    "top_p": 0.9,
+                    "top_k": 15,
                 },
                 stream=False,
-                user='',
+                user="",
                 stop=[],
             )
         except InvalidAuthenticationError as e:
             raise CredentialsValidateFailedError(f"Invalid API key: {e}")
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: list[PromptMessageTool] | None = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool] | None = None,
+    ) -> int:
         return self._num_tokens_from_messages(prompt_messages, tools)
 
     def _num_tokens_from_messages(self, messages: list[PromptMessage], tools: list[PromptMessageTool]) -> int:
         """
-            Calculate num tokens for OpenLLM model
-            it's a generate model, so we just join them by spe
+        Calculate num tokens for OpenLLM model
+        it's a generate model, so we just join them by spe
         """
-        messages = ','.join([message.content for message in messages])
+        messages = ",".join([message.content for message in messages])
         return self._get_num_tokens_by_gpt2(messages)
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], 
-                model_parameters: dict, tools: list[PromptMessageTool] | None = None, 
-                stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-        -> LLMResult | Generator:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         client = OpenLLMGenerate()
         response = client.generate(
             model_name=model,
-            server_url=credentials['server_url'],
+            server_url=credentials["server_url"],
             prompt_messages=[self._convert_prompt_message_to_openllm_message(message) for message in prompt_messages],
             model_parameters=model_parameters,
             stop=stop,
             stream=stream,
-            user=user
+            user=user,
         )
 
         if stream:
-            return self._handle_chat_generate_stream_response(model=model, prompt_messages=prompt_messages, credentials=credentials, response=response)
-        return self._handle_chat_generate_response(model=model, prompt_messages=prompt_messages, credentials=credentials, response=response)
+            return self._handle_chat_generate_stream_response(
+                model=model, prompt_messages=prompt_messages, credentials=credentials, response=response
+            )
+        return self._handle_chat_generate_response(
+            model=model, prompt_messages=prompt_messages, credentials=credentials, response=response
+        )
 
     def _convert_prompt_message_to_openllm_message(self, prompt_message: PromptMessage) -> OpenLLMGenerateMessage:
         """
-            convert PromptMessage to OpenLLMGenerateMessage so that we can use OpenLLMGenerateMessage interface
+        convert PromptMessage to OpenLLMGenerateMessage so that we can use OpenLLMGenerateMessage interface
         """
         if isinstance(prompt_message, UserPromptMessage):
             return OpenLLMGenerateMessage(role=OpenLLMGenerateMessage.Role.USER.value, content=prompt_message.content)
         elif isinstance(prompt_message, AssistantPromptMessage):
-            return OpenLLMGenerateMessage(role=OpenLLMGenerateMessage.Role.ASSISTANT.value, content=prompt_message.content)
+            return OpenLLMGenerateMessage(
+                role=OpenLLMGenerateMessage.Role.ASSISTANT.value, content=prompt_message.content
+            )
         else:
-            raise NotImplementedError(f'Prompt message type {type(prompt_message)} is not supported')
+            raise NotImplementedError(f"Prompt message type {type(prompt_message)} is not supported")
 
-    def _handle_chat_generate_response(self, model: str, prompt_messages: list[PromptMessage], credentials: dict, response: OpenLLMGenerateMessage) -> LLMResult:
-        usage = self._calc_response_usage(model=model, credentials=credentials, 
-                                          prompt_tokens=response.usage['prompt_tokens'], 
-                                          completion_tokens=response.usage['completion_tokens']
-                                        )
+    def _handle_chat_generate_response(
+        self, model: str, prompt_messages: list[PromptMessage], credentials: dict, response: OpenLLMGenerateMessage
+    ) -> LLMResult:
+        usage = self._calc_response_usage(
+            model=model,
+            credentials=credentials,
+            prompt_tokens=response.usage["prompt_tokens"],
+            completion_tokens=response.usage["completion_tokens"],
+        )
         return LLMResult(
             model=model,
             prompt_messages=prompt_messages,
@@ -130,27 +157,29 @@ class OpenLLMLargeLanguageModel(LargeLanguageModel):
             usage=usage,
         )
 
-    def _handle_chat_generate_stream_response(self, model: str, prompt_messages: list[PromptMessage], 
-                                              credentials: dict, response: Generator[OpenLLMGenerateMessage, None, None]) \
-        -> Generator[LLMResultChunk, None, None]:
+    def _handle_chat_generate_stream_response(
+        self,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        credentials: dict,
+        response: Generator[OpenLLMGenerateMessage, None, None],
+    ) -> Generator[LLMResultChunk, None, None]:
         for message in response:
             if message.usage:
                 usage = self._calc_response_usage(
-                    model=model, credentials=credentials, 
-                    prompt_tokens=message.usage['prompt_tokens'], 
-                    completion_tokens=message.usage['completion_tokens']
+                    model=model,
+                    credentials=credentials,
+                    prompt_tokens=message.usage["prompt_tokens"],
+                    completion_tokens=message.usage["completion_tokens"],
                 )
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=0,
-                        message=AssistantPromptMessage(
-                            content=message.content,
-                            tool_calls=[]
-                        ),
+                        message=AssistantPromptMessage(content=message.content, tool_calls=[]),
                         usage=usage,
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
             else:
@@ -159,73 +188,55 @@ class OpenLLMLargeLanguageModel(LargeLanguageModel):
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=0,
-                        message=AssistantPromptMessage(
-                            content=message.content,
-                            tool_calls=[]
-                        ),
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        message=AssistantPromptMessage(content=message.content, tool_calls=[]),
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
 
-
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
         rules = [
             ParameterRule(
-                name='temperature',
+                name="temperature",
                 type=ParameterType.FLOAT,
-                use_template='temperature',
-                label=I18nObject(
-                    zh_Hans='温度',
-                    en_US='Temperature'
-                )
+                use_template="temperature",
+                label=I18nObject(zh_Hans="温度", en_US="Temperature"),
             ),
             ParameterRule(
-                name='top_p',
+                name="top_p",
                 type=ParameterType.FLOAT,
-                use_template='top_p',
-                label=I18nObject(
-                    zh_Hans='Top P',
-                    en_US='Top P'
-                )
+                use_template="top_p",
+                label=I18nObject(zh_Hans="Top P", en_US="Top P"),
             ),
             ParameterRule(
-                name='top_k',
+                name="top_k",
                 type=ParameterType.INT,
-                use_template='top_k',
+                use_template="top_k",
                 min=1,
                 default=1,
-                label=I18nObject(
-                    zh_Hans='Top K',
-                    en_US='Top K'
-                )
+                label=I18nObject(zh_Hans="Top K", en_US="Top K"),
             ),
             ParameterRule(
-                name='max_tokens',
+                name="max_tokens",
                 type=ParameterType.INT,
-                use_template='max_tokens',
+                use_template="max_tokens",
                 min=1,
                 default=512,
-                label=I18nObject(
-                    zh_Hans='最大生成长度',
-                    en_US='Max Tokens'
-                )
-            )
+                label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"),
+            ),
         ]
 
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
-            model_properties={ 
+            model_properties={
                 ModelPropertyKey.MODE: LLMMode.COMPLETION.value,
             },
-            parameter_rules=rules
+            parameter_rules=rules,
         )
 
         return entity
@@ -241,22 +252,13 @@ class OpenLLMLargeLanguageModel(LargeLanguageModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-            ],
-            InvokeServerUnavailableError: [
-                InternalServerError
-            ],
-            InvokeRateLimitError: [
-                RateLimitReachedError
-            ],
+            InvokeConnectionError: [],
+            InvokeServerUnavailableError: [InternalServerError],
+            InvokeRateLimitError: [RateLimitReachedError],
             InvokeAuthorizationError: [
                 InvalidAuthenticationError,
                 InsufficientAccountBalanceError,
                 InvalidAPIKeyError,
             ],
-            InvokeBadRequestError: [
-                BadRequestError,
-                KeyError
-            ]
+            InvokeBadRequestError: [BadRequestError, KeyError],
         }
-
diff --git a/api/core/model_runtime/model_providers/openllm/llm/openllm_generate.py b/api/core/model_runtime/model_providers/openllm/llm/openllm_generate.py
index 1c3f084207..351dcced15 100644
--- a/api/core/model_runtime/model_providers/openllm/llm/openllm_generate.py
+++ b/api/core/model_runtime/model_providers/openllm/llm/openllm_generate.py
@@ -15,32 +15,38 @@ from core.model_runtime.model_providers.openllm.llm.openllm_generate_errors impo
 
 class OpenLLMGenerateMessage:
     class Role(Enum):
-        USER = 'user'
-        ASSISTANT = 'assistant'
+        USER = "user"
+        ASSISTANT = "assistant"
 
     role: str = Role.USER.value
     content: str
     usage: dict[str, int] = None
-    stop_reason: str = ''
+    stop_reason: str = ""
 
     def to_dict(self) -> dict[str, Any]:
         return {
-            'role': self.role,
-            'content': self.content,
+            "role": self.role,
+            "content": self.content,
         }
-    
-    def __init__(self, content: str, role: str = 'user') -> None:
+
+    def __init__(self, content: str, role: str = "user") -> None:
         self.content = content
         self.role = role
 
 
 class OpenLLMGenerate:
     def generate(
-            self, server_url: str, model_name: str, stream: bool, model_parameters: dict[str, Any],
-            stop: list[str], prompt_messages: list[OpenLLMGenerateMessage], user: str,
+        self,
+        server_url: str,
+        model_name: str,
+        stream: bool,
+        model_parameters: dict[str, Any],
+        stop: list[str],
+        prompt_messages: list[OpenLLMGenerateMessage],
+        user: str,
     ) -> Union[Generator[OpenLLMGenerateMessage, None, None], OpenLLMGenerateMessage]:
         if not server_url:
-            raise InvalidAuthenticationError('Invalid server URL')
+            raise InvalidAuthenticationError("Invalid server URL")
 
         default_llm_config = {
             "max_new_tokens": 128,
@@ -72,40 +78,37 @@ class OpenLLMGenerate:
             "frequency_penalty": 0,
             "use_beam_search": False,
             "ignore_eos": False,
-            "skip_special_tokens": True
+            "skip_special_tokens": True,
         }
 
-        if 'max_tokens' in model_parameters and type(model_parameters['max_tokens']) == int:
-            default_llm_config['max_new_tokens'] = model_parameters['max_tokens']
+        if "max_tokens" in model_parameters and type(model_parameters["max_tokens"]) == int:
+            default_llm_config["max_new_tokens"] = model_parameters["max_tokens"]
 
-        if 'temperature' in model_parameters and type(model_parameters['temperature']) == float:
-            default_llm_config['temperature'] = model_parameters['temperature']
+        if "temperature" in model_parameters and type(model_parameters["temperature"]) == float:
+            default_llm_config["temperature"] = model_parameters["temperature"]
 
-        if 'top_p' in model_parameters and type(model_parameters['top_p']) == float:
-            default_llm_config['top_p'] = model_parameters['top_p']
+        if "top_p" in model_parameters and type(model_parameters["top_p"]) == float:
+            default_llm_config["top_p"] = model_parameters["top_p"]
 
-        if 'top_k' in model_parameters and type(model_parameters['top_k']) == int:
-            default_llm_config['top_k'] = model_parameters['top_k']
+        if "top_k" in model_parameters and type(model_parameters["top_k"]) == int:
+            default_llm_config["top_k"] = model_parameters["top_k"]
 
-        if 'use_cache' in model_parameters and type(model_parameters['use_cache']) == bool:
-            default_llm_config['use_cache'] = model_parameters['use_cache']
+        if "use_cache" in model_parameters and type(model_parameters["use_cache"]) == bool:
+            default_llm_config["use_cache"] = model_parameters["use_cache"]
 
-        headers = {
-            'Content-Type': 'application/json',
-            'accept': 'application/json'
-        }
+        headers = {"Content-Type": "application/json", "accept": "application/json"}
 
         if stream:
-            url = f'{server_url}/v1/generate_stream'
+            url = f"{server_url}/v1/generate_stream"
             timeout = 10
         else:
-            url = f'{server_url}/v1/generate'
+            url = f"{server_url}/v1/generate"
             timeout = 120
 
         data = {
-            'stop': stop if stop else [],
-            'prompt': '\n'.join([message.content for message in prompt_messages]),
-            'llm_config': default_llm_config,
+            "stop": stop or [],
+            "prompt": "\n".join([message.content for message in prompt_messages]),
+            "llm_config": default_llm_config,
         }
 
         try:
@@ -113,10 +116,10 @@ class OpenLLMGenerate:
         except (ConnectionError, InvalidSchema, MissingSchema) as e:
             # cloud not connect to the server
             raise InvalidAuthenticationError(f"Invalid server URL: {e}")
-        
+
         if not response.ok:
             resp = response.json()
-            msg = resp['msg']
+            msg = resp["msg"]
             if response.status_code == 400:
                 raise BadRequestError(msg)
             elif response.status_code == 404:
@@ -125,69 +128,71 @@ class OpenLLMGenerate:
                 raise InternalServerError(msg)
             else:
                 raise InternalServerError(msg)
-            
+
         if stream:
             return self._handle_chat_stream_generate_response(response)
         return self._handle_chat_generate_response(response)
-        
+
     def _handle_chat_generate_response(self, response: Response) -> OpenLLMGenerateMessage:
         try:
             data = response.json()
         except Exception as e:
             raise InternalServerError(f"Failed to convert response to json: {e} with text: {response.text}")
 
-        message = data['outputs'][0]
-        text = message['text']
-        token_ids = message['token_ids']
-        prompt_token_ids = data['prompt_token_ids']
-        stop_reason = message['finish_reason']
+        message = data["outputs"][0]
+        text = message["text"]
+        token_ids = message["token_ids"]
+        prompt_token_ids = data["prompt_token_ids"]
+        stop_reason = message["finish_reason"]
 
         message = OpenLLMGenerateMessage(content=text, role=OpenLLMGenerateMessage.Role.ASSISTANT.value)
         message.stop_reason = stop_reason
         message.usage = {
-            'prompt_tokens': len(prompt_token_ids),
-            'completion_tokens': len(token_ids),
-            'total_tokens': len(prompt_token_ids) + len(token_ids),
+            "prompt_tokens": len(prompt_token_ids),
+            "completion_tokens": len(token_ids),
+            "total_tokens": len(prompt_token_ids) + len(token_ids),
         }
 
         return message
 
-    def _handle_chat_stream_generate_response(self, response: Response) -> Generator[OpenLLMGenerateMessage, None, None]:
+    def _handle_chat_stream_generate_response(
+        self, response: Response
+    ) -> Generator[OpenLLMGenerateMessage, None, None]:
         completion_usage = 0
 
         for line in response.iter_lines():
             if not line:
                 continue
 
-            line: str = line.decode('utf-8')
-            if line.startswith('data: '):
+            line: str = line.decode("utf-8")
+            if line.startswith("data: "):
                 line = line[6:].strip()
 
-            if line == '[DONE]':
+            if line == "[DONE]":
                 return
 
             try:
                 data = loads(line)
             except Exception as e:
                 raise InternalServerError(f"Failed to convert response to json: {e} with text: {line}")
-            
-            output = data['outputs']
+
+            output = data["outputs"]
 
             for choice in output:
-                text = choice['text']
-                token_ids = choice['token_ids']
+                text = choice["text"]
+                token_ids = choice["token_ids"]
 
                 completion_usage += len(token_ids)
                 message = OpenLLMGenerateMessage(content=text, role=OpenLLMGenerateMessage.Role.ASSISTANT.value)
 
-                if choice.get('finish_reason'):
-                    finish_reason = choice['finish_reason']
-                    prompt_token_ids = data['prompt_token_ids']
+                if choice.get("finish_reason"):
+                    finish_reason = choice["finish_reason"]
+                    prompt_token_ids = data["prompt_token_ids"]
                     message.stop_reason = finish_reason
                     message.usage = {
-                        'prompt_tokens': len(prompt_token_ids),
-                        'completion_tokens': completion_usage,
-                        'total_tokens': completion_usage + len(prompt_token_ids),
+                        "prompt_tokens": len(prompt_token_ids),
+                        "completion_tokens": completion_usage,
+                        "total_tokens": completion_usage + len(prompt_token_ids),
                     }
-                    
-                yield message
\ No newline at end of file
+
+                yield message
diff --git a/api/core/model_runtime/model_providers/openllm/llm/openllm_generate_errors.py b/api/core/model_runtime/model_providers/openllm/llm/openllm_generate_errors.py
index d9d279e6ca..309b5cf413 100644
--- a/api/core/model_runtime/model_providers/openllm/llm/openllm_generate_errors.py
+++ b/api/core/model_runtime/model_providers/openllm/llm/openllm_generate_errors.py
@@ -1,17 +1,22 @@
 class InvalidAuthenticationError(Exception):
     pass
 
+
 class InvalidAPIKeyError(Exception):
     pass
 
+
 class RateLimitReachedError(Exception):
     pass
 
+
 class InsufficientAccountBalanceError(Exception):
     pass
 
+
 class InternalServerError(Exception):
     pass
 
+
 class BadRequestError(Exception):
-    pass
\ No newline at end of file
+    pass
diff --git a/api/core/model_runtime/model_providers/openllm/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/openllm/text_embedding/text_embedding.py
index 4dbd0678e7..00e583cc79 100644
--- a/api/core/model_runtime/model_providers/openllm/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/openllm/text_embedding/text_embedding.py
@@ -23,9 +23,10 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
     """
     Model class for OpenLLM text embedding model.
     """
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -35,16 +36,13 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
-        server_url = credentials['server_url']
+        server_url = credentials["server_url"]
         if not server_url:
-            raise CredentialsValidateFailedError('server_url is required')
-        
-        headers = {
-            'Content-Type': 'application/json',
-            'accept': 'application/json'
-        }
+            raise CredentialsValidateFailedError("server_url is required")
 
-        url = f'{server_url}/v1/embeddings'
+        headers = {"Content-Type": "application/json", "accept": "application/json"}
+
+        url = f"{server_url}/v1/embeddings"
 
         data = texts
         try:
@@ -54,7 +52,7 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
             raise InvokeAuthorizationError(f"Invalid server URL: {e}")
         except Exception as e:
             raise InvokeConnectionError(str(e))
-        
+
         if response.status_code != 200:
             if response.status_code == 400:
                 raise InvokeBadRequestError(response.text)
@@ -62,21 +60,17 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
                 raise InvokeAuthorizationError(response.text)
             elif response.status_code == 500:
                 raise InvokeServerUnavailableError(response.text)
-        
+
         try:
             resp = response.json()[0]
-            embeddings = resp['embeddings']
-            total_tokens = resp['num_tokens']
+            embeddings = resp["embeddings"]
+            total_tokens = resp["num_tokens"]
         except KeyError as e:
             raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
 
         usage = self._calc_response_usage(model=model, credentials=credentials, tokens=total_tokens)
 
-        result = TextEmbeddingResult(
-            model=model,
-            embeddings=embeddings,
-            usage=usage
-        )
+        result = TextEmbeddingResult(model=model, embeddings=embeddings, usage=usage)
 
         return result
 
@@ -104,9 +98,9 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         try:
-            self._invoke(model=model, credentials=credentials, texts=['ping'])
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
         except InvokeAuthorizationError:
-            raise CredentialsValidateFailedError('Invalid server_url')
+            raise CredentialsValidateFailedError("Invalid server_url")
 
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
@@ -119,23 +113,13 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                KeyError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [KeyError],
         }
-    
+
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -147,10 +131,7 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -161,7 +142,7 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/openrouter/llm/gpt-3.5-turbo.yaml b/api/core/model_runtime/model_providers/openrouter/llm/gpt-3.5-turbo.yaml
index 1737c50bb1..186c1cc663 100644
--- a/api/core/model_runtime/model_providers/openrouter/llm/gpt-3.5-turbo.yaml
+++ b/api/core/model_runtime/model_providers/openrouter/llm/gpt-3.5-turbo.yaml
@@ -26,7 +26,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openrouter/llm/gpt-4-32k.yaml b/api/core/model_runtime/model_providers/openrouter/llm/gpt-4-32k.yaml
index 2d55cf8565..8c2989b300 100644
--- a/api/core/model_runtime/model_providers/openrouter/llm/gpt-4-32k.yaml
+++ b/api/core/model_runtime/model_providers/openrouter/llm/gpt-4-32k.yaml
@@ -41,7 +41,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openrouter/llm/gpt-4.yaml b/api/core/model_runtime/model_providers/openrouter/llm/gpt-4.yaml
index 12015f6f64..ef19d4f6f0 100644
--- a/api/core/model_runtime/model_providers/openrouter/llm/gpt-4.yaml
+++ b/api/core/model_runtime/model_providers/openrouter/llm/gpt-4.yaml
@@ -41,7 +41,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o-2024-08-06.yaml b/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o-2024-08-06.yaml
index cf2de0f73a..0be325f55b 100644
--- a/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o-2024-08-06.yaml
+++ b/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o-2024-08-06.yaml
@@ -28,7 +28,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o-mini.yaml b/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o-mini.yaml
index de0bad4136..3b1d95643d 100644
--- a/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o-mini.yaml
+++ b/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o-mini.yaml
@@ -27,7 +27,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o.yaml b/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o.yaml
index 6945402c72..a8c97efdd6 100644
--- a/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o.yaml
+++ b/api/core/model_runtime/model_providers/openrouter/llm/gpt-4o.yaml
@@ -27,7 +27,7 @@ parameter_rules:
   - name: response_format
     label:
       zh_Hans: 回复格式
-      en_US: response_format
+      en_US: Response Format
     type: string
     help:
       zh_Hans: 指定模型必须输出的格式
diff --git a/api/core/model_runtime/model_providers/openrouter/llm/llm.py b/api/core/model_runtime/model_providers/openrouter/llm/llm.py
index e78ac4caf1..b6bb249a04 100644
--- a/api/core/model_runtime/model_providers/openrouter/llm/llm.py
+++ b/api/core/model_runtime/model_providers/openrouter/llm/llm.py
@@ -8,18 +8,22 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
 
 
 class OpenRouterLargeLanguageModel(OAIAPICompatLargeLanguageModel):
-
     def _update_credential(self, model: str, credentials: dict):
-        credentials['endpoint_url'] = "https://openrouter.ai/api/v1"
-        credentials['mode'] = self.get_model_mode(model).value
-        credentials['function_calling_type'] = 'tool_call'
-        return
+        credentials["endpoint_url"] = "https://openrouter.ai/api/v1"
+        credentials["mode"] = self.get_model_mode(model).value
+        credentials["function_calling_type"] = "tool_call"
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._update_credential(model, credentials)
 
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
@@ -29,9 +33,17 @@ class OpenRouterLargeLanguageModel(OAIAPICompatLargeLanguageModel):
 
         return super().validate_credentials(model, credentials)
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
-                  tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                  stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._update_credential(model, credentials)
 
         return super()._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
@@ -41,8 +53,13 @@ class OpenRouterLargeLanguageModel(OAIAPICompatLargeLanguageModel):
 
         return super().get_customizable_model_schema(model, credentials)
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         self._update_credential(model, credentials)
 
         return super().get_num_tokens(model, credentials, prompt_messages, tools)
diff --git a/api/core/model_runtime/model_providers/openrouter/openrouter.py b/api/core/model_runtime/model_providers/openrouter/openrouter.py
index 613f71deb1..2e59ab5059 100644
--- a/api/core/model_runtime/model_providers/openrouter/openrouter.py
+++ b/api/core/model_runtime/model_providers/openrouter/openrouter.py
@@ -8,17 +8,13 @@ logger = logging.getLogger(__name__)
 
 
 class OpenRouterProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(
-                model='openai/gpt-3.5-turbo',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="openai/gpt-3.5-turbo", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
-            raise ex
\ No newline at end of file
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
+            raise ex
diff --git a/api/core/model_runtime/model_providers/perfxcloud/llm/llm.py b/api/core/model_runtime/model_providers/perfxcloud/llm/llm.py
index c9116bf685..89cac665aa 100644
--- a/api/core/model_runtime/model_providers/perfxcloud/llm/llm.py
+++ b/api/core/model_runtime/model_providers/perfxcloud/llm/llm.py
@@ -13,11 +13,17 @@ from core.model_runtime.model_providers.openai.llm.llm import OpenAILargeLanguag
 
 
 class PerfXCloudLargeLanguageModel(OpenAILargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
 
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
@@ -27,8 +33,7 @@ class PerfXCloudLargeLanguageModel(OpenAILargeLanguageModel):
         super().validate_credentials(model, credentials)
 
     # refactored from openai model runtime, use cl100k_base for calculate token number
-    def _num_tokens_from_string(self, model: str, text: str,
-                                tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_string(self, model: str, text: str, tools: Optional[list[PromptMessageTool]] = None) -> int:
         """
         Calculate num tokens for text completion model with tiktoken package.
 
@@ -46,8 +51,9 @@ class PerfXCloudLargeLanguageModel(OpenAILargeLanguageModel):
         return num_tokens
 
     # refactored from openai model runtime, use cl100k_base for calculate token number
-    def _num_tokens_from_messages(self, model: str, messages: list[PromptMessage],
-                                  tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_messages(
+        self, model: str, messages: list[PromptMessage], tools: Optional[list[PromptMessageTool]] = None
+    ) -> int:
         """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
 
         Official documentation: https://github.com/openai/openai-cookbook/blob/
@@ -67,10 +73,10 @@ class PerfXCloudLargeLanguageModel(OpenAILargeLanguageModel):
                 #  which need to download the image and then get the resolution for calculation,
                 #  and will increase the request delay
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
 
                     value = text
 
@@ -101,10 +107,10 @@ class PerfXCloudLargeLanguageModel(OpenAILargeLanguageModel):
 
     @staticmethod
     def _add_custom_parameters(credentials: dict) -> None:
-        credentials['mode'] = 'chat'
-        credentials['openai_api_key']=credentials['api_key']
-        if 'endpoint_url' not in credentials or credentials['endpoint_url'] == "":
-            credentials['openai_api_base']='https://cloud.perfxlab.cn'
+        credentials["mode"] = "chat"
+        credentials["openai_api_key"] = credentials["api_key"]
+        if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
+            credentials["openai_api_base"] = "https://cloud.perfxlab.cn"
         else:
-            parsed_url = urlparse(credentials['endpoint_url'])
-            credentials['openai_api_base']=f"{parsed_url.scheme}://{parsed_url.netloc}"
+            parsed_url = urlparse(credentials["endpoint_url"])
+            credentials["openai_api_base"] = f"{parsed_url.scheme}://{parsed_url.netloc}"
diff --git a/api/core/model_runtime/model_providers/perfxcloud/perfxcloud.py b/api/core/model_runtime/model_providers/perfxcloud/perfxcloud.py
index 0854ef5185..450d22fb75 100644
--- a/api/core/model_runtime/model_providers/perfxcloud/perfxcloud.py
+++ b/api/core/model_runtime/model_providers/perfxcloud/perfxcloud.py
@@ -8,7 +8,6 @@ logger = logging.getLogger(__name__)
 
 
 class PerfXCloudProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -21,12 +20,9 @@ class PerfXCloudProvider(ModelProvider):
 
             # Use `Qwen2_72B_Chat_GPTQ_Int4` model for validate,
             # no matter what model you pass in, text completion model or chat model
-            model_instance.validate_credentials(
-                model='Qwen2-72B-Instruct-GPTQ-Int4',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="Qwen2-72B-Instruct-GPTQ-Int4", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py
index 11d57e3749..b62a2d2aaf 100644
--- a/api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py
@@ -19,17 +19,17 @@ from core.model_runtime.entities.model_entities import (
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
-from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOAI_API_Compat
+from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOaiApiCompat
 
 
-class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
+class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
     """
     Model class for an OpenAI API-compatible text embedding model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -39,30 +39,28 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
-       
-        # Prepare headers and payload for the request
-        headers = {
-            'Content-Type': 'application/json'
-        }
 
-        api_key = credentials.get('api_key')
+        # Prepare headers and payload for the request
+        headers = {"Content-Type": "application/json"}
+
+        api_key = credentials.get("api_key")
         if api_key:
             headers["Authorization"] = f"Bearer {api_key}"
 
-        if 'endpoint_url' not in credentials or credentials['endpoint_url'] == "":
-            endpoint_url='https://cloud.perfxlab.cn/v1/'
+        if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
+            endpoint_url = "https://cloud.perfxlab.cn/v1/"
         else:
-            endpoint_url = credentials.get('endpoint_url')
-            if not endpoint_url.endswith('/'):
-                endpoint_url += '/'
+            endpoint_url = credentials.get("endpoint_url")
+            if not endpoint_url.endswith("/"):
+                endpoint_url += "/"
 
-        endpoint_url = urljoin(endpoint_url, 'embeddings')
+        endpoint_url = urljoin(endpoint_url, "embeddings")
 
         extra_model_kwargs = {}
         if user:
-            extra_model_kwargs['user'] = user
+            extra_model_kwargs["user"] = user
 
-        extra_model_kwargs['encoding_format'] = 'float'
+        extra_model_kwargs["encoding_format"] = "float"
 
         # get model properties
         context_size = self._get_context_size(model, credentials)
@@ -73,7 +71,6 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
         used_tokens = 0
 
         for i, text in enumerate(texts):
-
             # Here token count is only an approximation based on the GPT2 tokenizer
             # TODO: Optimize for better token estimation and chunking
             num_tokens = self._get_num_tokens_by_gpt2(text)
@@ -81,7 +78,7 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
             if num_tokens >= context_size:
                 cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
                 # if num tokens is larger than context length, only use the start
-                inputs.append(text[0: cutoff])
+                inputs.append(text[0:cutoff])
             else:
                 inputs.append(text)
             indices += [i]
@@ -91,42 +88,25 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
 
         for i in _iter:
             # Prepare the payload for the request
-            payload = {
-                'input': inputs[i: i + max_chunks],
-                'model': model,
-                **extra_model_kwargs
-            }
+            payload = {"input": inputs[i : i + max_chunks], "model": model, **extra_model_kwargs}
 
             # Make the request to the OpenAI API
-            response = requests.post(
-                endpoint_url,
-                headers=headers,
-                data=json.dumps(payload),
-                timeout=(10, 300)
-            )
+            response = requests.post(endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300))
 
             response.raise_for_status()  # Raise an exception for HTTP errors
             response_data = response.json()
 
             # Extract embeddings and used tokens from the response
-            embeddings_batch = [data['embedding'] for data in response_data['data']]
-            embedding_used_tokens = response_data['usage']['total_tokens']
+            embeddings_batch = [data["embedding"] for data in response_data["data"]]
+            embedding_used_tokens = response_data["usage"]["total_tokens"]
 
             used_tokens += embedding_used_tokens
             batched_embeddings += embeddings_batch
 
         # calc usage
-        usage = self._calc_response_usage(
-            model=model,
-            credentials=credentials,
-            tokens=used_tokens
-        )
-        
-        return TextEmbeddingResult(
-            embeddings=batched_embeddings,
-            usage=usage,
-            model=model
-        )
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
+
+        return TextEmbeddingResult(embeddings=batched_embeddings, usage=usage, model=model)
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
@@ -148,48 +128,38 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
         :return:
         """
         try:
-            headers = {
-                'Content-Type': 'application/json'
-            }
+            headers = {"Content-Type": "application/json"}
 
-            api_key = credentials.get('api_key')
+            api_key = credentials.get("api_key")
 
             if api_key:
                 headers["Authorization"] = f"Bearer {api_key}"
 
-            if 'endpoint_url' not in credentials or credentials['endpoint_url'] == "":
-                endpoint_url='https://cloud.perfxlab.cn/v1/'
+            if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
+                endpoint_url = "https://cloud.perfxlab.cn/v1/"
             else:
-                endpoint_url = credentials.get('endpoint_url')
-                if not endpoint_url.endswith('/'):
-                    endpoint_url += '/'
+                endpoint_url = credentials.get("endpoint_url")
+                if not endpoint_url.endswith("/"):
+                    endpoint_url += "/"
 
-            endpoint_url = urljoin(endpoint_url, 'embeddings')
+            endpoint_url = urljoin(endpoint_url, "embeddings")
 
-            payload = {
-                'input': 'ping',
-                'model': model
-            }
+            payload = {"input": "ping", "model": model}
 
-            response = requests.post(
-                url=endpoint_url,
-                headers=headers,
-                data=json.dumps(payload),
-                timeout=(10, 300)
-            )
+            response = requests.post(url=endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300))
 
             if response.status_code != 200:
                 raise CredentialsValidateFailedError(
-                    f'Credentials validation failed with status code {response.status_code}')
+                    f"Credentials validation failed with status code {response.status_code}"
+                )
 
             try:
                 json_result = response.json()
             except json.JSONDecodeError as e:
-                raise CredentialsValidateFailedError('Credentials validation failed: JSON decode error')
+                raise CredentialsValidateFailedError("Credentials validation failed: JSON decode error")
 
-            if 'model' not in json_result:
-                raise CredentialsValidateFailedError(
-                    'Credentials validation failed: invalid response')
+            if "model" not in json_result:
+                raise CredentialsValidateFailedError("Credentials validation failed: invalid response")
         except CredentialsValidateFailedError:
             raise
         except Exception as ex:
@@ -197,7 +167,7 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            generate custom model entities from credentials
+        generate custom model entities from credentials
         """
         entity = AIModelEntity(
             model=model,
@@ -205,20 +175,19 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size')),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],
             pricing=PriceConfig(
-                input=Decimal(credentials.get('input_price', 0)),
-                unit=Decimal(credentials.get('unit', 0)),
-                currency=credentials.get('currency', "USD")
-            )
+                input=Decimal(credentials.get("input_price", 0)),
+                unit=Decimal(credentials.get("unit", 0)),
+                currency=credentials.get("currency", "USD"),
+            ),
         )
 
         return entity
 
-
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -230,10 +199,7 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -244,7 +210,7 @@ class OAICompatEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/replicate/_common.py b/api/core/model_runtime/model_providers/replicate/_common.py
index 29d8427d8e..915f6e0eef 100644
--- a/api/core/model_runtime/model_providers/replicate/_common.py
+++ b/api/core/model_runtime/model_providers/replicate/_common.py
@@ -4,12 +4,6 @@ from core.model_runtime.errors.invoke import InvokeBadRequestError, InvokeError
 
 
 class _CommonReplicate:
-
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
-        return {
-            InvokeBadRequestError: [
-                ReplicateError,
-                ModelError
-            ]
-        }
+        return {InvokeBadRequestError: [ReplicateError, ModelError]}
diff --git a/api/core/model_runtime/model_providers/replicate/llm/llm.py b/api/core/model_runtime/model_providers/replicate/llm/llm.py
index 31b81a829e..3641b35dc0 100644
--- a/api/core/model_runtime/model_providers/replicate/llm/llm.py
+++ b/api/core/model_runtime/model_providers/replicate/llm/llm.py
@@ -28,16 +28,22 @@ from core.model_runtime.model_providers.replicate._common import _CommonReplicat
 
 
 class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
+        model_version = ""
+        if "model_version" in credentials:
+            model_version = credentials["model_version"]
 
-    def _invoke(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None, stream: bool = True,
-                user: Optional[str] = None) -> Union[LLMResult, Generator]:
-
-        model_version = ''
-        if 'model_version' in credentials:
-            model_version = credentials['model_version']
-
-        client = ReplicateClient(api_token=credentials['replicate_api_token'], timeout=30)
+        client = ReplicateClient(api_token=credentials["replicate_api_token"], timeout=30)
         model_info = client.models.get(model)
 
         if model_version:
@@ -48,39 +54,43 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
         inputs = {**model_parameters}
 
         if prompt_messages[0].role == PromptMessageRole.SYSTEM:
-            if 'system_prompt' in model_info_version.openapi_schema['components']['schemas']['Input']['properties']:
-                inputs['system_prompt'] = prompt_messages[0].content
-            inputs['prompt'] = prompt_messages[1].content
+            if "system_prompt" in model_info_version.openapi_schema["components"]["schemas"]["Input"]["properties"]:
+                inputs["system_prompt"] = prompt_messages[0].content
+            inputs["prompt"] = prompt_messages[1].content
         else:
-            inputs['prompt'] = prompt_messages[0].content
+            inputs["prompt"] = prompt_messages[0].content
 
-        prediction = client.predictions.create(
-            version=model_info_version, input=inputs
-        )
+        prediction = client.predictions.create(version=model_info_version, input=inputs)
 
         if stream:
             return self._handle_generate_stream_response(model, credentials, prediction, stop, prompt_messages)
         return self._handle_generate_response(model, credentials, prediction, stop, prompt_messages)
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         prompt = self._convert_messages_to_prompt(prompt_messages)
         return self._get_num_tokens_by_gpt2(prompt)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        if 'replicate_api_token' not in credentials:
-            raise CredentialsValidateFailedError('Replicate Access Token must be provided.')
+        if "replicate_api_token" not in credentials:
+            raise CredentialsValidateFailedError("Replicate Access Token must be provided.")
 
-        model_version = ''
-        if 'model_version' in credentials:
-            model_version = credentials['model_version']
+        model_version = ""
+        if "model_version" in credentials:
+            model_version = credentials["model_version"]
 
         if model.count("/") != 1:
-            raise CredentialsValidateFailedError('Replicate Model Name must be provided, '
-                                                 'format: {user_name}/{model_name}')
+            raise CredentialsValidateFailedError(
+                "Replicate Model Name must be provided, format: {user_name}/{model_name}"
+            )
 
         try:
-            client = ReplicateClient(api_token=credentials['replicate_api_token'], timeout=30)
+            client = ReplicateClient(api_token=credentials["replicate_api_token"], timeout=30)
             model_info = client.models.get(model)
 
             if model_version:
@@ -91,45 +101,44 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
             self._check_text_generation_model(model_info_version, model, model_version, model_info.description)
         except ReplicateError as e:
             raise CredentialsValidateFailedError(
-                f"Model {model}:{model_version} not exists, cause: {e.__class__.__name__}:{str(e)}")
+                f"Model {model}:{model_version} not exists, cause: {e.__class__.__name__}:{str(e)}"
+            )
         except Exception as e:
             raise CredentialsValidateFailedError(str(e))
 
     @staticmethod
     def _check_text_generation_model(model_info_version, model_name, version, description):
-        if 'language model' in description.lower():
+        if "language model" in description.lower():
             return
 
-        if 'temperature' not in model_info_version.openapi_schema['components']['schemas']['Input']['properties'] \
-                or 'top_p' not in model_info_version.openapi_schema['components']['schemas']['Input']['properties'] \
-                or 'top_k' not in model_info_version.openapi_schema['components']['schemas']['Input']['properties']:
+        if (
+            "temperature" not in model_info_version.openapi_schema["components"]["schemas"]["Input"]["properties"]
+            or "top_p" not in model_info_version.openapi_schema["components"]["schemas"]["Input"]["properties"]
+            or "top_k" not in model_info_version.openapi_schema["components"]["schemas"]["Input"]["properties"]
+        ):
             raise CredentialsValidateFailedError(f"Model {model_name}:{version} is not a Text Generation model.")
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
-        model_type = LLMMode.CHAT if model.endswith('-chat') else LLMMode.COMPLETION
+        model_type = LLMMode.CHAT if model.endswith("-chat") else LLMMode.COMPLETION
 
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
-            model_properties={
-                ModelPropertyKey.MODE: model_type.value
-            },
-            parameter_rules=self._get_customizable_model_parameter_rules(model, credentials)
+            model_properties={ModelPropertyKey.MODE: model_type.value},
+            parameter_rules=self._get_customizable_model_parameter_rules(model, credentials),
         )
 
         return entity
 
     @classmethod
     def _get_customizable_model_parameter_rules(cls, model: str, credentials: dict) -> list[ParameterRule]:
-        model_version = ''
-        if 'model_version' in credentials:
-            model_version = credentials['model_version']
+        model_version = ""
+        if "model_version" in credentials:
+            model_version = credentials["model_version"]
 
-        client = ReplicateClient(api_token=credentials['replicate_api_token'], timeout=30)
+        client = ReplicateClient(api_token=credentials["replicate_api_token"], timeout=30)
         model_info = client.models.get(model)
 
         if model_version:
@@ -140,15 +149,13 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
         parameter_rules = []
 
         input_properties = sorted(
-            model_info_version.openapi_schema["components"]["schemas"]["Input"][
-                "properties"
-            ].items(),
+            model_info_version.openapi_schema["components"]["schemas"]["Input"]["properties"].items(),
             key=lambda item: item[1].get("x-order", 0),
         )
 
         for key, value in input_properties:
-            if key not in ['system_prompt', 'prompt'] and 'stop' not in key:
-                value_type = value.get('type')
+            if key not in {"system_prompt", "prompt"} and "stop" not in key:
+                value_type = value.get("type")
 
                 if not value_type:
                     continue
@@ -157,28 +164,28 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
 
                 rule = ParameterRule(
                     name=key,
-                    label={
-                        'en_US': value['title']
-                    },
+                    label={"en_US": value["title"]},
                     type=param_type,
                     help={
-                        'en_US': value.get('description'),
+                        "en_US": value.get("description"),
                     },
                     required=False,
-                    default=value.get('default'),
-                    min=value.get('minimum'),
-                    max=value.get('maximum')
+                    default=value.get("default"),
+                    min=value.get("minimum"),
+                    max=value.get("maximum"),
                 )
                 parameter_rules.append(rule)
 
         return parameter_rules
 
-    def _handle_generate_stream_response(self,
-                                         model: str,
-                                         credentials: dict,
-                                         prediction: Prediction,
-                                         stop: list[str],
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        prediction: Prediction,
+        stop: list[str],
+        prompt_messages: list[PromptMessage],
+    ) -> Generator:
         index = -1
         current_completion: str = ""
         stop_condition_reached = False
@@ -189,7 +196,7 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
         for output in prediction.output_iterator():
             current_completion += output
 
-            if not is_prediction_output_finished and prediction.status == 'succeeded':
+            if not is_prediction_output_finished and prediction.status == "succeeded":
                 prediction_output_length = len(prediction.output) - 1
                 is_prediction_output_finished = True
 
@@ -207,18 +214,13 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
 
             index += 1
 
-            assistant_prompt_message = AssistantPromptMessage(
-                content=output if output else ''
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=output or "")
 
             if index < prediction_output_length:
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=index,
-                        message=assistant_prompt_message
-                    )
+                    delta=LLMResultChunkDelta(index=index, message=assistant_prompt_message),
                 )
             else:
                 prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
@@ -229,15 +231,17 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=index,
-                        message=assistant_prompt_message,
-                        usage=usage
-                    )
+                    delta=LLMResultChunkDelta(index=index, message=assistant_prompt_message, usage=usage),
                 )
 
-    def _handle_generate_response(self, model: str, credentials: dict, prediction: Prediction, stop: list[str],
-                                  prompt_messages: list[PromptMessage]) -> LLMResult:
+    def _handle_generate_response(
+        self,
+        model: str,
+        credentials: dict,
+        prediction: Prediction,
+        stop: list[str],
+        prompt_messages: list[PromptMessage],
+    ) -> LLMResult:
         current_completion: str = ""
         stop_condition_reached = False
         for output in prediction.output_iterator():
@@ -255,9 +259,7 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
             if stop_condition_reached:
                 break
 
-        assistant_prompt_message = AssistantPromptMessage(
-            content=current_completion
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=current_completion)
 
         prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
         completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
@@ -275,21 +277,13 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
 
     @classmethod
     def _get_parameter_type(cls, param_type: str) -> str:
-        type_mapping = {
-            'integer': 'int',
-            'number': 'float',
-            'boolean': 'boolean',
-            'string': 'string'
-        }
+        type_mapping = {"integer": "int", "number": "float", "boolean": "boolean", "string": "string"}
         return type_mapping.get(param_type)
 
     def _convert_messages_to_prompt(self, messages: list[PromptMessage]) -> str:
         messages = messages.copy()  # don't mutate the original list
 
-        text = "".join(
-            self._convert_one_message_to_text(message)
-            for message in messages
-        )
+        text = "".join(self._convert_one_message_to_text(message) for message in messages)
 
         return text.rstrip()
 
diff --git a/api/core/model_runtime/model_providers/replicate/replicate.py b/api/core/model_runtime/model_providers/replicate/replicate.py
index 3a5c9b84a0..ca137579c9 100644
--- a/api/core/model_runtime/model_providers/replicate/replicate.py
+++ b/api/core/model_runtime/model_providers/replicate/replicate.py
@@ -6,6 +6,5 @@ logger = logging.getLogger(__name__)
 
 
 class ReplicateProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         pass
diff --git a/api/core/model_runtime/model_providers/replicate/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/replicate/text_embedding/text_embedding.py
index 0e4cdbf5bc..71b6fb99c4 100644
--- a/api/core/model_runtime/model_providers/replicate/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/replicate/text_embedding/text_embedding.py
@@ -13,32 +13,27 @@ from core.model_runtime.model_providers.replicate._common import _CommonReplicat
 
 
 class ReplicateEmbeddingModel(_CommonReplicate, TextEmbeddingModel):
-    def _invoke(self, model: str, credentials: dict, texts: list[str],
-                user: Optional[str] = None) -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
+        client = ReplicateClient(api_token=credentials["replicate_api_token"], timeout=30)
 
-        client = ReplicateClient(api_token=credentials['replicate_api_token'], timeout=30)
-
-        if 'model_version' in credentials:
-            model_version = credentials['model_version']
+        if "model_version" in credentials:
+            model_version = credentials["model_version"]
         else:
             model_info = client.models.get(model)
             model_version = model_info.latest_version.id
 
-        replicate_model_version = f'{model}:{model_version}'
+        replicate_model_version = f"{model}:{model_version}"
 
         text_input_key = self._get_text_input_key(model, model_version, client)
 
-        embeddings = self._generate_embeddings_by_text_input_key(client, replicate_model_version, text_input_key,
-                                                                 texts)
+        embeddings = self._generate_embeddings_by_text_input_key(client, replicate_model_version, text_input_key, texts)
 
         tokens = self.get_num_tokens(model, credentials, texts)
         usage = self._calc_response_usage(model, credentials, tokens)
 
-        return TextEmbeddingResult(
-            model=model,
-            embeddings=embeddings,
-            usage=usage
-        )
+        return TextEmbeddingResult(model=model, embeddings=embeddings, usage=usage)
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         num_tokens = 0
@@ -47,39 +42,35 @@ class ReplicateEmbeddingModel(_CommonReplicate, TextEmbeddingModel):
         return num_tokens
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        if 'replicate_api_token' not in credentials:
-            raise CredentialsValidateFailedError('Replicate Access Token must be provided.')
+        if "replicate_api_token" not in credentials:
+            raise CredentialsValidateFailedError("Replicate Access Token must be provided.")
 
         try:
-            client = ReplicateClient(api_token=credentials['replicate_api_token'], timeout=30)
+            client = ReplicateClient(api_token=credentials["replicate_api_token"], timeout=30)
 
-            if 'model_version' in credentials:
-                model_version = credentials['model_version']
+            if "model_version" in credentials:
+                model_version = credentials["model_version"]
             else:
                 model_info = client.models.get(model)
                 model_version = model_info.latest_version.id
 
-            replicate_model_version = f'{model}:{model_version}'
+            replicate_model_version = f"{model}:{model_version}"
 
             text_input_key = self._get_text_input_key(model, model_version, client)
 
-            self._generate_embeddings_by_text_input_key(client, replicate_model_version, text_input_key,
-                                                        ['Hello worlds!'])
+            self._generate_embeddings_by_text_input_key(
+                client, replicate_model_version, text_input_key, ["Hello worlds!"]
+            )
         except Exception as e:
             raise CredentialsValidateFailedError(str(e))
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TEXT_EMBEDDING,
-            model_properties={
-                'context_size': 4096,
-                'max_chunks': 1
-            }
+            model_properties={"context_size": 4096, "max_chunks": 1},
         )
         return entity
 
@@ -90,49 +81,45 @@ class ReplicateEmbeddingModel(_CommonReplicate, TextEmbeddingModel):
 
         # sort through the openapi schema to get the name of text, texts or inputs
         input_properties = sorted(
-            model_info_version.openapi_schema["components"]["schemas"]["Input"][
-                "properties"
-            ].items(),
+            model_info_version.openapi_schema["components"]["schemas"]["Input"]["properties"].items(),
             key=lambda item: item[1].get("x-order", 0),
         )
 
         for input_property in input_properties:
-            if input_property[0] in ('text', 'texts', 'inputs'):
+            if input_property[0] in {"text", "texts", "inputs"}:
                 text_input_key = input_property[0]
                 return text_input_key
 
-        return ''
+        return ""
 
     @staticmethod
-    def _generate_embeddings_by_text_input_key(client: ReplicateClient, replicate_model_version: str,
-                                               text_input_key: str, texts: list[str]) -> list[list[float]]:
-
-        if text_input_key in ('text', 'inputs'):
+    def _generate_embeddings_by_text_input_key(
+        client: ReplicateClient, replicate_model_version: str, text_input_key: str, texts: list[str]
+    ) -> list[list[float]]:
+        if text_input_key in {"text", "inputs"}:
             embeddings = []
             for text in texts:
-                result = client.run(replicate_model_version, input={
-                    text_input_key: text
-                })
-                embeddings.append(result[0].get('embedding'))
+                result = client.run(replicate_model_version, input={text_input_key: text})
+                embeddings.append(result[0].get("embedding"))
 
             return [list(map(float, e)) for e in embeddings]
-        elif 'texts' == text_input_key:
-            result = client.run(replicate_model_version, input={
-                'texts': json.dumps(texts),
-                "batch_size": 4,
-                "convert_to_numpy": False,
-                "normalize_embeddings": True
-            })
+        elif "texts" == text_input_key:
+            result = client.run(
+                replicate_model_version,
+                input={
+                    "texts": json.dumps(texts),
+                    "batch_size": 4,
+                    "convert_to_numpy": False,
+                    "normalize_embeddings": True,
+                },
+            )
             return result
         else:
-            raise ValueError(f'embeddings input key is invalid: {text_input_key}')
+            raise ValueError(f"embeddings input key is invalid: {text_input_key}")
 
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -143,7 +130,7 @@ class ReplicateEmbeddingModel(_CommonReplicate, TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/sagemaker/llm/llm.py b/api/core/model_runtime/model_providers/sagemaker/llm/llm.py
index f8e7757a96..2edd13d56d 100644
--- a/api/core/model_runtime/model_providers/sagemaker/llm/llm.py
+++ b/api/core/model_runtime/model_providers/sagemaker/llm/llm.py
@@ -1,17 +1,36 @@
 import json
 import logging
-from collections.abc import Generator
-from typing import Any, Optional, Union
+import re
+from collections.abc import Generator, Iterator
+from typing import Any, Optional, Union, cast
 
+# from openai.types.chat import ChatCompletion, ChatCompletionChunk
 import boto3
+from sagemaker import Predictor, serializers
+from sagemaker.session import Session
 
-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
+    ImagePromptMessageContent,
     PromptMessage,
+    PromptMessageContent,
+    PromptMessageContentType,
     PromptMessageTool,
+    SystemPromptMessage,
+    ToolPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import (
+    AIModelEntity,
+    FetchFrom,
+    I18nObject,
+    ModelFeature,
+    ModelPropertyKey,
+    ModelType,
+    ParameterRule,
+    ParameterType,
 )
-from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, I18nObject, ModelType
 from core.model_runtime.errors.invoke import (
     InvokeAuthorizationError,
     InvokeBadRequestError,
@@ -26,17 +45,160 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 logger = logging.getLogger(__name__)
 
 
+def inference(predictor, messages: list[dict[str, Any]], params: dict[str, Any], stop: list, stream=False):
+    """
+    params:
+    predictor : Sagemaker Predictor
+    messages (List[Dict[str,Any]]): message list。
+                messages = [
+                {"role": "system", "content":"please answer in Chinese"},
+                {"role": "user", "content": "who are you? what are you doing?"},
+            ]
+    params (Dict[str,Any]): model parameters for LLM。
+    stream (bool): False by default。
+
+    response:
+    result of inference if stream is False
+    Iterator of Chunks if stream is True
+    """
+    payload = {
+        "model": params.get("model_name"),
+        "stop": stop,
+        "messages": messages,
+        "stream": stream,
+        "max_tokens": params.get("max_new_tokens", params.get("max_tokens", 2048)),
+        "temperature": params.get("temperature", 0.1),
+        "top_p": params.get("top_p", 0.9),
+    }
+
+    if not stream:
+        response = predictor.predict(payload)
+        return response
+    else:
+        response_stream = predictor.predict_stream(payload)
+        return response_stream
+
+
 class SageMakerLargeLanguageModel(LargeLanguageModel):
     """
     Model class for Cohere large language model.
     """
-    sagemaker_client: Any = None
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    sagemaker_client: Any = None
+    sagemaker_sess: Any = None
+    predictor: Any = None
+
+    def _handle_chat_generate_response(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool],
+        resp: bytes,
+    ) -> LLMResult:
+        """
+        handle normal chat generate response
+        """
+        resp_obj = json.loads(resp.decode("utf-8"))
+        resp_str = resp_obj.get("choices")[0].get("message").get("content")
+
+        if len(resp_str) == 0:
+            raise InvokeServerUnavailableError("Empty response")
+
+        assistant_prompt_message = AssistantPromptMessage(content=resp_str, tool_calls=[])
+
+        prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
+        completion_tokens = self._num_tokens_from_messages(messages=[assistant_prompt_message], tools=tools)
+
+        usage = self._calc_response_usage(
+            model=model, credentials=credentials, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens
+        )
+
+        response = LLMResult(
+            model=model,
+            prompt_messages=prompt_messages,
+            system_fingerprint=None,
+            usage=usage,
+            message=assistant_prompt_message,
+        )
+
+        return response
+
+    def _handle_chat_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool],
+        resp: Iterator[bytes],
+    ) -> Generator:
+        """
+        handle stream chat generate response
+        """
+        full_response = ""
+        buffer = ""
+        for chunk_bytes in resp:
+            buffer += chunk_bytes.decode("utf-8")
+            last_idx = 0
+            for match in re.finditer(r"^data:\s*(.+?)(\n\n)", buffer):
+                try:
+                    data = json.loads(match.group(1).strip())
+                    last_idx = match.span()[1]
+
+                    if "content" in data["choices"][0]["delta"]:
+                        chunk_content = data["choices"][0]["delta"]["content"]
+                        assistant_prompt_message = AssistantPromptMessage(content=chunk_content, tool_calls=[])
+
+                        if data["choices"][0]["finish_reason"] is not None:
+                            temp_assistant_prompt_message = AssistantPromptMessage(content=full_response, tool_calls=[])
+                            prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
+                            completion_tokens = self._num_tokens_from_messages(
+                                messages=[temp_assistant_prompt_message], tools=[]
+                            )
+                            usage = self._calc_response_usage(
+                                model=model,
+                                credentials=credentials,
+                                prompt_tokens=prompt_tokens,
+                                completion_tokens=completion_tokens,
+                            )
+
+                            yield LLMResultChunk(
+                                model=model,
+                                prompt_messages=prompt_messages,
+                                system_fingerprint=None,
+                                delta=LLMResultChunkDelta(
+                                    index=0,
+                                    message=assistant_prompt_message,
+                                    finish_reason=data["choices"][0]["finish_reason"],
+                                    usage=usage,
+                                ),
+                            )
+                        else:
+                            yield LLMResultChunk(
+                                model=model,
+                                prompt_messages=prompt_messages,
+                                system_fingerprint=None,
+                                delta=LLMResultChunkDelta(index=0, message=assistant_prompt_message),
+                            )
+
+                            full_response += chunk_content
+                except (json.JSONDecodeError, KeyError, IndexError) as e:
+                    logger.info("json parse exception, content: {}".format(match.group(1).strip()))
+                    pass
+
+            buffer = buffer[last_idx:]
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -50,58 +212,153 @@ class SageMakerLargeLanguageModel(LargeLanguageModel):
         :param user: unique user id
         :return: full response or stream response chunk generator result
         """
-        # get model mode
-        model_mode = self.get_model_mode(model, credentials)
-
         if not self.sagemaker_client:
-            access_key = credentials.get('access_key')
-            secret_key = credentials.get('secret_key')
-            aws_region = credentials.get('aws_region')
+            access_key = credentials.get("access_key")
+            secret_key = credentials.get("secret_key")
+            aws_region = credentials.get("aws_region")
             if aws_region:
                 if access_key and secret_key:
-                    self.sagemaker_client = boto3.client("sagemaker-runtime", 
+                    self.sagemaker_client = boto3.client(
+                        "sagemaker-runtime",
                         aws_access_key_id=access_key,
                         aws_secret_access_key=secret_key,
-                        region_name=aws_region)
+                        region_name=aws_region,
+                    )
                 else:
                     self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
             else:
                 self.sagemaker_client = boto3.client("sagemaker-runtime")
 
+            sagemaker_session = Session(sagemaker_runtime_client=self.sagemaker_client)
+            self.predictor = Predictor(
+                endpoint_name=credentials.get("sagemaker_endpoint"),
+                sagemaker_session=sagemaker_session,
+                serializer=serializers.JSONSerializer(),
+            )
 
-        sagemaker_endpoint = credentials.get('sagemaker_endpoint')
-        response_model = self.sagemaker_client.invoke_endpoint(
-                    EndpointName=sagemaker_endpoint,
-                    Body=json.dumps(
-                    {
-                        "inputs": prompt_messages[0].content,
-                        "parameters": { "stop" : stop},
-                        "history" : []
-                    }
-                    ),
-                    ContentType="application/json",
-                )
-
-        assistant_text = response_model['Body'].read().decode('utf8')
-
-        # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_text
+        messages: list[dict[str, Any]] = [{"role": p.role.value, "content": p.content} for p in prompt_messages]
+        response = inference(
+            predictor=self.predictor, messages=messages, params=model_parameters, stop=stop, stream=stream
         )
 
-        usage = self._calc_response_usage(model, credentials, 0, 0)
+        if stream:
+            if tools and len(tools) > 0:
+                raise InvokeBadRequestError(f"{model}'s tool calls does not support stream mode")
 
-        response = LLMResult(
-            model=model,
-            prompt_messages=prompt_messages,
-            message=assistant_prompt_message,
-            usage=usage
+            return self._handle_chat_stream_response(
+                model=model, credentials=credentials, prompt_messages=prompt_messages, tools=tools, resp=response
+            )
+        return self._handle_chat_generate_response(
+            model=model, credentials=credentials, prompt_messages=prompt_messages, tools=tools, resp=response
         )
 
-        return response
+    def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict:
+        """
+        Convert PromptMessage to dict for OpenAI Compatibility API
+        """
+        if isinstance(message, UserPromptMessage):
+            message = cast(UserPromptMessage, message)
+            if isinstance(message.content, str):
+                message_dict = {"role": "user", "content": message.content}
+            else:
+                sub_messages = []
+                for message_content in message.content:
+                    if message_content.type == PromptMessageContentType.TEXT:
+                        message_content = cast(PromptMessageContent, message_content)
+                        sub_message_dict = {"type": "text", "text": message_content.data}
+                        sub_messages.append(sub_message_dict)
+                    elif message_content.type == PromptMessageContentType.IMAGE:
+                        message_content = cast(ImagePromptMessageContent, message_content)
+                        sub_message_dict = {
+                            "type": "image_url",
+                            "image_url": {"url": message_content.data, "detail": message_content.detail.value},
+                        }
+                        sub_messages.append(sub_message_dict)
+                message_dict = {"role": "user", "content": sub_messages}
+        elif isinstance(message, AssistantPromptMessage):
+            message = cast(AssistantPromptMessage, message)
+            message_dict = {"role": "assistant", "content": message.content}
+            if message.tool_calls and len(message.tool_calls) > 0:
+                message_dict["function_call"] = {
+                    "name": message.tool_calls[0].function.name,
+                    "arguments": message.tool_calls[0].function.arguments,
+                }
+        elif isinstance(message, SystemPromptMessage):
+            message = cast(SystemPromptMessage, message)
+            message_dict = {"role": "system", "content": message.content}
+        elif isinstance(message, ToolPromptMessage):
+            message = cast(ToolPromptMessage, message)
+            message_dict = {"tool_call_id": message.tool_call_id, "role": "tool", "content": message.content}
+        else:
+            raise ValueError(f"Unknown message type {type(message)}")
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+        return message_dict
+
+    def _num_tokens_from_messages(
+        self, messages: list[PromptMessage], tools: list[PromptMessageTool], is_completion_model: bool = False
+    ) -> int:
+        def tokens(text: str):
+            return self._get_num_tokens_by_gpt2(text)
+
+        if is_completion_model:
+            return sum(tokens(str(message.content)) for message in messages)
+
+        tokens_per_message = 3
+        tokens_per_name = 1
+
+        num_tokens = 0
+        messages_dict = [self._convert_prompt_message_to_dict(m) for m in messages]
+        for message in messages_dict:
+            num_tokens += tokens_per_message
+            for key, value in message.items():
+                if isinstance(value, list):
+                    text = ""
+                    for item in value:
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
+
+                    value = text
+
+                if key == "tool_calls":
+                    for tool_call in value:
+                        for t_key, t_value in tool_call.items():
+                            num_tokens += tokens(t_key)
+                            if t_key == "function":
+                                for f_key, f_value in t_value.items():
+                                    num_tokens += tokens(f_key)
+                                    num_tokens += tokens(f_value)
+                            else:
+                                num_tokens += tokens(t_key)
+                                num_tokens += tokens(t_value)
+                if key == "function_call":
+                    for t_key, t_value in value.items():
+                        num_tokens += tokens(t_key)
+                        if t_key == "function":
+                            for f_key, f_value in t_value.items():
+                                num_tokens += tokens(f_key)
+                                num_tokens += tokens(f_value)
+                        else:
+                            num_tokens += tokens(t_key)
+                            num_tokens += tokens(t_value)
+                else:
+                    num_tokens += tokens(str(value))
+
+                if key == "name":
+                    num_tokens += tokens_per_name
+        num_tokens += 3
+
+        if tools:
+            num_tokens += self._num_tokens_for_tools(tools)
+
+        return num_tokens
+
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -112,10 +369,8 @@ class SageMakerLargeLanguageModel(LargeLanguageModel):
         :return:
         """
         # get model mode
-        model_mode = self.get_model_mode(model)
-
         try:
-            return 0
+            return self._num_tokens_from_messages(prompt_messages, tools)
         except Exception as e:
             raise self._transform_invoke_error(e)
 
@@ -129,7 +384,7 @@ class SageMakerLargeLanguageModel(LargeLanguageModel):
         """
         try:
             # get model mode
-            model_mode = self.get_model_mode(model)
+            pass
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
@@ -144,95 +399,63 @@ class SageMakerLargeLanguageModel(LargeLanguageModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                InvokeBadRequestError,
-                KeyError,
-                ValueError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [InvokeBadRequestError, KeyError, ValueError],
         }
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
         rules = [
             ParameterRule(
-                name='temperature',
+                name="temperature",
                 type=ParameterType.FLOAT,
-                use_template='temperature',
-                label=I18nObject(
-                    zh_Hans='温度',
-                    en_US='Temperature'
-                ),
+                use_template="temperature",
+                label=I18nObject(zh_Hans="温度", en_US="Temperature"),
             ),
             ParameterRule(
-                name='top_p',
+                name="top_p",
                 type=ParameterType.FLOAT,
-                use_template='top_p',
-                label=I18nObject(
-                    zh_Hans='Top P',
-                    en_US='Top P'
-                )
+                use_template="top_p",
+                label=I18nObject(zh_Hans="Top P", en_US="Top P"),
             ),
             ParameterRule(
-                name='max_tokens',
+                name="max_tokens",
                 type=ParameterType.INT,
-                use_template='max_tokens',
+                use_template="max_tokens",
                 min=1,
-                max=credentials.get('context_length', 2048),
+                max=credentials.get("context_length", 2048),
                 default=512,
-                label=I18nObject(
-                    zh_Hans='最大生成长度',
-                    en_US='Max Tokens'
-                )
-            )
+                label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"),
+            ),
         ]
 
-        completion_type = LLMMode.value_of(credentials["mode"])
-
-        if completion_type == LLMMode.CHAT:
-            print(f"completion_type : {LLMMode.CHAT.value}") 
-
-        if completion_type == LLMMode.COMPLETION:
-            print(f"completion_type : {LLMMode.COMPLETION.value}") 
+        completion_type = LLMMode.value_of(credentials["mode"]).value
 
         features = []
 
-        support_function_call = credentials.get('support_function_call', False)
+        support_function_call = credentials.get("support_function_call", False)
         if support_function_call:
             features.append(ModelFeature.TOOL_CALL)
 
-        support_vision = credentials.get('support_vision', False)
+        support_vision = credentials.get("support_vision", False)
         if support_vision:
             features.append(ModelFeature.VISION)
 
-        context_length = credentials.get('context_length', 2048)
+        context_length = credentials.get("context_length", 2048)
 
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
             features=features,
-            model_properties={
-                ModelPropertyKey.MODE: completion_type,
-                ModelPropertyKey.CONTEXT_SIZE: context_length
-            },
-            parameter_rules=rules
+            model_properties={ModelPropertyKey.MODE: completion_type, ModelPropertyKey.CONTEXT_SIZE: context_length},
+            parameter_rules=rules,
         )
 
         return entity
diff --git a/api/core/model_runtime/model_providers/sagemaker/rerank/rerank.py b/api/core/model_runtime/model_providers/sagemaker/rerank/rerank.py
index 0b06f54ef1..959dff6a21 100644
--- a/api/core/model_runtime/model_providers/sagemaker/rerank/rerank.py
+++ b/api/core/model_runtime/model_providers/sagemaker/rerank/rerank.py
@@ -1,5 +1,6 @@
 import json
 import logging
+import operator
 from typing import Any, Optional
 
 import boto3
@@ -20,34 +21,36 @@ from core.model_runtime.model_providers.__base.rerank_model import RerankModel
 
 logger = logging.getLogger(__name__)
 
+
 class SageMakerRerankModel(RerankModel):
     """
-    Model class for Cohere rerank model.
+    Model class for SageMaker rerank model.
     """
+
     sagemaker_client: Any = None
 
-    def _sagemaker_rerank(self, query_input: str, docs: list[str], rerank_endpoint:str):
-        inputs = [query_input]*len(docs)
+    def _sagemaker_rerank(self, query_input: str, docs: list[str], rerank_endpoint: str):
+        inputs = [query_input] * len(docs)
         response_model = self.sagemaker_client.invoke_endpoint(
             EndpointName=rerank_endpoint,
-            Body=json.dumps(
-                {
-                    "inputs": inputs,
-                    "docs": docs
-                }
-            ),
+            Body=json.dumps({"inputs": inputs, "docs": docs}),
             ContentType="application/json",
         )
-        json_str = response_model['Body'].read().decode('utf8')
+        json_str = response_model["Body"].read().decode("utf8")
         json_obj = json.loads(json_str)
-        scores = json_obj['scores']
+        scores = json_obj["scores"]
         return scores if isinstance(scores, list) else [scores]
 
-
-    def _invoke(self, model: str, credentials: dict,
-                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
-                user: Optional[str] = None) \
-            -> RerankResult:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
         """
         Invoke rerank model
 
@@ -63,22 +66,21 @@ class SageMakerRerankModel(RerankModel):
         line = 0
         try:
             if len(docs) == 0:
-                return RerankResult(
-                    model=model,
-                    docs=docs
-                )
+                return RerankResult(model=model, docs=docs)
 
             line = 1
             if not self.sagemaker_client:
-                access_key = credentials.get('aws_access_key_id')
-                secret_key = credentials.get('aws_secret_access_key')
-                aws_region = credentials.get('aws_region')
+                access_key = credentials.get("aws_access_key_id")
+                secret_key = credentials.get("aws_secret_access_key")
+                aws_region = credentials.get("aws_region")
                 if aws_region:
                     if access_key and secret_key:
-                        self.sagemaker_client = boto3.client("sagemaker-runtime", 
+                        self.sagemaker_client = boto3.client(
+                            "sagemaker-runtime",
                             aws_access_key_id=access_key,
                             aws_secret_access_key=secret_key,
-                            region_name=aws_region)
+                            region_name=aws_region,
+                        )
                     else:
                         self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
                 else:
@@ -86,22 +88,20 @@ class SageMakerRerankModel(RerankModel):
 
             line = 2
 
-            sagemaker_endpoint = credentials.get('sagemaker_endpoint')
+            sagemaker_endpoint = credentials.get("sagemaker_endpoint")
             candidate_docs = []
 
             scores = self._sagemaker_rerank(query, docs, sagemaker_endpoint)
             for idx in range(len(scores)):
-                candidate_docs.append({"content" : docs[idx], "score": scores[idx]})
+                candidate_docs.append({"content": docs[idx], "score": scores[idx]})
 
-            sorted(candidate_docs, key=lambda x: x['score'], reverse=True)
+            sorted(candidate_docs, key=operator.itemgetter("score"), reverse=True)
 
             line = 3
             rerank_documents = []
             for idx, result in enumerate(candidate_docs):
                 rerank_document = RerankDocument(
-                    index=idx,
-                    text=result.get('content'),
-                    score=result.get('score', -100.0)
+                    index=idx, text=result.get("content"), score=result.get("score", -100.0)
                 )
 
                 if score_threshold is not None:
@@ -110,13 +110,10 @@ class SageMakerRerankModel(RerankModel):
                 else:
                     rerank_documents.append(rerank_document)
 
-            return RerankResult(
-                model=model,
-                docs=rerank_documents
-            )
+            return RerankResult(model=model, docs=rerank_documents)
 
         except Exception as e:
-            logger.exception(f'Exception {e}, line : {line}')
+            logger.exception(f"Exception {e}, line : {line}")
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
@@ -137,7 +134,7 @@ class SageMakerRerankModel(RerankModel):
                     "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
                     "are a political division controlled by the United States. Its capital is Saipan.",
                 ],
-                score_threshold=0.8
+                score_threshold=0.8,
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -153,38 +150,24 @@ class SageMakerRerankModel(RerankModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                InvokeBadRequestError,
-                KeyError,
-                ValueError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [InvokeBadRequestError, KeyError, ValueError],
         }
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.RERANK,
-            model_properties={ },
-            parameter_rules=[]
+            model_properties={},
+            parameter_rules=[],
         )
 
-        return entity
\ No newline at end of file
+        return entity
diff --git a/api/core/model_runtime/model_providers/sagemaker/sagemaker.py b/api/core/model_runtime/model_providers/sagemaker/sagemaker.py
index 02d05f406c..042155b152 100644
--- a/api/core/model_runtime/model_providers/sagemaker/sagemaker.py
+++ b/api/core/model_runtime/model_providers/sagemaker/sagemaker.py
@@ -1,4 +1,6 @@
 import logging
+import uuid
+from typing import IO, Any
 
 from core.model_runtime.model_providers.__base.model_provider import ModelProvider
 
@@ -15,3 +17,25 @@ class SageMakerProvider(ModelProvider):
         :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
         """
         pass
+
+
+def buffer_to_s3(s3_client: Any, file: IO[bytes], bucket: str, s3_prefix: str) -> str:
+    """
+    return s3_uri of this file
+    """
+    s3_key = f"{s3_prefix}{uuid.uuid4()}.mp3"
+    s3_client.put_object(Body=file.read(), Bucket=bucket, Key=s3_key, ContentType="audio/mp3")
+    return s3_key
+
+
+def generate_presigned_url(s3_client: Any, file: IO[bytes], bucket_name: str, s3_prefix: str, expiration=600) -> str:
+    object_key = buffer_to_s3(s3_client, file, bucket_name, s3_prefix)
+    try:
+        response = s3_client.generate_presigned_url(
+            "get_object", Params={"Bucket": bucket_name, "Key": object_key}, ExpiresIn=expiration
+        )
+    except Exception as e:
+        print(f"Error generating presigned URL: {e}")
+        return None
+
+    return response
diff --git a/api/core/model_runtime/model_providers/sagemaker/sagemaker.yaml b/api/core/model_runtime/model_providers/sagemaker/sagemaker.yaml
index 290cb0edab..87cd50f50c 100644
--- a/api/core/model_runtime/model_providers/sagemaker/sagemaker.yaml
+++ b/api/core/model_runtime/model_providers/sagemaker/sagemaker.yaml
@@ -21,6 +21,8 @@ supported_model_types:
   - llm
   - text-embedding
   - rerank
+  - speech2text
+  - tts
 configurate_methods:
   - customizable-model
 model_credential_schema:
@@ -45,14 +47,10 @@ model_credential_schema:
         zh_Hans: 选择对话类型
         en_US: Select completion mode
       options:
-        - value: completion
-          label:
-            en_US: Completion
-            zh_Hans: 补全
         - value: chat
           label:
             en_US: Chat
-            zh_Hans: 对话
+            zh_Hans: Chat
     - variable: sagemaker_endpoint
       label:
         en_US: sagemaker endpoint
@@ -61,6 +59,76 @@ model_credential_schema:
       placeholder:
         zh_Hans: 请输出你的Sagemaker推理端点
         en_US: Enter your Sagemaker Inference endpoint
+    - variable: audio_s3_cache_bucket
+      show_on:
+        - variable: __model_type
+          value: speech2text
+      label:
+        zh_Hans: 音频缓存桶(s3 bucket)
+        en_US: audio cache bucket(s3 bucket)
+      type: text-input
+      required: true
+      placeholder:
+        zh_Hans: sagemaker-us-east-1-******207838
+        en_US: sagemaker-us-east-1-*******7838
+    - variable: audio_model_type
+      show_on:
+        - variable: __model_type
+          value: tts
+      label:
+        en_US: Audio model type
+      type: select
+      required: true
+      placeholder:
+        zh_Hans: 语音模型类型
+        en_US: Audio model type
+      options:
+        - value: PresetVoice
+          label:
+            en_US: preset voice
+            zh_Hans: 内置音色
+        - value: CloneVoice
+          label:
+            en_US: clone voice
+            zh_Hans: 克隆音色
+        - value: CloneVoice_CrossLingual
+          label:
+            en_US: crosslingual clone voice
+            zh_Hans: 跨语种克隆音色
+        - value: InstructVoice
+          label:
+            en_US: Instruct voice
+            zh_Hans: 文字指令音色
+    - variable: prompt_audio
+      show_on:
+        - variable: __model_type
+          value: tts
+      label:
+        en_US: Mock Audio Source
+      type: text-input
+      required: false
+      placeholder:
+        zh_Hans: 被模仿的音色音频
+        en_US: source audio to be mocked
+    - variable: prompt_text
+      show_on:
+        - variable: __model_type
+          value: tts
+      label:
+        en_US: Prompt Audio Text
+      type: text-input
+      required: false
+      placeholder:
+        zh_Hans: 模仿音色的对应文本
+        en_US: text for the mocked source audio
+    - variable: instruct_text
+      show_on:
+        - variable: __model_type
+          value: tts
+      label:
+        en_US: instruct text for speaker
+      type: text-input
+      required: false
     - variable: aws_access_key_id
       required: false
       label:
diff --git a/api/core/model_runtime/model_providers/sagemaker/speech2text/__init__.py b/api/core/model_runtime/model_providers/sagemaker/speech2text/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/model_runtime/model_providers/sagemaker/speech2text/speech2text.py b/api/core/model_runtime/model_providers/sagemaker/speech2text/speech2text.py
new file mode 100644
index 0000000000..6aa8c9995f
--- /dev/null
+++ b/api/core/model_runtime/model_providers/sagemaker/speech2text/speech2text.py
@@ -0,0 +1,125 @@
+import json
+import logging
+from typing import IO, Any, Optional
+
+import boto3
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
+from core.model_runtime.model_providers.sagemaker.sagemaker import generate_presigned_url
+
+logger = logging.getLogger(__name__)
+
+
+class SageMakerSpeech2TextModel(Speech2TextModel):
+    """
+    Model class for Xinference speech to text model.
+    """
+
+    sagemaker_client: Any = None
+    s3_client: Any = None
+
+    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
+        """
+        Invoke speech2text model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param file: audio file
+        :param user: unique user id
+        :return: text for given audio file
+        """
+        asr_text = None
+
+        try:
+            if not self.sagemaker_client:
+                access_key = credentials.get("aws_access_key_id")
+                secret_key = credentials.get("aws_secret_access_key")
+                aws_region = credentials.get("aws_region")
+                if aws_region:
+                    if access_key and secret_key:
+                        self.sagemaker_client = boto3.client(
+                            "sagemaker-runtime",
+                            aws_access_key_id=access_key,
+                            aws_secret_access_key=secret_key,
+                            region_name=aws_region,
+                        )
+                        self.s3_client = boto3.client(
+                            "s3", aws_access_key_id=access_key, aws_secret_access_key=secret_key, region_name=aws_region
+                        )
+                    else:
+                        self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
+                        self.s3_client = boto3.client("s3", region_name=aws_region)
+                else:
+                    self.sagemaker_client = boto3.client("sagemaker-runtime")
+                    self.s3_client = boto3.client("s3")
+
+            s3_prefix = "dify/speech2text/"
+            sagemaker_endpoint = credentials.get("sagemaker_endpoint")
+            bucket = credentials.get("audio_s3_cache_bucket")
+
+            s3_presign_url = generate_presigned_url(self.s3_client, file, bucket, s3_prefix)
+            payload = {"audio_s3_presign_uri": s3_presign_url}
+
+            response_model = self.sagemaker_client.invoke_endpoint(
+                EndpointName=sagemaker_endpoint, Body=json.dumps(payload), ContentType="application/json"
+            )
+            json_str = response_model["Body"].read().decode("utf8")
+            json_obj = json.loads(json_str)
+            asr_text = json_obj["text"]
+        except Exception as e:
+            logger.exception(f"Exception {e}, line : {line}")
+
+        return asr_text
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        pass
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [InvokeBadRequestError, KeyError, ValueError],
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+        used to define customizable model schema
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.SPEECH2TEXT,
+            model_properties={},
+            parameter_rules=[],
+        )
+
+        return entity
diff --git a/api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py
index 4b2858b1a2..d55144f8a7 100644
--- a/api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py
@@ -10,21 +10,22 @@ from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
-        InvokeAuthorizationError,
-        InvokeBadRequestError,
-        InvokeConnectionError,
-        InvokeError,
-        InvokeRateLimitError,
-        InvokeServerUnavailableError,
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
 )
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
 
 BATCH_SIZE = 20
-CONTEXT_SIZE=8192
+CONTEXT_SIZE = 8192
 
 logger = logging.getLogger(__name__)
 
+
 def batch_generator(generator, batch_size):
     while True:
         batch = list(itertools.islice(generator, batch_size))
@@ -32,33 +33,28 @@ def batch_generator(generator, batch_size):
             break
         yield batch
 
+
 class SageMakerEmbeddingModel(TextEmbeddingModel):
     """
     Model class for Cohere text embedding model.
     """
+
     sagemaker_client: Any = None
 
-    def _sagemaker_embedding(self, sm_client, endpoint_name, content_list:list[str]):
+    def _sagemaker_embedding(self, sm_client, endpoint_name, content_list: list[str]):
         response_model = sm_client.invoke_endpoint(
             EndpointName=endpoint_name,
-            Body=json.dumps(
-                {
-                    "inputs": content_list,
-                    "parameters": {},
-                    "is_query" : False,
-                    "instruction" :  ''
-                }
-            ),
+            Body=json.dumps({"inputs": content_list, "parameters": {}, "is_query": False, "instruction": ""}),
             ContentType="application/json",
         )
-        json_str = response_model['Body'].read().decode('utf8')
+        json_str = response_model["Body"].read().decode("utf8")
         json_obj = json.loads(json_str)
-        embeddings = json_obj['embeddings']
+        embeddings = json_obj["embeddings"]
         return embeddings
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -72,25 +68,27 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
         try:
             line = 1
             if not self.sagemaker_client:
-                access_key = credentials.get('aws_access_key_id')
-                secret_key = credentials.get('aws_secret_access_key')
-                aws_region = credentials.get('aws_region')
+                access_key = credentials.get("aws_access_key_id")
+                secret_key = credentials.get("aws_secret_access_key")
+                aws_region = credentials.get("aws_region")
                 if aws_region:
                     if access_key and secret_key:
-                        self.sagemaker_client = boto3.client("sagemaker-runtime", 
+                        self.sagemaker_client = boto3.client(
+                            "sagemaker-runtime",
                             aws_access_key_id=access_key,
                             aws_secret_access_key=secret_key,
-                            region_name=aws_region)
+                            region_name=aws_region,
+                        )
                     else:
                         self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
                 else:
                     self.sagemaker_client = boto3.client("sagemaker-runtime")
 
             line = 2
-            sagemaker_endpoint = credentials.get('sagemaker_endpoint')
+            sagemaker_endpoint = credentials.get("sagemaker_endpoint")
 
             line = 3
-            truncated_texts = [ item[:CONTEXT_SIZE] for item in texts ]
+            truncated_texts = [item[:CONTEXT_SIZE] for item in texts]
 
             batches = batch_generator((text for text in truncated_texts), batch_size=BATCH_SIZE)
             all_embeddings = []
@@ -105,18 +103,14 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
             usage = self._calc_response_usage(
                 model=model,
                 credentials=credentials,
-                tokens=0 # It's not SAAS API, usage is meaningless
+                tokens=0,  # It's not SAAS API, usage is meaningless
             )
             line = 6
 
-            return TextEmbeddingResult(
-                embeddings=all_embeddings,
-                usage=usage,
-                model=model
-            )
+            return TextEmbeddingResult(embeddings=all_embeddings, usage=usage, model=model)
 
         except Exception as e:
-            logger.exception(f'Exception {e}, line : {line}')
+            logger.exception(f"Exception {e}, line : {line}")
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
@@ -153,10 +147,7 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -167,7 +158,7 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
@@ -175,40 +166,28 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                KeyError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [KeyError],
         }
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
-        
+
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TEXT_EMBEDDING,
             model_properties={
                 ModelPropertyKey.CONTEXT_SIZE: CONTEXT_SIZE,
                 ModelPropertyKey.MAX_CHUNKS: BATCH_SIZE,
             },
-            parameter_rules=[]
+            parameter_rules=[],
         )
 
         return entity
diff --git a/api/core/model_runtime/model_providers/sagemaker/tts/__init__.py b/api/core/model_runtime/model_providers/sagemaker/tts/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/model_runtime/model_providers/sagemaker/tts/tts.py b/api/core/model_runtime/model_providers/sagemaker/tts/tts.py
new file mode 100644
index 0000000000..a22bd6dd6e
--- /dev/null
+++ b/api/core/model_runtime/model_providers/sagemaker/tts/tts.py
@@ -0,0 +1,275 @@
+import concurrent.futures
+import copy
+import json
+import logging
+from enum import Enum
+from typing import Any, Optional
+
+import boto3
+import requests
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.model_providers.__base.tts_model import TTSModel
+
+logger = logging.getLogger(__name__)
+
+
+class TTSModelType(Enum):
+    PresetVoice = "PresetVoice"
+    CloneVoice = "CloneVoice"
+    CloneVoice_CrossLingual = "CloneVoice_CrossLingual"
+    InstructVoice = "InstructVoice"
+
+
+class SageMakerText2SpeechModel(TTSModel):
+    sagemaker_client: Any = None
+    s3_client: Any = None
+    comprehend_client: Any = None
+
+    def __init__(self):
+        # preset voices, need support custom voice
+        self.model_voices = {
+            "__default": {
+                "all": [
+                    {"name": "Default", "value": "default"},
+                ]
+            },
+            "CosyVoice": {
+                "zh-Hans": [
+                    {"name": "中文男", "value": "中文男"},
+                    {"name": "中文女", "value": "中文女"},
+                    {"name": "粤语女", "value": "粤语女"},
+                ],
+                "zh-Hant": [
+                    {"name": "中文男", "value": "中文男"},
+                    {"name": "中文女", "value": "中文女"},
+                    {"name": "粤语女", "value": "粤语女"},
+                ],
+                "en-US": [
+                    {"name": "英文男", "value": "英文男"},
+                    {"name": "英文女", "value": "英文女"},
+                ],
+                "ja-JP": [
+                    {"name": "日语男", "value": "日语男"},
+                ],
+                "ko-KR": [
+                    {"name": "韩语女", "value": "韩语女"},
+                ],
+            },
+        }
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        pass
+
+    def _detect_lang_code(self, content: str, map_dict: dict = None):
+        map_dict = {"zh": "<|zh|>", "en": "<|en|>", "ja": "<|jp|>", "zh-TW": "<|yue|>", "ko": "<|ko|>"}
+
+        response = self.comprehend_client.detect_dominant_language(Text=content)
+        language_code = response["Languages"][0]["LanguageCode"]
+
+        return map_dict.get(language_code, "<|zh|>")
+
+    def _build_tts_payload(
+        self,
+        model_type: str,
+        content_text: str,
+        model_role: str,
+        prompt_text: str,
+        prompt_audio: str,
+        instruct_text: str,
+    ):
+        if model_type == TTSModelType.PresetVoice.value and model_role:
+            return {"tts_text": content_text, "role": model_role}
+        if model_type == TTSModelType.CloneVoice.value and prompt_text and prompt_audio:
+            return {"tts_text": content_text, "prompt_text": prompt_text, "prompt_audio": prompt_audio}
+        if model_type == TTSModelType.CloneVoice_CrossLingual.value and prompt_audio:
+            lang_tag = self._detect_lang_code(content_text)
+            return {"tts_text": f"{content_text}", "prompt_audio": prompt_audio, "lang_tag": lang_tag}
+        if model_type == TTSModelType.InstructVoice.value and instruct_text and model_role:
+            return {"tts_text": content_text, "role": model_role, "instruct_text": instruct_text}
+
+        raise RuntimeError(f"Invalid params for {model_type}")
+
+    def _invoke(
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ):
+        """
+        _invoke text2speech model
+
+        :param model: model name
+        :param tenant_id: user tenant id
+        :param credentials: model credentials
+        :param voice: model timbre
+        :param content_text: text content to be translated
+        :param user: unique user id
+        :return: text translated to audio file
+        """
+        if not self.sagemaker_client:
+            access_key = credentials.get("aws_access_key_id")
+            secret_key = credentials.get("aws_secret_access_key")
+            aws_region = credentials.get("aws_region")
+            if aws_region:
+                if access_key and secret_key:
+                    self.sagemaker_client = boto3.client(
+                        "sagemaker-runtime",
+                        aws_access_key_id=access_key,
+                        aws_secret_access_key=secret_key,
+                        region_name=aws_region,
+                    )
+                    self.s3_client = boto3.client(
+                        "s3", aws_access_key_id=access_key, aws_secret_access_key=secret_key, region_name=aws_region
+                    )
+                    self.comprehend_client = boto3.client(
+                        "comprehend",
+                        aws_access_key_id=access_key,
+                        aws_secret_access_key=secret_key,
+                        region_name=aws_region,
+                    )
+                else:
+                    self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
+                    self.s3_client = boto3.client("s3", region_name=aws_region)
+                    self.comprehend_client = boto3.client("comprehend", region_name=aws_region)
+            else:
+                self.sagemaker_client = boto3.client("sagemaker-runtime")
+                self.s3_client = boto3.client("s3")
+                self.comprehend_client = boto3.client("comprehend")
+
+        model_type = credentials.get("audio_model_type", "PresetVoice")
+        prompt_text = credentials.get("prompt_text")
+        prompt_audio = credentials.get("prompt_audio")
+        instruct_text = credentials.get("instruct_text")
+        sagemaker_endpoint = credentials.get("sagemaker_endpoint")
+        payload = self._build_tts_payload(model_type, content_text, voice, prompt_text, prompt_audio, instruct_text)
+
+        return self._tts_invoke_streaming(model_type, payload, sagemaker_endpoint)
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+        used to define customizable model schema
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TTS,
+            model_properties={},
+            parameter_rules=[],
+        )
+
+        return entity
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [InvokeBadRequestError, KeyError, ValueError],
+        }
+
+    def _get_model_default_voice(self, model: str, credentials: dict) -> any:
+        return ""
+
+    def _get_model_word_limit(self, model: str, credentials: dict) -> int:
+        return 15
+
+    def _get_model_audio_type(self, model: str, credentials: dict) -> str:
+        return "mp3"
+
+    def _get_model_workers_limit(self, model: str, credentials: dict) -> int:
+        return 5
+
+    def get_tts_model_voices(self, model: str, credentials: dict, language: Optional[str] = None) -> list:
+        audio_model_name = "CosyVoice"
+        for key, voices in self.model_voices.items():
+            if key in audio_model_name:
+                if language and language in voices:
+                    return voices[language]
+                elif "all" in voices:
+                    return voices["all"]
+
+        return self.model_voices["__default"]["all"]
+
+    def _invoke_sagemaker(self, payload: dict, endpoint: str):
+        response_model = self.sagemaker_client.invoke_endpoint(
+            EndpointName=endpoint,
+            Body=json.dumps(payload),
+            ContentType="application/json",
+        )
+        json_str = response_model["Body"].read().decode("utf8")
+        json_obj = json.loads(json_str)
+        return json_obj
+
+    def _tts_invoke_streaming(self, model_type: str, payload: dict, sagemaker_endpoint: str) -> any:
+        """
+        _tts_invoke_streaming text2speech model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param content_text: text content to be translated
+        :param voice: model timbre
+        :return: text translated to audio file
+        """
+        try:
+            lang_tag = ""
+            if model_type == TTSModelType.CloneVoice_CrossLingual.value:
+                lang_tag = payload.pop("lang_tag")
+
+            word_limit = self._get_model_word_limit(model="", credentials={})
+            content_text = payload.get("tts_text")
+            if len(content_text) > word_limit:
+                split_sentences = self._split_text_into_sentences(content_text, max_length=word_limit)
+                sentences = [f"{lang_tag}{s}" for s in split_sentences if len(s)]
+                len_sent = len(sentences)
+                executor = concurrent.futures.ThreadPoolExecutor(max_workers=min(4, len_sent))
+                payloads = [copy.deepcopy(payload) for i in range(len_sent)]
+                for idx in range(len_sent):
+                    payloads[idx]["tts_text"] = sentences[idx]
+
+                futures = [
+                    executor.submit(
+                        self._invoke_sagemaker,
+                        payload=payload,
+                        endpoint=sagemaker_endpoint,
+                    )
+                    for payload in payloads
+                ]
+
+                for future in futures:
+                    resp = future.result()
+                    audio_bytes = requests.get(resp.get("s3_presign_url")).content
+                    for i in range(0, len(audio_bytes), 1024):
+                        yield audio_bytes[i : i + 1024]
+            else:
+                resp = self._invoke_sagemaker(payload, sagemaker_endpoint)
+                audio_bytes = requests.get(resp.get("s3_presign_url")).content
+
+                for i in range(0, len(audio_bytes), 1024):
+                    yield audio_bytes[i : i + 1024]
+        except Exception as ex:
+            raise InvokeBadRequestError(str(ex))
diff --git a/api/core/model_runtime/model_providers/siliconflow/llm/llm.py b/api/core/model_runtime/model_providers/siliconflow/llm/llm.py
index a9ce7b98c3..c1868b6ad0 100644
--- a/api/core/model_runtime/model_providers/siliconflow/llm/llm.py
+++ b/api/core/model_runtime/model_providers/siliconflow/llm/llm.py
@@ -7,11 +7,17 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
 
 
 class SiliconflowLargeLanguageModel(OAIAPICompatLargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
@@ -21,5 +27,5 @@ class SiliconflowLargeLanguageModel(OAIAPICompatLargeLanguageModel):
 
     @classmethod
     def _add_custom_parameters(cls, credentials: dict) -> None:
-        credentials['mode'] = 'chat'
-        credentials['endpoint_url'] = 'https://api.siliconflow.cn/v1'
+        credentials["mode"] = "chat"
+        credentials["endpoint_url"] = "https://api.siliconflow.cn/v1"
diff --git a/api/core/model_runtime/model_providers/siliconflow/rerank/rerank.py b/api/core/model_runtime/model_providers/siliconflow/rerank/rerank.py
index 6835915816..58b033d28a 100644
--- a/api/core/model_runtime/model_providers/siliconflow/rerank/rerank.py
+++ b/api/core/model_runtime/model_providers/siliconflow/rerank/rerank.py
@@ -16,39 +16,38 @@ from core.model_runtime.model_providers.__base.rerank_model import RerankModel
 
 
 class SiliconflowRerankModel(RerankModel):
-
-    def _invoke(self, model: str, credentials: dict, query: str, docs: list[str],
-                score_threshold: Optional[float] = None, top_n: Optional[int] = None,
-                user: Optional[str] = None) -> RerankResult:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
         if len(docs) == 0:
             return RerankResult(model=model, docs=[])
 
-        base_url = credentials.get('base_url', 'https://api.siliconflow.cn/v1')
-        if base_url.endswith('/'):
-            base_url = base_url[:-1]
+        base_url = credentials.get("base_url", "https://api.siliconflow.cn/v1")
+        base_url = base_url.removesuffix("/")
         try:
             response = httpx.post(
-                base_url + '/rerank',
-                json={
-                    "model": model,
-                    "query": query,
-                    "documents": docs,
-                    "top_n": top_n,
-                    "return_documents": True
-                },
-                headers={"Authorization": f"Bearer {credentials.get('api_key')}"}
+                base_url + "/rerank",
+                json={"model": model, "query": query, "documents": docs, "top_n": top_n, "return_documents": True},
+                headers={"Authorization": f"Bearer {credentials.get('api_key')}"},
             )
             response.raise_for_status()
             results = response.json()
 
             rerank_documents = []
-            for result in results['results']:
+            for result in results["results"]:
                 rerank_document = RerankDocument(
-                    index=result['index'],
-                    text=result['document']['text'],
-                    score=result['relevance_score'],
+                    index=result["index"],
+                    text=result["document"]["text"],
+                    score=result["relevance_score"],
                 )
-                if score_threshold is None or result['relevance_score'] >= score_threshold:
+                if score_threshold is None or result["relevance_score"] >= score_threshold:
                     rerank_documents.append(rerank_document)
 
             return RerankResult(model=model, docs=rerank_documents)
@@ -57,7 +56,6 @@ class SiliconflowRerankModel(RerankModel):
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         try:
-
             self._invoke(
                 model=model,
                 credentials=credentials,
@@ -68,7 +66,7 @@ class SiliconflowRerankModel(RerankModel):
                     "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
                     "are a political division controlled by the United States. Its capital is Saipan.",
                 ],
-                score_threshold=0.8
+                score_threshold=0.8,
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -83,5 +81,5 @@ class SiliconflowRerankModel(RerankModel):
             InvokeServerUnavailableError: [httpx.RemoteProtocolError],
             InvokeRateLimitError: [],
             InvokeAuthorizationError: [httpx.HTTPStatusError],
-            InvokeBadRequestError: [httpx.RequestError]
-        }
\ No newline at end of file
+            InvokeBadRequestError: [httpx.RequestError],
+        }
diff --git a/api/core/model_runtime/model_providers/siliconflow/siliconflow.py b/api/core/model_runtime/model_providers/siliconflow/siliconflow.py
index dd0eea362a..e121ab8c7e 100644
--- a/api/core/model_runtime/model_providers/siliconflow/siliconflow.py
+++ b/api/core/model_runtime/model_providers/siliconflow/siliconflow.py
@@ -8,7 +8,6 @@ logger = logging.getLogger(__name__)
 
 
 class SiliconflowProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -19,12 +18,9 @@ class SiliconflowProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(
-                model='deepseek-ai/DeepSeek-V2-Chat',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="deepseek-ai/DeepSeek-V2-Chat", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/siliconflow/speech2text/speech2text.py b/api/core/model_runtime/model_providers/siliconflow/speech2text/speech2text.py
index 6ad3cab587..8d1932863e 100644
--- a/api/core/model_runtime/model_providers/siliconflow/speech2text/speech2text.py
+++ b/api/core/model_runtime/model_providers/siliconflow/speech2text/speech2text.py
@@ -8,9 +8,7 @@ class SiliconflowSpeech2TextModel(OAICompatSpeech2TextModel):
     Model class for Siliconflow Speech to text model.
     """
 
-    def _invoke(
-            self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None
-    ) -> str:
+    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
         """
         Invoke speech2text model
 
diff --git a/api/core/model_runtime/model_providers/siliconflow/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/siliconflow/text_embedding/text_embedding.py
index c58765cecb..6cdf4933b4 100644
--- a/api/core/model_runtime/model_providers/siliconflow/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/siliconflow/text_embedding/text_embedding.py
@@ -10,20 +10,21 @@ class SiliconflowTextEmbeddingModel(OAICompatEmbeddingModel):
     """
     Model class for Siliconflow text embedding model.
     """
+
     def validate_credentials(self, model: str, credentials: dict) -> None:
         self._add_custom_parameters(credentials)
         super().validate_credentials(model, credentials)
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         self._add_custom_parameters(credentials)
         return super()._invoke(model, credentials, texts, user)
-    
+
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         self._add_custom_parameters(credentials)
         return super().get_num_tokens(model, credentials, texts)
-    
+
     @classmethod
     def _add_custom_parameters(cls, credentials: dict) -> None:
-        credentials['endpoint_url'] = 'https://api.siliconflow.cn/v1'
\ No newline at end of file
+        credentials["endpoint_url"] = "https://api.siliconflow.cn/v1"
diff --git a/api/core/model_runtime/model_providers/spark/llm/_client.py b/api/core/model_runtime/model_providers/spark/llm/_client.py
index 10da265701..b99a657e71 100644
--- a/api/core/model_runtime/model_providers/spark/llm/_client.py
+++ b/api/core/model_runtime/model_providers/spark/llm/_client.py
@@ -15,51 +15,35 @@ import websocket
 
 class SparkLLMClient:
     def __init__(self, model: str, app_id: str, api_key: str, api_secret: str, api_domain: Optional[str] = None):
-        domain = 'spark-api.xf-yun.com'
-        endpoint = 'chat'
+        domain = "spark-api.xf-yun.com"
+        endpoint = "chat"
         if api_domain:
             domain = api_domain
-            if model == 'spark-v3':
-                endpoint = 'multimodal'
 
         model_api_configs = {
-            'spark-1.5': {
-                'version': 'v1.1',
-                'chat_domain': 'general'
-            },
-            'spark-2': {
-                'version': 'v2.1',
-                'chat_domain': 'generalv2'
-            },
-            'spark-3': {
-                'version': 'v3.1',
-                'chat_domain': 'generalv3'
-            },
-            'spark-3.5': {
-                'version': 'v3.5',
-                'chat_domain': 'generalv3.5'
-            },
-            'spark-4': {
-                'version': 'v4.0',
-                'chat_domain': '4.0Ultra'
-            }
+            "spark-lite": {"version": "v1.1", "chat_domain": "general"},
+            "spark-pro": {"version": "v3.1", "chat_domain": "generalv3"},
+            "spark-pro-128k": {"version": "pro-128k", "chat_domain": "pro-128k"},
+            "spark-max": {"version": "v3.5", "chat_domain": "generalv3.5"},
+            "spark-4.0-ultra": {"version": "v4.0", "chat_domain": "4.0Ultra"},
         }
 
-        api_version = model_api_configs[model]['version']
+        api_version = model_api_configs[model]["version"]
+
+        self.chat_domain = model_api_configs[model]["chat_domain"]
+
+        if model == "spark-pro-128k":
+            self.api_base = f"wss://{domain}/{endpoint}/{api_version}"
+        else:
+            self.api_base = f"wss://{domain}/{api_version}/{endpoint}"
 
-        self.chat_domain = model_api_configs[model]['chat_domain']
-        self.api_base = f"wss://{domain}/{api_version}/{endpoint}"
         self.app_id = app_id
         self.ws_url = self.create_url(
-            urlparse(self.api_base).netloc,
-            urlparse(self.api_base).path,
-            self.api_base,
-            api_key,
-            api_secret
+            urlparse(self.api_base).netloc, urlparse(self.api_base).path, self.api_base, api_key, api_secret
         )
 
         self.queue = queue.Queue()
-        self.blocking_message = ''
+        self.blocking_message = ""
 
     def create_url(self, host: str, path: str, api_base: str, api_key: str, api_secret: str) -> str:
         # generate timestamp by RFC1123
@@ -71,33 +55,32 @@ class SparkLLMClient:
         signature_origin += "GET " + path + " HTTP/1.1"
 
         # encrypt using hmac-sha256
-        signature_sha = hmac.new(api_secret.encode('utf-8'), signature_origin.encode('utf-8'),
-                                 digestmod=hashlib.sha256).digest()
+        signature_sha = hmac.new(
+            api_secret.encode("utf-8"), signature_origin.encode("utf-8"), digestmod=hashlib.sha256
+        ).digest()
 
-        signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding='utf-8')
+        signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding="utf-8")
 
-        authorization_origin = f'api_key="{api_key}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature_sha_base64}"'
+        authorization_origin = (
+            f'api_key="{api_key}", algorithm="hmac-sha256", headers="host date request-line",'
+            f' signature="{signature_sha_base64}"'
+        )
 
-        authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode(encoding='utf-8')
+        authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode(encoding="utf-8")
 
-        v = {
-            "authorization": authorization,
-            "date": date,
-            "host": host
-        }
+        v = {"authorization": authorization, "date": date, "host": host}
         # generate url
-        url = api_base + '?' + urlencode(v)
+        url = api_base + "?" + urlencode(v)
         return url
 
-    def run(self, messages: list, user_id: str,
-            model_kwargs: Optional[dict] = None, streaming: bool = False):
+    def run(self, messages: list, user_id: str, model_kwargs: Optional[dict] = None, streaming: bool = False):
         websocket.enableTrace(False)
         ws = websocket.WebSocketApp(
             self.ws_url,
             on_message=self.on_message,
             on_error=self.on_error,
             on_close=self.on_close,
-            on_open=self.on_open
+            on_open=self.on_open,
         )
         ws.messages = messages
         ws.user_id = user_id
@@ -106,86 +89,71 @@ class SparkLLMClient:
         ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE})
 
     def on_error(self, ws, error):
-        self.queue.put({
-            'status_code': error.status_code,
-            'error': error.resp_body.decode('utf-8')
-        })
+        self.queue.put({"status_code": error.status_code, "error": error.resp_body.decode("utf-8")})
         ws.close()
 
     def on_close(self, ws, close_status_code, close_reason):
-        self.queue.put({'done': True})
+        self.queue.put({"done": True})
 
     def on_open(self, ws):
-        self.blocking_message = ''
-        data = json.dumps(self.gen_params(
-            messages=ws.messages,
-            user_id=ws.user_id,
-            model_kwargs=ws.model_kwargs
-        ))
+        self.blocking_message = ""
+        data = json.dumps(self.gen_params(messages=ws.messages, user_id=ws.user_id, model_kwargs=ws.model_kwargs))
         ws.send(data)
 
     def on_message(self, ws, message):
         data = json.loads(message)
-        code = data['header']['code']
+        code = data["header"]["code"]
         if code != 0:
-            self.queue.put({
-                'status_code': 400,
-                'error': f"Code: {code}, Error: {data['header']['message']}"
-            })
+            self.queue.put({"status_code": 400, "error": f"Code: {code}, Error: {data['header']['message']}"})
             ws.close()
         else:
             choices = data["payload"]["choices"]
             status = choices["status"]
             content = choices["text"][0]["content"]
             if ws.streaming:
-                self.queue.put({'data': content})
+                self.queue.put({"data": content})
             else:
                 self.blocking_message += content
 
             if status == 2:
                 if not ws.streaming:
-                    self.queue.put({'data': self.blocking_message})
+                    self.queue.put({"data": self.blocking_message})
                 ws.close()
 
-    def gen_params(self, messages: list, user_id: str,
-                   model_kwargs: Optional[dict] = None) -> dict:
+    def gen_params(self, messages: list, user_id: str, model_kwargs: Optional[dict] = None) -> dict:
         data = {
             "header": {
                 "app_id": self.app_id,
                 # resolve this error message => $.header.uid' length must be less or equal than 32
-                "uid": user_id[:32] if user_id else None
+                "uid": user_id[:32] if user_id else None,
             },
-            "parameter": {
-                "chat": {
-                    "domain": self.chat_domain
-                }
-            },
-            "payload": {
-                "message": {
-                    "text": messages
-                }
-            }
+            "parameter": {"chat": {"domain": self.chat_domain}},
+            "payload": {"message": {"text": messages}},
         }
 
         if model_kwargs:
-            data['parameter']['chat'].update(model_kwargs)
+            data["parameter"]["chat"].update(model_kwargs)
 
         return data
 
     def subscribe(self):
         while True:
             content = self.queue.get()
-            if 'error' in content:
-                if content['status_code'] == 401:
-                    raise SparkError('[Spark] The credentials you provided are incorrect. '
-                                     'Please double-check and fill them in again.')
-                elif content['status_code'] == 403:
-                    raise SparkError("[Spark] Sorry, the credentials you provided are access denied. "
-                                     "Please try again after obtaining the necessary permissions.")
+            if "error" in content:
+                if content["status_code"] == 401:
+                    raise SparkError(
+                        "[Spark] The credentials you provided are incorrect. "
+                        "Please double-check and fill them in again."
+                    )
+                elif content["status_code"] == 403:
+                    raise SparkError(
+                        "[Spark] Sorry, the credentials you provided are access denied. "
+                        "Please try again after obtaining the necessary permissions."
+                    )
                 else:
                     raise SparkError(f"[Spark] code: {content['status_code']}, error: {content['error']}")
 
-            if 'data' not in content:
+            if "data" not in content:
                 break
             yield content
 
diff --git a/api/core/model_runtime/model_providers/spark/llm/_position.yaml b/api/core/model_runtime/model_providers/spark/llm/_position.yaml
index e49ee97db7..458397f2aa 100644
--- a/api/core/model_runtime/model_providers/spark/llm/_position.yaml
+++ b/api/core/model_runtime/model_providers/spark/llm/_position.yaml
@@ -1,3 +1,8 @@
+- spark-4.0-ultra
+- spark-max
+- spark-pro-128k
+- spark-pro
+- spark-lite
 - spark-4
 - spark-3.5
 - spark-3
diff --git a/api/core/model_runtime/model_providers/spark/llm/llm.py b/api/core/model_runtime/model_providers/spark/llm/llm.py
index 65beae517c..57193dc031 100644
--- a/api/core/model_runtime/model_providers/spark/llm/llm.py
+++ b/api/core/model_runtime/model_providers/spark/llm/llm.py
@@ -25,12 +25,17 @@ from ._client import SparkLLMClient
 
 
 class SparkLargeLanguageModel(LargeLanguageModel):
-
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -47,8 +52,13 @@ class SparkLargeLanguageModel(LargeLanguageModel):
         # invoke model
         return self._generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -80,15 +90,21 @@ class SparkLargeLanguageModel(LargeLanguageModel):
                 model_parameters={
                     "temperature": 0.5,
                 },
-                stream=False
+                stream=False,
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict,
-                  stop: Optional[list[str]] = None, stream: bool = True,
-                  user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -103,7 +119,7 @@ class SparkLargeLanguageModel(LargeLanguageModel):
         """
         extra_model_kwargs = {}
         if stop:
-            extra_model_kwargs['stop_sequences'] = stop
+            extra_model_kwargs["stop_sequences"] = stop
 
         # transform credentials to kwargs for model instance
         credentials_kwargs = self._to_credential_kwargs(credentials)
@@ -113,21 +129,33 @@ class SparkLargeLanguageModel(LargeLanguageModel):
             **credentials_kwargs,
         )
 
-        thread = threading.Thread(target=client.run, args=(
-            [{ 'role': prompt_message.role.value, 'content': prompt_message.content } for prompt_message in prompt_messages],
-            user,
-            model_parameters,
-            stream
-        ))
+        thread = threading.Thread(
+            target=client.run,
+            args=(
+                [
+                    {"role": prompt_message.role.value, "content": prompt_message.content}
+                    for prompt_message in prompt_messages
+                ],
+                user,
+                model_parameters,
+                stream,
+            ),
+        )
         thread.start()
 
         if stream:
             return self._handle_generate_stream_response(thread, model, credentials, client, prompt_messages)
 
         return self._handle_generate_response(thread, model, credentials, client, prompt_messages)
-        
-    def _handle_generate_response(self, thread: threading.Thread, model: str, credentials: dict, client: SparkLLMClient,
-                                  prompt_messages: list[PromptMessage]) -> LLMResult:
+
+    def _handle_generate_response(
+        self,
+        thread: threading.Thread,
+        model: str,
+        credentials: dict,
+        client: SparkLLMClient,
+        prompt_messages: list[PromptMessage],
+    ) -> LLMResult:
         """
         Handle llm response
 
@@ -140,7 +168,7 @@ class SparkLargeLanguageModel(LargeLanguageModel):
 
         for content in client.subscribe():
             if isinstance(content, dict):
-                delta = content['data']
+                delta = content["data"]
             else:
                 delta = content
 
@@ -148,9 +176,7 @@ class SparkLargeLanguageModel(LargeLanguageModel):
 
         thread.join()
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=completion
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=completion)
 
         # calculate num tokens
         prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
@@ -168,9 +194,15 @@ class SparkLargeLanguageModel(LargeLanguageModel):
         )
 
         return result
-    
-    def _handle_generate_stream_response(self, thread: threading.Thread, model: str, credentials: dict, client: SparkLLMClient,
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+
+    def _handle_generate_stream_response(
+        self,
+        thread: threading.Thread,
+        model: str,
+        credentials: dict,
+        client: SparkLLMClient,
+        prompt_messages: list[PromptMessage],
+    ) -> Generator:
         """
         Handle llm stream response
 
@@ -183,12 +215,12 @@ class SparkLargeLanguageModel(LargeLanguageModel):
         """
         for index, content in enumerate(client.subscribe()):
             if isinstance(content, dict):
-                delta = content['data']
+                delta = content["data"]
             else:
                 delta = content
 
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta if delta else '',
+                content=delta or "",
             )
 
             prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
@@ -199,11 +231,7 @@ class SparkLargeLanguageModel(LargeLanguageModel):
             yield LLMResultChunk(
                 model=model,
                 prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=index,
-                    message=assistant_prompt_message,
-                    usage=usage
-                )
+                delta=LLMResultChunkDelta(index=index, message=assistant_prompt_message, usage=usage),
             )
 
         thread.join()
@@ -216,9 +244,9 @@ class SparkLargeLanguageModel(LargeLanguageModel):
         :return:
         """
         credentials_kwargs = {
-            "app_id": credentials['app_id'],
-            "api_secret": credentials['api_secret'],
-            "api_key": credentials['api_key'],
+            "app_id": credentials["app_id"],
+            "api_secret": credentials["api_secret"],
+            "api_key": credentials["api_key"],
         }
 
         return credentials_kwargs
@@ -244,7 +272,7 @@ class SparkLargeLanguageModel(LargeLanguageModel):
             raise ValueError(f"Got unknown type {message}")
 
         return message_text
-    
+
     def _convert_messages_to_prompt(self, messages: list[PromptMessage]) -> str:
         """
         Format a list of messages into a full prompt for the Anthropic model
@@ -254,10 +282,7 @@ class SparkLargeLanguageModel(LargeLanguageModel):
         """
         messages = messages.copy()  # don't mutate the original list
 
-        text = "".join(
-            self._convert_one_message_to_text(message)
-            for message in messages
-        )
+        text = "".join(self._convert_one_message_to_text(message) for message in messages)
 
         # trim off the trailing ' ' that might come from the "Assistant: "
         return text.rstrip()
@@ -277,5 +302,5 @@ class SparkLargeLanguageModel(LargeLanguageModel):
             InvokeServerUnavailableError: [],
             InvokeRateLimitError: [],
             InvokeAuthorizationError: [],
-            InvokeBadRequestError: []
+            InvokeBadRequestError: [],
         }
diff --git a/api/core/model_runtime/model_providers/spark/llm/spark-1.5.yaml b/api/core/model_runtime/model_providers/spark/llm/spark-1.5.yaml
index 41b8765fe6..fcd65c24e0 100644
--- a/api/core/model_runtime/model_providers/spark/llm/spark-1.5.yaml
+++ b/api/core/model_runtime/model_providers/spark/llm/spark-1.5.yaml
@@ -1,4 +1,5 @@
 model: spark-1.5
+deprecated: true
 label:
   en_US: Spark V1.5
 model_type: llm
diff --git a/api/core/model_runtime/model_providers/spark/llm/spark-3.5.yaml b/api/core/model_runtime/model_providers/spark/llm/spark-3.5.yaml
index 6d24932ea8..86617a53d0 100644
--- a/api/core/model_runtime/model_providers/spark/llm/spark-3.5.yaml
+++ b/api/core/model_runtime/model_providers/spark/llm/spark-3.5.yaml
@@ -1,4 +1,5 @@
 model: spark-3.5
+deprecated: true
 label:
   en_US: Spark V3.5
 model_type: llm
diff --git a/api/core/model_runtime/model_providers/spark/llm/spark-3.yaml b/api/core/model_runtime/model_providers/spark/llm/spark-3.yaml
index 2ef9e10f45..9f296c684d 100644
--- a/api/core/model_runtime/model_providers/spark/llm/spark-3.yaml
+++ b/api/core/model_runtime/model_providers/spark/llm/spark-3.yaml
@@ -1,4 +1,5 @@
 model: spark-3
+deprecated: true
 label:
   en_US: Spark V3.0
 model_type: llm
diff --git a/api/core/model_runtime/model_providers/spark/llm/spark-4.0-ultra.yaml b/api/core/model_runtime/model_providers/spark/llm/spark-4.0-ultra.yaml
new file mode 100644
index 0000000000..bbf85764f1
--- /dev/null
+++ b/api/core/model_runtime/model_providers/spark/llm/spark-4.0-ultra.yaml
@@ -0,0 +1,42 @@
+model: spark-4.0-ultra
+label:
+  en_US: Spark 4.0 Ultra
+model_type: llm
+model_properties:
+  mode: chat
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 0.5
+    help:
+      zh_Hans: 核采样阈值。用于决定结果随机性，取值越高随机性越强即相同的问题得到的不同答案的可能性越高。
+      en_US: Kernel sampling threshold. Used to determine the randomness of the results. The higher the value, the stronger the randomness, that is, the higher the possibility of getting different answers to the same question.
+  - name: max_tokens
+    use_template: max_tokens
+    default: 4096
+    min: 1
+    max: 8192
+    help:
+      zh_Hans: 模型回答的tokens的最大长度。
+      en_US: Maximum length of tokens for the model response.
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    default: 4
+    min: 1
+    max: 6
+    help:
+      zh_Hans: 从 k 个候选中随机选择一个（非等概率）。
+      en_US: Randomly select one from k candidates (non-equal probability).
+    required: false
+  - name: show_ref_label
+    label:
+      zh_Hans: 联网检索
+      en_US: web search
+    type: boolean
+    default: false
+    help:
+      zh_Hans: 该参数仅4.0 Ultra版本支持，当设置为true时，如果输入内容触发联网检索插件，会先返回检索信源列表，然后再返回星火回复结果，否则仅返回星火回复结果
+      en_US: The parameter is only supported in the 4.0 Ultra version. When set to true, if the input triggers the online search plugin, it will first return a list of search sources and then return the Spark response. Otherwise, it will only return the Spark response.
diff --git a/api/core/model_runtime/model_providers/spark/llm/spark-4.yaml b/api/core/model_runtime/model_providers/spark/llm/spark-4.yaml
index 4b0bf27029..4b5529e81c 100644
--- a/api/core/model_runtime/model_providers/spark/llm/spark-4.yaml
+++ b/api/core/model_runtime/model_providers/spark/llm/spark-4.yaml
@@ -1,4 +1,5 @@
 model: spark-4
+deprecated: true
 label:
   en_US: Spark V4.0
 model_type: llm
diff --git a/api/core/model_runtime/model_providers/spark/llm/spark-lite.yaml b/api/core/model_runtime/model_providers/spark/llm/spark-lite.yaml
new file mode 100644
index 0000000000..1f6141a816
--- /dev/null
+++ b/api/core/model_runtime/model_providers/spark/llm/spark-lite.yaml
@@ -0,0 +1,33 @@
+model: spark-lite
+label:
+  en_US: Spark Lite
+model_type: llm
+model_properties:
+  mode: chat
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 0.5
+    help:
+      zh_Hans: 核采样阈值。用于决定结果随机性，取值越高随机性越强即相同的问题得到的不同答案的可能性越高。
+      en_US: Kernel sampling threshold. Used to determine the randomness of the results. The higher the value, the stronger the randomness, that is, the higher the possibility of getting different answers to the same question.
+  - name: max_tokens
+    use_template: max_tokens
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 模型回答的tokens的最大长度。
+      en_US: Maximum length of tokens for the model response.
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    default: 4
+    min: 1
+    max: 6
+    help:
+      zh_Hans: 从 k 个候选中随机选择一个（非等概率）。
+      en_US: Randomly select one from k candidates (non-equal probability).
+    required: false
diff --git a/api/core/model_runtime/model_providers/spark/llm/spark-max.yaml b/api/core/model_runtime/model_providers/spark/llm/spark-max.yaml
new file mode 100644
index 0000000000..71eb2b86d3
--- /dev/null
+++ b/api/core/model_runtime/model_providers/spark/llm/spark-max.yaml
@@ -0,0 +1,33 @@
+model: spark-max
+label:
+  en_US: Spark Max
+model_type: llm
+model_properties:
+  mode: chat
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 0.5
+    help:
+      zh_Hans: 核采样阈值。用于决定结果随机性，取值越高随机性越强即相同的问题得到的不同答案的可能性越高。
+      en_US: Kernel sampling threshold. Used to determine the randomness of the results. The higher the value, the stronger the randomness, that is, the higher the possibility of getting different answers to the same question.
+  - name: max_tokens
+    use_template: max_tokens
+    default: 4096
+    min: 1
+    max: 8192
+    help:
+      zh_Hans: 模型回答的tokens的最大长度。
+      en_US: Maximum length of tokens for the model response.
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    default: 4
+    min: 1
+    max: 6
+    help:
+      zh_Hans: 从 k 个候选中随机选择一个（非等概率）。
+      en_US: Randomly select one from k candidates (non-equal probability).
+    required: false
diff --git a/api/core/model_runtime/model_providers/spark/llm/spark-pro-128k.yaml b/api/core/model_runtime/model_providers/spark/llm/spark-pro-128k.yaml
new file mode 100644
index 0000000000..da1fead6da
--- /dev/null
+++ b/api/core/model_runtime/model_providers/spark/llm/spark-pro-128k.yaml
@@ -0,0 +1,33 @@
+model: spark-pro-128k
+label:
+  en_US: Spark Pro-128K
+model_type: llm
+model_properties:
+  mode: chat
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 0.5
+    help:
+      zh_Hans: 核采样阈值。用于决定结果随机性，取值越高随机性越强即相同的问题得到的不同答案的可能性越高。
+      en_US: Kernel sampling threshold. Used to determine the randomness of the results. The higher the value, the stronger the randomness, that is, the higher the possibility of getting different answers to the same question.
+  - name: max_tokens
+    use_template: max_tokens
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 模型回答的tokens的最大长度。
+      en_US: Maximum length of tokens for the model response.
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    default: 4
+    min: 1
+    max: 6
+    help:
+      zh_Hans: 从 k 个候选中随机选择一个（非等概率）。
+      en_US: Randomly select one from k candidates (non-equal probability).
+    required: false
diff --git a/api/core/model_runtime/model_providers/spark/llm/spark-pro.yaml b/api/core/model_runtime/model_providers/spark/llm/spark-pro.yaml
new file mode 100644
index 0000000000..9ee479f15b
--- /dev/null
+++ b/api/core/model_runtime/model_providers/spark/llm/spark-pro.yaml
@@ -0,0 +1,33 @@
+model: spark-pro
+label:
+  en_US: Spark Pro
+model_type: llm
+model_properties:
+  mode: chat
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 0.5
+    help:
+      zh_Hans: 核采样阈值。用于决定结果随机性，取值越高随机性越强即相同的问题得到的不同答案的可能性越高。
+      en_US: Kernel sampling threshold. Used to determine the randomness of the results. The higher the value, the stronger the randomness, that is, the higher the possibility of getting different answers to the same question.
+  - name: max_tokens
+    use_template: max_tokens
+    default: 4096
+    min: 1
+    max: 8192
+    help:
+      zh_Hans: 模型回答的tokens的最大长度。
+      en_US: Maximum length of tokens for the model response.
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    default: 4
+    min: 1
+    max: 6
+    help:
+      zh_Hans: 从 k 个候选中随机选择一个（非等概率）。
+      en_US: Randomly select one from k candidates (non-equal probability).
+    required: false
diff --git a/api/core/model_runtime/model_providers/stepfun/llm/llm.py b/api/core/model_runtime/model_providers/stepfun/llm/llm.py
index 6f6ffc8faa..dab666e4d0 100644
--- a/api/core/model_runtime/model_providers/stepfun/llm/llm.py
+++ b/api/core/model_runtime/model_providers/stepfun/llm/llm.py
@@ -30,11 +30,17 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
 
 
 class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
         self._add_function_call(model, credentials)
         user = user[:32] if user else None
@@ -49,51 +55,51 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
             model=model,
             label=I18nObject(en_US=model, zh_Hans=model),
             model_type=ModelType.LLM,
-            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL] 
-                if credentials.get('function_calling_type') == 'tool_call' 
-                else [],
+            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL]
+            if credentials.get("function_calling_type") == "tool_call"
+            else [],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', 8000)),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 8000)),
                 ModelPropertyKey.MODE: LLMMode.CHAT.value,
             },
             parameter_rules=[
                 ParameterRule(
-                    name='temperature',
-                    use_template='temperature',
-                    label=I18nObject(en_US='Temperature', zh_Hans='温度'),
+                    name="temperature",
+                    use_template="temperature",
+                    label=I18nObject(en_US="Temperature", zh_Hans="温度"),
                     type=ParameterType.FLOAT,
                 ),
                 ParameterRule(
-                    name='max_tokens',
-                    use_template='max_tokens',
+                    name="max_tokens",
+                    use_template="max_tokens",
                     default=512,
                     min=1,
-                    max=int(credentials.get('max_tokens', 1024)),
-                    label=I18nObject(en_US='Max Tokens', zh_Hans='最大标记'),
+                    max=int(credentials.get("max_tokens", 1024)),
+                    label=I18nObject(en_US="Max Tokens", zh_Hans="最大标记"),
                     type=ParameterType.INT,
                 ),
                 ParameterRule(
-                    name='top_p',
-                    use_template='top_p',
-                    label=I18nObject(en_US='Top P', zh_Hans='Top P'),
+                    name="top_p",
+                    use_template="top_p",
+                    label=I18nObject(en_US="Top P", zh_Hans="Top P"),
                     type=ParameterType.FLOAT,
                 ),
-            ]
+            ],
         )
 
     def _add_custom_parameters(self, credentials: dict) -> None:
-        credentials['mode'] = 'chat'
-        credentials['endpoint_url'] = 'https://api.stepfun.com/v1'
+        credentials["mode"] = "chat"
+        credentials["endpoint_url"] = "https://api.stepfun.com/v1"
 
     def _add_function_call(self, model: str, credentials: dict) -> None:
         model_schema = self.get_model_schema(model, credentials)
-        if model_schema and {
-            ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL
-        }.intersection(model_schema.features or []):
-            credentials['function_calling_type'] = 'tool_call'
+        if model_schema and {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL}.intersection(
+            model_schema.features or []
+        ):
+            credentials["function_calling_type"] = "tool_call"
 
-    def _convert_prompt_message_to_dict(self, message: PromptMessage,credentials: Optional[dict] = None) -> dict:
+    def _convert_prompt_message_to_dict(self, message: PromptMessage, credentials: Optional[dict] = None) -> dict:
         """
         Convert PromptMessage to dict for OpenAI API format
         """
@@ -106,10 +112,7 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
                         message_content = cast(PromptMessageContent, message_content)
-                        sub_message_dict = {
-                            "type": "text",
-                            "text": message_content.data
-                        }
+                        sub_message_dict = {"type": "text", "text": message_content.data}
                         sub_messages.append(sub_message_dict)
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
@@ -117,7 +120,7 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                             "type": "image_url",
                             "image_url": {
                                 "url": message_content.data,
-                            }
+                            },
                         }
                         sub_messages.append(sub_message_dict)
                 message_dict = {"role": "user", "content": sub_messages}
@@ -127,14 +130,16 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
             if message.tool_calls:
                 message_dict["tool_calls"] = []
                 for function_call in message.tool_calls:
-                    message_dict["tool_calls"].append({
-                        "id": function_call.id,
-                        "type": function_call.type,
-                        "function": {
-                            "name": function_call.function.name,
-                            "arguments": function_call.function.arguments
+                    message_dict["tool_calls"].append(
+                        {
+                            "id": function_call.id,
+                            "type": function_call.type,
+                            "function": {
+                                "name": function_call.function.name,
+                                "arguments": function_call.function.arguments,
+                            },
                         }
-                    })
+                    )
         elif isinstance(message, ToolPromptMessage):
             message = cast(ToolPromptMessage, message)
             message_dict = {"role": "tool", "content": message.content, "tool_call_id": message.tool_call_id}
@@ -160,21 +165,26 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         if response_tool_calls:
             for response_tool_call in response_tool_calls:
                 function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                    name=response_tool_call["function"]["name"] if response_tool_call.get("function", {}).get("name") else "",
-                    arguments=response_tool_call["function"]["arguments"] if response_tool_call.get("function", {}).get("arguments") else ""
+                    name=response_tool_call["function"]["name"]
+                    if response_tool_call.get("function", {}).get("name")
+                    else "",
+                    arguments=response_tool_call["function"]["arguments"]
+                    if response_tool_call.get("function", {}).get("arguments")
+                    else "",
                 )
 
                 tool_call = AssistantPromptMessage.ToolCall(
                     id=response_tool_call["id"] if response_tool_call.get("id") else "",
                     type=response_tool_call["type"] if response_tool_call.get("type") else "",
-                    function=function
+                    function=function,
                 )
                 tool_calls.append(tool_call)
 
         return tool_calls
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: requests.Response,
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
+    ) -> Generator:
         """
         Handle llm stream response
 
@@ -184,11 +194,12 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: llm response chunk generator
         """
-        full_assistant_content = ''
+        full_assistant_content = ""
         chunk_index = 0
 
-        def create_final_llm_result_chunk(index: int, message: AssistantPromptMessage, finish_reason: str) \
-                -> LLMResultChunk:
+        def create_final_llm_result_chunk(
+            index: int, message: AssistantPromptMessage, finish_reason: str
+        ) -> LLMResultChunk:
             # calculate num tokens
             prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
             completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
@@ -199,12 +210,7 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
             return LLMResultChunk(
                 model=model,
                 prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=index,
-                    message=message,
-                    finish_reason=finish_reason,
-                    usage=usage
-                )
+                delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
             )
 
         tools_calls: list[AssistantPromptMessage.ToolCall] = []
@@ -218,9 +224,9 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                 tool_call = next((tool_call for tool_call in tools_calls if tool_call.function.name == tool_name), None)
                 if tool_call is None:
                     tool_call = AssistantPromptMessage.ToolCall(
-                        id='',
-                        type='',
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tool_name, arguments="")
+                        id="",
+                        type="",
+                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tool_name, arguments=""),
                     )
                     tools_calls.append(tool_call)
 
@@ -242,9 +248,9 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         for chunk in response.iter_lines(decode_unicode=True, delimiter="\n\n"):
             if chunk:
                 # ignore sse comments
-                if chunk.startswith(':'):
+                if chunk.startswith(":"):
                     continue
-                decoded_chunk = chunk.strip().lstrip('data: ').lstrip()
+                decoded_chunk = chunk.strip().lstrip("data: ").lstrip()
                 chunk_json = None
                 try:
                     chunk_json = json.loads(decoded_chunk)
@@ -253,21 +259,21 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                     yield create_final_llm_result_chunk(
                         index=chunk_index + 1,
                         message=AssistantPromptMessage(content=""),
-                        finish_reason="Non-JSON encountered."
+                        finish_reason="Non-JSON encountered.",
                     )
                     break
-                if not chunk_json or len(chunk_json['choices']) == 0:
+                if not chunk_json or len(chunk_json["choices"]) == 0:
                     continue
 
-                choice = chunk_json['choices'][0]
-                finish_reason = chunk_json['choices'][0].get('finish_reason')
+                choice = chunk_json["choices"][0]
+                finish_reason = chunk_json["choices"][0].get("finish_reason")
                 chunk_index += 1
 
-                if 'delta' in choice:
-                    delta = choice['delta']
-                    delta_content = delta.get('content')
+                if "delta" in choice:
+                    delta = choice["delta"]
+                    delta_content = delta.get("content")
 
-                    assistant_message_tool_calls = delta.get('tool_calls', None)
+                    assistant_message_tool_calls = delta.get("tool_calls", None)
                     # assistant_message_function_call = delta.delta.function_call
 
                     # extract tool calls from response
@@ -275,19 +281,18 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                         tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
                         increase_tool_call(tool_calls)
 
-                    if delta_content is None or delta_content == '':
+                    if delta_content is None or delta_content == "":
                         continue
 
                     # transform assistant message to prompt message
                     assistant_prompt_message = AssistantPromptMessage(
-                        content=delta_content,
-                        tool_calls=tool_calls if assistant_message_tool_calls else []
+                        content=delta_content, tool_calls=tool_calls if assistant_message_tool_calls else []
                     )
 
                     full_assistant_content += delta_content
-                elif 'text' in choice:
-                    choice_text = choice.get('text', '')
-                    if choice_text == '':
+                elif "text" in choice:
+                    choice_text = choice.get("text", "")
+                    if choice_text == "":
                         continue
 
                     # transform assistant message to prompt message
@@ -303,26 +308,21 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=chunk_index,
                         message=assistant_prompt_message,
-                    )
+                    ),
                 )
 
             chunk_index += 1
-        
+
         if tools_calls:
             yield LLMResultChunk(
                 model=model,
                 prompt_messages=prompt_messages,
                 delta=LLMResultChunkDelta(
                     index=chunk_index,
-                    message=AssistantPromptMessage(
-                        tool_calls=tools_calls,
-                        content=""
-                    ),
-                )
+                    message=AssistantPromptMessage(tool_calls=tools_calls, content=""),
+                ),
             )
 
         yield create_final_llm_result_chunk(
-            index=chunk_index,
-            message=AssistantPromptMessage(content=""),
-            finish_reason=finish_reason
-        )
\ No newline at end of file
+            index=chunk_index, message=AssistantPromptMessage(content=""), finish_reason=finish_reason
+        )
diff --git a/api/core/model_runtime/model_providers/stepfun/stepfun.py b/api/core/model_runtime/model_providers/stepfun/stepfun.py
index 50b17392b5..e1c41a9153 100644
--- a/api/core/model_runtime/model_providers/stepfun/stepfun.py
+++ b/api/core/model_runtime/model_providers/stepfun/stepfun.py
@@ -8,7 +8,6 @@ logger = logging.getLogger(__name__)
 
 
 class StepfunProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -19,12 +18,9 @@ class StepfunProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(
-                model='step-1-8k',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="step-1-8k", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/tencent/speech2text/flash_recognizer.py b/api/core/model_runtime/model_providers/tencent/speech2text/flash_recognizer.py
index c3e3b7c258..c3c21793e8 100644
--- a/api/core/model_runtime/model_providers/tencent/speech2text/flash_recognizer.py
+++ b/api/core/model_runtime/model_providers/tencent/speech2text/flash_recognizer.py
@@ -1,6 +1,7 @@
 import base64
 import hashlib
 import hmac
+import operator
 import time
 
 import requests
@@ -67,10 +68,10 @@ class FlashRecognitionRequest:
 
 class FlashRecognizer:
     """
-    reponse:
+    response:
     request_id        string
-    status            Integer    
-    message           String    
+    status            Integer
+    message           String
     audio_duration    Integer
     flash_result      Result Array
 
@@ -81,16 +82,16 @@ class FlashRecognizer:
 
     Sentence:
     text              String
-    start_time        Integer    
-    end_time          Integer    
-    speaker_id        Integer    
+    start_time        Integer
+    end_time          Integer
+    speaker_id        Integer
     word_list         Word Array
 
     Word:
-    word              String 
-    start_time        Integer 
-    end_time          Integer 
-    stable_flag：     Integer 
+    word              String
+    start_time        Integer
+    end_time          Integer
+    stable_flag：     Integer
     """
 
     def __init__(self, appid, credential):
@@ -100,13 +101,13 @@ class FlashRecognizer:
     def _format_sign_string(self, param):
         signstr = "POSTasr.cloud.tencent.com/asr/flash/v1/"
         for t in param:
-            if 'appid' in t:
+            if "appid" in t:
                 signstr += str(t[1])
                 break
         signstr += "?"
         for x in param:
             tmp = x
-            if 'appid' in x:
+            if "appid" in x:
                 continue
             for t in tmp:
                 signstr += str(t)
@@ -121,31 +122,38 @@ class FlashRecognizer:
         return header
 
     def _sign(self, signstr, secret_key):
-        hmacstr = hmac.new(secret_key.encode('utf-8'),
-                           signstr.encode('utf-8'), hashlib.sha1).digest()
+        hmacstr = hmac.new(secret_key.encode("utf-8"), signstr.encode("utf-8"), hashlib.sha1).digest()
         s = base64.b64encode(hmacstr)
-        s = s.decode('utf-8')
+        s = s.decode("utf-8")
         return s
 
     def _build_req_with_signature(self, secret_key, params, header):
-        query = sorted(params.items(), key=lambda d: d[0])
+        query = sorted(params.items(), key=operator.itemgetter(0))
         signstr = self._format_sign_string(query)
         signature = self._sign(signstr, secret_key)
         header["Authorization"] = signature
-        requrl = "https://"
-        requrl += signstr[4::]
-        return requrl
+        req_url = "https://"
+        req_url += signstr[4::]
+        return req_url
 
     def _create_query_arr(self, req):
         return {
-            'appid': self.appid, 'secretid': self.credential.secret_id, 'timestamp': str(int(time.time())),
-             'engine_type': req.engine_type, 'voice_format': req.voice_format,
-             'speaker_diarization': req.speaker_diarization, 'hotword_id': req.hotword_id,
-             'customization_id': req.customization_id, 'filter_dirty': req.filter_dirty,
-             'filter_modal': req.filter_modal, 'filter_punc': req.filter_punc,
-             'convert_num_mode': req.convert_num_mode, 'word_info': req.word_info,
-             'first_channel_only': req.first_channel_only, 'reinforce_hotword': req.reinforce_hotword,
-             'sentence_max_length': req.sentence_max_length
+            "appid": self.appid,
+            "secretid": self.credential.secret_id,
+            "timestamp": str(int(time.time())),
+            "engine_type": req.engine_type,
+            "voice_format": req.voice_format,
+            "speaker_diarization": req.speaker_diarization,
+            "hotword_id": req.hotword_id,
+            "customization_id": req.customization_id,
+            "filter_dirty": req.filter_dirty,
+            "filter_modal": req.filter_modal,
+            "filter_punc": req.filter_punc,
+            "convert_num_mode": req.convert_num_mode,
+            "word_info": req.word_info,
+            "first_channel_only": req.first_channel_only,
+            "reinforce_hotword": req.reinforce_hotword,
+            "sentence_max_length": req.sentence_max_length,
         }
 
     def recognize(self, req, data):
diff --git a/api/core/model_runtime/model_providers/tencent/speech2text/speech2text.py b/api/core/model_runtime/model_providers/tencent/speech2text/speech2text.py
index 00ec5aa9c8..5b427663ca 100644
--- a/api/core/model_runtime/model_providers/tencent/speech2text/speech2text.py
+++ b/api/core/model_runtime/model_providers/tencent/speech2text/speech2text.py
@@ -18,9 +18,7 @@ from core.model_runtime.model_providers.tencent.speech2text.flash_recognizer imp
 
 
 class TencentSpeech2TextModel(Speech2TextModel):
-    def _invoke(self, model: str, credentials: dict,
-                file: IO[bytes], user: Optional[str] = None) \
-            -> str:
+    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
         """
         Invoke speech2text model
 
@@ -43,7 +41,7 @@ class TencentSpeech2TextModel(Speech2TextModel):
         try:
             audio_file_path = self._get_demo_file_path()
 
-            with open(audio_file_path, 'rb') as audio_file:
+            with open(audio_file_path, "rb") as audio_file:
                 self._speech2text_invoke(model, credentials, audio_file)
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -83,10 +81,6 @@ class TencentSpeech2TextModel(Speech2TextModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                requests.exceptions.ConnectionError
-            ],
-            InvokeAuthorizationError: [
-                CredentialsValidateFailedError
-            ]
+            InvokeConnectionError: [requests.exceptions.ConnectionError],
+            InvokeAuthorizationError: [CredentialsValidateFailedError],
         }
diff --git a/api/core/model_runtime/model_providers/tencent/tencent.py b/api/core/model_runtime/model_providers/tencent/tencent.py
index dd9f90bb47..79c6f577b8 100644
--- a/api/core/model_runtime/model_providers/tencent/tencent.py
+++ b/api/core/model_runtime/model_providers/tencent/tencent.py
@@ -18,12 +18,9 @@ class TencentProvider(ModelProvider):
         """
         try:
             model_instance = self.get_model_instance(ModelType.SPEECH2TEXT)
-            model_instance.validate_credentials(
-                model='tencent',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="tencent", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/togetherai/llm/llm.py b/api/core/model_runtime/model_providers/togetherai/llm/llm.py
index bb802d4071..b96d43979e 100644
--- a/api/core/model_runtime/model_providers/togetherai/llm/llm.py
+++ b/api/core/model_runtime/model_providers/togetherai/llm/llm.py
@@ -22,16 +22,21 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
 
 
 class TogetherAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
-
     def _update_endpoint_url(self, credentials: dict):
-        credentials['endpoint_url'] = "https://api.together.xyz/v1"
+        credentials["endpoint_url"] = "https://api.together.xyz/v1"
         return credentials
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         cred_with_endpoint = self._update_endpoint_url(credentials=credentials)
 
         return super()._invoke(model, cred_with_endpoint, prompt_messages, model_parameters, tools, stop, stream, user)
@@ -41,12 +46,22 @@ class TogetherAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
 
         return super().validate_credentials(model, cred_with_endpoint)
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
-                  tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                  stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         cred_with_endpoint = self._update_endpoint_url(credentials=credentials)
 
-        return super()._generate(model, cred_with_endpoint, prompt_messages, model_parameters, tools, stop, stream, user)
+        return super()._generate(
+            model, cred_with_endpoint, prompt_messages, model_parameters, tools, stop, stream, user
+        )
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         cred_with_endpoint = self._update_endpoint_url(credentials=credentials)
@@ -61,45 +76,45 @@ class TogetherAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             features=features,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(cred_with_endpoint.get('context_size', "4096")),
-                ModelPropertyKey.MODE: cred_with_endpoint.get('mode'),
+                ModelPropertyKey.CONTEXT_SIZE: int(cred_with_endpoint.get("context_size", "4096")),
+                ModelPropertyKey.MODE: cred_with_endpoint.get("mode"),
             },
             parameter_rules=[
                 ParameterRule(
                     name=DefaultParameterName.TEMPERATURE.value,
                     label=I18nObject(en_US="Temperature"),
                     type=ParameterType.FLOAT,
-                    default=float(cred_with_endpoint.get('temperature', 0.7)),
+                    default=float(cred_with_endpoint.get("temperature", 0.7)),
                     min=0,
                     max=2,
-                    precision=2
+                    precision=2,
                 ),
                 ParameterRule(
                     name=DefaultParameterName.TOP_P.value,
                     label=I18nObject(en_US="Top P"),
                     type=ParameterType.FLOAT,
-                    default=float(cred_with_endpoint.get('top_p', 1)),
+                    default=float(cred_with_endpoint.get("top_p", 1)),
                     min=0,
                     max=1,
-                    precision=2
+                    precision=2,
                 ),
                 ParameterRule(
                     name=TOP_K,
                     label=I18nObject(en_US="Top K"),
                     type=ParameterType.INT,
-                    default=int(cred_with_endpoint.get('top_k', 50)),
+                    default=int(cred_with_endpoint.get("top_k", 50)),
                     min=-2147483647,
                     max=2147483647,
-                    precision=0
+                    precision=0,
                 ),
                 ParameterRule(
                     name=REPETITION_PENALTY,
                     label=I18nObject(en_US="Repetition Penalty"),
                     type=ParameterType.FLOAT,
-                    default=float(cred_with_endpoint.get('repetition_penalty', 1)),
+                    default=float(cred_with_endpoint.get("repetition_penalty", 1)),
                     min=-3.4,
                     max=3.4,
-                    precision=1
+                    precision=1,
                 ),
                 ParameterRule(
                     name=DefaultParameterName.MAX_TOKENS.value,
@@ -107,46 +122,49 @@ class TogetherAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
                     type=ParameterType.INT,
                     default=512,
                     min=1,
-                    max=int(cred_with_endpoint.get('max_tokens_to_sample', 4096)),
+                    max=int(cred_with_endpoint.get("max_tokens_to_sample", 4096)),
                 ),
                 ParameterRule(
                     name=DefaultParameterName.FREQUENCY_PENALTY.value,
                     label=I18nObject(en_US="Frequency Penalty"),
                     type=ParameterType.FLOAT,
-                    default=float(credentials.get('frequency_penalty', 0)),
+                    default=float(credentials.get("frequency_penalty", 0)),
                     min=-2,
-                    max=2
+                    max=2,
                 ),
                 ParameterRule(
                     name=DefaultParameterName.PRESENCE_PENALTY.value,
                     label=I18nObject(en_US="Presence Penalty"),
                     type=ParameterType.FLOAT,
-                    default=float(credentials.get('presence_penalty', 0)),
+                    default=float(credentials.get("presence_penalty", 0)),
                     min=-2,
-                    max=2
+                    max=2,
                 ),
             ],
             pricing=PriceConfig(
-                input=Decimal(cred_with_endpoint.get('input_price', 0)),
-                output=Decimal(cred_with_endpoint.get('output_price', 0)),
-                unit=Decimal(cred_with_endpoint.get('unit', 0)),
-                currency=cred_with_endpoint.get('currency', "USD")
+                input=Decimal(cred_with_endpoint.get("input_price", 0)),
+                output=Decimal(cred_with_endpoint.get("output_price", 0)),
+                unit=Decimal(cred_with_endpoint.get("unit", 0)),
+                currency=cred_with_endpoint.get("currency", "USD"),
             ),
         )
 
-        if cred_with_endpoint['mode'] == 'chat':
+        if cred_with_endpoint["mode"] == "chat":
             entity.model_properties[ModelPropertyKey.MODE] = LLMMode.CHAT.value
-        elif cred_with_endpoint['mode'] == 'completion':
+        elif cred_with_endpoint["mode"] == "completion":
             entity.model_properties[ModelPropertyKey.MODE] = LLMMode.COMPLETION.value
         else:
             raise ValueError(f"Unknown completion type {cred_with_endpoint['completion_type']}")
 
         return entity
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         cred_with_endpoint = self._update_endpoint_url(credentials=credentials)
 
         return super().get_num_tokens(model, cred_with_endpoint, prompt_messages, tools)
-
-
diff --git a/api/core/model_runtime/model_providers/togetherai/togetherai.py b/api/core/model_runtime/model_providers/togetherai/togetherai.py
index ffce4794e7..aa4100a7c9 100644
--- a/api/core/model_runtime/model_providers/togetherai/togetherai.py
+++ b/api/core/model_runtime/model_providers/togetherai/togetherai.py
@@ -6,6 +6,5 @@ logger = logging.getLogger(__name__)
 
 
 class TogetherAIProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         pass
diff --git a/api/core/model_runtime/model_providers/tongyi/_common.py b/api/core/model_runtime/model_providers/tongyi/_common.py
index fab18b41fd..8a50c7aa05 100644
--- a/api/core/model_runtime/model_providers/tongyi/_common.py
+++ b/api/core/model_runtime/model_providers/tongyi/_common.py
@@ -21,7 +21,7 @@ class _CommonTongyi:
     @staticmethod
     def _to_credential_kwargs(credentials: dict) -> dict:
         credentials_kwargs = {
-            "dashscope_api_key": credentials['dashscope_api_key'],
+            "dashscope_api_key": credentials["dashscope_api_key"],
         }
 
         return credentials_kwargs
@@ -51,5 +51,5 @@ class _CommonTongyi:
                 InvalidParameter,
                 UnsupportedModel,
                 UnsupportedHTTPMethod,
-            ]
+            ],
         }
diff --git a/api/core/model_runtime/model_providers/tongyi/llm/llm.py b/api/core/model_runtime/model_providers/tongyi/llm/llm.py
index 4e1bb0a5a4..1d4eba6668 100644
--- a/api/core/model_runtime/model_providers/tongyi/llm/llm.py
+++ b/api/core/model_runtime/model_providers/tongyi/llm/llm.py
@@ -4,6 +4,7 @@ import tempfile
 import uuid
 from collections.abc import Generator
 from http import HTTPStatus
+from pathlib import Path
 from typing import Optional, Union, cast
 
 from dashscope import Generation, MultiModalConversation, get_tokenizer
@@ -17,7 +18,6 @@ from dashscope.common.error import (
     UnsupportedModel,
 )
 
-from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -46,11 +46,17 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 class TongyiLargeLanguageModel(LargeLanguageModel):
     tokenizers = {}
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -64,90 +70,16 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         :param user: unique user id
         :return: full response or stream response chunk generator result
         """
-        # invoke model
+        # invoke model without code wrapper
         return self._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
-    def _code_block_mode_wrapper(self, model: str, credentials: dict,
-                                 prompt_messages: list[PromptMessage], model_parameters: dict,
-                                 tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
-                                 stream: bool = True, user: str | None = None, callbacks: list[Callback] = None) \
-            -> LLMResult | Generator:
-        """
-        Wrapper for code block mode
-        """
-        block_prompts = """You should always follow the instructions and output a valid {{block}} object.
-The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
-if you are not sure about the structure.
-
-<instructions>
-{{instructions}}
-</instructions>
-You should also complete the text started with ``` but not tell ``` directly.
-"""
-
-        code_block = model_parameters.get("response_format", "")
-        if not code_block:
-            return self._invoke(
-                model=model,
-                credentials=credentials,
-                prompt_messages=prompt_messages,
-                model_parameters=model_parameters,
-                tools=tools,
-                stop=stop,
-                stream=stream,
-                user=user
-            )
-
-        model_parameters.pop("response_format")
-        stop = stop or []
-        stop.extend(["\n```", "```\n"])
-        block_prompts = block_prompts.replace("{{block}}", code_block)
-
-        # check if there is a system message
-        if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
-            # override the system message
-            prompt_messages[0] = SystemPromptMessage(
-                content=block_prompts
-                .replace("{{instructions}}", prompt_messages[0].content)
-            )
-        else:
-            # insert the system message
-            prompt_messages.insert(0, SystemPromptMessage(
-                content=block_prompts
-                .replace("{{instructions}}", f"Please output a valid {code_block} with markdown codeblocks.")
-            ))
-
-        if len(prompt_messages) > 0 and isinstance(prompt_messages[-1], UserPromptMessage):
-            # add ```JSON\n to the last message
-            prompt_messages[-1].content += f"\n```{code_block}\n"
-        else:
-            # append a user message
-            prompt_messages.append(UserPromptMessage(
-                content=f"```{code_block}\n"
-            ))
-
-        response = self._invoke(
-            model=model,
-            credentials=credentials,
-            prompt_messages=prompt_messages,
-            model_parameters=model_parameters,
-            tools=tools,
-            stop=stop,
-            stream=stream,
-            user=user
-        )
-
-        if isinstance(response, Generator):
-            return self._code_block_mode_stream_processor_with_backtick(
-                model=model,
-                prompt_messages=prompt_messages,
-                input_generator=response
-            )
-
-        return response
-
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -157,10 +89,10 @@ You should also complete the text started with ``` but not tell ``` directly.
         :param tools: tools for tool calling
         :return:
         """
-        if model in ['qwen-turbo-chat', 'qwen-plus-chat']:
-            model = model.replace('-chat', '')
-        if model == 'farui-plus':
-            model = 'qwen-farui-plus'
+        if model in {"qwen-turbo-chat", "qwen-plus-chat"}:
+            model = model.replace("-chat", "")
+        if model == "farui-plus":
+            model = "qwen-farui-plus"
 
         if model in self.tokenizers:
             tokenizer = self.tokenizers[model]
@@ -191,16 +123,22 @@ You should also complete the text started with ``` but not tell ``` directly.
                 model_parameters={
                     "temperature": 0.5,
                 },
-                stream=False
+                stream=False,
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict,
-                  tools: Optional[list[PromptMessageTool]] = None,
-                  stop: Optional[list[str]] = None, stream: bool = True,
-                  user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -219,18 +157,18 @@ You should also complete the text started with ``` but not tell ``` directly.
 
         mode = self.get_model_mode(model, credentials)
 
-        if model in ['qwen-turbo-chat', 'qwen-plus-chat']:
-            model = model.replace('-chat', '')
+        if model in {"qwen-turbo-chat", "qwen-plus-chat"}:
+            model = model.replace("-chat", "")
 
         extra_model_kwargs = {}
         if tools:
-            extra_model_kwargs['tools'] = self._convert_tools(tools)
+            extra_model_kwargs["tools"] = self._convert_tools(tools)
 
         if stop:
-            extra_model_kwargs['stop'] = stop
+            extra_model_kwargs["stop"] = stop
 
         params = {
-            'model': model,
+            "model": model,
             **model_parameters,
             **credentials_kwargs,
             **extra_model_kwargs,
@@ -238,23 +176,22 @@ You should also complete the text started with ``` but not tell ``` directly.
 
         model_schema = self.get_model_schema(model, credentials)
         if ModelFeature.VISION in (model_schema.features or []):
-            params['messages'] = self._convert_prompt_messages_to_tongyi_messages(prompt_messages, rich_content=True)
+            params["messages"] = self._convert_prompt_messages_to_tongyi_messages(prompt_messages, rich_content=True)
 
             response = MultiModalConversation.call(**params, stream=stream)
         else:
             # nothing different between chat model and completion model in tongyi
-            params['messages'] = self._convert_prompt_messages_to_tongyi_messages(prompt_messages)
-            response = Generation.call(**params,
-                                       result_format='message',
-                                       stream=stream)
+            params["messages"] = self._convert_prompt_messages_to_tongyi_messages(prompt_messages)
+            response = Generation.call(**params, result_format="message", stream=stream)
 
         if stream:
             return self._handle_generate_stream_response(model, credentials, response, prompt_messages)
 
         return self._handle_generate_response(model, credentials, response, prompt_messages)
 
-    def _handle_generate_response(self, model: str, credentials: dict, response: GenerationResponse,
-                                  prompt_messages: list[PromptMessage]) -> LLMResult:
+    def _handle_generate_response(
+        self, model: str, credentials: dict, response: GenerationResponse, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
         """
         Handle llm response
 
@@ -264,10 +201,8 @@ You should also complete the text started with ``` but not tell ``` directly.
         :param prompt_messages: prompt messages
         :return: llm response
         """
-        if response.status_code != 200 and response.status_code != HTTPStatus.OK:
-            raise ServiceUnavailableError(
-                response.message
-            )
+        if response.status_code not in {200, HTTPStatus.OK}:
+            raise ServiceUnavailableError(response.message)
         # transform assistant message to prompt message
         assistant_prompt_message = AssistantPromptMessage(
             content=response.output.choices[0].message.content,
@@ -286,9 +221,13 @@ You should also complete the text started with ``` but not tell ``` directly.
 
         return result
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict,
-                                         responses: Generator[GenerationResponse, None, None],
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        responses: Generator[GenerationResponse, None, None],
+        prompt_messages: list[PromptMessage],
+    ) -> Generator:
         """
         Handle llm stream response
 
@@ -298,10 +237,10 @@ You should also complete the text started with ``` but not tell ``` directly.
         :param prompt_messages: prompt messages
         :return: llm response chunk generator result
         """
-        full_text = ''
+        full_text = ""
         tool_calls = []
         for index, response in enumerate(responses):
-            if response.status_code != 200 and response.status_code != HTTPStatus.OK:
+            if response.status_code not in {200, HTTPStatus.OK}:
                 raise ServiceUnavailableError(
                     f"Failed to invoke model {model}, status code: {response.status_code}, "
                     f"message: {response.message}"
@@ -309,22 +248,22 @@ You should also complete the text started with ``` but not tell ``` directly.
 
             resp_finish_reason = response.output.choices[0].finish_reason
 
-            if resp_finish_reason is not None and resp_finish_reason != 'null':
+            if resp_finish_reason is not None and resp_finish_reason != "null":
                 resp_content = response.output.choices[0].message.content
 
                 assistant_prompt_message = AssistantPromptMessage(
-                    content='',
+                    content="",
                 )
 
-                if 'tool_calls' in response.output.choices[0].message:
-                    tool_calls = response.output.choices[0].message['tool_calls']
+                if "tool_calls" in response.output.choices[0].message:
+                    tool_calls = response.output.choices[0].message["tool_calls"]
                 elif resp_content:
                     # special for qwen-vl
                     if isinstance(resp_content, list):
-                        resp_content = resp_content[0]['text']
+                        resp_content = resp_content[0]["text"]
 
                     # transform assistant message to prompt message
-                    assistant_prompt_message.content = resp_content.replace(full_text, '', 1)
+                    assistant_prompt_message.content = resp_content.replace(full_text, "", 1)
 
                     full_text = resp_content
 
@@ -332,12 +271,11 @@ You should also complete the text started with ``` but not tell ``` directly.
                     message_tool_calls = []
                     for tool_call_obj in tool_calls:
                         message_tool_call = AssistantPromptMessage.ToolCall(
-                            id=tool_call_obj['function']['name'],
-                            type='function',
+                            id=tool_call_obj["function"]["name"],
+                            type="function",
                             function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                                name=tool_call_obj['function']['name'],
-                                arguments=tool_call_obj['function']['arguments']
-                            )
+                                name=tool_call_obj["function"]["name"], arguments=tool_call_obj["function"]["arguments"]
+                            ),
                         )
                         message_tool_calls.append(message_tool_call)
 
@@ -351,26 +289,23 @@ You should also complete the text started with ``` but not tell ``` directly.
                     model=model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
-                        index=index,
-                        message=assistant_prompt_message,
-                        finish_reason=resp_finish_reason,
-                        usage=usage
-                    )
+                        index=index, message=assistant_prompt_message, finish_reason=resp_finish_reason, usage=usage
+                    ),
                 )
             else:
                 resp_content = response.output.choices[0].message.content
                 if not resp_content:
-                    if 'tool_calls' in response.output.choices[0].message:
-                        tool_calls = response.output.choices[0].message['tool_calls']
+                    if "tool_calls" in response.output.choices[0].message:
+                        tool_calls = response.output.choices[0].message["tool_calls"]
                     continue
 
                 # special for qwen-vl
                 if isinstance(resp_content, list):
-                    resp_content = resp_content[0]['text']
+                    resp_content = resp_content[0]["text"]
 
                 # transform assistant message to prompt message
                 assistant_prompt_message = AssistantPromptMessage(
-                    content=resp_content.replace(full_text, '', 1),
+                    content=resp_content.replace(full_text, "", 1),
                 )
 
                 full_text = resp_content
@@ -378,10 +313,7 @@ You should also complete the text started with ``` but not tell ``` directly.
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=index,
-                        message=assistant_prompt_message
-                    )
+                    delta=LLMResultChunkDelta(index=index, message=assistant_prompt_message),
                 )
 
     def _to_credential_kwargs(self, credentials: dict) -> dict:
@@ -392,7 +324,7 @@ You should also complete the text started with ``` but not tell ``` directly.
         :return:
         """
         credentials_kwargs = {
-            "api_key": credentials['dashscope_api_key'],
+            "api_key": credentials["dashscope_api_key"],
         }
 
         return credentials_kwargs
@@ -419,9 +351,7 @@ You should also complete the text started with ``` but not tell ``` directly.
                         break
         elif isinstance(message, AssistantPromptMessage):
             message_text = f"{ai_prompt} {content}"
-        elif isinstance(message, SystemPromptMessage):
-            message_text = content
-        elif isinstance(message, ToolPromptMessage):
+        elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
             message_text = content
         else:
             raise ValueError(f"Got unknown type {message}")
@@ -437,16 +367,14 @@ You should also complete the text started with ``` but not tell ``` directly.
         """
         messages = messages.copy()  # don't mutate the original list
 
-        text = "".join(
-            self._convert_one_message_to_text(message)
-            for message in messages
-        )
+        text = "".join(self._convert_one_message_to_text(message) for message in messages)
 
         # trim off the trailing ' ' that might come from the "Assistant: "
         return text.rstrip()
 
-    def _convert_prompt_messages_to_tongyi_messages(self, prompt_messages: list[PromptMessage],
-                                                    rich_content: bool = False) -> list[dict]:
+    def _convert_prompt_messages_to_tongyi_messages(
+        self, prompt_messages: list[PromptMessage], rich_content: bool = False
+    ) -> list[dict]:
         """
         Convert prompt messages to tongyi messages
 
@@ -456,24 +384,28 @@ You should also complete the text started with ``` but not tell ``` directly.
         tongyi_messages = []
         for prompt_message in prompt_messages:
             if isinstance(prompt_message, SystemPromptMessage):
-                tongyi_messages.append({
-                    'role': 'system',
-                    'content': prompt_message.content if not rich_content else [{"text": prompt_message.content}],
-                })
+                tongyi_messages.append(
+                    {
+                        "role": "system",
+                        "content": prompt_message.content if not rich_content else [{"text": prompt_message.content}],
+                    }
+                )
             elif isinstance(prompt_message, UserPromptMessage):
                 if isinstance(prompt_message.content, str):
-                    tongyi_messages.append({
-                        'role': 'user',
-                        'content': prompt_message.content if not rich_content else [{"text": prompt_message.content}],
-                    })
+                    tongyi_messages.append(
+                        {
+                            "role": "user",
+                            "content": prompt_message.content
+                            if not rich_content
+                            else [{"text": prompt_message.content}],
+                        }
+                    )
                 else:
                     sub_messages = []
                     for message_content in prompt_message.content:
                         if message_content.type == PromptMessageContentType.TEXT:
                             message_content = cast(TextPromptMessageContent, message_content)
-                            sub_message_dict = {
-                                "text": message_content.data
-                            }
+                            sub_message_dict = {"text": message_content.data}
                             sub_messages.append(sub_message_dict)
                         elif message_content.type == PromptMessageContentType.IMAGE:
                             message_content = cast(ImagePromptMessageContent, message_content)
@@ -483,35 +415,25 @@ You should also complete the text started with ``` but not tell ``` directly.
                                 # convert image base64 data to file in /tmp
                                 image_url = self._save_base64_image_to_file(message_content.data)
 
-                            sub_message_dict = {
-                                "image": image_url
-                            }
+                            sub_message_dict = {"image": image_url}
                             sub_messages.append(sub_message_dict)
 
                     # resort sub_messages to ensure text is always at last
-                    sub_messages = sorted(sub_messages, key=lambda x: 'text' in x)
+                    sub_messages = sorted(sub_messages, key=lambda x: "text" in x)
 
-                    tongyi_messages.append({
-                        'role': 'user',
-                        'content': sub_messages
-                    })
+                    tongyi_messages.append({"role": "user", "content": sub_messages})
             elif isinstance(prompt_message, AssistantPromptMessage):
                 content = prompt_message.content
                 if not content:
-                    content = ' '
-                message = {
-                    'role': 'assistant',
-                    'content': content if not rich_content else [{"text": content}]
-                }
+                    content = " "
+                message = {"role": "assistant", "content": content if not rich_content else [{"text": content}]}
                 if prompt_message.tool_calls:
-                    message['tool_calls'] = [tool_call.model_dump() for tool_call in prompt_message.tool_calls]
+                    message["tool_calls"] = [tool_call.model_dump() for tool_call in prompt_message.tool_calls]
                 tongyi_messages.append(message)
             elif isinstance(prompt_message, ToolPromptMessage):
-                tongyi_messages.append({
-                    "role": "tool",
-                    "content": prompt_message.content,
-                    "name": prompt_message.tool_call_id
-                })
+                tongyi_messages.append(
+                    {"role": "tool", "content": prompt_message.content, "name": prompt_message.tool_call_id}
+                )
             else:
                 raise ValueError(f"Got unknown type {prompt_message}")
 
@@ -526,15 +448,14 @@ You should also complete the text started with ``` but not tell ``` directly.
         :return: image file path
         """
         # get mime type and encoded string
-        mime_type, encoded_string = base64_image.split(',')[0].split(';')[0].split(':')[1], base64_image.split(',')[1]
+        mime_type, encoded_string = base64_image.split(",")[0].split(";")[0].split(":")[1], base64_image.split(",")[1]
 
         # save image to file
         temp_dir = tempfile.gettempdir()
 
         file_path = os.path.join(temp_dir, f"{uuid.uuid4()}.{mime_type.split('/')[1]}")
 
-        with open(file_path, "wb") as image_file:
-            image_file.write(base64.b64decode(encoded_string))
+        Path(file_path).write_bytes(base64.b64decode(encoded_string))
 
         return f"file://{file_path}"
 
@@ -544,19 +465,18 @@ You should also complete the text started with ``` but not tell ``` directly.
         """
         tool_definitions = []
         for tool in tools:
-            properties = tool.parameters['properties']
-            required_properties = tool.parameters['required']
+            properties = tool.parameters["properties"]
+            required_properties = tool.parameters["required"]
 
             properties_definitions = {}
             for p_key, p_val in properties.items():
-                desc = p_val['description']
-                if 'enum' in p_val:
-                    desc += (f"; Only accepts one of the following predefined options: "
-                             f"[{', '.join(p_val['enum'])}]")
+                desc = p_val["description"]
+                if "enum" in p_val:
+                    desc += f"; Only accepts one of the following predefined options: [{', '.join(p_val['enum'])}]"
 
                 properties_definitions[p_key] = {
-                    'description': desc,
-                    'type': p_val['type'],
+                    "description": desc,
+                    "type": p_val["type"],
                 }
 
             tool_definition = {
@@ -565,8 +485,8 @@ You should also complete the text started with ``` but not tell ``` directly.
                     "name": tool.name,
                     "description": tool.description,
                     "parameters": properties_definitions,
-                    "required": required_properties
-                }
+                    "required": required_properties,
+                },
             }
 
             tool_definitions.append(tool_definition)
@@ -598,5 +518,5 @@ You should also complete the text started with ``` but not tell ``` directly.
                 InvalidParameter,
                 UnsupportedModel,
                 UnsupportedHTTPMethod,
-            ]
+            ],
         }
diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-long.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-long.yaml
index b2cf3dd486..33b3435eb6 100644
--- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-long.yaml
+++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-long.yaml
@@ -24,7 +24,7 @@ parameter_rules:
     type: int
     default: 2000
     min: 1
-    max: 2000
+    max: 6000
     help:
       zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
       en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
diff --git a/api/core/model_runtime/model_providers/tongyi/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/tongyi/text_embedding/text_embedding.py
index 97dcb72f7c..5783d2e383 100644
--- a/api/core/model_runtime/model_providers/tongyi/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/tongyi/text_embedding/text_embedding.py
@@ -46,7 +46,6 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
         used_tokens = 0
 
         for i, text in enumerate(texts):
-
             # Here token count is only an approximation based on the GPT2 tokenizer
             num_tokens = self._get_num_tokens_by_gpt2(text)
 
@@ -71,12 +70,8 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
             batched_embeddings += embeddings_batch
 
         # calc usage
-        usage = self._calc_response_usage(
-            model=model, credentials=credentials, tokens=used_tokens
-        )
-        return TextEmbeddingResult(
-            embeddings=batched_embeddings, usage=usage, model=model
-        )
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
+        return TextEmbeddingResult(embeddings=batched_embeddings, usage=usage, model=model)
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
@@ -108,16 +103,12 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
             credentials_kwargs = self._to_credential_kwargs(credentials)
 
             # call embedding model
-            self.embed_documents(
-                credentials_kwargs=credentials_kwargs, model=model, texts=["ping"]
-            )
+            self.embed_documents(credentials_kwargs=credentials_kwargs, model=model, texts=["ping"])
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
     @staticmethod
-    def embed_documents(
-        credentials_kwargs: dict, model: str, texts: list[str]
-    ) -> tuple[list[list[float]], int]:
+    def embed_documents(credentials_kwargs: dict, model: str, texts: list[str]) -> tuple[list[list[float]], int]:
         """Call out to Tongyi's embedding endpoint.
 
         Args:
@@ -145,7 +136,7 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
                     raise ValueError("Embedding data is missing in the response.")
             else:
                 raise ValueError("Response output is missing or does not contain embeddings.")
-            
+
             if response.usage and "total_tokens" in response.usage:
                 embedding_used_tokens += response.usage["total_tokens"]
             else:
@@ -153,9 +144,7 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
 
         return [list(map(float, e)) for e in embeddings], embedding_used_tokens
 
-    def _calc_response_usage(
-        self, model: str, credentials: dict, tokens: int
-    ) -> EmbeddingUsage:
+    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
 
diff --git a/api/core/model_runtime/model_providers/tongyi/tongyi.py b/api/core/model_runtime/model_providers/tongyi/tongyi.py
index d5e25e6ecf..a084512de9 100644
--- a/api/core/model_runtime/model_providers/tongyi/tongyi.py
+++ b/api/core/model_runtime/model_providers/tongyi/tongyi.py
@@ -20,12 +20,9 @@ class TongyiProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.LLM)
 
             # Use `qwen-turbo` model for validate,
-            model_instance.validate_credentials(
-                model='qwen-turbo',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="qwen-turbo", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/tongyi/tts/tts.py b/api/core/model_runtime/model_providers/tongyi/tts/tts.py
index 664b02cd92..48a38897a8 100644
--- a/api/core/model_runtime/model_providers/tongyi/tts/tts.py
+++ b/api/core/model_runtime/model_providers/tongyi/tts/tts.py
@@ -18,8 +18,9 @@ class TongyiText2SpeechModel(_CommonTongyi, TTSModel):
     Model class for Tongyi Speech to text model.
     """
 
-    def _invoke(self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str,
-                user: Optional[str] = None) -> any:
+    def _invoke(
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ) -> any:
         """
         _invoke text2speech model
 
@@ -31,14 +32,12 @@ class TongyiText2SpeechModel(_CommonTongyi, TTSModel):
         :param user: unique user id
         :return: text translated to audio file
         """
-        if not voice or voice not in [d['value'] for d in
-                                      self.get_tts_model_voices(model=model, credentials=credentials)]:
+        if not voice or voice not in [
+            d["value"] for d in self.get_tts_model_voices(model=model, credentials=credentials)
+        ]:
             voice = self._get_model_default_voice(model, credentials)
 
-        return self._tts_invoke_streaming(model=model,
-                                          credentials=credentials,
-                                          content_text=content_text,
-                                          voice=voice)
+        return self._tts_invoke_streaming(model=model, credentials=credentials, content_text=content_text, voice=voice)
 
     def validate_credentials(self, model: str, credentials: dict, user: Optional[str] = None) -> None:
         """
@@ -53,14 +52,13 @@ class TongyiText2SpeechModel(_CommonTongyi, TTSModel):
             self._tts_invoke_streaming(
                 model=model,
                 credentials=credentials,
-                content_text='Hello Dify!',
+                content_text="Hello Dify!",
                 voice=self._get_model_default_voice(model, credentials),
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str,
-                              voice: str) -> any:
+    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
         """
         _tts_invoke_streaming text2speech model
 
@@ -82,15 +80,21 @@ class TongyiText2SpeechModel(_CommonTongyi, TTSModel):
                 else:
                     sentences = list(self._split_text_into_sentences(org_text=content, max_length=wl))
                 for sentence in sentences:
-                    SpeechSynthesizer.call(model=v, sample_rate=16000,
-                                           api_key=api_key,
-                                           text=sentence.strip(),
-                                           callback=cb,
-                                           format=at, word_timestamp_enabled=True,
-                                           phoneme_timestamp_enabled=True)
+                    SpeechSynthesizer.call(
+                        model=v,
+                        sample_rate=16000,
+                        api_key=api_key,
+                        text=sentence.strip(),
+                        callback=cb,
+                        format=at,
+                        word_timestamp_enabled=True,
+                        phoneme_timestamp_enabled=True,
+                    )
 
-            threading.Thread(target=invoke_remote, args=(
-                content_text, voice, credentials.get('dashscope_api_key'), callback, audio_type, word_limit)).start()
+            threading.Thread(
+                target=invoke_remote,
+                args=(content_text, voice, credentials.get("dashscope_api_key"), callback, audio_type, word_limit),
+            ).start()
 
             while True:
                 audio = audio_queue.get()
@@ -112,16 +116,18 @@ class TongyiText2SpeechModel(_CommonTongyi, TTSModel):
         :param audio_type: audio file type
         :return: text translated to audio file
         """
-        response = dashscope.audio.tts.SpeechSynthesizer.call(model=voice, sample_rate=48000,
-                                                              api_key=credentials.get('dashscope_api_key'),
-                                                              text=sentence.strip(),
-                                                              format=audio_type)
+        response = dashscope.audio.tts.SpeechSynthesizer.call(
+            model=voice,
+            sample_rate=48000,
+            api_key=credentials.get("dashscope_api_key"),
+            text=sentence.strip(),
+            format=audio_type,
+        )
         if isinstance(response.get_audio_data(), bytes):
             return response.get_audio_data()
 
 
 class Callback(ResultCallback):
-
     def __init__(self, queue: Queue):
         self._queue = queue
 
diff --git a/api/core/model_runtime/model_providers/triton_inference_server/llm/llm.py b/api/core/model_runtime/model_providers/triton_inference_server/llm/llm.py
index 95272a41c2..cf7e3f14be 100644
--- a/api/core/model_runtime/model_providers/triton_inference_server/llm/llm.py
+++ b/api/core/model_runtime/model_providers/triton_inference_server/llm/llm.py
@@ -33,198 +33,223 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 
 
 class TritonInferenceAILargeLanguageModel(LargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], 
-                model_parameters: dict, tools: list[PromptMessageTool] | None = None, 
-                stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-        -> LLMResult | Generator:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         """
-            invoke LLM
+        invoke LLM
 
-            see `core.model_runtime.model_providers.__base.large_language_model.LargeLanguageModel._invoke`
+        see `core.model_runtime.model_providers.__base.large_language_model.LargeLanguageModel._invoke`
         """
         return self._generate(
-            model=model, credentials=credentials, prompt_messages=prompt_messages, model_parameters=model_parameters,
-            tools=tools, stop=stop, stream=stream, user=user,
+            model=model,
+            credentials=credentials,
+            prompt_messages=prompt_messages,
+            model_parameters=model_parameters,
+            tools=tools,
+            stop=stop,
+            stream=stream,
+            user=user,
         )
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
-            validate credentials
+        validate credentials
         """
-        if 'server_url' not in credentials:
-            raise CredentialsValidateFailedError('server_url is required in credentials')
-        
+        if "server_url" not in credentials:
+            raise CredentialsValidateFailedError("server_url is required in credentials")
+
         try:
-            self._invoke(model=model, credentials=credentials, prompt_messages=[
-                UserPromptMessage(content='ping')
-            ], model_parameters={}, stream=False)
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                prompt_messages=[UserPromptMessage(content="ping")],
+                model_parameters={},
+                stream=False,
+            )
         except InvokeError as ex:
-            raise CredentialsValidateFailedError(f'An error occurred during connection: {str(ex)}')
+            raise CredentialsValidateFailedError(f"An error occurred during connection: {str(ex)}")
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: list[PromptMessageTool] | None = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool] | None = None,
+    ) -> int:
         """
-            get number of tokens
+        get number of tokens
 
-            cause TritonInference LLM is a customized model, we could net detect which tokenizer to use
-            so we just take the GPT2 tokenizer as default
+        cause TritonInference LLM is a customized model, we could net detect which tokenizer to use
+        so we just take the GPT2 tokenizer as default
         """
         return self._get_num_tokens_by_gpt2(self._convert_prompt_message_to_text(prompt_messages))
-    
+
     def _convert_prompt_message_to_text(self, message: list[PromptMessage]) -> str:
         """
-            convert prompt message to text
+        convert prompt message to text
         """
-        text = ''
+        text = ""
         for item in message:
             if isinstance(item, UserPromptMessage):
-                text += f'User: {item.content}'
+                text += f"User: {item.content}"
             elif isinstance(item, SystemPromptMessage):
-                text += f'System: {item.content}'
+                text += f"System: {item.content}"
             elif isinstance(item, AssistantPromptMessage):
-                text += f'Assistant: {item.content}'
+                text += f"Assistant: {item.content}"
             else:
-                raise NotImplementedError(f'PromptMessage type {type(item)} is not supported')
+                raise NotImplementedError(f"PromptMessage type {type(item)} is not supported")
         return text
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
         rules = [
             ParameterRule(
-                name='temperature',
+                name="temperature",
                 type=ParameterType.FLOAT,
-                use_template='temperature',
-                label=I18nObject(
-                    zh_Hans='温度',
-                    en_US='Temperature'
-                ),
+                use_template="temperature",
+                label=I18nObject(zh_Hans="温度", en_US="Temperature"),
             ),
             ParameterRule(
-                name='top_p',
+                name="top_p",
                 type=ParameterType.FLOAT,
-                use_template='top_p',
-                label=I18nObject(
-                    zh_Hans='Top P',
-                    en_US='Top P'
-                )
+                use_template="top_p",
+                label=I18nObject(zh_Hans="Top P", en_US="Top P"),
             ),
             ParameterRule(
-                name='max_tokens',
+                name="max_tokens",
                 type=ParameterType.INT,
-                use_template='max_tokens',
+                use_template="max_tokens",
                 min=1,
-                max=int(credentials.get('context_length', 2048)),
-                default=min(512, int(credentials.get('context_length', 2048))),
-                label=I18nObject(
-                    zh_Hans='最大生成长度',
-                    en_US='Max Tokens'
-                )
-            )
+                max=int(credentials.get("context_length", 2048)),
+                default=min(512, int(credentials.get("context_length", 2048))),
+                label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"),
+            ),
         ]
 
         completion_type = None
 
-        if 'completion_type' in credentials:
-            if credentials['completion_type'] == 'chat':
+        if "completion_type" in credentials:
+            if credentials["completion_type"] == "chat":
                 completion_type = LLMMode.CHAT.value
-            elif credentials['completion_type'] == 'completion':
+            elif credentials["completion_type"] == "completion":
                 completion_type = LLMMode.COMPLETION.value
             else:
                 raise ValueError(f'completion_type {credentials["completion_type"]} is not supported')
-        
+
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             parameter_rules=rules,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
             model_properties={
                 ModelPropertyKey.MODE: completion_type,
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_length', 2048)),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_length", 2048)),
             },
         )
 
         return entity
-    
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], 
-                 model_parameters: dict,
-                 tools: list[PromptMessageTool] | None = None, 
-                 stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
+
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         """
-            generate text from LLM
+        generate text from LLM
         """
-        if 'server_url' not in credentials:
-            raise CredentialsValidateFailedError('server_url is required in credentials')
-        
-        if 'stream' in credentials and not bool(credentials['stream']) and stream:
-            raise ValueError(f'stream is not supported by model {model}')
+        if "server_url" not in credentials:
+            raise CredentialsValidateFailedError("server_url is required in credentials")
+
+        if "stream" in credentials and not bool(credentials["stream"]) and stream:
+            raise ValueError(f"stream is not supported by model {model}")
 
         try:
             parameters = {}
-            if 'temperature' in model_parameters:
-                parameters['temperature'] = model_parameters['temperature']
-            if 'top_p' in model_parameters:
-                parameters['top_p'] = model_parameters['top_p']
-            if 'top_k' in model_parameters:
-                parameters['top_k'] = model_parameters['top_k']
-            if 'presence_penalty' in model_parameters:
-                parameters['presence_penalty'] = model_parameters['presence_penalty']
-            if 'frequency_penalty' in model_parameters:
-                parameters['frequency_penalty'] = model_parameters['frequency_penalty']
+            if "temperature" in model_parameters:
+                parameters["temperature"] = model_parameters["temperature"]
+            if "top_p" in model_parameters:
+                parameters["top_p"] = model_parameters["top_p"]
+            if "top_k" in model_parameters:
+                parameters["top_k"] = model_parameters["top_k"]
+            if "presence_penalty" in model_parameters:
+                parameters["presence_penalty"] = model_parameters["presence_penalty"]
+            if "frequency_penalty" in model_parameters:
+                parameters["frequency_penalty"] = model_parameters["frequency_penalty"]
 
-            response = post(str(URL(credentials['server_url']) / 'v2' / 'models' / model / 'generate'), json={
-                'text_input': self._convert_prompt_message_to_text(prompt_messages),
-                'max_tokens': model_parameters.get('max_tokens', 512),
-                'parameters': {
-                    'stream': False,
-                    **parameters
+            response = post(
+                str(URL(credentials["server_url"]) / "v2" / "models" / model / "generate"),
+                json={
+                    "text_input": self._convert_prompt_message_to_text(prompt_messages),
+                    "max_tokens": model_parameters.get("max_tokens", 512),
+                    "parameters": {"stream": False, **parameters},
                 },
-            }, timeout=(10, 120))
+                timeout=(10, 120),
+            )
             response.raise_for_status()
             if response.status_code != 200:
-                raise InvokeBadRequestError(f'Invoke failed with status code {response.status_code}, {response.text}')
-            
+                raise InvokeBadRequestError(f"Invoke failed with status code {response.status_code}, {response.text}")
+
             if stream:
-                return self._handle_chat_stream_response(model=model, credentials=credentials, prompt_messages=prompt_messages,
-                                                        tools=tools, resp=response)
-            return self._handle_chat_generate_response(model=model, credentials=credentials, prompt_messages=prompt_messages,
-                                                        tools=tools, resp=response)
+                return self._handle_chat_stream_response(
+                    model=model, credentials=credentials, prompt_messages=prompt_messages, tools=tools, resp=response
+                )
+            return self._handle_chat_generate_response(
+                model=model, credentials=credentials, prompt_messages=prompt_messages, tools=tools, resp=response
+            )
         except Exception as ex:
-            raise InvokeConnectionError(f'An error occurred during connection: {str(ex)}')
-        
-    def _handle_chat_generate_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                                        tools: list[PromptMessageTool],
-                                        resp: Response) -> LLMResult:
+            raise InvokeConnectionError(f"An error occurred during connection: {str(ex)}")
+
+    def _handle_chat_generate_response(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool],
+        resp: Response,
+    ) -> LLMResult:
         """
-            handle normal chat generate response
+        handle normal chat generate response
         """
-        text = resp.json()['text_output']
+        text = resp.json()["text_output"]
 
         usage = LLMUsage.empty_usage()
         usage.prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
         usage.completion_tokens = self._get_num_tokens_by_gpt2(text)
 
         return LLMResult(
-            model=model,
-            prompt_messages=prompt_messages,
-            message=AssistantPromptMessage(
-                content=text
-            ),
-            usage=usage
+            model=model, prompt_messages=prompt_messages, message=AssistantPromptMessage(content=text), usage=usage
         )
 
-    def _handle_chat_stream_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                                        tools: list[PromptMessageTool],
-                                        resp: Response) -> Generator:
+    def _handle_chat_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool],
+        resp: Response,
+    ) -> Generator:
         """
-            handle normal chat generate response
+        handle normal chat generate response
         """
-        text = resp.json()['text_output']
+        text = resp.json()["text_output"]
 
         usage = LLMUsage.empty_usage()
         usage.prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
@@ -233,13 +258,7 @@ class TritonInferenceAILargeLanguageModel(LargeLanguageModel):
         yield LLMResultChunk(
             model=model,
             prompt_messages=prompt_messages,
-            delta=LLMResultChunkDelta(
-                index=0,
-                message=AssistantPromptMessage(
-                    content=text
-                ),
-                usage=usage
-            )
+            delta=LLMResultChunkDelta(index=0, message=AssistantPromptMessage(content=text), usage=usage),
         )
 
     @property
@@ -253,15 +272,9 @@ class TritonInferenceAILargeLanguageModel(LargeLanguageModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-            ],
-            InvokeServerUnavailableError: [
-            ],
-            InvokeRateLimitError: [
-            ],
-            InvokeAuthorizationError: [
-            ],
-            InvokeBadRequestError: [
-                ValueError
-            ]
-        }
\ No newline at end of file
+            InvokeConnectionError: [],
+            InvokeServerUnavailableError: [],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [],
+            InvokeBadRequestError: [ValueError],
+        }
diff --git a/api/core/model_runtime/model_providers/triton_inference_server/triton_inference_server.py b/api/core/model_runtime/model_providers/triton_inference_server/triton_inference_server.py
index 06846825ab..d85f7c82e7 100644
--- a/api/core/model_runtime/model_providers/triton_inference_server/triton_inference_server.py
+++ b/api/core/model_runtime/model_providers/triton_inference_server/triton_inference_server.py
@@ -4,6 +4,7 @@ from core.model_runtime.model_providers.__base.model_provider import ModelProvid
 
 logger = logging.getLogger(__name__)
 
+
 class XinferenceAIProvider(ModelProvider):
     def validate_provider_credentials(self, credentials: dict) -> None:
         pass
diff --git a/api/core/model_runtime/model_providers/upstage/_common.py b/api/core/model_runtime/model_providers/upstage/_common.py
index 13b73181e9..47ebaccd84 100644
--- a/api/core/model_runtime/model_providers/upstage/_common.py
+++ b/api/core/model_runtime/model_providers/upstage/_common.py
@@ -1,4 +1,3 @@
-
 from collections.abc import Mapping
 
 import openai
@@ -20,13 +19,13 @@ class _CommonUpstage:
         Transform credentials to kwargs for model instance
 
         :param credentials:
-        :return: 
+        :return:
         """
         credentials_kwargs = {
-            "api_key": credentials['upstage_api_key'],
+            "api_key": credentials["upstage_api_key"],
             "base_url": "https://api.upstage.ai/v1/solar",
             "timeout": Timeout(315.0, read=300.0, write=20.0, connect=10.0),
-            "max_retries": 1
+            "max_retries": 1,
         }
 
         return credentials_kwargs
@@ -53,5 +52,3 @@ class _CommonUpstage:
                 openai.APIError,
             ],
         }
-        
-
diff --git a/api/core/model_runtime/model_providers/upstage/llm/llm.py b/api/core/model_runtime/model_providers/upstage/llm/llm.py
index d1ed4619d6..a18ee90624 100644
--- a/api/core/model_runtime/model_providers/upstage/llm/llm.py
+++ b/api/core/model_runtime/model_providers/upstage/llm/llm.py
@@ -34,17 +34,25 @@ if you are not sure about the structure.
 <instructions>
 {{instructions}}
 </instructions>
-"""
+"""  # noqa: E501
+
 
 class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
     """
-    Model class for Upstage large language model. 
+    Model class for Upstage large language model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -67,15 +75,25 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
             tools=tools,
             stop=stop,
             stream=stream,
-            user=user
+            user=user,
         )
 
-    def _code_block_mode_wrapper(self,
-                                 model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None, callbacks: Optional[list[Callback]] = None) -> Union[LLMResult, Generator]:
+    def _code_block_mode_wrapper(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Code block mode wrapper for invoking large language model
         """
-        if 'response_format' in model_parameters and model_parameters['response_format'] in ['JSON', 'XML']:
+        if "response_format" in model_parameters and model_parameters["response_format"] in {"JSON", "XML"}:
             stop = stop or []
             self._transform_chat_json_prompts(
                 model=model,
@@ -86,9 +104,9 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
                 stop=stop,
                 stream=stream,
                 user=user,
-                response_format=model_parameters['response_format']
+                response_format=model_parameters["response_format"],
             )
-            model_parameters.pop('response_format')
+            model_parameters.pop("response_format")
 
             return self._invoke(
                 model=model,
@@ -98,15 +116,23 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
                 tools=tools,
                 stop=stop,
                 stream=stream,
-                user=user
+                user=user,
             )
 
-    def _transform_chat_json_prompts(self, model: str, credentials: dict,
-                                     prompt_messages: list[PromptMessage], model_parameters: dict,
-                                     tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
-                                     stream: bool = True, user: str | None = None, response_format: str = 'JSON') -> None:
+    def _transform_chat_json_prompts(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+        response_format: str = "JSON",
+    ) -> None:
         """
-        Transform json prompts 
+        Transform json prompts
         """
         if stop is None:
             stop = []
@@ -117,20 +143,29 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
 
         if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
             prompt_messages[0] = SystemPromptMessage(
-                content=UPSTAGE_BLOCK_MODE_PROMPT
-                    .replace("{{instructions}}", prompt_messages[0].content)
-                    .replace("{{block}}", response_format)
+                content=UPSTAGE_BLOCK_MODE_PROMPT.replace("{{instructions}}", prompt_messages[0].content).replace(
+                    "{{block}}", response_format
+                )
             )
             prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}\n"))
         else:
-            prompt_messages.insert(0, SystemPromptMessage(
-                                   content=UPSTAGE_BLOCK_MODE_PROMPT
-                                       .replace("{{instructions}}", f"Please output a valid {response_format} object.")
-                                       .replace("{{block}}", response_format)
-            ))
+            prompt_messages.insert(
+                0,
+                SystemPromptMessage(
+                    content=UPSTAGE_BLOCK_MODE_PROMPT.replace(
+                        "{{instructions}}", f"Please output a valid {response_format} object."
+                    ).replace("{{block}}", response_format)
+                ),
+            )
             prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -155,30 +190,31 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
             client = OpenAI(**credentials_kwargs)
 
             client.chat.completions.create(
-                messages=[{"role": "user", "content": "ping"}],
-                model=model,
-                temperature=0,
-                max_tokens=10,
-                stream=False
+                messages=[{"role": "user", "content": "ping"}], model=model, temperature=0, max_tokens=10, stream=False
             )
         except Exception as e:
             raise CredentialsValidateFailedError(str(e))
 
-    def _chat_generate(self, model: str, credentials: dict,
-                       prompt_messages: list[PromptMessage], model_parameters: dict,
-                       tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                       stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _chat_generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         credentials_kwargs = self._to_credential_kwargs(credentials)
         client = OpenAI(**credentials_kwargs)
 
         extra_model_kwargs = {}
 
         if tools:
-            extra_model_kwargs["functions"] = [{
-                "name": tool.name,
-                "description": tool.description,
-                "parameters": tool.parameters
-            } for tool in tools]
+            extra_model_kwargs["functions"] = [
+                {"name": tool.name, "description": tool.description, "parameters": tool.parameters} for tool in tools
+            ]
 
         if stop:
             extra_model_kwargs["stop"] = stop
@@ -198,10 +234,15 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
         if stream:
             return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages, tools)
         return self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
-        
-    def _handle_chat_generate_response(self, model: str, credentials: dict, response: ChatCompletion,
-                                       prompt_messages: list[PromptMessage],
-                                       tools: Optional[list[PromptMessageTool]] = None) -> LLMResult:
+
+    def _handle_chat_generate_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: ChatCompletion,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> LLMResult:
         """
         Handle llm chat response
 
@@ -222,10 +263,7 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
         tool_calls = [function_call] if function_call else []
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_message.content,
-            tool_calls=tool_calls
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_message.content, tool_calls=tool_calls)
 
         # calculate num tokens
         if response.usage:
@@ -251,9 +289,14 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
 
         return response
 
-    def _handle_chat_generate_stream_response(self, model: str, credentials: dict, response: Stream[ChatCompletionChunk],
-                                              prompt_messages: list[PromptMessage],
-                                              tools: Optional[list[PromptMessageTool]] = None) -> Generator:
+    def _handle_chat_generate_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: Stream[ChatCompletionChunk],
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> Generator:
         """
         Handle llm chat stream response
 
@@ -263,7 +306,7 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
         :param tools: tools for tool calling
         :return: llm response chunk generator
         """
-        full_assistant_content = ''
+        full_assistant_content = ""
         delta_assistant_message_function_call_storage: Optional[ChoiceDeltaFunctionCall] = None
         prompt_tokens = 0
         completion_tokens = 0
@@ -273,8 +316,8 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
             prompt_messages=prompt_messages,
             delta=LLMResultChunkDelta(
                 index=0,
-                message=AssistantPromptMessage(content=''),
-            )
+                message=AssistantPromptMessage(content=""),
+            ),
         )
 
         for chunk in response:
@@ -288,8 +331,11 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
             delta = chunk.choices[0]
             has_finish_reason = delta.finish_reason is not None
 
-            if not has_finish_reason and (delta.delta.content is None or delta.delta.content == '') and \
-                delta.delta.function_call is None:
+            if (
+                not has_finish_reason
+                and (delta.delta.content is None or delta.delta.content == "")
+                and delta.delta.function_call is None
+            ):
                 continue
 
             # assistant_message_tool_calls = delta.delta.tool_calls
@@ -311,7 +357,7 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
                     # start of stream function call
                     delta_assistant_message_function_call_storage = assistant_message_function_call
                     if delta_assistant_message_function_call_storage.arguments is None:
-                        delta_assistant_message_function_call_storage.arguments = ''
+                        delta_assistant_message_function_call_storage.arguments = ""
                     if not has_finish_reason:
                         continue
 
@@ -322,12 +368,9 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
                 final_tool_calls.extend(tool_calls)
 
             # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else '',
-                tool_calls=tool_calls
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
 
-            full_assistant_content += delta.delta.content if delta.delta.content else ''
+            full_assistant_content += delta.delta.content or ""
 
             if has_finish_reason:
                 final_chunk = LLMResultChunk(
@@ -338,7 +381,7 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
                         index=delta.index,
                         message=assistant_prompt_message,
                         finish_reason=delta.finish_reason,
-                    )
+                    ),
                 )
             else:
                 yield LLMResultChunk(
@@ -348,7 +391,7 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
                     delta=LLMResultChunkDelta(
                         index=delta.index,
                         message=assistant_prompt_message,
-                    )
+                    ),
                 )
 
         if not prompt_tokens:
@@ -356,8 +399,7 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
 
         if not completion_tokens:
             full_assistant_prompt_message = AssistantPromptMessage(
-                content=full_assistant_content,
-                tool_calls=final_tool_calls
+                content=full_assistant_content, tool_calls=final_tool_calls
             )
             completion_tokens = self._num_tokens_from_messages(model, [full_assistant_prompt_message])
 
@@ -367,9 +409,9 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
 
         yield final_chunk
 
-    def _extract_response_tool_calls(self,
-                                     response_tool_calls: list[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]) \
-            -> list[AssistantPromptMessage.ToolCall]:
+    def _extract_response_tool_calls(
+        self, response_tool_calls: list[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]
+    ) -> list[AssistantPromptMessage.ToolCall]:
         """
         Extract tool calls from response
 
@@ -380,21 +422,19 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
         if response_tool_calls:
             for response_tool_call in response_tool_calls:
                 function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                    name=response_tool_call.function.name,
-                    arguments=response_tool_call.function.arguments
+                    name=response_tool_call.function.name, arguments=response_tool_call.function.arguments
                 )
 
                 tool_call = AssistantPromptMessage.ToolCall(
-                    id=response_tool_call.id,
-                    type=response_tool_call.type,
-                    function=function
+                    id=response_tool_call.id, type=response_tool_call.type, function=function
                 )
                 tool_calls.append(tool_call)
 
         return tool_calls
 
-    def _extract_response_function_call(self, response_function_call: FunctionCall | ChoiceDeltaFunctionCall) \
-            -> AssistantPromptMessage.ToolCall:
+    def _extract_response_function_call(
+        self, response_function_call: FunctionCall | ChoiceDeltaFunctionCall
+    ) -> AssistantPromptMessage.ToolCall:
         """
         Extract function call from response
 
@@ -404,14 +444,11 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
         tool_call = None
         if response_function_call:
             function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                name=response_function_call.name,
-                arguments=response_function_call.arguments
+                name=response_function_call.name, arguments=response_function_call.arguments
             )
 
             tool_call = AssistantPromptMessage.ToolCall(
-                id=response_function_call.name,
-                type="function",
-                function=function
+                id=response_function_call.name, type="function", function=function
             )
 
         return tool_call
@@ -429,19 +466,13 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
                         message_content = cast(TextPromptMessageContent, message_content)
-                        sub_message_dict = {
-                            "type": "text",
-                            "text": message_content.data
-                        }
+                        sub_message_dict = {"type": "text", "text": message_content.data}
                         sub_messages.append(sub_message_dict)
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
                         sub_message_dict = {
                             "type": "image_url",
-                            "image_url": {
-                                "url": message_content.data,
-                                "detail": message_content.detail.value
-                            }
+                            "image_url": {"url": message_content.data, "detail": message_content.detail.value},
                         }
                         sub_messages.append(sub_message_dict)
 
@@ -467,11 +498,7 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
             #     "content": message.content,
             #     "tool_call_id": message.tool_call_id
             # }
-            message_dict = {
-                "role": "function",
-                "content": message.content,
-                "name": message.tool_call_id
-            }
+            message_dict = {"role": "function", "content": message.content, "name": message.tool_call_id}
         else:
             raise ValueError(f"Got unknown type {message}")
 
@@ -483,16 +510,17 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
     def _get_tokenizer(self) -> Tokenizer:
         return Tokenizer.from_pretrained("upstage/solar-1-mini-tokenizer")
 
-    def _num_tokens_from_messages(self, model: str, messages: list[PromptMessage],
-                                  tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_messages(
+        self, model: str, messages: list[PromptMessage], tools: Optional[list[PromptMessageTool]] = None
+    ) -> int:
         """
         Calculate num tokens for solar with Huggingface Solar tokenizer.
-        Solar tokenizer is opened in huggingface https://huggingface.co/upstage/solar-1-mini-tokenizer 
+        Solar tokenizer is opened in huggingface https://huggingface.co/upstage/solar-1-mini-tokenizer
         """
         tokenizer = self._get_tokenizer()
-        tokens_per_message = 5 # <|im_start|>{role}\n{message}<|im_end|>
-        tokens_prefix = 1 # <|startoftext|>
-        tokens_suffix = 3 # <|im_start|>assistant\n
+        tokens_per_message = 5  # <|im_start|>{role}\n{message}<|im_end|>
+        tokens_prefix = 1  # <|startoftext|>
+        tokens_suffix = 3  # <|im_start|>assistant\n
 
         num_tokens = 0
         num_tokens += tokens_prefix
@@ -502,10 +530,10 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
             num_tokens += tokens_per_message
             for key, value in message.items():
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
                     value = text
 
                 if key == "tool_calls":
@@ -538,37 +566,37 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
         """
         num_tokens = 0
         for tool in tools:
-            num_tokens += len(tokenizer.encode('type'))
-            num_tokens += len(tokenizer.encode('function'))
+            num_tokens += len(tokenizer.encode("type"))
+            num_tokens += len(tokenizer.encode("function"))
 
             # calculate num tokens for function object
-            num_tokens += len(tokenizer.encode('name'))
+            num_tokens += len(tokenizer.encode("name"))
             num_tokens += len(tokenizer.encode(tool.name))
-            num_tokens += len(tokenizer.encode('description'))
+            num_tokens += len(tokenizer.encode("description"))
             num_tokens += len(tokenizer.encode(tool.description))
             parameters = tool.parameters
-            num_tokens += len(tokenizer.encode('parameters'))
-            if 'title' in parameters:
-                num_tokens += len(tokenizer.encode('title'))
+            num_tokens += len(tokenizer.encode("parameters"))
+            if "title" in parameters:
+                num_tokens += len(tokenizer.encode("title"))
                 num_tokens += len(tokenizer.encode(parameters.get("title")))
-            num_tokens += len(tokenizer.encode('type'))
+            num_tokens += len(tokenizer.encode("type"))
             num_tokens += len(tokenizer.encode(parameters.get("type")))
-            if 'properties' in parameters:
-                num_tokens += len(tokenizer.encode('properties'))
-                for key, value in parameters.get('properties').items():
+            if "properties" in parameters:
+                num_tokens += len(tokenizer.encode("properties"))
+                for key, value in parameters.get("properties").items():
                     num_tokens += len(tokenizer.encode(key))
                     for field_key, field_value in value.items():
                         num_tokens += len(tokenizer.encode(field_key))
-                        if field_key == 'enum':
+                        if field_key == "enum":
                             for enum_field in field_value:
                                 num_tokens += 3
                                 num_tokens += len(tokenizer.encode(enum_field))
                         else:
                             num_tokens += len(tokenizer.encode(field_key))
                             num_tokens += len(tokenizer.encode(str(field_value)))
-            if 'required' in parameters:
-                num_tokens += len(tokenizer.encode('required'))
-                for required_field in parameters['required']:
+            if "required" in parameters:
+                num_tokens += len(tokenizer.encode("required"))
+                for required_field in parameters["required"]:
                     num_tokens += 3
                     num_tokens += len(tokenizer.encode(required_field))
 
diff --git a/api/core/model_runtime/model_providers/upstage/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/upstage/text_embedding/text_embedding.py
index 05ae8665d6..edd4a36d98 100644
--- a/api/core/model_runtime/model_providers/upstage/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/upstage/text_embedding/text_embedding.py
@@ -18,6 +18,7 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel):
     """
     Model class for Upstage text embedding model.
     """
+
     def _get_tokenizer(self) -> Tokenizer:
         return Tokenizer.from_pretrained("upstage/solar-1-mini-tokenizer")
 
@@ -53,9 +54,9 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel):
         for i, text in enumerate(texts):
             token = tokenizer.encode(text, add_special_tokens=False).tokens
             for j in range(0, len(token), context_size):
-                tokens += [token[j:j+context_size]]
+                tokens += [token[j : j + context_size]]
                 indices += [i]
-        
+
         batched_embeddings = []
         _iter = range(0, len(tokens), max_chunks)
 
@@ -63,20 +64,20 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel):
             embeddings_batch, embedding_used_tokens = self._embedding_invoke(
                 model=model,
                 client=client,
-                texts=tokens[i:i+max_chunks],
+                texts=tokens[i : i + max_chunks],
                 extra_model_kwargs=extra_model_kwargs,
             )
 
             used_tokens += embedding_used_tokens
             batched_embeddings += embeddings_batch
-        
+
         results: list[list[list[float]]] = [[] for _ in range(len(texts))]
         num_tokens_in_batch: list[list[int]] = [[] for _ in range(len(texts))]
 
         for i in range(len(indices)):
             results[indices[i]].append(batched_embeddings[i])
             num_tokens_in_batch[indices[i]].append(len(tokens[i]))
-        
+
         for i in range(len(texts)):
             _result = results[i]
             if len(_result) == 0:
@@ -91,15 +92,11 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel):
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
             embeddings[i] = (average / np.linalg.norm(average)).tolist()
-        
-        usage = self._calc_response_usage(
-            model=model,
-            credentials=credentials,
-            tokens=used_tokens
-        )
+
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
 
         return TextEmbeddingResult(embeddings=embeddings, usage=usage, model=model)
-    
+
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         tokenizer = self._get_tokenizer()
         """
@@ -122,7 +119,7 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel):
             total_num_tokens += len(tokenized_text)
 
         return total_num_tokens
-    
+
     def validate_credentials(self, model: str, credentials: Mapping) -> None:
         """
         Validate model credentials
@@ -137,16 +134,13 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel):
             client = OpenAI(**credentials_kwargs)
 
             # call embedding model
-            self._embedding_invoke(
-                model=model,
-                client=client,
-                texts=['ping'],
-                extra_model_kwargs={}
-            )
+            self._embedding_invoke(model=model, client=client, texts=["ping"], extra_model_kwargs={})
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
-    
-    def _embedding_invoke(self, model: str, client: OpenAI, texts: Union[list[str], str], extra_model_kwargs: dict) -> tuple[list[list[float]], int]:
+
+    def _embedding_invoke(
+        self, model: str, client: OpenAI, texts: Union[list[str], str], extra_model_kwargs: dict
+    ) -> tuple[list[list[float]], int]:
         """
         Invoke embedding model
         :param model: model name
@@ -155,17 +149,19 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel):
         :param extra_model_kwargs: extra model kwargs
         :return: embeddings and used tokens
         """
-        response = client.embeddings.create(
-            model=model,
-            input=texts,
-            **extra_model_kwargs
-        )
+        response = client.embeddings.create(model=model, input=texts, **extra_model_kwargs)
+
+        if "encoding_format" in extra_model_kwargs and extra_model_kwargs["encoding_format"] == "base64":
+            return (
+                [
+                    list(np.frombuffer(base64.b64decode(embedding.embedding), dtype=np.float32))
+                    for embedding in response.data
+                ],
+                response.usage.total_tokens,
+            )
 
-        if 'encoding_format' in extra_model_kwargs and extra_model_kwargs['encoding_format'] == 'base64':
-            return ([list(np.frombuffer(base64.b64decode(embedding.embedding), dtype=np.float32)) for embedding in response.data], response.usage.total_tokens)
-        
         return [data.embedding for data in response.data], response.usage.total_tokens
-    
+
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -176,10 +172,7 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel):
         :return: usage
         """
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            tokens=tokens,
-            price_type=PriceType.INPUT
+            model=model, credentials=credentials, tokens=tokens, price_type=PriceType.INPUT
         )
 
         usage = EmbeddingUsage(
@@ -189,7 +182,7 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/upstage/upstage.py b/api/core/model_runtime/model_providers/upstage/upstage.py
index 56c91c0061..e45d4aae19 100644
--- a/api/core/model_runtime/model_providers/upstage/upstage.py
+++ b/api/core/model_runtime/model_providers/upstage/upstage.py
@@ -8,7 +8,6 @@ logger = logging.getLogger(__name__)
 
 
 class UpstageProvider(ModelProvider):
-    
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -19,14 +18,10 @@ class UpstageProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(
-                model="solar-1-mini-chat",
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="solar-1-mini-chat", credentials=credentials)
         except CredentialsValidateFailedError as e:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise e
         except Exception as e:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise e
-                
diff --git a/api/core/model_runtime/model_providers/vertex_ai/llm/llm.py b/api/core/model_runtime/model_providers/vertex_ai/llm/llm.py
index 1a7368a2cf..da69b7cdf3 100644
--- a/api/core/model_runtime/model_providers/vertex_ai/llm/llm.py
+++ b/api/core/model_runtime/model_providers/vertex_ai/llm/llm.py
@@ -5,7 +5,6 @@ import logging
 from collections.abc import Generator
 from typing import Optional, Union, cast
 
-import google.api_core.exceptions as exceptions
 import google.auth.transport.requests
 import vertexai.generative_models as glm
 from anthropic import AnthropicVertex, Stream
@@ -17,6 +16,7 @@ from anthropic.types import (
     MessageStopEvent,
     MessageStreamEvent,
 )
+from google.api_core import exceptions
 from google.cloud import aiplatform
 from google.oauth2 import service_account
 from PIL import Image
@@ -49,12 +49,17 @@ logger = logging.getLogger(__name__)
 
 
 class VertexAiLargeLanguageModel(LargeLanguageModel):
-
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -74,8 +79,16 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         # invoke Gemini model
         return self._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
-    def _generate_anthropic(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
-                stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate_anthropic(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke Anthropic large language model
 
@@ -92,7 +105,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         service_account_info = json.loads(base64.b64decode(credentials["vertex_service_account_key"]))
         project_id = credentials["vertex_project_id"]
         SCOPES = ["https://www.googleapis.com/auth/cloud-platform"]
-        token = ''
+        token = ""
 
         # get access token from service account credential
         if service_account_info:
@@ -101,41 +114,35 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
             credentials.refresh(request)
             token = credentials.token
 
-        # Vertex AI Anthropic Claude3 Opus model available in us-east5 region, Sonnet and Haiku available in us-central1 region
-        if 'opus' or 'claude-3-5-sonnet' in model:
-            location = 'us-east5'
+        # Vertex AI Anthropic Claude3 Opus model available in us-east5 region, Sonnet and Haiku available
+        # in us-central1 region
+        if "opus" in model or "claude-3-5-sonnet" in model:
+            location = "us-east5"
         else:
-            location = 'us-central1'
-        
+            location = "us-central1"
+
         # use access token to authenticate
         if token:
-            client = AnthropicVertex(
-                region=location, 
-                project_id=project_id,
-                access_token=token
-            )
-        # When access token is empty, try to use the Google Cloud VM's built-in service account or the GOOGLE_APPLICATION_CREDENTIALS environment variable
+            client = AnthropicVertex(region=location, project_id=project_id, access_token=token)
+        # When access token is empty, try to use the Google Cloud VM's built-in service account
+        # or the GOOGLE_APPLICATION_CREDENTIALS environment variable
         else:
             client = AnthropicVertex(
-                region=location, 
+                region=location,
                 project_id=project_id,
             )
 
         extra_model_kwargs = {}
         if stop:
-            extra_model_kwargs['stop_sequences'] = stop
+            extra_model_kwargs["stop_sequences"] = stop
 
         system, prompt_message_dicts = self._convert_claude_prompt_messages(prompt_messages)
 
         if system:
-            extra_model_kwargs['system'] = system
+            extra_model_kwargs["system"] = system
 
         response = client.messages.create(
-            model=model,
-            messages=prompt_message_dicts,
-            stream=stream,
-            **model_parameters,
-            **extra_model_kwargs
+            model=model, messages=prompt_message_dicts, stream=stream, **model_parameters, **extra_model_kwargs
         )
 
         if stream:
@@ -143,8 +150,9 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
 
         return self._handle_claude_response(model, credentials, response, prompt_messages)
 
-    def _handle_claude_response(self, model: str, credentials: dict, response: Message,
-                                prompt_messages: list[PromptMessage]) -> LLMResult:
+    def _handle_claude_response(
+        self, model: str, credentials: dict, response: Message, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
         """
         Handle llm chat response
 
@@ -156,9 +164,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         """
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=response.content[0].text
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=response.content[0].text)
 
         # calculate num tokens
         if response.usage:
@@ -175,16 +181,18 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
 
         # transform response
         response = LLMResult(
-            model=response.model,
-            prompt_messages=prompt_messages,
-            message=assistant_prompt_message,
-            usage=usage
+            model=response.model, prompt_messages=prompt_messages, message=assistant_prompt_message, usage=usage
         )
 
         return response
 
-    def _handle_claude_stream_response(self, model: str, credentials: dict, response: Stream[MessageStreamEvent],
-                                        prompt_messages: list[PromptMessage], ) -> Generator:
+    def _handle_claude_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        response: Stream[MessageStreamEvent],
+        prompt_messages: list[PromptMessage],
+    ) -> Generator:
         """
         Handle llm chat stream response
 
@@ -196,7 +204,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         """
 
         try:
-            full_assistant_content = ''
+            full_assistant_content = ""
             return_model = None
             input_tokens = 0
             output_tokens = 0
@@ -217,18 +225,16 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                         prompt_messages=prompt_messages,
                         delta=LLMResultChunkDelta(
                             index=index + 1,
-                            message=AssistantPromptMessage(
-                                content=''
-                            ),
+                            message=AssistantPromptMessage(content=""),
                             finish_reason=finish_reason,
-                            usage=usage
-                        )
+                            usage=usage,
+                        ),
                     )
                 elif isinstance(chunk, ContentBlockDeltaEvent):
-                    chunk_text = chunk.delta.text if chunk.delta.text else ''
+                    chunk_text = chunk.delta.text or ""
                     full_assistant_content += chunk_text
                     assistant_prompt_message = AssistantPromptMessage(
-                        content=chunk_text if chunk_text else '',
+                        content=chunk_text or "",
                     )
                     index = chunk.index
                     yield LLMResultChunk(
@@ -237,12 +243,14 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                         delta=LLMResultChunkDelta(
                             index=index,
                             message=assistant_prompt_message,
-                        )
+                        ),
                     )
         except Exception as ex:
             raise InvokeError(str(ex))
 
-    def _calc_claude_response_usage(self, model: str, credentials: dict, prompt_tokens: int, completion_tokens: int) -> LLMUsage:
+    def _calc_claude_response_usage(
+        self, model: str, credentials: dict, prompt_tokens: int, completion_tokens: int
+    ) -> LLMUsage:
         """
         Calculate response usage
 
@@ -262,10 +270,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
 
         # get completion price info
         completion_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.OUTPUT,
-            tokens=completion_tokens
+            model=model, credentials=credentials, price_type=PriceType.OUTPUT, tokens=completion_tokens
         )
 
         # transform usage
@@ -281,7 +286,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
             total_tokens=prompt_tokens + completion_tokens,
             total_price=prompt_price_info.total_amount + completion_price_info.total_amount,
             currency=prompt_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
@@ -295,13 +300,13 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         first_loop = True
         for message in prompt_messages:
             if isinstance(message, SystemPromptMessage):
-                message.content=message.content.strip()
+                message.content = message.content.strip()
                 if first_loop:
-                    system=message.content
-                    first_loop=False
+                    system = message.content
+                    first_loop = False
                 else:
-                    system+="\n"
-                    system+=message.content
+                    system += "\n"
+                    system += message.content
 
         prompt_message_dicts = []
         for message in prompt_messages:
@@ -323,10 +328,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
                         message_content = cast(TextPromptMessageContent, message_content)
-                        sub_message_dict = {
-                            "type": "text",
-                            "text": message_content.data
-                        }
+                        sub_message_dict = {"type": "text", "text": message_content.data}
                         sub_messages.append(sub_message_dict)
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
@@ -336,7 +338,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                                 image_content = requests.get(message_content.data).content
                                 with Image.open(io.BytesIO(image_content)) as img:
                                     mime_type = f"image/{img.format.lower()}"
-                                base64_data = base64.b64encode(image_content).decode('utf-8')
+                                base64_data = base64.b64encode(image_content).decode("utf-8")
                             except Exception as ex:
                                 raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")
                         else:
@@ -344,17 +346,15 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                             mime_type = data_split[0].replace("data:", "")
                             base64_data = data_split[1]
 
-                        if mime_type not in ["image/jpeg", "image/png", "image/gif", "image/webp"]:
-                            raise ValueError(f"Unsupported image type {mime_type}, "
-                                             f"only support image/jpeg, image/png, image/gif, and image/webp")
+                        if mime_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}:
+                            raise ValueError(
+                                f"Unsupported image type {mime_type}, "
+                                f"only support image/jpeg, image/png, image/gif, and image/webp"
+                            )
 
                         sub_message_dict = {
                             "type": "image",
-                            "source": {
-                                "type": "base64",
-                                "media_type": mime_type,
-                                "data": base64_data
-                            }
+                            "source": {"type": "base64", "media_type": mime_type, "data": base64_data},
                         }
                         sub_messages.append(sub_message_dict)
 
@@ -370,8 +370,13 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
 
         return message_dict
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -384,7 +389,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         prompt = self._convert_messages_to_prompt(prompt_messages)
 
         return self._get_num_tokens_by_gpt2(prompt)
-    
+
     def _convert_messages_to_prompt(self, messages: list[PromptMessage]) -> str:
         """
         Format a list of messages into a full prompt for the Google model
@@ -394,13 +399,10 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         """
         messages = messages.copy()  # don't mutate the original list
 
-        text = "".join(
-            self._convert_one_message_to_text(message)
-            for message in messages
-        )
+        text = "".join(self._convert_one_message_to_text(message) for message in messages)
 
         return text.rstrip()
-    
+
     def _convert_tools_to_glm_tool(self, tools: list[PromptMessageTool]) -> glm.Tool:
         """
         Convert tool messages to glm tools
@@ -416,14 +418,16 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                         type=glm.Type.OBJECT,
                         properties={
                             key: {
-                                'type_': value.get('type', 'string').upper(),
-                                'description': value.get('description', ''),
-                                'enum': value.get('enum', [])
-                            } for key, value in tool.parameters.get('properties', {}).items()
+                                "type_": value.get("type", "string").upper(),
+                                "description": value.get("description", ""),
+                                "enum": value.get("enum", []),
+                            }
+                            for key, value in tool.parameters.get("properties", {}).items()
                         },
-                        required=tool.parameters.get('required', [])
+                        required=tool.parameters.get("required", []),
                     ),
-                ) for tool in tools
+                )
+                for tool in tools
             ]
         )
 
@@ -435,20 +439,25 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         :param credentials: model credentials
         :return:
         """
-        
+
         try:
             ping_message = SystemPromptMessage(content="ping")
             self._generate(model, credentials, [ping_message], {"max_tokens_to_sample": 5})
-            
+
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
-            
 
-    def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict,
-                  tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None, 
-                  stream: bool = True, user: Optional[str] = None
-        ) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -462,7 +471,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         config_kwargs = model_parameters.copy()
-        config_kwargs['max_output_tokens'] = config_kwargs.pop('max_tokens_to_sample', None)
+        config_kwargs["max_output_tokens"] = config_kwargs.pop("max_tokens_to_sample", None)
 
         if stop:
             config_kwargs["stop_sequences"] = stop
@@ -494,26 +503,21 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                     else:
                         history.append(content)
 
-        safety_settings={
+        safety_settings = {
             HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
             HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
             HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
             HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
         }
 
-        google_model = glm.GenerativeModel(
-            model_name=model,
-            system_instruction=system_instruction
-        )
+        google_model = glm.GenerativeModel(model_name=model, system_instruction=system_instruction)
 
         response = google_model.generate_content(
             contents=history,
-            generation_config=glm.GenerationConfig(
-                **config_kwargs
-            ),
+            generation_config=glm.GenerationConfig(**config_kwargs),
             stream=stream,
             safety_settings=safety_settings,
-            tools=self._convert_tools_to_glm_tool(tools) if tools else None
+            tools=self._convert_tools_to_glm_tool(tools) if tools else None,
         )
 
         if stream:
@@ -521,8 +525,9 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
 
         return self._handle_generate_response(model, credentials, response, prompt_messages)
 
-    def _handle_generate_response(self, model: str, credentials: dict, response: glm.GenerationResponse,
-                                  prompt_messages: list[PromptMessage]) -> LLMResult:
+    def _handle_generate_response(
+        self, model: str, credentials: dict, response: glm.GenerationResponse, prompt_messages: list[PromptMessage]
+    ) -> LLMResult:
         """
         Handle llm response
 
@@ -533,9 +538,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         :return: llm response
         """
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=response.candidates[0].content.parts[0].text
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=response.candidates[0].content.parts[0].text)
 
         # calculate num tokens
         prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
@@ -554,8 +557,9 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
 
         return result
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: glm.GenerationResponse,
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, response: glm.GenerationResponse, prompt_messages: list[PromptMessage]
+    ) -> Generator:
         """
         Handle llm stream response
 
@@ -568,9 +572,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         index = -1
         for chunk in response:
             for part in chunk.candidates[0].content.parts:
-                assistant_prompt_message = AssistantPromptMessage(
-                    content=''
-                )
+                assistant_prompt_message = AssistantPromptMessage(content="")
 
                 if part.text:
                     assistant_prompt_message.content += part.text
@@ -579,35 +581,31 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                     assistant_prompt_message.tool_calls = [
                         AssistantPromptMessage.ToolCall(
                             id=part.function_call.name,
-                            type='function',
+                            type="function",
                             function=AssistantPromptMessage.ToolCall.ToolCallFunction(
                                 name=part.function_call.name,
-                                arguments=json.dumps(dict(part.function_call.args.items()))
-                            )
+                                arguments=json.dumps(dict(part.function_call.args.items())),
+                            ),
                         )
                     ]
 
                 index += 1
-    
-                if not hasattr(chunk, 'finish_reason') or not chunk.finish_reason:                    
+
+                if not hasattr(chunk, "finish_reason") or not chunk.finish_reason:
                     # transform assistant message to prompt message
                     yield LLMResultChunk(
                         model=model,
                         prompt_messages=prompt_messages,
-                        delta=LLMResultChunkDelta(
-                            index=index,
-                            message=assistant_prompt_message
-                        )
+                        delta=LLMResultChunkDelta(index=index, message=assistant_prompt_message),
                     )
                 else:
-                    
                     # calculate num tokens
                     prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
                     completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
 
                     # transform usage
                     usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
-                    
+
                     yield LLMResultChunk(
                         model=model,
                         prompt_messages=prompt_messages,
@@ -615,8 +613,8 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                             index=index,
                             message=assistant_prompt_message,
                             finish_reason=chunk.candidates[0].finish_reason,
-                            usage=usage
-                        )
+                            usage=usage,
+                        ),
                     )
 
     def _convert_one_message_to_text(self, message: PromptMessage) -> str:
@@ -631,17 +629,13 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
 
         content = message.content
         if isinstance(content, list):
-            content = "".join(
-                c.data for c in content if c.type != PromptMessageContentType.IMAGE
-            )
+            content = "".join(c.data for c in content if c.type != PromptMessageContentType.IMAGE)
 
         if isinstance(message, UserPromptMessage):
             message_text = f"{human_prompt} {content}"
         elif isinstance(message, AssistantPromptMessage):
             message_text = f"{ai_prompt} {content}"
-        elif isinstance(message, SystemPromptMessage):
-            message_text = f"{human_prompt} {content}"
-        elif isinstance(message, ToolPromptMessage):
+        elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
             message_text = f"{human_prompt} {content}"
         else:
             raise ValueError(f"Got unknown type {message}")
@@ -658,7 +652,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         if isinstance(message, UserPromptMessage):
             glm_content = glm.Content(role="user", parts=[])
 
-            if (isinstance(message.content, str)):
+            if isinstance(message.content, str):
                 glm_content = glm.Content(role="user", parts=[glm.Part.from_text(message.content)])
             else:
                 parts = []
@@ -666,8 +660,8 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                     if c.type == PromptMessageContentType.TEXT:
                         parts.append(glm.Part.from_text(c.data))
                     else:
-                        metadata, data = c.data.split(',', 1)
-                        mime_type = metadata.split(';', 1)[0].split(':')[1]
+                        metadata, data = c.data.split(",", 1)
+                        mime_type = metadata.split(";", 1)[0].split(":")[1]
                         parts.append(glm.Part.from_data(mime_type=mime_type, data=data))
                 glm_content = glm.Content(role="user", parts=parts)
             return glm_content
@@ -675,52 +669,58 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
             if message.content:
                 glm_content = glm.Content(role="model", parts=[glm.Part.from_text(message.content)])
             if message.tool_calls:
-                glm_content = glm.Content(role="model", parts=[glm.Part.from_function_response(glm.FunctionCall(
-                    name=message.tool_calls[0].function.name,
-                    args=json.loads(message.tool_calls[0].function.arguments),
-                ))])
+                glm_content = glm.Content(
+                    role="model",
+                    parts=[
+                        glm.Part.from_function_response(
+                            glm.FunctionCall(
+                                name=message.tool_calls[0].function.name,
+                                args=json.loads(message.tool_calls[0].function.arguments),
+                            )
+                        )
+                    ],
+                )
             return glm_content
         elif isinstance(message, ToolPromptMessage):
-            glm_content = glm.Content(role="function", parts=[glm.Part(function_response=glm.FunctionResponse(
-                    name=message.name,
-                    response={
-                        "response": message.content
-                    }
-                ))])
+            glm_content = glm.Content(
+                role="function",
+                parts=[
+                    glm.Part(
+                        function_response=glm.FunctionResponse(
+                            name=message.name, response={"response": message.content}
+                        )
+                    )
+                ],
+            )
             return glm_content
         else:
             raise ValueError(f"Got unknown type {message}")
-    
+
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         """
         Map model invoke error to unified error
-        The key is the ermd = gml.GenerativeModel(model)ror type thrown to the caller
-        The value is the md = gml.GenerativeModel(model)error type thrown by the model,
+        The key is the ermd = gml.GenerativeModel(model) error type thrown to the caller
+        The value is the md = gml.GenerativeModel(model) error type thrown by the model,
         which needs to be converted into a unified error type for the caller.
 
-        :return: Invoke emd = gml.GenerativeModel(model)rror mapping
+        :return: Invoke emd = gml.GenerativeModel(model) error mapping
         """
         return {
-            InvokeConnectionError: [
-                exceptions.RetryError
-            ],
+            InvokeConnectionError: [exceptions.RetryError],
             InvokeServerUnavailableError: [
                 exceptions.ServiceUnavailable,
                 exceptions.InternalServerError,
                 exceptions.BadGateway,
                 exceptions.GatewayTimeout,
-                exceptions.DeadlineExceeded
-            ],
-            InvokeRateLimitError: [
-                exceptions.ResourceExhausted,
-                exceptions.TooManyRequests
+                exceptions.DeadlineExceeded,
             ],
+            InvokeRateLimitError: [exceptions.ResourceExhausted, exceptions.TooManyRequests],
             InvokeAuthorizationError: [
                 exceptions.Unauthenticated,
                 exceptions.PermissionDenied,
                 exceptions.Unauthenticated,
-                exceptions.Forbidden
+                exceptions.Forbidden,
             ],
             InvokeBadRequestError: [
                 exceptions.BadRequest,
@@ -736,5 +736,5 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                 exceptions.PreconditionFailed,
                 exceptions.RequestRangeNotSatisfiable,
                 exceptions.Cancelled,
-            ]
+            ],
         }
diff --git a/api/core/model_runtime/model_providers/vertex_ai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/vertex_ai/text_embedding/text_embedding.py
index 2404ba5894..519373a7f3 100644
--- a/api/core/model_runtime/model_providers/vertex_ai/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/vertex_ai/text_embedding/text_embedding.py
@@ -29,9 +29,9 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel):
     Model class for Vertex AI text embedding model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -51,23 +51,12 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel):
 
         client = VertexTextEmbeddingModel.from_pretrained(model)
 
-        embeddings_batch, embedding_used_tokens = self._embedding_invoke(
-            client=client,
-            texts=texts
-        )
+        embeddings_batch, embedding_used_tokens = self._embedding_invoke(client=client, texts=texts)
 
         # calc usage
-        usage = self._calc_response_usage(
-            model=model,
-            credentials=credentials,
-            tokens=embedding_used_tokens
-        )
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=embedding_used_tokens)
 
-        return TextEmbeddingResult(
-            embeddings=embeddings_batch,
-            usage=usage,
-            model=model
-        )
+        return TextEmbeddingResult(embeddings=embeddings_batch, usage=usage, model=model)
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
@@ -115,15 +104,11 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel):
             client = VertexTextEmbeddingModel.from_pretrained(model)
 
             # call embedding model
-            self._embedding_invoke(
-                model=model,
-                client=client,
-                texts=['ping']
-            )
+            self._embedding_invoke(model=model, client=client, texts=["ping"])
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _embedding_invoke(self, client: VertexTextEmbeddingModel, texts: list[str]) -> [list[float], int]: # type: ignore
+    def _embedding_invoke(self, client: VertexTextEmbeddingModel, texts: list[str]) -> [list[float], int]:  # type: ignore
         """
         Invoke embedding model
 
@@ -154,10 +139,7 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -168,14 +150,14 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
-    
+
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            generate custom model entities from credentials
+        generate custom model entities from credentials
         """
         entity = AIModelEntity(
             model=model,
@@ -183,15 +165,15 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size')),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],
             pricing=PriceConfig(
-                input=Decimal(credentials.get('input_price', 0)),
-                unit=Decimal(credentials.get('unit', 0)),
-                currency=credentials.get('currency', "USD")
-            )
+                input=Decimal(credentials.get("input_price", 0)),
+                unit=Decimal(credentials.get("unit", 0)),
+                currency=credentials.get("currency", "USD"),
+            ),
         )
 
         return entity
diff --git a/api/core/model_runtime/model_providers/vertex_ai/vertex_ai.py b/api/core/model_runtime/model_providers/vertex_ai/vertex_ai.py
index 3cbfb088d1..466a86fd36 100644
--- a/api/core/model_runtime/model_providers/vertex_ai/vertex_ai.py
+++ b/api/core/model_runtime/model_providers/vertex_ai/vertex_ai.py
@@ -20,12 +20,9 @@ class VertexAiProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.LLM)
 
             # Use `gemini-1.0-pro-002` model for validate,
-            model_instance.validate_credentials(
-                model='gemini-1.0-pro-002',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="gemini-1.0-pro-002", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/client.py b/api/core/model_runtime/model_providers/volcengine_maas/client.py
index a4d89dabcb..cfe21e4b9f 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/client.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/client.py
@@ -69,31 +69,26 @@ class ArkClientV3:
     def from_credentials(cls, credentials):
         """Initialize the client using the credentials provided."""
         args = {
-            "base_url": credentials['api_endpoint_host'],
-            "region": credentials['volc_region'],
+            "base_url": credentials["api_endpoint_host"],
+            "region": credentials["volc_region"],
         }
         if credentials.get("auth_method") == "api_key":
             args = {
                 **args,
-                "api_key": credentials['volc_api_key'],
+                "api_key": credentials["volc_api_key"],
             }
         else:
             args = {
                 **args,
-                "ak": credentials['volc_access_key_id'],
-                "sk": credentials['volc_secret_access_key'],
+                "ak": credentials["volc_access_key_id"],
+                "sk": credentials["volc_secret_access_key"],
             }
 
         if cls.is_compatible_with_legacy(credentials):
-            args = {
-                **args,
-                "base_url": DEFAULT_V3_ENDPOINT
-            }
+            args = {**args, "base_url": DEFAULT_V3_ENDPOINT}
 
-        client = ArkClientV3(
-            **args
-        )
-        client.endpoint_id = credentials['endpoint_id']
+        client = ArkClientV3(**args)
+        client.endpoint_id = credentials["endpoint_id"]
         return client
 
     @staticmethod
@@ -107,54 +102,48 @@ class ArkClientV3:
                 content = []
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
-                        content.append(ChatCompletionContentPartTextParam(
-                            text=message_content.text,
-                            type='text',
-                        ))
+                        content.append(
+                            ChatCompletionContentPartTextParam(
+                                text=message_content.text,
+                                type="text",
+                            )
+                        )
                     elif message_content.type == PromptMessageContentType.IMAGE:
-                        message_content = cast(
-                            ImagePromptMessageContent, message_content)
-                        image_data = re.sub(
-                            r'^data:image\/[a-zA-Z]+;base64,', '', message_content.data)
-                        content.append(ChatCompletionContentPartImageParam(
-                            image_url=ImageURL(
-                                url=image_data,
-                                detail=message_content.detail.value,
-                            ),
-                            type='image_url',
-                        ))
-            message_dict = ChatCompletionUserMessageParam(
-                role='user',
-                content=content
-            )
+                        message_content = cast(ImagePromptMessageContent, message_content)
+                        image_data = re.sub(r"^data:image\/[a-zA-Z]+;base64,", "", message_content.data)
+                        content.append(
+                            ChatCompletionContentPartImageParam(
+                                image_url=ImageURL(
+                                    url=image_data,
+                                    detail=message_content.detail.value,
+                                ),
+                                type="image_url",
+                            )
+                        )
+            message_dict = ChatCompletionUserMessageParam(role="user", content=content)
         elif isinstance(message, AssistantPromptMessage):
             message = cast(AssistantPromptMessage, message)
             message_dict = ChatCompletionAssistantMessageParam(
                 content=message.content,
-                role='assistant',
-                tool_calls=None if not message.tool_calls else [
+                role="assistant",
+                tool_calls=None
+                if not message.tool_calls
+                else [
                     ChatCompletionMessageToolCallParam(
                         id=call.id,
-                        function=Function(
-                            name=call.function.name,
-                            arguments=call.function.arguments
-                        ),
-                        type='function'
-                    ) for call in message.tool_calls
-                ]
+                        function=Function(name=call.function.name, arguments=call.function.arguments),
+                        type="function",
+                    )
+                    for call in message.tool_calls
+                ],
             )
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
-            message_dict = ChatCompletionSystemMessageParam(
-                content=message.content,
-                role='system'
-            )
+            message_dict = ChatCompletionSystemMessageParam(content=message.content, role="system")
         elif isinstance(message, ToolPromptMessage):
             message = cast(ToolPromptMessage, message)
             message_dict = ChatCompletionToolMessageParam(
-                content=message.content,
-                role='tool',
-                tool_call_id=message.tool_call_id
+                content=message.content, role="tool", tool_call_id=message.tool_call_id
             )
         else:
             raise ValueError(f"Got unknown PromptMessage type {message}")
@@ -164,23 +153,25 @@ class ArkClientV3:
     @staticmethod
     def _convert_tool_prompt(message: PromptMessageTool) -> ChatCompletionToolParam:
         return ChatCompletionToolParam(
-            type='function',
+            type="function",
             function=FunctionDefinition(
                 name=message.name,
                 description=message.description,
                 parameters=message.parameters,
-            )
+            ),
         )
 
-    def chat(self, messages: list[PromptMessage],
-             tools: Optional[list[PromptMessageTool]] = None,
-             stop: Optional[list[str]] = None,
-             frequency_penalty: Optional[float] = None,
-             max_tokens: Optional[int] = None,
-             presence_penalty: Optional[float] = None,
-             top_p: Optional[float] = None,
-             temperature: Optional[float] = None,
-             ) -> ChatCompletion:
+    def chat(
+        self,
+        messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        frequency_penalty: Optional[float] = None,
+        max_tokens: Optional[int] = None,
+        presence_penalty: Optional[float] = None,
+        top_p: Optional[float] = None,
+        temperature: Optional[float] = None,
+    ) -> ChatCompletion:
         """Block chat"""
         return self.ark.chat.completions.create(
             model=self.endpoint_id,
@@ -194,15 +185,17 @@ class ArkClientV3:
             temperature=temperature,
         )
 
-    def stream_chat(self, messages: list[PromptMessage],
-                    tools: Optional[list[PromptMessageTool]] = None,
-                    stop: Optional[list[str]] = None,
-                    frequency_penalty: Optional[float] = None,
-                    max_tokens: Optional[int] = None,
-                    presence_penalty: Optional[float] = None,
-                    top_p: Optional[float] = None,
-                    temperature: Optional[float] = None,
-                    ) -> Generator[ChatCompletionChunk]:
+    def stream_chat(
+        self,
+        messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        frequency_penalty: Optional[float] = None,
+        max_tokens: Optional[int] = None,
+        presence_penalty: Optional[float] = None,
+        top_p: Optional[float] = None,
+        temperature: Optional[float] = None,
+    ) -> Generator[ChatCompletionChunk]:
         """Stream chat"""
         chunks = self.ark.chat.completions.create(
             stream=True,
@@ -215,11 +208,9 @@ class ArkClientV3:
             presence_penalty=presence_penalty,
             top_p=top_p,
             temperature=temperature,
+            stream_options={"include_usage": True},
         )
-        for chunk in chunks:
-            if not chunk.choices:
-                continue
-            yield chunk
+        yield from chunks
 
     def embeddings(self, texts: list[str]) -> CreateEmbeddingResponse:
         return self.ark.embeddings.create(model=self.endpoint_id, input=texts)
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/legacy/client.py b/api/core/model_runtime/model_providers/volcengine_maas/legacy/client.py
index 1978c11680..266f1216f8 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/legacy/client.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/legacy/client.py
@@ -13,7 +13,7 @@ from core.model_runtime.entities.message_entities import (
     UserPromptMessage,
 )
 from core.model_runtime.model_providers.volcengine_maas.legacy.errors import wrap_error
-from core.model_runtime.model_providers.volcengine_maas.legacy.volc_sdk import ChatRole, MaasException, MaasService
+from core.model_runtime.model_providers.volcengine_maas.legacy.volc_sdk import ChatRole, MaasError, MaasService
 
 
 class MaaSClient(MaasService):
@@ -25,12 +25,12 @@ class MaaSClient(MaasService):
         self.endpoint_id = endpoint_id
 
     @classmethod
-    def from_credential(cls, credentials: dict) -> 'MaaSClient':
-        host = credentials['api_endpoint_host']
-        region = credentials['volc_region']
-        ak = credentials['volc_access_key_id']
-        sk = credentials['volc_secret_access_key']
-        endpoint_id = credentials['endpoint_id']
+    def from_credential(cls, credentials: dict) -> "MaaSClient":
+        host = credentials["api_endpoint_host"]
+        region = credentials["volc_region"]
+        ak = credentials["volc_access_key_id"]
+        sk = credentials["volc_secret_access_key"]
+        endpoint_id = credentials["endpoint_id"]
 
         client = cls(host, region)
         client.set_endpoint_id(endpoint_id)
@@ -40,8 +40,8 @@ class MaaSClient(MaasService):
 
     def chat(self, params: dict, messages: list[PromptMessage], stream=False, **extra_model_kwargs) -> Generator | dict:
         req = {
-            'parameters': params,
-            'messages': [self.convert_prompt_message_to_maas_message(prompt) for prompt in messages],
+            "parameters": params,
+            "messages": [self.convert_prompt_message_to_maas_message(prompt) for prompt in messages],
             **extra_model_kwargs,
         }
         if not stream:
@@ -55,9 +55,7 @@ class MaaSClient(MaasService):
         )
 
     def embeddings(self, texts: list[str]) -> dict:
-        req = {
-            'input': texts
-        }
+        req = {"input": texts}
         return super().embeddings(self.endpoint_id, req)
 
     @staticmethod
@@ -65,49 +63,40 @@ class MaaSClient(MaasService):
         if isinstance(message, UserPromptMessage):
             message = cast(UserPromptMessage, message)
             if isinstance(message.content, str):
-                message_dict = {"role": ChatRole.USER,
-                                "content": message.content}
+                message_dict = {"role": ChatRole.USER, "content": message.content}
             else:
                 content = []
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
-                        raise ValueError(
-                            'Content object type only support image_url')
+                        raise ValueError("Content object type only support image_url")
                     elif message_content.type == PromptMessageContentType.IMAGE:
-                        message_content = cast(
-                            ImagePromptMessageContent, message_content)
-                        image_data = re.sub(
-                            r'^data:image\/[a-zA-Z]+;base64,', '', message_content.data)
-                        content.append({
-                            'type': 'image_url',
-                            'image_url': {
-                                'url': '',
-                                'image_bytes': image_data,
-                                'detail': message_content.detail,
+                        message_content = cast(ImagePromptMessageContent, message_content)
+                        image_data = re.sub(r"^data:image\/[a-zA-Z]+;base64,", "", message_content.data)
+                        content.append(
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": "",
+                                    "image_bytes": image_data,
+                                    "detail": message_content.detail,
+                                },
                             }
-                        })
+                        )
 
-                message_dict = {'role': ChatRole.USER, 'content': content}
+                message_dict = {"role": ChatRole.USER, "content": content}
         elif isinstance(message, AssistantPromptMessage):
             message = cast(AssistantPromptMessage, message)
-            message_dict = {'role': ChatRole.ASSISTANT,
-                            'content': message.content}
+            message_dict = {"role": ChatRole.ASSISTANT, "content": message.content}
             if message.tool_calls:
-                message_dict['tool_calls'] = [
-                    {
-                        'name': call.function.name,
-                        'arguments': call.function.arguments
-                    } for call in message.tool_calls
+                message_dict["tool_calls"] = [
+                    {"name": call.function.name, "arguments": call.function.arguments} for call in message.tool_calls
                 ]
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
-            message_dict = {'role': ChatRole.SYSTEM,
-                            'content': message.content}
+            message_dict = {"role": ChatRole.SYSTEM, "content": message.content}
         elif isinstance(message, ToolPromptMessage):
             message = cast(ToolPromptMessage, message)
-            message_dict = {'role': ChatRole.FUNCTION,
-                            'content': message.content,
-                            'name': message.tool_call_id}
+            message_dict = {"role": ChatRole.FUNCTION, "content": message.content, "name": message.tool_call_id}
         else:
             raise ValueError(f"Got unknown PromptMessage type {message}")
 
@@ -117,7 +106,7 @@ class MaaSClient(MaasService):
     def wrap_exception(fn: Callable[[], dict | Generator]) -> dict | Generator:
         try:
             resp = fn()
-        except MaasException as e:
+        except MaasError as e:
             raise wrap_error(e)
 
         return resp
@@ -130,5 +119,5 @@ class MaaSClient(MaasService):
                 "name": tool.name,
                 "description": tool.description,
                 "parameters": tool.parameters,
-            }
+            },
         }
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/legacy/errors.py b/api/core/model_runtime/model_providers/volcengine_maas/legacy/errors.py
index 21ffaf1258..91dbe21a61 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/legacy/errors.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/legacy/errors.py
@@ -1,144 +1,144 @@
-from core.model_runtime.model_providers.volcengine_maas.legacy.volc_sdk import MaasException
+from core.model_runtime.model_providers.volcengine_maas.legacy.volc_sdk import MaasError
 
 
-class ClientSDKRequestError(MaasException):
+class ClientSDKRequestError(MaasError):
     pass
 
 
-class SignatureDoesNotMatch(MaasException):
+class SignatureDoesNotMatchError(MaasError):
     pass
 
 
-class RequestTimeout(MaasException):
+class RequestTimeoutError(MaasError):
     pass
 
 
-class ServiceConnectionTimeout(MaasException):
+class ServiceConnectionTimeoutError(MaasError):
     pass
 
 
-class MissingAuthenticationHeader(MaasException):
+class MissingAuthenticationHeaderError(MaasError):
     pass
 
 
-class AuthenticationHeaderIsInvalid(MaasException):
+class AuthenticationHeaderIsInvalidError(MaasError):
     pass
 
 
-class InternalServiceError(MaasException):
+class InternalServiceError(MaasError):
     pass
 
 
-class MissingParameter(MaasException):
+class MissingParameterError(MaasError):
     pass
 
 
-class InvalidParameter(MaasException):
+class InvalidParameterError(MaasError):
     pass
 
 
-class AuthenticationExpire(MaasException):
+class AuthenticationExpireError(MaasError):
     pass
 
 
-class EndpointIsInvalid(MaasException):
+class EndpointIsInvalidError(MaasError):
     pass
 
 
-class EndpointIsNotEnable(MaasException):
+class EndpointIsNotEnableError(MaasError):
     pass
 
 
-class ModelNotSupportStreamMode(MaasException):
+class ModelNotSupportStreamModeError(MaasError):
     pass
 
 
-class ReqTextExistRisk(MaasException):
+class ReqTextExistRiskError(MaasError):
     pass
 
 
-class RespTextExistRisk(MaasException):
+class RespTextExistRiskError(MaasError):
     pass
 
 
-class EndpointRateLimitExceeded(MaasException):
+class EndpointRateLimitExceededError(MaasError):
     pass
 
 
-class ServiceConnectionRefused(MaasException):
+class ServiceConnectionRefusedError(MaasError):
     pass
 
 
-class ServiceConnectionClosed(MaasException):
+class ServiceConnectionClosedError(MaasError):
     pass
 
 
-class UnauthorizedUserForEndpoint(MaasException):
+class UnauthorizedUserForEndpointError(MaasError):
     pass
 
 
-class InvalidEndpointWithNoURL(MaasException):
+class InvalidEndpointWithNoURLError(MaasError):
     pass
 
 
-class EndpointAccountRpmRateLimitExceeded(MaasException):
+class EndpointAccountRpmRateLimitExceededError(MaasError):
     pass
 
 
-class EndpointAccountTpmRateLimitExceeded(MaasException):
+class EndpointAccountTpmRateLimitExceededError(MaasError):
     pass
 
 
-class ServiceResourceWaitQueueFull(MaasException):
+class ServiceResourceWaitQueueFullError(MaasError):
     pass
 
 
-class EndpointIsPending(MaasException):
+class EndpointIsPendingError(MaasError):
     pass
 
 
-class ServiceNotOpen(MaasException):
+class ServiceNotOpenError(MaasError):
     pass
 
 
 AuthErrors = {
-    'SignatureDoesNotMatch': SignatureDoesNotMatch,
-    'MissingAuthenticationHeader': MissingAuthenticationHeader,
-    'AuthenticationHeaderIsInvalid': AuthenticationHeaderIsInvalid,
-    'AuthenticationExpire': AuthenticationExpire,
-    'UnauthorizedUserForEndpoint': UnauthorizedUserForEndpoint,
+    "SignatureDoesNotMatch": SignatureDoesNotMatchError,
+    "MissingAuthenticationHeader": MissingAuthenticationHeaderError,
+    "AuthenticationHeaderIsInvalid": AuthenticationHeaderIsInvalidError,
+    "AuthenticationExpire": AuthenticationExpireError,
+    "UnauthorizedUserForEndpoint": UnauthorizedUserForEndpointError,
 }
 
 BadRequestErrors = {
-    'MissingParameter': MissingParameter,
-    'InvalidParameter': InvalidParameter,
-    'EndpointIsInvalid': EndpointIsInvalid,
-    'EndpointIsNotEnable': EndpointIsNotEnable,
-    'ModelNotSupportStreamMode': ModelNotSupportStreamMode,
-    'ReqTextExistRisk': ReqTextExistRisk,
-    'RespTextExistRisk': RespTextExistRisk,
-    'InvalidEndpointWithNoURL': InvalidEndpointWithNoURL,
-    'ServiceNotOpen': ServiceNotOpen,
+    "MissingParameter": MissingParameterError,
+    "InvalidParameter": InvalidParameterError,
+    "EndpointIsInvalid": EndpointIsInvalidError,
+    "EndpointIsNotEnable": EndpointIsNotEnableError,
+    "ModelNotSupportStreamMode": ModelNotSupportStreamModeError,
+    "ReqTextExistRisk": ReqTextExistRiskError,
+    "RespTextExistRisk": RespTextExistRiskError,
+    "InvalidEndpointWithNoURL": InvalidEndpointWithNoURLError,
+    "ServiceNotOpen": ServiceNotOpenError,
 }
 
 RateLimitErrors = {
-    'EndpointRateLimitExceeded': EndpointRateLimitExceeded,
-    'EndpointAccountRpmRateLimitExceeded': EndpointAccountRpmRateLimitExceeded,
-    'EndpointAccountTpmRateLimitExceeded': EndpointAccountTpmRateLimitExceeded,
+    "EndpointRateLimitExceeded": EndpointRateLimitExceededError,
+    "EndpointAccountRpmRateLimitExceeded": EndpointAccountRpmRateLimitExceededError,
+    "EndpointAccountTpmRateLimitExceeded": EndpointAccountTpmRateLimitExceededError,
 }
 
 ServerUnavailableErrors = {
-    'InternalServiceError': InternalServiceError,
-    'EndpointIsPending': EndpointIsPending,
-    'ServiceResourceWaitQueueFull': ServiceResourceWaitQueueFull,
+    "InternalServiceError": InternalServiceError,
+    "EndpointIsPending": EndpointIsPendingError,
+    "ServiceResourceWaitQueueFull": ServiceResourceWaitQueueFullError,
 }
 
 ConnectionErrors = {
-    'ClientSDKRequestError': ClientSDKRequestError,
-    'RequestTimeout': RequestTimeout,
-    'ServiceConnectionTimeout': ServiceConnectionTimeout,
-    'ServiceConnectionRefused': ServiceConnectionRefused,
-    'ServiceConnectionClosed': ServiceConnectionClosed,
+    "ClientSDKRequestError": ClientSDKRequestError,
+    "RequestTimeout": RequestTimeoutError,
+    "ServiceConnectionTimeout": ServiceConnectionTimeoutError,
+    "ServiceConnectionRefused": ServiceConnectionRefusedError,
+    "ServiceConnectionClosed": ServiceConnectionClosedError,
 }
 
 ErrorCodeMap = {
@@ -150,7 +150,7 @@ ErrorCodeMap = {
 }
 
 
-def wrap_error(e: MaasException) -> Exception:
+def wrap_error(e: MaasError) -> Exception:
     if ErrorCodeMap.get(e.code):
         return ErrorCodeMap.get(e.code)(e.code_n, e.code, e.message, e.req_id)
     return e
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/__init__.py b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/__init__.py
index 64f342f16e..8b3eb157be 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/__init__.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/__init__.py
@@ -1,4 +1,4 @@
 from .common import ChatRole
-from .maas import MaasException, MaasService
+from .maas import MaasError, MaasService
 
-__all__ = ['MaasService', 'ChatRole', 'MaasException']
+__all__ = ["MaasService", "ChatRole", "MaasError"]
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/auth.py b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/auth.py
index 053432a089..c22bf8e76d 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/auth.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/auth.py
@@ -1,5 +1,6 @@
 # coding : utf-8
 import datetime
+from itertools import starmap
 
 import pytz
 
@@ -8,12 +9,12 @@ from .util import Util
 
 class MetaData:
     def __init__(self):
-        self.algorithm = ''
-        self.credential_scope = ''
-        self.signed_headers = ''
-        self.date = ''
-        self.region = ''
-        self.service = ''
+        self.algorithm = ""
+        self.credential_scope = ""
+        self.signed_headers = ""
+        self.date = ""
+        self.region = ""
+        self.service = ""
 
     def set_date(self, date):
         self.date = date
@@ -36,23 +37,23 @@ class MetaData:
 
 class SignResult:
     def __init__(self):
-        self.xdate = ''
-        self.xCredential = ''
-        self.xAlgorithm = ''
-        self.xSignedHeaders = ''
-        self.xSignedQueries = ''
-        self.xSignature = ''
-        self.xContextSha256 = ''
-        self.xSecurityToken = ''
+        self.xdate = ""
+        self.xCredential = ""
+        self.xAlgorithm = ""
+        self.xSignedHeaders = ""
+        self.xSignedQueries = ""
+        self.xSignature = ""
+        self.xContextSha256 = ""
+        self.xSecurityToken = ""
 
-        self.authorization = ''
+        self.authorization = ""
 
     def __str__(self):
-        return '\n'.join(['{}:{}'.format(*item) for item in self.__dict__.items()])
+        return "\n".join(list(starmap("{}:{}".format, self.__dict__.items())))
 
 
 class Credentials:
-    def __init__(self, ak, sk, service, region, session_token=''):
+    def __init__(self, ak, sk, service, region, session_token=""):
         self.ak = ak
         self.sk = sk
         self.service = service
@@ -72,73 +73,87 @@ class Credentials:
 class Signer:
     @staticmethod
     def sign(request, credentials):
-        if request.path == '':
-            request.path = '/'
-        if request.method != 'GET' and not ('Content-Type' in request.headers):
-            request.headers['Content-Type'] = 'application/x-www-form-urlencoded; charset=utf-8'
+        if request.path == "":
+            request.path = "/"
+        if request.method != "GET" and "Content-Type" not in request.headers:
+            request.headers["Content-Type"] = "application/x-www-form-urlencoded; charset=utf-8"
 
         format_date = Signer.get_current_format_date()
-        request.headers['X-Date'] = format_date
-        if credentials.session_token != '':
-            request.headers['X-Security-Token'] = credentials.session_token
+        request.headers["X-Date"] = format_date
+        if credentials.session_token != "":
+            request.headers["X-Security-Token"] = credentials.session_token
 
         md = MetaData()
-        md.set_algorithm('HMAC-SHA256')
+        md.set_algorithm("HMAC-SHA256")
         md.set_service(credentials.service)
         md.set_region(credentials.region)
         md.set_date(format_date[:8])
 
         hashed_canon_req = Signer.hashed_canonical_request_v4(request, md)
-        md.set_credential_scope('/'.join([md.date, md.region, md.service, 'request']))
+        md.set_credential_scope("/".join([md.date, md.region, md.service, "request"]))
 
-        signing_str = '\n'.join([md.algorithm, format_date, md.credential_scope, hashed_canon_req])
+        signing_str = "\n".join([md.algorithm, format_date, md.credential_scope, hashed_canon_req])
         signing_key = Signer.get_signing_secret_key_v4(credentials.sk, md.date, md.region, md.service)
         sign = Util.to_hex(Util.hmac_sha256(signing_key, signing_str))
-        request.headers['Authorization'] = Signer.build_auth_header_v4(sign, md, credentials)
-        return
+        request.headers["Authorization"] = Signer.build_auth_header_v4(sign, md, credentials)
 
     @staticmethod
     def hashed_canonical_request_v4(request, meta):
         body_hash = Util.sha256(request.body)
-        request.headers['X-Content-Sha256'] = body_hash
+        request.headers["X-Content-Sha256"] = body_hash
 
         signed_headers = {}
         for key in request.headers:
-            if key in ['Content-Type', 'Content-Md5', 'Host'] or key.startswith('X-'):
+            if key in {"Content-Type", "Content-Md5", "Host"} or key.startswith("X-"):
                 signed_headers[key.lower()] = request.headers[key]
 
-        if 'host' in signed_headers:
-            v = signed_headers['host']
-            if v.find(':') != -1:
-                split = v.split(':')
+        if "host" in signed_headers:
+            v = signed_headers["host"]
+            if v.find(":") != -1:
+                split = v.split(":")
                 port = split[1]
-                if str(port) == '80' or str(port) == '443':
-                    signed_headers['host'] = split[0]
+                if str(port) == "80" or str(port) == "443":
+                    signed_headers["host"] = split[0]
 
-        signed_str = ''
+        signed_str = ""
         for key in sorted(signed_headers.keys()):
-            signed_str += key + ':' + signed_headers[key] + '\n'
+            signed_str += key + ":" + signed_headers[key] + "\n"
 
-        meta.set_signed_headers(';'.join(sorted(signed_headers.keys())))
+        meta.set_signed_headers(";".join(sorted(signed_headers.keys())))
 
-        canonical_request = '\n'.join(
-            [request.method, Util.norm_uri(request.path), Util.norm_query(request.query), signed_str,
-             meta.signed_headers, body_hash])
+        canonical_request = "\n".join(
+            [
+                request.method,
+                Util.norm_uri(request.path),
+                Util.norm_query(request.query),
+                signed_str,
+                meta.signed_headers,
+                body_hash,
+            ]
+        )
 
         return Util.sha256(canonical_request)
 
     @staticmethod
     def get_signing_secret_key_v4(sk, date, region, service):
-        date = Util.hmac_sha256(bytes(sk, encoding='utf-8'), date)
+        date = Util.hmac_sha256(bytes(sk, encoding="utf-8"), date)
         region = Util.hmac_sha256(date, region)
         service = Util.hmac_sha256(region, service)
-        return Util.hmac_sha256(service, 'request')
+        return Util.hmac_sha256(service, "request")
 
     @staticmethod
     def build_auth_header_v4(signature, meta, credentials):
-        credential = credentials.ak + '/' + meta.credential_scope
-        return meta.algorithm + ' Credential=' + credential + ', SignedHeaders=' + meta.signed_headers + ', Signature=' + signature
+        credential = credentials.ak + "/" + meta.credential_scope
+        return (
+            meta.algorithm
+            + " Credential="
+            + credential
+            + ", SignedHeaders="
+            + meta.signed_headers
+            + ", Signature="
+            + signature
+        )
 
     @staticmethod
     def get_current_format_date():
-        return datetime.datetime.now(tz=pytz.timezone('UTC')).strftime("%Y%m%dT%H%M%SZ")
+        return datetime.datetime.now(tz=pytz.timezone("UTC")).strftime("%Y%m%dT%H%M%SZ")
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/service.py b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/service.py
index 7271ae63fd..33c41f3eb3 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/service.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/service.py
@@ -6,7 +6,7 @@ import requests
 
 from .auth import Signer
 
-VERSION = 'v1.0.137'
+VERSION = "v1.0.137"
 
 
 class Service:
@@ -31,7 +31,7 @@ class Service:
         self.service_info.scheme = scheme
 
     def get(self, api, params, doseq=0):
-        if not (api in self.api_info):
+        if api not in self.api_info:
             raise Exception("no such api")
         api_info = self.api_info[api]
 
@@ -40,52 +40,61 @@ class Service:
         Signer.sign(r, self.service_info.credentials)
 
         url = r.build(doseq)
-        resp = self.session.get(url, headers=r.headers,
-                                timeout=(self.service_info.connection_timeout, self.service_info.socket_timeout))
+        resp = self.session.get(
+            url, headers=r.headers, timeout=(self.service_info.connection_timeout, self.service_info.socket_timeout)
+        )
         if resp.status_code == 200:
             return resp.text
         else:
             raise Exception(resp.text)
 
     def post(self, api, params, form):
-        if not (api in self.api_info):
+        if api not in self.api_info:
             raise Exception("no such api")
         api_info = self.api_info[api]
         r = self.prepare_request(api_info, params)
-        r.headers['Content-Type'] = 'application/x-www-form-urlencoded'
+        r.headers["Content-Type"] = "application/x-www-form-urlencoded"
         r.form = self.merge(api_info.form, form)
         r.body = urlencode(r.form, True)
         Signer.sign(r, self.service_info.credentials)
 
         url = r.build()
 
-        resp = self.session.post(url, headers=r.headers, data=r.form,
-                                 timeout=(self.service_info.connection_timeout, self.service_info.socket_timeout))
+        resp = self.session.post(
+            url,
+            headers=r.headers,
+            data=r.form,
+            timeout=(self.service_info.connection_timeout, self.service_info.socket_timeout),
+        )
         if resp.status_code == 200:
             return resp.text
         else:
             raise Exception(resp.text)
 
     def json(self, api, params, body):
-        if not (api in self.api_info):
+        if api not in self.api_info:
             raise Exception("no such api")
         api_info = self.api_info[api]
         r = self.prepare_request(api_info, params)
-        r.headers['Content-Type'] = 'application/json'
+        r.headers["Content-Type"] = "application/json"
         r.body = body
 
         Signer.sign(r, self.service_info.credentials)
 
         url = r.build()
-        resp = self.session.post(url, headers=r.headers, data=r.body,
-                                 timeout=(self.service_info.connection_timeout, self.service_info.socket_timeout))
+        resp = self.session.post(
+            url,
+            headers=r.headers,
+            data=r.body,
+            timeout=(self.service_info.connection_timeout, self.service_info.socket_timeout),
+        )
         if resp.status_code == 200:
             return json.dumps(resp.json())
         else:
             raise Exception(resp.text.encode("utf-8"))
 
     def put(self, url, file_path, headers):
-        with open(file_path, 'rb') as f:
+        with open(file_path, "rb") as f:
             resp = self.session.put(url, headers=headers, data=f)
             if resp.status_code == 200:
                 return True, resp.text.encode("utf-8")
@@ -105,7 +114,7 @@ class Service:
                 params[key] = str(params[key])
             elif type(params[key]) == list:
                 if not doseq:
-                    params[key] = ','.join(params[key])
+                    params[key] = ",".join(params[key])
 
         connection_timeout = self.service_info.connection_timeout
         socket_timeout = self.service_info.socket_timeout
@@ -117,8 +126,8 @@ class Service:
         r.set_socket_timeout(socket_timeout)
 
         headers = self.merge(api_info.header, self.service_info.header)
-        headers['Host'] = self.service_info.host
-        headers['User-Agent'] = 'volc-sdk-python/' + VERSION
+        headers["Host"] = self.service_info.host
+        headers["User-Agent"] = "volc-sdk-python/" + VERSION
         r.set_headers(headers)
 
         query = self.merge(api_info.query, params)
@@ -143,13 +152,13 @@ class Service:
 
 class Request:
     def __init__(self):
-        self.schema = ''
-        self.method = ''
-        self.host = ''
-        self.path = ''
+        self.schema = ""
+        self.method = ""
+        self.host = ""
+        self.path = ""
         self.headers = OrderedDict()
         self.query = OrderedDict()
-        self.body = ''
+        self.body = ""
         self.form = {}
         self.connection_timeout = 0
         self.socket_timeout = 0
@@ -182,11 +191,11 @@ class Request:
         self.socket_timeout = socket_timeout
 
     def build(self, doseq=0):
-        return self.schema + '://' + self.host + self.path + '?' + urlencode(self.query, doseq)
+        return self.schema + "://" + self.host + self.path + "?" + urlencode(self.query, doseq)
 
 
 class ServiceInfo:
-    def __init__(self, host, header, credentials, connection_timeout, socket_timeout, scheme='http'):
+    def __init__(self, host, header, credentials, connection_timeout, socket_timeout, scheme="http"):
         self.host = host
         self.header = header
         self.credentials = credentials
@@ -204,4 +213,4 @@ class ApiInfo:
         self.header = header
 
     def __str__(self):
-        return 'method: ' + self.method + ', path: ' + self.path
+        return "method: " + self.method + ", path: " + self.path
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/util.py b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/util.py
index 7eb5fdfa91..178d63714e 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/util.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/util.py
@@ -1,5 +1,6 @@
 import hashlib
 import hmac
+import operator
 from functools import reduce
 from urllib.parse import quote
 
@@ -7,28 +8,28 @@ from urllib.parse import quote
 class Util:
     @staticmethod
     def norm_uri(path):
-        return quote(path).replace('%2F', '/').replace('+', '%20')
+        return quote(path).replace("%2F", "/").replace("+", "%20")
 
     @staticmethod
     def norm_query(params):
-        query = ''
+        query = ""
         for key in sorted(params.keys()):
             if type(params[key]) == list:
                 for k in params[key]:
-                    query = query + quote(key, safe='-_.~') + '=' + quote(k, safe='-_.~') + '&'
+                    query = query + quote(key, safe="-_.~") + "=" + quote(k, safe="-_.~") + "&"
             else:
-                query = query + quote(key, safe='-_.~') + '=' + quote(params[key], safe='-_.~') + '&'
+                query = query + quote(key, safe="-_.~") + "=" + quote(params[key], safe="-_.~") + "&"
         query = query[:-1]
-        return query.replace('+', '%20')
+        return query.replace("+", "%20")
 
     @staticmethod
     def hmac_sha256(key, content):
-        return hmac.new(key, bytes(content, encoding='utf-8'), hashlib.sha256).digest()
+        return hmac.new(key, bytes(content, encoding="utf-8"), hashlib.sha256).digest()
 
     @staticmethod
     def sha256(content):
         if isinstance(content, str) is True:
-            return hashlib.sha256(content.encode('utf-8')).hexdigest()
+            return hashlib.sha256(content.encode("utf-8")).hexdigest()
         else:
             return hashlib.sha256(content).hexdigest()
 
@@ -36,8 +37,8 @@ class Util:
     def to_hex(content):
         lst = []
         for ch in content:
-            hv = hex(ch).replace('0x', '')
+            hv = hex(ch).replace("0x", "")
             if len(hv) == 1:
-                hv = '0' + hv
+                hv = "0" + hv
             lst.append(hv)
-        return reduce(lambda x, y: x + y, lst)
+        return reduce(operator.add, lst)
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/common.py b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/common.py
index 8b14d026d9..3825fd6574 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/common.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/common.py
@@ -43,9 +43,7 @@ def json_to_object(json_str, req_id=None):
 
 
 def gen_req_id():
-    return datetime.now().strftime("%Y%m%d%H%M%S") + format(
-        random.randint(0, 2 ** 64 - 1), "020X"
-    )
+    return datetime.now().strftime("%Y%m%d%H%M%S") + format(random.randint(0, 2**64 - 1), "020X")
 
 
 class SSEDecoder:
@@ -53,13 +51,13 @@ class SSEDecoder:
         self.source = source
 
     def _read(self):
-        data = b''
+        data = b""
         for chunk in self.source:
             for line in chunk.splitlines(True):
                 data += line
-                if data.endswith((b'\r\r', b'\n\n', b'\r\n\r\n')):
+                if data.endswith((b"\r\r", b"\n\n", b"\r\n\r\n")):
                     yield data
-                    data = b''
+                    data = b""
         if data:
             yield data
 
@@ -67,13 +65,13 @@ class SSEDecoder:
         for chunk in self._read():
             for line in chunk.splitlines():
                 # skip comment
-                if line.startswith(b':'):
+                if line.startswith(b":"):
                     continue
 
-                if b':' in line:
-                    field, value = line.split(b':', 1)
+                if b":" in line:
+                    field, value = line.split(b":", 1)
                 else:
-                    field, value = line, b''
+                    field, value = line, b""
 
-                if field == b'data' and len(value) > 0:
+                if field == b"data" and len(value) > 0:
                     yield value
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/maas.py b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/maas.py
index 3cbe9d9f09..a3836685f1 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/maas.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/maas.py
@@ -9,9 +9,7 @@ from .common import SSEDecoder, dict_to_object, gen_req_id, json_to_object
 
 class MaasService(Service):
     def __init__(self, host, region, connection_timeout=60, socket_timeout=60):
-        service_info = self.get_service_info(
-            host, region, connection_timeout, socket_timeout
-        )
+        service_info = self.get_service_info(host, region, connection_timeout, socket_timeout)
         self._apikey = None
         api_info = self.get_api_info()
         super().__init__(service_info, api_info)
@@ -35,9 +33,7 @@ class MaasService(Service):
     def get_api_info():
         api_info = {
             "chat": ApiInfo("POST", "/api/v2/endpoint/{endpoint_id}/chat", {}, {}, {}),
-            "embeddings": ApiInfo(
-                "POST", "/api/v2/endpoint/{endpoint_id}/embeddings", {}, {}, {}
-            ),
+            "embeddings": ApiInfo("POST", "/api/v2/endpoint/{endpoint_id}/embeddings", {}, {}, {}),
         }
         return api_info
 
@@ -52,9 +48,7 @@ class MaasService(Service):
 
         try:
             req["stream"] = True
-            res = self._call(
-                endpoint_id, "chat", req_id, {}, json.dumps(req).encode("utf-8"), apikey, stream=True
-            )
+            res = self._call(endpoint_id, "chat", req_id, {}, json.dumps(req).encode("utf-8"), apikey, stream=True)
 
             decoder = SSEDecoder(res)
 
@@ -64,13 +58,12 @@ class MaasService(Service):
                         return
 
                     try:
-                        res = json_to_object(
-                            str(data, encoding="utf-8"), req_id=req_id)
+                        res = json_to_object(str(data, encoding="utf-8"), req_id=req_id)
                     except Exception:
                         raise
 
                     if res.error is not None and res.error.code_n != 0:
-                        raise MaasException(
+                        raise MaasError(
                             res.error.code_n,
                             res.error.code,
                             res.error.message,
@@ -79,7 +72,7 @@ class MaasService(Service):
                     yield res
 
             return iter_fn()
-        except MaasException:
+        except MaasError:
             raise
         except Exception as e:
             raise new_client_sdk_request_error(str(e))
@@ -95,29 +88,28 @@ class MaasService(Service):
         apikey = self._apikey
 
         try:
-            res = self._call(endpoint_id, api, req_id, params,
-                             json.dumps(req).encode("utf-8"), apikey)
+            res = self._call(endpoint_id, api, req_id, params, json.dumps(req).encode("utf-8"), apikey)
             resp = dict_to_object(res.json())
             if resp and isinstance(resp, dict):
                 resp["req_id"] = req_id
             return resp
 
-        except MaasException as e:
+        except MaasError as e:
             raise e
         except Exception as e:
             raise new_client_sdk_request_error(str(e), req_id)
 
     def _validate(self, api, req_id):
         credentials_exist = (
-            self.service_info.credentials is not None and
-            self.service_info.credentials.sk is not None and
-            self.service_info.credentials.ak is not None
+            self.service_info.credentials is not None
+            and self.service_info.credentials.sk is not None
+            and self.service_info.credentials.ak is not None
         )
 
         if not self._apikey and not credentials_exist:
             raise new_client_sdk_request_error("no valid credential", req_id)
 
-        if not (api in self.api_info):
+        if api not in self.api_info:
             raise new_client_sdk_request_error("no such api", req_id)
 
     def _call(self, endpoint_id, api, req_id, params, body, apikey=None, stream=False):
@@ -150,22 +142,19 @@ class MaasService(Service):
             raw = res.text.encode()
             res.close()
             try:
-                resp = json_to_object(
-                    str(raw, encoding="utf-8"), req_id=req_id)
+                resp = json_to_object(str(raw, encoding="utf-8"), req_id=req_id)
             except Exception:
                 raise new_client_sdk_request_error(raw, req_id)
 
             if resp.error:
-                raise MaasException(
-                    resp.error.code_n, resp.error.code, resp.error.message, req_id
-                )
+                raise MaasError(resp.error.code_n, resp.error.code, resp.error.message, req_id)
             else:
                 raise new_client_sdk_request_error(resp, req_id)
 
         return res
 
 
-class MaasException(Exception):
+class MaasError(Exception):
     def __init__(self, code_n, code, message, req_id):
         self.code_n = code_n
         self.code = code
@@ -173,15 +162,17 @@ class MaasException(Exception):
         self.req_id = req_id
 
     def __str__(self):
-        return ("Detailed exception information is listed below.\n" +
-                "req_id: {}\n" +
-                "code_n: {}\n" +
-                "code: {}\n" +
-                "message: {}").format(self.req_id, self.code_n, self.code, self.message)
+        return (
+            "Detailed exception information is listed below.\n"
+            + "req_id: {}\n"
+            + "code_n: {}\n"
+            + "code: {}\n"
+            + "message: {}"
+        ).format(self.req_id, self.code_n, self.code, self.message)
 
 
 def new_client_sdk_request_error(raw, req_id=""):
-    return MaasException(1709701, "ClientSDKRequestError", "MaaS SDK request error: {}".format(raw), req_id)
+    return MaasError(1709701, "ClientSDKRequestError", "MaaS SDK request error: {}".format(raw), req_id)
 
 
 class BinaryResponseContent:
@@ -189,25 +180,19 @@ class BinaryResponseContent:
         self.response = response
         self.request_id = request_id
 
-    def stream_to_file(
-            self,
-            file: str
-    ) -> None:
+    def stream_to_file(self, file: str) -> None:
         is_first = True
-        error_bytes = b''
+        error_bytes = b""
         with open(file, mode="wb") as f:
             for data in self.response:
-                if len(error_bytes) > 0 or (is_first and "\"error\":" in str(data)):
+                if len(error_bytes) > 0 or (is_first and '"error":' in str(data)):
                     error_bytes += data
                 else:
                     f.write(data)
 
         if len(error_bytes) > 0:
-            resp = json_to_object(
-                str(error_bytes, encoding="utf-8"), req_id=self.request_id)
-            raise MaasException(
-                resp.error.code_n, resp.error.code, resp.error.message, self.request_id
-            )
+            resp = json_to_object(str(error_bytes, encoding="utf-8"), req_id=self.request_id)
+            raise MaasError(resp.error.code_n, resp.error.code, resp.error.message, self.request_id)
 
     def iter_bytes(self) -> Iterator[bytes]:
         yield from self.response
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/llm/llm.py b/api/core/model_runtime/model_providers/volcengine_maas/llm/llm.py
index 996c66e604..dec6c9d789 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/llm/llm.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/llm/llm.py
@@ -35,7 +35,7 @@ from core.model_runtime.model_providers.volcengine_maas.legacy.errors import (
     AuthErrors,
     BadRequestErrors,
     ConnectionErrors,
-    MaasException,
+    MaasError,
     RateLimitErrors,
     ServerUnavailableErrors,
 )
@@ -49,10 +49,17 @@ logger = logging.getLogger(__name__)
 
 
 class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                model_parameters: dict, tools: list[PromptMessageTool] | None = None,
-                stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         if ArkClientV3.is_legacy(credentials):
             return self._generate_v2(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
         return self._generate_v3(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
@@ -71,27 +78,36 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
         try:
             client.chat(
                 {
-                    'max_new_tokens': 16,
-                    'temperature': 0.7,
-                    'top_p': 0.9,
-                    'top_k': 15,
+                    "max_new_tokens": 16,
+                    "temperature": 0.7,
+                    "top_p": 0.9,
+                    "top_k": 15,
                 },
-                [UserPromptMessage(content='ping\nAnswer: ')],
+                [UserPromptMessage(content="ping\nAnswer: ")],
             )
-        except MaasException as e:
+        except MaasError as e:
             raise CredentialsValidateFailedError(e.message)
 
     @staticmethod
     def _validate_credentials_v3(credentials: dict) -> None:
         client = ArkClientV3.from_credentials(credentials)
         try:
-            client.chat(max_tokens=16, temperature=0.7, top_p=0.9,
-                        messages=[UserPromptMessage(content='ping\nAnswer: ')], )
+            client.chat(
+                max_tokens=16,
+                temperature=0.7,
+                top_p=0.9,
+                messages=[UserPromptMessage(content="ping\nAnswer: ")],
+            )
         except Exception as e:
             raise CredentialsValidateFailedError(e)
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: list[PromptMessageTool] | None = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool] | None = None,
+    ) -> int:
         if ArkClientV3.is_legacy(credentials):
             return self._get_num_tokens_v2(prompt_messages)
         return self._get_num_tokens_v3(prompt_messages)
@@ -100,8 +116,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
         if len(messages) == 0:
             return 0
         num_tokens = 0
-        messages_dict = [
-            MaaSClient.convert_prompt_message_to_maas_message(m) for m in messages]
+        messages_dict = [MaaSClient.convert_prompt_message_to_maas_message(m) for m in messages]
         for message in messages_dict:
             for key, value in message.items():
                 num_tokens += self._get_num_tokens_by_gpt2(str(key))
@@ -113,8 +128,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
         if len(messages) == 0:
             return 0
         num_tokens = 0
-        messages_dict = [
-            ArkClientV3.convert_prompt_message(m) for m in messages]
+        messages_dict = [ArkClientV3.convert_prompt_message(m) for m in messages]
         for message in messages_dict:
             for key, value in message.items():
                 num_tokens += self._get_num_tokens_by_gpt2(str(key))
@@ -122,118 +136,126 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
 
         return num_tokens
 
-    def _generate_v2(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                     model_parameters: dict, tools: list[PromptMessageTool] | None = None,
-                     stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
-
+    def _generate_v2(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         client = MaaSClient.from_credential(credentials)
         req_params = get_v2_req_params(credentials, model_parameters, stop)
         extra_model_kwargs = {}
         if tools:
-            extra_model_kwargs['tools'] = [
-                MaaSClient.transform_tool_prompt_to_maas_config(tool) for tool in tools
-            ]
-        resp = MaaSClient.wrap_exception(
-            lambda: client.chat(req_params, prompt_messages, stream, **extra_model_kwargs))
+            extra_model_kwargs["tools"] = [MaaSClient.transform_tool_prompt_to_maas_config(tool) for tool in tools]
+        resp = MaaSClient.wrap_exception(lambda: client.chat(req_params, prompt_messages, stream, **extra_model_kwargs))
 
         def _handle_stream_chat_response() -> Generator:
             for index, r in enumerate(resp):
-                choices = r['choices']
+                choices = r["choices"]
                 if not choices:
                     continue
                 choice = choices[0]
-                message = choice['message']
+                message = choice["message"]
                 usage = None
-                if r.get('usage'):
-                    usage = self._calc_response_usage(model=model, credentials=credentials,
-                                                      prompt_tokens=r['usage']['prompt_tokens'],
-                                                      completion_tokens=r['usage']['completion_tokens']
-                                                      )
+                if r.get("usage"):
+                    usage = self._calc_response_usage(
+                        model=model,
+                        credentials=credentials,
+                        prompt_tokens=r["usage"]["prompt_tokens"],
+                        completion_tokens=r["usage"]["completion_tokens"],
+                    )
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=index,
-                        message=AssistantPromptMessage(
-                            content=message['content'] if message['content'] else '',
-                            tool_calls=[]
-                        ),
+                        message=AssistantPromptMessage(content=message["content"] or "", tool_calls=[]),
                         usage=usage,
-                        finish_reason=choice.get('finish_reason'),
+                        finish_reason=choice.get("finish_reason"),
                     ),
                 )
 
         def _handle_chat_response() -> LLMResult:
-            choices = resp['choices']
+            choices = resp["choices"]
             if not choices:
                 raise ValueError("No choices found")
 
             choice = choices[0]
-            message = choice['message']
+            message = choice["message"]
 
             # parse tool calls
             tool_calls = []
-            if message['tool_calls']:
-                for call in message['tool_calls']:
+            if message["tool_calls"]:
+                for call in message["tool_calls"]:
                     tool_call = AssistantPromptMessage.ToolCall(
-                        id=call['function']['name'],
-                        type=call['type'],
+                        id=call["function"]["name"],
+                        type=call["type"],
                         function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                            name=call['function']['name'],
-                            arguments=call['function']['arguments']
-                        )
+                            name=call["function"]["name"], arguments=call["function"]["arguments"]
+                        ),
                     )
                     tool_calls.append(tool_call)
 
-            usage = resp['usage']
+            usage = resp["usage"]
             return LLMResult(
                 model=model,
                 prompt_messages=prompt_messages,
                 message=AssistantPromptMessage(
-                    content=message['content'] if message['content'] else '',
+                    content=message["content"] or "",
                     tool_calls=tool_calls,
                 ),
-                usage=self._calc_response_usage(model=model, credentials=credentials,
-                                                prompt_tokens=usage['prompt_tokens'],
-                                                completion_tokens=usage['completion_tokens']
-                                                ),
+                usage=self._calc_response_usage(
+                    model=model,
+                    credentials=credentials,
+                    prompt_tokens=usage["prompt_tokens"],
+                    completion_tokens=usage["completion_tokens"],
+                ),
             )
 
         if not stream:
             return _handle_chat_response()
         return _handle_stream_chat_response()
 
-    def _generate_v3(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                     model_parameters: dict, tools: list[PromptMessageTool] | None = None,
-                     stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
-
+    def _generate_v3(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         client = ArkClientV3.from_credentials(credentials)
         req_params = get_v3_req_params(credentials, model_parameters, stop)
         if tools:
-            req_params['tools'] = tools
+            req_params["tools"] = tools
 
         def _handle_stream_chat_response(chunks: Generator[ChatCompletionChunk]) -> Generator:
             for chunk in chunks:
-                if not chunk.choices:
-                    continue
-                choice = chunk.choices[0]
-
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
-                        index=choice.index,
+                        index=0,
                         message=AssistantPromptMessage(
-                            content=choice.delta.content,
-                            tool_calls=[]
+                            content=chunk.choices[0].delta.content if chunk.choices else "", tool_calls=[]
                         ),
-                        usage=self._calc_response_usage(model=model, credentials=credentials,
-                                                        prompt_tokens=chunk.usage.prompt_tokens,
-                                                        completion_tokens=chunk.usage.completion_tokens
-                                                        ) if chunk.usage else None,
-                        finish_reason=choice.finish_reason,
+                        usage=self._calc_response_usage(
+                            model=model,
+                            credentials=credentials,
+                            prompt_tokens=chunk.usage.prompt_tokens,
+                            completion_tokens=chunk.usage.completion_tokens,
+                        )
+                        if chunk.usage
+                        else None,
+                        finish_reason=chunk.choices[0].finish_reason if chunk.choices else None,
                     ),
                 )
 
@@ -248,9 +270,8 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                         id=call.id,
                         type=call.type,
                         function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                            name=call.function.name,
-                            arguments=call.function.arguments
-                        )
+                            name=call.function.name, arguments=call.function.arguments
+                        ),
                     )
                     tool_calls.append(tool_call)
 
@@ -259,13 +280,15 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                 model=model,
                 prompt_messages=prompt_messages,
                 message=AssistantPromptMessage(
-                    content=message.content if message.content else "",
+                    content=message.content or "",
                     tool_calls=tool_calls,
                 ),
-                usage=self._calc_response_usage(model=model, credentials=credentials,
-                                                prompt_tokens=usage.prompt_tokens,
-                                                completion_tokens=usage.completion_tokens
-                                                ),
+                usage=self._calc_response_usage(
+                    model=model,
+                    credentials=credentials,
+                    prompt_tokens=usage.prompt_tokens,
+                    completion_tokens=usage.completion_tokens,
+                ),
             )
 
         if not stream:
@@ -277,72 +300,56 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
         model_config = get_model_config(credentials)
 
         rules = [
             ParameterRule(
-                name='temperature',
+                name="temperature",
                 type=ParameterType.FLOAT,
-                use_template='temperature',
-                label=I18nObject(
-                    zh_Hans='温度',
-                    en_US='Temperature'
-                )
+                use_template="temperature",
+                label=I18nObject(zh_Hans="温度", en_US="Temperature"),
             ),
             ParameterRule(
-                name='top_p',
+                name="top_p",
                 type=ParameterType.FLOAT,
-                use_template='top_p',
-                label=I18nObject(
-                    zh_Hans='Top P',
-                    en_US='Top P'
-                )
+                use_template="top_p",
+                label=I18nObject(zh_Hans="Top P", en_US="Top P"),
             ),
             ParameterRule(
-                name='top_k',
-                type=ParameterType.INT,
-                min=1,
-                default=1,
-                label=I18nObject(
-                    zh_Hans='Top K',
-                    en_US='Top K'
-                )
+                name="top_k", type=ParameterType.INT, min=1, default=1, label=I18nObject(zh_Hans="Top K", en_US="Top K")
             ),
             ParameterRule(
-                name='presence_penalty',
+                name="presence_penalty",
                 type=ParameterType.FLOAT,
-                use_template='presence_penalty',
+                use_template="presence_penalty",
                 label=I18nObject(
-                    en_US='Presence Penalty',
-                    zh_Hans='存在惩罚',
+                    en_US="Presence Penalty",
+                    zh_Hans="存在惩罚",
                 ),
                 min=-2.0,
                 max=2.0,
             ),
             ParameterRule(
-                name='frequency_penalty',
+                name="frequency_penalty",
                 type=ParameterType.FLOAT,
-                use_template='frequency_penalty',
+                use_template="frequency_penalty",
                 label=I18nObject(
-                    en_US='Frequency Penalty',
-                    zh_Hans='频率惩罚',
+                    en_US="Frequency Penalty",
+                    zh_Hans="频率惩罚",
                 ),
                 min=-2.0,
                 max=2.0,
             ),
             ParameterRule(
-                name='max_tokens',
+                name="max_tokens",
                 type=ParameterType.INT,
-                use_template='max_tokens',
+                use_template="max_tokens",
                 min=1,
                 max=model_config.properties.max_tokens,
                 default=512,
-                label=I18nObject(
-                    zh_Hans='最大生成长度',
-                    en_US='Max Tokens'
-                )
+                label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"),
             ),
         ]
 
@@ -352,9 +359,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
 
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
             model_properties=model_properties,
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/llm/models.py b/api/core/model_runtime/model_providers/volcengine_maas/llm/models.py
index a882f68a36..d8be14b024 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/llm/models.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/llm/models.py
@@ -16,138 +16,127 @@ class ModelConfig(BaseModel):
 
 
 configs: dict[str, ModelConfig] = {
-    'Doubao-pro-4k': ModelConfig(
+    "Doubao-pro-4k": ModelConfig(
         properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[ModelFeature.TOOL_CALL],
     ),
-    'Doubao-lite-4k': ModelConfig(
+    "Doubao-lite-4k": ModelConfig(
         properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[ModelFeature.TOOL_CALL],
     ),
-    'Doubao-pro-32k': ModelConfig(
+    "Doubao-pro-32k": ModelConfig(
         properties=ModelProperties(context_size=32768, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[ModelFeature.TOOL_CALL],
     ),
-    'Doubao-lite-32k': ModelConfig(
+    "Doubao-lite-32k": ModelConfig(
         properties=ModelProperties(context_size=32768, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[ModelFeature.TOOL_CALL],
     ),
-    'Doubao-pro-128k': ModelConfig(
+    "Doubao-pro-128k": ModelConfig(
         properties=ModelProperties(context_size=131072, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[ModelFeature.TOOL_CALL],
     ),
-    'Doubao-lite-128k': ModelConfig(
-        properties=ModelProperties(context_size=131072, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[]
+    "Doubao-lite-128k": ModelConfig(
+        properties=ModelProperties(context_size=131072, max_tokens=4096, mode=LLMMode.CHAT), features=[]
     ),
-    'Skylark2-pro-4k': ModelConfig(
-        properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[]
+    "Skylark2-pro-4k": ModelConfig(
+        properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT), features=[]
     ),
-    'Llama3-8B': ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT),
-        features=[]
+    "Llama3-8B": ModelConfig(
+        properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT), features=[]
     ),
-    'Llama3-70B': ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT),
-        features=[]
+    "Llama3-70B": ModelConfig(
+        properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT), features=[]
     ),
-    'Moonshot-v1-8k': ModelConfig(
+    "Moonshot-v1-8k": ModelConfig(
         properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[ModelFeature.TOOL_CALL],
     ),
-    'Moonshot-v1-32k': ModelConfig(
+    "Moonshot-v1-32k": ModelConfig(
         properties=ModelProperties(context_size=32768, max_tokens=16384, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[ModelFeature.TOOL_CALL],
     ),
-    'Moonshot-v1-128k': ModelConfig(
+    "Moonshot-v1-128k": ModelConfig(
         properties=ModelProperties(context_size=131072, max_tokens=65536, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[ModelFeature.TOOL_CALL],
     ),
-    'GLM3-130B': ModelConfig(
+    "GLM3-130B": ModelConfig(
         properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[ModelFeature.TOOL_CALL],
     ),
-    'GLM3-130B-Fin': ModelConfig(
+    "GLM3-130B-Fin": ModelConfig(
         properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[ModelFeature.TOOL_CALL],
+    ),
+    "Mistral-7B": ModelConfig(
+        properties=ModelProperties(context_size=8192, max_tokens=2048, mode=LLMMode.CHAT), features=[]
     ),
-    'Mistral-7B': ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=2048, mode=LLMMode.CHAT),
-        features=[]
-    )
 }
 
 
 def get_model_config(credentials: dict) -> ModelConfig:
-    base_model = credentials.get('base_model_name', '')
+    base_model = credentials.get("base_model_name", "")
     model_configs = configs.get(base_model)
     if not model_configs:
         return ModelConfig(
             properties=ModelProperties(
-                context_size=int(credentials.get('context_size', 0)),
-                max_tokens=int(credentials.get('max_tokens', 0)),
-                mode=LLMMode.value_of(credentials.get('mode', 'chat')),
+                context_size=int(credentials.get("context_size", 0)),
+                max_tokens=int(credentials.get("max_tokens", 0)),
+                mode=LLMMode.value_of(credentials.get("mode", "chat")),
             ),
-            features=[]
+            features=[],
         )
     return model_configs
 
 
-def get_v2_req_params(credentials: dict, model_parameters: dict,
-                      stop: list[str] | None = None):
+def get_v2_req_params(credentials: dict, model_parameters: dict, stop: list[str] | None = None):
     req_params = {}
     # predefined properties
     model_configs = get_model_config(credentials)
     if model_configs:
-        req_params['max_prompt_tokens'] = model_configs.properties.context_size
-        req_params['max_new_tokens'] = model_configs.properties.max_tokens
+        req_params["max_prompt_tokens"] = model_configs.properties.context_size
+        req_params["max_new_tokens"] = model_configs.properties.max_tokens
 
     # model parameters
-    if model_parameters.get('max_tokens'):
-        req_params['max_new_tokens'] = model_parameters.get('max_tokens')
-    if model_parameters.get('temperature'):
-        req_params['temperature'] = model_parameters.get('temperature')
-    if model_parameters.get('top_p'):
-        req_params['top_p'] = model_parameters.get('top_p')
-    if model_parameters.get('top_k'):
-        req_params['top_k'] = model_parameters.get('top_k')
-    if model_parameters.get('presence_penalty'):
-        req_params['presence_penalty'] = model_parameters.get(
-            'presence_penalty')
-    if model_parameters.get('frequency_penalty'):
-        req_params['frequency_penalty'] = model_parameters.get(
-            'frequency_penalty')
+    if model_parameters.get("max_tokens"):
+        req_params["max_new_tokens"] = model_parameters.get("max_tokens")
+    if model_parameters.get("temperature"):
+        req_params["temperature"] = model_parameters.get("temperature")
+    if model_parameters.get("top_p"):
+        req_params["top_p"] = model_parameters.get("top_p")
+    if model_parameters.get("top_k"):
+        req_params["top_k"] = model_parameters.get("top_k")
+    if model_parameters.get("presence_penalty"):
+        req_params["presence_penalty"] = model_parameters.get("presence_penalty")
+    if model_parameters.get("frequency_penalty"):
+        req_params["frequency_penalty"] = model_parameters.get("frequency_penalty")
 
     if stop:
-        req_params['stop'] = stop
+        req_params["stop"] = stop
 
     return req_params
 
 
-def get_v3_req_params(credentials: dict, model_parameters: dict,
-                      stop: list[str] | None = None):
+def get_v3_req_params(credentials: dict, model_parameters: dict, stop: list[str] | None = None):
     req_params = {}
     # predefined properties
     model_configs = get_model_config(credentials)
     if model_configs:
-        req_params['max_tokens'] = model_configs.properties.max_tokens
+        req_params["max_tokens"] = model_configs.properties.max_tokens
 
     # model parameters
-    if model_parameters.get('max_tokens'):
-        req_params['max_tokens'] = model_parameters.get('max_tokens')
-    if model_parameters.get('temperature'):
-        req_params['temperature'] = model_parameters.get('temperature')
-    if model_parameters.get('top_p'):
-        req_params['top_p'] = model_parameters.get('top_p')
-    if model_parameters.get('presence_penalty'):
-        req_params['presence_penalty'] = model_parameters.get(
-            'presence_penalty')
-    if model_parameters.get('frequency_penalty'):
-        req_params['frequency_penalty'] = model_parameters.get(
-            'frequency_penalty')
+    if model_parameters.get("max_tokens"):
+        req_params["max_tokens"] = model_parameters.get("max_tokens")
+    if model_parameters.get("temperature"):
+        req_params["temperature"] = model_parameters.get("temperature")
+    if model_parameters.get("top_p"):
+        req_params["top_p"] = model_parameters.get("top_p")
+    if model_parameters.get("presence_penalty"):
+        req_params["presence_penalty"] = model_parameters.get("presence_penalty")
+    if model_parameters.get("frequency_penalty"):
+        req_params["frequency_penalty"] = model_parameters.get("frequency_penalty")
 
     if stop:
-        req_params['stop'] = stop
+        req_params["stop"] = stop
 
     return req_params
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/models.py b/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/models.py
index 74cf26247c..ce4f0c3ab1 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/models.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/models.py
@@ -11,20 +11,18 @@ class ModelConfig(BaseModel):
 
 
 ModelConfigs = {
-    'Doubao-embedding': ModelConfig(
-        properties=ModelProperties(context_size=4096, max_chunks=32)
-    ),
+    "Doubao-embedding": ModelConfig(properties=ModelProperties(context_size=4096, max_chunks=32)),
 }
 
 
 def get_model_config(credentials: dict) -> ModelConfig:
-    base_model = credentials.get('base_model_name', '')
+    base_model = credentials.get("base_model_name", "")
     model_configs = ModelConfigs.get(base_model)
     if not model_configs:
         return ModelConfig(
             properties=ModelProperties(
-                context_size=int(credentials.get('context_size', 0)),
-                max_chunks=int(credentials.get('max_chunks', 0)),
+                context_size=int(credentials.get("context_size", 0)),
+                max_chunks=int(credentials.get("max_chunks", 0)),
             )
         )
     return model_configs
diff --git a/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/text_embedding.py
index d54aeeb0b1..9cba2cb879 100644
--- a/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/text_embedding.py
@@ -28,7 +28,7 @@ from core.model_runtime.model_providers.volcengine_maas.legacy.errors import (
     AuthErrors,
     BadRequestErrors,
     ConnectionErrors,
-    MaasException,
+    MaasError,
     RateLimitErrors,
     ServerUnavailableErrors,
 )
@@ -40,9 +40,9 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
     Model class for VolcengineMaaS text embedding model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -57,37 +57,27 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
 
         return self._generate_v3(model, credentials, texts, user)
 
-    def _generate_v2(self, model: str, credentials: dict,
-                     texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _generate_v2(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         client = MaaSClient.from_credential(credentials)
         resp = MaaSClient.wrap_exception(lambda: client.embeddings(texts))
 
-        usage = self._calc_response_usage(
-            model=model, credentials=credentials, tokens=resp['usage']['total_tokens'])
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=resp["usage"]["total_tokens"])
 
-        result = TextEmbeddingResult(
-            model=model,
-            embeddings=[v['embedding'] for v in resp['data']],
-            usage=usage
-        )
+        result = TextEmbeddingResult(model=model, embeddings=[v["embedding"] for v in resp["data"]], usage=usage)
 
         return result
 
-    def _generate_v3(self, model: str, credentials: dict,
-                     texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _generate_v3(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         client = ArkClientV3.from_credentials(credentials)
         resp = client.embeddings(texts)
 
-        usage = self._calc_response_usage(
-            model=model, credentials=credentials, tokens=resp.usage.total_tokens)
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=resp.usage.total_tokens)
 
-        result = TextEmbeddingResult(
-            model=model,
-            embeddings=[v.embedding for v in resp.data],
-            usage=usage
-        )
+        result = TextEmbeddingResult(model=model, embeddings=[v.embedding for v in resp.data], usage=usage)
 
         return result
 
@@ -120,13 +110,13 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
 
     def _validate_credentials_v2(self, model: str, credentials: dict) -> None:
         try:
-            self._invoke(model=model, credentials=credentials, texts=['ping'])
-        except MaasException as e:
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
+        except MaasError as e:
             raise CredentialsValidateFailedError(e.message)
 
     def _validate_credentials_v3(self, model: str, credentials: dict) -> None:
         try:
-            self._invoke(model=model, credentials=credentials, texts=['ping'])
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
         except Exception as e:
             raise CredentialsValidateFailedError(e)
 
@@ -150,12 +140,12 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
-            generate custom model entities from credentials
+        generate custom model entities from credentials
         """
         model_config = get_model_config(credentials)
         model_properties = {
             ModelPropertyKey.CONTEXT_SIZE: model_config.properties.context_size,
-            ModelPropertyKey.MAX_CHUNKS: model_config.properties.max_chunks
+            ModelPropertyKey.MAX_CHUNKS: model_config.properties.max_chunks,
         }
         entity = AIModelEntity(
             model=model,
@@ -165,10 +155,10 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
             model_properties=model_properties,
             parameter_rules=[],
             pricing=PriceConfig(
-                input=Decimal(credentials.get('input_price', 0)),
-                unit=Decimal(credentials.get('unit', 0)),
-                currency=credentials.get('currency', "USD")
-            )
+                input=Decimal(credentials.get("input_price", 0)),
+                unit=Decimal(credentials.get("unit", 0)),
+                currency=credentials.get("currency", "USD"),
+            ),
         )
 
         return entity
@@ -184,10 +174,7 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -198,7 +185,7 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/wenxin/_common.py b/api/core/model_runtime/model_providers/wenxin/_common.py
index 0230c78b75..d72d1bd83a 100644
--- a/api/core/model_runtime/model_providers/wenxin/_common.py
+++ b/api/core/model_runtime/model_providers/wenxin/_common.py
@@ -11,7 +11,7 @@ from core.model_runtime.model_providers.wenxin.wenxin_errors import (
     RateLimitReachedError,
 )
 
-baidu_access_tokens: dict[str, 'BaiduAccessToken'] = {}
+baidu_access_tokens: dict[str, "BaiduAccessToken"] = {}
 baidu_access_tokens_lock = Lock()
 
 
@@ -22,49 +22,46 @@ class BaiduAccessToken:
 
     def __init__(self, api_key: str) -> None:
         self.api_key = api_key
-        self.access_token = ''
+        self.access_token = ""
         self.expires = datetime.now() + timedelta(days=3)
 
     @staticmethod
     def _get_access_token(api_key: str, secret_key: str) -> str:
         """
-            request access token from Baidu
+        request access token from Baidu
         """
         try:
             response = post(
-                url=f'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={api_key}&client_secret={secret_key}',
-                headers={
-                    'Content-Type': 'application/json',
-                    'Accept': 'application/json'
-                },
+                url=f"https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={api_key}&client_secret={secret_key}",
+                headers={"Content-Type": "application/json", "Accept": "application/json"},
             )
         except Exception as e:
-            raise InvalidAuthenticationError(f'Failed to get access token from Baidu: {e}')
+            raise InvalidAuthenticationError(f"Failed to get access token from Baidu: {e}")
 
         resp = response.json()
-        if 'error' in resp:
-            if resp['error'] == 'invalid_client':
+        if "error" in resp:
+            if resp["error"] == "invalid_client":
                 raise InvalidAPIKeyError(f'Invalid API key or secret key: {resp["error_description"]}')
-            elif resp['error'] == 'unknown_error':
+            elif resp["error"] == "unknown_error":
                 raise InternalServerError(f'Internal server error: {resp["error_description"]}')
-            elif resp['error'] == 'invalid_request':
+            elif resp["error"] == "invalid_request":
                 raise BadRequestError(f'Bad request: {resp["error_description"]}')
-            elif resp['error'] == 'rate_limit_exceeded':
+            elif resp["error"] == "rate_limit_exceeded":
                 raise RateLimitReachedError(f'Rate limit reached: {resp["error_description"]}')
             else:
                 raise Exception(f'Unknown error: {resp["error_description"]}')
 
-        return resp['access_token']
+        return resp["access_token"]
 
     @staticmethod
-    def get_access_token(api_key: str, secret_key: str) -> 'BaiduAccessToken':
+    def get_access_token(api_key: str, secret_key: str) -> "BaiduAccessToken":
         """
-            LLM from Baidu requires access token to invoke the API.
-            however, we have api_key and secret_key, and access token is valid for 30 days.
-            so we can cache the access token for 3 days. (avoid memory leak)
+        LLM from Baidu requires access token to invoke the API.
+        however, we have api_key and secret_key, and access token is valid for 30 days.
+        so we can cache the access token for 3 days. (avoid memory leak)
 
-            it may be more efficient to use a ticker to refresh access token, but it will cause
-            more complexity, so we just refresh access tokens when get_access_token is called.
+        it may be more efficient to use a ticker to refresh access token, but it will cause
+        more complexity, so we just refresh access tokens when get_access_token is called.
         """
 
         # loop up cache, remove expired access token
@@ -79,11 +76,13 @@ class BaiduAccessToken:
             # if access token not in cache, request it
             token = BaiduAccessToken(api_key)
             baidu_access_tokens[api_key] = token
-            # release it to enhance performance
-            # btw, _get_access_token will raise exception if failed, release lock here to avoid deadlock
-            baidu_access_tokens_lock.release()
-            # try to get access token
-            token_str = BaiduAccessToken._get_access_token(api_key, secret_key)
+            try:
+                # try to get access token
+                token_str = BaiduAccessToken._get_access_token(api_key, secret_key)
+            finally:
+                # release it to enhance performance
+                # btw, _get_access_token will raise exception if failed, release lock here to avoid deadlock
+                baidu_access_tokens_lock.release()
             token.access_token = token_str
             token.expires = now + timedelta(days=3)
             return token
@@ -96,49 +95,49 @@ class BaiduAccessToken:
 
 class _CommonWenxin:
     api_bases = {
-        'ernie-bot': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-4k-0205',
-        'ernie-bot-4': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro',
-        'ernie-bot-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions',
-        'ernie-bot-turbo': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant',
-        'ernie-3.5-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions',
-        'ernie-3.5-8k-0205': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-8k-0205',
-        'ernie-3.5-8k-1222': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-8k-1222',
-        'ernie-3.5-4k-0205': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-4k-0205',
-        'ernie-3.5-128k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-128k',
-        'ernie-4.0-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro',
-        'ernie-4.0-8k-latest': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro',
-        'ernie-speed-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie_speed',
-        'ernie-speed-128k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-speed-128k',
-        'ernie-speed-appbuilder': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ai_apaas',
-        'ernie-lite-8k-0922': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant',
-        'ernie-lite-8k-0308': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-lite-8k',
-        'ernie-character-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-char-8k',
-        'ernie-character-8k-0321': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-char-8k',
-        'ernie-4.0-turbo-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k',
-        'ernie-4.0-turbo-8k-preview': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k-preview',
-        'yi_34b_chat': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/yi_34b_chat',
-        'embedding-v1': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/embedding-v1',
-        'bge-large-en': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/bge_large_en',
-        'bge-large-zh': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/bge_large_zh',
-        'tao-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/tao_8k',
+        "ernie-bot": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-4k-0205",
+        "ernie-bot-4": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro",
+        "ernie-bot-8k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions",
+        "ernie-bot-turbo": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant",
+        "ernie-3.5-8k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions",
+        "ernie-3.5-8k-0205": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-8k-0205",
+        "ernie-3.5-8k-1222": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-8k-1222",
+        "ernie-3.5-4k-0205": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-4k-0205",
+        "ernie-3.5-128k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-128k",
+        "ernie-4.0-8k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro",
+        "ernie-4.0-8k-latest": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro",
+        "ernie-speed-8k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie_speed",
+        "ernie-speed-128k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-speed-128k",
+        "ernie-speed-appbuilder": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ai_apaas",
+        "ernie-lite-8k-0922": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant",
+        "ernie-lite-8k-0308": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-lite-8k",
+        "ernie-character-8k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-char-8k",
+        "ernie-character-8k-0321": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-char-8k",
+        "ernie-4.0-turbo-8k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k",
+        "ernie-4.0-turbo-8k-preview": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k-preview",
+        "yi_34b_chat": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/yi_34b_chat",
+        "embedding-v1": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/embedding-v1",
+        "bge-large-en": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/bge_large_en",
+        "bge-large-zh": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/bge_large_zh",
+        "tao-8k": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/tao_8k",
     }
 
     function_calling_supports = [
-        'ernie-bot',
-        'ernie-bot-8k',
-        'ernie-3.5-8k',
-        'ernie-3.5-8k-0205',
-        'ernie-3.5-8k-1222',
-        'ernie-3.5-4k-0205',
-        'ernie-3.5-128k',
-        'ernie-4.0-8k',
-        'ernie-4.0-turbo-8k',
-        'ernie-4.0-turbo-8k-preview',
-        'yi_34b_chat'
+        "ernie-bot",
+        "ernie-bot-8k",
+        "ernie-3.5-8k",
+        "ernie-3.5-8k-0205",
+        "ernie-3.5-8k-1222",
+        "ernie-3.5-4k-0205",
+        "ernie-3.5-128k",
+        "ernie-4.0-8k",
+        "ernie-4.0-turbo-8k",
+        "ernie-4.0-turbo-8k-preview",
+        "yi_34b_chat",
     ]
 
-    api_key: str = ''
-    secret_key: str = ''
+    api_key: str = ""
+    secret_key: str = ""
 
     def __init__(self, api_key: str, secret_key: str):
         self.api_key = api_key
@@ -146,10 +145,7 @@ class _CommonWenxin:
 
     @staticmethod
     def _to_credential_kwargs(credentials: dict) -> dict:
-        credentials_kwargs = {
-            "api_key": credentials['api_key'],
-            "secret_key": credentials['secret_key']
-        }
+        credentials_kwargs = {"api_key": credentials["api_key"], "secret_key": credentials["secret_key"]}
         return credentials_kwargs
 
     def _handle_error(self, code: int, msg: str):
@@ -185,13 +181,13 @@ class _CommonWenxin:
             336105: BadRequestError,
             336200: InternalServerError,
             336303: BadRequestError,
-            337006: BadRequestError
+            337006: BadRequestError,
         }
 
         if code in error_map:
             raise error_map[code](msg)
         else:
-            raise InternalServerError(f'Unknown error: {msg}')
+            raise InternalServerError(f"Unknown error: {msg}")
 
     def _get_access_token(self) -> str:
         token = BaiduAccessToken.get_access_token(self.api_key, self.secret_key)
diff --git a/api/core/model_runtime/model_providers/wenxin/llm/ernie_bot.py b/api/core/model_runtime/model_providers/wenxin/llm/ernie_bot.py
index 8109949b1d..07b970f810 100644
--- a/api/core/model_runtime/model_providers/wenxin/llm/ernie_bot.py
+++ b/api/core/model_runtime/model_providers/wenxin/llm/ernie_bot.py
@@ -15,33 +15,39 @@ from core.model_runtime.model_providers.wenxin.wenxin_errors import (
 
 class ErnieMessage:
     class Role(Enum):
-        USER = 'user'
-        ASSISTANT = 'assistant'
-        FUNCTION = 'function'
-        SYSTEM = 'system'
+        USER = "user"
+        ASSISTANT = "assistant"
+        FUNCTION = "function"
+        SYSTEM = "system"
 
     role: str = Role.USER.value
     content: str
     usage: dict[str, int] = None
-    stop_reason: str = ''
+    stop_reason: str = ""
 
     def to_dict(self) -> dict[str, Any]:
         return {
-            'role': self.role,
-            'content': self.content,
+            "role": self.role,
+            "content": self.content,
         }
 
-    def __init__(self, content: str, role: str = 'user') -> None:
+    def __init__(self, content: str, role: str = "user") -> None:
         self.content = content
         self.role = role
 
+
 class ErnieBotModel(_CommonWenxin):
-
-    def generate(self, model: str, stream: bool, messages: list[ErnieMessage],
-                 parameters: dict[str, Any], timeout: int, tools: list[PromptMessageTool], \
-                 stop: list[str], user: str) \
-        -> Union[Generator[ErnieMessage, None, None], ErnieMessage]:
-
+    def generate(
+        self,
+        model: str,
+        stream: bool,
+        messages: list[ErnieMessage],
+        parameters: dict[str, Any],
+        timeout: int,
+        tools: list[PromptMessageTool],
+        stop: list[str],
+        user: str,
+    ) -> Union[Generator[ErnieMessage, None, None], ErnieMessage]:
         # check parameters
         self._check_parameters(model, parameters, tools, stop)
 
@@ -49,22 +55,23 @@ class ErnieBotModel(_CommonWenxin):
         access_token = self._get_access_token()
 
         # generate request body
-        url = f'{self.api_bases[model]}?access_token={access_token}'
+        url = f"{self.api_bases[model]}?access_token={access_token}"
 
         # clone messages
         messages_cloned = self._copy_messages(messages=messages)
 
         # build body
-        body = self._build_request_body(model, messages=messages_cloned, stream=stream,
-                                        parameters=parameters, tools=tools, stop=stop, user=user)
+        body = self._build_request_body(
+            model, messages=messages_cloned, stream=stream, parameters=parameters, tools=tools, stop=stop, user=user
+        )
         headers = {
-            'Content-Type': 'application/json',
+            "Content-Type": "application/json",
         }
 
         resp = post(url=url, data=dumps(body), headers=headers, stream=stream)
 
         if resp.status_code != 200:
-            raise InternalServerError(f'Failed to invoke ernie bot: {resp.text}')
+            raise InternalServerError(f"Failed to invoke ernie bot: {resp.text}")
 
         if stream:
             return self._handle_chat_stream_generate_response(resp)
@@ -73,10 +80,11 @@ class ErnieBotModel(_CommonWenxin):
     def _copy_messages(self, messages: list[ErnieMessage]) -> list[ErnieMessage]:
         return [ErnieMessage(message.content, message.role) for message in messages]
 
-    def _check_parameters(self, model: str, parameters: dict[str, Any],
-                          tools: list[PromptMessageTool], stop: list[str]) -> None:
+    def _check_parameters(
+        self, model: str, parameters: dict[str, Any], tools: list[PromptMessageTool], stop: list[str]
+    ) -> None:
         if model not in self.api_bases:
-            raise BadRequestError(f'Invalid model: {model}')
+            raise BadRequestError(f"Invalid model: {model}")
 
         # if model not in self.function_calling_supports and tools is not None and len(tools) > 0:
         #     raise BadRequestError(f'Model {model} does not support calling function.')
@@ -85,86 +93,106 @@ class ErnieBotModel(_CommonWenxin):
         # so, we just disable function calling for now.
 
         if tools is not None and len(tools) > 0:
-            raise BadRequestError('function calling is not supported yet.')
+            raise BadRequestError("function calling is not supported yet.")
 
         if stop is not None:
             if len(stop) > 4:
-                raise BadRequestError('stop list should not exceed 4 items.')
+                raise BadRequestError("stop list should not exceed 4 items.")
 
             for s in stop:
                 if len(s) > 20:
-                    raise BadRequestError('stop item should not exceed 20 characters.')
+                    raise BadRequestError("stop item should not exceed 20 characters.")
 
-    def _build_request_body(self, model: str, messages: list[ErnieMessage], stream: bool, parameters: dict[str, Any],
-                            tools: list[PromptMessageTool], stop: list[str], user: str) -> dict[str, Any]:
+    def _build_request_body(
+        self,
+        model: str,
+        messages: list[ErnieMessage],
+        stream: bool,
+        parameters: dict[str, Any],
+        tools: list[PromptMessageTool],
+        stop: list[str],
+        user: str,
+    ) -> dict[str, Any]:
         # if model in self.function_calling_supports:
         #     return self._build_function_calling_request_body(model, messages, parameters, tools, stop, user)
         return self._build_chat_request_body(model, messages, stream, parameters, stop, user)
 
-    def _build_function_calling_request_body(self, model: str, messages: list[ErnieMessage], stream: bool,
-                                                parameters: dict[str, Any], tools: list[PromptMessageTool],
-                                                stop: list[str], user: str) \
-        -> dict[str, Any]:
+    def _build_function_calling_request_body(
+        self,
+        model: str,
+        messages: list[ErnieMessage],
+        stream: bool,
+        parameters: dict[str, Any],
+        tools: list[PromptMessageTool],
+        stop: list[str],
+        user: str,
+    ) -> dict[str, Any]:
         if len(messages) % 2 == 0:
-            raise BadRequestError('The number of messages should be odd.')
-        if messages[0].role == 'function':
-            raise BadRequestError('The first message should be user message.')
+            raise BadRequestError("The number of messages should be odd.")
+        if messages[0].role == "function":
+            raise BadRequestError("The first message should be user message.")
 
         """
         TODO: implement function calling
         """
 
-    def _build_chat_request_body(self, model: str, messages: list[ErnieMessage], stream: bool,
-                                 parameters: dict[str, Any], stop: list[str], user: str) \
-        -> dict[str, Any]:
+    def _build_chat_request_body(
+        self,
+        model: str,
+        messages: list[ErnieMessage],
+        stream: bool,
+        parameters: dict[str, Any],
+        stop: list[str],
+        user: str,
+    ) -> dict[str, Any]:
         if len(messages) == 0:
-            raise BadRequestError('The number of messages should not be zero.')
+            raise BadRequestError("The number of messages should not be zero.")
 
         # check if the first element is system, shift it
-        system_message = ''
-        if messages[0].role == 'system':
+        system_message = ""
+        if messages[0].role == "system":
             message = messages.pop(0)
             system_message = message.content
 
         if len(messages) % 2 == 0:
-            raise BadRequestError('The number of messages should be odd.')
-        if messages[0].role != 'user':
-            raise BadRequestError('The first message should be user message.')
+            raise BadRequestError("The number of messages should be odd.")
+        if messages[0].role != "user":
+            raise BadRequestError("The first message should be user message.")
         body = {
-            'messages': [message.to_dict() for message in messages],
-            'stream': stream,
-            'stop': stop,
-            'user_id': user,
-            **parameters
+            "messages": [message.to_dict() for message in messages],
+            "stream": stream,
+            "stop": stop,
+            "user_id": user,
+            **parameters,
         }
 
-        if 'max_tokens' in parameters and type(parameters['max_tokens']) == int:
-            body['max_output_tokens'] = parameters['max_tokens']
+        if "max_tokens" in parameters and type(parameters["max_tokens"]) == int:
+            body["max_output_tokens"] = parameters["max_tokens"]
 
-        if 'presence_penalty' in parameters and type(parameters['presence_penalty']) == float:
-            body['penalty_score'] = parameters['presence_penalty']
+        if "presence_penalty" in parameters and type(parameters["presence_penalty"]) == float:
+            body["penalty_score"] = parameters["presence_penalty"]
 
         if system_message:
-            body['system'] = system_message
+            body["system"] = system_message
 
         return body
 
     def _handle_chat_generate_response(self, response: Response) -> ErnieMessage:
         data = response.json()
-        if 'error_code' in data:
-            code = data['error_code']
-            msg = data['error_msg']
+        if "error_code" in data:
+            code = data["error_code"]
+            msg = data["error_msg"]
             # raise error
             self._handle_error(code, msg)
 
-        result = data['result']
-        usage = data['usage']
+        result = data["result"]
+        usage = data["usage"]
 
-        message = ErnieMessage(content=result, role='assistant')
+        message = ErnieMessage(content=result, role="assistant")
         message.usage = {
-            'prompt_tokens': usage['prompt_tokens'],
-            'completion_tokens': usage['completion_tokens'],
-            'total_tokens': usage['total_tokens']
+            "prompt_tokens": usage["prompt_tokens"],
+            "completion_tokens": usage["completion_tokens"],
+            "total_tokens": usage["total_tokens"],
         }
 
         return message
@@ -173,19 +201,19 @@ class ErnieBotModel(_CommonWenxin):
         for line in response.iter_lines():
             if len(line) == 0:
                 continue
-            line = line.decode('utf-8')
-            if line[0] == '{':
+            line = line.decode("utf-8")
+            if line[0] == "{":
                 try:
                     data = loads(line)
-                    if 'error_code' in data:
-                        code = data['error_code']
-                        msg = data['error_msg']
+                    if "error_code" in data:
+                        code = data["error_code"]
+                        msg = data["error_msg"]
                         # raise error
                         self._handle_error(code, msg)
                 except Exception as e:
-                    raise InternalServerError(f'Failed to parse response: {e}')
+                    raise InternalServerError(f"Failed to parse response: {e}")
 
-            if line.startswith('data:'):
+            if line.startswith("data:"):
                 line = line[5:].strip()
             else:
                 continue
@@ -195,23 +223,23 @@ class ErnieBotModel(_CommonWenxin):
             try:
                 data = loads(line)
             except Exception as e:
-                raise InternalServerError(f'Failed to parse response: {e}')
+                raise InternalServerError(f"Failed to parse response: {e}")
 
-            result = data['result']
-            is_end = data['is_end']
+            result = data["result"]
+            is_end = data["is_end"]
 
             if is_end:
-                usage = data['usage']
-                finish_reason = data.get('finish_reason', None)
-                message = ErnieMessage(content=result, role='assistant')
+                usage = data["usage"]
+                finish_reason = data.get("finish_reason", None)
+                message = ErnieMessage(content=result, role="assistant")
                 message.usage = {
-                    'prompt_tokens': usage['prompt_tokens'],
-                    'completion_tokens': usage['completion_tokens'],
-                    'total_tokens': usage['total_tokens']
+                    "prompt_tokens": usage["prompt_tokens"],
+                    "completion_tokens": usage["completion_tokens"],
+                    "total_tokens": usage["total_tokens"],
                 }
                 message.stop_reason = finish_reason
 
                 yield message
             else:
-                message = ErnieMessage(content=result, role='assistant')
+                message = ErnieMessage(content=result, role="assistant")
                 yield message
diff --git a/api/core/model_runtime/model_providers/wenxin/llm/llm.py b/api/core/model_runtime/model_providers/wenxin/llm/llm.py
index 140606298c..f7c160b6b4 100644
--- a/api/core/model_runtime/model_providers/wenxin/llm/llm.py
+++ b/api/core/model_runtime/model_providers/wenxin/llm/llm.py
@@ -28,44 +28,84 @@ if you are not sure about the structure.
 </instructions>
 
 You should also complete the text started with ``` but not tell ``` directly.
-"""
+"""  # noqa: E501
+
 
 class ErnieBotLargeLanguageModel(LargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict, 
-                prompt_messages: list[PromptMessage], model_parameters: dict, 
-                tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
-                stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
-        return self._generate(model=model, credentials=credentials, prompt_messages=prompt_messages,
-                                model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
+        return self._generate(
+            model=model,
+            credentials=credentials,
+            prompt_messages=prompt_messages,
+            model_parameters=model_parameters,
+            tools=tools,
+            stop=stop,
+            stream=stream,
+            user=user,
+        )
 
-    def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                           model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
-                           stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,
-                           callbacks: list[Callback] = None) -> Union[LLMResult, Generator]:
+    def _code_block_mode_wrapper(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: list[Callback] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Code block mode wrapper for invoking large language model
         """
-        if 'response_format' in model_parameters and model_parameters['response_format'] in ['JSON', 'XML']:
-            response_format = model_parameters['response_format']
+        if "response_format" in model_parameters and model_parameters["response_format"] in {"JSON", "XML"}:
+            response_format = model_parameters["response_format"]
             stop = stop or []
-            self._transform_json_prompts(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user, response_format)
-            model_parameters.pop('response_format')
+            self._transform_json_prompts(
+                model, credentials, prompt_messages, model_parameters, tools, stop, stream, user, response_format
+            )
+            model_parameters.pop("response_format")
             if stream:
                 return self._code_block_mode_stream_processor(
                     model=model,
                     prompt_messages=prompt_messages,
-                    input_generator=self._invoke(model=model, credentials=credentials, prompt_messages=prompt_messages,
-                                                    model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
+                    input_generator=self._invoke(
+                        model=model,
+                        credentials=credentials,
+                        prompt_messages=prompt_messages,
+                        model_parameters=model_parameters,
+                        tools=tools,
+                        stop=stop,
+                        stream=stream,
+                        user=user,
+                    ),
                 )
-            
+
         return self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
-    def _transform_json_prompts(self, model: str, credentials: dict, 
-                                prompt_messages: list[PromptMessage], model_parameters: dict, 
-                                tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
-                                stream: bool = True, user: str | None = None, response_format: str = 'JSON') \
-                            -> None:
+    def _transform_json_prompts(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+        response_format: str = "JSON",
+    ) -> None:
         """
         Transform json prompts to model prompts
         """
@@ -74,34 +114,44 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
         if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
             # override the system message
             prompt_messages[0] = SystemPromptMessage(
-                content=ERNIE_BOT_BLOCK_MODE_PROMPT
-                    .replace("{{instructions}}", prompt_messages[0].content)
-                    .replace("{{block}}", response_format)
+                content=ERNIE_BOT_BLOCK_MODE_PROMPT.replace("{{instructions}}", prompt_messages[0].content).replace(
+                    "{{block}}", response_format
+                )
             )
         else:
             # insert the system message
-            prompt_messages.insert(0, SystemPromptMessage(
-                content=ERNIE_BOT_BLOCK_MODE_PROMPT
-                    .replace("{{instructions}}", f"Please output a valid {response_format} object.")
-                    .replace("{{block}}", response_format)
-            ))
+            prompt_messages.insert(
+                0,
+                SystemPromptMessage(
+                    content=ERNIE_BOT_BLOCK_MODE_PROMPT.replace(
+                        "{{instructions}}", f"Please output a valid {response_format} object."
+                    ).replace("{{block}}", response_format)
+                ),
+            )
 
         if len(prompt_messages) > 0 and isinstance(prompt_messages[-1], UserPromptMessage):
             # add ```JSON\n to the last message
             prompt_messages[-1].content += "\n```JSON\n{\n"
         else:
             # append a user message
-            prompt_messages.append(UserPromptMessage(
-                content="```JSON\n{\n"
-            ))
+            prompt_messages.append(UserPromptMessage(content="```JSON\n{\n"))
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: list[PromptMessageTool] | None = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool] | None = None,
+    ) -> int:
         # tools is not supported yet
         return self._num_tokens_from_messages(prompt_messages)
 
-    def _num_tokens_from_messages(self, messages: list[PromptMessage],) -> int:
+    def _num_tokens_from_messages(
+        self,
+        messages: list[PromptMessage],
+    ) -> int:
         """Calculate num tokens for baichuan model"""
+
         def tokens(text: str):
             return self._get_num_tokens_by_gpt2(text)
 
@@ -113,10 +163,10 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
             num_tokens += tokens_per_message
             for key, value in message.items():
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
 
                     value = text
 
@@ -126,36 +176,53 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
         return num_tokens
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        api_key = credentials['api_key']
-        secret_key = credentials['secret_key']
+        api_key = credentials["api_key"]
+        secret_key = credentials["secret_key"]
         try:
             BaiduAccessToken.get_access_token(api_key, secret_key)
         except Exception as e:
-            raise CredentialsValidateFailedError(f'Credentials validation failed: {e}')
+            raise CredentialsValidateFailedError(f"Credentials validation failed: {e}")
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], 
-                 model_parameters: dict, tools: list[PromptMessageTool] | None = None, 
-                 stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         instance = ErnieBotModel(
-            api_key=credentials['api_key'],
-            secret_key=credentials['secret_key'],
+            api_key=credentials["api_key"],
+            secret_key=credentials["secret_key"],
         )
 
-        user = user if user else 'ErnieBotDefault'
+        user = user or "ErnieBotDefault"
 
         # convert prompt messages to baichuan messages
         messages = [
             ErnieMessage(
-                content=message.content if isinstance(message.content, str) else ''.join([
-                    content.data for content in message.content
-                ]),
-                role=message.role.value
-            ) for message in prompt_messages
+                content=message.content
+                if isinstance(message.content, str)
+                else "".join([content.data for content in message.content]),
+                role=message.role.value,
+            )
+            for message in prompt_messages
         ]
 
         # invoke model
-        response = instance.generate(model=model, stream=stream, messages=messages, parameters=model_parameters, timeout=60, tools=tools, stop=stop, user=user)
+        response = instance.generate(
+            model=model,
+            stream=stream,
+            messages=messages,
+            parameters=model_parameters,
+            timeout=60,
+            tools=tools,
+            stop=stop,
+            user=user,
+        )
 
         if stream:
             return self._handle_chat_generate_stream_response(model, prompt_messages, credentials, response)
@@ -180,43 +247,49 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
             message_dict = {"role": "system", "content": message.content}
         else:
             raise ValueError(f"Unknown message type {type(message)}")
-        
+
         return message_dict
 
-    def _handle_chat_generate_response(self, model: str,
-                                       prompt_messages: list[PromptMessage],
-                                       credentials: dict,
-                                       response: ErnieMessage) -> LLMResult:
+    def _handle_chat_generate_response(
+        self, model: str, prompt_messages: list[PromptMessage], credentials: dict, response: ErnieMessage
+    ) -> LLMResult:
         # convert baichuan message to llm result
-        usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=response.usage['prompt_tokens'], completion_tokens=response.usage['completion_tokens'])
+        usage = self._calc_response_usage(
+            model=model,
+            credentials=credentials,
+            prompt_tokens=response.usage["prompt_tokens"],
+            completion_tokens=response.usage["completion_tokens"],
+        )
         return LLMResult(
             model=model,
             prompt_messages=prompt_messages,
-            message=AssistantPromptMessage(
-                content=response.content,
-                tool_calls=[]
-            ),
+            message=AssistantPromptMessage(content=response.content, tool_calls=[]),
             usage=usage,
         )
 
-    def _handle_chat_generate_stream_response(self, model: str,
-                                              prompt_messages: list[PromptMessage],
-                                              credentials: dict,
-                                              response: Generator[ErnieMessage, None, None]) -> Generator:
+    def _handle_chat_generate_stream_response(
+        self,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        credentials: dict,
+        response: Generator[ErnieMessage, None, None],
+    ) -> Generator:
         for message in response:
             if message.usage:
-                usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=message.usage['prompt_tokens'], completion_tokens=message.usage['completion_tokens'])
+                usage = self._calc_response_usage(
+                    model=model,
+                    credentials=credentials,
+                    prompt_tokens=message.usage["prompt_tokens"],
+                    completion_tokens=message.usage["completion_tokens"],
+                )
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=0,
-                        message=AssistantPromptMessage(
-                            content=message.content,
-                            tool_calls=[]
-                        ),
+                        message=AssistantPromptMessage(content=message.content, tool_calls=[]),
                         usage=usage,
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
             else:
@@ -225,11 +298,8 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=0,
-                        message=AssistantPromptMessage(
-                            content=message.content,
-                            tool_calls=[]
-                        ),
-                        finish_reason=message.stop_reason if message.stop_reason else None,
+                        message=AssistantPromptMessage(content=message.content, tool_calls=[]),
+                        finish_reason=message.stop_reason or None,
                     ),
                 )
 
diff --git a/api/core/model_runtime/model_providers/wenxin/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/wenxin/text_embedding/text_embedding.py
index 10ac1a1861..4d6f6dccd0 100644
--- a/api/core/model_runtime/model_providers/wenxin/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/wenxin/text_embedding/text_embedding.py
@@ -29,38 +29,38 @@ class TextEmbedding:
 class WenxinTextEmbedding(_CommonWenxin, TextEmbedding):
     def embed_documents(self, model: str, texts: list[str], user: str) -> (list[list[float]], int, int):
         access_token = self._get_access_token()
-        url = f'{self.api_bases[model]}?access_token={access_token}'
+        url = f"{self.api_bases[model]}?access_token={access_token}"
         body = self._build_embed_request_body(model, texts, user)
         headers = {
-            'Content-Type': 'application/json',
+            "Content-Type": "application/json",
         }
 
         resp = post(url, data=dumps(body), headers=headers)
         if resp.status_code != 200:
-            raise InternalServerError(f'Failed to invoke ernie bot: {resp.text}')
+            raise InternalServerError(f"Failed to invoke ernie bot: {resp.text}")
         return self._handle_embed_response(model, resp)
 
     def _build_embed_request_body(self, model: str, texts: list[str], user: str) -> dict[str, Any]:
         if len(texts) == 0:
-            raise BadRequestError('The number of texts should not be zero.')
+            raise BadRequestError("The number of texts should not be zero.")
         body = {
-            'input': texts,
-            'user_id': user,
+            "input": texts,
+            "user_id": user,
         }
         return body
 
     def _handle_embed_response(self, model: str, response: Response) -> (list[list[float]], int, int):
         data = response.json()
-        if 'error_code' in data:
-            code = data['error_code']
-            msg = data['error_msg']
+        if "error_code" in data:
+            code = data["error_code"]
+            msg = data["error_msg"]
             # raise error
             self._handle_error(code, msg)
 
-        embeddings = [v['embedding'] for v in data['data']]
-        _usage = data['usage']
-        tokens = _usage['prompt_tokens']
-        total_tokens = _usage['total_tokens']
+        embeddings = [v["embedding"] for v in data["data"]]
+        _usage = data["usage"]
+        tokens = _usage["prompt_tokens"]
+        total_tokens = _usage["total_tokens"]
 
         return embeddings, tokens, total_tokens
 
@@ -69,22 +69,23 @@ class WenxinTextEmbeddingModel(TextEmbeddingModel):
     def _create_text_embedding(self, api_key: str, secret_key: str) -> TextEmbedding:
         return WenxinTextEmbedding(api_key, secret_key)
 
-    def _invoke(self, model: str, credentials: dict, texts: list[str],
-                user: Optional[str] = None) -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
-                Invoke text embedding model
+        Invoke text embedding model
 
-                :param model: model name
-                :param credentials: model credentials
-                :param texts: texts to embed
-                :param user: unique user id
-                :return: embeddings result
-                """
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :return: embeddings result
+        """
 
-        api_key = credentials['api_key']
-        secret_key = credentials['secret_key']
+        api_key = credentials["api_key"]
+        secret_key = credentials["secret_key"]
         embedding: TextEmbedding = self._create_text_embedding(api_key, secret_key)
-        user = user if user else 'ErnieBotDefault'
+        user = user or "ErnieBotDefault"
 
         context_size = self._get_context_size(model, credentials)
         max_chunks = self._get_max_chunks(model, credentials)
@@ -94,7 +95,6 @@ class WenxinTextEmbeddingModel(TextEmbeddingModel):
         used_total_tokens = 0
 
         for i, text in enumerate(texts):
-
             # Here token count is only an approximation based on the GPT2 tokenizer
             num_tokens = self._get_num_tokens_by_gpt2(text)
 
@@ -110,9 +110,8 @@ class WenxinTextEmbeddingModel(TextEmbeddingModel):
         _iter = range(0, len(inputs), max_chunks)
         for i in _iter:
             embeddings_batch, _used_tokens, _total_used_tokens = embedding.embed_documents(
-                model,
-                inputs[i: i + max_chunks],
-                user)
+                model, inputs[i : i + max_chunks], user
+            )
             used_tokens += _used_tokens
             used_total_tokens += _total_used_tokens
             batched_embeddings += embeddings_batch
@@ -142,12 +141,12 @@ class WenxinTextEmbeddingModel(TextEmbeddingModel):
         return total_num_tokens
 
     def validate_credentials(self, model: str, credentials: Mapping) -> None:
-        api_key = credentials['api_key']
-        secret_key = credentials['secret_key']
+        api_key = credentials["api_key"]
+        secret_key = credentials["secret_key"]
         try:
             BaiduAccessToken.get_access_token(api_key, secret_key)
         except Exception as e:
-            raise CredentialsValidateFailedError(f'Credentials validation failed: {e}')
+            raise CredentialsValidateFailedError(f"Credentials validation failed: {e}")
 
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
@@ -164,10 +163,7 @@ class WenxinTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -178,7 +174,7 @@ class WenxinTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/wenxin/wenxin.py b/api/core/model_runtime/model_providers/wenxin/wenxin.py
index 04845d06bc..895af20bc8 100644
--- a/api/core/model_runtime/model_providers/wenxin/wenxin.py
+++ b/api/core/model_runtime/model_providers/wenxin/wenxin.py
@@ -6,6 +6,7 @@ from core.model_runtime.model_providers.__base.model_provider import ModelProvid
 
 logger = logging.getLogger(__name__)
 
+
 class WenxinProvider(ModelProvider):
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
@@ -19,12 +20,9 @@ class WenxinProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.LLM)
 
             # Use `ernie-bot` model for validate,
-            model_instance.validate_credentials(
-                model='ernie-bot',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="ernie-bot", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/wenxin/wenxin_errors.py b/api/core/model_runtime/model_providers/wenxin/wenxin_errors.py
index 0fbd0f55ec..bd074e0477 100644
--- a/api/core/model_runtime/model_providers/wenxin/wenxin_errors.py
+++ b/api/core/model_runtime/model_providers/wenxin/wenxin_errors.py
@@ -18,40 +18,37 @@ def invoke_error_mapping() -> dict[type[InvokeError], list[type[Exception]]]:
     :return: Invoke error mapping
     """
     return {
-        InvokeConnectionError: [
-        ],
-        InvokeServerUnavailableError: [
-            InternalServerError
-        ],
-        InvokeRateLimitError: [
-            RateLimitReachedError
-        ],
+        InvokeConnectionError: [],
+        InvokeServerUnavailableError: [InternalServerError],
+        InvokeRateLimitError: [RateLimitReachedError],
         InvokeAuthorizationError: [
             InvalidAuthenticationError,
-            InsufficientAccountBalance,
+            InsufficientAccountBalanceError,
             InvalidAPIKeyError,
         ],
-        InvokeBadRequestError: [
-            BadRequestError,
-            KeyError
-        ]
+        InvokeBadRequestError: [BadRequestError, KeyError],
     }
 
 
 class InvalidAuthenticationError(Exception):
     pass
 
+
 class InvalidAPIKeyError(Exception):
     pass
 
+
 class RateLimitReachedError(Exception):
     pass
 
-class InsufficientAccountBalance(Exception):
+
+class InsufficientAccountBalanceError(Exception):
     pass
 
+
 class InternalServerError(Exception):
     pass
 
+
 class BadRequestError(Exception):
-    pass
\ No newline at end of file
+    pass
diff --git a/api/core/model_runtime/model_providers/xinference/llm/llm.py b/api/core/model_runtime/model_providers/xinference/llm/llm.py
index 4760e8f118..4fadda5df5 100644
--- a/api/core/model_runtime/model_providers/xinference/llm/llm.py
+++ b/api/core/model_runtime/model_providers/xinference/llm/llm.py
@@ -65,88 +65,109 @@ from core.model_runtime.utils import helper
 
 
 class XinferenceAILargeLanguageModel(LargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                model_parameters: dict, tools: list[PromptMessageTool] | None = None,
-                stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         """
-            invoke LLM
+        invoke LLM
 
-            see `core.model_runtime.model_providers.__base.large_language_model.LargeLanguageModel._invoke`
+        see `core.model_runtime.model_providers.__base.large_language_model.LargeLanguageModel._invoke`
         """
-        if 'temperature' in model_parameters:
-            if model_parameters['temperature'] < 0.01:
-                model_parameters['temperature'] = 0.01
-            elif model_parameters['temperature'] > 1.0:
-                model_parameters['temperature'] = 0.99
+        if "temperature" in model_parameters:
+            if model_parameters["temperature"] < 0.01:
+                model_parameters["temperature"] = 0.01
+            elif model_parameters["temperature"] > 1.0:
+                model_parameters["temperature"] = 0.99
 
         return self._generate(
-            model=model, credentials=credentials, prompt_messages=prompt_messages, model_parameters=model_parameters,
-            tools=tools, stop=stop, stream=stream, user=user,
+            model=model,
+            credentials=credentials,
+            prompt_messages=prompt_messages,
+            model_parameters=model_parameters,
+            tools=tools,
+            stop=stop,
+            stream=stream,
+            user=user,
             extra_model_kwargs=XinferenceHelper.get_xinference_extra_parameter(
-                server_url=credentials['server_url'],
-                model_uid=credentials['model_uid'],
-                api_key=credentials.get('api_key'),
-            )
+                server_url=credentials["server_url"],
+                model_uid=credentials["model_uid"],
+                api_key=credentials.get("api_key"),
+            ),
         )
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
-            validate credentials
+        validate credentials
 
-            credentials should be like:
-            {
-                'model_type': 'text-generation',
-                'server_url': 'server url',
-                'model_uid': 'model uid',
-            }
+        credentials should be like:
+        {
+            'model_type': 'text-generation',
+            'server_url': 'server url',
+            'model_uid': 'model uid',
+        }
         """
         try:
-            if "/" in credentials['model_uid'] or "?" in credentials['model_uid'] or "#" in credentials['model_uid']:
+            if "/" in credentials["model_uid"] or "?" in credentials["model_uid"] or "#" in credentials["model_uid"]:
                 raise CredentialsValidateFailedError("model_uid should not contain /, ?, or #")
 
             extra_param = XinferenceHelper.get_xinference_extra_parameter(
-                server_url=credentials['server_url'],
-                model_uid=credentials['model_uid'],
-                api_key=credentials.get('api_key')
+                server_url=credentials["server_url"],
+                model_uid=credentials["model_uid"],
+                api_key=credentials.get("api_key"),
             )
-            if 'completion_type' not in credentials:
-                if 'chat' in extra_param.model_ability:
-                    credentials['completion_type'] = 'chat'
-                elif 'generate' in extra_param.model_ability:
-                    credentials['completion_type'] = 'completion'
+            if "completion_type" not in credentials:
+                if "chat" in extra_param.model_ability:
+                    credentials["completion_type"] = "chat"
+                elif "generate" in extra_param.model_ability:
+                    credentials["completion_type"] = "completion"
                 else:
                     raise ValueError(
-                        f'xinference model ability {extra_param.model_ability} is not supported, check if you have the right model type')
+                        f"xinference model ability {extra_param.model_ability} is not supported,"
+                        f" check if you have the right model type"
+                    )
 
             if extra_param.support_function_call:
-                credentials['support_function_call'] = True
+                credentials["support_function_call"] = True
 
             if extra_param.support_vision:
-                credentials['support_vision'] = True
+                credentials["support_vision"] = True
 
             if extra_param.context_length:
-                credentials['context_length'] = extra_param.context_length
+                credentials["context_length"] = extra_param.context_length
 
         except RuntimeError as e:
-            raise CredentialsValidateFailedError(f'Xinference credentials validate failed: {e}')
+            raise CredentialsValidateFailedError(f"Xinference credentials validate failed: {e}")
         except KeyError as e:
-            raise CredentialsValidateFailedError(f'Xinference credentials validate failed: {e}')
+            raise CredentialsValidateFailedError(f"Xinference credentials validate failed: {e}")
         except Exception as e:
             raise e
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: list[PromptMessageTool] | None = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool] | None = None,
+    ) -> int:
         """
-            get number of tokens
+        get number of tokens
 
-            cause XinferenceAI LLM is a customized model, we could net detect which tokenizer to use
-            so we just take the GPT2 tokenizer as default
+        cause XinferenceAI LLM is a customized model, we could net detect which tokenizer to use
+        so we just take the GPT2 tokenizer as default
         """
         return self._num_tokens_from_messages(prompt_messages, tools)
 
-    def _num_tokens_from_messages(self, messages: list[PromptMessage], tools: list[PromptMessageTool],
-                                  is_completion_model: bool = False) -> int:
+    def _num_tokens_from_messages(
+        self, messages: list[PromptMessage], tools: list[PromptMessageTool], is_completion_model: bool = False
+    ) -> int:
         def tokens(text: str):
             return self._get_num_tokens_by_gpt2(text)
 
@@ -162,10 +183,10 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             num_tokens += tokens_per_message
             for key, value in message.items():
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
 
                     value = text
 
@@ -217,30 +238,30 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         num_tokens = 0
         for tool in tools:
             # calculate num tokens for function object
-            num_tokens += tokens('name')
+            num_tokens += tokens("name")
             num_tokens += tokens(tool.name)
-            num_tokens += tokens('description')
+            num_tokens += tokens("description")
             num_tokens += tokens(tool.description)
             parameters = tool.parameters
-            num_tokens += tokens('parameters')
-            num_tokens += tokens('type')
+            num_tokens += tokens("parameters")
+            num_tokens += tokens("type")
             num_tokens += tokens(parameters.get("type"))
-            if 'properties' in parameters:
-                num_tokens += tokens('properties')
-                for key, value in parameters.get('properties').items():
+            if "properties" in parameters:
+                num_tokens += tokens("properties")
+                for key, value in parameters.get("properties").items():
                     num_tokens += tokens(key)
                     for field_key, field_value in value.items():
                         num_tokens += tokens(field_key)
-                        if field_key == 'enum':
+                        if field_key == "enum":
                             for enum_field in field_value:
                                 num_tokens += 3
                                 num_tokens += tokens(enum_field)
                         else:
                             num_tokens += tokens(field_key)
                             num_tokens += tokens(str(field_value))
-            if 'required' in parameters:
-                num_tokens += tokens('required')
-                for required_field in parameters['required']:
+            if "required" in parameters:
+                num_tokens += tokens("required")
+                for required_field in parameters["required"]:
                     num_tokens += 3
                     num_tokens += tokens(required_field)
 
@@ -248,18 +269,14 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
     def _convert_prompt_message_to_text(self, message: list[PromptMessage]) -> str:
         """
-            convert prompt message to text
+        convert prompt message to text
         """
-        text = ''
+        text = ""
         for item in message:
-            if isinstance(item, UserPromptMessage):
-                text += item.content
-            elif isinstance(item, SystemPromptMessage):
-                text += item.content
-            elif isinstance(item, AssistantPromptMessage):
+            if isinstance(item, UserPromptMessage | SystemPromptMessage | AssistantPromptMessage):
                 text += item.content
             else:
-                raise NotImplementedError(f'PromptMessage type {type(item)} is not supported')
+                raise NotImplementedError(f"PromptMessage type {type(item)} is not supported")
         return text
 
     def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict:
@@ -275,19 +292,13 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
                         message_content = cast(PromptMessageContent, message_content)
-                        sub_message_dict = {
-                            "type": "text",
-                            "text": message_content.data
-                        }
+                        sub_message_dict = {"type": "text", "text": message_content.data}
                         sub_messages.append(sub_message_dict)
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
                         sub_message_dict = {
                             "type": "image_url",
-                            "image_url": {
-                                "url": message_content.data,
-                                "detail": message_content.detail.value
-                            }
+                            "image_url": {"url": message_content.data, "detail": message_content.detail.value},
                         }
                         sub_messages.append(sub_message_dict)
                 message_dict = {"role": "user", "content": sub_messages}
@@ -297,7 +308,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             if message.tool_calls and len(message.tool_calls) > 0:
                 message_dict["function_call"] = {
                     "name": message.tool_calls[0].function.name,
-                    "arguments": message.tool_calls[0].function.arguments
+                    "arguments": message.tool_calls[0].function.arguments,
                 }
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
@@ -312,151 +323,145 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
         rules = [
             ParameterRule(
-                name='temperature',
+                name="temperature",
                 type=ParameterType.FLOAT,
-                use_template='temperature',
-                label=I18nObject(
-                    zh_Hans='温度',
-                    en_US='Temperature'
-                ),
+                use_template="temperature",
+                label=I18nObject(zh_Hans="温度", en_US="Temperature"),
             ),
             ParameterRule(
-                name='top_p',
+                name="top_p",
                 type=ParameterType.FLOAT,
-                use_template='top_p',
-                label=I18nObject(
-                    zh_Hans='Top P',
-                    en_US='Top P'
-                )
+                use_template="top_p",
+                label=I18nObject(zh_Hans="Top P", en_US="Top P"),
             ),
             ParameterRule(
-                name='max_tokens',
+                name="max_tokens",
                 type=ParameterType.INT,
-                use_template='max_tokens',
+                use_template="max_tokens",
                 min=1,
-                max=credentials.get('context_length', 2048),
+                max=credentials.get("context_length", 2048),
                 default=512,
-                label=I18nObject(
-                    zh_Hans='最大生成长度',
-                    en_US='Max Tokens'
-                )
+                label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"),
             ),
             ParameterRule(
                 name=DefaultParameterName.PRESENCE_PENALTY,
                 use_template=DefaultParameterName.PRESENCE_PENALTY,
                 type=ParameterType.FLOAT,
                 label=I18nObject(
-                    en_US='Presence Penalty',
-                    zh_Hans='存在惩罚',
+                    en_US="Presence Penalty",
+                    zh_Hans="存在惩罚",
                 ),
                 required=False,
                 help=I18nObject(
-                    en_US='Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they '
-                          'appear in the text so far, increasing the model\'s likelihood to talk about new topics.',
-                    zh_Hans='介于 -2.0 和 2.0 之间的数字。正值会根据新词是否已出现在文本中对其进行惩罚，从而增加模型谈论新话题的可能性。'
+                    en_US="Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they "
+                    "appear in the text so far, increasing the model's likelihood to talk about new topics.",
+                    zh_Hans="介于 -2.0 和 2.0 之间的数字。正值会根据新词是否已出现在文本中对其进行惩罚，"
+                    "从而增加模型谈论新话题的可能性。",
                 ),
                 default=0.0,
                 min=-2.0,
                 max=2.0,
-                precision=2
+                precision=2,
             ),
             ParameterRule(
                 name=DefaultParameterName.FREQUENCY_PENALTY,
                 use_template=DefaultParameterName.FREQUENCY_PENALTY,
                 type=ParameterType.FLOAT,
                 label=I18nObject(
-                    en_US='Frequency Penalty',
-                    zh_Hans='频率惩罚',
+                    en_US="Frequency Penalty",
+                    zh_Hans="频率惩罚",
                 ),
                 required=False,
                 help=I18nObject(
-                    en_US='Number between -2.0 and 2.0. Positive values penalize new tokens based on their '
-                          'existing frequency in the text so far, decreasing the model\'s likelihood to repeat the '
-                          'same line verbatim.',
-                    zh_Hans='介于 -2.0 和 2.0 之间的数字。正值会根据新词在文本中的现有频率对其进行惩罚，从而降低模型逐字重复相同内容的可能性。'
+                    en_US="Number between -2.0 and 2.0. Positive values penalize new tokens based on their "
+                    "existing frequency in the text so far, decreasing the model's likelihood to repeat the "
+                    "same line verbatim.",
+                    zh_Hans="介于 -2.0 和 2.0 之间的数字。正值会根据新词在文本中的现有频率对其进行惩罚，"
+                    "从而降低模型逐字重复相同内容的可能性。",
                 ),
                 default=0.0,
                 min=-2.0,
                 max=2.0,
-                precision=2
-            )
+                precision=2,
+            ),
         ]
 
         completion_type = None
 
-        if 'completion_type' in credentials:
-            if credentials['completion_type'] == 'chat':
+        if "completion_type" in credentials:
+            if credentials["completion_type"] == "chat":
                 completion_type = LLMMode.CHAT.value
-            elif credentials['completion_type'] == 'completion':
+            elif credentials["completion_type"] == "completion":
                 completion_type = LLMMode.COMPLETION.value
             else:
                 raise ValueError(f'completion_type {credentials["completion_type"]} is not supported')
         else:
             extra_args = XinferenceHelper.get_xinference_extra_parameter(
-                server_url=credentials['server_url'],
-                model_uid=credentials['model_uid'],
-                api_key=credentials.get('api_key')
+                server_url=credentials["server_url"],
+                model_uid=credentials["model_uid"],
+                api_key=credentials.get("api_key"),
             )
 
-            if 'chat' in extra_args.model_ability:
+            if "chat" in extra_args.model_ability:
                 completion_type = LLMMode.CHAT.value
-            elif 'generate' in extra_args.model_ability:
+            elif "generate" in extra_args.model_ability:
                 completion_type = LLMMode.COMPLETION.value
             else:
-                raise ValueError(f'xinference model ability {extra_args.model_ability} is not supported')
+                raise ValueError(f"xinference model ability {extra_args.model_ability} is not supported")
 
         features = []
 
-        support_function_call = credentials.get('support_function_call', False)
+        support_function_call = credentials.get("support_function_call", False)
         if support_function_call:
             features.append(ModelFeature.TOOL_CALL)
 
-        support_vision = credentials.get('support_vision', False)
+        support_vision = credentials.get("support_vision", False)
         if support_vision:
             features.append(ModelFeature.VISION)
 
-        context_length = credentials.get('context_length', 2048)
+        context_length = credentials.get("context_length", 2048)
 
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
             features=features,
-            model_properties={
-                ModelPropertyKey.MODE: completion_type,
-                ModelPropertyKey.CONTEXT_SIZE: context_length
-            },
-            parameter_rules=rules
+            model_properties={ModelPropertyKey.MODE: completion_type, ModelPropertyKey.CONTEXT_SIZE: context_length},
+            parameter_rules=rules,
         )
 
         return entity
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                  model_parameters: dict, extra_model_kwargs: XinferenceModelExtraParameter,
-                  tools: list[PromptMessageTool] | None = None,
-                  stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
-            -> LLMResult | Generator:
+    def _generate(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        extra_model_kwargs: XinferenceModelExtraParameter,
+        tools: list[PromptMessageTool] | None = None,
+        stop: list[str] | None = None,
+        stream: bool = True,
+        user: str | None = None,
+    ) -> LLMResult | Generator:
         """
-            generate text from LLM
+        generate text from LLM
 
-            see `core.model_runtime.model_providers.__base.large_language_model.LargeLanguageModel._generate`
+        see `core.model_runtime.model_providers.__base.large_language_model.LargeLanguageModel._generate`
 
-            extra_model_kwargs can be got by `XinferenceHelper.get_xinference_extra_parameter`
+        extra_model_kwargs can be got by `XinferenceHelper.get_xinference_extra_parameter`
         """
-        if 'server_url' not in credentials:
-            raise CredentialsValidateFailedError('server_url is required in credentials')
+        if "server_url" not in credentials:
+            raise CredentialsValidateFailedError("server_url is required in credentials")
 
-        if credentials['server_url'].endswith('/'):
-            credentials['server_url'] = credentials['server_url'][:-1]
+        credentials["server_url"] = credentials["server_url"].removesuffix("/")
 
-        api_key = credentials.get('api_key') or "abc"
+        api_key = credentials.get("api_key") or "abc"
 
         client = OpenAI(
             base_url=f'{credentials["server_url"]}/v1',
@@ -466,34 +471,29 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         )
 
         xinference_client = Client(
-            base_url=credentials['server_url'],
-            api_key=credentials.get('api_key'),
+            base_url=credentials["server_url"],
+            api_key=credentials.get("api_key"),
         )
 
-        xinference_model = xinference_client.get_model(credentials['model_uid'])
+        xinference_model = xinference_client.get_model(credentials["model_uid"])
 
         generate_config = {
-            'temperature': model_parameters.get('temperature', 1.0),
-            'top_p': model_parameters.get('top_p', 0.7),
-            'max_tokens': model_parameters.get('max_tokens', 512),
-            'presence_penalty': model_parameters.get('presence_penalty', 0.0),
-            'frequency_penalty': model_parameters.get('frequency_penalty', 0.0),
+            "temperature": model_parameters.get("temperature", 1.0),
+            "top_p": model_parameters.get("top_p", 0.7),
+            "max_tokens": model_parameters.get("max_tokens", 512),
+            "presence_penalty": model_parameters.get("presence_penalty", 0.0),
+            "frequency_penalty": model_parameters.get("frequency_penalty", 0.0),
         }
 
         if stop:
-            generate_config['stop'] = stop
+            generate_config["stop"] = stop
 
         if tools and len(tools) > 0:
-            generate_config['tools'] = [
-                {
-                    'type': 'function',
-                    'function': helper.dump_model(tool)
-                } for tool in tools
-            ]
-        vision = credentials.get('support_vision', False)
+            generate_config["tools"] = [{"type": "function", "function": helper.dump_model(tool)} for tool in tools]
+        vision = credentials.get("support_vision", False)
         if isinstance(xinference_model, RESTfulChatModelHandle | RESTfulChatglmCppChatModelHandle):
             resp = client.chat.completions.create(
-                model=credentials['model_uid'],
+                model=credentials["model_uid"],
                 messages=[self._convert_prompt_message_to_dict(message) for message in prompt_messages],
                 stream=stream,
                 user=user,
@@ -501,34 +501,34 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             )
             if stream:
                 if tools and len(tools) > 0:
-                    raise InvokeBadRequestError('xinference tool calls does not support stream mode')
-                return self._handle_chat_stream_response(model=model, credentials=credentials,
-                                                         prompt_messages=prompt_messages,
-                                                         tools=tools, resp=resp)
-            return self._handle_chat_generate_response(model=model, credentials=credentials,
-                                                       prompt_messages=prompt_messages,
-                                                       tools=tools, resp=resp)
+                    raise InvokeBadRequestError("xinference tool calls does not support stream mode")
+                return self._handle_chat_stream_response(
+                    model=model, credentials=credentials, prompt_messages=prompt_messages, tools=tools, resp=resp
+                )
+            return self._handle_chat_generate_response(
+                model=model, credentials=credentials, prompt_messages=prompt_messages, tools=tools, resp=resp
+            )
         elif isinstance(xinference_model, RESTfulGenerateModelHandle):
             resp = client.completions.create(
-                model=credentials['model_uid'],
+                model=credentials["model_uid"],
                 prompt=self._convert_prompt_message_to_text(prompt_messages),
                 stream=stream,
                 user=user,
                 **generate_config,
             )
             if stream:
-                return self._handle_completion_stream_response(model=model, credentials=credentials,
-                                                               prompt_messages=prompt_messages,
-                                                               tools=tools, resp=resp)
-            return self._handle_completion_generate_response(model=model, credentials=credentials,
-                                                             prompt_messages=prompt_messages,
-                                                             tools=tools, resp=resp)
+                return self._handle_completion_stream_response(
+                    model=model, credentials=credentials, prompt_messages=prompt_messages, tools=tools, resp=resp
+                )
+            return self._handle_completion_generate_response(
+                model=model, credentials=credentials, prompt_messages=prompt_messages, tools=tools, resp=resp
+            )
         else:
-            raise NotImplementedError(f'xinference model handle type {type(xinference_model)} is not supported')
+            raise NotImplementedError(f"xinference model handle type {type(xinference_model)} is not supported")
 
-    def _extract_response_tool_calls(self,
-                                     response_tool_calls: list[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]) \
-            -> list[AssistantPromptMessage.ToolCall]:
+    def _extract_response_tool_calls(
+        self, response_tool_calls: list[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]
+    ) -> list[AssistantPromptMessage.ToolCall]:
         """
         Extract tool calls from response
 
@@ -539,21 +539,19 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         if response_tool_calls:
             for response_tool_call in response_tool_calls:
                 function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                    name=response_tool_call.function.name,
-                    arguments=response_tool_call.function.arguments
+                    name=response_tool_call.function.name, arguments=response_tool_call.function.arguments
                 )
 
                 tool_call = AssistantPromptMessage.ToolCall(
-                    id=response_tool_call.id,
-                    type=response_tool_call.type,
-                    function=function
+                    id=response_tool_call.id, type=response_tool_call.type, function=function
                 )
                 tool_calls.append(tool_call)
 
         return tool_calls
 
-    def _extract_response_function_call(self, response_function_call: FunctionCall | ChoiceDeltaFunctionCall) \
-            -> AssistantPromptMessage.ToolCall:
+    def _extract_response_function_call(
+        self, response_function_call: FunctionCall | ChoiceDeltaFunctionCall
+    ) -> AssistantPromptMessage.ToolCall:
         """
         Extract function call from response
 
@@ -563,23 +561,25 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         tool_call = None
         if response_function_call:
             function = AssistantPromptMessage.ToolCall.ToolCallFunction(
-                name=response_function_call.name,
-                arguments=response_function_call.arguments
+                name=response_function_call.name, arguments=response_function_call.arguments
             )
 
             tool_call = AssistantPromptMessage.ToolCall(
-                id=response_function_call.name,
-                type="function",
-                function=function
+                id=response_function_call.name, type="function", function=function
             )
 
         return tool_call
 
-    def _handle_chat_generate_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                                       tools: list[PromptMessageTool],
-                                       resp: ChatCompletion) -> LLMResult:
+    def _handle_chat_generate_response(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool],
+        resp: ChatCompletion,
+    ) -> LLMResult:
         """
-            handle normal chat generate response
+        handle normal chat generate response
         """
         if len(resp.choices) == 0:
             raise InvokeServerUnavailableError("Empty response")
@@ -588,22 +588,22 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
         # convert tool call to assistant message tool call
         tool_calls = assistant_message.tool_calls
-        assistant_prompt_message_tool_calls = self._extract_response_tool_calls(tool_calls if tool_calls else [])
+        assistant_prompt_message_tool_calls = self._extract_response_tool_calls(tool_calls or [])
         function_call = assistant_message.function_call
         if function_call:
             assistant_prompt_message_tool_calls += [self._extract_response_function_call(function_call)]
 
         # transform assistant message to prompt message
         assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_message.content,
-            tool_calls=assistant_prompt_message_tool_calls
+            content=assistant_message.content, tool_calls=assistant_prompt_message_tool_calls
         )
 
         prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
         completion_tokens = self._num_tokens_from_messages(messages=[assistant_prompt_message], tools=tools)
 
-        usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=prompt_tokens,
-                                          completion_tokens=completion_tokens)
+        usage = self._calc_response_usage(
+            model=model, credentials=credentials, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens
+        )
 
         response = LLMResult(
             model=model,
@@ -615,13 +615,18 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
         return response
 
-    def _handle_chat_stream_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                                     tools: list[PromptMessageTool],
-                                     resp: Iterator[ChatCompletionChunk]) -> Generator:
+    def _handle_chat_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool],
+        resp: Iterator[ChatCompletionChunk],
+    ) -> Generator:
         """
-            handle stream chat generate response
+        handle stream chat generate response
         """
-        full_response = ''
+        full_response = ""
 
         for chunk in resp:
             if len(chunk.choices) == 0:
@@ -629,7 +634,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
             delta = chunk.choices[0]
 
-            if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ''):
+            if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ""):
                 continue
 
             # check if there is a tool call in the response
@@ -646,32 +651,31 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else '',
-                tool_calls=assistant_message_tool_calls
+                content=delta.delta.content or "", tool_calls=assistant_message_tool_calls
             )
 
             if delta.finish_reason is not None:
                 # temp_assistant_prompt_message is used to calculate usage
                 temp_assistant_prompt_message = AssistantPromptMessage(
-                    content=full_response,
-                    tool_calls=assistant_message_tool_calls
+                    content=full_response, tool_calls=assistant_message_tool_calls
                 )
 
                 prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
                 completion_tokens = self._num_tokens_from_messages(messages=[temp_assistant_prompt_message], tools=[])
 
-                usage = self._calc_response_usage(model=model, credentials=credentials,
-                                                  prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
+                usage = self._calc_response_usage(
+                    model=model,
+                    credentials=credentials,
+                    prompt_tokens=prompt_tokens,
+                    completion_tokens=completion_tokens,
+                )
 
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
                     system_fingerprint=chunk.system_fingerprint,
                     delta=LLMResultChunkDelta(
-                        index=0,
-                        message=assistant_prompt_message,
-                        finish_reason=delta.finish_reason,
-                        usage=usage
+                        index=0, message=assistant_prompt_message, finish_reason=delta.finish_reason, usage=usage
                     ),
                 )
             else:
@@ -687,11 +691,16 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
                 full_response += delta.delta.content
 
-    def _handle_completion_generate_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                                             tools: list[PromptMessageTool],
-                                             resp: Completion) -> LLMResult:
+    def _handle_completion_generate_response(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool],
+        resp: Completion,
+    ) -> LLMResult:
         """
-            handle normal completion generate response
+        handle normal completion generate response
         """
         if len(resp.choices) == 0:
             raise InvokeServerUnavailableError("Empty response")
@@ -699,14 +708,9 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         assistant_message = resp.choices[0].text
 
         # transform assistant message to prompt message
-        assistant_prompt_message = AssistantPromptMessage(
-            content=assistant_message,
-            tool_calls=[]
-        )
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_message, tool_calls=[])
 
-        prompt_tokens = self._get_num_tokens_by_gpt2(
-            self._convert_prompt_message_to_text(prompt_messages)
-        )
+        prompt_tokens = self._get_num_tokens_by_gpt2(self._convert_prompt_message_to_text(prompt_messages))
         completion_tokens = self._num_tokens_from_messages(
             messages=[assistant_prompt_message], tools=[], is_completion_model=True
         )
@@ -724,13 +728,18 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
         return response
 
-    def _handle_completion_stream_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                                           tools: list[PromptMessageTool],
-                                           resp: Iterator[Completion]) -> Generator:
+    def _handle_completion_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool],
+        resp: Iterator[Completion],
+    ) -> Generator:
         """
-            handle stream completion generate response
+        handle stream completion generate response
         """
-        full_response = ''
+        full_response = ""
 
         for chunk in resp:
             if len(chunk.choices) == 0:
@@ -739,40 +748,33 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             delta = chunk.choices[0]
 
             # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(
-                content=delta.text if delta.text else '',
-                tool_calls=[]
-            )
+            assistant_prompt_message = AssistantPromptMessage(content=delta.text or "", tool_calls=[])
 
             if delta.finish_reason is not None:
                 # temp_assistant_prompt_message is used to calculate usage
-                temp_assistant_prompt_message = AssistantPromptMessage(
-                    content=full_response,
-                    tool_calls=[]
-                )
+                temp_assistant_prompt_message = AssistantPromptMessage(content=full_response, tool_calls=[])
 
-                prompt_tokens = self._get_num_tokens_by_gpt2(
-                    self._convert_prompt_message_to_text(prompt_messages)
-                )
+                prompt_tokens = self._get_num_tokens_by_gpt2(self._convert_prompt_message_to_text(prompt_messages))
                 completion_tokens = self._num_tokens_from_messages(
                     messages=[temp_assistant_prompt_message], tools=[], is_completion_model=True
                 )
-                usage = self._calc_response_usage(model=model, credentials=credentials,
-                                                  prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
+                usage = self._calc_response_usage(
+                    model=model,
+                    credentials=credentials,
+                    prompt_tokens=prompt_tokens,
+                    completion_tokens=completion_tokens,
+                )
 
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
                     system_fingerprint=chunk.system_fingerprint,
                     delta=LLMResultChunkDelta(
-                        index=0,
-                        message=assistant_prompt_message,
-                        finish_reason=delta.finish_reason,
-                        usage=usage
+                        index=0, message=assistant_prompt_message, finish_reason=delta.finish_reason, usage=usage
                     ),
                 )
             else:
-                if delta.text is None or delta.text == '':
+                if delta.text is None or delta.text == "":
                     continue
 
                 yield LLMResultChunk(
@@ -807,15 +809,9 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
                 ConflictError,
                 NotFoundError,
                 UnprocessableEntityError,
-                PermissionDeniedError
+                PermissionDeniedError,
             ],
-            InvokeRateLimitError: [
-                RateLimitError
-            ],
-            InvokeAuthorizationError: [
-                AuthenticationError
-            ],
-            InvokeBadRequestError: [
-                ValueError
-            ]
+            InvokeRateLimitError: [RateLimitError],
+            InvokeAuthorizationError: [AuthenticationError],
+            InvokeBadRequestError: [ValueError],
         }
diff --git a/api/core/model_runtime/model_providers/xinference/rerank/rerank.py b/api/core/model_runtime/model_providers/xinference/rerank/rerank.py
index d809537479..8f18bc42d2 100644
--- a/api/core/model_runtime/model_providers/xinference/rerank/rerank.py
+++ b/api/core/model_runtime/model_providers/xinference/rerank/rerank.py
@@ -22,10 +22,16 @@ class XinferenceRerankModel(RerankModel):
     Model class for Xinference rerank model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
-                user: Optional[str] = None) \
-            -> RerankResult:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
         """
         Invoke rerank model
 
@@ -39,24 +45,15 @@ class XinferenceRerankModel(RerankModel):
         :return: rerank result
         """
         if len(docs) == 0:
-            return RerankResult(
-                model=model,
-                docs=[]
-            )
+            return RerankResult(model=model, docs=[])
 
-        server_url = credentials['server_url']
-        model_uid = credentials['model_uid']
-        api_key = credentials.get('api_key')
-        if server_url.endswith('/'):
-            server_url = server_url[:-1]
-        auth_headers = {'Authorization': f'Bearer {api_key}'} if api_key else {}
+        server_url = credentials["server_url"]
+        model_uid = credentials["model_uid"]
+        api_key = credentials.get("api_key")
+        server_url = server_url.removesuffix("/")
+        auth_headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
 
-        params = {
-            'documents': docs,
-            'query': query,
-            'top_n': top_n,
-            'return_documents': True
-        }
+        params = {"documents": docs, "query": query, "top_n": top_n, "return_documents": True}
         try:
             handle = RESTfulRerankModelHandle(model_uid, server_url, auth_headers)
             response = handle.rerank(**params)
@@ -69,27 +66,24 @@ class XinferenceRerankModel(RerankModel):
             response = handle.rerank(**params)
 
         rerank_documents = []
-        for idx, result in enumerate(response['results']):
+        for idx, result in enumerate(response["results"]):
             # format document
-            index = result['index']
-            page_content = result['document'] if isinstance(result['document'], str) else result['document']['text']
+            index = result["index"]
+            page_content = result["document"] if isinstance(result["document"], str) else result["document"]["text"]
             rerank_document = RerankDocument(
                 index=index,
                 text=page_content,
-                score=result['relevance_score'],
+                score=result["relevance_score"],
             )
 
             # score threshold check
             if score_threshold is not None:
-                if result['relevance_score'] >= score_threshold:
+                if result["relevance_score"] >= score_threshold:
                     rerank_documents.append(rerank_document)
             else:
                 rerank_documents.append(rerank_document)
 
-        return RerankResult(
-            model=model,
-            docs=rerank_documents
-        )
+        return RerankResult(model=model, docs=rerank_documents)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
@@ -100,34 +94,34 @@ class XinferenceRerankModel(RerankModel):
         :return:
         """
         try:
-            if "/" in credentials['model_uid'] or "?" in credentials['model_uid'] or "#" in credentials['model_uid']:
+            if "/" in credentials["model_uid"] or "?" in credentials["model_uid"] or "#" in credentials["model_uid"]:
                 raise CredentialsValidateFailedError("model_uid should not contain /, ?, or #")
 
-            if credentials['server_url'].endswith('/'):
-                credentials['server_url'] = credentials['server_url'][:-1]
+            credentials["server_url"] = credentials["server_url"].removesuffix("/")
 
             # initialize client
             client = Client(
-                base_url=credentials['server_url'],
-                api_key=credentials.get('api_key'),
+                base_url=credentials["server_url"],
+                api_key=credentials.get("api_key"),
             )
 
-            xinference_client = client.get_model(model_uid=credentials['model_uid'])
+            xinference_client = client.get_model(model_uid=credentials["model_uid"])
 
             if not isinstance(xinference_client, RESTfulRerankModelHandle):
                 raise InvokeBadRequestError(
-                    'please check model type, the model you want to invoke is not a rerank model')
+                    "please check model type, the model you want to invoke is not a rerank model"
+                )
 
             self.invoke(
                 model=model,
                 credentials=credentials,
                 query="Whose kasumi",
                 docs=[
-                    "Kasumi is a girl's name of Japanese origin meaning \"mist\".",
+                    'Kasumi is a girl\'s name of Japanese origin meaning "mist".',
                     "Her music is a kawaii bass, a mix of future bass, pop, and kawaii music ",
-                    "and she leads a team named PopiParty."
+                    "and she leads a team named PopiParty.",
                 ],
-                score_threshold=0.8
+                score_threshold=0.8,
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -143,53 +137,38 @@ class XinferenceRerankModel(RerankModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                InvokeBadRequestError,
-                KeyError,
-                ValueError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [InvokeBadRequestError, KeyError, ValueError],
         }
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.RERANK,
             model_properties={},
-            parameter_rules=[]
+            parameter_rules=[],
         )
 
         return entity
 
 
 class RESTfulRerankModelHandleWithoutExtraParameter(RESTfulRerankModelHandle):
-
     def rerank(
-            self,
-            documents: list[str],
-            query: str,
-            top_n: Optional[int] = None,
-            max_chunks_per_doc: Optional[int] = None,
-            return_documents: Optional[bool] = None,
-            **kwargs
+        self,
+        documents: list[str],
+        query: str,
+        top_n: Optional[int] = None,
+        max_chunks_per_doc: Optional[int] = None,
+        return_documents: Optional[bool] = None,
+        **kwargs,
     ):
         url = f"{self._base_url}/v1/rerank"
         request_body = {
@@ -205,8 +184,6 @@ class RESTfulRerankModelHandleWithoutExtraParameter(RESTfulRerankModelHandle):
 
         response = requests.post(url, json=request_body, headers=self.auth_headers)
         if response.status_code != 200:
-            raise InvokeServerUnavailableError(
-                f"Failed to rerank documents, detail: {response.json()['detail']}"
-            )
+            raise InvokeServerUnavailableError(f"Failed to rerank documents, detail: {response.json()['detail']}")
         response_data = response.json()
         return response_data
diff --git a/api/core/model_runtime/model_providers/xinference/speech2text/speech2text.py b/api/core/model_runtime/model_providers/xinference/speech2text/speech2text.py
index 62b77f22e5..a6c5b8a0a5 100644
--- a/api/core/model_runtime/model_providers/xinference/speech2text/speech2text.py
+++ b/api/core/model_runtime/model_providers/xinference/speech2text/speech2text.py
@@ -21,9 +21,7 @@ class XinferenceSpeech2TextModel(Speech2TextModel):
     Model class for Xinference speech to text model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                file: IO[bytes], user: Optional[str] = None) \
-            -> str:
+    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
         """
         Invoke speech2text model
 
@@ -44,27 +42,27 @@ class XinferenceSpeech2TextModel(Speech2TextModel):
         :return:
         """
         try:
-            if "/" in credentials['model_uid'] or "?" in credentials['model_uid'] or "#" in credentials['model_uid']:
+            if "/" in credentials["model_uid"] or "?" in credentials["model_uid"] or "#" in credentials["model_uid"]:
                 raise CredentialsValidateFailedError("model_uid should not contain /, ?, or #")
 
-            if credentials['server_url'].endswith('/'):
-                credentials['server_url'] = credentials['server_url'][:-1]
+            credentials["server_url"] = credentials["server_url"].removesuffix("/")
 
             # initialize client
             client = Client(
-                base_url=credentials['server_url'],
-                api_key=credentials.get('api_key'),
+                base_url=credentials["server_url"],
+                api_key=credentials.get("api_key"),
             )
 
-            xinference_client = client.get_model(model_uid=credentials['model_uid'])
+            xinference_client = client.get_model(model_uid=credentials["model_uid"])
 
             if not isinstance(xinference_client, RESTfulAudioModelHandle):
                 raise InvokeBadRequestError(
-                    'please check model type, the model you want to invoke is not a audio model')
+                    "please check model type, the model you want to invoke is not a audio model"
+                )
 
             audio_file_path = self._get_demo_file_path()
 
-            with open(audio_file_path, 'rb') as audio_file:
+            with open(audio_file_path, "rb") as audio_file:
                 self.invoke(model, credentials, audio_file)
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -80,23 +78,11 @@ class XinferenceSpeech2TextModel(Speech2TextModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                InvokeBadRequestError,
-                KeyError,
-                ValueError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [InvokeBadRequestError, KeyError, ValueError],
         }
 
     def _speech2text_invoke(
@@ -114,29 +100,28 @@ class XinferenceSpeech2TextModel(Speech2TextModel):
 
         :param model: model name
         :param credentials: model credentials
-        :param file: The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpe            g,mpga, m4a, ogg, wav, or webm.
+        :param file: The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpeg,
+          mpga, m4a, ogg, wav, or webm.
         :param language: The language of the input audio. Supplying the input language in ISO-639-1
         :param prompt: An optional text to guide the model's style or continue a previous audio segment.
             The prompt should match the audio language.
-        :param response_format: The format of the transcript output, in one of these options: json, text, srt, verbose            _json, or vtt.
-        :param temperature: The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output mor            e random,while lower values like 0.2 will make it more focused and deterministic.If set to 0, the model wi            ll use log probability to automatically increase the temperature until certain thresholds are hit.
+        :param response_format: The format of the transcript output, in one of these options: json, text, srt,
+          verbose_json, or vtt.
+        :param temperature: The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more
+          random,while lower values like 0.2 will make it more focused and deterministic.If set to 0, the model will use
+          log probability to automatically increase the temperature until certain thresholds are hit.
         :return: text for given audio file
         """
-        server_url = credentials['server_url']
-        model_uid = credentials['model_uid']
-        api_key = credentials.get('api_key')
-        if server_url.endswith('/'):
-            server_url = server_url[:-1]
-        auth_headers = {'Authorization': f'Bearer {api_key}'} if api_key else {}
+        server_url = credentials["server_url"]
+        model_uid = credentials["model_uid"]
+        api_key = credentials.get("api_key")
+        server_url = server_url.removesuffix("/")
+        auth_headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
 
         try:
             handle = RESTfulAudioModelHandle(model_uid, server_url, auth_headers)
             response = handle.transcriptions(
-                audio=file,
-                language=language,
-                prompt=prompt,
-                response_format=response_format,
-                temperature=temperature
+                audio=file, language=language, prompt=prompt, response_format=response_format, temperature=temperature
             )
         except RuntimeError as e:
             raise InvokeServerUnavailableError(str(e))
@@ -145,17 +130,15 @@ class XinferenceSpeech2TextModel(Speech2TextModel):
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.SPEECH2TEXT,
-            model_properties={ },
-            parameter_rules=[]
+            model_properties={},
+            parameter_rules=[],
         )
 
         return entity
diff --git a/api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py
index 3a8d704c25..8043af1d6c 100644
--- a/api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py
@@ -23,9 +23,10 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
     """
     Model class for Xinference text embedding model.
     """
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -41,12 +42,11 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
-        server_url = credentials['server_url']
-        model_uid = credentials['model_uid']
-        api_key = credentials.get('api_key')
-        if server_url.endswith('/'):
-            server_url = server_url[:-1]
-        auth_headers = {'Authorization': f'Bearer {api_key}'} if api_key else {}
+        server_url = credentials["server_url"]
+        model_uid = credentials["model_uid"]
+        api_key = credentials.get("api_key")
+        server_url = server_url.removesuffix("/")
+        auth_headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
 
         try:
             handle = RESTfulEmbeddingModelHandle(model_uid, server_url, auth_headers)
@@ -70,13 +70,11 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
             embedding: List[float]
         """
 
-        usage = embeddings['usage']
-        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage['total_tokens'])
+        usage = embeddings["usage"]
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage["total_tokens"])
 
         result = TextEmbeddingResult(
-            model=model,
-            embeddings=[embedding['embedding'] for embedding in embeddings['data']],
-            usage=usage
+            model=model, embeddings=[embedding["embedding"] for embedding in embeddings["data"]], usage=usage
         )
 
         return result
@@ -105,12 +103,12 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         try:
-            if "/" in credentials['model_uid'] or "?" in credentials['model_uid'] or "#" in credentials['model_uid']:
+            if "/" in credentials["model_uid"] or "?" in credentials["model_uid"] or "#" in credentials["model_uid"]:
                 raise CredentialsValidateFailedError("model_uid should not contain /, ?, or #")
 
-            server_url = credentials['server_url']
-            model_uid = credentials['model_uid']
-            api_key = credentials.get('api_key')
+            server_url = credentials["server_url"]
+            model_uid = credentials["model_uid"]
+            api_key = credentials.get("api_key")
             extra_args = XinferenceHelper.get_xinference_extra_parameter(
                 server_url=server_url,
                 model_uid=model_uid,
@@ -118,9 +116,8 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
             )
 
             if extra_args.max_tokens:
-                credentials['max_tokens'] = extra_args.max_tokens
-            if server_url.endswith('/'):
-                server_url = server_url[:-1]
+                credentials["max_tokens"] = extra_args.max_tokens
+            server_url = server_url.removesuffix("/")
 
             client = Client(
                 base_url=server_url,
@@ -133,32 +130,24 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
                 raise InvokeAuthorizationError(e)
 
             if not isinstance(handle, RESTfulEmbeddingModelHandle):
-                raise InvokeBadRequestError('please check model type, the model you want to invoke is not a text embedding model')
+                raise InvokeBadRequestError(
+                    "please check model type, the model you want to invoke is not a text embedding model"
+                )
 
-            self._invoke(model=model, credentials=credentials, texts=['ping'])
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
         except InvokeAuthorizationError as e:
-            raise CredentialsValidateFailedError(f'Failed to validate credentials for model {model}: {e}')
+            raise CredentialsValidateFailedError(f"Failed to validate credentials for model {model}: {e}")
         except RuntimeError as e:
             raise CredentialsValidateFailedError(e)
 
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                KeyError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [KeyError],
         }
 
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
@@ -172,10 +161,7 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -186,28 +172,26 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
 
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TEXT_EMBEDDING,
             model_properties={
                 ModelPropertyKey.MAX_CHUNKS: 1,
-                ModelPropertyKey.CONTEXT_SIZE: 'max_tokens' in credentials and credentials['max_tokens'] or 512,
+                ModelPropertyKey.CONTEXT_SIZE: "max_tokens" in credentials and credentials["max_tokens"] or 512,
             },
-            parameter_rules=[]
+            parameter_rules=[],
         )
 
         return entity
diff --git a/api/core/model_runtime/model_providers/xinference/tts/tts.py b/api/core/model_runtime/model_providers/xinference/tts/tts.py
index 8cc99fef7c..10538b5788 100644
--- a/api/core/model_runtime/model_providers/xinference/tts/tts.py
+++ b/api/core/model_runtime/model_providers/xinference/tts/tts.py
@@ -19,92 +19,90 @@ from core.model_runtime.model_providers.xinference.xinference_helper import Xinf
 
 
 class XinferenceText2SpeechModel(TTSModel):
-
     def __init__(self):
         # preset voices, need support custom voice
         self.model_voices = {
-            '__default': {
-                'all': [
-                    {'name': 'Default', 'value': 'default'},
+            "__default": {
+                "all": [
+                    {"name": "Default", "value": "default"},
                 ]
             },
-            'ChatTTS': {
-                'all': [
-                    {'name': 'Alloy', 'value': 'alloy'},
-                    {'name': 'Echo', 'value': 'echo'},
-                    {'name': 'Fable', 'value': 'fable'},
-                    {'name': 'Onyx', 'value': 'onyx'},
-                    {'name': 'Nova', 'value': 'nova'},
-                    {'name': 'Shimmer', 'value': 'shimmer'},
+            "ChatTTS": {
+                "all": [
+                    {"name": "Alloy", "value": "alloy"},
+                    {"name": "Echo", "value": "echo"},
+                    {"name": "Fable", "value": "fable"},
+                    {"name": "Onyx", "value": "onyx"},
+                    {"name": "Nova", "value": "nova"},
+                    {"name": "Shimmer", "value": "shimmer"},
                 ]
             },
-            'CosyVoice': {
-                'zh-Hans': [
-                    {'name': '中文男', 'value': '中文男'},
-                    {'name': '中文女', 'value': '中文女'},
-                    {'name': '粤语女', 'value': '粤语女'},
+            "CosyVoice": {
+                "zh-Hans": [
+                    {"name": "中文男", "value": "中文男"},
+                    {"name": "中文女", "value": "中文女"},
+                    {"name": "粤语女", "value": "粤语女"},
                 ],
-                'zh-Hant': [
-                    {'name': '中文男', 'value': '中文男'},
-                    {'name': '中文女', 'value': '中文女'},
-                    {'name': '粤语女', 'value': '粤语女'},
+                "zh-Hant": [
+                    {"name": "中文男", "value": "中文男"},
+                    {"name": "中文女", "value": "中文女"},
+                    {"name": "粤语女", "value": "粤语女"},
                 ],
-                'en-US': [
-                    {'name': '英文男', 'value': '英文男'},
-                    {'name': '英文女', 'value': '英文女'},
+                "en-US": [
+                    {"name": "英文男", "value": "英文男"},
+                    {"name": "英文女", "value": "英文女"},
                 ],
-                'ja-JP': [
-                    {'name': '日语男', 'value': '日语男'},
+                "ja-JP": [
+                    {"name": "日语男", "value": "日语男"},
                 ],
-                'ko-KR': [
-                    {'name': '韩语女', 'value': '韩语女'},
-                ]
-            }
+                "ko-KR": [
+                    {"name": "韩语女", "value": "韩语女"},
+                ],
+            },
         }
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
-                Validate model credentials
+        Validate model credentials
 
-                :param model: model name
-                :param credentials: model credentials
-                :return:
-                """
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
         try:
-            if ("/" in credentials['model_uid'] or
-                    "?" in credentials['model_uid'] or
-                    "#" in credentials['model_uid']):
+            if "/" in credentials["model_uid"] or "?" in credentials["model_uid"] or "#" in credentials["model_uid"]:
                 raise CredentialsValidateFailedError("model_uid should not contain /, ?, or #")
 
-            if credentials['server_url'].endswith('/'):
-                credentials['server_url'] = credentials['server_url'][:-1]
+            credentials["server_url"] = credentials["server_url"].removesuffix("/")
 
             extra_param = XinferenceHelper.get_xinference_extra_parameter(
-                server_url=credentials['server_url'],
-                model_uid=credentials['model_uid'],
-                api_key=credentials.get('api_key'),
+                server_url=credentials["server_url"],
+                model_uid=credentials["model_uid"],
+                api_key=credentials.get("api_key"),
             )
 
-            if 'text-to-audio' not in extra_param.model_ability:
+            if "text-to-audio" not in extra_param.model_ability:
                 raise InvokeBadRequestError(
-                    'please check model type, the model you want to invoke is not a text-to-audio model')
+                    "please check model type, the model you want to invoke is not a text-to-audio model"
+                )
 
             if extra_param.model_family and extra_param.model_family in self.model_voices:
-                credentials['audio_model_name'] = extra_param.model_family
+                credentials["audio_model_name"] = extra_param.model_family
             else:
-                credentials['audio_model_name'] = '__default'
+                credentials["audio_model_name"] = "__default"
 
             self._tts_invoke_streaming(
                 model=model,
                 credentials=credentials,
-                content_text='Hello Dify!',
+                content_text="Hello Dify!",
                 voice=self._get_model_default_voice(model, credentials),
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _invoke(self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str,
-                user: Optional[str] = None):
+    def _invoke(
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ):
         """
         _invoke text2speech model
 
@@ -120,18 +118,16 @@ class XinferenceText2SpeechModel(TTSModel):
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
-            used to define customizable model schema
+        used to define customizable model schema
         """
 
         entity = AIModelEntity(
             model=model,
-            label=I18nObject(
-                en_US=model
-            ),
+            label=I18nObject(en_US=model),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TTS,
             model_properties={},
-            parameter_rules=[]
+            parameter_rules=[],
         )
 
         return entity
@@ -147,40 +143,28 @@ class XinferenceText2SpeechModel(TTSModel):
         :return: Invoke error mapping
         """
         return {
-            InvokeConnectionError: [
-                InvokeConnectionError
-            ],
-            InvokeServerUnavailableError: [
-                InvokeServerUnavailableError
-            ],
-            InvokeRateLimitError: [
-                InvokeRateLimitError
-            ],
-            InvokeAuthorizationError: [
-                InvokeAuthorizationError
-            ],
-            InvokeBadRequestError: [
-                InvokeBadRequestError,
-                KeyError,
-                ValueError
-            ]
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [InvokeBadRequestError, KeyError, ValueError],
         }
 
     def get_tts_model_voices(self, model: str, credentials: dict, language: Optional[str] = None) -> list:
-        audio_model_name = credentials.get('audio_model_name', '__default')
+        audio_model_name = credentials.get("audio_model_name", "__default")
         for key, voices in self.model_voices.items():
             if key in audio_model_name:
                 if language and language in voices:
                     return voices[language]
-                elif 'all' in voices:
-                    return voices['all']
+                elif "all" in voices:
+                    return voices["all"]
                 else:
                     all_voices = []
                     for lang, lang_voices in voices.items():
                         all_voices.extend(lang_voices)
                     return all_voices
 
-        return self.model_voices['__default']['all']
+        return self.model_voices["__default"]["all"]
 
     def _get_model_default_voice(self, model: str, credentials: dict) -> any:
         return ""
@@ -194,8 +178,7 @@ class XinferenceText2SpeechModel(TTSModel):
     def _get_model_workers_limit(self, model: str, credentials: dict) -> int:
         return 5
 
-    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str,
-                              voice: str) -> any:
+    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
         """
         _tts_invoke_streaming text2speech model
 
@@ -205,48 +188,41 @@ class XinferenceText2SpeechModel(TTSModel):
         :param voice: model timbre
         :return: text translated to audio file
         """
-        if credentials['server_url'].endswith('/'):
-            credentials['server_url'] = credentials['server_url'][:-1]
+        credentials["server_url"] = credentials["server_url"].removesuffix("/")
 
         try:
-            api_key = credentials.get('api_key')
-            auth_headers = {'Authorization': f'Bearer {api_key}'} if api_key else {}
+            api_key = credentials.get("api_key")
+            auth_headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
             handle = RESTfulAudioModelHandle(
-                credentials['model_uid'], credentials['server_url'], auth_headers=auth_headers
+                credentials["model_uid"], credentials["server_url"], auth_headers=auth_headers
             )
 
-            model_support_voice = [x.get("value") for x in
-                                   self.get_tts_model_voices(model=model, credentials=credentials)]
+            model_support_voice = [
+                x.get("value") for x in self.get_tts_model_voices(model=model, credentials=credentials)
+            ]
             if not voice or voice not in model_support_voice:
                 voice = self._get_model_default_voice(model, credentials)
             word_limit = self._get_model_word_limit(model, credentials)
             if len(content_text) > word_limit:
                 sentences = self._split_text_into_sentences(content_text, max_length=word_limit)
                 executor = concurrent.futures.ThreadPoolExecutor(max_workers=min(3, len(sentences)))
-                futures = [executor.submit(
-                    handle.speech,
-                    input=sentences[i],
-                    voice=voice,
-                    response_format="mp3",
-                    speed=1.0,
-                    stream=False
-                )
-                    for i in range(len(sentences))]
+                futures = [
+                    executor.submit(
+                        handle.speech, input=sentences[i], voice=voice, response_format="mp3", speed=1.0, stream=False
+                    )
+                    for i in range(len(sentences))
+                ]
 
-                for index, future in enumerate(futures):
+                for future in futures:
                     response = future.result()
                     for i in range(0, len(response), 1024):
-                        yield response[i:i + 1024]
+                        yield response[i : i + 1024]
             else:
                 response = handle.speech(
-                    input=content_text.strip(),
-                    voice=voice,
-                    response_format="mp3",
-                    speed=1.0,
-                    stream=False
+                    input=content_text.strip(), voice=voice, response_format="mp3", speed=1.0, stream=False
                 )
 
                 for i in range(0, len(response), 1024):
-                    yield response[i:i + 1024]
+                    yield response[i : i + 1024]
         except Exception as ex:
             raise InvokeBadRequestError(str(ex))
diff --git a/api/core/model_runtime/model_providers/xinference/xinference_helper.py b/api/core/model_runtime/model_providers/xinference/xinference_helper.py
index 75161ad376..619ee1492a 100644
--- a/api/core/model_runtime/model_providers/xinference/xinference_helper.py
+++ b/api/core/model_runtime/model_providers/xinference/xinference_helper.py
@@ -18,9 +18,17 @@ class XinferenceModelExtraParameter:
     support_vision: bool = False
     model_family: Optional[str]
 
-    def __init__(self, model_format: str, model_handle_type: str, model_ability: list[str],
-                 support_function_call: bool, support_vision: bool, max_tokens: int, context_length: int,
-                 model_family: Optional[str]) -> None:
+    def __init__(
+        self,
+        model_format: str,
+        model_handle_type: str,
+        model_ability: list[str],
+        support_function_call: bool,
+        support_vision: bool,
+        max_tokens: int,
+        context_length: int,
+        model_family: Optional[str],
+    ) -> None:
         self.model_format = model_format
         self.model_handle_type = model_handle_type
         self.model_ability = model_ability
@@ -30,9 +38,11 @@ class XinferenceModelExtraParameter:
         self.context_length = context_length
         self.model_family = model_family
 
+
 cache = {}
 cache_lock = Lock()
 
+
 class XinferenceHelper:
     @staticmethod
     def get_xinference_extra_parameter(server_url: str, model_uid: str, api_key: str) -> XinferenceModelExtraParameter:
@@ -40,16 +50,16 @@ class XinferenceHelper:
         with cache_lock:
             if model_uid not in cache:
                 cache[model_uid] = {
-                    'expires': time() + 300,
-                    'value': XinferenceHelper._get_xinference_extra_parameter(server_url, model_uid, api_key)
+                    "expires": time() + 300,
+                    "value": XinferenceHelper._get_xinference_extra_parameter(server_url, model_uid, api_key),
                 }
-            return cache[model_uid]['value']
+            return cache[model_uid]["value"]
 
     @staticmethod
     def _clean_cache() -> None:
         try:
             with cache_lock:
-                expired_keys = [model_uid for model_uid, model in cache.items() if model['expires'] < time()]
+                expired_keys = [model_uid for model_uid, model in cache.items() if model["expires"] < time()]
                 for model_uid in expired_keys:
                     del cache[model_uid]
         except RuntimeError as e:
@@ -58,55 +68,59 @@ class XinferenceHelper:
     @staticmethod
     def _get_xinference_extra_parameter(server_url: str, model_uid: str, api_key: str) -> XinferenceModelExtraParameter:
         """
-            get xinference model extra parameter like model_format and model_handle_type
+        get xinference model extra parameter like model_format and model_handle_type
         """
 
         if not model_uid or not model_uid.strip() or not server_url or not server_url.strip():
-            raise RuntimeError('model_uid is empty')
+            raise RuntimeError("model_uid is empty")
 
-        url = str(URL(server_url) / 'v1' / 'models' / model_uid)
+        url = str(URL(server_url) / "v1" / "models" / model_uid)
 
-        # this method is surrounded by a lock, and default requests may hang forever, so we just set a Adapter with max_retries=3
+        # this method is surrounded by a lock, and default requests may hang forever,
+        # so we just set a Adapter with max_retries=3
         session = Session()
-        session.mount('http://', HTTPAdapter(max_retries=3))
-        session.mount('https://', HTTPAdapter(max_retries=3))
-        headers = {'Authorization': f'Bearer {api_key}'} if api_key else {}
+        session.mount("http://", HTTPAdapter(max_retries=3))
+        session.mount("https://", HTTPAdapter(max_retries=3))
+        headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
 
         try:
             response = session.get(url, headers=headers, timeout=10)
         except (MissingSchema, ConnectionError, Timeout) as e:
-            raise RuntimeError(f'get xinference model extra parameter failed, url: {url}, error: {e}')
+            raise RuntimeError(f"get xinference model extra parameter failed, url: {url}, error: {e}")
         if response.status_code != 200:
-            raise RuntimeError(f'get xinference model extra parameter failed, status code: {response.status_code}, response: {response.text}')
+            raise RuntimeError(
+                f"get xinference model extra parameter failed, status code: {response.status_code},"
+                f" response: {response.text}"
+            )
 
         response_json = response.json()
 
-        model_format = response_json.get('model_format', 'ggmlv3')
-        model_ability = response_json.get('model_ability', [])
-        model_family = response_json.get('model_family', None)
+        model_format = response_json.get("model_format", "ggmlv3")
+        model_ability = response_json.get("model_ability", [])
+        model_family = response_json.get("model_family", None)
 
-        if response_json.get('model_type') == 'embedding':
-            model_handle_type = 'embedding'
-        elif response_json.get('model_type') == 'audio':
-            model_handle_type = 'audio'
-            if model_family and model_family in ['ChatTTS', 'CosyVoice']:
-                model_ability.append('text-to-audio')
+        if response_json.get("model_type") == "embedding":
+            model_handle_type = "embedding"
+        elif response_json.get("model_type") == "audio":
+            model_handle_type = "audio"
+            if model_family and model_family in {"ChatTTS", "CosyVoice", "FishAudio"}:
+                model_ability.append("text-to-audio")
             else:
-                model_ability.append('audio-to-text')
-        elif model_format == 'ggmlv3' and 'chatglm' in response_json['model_name']:
-            model_handle_type = 'chatglm'
-        elif 'generate' in model_ability:
-            model_handle_type = 'generate'
-        elif 'chat' in model_ability:
-            model_handle_type = 'chat'
+                model_ability.append("audio-to-text")
+        elif model_format == "ggmlv3" and "chatglm" in response_json["model_name"]:
+            model_handle_type = "chatglm"
+        elif "generate" in model_ability:
+            model_handle_type = "generate"
+        elif "chat" in model_ability:
+            model_handle_type = "chat"
         else:
-            raise NotImplementedError('xinference model handle type is not supported')
+            raise NotImplementedError("xinference model handle type is not supported")
 
-        support_function_call = 'tools' in model_ability
-        support_vision = 'vision' in model_ability
-        max_tokens = response_json.get('max_tokens', 512)
+        support_function_call = "tools" in model_ability
+        support_vision = "vision" in model_ability
+        max_tokens = response_json.get("max_tokens", 512)
 
-        context_length = response_json.get('context_length', 2048)
+        context_length = response_json.get("context_length", 2048)
 
         return XinferenceModelExtraParameter(
             model_format=model_format,
@@ -116,5 +130,5 @@ class XinferenceHelper:
             support_vision=support_vision,
             max_tokens=max_tokens,
             context_length=context_length,
-            model_family=model_family
+            model_family=model_family,
         )
diff --git a/api/core/model_runtime/model_providers/yi/llm/llm.py b/api/core/model_runtime/model_providers/yi/llm/llm.py
index d33f38333b..5ab7fd126e 100644
--- a/api/core/model_runtime/model_providers/yi/llm/llm.py
+++ b/api/core/model_runtime/model_providers/yi/llm/llm.py
@@ -14,11 +14,17 @@ from core.model_runtime.model_providers.openai.llm.llm import OpenAILargeLanguag
 
 
 class YiLargeLanguageModel(OpenAILargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
 
         # yi-vl-plus not support system prompt yet.
@@ -27,7 +33,9 @@ class YiLargeLanguageModel(OpenAILargeLanguageModel):
             for message in prompt_messages:
                 if not isinstance(message, SystemPromptMessage):
                     prompt_message_except_system.append(message)
-            return super()._invoke(model, credentials, prompt_message_except_system, model_parameters, tools, stop, stream)
+            return super()._invoke(
+                model, credentials, prompt_message_except_system, model_parameters, tools, stop, stream
+            )
 
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
@@ -36,8 +44,7 @@ class YiLargeLanguageModel(OpenAILargeLanguageModel):
         super().validate_credentials(model, credentials)
 
     # refactored from openai model runtime, use cl100k_base for calculate token number
-    def _num_tokens_from_string(self, model: str, text: str,
-                                tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_string(self, model: str, text: str, tools: Optional[list[PromptMessageTool]] = None) -> int:
         """
         Calculate num tokens for text completion model with tiktoken package.
 
@@ -55,8 +62,9 @@ class YiLargeLanguageModel(OpenAILargeLanguageModel):
         return num_tokens
 
     # refactored from openai model runtime, use cl100k_base for calculate token number
-    def _num_tokens_from_messages(self, model: str, messages: list[PromptMessage],
-                                  tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def _num_tokens_from_messages(
+        self, model: str, messages: list[PromptMessage], tools: Optional[list[PromptMessageTool]] = None
+    ) -> int:
         """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
 
         Official documentation: https://github.com/openai/openai-cookbook/blob/
@@ -76,10 +84,10 @@ class YiLargeLanguageModel(OpenAILargeLanguageModel):
                 #  which need to download the image and then get the resolution for calculation,
                 #  and will increase the request delay
                 if isinstance(value, list):
-                    text = ''
+                    text = ""
                     for item in value:
-                        if isinstance(item, dict) and item['type'] == 'text':
-                            text += item['text']
+                        if isinstance(item, dict) and item["type"] == "text":
+                            text += item["text"]
 
                     value = text
 
@@ -110,10 +118,10 @@ class YiLargeLanguageModel(OpenAILargeLanguageModel):
 
     @staticmethod
     def _add_custom_parameters(credentials: dict) -> None:
-        credentials['mode'] = 'chat'
-        credentials['openai_api_key']=credentials['api_key']
-        if 'endpoint_url' not in credentials or credentials['endpoint_url'] == "":
-            credentials['openai_api_base']='https://api.lingyiwanwu.com'
+        credentials["mode"] = "chat"
+        credentials["openai_api_key"] = credentials["api_key"]
+        if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
+            credentials["openai_api_base"] = "https://api.lingyiwanwu.com"
         else:
-            parsed_url = urlparse(credentials['endpoint_url'])
-            credentials['openai_api_base']=f"{parsed_url.scheme}://{parsed_url.netloc}"
+            parsed_url = urlparse(credentials["endpoint_url"])
+            credentials["openai_api_base"] = f"{parsed_url.scheme}://{parsed_url.netloc}"
diff --git a/api/core/model_runtime/model_providers/yi/yi.py b/api/core/model_runtime/model_providers/yi/yi.py
index 691c7aa371..9599acb22b 100644
--- a/api/core/model_runtime/model_providers/yi/yi.py
+++ b/api/core/model_runtime/model_providers/yi/yi.py
@@ -8,7 +8,6 @@ logger = logging.getLogger(__name__)
 
 
 class YiProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -21,12 +20,9 @@ class YiProvider(ModelProvider):
 
             # Use `yi-34b-chat-0205` model for validate,
             # no matter what model you pass in, text completion model or chat model
-            model_instance.validate_credentials(
-                model='yi-34b-chat-0205',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="yi-34b-chat-0205", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/zhinao/llm/llm.py b/api/core/model_runtime/model_providers/zhinao/llm/llm.py
index 6930a5ed01..befc3de021 100644
--- a/api/core/model_runtime/model_providers/zhinao/llm/llm.py
+++ b/api/core/model_runtime/model_providers/zhinao/llm/llm.py
@@ -7,11 +7,17 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
 
 
 class ZhinaoLargeLanguageModel(OAIAPICompatLargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
@@ -21,5 +27,5 @@ class ZhinaoLargeLanguageModel(OAIAPICompatLargeLanguageModel):
 
     @classmethod
     def _add_custom_parameters(cls, credentials: dict) -> None:
-        credentials['mode'] = 'chat'
-        credentials['endpoint_url'] = 'https://api.360.cn/v1'
+        credentials["mode"] = "chat"
+        credentials["endpoint_url"] = "https://api.360.cn/v1"
diff --git a/api/core/model_runtime/model_providers/zhinao/zhinao.py b/api/core/model_runtime/model_providers/zhinao/zhinao.py
index 44b36c9f51..2a263292f9 100644
--- a/api/core/model_runtime/model_providers/zhinao/zhinao.py
+++ b/api/core/model_runtime/model_providers/zhinao/zhinao.py
@@ -8,7 +8,6 @@ logger = logging.getLogger(__name__)
 
 
 class ZhinaoProvider(ModelProvider):
-
     def validate_provider_credentials(self, credentials: dict) -> None:
         """
         Validate provider credentials
@@ -21,12 +20,9 @@ class ZhinaoProvider(ModelProvider):
 
             # Use `360gpt-turbo` model for validate,
             # no matter what model you pass in, text completion model or chat model
-            model_instance.validate_credentials(
-                model='360gpt-turbo',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="360gpt-turbo", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/zhipuai/_common.py b/api/core/model_runtime/model_providers/zhipuai/_common.py
index 3412d8100f..fa95232f71 100644
--- a/api/core/model_runtime/model_providers/zhipuai/_common.py
+++ b/api/core/model_runtime/model_providers/zhipuai/_common.py
@@ -17,8 +17,7 @@ class _CommonZhipuaiAI:
         :return:
         """
         credentials_kwargs = {
-            "api_key": credentials['api_key'] if 'api_key' in credentials else 
-                        credentials.get("zhipuai_api_key"),
+            "api_key": credentials["api_key"] if "api_key" in credentials else credentials.get("zhipuai_api_key"),
         }
 
         return credentials_kwargs
@@ -38,5 +37,5 @@ class _CommonZhipuaiAI:
             InvokeServerUnavailableError: [],
             InvokeRateLimitError: [],
             InvokeAuthorizationError: [],
-            InvokeBadRequestError: []
+            InvokeBadRequestError: [],
         }
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/chatglm_turbo.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/chatglm_turbo.yaml
index 8f51f80967..fcd5c5ef64 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/chatglm_turbo.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/chatglm_turbo.yaml
@@ -19,15 +19,24 @@ parameter_rules:
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
-  - name: incremental
+  - name: do_sample
     label:
-      zh_Hans: 增量返回
-      en_US: Incremental
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
     type: boolean
     help:
-      zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
-      en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
-    required: false
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: return_type
     label:
       zh_Hans: 回复类型
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-0520.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-0520.yaml
index 8391278e4f..b1f9b7485c 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-0520.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-0520.yaml
@@ -23,20 +23,29 @@ parameter_rules:
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
-  - name: incremental
+  - name: do_sample
     label:
-      zh_Hans: 增量返回
-      en_US: Incremental
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
     type: boolean
     help:
-      zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
-      en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
-    required: false
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024
     min: 1
-    max: 8192
+    max: 4095
 pricing:
   input: '0.1'
   output: '0.1'
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-air.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-air.yaml
index 7caebd3e4b..4e7d5fd3cc 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-air.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-air.yaml
@@ -23,20 +23,29 @@ parameter_rules:
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
-  - name: incremental
+  - name: do_sample
     label:
-      zh_Hans: 增量返回
-      en_US: Incremental
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
     type: boolean
     help:
-      zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
-      en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
-    required: false
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024
     min: 1
-    max: 8192
+    max: 4095
 pricing:
   input: '0.001'
   output: '0.001'
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-airx.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-airx.yaml
index dc123913de..14f17db5d6 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-airx.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-airx.yaml
@@ -23,20 +23,29 @@ parameter_rules:
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
-  - name: incremental
+  - name: do_sample
     label:
-      zh_Hans: 增量返回
-      en_US: Incremental
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
     type: boolean
     help:
-      zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
-      en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
-    required: false
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024
     min: 1
-    max: 8192
+    max: 4095
 pricing:
   input: '0.01'
   output: '0.01'
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flash.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flash.yaml
index 0e3c001f06..3361474d73 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flash.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flash.yaml
@@ -23,20 +23,29 @@ parameter_rules:
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
-  - name: incremental
+  - name: do_sample
     label:
-      zh_Hans: 增量返回
-      en_US: Incremental
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
     type: boolean
     help:
-      zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
-      en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
-    required: false
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024
     min: 1
-    max: 8192
+    max: 4095
 pricing:
   input: '0'
   output: '0'
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml
index b0f95c0a68..bf0135d198 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml
@@ -23,15 +23,24 @@ parameter_rules:
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
-  - name: incremental
+  - name: do_sample
     label:
-      zh_Hans: 增量返回
-      en_US: Incremental
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
     type: boolean
     help:
-      zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
-      en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
-    required: false
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml
index 271eecf199..ab4b32dd82 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml
@@ -23,20 +23,29 @@ parameter_rules:
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
-  - name: incremental
+  - name: do_sample
     label:
-      zh_Hans: 增量返回
-      en_US: Incremental
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
     type: boolean
     help:
-      zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
-      en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
-    required: false
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024
     min: 1
-    max: 8192
+    max: 4095
 pricing:
   input: '0.1'
   output: '0.1'
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_long.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_long.yaml
index 150e07b60a..d1b01731f5 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_long.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_long.yaml
@@ -26,11 +26,29 @@ parameter_rules:
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
+  - name: do_sample
+    label:
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
+    type: boolean
+    help:
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024
     min: 1
-    max: 8192
+    max: 4095
 pricing:
   input: '0.001'
   output: '0.001'
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_plus.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_plus.yaml
index 237a951cd5..9ede308f18 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_plus.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_plus.yaml
@@ -23,20 +23,29 @@ parameter_rules:
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
-  - name: incremental
+  - name: do_sample
     label:
-      zh_Hans: 增量返回
-      en_US: Incremental
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
     type: boolean
     help:
-      zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
-      en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
-    required: false
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024
     min: 1
-    max: 8192
+    max: 4095
 pricing:
   input: '0.05'
   output: '0.05'
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml
index c7a4093d7a..28286580a7 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml
@@ -17,19 +17,28 @@ parameter_rules:
       en_US: Sampling temperature, controls the randomness of the output, must be a positive number. The value range is (0.0,1.0], which cannot be equal to 0. The default value is 0.95. The larger the value, the more random and creative the output will be; the smaller the value, The output will be more stable or certain. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
   - name: top_p
     use_template: top_p
-    default: 0.7
+    default: 0.6
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
-  - name: incremental
+  - name: do_sample
     label:
-      zh_Hans: 增量返回
-      en_US: Incremental
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
     type: boolean
     help:
-      zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
-      en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
-    required: false
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_plus.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_plus.yaml
index a7aee5b4ca..4c5fa24034 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_plus.yaml
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_plus.yaml
@@ -17,19 +17,28 @@ parameter_rules:
       en_US: Sampling temperature, controls the randomness of the output, must be a positive number. The value range is (0.0,1.0], which cannot be equal to 0. The default value is 0.95. The larger the value, the more random and creative the output will be; the smaller the value, The output will be more stable or certain. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
   - name: top_p
     use_template: top_p
-    default: 0.7
+    default: 0.6
     help:
       zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
       en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
-  - name: incremental
+  - name: do_sample
     label:
-      zh_Hans: 增量返回
-      en_US: Incremental
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
     type: boolean
     help:
-      zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
-      en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
-    required: false
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: stream
+    label:
+      zh_Hans: 流处理
+      en_US: Event Stream
+    type: boolean
+    help:
+      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
+      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
+    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024
diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/llm.py b/api/core/model_runtime/model_providers/zhipuai/llm/llm.py
index b2cdc7ad7a..ea331701ab 100644
--- a/api/core/model_runtime/model_providers/zhipuai/llm/llm.py
+++ b/api/core/model_runtime/model_providers/zhipuai/llm/llm.py
@@ -31,16 +31,21 @@ And you should always end the block with a "```" to indicate the end of the JSON
 {{instructions}}
 </instructions>
 
-```JSON"""
+```JSON"""  # noqa: E501
 
 
 class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
-
-    def _invoke(self, model: str, credentials: dict,
-                prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                stream: bool = True, user: Optional[str] = None) \
-            -> Union[LLMResult, Generator]:
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -62,9 +67,9 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         # self._transform_json_prompts(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
         return self._generate(model, credentials_kwargs, prompt_messages, model_parameters, tools, stop, stream, user)
 
-    # def _transform_json_prompts(self, model: str, credentials: dict, 
-    #                             prompt_messages: list[PromptMessage], model_parameters: dict, 
-    #                             tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
+    # def _transform_json_prompts(self, model: str, credentials: dict,
+    #                             prompt_messages: list[PromptMessage], model_parameters: dict,
+    #                             tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
     #                             stream: bool = True, user: str | None = None) \
     #                         -> None:
     #     """
@@ -94,8 +99,13 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
     #             content="```JSON\n"
     #         ))
 
-    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
         """
         Get number of tokens for given prompt messages
 
@@ -130,16 +140,22 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
                     "temperature": 0.5,
                 },
                 tools=[],
-                stream=False
+                stream=False,
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _generate(self, model: str, credentials_kwargs: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict,
-                  tools: Optional[list[PromptMessageTool]] = None,
-                  stop: Optional[list[str]] = None, stream: bool = True,
-                  user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _generate(
+        self,
+        model: str,
+        credentials_kwargs: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+    ) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
 
@@ -154,15 +170,13 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         """
         extra_model_kwargs = {}
         # request to glm-4v-plus with stop words will always response "finish_reason":"network_error"
-        if stop and model!= 'glm-4v-plus':
-            extra_model_kwargs['stop'] = stop
+        if stop and model != "glm-4v-plus":
+            extra_model_kwargs["stop"] = stop
 
-        client = ZhipuAI(
-            api_key=credentials_kwargs['api_key']
-        )
+        client = ZhipuAI(api_key=credentials_kwargs["api_key"])
 
         if len(prompt_messages) == 0:
-            raise ValueError('At least one message is required')
+            raise ValueError("At least one message is required")
 
         if prompt_messages[0].role == PromptMessageRole.SYSTEM:
             if not prompt_messages[0].content:
@@ -172,13 +186,13 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         new_prompt_messages: list[PromptMessage] = []
         for prompt_message in prompt_messages:
             copy_prompt_message = prompt_message.copy()
-            if copy_prompt_message.role in [PromptMessageRole.USER, PromptMessageRole.SYSTEM, PromptMessageRole.TOOL]:
+            if copy_prompt_message.role in {PromptMessageRole.USER, PromptMessageRole.SYSTEM, PromptMessageRole.TOOL}:
                 if isinstance(copy_prompt_message.content, list):
                     # check if model is 'glm-4v'
-                    if model not in ('glm-4v', 'glm-4v-plus'):
+                    if model not in {"glm-4v", "glm-4v-plus"}:
                         # not support list message
                         continue
-                    # get image and 
+                    # get image and
                     if not isinstance(copy_prompt_message, UserPromptMessage):
                         # not support system message
                         continue
@@ -188,13 +202,14 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
                     # not support image message
                     continue
 
-                if new_prompt_messages and new_prompt_messages[-1].role == PromptMessageRole.USER and \
-                        copy_prompt_message.role == PromptMessageRole.USER:
+                if (
+                    new_prompt_messages
+                    and new_prompt_messages[-1].role == PromptMessageRole.USER
+                    and copy_prompt_message.role == PromptMessageRole.USER
+                ):
                     new_prompt_messages[-1].content += "\n\n" + copy_prompt_message.content
                 else:
-                    if copy_prompt_message.role == PromptMessageRole.USER:
-                        new_prompt_messages.append(copy_prompt_message)
-                    elif copy_prompt_message.role == PromptMessageRole.TOOL:
+                    if copy_prompt_message.role in {PromptMessageRole.USER, PromptMessageRole.TOOL}:
                         new_prompt_messages.append(copy_prompt_message)
                     elif copy_prompt_message.role == PromptMessageRole.SYSTEM:
                         new_prompt_message = SystemPromptMessage(content=copy_prompt_message.content)
@@ -208,77 +223,66 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
                 else:
                     new_prompt_messages.append(copy_prompt_message)
 
-        if model == 'glm-4v' or model == 'glm-4v-plus':
+        if model in {"glm-4v", "glm-4v-plus"}:
             params = self._construct_glm_4v_parameter(model, new_prompt_messages, model_parameters)
         else:
-            params = {
-                'model': model,
-                'messages': [],
-                **model_parameters
-            }
+            params = {"model": model, "messages": [], **model_parameters}
             # glm model
-            if not model.startswith('chatglm'):
-
+            if not model.startswith("chatglm"):
                 for prompt_message in new_prompt_messages:
                     if prompt_message.role == PromptMessageRole.TOOL:
-                        params['messages'].append({
-                            'role': 'tool',
-                            'content': prompt_message.content,
-                            'tool_call_id': prompt_message.tool_call_id
-                        })
+                        params["messages"].append(
+                            {
+                                "role": "tool",
+                                "content": prompt_message.content,
+                                "tool_call_id": prompt_message.tool_call_id,
+                            }
+                        )
                     elif isinstance(prompt_message, AssistantPromptMessage):
                         if prompt_message.tool_calls:
-                            params['messages'].append({
-                                'role': 'assistant',
-                                'content': prompt_message.content,
-                                'tool_calls': [
-                                    {
-                                        'id': tool_call.id,
-                                        'type': tool_call.type,
-                                        'function': {
-                                            'name': tool_call.function.name,
-                                            'arguments': tool_call.function.arguments
+                            params["messages"].append(
+                                {
+                                    "role": "assistant",
+                                    "content": prompt_message.content,
+                                    "tool_calls": [
+                                        {
+                                            "id": tool_call.id,
+                                            "type": tool_call.type,
+                                            "function": {
+                                                "name": tool_call.function.name,
+                                                "arguments": tool_call.function.arguments,
+                                            },
                                         }
-                                    } for tool_call in prompt_message.tool_calls
-                                ]
-                            })
+                                        for tool_call in prompt_message.tool_calls
+                                    ],
+                                }
+                            )
                         else:
-                            params['messages'].append({
-                                'role': 'assistant',
-                                'content': prompt_message.content
-                            })
+                            params["messages"].append({"role": "assistant", "content": prompt_message.content})
                     else:
-                        params['messages'].append({
-                            'role': prompt_message.role.value,
-                            'content': prompt_message.content
-                        })
+                        params["messages"].append(
+                            {"role": prompt_message.role.value, "content": prompt_message.content}
+                        )
             else:
                 # chatglm model
                 for prompt_message in new_prompt_messages:
                     # merge system message to user message
-                    if prompt_message.role == PromptMessageRole.SYSTEM or \
-                            prompt_message.role == PromptMessageRole.TOOL or \
-                            prompt_message.role == PromptMessageRole.USER:
-                        if len(params['messages']) > 0 and params['messages'][-1]['role'] == 'user':
-                            params['messages'][-1]['content'] += "\n\n" + prompt_message.content
+                    if prompt_message.role in {
+                        PromptMessageRole.SYSTEM,
+                        PromptMessageRole.TOOL,
+                        PromptMessageRole.USER,
+                    }:
+                        if len(params["messages"]) > 0 and params["messages"][-1]["role"] == "user":
+                            params["messages"][-1]["content"] += "\n\n" + prompt_message.content
                         else:
-                            params['messages'].append({
-                                'role': 'user',
-                                'content': prompt_message.content
-                            })
+                            params["messages"].append({"role": "user", "content": prompt_message.content})
                     else:
-                        params['messages'].append({
-                            'role': prompt_message.role.value,
-                            'content': prompt_message.content
-                        })
+                        params["messages"].append(
+                            {"role": prompt_message.role.value, "content": prompt_message.content}
+                        )
 
         if tools and len(tools) > 0:
-            params['tools'] = [
-                {
-                    'type': 'function',
-                    'function': helper.dump_model(tool)
-                } for tool in tools
-            ]
+            params["tools"] = [{"type": "function", "function": helper.dump_model(tool)} for tool in tools]
 
         if stream:
             response = client.chat.completions.create(stream=stream, **params, **extra_model_kwargs)
@@ -287,47 +291,41 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         response = client.chat.completions.create(**params, **extra_model_kwargs)
         return self._handle_generate_response(model, credentials_kwargs, tools, response, prompt_messages)
 
-    def _construct_glm_4v_parameter(self, model: str, prompt_messages: list[PromptMessage],
-                                    model_parameters: dict):
+    def _construct_glm_4v_parameter(self, model: str, prompt_messages: list[PromptMessage], model_parameters: dict):
         messages = [
-            {
-                'role': message.role.value,
-                'content': self._construct_glm_4v_messages(message.content)
-            }
+            {"role": message.role.value, "content": self._construct_glm_4v_messages(message.content)}
             for message in prompt_messages
         ]
 
-        params = {
-            'model': model,
-            'messages': messages,
-            **model_parameters
-        }
+        params = {"model": model, "messages": messages, **model_parameters}
 
         return params
 
     def _construct_glm_4v_messages(self, prompt_message: Union[str, list[PromptMessageContent]]) -> list[dict]:
         if isinstance(prompt_message, str):
-            return [{'type': 'text', 'text': prompt_message}]
+            return [{"type": "text", "text": prompt_message}]
 
         return [
-            {'type': 'image_url', 'image_url': {'url': self._remove_image_header(item.data)}}
-            if item.type == PromptMessageContentType.IMAGE else
-            {'type': 'text', 'text': item.data}
-
+            {"type": "image_url", "image_url": {"url": self._remove_image_header(item.data)}}
+            if item.type == PromptMessageContentType.IMAGE
+            else {"type": "text", "text": item.data}
             for item in prompt_message
         ]
 
     def _remove_image_header(self, image: str) -> str:
-        if image.startswith('data:image'):
-            return image.split(',')[1]
+        if image.startswith("data:image"):
+            return image.split(",")[1]
 
         return image
 
-    def _handle_generate_response(self, model: str,
-                                  credentials: dict,
-                                  tools: Optional[list[PromptMessageTool]],
-                                  response: Completion,
-                                  prompt_messages: list[PromptMessage]) -> LLMResult:
+    def _handle_generate_response(
+        self,
+        model: str,
+        credentials: dict,
+        tools: Optional[list[PromptMessageTool]],
+        response: Completion,
+        prompt_messages: list[PromptMessage],
+    ) -> LLMResult:
         """
         Handle llm response
 
@@ -336,12 +334,12 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: llm response
         """
-        text = ''
+        text = ""
         assistant_tool_calls: list[AssistantPromptMessage.ToolCall] = []
         for choice in response.choices:
             if choice.message.tool_calls:
                 for tool_call in choice.message.tool_calls:
-                    if tool_call.type == 'function':
+                    if tool_call.type == "function":
                         assistant_tool_calls.append(
                             AssistantPromptMessage.ToolCall(
                                 id=tool_call.id,
@@ -349,11 +347,11 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
                                 function=AssistantPromptMessage.ToolCall.ToolCallFunction(
                                     name=tool_call.function.name,
                                     arguments=tool_call.function.arguments,
-                                )
+                                ),
                             )
                         )
 
-            text += choice.message.content or ''
+            text += choice.message.content or ""
 
         prompt_usage = response.usage.prompt_tokens
         completion_usage = response.usage.completion_tokens
@@ -365,20 +363,20 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         result = LLMResult(
             model=model,
             prompt_messages=prompt_messages,
-            message=AssistantPromptMessage(
-                content=text,
-                tool_calls=assistant_tool_calls
-            ),
+            message=AssistantPromptMessage(content=text, tool_calls=assistant_tool_calls),
             usage=usage,
         )
 
         return result
 
-    def _handle_generate_stream_response(self, model: str,
-                                         credentials: dict,
-                                         tools: Optional[list[PromptMessageTool]],
-                                         responses: Generator[ChatCompletionChunk, None, None],
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_generate_stream_response(
+        self,
+        model: str,
+        credentials: dict,
+        tools: Optional[list[PromptMessageTool]],
+        responses: Generator[ChatCompletionChunk, None, None],
+        prompt_messages: list[PromptMessage],
+    ) -> Generator:
         """
         Handle llm stream response
 
@@ -387,19 +385,19 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: llm response chunk generator result
         """
-        full_assistant_content = ''
+        full_assistant_content = ""
         for chunk in responses:
             if len(chunk.choices) == 0:
                 continue
 
             delta = chunk.choices[0]
 
-            if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ''):
+            if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ""):
                 continue
 
             assistant_tool_calls: list[AssistantPromptMessage.ToolCall] = []
             for tool_call in delta.delta.tool_calls or []:
-                if tool_call.type == 'function':
+                if tool_call.type == "function":
                     assistant_tool_calls.append(
                         AssistantPromptMessage.ToolCall(
                             id=tool_call.id,
@@ -407,17 +405,16 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
                             function=AssistantPromptMessage.ToolCall.ToolCallFunction(
                                 name=tool_call.function.name,
                                 arguments=tool_call.function.arguments,
-                            )
+                            ),
                         )
                     )
 
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content if delta.delta.content else '',
-                tool_calls=assistant_tool_calls
+                content=delta.delta.content or "", tool_calls=assistant_tool_calls
             )
 
-            full_assistant_content += delta.delta.content if delta.delta.content else ''
+            full_assistant_content += delta.delta.content or ""
 
             if delta.finish_reason is not None and chunk.usage is not None:
                 completion_tokens = chunk.usage.completion_tokens
@@ -429,24 +426,22 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
                 yield LLMResultChunk(
                     model=chunk.model,
                     prompt_messages=prompt_messages,
-                    system_fingerprint='',
+                    system_fingerprint="",
                     delta=LLMResultChunkDelta(
                         index=delta.index,
                         message=assistant_prompt_message,
                         finish_reason=delta.finish_reason,
-                        usage=usage
-                    )
+                        usage=usage,
+                    ),
                 )
             else:
                 yield LLMResultChunk(
                     model=chunk.model,
                     prompt_messages=prompt_messages,
-                    system_fingerprint='',
+                    system_fingerprint="",
                     delta=LLMResultChunkDelta(
-                        index=delta.index,
-                        message=assistant_prompt_message,
-                        finish_reason=delta.finish_reason
-                    )
+                        index=delta.index, message=assistant_prompt_message, finish_reason=delta.finish_reason
+                    ),
                 )
 
     def _convert_one_message_to_text(self, message: PromptMessage) -> str:
@@ -464,27 +459,23 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
             message_text = f"{human_prompt} {content}"
         elif isinstance(message, AssistantPromptMessage):
             message_text = f"{ai_prompt} {content}"
-        elif isinstance(message, SystemPromptMessage):
-            message_text = content
-        elif isinstance(message, ToolPromptMessage):
+        elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
             message_text = content
         else:
             raise ValueError(f"Got unknown type {message}")
 
         return message_text
 
-    def _convert_messages_to_prompt(self, messages: list[PromptMessage],
-                                    tools: Optional[list[PromptMessageTool]] = None) -> str:
+    def _convert_messages_to_prompt(
+        self, messages: list[PromptMessage], tools: Optional[list[PromptMessageTool]] = None
+    ) -> str:
         """
         :param messages: List of PromptMessage to combine.
         :return: Combined string with necessary human_prompt and ai_prompt tags.
         """
         messages = messages.copy()  # don't mutate the original list
 
-        text = "".join(
-            self._convert_one_message_to_text(message)
-            for message in messages
-        )
+        text = "".join(self._convert_one_message_to_text(message) for message in messages)
 
         if tools and len(tools) > 0:
             text += "\n\nTools:"
diff --git a/api/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py
index 0f9fecfc72..ee20954381 100644
--- a/api/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py
+++ b/api/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py
@@ -14,9 +14,9 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
     Model class for ZhipuAI text embedding model.
     """
 
-    def _invoke(self, model: str, credentials: dict,
-                texts: list[str], user: Optional[str] = None) \
-            -> TextEmbeddingResult:
+    def _invoke(
+        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+    ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
 
@@ -27,16 +27,14 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
         :return: embeddings result
         """
         credentials_kwargs = self._to_credential_kwargs(credentials)
-        client = ZhipuAI(
-            api_key=credentials_kwargs['api_key']
-        )
+        client = ZhipuAI(api_key=credentials_kwargs["api_key"])
 
         embeddings, embedding_used_tokens = self.embed_documents(model, client, texts)
 
         return TextEmbeddingResult(
             embeddings=embeddings,
             usage=self._calc_response_usage(model, credentials_kwargs, embedding_used_tokens),
-            model=model
+            model=model,
         )
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
@@ -50,7 +48,7 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
         """
         if len(texts) == 0:
             return 0
-        
+
         total_num_tokens = 0
         for text in texts:
             total_num_tokens += self._get_num_tokens_by_gpt2(text)
@@ -68,15 +66,13 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
         try:
             # transform credentials to kwargs for model instance
             credentials_kwargs = self._to_credential_kwargs(credentials)
-            client = ZhipuAI(
-                api_key=credentials_kwargs['api_key']
-            )
+            client = ZhipuAI(api_key=credentials_kwargs["api_key"])
 
             # call embedding model
             self.embed_documents(
                 model=model,
                 client=client,
-                texts=['ping'],
+                texts=["ping"],
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -100,7 +96,7 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
             embedding_used_tokens += response.usage.total_tokens
 
         return [list(map(float, e)) for e in embeddings], embedding_used_tokens
-    
+
     def embed_query(self, text: str) -> list[float]:
         """Call out to ZhipuAI's embedding endpoint.
 
@@ -111,8 +107,8 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
             Embeddings for the text.
         """
         return self.embed_documents([text])[0]
-    
-    def _calc_response_usage(self, model: str,credentials: dict, tokens: int) -> EmbeddingUsage:
+
+    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage
 
@@ -122,10 +118,7 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
         """
         # get input price info
         input_price_info = self.get_price(
-            model=model,
-            credentials=credentials,
-            price_type=PriceType.INPUT,
-            tokens=tokens
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
         )
 
         # transform usage
@@ -136,7 +129,7 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at
+            latency=time.perf_counter() - self.started_at,
         )
 
         return usage
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai.py
index c517d2dba5..e75aad6eb0 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai.py
@@ -19,12 +19,9 @@ class ZhipuaiProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            model_instance.validate_credentials(
-                model='glm-4',
-                credentials=credentials
-            )
+            model_instance.validate_credentials(model="glm-4", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
             raise ex
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/__init__.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/__init__.py
index 4dcd03f551..bf9b093cb3 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/__init__.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/__init__.py
@@ -1,4 +1,3 @@
-
 from .__version__ import __version__
 from ._client import ZhipuAI
 from .core._errors import (
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/__version__.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/__version__.py
index eb0ad332ca..659f38d7ff 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/__version__.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/__version__.py
@@ -1,2 +1 @@
-
-__version__ = 'v2.0.1'
\ No newline at end of file
+__version__ = "v2.0.1"
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/_client.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/_client.py
index 6588d1dd68..df9e506095 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/_client.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/_client.py
@@ -20,14 +20,14 @@ class ZhipuAI(HttpClient):
     api_key: str
 
     def __init__(
-            self,
-            *,
-            api_key: str | None = None,
-            base_url: str | httpx.URL | None = None,
-            timeout: Union[float, Timeout, None, NotGiven] = NOT_GIVEN,
-            max_retries: int = ZHIPUAI_DEFAULT_MAX_RETRIES,
-            http_client: httpx.Client | None = None,
-            custom_headers: Mapping[str, str] | None = None
+        self,
+        *,
+        api_key: str | None = None,
+        base_url: str | httpx.URL | None = None,
+        timeout: Union[float, Timeout, None, NotGiven] = NOT_GIVEN,
+        max_retries: int = ZHIPUAI_DEFAULT_MAX_RETRIES,
+        http_client: httpx.Client | None = None,
+        custom_headers: Mapping[str, str] | None = None,
     ) -> None:
         if api_key is None:
             raise ZhipuAIError("No api_key provided, please provide it through parameters or environment variables")
@@ -38,6 +38,7 @@ class ZhipuAI(HttpClient):
         if base_url is None:
             base_url = "https://open.bigmodel.cn/api/paas/v4"
         from .__version__ import __version__
+
         super().__init__(
             version=__version__,
             base_url=base_url,
@@ -58,9 +59,7 @@ class ZhipuAI(HttpClient):
         return {"Authorization": f"{_jwt_token.generate_token(api_key)}"}
 
     def __del__(self) -> None:
-        if (not hasattr(self, "_has_custom_http_client")
-                or not hasattr(self, "close")
-                or not hasattr(self, "_client")):
+        if not hasattr(self, "_has_custom_http_client") or not hasattr(self, "close") or not hasattr(self, "_client"):
             # if the '__init__' method raised an error, self would not have client attr
             return
 
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/async_completions.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/async_completions.py
index dab6dac5fe..1f80119739 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/async_completions.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/async_completions.py
@@ -17,25 +17,24 @@ class AsyncCompletions(BaseAPI):
     def __init__(self, client: ZhipuAI) -> None:
         super().__init__(client)
 
-
     def create(
-            self,
-            *,
-            model: str,
-            request_id: Optional[str] | NotGiven = NOT_GIVEN,
-            do_sample: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
-            temperature: Optional[float] | NotGiven = NOT_GIVEN,
-            top_p: Optional[float] | NotGiven = NOT_GIVEN,
-            max_tokens: int | NotGiven = NOT_GIVEN,
-            seed: int | NotGiven = NOT_GIVEN,
-            messages: Union[str, list[str], list[int], list[list[int]], None],
-            stop: Optional[Union[str, list[str], None]] | NotGiven = NOT_GIVEN,
-            sensitive_word_check: Optional[object] | NotGiven = NOT_GIVEN,
-            tools: Optional[object] | NotGiven = NOT_GIVEN,
-            tool_choice: str | NotGiven = NOT_GIVEN,
-            extra_headers: Headers | None = None,
-            disable_strict_validation: Optional[bool] | None = None,
-            timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+        self,
+        *,
+        model: str,
+        request_id: Optional[str] | NotGiven = NOT_GIVEN,
+        do_sample: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
+        temperature: Optional[float] | NotGiven = NOT_GIVEN,
+        top_p: Optional[float] | NotGiven = NOT_GIVEN,
+        max_tokens: int | NotGiven = NOT_GIVEN,
+        seed: int | NotGiven = NOT_GIVEN,
+        messages: Union[str, list[str], list[int], list[list[int]], None],
+        stop: Optional[Union[str, list[str], None]] | NotGiven = NOT_GIVEN,
+        sensitive_word_check: Optional[object] | NotGiven = NOT_GIVEN,
+        tools: Optional[object] | NotGiven = NOT_GIVEN,
+        tool_choice: str | NotGiven = NOT_GIVEN,
+        extra_headers: Headers | None = None,
+        disable_strict_validation: Optional[bool] | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> AsyncTaskStatus:
         _cast_type = AsyncTaskStatus
 
@@ -57,9 +56,7 @@ class AsyncCompletions(BaseAPI):
                 "tools": tools,
                 "tool_choice": tool_choice,
             },
-            options=make_user_request_input(
-                extra_headers=extra_headers, timeout=timeout
-            ),
+            options=make_user_request_input(extra_headers=extra_headers, timeout=timeout),
             cast_type=_cast_type,
             enable_stream=False,
         )
@@ -71,16 +68,11 @@ class AsyncCompletions(BaseAPI):
         disable_strict_validation: Optional[bool] | None = None,
         timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> Union[AsyncCompletion, AsyncTaskStatus]:
-        _cast_type = Union[AsyncCompletion,AsyncTaskStatus]
+        _cast_type = Union[AsyncCompletion, AsyncTaskStatus]
         if disable_strict_validation:
             _cast_type = object
         return self._get(
             path=f"/async-result/{id}",
             cast_type=_cast_type,
-            options=make_user_request_input(
-                extra_headers=extra_headers,
-                timeout=timeout
-            )
+            options=make_user_request_input(extra_headers=extra_headers, timeout=timeout),
         )
-
-
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/completions.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/completions.py
index 5c4ed4d1ba..ec29f33864 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/completions.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/completions.py
@@ -20,24 +20,24 @@ class Completions(BaseAPI):
         super().__init__(client)
 
     def create(
-            self,
-            *,
-            model: str,
-            request_id: Optional[str] | NotGiven = NOT_GIVEN,
-            do_sample: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
-            stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
-            temperature: Optional[float] | NotGiven = NOT_GIVEN,
-            top_p: Optional[float] | NotGiven = NOT_GIVEN,
-            max_tokens: int | NotGiven = NOT_GIVEN,
-            seed: int | NotGiven = NOT_GIVEN,
-            messages: Union[str, list[str], list[int], object, None],
-            stop: Optional[Union[str, list[str], None]] | NotGiven = NOT_GIVEN,
-            sensitive_word_check: Optional[object] | NotGiven = NOT_GIVEN,
-            tools: Optional[object] | NotGiven = NOT_GIVEN,
-            tool_choice: str | NotGiven = NOT_GIVEN,
-            extra_headers: Headers | None = None,
-            disable_strict_validation: Optional[bool] | None = None,
-            timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+        self,
+        *,
+        model: str,
+        request_id: Optional[str] | NotGiven = NOT_GIVEN,
+        do_sample: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
+        stream: Optional[Literal[False]] | Literal[True] | NotGiven = NOT_GIVEN,
+        temperature: Optional[float] | NotGiven = NOT_GIVEN,
+        top_p: Optional[float] | NotGiven = NOT_GIVEN,
+        max_tokens: int | NotGiven = NOT_GIVEN,
+        seed: int | NotGiven = NOT_GIVEN,
+        messages: Union[str, list[str], list[int], object, None],
+        stop: Optional[Union[str, list[str], None]] | NotGiven = NOT_GIVEN,
+        sensitive_word_check: Optional[object] | NotGiven = NOT_GIVEN,
+        tools: Optional[object] | NotGiven = NOT_GIVEN,
+        tool_choice: str | NotGiven = NOT_GIVEN,
+        extra_headers: Headers | None = None,
+        disable_strict_validation: Optional[bool] | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> Completion | StreamResponse[ChatCompletionChunk]:
         _cast_type = Completion
         _stream_cls = StreamResponse[ChatCompletionChunk]
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/embeddings.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/embeddings.py
index 35d54592fd..2308a20451 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/embeddings.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/embeddings.py
@@ -18,16 +18,16 @@ class Embeddings(BaseAPI):
         super().__init__(client)
 
     def create(
-            self,
-            *,
-            input: Union[str, list[str], list[int], list[list[int]]],
-            model: Union[str],
-            encoding_format: str | NotGiven = NOT_GIVEN,
-            user: str | NotGiven = NOT_GIVEN,
-            sensitive_word_check: Optional[object] | NotGiven = NOT_GIVEN,
-            extra_headers: Headers | None = None,
-            disable_strict_validation: Optional[bool] | None = None,
-            timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+        self,
+        *,
+        input: Union[str, list[str], list[int], list[list[int]]],
+        model: Union[str],
+        encoding_format: str | NotGiven = NOT_GIVEN,
+        user: str | NotGiven = NOT_GIVEN,
+        sensitive_word_check: Optional[object] | NotGiven = NOT_GIVEN,
+        extra_headers: Headers | None = None,
+        disable_strict_validation: Optional[bool] | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> EmbeddingsResponded:
         _cast_type = EmbeddingsResponded
         if disable_strict_validation:
@@ -41,9 +41,7 @@ class Embeddings(BaseAPI):
                 "user": user,
                 "sensitive_word_check": sensitive_word_check,
             },
-            options=make_user_request_input(
-                extra_headers=extra_headers, timeout=timeout
-            ),
+            options=make_user_request_input(extra_headers=extra_headers, timeout=timeout),
             cast_type=_cast_type,
             enable_stream=False,
         )
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/files.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/files.py
index 5deb8d08f3..f2ac74bffa 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/files.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/files.py
@@ -17,17 +17,16 @@ __all__ = ["Files"]
 
 
 class Files(BaseAPI):
-
     def __init__(self, client: ZhipuAI) -> None:
         super().__init__(client)
 
     def create(
-            self,
-            *,
-            file: FileTypes,
-            purpose: str,
-            extra_headers: Headers | None = None,
-            timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+        self,
+        *,
+        file: FileTypes,
+        purpose: str,
+        extra_headers: Headers | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> FileObject:
         if not is_file_content(file):
             prefix = f"Expected file input `{file!r}`"
@@ -44,21 +43,19 @@ class Files(BaseAPI):
                 "purpose": purpose,
             },
             files=files,
-            options=make_user_request_input(
-                extra_headers=extra_headers, timeout=timeout
-            ),
+            options=make_user_request_input(extra_headers=extra_headers, timeout=timeout),
             cast_type=FileObject,
         )
 
     def list(
-            self,
-            *,
-            purpose: str | NotGiven = NOT_GIVEN,
-            limit: int  | NotGiven = NOT_GIVEN,
-            after: str | NotGiven = NOT_GIVEN,
-            order: str | NotGiven = NOT_GIVEN,
-            extra_headers: Headers | None = None,
-            timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+        self,
+        *,
+        purpose: str | NotGiven = NOT_GIVEN,
+        limit: int | NotGiven = NOT_GIVEN,
+        after: str | NotGiven = NOT_GIVEN,
+        order: str | NotGiven = NOT_GIVEN,
+        extra_headers: Headers | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> ListOfFileObject:
         return self._get(
             "/files",
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/fine_tuning/fine_tuning.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/fine_tuning/fine_tuning.py
index dc54a9ca45..dc30bd33ed 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/fine_tuning/fine_tuning.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/fine_tuning/fine_tuning.py
@@ -13,4 +13,3 @@ class FineTuning(BaseAPI):
     def __init__(self, client: "ZhipuAI") -> None:
         super().__init__(client)
         self.jobs = Jobs(client)
-
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/fine_tuning/jobs.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/fine_tuning/jobs.py
index b860de192a..3d2e9208a1 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/fine_tuning/jobs.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/fine_tuning/jobs.py
@@ -16,21 +16,20 @@ __all__ = ["Jobs"]
 
 
 class Jobs(BaseAPI):
-
     def __init__(self, client: ZhipuAI) -> None:
         super().__init__(client)
 
     def create(
-            self,
-            *,
-            model: str,
-            training_file: str,
-            hyperparameters: job_create_params.Hyperparameters | NotGiven = NOT_GIVEN,
-            suffix: Optional[str] | NotGiven = NOT_GIVEN,
-            request_id: Optional[str] | NotGiven = NOT_GIVEN,
-            validation_file: Optional[str] | NotGiven = NOT_GIVEN,
-            extra_headers: Headers | None = None,
-            timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+        self,
+        *,
+        model: str,
+        training_file: str,
+        hyperparameters: job_create_params.Hyperparameters | NotGiven = NOT_GIVEN,
+        suffix: Optional[str] | NotGiven = NOT_GIVEN,
+        request_id: Optional[str] | NotGiven = NOT_GIVEN,
+        validation_file: Optional[str] | NotGiven = NOT_GIVEN,
+        extra_headers: Headers | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> FineTuningJob:
         return self._post(
             "/fine_tuning/jobs",
@@ -42,34 +41,30 @@ class Jobs(BaseAPI):
                 "validation_file": validation_file,
                 "request_id": request_id,
             },
-            options=make_user_request_input(
-                extra_headers=extra_headers, timeout=timeout
-            ),
+            options=make_user_request_input(extra_headers=extra_headers, timeout=timeout),
             cast_type=FineTuningJob,
         )
 
     def retrieve(
-            self,
-            fine_tuning_job_id: str,
-            *,
-            extra_headers: Headers | None = None,
-            timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+        self,
+        fine_tuning_job_id: str,
+        *,
+        extra_headers: Headers | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> FineTuningJob:
         return self._get(
             f"/fine_tuning/jobs/{fine_tuning_job_id}",
-            options=make_user_request_input(
-                extra_headers=extra_headers, timeout=timeout
-            ),
+            options=make_user_request_input(extra_headers=extra_headers, timeout=timeout),
             cast_type=FineTuningJob,
         )
 
     def list(
-            self,
-            *,
-            after: str | NotGiven = NOT_GIVEN,
-            limit: int | NotGiven = NOT_GIVEN,
-            extra_headers: Headers | None = None,
-            timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+        self,
+        *,
+        after: str | NotGiven = NOT_GIVEN,
+        limit: int | NotGiven = NOT_GIVEN,
+        extra_headers: Headers | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> ListOfFineTuningJob:
         return self._get(
             "/fine_tuning/jobs",
@@ -93,7 +88,6 @@ class Jobs(BaseAPI):
         extra_headers: Headers | None = None,
         timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> FineTuningJobEvent:
-
         return self._get(
             f"/fine_tuning/jobs/{fine_tuning_job_id}/events",
             cast_type=FineTuningJobEvent,
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/images.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/images.py
index 8eae1216d0..2692b093af 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/images.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/images.py
@@ -18,21 +18,21 @@ class Images(BaseAPI):
         super().__init__(client)
 
     def generations(
-            self,
-            *,
-            prompt: str,
-            model: str | NotGiven = NOT_GIVEN,
-            n: Optional[int] | NotGiven = NOT_GIVEN,
-            quality: Optional[str] | NotGiven = NOT_GIVEN,
-            response_format: Optional[str] | NotGiven = NOT_GIVEN,
-            size: Optional[str] | NotGiven = NOT_GIVEN,
-            style: Optional[str] | NotGiven = NOT_GIVEN,
-            user: str | NotGiven = NOT_GIVEN,
-            request_id: Optional[str] | NotGiven = NOT_GIVEN,
-            extra_headers: Headers | None = None,
-            extra_body: Body | None = None,
-            disable_strict_validation: Optional[bool] | None = None,
-            timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+        self,
+        *,
+        prompt: str,
+        model: str | NotGiven = NOT_GIVEN,
+        n: Optional[int] | NotGiven = NOT_GIVEN,
+        quality: Optional[str] | NotGiven = NOT_GIVEN,
+        response_format: Optional[str] | NotGiven = NOT_GIVEN,
+        size: Optional[str] | NotGiven = NOT_GIVEN,
+        style: Optional[str] | NotGiven = NOT_GIVEN,
+        user: str | NotGiven = NOT_GIVEN,
+        request_id: Optional[str] | NotGiven = NOT_GIVEN,
+        extra_headers: Headers | None = None,
+        extra_body: Body | None = None,
+        disable_strict_validation: Optional[bool] | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
     ) -> ImagesResponded:
         _cast_type = ImagesResponded
         if disable_strict_validation:
@@ -50,11 +50,7 @@ class Images(BaseAPI):
                 "user": user,
                 "request_id": request_id,
             },
-            options=make_user_request_input(
-                extra_headers=extra_headers,
-                extra_body=extra_body,
-                timeout=timeout
-            ),
+            options=make_user_request_input(extra_headers=extra_headers, extra_body=extra_body, timeout=timeout),
             cast_type=_cast_type,
             enable_stream=False,
         )
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_base_type.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_base_type.py
index b7cf6bb7fd..7a91f9b796 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_base_type.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_base_type.py
@@ -75,7 +75,8 @@ Headers = Mapping[str, Union[str, Omit]]
 
 ResponseT = TypeVar(
     "ResponseT",
-    bound="Union[str, None, BaseModel, list[Any], Dict[str, Any], Response, UnknownResponse, ModelBuilderProtocol, BinaryResponseContent]",
+    bound="Union[str, None, BaseModel, list[Any], Dict[str, Any], Response, UnknownResponse, ModelBuilderProtocol,"
+    " BinaryResponseContent]",
 )
 
 # for user input files
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_errors.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_errors.py
index a2a438b8f3..1027c1bc5b 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_errors.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_errors.py
@@ -17,7 +17,10 @@ __all__ = [
 
 
 class ZhipuAIError(Exception):
-    def __init__(self, message: str, ) -> None:
+    def __init__(
+        self,
+        message: str,
+    ) -> None:
         super().__init__(message)
 
 
@@ -31,24 +34,19 @@ class APIStatusError(Exception):
         self.status_code = response.status_code
 
 
-class APIRequestFailedError(APIStatusError):
-    ...
+class APIRequestFailedError(APIStatusError): ...
 
 
-class APIAuthenticationError(APIStatusError):
-    ...
+class APIAuthenticationError(APIStatusError): ...
 
 
-class APIReachLimitError(APIStatusError):
-    ...
+class APIReachLimitError(APIStatusError): ...
 
 
-class APIInternalError(APIStatusError):
-    ...
+class APIInternalError(APIStatusError): ...
 
 
-class APIServerFlowExceedError(APIStatusError):
-    ...
+class APIServerFlowExceedError(APIStatusError): ...
 
 
 class APIResponseError(Exception):
@@ -67,16 +65,11 @@ class APIResponseValidationError(APIResponseError):
     status_code: int
     response: httpx.Response
 
-    def __init__(
-            self,
-            response: httpx.Response,
-            json_data: object | None, *,
-            message: str | None = None
-    ) -> None:
+    def __init__(self, response: httpx.Response, json_data: object | None, *, message: str | None = None) -> None:
         super().__init__(
             message=message or "Data returned by API invalid for expected schema.",
             request=response.request,
-            json_data=json_data
+            json_data=json_data,
         )
         self.response = response
         self.status_code = response.status_code
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_http_client.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_http_client.py
index 263fe82990..5f7f6d04f2 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_http_client.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_http_client.py
@@ -30,6 +30,8 @@ def _merge_map(map1: Mapping, map2: Mapping) -> Mapping:
     return {key: val for key, val in merged.items() if val is not None}
 
 
+from itertools import starmap
+
 from httpx._config import DEFAULT_TIMEOUT_CONFIG as HTTPX_DEFAULT_TIMEOUT
 
 ZHIPUAI_DEFAULT_TIMEOUT = httpx.Timeout(timeout=300.0, connect=8.0)
@@ -48,13 +50,13 @@ class HttpClient:
     _default_stream_cls: type[StreamResponse[Any]] | None = None
 
     def __init__(
-            self,
-            *,
-            version: str,
-            base_url: URL,
-            timeout: Union[float, Timeout, None],
-            custom_httpx_client: httpx.Client | None = None,
-            custom_headers: Mapping[str, str] | None = None,
+        self,
+        *,
+        version: str,
+        base_url: URL,
+        timeout: Union[float, Timeout, None],
+        custom_httpx_client: httpx.Client | None = None,
+        custom_headers: Mapping[str, str] | None = None,
     ) -> None:
         if timeout is None or isinstance(timeout, NotGiven):
             if custom_httpx_client and custom_httpx_client.timeout != HTTPX_DEFAULT_TIMEOUT:
@@ -76,7 +78,6 @@ class HttpClient:
         self._custom_headers = custom_headers or {}
 
     def _prepare_url(self, url: str) -> URL:
-
         sub_url = URL(url)
         if sub_url.is_relative_url:
             request_raw_url = self._base_url.raw_path + sub_url.raw_path.lstrip(b"/")
@@ -86,16 +87,15 @@ class HttpClient:
 
     @property
     def _default_headers(self):
-        return \
-            {
-                "Accept": "application/json",
-                "Content-Type": "application/json; charset=UTF-8",
-                "ZhipuAI-SDK-Ver": self._version,
-                "source_type": "zhipu-sdk-python",
-                "x-request-sdk": "zhipu-sdk-python",
-                **self._auth_headers,
-                **self._custom_headers,
-            }
+        return {
+            "Accept": "application/json",
+            "Content-Type": "application/json; charset=UTF-8",
+            "ZhipuAI-SDK-Ver": self._version,
+            "source_type": "zhipu-sdk-python",
+            "x-request-sdk": "zhipu-sdk-python",
+            **self._auth_headers,
+            **self._custom_headers,
+        }
 
     @property
     def _auth_headers(self):
@@ -109,10 +109,7 @@ class HttpClient:
 
         return httpx_headers
 
-    def _prepare_request(
-            self,
-            request_param: ClientRequestParam
-    ) -> httpx.Request:
+    def _prepare_request(self, request_param: ClientRequestParam) -> httpx.Request:
         kwargs: dict[str, Any] = {}
         json_data = request_param.json_data
         headers = self._prepare_headers(request_param)
@@ -135,16 +132,16 @@ class HttpClient:
             **kwargs,
         )
 
-    def _object_to_formfata(self, key: str, value: Data | Mapping[object, object]) -> list[tuple[str, str]]:
+    def _object_to_formdata(self, key: str, value: Data | Mapping[object, object]) -> list[tuple[str, str]]:
         items = []
 
         if isinstance(value, Mapping):
             for k, v in value.items():
-                items.extend(self._object_to_formfata(f"{key}[{k}]", v))
+                items.extend(self._object_to_formdata(f"{key}[{k}]", v))
             return items
         if isinstance(value, list | tuple):
             for v in value:
-                items.extend(self._object_to_formfata(key + "[]", v))
+                items.extend(self._object_to_formdata(key + "[]", v))
             return items
 
         def _primitive_value_to_str(val) -> str:
@@ -164,8 +161,7 @@ class HttpClient:
         return [(key, str_data)]
 
     def _make_multipartform(self, data: Mapping[object, object]) -> dict[str, object]:
-
-        items = flatten([self._object_to_formfata(k, v) for k, v in data.items()])
+        items = flatten(list(starmap(self._object_to_formdata, data.items())))
 
         serialized: dict[str, object] = {}
         for key, value in items:
@@ -175,30 +171,25 @@ class HttpClient:
         return serialized
 
     def _parse_response(
-            self,
-            *,
-            cast_type: type[ResponseT],
-            response: httpx.Response,
-            enable_stream: bool,
-            request_param: ClientRequestParam,
-            stream_cls: type[StreamResponse[Any]] | None = None,
+        self,
+        *,
+        cast_type: type[ResponseT],
+        response: httpx.Response,
+        enable_stream: bool,
+        request_param: ClientRequestParam,
+        stream_cls: type[StreamResponse[Any]] | None = None,
     ) -> HttpResponse:
-
         http_response = HttpResponse(
-            raw_response=response,
-            cast_type=cast_type,
-            client=self,
-            enable_stream=enable_stream,
-            stream_cls=stream_cls
+            raw_response=response, cast_type=cast_type, client=self, enable_stream=enable_stream, stream_cls=stream_cls
         )
         return http_response.parse()
 
     def _process_response_data(
-            self,
-            *,
-            data: object,
-            cast_type: type[ResponseT],
-            response: httpx.Response,
+        self,
+        *,
+        data: object,
+        cast_type: type[ResponseT],
+        response: httpx.Response,
     ) -> ResponseT:
         if data is None:
             return cast(ResponseT, None)
@@ -225,12 +216,12 @@ class HttpClient:
 
     @retry(stop=stop_after_attempt(ZHIPUAI_DEFAULT_MAX_RETRIES))
     def request(
-            self,
-            *,
-            cast_type: type[ResponseT],
-            params: ClientRequestParam,
-            enable_stream: bool = False,
-            stream_cls: type[StreamResponse[Any]] | None = None,
+        self,
+        *,
+        cast_type: type[ResponseT],
+        params: ClientRequestParam,
+        enable_stream: bool = False,
+        stream_cls: type[StreamResponse[Any]] | None = None,
     ) -> ResponseT | StreamResponse:
         request = self._prepare_request(params)
 
@@ -259,81 +250,79 @@ class HttpClient:
         )
 
     def get(
-            self,
-            path: str,
-            *,
-            cast_type: type[ResponseT],
-            options: UserRequestInput = {},
-            enable_stream: bool = False,
+        self,
+        path: str,
+        *,
+        cast_type: type[ResponseT],
+        options: UserRequestInput = {},
+        enable_stream: bool = False,
     ) -> ResponseT | StreamResponse:
         opts = ClientRequestParam.construct(method="get", url=path, **options)
-        return self.request(
-            cast_type=cast_type, params=opts,
-            enable_stream=enable_stream
-        )
+        return self.request(cast_type=cast_type, params=opts, enable_stream=enable_stream)
 
     def post(
-            self,
-            path: str,
-            *,
-            body: Body | None = None,
-            cast_type: type[ResponseT],
-            options: UserRequestInput = {},
-            files: RequestFiles | None = None,
-            enable_stream: bool = False,
-            stream_cls: type[StreamResponse[Any]] | None = None,
+        self,
+        path: str,
+        *,
+        body: Body | None = None,
+        cast_type: type[ResponseT],
+        options: UserRequestInput = {},
+        files: RequestFiles | None = None,
+        enable_stream: bool = False,
+        stream_cls: type[StreamResponse[Any]] | None = None,
     ) -> ResponseT | StreamResponse:
-        opts = ClientRequestParam.construct(method="post", json_data=body, files=make_httpx_files(files), url=path,
-                                            **options)
-
-        return self.request(
-            cast_type=cast_type, params=opts,
-            enable_stream=enable_stream,
-            stream_cls=stream_cls
+        opts = ClientRequestParam.construct(
+            method="post", json_data=body, files=make_httpx_files(files), url=path, **options
         )
 
+        return self.request(cast_type=cast_type, params=opts, enable_stream=enable_stream, stream_cls=stream_cls)
+
     def patch(
-            self,
-            path: str,
-            *,
-            body: Body | None = None,
-            cast_type: type[ResponseT],
-            options: UserRequestInput = {},
+        self,
+        path: str,
+        *,
+        body: Body | None = None,
+        cast_type: type[ResponseT],
+        options: UserRequestInput = {},
     ) -> ResponseT:
         opts = ClientRequestParam.construct(method="patch", url=path, json_data=body, **options)
 
         return self.request(
-            cast_type=cast_type, params=opts,
+            cast_type=cast_type,
+            params=opts,
         )
 
     def put(
-            self,
-            path: str,
-            *,
-            body: Body | None = None,
-            cast_type: type[ResponseT],
-            options: UserRequestInput = {},
-            files: RequestFiles | None = None,
+        self,
+        path: str,
+        *,
+        body: Body | None = None,
+        cast_type: type[ResponseT],
+        options: UserRequestInput = {},
+        files: RequestFiles | None = None,
     ) -> ResponseT | StreamResponse:
-        opts = ClientRequestParam.construct(method="put", url=path, json_data=body, files=make_httpx_files(files),
-                                            **options)
+        opts = ClientRequestParam.construct(
+            method="put", url=path, json_data=body, files=make_httpx_files(files), **options
+        )
 
         return self.request(
-            cast_type=cast_type, params=opts,
+            cast_type=cast_type,
+            params=opts,
         )
 
     def delete(
-            self,
-            path: str,
-            *,
-            body: Body | None = None,
-            cast_type: type[ResponseT],
-            options: UserRequestInput = {},
+        self,
+        path: str,
+        *,
+        body: Body | None = None,
+        cast_type: type[ResponseT],
+        options: UserRequestInput = {},
     ) -> ResponseT | StreamResponse:
         opts = ClientRequestParam.construct(method="delete", url=path, json_data=body, **options)
 
         return self.request(
-            cast_type=cast_type, params=opts,
+            cast_type=cast_type,
+            params=opts,
         )
 
     def _make_status_error(self, response) -> APIStatusError:
@@ -355,11 +344,11 @@ class HttpClient:
 
 
 def make_user_request_input(
-        max_retries: int | None = None,
-        timeout: float | Timeout | None | NotGiven = NOT_GIVEN,
-        extra_headers: Headers = None,
-        extra_body: Body | None = None,
-        query: Query | None = None,
+    max_retries: int | None = None,
+    timeout: float | Timeout | None | NotGiven = NOT_GIVEN,
+    extra_headers: Headers = None,
+    extra_body: Body | None = None,
+    query: Query | None = None,
 ) -> UserRequestInput:
     options: UserRequestInput = {}
 
@@ -368,7 +357,7 @@ def make_user_request_input(
     if max_retries is not None:
         options["max_retries"] = max_retries
     if not isinstance(timeout, NotGiven):
-        options['timeout'] = timeout
+        options["timeout"] = timeout
     if query is not None:
         options["params"] = query
     if extra_body is not None:
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_request_opt.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_request_opt.py
index a3f49ba846..ac459151fc 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_request_opt.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_request_opt.py
@@ -35,17 +35,14 @@ class ClientRequestParam:
 
     @classmethod
     def construct(  # type: ignore
-            cls,
-            _fields_set: set[str] | None = None,
-            **values: Unpack[UserRequestInput],
-    ) -> ClientRequestParam :
-        kwargs: dict[str, Any] = {
-            key: remove_notgiven_indict(value) for key, value in values.items()
-        }
+        cls,
+        _fields_set: set[str] | None = None,
+        **values: Unpack[UserRequestInput],
+    ) -> ClientRequestParam:
+        kwargs: dict[str, Any] = {key: remove_notgiven_indict(value) for key, value in values.items()}
         client = cls()
         client.__dict__.update(kwargs)
 
         return client
 
     model_construct = construct
-
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_response.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_response.py
index 2f831b6fc9..56e60a7934 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_response.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_response.py
@@ -26,13 +26,13 @@ class HttpResponse(Generic[R]):
     http_response: httpx.Response
 
     def __init__(
-            self,
-            *,
-            raw_response: httpx.Response,
-            cast_type: type[R],
-            client: HttpClient,
-            enable_stream: bool = False,
-            stream_cls: type[StreamResponse[Any]] | None = None,
+        self,
+        *,
+        raw_response: httpx.Response,
+        cast_type: type[R],
+        client: HttpClient,
+        enable_stream: bool = False,
+        stream_cls: type[StreamResponse[Any]] | None = None,
     ) -> None:
         self._cast_type = cast_type
         self._client = client
@@ -52,8 +52,8 @@ class HttpResponse(Generic[R]):
                 self._stream_cls(
                     cast_type=cast(type, get_args(self._stream_cls)[0]),
                     response=self.http_response,
-                    client=self._client
-                )
+                    client=self._client,
+                ),
             )
             return self._parsed
         cast_type = self._cast_type
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_sse_client.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_sse_client.py
index 66afbfd107..ec2745d059 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_sse_client.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_sse_client.py
@@ -16,16 +16,15 @@ if TYPE_CHECKING:
 
 
 class StreamResponse(Generic[ResponseT]):
-
     response: httpx.Response
     _cast_type: type[ResponseT]
 
     def __init__(
-            self,
-            *,
-            cast_type: type[ResponseT],
-            response: httpx.Response,
-            client: HttpClient,
+        self,
+        *,
+        cast_type: type[ResponseT],
+        response: httpx.Response,
+        client: HttpClient,
     ) -> None:
         self.response = response
         self._cast_type = cast_type
@@ -39,7 +38,6 @@ class StreamResponse(Generic[ResponseT]):
         yield from self._stream_chunks
 
     def __stream__(self) -> Iterator[ResponseT]:
-
         sse_line_parser = SSELineParser()
         iterator = sse_line_parser.iter_lines(self.response.iter_lines())
 
@@ -63,11 +61,7 @@ class StreamResponse(Generic[ResponseT]):
 
 class Event:
     def __init__(
-            self,
-            event: str | None = None,
-            data: str | None = None,
-            id: str | None = None,
-            retry: int | None = None
+        self, event: str | None = None, data: str | None = None, id: str | None = None, retry: int | None = None
     ):
         self._event = event
         self._data = data
@@ -76,21 +70,28 @@ class Event:
 
     def __repr__(self):
         data_len = len(self._data) if self._data else 0
-        return f"Event(event={self._event}, data={self._data} ,data_length={data_len}, id={self._id}, retry={self._retry}"
+        return (
+            f"Event(event={self._event}, data={self._data} ,data_length={data_len}, id={self._id}, retry={self._retry}"
+        )
 
     @property
-    def event(self): return self._event
+    def event(self):
+        return self._event
 
     @property
-    def data(self): return self._data
+    def data(self):
+        return self._data
 
-    def json_data(self): return json.loads(self._data)
+    def json_data(self):
+        return json.loads(self._data)
 
     @property
-    def id(self): return self._id
+    def id(self):
+        return self._id
 
     @property
-    def retry(self): return self._retry
+    def retry(self):
+        return self._retry
 
 
 class SSELineParser:
@@ -107,19 +108,11 @@ class SSELineParser:
 
     def iter_lines(self, lines: Iterator[str]) -> Iterator[Event]:
         for line in lines:
-            line = line.rstrip('\n')
+            line = line.rstrip("\n")
             if not line:
-                if self._event is None and \
-                        not self._data and \
-                        self._id is None and \
-                        self._retry is None:
+                if self._event is None and not self._data and self._id is None and self._retry is None:
                     continue
-                sse_event = Event(
-                    event=self._event,
-                    data='\n'.join(self._data),
-                    id=self._id,
-                    retry=self._retry
-                )
+                sse_event = Event(event=self._event, data="\n".join(self._data), id=self._id, retry=self._retry)
                 self._event = None
                 self._data = []
                 self._id = None
@@ -134,8 +127,7 @@ class SSELineParser:
 
         field, _p, value = line.partition(":")
 
-        if value.startswith(' '):
-            value = value[1:]
+        value = value.removeprefix(" ")
         if field == "data":
             self._data.append(value)
         elif field == "event":
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/chat/async_chat_completion.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/chat/async_chat_completion.py
index f22f32d251..a0645b0916 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/chat/async_chat_completion.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/chat/async_chat_completion.py
@@ -20,4 +20,4 @@ class AsyncCompletion(BaseModel):
     model: Optional[str] = None
     task_status: str
     choices: list[CompletionChoice]
-    usage: CompletionUsage
\ No newline at end of file
+    usage: CompletionUsage
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/chat/chat_completion.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/chat/chat_completion.py
index b2a847c50c..4b3a929a2b 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/chat/chat_completion.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/chat/chat_completion.py
@@ -41,5 +41,3 @@ class Completion(BaseModel):
     request_id: Optional[str] = None
     id: Optional[str] = None
     usage: CompletionUsage
-
-
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/file_object.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/file_object.py
index 917bda7576..75f76fe969 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/file_object.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/file_object.py
@@ -6,7 +6,6 @@ __all__ = ["FileObject"]
 
 
 class FileObject(BaseModel):
-
     id: Optional[str] = None
     bytes: Optional[int] = None
     created_at: Optional[int] = None
@@ -18,7 +17,6 @@ class FileObject(BaseModel):
 
 
 class ListOfFileObject(BaseModel):
-
     object: Optional[str] = None
     data: list[FileObject]
     has_more: Optional[bool] = None
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/fine_tuning/__init__.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/fine_tuning/__init__.py
index af0991892e..416f516ef7 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/fine_tuning/__init__.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/fine_tuning/__init__.py
@@ -1,5 +1,4 @@
 from __future__ import annotations
 
-from .fine_tuning_job import FineTuningJob as FineTuningJob
-from .fine_tuning_job import ListOfFineTuningJob as ListOfFineTuningJob
-from .fine_tuning_job_event import FineTuningJobEvent as FineTuningJobEvent
+from .fine_tuning_job import FineTuningJob, ListOfFineTuningJob
+from .fine_tuning_job_event import FineTuningJobEvent
diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/fine_tuning/fine_tuning_job.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/fine_tuning/fine_tuning_job.py
index 71c00eaff0..1d3930286b 100644
--- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/fine_tuning/fine_tuning_job.py
+++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/fine_tuning/fine_tuning_job.py
@@ -2,7 +2,7 @@ from typing import Optional, Union
 
 from pydantic import BaseModel
 
-__all__ = ["FineTuningJob", "Error", "Hyperparameters", "ListOfFineTuningJob" ]
+__all__ = ["FineTuningJob", "Error", "Hyperparameters", "ListOfFineTuningJob"]
 
 
 class Error(BaseModel):
diff --git a/api/core/model_runtime/schema_validators/common_validator.py b/api/core/model_runtime/schema_validators/common_validator.py
index fe705d6943..029ec1a581 100644
--- a/api/core/model_runtime/schema_validators/common_validator.py
+++ b/api/core/model_runtime/schema_validators/common_validator.py
@@ -4,9 +4,9 @@ from core.model_runtime.entities.provider_entities import CredentialFormSchema,
 
 
 class CommonValidator:
-    def _validate_and_filter_credential_form_schemas(self,
-                                                     credential_form_schemas: list[CredentialFormSchema],
-                                                     credentials: dict) -> dict:
+    def _validate_and_filter_credential_form_schemas(
+        self, credential_form_schemas: list[CredentialFormSchema], credentials: dict
+    ) -> dict:
         need_validate_credential_form_schema_map = {}
         for credential_form_schema in credential_form_schemas:
             if not credential_form_schema.show_on:
@@ -36,8 +36,9 @@ class CommonValidator:
 
         return validated_credentials
 
-    def _validate_credential_form_schema(self, credential_form_schema: CredentialFormSchema, credentials: dict) \
-            -> Optional[str]:
+    def _validate_credential_form_schema(
+        self, credential_form_schema: CredentialFormSchema, credentials: dict
+    ) -> Optional[str]:
         """
         Validate credential form schema
 
@@ -49,7 +50,7 @@ class CommonValidator:
         if credential_form_schema.variable not in credentials or not credentials[credential_form_schema.variable]:
             # If required is True, an exception is thrown
             if credential_form_schema.required:
-                raise ValueError(f'Variable {credential_form_schema.variable} is required')
+                raise ValueError(f"Variable {credential_form_schema.variable} is required")
             else:
                 # Get the value of default
                 if credential_form_schema.default:
@@ -65,23 +66,26 @@ class CommonValidator:
         # If max_length=0, no validation is performed
         if credential_form_schema.max_length:
             if len(value) > credential_form_schema.max_length:
-                raise ValueError(f'Variable {credential_form_schema.variable} length should not greater than {credential_form_schema.max_length}')
+                raise ValueError(
+                    f"Variable {credential_form_schema.variable} length should not"
+                    f" greater than {credential_form_schema.max_length}"
+                )
 
         # check the type of value
         if not isinstance(value, str):
-            raise ValueError(f'Variable {credential_form_schema.variable} should be string')
+            raise ValueError(f"Variable {credential_form_schema.variable} should be string")
 
-        if credential_form_schema.type in [FormType.SELECT, FormType.RADIO]:
+        if credential_form_schema.type in {FormType.SELECT, FormType.RADIO}:
             # If the value is in options, no validation is performed
             if credential_form_schema.options:
                 if value not in [option.value for option in credential_form_schema.options]:
-                    raise ValueError(f'Variable {credential_form_schema.variable} is not in options')
+                    raise ValueError(f"Variable {credential_form_schema.variable} is not in options")
 
         if credential_form_schema.type == FormType.SWITCH:
             # If the value is not in ['true', 'false'], an exception is thrown
-            if value.lower() not in ['true', 'false']:
-                raise ValueError(f'Variable {credential_form_schema.variable} should be true or false')
+            if value.lower() not in {"true", "false"}:
+                raise ValueError(f"Variable {credential_form_schema.variable} should be true or false")
 
-            value = True if value.lower() == 'true' else False
+            value = True if value.lower() == "true" else False
 
         return value
diff --git a/api/core/model_runtime/schema_validators/model_credential_schema_validator.py b/api/core/model_runtime/schema_validators/model_credential_schema_validator.py
index c4786fad5d..7d1644d134 100644
--- a/api/core/model_runtime/schema_validators/model_credential_schema_validator.py
+++ b/api/core/model_runtime/schema_validators/model_credential_schema_validator.py
@@ -4,7 +4,6 @@ from core.model_runtime.schema_validators.common_validator import CommonValidato
 
 
 class ModelCredentialSchemaValidator(CommonValidator):
-
     def __init__(self, model_type: ModelType, model_credential_schema: ModelCredentialSchema):
         self.model_type = model_type
         self.model_credential_schema = model_credential_schema
diff --git a/api/core/model_runtime/schema_validators/provider_credential_schema_validator.py b/api/core/model_runtime/schema_validators/provider_credential_schema_validator.py
index c945016534..6dff2428ca 100644
--- a/api/core/model_runtime/schema_validators/provider_credential_schema_validator.py
+++ b/api/core/model_runtime/schema_validators/provider_credential_schema_validator.py
@@ -3,7 +3,6 @@ from core.model_runtime.schema_validators.common_validator import CommonValidato
 
 
 class ProviderCredentialSchemaValidator(CommonValidator):
-
     def __init__(self, provider_credential_schema: ProviderCredentialSchema):
         self.provider_credential_schema = provider_credential_schema
 
diff --git a/api/core/model_runtime/utils/encoders.py b/api/core/model_runtime/utils/encoders.py
index 5078f00bfa..ec1bad5698 100644
--- a/api/core/model_runtime/utils/encoders.py
+++ b/api/core/model_runtime/utils/encoders.py
@@ -18,11 +18,10 @@ from pydantic_core import Url
 from pydantic_extra_types.color import Color
 
 
-def _model_dump(
-    model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any
-) -> Any:
+def _model_dump(model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any) -> Any:
     return model.model_dump(mode=mode, **kwargs)
 
+
 # Taken from Pydantic v1 as is
 def isoformat(o: Union[datetime.date, datetime.time]) -> str:
     return o.isoformat()
@@ -82,11 +81,9 @@ ENCODERS_BY_TYPE: dict[type[Any], Callable[[Any], Any]] = {
 
 
 def generate_encoders_by_class_tuples(
-    type_encoder_map: dict[Any, Callable[[Any], Any]]
+    type_encoder_map: dict[Any, Callable[[Any], Any]],
 ) -> dict[Callable[[Any], Any], tuple[Any, ...]]:
-    encoders_by_class_tuples: dict[Callable[[Any], Any], tuple[Any, ...]] = defaultdict(
-        tuple
-    )
+    encoders_by_class_tuples: dict[Callable[[Any], Any], tuple[Any, ...]] = defaultdict(tuple)
     for type_, encoder in type_encoder_map.items():
         encoders_by_class_tuples[encoder] += (type_,)
     return encoders_by_class_tuples
@@ -149,17 +146,13 @@ def jsonable_encoder(
     if isinstance(obj, str | int | float | type(None)):
         return obj
     if isinstance(obj, Decimal):
-        return format(obj, 'f')
+        return format(obj, "f")
     if isinstance(obj, dict):
         encoded_dict = {}
         allowed_keys = set(obj.keys())
         for key, value in obj.items():
             if (
-                (
-                    not sqlalchemy_safe
-                    or (not isinstance(key, str))
-                    or (not key.startswith("_sa"))
-                )
+                (not sqlalchemy_safe or (not isinstance(key, str)) or (not key.startswith("_sa")))
                 and (value is not None or not exclude_none)
                 and key in allowed_keys
             ):
diff --git a/api/core/model_runtime/utils/helper.py b/api/core/model_runtime/utils/helper.py
index c68a554471..2067092d80 100644
--- a/api/core/model_runtime/utils/helper.py
+++ b/api/core/model_runtime/utils/helper.py
@@ -3,7 +3,7 @@ from pydantic import BaseModel
 
 
 def dump_model(model: BaseModel) -> dict:
-    if hasattr(pydantic, 'model_dump'):
+    if hasattr(pydantic, "model_dump"):
         return pydantic.model_dump(model)
     else:
         return model.model_dump()
diff --git a/api/core/moderation/api/api.py b/api/core/moderation/api/api.py
index f96e2a1c21..094ad78636 100644
--- a/api/core/moderation/api/api.py
+++ b/api/core/moderation/api/api.py
@@ -44,32 +44,29 @@ class ApiModeration(Moderation):
         flagged = False
         preset_response = ""
 
-        if self.config['inputs_config']['enabled']:
-            params = ModerationInputParams(
-                app_id=self.app_id,
-                inputs=inputs,
-                query=query
-            )
+        if self.config["inputs_config"]["enabled"]:
+            params = ModerationInputParams(app_id=self.app_id, inputs=inputs, query=query)
 
             result = self._get_config_by_requestor(APIBasedExtensionPoint.APP_MODERATION_INPUT, params.model_dump())
             return ModerationInputsResult(**result)
 
-        return ModerationInputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
+        return ModerationInputsResult(
+            flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response
+        )
 
     def moderation_for_outputs(self, text: str) -> ModerationOutputsResult:
         flagged = False
         preset_response = ""
 
-        if self.config['outputs_config']['enabled']:
-            params = ModerationOutputParams(
-                app_id=self.app_id,
-                text=text
-            )
+        if self.config["outputs_config"]["enabled"]:
+            params = ModerationOutputParams(app_id=self.app_id, text=text)
 
             result = self._get_config_by_requestor(APIBasedExtensionPoint.APP_MODERATION_OUTPUT, params.model_dump())
             return ModerationOutputsResult(**result)
 
-        return ModerationOutputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
+        return ModerationOutputsResult(
+            flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response
+        )
 
     def _get_config_by_requestor(self, extension_point: APIBasedExtensionPoint, params: dict) -> dict:
         extension = self._get_api_based_extension(self.tenant_id, self.config.get("api_based_extension_id"))
@@ -80,9 +77,10 @@ class ApiModeration(Moderation):
 
     @staticmethod
     def _get_api_based_extension(tenant_id: str, api_based_extension_id: str) -> APIBasedExtension:
-        extension = db.session.query(APIBasedExtension).filter(
-            APIBasedExtension.tenant_id == tenant_id,
-            APIBasedExtension.id == api_based_extension_id
-        ).first()
+        extension = (
+            db.session.query(APIBasedExtension)
+            .filter(APIBasedExtension.tenant_id == tenant_id, APIBasedExtension.id == api_based_extension_id)
+            .first()
+        )
 
         return extension
diff --git a/api/core/moderation/base.py b/api/core/moderation/base.py
index 9a369a9f87..60898d5547 100644
--- a/api/core/moderation/base.py
+++ b/api/core/moderation/base.py
@@ -8,8 +8,8 @@ from core.extension.extensible import Extensible, ExtensionModule
 
 
 class ModerationAction(Enum):
-    DIRECT_OUTPUT = 'direct_output'
-    OVERRIDED = 'overrided'
+    DIRECT_OUTPUT = "direct_output"
+    OVERRIDDEN = "overridden"
 
 
 class ModerationInputsResult(BaseModel):
@@ -31,6 +31,7 @@ class Moderation(Extensible, ABC):
     """
     The base class of moderation.
     """
+
     module: ExtensionModule = ExtensionModule.MODERATION
 
     def __init__(self, app_id: str, tenant_id: str, config: Optional[dict] = None) -> None:
@@ -75,7 +76,7 @@ class Moderation(Extensible, ABC):
         raise NotImplementedError
 
     @classmethod
-    def _validate_inputs_and_outputs_config(self, config: dict, is_preset_response_required: bool) -> None:
+    def _validate_inputs_and_outputs_config(cls, config: dict, is_preset_response_required: bool) -> None:
         # inputs_config
         inputs_config = config.get("inputs_config")
         if not isinstance(inputs_config, dict):
@@ -110,5 +111,5 @@ class Moderation(Extensible, ABC):
                 raise ValueError("outputs_config.preset_response must be less than 100 characters")
 
 
-class ModerationException(Exception):
+class ModerationError(Exception):
     pass
diff --git a/api/core/moderation/input_moderation.py b/api/core/moderation/input_moderation.py
index 8157b300b1..46d3963bd0 100644
--- a/api/core/moderation/input_moderation.py
+++ b/api/core/moderation/input_moderation.py
@@ -2,7 +2,7 @@ import logging
 from typing import Optional
 
 from core.app.app_config.entities import AppConfig
-from core.moderation.base import ModerationAction, ModerationException
+from core.moderation.base import ModerationAction, ModerationError
 from core.moderation.factory import ModerationFactory
 from core.ops.entities.trace_entity import TraceTaskName
 from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
@@ -13,13 +13,14 @@ logger = logging.getLogger(__name__)
 
 class InputModeration:
     def check(
-        self, app_id: str,
+        self,
+        app_id: str,
         tenant_id: str,
         app_config: AppConfig,
         inputs: dict,
         query: str,
         message_id: str,
-        trace_manager: Optional[TraceQueueManager] = None
+        trace_manager: Optional[TraceQueueManager] = None,
     ) -> tuple[bool, dict, str]:
         """
         Process sensitive_word_avoidance.
@@ -39,10 +40,7 @@ class InputModeration:
         moderation_type = sensitive_word_avoidance_config.type
 
         moderation_factory = ModerationFactory(
-            name=moderation_type,
-            app_id=app_id,
-            tenant_id=tenant_id,
-            config=sensitive_word_avoidance_config.config
+            name=moderation_type, app_id=app_id, tenant_id=tenant_id, config=sensitive_word_avoidance_config.config
         )
 
         with measure_time() as timer:
@@ -55,7 +53,7 @@ class InputModeration:
                     message_id=message_id,
                     moderation_result=moderation_result,
                     inputs=inputs,
-                    timer=timer
+                    timer=timer,
                 )
             )
 
@@ -63,8 +61,8 @@ class InputModeration:
             return False, inputs, query
 
         if moderation_result.action == ModerationAction.DIRECT_OUTPUT:
-            raise ModerationException(moderation_result.preset_response)
-        elif moderation_result.action == ModerationAction.OVERRIDED:
+            raise ModerationError(moderation_result.preset_response)
+        elif moderation_result.action == ModerationAction.OVERRIDDEN:
             inputs = moderation_result.inputs
             query = moderation_result.query
 
diff --git a/api/core/moderation/keywords/keywords.py b/api/core/moderation/keywords/keywords.py
index ca562ad987..dc6a7ec564 100644
--- a/api/core/moderation/keywords/keywords.py
+++ b/api/core/moderation/keywords/keywords.py
@@ -25,41 +25,38 @@ class KeywordsModeration(Moderation):
         flagged = False
         preset_response = ""
 
-        if self.config['inputs_config']['enabled']:
-            preset_response = self.config['inputs_config']['preset_response']
+        if self.config["inputs_config"]["enabled"]:
+            preset_response = self.config["inputs_config"]["preset_response"]
 
             if query:
-                inputs['query__'] = query
+                inputs["query__"] = query
 
             # Filter out empty values
-            keywords_list = [keyword for keyword in self.config['keywords'].split('\n') if keyword]
+            keywords_list = [keyword for keyword in self.config["keywords"].split("\n") if keyword]
 
             flagged = self._is_violated(inputs, keywords_list)
 
-        return ModerationInputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
+        return ModerationInputsResult(
+            flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response
+        )
 
     def moderation_for_outputs(self, text: str) -> ModerationOutputsResult:
         flagged = False
         preset_response = ""
 
-        if self.config['outputs_config']['enabled']:
+        if self.config["outputs_config"]["enabled"]:
             # Filter out empty values
-            keywords_list = [keyword for keyword in self.config['keywords'].split('\n') if keyword]
+            keywords_list = [keyword for keyword in self.config["keywords"].split("\n") if keyword]
 
-            flagged = self._is_violated({'text': text}, keywords_list)
-            preset_response = self.config['outputs_config']['preset_response']
+            flagged = self._is_violated({"text": text}, keywords_list)
+            preset_response = self.config["outputs_config"]["preset_response"]
 
-        return ModerationOutputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
+        return ModerationOutputsResult(
+            flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response
+        )
 
     def _is_violated(self, inputs: dict, keywords_list: list) -> bool:
-        for value in inputs.values():
-            if self._check_keywords_in_value(keywords_list, value):
-                return True
+        return any(self._check_keywords_in_value(keywords_list, value) for value in inputs.values())
 
-        return False
-
-    def _check_keywords_in_value(self, keywords_list, value):
-        for keyword in keywords_list:
-            if keyword.lower() in value.lower():
-                return True
-        return False
+    def _check_keywords_in_value(self, keywords_list, value) -> bool:
+        return any(keyword.lower() in value.lower() for keyword in keywords_list)
diff --git a/api/core/moderation/openai_moderation/openai_moderation.py b/api/core/moderation/openai_moderation/openai_moderation.py
index fee51007eb..6465de23b9 100644
--- a/api/core/moderation/openai_moderation/openai_moderation.py
+++ b/api/core/moderation/openai_moderation/openai_moderation.py
@@ -21,37 +21,36 @@ class OpenAIModeration(Moderation):
         flagged = False
         preset_response = ""
 
-        if self.config['inputs_config']['enabled']:
-            preset_response = self.config['inputs_config']['preset_response']
+        if self.config["inputs_config"]["enabled"]:
+            preset_response = self.config["inputs_config"]["preset_response"]
 
             if query:
-                inputs['query__'] = query
+                inputs["query__"] = query
             flagged = self._is_violated(inputs)
 
-        return ModerationInputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
+        return ModerationInputsResult(
+            flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response
+        )
 
     def moderation_for_outputs(self, text: str) -> ModerationOutputsResult:
         flagged = False
         preset_response = ""
 
-        if self.config['outputs_config']['enabled']:
-            flagged = self._is_violated({'text': text})
-            preset_response = self.config['outputs_config']['preset_response']
+        if self.config["outputs_config"]["enabled"]:
+            flagged = self._is_violated({"text": text})
+            preset_response = self.config["outputs_config"]["preset_response"]
 
-        return ModerationOutputsResult(flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response)
+        return ModerationOutputsResult(
+            flagged=flagged, action=ModerationAction.DIRECT_OUTPUT, preset_response=preset_response
+        )
 
     def _is_violated(self, inputs: dict):
-        text = '\n'.join(str(inputs.values()))
+        text = "\n".join(str(inputs.values()))
         model_manager = ModelManager()
         model_instance = model_manager.get_model_instance(
-            tenant_id=self.tenant_id,
-            provider="openai",
-            model_type=ModelType.MODERATION,
-            model="text-moderation-stable"
+            tenant_id=self.tenant_id, provider="openai", model_type=ModelType.MODERATION, model="text-moderation-stable"
         )
 
-        openai_moderation = model_instance.invoke_moderation(
-            text=text
-        )
+        openai_moderation = model_instance.invoke_moderation(text=text)
 
         return openai_moderation
diff --git a/api/core/moderation/output_moderation.py b/api/core/moderation/output_moderation.py
index 9a4d8db4e2..d8d794be18 100644
--- a/api/core/moderation/output_moderation.py
+++ b/api/core/moderation/output_moderation.py
@@ -29,18 +29,18 @@ class OutputModeration(BaseModel):
 
     thread: Optional[threading.Thread] = None
     thread_running: bool = True
-    buffer: str = ''
+    buffer: str = ""
     is_final_chunk: bool = False
     final_output: Optional[str] = None
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
-    def should_direct_output(self):
+    def should_direct_output(self) -> bool:
         return self.final_output is not None
 
-    def get_final_output(self):
-        return self.final_output
+    def get_final_output(self) -> str:
+        return self.final_output or ""
 
-    def append_new_token(self, token: str):
+    def append_new_token(self, token: str) -> None:
         self.buffer += token
 
         if not self.thread:
@@ -50,11 +50,7 @@ class OutputModeration(BaseModel):
         self.buffer = completion
         self.is_final_chunk = True
 
-        result = self.moderation(
-            tenant_id=self.tenant_id,
-            app_id=self.app_id,
-            moderation_buffer=completion
-        )
+        result = self.moderation(tenant_id=self.tenant_id, app_id=self.app_id, moderation_buffer=completion)
 
         if not result or not result.flagged:
             return completion
@@ -65,21 +61,19 @@ class OutputModeration(BaseModel):
             final_output = result.text
 
         if public_event:
-            self.queue_manager.publish(
-                QueueMessageReplaceEvent(
-                    text=final_output
-                ),
-                PublishFrom.TASK_PIPELINE
-            )
+            self.queue_manager.publish(QueueMessageReplaceEvent(text=final_output), PublishFrom.TASK_PIPELINE)
 
         return final_output
 
     def start_thread(self) -> threading.Thread:
         buffer_size = dify_config.MODERATION_BUFFER_SIZE
-        thread = threading.Thread(target=self.worker, kwargs={
-            'flask_app': current_app._get_current_object(),
-            'buffer_size': buffer_size if buffer_size > 0 else dify_config.MODERATION_BUFFER_SIZE
-        })
+        thread = threading.Thread(
+            target=self.worker,
+            kwargs={
+                "flask_app": current_app._get_current_object(),
+                "buffer_size": buffer_size if buffer_size > 0 else dify_config.MODERATION_BUFFER_SIZE,
+            },
+        )
 
         thread.start()
 
@@ -104,9 +98,7 @@ class OutputModeration(BaseModel):
                 current_length = buffer_length
 
                 result = self.moderation(
-                    tenant_id=self.tenant_id,
-                    app_id=self.app_id,
-                    moderation_buffer=moderation_buffer
+                    tenant_id=self.tenant_id, app_id=self.app_id, moderation_buffer=moderation_buffer
                 )
 
                 if not result or not result.flagged:
@@ -116,16 +108,11 @@ class OutputModeration(BaseModel):
                     final_output = result.preset_response
                     self.final_output = final_output
                 else:
-                    final_output = result.text + self.buffer[len(moderation_buffer):]
+                    final_output = result.text + self.buffer[len(moderation_buffer) :]
 
                 # trigger replace event
                 if self.thread_running:
-                    self.queue_manager.publish(
-                        QueueMessageReplaceEvent(
-                            text=final_output
-                        ),
-                        PublishFrom.TASK_PIPELINE
-                    )
+                    self.queue_manager.publish(QueueMessageReplaceEvent(text=final_output), PublishFrom.TASK_PIPELINE)
 
                 if result.action == ModerationAction.DIRECT_OUTPUT:
                     break
@@ -133,10 +120,7 @@ class OutputModeration(BaseModel):
     def moderation(self, tenant_id: str, app_id: str, moderation_buffer: str) -> Optional[ModerationOutputsResult]:
         try:
             moderation_factory = ModerationFactory(
-                name=self.rule.type,
-                app_id=app_id,
-                tenant_id=tenant_id,
-                config=self.rule.config
+                name=self.rule.type, app_id=app_id, tenant_id=tenant_id, config=self.rule.config
             )
 
             result: ModerationOutputsResult = moderation_factory.moderation_for_outputs(moderation_buffer)
diff --git a/api/core/ops/base_trace_instance.py b/api/core/ops/base_trace_instance.py
index c7af8e2963..f7b882fc71 100644
--- a/api/core/ops/base_trace_instance.py
+++ b/api/core/ops/base_trace_instance.py
@@ -23,4 +23,4 @@ class BaseTraceInstance(ABC):
         Abstract method to trace activities.
         Subclasses must implement specific tracing logic for activities.
         """
-        ...
\ No newline at end of file
+        ...
diff --git a/api/core/ops/entities/config_entity.py b/api/core/ops/entities/config_entity.py
index 221e6239ab..5c79867571 100644
--- a/api/core/ops/entities/config_entity.py
+++ b/api/core/ops/entities/config_entity.py
@@ -4,14 +4,15 @@ from pydantic import BaseModel, ValidationInfo, field_validator
 
 
 class TracingProviderEnum(Enum):
-    LANGFUSE = 'langfuse'
-    LANGSMITH = 'langsmith'
+    LANGFUSE = "langfuse"
+    LANGSMITH = "langsmith"
 
 
 class BaseTracingConfig(BaseModel):
     """
     Base model class for tracing
     """
+
     ...
 
 
@@ -19,16 +20,18 @@ class LangfuseConfig(BaseTracingConfig):
     """
     Model class for Langfuse tracing config.
     """
+
     public_key: str
     secret_key: str
-    host: str = 'https://api.langfuse.com'
+    host: str = "https://api.langfuse.com"
 
     @field_validator("host")
+    @classmethod
     def set_value(cls, v, info: ValidationInfo):
         if v is None or v == "":
-            v = 'https://api.langfuse.com'
-        if not v.startswith('https://') and not v.startswith('http://'):
-            raise ValueError('host must start with https:// or http://')
+            v = "https://api.langfuse.com"
+        if not v.startswith("https://") and not v.startswith("http://"):
+            raise ValueError("host must start with https:// or http://")
 
         return v
 
@@ -37,15 +40,17 @@ class LangSmithConfig(BaseTracingConfig):
     """
     Model class for Langsmith tracing config.
     """
+
     api_key: str
     project: str
-    endpoint: str = 'https://api.smith.langchain.com'
+    endpoint: str = "https://api.smith.langchain.com"
 
     @field_validator("endpoint")
+    @classmethod
     def set_value(cls, v, info: ValidationInfo):
         if v is None or v == "":
-            v = 'https://api.smith.langchain.com'
-        if not v.startswith('https://'):
-            raise ValueError('endpoint must start with https://')
+            v = "https://api.smith.langchain.com"
+        if not v.startswith("https://"):
+            raise ValueError("endpoint must start with https://")
 
         return v
diff --git a/api/core/ops/entities/trace_entity.py b/api/core/ops/entities/trace_entity.py
index a1443f0691..f27a0af6e0 100644
--- a/api/core/ops/entities/trace_entity.py
+++ b/api/core/ops/entities/trace_entity.py
@@ -15,6 +15,7 @@ class BaseTraceInfo(BaseModel):
     metadata: dict[str, Any]
 
     @field_validator("inputs", "outputs")
+    @classmethod
     def ensure_type(cls, v):
         if v is None:
             return None
@@ -23,6 +24,7 @@ class BaseTraceInfo(BaseModel):
         else:
             return ""
 
+
 class WorkflowTraceInfo(BaseTraceInfo):
     workflow_data: Any
     conversation_id: Optional[str] = None
@@ -98,23 +100,24 @@ class GenerateNameTraceInfo(BaseTraceInfo):
     conversation_id: Optional[str] = None
     tenant_id: str
 
+
 trace_info_info_map = {
-    'WorkflowTraceInfo': WorkflowTraceInfo,
-    'MessageTraceInfo': MessageTraceInfo,
-    'ModerationTraceInfo': ModerationTraceInfo,
-    'SuggestedQuestionTraceInfo': SuggestedQuestionTraceInfo,
-    'DatasetRetrievalTraceInfo': DatasetRetrievalTraceInfo,
-    'ToolTraceInfo': ToolTraceInfo,
-    'GenerateNameTraceInfo': GenerateNameTraceInfo,
+    "WorkflowTraceInfo": WorkflowTraceInfo,
+    "MessageTraceInfo": MessageTraceInfo,
+    "ModerationTraceInfo": ModerationTraceInfo,
+    "SuggestedQuestionTraceInfo": SuggestedQuestionTraceInfo,
+    "DatasetRetrievalTraceInfo": DatasetRetrievalTraceInfo,
+    "ToolTraceInfo": ToolTraceInfo,
+    "GenerateNameTraceInfo": GenerateNameTraceInfo,
 }
 
 
 class TraceTaskName(str, Enum):
-    CONVERSATION_TRACE = 'conversation'
-    WORKFLOW_TRACE = 'workflow'
-    MESSAGE_TRACE = 'message'
-    MODERATION_TRACE = 'moderation'
-    SUGGESTED_QUESTION_TRACE = 'suggested_question'
-    DATASET_RETRIEVAL_TRACE = 'dataset_retrieval'
-    TOOL_TRACE = 'tool'
-    GENERATE_NAME_TRACE = 'generate_conversation_name'
+    CONVERSATION_TRACE = "conversation"
+    WORKFLOW_TRACE = "workflow"
+    MESSAGE_TRACE = "message"
+    MODERATION_TRACE = "moderation"
+    SUGGESTED_QUESTION_TRACE = "suggested_question"
+    DATASET_RETRIEVAL_TRACE = "dataset_retrieval"
+    TOOL_TRACE = "tool"
+    GENERATE_NAME_TRACE = "generate_conversation_name"
diff --git a/api/core/ops/langfuse_trace/entities/langfuse_trace_entity.py b/api/core/ops/langfuse_trace/entities/langfuse_trace_entity.py
index af7661f0af..447b799f1f 100644
--- a/api/core/ops/langfuse_trace/entities/langfuse_trace_entity.py
+++ b/api/core/ops/langfuse_trace/entities/langfuse_trace_entity.py
@@ -101,6 +101,7 @@ class LangfuseTrace(BaseModel):
     )
 
     @field_validator("input", "output")
+    @classmethod
     def ensure_dict(cls, v, info: ValidationInfo):
         field_name = info.field_name
         return validate_input_output(v, field_name)
@@ -171,6 +172,7 @@ class LangfuseSpan(BaseModel):
     )
 
     @field_validator("input", "output")
+    @classmethod
     def ensure_dict(cls, v, info: ValidationInfo):
         field_name = info.field_name
         return validate_input_output(v, field_name)
@@ -196,6 +198,7 @@ class GenerationUsage(BaseModel):
     totalCost: Optional[float] = None
 
     @field_validator("input", "output")
+    @classmethod
     def ensure_dict(cls, v, info: ValidationInfo):
         field_name = info.field_name
         return validate_input_output(v, field_name)
@@ -273,6 +276,7 @@ class LangfuseGeneration(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
     @field_validator("input", "output")
+    @classmethod
     def ensure_dict(cls, v, info: ValidationInfo):
         field_name = info.field_name
         return validate_input_output(v, field_name)
diff --git a/api/core/ops/langfuse_trace/langfuse_trace.py b/api/core/ops/langfuse_trace/langfuse_trace.py
index a0f3ac7f86..6aefbec9aa 100644
--- a/api/core/ops/langfuse_trace/langfuse_trace.py
+++ b/api/core/ops/langfuse_trace/langfuse_trace.py
@@ -65,7 +65,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             self.generate_name_trace(trace_info)
 
     def workflow_trace(self, trace_info: WorkflowTraceInfo):
-        trace_id = trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id
+        trace_id = trace_info.workflow_app_log_id or trace_info.workflow_run_id
         user_id = trace_info.metadata.get("user_id")
         if trace_info.message_id:
             trace_id = trace_info.message_id
@@ -84,7 +84,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             )
             self.add_trace(langfuse_trace_data=trace_data)
             workflow_span_data = LangfuseSpan(
-                id=(trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id),
+                id=(trace_info.workflow_app_log_id or trace_info.workflow_run_id),
                 name=TraceTaskName.WORKFLOW_TRACE.value,
                 input=trace_info.workflow_run_inputs,
                 output=trace_info.workflow_run_outputs,
@@ -93,7 +93,7 @@ class LangFuseDataTrace(BaseTraceInstance):
                 end_time=trace_info.end_time,
                 metadata=trace_info.metadata,
                 level=LevelEnum.DEFAULT if trace_info.error == "" else LevelEnum.ERROR,
-                status_message=trace_info.error if trace_info.error else "",
+                status_message=trace_info.error or "",
             )
             self.add_span(langfuse_span_data=workflow_span_data)
         else:
@@ -143,7 +143,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             else:
                 inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
             outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
-            created_at = node_execution.created_at if node_execution.created_at else datetime.now()
+            created_at = node_execution.created_at or datetime.now()
             elapsed_time = node_execution.elapsed_time
             finished_at = created_at + timedelta(seconds=elapsed_time)
 
@@ -172,10 +172,8 @@ class LangFuseDataTrace(BaseTraceInstance):
                     end_time=finished_at,
                     metadata=metadata,
                     level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
-                    status_message=trace_info.error if trace_info.error else "",
-                    parent_observation_id=(
-                        trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id
-                    ),
+                    status_message=trace_info.error or "",
+                    parent_observation_id=(trace_info.workflow_app_log_id or trace_info.workflow_run_id),
                 )
             else:
                 span_data = LangfuseSpan(
@@ -188,7 +186,7 @@ class LangFuseDataTrace(BaseTraceInstance):
                     end_time=finished_at,
                     metadata=metadata,
                     level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
-                    status_message=trace_info.error if trace_info.error else "",
+                    status_message=trace_info.error or "",
                 )
 
             self.add_span(langfuse_span_data=span_data)
@@ -212,7 +210,7 @@ class LangFuseDataTrace(BaseTraceInstance):
                     output=outputs,
                     metadata=metadata,
                     level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
-                    status_message=trace_info.error if trace_info.error else "",
+                    status_message=trace_info.error or "",
                     usage=generation_usage,
                 )
 
@@ -277,7 +275,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             output=message_data.answer,
             metadata=metadata,
             level=(LevelEnum.DEFAULT if message_data.status != "error" else LevelEnum.ERROR),
-            status_message=message_data.error if message_data.error else "",
+            status_message=message_data.error or "",
             usage=generation_usage,
         )
 
@@ -319,7 +317,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             end_time=trace_info.end_time,
             metadata=trace_info.metadata,
             level=(LevelEnum.DEFAULT if message_data.status != "error" else LevelEnum.ERROR),
-            status_message=message_data.error if message_data.error else "",
+            status_message=message_data.error or "",
             usage=generation_usage,
         )
 
diff --git a/api/core/ops/langsmith_trace/entities/langsmith_trace_entity.py b/api/core/ops/langsmith_trace/entities/langsmith_trace_entity.py
index f3fc46d99a..05c932fb99 100644
--- a/api/core/ops/langsmith_trace/entities/langsmith_trace_entity.py
+++ b/api/core/ops/langsmith_trace/entities/langsmith_trace_entity.py
@@ -35,49 +35,32 @@ class LangSmithRunModel(LangSmithTokenUsage, LangSmithMultiModel):
     run_type: LangSmithRunType = Field(..., description="Type of the run")
     start_time: Optional[datetime | str] = Field(None, description="Start time of the run")
     end_time: Optional[datetime | str] = Field(None, description="End time of the run")
-    extra: Optional[dict[str, Any]] = Field(
-        None, description="Extra information of the run"
-    )
+    extra: Optional[dict[str, Any]] = Field(None, description="Extra information of the run")
     error: Optional[str] = Field(None, description="Error message of the run")
-    serialized: Optional[dict[str, Any]] = Field(
-        None, description="Serialized data of the run"
-    )
+    serialized: Optional[dict[str, Any]] = Field(None, description="Serialized data of the run")
     parent_run_id: Optional[str] = Field(None, description="Parent run ID")
-    events: Optional[list[dict[str, Any]]] = Field(
-        None, description="Events associated with the run"
-    )
+    events: Optional[list[dict[str, Any]]] = Field(None, description="Events associated with the run")
     tags: Optional[list[str]] = Field(None, description="Tags associated with the run")
-    trace_id: Optional[str] = Field(
-        None, description="Trace ID associated with the run"
-    )
+    trace_id: Optional[str] = Field(None, description="Trace ID associated with the run")
     dotted_order: Optional[str] = Field(None, description="Dotted order of the run")
     id: Optional[str] = Field(None, description="ID of the run")
-    session_id: Optional[str] = Field(
-        None, description="Session ID associated with the run"
-    )
-    session_name: Optional[str] = Field(
-        None, description="Session name associated with the run"
-    )
-    reference_example_id: Optional[str] = Field(
-        None, description="Reference example ID associated with the run"
-    )
-    input_attachments: Optional[dict[str, Any]] = Field(
-        None, description="Input attachments of the run"
-    )
-    output_attachments: Optional[dict[str, Any]] = Field(
-        None, description="Output attachments of the run"
-    )
+    session_id: Optional[str] = Field(None, description="Session ID associated with the run")
+    session_name: Optional[str] = Field(None, description="Session name associated with the run")
+    reference_example_id: Optional[str] = Field(None, description="Reference example ID associated with the run")
+    input_attachments: Optional[dict[str, Any]] = Field(None, description="Input attachments of the run")
+    output_attachments: Optional[dict[str, Any]] = Field(None, description="Output attachments of the run")
 
     @field_validator("inputs", "outputs")
+    @classmethod
     def ensure_dict(cls, v, info: ValidationInfo):
         field_name = info.field_name
         values = info.data
         if v == {} or v is None:
             return v
         usage_metadata = {
-            "input_tokens": values.get('input_tokens', 0),
-            "output_tokens": values.get('output_tokens', 0),
-            "total_tokens": values.get('total_tokens', 0),
+            "input_tokens": values.get("input_tokens", 0),
+            "output_tokens": values.get("output_tokens", 0),
+            "total_tokens": values.get("total_tokens", 0),
         }
         file_list = values.get("file_list", [])
         if isinstance(v, str):
@@ -133,6 +116,7 @@ class LangSmithRunModel(LangSmithTokenUsage, LangSmithMultiModel):
             return v
         return v
 
+    @classmethod
     @field_validator("start_time", "end_time")
     def format_time(cls, v, info: ValidationInfo):
         if not isinstance(v, datetime):
@@ -143,25 +127,15 @@ class LangSmithRunModel(LangSmithTokenUsage, LangSmithMultiModel):
 
 class LangSmithRunUpdateModel(BaseModel):
     run_id: str = Field(..., description="ID of the run")
-    trace_id: Optional[str] = Field(
-        None, description="Trace ID associated with the run"
-    )
+    trace_id: Optional[str] = Field(None, description="Trace ID associated with the run")
     dotted_order: Optional[str] = Field(None, description="Dotted order of the run")
     parent_run_id: Optional[str] = Field(None, description="Parent run ID")
     end_time: Optional[datetime | str] = Field(None, description="End time of the run")
     error: Optional[str] = Field(None, description="Error message of the run")
     inputs: Optional[dict[str, Any]] = Field(None, description="Inputs of the run")
     outputs: Optional[dict[str, Any]] = Field(None, description="Outputs of the run")
-    events: Optional[list[dict[str, Any]]] = Field(
-        None, description="Events associated with the run"
-    )
+    events: Optional[list[dict[str, Any]]] = Field(None, description="Events associated with the run")
     tags: Optional[list[str]] = Field(None, description="Tags associated with the run")
-    extra: Optional[dict[str, Any]] = Field(
-        None, description="Extra information of the run"
-    )
-    input_attachments: Optional[dict[str, Any]] = Field(
-        None, description="Input attachments of the run"
-    )
-    output_attachments: Optional[dict[str, Any]] = Field(
-        None, description="Output attachments of the run"
-    )
+    extra: Optional[dict[str, Any]] = Field(None, description="Extra information of the run")
+    input_attachments: Optional[dict[str, Any]] = Field(None, description="Input attachments of the run")
+    output_attachments: Optional[dict[str, Any]] = Field(None, description="Output attachments of the run")
diff --git a/api/core/ops/langsmith_trace/langsmith_trace.py b/api/core/ops/langsmith_trace/langsmith_trace.py
index cc242905bd..37cbea13fd 100644
--- a/api/core/ops/langsmith_trace/langsmith_trace.py
+++ b/api/core/ops/langsmith_trace/langsmith_trace.py
@@ -1,9 +1,11 @@
 import json
 import logging
 import os
+import uuid
 from datetime import datetime, timedelta
 
 from langsmith import Client
+from langsmith.schemas import RunBase
 
 from core.ops.base_trace_instance import BaseTraceInstance
 from core.ops.entities.config_entity import LangSmithConfig
@@ -80,7 +82,7 @@ class LangSmithDataTrace(BaseTraceInstance):
         langsmith_run = LangSmithRunModel(
             file_list=trace_info.file_list,
             total_tokens=trace_info.total_tokens,
-            id=trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id,
+            id=trace_info.workflow_app_log_id or trace_info.workflow_run_id,
             name=TraceTaskName.WORKFLOW_TRACE.value,
             inputs=trace_info.workflow_run_inputs,
             run_type=LangSmithRunType.tool,
@@ -92,7 +94,7 @@ class LangSmithDataTrace(BaseTraceInstance):
             },
             error=trace_info.error,
             tags=["workflow"],
-            parent_run_id=trace_info.message_id if trace_info.message_id else None,
+            parent_run_id=trace_info.message_id or None,
         )
 
         self.add_run(langsmith_run)
@@ -131,7 +133,7 @@ class LangSmithDataTrace(BaseTraceInstance):
             else:
                 inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
             outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
-            created_at = node_execution.created_at if node_execution.created_at else datetime.now()
+            created_at = node_execution.created_at or datetime.now()
             elapsed_time = node_execution.elapsed_time
             finished_at = created_at + timedelta(seconds=elapsed_time)
 
@@ -157,8 +159,8 @@ class LangSmithDataTrace(BaseTraceInstance):
                 run_type = LangSmithRunType.llm
                 metadata.update(
                     {
-                        'ls_provider': process_data.get('model_provider', ''),
-                        'ls_model_name': process_data.get('model_name', ''),
+                        "ls_provider": process_data.get("model_provider", ""),
+                        "ls_model_name": process_data.get("model_name", ""),
                     }
                 )
             elif node_type == "knowledge-retrieval":
@@ -178,9 +180,7 @@ class LangSmithDataTrace(BaseTraceInstance):
                 extra={
                     "metadata": metadata,
                 },
-                parent_run_id=trace_info.workflow_app_log_id
-                if trace_info.workflow_app_log_id
-                else trace_info.workflow_run_id,
+                parent_run_id=trace_info.workflow_app_log_id or trace_info.workflow_run_id,
                 tags=["node_execution"],
             )
 
@@ -371,3 +371,22 @@ class LangSmithDataTrace(BaseTraceInstance):
         except Exception as e:
             logger.debug(f"LangSmith API check failed: {str(e)}")
             raise ValueError(f"LangSmith API check failed: {str(e)}")
+
+    def get_project_url(self):
+        try:
+            run_data = RunBase(
+                id=uuid.uuid4(),
+                name="tool",
+                inputs={"input": "test"},
+                outputs={"output": "test"},
+                run_type=LangSmithRunType.tool,
+                start_time=datetime.now(),
+            )
+
+            project_url = self.langsmith_client.get_run_url(
+                run=run_data, project_id=self.project_id, project_name=self.project_name
+            )
+            return project_url.split("/r/")[0]
+        except Exception as e:
+            logger.debug(f"LangSmith get run url failed: {str(e)}")
+            raise ValueError(f"LangSmith get run url failed: {str(e)}")
diff --git a/api/core/ops/ops_trace_manager.py b/api/core/ops/ops_trace_manager.py
index 1416d6bd2d..6f17bade97 100644
--- a/api/core/ops/ops_trace_manager.py
+++ b/api/core/ops/ops_trace_manager.py
@@ -36,17 +36,17 @@ from tasks.ops_trace_task import process_trace_tasks
 
 provider_config_map = {
     TracingProviderEnum.LANGFUSE.value: {
-        'config_class': LangfuseConfig,
-        'secret_keys': ['public_key', 'secret_key'],
-        'other_keys': ['host', 'project_key'],
-        'trace_instance': LangFuseDataTrace
+        "config_class": LangfuseConfig,
+        "secret_keys": ["public_key", "secret_key"],
+        "other_keys": ["host", "project_key"],
+        "trace_instance": LangFuseDataTrace,
     },
     TracingProviderEnum.LANGSMITH.value: {
-        'config_class': LangSmithConfig,
-        'secret_keys': ['api_key'],
-        'other_keys': ['project', 'endpoint'],
-        'trace_instance': LangSmithDataTrace
-    }
+        "config_class": LangSmithConfig,
+        "secret_keys": ["api_key"],
+        "other_keys": ["project", "endpoint"],
+        "trace_instance": LangSmithDataTrace,
+    },
 }
 
 
@@ -64,14 +64,17 @@ class OpsTraceManager:
         :return: encrypted tracing configuration
         """
         # Get the configuration class and the keys that require encryption
-        config_class, secret_keys, other_keys = provider_config_map[tracing_provider]['config_class'], \
-            provider_config_map[tracing_provider]['secret_keys'], provider_config_map[tracing_provider]['other_keys']
+        config_class, secret_keys, other_keys = (
+            provider_config_map[tracing_provider]["config_class"],
+            provider_config_map[tracing_provider]["secret_keys"],
+            provider_config_map[tracing_provider]["other_keys"],
+        )
 
         new_config = {}
         # Encrypt necessary keys
         for key in secret_keys:
             if key in tracing_config:
-                if '*' in tracing_config[key]:
+                if "*" in tracing_config[key]:
                     # If the key contains '*', retain the original value from the current config
                     new_config[key] = current_trace_config.get(key, tracing_config[key])
                 else:
@@ -94,8 +97,11 @@ class OpsTraceManager:
         :param tracing_config: tracing config
         :return:
         """
-        config_class, secret_keys, other_keys = provider_config_map[tracing_provider]['config_class'], \
-            provider_config_map[tracing_provider]['secret_keys'], provider_config_map[tracing_provider]['other_keys']
+        config_class, secret_keys, other_keys = (
+            provider_config_map[tracing_provider]["config_class"],
+            provider_config_map[tracing_provider]["secret_keys"],
+            provider_config_map[tracing_provider]["other_keys"],
+        )
         new_config = {}
         for key in secret_keys:
             if key in tracing_config:
@@ -114,8 +120,11 @@ class OpsTraceManager:
         :param decrypt_tracing_config: tracing config
         :return:
         """
-        config_class, secret_keys, other_keys = provider_config_map[tracing_provider]['config_class'], \
-            provider_config_map[tracing_provider]['secret_keys'], provider_config_map[tracing_provider]['other_keys']
+        config_class, secret_keys, other_keys = (
+            provider_config_map[tracing_provider]["config_class"],
+            provider_config_map[tracing_provider]["secret_keys"],
+            provider_config_map[tracing_provider]["other_keys"],
+        )
         new_config = {}
         for key in secret_keys:
             if key in decrypt_tracing_config:
@@ -133,9 +142,11 @@ class OpsTraceManager:
         :param tracing_provider: tracing provider
         :return:
         """
-        trace_config_data: TraceAppConfig = db.session.query(TraceAppConfig).filter(
-            TraceAppConfig.app_id == app_id, TraceAppConfig.tracing_provider == tracing_provider
-        ).first()
+        trace_config_data: TraceAppConfig = (
+            db.session.query(TraceAppConfig)
+            .filter(TraceAppConfig.app_id == app_id, TraceAppConfig.tracing_provider == tracing_provider)
+            .first()
+        )
 
         if not trace_config_data:
             return None
@@ -164,21 +175,21 @@ class OpsTraceManager:
         if app_id is None:
             return None
 
-        app: App = db.session.query(App).filter(
-            App.id == app_id
-        ).first()
+        app: App = db.session.query(App).filter(App.id == app_id).first()
         app_ops_trace_config = json.loads(app.tracing) if app.tracing else None
 
         if app_ops_trace_config is not None:
-            tracing_provider = app_ops_trace_config.get('tracing_provider')
+            tracing_provider = app_ops_trace_config.get("tracing_provider")
         else:
             return None
 
         # decrypt_token
         decrypt_trace_config = cls.get_decrypted_tracing_config(app_id, tracing_provider)
-        if app_ops_trace_config.get('enabled'):
-            trace_instance, config_class = provider_config_map[tracing_provider]['trace_instance'], \
-                provider_config_map[tracing_provider]['config_class']
+        if app_ops_trace_config.get("enabled"):
+            trace_instance, config_class = (
+                provider_config_map[tracing_provider]["trace_instance"],
+                provider_config_map[tracing_provider]["config_class"],
+            )
             tracing_instance = trace_instance(config_class(**decrypt_trace_config))
             return tracing_instance
 
@@ -192,9 +203,11 @@ class OpsTraceManager:
         conversation_data = db.session.query(Conversation).filter(Conversation.id == conversation_id).first()
 
         if conversation_data.app_model_config_id:
-            app_model_config = db.session.query(AppModelConfig).filter(
-                AppModelConfig.id == conversation_data.app_model_config_id
-            ).first()
+            app_model_config = (
+                db.session.query(AppModelConfig)
+                .filter(AppModelConfig.id == conversation_data.app_model_config_id)
+                .first()
+            )
         elif conversation_data.app_model_config_id is None and conversation_data.override_model_configs:
             app_model_config = conversation_data.override_model_configs
 
@@ -210,7 +223,7 @@ class OpsTraceManager:
         :return:
         """
         # auth check
-        if tracing_provider not in provider_config_map.keys() and tracing_provider is not None:
+        if tracing_provider not in provider_config_map and tracing_provider is not None:
             raise ValueError(f"Invalid tracing provider: {tracing_provider}")
 
         app_config: App = db.session.query(App).filter(App.id == app_id).first()
@@ -231,10 +244,7 @@ class OpsTraceManager:
         """
         app: App = db.session.query(App).filter(App.id == app_id).first()
         if not app.tracing:
-            return {
-                "enabled": False,
-                "tracing_provider": None
-            }
+            return {"enabled": False, "tracing_provider": None}
         app_trace_config = json.loads(app.tracing)
         return app_trace_config
 
@@ -246,8 +256,10 @@ class OpsTraceManager:
         :param tracing_provider: tracing provider
         :return:
         """
-        config_type, trace_instance = provider_config_map[tracing_provider]['config_class'], \
-            provider_config_map[tracing_provider]['trace_instance']
+        config_type, trace_instance = (
+            provider_config_map[tracing_provider]["config_class"],
+            provider_config_map[tracing_provider]["trace_instance"],
+        )
         tracing_config = config_type(**tracing_config)
         return trace_instance(tracing_config).api_check()
 
@@ -259,11 +271,28 @@ class OpsTraceManager:
         :param tracing_provider: tracing provider
         :return:
         """
-        config_type, trace_instance = provider_config_map[tracing_provider]['config_class'], \
-            provider_config_map[tracing_provider]['trace_instance']
+        config_type, trace_instance = (
+            provider_config_map[tracing_provider]["config_class"],
+            provider_config_map[tracing_provider]["trace_instance"],
+        )
         tracing_config = config_type(**tracing_config)
         return trace_instance(tracing_config).get_project_key()
 
+    @staticmethod
+    def get_trace_config_project_url(tracing_config: dict, tracing_provider: str):
+        """
+        get trace config is project key
+        :param tracing_config: tracing config
+        :param tracing_provider: tracing provider
+        :return:
+        """
+        config_type, trace_instance = (
+            provider_config_map[tracing_provider]["config_class"],
+            provider_config_map[tracing_provider]["trace_instance"],
+        )
+        tracing_config = config_type(**tracing_config)
+        return trace_instance(tracing_config).get_project_url()
+
 
 class TraceTask:
     def __init__(
@@ -274,7 +303,7 @@ class TraceTask:
         conversation_id: Optional[str] = None,
         user_id: Optional[str] = None,
         timer: Optional[Any] = None,
-        **kwargs
+        **kwargs,
     ):
         self.trace_type = trace_type
         self.message_id = message_id
@@ -297,9 +326,7 @@ class TraceTask:
                 self.workflow_run, self.conversation_id, self.user_id
             ),
             TraceTaskName.MESSAGE_TRACE: lambda: self.message_trace(self.message_id),
-            TraceTaskName.MODERATION_TRACE: lambda: self.moderation_trace(
-                self.message_id, self.timer, **self.kwargs
-            ),
+            TraceTaskName.MODERATION_TRACE: lambda: self.moderation_trace(self.message_id, self.timer, **self.kwargs),
             TraceTaskName.SUGGESTED_QUESTION_TRACE: lambda: self.suggested_question_trace(
                 self.message_id, self.timer, **self.kwargs
             ),
@@ -324,32 +351,29 @@ class TraceTask:
         workflow_run_id = workflow_run.id
         workflow_run_elapsed_time = workflow_run.elapsed_time
         workflow_run_status = workflow_run.status
-        workflow_run_inputs = (
-            json.loads(workflow_run.inputs) if workflow_run.inputs else {}
-        )
-        workflow_run_outputs = (
-            json.loads(workflow_run.outputs) if workflow_run.outputs else {}
-        )
+        workflow_run_inputs = json.loads(workflow_run.inputs) if workflow_run.inputs else {}
+        workflow_run_outputs = json.loads(workflow_run.outputs) if workflow_run.outputs else {}
         workflow_run_version = workflow_run.version
-        error = workflow_run.error if workflow_run.error else ""
+        error = workflow_run.error or ""
 
         total_tokens = workflow_run.total_tokens
 
-        file_list = workflow_run_inputs.get("sys.file") if workflow_run_inputs.get("sys.file") else []
+        file_list = workflow_run_inputs.get("sys.file") or []
         query = workflow_run_inputs.get("query") or workflow_run_inputs.get("sys.query") or ""
 
         # get workflow_app_log_id
-        workflow_app_log_data = db.session.query(WorkflowAppLog).filter_by(
-            tenant_id=tenant_id,
-            app_id=workflow_run.app_id,
-            workflow_run_id=workflow_run.id
-        ).first()
+        workflow_app_log_data = (
+            db.session.query(WorkflowAppLog)
+            .filter_by(tenant_id=tenant_id, app_id=workflow_run.app_id, workflow_run_id=workflow_run.id)
+            .first()
+        )
         workflow_app_log_id = str(workflow_app_log_data.id) if workflow_app_log_data else None
         # get message_id
-        message_data = db.session.query(Message.id).filter_by(
-            conversation_id=conversation_id,
-            workflow_run_id=workflow_run_id
-        ).first()
+        message_data = (
+            db.session.query(Message.id)
+            .filter_by(conversation_id=conversation_id, workflow_run_id=workflow_run_id)
+            .first()
+        )
         message_id = str(message_data.id) if message_data else None
 
         metadata = {
@@ -428,7 +452,7 @@ class TraceTask:
             message_tokens=message_tokens,
             answer_tokens=message_data.answer_tokens,
             total_tokens=message_tokens + message_data.answer_tokens,
-            error=message_data.error if message_data.error else "",
+            error=message_data.error or "",
             inputs=inputs,
             outputs=message_data.answer,
             file_list=file_list,
@@ -457,13 +481,13 @@ class TraceTask:
         # get workflow_app_log_id
         workflow_app_log_id = None
         if message_data.workflow_run_id:
-            workflow_app_log_data = db.session.query(WorkflowAppLog).filter_by(
-                workflow_run_id=message_data.workflow_run_id
-            ).first()
+            workflow_app_log_data = (
+                db.session.query(WorkflowAppLog).filter_by(workflow_run_id=message_data.workflow_run_id).first()
+            )
             workflow_app_log_id = str(workflow_app_log_data.id) if workflow_app_log_data else None
 
         moderation_trace_info = ModerationTraceInfo(
-            message_id=workflow_app_log_id if workflow_app_log_id else message_id,
+            message_id=workflow_app_log_id or message_id,
             inputs=inputs,
             message_data=message_data.to_dict(),
             flagged=moderation_result.flagged,
@@ -497,13 +521,13 @@ class TraceTask:
         # get workflow_app_log_id
         workflow_app_log_id = None
         if message_data.workflow_run_id:
-            workflow_app_log_data = db.session.query(WorkflowAppLog).filter_by(
-                workflow_run_id=message_data.workflow_run_id
-            ).first()
+            workflow_app_log_data = (
+                db.session.query(WorkflowAppLog).filter_by(workflow_run_id=message_data.workflow_run_id).first()
+            )
             workflow_app_log_id = str(workflow_app_log_data.id) if workflow_app_log_data else None
 
         suggested_question_trace_info = SuggestedQuestionTraceInfo(
-            message_id=workflow_app_log_id if workflow_app_log_id else message_id,
+            message_id=workflow_app_log_id or message_id,
             message_data=message_data.to_dict(),
             inputs=message_data.message,
             outputs=message_data.answer,
@@ -545,7 +569,7 @@ class TraceTask:
 
         dataset_retrieval_trace_info = DatasetRetrievalTraceInfo(
             message_id=message_id,
-            inputs=message_data.query if message_data.query else message_data.inputs,
+            inputs=message_data.query or message_data.inputs,
             documents=[doc.model_dump() for doc in documents],
             start_time=timer.get("start"),
             end_time=timer.get("end"),
@@ -556,9 +580,9 @@ class TraceTask:
         return dataset_retrieval_trace_info
 
     def tool_trace(self, message_id, timer, **kwargs):
-        tool_name = kwargs.get('tool_name')
-        tool_inputs = kwargs.get('tool_inputs')
-        tool_outputs = kwargs.get('tool_outputs')
+        tool_name = kwargs.get("tool_name")
+        tool_inputs = kwargs.get("tool_inputs")
+        tool_outputs = kwargs.get("tool_outputs")
         message_data = get_message_data(message_id)
         if not message_data:
             return {}
@@ -573,11 +597,11 @@ class TraceTask:
             if tool_name in agent_thought.tools:
                 created_time = agent_thought.created_at
                 tool_meta_data = agent_thought.tool_meta.get(tool_name, {})
-                tool_config = tool_meta_data.get('tool_config', {})
-                time_cost = tool_meta_data.get('time_cost', 0)
+                tool_config = tool_meta_data.get("tool_config", {})
+                time_cost = tool_meta_data.get("time_cost", 0)
                 end_time = created_time + timedelta(seconds=time_cost)
-                error = tool_meta_data.get('error', "")
-                tool_parameters = tool_meta_data.get('tool_parameters', {})
+                error = tool_meta_data.get("error", "")
+                tool_parameters = tool_meta_data.get("tool_parameters", {})
         metadata = {
             "message_id": message_id,
             "tool_name": tool_name,
@@ -671,8 +695,7 @@ class TraceQueueManager:
             self.start_timer()
 
     def add_trace_task(self, trace_task: TraceTask):
-        global trace_manager_timer
-        global trace_manager_queue
+        global trace_manager_timer, trace_manager_queue
         try:
             if self.trace_instance:
                 trace_task.app_id = self.app_id
@@ -702,9 +725,7 @@ class TraceQueueManager:
     def start_timer(self):
         global trace_manager_timer
         if trace_manager_timer is None or not trace_manager_timer.is_alive():
-            trace_manager_timer = threading.Timer(
-                trace_manager_interval, self.run
-            )
+            trace_manager_timer = threading.Timer(trace_manager_interval, self.run)
             trace_manager_timer.name = f"trace_manager_timer_{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())}"
             trace_manager_timer.daemon = False
             trace_manager_timer.start()
diff --git a/api/core/ops/utils.py b/api/core/ops/utils.py
index 3b2e04abb7..498685b342 100644
--- a/api/core/ops/utils.py
+++ b/api/core/ops/utils.py
@@ -20,19 +20,19 @@ def get_message_data(message_id):
 
 @contextmanager
 def measure_time():
-    timing_info = {'start': datetime.now(), 'end': None}
+    timing_info = {"start": datetime.now(), "end": None}
     try:
         yield timing_info
     finally:
-        timing_info['end'] = datetime.now()
+        timing_info["end"] = datetime.now()
 
 
 def replace_text_with_content(data):
     if isinstance(data, dict):
         new_data = {}
         for key, value in data.items():
-            if key == 'text':
-                new_data['content'] = value
+            if key == "text":
+                new_data["content"] = value
             else:
                 new_data[key] = replace_text_with_content(value)
         return new_data
diff --git a/api/core/prompt/advanced_prompt_transform.py b/api/core/prompt/advanced_prompt_transform.py
index 22420fea2c..ce8038d14e 100644
--- a/api/core/prompt/advanced_prompt_transform.py
+++ b/api/core/prompt/advanced_prompt_transform.py
@@ -22,18 +22,22 @@ class AdvancedPromptTransform(PromptTransform):
     """
     Advanced Prompt Transform for Workflow LLM Node.
     """
+
     def __init__(self, with_variable_tmpl: bool = False) -> None:
         self.with_variable_tmpl = with_variable_tmpl
 
-    def get_prompt(self, prompt_template: Union[list[ChatModelMessage], CompletionModelPromptTemplate],
-                   inputs: dict,
-                   query: str,
-                   files: list[FileVar],
-                   context: Optional[str],
-                   memory_config: Optional[MemoryConfig],
-                   memory: Optional[TokenBufferMemory],
-                   model_config: ModelConfigWithCredentialsEntity,
-                   query_prompt_template: Optional[str] = None) -> list[PromptMessage]:
+    def get_prompt(
+        self,
+        prompt_template: Union[list[ChatModelMessage], CompletionModelPromptTemplate],
+        inputs: dict,
+        query: str,
+        files: list[FileVar],
+        context: Optional[str],
+        memory_config: Optional[MemoryConfig],
+        memory: Optional[TokenBufferMemory],
+        model_config: ModelConfigWithCredentialsEntity,
+        query_prompt_template: Optional[str] = None,
+    ) -> list[PromptMessage]:
         inputs = {key: str(value) for key, value in inputs.items()}
 
         prompt_messages = []
@@ -48,7 +52,7 @@ class AdvancedPromptTransform(PromptTransform):
                 context=context,
                 memory_config=memory_config,
                 memory=memory,
-                model_config=model_config
+                model_config=model_config,
             )
         elif model_mode == ModelMode.CHAT:
             prompt_messages = self._get_chat_model_prompt_messages(
@@ -60,20 +64,22 @@ class AdvancedPromptTransform(PromptTransform):
                 context=context,
                 memory_config=memory_config,
                 memory=memory,
-                model_config=model_config
+                model_config=model_config,
             )
 
         return prompt_messages
 
-    def _get_completion_model_prompt_messages(self,
-                                              prompt_template: CompletionModelPromptTemplate,
-                                              inputs: dict,
-                                              query: Optional[str],
-                                              files: list[FileVar],
-                                              context: Optional[str],
-                                              memory_config: Optional[MemoryConfig],
-                                              memory: Optional[TokenBufferMemory],
-                                              model_config: ModelConfigWithCredentialsEntity) -> list[PromptMessage]:
+    def _get_completion_model_prompt_messages(
+        self,
+        prompt_template: CompletionModelPromptTemplate,
+        inputs: dict,
+        query: Optional[str],
+        files: list[FileVar],
+        context: Optional[str],
+        memory_config: Optional[MemoryConfig],
+        memory: Optional[TokenBufferMemory],
+        model_config: ModelConfigWithCredentialsEntity,
+    ) -> list[PromptMessage]:
         """
         Get completion model prompt messages.
         """
@@ -81,7 +87,7 @@ class AdvancedPromptTransform(PromptTransform):
 
         prompt_messages = []
 
-        if prompt_template.edition_type == 'basic' or not prompt_template.edition_type:
+        if prompt_template.edition_type == "basic" or not prompt_template.edition_type:
             prompt_template = PromptTemplateParser(template=raw_prompt, with_variable_tmpl=self.with_variable_tmpl)
             prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
 
@@ -96,15 +102,13 @@ class AdvancedPromptTransform(PromptTransform):
                     role_prefix=role_prefix,
                     prompt_template=prompt_template,
                     prompt_inputs=prompt_inputs,
-                    model_config=model_config
+                    model_config=model_config,
                 )
 
             if query:
                 prompt_inputs = self._set_query_variable(query, prompt_template, prompt_inputs)
 
-            prompt = prompt_template.format(
-                prompt_inputs
-            )
+            prompt = prompt_template.format(prompt_inputs)
         else:
             prompt = raw_prompt
             prompt_inputs = inputs
@@ -122,16 +126,18 @@ class AdvancedPromptTransform(PromptTransform):
 
         return prompt_messages
 
-    def _get_chat_model_prompt_messages(self,
-                                        prompt_template: list[ChatModelMessage],
-                                        inputs: dict,
-                                        query: Optional[str],
-                                        files: list[FileVar],
-                                        context: Optional[str],
-                                        memory_config: Optional[MemoryConfig],
-                                        memory: Optional[TokenBufferMemory],
-                                        model_config: ModelConfigWithCredentialsEntity,
-                                        query_prompt_template: Optional[str] = None) -> list[PromptMessage]:
+    def _get_chat_model_prompt_messages(
+        self,
+        prompt_template: list[ChatModelMessage],
+        inputs: dict,
+        query: Optional[str],
+        files: list[FileVar],
+        context: Optional[str],
+        memory_config: Optional[MemoryConfig],
+        memory: Optional[TokenBufferMemory],
+        model_config: ModelConfigWithCredentialsEntity,
+        query_prompt_template: Optional[str] = None,
+    ) -> list[PromptMessage]:
         """
         Get chat model prompt messages.
         """
@@ -142,22 +148,20 @@ class AdvancedPromptTransform(PromptTransform):
         for prompt_item in raw_prompt_list:
             raw_prompt = prompt_item.text
 
-            if prompt_item.edition_type == 'basic' or not prompt_item.edition_type:
+            if prompt_item.edition_type == "basic" or not prompt_item.edition_type:
                 prompt_template = PromptTemplateParser(template=raw_prompt, with_variable_tmpl=self.with_variable_tmpl)
                 prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
 
                 prompt_inputs = self._set_context_variable(context, prompt_template, prompt_inputs)
 
-                prompt = prompt_template.format(
-                    prompt_inputs
-                )
-            elif prompt_item.edition_type == 'jinja2':
+                prompt = prompt_template.format(prompt_inputs)
+            elif prompt_item.edition_type == "jinja2":
                 prompt = raw_prompt
                 prompt_inputs = inputs
 
                 prompt = Jinja2Formatter.format(prompt, prompt_inputs)
             else:
-                raise ValueError(f'Invalid edition type: {prompt_item.edition_type}')
+                raise ValueError(f"Invalid edition type: {prompt_item.edition_type}")
 
             if prompt_item.role == PromptMessageRole.USER:
                 prompt_messages.append(UserPromptMessage(content=prompt))
@@ -168,17 +172,14 @@ class AdvancedPromptTransform(PromptTransform):
 
         if query and query_prompt_template:
             prompt_template = PromptTemplateParser(
-                template=query_prompt_template,
-                with_variable_tmpl=self.with_variable_tmpl
+                template=query_prompt_template, with_variable_tmpl=self.with_variable_tmpl
             )
             prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
-            prompt_inputs['#sys.query#'] = query
+            prompt_inputs["#sys.query#"] = query
 
             prompt_inputs = self._set_context_variable(context, prompt_template, prompt_inputs)
 
-            query = prompt_template.format(
-                prompt_inputs
-            )
+            query = prompt_template.format(prompt_inputs)
 
         if memory and memory_config:
             prompt_messages = self._append_chat_histories(memory, memory_config, prompt_messages, model_config)
@@ -203,7 +204,7 @@ class AdvancedPromptTransform(PromptTransform):
 
                     last_message.content = prompt_message_contents
                 else:
-                    prompt_message_contents = [TextPromptMessageContent(data='')]  # not for query
+                    prompt_message_contents = [TextPromptMessageContent(data="")]  # not for query
                     for file in files:
                         prompt_message_contents.append(file.prompt_message_content)
 
@@ -220,38 +221,39 @@ class AdvancedPromptTransform(PromptTransform):
         return prompt_messages
 
     def _set_context_variable(self, context: str, prompt_template: PromptTemplateParser, prompt_inputs: dict) -> dict:
-        if '#context#' in prompt_template.variable_keys:
+        if "#context#" in prompt_template.variable_keys:
             if context:
-                prompt_inputs['#context#'] = context
+                prompt_inputs["#context#"] = context
             else:
-                prompt_inputs['#context#'] = ''
+                prompt_inputs["#context#"] = ""
 
         return prompt_inputs
 
     def _set_query_variable(self, query: str, prompt_template: PromptTemplateParser, prompt_inputs: dict) -> dict:
-        if '#query#' in prompt_template.variable_keys:
+        if "#query#" in prompt_template.variable_keys:
             if query:
-                prompt_inputs['#query#'] = query
+                prompt_inputs["#query#"] = query
             else:
-                prompt_inputs['#query#'] = ''
+                prompt_inputs["#query#"] = ""
 
         return prompt_inputs
 
-    def _set_histories_variable(self, memory: TokenBufferMemory,
-                                memory_config: MemoryConfig,
-                                raw_prompt: str,
-                                role_prefix: MemoryConfig.RolePrefix,
-                                prompt_template: PromptTemplateParser,
-                                prompt_inputs: dict,
-                                model_config: ModelConfigWithCredentialsEntity) -> dict:
-        if '#histories#' in prompt_template.variable_keys:
+    def _set_histories_variable(
+        self,
+        memory: TokenBufferMemory,
+        memory_config: MemoryConfig,
+        raw_prompt: str,
+        role_prefix: MemoryConfig.RolePrefix,
+        prompt_template: PromptTemplateParser,
+        prompt_inputs: dict,
+        model_config: ModelConfigWithCredentialsEntity,
+    ) -> dict:
+        if "#histories#" in prompt_template.variable_keys:
             if memory:
-                inputs = {'#histories#': '', **prompt_inputs}
+                inputs = {"#histories#": "", **prompt_inputs}
                 prompt_template = PromptTemplateParser(template=raw_prompt, with_variable_tmpl=self.with_variable_tmpl)
                 prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
-                tmp_human_message = UserPromptMessage(
-                    content=prompt_template.format(prompt_inputs)
-                )
+                tmp_human_message = UserPromptMessage(content=prompt_template.format(prompt_inputs))
 
                 rest_tokens = self._calculate_rest_token([tmp_human_message], model_config)
 
@@ -260,10 +262,10 @@ class AdvancedPromptTransform(PromptTransform):
                     memory_config=memory_config,
                     max_token_limit=rest_tokens,
                     human_prefix=role_prefix.user,
-                    ai_prefix=role_prefix.assistant
+                    ai_prefix=role_prefix.assistant,
                 )
-                prompt_inputs['#histories#'] = histories
+                prompt_inputs["#histories#"] = histories
             else:
-                prompt_inputs['#histories#'] = ''
+                prompt_inputs["#histories#"] = ""
 
         return prompt_inputs
diff --git a/api/core/prompt/agent_history_prompt_transform.py b/api/core/prompt/agent_history_prompt_transform.py
index af0075ea91..caa1793ea8 100644
--- a/api/core/prompt/agent_history_prompt_transform.py
+++ b/api/core/prompt/agent_history_prompt_transform.py
@@ -17,12 +17,14 @@ class AgentHistoryPromptTransform(PromptTransform):
     """
     History Prompt Transform for Agent App
     """
-    def __init__(self,
-                 model_config: ModelConfigWithCredentialsEntity,
-                 prompt_messages: list[PromptMessage],
-                 history_messages: list[PromptMessage],
-                 memory: Optional[TokenBufferMemory] = None,
-                 ):
+
+    def __init__(
+        self,
+        model_config: ModelConfigWithCredentialsEntity,
+        prompt_messages: list[PromptMessage],
+        history_messages: list[PromptMessage],
+        memory: Optional[TokenBufferMemory] = None,
+    ):
         self.model_config = model_config
         self.prompt_messages = prompt_messages
         self.history_messages = history_messages
@@ -45,9 +47,7 @@ class AgentHistoryPromptTransform(PromptTransform):
         model_type_instance = cast(LargeLanguageModel, model_type_instance)
 
         curr_message_tokens = model_type_instance.get_num_tokens(
-            self.memory.model_instance.model,
-            self.memory.model_instance.credentials,
-            self.history_messages
+            self.memory.model_instance.model, self.memory.model_instance.credentials, self.history_messages
         )
         if curr_message_tokens <= max_token_limit:
             return self.history_messages
@@ -63,9 +63,7 @@ class AgentHistoryPromptTransform(PromptTransform):
             # a message is start with UserPromptMessage
             if isinstance(prompt_message, UserPromptMessage):
                 curr_message_tokens = model_type_instance.get_num_tokens(
-                    self.memory.model_instance.model,
-                    self.memory.model_instance.credentials,
-                    prompt_messages
+                    self.memory.model_instance.model, self.memory.model_instance.credentials, prompt_messages
                 )
                 # if current message token is overflow, drop all the prompts in current message and break
                 if curr_message_tokens > max_token_limit:
diff --git a/api/core/prompt/entities/advanced_prompt_entities.py b/api/core/prompt/entities/advanced_prompt_entities.py
index 61df69163c..c8e7b414df 100644
--- a/api/core/prompt/entities/advanced_prompt_entities.py
+++ b/api/core/prompt/entities/advanced_prompt_entities.py
@@ -9,27 +9,31 @@ class ChatModelMessage(BaseModel):
     """
     Chat Message.
     """
+
     text: str
     role: PromptMessageRole
-    edition_type: Optional[Literal['basic', 'jinja2']] = None
+    edition_type: Optional[Literal["basic", "jinja2"]] = None
 
 
 class CompletionModelPromptTemplate(BaseModel):
     """
     Completion Model Prompt Template.
     """
+
     text: str
-    edition_type: Optional[Literal['basic', 'jinja2']] = None
+    edition_type: Optional[Literal["basic", "jinja2"]] = None
 
 
 class MemoryConfig(BaseModel):
     """
     Memory Config.
     """
+
     class RolePrefix(BaseModel):
         """
         Role Prefix.
         """
+
         user: str
         assistant: str
 
@@ -37,6 +41,7 @@ class MemoryConfig(BaseModel):
         """
         Window Config.
         """
+
         enabled: bool
         size: Optional[int] = None
 
diff --git a/api/core/prompt/prompt_templates/advanced_prompt_templates.py b/api/core/prompt/prompt_templates/advanced_prompt_templates.py
index da40534d99..0ab7f526cc 100644
--- a/api/core/prompt/prompt_templates/advanced_prompt_templates.py
+++ b/api/core/prompt/prompt_templates/advanced_prompt_templates.py
@@ -1,83 +1,45 @@
-CONTEXT = "Use the following context as your learned knowledge, inside <context></context> XML tags.\n\n<context>\n{{#context#}}\n</context>\n\nWhen answer to user:\n- If you don't know, just say that you don't know.\n- If you don't know when you are not sure, ask for clarification.\nAvoid mentioning that you obtained the information from the context.\nAnd answer according to the language of the user's question.\n"
+CONTEXT = "Use the following context as your learned knowledge, inside <context></context> XML tags.\n\n<context>\n{{#context#}}\n</context>\n\nWhen answer to user:\n- If you don't know, just say that you don't know.\n- If you don't know when you are not sure, ask for clarification.\nAvoid mentioning that you obtained the information from the context.\nAnd answer according to the language of the user's question.\n"  # noqa: E501
 
-BAICHUAN_CONTEXT = "用户在与一个客观的助手对话。助手会尊重找到的材料，给出全面专业的解释，但不会过度演绎。同时回答中不会暴露引用的材料：\n\n```\n{{#context#}}\n```\n\n"
+BAICHUAN_CONTEXT = "用户在与一个客观的助手对话。助手会尊重找到的材料，给出全面专业的解释，但不会过度演绎。同时回答中不会暴露引用的材料：\n\n```\n{{#context#}}\n```\n\n"  # noqa: E501
 
 CHAT_APP_COMPLETION_PROMPT_CONFIG = {
     "completion_prompt_config": {
         "prompt": {
-            "text": "{{#pre_prompt#}}\nHere is the chat histories between human and assistant, inside <histories></histories> XML tags.\n\n<histories>\n{{#histories#}}\n</histories>\n\n\nHuman: {{#query#}}\n\nAssistant: "
+            "text": "{{#pre_prompt#}}\nHere is the chat histories between human and assistant, inside <histories></histories> XML tags.\n\n<histories>\n{{#histories#}}\n</histories>\n\n\nHuman: {{#query#}}\n\nAssistant: "  # noqa: E501
         },
-        "conversation_histories_role": {
-            "user_prefix": "Human",
-            "assistant_prefix": "Assistant"
-        }
+        "conversation_histories_role": {"user_prefix": "Human", "assistant_prefix": "Assistant"},
     },
-    "stop": ["Human:"]
+    "stop": ["Human:"],
 }
 
-CHAT_APP_CHAT_PROMPT_CONFIG = { 
-    "chat_prompt_config": {
-        "prompt": [{
-            "role": "system",
-            "text": "{{#pre_prompt#}}"
-        }]
-    }
-}
+CHAT_APP_CHAT_PROMPT_CONFIG = {"chat_prompt_config": {"prompt": [{"role": "system", "text": "{{#pre_prompt#}}"}]}}
 
-COMPLETION_APP_CHAT_PROMPT_CONFIG = {
-    "chat_prompt_config": {
-        "prompt": [{
-            "role": "user",
-            "text": "{{#pre_prompt#}}"
-        }]
-    }
-}
+COMPLETION_APP_CHAT_PROMPT_CONFIG = {"chat_prompt_config": {"prompt": [{"role": "user", "text": "{{#pre_prompt#}}"}]}}
 
 COMPLETION_APP_COMPLETION_PROMPT_CONFIG = {
-    "completion_prompt_config": {
-        "prompt": {
-            "text": "{{#pre_prompt#}}"
-        }
-    },
-    "stop": ["Human:"]
+    "completion_prompt_config": {"prompt": {"text": "{{#pre_prompt#}}"}},
+    "stop": ["Human:"],
 }
 
 BAICHUAN_CHAT_APP_COMPLETION_PROMPT_CONFIG = {
     "completion_prompt_config": {
         "prompt": {
-            "text": "{{#pre_prompt#}}\n\n用户和助手的历史对话内容如下：\n```\n{{#histories#}}\n```\n\n\n\n用户：{{#query#}}"
+            "text": "{{#pre_prompt#}}\n\n用户和助手的历史对话内容如下：\n```\n{{#histories#}}\n```\n\n\n\n用户：{{#query#}}"  # noqa: E501
         },
-        "conversation_histories_role": {
-            "user_prefix": "用户",
-            "assistant_prefix": "助手"
-        }
+        "conversation_histories_role": {"user_prefix": "用户", "assistant_prefix": "助手"},
     },
-    "stop": ["用户:"]
+    "stop": ["用户:"],
 }
 
-BAICHUAN_CHAT_APP_CHAT_PROMPT_CONFIG = { 
-    "chat_prompt_config": {
-        "prompt": [{
-            "role": "system",
-            "text": "{{#pre_prompt#}}"
-        }]
-    }
+BAICHUAN_CHAT_APP_CHAT_PROMPT_CONFIG = {
+    "chat_prompt_config": {"prompt": [{"role": "system", "text": "{{#pre_prompt#}}"}]}
 }
 
 BAICHUAN_COMPLETION_APP_CHAT_PROMPT_CONFIG = {
-    "chat_prompt_config": {
-        "prompt": [{
-            "role": "user",
-            "text": "{{#pre_prompt#}}"
-        }]
-    }
+    "chat_prompt_config": {"prompt": [{"role": "user", "text": "{{#pre_prompt#}}"}]}
 }
 
 BAICHUAN_COMPLETION_APP_COMPLETION_PROMPT_CONFIG = {
-    "completion_prompt_config": {
-        "prompt": {
-            "text": "{{#pre_prompt#}}"
-        }
-    },
-    "stop": ["用户:"]
+    "completion_prompt_config": {"prompt": {"text": "{{#pre_prompt#}}"}},
+    "stop": ["用户:"],
 }
diff --git a/api/core/prompt/prompt_transform.py b/api/core/prompt/prompt_transform.py
index b86d3fa815..87acdb3c49 100644
--- a/api/core/prompt/prompt_transform.py
+++ b/api/core/prompt/prompt_transform.py
@@ -9,75 +9,78 @@ from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 
 
 class PromptTransform:
-    def _append_chat_histories(self, memory: TokenBufferMemory,
-                               memory_config: MemoryConfig,
-                               prompt_messages: list[PromptMessage],
-                               model_config: ModelConfigWithCredentialsEntity) -> list[PromptMessage]:
+    def _append_chat_histories(
+        self,
+        memory: TokenBufferMemory,
+        memory_config: MemoryConfig,
+        prompt_messages: list[PromptMessage],
+        model_config: ModelConfigWithCredentialsEntity,
+    ) -> list[PromptMessage]:
         rest_tokens = self._calculate_rest_token(prompt_messages, model_config)
         histories = self._get_history_messages_list_from_memory(memory, memory_config, rest_tokens)
         prompt_messages.extend(histories)
 
         return prompt_messages
 
-    def _calculate_rest_token(self, prompt_messages: list[PromptMessage],
-                              model_config: ModelConfigWithCredentialsEntity) -> int:
+    def _calculate_rest_token(
+        self, prompt_messages: list[PromptMessage], model_config: ModelConfigWithCredentialsEntity
+    ) -> int:
         rest_tokens = 2000
 
         model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
         if model_context_tokens:
             model_instance = ModelInstance(
-                provider_model_bundle=model_config.provider_model_bundle,
-                model=model_config.model
+                provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
             )
 
-            curr_message_tokens = model_instance.get_llm_num_tokens(
-                prompt_messages
-            )
+            curr_message_tokens = model_instance.get_llm_num_tokens(prompt_messages)
 
             max_tokens = 0
             for parameter_rule in model_config.model_schema.parameter_rules:
-                if (parameter_rule.name == 'max_tokens'
-                        or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
-                    max_tokens = (model_config.parameters.get(parameter_rule.name)
-                                  or model_config.parameters.get(parameter_rule.use_template)) or 0
+                if parameter_rule.name == "max_tokens" or (
+                    parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+                ):
+                    max_tokens = (
+                        model_config.parameters.get(parameter_rule.name)
+                        or model_config.parameters.get(parameter_rule.use_template)
+                    ) or 0
 
             rest_tokens = model_context_tokens - max_tokens - curr_message_tokens
             rest_tokens = max(rest_tokens, 0)
 
         return rest_tokens
 
-    def _get_history_messages_from_memory(self, memory: TokenBufferMemory,
-                                          memory_config: MemoryConfig,
-                                          max_token_limit: int,
-                                          human_prefix: Optional[str] = None,
-                                          ai_prefix: Optional[str] = None) -> str:
+    def _get_history_messages_from_memory(
+        self,
+        memory: TokenBufferMemory,
+        memory_config: MemoryConfig,
+        max_token_limit: int,
+        human_prefix: Optional[str] = None,
+        ai_prefix: Optional[str] = None,
+    ) -> str:
         """Get memory messages."""
-        kwargs = {
-            "max_token_limit": max_token_limit
-        }
+        kwargs = {"max_token_limit": max_token_limit}
 
         if human_prefix:
-            kwargs['human_prefix'] = human_prefix
+            kwargs["human_prefix"] = human_prefix
 
         if ai_prefix:
-            kwargs['ai_prefix'] = ai_prefix
+            kwargs["ai_prefix"] = ai_prefix
 
         if memory_config.window.enabled and memory_config.window.size is not None and memory_config.window.size > 0:
-            kwargs['message_limit'] = memory_config.window.size
+            kwargs["message_limit"] = memory_config.window.size
 
-        return memory.get_history_prompt_text(
-            **kwargs
-        )
+        return memory.get_history_prompt_text(**kwargs)
 
-    def _get_history_messages_list_from_memory(self, memory: TokenBufferMemory,
-                                               memory_config: MemoryConfig,
-                                               max_token_limit: int) -> list[PromptMessage]:
+    def _get_history_messages_list_from_memory(
+        self, memory: TokenBufferMemory, memory_config: MemoryConfig, max_token_limit: int
+    ) -> list[PromptMessage]:
         """Get memory messages."""
         return memory.get_history_prompt_messages(
             max_token_limit=max_token_limit,
             message_limit=memory_config.window.size
-            if (memory_config.window.enabled
-                and memory_config.window.size is not None
-                and memory_config.window.size > 0)
-            else None
+            if (
+                memory_config.window.enabled and memory_config.window.size is not None and memory_config.window.size > 0
+            )
+            else None,
         )
diff --git a/api/core/prompt/simple_prompt_transform.py b/api/core/prompt/simple_prompt_transform.py
index fd7ed0181b..7479560520 100644
--- a/api/core/prompt/simple_prompt_transform.py
+++ b/api/core/prompt/simple_prompt_transform.py
@@ -22,11 +22,11 @@ if TYPE_CHECKING:
 
 
 class ModelMode(enum.Enum):
-    COMPLETION = 'completion'
-    CHAT = 'chat'
+    COMPLETION = "completion"
+    CHAT = "chat"
 
     @classmethod
-    def value_of(cls, value: str) -> 'ModelMode':
+    def value_of(cls, value: str) -> "ModelMode":
         """
         Get value of given mode.
 
@@ -36,7 +36,7 @@ class ModelMode(enum.Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid mode value {value}')
+        raise ValueError(f"invalid mode value {value}")
 
 
 prompt_file_contents = {}
@@ -47,16 +47,17 @@ class SimplePromptTransform(PromptTransform):
     Simple Prompt Transform for Chatbot App Basic Mode.
     """
 
-    def get_prompt(self,
-                   app_mode: AppMode,
-                   prompt_template_entity: PromptTemplateEntity,
-                   inputs: dict,
-                   query: str,
-                   files: list["FileVar"],
-                   context: Optional[str],
-                   memory: Optional[TokenBufferMemory],
-                   model_config: ModelConfigWithCredentialsEntity) -> \
-            tuple[list[PromptMessage], Optional[list[str]]]:
+    def get_prompt(
+        self,
+        app_mode: AppMode,
+        prompt_template_entity: PromptTemplateEntity,
+        inputs: dict,
+        query: str,
+        files: list["FileVar"],
+        context: Optional[str],
+        memory: Optional[TokenBufferMemory],
+        model_config: ModelConfigWithCredentialsEntity,
+    ) -> tuple[list[PromptMessage], Optional[list[str]]]:
         inputs = {key: str(value) for key, value in inputs.items()}
 
         model_mode = ModelMode.value_of(model_config.mode)
@@ -69,7 +70,7 @@ class SimplePromptTransform(PromptTransform):
                 files=files,
                 context=context,
                 memory=memory,
-                model_config=model_config
+                model_config=model_config,
             )
         else:
             prompt_messages, stops = self._get_completion_model_prompt_messages(
@@ -80,19 +81,21 @@ class SimplePromptTransform(PromptTransform):
                 files=files,
                 context=context,
                 memory=memory,
-                model_config=model_config
+                model_config=model_config,
             )
 
         return prompt_messages, stops
 
-    def get_prompt_str_and_rules(self, app_mode: AppMode,
-                                 model_config: ModelConfigWithCredentialsEntity,
-                                 pre_prompt: str,
-                                 inputs: dict,
-                                 query: Optional[str] = None,
-                                 context: Optional[str] = None,
-                                 histories: Optional[str] = None,
-                                 ) -> tuple[str, dict]:
+    def get_prompt_str_and_rules(
+        self,
+        app_mode: AppMode,
+        model_config: ModelConfigWithCredentialsEntity,
+        pre_prompt: str,
+        inputs: dict,
+        query: Optional[str] = None,
+        context: Optional[str] = None,
+        histories: Optional[str] = None,
+    ) -> tuple[str, dict]:
         # get prompt template
         prompt_template_config = self.get_prompt_template(
             app_mode=app_mode,
@@ -101,74 +104,75 @@ class SimplePromptTransform(PromptTransform):
             pre_prompt=pre_prompt,
             has_context=context is not None,
             query_in_prompt=query is not None,
-            with_memory_prompt=histories is not None
+            with_memory_prompt=histories is not None,
         )
 
-        variables = {k: inputs[k] for k in prompt_template_config['custom_variable_keys'] if k in inputs}
+        variables = {k: inputs[k] for k in prompt_template_config["custom_variable_keys"] if k in inputs}
 
-        for v in prompt_template_config['special_variable_keys']:
+        for v in prompt_template_config["special_variable_keys"]:
             # support #context#, #query# and #histories#
-            if v == '#context#':
-                variables['#context#'] = context if context else ''
-            elif v == '#query#':
-                variables['#query#'] = query if query else ''
-            elif v == '#histories#':
-                variables['#histories#'] = histories if histories else ''
+            if v == "#context#":
+                variables["#context#"] = context or ""
+            elif v == "#query#":
+                variables["#query#"] = query or ""
+            elif v == "#histories#":
+                variables["#histories#"] = histories or ""
 
-        prompt_template = prompt_template_config['prompt_template']
+        prompt_template = prompt_template_config["prompt_template"]
         prompt = prompt_template.format(variables)
 
-        return prompt, prompt_template_config['prompt_rules']
+        return prompt, prompt_template_config["prompt_rules"]
 
-    def get_prompt_template(self, app_mode: AppMode,
-                            provider: str,
-                            model: str,
-                            pre_prompt: str,
-                            has_context: bool,
-                            query_in_prompt: bool,
-                            with_memory_prompt: bool = False) -> dict:
-        prompt_rules = self._get_prompt_rule(
-            app_mode=app_mode,
-            provider=provider,
-            model=model
-        )
+    def get_prompt_template(
+        self,
+        app_mode: AppMode,
+        provider: str,
+        model: str,
+        pre_prompt: str,
+        has_context: bool,
+        query_in_prompt: bool,
+        with_memory_prompt: bool = False,
+    ) -> dict:
+        prompt_rules = self._get_prompt_rule(app_mode=app_mode, provider=provider, model=model)
 
         custom_variable_keys = []
         special_variable_keys = []
 
-        prompt = ''
-        for order in prompt_rules['system_prompt_orders']:
-            if order == 'context_prompt' and has_context:
-                prompt += prompt_rules['context_prompt']
-                special_variable_keys.append('#context#')
-            elif order == 'pre_prompt' and pre_prompt:
-                prompt += pre_prompt + '\n'
+        prompt = ""
+        for order in prompt_rules["system_prompt_orders"]:
+            if order == "context_prompt" and has_context:
+                prompt += prompt_rules["context_prompt"]
+                special_variable_keys.append("#context#")
+            elif order == "pre_prompt" and pre_prompt:
+                prompt += pre_prompt + "\n"
                 pre_prompt_template = PromptTemplateParser(template=pre_prompt)
                 custom_variable_keys = pre_prompt_template.variable_keys
-            elif order == 'histories_prompt' and with_memory_prompt:
-                prompt += prompt_rules['histories_prompt']
-                special_variable_keys.append('#histories#')
+            elif order == "histories_prompt" and with_memory_prompt:
+                prompt += prompt_rules["histories_prompt"]
+                special_variable_keys.append("#histories#")
 
         if query_in_prompt:
-            prompt += prompt_rules.get('query_prompt', '{{#query#}}')
-            special_variable_keys.append('#query#')
+            prompt += prompt_rules.get("query_prompt", "{{#query#}}")
+            special_variable_keys.append("#query#")
 
         return {
             "prompt_template": PromptTemplateParser(template=prompt),
             "custom_variable_keys": custom_variable_keys,
             "special_variable_keys": special_variable_keys,
-            "prompt_rules": prompt_rules
+            "prompt_rules": prompt_rules,
         }
 
-    def _get_chat_model_prompt_messages(self, app_mode: AppMode,
-                                        pre_prompt: str,
-                                        inputs: dict,
-                                        query: str,
-                                        context: Optional[str],
-                                        files: list["FileVar"],
-                                        memory: Optional[TokenBufferMemory],
-                                        model_config: ModelConfigWithCredentialsEntity) \
-            -> tuple[list[PromptMessage], Optional[list[str]]]:
+    def _get_chat_model_prompt_messages(
+        self,
+        app_mode: AppMode,
+        pre_prompt: str,
+        inputs: dict,
+        query: str,
+        context: Optional[str],
+        files: list["FileVar"],
+        memory: Optional[TokenBufferMemory],
+        model_config: ModelConfigWithCredentialsEntity,
+    ) -> tuple[list[PromptMessage], Optional[list[str]]]:
         prompt_messages = []
 
         # get prompt
@@ -178,7 +182,7 @@ class SimplePromptTransform(PromptTransform):
             pre_prompt=pre_prompt,
             inputs=inputs,
             query=None,
-            context=context
+            context=context,
         )
 
         if prompt and query:
@@ -193,7 +197,7 @@ class SimplePromptTransform(PromptTransform):
                     )
                 ),
                 prompt_messages=prompt_messages,
-                model_config=model_config
+                model_config=model_config,
             )
 
         if query:
@@ -203,15 +207,17 @@ class SimplePromptTransform(PromptTransform):
 
         return prompt_messages, None
 
-    def _get_completion_model_prompt_messages(self, app_mode: AppMode,
-                                              pre_prompt: str,
-                                              inputs: dict,
-                                              query: str,
-                                              context: Optional[str],
-                                              files: list["FileVar"],
-                                              memory: Optional[TokenBufferMemory],
-                                              model_config: ModelConfigWithCredentialsEntity) \
-            -> tuple[list[PromptMessage], Optional[list[str]]]:
+    def _get_completion_model_prompt_messages(
+        self,
+        app_mode: AppMode,
+        pre_prompt: str,
+        inputs: dict,
+        query: str,
+        context: Optional[str],
+        files: list["FileVar"],
+        memory: Optional[TokenBufferMemory],
+        model_config: ModelConfigWithCredentialsEntity,
+    ) -> tuple[list[PromptMessage], Optional[list[str]]]:
         # get prompt
         prompt, prompt_rules = self.get_prompt_str_and_rules(
             app_mode=app_mode,
@@ -219,13 +225,11 @@ class SimplePromptTransform(PromptTransform):
             pre_prompt=pre_prompt,
             inputs=inputs,
             query=query,
-            context=context
+            context=context,
         )
 
         if memory:
-            tmp_human_message = UserPromptMessage(
-                content=prompt
-            )
+            tmp_human_message = UserPromptMessage(content=prompt)
 
             rest_tokens = self._calculate_rest_token([tmp_human_message], model_config)
             histories = self._get_history_messages_from_memory(
@@ -236,8 +240,8 @@ class SimplePromptTransform(PromptTransform):
                     )
                 ),
                 max_token_limit=rest_tokens,
-                human_prefix=prompt_rules.get('human_prefix', 'Human'),
-                ai_prefix=prompt_rules.get('assistant_prefix', 'Assistant')
+                human_prefix=prompt_rules.get("human_prefix", "Human"),
+                ai_prefix=prompt_rules.get("assistant_prefix", "Assistant"),
             )
 
             # get prompt
@@ -248,10 +252,10 @@ class SimplePromptTransform(PromptTransform):
                 inputs=inputs,
                 query=query,
                 context=context,
-                histories=histories
+                histories=histories,
             )
 
-        stops = prompt_rules.get('stops')
+        stops = prompt_rules.get("stops")
         if stops is not None and len(stops) == 0:
             stops = None
 
@@ -277,22 +281,18 @@ class SimplePromptTransform(PromptTransform):
         :param model: model name
         :return:
         """
-        prompt_file_name = self._prompt_file_name(
-            app_mode=app_mode,
-            provider=provider,
-            model=model
-        )
+        prompt_file_name = self._prompt_file_name(app_mode=app_mode, provider=provider, model=model)
 
         # Check if the prompt file is already loaded
         if prompt_file_name in prompt_file_contents:
             return prompt_file_contents[prompt_file_name]
 
         # Get the absolute path of the subdirectory
-        prompt_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'prompt_templates')
-        json_file_path = os.path.join(prompt_path, f'{prompt_file_name}.json')
+        prompt_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "prompt_templates")
+        json_file_path = os.path.join(prompt_path, f"{prompt_file_name}.json")
 
         # Open the JSON file and read its content
-        with open(json_file_path, encoding='utf-8') as json_file:
+        with open(json_file_path, encoding="utf-8") as json_file:
             content = json.load(json_file)
 
             # Store the content of the prompt file
@@ -303,21 +303,21 @@ class SimplePromptTransform(PromptTransform):
     def _prompt_file_name(self, app_mode: AppMode, provider: str, model: str) -> str:
         # baichuan
         is_baichuan = False
-        if provider == 'baichuan':
+        if provider == "baichuan":
             is_baichuan = True
         else:
             baichuan_supported_providers = ["huggingface_hub", "openllm", "xinference"]
-            if provider in baichuan_supported_providers and 'baichuan' in model.lower():
+            if provider in baichuan_supported_providers and "baichuan" in model.lower():
                 is_baichuan = True
 
         if is_baichuan:
             if app_mode == AppMode.COMPLETION:
-                return 'baichuan_completion'
+                return "baichuan_completion"
             else:
-                return 'baichuan_chat'
+                return "baichuan_chat"
 
         # common
         if app_mode == AppMode.COMPLETION:
-            return 'common_completion'
+            return "common_completion"
         else:
-            return 'common_chat'
+            return "common_chat"
diff --git a/api/core/prompt/utils/prompt_message_util.py b/api/core/prompt/utils/prompt_message_util.py
index befdceeda5..29494db221 100644
--- a/api/core/prompt/utils/prompt_message_util.py
+++ b/api/core/prompt/utils/prompt_message_util.py
@@ -25,26 +25,29 @@ class PromptMessageUtil:
             tool_calls = []
             for prompt_message in prompt_messages:
                 if prompt_message.role == PromptMessageRole.USER:
-                    role = 'user'
+                    role = "user"
                 elif prompt_message.role == PromptMessageRole.ASSISTANT:
-                    role = 'assistant'
+                    role = "assistant"
                     if isinstance(prompt_message, AssistantPromptMessage):
-                        tool_calls = [{
-                            'id': tool_call.id,
-                            'type': 'function',
-                            'function': {
-                                'name': tool_call.function.name,
-                                'arguments': tool_call.function.arguments,
+                        tool_calls = [
+                            {
+                                "id": tool_call.id,
+                                "type": "function",
+                                "function": {
+                                    "name": tool_call.function.name,
+                                    "arguments": tool_call.function.arguments,
+                                },
                             }
-                        } for tool_call in prompt_message.tool_calls]
+                            for tool_call in prompt_message.tool_calls
+                        ]
                 elif prompt_message.role == PromptMessageRole.SYSTEM:
-                    role = 'system'
+                    role = "system"
                 elif prompt_message.role == PromptMessageRole.TOOL:
-                    role = 'tool'
+                    role = "tool"
                 else:
                     continue
 
-                text = ''
+                text = ""
                 files = []
                 if isinstance(prompt_message.content, list):
                     for content in prompt_message.content:
@@ -53,27 +56,25 @@ class PromptMessageUtil:
                             text += content.data
                         else:
                             content = cast(ImagePromptMessageContent, content)
-                            files.append({
-                                "type": 'image',
-                                "data": content.data[:10] + '...[TRUNCATED]...' + content.data[-10:],
-                                "detail": content.detail.value
-                            })
+                            files.append(
+                                {
+                                    "type": "image",
+                                    "data": content.data[:10] + "...[TRUNCATED]..." + content.data[-10:],
+                                    "detail": content.detail.value,
+                                }
+                            )
                 else:
                     text = prompt_message.content
 
-                prompt = {
-                    "role": role,
-                    "text": text,
-                    "files": files
-                }
-                
+                prompt = {"role": role, "text": text, "files": files}
+
                 if tool_calls:
-                    prompt['tool_calls'] = tool_calls
+                    prompt["tool_calls"] = tool_calls
 
                 prompts.append(prompt)
         else:
             prompt_message = prompt_messages[0]
-            text = ''
+            text = ""
             files = []
             if isinstance(prompt_message.content, list):
                 for content in prompt_message.content:
@@ -82,21 +83,23 @@ class PromptMessageUtil:
                         text += content.data
                     else:
                         content = cast(ImagePromptMessageContent, content)
-                        files.append({
-                            "type": 'image',
-                            "data": content.data[:10] + '...[TRUNCATED]...' + content.data[-10:],
-                            "detail": content.detail.value
-                        })
+                        files.append(
+                            {
+                                "type": "image",
+                                "data": content.data[:10] + "...[TRUNCATED]..." + content.data[-10:],
+                                "detail": content.detail.value,
+                            }
+                        )
             else:
                 text = prompt_message.content
 
             params = {
-                "role": 'user',
+                "role": "user",
                 "text": text,
             }
 
             if files:
-                params['files'] = files
+                params["files"] = files
 
             prompts.append(params)
 
diff --git a/api/core/prompt/utils/prompt_template_parser.py b/api/core/prompt/utils/prompt_template_parser.py
index 3e68492df2..8111559675 100644
--- a/api/core/prompt/utils/prompt_template_parser.py
+++ b/api/core/prompt/utils/prompt_template_parser.py
@@ -38,8 +38,8 @@ class PromptTemplateParser:
             return value
 
         prompt = re.sub(self.regex, replacer, self.template)
-        return re.sub(r'<\|.*?\|>', '', prompt)
+        return re.sub(r"<\|.*?\|>", "", prompt)
 
     @classmethod
     def remove_template_variables(cls, text: str, with_variable_tmpl: bool = False):
-        return re.sub(WITH_VARIABLE_TMPL_REGEX if with_variable_tmpl else REGEX, r'{\1}', text)
+        return re.sub(WITH_VARIABLE_TMPL_REGEX if with_variable_tmpl else REGEX, r"{\1}", text)
diff --git a/api/core/provider_manager.py b/api/core/provider_manager.py
index 67eee2c294..3a1fe300df 100644
--- a/api/core/provider_manager.py
+++ b/api/core/provider_manager.py
@@ -90,8 +90,7 @@ class ProviderManager:
 
         # Initialize trial provider records if not exist
         provider_name_to_provider_records_dict = self._init_trial_provider_records(
-            tenant_id,
-            provider_name_to_provider_records_dict
+            tenant_id, provider_name_to_provider_records_dict
         )
 
         # Get all provider model records of the workspace
@@ -107,22 +106,20 @@ class ProviderManager:
         provider_name_to_provider_model_settings_dict = self._get_all_provider_model_settings(tenant_id)
 
         # Get All load balancing configs
-        provider_name_to_provider_load_balancing_model_configs_dict \
-            = self._get_all_provider_load_balancing_configs(tenant_id)
-
-        provider_configurations = ProviderConfigurations(
-            tenant_id=tenant_id
+        provider_name_to_provider_load_balancing_model_configs_dict = self._get_all_provider_load_balancing_configs(
+            tenant_id
         )
 
+        provider_configurations = ProviderConfigurations(tenant_id=tenant_id)
+
         # Construct ProviderConfiguration objects for each provider
         for provider_entity in provider_entities:
-
             # handle include, exclude
             if is_filtered(
-                    include_set=dify_config.POSITION_PROVIDER_INCLUDES_SET,
-                    exclude_set=dify_config.POSITION_PROVIDER_EXCLUDES_SET,
-                    data=provider_entity,
-                    name_func=lambda x: x.provider,
+                include_set=dify_config.POSITION_PROVIDER_INCLUDES_SET,
+                exclude_set=dify_config.POSITION_PROVIDER_EXCLUDES_SET,
+                data=provider_entity,
+                name_func=lambda x: x.provider,
             ):
                 continue
 
@@ -132,18 +129,11 @@ class ProviderManager:
 
             # Convert to custom configuration
             custom_configuration = self._to_custom_configuration(
-                tenant_id,
-                provider_entity,
-                provider_records,
-                provider_model_records
+                tenant_id, provider_entity, provider_records, provider_model_records
             )
 
             # Convert to system configuration
-            system_configuration = self._to_system_configuration(
-                tenant_id,
-                provider_entity,
-                provider_records
-            )
+            system_configuration = self._to_system_configuration(tenant_id, provider_entity, provider_records)
 
             # Get preferred provider type
             preferred_provider_type_record = provider_name_to_preferred_model_provider_records_dict.get(provider_name)
@@ -173,14 +163,15 @@ class ProviderManager:
             provider_model_settings = provider_name_to_provider_model_settings_dict.get(provider_name)
 
             # Get provider load balancing configs
-            provider_load_balancing_configs \
-                = provider_name_to_provider_load_balancing_model_configs_dict.get(provider_name)
+            provider_load_balancing_configs = provider_name_to_provider_load_balancing_model_configs_dict.get(
+                provider_name
+            )
 
             # Convert to model settings
             model_settings = self._to_model_settings(
                 provider_entity=provider_entity,
                 provider_model_settings=provider_model_settings,
-                load_balancing_model_configs=provider_load_balancing_configs
+                load_balancing_model_configs=provider_load_balancing_configs,
             )
 
             provider_configuration = ProviderConfiguration(
@@ -190,7 +181,7 @@ class ProviderManager:
                 using_provider_type=using_provider_type,
                 system_configuration=system_configuration,
                 custom_configuration=custom_configuration,
-                model_settings=model_settings
+                model_settings=model_settings,
             )
 
             provider_configurations[provider_name] = provider_configuration
@@ -219,7 +210,7 @@ class ProviderManager:
         return ProviderModelBundle(
             configuration=provider_configuration,
             provider_instance=provider_instance,
-            model_type_instance=model_type_instance
+            model_type_instance=model_type_instance,
         )
 
     def get_default_model(self, tenant_id: str, model_type: ModelType) -> Optional[DefaultModelEntity]:
@@ -231,11 +222,14 @@ class ProviderManager:
         :return:
         """
         # Get the corresponding TenantDefaultModel record
-        default_model = db.session.query(TenantDefaultModel) \
+        default_model = (
+            db.session.query(TenantDefaultModel)
             .filter(
-            TenantDefaultModel.tenant_id == tenant_id,
-            TenantDefaultModel.model_type == model_type.to_origin_model_type()
-        ).first()
+                TenantDefaultModel.tenant_id == tenant_id,
+                TenantDefaultModel.model_type == model_type.to_origin_model_type(),
+            )
+            .first()
+        )
 
         # If it does not exist, get the first available provider model from get_configurations
         # and update the TenantDefaultModel record
@@ -244,20 +238,18 @@ class ProviderManager:
             provider_configurations = self.get_configurations(tenant_id)
 
             # get available models from provider_configurations
-            available_models = provider_configurations.get_models(
-                model_type=model_type,
-                only_active=True
-            )
+            available_models = provider_configurations.get_models(model_type=model_type, only_active=True)
 
             if available_models:
-                available_model = next((model for model in available_models if model.model == "gpt-4"),
-                                       available_models[0])
+                available_model = next(
+                    (model for model in available_models if model.model == "gpt-4"), available_models[0]
+                )
 
                 default_model = TenantDefaultModel(
                     tenant_id=tenant_id,
                     model_type=model_type.to_origin_model_type(),
                     provider_name=available_model.provider.provider,
-                    model_name=available_model.model
+                    model_name=available_model.model,
                 )
                 db.session.add(default_model)
                 db.session.commit()
@@ -276,8 +268,8 @@ class ProviderManager:
                 label=provider_schema.label,
                 icon_small=provider_schema.icon_small,
                 icon_large=provider_schema.icon_large,
-                supported_model_types=provider_schema.supported_model_types
-            )
+                supported_model_types=provider_schema.supported_model_types,
+            ),
         )
 
     def get_first_provider_first_model(self, tenant_id: str, model_type: ModelType) -> tuple[str, str]:
@@ -291,15 +283,13 @@ class ProviderManager:
         provider_configurations = self.get_configurations(tenant_id)
 
         # get available models from provider_configurations
-        all_models = provider_configurations.get_models(
-            model_type=model_type,
-            only_active=False
-        )
+        all_models = provider_configurations.get_models(model_type=model_type, only_active=False)
 
         return all_models[0].provider.provider, all_models[0].model
 
-    def update_default_model_record(self, tenant_id: str, model_type: ModelType, provider: str, model: str) \
-            -> TenantDefaultModel:
+    def update_default_model_record(
+        self, tenant_id: str, model_type: ModelType, provider: str, model: str
+    ) -> TenantDefaultModel:
         """
         Update default model record.
 
@@ -314,10 +304,7 @@ class ProviderManager:
             raise ValueError(f"Provider {provider} does not exist.")
 
         # get available models from provider_configurations
-        available_models = provider_configurations.get_models(
-            model_type=model_type,
-            only_active=True
-        )
+        available_models = provider_configurations.get_models(model_type=model_type, only_active=True)
 
         # check if the model is exist in available models
         model_names = [model.model for model in available_models]
@@ -325,11 +312,14 @@ class ProviderManager:
             raise ValueError(f"Model {model} does not exist.")
 
         # Get the list of available models from get_configurations and check if it is LLM
-        default_model = db.session.query(TenantDefaultModel) \
+        default_model = (
+            db.session.query(TenantDefaultModel)
             .filter(
-            TenantDefaultModel.tenant_id == tenant_id,
-            TenantDefaultModel.model_type == model_type.to_origin_model_type()
-        ).first()
+                TenantDefaultModel.tenant_id == tenant_id,
+                TenantDefaultModel.model_type == model_type.to_origin_model_type(),
+            )
+            .first()
+        )
 
         # create or update TenantDefaultModel record
         if default_model:
@@ -358,11 +348,7 @@ class ProviderManager:
         :param tenant_id: workspace id
         :return:
         """
-        providers = db.session.query(Provider) \
-            .filter(
-            Provider.tenant_id == tenant_id,
-            Provider.is_valid == True
-        ).all()
+        providers = db.session.query(Provider).filter(Provider.tenant_id == tenant_id, Provider.is_valid == True).all()
 
         provider_name_to_provider_records_dict = defaultdict(list)
         for provider in providers:
@@ -379,11 +365,11 @@ class ProviderManager:
         :return:
         """
         # Get all provider model records of the workspace
-        provider_models = db.session.query(ProviderModel) \
-            .filter(
-            ProviderModel.tenant_id == tenant_id,
-            ProviderModel.is_valid == True
-        ).all()
+        provider_models = (
+            db.session.query(ProviderModel)
+            .filter(ProviderModel.tenant_id == tenant_id, ProviderModel.is_valid == True)
+            .all()
+        )
 
         provider_name_to_provider_model_records_dict = defaultdict(list)
         for provider_model in provider_models:
@@ -399,10 +385,11 @@ class ProviderManager:
         :param tenant_id: workspace id
         :return:
         """
-        preferred_provider_types = db.session.query(TenantPreferredModelProvider) \
-            .filter(
-            TenantPreferredModelProvider.tenant_id == tenant_id
-        ).all()
+        preferred_provider_types = (
+            db.session.query(TenantPreferredModelProvider)
+            .filter(TenantPreferredModelProvider.tenant_id == tenant_id)
+            .all()
+        )
 
         provider_name_to_preferred_provider_type_records_dict = {
             preferred_provider_type.provider_name: preferred_provider_type
@@ -419,15 +406,17 @@ class ProviderManager:
         :param tenant_id: workspace id
         :return:
         """
-        provider_model_settings = db.session.query(ProviderModelSetting) \
-            .filter(
-            ProviderModelSetting.tenant_id == tenant_id
-        ).all()
+        provider_model_settings = (
+            db.session.query(ProviderModelSetting).filter(ProviderModelSetting.tenant_id == tenant_id).all()
+        )
 
         provider_name_to_provider_model_settings_dict = defaultdict(list)
         for provider_model_setting in provider_model_settings:
-            (provider_name_to_provider_model_settings_dict[provider_model_setting.provider_name]
-             .append(provider_model_setting))
+            (
+                provider_name_to_provider_model_settings_dict[provider_model_setting.provider_name].append(
+                    provider_model_setting
+                )
+            )
 
         return provider_name_to_provider_model_settings_dict
 
@@ -445,27 +434,30 @@ class ProviderManager:
             model_load_balancing_enabled = FeatureService.get_features(tenant_id).model_load_balancing_enabled
             redis_client.setex(cache_key, 120, str(model_load_balancing_enabled))
         else:
-            cache_result = cache_result.decode('utf-8')
-            model_load_balancing_enabled = cache_result == 'True'
+            cache_result = cache_result.decode("utf-8")
+            model_load_balancing_enabled = cache_result == "True"
 
         if not model_load_balancing_enabled:
             return {}
 
-        provider_load_balancing_configs = db.session.query(LoadBalancingModelConfig) \
-            .filter(
-            LoadBalancingModelConfig.tenant_id == tenant_id
-        ).all()
+        provider_load_balancing_configs = (
+            db.session.query(LoadBalancingModelConfig).filter(LoadBalancingModelConfig.tenant_id == tenant_id).all()
+        )
 
         provider_name_to_provider_load_balancing_model_configs_dict = defaultdict(list)
         for provider_load_balancing_config in provider_load_balancing_configs:
-            (provider_name_to_provider_load_balancing_model_configs_dict[provider_load_balancing_config.provider_name]
-             .append(provider_load_balancing_config))
+            (
+                provider_name_to_provider_load_balancing_model_configs_dict[
+                    provider_load_balancing_config.provider_name
+                ].append(provider_load_balancing_config)
+            )
 
         return provider_name_to_provider_load_balancing_model_configs_dict
 
     @staticmethod
-    def _init_trial_provider_records(tenant_id: str,
-                                     provider_name_to_provider_records_dict: dict[str, list]) -> dict[str, list]:
+    def _init_trial_provider_records(
+        tenant_id: str, provider_name_to_provider_records_dict: dict[str, list]
+    ) -> dict[str, list]:
         """
         Initialize trial provider records if not exists.
 
@@ -489,8 +481,9 @@ class ProviderManager:
                 if provider_record.provider_type != ProviderType.SYSTEM.value:
                     continue
 
-                provider_quota_to_provider_record_dict[ProviderQuotaType.value_of(provider_record.quota_type)] \
-                    = provider_record
+                provider_quota_to_provider_record_dict[ProviderQuotaType.value_of(provider_record.quota_type)] = (
+                    provider_record
+                )
 
             for quota in configuration.quotas:
                 if quota.quota_type == ProviderQuotaType.TRIAL:
@@ -504,19 +497,22 @@ class ProviderManager:
                                 quota_type=ProviderQuotaType.TRIAL.value,
                                 quota_limit=quota.quota_limit,
                                 quota_used=0,
-                                is_valid=True
+                                is_valid=True,
                             )
                             db.session.add(provider_record)
                             db.session.commit()
                         except IntegrityError:
                             db.session.rollback()
-                            provider_record = db.session.query(Provider) \
+                            provider_record = (
+                                db.session.query(Provider)
                                 .filter(
-                                Provider.tenant_id == tenant_id,
-                                Provider.provider_name == provider_name,
-                                Provider.provider_type == ProviderType.SYSTEM.value,
-                                Provider.quota_type == ProviderQuotaType.TRIAL.value
-                            ).first()
+                                    Provider.tenant_id == tenant_id,
+                                    Provider.provider_name == provider_name,
+                                    Provider.provider_type == ProviderType.SYSTEM.value,
+                                    Provider.quota_type == ProviderQuotaType.TRIAL.value,
+                                )
+                                .first()
+                            )
 
                             if provider_record and not provider_record.is_valid:
                                 provider_record.is_valid = True
@@ -526,11 +522,13 @@ class ProviderManager:
 
         return provider_name_to_provider_records_dict
 
-    def _to_custom_configuration(self,
-                                 tenant_id: str,
-                                 provider_entity: ProviderEntity,
-                                 provider_records: list[Provider],
-                                 provider_model_records: list[ProviderModel]) -> CustomConfiguration:
+    def _to_custom_configuration(
+        self,
+        tenant_id: str,
+        provider_entity: ProviderEntity,
+        provider_records: list[Provider],
+        provider_model_records: list[ProviderModel],
+    ) -> CustomConfiguration:
         """
         Convert to custom configuration.
 
@@ -543,7 +541,8 @@ class ProviderManager:
         # Get provider credential secret variables
         provider_credential_secret_variables = self._extract_secret_variables(
             provider_entity.provider_credential_schema.credential_form_schemas
-            if provider_entity.provider_credential_schema else []
+            if provider_entity.provider_credential_schema
+            else []
         )
 
         # Get custom provider record
@@ -563,7 +562,7 @@ class ProviderManager:
             provider_credentials_cache = ProviderCredentialsCache(
                 tenant_id=tenant_id,
                 identity_id=custom_provider_record.id,
-                cache_type=ProviderCredentialsCacheType.PROVIDER
+                cache_type=ProviderCredentialsCacheType.PROVIDER,
             )
 
             # Get cached provider credentials
@@ -572,11 +571,11 @@ class ProviderManager:
             if not cached_provider_credentials:
                 try:
                     # fix origin data
-                    if (custom_provider_record.encrypted_config
-                            and not custom_provider_record.encrypted_config.startswith("{")):
-                        provider_credentials = {
-                            "openai_api_key": custom_provider_record.encrypted_config
-                        }
+                    if (
+                        custom_provider_record.encrypted_config
+                        and not custom_provider_record.encrypted_config.startswith("{")
+                    ):
+                        provider_credentials = {"openai_api_key": custom_provider_record.encrypted_config}
                     else:
                         provider_credentials = json.loads(custom_provider_record.encrypted_config)
                 except JSONDecodeError:
@@ -590,28 +589,23 @@ class ProviderManager:
                     if variable in provider_credentials:
                         try:
                             provider_credentials[variable] = encrypter.decrypt_token_with_decoding(
-                                provider_credentials.get(variable),
-                                self.decoding_rsa_key,
-                                self.decoding_cipher_rsa
+                                provider_credentials.get(variable), self.decoding_rsa_key, self.decoding_cipher_rsa
                             )
                         except ValueError:
                             pass
 
                 # cache provider credentials
-                provider_credentials_cache.set(
-                    credentials=provider_credentials
-                )
+                provider_credentials_cache.set(credentials=provider_credentials)
             else:
                 provider_credentials = cached_provider_credentials
 
-            custom_provider_configuration = CustomProviderConfiguration(
-                credentials=provider_credentials
-            )
+            custom_provider_configuration = CustomProviderConfiguration(credentials=provider_credentials)
 
         # Get provider model credential secret variables
         model_credential_secret_variables = self._extract_secret_variables(
             provider_entity.model_credential_schema.credential_form_schemas
-            if provider_entity.model_credential_schema else []
+            if provider_entity.model_credential_schema
+            else []
         )
 
         # Get custom provider model credentials
@@ -621,9 +615,7 @@ class ProviderManager:
                 continue
 
             provider_model_credentials_cache = ProviderCredentialsCache(
-                tenant_id=tenant_id,
-                identity_id=provider_model_record.id,
-                cache_type=ProviderCredentialsCacheType.MODEL
+                tenant_id=tenant_id, identity_id=provider_model_record.id, cache_type=ProviderCredentialsCacheType.MODEL
             )
 
             # Get cached provider model credentials
@@ -645,15 +637,13 @@ class ProviderManager:
                             provider_model_credentials[variable] = encrypter.decrypt_token_with_decoding(
                                 provider_model_credentials.get(variable),
                                 self.decoding_rsa_key,
-                                self.decoding_cipher_rsa
+                                self.decoding_cipher_rsa,
                             )
                         except ValueError:
                             pass
 
                 # cache provider model credentials
-                provider_model_credentials_cache.set(
-                    credentials=provider_model_credentials
-                )
+                provider_model_credentials_cache.set(credentials=provider_model_credentials)
             else:
                 provider_model_credentials = cached_provider_model_credentials
 
@@ -661,19 +651,15 @@ class ProviderManager:
                 CustomModelConfiguration(
                     model=provider_model_record.model_name,
                     model_type=ModelType.value_of(provider_model_record.model_type),
-                    credentials=provider_model_credentials
+                    credentials=provider_model_credentials,
                 )
             )
 
-        return CustomConfiguration(
-            provider=custom_provider_configuration,
-            models=custom_model_configurations
-        )
+        return CustomConfiguration(provider=custom_provider_configuration, models=custom_model_configurations)
 
-    def _to_system_configuration(self,
-                                 tenant_id: str,
-                                 provider_entity: ProviderEntity,
-                                 provider_records: list[Provider]) -> SystemConfiguration:
+    def _to_system_configuration(
+        self, tenant_id: str, provider_entity: ProviderEntity, provider_records: list[Provider]
+    ) -> SystemConfiguration:
         """
         Convert to system configuration.
 
@@ -685,11 +671,11 @@ class ProviderManager:
         # Get hosting configuration
         hosting_configuration = ext_hosting_provider.hosting_configuration
 
-        if provider_entity.provider not in hosting_configuration.provider_map \
-                or not hosting_configuration.provider_map.get(provider_entity.provider).enabled:
-            return SystemConfiguration(
-                enabled=False
-            )
+        if (
+            provider_entity.provider not in hosting_configuration.provider_map
+            or not hosting_configuration.provider_map.get(provider_entity.provider).enabled
+        ):
+            return SystemConfiguration(enabled=False)
 
         provider_hosting_configuration = hosting_configuration.provider_map.get(provider_entity.provider)
 
@@ -699,8 +685,9 @@ class ProviderManager:
             if provider_record.provider_type != ProviderType.SYSTEM.value:
                 continue
 
-            quota_type_to_provider_records_dict[ProviderQuotaType.value_of(provider_record.quota_type)] \
-                = provider_record
+            quota_type_to_provider_records_dict[ProviderQuotaType.value_of(provider_record.quota_type)] = (
+                provider_record
+            )
 
         quota_configurations = []
         for provider_quota in provider_hosting_configuration.quotas:
@@ -712,7 +699,7 @@ class ProviderManager:
                         quota_used=0,
                         quota_limit=0,
                         is_valid=False,
-                        restrict_models=provider_quota.restrict_models
+                        restrict_models=provider_quota.restrict_models,
                     )
                 else:
                     continue
@@ -724,16 +711,15 @@ class ProviderManager:
                     quota_unit=provider_hosting_configuration.quota_unit,
                     quota_used=provider_record.quota_used,
                     quota_limit=provider_record.quota_limit,
-                    is_valid=provider_record.quota_limit > provider_record.quota_used or provider_record.quota_limit == -1,
-                    restrict_models=provider_quota.restrict_models
+                    is_valid=provider_record.quota_limit > provider_record.quota_used
+                    or provider_record.quota_limit == -1,
+                    restrict_models=provider_quota.restrict_models,
                 )
 
             quota_configurations.append(quota_configuration)
 
         if len(quota_configurations) == 0:
-            return SystemConfiguration(
-                enabled=False
-            )
+            return SystemConfiguration(enabled=False)
 
         current_quota_type = self._choice_current_using_quota_type(quota_configurations)
 
@@ -745,7 +731,7 @@ class ProviderManager:
                 provider_credentials_cache = ProviderCredentialsCache(
                     tenant_id=tenant_id,
                     identity_id=provider_record.id,
-                    cache_type=ProviderCredentialsCacheType.PROVIDER
+                    cache_type=ProviderCredentialsCacheType.PROVIDER,
                 )
 
                 # Get cached provider credentials
@@ -760,7 +746,8 @@ class ProviderManager:
                     # Get provider credential secret variables
                     provider_credential_secret_variables = self._extract_secret_variables(
                         provider_entity.provider_credential_schema.credential_form_schemas
-                        if provider_entity.provider_credential_schema else []
+                        if provider_entity.provider_credential_schema
+                        else []
                     )
 
                     # Get decoding rsa key and cipher for decrypting credentials
@@ -771,9 +758,7 @@ class ProviderManager:
                         if variable in provider_credentials:
                             try:
                                 provider_credentials[variable] = encrypter.decrypt_token_with_decoding(
-                                    provider_credentials.get(variable),
-                                    self.decoding_rsa_key,
-                                    self.decoding_cipher_rsa
+                                    provider_credentials.get(variable), self.decoding_rsa_key, self.decoding_cipher_rsa
                                 )
                             except ValueError:
                                 pass
@@ -781,9 +766,7 @@ class ProviderManager:
                     current_using_credentials = provider_credentials
 
                     # cache provider credentials
-                    provider_credentials_cache.set(
-                        credentials=current_using_credentials
-                    )
+                    provider_credentials_cache.set(credentials=current_using_credentials)
                 else:
                     current_using_credentials = cached_provider_credentials
             else:
@@ -794,7 +777,7 @@ class ProviderManager:
             enabled=True,
             current_quota_type=current_quota_type,
             quota_configurations=quota_configurations,
-            credentials=current_using_credentials
+            credentials=current_using_credentials,
         )
 
     @staticmethod
@@ -809,8 +792,7 @@ class ProviderManager:
         """
         # convert to dict
         quota_type_to_quota_configuration_dict = {
-            quota_configuration.quota_type: quota_configuration
-            for quota_configuration in quota_configurations
+            quota_configuration.quota_type: quota_configuration for quota_configuration in quota_configurations
         }
 
         last_quota_configuration = None
@@ -823,7 +805,7 @@ class ProviderManager:
         if last_quota_configuration:
             return last_quota_configuration.quota_type
 
-        raise ValueError('No quota type available')
+        raise ValueError("No quota type available")
 
     @staticmethod
     def _extract_secret_variables(credential_form_schemas: list[CredentialFormSchema]) -> list[str]:
@@ -840,10 +822,12 @@ class ProviderManager:
 
         return secret_input_form_variables
 
-    def _to_model_settings(self, provider_entity: ProviderEntity,
-                           provider_model_settings: Optional[list[ProviderModelSetting]] = None,
-                           load_balancing_model_configs: Optional[list[LoadBalancingModelConfig]] = None) \
-            -> list[ModelSettings]:
+    def _to_model_settings(
+        self,
+        provider_entity: ProviderEntity,
+        provider_model_settings: Optional[list[ProviderModelSetting]] = None,
+        load_balancing_model_configs: Optional[list[LoadBalancingModelConfig]] = None,
+    ) -> list[ModelSettings]:
         """
         Convert to model settings.
         :param provider_entity: provider entity
@@ -854,7 +838,8 @@ class ProviderManager:
         # Get provider model credential secret variables
         model_credential_secret_variables = self._extract_secret_variables(
             provider_entity.model_credential_schema.credential_form_schemas
-            if provider_entity.model_credential_schema else []
+            if provider_entity.model_credential_schema
+            else []
         )
 
         model_settings = []
@@ -865,24 +850,28 @@ class ProviderManager:
             load_balancing_configs = []
             if provider_model_setting.load_balancing_enabled and load_balancing_model_configs:
                 for load_balancing_model_config in load_balancing_model_configs:
-                    if (load_balancing_model_config.model_name == provider_model_setting.model_name
-                            and load_balancing_model_config.model_type == provider_model_setting.model_type):
+                    if (
+                        load_balancing_model_config.model_name == provider_model_setting.model_name
+                        and load_balancing_model_config.model_type == provider_model_setting.model_type
+                    ):
                         if not load_balancing_model_config.enabled:
                             continue
 
                         if not load_balancing_model_config.encrypted_config:
                             if load_balancing_model_config.name == "__inherit__":
-                                load_balancing_configs.append(ModelLoadBalancingConfiguration(
-                                    id=load_balancing_model_config.id,
-                                    name=load_balancing_model_config.name,
-                                    credentials={}
-                                ))
+                                load_balancing_configs.append(
+                                    ModelLoadBalancingConfiguration(
+                                        id=load_balancing_model_config.id,
+                                        name=load_balancing_model_config.name,
+                                        credentials={},
+                                    )
+                                )
                             continue
 
                         provider_model_credentials_cache = ProviderCredentialsCache(
                             tenant_id=load_balancing_model_config.tenant_id,
                             identity_id=load_balancing_model_config.id,
-                            cache_type=ProviderCredentialsCacheType.LOAD_BALANCING_MODEL
+                            cache_type=ProviderCredentialsCacheType.LOAD_BALANCING_MODEL,
                         )
 
                         # Get cached provider model credentials
@@ -897,7 +886,8 @@ class ProviderManager:
                             # Get decoding rsa key and cipher for decrypting credentials
                             if self.decoding_rsa_key is None or self.decoding_cipher_rsa is None:
                                 self.decoding_rsa_key, self.decoding_cipher_rsa = encrypter.get_decrypt_decoding(
-                                    load_balancing_model_config.tenant_id)
+                                    load_balancing_model_config.tenant_id
+                                )
 
                             for variable in model_credential_secret_variables:
                                 if variable in provider_model_credentials:
@@ -905,30 +895,30 @@ class ProviderManager:
                                         provider_model_credentials[variable] = encrypter.decrypt_token_with_decoding(
                                             provider_model_credentials.get(variable),
                                             self.decoding_rsa_key,
-                                            self.decoding_cipher_rsa
+                                            self.decoding_cipher_rsa,
                                         )
                                     except ValueError:
                                         pass
 
                             # cache provider model credentials
-                            provider_model_credentials_cache.set(
-                                credentials=provider_model_credentials
-                            )
+                            provider_model_credentials_cache.set(credentials=provider_model_credentials)
                         else:
                             provider_model_credentials = cached_provider_model_credentials
 
-                        load_balancing_configs.append(ModelLoadBalancingConfiguration(
-                            id=load_balancing_model_config.id,
-                            name=load_balancing_model_config.name,
-                            credentials=provider_model_credentials
-                        ))
+                        load_balancing_configs.append(
+                            ModelLoadBalancingConfiguration(
+                                id=load_balancing_model_config.id,
+                                name=load_balancing_model_config.name,
+                                credentials=provider_model_credentials,
+                            )
+                        )
 
             model_settings.append(
                 ModelSettings(
                     model=provider_model_setting.model_name,
                     model_type=ModelType.value_of(provider_model_setting.model_type),
                     enabled=provider_model_setting.enabled,
-                    load_balancing_configs=load_balancing_configs if len(load_balancing_configs) > 1 else []
+                    load_balancing_configs=load_balancing_configs if len(load_balancing_configs) > 1 else [],
                 )
             )
 
diff --git a/api/core/rag/cleaner/clean_processor.py b/api/core/rag/cleaner/clean_processor.py
index eaad0e0f4c..3c6ab2e4cf 100644
--- a/api/core/rag/cleaner/clean_processor.py
+++ b/api/core/rag/cleaner/clean_processor.py
@@ -2,37 +2,35 @@ import re
 
 
 class CleanProcessor:
-
     @classmethod
     def clean(cls, text: str, process_rule: dict) -> str:
         # default clean
         # remove invalid symbol
-        text = re.sub(r'<\|', '<', text)
-        text = re.sub(r'\|>', '>', text)
-        text = re.sub(r'[\x00-\x08\x0B\x0C\x0E-\x1F\x7F\xEF\xBF\xBE]', '', text)
+        text = re.sub(r"<\|", "<", text)
+        text = re.sub(r"\|>", ">", text)
+        text = re.sub(r"[\x00-\x08\x0B\x0C\x0E-\x1F\x7F\xEF\xBF\xBE]", "", text)
         # Unicode  U+FFFE
-        text = re.sub('\uFFFE', '', text)
+        text = re.sub("\ufffe", "", text)
 
-        rules = process_rule['rules'] if process_rule else None
-        if 'pre_processing_rules' in rules:
+        rules = process_rule["rules"] if process_rule else None
+        if "pre_processing_rules" in rules:
             pre_processing_rules = rules["pre_processing_rules"]
             for pre_processing_rule in pre_processing_rules:
                 if pre_processing_rule["id"] == "remove_extra_spaces" and pre_processing_rule["enabled"] is True:
                     # Remove extra spaces
-                    pattern = r'\n{3,}'
-                    text = re.sub(pattern, '\n\n', text)
-                    pattern = r'[\t\f\r\x20\u00a0\u1680\u180e\u2000-\u200a\u202f\u205f\u3000]{2,}'
-                    text = re.sub(pattern, ' ', text)
+                    pattern = r"\n{3,}"
+                    text = re.sub(pattern, "\n\n", text)
+                    pattern = r"[\t\f\r\x20\u00a0\u1680\u180e\u2000-\u200a\u202f\u205f\u3000]{2,}"
+                    text = re.sub(pattern, " ", text)
                 elif pre_processing_rule["id"] == "remove_urls_emails" and pre_processing_rule["enabled"] is True:
                     # Remove email
-                    pattern = r'([a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+)'
-                    text = re.sub(pattern, '', text)
+                    pattern = r"([a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+)"
+                    text = re.sub(pattern, "", text)
 
                     # Remove URL
-                    pattern = r'https?://[^\s]+'
-                    text = re.sub(pattern, '', text)
+                    pattern = r"https?://[^\s]+"
+                    text = re.sub(pattern, "", text)
         return text
 
     def filter_string(self, text):
-
         return text
diff --git a/api/core/rag/cleaner/cleaner_base.py b/api/core/rag/cleaner/cleaner_base.py
index 523bd904f2..d3bc2f765e 100644
--- a/api/core/rag/cleaner/cleaner_base.py
+++ b/api/core/rag/cleaner/cleaner_base.py
@@ -1,12 +1,11 @@
 """Abstract interface for document cleaner implementations."""
+
 from abc import ABC, abstractmethod
 
 
 class BaseCleaner(ABC):
-    """Interface for clean chunk content.
-    """
+    """Interface for clean chunk content."""
 
     @abstractmethod
     def clean(self, content: str):
         raise NotImplementedError
-
diff --git a/api/core/rag/cleaner/unstructured/unstructured_extra_whitespace_cleaner.py b/api/core/rag/cleaner/unstructured/unstructured_extra_whitespace_cleaner.py
index 6a0b8c9046..167a919e69 100644
--- a/api/core/rag/cleaner/unstructured/unstructured_extra_whitespace_cleaner.py
+++ b/api/core/rag/cleaner/unstructured/unstructured_extra_whitespace_cleaner.py
@@ -1,9 +1,9 @@
 """Abstract interface for document clean implementations."""
+
 from core.rag.cleaner.cleaner_base import BaseCleaner
 
 
 class UnstructuredNonAsciiCharsCleaner(BaseCleaner):
-
     def clean(self, content) -> str:
         """clean document content."""
         from unstructured.cleaners.core import clean_extra_whitespace
diff --git a/api/core/rag/cleaner/unstructured/unstructured_group_broken_paragraphs_cleaner.py b/api/core/rag/cleaner/unstructured/unstructured_group_broken_paragraphs_cleaner.py
index 6fc3a408da..9c682d29db 100644
--- a/api/core/rag/cleaner/unstructured/unstructured_group_broken_paragraphs_cleaner.py
+++ b/api/core/rag/cleaner/unstructured/unstructured_group_broken_paragraphs_cleaner.py
@@ -1,9 +1,9 @@
 """Abstract interface for document clean implementations."""
+
 from core.rag.cleaner.cleaner_base import BaseCleaner
 
 
 class UnstructuredGroupBrokenParagraphsCleaner(BaseCleaner):
-
     def clean(self, content) -> str:
         """clean document content."""
         import re
diff --git a/api/core/rag/cleaner/unstructured/unstructured_non_ascii_chars_cleaner.py b/api/core/rag/cleaner/unstructured/unstructured_non_ascii_chars_cleaner.py
index ca1ae8dfd1..0cdbb171e1 100644
--- a/api/core/rag/cleaner/unstructured/unstructured_non_ascii_chars_cleaner.py
+++ b/api/core/rag/cleaner/unstructured/unstructured_non_ascii_chars_cleaner.py
@@ -1,12 +1,12 @@
 """Abstract interface for document clean implementations."""
+
 from core.rag.cleaner.cleaner_base import BaseCleaner
 
 
 class UnstructuredNonAsciiCharsCleaner(BaseCleaner):
-
     def clean(self, content) -> str:
         """clean document content."""
         from unstructured.cleaners.core import clean_non_ascii_chars
 
-        # Returns "This text containsnon-ascii characters!"
+        # Returns "This text contains non-ascii characters!"
         return clean_non_ascii_chars(content)
diff --git a/api/core/rag/cleaner/unstructured/unstructured_replace_unicode_quotes_cleaner.py b/api/core/rag/cleaner/unstructured/unstructured_replace_unicode_quotes_cleaner.py
index 974a28fef1..9f42044a2d 100644
--- a/api/core/rag/cleaner/unstructured/unstructured_replace_unicode_quotes_cleaner.py
+++ b/api/core/rag/cleaner/unstructured/unstructured_replace_unicode_quotes_cleaner.py
@@ -1,11 +1,12 @@
 """Abstract interface for document clean implementations."""
+
 from core.rag.cleaner.cleaner_base import BaseCleaner
 
 
 class UnstructuredNonAsciiCharsCleaner(BaseCleaner):
-
     def clean(self, content) -> str:
         """Replaces unicode quote characters, such as the \x91 character in a string."""
 
         from unstructured.cleaners.core import replace_unicode_quotes
+
         return replace_unicode_quotes(content)
diff --git a/api/core/rag/cleaner/unstructured/unstructured_translate_text_cleaner.py b/api/core/rag/cleaner/unstructured/unstructured_translate_text_cleaner.py
index dfaf3a2787..32ae7217e8 100644
--- a/api/core/rag/cleaner/unstructured/unstructured_translate_text_cleaner.py
+++ b/api/core/rag/cleaner/unstructured/unstructured_translate_text_cleaner.py
@@ -1,9 +1,9 @@
 """Abstract interface for document clean implementations."""
+
 from core.rag.cleaner.cleaner_base import BaseCleaner
 
 
 class UnstructuredTranslateTextCleaner(BaseCleaner):
-
     def clean(self, content) -> str:
         """clean document content."""
         from unstructured.cleaners.translate import translate_text
diff --git a/api/core/rag/data_post_processor/data_post_processor.py b/api/core/rag/data_post_processor/data_post_processor.py
index ad9ee4f7cf..b1d6f93cff 100644
--- a/api/core/rag/data_post_processor/data_post_processor.py
+++ b/api/core/rag/data_post_processor/data_post_processor.py
@@ -12,17 +12,27 @@ from core.rag.rerank.weight_rerank import WeightRerankRunner
 
 
 class DataPostProcessor:
-    """Interface for data post-processing document.
-    """
+    """Interface for data post-processing document."""
 
-    def __init__(self, tenant_id: str, reranking_mode: str,
-                 reranking_model: Optional[dict] = None, weights: Optional[dict] = None,
-                 reorder_enabled: bool = False):
+    def __init__(
+        self,
+        tenant_id: str,
+        reranking_mode: str,
+        reranking_model: Optional[dict] = None,
+        weights: Optional[dict] = None,
+        reorder_enabled: bool = False,
+    ):
         self.rerank_runner = self._get_rerank_runner(reranking_mode, tenant_id, reranking_model, weights)
         self.reorder_runner = self._get_reorder_runner(reorder_enabled)
 
-    def invoke(self, query: str, documents: list[Document], score_threshold: Optional[float] = None,
-               top_n: Optional[int] = None, user: Optional[str] = None) -> list[Document]:
+    def invoke(
+        self,
+        query: str,
+        documents: list[Document],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> list[Document]:
         if self.rerank_runner:
             documents = self.rerank_runner.run(query, documents, score_threshold, top_n, user)
 
@@ -31,21 +41,26 @@ class DataPostProcessor:
 
         return documents
 
-    def _get_rerank_runner(self, reranking_mode: str, tenant_id: str, reranking_model: Optional[dict] = None,
-                           weights: Optional[dict] = None) -> Optional[RerankModelRunner | WeightRerankRunner]:
+    def _get_rerank_runner(
+        self,
+        reranking_mode: str,
+        tenant_id: str,
+        reranking_model: Optional[dict] = None,
+        weights: Optional[dict] = None,
+    ) -> Optional[RerankModelRunner | WeightRerankRunner]:
         if reranking_mode == RerankMode.WEIGHTED_SCORE.value and weights:
             return WeightRerankRunner(
                 tenant_id,
                 Weights(
                     vector_setting=VectorSetting(
-                        vector_weight=weights['vector_setting']['vector_weight'],
-                        embedding_provider_name=weights['vector_setting']['embedding_provider_name'],
-                        embedding_model_name=weights['vector_setting']['embedding_model_name'],
+                        vector_weight=weights["vector_setting"]["vector_weight"],
+                        embedding_provider_name=weights["vector_setting"]["embedding_provider_name"],
+                        embedding_model_name=weights["vector_setting"]["embedding_model_name"],
                     ),
                     keyword_setting=KeywordSetting(
-                        keyword_weight=weights['keyword_setting']['keyword_weight'],
-                    )
-                )
+                        keyword_weight=weights["keyword_setting"]["keyword_weight"],
+                    ),
+                ),
             )
         elif reranking_mode == RerankMode.RERANKING_MODEL.value:
             if reranking_model:
@@ -53,9 +68,9 @@ class DataPostProcessor:
                     model_manager = ModelManager()
                     rerank_model_instance = model_manager.get_model_instance(
                         tenant_id=tenant_id,
-                        provider=reranking_model['reranking_provider_name'],
+                        provider=reranking_model["reranking_provider_name"],
                         model_type=ModelType.RERANK,
-                        model=reranking_model['reranking_model_name']
+                        model=reranking_model["reranking_model_name"],
                     )
                 except InvokeAuthorizationError:
                     return None
@@ -67,5 +82,3 @@ class DataPostProcessor:
         if reorder_enabled:
             return ReorderRunner()
         return None
-
-
diff --git a/api/core/rag/data_post_processor/reorder.py b/api/core/rag/data_post_processor/reorder.py
index 71297588a4..a9a0885241 100644
--- a/api/core/rag/data_post_processor/reorder.py
+++ b/api/core/rag/data_post_processor/reorder.py
@@ -2,7 +2,6 @@ from core.rag.models.document import Document
 
 
 class ReorderRunner:
-
     def run(self, documents: list[Document]) -> list[Document]:
         # Retrieve elements from odd indices (0, 2, 4, etc.) of the documents list
         odd_elements = documents[::2]
diff --git a/api/core/rag/datasource/keyword/jieba/jieba.py b/api/core/rag/datasource/keyword/jieba/jieba.py
index a3714c2fd3..3073100746 100644
--- a/api/core/rag/datasource/keyword/jieba/jieba.py
+++ b/api/core/rag/datasource/keyword/jieba/jieba.py
@@ -24,37 +24,42 @@ class Jieba(BaseKeyword):
         self._config = KeywordTableConfig()
 
     def create(self, texts: list[Document], **kwargs) -> BaseKeyword:
-        lock_name = 'keyword_indexing_lock_{}'.format(self.dataset.id)
+        lock_name = "keyword_indexing_lock_{}".format(self.dataset.id)
         with redis_client.lock(lock_name, timeout=600):
             keyword_table_handler = JiebaKeywordTableHandler()
             keyword_table = self._get_dataset_keyword_table()
             for text in texts:
-                keywords = keyword_table_handler.extract_keywords(text.page_content, self._config.max_keywords_per_chunk)
-                self._update_segment_keywords(self.dataset.id, text.metadata['doc_id'], list(keywords))
-                keyword_table = self._add_text_to_keyword_table(keyword_table, text.metadata['doc_id'], list(keywords))
+                keywords = keyword_table_handler.extract_keywords(
+                    text.page_content, self._config.max_keywords_per_chunk
+                )
+                self._update_segment_keywords(self.dataset.id, text.metadata["doc_id"], list(keywords))
+                keyword_table = self._add_text_to_keyword_table(keyword_table, text.metadata["doc_id"], list(keywords))
 
             self._save_dataset_keyword_table(keyword_table)
 
             return self
 
     def add_texts(self, texts: list[Document], **kwargs):
-        lock_name = 'keyword_indexing_lock_{}'.format(self.dataset.id)
+        lock_name = "keyword_indexing_lock_{}".format(self.dataset.id)
         with redis_client.lock(lock_name, timeout=600):
             keyword_table_handler = JiebaKeywordTableHandler()
 
             keyword_table = self._get_dataset_keyword_table()
-            keywords_list = kwargs.get('keywords_list', None)
+            keywords_list = kwargs.get("keywords_list", None)
             for i in range(len(texts)):
                 text = texts[i]
                 if keywords_list:
                     keywords = keywords_list[i]
                     if not keywords:
-                        keywords = keyword_table_handler.extract_keywords(text.page_content,
-                                                                          self._config.max_keywords_per_chunk)
+                        keywords = keyword_table_handler.extract_keywords(
+                            text.page_content, self._config.max_keywords_per_chunk
+                        )
                 else:
-                    keywords = keyword_table_handler.extract_keywords(text.page_content, self._config.max_keywords_per_chunk)
-                self._update_segment_keywords(self.dataset.id, text.metadata['doc_id'], list(keywords))
-                keyword_table = self._add_text_to_keyword_table(keyword_table, text.metadata['doc_id'], list(keywords))
+                    keywords = keyword_table_handler.extract_keywords(
+                        text.page_content, self._config.max_keywords_per_chunk
+                    )
+                self._update_segment_keywords(self.dataset.id, text.metadata["doc_id"], list(keywords))
+                keyword_table = self._add_text_to_keyword_table(keyword_table, text.metadata["doc_id"], list(keywords))
 
             self._save_dataset_keyword_table(keyword_table)
 
@@ -63,97 +68,91 @@ class Jieba(BaseKeyword):
         return id in set.union(*keyword_table.values())
 
     def delete_by_ids(self, ids: list[str]) -> None:
-        lock_name = 'keyword_indexing_lock_{}'.format(self.dataset.id)
+        lock_name = "keyword_indexing_lock_{}".format(self.dataset.id)
         with redis_client.lock(lock_name, timeout=600):
             keyword_table = self._get_dataset_keyword_table()
             keyword_table = self._delete_ids_from_keyword_table(keyword_table, ids)
 
             self._save_dataset_keyword_table(keyword_table)
 
-    def search(
-            self, query: str,
-            **kwargs: Any
-    ) -> list[Document]:
+    def search(self, query: str, **kwargs: Any) -> list[Document]:
         keyword_table = self._get_dataset_keyword_table()
 
-        k = kwargs.get('top_k', 4)
+        k = kwargs.get("top_k", 4)
 
         sorted_chunk_indices = self._retrieve_ids_by_query(keyword_table, query, k)
 
         documents = []
         for chunk_index in sorted_chunk_indices:
-            segment = db.session.query(DocumentSegment).filter(
-                DocumentSegment.dataset_id == self.dataset.id,
-                DocumentSegment.index_node_id == chunk_index
-            ).first()
+            segment = (
+                db.session.query(DocumentSegment)
+                .filter(DocumentSegment.dataset_id == self.dataset.id, DocumentSegment.index_node_id == chunk_index)
+                .first()
+            )
 
             if segment:
-
-                documents.append(Document(
-                    page_content=segment.content,
-                    metadata={
-                        "doc_id": chunk_index,
-                        "doc_hash": segment.index_node_hash,
-                        "document_id": segment.document_id,
-                        "dataset_id": segment.dataset_id,
-                    }
-                ))
+                documents.append(
+                    Document(
+                        page_content=segment.content,
+                        metadata={
+                            "doc_id": chunk_index,
+                            "doc_hash": segment.index_node_hash,
+                            "document_id": segment.document_id,
+                            "dataset_id": segment.dataset_id,
+                        },
+                    )
+                )
 
         return documents
 
     def delete(self) -> None:
-        lock_name = 'keyword_indexing_lock_{}'.format(self.dataset.id)
+        lock_name = "keyword_indexing_lock_{}".format(self.dataset.id)
         with redis_client.lock(lock_name, timeout=600):
             dataset_keyword_table = self.dataset.dataset_keyword_table
             if dataset_keyword_table:
                 db.session.delete(dataset_keyword_table)
                 db.session.commit()
-                if dataset_keyword_table.data_source_type != 'database':
-                    file_key = 'keyword_files/' + self.dataset.tenant_id + '/' + self.dataset.id + '.txt'
+                if dataset_keyword_table.data_source_type != "database":
+                    file_key = "keyword_files/" + self.dataset.tenant_id + "/" + self.dataset.id + ".txt"
                     storage.delete(file_key)
 
     def _save_dataset_keyword_table(self, keyword_table):
         keyword_table_dict = {
-            '__type__': 'keyword_table',
-            '__data__': {
-                "index_id": self.dataset.id,
-                "summary": None,
-                "table": keyword_table
-            }
+            "__type__": "keyword_table",
+            "__data__": {"index_id": self.dataset.id, "summary": None, "table": keyword_table},
         }
         dataset_keyword_table = self.dataset.dataset_keyword_table
         keyword_data_source_type = dataset_keyword_table.data_source_type
-        if keyword_data_source_type == 'database':
+        if keyword_data_source_type == "database":
             dataset_keyword_table.keyword_table = json.dumps(keyword_table_dict, cls=SetEncoder)
             db.session.commit()
         else:
-            file_key = 'keyword_files/' + self.dataset.tenant_id + '/' + self.dataset.id + '.txt'
+            file_key = "keyword_files/" + self.dataset.tenant_id + "/" + self.dataset.id + ".txt"
             if storage.exists(file_key):
                 storage.delete(file_key)
-            storage.save(file_key, json.dumps(keyword_table_dict, cls=SetEncoder).encode('utf-8'))
+            storage.save(file_key, json.dumps(keyword_table_dict, cls=SetEncoder).encode("utf-8"))
 
     def _get_dataset_keyword_table(self) -> Optional[dict]:
         dataset_keyword_table = self.dataset.dataset_keyword_table
         if dataset_keyword_table:
             keyword_table_dict = dataset_keyword_table.keyword_table_dict
             if keyword_table_dict:
-                return keyword_table_dict['__data__']['table']
+                return keyword_table_dict["__data__"]["table"]
         else:
             keyword_data_source_type = dify_config.KEYWORD_DATA_SOURCE_TYPE
             dataset_keyword_table = DatasetKeywordTable(
                 dataset_id=self.dataset.id,
-                keyword_table='',
+                keyword_table="",
                 data_source_type=keyword_data_source_type,
             )
-            if keyword_data_source_type == 'database':
-                dataset_keyword_table.keyword_table = json.dumps({
-                    '__type__': 'keyword_table',
-                    '__data__': {
-                        "index_id": self.dataset.id,
-                        "summary": None,
-                        "table": {}
-                    }
-                }, cls=SetEncoder)
+            if keyword_data_source_type == "database":
+                dataset_keyword_table.keyword_table = json.dumps(
+                    {
+                        "__type__": "keyword_table",
+                        "__data__": {"index_id": self.dataset.id, "summary": None, "table": {}},
+                    },
+                    cls=SetEncoder,
+                )
             db.session.add(dataset_keyword_table)
             db.session.commit()
 
@@ -174,9 +173,7 @@ class Jieba(BaseKeyword):
         keywords_to_delete = set()
         for keyword, node_idxs in keyword_table.items():
             if node_idxs_to_delete.intersection(node_idxs):
-                keyword_table[keyword] = node_idxs.difference(
-                    node_idxs_to_delete
-                )
+                keyword_table[keyword] = node_idxs.difference(node_idxs_to_delete)
                 if not keyword_table[keyword]:
                     keywords_to_delete.add(keyword)
 
@@ -202,13 +199,14 @@ class Jieba(BaseKeyword):
             reverse=True,
         )
 
-        return sorted_chunk_indices[: k]
+        return sorted_chunk_indices[:k]
 
     def _update_segment_keywords(self, dataset_id: str, node_id: str, keywords: list[str]):
-        document_segment = db.session.query(DocumentSegment).filter(
-            DocumentSegment.dataset_id == dataset_id,
-            DocumentSegment.index_node_id == node_id
-        ).first()
+        document_segment = (
+            db.session.query(DocumentSegment)
+            .filter(DocumentSegment.dataset_id == dataset_id, DocumentSegment.index_node_id == node_id)
+            .first()
+        )
         if document_segment:
             document_segment.keywords = keywords
             db.session.add(document_segment)
@@ -224,14 +222,14 @@ class Jieba(BaseKeyword):
         keyword_table_handler = JiebaKeywordTableHandler()
         keyword_table = self._get_dataset_keyword_table()
         for pre_segment_data in pre_segment_data_list:
-            segment = pre_segment_data['segment']
-            if pre_segment_data['keywords']:
-                segment.keywords = pre_segment_data['keywords']
-                keyword_table = self._add_text_to_keyword_table(keyword_table, segment.index_node_id,
-                                                                pre_segment_data['keywords'])
+            segment = pre_segment_data["segment"]
+            if pre_segment_data["keywords"]:
+                segment.keywords = pre_segment_data["keywords"]
+                keyword_table = self._add_text_to_keyword_table(
+                    keyword_table, segment.index_node_id, pre_segment_data["keywords"]
+                )
             else:
-                keywords = keyword_table_handler.extract_keywords(segment.content,
-                                                                  self._config.max_keywords_per_chunk)
+                keywords = keyword_table_handler.extract_keywords(segment.content, self._config.max_keywords_per_chunk)
                 segment.keywords = list(keywords)
                 keyword_table = self._add_text_to_keyword_table(keyword_table, segment.index_node_id, list(keywords))
         self._save_dataset_keyword_table(keyword_table)
diff --git a/api/core/rag/datasource/keyword/jieba/jieba_keyword_table_handler.py b/api/core/rag/datasource/keyword/jieba/jieba_keyword_table_handler.py
index ad669ef515..4b1ade8e3f 100644
--- a/api/core/rag/datasource/keyword/jieba/jieba_keyword_table_handler.py
+++ b/api/core/rag/datasource/keyword/jieba/jieba_keyword_table_handler.py
@@ -8,7 +8,6 @@ from core.rag.datasource.keyword.jieba.stopwords import STOPWORDS
 
 
 class JiebaKeywordTableHandler:
-
     def __init__(self):
         default_tfidf.stop_words = STOPWORDS
 
@@ -30,4 +29,4 @@ class JiebaKeywordTableHandler:
             if len(sub_tokens) > 1:
                 results.update({w for w in sub_tokens if w not in list(STOPWORDS)})
 
-        return results
\ No newline at end of file
+        return results
diff --git a/api/core/rag/datasource/keyword/jieba/stopwords.py b/api/core/rag/datasource/keyword/jieba/stopwords.py
index c616a15cf0..9abe78d6ef 100644
--- a/api/core/rag/datasource/keyword/jieba/stopwords.py
+++ b/api/core/rag/datasource/keyword/jieba/stopwords.py
@@ -1,90 +1,1380 @@
 STOPWORDS = {
-    "during", "when", "but", "then", "further", "isn", "mustn't", "until", "own", "i", "couldn", "y", "only", "you've",
-    "ours", "who", "where", "ourselves", "has", "to", "was", "didn't", "themselves", "if", "against", "through", "her",
-    "an", "your", "can", "those", "didn", "about", "aren't", "shan't", "be", "not", "these", "again", "so", "t",
-    "theirs", "weren", "won't", "won", "itself", "just", "same", "while", "why", "doesn", "aren", "him", "haven",
-    "for", "you'll", "that", "we", "am", "d", "by", "having", "wasn't", "than", "weren't", "out", "from", "now",
-    "their", "too", "hadn", "o", "needn", "most", "it", "under", "needn't", "any", "some", "few", "ll", "hers", "which",
-    "m", "you're", "off", "other", "had", "she", "you'd", "do", "you", "does", "s", "will", "each", "wouldn't", "hasn't",
-    "such", "more", "whom", "she's", "my", "yours", "yourself", "of", "on", "very", "hadn't", "with", "yourselves",
-    "been", "ma", "them", "mightn't", "shan", "mustn", "they", "what", "both", "that'll", "how", "is", "he", "because",
-    "down", "haven't", "are", "no", "it's", "our", "being", "the", "or", "above", "myself", "once", "don't", "doesn't",
-    "as", "nor", "here", "herself", "hasn", "mightn", "have", "its", "all", "were", "ain", "this", "at", "after",
-    "over", "shouldn't", "into", "before", "don", "wouldn", "re", "couldn't", "wasn", "in", "should", "there",
-    "himself", "isn't", "should've", "doing", "ve", "shouldn", "a", "did", "and", "his", "between", "me", "up", "below",
-    "人民", "末##末", "啊", "阿", "哎", "哎呀", "哎哟", "唉", "俺", "俺们", "按", "按照", "吧", "吧哒", "把", "罢了", "被", "本",
-    "本着", "比", "比方", "比如", "鄙人", "彼", "彼此", "边", "别", "别的", "别说", "并", "并且", "不比", "不成", "不单", "不但",
-    "不独", "不管", "不光", "不过", "不仅", "不拘", "不论", "不怕", "不然", "不如", "不特", "不惟", "不问", "不只", "朝", "朝着",
-    "趁", "趁着", "乘", "冲", "除", "除此之外", "除非", "除了", "此", "此间", "此外", "从", "从而", "打", "待", "但", "但是", "当",
-    "当着", "到", "得", "的", "的话", "等", "等等", "地", "第", "叮咚", "对", "对于", "多", "多少", "而", "而况", "而且", "而是",
-    "而外", "而言", "而已", "尔后", "反过来", "反过来说", "反之", "非但", "非徒", "否则", "嘎", "嘎登", "该", "赶", "个", "各",
-    "各个", "各位", "各种", "各自", "给", "根据", "跟", "故", "故此", "固然", "关于", "管", "归", "果然", "果真", "过", "哈",
-    "哈哈", "呵", "和", "何", "何处", "何况", "何时", "嘿", "哼", "哼唷", "呼哧", "乎", "哗", "还是", "还有", "换句话说", "换言之",
-    "或", "或是", "或者", "极了", "及", "及其", "及至", "即", "即便", "即或", "即令", "即若", "即使", "几", "几时", "己", "既",
-    "既然", "既是", "继而", "加之", "假如", "假若", "假使", "鉴于", "将", "较", "较之", "叫", "接着", "结果", "借", "紧接着",
-    "进而", "尽", "尽管", "经", "经过", "就", "就是", "就是说", "据", "具体地说", "具体说来", "开始", "开外", "靠", "咳", "可",
-    "可见", "可是", "可以", "况且", "啦", "来", "来着", "离", "例如", "哩", "连", "连同", "两者", "了", "临", "另", "另外",
-    "另一方面", "论", "嘛", "吗", "慢说", "漫说", "冒", "么", "每", "每当", "们", "莫若", "某", "某个", "某些", "拿", "哪",
-    "哪边", "哪儿", "哪个", "哪里", "哪年", "哪怕", "哪天", "哪些", "哪样", "那", "那边", "那儿", "那个", "那会儿", "那里", "那么",
-    "那么些", "那么样", "那时", "那些", "那样", "乃", "乃至", "呢", "能", "你", "你们", "您", "宁", "宁可", "宁肯", "宁愿", "哦",
-    "呕", "啪达", "旁人", "呸", "凭", "凭借", "其", "其次", "其二", "其他", "其它", "其一", "其余", "其中", "起", "起见", "岂但",
-    "恰恰相反", "前后", "前者", "且", "然而", "然后", "然则", "让", "人家", "任", "任何", "任凭", "如", "如此", "如果", "如何",
-    "如其", "如若", "如上所述", "若", "若非", "若是", "啥", "上下", "尚且", "设若", "设使", "甚而", "甚么", "甚至", "省得", "时候",
-    "什么", "什么样", "使得", "是", "是的", "首先", "谁", "谁知", "顺", "顺着", "似的", "虽", "虽然", "虽说", "虽则", "随", "随着",
-    "所", "所以", "他", "他们", "他人", "它", "它们", "她", "她们", "倘", "倘或", "倘然", "倘若", "倘使", "腾", "替", "通过", "同",
-    "同时", "哇", "万一", "往", "望", "为", "为何", "为了", "为什么", "为着", "喂", "嗡嗡", "我", "我们", "呜", "呜呼", "乌乎",
-    "无论", "无宁", "毋宁", "嘻", "吓", "相对而言", "像", "向", "向着", "嘘", "呀", "焉", "沿", "沿着", "要", "要不", "要不然",
-    "要不是", "要么", "要是", "也", "也罢", "也好", "一", "一般", "一旦", "一方面", "一来", "一切", "一样", "一则", "依", "依照",
-    "矣", "以", "以便", "以及", "以免", "以至", "以至于", "以致", "抑或", "因", "因此", "因而", "因为", "哟", "用", "由",
-    "由此可见", "由于", "有", "有的", "有关", "有些", "又", "于", "于是", "于是乎", "与", "与此同时", "与否", "与其", "越是",
-    "云云", "哉", "再说", "再者", "在", "在下", "咱", "咱们", "则", "怎", "怎么", "怎么办", "怎么样", "怎样", "咋", "照", "照着",
-    "者", "这", "这边", "这儿", "这个", "这会儿", "这就是说", "这里", "这么", "这么点儿", "这么些", "这么样", "这时", "这些", "这样",
-    "正如", "吱", "之", "之类", "之所以", "之一", "只是", "只限", "只要", "只有", "至", "至于", "诸位", "着", "着呢", "自", "自从",
-    "自个儿", "自各儿", "自己", "自家", "自身", "综上所述", "总的来看", "总的来说", "总的说来", "总而言之", "总之", "纵", "纵令",
-    "纵然", "纵使", "遵照", "作为", "兮", "呃", "呗", "咚", "咦", "喏", "啐", "喔唷", "嗬", "嗯", "嗳", "~", "!", ".", ":",
-    "\"", "'", "(", ")", "*", "A", "白", "社会主义", "--", "..", ">>", " [", " ]", "", "<", ">", "/", "\\", "|", "-", "_",
-    "+", "=", "&", "^", "%", "#", "@", "`", ";", "$", "（", "）", "——", "—", "￥", "·", "...", "‘", "’", "〉", "〈", "…",
-    "　", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "０", "１", "２", "３", "４", "５", "６", "７", "８", "９", "二",
-    "三", "四", "五", "六", "七", "八", "九", "零", "＞", "＜", "＠", "＃", "＄", "％", "︿", "＆", "＊", "＋", "～", "｜", "［",
-    "］", "｛", "｝", "啊哈", "啊呀", "啊哟", "挨次", "挨个", "挨家挨户", "挨门挨户", "挨门逐户", "挨着", "按理", "按期", "按时",
-    "按说", "暗地里", "暗中", "暗自", "昂然", "八成", "白白", "半", "梆", "保管", "保险", "饱", "背地里", "背靠背", "倍感", "倍加",
-    "本人", "本身", "甭", "比起", "比如说", "比照", "毕竟", "必", "必定", "必将", "必须", "便", "别人", "并非", "并肩", "并没",
-    "并没有", "并排", "并无", "勃然", "不", "不必", "不常", "不大", "不但...而且", "不得", "不得不", "不得了", "不得已", "不迭",
-    "不定", "不对", "不妨", "不管怎样", "不会", "不仅...而且", "不仅仅", "不仅仅是", "不经意", "不可开交", "不可抗拒", "不力", "不了",
-    "不料", "不满", "不免", "不能不", "不起", "不巧", "不然的话", "不日", "不少", "不胜", "不时", "不是", "不同", "不能", "不要",
-    "不外", "不外乎", "不下", "不限", "不消", "不已", "不亦乐乎", "不由得", "不再", "不择手段", "不怎么", "不曾", "不知不觉", "不止",
-    "不止一次", "不至于", "才", "才能", "策略地", "差不多", "差一点", "常", "常常", "常言道", "常言说", "常言说得好", "长此下去",
-    "长话短说", "长期以来", "长线", "敞开儿", "彻夜", "陈年", "趁便", "趁机", "趁热", "趁势", "趁早", "成年", "成年累月", "成心",
-    "乘机", "乘胜", "乘势", "乘隙", "乘虚", "诚然", "迟早", "充分", "充其极", "充其量", "抽冷子", "臭", "初", "出", "出来", "出去",
-    "除此", "除此而外", "除此以外", "除开", "除去", "除却", "除外", "处处", "川流不息", "传", "传说", "传闻", "串行", "纯", "纯粹",
-    "此后", "此中", "次第", "匆匆", "从不", "从此", "从此以后", "从古到今", "从古至今", "从今以后", "从宽", "从来", "从轻", "从速",
-    "从头", "从未", "从无到有", "从小", "从新", "从严", "从优", "从早到晚", "从中", "从重", "凑巧", "粗", "存心", "达旦", "打从",
-    "打开天窗说亮话", "大", "大不了", "大大", "大抵", "大都", "大多", "大凡", "大概", "大家", "大举", "大略", "大面儿上", "大事",
-    "大体", "大体上", "大约", "大张旗鼓", "大致", "呆呆地", "带", "殆", "待到", "单", "单纯", "单单", "但愿", "弹指之间", "当场",
-    "当儿", "当即", "当口儿", "当然", "当庭", "当头", "当下", "当真", "当中", "倒不如", "倒不如说", "倒是", "到处", "到底", "到了儿",
-    "到目前为止", "到头", "到头来", "得起", "得天独厚", "的确", "等到", "叮当", "顶多", "定", "动不动", "动辄", "陡然", "都", "独",
-    "独自", "断然", "顿时", "多次", "多多", "多多少少", "多多益善", "多亏", "多年来", "多年前", "而后", "而论", "而又", "尔等",
-    "二话不说", "二话没说", "反倒", "反倒是", "反而", "反手", "反之亦然", "反之则", "方", "方才", "方能", "放量", "非常", "非得",
-    "分期", "分期分批", "分头", "奋勇", "愤然", "风雨无阻", "逢", "弗", "甫", "嘎嘎", "该当", "概", "赶快", "赶早不赶晚", "敢",
-    "敢情", "敢于", "刚", "刚才", "刚好", "刚巧", "高低", "格外", "隔日", "隔夜", "个人", "各式", "更", "更加", "更进一步", "更为",
-    "公然", "共", "共总", "够瞧的", "姑且", "古来", "故而", "故意", "固", "怪", "怪不得", "惯常", "光", "光是", "归根到底",
-    "归根结底", "过于", "毫不", "毫无", "毫无保留地", "毫无例外", "好在", "何必", "何尝", "何妨", "何苦", "何乐而不为", "何须",
-    "何止", "很", "很多", "很少", "轰然", "后来", "呼啦", "忽地", "忽然", "互", "互相", "哗啦", "话说", "还", "恍然", "会", "豁然",
-    "活", "伙同", "或多或少", "或许", "基本", "基本上", "基于", "极", "极大", "极度", "极端", "极力", "极其", "极为", "急匆匆",
-    "即将", "即刻", "即是说", "几度", "几番", "几乎", "几经", "既...又", "继之", "加上", "加以", "间或", "简而言之", "简言之",
-    "简直", "见", "将才", "将近", "将要", "交口", "较比", "较为", "接连不断", "接下来", "皆可", "截然", "截至", "藉以", "借此",
-    "借以", "届时", "仅", "仅仅", "谨", "进来", "进去", "近", "近几年来", "近来", "近年来", "尽管如此", "尽可能", "尽快", "尽量",
-    "尽然", "尽如人意", "尽心竭力", "尽心尽力", "尽早", "精光", "经常", "竟", "竟然", "究竟", "就此", "就地", "就算", "居然", "局外",
-    "举凡", "据称", "据此", "据实", "据说", "据我所知", "据悉", "具体来说", "决不", "决非", "绝", "绝不", "绝顶", "绝对", "绝非",
-    "均", "喀", "看", "看来", "看起来", "看上去", "看样子", "可好", "可能", "恐怕", "快", "快要", "来不及", "来得及", "来讲",
-    "来看", "拦腰", "牢牢", "老", "老大", "老老实实", "老是", "累次", "累年", "理当", "理该", "理应", "历", "立", "立地", "立刻",
-    "立马", "立时", "联袂", "连连", "连日", "连日来", "连声", "连袂", "临到", "另方面", "另行", "另一个", "路经", "屡", "屡次",
-    "屡次三番", "屡屡", "缕缕", "率尔", "率然", "略", "略加", "略微", "略为", "论说", "马上", "蛮", "满", "没", "没有", "每逢",
-    "每每", "每时每刻", "猛然", "猛然间", "莫", "莫不", "莫非", "莫如", "默默地", "默然", "呐", "那末", "奈", "难道", "难得", "难怪",
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+    "初",
+    "出",
+    "出来",
+    "出去",
+    "除此",
+    "除此而外",
+    "除此以外",
+    "除开",
+    "除去",
+    "除却",
+    "除外",
+    "处处",
+    "川流不息",
+    "传",
+    "传说",
+    "传闻",
+    "串行",
+    "纯",
+    "纯粹",
+    "此后",
+    "此中",
+    "次第",
+    "匆匆",
+    "从不",
+    "从此",
+    "从此以后",
+    "从古到今",
+    "从古至今",
+    "从今以后",
+    "从宽",
+    "从来",
+    "从轻",
+    "从速",
+    "从头",
+    "从未",
+    "从无到有",
+    "从小",
+    "从新",
+    "从严",
+    "从优",
+    "从早到晚",
+    "从中",
+    "从重",
+    "凑巧",
+    "粗",
+    "存心",
+    "达旦",
+    "打从",
+    "打开天窗说亮话",
+    "大",
+    "大不了",
+    "大大",
+    "大抵",
+    "大都",
+    "大多",
+    "大凡",
+    "大概",
+    "大家",
+    "大举",
+    "大略",
+    "大面儿上",
+    "大事",
+    "大体",
+    "大体上",
+    "大约",
+    "大张旗鼓",
+    "大致",
+    "呆呆地",
+    "带",
+    "殆",
+    "待到",
+    "单",
+    "单纯",
+    "单单",
+    "但愿",
+    "弹指之间",
+    "当场",
+    "当儿",
+    "当即",
+    "当口儿",
+    "当然",
+    "当庭",
+    "当头",
+    "当下",
+    "当真",
+    "当中",
+    "倒不如",
+    "倒不如说",
+    "倒是",
+    "到处",
+    "到底",
+    "到了儿",
+    "到目前为止",
+    "到头",
+    "到头来",
+    "得起",
+    "得天独厚",
+    "的确",
+    "等到",
+    "叮当",
+    "顶多",
+    "定",
+    "动不动",
+    "动辄",
+    "陡然",
+    "都",
+    "独",
+    "独自",
+    "断然",
+    "顿时",
+    "多次",
+    "多多",
+    "多多少少",
+    "多多益善",
+    "多亏",
+    "多年来",
+    "多年前",
+    "而后",
+    "而论",
+    "而又",
+    "尔等",
+    "二话不说",
+    "二话没说",
+    "反倒",
+    "反倒是",
+    "反而",
+    "反手",
+    "反之亦然",
+    "反之则",
+    "方",
+    "方才",
+    "方能",
+    "放量",
+    "非常",
+    "非得",
+    "分期",
+    "分期分批",
+    "分头",
+    "奋勇",
+    "愤然",
+    "风雨无阻",
+    "逢",
+    "弗",
+    "甫",
+    "嘎嘎",
+    "该当",
+    "概",
+    "赶快",
+    "赶早不赶晚",
+    "敢",
+    "敢情",
+    "敢于",
+    "刚",
+    "刚才",
+    "刚好",
+    "刚巧",
+    "高低",
+    "格外",
+    "隔日",
+    "隔夜",
+    "个人",
+    "各式",
+    "更",
+    "更加",
+    "更进一步",
+    "更为",
+    "公然",
+    "共",
+    "共总",
+    "够瞧的",
+    "姑且",
+    "古来",
+    "故而",
+    "故意",
+    "固",
+    "怪",
+    "怪不得",
+    "惯常",
+    "光",
+    "光是",
+    "归根到底",
+    "归根结底",
+    "过于",
+    "毫不",
+    "毫无",
+    "毫无保留地",
+    "毫无例外",
+    "好在",
+    "何必",
+    "何尝",
+    "何妨",
+    "何苦",
+    "何乐而不为",
+    "何须",
+    "何止",
+    "很",
+    "很多",
+    "很少",
+    "轰然",
+    "后来",
+    "呼啦",
+    "忽地",
+    "忽然",
+    "互",
+    "互相",
+    "哗啦",
+    "话说",
+    "还",
+    "恍然",
+    "会",
+    "豁然",
+    "活",
+    "伙同",
+    "或多或少",
+    "或许",
+    "基本",
+    "基本上",
+    "基于",
+    "极",
+    "极大",
+    "极度",
+    "极端",
+    "极力",
+    "极其",
+    "极为",
+    "急匆匆",
+    "即将",
+    "即刻",
+    "即是说",
+    "几度",
+    "几番",
+    "几乎",
+    "几经",
+    "既...又",
+    "继之",
+    "加上",
+    "加以",
+    "间或",
+    "简而言之",
+    "简言之",
+    "简直",
+    "见",
+    "将才",
+    "将近",
+    "将要",
+    "交口",
+    "较比",
+    "较为",
+    "接连不断",
+    "接下来",
+    "皆可",
+    "截然",
+    "截至",
+    "藉以",
+    "借此",
+    "借以",
+    "届时",
+    "仅",
+    "仅仅",
+    "谨",
+    "进来",
+    "进去",
+    "近",
+    "近几年来",
+    "近来",
+    "近年来",
+    "尽管如此",
+    "尽可能",
+    "尽快",
+    "尽量",
+    "尽然",
+    "尽如人意",
+    "尽心竭力",
+    "尽心尽力",
+    "尽早",
+    "精光",
+    "经常",
+    "竟",
+    "竟然",
+    "究竟",
+    "就此",
+    "就地",
+    "就算",
+    "居然",
+    "局外",
+    "举凡",
+    "据称",
+    "据此",
+    "据实",
+    "据说",
+    "据我所知",
+    "据悉",
+    "具体来说",
+    "决不",
+    "决非",
+    "绝",
+    "绝不",
+    "绝顶",
+    "绝对",
+    "绝非",
+    "均",
+    "喀",
+    "看",
+    "看来",
+    "看起来",
+    "看上去",
+    "看样子",
+    "可好",
+    "可能",
+    "恐怕",
+    "快",
+    "快要",
+    "来不及",
+    "来得及",
+    "来讲",
+    "来看",
+    "拦腰",
+    "牢牢",
+    "老",
+    "老大",
+    "老老实实",
+    "老是",
+    "累次",
+    "累年",
+    "理当",
+    "理该",
+    "理应",
+    "历",
+    "立",
+    "立地",
+    "立刻",
+    "立马",
+    "立时",
+    "联袂",
+    "连连",
+    "连日",
+    "连日来",
+    "连声",
+    "连袂",
+    "临到",
+    "另方面",
+    "另行",
+    "另一个",
+    "路经",
+    "屡",
+    "屡次",
+    "屡次三番",
+    "屡屡",
+    "缕缕",
+    "率尔",
+    "率然",
+    "略",
+    "略加",
+    "略微",
+    "略为",
+    "论说",
+    "马上",
+    "蛮",
+    "满",
+    "没",
+    "没有",
+    "每逢",
+    "每每",
+    "每时每刻",
+    "猛然",
+    "猛然间",
+    "莫",
+    "莫不",
+    "莫非",
+    "莫如",
+    "默默地",
+    "默然",
+    "呐",
+    "那末",
+    "奈",
+    "难道",
+    "难得",
+    "难怪",
+    "难说",
+    "内",
+    "年复一年",
+    "凝神",
+    "偶而",
+    "偶尔",
+    "怕",
+    "砰",
+    "碰巧",
+    "譬如",
+    "偏偏",
+    "乒",
+    "平素",
+    "颇",
+    "迫于",
+    "扑通",
+    "其后",
+    "其实",
+    "奇",
+    "齐",
+    "起初",
+    "起来",
+    "起首",
+    "起头",
+    "起先",
+    "岂",
+    "岂非",
+    "岂止",
+    "迄",
+    "恰逢",
+    "恰好",
+    "恰恰",
+    "恰巧",
+    "恰如",
+    "恰似",
+    "千",
+    "千万",
+    "千万千万",
+    "切",
+    "切不可",
+    "切莫",
+    "切切",
+    "切勿",
+    "窃",
+    "亲口",
+    "亲身",
+    "亲手",
+    "亲眼",
+    "亲自",
+    "顷",
+    "顷刻",
+    "顷刻间",
+    "顷刻之间",
+    "请勿",
+    "穷年累月",
+    "取道",
+    "去",
+    "权时",
+    "全都",
+    "全力",
+    "全年",
+    "全然",
+    "全身心",
+    "然",
+    "人人",
+    "仍",
+    "仍旧",
+    "仍然",
+    "日复一日",
+    "日见",
+    "日渐",
+    "日益",
+    "日臻",
+    "如常",
+    "如此等等",
+    "如次",
+    "如今",
+    "如期",
+    "如前所述",
+    "如上",
+    "如下",
+    "汝",
+    "三番两次",
+    "三番五次",
+    "三天两头",
+    "瑟瑟",
+    "沙沙",
+    "上",
+    "上来",
+    "上去",
+    "一个",
+    "月",
+    "日",
+    "\n",
 }
diff --git a/api/core/rag/datasource/keyword/keyword_base.py b/api/core/rag/datasource/keyword/keyword_base.py
index b77c6562b2..4b9ec460e6 100644
--- a/api/core/rag/datasource/keyword/keyword_base.py
+++ b/api/core/rag/datasource/keyword/keyword_base.py
@@ -8,7 +8,6 @@ from models.dataset import Dataset
 
 
 class BaseKeyword(ABC):
-
     def __init__(self, dataset: Dataset):
         self.dataset = dataset
 
@@ -31,15 +30,12 @@ class BaseKeyword(ABC):
     def delete(self) -> None:
         raise NotImplementedError
 
-    def search(
-            self, query: str,
-            **kwargs: Any
-    ) -> list[Document]:
+    def search(self, query: str, **kwargs: Any) -> list[Document]:
         raise NotImplementedError
 
     def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
-        for text in texts[:]:
-            doc_id = text.metadata['doc_id']
+        for text in texts.copy():
+            doc_id = text.metadata["doc_id"]
             exists_duplicate_node = self.text_exists(doc_id)
             if exists_duplicate_node:
                 texts.remove(text)
@@ -47,4 +43,4 @@ class BaseKeyword(ABC):
         return texts
 
     def _get_uuids(self, texts: list[Document]) -> list[str]:
-        return [text.metadata['doc_id'] for text in texts]
+        return [text.metadata["doc_id"] for text in texts]
diff --git a/api/core/rag/datasource/keyword/keyword_factory.py b/api/core/rag/datasource/keyword/keyword_factory.py
index 6ac610f82b..3c99f33be6 100644
--- a/api/core/rag/datasource/keyword/keyword_factory.py
+++ b/api/core/rag/datasource/keyword/keyword_factory.py
@@ -20,9 +20,7 @@ class Keyword:
             raise ValueError("Keyword store must be specified.")
 
         if keyword_type == "jieba":
-            return Jieba(
-                dataset=self._dataset
-            )
+            return Jieba(dataset=self._dataset)
         else:
             raise ValueError(f"Keyword store {keyword_type} is not supported.")
 
@@ -41,10 +39,7 @@ class Keyword:
     def delete(self) -> None:
         self._keyword_processor.delete()
 
-    def search(
-            self, query: str,
-            **kwargs: Any
-    ) -> list[Document]:
+    def search(self, query: str, **kwargs: Any) -> list[Document]:
         return self._keyword_processor.search(query, **kwargs)
 
     def __getattr__(self, name):
diff --git a/api/core/rag/datasource/retrieval_service.py b/api/core/rag/datasource/retrieval_service.py
index 4e56df34ae..9819278491 100644
--- a/api/core/rag/datasource/retrieval_service.py
+++ b/api/core/rag/datasource/retrieval_service.py
@@ -7,25 +7,21 @@ from core.rag.data_post_processor.data_post_processor import DataPostProcessor
 from core.rag.datasource.keyword.keyword_factory import Keyword
 from core.rag.datasource.vdb.vector_factory import Vector
 from core.rag.rerank.constants.rerank_mode import RerankMode
-from core.rag.retrieval.retrival_methods import RetrievalMethod
+from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from extensions.ext_database import db
 from models.dataset import Dataset
 from services.external_knowledge_service import ExternalDatasetService
 
 default_retrieval_model = {
-    'search_method': RetrievalMethod.SEMANTIC_SEARCH.value,
-    'reranking_enable': False,
-    'reranking_model': {
-        'reranking_provider_name': '',
-        'reranking_model_name': ''
-    },
-    'top_k': 2,
-    'score_threshold_enabled': False
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "reranking_enable": False,
+    "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
+    "top_k": 2,
+    "score_threshold_enabled": False,
 }
 
 
 class RetrievalService:
-
     @classmethod
     def retrieve(cls, retrival_method: str, dataset_id: str, query: str,
                  top_k: int, score_threshold: Optional[float] = .0,
@@ -113,95 +109,104 @@ class RetrievalService:
             return all_documents
 
     @classmethod
-    def keyword_search(cls, flask_app: Flask, dataset_id: str, query: str,
-                       top_k: int, all_documents: list, exceptions: list):
+    def keyword_search(
+        cls, flask_app: Flask, dataset_id: str, query: str, top_k: int, all_documents: list, exceptions: list
+    ):
         with flask_app.app_context():
             try:
-                dataset = db.session.query(Dataset).filter(
-                    Dataset.id == dataset_id
-                ).first()
+                dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
 
-                keyword = Keyword(
-                    dataset=dataset
-                )
+                keyword = Keyword(dataset=dataset)
 
-                documents = keyword.search(
-                    cls.escape_query_for_search(query),
-                    top_k=top_k
-                )
+                documents = keyword.search(cls.escape_query_for_search(query), top_k=top_k)
                 all_documents.extend(documents)
             except Exception as e:
                 exceptions.append(str(e))
 
     @classmethod
-    def embedding_search(cls, flask_app: Flask, dataset_id: str, query: str,
-                         top_k: int, score_threshold: Optional[float], reranking_model: Optional[dict],
-                         all_documents: list, retrival_method: str, exceptions: list):
+    def embedding_search(
+        cls,
+        flask_app: Flask,
+        dataset_id: str,
+        query: str,
+        top_k: int,
+        score_threshold: Optional[float],
+        reranking_model: Optional[dict],
+        all_documents: list,
+        retrieval_method: str,
+        exceptions: list,
+    ):
         with flask_app.app_context():
             try:
-                dataset = db.session.query(Dataset).filter(
-                    Dataset.id == dataset_id
-                ).first()
+                dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
 
-                vector = Vector(
-                    dataset=dataset
-                )
+                vector = Vector(dataset=dataset)
 
                 documents = vector.search_by_vector(
                     cls.escape_query_for_search(query),
-                    search_type='similarity_score_threshold',
+                    search_type="similarity_score_threshold",
                     top_k=top_k,
                     score_threshold=score_threshold,
-                    filter={
-                        'group_id': [dataset.id]
-                    }
+                    filter={"group_id": [dataset.id]},
                 )
 
                 if documents:
-                    if reranking_model and reranking_model.get('reranking_model_name') and reranking_model.get('reranking_provider_name') and retrival_method == RetrievalMethod.SEMANTIC_SEARCH.value:
-                        data_post_processor = DataPostProcessor(str(dataset.tenant_id),
-                                                                RerankMode.RERANKING_MODEL.value,
-                                                                reranking_model, None, False)
-                        all_documents.extend(data_post_processor.invoke(
-                            query=query,
-                            documents=documents,
-                            score_threshold=score_threshold,
-                            top_n=len(documents)
-                        ))
+                    if (
+                        reranking_model
+                        and reranking_model.get("reranking_model_name")
+                        and reranking_model.get("reranking_provider_name")
+                        and retrieval_method == RetrievalMethod.SEMANTIC_SEARCH.value
+                    ):
+                        data_post_processor = DataPostProcessor(
+                            str(dataset.tenant_id), RerankMode.RERANKING_MODEL.value, reranking_model, None, False
+                        )
+                        all_documents.extend(
+                            data_post_processor.invoke(
+                                query=query, documents=documents, score_threshold=score_threshold, top_n=len(documents)
+                            )
+                        )
                     else:
                         all_documents.extend(documents)
             except Exception as e:
                 exceptions.append(str(e))
 
     @classmethod
-    def full_text_index_search(cls, flask_app: Flask, dataset_id: str, query: str,
-                               top_k: int, score_threshold: Optional[float], reranking_model: Optional[dict],
-                               all_documents: list, retrival_method: str, exceptions: list):
+    def full_text_index_search(
+        cls,
+        flask_app: Flask,
+        dataset_id: str,
+        query: str,
+        top_k: int,
+        score_threshold: Optional[float],
+        reranking_model: Optional[dict],
+        all_documents: list,
+        retrieval_method: str,
+        exceptions: list,
+    ):
         with flask_app.app_context():
             try:
-                dataset = db.session.query(Dataset).filter(
-                    Dataset.id == dataset_id
-                ).first()
+                dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
 
                 vector_processor = Vector(
                     dataset=dataset,
                 )
 
-                documents = vector_processor.search_by_full_text(
-                    cls.escape_query_for_search(query),
-                    top_k=top_k
-                )
+                documents = vector_processor.search_by_full_text(cls.escape_query_for_search(query), top_k=top_k)
                 if documents:
-                    if reranking_model and reranking_model.get('reranking_model_name') and reranking_model.get('reranking_provider_name') and retrival_method == RetrievalMethod.FULL_TEXT_SEARCH.value:
-                        data_post_processor = DataPostProcessor(str(dataset.tenant_id),
-                                                                RerankMode.RERANKING_MODEL.value,
-                                                                reranking_model, None, False)
-                        all_documents.extend(data_post_processor.invoke(
-                            query=query,
-                            documents=documents,
-                            score_threshold=score_threshold,
-                            top_n=len(documents)
-                        ))
+                    if (
+                        reranking_model
+                        and reranking_model.get("reranking_model_name")
+                        and reranking_model.get("reranking_provider_name")
+                        and retrieval_method == RetrievalMethod.FULL_TEXT_SEARCH.value
+                    ):
+                        data_post_processor = DataPostProcessor(
+                            str(dataset.tenant_id), RerankMode.RERANKING_MODEL.value, reranking_model, None, False
+                        )
+                        all_documents.extend(
+                            data_post_processor.invoke(
+                                query=query, documents=documents, score_threshold=score_threshold, top_n=len(documents)
+                            )
+                        )
                     else:
                         all_documents.extend(documents)
             except Exception as e:
@@ -209,4 +214,4 @@ class RetrievalService:
 
     @staticmethod
     def escape_query_for_search(query: str) -> str:
-        return query.replace('"', '\\"')
\ No newline at end of file
+        return query.replace('"', '\\"')
diff --git a/api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py b/api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py
index b78e2a59b1..612542dab1 100644
--- a/api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py
+++ b/api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py
@@ -29,6 +29,7 @@ class AnalyticdbConfig(BaseModel):
     namespace_password: str = (None,)
     metrics: str = ("cosine",)
     read_timeout: int = 60000
+
     def to_analyticdb_client_params(self):
         return {
             "access_key_id": self.access_key_id,
@@ -37,6 +38,7 @@ class AnalyticdbConfig(BaseModel):
             "read_timeout": self.read_timeout,
         }
 
+
 class AnalyticdbVector(BaseVector):
     _instance = None
     _init = False
@@ -57,9 +59,7 @@ class AnalyticdbVector(BaseVector):
         except:
             raise ImportError(_import_err_msg)
         self.config = config
-        self._client_config = open_api_models.Config(
-            user_agent="dify", **config.to_analyticdb_client_params()
-        )
+        self._client_config = open_api_models.Config(user_agent="dify", **config.to_analyticdb_client_params())
         self._client = Client(self._client_config)
         self._initialize()
         AnalyticdbVector._init = True
@@ -77,6 +77,7 @@ class AnalyticdbVector(BaseVector):
 
     def _initialize_vector_database(self) -> None:
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
         request = gpdb_20160503_models.InitVectorDatabaseRequest(
             dbinstance_id=self.config.instance_id,
             region_id=self.config.region_id,
@@ -88,6 +89,7 @@ class AnalyticdbVector(BaseVector):
     def _create_namespace_if_not_exists(self) -> None:
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
         from Tea.exceptions import TeaException
+
         try:
             request = gpdb_20160503_models.DescribeNamespaceRequest(
                 dbinstance_id=self.config.instance_id,
@@ -109,13 +111,12 @@ class AnalyticdbVector(BaseVector):
                 )
                 self._client.create_namespace(request)
             else:
-                raise ValueError(
-                    f"failed to create namespace {self.config.namespace}: {e}"
-                )
+                raise ValueError(f"failed to create namespace {self.config.namespace}: {e}")
 
     def _create_collection_if_not_exists(self, embedding_dimension: int):
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
         from Tea.exceptions import TeaException
+
         cache_key = f"vector_indexing_{self._collection_name}"
         lock_name = f"{cache_key}_lock"
         with redis_client.lock(lock_name, timeout=20):
@@ -149,9 +150,7 @@ class AnalyticdbVector(BaseVector):
                     )
                     self._client.create_collection(request)
                 else:
-                    raise ValueError(
-                        f"failed to create collection {self._collection_name}: {e}"
-                    )
+                    raise ValueError(f"failed to create collection {self._collection_name}: {e}")
             redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
     def get_type(self) -> str:
@@ -162,10 +161,9 @@ class AnalyticdbVector(BaseVector):
         self._create_collection_if_not_exists(dimension)
         self.add_texts(texts, embeddings)
 
-    def add_texts(
-        self, documents: list[Document], embeddings: list[list[float]], **kwargs
-    ):
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
         rows: list[gpdb_20160503_models.UpsertCollectionDataRequestRows] = []
         for doc, embedding in zip(documents, embeddings, strict=True):
             metadata = {
@@ -191,6 +189,7 @@ class AnalyticdbVector(BaseVector):
 
     def text_exists(self, id: str) -> bool:
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
         request = gpdb_20160503_models.QueryCollectionDataRequest(
             dbinstance_id=self.config.instance_id,
             region_id=self.config.region_id,
@@ -202,13 +201,14 @@ class AnalyticdbVector(BaseVector):
             vector=None,
             content=None,
             top_k=1,
-            filter=f"ref_doc_id='{id}'"
+            filter=f"ref_doc_id='{id}'",
         )
         response = self._client.query_collection_data(request)
         return len(response.body.matches.match) > 0
 
     def delete_by_ids(self, ids: list[str]) -> None:
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
         ids_str = ",".join(f"'{id}'" for id in ids)
         ids_str = f"({ids_str})"
         request = gpdb_20160503_models.DeleteCollectionDataRequest(
@@ -224,6 +224,7 @@ class AnalyticdbVector(BaseVector):
 
     def delete_by_metadata_field(self, key: str, value: str) -> None:
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
         request = gpdb_20160503_models.DeleteCollectionDataRequest(
             dbinstance_id=self.config.instance_id,
             region_id=self.config.region_id,
@@ -235,15 +236,10 @@ class AnalyticdbVector(BaseVector):
         )
         self._client.delete_collection_data(request)
 
-    def search_by_vector(
-        self, query_vector: list[float], **kwargs: Any
-    ) -> list[Document]:
+    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-        score_threshold = (
-            kwargs.get("score_threshold", 0.0)
-            if kwargs.get("score_threshold", 0.0)
-            else 0.0
-        )
+
+        score_threshold = kwargs.get("score_threshold") or 0.0
         request = gpdb_20160503_models.QueryCollectionDataRequest(
             dbinstance_id=self.config.instance_id,
             region_id=self.config.region_id,
@@ -270,11 +266,8 @@ class AnalyticdbVector(BaseVector):
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-        score_threshold = (
-            kwargs.get("score_threshold", 0.0)
-            if kwargs.get("score_threshold", 0.0)
-            else 0.0
-        )
+
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
         request = gpdb_20160503_models.QueryCollectionDataRequest(
             dbinstance_id=self.config.instance_id,
             region_id=self.config.region_id,
@@ -304,6 +297,7 @@ class AnalyticdbVector(BaseVector):
     def delete(self) -> None:
         try:
             from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
             request = gpdb_20160503_models.DeleteCollectionRequest(
                 collection=self._collection_name,
                 dbinstance_id=self.config.instance_id,
@@ -315,19 +309,16 @@ class AnalyticdbVector(BaseVector):
         except Exception as e:
             raise e
 
+
 class AnalyticdbVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings):
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict["vector_store"][
-                "class_prefix"
-            ]
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix.lower()
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.ANALYTICDB, collection_name)
-            )
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.ANALYTICDB, collection_name))
 
         # handle optional params
         if dify_config.ANALYTICDB_KEY_ID is None:
diff --git a/api/core/rag/datasource/vdb/chroma/chroma_vector.py b/api/core/rag/datasource/vdb/chroma/chroma_vector.py
index 3629887b44..610aa498ab 100644
--- a/api/core/rag/datasource/vdb/chroma/chroma_vector.py
+++ b/api/core/rag/datasource/vdb/chroma/chroma_vector.py
@@ -27,21 +27,20 @@ class ChromaConfig(BaseModel):
         settings = Settings(
             # auth
             chroma_client_auth_provider=self.auth_provider,
-            chroma_client_auth_credentials=self.auth_credentials
+            chroma_client_auth_credentials=self.auth_credentials,
         )
 
         return {
-            'host': self.host,
-            'port': self.port,
-            'ssl': False,
-            'tenant': self.tenant,
-            'database': self.database,
-            'settings': settings,
+            "host": self.host,
+            "port": self.port,
+            "ssl": False,
+            "tenant": self.tenant,
+            "database": self.database,
+            "settings": settings,
         }
 
 
 class ChromaVector(BaseVector):
-
     def __init__(self, collection_name: str, config: ChromaConfig):
         super().__init__(collection_name)
         self._client_config = config
@@ -58,9 +57,9 @@ class ChromaVector(BaseVector):
             self.add_texts(texts, embeddings, **kwargs)
 
     def create_collection(self, collection_name: str):
-        lock_name = 'vector_indexing_lock_{}'.format(collection_name)
+        lock_name = "vector_indexing_lock_{}".format(collection_name)
         with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
             if redis_client.get(collection_exist_cache_key):
                 return
             self._client.get_or_create_collection(collection_name)
@@ -76,7 +75,7 @@ class ChromaVector(BaseVector):
 
     def delete_by_metadata_field(self, key: str, value: str):
         collection = self._client.get_or_create_collection(self._collection_name)
-        collection.delete(where={key: {'$eq': value}})
+        collection.delete(where={key: {"$eq": value}})
 
     def delete(self):
         self._client.delete_collection(self._collection_name)
@@ -93,26 +92,26 @@ class ChromaVector(BaseVector):
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         collection = self._client.get_or_create_collection(self._collection_name)
         results: QueryResult = collection.query(query_embeddings=query_vector, n_results=kwargs.get("top_k", 4))
-        score_threshold = kwargs.get("score_threshold", .0) if kwargs.get('score_threshold', .0) else 0.0
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
 
-        ids: list[str] = results['ids'][0]
-        documents: list[str] = results['documents'][0]
-        metadatas: dict[str, Any] = results['metadatas'][0]
-        distances: list[float] = results['distances'][0]
+        ids: list[str] = results["ids"][0]
+        documents: list[str] = results["documents"][0]
+        metadatas: dict[str, Any] = results["metadatas"][0]
+        distances: list[float] = results["distances"][0]
 
         docs = []
         for index in range(len(ids)):
             distance = distances[index]
             metadata = metadatas[index]
             if distance >= score_threshold:
-                metadata['score'] = distance
+                metadata["score"] = distance
                 doc = Document(
                     page_content=documents[index],
                     metadata=metadata,
                 )
                 docs.append(doc)
-         # Sort the documents by score in descending order
-        docs = sorted(docs, key=lambda x: x.metadata['score'], reverse=True)
+        # Sort the documents by score in descending order
+        docs = sorted(docs, key=lambda x: x.metadata["score"], reverse=True)
         return docs
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
@@ -123,15 +122,12 @@ class ChromaVector(BaseVector):
 class ChromaVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> BaseVector:
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix.lower()
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
-            index_struct_dict = {
-                "type": VectorType.CHROMA,
-                "vector_store": {"class_prefix": collection_name}
-            }
+            index_struct_dict = {"type": VectorType.CHROMA, "vector_store": {"class_prefix": collection_name}}
             dataset.index_struct = json.dumps(index_struct_dict)
 
         return ChromaVector(
diff --git a/api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py b/api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py
index 233539756f..8d57855120 100644
--- a/api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py
+++ b/api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py
@@ -26,15 +26,16 @@ class ElasticSearchConfig(BaseModel):
     username: str
     password: str
 
-    @model_validator(mode='before')
+    @model_validator(mode="before")
+    @classmethod
     def validate_config(cls, values: dict) -> dict:
-        if not values['host']:
+        if not values["host"]:
             raise ValueError("config HOST is required")
-        if not values['port']:
+        if not values["port"]:
             raise ValueError("config PORT is required")
-        if not values['username']:
+        if not values["username"]:
             raise ValueError("config USERNAME is required")
-        if not values['password']:
+        if not values["password"]:
             raise ValueError("config PASSWORD is required")
         return values
 
@@ -50,10 +51,10 @@ class ElasticSearchVector(BaseVector):
     def _init_client(self, config: ElasticSearchConfig) -> Elasticsearch:
         try:
             parsed_url = urlparse(config.host)
-            if parsed_url.scheme in ['http', 'https']:
-                hosts = f'{config.host}:{config.port}'
+            if parsed_url.scheme in {"http", "https"}:
+                hosts = f"{config.host}:{config.port}"
             else:
-                hosts = f'http://{config.host}:{config.port}'
+                hosts = f"http://{config.host}:{config.port}"
             client = Elasticsearch(
                 hosts=hosts,
                 basic_auth=(config.username, config.password),
@@ -68,45 +69,41 @@ class ElasticSearchVector(BaseVector):
 
     def _get_version(self) -> str:
         info = self._client.info()
-        return info['version']['number']
+        return info["version"]["number"]
 
     def _check_version(self):
-        if self._version < '8.0.0':
+        if self._version < "8.0.0":
             raise ValueError("Elasticsearch vector database version must be greater than 8.0.0")
 
     def get_type(self) -> str:
-        return 'elasticsearch'
+        return "elasticsearch"
 
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
         uuids = self._get_uuids(documents)
         for i in range(len(documents)):
-            self._client.index(index=self._collection_name,
-                               id=uuids[i],
-                               document={
-                                   Field.CONTENT_KEY.value: documents[i].page_content,
-                                   Field.VECTOR.value: embeddings[i] if embeddings[i] else None,
-                                   Field.METADATA_KEY.value: documents[i].metadata if documents[i].metadata else {}
-                               })
+            self._client.index(
+                index=self._collection_name,
+                id=uuids[i],
+                document={
+                    Field.CONTENT_KEY.value: documents[i].page_content,
+                    Field.VECTOR.value: embeddings[i] or None,
+                    Field.METADATA_KEY.value: documents[i].metadata or {},
+                },
+            )
         self._client.indices.refresh(index=self._collection_name)
         return uuids
 
     def text_exists(self, id: str) -> bool:
-        return self._client.exists(index=self._collection_name, id=id).__bool__()
+        return bool(self._client.exists(index=self._collection_name, id=id))
 
     def delete_by_ids(self, ids: list[str]) -> None:
         for id in ids:
             self._client.delete(index=self._collection_name, id=id)
 
     def delete_by_metadata_field(self, key: str, value: str) -> None:
-        query_str = {
-            'query': {
-                'match': {
-                    f'metadata.{key}': f'{value}'
-                }
-            }
-        }
+        query_str = {"query": {"match": {f"metadata.{key}": f"{value}"}}}
         results = self._client.search(index=self._collection_name, body=query_str)
-        ids = [hit['_id'] for hit in results['hits']['hits']]
+        ids = [hit["_id"] for hit in results["hits"]["hits"]]
         if ids:
             self.delete_by_ids(ids)
 
@@ -115,44 +112,44 @@ class ElasticSearchVector(BaseVector):
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 10)
-        knn = {
-            "field": Field.VECTOR.value,
-            "query_vector": query_vector,
-            "k": top_k
-        }
+        knn = {"field": Field.VECTOR.value, "query_vector": query_vector, "k": top_k}
 
         results = self._client.search(index=self._collection_name, knn=knn, size=top_k)
 
         docs_and_scores = []
-        for hit in results['hits']['hits']:
+        for hit in results["hits"]["hits"]:
             docs_and_scores.append(
-                (Document(page_content=hit['_source'][Field.CONTENT_KEY.value],
-                          vector=hit['_source'][Field.VECTOR.value],
-                          metadata=hit['_source'][Field.METADATA_KEY.value]), hit['_score']))
+                (
+                    Document(
+                        page_content=hit["_source"][Field.CONTENT_KEY.value],
+                        vector=hit["_source"][Field.VECTOR.value],
+                        metadata=hit["_source"][Field.METADATA_KEY.value],
+                    ),
+                    hit["_score"],
+                )
+            )
 
         docs = []
         for doc, score in docs_and_scores:
-            score_threshold = kwargs.get("score_threshold", .0) if kwargs.get('score_threshold', .0) else 0.0
+            score_threshold = float(kwargs.get("score_threshold") or 0.0)
             if score > score_threshold:
-                doc.metadata['score'] = score
+                doc.metadata["score"] = score
             docs.append(doc)
 
         return docs
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        query_str = {
-            "match": {
-                Field.CONTENT_KEY.value: query
-            }
-        }
+        query_str = {"match": {Field.CONTENT_KEY.value: query}}
         results = self._client.search(index=self._collection_name, query=query_str)
         docs = []
-        for hit in results['hits']['hits']:
-            docs.append(Document(
-                page_content=hit['_source'][Field.CONTENT_KEY.value],
-                vector=hit['_source'][Field.VECTOR.value],
-                metadata=hit['_source'][Field.METADATA_KEY.value],
-            ))
+        for hit in results["hits"]["hits"]:
+            docs.append(
+                Document(
+                    page_content=hit["_source"][Field.CONTENT_KEY.value],
+                    vector=hit["_source"][Field.VECTOR.value],
+                    metadata=hit["_source"][Field.METADATA_KEY.value],
+                )
+            )
 
         return docs
 
@@ -162,11 +159,11 @@ class ElasticSearchVector(BaseVector):
         self.add_texts(texts, embeddings, **kwargs)
 
     def create_collection(
-            self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
+        self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
     ):
-        lock_name = f'vector_indexing_lock_{self._collection_name}'
+        lock_name = f"vector_indexing_lock_{self._collection_name}"
         with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = f'vector_indexing_{self._collection_name}'
+            collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
             if redis_client.get(collection_exist_cache_key):
                 logger.info(f"Collection {self._collection_name} already exists.")
                 return
@@ -179,14 +176,14 @@ class ElasticSearchVector(BaseVector):
                         Field.VECTOR.value: {  # Make sure the dimension is correct here
                             "type": "dense_vector",
                             "dims": dim,
-                            "similarity": "cosine"
+                            "similarity": "cosine",
                         },
                         Field.METADATA_KEY.value: {
                             "type": "object",
                             "properties": {
                                 "doc_id": {"type": "keyword"}  # Map doc_id to keyword type
-                            }
-                        }
+                            },
+                        },
                     }
                 }
                 self._client.indices.create(index=self._collection_name, mappings=mappings)
@@ -197,22 +194,21 @@ class ElasticSearchVector(BaseVector):
 class ElasticSearchVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> ElasticSearchVector:
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.ELASTICSEARCH, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.ELASTICSEARCH, collection_name))
 
         config = current_app.config
         return ElasticSearchVector(
             index_name=collection_name,
             config=ElasticSearchConfig(
-                host=config.get('ELASTICSEARCH_HOST'),
-                port=config.get('ELASTICSEARCH_PORT'),
-                username=config.get('ELASTICSEARCH_USERNAME'),
-                password=config.get('ELASTICSEARCH_PASSWORD'),
+                host=config.get("ELASTICSEARCH_HOST"),
+                port=config.get("ELASTICSEARCH_PORT"),
+                username=config.get("ELASTICSEARCH_USERNAME"),
+                password=config.get("ELASTICSEARCH_PASSWORD"),
             ),
-            attributes=[]
+            attributes=[],
         )
diff --git a/api/core/rag/datasource/vdb/milvus/milvus_vector.py b/api/core/rag/datasource/vdb/milvus/milvus_vector.py
index cfc533ed33..bdca59f869 100644
--- a/api/core/rag/datasource/vdb/milvus/milvus_vector.py
+++ b/api/core/rag/datasource/vdb/milvus/milvus_vector.py
@@ -1,10 +1,9 @@
 import json
 import logging
 from typing import Any, Optional
-from uuid import uuid4
 
 from pydantic import BaseModel, model_validator
-from pymilvus import MilvusClient, MilvusException, connections
+from pymilvus import MilvusClient, MilvusException
 from pymilvus.milvus_client import IndexParams
 
 from configs import dify_config
@@ -21,55 +20,47 @@ logger = logging.getLogger(__name__)
 
 
 class MilvusConfig(BaseModel):
-    host: str
-    port: int
+    uri: str
+    token: Optional[str] = None
     user: str
     password: str
-    secure: bool = False
     batch_size: int = 100
     database: str = "default"
 
-    @model_validator(mode='before')
+    @model_validator(mode="before")
+    @classmethod
     def validate_config(cls, values: dict) -> dict:
-        if not values.get('host'):
-            raise ValueError("config MILVUS_HOST is required")
-        if not values.get('port'):
-            raise ValueError("config MILVUS_PORT is required")
-        if not values.get('user'):
+        if not values.get("uri"):
+            raise ValueError("config MILVUS_URI is required")
+        if not values.get("user"):
             raise ValueError("config MILVUS_USER is required")
-        if not values.get('password'):
+        if not values.get("password"):
             raise ValueError("config MILVUS_PASSWORD is required")
         return values
 
     def to_milvus_params(self):
         return {
-            'host': self.host,
-            'port': self.port,
-            'user': self.user,
-            'password': self.password,
-            'secure': self.secure,
-            'db_name': self.database,
+            "uri": self.uri,
+            "token": self.token,
+            "user": self.user,
+            "password": self.password,
+            "db_name": self.database,
         }
 
 
 class MilvusVector(BaseVector):
-
     def __init__(self, collection_name: str, config: MilvusConfig):
         super().__init__(collection_name)
         self._client_config = config
         self._client = self._init_client(config)
-        self._consistency_level = 'Session'
+        self._consistency_level = "Session"
         self._fields = []
 
     def get_type(self) -> str:
         return VectorType.MILVUS
 
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
-        index_params = {
-            'metric_type': 'IP',
-            'index_type': "HNSW",
-            'params': {"M": 8, "efConstruction": 64}
-        }
+        index_params = {"metric_type": "IP", "index_type": "HNSW", "params": {"M": 8, "efConstruction": 64}}
         metadatas = [d.metadata for d in texts]
         self.create_collection(embeddings, metadatas, index_params)
         self.add_texts(texts, embeddings)
@@ -80,7 +71,7 @@ class MilvusVector(BaseVector):
             insert_dict = {
                 Field.CONTENT_KEY.value: documents[i].page_content,
                 Field.VECTOR.value: embeddings[i],
-                Field.METADATA_KEY.value: documents[i].metadata
+                Field.METADATA_KEY.value: documents[i].metadata,
             }
             insert_dict_list.append(insert_dict)
         # Total insert count
@@ -89,111 +80,70 @@ class MilvusVector(BaseVector):
         pks: list[str] = []
 
         for i in range(0, total_count, 1000):
-            batch_insert_list = insert_dict_list[i:i + 1000]
+            batch_insert_list = insert_dict_list[i : i + 1000]
             # Insert into the collection.
             try:
                 ids = self._client.insert(collection_name=self._collection_name, data=batch_insert_list)
                 pks.extend(ids)
             except MilvusException as e:
-                logger.error(
-                    "Failed to insert batch starting at entity: %s/%s", i, total_count
-                )
+                logger.error("Failed to insert batch starting at entity: %s/%s", i, total_count)
                 raise e
         return pks
 
     def get_ids_by_metadata_field(self, key: str, value: str):
-        result = self._client.query(collection_name=self._collection_name,
-                                    filter=f'metadata["{key}"] == "{value}"',
-                                    output_fields=["id"])
+        result = self._client.query(
+            collection_name=self._collection_name, filter=f'metadata["{key}"] == "{value}"', output_fields=["id"]
+        )
         if result:
             return [item["id"] for item in result]
         else:
             return None
 
     def delete_by_metadata_field(self, key: str, value: str):
-        alias = uuid4().hex
-        if self._client_config.secure:
-            uri = "https://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-        else:
-            uri = "http://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-        connections.connect(alias=alias, uri=uri, user=self._client_config.user, password=self._client_config.password,
-                            db_name=self._client_config.database)
-
-        from pymilvus import utility
-        if utility.has_collection(self._collection_name, using=alias):
-
+        if self._client.has_collection(self._collection_name):
             ids = self.get_ids_by_metadata_field(key, value)
             if ids:
                 self._client.delete(collection_name=self._collection_name, pks=ids)
 
     def delete_by_ids(self, ids: list[str]) -> None:
-        alias = uuid4().hex
-        if self._client_config.secure:
-            uri = "https://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-        else:
-            uri = "http://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-        connections.connect(alias=alias, uri=uri, user=self._client_config.user, password=self._client_config.password,
-                            db_name=self._client_config.database)
-
-        from pymilvus import utility
-        if utility.has_collection(self._collection_name, using=alias):
-
-            result = self._client.query(collection_name=self._collection_name,
-                                        filter=f'metadata["doc_id"] in {ids}',
-                                        output_fields=["id"])
+        if self._client.has_collection(self._collection_name):
+            result = self._client.query(
+                collection_name=self._collection_name, filter=f'metadata["doc_id"] in {ids}', output_fields=["id"]
+            )
             if result:
                 ids = [item["id"] for item in result]
                 self._client.delete(collection_name=self._collection_name, pks=ids)
 
     def delete(self) -> None:
-        alias = uuid4().hex
-        if self._client_config.secure:
-            uri = "https://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-        else:
-            uri = "http://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-        connections.connect(alias=alias, uri=uri, user=self._client_config.user, password=self._client_config.password,
-                            db_name=self._client_config.database)
-
-        from pymilvus import utility
-        if utility.has_collection(self._collection_name, using=alias):
-            utility.drop_collection(self._collection_name, None, using=alias)
+        if self._client.has_collection(self._collection_name):
+            self._client.drop_collection(self._collection_name, None)
 
     def text_exists(self, id: str) -> bool:
-        alias = uuid4().hex
-        if self._client_config.secure:
-            uri = "https://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-        else:
-            uri = "http://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-        connections.connect(alias=alias, uri=uri, user=self._client_config.user, password=self._client_config.password,
-                            db_name=self._client_config.database)
-
-        from pymilvus import utility
-        if not utility.has_collection(self._collection_name, using=alias):
+        if not self._client.has_collection(self._collection_name):
             return False
 
-        result = self._client.query(collection_name=self._collection_name,
-                                    filter=f'metadata["doc_id"] == "{id}"',
-                                    output_fields=["id"])
+        result = self._client.query(
+            collection_name=self._collection_name, filter=f'metadata["doc_id"] == "{id}"', output_fields=["id"]
+        )
 
         return len(result) > 0
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
-
         # Set search parameters.
-        results = self._client.search(collection_name=self._collection_name,
-                                      data=[query_vector],
-                                      limit=kwargs.get('top_k', 4),
-                                      output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
-                                      )
+        results = self._client.search(
+            collection_name=self._collection_name,
+            data=[query_vector],
+            limit=kwargs.get("top_k", 4),
+            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+        )
         # Organize results.
         docs = []
         for result in results[0]:
-            metadata = result['entity'].get(Field.METADATA_KEY.value)
-            metadata['score'] = result['distance']
-            score_threshold = kwargs.get('score_threshold') if kwargs.get('score_threshold') else 0.0
-            if result['distance'] > score_threshold:
-                doc = Document(page_content=result['entity'].get(Field.CONTENT_KEY.value),
-                               metadata=metadata)
+            metadata = result["entity"].get(Field.METADATA_KEY.value)
+            metadata["score"] = result["distance"]
+            score_threshold = float(kwargs.get("score_threshold") or 0.0)
+            if result["distance"] > score_threshold:
+                doc = Document(page_content=result["entity"].get(Field.CONTENT_KEY.value), metadata=metadata)
                 docs.append(doc)
         return docs
 
@@ -202,23 +152,15 @@ class MilvusVector(BaseVector):
         return []
 
     def create_collection(
-            self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
+        self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
     ):
-        lock_name = 'vector_indexing_lock_{}'.format(self._collection_name)
+        lock_name = "vector_indexing_lock_{}".format(self._collection_name)
         with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
             if redis_client.get(collection_exist_cache_key):
                 return
             # Grab the existing collection if it exists
-            from pymilvus import utility
-            alias = uuid4().hex
-            if self._client_config.secure:
-                uri = "https://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-            else:
-                uri = "http://" + str(self._client_config.host) + ":" + str(self._client_config.port)
-            connections.connect(alias=alias, uri=uri, user=self._client_config.user,
-                                password=self._client_config.password, db_name=self._client_config.database)
-            if not utility.has_collection(self._collection_name, using=alias):
+            if not self._client.has_collection(self._collection_name):
                 from pymilvus import CollectionSchema, DataType, FieldSchema
                 from pymilvus.orm.types import infer_dtype_bydata
 
@@ -229,19 +171,11 @@ class MilvusVector(BaseVector):
                     fields.append(FieldSchema(Field.METADATA_KEY.value, DataType.JSON, max_length=65_535))
 
                 # Create the text field
-                fields.append(
-                    FieldSchema(Field.CONTENT_KEY.value, DataType.VARCHAR, max_length=65_535)
-                )
+                fields.append(FieldSchema(Field.CONTENT_KEY.value, DataType.VARCHAR, max_length=65_535))
                 # Create the primary key field
-                fields.append(
-                    FieldSchema(
-                        Field.PRIMARY_KEY.value, DataType.INT64, is_primary=True, auto_id=True
-                    )
-                )
+                fields.append(FieldSchema(Field.PRIMARY_KEY.value, DataType.INT64, is_primary=True, auto_id=True))
                 # Create the vector field, supports binary or float vectors
-                fields.append(
-                    FieldSchema(Field.VECTOR.value, infer_dtype_bydata(embeddings[0]), dim=dim)
-                )
+                fields.append(FieldSchema(Field.VECTOR.value, infer_dtype_bydata(embeddings[0]), dim=dim))
 
                 # Create the schema for the collection
                 schema = CollectionSchema(fields)
@@ -257,39 +191,36 @@ class MilvusVector(BaseVector):
 
                 # Create the collection
                 collection_name = self._collection_name
-                self._client.create_collection(collection_name=collection_name,
-                                               schema=schema, index_params=index_params_obj,
-                                               consistency_level=self._consistency_level)
+                self._client.create_collection(
+                    collection_name=collection_name,
+                    schema=schema,
+                    index_params=index_params_obj,
+                    consistency_level=self._consistency_level,
+                )
             redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
     def _init_client(self, config) -> MilvusClient:
-        if config.secure:
-            uri = "https://" + str(config.host) + ":" + str(config.port)
-        else:
-            uri = "http://" + str(config.host) + ":" + str(config.port)
-        client = MilvusClient(uri=uri, user=config.user, password=config.password, db_name=config.database)
+        client = MilvusClient(uri=config.uri, user=config.user, password=config.password, db_name=config.database)
         return client
 
 
 class MilvusVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> MilvusVector:
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.MILVUS, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.MILVUS, collection_name))
 
         return MilvusVector(
             collection_name=collection_name,
             config=MilvusConfig(
-                host=dify_config.MILVUS_HOST,
-                port=dify_config.MILVUS_PORT,
+                uri=dify_config.MILVUS_URI,
+                token=dify_config.MILVUS_TOKEN,
                 user=dify_config.MILVUS_USER,
                 password=dify_config.MILVUS_PASSWORD,
-                secure=dify_config.MILVUS_SECURE,
                 database=dify_config.MILVUS_DATABASE,
-            )
+            ),
         )
diff --git a/api/core/rag/datasource/vdb/myscale/myscale_vector.py b/api/core/rag/datasource/vdb/myscale/myscale_vector.py
index 05e75effef..2320a69a30 100644
--- a/api/core/rag/datasource/vdb/myscale/myscale_vector.py
+++ b/api/core/rag/datasource/vdb/myscale/myscale_vector.py
@@ -31,12 +31,11 @@ class SortOrder(Enum):
 
 
 class MyScaleVector(BaseVector):
-
     def __init__(self, collection_name: str, config: MyScaleConfig, metric: str = "Cosine"):
         super().__init__(collection_name)
         self._config = config
         self._metric = metric
-        self._vec_order = SortOrder.ASC if metric.upper() in ["COSINE", "L2"] else SortOrder.DESC
+        self._vec_order = SortOrder.ASC if metric.upper() in {"COSINE", "L2"} else SortOrder.DESC
         self._client = get_client(
             host=config.host,
             port=config.port,
@@ -80,7 +79,7 @@ class MyScaleVector(BaseVector):
                 doc_id,
                 self.escape_str(doc.page_content),
                 embeddings[i],
-                json.dumps(doc.metadata) if doc.metadata else {}
+                json.dumps(doc.metadata) if doc.metadata else {},
             )
             values.append(str(row))
             ids.append(doc_id)
@@ -93,7 +92,7 @@ class MyScaleVector(BaseVector):
 
     @staticmethod
     def escape_str(value: Any) -> str:
-        return "".join(" " if c in ("\\", "'") else c for c in str(value))
+        return "".join(" " if c in {"\\", "'"} else c for c in str(value))
 
     def text_exists(self, id: str) -> bool:
         results = self._client.query(f"SELECT id FROM {self._config.database}.{self._collection_name} WHERE id='{id}'")
@@ -101,7 +100,8 @@ class MyScaleVector(BaseVector):
 
     def delete_by_ids(self, ids: list[str]) -> None:
         self._client.command(
-            f"DELETE FROM {self._config.database}.{self._collection_name} WHERE id IN {str(tuple(ids))}")
+            f"DELETE FROM {self._config.database}.{self._collection_name} WHERE id IN {str(tuple(ids))}"
+        )
 
     def get_ids_by_metadata_field(self, key: str, value: str):
         rows = self._client.query(
@@ -122,9 +122,12 @@ class MyScaleVector(BaseVector):
 
     def _search(self, dist: str, order: SortOrder, **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 5)
-        score_threshold = kwargs.get('score_threshold') or 0.0
-        where_str = f"WHERE dist < {1 - score_threshold}" if \
-            self._metric.upper() == "COSINE" and order == SortOrder.ASC and score_threshold > 0.0 else ""
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
+        where_str = (
+            f"WHERE dist < {1 - score_threshold}"
+            if self._metric.upper() == "COSINE" and order == SortOrder.ASC and score_threshold > 0.0
+            else ""
+        )
         sql = f"""
             SELECT text, vector, metadata, {dist} as dist FROM {self._config.database}.{self._collection_name}
             {where_str} ORDER BY dist {order.value} LIMIT {top_k}
@@ -133,7 +136,7 @@ class MyScaleVector(BaseVector):
             return [
                 Document(
                     page_content=r["text"],
-                    vector=r['vector'],
+                    vector=r["vector"],
                     metadata=r["metadata"],
                 )
                 for r in self._client.query(sql).named_results()
@@ -149,13 +152,12 @@ class MyScaleVector(BaseVector):
 class MyScaleVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> MyScaleVector:
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix.lower()
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.MYSCALE, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.MYSCALE, collection_name))
 
         return MyScaleVector(
             collection_name=collection_name,
diff --git a/api/core/rag/datasource/vdb/opensearch/opensearch_vector.py b/api/core/rag/datasource/vdb/opensearch/opensearch_vector.py
index c95d202173..8d2e0a86ab 100644
--- a/api/core/rag/datasource/vdb/opensearch/opensearch_vector.py
+++ b/api/core/rag/datasource/vdb/opensearch/opensearch_vector.py
@@ -28,11 +28,12 @@ class OpenSearchConfig(BaseModel):
     password: Optional[str] = None
     secure: bool = False
 
-    @model_validator(mode='before')
+    @model_validator(mode="before")
+    @classmethod
     def validate_config(cls, values: dict) -> dict:
-        if not values.get('host'):
+        if not values.get("host"):
             raise ValueError("config OPENSEARCH_HOST is required")
-        if not values.get('port'):
+        if not values.get("port"):
             raise ValueError("config OPENSEARCH_PORT is required")
         return values
 
@@ -44,19 +45,18 @@ class OpenSearchConfig(BaseModel):
 
     def to_opensearch_params(self) -> dict[str, Any]:
         params = {
-            'hosts': [{'host': self.host, 'port': self.port}],
-            'use_ssl': self.secure,
-            'verify_certs': self.secure,
+            "hosts": [{"host": self.host, "port": self.port}],
+            "use_ssl": self.secure,
+            "verify_certs": self.secure,
         }
         if self.user and self.password:
-            params['http_auth'] = (self.user, self.password)
+            params["http_auth"] = (self.user, self.password)
         if self.secure:
-            params['ssl_context'] = self.create_ssl_context()
+            params["ssl_context"] = self.create_ssl_context()
         return params
 
 
 class OpenSearchVector(BaseVector):
-
     def __init__(self, collection_name: str, config: OpenSearchConfig):
         super().__init__(collection_name)
         self._client_config = config
@@ -81,7 +81,7 @@ class OpenSearchVector(BaseVector):
                     Field.CONTENT_KEY.value: documents[i].page_content,
                     Field.VECTOR.value: embeddings[i],  # Make sure you pass an array here
                     Field.METADATA_KEY.value: documents[i].metadata,
-                }
+                },
             }
             actions.append(action)
 
@@ -90,8 +90,8 @@ class OpenSearchVector(BaseVector):
     def get_ids_by_metadata_field(self, key: str, value: str):
         query = {"query": {"term": {f"{Field.METADATA_KEY.value}.{key}": value}}}
         response = self._client.search(index=self._collection_name.lower(), body=query)
-        if response['hits']['hits']:
-            return [hit['_id'] for hit in response['hits']['hits']]
+        if response["hits"]["hits"]:
+            return [hit["_id"] for hit in response["hits"]["hits"]]
         else:
             return None
 
@@ -110,7 +110,7 @@ class OpenSearchVector(BaseVector):
         actual_ids = []
 
         for doc_id in ids:
-            es_ids = self.get_ids_by_metadata_field('doc_id', doc_id)
+            es_ids = self.get_ids_by_metadata_field("doc_id", doc_id)
             if es_ids:
                 actual_ids.extend(es_ids)
             else:
@@ -122,9 +122,9 @@ class OpenSearchVector(BaseVector):
                 helpers.bulk(self._client, actions)
             except BulkIndexError as e:
                 for error in e.errors:
-                    delete_error = error.get('delete', {})
-                    status = delete_error.get('status')
-                    doc_id = delete_error.get('_id')
+                    delete_error = error.get("delete", {})
+                    status = delete_error.get("status")
+                    doc_id = delete_error.get("_id")
 
                     if status == 404:
                         logger.warning(f"Document not found for deletion: {doc_id}")
@@ -151,15 +151,8 @@ class OpenSearchVector(BaseVector):
             raise ValueError("All elements in query_vector should be floats")
 
         query = {
-            "size": kwargs.get('top_k', 4),
-            "query": {
-                "knn": {
-                    Field.VECTOR.value: {
-                        Field.VECTOR.value: query_vector,
-                        "k": kwargs.get('top_k', 4)
-                    }
-                }
-            }
+            "size": kwargs.get("top_k", 4),
+            "query": {"knn": {Field.VECTOR.value: {Field.VECTOR.value: query_vector, "k": kwargs.get("top_k", 4)}}},
         }
 
         try:
@@ -169,17 +162,17 @@ class OpenSearchVector(BaseVector):
             raise
 
         docs = []
-        for hit in response['hits']['hits']:
-            metadata = hit['_source'].get(Field.METADATA_KEY.value, {})
+        for hit in response["hits"]["hits"]:
+            metadata = hit["_source"].get(Field.METADATA_KEY.value, {})
 
             # Make sure metadata is a dictionary
             if metadata is None:
                 metadata = {}
 
-            metadata['score'] = hit['_score']
-            score_threshold = kwargs.get('score_threshold') if kwargs.get('score_threshold') else 0.0
-            if hit['_score'] > score_threshold:
-                doc = Document(page_content=hit['_source'].get(Field.CONTENT_KEY.value), metadata=metadata)
+            metadata["score"] = hit["_score"]
+            score_threshold = float(kwargs.get("score_threshold") or 0.0)
+            if hit["_score"] > score_threshold:
+                doc = Document(page_content=hit["_source"].get(Field.CONTENT_KEY.value), metadata=metadata)
                 docs.append(doc)
 
         return docs
@@ -190,32 +183,28 @@ class OpenSearchVector(BaseVector):
         response = self._client.search(index=self._collection_name.lower(), body=full_text_query)
 
         docs = []
-        for hit in response['hits']['hits']:
-            metadata = hit['_source'].get(Field.METADATA_KEY.value)
-            vector = hit['_source'].get(Field.VECTOR.value)
-            page_content = hit['_source'].get(Field.CONTENT_KEY.value)
+        for hit in response["hits"]["hits"]:
+            metadata = hit["_source"].get(Field.METADATA_KEY.value)
+            vector = hit["_source"].get(Field.VECTOR.value)
+            page_content = hit["_source"].get(Field.CONTENT_KEY.value)
             doc = Document(page_content=page_content, vector=vector, metadata=metadata)
             docs.append(doc)
 
         return docs
 
     def create_collection(
-            self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
+        self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
     ):
-        lock_name = f'vector_indexing_lock_{self._collection_name.lower()}'
+        lock_name = f"vector_indexing_lock_{self._collection_name.lower()}"
         with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = f'vector_indexing_{self._collection_name.lower()}'
+            collection_exist_cache_key = f"vector_indexing_{self._collection_name.lower()}"
             if redis_client.get(collection_exist_cache_key):
                 logger.info(f"Collection {self._collection_name.lower()} already exists.")
                 return
 
             if not self._client.indices.exists(index=self._collection_name.lower()):
                 index_body = {
-                    "settings": {
-                        "index": {
-                            "knn": True
-                        }
-                    },
+                    "settings": {"index": {"knn": True}},
                     "mappings": {
                         "properties": {
                             Field.CONTENT_KEY.value: {"type": "text"},
@@ -226,20 +215,17 @@ class OpenSearchVector(BaseVector):
                                     "name": "hnsw",
                                     "space_type": "l2",
                                     "engine": "faiss",
-                                    "parameters": {
-                                        "ef_construction": 64,
-                                        "m": 8
-                                    }
-                                }
+                                    "parameters": {"ef_construction": 64, "m": 8},
+                                },
                             },
                             Field.METADATA_KEY.value: {
                                 "type": "object",
                                 "properties": {
                                     "doc_id": {"type": "keyword"}  # Map doc_id to keyword type
-                                }
-                            }
+                                },
+                            },
                         }
-                    }
+                    },
                 }
 
                 self._client.indices.create(index=self._collection_name.lower(), body=index_body)
@@ -248,17 +234,14 @@ class OpenSearchVector(BaseVector):
 
 
 class OpenSearchVectorFactory(AbstractVectorFactory):
-
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> OpenSearchVector:
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix.lower()
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.OPENSEARCH, collection_name))
-
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.OPENSEARCH, collection_name))
 
         open_search_config = OpenSearchConfig(
             host=dify_config.OPENSEARCH_HOST,
@@ -268,7 +251,4 @@ class OpenSearchVectorFactory(AbstractVectorFactory):
             secure=dify_config.OPENSEARCH_SECURE,
         )
 
-        return OpenSearchVector(
-            collection_name=collection_name,
-            config=open_search_config
-        )
+        return OpenSearchVector(collection_name=collection_name, config=open_search_config)
diff --git a/api/core/rag/datasource/vdb/oracle/oraclevector.py b/api/core/rag/datasource/vdb/oracle/oraclevector.py
index aa2c6171c3..77ec45b4d3 100644
--- a/api/core/rag/datasource/vdb/oracle/oraclevector.py
+++ b/api/core/rag/datasource/vdb/oracle/oraclevector.py
@@ -31,7 +31,8 @@ class OracleVectorConfig(BaseModel):
     password: str
     database: str
 
-    @model_validator(mode='before')
+    @model_validator(mode="before")
+    @classmethod
     def validate_config(cls, values: dict) -> dict:
         if not values["host"]:
             raise ValueError("config ORACLE_HOST is required")
@@ -103,9 +104,16 @@ class OracleVector(BaseVector):
                 arraysize=cursor.arraysize,
                 outconverter=self.numpy_converter_out,
             )
-    def _create_connection_pool(self, config: OracleVectorConfig):
-        return oracledb.create_pool(user=config.user, password=config.password, dsn="{}:{}/{}".format(config.host, config.port, config.database), min=1, max=50, increment=1)
 
+    def _create_connection_pool(self, config: OracleVectorConfig):
+        return oracledb.create_pool(
+            user=config.user,
+            password=config.password,
+            dsn="{}:{}/{}".format(config.host, config.port, config.database),
+            min=1,
+            max=50,
+            increment=1,
+        )
 
     @contextmanager
     def _get_cursor(self):
@@ -136,13 +144,15 @@ class OracleVector(BaseVector):
                     doc_id,
                     doc.page_content,
                     json.dumps(doc.metadata),
-                    #array.array("f", embeddings[i]),
+                    # array.array("f", embeddings[i]),
                     numpy.array(embeddings[i]),
                 )
             )
-        #print(f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)")
+        # print(f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)")
         with self._get_cursor() as cur:
-            cur.executemany(f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)", values)
+            cur.executemany(
+                f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)", values
+            )
         return pks
 
     def text_exists(self, id: str) -> bool:
@@ -157,7 +167,8 @@ class OracleVector(BaseVector):
             for record in cur:
                 docs.append(Document(page_content=record[1], metadata=record[0]))
         return docs
-    #def get_ids_by_metadata_field(self, key: str, value: str):
+
+    # def get_ids_by_metadata_field(self, key: str, value: str):
     #    with self._get_cursor() as cur:
     #        cur.execute(f"SELECT id FROM {self.table_name} d WHERE d.meta.{key}='{value}'" )
     #        idss = []
@@ -184,10 +195,12 @@ class OracleVector(BaseVector):
         top_k = kwargs.get("top_k", 5)
         with self._get_cursor() as cur:
             cur.execute(
-                f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name} ORDER BY distance fetch first {top_k} rows only" ,[numpy.array(query_vector)]
+                f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name}"
+                f" ORDER BY distance fetch first {top_k} rows only",
+                [numpy.array(query_vector)],
             )
             docs = []
-            score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+            score_threshold = float(kwargs.get("score_threshold") or 0.0)
             for record in cur:
                 metadata, text, distance = record
                 score = 1 - distance
@@ -199,10 +212,10 @@ class OracleVector(BaseVector):
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 5)
         # just not implement fetch by score_threshold now, may be later
-        score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
         if len(query) > 0:
             # Check which language the query is in
-            zh_pattern = re.compile('[\u4e00-\u9fa5]+')
+            zh_pattern = re.compile("[\u4e00-\u9fa5]+")
             match = zh_pattern.search(query)
             entities = []
             #  match: query condition maybe is a chinese sentence, so using Jieba split,else using nltk split
@@ -210,7 +223,7 @@ class OracleVector(BaseVector):
                 words = pseg.cut(query)
                 current_entity = ""
                 for word, pos in words:
-                    if pos == 'nr' or pos == 'Ng' or pos == 'eng' or pos == 'nz' or pos == 'n' or pos == 'ORG' or pos == 'v':  # nr: 人名, ns: 地名, nt: 机构名
+                    if pos in {"nr", "Ng", "eng", "nz", "n", "ORG", "v"}:  # nr: 人名, ns: 地名, nt: 机构名
                         current_entity += word
                     else:
                         if current_entity:
@@ -220,22 +233,23 @@ class OracleVector(BaseVector):
                     entities.append(current_entity)
             else:
                 try:
-                    nltk.data.find('tokenizers/punkt')
-                    nltk.data.find('corpora/stopwords')
+                    nltk.data.find("tokenizers/punkt")
+                    nltk.data.find("corpora/stopwords")
                 except LookupError:
-                    nltk.download('punkt')
-                    nltk.download('stopwords')
+                    nltk.download("punkt")
+                    nltk.download("stopwords")
                     print("run download")
-                e_str = re.sub(r'[^\w ]', '', query)
+                e_str = re.sub(r"[^\w ]", "", query)
                 all_tokens = nltk.word_tokenize(e_str)
-                stop_words = stopwords.words('english')
+                stop_words = stopwords.words("english")
                 for token in all_tokens:
                     if token not in stop_words:
                         entities.append(token)
             with self._get_cursor() as cur:
                 cur.execute(
-                    f"select meta, text, embedding FROM {self.table_name} WHERE CONTAINS(text, :1, 1) > 0 order by score(1) desc fetch first {top_k} rows only",
-                    [" ACCUM ".join(entities)]
+                    f"select meta, text, embedding FROM {self.table_name}"
+                    f" WHERE CONTAINS(text, :1, 1) > 0 order by score(1) desc fetch first {top_k} rows only",
+                    [" ACCUM ".join(entities)],
                 )
                 docs = []
                 for record in cur:
@@ -273,8 +287,7 @@ class OracleVectorFactory(AbstractVectorFactory):
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.ORACLE, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.ORACLE, collection_name))
 
         return OracleVector(
             collection_name=collection_name,
diff --git a/api/core/rag/datasource/vdb/pgvecto_rs/pgvecto_rs.py b/api/core/rag/datasource/vdb/pgvecto_rs/pgvecto_rs.py
index a48224070f..de2d65b223 100644
--- a/api/core/rag/datasource/vdb/pgvecto_rs/pgvecto_rs.py
+++ b/api/core/rag/datasource/vdb/pgvecto_rs/pgvecto_rs.py
@@ -31,27 +31,29 @@ class PgvectoRSConfig(BaseModel):
     password: str
     database: str
 
-    @model_validator(mode='before')
+    @model_validator(mode="before")
+    @classmethod
     def validate_config(cls, values: dict) -> dict:
-        if not values['host']:
+        if not values["host"]:
             raise ValueError("config PGVECTO_RS_HOST is required")
-        if not values['port']:
+        if not values["port"]:
             raise ValueError("config PGVECTO_RS_PORT is required")
-        if not values['user']:
+        if not values["user"]:
             raise ValueError("config PGVECTO_RS_USER is required")
-        if not values['password']:
+        if not values["password"]:
             raise ValueError("config PGVECTO_RS_PASSWORD is required")
-        if not values['database']:
+        if not values["database"]:
             raise ValueError("config PGVECTO_RS_DATABASE is required")
         return values
 
 
 class PGVectoRS(BaseVector):
-
     def __init__(self, collection_name: str, config: PgvectoRSConfig, dim: int):
         super().__init__(collection_name)
         self._client_config = config
-        self._url = f"postgresql+psycopg2://{config.user}:{config.password}@{config.host}:{config.port}/{config.database}"
+        self._url = (
+            f"postgresql+psycopg2://{config.user}:{config.password}@{config.host}:{config.port}/{config.database}"
+        )
         self._client = create_engine(self._url)
         with Session(self._client) as session:
             session.execute(text("CREATE EXTENSION IF NOT EXISTS vectors"))
@@ -80,9 +82,9 @@ class PGVectoRS(BaseVector):
         self.add_texts(texts, embeddings)
 
     def create_collection(self, dimension: int):
-        lock_name = 'vector_indexing_lock_{}'.format(self._collection_name)
+        lock_name = "vector_indexing_lock_{}".format(self._collection_name)
         with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
             if redis_client.get(collection_exist_cache_key):
                 return
             index_name = f"{self._collection_name}_embedding_index"
@@ -133,9 +135,7 @@ class PGVectoRS(BaseVector):
     def get_ids_by_metadata_field(self, key: str, value: str):
         result = None
         with Session(self._client) as session:
-            select_statement = sql_text(
-                f"SELECT id FROM {self._collection_name} WHERE meta->>'{key}' = '{value}'; "
-            )
+            select_statement = sql_text(f"SELECT id FROM {self._collection_name} WHERE meta->>'{key}' = '{value}'; ")
             result = session.execute(select_statement).fetchall()
         if result:
             return [item[0] for item in result]
@@ -143,12 +143,11 @@ class PGVectoRS(BaseVector):
             return None
 
     def delete_by_metadata_field(self, key: str, value: str):
-
         ids = self.get_ids_by_metadata_field(key, value)
         if ids:
             with Session(self._client) as session:
                 select_statement = sql_text(f"DELETE FROM {self._collection_name} WHERE id = ANY(:ids)")
-                session.execute(select_statement, {'ids': ids})
+                session.execute(select_statement, {"ids": ids})
                 session.commit()
 
     def delete_by_ids(self, ids: list[str]) -> None:
@@ -156,13 +155,13 @@ class PGVectoRS(BaseVector):
             select_statement = sql_text(
                 f"SELECT id FROM {self._collection_name} WHERE meta->>'doc_id' = ANY (:doc_ids); "
             )
-            result = session.execute(select_statement, {'doc_ids': ids}).fetchall()
+            result = session.execute(select_statement, {"doc_ids": ids}).fetchall()
         if result:
             ids = [item[0] for item in result]
             if ids:
                 with Session(self._client) as session:
                     select_statement = sql_text(f"DELETE FROM {self._collection_name} WHERE id = ANY(:ids)")
-                    session.execute(select_statement, {'ids': ids})
+                    session.execute(select_statement, {"ids": ids})
                     session.commit()
 
     def delete(self) -> None:
@@ -187,7 +186,7 @@ class PGVectoRS(BaseVector):
                         query_vector,
                     ).label("distance"),
                 )
-                .limit(kwargs.get('top_k', 2))
+                .limit(kwargs.get("top_k", 2))
                 .order_by("distance")
             )
             res = session.execute(stmt)
@@ -198,11 +197,10 @@ class PGVectoRS(BaseVector):
         for record, dis in results:
             metadata = record.meta
             score = 1 - dis
-            metadata['score'] = score
-            score_threshold = kwargs.get('score_threshold') if kwargs.get('score_threshold') else 0.0
+            metadata["score"] = score
+            score_threshold = float(kwargs.get("score_threshold") or 0.0)
             if score > score_threshold:
-                doc = Document(page_content=record.text,
-                               metadata=metadata)
+                doc = Document(page_content=record.text, metadata=metadata)
                 docs.append(doc)
         return docs
 
@@ -225,13 +223,12 @@ class PGVectoRS(BaseVector):
 class PGVectoRSFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> PGVectoRS:
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix.lower()
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.WEAVIATE, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.WEAVIATE, collection_name))
         dim = len(embeddings.embed_query("pgvecto_rs"))
 
         return PGVectoRS(
@@ -243,5 +240,5 @@ class PGVectoRSFactory(AbstractVectorFactory):
                 password=dify_config.PGVECTO_RS_PASSWORD,
                 database=dify_config.PGVECTO_RS_DATABASE,
             ),
-            dim=dim
+            dim=dim,
         )
diff --git a/api/core/rag/datasource/vdb/pgvector/pgvector.py b/api/core/rag/datasource/vdb/pgvector/pgvector.py
index c9f2f35af0..79879d4f63 100644
--- a/api/core/rag/datasource/vdb/pgvector/pgvector.py
+++ b/api/core/rag/datasource/vdb/pgvector/pgvector.py
@@ -24,7 +24,8 @@ class PGVectorConfig(BaseModel):
     password: str
     database: str
 
-    @model_validator(mode='before')
+    @model_validator(mode="before")
+    @classmethod
     def validate_config(cls, values: dict) -> dict:
         if not values["host"]:
             raise ValueError("config PGVECTOR_HOST is required")
@@ -138,11 +139,12 @@ class PGVector(BaseVector):
 
         with self._get_cursor() as cur:
             cur.execute(
-                f"SELECT meta, text, embedding <=> %s AS distance FROM {self.table_name} ORDER BY distance LIMIT {top_k}",
+                f"SELECT meta, text, embedding <=> %s AS distance FROM {self.table_name}"
+                f" ORDER BY distance LIMIT {top_k}",
                 (json.dumps(query_vector),),
             )
             docs = []
-            score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
+            score_threshold = float(kwargs.get("score_threshold") or 0.0)
             for record in cur:
                 metadata, text, distance = record
                 score = 1 - distance
@@ -201,8 +203,7 @@ class PGVectorFactory(AbstractVectorFactory):
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.PGVECTOR, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.PGVECTOR, collection_name))
 
         return PGVector(
             collection_name=collection_name,
diff --git a/api/core/rag/datasource/vdb/qdrant/qdrant_vector.py b/api/core/rag/datasource/vdb/qdrant/qdrant_vector.py
index 297bff928e..f418e3ca05 100644
--- a/api/core/rag/datasource/vdb/qdrant/qdrant_vector.py
+++ b/api/core/rag/datasource/vdb/qdrant/qdrant_vector.py
@@ -48,28 +48,25 @@ class QdrantConfig(BaseModel):
     prefer_grpc: bool = False
 
     def to_qdrant_params(self):
-        if self.endpoint and self.endpoint.startswith('path:'):
-            path = self.endpoint.replace('path:', '')
+        if self.endpoint and self.endpoint.startswith("path:"):
+            path = self.endpoint.replace("path:", "")
             if not os.path.isabs(path):
                 path = os.path.join(self.root_path, path)
 
-            return {
-                'path': path
-            }
+            return {"path": path}
         else:
             return {
-                'url': self.endpoint,
-                'api_key': self.api_key,
-                'timeout': self.timeout,
-                'verify': self.endpoint.startswith('https'),
-                'grpc_port': self.grpc_port,
-                'prefer_grpc': self.prefer_grpc
+                "url": self.endpoint,
+                "api_key": self.api_key,
+                "timeout": self.timeout,
+                "verify": self.endpoint.startswith("https"),
+                "grpc_port": self.grpc_port,
+                "prefer_grpc": self.prefer_grpc,
             }
 
 
 class QdrantVector(BaseVector):
-
-    def __init__(self, collection_name: str, group_id: str, config: QdrantConfig, distance_func: str = 'Cosine'):
+    def __init__(self, collection_name: str, group_id: str, config: QdrantConfig, distance_func: str = "Cosine"):
         super().__init__(collection_name)
         self._client_config = config
         self._client = qdrant_client.QdrantClient(**self._client_config.to_qdrant_params())
@@ -80,10 +77,7 @@ class QdrantVector(BaseVector):
         return VectorType.QDRANT
 
     def to_index_struct(self) -> dict:
-        return {
-            "type": self.get_type(),
-            "vector_store": {"class_prefix": self._collection_name}
-        }
+        return {"type": self.get_type(), "vector_store": {"class_prefix": self._collection_name}}
 
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
         if texts:
@@ -97,9 +91,9 @@ class QdrantVector(BaseVector):
             self.add_texts(texts, embeddings, **kwargs)
 
     def create_collection(self, collection_name: str, vector_size: int):
-        lock_name = 'vector_indexing_lock_{}'.format(collection_name)
+        lock_name = "vector_indexing_lock_{}".format(collection_name)
         with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
             if redis_client.get(collection_exist_cache_key):
                 return
             collection_name = collection_name or uuid.uuid4().hex
@@ -110,12 +104,19 @@ class QdrantVector(BaseVector):
                 all_collection_name.append(collection.name)
             if collection_name not in all_collection_name:
                 from qdrant_client.http import models as rest
+
                 vectors_config = rest.VectorParams(
                     size=vector_size,
                     distance=rest.Distance[self._distance_func],
                 )
-                hnsw_config = HnswConfigDiff(m=0, payload_m=16, ef_construct=100, full_scan_threshold=10000,
-                                             max_indexing_threads=0, on_disk=False)
+                hnsw_config = HnswConfigDiff(
+                    m=0,
+                    payload_m=16,
+                    ef_construct=100,
+                    full_scan_threshold=10000,
+                    max_indexing_threads=0,
+                    on_disk=False,
+                )
                 self._client.recreate_collection(
                     collection_name=collection_name,
                     vectors_config=vectors_config,
@@ -124,21 +125,24 @@ class QdrantVector(BaseVector):
                 )
 
                 # create group_id payload index
-                self._client.create_payload_index(collection_name, Field.GROUP_KEY.value,
-                                                  field_schema=PayloadSchemaType.KEYWORD)
+                self._client.create_payload_index(
+                    collection_name, Field.GROUP_KEY.value, field_schema=PayloadSchemaType.KEYWORD
+                )
                 # create doc_id payload index
-                self._client.create_payload_index(collection_name, Field.DOC_ID.value,
-                                                  field_schema=PayloadSchemaType.KEYWORD)
+                self._client.create_payload_index(
+                    collection_name, Field.DOC_ID.value, field_schema=PayloadSchemaType.KEYWORD
+                )
                 # create full text index
                 text_index_params = TextIndexParams(
                     type=TextIndexType.TEXT,
                     tokenizer=TokenizerType.MULTILINGUAL,
                     min_token_len=2,
                     max_token_len=20,
-                    lowercase=True
+                    lowercase=True,
+                )
+                self._client.create_payload_index(
+                    collection_name, Field.CONTENT_KEY.value, field_schema=text_index_params
                 )
-                self._client.create_payload_index(collection_name, Field.CONTENT_KEY.value,
-                                                  field_schema=text_index_params)
             redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
@@ -147,26 +151,23 @@ class QdrantVector(BaseVector):
         metadatas = [d.metadata for d in documents]
 
         added_ids = []
-        for batch_ids, points in self._generate_rest_batches(
-                texts, embeddings, metadatas, uuids, 64, self._group_id
-        ):
-            self._client.upsert(
-                collection_name=self._collection_name, points=points
-            )
+        for batch_ids, points in self._generate_rest_batches(texts, embeddings, metadatas, uuids, 64, self._group_id):
+            self._client.upsert(collection_name=self._collection_name, points=points)
             added_ids.extend(batch_ids)
 
         return added_ids
 
     def _generate_rest_batches(
-            self,
-            texts: Iterable[str],
-            embeddings: list[list[float]],
-            metadatas: Optional[list[dict]] = None,
-            ids: Optional[Sequence[str]] = None,
-            batch_size: int = 64,
-            group_id: Optional[str] = None,
+        self,
+        texts: Iterable[str],
+        embeddings: list[list[float]],
+        metadatas: Optional[list[dict]] = None,
+        ids: Optional[Sequence[str]] = None,
+        batch_size: int = 64,
+        group_id: Optional[str] = None,
     ) -> Generator[tuple[list[str], list[rest.PointStruct]], None, None]:
         from qdrant_client.http import models as rest
+
         texts_iterator = iter(texts)
         embeddings_iterator = iter(embeddings)
         metadatas_iterator = iter(metadatas or [])
@@ -203,13 +204,13 @@ class QdrantVector(BaseVector):
 
     @classmethod
     def _build_payloads(
-            cls,
-            texts: Iterable[str],
-            metadatas: Optional[list[dict]],
-            content_payload_key: str,
-            metadata_payload_key: str,
-            group_id: str,
-            group_payload_key: str
+        cls,
+        texts: Iterable[str],
+        metadatas: Optional[list[dict]],
+        content_payload_key: str,
+        metadata_payload_key: str,
+        group_id: str,
+        group_payload_key: str,
     ) -> list[dict]:
         payloads = []
         for i, text in enumerate(texts):
@@ -219,18 +220,11 @@ class QdrantVector(BaseVector):
                     "calling .from_texts or .add_texts on Qdrant instance."
                 )
             metadata = metadatas[i] if metadatas is not None else None
-            payloads.append(
-                {
-                    content_payload_key: text,
-                    metadata_payload_key: metadata,
-                    group_payload_key: group_id
-                }
-            )
+            payloads.append({content_payload_key: text, metadata_payload_key: metadata, group_payload_key: group_id})
 
         return payloads
 
     def delete_by_metadata_field(self, key: str, value: str):
-
         from qdrant_client.http import models
         from qdrant_client.http.exceptions import UnexpectedResponse
 
@@ -248,9 +242,7 @@ class QdrantVector(BaseVector):
 
             self._client.delete(
                 collection_name=self._collection_name,
-                points_selector=FilterSelector(
-                    filter=filter
-                ),
+                points_selector=FilterSelector(filter=filter),
             )
         except UnexpectedResponse as e:
             # Collection does not exist, so return
@@ -275,9 +267,7 @@ class QdrantVector(BaseVector):
             )
             self._client.delete(
                 collection_name=self._collection_name,
-                points_selector=FilterSelector(
-                    filter=filter
-                ),
+                points_selector=FilterSelector(filter=filter),
             )
         except UnexpectedResponse as e:
             # Collection does not exist, so return
@@ -288,7 +278,6 @@ class QdrantVector(BaseVector):
                 raise e
 
     def delete_by_ids(self, ids: list[str]) -> None:
-
         from qdrant_client.http import models
         from qdrant_client.http.exceptions import UnexpectedResponse
 
@@ -304,9 +293,7 @@ class QdrantVector(BaseVector):
                 )
                 self._client.delete(
                     collection_name=self._collection_name,
-                    points_selector=FilterSelector(
-                        filter=filter
-                    ),
+                    points_selector=FilterSelector(filter=filter),
                 )
             except UnexpectedResponse as e:
                 # Collection does not exist, so return
@@ -324,15 +311,13 @@ class QdrantVector(BaseVector):
             all_collection_name.append(collection.name)
         if self._collection_name not in all_collection_name:
             return False
-        response = self._client.retrieve(
-            collection_name=self._collection_name,
-            ids=[id]
-        )
+        response = self._client.retrieve(collection_name=self._collection_name, ids=[id])
 
         return len(response) > 0
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         from qdrant_client.http import models
+
         filter = models.Filter(
             must=[
                 models.FieldCondition(
@@ -348,22 +333,22 @@ class QdrantVector(BaseVector):
             limit=kwargs.get("top_k", 4),
             with_payload=True,
             with_vectors=True,
-            score_threshold=kwargs.get("score_threshold", .0)
+            score_threshold=float(kwargs.get("score_threshold") or 0.0),
         )
         docs = []
         for result in results:
             metadata = result.payload.get(Field.METADATA_KEY.value) or {}
             # duplicate check score threshold
-            score_threshold = kwargs.get("score_threshold", .0) if kwargs.get('score_threshold', .0) else 0.0
+            score_threshold = float(kwargs.get("score_threshold") or 0.0)
             if result.score > score_threshold:
-                metadata['score'] = result.score
+                metadata["score"] = result.score
                 doc = Document(
                     page_content=result.payload.get(Field.CONTENT_KEY.value),
                     metadata=metadata,
                 )
                 docs.append(doc)
         # Sort the documents by score in descending order
-        docs = sorted(docs, key=lambda x: x.metadata['score'], reverse=True)
+        docs = sorted(docs, key=lambda x: x.metadata["score"], reverse=True)
         return docs
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
@@ -372,6 +357,7 @@ class QdrantVector(BaseVector):
             List of documents most similar to the query text and distance for each.
         """
         from qdrant_client.http import models
+
         scroll_filter = models.Filter(
             must=[
                 models.FieldCondition(
@@ -381,24 +367,21 @@ class QdrantVector(BaseVector):
                 models.FieldCondition(
                     key="page_content",
                     match=models.MatchText(text=query),
-                )
+                ),
             ]
         )
         response = self._client.scroll(
             collection_name=self._collection_name,
             scroll_filter=scroll_filter,
-            limit=kwargs.get('top_k', 2),
+            limit=kwargs.get("top_k", 2),
             with_payload=True,
-            with_vectors=True
-
+            with_vectors=True,
         )
         results = response[0]
         documents = []
         for result in results:
             if result:
-                document = self._document_from_scored_point(
-                    result, Field.CONTENT_KEY.value, Field.METADATA_KEY.value
-                )
+                document = self._document_from_scored_point(result, Field.CONTENT_KEY.value, Field.METADATA_KEY.value)
                 documents.append(document)
 
         return documents
@@ -410,10 +393,10 @@ class QdrantVector(BaseVector):
 
     @classmethod
     def _document_from_scored_point(
-            cls,
-            scored_point: Any,
-            content_payload_key: str,
-            metadata_payload_key: str,
+        cls,
+        scored_point: Any,
+        content_payload_key: str,
+        metadata_payload_key: str,
     ) -> Document:
         return Document(
             page_content=scored_point.payload.get(content_payload_key),
@@ -425,24 +408,25 @@ class QdrantVector(BaseVector):
 class QdrantVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> QdrantVector:
         if dataset.collection_binding_id:
-            dataset_collection_binding = db.session.query(DatasetCollectionBinding). \
-                filter(DatasetCollectionBinding.id == dataset.collection_binding_id). \
-                one_or_none()
+            dataset_collection_binding = (
+                db.session.query(DatasetCollectionBinding)
+                .filter(DatasetCollectionBinding.id == dataset.collection_binding_id)
+                .one_or_none()
+            )
             if dataset_collection_binding:
                 collection_name = dataset_collection_binding.collection_name
             else:
-                raise ValueError('Dataset Collection Bindings is not exist!')
+                raise ValueError("Dataset Collection Bindings is not exist!")
         else:
             if dataset.index_struct_dict:
-                class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+                class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
                 collection_name = class_prefix
             else:
                 dataset_id = dataset.id
                 collection_name = Dataset.gen_collection_name_by_id(dataset_id)
 
         if not dataset.index_struct_dict:
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.QDRANT, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.QDRANT, collection_name))
 
         config = current_app.config
         return QdrantVector(
@@ -454,6 +438,6 @@ class QdrantVectorFactory(AbstractVectorFactory):
                 root_path=config.root_path,
                 timeout=dify_config.QDRANT_CLIENT_TIMEOUT,
                 grpc_port=dify_config.QDRANT_GRPC_PORT,
-                prefer_grpc=dify_config.QDRANT_GRPC_ENABLED
-            )
+                prefer_grpc=dify_config.QDRANT_GRPC_ENABLED,
+            ),
         )
diff --git a/api/core/rag/datasource/vdb/relyt/relyt_vector.py b/api/core/rag/datasource/vdb/relyt/relyt_vector.py
index 63ad0682d7..f47f75718a 100644
--- a/api/core/rag/datasource/vdb/relyt/relyt_vector.py
+++ b/api/core/rag/datasource/vdb/relyt/relyt_vector.py
@@ -33,28 +33,30 @@ class RelytConfig(BaseModel):
     password: str
     database: str
 
-    @model_validator(mode='before')
+    @model_validator(mode="before")
+    @classmethod
     def validate_config(cls, values: dict) -> dict:
-        if not values['host']:
+        if not values["host"]:
             raise ValueError("config RELYT_HOST is required")
-        if not values['port']:
+        if not values["port"]:
             raise ValueError("config RELYT_PORT is required")
-        if not values['user']:
+        if not values["user"]:
             raise ValueError("config RELYT_USER is required")
-        if not values['password']:
+        if not values["password"]:
             raise ValueError("config RELYT_PASSWORD is required")
-        if not values['database']:
+        if not values["database"]:
             raise ValueError("config RELYT_DATABASE is required")
         return values
 
 
 class RelytVector(BaseVector):
-
     def __init__(self, collection_name: str, config: RelytConfig, group_id: str):
         super().__init__(collection_name)
         self.embedding_dimension = 1536
         self._client_config = config
-        self._url = f"postgresql+psycopg2://{config.user}:{config.password}@{config.host}:{config.port}/{config.database}"
+        self._url = (
+            f"postgresql+psycopg2://{config.user}:{config.password}@{config.host}:{config.port}/{config.database}"
+        )
         self.client = create_engine(self._url)
         self._fields = []
         self._group_id = group_id
@@ -70,9 +72,9 @@ class RelytVector(BaseVector):
         self.add_texts(texts, embeddings)
 
     def create_collection(self, dimension: int):
-        lock_name = 'vector_indexing_lock_{}'.format(self._collection_name)
+        lock_name = "vector_indexing_lock_{}".format(self._collection_name)
         with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
             if redis_client.get(collection_exist_cache_key):
                 return
             index_name = f"{self._collection_name}_embedding_index"
@@ -110,7 +112,7 @@ class RelytVector(BaseVector):
         ids = [str(uuid.uuid1()) for _ in documents]
         metadatas = [d.metadata for d in documents]
         for metadata in metadatas:
-            metadata['group_id'] = self._group_id
+            metadata["group_id"] = self._group_id
         texts = [d.page_content for d in documents]
 
         # Define the table schema
@@ -125,29 +127,26 @@ class RelytVector(BaseVector):
         )
 
         chunks_table_data = []
-        with self.client.connect() as conn:
-            with conn.begin():
-                for document, metadata, chunk_id, embedding in zip(
-                        texts, metadatas, ids, embeddings
-                ):
-                    chunks_table_data.append(
-                        {
-                            "id": chunk_id,
-                            "embedding": embedding,
-                            "document": document,
-                            "metadata": metadata,
-                        }
-                    )
+        with self.client.connect() as conn, conn.begin():
+            for document, metadata, chunk_id, embedding in zip(texts, metadatas, ids, embeddings):
+                chunks_table_data.append(
+                    {
+                        "id": chunk_id,
+                        "embedding": embedding,
+                        "document": document,
+                        "metadata": metadata,
+                    }
+                )
 
-                    # Execute the batch insert when the batch size is reached
-                    if len(chunks_table_data) == 500:
-                        conn.execute(insert(chunks_table).values(chunks_table_data))
-                        # Clear the chunks_table_data list for the next batch
-                        chunks_table_data.clear()
-
-                # Insert any remaining records that didn't make up a full batch
-                if chunks_table_data:
+                # Execute the batch insert when the batch size is reached
+                if len(chunks_table_data) == 500:
                     conn.execute(insert(chunks_table).values(chunks_table_data))
+                    # Clear the chunks_table_data list for the next batch
+                    chunks_table_data.clear()
+
+            # Insert any remaining records that didn't make up a full batch
+            if chunks_table_data:
+                conn.execute(insert(chunks_table).values(chunks_table_data))
 
         return ids
 
@@ -186,25 +185,22 @@ class RelytVector(BaseVector):
         )
 
         try:
-            with self.client.connect() as conn:
-                with conn.begin():
-                    delete_condition = chunks_table.c.id.in_(ids)
-                    conn.execute(chunks_table.delete().where(delete_condition))
-                    return True
+            with self.client.connect() as conn, conn.begin():
+                delete_condition = chunks_table.c.id.in_(ids)
+                conn.execute(chunks_table.delete().where(delete_condition))
+                return True
         except Exception as e:
             print("Delete operation failed:", str(e))
             return False
 
     def delete_by_metadata_field(self, key: str, value: str):
-
         ids = self.get_ids_by_metadata_field(key, value)
         if ids:
             self.delete_by_uuids(ids)
 
     def delete_by_ids(self, ids: list[str]) -> None:
-
         with Session(self.client) as session:
-            ids_str = ','.join(f"'{doc_id}'" for doc_id in ids)
+            ids_str = ",".join(f"'{doc_id}'" for doc_id in ids)
             select_statement = sql_text(
                 f"""SELECT id FROM "{self._collection_name}" WHERE metadata->>'doc_id' in ({ids_str}); """
             )
@@ -228,38 +224,34 @@ class RelytVector(BaseVector):
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         results = self.similarity_search_with_score_by_vector(
-            k=int(kwargs.get('top_k')),
-            embedding=query_vector,
-            filter=kwargs.get('filter')
+            k=int(kwargs.get("top_k")), embedding=query_vector, filter=kwargs.get("filter")
         )
 
         # Organize results.
         docs = []
         for document, score in results:
-            score_threshold = kwargs.get('score_threshold') if kwargs.get('score_threshold') else 0.0
+            score_threshold = float(kwargs.get("score_threshold") or 0.0)
             if 1 - score > score_threshold:
                 docs.append(document)
         return docs
 
     def similarity_search_with_score_by_vector(
-            self,
-            embedding: list[float],
-            k: int = 4,
-            filter: Optional[dict] = None,
+        self,
+        embedding: list[float],
+        k: int = 4,
+        filter: Optional[dict] = None,
     ) -> list[tuple[Document, float]]:
         # Add the filter if provided
         try:
             from sqlalchemy.engine import Row
         except ImportError:
-            raise ImportError(
-                "Could not import Row from sqlalchemy.engine. "
-                "Please 'pip install sqlalchemy>=1.4'."
-            )
+            raise ImportError("Could not import Row from sqlalchemy.engine. Please 'pip install sqlalchemy>=1.4'.")
 
         filter_condition = ""
         if filter is not None:
             conditions = [
-                f"metadata->>{key!r} in ({', '.join(map(repr, value))})" if len(value) > 1
+                f"metadata->>{key!r} in ({', '.join(map(repr, value))})"
+                if len(value) > 1
                 else f"metadata->>{key!r} = {value[0]!r}"
                 for key, value in filter.items()
             ]
@@ -305,13 +297,12 @@ class RelytVector(BaseVector):
 class RelytVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> RelytVector:
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.RELYT, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.RELYT, collection_name))
 
         return RelytVector(
             collection_name=collection_name,
@@ -322,5 +313,5 @@ class RelytVectorFactory(AbstractVectorFactory):
                 password=dify_config.RELYT_PASSWORD,
                 database=dify_config.RELYT_DATABASE,
             ),
-            group_id=dataset.id
+            group_id=dataset.id,
         )
diff --git a/api/core/rag/datasource/vdb/tencent/tencent_vector.py b/api/core/rag/datasource/vdb/tencent/tencent_vector.py
index 3325a1028e..faa373017b 100644
--- a/api/core/rag/datasource/vdb/tencent/tencent_vector.py
+++ b/api/core/rag/datasource/vdb/tencent/tencent_vector.py
@@ -25,16 +25,11 @@ class TencentConfig(BaseModel):
     database: Optional[str]
     index_type: str = "HNSW"
     metric_type: str = "L2"
-    shard: int = 1,
-    replicas: int = 2,
+    shard: int = (1,)
+    replicas: int = (2,)
 
     def to_tencent_params(self):
-        return {
-            'url': self.url,
-            'username': self.username,
-            'key': self.api_key,
-            'timeout': self.timeout
-        }
+        return {"url": self.url, "username": self.username, "key": self.api_key, "timeout": self.timeout}
 
 
 class TencentVector(BaseVector):
@@ -61,25 +56,19 @@ class TencentVector(BaseVector):
             return self._client.create_database(database_name=self._client_config.database)
 
     def get_type(self) -> str:
-        return 'tencent'
+        return "tencent"
 
     def to_index_struct(self) -> dict:
-        return {
-            "type": self.get_type(),
-            "vector_store": {"class_prefix": self._collection_name}
-        }
+        return {"type": self.get_type(), "vector_store": {"class_prefix": self._collection_name}}
 
     def _has_collection(self) -> bool:
         collections = self._db.list_collections()
-        for collection in collections:
-            if collection.collection_name == self._collection_name:
-                return True
-        return False
+        return any(collection.collection_name == self._collection_name for collection in collections)
 
     def _create_collection(self, dimension: int) -> None:
-        lock_name = 'vector_indexing_lock_{}'.format(self._collection_name)
+        lock_name = "vector_indexing_lock_{}".format(self._collection_name)
         with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
             if redis_client.get(collection_exist_cache_key):
                 return
 
@@ -101,9 +90,7 @@ class TencentVector(BaseVector):
                 raise ValueError("unsupported metric_type")
             params = vdb_index.HNSWParams(m=16, efconstruction=200)
             index = vdb_index.Index(
-                vdb_index.FilterIndex(
-                    self.field_id, enum.FieldType.String, enum.IndexType.PRIMARY_KEY
-                ),
+                vdb_index.FilterIndex(self.field_id, enum.FieldType.String, enum.IndexType.PRIMARY_KEY),
                 vdb_index.VectorIndex(
                     self.field_vector,
                     dimension,
@@ -111,12 +98,8 @@ class TencentVector(BaseVector):
                     metric_type,
                     params,
                 ),
-                vdb_index.FilterIndex(
-                    self.field_text, enum.FieldType.String, enum.IndexType.FILTER
-                ),
-                vdb_index.FilterIndex(
-                    self.field_metadata, enum.FieldType.String, enum.IndexType.FILTER
-                ),
+                vdb_index.FilterIndex(self.field_text, enum.FieldType.String, enum.IndexType.FILTER),
+                vdb_index.FilterIndex(self.field_metadata, enum.FieldType.String, enum.IndexType.FILTER),
             )
 
             self._db.create_collection(
@@ -163,15 +146,14 @@ class TencentVector(BaseVector):
         self._db.collection(self._collection_name).delete(filter=Filter(Filter.In(key, [value])))
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
-
-        res = self._db.collection(self._collection_name).search(vectors=[query_vector],
-                                                                params=document.HNSWSearchParams(
-                                                                    ef=kwargs.get("ef", 10)),
-                                                                retrieve_vector=False,
-                                                                limit=kwargs.get('top_k', 4),
-                                                                timeout=self._client_config.timeout,
-                                                                )
-        score_threshold = kwargs.get("score_threshold", .0) if kwargs.get('score_threshold', .0) else 0.0
+        res = self._db.collection(self._collection_name).search(
+            vectors=[query_vector],
+            params=document.HNSWSearchParams(ef=kwargs.get("ef", 10)),
+            retrieve_vector=False,
+            limit=kwargs.get("top_k", 4),
+            timeout=self._client_config.timeout,
+        )
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
         return self._get_search_res(res, score_threshold)
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
@@ -200,15 +182,13 @@ class TencentVector(BaseVector):
 
 class TencentVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> TencentVector:
-
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix.lower()
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.TENCENT, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.TENCENT, collection_name))
 
         return TencentVector(
             collection_name=collection_name,
@@ -220,5 +200,5 @@ class TencentVectorFactory(AbstractVectorFactory):
                 database=dify_config.TENCENT_VECTOR_DB_DATABASE,
                 shard=dify_config.TENCENT_VECTOR_DB_SHARD,
                 replicas=dify_config.TENCENT_VECTOR_DB_REPLICAS,
-            )
+            ),
         )
diff --git a/api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py b/api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py
index d3685c0991..20490d3215 100644
--- a/api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py
+++ b/api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py
@@ -28,47 +28,57 @@ class TiDBVectorConfig(BaseModel):
     database: str
     program_name: str
 
-    @model_validator(mode='before')
+    @model_validator(mode="before")
+    @classmethod
     def validate_config(cls, values: dict) -> dict:
-        if not values['host']:
+        if not values["host"]:
             raise ValueError("config TIDB_VECTOR_HOST is required")
-        if not values['port']:
+        if not values["port"]:
             raise ValueError("config TIDB_VECTOR_PORT is required")
-        if not values['user']:
+        if not values["user"]:
             raise ValueError("config TIDB_VECTOR_USER is required")
-        if not values['password']:
+        if not values["password"]:
             raise ValueError("config TIDB_VECTOR_PASSWORD is required")
-        if not values['database']:
+        if not values["database"]:
             raise ValueError("config TIDB_VECTOR_DATABASE is required")
-        if not values['program_name']:
+        if not values["program_name"]:
             raise ValueError("config APPLICATION_NAME is required")
         return values
 
 
 class TiDBVector(BaseVector):
-
     def get_type(self) -> str:
         return VectorType.TIDB_VECTOR
 
     def _table(self, dim: int) -> Table:
         from tidb_vector.sqlalchemy import VectorType
+
         return Table(
             self._collection_name,
             self._orm_base.metadata,
-            Column('id', String(36), primary_key=True, nullable=False),
-            Column("vector", VectorType(dim), nullable=False, comment="" if self._distance_func is None else f"hnsw(distance={self._distance_func})"),
+            Column("id", String(36), primary_key=True, nullable=False),
+            Column(
+                "vector",
+                VectorType(dim),
+                nullable=False,
+                comment="" if self._distance_func is None else f"hnsw(distance={self._distance_func})",
+            ),
             Column("text", TEXT, nullable=False),
             Column("meta", JSON, nullable=False),
             Column("create_time", DateTime, server_default=sqlalchemy.text("CURRENT_TIMESTAMP")),
-            Column("update_time", DateTime, server_default=sqlalchemy.text("CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP")),
-            extend_existing=True
+            Column(
+                "update_time", DateTime, server_default=sqlalchemy.text("CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP")
+            ),
+            extend_existing=True,
         )
 
-    def __init__(self, collection_name: str, config: TiDBVectorConfig, distance_func: str = 'cosine'):
+    def __init__(self, collection_name: str, config: TiDBVectorConfig, distance_func: str = "cosine"):
         super().__init__(collection_name)
         self._client_config = config
-        self._url = (f"mysql+pymysql://{config.user}:{config.password}@{config.host}:{config.port}/{config.database}?"
-                     f"ssl_verify_cert=true&ssl_verify_identity=true&program_name={config.program_name}")
+        self._url = (
+            f"mysql+pymysql://{config.user}:{config.password}@{config.host}:{config.port}/{config.database}?"
+            f"ssl_verify_cert=true&ssl_verify_identity=true&program_name={config.program_name}"
+        )
         self._distance_func = distance_func.lower()
         self._engine = create_engine(self._url)
         self._orm_base = declarative_base()
@@ -83,9 +93,9 @@ class TiDBVector(BaseVector):
 
     def _create_collection(self, dimension: int):
         logger.info("_create_collection, collection_name " + self._collection_name)
-        lock_name = 'vector_indexing_lock_{}'.format(self._collection_name)
+        lock_name = "vector_indexing_lock_{}".format(self._collection_name)
         with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
             if redis_client.get(collection_exist_cache_key):
                 return
             with Session(self._engine) as session:
@@ -114,31 +124,28 @@ class TiDBVector(BaseVector):
         texts = [d.page_content for d in documents]
 
         chunks_table_data = []
-        with self._engine.connect() as conn:
-            with conn.begin():
-                for id, text, meta, embedding in zip(
-                        ids, texts, metas, embeddings
-                ):
-                    chunks_table_data.append({"id": id, "vector": embedding, "text": text, "meta": meta})
+        with self._engine.connect() as conn, conn.begin():
+            for id, text, meta, embedding in zip(ids, texts, metas, embeddings):
+                chunks_table_data.append({"id": id, "vector": embedding, "text": text, "meta": meta})
 
-                    # Execute the batch insert when the batch size is reached
-                    if len(chunks_table_data) == 500:
-                        conn.execute(insert(table).values(chunks_table_data))
-                        # Clear the chunks_table_data list for the next batch
-                        chunks_table_data.clear()
-
-                # Insert any remaining records that didn't make up a full batch
-                if chunks_table_data:
+                # Execute the batch insert when the batch size is reached
+                if len(chunks_table_data) == 500:
                     conn.execute(insert(table).values(chunks_table_data))
+                    # Clear the chunks_table_data list for the next batch
+                    chunks_table_data.clear()
+
+            # Insert any remaining records that didn't make up a full batch
+            if chunks_table_data:
+                conn.execute(insert(table).values(chunks_table_data))
         return ids
 
     def text_exists(self, id: str) -> bool:
-        result = self.get_ids_by_metadata_field('doc_id', id)
+        result = self.get_ids_by_metadata_field("doc_id", id)
         return bool(result)
 
     def delete_by_ids(self, ids: list[str]) -> None:
         with Session(self._engine) as session:
-            ids_str = ','.join(f"'{doc_id}'" for doc_id in ids)
+            ids_str = ",".join(f"'{doc_id}'" for doc_id in ids)
             select_statement = sql_text(
                 f"""SELECT id FROM {self._collection_name} WHERE meta->>'$.doc_id' in ({ids_str}); """
             )
@@ -152,11 +159,10 @@ class TiDBVector(BaseVector):
             raise ValueError("No ids provided to delete.")
         table = self._table(self._dimension)
         try:
-            with self._engine.connect() as conn:
-                with conn.begin():
-                    delete_condition = table.c.id.in_(ids)
-                    conn.execute(table.delete().where(delete_condition))
-                    return True
+            with self._engine.connect() as conn, conn.begin():
+                delete_condition = table.c.id.in_(ids)
+                conn.execute(table.delete().where(delete_condition))
+                return True
         except Exception as e:
             print("Delete operation failed:", str(e))
             return False
@@ -179,21 +185,23 @@ class TiDBVector(BaseVector):
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 5)
-        score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
-        filter = kwargs.get('filter')
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
+        filter = kwargs.get("filter")
         distance = 1 - score_threshold
 
         query_vector_str = ", ".join(format(x) for x in query_vector)
         query_vector_str = "[" + query_vector_str + "]"
-        logger.debug(f"_collection_name: {self._collection_name}, score_threshold: {score_threshold}, distance: {distance}")
+        logger.debug(
+            f"_collection_name: {self._collection_name}, score_threshold: {score_threshold}, distance: {distance}"
+        )
 
         docs = []
-        if self._distance_func == 'l2':
-            tidb_func = 'Vec_l2_distance'
-        elif self._distance_func == 'cosine':
-            tidb_func = 'Vec_Cosine_distance'
+        if self._distance_func == "l2":
+            tidb_func = "Vec_l2_distance"
+        elif self._distance_func == "cosine":
+            tidb_func = "Vec_Cosine_distance"
         else:
-            tidb_func = 'Vec_Cosine_distance'
+            tidb_func = "Vec_Cosine_distance"
 
         with Session(self._engine) as session:
             select_statement = sql_text(
@@ -208,7 +216,7 @@ class TiDBVector(BaseVector):
             results = [(row[0], row[1], row[2]) for row in res]
             for meta, text, distance in results:
                 metadata = json.loads(meta)
-                metadata['score'] = 1 - distance
+                metadata["score"] = 1 - distance
                 docs.append(Document(page_content=text, metadata=metadata))
         return docs
 
@@ -224,15 +232,13 @@ class TiDBVector(BaseVector):
 
 class TiDBVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> TiDBVector:
-
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix.lower()
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.TIDB_VECTOR, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.TIDB_VECTOR, collection_name))
 
         return TiDBVector(
             collection_name=collection_name,
diff --git a/api/core/rag/datasource/vdb/vector_base.py b/api/core/rag/datasource/vdb/vector_base.py
index 3f70e8b608..1a0dc7f48b 100644
--- a/api/core/rag/datasource/vdb/vector_base.py
+++ b/api/core/rag/datasource/vdb/vector_base.py
@@ -7,7 +7,6 @@ from core.rag.models.document import Document
 
 
 class BaseVector(ABC):
-
     def __init__(self, collection_name: str):
         self._collection_name = collection_name
 
@@ -39,26 +38,19 @@ class BaseVector(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def search_by_vector(
-            self,
-            query_vector: list[float],
-            **kwargs: Any
-    ) -> list[Document]:
+    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         raise NotImplementedError
 
     @abstractmethod
-    def search_by_full_text(
-            self, query: str,
-            **kwargs: Any
-    ) -> list[Document]:
+    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         raise NotImplementedError
 
     def delete(self) -> None:
         raise NotImplementedError
 
     def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
-        for text in texts[:]:
-            doc_id = text.metadata['doc_id']
+        for text in texts.copy():
+            doc_id = text.metadata["doc_id"]
             exists_duplicate_node = self.text_exists(doc_id)
             if exists_duplicate_node:
                 texts.remove(text)
@@ -66,7 +58,7 @@ class BaseVector(ABC):
         return texts
 
     def _get_uuids(self, texts: list[Document]) -> list[str]:
-        return [text.metadata['doc_id'] for text in texts]
+        return [text.metadata["doc_id"] for text in texts]
 
     @property
     def collection_name(self):
diff --git a/api/core/rag/datasource/vdb/vector_factory.py b/api/core/rag/datasource/vdb/vector_factory.py
index 3e9ca8e1fe..ca90233b7f 100644
--- a/api/core/rag/datasource/vdb/vector_factory.py
+++ b/api/core/rag/datasource/vdb/vector_factory.py
@@ -20,17 +20,14 @@ class AbstractVectorFactory(ABC):
 
     @staticmethod
     def gen_index_struct_dict(vector_type: VectorType, collection_name: str) -> dict:
-        index_struct_dict = {
-            "type": vector_type,
-            "vector_store": {"class_prefix": collection_name}
-        }
+        index_struct_dict = {"type": vector_type, "vector_store": {"class_prefix": collection_name}}
         return index_struct_dict
 
 
 class Vector:
     def __init__(self, dataset: Dataset, attributes: list = None):
         if attributes is None:
-            attributes = ['doc_id', 'dataset_id', 'document_id', 'doc_hash']
+            attributes = ["doc_id", "dataset_id", "document_id", "doc_hash"]
         self._dataset = dataset
         self._embeddings = self._get_embeddings()
         self._attributes = attributes
@@ -39,7 +36,7 @@ class Vector:
     def _init_vector(self) -> BaseVector:
         vector_type = dify_config.VECTOR_STORE
         if self._dataset.index_struct_dict:
-            vector_type = self._dataset.index_struct_dict['type']
+            vector_type = self._dataset.index_struct_dict["type"]
 
         if not vector_type:
             raise ValueError("Vector store must be specified.")
@@ -52,45 +49,59 @@ class Vector:
         match vector_type:
             case VectorType.CHROMA:
                 from core.rag.datasource.vdb.chroma.chroma_vector import ChromaVectorFactory
+
                 return ChromaVectorFactory
             case VectorType.MILVUS:
                 from core.rag.datasource.vdb.milvus.milvus_vector import MilvusVectorFactory
+
                 return MilvusVectorFactory
             case VectorType.MYSCALE:
                 from core.rag.datasource.vdb.myscale.myscale_vector import MyScaleVectorFactory
+
                 return MyScaleVectorFactory
             case VectorType.PGVECTOR:
                 from core.rag.datasource.vdb.pgvector.pgvector import PGVectorFactory
+
                 return PGVectorFactory
             case VectorType.PGVECTO_RS:
                 from core.rag.datasource.vdb.pgvecto_rs.pgvecto_rs import PGVectoRSFactory
+
                 return PGVectoRSFactory
             case VectorType.QDRANT:
                 from core.rag.datasource.vdb.qdrant.qdrant_vector import QdrantVectorFactory
+
                 return QdrantVectorFactory
             case VectorType.RELYT:
                 from core.rag.datasource.vdb.relyt.relyt_vector import RelytVectorFactory
+
                 return RelytVectorFactory
             case VectorType.ELASTICSEARCH:
                 from core.rag.datasource.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
+
                 return ElasticSearchVectorFactory
             case VectorType.TIDB_VECTOR:
                 from core.rag.datasource.vdb.tidb_vector.tidb_vector import TiDBVectorFactory
+
                 return TiDBVectorFactory
             case VectorType.WEAVIATE:
                 from core.rag.datasource.vdb.weaviate.weaviate_vector import WeaviateVectorFactory
+
                 return WeaviateVectorFactory
             case VectorType.TENCENT:
                 from core.rag.datasource.vdb.tencent.tencent_vector import TencentVectorFactory
+
                 return TencentVectorFactory
             case VectorType.ORACLE:
                 from core.rag.datasource.vdb.oracle.oraclevector import OracleVectorFactory
+
                 return OracleVectorFactory
             case VectorType.OPENSEARCH:
                 from core.rag.datasource.vdb.opensearch.opensearch_vector import OpenSearchVectorFactory
+
                 return OpenSearchVectorFactory
             case VectorType.ANALYTICDB:
                 from core.rag.datasource.vdb.analyticdb.analyticdb_vector import AnalyticdbVectorFactory
+
                 return AnalyticdbVectorFactory
             case _:
                 raise ValueError(f"Vector store {vector_type} is not supported.")
@@ -98,21 +109,14 @@ class Vector:
     def create(self, texts: list = None, **kwargs):
         if texts:
             embeddings = self._embeddings.embed_documents([document.page_content for document in texts])
-            self._vector_processor.create(
-                texts=texts,
-                embeddings=embeddings,
-                **kwargs
-            )
+            self._vector_processor.create(texts=texts, embeddings=embeddings, **kwargs)
 
     def add_texts(self, documents: list[Document], **kwargs):
-        if kwargs.get('duplicate_check', False):
+        if kwargs.get("duplicate_check", False):
             documents = self._filter_duplicate_texts(documents)
+
         embeddings = self._embeddings.embed_documents([document.page_content for document in documents])
-        self._vector_processor.create(
-            texts=documents,
-            embeddings=embeddings,
-            **kwargs
-        )
+        self._vector_processor.create(texts=documents, embeddings=embeddings, **kwargs)
 
     def text_exists(self, id: str) -> bool:
         return self._vector_processor.text_exists(id)
@@ -123,24 +127,18 @@ class Vector:
     def delete_by_metadata_field(self, key: str, value: str) -> None:
         self._vector_processor.delete_by_metadata_field(key, value)
 
-    def search_by_vector(
-            self, query: str,
-            **kwargs: Any
-    ) -> list[Document]:
+    def search_by_vector(self, query: str, **kwargs: Any) -> list[Document]:
         query_vector = self._embeddings.embed_query(query)
         return self._vector_processor.search_by_vector(query_vector, **kwargs)
 
-    def search_by_full_text(
-            self, query: str,
-            **kwargs: Any
-    ) -> list[Document]:
+    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         return self._vector_processor.search_by_full_text(query, **kwargs)
 
     def delete(self) -> None:
         self._vector_processor.delete()
         # delete collection redis cache
         if self._vector_processor.collection_name:
-            collection_exist_cache_key = 'vector_indexing_{}'.format(self._vector_processor.collection_name)
+            collection_exist_cache_key = "vector_indexing_{}".format(self._vector_processor.collection_name)
             redis_client.delete(collection_exist_cache_key)
 
     def _get_embeddings(self) -> Embeddings:
@@ -150,14 +148,13 @@ class Vector:
             tenant_id=self._dataset.tenant_id,
             provider=self._dataset.embedding_model_provider,
             model_type=ModelType.TEXT_EMBEDDING,
-            model=self._dataset.embedding_model
-
+            model=self._dataset.embedding_model,
         )
         return CacheEmbedding(embedding_model)
 
     def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
-        for text in texts[:]:
-            doc_id = text.metadata['doc_id']
+        for text in texts.copy():
+            doc_id = text.metadata["doc_id"]
             exists_duplicate_node = self.text_exists(doc_id)
             if exists_duplicate_node:
                 texts.remove(text)
diff --git a/api/core/rag/datasource/vdb/vector_type.py b/api/core/rag/datasource/vdb/vector_type.py
index 317ca6abc8..ba04ea879d 100644
--- a/api/core/rag/datasource/vdb/vector_type.py
+++ b/api/core/rag/datasource/vdb/vector_type.py
@@ -2,17 +2,17 @@ from enum import Enum
 
 
 class VectorType(str, Enum):
-    ANALYTICDB = 'analyticdb'
-    CHROMA = 'chroma'
-    MILVUS = 'milvus'
-    MYSCALE = 'myscale'
-    PGVECTOR = 'pgvector'
-    PGVECTO_RS = 'pgvecto-rs'
-    QDRANT = 'qdrant'
-    RELYT = 'relyt'
-    TIDB_VECTOR = 'tidb_vector'
-    WEAVIATE = 'weaviate'
-    OPENSEARCH = 'opensearch'
-    TENCENT = 'tencent'
-    ORACLE = 'oracle'
-    ELASTICSEARCH = 'elasticsearch'
+    ANALYTICDB = "analyticdb"
+    CHROMA = "chroma"
+    MILVUS = "milvus"
+    MYSCALE = "myscale"
+    PGVECTOR = "pgvector"
+    PGVECTO_RS = "pgvecto-rs"
+    QDRANT = "qdrant"
+    RELYT = "relyt"
+    TIDB_VECTOR = "tidb_vector"
+    WEAVIATE = "weaviate"
+    OPENSEARCH = "opensearch"
+    TENCENT = "tencent"
+    ORACLE = "oracle"
+    ELASTICSEARCH = "elasticsearch"
diff --git a/api/core/rag/datasource/vdb/weaviate/weaviate_vector.py b/api/core/rag/datasource/vdb/weaviate/weaviate_vector.py
index 205fe850c3..6eee344b9b 100644
--- a/api/core/rag/datasource/vdb/weaviate/weaviate_vector.py
+++ b/api/core/rag/datasource/vdb/weaviate/weaviate_vector.py
@@ -22,15 +22,15 @@ class WeaviateConfig(BaseModel):
     api_key: Optional[str] = None
     batch_size: int = 100
 
-    @model_validator(mode='before')
+    @model_validator(mode="before")
+    @classmethod
     def validate_config(cls, values: dict) -> dict:
-        if not values['endpoint']:
+        if not values["endpoint"]:
             raise ValueError("config WEAVIATE_ENDPOINT is required")
         return values
 
 
 class WeaviateVector(BaseVector):
-
     def __init__(self, collection_name: str, config: WeaviateConfig, attributes: list):
         super().__init__(collection_name)
         self._client = self._init_client(config)
@@ -43,10 +43,7 @@ class WeaviateVector(BaseVector):
 
         try:
             client = weaviate.Client(
-                url=config.endpoint,
-                auth_client_secret=auth_config,
-                timeout_config=(5, 60),
-                startup_period=None
+                url=config.endpoint, auth_client_secret=auth_config, timeout_config=(5, 60), startup_period=None
             )
         except requests.exceptions.ConnectionError:
             raise ConnectionError("Vector database connection error")
@@ -68,10 +65,10 @@ class WeaviateVector(BaseVector):
 
     def get_collection_name(self, dataset: Dataset) -> str:
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
-            if not class_prefix.endswith('_Node'):
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
+            if not class_prefix.endswith("_Node"):
                 # original class_prefix
-                class_prefix += '_Node'
+                class_prefix += "_Node"
 
             return class_prefix
 
@@ -79,10 +76,7 @@ class WeaviateVector(BaseVector):
         return Dataset.gen_collection_name_by_id(dataset_id)
 
     def to_index_struct(self) -> dict:
-        return {
-            "type": self.get_type(),
-            "vector_store": {"class_prefix": self._collection_name}
-        }
+        return {"type": self.get_type(), "vector_store": {"class_prefix": self._collection_name}}
 
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
         # create collection
@@ -91,9 +85,9 @@ class WeaviateVector(BaseVector):
         self.add_texts(texts, embeddings)
 
     def _create_collection(self):
-        lock_name = 'vector_indexing_lock_{}'.format(self._collection_name)
+        lock_name = "vector_indexing_lock_{}".format(self._collection_name)
         with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = 'vector_indexing_{}'.format(self._collection_name)
+            collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
             if redis_client.get(collection_exist_cache_key):
                 return
             schema = self._default_schema(self._collection_name)
@@ -129,17 +123,9 @@ class WeaviateVector(BaseVector):
         # check whether the index already exists
         schema = self._default_schema(self._collection_name)
         if self._client.schema.contains(schema):
-            where_filter = {
-                "operator": "Equal",
-                "path": [key],
-                "valueText": value
-            }
+            where_filter = {"operator": "Equal", "path": [key], "valueText": value}
 
-            self._client.batch.delete_objects(
-                class_name=self._collection_name,
-                where=where_filter,
-                output='minimal'
-            )
+            self._client.batch.delete_objects(class_name=self._collection_name, where=where_filter, output="minimal")
 
     def delete(self):
         # check whether the index already exists
@@ -154,11 +140,19 @@ class WeaviateVector(BaseVector):
         # check whether the index already exists
         if not self._client.schema.contains(schema):
             return False
-        result = self._client.query.get(collection_name).with_additional(["id"]).with_where({
-            "path": ["doc_id"],
-            "operator": "Equal",
-            "valueText": id,
-        }).with_limit(1).do()
+        result = (
+            self._client.query.get(collection_name)
+            .with_additional(["id"])
+            .with_where(
+                {
+                    "path": ["doc_id"],
+                    "operator": "Equal",
+                    "valueText": id,
+                }
+            )
+            .with_limit(1)
+            .do()
+        )
 
         if "errors" in result:
             raise ValueError(f"Error during query: {result['errors']}")
@@ -211,13 +205,13 @@ class WeaviateVector(BaseVector):
 
         docs = []
         for doc, score in docs_and_scores:
-            score_threshold = kwargs.get("score_threshold", .0) if kwargs.get('score_threshold', .0) else 0.0
+            score_threshold = float(kwargs.get("score_threshold") or 0.0)
             # check score threshold
             if score > score_threshold:
-                doc.metadata['score'] = score
+                doc.metadata["score"] = score
                 docs.append(doc)
         # Sort the documents by score in descending order
-        docs = sorted(docs, key=lambda x: x.metadata['score'], reverse=True)
+        docs = sorted(docs, key=lambda x: x.metadata["score"], reverse=True)
         return docs
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
@@ -240,15 +234,15 @@ class WeaviateVector(BaseVector):
         if kwargs.get("where_filter"):
             query_obj = query_obj.with_where(kwargs.get("where_filter"))
         query_obj = query_obj.with_additional(["vector"])
-        properties = ['text']
-        result = query_obj.with_bm25(query=query, properties=properties).with_limit(kwargs.get('top_k', 2)).do()
+        properties = ["text"]
+        result = query_obj.with_bm25(query=query, properties=properties).with_limit(kwargs.get("top_k", 2)).do()
         if "errors" in result:
             raise ValueError(f"Error during query: {result['errors']}")
         docs = []
         for res in result["data"]["Get"][collection_name]:
             text = res.pop(Field.TEXT_KEY.value)
-            additional = res.pop('_additional')
-            docs.append(Document(page_content=text, vector=additional['vector'], metadata=res))
+            additional = res.pop("_additional")
+            docs.append(Document(page_content=text, vector=additional["vector"], metadata=res))
         return docs
 
     def _default_schema(self, index_name: str) -> dict:
@@ -271,20 +265,19 @@ class WeaviateVector(BaseVector):
 class WeaviateVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> WeaviateVector:
         if dataset.index_struct_dict:
-            class_prefix: str = dataset.index_struct_dict['vector_store']['class_prefix']
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix
         else:
             dataset_id = dataset.id
             collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-            dataset.index_struct = json.dumps(
-                self.gen_index_struct_dict(VectorType.WEAVIATE, collection_name))
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.WEAVIATE, collection_name))
 
         return WeaviateVector(
             collection_name=collection_name,
             config=WeaviateConfig(
                 endpoint=dify_config.WEAVIATE_ENDPOINT,
                 api_key=dify_config.WEAVIATE_API_KEY,
-                batch_size=dify_config.WEAVIATE_BATCH_SIZE
+                batch_size=dify_config.WEAVIATE_BATCH_SIZE,
             ),
-            attributes=attributes
+            attributes=attributes,
         )
diff --git a/api/core/rag/docstore/dataset_docstore.py b/api/core/rag/docstore/dataset_docstore.py
index 96a15be742..319a2612c7 100644
--- a/api/core/rag/docstore/dataset_docstore.py
+++ b/api/core/rag/docstore/dataset_docstore.py
@@ -12,10 +12,10 @@ from models.dataset import Dataset, DocumentSegment
 
 class DatasetDocumentStore:
     def __init__(
-            self,
-            dataset: Dataset,
-            user_id: str,
-            document_id: Optional[str] = None,
+        self,
+        dataset: Dataset,
+        user_id: str,
+        document_id: Optional[str] = None,
     ):
         self._dataset = dataset
         self._user_id = user_id
@@ -41,9 +41,9 @@ class DatasetDocumentStore:
 
     @property
     def docs(self) -> dict[str, Document]:
-        document_segments = db.session.query(DocumentSegment).filter(
-            DocumentSegment.dataset_id == self._dataset.id
-        ).all()
+        document_segments = (
+            db.session.query(DocumentSegment).filter(DocumentSegment.dataset_id == self._dataset.id).all()
+        )
 
         output = {}
         for document_segment in document_segments:
@@ -55,48 +55,45 @@ class DatasetDocumentStore:
                     "doc_hash": document_segment.index_node_hash,
                     "document_id": document_segment.document_id,
                     "dataset_id": document_segment.dataset_id,
-                }
+                },
             )
 
         return output
 
-    def add_documents(
-            self, docs: Sequence[Document], allow_update: bool = True
-    ) -> None:
-        max_position = db.session.query(func.max(DocumentSegment.position)).filter(
-            DocumentSegment.document_id == self._document_id
-        ).scalar()
+    def add_documents(self, docs: Sequence[Document], allow_update: bool = True) -> None:
+        max_position = (
+            db.session.query(func.max(DocumentSegment.position))
+            .filter(DocumentSegment.document_id == self._document_id)
+            .scalar()
+        )
 
         if max_position is None:
             max_position = 0
         embedding_model = None
-        if self._dataset.indexing_technique == 'high_quality':
+        if self._dataset.indexing_technique == "high_quality":
             model_manager = ModelManager()
             embedding_model = model_manager.get_model_instance(
                 tenant_id=self._dataset.tenant_id,
                 provider=self._dataset.embedding_model_provider,
                 model_type=ModelType.TEXT_EMBEDDING,
-                model=self._dataset.embedding_model
+                model=self._dataset.embedding_model,
             )
 
         for doc in docs:
             if not isinstance(doc, Document):
                 raise ValueError("doc must be a Document")
 
-            segment_document = self.get_document_segment(doc_id=doc.metadata['doc_id'])
+            segment_document = self.get_document_segment(doc_id=doc.metadata["doc_id"])
 
             # NOTE: doc could already exist in the store, but we overwrite it
             if not allow_update and segment_document:
                 raise ValueError(
-                    f"doc_id {doc.metadata['doc_id']} already exists. "
-                    "Set allow_update to True to overwrite."
+                    f"doc_id {doc.metadata['doc_id']} already exists. Set allow_update to True to overwrite."
                 )
 
             # calc embedding use tokens
             if embedding_model:
-                tokens = embedding_model.get_text_embedding_num_tokens(
-                    texts=[doc.page_content]
-                )
+                tokens = embedding_model.get_text_embedding_num_tokens(texts=[doc.page_content])
             else:
                 tokens = 0
 
@@ -107,8 +104,8 @@ class DatasetDocumentStore:
                     tenant_id=self._dataset.tenant_id,
                     dataset_id=self._dataset.id,
                     document_id=self._document_id,
-                    index_node_id=doc.metadata['doc_id'],
-                    index_node_hash=doc.metadata['doc_hash'],
+                    index_node_id=doc.metadata["doc_id"],
+                    index_node_hash=doc.metadata["doc_hash"],
                     position=max_position,
                     content=doc.page_content,
                     word_count=len(doc.page_content),
@@ -116,15 +113,15 @@ class DatasetDocumentStore:
                     enabled=False,
                     created_by=self._user_id,
                 )
-                if doc.metadata.get('answer'):
-                    segment_document.answer = doc.metadata.pop('answer', '')
+                if doc.metadata.get("answer"):
+                    segment_document.answer = doc.metadata.pop("answer", "")
 
                 db.session.add(segment_document)
             else:
                 segment_document.content = doc.page_content
-                if doc.metadata.get('answer'):
-                    segment_document.answer = doc.metadata.pop('answer', '')
-                segment_document.index_node_hash = doc.metadata['doc_hash']
+                if doc.metadata.get("answer"):
+                    segment_document.answer = doc.metadata.pop("answer", "")
+                segment_document.index_node_hash = doc.metadata["doc_hash"]
                 segment_document.word_count = len(doc.page_content)
                 segment_document.tokens = tokens
 
@@ -135,9 +132,7 @@ class DatasetDocumentStore:
         result = self.get_document_segment(doc_id)
         return result is not None
 
-    def get_document(
-            self, doc_id: str, raise_error: bool = True
-    ) -> Optional[Document]:
+    def get_document(self, doc_id: str, raise_error: bool = True) -> Optional[Document]:
         document_segment = self.get_document_segment(doc_id)
 
         if document_segment is None:
@@ -153,7 +148,7 @@ class DatasetDocumentStore:
                 "doc_hash": document_segment.index_node_hash,
                 "document_id": document_segment.document_id,
                 "dataset_id": document_segment.dataset_id,
-            }
+            },
         )
 
     def delete_document(self, doc_id: str, raise_error: bool = True) -> None:
@@ -188,9 +183,10 @@ class DatasetDocumentStore:
         return document_segment.index_node_hash
 
     def get_document_segment(self, doc_id: str) -> DocumentSegment:
-        document_segment = db.session.query(DocumentSegment).filter(
-            DocumentSegment.dataset_id == self._dataset.id,
-            DocumentSegment.index_node_id == doc_id
-        ).first()
+        document_segment = (
+            db.session.query(DocumentSegment)
+            .filter(DocumentSegment.dataset_id == self._dataset.id, DocumentSegment.index_node_id == doc_id)
+            .first()
+        )
 
         return document_segment
diff --git a/api/core/rag/extractor/blod/blod.py b/api/core/rag/extractor/blob/blob.py
similarity index 96%
rename from api/core/rag/extractor/blod/blod.py
rename to api/core/rag/extractor/blob/blob.py
index abfdafcfa2..e46ab8b7fd 100644
--- a/api/core/rag/extractor/blod/blod.py
+++ b/api/core/rag/extractor/blob/blob.py
@@ -4,6 +4,7 @@ The goal is to facilitate decoupling of content loading from content parsing cod
 
 In addition, content loading code should provide a lazy loading interface by default.
 """
+
 from __future__ import annotations
 
 import contextlib
@@ -11,7 +12,7 @@ import mimetypes
 from abc import ABC, abstractmethod
 from collections.abc import Generator, Iterable, Mapping
 from io import BufferedReader, BytesIO
-from pathlib import PurePath
+from pathlib import Path, PurePath
 from typing import Any, Optional, Union
 
 from pydantic import BaseModel, ConfigDict, model_validator
@@ -55,8 +56,7 @@ class Blob(BaseModel):
     def as_string(self) -> str:
         """Read data as a string."""
         if self.data is None and self.path:
-            with open(str(self.path), encoding=self.encoding) as f:
-                return f.read()
+            return Path(str(self.path)).read_text(encoding=self.encoding)
         elif isinstance(self.data, bytes):
             return self.data.decode(self.encoding)
         elif isinstance(self.data, str):
@@ -71,8 +71,7 @@ class Blob(BaseModel):
         elif isinstance(self.data, str):
             return self.data.encode(self.encoding)
         elif self.data is None and self.path:
-            with open(str(self.path), "rb") as f:
-                return f.read()
+            return Path(str(self.path)).read_bytes()
         else:
             raise ValueError(f"Unable to get bytes for blob {self}")
 
diff --git a/api/core/rag/extractor/csv_extractor.py b/api/core/rag/extractor/csv_extractor.py
index 0470569f39..5b67403902 100644
--- a/api/core/rag/extractor/csv_extractor.py
+++ b/api/core/rag/extractor/csv_extractor.py
@@ -1,4 +1,5 @@
 """Abstract interface for document loader implementations."""
+
 import csv
 from typing import Optional
 
@@ -18,12 +19,12 @@ class CSVExtractor(BaseExtractor):
     """
 
     def __init__(
-            self,
-            file_path: str,
-            encoding: Optional[str] = None,
-            autodetect_encoding: bool = False,
-            source_column: Optional[str] = None,
-            csv_args: Optional[dict] = None,
+        self,
+        file_path: str,
+        encoding: Optional[str] = None,
+        autodetect_encoding: bool = False,
+        source_column: Optional[str] = None,
+        csv_args: Optional[dict] = None,
     ):
         """Initialize with file path."""
         self._file_path = file_path
@@ -57,7 +58,7 @@ class CSVExtractor(BaseExtractor):
         docs = []
         try:
             # load csv file into pandas dataframe
-            df = pd.read_csv(csvfile, on_bad_lines='skip', **self.csv_args)
+            df = pd.read_csv(csvfile, on_bad_lines="skip", **self.csv_args)
 
             # check source column exists
             if self.source_column and self.source_column not in df.columns:
@@ -67,7 +68,7 @@ class CSVExtractor(BaseExtractor):
 
             for i, row in df.iterrows():
                 content = ";".join(f"{col.strip()}: {str(row[col]).strip()}" for col in df.columns)
-                source = row[self.source_column] if self.source_column else ''
+                source = row[self.source_column] if self.source_column else ""
                 metadata = {"source": source, "row": i}
                 doc = Document(page_content=content, metadata=metadata)
                 docs.append(doc)
diff --git a/api/core/rag/extractor/entity/extract_setting.py b/api/core/rag/extractor/entity/extract_setting.py
index 7479b1d97b..3692b5d19d 100644
--- a/api/core/rag/extractor/entity/extract_setting.py
+++ b/api/core/rag/extractor/entity/extract_setting.py
@@ -10,6 +10,7 @@ class NotionInfo(BaseModel):
     """
     Notion import info.
     """
+
     notion_workspace_id: str
     notion_obj_id: str
     notion_page_type: str
@@ -25,6 +26,7 @@ class WebsiteInfo(BaseModel):
     """
     website import info.
     """
+
     provider: str
     job_id: str
     url: str
@@ -43,6 +45,7 @@ class ExtractSetting(BaseModel):
     """
     Model class for provider response.
     """
+
     datasource_type: str
     upload_file: Optional[UploadFile] = None
     notion_info: Optional[NotionInfo] = None
diff --git a/api/core/rag/extractor/excel_extractor.py b/api/core/rag/extractor/excel_extractor.py
index f0c302a619..fc33165719 100644
--- a/api/core/rag/extractor/excel_extractor.py
+++ b/api/core/rag/extractor/excel_extractor.py
@@ -1,4 +1,5 @@
 """Abstract interface for document loader implementations."""
+
 import os
 from typing import Optional
 
@@ -17,59 +18,60 @@ class ExcelExtractor(BaseExtractor):
         file_path: Path to the file to load.
     """
 
-    def __init__(
-            self,
-            file_path: str,
-            encoding: Optional[str] = None,
-            autodetect_encoding: bool = False
-    ):
+    def __init__(self, file_path: str, encoding: Optional[str] = None, autodetect_encoding: bool = False):
         """Initialize with file path."""
         self._file_path = file_path
         self._encoding = encoding
         self._autodetect_encoding = autodetect_encoding
 
     def extract(self) -> list[Document]:
-        """ Load from Excel file in xls or xlsx format using Pandas and openpyxl."""
+        """Load from Excel file in xls or xlsx format using Pandas and openpyxl."""
         documents = []
         file_extension = os.path.splitext(self._file_path)[-1].lower()
 
-        if file_extension == '.xlsx':
+        if file_extension == ".xlsx":
             wb = load_workbook(self._file_path, data_only=True)
             for sheet_name in wb.sheetnames:
                 sheet = wb[sheet_name]
                 data = sheet.values
-                cols = next(data)
+                try:
+                    cols = next(data)
+                except StopIteration:
+                    continue
                 df = pd.DataFrame(data, columns=cols)
 
-                df.dropna(how='all', inplace=True)
+                df.dropna(how="all", inplace=True)
 
                 for index, row in df.iterrows():
                     page_content = []
                     for col_index, (k, v) in enumerate(row.items()):
                         if pd.notna(v):
-                            cell = sheet.cell(row=index + 2,
-                                              column=col_index + 1)  # +2 to account for header and 1-based index
+                            cell = sheet.cell(
+                                row=index + 2, column=col_index + 1
+                            )  # +2 to account for header and 1-based index
                             if cell.hyperlink:
                                 value = f"[{v}]({cell.hyperlink.target})"
                                 page_content.append(f'"{k}":"{value}"')
                             else:
                                 page_content.append(f'"{k}":"{v}"')
-                    documents.append(Document(page_content=';'.join(page_content),
-                                              metadata={'source': self._file_path}))
+                    documents.append(
+                        Document(page_content=";".join(page_content), metadata={"source": self._file_path})
+                    )
 
-        elif file_extension == '.xls':
-            excel_file = pd.ExcelFile(self._file_path, engine='xlrd')
+        elif file_extension == ".xls":
+            excel_file = pd.ExcelFile(self._file_path, engine="xlrd")
             for sheet_name in excel_file.sheet_names:
                 df = excel_file.parse(sheet_name=sheet_name)
-                df.dropna(how='all', inplace=True)
+                df.dropna(how="all", inplace=True)
 
                 for _, row in df.iterrows():
                     page_content = []
                     for k, v in row.items():
                         if pd.notna(v):
                             page_content.append(f'"{k}":"{v}"')
-                    documents.append(Document(page_content=';'.join(page_content),
-                                              metadata={'source': self._file_path}))
+                    documents.append(
+                        Document(page_content=";".join(page_content), metadata={"source": self._file_path})
+                    )
         else:
             raise ValueError(f"Unsupported file extension: {file_extension}")
 
diff --git a/api/core/rag/extractor/extract_processor.py b/api/core/rag/extractor/extract_processor.py
index f7a08135f5..fe7eaa32e6 100644
--- a/api/core/rag/extractor/extract_processor.py
+++ b/api/core/rag/extractor/extract_processor.py
@@ -29,61 +29,62 @@ from core.rag.models.document import Document
 from extensions.ext_storage import storage
 from models.model import UploadFile
 
-SUPPORT_URL_CONTENT_TYPES = ['application/pdf', 'text/plain', 'application/json']
-USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+SUPPORT_URL_CONTENT_TYPES = ["application/pdf", "text/plain", "application/json"]
+USER_AGENT = (
+    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124"
+    " Safari/537.36"
+)
 
 
 class ExtractProcessor:
     @classmethod
-    def load_from_upload_file(cls, upload_file: UploadFile, return_text: bool = False, is_automatic: bool = False) \
-            -> Union[list[Document], str]:
+    def load_from_upload_file(
+        cls, upload_file: UploadFile, return_text: bool = False, is_automatic: bool = False
+    ) -> Union[list[Document], str]:
         extract_setting = ExtractSetting(
-            datasource_type="upload_file",
-            upload_file=upload_file,
-            document_model='text_model'
+            datasource_type="upload_file", upload_file=upload_file, document_model="text_model"
         )
         if return_text:
-            delimiter = '\n'
+            delimiter = "\n"
             return delimiter.join([document.page_content for document in cls.extract(extract_setting, is_automatic)])
         else:
             return cls.extract(extract_setting, is_automatic)
 
     @classmethod
     def load_from_url(cls, url: str, return_text: bool = False) -> Union[list[Document], str]:
-        response = ssrf_proxy.get(url, headers={
-            "User-Agent": USER_AGENT
-        })
+        response = ssrf_proxy.get(url, headers={"User-Agent": USER_AGENT})
 
         with tempfile.TemporaryDirectory() as temp_dir:
             suffix = Path(url).suffix
-            if not suffix and suffix != '.':
+            if not suffix and suffix != ".":
                 # get content-type
-                if response.headers.get('Content-Type'):
-                    suffix = '.' + response.headers.get('Content-Type').split('/')[-1]
+                if response.headers.get("Content-Type"):
+                    suffix = "." + response.headers.get("Content-Type").split("/")[-1]
                 else:
-                    content_disposition = response.headers.get('Content-Disposition')
+                    content_disposition = response.headers.get("Content-Disposition")
                     filename_match = re.search(r'filename="([^"]+)"', content_disposition)
                     if filename_match:
                         filename = unquote(filename_match.group(1))
-                        suffix = '.' + re.search(r'\.(\w+)$', filename).group(1)
+                        suffix = "." + re.search(r"\.(\w+)$", filename).group(1)
 
             file_path = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
-            with open(file_path, 'wb') as file:
-                file.write(response.content)
-            extract_setting = ExtractSetting(
-                datasource_type="upload_file",
-                document_model='text_model'
-            )
+            Path(file_path).write_bytes(response.content)
+            extract_setting = ExtractSetting(datasource_type="upload_file", document_model="text_model")
             if return_text:
-                delimiter = '\n'
-                return delimiter.join([document.page_content for document in cls.extract(
-                    extract_setting=extract_setting, file_path=file_path)])
+                delimiter = "\n"
+                return delimiter.join(
+                    [
+                        document.page_content
+                        for document in cls.extract(extract_setting=extract_setting, file_path=file_path)
+                    ]
+                )
             else:
                 return cls.extract(extract_setting=extract_setting, file_path=file_path)
 
     @classmethod
-    def extract(cls, extract_setting: ExtractSetting, is_automatic: bool = False,
-                file_path: str = None) -> list[Document]:
+    def extract(
+        cls, extract_setting: ExtractSetting, is_automatic: bool = False, file_path: str = None
+    ) -> list[Document]:
         if extract_setting.datasource_type == DatasourceType.FILE.value:
             with tempfile.TemporaryDirectory() as temp_dir:
                 if not file_path:
@@ -96,50 +97,56 @@ class ExtractProcessor:
                 etl_type = dify_config.ETL_TYPE
                 unstructured_api_url = dify_config.UNSTRUCTURED_API_URL
                 unstructured_api_key = dify_config.UNSTRUCTURED_API_KEY
-                if etl_type == 'Unstructured':
-                    if file_extension == '.xlsx' or file_extension == '.xls':
+                if etl_type == "Unstructured":
+                    if file_extension in {".xlsx", ".xls"}:
                         extractor = ExcelExtractor(file_path)
-                    elif file_extension == '.pdf':
+                    elif file_extension == ".pdf":
                         extractor = PdfExtractor(file_path)
-                    elif file_extension in ['.md', '.markdown']:
-                        extractor = UnstructuredMarkdownExtractor(file_path, unstructured_api_url) if is_automatic \
+                    elif file_extension in {".md", ".markdown"}:
+                        extractor = (
+                            UnstructuredMarkdownExtractor(file_path, unstructured_api_url)
+                            if is_automatic
                             else MarkdownExtractor(file_path, autodetect_encoding=True)
-                    elif file_extension in ['.htm', '.html']:
+                        )
+                    elif file_extension in {".htm", ".html"}:
                         extractor = HtmlExtractor(file_path)
-                    elif file_extension in ['.docx']:
+                    elif file_extension == ".docx":
                         extractor = WordExtractor(file_path, upload_file.tenant_id, upload_file.created_by)
-                    elif file_extension == '.csv':
+                    elif file_extension == ".csv":
                         extractor = CSVExtractor(file_path, autodetect_encoding=True)
-                    elif file_extension == '.msg':
+                    elif file_extension == ".msg":
                         extractor = UnstructuredMsgExtractor(file_path, unstructured_api_url)
-                    elif file_extension == '.eml':
+                    elif file_extension == ".eml":
                         extractor = UnstructuredEmailExtractor(file_path, unstructured_api_url)
-                    elif file_extension == '.ppt':
+                    elif file_extension == ".ppt":
                         extractor = UnstructuredPPTExtractor(file_path, unstructured_api_url, unstructured_api_key)
-                    elif file_extension == '.pptx':
+                    elif file_extension == ".pptx":
                         extractor = UnstructuredPPTXExtractor(file_path, unstructured_api_url)
-                    elif file_extension == '.xml':
+                    elif file_extension == ".xml":
                         extractor = UnstructuredXmlExtractor(file_path, unstructured_api_url)
-                    elif file_extension == 'epub':
+                    elif file_extension == "epub":
                         extractor = UnstructuredEpubExtractor(file_path, unstructured_api_url)
                     else:
                         # txt
-                        extractor = UnstructuredTextExtractor(file_path, unstructured_api_url) if is_automatic \
+                        extractor = (
+                            UnstructuredTextExtractor(file_path, unstructured_api_url)
+                            if is_automatic
                             else TextExtractor(file_path, autodetect_encoding=True)
+                        )
                 else:
-                    if file_extension == '.xlsx' or file_extension == '.xls':
+                    if file_extension in {".xlsx", ".xls"}:
                         extractor = ExcelExtractor(file_path)
-                    elif file_extension == '.pdf':
+                    elif file_extension == ".pdf":
                         extractor = PdfExtractor(file_path)
-                    elif file_extension in ['.md', '.markdown']:
+                    elif file_extension in {".md", ".markdown"}:
                         extractor = MarkdownExtractor(file_path, autodetect_encoding=True)
-                    elif file_extension in ['.htm', '.html']:
+                    elif file_extension in {".htm", ".html"}:
                         extractor = HtmlExtractor(file_path)
-                    elif file_extension in ['.docx']:
+                    elif file_extension == ".docx":
                         extractor = WordExtractor(file_path, upload_file.tenant_id, upload_file.created_by)
-                    elif file_extension == '.csv':
+                    elif file_extension == ".csv":
                         extractor = CSVExtractor(file_path, autodetect_encoding=True)
-                    elif file_extension == 'epub':
+                    elif file_extension == "epub":
                         extractor = UnstructuredEpubExtractor(file_path)
                     else:
                         # txt
@@ -155,13 +162,13 @@ class ExtractProcessor:
             )
             return extractor.extract()
         elif extract_setting.datasource_type == DatasourceType.WEBSITE.value:
-            if extract_setting.website_info.provider == 'firecrawl':
+            if extract_setting.website_info.provider == "firecrawl":
                 extractor = FirecrawlWebExtractor(
                     url=extract_setting.website_info.url,
                     job_id=extract_setting.website_info.job_id,
                     tenant_id=extract_setting.website_info.tenant_id,
                     mode=extract_setting.website_info.mode,
-                    only_main_content=extract_setting.website_info.only_main_content
+                    only_main_content=extract_setting.website_info.only_main_content,
                 )
                 return extractor.extract()
             else:
diff --git a/api/core/rag/extractor/extractor_base.py b/api/core/rag/extractor/extractor_base.py
index c490e59332..582eca94df 100644
--- a/api/core/rag/extractor/extractor_base.py
+++ b/api/core/rag/extractor/extractor_base.py
@@ -1,12 +1,11 @@
 """Abstract interface for document loader implementations."""
+
 from abc import ABC, abstractmethod
 
 
 class BaseExtractor(ABC):
-    """Interface for extract files.
-    """
+    """Interface for extract files."""
 
     @abstractmethod
     def extract(self):
         raise NotImplementedError
-
diff --git a/api/core/rag/extractor/firecrawl/firecrawl_app.py b/api/core/rag/extractor/firecrawl/firecrawl_app.py
index 2b85ad9739..17c2087a0a 100644
--- a/api/core/rag/extractor/firecrawl/firecrawl_app.py
+++ b/api/core/rag/extractor/firecrawl/firecrawl_app.py
@@ -9,108 +9,98 @@ from extensions.ext_storage import storage
 class FirecrawlApp:
     def __init__(self, api_key=None, base_url=None):
         self.api_key = api_key
-        self.base_url = base_url or 'https://api.firecrawl.dev'
-        if self.api_key is None and self.base_url == 'https://api.firecrawl.dev':
-            raise ValueError('No API key provided')
+        self.base_url = base_url or "https://api.firecrawl.dev"
+        if self.api_key is None and self.base_url == "https://api.firecrawl.dev":
+            raise ValueError("No API key provided")
 
     def scrape_url(self, url, params=None) -> dict:
-        headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f'Bearer {self.api_key}'
-        }
-        json_data = {'url': url}
+        headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"}
+        json_data = {"url": url}
         if params:
             json_data.update(params)
-        response = requests.post(
-            f'{self.base_url}/v0/scrape',
-            headers=headers,
-            json=json_data
-        )
+        response = requests.post(f"{self.base_url}/v0/scrape", headers=headers, json=json_data)
         if response.status_code == 200:
             response = response.json()
-            if response['success'] == True:
-                data = response['data']
+            if response["success"] == True:
+                data = response["data"]
                 return {
-                    'title': data.get('metadata').get('title'),
-                    'description': data.get('metadata').get('description'),
-                    'source_url': data.get('metadata').get('sourceURL'),
-                    'markdown': data.get('markdown')
+                    "title": data.get("metadata").get("title"),
+                    "description": data.get("metadata").get("description"),
+                    "source_url": data.get("metadata").get("sourceURL"),
+                    "markdown": data.get("markdown"),
                 }
             else:
                 raise Exception(f'Failed to scrape URL. Error: {response["error"]}')
 
-        elif response.status_code in [402, 409, 500]:
-            error_message = response.json().get('error', 'Unknown error occurred')
-            raise Exception(f'Failed to scrape URL. Status code: {response.status_code}. Error: {error_message}')
+        elif response.status_code in {402, 409, 500}:
+            error_message = response.json().get("error", "Unknown error occurred")
+            raise Exception(f"Failed to scrape URL. Status code: {response.status_code}. Error: {error_message}")
         else:
-            raise Exception(f'Failed to scrape URL. Status code: {response.status_code}')
+            raise Exception(f"Failed to scrape URL. Status code: {response.status_code}")
 
     def crawl_url(self, url, params=None) -> str:
         headers = self._prepare_headers()
-        json_data = {'url': url}
+        json_data = {"url": url}
         if params:
             json_data.update(params)
-        response = self._post_request(f'{self.base_url}/v0/crawl', json_data, headers)
+        response = self._post_request(f"{self.base_url}/v0/crawl", json_data, headers)
         if response.status_code == 200:
-            job_id = response.json().get('jobId')
+            job_id = response.json().get("jobId")
             return job_id
         else:
-            self._handle_error(response, 'start crawl job')
+            self._handle_error(response, "start crawl job")
 
     def check_crawl_status(self, job_id) -> dict:
         headers = self._prepare_headers()
-        response = self._get_request(f'{self.base_url}/v0/crawl/status/{job_id}', headers)
+        response = self._get_request(f"{self.base_url}/v0/crawl/status/{job_id}", headers)
         if response.status_code == 200:
             crawl_status_response = response.json()
-            if crawl_status_response.get('status') == 'completed':
-                total = crawl_status_response.get('total', 0)
+            if crawl_status_response.get("status") == "completed":
+                total = crawl_status_response.get("total", 0)
                 if total == 0:
-                    raise Exception('Failed to check crawl status. Error: No page found')
-                data = crawl_status_response.get('data', [])
+                    raise Exception("Failed to check crawl status. Error: No page found")
+                data = crawl_status_response.get("data", [])
                 url_data_list = []
                 for item in data:
-                    if isinstance(item, dict) and 'metadata' in item and 'markdown' in item:
+                    if isinstance(item, dict) and "metadata" in item and "markdown" in item:
                         url_data = {
-                            'title': item.get('metadata').get('title'),
-                            'description': item.get('metadata').get('description'),
-                            'source_url': item.get('metadata').get('sourceURL'),
-                            'markdown': item.get('markdown')
+                            "title": item.get("metadata").get("title"),
+                            "description": item.get("metadata").get("description"),
+                            "source_url": item.get("metadata").get("sourceURL"),
+                            "markdown": item.get("markdown"),
                         }
                         url_data_list.append(url_data)
                 if url_data_list:
-                    file_key = 'website_files/' + job_id + '.txt'
+                    file_key = "website_files/" + job_id + ".txt"
                     if storage.exists(file_key):
                         storage.delete(file_key)
-                    storage.save(file_key, json.dumps(url_data_list).encode('utf-8'))
+                    storage.save(file_key, json.dumps(url_data_list).encode("utf-8"))
                 return {
-                    'status': 'completed',
-                    'total': crawl_status_response.get('total'),
-                    'current': crawl_status_response.get('current'),
-                    'data': url_data_list
+                    "status": "completed",
+                    "total": crawl_status_response.get("total"),
+                    "current": crawl_status_response.get("current"),
+                    "data": url_data_list,
                 }
 
             else:
                 return {
-                    'status': crawl_status_response.get('status'),
-                    'total': crawl_status_response.get('total'),
-                    'current': crawl_status_response.get('current'),
-                    'data': []
+                    "status": crawl_status_response.get("status"),
+                    "total": crawl_status_response.get("total"),
+                    "current": crawl_status_response.get("current"),
+                    "data": [],
                 }
 
         else:
-            self._handle_error(response, 'check crawl status')
+            self._handle_error(response, "check crawl status")
 
     def _prepare_headers(self):
-        return {
-            'Content-Type': 'application/json',
-            'Authorization': f'Bearer {self.api_key}'
-        }
+        return {"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"}
 
     def _post_request(self, url, data, headers, retries=3, backoff_factor=0.5):
         for attempt in range(retries):
             response = requests.post(url, headers=headers, json=data)
             if response.status_code == 502:
-                time.sleep(backoff_factor * (2 ** attempt))
+                time.sleep(backoff_factor * (2**attempt))
             else:
                 return response
         return response
@@ -119,13 +109,11 @@ class FirecrawlApp:
         for attempt in range(retries):
             response = requests.get(url, headers=headers)
             if response.status_code == 502:
-                time.sleep(backoff_factor * (2 ** attempt))
+                time.sleep(backoff_factor * (2**attempt))
             else:
                 return response
         return response
 
     def _handle_error(self, response, action):
-        error_message = response.json().get('error', 'Unknown error occurred')
-        raise Exception(f'Failed to {action}. Status code: {response.status_code}. Error: {error_message}')
-
-
+        error_message = response.json().get("error", "Unknown error occurred")
+        raise Exception(f"Failed to {action}. Status code: {response.status_code}. Error: {error_message}")
diff --git a/api/core/rag/extractor/firecrawl/firecrawl_web_extractor.py b/api/core/rag/extractor/firecrawl/firecrawl_web_extractor.py
index 8e2f107e5e..b33ce167c2 100644
--- a/api/core/rag/extractor/firecrawl/firecrawl_web_extractor.py
+++ b/api/core/rag/extractor/firecrawl/firecrawl_web_extractor.py
@@ -5,7 +5,7 @@ from services.website_service import WebsiteService
 
 class FirecrawlWebExtractor(BaseExtractor):
     """
-    Crawl and scrape websites and return content in clean llm-ready markdown. 
+    Crawl and scrape websites and return content in clean llm-ready markdown.
 
 
     Args:
@@ -15,14 +15,7 @@ class FirecrawlWebExtractor(BaseExtractor):
         mode: The mode of operation. Defaults to 'scrape'. Options are 'crawl', 'scrape' and 'crawl_return_urls'.
     """
 
-    def __init__(
-            self,
-            url: str,
-            job_id: str,
-            tenant_id: str,
-            mode: str = 'crawl',
-            only_main_content: bool = False
-    ):
+    def __init__(self, url: str, job_id: str, tenant_id: str, mode: str = "crawl", only_main_content: bool = False):
         """Initialize with url, api_key, base_url and mode."""
         self._url = url
         self.job_id = job_id
@@ -33,28 +26,31 @@ class FirecrawlWebExtractor(BaseExtractor):
     def extract(self) -> list[Document]:
         """Extract content from the URL."""
         documents = []
-        if self.mode == 'crawl':
-            crawl_data = WebsiteService.get_crawl_url_data(self.job_id, 'firecrawl', self._url, self.tenant_id)
+        if self.mode == "crawl":
+            crawl_data = WebsiteService.get_crawl_url_data(self.job_id, "firecrawl", self._url, self.tenant_id)
             if crawl_data is None:
                 return []
-            document = Document(page_content=crawl_data.get('markdown', ''),
-                                metadata={
-                                    'source_url': crawl_data.get('source_url'),
-                                    'description': crawl_data.get('description'),
-                                    'title': crawl_data.get('title')
-                                }
-                                )
+            document = Document(
+                page_content=crawl_data.get("markdown", ""),
+                metadata={
+                    "source_url": crawl_data.get("source_url"),
+                    "description": crawl_data.get("description"),
+                    "title": crawl_data.get("title"),
+                },
+            )
             documents.append(document)
-        elif self.mode == 'scrape':
-            scrape_data = WebsiteService.get_scrape_url_data('firecrawl', self._url, self.tenant_id,
-                                                             self.only_main_content)
+        elif self.mode == "scrape":
+            scrape_data = WebsiteService.get_scrape_url_data(
+                "firecrawl", self._url, self.tenant_id, self.only_main_content
+            )
 
-            document = Document(page_content=scrape_data.get('markdown', ''),
-                                metadata={
-                                    'source_url': scrape_data.get('source_url'),
-                                    'description': scrape_data.get('description'),
-                                    'title': scrape_data.get('title')
-                                }
-                                )
+            document = Document(
+                page_content=scrape_data.get("markdown", ""),
+                metadata={
+                    "source_url": scrape_data.get("source_url"),
+                    "description": scrape_data.get("description"),
+                    "title": scrape_data.get("title"),
+                },
+            )
             documents.append(document)
         return documents
diff --git a/api/core/rag/extractor/helpers.py b/api/core/rag/extractor/helpers.py
index 0c17a47b32..69ca9d5d63 100644
--- a/api/core/rag/extractor/helpers.py
+++ b/api/core/rag/extractor/helpers.py
@@ -1,6 +1,7 @@
 """Document loader helpers."""
 
 import concurrent.futures
+from pathlib import Path
 from typing import NamedTuple, Optional, cast
 
 
@@ -28,8 +29,7 @@ def detect_file_encodings(file_path: str, timeout: int = 5) -> list[FileEncoding
     import chardet
 
     def read_and_detect(file_path: str) -> list[dict]:
-        with open(file_path, "rb") as f:
-            rawdata = f.read()
+        rawdata = Path(file_path).read_bytes()
         return cast(list[dict], chardet.detect_all(rawdata))
 
     with concurrent.futures.ThreadPoolExecutor() as executor:
@@ -37,9 +37,7 @@ def detect_file_encodings(file_path: str, timeout: int = 5) -> list[FileEncoding
         try:
             encodings = future.result(timeout=timeout)
         except concurrent.futures.TimeoutError:
-            raise TimeoutError(
-                f"Timeout reached while detecting encoding for {file_path}"
-            )
+            raise TimeoutError(f"Timeout reached while detecting encoding for {file_path}")
 
     if all(encoding["encoding"] is None for encoding in encodings):
         raise RuntimeError(f"Could not detect encoding for {file_path}")
diff --git a/api/core/rag/extractor/html_extractor.py b/api/core/rag/extractor/html_extractor.py
index ceb5306255..560c2d1d84 100644
--- a/api/core/rag/extractor/html_extractor.py
+++ b/api/core/rag/extractor/html_extractor.py
@@ -1,4 +1,5 @@
 """Abstract interface for document loader implementations."""
+
 from bs4 import BeautifulSoup
 
 from core.rag.extractor.extractor_base import BaseExtractor
@@ -6,7 +7,6 @@ from core.rag.models.document import Document
 
 
 class HtmlExtractor(BaseExtractor):
-
     """
     Load html files.
 
@@ -15,10 +15,7 @@ class HtmlExtractor(BaseExtractor):
         file_path: Path to the file to load.
     """
 
-    def __init__(
-        self,
-        file_path: str
-    ):
+    def __init__(self, file_path: str):
         """Initialize with file path."""
         self._file_path = file_path
 
@@ -27,8 +24,8 @@ class HtmlExtractor(BaseExtractor):
 
     def _load_as_text(self) -> str:
         with open(self._file_path, "rb") as fp:
-            soup = BeautifulSoup(fp, 'html.parser')
+            soup = BeautifulSoup(fp, "html.parser")
             text = soup.get_text()
-            text = text.strip() if text else ''
+            text = text.strip() if text else ""
 
-        return text
\ No newline at end of file
+        return text
diff --git a/api/core/rag/extractor/markdown_extractor.py b/api/core/rag/extractor/markdown_extractor.py
index b24cf2e170..849852ac23 100644
--- a/api/core/rag/extractor/markdown_extractor.py
+++ b/api/core/rag/extractor/markdown_extractor.py
@@ -1,5 +1,7 @@
 """Abstract interface for document loader implementations."""
+
 import re
+from pathlib import Path
 from typing import Optional, cast
 
 from core.rag.extractor.extractor_base import BaseExtractor
@@ -16,12 +18,12 @@ class MarkdownExtractor(BaseExtractor):
     """
 
     def __init__(
-            self,
-            file_path: str,
-            remove_hyperlinks: bool = False,
-            remove_images: bool = False,
-            encoding: Optional[str] = None,
-            autodetect_encoding: bool = True,
+        self,
+        file_path: str,
+        remove_hyperlinks: bool = False,
+        remove_images: bool = False,
+        encoding: Optional[str] = None,
+        autodetect_encoding: bool = True,
     ):
         """Initialize with file path."""
         self._file_path = file_path
@@ -78,13 +80,10 @@ class MarkdownExtractor(BaseExtractor):
         if current_header is not None:
             # pass linting, assert keys are defined
             markdown_tups = [
-                (re.sub(r"#", "", cast(str, key)).strip(), re.sub(r"<.*?>", "", value))
-                for key, value in markdown_tups
+                (re.sub(r"#", "", cast(str, key)).strip(), re.sub(r"<.*?>", "", value)) for key, value in markdown_tups
             ]
         else:
-            markdown_tups = [
-                (key, re.sub("\n", "", value)) for key, value in markdown_tups
-            ]
+            markdown_tups = [(key, re.sub("\n", "", value)) for key, value in markdown_tups]
 
         return markdown_tups
 
@@ -104,15 +103,13 @@ class MarkdownExtractor(BaseExtractor):
         """Parse file into tuples."""
         content = ""
         try:
-            with open(filepath, encoding=self._encoding) as f:
-                content = f.read()
+            content = Path(filepath).read_text(encoding=self._encoding)
         except UnicodeDecodeError as e:
             if self._autodetect_encoding:
                 detected_encodings = detect_file_encodings(filepath)
                 for encoding in detected_encodings:
                     try:
-                        with open(filepath, encoding=encoding.encoding) as f:
-                            content = f.read()
+                        content = Path(filepath).read_text(encoding=encoding.encoding)
                         break
                     except UnicodeDecodeError:
                         continue
diff --git a/api/core/rag/extractor/notion_extractor.py b/api/core/rag/extractor/notion_extractor.py
index 2b12b8a4b2..87a4ce08bf 100644
--- a/api/core/rag/extractor/notion_extractor.py
+++ b/api/core/rag/extractor/notion_extractor.py
@@ -21,22 +21,21 @@ RETRIEVE_PAGE_URL_TMPL = "https://api.notion.com/v1/pages/{page_id}"
 RETRIEVE_DATABASE_URL_TMPL = "https://api.notion.com/v1/databases/{database_id}"
 # if user want split by headings, use the corresponding splitter
 HEADING_SPLITTER = {
-    'heading_1': '# ',
-    'heading_2': '## ',
-    'heading_3': '### ',
+    "heading_1": "# ",
+    "heading_2": "## ",
+    "heading_3": "### ",
 }
 
+
 class NotionExtractor(BaseExtractor):
-
     def __init__(
-            self,
-            notion_workspace_id: str,
-            notion_obj_id: str,
-            notion_page_type: str,
-            tenant_id: str,
-            document_model: Optional[DocumentModel] = None,
-            notion_access_token: Optional[str] = None,
-
+        self,
+        notion_workspace_id: str,
+        notion_obj_id: str,
+        notion_page_type: str,
+        tenant_id: str,
+        document_model: Optional[DocumentModel] = None,
+        notion_access_token: Optional[str] = None,
     ):
         self._notion_access_token = None
         self._document_model = document_model
@@ -46,46 +45,38 @@ class NotionExtractor(BaseExtractor):
         if notion_access_token:
             self._notion_access_token = notion_access_token
         else:
-            self._notion_access_token = self._get_access_token(tenant_id,
-                                                               self._notion_workspace_id)
+            self._notion_access_token = self._get_access_token(tenant_id, self._notion_workspace_id)
             if not self._notion_access_token:
                 integration_token = dify_config.NOTION_INTEGRATION_TOKEN
                 if integration_token is None:
                     raise ValueError(
-                        "Must specify `integration_token` or set environment "
-                        "variable `NOTION_INTEGRATION_TOKEN`."
+                        "Must specify `integration_token` or set environment variable `NOTION_INTEGRATION_TOKEN`."
                     )
 
                 self._notion_access_token = integration_token
 
     def extract(self) -> list[Document]:
-        self.update_last_edited_time(
-            self._document_model
-        )
+        self.update_last_edited_time(self._document_model)
 
         text_docs = self._load_data_as_documents(self._notion_obj_id, self._notion_page_type)
 
         return text_docs
 
-    def _load_data_as_documents(
-            self, notion_obj_id: str, notion_page_type: str
-    ) -> list[Document]:
+    def _load_data_as_documents(self, notion_obj_id: str, notion_page_type: str) -> list[Document]:
         docs = []
-        if notion_page_type == 'database':
+        if notion_page_type == "database":
             # get all the pages in the database
             page_text_documents = self._get_notion_database_data(notion_obj_id)
             docs.extend(page_text_documents)
-        elif notion_page_type == 'page':
+        elif notion_page_type == "page":
             page_text_list = self._get_notion_block_data(notion_obj_id)
-            docs.append(Document(page_content='\n'.join(page_text_list)))
+            docs.append(Document(page_content="\n".join(page_text_list)))
         else:
             raise ValueError("notion page type not supported")
 
         return docs
 
-    def _get_notion_database_data(
-            self, database_id: str, query_dict: dict[str, Any] = {}
-    ) -> list[Document]:
+    def _get_notion_database_data(self, database_id: str, query_dict: dict[str, Any] = {}) -> list[Document]:
         """Get all the pages from a Notion database."""
         res = requests.post(
             DATABASE_URL_TMPL.format(database_id=database_id),
@@ -100,50 +91,50 @@ class NotionExtractor(BaseExtractor):
         data = res.json()
 
         database_content = []
-        if 'results' not in data or data["results"] is None:
+        if "results" not in data or data["results"] is None:
             return []
         for result in data["results"]:
-            properties = result['properties']
+            properties = result["properties"]
             data = {}
             for property_name, property_value in properties.items():
-                type = property_value['type']
-                if type == 'multi_select':
+                type = property_value["type"]
+                if type == "multi_select":
                     value = []
                     multi_select_list = property_value[type]
                     for multi_select in multi_select_list:
-                        value.append(multi_select['name'])
-                elif type == 'rich_text' or type == 'title':
+                        value.append(multi_select["name"])
+                elif type in {"rich_text", "title"}:
                     if len(property_value[type]) > 0:
-                        value = property_value[type][0]['plain_text']
+                        value = property_value[type][0]["plain_text"]
                     else:
-                        value = ''
-                elif type == 'select' or type == 'status':
+                        value = ""
+                elif type in {"select", "status"}:
                     if property_value[type]:
-                        value = property_value[type]['name']
+                        value = property_value[type]["name"]
                     else:
-                        value = ''
+                        value = ""
                 else:
                     value = property_value[type]
                 data[property_name] = value
             row_dict = {k: v for k, v in data.items() if v}
-            row_content = ''
+            row_content = ""
             for key, value in row_dict.items():
                 if isinstance(value, dict):
                     value_dict = {k: v for k, v in value.items() if v}
-                    value_content = ''.join(f'{k}:{v} ' for k, v in value_dict.items())
-                    row_content = row_content + f'{key}:{value_content}\n'
+                    value_content = "".join(f"{k}:{v} " for k, v in value_dict.items())
+                    row_content = row_content + f"{key}:{value_content}\n"
                 else:
-                    row_content = row_content + f'{key}:{value}\n'
+                    row_content = row_content + f"{key}:{value}\n"
             database_content.append(row_content)
 
-        return [Document(page_content='\n'.join(database_content))]
+        return [Document(page_content="\n".join(database_content))]
 
     def _get_notion_block_data(self, page_id: str) -> list[str]:
         result_lines_arr = []
         start_cursor = None
         block_url = BLOCK_CHILD_URL_TMPL.format(block_id=page_id)
         while True:
-            query_dict: dict[str, Any] = {} if not start_cursor else {'start_cursor': start_cursor}
+            query_dict: dict[str, Any] = {} if not start_cursor else {"start_cursor": start_cursor}
             res = requests.request(
                 "GET",
                 block_url,
@@ -152,14 +143,14 @@ class NotionExtractor(BaseExtractor):
                     "Content-Type": "application/json",
                     "Notion-Version": "2022-06-28",
                 },
-                params=query_dict
+                params=query_dict,
             )
             data = res.json()
             for result in data["results"]:
                 result_type = result["type"]
                 result_obj = result[result_type]
                 cur_result_text_arr = []
-                if result_type == 'table':
+                if result_type == "table":
                     result_block_id = result["id"]
                     text = self._read_table_rows(result_block_id)
                     text += "\n\n"
@@ -175,17 +166,15 @@ class NotionExtractor(BaseExtractor):
                     result_block_id = result["id"]
                     has_children = result["has_children"]
                     block_type = result["type"]
-                    if has_children and block_type != 'child_page':
-                        children_text = self._read_block(
-                            result_block_id, num_tabs=1
-                        )
+                    if has_children and block_type != "child_page":
+                        children_text = self._read_block(result_block_id, num_tabs=1)
                         cur_result_text_arr.append(children_text)
 
                     cur_result_text = "\n".join(cur_result_text_arr)
                     if result_type in HEADING_SPLITTER:
                         result_lines_arr.append(f"{HEADING_SPLITTER[result_type]}{cur_result_text}")
                     else:
-                        result_lines_arr.append(cur_result_text + '\n\n')
+                        result_lines_arr.append(cur_result_text + "\n\n")
 
             if data["next_cursor"] is None:
                 break
@@ -199,7 +188,7 @@ class NotionExtractor(BaseExtractor):
         start_cursor = None
         block_url = BLOCK_CHILD_URL_TMPL.format(block_id=block_id)
         while True:
-            query_dict: dict[str, Any] = {} if not start_cursor else {'start_cursor': start_cursor}
+            query_dict: dict[str, Any] = {} if not start_cursor else {"start_cursor": start_cursor}
 
             res = requests.request(
                 "GET",
@@ -209,16 +198,16 @@ class NotionExtractor(BaseExtractor):
                     "Content-Type": "application/json",
                     "Notion-Version": "2022-06-28",
                 },
-                params=query_dict
+                params=query_dict,
             )
             data = res.json()
-            if 'results' not in data or data["results"] is None:
+            if "results" not in data or data["results"] is None:
                 break
             for result in data["results"]:
                 result_type = result["type"]
                 result_obj = result[result_type]
                 cur_result_text_arr = []
-                if result_type == 'table':
+                if result_type == "table":
                     result_block_id = result["id"]
                     text = self._read_table_rows(result_block_id)
                     result_lines_arr.append(text)
@@ -233,17 +222,15 @@ class NotionExtractor(BaseExtractor):
                     result_block_id = result["id"]
                     has_children = result["has_children"]
                     block_type = result["type"]
-                    if has_children and block_type != 'child_page':
-                        children_text = self._read_block(
-                            result_block_id, num_tabs=num_tabs + 1
-                        )
+                    if has_children and block_type != "child_page":
+                        children_text = self._read_block(result_block_id, num_tabs=num_tabs + 1)
                         cur_result_text_arr.append(children_text)
 
                     cur_result_text = "\n".join(cur_result_text_arr)
                     if result_type in HEADING_SPLITTER:
-                        result_lines_arr.append(f'{HEADING_SPLITTER[result_type]}{cur_result_text}')
+                        result_lines_arr.append(f"{HEADING_SPLITTER[result_type]}{cur_result_text}")
                     else:
-                        result_lines_arr.append(cur_result_text + '\n\n')
+                        result_lines_arr.append(cur_result_text + "\n\n")
 
             if data["next_cursor"] is None:
                 break
@@ -260,7 +247,7 @@ class NotionExtractor(BaseExtractor):
         start_cursor = None
         block_url = BLOCK_CHILD_URL_TMPL.format(block_id=block_id)
         while not done:
-            query_dict: dict[str, Any] = {} if not start_cursor else {'start_cursor': start_cursor}
+            query_dict: dict[str, Any] = {} if not start_cursor else {"start_cursor": start_cursor}
 
             res = requests.request(
                 "GET",
@@ -270,28 +257,28 @@ class NotionExtractor(BaseExtractor):
                     "Content-Type": "application/json",
                     "Notion-Version": "2022-06-28",
                 },
-                params=query_dict
+                params=query_dict,
             )
             data = res.json()
             # get table headers text
             table_header_cell_texts = []
-            tabel_header_cells = data["results"][0]['table_row']['cells']
-            for tabel_header_cell in tabel_header_cells:
-                if tabel_header_cell:
-                    for table_header_cell_text in tabel_header_cell:
+            table_header_cells = data["results"][0]["table_row"]["cells"]
+            for table_header_cell in table_header_cells:
+                if table_header_cell:
+                    for table_header_cell_text in table_header_cell:
                         text = table_header_cell_text["text"]["content"]
                         table_header_cell_texts.append(text)
                 else:
-                    table_header_cell_texts.append('')
+                    table_header_cell_texts.append("")
             # Initialize Markdown table with headers
             markdown_table = "| " + " | ".join(table_header_cell_texts) + " |\n"
-            markdown_table += "| " + " | ".join(['---'] * len(table_header_cell_texts)) + " |\n"
+            markdown_table += "| " + " | ".join(["---"] * len(table_header_cell_texts)) + " |\n"
 
             # Process data to format each row in Markdown table format
             results = data["results"]
             for i in range(len(results) - 1):
                 column_texts = []
-                table_column_cells = data["results"][i + 1]['table_row']['cells']
+                table_column_cells = data["results"][i + 1]["table_row"]["cells"]
                 for j in range(len(table_column_cells)):
                     if table_column_cells[j]:
                         for table_column_cell_text in table_column_cells[j]:
@@ -315,10 +302,8 @@ class NotionExtractor(BaseExtractor):
 
         last_edited_time = self.get_notion_last_edited_time()
         data_source_info = document_model.data_source_info_dict
-        data_source_info['last_edited_time'] = last_edited_time
-        update_params = {
-            DocumentModel.data_source_info: json.dumps(data_source_info)
-        }
+        data_source_info["last_edited_time"] = last_edited_time
+        update_params = {DocumentModel.data_source_info: json.dumps(data_source_info)}
 
         DocumentModel.query.filter_by(id=document_model.id).update(update_params)
         db.session.commit()
@@ -326,7 +311,7 @@ class NotionExtractor(BaseExtractor):
     def get_notion_last_edited_time(self) -> str:
         obj_id = self._notion_obj_id
         page_type = self._notion_page_type
-        if page_type == 'database':
+        if page_type == "database":
             retrieve_page_url = RETRIEVE_DATABASE_URL_TMPL.format(database_id=obj_id)
         else:
             retrieve_page_url = RETRIEVE_PAGE_URL_TMPL.format(page_id=obj_id)
@@ -341,7 +326,7 @@ class NotionExtractor(BaseExtractor):
                 "Content-Type": "application/json",
                 "Notion-Version": "2022-06-28",
             },
-            json=query_dict
+            json=query_dict,
         )
 
         data = res.json()
@@ -352,14 +337,16 @@ class NotionExtractor(BaseExtractor):
         data_source_binding = DataSourceOauthBinding.query.filter(
             db.and_(
                 DataSourceOauthBinding.tenant_id == tenant_id,
-                DataSourceOauthBinding.provider == 'notion',
+                DataSourceOauthBinding.provider == "notion",
                 DataSourceOauthBinding.disabled == False,
-                DataSourceOauthBinding.source_info['workspace_id'] == f'"{notion_workspace_id}"'
+                DataSourceOauthBinding.source_info["workspace_id"] == f'"{notion_workspace_id}"',
             )
         ).first()
 
         if not data_source_binding:
-            raise Exception(f'No notion data source binding found for tenant {tenant_id} '
-                            f'and notion workspace {notion_workspace_id}')
+            raise Exception(
+                f"No notion data source binding found for tenant {tenant_id} "
+                f"and notion workspace {notion_workspace_id}"
+            )
 
         return data_source_binding.access_token
diff --git a/api/core/rag/extractor/pdf_extractor.py b/api/core/rag/extractor/pdf_extractor.py
index cbb2655390..57cb9610ba 100644
--- a/api/core/rag/extractor/pdf_extractor.py
+++ b/api/core/rag/extractor/pdf_extractor.py
@@ -1,8 +1,9 @@
 """Abstract interface for document loader implementations."""
+
 from collections.abc import Iterator
 from typing import Optional
 
-from core.rag.extractor.blod.blod import Blob
+from core.rag.extractor.blob.blob import Blob
 from core.rag.extractor.extractor_base import BaseExtractor
 from core.rag.models.document import Document
 from extensions.ext_storage import storage
@@ -16,21 +17,17 @@ class PdfExtractor(BaseExtractor):
         file_path: Path to the file to load.
     """
 
-    def __init__(
-            self,
-            file_path: str,
-            file_cache_key: Optional[str] = None
-    ):
+    def __init__(self, file_path: str, file_cache_key: Optional[str] = None):
         """Initialize with file path."""
         self._file_path = file_path
         self._file_cache_key = file_cache_key
 
     def extract(self) -> list[Document]:
-        plaintext_file_key = ''
+        plaintext_file_key = ""
         plaintext_file_exists = False
         if self._file_cache_key:
             try:
-                text = storage.load(self._file_cache_key).decode('utf-8')
+                text = storage.load(self._file_cache_key).decode("utf-8")
                 plaintext_file_exists = True
                 return [Document(page_content=text)]
             except FileNotFoundError:
@@ -43,12 +40,12 @@ class PdfExtractor(BaseExtractor):
 
         # save plaintext file for caching
         if not plaintext_file_exists and plaintext_file_key:
-            storage.save(plaintext_file_key, text.encode('utf-8'))
+            storage.save(plaintext_file_key, text.encode("utf-8"))
 
         return documents
 
     def load(
-            self,
+        self,
     ) -> Iterator[Document]:
         """Lazy load given path as pages."""
         blob = Blob.from_path(self._file_path)
diff --git a/api/core/rag/extractor/text_extractor.py b/api/core/rag/extractor/text_extractor.py
index ac5d0920cf..b2b51d71d7 100644
--- a/api/core/rag/extractor/text_extractor.py
+++ b/api/core/rag/extractor/text_extractor.py
@@ -1,4 +1,6 @@
 """Abstract interface for document loader implementations."""
+
+from pathlib import Path
 from typing import Optional
 
 from core.rag.extractor.extractor_base import BaseExtractor
@@ -14,12 +16,7 @@ class TextExtractor(BaseExtractor):
         file_path: Path to the file to load.
     """
 
-    def __init__(
-            self,
-            file_path: str,
-            encoding: Optional[str] = None,
-            autodetect_encoding: bool = False
-    ):
+    def __init__(self, file_path: str, encoding: Optional[str] = None, autodetect_encoding: bool = False):
         """Initialize with file path."""
         self._file_path = file_path
         self._encoding = encoding
@@ -29,15 +26,13 @@ class TextExtractor(BaseExtractor):
         """Load from file path."""
         text = ""
         try:
-            with open(self._file_path, encoding=self._encoding) as f:
-                text = f.read()
+            text = Path(self._file_path).read_text(encoding=self._encoding)
         except UnicodeDecodeError as e:
             if self._autodetect_encoding:
                 detected_encodings = detect_file_encodings(self._file_path)
                 for encoding in detected_encodings:
                     try:
-                        with open(self._file_path, encoding=encoding.encoding) as f:
-                            text = f.read()
+                        text = Path(self._file_path).read_text(encoding=encoding.encoding)
                         break
                     except UnicodeDecodeError:
                         continue
diff --git a/api/core/rag/extractor/unstructured/unstructured_doc_extractor.py b/api/core/rag/extractor/unstructured/unstructured_doc_extractor.py
index 0323b14a4a..a525c9e9e3 100644
--- a/api/core/rag/extractor/unstructured/unstructured_doc_extractor.py
+++ b/api/core/rag/extractor/unstructured/unstructured_doc_extractor.py
@@ -8,13 +8,12 @@ logger = logging.getLogger(__name__)
 
 
 class UnstructuredWordExtractor(BaseExtractor):
-    """Loader that uses unstructured to load word documents.
-    """
+    """Loader that uses unstructured to load word documents."""
 
     def __init__(
-            self,
-            file_path: str,
-            api_url: str,
+        self,
+        file_path: str,
+        api_url: str,
     ):
         """Initialize with file path."""
         self._file_path = file_path
@@ -24,9 +23,7 @@ class UnstructuredWordExtractor(BaseExtractor):
         from unstructured.__version__ import __version__ as __unstructured_version__
         from unstructured.file_utils.filetype import FileType, detect_filetype
 
-        unstructured_version = tuple(
-            int(x) for x in __unstructured_version__.split(".")
-        )
+        unstructured_version = tuple(int(x) for x in __unstructured_version__.split("."))
         # check the file extension
         try:
             import magic  # noqa: F401
@@ -53,6 +50,7 @@ class UnstructuredWordExtractor(BaseExtractor):
             elements = partition_docx(filename=self._file_path)
 
         from unstructured.chunking.title import chunk_by_title
+
         chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
         documents = []
         for chunk in chunks:
diff --git a/api/core/rag/extractor/unstructured/unstructured_eml_extractor.py b/api/core/rag/extractor/unstructured/unstructured_eml_extractor.py
index 2e704f187d..34c6811b67 100644
--- a/api/core/rag/extractor/unstructured/unstructured_eml_extractor.py
+++ b/api/core/rag/extractor/unstructured/unstructured_eml_extractor.py
@@ -26,6 +26,7 @@ class UnstructuredEmailExtractor(BaseExtractor):
 
     def extract(self) -> list[Document]:
         from unstructured.partition.email import partition_email
+
         elements = partition_email(filename=self._file_path)
 
         # noinspection PyBroadException
@@ -34,15 +35,16 @@ class UnstructuredEmailExtractor(BaseExtractor):
                 element_text = element.text.strip()
 
                 padding_needed = 4 - len(element_text) % 4
-                element_text += '=' * padding_needed
+                element_text += "=" * padding_needed
 
                 element_decode = base64.b64decode(element_text)
-                soup = BeautifulSoup(element_decode.decode('utf-8'), 'html.parser')
+                soup = BeautifulSoup(element_decode.decode("utf-8"), "html.parser")
                 element.text = soup.get_text()
         except Exception:
             pass
 
         from unstructured.chunking.title import chunk_by_title
+
         chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
         documents = []
         for chunk in chunks:
diff --git a/api/core/rag/extractor/unstructured/unstructured_epub_extractor.py b/api/core/rag/extractor/unstructured/unstructured_epub_extractor.py
index 44cf958ea2..fa50fa76b2 100644
--- a/api/core/rag/extractor/unstructured/unstructured_epub_extractor.py
+++ b/api/core/rag/extractor/unstructured/unstructured_epub_extractor.py
@@ -28,6 +28,7 @@ class UnstructuredEpubExtractor(BaseExtractor):
 
         elements = partition_epub(filename=self._file_path, xml_keep_tags=True)
         from unstructured.chunking.title import chunk_by_title
+
         chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
         documents = []
         for chunk in chunks:
diff --git a/api/core/rag/extractor/unstructured/unstructured_markdown_extractor.py b/api/core/rag/extractor/unstructured/unstructured_markdown_extractor.py
index 144b4e0c1d..fc3ff10693 100644
--- a/api/core/rag/extractor/unstructured/unstructured_markdown_extractor.py
+++ b/api/core/rag/extractor/unstructured/unstructured_markdown_extractor.py
@@ -38,6 +38,7 @@ class UnstructuredMarkdownExtractor(BaseExtractor):
 
         elements = partition_md(filename=self._file_path)
         from unstructured.chunking.title import chunk_by_title
+
         chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
         documents = []
         for chunk in chunks:
diff --git a/api/core/rag/extractor/unstructured/unstructured_msg_extractor.py b/api/core/rag/extractor/unstructured/unstructured_msg_extractor.py
index ad09b79eb0..8091e83e85 100644
--- a/api/core/rag/extractor/unstructured/unstructured_msg_extractor.py
+++ b/api/core/rag/extractor/unstructured/unstructured_msg_extractor.py
@@ -14,11 +14,7 @@ class UnstructuredMsgExtractor(BaseExtractor):
         file_path: Path to the file to load.
     """
 
-    def __init__(
-        self,
-        file_path: str,
-        api_url: str
-    ):
+    def __init__(self, file_path: str, api_url: str):
         """Initialize with file path."""
         self._file_path = file_path
         self._api_url = api_url
@@ -28,6 +24,7 @@ class UnstructuredMsgExtractor(BaseExtractor):
 
         elements = partition_msg(filename=self._file_path)
         from unstructured.chunking.title import chunk_by_title
+
         chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
         documents = []
         for chunk in chunks:
diff --git a/api/core/rag/extractor/unstructured/unstructured_ppt_extractor.py b/api/core/rag/extractor/unstructured/unstructured_ppt_extractor.py
index d354b593ed..b69394b3b1 100644
--- a/api/core/rag/extractor/unstructured/unstructured_ppt_extractor.py
+++ b/api/core/rag/extractor/unstructured/unstructured_ppt_extractor.py
@@ -14,12 +14,7 @@ class UnstructuredPPTExtractor(BaseExtractor):
         file_path: Path to the file to load.
     """
 
-    def __init__(
-            self,
-            file_path: str,
-            api_url: str,
-            api_key: str
-    ):
+    def __init__(self, file_path: str, api_url: str, api_key: str):
         """Initialize with file path."""
         self._file_path = file_path
         self._api_url = api_url
diff --git a/api/core/rag/extractor/unstructured/unstructured_pptx_extractor.py b/api/core/rag/extractor/unstructured/unstructured_pptx_extractor.py
index 6fcbb5feb9..6ed4a0dfb3 100644
--- a/api/core/rag/extractor/unstructured/unstructured_pptx_extractor.py
+++ b/api/core/rag/extractor/unstructured/unstructured_pptx_extractor.py
@@ -14,11 +14,7 @@ class UnstructuredPPTXExtractor(BaseExtractor):
         file_path: Path to the file to load.
     """
 
-    def __init__(
-            self,
-            file_path: str,
-            api_url: str
-    ):
+    def __init__(self, file_path: str, api_url: str):
         """Initialize with file path."""
         self._file_path = file_path
         self._api_url = api_url
diff --git a/api/core/rag/extractor/unstructured/unstructured_text_extractor.py b/api/core/rag/extractor/unstructured/unstructured_text_extractor.py
index f4a4adbc16..22dfdd2075 100644
--- a/api/core/rag/extractor/unstructured/unstructured_text_extractor.py
+++ b/api/core/rag/extractor/unstructured/unstructured_text_extractor.py
@@ -14,11 +14,7 @@ class UnstructuredTextExtractor(BaseExtractor):
         file_path: Path to the file to load.
     """
 
-    def __init__(
-        self,
-        file_path: str,
-        api_url: str
-    ):
+    def __init__(self, file_path: str, api_url: str):
         """Initialize with file path."""
         self._file_path = file_path
         self._api_url = api_url
@@ -28,6 +24,7 @@ class UnstructuredTextExtractor(BaseExtractor):
 
         elements = partition_text(filename=self._file_path)
         from unstructured.chunking.title import chunk_by_title
+
         chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
         documents = []
         for chunk in chunks:
diff --git a/api/core/rag/extractor/unstructured/unstructured_xml_extractor.py b/api/core/rag/extractor/unstructured/unstructured_xml_extractor.py
index 6aef8e0f7e..3bffc01fbf 100644
--- a/api/core/rag/extractor/unstructured/unstructured_xml_extractor.py
+++ b/api/core/rag/extractor/unstructured/unstructured_xml_extractor.py
@@ -14,11 +14,7 @@ class UnstructuredXmlExtractor(BaseExtractor):
         file_path: Path to the file to load.
     """
 
-    def __init__(
-        self,
-        file_path: str,
-        api_url: str
-    ):
+    def __init__(self, file_path: str, api_url: str):
         """Initialize with file path."""
         self._file_path = file_path
         self._api_url = api_url
@@ -28,6 +24,7 @@ class UnstructuredXmlExtractor(BaseExtractor):
 
         elements = partition_xml(filename=self._file_path, xml_keep_tags=True)
         from unstructured.chunking.title import chunk_by_title
+
         chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
         documents = []
         for chunk in chunks:
diff --git a/api/core/rag/extractor/word_extractor.py b/api/core/rag/extractor/word_extractor.py
index 15822867bb..7352ef378b 100644
--- a/api/core/rag/extractor/word_extractor.py
+++ b/api/core/rag/extractor/word_extractor.py
@@ -1,4 +1,5 @@
 """Abstract interface for document loader implementations."""
+
 import datetime
 import logging
 import mimetypes
@@ -6,8 +7,8 @@ import os
 import re
 import tempfile
 import uuid
-import xml.etree.ElementTree as ET
 from urllib.parse import urlparse
+from xml.etree import ElementTree
 
 import requests
 from docx import Document as DocxDocument
@@ -21,6 +22,7 @@ from models.model import UploadFile
 
 logger = logging.getLogger(__name__)
 
+
 class WordExtractor(BaseExtractor):
     """Load docx files.
 
@@ -43,12 +45,11 @@ class WordExtractor(BaseExtractor):
             r = requests.get(self.file_path)
 
             if r.status_code != 200:
-                raise ValueError(
-                    f"Check the url of your file; returned status code {r.status_code}"
-                )
+                raise ValueError(f"Check the url of your file; returned status code {r.status_code}")
 
             self.web_path = self.file_path
-            self.temp_file = tempfile.NamedTemporaryFile()
+            # TODO: use a better way to handle the file
+            self.temp_file = tempfile.NamedTemporaryFile()  # noqa: SIM115
             self.temp_file.write(r.content)
             self.file_path = self.temp_file.name
         elif not os.path.isfile(self.file_path):
@@ -60,11 +61,13 @@ class WordExtractor(BaseExtractor):
 
     def extract(self) -> list[Document]:
         """Load given path as single page."""
-        content = self.parse_docx(self.file_path, 'storage')
-        return [Document(
-            page_content=content,
-            metadata={"source": self.file_path},
-        )]
+        content = self.parse_docx(self.file_path, "storage")
+        return [
+            Document(
+                page_content=content,
+                metadata={"source": self.file_path},
+            )
+        ]
 
     @staticmethod
     def _is_valid_url(url: str) -> bool:
@@ -84,18 +87,18 @@ class WordExtractor(BaseExtractor):
                     url = rel.reltype
                     response = requests.get(url, stream=True)
                     if response.status_code == 200:
-                        image_ext = mimetypes.guess_extension(response.headers['Content-Type'])
+                        image_ext = mimetypes.guess_extension(response.headers["Content-Type"])
                         file_uuid = str(uuid.uuid4())
-                        file_key = 'image_files/' + self.tenant_id + '/' + file_uuid + '.' + image_ext
+                        file_key = "image_files/" + self.tenant_id + "/" + file_uuid + "." + image_ext
                         mime_type, _ = mimetypes.guess_type(file_key)
                         storage.save(file_key, response.content)
                     else:
                         continue
                 else:
-                    image_ext = rel.target_ref.split('.')[-1]
+                    image_ext = rel.target_ref.split(".")[-1]
                     # user uuid as file name
                     file_uuid = str(uuid.uuid4())
-                    file_key = 'image_files/' + self.tenant_id + '/' + file_uuid + '.' + image_ext
+                    file_key = "image_files/" + self.tenant_id + "/" + file_uuid + "." + image_ext
                     mime_type, _ = mimetypes.guess_type(file_key)
 
                     storage.save(file_key, rel.target_part.blob)
@@ -112,12 +115,14 @@ class WordExtractor(BaseExtractor):
                     created_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
                     used=True,
                     used_by=self.user_id,
-                    used_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
+                    used_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None),
                 )
 
                 db.session.add(upload_file)
                 db.session.commit()
-                image_map[rel.target_part] = f"![image]({dify_config.CONSOLE_API_URL}/files/{upload_file.id}/image-preview)"
+                image_map[rel.target_part] = (
+                    f"![image]({dify_config.CONSOLE_API_URL}/files/{upload_file.id}/image-preview)"
+                )
 
         return image_map
 
@@ -148,7 +153,7 @@ class WordExtractor(BaseExtractor):
             if col_index >= total_cols:
                 break
             cell_content = self._parse_cell(cell, image_map).strip()
-            cell_colspan = cell.grid_span if cell.grid_span else 1
+            cell_colspan = cell.grid_span or 1
             for i in range(cell_colspan):
                 if col_index + i < total_cols:
                     row_cells[col_index + i] = cell_content if i == 0 else ""
@@ -167,8 +172,8 @@ class WordExtractor(BaseExtractor):
     def _parse_cell_paragraph(self, paragraph, image_map):
         paragraph_content = []
         for run in paragraph.runs:
-            if run.element.xpath('.//a:blip'):
-                for blip in run.element.xpath('.//a:blip'):
+            if run.element.xpath(".//a:blip"):
+                for blip in run.element.xpath(".//a:blip"):
                     image_id = blip.get("{http://schemas.openxmlformats.org/officeDocument/2006/relationships}embed")
                     if not image_id:
                         continue
@@ -184,16 +189,16 @@ class WordExtractor(BaseExtractor):
     def _parse_paragraph(self, paragraph, image_map):
         paragraph_content = []
         for run in paragraph.runs:
-            if run.element.xpath('.//a:blip'):
-                for blip in run.element.xpath('.//a:blip'):
-                    embed_id = blip.get('{http://schemas.openxmlformats.org/officeDocument/2006/relationships}embed')
+            if run.element.xpath(".//a:blip"):
+                for blip in run.element.xpath(".//a:blip"):
+                    embed_id = blip.get("{http://schemas.openxmlformats.org/officeDocument/2006/relationships}embed")
                     if embed_id:
                         rel_target = run.part.rels[embed_id].target_ref
                         if rel_target in image_map:
                             paragraph_content.append(image_map[rel_target])
             if run.text.strip():
                 paragraph_content.append(run.text.strip())
-        return ' '.join(paragraph_content) if paragraph_content else ''
+        return " ".join(paragraph_content) if paragraph_content else ""
 
     def parse_docx(self, docx_path, image_folder):
         doc = DocxDocument(docx_path)
@@ -204,60 +209,59 @@ class WordExtractor(BaseExtractor):
         image_map = self._extract_images_from_docx(doc, image_folder)
 
         hyperlinks_url = None
-        url_pattern = re.compile(r'http://[^\s+]+//|https://[^\s+]+')
+        url_pattern = re.compile(r"http://[^\s+]+//|https://[^\s+]+")
         for para in doc.paragraphs:
             for run in para.runs:
                 if run.text and hyperlinks_url:
-                    result = f'  [{run.text}]({hyperlinks_url})  '
+                    result = f"  [{run.text}]({hyperlinks_url})  "
                     run.text = result
                     hyperlinks_url = None
-                if 'HYPERLINK' in run.element.xml:
+                if "HYPERLINK" in run.element.xml:
                     try:
-                        xml = ET.XML(run.element.xml)
+                        xml = ElementTree.XML(run.element.xml)
                         x_child = [c for c in xml.iter() if c is not None]
                         for x in x_child:
                             if x_child is None:
                                 continue
-                            if x.tag.endswith('instrText'):
+                            if x.tag.endswith("instrText"):
                                 for i in url_pattern.findall(x.text):
                                     hyperlinks_url = str(i)
                     except Exception as e:
                         logger.error(e)
 
-
-
-
         def parse_paragraph(paragraph):
             paragraph_content = []
             for run in paragraph.runs:
-                if hasattr(run.element, 'tag') and isinstance(element.tag, str) and run.element.tag.endswith('r'):
+                if hasattr(run.element, "tag") and isinstance(element.tag, str) and run.element.tag.endswith("r"):
                     drawing_elements = run.element.findall(
-                        './/{http://schemas.openxmlformats.org/wordprocessingml/2006/main}drawing')
+                        ".//{http://schemas.openxmlformats.org/wordprocessingml/2006/main}drawing"
+                    )
                     for drawing in drawing_elements:
                         blip_elements = drawing.findall(
-                            './/{http://schemas.openxmlformats.org/drawingml/2006/main}blip')
+                            ".//{http://schemas.openxmlformats.org/drawingml/2006/main}blip"
+                        )
                         for blip in blip_elements:
                             embed_id = blip.get(
-                                '{http://schemas.openxmlformats.org/officeDocument/2006/relationships}embed')
+                                "{http://schemas.openxmlformats.org/officeDocument/2006/relationships}embed"
+                            )
                             if embed_id:
                                 image_part = doc.part.related_parts.get(embed_id)
                                 if image_part in image_map:
                                     paragraph_content.append(image_map[image_part])
                 if run.text.strip():
                     paragraph_content.append(run.text.strip())
-            return ''.join(paragraph_content) if paragraph_content else ''
+            return "".join(paragraph_content) if paragraph_content else ""
 
         paragraphs = doc.paragraphs.copy()
         tables = doc.tables.copy()
         for element in doc.element.body:
-            if hasattr(element, 'tag'):
-                if isinstance(element.tag, str) and element.tag.endswith('p'):  # paragraph
+            if hasattr(element, "tag"):
+                if isinstance(element.tag, str) and element.tag.endswith("p"):  # paragraph
                     para = paragraphs.pop(0)
                     parsed_paragraph = parse_paragraph(para)
                     if parsed_paragraph:
                         content.append(parsed_paragraph)
-                elif isinstance(element.tag, str) and element.tag.endswith('tbl'):  # table
+                elif isinstance(element.tag, str) and element.tag.endswith("tbl"):  # table
                     table = tables.pop(0)
                     content.append(self._table_to_markdown(table, image_map))
-        return '\n'.join(content)
-
+        return "\n".join(content)
diff --git a/api/core/rag/index_processor/index_processor_base.py b/api/core/rag/index_processor/index_processor_base.py
index 176d0c1ed6..be857bd122 100644
--- a/api/core/rag/index_processor/index_processor_base.py
+++ b/api/core/rag/index_processor/index_processor_base.py
@@ -1,4 +1,5 @@
 """Abstract interface for document loader implementations."""
+
 from abc import ABC, abstractmethod
 from typing import Optional
 
@@ -15,8 +16,7 @@ from models.dataset import Dataset, DatasetProcessRule
 
 
 class BaseIndexProcessor(ABC):
-    """Interface for extract files.
-    """
+    """Interface for extract files."""
 
     @abstractmethod
     def extract(self, extract_setting: ExtractSetting, **kwargs) -> list[Document]:
@@ -34,18 +34,24 @@ class BaseIndexProcessor(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def retrieve(self, retrival_method: str, query: str, dataset: Dataset, top_k: int,
-                 score_threshold: float, reranking_model: dict) -> list[Document]:
+    def retrieve(
+        self,
+        retrieval_method: str,
+        query: str,
+        dataset: Dataset,
+        top_k: int,
+        score_threshold: float,
+        reranking_model: dict,
+    ) -> list[Document]:
         raise NotImplementedError
 
-    def _get_splitter(self, processing_rule: dict,
-                      embedding_model_instance: Optional[ModelInstance]) -> TextSplitter:
+    def _get_splitter(self, processing_rule: dict, embedding_model_instance: Optional[ModelInstance]) -> TextSplitter:
         """
         Get the NodeParser object according to the processing rule.
         """
-        if processing_rule['mode'] == "custom":
+        if processing_rule["mode"] == "custom":
             # The user-defined segmentation rule
-            rules = processing_rule['rules']
+            rules = processing_rule["rules"]
             segmentation = rules["segmentation"]
             max_segmentation_tokens_length = dify_config.INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH
             if segmentation["max_tokens"] < 50 or segmentation["max_tokens"] > max_segmentation_tokens_length:
@@ -53,22 +59,22 @@ class BaseIndexProcessor(ABC):
 
             separator = segmentation["separator"]
             if separator:
-                separator = separator.replace('\\n', '\n')
+                separator = separator.replace("\\n", "\n")
 
             character_splitter = FixedRecursiveCharacterTextSplitter.from_encoder(
                 chunk_size=segmentation["max_tokens"],
-                chunk_overlap=segmentation.get('chunk_overlap', 0) or 0,
+                chunk_overlap=segmentation.get("chunk_overlap", 0) or 0,
                 fixed_separator=separator,
                 separators=["\n\n", "。", ". ", " ", ""],
-                embedding_model_instance=embedding_model_instance
+                embedding_model_instance=embedding_model_instance,
             )
         else:
             # Automatic segmentation
             character_splitter = EnhanceRecursiveCharacterTextSplitter.from_encoder(
-                chunk_size=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['max_tokens'],
-                chunk_overlap=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['chunk_overlap'],
+                chunk_size=DatasetProcessRule.AUTOMATIC_RULES["segmentation"]["max_tokens"],
+                chunk_overlap=DatasetProcessRule.AUTOMATIC_RULES["segmentation"]["chunk_overlap"],
                 separators=["\n\n", "。", ". ", " ", ""],
-                embedding_model_instance=embedding_model_instance
+                embedding_model_instance=embedding_model_instance,
             )
 
         return character_splitter
diff --git a/api/core/rag/index_processor/index_processor_factory.py b/api/core/rag/index_processor/index_processor_factory.py
index df43a64910..9b855ece2c 100644
--- a/api/core/rag/index_processor/index_processor_factory.py
+++ b/api/core/rag/index_processor/index_processor_factory.py
@@ -7,8 +7,7 @@ from core.rag.index_processor.processor.qa_index_processor import QAIndexProcess
 
 
 class IndexProcessorFactory:
-    """IndexProcessorInit.
-    """
+    """IndexProcessorInit."""
 
     def __init__(self, index_type: str):
         self._index_type = index_type
@@ -22,7 +21,6 @@ class IndexProcessorFactory:
         if self._index_type == IndexType.PARAGRAPH_INDEX.value:
             return ParagraphIndexProcessor()
         elif self._index_type == IndexType.QA_INDEX.value:
-
             return QAIndexProcessor()
         else:
             raise ValueError(f"Index type {self._index_type} is not supported.")
diff --git a/api/core/rag/index_processor/processor/paragraph_index_processor.py b/api/core/rag/index_processor/processor/paragraph_index_processor.py
index 5fbc319fd6..ed5712220f 100644
--- a/api/core/rag/index_processor/processor/paragraph_index_processor.py
+++ b/api/core/rag/index_processor/processor/paragraph_index_processor.py
@@ -1,4 +1,5 @@
 """Paragraph index processor."""
+
 import uuid
 from typing import Optional
 
@@ -15,34 +16,33 @@ from models.dataset import Dataset
 
 
 class ParagraphIndexProcessor(BaseIndexProcessor):
-
     def extract(self, extract_setting: ExtractSetting, **kwargs) -> list[Document]:
-
-        text_docs = ExtractProcessor.extract(extract_setting=extract_setting,
-                                             is_automatic=kwargs.get('process_rule_mode') == "automatic")
+        text_docs = ExtractProcessor.extract(
+            extract_setting=extract_setting, is_automatic=kwargs.get("process_rule_mode") == "automatic"
+        )
 
         return text_docs
 
     def transform(self, documents: list[Document], **kwargs) -> list[Document]:
         # Split the text documents into nodes.
-        splitter = self._get_splitter(processing_rule=kwargs.get('process_rule'),
-                                      embedding_model_instance=kwargs.get('embedding_model_instance'))
+        splitter = self._get_splitter(
+            processing_rule=kwargs.get("process_rule"), embedding_model_instance=kwargs.get("embedding_model_instance")
+        )
         all_documents = []
         for document in documents:
             # document clean
-            document_text = CleanProcessor.clean(document.page_content, kwargs.get('process_rule'))
+            document_text = CleanProcessor.clean(document.page_content, kwargs.get("process_rule"))
             document.page_content = document_text
             # parse document to nodes
             document_nodes = splitter.split_documents([document])
             split_documents = []
             for document_node in document_nodes:
-
                 if document_node.page_content.strip():
                     doc_id = str(uuid.uuid4())
                     hash = helper.generate_text_hash(document_node.page_content)
-                    document_node.metadata['doc_id'] = doc_id
-                    document_node.metadata['doc_hash'] = hash
-                    # delete Spliter character
+                    document_node.metadata["doc_id"] = doc_id
+                    document_node.metadata["doc_hash"] = hash
+                    # delete Splitter character
                     page_content = document_node.page_content
                     if page_content.startswith(".") or page_content.startswith("。"):
                         page_content = page_content[1:].strip()
@@ -55,7 +55,7 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
         return all_documents
 
     def load(self, dataset: Dataset, documents: list[Document], with_keywords: bool = True):
-        if dataset.indexing_technique == 'high_quality':
+        if dataset.indexing_technique == "high_quality":
             vector = Vector(dataset)
             vector.create(documents)
         if with_keywords:
@@ -63,7 +63,7 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
             keyword.create(documents)
 
     def clean(self, dataset: Dataset, node_ids: Optional[list[str]], with_keywords: bool = True):
-        if dataset.indexing_technique == 'high_quality':
+        if dataset.indexing_technique == "high_quality":
             vector = Vector(dataset)
             if node_ids:
                 vector.delete_by_ids(node_ids)
@@ -76,17 +76,29 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
             else:
                 keyword.delete()
 
-    def retrieve(self, retrival_method: str, query: str, dataset: Dataset, top_k: int,
-                 score_threshold: float, reranking_model: dict) -> list[Document]:
+    def retrieve(
+        self,
+        retrieval_method: str,
+        query: str,
+        dataset: Dataset,
+        top_k: int,
+        score_threshold: float,
+        reranking_model: dict,
+    ) -> list[Document]:
         # Set search parameters.
-        results = RetrievalService.retrieve(retrival_method=retrival_method, dataset_id=dataset.id, query=query,
-                                            top_k=top_k, score_threshold=score_threshold,
-                                            reranking_model=reranking_model)
+        results = RetrievalService.retrieve(
+            retrieval_method=retrieval_method,
+            dataset_id=dataset.id,
+            query=query,
+            top_k=top_k,
+            score_threshold=score_threshold,
+            reranking_model=reranking_model,
+        )
         # Organize results.
         docs = []
         for result in results:
             metadata = result.metadata
-            metadata['score'] = result.score
+            metadata["score"] = result.score
             if result.score > score_threshold:
                 doc = Document(page_content=result.page_content, metadata=metadata)
                 docs.append(doc)
diff --git a/api/core/rag/index_processor/processor/qa_index_processor.py b/api/core/rag/index_processor/processor/qa_index_processor.py
index 139bfe15f3..1dbc473281 100644
--- a/api/core/rag/index_processor/processor/qa_index_processor.py
+++ b/api/core/rag/index_processor/processor/qa_index_processor.py
@@ -1,4 +1,5 @@
 """Paragraph index processor."""
+
 import logging
 import re
 import threading
@@ -23,34 +24,34 @@ from models.dataset import Dataset
 
 class QAIndexProcessor(BaseIndexProcessor):
     def extract(self, extract_setting: ExtractSetting, **kwargs) -> list[Document]:
-
-        text_docs = ExtractProcessor.extract(extract_setting=extract_setting,
-                                             is_automatic=kwargs.get('process_rule_mode') == "automatic")
+        text_docs = ExtractProcessor.extract(
+            extract_setting=extract_setting, is_automatic=kwargs.get("process_rule_mode") == "automatic"
+        )
         return text_docs
 
     def transform(self, documents: list[Document], **kwargs) -> list[Document]:
-        splitter = self._get_splitter(processing_rule=kwargs.get('process_rule'),
-                                      embedding_model_instance=kwargs.get('embedding_model_instance'))
+        splitter = self._get_splitter(
+            processing_rule=kwargs.get("process_rule"), embedding_model_instance=kwargs.get("embedding_model_instance")
+        )
 
         # Split the text documents into nodes.
         all_documents = []
         all_qa_documents = []
         for document in documents:
             # document clean
-            document_text = CleanProcessor.clean(document.page_content, kwargs.get('process_rule'))
+            document_text = CleanProcessor.clean(document.page_content, kwargs.get("process_rule"))
             document.page_content = document_text
 
             # parse document to nodes
             document_nodes = splitter.split_documents([document])
             split_documents = []
             for document_node in document_nodes:
-
                 if document_node.page_content.strip():
                     doc_id = str(uuid.uuid4())
                     hash = helper.generate_text_hash(document_node.page_content)
-                    document_node.metadata['doc_id'] = doc_id
-                    document_node.metadata['doc_hash'] = hash
-                    # delete Spliter character
+                    document_node.metadata["doc_id"] = doc_id
+                    document_node.metadata["doc_hash"] = hash
+                    # delete Splitter character
                     page_content = document_node.page_content
                     if page_content.startswith(".") or page_content.startswith("。"):
                         page_content = page_content[1:]
@@ -61,14 +62,18 @@ class QAIndexProcessor(BaseIndexProcessor):
             all_documents.extend(split_documents)
         for i in range(0, len(all_documents), 10):
             threads = []
-            sub_documents = all_documents[i:i + 10]
+            sub_documents = all_documents[i : i + 10]
             for doc in sub_documents:
-                document_format_thread = threading.Thread(target=self._format_qa_document, kwargs={
-                    'flask_app': current_app._get_current_object(),
-                    'tenant_id': kwargs.get('tenant_id'),
-                    'document_node': doc,
-                    'all_qa_documents': all_qa_documents,
-                    'document_language': kwargs.get('doc_language', 'English')})
+                document_format_thread = threading.Thread(
+                    target=self._format_qa_document,
+                    kwargs={
+                        "flask_app": current_app._get_current_object(),
+                        "tenant_id": kwargs.get("tenant_id"),
+                        "document_node": doc,
+                        "all_qa_documents": all_qa_documents,
+                        "document_language": kwargs.get("doc_language", "English"),
+                    },
+                )
                 threads.append(document_format_thread)
                 document_format_thread.start()
             for thread in threads:
@@ -76,9 +81,8 @@ class QAIndexProcessor(BaseIndexProcessor):
         return all_qa_documents
 
     def format_by_template(self, file: FileStorage, **kwargs) -> list[Document]:
-
         # check file type
-        if not file.filename.endswith('.csv'):
+        if not file.filename.endswith(".csv"):
             raise ValueError("Invalid file type. Only CSV files are allowed")
 
         try:
@@ -86,7 +90,7 @@ class QAIndexProcessor(BaseIndexProcessor):
             df = pd.read_csv(file)
             text_docs = []
             for index, row in df.iterrows():
-                data = Document(page_content=row[0], metadata={'answer': row[1]})
+                data = Document(page_content=row[0], metadata={"answer": row[1]})
                 text_docs.append(data)
             if len(text_docs) == 0:
                 raise ValueError("The CSV file is empty.")
@@ -96,7 +100,7 @@ class QAIndexProcessor(BaseIndexProcessor):
         return text_docs
 
     def load(self, dataset: Dataset, documents: list[Document], with_keywords: bool = True):
-        if dataset.indexing_technique == 'high_quality':
+        if dataset.indexing_technique == "high_quality":
             vector = Vector(dataset)
             vector.create(documents)
 
@@ -107,17 +111,29 @@ class QAIndexProcessor(BaseIndexProcessor):
         else:
             vector.delete()
 
-    def retrieve(self, retrival_method: str, query: str, dataset: Dataset, top_k: int,
-                 score_threshold: float, reranking_model: dict):
+    def retrieve(
+        self,
+        retrieval_method: str,
+        query: str,
+        dataset: Dataset,
+        top_k: int,
+        score_threshold: float,
+        reranking_model: dict,
+    ):
         # Set search parameters.
-        results = RetrievalService.retrieve(retrival_method=retrival_method, dataset_id=dataset.id, query=query,
-                                            top_k=top_k, score_threshold=score_threshold,
-                                            reranking_model=reranking_model)
+        results = RetrievalService.retrieve(
+            retrieval_method=retrieval_method,
+            dataset_id=dataset.id,
+            query=query,
+            top_k=top_k,
+            score_threshold=score_threshold,
+            reranking_model=reranking_model,
+        )
         # Organize results.
         docs = []
         for result in results:
             metadata = result.metadata
-            metadata['score'] = result.score
+            metadata["score"] = result.score
             if result.score > score_threshold:
                 doc = Document(page_content=result.page_content, metadata=metadata)
                 docs.append(doc)
@@ -134,12 +150,12 @@ class QAIndexProcessor(BaseIndexProcessor):
                 document_qa_list = self._format_split_text(response)
                 qa_documents = []
                 for result in document_qa_list:
-                    qa_document = Document(page_content=result['question'], metadata=document_node.metadata.copy())
+                    qa_document = Document(page_content=result["question"], metadata=document_node.metadata.copy())
                     doc_id = str(uuid.uuid4())
-                    hash = helper.generate_text_hash(result['question'])
-                    qa_document.metadata['answer'] = result['answer']
-                    qa_document.metadata['doc_id'] = doc_id
-                    qa_document.metadata['doc_hash'] = hash
+                    hash = helper.generate_text_hash(result["question"])
+                    qa_document.metadata["answer"] = result["answer"]
+                    qa_document.metadata["doc_id"] = doc_id
+                    qa_document.metadata["doc_hash"] = hash
                     qa_documents.append(qa_document)
                 format_documents.extend(qa_documents)
             except Exception as e:
@@ -151,10 +167,4 @@ class QAIndexProcessor(BaseIndexProcessor):
         regex = r"Q\d+:\s*(.*?)\s*A\d+:\s*([\s\S]*?)(?=Q\d+:|$)"
         matches = re.findall(regex, text, re.UNICODE)
 
-        return [
-            {
-                "question": q,
-                "answer": re.sub(r"\n\s*", "\n", a.strip())
-            }
-            for q, a in matches if q and a
-        ]
+        return [{"question": q, "answer": re.sub(r"\n\s*", "\n", a.strip())} for q, a in matches if q and a]
diff --git a/api/core/rag/models/document.py b/api/core/rag/models/document.py
index 6f3c1c5d34..0ff1fdb81c 100644
--- a/api/core/rag/models/document.py
+++ b/api/core/rag/models/document.py
@@ -55,9 +55,7 @@ class BaseDocumentTransformer(ABC):
     """
 
     @abstractmethod
-    def transform_documents(
-        self, documents: Sequence[Document], **kwargs: Any
-    ) -> Sequence[Document]:
+    def transform_documents(self, documents: Sequence[Document], **kwargs: Any) -> Sequence[Document]:
         """Transform a list of documents.
 
         Args:
@@ -68,9 +66,7 @@ class BaseDocumentTransformer(ABC):
         """
 
     @abstractmethod
-    async def atransform_documents(
-        self, documents: Sequence[Document], **kwargs: Any
-    ) -> Sequence[Document]:
+    async def atransform_documents(self, documents: Sequence[Document], **kwargs: Any) -> Sequence[Document]:
         """Asynchronously transform a list of documents.
 
         Args:
diff --git a/api/core/rag/rerank/constants/rerank_mode.py b/api/core/rag/rerank/constants/rerank_mode.py
index afbb9fd89d..d4894e3cc6 100644
--- a/api/core/rag/rerank/constants/rerank_mode.py
+++ b/api/core/rag/rerank/constants/rerank_mode.py
@@ -2,7 +2,5 @@ from enum import Enum
 
 
 class RerankMode(Enum):
-
-    RERANKING_MODEL = 'reranking_model'
-    WEIGHTED_SCORE = 'weighted_score'
-
+    RERANKING_MODEL = "reranking_model"
+    WEIGHTED_SCORE = "weighted_score"
diff --git a/api/core/rag/rerank/rerank_model.py b/api/core/rag/rerank/rerank_model.py
index d9067da288..6356ff87ab 100644
--- a/api/core/rag/rerank/rerank_model.py
+++ b/api/core/rag/rerank/rerank_model.py
@@ -8,8 +8,14 @@ class RerankModelRunner:
     def __init__(self, rerank_model_instance: ModelInstance) -> None:
         self.rerank_model_instance = rerank_model_instance
 
-    def run(self, query: str, documents: list[Document], score_threshold: Optional[float] = None,
-            top_n: Optional[int] = None, user: Optional[str] = None) -> list[Document]:
+    def run(
+        self,
+        query: str,
+        documents: list[Document],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> list[Document]:
         """
         Run rerank model
         :param query: search query
@@ -23,19 +29,15 @@ class RerankModelRunner:
         doc_id = []
         unique_documents = []
         for document in documents:
-            if document.metadata['doc_id'] not in doc_id:
-                doc_id.append(document.metadata['doc_id'])
+            if document.metadata["doc_id"] not in doc_id:
+                doc_id.append(document.metadata["doc_id"])
                 docs.append(document.page_content)
                 unique_documents.append(document)
 
         documents = unique_documents
 
         rerank_result = self.rerank_model_instance.invoke_rerank(
-            query=query,
-            docs=docs,
-            score_threshold=score_threshold,
-            top_n=top_n,
-            user=user
+            query=query, docs=docs, score_threshold=score_threshold, top_n=top_n, user=user
         )
 
         rerank_documents = []
@@ -45,12 +47,12 @@ class RerankModelRunner:
             rerank_document = Document(
                 page_content=result.text,
                 metadata={
-                    "doc_id": documents[result.index].metadata['doc_id'],
-                    "doc_hash": documents[result.index].metadata['doc_hash'],
-                    "document_id": documents[result.index].metadata['document_id'],
-                    "dataset_id": documents[result.index].metadata['dataset_id'],
-                    'score': result.score
-                }
+                    "doc_id": documents[result.index].metadata["doc_id"],
+                    "doc_hash": documents[result.index].metadata["doc_hash"],
+                    "document_id": documents[result.index].metadata["document_id"],
+                    "dataset_id": documents[result.index].metadata["dataset_id"],
+                    "score": result.score,
+                },
             )
             rerank_documents.append(rerank_document)
 
diff --git a/api/core/rag/rerank/weight_rerank.py b/api/core/rag/rerank/weight_rerank.py
index d8a7873982..16d6b879a4 100644
--- a/api/core/rag/rerank/weight_rerank.py
+++ b/api/core/rag/rerank/weight_rerank.py
@@ -13,13 +13,18 @@ from core.rag.rerank.entity.weight import VectorSetting, Weights
 
 
 class WeightRerankRunner:
-
     def __init__(self, tenant_id: str, weights: Weights) -> None:
         self.tenant_id = tenant_id
         self.weights = weights
 
-    def run(self, query: str, documents: list[Document], score_threshold: Optional[float] = None,
-            top_n: Optional[int] = None, user: Optional[str] = None) -> list[Document]:
+    def run(
+        self,
+        query: str,
+        documents: list[Document],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> list[Document]:
         """
         Run rerank model
         :param query: search query
@@ -34,8 +39,8 @@ class WeightRerankRunner:
         doc_id = []
         unique_documents = []
         for document in documents:
-            if document.metadata['doc_id'] not in doc_id:
-                doc_id.append(document.metadata['doc_id'])
+            if document.metadata["doc_id"] not in doc_id:
+                doc_id.append(document.metadata["doc_id"])
                 docs.append(document.page_content)
                 unique_documents.append(document)
 
@@ -47,13 +52,15 @@ class WeightRerankRunner:
         query_vector_scores = self._calculate_cosine(self.tenant_id, query, documents, self.weights.vector_setting)
         for document, query_score, query_vector_score in zip(documents, query_scores, query_vector_scores):
             # format document
-            score = self.weights.vector_setting.vector_weight * query_vector_score + \
-                    self.weights.keyword_setting.keyword_weight * query_score
+            score = (
+                self.weights.vector_setting.vector_weight * query_vector_score
+                + self.weights.keyword_setting.keyword_weight * query_score
+            )
             if score_threshold and score < score_threshold:
                 continue
-            document.metadata['score'] = score
+            document.metadata["score"] = score
             rerank_documents.append(document)
-        rerank_documents = sorted(rerank_documents, key=lambda x: x.metadata['score'], reverse=True)
+        rerank_documents = sorted(rerank_documents, key=lambda x: x.metadata["score"], reverse=True)
         return rerank_documents[:top_n] if top_n else rerank_documents
 
     def _calculate_keyword_score(self, query: str, documents: list[Document]) -> list[float]:
@@ -70,7 +77,7 @@ class WeightRerankRunner:
         for document in documents:
             # get the document keywords
             document_keywords = keyword_table_handler.extract_keywords(document.page_content, None)
-            document.metadata['keywords'] = document_keywords
+            document.metadata["keywords"] = document_keywords
             documents_keywords.append(document_keywords)
 
         # Counter query keywords(TF)
@@ -113,8 +120,8 @@ class WeightRerankRunner:
             intersection = set(vec1.keys()) & set(vec2.keys())
             numerator = sum(vec1[x] * vec2[x] for x in intersection)
 
-            sum1 = sum(vec1[x] ** 2 for x in vec1.keys())
-            sum2 = sum(vec2[x] ** 2 for x in vec2.keys())
+            sum1 = sum(vec1[x] ** 2 for x in vec1)
+            sum2 = sum(vec2[x] ** 2 for x in vec2)
             denominator = math.sqrt(sum1) * math.sqrt(sum2)
 
             if not denominator:
@@ -132,8 +139,9 @@ class WeightRerankRunner:
 
         return similarities
 
-    def _calculate_cosine(self, tenant_id: str, query: str, documents: list[Document],
-                          vector_setting: VectorSetting) -> list[float]:
+    def _calculate_cosine(
+        self, tenant_id: str, query: str, documents: list[Document], vector_setting: VectorSetting
+    ) -> list[float]:
         """
         Calculate Cosine scores
         :param query: search query
@@ -149,15 +157,14 @@ class WeightRerankRunner:
             tenant_id=tenant_id,
             provider=vector_setting.embedding_provider_name,
             model_type=ModelType.TEXT_EMBEDDING,
-            model=vector_setting.embedding_model_name
-
+            model=vector_setting.embedding_model_name,
         )
         cache_embedding = CacheEmbedding(embedding_model)
         query_vector = cache_embedding.embed_query(query)
         for document in documents:
             # calculate cosine similarity
-            if 'score' in document.metadata:
-                query_vector_scores.append(document.metadata['score'])
+            if "score" in document.metadata:
+                query_vector_scores.append(document.metadata["score"])
             else:
                 # transform to NumPy
                 vec1 = np.array(query_vector)
diff --git a/api/core/rag/retrieval/dataset_retrieval.py b/api/core/rag/retrieval/dataset_retrieval.py
index c970e3dafa..286ecd4c03 100644
--- a/api/core/rag/retrieval/dataset_retrieval.py
+++ b/api/core/rag/retrieval/dataset_retrieval.py
@@ -21,7 +21,7 @@ from core.rag.data_post_processor.data_post_processor import DataPostProcessor
 from core.rag.datasource.keyword.jieba.jieba_keyword_table_handler import JiebaKeywordTableHandler
 from core.rag.datasource.retrieval_service import RetrievalService
 from core.rag.models.document import Document
-from core.rag.retrieval.retrival_methods import RetrievalMethod
+from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.rag.retrieval.router.multi_dataset_function_call_router import FunctionCallMultiDatasetRouter
 from core.rag.retrieval.router.multi_dataset_react_route import ReactMultiDatasetRouter
 from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
@@ -32,14 +32,11 @@ from models.dataset import Dataset, DatasetQuery, DocumentSegment
 from models.dataset import Document as DatasetDocument
 
 default_retrieval_model = {
-    'search_method': RetrievalMethod.SEMANTIC_SEARCH.value,
-    'reranking_enable': False,
-    'reranking_model': {
-        'reranking_provider_name': '',
-        'reranking_model_name': ''
-    },
-    'top_k': 2,
-    'score_threshold_enabled': False
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "reranking_enable": False,
+    "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
+    "top_k": 2,
+    "score_threshold_enabled": False,
 }
 
 
@@ -48,15 +45,18 @@ class DatasetRetrieval:
         self.application_generate_entity = application_generate_entity
 
     def retrieve(
-            self, app_id: str, user_id: str, tenant_id: str,
-            model_config: ModelConfigWithCredentialsEntity,
-            config: DatasetEntity,
-            query: str,
-            invoke_from: InvokeFrom,
-            show_retrieve_source: bool,
-            hit_callback: DatasetIndexToolCallbackHandler,
-            message_id: str,
-            memory: Optional[TokenBufferMemory] = None,
+        self,
+        app_id: str,
+        user_id: str,
+        tenant_id: str,
+        model_config: ModelConfigWithCredentialsEntity,
+        config: DatasetEntity,
+        query: str,
+        invoke_from: InvokeFrom,
+        show_retrieve_source: bool,
+        hit_callback: DatasetIndexToolCallbackHandler,
+        message_id: str,
+        memory: Optional[TokenBufferMemory] = None,
     ) -> Optional[str]:
         """
         Retrieve dataset.
@@ -84,16 +84,12 @@ class DatasetRetrieval:
 
         model_manager = ModelManager()
         model_instance = model_manager.get_model_instance(
-            tenant_id=tenant_id,
-            model_type=ModelType.LLM,
-            provider=model_config.provider,
-            model=model_config.model
+            tenant_id=tenant_id, model_type=ModelType.LLM, provider=model_config.provider, model=model_config.model
         )
 
         # get model schema
         model_schema = model_type_instance.get_model_schema(
-            model=model_config.model,
-            credentials=model_config.credentials
+            model=model_config.model, credentials=model_config.credentials
         )
 
         if not model_schema:
@@ -102,39 +98,46 @@ class DatasetRetrieval:
         planning_strategy = PlanningStrategy.REACT_ROUTER
         features = model_schema.features
         if features:
-            if ModelFeature.TOOL_CALL in features \
-                    or ModelFeature.MULTI_TOOL_CALL in features:
+            if ModelFeature.TOOL_CALL in features or ModelFeature.MULTI_TOOL_CALL in features:
                 planning_strategy = PlanningStrategy.ROUTER
         available_datasets = []
         for dataset_id in dataset_ids:
             # get dataset from dataset id
-            dataset = db.session.query(Dataset).filter(
-                Dataset.tenant_id == tenant_id,
-                Dataset.id == dataset_id
-            ).first()
+            dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
 
             # pass if dataset is not available
             if not dataset:
                 continue
 
             # pass if dataset is not available
-            if (dataset and dataset.available_document_count == 0
-                    and dataset.available_document_count == 0):
+            if dataset and dataset.available_document_count == 0 and dataset.available_document_count == 0:
                 continue
 
             available_datasets.append(dataset)
         all_documents = []
-        user_from = 'account' if invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end_user'
+        user_from = "account" if invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER} else "end_user"
         if retrieve_config.retrieve_strategy == DatasetRetrieveConfigEntity.RetrieveStrategy.SINGLE:
             all_documents = self.single_retrieve(
-                app_id, tenant_id, user_id, user_from, available_datasets, query,
+                app_id,
+                tenant_id,
+                user_id,
+                user_from,
+                available_datasets,
+                query,
                 model_instance,
-                model_config, planning_strategy, message_id
+                model_config,
+                planning_strategy,
+                message_id,
             )
         elif retrieve_config.retrieve_strategy == DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE:
             all_documents = self.multiple_retrieve(
-                app_id, tenant_id, user_id, user_from,
-                available_datasets, query, retrieve_config.top_k,
+                app_id,
+                tenant_id,
+                user_id,
+                user_from,
+                available_datasets,
+                query,
+                retrieve_config.top_k,
                 retrieve_config.score_threshold,
                 retrieve_config.rerank_mode,
                 retrieve_config.reranking_model,
@@ -145,89 +148,89 @@ class DatasetRetrieval:
 
         document_score_list = {}
         for item in all_documents:
-            if item.metadata.get('score'):
-                document_score_list[item.metadata['doc_id']] = item.metadata['score']
+            if item.metadata.get("score"):
+                document_score_list[item.metadata["doc_id"]] = item.metadata["score"]
 
         document_context_list = []
-        index_node_ids = [document.metadata['doc_id'] for document in all_documents]
+        index_node_ids = [document.metadata["doc_id"] for document in all_documents]
         segments = DocumentSegment.query.filter(
             DocumentSegment.dataset_id.in_(dataset_ids),
             DocumentSegment.completed_at.isnot(None),
-            DocumentSegment.status == 'completed',
+            DocumentSegment.status == "completed",
             DocumentSegment.enabled == True,
-            DocumentSegment.index_node_id.in_(index_node_ids)
+            DocumentSegment.index_node_id.in_(index_node_ids),
         ).all()
 
         if segments:
             index_node_id_to_position = {id: position for position, id in enumerate(index_node_ids)}
-            sorted_segments = sorted(segments,
-                                     key=lambda segment: index_node_id_to_position.get(segment.index_node_id,
-                                                                                       float('inf')))
+            sorted_segments = sorted(
+                segments, key=lambda segment: index_node_id_to_position.get(segment.index_node_id, float("inf"))
+            )
             for segment in sorted_segments:
                 if segment.answer:
-                    document_context_list.append(f'question:{segment.get_sign_content()} answer:{segment.answer}')
+                    document_context_list.append(f"question:{segment.get_sign_content()} answer:{segment.answer}")
                 else:
                     document_context_list.append(segment.get_sign_content())
             if show_retrieve_source:
                 context_list = []
                 resource_number = 1
                 for segment in sorted_segments:
-                    dataset = Dataset.query.filter_by(
-                        id=segment.dataset_id
+                    dataset = Dataset.query.filter_by(id=segment.dataset_id).first()
+                    document = DatasetDocument.query.filter(
+                        DatasetDocument.id == segment.document_id,
+                        DatasetDocument.enabled == True,
+                        DatasetDocument.archived == False,
                     ).first()
-                    document = DatasetDocument.query.filter(DatasetDocument.id == segment.document_id,
-                                                            DatasetDocument.enabled == True,
-                                                            DatasetDocument.archived == False,
-                                                            ).first()
                     if dataset and document:
                         source = {
-                            'position': resource_number,
-                            'dataset_id': dataset.id,
-                            'dataset_name': dataset.name,
-                            'document_id': document.id,
-                            'document_name': document.name,
-                            'data_source_type': document.data_source_type,
-                            'segment_id': segment.id,
-                            'retriever_from': invoke_from.to_source(),
-                            'score': document_score_list.get(segment.index_node_id, None)
+                            "position": resource_number,
+                            "dataset_id": dataset.id,
+                            "dataset_name": dataset.name,
+                            "document_id": document.id,
+                            "document_name": document.name,
+                            "data_source_type": document.data_source_type,
+                            "segment_id": segment.id,
+                            "retriever_from": invoke_from.to_source(),
+                            "score": document_score_list.get(segment.index_node_id, None),
                         }
 
-                        if invoke_from.to_source() == 'dev':
-                            source['hit_count'] = segment.hit_count
-                            source['word_count'] = segment.word_count
-                            source['segment_position'] = segment.position
-                            source['index_node_hash'] = segment.index_node_hash
+                        if invoke_from.to_source() == "dev":
+                            source["hit_count"] = segment.hit_count
+                            source["word_count"] = segment.word_count
+                            source["segment_position"] = segment.position
+                            source["index_node_hash"] = segment.index_node_hash
                         if segment.answer:
-                            source['content'] = f'question:{segment.content} \nanswer:{segment.answer}'
+                            source["content"] = f"question:{segment.content} \nanswer:{segment.answer}"
                         else:
-                            source['content'] = segment.content
+                            source["content"] = segment.content
                         context_list.append(source)
                     resource_number += 1
                 if hit_callback:
                     hit_callback.return_retriever_resource_info(context_list)
 
             return str("\n".join(document_context_list))
-        return ''
+        return ""
 
     def single_retrieve(
-            self, app_id: str,
-            tenant_id: str,
-            user_id: str,
-            user_from: str,
-            available_datasets: list,
-            query: str,
-            model_instance: ModelInstance,
-            model_config: ModelConfigWithCredentialsEntity,
-            planning_strategy: PlanningStrategy,
-            message_id: Optional[str] = None,
+        self,
+        app_id: str,
+        tenant_id: str,
+        user_id: str,
+        user_from: str,
+        available_datasets: list,
+        query: str,
+        model_instance: ModelInstance,
+        model_config: ModelConfigWithCredentialsEntity,
+        planning_strategy: PlanningStrategy,
+        message_id: Optional[str] = None,
     ):
         tools = []
         for dataset in available_datasets:
             description = dataset.description
             if not description:
-                description = 'useful for when you want to answer queries about the ' + dataset.name
+                description = "useful for when you want to answer queries about the " + dataset.name
 
-            description = description.replace('\n', '').replace('\r', '')
+            description = description.replace("\n", "").replace("\r", "")
             message_tool = PromptMessageTool(
                 name=dataset.id,
                 description=description,
@@ -235,14 +238,15 @@ class DatasetRetrieval:
                     "type": "object",
                     "properties": {},
                     "required": [],
-                }
+                },
             )
             tools.append(message_tool)
         dataset_id = None
         if planning_strategy == PlanningStrategy.REACT_ROUTER:
             react_multi_dataset_router = ReactMultiDatasetRouter()
-            dataset_id = react_multi_dataset_router.invoke(query, tools, model_config, model_instance,
-                                                           user_id, tenant_id)
+            dataset_id = react_multi_dataset_router.invoke(
+                query, tools, model_config, model_instance, user_id, tenant_id
+            )
 
         elif planning_strategy == PlanningStrategy.ROUTER:
             function_call_router = FunctionCallMultiDatasetRouter()
@@ -250,61 +254,61 @@ class DatasetRetrieval:
 
         if dataset_id:
             # get retrieval model config
-            dataset = db.session.query(Dataset).filter(
-                Dataset.id == dataset_id
-            ).first()
+            dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
             if dataset:
-                retrieval_model_config = dataset.retrieval_model \
-                    if dataset.retrieval_model else default_retrieval_model
+                retrieval_model_config = dataset.retrieval_model or default_retrieval_model
 
                 # get top k
-                top_k = retrieval_model_config['top_k']
+                top_k = retrieval_model_config["top_k"]
                 # get retrieval method
                 if dataset.indexing_technique == "economy":
-                    retrival_method = 'keyword_search'
+                    retrieval_method = "keyword_search"
                 else:
-                    retrival_method = retrieval_model_config['search_method']
+                    retrieval_method = retrieval_model_config["search_method"]
                 # get reranking model
-                reranking_model = retrieval_model_config['reranking_model'] \
-                    if retrieval_model_config['reranking_enable'] else None
+                reranking_model = (
+                    retrieval_model_config["reranking_model"] if retrieval_model_config["reranking_enable"] else None
+                )
                 # get score threshold
-                score_threshold = .0
+                score_threshold = 0.0
                 score_threshold_enabled = retrieval_model_config.get("score_threshold_enabled")
                 if score_threshold_enabled:
                     score_threshold = retrieval_model_config.get("score_threshold")
 
                 with measure_time() as timer:
                     results = RetrievalService.retrieve(
-                        retrival_method=retrival_method, dataset_id=dataset.id,
+                        retrieval_method=retrieval_method,
+                        dataset_id=dataset.id,
                         query=query,
-                        top_k=top_k, score_threshold=score_threshold,
+                        top_k=top_k,
+                        score_threshold=score_threshold,
                         reranking_model=reranking_model,
-                        reranking_mode=retrieval_model_config.get('reranking_mode', 'reranking_model'),
-                        weights=retrieval_model_config.get('weights', None),
+                        reranking_mode=retrieval_model_config.get("reranking_mode", "reranking_model"),
+                        weights=retrieval_model_config.get("weights", None),
                     )
                 self._on_query(query, [dataset_id], app_id, user_from, user_id)
 
                 if results:
-                    self._on_retrival_end(results, message_id, timer)
+                    self._on_retrieval_end(results, message_id, timer)
 
                 return results
         return []
 
     def multiple_retrieve(
-            self,
-            app_id: str,
-            tenant_id: str,
-            user_id: str,
-            user_from: str,
-            available_datasets: list,
-            query: str,
-            top_k: int,
-            score_threshold: float,
-            reranking_mode: str,
-            reranking_model: Optional[dict] = None,
-            weights: Optional[dict] = None,
-            reranking_enable: bool = True,
-            message_id: Optional[str] = None,
+        self,
+        app_id: str,
+        tenant_id: str,
+        user_id: str,
+        user_from: str,
+        available_datasets: list,
+        query: str,
+        top_k: int,
+        score_threshold: float,
+        reranking_mode: str,
+        reranking_model: Optional[dict] = None,
+        weights: Optional[dict] = None,
+        reranking_enable: bool = True,
+        message_id: Optional[str] = None,
     ):
         threads = []
         all_documents = []
@@ -312,13 +316,16 @@ class DatasetRetrieval:
         index_type = None
         for dataset in available_datasets:
             index_type = dataset.indexing_technique
-            retrieval_thread = threading.Thread(target=self._retriever, kwargs={
-                'flask_app': current_app._get_current_object(),
-                'dataset_id': dataset.id,
-                'query': query,
-                'top_k': top_k,
-                'all_documents': all_documents,
-            })
+            retrieval_thread = threading.Thread(
+                target=self._retriever,
+                kwargs={
+                    "flask_app": current_app._get_current_object(),
+                    "dataset_id": dataset.id,
+                    "query": query,
+                    "top_k": top_k,
+                    "all_documents": all_documents,
+                },
+            )
             threads.append(retrieval_thread)
             retrieval_thread.start()
         for thread in threads:
@@ -327,16 +334,10 @@ class DatasetRetrieval:
         with measure_time() as timer:
             if reranking_enable:
                 # do rerank for searched documents
-                data_post_processor = DataPostProcessor(
-                    tenant_id, reranking_mode,
-                    reranking_model, weights, False
-                )
+                data_post_processor = DataPostProcessor(tenant_id, reranking_mode, reranking_model, weights, False)
 
                 all_documents = data_post_processor.invoke(
-                    query=query,
-                    documents=all_documents,
-                    score_threshold=score_threshold,
-                    top_n=top_k
+                    query=query, documents=all_documents, score_threshold=score_threshold, top_n=top_k
                 )
             else:
                 if index_type == "economy":
@@ -347,40 +348,36 @@ class DatasetRetrieval:
         self._on_query(query, dataset_ids, app_id, user_from, user_id)
 
         if all_documents:
-            self._on_retrival_end(all_documents, message_id, timer)
+            self._on_retrieval_end(all_documents, message_id, timer)
 
         return all_documents
 
-    def _on_retrival_end(
+    def _on_retrieval_end(
         self, documents: list[Document], message_id: Optional[str] = None, timer: Optional[dict] = None
     ) -> None:
-        """Handle retrival end."""
+        """Handle retrieval end."""
         for document in documents:
             query = db.session.query(DocumentSegment).filter(
-                DocumentSegment.index_node_id == document.metadata['doc_id']
+                DocumentSegment.index_node_id == document.metadata["doc_id"]
             )
 
             # if 'dataset_id' in document.metadata:
-            if 'dataset_id' in document.metadata:
-                query = query.filter(DocumentSegment.dataset_id == document.metadata['dataset_id'])
+            if "dataset_id" in document.metadata:
+                query = query.filter(DocumentSegment.dataset_id == document.metadata["dataset_id"])
 
             # add hit count to document segment
-            query.update(
-                {DocumentSegment.hit_count: DocumentSegment.hit_count + 1},
-                synchronize_session=False
-            )
+            query.update({DocumentSegment.hit_count: DocumentSegment.hit_count + 1}, synchronize_session=False)
 
             db.session.commit()
 
         # get tracing instance
-        trace_manager: TraceQueueManager = self.application_generate_entity.trace_manager if self.application_generate_entity else None
+        trace_manager: TraceQueueManager = (
+            self.application_generate_entity.trace_manager if self.application_generate_entity else None
+        )
         if trace_manager:
             trace_manager.add_trace_task(
                 TraceTask(
-                    TraceTaskName.DATASET_RETRIEVAL_TRACE,
-                    message_id=message_id,
-                    documents=documents,
-                    timer=timer
+                    TraceTaskName.DATASET_RETRIEVAL_TRACE, message_id=message_id, documents=documents, timer=timer
                 )
             )
 
@@ -395,10 +392,10 @@ class DatasetRetrieval:
             dataset_query = DatasetQuery(
                 dataset_id=dataset_id,
                 content=query,
-                source='app',
+                source="app",
                 source_app_id=app_id,
                 created_by_role=user_from,
-                created_by=user_id
+                created_by=user_id,
             )
             dataset_queries.append(dataset_query)
         if dataset_queries:
@@ -407,50 +404,50 @@ class DatasetRetrieval:
 
     def _retriever(self, flask_app: Flask, dataset_id: str, query: str, top_k: int, all_documents: list):
         with flask_app.app_context():
-            dataset = db.session.query(Dataset).filter(
-                Dataset.id == dataset_id
-            ).first()
+            dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
 
             if not dataset:
                 return []
 
             # get retrieval model , if the model is not setting , using default
-            retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+            retrieval_model = dataset.retrieval_model or default_retrieval_model
 
             if dataset.indexing_technique == "economy":
                 # use keyword table query
-                documents = RetrievalService.retrieve(retrival_method='keyword_search',
-                                                      dataset_id=dataset.id,
-                                                      query=query,
-                                                      top_k=top_k
-                                                      )
+                documents = RetrievalService.retrieve(
+                    retrieval_method="keyword_search", dataset_id=dataset.id, query=query, top_k=top_k
+                )
                 if documents:
                     all_documents.extend(documents)
             else:
                 if top_k > 0:
                     # retrieval source
-                    documents = RetrievalService.retrieve(retrival_method=retrieval_model['search_method'],
-                                                          dataset_id=dataset.id,
-                                                          query=query,
-                                                          top_k=top_k,
-                                                          score_threshold=retrieval_model.get('score_threshold', .0)
-                                                          if retrieval_model['score_threshold_enabled'] else None,
-                                                          reranking_model=retrieval_model.get('reranking_model', None)
-                                                          if retrieval_model['reranking_enable'] else None,
-                                                          reranking_mode=retrieval_model.get('reranking_mode')
-                                                          if retrieval_model.get('reranking_mode') else 'reranking_model',
-                                                          weights=retrieval_model.get('weights', None),
-                                                          )
+                    documents = RetrievalService.retrieve(
+                        retrieval_method=retrieval_model["search_method"],
+                        dataset_id=dataset.id,
+                        query=query,
+                        top_k=retrieval_model.get("top_k") or 2,
+                        score_threshold=retrieval_model.get("score_threshold", 0.0)
+                        if retrieval_model["score_threshold_enabled"]
+                        else 0.0,
+                        reranking_model=retrieval_model.get("reranking_model", None)
+                        if retrieval_model["reranking_enable"]
+                        else None,
+                        reranking_mode=retrieval_model.get("reranking_mode") or "reranking_model",
+                        weights=retrieval_model.get("weights", None),
+                    )
 
                     all_documents.extend(documents)
 
-    def to_dataset_retriever_tool(self, tenant_id: str,
-                                  dataset_ids: list[str],
-                                  retrieve_config: DatasetRetrieveConfigEntity,
-                                  return_resource: bool,
-                                  invoke_from: InvokeFrom,
-                                  hit_callback: DatasetIndexToolCallbackHandler) \
-            -> Optional[list[DatasetRetrieverBaseTool]]:
+    def to_dataset_retriever_tool(
+        self,
+        tenant_id: str,
+        dataset_ids: list[str],
+        retrieve_config: DatasetRetrieveConfigEntity,
+        return_resource: bool,
+        invoke_from: InvokeFrom,
+        hit_callback: DatasetIndexToolCallbackHandler,
+    ) -> Optional[list[DatasetRetrieverBaseTool]]:
         """
         A dataset tool is a tool that can be used to retrieve information from a dataset
         :param tenant_id: tenant id
@@ -464,18 +461,14 @@ class DatasetRetrieval:
         available_datasets = []
         for dataset_id in dataset_ids:
             # get dataset from dataset id
-            dataset = db.session.query(Dataset).filter(
-                Dataset.tenant_id == tenant_id,
-                Dataset.id == dataset_id
-            ).first()
+            dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
 
             # pass if dataset is not available
             if not dataset:
                 continue
 
             # pass if dataset is not available
-            if (dataset and dataset.available_document_count == 0
-                    and dataset.available_document_count == 0):
+            if dataset and dataset.available_document_count == 0 and dataset.available_document_count == 0:
                 continue
 
             available_datasets.append(dataset)
@@ -483,22 +476,18 @@ class DatasetRetrieval:
         if retrieve_config.retrieve_strategy == DatasetRetrieveConfigEntity.RetrieveStrategy.SINGLE:
             # get retrieval model config
             default_retrieval_model = {
-                'search_method': RetrievalMethod.SEMANTIC_SEARCH.value,
-                'reranking_enable': False,
-                'reranking_model': {
-                    'reranking_provider_name': '',
-                    'reranking_model_name': ''
-                },
-                'top_k': 2,
-                'score_threshold_enabled': False
+                "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+                "reranking_enable": False,
+                "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
+                "top_k": 2,
+                "score_threshold_enabled": False,
             }
 
             for dataset in available_datasets:
-                retrieval_model_config = dataset.retrieval_model \
-                    if dataset.retrieval_model else default_retrieval_model
+                retrieval_model_config = dataset.retrieval_model or default_retrieval_model
 
                 # get top k
-                top_k = retrieval_model_config['top_k']
+                top_k = retrieval_model_config["top_k"]
 
                 # get score threshold
                 score_threshold = None
@@ -512,7 +501,7 @@ class DatasetRetrieval:
                     score_threshold=score_threshold,
                     hit_callbacks=[hit_callback],
                     return_resource=return_resource,
-                    retriever_from=invoke_from.to_source()
+                    retriever_from=invoke_from.to_source(),
                 )
 
                 tools.append(tool)
@@ -525,8 +514,8 @@ class DatasetRetrieval:
                 hit_callbacks=[hit_callback],
                 return_resource=return_resource,
                 retriever_from=invoke_from.to_source(),
-                reranking_provider_name=retrieve_config.reranking_model.get('reranking_provider_name'),
-                reranking_model_name=retrieve_config.reranking_model.get('reranking_model_name')
+                reranking_provider_name=retrieve_config.reranking_model.get("reranking_provider_name"),
+                reranking_model_name=retrieve_config.reranking_model.get("reranking_model_name"),
             )
 
             tools.append(tool)
@@ -547,7 +536,7 @@ class DatasetRetrieval:
         for document in documents:
             # get the document keywords
             document_keywords = keyword_table_handler.extract_keywords(document.page_content, None)
-            document.metadata['keywords'] = document_keywords
+            document.metadata["keywords"] = document_keywords
             documents_keywords.append(document_keywords)
 
         # Counter query keywords(TF)
@@ -590,8 +579,8 @@ class DatasetRetrieval:
             intersection = set(vec1.keys()) & set(vec2.keys())
             numerator = sum(vec1[x] * vec2[x] for x in intersection)
 
-            sum1 = sum(vec1[x] ** 2 for x in vec1.keys())
-            sum2 = sum(vec2[x] ** 2 for x in vec2.keys())
+            sum1 = sum(vec1[x] ** 2 for x in vec1)
+            sum2 = sum(vec2[x] ** 2 for x in vec2)
             denominator = math.sqrt(sum1) * math.sqrt(sum2)
 
             if not denominator:
@@ -606,21 +595,19 @@ class DatasetRetrieval:
 
         for document, score in zip(documents, similarities):
             # format document
-            document.metadata['score'] = score
-        documents = sorted(documents, key=lambda x: x.metadata['score'], reverse=True)
+            document.metadata["score"] = score
+        documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True)
         return documents[:top_k] if top_k else documents
 
-    def calculate_vector_score(self, all_documents: list[Document],
-                               top_k: int, score_threshold: float) -> list[Document]:
+    def calculate_vector_score(
+        self, all_documents: list[Document], top_k: int, score_threshold: float
+    ) -> list[Document]:
         filter_documents = []
         for document in all_documents:
-            if score_threshold is None or document.metadata['score'] >= score_threshold:
+            if score_threshold is None or document.metadata["score"] >= score_threshold:
                 filter_documents.append(document)
 
         if not filter_documents:
             return []
-        filter_documents = sorted(filter_documents, key=lambda x: x.metadata['score'], reverse=True)
+        filter_documents = sorted(filter_documents, key=lambda x: x.metadata["score"], reverse=True)
         return filter_documents[:top_k] if top_k else filter_documents
-
-
-
diff --git a/api/core/rag/retrieval/output_parser/structured_chat.py b/api/core/rag/retrieval/output_parser/structured_chat.py
index 60770bd4c6..7fc78bce83 100644
--- a/api/core/rag/retrieval/output_parser/structured_chat.py
+++ b/api/core/rag/retrieval/output_parser/structured_chat.py
@@ -16,9 +16,7 @@ class StructuredChatOutputParser:
                 if response["action"] == "Final Answer":
                     return ReactFinish({"output": response["action_input"]}, text)
                 else:
-                    return ReactAction(
-                        response["action"], response.get("action_input", {}), text
-                    )
+                    return ReactAction(response["action"], response.get("action_input", {}), text)
             else:
                 return ReactFinish({"output": text}, text)
         except Exception as e:
diff --git a/api/core/rag/retrieval/retrival_methods.py b/api/core/rag/retrieval/retrieval_methods.py
similarity index 79%
rename from api/core/rag/retrieval/retrival_methods.py
rename to api/core/rag/retrieval/retrieval_methods.py
index 12aa28a51c..eaa00bca88 100644
--- a/api/core/rag/retrieval/retrival_methods.py
+++ b/api/core/rag/retrieval/retrieval_methods.py
@@ -2,9 +2,9 @@ from enum import Enum
 
 
 class RetrievalMethod(Enum):
-    SEMANTIC_SEARCH = 'semantic_search'
-    FULL_TEXT_SEARCH = 'full_text_search'
-    HYBRID_SEARCH = 'hybrid_search'
+    SEMANTIC_SEARCH = "semantic_search"
+    FULL_TEXT_SEARCH = "full_text_search"
+    HYBRID_SEARCH = "hybrid_search"
 
     @staticmethod
     def is_support_semantic_search(retrieval_method: str) -> bool:
diff --git a/api/core/rag/retrieval/router/multi_dataset_function_call_router.py b/api/core/rag/retrieval/router/multi_dataset_function_call_router.py
index 84e53952ac..06147fe7b5 100644
--- a/api/core/rag/retrieval/router/multi_dataset_function_call_router.py
+++ b/api/core/rag/retrieval/router/multi_dataset_function_call_router.py
@@ -6,14 +6,12 @@ from core.model_runtime.entities.message_entities import PromptMessageTool, Syst
 
 
 class FunctionCallMultiDatasetRouter:
-
     def invoke(
-            self,
-            query: str,
-            dataset_tools: list[PromptMessageTool],
-            model_config: ModelConfigWithCredentialsEntity,
-            model_instance: ModelInstance,
-
+        self,
+        query: str,
+        dataset_tools: list[PromptMessageTool],
+        model_config: ModelConfigWithCredentialsEntity,
+        model_instance: ModelInstance,
     ) -> Union[str, None]:
         """Given input, decided what to do.
         Returns:
@@ -26,22 +24,18 @@ class FunctionCallMultiDatasetRouter:
 
         try:
             prompt_messages = [
-                SystemPromptMessage(content='You are a helpful AI assistant.'),
-                UserPromptMessage(content=query)
+                SystemPromptMessage(content="You are a helpful AI assistant."),
+                UserPromptMessage(content=query),
             ]
             result = model_instance.invoke_llm(
                 prompt_messages=prompt_messages,
                 tools=dataset_tools,
                 stream=False,
-                model_parameters={
-                    'temperature': 0.2,
-                    'top_p': 0.3,
-                    'max_tokens': 1500
-                }
+                model_parameters={"temperature": 0.2, "top_p": 0.3, "max_tokens": 1500},
             )
             if result.message.tool_calls:
                 # get retrieval model config
                 return result.message.tool_calls[0].function.name
             return None
         except Exception as e:
-            return None
\ No newline at end of file
+            return None
diff --git a/api/core/rag/retrieval/router/multi_dataset_react_route.py b/api/core/rag/retrieval/router/multi_dataset_react_route.py
index 92f24277c1..a0494adc60 100644
--- a/api/core/rag/retrieval/router/multi_dataset_react_route.py
+++ b/api/core/rag/retrieval/router/multi_dataset_react_route.py
@@ -14,7 +14,7 @@ from core.workflow.nodes.llm.llm_node import LLMNode
 PREFIX = """Respond to the human as helpfully and accurately as possible. You have access to the following tools:"""
 
 SUFFIX = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
-Thought:"""
+Thought:"""  # noqa: E501
 
 FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
 The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
@@ -46,20 +46,18 @@ Action:
   "action": "Final Answer",
   "action_input": "Final response to human"
 }}
-```"""
+```"""  # noqa: E501
 
 
 class ReactMultiDatasetRouter:
-
     def invoke(
-            self,
-            query: str,
-            dataset_tools: list[PromptMessageTool],
-            model_config: ModelConfigWithCredentialsEntity,
-            model_instance: ModelInstance,
-            user_id: str,
-            tenant_id: str
-
+        self,
+        query: str,
+        dataset_tools: list[PromptMessageTool],
+        model_config: ModelConfigWithCredentialsEntity,
+        model_instance: ModelInstance,
+        user_id: str,
+        tenant_id: str,
     ) -> Union[str, None]:
         """Given input, decided what to do.
         Returns:
@@ -71,23 +69,28 @@ class ReactMultiDatasetRouter:
             return dataset_tools[0].name
 
         try:
-            return self._react_invoke(query=query, model_config=model_config,
-                                      model_instance=model_instance,
-                                      tools=dataset_tools, user_id=user_id, tenant_id=tenant_id)
+            return self._react_invoke(
+                query=query,
+                model_config=model_config,
+                model_instance=model_instance,
+                tools=dataset_tools,
+                user_id=user_id,
+                tenant_id=tenant_id,
+            )
         except Exception as e:
             return None
 
     def _react_invoke(
-            self,
-            query: str,
-            model_config: ModelConfigWithCredentialsEntity,
-            model_instance: ModelInstance,
-            tools: Sequence[PromptMessageTool],
-            user_id: str,
-            tenant_id: str,
-            prefix: str = PREFIX,
-            suffix: str = SUFFIX,
-            format_instructions: str = FORMAT_INSTRUCTIONS,
+        self,
+        query: str,
+        model_config: ModelConfigWithCredentialsEntity,
+        model_instance: ModelInstance,
+        tools: Sequence[PromptMessageTool],
+        user_id: str,
+        tenant_id: str,
+        prefix: str = PREFIX,
+        suffix: str = SUFFIX,
+        format_instructions: str = FORMAT_INSTRUCTIONS,
     ) -> Union[str, None]:
         if model_config.mode == "chat":
             prompt = self.create_chat_prompt(
@@ -103,18 +106,18 @@ class ReactMultiDatasetRouter:
                 prefix=prefix,
                 format_instructions=format_instructions,
             )
-        stop = ['Observation:']
+        stop = ["Observation:"]
         # handle invoke result
         prompt_transform = AdvancedPromptTransform()
         prompt_messages = prompt_transform.get_prompt(
             prompt_template=prompt,
             inputs={},
-            query='',
+            query="",
             files=[],
-            context='',
+            context="",
             memory_config=None,
             memory=None,
-            model_config=model_config
+            model_config=model_config,
         )
         result_text, usage = self._invoke_llm(
             completion_param=model_config.parameters,
@@ -122,7 +125,7 @@ class ReactMultiDatasetRouter:
             prompt_messages=prompt_messages,
             stop=stop,
             user_id=user_id,
-            tenant_id=tenant_id
+            tenant_id=tenant_id,
         )
         output_parser = StructuredChatOutputParser()
         react_decision = output_parser.parse(result_text)
@@ -130,17 +133,21 @@ class ReactMultiDatasetRouter:
             return react_decision.tool
         return None
 
-    def _invoke_llm(self, completion_param: dict,
-                    model_instance: ModelInstance,
-                    prompt_messages: list[PromptMessage],
-                    stop: list[str], user_id: str, tenant_id: str
-                    ) -> tuple[str, LLMUsage]:
+    def _invoke_llm(
+        self,
+        completion_param: dict,
+        model_instance: ModelInstance,
+        prompt_messages: list[PromptMessage],
+        stop: list[str],
+        user_id: str,
+        tenant_id: str,
+    ) -> tuple[str, LLMUsage]:
         """
-            Invoke large language model
-            :param model_instance: model instance
-            :param prompt_messages: prompt messages
-            :param stop: stop
-            :return:
+        Invoke large language model
+        :param model_instance: model instance
+        :param prompt_messages: prompt messages
+        :param stop: stop
+        :return:
         """
         invoke_result = model_instance.invoke_llm(
             prompt_messages=prompt_messages,
@@ -151,9 +158,7 @@ class ReactMultiDatasetRouter:
         )
 
         # handle invoke result
-        text, usage = self._handle_invoke_result(
-            invoke_result=invoke_result
-        )
+        text, usage = self._handle_invoke_result(invoke_result=invoke_result)
 
         # deduct quota
         LLMNode.deduct_llm_quota(tenant_id=tenant_id, model_instance=model_instance, usage=usage)
@@ -168,7 +173,7 @@ class ReactMultiDatasetRouter:
         """
         model = None
         prompt_messages = []
-        full_text = ''
+        full_text = ""
         usage = None
         for result in invoke_result:
             text = result.delta.message.content
@@ -189,40 +194,36 @@ class ReactMultiDatasetRouter:
         return full_text, usage
 
     def create_chat_prompt(
-            self,
-            query: str,
-            tools: Sequence[PromptMessageTool],
-            prefix: str = PREFIX,
-            suffix: str = SUFFIX,
-            format_instructions: str = FORMAT_INSTRUCTIONS,
+        self,
+        query: str,
+        tools: Sequence[PromptMessageTool],
+        prefix: str = PREFIX,
+        suffix: str = SUFFIX,
+        format_instructions: str = FORMAT_INSTRUCTIONS,
     ) -> list[ChatModelMessage]:
         tool_strings = []
         for tool in tools:
             tool_strings.append(
-                f"{tool.name}: {tool.description}, args: {{'query': {{'title': 'Query', 'description': 'Query for the dataset to be used to retrieve the dataset.', 'type': 'string'}}}}")
+                f"{tool.name}: {tool.description}, args: {{'query': {{'title': 'Query',"
+                f" 'description': 'Query for the dataset to be used to retrieve the dataset.', 'type': 'string'}}}}"
+            )
         formatted_tools = "\n".join(tool_strings)
         unique_tool_names = {tool.name for tool in tools}
         tool_names = ", ".join('"' + name + '"' for name in unique_tool_names)
         format_instructions = format_instructions.format(tool_names=tool_names)
         template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
         prompt_messages = []
-        system_prompt_messages = ChatModelMessage(
-            role=PromptMessageRole.SYSTEM,
-            text=template
-        )
+        system_prompt_messages = ChatModelMessage(role=PromptMessageRole.SYSTEM, text=template)
         prompt_messages.append(system_prompt_messages)
-        user_prompt_message = ChatModelMessage(
-            role=PromptMessageRole.USER,
-            text=query
-        )
+        user_prompt_message = ChatModelMessage(role=PromptMessageRole.USER, text=query)
         prompt_messages.append(user_prompt_message)
         return prompt_messages
 
     def create_completion_prompt(
-            self,
-            tools: Sequence[PromptMessageTool],
-            prefix: str = PREFIX,
-            format_instructions: str = FORMAT_INSTRUCTIONS,
+        self,
+        tools: Sequence[PromptMessageTool],
+        prefix: str = PREFIX,
+        format_instructions: str = FORMAT_INSTRUCTIONS,
     ) -> CompletionModelPromptTemplate:
         """Create prompt in the style of the zero shot agent.
 
@@ -236,7 +237,7 @@ class ReactMultiDatasetRouter:
         suffix = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
 Question: {input}
 Thought: {agent_scratchpad}
-"""
+"""  # noqa: E501
 
         tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
         tool_names = ", ".join([tool.name for tool in tools])
diff --git a/api/core/rag/splitter/fixed_text_splitter.py b/api/core/rag/splitter/fixed_text_splitter.py
index 6a0804f890..53032b34d5 100644
--- a/api/core/rag/splitter/fixed_text_splitter.py
+++ b/api/core/rag/splitter/fixed_text_splitter.py
@@ -1,4 +1,5 @@
 """Functionality for splitting text."""
+
 from __future__ import annotations
 
 from typing import Any, Optional
@@ -18,31 +19,29 @@ from core.rag.splitter.text_splitter import (
 
 class EnhanceRecursiveCharacterTextSplitter(RecursiveCharacterTextSplitter):
     """
-        This class is used to implement from_gpt2_encoder, to prevent using of tiktoken
+    This class is used to implement from_gpt2_encoder, to prevent using of tiktoken
     """
 
     @classmethod
     def from_encoder(
-            cls: type[TS],
-            embedding_model_instance: Optional[ModelInstance],
-            allowed_special: Union[Literal[all], Set[str]] = set(),
-            disallowed_special: Union[Literal[all], Collection[str]] = "all",
-            **kwargs: Any,
+        cls: type[TS],
+        embedding_model_instance: Optional[ModelInstance],
+        allowed_special: Union[Literal[all], Set[str]] = set(),
+        disallowed_special: Union[Literal[all], Collection[str]] = "all",
+        **kwargs: Any,
     ):
         def _token_encoder(text: str) -> int:
             if not text:
                 return 0
 
             if embedding_model_instance:
-                return embedding_model_instance.get_text_embedding_num_tokens(
-                    texts=[text]
-                )
+                return embedding_model_instance.get_text_embedding_num_tokens(texts=[text])
             else:
                 return GPT2Tokenizer.get_num_tokens(text)
 
         if issubclass(cls, TokenTextSplitter):
             extra_kwargs = {
-                "model_name": embedding_model_instance.model if embedding_model_instance else 'gpt2',
+                "model_name": embedding_model_instance.model if embedding_model_instance else "gpt2",
                 "allowed_special": allowed_special,
                 "disallowed_special": disallowed_special,
             }
@@ -93,17 +92,21 @@ class FixedRecursiveCharacterTextSplitter(EnhanceRecursiveCharacterTextSplitter)
             splits = list(text)
         # Now go merging things, recursively splitting longer texts.
         _good_splits = []
+        _good_splits_lengths = []  # cache the lengths of the splits
         for s in splits:
-            if self._length_function(s) < self._chunk_size:
+            s_len = self._length_function(s)
+            if s_len < self._chunk_size:
                 _good_splits.append(s)
+                _good_splits_lengths.append(s_len)
             else:
                 if _good_splits:
-                    merged_text = self._merge_splits(_good_splits, separator)
+                    merged_text = self._merge_splits(_good_splits, separator, _good_splits_lengths)
                     final_chunks.extend(merged_text)
                     _good_splits = []
+                    _good_splits_lengths = []
                 other_info = self.recursive_split_text(s)
                 final_chunks.extend(other_info)
         if _good_splits:
-            merged_text = self._merge_splits(_good_splits, separator)
+            merged_text = self._merge_splits(_good_splits, separator, _good_splits_lengths)
             final_chunks.extend(merged_text)
         return final_chunks
diff --git a/api/core/rag/splitter/text_splitter.py b/api/core/rag/splitter/text_splitter.py
index 943f9918a7..7dd62f8de1 100644
--- a/api/core/rag/splitter/text_splitter.py
+++ b/api/core/rag/splitter/text_splitter.py
@@ -22,9 +22,7 @@ logger = logging.getLogger(__name__)
 TS = TypeVar("TS", bound="TextSplitter")
 
 
-def _split_text_with_regex(
-        text: str, separator: str, keep_separator: bool
-) -> list[str]:
+def _split_text_with_regex(text: str, separator: str, keep_separator: bool) -> list[str]:
     # Now that we have the separator, split the text
     if separator:
         if keep_separator:
@@ -37,19 +35,19 @@ def _split_text_with_regex(
             splits = re.split(separator, text)
     else:
         splits = list(text)
-    return [s for s in splits if (s != "" and s != '\n')]
+    return [s for s in splits if (s not in {"", "\n"})]
 
 
 class TextSplitter(BaseDocumentTransformer, ABC):
     """Interface for splitting text into chunks."""
 
     def __init__(
-            self,
-            chunk_size: int = 4000,
-            chunk_overlap: int = 200,
-            length_function: Callable[[str], int] = len,
-            keep_separator: bool = False,
-            add_start_index: bool = False,
+        self,
+        chunk_size: int = 4000,
+        chunk_overlap: int = 200,
+        length_function: Callable[[str], int] = len,
+        keep_separator: bool = False,
+        add_start_index: bool = False,
     ) -> None:
         """Create a new TextSplitter.
 
@@ -62,8 +60,7 @@ class TextSplitter(BaseDocumentTransformer, ABC):
         """
         if chunk_overlap > chunk_size:
             raise ValueError(
-                f"Got a larger chunk overlap ({chunk_overlap}) than chunk size "
-                f"({chunk_size}), should be smaller."
+                f"Got a larger chunk overlap ({chunk_overlap}) than chunk size ({chunk_size}), should be smaller."
             )
         self._chunk_size = chunk_size
         self._chunk_overlap = chunk_overlap
@@ -75,9 +72,7 @@ class TextSplitter(BaseDocumentTransformer, ABC):
     def split_text(self, text: str) -> list[str]:
         """Split text into multiple components."""
 
-    def create_documents(
-            self, texts: list[str], metadatas: Optional[list[dict]] = None
-    ) -> list[Document]:
+    def create_documents(self, texts: list[str], metadatas: Optional[list[dict]] = None) -> list[Document]:
         """Create documents from a list of texts."""
         _metadatas = metadatas or [{}] * len(texts)
         documents = []
@@ -119,14 +114,10 @@ class TextSplitter(BaseDocumentTransformer, ABC):
         index = 0
         for d in splits:
             _len = lengths[index]
-            if (
-                    total + _len + (separator_len if len(current_doc) > 0 else 0)
-                    > self._chunk_size
-            ):
+            if total + _len + (separator_len if len(current_doc) > 0 else 0) > self._chunk_size:
                 if total > self._chunk_size:
                     logger.warning(
-                        f"Created a chunk of size {total}, "
-                        f"which is longer than the specified {self._chunk_size}"
+                        f"Created a chunk of size {total}, which is longer than the specified {self._chunk_size}"
                     )
                 if len(current_doc) > 0:
                     doc = self._join_docs(current_doc, separator)
@@ -136,13 +127,9 @@ class TextSplitter(BaseDocumentTransformer, ABC):
                     # - we have a larger chunk than in the chunk overlap
                     # - or if we still have any chunks and the length is long
                     while total > self._chunk_overlap or (
-                            total + _len + (separator_len if len(current_doc) > 0 else 0)
-                            > self._chunk_size
-                            and total > 0
+                        total + _len + (separator_len if len(current_doc) > 0 else 0) > self._chunk_size and total > 0
                     ):
-                        total -= self._length_function(current_doc[0]) + (
-                            separator_len if len(current_doc) > 1 else 0
-                        )
+                        total -= self._length_function(current_doc[0]) + (separator_len if len(current_doc) > 1 else 0)
                         current_doc = current_doc[1:]
             current_doc.append(d)
             total += _len + (separator_len if len(current_doc) > 1 else 0)
@@ -159,28 +146,25 @@ class TextSplitter(BaseDocumentTransformer, ABC):
             from transformers import PreTrainedTokenizerBase
 
             if not isinstance(tokenizer, PreTrainedTokenizerBase):
-                raise ValueError(
-                    "Tokenizer received was not an instance of PreTrainedTokenizerBase"
-                )
+                raise ValueError("Tokenizer received was not an instance of PreTrainedTokenizerBase")
 
             def _huggingface_tokenizer_length(text: str) -> int:
                 return len(tokenizer.encode(text))
 
         except ImportError:
             raise ValueError(
-                "Could not import transformers python package. "
-                "Please install it with `pip install transformers`."
+                "Could not import transformers python package. Please install it with `pip install transformers`."
             )
         return cls(length_function=_huggingface_tokenizer_length, **kwargs)
 
     @classmethod
     def from_tiktoken_encoder(
-            cls: type[TS],
-            encoding_name: str = "gpt2",
-            model_name: Optional[str] = None,
-            allowed_special: Union[Literal["all"], Set[str]] = set(),
-            disallowed_special: Union[Literal["all"], Collection[str]] = "all",
-            **kwargs: Any,
+        cls: type[TS],
+        encoding_name: str = "gpt2",
+        model_name: Optional[str] = None,
+        allowed_special: Union[Literal["all"], Set[str]] = set(),
+        disallowed_special: Union[Literal["all"], Collection[str]] = "all",
+        **kwargs: Any,
     ) -> TS:
         """Text splitter that uses tiktoken encoder to count length."""
         try:
@@ -217,15 +201,11 @@ class TextSplitter(BaseDocumentTransformer, ABC):
 
         return cls(length_function=_tiktoken_encoder, **kwargs)
 
-    def transform_documents(
-            self, documents: Sequence[Document], **kwargs: Any
-    ) -> Sequence[Document]:
+    def transform_documents(self, documents: Sequence[Document], **kwargs: Any) -> Sequence[Document]:
         """Transform sequence of documents by splitting them."""
         return self.split_documents(list(documents))
 
-    async def atransform_documents(
-            self, documents: Sequence[Document], **kwargs: Any
-    ) -> Sequence[Document]:
+    async def atransform_documents(self, documents: Sequence[Document], **kwargs: Any) -> Sequence[Document]:
         """Asynchronously transform a sequence of documents by splitting them."""
         raise NotImplementedError
 
@@ -243,7 +223,10 @@ class CharacterTextSplitter(TextSplitter):
         # First we naively split the large input into a bunch of smaller ones.
         splits = _split_text_with_regex(text, self._separator, self._keep_separator)
         _separator = "" if self._keep_separator else self._separator
-        return self._merge_splits(splits, _separator)
+        _good_splits_lengths = []  # cache the lengths of the splits
+        for split in splits:
+            _good_splits_lengths.append(self._length_function(split))
+        return self._merge_splits(splits, _separator, _good_splits_lengths)
 
 
 class LineType(TypedDict):
@@ -264,9 +247,7 @@ class HeaderType(TypedDict):
 class MarkdownHeaderTextSplitter:
     """Splitting markdown files based on specified headers."""
 
-    def __init__(
-            self, headers_to_split_on: list[tuple[str, str]], return_each_line: bool = False
-    ):
+    def __init__(self, headers_to_split_on: list[tuple[str, str]], return_each_line: bool = False):
         """Create a new MarkdownHeaderTextSplitter.
 
         Args:
@@ -277,9 +258,7 @@ class MarkdownHeaderTextSplitter:
         self.return_each_line = return_each_line
         # Given the headers we want to split on,
         # (e.g., "#, ##, etc") order by length
-        self.headers_to_split_on = sorted(
-            headers_to_split_on, key=lambda split: len(split[0]), reverse=True
-        )
+        self.headers_to_split_on = sorted(headers_to_split_on, key=lambda split: len(split[0]), reverse=True)
 
     def aggregate_lines_to_chunks(self, lines: list[LineType]) -> list[Document]:
         """Combine lines with common metadata into chunks
@@ -289,10 +268,7 @@ class MarkdownHeaderTextSplitter:
         aggregated_chunks: list[LineType] = []
 
         for line in lines:
-            if (
-                    aggregated_chunks
-                    and aggregated_chunks[-1]["metadata"] == line["metadata"]
-            ):
+            if aggregated_chunks and aggregated_chunks[-1]["metadata"] == line["metadata"]:
                 # If the last line in the aggregated list
                 # has the same metadata as the current line,
                 # append the current content to the last lines's content
@@ -301,10 +277,7 @@ class MarkdownHeaderTextSplitter:
                 # Otherwise, append the current line to the aggregated list
                 aggregated_chunks.append(line)
 
-        return [
-            Document(page_content=chunk["content"], metadata=chunk["metadata"])
-            for chunk in aggregated_chunks
-        ]
+        return [Document(page_content=chunk["content"], metadata=chunk["metadata"]) for chunk in aggregated_chunks]
 
     def split_text(self, text: str) -> list[Document]:
         """Split markdown file
@@ -329,10 +302,9 @@ class MarkdownHeaderTextSplitter:
             for sep, name in self.headers_to_split_on:
                 # Check if line starts with a header that we intend to split on
                 if stripped_line.startswith(sep) and (
-                        # Header with no text OR header is followed by space
-                        # Both are valid conditions that sep is being used a header
-                        len(stripped_line) == len(sep)
-                        or stripped_line[len(sep)] == " "
+                    # Header with no text OR header is followed by space
+                    # Both are valid conditions that sep is being used a header
+                    len(stripped_line) == len(sep) or stripped_line[len(sep)] == " "
                 ):
                     # Ensure we are tracking the header as metadata
                     if name is not None:
@@ -340,10 +312,7 @@ class MarkdownHeaderTextSplitter:
                         current_header_level = sep.count("#")
 
                         # Pop out headers of lower or same level from the stack
-                        while (
-                                header_stack
-                                and header_stack[-1]["level"] >= current_header_level
-                        ):
+                        while header_stack and header_stack[-1]["level"] >= current_header_level:
                             # We have encountered a new header
                             # at the same or higher level
                             popped_header = header_stack.pop()
@@ -356,7 +325,7 @@ class MarkdownHeaderTextSplitter:
                         header: HeaderType = {
                             "level": current_header_level,
                             "name": name,
-                            "data": stripped_line[len(sep):].strip(),
+                            "data": stripped_line[len(sep) :].strip(),
                         }
                         header_stack.append(header)
                         # Update initial_metadata with the current header
@@ -389,9 +358,7 @@ class MarkdownHeaderTextSplitter:
             current_metadata = initial_metadata.copy()
 
         if current_content:
-            lines_with_metadata.append(
-                {"content": "\n".join(current_content), "metadata": current_metadata}
-            )
+            lines_with_metadata.append({"content": "\n".join(current_content), "metadata": current_metadata})
 
         # lines_with_metadata has each line with associated header metadata
         # aggregate these into chunks based on common metadata
@@ -399,8 +366,7 @@ class MarkdownHeaderTextSplitter:
             return self.aggregate_lines_to_chunks(lines_with_metadata)
         else:
             return [
-                Document(page_content=chunk["content"], metadata=chunk["metadata"])
-                for chunk in lines_with_metadata
+                Document(page_content=chunk["content"], metadata=chunk["metadata"]) for chunk in lines_with_metadata
             ]
 
 
@@ -433,12 +399,12 @@ class TokenTextSplitter(TextSplitter):
     """Splitting text to tokens using model tokenizer."""
 
     def __init__(
-            self,
-            encoding_name: str = "gpt2",
-            model_name: Optional[str] = None,
-            allowed_special: Union[Literal["all"], Set[str]] = set(),
-            disallowed_special: Union[Literal["all"], Collection[str]] = "all",
-            **kwargs: Any,
+        self,
+        encoding_name: str = "gpt2",
+        model_name: Optional[str] = None,
+        allowed_special: Union[Literal["all"], Set[str]] = set(),
+        disallowed_special: Union[Literal["all"], Collection[str]] = "all",
+        **kwargs: Any,
     ) -> None:
         """Create a new TextSplitter."""
         super().__init__(**kwargs)
@@ -485,10 +451,10 @@ class RecursiveCharacterTextSplitter(TextSplitter):
     """
 
     def __init__(
-            self,
-            separators: Optional[list[str]] = None,
-            keep_separator: bool = True,
-            **kwargs: Any,
+        self,
+        separators: Optional[list[str]] = None,
+        keep_separator: bool = True,
+        **kwargs: Any,
     ) -> None:
         """Create a new TextSplitter."""
         super().__init__(keep_separator=keep_separator, **kwargs)
@@ -505,7 +471,7 @@ class RecursiveCharacterTextSplitter(TextSplitter):
                 break
             if re.search(_s, text):
                 separator = _s
-                new_separators = separators[i + 1:]
+                new_separators = separators[i + 1 :]
                 break
 
         splits = _split_text_with_regex(text, separator, self._keep_separator)
diff --git a/api/core/tools/entities/api_entities.py b/api/core/tools/entities/api_entities.py
index 2b01b8fd8e..b988a588e9 100644
--- a/api/core/tools/entities/api_entities.py
+++ b/api/core/tools/entities/api_entities.py
@@ -10,23 +10,23 @@ from core.tools.tool.tool import ToolParameter
 
 class UserTool(BaseModel):
     author: str
-    name: str # identifier
-    label: I18nObject # label
+    name: str  # identifier
+    label: I18nObject  # label
     description: I18nObject
     parameters: Optional[list[ToolParameter]] = None
     labels: list[str] = None
 
-UserToolProviderTypeLiteral = Optional[Literal[
-    'builtin', 'api', 'workflow'
-]]
+
+UserToolProviderTypeLiteral = Optional[Literal["builtin", "api", "workflow"]]
+
 
 class UserToolProvider(BaseModel):
     id: str
     author: str
-    name: str # identifier
+    name: str  # identifier
     description: I18nObject
     icon: str
-    label: I18nObject # label
+    label: I18nObject  # label
     type: ToolProviderType
     masked_credentials: Optional[dict] = None
     original_credentials: Optional[dict] = None
@@ -40,26 +40,27 @@ class UserToolProvider(BaseModel):
         # overwrite tool parameter types for temp fix
         tools = jsonable_encoder(self.tools)
         for tool in tools:
-            if tool.get('parameters'):
-                for parameter in tool.get('parameters'):
-                    if parameter.get('type') == ToolParameter.ToolParameterType.FILE.value:
-                        parameter['type'] = 'files'
+            if tool.get("parameters"):
+                for parameter in tool.get("parameters"):
+                    if parameter.get("type") == ToolParameter.ToolParameterType.FILE.value:
+                        parameter["type"] = "files"
         # -------------
 
         return {
-            'id': self.id,
-            'author': self.author,
-            'name': self.name,
-            'description': self.description.to_dict(),
-            'icon': self.icon,
-            'label': self.label.to_dict(),
-            'type': self.type.value,
-            'team_credentials': self.masked_credentials,
-            'is_team_authorization': self.is_team_authorization,
-            'allow_delete': self.allow_delete,
-            'tools': tools,
-            'labels': self.labels,
+            "id": self.id,
+            "author": self.author,
+            "name": self.name,
+            "description": self.description.to_dict(),
+            "icon": self.icon,
+            "label": self.label.to_dict(),
+            "type": self.type.value,
+            "team_credentials": self.masked_credentials,
+            "is_team_authorization": self.is_team_authorization,
+            "allow_delete": self.allow_delete,
+            "tools": tools,
+            "labels": self.labels,
         }
 
+
 class UserToolProviderCredentials(BaseModel):
-    credentials: dict[str, ToolProviderCredentials]
\ No newline at end of file
+    credentials: dict[str, ToolProviderCredentials]
diff --git a/api/core/tools/entities/common_entities.py b/api/core/tools/entities/common_entities.py
index 55e31e8c35..37a926697b 100644
--- a/api/core/tools/entities/common_entities.py
+++ b/api/core/tools/entities/common_entities.py
@@ -7,6 +7,7 @@ class I18nObject(BaseModel):
     """
     Model class for i18n object.
     """
+
     zh_Hans: Optional[str] = None
     pt_BR: Optional[str] = None
     en_US: str
@@ -19,8 +20,4 @@ class I18nObject(BaseModel):
             self.pt_BR = self.en_US
 
     def to_dict(self) -> dict:
-        return {
-            'zh_Hans': self.zh_Hans,
-            'en_US': self.en_US,
-            'pt_BR': self.pt_BR
-        }
+        return {"zh_Hans": self.zh_Hans, "en_US": self.en_US, "pt_BR": self.pt_BR}
diff --git a/api/core/tools/entities/tool_bundle.py b/api/core/tools/entities/tool_bundle.py
index d18d27fb02..0c15b2a371 100644
--- a/api/core/tools/entities/tool_bundle.py
+++ b/api/core/tools/entities/tool_bundle.py
@@ -7,8 +7,10 @@ from core.tools.entities.tool_entities import ToolParameter
 
 class ApiToolBundle(BaseModel):
     """
-    This class is used to store the schema information of an api based tool. such as the url, the method, the parameters, etc.
+    This class is used to store the schema information of an api based tool.
+     such as the url, the method, the parameters, etc.
     """
+
     # server_url
     server_url: str
     # method
diff --git a/api/core/tools/entities/tool_entities.py b/api/core/tools/entities/tool_entities.py
index e31dec55d2..02b8b35be7 100644
--- a/api/core/tools/entities/tool_entities.py
+++ b/api/core/tools/entities/tool_entities.py
@@ -7,27 +7,29 @@ from core.tools.entities.common_entities import I18nObject
 
 
 class ToolLabelEnum(Enum):
-    SEARCH = 'search'
-    IMAGE = 'image'
-    VIDEOS = 'videos'
-    WEATHER = 'weather'
-    FINANCE = 'finance'
-    DESIGN = 'design'
-    TRAVEL = 'travel'
-    SOCIAL = 'social'
-    NEWS = 'news'
-    MEDICAL = 'medical'
-    PRODUCTIVITY = 'productivity'
-    EDUCATION = 'education'
-    BUSINESS = 'business'
-    ENTERTAINMENT = 'entertainment'
-    UTILITIES = 'utilities'
-    OTHER = 'other'
+    SEARCH = "search"
+    IMAGE = "image"
+    VIDEOS = "videos"
+    WEATHER = "weather"
+    FINANCE = "finance"
+    DESIGN = "design"
+    TRAVEL = "travel"
+    SOCIAL = "social"
+    NEWS = "news"
+    MEDICAL = "medical"
+    PRODUCTIVITY = "productivity"
+    EDUCATION = "education"
+    BUSINESS = "business"
+    ENTERTAINMENT = "entertainment"
+    UTILITIES = "utilities"
+    OTHER = "other"
+
 
 class ToolProviderType(Enum):
     """
-        Enum class for tool provider
+    Enum class for tool provider
     """
+
     BUILT_IN = "builtin"
     WORKFLOW = "workflow"
     API = "api"
@@ -35,7 +37,7 @@ class ToolProviderType(Enum):
     DATASET_RETRIEVAL = "dataset-retrieval"
 
     @classmethod
-    def value_of(cls, value: str) -> 'ToolProviderType':
+    def value_of(cls, value: str) -> "ToolProviderType":
         """
         Get value of given mode.
 
@@ -45,19 +47,21 @@ class ToolProviderType(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid mode value {value}')
+        raise ValueError(f"invalid mode value {value}")
+
 
 class ApiProviderSchemaType(Enum):
     """
     Enum class for api provider schema type.
     """
+
     OPENAPI = "openapi"
     SWAGGER = "swagger"
     OPENAI_PLUGIN = "openai_plugin"
     OPENAI_ACTIONS = "openai_actions"
 
     @classmethod
-    def value_of(cls, value: str) -> 'ApiProviderSchemaType':
+    def value_of(cls, value: str) -> "ApiProviderSchemaType":
         """
         Get value of given mode.
 
@@ -67,17 +71,19 @@ class ApiProviderSchemaType(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid mode value {value}')
+        raise ValueError(f"invalid mode value {value}")
+
 
 class ApiProviderAuthType(Enum):
     """
     Enum class for api provider auth type.
     """
+
     NONE = "none"
     API_KEY = "api_key"
 
     @classmethod
-    def value_of(cls, value: str) -> 'ApiProviderAuthType':
+    def value_of(cls, value: str) -> "ApiProviderAuthType":
         """
         Get value of given mode.
 
@@ -87,7 +93,8 @@ class ApiProviderAuthType(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid mode value {value}')
+        raise ValueError(f"invalid mode value {value}")
+
 
 class ToolInvokeMessage(BaseModel):
     class MessageType(Enum):
@@ -105,19 +112,21 @@ class ToolInvokeMessage(BaseModel):
     """
     message: str | bytes | dict | None = None
     meta: dict[str, Any] | None = None
-    save_as: str = ''
+    save_as: str = ""
+
 
 class ToolInvokeMessageBinary(BaseModel):
     mimetype: str = Field(..., description="The mimetype of the binary")
     url: str = Field(..., description="The url of the binary")
-    save_as: str = ''
+    save_as: str = ""
     file_var: Optional[dict[str, Any]] = None
 
+
 class ToolParameterOption(BaseModel):
     value: str = Field(..., description="The value of the option")
     label: I18nObject = Field(..., description="The label of the option")
 
-    @field_validator('value', mode='before')
+    @field_validator("value", mode="before")
     @classmethod
     def transform_id_to_str(cls, value) -> str:
         if not isinstance(value, str):
@@ -136,9 +145,9 @@ class ToolParameter(BaseModel):
         FILE = "file"
 
     class ToolParameterForm(Enum):
-        SCHEMA = "schema" # should be set while adding tool
-        FORM = "form"     # should be set before invoking tool
-        LLM = "llm"       # will be set by LLM
+        SCHEMA = "schema"  # should be set while adding tool
+        FORM = "form"  # should be set before invoking tool
+        LLM = "llm"  # will be set by LLM
 
     name: str = Field(..., description="The name of the parameter")
     label: I18nObject = Field(..., description="The label presented to the user")
@@ -154,25 +163,32 @@ class ToolParameter(BaseModel):
     options: Optional[list[ToolParameterOption]] = None
 
     @classmethod
-    def get_simple_instance(cls,
-                       name: str, llm_description: str, type: ToolParameterType,
-                       required: bool, options: Optional[list[str]] = None) -> 'ToolParameter':
+    def get_simple_instance(
+        cls,
+        name: str,
+        llm_description: str,
+        type: ToolParameterType,
+        required: bool,
+        options: Optional[list[str]] = None,
+    ) -> "ToolParameter":
         """
-            get a simple tool parameter
+        get a simple tool parameter
 
-            :param name: the name of the parameter
-            :param llm_description: the description presented to the LLM
-            :param type: the type of the parameter
-            :param required: if the parameter is required
-            :param options: the options of the parameter
+        :param name: the name of the parameter
+        :param llm_description: the description presented to the LLM
+        :param type: the type of the parameter
+        :param required: if the parameter is required
+        :param options: the options of the parameter
         """
         # convert options to ToolParameterOption
         if options:
-            options = [ToolParameterOption(value=option, label=I18nObject(en_US=option, zh_Hans=option)) for option in options]
+            options = [
+                ToolParameterOption(value=option, label=I18nObject(en_US=option, zh_Hans=option)) for option in options
+            ]
         return cls(
             name=name,
-            label=I18nObject(en_US='', zh_Hans=''),
-            human_description=I18nObject(en_US='', zh_Hans=''),
+            label=I18nObject(en_US="", zh_Hans=""),
+            human_description=I18nObject(en_US="", zh_Hans=""),
             type=type,
             form=cls.ToolParameterForm.LLM,
             llm_description=llm_description,
@@ -180,18 +196,24 @@ class ToolParameter(BaseModel):
             options=options,
         )
 
+
 class ToolProviderIdentity(BaseModel):
     author: str = Field(..., description="The author of the tool")
     name: str = Field(..., description="The name of the tool")
     description: I18nObject = Field(..., description="The description of the tool")
     icon: str = Field(..., description="The icon of the tool")
     label: I18nObject = Field(..., description="The label of the tool")
-    tags: Optional[list[ToolLabelEnum]] = Field(default=[], description="The tags of the tool", )
+    tags: Optional[list[ToolLabelEnum]] = Field(
+        default=[],
+        description="The tags of the tool",
+    )
+
 
 class ToolDescription(BaseModel):
     human: I18nObject = Field(..., description="The description presented to the user")
     llm: str = Field(..., description="The description presented to the LLM")
 
+
 class ToolIdentity(BaseModel):
     author: str = Field(..., description="The author of the tool")
     name: str = Field(..., description="The name of the tool")
@@ -199,10 +221,12 @@ class ToolIdentity(BaseModel):
     provider: str = Field(..., description="The provider of the tool")
     icon: Optional[str] = None
 
+
 class ToolCredentialsOption(BaseModel):
     value: str = Field(..., description="The value of the option")
     label: I18nObject = Field(..., description="The label of the option")
 
+
 class ToolProviderCredentials(BaseModel):
     class CredentialsType(Enum):
         SECRET_INPUT = "secret-input"
@@ -221,7 +245,7 @@ class ToolProviderCredentials(BaseModel):
             for mode in cls:
                 if mode.value == value:
                     return mode
-            raise ValueError(f'invalid mode value {value}')
+            raise ValueError(f"invalid mode value {value}")
 
         @staticmethod
         def default(value: str) -> str:
@@ -239,33 +263,38 @@ class ToolProviderCredentials(BaseModel):
 
     def to_dict(self) -> dict:
         return {
-            'name': self.name,
-            'type': self.type.value,
-            'required': self.required,
-            'default': self.default,
-            'options': self.options,
-            'help': self.help.to_dict() if self.help else None,
-            'label': self.label.to_dict(),
-            'url': self.url,
-            'placeholder': self.placeholder.to_dict() if self.placeholder else None,
+            "name": self.name,
+            "type": self.type.value,
+            "required": self.required,
+            "default": self.default,
+            "options": self.options,
+            "help": self.help.to_dict() if self.help else None,
+            "label": self.label.to_dict(),
+            "url": self.url,
+            "placeholder": self.placeholder.to_dict() if self.placeholder else None,
         }
 
+
 class ToolRuntimeVariableType(Enum):
     TEXT = "text"
     IMAGE = "image"
 
+
 class ToolRuntimeVariable(BaseModel):
     type: ToolRuntimeVariableType = Field(..., description="The type of the variable")
     name: str = Field(..., description="The name of the variable")
     position: int = Field(..., description="The position of the variable")
     tool_name: str = Field(..., description="The name of the tool")
 
+
 class ToolRuntimeTextVariable(ToolRuntimeVariable):
     value: str = Field(..., description="The value of the variable")
 
+
 class ToolRuntimeImageVariable(ToolRuntimeVariable):
     value: str = Field(..., description="The path of the image")
 
+
 class ToolRuntimeVariablePool(BaseModel):
     conversation_id: str = Field(..., description="The conversation id")
     user_id: str = Field(..., description="The user id")
@@ -274,26 +303,26 @@ class ToolRuntimeVariablePool(BaseModel):
     pool: list[ToolRuntimeVariable] = Field(..., description="The pool of variables")
 
     def __init__(self, **data: Any):
-        pool = data.get('pool', [])
+        pool = data.get("pool", [])
         # convert pool into correct type
         for index, variable in enumerate(pool):
-            if variable['type'] == ToolRuntimeVariableType.TEXT.value:
+            if variable["type"] == ToolRuntimeVariableType.TEXT.value:
                 pool[index] = ToolRuntimeTextVariable(**variable)
-            elif variable['type'] == ToolRuntimeVariableType.IMAGE.value:
+            elif variable["type"] == ToolRuntimeVariableType.IMAGE.value:
                 pool[index] = ToolRuntimeImageVariable(**variable)
         super().__init__(**data)
 
     def dict(self) -> dict:
         return {
-            'conversation_id': self.conversation_id,
-            'user_id': self.user_id,
-            'tenant_id': self.tenant_id,
-            'pool': [variable.model_dump() for variable in self.pool],
+            "conversation_id": self.conversation_id,
+            "user_id": self.user_id,
+            "tenant_id": self.tenant_id,
+            "pool": [variable.model_dump() for variable in self.pool],
         }
 
     def set_text(self, tool_name: str, name: str, value: str) -> None:
         """
-            set a text variable
+        set a text variable
         """
         for variable in self.pool:
             if variable.name == name:
@@ -314,10 +343,10 @@ class ToolRuntimeVariablePool(BaseModel):
 
     def set_file(self, tool_name: str, value: str, name: str = None) -> None:
         """
-            set an image variable
+        set an image variable
 
-            :param tool_name: the name of the tool
-            :param value: the id of the file
+        :param tool_name: the name of the tool
+        :param value: the id of the file
         """
         # check how many image variables are there
         image_variable_count = 0
@@ -345,22 +374,27 @@ class ToolRuntimeVariablePool(BaseModel):
 
         self.pool.append(variable)
 
+
 class ModelToolPropertyKey(Enum):
     IMAGE_PARAMETER_NAME = "image_parameter_name"
 
+
 class ModelToolConfiguration(BaseModel):
     """
     Model tool configuration
     """
+
     type: str = Field(..., description="The type of the model tool")
     model: str = Field(..., description="The model")
     label: I18nObject = Field(..., description="The label of the model tool")
     properties: dict[ModelToolPropertyKey, Any] = Field(..., description="The properties of the model tool")
 
+
 class ModelToolProviderConfiguration(BaseModel):
     """
     Model tool provider configuration
     """
+
     provider: str = Field(..., description="The provider of the model tool")
     models: list[ModelToolConfiguration] = Field(..., description="The models of the model tool")
     label: I18nObject = Field(..., description="The label of the model tool")
@@ -370,27 +404,30 @@ class WorkflowToolParameterConfiguration(BaseModel):
     """
     Workflow tool configuration
     """
+
     name: str = Field(..., description="The name of the parameter")
     description: str = Field(..., description="The description of the parameter")
     form: ToolParameter.ToolParameterForm = Field(..., description="The form of the parameter")
 
+
 class ToolInvokeMeta(BaseModel):
     """
     Tool invoke meta
     """
+
     time_cost: float = Field(..., description="The time cost of the tool invoke")
     error: Optional[str] = None
     tool_config: Optional[dict] = None
 
     @classmethod
-    def empty(cls) -> 'ToolInvokeMeta':
+    def empty(cls) -> "ToolInvokeMeta":
         """
         Get an empty instance of ToolInvokeMeta
         """
         return cls(time_cost=0.0, error=None, tool_config={})
 
     @classmethod
-    def error_instance(cls, error: str) -> 'ToolInvokeMeta':
+    def error_instance(cls, error: str) -> "ToolInvokeMeta":
         """
         Get an instance of ToolInvokeMeta with error
         """
@@ -398,22 +435,26 @@ class ToolInvokeMeta(BaseModel):
 
     def to_dict(self) -> dict:
         return {
-            'time_cost': self.time_cost,
-            'error': self.error,
-            'tool_config': self.tool_config,
+            "time_cost": self.time_cost,
+            "error": self.error,
+            "tool_config": self.tool_config,
         }
 
+
 class ToolLabel(BaseModel):
     """
     Tool label
     """
+
     name: str = Field(..., description="The name of the tool")
     label: I18nObject = Field(..., description="The label of the tool")
     icon: str = Field(..., description="The icon of the tool")
 
+
 class ToolInvokeFrom(Enum):
     """
     Enum class for tool invoke
     """
+
     WORKFLOW = "workflow"
     AGENT = "agent"
diff --git a/api/core/tools/entities/values.py b/api/core/tools/entities/values.py
index d0be5e9355..f460df7e25 100644
--- a/api/core/tools/entities/values.py
+++ b/api/core/tools/entities/values.py
@@ -2,73 +2,109 @@ from core.tools.entities.common_entities import I18nObject
 from core.tools.entities.tool_entities import ToolLabel, ToolLabelEnum
 
 ICONS = {
-    ToolLabelEnum.SEARCH: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+    ToolLabelEnum.SEARCH: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M7.33398 1.3335C10.646 1.3335 13.334 4.0215 13.334 7.3335C13.334 10.6455 10.646 13.3335 7.33398 13.3335C4.02198 13.3335 1.33398 10.6455 1.33398 7.3335C1.33398 4.0215 4.02198 1.3335 7.33398 1.3335ZM7.33398 12.0002C9.91232 12.0002 12.0007 9.91183 12.0007 7.3335C12.0007 4.75516 9.91232 2.66683 7.33398 2.66683C4.75565 2.66683 2.66732 4.75516 2.66732 7.3335C2.66732 9.91183 4.75565 12.0002 7.33398 12.0002ZM12.9909 12.0476L14.8764 13.9332L13.9337 14.876L12.0481 12.9904L12.9909 12.0476Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.IMAGE: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.IMAGE: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M13.0514 9.71752L10.4718 7.13792C10.2115 6.87752 9.78932 6.87752 9.52898 7.13792L4.57721 12.0897C3.4097 11.1113 2.66732 9.64232 2.66732 7.99992C2.66732 5.0544 5.05513 2.66659 8.00065 2.66659C10.9462 2.66659 13.334 5.0544 13.334 7.99992C13.334 8.60085 13.2346 9.17852 13.0514 9.71752ZM5.72683 12.8257L10.0004 8.55212L12.4259 10.9777C11.4668 12.4001 9.84152 13.3331 8.00038 13.3331C7.18632 13.3331 6.41628 13.1511 5.72683 12.8257ZM8.00065 14.6666C11.6825 14.6666 14.6673 11.6818 14.6673 7.99992C14.6673 4.31802 11.6825 1.33325 8.00065 1.33325C4.31875 1.33325 1.33398 4.31802 1.33398 7.99992C1.33398 11.6818 4.31875 14.6666 8.00065 14.6666ZM7.33398 6.66658C7.33398 7.40299 6.73705 7.99992 6.00065 7.99992C5.26427 7.99992 4.66732 7.40299 4.66732 6.66658C4.66732 5.9302 5.26427 5.33325 6.00065 5.33325C6.73705 5.33325 7.33398 5.9302 7.33398 6.66658Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.VIDEOS: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.VIDEOS: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M8.00065 13.3333H13.334V14.6666H8.00065C4.31875 14.6666 1.33398 11.6818 1.33398 7.99992C1.33398 4.31802 4.31875 1.33325 8.00065 1.33325C11.6825 1.33325 14.6673 4.31802 14.6673 7.99992C14.6673 9.50072 14.1714 10.8857 13.3345 11.9999H11.5284C12.6356 11.0227 13.334 9.59285 13.334 7.99992C13.334 5.0544 10.9462 2.66659 8.00065 2.66659C5.05513 2.66659 2.66732 5.0544 2.66732 7.99992C2.66732 10.9455 5.05513 13.3333 8.00065 13.3333ZM8.00065 6.66658C7.26425 6.66658 6.66732 6.06963 6.66732 5.33325C6.66732 4.59687 7.26425 3.99992 8.00065 3.99992C8.73705 3.99992 9.33398 4.59687 9.33398 5.33325C9.33398 6.06963 8.73705 6.66658 8.00065 6.66658ZM5.33398 9.33325C4.5976 9.33325 4.00065 8.73632 4.00065 7.99992C4.00065 7.26352 4.5976 6.66658 5.33398 6.66658C6.07036 6.66658 6.66732 7.26352 6.66732 7.99992C6.66732 8.73632 6.07036 9.33325 5.33398 9.33325ZM10.6673 9.33325C9.93092 9.33325 9.33398 8.73632 9.33398 7.99992C9.33398 7.26352 9.93092 6.66658 10.6673 6.66658C11.4037 6.66658 12.0007 7.26352 12.0007 7.99992C12.0007 8.73632 11.4037 9.33325 10.6673 9.33325ZM8.00065 11.9999C7.26425 11.9999 6.66732 11.403 6.66732 10.6666C6.66732 9.93018 7.26425 9.33325 8.00065 9.33325C8.73705 9.33325 9.33398 9.93018 9.33398 10.6666C9.33398 11.403 8.73705 11.9999 8.00065 11.9999Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.WEATHER: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.WEATHER: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M6.6553 3.37344C7.42088 2.1484 8.78162 1.3335 10.3327 1.3335C12.7259 1.3335 14.666 3.2736 14.666 5.66683C14.666 6.38704 14.4903 7.06623 14.1794 7.66383C14.8894 8.3325 15.3327 9.28123 15.3327 10.3335C15.3327 12.3586 13.6911 14.0002 11.666 14.0002H5.99935C3.05383 14.0002 0.666016 11.6124 0.666016 8.66683C0.666016 5.72131 3.05383 3.3335 5.99935 3.3335C6.22143 3.3335 6.44034 3.34707 6.6553 3.37344ZM8.03628 3.73629C9.37768 4.29108 10.4435 5.37735 10.9711 6.73256C11.1961 6.68943 11.4284 6.66683 11.666 6.66683C12.1561 6.66683 12.6237 6.76296 13.0511 6.93743C13.2317 6.55162 13.3327 6.12102 13.3327 5.66683C13.3327 4.00998 11.9895 2.66683 10.3327 2.66683C9.41115 2.66683 8.58662 3.08236 8.03628 3.73629ZM11.666 12.6668C12.9547 12.6668 13.9993 11.6222 13.9993 10.3335C13.9993 9.04483 12.9547 8.00016 11.666 8.00016C11.013 8.00016 10.4227 8.26836 9.99922 8.70063C9.99928 8.68936 9.99935 8.6781 9.99935 8.66683C9.99935 6.45769 8.20848 4.66683 5.99935 4.66683C3.79021 4.66683 1.99935 6.45769 1.99935 8.66683C1.99935 10.876 3.79021 12.6668 5.99935 12.6668H11.666Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.FINANCE: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.FINANCE: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M8.00262 14.6685C4.32071 14.6685 1.33594 11.6838 1.33594 8.00184C1.33594 4.31997 4.32071 1.33521 8.00262 1.33521C11.6845 1.33521 14.6693 4.31997 14.6693 8.00184C14.6693 11.6838 11.6845 14.6685 8.00262 14.6685ZM8.00262 13.3352C10.9482 13.3352 13.336 10.9474 13.336 8.00184C13.336 5.05635 10.9482 2.66854 8.00262 2.66854C5.05708 2.66854 2.66927 5.05635 2.66927 8.00184C2.66927 10.9474 5.05708 13.3352 8.00262 13.3352ZM5.66927 9.33517H9.33595C9.52002 9.33517 9.66928 9.18597 9.66928 9.00184C9.66928 8.81777 9.52002 8.66851 9.33595 8.66851H6.66928C5.7488 8.66851 5.0026 7.92237 5.0026 7.00184C5.0026 6.08139 5.7488 5.33521 6.66928 5.33521H7.33595V4.00187H8.66928V5.33521H10.336V6.66851H6.66928C6.48518 6.66851 6.33594 6.81777 6.33594 7.00184C6.33594 7.18597 6.48518 7.33517 6.66928 7.33517H9.33595C10.2564 7.33517 11.0026 8.08137 11.0026 9.00184C11.0026 9.92237 10.2564 10.6685 9.33595 10.6685H8.66928V12.0018H7.33595V10.6685H5.66927V9.33517Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.DESIGN: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.DESIGN: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M4.70152 9.41416L3.2873 10.8284L5.17292 12.714L12.7154 5.17154L10.8298 3.28592L9.41557 4.70013L10.3584 5.64295L9.41557 6.58575L8.47277 5.64295L7.52997 6.58575L8.47277 7.52856L7.52997 8.47136L6.58713 7.52856L5.64433 8.47136L6.58713 9.41416L5.64433 10.357L4.70152 9.41416ZM11.3012 1.87171L14.1296 4.70013C14.39 4.96049 14.39 5.38259 14.1296 5.64295L5.64433 14.1282C5.38397 14.3886 4.96187 14.3886 4.70152 14.1282L1.87309 11.2998C1.61274 11.0394 1.61274 10.6174 1.87309 10.357L10.3584 1.87171C10.6187 1.61136 11.0408 1.61136 11.3012 1.87171ZM9.41557 12.2423L10.3584 11.2995L11.8534 12.7945H12.7962V11.8517L11.3012 10.3567L12.244 9.41383L14.0011 11.171V13.9999H11.1732L9.41557 12.2423ZM3.75861 6.58533L1.87299 4.69971C1.61265 4.43937 1.61265 4.01725 1.87299 3.75691L3.75861 1.87129C4.01896 1.61094 4.44107 1.61094 4.70142 1.87129L6.58704 3.75691L5.64423 4.69971L4.23002 3.2855L3.28721 4.22831L4.70142 5.64253L3.75861 6.58533Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.TRAVEL: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.TRAVEL: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M9.44839 2C9.80198 2 10.1411 2.14047 10.3912 2.39053L13.6101 5.60947C13.8602 5.85953 14.0007 6.19866 14.0007 6.55229V11.3333H15.334V12.6667L9.91652 12.6672C9.62032 13.8171 8.57638 14.6667 7.33398 14.6667C6.0916 14.6667 5.04766 13.8171 4.75146 12.6672L2.00065 12.6667C1.63246 12.6667 1.33398 12.3682 1.33398 12V3.33333C1.33398 2.59695 1.93094 2 2.66732 2H9.44839ZM7.33398 10.6667C6.5976 10.6667 6.00065 11.2636 6.00065 12C6.00065 12.7364 6.5976 13.3333 7.33398 13.3333C8.07038 13.3333 8.66732 12.7364 8.66732 12C8.66732 11.2636 8.07038 10.6667 7.33398 10.6667ZM9.44839 3.33333H2.66732V11.3333L4.75128 11.3335C5.04726 10.1833 6.09136 9.33333 7.33398 9.33333C8.57658 9.33333 9.62072 10.1833 9.91665 11.3335L12.6673 11.3333V6.55229L9.44839 3.33333ZM9.33398 4.66667V8.66667H4.00065V4.66667H9.33398ZM8.00065 6H5.33398V7.33333H8.00065V6Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.SOCIAL: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.SOCIAL: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M13.334 7.99992C13.334 5.0544 10.9462 2.66659 8.00065 2.66659C5.05513 2.66659 2.66732 5.0544 2.66732 7.99992C2.66732 10.9455 5.05513 13.3333 8.00065 13.3333C9.09518 13.3333 10.1127 13.0035 10.9594 12.438L11.699 13.5475C10.6408 14.2545 9.36885 14.6666 8.00065 14.6666C4.31875 14.6666 1.33398 11.6818 1.33398 7.99992C1.33398 4.31802 4.31875 1.33325 8.00065 1.33325C11.6825 1.33325 14.6673 4.31802 14.6673 7.99992V8.99992C14.6673 10.2886 13.6227 11.3333 12.334 11.3333C11.5312 11.3333 10.8231 10.9278 10.4032 10.3105C9.79678 10.9409 8.94452 11.3333 8.00065 11.3333C6.1597 11.3333 4.66732 9.84085 4.66732 7.99992C4.66732 6.15897 6.1597 4.66658 8.00065 4.66658C8.75118 4.66658 9.44378 4.91464 10.001 5.33325H11.334V8.99992C11.334 9.55219 11.7817 9.99992 12.334 9.99992C12.8863 9.99992 13.334 9.55219 13.334 8.99992V7.99992ZM8.00065 5.99992C6.89605 5.99992 6.00065 6.89532 6.00065 7.99992C6.00065 9.10452 6.89605 9.99992 8.00065 9.99992C9.10525 9.99992 10.0007 9.10452 10.0007 7.99992C10.0007 6.89532 9.10525 5.99992 8.00065 5.99992Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.NEWS: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.NEWS: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M10.6673 13.3335V2.66683H2.66732V12.6668C2.66732 13.035 2.9658 13.3335 3.33398 13.3335H10.6673ZM12.6673 14.6668H3.33398C2.22942 14.6668 1.33398 13.7714 1.33398 12.6668V2.00016C1.33398 1.63198 1.63246 1.3335 2.00065 1.3335H11.334C11.7022 1.3335 12.0007 1.63198 12.0007 2.00016V6.66683H14.6673V12.6668C14.6673 13.7714 13.7719 14.6668 12.6673 14.6668ZM12.0007 8.00016V12.6668C12.0007 13.035 12.2991 13.3335 12.6673 13.3335C13.0355 13.3335 13.334 13.035 13.334 12.6668V8.00016H12.0007ZM4.00065 4.00016H8.00065V8.00016H4.00065V4.00016ZM5.33398 5.3335V6.66683H6.66732V5.3335H5.33398ZM4.00065 8.66683H9.33398V10.0002H4.00065V8.66683ZM4.00065 10.6668H9.33398V12.0002H4.00065V10.6668Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.MEDICAL: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.MEDICAL: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M8.79747 1.51186L10.9641 5.26464C11.1482 5.5835 11.0389 5.99122 10.7201 6.17532L9.85373 6.67474L10.5207 7.83001L9.366 8.49668L8.699 7.34141L7.83333 7.84201C7.51447 8.02608 7.10673 7.91681 6.92267 7.59794L5.69747 5.47632C4.32922 5.89145 3.33333 7.16268 3.33333 8.66654C3.33333 9.08348 3.40987 9.48248 3.54965 9.85034C4.06613 9.52254 4.67762 9.33321 5.33333 9.33321C6.45605 9.33321 7.44913 9.88828 8.05313 10.7389L13.1787 7.78014L13.8454 8.93488L8.5932 11.9672C8.64133 12.1927 8.66667 12.4267 8.66667 12.6665C8.66667 12.895 8.64367 13.1181 8.59993 13.3337L14 13.3332V14.6665L2.66703 14.6673C2.2482 14.1101 2 13.4173 2 12.6665C2 11.9951 2.19855 11.3699 2.54014 10.8467C2.19517 10.1964 2 9.45428 2 8.66654C2 6.66968 3.25421 4.96575 5.01785 4.29953L4.75598 3.84519C4.38779 3.20747 4.60629 2.39202 5.24402 2.02382L6.97607 1.02382C7.6138 0.655637 8.42927 0.874138 8.79747 1.51186ZM5.33333 10.6665C4.22877 10.6665 3.33333 11.562 3.33333 12.6665C3.33333 12.9003 3.37343 13.1247 3.44711 13.3331H7.21953C7.29327 13.1247 7.33333 12.9003 7.33333 12.6665C7.33333 11.562 6.4379 10.6665 5.33333 10.6665ZM7.64273 2.17852L5.91068 3.17852L7.744 6.35395L9.47607 5.35395L7.64273 2.17852Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.PRODUCTIVITY: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.PRODUCTIVITY: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M6.64807 11.9999H9.35062C9.43862 11.1989 9.84742 10.5376 10.5111 9.81499C10.5858 9.73365 11.0652 9.23752 11.1221 9.16665C11.6872 8.46199 11.9993 7.58992 11.9993 6.66659C11.9993 4.45745 10.2085 2.66659 7.99935 2.66659C5.79021 2.66659 3.99935 4.45745 3.99935 6.66659C3.99935 7.58945 4.31118 8.46105 4.87576 9.16552C4.93271 9.23659 5.41322 9.73405 5.48704 9.81445C6.15112 10.5375 6.56004 11.1989 6.64807 11.9999ZM9.33268 13.3333H6.66602V13.9999H9.33268V13.3333ZM3.83532 9.99939C3.10365 9.08639 2.66602 7.92759 2.66602 6.66659C2.66602 3.72107 5.05383 1.33325 7.99935 1.33325C10.9449 1.33325 13.3327 3.72107 13.3327 6.66659C13.3327 7.92825 12.8945 9.08759 12.1622 10.0009C11.7487 10.5165 10.666 11.3333 10.666 12.3333V13.9999C10.666 14.7363 10.0691 15.3333 9.33268 15.3333H6.66602C5.92964 15.3333 5.33268 14.7363 5.33268 13.9999V12.3333C5.33268 11.3333 4.24907 10.5157 3.83532 9.99939ZM8.66602 6.66979H10.3327L7.33268 10.6698V8.00312H5.66602L8.66602 3.99992V6.66979Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.EDUCATION: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.EDUCATION: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M14 2.66683H4.66667C3.93029 2.66683 3.33333 3.26378 3.33333 4.00016C3.33333 4.73654 3.93029 5.3335 4.66667 5.3335H14V14.0002C14 14.3684 13.7015 14.6668 13.3333 14.6668H4.66667C3.19391 14.6668 2 13.4729 2 12.0002V4.00016C2 2.5274 3.19391 1.3335 4.66667 1.3335H13.3333C13.7015 1.3335 14 1.63198 14 2.00016V2.66683ZM3.33333 12.0002C3.33333 12.7366 3.93029 13.3335 4.66667 13.3335H12.6667V6.66683H4.66667C4.18095 6.66683 3.72557 6.53697 3.33333 6.31008V12.0002ZM13.3333 4.66683H4.66667C4.29848 4.66683 4 4.36835 4 4.00016C4 3.63198 4.29848 3.3335 4.66667 3.3335H13.3333V4.66683Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.BUSINESS: '''<svg xmlns="http://www.w3.org/2000/svg" width="14" height="14" viewBox="0 0 14 14" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.BUSINESS: """<svg xmlns="http://www.w3.org/2000/svg" width="14" height="14" viewBox="0 0 14 14" fill="none">
   <path d="M3.66732 3.33341V1.33341C3.66732 0.965228 3.9658 0.666748 4.33398 0.666748H9.66732C10.0355 0.666748 10.334 0.965228 10.334 1.33341V3.33341H13.0007C13.3689 3.33341 13.6673 3.63189 13.6673 4.00008V13.3334C13.6673 13.7016 13.3689 14.0001 13.0007 14.0001H1.00065C0.632464 14.0001 0.333984 13.7016 0.333984 13.3334V4.00008C0.333984 3.63189 0.632464 3.33341 1.00065 3.33341H3.66732ZM12.334 8.66675H1.66732V12.6667H12.334V8.66675ZM12.334 4.66675H1.66732V7.33341H3.66732V6.00008H5.00065V7.33341H9.00065V6.00008H10.334V7.33341H12.334V4.66675ZM5.00065 2.00008V3.33341H9.00065V2.00008H5.00065Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.ENTERTAINMENT: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.ENTERTAINMENT: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M11.3327 2.66675C13.5418 2.66675 15.3327 4.45761 15.3327 6.66675V9.33342C15.3327 11.5425 13.5418 13.3334 11.3327 13.3334H4.66602C2.45688 13.3334 0.666016 11.5425 0.666016 9.33342V6.66675C0.666016 4.45761 2.45688 2.66675 4.66602 2.66675H11.3327ZM11.3327 4.00008H4.66602C3.23788 4.00008 2.07196 5.12273 2.00262 6.53365L1.99935 6.66675V9.33342C1.99935 10.7615 3.122 11.9275 4.53292 11.9968L4.66602 12.0001H11.3327C12.7608 12.0001 13.9267 10.8774 13.9961 9.46648L13.9993 9.33342V6.66675C13.9993 5.23861 12.8767 4.07269 11.4657 4.00335L11.3327 4.00008ZM6.66602 6.00008V7.33342H7.99935V8.66675H6.66535L6.66602 10.0001H5.33268L5.33202 8.66675H3.99935V7.33342H5.33268V6.00008H6.66602ZM11.9993 8.66675V10.0001H10.666V8.66675H11.9993ZM10.666 6.00008V7.33342H9.33268V6.00008H10.666Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.UTILITIES: '''<svg xmlns="http://www.w3.org/2000/svg" width="13" height="15" viewBox="0 0 13 15" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.UTILITIES: """<svg xmlns="http://www.w3.org/2000/svg" width="13" height="15" viewBox="0 0 13 15" fill="none">
   <path d="M12.3346 0.333252C12.7028 0.333252 13.0013 0.631732 13.0013 0.999919V4.33325C13.0013 4.70144 12.7028 4.99992 12.3346 4.99992H9.0013V13.6666C9.0013 14.0348 8.70284 14.3333 8.33463 14.3333H5.66797C5.29978 14.3333 5.0013 14.0348 5.0013 13.6666V4.99992H1.33464C0.966449 4.99992 0.667969 4.70144 0.667969 4.33325V2.74527C0.667969 2.49276 0.810635 2.26192 1.0365 2.14899L4.66797 0.333252H12.3346ZM9.0013 1.66659H4.98273L2.0013 3.1573V3.66659H6.33464V12.9999H7.66797V3.66659H9.0013V1.66659ZM11.668 1.66659H10.3346V3.66659H11.668V1.66659Z" fill="#344054"/>
-</svg>''',
-    ToolLabelEnum.OTHER: '''<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
+</svg>""",  # noqa: E501
+    ToolLabelEnum.OTHER: """<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
   <path d="M8.00052 0.666748L4.00065 7.33342H12.0007L8.00052 0.666748ZM8.00052 3.25828L9.64572 6.00008H6.35553L8.00052 3.25828ZM4.50065 13.3334C3.48813 13.3334 2.66732 12.5126 2.66732 11.5001C2.66732 10.4875 3.48813 9.66675 4.50065 9.66675C5.51317 9.66675 6.33398 10.4875 6.33398 11.5001C6.33398 12.5126 5.51317 13.3334 4.50065 13.3334ZM4.50065 14.6667C6.24955 14.6667 7.66732 13.249 7.66732 11.5001C7.66732 9.75115 6.24955 8.33342 4.50065 8.33342C2.75175 8.33342 1.33398 9.75115 1.33398 11.5001C1.33398 13.249 2.75175 14.6667 4.50065 14.6667ZM10.0007 10.3334V13.0001H12.6673V10.3334H10.0007ZM8.66732 14.3334V9.00008H14.0007V14.3334H8.66732Z" fill="#344054"/>
-</svg>'''
+</svg>""",  # noqa: E501
 }
 
 default_tool_label_dict = {
-    ToolLabelEnum.SEARCH: ToolLabel(name='search', label=I18nObject(en_US='Search', zh_Hans='搜索'), icon=ICONS[ToolLabelEnum.SEARCH]),
-    ToolLabelEnum.IMAGE: ToolLabel(name='image', label=I18nObject(en_US='Image', zh_Hans='图片'), icon=ICONS[ToolLabelEnum.IMAGE]),
-    ToolLabelEnum.VIDEOS: ToolLabel(name='videos', label=I18nObject(en_US='Videos', zh_Hans='视频'), icon=ICONS[ToolLabelEnum.VIDEOS]),
-    ToolLabelEnum.WEATHER: ToolLabel(name='weather', label=I18nObject(en_US='Weather', zh_Hans='天气'), icon=ICONS[ToolLabelEnum.WEATHER]),
-    ToolLabelEnum.FINANCE: ToolLabel(name='finance', label=I18nObject(en_US='Finance', zh_Hans='金融'), icon=ICONS[ToolLabelEnum.FINANCE]),
-    ToolLabelEnum.DESIGN: ToolLabel(name='design', label=I18nObject(en_US='Design', zh_Hans='设计'), icon=ICONS[ToolLabelEnum.DESIGN]),
-    ToolLabelEnum.TRAVEL: ToolLabel(name='travel', label=I18nObject(en_US='Travel', zh_Hans='旅行'), icon=ICONS[ToolLabelEnum.TRAVEL]),
-    ToolLabelEnum.SOCIAL: ToolLabel(name='social', label=I18nObject(en_US='Social', zh_Hans='社交'), icon=ICONS[ToolLabelEnum.SOCIAL]),
-    ToolLabelEnum.NEWS: ToolLabel(name='news', label=I18nObject(en_US='News', zh_Hans='新闻'), icon=ICONS[ToolLabelEnum.NEWS]),
-    ToolLabelEnum.MEDICAL: ToolLabel(name='medical', label=I18nObject(en_US='Medical', zh_Hans='医疗'), icon=ICONS[ToolLabelEnum.MEDICAL]),
-    ToolLabelEnum.PRODUCTIVITY: ToolLabel(name='productivity', label=I18nObject(en_US='Productivity', zh_Hans='生产力'), icon=ICONS[ToolLabelEnum.PRODUCTIVITY]),
-    ToolLabelEnum.EDUCATION: ToolLabel(name='education', label=I18nObject(en_US='Education', zh_Hans='教育'), icon=ICONS[ToolLabelEnum.EDUCATION]),
-    ToolLabelEnum.BUSINESS: ToolLabel(name='business', label=I18nObject(en_US='Business', zh_Hans='商业'), icon=ICONS[ToolLabelEnum.BUSINESS]),
-    ToolLabelEnum.ENTERTAINMENT: ToolLabel(name='entertainment', label=I18nObject(en_US='Entertainment', zh_Hans='娱乐'), icon=ICONS[ToolLabelEnum.ENTERTAINMENT]),
-    ToolLabelEnum.UTILITIES: ToolLabel(name='utilities', label=I18nObject(en_US='Utilities', zh_Hans='工具'), icon=ICONS[ToolLabelEnum.UTILITIES]),
-    ToolLabelEnum.OTHER: ToolLabel(name='other', label=I18nObject(en_US='Other', zh_Hans='其他'), icon=ICONS[ToolLabelEnum.OTHER]),
+    ToolLabelEnum.SEARCH: ToolLabel(
+        name="search", label=I18nObject(en_US="Search", zh_Hans="搜索"), icon=ICONS[ToolLabelEnum.SEARCH]
+    ),
+    ToolLabelEnum.IMAGE: ToolLabel(
+        name="image", label=I18nObject(en_US="Image", zh_Hans="图片"), icon=ICONS[ToolLabelEnum.IMAGE]
+    ),
+    ToolLabelEnum.VIDEOS: ToolLabel(
+        name="videos", label=I18nObject(en_US="Videos", zh_Hans="视频"), icon=ICONS[ToolLabelEnum.VIDEOS]
+    ),
+    ToolLabelEnum.WEATHER: ToolLabel(
+        name="weather", label=I18nObject(en_US="Weather", zh_Hans="天气"), icon=ICONS[ToolLabelEnum.WEATHER]
+    ),
+    ToolLabelEnum.FINANCE: ToolLabel(
+        name="finance", label=I18nObject(en_US="Finance", zh_Hans="金融"), icon=ICONS[ToolLabelEnum.FINANCE]
+    ),
+    ToolLabelEnum.DESIGN: ToolLabel(
+        name="design", label=I18nObject(en_US="Design", zh_Hans="设计"), icon=ICONS[ToolLabelEnum.DESIGN]
+    ),
+    ToolLabelEnum.TRAVEL: ToolLabel(
+        name="travel", label=I18nObject(en_US="Travel", zh_Hans="旅行"), icon=ICONS[ToolLabelEnum.TRAVEL]
+    ),
+    ToolLabelEnum.SOCIAL: ToolLabel(
+        name="social", label=I18nObject(en_US="Social", zh_Hans="社交"), icon=ICONS[ToolLabelEnum.SOCIAL]
+    ),
+    ToolLabelEnum.NEWS: ToolLabel(
+        name="news", label=I18nObject(en_US="News", zh_Hans="新闻"), icon=ICONS[ToolLabelEnum.NEWS]
+    ),
+    ToolLabelEnum.MEDICAL: ToolLabel(
+        name="medical", label=I18nObject(en_US="Medical", zh_Hans="医疗"), icon=ICONS[ToolLabelEnum.MEDICAL]
+    ),
+    ToolLabelEnum.PRODUCTIVITY: ToolLabel(
+        name="productivity",
+        label=I18nObject(en_US="Productivity", zh_Hans="生产力"),
+        icon=ICONS[ToolLabelEnum.PRODUCTIVITY],
+    ),
+    ToolLabelEnum.EDUCATION: ToolLabel(
+        name="education", label=I18nObject(en_US="Education", zh_Hans="教育"), icon=ICONS[ToolLabelEnum.EDUCATION]
+    ),
+    ToolLabelEnum.BUSINESS: ToolLabel(
+        name="business", label=I18nObject(en_US="Business", zh_Hans="商业"), icon=ICONS[ToolLabelEnum.BUSINESS]
+    ),
+    ToolLabelEnum.ENTERTAINMENT: ToolLabel(
+        name="entertainment",
+        label=I18nObject(en_US="Entertainment", zh_Hans="娱乐"),
+        icon=ICONS[ToolLabelEnum.ENTERTAINMENT],
+    ),
+    ToolLabelEnum.UTILITIES: ToolLabel(
+        name="utilities", label=I18nObject(en_US="Utilities", zh_Hans="工具"), icon=ICONS[ToolLabelEnum.UTILITIES]
+    ),
+    ToolLabelEnum.OTHER: ToolLabel(
+        name="other", label=I18nObject(en_US="Other", zh_Hans="其他"), icon=ICONS[ToolLabelEnum.OTHER]
+    ),
 }
 
 default_tool_labels = [v for k, v in default_tool_label_dict.items()]
diff --git a/api/core/tools/errors.py b/api/core/tools/errors.py
index 9fd8322db1..6febf137b0 100644
--- a/api/core/tools/errors.py
+++ b/api/core/tools/errors.py
@@ -4,23 +4,30 @@ from core.tools.entities.tool_entities import ToolInvokeMeta
 class ToolProviderNotFoundError(ValueError):
     pass
 
+
 class ToolNotFoundError(ValueError):
     pass
 
+
 class ToolParameterValidationError(ValueError):
     pass
 
+
 class ToolProviderCredentialValidationError(ValueError):
     pass
 
+
 class ToolNotSupportedError(ValueError):
     pass
 
+
 class ToolInvokeError(ValueError):
     pass
 
+
 class ToolApiSchemaError(ValueError):
     pass
 
+
 class ToolEngineInvokeError(Exception):
-    meta: ToolInvokeMeta
\ No newline at end of file
+    meta: ToolInvokeMeta
diff --git a/api/core/tools/provider/api_tool_provider.py b/api/core/tools/provider/api_tool_provider.py
index ae80ad2114..d99314e33a 100644
--- a/api/core/tools/provider/api_tool_provider.py
+++ b/api/core/tools/provider/api_tool_provider.py
@@ -1,4 +1,3 @@
-
 from core.tools.entities.common_entities import I18nObject
 from core.tools.entities.tool_bundle import ApiToolBundle
 from core.tools.entities.tool_entities import (
@@ -18,85 +17,69 @@ class ApiToolProviderController(ToolProviderController):
     provider_id: str
 
     @staticmethod
-    def from_db(db_provider: ApiToolProvider, auth_type: ApiProviderAuthType) -> 'ApiToolProviderController':
+    def from_db(db_provider: ApiToolProvider, auth_type: ApiProviderAuthType) -> "ApiToolProviderController":
         credentials_schema = {
-            'auth_type': ToolProviderCredentials(
-                name='auth_type',
+            "auth_type": ToolProviderCredentials(
+                name="auth_type",
                 required=True,
                 type=ToolProviderCredentials.CredentialsType.SELECT,
                 options=[
-                    ToolCredentialsOption(value='none', label=I18nObject(en_US='None', zh_Hans='无')),
-                    ToolCredentialsOption(value='api_key', label=I18nObject(en_US='api_key', zh_Hans='api_key'))
+                    ToolCredentialsOption(value="none", label=I18nObject(en_US="None", zh_Hans="无")),
+                    ToolCredentialsOption(value="api_key", label=I18nObject(en_US="api_key", zh_Hans="api_key")),
                 ],
-                default='none',
-                help=I18nObject(
-                    en_US='The auth type of the api provider',
-                    zh_Hans='api provider 的认证类型'
-                )
+                default="none",
+                help=I18nObject(en_US="The auth type of the api provider", zh_Hans="api provider 的认证类型"),
             )
         }
         if auth_type == ApiProviderAuthType.API_KEY:
             credentials_schema = {
                 **credentials_schema,
-                'api_key_header': ToolProviderCredentials(
-                    name='api_key_header',
+                "api_key_header": ToolProviderCredentials(
+                    name="api_key_header",
                     required=False,
-                    default='api_key',
+                    default="api_key",
                     type=ToolProviderCredentials.CredentialsType.TEXT_INPUT,
-                    help=I18nObject(
-                        en_US='The header name of the api key',
-                        zh_Hans='携带 api key 的 header 名称'
-                    )
+                    help=I18nObject(en_US="The header name of the api key", zh_Hans="携带 api key 的 header 名称"),
                 ),
-                'api_key_value': ToolProviderCredentials(
-                    name='api_key_value',
+                "api_key_value": ToolProviderCredentials(
+                    name="api_key_value",
                     required=True,
                     type=ToolProviderCredentials.CredentialsType.SECRET_INPUT,
-                    help=I18nObject(
-                        en_US='The api key',
-                        zh_Hans='api key的值'
-                    )
+                    help=I18nObject(en_US="The api key", zh_Hans="api key的值"),
                 ),
-                'api_key_header_prefix': ToolProviderCredentials(
-                    name='api_key_header_prefix',
+                "api_key_header_prefix": ToolProviderCredentials(
+                    name="api_key_header_prefix",
                     required=False,
-                    default='basic',
+                    default="basic",
                     type=ToolProviderCredentials.CredentialsType.SELECT,
-                    help=I18nObject(
-                        en_US='The prefix of the api key header',
-                        zh_Hans='api key header 的前缀'
-                    ),
+                    help=I18nObject(en_US="The prefix of the api key header", zh_Hans="api key header 的前缀"),
                     options=[
-                        ToolCredentialsOption(value='basic', label=I18nObject(en_US='Basic', zh_Hans='Basic')),
-                        ToolCredentialsOption(value='bearer', label=I18nObject(en_US='Bearer', zh_Hans='Bearer')),
-                        ToolCredentialsOption(value='custom', label=I18nObject(en_US='Custom', zh_Hans='Custom'))
-                    ]
-                )
+                        ToolCredentialsOption(value="basic", label=I18nObject(en_US="Basic", zh_Hans="Basic")),
+                        ToolCredentialsOption(value="bearer", label=I18nObject(en_US="Bearer", zh_Hans="Bearer")),
+                        ToolCredentialsOption(value="custom", label=I18nObject(en_US="Custom", zh_Hans="Custom")),
+                    ],
+                ),
             }
         elif auth_type == ApiProviderAuthType.NONE:
             pass
         else:
-            raise ValueError(f'invalid auth type {auth_type}')
+            raise ValueError(f"invalid auth type {auth_type}")
 
-        user_name = db_provider.user.name if db_provider.user_id else ''
+        user_name = db_provider.user.name if db_provider.user_id else ""
 
-        return ApiToolProviderController(**{
-            'identity': {
-                'author': user_name,
-                'name': db_provider.name,
-                'label': {
-                    'en_US': db_provider.name,
-                    'zh_Hans': db_provider.name
+        return ApiToolProviderController(
+            **{
+                "identity": {
+                    "author": user_name,
+                    "name": db_provider.name,
+                    "label": {"en_US": db_provider.name, "zh_Hans": db_provider.name},
+                    "description": {"en_US": db_provider.description, "zh_Hans": db_provider.description},
+                    "icon": db_provider.icon,
                 },
-                'description': {
-                    'en_US': db_provider.description,
-                    'zh_Hans': db_provider.description
-                },
-                'icon': db_provider.icon,
-            },
-            'credentials_schema': credentials_schema,
-            'provider_id': db_provider.id or '',
-        })
+                "credentials_schema": credentials_schema,
+                "provider_id": db_provider.id or "",
+            }
+        )
 
     @property
     def provider_type(self) -> ToolProviderType:
@@ -104,39 +87,35 @@ class ApiToolProviderController(ToolProviderController):
 
     def _parse_tool_bundle(self, tool_bundle: ApiToolBundle) -> ApiTool:
         """
-            parse tool bundle to tool
+        parse tool bundle to tool
 
-            :param tool_bundle: the tool bundle
-            :return: the tool
+        :param tool_bundle: the tool bundle
+        :return: the tool
         """
-        return ApiTool(**{
-            'api_bundle': tool_bundle,
-            'identity' : {
-                'author': tool_bundle.author,
-                'name': tool_bundle.operation_id,
-                'label': {
-                    'en_US': tool_bundle.operation_id,
-                    'zh_Hans': tool_bundle.operation_id
+        return ApiTool(
+            **{
+                "api_bundle": tool_bundle,
+                "identity": {
+                    "author": tool_bundle.author,
+                    "name": tool_bundle.operation_id,
+                    "label": {"en_US": tool_bundle.operation_id, "zh_Hans": tool_bundle.operation_id},
+                    "icon": self.identity.icon,
+                    "provider": self.provider_id,
                 },
-                'icon': self.identity.icon,
-                'provider': self.provider_id,
-            },
-            'description': {
-                'human': {
-                    'en_US': tool_bundle.summary or '',
-                    'zh_Hans': tool_bundle.summary or ''
+                "description": {
+                    "human": {"en_US": tool_bundle.summary or "", "zh_Hans": tool_bundle.summary or ""},
+                    "llm": tool_bundle.summary or "",
                 },
-                'llm': tool_bundle.summary or ''
-            },
-            'parameters' : tool_bundle.parameters if tool_bundle.parameters else [],
-        })
+                "parameters": tool_bundle.parameters or [],
+            }
+        )
 
     def load_bundled_tools(self, tools: list[ApiToolBundle]) -> list[ApiTool]:
         """
-            load bundled tools
+        load bundled tools
 
-            :param tools: the bundled tools
-            :return: the tools
+        :param tools: the bundled tools
+        :return: the tools
         """
         self.tools = [self._parse_tool_bundle(tool) for tool in tools]
 
@@ -144,22 +123,23 @@ class ApiToolProviderController(ToolProviderController):
 
     def get_tools(self, user_id: str, tenant_id: str) -> list[ApiTool]:
         """
-            fetch tools from database
+        fetch tools from database
 
-            :param user_id: the user id
-            :param tenant_id: the tenant id
-            :return: the tools
+        :param user_id: the user id
+        :param tenant_id: the tenant id
+        :return: the tools
         """
         if self.tools is not None:
             return self.tools
-        
+
         tools: list[Tool] = []
 
         # get tenant api providers
-        db_providers: list[ApiToolProvider] = db.session.query(ApiToolProvider).filter(
-            ApiToolProvider.tenant_id == tenant_id,
-            ApiToolProvider.name == self.identity.name
-        ).all()
+        db_providers: list[ApiToolProvider] = (
+            db.session.query(ApiToolProvider)
+            .filter(ApiToolProvider.tenant_id == tenant_id, ApiToolProvider.name == self.identity.name)
+            .all()
+        )
 
         if db_providers and len(db_providers) != 0:
             for db_provider in db_providers:
@@ -167,16 +147,16 @@ class ApiToolProviderController(ToolProviderController):
                     assistant_tool = self._parse_tool_bundle(tool)
                     assistant_tool.is_team_authorization = True
                     tools.append(assistant_tool)
-        
+
         self.tools = tools
         return tools
-    
+
     def get_tool(self, tool_name: str) -> ApiTool:
         """
-            get tool by name
+        get tool by name
 
-            :param tool_name: the name of the tool
-            :return: the tool
+        :param tool_name: the name of the tool
+        :return: the tool
         """
         if self.tools is None:
             self.get_tools()
@@ -185,4 +165,4 @@ class ApiToolProviderController(ToolProviderController):
             if tool.identity.name == tool_name:
                 return tool
 
-        raise ValueError(f'tool {tool_name} not found')
\ No newline at end of file
+        raise ValueError(f"tool {tool_name} not found")
diff --git a/api/core/tools/provider/app_tool_provider.py b/api/core/tools/provider/app_tool_provider.py
index 2d472e0a93..09f328cd1f 100644
--- a/api/core/tools/provider/app_tool_provider.py
+++ b/api/core/tools/provider/app_tool_provider.py
@@ -11,11 +11,12 @@ from models.tools import PublishedAppTool
 
 logger = logging.getLogger(__name__)
 
+
 class AppToolProviderEntity(ToolProviderController):
     @property
     def provider_type(self) -> ToolProviderType:
         return ToolProviderType.APP
-    
+
     def _validate_credentials(self, tool_name: str, credentials: dict[str, Any]) -> None:
         pass
 
@@ -23,9 +24,13 @@ class AppToolProviderEntity(ToolProviderController):
         pass
 
     def get_tools(self, user_id: str) -> list[Tool]:
-        db_tools: list[PublishedAppTool] = db.session.query(PublishedAppTool).filter(
-            PublishedAppTool.user_id == user_id,
-        ).all()
+        db_tools: list[PublishedAppTool] = (
+            db.session.query(PublishedAppTool)
+            .filter(
+                PublishedAppTool.user_id == user_id,
+            )
+            .all()
+        )
 
         if not db_tools or len(db_tools) == 0:
             return []
@@ -34,23 +39,17 @@ class AppToolProviderEntity(ToolProviderController):
 
         for db_tool in db_tools:
             tool = {
-                'identity': {
-                    'author': db_tool.author,
-                    'name': db_tool.tool_name,
-                    'label': {
-                        'en_US': db_tool.tool_name,
-                        'zh_Hans': db_tool.tool_name
-                    },
-                    'icon': ''
+                "identity": {
+                    "author": db_tool.author,
+                    "name": db_tool.tool_name,
+                    "label": {"en_US": db_tool.tool_name, "zh_Hans": db_tool.tool_name},
+                    "icon": "",
                 },
-                'description': {
-                    'human': {
-                        'en_US': db_tool.description_i18n.en_US,
-                        'zh_Hans': db_tool.description_i18n.zh_Hans
-                    },
-                    'llm': db_tool.llm_description
+                "description": {
+                    "human": {"en_US": db_tool.description_i18n.en_US, "zh_Hans": db_tool.description_i18n.zh_Hans},
+                    "llm": db_tool.llm_description,
                 },
-                'parameters': []
+                "parameters": [],
             }
             # get app from db
             app: App = db_tool.app
@@ -64,52 +63,41 @@ class AppToolProviderEntity(ToolProviderController):
             for input_form in user_input_form_list:
                 # get type
                 form_type = input_form.keys()[0]
-                default = input_form[form_type]['default']
-                required = input_form[form_type]['required']
-                label = input_form[form_type]['label']
-                variable_name = input_form[form_type]['variable_name']
-                options = input_form[form_type].get('options', [])
-                if form_type == 'paragraph' or form_type == 'text-input':
-                    tool['parameters'].append(ToolParameter(
-                        name=variable_name,
-                        label=I18nObject(
-                            en_US=label,
-                            zh_Hans=label
-                        ),
-                        human_description=I18nObject(
-                            en_US=label,
-                            zh_Hans=label
-                        ),
-                        llm_description=label,
-                        form=ToolParameter.ToolParameterForm.FORM,
-                        type=ToolParameter.ToolParameterType.STRING,
-                        required=required,
-                        default=default
-                    ))
-                elif form_type == 'select':
-                    tool['parameters'].append(ToolParameter(
-                        name=variable_name,
-                        label=I18nObject(
-                            en_US=label,
-                            zh_Hans=label
-                        ),
-                        human_description=I18nObject(
-                            en_US=label,
-                            zh_Hans=label
-                        ),
-                        llm_description=label,
-                        form=ToolParameter.ToolParameterForm.FORM,
-                        type=ToolParameter.ToolParameterType.SELECT,
-                        required=required,
-                        default=default,
-                        options=[ToolParameterOption(
-                            value=option,
-                            label=I18nObject(
-                                en_US=option,
-                                zh_Hans=option
-                            )
-                        ) for option in options]
-                    ))
+                default = input_form[form_type]["default"]
+                required = input_form[form_type]["required"]
+                label = input_form[form_type]["label"]
+                variable_name = input_form[form_type]["variable_name"]
+                options = input_form[form_type].get("options", [])
+                if form_type in {"paragraph", "text-input"}:
+                    tool["parameters"].append(
+                        ToolParameter(
+                            name=variable_name,
+                            label=I18nObject(en_US=label, zh_Hans=label),
+                            human_description=I18nObject(en_US=label, zh_Hans=label),
+                            llm_description=label,
+                            form=ToolParameter.ToolParameterForm.FORM,
+                            type=ToolParameter.ToolParameterType.STRING,
+                            required=required,
+                            default=default,
+                        )
+                    )
+                elif form_type == "select":
+                    tool["parameters"].append(
+                        ToolParameter(
+                            name=variable_name,
+                            label=I18nObject(en_US=label, zh_Hans=label),
+                            human_description=I18nObject(en_US=label, zh_Hans=label),
+                            llm_description=label,
+                            form=ToolParameter.ToolParameterForm.FORM,
+                            type=ToolParameter.ToolParameterType.SELECT,
+                            required=required,
+                            default=default,
+                            options=[
+                                ToolParameterOption(value=option, label=I18nObject(en_US=option, zh_Hans=option))
+                                for option in options
+                            ],
+                        )
+                    )
 
             tools.append(Tool(**tool))
-        return tools
\ No newline at end of file
+        return tools
diff --git a/api/core/tools/provider/builtin/_positions.py b/api/core/tools/provider/builtin/_positions.py
index 062668fc5b..5c10f72fda 100644
--- a/api/core/tools/provider/builtin/_positions.py
+++ b/api/core/tools/provider/builtin/_positions.py
@@ -10,7 +10,7 @@ class BuiltinToolProviderSort:
     @classmethod
     def sort(cls, providers: list[UserToolProvider]) -> list[UserToolProvider]:
         if not cls._position:
-            cls._position = get_tool_position_map(os.path.join(os.path.dirname(__file__), '..'))
+            cls._position = get_tool_position_map(os.path.join(os.path.dirname(__file__), ".."))
 
         def name_func(provider: UserToolProvider) -> str:
             return provider.name
diff --git a/api/core/tools/provider/builtin/aippt/aippt.py b/api/core/tools/provider/builtin/aippt/aippt.py
index 25133c51df..e0cbbd2992 100644
--- a/api/core/tools/provider/builtin/aippt/aippt.py
+++ b/api/core/tools/provider/builtin/aippt/aippt.py
@@ -6,6 +6,6 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class AIPPTProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
         try:
-            AIPPTGenerateTool._get_api_token(credentials, user_id='__dify_system__')
+            AIPPTGenerateTool._get_api_token(credentials, user_id="__dify_system__")
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/aippt/tools/aippt.py b/api/core/tools/provider/builtin/aippt/tools/aippt.py
index 8d6883a3b1..dd9371f70d 100644
--- a/api/core/tools/provider/builtin/aippt/tools/aippt.py
+++ b/api/core/tools/provider/builtin/aippt/tools/aippt.py
@@ -20,16 +20,16 @@ class AIPPTGenerateTool(BuiltinTool):
     A tool for generating a ppt
     """
 
-    _api_base_url = URL('https://co.aippt.cn/api')
+    _api_base_url = URL("https://co.aippt.cn/api")
     _api_token_cache = {}
-    _api_token_cache_lock:Optional[Lock] = None
+    _api_token_cache_lock: Optional[Lock] = None
     _style_cache = {}
-    _style_cache_lock:Optional[Lock] = None
+    _style_cache_lock: Optional[Lock] = None
 
     _task = {}
     _task_type_map = {
-        'auto': 1,
-        'markdown': 7,
+        "auto": 1,
+        "markdown": 7,
     }
 
     def __init__(self, **kwargs: Any):
@@ -46,67 +46,58 @@ class AIPPTGenerateTool(BuiltinTool):
             tool_parameters (dict[str, Any]): The parameters for the tool
 
         Returns:
-            ToolInvokeMessage | list[ToolInvokeMessage]: The result of the tool invocation, which can be a single message or a list of messages.
+            ToolInvokeMessage | list[ToolInvokeMessage]: The result of the tool invocation,
+             which can be a single message or a list of messages.
         """
-        title = tool_parameters.get('title', '')
+        title = tool_parameters.get("title", "")
         if not title:
-            return self.create_text_message('Please provide a title for the ppt')
-        
-        model = tool_parameters.get('model', 'aippt')
+            return self.create_text_message("Please provide a title for the ppt")
+
+        model = tool_parameters.get("model", "aippt")
         if not model:
-            return self.create_text_message('Please provide a model for the ppt')
-        
-        outline = tool_parameters.get('outline', '')
+            return self.create_text_message("Please provide a model for the ppt")
+
+        outline = tool_parameters.get("outline", "")
 
         # create task
         task_id = self._create_task(
-            type=self._task_type_map['auto' if not outline else 'markdown'],
+            type=self._task_type_map["auto" if not outline else "markdown"],
             title=title,
             content=outline,
-            user_id=user_id
+            user_id=user_id,
         )
 
         # get suit
-        color = tool_parameters.get('color')
-        style = tool_parameters.get('style')
+        color = tool_parameters.get("color")
+        style = tool_parameters.get("style")
 
-        if color == '__default__':
-            color_id = ''
+        if color == "__default__":
+            color_id = ""
         else:
-            color_id = int(color.split('-')[1])
+            color_id = int(color.split("-")[1])
 
-        if style == '__default__':
-            style_id = ''
+        if style == "__default__":
+            style_id = ""
         else:
-            style_id = int(style.split('-')[1])
+            style_id = int(style.split("-")[1])
 
         suit_id = self._get_suit(style_id=style_id, colour_id=color_id)
 
         # generate outline
         if not outline:
-            self._generate_outline(
-                task_id=task_id,
-                model=model,
-                user_id=user_id
-            )
+            self._generate_outline(task_id=task_id, model=model, user_id=user_id)
 
             # generate content
-            self._generate_content(
-                task_id=task_id,
-                model=model,
-                user_id=user_id
-            )
+            self._generate_content(task_id=task_id, model=model, user_id=user_id)
 
         # generate ppt
-        _, ppt_url = self._generate_ppt(
-            task_id=task_id,
-            suit_id=suit_id,
-            user_id=user_id
-        )
+        _, ppt_url = self._generate_ppt(task_id=task_id, suit_id=suit_id, user_id=user_id)
 
-        return self.create_text_message('''the ppt has been created successfully,'''
-                                 f'''the ppt url is {ppt_url}'''
-                                 '''please give the ppt url to user and direct user to download it.''')
+        return self.create_text_message(
+            """the ppt has been created successfully,"""
+            f"""the ppt url is {ppt_url}"""
+            """please give the ppt url to user and direct user to download it."""
+        )
 
     def _create_task(self, type: int, title: str, content: str, user_id: str) -> str:
         """
@@ -119,129 +110,121 @@ class AIPPTGenerateTool(BuiltinTool):
         :return: the task ID
         """
         headers = {
-            'x-channel': '',
-            'x-api-key': self.runtime.credentials['aippt_access_key'],
-            'x-token': self._get_api_token(credentials=self.runtime.credentials, user_id=user_id),
+            "x-channel": "",
+            "x-api-key": self.runtime.credentials["aippt_access_key"],
+            "x-token": self._get_api_token(credentials=self.runtime.credentials, user_id=user_id),
         }
         response = post(
-            str(self._api_base_url / 'ai' / 'chat' / 'v2' / 'task'),
+            str(self._api_base_url / "ai" / "chat" / "v2" / "task"),
             headers=headers,
-            files={
-                'type': ('', str(type)),
-                'title': ('', title),
-                'content': ('', content)
-            }
+            files={"type": ("", str(type)), "title": ("", title), "content": ("", content)},
         )
 
         if response.status_code != 200:
-            raise Exception(f'Failed to connect to aippt: {response.text}')
-        
+            raise Exception(f"Failed to connect to aippt: {response.text}")
+
         response = response.json()
-        if response.get('code') != 0:
+        if response.get("code") != 0:
             raise Exception(f'Failed to create task: {response.get("msg")}')
 
-        return response.get('data', {}).get('id')
-    
+        return response.get("data", {}).get("id")
+
     def _generate_outline(self, task_id: str, model: str, user_id: str) -> str:
-        api_url = self._api_base_url / 'ai' / 'chat' / 'outline' if model == 'aippt' else \
-            self._api_base_url / 'ai' / 'chat' / 'wx' / 'outline'
-        api_url %= {'task_id': task_id}
+        api_url = (
+            self._api_base_url / "ai" / "chat" / "outline"
+            if model == "aippt"
+            else self._api_base_url / "ai" / "chat" / "wx" / "outline"
+        )
+        api_url %= {"task_id": task_id}
 
         headers = {
-            'x-channel': '',
-            'x-api-key': self.runtime.credentials['aippt_access_key'],
-            'x-token': self._get_api_token(credentials=self.runtime.credentials, user_id=user_id),
+            "x-channel": "",
+            "x-api-key": self.runtime.credentials["aippt_access_key"],
+            "x-token": self._get_api_token(credentials=self.runtime.credentials, user_id=user_id),
         }
 
-        response = requests_get(
-            url=api_url,
-            headers=headers,
-            stream=True,
-            timeout=(10, 60)
-        )
+        response = requests_get(url=api_url, headers=headers, stream=True, timeout=(10, 60))
 
         if response.status_code != 200:
-            raise Exception(f'Failed to connect to aippt: {response.text}')
-        
-        outline = ''
-        for chunk in response.iter_lines(delimiter=b'\n\n'):
+            raise Exception(f"Failed to connect to aippt: {response.text}")
+
+        outline = ""
+        for chunk in response.iter_lines(delimiter=b"\n\n"):
             if not chunk:
                 continue
-            
-            event = ''
-            lines = chunk.decode('utf-8').split('\n')
+
+            event = ""
+            lines = chunk.decode("utf-8").split("\n")
             for line in lines:
-                if line.startswith('event:'):
+                if line.startswith("event:"):
                     event = line[6:]
-                elif line.startswith('data:'):
+                elif line.startswith("data:"):
                     data = line[5:]
-                    if event == 'message':
+                    if event == "message":
                         try:
                             data = json_loads(data)
-                            outline += data.get('content', '')
+                            outline += data.get("content", "")
                         except Exception as e:
                             pass
-                    elif event == 'close':
+                    elif event == "close":
                         break
-                    elif event == 'error' or event == 'filter':
-                        raise Exception(f'Failed to generate outline: {data}')
-                    
+                    elif event in {"error", "filter"}:
+                        raise Exception(f"Failed to generate outline: {data}")
+
         return outline
-    
+
     def _generate_content(self, task_id: str, model: str, user_id: str) -> str:
-        api_url = self._api_base_url / 'ai' / 'chat' / 'content' if model == 'aippt' else \
-            self._api_base_url / 'ai' / 'chat' / 'wx' / 'content'
-        api_url %= {'task_id': task_id}
+        api_url = (
+            self._api_base_url / "ai" / "chat" / "content"
+            if model == "aippt"
+            else self._api_base_url / "ai" / "chat" / "wx" / "content"
+        )
+        api_url %= {"task_id": task_id}
 
         headers = {
-            'x-channel': '',
-            'x-api-key': self.runtime.credentials['aippt_access_key'],
-            'x-token': self._get_api_token(credentials=self.runtime.credentials, user_id=user_id),
+            "x-channel": "",
+            "x-api-key": self.runtime.credentials["aippt_access_key"],
+            "x-token": self._get_api_token(credentials=self.runtime.credentials, user_id=user_id),
         }
 
-        response = requests_get(
-            url=api_url,
-            headers=headers,
-            stream=True,
-            timeout=(10, 60)
-        )
+        response = requests_get(url=api_url, headers=headers, stream=True, timeout=(10, 60))
 
         if response.status_code != 200:
-            raise Exception(f'Failed to connect to aippt: {response.text}')
-        
-        if model == 'aippt':
-            content = ''
-            for chunk in response.iter_lines(delimiter=b'\n\n'):
+            raise Exception(f"Failed to connect to aippt: {response.text}")
+
+        if model == "aippt":
+            content = ""
+            for chunk in response.iter_lines(delimiter=b"\n\n"):
                 if not chunk:
                     continue
-                
-                event = ''
-                lines = chunk.decode('utf-8').split('\n')
+
+                event = ""
+                lines = chunk.decode("utf-8").split("\n")
                 for line in lines:
-                    if line.startswith('event:'):
+                    if line.startswith("event:"):
                         event = line[6:]
-                    elif line.startswith('data:'):
+                    elif line.startswith("data:"):
                         data = line[5:]
-                        if event == 'message':
+                        if event == "message":
                             try:
                                 data = json_loads(data)
-                                content += data.get('content', '')
+                                content += data.get("content", "")
                             except Exception as e:
                                 pass
-                        elif event == 'close':
+                        elif event == "close":
                             break
-                        elif event == 'error' or event == 'filter':
-                            raise Exception(f'Failed to generate content: {data}')
-                        
+                        elif event in {"error", "filter"}:
+                            raise Exception(f"Failed to generate content: {data}")
+
             return content
-        elif model == 'wenxin':
+        elif model == "wenxin":
             response = response.json()
-            if response.get('code') != 0:
+            if response.get("code") != 0:
                 raise Exception(f'Failed to generate content: {response.get("msg")}')
-            
-            return response.get('data', '')
-        
-        return ''
+
+            return response.get("data", "")
+
+        return ""
 
     def _generate_ppt(self, task_id: str, suit_id: int, user_id) -> tuple[str, str]:
         """
@@ -252,83 +235,73 @@ class AIPPTGenerateTool(BuiltinTool):
         :return: the cover url of the ppt and the ppt url
         """
         headers = {
-            'x-channel': '',
-            'x-api-key': self.runtime.credentials['aippt_access_key'],
-            'x-token': self._get_api_token(credentials=self.runtime.credentials, user_id=user_id),
+            "x-channel": "",
+            "x-api-key": self.runtime.credentials["aippt_access_key"],
+            "x-token": self._get_api_token(credentials=self.runtime.credentials, user_id=user_id),
         }
 
         response = post(
-            str(self._api_base_url / 'design' / 'v2' / 'save'),
+            str(self._api_base_url / "design" / "v2" / "save"),
             headers=headers,
-            data={
-                'task_id': task_id,
-                'template_id': suit_id
-            }
+            data={"task_id": task_id, "template_id": suit_id},
         )
 
         if response.status_code != 200:
-            raise Exception(f'Failed to connect to aippt: {response.text}')
-        
+            raise Exception(f"Failed to connect to aippt: {response.text}")
+
         response = response.json()
-        if response.get('code') != 0:
+        if response.get("code") != 0:
             raise Exception(f'Failed to generate ppt: {response.get("msg")}')
-        
-        id = response.get('data', {}).get('id')
-        cover_url = response.get('data', {}).get('cover_url')
+
+        id = response.get("data", {}).get("id")
+        cover_url = response.get("data", {}).get("cover_url")
 
         response = post(
-            str(self._api_base_url / 'download' / 'export' / 'file'),
+            str(self._api_base_url / "download" / "export" / "file"),
             headers=headers,
-            data={
-                'id': id,
-                'format': 'ppt',
-                'files_to_zip': False,
-                'edit': True
-            }
+            data={"id": id, "format": "ppt", "files_to_zip": False, "edit": True},
         )
 
         if response.status_code != 200:
-            raise Exception(f'Failed to connect to aippt: {response.text}')
-        
+            raise Exception(f"Failed to connect to aippt: {response.text}")
+
         response = response.json()
-        if response.get('code') != 0:
+        if response.get("code") != 0:
             raise Exception(f'Failed to generate ppt: {response.get("msg")}')
-        
-        export_code = response.get('data')
+
+        export_code = response.get("data")
         if not export_code:
-            raise Exception('Failed to generate ppt, the export code is empty')
-        
+            raise Exception("Failed to generate ppt, the export code is empty")
+
         current_iteration = 0
         while current_iteration < 50:
             # get ppt url
             response = post(
-                str(self._api_base_url / 'download' / 'export' / 'file' / 'result'),
+                str(self._api_base_url / "download" / "export" / "file" / "result"),
                 headers=headers,
-                data={
-                    'task_key': export_code
-                }
+                data={"task_key": export_code},
             )
 
             if response.status_code != 200:
-                raise Exception(f'Failed to connect to aippt: {response.text}')
-            
+                raise Exception(f"Failed to connect to aippt: {response.text}")
+
             response = response.json()
-            if response.get('code') != 0:
+            if response.get("code") != 0:
                 raise Exception(f'Failed to generate ppt: {response.get("msg")}')
-            
-            if response.get('msg') == '导出中':
+
+            if response.get("msg") == "导出中":
                 current_iteration += 1
                 sleep(2)
                 continue
-            
-            ppt_url = response.get('data', [])
+
+            ppt_url = response.get("data", [])
             if len(ppt_url) == 0:
-                raise Exception('Failed to generate ppt, the ppt url is empty')
-            
+                raise Exception("Failed to generate ppt, the ppt url is empty")
+
             return cover_url, ppt_url[0]
-        
-        raise Exception('Failed to generate ppt, the export is timeout')
-        
+
+        raise Exception("Failed to generate ppt, the export is timeout")
+
     @classmethod
     def _get_api_token(cls, credentials: dict[str, str], user_id: str) -> str:
         """
@@ -337,53 +310,43 @@ class AIPPTGenerateTool(BuiltinTool):
         :param credentials: the credentials
         :return: the API token
         """
-        access_key = credentials['aippt_access_key']
-        secret_key = credentials['aippt_secret_key']
+        access_key = credentials["aippt_access_key"]
+        secret_key = credentials["aippt_secret_key"]
 
-        cache_key = f'{access_key}#@#{user_id}'
+        cache_key = f"{access_key}#@#{user_id}"
 
         with cls._api_token_cache_lock:
             # clear expired tokens
             now = time()
             for key in list(cls._api_token_cache.keys()):
-                if cls._api_token_cache[key]['expire'] < now:
+                if cls._api_token_cache[key]["expire"] < now:
                     del cls._api_token_cache[key]
 
             if cache_key in cls._api_token_cache:
-                return cls._api_token_cache[cache_key]['token']
-            
+                return cls._api_token_cache[cache_key]["token"]
+
         # get token
         headers = {
-            'x-api-key': access_key,
-            'x-timestamp': str(int(now)),
-            'x-signature': cls._calculate_sign(access_key, secret_key, int(now))
+            "x-api-key": access_key,
+            "x-timestamp": str(int(now)),
+            "x-signature": cls._calculate_sign(access_key, secret_key, int(now)),
         }
 
-        param = {
-            'uid': user_id,
-            'channel': ''
-        }
+        param = {"uid": user_id, "channel": ""}
 
-        response = get(
-            str(cls._api_base_url / 'grant' / 'token'),
-            params=param,
-            headers=headers
-        )
+        response = get(str(cls._api_base_url / "grant" / "token"), params=param, headers=headers)
 
         if response.status_code != 200:
-            raise Exception(f'Failed to connect to aippt: {response.text}')
+            raise Exception(f"Failed to connect to aippt: {response.text}")
         response = response.json()
-        if response.get('code') != 0:
+        if response.get("code") != 0:
             raise Exception(f'Failed to connect to aippt: {response.get("msg")}')
-        
-        token = response.get('data', {}).get('token')
-        expire = response.get('data', {}).get('time_expire')
+
+        token = response.get("data", {}).get("token")
+        expire = response.get("data", {}).get("time_expire")
 
         with cls._api_token_cache_lock:
-            cls._api_token_cache[cache_key] = {
-                'token': token,
-                'expire': now + expire
-            }
+            cls._api_token_cache[cache_key] = {"token": token, "expire": now + expire}
 
         return token
 
@@ -391,11 +354,9 @@ class AIPPTGenerateTool(BuiltinTool):
     def _calculate_sign(cls, access_key: str, secret_key: str, timestamp: int) -> str:
         return b64encode(
             hmac_new(
-                key=secret_key.encode('utf-8'), 
-                msg=f'GET@/api/grant/token/@{timestamp}'.encode(),
-                digestmod=sha1
+                key=secret_key.encode("utf-8"), msg=f"GET@/api/grant/token/@{timestamp}".encode(), digestmod=sha1
             ).digest()
-        ).decode('utf-8')
+        ).decode("utf-8")
 
     @classmethod
     def _get_styles(cls, credentials: dict[str, str], user_id: str) -> tuple[list[dict], list[dict]]:
@@ -408,47 +369,46 @@ class AIPPTGenerateTool(BuiltinTool):
             # clear expired styles
             now = time()
             for key in list(cls._style_cache.keys()):
-                if cls._style_cache[key]['expire'] < now:
+                if cls._style_cache[key]["expire"] < now:
                     del cls._style_cache[key]
 
             key = f'{credentials["aippt_access_key"]}#@#{user_id}'
             if key in cls._style_cache:
-                return cls._style_cache[key]['colors'], cls._style_cache[key]['styles']
+                return cls._style_cache[key]["colors"], cls._style_cache[key]["styles"]
 
         headers = {
-            'x-channel': '',
-            'x-api-key': credentials['aippt_access_key'],
-            'x-token': cls._get_api_token(credentials=credentials, user_id=user_id)
+            "x-channel": "",
+            "x-api-key": credentials["aippt_access_key"],
+            "x-token": cls._get_api_token(credentials=credentials, user_id=user_id),
         }
-        response = get(
-            str(cls._api_base_url / 'template_component' / 'suit' / 'select'),
-            headers=headers
-        )
+        response = get(str(cls._api_base_url / "template_component" / "suit" / "select"), headers=headers)
 
         if response.status_code != 200:
-            raise Exception(f'Failed to connect to aippt: {response.text}')
-        
+            raise Exception(f"Failed to connect to aippt: {response.text}")
+
         response = response.json()
 
-        if response.get('code') != 0:
+        if response.get("code") != 0:
             raise Exception(f'Failed to connect to aippt: {response.get("msg")}')
-        
-        colors = [{
-            'id': f'id-{item.get("id")}',
-            'name': item.get('name'),
-            'en_name': item.get('en_name', item.get('name')),
-        } for item in response.get('data', {}).get('colour') or []]
-        styles = [{
-            'id': f'id-{item.get("id")}',
-            'name': item.get('title'),
-        } for item in response.get('data', {}).get('suit_style') or []]
+
+        colors = [
+            {
+                "id": f'id-{item.get("id")}',
+                "name": item.get("name"),
+                "en_name": item.get("en_name", item.get("name")),
+            }
+            for item in response.get("data", {}).get("colour") or []
+        ]
+        styles = [
+            {
+                "id": f'id-{item.get("id")}',
+                "name": item.get("title"),
+            }
+            for item in response.get("data", {}).get("suit_style") or []
+        ]
 
         with cls._style_cache_lock:
-            cls._style_cache[key] = {
-                'colors': colors,
-                'styles': styles,
-                'expire': now + 60 * 60
-            }
+            cls._style_cache[key] = {"colors": colors, "styles": styles, "expire": now + 60 * 60}
 
         return colors, styles
 
@@ -459,44 +419,39 @@ class AIPPTGenerateTool(BuiltinTool):
         :param credentials: the credentials
         :return: Tuple[list[dict[id, color]], list[dict[id, style]]
         """
-        if not self.runtime.credentials.get('aippt_access_key') or not self.runtime.credentials.get('aippt_secret_key'):
-            raise Exception('Please provide aippt credentials')
+        if not self.runtime.credentials.get("aippt_access_key") or not self.runtime.credentials.get("aippt_secret_key"):
+            raise Exception("Please provide aippt credentials")
 
         return self._get_styles(credentials=self.runtime.credentials, user_id=user_id)
-    
+
     def _get_suit(self, style_id: int, colour_id: int) -> int:
         """
         Get suit
         """
         headers = {
-            'x-channel': '',
-            'x-api-key': self.runtime.credentials['aippt_access_key'],
-            'x-token': self._get_api_token(credentials=self.runtime.credentials, user_id='__dify_system__')
+            "x-channel": "",
+            "x-api-key": self.runtime.credentials["aippt_access_key"],
+            "x-token": self._get_api_token(credentials=self.runtime.credentials, user_id="__dify_system__"),
         }
         response = get(
-            str(self._api_base_url / 'template_component' / 'suit' / 'search'),
+            str(self._api_base_url / "template_component" / "suit" / "search"),
             headers=headers,
-            params={
-                'style_id': style_id,
-                'colour_id': colour_id,
-                'page': 1,
-                'page_size': 1
-            }
+            params={"style_id": style_id, "colour_id": colour_id, "page": 1, "page_size": 1},
         )
 
         if response.status_code != 200:
-            raise Exception(f'Failed to connect to aippt: {response.text}')
-        
+            raise Exception(f"Failed to connect to aippt: {response.text}")
+
         response = response.json()
 
-        if response.get('code') != 0:
+        if response.get("code") != 0:
             raise Exception(f'Failed to connect to aippt: {response.get("msg")}')
-        
-        if len(response.get('data', {}).get('list') or []) > 0:
-            return response.get('data', {}).get('list')[0].get('id')
-        
-        raise Exception('Failed to get suit, the suit does not exist, please check the style and color')
-    
+
+        if len(response.get("data", {}).get("list") or []) > 0:
+            return response.get("data", {}).get("list")[0].get("id")
+
+        raise Exception("Failed to get suit, the suit does not exist, please check the style and color")
+
     def get_runtime_parameters(self) -> list[ToolParameter]:
         """
         Get runtime parameters
@@ -504,43 +459,40 @@ class AIPPTGenerateTool(BuiltinTool):
         Override this method to add runtime parameters to the tool.
         """
         try:
-            colors, styles = self.get_styles(user_id='__dify_system__')
+            colors, styles = self.get_styles(user_id="__dify_system__")
         except Exception as e:
-            colors, styles = [
-                {'id': '-1', 'name': '__default__', 'en_name': '__default__'}
-            ], [
-                {'id': '-1', 'name': '__default__', 'en_name': '__default__'}
-            ]
+            colors, styles = (
+                [{"id": "-1", "name": "__default__", "en_name": "__default__"}],
+                [{"id": "-1", "name": "__default__", "en_name": "__default__"}],
+            )
 
         return [
             ToolParameter(
-                name='color',
-                label=I18nObject(zh_Hans='颜色', en_US='Color'),
-                human_description=I18nObject(zh_Hans='颜色', en_US='Color'),
+                name="color",
+                label=I18nObject(zh_Hans="颜色", en_US="Color"),
+                human_description=I18nObject(zh_Hans="颜色", en_US="Color"),
                 type=ToolParameter.ToolParameterType.SELECT,
                 form=ToolParameter.ToolParameterForm.FORM,
                 required=False,
-                default=colors[0]['id'],
+                default=colors[0]["id"],
                 options=[
                     ToolParameterOption(
-                        value=color['id'],
-                        label=I18nObject(zh_Hans=color['name'], en_US=color['en_name'])
-                    ) for color in colors
-                ]
+                        value=color["id"], label=I18nObject(zh_Hans=color["name"], en_US=color["en_name"])
+                    )
+                    for color in colors
+                ],
             ),
             ToolParameter(
-                name='style',
-                label=I18nObject(zh_Hans='风格', en_US='Style'),
-                human_description=I18nObject(zh_Hans='风格', en_US='Style'),
+                name="style",
+                label=I18nObject(zh_Hans="风格", en_US="Style"),
+                human_description=I18nObject(zh_Hans="风格", en_US="Style"),
                 type=ToolParameter.ToolParameterType.SELECT,
                 form=ToolParameter.ToolParameterForm.FORM,
                 required=False,
-                default=styles[0]['id'],
+                default=styles[0]["id"],
                 options=[
-                    ToolParameterOption(
-                        value=style['id'],
-                        label=I18nObject(zh_Hans=style['name'], en_US=style['name'])
-                    ) for style in styles
-                ]
+                    ToolParameterOption(value=style["id"], label=I18nObject(zh_Hans=style["name"], en_US=style["name"]))
+                    for style in styles
+                ],
             ),
-        ]
\ No newline at end of file
+        ]
diff --git a/api/core/tools/provider/builtin/alphavantage/alphavantage.py b/api/core/tools/provider/builtin/alphavantage/alphavantage.py
index 01f2acfb5b..a84630e5aa 100644
--- a/api/core/tools/provider/builtin/alphavantage/alphavantage.py
+++ b/api/core/tools/provider/builtin/alphavantage/alphavantage.py
@@ -13,7 +13,7 @@ class AlphaVantageProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "code": "AAPL",  # Apple Inc.
                 },
diff --git a/api/core/tools/provider/builtin/alphavantage/tools/query_stock.py b/api/core/tools/provider/builtin/alphavantage/tools/query_stock.py
index 5c379b746d..d06611acd0 100644
--- a/api/core/tools/provider/builtin/alphavantage/tools/query_stock.py
+++ b/api/core/tools/provider/builtin/alphavantage/tools/query_stock.py
@@ -9,17 +9,16 @@ ALPHAVANTAGE_API_URL = "https://www.alphavantage.co/query"
 
 
 class QueryStockTool(BuiltinTool):
-
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
-        stock_code = tool_parameters.get('code', '')
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        stock_code = tool_parameters.get("code", "")
         if not stock_code:
-            return self.create_text_message('Please tell me your stock code')
+            return self.create_text_message("Please tell me your stock code")
 
-        if 'api_key' not in self.runtime.credentials or not self.runtime.credentials.get('api_key'):
+        if "api_key" not in self.runtime.credentials or not self.runtime.credentials.get("api_key"):
             return self.create_text_message("Alpha Vantage API key is required.")
 
         params = {
@@ -27,7 +26,7 @@ class QueryStockTool(BuiltinTool):
             "symbol": stock_code,
             "outputsize": "compact",
             "datatype": "json",
-            "apikey": self.runtime.credentials['api_key']
+            "apikey": self.runtime.credentials["api_key"],
         }
         response = requests.get(url=ALPHAVANTAGE_API_URL, params=params)
         response.raise_for_status()
@@ -35,15 +34,15 @@ class QueryStockTool(BuiltinTool):
         return self.create_json_message(result)
 
     def _handle_response(self, response: dict[str, Any]) -> dict[str, Any]:
-        result = response.get('Time Series (Daily)', {})
+        result = response.get("Time Series (Daily)", {})
         if not result:
             return {}
         stock_result = {}
         for k, v in result.items():
             stock_result[k] = {}
-            stock_result[k]['open'] = v.get('1. open')
-            stock_result[k]['high'] = v.get('2. high')
-            stock_result[k]['low'] = v.get('3. low')
-            stock_result[k]['close'] = v.get('4. close')
-            stock_result[k]['volume'] = v.get('5. volume')
+            stock_result[k]["open"] = v.get("1. open")
+            stock_result[k]["high"] = v.get("2. high")
+            stock_result[k]["low"] = v.get("3. low")
+            stock_result[k]["close"] = v.get("4. close")
+            stock_result[k]["volume"] = v.get("5. volume")
         return stock_result
diff --git a/api/core/tools/provider/builtin/arxiv/arxiv.py b/api/core/tools/provider/builtin/arxiv/arxiv.py
index 707fc69be3..ebb2d1a8c4 100644
--- a/api/core/tools/provider/builtin/arxiv/arxiv.py
+++ b/api/core/tools/provider/builtin/arxiv/arxiv.py
@@ -11,11 +11,10 @@ class ArxivProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "query": "John Doe",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/arxiv/tools/arxiv_search.py b/api/core/tools/provider/builtin/arxiv/tools/arxiv_search.py
index ce28373880..2d65ba2d6f 100644
--- a/api/core/tools/provider/builtin/arxiv/tools/arxiv_search.py
+++ b/api/core/tools/provider/builtin/arxiv/tools/arxiv_search.py
@@ -8,6 +8,8 @@ from core.tools.entities.tool_entities import ToolInvokeMessage
 from core.tools.tool.builtin_tool import BuiltinTool
 
 logger = logging.getLogger(__name__)
+
+
 class ArxivAPIWrapper(BaseModel):
     """Wrapper around ArxivAPI.
 
@@ -86,11 +88,13 @@ class ArxivAPIWrapper(BaseModel):
 
 class ArxivSearchInput(BaseModel):
     query: str = Field(..., description="Search query.")
-    
+
+
 class ArxivSearchTool(BuiltinTool):
     """
     A tool for searching articles on Arxiv.
     """
+
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage | list[ToolInvokeMessage]:
         """
         Invokes the Arxiv search tool with the given user ID and tool parameters.
@@ -100,15 +104,16 @@ class ArxivSearchTool(BuiltinTool):
             tool_parameters (dict[str, Any]): The parameters for the tool, including the 'query' parameter.
 
         Returns:
-            ToolInvokeMessage | list[ToolInvokeMessage]: The result of the tool invocation, which can be a single message or a list of messages.
+            ToolInvokeMessage | list[ToolInvokeMessage]: The result of the tool invocation,
+             which can be a single message or a list of messages.
         """
-        query = tool_parameters.get('query', '')
+        query = tool_parameters.get("query", "")
 
         if not query:
-            return self.create_text_message('Please input query')
-        
+            return self.create_text_message("Please input query")
+
         arxiv = ArxivAPIWrapper()
-        
+
         response = arxiv.run(query)
-        
+
         return self.create_text_message(self.summary(user_id=user_id, content=response))
diff --git a/api/core/tools/provider/builtin/aws/aws.py b/api/core/tools/provider/builtin/aws/aws.py
index 13ede96015..f81b5dbd27 100644
--- a/api/core/tools/provider/builtin/aws/aws.py
+++ b/api/core/tools/provider/builtin/aws/aws.py
@@ -11,15 +11,14 @@ class SageMakerProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
-                    "sagemaker_endpoint" : "",
+                    "sagemaker_endpoint": "",
                     "query": "misaka mikoto",
-                    "candidate_texts" : "hello$$$hello world",
-                    "topk" : 5,
-                    "aws_region" : ""
+                    "candidate_texts": "hello$$$hello world",
+                    "topk": 5,
+                    "aws_region": "",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/aws/tools/apply_guardrail.py b/api/core/tools/provider/builtin/aws/tools/apply_guardrail.py
index 9c006733bd..a04f5c0fe9 100644
--- a/api/core/tools/provider/builtin/aws/tools/apply_guardrail.py
+++ b/api/core/tools/provider/builtin/aws/tools/apply_guardrail.py
@@ -3,6 +3,7 @@ import logging
 from typing import Any, Union
 
 import boto3
+from botocore.exceptions import BotoCoreError
 from pydantic import BaseModel, Field
 
 from core.tools.entities.tool_entities import ToolInvokeMessage
@@ -11,40 +12,43 @@ from core.tools.tool.builtin_tool import BuiltinTool
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+
 class GuardrailParameters(BaseModel):
     guardrail_id: str = Field(..., description="The identifier of the guardrail")
     guardrail_version: str = Field(..., description="The version of the guardrail")
     source: str = Field(..., description="The source of the content")
     text: str = Field(..., description="The text to apply the guardrail to")
-    aws_region: str = Field(default="us-east-1", description="AWS region for the Bedrock client")
+    aws_region: str = Field(..., description="AWS region for the Bedrock client")
+
 
 class ApplyGuardrailTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
         Invoke the ApplyGuardrail tool
         """
         try:
             # Validate and parse input parameters
             params = GuardrailParameters(**tool_parameters)
-            
+
             # Initialize AWS client
-            bedrock_client = boto3.client('bedrock-runtime', region_name=params.aws_region)
+            bedrock_client = boto3.client("bedrock-runtime", region_name=params.aws_region)
 
             # Apply guardrail
             response = bedrock_client.apply_guardrail(
                 guardrailIdentifier=params.guardrail_id,
                 guardrailVersion=params.guardrail_version,
                 source=params.source,
-                content=[{"text": {"text": params.text}}]
+                content=[{"text": {"text": params.text}}],
             )
 
+            logger.info(f"Raw response from AWS: {json.dumps(response, indent=2)}")
+
             # Check for empty response
             if not response:
                 return self.create_text_message(text="Received empty response from AWS Bedrock.")
-           
+
             # Process the result
             action = response.get("action", "No action specified")
             outputs = response.get("outputs", [])
@@ -55,9 +59,12 @@ class ApplyGuardrailTool(BuiltinTool):
             formatted_assessments = []
             for assessment in assessments:
                 for policy_type, policy_data in assessment.items():
-                    if isinstance(policy_data, dict) and 'topics' in policy_data:
-                        for topic in policy_data['topics']:
-                            formatted_assessments.append(f"Policy: {policy_type}, Topic: {topic['name']}, Type: {topic['type']}, Action: {topic['action']}")
+                    if isinstance(policy_data, dict) and "topics" in policy_data:
+                        for topic in policy_data["topics"]:
+                            formatted_assessments.append(
+                                f"Policy: {policy_type}, Topic: {topic['name']}, Type: {topic['type']},"
+                                f" Action: {topic['action']}"
+                            )
                     else:
                         formatted_assessments.append(f"Policy: {policy_type}, Data: {policy_data}")
 
@@ -65,19 +72,19 @@ class ApplyGuardrailTool(BuiltinTool):
             result += f"Output: {output}\n "
             if formatted_assessments:
                 result += "Assessments:\n " + "\n ".join(formatted_assessments) + "\n "
-#           result += f"Full response: {json.dumps(response, indent=2, ensure_ascii=False)}"
+            #           result += f"Full response: {json.dumps(response, indent=2, ensure_ascii=False)}"
 
             return self.create_text_message(text=result)
 
-        except boto3.exceptions.BotoCoreError as e:
-            error_message = f'AWS service error: {str(e)}'
+        except BotoCoreError as e:
+            error_message = f"AWS service error: {str(e)}"
             logger.error(error_message, exc_info=True)
             return self.create_text_message(text=error_message)
         except json.JSONDecodeError as e:
-            error_message = f'JSON parsing error: {str(e)}'
+            error_message = f"JSON parsing error: {str(e)}"
             logger.error(error_message, exc_info=True)
             return self.create_text_message(text=error_message)
         except Exception as e:
-            error_message = f'An unexpected error occurred: {str(e)}'
+            error_message = f"An unexpected error occurred: {str(e)}"
             logger.error(error_message, exc_info=True)
             return self.create_text_message(text=error_message)
diff --git a/api/core/tools/provider/builtin/aws/tools/apply_guardrail.yaml b/api/core/tools/provider/builtin/aws/tools/apply_guardrail.yaml
index 2b7c8abb44..66044e4ea8 100644
--- a/api/core/tools/provider/builtin/aws/tools/apply_guardrail.yaml
+++ b/api/core/tools/provider/builtin/aws/tools/apply_guardrail.yaml
@@ -54,3 +54,14 @@ parameters:
       zh_Hans: 用于请求护栏审查的内容，可以是用户输入或 LLM 输出。
     llm_description: The content used for requesting guardrail review, which can be either user input or LLM output.
     form: llm
+  - name: aws_region
+    type: string
+    required: true
+    label:
+      en_US: AWS Region
+      zh_Hans: AWS 区域
+    human_description:
+      en_US: Please enter the AWS region for the Bedrock client, for example 'us-east-1'.
+      zh_Hans: 请输入 Bedrock 客户端的 AWS 区域，例如 'us-east-1'。
+    llm_description: Please enter the AWS region for the Bedrock client, for example 'us-east-1'.
+    form: form
diff --git a/api/core/tools/provider/builtin/aws/tools/lambda_translate_utils.py b/api/core/tools/provider/builtin/aws/tools/lambda_translate_utils.py
index 005ba3deb5..48755753ac 100644
--- a/api/core/tools/provider/builtin/aws/tools/lambda_translate_utils.py
+++ b/api/core/tools/provider/builtin/aws/tools/lambda_translate_utils.py
@@ -11,78 +11,81 @@ class LambdaTranslateUtilsTool(BuiltinTool):
     lambda_client: Any = None
 
     def _invoke_lambda(self, text_content, src_lang, dest_lang, model_id, dictionary_name, request_type, lambda_name):
-        msg = { 
-            "src_content":text_content, 
-            "src_lang": src_lang, 
-            "dest_lang":dest_lang, 
+        msg = {
+            "src_content": text_content,
+            "src_lang": src_lang,
+            "dest_lang": dest_lang,
             "dictionary_id": dictionary_name,
-            "request_type" : request_type, 
-            "model_id" : model_id
+            "request_type": request_type,
+            "model_id": model_id,
         }
 
-        invoke_response = self.lambda_client.invoke(FunctionName=lambda_name,
-                                               InvocationType='RequestResponse',
-                                               Payload=json.dumps(msg))
-        response_body = invoke_response['Payload']
+        invoke_response = self.lambda_client.invoke(
+            FunctionName=lambda_name, InvocationType="RequestResponse", Payload=json.dumps(msg)
+        )
+        response_body = invoke_response["Payload"]
 
         response_str = response_body.read().decode("unicode_escape")
 
         return response_str
 
-    def _invoke(self, 
-                user_id: str, 
-               tool_parameters: dict[str, Any], 
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         line = 0
         try:
             if not self.lambda_client:
-                aws_region = tool_parameters.get('aws_region')
+                aws_region = tool_parameters.get("aws_region")
                 if aws_region:
                     self.lambda_client = boto3.client("lambda", region_name=aws_region)
                 else:
                     self.lambda_client = boto3.client("lambda")
 
             line = 1
-            text_content = tool_parameters.get('text_content', '')
+            text_content = tool_parameters.get("text_content", "")
             if not text_content:
-                return self.create_text_message('Please input text_content')
-            
+                return self.create_text_message("Please input text_content")
+
             line = 2
-            src_lang = tool_parameters.get('src_lang', '')
+            src_lang = tool_parameters.get("src_lang", "")
             if not src_lang:
-                return self.create_text_message('Please input src_lang')
-            
+                return self.create_text_message("Please input src_lang")
+
             line = 3
-            dest_lang = tool_parameters.get('dest_lang', '')
+            dest_lang = tool_parameters.get("dest_lang", "")
             if not dest_lang:
-                return self.create_text_message('Please input dest_lang')
-            
+                return self.create_text_message("Please input dest_lang")
+
             line = 4
-            lambda_name = tool_parameters.get('lambda_name', '')
+            lambda_name = tool_parameters.get("lambda_name", "")
             if not lambda_name:
-                return self.create_text_message('Please input lambda_name')
-            
+                return self.create_text_message("Please input lambda_name")
+
             line = 5
-            request_type = tool_parameters.get('request_type', '')
+            request_type = tool_parameters.get("request_type", "")
             if not request_type:
-                return self.create_text_message('Please input request_type')
-            
+                return self.create_text_message("Please input request_type")
+
             line = 6
-            model_id = tool_parameters.get('model_id', '')
+            model_id = tool_parameters.get("model_id", "")
             if not model_id:
-                return self.create_text_message('Please input model_id')
+                return self.create_text_message("Please input model_id")
 
             line = 7
-            dictionary_name = tool_parameters.get('dictionary_name', '')
+            dictionary_name = tool_parameters.get("dictionary_name", "")
             if not dictionary_name:
-                return self.create_text_message('Please input dictionary_name')
-            
-            result = self._invoke_lambda(text_content, src_lang, dest_lang, model_id, dictionary_name, request_type, lambda_name)
+                return self.create_text_message("Please input dictionary_name")
+
+            result = self._invoke_lambda(
+                text_content, src_lang, dest_lang, model_id, dictionary_name, request_type, lambda_name
+            )
 
             return self.create_text_message(text=result)
 
         except Exception as e:
-            return self.create_text_message(f'Exception {str(e)}, line : {line}')
+            return self.create_text_message(f"Exception {str(e)}, line : {line}")
diff --git a/api/core/tools/provider/builtin/aws/tools/lambda_translate_utils.yaml b/api/core/tools/provider/builtin/aws/tools/lambda_translate_utils.yaml
index a35c9f49fb..3bb133c7ec 100644
--- a/api/core/tools/provider/builtin/aws/tools/lambda_translate_utils.yaml
+++ b/api/core/tools/provider/builtin/aws/tools/lambda_translate_utils.yaml
@@ -10,7 +10,7 @@ description:
   human:
     en_US: A util tools for LLM translation, extra deployment is needed on AWS. Please refer Github Repo - https://github.com/ybalbert001/dynamodb-rag
     zh_Hans: 大语言模型翻译工具(专词映射获取)，需要在AWS上进行额外部署，可参考Github Repo - https://github.com/ybalbert001/dynamodb-rag
-    pt_BR: A util tools for LLM translation, specfic Lambda Function deployment is needed on AWS. Please refer Github Repo - https://github.com/ybalbert001/dynamodb-rag
+    pt_BR: A util tools for LLM translation, specific Lambda Function deployment is needed on AWS. Please refer Github Repo - https://github.com/ybalbert001/dynamodb-rag
   llm: A util tools for translation.
 parameters:
   - name: text_content
diff --git a/api/core/tools/provider/builtin/aws/tools/lambda_yaml_to_json.py b/api/core/tools/provider/builtin/aws/tools/lambda_yaml_to_json.py
new file mode 100644
index 0000000000..f43f3b6fe0
--- /dev/null
+++ b/api/core/tools/provider/builtin/aws/tools/lambda_yaml_to_json.py
@@ -0,0 +1,70 @@
+import json
+import logging
+from typing import Any, Union
+
+import boto3
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+console_handler = logging.StreamHandler()
+logger.addHandler(console_handler)
+
+
+class LambdaYamlToJsonTool(BuiltinTool):
+    lambda_client: Any = None
+
+    def _invoke_lambda(self, lambda_name: str, yaml_content: str) -> str:
+        msg = {"body": yaml_content}
+        logger.info(json.dumps(msg))
+
+        invoke_response = self.lambda_client.invoke(
+            FunctionName=lambda_name, InvocationType="RequestResponse", Payload=json.dumps(msg)
+        )
+        response_body = invoke_response["Payload"]
+
+        response_str = response_body.read().decode("utf-8")
+        resp_json = json.loads(response_str)
+
+        logger.info(resp_json)
+        if resp_json["statusCode"] != 200:
+            raise Exception(f"Invalid status code: {response_str}")
+
+        return resp_json["body"]
+
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        invoke tools
+        """
+        try:
+            if not self.lambda_client:
+                aws_region = tool_parameters.get("aws_region")  # todo: move aws_region out, and update client region
+                if aws_region:
+                    self.lambda_client = boto3.client("lambda", region_name=aws_region)
+                else:
+                    self.lambda_client = boto3.client("lambda")
+
+            yaml_content = tool_parameters.get("yaml_content", "")
+            if not yaml_content:
+                return self.create_text_message("Please input yaml_content")
+
+            lambda_name = tool_parameters.get("lambda_name", "")
+            if not lambda_name:
+                return self.create_text_message("Please input lambda_name")
+            logger.debug(f"{json.dumps(tool_parameters, indent=2, ensure_ascii=False)}")
+
+            result = self._invoke_lambda(lambda_name, yaml_content)
+            logger.debug(result)
+
+            return self.create_text_message(result)
+        except Exception as e:
+            return self.create_text_message(f"Exception: {str(e)}")
+
+        console_handler.flush()
diff --git a/api/core/tools/provider/builtin/aws/tools/lambda_yaml_to_json.yaml b/api/core/tools/provider/builtin/aws/tools/lambda_yaml_to_json.yaml
new file mode 100644
index 0000000000..919c285348
--- /dev/null
+++ b/api/core/tools/provider/builtin/aws/tools/lambda_yaml_to_json.yaml
@@ -0,0 +1,53 @@
+identity:
+  name: lambda_yaml_to_json
+  author: AWS
+  label:
+    en_US: LambdaYamlToJson
+    zh_Hans: LambdaYamlToJson
+    pt_BR: LambdaYamlToJson
+  icon: icon.svg
+description:
+  human:
+    en_US: A tool to convert yaml to json using AWS Lambda.
+    zh_Hans: 将 YAML 转为 JSON 的工具（通过AWS Lambda）。
+    pt_BR: A tool to convert yaml to json using AWS Lambda.
+  llm: A tool to convert yaml to json.
+parameters:
+  - name: yaml_content
+    type: string
+    required: true
+    label:
+      en_US: YAML content to convert for
+      zh_Hans: YAML 内容
+      pt_BR: YAML content to convert for
+    human_description:
+      en_US: YAML content to convert for
+      zh_Hans: YAML 内容
+      pt_BR: YAML content to convert for
+    llm_description: YAML content to convert for
+    form: llm
+  - name: aws_region
+    type: string
+    required: false
+    label:
+      en_US: region of lambda
+      zh_Hans: Lambda 所在的region
+      pt_BR: region of lambda
+    human_description:
+      en_US: region of lambda
+      zh_Hans: Lambda 所在的region
+      pt_BR: region of lambda
+    llm_description: region of lambda
+    form: form
+  - name: lambda_name
+    type: string
+    required: false
+    label:
+      en_US: name of lambda
+      zh_Hans: Lambda 名称
+      pt_BR: name of lambda
+    human_description:
+      en_US: name of lambda
+      zh_Hans: Lambda 名称
+      pt_BR: name of lambda
+    form: form
diff --git a/api/core/tools/provider/builtin/aws/tools/sagemaker_text_rerank.py b/api/core/tools/provider/builtin/aws/tools/sagemaker_text_rerank.py
index d4bc446e5b..bffcd058b5 100644
--- a/api/core/tools/provider/builtin/aws/tools/sagemaker_text_rerank.py
+++ b/api/core/tools/provider/builtin/aws/tools/sagemaker_text_rerank.py
@@ -1,4 +1,5 @@
 import json
+import operator
 from typing import Any, Union
 
 import boto3
@@ -9,37 +10,33 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 class SageMakerReRankTool(BuiltinTool):
     sagemaker_client: Any = None
-    sagemaker_endpoint:str = None
-    topk:int = None
+    sagemaker_endpoint: str = None
+    topk: int = None
 
-    def _sagemaker_rerank(self, query_input: str, docs: list[str], rerank_endpoint:str):
-        inputs = [query_input]*len(docs)
+    def _sagemaker_rerank(self, query_input: str, docs: list[str], rerank_endpoint: str):
+        inputs = [query_input] * len(docs)
         response_model = self.sagemaker_client.invoke_endpoint(
             EndpointName=rerank_endpoint,
-            Body=json.dumps(
-                {
-                    "inputs": inputs,
-                    "docs": docs
-                }
-            ),
+            Body=json.dumps({"inputs": inputs, "docs": docs}),
             ContentType="application/json",
         )
-        json_str = response_model['Body'].read().decode('utf8')
+        json_str = response_model["Body"].read().decode("utf8")
         json_obj = json.loads(json_str)
-        scores = json_obj['scores']
+        scores = json_obj["scores"]
         return scores if isinstance(scores, list) else [scores]
 
-    def _invoke(self, 
-                user_id: str, 
-               tool_parameters: dict[str, Any], 
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         line = 0
         try:
             if not self.sagemaker_client:
-                aws_region = tool_parameters.get('aws_region')
+                aws_region = tool_parameters.get("aws_region")
                 if aws_region:
                     self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
                 else:
@@ -47,25 +44,25 @@ class SageMakerReRankTool(BuiltinTool):
 
             line = 1
             if not self.sagemaker_endpoint:
-                self.sagemaker_endpoint = tool_parameters.get('sagemaker_endpoint')
+                self.sagemaker_endpoint = tool_parameters.get("sagemaker_endpoint")
 
             line = 2
             if not self.topk:
-                self.topk = tool_parameters.get('topk', 5)
+                self.topk = tool_parameters.get("topk", 5)
 
             line = 3
-            query = tool_parameters.get('query', '')
+            query = tool_parameters.get("query", "")
             if not query:
-                return self.create_text_message('Please input query')
-            
+                return self.create_text_message("Please input query")
+
             line = 4
-            candidate_texts = tool_parameters.get('candidate_texts')
+            candidate_texts = tool_parameters.get("candidate_texts")
             if not candidate_texts:
-                return self.create_text_message('Please input candidate_texts')
-            
+                return self.create_text_message("Please input candidate_texts")
+
             line = 5
             candidate_docs = json.loads(candidate_texts)
-            docs = [ item.get('content') for item in candidate_docs ]
+            docs = [item.get("content") for item in candidate_docs]
 
             line = 6
             scores = self._sagemaker_rerank(query_input=query, docs=docs, rerank_endpoint=self.sagemaker_endpoint)
@@ -75,12 +72,10 @@ class SageMakerReRankTool(BuiltinTool):
                 candidate_docs[idx]["score"] = scores[idx]
 
             line = 8
-            sorted_candidate_docs = sorted(candidate_docs, key=lambda x: x['score'], reverse=True)
+            sorted_candidate_docs = sorted(candidate_docs, key=operator.itemgetter("score"), reverse=True)
 
             line = 9
-            results_str = json.dumps(sorted_candidate_docs[:self.topk], ensure_ascii=False)
-            return self.create_text_message(text=results_str)
-            
+            return [self.create_json_message(res) for res in sorted_candidate_docs[: self.topk]]
+
         except Exception as e:
-            return self.create_text_message(f'Exception {str(e)}, line : {line}')
-    
\ No newline at end of file
+            return self.create_text_message(f"Exception {str(e)}, line : {line}")
diff --git a/api/core/tools/provider/builtin/aws/tools/sagemaker_tts.py b/api/core/tools/provider/builtin/aws/tools/sagemaker_tts.py
new file mode 100644
index 0000000000..bceeaab745
--- /dev/null
+++ b/api/core/tools/provider/builtin/aws/tools/sagemaker_tts.py
@@ -0,0 +1,101 @@
+import json
+from enum import Enum
+from typing import Any, Union
+
+import boto3
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class TTSModelType(Enum):
+    PresetVoice = "PresetVoice"
+    CloneVoice = "CloneVoice"
+    CloneVoice_CrossLingual = "CloneVoice_CrossLingual"
+    InstructVoice = "InstructVoice"
+
+
+class SageMakerTTSTool(BuiltinTool):
+    sagemaker_client: Any = None
+    sagemaker_endpoint: str = None
+    s3_client: Any = None
+    comprehend_client: Any = None
+
+    def _detect_lang_code(self, content: str, map_dict: dict = None):
+        map_dict = {"zh": "<|zh|>", "en": "<|en|>", "ja": "<|jp|>", "zh-TW": "<|yue|>", "ko": "<|ko|>"}
+
+        response = self.comprehend_client.detect_dominant_language(Text=content)
+        language_code = response["Languages"][0]["LanguageCode"]
+        return map_dict.get(language_code, "<|zh|>")
+
+    def _build_tts_payload(
+        self,
+        model_type: str,
+        content_text: str,
+        model_role: str,
+        prompt_text: str,
+        prompt_audio: str,
+        instruct_text: str,
+    ):
+        if model_type == TTSModelType.PresetVoice.value and model_role:
+            return {"tts_text": content_text, "role": model_role}
+        if model_type == TTSModelType.CloneVoice.value and prompt_text and prompt_audio:
+            return {"tts_text": content_text, "prompt_text": prompt_text, "prompt_audio": prompt_audio}
+        if model_type == TTSModelType.CloneVoice_CrossLingual.value and prompt_audio:
+            lang_tag = self._detect_lang_code(content_text)
+            return {"tts_text": f"{content_text}", "prompt_audio": prompt_audio, "lang_tag": lang_tag}
+        if model_type == TTSModelType.InstructVoice.value and instruct_text and model_role:
+            return {"tts_text": content_text, "role": model_role, "instruct_text": instruct_text}
+
+        raise RuntimeError(f"Invalid params for {model_type}")
+
+    def _invoke_sagemaker(self, payload: dict, endpoint: str):
+        response_model = self.sagemaker_client.invoke_endpoint(
+            EndpointName=endpoint,
+            Body=json.dumps(payload),
+            ContentType="application/json",
+        )
+        json_str = response_model["Body"].read().decode("utf8")
+        json_obj = json.loads(json_str)
+        return json_obj
+
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        invoke tools
+        """
+        try:
+            if not self.sagemaker_client:
+                aws_region = tool_parameters.get("aws_region")
+                if aws_region:
+                    self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
+                    self.s3_client = boto3.client("s3", region_name=aws_region)
+                    self.comprehend_client = boto3.client("comprehend", region_name=aws_region)
+                else:
+                    self.sagemaker_client = boto3.client("sagemaker-runtime")
+                    self.s3_client = boto3.client("s3")
+                    self.comprehend_client = boto3.client("comprehend")
+
+            if not self.sagemaker_endpoint:
+                self.sagemaker_endpoint = tool_parameters.get("sagemaker_endpoint")
+
+            tts_text = tool_parameters.get("tts_text")
+            tts_infer_type = tool_parameters.get("tts_infer_type")
+
+            voice = tool_parameters.get("voice")
+            mock_voice_audio = tool_parameters.get("mock_voice_audio")
+            mock_voice_text = tool_parameters.get("mock_voice_text")
+            voice_instruct_prompt = tool_parameters.get("voice_instruct_prompt")
+            payload = self._build_tts_payload(
+                tts_infer_type, tts_text, voice, mock_voice_text, mock_voice_audio, voice_instruct_prompt
+            )
+
+            result = self._invoke_sagemaker(payload, self.sagemaker_endpoint)
+
+            return self.create_text_message(text=result["s3_presign_url"])
+
+        except Exception as e:
+            return self.create_text_message(f"Exception {str(e)}")
diff --git a/api/core/tools/provider/builtin/aws/tools/sagemaker_tts.yaml b/api/core/tools/provider/builtin/aws/tools/sagemaker_tts.yaml
new file mode 100644
index 0000000000..a6a61dd4aa
--- /dev/null
+++ b/api/core/tools/provider/builtin/aws/tools/sagemaker_tts.yaml
@@ -0,0 +1,149 @@
+identity:
+  name: sagemaker_tts
+  author: AWS
+  label:
+    en_US: SagemakerTTS
+    zh_Hans: Sagemaker语音合成
+    pt_BR: SagemakerTTS
+  icon: icon.svg
+description:
+  human:
+    en_US: A tool for Speech synthesis - https://github.com/aws-samples/dify-aws-tool
+    zh_Hans: Sagemaker语音合成工具, 请参考 Github Repo - https://github.com/aws-samples/dify-aws-tool上的部署脚本
+    pt_BR: A tool for Speech synthesis.
+  llm: A tool for Speech synthesis. You can find deploy notebook on Github Repo - https://github.com/aws-samples/dify-aws-tool
+parameters:
+  - name: sagemaker_endpoint
+    type: string
+    required: true
+    label:
+      en_US: sagemaker endpoint for tts
+      zh_Hans: 语音生成的SageMaker端点
+      pt_BR: sagemaker endpoint for tts
+    human_description:
+      en_US: sagemaker endpoint for tts
+      zh_Hans: 语音生成的SageMaker端点
+      pt_BR: sagemaker endpoint for tts
+    llm_description: sagemaker endpoint for tts
+    form: form
+  - name: tts_text
+    type: string
+    required: true
+    label:
+      en_US: tts text
+      zh_Hans: 语音合成原文
+      pt_BR: tts text
+    human_description:
+      en_US: tts text
+      zh_Hans: 语音合成原文
+      pt_BR: tts text
+    llm_description: tts text
+    form: llm
+  - name: tts_infer_type
+    type: select
+    required: false
+    label:
+      en_US: tts infer type
+      zh_Hans: 合成方式
+      pt_BR: tts infer type
+    human_description:
+      en_US: tts infer type
+      zh_Hans: 合成方式
+      pt_BR: tts infer type
+    llm_description: tts infer type
+    options:
+      - value: PresetVoice
+        label:
+          en_US: preset voice
+          zh_Hans: 预置音色
+      - value: CloneVoice
+        label:
+          en_US: clone voice
+          zh_Hans: 克隆音色
+      - value: CloneVoice_CrossLingual
+        label:
+          en_US: clone crossLingual voice
+          zh_Hans: 克隆音色(跨语言)
+      - value: InstructVoice
+        label:
+          en_US: instruct voice
+          zh_Hans: 指令音色
+    form: form
+  - name: voice
+    type: select
+    required: false
+    label:
+      en_US: preset voice
+      zh_Hans: 预置音色
+      pt_BR: preset voice
+    human_description:
+      en_US: preset voice
+      zh_Hans: 预置音色
+      pt_BR: preset voice
+    llm_description: preset voice
+    options:
+      - value: 中文男
+        label:
+          en_US: zh-cn male
+          zh_Hans: 中文男
+      - value: 中文女
+        label:
+          en_US: zh-cn female
+          zh_Hans: 中文女
+      - value: 粤语女
+        label:
+          en_US: zh-TW female
+          zh_Hans: 粤语女
+    form: form
+  - name: mock_voice_audio
+    type: string
+    required: false
+    label:
+      en_US: clone voice link
+      zh_Hans: 克隆音频链接
+      pt_BR: clone voice link
+    human_description:
+      en_US: clone voice link
+      zh_Hans: 克隆音频链接
+      pt_BR: clone voice link
+    llm_description: clone voice link
+    form: llm
+  - name: mock_voice_text
+    type: string
+    required: false
+    label:
+      en_US: text of clone voice
+      zh_Hans: 克隆音频对应文本
+      pt_BR: text of clone voice
+    human_description:
+      en_US: text of clone voice
+      zh_Hans: 克隆音频对应文本
+      pt_BR: text of clone voice
+    llm_description: text of clone voice
+    form: llm
+  - name: voice_instruct_prompt
+    type: string
+    required: false
+    label:
+      en_US: instruct prompt for voice
+      zh_Hans: 音色指令文本
+      pt_BR: instruct prompt for voice
+    human_description:
+      en_US: instruct prompt for voice
+      zh_Hans: 音色指令文本
+      pt_BR: instruct prompt for voice
+    llm_description: instruct prompt for voice
+    form: llm
+  - name: aws_region
+    type: string
+    required: false
+    label:
+      en_US: region of sagemaker endpoint
+      zh_Hans: SageMaker 端点所在的region
+      pt_BR: region of sagemaker endpoint
+    human_description:
+      en_US: region of sagemaker endpoint
+      zh_Hans: SageMaker 端点所在的region
+      pt_BR: region of sagemaker endpoint
+    llm_description: region of sagemaker endpoint
+    form: form
diff --git a/api/core/tools/provider/builtin/azuredalle/azuredalle.py b/api/core/tools/provider/builtin/azuredalle/azuredalle.py
index 2981a54d3c..1fab0d03a2 100644
--- a/api/core/tools/provider/builtin/azuredalle/azuredalle.py
+++ b/api/core/tools/provider/builtin/azuredalle/azuredalle.py
@@ -13,12 +13,8 @@ class AzureDALLEProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
-                tool_parameters={
-                    "prompt": "cute girl, blue eyes, white hair, anime style",
-                    "size": "square",
-                    "n": 1
-                },
+                user_id="",
+                tool_parameters={"prompt": "cute girl, blue eyes, white hair, anime style", "size": "square", "n": 1},
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/azuredalle/tools/dalle3.py b/api/core/tools/provider/builtin/azuredalle/tools/dalle3.py
index 2ffdd38b72..cfa3cfb092 100644
--- a/api/core/tools/provider/builtin/azuredalle/tools/dalle3.py
+++ b/api/core/tools/provider/builtin/azuredalle/tools/dalle3.py
@@ -9,47 +9,48 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class DallE3Tool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         client = AzureOpenAI(
-            api_version=self.runtime.credentials['azure_openai_api_version'],
-            azure_endpoint=self.runtime.credentials['azure_openai_base_url'],
-            api_key=self.runtime.credentials['azure_openai_api_key'],
+            api_version=self.runtime.credentials["azure_openai_api_version"],
+            azure_endpoint=self.runtime.credentials["azure_openai_base_url"],
+            api_key=self.runtime.credentials["azure_openai_api_key"],
         )
 
         SIZE_MAPPING = {
-            'square': '1024x1024',
-            'vertical': '1024x1792',
-            'horizontal': '1792x1024',
+            "square": "1024x1024",
+            "vertical": "1024x1792",
+            "horizontal": "1792x1024",
         }
 
         # prompt
-        prompt = tool_parameters.get('prompt', '')
+        prompt = tool_parameters.get("prompt", "")
         if not prompt:
-            return self.create_text_message('Please input prompt')
+            return self.create_text_message("Please input prompt")
         # get size
-        size = SIZE_MAPPING[tool_parameters.get('size', 'square')]
+        size = SIZE_MAPPING[tool_parameters.get("size", "square")]
         # get n
-        n = tool_parameters.get('n', 1)
+        n = tool_parameters.get("n", 1)
         # get quality
-        quality = tool_parameters.get('quality', 'standard')
-        if quality not in ['standard', 'hd']:
-            return self.create_text_message('Invalid quality')
+        quality = tool_parameters.get("quality", "standard")
+        if quality not in {"standard", "hd"}:
+            return self.create_text_message("Invalid quality")
         # get style
-        style = tool_parameters.get('style', 'vivid')
-        if style not in ['natural', 'vivid']:
-            return self.create_text_message('Invalid style')
+        style = tool_parameters.get("style", "vivid")
+        if style not in {"natural", "vivid"}:
+            return self.create_text_message("Invalid style")
         # set extra body
-        seed_id = tool_parameters.get('seed_id', self._generate_random_id(8))
-        extra_body = {'seed': seed_id}
+        seed_id = tool_parameters.get("seed_id", self._generate_random_id(8))
+        extra_body = {"seed": seed_id}
 
         # call openapi dalle3
-        model = self.runtime.credentials['azure_openai_api_model_name']
+        model = self.runtime.credentials["azure_openai_api_model_name"]
         response = client.images.generate(
             prompt=prompt,
             model=model,
@@ -58,21 +59,25 @@ class DallE3Tool(BuiltinTool):
             extra_body=extra_body,
             style=style,
             quality=quality,
-            response_format='b64_json'
+            response_format="b64_json",
         )
 
         result = []
 
         for image in response.data:
-            result.append(self.create_blob_message(blob=b64decode(image.b64_json),
-                                                   meta={'mime_type': 'image/png'},
-                                                   save_as=self.VARIABLE_KEY.IMAGE.value))
-        result.append(self.create_text_message(f'\nGenerate image source to Seed ID: {seed_id}'))
+            result.append(
+                self.create_blob_message(
+                    blob=b64decode(image.b64_json),
+                    meta={"mime_type": "image/png"},
+                    save_as=self.VariableKey.IMAGE.value,
+                )
+            )
+        result.append(self.create_text_message(f"\nGenerate image source to Seed ID: {seed_id}"))
 
         return result
 
     @staticmethod
     def _generate_random_id(length=8):
-        characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'
-        random_id = ''.join(random.choices(characters, k=length))
+        characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"
+        random_id = "".join(random.choices(characters, k=length))
         return random_id
diff --git a/api/core/tools/provider/builtin/bing/tools/bing_web_search.py b/api/core/tools/provider/builtin/bing/tools/bing_web_search.py
index f85a5ed472..8bed2c556c 100644
--- a/api/core/tools/provider/builtin/bing/tools/bing_web_search.py
+++ b/api/core/tools/provider/builtin/bing/tools/bing_web_search.py
@@ -8,142 +8,135 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class BingSearchTool(BuiltinTool):
-    url: str = 'https://api.bing.microsoft.com/v7.0/search'
+    url: str = "https://api.bing.microsoft.com/v7.0/search"
 
-    def _invoke_bing(self, 
-                     user_id: str,
-                     server_url: str,
-                     subscription_key: str, query: str, limit: int, 
-                     result_type: str, market: str, lang: str, 
-                     filters: list[str]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke_bing(
+        self,
+        user_id: str,
+        server_url: str,
+        subscription_key: str,
+        query: str,
+        limit: int,
+        result_type: str,
+        market: str,
+        lang: str,
+        filters: list[str],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke bing search
+        invoke bing search
         """
-        market_code = f'{lang}-{market}'
-        accept_language = f'{lang},{market_code};q=0.9'
-        headers = {
-            'Ocp-Apim-Subscription-Key': subscription_key,
-            'Accept-Language': accept_language
-        }
+        market_code = f"{lang}-{market}"
+        accept_language = f"{lang},{market_code};q=0.9"
+        headers = {"Ocp-Apim-Subscription-Key": subscription_key, "Accept-Language": accept_language}
 
         query = quote(query)
         server_url = f'{server_url}?q={query}&mkt={market_code}&count={limit}&responseFilter={",".join(filters)}'
         response = get(server_url, headers=headers)
 
         if response.status_code != 200:
-            raise Exception(f'Error {response.status_code}: {response.text}')
-        
-        response = response.json()
-        search_results = response['webPages']['value'][:limit] if 'webPages' in response else []
-        related_searches = response['relatedSearches']['value'] if 'relatedSearches' in response else []
-        entities = response['entities']['value'] if 'entities' in response else []
-        news = response['news']['value'] if 'news' in response else []
-        computation = response['computation']['value'] if 'computation' in response else None
+            raise Exception(f"Error {response.status_code}: {response.text}")
 
-        if result_type == 'link':
+        response = response.json()
+        search_results = response["webPages"]["value"][:limit] if "webPages" in response else []
+        related_searches = response["relatedSearches"]["value"] if "relatedSearches" in response else []
+        entities = response["entities"]["value"] if "entities" in response else []
+        news = response["news"]["value"] if "news" in response else []
+        computation = response["computation"]["value"] if "computation" in response else None
+
+        if result_type == "link":
             results = []
             if search_results:
                 for result in search_results:
                     url = f': {result["url"]}' if "url" in result else ""
-                    results.append(self.create_text_message(
-                        text=f'{result["name"]}{url}'
-                    ))
-
+                    results.append(self.create_text_message(text=f'{result["name"]}{url}'))
 
             if entities:
                 for entity in entities:
                     url = f': {entity["url"]}' if "url" in entity else ""
-                    results.append(self.create_text_message(
-                        text=f'{entity.get("name", "")}{url}'
-                    ))
+                    results.append(self.create_text_message(text=f'{entity.get("name", "")}{url}'))
 
             if news:
                 for news_item in news:
                     url = f': {news_item["url"]}' if "url" in news_item else ""
-                    results.append(self.create_text_message(
-                        text=f'{news_item.get("name", "")}{url}'
-                    ))
+                    results.append(self.create_text_message(text=f'{news_item.get("name", "")}{url}'))
 
             if related_searches:
                 for related in related_searches:
                     url = f': {related["displayText"]}' if "displayText" in related else ""
-                    results.append(self.create_text_message(
-                        text=f'{related.get("displayText", "")}{url}'
-                    ))
-                    
+                    results.append(self.create_text_message(text=f'{related.get("displayText", "")}{url}'))
+
             return results
         else:
             # construct text
-            text = ''
+            text = ""
             if search_results:
                 for i, result in enumerate(search_results):
-                    text += f'{i+1}: {result.get("name", "")} - {result.get("snippet", "")}\n'
+                    text += f'{i + 1}: {result.get("name", "")} - {result.get("snippet", "")}\n'
 
-            if computation and 'expression' in computation and 'value' in computation:
-                text += '\nComputation:\n'
+            if computation and "expression" in computation and "value" in computation:
+                text += "\nComputation:\n"
                 text += f'{computation["expression"]} = {computation["value"]}\n'
 
             if entities:
-                text += '\nEntities:\n'
+                text += "\nEntities:\n"
                 for entity in entities:
                     url = f'- {entity["url"]}' if "url" in entity else ""
                     text += f'{entity.get("name", "")}{url}\n'
 
             if news:
-                text += '\nNews:\n'
+                text += "\nNews:\n"
                 for news_item in news:
                     url = f'- {news_item["url"]}' if "url" in news_item else ""
                     text += f'{news_item.get("name", "")}{url}\n'
 
             if related_searches:
-                text += '\n\nRelated Searches:\n'
+                text += "\n\nRelated Searches:\n"
                 for related in related_searches:
                     url = f'- {related["webSearchUrl"]}' if "webSearchUrl" in related else ""
                     text += f'{related.get("displayText", "")}{url}\n'
 
             return self.create_text_message(text=self.summary(user_id=user_id, content=text))
-        
 
     def validate_credentials(self, credentials: dict[str, Any], tool_parameters: dict[str, Any]) -> None:
-        key = credentials.get('subscription_key')
+        key = credentials.get("subscription_key")
         if not key:
-            raise Exception('subscription_key is required')
-        
-        server_url = credentials.get('server_url')
+            raise Exception("subscription_key is required")
+
+        server_url = credentials.get("server_url")
         if not server_url:
             server_url = self.url
 
-        query = tool_parameters.get('query')
+        query = tool_parameters.get("query")
         if not query:
-            raise Exception('query is required')
-        
-        limit = min(tool_parameters.get('limit', 5), 10)
-        result_type = tool_parameters.get('result_type', 'text') or 'text'
+            raise Exception("query is required")
 
-        market = tool_parameters.get('market', 'US')
-        lang = tool_parameters.get('language', 'en')
+        limit = min(tool_parameters.get("limit", 5), 10)
+        result_type = tool_parameters.get("result_type", "text") or "text"
+
+        market = tool_parameters.get("market", "US")
+        lang = tool_parameters.get("language", "en")
         filter = []
 
-        if credentials.get('allow_entities', False):
-            filter.append('Entities')
+        if credentials.get("allow_entities", False):
+            filter.append("Entities")
 
-        if credentials.get('allow_computation', False):
-            filter.append('Computation')
+        if credentials.get("allow_computation", False):
+            filter.append("Computation")
 
-        if credentials.get('allow_news', False):
-            filter.append('News')
+        if credentials.get("allow_news", False):
+            filter.append("News")
 
-        if credentials.get('allow_related_searches', False):
-            filter.append('RelatedSearches')
+        if credentials.get("allow_related_searches", False):
+            filter.append("RelatedSearches")
 
-        if credentials.get('allow_web_pages', False):
-            filter.append('WebPages')
+        if credentials.get("allow_web_pages", False):
+            filter.append("WebPages")
 
         if not filter:
-            raise Exception('At least one filter is required')
-        
+            raise Exception("At least one filter is required")
+
         self._invoke_bing(
-            user_id='test',
+            user_id="test",
             server_url=server_url,
             subscription_key=key,
             query=query,
@@ -151,50 +144,51 @@ class BingSearchTool(BuiltinTool):
             result_type=result_type,
             market=market,
             lang=lang,
-            filters=filter
+            filters=filter,
         )
-        
-    def _invoke(self, 
-                user_id: str,
-               tool_parameters: dict[str, Any], 
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
 
-        key = self.runtime.credentials.get('subscription_key', None)
+        key = self.runtime.credentials.get("subscription_key", None)
         if not key:
-            raise Exception('subscription_key is required')
-        
-        server_url = self.runtime.credentials.get('server_url', None)
+            raise Exception("subscription_key is required")
+
+        server_url = self.runtime.credentials.get("server_url", None)
         if not server_url:
             server_url = self.url
-        
-        query = tool_parameters.get('query')
+
+        query = tool_parameters.get("query")
         if not query:
-            raise Exception('query is required')
-        
-        limit = min(tool_parameters.get('limit', 5), 10)
-        result_type = tool_parameters.get('result_type', 'text') or 'text'
-        
-        market = tool_parameters.get('market', 'US')
-        lang = tool_parameters.get('language', 'en')
+            raise Exception("query is required")
+
+        limit = min(tool_parameters.get("limit", 5), 10)
+        result_type = tool_parameters.get("result_type", "text") or "text"
+
+        market = tool_parameters.get("market", "US")
+        lang = tool_parameters.get("language", "en")
         filter = []
 
-        if tool_parameters.get('enable_computation', False):
-            filter.append('Computation')
-        if tool_parameters.get('enable_entities', False):
-            filter.append('Entities')
-        if tool_parameters.get('enable_news', False):
-            filter.append('News')
-        if tool_parameters.get('enable_related_search', False):
-            filter.append('RelatedSearches')
-        if tool_parameters.get('enable_webpages', False):
-            filter.append('WebPages')
+        if tool_parameters.get("enable_computation", False):
+            filter.append("Computation")
+        if tool_parameters.get("enable_entities", False):
+            filter.append("Entities")
+        if tool_parameters.get("enable_news", False):
+            filter.append("News")
+        if tool_parameters.get("enable_related_search", False):
+            filter.append("RelatedSearches")
+        if tool_parameters.get("enable_webpages", False):
+            filter.append("WebPages")
 
         if not filter:
-            raise Exception('At least one filter is required')
-        
+            raise Exception("At least one filter is required")
+
         return self._invoke_bing(
             user_id=user_id,
             server_url=server_url,
@@ -204,5 +198,5 @@ class BingSearchTool(BuiltinTool):
             result_type=result_type,
             market=market,
             lang=lang,
-            filters=filter
-        )
\ No newline at end of file
+            filters=filter,
+        )
diff --git a/api/core/tools/provider/builtin/brave/brave.py b/api/core/tools/provider/builtin/brave/brave.py
index e5eada80ee..c24ee67334 100644
--- a/api/core/tools/provider/builtin/brave/brave.py
+++ b/api/core/tools/provider/builtin/brave/brave.py
@@ -13,11 +13,10 @@ class BraveProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "query": "Sachin Tendulkar",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/brave/brave.yaml b/api/core/tools/provider/builtin/brave/brave.yaml
index 93d315f839..2b0dcc0188 100644
--- a/api/core/tools/provider/builtin/brave/brave.yaml
+++ b/api/core/tools/provider/builtin/brave/brave.yaml
@@ -29,3 +29,11 @@ credentials_for_provider:
       zh_Hans: 从 Brave 获取您的 Brave Search API key
       pt_BR: Get your Brave Search API key from Brave
     url: https://brave.com/search/api/
+  base_url:
+    type: text-input
+    required: false
+    label:
+      en_US: Brave server's Base URL
+      zh_Hans: Brave服务器的API URL
+    placeholder:
+      en_US: https://api.search.brave.com/res/v1/web/search
diff --git a/api/core/tools/provider/builtin/brave/tools/brave_search.py b/api/core/tools/provider/builtin/brave/tools/brave_search.py
index 21cbf2c7da..c34362ae52 100644
--- a/api/core/tools/provider/builtin/brave/tools/brave_search.py
+++ b/api/core/tools/provider/builtin/brave/tools/brave_search.py
@@ -7,6 +7,8 @@ from pydantic import BaseModel, Field
 from core.tools.entities.tool_entities import ToolInvokeMessage
 from core.tools.tool.builtin_tool import BuiltinTool
 
+BRAVE_BASE_URL = "https://api.search.brave.com/res/v1/web/search"
+
 
 class BraveSearchWrapper(BaseModel):
     """Wrapper around the Brave search engine."""
@@ -15,8 +17,10 @@ class BraveSearchWrapper(BaseModel):
     """The API key to use for the Brave search engine."""
     search_kwargs: dict = Field(default_factory=dict)
     """Additional keyword arguments to pass to the search request."""
-    base_url: str = "https://api.search.brave.com/res/v1/web/search"
+    base_url: str = BRAVE_BASE_URL
     """The base URL for the Brave search engine."""
+    ensure_ascii: bool = True
+    """Ensure the JSON output is ASCII encoded."""
 
     def run(self, query: str) -> str:
         """Query the Brave search engine and return the results as a JSON string.
@@ -36,8 +40,8 @@ class BraveSearchWrapper(BaseModel):
             }
             for item in web_search_results
         ]
-        return json.dumps(final_results)
-    
+        return json.dumps(final_results, ensure_ascii=self.ensure_ascii)
+
     def _search_request(self, query: str) -> list[dict]:
         headers = {
             "X-Subscription-Token": self.api_key,
@@ -55,6 +59,7 @@ class BraveSearchWrapper(BaseModel):
 
         return response.json().get("web", {}).get("results", [])
 
+
 class BraveSearch(BaseModel):
     """Tool that queries the BraveSearch."""
 
@@ -68,7 +73,7 @@ class BraveSearch(BaseModel):
 
     @classmethod
     def from_api_key(
-        cls, api_key: str, search_kwargs: Optional[dict] = None, **kwargs: Any
+        cls, api_key: str, base_url: str, search_kwargs: Optional[dict] = None, ensure_ascii: bool = True, **kwargs: Any
     ) -> "BraveSearch":
         """Create a tool from an api key.
 
@@ -80,7 +85,9 @@ class BraveSearch(BaseModel):
         Returns:
             A tool.
         """
-        wrapper = BraveSearchWrapper(api_key=api_key, search_kwargs=search_kwargs or {})
+        wrapper = BraveSearchWrapper(
+            api_key=api_key, base_url=base_url, search_kwargs=search_kwargs or {}, ensure_ascii=ensure_ascii
+        )
         return cls(search_wrapper=wrapper, **kwargs)
 
     def _run(
@@ -90,6 +97,7 @@ class BraveSearch(BaseModel):
         """Use the tool."""
         return self.search_wrapper.run(query)
 
+
 class BraveSearchTool(BuiltinTool):
     """
     Tool for performing a search using Brave search engine.
@@ -106,14 +114,21 @@ class BraveSearchTool(BuiltinTool):
         Returns:
             ToolInvokeMessage | list[ToolInvokeMessage]: The result of the tool invocation.
         """
-        query = tool_parameters.get('query', '')
-        count = tool_parameters.get('count', 3)
-        api_key = self.runtime.credentials['brave_search_api_key']
+        query = tool_parameters.get("query", "")
+        count = tool_parameters.get("count", 3)
+        api_key = self.runtime.credentials["brave_search_api_key"]
+        base_url = self.runtime.credentials.get("base_url", BRAVE_BASE_URL)
+        ensure_ascii = tool_parameters.get("ensure_ascii", True)
+
+        if len(base_url) == 0:
+            base_url = BRAVE_BASE_URL
 
         if not query:
-            return self.create_text_message('Please input query')
+            return self.create_text_message("Please input query")
 
-        tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={"count": count})
+        tool = BraveSearch.from_api_key(
+            api_key=api_key, base_url=base_url, search_kwargs={"count": count}, ensure_ascii=ensure_ascii
+        )
 
         results = tool._run(query)
 
@@ -121,4 +136,3 @@ class BraveSearchTool(BuiltinTool):
             return self.create_text_message(f"No results found for '{query}' in Tavily")
         else:
             return self.create_text_message(text=results)
-
diff --git a/api/core/tools/provider/builtin/brave/tools/brave_search.yaml b/api/core/tools/provider/builtin/brave/tools/brave_search.yaml
index b2a734c12d..5222a375f8 100644
--- a/api/core/tools/provider/builtin/brave/tools/brave_search.yaml
+++ b/api/core/tools/provider/builtin/brave/tools/brave_search.yaml
@@ -39,3 +39,15 @@ parameters:
       pt_BR: O número de resultados de pesquisa a serem retornados, permitindo que os usuários controlem a amplitude de sua saída de pesquisa.
     llm_description: Specifies the amount of search results to be displayed, offering users the ability to adjust the scope of their search findings.
     form: llm
+  - name: ensure_ascii
+    type: boolean
+    default: true
+    label:
+      en_US: Ensure ASCII
+      zh_Hans: 确保 ASCII
+      pt_BR: Ensure ASCII
+    human_description:
+      en_US: Ensure the JSON output is ASCII encoded
+      zh_Hans: 确保输出的 JSON 是 ASCII 编码
+      pt_BR: Ensure the JSON output is ASCII encoded
+    form: form
diff --git a/api/core/tools/provider/builtin/chart/chart.py b/api/core/tools/provider/builtin/chart/chart.py
index 0865bc700a..8a24d33428 100644
--- a/api/core/tools/provider/builtin/chart/chart.py
+++ b/api/core/tools/provider/builtin/chart/chart.py
@@ -7,16 +7,34 @@ from core.tools.provider.builtin.chart.tools.line import LinearChartTool
 from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
 
 # use a business theme
-plt.style.use('seaborn-v0_8-darkgrid')
-plt.rcParams['axes.unicode_minus'] = False
+plt.style.use("seaborn-v0_8-darkgrid")
+plt.rcParams["axes.unicode_minus"] = False
+
 
 def init_fonts():
     fonts = findSystemFonts()
 
     popular_unicode_fonts = [
-        'Arial Unicode MS', 'DejaVu Sans', 'DejaVu Sans Mono', 'DejaVu Serif', 'FreeMono', 'FreeSans', 'FreeSerif',
-        'Liberation Mono', 'Liberation Sans', 'Liberation Serif', 'Noto Mono', 'Noto Sans', 'Noto Serif', 'Open Sans',
-        'Roboto', 'Source Code Pro', 'Source Sans Pro', 'Source Serif Pro', 'Ubuntu', 'Ubuntu Mono'
+        "Arial Unicode MS",
+        "DejaVu Sans",
+        "DejaVu Sans Mono",
+        "DejaVu Serif",
+        "FreeMono",
+        "FreeSans",
+        "FreeSerif",
+        "Liberation Mono",
+        "Liberation Sans",
+        "Liberation Serif",
+        "Noto Mono",
+        "Noto Sans",
+        "Noto Serif",
+        "Open Sans",
+        "Roboto",
+        "Source Code Pro",
+        "Source Sans Pro",
+        "Source Serif Pro",
+        "Ubuntu",
+        "Ubuntu Mono",
     ]
 
     supported_fonts = []
@@ -25,21 +43,23 @@ def init_fonts():
         try:
             font = TTFont(font_path)
             # get family name
-            family_name = font['name'].getName(1, 3, 1).toUnicode()
+            family_name = font["name"].getName(1, 3, 1).toUnicode()
             if family_name in popular_unicode_fonts:
                 supported_fonts.append(family_name)
         except:
             pass
 
-    plt.rcParams['font.family'] = 'sans-serif'
+    plt.rcParams["font.family"] = "sans-serif"
     # sort by order of popular_unicode_fonts
     for font in popular_unicode_fonts:
         if font in supported_fonts:
-            plt.rcParams['font.sans-serif'] = font
+            plt.rcParams["font.sans-serif"] = font
             break
-    
+
+
 init_fonts()
 
+
 class ChartProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
         try:
@@ -48,11 +68,10 @@ class ChartProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "data": "1,3,5,7,9,2,4,6,8,10",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/chart/tools/bar.py b/api/core/tools/provider/builtin/chart/tools/bar.py
index 749ec761c6..3a47c0cfc0 100644
--- a/api/core/tools/provider/builtin/chart/tools/bar.py
+++ b/api/core/tools/provider/builtin/chart/tools/bar.py
@@ -8,12 +8,13 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class BarChartTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) \
-          -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        data = tool_parameters.get('data', '')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        data = tool_parameters.get("data", "")
         if not data:
-            return self.create_text_message('Please input data')
-        data = data.split(';')
+            return self.create_text_message("Please input data")
+        data = data.split(";")
 
         # if all data is int, convert to int
         if all(i.isdigit() for i in data):
@@ -21,29 +22,27 @@ class BarChartTool(BuiltinTool):
         else:
             data = [float(i) for i in data]
 
-        axis = tool_parameters.get('x_axis') or None
+        axis = tool_parameters.get("x_axis") or None
         if axis:
-            axis = axis.split(';')
+            axis = axis.split(";")
             if len(axis) != len(data):
                 axis = None
 
         flg, ax = plt.subplots(figsize=(10, 8))
 
         if axis:
-            axis = [label[:10] + '...' if len(label) > 10 else label for label in axis]
-            ax.set_xticklabels(axis, rotation=45, ha='right')
+            axis = [label[:10] + "..." if len(label) > 10 else label for label in axis]
+            ax.set_xticklabels(axis, rotation=45, ha="right")
             ax.bar(axis, data)
         else:
             ax.bar(range(len(data)), data)
 
         buf = io.BytesIO()
-        flg.savefig(buf, format='png')
+        flg.savefig(buf, format="png")
         buf.seek(0)
         plt.close(flg)
 
         return [
-            self.create_text_message('the bar chart is saved as an image.'),
-            self.create_blob_message(blob=buf.read(),
-                                    meta={'mime_type': 'image/png'})
+            self.create_text_message("the bar chart is saved as an image."),
+            self.create_blob_message(blob=buf.read(), meta={"mime_type": "image/png"}),
         ]
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/chart/tools/line.py b/api/core/tools/provider/builtin/chart/tools/line.py
index 608bd6623c..39e8caac7e 100644
--- a/api/core/tools/provider/builtin/chart/tools/line.py
+++ b/api/core/tools/provider/builtin/chart/tools/line.py
@@ -8,18 +8,19 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class LinearChartTool(BuiltinTool):
-    def _invoke(self, 
-                user_id: str, 
-               tool_parameters: dict[str, Any], 
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        data = tool_parameters.get('data', '')
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        data = tool_parameters.get("data", "")
         if not data:
-            return self.create_text_message('Please input data')
-        data = data.split(';')
+            return self.create_text_message("Please input data")
+        data = data.split(";")
 
-        axis = tool_parameters.get('x_axis') or None
+        axis = tool_parameters.get("x_axis") or None
         if axis:
-            axis = axis.split(';')
+            axis = axis.split(";")
             if len(axis) != len(data):
                 axis = None
 
@@ -32,20 +33,18 @@ class LinearChartTool(BuiltinTool):
         flg, ax = plt.subplots(figsize=(10, 8))
 
         if axis:
-            axis = [label[:10] + '...' if len(label) > 10 else label for label in axis]
-            ax.set_xticklabels(axis, rotation=45, ha='right')
+            axis = [label[:10] + "..." if len(label) > 10 else label for label in axis]
+            ax.set_xticklabels(axis, rotation=45, ha="right")
             ax.plot(axis, data)
         else:
             ax.plot(data)
 
         buf = io.BytesIO()
-        flg.savefig(buf, format='png')
+        flg.savefig(buf, format="png")
         buf.seek(0)
         plt.close(flg)
 
         return [
-            self.create_text_message('the linear chart is saved as an image.'),
-            self.create_blob_message(blob=buf.read(),
-                                    meta={'mime_type': 'image/png'})
+            self.create_text_message("the linear chart is saved as an image."),
+            self.create_blob_message(blob=buf.read(), meta={"mime_type": "image/png"}),
         ]
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/chart/tools/pie.py b/api/core/tools/provider/builtin/chart/tools/pie.py
index 4c551229e9..2c3b8a733e 100644
--- a/api/core/tools/provider/builtin/chart/tools/pie.py
+++ b/api/core/tools/provider/builtin/chart/tools/pie.py
@@ -8,15 +8,16 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class PieChartTool(BuiltinTool):
-    def _invoke(self, 
-                user_id: str, 
-               tool_parameters: dict[str, Any], 
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        data = tool_parameters.get('data', '')
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        data = tool_parameters.get("data", "")
         if not data:
-            return self.create_text_message('Please input data')
-        data = data.split(';')
-        categories = tool_parameters.get('categories') or None
+            return self.create_text_message("Please input data")
+        data = data.split(";")
+        categories = tool_parameters.get("categories") or None
 
         # if all data is int, convert to int
         if all(i.isdigit() for i in data):
@@ -27,7 +28,7 @@ class PieChartTool(BuiltinTool):
         flg, ax = plt.subplots()
 
         if categories:
-            categories = categories.split(';')
+            categories = categories.split(";")
             if len(categories) != len(data):
                 categories = None
 
@@ -37,12 +38,11 @@ class PieChartTool(BuiltinTool):
             ax.pie(data)
 
         buf = io.BytesIO()
-        flg.savefig(buf, format='png')
+        flg.savefig(buf, format="png")
         buf.seek(0)
         plt.close(flg)
 
         return [
-            self.create_text_message('the pie chart is saved as an image.'),
-            self.create_blob_message(blob=buf.read(),
-                                    meta={'mime_type': 'image/png'})
-        ]
\ No newline at end of file
+            self.create_text_message("the pie chart is saved as an image."),
+            self.create_blob_message(blob=buf.read(), meta={"mime_type": "image/png"}),
+        ]
diff --git a/api/core/tools/provider/builtin/code/tools/simple_code.py b/api/core/tools/provider/builtin/code/tools/simple_code.py
index 37645bf0d0..632c9fc7f1 100644
--- a/api/core/tools/provider/builtin/code/tools/simple_code.py
+++ b/api/core/tools/provider/builtin/code/tools/simple_code.py
@@ -8,15 +8,15 @@ from core.tools.tool.builtin_tool import BuiltinTool
 class SimpleCode(BuiltinTool):
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage | list[ToolInvokeMessage]:
         """
-            invoke simple code
+        invoke simple code
         """
 
-        language = tool_parameters.get('language', CodeLanguage.PYTHON3)
-        code = tool_parameters.get('code', '')
+        language = tool_parameters.get("language", CodeLanguage.PYTHON3)
+        code = tool_parameters.get("code", "")
 
-        if language not in [CodeLanguage.PYTHON3, CodeLanguage.JAVASCRIPT]:
-            raise ValueError(f'Only python3 and javascript are supported, not {language}')
-        
-        result = CodeExecutor.execute_code(language, '', code)
+        if language not in {CodeLanguage.PYTHON3, CodeLanguage.JAVASCRIPT}:
+            raise ValueError(f"Only python3 and javascript are supported, not {language}")
 
-        return self.create_text_message(result)
\ No newline at end of file
+        result = CodeExecutor.execute_code(language, "", code)
+
+        return self.create_text_message(result)
diff --git a/api/core/tools/provider/builtin/cogview/cogview.py b/api/core/tools/provider/builtin/cogview/cogview.py
index 801817ec06..6941ce8649 100644
--- a/api/core/tools/provider/builtin/cogview/cogview.py
+++ b/api/core/tools/provider/builtin/cogview/cogview.py
@@ -1,4 +1,5 @@
-""" Provide the input parameters type for the cogview provider class """
+"""Provide the input parameters type for the cogview provider class"""
+
 from typing import Any
 
 from core.tools.errors import ToolProviderCredentialValidationError
@@ -7,7 +8,8 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 
 
 class COGVIEWProvider(BuiltinToolProviderController):
-    """ cogview provider """
+    """cogview provider"""
+
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
             CogView3Tool().fork_tool_runtime(
@@ -15,13 +17,12 @@ class COGVIEWProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "prompt": "一个城市在水晶瓶中欢快生活的场景，水彩画风格，展现出微观与珠宝般的美丽。",
                     "size": "square",
-                    "n": 1
+                    "n": 1,
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e)) from e
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/cogview/tools/cogview3.py b/api/core/tools/provider/builtin/cogview/tools/cogview3.py
index 89ffcf3347..9039708588 100644
--- a/api/core/tools/provider/builtin/cogview/tools/cogview3.py
+++ b/api/core/tools/provider/builtin/cogview/tools/cogview3.py
@@ -7,43 +7,42 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class CogView3Tool(BuiltinTool):
-    """ CogView3 Tool """
+    """CogView3 Tool"""
 
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
         Invoke CogView3 tool
         """
         client = ZhipuAI(
-            base_url=self.runtime.credentials['zhipuai_base_url'],
-            api_key=self.runtime.credentials['zhipuai_api_key'],
+            base_url=self.runtime.credentials["zhipuai_base_url"],
+            api_key=self.runtime.credentials["zhipuai_api_key"],
         )
         size_mapping = {
-            'square': '1024x1024',
-            'vertical': '1024x1792',
-            'horizontal': '1792x1024',
+            "square": "1024x1024",
+            "vertical": "1024x1792",
+            "horizontal": "1792x1024",
         }
         # prompt
-        prompt = tool_parameters.get('prompt', '')
+        prompt = tool_parameters.get("prompt", "")
         if not prompt:
-            return self.create_text_message('Please input prompt')
+            return self.create_text_message("Please input prompt")
         # get size
-        size = size_mapping[tool_parameters.get('size', 'square')]
+        size = size_mapping[tool_parameters.get("size", "square")]
         # get n
-        n = tool_parameters.get('n', 1)
+        n = tool_parameters.get("n", 1)
         # get quality
-        quality = tool_parameters.get('quality', 'standard')
-        if quality not in ['standard', 'hd']:
-            return self.create_text_message('Invalid quality')
+        quality = tool_parameters.get("quality", "standard")
+        if quality not in {"standard", "hd"}:
+            return self.create_text_message("Invalid quality")
         # get style
-        style = tool_parameters.get('style', 'vivid')
-        if style not in ['natural', 'vivid']:
-            return self.create_text_message('Invalid style')
+        style = tool_parameters.get("style", "vivid")
+        if style not in {"natural", "vivid"}:
+            return self.create_text_message("Invalid style")
         # set extra body
-        seed_id = tool_parameters.get('seed_id', self._generate_random_id(8))
-        extra_body = {'seed': seed_id}
+        seed_id = tool_parameters.get("seed_id", self._generate_random_id(8))
+        extra_body = {"seed": seed_id}
         response = client.images.generations(
             prompt=prompt,
             model="cogview-3",
@@ -52,18 +51,22 @@ class CogView3Tool(BuiltinTool):
             extra_body=extra_body,
             style=style,
             quality=quality,
-            response_format='b64_json'
+            response_format="b64_json",
         )
         result = []
         for image in response.data:
             result.append(self.create_image_message(image=image.url))
-            result.append(self.create_json_message({
-                "url": image.url,
-            }))
+            result.append(
+                self.create_json_message(
+                    {
+                        "url": image.url,
+                    }
+                )
+            )
         return result
 
     @staticmethod
     def _generate_random_id(length=8):
-        characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'
-        random_id = ''.join(random.choices(characters, k=length))
+        characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"
+        random_id = "".join(random.choices(characters, k=length))
         return random_id
diff --git a/api/core/tools/provider/builtin/comfyui/_assets/icon.png b/api/core/tools/provider/builtin/comfyui/_assets/icon.png
new file mode 100644
index 0000000000..958ec5c5cf
Binary files /dev/null and b/api/core/tools/provider/builtin/comfyui/_assets/icon.png differ
diff --git a/api/core/tools/provider/builtin/comfyui/comfyui.py b/api/core/tools/provider/builtin/comfyui/comfyui.py
new file mode 100644
index 0000000000..7013a0b93c
--- /dev/null
+++ b/api/core/tools/provider/builtin/comfyui/comfyui.py
@@ -0,0 +1,17 @@
+from typing import Any
+
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.provider.builtin.comfyui.tools.comfyui_stable_diffusion import ComfyuiStableDiffusionTool
+from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
+
+
+class ComfyUIProvider(BuiltinToolProviderController):
+    def _validate_credentials(self, credentials: dict[str, Any]) -> None:
+        try:
+            ComfyuiStableDiffusionTool().fork_tool_runtime(
+                runtime={
+                    "credentials": credentials,
+                }
+            ).validate_models()
+        except Exception as e:
+            raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/comfyui/comfyui.yaml b/api/core/tools/provider/builtin/comfyui/comfyui.yaml
new file mode 100644
index 0000000000..066fd85308
--- /dev/null
+++ b/api/core/tools/provider/builtin/comfyui/comfyui.yaml
@@ -0,0 +1,42 @@
+identity:
+  author: Qun
+  name: comfyui
+  label:
+    en_US: ComfyUI
+    zh_Hans: ComfyUI
+    pt_BR: ComfyUI
+  description:
+    en_US: ComfyUI is a tool for generating images which can be deployed locally.
+    zh_Hans: ComfyUI 是一个可以在本地部署的图片生成的工具。
+    pt_BR: ComfyUI is a tool for generating images which can be deployed locally.
+  icon: icon.png
+  tags:
+    - image
+credentials_for_provider:
+  base_url:
+    type: text-input
+    required: true
+    label:
+      en_US: Base URL
+      zh_Hans: ComfyUI服务器的Base URL
+      pt_BR: Base URL
+    placeholder:
+      en_US: Please input your ComfyUI server's Base URL
+      zh_Hans: 请输入你的 ComfyUI 服务器的 Base URL
+      pt_BR: Please input your ComfyUI server's Base URL
+  model:
+    type: text-input
+    required: true
+    label:
+      en_US: Model with suffix
+      zh_Hans: 模型, 需要带后缀
+      pt_BR: Model with suffix
+    placeholder:
+      en_US: Please input your model
+      zh_Hans: 请输入你的模型名称
+      pt_BR: Please input your model
+    help:
+      en_US: The checkpoint name of the ComfyUI server, e.g. xxx.safetensors
+      zh_Hans: ComfyUI服务器的模型名称, 比如 xxx.safetensors
+      pt_BR: The checkpoint name of the ComfyUI server, e.g. xxx.safetensors
+    url: https://docs.dify.ai/tutorials/tool-configuration/comfyui
diff --git a/api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.py b/api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.py
new file mode 100644
index 0000000000..b9b52c0b4d
--- /dev/null
+++ b/api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.py
@@ -0,0 +1,475 @@
+import json
+import os
+import random
+import uuid
+from copy import deepcopy
+from enum import Enum
+from typing import Any, Union
+
+import websocket
+from httpx import get, post
+from yarl import URL
+
+from core.tools.entities.common_entities import I18nObject
+from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter, ToolParameterOption
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.tool.builtin_tool import BuiltinTool
+
+SD_TXT2IMG_OPTIONS = {}
+LORA_NODE = {
+    "inputs": {"lora_name": "", "strength_model": 1, "strength_clip": 1, "model": ["11", 0], "clip": ["11", 1]},
+    "class_type": "LoraLoader",
+    "_meta": {"title": "Load LoRA"},
+}
+FluxGuidanceNode = {
+    "inputs": {"guidance": 3.5, "conditioning": ["6", 0]},
+    "class_type": "FluxGuidance",
+    "_meta": {"title": "FluxGuidance"},
+}
+
+
+class ModelType(Enum):
+    SD15 = 1
+    SDXL = 2
+    SD3 = 3
+    FLUX = 4
+
+
+class ComfyuiStableDiffusionTool(BuiltinTool):
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        invoke tools
+        """
+        # base url
+        base_url = self.runtime.credentials.get("base_url", "")
+        if not base_url:
+            return self.create_text_message("Please input base_url")
+
+        if tool_parameters.get("model"):
+            self.runtime.credentials["model"] = tool_parameters["model"]
+
+        model = self.runtime.credentials.get("model", None)
+        if not model:
+            return self.create_text_message("Please input model")
+
+        # prompt
+        prompt = tool_parameters.get("prompt", "")
+        if not prompt:
+            return self.create_text_message("Please input prompt")
+
+        # get negative prompt
+        negative_prompt = tool_parameters.get("negative_prompt", "")
+
+        # get size
+        width = tool_parameters.get("width", 1024)
+        height = tool_parameters.get("height", 1024)
+
+        # get steps
+        steps = tool_parameters.get("steps", 1)
+
+        # get sampler_name
+        sampler_name = tool_parameters.get("sampler_name", "euler")
+
+        # scheduler
+        scheduler = tool_parameters.get("scheduler", "normal")
+
+        # get cfg
+        cfg = tool_parameters.get("cfg", 7.0)
+
+        # get model type
+        model_type = tool_parameters.get("model_type", ModelType.SD15.name)
+
+        # get lora
+        # supports up to 3 loras
+        lora_list = []
+        lora_strength_list = []
+        if tool_parameters.get("lora_1"):
+            lora_list.append(tool_parameters["lora_1"])
+            lora_strength_list.append(tool_parameters.get("lora_strength_1", 1))
+        if tool_parameters.get("lora_2"):
+            lora_list.append(tool_parameters["lora_2"])
+            lora_strength_list.append(tool_parameters.get("lora_strength_2", 1))
+        if tool_parameters.get("lora_3"):
+            lora_list.append(tool_parameters["lora_3"])
+            lora_strength_list.append(tool_parameters.get("lora_strength_3", 1))
+
+        return self.text2img(
+            base_url=base_url,
+            model=model,
+            model_type=model_type,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            width=width,
+            height=height,
+            steps=steps,
+            sampler_name=sampler_name,
+            scheduler=scheduler,
+            cfg=cfg,
+            lora_list=lora_list,
+            lora_strength_list=lora_strength_list,
+        )
+
+    def get_checkpoints(self) -> list[str]:
+        """
+        get checkpoints
+        """
+        try:
+            base_url = self.runtime.credentials.get("base_url", None)
+            if not base_url:
+                return []
+            api_url = str(URL(base_url) / "models" / "checkpoints")
+            response = get(url=api_url, timeout=(2, 10))
+            if response.status_code != 200:
+                return []
+            else:
+                return response.json()
+        except Exception as e:
+            return []
+
+    def get_loras(self) -> list[str]:
+        """
+        get loras
+        """
+        try:
+            base_url = self.runtime.credentials.get("base_url", None)
+            if not base_url:
+                return []
+            api_url = str(URL(base_url) / "models" / "loras")
+            response = get(url=api_url, timeout=(2, 10))
+            if response.status_code != 200:
+                return []
+            else:
+                return response.json()
+        except Exception as e:
+            return []
+
+    def get_sample_methods(self) -> tuple[list[str], list[str]]:
+        """
+        get sample method
+        """
+        try:
+            base_url = self.runtime.credentials.get("base_url", None)
+            if not base_url:
+                return [], []
+            api_url = str(URL(base_url) / "object_info" / "KSampler")
+            response = get(url=api_url, timeout=(2, 10))
+            if response.status_code != 200:
+                return [], []
+            else:
+                data = response.json()["KSampler"]["input"]["required"]
+                return data["sampler_name"][0], data["scheduler"][0]
+        except Exception as e:
+            return [], []
+
+    def validate_models(self) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        validate models
+        """
+        try:
+            base_url = self.runtime.credentials.get("base_url", None)
+            if not base_url:
+                raise ToolProviderCredentialValidationError("Please input base_url")
+            model = self.runtime.credentials.get("model", None)
+            if not model:
+                raise ToolProviderCredentialValidationError("Please input model")
+
+            api_url = str(URL(base_url) / "models" / "checkpoints")
+            response = get(url=api_url, timeout=(2, 10))
+            if response.status_code != 200:
+                raise ToolProviderCredentialValidationError("Failed to get models")
+            else:
+                models = response.json()
+                if len([d for d in models if d == model]) > 0:
+                    return self.create_text_message(json.dumps(models))
+                else:
+                    raise ToolProviderCredentialValidationError(f"model {model} does not exist")
+        except Exception as e:
+            raise ToolProviderCredentialValidationError(f"Failed to get models, {e}")
+
+    def get_history(self, base_url, prompt_id):
+        """
+        get history
+        """
+        url = str(URL(base_url) / "history")
+        respond = get(url, params={"prompt_id": prompt_id}, timeout=(2, 10))
+        return respond.json()
+
+    def download_image(self, base_url, filename, subfolder, folder_type):
+        """
+        download image
+        """
+        url = str(URL(base_url) / "view")
+        response = get(url, params={"filename": filename, "subfolder": subfolder, "type": folder_type}, timeout=(2, 10))
+        return response.content
+
+    def queue_prompt_image(self, base_url, client_id, prompt):
+        """
+        send prompt task and rotate
+        """
+        # initiate task execution
+        url = str(URL(base_url) / "prompt")
+        respond = post(url, data=json.dumps({"client_id": client_id, "prompt": prompt}), timeout=(2, 10))
+        prompt_id = respond.json()["prompt_id"]
+
+        ws = websocket.WebSocket()
+        if "https" in base_url:
+            ws_url = base_url.replace("https", "ws")
+        else:
+            ws_url = base_url.replace("http", "ws")
+        ws.connect(str(URL(f"{ws_url}") / "ws") + f"?clientId={client_id}", timeout=120)
+
+        # websocket rotate execution status
+        output_images = {}
+        while True:
+            out = ws.recv()
+            if isinstance(out, str):
+                message = json.loads(out)
+                if message["type"] == "executing":
+                    data = message["data"]
+                    if data["node"] is None and data["prompt_id"] == prompt_id:
+                        break  # Execution is done
+                elif message["type"] == "status":
+                    data = message["data"]
+                    if data["status"]["exec_info"]["queue_remaining"] == 0 and data.get("sid"):
+                        break  # Execution is done
+            else:
+                continue  # previews are binary data
+
+        # download image when execution finished
+        history = self.get_history(base_url, prompt_id)[prompt_id]
+        for o in history["outputs"]:
+            for node_id in history["outputs"]:
+                node_output = history["outputs"][node_id]
+                if "images" in node_output:
+                    images_output = []
+                    for image in node_output["images"]:
+                        image_data = self.download_image(base_url, image["filename"], image["subfolder"], image["type"])
+                        images_output.append(image_data)
+                    output_images[node_id] = images_output
+
+        ws.close()
+
+        return output_images
+
+    def text2img(
+        self,
+        base_url: str,
+        model: str,
+        model_type: str,
+        prompt: str,
+        negative_prompt: str,
+        width: int,
+        height: int,
+        steps: int,
+        sampler_name: str,
+        scheduler: str,
+        cfg: float,
+        lora_list: list,
+        lora_strength_list: list,
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        generate image
+        """
+        if not SD_TXT2IMG_OPTIONS:
+            current_dir = os.path.dirname(os.path.realpath(__file__))
+            with open(os.path.join(current_dir, "txt2img.json")) as file:
+                SD_TXT2IMG_OPTIONS.update(json.load(file))
+
+        draw_options = deepcopy(SD_TXT2IMG_OPTIONS)
+        draw_options["3"]["inputs"]["steps"] = steps
+        draw_options["3"]["inputs"]["sampler_name"] = sampler_name
+        draw_options["3"]["inputs"]["scheduler"] = scheduler
+        draw_options["3"]["inputs"]["cfg"] = cfg
+        # generate different image when using same prompt next time
+        draw_options["3"]["inputs"]["seed"] = random.randint(0, 100000000)
+        draw_options["4"]["inputs"]["ckpt_name"] = model
+        draw_options["5"]["inputs"]["width"] = width
+        draw_options["5"]["inputs"]["height"] = height
+        draw_options["6"]["inputs"]["text"] = prompt
+        draw_options["7"]["inputs"]["text"] = negative_prompt
+        # if the model is SD3 or FLUX series, the Latent class should be corresponding to SD3 Latent
+        if model_type in (ModelType.SD3.name, ModelType.FLUX.name):
+            draw_options["5"]["class_type"] = "EmptySD3LatentImage"
+
+        if lora_list:
+            # last Lora node link to KSampler node
+            draw_options["3"]["inputs"]["model"][0] = "10"
+            # last Lora node link to positive and negative Clip node
+            draw_options["6"]["inputs"]["clip"][0] = "10"
+            draw_options["7"]["inputs"]["clip"][0] = "10"
+            # every Lora node link to next Lora node, and Checkpoints node link to first Lora node
+            for i, (lora, strength) in enumerate(zip(lora_list, lora_strength_list), 10):
+                if i - 10 == len(lora_list) - 1:
+                    next_node_id = "4"
+                else:
+                    next_node_id = str(i + 1)
+                lora_node = deepcopy(LORA_NODE)
+                lora_node["inputs"]["lora_name"] = lora
+                lora_node["inputs"]["strength_model"] = strength
+                lora_node["inputs"]["strength_clip"] = strength
+                lora_node["inputs"]["model"][0] = next_node_id
+                lora_node["inputs"]["clip"][0] = next_node_id
+                draw_options[str(i)] = lora_node
+
+        # FLUX need to add FluxGuidance Node
+        if model_type == ModelType.FLUX.name:
+            last_node_id = str(10 + len(lora_list))
+            draw_options[last_node_id] = deepcopy(FluxGuidanceNode)
+            draw_options[last_node_id]["inputs"]["conditioning"][0] = "6"
+            draw_options["3"]["inputs"]["positive"][0] = last_node_id
+
+        try:
+            client_id = str(uuid.uuid4())
+            result = self.queue_prompt_image(base_url, client_id, prompt=draw_options)
+
+            # get first image
+            image = b""
+            for node in result:
+                for img in result[node]:
+                    if img:
+                        image = img
+                        break
+
+            return self.create_blob_message(
+                blob=image, meta={"mime_type": "image/png"}, save_as=self.VARIABLE_KEY.IMAGE.value
+            )
+
+        except Exception as e:
+            return self.create_text_message(f"Failed to generate image: {str(e)}")
+
+    def get_runtime_parameters(self) -> list[ToolParameter]:
+        parameters = [
+            ToolParameter(
+                name="prompt",
+                label=I18nObject(en_US="Prompt", zh_Hans="Prompt"),
+                human_description=I18nObject(
+                    en_US="Image prompt, you can check the official documentation of Stable Diffusion",
+                    zh_Hans="图像提示词，您可以查看 Stable Diffusion 的官方文档",
+                ),
+                type=ToolParameter.ToolParameterType.STRING,
+                form=ToolParameter.ToolParameterForm.LLM,
+                llm_description="Image prompt of Stable Diffusion, you should describe the image "
+                "you want to generate as a list of words as possible as detailed, "
+                "the prompt must be written in English.",
+                required=True,
+            ),
+        ]
+        if self.runtime.credentials:
+            try:
+                models = self.get_checkpoints()
+                if len(models) != 0:
+                    parameters.append(
+                        ToolParameter(
+                            name="model",
+                            label=I18nObject(en_US="Model", zh_Hans="Model"),
+                            human_description=I18nObject(
+                                en_US="Model of Stable Diffusion or FLUX, "
+                                "you can check the official documentation of Stable Diffusion or FLUX",
+                                zh_Hans="Stable Diffusion 或者 FLUX 的模型，您可以查看 Stable Diffusion 的官方文档",
+                            ),
+                            type=ToolParameter.ToolParameterType.SELECT,
+                            form=ToolParameter.ToolParameterForm.FORM,
+                            llm_description="Model of Stable Diffusion or FLUX, "
+                            "you can check the official documentation of Stable Diffusion or FLUX",
+                            required=True,
+                            default=models[0],
+                            options=[
+                                ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i)) for i in models
+                            ],
+                        )
+                    )
+                loras = self.get_loras()
+                if len(loras) != 0:
+                    for n in range(1, 4):
+                        parameters.append(
+                            ToolParameter(
+                                name=f"lora_{n}",
+                                label=I18nObject(en_US=f"Lora {n}", zh_Hans=f"Lora {n}"),
+                                human_description=I18nObject(
+                                    en_US="Lora of Stable Diffusion, "
+                                    "you can check the official documentation of Stable Diffusion",
+                                    zh_Hans="Stable Diffusion 的 Lora 模型，您可以查看 Stable Diffusion 的官方文档",
+                                ),
+                                type=ToolParameter.ToolParameterType.SELECT,
+                                form=ToolParameter.ToolParameterForm.FORM,
+                                llm_description="Lora of Stable Diffusion, "
+                                "you can check the official documentation of "
+                                "Stable Diffusion",
+                                required=False,
+                                options=[
+                                    ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i)) for i in loras
+                                ],
+                            )
+                        )
+                sample_methods, schedulers = self.get_sample_methods()
+                if len(sample_methods) != 0:
+                    parameters.append(
+                        ToolParameter(
+                            name="sampler_name",
+                            label=I18nObject(en_US="Sampling method", zh_Hans="Sampling method"),
+                            human_description=I18nObject(
+                                en_US="Sampling method of Stable Diffusion, "
+                                "you can check the official documentation of Stable Diffusion",
+                                zh_Hans="Stable Diffusion 的Sampling method，您可以查看 Stable Diffusion 的官方文档",
+                            ),
+                            type=ToolParameter.ToolParameterType.SELECT,
+                            form=ToolParameter.ToolParameterForm.FORM,
+                            llm_description="Sampling method of Stable Diffusion, "
+                            "you can check the official documentation of Stable Diffusion",
+                            required=True,
+                            default=sample_methods[0],
+                            options=[
+                                ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i))
+                                for i in sample_methods
+                            ],
+                        )
+                    )
+                if len(schedulers) != 0:
+                    parameters.append(
+                        ToolParameter(
+                            name="scheduler",
+                            label=I18nObject(en_US="Scheduler", zh_Hans="Scheduler"),
+                            human_description=I18nObject(
+                                en_US="Scheduler of Stable Diffusion, "
+                                "you can check the official documentation of Stable Diffusion",
+                                zh_Hans="Stable Diffusion 的Scheduler，您可以查看 Stable Diffusion 的官方文档",
+                            ),
+                            type=ToolParameter.ToolParameterType.SELECT,
+                            form=ToolParameter.ToolParameterForm.FORM,
+                            llm_description="Scheduler of Stable Diffusion, "
+                            "you can check the official documentation of Stable Diffusion",
+                            required=True,
+                            default=schedulers[0],
+                            options=[
+                                ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i)) for i in schedulers
+                            ],
+                        )
+                    )
+                parameters.append(
+                    ToolParameter(
+                        name="model_type",
+                        label=I18nObject(en_US="Model Type", zh_Hans="Model Type"),
+                        human_description=I18nObject(
+                            en_US="Model Type of Stable Diffusion or Flux, "
+                            "you can check the official documentation of Stable Diffusion or Flux",
+                            zh_Hans="Stable Diffusion 或 FLUX 的模型类型，"
+                            "您可以查看 Stable Diffusion 或 Flux 的官方文档",
+                        ),
+                        type=ToolParameter.ToolParameterType.SELECT,
+                        form=ToolParameter.ToolParameterForm.FORM,
+                        llm_description="Model Type of Stable Diffusion or Flux, "
+                        "you can check the official documentation of Stable Diffusion or Flux",
+                        required=True,
+                        default=ModelType.SD15.name,
+                        options=[
+                            ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i))
+                            for i in ModelType.__members__
+                        ],
+                    )
+                )
+            except:
+                pass
+
+        return parameters
diff --git a/api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.yaml b/api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.yaml
new file mode 100644
index 0000000000..4f4a6942b3
--- /dev/null
+++ b/api/core/tools/provider/builtin/comfyui/tools/comfyui_stable_diffusion.yaml
@@ -0,0 +1,212 @@
+identity:
+  name: txt2img workflow
+  author: Qun
+  label:
+    en_US: Txt2Img Workflow
+    zh_Hans: Txt2Img Workflow
+    pt_BR: Txt2Img Workflow
+description:
+  human:
+    en_US: a pre-defined comfyui workflow that can use one model and up to 3 loras to generate images. Support SD1.5, SDXL, SD3 and FLUX which contain text encoders/clip, but does not support models that requires a triple clip loader.
+    zh_Hans: 一个预定义的 ComfyUI 工作流，可以使用一个模型和最多3个loras来生成图像。支持包含文本编码器/clip的SD1.5、SDXL、SD3和FLUX，但不支持需要clip加载器的模型。
+    pt_BR: a pre-defined comfyui workflow that can use one model and up to 3 loras to generate images. Support SD1.5, SDXL, SD3 and FLUX which contain text encoders/clip, but does not support models that requires a triple clip loader.
+  llm: draw the image you want based on your prompt.
+parameters:
+  - name: prompt
+    type: string
+    required: true
+    label:
+      en_US: Prompt
+      zh_Hans: 提示词
+      pt_BR: Prompt
+    human_description:
+      en_US: Image prompt, you can check the official documentation of Stable Diffusion or FLUX
+      zh_Hans: 图像提示词，您可以查看 Stable Diffusion 或者 FLUX 的官方文档
+      pt_BR: Image prompt, you can check the official documentation of Stable Diffusion or FLUX
+    llm_description: Image prompt of Stable Diffusion, you should describe the image you want to generate as a list of words as possible as detailed, the prompt must be written in English.
+    form: llm
+  - name: model
+    type: string
+    required: true
+    label:
+      en_US: Model Name
+      zh_Hans: 模型名称
+      pt_BR: Model Name
+    human_description:
+      en_US: Model Name
+      zh_Hans: 模型名称
+      pt_BR: Model Name
+    form: form
+  - name: model_type
+    type: string
+    required: true
+    label:
+      en_US: Model Type
+      zh_Hans: 模型类型
+      pt_BR: Model Type
+    human_description:
+      en_US: Model Type
+      zh_Hans: 模型类型
+      pt_BR: Model Type
+    form: form
+  - name: lora_1
+    type: string
+    required: false
+    label:
+      en_US: Lora 1
+      zh_Hans: Lora 1
+      pt_BR: Lora 1
+    human_description:
+      en_US: Lora 1
+      zh_Hans: Lora 1
+      pt_BR: Lora 1
+    form: form
+  - name: lora_strength_1
+    type: number
+    required: false
+    label:
+      en_US: Lora Strength 1
+      zh_Hans: Lora Strength 1
+      pt_BR: Lora Strength 1
+    human_description:
+      en_US: Lora Strength 1
+      zh_Hans: Lora模型的权重
+      pt_BR: Lora Strength 1
+    form: form
+  - name: steps
+    type: number
+    required: false
+    label:
+      en_US: Steps
+      zh_Hans: Steps
+      pt_BR: Steps
+    human_description:
+      en_US: Steps
+      zh_Hans: Steps
+      pt_BR: Steps
+    form: form
+    default: 20
+  - name: width
+    type: number
+    required: false
+    label:
+      en_US: Width
+      zh_Hans: Width
+      pt_BR: Width
+    human_description:
+      en_US: Width
+      zh_Hans: Width
+      pt_BR: Width
+    form: form
+    default: 1024
+  - name: height
+    type: number
+    required: false
+    label:
+      en_US: Height
+      zh_Hans: Height
+      pt_BR: Height
+    human_description:
+      en_US: Height
+      zh_Hans: Height
+      pt_BR: Height
+    form: form
+    default: 1024
+  - name: negative_prompt
+    type: string
+    required: false
+    label:
+      en_US: Negative prompt
+      zh_Hans: Negative prompt
+      pt_BR: Negative prompt
+    human_description:
+      en_US: Negative prompt
+      zh_Hans: Negative prompt
+      pt_BR: Negative prompt
+    form: form
+    default: bad art, ugly, deformed, watermark, duplicated, discontinuous lines
+  - name: cfg
+    type: number
+    required: false
+    label:
+      en_US: CFG Scale
+      zh_Hans: CFG Scale
+      pt_BR: CFG Scale
+    human_description:
+      en_US: CFG Scale
+      zh_Hans: 提示词相关性(CFG Scale)
+      pt_BR: CFG Scale
+    form: form
+    default: 7.0
+  - name: sampler_name
+    type: string
+    required: false
+    label:
+      en_US: Sampling method
+      zh_Hans: Sampling method
+      pt_BR: Sampling method
+    human_description:
+      en_US: Sampling method
+      zh_Hans: Sampling method
+      pt_BR: Sampling method
+    form: form
+  - name: scheduler
+    type: string
+    required: false
+    label:
+      en_US: Scheduler
+      zh_Hans: Scheduler
+      pt_BR: Scheduler
+    human_description:
+      en_US: Scheduler
+      zh_Hans: Scheduler
+      pt_BR: Scheduler
+    form: form
+  - name: lora_2
+    type: string
+    required: false
+    label:
+      en_US: Lora 2
+      zh_Hans: Lora 2
+      pt_BR: Lora 2
+    human_description:
+      en_US: Lora 2
+      zh_Hans: Lora 2
+      pt_BR: Lora 2
+    form: form
+  - name: lora_strength_2
+    type: number
+    required: false
+    label:
+      en_US: Lora Strength 2
+      zh_Hans: Lora Strength 2
+      pt_BR: Lora Strength 2
+    human_description:
+      en_US: Lora Strength 2
+      zh_Hans: Lora模型的权重
+      pt_BR: Lora Strength 2
+    form: form
+  - name: lora_3
+    type: string
+    required: false
+    label:
+      en_US: Lora 3
+      zh_Hans: Lora 3
+      pt_BR: Lora 3
+    human_description:
+      en_US: Lora 3
+      zh_Hans: Lora 3
+      pt_BR: Lora 3
+    form: form
+  - name: lora_strength_3
+    type: number
+    required: false
+    label:
+      en_US: Lora Strength 3
+      zh_Hans: Lora Strength 3
+      pt_BR: Lora Strength 3
+    human_description:
+      en_US: Lora Strength 3
+      zh_Hans: Lora模型的权重
+      pt_BR: Lora Strength 3
+    form: form
diff --git a/api/core/tools/provider/builtin/comfyui/tools/txt2img.json b/api/core/tools/provider/builtin/comfyui/tools/txt2img.json
new file mode 100644
index 0000000000..8ea869ff10
--- /dev/null
+++ b/api/core/tools/provider/builtin/comfyui/tools/txt2img.json
@@ -0,0 +1,107 @@
+{
+  "3": {
+    "inputs": {
+      "seed": 156680208700286,
+      "steps": 20,
+      "cfg": 8,
+      "sampler_name": "euler",
+      "scheduler": "normal",
+      "denoise": 1,
+      "model": [
+        "4",
+        0
+      ],
+      "positive": [
+        "6",
+        0
+      ],
+      "negative": [
+        "7",
+        0
+      ],
+      "latent_image": [
+        "5",
+        0
+      ]
+    },
+    "class_type": "KSampler",
+    "_meta": {
+      "title": "KSampler"
+    }
+  },
+  "4": {
+    "inputs": {
+      "ckpt_name": "3dAnimationDiffusion_v10.safetensors"
+    },
+    "class_type": "CheckpointLoaderSimple",
+    "_meta": {
+      "title": "Load Checkpoint"
+    }
+  },
+  "5": {
+    "inputs": {
+      "width": 512,
+      "height": 512,
+      "batch_size": 1
+    },
+    "class_type": "EmptyLatentImage",
+    "_meta": {
+      "title": "Empty Latent Image"
+    }
+  },
+  "6": {
+    "inputs": {
+      "text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
+      "clip": [
+        "4",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "7": {
+    "inputs": {
+      "text": "text, watermark",
+      "clip": [
+        "4",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "CLIP Text Encode (Prompt)"
+    }
+  },
+  "8": {
+    "inputs": {
+      "samples": [
+        "3",
+        0
+      ],
+      "vae": [
+        "4",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "9": {
+    "inputs": {
+      "filename_prefix": "ComfyUI",
+      "images": [
+        "8",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "Save Image"
+    }
+  }
+}
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/crossref/crossref.py b/api/core/tools/provider/builtin/crossref/crossref.py
index 404e483e0d..8ba3c1b48a 100644
--- a/api/core/tools/provider/builtin/crossref/crossref.py
+++ b/api/core/tools/provider/builtin/crossref/crossref.py
@@ -11,9 +11,9 @@ class CrossRefProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
-                    "doi": '10.1007/s00894-022-05373-8',
+                    "doi": "10.1007/s00894-022-05373-8",
                 },
             )
         except Exception as e:
diff --git a/api/core/tools/provider/builtin/crossref/tools/query_doi.py b/api/core/tools/provider/builtin/crossref/tools/query_doi.py
index a43c0989e4..746139dd69 100644
--- a/api/core/tools/provider/builtin/crossref/tools/query_doi.py
+++ b/api/core/tools/provider/builtin/crossref/tools/query_doi.py
@@ -11,15 +11,18 @@ class CrossRefQueryDOITool(BuiltinTool):
     """
     Tool for querying the metadata of a publication using its DOI.
     """
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        doi = tool_parameters.get('doi')
+
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        doi = tool_parameters.get("doi")
         if not doi:
-            raise ToolParameterValidationError('doi is required.')
+            raise ToolParameterValidationError("doi is required.")
         # doc: https://github.com/CrossRef/rest-api-doc
         url = f"https://api.crossref.org/works/{doi}"
         response = requests.get(url)
         response.raise_for_status()
         response = response.json()
-        message = response.get('message', {})
+        message = response.get("message", {})
 
         return self.create_json_message(message)
diff --git a/api/core/tools/provider/builtin/crossref/tools/query_title.py b/api/core/tools/provider/builtin/crossref/tools/query_title.py
index 946aa6dc94..e245238183 100644
--- a/api/core/tools/provider/builtin/crossref/tools/query_title.py
+++ b/api/core/tools/provider/builtin/crossref/tools/query_title.py
@@ -12,16 +12,16 @@ def convert_time_str_to_seconds(time_str: str) -> int:
     Convert a time string to seconds.
     example: 1s -> 1,  1m30s -> 90, 1h30m -> 5400, 1h30m30s -> 5430
     """
-    time_str = time_str.lower().strip().replace(' ', '')
+    time_str = time_str.lower().strip().replace(" ", "")
     seconds = 0
-    if 'h' in time_str:
-        hours, time_str = time_str.split('h')
+    if "h" in time_str:
+        hours, time_str = time_str.split("h")
         seconds += int(hours) * 3600
-    if 'm' in time_str:
-        minutes, time_str = time_str.split('m')
+    if "m" in time_str:
+        minutes, time_str = time_str.split("m")
         seconds += int(minutes) * 60
-    if 's' in time_str:
-        seconds += int(time_str.replace('s', ''))
+    if "s" in time_str:
+        seconds += int(time_str.replace("s", ""))
     return seconds
 
 
@@ -30,6 +30,7 @@ class CrossRefQueryTitleAPI:
     Tool for querying the metadata of a publication using its title.
     Crossref API doc: https://github.com/CrossRef/rest-api-doc
     """
+
     query_url_template: str = "https://api.crossref.org/works?query.bibliographic={query}&rows={rows}&offset={offset}&sort={sort}&order={order}&mailto={mailto}"
     rate_limit: int = 50
     rate_interval: float = 1
@@ -38,7 +39,15 @@ class CrossRefQueryTitleAPI:
     def __init__(self, mailto: str):
         self.mailto = mailto
 
-    def _query(self, query: str, rows: int = 5, offset: int = 0, sort: str = 'relevance', order: str = 'desc', fuzzy_query: bool = False) -> list[dict]:
+    def _query(
+        self,
+        query: str,
+        rows: int = 5,
+        offset: int = 0,
+        sort: str = "relevance",
+        order: str = "desc",
+        fuzzy_query: bool = False,
+    ) -> list[dict]:
         """
         Query the metadata of a publication using its title.
         :param query: the title of the publication
@@ -47,33 +56,37 @@ class CrossRefQueryTitleAPI:
         :param order: the sort order
         :param fuzzy_query: whether to return all items that match the query
         """
-        url = self.query_url_template.format(query=query, rows=rows, offset=offset, sort=sort, order=order, mailto=self.mailto)
+        url = self.query_url_template.format(
+            query=query, rows=rows, offset=offset, sort=sort, order=order, mailto=self.mailto
+        )
         response = requests.get(url)
         response.raise_for_status()
-        rate_limit = int(response.headers['x-ratelimit-limit'])
+        rate_limit = int(response.headers["x-ratelimit-limit"])
         # convert time string to seconds
-        rate_interval = convert_time_str_to_seconds(response.headers['x-ratelimit-interval'])
+        rate_interval = convert_time_str_to_seconds(response.headers["x-ratelimit-interval"])
 
         self.rate_limit = rate_limit
         self.rate_interval = rate_interval
 
         response = response.json()
-        if response['status'] != 'ok':
+        if response["status"] != "ok":
             return []
 
-        message = response['message']
+        message = response["message"]
         if fuzzy_query:
             # fuzzy query return all items
-            return message['items']
+            return message["items"]
         else:
-            for paper in message['items']:
-                title = paper['title'][0]
+            for paper in message["items"]:
+                title = paper["title"][0]
                 if title.lower() != query.lower():
                     continue
                 return [paper]
         return []
 
-    def query(self, query: str, rows: int = 5, sort: str = 'relevance', order: str = 'desc', fuzzy_query: bool = False) -> list[dict]:
+    def query(
+        self, query: str, rows: int = 5, sort: str = "relevance", order: str = "desc", fuzzy_query: bool = False
+    ) -> list[dict]:
         """
         Query the metadata of a publication using its title.
         :param query: the title of the publication
@@ -89,7 +102,14 @@ class CrossRefQueryTitleAPI:
             results = []
 
             for i in range(query_times):
-                result = self._query(query, rows=self.rate_limit, offset=i * self.rate_limit, sort=sort, order=order, fuzzy_query=fuzzy_query)
+                result = self._query(
+                    query,
+                    rows=self.rate_limit,
+                    offset=i * self.rate_limit,
+                    sort=sort,
+                    order=order,
+                    fuzzy_query=fuzzy_query,
+                )
                 if fuzzy_query:
                     results.extend(result)
                 else:
@@ -107,13 +127,16 @@ class CrossRefQueryTitleTool(BuiltinTool):
     """
     Tool for querying the metadata of a publication using its title.
     """
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        query = tool_parameters.get('query')
-        fuzzy_query = tool_parameters.get('fuzzy_query', False)
-        rows = tool_parameters.get('rows', 3)
-        sort = tool_parameters.get('sort', 'relevance')
-        order = tool_parameters.get('order', 'desc')
-        mailto = self.runtime.credentials['mailto']
+
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        query = tool_parameters.get("query")
+        fuzzy_query = tool_parameters.get("fuzzy_query", False)
+        rows = tool_parameters.get("rows", 3)
+        sort = tool_parameters.get("sort", "relevance")
+        order = tool_parameters.get("order", "desc")
+        mailto = self.runtime.credentials["mailto"]
 
         result = CrossRefQueryTitleAPI(mailto).query(query, rows, sort, order, fuzzy_query)
 
diff --git a/api/core/tools/provider/builtin/dalle/dalle.py b/api/core/tools/provider/builtin/dalle/dalle.py
index 1c8019364d..5bd16e49e8 100644
--- a/api/core/tools/provider/builtin/dalle/dalle.py
+++ b/api/core/tools/provider/builtin/dalle/dalle.py
@@ -13,13 +13,8 @@ class DALLEProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
-                tool_parameters={
-                    "prompt": "cute girl, blue eyes, white hair, anime style",
-                    "size": "small",
-                    "n": 1
-                },
+                user_id="",
+                tool_parameters={"prompt": "cute girl, blue eyes, white hair, anime style", "size": "small", "n": 1},
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/dalle/dalle.yaml b/api/core/tools/provider/builtin/dalle/dalle.yaml
index f09a9177f2..37cf93c28a 100644
--- a/api/core/tools/provider/builtin/dalle/dalle.yaml
+++ b/api/core/tools/provider/builtin/dalle/dalle.yaml
@@ -29,7 +29,7 @@ credentials_for_provider:
       en_US: Please input your OpenAI API key
       zh_Hans: 请输入你的 OpenAI API key
       pt_BR: Please input your OpenAI API key
-  openai_organizaion_id:
+  openai_organization_id:
     type: text-input
     required: false
     label:
diff --git a/api/core/tools/provider/builtin/dalle/tools/dalle2.py b/api/core/tools/provider/builtin/dalle/tools/dalle2.py
index 450e782281..fbd7397292 100644
--- a/api/core/tools/provider/builtin/dalle/tools/dalle2.py
+++ b/api/core/tools/provider/builtin/dalle/tools/dalle2.py
@@ -9,59 +9,58 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class DallE2Tool(BuiltinTool):
-    def _invoke(self, 
-                user_id: str, 
-               tool_parameters: dict[str, Any], 
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        openai_organization = self.runtime.credentials.get('openai_organizaion_id', None)
+        openai_organization = self.runtime.credentials.get("openai_organization_id", None)
         if not openai_organization:
             openai_organization = None
-        openai_base_url = self.runtime.credentials.get('openai_base_url', None)
+        openai_base_url = self.runtime.credentials.get("openai_base_url", None)
         if not openai_base_url:
             openai_base_url = None
         else:
-            openai_base_url = str(URL(openai_base_url) / 'v1')
+            openai_base_url = str(URL(openai_base_url) / "v1")
 
         client = OpenAI(
-            api_key=self.runtime.credentials['openai_api_key'],
+            api_key=self.runtime.credentials["openai_api_key"],
             base_url=openai_base_url,
-            organization=openai_organization
+            organization=openai_organization,
         )
 
         SIZE_MAPPING = {
-            'small': '256x256',
-            'medium': '512x512',
-            'large': '1024x1024',
+            "small": "256x256",
+            "medium": "512x512",
+            "large": "1024x1024",
         }
 
         # prompt
-        prompt = tool_parameters.get('prompt', '')
+        prompt = tool_parameters.get("prompt", "")
         if not prompt:
-            return self.create_text_message('Please input prompt')
-        
+            return self.create_text_message("Please input prompt")
+
         # get size
-        size = SIZE_MAPPING[tool_parameters.get('size', 'large')]
+        size = SIZE_MAPPING[tool_parameters.get("size", "large")]
 
         # get n
-        n = tool_parameters.get('n', 1)
+        n = tool_parameters.get("n", 1)
 
         # call openapi dalle2
-        response = client.images.generate(
-            prompt=prompt,
-            model='dall-e-2',
-            size=size,
-            n=n,
-            response_format='b64_json'
-        )
+        response = client.images.generate(prompt=prompt, model="dall-e-2", size=size, n=n, response_format="b64_json")
 
         result = []
 
         for image in response.data:
-            result.append(self.create_blob_message(blob=b64decode(image.b64_json), 
-                                                   meta={ 'mime_type': 'image/png' },
-                                                    save_as=self.VARIABLE_KEY.IMAGE.value))
+            result.append(
+                self.create_blob_message(
+                    blob=b64decode(image.b64_json),
+                    meta={"mime_type": "image/png"},
+                    save_as=self.VariableKey.IMAGE.value,
+                )
+            )
 
         return result
diff --git a/api/core/tools/provider/builtin/dalle/tools/dalle3.py b/api/core/tools/provider/builtin/dalle/tools/dalle3.py
index f985deade5..a8c647d71e 100644
--- a/api/core/tools/provider/builtin/dalle/tools/dalle3.py
+++ b/api/core/tools/provider/builtin/dalle/tools/dalle3.py
@@ -10,69 +10,64 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class DallE3Tool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        openai_organization = self.runtime.credentials.get('openai_organizaion_id', None)
+        openai_organization = self.runtime.credentials.get("openai_organization_id", None)
         if not openai_organization:
             openai_organization = None
-        openai_base_url = self.runtime.credentials.get('openai_base_url', None)
+        openai_base_url = self.runtime.credentials.get("openai_base_url", None)
         if not openai_base_url:
             openai_base_url = None
         else:
-            openai_base_url = str(URL(openai_base_url) / 'v1')
+            openai_base_url = str(URL(openai_base_url) / "v1")
 
         client = OpenAI(
-            api_key=self.runtime.credentials['openai_api_key'],
+            api_key=self.runtime.credentials["openai_api_key"],
             base_url=openai_base_url,
-            organization=openai_organization
+            organization=openai_organization,
         )
 
         SIZE_MAPPING = {
-            'square': '1024x1024',
-            'vertical': '1024x1792',
-            'horizontal': '1792x1024',
+            "square": "1024x1024",
+            "vertical": "1024x1792",
+            "horizontal": "1792x1024",
         }
 
         # prompt
-        prompt = tool_parameters.get('prompt', '')
+        prompt = tool_parameters.get("prompt", "")
         if not prompt:
-            return self.create_text_message('Please input prompt')
+            return self.create_text_message("Please input prompt")
         # get size
-        size = SIZE_MAPPING[tool_parameters.get('size', 'square')]
+        size = SIZE_MAPPING[tool_parameters.get("size", "square")]
         # get n
-        n = tool_parameters.get('n', 1)
+        n = tool_parameters.get("n", 1)
         # get quality
-        quality = tool_parameters.get('quality', 'standard')
-        if quality not in ['standard', 'hd']:
-            return self.create_text_message('Invalid quality')
+        quality = tool_parameters.get("quality", "standard")
+        if quality not in {"standard", "hd"}:
+            return self.create_text_message("Invalid quality")
         # get style
-        style = tool_parameters.get('style', 'vivid')
-        if style not in ['natural', 'vivid']:
-            return self.create_text_message('Invalid style')
+        style = tool_parameters.get("style", "vivid")
+        if style not in {"natural", "vivid"}:
+            return self.create_text_message("Invalid style")
 
         # call openapi dalle3
         response = client.images.generate(
-            prompt=prompt,
-            model='dall-e-3',
-            size=size,
-            n=n,
-            style=style,
-            quality=quality,
-            response_format='b64_json'
+            prompt=prompt, model="dall-e-3", size=size, n=n, style=style, quality=quality, response_format="b64_json"
         )
 
         result = []
 
         for image in response.data:
             mime_type, blob_image = DallE3Tool._decode_image(image.b64_json)
-            blob_message = self.create_blob_message(blob=blob_image,
-                                                    meta={'mime_type': mime_type},
-                                                    save_as=self.VARIABLE_KEY.IMAGE.value)
+            blob_message = self.create_blob_message(
+                blob=blob_image, meta={"mime_type": mime_type}, save_as=self.VariableKey.IMAGE.value
+            )
             result.append(blob_message)
         return result
 
@@ -86,7 +81,7 @@ class DallE3Tool(BuiltinTool):
         :return: A tuple containing the MIME type and the decoded image bytes
         """
         if DallE3Tool._is_plain_base64(base64_image):
-            return 'image/png', base64.b64decode(base64_image)
+            return "image/png", base64.b64decode(base64_image)
         else:
             return DallE3Tool._extract_mime_and_data(base64_image)
 
@@ -98,7 +93,7 @@ class DallE3Tool(BuiltinTool):
         :param encoded_str: Base64 encoded image string
         :return: True if the string is plain base64, False otherwise
         """
-        return not encoded_str.startswith('data:image')
+        return not encoded_str.startswith("data:image")
 
     @staticmethod
     def _extract_mime_and_data(encoded_str: str) -> tuple[str, bytes]:
@@ -108,13 +103,13 @@ class DallE3Tool(BuiltinTool):
         :param encoded_str: Base64 encoded image string with MIME type prefix
         :return: A tuple containing the MIME type and the decoded image bytes
         """
-        mime_type = encoded_str.split(';')[0].split(':')[1]
-        image_data_base64 = encoded_str.split(',')[1]
+        mime_type = encoded_str.split(";")[0].split(":")[1]
+        image_data_base64 = encoded_str.split(",")[1]
         decoded_data = base64.b64decode(image_data_base64)
         return mime_type, decoded_data
 
     @staticmethod
     def _generate_random_id(length=8):
-        characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'
-        random_id = ''.join(random.choices(characters, k=length))
+        characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"
+        random_id = "".join(random.choices(characters, k=length))
         return random_id
diff --git a/api/core/tools/provider/builtin/devdocs/devdocs.py b/api/core/tools/provider/builtin/devdocs/devdocs.py
index 95d7939d0d..446c1e5489 100644
--- a/api/core/tools/provider/builtin/devdocs/devdocs.py
+++ b/api/core/tools/provider/builtin/devdocs/devdocs.py
@@ -11,7 +11,7 @@ class DevDocsProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "doc": "python~3.12",
                     "topic": "library/code",
@@ -19,4 +19,3 @@ class DevDocsProvider(BuiltinToolProviderController):
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/devdocs/tools/searchDevDocs.py b/api/core/tools/provider/builtin/devdocs/tools/searchDevDocs.py
index 1a244c5db3..57cf6d7a30 100644
--- a/api/core/tools/provider/builtin/devdocs/tools/searchDevDocs.py
+++ b/api/core/tools/provider/builtin/devdocs/tools/searchDevDocs.py
@@ -13,7 +13,9 @@ class SearchDevDocsInput(BaseModel):
 
 
 class SearchDevDocsTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
         Invokes the DevDocs search tool with the given user ID and tool parameters.
 
@@ -22,15 +24,16 @@ class SearchDevDocsTool(BuiltinTool):
             tool_parameters (dict[str, Any]): The parameters for the tool, including 'doc' and 'topic'.
 
         Returns:
-            ToolInvokeMessage | list[ToolInvokeMessage]: The result of the tool invocation, which can be a single message or a list of messages.
+            ToolInvokeMessage | list[ToolInvokeMessage]: The result of the tool invocation,
+             which can be a single message or a list of messages.
         """
-        doc = tool_parameters.get('doc', '')
-        topic = tool_parameters.get('topic', '')
+        doc = tool_parameters.get("doc", "")
+        topic = tool_parameters.get("topic", "")
 
         if not doc:
-            return self.create_text_message('Please provide the documentation name.')
+            return self.create_text_message("Please provide the documentation name.")
         if not topic:
-            return self.create_text_message('Please provide the topic path.')
+            return self.create_text_message("Please provide the topic path.")
 
         url = f"https://documents.devdocs.io/{doc}/{topic}.html"
         response = requests.get(url)
@@ -39,4 +42,6 @@ class SearchDevDocsTool(BuiltinTool):
             content = response.text
             return self.create_text_message(self.summary(user_id=user_id, content=content))
         else:
-            return self.create_text_message(f"Failed to retrieve the documentation. Status code: {response.status_code}")
\ No newline at end of file
+            return self.create_text_message(
+                f"Failed to retrieve the documentation. Status code: {response.status_code}"
+            )
diff --git a/api/core/tools/provider/builtin/did/did.py b/api/core/tools/provider/builtin/did/did.py
index b4bf172131..5af78794f6 100644
--- a/api/core/tools/provider/builtin/did/did.py
+++ b/api/core/tools/provider/builtin/did/did.py
@@ -7,15 +7,12 @@ class DIDProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
         try:
             # Example validation using the D-ID talks tool
-            TalksTool().fork_tool_runtime(
-                runtime={"credentials": credentials}
-            ).invoke(
-                user_id='',
+            TalksTool().fork_tool_runtime(runtime={"credentials": credentials}).invoke(
+                user_id="",
                 tool_parameters={
                     "source_url": "https://www.d-id.com/wp-content/uploads/2023/11/Hero-image-1.png",
                     "text_input": "Hello, welcome to use D-ID tool in Dify",
-                }
+                },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/did/did_appx.py b/api/core/tools/provider/builtin/did/did_appx.py
index 964e82b729..c68878630d 100644
--- a/api/core/tools/provider/builtin/did/did_appx.py
+++ b/api/core/tools/provider/builtin/did/did_appx.py
@@ -12,14 +12,14 @@ logger = logging.getLogger(__name__)
 class DIDApp:
     def __init__(self, api_key: str | None = None, base_url: str | None = None):
         self.api_key = api_key
-        self.base_url = base_url or 'https://api.d-id.com'
+        self.base_url = base_url or "https://api.d-id.com"
         if not self.api_key:
-            raise ValueError('API key is required')
+            raise ValueError("API key is required")
 
     def _prepare_headers(self, idempotency_key: str | None = None):
-        headers = {'Content-Type': 'application/json', 'Authorization': f'Basic {self.api_key}'}
+        headers = {"Content-Type": "application/json", "Authorization": f"Basic {self.api_key}"}
         if idempotency_key:
-            headers['Idempotency-Key'] = idempotency_key
+            headers["Idempotency-Key"] = idempotency_key
         return headers
 
     def _request(
@@ -44,44 +44,44 @@ class DIDApp:
         return None
 
     def talks(self, wait: bool = True, poll_interval: int = 5, idempotency_key: str | None = None, **kwargs):
-        endpoint = f'{self.base_url}/talks'
+        endpoint = f"{self.base_url}/talks"
         headers = self._prepare_headers(idempotency_key)
-        data = kwargs['params']
-        logger.debug(f'Send request to {endpoint=} body={data}')
-        response = self._request('POST', endpoint, data, headers)
+        data = kwargs["params"]
+        logger.debug(f"Send request to {endpoint=} body={data}")
+        response = self._request("POST", endpoint, data, headers)
         if response is None:
-            raise HTTPError('Failed to initiate D-ID talks after multiple retries')
-        id: str = response['id']
+            raise HTTPError("Failed to initiate D-ID talks after multiple retries")
+        id: str = response["id"]
         if wait:
-            return self._monitor_job_status(id=id, target='talks', poll_interval=poll_interval)
+            return self._monitor_job_status(id=id, target="talks", poll_interval=poll_interval)
         return id
 
     def animations(self, wait: bool = True, poll_interval: int = 5, idempotency_key: str | None = None, **kwargs):
-        endpoint = f'{self.base_url}/animations'
+        endpoint = f"{self.base_url}/animations"
         headers = self._prepare_headers(idempotency_key)
-        data = kwargs['params']
-        logger.debug(f'Send request to {endpoint=} body={data}')
-        response = self._request('POST', endpoint, data, headers)
+        data = kwargs["params"]
+        logger.debug(f"Send request to {endpoint=} body={data}")
+        response = self._request("POST", endpoint, data, headers)
         if response is None:
-            raise HTTPError('Failed to initiate D-ID talks after multiple retries')
-        id: str = response['id']
+            raise HTTPError("Failed to initiate D-ID talks after multiple retries")
+        id: str = response["id"]
         if wait:
-            return self._monitor_job_status(target='animations', id=id, poll_interval=poll_interval)
+            return self._monitor_job_status(target="animations", id=id, poll_interval=poll_interval)
         return id
 
     def check_did_status(self, target: str, id: str):
-        endpoint = f'{self.base_url}/{target}/{id}'
+        endpoint = f"{self.base_url}/{target}/{id}"
         headers = self._prepare_headers()
-        response = self._request('GET', endpoint, headers=headers)
+        response = self._request("GET", endpoint, headers=headers)
         if response is None:
-            raise HTTPError(f'Failed to check status for talks {id} after multiple retries')
+            raise HTTPError(f"Failed to check status for talks {id} after multiple retries")
         return response
 
     def _monitor_job_status(self, target: str, id: str, poll_interval: int):
         while True:
             status = self.check_did_status(target=target, id=id)
-            if status['status'] == 'done':
+            if status["status"] == "done":
                 return status
-            elif status['status'] == 'error' or status['status'] == 'rejected':
-                raise HTTPError(f'Talks {id} failed: {status["status"]} {status.get("error",{}).get("description")}')
+            elif status["status"] == "error" or status["status"] == "rejected":
+                raise HTTPError(f'Talks {id} failed: {status["status"]} {status.get("error", {}).get("description")}')
             time.sleep(poll_interval)
diff --git a/api/core/tools/provider/builtin/did/tools/animations.py b/api/core/tools/provider/builtin/did/tools/animations.py
index e1d9de603f..bc9d17e40d 100644
--- a/api/core/tools/provider/builtin/did/tools/animations.py
+++ b/api/core/tools/provider/builtin/did/tools/animations.py
@@ -10,33 +10,33 @@ class AnimationsTool(BuiltinTool):
     def _invoke(
         self, user_id: str, tool_parameters: dict[str, Any]
     ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        app = DIDApp(api_key=self.runtime.credentials['did_api_key'], base_url=self.runtime.credentials['base_url'])
+        app = DIDApp(api_key=self.runtime.credentials["did_api_key"], base_url=self.runtime.credentials["base_url"])
 
-        driver_expressions_str = tool_parameters.get('driver_expressions')
+        driver_expressions_str = tool_parameters.get("driver_expressions")
         driver_expressions = json.loads(driver_expressions_str) if driver_expressions_str else None
 
         config = {
-            'stitch': tool_parameters.get('stitch', True),
-            'mute': tool_parameters.get('mute'),
-            'result_format': tool_parameters.get('result_format') or 'mp4',
+            "stitch": tool_parameters.get("stitch", True),
+            "mute": tool_parameters.get("mute"),
+            "result_format": tool_parameters.get("result_format") or "mp4",
         }
-        config = {k: v for k, v in config.items() if v is not None and v != ''}
+        config = {k: v for k, v in config.items() if v is not None and v != ""}
 
         options = {
-            'source_url': tool_parameters['source_url'],
-            'driver_url': tool_parameters.get('driver_url'),
-            'config': config,
+            "source_url": tool_parameters["source_url"],
+            "driver_url": tool_parameters.get("driver_url"),
+            "config": config,
         }
-        options = {k: v for k, v in options.items() if v is not None and v != ''}
+        options = {k: v for k, v in options.items() if v is not None and v != ""}
 
-        if not options.get('source_url'):
-            raise ValueError('Source URL is required')
+        if not options.get("source_url"):
+            raise ValueError("Source URL is required")
 
-        if config.get('logo_url'):
-            if not config.get('logo_x'):
-                raise ValueError('Logo X position is required when logo URL is provided')
-            if not config.get('logo_y'):
-                raise ValueError('Logo Y position is required when logo URL is provided')
+        if config.get("logo_url"):
+            if not config.get("logo_x"):
+                raise ValueError("Logo X position is required when logo URL is provided")
+            if not config.get("logo_y"):
+                raise ValueError("Logo Y position is required when logo URL is provided")
 
         animations_result = app.animations(params=options, wait=True)
 
@@ -44,6 +44,6 @@ class AnimationsTool(BuiltinTool):
             animations_result = json.dumps(animations_result, ensure_ascii=False, indent=4)
 
         if not animations_result:
-            return self.create_text_message('D-ID animations request failed.')
+            return self.create_text_message("D-ID animations request failed.")
 
         return self.create_text_message(animations_result)
diff --git a/api/core/tools/provider/builtin/did/tools/talks.py b/api/core/tools/provider/builtin/did/tools/talks.py
index 06b2c4cb2f..d6f0c7ff17 100644
--- a/api/core/tools/provider/builtin/did/tools/talks.py
+++ b/api/core/tools/provider/builtin/did/tools/talks.py
@@ -10,49 +10,49 @@ class TalksTool(BuiltinTool):
     def _invoke(
         self, user_id: str, tool_parameters: dict[str, Any]
     ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        app = DIDApp(api_key=self.runtime.credentials['did_api_key'], base_url=self.runtime.credentials['base_url'])
+        app = DIDApp(api_key=self.runtime.credentials["did_api_key"], base_url=self.runtime.credentials["base_url"])
 
-        driver_expressions_str = tool_parameters.get('driver_expressions')
+        driver_expressions_str = tool_parameters.get("driver_expressions")
         driver_expressions = json.loads(driver_expressions_str) if driver_expressions_str else None
 
         script = {
-            'type': tool_parameters.get('script_type') or 'text',
-            'input': tool_parameters.get('text_input'),
-            'audio_url': tool_parameters.get('audio_url'),
-            'reduce_noise': tool_parameters.get('audio_reduce_noise', False),
+            "type": tool_parameters.get("script_type") or "text",
+            "input": tool_parameters.get("text_input"),
+            "audio_url": tool_parameters.get("audio_url"),
+            "reduce_noise": tool_parameters.get("audio_reduce_noise", False),
         }
-        script = {k: v for k, v in script.items() if v is not None and v != ''}
+        script = {k: v for k, v in script.items() if v is not None and v != ""}
         config = {
-            'stitch': tool_parameters.get('stitch', True),
-            'sharpen': tool_parameters.get('sharpen'),
-            'fluent': tool_parameters.get('fluent'),
-            'result_format': tool_parameters.get('result_format') or 'mp4',
-            'pad_audio': tool_parameters.get('pad_audio'),
-            'driver_expressions': driver_expressions,
+            "stitch": tool_parameters.get("stitch", True),
+            "sharpen": tool_parameters.get("sharpen"),
+            "fluent": tool_parameters.get("fluent"),
+            "result_format": tool_parameters.get("result_format") or "mp4",
+            "pad_audio": tool_parameters.get("pad_audio"),
+            "driver_expressions": driver_expressions,
         }
-        config = {k: v for k, v in config.items() if v is not None and v != ''}
+        config = {k: v for k, v in config.items() if v is not None and v != ""}
 
         options = {
-            'source_url': tool_parameters['source_url'],
-            'driver_url': tool_parameters.get('driver_url'),
-            'script': script,
-            'config': config,
+            "source_url": tool_parameters["source_url"],
+            "driver_url": tool_parameters.get("driver_url"),
+            "script": script,
+            "config": config,
         }
-        options = {k: v for k, v in options.items() if v is not None and v != ''}
+        options = {k: v for k, v in options.items() if v is not None and v != ""}
 
-        if not options.get('source_url'):
-            raise ValueError('Source URL is required')
+        if not options.get("source_url"):
+            raise ValueError("Source URL is required")
 
-        if script.get('type') == 'audio':
-            script.pop('input', None)
-            if not script.get('audio_url'):
-                raise ValueError('Audio URL is required for audio script type')
+        if script.get("type") == "audio":
+            script.pop("input", None)
+            if not script.get("audio_url"):
+                raise ValueError("Audio URL is required for audio script type")
 
-        if script.get('type') == 'text':
-            script.pop('audio_url', None)
-            script.pop('reduce_noise', None)
-            if not script.get('input'):
-                raise ValueError('Text input is required for text script type')
+        if script.get("type") == "text":
+            script.pop("audio_url", None)
+            script.pop("reduce_noise", None)
+            if not script.get("input"):
+                raise ValueError("Text input is required for text script type")
 
         talks_result = app.talks(params=options, wait=True)
 
@@ -60,6 +60,6 @@ class TalksTool(BuiltinTool):
             talks_result = json.dumps(talks_result, ensure_ascii=False, indent=4)
 
         if not talks_result:
-            return self.create_text_message('D-ID talks request failed.')
+            return self.create_text_message("D-ID talks request failed.")
 
         return self.create_text_message(talks_result)
diff --git a/api/core/tools/provider/builtin/dingtalk/tools/dingtalk_group_bot.py b/api/core/tools/provider/builtin/dingtalk/tools/dingtalk_group_bot.py
index c247c3bd6b..f33ad5be59 100644
--- a/api/core/tools/provider/builtin/dingtalk/tools/dingtalk_group_bot.py
+++ b/api/core/tools/provider/builtin/dingtalk/tools/dingtalk_group_bot.py
@@ -13,38 +13,43 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class DingTalkGroupBotTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
-            Dingtalk custom group robot API docs:
-            https://open.dingtalk.com/document/orgapp/custom-robot-access
+        invoke tools
+        Dingtalk custom group robot API docs:
+        https://open.dingtalk.com/document/orgapp/custom-robot-access
         """
-        content = tool_parameters.get('content')
+        content = tool_parameters.get("content")
         if not content:
-            return self.create_text_message('Invalid parameter content')
+            return self.create_text_message("Invalid parameter content")
 
-        access_token = tool_parameters.get('access_token')
+        access_token = tool_parameters.get("access_token")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token. '
-                                            'Regarding information about security details,'
-                                            'please refer to the DingTalk docs:'
-                                            'https://open.dingtalk.com/document/robots/customize-robot-security-settings')
+            return self.create_text_message(
+                "Invalid parameter access_token. "
+                "Regarding information about security details,"
+                "please refer to the DingTalk docs:"
+                "https://open.dingtalk.com/document/robots/customize-robot-security-settings"
+            )
 
-        sign_secret = tool_parameters.get('sign_secret')
+        sign_secret = tool_parameters.get("sign_secret")
         if not sign_secret:
-            return self.create_text_message('Invalid parameter sign_secret. '
-                                            'Regarding information about security details,'
-                                            'please refer to the DingTalk docs:'
-                                            'https://open.dingtalk.com/document/robots/customize-robot-security-settings')
+            return self.create_text_message(
+                "Invalid parameter sign_secret. "
+                "Regarding information about security details,"
+                "please refer to the DingTalk docs:"
+                "https://open.dingtalk.com/document/robots/customize-robot-security-settings"
+            )
 
-        msgtype = 'text'
-        api_url = 'https://oapi.dingtalk.com/robot/send'
+        msgtype = "text"
+        api_url = "https://oapi.dingtalk.com/robot/send"
         headers = {
-            'Content-Type': 'application/json',
+            "Content-Type": "application/json",
         }
         params = {
-            'access_token': access_token,
+            "access_token": access_token,
         }
 
         self._apply_security_mechanism(params, sign_secret)
@@ -53,7 +58,7 @@ class DingTalkGroupBotTool(BuiltinTool):
             "msgtype": msgtype,
             "text": {
                 "content": content,
-            }
+            },
         }
 
         try:
@@ -62,7 +67,8 @@ class DingTalkGroupBotTool(BuiltinTool):
                 return self.create_text_message("Text message sent successfully")
             else:
                 return self.create_text_message(
-                    f"Failed to send the text message, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to send the text message, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to send message to group chat bot. {}".format(e))
 
@@ -70,14 +76,14 @@ class DingTalkGroupBotTool(BuiltinTool):
     def _apply_security_mechanism(params: dict[str, Any], sign_secret: str):
         try:
             timestamp = str(round(time.time() * 1000))
-            secret_enc = sign_secret.encode('utf-8')
-            string_to_sign = f'{timestamp}\n{sign_secret}'
-            string_to_sign_enc = string_to_sign.encode('utf-8')
+            secret_enc = sign_secret.encode("utf-8")
+            string_to_sign = f"{timestamp}\n{sign_secret}"
+            string_to_sign_enc = string_to_sign.encode("utf-8")
             hmac_code = hmac.new(secret_enc, string_to_sign_enc, digestmod=hashlib.sha256).digest()
             sign = urllib.parse.quote_plus(base64.b64encode(hmac_code))
 
-            params['timestamp'] = timestamp
-            params['sign'] = sign
+            params["timestamp"] = timestamp
+            params["sign"] = sign
         except Exception:
             msg = "Failed to apply security mechanism to the request."
             logging.exception(msg)
diff --git a/api/core/tools/provider/builtin/duckduckgo/duckduckgo.py b/api/core/tools/provider/builtin/duckduckgo/duckduckgo.py
index 2292e89fa6..8269167127 100644
--- a/api/core/tools/provider/builtin/duckduckgo/duckduckgo.py
+++ b/api/core/tools/provider/builtin/duckduckgo/duckduckgo.py
@@ -11,11 +11,10 @@ class DuckDuckGoProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "query": "John Doe",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_ai.py b/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_ai.py
index 878b0d8645..8bdd638f4a 100644
--- a/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_ai.py
+++ b/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_ai.py
@@ -13,8 +13,8 @@ class DuckDuckGoAITool(BuiltinTool):
 
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
         query_dict = {
-            "keywords": tool_parameters.get('query'),
-            "model": tool_parameters.get('model'),
+            "keywords": tool_parameters.get("query"),
+            "model": tool_parameters.get("model"),
         }
         response = DDGS().chat(**query_dict)
         return self.create_text_message(text=response)
diff --git a/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_img.py b/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_img.py
index bca53f6b4b..396570248a 100644
--- a/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_img.py
+++ b/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_img.py
@@ -14,18 +14,17 @@ class DuckDuckGoImageSearchTool(BuiltinTool):
 
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> list[ToolInvokeMessage]:
         query_dict = {
-            "keywords": tool_parameters.get('query'),
-            "timelimit": tool_parameters.get('timelimit'),
-            "size": tool_parameters.get('size'),
-            "max_results": tool_parameters.get('max_results'),
+            "keywords": tool_parameters.get("query"),
+            "timelimit": tool_parameters.get("timelimit"),
+            "size": tool_parameters.get("size"),
+            "max_results": tool_parameters.get("max_results"),
         }
         response = DDGS().images(**query_dict)
         result = []
         for res in response:
-            res['transfer_method'] = FileTransferMethod.REMOTE_URL
-            msg = ToolInvokeMessage(type=ToolInvokeMessage.MessageType.IMAGE_LINK,
-                                    message=res.get('image'),
-                                    save_as='',
-                                    meta=res)
+            res["transfer_method"] = FileTransferMethod.REMOTE_URL
+            msg = ToolInvokeMessage(
+                type=ToolInvokeMessage.MessageType.IMAGE_LINK, message=res.get("image"), save_as="", meta=res
+            )
             result.append(msg)
         return result
diff --git a/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_search.py b/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_search.py
index dfaeb734d8..cbd65d2e77 100644
--- a/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_search.py
+++ b/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_search.py
@@ -21,10 +21,11 @@ class DuckDuckGoSearchTool(BuiltinTool):
     """
     Tool for performing a search using DuckDuckGo search engine.
     """
+
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage | list[ToolInvokeMessage]:
-        query = tool_parameters.get('query')
-        max_results = tool_parameters.get('max_results', 5)
-        require_summary = tool_parameters.get('require_summary', False)
+        query = tool_parameters.get("query")
+        max_results = tool_parameters.get("max_results", 5)
+        require_summary = tool_parameters.get("require_summary", False)
         response = DDGS().text(query, max_results=max_results)
         if require_summary:
             results = "\n".join([res.get("body") for res in response])
@@ -34,7 +35,11 @@ class DuckDuckGoSearchTool(BuiltinTool):
 
     def summary_results(self, user_id: str, content: str, query: str) -> str:
         prompt = SUMMARY_PROMPT.format(query=query, content=content)
-        summary = self.invoke_model(user_id=user_id, prompt_messages=[
-            SystemPromptMessage(content=prompt),
-        ], stop=[])
+        summary = self.invoke_model(
+            user_id=user_id,
+            prompt_messages=[
+                SystemPromptMessage(content=prompt),
+            ],
+            stop=[],
+        )
         return summary.message.content
diff --git a/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_translate.py b/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_translate.py
index 9822b37cf0..396ce21b18 100644
--- a/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_translate.py
+++ b/api/core/tools/provider/builtin/duckduckgo/tools/ddgo_translate.py
@@ -13,8 +13,8 @@ class DuckDuckGoTranslateTool(BuiltinTool):
 
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
         query_dict = {
-            "keywords": tool_parameters.get('query'),
-            "to": tool_parameters.get('translate_to'),
+            "keywords": tool_parameters.get("query"),
+            "to": tool_parameters.get("translate_to"),
         }
-        response = DDGS().translate(**query_dict)[0].get('translated', 'Unable to translate!')
+        response = DDGS().translate(**query_dict)[0].get("translated", "Unable to translate!")
         return self.create_text_message(text=response)
diff --git a/api/core/tools/provider/builtin/feishu/tools/feishu_group_bot.py b/api/core/tools/provider/builtin/feishu/tools/feishu_group_bot.py
index e8ab02f55e..e82da8ca53 100644
--- a/api/core/tools/provider/builtin/feishu/tools/feishu_group_bot.py
+++ b/api/core/tools/provider/builtin/feishu/tools/feishu_group_bot.py
@@ -8,35 +8,35 @@ from core.tools.utils.uuid_utils import is_valid_uuid
 
 
 class FeishuGroupBotTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
-            API document: https://open.feishu.cn/document/client-docs/bot-v3/add-custom-bot
+        invoke tools
+        API document: https://open.feishu.cn/document/client-docs/bot-v3/add-custom-bot
         """
 
         url = "https://open.feishu.cn/open-apis/bot/v2/hook"
 
-        content = tool_parameters.get('content', '')
+        content = tool_parameters.get("content", "")
         if not content:
-            return self.create_text_message('Invalid parameter content')
+            return self.create_text_message("Invalid parameter content")
 
-        hook_key = tool_parameters.get('hook_key', '')
+        hook_key = tool_parameters.get("hook_key", "")
         if not is_valid_uuid(hook_key):
-            return self.create_text_message(
-                f'Invalid parameter hook_key ${hook_key}, not a valid UUID')
+            return self.create_text_message(f"Invalid parameter hook_key ${hook_key}, not a valid UUID")
 
-        msg_type = 'text'
-        api_url = f'{url}/{hook_key}'
+        msg_type = "text"
+        api_url = f"{url}/{hook_key}"
         headers = {
-            'Content-Type': 'application/json',
+            "Content-Type": "application/json",
         }
         params = {}
         payload = {
             "msg_type": msg_type,
             "content": {
                 "text": content,
-            }
+            },
         }
 
         try:
@@ -45,6 +45,7 @@ class FeishuGroupBotTool(BuiltinTool):
                 return self.create_text_message("Text message sent successfully")
             else:
                 return self.create_text_message(
-                    f"Failed to send the text message, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to send the text message, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
-            return self.create_text_message("Failed to send message to group chat bot. {}".format(e))
\ No newline at end of file
+            return self.create_text_message("Failed to send message to group chat bot. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/feishu_base.py b/api/core/tools/provider/builtin/feishu_base/feishu_base.py
index febb769ff8..04056af53b 100644
--- a/api/core/tools/provider/builtin/feishu_base/feishu_base.py
+++ b/api/core/tools/provider/builtin/feishu_base/feishu_base.py
@@ -5,4 +5,4 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class FeishuBaseProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
         GetTenantAccessTokenTool()
-        pass
\ No newline at end of file
+        pass
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/add_base_record.py b/api/core/tools/provider/builtin/feishu_base/tools/add_base_record.py
index be43b43ce4..4a605fbffe 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/add_base_record.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/add_base_record.py
@@ -8,45 +8,49 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class AddBaseRecordTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/bitable/v1/apps/{app_token}/tables/{table_id}/records"
 
-        access_token = tool_parameters.get('Authorization', '')
+        access_token = tool_parameters.get("Authorization", "")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token')
+            return self.create_text_message("Invalid parameter access_token")
 
-        app_token = tool_parameters.get('app_token', '')
+        app_token = tool_parameters.get("app_token", "")
         if not app_token:
-            return self.create_text_message('Invalid parameter app_token')
+            return self.create_text_message("Invalid parameter app_token")
 
-        table_id = tool_parameters.get('table_id', '')
+        table_id = tool_parameters.get("table_id", "")
         if not table_id:
-            return self.create_text_message('Invalid parameter table_id')
+            return self.create_text_message("Invalid parameter table_id")
 
-        fields = tool_parameters.get('fields', '')
+        fields = tool_parameters.get("fields", "")
         if not fields:
-            return self.create_text_message('Invalid parameter fields')
+            return self.create_text_message("Invalid parameter fields")
 
         headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {access_token}",
         }
 
         params = {}
-        payload = {
-            "fields": json.loads(fields)
-        }
+        payload = {"fields": json.loads(fields)}
 
         try:
-            res = httpx.post(url.format(app_token=app_token, table_id=table_id), headers=headers, params=params,
-                             json=payload, timeout=30)
+            res = httpx.post(
+                url.format(app_token=app_token, table_id=table_id),
+                headers=headers,
+                params=params,
+                json=payload,
+                timeout=30,
+            )
             res_json = res.json()
             if res.is_success:
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to add base record, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to add base record, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to add base record. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/create_base.py b/api/core/tools/provider/builtin/feishu_base/tools/create_base.py
index 639644e7f0..6b755e2007 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/create_base.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/create_base.py
@@ -8,28 +8,25 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class CreateBaseTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/bitable/v1/apps"
 
-        access_token = tool_parameters.get('Authorization', '')
+        access_token = tool_parameters.get("Authorization", "")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token')
+            return self.create_text_message("Invalid parameter access_token")
 
-        name = tool_parameters.get('name', '')
-        folder_token = tool_parameters.get('folder_token', '')
+        name = tool_parameters.get("name", "")
+        folder_token = tool_parameters.get("folder_token", "")
 
         headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {access_token}",
         }
 
         params = {}
-        payload = {
-            "name": name,
-            "folder_token": folder_token
-        }
+        payload = {"name": name, "folder_token": folder_token}
 
         try:
             res = httpx.post(url, headers=headers, params=params, json=payload, timeout=30)
@@ -38,6 +35,7 @@ class CreateBaseTool(BuiltinTool):
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to create base, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to create base, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to create base. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/create_base_table.py b/api/core/tools/provider/builtin/feishu_base/tools/create_base_table.py
index e9062e8730..b05d700113 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/create_base_table.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/create_base_table.py
@@ -8,37 +8,32 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class CreateBaseTableTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/bitable/v1/apps/{app_token}/tables"
 
-        access_token = tool_parameters.get('Authorization', '')
+        access_token = tool_parameters.get("Authorization", "")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token')
+            return self.create_text_message("Invalid parameter access_token")
 
-        app_token = tool_parameters.get('app_token', '')
+        app_token = tool_parameters.get("app_token", "")
         if not app_token:
-            return self.create_text_message('Invalid parameter app_token')
+            return self.create_text_message("Invalid parameter app_token")
 
-        name = tool_parameters.get('name', '')
+        name = tool_parameters.get("name", "")
 
-        fields = tool_parameters.get('fields', '')
+        fields = tool_parameters.get("fields", "")
         if not fields:
-            return self.create_text_message('Invalid parameter fields')
+            return self.create_text_message("Invalid parameter fields")
 
         headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {access_token}",
         }
 
         params = {}
-        payload = {
-            "table": {
-                "name": name,
-                "fields": json.loads(fields)
-            }
-        }
+        payload = {"table": {"name": name, "fields": json.loads(fields)}}
 
         try:
             res = httpx.post(url.format(app_token=app_token), headers=headers, params=params, json=payload, timeout=30)
@@ -47,6 +42,7 @@ class CreateBaseTableTool(BuiltinTool):
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to create base table, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to create base table, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to create base table. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/delete_base_records.py b/api/core/tools/provider/builtin/feishu_base/tools/delete_base_records.py
index aa13aad6fa..862eb2171b 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/delete_base_records.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/delete_base_records.py
@@ -8,45 +8,49 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class DeleteBaseRecordsTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/bitable/v1/apps/{app_token}/tables/{table_id}/records/batch_delete"
 
-        access_token = tool_parameters.get('Authorization', '')
+        access_token = tool_parameters.get("Authorization", "")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token')
+            return self.create_text_message("Invalid parameter access_token")
 
-        app_token = tool_parameters.get('app_token', '')
+        app_token = tool_parameters.get("app_token", "")
         if not app_token:
-            return self.create_text_message('Invalid parameter app_token')
+            return self.create_text_message("Invalid parameter app_token")
 
-        table_id = tool_parameters.get('table_id', '')
+        table_id = tool_parameters.get("table_id", "")
         if not table_id:
-            return self.create_text_message('Invalid parameter table_id')
+            return self.create_text_message("Invalid parameter table_id")
 
-        record_ids = tool_parameters.get('record_ids', '')
+        record_ids = tool_parameters.get("record_ids", "")
         if not record_ids:
-            return self.create_text_message('Invalid parameter record_ids')
+            return self.create_text_message("Invalid parameter record_ids")
 
         headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {access_token}",
         }
 
         params = {}
-        payload = {
-            "records": json.loads(record_ids)
-        }
+        payload = {"records": json.loads(record_ids)}
 
         try:
-            res = httpx.post(url.format(app_token=app_token, table_id=table_id), headers=headers, params=params,
-                             json=payload, timeout=30)
+            res = httpx.post(
+                url.format(app_token=app_token, table_id=table_id),
+                headers=headers,
+                params=params,
+                json=payload,
+                timeout=30,
+            )
             res_json = res.json()
             if res.is_success:
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to delete base records, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to delete base records, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to delete base records. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/delete_base_tables.py b/api/core/tools/provider/builtin/feishu_base/tools/delete_base_tables.py
index c4280ebc21..f512186303 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/delete_base_tables.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/delete_base_tables.py
@@ -8,32 +8,30 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class DeleteBaseTablesTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/bitable/v1/apps/{app_token}/tables/batch_delete"
 
-        access_token = tool_parameters.get('Authorization', '')
+        access_token = tool_parameters.get("Authorization", "")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token')
+            return self.create_text_message("Invalid parameter access_token")
 
-        app_token = tool_parameters.get('app_token', '')
+        app_token = tool_parameters.get("app_token", "")
         if not app_token:
-            return self.create_text_message('Invalid parameter app_token')
+            return self.create_text_message("Invalid parameter app_token")
 
-        table_ids = tool_parameters.get('table_ids', '')
+        table_ids = tool_parameters.get("table_ids", "")
         if not table_ids:
-            return self.create_text_message('Invalid parameter table_ids')
+            return self.create_text_message("Invalid parameter table_ids")
 
         headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {access_token}",
         }
 
         params = {}
-        payload = {
-            "table_ids": json.loads(table_ids)
-        }
+        payload = {"table_ids": json.loads(table_ids)}
 
         try:
             res = httpx.post(url.format(app_token=app_token), headers=headers, params=params, json=payload, timeout=30)
@@ -42,6 +40,7 @@ class DeleteBaseTablesTool(BuiltinTool):
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to delete base tables, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to delete base tables, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to delete base tables. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/get_base_info.py b/api/core/tools/provider/builtin/feishu_base/tools/get_base_info.py
index de70f2ed93..f664bbeed0 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/get_base_info.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/get_base_info.py
@@ -8,22 +8,22 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class GetBaseInfoTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/bitable/v1/apps/{app_token}"
 
-        access_token = tool_parameters.get('Authorization', '')
+        access_token = tool_parameters.get("Authorization", "")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token')
+            return self.create_text_message("Invalid parameter access_token")
 
-        app_token = tool_parameters.get('app_token', '')
+        app_token = tool_parameters.get("app_token", "")
         if not app_token:
-            return self.create_text_message('Invalid parameter app_token')
+            return self.create_text_message("Invalid parameter app_token")
 
         headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {access_token}",
         }
 
         try:
@@ -33,6 +33,7 @@ class GetBaseInfoTool(BuiltinTool):
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to get base info, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to get base info, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to get base info. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/get_tenant_access_token.py b/api/core/tools/provider/builtin/feishu_base/tools/get_tenant_access_token.py
index 88507bda60..2ea61d0068 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/get_tenant_access_token.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/get_tenant_access_token.py
@@ -8,27 +8,24 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class GetTenantAccessTokenTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal"
 
-        app_id = tool_parameters.get('app_id', '')
+        app_id = tool_parameters.get("app_id", "")
         if not app_id:
-            return self.create_text_message('Invalid parameter app_id')
+            return self.create_text_message("Invalid parameter app_id")
 
-        app_secret = tool_parameters.get('app_secret', '')
+        app_secret = tool_parameters.get("app_secret", "")
         if not app_secret:
-            return self.create_text_message('Invalid parameter app_secret')
+            return self.create_text_message("Invalid parameter app_secret")
 
         headers = {
-            'Content-Type': 'application/json',
+            "Content-Type": "application/json",
         }
         params = {}
-        payload = {
-            "app_id": app_id,
-            "app_secret": app_secret
-        }
+        payload = {"app_id": app_id, "app_secret": app_secret}
 
         """
         {
@@ -45,6 +42,7 @@ class GetTenantAccessTokenTool(BuiltinTool):
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to get tenant access token, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to get tenant access token, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to get tenant access token. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/list_base_records.py b/api/core/tools/provider/builtin/feishu_base/tools/list_base_records.py
index 2a4229f137..e579d02f69 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/list_base_records.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/list_base_records.py
@@ -8,31 +8,31 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class ListBaseRecordsTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/bitable/v1/apps/{app_token}/tables/{table_id}/records/search"
 
-        access_token = tool_parameters.get('Authorization', '')
+        access_token = tool_parameters.get("Authorization", "")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token')
+            return self.create_text_message("Invalid parameter access_token")
 
-        app_token = tool_parameters.get('app_token', '')
+        app_token = tool_parameters.get("app_token", "")
         if not app_token:
-            return self.create_text_message('Invalid parameter app_token')
+            return self.create_text_message("Invalid parameter app_token")
 
-        table_id = tool_parameters.get('table_id', '')
+        table_id = tool_parameters.get("table_id", "")
         if not table_id:
-            return self.create_text_message('Invalid parameter table_id')
+            return self.create_text_message("Invalid parameter table_id")
 
-        page_token = tool_parameters.get('page_token', '')
-        page_size = tool_parameters.get('page_size', '')
-        sort_condition = tool_parameters.get('sort_condition', '')
-        filter_condition = tool_parameters.get('filter_condition', '')
+        page_token = tool_parameters.get("page_token", "")
+        page_size = tool_parameters.get("page_size", "")
+        sort_condition = tool_parameters.get("sort_condition", "")
+        filter_condition = tool_parameters.get("filter_condition", "")
 
         headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {access_token}",
         }
 
         params = {
@@ -40,22 +40,26 @@ class ListBaseRecordsTool(BuiltinTool):
             "page_size": page_size,
         }
 
-        payload = {
-            "automatic_fields": True
-        }
+        payload = {"automatic_fields": True}
         if sort_condition:
             payload["sort"] = json.loads(sort_condition)
         if filter_condition:
             payload["filter"] = json.loads(filter_condition)
 
         try:
-            res = httpx.post(url.format(app_token=app_token, table_id=table_id), headers=headers, params=params,
-                             json=payload, timeout=30)
+            res = httpx.post(
+                url.format(app_token=app_token, table_id=table_id),
+                headers=headers,
+                params=params,
+                json=payload,
+                timeout=30,
+            )
             res_json = res.json()
             if res.is_success:
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to list base records, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to list base records, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to list base records. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/list_base_tables.py b/api/core/tools/provider/builtin/feishu_base/tools/list_base_tables.py
index 6d82490eb3..4ec9a476bc 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/list_base_tables.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/list_base_tables.py
@@ -8,25 +8,25 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class ListBaseTablesTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/bitable/v1/apps/{app_token}/tables"
 
-        access_token = tool_parameters.get('Authorization', '')
+        access_token = tool_parameters.get("Authorization", "")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token')
+            return self.create_text_message("Invalid parameter access_token")
 
-        app_token = tool_parameters.get('app_token', '')
+        app_token = tool_parameters.get("app_token", "")
         if not app_token:
-            return self.create_text_message('Invalid parameter app_token')
+            return self.create_text_message("Invalid parameter app_token")
 
-        page_token = tool_parameters.get('page_token', '')
-        page_size = tool_parameters.get('page_size', '')
+        page_token = tool_parameters.get("page_token", "")
+        page_size = tool_parameters.get("page_size", "")
 
         headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {access_token}",
         }
 
         params = {
@@ -41,6 +41,7 @@ class ListBaseTablesTool(BuiltinTool):
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to list base tables, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to list base tables, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to list base tables. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/read_base_record.py b/api/core/tools/provider/builtin/feishu_base/tools/read_base_record.py
index bb4bd6c3a6..fb818f8380 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/read_base_record.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/read_base_record.py
@@ -8,40 +8,42 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class ReadBaseRecordTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/bitable/v1/apps/{app_token}/tables/{table_id}/records/{record_id}"
 
-        access_token = tool_parameters.get('Authorization', '')
+        access_token = tool_parameters.get("Authorization", "")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token')
+            return self.create_text_message("Invalid parameter access_token")
 
-        app_token = tool_parameters.get('app_token', '')
+        app_token = tool_parameters.get("app_token", "")
         if not app_token:
-            return self.create_text_message('Invalid parameter app_token')
+            return self.create_text_message("Invalid parameter app_token")
 
-        table_id = tool_parameters.get('table_id', '')
+        table_id = tool_parameters.get("table_id", "")
         if not table_id:
-            return self.create_text_message('Invalid parameter table_id')
+            return self.create_text_message("Invalid parameter table_id")
 
-        record_id = tool_parameters.get('record_id', '')
+        record_id = tool_parameters.get("record_id", "")
         if not record_id:
-            return self.create_text_message('Invalid parameter record_id')
+            return self.create_text_message("Invalid parameter record_id")
 
         headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {access_token}",
         }
 
         try:
-            res = httpx.get(url.format(app_token=app_token, table_id=table_id, record_id=record_id), headers=headers,
-                            timeout=30)
+            res = httpx.get(
+                url.format(app_token=app_token, table_id=table_id, record_id=record_id), headers=headers, timeout=30
+            )
             res_json = res.json()
             if res.is_success:
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to read base record, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to read base record, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to read base record. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_base/tools/update_base_record.py b/api/core/tools/provider/builtin/feishu_base/tools/update_base_record.py
index 6551053ce2..6d7e33f3ff 100644
--- a/api/core/tools/provider/builtin/feishu_base/tools/update_base_record.py
+++ b/api/core/tools/provider/builtin/feishu_base/tools/update_base_record.py
@@ -8,49 +8,53 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class UpdateBaseRecordTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         url = "https://open.feishu.cn/open-apis/bitable/v1/apps/{app_token}/tables/{table_id}/records/{record_id}"
 
-        access_token = tool_parameters.get('Authorization', '')
+        access_token = tool_parameters.get("Authorization", "")
         if not access_token:
-            return self.create_text_message('Invalid parameter access_token')
+            return self.create_text_message("Invalid parameter access_token")
 
-        app_token = tool_parameters.get('app_token', '')
+        app_token = tool_parameters.get("app_token", "")
         if not app_token:
-            return self.create_text_message('Invalid parameter app_token')
+            return self.create_text_message("Invalid parameter app_token")
 
-        table_id = tool_parameters.get('table_id', '')
+        table_id = tool_parameters.get("table_id", "")
         if not table_id:
-            return self.create_text_message('Invalid parameter table_id')
+            return self.create_text_message("Invalid parameter table_id")
 
-        record_id = tool_parameters.get('record_id', '')
+        record_id = tool_parameters.get("record_id", "")
         if not record_id:
-            return self.create_text_message('Invalid parameter record_id')
+            return self.create_text_message("Invalid parameter record_id")
 
-        fields = tool_parameters.get('fields', '')
+        fields = tool_parameters.get("fields", "")
         if not fields:
-            return self.create_text_message('Invalid parameter fields')
+            return self.create_text_message("Invalid parameter fields")
 
         headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f"Bearer {access_token}",
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {access_token}",
         }
 
         params = {}
-        payload = {
-            "fields": json.loads(fields)
-        }
+        payload = {"fields": json.loads(fields)}
 
         try:
-            res = httpx.put(url.format(app_token=app_token, table_id=table_id, record_id=record_id), headers=headers,
-                            params=params, json=payload, timeout=30)
+            res = httpx.put(
+                url.format(app_token=app_token, table_id=table_id, record_id=record_id),
+                headers=headers,
+                params=params,
+                json=payload,
+                timeout=30,
+            )
             res_json = res.json()
             if res.is_success:
                 return self.create_text_message(text=json.dumps(res_json))
             else:
                 return self.create_text_message(
-                    f"Failed to update base record, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to update base record, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to update base record. {}".format(e))
diff --git a/api/core/tools/provider/builtin/feishu_document/feishu_document.py b/api/core/tools/provider/builtin/feishu_document/feishu_document.py
index c4f8f26e2c..b0a1e393eb 100644
--- a/api/core/tools/provider/builtin/feishu_document/feishu_document.py
+++ b/api/core/tools/provider/builtin/feishu_document/feishu_document.py
@@ -5,11 +5,11 @@ from core.tools.utils.feishu_api_utils import FeishuRequest
 
 class FeishuDocumentProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
-        app_id = credentials.get('app_id')
-        app_secret = credentials.get('app_secret')
+        app_id = credentials.get("app_id")
+        app_secret = credentials.get("app_secret")
         if not app_id or not app_secret:
             raise ToolProviderCredentialValidationError("app_id and app_secret is required")
         try:
             assert FeishuRequest(app_id, app_secret).tenant_access_token is not None
         except Exception as e:
-            raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
+            raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/feishu_document/tools/create_document.py b/api/core/tools/provider/builtin/feishu_document/tools/create_document.py
index 0ff82e621b..090a0828e8 100644
--- a/api/core/tools/provider/builtin/feishu_document/tools/create_document.py
+++ b/api/core/tools/provider/builtin/feishu_document/tools/create_document.py
@@ -7,13 +7,13 @@ from core.tools.utils.feishu_api_utils import FeishuRequest
 
 class CreateDocumentTool(BuiltinTool):
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
-        app_id = self.runtime.credentials.get('app_id')
-        app_secret = self.runtime.credentials.get('app_secret')
+        app_id = self.runtime.credentials.get("app_id")
+        app_secret = self.runtime.credentials.get("app_secret")
         client = FeishuRequest(app_id, app_secret)
 
-        title = tool_parameters.get('title')
-        content = tool_parameters.get('content')
-        folder_token = tool_parameters.get('folder_token')
+        title = tool_parameters.get("title")
+        content = tool_parameters.get("content")
+        folder_token = tool_parameters.get("folder_token")
 
         res = client.create_document(title, content, folder_token)
         return self.create_json_message(res)
diff --git a/api/core/tools/provider/builtin/feishu_document/tools/get_document_raw_content.py b/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.py
similarity index 50%
rename from api/core/tools/provider/builtin/feishu_document/tools/get_document_raw_content.py
rename to api/core/tools/provider/builtin/feishu_document/tools/get_document_content.py
index 16ef90908b..c94a5f70ed 100644
--- a/api/core/tools/provider/builtin/feishu_document/tools/get_document_raw_content.py
+++ b/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.py
@@ -7,11 +7,13 @@ from core.tools.utils.feishu_api_utils import FeishuRequest
 
 class GetDocumentRawContentTool(BuiltinTool):
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
-        app_id = self.runtime.credentials.get('app_id')
-        app_secret = self.runtime.credentials.get('app_secret')
+        app_id = self.runtime.credentials.get("app_id")
+        app_secret = self.runtime.credentials.get("app_secret")
         client = FeishuRequest(app_id, app_secret)
 
-        document_id = tool_parameters.get('document_id')
+        document_id = tool_parameters.get("document_id")
+        mode = tool_parameters.get("mode")
+        lang = tool_parameters.get("lang", 0)
 
-        res = client.get_document_raw_content(document_id)
-        return self.create_json_message(res)
\ No newline at end of file
+        res = client.get_document_content(document_id, mode, lang)
+        return self.create_json_message(res)
diff --git a/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.yaml b/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.yaml
new file mode 100644
index 0000000000..51eda73a60
--- /dev/null
+++ b/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.yaml
@@ -0,0 +1,49 @@
+identity:
+  name: get_document_content
+  author: Doug Lea
+  label:
+    en_US: Get Document Content
+    zh_Hans: 获取飞书云文档的内容
+description:
+  human:
+    en_US: Get document content
+    zh_Hans: 获取飞书云文档的内容
+  llm: A tool for retrieving content from Feishu cloud documents.
+parameters:
+  - name: document_id
+    type: string
+    required: true
+    label:
+      en_US: document_id
+      zh_Hans: 飞书文档的唯一标识
+    human_description:
+      en_US: Unique identifier for a Feishu document. You can also input the document's URL.
+      zh_Hans: 飞书文档的唯一标识，支持输入文档的 URL。
+    llm_description: 飞书文档的唯一标识，支持输入文档的 URL。
+    form: llm
+
+  - name: mode
+    type: string
+    required: false
+    label:
+      en_US: mode
+      zh_Hans: 文档返回格式
+    human_description:
+      en_US: Format of the document return, optional values are text, markdown, can be empty, default is markdown.
+      zh_Hans: 文档返回格式，可选值有 text、markdown，可以为空，默认值为 markdown。
+    llm_description: 文档返回格式，可选值有 text、markdown，可以为空，默认值为 markdown。
+    form: llm
+
+  - name: lang
+    type: number
+    required: false
+    default: 0
+    label:
+      en_US: lang
+      zh_Hans: 指定@用户的语言
+    human_description:
+      en_US: |
+        Specifies the language for MentionUser, optional values are [0, 1]. 0: User's default name, 1: User's English name, default is 0.
+      zh_Hans: 指定返回的 MentionUser，即 @用户 的语言，可选值有 [0,1]。0：该用户的默认名称，1：该用户的英文名称，默认值为 0。
+    llm_description: 指定返回的 MentionUser，即 @用户 的语言，可选值有 [0,1]。0：该用户的默认名称，1：该用户的英文名称，默认值为 0。
+    form: llm
diff --git a/api/core/tools/provider/builtin/feishu_document/tools/get_document_raw_content.yaml b/api/core/tools/provider/builtin/feishu_document/tools/get_document_raw_content.yaml
deleted file mode 100644
index e5b0937e03..0000000000
--- a/api/core/tools/provider/builtin/feishu_document/tools/get_document_raw_content.yaml
+++ /dev/null
@@ -1,23 +0,0 @@
-identity:
-  name: get_document_raw_content
-  author: Doug Lea
-  label:
-    en_US: Get Document Raw Content
-    zh_Hans: 获取文档纯文本内容
-description:
-  human:
-    en_US: Get document raw content
-    zh_Hans: 获取文档纯文本内容
-  llm: A tool for getting the plain text content of Feishu documents
-parameters:
-  - name: document_id
-    type: string
-    required: true
-    label:
-      en_US: document_id
-      zh_Hans: 飞书文档的唯一标识
-    human_description:
-      en_US: Unique ID of Feishu document document_id
-      zh_Hans: 飞书文档的唯一标识 document_id
-    llm_description: 飞书文档的唯一标识 document_id
-    form: llm
diff --git a/api/core/tools/provider/builtin/feishu_document/tools/list_document_block.yaml b/api/core/tools/provider/builtin/feishu_document/tools/list_document_block.yaml
deleted file mode 100644
index d51e5a837c..0000000000
--- a/api/core/tools/provider/builtin/feishu_document/tools/list_document_block.yaml
+++ /dev/null
@@ -1,48 +0,0 @@
-identity:
-  name: list_document_block
-  author: Doug Lea
-  label:
-    en_US: List Document Block
-    zh_Hans: 获取飞书文档所有块
-description:
-  human:
-    en_US: List document block
-    zh_Hans: 获取飞书文档所有块的富文本内容并分页返回。
-  llm: A tool to get all blocks of Feishu documents
-parameters:
-  - name: document_id
-    type: string
-    required: true
-    label:
-      en_US: document_id
-      zh_Hans: 飞书文档的唯一标识
-    human_description:
-      en_US: Unique ID of Feishu document document_id
-      zh_Hans: 飞书文档的唯一标识 document_id
-    llm_description: 飞书文档的唯一标识 document_id
-    form: llm
-
-  - name: page_size
-    type: number
-    required: false
-    default: 500
-    label:
-      en_US: page_size
-      zh_Hans: 分页大小
-    human_description:
-      en_US: Paging size, the default and maximum value is 500.
-      zh_Hans: 分页大小, 默认值和最大值为 500。
-    llm_description: 分页大小, 表示一次请求最多返回多少条数据，默认值和最大值为 500。
-    form: llm
-
-  - name: page_token
-    type: string
-    required: false
-    label:
-      en_US: page_token
-      zh_Hans: 分页标记
-    human_description:
-      en_US: Pagination tag, used to paginate query results so that more items can be obtained in the next traversal.
-      zh_Hans: 分页标记，用于分页查询结果，以便下次遍历时获取更多项。
-    llm_description: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。
-    form: llm
diff --git a/api/core/tools/provider/builtin/feishu_document/tools/list_document_block.py b/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.py
similarity index 53%
rename from api/core/tools/provider/builtin/feishu_document/tools/list_document_block.py
rename to api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.py
index 97d17bdb04..572a7abf28 100644
--- a/api/core/tools/provider/builtin/feishu_document/tools/list_document_block.py
+++ b/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.py
@@ -7,13 +7,13 @@ from core.tools.utils.feishu_api_utils import FeishuRequest
 
 class ListDocumentBlockTool(BuiltinTool):
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
-        app_id = self.runtime.credentials.get('app_id')
-        app_secret = self.runtime.credentials.get('app_secret')
+        app_id = self.runtime.credentials.get("app_id")
+        app_secret = self.runtime.credentials.get("app_secret")
         client = FeishuRequest(app_id, app_secret)
 
-        document_id = tool_parameters.get('document_id')
-        page_size = tool_parameters.get('page_size', 500)
-        page_token = tool_parameters.get('page_token', '')
+        document_id = tool_parameters.get("document_id")
+        page_size = tool_parameters.get("page_size", 500)
+        page_token = tool_parameters.get("page_token", "")
 
-        res = client.list_document_block(document_id, page_token, page_size)
+        res = client.list_document_blocks(document_id, page_token, page_size)
         return self.create_json_message(res)
diff --git a/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.yaml b/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.yaml
new file mode 100644
index 0000000000..019ac98390
--- /dev/null
+++ b/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.yaml
@@ -0,0 +1,74 @@
+identity:
+  name: list_document_blocks
+  author: Doug Lea
+  label:
+    en_US: List Document Blocks
+    zh_Hans: 获取飞书文档所有块
+description:
+  human:
+    en_US: List document blocks
+    zh_Hans: 获取飞书文档所有块的富文本内容并分页返回
+  llm: A tool to get all blocks of Feishu documents
+parameters:
+  - name: document_id
+    type: string
+    required: true
+    label:
+      en_US: document_id
+      zh_Hans: 飞书文档的唯一标识
+    human_description:
+      en_US: Unique identifier for a Feishu document. You can also input the document's URL.
+      zh_Hans: 飞书文档的唯一标识，支持输入文档的 URL。
+    llm_description: 飞书文档的唯一标识，支持输入文档的 URL。
+    form: llm
+
+  - name: user_id_type
+    type: select
+    required: false
+    options:
+      - value: open_id
+        label:
+          en_US: open_id
+          zh_Hans: open_id
+      - value: union_id
+        label:
+          en_US: union_id
+          zh_Hans: union_id
+      - value: user_id
+        label:
+          en_US: user_id
+          zh_Hans: user_id
+    default: "open_id"
+    label:
+      en_US: user_id_type
+      zh_Hans: 用户 ID 类型
+    human_description:
+      en_US: User ID type, optional values are open_id, union_id, user_id, with a default value of open_id.
+      zh_Hans: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。
+    llm_description: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。
+    form: llm
+
+  - name: page_size
+    type: number
+    required: false
+    default: "500"
+    label:
+      en_US: page_size
+      zh_Hans: 分页大小
+    human_description:
+      en_US: Paging size, the default and maximum value is 500.
+      zh_Hans: 分页大小, 默认值和最大值为 500。
+    llm_description: 分页大小, 表示一次请求最多返回多少条数据，默认值和最大值为 500。
+    form: llm
+
+  - name: page_token
+    type: string
+    required: false
+    label:
+      en_US: page_token
+      zh_Hans: 分页标记
+    human_description:
+      en_US: Pagination token used to navigate through query results, allowing retrieval of additional items in subsequent requests.
+      zh_Hans: 分页标记，用于分页查询结果，以便下次遍历时获取更多项。
+    llm_description: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。
+    form: llm
diff --git a/api/core/tools/provider/builtin/feishu_document/tools/write_document.py b/api/core/tools/provider/builtin/feishu_document/tools/write_document.py
index 914a44dce6..6061250e48 100644
--- a/api/core/tools/provider/builtin/feishu_document/tools/write_document.py
+++ b/api/core/tools/provider/builtin/feishu_document/tools/write_document.py
@@ -7,13 +7,13 @@ from core.tools.utils.feishu_api_utils import FeishuRequest
 
 class CreateDocumentTool(BuiltinTool):
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
-        app_id = self.runtime.credentials.get('app_id')
-        app_secret = self.runtime.credentials.get('app_secret')
+        app_id = self.runtime.credentials.get("app_id")
+        app_secret = self.runtime.credentials.get("app_secret")
         client = FeishuRequest(app_id, app_secret)
 
-        document_id = tool_parameters.get('document_id')
-        content = tool_parameters.get('content')
-        position = tool_parameters.get('position')
+        document_id = tool_parameters.get("document_id")
+        content = tool_parameters.get("content")
+        position = tool_parameters.get("position")
 
         res = client.write_document(document_id, content, position)
         return self.create_json_message(res)
diff --git a/api/core/tools/provider/builtin/feishu_document/tools/write_document.yaml b/api/core/tools/provider/builtin/feishu_document/tools/write_document.yaml
index 8ee219d4a7..4282e3dcf3 100644
--- a/api/core/tools/provider/builtin/feishu_document/tools/write_document.yaml
+++ b/api/core/tools/provider/builtin/feishu_document/tools/write_document.yaml
@@ -17,33 +17,35 @@ parameters:
       en_US: document_id
       zh_Hans: 飞书文档的唯一标识
     human_description:
-      en_US: Unique ID of Feishu document document_id
-      zh_Hans: 飞书文档的唯一标识 document_id
-    llm_description: 飞书文档的唯一标识 document_id
+      en_US: Unique identifier for a Feishu document. You can also input the document's URL.
+      zh_Hans: 飞书文档的唯一标识，支持输入文档的 URL。
+    llm_description: 飞书文档的唯一标识，支持输入文档的 URL。
     form: llm
 
   - name: content
     type: string
     required: true
     label:
-      en_US: document content
-      zh_Hans: 文档内容
+      en_US: Plain text or Markdown content
+      zh_Hans: 纯文本或 Markdown 内容
     human_description:
-      en_US: Document content, supports markdown syntax, can be empty.
-      zh_Hans: 文档内容，支持 markdown 语法，可以为空。
-    llm_description:
+      en_US: Plain text or Markdown content. Note that embedded tables in the document should not have merged cells.
+      zh_Hans: 纯文本或 Markdown 内容。注意文档的内嵌套表格不允许有单元格合并。
+    llm_description: 纯文本或 Markdown 内容，注意文档的内嵌套表格不允许有单元格合并。
     form: llm
 
   - name: position
-    type: select
-    required: true
-    default: start
+    type: string
+    required: false
     label:
-      en_US: Choose where to add content
-      zh_Hans: 选择添加内容的位置
+      en_US: position
+      zh_Hans: 添加位置
     human_description:
-      en_US: Please fill in start or end to add content at the beginning or end of the document respectively.
-      zh_Hans: 请填入 start 或 end, 分别表示在文档开头(start)或结尾(end)添加内容。
+      en_US: |
+        Enumeration values: start or end. Use 'start' to add content at the beginning of the document, and 'end' to add content at the end. The default value is 'end'.
+      zh_Hans: 枚举值：start 或 end。使用 'start' 在文档开头添加内容，使用 'end' 在文档结尾添加内容，默认值为 'end'。
+    llm_description: |
+      枚举值 start、end，start: 在文档开头添加内容；end: 在文档结尾添加内容，默认值为 end。
     form: llm
     options:
       - value: start
@@ -54,3 +56,4 @@ parameters:
         label:
           en_US: end
           zh_Hans: 在文档结尾添加内容
+    default: start
diff --git a/api/core/tools/provider/builtin/feishu_message/feishu_message.py b/api/core/tools/provider/builtin/feishu_message/feishu_message.py
index 6d7fed330c..7b3adb9293 100644
--- a/api/core/tools/provider/builtin/feishu_message/feishu_message.py
+++ b/api/core/tools/provider/builtin/feishu_message/feishu_message.py
@@ -5,11 +5,11 @@ from core.tools.utils.feishu_api_utils import FeishuRequest
 
 class FeishuMessageProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
-        app_id = credentials.get('app_id')
-        app_secret = credentials.get('app_secret')
+        app_id = credentials.get("app_id")
+        app_secret = credentials.get("app_secret")
         if not app_id or not app_secret:
             raise ToolProviderCredentialValidationError("app_id and app_secret is required")
         try:
             assert FeishuRequest(app_id, app_secret).tenant_access_token is not None
         except Exception as e:
-            raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
+            raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/feishu_message/tools/send_bot_message.py b/api/core/tools/provider/builtin/feishu_message/tools/send_bot_message.py
index 74f6866ba3..1dd315d0e2 100644
--- a/api/core/tools/provider/builtin/feishu_message/tools/send_bot_message.py
+++ b/api/core/tools/provider/builtin/feishu_message/tools/send_bot_message.py
@@ -7,14 +7,14 @@ from core.tools.utils.feishu_api_utils import FeishuRequest
 
 class SendBotMessageTool(BuiltinTool):
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
-        app_id = self.runtime.credentials.get('app_id')
-        app_secret = self.runtime.credentials.get('app_secret')
+        app_id = self.runtime.credentials.get("app_id")
+        app_secret = self.runtime.credentials.get("app_secret")
         client = FeishuRequest(app_id, app_secret)
 
-        receive_id_type = tool_parameters.get('receive_id_type')
-        receive_id = tool_parameters.get('receive_id')
-        msg_type = tool_parameters.get('msg_type')
-        content = tool_parameters.get('content')
+        receive_id_type = tool_parameters.get("receive_id_type")
+        receive_id = tool_parameters.get("receive_id")
+        msg_type = tool_parameters.get("msg_type")
+        content = tool_parameters.get("content")
 
         res = client.send_bot_message(receive_id_type, receive_id, msg_type, content)
         return self.create_json_message(res)
diff --git a/api/core/tools/provider/builtin/feishu_message/tools/send_webhook_message.py b/api/core/tools/provider/builtin/feishu_message/tools/send_webhook_message.py
index 7159f59ffa..44e70e0a15 100644
--- a/api/core/tools/provider/builtin/feishu_message/tools/send_webhook_message.py
+++ b/api/core/tools/provider/builtin/feishu_message/tools/send_webhook_message.py
@@ -6,14 +6,14 @@ from core.tools.utils.feishu_api_utils import FeishuRequest
 
 
 class SendWebhookMessageTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) ->ToolInvokeMessage:
-        app_id = self.runtime.credentials.get('app_id')
-        app_secret = self.runtime.credentials.get('app_secret')
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
+        app_id = self.runtime.credentials.get("app_id")
+        app_secret = self.runtime.credentials.get("app_secret")
         client = FeishuRequest(app_id, app_secret)
 
-        webhook = tool_parameters.get('webhook')
-        msg_type = tool_parameters.get('msg_type')
-        content = tool_parameters.get('content')
+        webhook = tool_parameters.get("webhook")
+        msg_type = tool_parameters.get("msg_type")
+        content = tool_parameters.get("content")
 
         res = client.send_webhook_message(webhook, msg_type, content)
         return self.create_json_message(res)
diff --git a/api/core/tools/provider/builtin/firecrawl/firecrawl.py b/api/core/tools/provider/builtin/firecrawl/firecrawl.py
index 24dc35759d..01455d7206 100644
--- a/api/core/tools/provider/builtin/firecrawl/firecrawl.py
+++ b/api/core/tools/provider/builtin/firecrawl/firecrawl.py
@@ -7,15 +7,8 @@ class FirecrawlProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
         try:
             # Example validation using the ScrapeTool, only scraping title for minimize content
-            ScrapeTool().fork_tool_runtime(
-                runtime={"credentials": credentials}
-            ).invoke(
-                user_id='',
-                tool_parameters={
-                    "url": "https://google.com",
-                    "onlyIncludeTags": 'title'
-                }
+            ScrapeTool().fork_tool_runtime(runtime={"credentials": credentials}).invoke(
+                user_id="", tool_parameters={"url": "https://google.com", "onlyIncludeTags": "title"}
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/firecrawl/firecrawl_appx.py b/api/core/tools/provider/builtin/firecrawl/firecrawl_appx.py
index 3b3f78731b..d9fb6f04bc 100644
--- a/api/core/tools/provider/builtin/firecrawl/firecrawl_appx.py
+++ b/api/core/tools/provider/builtin/firecrawl/firecrawl_appx.py
@@ -13,85 +13,83 @@ logger = logging.getLogger(__name__)
 class FirecrawlApp:
     def __init__(self, api_key: str | None = None, base_url: str | None = None):
         self.api_key = api_key
-        self.base_url = base_url or 'https://api.firecrawl.dev'
+        self.base_url = base_url or "https://api.firecrawl.dev"
         if not self.api_key:
             raise ValueError("API key is required")
 
     def _prepare_headers(self, idempotency_key: str | None = None):
-        headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f'Bearer {self.api_key}'
-        }
+        headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"}
         if idempotency_key:
-            headers['Idempotency-Key'] = idempotency_key
+            headers["Idempotency-Key"] = idempotency_key
         return headers
 
     def _request(
-            self,
-            method: str,
-            url: str,
-            data: Mapping[str, Any] | None = None,
-            headers: Mapping[str, str] | None = None,
-            retries: int = 3,
-            backoff_factor: float = 0.3,
+        self,
+        method: str,
+        url: str,
+        data: Mapping[str, Any] | None = None,
+        headers: Mapping[str, str] | None = None,
+        retries: int = 3,
+        backoff_factor: float = 0.3,
     ) -> Mapping[str, Any] | None:
         if not headers:
             headers = self._prepare_headers()
         for i in range(retries):
             try:
                 response = requests.request(method, url, json=data, headers=headers)
-                response.raise_for_status()
                 return response.json()
-            except requests.exceptions.RequestException as e:
+            except requests.exceptions.RequestException:
                 if i < retries - 1:
-                    time.sleep(backoff_factor * (2 ** i))
+                    time.sleep(backoff_factor * (2**i))
                 else:
                     raise
         return None
 
     def scrape_url(self, url: str, **kwargs):
-        endpoint = f'{self.base_url}/v0/scrape'
-        data = {'url': url, **kwargs}
+        endpoint = f"{self.base_url}/v1/scrape"
+        data = {"url": url, **kwargs}
         logger.debug(f"Sent request to {endpoint=} body={data}")
-        response = self._request('POST', endpoint, data)
+        response = self._request("POST", endpoint, data)
         if response is None:
             raise HTTPError("Failed to scrape URL after multiple retries")
         return response
 
-    def search(self, query: str, **kwargs):
-        endpoint = f'{self.base_url}/v0/search'
-        data = {'query': query, **kwargs}
+    def map(self, url: str, **kwargs):
+        endpoint = f"{self.base_url}/v1/map"
+        data = {"url": url, **kwargs}
         logger.debug(f"Sent request to {endpoint=} body={data}")
-        response = self._request('POST', endpoint, data)
+        response = self._request("POST", endpoint, data)
         if response is None:
-            raise HTTPError("Failed to perform search after multiple retries")
+            raise HTTPError("Failed to perform map after multiple retries")
         return response
 
     def crawl_url(
-            self, url: str, wait: bool = True, poll_interval: int = 5, idempotency_key: str | None = None, **kwargs
+        self, url: str, wait: bool = True, poll_interval: int = 5, idempotency_key: str | None = None, **kwargs
     ):
-        endpoint = f'{self.base_url}/v0/crawl'
+        endpoint = f"{self.base_url}/v1/crawl"
         headers = self._prepare_headers(idempotency_key)
-        data = {'url': url, **kwargs}
+        data = {"url": url, **kwargs}
         logger.debug(f"Sent request to {endpoint=} body={data}")
-        response = self._request('POST', endpoint, data, headers)
+        response = self._request("POST", endpoint, data, headers)
         if response is None:
             raise HTTPError("Failed to initiate crawl after multiple retries")
-        job_id: str = response['jobId']
+        elif response.get("success") == False:
+            raise HTTPError(f'Failed to crawl: {response.get("error")}')
+        job_id: str = response["id"]
         if wait:
             return self._monitor_job_status(job_id=job_id, poll_interval=poll_interval)
         return response
 
     def check_crawl_status(self, job_id: str):
-        endpoint = f'{self.base_url}/v0/crawl/status/{job_id}'
-        response = self._request('GET', endpoint)
+        endpoint = f"{self.base_url}/v1/crawl/{job_id}"
+        response = self._request("GET", endpoint)
         if response is None:
             raise HTTPError(f"Failed to check status for job {job_id} after multiple retries")
         return response
 
     def cancel_crawl_job(self, job_id: str):
-        endpoint = f'{self.base_url}/v0/crawl/cancel/{job_id}'
-        response = self._request('DELETE', endpoint)
+        endpoint = f"{self.base_url}/v1/crawl/{job_id}"
+        response = self._request("DELETE", endpoint)
         if response is None:
             raise HTTPError(f"Failed to cancel job {job_id} after multiple retries")
         return response
@@ -99,9 +97,9 @@ class FirecrawlApp:
     def _monitor_job_status(self, job_id: str, poll_interval: int):
         while True:
             status = self.check_crawl_status(job_id)
-            if status['status'] == 'completed':
+            if status["status"] == "completed":
                 return status
-            elif status['status'] == 'failed':
+            elif status["status"] == "failed":
                 raise HTTPError(f'Job {job_id} failed: {status["error"]}')
             time.sleep(poll_interval)
 
@@ -109,7 +107,7 @@ class FirecrawlApp:
 def get_array_params(tool_parameters: dict[str, Any], key):
     param = tool_parameters.get(key)
     if param:
-        return param.split(',')
+        return param.split(",")
 
 
 def get_json_params(tool_parameters: dict[str, Any], key):
@@ -119,6 +117,6 @@ def get_json_params(tool_parameters: dict[str, Any], key):
             # support both single quotes and double quotes
             param = param.replace("'", '"')
             param = json.loads(param)
-        except:
+        except Exception:
             raise ValueError(f"Invalid {key} format.")
         return param
diff --git a/api/core/tools/provider/builtin/firecrawl/tools/crawl.py b/api/core/tools/provider/builtin/firecrawl/tools/crawl.py
index 08c40a4064..9675b8eb91 100644
--- a/api/core/tools/provider/builtin/firecrawl/tools/crawl.py
+++ b/api/core/tools/provider/builtin/firecrawl/tools/crawl.py
@@ -8,41 +8,38 @@ from core.tools.tool.builtin_tool import BuiltinTool
 class CrawlTool(BuiltinTool):
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
         """
-        the crawlerOptions and pageOptions comes from doc here:
+        the api doc:
         https://docs.firecrawl.dev/api-reference/endpoint/crawl
         """
-        app = FirecrawlApp(api_key=self.runtime.credentials['firecrawl_api_key'],
-                           base_url=self.runtime.credentials['base_url'])
-        crawlerOptions = {}
-        pageOptions = {}
-
-        wait_for_results = tool_parameters.get('wait_for_results', True)
-
-        crawlerOptions['excludes'] = get_array_params(tool_parameters, 'excludes')
-        crawlerOptions['includes'] = get_array_params(tool_parameters, 'includes')
-        crawlerOptions['returnOnlyUrls'] = tool_parameters.get('returnOnlyUrls', False)
-        crawlerOptions['maxDepth'] = tool_parameters.get('maxDepth')
-        crawlerOptions['mode'] = tool_parameters.get('mode')
-        crawlerOptions['ignoreSitemap'] = tool_parameters.get('ignoreSitemap', False)
-        crawlerOptions['limit'] = tool_parameters.get('limit', 5)
-        crawlerOptions['allowBackwardCrawling'] = tool_parameters.get('allowBackwardCrawling', False)
-        crawlerOptions['allowExternalContentLinks'] = tool_parameters.get('allowExternalContentLinks', False)
-
-        pageOptions['headers'] = get_json_params(tool_parameters, 'headers')
-        pageOptions['includeHtml'] = tool_parameters.get('includeHtml', False)
-        pageOptions['includeRawHtml'] = tool_parameters.get('includeRawHtml', False)
-        pageOptions['onlyIncludeTags'] = get_array_params(tool_parameters, 'onlyIncludeTags')
-        pageOptions['removeTags'] = get_array_params(tool_parameters, 'removeTags')
-        pageOptions['onlyMainContent'] = tool_parameters.get('onlyMainContent', False)
-        pageOptions['replaceAllPathsWithAbsolutePaths'] = tool_parameters.get('replaceAllPathsWithAbsolutePaths', False)
-        pageOptions['screenshot'] = tool_parameters.get('screenshot', False)
-        pageOptions['waitFor'] = tool_parameters.get('waitFor', 0)
-
-        crawl_result = app.crawl_url(
-            url=tool_parameters['url'],
-            wait=wait_for_results,
-            crawlerOptions=crawlerOptions,
-            pageOptions=pageOptions
+        app = FirecrawlApp(
+            api_key=self.runtime.credentials["firecrawl_api_key"], base_url=self.runtime.credentials["base_url"]
         )
 
+        scrapeOptions = {}
+        payload = {}
+
+        wait_for_results = tool_parameters.get("wait_for_results", True)
+
+        payload["excludePaths"] = get_array_params(tool_parameters, "excludePaths")
+        payload["includePaths"] = get_array_params(tool_parameters, "includePaths")
+        payload["maxDepth"] = tool_parameters.get("maxDepth")
+        payload["ignoreSitemap"] = tool_parameters.get("ignoreSitemap", False)
+        payload["limit"] = tool_parameters.get("limit", 5)
+        payload["allowBackwardLinks"] = tool_parameters.get("allowBackwardLinks", False)
+        payload["allowExternalLinks"] = tool_parameters.get("allowExternalLinks", False)
+        payload["webhook"] = tool_parameters.get("webhook")
+
+        scrapeOptions["formats"] = get_array_params(tool_parameters, "formats")
+        scrapeOptions["headers"] = get_json_params(tool_parameters, "headers")
+        scrapeOptions["includeTags"] = get_array_params(tool_parameters, "includeTags")
+        scrapeOptions["excludeTags"] = get_array_params(tool_parameters, "excludeTags")
+        scrapeOptions["onlyMainContent"] = tool_parameters.get("onlyMainContent", False)
+        scrapeOptions["waitFor"] = tool_parameters.get("waitFor", 0)
+        scrapeOptions = {k: v for k, v in scrapeOptions.items() if v not in {None, ""}}
+        payload["scrapeOptions"] = scrapeOptions or None
+
+        payload = {k: v for k, v in payload.items() if v not in {None, ""}}
+
+        crawl_result = app.crawl_url(url=tool_parameters["url"], wait=wait_for_results, **payload)
+
         return self.create_json_message(crawl_result)
diff --git a/api/core/tools/provider/builtin/firecrawl/tools/crawl.yaml b/api/core/tools/provider/builtin/firecrawl/tools/crawl.yaml
index 0c5399f973..0d7dbcac20 100644
--- a/api/core/tools/provider/builtin/firecrawl/tools/crawl.yaml
+++ b/api/core/tools/provider/builtin/firecrawl/tools/crawl.yaml
@@ -31,8 +31,21 @@ parameters:
       en_US: If you choose not to wait, it will directly return a job ID. You can use this job ID to check the crawling results or cancel the crawling task, which is usually very useful for a large-scale crawling task.
       zh_Hans: 如果选择不等待，则会直接返回一个job_id，可以通过job_id查询爬取结果或取消爬取任务，这通常对于一个大型爬取任务来说非常有用。
     form: form
-############## Crawl Options #######################
-  - name: includes
+############## Payload #######################
+  - name: excludePaths
+    type: string
+    label:
+      en_US: URL patterns to exclude
+      zh_Hans: 要排除的URL模式
+    placeholder:
+      en_US: Use commas to separate multiple tags
+      zh_Hans: 多个标签时使用半角逗号分隔
+    human_description:
+      en_US: |
+        Pages matching these patterns will be skipped. Example: blog/*, about/*
+      zh_Hans: 匹配这些模式的页面将被跳过。示例：blog/*, about/*
+    form: form
+  - name: includePaths
     type: string
     required: false
     label:
@@ -46,30 +59,6 @@ parameters:
        Only pages matching these patterns will be crawled. Example: blog/*, about/*
       zh_Hans: 只有与这些模式匹配的页面才会被爬取。示例：blog/*, about/*
     form: form
-  - name: excludes
-    type: string
-    label:
-      en_US: URL patterns to exclude
-      zh_Hans: 要排除的URL模式
-    placeholder:
-      en_US: Use commas to separate multiple tags
-      zh_Hans: 多个标签时使用半角逗号分隔
-    human_description:
-      en_US: |
-       Pages matching these patterns will be skipped. Example: blog/*, about/*
-      zh_Hans: 匹配这些模式的页面将被跳过。示例：blog/*, about/*
-    form: form
-  - name: returnOnlyUrls
-    type: boolean
-    default: false
-    label:
-      en_US: return Only Urls
-      zh_Hans: 仅返回URL
-    human_description:
-      en_US: |
-       If true, returns only the URLs as a list on the crawl status. Attention: the return response will be a list of URLs inside the data, not a list of documents.
-      zh_Hans: 只返回爬取到的网页链接，而不是网页内容本身。
-    form: form
   - name: maxDepth
     type: number
     label:
@@ -80,27 +69,10 @@ parameters:
       zh_Hans: 相对于输入的URL，爬取的最大深度。maxDepth为0时，仅抓取输入的URL。maxDepth为1时，抓取输入的URL以及所有一级深层页面。maxDepth为2时，抓取输入的URL以及所有两级深层页面。更高值遵循相同模式。
     form: form
     min: 0
-  - name: mode
-    type: select
-    required: false
-    form: form
-    options:
-      - value: default
-        label:
-          en_US: default
-      - value: fast
-        label:
-          en_US: fast
-    default: default
-    label:
-      en_US: Crawl Mode
-      zh_Hans: 爬取模式
-    human_description:
-      en_US: The crawling mode to use. Fast mode crawls 4x faster websites without sitemap, but may not be as accurate and shouldn't be used in heavy js-rendered websites.
-      zh_Hans: 使用fast模式将不会使用其站点地图，比普通模式快4倍，但是可能不够准确，也不适用于大量js渲染的网站。
+    default: 2
   - name: ignoreSitemap
     type: boolean
-    default: false
+    default: true
     label:
       en_US: ignore Sitemap
       zh_Hans: 忽略站点地图
@@ -120,7 +92,7 @@ parameters:
     form: form
     min: 1
     default: 5
-  - name: allowBackwardCrawling
+  - name: allowBackwardLinks
     type: boolean
     default: false
     label:
@@ -130,7 +102,7 @@ parameters:
       en_US: Enables the crawler to navigate from a specific URL to previously linked pages. For instance, from 'example.com/product/123' back to 'example.com/product'
       zh_Hans: 使爬虫能够从特定URL导航到之前链接的页面。例如，从'example.com/product/123'返回到'example.com/product'
     form: form
-  - name: allowExternalContentLinks
+  - name: allowExternalLinks
     type: boolean
     default: false
     label:
@@ -140,7 +112,30 @@ parameters:
       en_US: Allows the crawler to follow links to external websites.
       zh_Hans:
     form: form
-############## Page Options #######################
+  - name: webhook
+    type: string
+    label:
+      en_US: webhook
+    human_description:
+      en_US: |
+       The URL to send the webhook to. This will trigger for crawl started (crawl.started) ,every page crawled (crawl.page) and when the crawl is completed (crawl.completed or crawl.failed). The response will be the same as the /scrape endpoint.
+      zh_Hans: 发送Webhook的URL。这将在开始爬取（crawl.started）、每爬取一个页面（crawl.page）以及爬取完成（crawl.completed或crawl.failed）时触发。响应将与/scrape端点相同。
+    form: form
+############## Scrape Options #######################
+  - name: formats
+    type: string
+    label:
+      en_US: Formats
+      zh_Hans: 结果的格式
+    placeholder:
+      en_US: Use commas to separate multiple tags
+      zh_Hans: 多个标签时使用半角逗号分隔
+    human_description:
+      en_US: |
+        Formats to include in the output. Available options: markdown, html, rawHtml, links, screenshot
+      zh_Hans: |
+        输出中应包含的格式。可以填入: markdown, html, rawHtml, links, screenshot
+    form: form
   - name: headers
     type: string
     label:
@@ -155,30 +150,10 @@ parameters:
       en_US: Please enter an object that can be serialized in JSON
       zh_Hans: 请输入可以json序列化的对象
     form: form
-  - name: includeHtml
-    type: boolean
-    default: false
-    label:
-      en_US: include Html
-      zh_Hans: 包含HTML
-    human_description:
-      en_US: Include the HTML version of the content on page. Will output a html key in the response.
-      zh_Hans: 返回中包含一个HTML版本的内容，将以html键返回。
-    form: form
-  - name: includeRawHtml
-    type: boolean
-    default: false
-    label:
-      en_US: include Raw Html
-      zh_Hans: 包含原始HTML
-    human_description:
-      en_US: Include the raw HTML content of the page. Will output a rawHtml key in the response.
-      zh_Hans: 返回中包含一个原始HTML版本的内容，将以rawHtml键返回。
-    form: form
-  - name: onlyIncludeTags
+  - name: includeTags
     type: string
     label:
-      en_US: only Include Tags
+      en_US: Include Tags
       zh_Hans: 仅抓取这些标签
     placeholder:
       en_US: Use commas to separate multiple tags
@@ -189,6 +164,20 @@ parameters:
       zh_Hans: |
         仅在最终输出中包含HTML页面的这些标签，可以通过标签名、类或ID来设定，使用逗号分隔值。示例：script, .ad, #footer
     form: form
+  - name: excludeTags
+    type: string
+    label:
+      en_US: Exclude Tags
+      zh_Hans: 要移除这些标签
+    human_description:
+      en_US: |
+        Tags, classes and ids to remove from the page. Use comma separated values. Example: script, .ad, #footer
+      zh_Hans: |
+        要在最终输出中移除HTML页面的这些标签，可以通过标签名、类或ID来设定，使用逗号分隔值。示例：script, .ad, #footer
+    placeholder:
+      en_US: Use commas to separate multiple tags
+      zh_Hans: 多个标签时使用半角逗号分隔
+    form: form
   - name: onlyMainContent
     type: boolean
     default: false
@@ -199,40 +188,6 @@ parameters:
       en_US: Only return the main content of the page excluding headers, navs, footers, etc.
       zh_Hans: 只返回页面的主要内容，不包括头部、导航栏、尾部等。
     form: form
-  - name: removeTags
-    type: string
-    label:
-      en_US: remove Tags
-      zh_Hans: 要移除这些标签
-    human_description:
-      en_US: |
-        Tags, classes and ids to remove from the page. Use comma separated values. Example: script, .ad, #footer
-      zh_Hans: |
-        要在最终输出中移除HTML页面的这些标签，可以通过标签名、类或ID来设定，使用逗号分隔值。示例：script, .ad, #footer
-    placeholder:
-      en_US: Use commas to separate multiple tags
-      zh_Hans: 多个标签时使用半角逗号分隔
-    form: form
-  - name: replaceAllPathsWithAbsolutePaths
-    type: boolean
-    default: false
-    label:
-      en_US: All AbsolutePaths
-      zh_Hans: 使用绝对路径
-    human_description:
-      en_US: Replace all relative paths with absolute paths for images and links.
-      zh_Hans: 将所有图片和链接的相对路径替换为绝对路径。
-    form: form
-  - name: screenshot
-    type: boolean
-    default: false
-    label:
-      en_US: screenshot
-      zh_Hans: 截图
-    human_description:
-      en_US: Include a screenshot of the top of the page that you are scraping.
-      zh_Hans: 提供正在抓取的页面的顶部的截图。
-    form: form
   - name: waitFor
     type: number
     min: 0
diff --git a/api/core/tools/provider/builtin/firecrawl/tools/crawl_job.py b/api/core/tools/provider/builtin/firecrawl/tools/crawl_job.py
index fa6c1f87ee..0d2486c7ca 100644
--- a/api/core/tools/provider/builtin/firecrawl/tools/crawl_job.py
+++ b/api/core/tools/provider/builtin/firecrawl/tools/crawl_job.py
@@ -7,14 +7,15 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 class CrawlJobTool(BuiltinTool):
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
-        app = FirecrawlApp(api_key=self.runtime.credentials['firecrawl_api_key'],
-                           base_url=self.runtime.credentials['base_url'])
-        operation = tool_parameters.get('operation', 'get')
-        if operation == 'get':
-            result = app.check_crawl_status(job_id=tool_parameters['job_id'])
-        elif operation == 'cancel':
-            result = app.cancel_crawl_job(job_id=tool_parameters['job_id'])
+        app = FirecrawlApp(
+            api_key=self.runtime.credentials["firecrawl_api_key"], base_url=self.runtime.credentials["base_url"]
+        )
+        operation = tool_parameters.get("operation", "get")
+        if operation == "get":
+            result = app.check_crawl_status(job_id=tool_parameters["job_id"])
+        elif operation == "cancel":
+            result = app.cancel_crawl_job(job_id=tool_parameters["job_id"])
         else:
-            raise ValueError(f'Invalid operation: {operation}')
+            raise ValueError(f"Invalid operation: {operation}")
 
         return self.create_json_message(result)
diff --git a/api/core/tools/provider/builtin/firecrawl/tools/map.py b/api/core/tools/provider/builtin/firecrawl/tools/map.py
new file mode 100644
index 0000000000..bdfb5faeb8
--- /dev/null
+++ b/api/core/tools/provider/builtin/firecrawl/tools/map.py
@@ -0,0 +1,25 @@
+from typing import Any
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.provider.builtin.firecrawl.firecrawl_appx import FirecrawlApp
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class MapTool(BuiltinTool):
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
+        """
+        the api doc:
+        https://docs.firecrawl.dev/api-reference/endpoint/map
+        """
+        app = FirecrawlApp(
+            api_key=self.runtime.credentials["firecrawl_api_key"], base_url=self.runtime.credentials["base_url"]
+        )
+        payload = {}
+        payload["search"] = tool_parameters.get("search")
+        payload["ignoreSitemap"] = tool_parameters.get("ignoreSitemap", True)
+        payload["includeSubdomains"] = tool_parameters.get("includeSubdomains", False)
+        payload["limit"] = tool_parameters.get("limit", 5000)
+
+        map_result = app.map(url=tool_parameters["url"], **payload)
+
+        return self.create_json_message(map_result)
diff --git a/api/core/tools/provider/builtin/firecrawl/tools/map.yaml b/api/core/tools/provider/builtin/firecrawl/tools/map.yaml
new file mode 100644
index 0000000000..9913756983
--- /dev/null
+++ b/api/core/tools/provider/builtin/firecrawl/tools/map.yaml
@@ -0,0 +1,59 @@
+identity:
+  name: map
+  author: hjlarry
+  label:
+    en_US: Map
+    zh_Hans: 地图式快爬
+description:
+  human:
+    en_US: Input a website and get all the urls on the website - extremly fast
+    zh_Hans: 输入一个网站，快速获取网站上的所有网址。
+  llm: Input a website and get all the urls on the website - extremly fast
+parameters:
+  - name: url
+    type: string
+    required: true
+    label:
+      en_US: Start URL
+      zh_Hans: 起始URL
+    human_description:
+      en_US: The base URL to start crawling from.
+      zh_Hans: 要爬取网站的起始URL。
+    llm_description: The URL of the website that needs to be crawled. This is a required parameter.
+    form: llm
+  - name: search
+    type: string
+    label:
+      en_US: search
+      zh_Hans: 搜索查询
+    human_description:
+      en_US: Search query to use for mapping. During the Alpha phase, the 'smart' part of the search functionality is limited to 100 search results. However, if map finds more results, there is no limit applied.
+      zh_Hans: 用于映射的搜索查询。在Alpha阶段，搜索功能的“智能”部分限制为最多100个搜索结果。然而，如果地图找到了更多结果，则不施加任何限制。
+    llm_description: Search query to use for mapping. During the Alpha phase, the 'smart' part of the search functionality is limited to 100 search results. However, if map finds more results, there is no limit applied.
+    form: llm
+############## Page Options #######################
+  - name: ignoreSitemap
+    type: boolean
+    default: true
+    label:
+      en_US: ignore Sitemap
+      zh_Hans: 忽略站点地图
+    human_description:
+      en_US: Ignore the website sitemap when crawling.
+      zh_Hans: 爬取时忽略网站站点地图。
+    form: form
+  - name: includeSubdomains
+    type: boolean
+    default: false
+    label:
+      en_US: include Subdomains
+      zh_Hans: 包含子域名
+    form: form
+  - name: limit
+    type: number
+    min: 0
+    default: 5000
+    label:
+      en_US: Maximum results
+      zh_Hans: 最大结果数量
+    form: form
diff --git a/api/core/tools/provider/builtin/firecrawl/tools/scrape.py b/api/core/tools/provider/builtin/firecrawl/tools/scrape.py
index 91412da548..538b4a1fcb 100644
--- a/api/core/tools/provider/builtin/firecrawl/tools/scrape.py
+++ b/api/core/tools/provider/builtin/firecrawl/tools/scrape.py
@@ -6,34 +6,34 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class ScrapeTool(BuiltinTool):
-
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> list[ToolInvokeMessage]:
         """
-        the pageOptions and extractorOptions comes from doc here:
+        the api doc:
         https://docs.firecrawl.dev/api-reference/endpoint/scrape
         """
-        app = FirecrawlApp(api_key=self.runtime.credentials['firecrawl_api_key'],
-                           base_url=self.runtime.credentials['base_url'])
+        app = FirecrawlApp(
+            api_key=self.runtime.credentials["firecrawl_api_key"], base_url=self.runtime.credentials["base_url"]
+        )
 
-        pageOptions = {}
-        extractorOptions = {}
+        payload = {}
+        extract = {}
 
-        pageOptions['headers'] = get_json_params(tool_parameters, 'headers')
-        pageOptions['includeHtml'] = tool_parameters.get('includeHtml', False)
-        pageOptions['includeRawHtml'] = tool_parameters.get('includeRawHtml', False)
-        pageOptions['onlyIncludeTags'] = get_array_params(tool_parameters, 'onlyIncludeTags')
-        pageOptions['removeTags'] = get_array_params(tool_parameters, 'removeTags')
-        pageOptions['onlyMainContent'] = tool_parameters.get('onlyMainContent', False)
-        pageOptions['replaceAllPathsWithAbsolutePaths'] = tool_parameters.get('replaceAllPathsWithAbsolutePaths', False)
-        pageOptions['screenshot'] = tool_parameters.get('screenshot', False)
-        pageOptions['waitFor'] = tool_parameters.get('waitFor', 0)
+        payload["formats"] = get_array_params(tool_parameters, "formats")
+        payload["onlyMainContent"] = tool_parameters.get("onlyMainContent", True)
+        payload["includeTags"] = get_array_params(tool_parameters, "includeTags")
+        payload["excludeTags"] = get_array_params(tool_parameters, "excludeTags")
+        payload["headers"] = get_json_params(tool_parameters, "headers")
+        payload["waitFor"] = tool_parameters.get("waitFor", 0)
+        payload["timeout"] = tool_parameters.get("timeout", 30000)
 
-        extractorOptions['mode'] = tool_parameters.get('mode', '')
-        extractorOptions['extractionPrompt'] = tool_parameters.get('extractionPrompt', '')
-        extractorOptions['extractionSchema'] = get_json_params(tool_parameters, 'extractionSchema')
+        extract["schema"] = get_json_params(tool_parameters, "schema")
+        extract["systemPrompt"] = tool_parameters.get("systemPrompt")
+        extract["prompt"] = tool_parameters.get("prompt")
+        extract = {k: v for k, v in extract.items() if v not in {None, ""}}
+        payload["extract"] = extract or None
 
-        crawl_result = app.scrape_url(url=tool_parameters['url'],
-                                      pageOptions=pageOptions,
-                                      extractorOptions=extractorOptions)
+        payload = {k: v for k, v in payload.items() if v not in {None, ""}}
 
-        return self.create_json_message(crawl_result)
+        crawl_result = app.scrape_url(url=tool_parameters["url"], **payload)
+        markdown_result = crawl_result.get("data", {}).get("markdown", "")
+        return [self.create_text_message(markdown_result), self.create_json_message(crawl_result)]
diff --git a/api/core/tools/provider/builtin/firecrawl/tools/scrape.yaml b/api/core/tools/provider/builtin/firecrawl/tools/scrape.yaml
index 598429de5e..8f1f1348a4 100644
--- a/api/core/tools/provider/builtin/firecrawl/tools/scrape.yaml
+++ b/api/core/tools/provider/builtin/firecrawl/tools/scrape.yaml
@@ -6,8 +6,8 @@ identity:
     zh_Hans: 单页面抓取
 description:
   human:
-    en_US: Extract data from a single URL.
-    zh_Hans: 从单个URL抓取数据。
+    en_US: Turn any url into clean data.
+    zh_Hans: 将任何网址转换为干净的数据。
   llm: This tool is designed to scrape URL and output the content in Markdown format.
 parameters:
   - name: url
@@ -21,7 +21,59 @@ parameters:
       zh_Hans: 要抓取并提取数据的网站URL。
     llm_description: The URL of the website that needs to be crawled. This is a required parameter.
     form: llm
-############## Page Options #######################
+############## Payload #######################
+  - name: formats
+    type: string
+    label:
+      en_US: Formats
+      zh_Hans: 结果的格式
+    placeholder:
+      en_US: Use commas to separate multiple tags
+      zh_Hans: 多个标签时使用半角逗号分隔
+    human_description:
+      en_US: |
+        Formats to include in the output. Available options: markdown, html, rawHtml, links, screenshot, extract, screenshot@fullPage
+      zh_Hans: |
+        输出中应包含的格式。可以填入: markdown, html, rawHtml, links, screenshot, extract, screenshot@fullPage
+    form: form
+  - name: onlyMainContent
+    type: boolean
+    default: false
+    label:
+      en_US: only Main Content
+      zh_Hans: 仅抓取主要内容
+    human_description:
+      en_US: Only return the main content of the page excluding headers, navs, footers, etc.
+      zh_Hans: 只返回页面的主要内容，不包括头部、导航栏、尾部等。
+    form: form
+  - name: includeTags
+    type: string
+    label:
+      en_US: Include Tags
+      zh_Hans: 仅抓取这些标签
+    placeholder:
+      en_US: Use commas to separate multiple tags
+      zh_Hans: 多个标签时使用半角逗号分隔
+    human_description:
+      en_US: |
+        Only include tags, classes and ids from the page in the final output. Use comma separated values. Example: script, .ad, #footer
+      zh_Hans: |
+        仅在最终输出中包含HTML页面的这些标签，可以通过标签名、类或ID来设定，使用逗号分隔值。示例：script, .ad, #footer
+    form: form
+  - name: excludeTags
+    type: string
+    label:
+      en_US: Exclude Tags
+      zh_Hans: 要移除这些标签
+    human_description:
+      en_US: |
+        Tags, classes and ids to remove from the page. Use comma separated values. Example: script, .ad, #footer
+      zh_Hans: |
+        要在最终输出中移除HTML页面的这些标签，可以通过标签名、类或ID来设定，使用逗号分隔值。示例：script, .ad, #footer
+    placeholder:
+      en_US: Use commas to separate multiple tags
+      zh_Hans: 多个标签时使用半角逗号分隔
+    form: form
   - name: headers
     type: string
     label:
@@ -36,87 +88,10 @@ parameters:
       en_US: Please enter an object that can be serialized in JSON
       zh_Hans: 请输入可以json序列化的对象
     form: form
-  - name: includeHtml
-    type: boolean
-    default: false
-    label:
-      en_US: include Html
-      zh_Hans: 包含HTML
-    human_description:
-      en_US: Include the HTML version of the content on page. Will output a html key in the response.
-      zh_Hans: 返回中包含一个HTML版本的内容，将以html键返回。
-    form: form
-  - name: includeRawHtml
-    type: boolean
-    default: false
-    label:
-      en_US: include Raw Html
-      zh_Hans: 包含原始HTML
-    human_description:
-      en_US: Include the raw HTML content of the page. Will output a rawHtml key in the response.
-      zh_Hans: 返回中包含一个原始HTML版本的内容，将以rawHtml键返回。
-    form: form
-  - name: onlyIncludeTags
-    type: string
-    label:
-      en_US: only Include Tags
-      zh_Hans: 仅抓取这些标签
-    placeholder:
-      en_US: Use commas to separate multiple tags
-      zh_Hans: 多个标签时使用半角逗号分隔
-    human_description:
-      en_US: |
-        Only include tags, classes and ids from the page in the final output. Use comma separated values. Example: script, .ad, #footer
-      zh_Hans: |
-        仅在最终输出中包含HTML页面的这些标签，可以通过标签名、类或ID来设定，使用逗号分隔值。示例：script, .ad, #footer
-    form: form
-  - name: onlyMainContent
-    type: boolean
-    default: false
-    label:
-      en_US: only Main Content
-      zh_Hans: 仅抓取主要内容
-    human_description:
-      en_US: Only return the main content of the page excluding headers, navs, footers, etc.
-      zh_Hans: 只返回页面的主要内容，不包括头部、导航栏、尾部等。
-    form: form
-  - name: removeTags
-    type: string
-    label:
-      en_US: remove Tags
-      zh_Hans: 要移除这些标签
-    human_description:
-      en_US: |
-        Tags, classes and ids to remove from the page. Use comma separated values. Example: script, .ad, #footer
-      zh_Hans: |
-        要在最终输出中移除HTML页面的这些标签，可以通过标签名、类或ID来设定，使用逗号分隔值。示例：script, .ad, #footer
-    placeholder:
-      en_US: Use commas to separate multiple tags
-      zh_Hans: 多个标签时使用半角逗号分隔
-    form: form
-  - name: replaceAllPathsWithAbsolutePaths
-    type: boolean
-    default: false
-    label:
-      en_US: All AbsolutePaths
-      zh_Hans: 使用绝对路径
-    human_description:
-      en_US: Replace all relative paths with absolute paths for images and links.
-      zh_Hans: 将所有图片和链接的相对路径替换为绝对路径。
-    form: form
-  - name: screenshot
-    type: boolean
-    default: false
-    label:
-      en_US: screenshot
-      zh_Hans: 截图
-    human_description:
-      en_US: Include a screenshot of the top of the page that you are scraping.
-      zh_Hans: 提供正在抓取的页面的顶部的截图。
-    form: form
   - name: waitFor
     type: number
     min: 0
+    default: 0
     label:
       en_US: wait For
       zh_Hans: 等待时间
@@ -124,57 +99,54 @@ parameters:
       en_US: Wait x amount of milliseconds for the page to load to fetch content.
       zh_Hans: 等待x毫秒以使页面加载并获取内容。
     form: form
+  - name: timeout
+    type: number
+    min: 0
+    default: 30000
+    label:
+      en_US: Timeout
+    human_description:
+      en_US: Timeout in milliseconds for the request.
+      zh_Hans: 请求的超时时间（以毫秒为单位）。
+    form: form
 ############## Extractor Options #######################
-  - name: mode
-    type: select
-    options:
-      - value: markdown
-        label:
-          en_US: markdown
-      - value: llm-extraction
-        label:
-          en_US: llm-extraction
-      - value: llm-extraction-from-raw-html
-        label:
-          en_US: llm-extraction-from-raw-html
-      - value: llm-extraction-from-markdown
-        label:
-          en_US: llm-extraction-from-markdown
-    label:
-      en_US: Extractor Mode
-      zh_Hans: 提取模式
-    human_description:
-      en_US: |
-       The extraction mode to use. 'markdown': Returns the scraped markdown content, does not perform LLM extraction. 'llm-extraction': Extracts information from the cleaned and parsed content using LLM.
-      zh_Hans: 使用的提取模式。“markdown”：返回抓取的markdown内容，不执行LLM提取。“llm-extractioin”：使用LLM按Extractor Schema从内容中提取信息。
-    form: form
-  - name: extractionPrompt
-    type: string
-    label:
-      en_US: Extractor Prompt
-      zh_Hans: 提取时的提示词
-    human_description:
-      en_US: A prompt describing what information to extract from the page, applicable for LLM extraction modes.
-      zh_Hans: 当使用LLM提取模式时，用于给LLM描述提取规则。
-    form: form
-  - name: extractionSchema
+  - name: schema
     type: string
     label:
       en_US: Extractor Schema
       zh_Hans: 提取时的结构
     placeholder:
       en_US: Please enter an object that can be serialized in JSON
+      zh_Hans: 请输入可以json序列化的对象
     human_description:
       en_US: |
-        The schema for the data to be extracted, required only for LLM extraction modes. Example: {
+        The schema for the data to be extracted. Example: {
             "type": "object",
             "properties": {"company_mission": {"type": "string"}},
             "required": ["company_mission"]
         }
       zh_Hans: |
-       当使用LLM提取模式时，使用该结构去提取，示例：{
+       使用该结构去提取，示例：{
             "type": "object",
             "properties": {"company_mission": {"type": "string"}},
             "required": ["company_mission"]
         }
     form: form
+  - name: systemPrompt
+    type: string
+    label:
+      en_US: Extractor System Prompt
+      zh_Hans: 提取时的系统提示词
+    human_description:
+      en_US: The system prompt to use for the extraction.
+      zh_Hans: 用于提取的系统提示。
+    form: form
+  - name: prompt
+    type: string
+    label:
+      en_US: Extractor Prompt
+      zh_Hans: 提取时的提示词
+    human_description:
+      en_US: The prompt to use for the extraction without a schema.
+      zh_Hans: 用于无schema时提取的提示词
+    form: form
diff --git a/api/core/tools/provider/builtin/firecrawl/tools/search.py b/api/core/tools/provider/builtin/firecrawl/tools/search.py
deleted file mode 100644
index e2b2ac6b4d..0000000000
--- a/api/core/tools/provider/builtin/firecrawl/tools/search.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from typing import Any
-
-from core.tools.entities.tool_entities import ToolInvokeMessage
-from core.tools.provider.builtin.firecrawl.firecrawl_appx import FirecrawlApp
-from core.tools.tool.builtin_tool import BuiltinTool
-
-
-class SearchTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
-        """
-        the pageOptions and searchOptions comes from doc here:
-        https://docs.firecrawl.dev/api-reference/endpoint/search
-        """
-        app = FirecrawlApp(api_key=self.runtime.credentials['firecrawl_api_key'],
-                           base_url=self.runtime.credentials['base_url'])
-        pageOptions = {}
-        pageOptions['onlyMainContent'] = tool_parameters.get('onlyMainContent', False)
-        pageOptions['fetchPageContent'] = tool_parameters.get('fetchPageContent', True)
-        pageOptions['includeHtml'] = tool_parameters.get('includeHtml', False)
-        pageOptions['includeRawHtml'] = tool_parameters.get('includeRawHtml', False)
-        searchOptions = {'limit': tool_parameters.get('limit')}
-        search_result = app.search(
-            query=tool_parameters['keyword'],
-            pageOptions=pageOptions,
-            searchOptions=searchOptions
-        )
-
-        return self.create_json_message(search_result)
diff --git a/api/core/tools/provider/builtin/firecrawl/tools/search.yaml b/api/core/tools/provider/builtin/firecrawl/tools/search.yaml
deleted file mode 100644
index 29df0cfaaa..0000000000
--- a/api/core/tools/provider/builtin/firecrawl/tools/search.yaml
+++ /dev/null
@@ -1,75 +0,0 @@
-identity:
-  name: search
-  author: ahasasjeb
-  label:
-    en_US: Search
-    zh_Hans: 搜索
-description:
-  human:
-    en_US: Search, and output in Markdown format
-    zh_Hans: 搜索，并且以Markdown格式输出
-  llm: This tool can perform online searches and convert the results to Markdown format.
-parameters:
-  - name: keyword
-    type: string
-    required: true
-    label:
-      en_US: keyword
-      zh_Hans: 关键词
-    human_description:
-      en_US: Input keywords to use Firecrawl API for search.
-      zh_Hans: 输入关键词即可使用Firecrawl API进行搜索。
-    llm_description: Efficiently extract keywords from user text.
-    form: llm
-############## Page Options #######################
-  - name: onlyMainContent
-    type: boolean
-    default: false
-    label:
-      en_US: only Main Content
-      zh_Hans: 仅抓取主要内容
-    human_description:
-      en_US: Only return the main content of the page excluding headers, navs, footers, etc.
-      zh_Hans: 只返回页面的主要内容，不包括头部、导航栏、尾部等。
-    form: form
-  - name: fetchPageContent
-    type: boolean
-    default: true
-    label:
-      en_US: fetch Page Content
-      zh_Hans: 抓取页面内容
-    human_description:
-      en_US: Fetch the content of each page. If false, defaults to a basic fast serp API.
-      zh_Hans: 获取每个页面的内容。如果为否，则使用基本的快速搜索结果页面API。
-    form: form
-  - name: includeHtml
-    type: boolean
-    default: false
-    label:
-      en_US: include Html
-      zh_Hans: 包含HTML
-    human_description:
-      en_US: Include the HTML version of the content on page. Will output a html key in the response.
-      zh_Hans: 返回中包含一个HTML版本的内容，将以html键返回。
-    form: form
-  - name: includeRawHtml
-    type: boolean
-    default: false
-    label:
-      en_US: include Raw Html
-      zh_Hans: 包含原始HTML
-    human_description:
-      en_US: Include the raw HTML content of the page. Will output a rawHtml key in the response.
-      zh_Hans: 返回中包含一个原始HTML版本的内容，将以rawHtml键返回。
-    form: form
-############## Search Options #######################
-  - name: limit
-    type: number
-    min: 0
-    label:
-      en_US: Maximum results
-      zh_Hans: 最大结果数量
-    human_description:
-      en_US: Maximum number of results. Max is 20 during beta.
-      zh_Hans: 最大结果数量。在测试阶段，最大为20。
-    form: form
diff --git a/api/core/tools/provider/builtin/gaode/gaode.py b/api/core/tools/provider/builtin/gaode/gaode.py
index b55d93e07b..49a8e537fb 100644
--- a/api/core/tools/provider/builtin/gaode/gaode.py
+++ b/api/core/tools/provider/builtin/gaode/gaode.py
@@ -9,17 +9,19 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class GaodeProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
         try:
-            if 'api_key' not in credentials or not credentials.get('api_key'):
+            if "api_key" not in credentials or not credentials.get("api_key"):
                 raise ToolProviderCredentialValidationError("Gaode API key is required.")
 
             try:
-                response = requests.get(url="https://restapi.amap.com/v3/geocode/geo?address={address}&key={apikey}"
-                                            "".format(address=urllib.parse.quote('广东省广州市天河区广州塔'),
-                                                      apikey=credentials.get('api_key')))
-                if response.status_code == 200 and (response.json()).get('info') == 'OK':
+                response = requests.get(
+                    url="https://restapi.amap.com/v3/geocode/geo?address={address}&key={apikey}".format(
+                        address=urllib.parse.quote("广东省广州市天河区广州塔"), apikey=credentials.get("api_key")
+                    )
+                )
+                if response.status_code == 200 and (response.json()).get("info") == "OK":
                     pass
                 else:
-                    raise ToolProviderCredentialValidationError((response.json()).get('info'))
+                    raise ToolProviderCredentialValidationError((response.json()).get("info"))
             except Exception as e:
                 raise ToolProviderCredentialValidationError("Gaode API Key is invalid. {}".format(e))
         except Exception as e:
diff --git a/api/core/tools/provider/builtin/gaode/tools/gaode_weather.py b/api/core/tools/provider/builtin/gaode/tools/gaode_weather.py
index efd11cedce..ea06e2ce61 100644
--- a/api/core/tools/provider/builtin/gaode/tools/gaode_weather.py
+++ b/api/core/tools/provider/builtin/gaode/tools/gaode_weather.py
@@ -8,50 +8,57 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class GaodeRepositoriesTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        city = tool_parameters.get('city', '')
+        city = tool_parameters.get("city", "")
         if not city:
-            return self.create_text_message('Please tell me your city')
+            return self.create_text_message("Please tell me your city")
 
-        if 'api_key' not in self.runtime.credentials or not self.runtime.credentials.get('api_key'):
+        if "api_key" not in self.runtime.credentials or not self.runtime.credentials.get("api_key"):
             return self.create_text_message("Gaode API key is required.")
 
         try:
             s = requests.session()
-            api_domain = 'https://restapi.amap.com/v3'
-            city_response = s.request(method='GET', headers={"Content-Type": "application/json; charset=utf-8"},
-                                      url="{url}/config/district?keywords={keywords}"
-                                          "&subdistrict=0&extensions=base&key={apikey}"
-                                          "".format(url=api_domain, keywords=city,
-                                                    apikey=self.runtime.credentials.get('api_key')))
+            api_domain = "https://restapi.amap.com/v3"
+            city_response = s.request(
+                method="GET",
+                headers={"Content-Type": "application/json; charset=utf-8"},
+                url="{url}/config/district?keywords={keywords}&subdistrict=0&extensions=base&key={apikey}".format(
+                    url=api_domain, keywords=city, apikey=self.runtime.credentials.get("api_key")
+                ),
+            )
             City_data = city_response.json()
-            if city_response.status_code == 200 and City_data.get('info') == 'OK':
-                if len(City_data.get('districts')) > 0:
-                    CityCode = City_data['districts'][0]['adcode']
-                    weatherInfo_response = s.request(method='GET',
-                                                     url="{url}/weather/weatherInfo?city={citycode}&extensions=all&key={apikey}&output=json"
-                                                         "".format(url=api_domain, citycode=CityCode,
-                                                                   apikey=self.runtime.credentials.get('api_key')))
+            if city_response.status_code == 200 and City_data.get("info") == "OK":
+                if len(City_data.get("districts")) > 0:
+                    CityCode = City_data["districts"][0]["adcode"]
+                    weatherInfo_response = s.request(
+                        method="GET",
+                        url="{url}/weather/weatherInfo?city={citycode}&extensions=all&key={apikey}&output=json"
+                        "".format(url=api_domain, citycode=CityCode, apikey=self.runtime.credentials.get("api_key")),
+                    )
                     weatherInfo_data = weatherInfo_response.json()
-                    if weatherInfo_response.status_code == 200 and weatherInfo_data.get('info') == 'OK':
+                    if weatherInfo_response.status_code == 200 and weatherInfo_data.get("info") == "OK":
                         contents = []
-                        if len(weatherInfo_data.get('forecasts')) > 0:
-                            for item in weatherInfo_data['forecasts'][0]['casts']:
+                        if len(weatherInfo_data.get("forecasts")) > 0:
+                            for item in weatherInfo_data["forecasts"][0]["casts"]:
                                 content = {}
-                                content['date'] = item.get('date')
-                                content['week'] = item.get('week')
-                                content['dayweather'] = item.get('dayweather')
-                                content['daytemp_float'] = item.get('daytemp_float')
-                                content['daywind'] = item.get('daywind')
-                                content['nightweather'] = item.get('nightweather')
-                                content['nighttemp_float'] = item.get('nighttemp_float')
+                                content["date"] = item.get("date")
+                                content["week"] = item.get("week")
+                                content["dayweather"] = item.get("dayweather")
+                                content["daytemp_float"] = item.get("daytemp_float")
+                                content["daywind"] = item.get("daywind")
+                                content["nightweather"] = item.get("nightweather")
+                                content["nighttemp_float"] = item.get("nighttemp_float")
                                 contents.append(content)
                             s.close()
-                            return self.create_text_message(self.summary(user_id=user_id, content=json.dumps(contents, ensure_ascii=False)))
+                            return self.create_text_message(
+                                self.summary(user_id=user_id, content=json.dumps(contents, ensure_ascii=False))
+                            )
             s.close()
-            return self.create_text_message(f'No weather information for {city} was found.')
+            return self.create_text_message(f"No weather information for {city} was found.")
         except Exception as e:
             return self.create_text_message("Gaode API Key and Api Version is invalid. {}".format(e))
diff --git a/api/core/tools/provider/builtin/getimgai/getimgai.py b/api/core/tools/provider/builtin/getimgai/getimgai.py
index c81d5fa333..bbd07d120f 100644
--- a/api/core/tools/provider/builtin/getimgai/getimgai.py
+++ b/api/core/tools/provider/builtin/getimgai/getimgai.py
@@ -7,16 +7,13 @@ class GetImgAIProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
         try:
             # Example validation using the text2image tool
-            Text2ImageTool().fork_tool_runtime(
-                runtime={"credentials": credentials}
-            ).invoke(
-                user_id='',
+            Text2ImageTool().fork_tool_runtime(runtime={"credentials": credentials}).invoke(
+                user_id="",
                 tool_parameters={
                     "prompt": "A fire egg",
                     "response_format": "url",
                     "style": "photorealism",
-                }
+                },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/getimgai/getimgai_appx.py b/api/core/tools/provider/builtin/getimgai/getimgai_appx.py
index e28c57649c..0e95a5f654 100644
--- a/api/core/tools/provider/builtin/getimgai/getimgai_appx.py
+++ b/api/core/tools/provider/builtin/getimgai/getimgai_appx.py
@@ -8,18 +8,16 @@ from requests.exceptions import HTTPError
 
 logger = logging.getLogger(__name__)
 
+
 class GetImgAIApp:
     def __init__(self, api_key: str | None = None, base_url: str | None = None):
         self.api_key = api_key
-        self.base_url = base_url or 'https://api.getimg.ai/v1'
+        self.base_url = base_url or "https://api.getimg.ai/v1"
         if not self.api_key:
             raise ValueError("API key is required")
 
     def _prepare_headers(self):
-        headers = {
-            'Content-Type': 'application/json',
-            'Authorization': f'Bearer {self.api_key}'
-        }
+        headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"}
         return headers
 
     def _request(
@@ -38,22 +36,20 @@ class GetImgAIApp:
                 return response.json()
             except requests.exceptions.RequestException as e:
                 if i < retries - 1 and isinstance(e, HTTPError) and e.response.status_code >= 500:
-                    time.sleep(backoff_factor * (2 ** i))
+                    time.sleep(backoff_factor * (2**i))
                 else:
                     raise
         return None
 
-    def text2image(
-        self, mode: str, **kwargs
-    ):
-        data = kwargs['params']
-        if not data.get('prompt'):
+    def text2image(self, mode: str, **kwargs):
+        data = kwargs["params"]
+        if not data.get("prompt"):
             raise ValueError("Prompt is required")
 
-        endpoint = f'{self.base_url}/{mode}/text-to-image'
+        endpoint = f"{self.base_url}/{mode}/text-to-image"
         headers = self._prepare_headers()
         logger.debug(f"Send request to {endpoint=} body={data}")
-        response = self._request('POST', endpoint, data, headers)
+        response = self._request("POST", endpoint, data, headers)
         if response is None:
             raise HTTPError("Failed to initiate getimg.ai after multiple retries")
         return response
diff --git a/api/core/tools/provider/builtin/getimgai/tools/text2image.py b/api/core/tools/provider/builtin/getimgai/tools/text2image.py
index dad7314479..c556749552 100644
--- a/api/core/tools/provider/builtin/getimgai/tools/text2image.py
+++ b/api/core/tools/provider/builtin/getimgai/tools/text2image.py
@@ -7,28 +7,28 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class Text2ImageTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        app = GetImgAIApp(api_key=self.runtime.credentials['getimg_api_key'], base_url=self.runtime.credentials['base_url'])
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        app = GetImgAIApp(
+            api_key=self.runtime.credentials["getimg_api_key"], base_url=self.runtime.credentials["base_url"]
+        )
 
         options = {
-            'style': tool_parameters.get('style'),
-            'prompt': tool_parameters.get('prompt'),
-            'aspect_ratio': tool_parameters.get('aspect_ratio'),
-            'output_format': tool_parameters.get('output_format', 'jpeg'),
-            'response_format': tool_parameters.get('response_format', 'url'),
-            'width': tool_parameters.get('width'),
-            'height': tool_parameters.get('height'),
-            'steps': tool_parameters.get('steps'),
-            'negative_prompt': tool_parameters.get('negative_prompt'),
-            'prompt_2': tool_parameters.get('prompt_2'),
+            "style": tool_parameters.get("style"),
+            "prompt": tool_parameters.get("prompt"),
+            "aspect_ratio": tool_parameters.get("aspect_ratio"),
+            "output_format": tool_parameters.get("output_format", "jpeg"),
+            "response_format": tool_parameters.get("response_format", "url"),
+            "width": tool_parameters.get("width"),
+            "height": tool_parameters.get("height"),
+            "steps": tool_parameters.get("steps"),
+            "negative_prompt": tool_parameters.get("negative_prompt"),
+            "prompt_2": tool_parameters.get("prompt_2"),
         }
         options = {k: v for k, v in options.items() if v}
 
-        text2image_result = app.text2image(
-            mode=tool_parameters.get('mode', 'essential-v2'),
-            params=options,
-            wait=True
-        )
+        text2image_result = app.text2image(mode=tool_parameters.get("mode", "essential-v2"), params=options, wait=True)
 
         if not isinstance(text2image_result, str):
             text2image_result = json.dumps(text2image_result, ensure_ascii=False, indent=4)
diff --git a/api/core/tools/provider/builtin/github/github.py b/api/core/tools/provider/builtin/github/github.py
index 9275504208..87a34ac3e8 100644
--- a/api/core/tools/provider/builtin/github/github.py
+++ b/api/core/tools/provider/builtin/github/github.py
@@ -4,28 +4,28 @@ from core.tools.errors import ToolProviderCredentialValidationError
 from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
 
 
-class GihubProvider(BuiltinToolProviderController):
+class GithubProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
         try:
-            if 'access_tokens' not in credentials or not credentials.get('access_tokens'):
+            if "access_tokens" not in credentials or not credentials.get("access_tokens"):
                 raise ToolProviderCredentialValidationError("Github API Access Tokens is required.")
-            if 'api_version' not in credentials or not credentials.get('api_version'):
-                api_version = '2022-11-28'
+            if "api_version" not in credentials or not credentials.get("api_version"):
+                api_version = "2022-11-28"
             else:
-                api_version = credentials.get('api_version')
+                api_version = credentials.get("api_version")
 
             try:
                 headers = {
                     "Content-Type": "application/vnd.github+json",
                     "Authorization": f"Bearer {credentials.get('access_tokens')}",
-                    "X-GitHub-Api-Version": api_version
+                    "X-GitHub-Api-Version": api_version,
                 }
 
                 response = requests.get(
-                    url="https://api.github.com/search/users?q={account}".format(account='charli117'),
-                    headers=headers)
+                    url="https://api.github.com/search/users?q={account}".format(account="charli117"), headers=headers
+                )
                 if response.status_code != 200:
-                    raise ToolProviderCredentialValidationError((response.json()).get('message'))
+                    raise ToolProviderCredentialValidationError((response.json()).get("message"))
             except Exception as e:
                 raise ToolProviderCredentialValidationError("Github API Key and Api Version is invalid. {}".format(e))
         except Exception as e:
diff --git a/api/core/tools/provider/builtin/github/tools/github_repositories.py b/api/core/tools/provider/builtin/github/tools/github_repositories.py
index a2f1e07fd4..32f9922e65 100644
--- a/api/core/tools/provider/builtin/github/tools/github_repositories.py
+++ b/api/core/tools/provider/builtin/github/tools/github_repositories.py
@@ -9,54 +9,62 @@ from core.tools.entities.tool_entities import ToolInvokeMessage
 from core.tools.tool.builtin_tool import BuiltinTool
 
 
-class GihubRepositoriesTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+class GithubRepositoriesTool(BuiltinTool):
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        top_n = tool_parameters.get('top_n', 5)
-        query = tool_parameters.get('query', '')
+        top_n = tool_parameters.get("top_n", 5)
+        query = tool_parameters.get("query", "")
         if not query:
-            return self.create_text_message('Please input symbol')
+            return self.create_text_message("Please input symbol")
 
-        if 'access_tokens' not in self.runtime.credentials or not self.runtime.credentials.get('access_tokens'):
+        if "access_tokens" not in self.runtime.credentials or not self.runtime.credentials.get("access_tokens"):
             return self.create_text_message("Github API Access Tokens is required.")
-        if 'api_version' not in self.runtime.credentials or not self.runtime.credentials.get('api_version'):
-            api_version = '2022-11-28'
+        if "api_version" not in self.runtime.credentials or not self.runtime.credentials.get("api_version"):
+            api_version = "2022-11-28"
         else:
-            api_version = self.runtime.credentials.get('api_version')
+            api_version = self.runtime.credentials.get("api_version")
 
         try:
             headers = {
                 "Content-Type": "application/vnd.github+json",
                 "Authorization": f"Bearer {self.runtime.credentials.get('access_tokens')}",
-                "X-GitHub-Api-Version": api_version
+                "X-GitHub-Api-Version": api_version,
             }
             s = requests.session()
-            api_domain = 'https://api.github.com'
-            response = s.request(method='GET', headers=headers,
-                                 url=f"{api_domain}/search/repositories?"
-                                     f"q={quote(query)}&sort=stars&per_page={top_n}&order=desc")
+            api_domain = "https://api.github.com"
+            response = s.request(
+                method="GET",
+                headers=headers,
+                url=f"{api_domain}/search/repositories?q={quote(query)}&sort=stars&per_page={top_n}&order=desc",
+            )
             response_data = response.json()
-            if response.status_code == 200 and isinstance(response_data.get('items'), list):
+            if response.status_code == 200 and isinstance(response_data.get("items"), list):
                 contents = []
-                if len(response_data.get('items')) > 0:
-                    for item in response_data.get('items'):
+                if len(response_data.get("items")) > 0:
+                    for item in response_data.get("items"):
                         content = {}
-                        updated_at_object = datetime.strptime(item['updated_at'], "%Y-%m-%dT%H:%M:%SZ")
-                        content['owner'] = item['owner']['login']
-                        content['name'] = item['name']
-                        content['description'] = item['description'][:100] + '...' if len(item['description']) > 100 else item['description']
-                        content['url'] = item['html_url']
-                        content['star'] = item['watchers']
-                        content['forks'] = item['forks']
-                        content['updated'] = updated_at_object.strftime("%Y-%m-%d")
+                        updated_at_object = datetime.strptime(item["updated_at"], "%Y-%m-%dT%H:%M:%SZ")
+                        content["owner"] = item["owner"]["login"]
+                        content["name"] = item["name"]
+                        content["description"] = (
+                            item["description"][:100] + "..." if len(item["description"]) > 100 else item["description"]
+                        )
+                        content["url"] = item["html_url"]
+                        content["star"] = item["watchers"]
+                        content["forks"] = item["forks"]
+                        content["updated"] = updated_at_object.strftime("%Y-%m-%d")
                         contents.append(content)
                     s.close()
-                    return self.create_text_message(self.summary(user_id=user_id, content=json.dumps(contents, ensure_ascii=False)))
+                    return self.create_text_message(
+                        self.summary(user_id=user_id, content=json.dumps(contents, ensure_ascii=False))
+                    )
                 else:
-                    return self.create_text_message(f'No items related to {query} were found.')
+                    return self.create_text_message(f"No items related to {query} were found.")
             else:
-                return self.create_text_message((response.json()).get('message'))
+                return self.create_text_message((response.json()).get("message"))
         except Exception as e:
             return self.create_text_message("Github API Key and Api Version is invalid. {}".format(e))
diff --git a/api/core/tools/provider/builtin/gitlab/gitlab.py b/api/core/tools/provider/builtin/gitlab/gitlab.py
index 0c13ec662a..9bd4a0bd52 100644
--- a/api/core/tools/provider/builtin/gitlab/gitlab.py
+++ b/api/core/tools/provider/builtin/gitlab/gitlab.py
@@ -9,13 +9,13 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class GitlabProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
-            if 'access_tokens' not in credentials or not credentials.get('access_tokens'):
+            if "access_tokens" not in credentials or not credentials.get("access_tokens"):
                 raise ToolProviderCredentialValidationError("Gitlab Access Tokens is required.")
-            
-            if 'site_url' not in credentials or not credentials.get('site_url'):
-                site_url = 'https://gitlab.com'
+
+            if "site_url" not in credentials or not credentials.get("site_url"):
+                site_url = "https://gitlab.com"
             else:
-                site_url = credentials.get('site_url')
+                site_url = credentials.get("site_url")
 
             try:
                 headers = {
@@ -23,12 +23,10 @@ class GitlabProvider(BuiltinToolProviderController):
                     "Authorization": f"Bearer {credentials.get('access_tokens')}",
                 }
 
-                response = requests.get(
-                    url= f"{site_url}/api/v4/user",
-                    headers=headers)
+                response = requests.get(url=f"{site_url}/api/v4/user", headers=headers)
                 if response.status_code != 200:
-                    raise ToolProviderCredentialValidationError((response.json()).get('message'))
+                    raise ToolProviderCredentialValidationError((response.json()).get("message"))
             except Exception as e:
                 raise ToolProviderCredentialValidationError("Gitlab Access Tokens is invalid. {}".format(e))
         except Exception as e:
-            raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
+            raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/gitlab/tools/gitlab_commits.py b/api/core/tools/provider/builtin/gitlab/tools/gitlab_commits.py
index 880d722bda..45ab15f437 100644
--- a/api/core/tools/provider/builtin/gitlab/tools/gitlab_commits.py
+++ b/api/core/tools/provider/builtin/gitlab/tools/gitlab_commits.py
@@ -1,4 +1,5 @@
 import json
+import urllib.parse
 from datetime import datetime, timedelta
 from typing import Any, Union
 
@@ -9,103 +10,133 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class GitlabCommitsTool(BuiltinTool):
-    def _invoke(self, 
-                user_id: str,
-                tool_parameters: dict[str, Any]
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        project = tool_parameters.get("project", "")
+        repository = tool_parameters.get("repository", "")
+        employee = tool_parameters.get("employee", "")
+        start_time = tool_parameters.get("start_time", "")
+        end_time = tool_parameters.get("end_time", "")
+        change_type = tool_parameters.get("change_type", "all")
 
-        project = tool_parameters.get('project', '')
-        employee = tool_parameters.get('employee', '')
-        start_time = tool_parameters.get('start_time', '')
-        end_time = tool_parameters.get('end_time', '')
-        change_type = tool_parameters.get('change_type', 'all')
-
-        if not project:
-            return self.create_text_message('Project is required')
+        if not project and not repository:
+            return self.create_text_message("Either project or repository is required")
 
         if not start_time:
             start_time = (datetime.utcnow() - timedelta(days=1)).isoformat()
         if not end_time:
             end_time = datetime.utcnow().isoformat()
 
-        access_token = self.runtime.credentials.get('access_tokens')
-        site_url = self.runtime.credentials.get('site_url')
+        access_token = self.runtime.credentials.get("access_tokens")
+        site_url = self.runtime.credentials.get("site_url")
 
-        if 'access_tokens' not in self.runtime.credentials or not self.runtime.credentials.get('access_tokens'):
+        if "access_tokens" not in self.runtime.credentials or not self.runtime.credentials.get("access_tokens"):
             return self.create_text_message("Gitlab API Access Tokens is required.")
-        if 'site_url' not in self.runtime.credentials or not self.runtime.credentials.get('site_url'):
-            site_url = 'https://gitlab.com'
-        
+        if "site_url" not in self.runtime.credentials or not self.runtime.credentials.get("site_url"):
+            site_url = "https://gitlab.com"
+
         # Get commit content
-        result = self.fetch(user_id, site_url, access_token, project, employee, start_time, end_time, change_type)
+        if repository:
+            result = self.fetch_commits(
+                site_url, access_token, repository, employee, start_time, end_time, change_type, is_repository=True
+            )
+        else:
+            result = self.fetch_commits(
+                site_url, access_token, project, employee, start_time, end_time, change_type, is_repository=False
+            )
 
         return [self.create_json_message(item) for item in result]
-    
-    def fetch(self,user_id: str, site_url: str, access_token: str, project: str, employee: str = None, start_time: str = '', end_time: str = '', change_type: str = '') -> list[dict[str, Any]]:
+
+    def fetch_commits(
+        self,
+        site_url: str,
+        access_token: str,
+        identifier: str,
+        employee: str,
+        start_time: str,
+        end_time: str,
+        change_type: str,
+        is_repository: bool,
+    ) -> list[dict[str, Any]]:
         domain = site_url
         headers = {"PRIVATE-TOKEN": access_token}
         results = []
 
         try:
-            # Get all of projects
-            url = f"{domain}/api/v4/projects"
-            response = requests.get(url, headers=headers)
-            response.raise_for_status()
-            projects = response.json()
+            if is_repository:
+                # URL encode the repository path
+                encoded_identifier = urllib.parse.quote(identifier, safe="")
+                commits_url = f"{domain}/api/v4/projects/{encoded_identifier}/repository/commits"
+            else:
+                # Get all projects
+                url = f"{domain}/api/v4/projects"
+                response = requests.get(url, headers=headers)
+                response.raise_for_status()
+                projects = response.json()
 
-            filtered_projects = [p for p in projects if project == "*" or p['name'] == project]
+                filtered_projects = [p for p in projects if identifier == "*" or p["name"] == identifier]
 
-            for project in filtered_projects:
-                project_id = project['id']
-                project_name = project['name']
-                print(f"Project: {project_name}")
+                for project in filtered_projects:
+                    project_id = project["id"]
+                    project_name = project["name"]
+                    print(f"Project: {project_name}")
 
-                # Get all of proejct commits
-                commits_url = f"{domain}/api/v4/projects/{project_id}/repository/commits"
-                params = {
-                    'since': start_time,
-                    'until': end_time
-                }
-                if employee:
-                    params['author'] = employee
+                    commits_url = f"{domain}/api/v4/projects/{project_id}/repository/commits"
 
-                commits_response = requests.get(commits_url, headers=headers, params=params)
-                commits_response.raise_for_status()
-                commits = commits_response.json()
+            params = {"since": start_time, "until": end_time}
+            if employee:
+                params["author"] = employee
 
-                for commit in commits:
-                    commit_sha = commit['id']
-                    author_name = commit['author_name']
+            commits_response = requests.get(commits_url, headers=headers, params=params)
+            commits_response.raise_for_status()
+            commits = commits_response.json()
 
+            for commit in commits:
+                commit_sha = commit["id"]
+                author_name = commit["author_name"]
+
+                if is_repository:
+                    diff_url = f"{domain}/api/v4/projects/{encoded_identifier}/repository/commits/{commit_sha}/diff"
+                else:
                     diff_url = f"{domain}/api/v4/projects/{project_id}/repository/commits/{commit_sha}/diff"
-                    diff_response = requests.get(diff_url, headers=headers)
-                    diff_response.raise_for_status()
-                    diffs = diff_response.json()
-                    
-                    for diff in diffs:
-                        # Caculate code lines of changed
-                        added_lines = diff['diff'].count('\n+')
-                        removed_lines = diff['diff'].count('\n-')
-                        total_changes = added_lines + removed_lines
 
-                        if change_type == "new":
-                            if added_lines > 1:
-                                final_code = ''.join([line[1:] for line in diff['diff'].split('\n') if line.startswith('+') and not line.startswith('+++')])
-                                results.append({
-                                    "commit_sha": commit_sha,
-                                    "author_name": author_name,
-                                    "diff": final_code
-                                })
-                        else:
-                            if total_changes > 1:
-                                final_code = ''.join([line[1:] for line in diff['diff'].split('\n') if (line.startswith('+') or line.startswith('-')) and not line.startswith('+++') and not line.startswith('---')])
-                                final_code_escaped = json.dumps(final_code)[1:-1]  # Escape the final code
-                                results.append({
-                                    "commit_sha": commit_sha,
-                                    "author_name": author_name,
-                                    "diff": final_code_escaped
-                                })
+                diff_response = requests.get(diff_url, headers=headers)
+                diff_response.raise_for_status()
+                diffs = diff_response.json()
+
+                for diff in diffs:
+                    # Calculate code lines of changes
+                    added_lines = diff["diff"].count("\n+")
+                    removed_lines = diff["diff"].count("\n-")
+                    total_changes = added_lines + removed_lines
+
+                    if change_type == "new":
+                        if added_lines > 1:
+                            final_code = "".join(
+                                [
+                                    line[1:]
+                                    for line in diff["diff"].split("\n")
+                                    if line.startswith("+") and not line.startswith("+++")
+                                ]
+                            )
+                            results.append({"commit_sha": commit_sha, "author_name": author_name, "diff": final_code})
+                    else:
+                        if total_changes > 1:
+                            final_code = "".join(
+                                [
+                                    line[1:]
+                                    for line in diff["diff"].split("\n")
+                                    if (line.startswith("+") or line.startswith("-"))
+                                    and not line.startswith("+++")
+                                    and not line.startswith("---")
+                                ]
+                            )
+                            final_code_escaped = json.dumps(final_code)[1:-1]  # Escape the final code
+                            results.append(
+                                {"commit_sha": commit_sha, "author_name": author_name, "diff": final_code_escaped}
+                            )
         except requests.RequestException as e:
             print(f"Error fetching data from GitLab: {e}")
-        
-        return results
\ No newline at end of file
+
+        return results
diff --git a/api/core/tools/provider/builtin/gitlab/tools/gitlab_commits.yaml b/api/core/tools/provider/builtin/gitlab/tools/gitlab_commits.yaml
index dd4e31d663..669378ac97 100644
--- a/api/core/tools/provider/builtin/gitlab/tools/gitlab_commits.yaml
+++ b/api/core/tools/provider/builtin/gitlab/tools/gitlab_commits.yaml
@@ -6,7 +6,7 @@ identity:
     zh_Hans: GitLab 提交内容查询
 description:
   human:
-    en_US: A tool for query GitLab commits, Input should be a exists username or projec.
+    en_US: A tool for query GitLab commits, Input should be a exists username or project.
     zh_Hans: 一个用于查询 GitLab 代码提交内容的工具，输入的内容应该是一个已存在的用户名或者项目名。
   llm: A tool for query GitLab commits, Input should be a exists username or project.
 parameters:
@@ -21,9 +21,20 @@ parameters:
       zh_Hans: 员工用户名
     llm_description: User name for GitLab
     form: llm
+  - name: repository
+    type: string
+    required: false
+    label:
+      en_US: repository
+      zh_Hans: 仓库路径
+    human_description:
+      en_US: repository
+      zh_Hans: 仓库路径，以namespace/project_name的形式。
+    llm_description: Repository path for GitLab, like namespace/project_name.
+    form: llm
   - name: project
     type: string
-    required: true
+    required: false
     label:
       en_US: project
       zh_Hans: 项目名
diff --git a/api/core/tools/provider/builtin/gitlab/tools/gitlab_files.py b/api/core/tools/provider/builtin/gitlab/tools/gitlab_files.py
index 7fa1d0d112..1e77f3c6df 100644
--- a/api/core/tools/provider/builtin/gitlab/tools/gitlab_files.py
+++ b/api/core/tools/provider/builtin/gitlab/tools/gitlab_files.py
@@ -1,3 +1,4 @@
+import urllib.parse
 from typing import Any, Union
 
 import requests
@@ -7,47 +8,85 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class GitlabFilesTool(BuiltinTool):
-    def _invoke(self, 
-                user_id: str,
-                tool_parameters: dict[str, Any]
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        
-        project = tool_parameters.get('project', '')
-        branch = tool_parameters.get('branch', '')
-        path = tool_parameters.get('path', '')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        project = tool_parameters.get("project", "")
+        repository = tool_parameters.get("repository", "")
+        branch = tool_parameters.get("branch", "")
+        path = tool_parameters.get("path", "")
 
-
-        if not project:
-            return self.create_text_message('Project is required')
+        if not project and not repository:
+            return self.create_text_message("Either project or repository is required")
         if not branch:
-            return self.create_text_message('Branch is required')
-
+            return self.create_text_message("Branch is required")
         if not path:
-            return self.create_text_message('Path is required')
+            return self.create_text_message("Path is required")
 
-        access_token = self.runtime.credentials.get('access_tokens')
-        site_url = self.runtime.credentials.get('site_url')
+        access_token = self.runtime.credentials.get("access_tokens")
+        site_url = self.runtime.credentials.get("site_url")
 
-        if 'access_tokens' not in self.runtime.credentials or not self.runtime.credentials.get('access_tokens'):
+        if "access_tokens" not in self.runtime.credentials or not self.runtime.credentials.get("access_tokens"):
             return self.create_text_message("Gitlab API Access Tokens is required.")
-        if 'site_url' not in self.runtime.credentials or not self.runtime.credentials.get('site_url'):
-            site_url = 'https://gitlab.com'
-    
-        # Get project ID from project name
-        project_id = self.get_project_id(site_url, access_token, project)
-        if not project_id:
-            return self.create_text_message(f"Project '{project}' not found.")
+        if "site_url" not in self.runtime.credentials or not self.runtime.credentials.get("site_url"):
+            site_url = "https://gitlab.com"
 
-        # Get commit content
-        result = self.fetch(user_id, project_id, site_url, access_token, branch, path)
+        # Get file content
+        if repository:
+            result = self.fetch_files(site_url, access_token, repository, branch, path, is_repository=True)
+        else:
+            result = self.fetch_files(site_url, access_token, project, branch, path, is_repository=False)
 
         return [self.create_json_message(item) for item in result]
-    
-    def extract_project_name_and_path(self, path: str) -> tuple[str, str]:
-        parts = path.split('/', 1)
-        if len(parts) < 2:
-            return None, None
-        return parts[0], parts[1]
+
+    def fetch_files(
+        self, site_url: str, access_token: str, identifier: str, branch: str, path: str, is_repository: bool
+    ) -> list[dict[str, Any]]:
+        domain = site_url
+        headers = {"PRIVATE-TOKEN": access_token}
+        results = []
+
+        try:
+            if is_repository:
+                # URL encode the repository path
+                encoded_identifier = urllib.parse.quote(identifier, safe="")
+                tree_url = f"{domain}/api/v4/projects/{encoded_identifier}/repository/tree?path={path}&ref={branch}"
+            else:
+                # Get project ID from project name
+                project_id = self.get_project_id(site_url, access_token, identifier)
+                if not project_id:
+                    return self.create_text_message(f"Project '{identifier}' not found.")
+                tree_url = f"{domain}/api/v4/projects/{project_id}/repository/tree?path={path}&ref={branch}"
+
+            response = requests.get(tree_url, headers=headers)
+            response.raise_for_status()
+            items = response.json()
+
+            for item in items:
+                item_path = item["path"]
+                if item["type"] == "tree":  # It's a directory
+                    results.extend(
+                        self.fetch_files(site_url, access_token, identifier, branch, item_path, is_repository)
+                    )
+                else:  # It's a file
+                    if is_repository:
+                        file_url = (
+                            f"{domain}/api/v4/projects/{encoded_identifier}/repository/files"
+                            f"/{item_path}/raw?ref={branch}"
+                        )
+                    else:
+                        file_url = (
+                            f"{domain}/api/v4/projects/{project_id}/repository/files/{item_path}/raw?ref={branch}"
+                        )
+
+                    file_response = requests.get(file_url, headers=headers)
+                    file_response.raise_for_status()
+                    file_content = file_response.text
+                    results.append({"path": item_path, "branch": branch, "content": file_content})
+        except requests.RequestException as e:
+            print(f"Error fetching data from GitLab: {e}")
+
+        return results
 
     def get_project_id(self, site_url: str, access_token: str, project_name: str) -> Union[str, None]:
         headers = {"PRIVATE-TOKEN": access_token}
@@ -57,39 +96,8 @@ class GitlabFilesTool(BuiltinTool):
             response.raise_for_status()
             projects = response.json()
             for project in projects:
-                if project['name'] == project_name:
-                    return project['id']
+                if project["name"] == project_name:
+                    return project["id"]
         except requests.RequestException as e:
             print(f"Error fetching project ID from GitLab: {e}")
         return None
-    
-    def fetch(self,user_id: str, project_id: str, site_url: str, access_token: str, branch: str, path: str = None) -> list[dict[str, Any]]:
-        domain = site_url
-        headers = {"PRIVATE-TOKEN": access_token}
-        results = []
-
-        try:
-            # List files and directories in the given path
-            url = f"{domain}/api/v4/projects/{project_id}/repository/tree?path={path}&ref={branch}"
-            response = requests.get(url, headers=headers)
-            response.raise_for_status()
-            items = response.json()
-
-            for item in items:
-                item_path = item['path']
-                if item['type'] == 'tree':  # It's a directory
-                    results.extend(self.fetch(project_id, site_url, access_token, branch, item_path))
-                else:  # It's a file
-                    file_url = f"{domain}/api/v4/projects/{project_id}/repository/files/{item_path}/raw?ref={branch}"
-                    file_response = requests.get(file_url, headers=headers)
-                    file_response.raise_for_status()
-                    file_content = file_response.text
-                    results.append({
-                        "path": item_path,
-                        "branch": branch,
-                        "content": file_content
-                    })
-        except requests.RequestException as e:
-            print(f"Error fetching data from GitLab: {e}")
-        
-        return results
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/gitlab/tools/gitlab_files.yaml b/api/core/tools/provider/builtin/gitlab/tools/gitlab_files.yaml
index d99b6254c1..4c733673f1 100644
--- a/api/core/tools/provider/builtin/gitlab/tools/gitlab_files.yaml
+++ b/api/core/tools/provider/builtin/gitlab/tools/gitlab_files.yaml
@@ -10,9 +10,20 @@ description:
     zh_Hans: 一个用于查询 GitLab 文件的工具，输入的内容应该是分支和一个已存在文件或者文件夹路径。
   llm: A tool for query GitLab files, Input should be a exists file or directory path.
 parameters:
+  - name: repository
+    type: string
+    required: false
+    label:
+      en_US: repository
+      zh_Hans: 仓库路径
+    human_description:
+      en_US: repository
+      zh_Hans: 仓库路径，以namespace/project_name的形式。
+    llm_description: Repository path for GitLab, like namespace/project_name.
+    form: llm
   - name: project
     type: string
-    required: true
+    required: false
     label:
       en_US: project
       zh_Hans: 项目
diff --git a/api/core/tools/provider/builtin/google/google.py b/api/core/tools/provider/builtin/google/google.py
index 8f4b9a4a4e..6b5395f9d3 100644
--- a/api/core/tools/provider/builtin/google/google.py
+++ b/api/core/tools/provider/builtin/google/google.py
@@ -13,12 +13,8 @@ class GoogleProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
-                tool_parameters={
-                    "query": "test",
-                    "result_type": "link"
-                },
+                user_id="",
+                tool_parameters={"query": "test", "result_type": "link"},
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/google/tools/google_search.py b/api/core/tools/provider/builtin/google/tools/google_search.py
index 09d0326fb4..a9f65925d8 100644
--- a/api/core/tools/provider/builtin/google/tools/google_search.py
+++ b/api/core/tools/provider/builtin/google/tools/google_search.py
@@ -9,7 +9,6 @@ SERP_API_URL = "https://serpapi.com/search"
 
 
 class GoogleSearchTool(BuiltinTool):
-
     def _parse_response(self, response: dict) -> dict:
         result = {}
         if "knowledge_graph" in response:
@@ -17,25 +16,23 @@ class GoogleSearchTool(BuiltinTool):
             result["description"] = response["knowledge_graph"].get("description", "")
         if "organic_results" in response:
             result["organic_results"] = [
-                {
-                    "title": item.get("title", ""),
-                    "link": item.get("link", ""),
-                    "snippet": item.get("snippet", "")
-                }
+                {"title": item.get("title", ""), "link": item.get("link", ""), "snippet": item.get("snippet", "")}
                 for item in response["organic_results"]
             ]
         return result
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         params = {
-            "api_key": self.runtime.credentials['serpapi_api_key'],
-            "q": tool_parameters['query'],
+            "api_key": self.runtime.credentials["serpapi_api_key"],
+            "q": tool_parameters["query"],
             "engine": "google",
             "google_domain": "google.com",
             "gl": "us",
-            "hl": "en"
+            "hl": "en",
         }
         response = requests.get(url=SERP_API_URL, params=params)
         response.raise_for_status()
diff --git a/api/core/tools/provider/builtin/google_translate/google_translate.py b/api/core/tools/provider/builtin/google_translate/google_translate.py
index f6e1d65834..ea53aa4eeb 100644
--- a/api/core/tools/provider/builtin/google_translate/google_translate.py
+++ b/api/core/tools/provider/builtin/google_translate/google_translate.py
@@ -8,10 +8,6 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class JsonExtractProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
-            GoogleTranslate().invoke(user_id='',
-                                     tool_parameters={
-                                         "content": "这是一段测试文本",
-                                         "dest": "en"
-                                     })
+            GoogleTranslate().invoke(user_id="", tool_parameters={"content": "这是一段测试文本", "dest": "en"})
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/google_translate/tools/translate.py b/api/core/tools/provider/builtin/google_translate/tools/translate.py
index 4314182b06..ea3f2077d5 100644
--- a/api/core/tools/provider/builtin/google_translate/tools/translate.py
+++ b/api/core/tools/provider/builtin/google_translate/tools/translate.py
@@ -7,46 +7,41 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class GoogleTranslate(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        content = tool_parameters.get('content', '')
+        content = tool_parameters.get("content", "")
         if not content:
-            return self.create_text_message('Invalid parameter content')
+            return self.create_text_message("Invalid parameter content")
 
-        dest = tool_parameters.get('dest', '')
+        dest = tool_parameters.get("dest", "")
         if not dest:
-            return self.create_text_message('Invalid parameter destination language')
+            return self.create_text_message("Invalid parameter destination language")
 
         try:
             result = self._translate(content, dest)
             return self.create_text_message(str(result))
         except Exception:
-            return self.create_text_message('Translation service error, please check the network')
+            return self.create_text_message("Translation service error, please check the network")
 
     def _translate(self, content: str, dest: str) -> str:
         try:
             url = "https://translate.googleapis.com/translate_a/single"
-            params = {
-                "client": "gtx",
-                "sl": "auto",
-                "tl": dest,
-                "dt": "t",
-                "q": content
-            }
+            params = {"client": "gtx", "sl": "auto", "tl": dest, "dt": "t", "q": content}
 
             headers = {
-                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)"
+                " Chrome/91.0.4472.124 Safari/537.36"
             }
 
-            response_json = requests.get(
-                url, params=params, headers=headers).json()
+            response_json = requests.get(url, params=params, headers=headers).json()
             result = response_json[0]
-            translated_text = ''.join([item[0] for item in result if item[0]])
+            translated_text = "".join([item[0] for item in result if item[0]])
             return str(translated_text)
         except Exception as e:
             return str(e)
diff --git a/api/core/tools/provider/builtin/hap/hap.py b/api/core/tools/provider/builtin/hap/hap.py
index e0a48e05a5..cbdf950465 100644
--- a/api/core/tools/provider/builtin/hap/hap.py
+++ b/api/core/tools/provider/builtin/hap/hap.py
@@ -5,4 +5,4 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 
 class HapProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
-        pass
\ No newline at end of file
+        pass
diff --git a/api/core/tools/provider/builtin/hap/tools/add_worksheet_record.py b/api/core/tools/provider/builtin/hap/tools/add_worksheet_record.py
index 0e101dc67d..597adc91db 100644
--- a/api/core/tools/provider/builtin/hap/tools/add_worksheet_record.py
+++ b/api/core/tools/provider/builtin/hap/tools/add_worksheet_record.py
@@ -8,41 +8,40 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class AddWorksheetRecordTool(BuiltinTool):
-
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        
-        appkey = tool_parameters.get('appkey', '')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        appkey = tool_parameters.get("appkey", "")
         if not appkey:
-            return self.create_text_message('Invalid parameter App Key')
-        sign = tool_parameters.get('sign', '')
+            return self.create_text_message("Invalid parameter App Key")
+        sign = tool_parameters.get("sign", "")
         if not sign:
-            return self.create_text_message('Invalid parameter Sign')
-        worksheet_id = tool_parameters.get('worksheet_id', '')
+            return self.create_text_message("Invalid parameter Sign")
+        worksheet_id = tool_parameters.get("worksheet_id", "")
         if not worksheet_id:
-            return self.create_text_message('Invalid parameter Worksheet ID')
-        record_data = tool_parameters.get('record_data', '')
+            return self.create_text_message("Invalid parameter Worksheet ID")
+        record_data = tool_parameters.get("record_data", "")
         if not record_data:
-            return self.create_text_message('Invalid parameter Record Row Data')
-        
-        host = tool_parameters.get('host', '')
+            return self.create_text_message("Invalid parameter Record Row Data")
+
+        host = tool_parameters.get("host", "")
         if not host:
-            host = 'https://api.mingdao.com'
+            host = "https://api.mingdao.com"
         elif not host.startswith(("http://", "https://")):
-            return self.create_text_message('Invalid parameter Host Address')
+            return self.create_text_message("Invalid parameter Host Address")
         else:
-            host = f"{host[:-1] if host.endswith('/') else host}/api"
+            host = f"{host.removesuffix('/')}/api"
 
         url = f"{host}/v2/open/worksheet/addRow"
-        headers = {'Content-Type': 'application/json'}
+        headers = {"Content-Type": "application/json"}
         payload = {"appKey": appkey, "sign": sign, "worksheetId": worksheet_id}
 
         try:
-            payload['controls'] = json.loads(record_data)
+            payload["controls"] = json.loads(record_data)
             res = httpx.post(url, headers=headers, json=payload, timeout=60)
             res.raise_for_status()
             res_json = res.json()
-            if res_json.get('error_code') != 1:
+            if res_json.get("error_code") != 1:
                 return self.create_text_message(f"Failed to add the new record. {res_json['error_msg']}")
             return self.create_text_message(f"New record added successfully. The record ID is {res_json['data']}.")
         except httpx.RequestError as e:
diff --git a/api/core/tools/provider/builtin/hap/tools/delete_worksheet_record.py b/api/core/tools/provider/builtin/hap/tools/delete_worksheet_record.py
index ba25952c9f..5d42af4c49 100644
--- a/api/core/tools/provider/builtin/hap/tools/delete_worksheet_record.py
+++ b/api/core/tools/provider/builtin/hap/tools/delete_worksheet_record.py
@@ -7,43 +7,42 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class DeleteWorksheetRecordTool(BuiltinTool):
-
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        
-        appkey = tool_parameters.get('appkey', '')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        appkey = tool_parameters.get("appkey", "")
         if not appkey:
-            return self.create_text_message('Invalid parameter App Key')
-        sign = tool_parameters.get('sign', '')
+            return self.create_text_message("Invalid parameter App Key")
+        sign = tool_parameters.get("sign", "")
         if not sign:
-            return self.create_text_message('Invalid parameter Sign')
-        worksheet_id = tool_parameters.get('worksheet_id', '')
+            return self.create_text_message("Invalid parameter Sign")
+        worksheet_id = tool_parameters.get("worksheet_id", "")
         if not worksheet_id:
-            return self.create_text_message('Invalid parameter Worksheet ID')
-        row_id = tool_parameters.get('row_id', '')
+            return self.create_text_message("Invalid parameter Worksheet ID")
+        row_id = tool_parameters.get("row_id", "")
         if not row_id:
-            return self.create_text_message('Invalid parameter Record Row ID')
-        
-        host = tool_parameters.get('host', '')
+            return self.create_text_message("Invalid parameter Record Row ID")
+
+        host = tool_parameters.get("host", "")
         if not host:
-            host = 'https://api.mingdao.com'
+            host = "https://api.mingdao.com"
         elif not host.startswith(("http://", "https://")):
-            return self.create_text_message('Invalid parameter Host Address')
+            return self.create_text_message("Invalid parameter Host Address")
         else:
-            host = f"{host[:-1] if host.endswith('/') else host}/api"
+            host = f"{host.removesuffix('/')}/api"
 
         url = f"{host}/v2/open/worksheet/deleteRow"
-        headers = {'Content-Type': 'application/json'}
+        headers = {"Content-Type": "application/json"}
         payload = {"appKey": appkey, "sign": sign, "worksheetId": worksheet_id, "rowId": row_id}
 
         try:
             res = httpx.post(url, headers=headers, json=payload, timeout=30)
             res.raise_for_status()
             res_json = res.json()
-            if res_json.get('error_code') != 1:
+            if res_json.get("error_code") != 1:
                 return self.create_text_message(f"Failed to delete the record. {res_json['error_msg']}")
             return self.create_text_message("Successfully deleted the record.")
         except httpx.RequestError as e:
             return self.create_text_message(f"Failed to delete the record, request error: {e}")
         except Exception as e:
-            return self.create_text_message(f"Failed to delete the record, unexpected error: {e}")
\ No newline at end of file
+            return self.create_text_message(f"Failed to delete the record, unexpected error: {e}")
diff --git a/api/core/tools/provider/builtin/hap/tools/get_worksheet_fields.py b/api/core/tools/provider/builtin/hap/tools/get_worksheet_fields.py
index 2c46d9dd4e..6887b8b4e9 100644
--- a/api/core/tools/provider/builtin/hap/tools/get_worksheet_fields.py
+++ b/api/core/tools/provider/builtin/hap/tools/get_worksheet_fields.py
@@ -8,43 +8,42 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class GetWorksheetFieldsTool(BuiltinTool):
-
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        
-        appkey = tool_parameters.get('appkey', '')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        appkey = tool_parameters.get("appkey", "")
         if not appkey:
-            return self.create_text_message('Invalid parameter App Key')
-        sign = tool_parameters.get('sign', '')
+            return self.create_text_message("Invalid parameter App Key")
+        sign = tool_parameters.get("sign", "")
         if not sign:
-            return self.create_text_message('Invalid parameter Sign')
-        worksheet_id = tool_parameters.get('worksheet_id', '')
+            return self.create_text_message("Invalid parameter Sign")
+        worksheet_id = tool_parameters.get("worksheet_id", "")
         if not worksheet_id:
-            return self.create_text_message('Invalid parameter Worksheet ID')
-        
-        host = tool_parameters.get('host', '')
+            return self.create_text_message("Invalid parameter Worksheet ID")
+
+        host = tool_parameters.get("host", "")
         if not host:
-            host = 'https://api.mingdao.com'
+            host = "https://api.mingdao.com"
         elif not host.startswith(("http://", "https://")):
-            return self.create_text_message('Invalid parameter Host Address')
+            return self.create_text_message("Invalid parameter Host Address")
         else:
-            host = f"{host[:-1] if host.endswith('/') else host}/api"
+            host = f"{host.removesuffix('/')}/api"
 
         url = f"{host}/v2/open/worksheet/getWorksheetInfo"
-        headers = {'Content-Type': 'application/json'}
+        headers = {"Content-Type": "application/json"}
         payload = {"appKey": appkey, "sign": sign, "worksheetId": worksheet_id}
 
         try:
             res = httpx.post(url, headers=headers, json=payload, timeout=60)
             res.raise_for_status()
             res_json = res.json()
-            if res_json.get('error_code') != 1:
+            if res_json.get("error_code") != 1:
                 return self.create_text_message(f"Failed to get the worksheet information. {res_json['error_msg']}")
-            
-            fields_json, fields_table = self.get_controls(res_json['data']['controls'])
-            result_type = tool_parameters.get('result_type', 'table')
+
+            fields_json, fields_table = self.get_controls(res_json["data"]["controls"])
+            result_type = tool_parameters.get("result_type", "table")
             return self.create_text_message(
-                text=json.dumps(fields_json, ensure_ascii=False) if result_type == 'json' else fields_table
+                text=json.dumps(fields_json, ensure_ascii=False) if result_type == "json" else fields_table
             )
         except httpx.RequestError as e:
             return self.create_text_message(f"Failed to get the worksheet information, request error: {e}")
@@ -88,61 +87,66 @@ class GetWorksheetFieldsTool(BuiltinTool):
             50: "Text",
             51: "Query Record",
         }
-        return field_type_map.get(field_type_id, '')
+        return field_type_map.get(field_type_id, "")
 
     def get_controls(self, controls: list) -> dict:
         fields = []
-        fields_list = ['|fieldId|fieldName|fieldType|fieldTypeId|description|options|','|'+'---|'*6]
+        fields_list = ["|fieldId|fieldName|fieldType|fieldTypeId|description|options|", "|" + "---|" * 6]
         for control in controls:
-            if control['type'] in self._get_ignore_types():
+            if control["type"] in self._get_ignore_types():
                 continue
-            field_type_id = control['type']
-            field_type = self.get_field_type_by_id(control['type'])
+            field_type_id = control["type"]
+            field_type = self.get_field_type_by_id(control["type"])
             if field_type_id == 30:
-                source_type = control['sourceControl']['type']
+                source_type = control["sourceControl"]["type"]
                 if source_type in self._get_ignore_types():
                     continue
                 else:
                     field_type_id = source_type
                     field_type = self.get_field_type_by_id(source_type)
             field = {
-                'id': control['controlId'],
-                'name': control['controlName'],
-                'type': field_type,
-                'typeId': field_type_id,
-                'description': control['remark'].replace('\n', ' ').replace('\t', '  '),
-                'options': self._extract_options(control),
+                "id": control["controlId"],
+                "name": control["controlName"],
+                "type": field_type,
+                "typeId": field_type_id,
+                "description": control["remark"].replace("\n", " ").replace("\t", "  "),
+                "options": self._extract_options(control),
             }
             fields.append(field)
-            fields_list.append(f"|{field['id']}|{field['name']}|{field['type']}|{field['typeId']}|{field['description']}|{field['options'] if field['options'] else ''}|")
+            fields_list.append(
+                f"|{field['id']}|{field['name']}|{field['type']}|{field['typeId']}|{field['description']}"
+                f"|{field['options'] or ''}|"
+            )
 
-        fields.append({
-            'id': 'ctime',
-            'name': 'Created Time',
-            'type': self.get_field_type_by_id(16),
-            'typeId': 16,
-            'description': '',
-            'options': []
-        })
+        fields.append(
+            {
+                "id": "ctime",
+                "name": "Created Time",
+                "type": self.get_field_type_by_id(16),
+                "typeId": 16,
+                "description": "",
+                "options": [],
+            }
+        )
         fields_list.append("|ctime|Created Time|Date|16|||")
-        return fields, '\n'.join(fields_list)
+        return fields, "\n".join(fields_list)
 
     def _extract_options(self, control: dict) -> list:
         options = []
-        if control['type'] in [9, 10, 11]:
-            options.extend([{"key": opt['key'], "value": opt['value']} for opt in control.get('options', [])])
-        elif control['type'] in [28, 36]:
-            itemnames = control['advancedSetting'].get('itemnames')
-            if itemnames and itemnames.startswith('[{'):
+        if control["type"] in {9, 10, 11}:
+            options.extend([{"key": opt["key"], "value": opt["value"]} for opt in control.get("options", [])])
+        elif control["type"] in {28, 36}:
+            itemnames = control["advancedSetting"].get("itemnames")
+            if itemnames and itemnames.startswith("[{"):
                 try:
                     options = json.loads(itemnames)
                 except json.JSONDecodeError:
                     pass
-        elif control['type'] == 30:
-            source_type = control['sourceControl']['type']
+        elif control["type"] == 30:
+            source_type = control["sourceControl"]["type"]
             if source_type not in self._get_ignore_types():
-                options.extend([{"key": opt['key'], "value": opt['value']} for opt in control.get('options', [])])
+                options.extend([{"key": opt["key"], "value": opt["value"]} for opt in control.get("options", [])])
         return options
-    
+
     def _get_ignore_types(self):
-        return {14, 21, 22, 34, 42, 43, 45, 47, 49, 10010}
\ No newline at end of file
+        return {14, 21, 22, 34, 42, 43, 45, 47, 49, 10010}
diff --git a/api/core/tools/provider/builtin/hap/tools/get_worksheet_pivot_data.py b/api/core/tools/provider/builtin/hap/tools/get_worksheet_pivot_data.py
index 6bf1caa65e..26d7116869 100644
--- a/api/core/tools/provider/builtin/hap/tools/get_worksheet_pivot_data.py
+++ b/api/core/tools/provider/builtin/hap/tools/get_worksheet_pivot_data.py
@@ -8,64 +8,66 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class GetWorksheetPivotDataTool(BuiltinTool):
-
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        
-        appkey = tool_parameters.get('appkey', '')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        appkey = tool_parameters.get("appkey", "")
         if not appkey:
-            return self.create_text_message('Invalid parameter App Key')
-        sign = tool_parameters.get('sign', '')
+            return self.create_text_message("Invalid parameter App Key")
+        sign = tool_parameters.get("sign", "")
         if not sign:
-            return self.create_text_message('Invalid parameter Sign')
-        worksheet_id = tool_parameters.get('worksheet_id', '')
+            return self.create_text_message("Invalid parameter Sign")
+        worksheet_id = tool_parameters.get("worksheet_id", "")
         if not worksheet_id:
-            return self.create_text_message('Invalid parameter Worksheet ID')
-        x_column_fields = tool_parameters.get('x_column_fields', '')
-        if not x_column_fields or not x_column_fields.startswith('['):
-            return self.create_text_message('Invalid parameter Column Fields')
-        y_row_fields = tool_parameters.get('y_row_fields', '')
-        if y_row_fields and not y_row_fields.strip().startswith('['):
-            return self.create_text_message('Invalid parameter Row Fields')
+            return self.create_text_message("Invalid parameter Worksheet ID")
+        x_column_fields = tool_parameters.get("x_column_fields", "")
+        if not x_column_fields or not x_column_fields.startswith("["):
+            return self.create_text_message("Invalid parameter Column Fields")
+        y_row_fields = tool_parameters.get("y_row_fields", "")
+        if y_row_fields and not y_row_fields.strip().startswith("["):
+            return self.create_text_message("Invalid parameter Row Fields")
         elif not y_row_fields:
-            y_row_fields = '[]'
-        value_fields = tool_parameters.get('value_fields', '')
-        if not value_fields or not value_fields.strip().startswith('['):
-            return self.create_text_message('Invalid parameter Value Fields')
-        
-        host = tool_parameters.get('host', '')
+            y_row_fields = "[]"
+        value_fields = tool_parameters.get("value_fields", "")
+        if not value_fields or not value_fields.strip().startswith("["):
+            return self.create_text_message("Invalid parameter Value Fields")
+
+        host = tool_parameters.get("host", "")
         if not host:
-            host = 'https://api.mingdao.com'
+            host = "https://api.mingdao.com"
         elif not host.startswith(("http://", "https://")):
-            return self.create_text_message('Invalid parameter Host Address')
+            return self.create_text_message("Invalid parameter Host Address")
         else:
-            host = f"{host[:-1] if host.endswith('/') else host}/api"
+            host = f"{host.removesuffix('/')}/api"
 
         url = f"{host}/report/getPivotData"
-        headers = {'Content-Type': 'application/json'}
+        headers = {"Content-Type": "application/json"}
         payload = {"appKey": appkey, "sign": sign, "worksheetId": worksheet_id, "options": {"showTotal": True}}
 
         try:
             x_column_fields = json.loads(x_column_fields)
-            payload['columns'] = x_column_fields
+            payload["columns"] = x_column_fields
             y_row_fields = json.loads(y_row_fields)
-            if y_row_fields: payload['rows'] = y_row_fields
+            if y_row_fields:
+                payload["rows"] = y_row_fields
             value_fields = json.loads(value_fields)
-            payload['values'] = value_fields
-            sort_fields = tool_parameters.get('sort_fields', '')
-            if not sort_fields: sort_fields = '[]'
+            payload["values"] = value_fields
+            sort_fields = tool_parameters.get("sort_fields", "")
+            if not sort_fields:
+                sort_fields = "[]"
             sort_fields = json.loads(sort_fields)
-            if sort_fields: payload['options']['sort'] = sort_fields
+            if sort_fields:
+                payload["options"]["sort"] = sort_fields
             res = httpx.post(url, headers=headers, json=payload, timeout=60)
             res.raise_for_status()
             res_json = res.json()
-            if res_json.get('status') != 1:
+            if res_json.get("status") != 1:
                 return self.create_text_message(f"Failed to get the worksheet pivot data. {res_json['msg']}")
-            
-            pivot_json = self.generate_pivot_json(res_json['data'])
-            pivot_table = self.generate_pivot_table(res_json['data'])
-            result_type = tool_parameters.get('result_type', '')
-            text = pivot_table if result_type == 'table' else json.dumps(pivot_json, ensure_ascii=False)
+
+            pivot_json = self.generate_pivot_json(res_json["data"])
+            pivot_table = self.generate_pivot_table(res_json["data"])
+            result_type = tool_parameters.get("result_type", "")
+            text = pivot_table if result_type == "table" else json.dumps(pivot_json, ensure_ascii=False)
             return self.create_text_message(text)
         except httpx.RequestError as e:
             return self.create_text_message(f"Failed to get the worksheet pivot data, request error: {e}")
@@ -75,27 +77,31 @@ class GetWorksheetPivotDataTool(BuiltinTool):
             return self.create_text_message(f"Failed to get the worksheet pivot data, unexpected error: {e}")
 
     def generate_pivot_table(self, data: dict[str, Any]) -> str:
-        columns = data['metadata']['columns']
-        rows = data['metadata']['rows']
-        values = data['metadata']['values']
+        columns = data["metadata"]["columns"]
+        rows = data["metadata"]["rows"]
+        values = data["metadata"]["values"]
 
-        rows_data = data['data']
+        rows_data = data["data"]
 
-        header = ([row['displayName'] for row in rows] if rows else []) + [column['displayName'] for column in columns] + [value['displayName'] for value in values]
-        line = (['---'] * len(rows) if rows else []) + ['---'] * len(columns) + ['--:'] * len(values)
+        header = (
+            ([row["displayName"] for row in rows] if rows else [])
+            + [column["displayName"] for column in columns]
+            + [value["displayName"] for value in values]
+        )
+        line = (["---"] * len(rows) if rows else []) + ["---"] * len(columns) + ["--:"] * len(values)
 
         table = [header, line]
         for row in rows_data:
-            row_data = [self.replace_pipe(row['rows'][r['controlId']]) for r in rows] if rows else []
-            row_data.extend([self.replace_pipe(row['columns'][column['controlId']]) for column in columns])
-            row_data.extend([self.replace_pipe(str(row['values'][value['controlId']])) for value in values])
+            row_data = [self.replace_pipe(row["rows"][r["controlId"]]) for r in rows] if rows else []
+            row_data.extend([self.replace_pipe(row["columns"][column["controlId"]]) for column in columns])
+            row_data.extend([self.replace_pipe(str(row["values"][value["controlId"]])) for value in values])
             table.append(row_data)
 
-        return '\n'.join([('|'+'|'.join(row) +'|') for row in table])
-    
+        return "\n".join([("|" + "|".join(row) + "|") for row in table])
+
     def replace_pipe(self, text: str) -> str:
-        return text.replace('|', '▏').replace('\n', ' ')
-    
+        return text.replace("|", "▏").replace("\n", " ")
+
     def generate_pivot_json(self, data: dict[str, Any]) -> dict:
         fields = {
             "x-axis": [
@@ -103,13 +109,14 @@ class GetWorksheetPivotDataTool(BuiltinTool):
                 for column in data["metadata"]["columns"]
             ],
             "y-axis": [
-                {"fieldId": row["controlId"], "fieldName": row["displayName"]}
-                for row in data["metadata"]["rows"]
-            ] if data["metadata"]["rows"] else [],
+                {"fieldId": row["controlId"], "fieldName": row["displayName"]} for row in data["metadata"]["rows"]
+            ]
+            if data["metadata"]["rows"]
+            else [],
             "values": [
                 {"fieldId": value["controlId"], "fieldName": value["displayName"]}
                 for value in data["metadata"]["values"]
-            ]
+            ],
         }
         # fields = ([
         #     {"fieldId": row["controlId"], "fieldName": row["displayName"]}
@@ -123,8 +130,8 @@ class GetWorksheetPivotDataTool(BuiltinTool):
         # ]
         rows = []
         for row in data["data"]:
-            row_data = row["rows"] if row["rows"] else {}
+            row_data = row["rows"] or {}
             row_data.update(row["columns"])
             row_data.update(row["values"])
             rows.append(row_data)
-        return {"fields": fields, "rows": rows, "summary": data["metadata"]["totalRow"]}
\ No newline at end of file
+        return {"fields": fields, "rows": rows, "summary": data["metadata"]["totalRow"]}
diff --git a/api/core/tools/provider/builtin/hap/tools/list_worksheet_records.py b/api/core/tools/provider/builtin/hap/tools/list_worksheet_records.py
index dddc041cc1..d6ac3688b7 100644
--- a/api/core/tools/provider/builtin/hap/tools/list_worksheet_records.py
+++ b/api/core/tools/provider/builtin/hap/tools/list_worksheet_records.py
@@ -9,191 +9,213 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class ListWorksheetRecordsTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
-        appkey = tool_parameters.get('appkey', '')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        appkey = tool_parameters.get("appkey", "")
         if not appkey:
-            return self.create_text_message('Invalid parameter App Key')
+            return self.create_text_message("Invalid parameter App Key")
 
-        sign = tool_parameters.get('sign', '')
+        sign = tool_parameters.get("sign", "")
         if not sign:
-            return self.create_text_message('Invalid parameter Sign')
+            return self.create_text_message("Invalid parameter Sign")
 
-        worksheet_id = tool_parameters.get('worksheet_id', '')
+        worksheet_id = tool_parameters.get("worksheet_id", "")
         if not worksheet_id:
-            return self.create_text_message('Invalid parameter Worksheet ID')
+            return self.create_text_message("Invalid parameter Worksheet ID")
 
-        host = tool_parameters.get('host', '')
+        host = tool_parameters.get("host", "")
         if not host:
-            host = 'https://api.mingdao.com'
+            host = "https://api.mingdao.com"
         elif not (host.startswith("http://") or host.startswith("https://")):
-            return self.create_text_message('Invalid parameter Host Address')
+            return self.create_text_message("Invalid parameter Host Address")
         else:
-            host = f"{host[:-1] if host.endswith('/') else host}/api"
-        
+            host = f"{host.removesuffix('/')}/api"
+
         url_fields = f"{host}/v2/open/worksheet/getWorksheetInfo"
-        headers = {'Content-Type': 'application/json'}
+        headers = {"Content-Type": "application/json"}
         payload = {"appKey": appkey, "sign": sign, "worksheetId": worksheet_id}
 
-        field_ids = tool_parameters.get('field_ids', '')
+        field_ids = tool_parameters.get("field_ids", "")
 
         try:
             res = httpx.post(url_fields, headers=headers, json=payload, timeout=30)
             res_json = res.json()
             if res.is_success:
-                if res_json['error_code'] != 1:
-                    return self.create_text_message("Failed to get the worksheet information. {}".format(res_json['error_msg']))
+                if res_json["error_code"] != 1:
+                    return self.create_text_message(
+                        "Failed to get the worksheet information. {}".format(res_json["error_msg"])
+                    )
                 else:
-                    worksheet_name = res_json['data']['name']
-                    fields, schema, table_header = self.get_schema(res_json['data']['controls'], field_ids)
+                    worksheet_name = res_json["data"]["name"]
+                    fields, schema, table_header = self.get_schema(res_json["data"]["controls"], field_ids)
             else:
                 return self.create_text_message(
-                    f"Failed to get the worksheet information, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to get the worksheet information, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
-            return self.create_text_message("Failed to get the worksheet information, something went wrong: {}".format(e))
+            return self.create_text_message(
+                "Failed to get the worksheet information, something went wrong: {}".format(e)
+            )
 
         if field_ids:
-            payload['controls'] = [v.strip() for v in field_ids.split(',')] if field_ids else []
-        filters = tool_parameters.get('filters', '')
+            payload["controls"] = [v.strip() for v in field_ids.split(",")] if field_ids else []
+        filters = tool_parameters.get("filters", "")
         if filters:
-            payload['filters'] = json.loads(filters)
-        sort_id = tool_parameters.get('sort_id', '')
-        sort_is_asc = tool_parameters.get('sort_is_asc', False)
+            payload["filters"] = json.loads(filters)
+        sort_id = tool_parameters.get("sort_id", "")
+        sort_is_asc = tool_parameters.get("sort_is_asc", False)
         if sort_id:
-            payload['sortId'] = sort_id
-            payload['isAsc'] = sort_is_asc
-        limit = tool_parameters.get('limit', 50)
-        payload['pageSize'] = limit
-        page_index = tool_parameters.get('page_index', 1)
-        payload['pageIndex'] = page_index
-        payload['useControlId'] = True
-        payload['listType'] = 1
+            payload["sortId"] = sort_id
+            payload["isAsc"] = sort_is_asc
+        limit = tool_parameters.get("limit", 50)
+        payload["pageSize"] = limit
+        page_index = tool_parameters.get("page_index", 1)
+        payload["pageIndex"] = page_index
+        payload["useControlId"] = True
+        payload["listType"] = 1
 
         url = f"{host}/v2/open/worksheet/getFilterRows"
         try:
             res = httpx.post(url, headers=headers, json=payload, timeout=90)
             res_json = res.json()
             if res.is_success:
-                if res_json['error_code'] != 1:
-                    return self.create_text_message("Failed to get the records. {}".format(res_json['error_msg']))
+                if res_json["error_code"] != 1:
+                    return self.create_text_message("Failed to get the records. {}".format(res_json["error_msg"]))
                 else:
                     result = {
                         "fields": fields,
                         "rows": [],
                         "total": res_json.get("data", {}).get("total"),
-                        "payload": {key: payload[key] for key in ['worksheetId', 'controls', 'filters', 'sortId', 'isAsc', 'pageSize', 'pageIndex'] if key in payload}
+                        "payload": {
+                            key: payload[key]
+                            for key in [
+                                "worksheetId",
+                                "controls",
+                                "filters",
+                                "sortId",
+                                "isAsc",
+                                "pageSize",
+                                "pageIndex",
+                            ]
+                            if key in payload
+                        },
                     }
                     rows = res_json.get("data", {}).get("rows", [])
-                    result_type = tool_parameters.get('result_type', '')
-                    if not result_type: result_type = 'table'
-                    if result_type == 'json':
+                    result_type = tool_parameters.get("result_type", "")
+                    if not result_type:
+                        result_type = "table"
+                    if result_type == "json":
                         for row in rows:
-                            result['rows'].append(self.get_row_field_value(row, schema))
+                            result["rows"].append(self.get_row_field_value(row, schema))
                         return self.create_text_message(json.dumps(result, ensure_ascii=False))
                     else:
                         result_text = f"Found {result['total']} rows in worksheet \"{worksheet_name}\"."
-                        if result['total'] > 0:
-                            result_text += f" The following are {result['total'] if result['total'] < limit else limit} pieces of data presented in a table format:\n\n{table_header}"
+                        if result["total"] > 0:
+                            result_text += (
+                                f" The following are {min(limit, result['total'])}"
+                                f" pieces of data presented in a table format:\n\n{table_header}"
+                            )
                             for row in rows:
                                 result_values = []
                                 for f in fields:
-                                    result_values.append(self.handle_value_type(row[f['fieldId']], schema[f['fieldId']]))
-                                result_text += '\n|'+'|'.join(result_values)+'|'
+                                    result_values.append(
+                                        self.handle_value_type(row[f["fieldId"]], schema[f["fieldId"]])
+                                    )
+                                result_text += "\n|" + "|".join(result_values) + "|"
                         return self.create_text_message(result_text)
             else:
                 return self.create_text_message(
-                    f"Failed to get the records, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to get the records, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to get the records, something went wrong: {}".format(e))
 
-
     def get_row_field_value(self, row: dict, schema: dict):
         row_value = {"rowid": row["rowid"]}
         for field in schema:
             row_value[field] = self.handle_value_type(row[field], schema[field])
         return row_value
 
-
-    def get_schema(self, controls: list, fieldids: str): 
-        allow_fields = {v.strip() for v in fieldids.split(',')} if fieldids else set()
+    def get_schema(self, controls: list, fieldids: str):
+        allow_fields = {v.strip() for v in fieldids.split(",")} if fieldids else set()
         fields = []
         schema = {}
         field_names = []
         for control in controls:
             control_type_id = self.get_real_type_id(control)
-            if (control_type_id in self._get_ignore_types()) or (allow_fields and not control['controlId'] in allow_fields):
+            if (control_type_id in self._get_ignore_types()) or (
+                allow_fields and control["controlId"] not in allow_fields
+            ):
                 continue
             else:
-                fields.append({'fieldId': control['controlId'], 'fieldName': control['controlName']})
-                schema[control['controlId']] = {'typeId': control_type_id, 'options': self.set_option(control)}
-                field_names.append(control['controlName'])
-        if (not allow_fields or ('ctime' in allow_fields)):
-            fields.append({'fieldId': 'ctime', 'fieldName': 'Created Time'})
-            schema['ctime'] = {'typeId': 16, 'options': {}}
+                fields.append({"fieldId": control["controlId"], "fieldName": control["controlName"]})
+                schema[control["controlId"]] = {"typeId": control_type_id, "options": self.set_option(control)}
+                field_names.append(control["controlName"])
+        if not allow_fields or ("ctime" in allow_fields):
+            fields.append({"fieldId": "ctime", "fieldName": "Created Time"})
+            schema["ctime"] = {"typeId": 16, "options": {}}
             field_names.append("Created Time")
-        fields.append({'fieldId':'rowid', 'fieldName': 'Record Row ID'})
-        schema['rowid'] = {'typeId': 2, 'options': {}}
+        fields.append({"fieldId": "rowid", "fieldName": "Record Row ID"})
+        schema["rowid"] = {"typeId": 2, "options": {}}
         field_names.append("Record Row ID")
-        return fields, schema, '|'+'|'.join(field_names)+'|\n|'+'---|'*len(field_names)
-    
+        return fields, schema, "|" + "|".join(field_names) + "|\n|" + "---|" * len(field_names)
+
     def get_real_type_id(self, control: dict) -> int:
-        return control['sourceControlType'] if control['type'] == 30 else control['type']
-    
+        return control["sourceControlType"] if control["type"] == 30 else control["type"]
+
     def set_option(self, control: dict) -> dict:
         options = {}
-        if control.get('options'):
-            options = {option['key']: option['value'] for option in control['options']}
-        elif control.get('advancedSetting', {}).get('itemnames'):
+        if control.get("options"):
+            options = {option["key"]: option["value"] for option in control["options"]}
+        elif control.get("advancedSetting", {}).get("itemnames"):
             try:
-                itemnames = json.loads(control['advancedSetting']['itemnames'])
-                options = {item['key']: item['value'] for item in itemnames}
+                itemnames = json.loads(control["advancedSetting"]["itemnames"])
+                options = {item["key"]: item["value"] for item in itemnames}
             except json.JSONDecodeError:
                 pass
         return options
 
     def _get_ignore_types(self):
         return {14, 21, 22, 34, 42, 43, 45, 47, 49, 10010}
-    
+
     def handle_value_type(self, value, field):
         type_id = field.get("typeId")
         if type_id == 10:
             value = value if isinstance(value, str) else "、".join(value)
-        elif type_id in [28, 36]:
+        elif type_id in {28, 36}:
             value = field.get("options", {}).get(value, value)
-        elif type_id in [26, 27, 48, 14]:
+        elif type_id in {26, 27, 48, 14}:
             value = self.process_value(value)
-        elif type_id in [35, 29]:
+        elif type_id in {35, 29}:
             value = self.parse_cascade_or_associated(field, value)
         elif type_id == 40:
             value = self.parse_location(value)
-        return self.rich_text_to_plain_text(value) if value else ''
+        return self.rich_text_to_plain_text(value) if value else ""
 
     def process_value(self, value):
         if isinstance(value, str):
-            if value.startswith("[{\"accountId\""):
+            if value.startswith('[{"accountId"'):
                 value = json.loads(value)
-                value = ', '.join([item['fullname'] for item in value])
-            elif value.startswith("[{\"departmentId\""):
+                value = ", ".join([item["fullname"] for item in value])
+            elif value.startswith('[{"departmentId"'):
                 value = json.loads(value)
-                value = '、'.join([item['departmentName'] for item in value])
-            elif value.startswith("[{\"organizeId\""):
+                value = "、".join([item["departmentName"] for item in value])
+            elif value.startswith('[{"organizeId"'):
                 value = json.loads(value)
-                value = '、'.join([item['organizeName'] for item in value])
-            elif value.startswith("[{\"file_id\""):
-                value = ''
-            elif value == '[]':
-                value = ''
-        elif hasattr(value, 'accountId'):
-            value = value['fullname']
+                value = "、".join([item["organizeName"] for item in value])
+            elif value.startswith('[{"file_id"') or value == "[]":
+                value = ""
+        elif hasattr(value, "accountId"):
+            value = value["fullname"]
         return value
 
     def parse_cascade_or_associated(self, field, value):
-        if (field['typeId'] == 35 and value.startswith('[')) or (field['typeId'] == 29 and value.startswith('[{')):
+        if (field["typeId"] == 35 and value.startswith("[")) or (field["typeId"] == 29 and value.startswith("[{")):
             value = json.loads(value)
-            value = value[0]['name'] if len(value) > 0 else ''
+            value = value[0]["name"] if len(value) > 0 else ""
         else:
-            value = ''
+            value = ""
         return value
 
     def parse_location(self, value):
@@ -205,5 +227,5 @@ class ListWorksheetRecordsTool(BuiltinTool):
         return value
 
     def rich_text_to_plain_text(self, rich_text):
-        text = re.sub(r'<[^>]+>', '', rich_text) if '<' in rich_text else rich_text
-        return text.replace("|", "▏").replace("\n", " ")
\ No newline at end of file
+        text = re.sub(r"<[^>]+>", "", rich_text) if "<" in rich_text else rich_text
+        return text.replace("|", "▏").replace("\n", " ")
diff --git a/api/core/tools/provider/builtin/hap/tools/list_worksheets.py b/api/core/tools/provider/builtin/hap/tools/list_worksheets.py
index 960cbd10ac..4e852c0028 100644
--- a/api/core/tools/provider/builtin/hap/tools/list_worksheets.py
+++ b/api/core/tools/provider/builtin/hap/tools/list_worksheets.py
@@ -8,75 +8,76 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class ListWorksheetsTool(BuiltinTool):
-
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
-        appkey = tool_parameters.get('appkey', '')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        appkey = tool_parameters.get("appkey", "")
         if not appkey:
-            return self.create_text_message('Invalid parameter App Key')
-        sign = tool_parameters.get('sign', '')
+            return self.create_text_message("Invalid parameter App Key")
+        sign = tool_parameters.get("sign", "")
         if not sign:
-            return self.create_text_message('Invalid parameter Sign')
-        
-        host = tool_parameters.get('host', '')
+            return self.create_text_message("Invalid parameter Sign")
+
+        host = tool_parameters.get("host", "")
         if not host:
-            host = 'https://api.mingdao.com'
+            host = "https://api.mingdao.com"
         elif not (host.startswith("http://") or host.startswith("https://")):
-            return self.create_text_message('Invalid parameter Host Address')
+            return self.create_text_message("Invalid parameter Host Address")
         else:
-            host = f"{host[:-1] if host.endswith('/') else host}/api"
+            host = f"{host.removesuffix('/')}/api"
         url = f"{host}/v1/open/app/get"
 
-        result_type = tool_parameters.get('result_type', '')
+        result_type = tool_parameters.get("result_type", "")
         if not result_type:
-            result_type = 'table'
+            result_type = "table"
 
-        headers = { 'Content-Type': 'application/json' }
-        params = { "appKey": appkey, "sign": sign, }
+        headers = {"Content-Type": "application/json"}
+        params = {
+            "appKey": appkey,
+            "sign": sign,
+        }
         try:
             res = httpx.get(url, headers=headers, params=params, timeout=30)
             res_json = res.json()
             if res.is_success:
-                if res_json['error_code'] != 1:
-                    return self.create_text_message("Failed to access the application. {}".format(res_json['error_msg']))
+                if res_json["error_code"] != 1:
+                    return self.create_text_message(
+                        "Failed to access the application. {}".format(res_json["error_msg"])
+                    )
                 else:
-                    if result_type == 'json':
+                    if result_type == "json":
                         worksheets = []
-                        for section in res_json['data']['sections']:
+                        for section in res_json["data"]["sections"]:
                             worksheets.extend(self._extract_worksheets(section, result_type))
                         return self.create_text_message(text=json.dumps(worksheets, ensure_ascii=False))
                     else:
-                        worksheets = '|worksheetId|worksheetName|description|\n|---|---|---|'
-                        for section in res_json['data']['sections']:
+                        worksheets = "|worksheetId|worksheetName|description|\n|---|---|---|"
+                        for section in res_json["data"]["sections"]:
                             worksheets += self._extract_worksheets(section, result_type)
                         return self.create_text_message(worksheets)
 
             else:
                 return self.create_text_message(
-                    f"Failed to list worksheets, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to list worksheets, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to list worksheets, something went wrong: {}".format(e))
 
     def _extract_worksheets(self, section, type):
         items = []
-        tables = ''
-        for item in section.get('items', []):
-            if item.get('type') == 0 and (not 'notes' in item or item.get('notes') != 'NO'):
-                if type == 'json':
-                    filtered_item = {
-                        'id': item['id'],
-                        'name': item['name'],
-                        'notes': item.get('notes', '')
-                    }
+        tables = ""
+        for item in section.get("items", []):
+            if item.get("type") == 0 and ("notes" not in item or item.get("notes") != "NO"):
+                if type == "json":
+                    filtered_item = {"id": item["id"], "name": item["name"], "notes": item.get("notes", "")}
                     items.append(filtered_item)
                 else:
                     tables += f"\n|{item['id']}|{item['name']}|{item.get('notes', '')}|"
 
-        for child_section in section.get('childSections', []):
-            if type == 'json':
-                items.extend(self._extract_worksheets(child_section, 'json'))
+        for child_section in section.get("childSections", []):
+            if type == "json":
+                items.extend(self._extract_worksheets(child_section, "json"))
             else:
-                tables += self._extract_worksheets(child_section, 'table')
-        
-        return items if type == 'json' else tables
\ No newline at end of file
+                tables += self._extract_worksheets(child_section, "table")
+
+        return items if type == "json" else tables
diff --git a/api/core/tools/provider/builtin/hap/tools/update_worksheet_record.py b/api/core/tools/provider/builtin/hap/tools/update_worksheet_record.py
index 6ca1b98d90..971f3d37f6 100644
--- a/api/core/tools/provider/builtin/hap/tools/update_worksheet_record.py
+++ b/api/core/tools/provider/builtin/hap/tools/update_worksheet_record.py
@@ -8,44 +8,43 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class UpdateWorksheetRecordTool(BuiltinTool):
-
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        
-        appkey = tool_parameters.get('appkey', '')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        appkey = tool_parameters.get("appkey", "")
         if not appkey:
-            return self.create_text_message('Invalid parameter App Key')
-        sign = tool_parameters.get('sign', '')
+            return self.create_text_message("Invalid parameter App Key")
+        sign = tool_parameters.get("sign", "")
         if not sign:
-            return self.create_text_message('Invalid parameter Sign')
-        worksheet_id = tool_parameters.get('worksheet_id', '')
+            return self.create_text_message("Invalid parameter Sign")
+        worksheet_id = tool_parameters.get("worksheet_id", "")
         if not worksheet_id:
-            return self.create_text_message('Invalid parameter Worksheet ID')
-        row_id = tool_parameters.get('row_id', '')
+            return self.create_text_message("Invalid parameter Worksheet ID")
+        row_id = tool_parameters.get("row_id", "")
         if not row_id:
-            return self.create_text_message('Invalid parameter Record Row ID')
-        record_data = tool_parameters.get('record_data', '')
+            return self.create_text_message("Invalid parameter Record Row ID")
+        record_data = tool_parameters.get("record_data", "")
         if not record_data:
-            return self.create_text_message('Invalid parameter Record Row Data')
-        
-        host = tool_parameters.get('host', '')
+            return self.create_text_message("Invalid parameter Record Row Data")
+
+        host = tool_parameters.get("host", "")
         if not host:
-            host = 'https://api.mingdao.com'
+            host = "https://api.mingdao.com"
         elif not host.startswith(("http://", "https://")):
-            return self.create_text_message('Invalid parameter Host Address')
+            return self.create_text_message("Invalid parameter Host Address")
         else:
-            host = f"{host[:-1] if host.endswith('/') else host}/api"
+            host = f"{host.removesuffix('/')}/api"
 
         url = f"{host}/v2/open/worksheet/editRow"
-        headers = {'Content-Type': 'application/json'}
+        headers = {"Content-Type": "application/json"}
         payload = {"appKey": appkey, "sign": sign, "worksheetId": worksheet_id, "rowId": row_id}
 
         try:
-            payload['controls'] = json.loads(record_data)
+            payload["controls"] = json.loads(record_data)
             res = httpx.post(url, headers=headers, json=payload, timeout=60)
             res.raise_for_status()
             res_json = res.json()
-            if res_json.get('error_code') != 1:
+            if res_json.get("error_code") != 1:
                 return self.create_text_message(f"Failed to update the record. {res_json['error_msg']}")
             return self.create_text_message("Record updated successfully.")
         except httpx.RequestError as e:
diff --git a/api/core/tools/provider/builtin/jina/jina.py b/api/core/tools/provider/builtin/jina/jina.py
index 12e5058cdc..154e15db01 100644
--- a/api/core/tools/provider/builtin/jina/jina.py
+++ b/api/core/tools/provider/builtin/jina/jina.py
@@ -10,27 +10,29 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class GoogleProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
-            if credentials['api_key'] is None:
-                credentials['api_key'] = ''
+            if credentials["api_key"] is None:
+                credentials["api_key"] = ""
             else:
-                result = JinaReaderTool().fork_tool_runtime(
-                    runtime={
-                        "credentials": credentials,
-                    }
-                ).invoke(
-                    user_id='',
-                    tool_parameters={
-                        "url": "https://example.com",
-                    },
-                )[0]
+                result = (
+                    JinaReaderTool()
+                    .fork_tool_runtime(
+                        runtime={
+                            "credentials": credentials,
+                        }
+                    )
+                    .invoke(
+                        user_id="",
+                        tool_parameters={
+                            "url": "https://example.com",
+                        },
+                    )[0]
+                )
 
                 message = json.loads(result.message)
-                if message['code'] != 200:
-                    raise ToolProviderCredentialValidationError(message['message'])
+                if message["code"] != 200:
+                    raise ToolProviderCredentialValidationError(message["message"])
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
+
     def _get_tool_labels(self) -> list[ToolLabelEnum]:
-        return [
-            ToolLabelEnum.SEARCH, ToolLabelEnum.PRODUCTIVITY
-        ]
\ No newline at end of file
+        return [ToolLabelEnum.SEARCH, ToolLabelEnum.PRODUCTIVITY]
diff --git a/api/core/tools/provider/builtin/jina/tools/jina_reader.py b/api/core/tools/provider/builtin/jina/tools/jina_reader.py
index cee46cee23..0dd55c6529 100644
--- a/api/core/tools/provider/builtin/jina/tools/jina_reader.py
+++ b/api/core/tools/provider/builtin/jina/tools/jina_reader.py
@@ -9,26 +9,25 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class JinaReaderTool(BuiltinTool):
-    _jina_reader_endpoint = 'https://r.jina.ai/'
+    _jina_reader_endpoint = "https://r.jina.ai/"
 
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        url = tool_parameters['url']
+        url = tool_parameters["url"]
 
-        headers = {
-            'Accept': 'application/json'
-        }
+        headers = {"Accept": "application/json"}
 
-        if 'api_key' in self.runtime.credentials and self.runtime.credentials.get('api_key'):
-            headers['Authorization'] = "Bearer " + self.runtime.credentials.get('api_key')
+        if "api_key" in self.runtime.credentials and self.runtime.credentials.get("api_key"):
+            headers["Authorization"] = "Bearer " + self.runtime.credentials.get("api_key")
 
-        request_params = tool_parameters.get('request_params')
-        if request_params is not None and request_params != '':
+        request_params = tool_parameters.get("request_params")
+        if request_params is not None and request_params != "":
             try:
                 request_params = json.loads(request_params)
                 if not isinstance(request_params, dict):
@@ -36,40 +35,40 @@ class JinaReaderTool(BuiltinTool):
             except (json.JSONDecodeError, ValueError) as e:
                 raise ValueError(f"Invalid request_params: {e}")
 
-        target_selector = tool_parameters.get('target_selector')
-        if target_selector is not None and target_selector != '':
-            headers['X-Target-Selector'] = target_selector
+        target_selector = tool_parameters.get("target_selector")
+        if target_selector is not None and target_selector != "":
+            headers["X-Target-Selector"] = target_selector
 
-        wait_for_selector = tool_parameters.get('wait_for_selector')
-        if wait_for_selector is not None and wait_for_selector != '':
-            headers['X-Wait-For-Selector'] = wait_for_selector
+        wait_for_selector = tool_parameters.get("wait_for_selector")
+        if wait_for_selector is not None and wait_for_selector != "":
+            headers["X-Wait-For-Selector"] = wait_for_selector
 
-        if tool_parameters.get('image_caption', False):
-            headers['X-With-Generated-Alt'] = 'true'
+        if tool_parameters.get("image_caption", False):
+            headers["X-With-Generated-Alt"] = "true"
 
-        if tool_parameters.get('gather_all_links_at_the_end', False):
-            headers['X-With-Links-Summary'] = 'true'
+        if tool_parameters.get("gather_all_links_at_the_end", False):
+            headers["X-With-Links-Summary"] = "true"
 
-        if tool_parameters.get('gather_all_images_at_the_end', False):
-            headers['X-With-Images-Summary'] = 'true'
+        if tool_parameters.get("gather_all_images_at_the_end", False):
+            headers["X-With-Images-Summary"] = "true"
 
-        proxy_server = tool_parameters.get('proxy_server')
-        if proxy_server is not None and proxy_server != '':
-            headers['X-Proxy-Url'] = proxy_server
+        proxy_server = tool_parameters.get("proxy_server")
+        if proxy_server is not None and proxy_server != "":
+            headers["X-Proxy-Url"] = proxy_server
 
-        if tool_parameters.get('no_cache', False):
-            headers['X-No-Cache'] = 'true'
+        if tool_parameters.get("no_cache", False):
+            headers["X-No-Cache"] = "true"
 
-        max_retries = tool_parameters.get('max_retries', 3)
+        max_retries = tool_parameters.get("max_retries", 3)
         response = ssrf_proxy.get(
             str(URL(self._jina_reader_endpoint + url)),
             headers=headers,
             params=request_params,
             timeout=(10, 60),
-            max_retries=max_retries
+            max_retries=max_retries,
         )
 
-        if tool_parameters.get('summary', False):
+        if tool_parameters.get("summary", False):
             return self.create_text_message(self.summary(user_id, response.text))
 
         return self.create_text_message(response.text)
diff --git a/api/core/tools/provider/builtin/jina/tools/jina_search.py b/api/core/tools/provider/builtin/jina/tools/jina_search.py
index d4a81cd096..30af6de783 100644
--- a/api/core/tools/provider/builtin/jina/tools/jina_search.py
+++ b/api/core/tools/provider/builtin/jina/tools/jina_search.py
@@ -8,44 +8,39 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class JinaSearchTool(BuiltinTool):
-    _jina_search_endpoint = 'https://s.jina.ai/'
+    _jina_search_endpoint = "https://s.jina.ai/"
 
     def _invoke(
         self,
         user_id: str,
         tool_parameters: dict[str, Any],
     ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        query = tool_parameters['query']
+        query = tool_parameters["query"]
 
-        headers = {
-            'Accept': 'application/json'
-        }
+        headers = {"Accept": "application/json"}
 
-        if 'api_key' in self.runtime.credentials and self.runtime.credentials.get('api_key'):
-            headers['Authorization'] = "Bearer " + self.runtime.credentials.get('api_key')
+        if "api_key" in self.runtime.credentials and self.runtime.credentials.get("api_key"):
+            headers["Authorization"] = "Bearer " + self.runtime.credentials.get("api_key")
 
-        if tool_parameters.get('image_caption', False):
-            headers['X-With-Generated-Alt'] = 'true'
+        if tool_parameters.get("image_caption", False):
+            headers["X-With-Generated-Alt"] = "true"
 
-        if tool_parameters.get('gather_all_links_at_the_end', False):
-            headers['X-With-Links-Summary'] = 'true'
+        if tool_parameters.get("gather_all_links_at_the_end", False):
+            headers["X-With-Links-Summary"] = "true"
 
-        if tool_parameters.get('gather_all_images_at_the_end', False):
-            headers['X-With-Images-Summary'] = 'true'
+        if tool_parameters.get("gather_all_images_at_the_end", False):
+            headers["X-With-Images-Summary"] = "true"
 
-        proxy_server = tool_parameters.get('proxy_server')
-        if proxy_server is not None and proxy_server != '':
-            headers['X-Proxy-Url'] = proxy_server
+        proxy_server = tool_parameters.get("proxy_server")
+        if proxy_server is not None and proxy_server != "":
+            headers["X-Proxy-Url"] = proxy_server
 
-        if tool_parameters.get('no_cache', False):
-            headers['X-No-Cache'] = 'true'
+        if tool_parameters.get("no_cache", False):
+            headers["X-No-Cache"] = "true"
 
-        max_retries = tool_parameters.get('max_retries', 3)
+        max_retries = tool_parameters.get("max_retries", 3)
         response = ssrf_proxy.get(
-            str(URL(self._jina_search_endpoint + query)),
-            headers=headers,
-            timeout=(10, 60),
-            max_retries=max_retries
+            str(URL(self._jina_search_endpoint + query)), headers=headers, timeout=(10, 60), max_retries=max_retries
         )
 
         return self.create_text_message(response.text)
diff --git a/api/core/tools/provider/builtin/jina/tools/jina_tokenizer.py b/api/core/tools/provider/builtin/jina/tools/jina_tokenizer.py
index 0d018e3ca2..06dabcc9c2 100644
--- a/api/core/tools/provider/builtin/jina/tools/jina_tokenizer.py
+++ b/api/core/tools/provider/builtin/jina/tools/jina_tokenizer.py
@@ -6,33 +6,29 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class JinaTokenizerTool(BuiltinTool):
-    _jina_tokenizer_endpoint = 'https://tokenize.jina.ai/'
+    _jina_tokenizer_endpoint = "https://tokenize.jina.ai/"
 
     def _invoke(
         self,
         user_id: str,
         tool_parameters: dict[str, Any],
     ) -> ToolInvokeMessage:
-        content = tool_parameters['content']
-        body = {
-            "content": content
-        }
+        content = tool_parameters["content"]
+        body = {"content": content}
 
-        headers = {
-            'Content-Type': 'application/json'
-        }
+        headers = {"Content-Type": "application/json"}
 
-        if 'api_key' in self.runtime.credentials and self.runtime.credentials.get('api_key'):
-            headers['Authorization'] = "Bearer " + self.runtime.credentials.get('api_key')
+        if "api_key" in self.runtime.credentials and self.runtime.credentials.get("api_key"):
+            headers["Authorization"] = "Bearer " + self.runtime.credentials.get("api_key")
 
-        if tool_parameters.get('return_chunks', False):
-            body['return_chunks'] = True
-        
-        if tool_parameters.get('return_tokens', False):
-            body['return_tokens'] = True
-        
-        if tokenizer := tool_parameters.get('tokenizer'):
-            body['tokenizer'] = tokenizer
+        if tool_parameters.get("return_chunks", False):
+            body["return_chunks"] = True
+
+        if tool_parameters.get("return_tokens", False):
+            body["return_tokens"] = True
+
+        if tokenizer := tool_parameters.get("tokenizer"):
+            body["tokenizer"] = tokenizer
 
         response = ssrf_proxy.post(
             self._jina_tokenizer_endpoint,
diff --git a/api/core/tools/provider/builtin/json_process/json_process.py b/api/core/tools/provider/builtin/json_process/json_process.py
index f6eed3c628..10746210b5 100644
--- a/api/core/tools/provider/builtin/json_process/json_process.py
+++ b/api/core/tools/provider/builtin/json_process/json_process.py
@@ -8,10 +8,9 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class JsonExtractProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
-            JSONParseTool().invoke(user_id='',
-                                            tool_parameters={
-                                                'content': '{"name": "John", "age": 30, "city": "New York"}',
-                                                'json_filter': '$.name'
-                                            })
+            JSONParseTool().invoke(
+                user_id="",
+                tool_parameters={"content": '{"name": "John", "age": 30, "city": "New York"}', "json_filter": "$.name"},
+            )
         except Exception as e:
-            raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
+            raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/json_process/tools/delete.py b/api/core/tools/provider/builtin/json_process/tools/delete.py
index 1b49cfe2f3..fcab3d71a9 100644
--- a/api/core/tools/provider/builtin/json_process/tools/delete.py
+++ b/api/core/tools/provider/builtin/json_process/tools/delete.py
@@ -8,34 +8,35 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class JSONDeleteTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
         Invoke the JSON delete tool
         """
         # Get content
-        content = tool_parameters.get('content', '')
+        content = tool_parameters.get("content", "")
         if not content:
-            return self.create_text_message('Invalid parameter content')
+            return self.create_text_message("Invalid parameter content")
 
         # Get query
-        query = tool_parameters.get('query', '')
+        query = tool_parameters.get("query", "")
         if not query:
-            return self.create_text_message('Invalid parameter query')
+            return self.create_text_message("Invalid parameter query")
 
-        ensure_ascii = tool_parameters.get('ensure_ascii', True)
+        ensure_ascii = tool_parameters.get("ensure_ascii", True)
         try:
             result = self._delete(content, query, ensure_ascii)
             return self.create_text_message(str(result))
         except Exception as e:
-            return self.create_text_message(f'Failed to delete JSON content: {str(e)}')
+            return self.create_text_message(f"Failed to delete JSON content: {str(e)}")
 
     def _delete(self, origin_json: str, query: str, ensure_ascii: bool) -> str:
         try:
             input_data = json.loads(origin_json)
-            expr = parse('$.' + query.lstrip('$.'))  # Ensure query path starts with $
+            expr = parse("$." + query.lstrip("$."))  # Ensure query path starts with $
 
             matches = expr.find(input_data)
 
diff --git a/api/core/tools/provider/builtin/json_process/tools/insert.py b/api/core/tools/provider/builtin/json_process/tools/insert.py
index 48d1bdcab4..793c74e5f9 100644
--- a/api/core/tools/provider/builtin/json_process/tools/insert.py
+++ b/api/core/tools/provider/builtin/json_process/tools/insert.py
@@ -8,46 +8,49 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class JSONParseTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         # get content
-        content = tool_parameters.get('content', '')
+        content = tool_parameters.get("content", "")
         if not content:
-            return self.create_text_message('Invalid parameter content')
+            return self.create_text_message("Invalid parameter content")
 
         # get query
-        query = tool_parameters.get('query', '')
+        query = tool_parameters.get("query", "")
         if not query:
-            return self.create_text_message('Invalid parameter query')
+            return self.create_text_message("Invalid parameter query")
 
         # get new value
-        new_value = tool_parameters.get('new_value', '')
+        new_value = tool_parameters.get("new_value", "")
         if not new_value:
-            return self.create_text_message('Invalid parameter new_value')
+            return self.create_text_message("Invalid parameter new_value")
 
         # get insert position
-        index = tool_parameters.get('index')
+        index = tool_parameters.get("index")
 
         # get create path
-        create_path = tool_parameters.get('create_path', False)
+        create_path = tool_parameters.get("create_path", False)
 
         # get value decode.
         # if true, it will be decoded to an dict
-        value_decode = tool_parameters.get('value_decode', False)
+        value_decode = tool_parameters.get("value_decode", False)
 
-        ensure_ascii = tool_parameters.get('ensure_ascii', True)
+        ensure_ascii = tool_parameters.get("ensure_ascii", True)
         try:
             result = self._insert(content, query, new_value, ensure_ascii, value_decode, index, create_path)
             return self.create_text_message(str(result))
         except Exception:
-            return self.create_text_message('Failed to insert JSON content')
+            return self.create_text_message("Failed to insert JSON content")
 
-    def _insert(self, origin_json, query, new_value, ensure_ascii: bool, value_decode: bool, index=None, create_path=False):
+    def _insert(
+        self, origin_json, query, new_value, ensure_ascii: bool, value_decode: bool, index=None, create_path=False
+    ):
         try:
             input_data = json.loads(origin_json)
             expr = parse(query)
@@ -61,13 +64,13 @@ class JSONParseTool(BuiltinTool):
 
             if not matches and create_path:
                 # create new path
-                path_parts = query.strip('$').strip('.').split('.')
+                path_parts = query.strip("$").strip(".").split(".")
                 current = input_data
                 for i, part in enumerate(path_parts):
-                    if '[' in part and ']' in part:
+                    if "[" in part and "]" in part:
                         # process array index
-                        array_name, index = part.split('[')
-                        index = int(index.rstrip(']'))
+                        array_name, index = part.split("[")
+                        index = int(index.rstrip("]"))
                         if array_name not in current:
                             current[array_name] = []
                         while len(current[array_name]) <= index:
diff --git a/api/core/tools/provider/builtin/json_process/tools/parse.py b/api/core/tools/provider/builtin/json_process/tools/parse.py
index ecd39113ae..37cae40153 100644
--- a/api/core/tools/provider/builtin/json_process/tools/parse.py
+++ b/api/core/tools/provider/builtin/json_process/tools/parse.py
@@ -8,29 +8,30 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class JSONParseTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         # get content
-        content = tool_parameters.get('content', '')
+        content = tool_parameters.get("content", "")
         if not content:
-            return self.create_text_message('Invalid parameter content')
+            return self.create_text_message("Invalid parameter content")
 
         # get json filter
-        json_filter = tool_parameters.get('json_filter', '')
+        json_filter = tool_parameters.get("json_filter", "")
         if not json_filter:
-            return self.create_text_message('Invalid parameter json_filter')
+            return self.create_text_message("Invalid parameter json_filter")
 
-        ensure_ascii = tool_parameters.get('ensure_ascii', True)
+        ensure_ascii = tool_parameters.get("ensure_ascii", True)
         try:
             result = self._extract(content, json_filter, ensure_ascii)
             return self.create_text_message(str(result))
         except Exception:
-            return self.create_text_message('Failed to extract JSON content')
+            return self.create_text_message("Failed to extract JSON content")
 
     # Extract data from JSON content
     def _extract(self, content: str, json_filter: str, ensure_ascii: bool) -> str:
diff --git a/api/core/tools/provider/builtin/json_process/tools/replace.py b/api/core/tools/provider/builtin/json_process/tools/replace.py
index b19198aa93..383825c2d0 100644
--- a/api/core/tools/provider/builtin/json_process/tools/replace.py
+++ b/api/core/tools/provider/builtin/json_process/tools/replace.py
@@ -8,55 +8,60 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class JSONReplaceTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         # get content
-        content = tool_parameters.get('content', '')
+        content = tool_parameters.get("content", "")
         if not content:
-            return self.create_text_message('Invalid parameter content')
+            return self.create_text_message("Invalid parameter content")
 
         # get query
-        query = tool_parameters.get('query', '')
+        query = tool_parameters.get("query", "")
         if not query:
-            return self.create_text_message('Invalid parameter query')
+            return self.create_text_message("Invalid parameter query")
 
         # get replace value
-        replace_value = tool_parameters.get('replace_value', '')
+        replace_value = tool_parameters.get("replace_value", "")
         if not replace_value:
-            return self.create_text_message('Invalid parameter replace_value')
+            return self.create_text_message("Invalid parameter replace_value")
 
         # get replace model
-        replace_model = tool_parameters.get('replace_model', '')
+        replace_model = tool_parameters.get("replace_model", "")
         if not replace_model:
-            return self.create_text_message('Invalid parameter replace_model')
+            return self.create_text_message("Invalid parameter replace_model")
 
         # get value decode.
         # if true, it will be decoded to an dict
-        value_decode = tool_parameters.get('value_decode', False)
+        value_decode = tool_parameters.get("value_decode", False)
 
-        ensure_ascii = tool_parameters.get('ensure_ascii', True)
+        ensure_ascii = tool_parameters.get("ensure_ascii", True)
         try:
-            if replace_model == 'pattern':
+            if replace_model == "pattern":
                 # get replace pattern
-                replace_pattern = tool_parameters.get('replace_pattern', '')
+                replace_pattern = tool_parameters.get("replace_pattern", "")
                 if not replace_pattern:
-                    return self.create_text_message('Invalid parameter replace_pattern')
-                result = self._replace_pattern(content, query, replace_pattern, replace_value, ensure_ascii, value_decode)
-            elif replace_model == 'key':
+                    return self.create_text_message("Invalid parameter replace_pattern")
+                result = self._replace_pattern(
+                    content, query, replace_pattern, replace_value, ensure_ascii, value_decode
+                )
+            elif replace_model == "key":
                 result = self._replace_key(content, query, replace_value, ensure_ascii)
-            elif replace_model == 'value':
+            elif replace_model == "value":
                 result = self._replace_value(content, query, replace_value, ensure_ascii, value_decode)
             return self.create_text_message(str(result))
         except Exception:
-            return self.create_text_message('Failed to replace JSON content')
+            return self.create_text_message("Failed to replace JSON content")
 
     # Replace pattern
-    def _replace_pattern(self, content: str, query: str, replace_pattern: str, replace_value: str, ensure_ascii: bool, value_decode: bool) -> str:
+    def _replace_pattern(
+        self, content: str, query: str, replace_pattern: str, replace_value: str, ensure_ascii: bool, value_decode: bool
+    ) -> str:
         try:
             input_data = json.loads(content)
             expr = parse(query)
@@ -102,7 +107,9 @@ class JSONReplaceTool(BuiltinTool):
             return str(e)
 
     # Replace value
-    def _replace_value(self, content: str, query: str, replace_value: str, ensure_ascii: bool, value_decode: bool) -> str:
+    def _replace_value(
+        self, content: str, query: str, replace_value: str, ensure_ascii: bool, value_decode: bool
+    ) -> str:
         try:
             input_data = json.loads(content)
             expr = parse(query)
diff --git a/api/core/tools/provider/builtin/judge0ce/judge0ce.py b/api/core/tools/provider/builtin/judge0ce/judge0ce.py
index bac6576797..50db74dd9e 100644
--- a/api/core/tools/provider/builtin/judge0ce/judge0ce.py
+++ b/api/core/tools/provider/builtin/judge0ce/judge0ce.py
@@ -13,7 +13,7 @@ class Judge0CEProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "source_code": "print('hello world')",
                     "language_id": 71,
@@ -21,4 +21,3 @@ class Judge0CEProvider(BuiltinToolProviderController):
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/judge0ce/tools/executeCode.py b/api/core/tools/provider/builtin/judge0ce/tools/executeCode.py
index 6031687c03..b8d654ff63 100644
--- a/api/core/tools/provider/builtin/judge0ce/tools/executeCode.py
+++ b/api/core/tools/provider/builtin/judge0ce/tools/executeCode.py
@@ -9,11 +9,13 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class ExecuteCodeTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
         invoke tools
         """
-        api_key = self.runtime.credentials['X-RapidAPI-Key']
+        api_key = self.runtime.credentials["X-RapidAPI-Key"]
 
         url = "https://judge0-ce.p.rapidapi.com/submissions"
 
@@ -22,15 +24,15 @@ class ExecuteCodeTool(BuiltinTool):
         headers = {
             "Content-Type": "application/json",
             "X-RapidAPI-Key": api_key,
-            "X-RapidAPI-Host": "judge0-ce.p.rapidapi.com"
+            "X-RapidAPI-Host": "judge0-ce.p.rapidapi.com",
         }
 
         payload = {
-            "language_id": tool_parameters['language_id'],
-            "source_code": tool_parameters['source_code'],
-            "stdin": tool_parameters.get('stdin', ''),
-            "expected_output": tool_parameters.get('expected_output', ''),
-            "additional_files": tool_parameters.get('additional_files', ''),
+            "language_id": tool_parameters["language_id"],
+            "source_code": tool_parameters["source_code"],
+            "stdin": tool_parameters.get("stdin", ""),
+            "expected_output": tool_parameters.get("expected_output", ""),
+            "additional_files": tool_parameters.get("additional_files", ""),
         }
 
         response = post(url, data=json.dumps(payload), headers=headers, params=querystring)
@@ -38,22 +40,22 @@ class ExecuteCodeTool(BuiltinTool):
         if response.status_code != 201:
             raise Exception(response.text)
 
-        token = response.json()['token']
+        token = response.json()["token"]
 
         url = f"https://judge0-ce.p.rapidapi.com/submissions/{token}"
-        headers = {
-            "X-RapidAPI-Key": api_key
-        }
-        
+        headers = {"X-RapidAPI-Key": api_key}
+
         response = requests.get(url, headers=headers)
         if response.status_code == 200:
             result = response.json()
-            return self.create_text_message(text=f"stdout: {result.get('stdout', '')}\n"
-                             f"stderr: {result.get('stderr', '')}\n"
-                             f"compile_output: {result.get('compile_output', '')}\n"
-                             f"message: {result.get('message', '')}\n"
-                             f"status: {result['status']['description']}\n"
-                             f"time: {result.get('time', '')} seconds\n"
-                             f"memory: {result.get('memory', '')} bytes")
+            return self.create_text_message(
+                text=f"stdout: {result.get('stdout', '')}\n"
+                f"stderr: {result.get('stderr', '')}\n"
+                f"compile_output: {result.get('compile_output', '')}\n"
+                f"message: {result.get('message', '')}\n"
+                f"status: {result['status']['description']}\n"
+                f"time: {result.get('time', '')} seconds\n"
+                f"memory: {result.get('memory', '')} bytes"
+            )
         else:
-            return self.create_text_message(text=f"Error retrieving submission details: {response.text}")
\ No newline at end of file
+            return self.create_text_message(text=f"Error retrieving submission details: {response.text}")
diff --git a/api/core/tools/provider/builtin/maths/maths.py b/api/core/tools/provider/builtin/maths/maths.py
index 7226a5c168..d4b449ec87 100644
--- a/api/core/tools/provider/builtin/maths/maths.py
+++ b/api/core/tools/provider/builtin/maths/maths.py
@@ -9,9 +9,9 @@ class MathsProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
             EvaluateExpressionTool().invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
-                    'expression': '1+(2+3)*4',
+                    "expression": "1+(2+3)*4",
                 },
             )
         except Exception as e:
diff --git a/api/core/tools/provider/builtin/maths/tools/eval_expression.py b/api/core/tools/provider/builtin/maths/tools/eval_expression.py
index bf73ed6918..0c5b5e41cb 100644
--- a/api/core/tools/provider/builtin/maths/tools/eval_expression.py
+++ b/api/core/tools/provider/builtin/maths/tools/eval_expression.py
@@ -8,22 +8,23 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class EvaluateExpressionTool(BuiltinTool):
-    def _invoke(self, 
-                user_id: str,
-               tool_parameters: dict[str, Any], 
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         # get expression
-        expression = tool_parameters.get('expression', '').strip()
+        expression = tool_parameters.get("expression", "").strip()
         if not expression:
-            return self.create_text_message('Invalid expression')
+            return self.create_text_message("Invalid expression")
 
         try:
             result = ne.evaluate(expression)
             result_str = str(result)
         except Exception as e:
-            logging.exception(f'Error evaluating expression: {expression}')
-            return self.create_text_message(f'Invalid expression: {expression}, error: {str(e)}')
-        return self.create_text_message(f'The result of the expression "{expression}" is {result_str}')
\ No newline at end of file
+            logging.exception(f"Error evaluating expression: {expression}")
+            return self.create_text_message(f"Invalid expression: {expression}, error: {str(e)}")
+        return self.create_text_message(f'The result of the expression "{expression}" is {result_str}')
diff --git a/api/core/tools/provider/builtin/nominatim/nominatim.py b/api/core/tools/provider/builtin/nominatim/nominatim.py
index b6f29b5feb..5a24bed750 100644
--- a/api/core/tools/provider/builtin/nominatim/nominatim.py
+++ b/api/core/tools/provider/builtin/nominatim/nominatim.py
@@ -8,16 +8,20 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class NominatimProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
-            result = NominatimSearchTool().fork_tool_runtime(
-                runtime={
-                    "credentials": credentials,
-                }
-            ).invoke(
-                user_id='',
-                tool_parameters={
-                    'query': 'London',
-                    'limit': 1,
-                },
+            result = (
+                NominatimSearchTool()
+                .fork_tool_runtime(
+                    runtime={
+                        "credentials": credentials,
+                    }
+                )
+                .invoke(
+                    user_id="",
+                    tool_parameters={
+                        "query": "London",
+                        "limit": 1,
+                    },
+                )
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/nominatim/tools/nominatim_lookup.py b/api/core/tools/provider/builtin/nominatim/tools/nominatim_lookup.py
index e21ce14f54..ffa8ad0fcc 100644
--- a/api/core/tools/provider/builtin/nominatim/tools/nominatim_lookup.py
+++ b/api/core/tools/provider/builtin/nominatim/tools/nominatim_lookup.py
@@ -8,40 +8,33 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class NominatimLookupTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        osm_ids = tool_parameters.get('osm_ids', '')
-        
-        if not osm_ids:
-            return self.create_text_message('Please provide OSM IDs')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        osm_ids = tool_parameters.get("osm_ids", "")
 
-        params = {
-            'osm_ids': osm_ids,
-            'format': 'json',
-            'addressdetails': 1
-        }
-        
-        return self._make_request(user_id, 'lookup', params)
+        if not osm_ids:
+            return self.create_text_message("Please provide OSM IDs")
+
+        params = {"osm_ids": osm_ids, "format": "json", "addressdetails": 1}
+
+        return self._make_request(user_id, "lookup", params)
 
     def _make_request(self, user_id: str, endpoint: str, params: dict) -> ToolInvokeMessage:
-        base_url = self.runtime.credentials.get('base_url', 'https://nominatim.openstreetmap.org')
-        
+        base_url = self.runtime.credentials.get("base_url", "https://nominatim.openstreetmap.org")
+
         try:
-            headers = {
-                "User-Agent": "DifyNominatimTool/1.0"
-            }
+            headers = {"User-Agent": "DifyNominatimTool/1.0"}
             s = requests.session()
-            response = s.request(
-                method='GET',
-                headers=headers,
-                url=f"{base_url}/{endpoint}",
-                params=params
-            )
+            response = s.request(method="GET", headers=headers, url=f"{base_url}/{endpoint}", params=params)
             response_data = response.json()
-            
+
             if response.status_code == 200:
                 s.close()
-                return self.create_text_message(self.summary(user_id=user_id, content=json.dumps(response_data, ensure_ascii=False)))
+                return self.create_text_message(
+                    self.summary(user_id=user_id, content=json.dumps(response_data, ensure_ascii=False))
+                )
             else:
                 return self.create_text_message(f"Error: {response.status_code} - {response.text}")
         except Exception as e:
-            return self.create_text_message(f"An error occurred: {str(e)}")
\ No newline at end of file
+            return self.create_text_message(f"An error occurred: {str(e)}")
diff --git a/api/core/tools/provider/builtin/nominatim/tools/nominatim_reverse.py b/api/core/tools/provider/builtin/nominatim/tools/nominatim_reverse.py
index 438d5219e9..f46691e1a3 100644
--- a/api/core/tools/provider/builtin/nominatim/tools/nominatim_reverse.py
+++ b/api/core/tools/provider/builtin/nominatim/tools/nominatim_reverse.py
@@ -8,42 +8,34 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class NominatimReverseTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        lat = tool_parameters.get('lat')
-        lon = tool_parameters.get('lon')
-        
-        if lat is None or lon is None:
-            return self.create_text_message('Please provide both latitude and longitude')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        lat = tool_parameters.get("lat")
+        lon = tool_parameters.get("lon")
 
-        params = {
-            'lat': lat,
-            'lon': lon,
-            'format': 'json',
-            'addressdetails': 1
-        }
-        
-        return self._make_request(user_id, 'reverse', params)
+        if lat is None or lon is None:
+            return self.create_text_message("Please provide both latitude and longitude")
+
+        params = {"lat": lat, "lon": lon, "format": "json", "addressdetails": 1}
+
+        return self._make_request(user_id, "reverse", params)
 
     def _make_request(self, user_id: str, endpoint: str, params: dict) -> ToolInvokeMessage:
-        base_url = self.runtime.credentials.get('base_url', 'https://nominatim.openstreetmap.org')
-        
+        base_url = self.runtime.credentials.get("base_url", "https://nominatim.openstreetmap.org")
+
         try:
-            headers = {
-                "User-Agent": "DifyNominatimTool/1.0"
-            }
+            headers = {"User-Agent": "DifyNominatimTool/1.0"}
             s = requests.session()
-            response = s.request(
-                method='GET',
-                headers=headers,
-                url=f"{base_url}/{endpoint}",
-                params=params
-            )
+            response = s.request(method="GET", headers=headers, url=f"{base_url}/{endpoint}", params=params)
             response_data = response.json()
-            
+
             if response.status_code == 200:
                 s.close()
-                return self.create_text_message(self.summary(user_id=user_id, content=json.dumps(response_data, ensure_ascii=False)))
+                return self.create_text_message(
+                    self.summary(user_id=user_id, content=json.dumps(response_data, ensure_ascii=False))
+                )
             else:
                 return self.create_text_message(f"Error: {response.status_code} - {response.text}")
         except Exception as e:
-            return self.create_text_message(f"An error occurred: {str(e)}")
\ No newline at end of file
+            return self.create_text_message(f"An error occurred: {str(e)}")
diff --git a/api/core/tools/provider/builtin/nominatim/tools/nominatim_search.py b/api/core/tools/provider/builtin/nominatim/tools/nominatim_search.py
index 983cbc0e34..34851d86dc 100644
--- a/api/core/tools/provider/builtin/nominatim/tools/nominatim_search.py
+++ b/api/core/tools/provider/builtin/nominatim/tools/nominatim_search.py
@@ -8,42 +8,34 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class NominatimSearchTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        query = tool_parameters.get('query', '')
-        limit = tool_parameters.get('limit', 10)
-        
-        if not query:
-            return self.create_text_message('Please input a search query')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        query = tool_parameters.get("query", "")
+        limit = tool_parameters.get("limit", 10)
 
-        params = {
-            'q': query,
-            'format': 'json',
-            'limit': limit,
-            'addressdetails': 1
-        }
-        
-        return self._make_request(user_id, 'search', params)
+        if not query:
+            return self.create_text_message("Please input a search query")
+
+        params = {"q": query, "format": "json", "limit": limit, "addressdetails": 1}
+
+        return self._make_request(user_id, "search", params)
 
     def _make_request(self, user_id: str, endpoint: str, params: dict) -> ToolInvokeMessage:
-        base_url = self.runtime.credentials.get('base_url', 'https://nominatim.openstreetmap.org')
-        
+        base_url = self.runtime.credentials.get("base_url", "https://nominatim.openstreetmap.org")
+
         try:
-            headers = {
-                "User-Agent": "DifyNominatimTool/1.0"
-            }
+            headers = {"User-Agent": "DifyNominatimTool/1.0"}
             s = requests.session()
-            response = s.request(
-                method='GET',
-                headers=headers,
-                url=f"{base_url}/{endpoint}",
-                params=params
-            )
+            response = s.request(method="GET", headers=headers, url=f"{base_url}/{endpoint}", params=params)
             response_data = response.json()
-            
+
             if response.status_code == 200:
                 s.close()
-                return self.create_text_message(self.summary(user_id=user_id, content=json.dumps(response_data, ensure_ascii=False)))
+                return self.create_text_message(
+                    self.summary(user_id=user_id, content=json.dumps(response_data, ensure_ascii=False))
+                )
             else:
                 return self.create_text_message(f"Error: {response.status_code} - {response.text}")
         except Exception as e:
-            return self.create_text_message(f"An error occurred: {str(e)}")
\ No newline at end of file
+            return self.create_text_message(f"An error occurred: {str(e)}")
diff --git a/api/core/tools/provider/builtin/novitaai/_novita_tool_base.py b/api/core/tools/provider/builtin/novitaai/_novita_tool_base.py
index b753be4791..762e158459 100644
--- a/api/core/tools/provider/builtin/novitaai/_novita_tool_base.py
+++ b/api/core/tools/provider/builtin/novitaai/_novita_tool_base.py
@@ -12,10 +12,10 @@ class NovitaAiToolBase:
         if not loras_str:
             return []
 
-        loras_ori_list = lora_str.strip().split(';')
+        loras_ori_list = lora_str.strip().split(";")
         result_list = []
         for lora_str in loras_ori_list:
-            lora_info = lora_str.strip().split(',')
+            lora_info = lora_str.strip().split(",")
             lora = Txt2ImgV3LoRA(
                 model_name=lora_info[0].strip(),
                 strength=float(lora_info[1]),
@@ -28,43 +28,39 @@ class NovitaAiToolBase:
         if not embeddings_str:
             return []
 
-        embeddings_ori_list = embeddings_str.strip().split(';')
+        embeddings_ori_list = embeddings_str.strip().split(";")
         result_list = []
         for embedding_str in embeddings_ori_list:
-            embedding = Txt2ImgV3Embedding(
-                model_name=embedding_str.strip()
-            )
+            embedding = Txt2ImgV3Embedding(model_name=embedding_str.strip())
             result_list.append(embedding)
 
         return result_list
 
     def _extract_hires_fix(self, hires_fix_str: str):
-        hires_fix_info = hires_fix_str.strip().split(',')
-        if 'upscaler' in hires_fix_info:
+        hires_fix_info = hires_fix_str.strip().split(",")
+        if "upscaler" in hires_fix_info:
             hires_fix = Txt2ImgV3HiresFix(
                 target_width=int(hires_fix_info[0]),
                 target_height=int(hires_fix_info[1]),
                 strength=float(hires_fix_info[2]),
-                upscaler=hires_fix_info[3].strip()
+                upscaler=hires_fix_info[3].strip(),
             )
         else:
             hires_fix = Txt2ImgV3HiresFix(
                 target_width=int(hires_fix_info[0]),
                 target_height=int(hires_fix_info[1]),
-                strength=float(hires_fix_info[2])
+                strength=float(hires_fix_info[2]),
             )
 
         return hires_fix
 
     def _extract_refiner(self, switch_at: str):
-        refiner = Txt2ImgV3Refiner(
-            switch_at=float(switch_at)
-        )
+        refiner = Txt2ImgV3Refiner(switch_at=float(switch_at))
         return refiner
 
     def _is_hit_nsfw_detection(self, image: V3TaskImage, confidence_threshold: float) -> bool:
         """
-            is hit nsfw
+        is hit nsfw
         """
         if image.nsfw_detection_result is None:
             return False
diff --git a/api/core/tools/provider/builtin/novitaai/novitaai.py b/api/core/tools/provider/builtin/novitaai/novitaai.py
index 1e7d9757c3..d5e32eff29 100644
--- a/api/core/tools/provider/builtin/novitaai/novitaai.py
+++ b/api/core/tools/provider/builtin/novitaai/novitaai.py
@@ -8,23 +8,27 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class NovitaAIProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
-            result = NovitaAiTxt2ImgTool().fork_tool_runtime(
-                runtime={
-                    "credentials": credentials,
-                }
-            ).invoke(
-                user_id='',
-                tool_parameters={
-                    'model_name': 'cinenautXLATRUE_cinenautV10_392434.safetensors',
-                    'prompt': 'a futuristic city with flying cars',
-                    'negative_prompt': '',
-                    'width': 128,
-                    'height': 128,
-                    'image_num': 1,
-                    'guidance_scale': 7.5,
-                    'seed': -1,
-                    'steps': 1,
-                },
+            result = (
+                NovitaAiTxt2ImgTool()
+                .fork_tool_runtime(
+                    runtime={
+                        "credentials": credentials,
+                    }
+                )
+                .invoke(
+                    user_id="",
+                    tool_parameters={
+                        "model_name": "cinenautXLATRUE_cinenautV10_392434.safetensors",
+                        "prompt": "a futuristic city with flying cars",
+                        "negative_prompt": "",
+                        "width": 128,
+                        "height": 128,
+                        "image_num": 1,
+                        "guidance_scale": 7.5,
+                        "seed": -1,
+                        "steps": 1,
+                    },
+                )
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/novitaai/tools/novitaai_createtile.py b/api/core/tools/provider/builtin/novitaai/tools/novitaai_createtile.py
index e63c891957..0b4f2edff3 100644
--- a/api/core/tools/provider/builtin/novitaai/tools/novitaai_createtile.py
+++ b/api/core/tools/provider/builtin/novitaai/tools/novitaai_createtile.py
@@ -12,17 +12,18 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class NovitaAiCreateTileTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        if 'api_key' not in self.runtime.credentials or not self.runtime.credentials.get('api_key'):
+        if "api_key" not in self.runtime.credentials or not self.runtime.credentials.get("api_key"):
             raise ToolProviderCredentialValidationError("Novita AI API Key is required.")
 
-        api_key = self.runtime.credentials.get('api_key')
+        api_key = self.runtime.credentials.get("api_key")
 
         client = NovitaClient(api_key=api_key)
         param = self._process_parameters(tool_parameters)
@@ -30,21 +31,23 @@ class NovitaAiCreateTileTool(BuiltinTool):
 
         results = []
         results.append(
-            self.create_blob_message(blob=b64decode(client_result.image_file),
-                                     meta={'mime_type': f'image/{client_result.image_type}'},
-                                     save_as=self.VARIABLE_KEY.IMAGE.value)
+            self.create_blob_message(
+                blob=b64decode(client_result.image_file),
+                meta={"mime_type": f"image/{client_result.image_type}"},
+                save_as=self.VariableKey.IMAGE.value,
+            )
         )
 
         return results
 
     def _process_parameters(self, parameters: dict[str, Any]) -> dict[str, Any]:
         """
-            process parameters
+        process parameters
         """
         res_parameters = deepcopy(parameters)
 
         # delete none and empty
-        keys_to_delete = [k for k, v in res_parameters.items() if v is None or v == '']
+        keys_to_delete = [k for k, v in res_parameters.items() if v is None or v == ""]
         for k in keys_to_delete:
             del res_parameters[k]
 
diff --git a/api/core/tools/provider/builtin/novitaai/tools/novitaai_modelquery.py b/api/core/tools/provider/builtin/novitaai/tools/novitaai_modelquery.py
index ec2927675e..a200ee8123 100644
--- a/api/core/tools/provider/builtin/novitaai/tools/novitaai_modelquery.py
+++ b/api/core/tools/provider/builtin/novitaai/tools/novitaai_modelquery.py
@@ -12,127 +12,137 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class NovitaAiModelQueryTool(BuiltinTool):
-    _model_query_endpoint = 'https://api.novita.ai/v3/model'
+    _model_query_endpoint = "https://api.novita.ai/v3/model"
 
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        if 'api_key' not in self.runtime.credentials or not self.runtime.credentials.get('api_key'):
+        if "api_key" not in self.runtime.credentials or not self.runtime.credentials.get("api_key"):
             raise ToolProviderCredentialValidationError("Novita AI API Key is required.")
 
-        api_key = self.runtime.credentials.get('api_key')
-        headers = {
-            'Content-Type': 'application/json',
-            'Authorization': "Bearer " + api_key
-        }
+        api_key = self.runtime.credentials.get("api_key")
+        headers = {"Content-Type": "application/json", "Authorization": "Bearer " + api_key}
         params = self._process_parameters(tool_parameters)
-        result_type = params.get('result_type')
-        del params['result_type']
+        result_type = params.get("result_type")
+        del params["result_type"]
 
         models_data = self._query_models(
             models_data=[],
             headers=headers,
             params=params,
-            recursive=False if result_type == 'first sd_name' or result_type == 'first name sd_name pair' else True
+            recursive=result_type not in {"first sd_name", "first name sd_name pair"},
         )
 
-        result_str = ''
-        if result_type == 'first sd_name':
-            result_str = models_data[0]['sd_name_in_api'] if len(models_data) > 0 else ''
-        elif result_type == 'first name sd_name pair':
-            result_str = json.dumps({'name': models_data[0]['name'], 'sd_name': models_data[0]['sd_name_in_api']}) if len(models_data) > 0 else ''
-        elif result_type == 'sd_name array':
-            sd_name_array = [model['sd_name_in_api'] for model in models_data] if len(models_data) > 0 else []
+        result_str = ""
+        if result_type == "first sd_name":
+            result_str = models_data[0]["sd_name_in_api"] if len(models_data) > 0 else ""
+        elif result_type == "first name sd_name pair":
+            result_str = (
+                json.dumps({"name": models_data[0]["name"], "sd_name": models_data[0]["sd_name_in_api"]})
+                if len(models_data) > 0
+                else ""
+            )
+        elif result_type == "sd_name array":
+            sd_name_array = [model["sd_name_in_api"] for model in models_data] if len(models_data) > 0 else []
             result_str = json.dumps(sd_name_array)
-        elif result_type == 'name array':
-            name_array = [model['name'] for model in models_data] if len(models_data) > 0 else []
+        elif result_type == "name array":
+            name_array = [model["name"] for model in models_data] if len(models_data) > 0 else []
             result_str = json.dumps(name_array)
-        elif result_type == 'name sd_name pair array':
-            name_sd_name_pair_array = [{'name': model['name'], 'sd_name': model['sd_name_in_api']}
-                                       for model in models_data] if len(models_data) > 0 else []
+        elif result_type == "name sd_name pair array":
+            name_sd_name_pair_array = (
+                [{"name": model["name"], "sd_name": model["sd_name_in_api"]} for model in models_data]
+                if len(models_data) > 0
+                else []
+            )
             result_str = json.dumps(name_sd_name_pair_array)
-        elif result_type == 'whole info array':
+        elif result_type == "whole info array":
             result_str = json.dumps(models_data)
         else:
             raise NotImplementedError
 
         return self.create_text_message(result_str)
 
-    def _query_models(self, models_data: list, headers: dict[str, Any],
-                      params: dict[str, Any], pagination_cursor: str = '', recursive: bool = True) -> list:
+    def _query_models(
+        self,
+        models_data: list,
+        headers: dict[str, Any],
+        params: dict[str, Any],
+        pagination_cursor: str = "",
+        recursive: bool = True,
+    ) -> list:
         """
-            query models
+        query models
         """
         inside_params = deepcopy(params)
 
-        if pagination_cursor != '':
-            inside_params['pagination.cursor'] = pagination_cursor
+        if pagination_cursor != "":
+            inside_params["pagination.cursor"] = pagination_cursor
 
         response = ssrf_proxy.get(
-            url=str(URL(self._model_query_endpoint)),
-            headers=headers,
-            params=params,
-            timeout=(10, 60)
+            url=str(URL(self._model_query_endpoint)), headers=headers, params=params, timeout=(10, 60)
         )
 
         res_data = response.json()
 
-        models_data.extend(res_data['models'])
+        models_data.extend(res_data["models"])
 
-        res_data_len = len(res_data['models'])
-        if res_data_len == 0 or res_data_len < int(params['pagination.limit']) or recursive is False:
+        res_data_len = len(res_data["models"])
+        if res_data_len == 0 or res_data_len < int(params["pagination.limit"]) or recursive is False:
             # deduplicate
             df = DataFrame.from_dict(models_data)
-            df_unique = df.drop_duplicates(subset=['id'])
-            models_data = df_unique.to_dict('records')
+            df_unique = df.drop_duplicates(subset=["id"])
+            models_data = df_unique.to_dict("records")
             return models_data
 
         return self._query_models(
             models_data=models_data,
             headers=headers,
             params=inside_params,
-            pagination_cursor=res_data['pagination']['next_cursor']
+            pagination_cursor=res_data["pagination"]["next_cursor"],
         )
 
     def _process_parameters(self, parameters: dict[str, Any]) -> dict[str, Any]:
         """
-            process parameters
+        process parameters
         """
         process_parameters = deepcopy(parameters)
         res_parameters = {}
 
         # delete none or empty
-        keys_to_delete = [k for k, v in process_parameters.items() if v is None or v == '']
+        keys_to_delete = [k for k, v in process_parameters.items() if v is None or v == ""]
         for k in keys_to_delete:
             del process_parameters[k]
 
-        if 'query' in process_parameters and process_parameters.get('query') != 'unspecified':
-            res_parameters['filter.query'] = process_parameters['query']
+        if "query" in process_parameters and process_parameters.get("query") != "unspecified":
+            res_parameters["filter.query"] = process_parameters["query"]
 
-        if 'visibility' in process_parameters and process_parameters.get('visibility') != 'unspecified':
-            res_parameters['filter.visibility'] = process_parameters['visibility']
+        if "visibility" in process_parameters and process_parameters.get("visibility") != "unspecified":
+            res_parameters["filter.visibility"] = process_parameters["visibility"]
 
-        if 'source' in process_parameters and process_parameters.get('source') != 'unspecified':
-            res_parameters['filter.source'] = process_parameters['source']
+        if "source" in process_parameters and process_parameters.get("source") != "unspecified":
+            res_parameters["filter.source"] = process_parameters["source"]
 
-        if 'type' in process_parameters and process_parameters.get('type') != 'unspecified':
-            res_parameters['filter.types'] = process_parameters['type']
+        if "type" in process_parameters and process_parameters.get("type") != "unspecified":
+            res_parameters["filter.types"] = process_parameters["type"]
 
-        if 'is_sdxl' in process_parameters:
-            if process_parameters['is_sdxl'] == 'true':
-                res_parameters['filter.is_sdxl'] = True
-            elif process_parameters['is_sdxl'] == 'false':
-                res_parameters['filter.is_sdxl'] = False
+        if "is_sdxl" in process_parameters:
+            if process_parameters["is_sdxl"] == "true":
+                res_parameters["filter.is_sdxl"] = True
+            elif process_parameters["is_sdxl"] == "false":
+                res_parameters["filter.is_sdxl"] = False
 
-        res_parameters['result_type'] = process_parameters.get('result_type', 'first sd_name')
+        res_parameters["result_type"] = process_parameters.get("result_type", "first sd_name")
 
-        res_parameters['pagination.limit'] = 1 \
-            if res_parameters.get('result_type') == 'first sd_name' \
-            or res_parameters.get('result_type') == 'first name sd_name pair'\
+        res_parameters["pagination.limit"] = (
+            1
+            if res_parameters.get("result_type") == "first sd_name"
+            or res_parameters.get("result_type") == "first name sd_name pair"
             else 100
+        )
 
         return res_parameters
diff --git a/api/core/tools/provider/builtin/novitaai/tools/novitaai_txt2img.py b/api/core/tools/provider/builtin/novitaai/tools/novitaai_txt2img.py
index 5fef3d2da7..9c61eab9f9 100644
--- a/api/core/tools/provider/builtin/novitaai/tools/novitaai_txt2img.py
+++ b/api/core/tools/provider/builtin/novitaai/tools/novitaai_txt2img.py
@@ -13,17 +13,18 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class NovitaAiTxt2ImgTool(BuiltinTool, NovitaAiToolBase):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        if 'api_key' not in self.runtime.credentials or not self.runtime.credentials.get('api_key'):
+        if "api_key" not in self.runtime.credentials or not self.runtime.credentials.get("api_key"):
             raise ToolProviderCredentialValidationError("Novita AI API Key is required.")
 
-        api_key = self.runtime.credentials.get('api_key')
+        api_key = self.runtime.credentials.get("api_key")
 
         client = NovitaClient(api_key=api_key)
         param = self._process_parameters(tool_parameters)
@@ -32,56 +33,58 @@ class NovitaAiTxt2ImgTool(BuiltinTool, NovitaAiToolBase):
         results = []
         for image_encoded, image in zip(client_result.images_encoded, client_result.images):
             if self._is_hit_nsfw_detection(image, 0.8):
-                results = self.create_text_message(text='NSFW detected!')
+                results = self.create_text_message(text="NSFW detected!")
                 break
 
             results.append(
-                self.create_blob_message(blob=b64decode(image_encoded),
-                                         meta={'mime_type': f'image/{image.image_type}'},
-                                         save_as=self.VARIABLE_KEY.IMAGE.value)
+                self.create_blob_message(
+                    blob=b64decode(image_encoded),
+                    meta={"mime_type": f"image/{image.image_type}"},
+                    save_as=self.VariableKey.IMAGE.value,
+                )
             )
 
         return results
 
     def _process_parameters(self, parameters: dict[str, Any]) -> dict[str, Any]:
         """
-            process parameters
+        process parameters
         """
         res_parameters = deepcopy(parameters)
 
         # delete none and empty
-        keys_to_delete = [k for k, v in res_parameters.items() if v is None or v == '']
+        keys_to_delete = [k for k, v in res_parameters.items() if v is None or v == ""]
         for k in keys_to_delete:
             del res_parameters[k]
 
-        if 'clip_skip' in res_parameters and res_parameters.get('clip_skip') == 0:
-            del res_parameters['clip_skip']
+        if "clip_skip" in res_parameters and res_parameters.get("clip_skip") == 0:
+            del res_parameters["clip_skip"]
 
-        if 'refiner_switch_at' in res_parameters and res_parameters.get('refiner_switch_at') == 0:
-            del res_parameters['refiner_switch_at']
+        if "refiner_switch_at" in res_parameters and res_parameters.get("refiner_switch_at") == 0:
+            del res_parameters["refiner_switch_at"]
 
-        if 'enabled_enterprise_plan' in res_parameters:
-            res_parameters['enterprise_plan'] = {'enabled': res_parameters['enabled_enterprise_plan']}
-            del res_parameters['enabled_enterprise_plan']
+        if "enabled_enterprise_plan" in res_parameters:
+            res_parameters["enterprise_plan"] = {"enabled": res_parameters["enabled_enterprise_plan"]}
+            del res_parameters["enabled_enterprise_plan"]
 
-        if 'nsfw_detection_level' in res_parameters:
-            res_parameters['nsfw_detection_level'] = int(res_parameters['nsfw_detection_level'])
+        if "nsfw_detection_level" in res_parameters:
+            res_parameters["nsfw_detection_level"] = int(res_parameters["nsfw_detection_level"])
 
         # process loras
-        if 'loras' in res_parameters:
-            res_parameters['loras'] = self._extract_loras(res_parameters.get('loras'))
+        if "loras" in res_parameters:
+            res_parameters["loras"] = self._extract_loras(res_parameters.get("loras"))
 
         # process embeddings
-        if 'embeddings' in res_parameters:
-            res_parameters['embeddings'] = self._extract_embeddings(res_parameters.get('embeddings'))
+        if "embeddings" in res_parameters:
+            res_parameters["embeddings"] = self._extract_embeddings(res_parameters.get("embeddings"))
 
         # process hires_fix
-        if 'hires_fix' in res_parameters:
-            res_parameters['hires_fix'] = self._extract_hires_fix(res_parameters.get('hires_fix'))
+        if "hires_fix" in res_parameters:
+            res_parameters["hires_fix"] = self._extract_hires_fix(res_parameters.get("hires_fix"))
 
         # process refiner
-        if 'refiner_switch_at' in res_parameters:
-            res_parameters['refiner'] = self._extract_refiner(res_parameters.get('refiner_switch_at'))
-            del res_parameters['refiner_switch_at']
+        if "refiner_switch_at" in res_parameters:
+            res_parameters["refiner"] = self._extract_refiner(res_parameters.get("refiner_switch_at"))
+            del res_parameters["refiner_switch_at"]
 
         return res_parameters
diff --git a/api/core/tools/provider/builtin/onebot/onebot.py b/api/core/tools/provider/builtin/onebot/onebot.py
index 42f321e919..b8e5ed24d6 100644
--- a/api/core/tools/provider/builtin/onebot/onebot.py
+++ b/api/core/tools/provider/builtin/onebot/onebot.py
@@ -5,8 +5,6 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 
 
 class OneBotProvider(BuiltinToolProviderController):
-
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
-
         if not credentials.get("ob11_http_url"):
-            raise ToolProviderCredentialValidationError('OneBot HTTP URL is required.')
+            raise ToolProviderCredentialValidationError("OneBot HTTP URL is required.")
diff --git a/api/core/tools/provider/builtin/onebot/tools/send_group_msg.py b/api/core/tools/provider/builtin/onebot/tools/send_group_msg.py
index 2a1a9f86de..9c95bbc2ae 100644
--- a/api/core/tools/provider/builtin/onebot/tools/send_group_msg.py
+++ b/api/core/tools/provider/builtin/onebot/tools/send_group_msg.py
@@ -11,54 +11,29 @@ class SendGroupMsg(BuiltinTool):
     """OneBot v11 Tool: Send Group Message"""
 
     def _invoke(
-            self,
-            user_id: str,
-            tool_parameters: dict[str, Any]
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         # Get parameters
-        send_group_id = tool_parameters.get('group_id', '')
-        
-        message = tool_parameters.get('message', '')
+        send_group_id = tool_parameters.get("group_id", "")
+
+        message = tool_parameters.get("message", "")
         if not message:
-            return self.create_json_message(
-                {
-                    'error': 'Message is empty.'
-                }
-            )
-        
-        auto_escape = tool_parameters.get('auto_escape', False)
+            return self.create_json_message({"error": "Message is empty."})
+
+        auto_escape = tool_parameters.get("auto_escape", False)
 
         try:
-            url = URL(self.runtime.credentials['ob11_http_url']) / 'send_group_msg'
+            url = URL(self.runtime.credentials["ob11_http_url"]) / "send_group_msg"
 
             resp = requests.post(
                 url,
-                json={
-                    'group_id': send_group_id,
-                    'message': message,
-                    'auto_escape': auto_escape
-                },
-                headers={
-                    'Authorization': 'Bearer ' + self.runtime.credentials['access_token']
-                }
+                json={"group_id": send_group_id, "message": message, "auto_escape": auto_escape},
+                headers={"Authorization": "Bearer " + self.runtime.credentials["access_token"]},
             )
 
             if resp.status_code != 200:
-                return self.create_json_message(
-                    {
-                        'error': f'Failed to send group message: {resp.text}'
-                    }
-                )
+                return self.create_json_message({"error": f"Failed to send group message: {resp.text}"})
 
-            return self.create_json_message(
-                {
-                    'response': resp.json()
-                }
-            )
+            return self.create_json_message({"response": resp.json()})
         except Exception as e:
-            return self.create_json_message(
-                {
-                    'error': f'Failed to send group message: {e}'
-                }
-            )
+            return self.create_json_message({"error": f"Failed to send group message: {e}"})
diff --git a/api/core/tools/provider/builtin/onebot/tools/send_private_msg.py b/api/core/tools/provider/builtin/onebot/tools/send_private_msg.py
index 8ef4d72ab6..1174c7f07d 100644
--- a/api/core/tools/provider/builtin/onebot/tools/send_private_msg.py
+++ b/api/core/tools/provider/builtin/onebot/tools/send_private_msg.py
@@ -11,54 +11,29 @@ class SendPrivateMsg(BuiltinTool):
     """OneBot v11 Tool: Send Private Message"""
 
     def _invoke(
-            self,
-            user_id: str,
-            tool_parameters: dict[str, Any]
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         # Get parameters
-        send_user_id = tool_parameters.get('user_id', '')
-        
-        message = tool_parameters.get('message', '')
+        send_user_id = tool_parameters.get("user_id", "")
+
+        message = tool_parameters.get("message", "")
         if not message:
-            return self.create_json_message(
-                {
-                    'error': 'Message is empty.'
-                }
-            )
-        
-        auto_escape = tool_parameters.get('auto_escape', False)
+            return self.create_json_message({"error": "Message is empty."})
+
+        auto_escape = tool_parameters.get("auto_escape", False)
 
         try:
-            url = URL(self.runtime.credentials['ob11_http_url']) / 'send_private_msg'
+            url = URL(self.runtime.credentials["ob11_http_url"]) / "send_private_msg"
 
             resp = requests.post(
                 url,
-                json={
-                    'user_id': send_user_id,
-                    'message': message,
-                    'auto_escape': auto_escape
-                },
-                headers={
-                    'Authorization': 'Bearer ' + self.runtime.credentials['access_token']
-                }
+                json={"user_id": send_user_id, "message": message, "auto_escape": auto_escape},
+                headers={"Authorization": "Bearer " + self.runtime.credentials["access_token"]},
             )
 
             if resp.status_code != 200:
-                return self.create_json_message(
-                    {
-                        'error': f'Failed to send private message: {resp.text}'
-                    }
-                )
-        
-            return self.create_json_message(
-                {
-                    'response': resp.json()
-                }
-            )
+                return self.create_json_message({"error": f"Failed to send private message: {resp.text}"})
+
+            return self.create_json_message({"response": resp.json()})
         except Exception as e:
-            return self.create_json_message(
-                {
-                    'error': f'Failed to send private message: {e}'
-                }
-            )
\ No newline at end of file
+            return self.create_json_message({"error": f"Failed to send private message: {e}"})
diff --git a/api/core/tools/provider/builtin/openweather/openweather.py b/api/core/tools/provider/builtin/openweather/openweather.py
index a2827177a3..9e40249aba 100644
--- a/api/core/tools/provider/builtin/openweather/openweather.py
+++ b/api/core/tools/provider/builtin/openweather/openweather.py
@@ -5,7 +5,6 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 
 
 def query_weather(city="Beijing", units="metric", language="zh_cn", api_key=None):
-
     url = "https://api.openweathermap.org/data/2.5/weather"
     params = {"q": city, "appid": api_key, "units": units, "lang": language}
 
@@ -16,21 +15,15 @@ class OpenweatherProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
         try:
             if "api_key" not in credentials or not credentials.get("api_key"):
-                raise ToolProviderCredentialValidationError(
-                    "Open weather API key is required."
-                )
+                raise ToolProviderCredentialValidationError("Open weather API key is required.")
             apikey = credentials.get("api_key")
             try:
                 response = query_weather(api_key=apikey)
                 if response.status_code == 200:
                     pass
                 else:
-                    raise ToolProviderCredentialValidationError(
-                        (response.json()).get("info")
-                    )
+                    raise ToolProviderCredentialValidationError((response.json()).get("info"))
             except Exception as e:
-                raise ToolProviderCredentialValidationError(
-                    "Open weather API Key is invalid. {}".format(e)
-                )
+                raise ToolProviderCredentialValidationError("Open weather API Key is invalid. {}".format(e))
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/openweather/tools/weather.py b/api/core/tools/provider/builtin/openweather/tools/weather.py
index 536a3511f4..ed4ec487fa 100644
--- a/api/core/tools/provider/builtin/openweather/tools/weather.py
+++ b/api/core/tools/provider/builtin/openweather/tools/weather.py
@@ -17,10 +17,7 @@ class OpenweatherTool(BuiltinTool):
         city = tool_parameters.get("city", "")
         if not city:
             return self.create_text_message("Please tell me your city")
-        if (
-            "api_key" not in self.runtime.credentials
-            or not self.runtime.credentials.get("api_key")
-        ):
+        if "api_key" not in self.runtime.credentials or not self.runtime.credentials.get("api_key"):
             return self.create_text_message("OpenWeather API key is required.")
 
         units = tool_parameters.get("units", "metric")
@@ -29,7 +26,7 @@ class OpenweatherTool(BuiltinTool):
             # request URL
             url = "https://api.openweathermap.org/data/2.5/weather"
 
-            # request parmas
+            # request params
             params = {
                 "q": city,
                 "appid": self.runtime.credentials.get("api_key"),
@@ -39,12 +36,9 @@ class OpenweatherTool(BuiltinTool):
             response = requests.get(url, params=params)
 
             if response.status_code == 200:
-
                 data = response.json()
                 return self.create_text_message(
-                    self.summary(
-                        user_id=user_id, content=json.dumps(data, ensure_ascii=False)
-                    )
+                    self.summary(user_id=user_id, content=json.dumps(data, ensure_ascii=False))
                 )
             else:
                 error_message = {
@@ -55,6 +49,4 @@ class OpenweatherTool(BuiltinTool):
                 return json.dumps(error_message)
 
         except Exception as e:
-            return self.create_text_message(
-                "Openweather API Key is invalid. {}".format(e)
-            )
+            return self.create_text_message("Openweather API Key is invalid. {}".format(e))
diff --git a/api/core/tools/provider/builtin/perplexity/perplexity.py b/api/core/tools/provider/builtin/perplexity/perplexity.py
index ff91edf18d..80518853fb 100644
--- a/api/core/tools/provider/builtin/perplexity/perplexity.py
+++ b/api/core/tools/provider/builtin/perplexity/perplexity.py
@@ -11,34 +11,26 @@ class PerplexityProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         headers = {
             "Authorization": f"Bearer {credentials.get('perplexity_api_key')}",
-            "Content-Type": "application/json"
+            "Content-Type": "application/json",
         }
-        
+
         payload = {
             "model": "llama-3.1-sonar-small-128k-online",
             "messages": [
-                {
-                    "role": "system",
-                    "content": "You are a helpful assistant."
-                },
-                {
-                    "role": "user",
-                    "content": "Hello"
-                }
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": "Hello"},
             ],
             "max_tokens": 5,
             "temperature": 0.1,
             "top_p": 0.9,
-            "stream": False
+            "stream": False,
         }
 
         try:
             response = requests.post(PERPLEXITY_API_URL, json=payload, headers=headers)
             response.raise_for_status()
         except requests.RequestException as e:
-            raise ToolProviderCredentialValidationError(
-                f"Failed to validate Perplexity API key: {str(e)}"
-            )
+            raise ToolProviderCredentialValidationError(f"Failed to validate Perplexity API key: {str(e)}")
 
         if response.status_code != 200:
             raise ToolProviderCredentialValidationError(
diff --git a/api/core/tools/provider/builtin/perplexity/tools/perplexity_search.py b/api/core/tools/provider/builtin/perplexity/tools/perplexity_search.py
index 5b1a263f9b..5ed4b9ca99 100644
--- a/api/core/tools/provider/builtin/perplexity/tools/perplexity_search.py
+++ b/api/core/tools/provider/builtin/perplexity/tools/perplexity_search.py
@@ -8,65 +8,60 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 PERPLEXITY_API_URL = "https://api.perplexity.ai/chat/completions"
 
+
 class PerplexityAITool(BuiltinTool):
     def _parse_response(self, response: dict) -> dict:
         """Parse the response from Perplexity AI API"""
-        if 'choices' in response and len(response['choices']) > 0:
-            message = response['choices'][0]['message']
+        if "choices" in response and len(response["choices"]) > 0:
+            message = response["choices"][0]["message"]
             return {
-                'content': message.get('content', ''),
-                'role': message.get('role', ''),
-                'citations': response.get('citations', [])
+                "content": message.get("content", ""),
+                "role": message.get("role", ""),
+                "citations": response.get("citations", []),
             }
         else:
-            return {'content': 'Unable to get a valid response', 'role': 'assistant', 'citations': []}
+            return {"content": "Unable to get a valid response", "role": "assistant", "citations": []}
 
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         headers = {
             "Authorization": f"Bearer {self.runtime.credentials['perplexity_api_key']}",
-            "Content-Type": "application/json"
+            "Content-Type": "application/json",
         }
-        
+
         payload = {
-            "model": tool_parameters.get('model', 'llama-3.1-sonar-small-128k-online'),
+            "model": tool_parameters.get("model", "llama-3.1-sonar-small-128k-online"),
             "messages": [
-                {
-                    "role": "system",
-                    "content": "Be precise and concise."
-                },
-                {
-                    "role": "user",
-                    "content": tool_parameters['query']
-                }
+                {"role": "system", "content": "Be precise and concise."},
+                {"role": "user", "content": tool_parameters["query"]},
             ],
-            "max_tokens": tool_parameters.get('max_tokens', 4096),
-            "temperature": tool_parameters.get('temperature', 0.7),
-            "top_p": tool_parameters.get('top_p', 1),
-            "top_k": tool_parameters.get('top_k', 5),
-            "presence_penalty": tool_parameters.get('presence_penalty', 0),
-            "frequency_penalty": tool_parameters.get('frequency_penalty', 1),
-            "stream": False
+            "max_tokens": tool_parameters.get("max_tokens", 4096),
+            "temperature": tool_parameters.get("temperature", 0.7),
+            "top_p": tool_parameters.get("top_p", 1),
+            "top_k": tool_parameters.get("top_k", 5),
+            "presence_penalty": tool_parameters.get("presence_penalty", 0),
+            "frequency_penalty": tool_parameters.get("frequency_penalty", 1),
+            "stream": False,
         }
-        
-        if 'search_recency_filter' in tool_parameters:
-            payload['search_recency_filter'] = tool_parameters['search_recency_filter']
-        if 'return_citations' in tool_parameters:
-            payload['return_citations'] = tool_parameters['return_citations']
-        if 'search_domain_filter' in tool_parameters:
-            if isinstance(tool_parameters['search_domain_filter'], str):
-                payload['search_domain_filter'] = [tool_parameters['search_domain_filter']]
-            elif isinstance(tool_parameters['search_domain_filter'], list):
-                payload['search_domain_filter'] = tool_parameters['search_domain_filter']
-        
+
+        if "search_recency_filter" in tool_parameters:
+            payload["search_recency_filter"] = tool_parameters["search_recency_filter"]
+        if "return_citations" in tool_parameters:
+            payload["return_citations"] = tool_parameters["return_citations"]
+        if "search_domain_filter" in tool_parameters:
+            if isinstance(tool_parameters["search_domain_filter"], str):
+                payload["search_domain_filter"] = [tool_parameters["search_domain_filter"]]
+            elif isinstance(tool_parameters["search_domain_filter"], list):
+                payload["search_domain_filter"] = tool_parameters["search_domain_filter"]
 
         response = requests.post(url=PERPLEXITY_API_URL, json=payload, headers=headers)
         response.raise_for_status()
         valuable_res = self._parse_response(response.json())
-        
+
         return [
             self.create_json_message(valuable_res),
-            self.create_text_message(json.dumps(valuable_res, ensure_ascii=False, indent=2))
+            self.create_text_message(json.dumps(valuable_res, ensure_ascii=False, indent=2)),
         ]
diff --git a/api/core/tools/provider/builtin/pubmed/pubmed.py b/api/core/tools/provider/builtin/pubmed/pubmed.py
index 05cd171b87..ea3a477c30 100644
--- a/api/core/tools/provider/builtin/pubmed/pubmed.py
+++ b/api/core/tools/provider/builtin/pubmed/pubmed.py
@@ -11,11 +11,10 @@ class PubMedProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "query": "John Doe",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/pubmed/tools/pubmed_search.py b/api/core/tools/provider/builtin/pubmed/tools/pubmed_search.py
index 58811d65e6..3a4f374ea0 100644
--- a/api/core/tools/provider/builtin/pubmed/tools/pubmed_search.py
+++ b/api/core/tools/provider/builtin/pubmed/tools/pubmed_search.py
@@ -51,17 +51,12 @@ class PubMedAPIWrapper(BaseModel):
         try:
             # Retrieve the top-k results for the query
             docs = [
-                f"Published: {result['pub_date']}\nTitle: {result['title']}\n"
-                f"Summary: {result['summary']}"
+                f"Published: {result['pub_date']}\nTitle: {result['title']}\nSummary: {result['summary']}"
                 for result in self.load(query[: self.ARXIV_MAX_QUERY_LENGTH])
             ]
 
             # Join the results and limit the character count
-            return (
-                "\n\n".join(docs)[:self.doc_content_chars_max]
-                if docs
-                else "No good PubMed Result was found"
-            )
+            return "\n\n".join(docs)[: self.doc_content_chars_max] if docs else "No good PubMed Result was found"
         except Exception as ex:
             return f"PubMed exception: {ex}"
 
@@ -91,13 +86,7 @@ class PubMedAPIWrapper(BaseModel):
         return articles
 
     def retrieve_article(self, uid: str, webenv: str) -> dict:
-        url = (
-            self.base_url_efetch
-            + "db=pubmed&retmode=xml&id="
-            + uid
-            + "&webenv="
-            + webenv
-        )
+        url = self.base_url_efetch + "db=pubmed&retmode=xml&id=" + uid + "&webenv=" + webenv
 
         retry = 0
         while True:
@@ -108,10 +97,7 @@ class PubMedAPIWrapper(BaseModel):
                 if e.code == 429 and retry < self.max_retry:
                     # Too Many Requests error
                     # wait for an exponentially increasing amount of time
-                    print(
-                        f"Too Many Requests, "
-                        f"waiting for {self.sleep_time:.2f} seconds..."
-                    )
+                    print(f"Too Many Requests, waiting for {self.sleep_time:.2f} seconds...")
                     time.sleep(self.sleep_time)
                     self.sleep_time *= 2
                     retry += 1
@@ -125,27 +111,21 @@ class PubMedAPIWrapper(BaseModel):
         if "<ArticleTitle>" in xml_text and "</ArticleTitle>" in xml_text:
             start_tag = "<ArticleTitle>"
             end_tag = "</ArticleTitle>"
-            title = xml_text[
-                xml_text.index(start_tag) + len(start_tag) : xml_text.index(end_tag)
-            ]
+            title = xml_text[xml_text.index(start_tag) + len(start_tag) : xml_text.index(end_tag)]
 
         # Get abstract
         abstract = ""
         if "<AbstractText>" in xml_text and "</AbstractText>" in xml_text:
             start_tag = "<AbstractText>"
             end_tag = "</AbstractText>"
-            abstract = xml_text[
-                xml_text.index(start_tag) + len(start_tag) : xml_text.index(end_tag)
-            ]
+            abstract = xml_text[xml_text.index(start_tag) + len(start_tag) : xml_text.index(end_tag)]
 
         # Get publication date
         pub_date = ""
         if "<PubDate>" in xml_text and "</PubDate>" in xml_text:
             start_tag = "<PubDate>"
             end_tag = "</PubDate>"
-            pub_date = xml_text[
-                xml_text.index(start_tag) + len(start_tag) : xml_text.index(end_tag)
-            ]
+            pub_date = xml_text[xml_text.index(start_tag) + len(start_tag) : xml_text.index(end_tag)]
 
         # Return article as dictionary
         article = {
@@ -182,6 +162,7 @@ class PubmedQueryRun(BaseModel):
 class PubMedInput(BaseModel):
     query: str = Field(..., description="Search query.")
 
+
 class PubMedSearchTool(BuiltinTool):
     """
     Tool for performing a search using PubMed search engine.
@@ -198,14 +179,13 @@ class PubMedSearchTool(BuiltinTool):
         Returns:
             ToolInvokeMessage | list[ToolInvokeMessage]: The result of the tool invocation.
         """
-        query = tool_parameters.get('query', '')
+        query = tool_parameters.get("query", "")
 
         if not query:
-            return self.create_text_message('Please input query')
+            return self.create_text_message("Please input query")
 
         tool = PubmedQueryRun(args_schema=PubMedInput)
 
         result = tool._run(query)
 
         return self.create_text_message(self.summary(user_id=user_id, content=result))
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/qrcode/qrcode.py b/api/core/tools/provider/builtin/qrcode/qrcode.py
index 9fa7d01265..8466b9a26b 100644
--- a/api/core/tools/provider/builtin/qrcode/qrcode.py
+++ b/api/core/tools/provider/builtin/qrcode/qrcode.py
@@ -8,9 +8,6 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class QRCodeProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
-            QRCodeGeneratorTool().invoke(user_id='',
-                                         tool_parameters={
-                                            'content': 'Dify 123 😊'
-                                        })
+            QRCodeGeneratorTool().invoke(user_id="", tool_parameters={"content": "Dify 123 😊"})
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/qrcode/tools/qrcode_generator.py b/api/core/tools/provider/builtin/qrcode/tools/qrcode_generator.py
index 5eede98f5e..d8ca20bde6 100644
--- a/api/core/tools/provider/builtin/qrcode/tools/qrcode_generator.py
+++ b/api/core/tools/provider/builtin/qrcode/tools/qrcode_generator.py
@@ -13,43 +13,44 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 class QRCodeGeneratorTool(BuiltinTool):
     error_correction_levels: dict[str, int] = {
-        'L': ERROR_CORRECT_L,  # <=7%
-        'M': ERROR_CORRECT_M,  # <=15%
-        'Q': ERROR_CORRECT_Q,  # <=25%
-        'H': ERROR_CORRECT_H,  # <=30%
+        "L": ERROR_CORRECT_L,  # <=7%
+        "M": ERROR_CORRECT_M,  # <=15%
+        "Q": ERROR_CORRECT_Q,  # <=25%
+        "H": ERROR_CORRECT_H,  # <=30%
     }
 
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         # get text content
-        content = tool_parameters.get('content', '')
+        content = tool_parameters.get("content", "")
         if not content:
-            return self.create_text_message('Invalid parameter content')
+            return self.create_text_message("Invalid parameter content")
 
         # get border size
-        border = tool_parameters.get('border', 0)
+        border = tool_parameters.get("border", 0)
         if border < 0 or border > 100:
-            return self.create_text_message('Invalid parameter border')
+            return self.create_text_message("Invalid parameter border")
 
         # get error_correction
-        error_correction = tool_parameters.get('error_correction', '')
-        if error_correction not in self.error_correction_levels.keys():
-            return self.create_text_message('Invalid parameter error_correction')
+        error_correction = tool_parameters.get("error_correction", "")
+        if error_correction not in self.error_correction_levels:
+            return self.create_text_message("Invalid parameter error_correction")
 
         try:
             image = self._generate_qrcode(content, border, error_correction)
             image_bytes = self._image_to_byte_array(image)
-            return self.create_blob_message(blob=image_bytes,
-                                            meta={'mime_type': 'image/png'},
-                                            save_as=self.VARIABLE_KEY.IMAGE.value)
+            return self.create_blob_message(
+                blob=image_bytes, meta={"mime_type": "image/png"}, save_as=self.VariableKey.IMAGE.value
+            )
         except Exception:
-            logging.exception(f'Failed to generate QR code for content: {content}')
-            return self.create_text_message('Failed to generate QR code')
+            logging.exception(f"Failed to generate QR code for content: {content}")
+            return self.create_text_message("Failed to generate QR code")
 
     def _generate_qrcode(self, content: str, border: int, error_correction: str) -> BaseImage:
         qr = QRCode(
diff --git a/api/core/tools/provider/builtin/regex/regex.py b/api/core/tools/provider/builtin/regex/regex.py
index d38ae1b292..c498105979 100644
--- a/api/core/tools/provider/builtin/regex/regex.py
+++ b/api/core/tools/provider/builtin/regex/regex.py
@@ -9,10 +9,10 @@ class RegexProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
             RegexExpressionTool().invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
-                    'content': '1+(2+3)*4',
-                    'expression': r'(\d+)',
+                    "content": "1+(2+3)*4",
+                    "expression": r"(\d+)",
                 },
             )
         except Exception as e:
diff --git a/api/core/tools/provider/builtin/regex/tools/regex_extract.py b/api/core/tools/provider/builtin/regex/tools/regex_extract.py
index 5d8f013d0d..786b469404 100644
--- a/api/core/tools/provider/builtin/regex/tools/regex_extract.py
+++ b/api/core/tools/provider/builtin/regex/tools/regex_extract.py
@@ -6,22 +6,23 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class RegexExpressionTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-               tool_parameters: dict[str, Any],
-        ) ->  Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         # get expression
-        content = tool_parameters.get('content', '').strip()
+        content = tool_parameters.get("content", "").strip()
         if not content:
-            return self.create_text_message('Invalid content')
-        expression = tool_parameters.get('expression', '').strip()
+            return self.create_text_message("Invalid content")
+        expression = tool_parameters.get("expression", "").strip()
         if not expression:
-            return self.create_text_message('Invalid expression')
+            return self.create_text_message("Invalid expression")
         try:
             result = re.findall(expression, content)
             return self.create_text_message(str(result))
         except Exception as e:
-            return self.create_text_message(f'Failed to extract result, error: {str(e)}')
\ No newline at end of file
+            return self.create_text_message(f"Failed to extract result, error: {str(e)}")
diff --git a/api/core/tools/provider/builtin/searchapi/searchapi.py b/api/core/tools/provider/builtin/searchapi/searchapi.py
index 6fa4f05acd..109bba8b2d 100644
--- a/api/core/tools/provider/builtin/searchapi/searchapi.py
+++ b/api/core/tools/provider/builtin/searchapi/searchapi.py
@@ -13,11 +13,8 @@ class SearchAPIProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
-                tool_parameters={
-                    "query": "SearchApi dify",
-                    "result_type": "link"
-                },
+                user_id="",
+                tool_parameters={"query": "SearchApi dify", "result_type": "link"},
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/searchapi/tools/google.py b/api/core/tools/provider/builtin/searchapi/tools/google.py
index dd780aeadc..17e2978194 100644
--- a/api/core/tools/provider/builtin/searchapi/tools/google.py
+++ b/api/core/tools/provider/builtin/searchapi/tools/google.py
@@ -7,6 +7,7 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 SEARCH_API_URL = "https://www.searchapi.io/api/v1/search"
 
+
 class SearchAPI:
     """
     SearchAPI tool provider.
@@ -37,42 +38,45 @@ class SearchAPI:
         return {
             "engine": "google",
             "q": query,
-            **{key: value for key, value in kwargs.items() if value not in [None, ""]},
+            **{key: value for key, value in kwargs.items() if value not in {None, ""}},
         }
 
     @staticmethod
     def _process_response(res: dict, type: str) -> str:
         """Process response from SearchAPI."""
-        if "error" in res.keys():
+        if "error" in res:
             raise ValueError(f"Got error from SearchApi: {res['error']}")
 
         toret = ""
         if type == "text":
-            if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
+            if "answer_box" in res and "answer" in res["answer_box"]:
                 toret += res["answer_box"]["answer"] + "\n"
-            if "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
+            if "answer_box" in res and "snippet" in res["answer_box"]:
                 toret += res["answer_box"]["snippet"] + "\n"
-            if "knowledge_graph" in res.keys() and "description" in res["knowledge_graph"].keys():
+            if "knowledge_graph" in res and "description" in res["knowledge_graph"]:
                 toret += res["knowledge_graph"]["description"] + "\n"
-            if "organic_results" in res.keys() and "snippet" in res["organic_results"][0].keys():
+            if "organic_results" in res and "snippet" in res["organic_results"][0]:
                 for item in res["organic_results"]:
                     toret += "content: " + item["snippet"] + "\n" + "link: " + item["link"] + "\n"
             if toret == "":
                 toret = "No good search result found"
 
         elif type == "link":
-            if "answer_box" in res.keys() and "organic_result" in res["answer_box"].keys():
-                if "title" in res["answer_box"]["organic_result"].keys():
-                    toret = f"[{res['answer_box']['organic_result']['title']}]({res['answer_box']['organic_result']['link']})\n"
-            elif "organic_results" in res.keys() and "link" in res["organic_results"][0].keys():
+            if "answer_box" in res and "organic_result" in res["answer_box"]:
+                if "title" in res["answer_box"]["organic_result"]:
+                    toret = (
+                        f"[{res['answer_box']['organic_result']['title']}]"
+                        f"({res['answer_box']['organic_result']['link']})\n"
+                    )
+            elif "organic_results" in res and "link" in res["organic_results"][0]:
                 toret = ""
                 for item in res["organic_results"]:
                     toret += f"[{item['title']}]({item['link']})\n"
-            elif "related_questions" in res.keys() and "link" in res["related_questions"][0].keys():
+            elif "related_questions" in res and "link" in res["related_questions"][0]:
                 toret = ""
                 for item in res["related_questions"]:
                     toret += f"[{item['title']}]({item['link']})\n"
-            elif "related_searches" in res.keys() and "link" in res["related_searches"][0].keys():
+            elif "related_searches" in res and "link" in res["related_searches"][0]:
                 toret = ""
                 for item in res["related_searches"]:
                     toret += f"[{item['title']}]({item['link']})\n"
@@ -80,25 +84,29 @@ class SearchAPI:
                 toret = "No good search result found"
         return toret
 
+
 class GoogleTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
         Invoke the SearchApi tool.
         """
-        query = tool_parameters['query']
-        result_type = tool_parameters['result_type']
+        query = tool_parameters["query"]
+        result_type = tool_parameters["result_type"]
         num = tool_parameters.get("num", 10)
         google_domain = tool_parameters.get("google_domain", "google.com")
         gl = tool_parameters.get("gl", "us")
         hl = tool_parameters.get("hl", "en")
         location = tool_parameters.get("location")
 
-        api_key = self.runtime.credentials['searchapi_api_key']
-        result = SearchAPI(api_key).run(query, result_type=result_type, num=num, google_domain=google_domain, gl=gl, hl=hl, location=location)
+        api_key = self.runtime.credentials["searchapi_api_key"]
+        result = SearchAPI(api_key).run(
+            query, result_type=result_type, num=num, google_domain=google_domain, gl=gl, hl=hl, location=location
+        )
 
-        if result_type == 'text':
+        if result_type == "text":
             return self.create_text_message(text=result)
         return self.create_link_message(link=result)
diff --git a/api/core/tools/provider/builtin/searchapi/tools/google_jobs.py b/api/core/tools/provider/builtin/searchapi/tools/google_jobs.py
index 81c67c51a9..c478bc108b 100644
--- a/api/core/tools/provider/builtin/searchapi/tools/google_jobs.py
+++ b/api/core/tools/provider/builtin/searchapi/tools/google_jobs.py
@@ -7,6 +7,7 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 SEARCH_API_URL = "https://www.searchapi.io/api/v1/search"
 
+
 class SearchAPI:
     """
     SearchAPI tool provider.
@@ -37,41 +38,52 @@ class SearchAPI:
         return {
             "engine": "google_jobs",
             "q": query,
-            **{key: value for key, value in kwargs.items() if value not in [None, ""]},
+            **{key: value for key, value in kwargs.items() if value not in {None, ""}},
         }
 
     @staticmethod
     def _process_response(res: dict, type: str) -> str:
         """Process response from SearchAPI."""
-        if "error" in res.keys():
+        if "error" in res:
             raise ValueError(f"Got error from SearchApi: {res['error']}")
 
         toret = ""
         if type == "text":
-            if "jobs" in res.keys() and "title" in res["jobs"][0].keys():
+            if "jobs" in res and "title" in res["jobs"][0]:
                 for item in res["jobs"]:
-                    toret += "title: " + item["title"] + "\n" + "company_name: " + item["company_name"] + "content: " + item["description"] + "\n"
+                    toret += (
+                        "title: "
+                        + item["title"]
+                        + "\n"
+                        + "company_name: "
+                        + item["company_name"]
+                        + "content: "
+                        + item["description"]
+                        + "\n"
+                    )
             if toret == "":
                 toret = "No good search result found"
 
         elif type == "link":
-            if "jobs" in res.keys() and "apply_link" in res["jobs"][0].keys():
+            if "jobs" in res and "apply_link" in res["jobs"][0]:
                 for item in res["jobs"]:
                     toret += f"[{item['title']} - {item['company_name']}]({item['apply_link']})\n"
             else:
                 toret = "No good search result found"
         return toret
 
+
 class GoogleJobsTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
         Invoke the SearchApi tool.
         """
-        query = tool_parameters['query']
-        result_type = tool_parameters['result_type']
+        query = tool_parameters["query"]
+        result_type = tool_parameters["result_type"]
         is_remote = tool_parameters.get("is_remote")
         google_domain = tool_parameters.get("google_domain", "google.com")
         gl = tool_parameters.get("gl", "us")
@@ -80,9 +92,11 @@ class GoogleJobsTool(BuiltinTool):
 
         ltype = 1 if is_remote else None
 
-        api_key = self.runtime.credentials['searchapi_api_key']
-        result = SearchAPI(api_key).run(query, result_type=result_type, google_domain=google_domain, gl=gl, hl=hl, location=location, ltype=ltype)
+        api_key = self.runtime.credentials["searchapi_api_key"]
+        result = SearchAPI(api_key).run(
+            query, result_type=result_type, google_domain=google_domain, gl=gl, hl=hl, location=location, ltype=ltype
+        )
 
-        if result_type == 'text':
+        if result_type == "text":
             return self.create_text_message(text=result)
         return self.create_link_message(link=result)
diff --git a/api/core/tools/provider/builtin/searchapi/tools/google_news.py b/api/core/tools/provider/builtin/searchapi/tools/google_news.py
index 5d2657dddd..562bc01964 100644
--- a/api/core/tools/provider/builtin/searchapi/tools/google_news.py
+++ b/api/core/tools/provider/builtin/searchapi/tools/google_news.py
@@ -7,6 +7,7 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 SEARCH_API_URL = "https://www.searchapi.io/api/v1/search"
 
+
 class SearchAPI:
     """
     SearchAPI tool provider.
@@ -37,56 +38,60 @@ class SearchAPI:
         return {
             "engine": "google_news",
             "q": query,
-            **{key: value for key, value in kwargs.items() if value not in [None, ""]},
+            **{key: value for key, value in kwargs.items() if value not in {None, ""}},
         }
 
     @staticmethod
     def _process_response(res: dict, type: str) -> str:
         """Process response from SearchAPI."""
-        if "error" in res.keys():
+        if "error" in res:
             raise ValueError(f"Got error from SearchApi: {res['error']}")
 
         toret = ""
         if type == "text":
-            if "organic_results" in res.keys() and "snippet" in res["organic_results"][0].keys():
+            if "organic_results" in res and "snippet" in res["organic_results"][0]:
                 for item in res["organic_results"]:
                     toret += "content: " + item["snippet"] + "\n" + "link: " + item["link"] + "\n"
-            if "top_stories" in res.keys() and "title" in res["top_stories"][0].keys():
+            if "top_stories" in res and "title" in res["top_stories"][0]:
                 for item in res["top_stories"]:
                     toret += "title: " + item["title"] + "\n" + "link: " + item["link"] + "\n"
             if toret == "":
                 toret = "No good search result found"
 
         elif type == "link":
-            if "organic_results" in res.keys() and "title" in res["organic_results"][0].keys():
+            if "organic_results" in res and "title" in res["organic_results"][0]:
                 for item in res["organic_results"]:
                     toret += f"[{item['title']}]({item['link']})\n"
-            elif "top_stories" in res.keys() and "title" in res["top_stories"][0].keys():
+            elif "top_stories" in res and "title" in res["top_stories"][0]:
                 for item in res["top_stories"]:
                     toret += f"[{item['title']}]({item['link']})\n"
             else:
                 toret = "No good search result found"
         return toret
 
+
 class GoogleNewsTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
         Invoke the SearchApi tool.
         """
-        query = tool_parameters['query']
-        result_type = tool_parameters['result_type']
+        query = tool_parameters["query"]
+        result_type = tool_parameters["result_type"]
         num = tool_parameters.get("num", 10)
         google_domain = tool_parameters.get("google_domain", "google.com")
         gl = tool_parameters.get("gl", "us")
         hl = tool_parameters.get("hl", "en")
         location = tool_parameters.get("location")
 
-        api_key = self.runtime.credentials['searchapi_api_key']
-        result = SearchAPI(api_key).run(query, result_type=result_type, num=num, google_domain=google_domain, gl=gl, hl=hl, location=location)
+        api_key = self.runtime.credentials["searchapi_api_key"]
+        result = SearchAPI(api_key).run(
+            query, result_type=result_type, num=num, google_domain=google_domain, gl=gl, hl=hl, location=location
+        )
 
-        if result_type == 'text':
+        if result_type == "text":
             return self.create_text_message(text=result)
         return self.create_link_message(link=result)
diff --git a/api/core/tools/provider/builtin/searchapi/tools/youtube_transcripts.py b/api/core/tools/provider/builtin/searchapi/tools/youtube_transcripts.py
index 6345b33801..1867cf7be7 100644
--- a/api/core/tools/provider/builtin/searchapi/tools/youtube_transcripts.py
+++ b/api/core/tools/provider/builtin/searchapi/tools/youtube_transcripts.py
@@ -7,6 +7,7 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 SEARCH_API_URL = "https://www.searchapi.io/api/v1/search"
 
+
 class SearchAPI:
     """
     SearchAPI tool provider.
@@ -36,18 +37,18 @@ class SearchAPI:
         return {
             "engine": "youtube_transcripts",
             "video_id": video_id,
-            "lang": language if language else "en",
-            **{key: value for key, value in kwargs.items() if value not in [None, ""]},
+            "lang": language or "en",
+            **{key: value for key, value in kwargs.items() if value not in {None, ""}},
         }
 
     @staticmethod
     def _process_response(res: dict) -> str:
         """Process response from SearchAPI."""
-        if "error" in res.keys():
+        if "error" in res:
             raise ValueError(f"Got error from SearchApi: {res['error']}")
 
         toret = ""
-        if "transcripts" in res.keys() and "text" in res["transcripts"][0].keys():
+        if "transcripts" in res and "text" in res["transcripts"][0]:
             for item in res["transcripts"]:
                 toret += item["text"] + " "
         if toret == "":
@@ -55,18 +56,20 @@ class SearchAPI:
 
         return toret
 
+
 class YoutubeTranscriptsTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
         Invoke the SearchApi tool.
         """
-        video_id = tool_parameters['video_id']
-        language = tool_parameters.get('language', "en")
+        video_id = tool_parameters["video_id"]
+        language = tool_parameters.get("language", "en")
 
-        api_key = self.runtime.credentials['searchapi_api_key']
+        api_key = self.runtime.credentials["searchapi_api_key"]
         result = SearchAPI(api_key).run(video_id, language=language)
 
         return self.create_text_message(text=result)
diff --git a/api/core/tools/provider/builtin/searxng/searxng.py b/api/core/tools/provider/builtin/searxng/searxng.py
index ab354003e6..b7bbcc60b1 100644
--- a/api/core/tools/provider/builtin/searxng/searxng.py
+++ b/api/core/tools/provider/builtin/searxng/searxng.py
@@ -13,12 +13,8 @@ class SearXNGProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
-                tool_parameters={
-                    "query": "SearXNG",
-                    "limit": 1,
-                    "search_type": "general"
-                },
+                user_id="",
+                tool_parameters={"query": "SearXNG", "limit": 1, "search_type": "general"},
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/searxng/tools/searxng_search.py b/api/core/tools/provider/builtin/searxng/tools/searxng_search.py
index dc835a8e8c..c5e339a108 100644
--- a/api/core/tools/provider/builtin/searxng/tools/searxng_search.py
+++ b/api/core/tools/provider/builtin/searxng/tools/searxng_search.py
@@ -23,18 +23,21 @@ class SearXNGSearchTool(BuiltinTool):
             ToolInvokeMessage | list[ToolInvokeMessage]: The result of the tool invocation.
         """
 
-        host = self.runtime.credentials.get('searxng_base_url')
+        host = self.runtime.credentials.get("searxng_base_url")
         if not host:
-            raise Exception('SearXNG api is required')
+            raise Exception("SearXNG api is required")
 
-        response = requests.get(host, params={
-            "q": tool_parameters.get('query'),
-            "format": "json",
-            "categories": tool_parameters.get('search_type', 'general')
-        })
+        response = requests.get(
+            host,
+            params={
+                "q": tool_parameters.get("query"),
+                "format": "json",
+                "categories": tool_parameters.get("search_type", "general"),
+            },
+        )
 
         if response.status_code != 200:
-            raise Exception(f'Error {response.status_code}: {response.text}')
+            raise Exception(f"Error {response.status_code}: {response.text}")
 
         res = response.json().get("results", [])
         if not res:
diff --git a/api/core/tools/provider/builtin/serper/serper.py b/api/core/tools/provider/builtin/serper/serper.py
index 2a42109373..cb1d090a9d 100644
--- a/api/core/tools/provider/builtin/serper/serper.py
+++ b/api/core/tools/provider/builtin/serper/serper.py
@@ -13,11 +13,8 @@ class SerperProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
-                tool_parameters={
-                    "query": "test",
-                    "result_type": "link"
-                },
+                user_id="",
+                tool_parameters={"query": "test", "result_type": "link"},
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/serper/tools/serper_search.py b/api/core/tools/provider/builtin/serper/tools/serper_search.py
index 24facaf4ec..7baebbf958 100644
--- a/api/core/tools/provider/builtin/serper/tools/serper_search.py
+++ b/api/core/tools/provider/builtin/serper/tools/serper_search.py
@@ -9,7 +9,6 @@ SERPER_API_URL = "https://google.serper.dev/search"
 
 
 class SerperSearchTool(BuiltinTool):
-
     def _parse_response(self, response: dict) -> dict:
         result = {}
         if "knowledgeGraph" in response:
@@ -17,28 +16,19 @@ class SerperSearchTool(BuiltinTool):
             result["description"] = response["knowledgeGraph"].get("description", "")
         if "organic" in response:
             result["organic"] = [
-                {
-                    "title": item.get("title", ""),
-                    "link": item.get("link", ""),
-                    "snippet": item.get("snippet", "")
-                }
+                {"title": item.get("title", ""), "link": item.get("link", ""), "snippet": item.get("snippet", "")}
                 for item in response["organic"]
             ]
         return result
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        params = {
-            "q": tool_parameters['query'],
-            "gl": "us",
-            "hl": "en"
-        }
-        headers = {
-             'X-API-KEY': self.runtime.credentials['serperapi_api_key'],
-             'Content-Type': 'application/json'
-        }
-        response = requests.get(url=SERPER_API_URL, params=params,headers=headers)
+
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        params = {"q": tool_parameters["query"], "gl": "us", "hl": "en"}
+        headers = {"X-API-KEY": self.runtime.credentials["serperapi_api_key"], "Content-Type": "application/json"}
+        response = requests.get(url=SERPER_API_URL, params=params, headers=headers)
         response.raise_for_status()
         valuable_res = self._parse_response(response.json())
         return self.create_json_message(valuable_res)
diff --git a/api/core/tools/provider/builtin/siliconflow/siliconflow.py b/api/core/tools/provider/builtin/siliconflow/siliconflow.py
index 0df78280df..37a0b0755b 100644
--- a/api/core/tools/provider/builtin/siliconflow/siliconflow.py
+++ b/api/core/tools/provider/builtin/siliconflow/siliconflow.py
@@ -14,6 +14,4 @@ class SiliconflowProvider(BuiltinToolProviderController):
 
         response = requests.get(url, headers=headers)
         if response.status_code != 200:
-            raise ToolProviderCredentialValidationError(
-                "SiliconFlow API key is invalid"
-            )
+            raise ToolProviderCredentialValidationError("SiliconFlow API key is invalid")
diff --git a/api/core/tools/provider/builtin/siliconflow/tools/flux.py b/api/core/tools/provider/builtin/siliconflow/tools/flux.py
index ed9f4be574..0d16ff385e 100644
--- a/api/core/tools/provider/builtin/siliconflow/tools/flux.py
+++ b/api/core/tools/provider/builtin/siliconflow/tools/flux.py
@@ -5,17 +5,16 @@ import requests
 from core.tools.entities.tool_entities import ToolInvokeMessage
 from core.tools.tool.builtin_tool import BuiltinTool
 
-FLUX_URL = (
-    "https://api.siliconflow.cn/v1/black-forest-labs/FLUX.1-schnell/text-to-image"
-)
+FLUX_URL = {
+    "schnell": "https://api.siliconflow.cn/v1/black-forest-labs/FLUX.1-schnell/text-to-image",
+    "dev": "https://api.siliconflow.cn/v1/image/generations",
+}
 
 
 class FluxTool(BuiltinTool):
-
     def _invoke(
         self, user_id: str, tool_parameters: dict[str, Any]
     ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
         headers = {
             "accept": "application/json",
             "content-type": "application/json",
@@ -28,17 +27,17 @@ class FluxTool(BuiltinTool):
             "seed": tool_parameters.get("seed"),
             "num_inference_steps": tool_parameters.get("num_inference_steps", 20),
         }
+        model = tool_parameters.get("model", "schnell")
+        url = FLUX_URL.get(model)
+        if model == "dev":
+            payload["model"] = "black-forest-labs/FLUX.1-dev"
 
-        response = requests.post(FLUX_URL, json=payload, headers=headers)
+        response = requests.post(url, json=payload, headers=headers)
         if response.status_code != 200:
             return self.create_text_message(f"Got Error Response:{response.text}")
 
         res = response.json()
         result = [self.create_json_message(res)]
         for image in res.get("images", []):
-            result.append(
-                self.create_image_message(
-                    image=image.get("url"), save_as=self.VARIABLE_KEY.IMAGE.value
-                )
-            )
+            result.append(self.create_image_message(image=image.get("url"), save_as=self.VariableKey.IMAGE.value))
         return result
diff --git a/api/core/tools/provider/builtin/siliconflow/tools/flux.yaml b/api/core/tools/provider/builtin/siliconflow/tools/flux.yaml
index 2a0698700c..d06b9bf3e1 100644
--- a/api/core/tools/provider/builtin/siliconflow/tools/flux.yaml
+++ b/api/core/tools/provider/builtin/siliconflow/tools/flux.yaml
@@ -6,8 +6,8 @@ identity:
   icon: icon.svg
 description:
   human:
-    en_US: Generate image via SiliconFlow's flux schnell.
-  llm: This tool is used to generate image from prompt via SiliconFlow's flux schnell model.
+    en_US: Generate image via SiliconFlow's flux model.
+  llm: This tool is used to generate image from prompt via SiliconFlow's flux model.
 parameters:
   - name: prompt
     type: string
@@ -17,9 +17,24 @@ parameters:
       zh_Hans: 提示词
     human_description:
       en_US: The text prompt used to generate the image.
-      zh_Hans: 用于生成图片的文字提示词
+      zh_Hans: 建议用英文的生成图片提示词以获得更好的生成效果。
     llm_description: this prompt text will be used to generate image.
     form: llm
+  - name: model
+    type: select
+    required: true
+    options:
+      - value: schnell
+        label:
+          en_US: Flux.1-schnell
+      - value: dev
+        label:
+          en_US: Flux.1-dev
+    default: schnell
+    label:
+      en_US: Choose Image Model
+      zh_Hans: 选择生成图片的模型
+    form: form
   - name: image_size
     type: select
     required: true
diff --git a/api/core/tools/provider/builtin/siliconflow/tools/stable_diffusion.py b/api/core/tools/provider/builtin/siliconflow/tools/stable_diffusion.py
index e8134a6565..d6a0b03d1b 100644
--- a/api/core/tools/provider/builtin/siliconflow/tools/stable_diffusion.py
+++ b/api/core/tools/provider/builtin/siliconflow/tools/stable_diffusion.py
@@ -12,11 +12,9 @@ SDURL = {
 
 
 class StableDiffusionTool(BuiltinTool):
-
     def _invoke(
         self, user_id: str, tool_parameters: dict[str, Any]
     ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-
         headers = {
             "accept": "application/json",
             "content-type": "application/json",
@@ -43,9 +41,5 @@ class StableDiffusionTool(BuiltinTool):
         res = response.json()
         result = [self.create_json_message(res)]
         for image in res.get("images", []):
-            result.append(
-                self.create_image_message(
-                    image=image.get("url"), save_as=self.VARIABLE_KEY.IMAGE.value
-                )
-            )
+            result.append(self.create_image_message(image=image.get("url"), save_as=self.VariableKey.IMAGE.value))
         return result
diff --git a/api/core/tools/provider/builtin/slack/tools/slack_webhook.py b/api/core/tools/provider/builtin/slack/tools/slack_webhook.py
index f47557f2ef..85e0de7675 100644
--- a/api/core/tools/provider/builtin/slack/tools/slack_webhook.py
+++ b/api/core/tools/provider/builtin/slack/tools/slack_webhook.py
@@ -7,25 +7,27 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class SlackWebhookTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            Incoming Webhooks
-            API Document: https://api.slack.com/messaging/webhooks
+        Incoming Webhooks
+        API Document: https://api.slack.com/messaging/webhooks
         """
 
-        content = tool_parameters.get('content', '')
+        content = tool_parameters.get("content", "")
         if not content:
-            return self.create_text_message('Invalid parameter content')
+            return self.create_text_message("Invalid parameter content")
 
-        webhook_url = tool_parameters.get('webhook_url', '')
+        webhook_url = tool_parameters.get("webhook_url", "")
 
-        if not webhook_url.startswith('https://hooks.slack.com/'):
+        if not webhook_url.startswith("https://hooks.slack.com/"):
             return self.create_text_message(
-                f'Invalid parameter webhook_url ${webhook_url}, not a valid Slack webhook URL')
+                f"Invalid parameter webhook_url ${webhook_url}, not a valid Slack webhook URL"
+            )
 
         headers = {
-            'Content-Type': 'application/json',
+            "Content-Type": "application/json",
         }
         params = {}
         payload = {
@@ -38,6 +40,7 @@ class SlackWebhookTool(BuiltinTool):
                 return self.create_text_message("Text message was sent successfully")
             else:
                 return self.create_text_message(
-                    f"Failed to send the text message, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to send the text message, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
-            return self.create_text_message("Failed to send message through webhook. {}".format(e))
\ No newline at end of file
+            return self.create_text_message("Failed to send message through webhook. {}".format(e))
diff --git a/api/core/tools/provider/builtin/spark/spark.py b/api/core/tools/provider/builtin/spark/spark.py
index cb8e69a59f..e0b1a58a3f 100644
--- a/api/core/tools/provider/builtin/spark/spark.py
+++ b/api/core/tools/provider/builtin/spark/spark.py
@@ -29,12 +29,8 @@ class SparkProvider(BuiltinToolProviderController):
                     #  0 success，
                     pass
                 else:
-                    raise ToolProviderCredentialValidationError(
-                        "image generate error, code:{}".format(code)
-                    )
+                    raise ToolProviderCredentialValidationError("image generate error, code:{}".format(code))
             except Exception as e:
-                raise ToolProviderCredentialValidationError(
-                    "APPID APISecret APIKey is invalid. {}".format(e)
-                )
+                raise ToolProviderCredentialValidationError("APPID APISecret APIKey is invalid. {}".format(e))
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/spark/tools/spark_img_generation.py b/api/core/tools/provider/builtin/spark/tools/spark_img_generation.py
index a977af2b76..81d9e8d941 100644
--- a/api/core/tools/provider/builtin/spark/tools/spark_img_generation.py
+++ b/api/core/tools/provider/builtin/spark/tools/spark_img_generation.py
@@ -15,16 +15,16 @@ from core.tools.entities.tool_entities import ToolInvokeMessage
 from core.tools.tool.builtin_tool import BuiltinTool
 
 
-class AssembleHeaderException(Exception):
+class AssembleHeaderError(Exception):
     def __init__(self, msg):
         self.message = msg
 
 
 class Url:
-    def __init__(this, host, path, schema):
-        this.host = host
-        this.path = path
-        this.schema = schema
+    def __init__(self, host, path, schema):
+        self.host = host
+        self.path = path
+        self.schema = schema
 
 
 # calculate sha256 and encode to base64
@@ -35,49 +35,46 @@ def sha256base64(data):
     return digest
 
 
-def parse_url(requset_url):
-    stidx = requset_url.index("://")
-    host = requset_url[stidx + 3 :]
-    schema = requset_url[: stidx + 3]
+def parse_url(request_url):
+    stidx = request_url.index("://")
+    host = request_url[stidx + 3 :]
+    schema = request_url[: stidx + 3]
     edidx = host.index("/")
     if edidx <= 0:
-        raise AssembleHeaderException("invalid request url:" + requset_url)
+        raise AssembleHeaderError("invalid request url:" + request_url)
     path = host[edidx:]
     host = host[:edidx]
     u = Url(host, path, schema)
     return u
 
-def assemble_ws_auth_url(requset_url, method="GET", api_key="", api_secret=""):
-    u = parse_url(requset_url)
+
+def assemble_ws_auth_url(request_url, method="GET", api_key="", api_secret=""):
+    u = parse_url(request_url)
     host = u.host
     path = u.path
     now = datetime.now()
     date = format_date_time(mktime(now.timetuple()))
-    signature_origin = "host: {}\ndate: {}\n{} {} HTTP/1.1".format(
-        host, date, method, path
-    )
+    signature_origin = "host: {}\ndate: {}\n{} {} HTTP/1.1".format(host, date, method, path)
     signature_sha = hmac.new(
         api_secret.encode("utf-8"),
         signature_origin.encode("utf-8"),
         digestmod=hashlib.sha256,
     ).digest()
     signature_sha = base64.b64encode(signature_sha).decode(encoding="utf-8")
-    authorization_origin = f'api_key="{api_key}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature_sha}"'
-
-    authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode(
-        encoding="utf-8"
+    authorization_origin = (
+        f'api_key="{api_key}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature_sha}"'
     )
+
+    authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode(encoding="utf-8")
     values = {"host": host, "date": date, "authorization": authorization}
 
-    return requset_url + "?" + urlencode(values)
+    return request_url + "?" + urlencode(values)
 
 
 def get_body(appid, text):
     body = {
         "header": {"app_id": appid, "uid": "123456789"},
-        "parameter": {
-            "chat": {"domain": "general", "temperature": 0.5, "max_tokens": 4096}
-        },
+        "parameter": {"chat": {"domain": "general", "temperature": 0.5, "max_tokens": 4096}},
         "payload": {"message": {"text": [{"role": "user", "content": text}]}},
     }
     return body
@@ -85,13 +82,9 @@ def get_body(appid, text):
 
 def spark_response(text, appid, apikey, apisecret):
     host = "http://spark-api.cn-huabei-1.xf-yun.com/v2.1/tti"
-    url = assemble_ws_auth_url(
-        host, method="POST", api_key=apikey, api_secret=apisecret
-    )
+    url = assemble_ws_auth_url(host, method="POST", api_key=apikey, api_secret=apisecret)
     content = get_body(appid, text)
-    response = requests.post(
-        url, json=content, headers={"content-type": "application/json"}
-    ).text
+    response = requests.post(url, json=content, headers={"content-type": "application/json"}).text
     return response
 
 
@@ -105,19 +98,11 @@ class SparkImgGeneratorTool(BuiltinTool):
         invoke tools
         """
 
-        if "APPID" not in self.runtime.credentials or not self.runtime.credentials.get(
-            "APPID"
-        ):
+        if "APPID" not in self.runtime.credentials or not self.runtime.credentials.get("APPID"):
             return self.create_text_message("APPID  is required.")
-        if (
-            "APISecret" not in self.runtime.credentials
-            or not self.runtime.credentials.get("APISecret")
-        ):
+        if "APISecret" not in self.runtime.credentials or not self.runtime.credentials.get("APISecret"):
             return self.create_text_message("APISecret  is required.")
-        if (
-            "APIKey" not in self.runtime.credentials
-            or not self.runtime.credentials.get("APIKey")
-        ):
+        if "APIKey" not in self.runtime.credentials or not self.runtime.credentials.get("APIKey"):
             return self.create_text_message("APIKey  is required.")
 
         prompt = tool_parameters.get("prompt", "")
@@ -130,7 +115,7 @@ class SparkImgGeneratorTool(BuiltinTool):
                 self.create_blob_message(
                     blob=b64decode(image["base64_image"]),
                     meta={"mime_type": "image/png"},
-                    save_as=self.VARIABLE_KEY.IMAGE.value,
+                    save_as=self.VariableKey.IMAGE.value,
                 )
             )
         return result
diff --git a/api/core/tools/provider/builtin/spider/spider.py b/api/core/tools/provider/builtin/spider/spider.py
index 5bcc56a724..5959555318 100644
--- a/api/core/tools/provider/builtin/spider/spider.py
+++ b/api/core/tools/provider/builtin/spider/spider.py
@@ -8,13 +8,13 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class SpiderProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
-            app = Spider(api_key=credentials['spider_api_key'])
-            app.scrape_url(url='https://spider.cloud')
+            app = Spider(api_key=credentials["spider_api_key"])
+            app.scrape_url(url="https://spider.cloud")
         except AttributeError as e:
             # Handle cases where NoneType is not iterable, which might indicate API issues
-            if 'NoneType' in str(e) and 'not iterable' in str(e):
-                raise ToolProviderCredentialValidationError('API is currently down, try again in 15 minutes', str(e))
+            if "NoneType" in str(e) and "not iterable" in str(e):
+                raise ToolProviderCredentialValidationError("API is currently down, try again in 15 minutes", str(e))
             else:
-                raise ToolProviderCredentialValidationError('An unexpected error occurred.', str(e))
+                raise ToolProviderCredentialValidationError("An unexpected error occurred.", str(e))
         except Exception as e:
-            raise ToolProviderCredentialValidationError('An unexpected error occurred.', str(e))
+            raise ToolProviderCredentialValidationError("An unexpected error occurred.", str(e))
diff --git a/api/core/tools/provider/builtin/spider/spiderApp.py b/api/core/tools/provider/builtin/spider/spiderApp.py
index f0ed64867a..4bc446a1a0 100644
--- a/api/core/tools/provider/builtin/spider/spiderApp.py
+++ b/api/core/tools/provider/builtin/spider/spiderApp.py
@@ -65,9 +65,7 @@ class Spider:
         :return: The JSON response or the raw response stream if stream is True.
         """
         headers = self._prepare_headers(content_type)
-        response = self._post_request(
-            f"https://api.spider.cloud/v1/{endpoint}", data, headers, stream
-        )
+        response = self._post_request(f"https://api.spider.cloud/v1/{endpoint}", data, headers, stream)
 
         if stream:
             return response
@@ -76,9 +74,7 @@ class Spider:
         else:
             self._handle_error(response, f"post to {endpoint}")
 
-    def api_get(
-        self, endpoint: str, stream: bool, content_type: str = "application/json"
-    ):
+    def api_get(self, endpoint: str, stream: bool, content_type: str = "application/json"):
         """
         Send a GET request to the specified endpoint.
 
@@ -86,9 +82,7 @@ class Spider:
         :return: The JSON decoded response.
         """
         headers = self._prepare_headers(content_type)
-        response = self._get_request(
-            f"https://api.spider.cloud/v1/{endpoint}", headers, stream
-        )
+        response = self._get_request(f"https://api.spider.cloud/v1/{endpoint}", headers, stream)
         if response.status_code == 200:
             return response.json()
         else:
@@ -120,14 +114,12 @@ class Spider:
 
         # Add { "return_format": "markdown" } to the params if not already present
         if "return_format" not in params:
-            params["return_format"] = "markdown"    
+            params["return_format"] = "markdown"
 
         # Set limit to 1
         params["limit"] = 1
 
-        return self.api_post(
-            "crawl", {"url": url, **(params or {})}, stream, content_type
-        )
+        return self.api_post("crawl", {"url": url, **(params or {})}, stream, content_type)
 
     def crawl_url(
         self,
@@ -150,9 +142,7 @@ class Spider:
         if "return_format" not in params:
             params["return_format"] = "markdown"
 
-        return self.api_post(
-            "crawl", {"url": url, **(params or {})}, stream, content_type
-        )
+        return self.api_post("crawl", {"url": url, **(params or {})}, stream, content_type)
 
     def links(
         self,
@@ -168,9 +158,7 @@ class Spider:
         :param params: Optional parameters for the link retrieval request.
         :return: JSON response containing the links.
         """
-        return self.api_post(
-            "links", {"url": url, **(params or {})}, stream, content_type
-        )
+        return self.api_post("links", {"url": url, **(params or {})}, stream, content_type)
 
     def extract_contacts(
         self,
@@ -207,9 +195,7 @@ class Spider:
         :param params: Optional parameters to guide the labeling process.
         :return: JSON response with labeled data.
         """
-        return self.api_post(
-            "pipeline/label", {"url": url, **(params or {})}, stream, content_type
-        )
+        return self.api_post("pipeline/label", {"url": url, **(params or {})}, stream, content_type)
 
     def _prepare_headers(self, content_type: str = "application/json"):
         return {
@@ -228,12 +214,8 @@ class Spider:
         return requests.delete(url, headers=headers, stream=stream)
 
     def _handle_error(self, response, action):
-        if response.status_code in [402, 409, 500]:
+        if response.status_code in {402, 409, 500}:
             error_message = response.json().get("error", "Unknown error occurred")
-            raise Exception(
-                f"Failed to {action}. Status code: {response.status_code}. Error: {error_message}"
-            )
+            raise Exception(f"Failed to {action}. Status code: {response.status_code}. Error: {error_message}")
         else:
-            raise Exception(
-                f"Unexpected error occurred while trying to {action}. Status code: {response.status_code}"
-            )
+            raise Exception(f"Unexpected error occurred while trying to {action}. Status code: {response.status_code}")
diff --git a/api/core/tools/provider/builtin/spider/tools/scraper_crawler.py b/api/core/tools/provider/builtin/spider/tools/scraper_crawler.py
index 64bbcc10cc..20d2daef55 100644
--- a/api/core/tools/provider/builtin/spider/tools/scraper_crawler.py
+++ b/api/core/tools/provider/builtin/spider/tools/scraper_crawler.py
@@ -6,42 +6,44 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class ScrapeTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         # initialize the app object with the api key
-        app = Spider(api_key=self.runtime.credentials['spider_api_key'])
+        app = Spider(api_key=self.runtime.credentials["spider_api_key"])
+
+        url = tool_parameters["url"]
+        mode = tool_parameters["mode"]
 
-        url = tool_parameters['url']
-        mode = tool_parameters['mode']
-        
         options = {
-            'limit': tool_parameters.get('limit', 0),
-            'depth': tool_parameters.get('depth', 0),
-            'blacklist': tool_parameters.get('blacklist', '').split(',') if tool_parameters.get('blacklist') else [],
-            'whitelist': tool_parameters.get('whitelist', '').split(',') if tool_parameters.get('whitelist') else [],
-            'readability': tool_parameters.get('readability', False),
+            "limit": tool_parameters.get("limit", 0),
+            "depth": tool_parameters.get("depth", 0),
+            "blacklist": tool_parameters.get("blacklist", "").split(",") if tool_parameters.get("blacklist") else [],
+            "whitelist": tool_parameters.get("whitelist", "").split(",") if tool_parameters.get("whitelist") else [],
+            "readability": tool_parameters.get("readability", False),
         }
 
         result = ""
 
         try:
-            if mode == 'scrape':
+            if mode == "scrape":
                 scrape_result = app.scrape_url(
-                    url=url, 
+                    url=url,
                     params=options,
                 )
 
                 for i in scrape_result:
-                    result += "URL: " + i.get('url', '') + "\n"
-                    result += "CONTENT: " + i.get('content', '') + "\n\n"
-            elif mode == 'crawl':
+                    result += "URL: " + i.get("url", "") + "\n"
+                    result += "CONTENT: " + i.get("content", "") + "\n\n"
+            elif mode == "crawl":
                 crawl_result = app.crawl_url(
-                    url=tool_parameters['url'], 
+                    url=tool_parameters["url"],
                     params=options,
                 )
                 for i in crawl_result:
-                    result += "URL: " + i.get('url', '') + "\n"
-                    result += "CONTENT: " + i.get('content', '') + "\n\n"
+                    result += "URL: " + i.get("url", "") + "\n"
+                    result += "CONTENT: " + i.get("content", "") + "\n\n"
         except Exception as e:
-            return self.create_text_message("An error occured", str(e))
+            return self.create_text_message("An error occurred", str(e))
 
         return self.create_text_message(result)
diff --git a/api/core/tools/provider/builtin/stability/stability.py b/api/core/tools/provider/builtin/stability/stability.py
index b31d786178..f09d81ac27 100644
--- a/api/core/tools/provider/builtin/stability/stability.py
+++ b/api/core/tools/provider/builtin/stability/stability.py
@@ -8,6 +8,7 @@ class StabilityToolProvider(BuiltinToolProviderController, BaseStabilityAuthoriz
     """
     This class is responsible for providing the stability tool.
     """
+
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         """
         This method is responsible for validating the credentials.
diff --git a/api/core/tools/provider/builtin/stability/tools/base.py b/api/core/tools/provider/builtin/stability/tools/base.py
index a4788fd869..c3b7edbefa 100644
--- a/api/core/tools/provider/builtin/stability/tools/base.py
+++ b/api/core/tools/provider/builtin/stability/tools/base.py
@@ -9,26 +9,23 @@ class BaseStabilityAuthorization:
         """
         This method is responsible for validating the credentials.
         """
-        api_key = credentials.get('api_key', '')
+        api_key = credentials.get("api_key", "")
         if not api_key:
-            raise ToolProviderCredentialValidationError('API key is required.')
-        
+            raise ToolProviderCredentialValidationError("API key is required.")
+
         response = requests.get(
-            URL('https://api.stability.ai') / 'v1' / 'user' / 'account', 
+            URL("https://api.stability.ai") / "v1" / "user" / "account",
             headers=self.generate_authorization_headers(credentials),
-            timeout=(5, 30)
+            timeout=(5, 30),
         )
 
         if not response.ok:
-            raise ToolProviderCredentialValidationError('Invalid API key.')
+            raise ToolProviderCredentialValidationError("Invalid API key.")
 
         return True
-    
+
     def generate_authorization_headers(self, credentials: dict) -> dict[str, str]:
         """
         This method is responsible for generating the authorization headers.
         """
-        return {
-            'Authorization': f'Bearer {credentials.get("api_key", "")}'
-        }
-    
\ No newline at end of file
+        return {"Authorization": f'Bearer {credentials.get("api_key", "")}'}
diff --git a/api/core/tools/provider/builtin/stability/tools/text2image.py b/api/core/tools/provider/builtin/stability/tools/text2image.py
index 41236f7b43..6bcf315484 100644
--- a/api/core/tools/provider/builtin/stability/tools/text2image.py
+++ b/api/core/tools/provider/builtin/stability/tools/text2image.py
@@ -11,10 +11,11 @@ class StableDiffusionTool(BuiltinTool, BaseStabilityAuthorization):
     """
     This class is responsible for providing the stable diffusion tool.
     """
+
     model_endpoint_map: dict[str, str] = {
-        'sd3': 'https://api.stability.ai/v2beta/stable-image/generate/sd3',
-        'sd3-turbo': 'https://api.stability.ai/v2beta/stable-image/generate/sd3',
-        'core': 'https://api.stability.ai/v2beta/stable-image/generate/core',
+        "sd3": "https://api.stability.ai/v2beta/stable-image/generate/sd3",
+        "sd3-turbo": "https://api.stability.ai/v2beta/stable-image/generate/sd3",
+        "core": "https://api.stability.ai/v2beta/stable-image/generate/core",
     }
 
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage | list[ToolInvokeMessage]:
@@ -22,39 +23,34 @@ class StableDiffusionTool(BuiltinTool, BaseStabilityAuthorization):
         Invoke the tool.
         """
         payload = {
-            'prompt': tool_parameters.get('prompt', ''),
-            'aspect_ratio': tool_parameters.get('aspect_ratio', '16:9') or tool_parameters.get('aspect_radio', '16:9'),
-            'mode': 'text-to-image',
-            'seed': tool_parameters.get('seed', 0),
-            'output_format': 'png',
+            "prompt": tool_parameters.get("prompt", ""),
+            "aspect_ratio": tool_parameters.get("aspect_ratio", "16:9") or tool_parameters.get("aspect_radio", "16:9"),
+            "mode": "text-to-image",
+            "seed": tool_parameters.get("seed", 0),
+            "output_format": "png",
         }
 
-        model = tool_parameters.get('model', 'core')
+        model = tool_parameters.get("model", "core")
 
-        if model in ['sd3', 'sd3-turbo']:
-            payload['model'] = tool_parameters.get('model')
+        if model in {"sd3", "sd3-turbo"}:
+            payload["model"] = tool_parameters.get("model")
 
-        if not model == 'sd3-turbo':
-            payload['negative_prompt'] = tool_parameters.get('negative_prompt', '')
+        if model != "sd3-turbo":
+            payload["negative_prompt"] = tool_parameters.get("negative_prompt", "")
 
         response = post(
-            self.model_endpoint_map[tool_parameters.get('model', 'core')],
+            self.model_endpoint_map[tool_parameters.get("model", "core")],
             headers={
-                'accept': 'image/*',
+                "accept": "image/*",
                 **self.generate_authorization_headers(self.runtime.credentials),
             },
-            files={
-                key: (None, str(value)) for key, value in payload.items()
-            },
-            timeout=(5, 30)
+            files={key: (None, str(value)) for key, value in payload.items()},
+            timeout=(5, 30),
         )
 
         if not response.status_code == 200:
             raise Exception(response.text)
-        
+
         return self.create_blob_message(
-            blob=response.content, meta={
-                'mime_type': 'image/png'
-            },
-            save_as=self.VARIABLE_KEY.IMAGE.value
+            blob=response.content, meta={"mime_type": "image/png"}, save_as=self.VariableKey.IMAGE.value
         )
diff --git a/api/core/tools/provider/builtin/stablediffusion/stablediffusion.py b/api/core/tools/provider/builtin/stablediffusion/stablediffusion.py
index 317d705f7c..abaa297cf3 100644
--- a/api/core/tools/provider/builtin/stablediffusion/stablediffusion.py
+++ b/api/core/tools/provider/builtin/stablediffusion/stablediffusion.py
@@ -15,4 +15,3 @@ class StableDiffusionProvider(BuiltinToolProviderController):
             ).validate_models()
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/stablediffusion/tools/stable_diffusion.py b/api/core/tools/provider/builtin/stablediffusion/tools/stable_diffusion.py
index 4be9207d66..64fdc961b4 100644
--- a/api/core/tools/provider/builtin/stablediffusion/tools/stable_diffusion.py
+++ b/api/core/tools/provider/builtin/stablediffusion/tools/stable_diffusion.py
@@ -18,19 +18,17 @@ DRAW_TEXT_OPTIONS = {
     # Prompts
     "prompt": "",
     "negative_prompt": "",
-    # "styles": [],                
-    # Seeds 
+    # "styles": [],
+    # Seeds
     "seed": -1,
     "subseed": -1,
     "subseed_strength": 0,
     "seed_resize_from_h": -1,
     "seed_resize_from_w": -1,
-
     # Samplers
     "sampler_name": "DPM++ 2M",
     # "scheduler": "",
     # "sampler_index": "Automatic",
-
     # Latent Space Options
     "batch_size": 1,
     "n_iter": 1,
@@ -42,9 +40,9 @@ DRAW_TEXT_OPTIONS = {
     # "tiling": True,
     "do_not_save_samples": False,
     "do_not_save_grid": False,
-    # "eta": 0,                      
-    # "denoising_strength": 0.75,   
-    # "s_min_uncond": 0,             
+    # "eta": 0,
+    # "denoising_strength": 0.75,
+    # "s_min_uncond": 0,
     # "s_churn": 0,
     # "s_tmax": 0,
     # "s_tmin": 0,
@@ -73,7 +71,6 @@ DRAW_TEXT_OPTIONS = {
     "hr_negative_prompt": "",
     # Task Options
     # "force_task_id": "",
-
     # Script Options
     # "script_name": "",
     "script_args": [],
@@ -82,152 +79,150 @@ DRAW_TEXT_OPTIONS = {
     "save_images": False,
     "alwayson_scripts": {},
     # "infotext": "",
-    
 }
 
 
 class StableDiffusionTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) \
-        -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         # base url
-        base_url = self.runtime.credentials.get('base_url', None)
+        base_url = self.runtime.credentials.get("base_url", None)
         if not base_url:
-            return self.create_text_message('Please input base_url')
+            return self.create_text_message("Please input base_url")
 
-        if tool_parameters.get('model'):
-            self.runtime.credentials['model'] = tool_parameters['model']
+        if tool_parameters.get("model"):
+            self.runtime.credentials["model"] = tool_parameters["model"]
 
-        model = self.runtime.credentials.get('model', None)
+        model = self.runtime.credentials.get("model", None)
         if not model:
-            return self.create_text_message('Please input model')
-        
+            return self.create_text_message("Please input model")
+
         # set model
         try:
-            url = str(URL(base_url) / 'sdapi' / 'v1' / 'options')
-            response = post(url, data=json.dumps({
-                'sd_model_checkpoint': model
-            }))
+            url = str(URL(base_url) / "sdapi" / "v1" / "options")
+            response = post(url, data=json.dumps({"sd_model_checkpoint": model}))
             if response.status_code != 200:
-                raise ToolProviderCredentialValidationError('Failed to set model, please tell user to set model')
+                raise ToolProviderCredentialValidationError("Failed to set model, please tell user to set model")
         except Exception as e:
-            raise ToolProviderCredentialValidationError('Failed to set model, please tell user to set model')
+            raise ToolProviderCredentialValidationError("Failed to set model, please tell user to set model")
 
         # get image id and image variable
-        image_id = tool_parameters.get('image_id', '')
+        image_id = tool_parameters.get("image_id", "")
         image_variable = self.get_default_image_variable()
         # Return text2img if there's no image ID or no image variable
         if not image_id or not image_variable:
-            return self.text2img(base_url=base_url,tool_parameters=tool_parameters)
+            return self.text2img(base_url=base_url, tool_parameters=tool_parameters)
 
         # Proceed with image-to-image generation
-        return self.img2img(base_url=base_url,tool_parameters=tool_parameters)
+        return self.img2img(base_url=base_url, tool_parameters=tool_parameters)
 
     def validate_models(self) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            validate models
+        validate models
         """
         try:
-            base_url = self.runtime.credentials.get('base_url', None)
+            base_url = self.runtime.credentials.get("base_url", None)
             if not base_url:
-                raise ToolProviderCredentialValidationError('Please input base_url')
-            model = self.runtime.credentials.get('model', None)
+                raise ToolProviderCredentialValidationError("Please input base_url")
+            model = self.runtime.credentials.get("model", None)
             if not model:
-                raise ToolProviderCredentialValidationError('Please input model')
+                raise ToolProviderCredentialValidationError("Please input model")
 
-            api_url = str(URL(base_url) / 'sdapi' / 'v1' / 'sd-models')
+            api_url = str(URL(base_url) / "sdapi" / "v1" / "sd-models")
             response = get(url=api_url, timeout=10)
             if response.status_code == 404:
                 # try draw a picture
                 self._invoke(
-                    user_id='test',
+                    user_id="test",
                     tool_parameters={
-                        'prompt': 'a cat',
-                        'width': 1024,
-                        'height': 1024,
-                        'steps': 1,
-                        'lora': '',
-                    }
+                        "prompt": "a cat",
+                        "width": 1024,
+                        "height": 1024,
+                        "steps": 1,
+                        "lora": "",
+                    },
                 )
             elif response.status_code != 200:
-                raise ToolProviderCredentialValidationError('Failed to get models')
+                raise ToolProviderCredentialValidationError("Failed to get models")
             else:
-                models = [d['model_name'] for d in response.json()]
+                models = [d["model_name"] for d in response.json()]
                 if len([d for d in models if d == model]) > 0:
                     return self.create_text_message(json.dumps(models))
                 else:
-                    raise ToolProviderCredentialValidationError(f'model {model} does not exist')
+                    raise ToolProviderCredentialValidationError(f"model {model} does not exist")
         except Exception as e:
-            raise ToolProviderCredentialValidationError(f'Failed to get models, {e}')
+            raise ToolProviderCredentialValidationError(f"Failed to get models, {e}")
 
     def get_sd_models(self) -> list[str]:
         """
-            get sd models
+        get sd models
         """
         try:
-            base_url = self.runtime.credentials.get('base_url', None)
+            base_url = self.runtime.credentials.get("base_url", None)
             if not base_url:
                 return []
-            api_url = str(URL(base_url) / 'sdapi' / 'v1' / 'sd-models')
+            api_url = str(URL(base_url) / "sdapi" / "v1" / "sd-models")
             response = get(url=api_url, timeout=(2, 10))
             if response.status_code != 200:
                 return []
             else:
-                return [d['model_name'] for d in response.json()]
-        except Exception as e:
-            return []
-        
-    def get_sample_methods(self) -> list[str]:
-        """
-            get sample method
-        """
-        try:
-            base_url = self.runtime.credentials.get('base_url', None)
-            if not base_url:
-                return []
-            api_url = str(URL(base_url) / 'sdapi' / 'v1' / 'samplers')
-            response = get(url=api_url, timeout=(2, 10))
-            if response.status_code != 200:
-                return []
-            else:
-                return [d['name'] for d in response.json()]
+                return [d["model_name"] for d in response.json()]
         except Exception as e:
             return []
 
-    def img2img(self, base_url: str, tool_parameters: dict[str, Any]) \
-        -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def get_sample_methods(self) -> list[str]:
         """
-            generate image
+        get sample method
+        """
+        try:
+            base_url = self.runtime.credentials.get("base_url", None)
+            if not base_url:
+                return []
+            api_url = str(URL(base_url) / "sdapi" / "v1" / "samplers")
+            response = get(url=api_url, timeout=(2, 10))
+            if response.status_code != 200:
+                return []
+            else:
+                return [d["name"] for d in response.json()]
+        except Exception as e:
+            return []
+
+    def img2img(
+        self, base_url: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        generate image
         """
 
         # Fetch the binary data of the image
         image_variable = self.get_default_image_variable()
         image_binary = self.get_variable_file(image_variable.name)
         if not image_binary:
-            return self.create_text_message('Image not found, please request user to generate image firstly.')
+            return self.create_text_message("Image not found, please request user to generate image firstly.")
 
         # Convert image to RGB and save as PNG
         try:
-            with Image.open(io.BytesIO(image_binary)) as image:
-                with io.BytesIO() as buffer:
-                    image.convert("RGB").save(buffer, format="PNG")
-                    image_binary = buffer.getvalue()
+            with Image.open(io.BytesIO(image_binary)) as image, io.BytesIO() as buffer:
+                image.convert("RGB").save(buffer, format="PNG")
+                image_binary = buffer.getvalue()
         except Exception as e:
             return self.create_text_message(f"Failed to process the image: {str(e)}")
 
         # copy draw options
         draw_options = deepcopy(DRAW_TEXT_OPTIONS)
         # set image options
-        model = tool_parameters.get('model', '')
+        model = tool_parameters.get("model", "")
         draw_options_image = {
-            "init_images": [b64encode(image_binary).decode('utf-8')],
+            "init_images": [b64encode(image_binary).decode("utf-8")],
             "denoising_strength": 0.9,
             "restore_faces": False,
             "script_args": [],
             "override_settings": {"sd_model_checkpoint": model},
-            "resize_mode":0,
+            "resize_mode": 0,
             "image_cfg_scale": 0,
             # "mask": None,
             "mask_blur_x": 4,
@@ -247,136 +242,149 @@ class StableDiffusionTool(BuiltinTool):
         draw_options.update(tool_parameters)
 
         # get prompt lora model
-        prompt = tool_parameters.get('prompt', '')
-        lora = tool_parameters.get('lora', '')
-        model = tool_parameters.get('model', '')
+        prompt = tool_parameters.get("prompt", "")
+        lora = tool_parameters.get("lora", "")
+        model = tool_parameters.get("model", "")
         if lora:
-            draw_options['prompt'] = f'{lora},{prompt}'
+            draw_options["prompt"] = f"{lora},{prompt}"
         else:
-            draw_options['prompt'] = prompt
+            draw_options["prompt"] = prompt
 
         try:
-            url = str(URL(base_url) / 'sdapi' / 'v1' / 'img2img') 
+            url = str(URL(base_url) / "sdapi" / "v1" / "img2img")
             response = post(url, data=json.dumps(draw_options), timeout=120)
             if response.status_code != 200:
-                return self.create_text_message('Failed to generate image')
-            
-            image = response.json()['images'][0]
+                return self.create_text_message("Failed to generate image")
+
+            image = response.json()["images"][0]
+
+            return self.create_blob_message(
+                blob=b64decode(image), meta={"mime_type": "image/png"}, save_as=self.VariableKey.IMAGE.value
+            )
 
-            return self.create_blob_message(blob=b64decode(image), 
-                                            meta={ 'mime_type': 'image/png' },
-                                            save_as=self.VARIABLE_KEY.IMAGE.value)
-            
         except Exception as e:
-            return self.create_text_message('Failed to generate image')
+            return self.create_text_message("Failed to generate image")
 
-    def text2img(self, base_url: str, tool_parameters: dict[str, Any]) \
-        -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def text2img(
+        self, base_url: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            generate image
+        generate image
         """
         # copy draw options
         draw_options = deepcopy(DRAW_TEXT_OPTIONS)
         draw_options.update(tool_parameters)
         # get prompt lora model
-        prompt = tool_parameters.get('prompt', '')
-        lora = tool_parameters.get('lora', '')
-        model = tool_parameters.get('model', '')
+        prompt = tool_parameters.get("prompt", "")
+        lora = tool_parameters.get("lora", "")
+        model = tool_parameters.get("model", "")
         if lora:
-            draw_options['prompt'] = f'{lora},{prompt}'
+            draw_options["prompt"] = f"{lora},{prompt}"
         else:
-            draw_options['prompt'] = prompt
-        draw_options['override_settings']['sd_model_checkpoint'] = model
+            draw_options["prompt"] = prompt
+        draw_options["override_settings"]["sd_model_checkpoint"] = model
 
-        
         try:
-            url = str(URL(base_url) / 'sdapi' / 'v1' / 'txt2img')
+            url = str(URL(base_url) / "sdapi" / "v1" / "txt2img")
             response = post(url, data=json.dumps(draw_options), timeout=120)
             if response.status_code != 200:
-                return self.create_text_message('Failed to generate image')
-            
-            image = response.json()['images'][0]
+                return self.create_text_message("Failed to generate image")
+
+            image = response.json()["images"][0]
+
+            return self.create_blob_message(
+                blob=b64decode(image), meta={"mime_type": "image/png"}, save_as=self.VariableKey.IMAGE.value
+            )
 
-            return self.create_blob_message(blob=b64decode(image), 
-                                            meta={ 'mime_type': 'image/png' },
-                                            save_as=self.VARIABLE_KEY.IMAGE.value)
-            
         except Exception as e:
-            return self.create_text_message('Failed to generate image')
+            return self.create_text_message("Failed to generate image")
 
     def get_runtime_parameters(self) -> list[ToolParameter]:
         parameters = [
-            ToolParameter(name='prompt',
-                         label=I18nObject(en_US='Prompt', zh_Hans='Prompt'),
-                         human_description=I18nObject(
-                             en_US='Image prompt, you can check the official documentation of Stable Diffusion',
-                             zh_Hans='图像提示词，您可以查看 Stable Diffusion 的官方文档',
-                         ),
-                         type=ToolParameter.ToolParameterType.STRING,
-                         form=ToolParameter.ToolParameterForm.LLM,
-                         llm_description='Image prompt of Stable Diffusion, you should describe the image you want to generate as a list of words as possible as detailed, the prompt must be written in English.',
-                         required=True),
+            ToolParameter(
+                name="prompt",
+                label=I18nObject(en_US="Prompt", zh_Hans="Prompt"),
+                human_description=I18nObject(
+                    en_US="Image prompt, you can check the official documentation of Stable Diffusion",
+                    zh_Hans="图像提示词，您可以查看 Stable Diffusion 的官方文档",
+                ),
+                type=ToolParameter.ToolParameterType.STRING,
+                form=ToolParameter.ToolParameterForm.LLM,
+                llm_description="Image prompt of Stable Diffusion, you should describe the image you want to generate"
+                " as a list of words as possible as detailed, the prompt must be written in English.",
+                required=True,
+            ),
         ]
         if len(self.list_default_image_variables()) != 0:
             parameters.append(
-                ToolParameter(name='image_id',
-                             label=I18nObject(en_US='image_id', zh_Hans='image_id'),
-                             human_description=I18nObject(
-                                en_US='Image id of the image you want to generate based on, if you want to generate image based on the default image, you can leave this field empty.',
-                                zh_Hans='您想要生成的图像的图像 ID，如果您想要基于默认图像生成图像，则可以将此字段留空。',
-                             ),
-                             type=ToolParameter.ToolParameterType.STRING,
-                             form=ToolParameter.ToolParameterForm.LLM,
-                             llm_description='Image id of the original image, you can leave this field empty if you want to generate a new image.',
-                             required=True,
-                             options=[ToolParameterOption(
-                                 value=i.name,
-                                 label=I18nObject(en_US=i.name, zh_Hans=i.name)
-                             ) for i in self.list_default_image_variables()])
+                ToolParameter(
+                    name="image_id",
+                    label=I18nObject(en_US="image_id", zh_Hans="image_id"),
+                    human_description=I18nObject(
+                        en_US="Image id of the image you want to generate based on, if you want to generate image based"
+                        " on the default image, you can leave this field empty.",
+                        zh_Hans="您想要生成的图像的图像 ID，如果您想要基于默认图像生成图像，则可以将此字段留空。",
+                    ),
+                    type=ToolParameter.ToolParameterType.STRING,
+                    form=ToolParameter.ToolParameterForm.LLM,
+                    llm_description="Image id of the original image, you can leave this field empty if you want to"
+                    " generate a new image.",
+                    required=True,
+                    options=[
+                        ToolParameterOption(value=i.name, label=I18nObject(en_US=i.name, zh_Hans=i.name))
+                        for i in self.list_default_image_variables()
+                    ],
+                )
             )
-        
+
         if self.runtime.credentials:
             try:
                 models = self.get_sd_models()
                 if len(models) != 0:
                     parameters.append(
-                        ToolParameter(name='model',
-                                     label=I18nObject(en_US='Model', zh_Hans='Model'),
-                                     human_description=I18nObject(
-                                        en_US='Model of Stable Diffusion, you can check the official documentation of Stable Diffusion',
-                                        zh_Hans='Stable Diffusion 的模型，您可以查看 Stable Diffusion 的官方文档',
-                                     ),
-                                     type=ToolParameter.ToolParameterType.SELECT,
-                                     form=ToolParameter.ToolParameterForm.FORM,
-                                     llm_description='Model of Stable Diffusion, you can check the official documentation of Stable Diffusion',
-                                     required=True,
-                                     default=models[0],
-                                     options=[ToolParameterOption(
-                                         value=i,
-                                         label=I18nObject(en_US=i, zh_Hans=i)
-                                     ) for i in models])
+                        ToolParameter(
+                            name="model",
+                            label=I18nObject(en_US="Model", zh_Hans="Model"),
+                            human_description=I18nObject(
+                                en_US="Model of Stable Diffusion, you can check the official documentation"
+                                " of Stable Diffusion",
+                                zh_Hans="Stable Diffusion 的模型，您可以查看 Stable Diffusion 的官方文档",
+                            ),
+                            type=ToolParameter.ToolParameterType.SELECT,
+                            form=ToolParameter.ToolParameterForm.FORM,
+                            llm_description="Model of Stable Diffusion, you can check the official documentation"
+                            " of Stable Diffusion",
+                            required=True,
+                            default=models[0],
+                            options=[
+                                ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i)) for i in models
+                            ],
+                        )
                     )
-                    
+
             except:
                 pass
-            
+
             sample_methods = self.get_sample_methods()
             if len(sample_methods) != 0:
                 parameters.append(
-                        ToolParameter(name='sampler_name',
-                                     label=I18nObject(en_US='Sampling method', zh_Hans='Sampling method'),
-                                     human_description=I18nObject(
-                                        en_US='Sampling method of Stable Diffusion, you can check the official documentation of Stable Diffusion',
-                                        zh_Hans='Stable Diffusion 的Sampling method，您可以查看 Stable Diffusion 的官方文档',
-                                     ),
-                                     type=ToolParameter.ToolParameterType.SELECT,
-                                     form=ToolParameter.ToolParameterForm.FORM,
-                                     llm_description='Sampling method of Stable Diffusion, you can check the official documentation of Stable Diffusion',
-                                     required=True,
-                                     default=sample_methods[0],
-                                     options=[ToolParameterOption(
-                                         value=i,
-                                         label=I18nObject(en_US=i, zh_Hans=i)
-                                     ) for i in sample_methods])
+                    ToolParameter(
+                        name="sampler_name",
+                        label=I18nObject(en_US="Sampling method", zh_Hans="Sampling method"),
+                        human_description=I18nObject(
+                            en_US="Sampling method of Stable Diffusion, you can check the official documentation"
+                            " of Stable Diffusion",
+                            zh_Hans="Stable Diffusion 的Sampling method，您可以查看 Stable Diffusion 的官方文档",
+                        ),
+                        type=ToolParameter.ToolParameterType.SELECT,
+                        form=ToolParameter.ToolParameterForm.FORM,
+                        llm_description="Sampling method of Stable Diffusion, you can check the official documentation"
+                        " of Stable Diffusion",
+                        required=True,
+                        default=sample_methods[0],
+                        options=[
+                            ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i)) for i in sample_methods
+                        ],
                     )
+                )
         return parameters
diff --git a/api/core/tools/provider/builtin/stackexchange/stackexchange.py b/api/core/tools/provider/builtin/stackexchange/stackexchange.py
index de64c84997..9680c633cc 100644
--- a/api/core/tools/provider/builtin/stackexchange/stackexchange.py
+++ b/api/core/tools/provider/builtin/stackexchange/stackexchange.py
@@ -11,16 +11,15 @@ class StackExchangeProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "intitle": "Test",
-                    "sort": "relevance",  
+                    "sort": "relevance",
                     "order": "desc",
                     "site": "stackoverflow",
                     "accepted": True,
-                    "pagesize": 1
+                    "pagesize": 1,
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/stackexchange/tools/fetchAnsByStackExQuesID.py b/api/core/tools/provider/builtin/stackexchange/tools/fetchAnsByStackExQuesID.py
index f8e1710844..5345320095 100644
--- a/api/core/tools/provider/builtin/stackexchange/tools/fetchAnsByStackExQuesID.py
+++ b/api/core/tools/provider/builtin/stackexchange/tools/fetchAnsByStackExQuesID.py
@@ -17,7 +17,9 @@ class FetchAnsByStackExQuesIDInput(BaseModel):
 
 
 class FetchAnsByStackExQuesIDTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         input = FetchAnsByStackExQuesIDInput(**tool_parameters)
 
         params = {
@@ -26,7 +28,7 @@ class FetchAnsByStackExQuesIDTool(BuiltinTool):
             "order": input.order,
             "sort": input.sort,
             "pagesize": input.pagesize,
-            "page": input.page
+            "page": input.page,
         }
 
         response = requests.get(f"https://api.stackexchange.com/2.3/questions/{input.id}/answers", params=params)
@@ -34,4 +36,4 @@ class FetchAnsByStackExQuesIDTool(BuiltinTool):
         if response.status_code == 200:
             return self.create_text_message(self.summary(user_id=user_id, content=response.text))
         else:
-            return self.create_text_message(f"API request failed with status code {response.status_code}")
\ No newline at end of file
+            return self.create_text_message(f"API request failed with status code {response.status_code}")
diff --git a/api/core/tools/provider/builtin/stackexchange/tools/searchStackExQuestions.py b/api/core/tools/provider/builtin/stackexchange/tools/searchStackExQuestions.py
index 8436433c32..4a25a808ad 100644
--- a/api/core/tools/provider/builtin/stackexchange/tools/searchStackExQuestions.py
+++ b/api/core/tools/provider/builtin/stackexchange/tools/searchStackExQuestions.py
@@ -9,26 +9,28 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 class SearchStackExQuestionsInput(BaseModel):
     intitle: str = Field(..., description="The search query.")
-    sort: str = Field(..., description="The sort order - relevance, activity, votes, creation.")  
+    sort: str = Field(..., description="The sort order - relevance, activity, votes, creation.")
     order: str = Field(..., description="asc or desc")
     site: str = Field(..., description="The Stack Exchange site.")
     tagged: str = Field(None, description="Semicolon-separated tags to include.")
     nottagged: str = Field(None, description="Semicolon-separated tags to exclude.")
-    accepted: bool = Field(..., description="true for only accepted answers, false otherwise") 
+    accepted: bool = Field(..., description="true for only accepted answers, false otherwise")
     pagesize: int = Field(..., description="Number of results per page")
 
 
 class SearchStackExQuestionsTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         input = SearchStackExQuestionsInput(**tool_parameters)
 
         params = {
             "intitle": input.intitle,
             "sort": input.sort,
-            "order": input.order,  
+            "order": input.order,
             "site": input.site,
             "accepted": input.accepted,
-            "pagesize": input.pagesize
+            "pagesize": input.pagesize,
         }
         if input.tagged:
             params["tagged"] = input.tagged
@@ -40,4 +42,4 @@ class SearchStackExQuestionsTool(BuiltinTool):
         if response.status_code == 200:
             return self.create_text_message(self.summary(user_id=user_id, content=response.text))
         else:
-            return self.create_text_message(f"API request failed with status code {response.status_code}")
\ No newline at end of file
+            return self.create_text_message(f"API request failed with status code {response.status_code}")
diff --git a/api/core/tools/provider/builtin/stepfun/stepfun.py b/api/core/tools/provider/builtin/stepfun/stepfun.py
index e809b04546..b24f730c95 100644
--- a/api/core/tools/provider/builtin/stepfun/stepfun.py
+++ b/api/core/tools/provider/builtin/stepfun/stepfun.py
@@ -13,13 +13,12 @@ class StepfunProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "prompt": "cute girl, blue eyes, white hair, anime style",
                     "size": "1024x1024",
-                    "n": 1
+                    "n": 1,
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/stepfun/tools/image.py b/api/core/tools/provider/builtin/stepfun/tools/image.py
index c571f54675..0b92b122bf 100644
--- a/api/core/tools/provider/builtin/stepfun/tools/image.py
+++ b/api/core/tools/provider/builtin/stepfun/tools/image.py
@@ -9,61 +9,67 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class StepfunTool(BuiltinTool):
-    """ Stepfun Image Generation Tool """
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    """Stepfun Image Generation Tool"""
+
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        base_url = self.runtime.credentials.get('stepfun_base_url', 'https://api.stepfun.com')
-        base_url = str(URL(base_url) / 'v1')
+        base_url = self.runtime.credentials.get("stepfun_base_url", "https://api.stepfun.com")
+        base_url = str(URL(base_url) / "v1")
 
         client = OpenAI(
-            api_key=self.runtime.credentials['stepfun_api_key'],
+            api_key=self.runtime.credentials["stepfun_api_key"],
             base_url=base_url,
         )
 
         extra_body = {}
-        model = tool_parameters.get('model', 'step-1x-medium')
+        model = tool_parameters.get("model", "step-1x-medium")
         if not model:
-            return self.create_text_message('Please input model name')
+            return self.create_text_message("Please input model name")
         # prompt
-        prompt = tool_parameters.get('prompt', '')
+        prompt = tool_parameters.get("prompt", "")
         if not prompt:
-            return self.create_text_message('Please input prompt')
+            return self.create_text_message("Please input prompt")
 
-        seed = tool_parameters.get('seed', 0)
+        seed = tool_parameters.get("seed", 0)
         if seed > 0:
-            extra_body['seed'] = seed
-        steps = tool_parameters.get('steps', 0)
+            extra_body["seed"] = seed
+        steps = tool_parameters.get("steps", 0)
         if steps > 0:
-            extra_body['steps'] = steps
-        negative_prompt = tool_parameters.get('negative_prompt', '')
+            extra_body["steps"] = steps
+        negative_prompt = tool_parameters.get("negative_prompt", "")
         if negative_prompt:
-            extra_body['negative_prompt'] = negative_prompt
+            extra_body["negative_prompt"] = negative_prompt
 
         # call openapi stepfun model
         response = client.images.generate(
             prompt=prompt,
             model=model,
-            size=tool_parameters.get('size', '1024x1024'),
-            n=tool_parameters.get('n', 1),
-            extra_body= extra_body
+            size=tool_parameters.get("size", "1024x1024"),
+            n=tool_parameters.get("n", 1),
+            extra_body=extra_body,
         )
         print(response)
 
         result = []
         for image in response.data:
             result.append(self.create_image_message(image=image.url))
-            result.append(self.create_json_message({
-                "url": image.url,
-            }))
+            result.append(
+                self.create_json_message(
+                    {
+                        "url": image.url,
+                    }
+                )
+            )
         return result
 
     @staticmethod
     def _generate_random_id(length=8):
-        characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'
-        random_id = ''.join(random.choices(characters, k=length))
+        characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"
+        random_id = "".join(random.choices(characters, k=length))
         return random_id
diff --git a/api/core/tools/provider/builtin/tavily/tavily.py b/api/core/tools/provider/builtin/tavily/tavily.py
index e376d99d6b..a702b0a74e 100644
--- a/api/core/tools/provider/builtin/tavily/tavily.py
+++ b/api/core/tools/provider/builtin/tavily/tavily.py
@@ -13,7 +13,7 @@ class TavilyProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "query": "Sachin Tendulkar",
                     "search_depth": "basic",
@@ -22,9 +22,8 @@ class TavilyProvider(BuiltinToolProviderController):
                     "include_raw_content": False,
                     "max_results": 5,
                     "include_domains": "",
-                    "exclude_domains": ""
+                    "exclude_domains": "",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/tavily/tools/tavily_search.py b/api/core/tools/provider/builtin/tavily/tools/tavily_search.py
index 0200df3c8a..ca6d8633e4 100644
--- a/api/core/tools/provider/builtin/tavily/tools/tavily_search.py
+++ b/api/core/tools/provider/builtin/tavily/tools/tavily_search.py
@@ -36,15 +36,23 @@ class TavilySearch:
 
         """
         params["api_key"] = self.api_key
-        if 'exclude_domains' in params and isinstance(params['exclude_domains'], str) and params['exclude_domains'] != 'None':
-            params['exclude_domains'] = params['exclude_domains'].split()
+        if (
+            "exclude_domains" in params
+            and isinstance(params["exclude_domains"], str)
+            and params["exclude_domains"] != "None"
+        ):
+            params["exclude_domains"] = params["exclude_domains"].split()
         else:
-            params['exclude_domains'] = []
-        if 'include_domains' in params and isinstance(params['include_domains'], str) and params['include_domains'] != 'None':
-            params['include_domains'] = params['include_domains'].split()
+            params["exclude_domains"] = []
+        if (
+            "include_domains" in params
+            and isinstance(params["include_domains"], str)
+            and params["include_domains"] != "None"
+        ):
+            params["include_domains"] = params["include_domains"].split()
         else:
-            params['include_domains'] = []
-        
+            params["include_domains"] = []
+
         response = requests.post(f"{TAVILY_API_URL}/search", json=params)
         response.raise_for_status()
         return response.json()
@@ -91,9 +99,7 @@ class TavilySearchTool(BuiltinTool):
     A tool for searching Tavily using a given query.
     """
 
-    def _invoke(
-        self, user_id: str, tool_parameters: dict[str, Any]
-    ) -> ToolInvokeMessage | list[ToolInvokeMessage]:
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage | list[ToolInvokeMessage]:
         """
         Invokes the Tavily search tool with the given user ID and tool parameters.
 
@@ -115,4 +121,4 @@ class TavilySearchTool(BuiltinTool):
         if not results:
             return self.create_text_message(f"No results found for '{query}' in Tavily")
         else:
-            return self.create_text_message(text=results)
\ No newline at end of file
+            return self.create_text_message(text=results)
diff --git a/api/core/tools/provider/builtin/tianditu/tianditu.py b/api/core/tools/provider/builtin/tianditu/tianditu.py
index 1f96be06b0..cb7d7bd8bb 100644
--- a/api/core/tools/provider/builtin/tianditu/tianditu.py
+++ b/api/core/tools/provider/builtin/tianditu/tianditu.py
@@ -12,10 +12,12 @@ class TiandituProvider(BuiltinToolProviderController):
                 runtime={
                     "credentials": credentials,
                 }
-            ).invoke(user_id='',
-                        tool_parameters={
-                        'content': '北京',
-                        'specify': '156110000',
-                    })
+            ).invoke(
+                user_id="",
+                tool_parameters={
+                    "content": "北京",
+                    "specify": "156110000",
+                },
+            )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/tianditu/tools/geocoder.py b/api/core/tools/provider/builtin/tianditu/tools/geocoder.py
index 484a3768c8..690a0aed6f 100644
--- a/api/core/tools/provider/builtin/tianditu/tools/geocoder.py
+++ b/api/core/tools/provider/builtin/tianditu/tools/geocoder.py
@@ -8,26 +8,26 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class GeocoderTool(BuiltinTool):
-    
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        base_url = 'http://api.tianditu.gov.cn/geocoder'
-        
-        keyword = tool_parameters.get('keyword', '')
+        base_url = "http://api.tianditu.gov.cn/geocoder"
+
+        keyword = tool_parameters.get("keyword", "")
         if not keyword:
-            return self.create_text_message('Invalid parameter keyword')
-        
-        tk = self.runtime.credentials['tianditu_api_key']
-        
+            return self.create_text_message("Invalid parameter keyword")
+
+        tk = self.runtime.credentials["tianditu_api_key"]
+
         params = {
-            'keyWord': keyword,
+            "keyWord": keyword,
         }
-        
-        result = requests.get(base_url + '?ds=' + json.dumps(params, ensure_ascii=False) + '&tk=' + tk).json()
+
+        result = requests.get(base_url + "?ds=" + json.dumps(params, ensure_ascii=False) + "&tk=" + tk).json()
 
         return self.create_json_message(result)
diff --git a/api/core/tools/provider/builtin/tianditu/tools/poisearch.py b/api/core/tools/provider/builtin/tianditu/tools/poisearch.py
index 08a5b8ef42..798dd94d33 100644
--- a/api/core/tools/provider/builtin/tianditu/tools/poisearch.py
+++ b/api/core/tools/provider/builtin/tianditu/tools/poisearch.py
@@ -8,38 +8,51 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class PoiSearchTool(BuiltinTool):
-    
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        geocoder_base_url = 'http://api.tianditu.gov.cn/geocoder'
-        base_url = 'http://api.tianditu.gov.cn/v2/search'
-        
-        keyword = tool_parameters.get('keyword', '')
+        geocoder_base_url = "http://api.tianditu.gov.cn/geocoder"
+        base_url = "http://api.tianditu.gov.cn/v2/search"
+
+        keyword = tool_parameters.get("keyword", "")
         if not keyword:
-            return self.create_text_message('Invalid parameter keyword')
-        
-        baseAddress = tool_parameters.get('baseAddress', '')
+            return self.create_text_message("Invalid parameter keyword")
+
+        baseAddress = tool_parameters.get("baseAddress", "")
         if not baseAddress:
-            return self.create_text_message('Invalid parameter baseAddress')
-        
-        tk = self.runtime.credentials['tianditu_api_key']
-        
-        base_coords = requests.get(geocoder_base_url + '?ds=' + json.dumps({'keyWord': baseAddress,}, ensure_ascii=False) + '&tk=' + tk).json()
-        
+            return self.create_text_message("Invalid parameter baseAddress")
+
+        tk = self.runtime.credentials["tianditu_api_key"]
+
+        base_coords = requests.get(
+            geocoder_base_url
+            + "?ds="
+            + json.dumps(
+                {
+                    "keyWord": baseAddress,
+                },
+                ensure_ascii=False,
+            )
+            + "&tk="
+            + tk
+        ).json()
+
         params = {
-            'keyWord': keyword,
-            'queryRadius': 5000,
-            'queryType': 3,
-            'pointLonlat': base_coords['location']['lon'] + ',' + base_coords['location']['lat'],
-            'start': 0,
-            'count': 100,
+            "keyWord": keyword,
+            "queryRadius": 5000,
+            "queryType": 3,
+            "pointLonlat": base_coords["location"]["lon"] + "," + base_coords["location"]["lat"],
+            "start": 0,
+            "count": 100,
         }
-        
-        result = requests.get(base_url + '?postStr=' + json.dumps(params, ensure_ascii=False) + '&type=query&tk=' + tk).json()
+
+        result = requests.get(
+            base_url + "?postStr=" + json.dumps(params, ensure_ascii=False) + "&type=query&tk=" + tk
+        ).json()
 
         return self.create_json_message(result)
diff --git a/api/core/tools/provider/builtin/tianditu/tools/staticmap.py b/api/core/tools/provider/builtin/tianditu/tools/staticmap.py
index ecac4404ca..aeaef08805 100644
--- a/api/core/tools/provider/builtin/tianditu/tools/staticmap.py
+++ b/api/core/tools/provider/builtin/tianditu/tools/staticmap.py
@@ -8,29 +8,42 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class PoiSearchTool(BuiltinTool):
-    
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        
-        geocoder_base_url = 'http://api.tianditu.gov.cn/geocoder'
-        base_url = 'http://api.tianditu.gov.cn/staticimage'
-        
-        keyword = tool_parameters.get('keyword', '')
-        if not keyword:
-            return self.create_text_message('Invalid parameter keyword')
-        
-        tk = self.runtime.credentials['tianditu_api_key']
-        
-        keyword_coords = requests.get(geocoder_base_url + '?ds=' + json.dumps({'keyWord': keyword,}, ensure_ascii=False) + '&tk=' + tk).json()
-        coords = keyword_coords['location']['lon'] + ',' + keyword_coords['location']['lat']
-        
-        result = requests.get(base_url + '?center=' + coords + '&markers=' + coords + '&width=400&height=300&zoom=14&tk=' + tk).content
 
-        return self.create_blob_message(blob=result,
-                                            meta={'mime_type': 'image/png'},
-                                            save_as=self.VARIABLE_KEY.IMAGE.value)
+        geocoder_base_url = "http://api.tianditu.gov.cn/geocoder"
+        base_url = "http://api.tianditu.gov.cn/staticimage"
+
+        keyword = tool_parameters.get("keyword", "")
+        if not keyword:
+            return self.create_text_message("Invalid parameter keyword")
+
+        tk = self.runtime.credentials["tianditu_api_key"]
+
+        keyword_coords = requests.get(
+            geocoder_base_url
+            + "?ds="
+            + json.dumps(
+                {
+                    "keyWord": keyword,
+                },
+                ensure_ascii=False,
+            )
+            + "&tk="
+            + tk
+        ).json()
+        coords = keyword_coords["location"]["lon"] + "," + keyword_coords["location"]["lat"]
+
+        result = requests.get(
+            base_url + "?center=" + coords + "&markers=" + coords + "&width=400&height=300&zoom=14&tk=" + tk
+        ).content
+
+        return self.create_blob_message(
+            blob=result, meta={"mime_type": "image/png"}, save_as=self.VariableKey.IMAGE.value
+        )
diff --git a/api/core/tools/provider/builtin/time/time.py b/api/core/tools/provider/builtin/time/time.py
index 833ae194ef..e4df8d616c 100644
--- a/api/core/tools/provider/builtin/time/time.py
+++ b/api/core/tools/provider/builtin/time/time.py
@@ -9,9 +9,8 @@ class WikiPediaProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         try:
             CurrentTimeTool().invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={},
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/time/tools/current_time.py b/api/core/tools/provider/builtin/time/tools/current_time.py
index 90c01665e6..cc38739c16 100644
--- a/api/core/tools/provider/builtin/time/tools/current_time.py
+++ b/api/core/tools/provider/builtin/time/tools/current_time.py
@@ -8,21 +8,22 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class CurrentTimeTool(BuiltinTool):
-    def _invoke(self, 
-                user_id: str,
-               tool_parameters: dict[str, Any], 
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         # get timezone
-        tz = tool_parameters.get('timezone', 'UTC')
-        fm = tool_parameters.get('format') or '%Y-%m-%d %H:%M:%S %Z'
-        if tz == 'UTC':
-            return self.create_text_message(f'{datetime.now(timezone.utc).strftime(fm)}')
-        
+        tz = tool_parameters.get("timezone", "UTC")
+        fm = tool_parameters.get("format") or "%Y-%m-%d %H:%M:%S %Z"
+        if tz == "UTC":
+            return self.create_text_message(f"{datetime.now(timezone.utc).strftime(fm)}")
+
         try:
             tz = pytz_timezone(tz)
         except:
-            return self.create_text_message(f'Invalid timezone: {tz}')
-        return self.create_text_message(f'{datetime.now(tz).strftime(fm)}')
\ No newline at end of file
+            return self.create_text_message(f"Invalid timezone: {tz}")
+        return self.create_text_message(f"{datetime.now(tz).strftime(fm)}")
diff --git a/api/core/tools/provider/builtin/time/tools/weekday.py b/api/core/tools/provider/builtin/time/tools/weekday.py
index 4461cb5a32..b327e54e17 100644
--- a/api/core/tools/provider/builtin/time/tools/weekday.py
+++ b/api/core/tools/provider/builtin/time/tools/weekday.py
@@ -7,25 +7,26 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class WeekdayTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            Calculate the day of the week for a given date
+        Calculate the day of the week for a given date
         """
-        year = tool_parameters.get('year')
-        month = tool_parameters.get('month')
-        day = tool_parameters.get('day')
+        year = tool_parameters.get("year")
+        month = tool_parameters.get("month")
+        day = tool_parameters.get("day")
 
         date_obj = self.convert_datetime(year, month, day)
         if not date_obj:
-            return self.create_text_message(f'Invalid date: Year {year}, Month {month}, Day {day}.')
+            return self.create_text_message(f"Invalid date: Year {year}, Month {month}, Day {day}.")
 
         weekday_name = calendar.day_name[date_obj.weekday()]
         month_name = calendar.month_name[month]
         readable_date = f"{month_name} {date_obj.day}, {date_obj.year}"
-        return self.create_text_message(f'{readable_date} is {weekday_name}.')
+        return self.create_text_message(f"{readable_date} is {weekday_name}.")
 
     @staticmethod
     def convert_datetime(year, month, day) -> datetime | None:
diff --git a/api/core/tools/provider/builtin/trello/tools/create_board.py b/api/core/tools/provider/builtin/trello/tools/create_board.py
index 2655602afa..5a61d22157 100644
--- a/api/core/tools/provider/builtin/trello/tools/create_board.py
+++ b/api/core/tools/provider/builtin/trello/tools/create_board.py
@@ -22,19 +22,15 @@ class CreateBoardTool(BuiltinTool):
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        board_name = tool_parameters.get('name')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        board_name = tool_parameters.get("name")
 
         if not (api_key and token and board_name):
             return self.create_text_message("Missing required parameters: API key, token, or board name.")
 
         url = "https://api.trello.com/1/boards/"
-        query_params = {
-            'name': board_name,
-            'key': api_key,
-            'token': token
-        }
+        query_params = {"name": board_name, "key": api_key, "token": token}
 
         try:
             response = requests.post(url, params=query_params)
@@ -43,5 +39,6 @@ class CreateBoardTool(BuiltinTool):
             return self.create_text_message("Failed to create board")
 
         board = response.json()
-        return self.create_text_message(text=f"Board created successfully! Board name: {board['name']}, ID: {board['id']}")
-
+        return self.create_text_message(
+            text=f"Board created successfully! Board name: {board['name']}, ID: {board['id']}"
+        )
diff --git a/api/core/tools/provider/builtin/trello/tools/create_list_on_board.py b/api/core/tools/provider/builtin/trello/tools/create_list_on_board.py
index f5b156cb44..b32b0124dd 100644
--- a/api/core/tools/provider/builtin/trello/tools/create_list_on_board.py
+++ b/api/core/tools/provider/builtin/trello/tools/create_list_on_board.py
@@ -17,25 +17,22 @@ class CreateListOnBoardTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation, including the board ID and list name.
+            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation,
+             including the board ID and list name.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        board_id = tool_parameters.get('id')
-        list_name = tool_parameters.get('name')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        board_id = tool_parameters.get("id")
+        list_name = tool_parameters.get("name")
 
         if not (api_key and token and board_id and list_name):
             return self.create_text_message("Missing required parameters: API key, token, board ID, or list name.")
 
         url = f"https://api.trello.com/1/boards/{board_id}/lists"
-        params = {
-            'name': list_name,
-            'key': api_key,
-            'token': token
-        }
+        params = {"name": list_name, "key": api_key, "token": token}
 
         try:
             response = requests.post(url, params=params)
@@ -44,5 +41,6 @@ class CreateListOnBoardTool(BuiltinTool):
             return self.create_text_message("Failed to create list")
 
         new_list = response.json()
-        return self.create_text_message(text=f"List '{new_list['name']}' created successfully with Id {new_list['id']} on board {board_id}.")
-
+        return self.create_text_message(
+            text=f"List '{new_list['name']}' created successfully with Id {new_list['id']} on board {board_id}."
+        )
diff --git a/api/core/tools/provider/builtin/trello/tools/create_new_card_on_board.py b/api/core/tools/provider/builtin/trello/tools/create_new_card_on_board.py
index 74b73b40e5..e98efb81ca 100644
--- a/api/core/tools/provider/builtin/trello/tools/create_new_card_on_board.py
+++ b/api/core/tools/provider/builtin/trello/tools/create_new_card_on_board.py
@@ -17,20 +17,21 @@ class CreateNewCardOnBoardTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool, None]]): The parameters for the tool invocation, including details for the new card.
+            tool_parameters (dict[str, Union[str, int, bool, None]]): The parameters for the tool invocation,
+             including details for the new card.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
 
         # Ensure required parameters are present
-        if 'name' not in tool_parameters or 'idList' not in tool_parameters:
+        if "name" not in tool_parameters or "idList" not in tool_parameters:
             return self.create_text_message("Missing required parameters: name or idList.")
 
         url = "https://api.trello.com/1/cards"
-        params = {**tool_parameters, 'key': api_key, 'token': token}
+        params = {**tool_parameters, "key": api_key, "token": token}
 
         try:
             response = requests.post(url, params=params)
@@ -39,5 +40,6 @@ class CreateNewCardOnBoardTool(BuiltinTool):
         except requests.exceptions.RequestException as e:
             return self.create_text_message("Failed to create card")
 
-        return self.create_text_message(text=f"New card '{new_card['name']}' created successfully with ID {new_card['id']}.")
-
+        return self.create_text_message(
+            text=f"New card '{new_card['name']}' created successfully with ID {new_card['id']}."
+        )
diff --git a/api/core/tools/provider/builtin/trello/tools/delete_board.py b/api/core/tools/provider/builtin/trello/tools/delete_board.py
index 29df3fda2d..7fc9d1f13c 100644
--- a/api/core/tools/provider/builtin/trello/tools/delete_board.py
+++ b/api/core/tools/provider/builtin/trello/tools/delete_board.py
@@ -17,14 +17,15 @@ class DeleteBoardTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation, including the board ID.
+            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation,
+             including the board ID.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        board_id = tool_parameters.get('boardId')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        board_id = tool_parameters.get("boardId")
 
         if not (api_key and token and board_id):
             return self.create_text_message("Missing required parameters: API key, token, or board ID.")
@@ -38,4 +39,3 @@ class DeleteBoardTool(BuiltinTool):
             return self.create_text_message("Failed to delete board")
 
         return self.create_text_message(text=f"Board with ID {board_id} deleted successfully.")
-
diff --git a/api/core/tools/provider/builtin/trello/tools/delete_card.py b/api/core/tools/provider/builtin/trello/tools/delete_card.py
index 2ced5f6c14..1de98d639e 100644
--- a/api/core/tools/provider/builtin/trello/tools/delete_card.py
+++ b/api/core/tools/provider/builtin/trello/tools/delete_card.py
@@ -17,14 +17,15 @@ class DeleteCardByIdTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation, including the card ID.
+            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation,
+             including the card ID.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        card_id = tool_parameters.get('id')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        card_id = tool_parameters.get("id")
 
         if not (api_key and token and card_id):
             return self.create_text_message("Missing required parameters: API key, token, or card ID.")
@@ -38,4 +39,3 @@ class DeleteCardByIdTool(BuiltinTool):
             return self.create_text_message("Failed to delete card")
 
         return self.create_text_message(text=f"Card with ID {card_id} has been successfully deleted.")
-
diff --git a/api/core/tools/provider/builtin/trello/tools/fetch_all_boards.py b/api/core/tools/provider/builtin/trello/tools/fetch_all_boards.py
index f9d554c6fb..0c5ed9ea85 100644
--- a/api/core/tools/provider/builtin/trello/tools/fetch_all_boards.py
+++ b/api/core/tools/provider/builtin/trello/tools/fetch_all_boards.py
@@ -28,9 +28,7 @@ class FetchAllBoardsTool(BuiltinTool):
         token = self.runtime.credentials.get("trello_api_token")
 
         if not (api_key and token):
-            return self.create_text_message(
-                "Missing Trello API key or token in credentials."
-            )
+            return self.create_text_message("Missing Trello API key or token in credentials.")
 
         # Including board filter in the request if provided
         board_filter = tool_parameters.get("boards", "open")
@@ -48,7 +46,5 @@ class FetchAllBoardsTool(BuiltinTool):
             return self.create_text_message("No boards found in Trello.")
 
         # Creating a string with both board names and IDs
-        boards_info = ", ".join(
-            [f"{board['name']} (ID: {board['id']})" for board in boards]
-        )
+        boards_info = ", ".join([f"{board['name']} (ID: {board['id']})" for board in boards])
         return self.create_text_message(text=f"Boards: {boards_info}")
diff --git a/api/core/tools/provider/builtin/trello/tools/get_board_actions.py b/api/core/tools/provider/builtin/trello/tools/get_board_actions.py
index 5678d8f8d7..cabc7ce093 100644
--- a/api/core/tools/provider/builtin/trello/tools/get_board_actions.py
+++ b/api/core/tools/provider/builtin/trello/tools/get_board_actions.py
@@ -17,14 +17,15 @@ class GetBoardActionsTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation, including the board ID.
+            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation,
+             including the board ID.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        board_id = tool_parameters.get('boardId')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        board_id = tool_parameters.get("boardId")
 
         if not (api_key and token and board_id):
             return self.create_text_message("Missing required parameters: API key, token, or board ID.")
@@ -38,6 +39,7 @@ class GetBoardActionsTool(BuiltinTool):
         except requests.exceptions.RequestException as e:
             return self.create_text_message("Failed to retrieve board actions")
 
-        actions_summary = "\n".join([f"{action['type']}: {action.get('data', {}).get('text', 'No details available')}" for action in actions])
+        actions_summary = "\n".join(
+            [f"{action['type']}: {action.get('data', {}).get('text', 'No details available')}" for action in actions]
+        )
         return self.create_text_message(text=f"Actions for Board ID {board_id}:\n{actions_summary}")
-
diff --git a/api/core/tools/provider/builtin/trello/tools/get_board_by_id.py b/api/core/tools/provider/builtin/trello/tools/get_board_by_id.py
index ee6cb065e5..fe42cd9c5c 100644
--- a/api/core/tools/provider/builtin/trello/tools/get_board_by_id.py
+++ b/api/core/tools/provider/builtin/trello/tools/get_board_by_id.py
@@ -17,14 +17,15 @@ class GetBoardByIdTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation, including the board ID.
+            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation,
+             including the board ID.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        board_id = tool_parameters.get('boardId')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        board_id = tool_parameters.get("boardId")
 
         if not (api_key and token and board_id):
             return self.create_text_message("Missing required parameters: API key, token, or board ID.")
@@ -63,4 +64,3 @@ class GetBoardByIdTool(BuiltinTool):
             f"Background Color: {board['prefs']['backgroundColor']}"
         )
         return details
-
diff --git a/api/core/tools/provider/builtin/trello/tools/get_board_cards.py b/api/core/tools/provider/builtin/trello/tools/get_board_cards.py
index 1abb688750..ff2b1221e7 100644
--- a/api/core/tools/provider/builtin/trello/tools/get_board_cards.py
+++ b/api/core/tools/provider/builtin/trello/tools/get_board_cards.py
@@ -17,14 +17,15 @@ class GetBoardCardsTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation, including the board ID.
+            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation,
+             including the board ID.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        board_id = tool_parameters.get('boardId')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        board_id = tool_parameters.get("boardId")
 
         if not (api_key and token and board_id):
             return self.create_text_message("Missing required parameters: API key, token, or board ID.")
@@ -40,4 +41,3 @@ class GetBoardCardsTool(BuiltinTool):
 
         cards_summary = "\n".join([f"{card['name']} (ID: {card['id']})" for card in cards])
         return self.create_text_message(text=f"Cards for Board ID {board_id}:\n{cards_summary}")
-
diff --git a/api/core/tools/provider/builtin/trello/tools/get_filterd_board_cards.py b/api/core/tools/provider/builtin/trello/tools/get_filterd_board_cards.py
index 375ead5b1d..3d7f9f4ad1 100644
--- a/api/core/tools/provider/builtin/trello/tools/get_filterd_board_cards.py
+++ b/api/core/tools/provider/builtin/trello/tools/get_filterd_board_cards.py
@@ -17,15 +17,16 @@ class GetFilteredBoardCardsTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation, including the board ID and filter.
+            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation,
+             including the board ID and filter.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        board_id = tool_parameters.get('boardId')
-        filter = tool_parameters.get('filter')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        board_id = tool_parameters.get("boardId")
+        filter = tool_parameters.get("filter")
 
         if not (api_key and token and board_id and filter):
             return self.create_text_message("Missing required parameters: API key, token, board ID, or filter.")
@@ -40,5 +41,6 @@ class GetFilteredBoardCardsTool(BuiltinTool):
             return self.create_text_message("Failed to retrieve filtered cards")
 
         card_details = "\n".join([f"{card['name']} (ID: {card['id']})" for card in filtered_cards])
-        return self.create_text_message(text=f"Filtered Cards for Board ID {board_id} with Filter '{filter}':\n{card_details}")
-
+        return self.create_text_message(
+            text=f"Filtered Cards for Board ID {board_id} with Filter '{filter}':\n{card_details}"
+        )
diff --git a/api/core/tools/provider/builtin/trello/tools/get_lists_on_board.py b/api/core/tools/provider/builtin/trello/tools/get_lists_on_board.py
index 7b9b9cf24b..ccf404068f 100644
--- a/api/core/tools/provider/builtin/trello/tools/get_lists_on_board.py
+++ b/api/core/tools/provider/builtin/trello/tools/get_lists_on_board.py
@@ -17,14 +17,15 @@ class GetListsFromBoardTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation, including the board ID.
+            tool_parameters (dict[str, Union[str, int, bool]]): The parameters for the tool invocation,
+             including the board ID.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        board_id = tool_parameters.get('boardId')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        board_id = tool_parameters.get("boardId")
 
         if not (api_key and token and board_id):
             return self.create_text_message("Missing required parameters: API key, token, or board ID.")
@@ -40,4 +41,3 @@ class GetListsFromBoardTool(BuiltinTool):
 
         lists_info = "\n".join([f"{list['name']} (ID: {list['id']})" for list in lists])
         return self.create_text_message(text=f"Lists on Board ID {board_id}:\n{lists_info}")
-
diff --git a/api/core/tools/provider/builtin/trello/tools/update_board.py b/api/core/tools/provider/builtin/trello/tools/update_board.py
index 7ad6ac2e64..1e358b00f4 100644
--- a/api/core/tools/provider/builtin/trello/tools/update_board.py
+++ b/api/core/tools/provider/builtin/trello/tools/update_board.py
@@ -17,14 +17,15 @@ class UpdateBoardByIdTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool, None]]): The parameters for the tool invocation, including board ID and updates.
+            tool_parameters (dict[str, Union[str, int, bool, None]]): The parameters for the tool invocation,
+             including board ID and updates.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        board_id = tool_parameters.pop('boardId', None)
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        board_id = tool_parameters.pop("boardId", None)
 
         if not (api_key and token and board_id):
             return self.create_text_message("Missing required parameters: API key, token, or board ID.")
@@ -33,8 +34,8 @@ class UpdateBoardByIdTool(BuiltinTool):
 
         # Removing parameters not intended for update action or with None value
         params = {k: v for k, v in tool_parameters.items() if v is not None}
-        params['key'] = api_key
-        params['token'] = token
+        params["key"] = api_key
+        params["token"] = token
 
         try:
             response = requests.put(url, params=params)
@@ -44,4 +45,3 @@ class UpdateBoardByIdTool(BuiltinTool):
 
         updated_board = response.json()
         return self.create_text_message(text=f"Board '{updated_board['name']}' updated successfully.")
-
diff --git a/api/core/tools/provider/builtin/trello/tools/update_card.py b/api/core/tools/provider/builtin/trello/tools/update_card.py
index 417344350c..d25fcbafaa 100644
--- a/api/core/tools/provider/builtin/trello/tools/update_card.py
+++ b/api/core/tools/provider/builtin/trello/tools/update_card.py
@@ -17,22 +17,23 @@ class UpdateCardByIdTool(BuiltinTool):
 
         Args:
             user_id (str): The ID of the user invoking the tool.
-            tool_parameters (dict[str, Union[str, int, bool, None]]): The parameters for the tool invocation, including the card ID and updates.
+            tool_parameters (dict[str, Union[str, int, bool, None]]): The parameters for the tool invocation,
+             including the card ID and updates.
 
         Returns:
             ToolInvokeMessage: The result of the tool invocation.
         """
-        api_key = self.runtime.credentials.get('trello_api_key')
-        token = self.runtime.credentials.get('trello_api_token')
-        card_id = tool_parameters.get('id')
+        api_key = self.runtime.credentials.get("trello_api_key")
+        token = self.runtime.credentials.get("trello_api_token")
+        card_id = tool_parameters.get("id")
 
         if not (api_key and token and card_id):
             return self.create_text_message("Missing required parameters: API key, token, or card ID.")
 
         # Constructing the URL and the payload for the PUT request
         url = f"https://api.trello.com/1/cards/{card_id}"
-        params = {k: v for k, v in tool_parameters.items() if v is not None and k != 'id'}
-        params.update({'key': api_key, 'token': token})
+        params = {k: v for k, v in tool_parameters.items() if v is not None and k != "id"}
+        params.update({"key": api_key, "token": token})
 
         try:
             response = requests.put(url, params=params)
diff --git a/api/core/tools/provider/builtin/trello/trello.py b/api/core/tools/provider/builtin/trello/trello.py
index 84ecd20803..e0dca50ec9 100644
--- a/api/core/tools/provider/builtin/trello/trello.py
+++ b/api/core/tools/provider/builtin/trello/trello.py
@@ -9,17 +9,17 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 class TrelloProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         """Validate Trello API credentials by making a test API call.
-        
+
         Args:
             credentials (dict[str, Any]): The Trello API credentials to validate.
-        
+
         Raises:
             ToolProviderCredentialValidationError: If the credentials are invalid.
         """
         api_key = credentials.get("trello_api_key")
         token = credentials.get("trello_api_token")
         url = f"https://api.trello.com/1/members/me?key={api_key}&token={token}"
-        
+
         try:
             response = requests.get(url)
             response.raise_for_status()  # Raises an HTTPError for bad responses
@@ -32,4 +32,3 @@ class TrelloProvider(BuiltinToolProviderController):
         except requests.exceptions.RequestException as e:
             # Handle other exceptions, such as connection errors
             raise ToolProviderCredentialValidationError("Error validating Trello credentials")
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/twilio/tools/send_message.py b/api/core/tools/provider/builtin/twilio/tools/send_message.py
index 1c52589956..5ee839baa5 100644
--- a/api/core/tools/provider/builtin/twilio/tools/send_message.py
+++ b/api/core/tools/provider/builtin/twilio/tools/send_message.py
@@ -32,17 +32,14 @@ class TwilioAPIWrapper(BaseModel):
         must be empty.
     """
 
-    @field_validator('client', mode='before')
+    @field_validator("client", mode="before")
     @classmethod
     def set_validator(cls, values: dict) -> dict:
         """Validate that api key and python package exists in environment."""
         try:
             from twilio.rest import Client
         except ImportError:
-            raise ImportError(
-                "Could not import twilio python package. "
-                "Please install it with `pip install twilio`."
-            )
+            raise ImportError("Could not import twilio python package. Please install it with `pip install twilio`.")
         account_sid = values.get("account_sid")
         auth_token = values.get("auth_token")
         values["from_number"] = values.get("from_number")
@@ -75,7 +72,8 @@ class SendMessageTool(BuiltinTool):
         tool_parameters (Dict[str, Any]): The parameters required for sending the message.
 
     Returns:
-        Union[ToolInvokeMessage, List[ToolInvokeMessage]]: The result of invoking the tool, which includes the status of the message sending operation.
+        Union[ToolInvokeMessage, List[ToolInvokeMessage]]: The result of invoking the tool,
+         which includes the status of the message sending operation.
     """
 
     def _invoke(
@@ -91,9 +89,7 @@ class SendMessageTool(BuiltinTool):
         if to_number.startswith("whatsapp:"):
             from_number = f"whatsapp: {from_number}"
 
-        twilio = TwilioAPIWrapper(
-            account_sid=account_sid, auth_token=auth_token, from_number=from_number
-        )
+        twilio = TwilioAPIWrapper(account_sid=account_sid, auth_token=auth_token, from_number=from_number)
 
         # Sending the message through Twilio
         result = twilio.run(message, to_number)
diff --git a/api/core/tools/provider/builtin/twilio/twilio.py b/api/core/tools/provider/builtin/twilio/twilio.py
index 06f276053a..b1d100aad9 100644
--- a/api/core/tools/provider/builtin/twilio/twilio.py
+++ b/api/core/tools/provider/builtin/twilio/twilio.py
@@ -14,7 +14,7 @@ class TwilioProvider(BuiltinToolProviderController):
             account_sid = credentials["account_sid"]
             auth_token = credentials["auth_token"]
             from_number = credentials["from_number"]
-            
+
             # Initialize twilio client
             client = Client(account_sid, auth_token)
 
@@ -27,4 +27,3 @@ class TwilioProvider(BuiltinToolProviderController):
             raise ToolProviderCredentialValidationError(f"Missing required credential: {e}") from e
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/vanna/tools/vanna.py b/api/core/tools/provider/builtin/vanna/tools/vanna.py
index a6efb0f79a..c90d766e48 100644
--- a/api/core/tools/provider/builtin/vanna/tools/vanna.py
+++ b/api/core/tools/provider/builtin/vanna/tools/vanna.py
@@ -38,7 +38,7 @@ class VannaTool(BuiltinTool):
         vn = VannaDefault(model=model, api_key=api_key)
 
         db_type = tool_parameters.get("db_type", "")
-        if db_type in ["Postgres", "MySQL", "Hive", "ClickHouse"]:
+        if db_type in {"Postgres", "MySQL", "Hive", "ClickHouse"}:
             if not db_name:
                 return self.create_text_message("Please input database name")
             if not username:
diff --git a/api/core/tools/provider/builtin/vanna/vanna.py b/api/core/tools/provider/builtin/vanna/vanna.py
index ab1fd71df5..84724e921a 100644
--- a/api/core/tools/provider/builtin/vanna/vanna.py
+++ b/api/core/tools/provider/builtin/vanna/vanna.py
@@ -13,13 +13,13 @@ class VannaProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "model": "chinook",
                     "db_type": "SQLite",
                     "url": "https://vanna.ai/Chinook.sqlite",
-                    "query": "What are the top 10 customers by sales?"
+                    "query": "What are the top 10 customers by sales?",
                 },
             )
         except Exception as e:
-            raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file
+            raise ToolProviderCredentialValidationError(str(e))
diff --git a/api/core/tools/provider/builtin/vectorizer/tools/test_data.py b/api/core/tools/provider/builtin/vectorizer/tools/test_data.py
index 1506ac0c9d..8effa9818a 100644
--- a/api/core/tools/provider/builtin/vectorizer/tools/test_data.py
+++ b/api/core/tools/provider/builtin/vectorizer/tools/test_data.py
@@ -1 +1 @@
-VECTORIZER_ICON_PNG = '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'
\ No newline at end of file
+VECTORIZER_ICON_PNG = "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"  # noqa: E501
diff --git a/api/core/tools/provider/builtin/vectorizer/tools/vectorizer.py b/api/core/tools/provider/builtin/vectorizer/tools/vectorizer.py
index c6ec198034..4bd601c0bd 100644
--- a/api/core/tools/provider/builtin/vectorizer/tools/vectorizer.py
+++ b/api/core/tools/provider/builtin/vectorizer/tools/vectorizer.py
@@ -10,65 +10,60 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class VectorizerTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) \
-        -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        api_key_name = self.runtime.credentials.get('api_key_name', None)
-        api_key_value = self.runtime.credentials.get('api_key_value', None)
-        mode = tool_parameters.get('mode', 'test')
-        if mode == 'production':
-            mode = 'preview'
+        api_key_name = self.runtime.credentials.get("api_key_name", None)
+        api_key_value = self.runtime.credentials.get("api_key_value", None)
+        mode = tool_parameters.get("mode", "test")
+        if mode == "production":
+            mode = "preview"
 
         if not api_key_name or not api_key_value:
-            raise ToolProviderCredentialValidationError('Please input api key name and value')
+            raise ToolProviderCredentialValidationError("Please input api key name and value")
 
-        image_id = tool_parameters.get('image_id', '')
+        image_id = tool_parameters.get("image_id", "")
         if not image_id:
-            return self.create_text_message('Please input image id')
-        
-        if image_id.startswith('__test_'):
+            return self.create_text_message("Please input image id")
+
+        if image_id.startswith("__test_"):
             image_binary = b64decode(VECTORIZER_ICON_PNG)
         else:
-            image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)
+            image_binary = self.get_variable_file(self.VariableKey.IMAGE)
             if not image_binary:
-                return self.create_text_message('Image not found, please request user to generate image firstly.')
+                return self.create_text_message("Image not found, please request user to generate image firstly.")
 
         response = post(
-            'https://vectorizer.ai/api/v1/vectorize',
-            files={
-                'image': image_binary
-            },
-            data={
-                'mode': mode
-            } if mode == 'test' else {},
-            auth=(api_key_name, api_key_value), 
-            timeout=30
+            "https://vectorizer.ai/api/v1/vectorize",
+            files={"image": image_binary},
+            data={"mode": mode} if mode == "test" else {},
+            auth=(api_key_name, api_key_value),
+            timeout=30,
         )
 
         if response.status_code != 200:
             raise Exception(response.text)
-        
+
         return [
-            self.create_text_message('the vectorized svg is saved as an image.'),
-            self.create_blob_message(blob=response.content,
-                                    meta={'mime_type': 'image/svg+xml'})
+            self.create_text_message("the vectorized svg is saved as an image."),
+            self.create_blob_message(blob=response.content, meta={"mime_type": "image/svg+xml"}),
         ]
-    
+
     def get_runtime_parameters(self) -> list[ToolParameter]:
         """
         override the runtime parameters
         """
         return [
             ToolParameter.get_simple_instance(
-                name='image_id',
-                llm_description=f'the image id that you want to vectorize, \
+                name="image_id",
+                llm_description=f"the image id that you want to vectorize, \
                     and the image id should be specified in \
-                        {[i.name for i in self.list_default_image_variables()]}',
+                        {[i.name for i in self.list_default_image_variables()]}",
                 type=ToolParameter.ToolParameterType.SELECT,
                 required=True,
-                options=[i.name for i in self.list_default_image_variables()]
+                options=[i.name for i in self.list_default_image_variables()],
             )
         ]
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/vectorizer/vectorizer.py b/api/core/tools/provider/builtin/vectorizer/vectorizer.py
index 3f89a83500..3b868572f9 100644
--- a/api/core/tools/provider/builtin/vectorizer/vectorizer.py
+++ b/api/core/tools/provider/builtin/vectorizer/vectorizer.py
@@ -13,12 +13,8 @@ class VectorizerProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
-                tool_parameters={
-                    "mode": "test",
-                    "image_id": "__test_123"
-                },
+                user_id="",
+                tool_parameters={"mode": "test", "image_id": "__test_123"},
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/webscraper/tools/webscraper.py b/api/core/tools/provider/builtin/webscraper/tools/webscraper.py
index 3d098e6768..12670b4b8b 100644
--- a/api/core/tools/provider/builtin/webscraper/tools/webscraper.py
+++ b/api/core/tools/provider/builtin/webscraper/tools/webscraper.py
@@ -6,23 +6,24 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class WebscraperTool(BuiltinTool):
-    def _invoke(self,
-                user_id: str,
-                tool_parameters: dict[str, Any],
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
         try:
-            url = tool_parameters.get('url', '')
-            user_agent = tool_parameters.get('user_agent', '')
+            url = tool_parameters.get("url", "")
+            user_agent = tool_parameters.get("user_agent", "")
             if not url:
-                return self.create_text_message('Please input url')
+                return self.create_text_message("Please input url")
 
             # get webpage
             result = self.get_url(url, user_agent=user_agent)
 
-            if tool_parameters.get('generate_summary'):
+            if tool_parameters.get("generate_summary"):
                 # summarize and return
                 return self.create_text_message(self.summary(user_id=user_id, content=result))
             else:
diff --git a/api/core/tools/provider/builtin/webscraper/webscraper.py b/api/core/tools/provider/builtin/webscraper/webscraper.py
index 1e60fdb293..3c51393ac6 100644
--- a/api/core/tools/provider/builtin/webscraper/webscraper.py
+++ b/api/core/tools/provider/builtin/webscraper/webscraper.py
@@ -13,12 +13,11 @@ class WebscraperProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
-                    'url': 'https://www.google.com',
-                    'user_agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 '
+                    "url": "https://www.google.com",
+                    "user_agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 ",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/websearch/tools/job_search.py b/api/core/tools/provider/builtin/websearch/tools/job_search.py
index 9128305922..293f4f6329 100644
--- a/api/core/tools/provider/builtin/websearch/tools/job_search.py
+++ b/api/core/tools/provider/builtin/websearch/tools/job_search.py
@@ -50,14 +50,16 @@ class SerplyApi:
         for job in jobs[:10]:
             try:
                 string.append(
-                    "\n".join([
-                        f"Position: {job['position']}",
-                        f"Employer: {job['employer']}",
-                        f"Location: {job['location']}",
-                        f"Link: {job['link']}",
-                        f"""Highest: {", ".join(list(job["highlights"]))}""",
-                        "---",
-                    ])
+                    "\n".join(
+                        [
+                            f"Position: {job['position']}",
+                            f"Employer: {job['employer']}",
+                            f"Location: {job['location']}",
+                            f"Link: {job['link']}",
+                            f"""Highest: {", ".join(list(job["highlights"]))}""",
+                            "---",
+                        ]
+                    )
                 )
             except KeyError:
                 continue
diff --git a/api/core/tools/provider/builtin/websearch/tools/news_search.py b/api/core/tools/provider/builtin/websearch/tools/news_search.py
index e9c0744f05..9b5482fe18 100644
--- a/api/core/tools/provider/builtin/websearch/tools/news_search.py
+++ b/api/core/tools/provider/builtin/websearch/tools/news_search.py
@@ -53,13 +53,15 @@ class SerplyApi:
                 r = requests.get(entry["link"])
                 final_link = r.history[-1].headers["Location"]
                 string.append(
-                    "\n".join([
-                        f"Title: {entry['title']}",
-                        f"Link: {final_link}",
-                        f"Source: {entry['source']['title']}",
-                        f"Published: {entry['published']}",
-                        "---",
-                    ])
+                    "\n".join(
+                        [
+                            f"Title: {entry['title']}",
+                            f"Link: {final_link}",
+                            f"Source: {entry['source']['title']}",
+                            f"Published: {entry['published']}",
+                            "---",
+                        ]
+                    )
                 )
             except KeyError:
                 continue
diff --git a/api/core/tools/provider/builtin/websearch/tools/scholar_search.py b/api/core/tools/provider/builtin/websearch/tools/scholar_search.py
index 0030a03c06..798d059b51 100644
--- a/api/core/tools/provider/builtin/websearch/tools/scholar_search.py
+++ b/api/core/tools/provider/builtin/websearch/tools/scholar_search.py
@@ -55,14 +55,16 @@ class SerplyApi:
                     link = article["link"]
                 authors = [author["name"] for author in article["author"]["authors"]]
                 string.append(
-                    "\n".join([
-                        f"Title: {article['title']}",
-                        f"Link: {link}",
-                        f"Description: {article['description']}",
-                        f"Cite: {article['cite']}",
-                        f"Authors: {', '.join(authors)}",
-                        "---",
-                    ])
+                    "\n".join(
+                        [
+                            f"Title: {article['title']}",
+                            f"Link: {link}",
+                            f"Description: {article['description']}",
+                            f"Cite: {article['cite']}",
+                            f"Authors: {', '.join(authors)}",
+                            "---",
+                        ]
+                    )
                 )
             except KeyError:
                 continue
diff --git a/api/core/tools/provider/builtin/websearch/tools/web_search.py b/api/core/tools/provider/builtin/websearch/tools/web_search.py
index 4f57c27caf..fe363ac7a4 100644
--- a/api/core/tools/provider/builtin/websearch/tools/web_search.py
+++ b/api/core/tools/provider/builtin/websearch/tools/web_search.py
@@ -49,12 +49,14 @@ class SerplyApi:
         for result in results:
             try:
                 string.append(
-                    "\n".join([
-                        f"Title: {result['title']}",
-                        f"Link: {result['link']}",
-                        f"Description: {result['description'].strip()}",
-                        "---",
-                    ])
+                    "\n".join(
+                        [
+                            f"Title: {result['title']}",
+                            f"Link: {result['link']}",
+                            f"Description: {result['description'].strip()}",
+                            "---",
+                        ]
+                    )
                 )
             except KeyError:
                 continue
diff --git a/api/core/tools/provider/builtin/wecom/tools/wecom_group_bot.py b/api/core/tools/provider/builtin/wecom/tools/wecom_group_bot.py
index fb44b70f4e..545d9f4f8d 100644
--- a/api/core/tools/provider/builtin/wecom/tools/wecom_group_bot.py
+++ b/api/core/tools/provider/builtin/wecom/tools/wecom_group_bot.py
@@ -8,41 +8,41 @@ from core.tools.utils.uuid_utils import is_valid_uuid
 
 
 class WecomGroupBotTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]
-                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        content = tool_parameters.get('content', '')
+        content = tool_parameters.get("content", "")
         if not content:
-            return self.create_text_message('Invalid parameter content')
+            return self.create_text_message("Invalid parameter content")
 
-        hook_key = tool_parameters.get('hook_key', '')
+        hook_key = tool_parameters.get("hook_key", "")
         if not is_valid_uuid(hook_key):
-            return self.create_text_message(
-                f'Invalid parameter hook_key ${hook_key}, not a valid UUID')
+            return self.create_text_message(f"Invalid parameter hook_key ${hook_key}, not a valid UUID")
 
-        message_type = tool_parameters.get('message_type', 'text')
-        if message_type == 'markdown':
+        message_type = tool_parameters.get("message_type", "text")
+        if message_type == "markdown":
             payload = {
-                "msgtype": 'markdown',
+                "msgtype": "markdown",
                 "markdown": {
                     "content": content,
-                }
+                },
             }
         else:
             payload = {
-                "msgtype": 'text',
+                "msgtype": "text",
                 "text": {
                     "content": content,
-                }
+                },
             }
-        api_url = 'https://qyapi.weixin.qq.com/cgi-bin/webhook/send'
+        api_url = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send"
         headers = {
-            'Content-Type': 'application/json',
+            "Content-Type": "application/json",
         }
         params = {
-            'key': hook_key,
+            "key": hook_key,
         }
 
         try:
@@ -51,6 +51,7 @@ class WecomGroupBotTool(BuiltinTool):
                 return self.create_text_message("Text message sent successfully")
             else:
                 return self.create_text_message(
-                    f"Failed to send the text message, status code: {res.status_code}, response: {res.text}")
+                    f"Failed to send the text message, status code: {res.status_code}, response: {res.text}"
+                )
         except Exception as e:
             return self.create_text_message("Failed to send message to group chat bot. {}".format(e))
diff --git a/api/core/tools/provider/builtin/wikipedia/tools/wikipedia_search.py b/api/core/tools/provider/builtin/wikipedia/tools/wikipedia_search.py
index 0796cd2392..cb88e9519a 100644
--- a/api/core/tools/provider/builtin/wikipedia/tools/wikipedia_search.py
+++ b/api/core/tools/provider/builtin/wikipedia/tools/wikipedia_search.py
@@ -27,7 +27,7 @@ class WikipediaAPIWrapper:
         self.doc_content_chars_max = doc_content_chars_max
 
     def run(self, query: str, lang: str = "") -> str:
-        if lang in wikipedia.languages().keys():
+        if lang in wikipedia.languages():
             self.lang = lang
 
         wikipedia.set_lang(self.lang)
@@ -83,7 +83,6 @@ class WikipediaQueryRun:
 
 
 class WikiPediaSearchTool(BuiltinTool):
-
     def _invoke(
         self,
         user_id: str,
diff --git a/api/core/tools/provider/builtin/wikipedia/wikipedia.py b/api/core/tools/provider/builtin/wikipedia/wikipedia.py
index f8038714a5..178bf7b0ce 100644
--- a/api/core/tools/provider/builtin/wikipedia/wikipedia.py
+++ b/api/core/tools/provider/builtin/wikipedia/wikipedia.py
@@ -11,11 +11,10 @@ class WikiPediaProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "query": "misaka mikoto",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/wolframalpha/tools/wolframalpha.py b/api/core/tools/provider/builtin/wolframalpha/tools/wolframalpha.py
index 8cb9c10ddf..9dc5bed824 100644
--- a/api/core/tools/provider/builtin/wolframalpha/tools/wolframalpha.py
+++ b/api/core/tools/provider/builtin/wolframalpha/tools/wolframalpha.py
@@ -8,29 +8,24 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class WolframAlphaTool(BuiltinTool):
-    _base_url = 'https://api.wolframalpha.com/v2/query'
+    _base_url = "https://api.wolframalpha.com/v2/query"
 
-    def _invoke(self, 
-                user_id: str, 
-               tool_parameters: dict[str, Any], 
-        ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        query = tool_parameters.get('query', '')
+        query = tool_parameters.get("query", "")
         if not query:
-            return self.create_text_message('Please input query')
-        appid = self.runtime.credentials.get('appid', '')
+            return self.create_text_message("Please input query")
+        appid = self.runtime.credentials.get("appid", "")
         if not appid:
-            raise ToolProviderCredentialValidationError('Please input appid')
-        
-        params = {
-            'appid': appid,
-            'input': query,
-            'includepodid': 'Result',
-            'format': 'plaintext',
-            'output': 'json'
-        }
+            raise ToolProviderCredentialValidationError("Please input appid")
+
+        params = {"appid": appid, "input": query, "includepodid": "Result", "format": "plaintext", "output": "json"}
 
         finished = False
         result = None
@@ -45,34 +40,33 @@ class WolframAlphaTool(BuiltinTool):
                 response_data = response.json()
             except Exception as e:
                 raise ToolInvokeError(str(e))
-            
-            if 'success' not in response_data['queryresult'] or response_data['queryresult']['success'] != True:
-                query_result = response_data.get('queryresult', {})
-                if query_result.get('error'):
-                    if 'msg' in query_result['error']:
-                        if query_result['error']['msg'] == 'Invalid appid':
-                            raise ToolProviderCredentialValidationError('Invalid appid')
-                raise ToolInvokeError('Failed to invoke tool')
-                    
-            if 'didyoumeans' in response_data['queryresult']:
-                # get the most likely interpretation
-                query = ''
-                max_score = 0
-                for didyoumean in response_data['queryresult']['didyoumeans']:
-                    if float(didyoumean['score']) > max_score:
-                        query = didyoumean['val']
-                        max_score = float(didyoumean['score'])
 
-                params['input'] = query
+            if "success" not in response_data["queryresult"] or response_data["queryresult"]["success"] != True:
+                query_result = response_data.get("queryresult", {})
+                if query_result.get("error"):
+                    if "msg" in query_result["error"]:
+                        if query_result["error"]["msg"] == "Invalid appid":
+                            raise ToolProviderCredentialValidationError("Invalid appid")
+                raise ToolInvokeError("Failed to invoke tool")
+
+            if "didyoumeans" in response_data["queryresult"]:
+                # get the most likely interpretation
+                query = ""
+                max_score = 0
+                for didyoumean in response_data["queryresult"]["didyoumeans"]:
+                    if float(didyoumean["score"]) > max_score:
+                        query = didyoumean["val"]
+                        max_score = float(didyoumean["score"])
+
+                params["input"] = query
             else:
                 finished = True
-                if 'souces' in response_data['queryresult']:
-                    return self.create_link_message(response_data['queryresult']['sources']['url'])
-                elif 'pods' in response_data['queryresult']:
-                    result = response_data['queryresult']['pods'][0]['subpods'][0]['plaintext']
+                if "souces" in response_data["queryresult"]:
+                    return self.create_link_message(response_data["queryresult"]["sources"]["url"])
+                elif "pods" in response_data["queryresult"]:
+                    result = response_data["queryresult"]["pods"][0]["subpods"][0]["plaintext"]
 
         if not finished or not result:
-            return self.create_text_message('No result found')
+            return self.create_text_message("No result found")
 
         return self.create_text_message(result)
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/wolframalpha/wolframalpha.py b/api/core/tools/provider/builtin/wolframalpha/wolframalpha.py
index ef1aac7ff2..7be288b538 100644
--- a/api/core/tools/provider/builtin/wolframalpha/wolframalpha.py
+++ b/api/core/tools/provider/builtin/wolframalpha/wolframalpha.py
@@ -13,11 +13,10 @@ class GoogleProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "query": "1+2+....+111",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/yahoo/tools/analytics.py b/api/core/tools/provider/builtin/yahoo/tools/analytics.py
index cf511ea894..f044fbe540 100644
--- a/api/core/tools/provider/builtin/yahoo/tools/analytics.py
+++ b/api/core/tools/provider/builtin/yahoo/tools/analytics.py
@@ -10,27 +10,28 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class YahooFinanceAnalyticsTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) \
-          -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        symbol = tool_parameters.get('symbol', '')
+        symbol = tool_parameters.get("symbol", "")
         if not symbol:
-            return self.create_text_message('Please input symbol')
-        
+            return self.create_text_message("Please input symbol")
+
         time_range = [None, None]
-        start_date = tool_parameters.get('start_date', '')
+        start_date = tool_parameters.get("start_date", "")
         if start_date:
             time_range[0] = start_date
         else:
-            time_range[0] = '1800-01-01'
+            time_range[0] = "1800-01-01"
 
-        end_date = tool_parameters.get('end_date', '')
+        end_date = tool_parameters.get("end_date", "")
         if end_date:
             time_range[1] = end_date
         else:
-            time_range[1] = datetime.now().strftime('%Y-%m-%d')
+            time_range[1] = datetime.now().strftime("%Y-%m-%d")
 
         stock_data = download(symbol, start=time_range[0], end=time_range[1])
         max_segments = min(15, len(stock_data))
@@ -41,30 +42,29 @@ class YahooFinanceAnalyticsTool(BuiltinTool):
             end_idx = (i + 1) * rows_per_segment if i < max_segments - 1 else len(stock_data)
             segment_data = stock_data.iloc[start_idx:end_idx]
             segment_summary = {
-                'Start Date': segment_data.index[0],
-                'End Date': segment_data.index[-1],
-                'Average Close': segment_data['Close'].mean(),
-                'Average Volume': segment_data['Volume'].mean(),
-                'Average Open': segment_data['Open'].mean(),
-                'Average High': segment_data['High'].mean(),
-                'Average Low': segment_data['Low'].mean(),
-                'Average Adj Close': segment_data['Adj Close'].mean(),
-                'Max Close': segment_data['Close'].max(),
-                'Min Close': segment_data['Close'].min(),
-                'Max Volume': segment_data['Volume'].max(),
-                'Min Volume': segment_data['Volume'].min(),
-                'Max Open': segment_data['Open'].max(),
-                'Min Open': segment_data['Open'].min(),
-                'Max High': segment_data['High'].max(),
-                'Min High': segment_data['High'].min(),
+                "Start Date": segment_data.index[0],
+                "End Date": segment_data.index[-1],
+                "Average Close": segment_data["Close"].mean(),
+                "Average Volume": segment_data["Volume"].mean(),
+                "Average Open": segment_data["Open"].mean(),
+                "Average High": segment_data["High"].mean(),
+                "Average Low": segment_data["Low"].mean(),
+                "Average Adj Close": segment_data["Adj Close"].mean(),
+                "Max Close": segment_data["Close"].max(),
+                "Min Close": segment_data["Close"].min(),
+                "Max Volume": segment_data["Volume"].max(),
+                "Min Volume": segment_data["Volume"].min(),
+                "Max Open": segment_data["Open"].max(),
+                "Min Open": segment_data["Open"].min(),
+                "Max High": segment_data["High"].max(),
+                "Min High": segment_data["High"].min(),
             }
-            
+
             summary_data.append(segment_summary)
 
         summary_df = pd.DataFrame(summary_data)
-        
+
         try:
             return self.create_text_message(str(summary_df.to_dict()))
         except (HTTPError, ReadTimeout):
-            return self.create_text_message('There is a internet connection problem. Please try again later.')
-    
\ No newline at end of file
+            return self.create_text_message("There is a internet connection problem. Please try again later.")
diff --git a/api/core/tools/provider/builtin/yahoo/tools/news.py b/api/core/tools/provider/builtin/yahoo/tools/news.py
index 4f2922ef3e..ff820430f9 100644
--- a/api/core/tools/provider/builtin/yahoo/tools/news.py
+++ b/api/core/tools/provider/builtin/yahoo/tools/news.py
@@ -8,40 +8,39 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class YahooFinanceSearchTickerTool(BuiltinTool):
-    def _invoke(self,user_id: str, tool_parameters: dict[str, Any]) \
-          -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        '''
-            invoke tools
-        '''
-        
-        query = tool_parameters.get('symbol', '')
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        invoke tools
+        """
+
+        query = tool_parameters.get("symbol", "")
         if not query:
-            return self.create_text_message('Please input symbol')
-        
+            return self.create_text_message("Please input symbol")
+
         try:
             return self.run(ticker=query, user_id=user_id)
         except (HTTPError, ReadTimeout):
-            return self.create_text_message('There is a internet connection problem. Please try again later.')
+            return self.create_text_message("There is a internet connection problem. Please try again later.")
 
     def run(self, ticker: str, user_id: str) -> ToolInvokeMessage:
         company = yfinance.Ticker(ticker)
         try:
             if company.isin is None:
-                return self.create_text_message(f'Company ticker {ticker} not found.')
+                return self.create_text_message(f"Company ticker {ticker} not found.")
         except (HTTPError, ReadTimeout, ConnectionError):
-            return self.create_text_message(f'Company ticker {ticker} not found.')
+            return self.create_text_message(f"Company ticker {ticker} not found.")
 
         links = []
         try:
-            links = [n['link'] for n in company.news if n['type'] == 'STORY']
+            links = [n["link"] for n in company.news if n["type"] == "STORY"]
         except (HTTPError, ReadTimeout, ConnectionError):
             if not links:
-                return self.create_text_message(f'There is nothing about {ticker} ticker')
+                return self.create_text_message(f"There is nothing about {ticker} ticker")
         if not links:
-            return self.create_text_message(f'No news found for company that searched with {ticker} ticker.')
-        
-        result = '\n\n'.join([
-            self.get_url(link) for link in links
-        ])
+            return self.create_text_message(f"No news found for company that searched with {ticker} ticker.")
+
+        result = "\n\n".join([self.get_url(link) for link in links])
 
         return self.create_text_message(self.summary(user_id=user_id, content=result))
diff --git a/api/core/tools/provider/builtin/yahoo/tools/ticker.py b/api/core/tools/provider/builtin/yahoo/tools/ticker.py
index 262fff3b25..dfc7e46047 100644
--- a/api/core/tools/provider/builtin/yahoo/tools/ticker.py
+++ b/api/core/tools/provider/builtin/yahoo/tools/ticker.py
@@ -8,19 +8,20 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class YahooFinanceSearchTickerTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) \
-          -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        query = tool_parameters.get('symbol', '')
+        query = tool_parameters.get("symbol", "")
         if not query:
-            return self.create_text_message('Please input symbol')
-        
+            return self.create_text_message("Please input symbol")
+
         try:
             return self.create_text_message(self.run(ticker=query))
         except (HTTPError, ReadTimeout):
-            return self.create_text_message('There is a internet connection problem. Please try again later.')
-    
+            return self.create_text_message("There is a internet connection problem. Please try again later.")
+
     def run(self, ticker: str) -> str:
-        return str(Ticker(ticker).info)
\ No newline at end of file
+        return str(Ticker(ticker).info)
diff --git a/api/core/tools/provider/builtin/yahoo/yahoo.py b/api/core/tools/provider/builtin/yahoo/yahoo.py
index 96dbc6c3d0..8d82084e76 100644
--- a/api/core/tools/provider/builtin/yahoo/yahoo.py
+++ b/api/core/tools/provider/builtin/yahoo/yahoo.py
@@ -11,11 +11,10 @@ class YahooFinanceProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "ticker": "MSFT",
                 },
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/youtube/tools/videos.py b/api/core/tools/provider/builtin/youtube/tools/videos.py
index 7a9b9fce4a..95dec2eac9 100644
--- a/api/core/tools/provider/builtin/youtube/tools/videos.py
+++ b/api/core/tools/provider/builtin/youtube/tools/videos.py
@@ -8,60 +8,67 @@ from core.tools.tool.builtin_tool import BuiltinTool
 
 
 class YoutubeVideosAnalyticsTool(BuiltinTool):
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) \
-          -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke tools
+        invoke tools
         """
-        channel = tool_parameters.get('channel', '')
+        channel = tool_parameters.get("channel", "")
         if not channel:
-            return self.create_text_message('Please input symbol')
-        
+            return self.create_text_message("Please input symbol")
+
         time_range = [None, None]
-        start_date = tool_parameters.get('start_date', '')
+        start_date = tool_parameters.get("start_date", "")
         if start_date:
             time_range[0] = start_date
         else:
-            time_range[0] = '1800-01-01'
+            time_range[0] = "1800-01-01"
 
-        end_date = tool_parameters.get('end_date', '')
+        end_date = tool_parameters.get("end_date", "")
         if end_date:
             time_range[1] = end_date
         else:
-            time_range[1] = datetime.now().strftime('%Y-%m-%d')
+            time_range[1] = datetime.now().strftime("%Y-%m-%d")
 
-        if 'google_api_key' not in self.runtime.credentials or not self.runtime.credentials['google_api_key']:
-            return self.create_text_message('Please input api key')
+        if "google_api_key" not in self.runtime.credentials or not self.runtime.credentials["google_api_key"]:
+            return self.create_text_message("Please input api key")
 
-        youtube = build('youtube', 'v3', developerKey=self.runtime.credentials['google_api_key'])
+        youtube = build("youtube", "v3", developerKey=self.runtime.credentials["google_api_key"])
 
         # try to get channel id
-        search_results = youtube.search().list(q=channel, type='channel', order='relevance', part='id').execute()
-        channel_id = search_results['items'][0]['id']['channelId']
+        search_results = youtube.search().list(q=channel, type="channel", order="relevance", part="id").execute()
+        channel_id = search_results["items"][0]["id"]["channelId"]
 
         start_date, end_date = time_range
 
-        start_date = datetime.strptime(start_date, '%Y-%m-%d').strftime('%Y-%m-%dT%H:%M:%SZ')
-        end_date = datetime.strptime(end_date, '%Y-%m-%d').strftime('%Y-%m-%dT%H:%M:%SZ')
+        start_date = datetime.strptime(start_date, "%Y-%m-%d").strftime("%Y-%m-%dT%H:%M:%SZ")
+        end_date = datetime.strptime(end_date, "%Y-%m-%d").strftime("%Y-%m-%dT%H:%M:%SZ")
 
         # get videos
-        time_range_videos = youtube.search().list(
-            part='snippet', channelId=channel_id, order='date', type='video', 
-            publishedAfter=start_date, 
-            publishedBefore=end_date
-        ).execute()
+        time_range_videos = (
+            youtube.search()
+            .list(
+                part="snippet",
+                channelId=channel_id,
+                order="date",
+                type="video",
+                publishedAfter=start_date,
+                publishedBefore=end_date,
+            )
+            .execute()
+        )
 
         def extract_video_data(video_list):
             data = []
-            for video in video_list['items']:
-                video_id = video['id']['videoId']
-                video_info = youtube.videos().list(part='snippet,statistics', id=video_id).execute()
-                title = video_info['items'][0]['snippet']['title']
-                views = video_info['items'][0]['statistics']['viewCount']
-                data.append({'Title': title, 'Views': views})
+            for video in video_list["items"]:
+                video_id = video["id"]["videoId"]
+                video_info = youtube.videos().list(part="snippet,statistics", id=video_id).execute()
+                title = video_info["items"][0]["snippet"]["title"]
+                views = video_info["items"][0]["statistics"]["viewCount"]
+                data.append({"Title": title, "Views": views})
             return data
 
         summary = extract_video_data(time_range_videos)
-        
+
         return self.create_text_message(str(summary))
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin/youtube/youtube.py b/api/core/tools/provider/builtin/youtube/youtube.py
index 83a4fccb32..aad876491c 100644
--- a/api/core/tools/provider/builtin/youtube/youtube.py
+++ b/api/core/tools/provider/builtin/youtube/youtube.py
@@ -11,7 +11,7 @@ class YahooFinanceProvider(BuiltinToolProviderController):
                     "credentials": credentials,
                 }
             ).invoke(
-                user_id='',
+                user_id="",
                 tool_parameters={
                     "channel": "TOKYO GIRLS COLLECTION",
                     "start_date": "2020-01-01",
@@ -20,4 +20,3 @@ class YahooFinanceProvider(BuiltinToolProviderController):
             )
         except Exception as e:
             raise ToolProviderCredentialValidationError(str(e))
-        
\ No newline at end of file
diff --git a/api/core/tools/provider/builtin_tool_provider.py b/api/core/tools/provider/builtin_tool_provider.py
index bcf41c90ed..ff022812ef 100644
--- a/api/core/tools/provider/builtin_tool_provider.py
+++ b/api/core/tools/provider/builtin_tool_provider.py
@@ -19,37 +19,39 @@ from core.tools.utils.yaml_utils import load_yaml_file
 
 class BuiltinToolProviderController(ToolProviderController):
     def __init__(self, **data: Any) -> None:
-        if self.provider_type == ToolProviderType.API or self.provider_type == ToolProviderType.APP:
+        if self.provider_type in {ToolProviderType.API, ToolProviderType.APP}:
             super().__init__(**data)
             return
-        
+
         # load provider yaml
-        provider = self.__class__.__module__.split('.')[-1]
-        yaml_path = path.join(path.dirname(path.realpath(__file__)), 'builtin', provider, f'{provider}.yaml')
+        provider = self.__class__.__module__.split(".")[-1]
+        yaml_path = path.join(path.dirname(path.realpath(__file__)), "builtin", provider, f"{provider}.yaml")
         try:
             provider_yaml = load_yaml_file(yaml_path, ignore_error=False)
         except Exception as e:
-            raise ToolProviderNotFoundError(f'can not load provider yaml for {provider}: {e}')
+            raise ToolProviderNotFoundError(f"can not load provider yaml for {provider}: {e}")
 
-        if 'credentials_for_provider' in provider_yaml and provider_yaml['credentials_for_provider'] is not None:
+        if "credentials_for_provider" in provider_yaml and provider_yaml["credentials_for_provider"] is not None:
             # set credentials name
-            for credential_name in provider_yaml['credentials_for_provider']:
-                provider_yaml['credentials_for_provider'][credential_name]['name'] = credential_name
+            for credential_name in provider_yaml["credentials_for_provider"]:
+                provider_yaml["credentials_for_provider"][credential_name]["name"] = credential_name
 
-        super().__init__(**{
-            'identity': provider_yaml['identity'],
-            'credentials_schema': provider_yaml.get('credentials_for_provider', None),
-        })
+        super().__init__(
+            **{
+                "identity": provider_yaml["identity"],
+                "credentials_schema": provider_yaml.get("credentials_for_provider", None),
+            }
+        )
 
     def _get_builtin_tools(self) -> list[Tool]:
         """
-            returns a list of tools that the provider can provide
+        returns a list of tools that the provider can provide
 
-            :return: list of tools
+        :return: list of tools
         """
         if self.tools:
             return self.tools
-        
+
         provider = self.identity.name
         tool_path = path.join(path.dirname(path.realpath(__file__)), "builtin", provider, "tools")
         # get all the yaml files in the tool path
@@ -62,155 +64,161 @@ class BuiltinToolProviderController(ToolProviderController):
 
             # get tool class, import the module
             assistant_tool_class = load_single_subclass_from_source(
-                module_name=f'core.tools.provider.builtin.{provider}.tools.{tool_name}',
-                script_path=path.join(path.dirname(path.realpath(__file__)),
-                                       'builtin', provider, 'tools', f'{tool_name}.py'),
-                parent_type=BuiltinTool)
+                module_name=f"core.tools.provider.builtin.{provider}.tools.{tool_name}",
+                script_path=path.join(
+                    path.dirname(path.realpath(__file__)), "builtin", provider, "tools", f"{tool_name}.py"
+                ),
+                parent_type=BuiltinTool,
+            )
             tool["identity"]["provider"] = provider
             tools.append(assistant_tool_class(**tool))
 
         self.tools = tools
         return tools
-    
+
     def get_credentials_schema(self) -> dict[str, ToolProviderCredentials]:
         """
-            returns the credentials schema of the provider
+        returns the credentials schema of the provider
 
-            :return: the credentials schema
+        :return: the credentials schema
         """
         if not self.credentials_schema:
             return {}
-        
+
         return self.credentials_schema.copy()
 
     def get_tools(self) -> list[Tool]:
         """
-            returns a list of tools that the provider can provide
+        returns a list of tools that the provider can provide
 
-            :return: list of tools
+        :return: list of tools
         """
         return self._get_builtin_tools()
-    
+
     def get_tool(self, tool_name: str) -> Tool:
         """
-            returns the tool that the provider can provide
+        returns the tool that the provider can provide
         """
         return next(filter(lambda x: x.identity.name == tool_name, self.get_tools()), None)
 
     def get_parameters(self, tool_name: str) -> list[ToolParameter]:
         """
-            returns the parameters of the tool
+        returns the parameters of the tool
 
-            :param tool_name: the name of the tool, defined in `get_tools`
-            :return: list of parameters
+        :param tool_name: the name of the tool, defined in `get_tools`
+        :return: list of parameters
         """
         tool = next(filter(lambda x: x.identity.name == tool_name, self.get_tools()), None)
         if tool is None:
-            raise ToolNotFoundError(f'tool {tool_name} not found')
+            raise ToolNotFoundError(f"tool {tool_name} not found")
         return tool.parameters
 
     @property
     def need_credentials(self) -> bool:
         """
-            returns whether the provider needs credentials
+        returns whether the provider needs credentials
 
-            :return: whether the provider needs credentials
+        :return: whether the provider needs credentials
         """
-        return self.credentials_schema is not None and \
-            len(self.credentials_schema) != 0
+        return self.credentials_schema is not None and len(self.credentials_schema) != 0
 
     @property
     def provider_type(self) -> ToolProviderType:
         """
-            returns the type of the provider
+        returns the type of the provider
 
-            :return: type of the provider
+        :return: type of the provider
         """
         return ToolProviderType.BUILT_IN
 
     @property
     def tool_labels(self) -> list[str]:
         """
-            returns the labels of the provider
+        returns the labels of the provider
 
-            :return: labels of the provider
+        :return: labels of the provider
         """
         label_enums = self._get_tool_labels()
         return [default_tool_label_dict[label].name for label in label_enums]
 
     def _get_tool_labels(self) -> list[ToolLabelEnum]:
         """
-            returns the labels of the provider
+        returns the labels of the provider
         """
         return self.identity.tags or []
 
     def validate_parameters(self, tool_id: int, tool_name: str, tool_parameters: dict[str, Any]) -> None:
         """
-            validate the parameters of the tool and set the default value if needed
+        validate the parameters of the tool and set the default value if needed
 
-            :param tool_name: the name of the tool, defined in `get_tools`
-            :param tool_parameters: the parameters of the tool
+        :param tool_name: the name of the tool, defined in `get_tools`
+        :param tool_parameters: the parameters of the tool
         """
         tool_parameters_schema = self.get_parameters(tool_name)
-        
+
         tool_parameters_need_to_validate: dict[str, ToolParameter] = {}
         for parameter in tool_parameters_schema:
             tool_parameters_need_to_validate[parameter.name] = parameter
 
         for parameter in tool_parameters:
             if parameter not in tool_parameters_need_to_validate:
-                raise ToolParameterValidationError(f'parameter {parameter} not found in tool {tool_name}')
-            
+                raise ToolParameterValidationError(f"parameter {parameter} not found in tool {tool_name}")
+
             # check type
             parameter_schema = tool_parameters_need_to_validate[parameter]
             if parameter_schema.type == ToolParameter.ToolParameterType.STRING:
                 if not isinstance(tool_parameters[parameter], str):
-                    raise ToolParameterValidationError(f'parameter {parameter} should be string')
-            
+                    raise ToolParameterValidationError(f"parameter {parameter} should be string")
+
             elif parameter_schema.type == ToolParameter.ToolParameterType.NUMBER:
                 if not isinstance(tool_parameters[parameter], int | float):
-                    raise ToolParameterValidationError(f'parameter {parameter} should be number')
-                
+                    raise ToolParameterValidationError(f"parameter {parameter} should be number")
+
                 if parameter_schema.min is not None and tool_parameters[parameter] < parameter_schema.min:
-                    raise ToolParameterValidationError(f'parameter {parameter} should be greater than {parameter_schema.min}')
-                
+                    raise ToolParameterValidationError(
+                        f"parameter {parameter} should be greater than {parameter_schema.min}"
+                    )
+
                 if parameter_schema.max is not None and tool_parameters[parameter] > parameter_schema.max:
-                    raise ToolParameterValidationError(f'parameter {parameter} should be less than {parameter_schema.max}')
-                
+                    raise ToolParameterValidationError(
+                        f"parameter {parameter} should be less than {parameter_schema.max}"
+                    )
+
             elif parameter_schema.type == ToolParameter.ToolParameterType.BOOLEAN:
                 if not isinstance(tool_parameters[parameter], bool):
-                    raise ToolParameterValidationError(f'parameter {parameter} should be boolean')
-                
+                    raise ToolParameterValidationError(f"parameter {parameter} should be boolean")
+
             elif parameter_schema.type == ToolParameter.ToolParameterType.SELECT:
                 if not isinstance(tool_parameters[parameter], str):
-                    raise ToolParameterValidationError(f'parameter {parameter} should be string')
-                
+                    raise ToolParameterValidationError(f"parameter {parameter} should be string")
+
                 options = parameter_schema.options
                 if not isinstance(options, list):
-                    raise ToolParameterValidationError(f'parameter {parameter} options should be list')
-                
+                    raise ToolParameterValidationError(f"parameter {parameter} options should be list")
+
                 if tool_parameters[parameter] not in [x.value for x in options]:
-                    raise ToolParameterValidationError(f'parameter {parameter} should be one of {options}')
-                
+                    raise ToolParameterValidationError(f"parameter {parameter} should be one of {options}")
+
             tool_parameters_need_to_validate.pop(parameter)
 
         for parameter in tool_parameters_need_to_validate:
             parameter_schema = tool_parameters_need_to_validate[parameter]
             if parameter_schema.required:
-                raise ToolParameterValidationError(f'parameter {parameter} is required')
-            
+                raise ToolParameterValidationError(f"parameter {parameter} is required")
+
             # the parameter is not set currently, set the default value if needed
             if parameter_schema.default is not None:
-                default_value = ToolParameterConverter.cast_parameter_by_type(parameter_schema.default,
-                                                                              parameter_schema.type)
+                default_value = ToolParameterConverter.cast_parameter_by_type(
+                    parameter_schema.default, parameter_schema.type
+                )
                 tool_parameters[parameter] = default_value
-    
+
     def validate_credentials(self, credentials: dict[str, Any]) -> None:
         """
-            validate the credentials of the provider
+        validate the credentials of the provider
 
-            :param tool_name: the name of the tool, defined in `get_tools`
-            :param credentials: the credentials of the tool
+        :param tool_name: the name of the tool, defined in `get_tools`
+        :param credentials: the credentials of the tool
         """
         # validate credentials format
         self.validate_credentials_format(credentials)
@@ -221,9 +229,9 @@ class BuiltinToolProviderController(ToolProviderController):
     @abstractmethod
     def _validate_credentials(self, credentials: dict[str, Any]) -> None:
         """
-            validate the credentials of the provider
+        validate the credentials of the provider
 
-            :param tool_name: the name of the tool, defined in `get_tools`
-            :param credentials: the credentials of the tool
+        :param tool_name: the name of the tool, defined in `get_tools`
+        :param credentials: the credentials of the tool
         """
         pass
diff --git a/api/core/tools/provider/tool_provider.py b/api/core/tools/provider/tool_provider.py
index ef1ace9c7c..05c88b904e 100644
--- a/api/core/tools/provider/tool_provider.py
+++ b/api/core/tools/provider/tool_provider.py
@@ -21,162 +21,174 @@ class ToolProviderController(BaseModel, ABC):
 
     def get_credentials_schema(self) -> dict[str, ToolProviderCredentials]:
         """
-            returns the credentials schema of the provider
+        returns the credentials schema of the provider
 
-            :return: the credentials schema
+        :return: the credentials schema
         """
         return self.credentials_schema.copy()
-    
+
     @abstractmethod
     def get_tools(self) -> list[Tool]:
         """
-            returns a list of tools that the provider can provide
+        returns a list of tools that the provider can provide
 
-            :return: list of tools
+        :return: list of tools
         """
         pass
 
     @abstractmethod
     def get_tool(self, tool_name: str) -> Tool:
         """
-            returns a tool that the provider can provide
+        returns a tool that the provider can provide
 
-            :return: tool
+        :return: tool
         """
         pass
 
     def get_parameters(self, tool_name: str) -> list[ToolParameter]:
         """
-            returns the parameters of the tool
+        returns the parameters of the tool
 
-            :param tool_name: the name of the tool, defined in `get_tools`
-            :return: list of parameters
+        :param tool_name: the name of the tool, defined in `get_tools`
+        :return: list of parameters
         """
         tool = next(filter(lambda x: x.identity.name == tool_name, self.get_tools()), None)
         if tool is None:
-            raise ToolNotFoundError(f'tool {tool_name} not found')
+            raise ToolNotFoundError(f"tool {tool_name} not found")
         return tool.parameters
 
     @property
     def provider_type(self) -> ToolProviderType:
         """
-            returns the type of the provider
+        returns the type of the provider
 
-            :return: type of the provider
+        :return: type of the provider
         """
         return ToolProviderType.BUILT_IN
 
     def validate_parameters(self, tool_id: int, tool_name: str, tool_parameters: dict[str, Any]) -> None:
         """
-            validate the parameters of the tool and set the default value if needed
+        validate the parameters of the tool and set the default value if needed
 
-            :param tool_name: the name of the tool, defined in `get_tools`
-            :param tool_parameters: the parameters of the tool
+        :param tool_name: the name of the tool, defined in `get_tools`
+        :param tool_parameters: the parameters of the tool
         """
         tool_parameters_schema = self.get_parameters(tool_name)
-        
+
         tool_parameters_need_to_validate: dict[str, ToolParameter] = {}
         for parameter in tool_parameters_schema:
             tool_parameters_need_to_validate[parameter.name] = parameter
 
         for parameter in tool_parameters:
             if parameter not in tool_parameters_need_to_validate:
-                raise ToolParameterValidationError(f'parameter {parameter} not found in tool {tool_name}')
-            
+                raise ToolParameterValidationError(f"parameter {parameter} not found in tool {tool_name}")
+
             # check type
             parameter_schema = tool_parameters_need_to_validate[parameter]
             if parameter_schema.type == ToolParameter.ToolParameterType.STRING:
                 if not isinstance(tool_parameters[parameter], str):
-                    raise ToolParameterValidationError(f'parameter {parameter} should be string')
-            
+                    raise ToolParameterValidationError(f"parameter {parameter} should be string")
+
             elif parameter_schema.type == ToolParameter.ToolParameterType.NUMBER:
                 if not isinstance(tool_parameters[parameter], int | float):
-                    raise ToolParameterValidationError(f'parameter {parameter} should be number')
-                
+                    raise ToolParameterValidationError(f"parameter {parameter} should be number")
+
                 if parameter_schema.min is not None and tool_parameters[parameter] < parameter_schema.min:
-                    raise ToolParameterValidationError(f'parameter {parameter} should be greater than {parameter_schema.min}')
-                
+                    raise ToolParameterValidationError(
+                        f"parameter {parameter} should be greater than {parameter_schema.min}"
+                    )
+
                 if parameter_schema.max is not None and tool_parameters[parameter] > parameter_schema.max:
-                    raise ToolParameterValidationError(f'parameter {parameter} should be less than {parameter_schema.max}')
-                
+                    raise ToolParameterValidationError(
+                        f"parameter {parameter} should be less than {parameter_schema.max}"
+                    )
+
             elif parameter_schema.type == ToolParameter.ToolParameterType.BOOLEAN:
                 if not isinstance(tool_parameters[parameter], bool):
-                    raise ToolParameterValidationError(f'parameter {parameter} should be boolean')
-                
+                    raise ToolParameterValidationError(f"parameter {parameter} should be boolean")
+
             elif parameter_schema.type == ToolParameter.ToolParameterType.SELECT:
                 if not isinstance(tool_parameters[parameter], str):
-                    raise ToolParameterValidationError(f'parameter {parameter} should be string')
-                
+                    raise ToolParameterValidationError(f"parameter {parameter} should be string")
+
                 options = parameter_schema.options
                 if not isinstance(options, list):
-                    raise ToolParameterValidationError(f'parameter {parameter} options should be list')
-                
+                    raise ToolParameterValidationError(f"parameter {parameter} options should be list")
+
                 if tool_parameters[parameter] not in [x.value for x in options]:
-                    raise ToolParameterValidationError(f'parameter {parameter} should be one of {options}')
-                
+                    raise ToolParameterValidationError(f"parameter {parameter} should be one of {options}")
+
             tool_parameters_need_to_validate.pop(parameter)
 
         for parameter in tool_parameters_need_to_validate:
             parameter_schema = tool_parameters_need_to_validate[parameter]
             if parameter_schema.required:
-                raise ToolParameterValidationError(f'parameter {parameter} is required')
-            
+                raise ToolParameterValidationError(f"parameter {parameter} is required")
+
             # the parameter is not set currently, set the default value if needed
             if parameter_schema.default is not None:
-                tool_parameters[parameter] = ToolParameterConverter.cast_parameter_by_type(parameter_schema.default,
-                                                                                           parameter_schema.type)
+                tool_parameters[parameter] = ToolParameterConverter.cast_parameter_by_type(
+                    parameter_schema.default, parameter_schema.type
+                )
 
     def validate_credentials_format(self, credentials: dict[str, Any]) -> None:
         """
-            validate the format of the credentials of the provider and set the default value if needed
+        validate the format of the credentials of the provider and set the default value if needed
 
-            :param credentials: the credentials of the tool
+        :param credentials: the credentials of the tool
         """
         credentials_schema = self.credentials_schema
         if credentials_schema is None:
             return
-        
+
         credentials_need_to_validate: dict[str, ToolProviderCredentials] = {}
         for credential_name in credentials_schema:
             credentials_need_to_validate[credential_name] = credentials_schema[credential_name]
 
         for credential_name in credentials:
             if credential_name not in credentials_need_to_validate:
-                raise ToolProviderCredentialValidationError(f'credential {credential_name} not found in provider {self.identity.name}')
-            
+                raise ToolProviderCredentialValidationError(
+                    f"credential {credential_name} not found in provider {self.identity.name}"
+                )
+
             # check type
             credential_schema = credentials_need_to_validate[credential_name]
-            if credential_schema == ToolProviderCredentials.CredentialsType.SECRET_INPUT or \
-                credential_schema == ToolProviderCredentials.CredentialsType.TEXT_INPUT:
+            if credential_schema.type in {
+                ToolProviderCredentials.CredentialsType.SECRET_INPUT,
+                ToolProviderCredentials.CredentialsType.TEXT_INPUT,
+            }:
                 if not isinstance(credentials[credential_name], str):
-                    raise ToolProviderCredentialValidationError(f'credential {credential_name} should be string')
-            
+                    raise ToolProviderCredentialValidationError(f"credential {credential_name} should be string")
+
             elif credential_schema.type == ToolProviderCredentials.CredentialsType.SELECT:
                 if not isinstance(credentials[credential_name], str):
-                    raise ToolProviderCredentialValidationError(f'credential {credential_name} should be string')
-                
+                    raise ToolProviderCredentialValidationError(f"credential {credential_name} should be string")
+
                 options = credential_schema.options
                 if not isinstance(options, list):
-                    raise ToolProviderCredentialValidationError(f'credential {credential_name} options should be list')
-                
+                    raise ToolProviderCredentialValidationError(f"credential {credential_name} options should be list")
+
                 if credentials[credential_name] not in [x.value for x in options]:
-                    raise ToolProviderCredentialValidationError(f'credential {credential_name} should be one of {options}')
-                
+                    raise ToolProviderCredentialValidationError(
+                        f"credential {credential_name} should be one of {options}"
+                    )
+
             credentials_need_to_validate.pop(credential_name)
 
         for credential_name in credentials_need_to_validate:
             credential_schema = credentials_need_to_validate[credential_name]
             if credential_schema.required:
-                raise ToolProviderCredentialValidationError(f'credential {credential_name} is required')
-            
+                raise ToolProviderCredentialValidationError(f"credential {credential_name} is required")
+
             # the credential is not set currently, set the default value if needed
             if credential_schema.default is not None:
                 default_value = credential_schema.default
                 # parse default value into the correct type
-                if credential_schema.type == ToolProviderCredentials.CredentialsType.SECRET_INPUT or \
-                    credential_schema.type == ToolProviderCredentials.CredentialsType.TEXT_INPUT or \
-                    credential_schema.type == ToolProviderCredentials.CredentialsType.SELECT:
+                if credential_schema.type in {
+                    ToolProviderCredentials.CredentialsType.SECRET_INPUT,
+                    ToolProviderCredentials.CredentialsType.TEXT_INPUT,
+                    ToolProviderCredentials.CredentialsType.SELECT,
+                }:
                     default_value = str(default_value)
 
                 credentials[credential_name] = default_value
-    
\ No newline at end of file
diff --git a/api/core/tools/provider/workflow_tool_provider.py b/api/core/tools/provider/workflow_tool_provider.py
index f14abac767..25eaf6a66a 100644
--- a/api/core/tools/provider/workflow_tool_provider.py
+++ b/api/core/tools/provider/workflow_tool_provider.py
@@ -30,29 +30,25 @@ class WorkflowToolProviderController(ToolProviderController):
     provider_id: str
 
     @classmethod
-    def from_db(cls, db_provider: WorkflowToolProvider) -> 'WorkflowToolProviderController':
+    def from_db(cls, db_provider: WorkflowToolProvider) -> "WorkflowToolProviderController":
         app = db_provider.app
 
         if not app:
-            raise ValueError('app not found')
+            raise ValueError("app not found")
 
-        controller = WorkflowToolProviderController(**{
-            'identity': {
-                'author': db_provider.user.name if db_provider.user_id and db_provider.user else '',
-                'name': db_provider.label,
-                'label': {
-                    'en_US': db_provider.label,
-                    'zh_Hans': db_provider.label
+        controller = WorkflowToolProviderController(
+            **{
+                "identity": {
+                    "author": db_provider.user.name if db_provider.user_id and db_provider.user else "",
+                    "name": db_provider.label,
+                    "label": {"en_US": db_provider.label, "zh_Hans": db_provider.label},
+                    "description": {"en_US": db_provider.description, "zh_Hans": db_provider.description},
+                    "icon": db_provider.icon,
                 },
-                'description': {
-                    'en_US': db_provider.description,
-                    'zh_Hans': db_provider.description
-                },
-                'icon': db_provider.icon,
-            },
-            'credentials_schema': {},
-            'provider_id': db_provider.id or '',
-        })
+                "credentials_schema": {},
+                "provider_id": db_provider.id or "",
+            }
+        )
 
         # init tools
 
@@ -66,25 +62,23 @@ class WorkflowToolProviderController(ToolProviderController):
 
     def _get_db_provider_tool(self, db_provider: WorkflowToolProvider, app: App) -> WorkflowTool:
         """
-            get db provider tool
-            :param db_provider: the db provider
-            :param app: the app
-            :return: the tool
+        get db provider tool
+        :param db_provider: the db provider
+        :param app: the app
+        :return: the tool
         """
-        workflow: Workflow = db.session.query(Workflow).filter(
-            Workflow.app_id == db_provider.app_id,
-            Workflow.version == db_provider.version
-        ).first()
+        workflow: Workflow = (
+            db.session.query(Workflow)
+            .filter(Workflow.app_id == db_provider.app_id, Workflow.version == db_provider.version)
+            .first()
+        )
         if not workflow:
-            raise ValueError('workflow not found')
+            raise ValueError("workflow not found")
 
         # fetch start node
         graph: dict = workflow.graph_dict
         features_dict: dict = workflow.features_dict
-        features = WorkflowAppConfigManager.convert_features(
-            config_dict=features_dict,
-            app_mode=AppMode.WORKFLOW
-        )
+        features = WorkflowAppConfigManager.convert_features(config_dict=features_dict, app_mode=AppMode.WORKFLOW)
 
         parameters = db_provider.parameter_configurations
         variables = WorkflowToolConfigurationUtils.get_workflow_graph_variables(graph)
@@ -101,51 +95,34 @@ class WorkflowToolProviderController(ToolProviderController):
                 parameter_type = None
                 options = None
                 if variable.type not in VARIABLE_TO_PARAMETER_TYPE_MAPPING:
-                    raise ValueError(f'unsupported variable type {variable.type}')
+                    raise ValueError(f"unsupported variable type {variable.type}")
                 parameter_type = VARIABLE_TO_PARAMETER_TYPE_MAPPING[variable.type]
 
                 if variable.type == VariableEntityType.SELECT and variable.options:
                     options = [
-                        ToolParameterOption(
-                            value=option,
-                            label=I18nObject(
-                                en_US=option,
-                                zh_Hans=option
-                            )
-                        ) for option in variable.options
+                        ToolParameterOption(value=option, label=I18nObject(en_US=option, zh_Hans=option))
+                        for option in variable.options
                     ]
 
                 workflow_tool_parameters.append(
                     ToolParameter(
                         name=parameter.name,
-                        label=I18nObject(
-                            en_US=variable.label,
-                            zh_Hans=variable.label
-                        ),
-                        human_description=I18nObject(
-                            en_US=parameter.description,
-                            zh_Hans=parameter.description
-                        ),
+                        label=I18nObject(en_US=variable.label, zh_Hans=variable.label),
+                        human_description=I18nObject(en_US=parameter.description, zh_Hans=parameter.description),
                         type=parameter_type,
                         form=parameter.form,
                         llm_description=parameter.description,
                         required=variable.required,
                         options=options,
-                        default=variable.default
+                        default=variable.default,
                     )
                 )
             elif features.file_upload:
                 workflow_tool_parameters.append(
                     ToolParameter(
                         name=parameter.name,
-                        label=I18nObject(
-                            en_US=parameter.name,
-                            zh_Hans=parameter.name
-                        ),
-                        human_description=I18nObject(
-                            en_US=parameter.description,
-                            zh_Hans=parameter.description
-                        ),
+                        label=I18nObject(en_US=parameter.name, zh_Hans=parameter.name),
+                        human_description=I18nObject(en_US=parameter.description, zh_Hans=parameter.description),
                         type=ToolParameter.ToolParameterType.FILE,
                         llm_description=parameter.description,
                         required=False,
@@ -153,53 +130,51 @@ class WorkflowToolProviderController(ToolProviderController):
                     )
                 )
             else:
-                raise ValueError('variable not found')
+                raise ValueError("variable not found")
 
         return WorkflowTool(
             identity=ToolIdentity(
-                author=user.name if user else '',
+                author=user.name if user else "",
                 name=db_provider.name,
-                label=I18nObject(
-                    en_US=db_provider.label,
-                    zh_Hans=db_provider.label
-                ),
+                label=I18nObject(en_US=db_provider.label, zh_Hans=db_provider.label),
                 provider=self.provider_id,
                 icon=db_provider.icon,
             ),
             description=ToolDescription(
-                human=I18nObject(
-                    en_US=db_provider.description,
-                    zh_Hans=db_provider.description
-                ),
+                human=I18nObject(en_US=db_provider.description, zh_Hans=db_provider.description),
                 llm=db_provider.description,
             ),
             parameters=workflow_tool_parameters,
             is_team_authorization=True,
             workflow_app_id=app.id,
             workflow_entities={
-                'app': app,
-                'workflow': workflow,
+                "app": app,
+                "workflow": workflow,
             },
             version=db_provider.version,
             workflow_call_depth=0,
-            label=db_provider.label
+            label=db_provider.label,
         )
 
     def get_tools(self, user_id: str, tenant_id: str) -> list[WorkflowTool]:
         """
-            fetch tools from database
+        fetch tools from database
 
-            :param user_id: the user id
-            :param tenant_id: the tenant id
-            :return: the tools
+        :param user_id: the user id
+        :param tenant_id: the tenant id
+        :return: the tools
         """
         if self.tools is not None:
             return self.tools
 
-        db_providers: WorkflowToolProvider = db.session.query(WorkflowToolProvider).filter(
-            WorkflowToolProvider.tenant_id == tenant_id,
-            WorkflowToolProvider.app_id == self.provider_id,
-        ).first()
+        db_providers: WorkflowToolProvider = (
+            db.session.query(WorkflowToolProvider)
+            .filter(
+                WorkflowToolProvider.tenant_id == tenant_id,
+                WorkflowToolProvider.app_id == self.provider_id,
+            )
+            .first()
+        )
 
         if not db_providers:
             return []
@@ -210,10 +185,10 @@ class WorkflowToolProviderController(ToolProviderController):
 
     def get_tool(self, tool_name: str) -> Optional[WorkflowTool]:
         """
-            get tool by name
+        get tool by name
 
-            :param tool_name: the name of the tool
-            :return: the tool
+        :param tool_name: the name of the tool
+        :return: the tool
         """
         if self.tools is None:
             return None
diff --git a/api/core/tools/tool/api_tool.py b/api/core/tools/tool/api_tool.py
index 38f10032e2..c779d704c3 100644
--- a/api/core/tools/tool/api_tool.py
+++ b/api/core/tools/tool/api_tool.py
@@ -5,15 +5,15 @@ from urllib.parse import urlencode
 
 import httpx
 
-import core.helper.ssrf_proxy as ssrf_proxy
+from core.helper import ssrf_proxy
 from core.tools.entities.tool_bundle import ApiToolBundle
 from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType
 from core.tools.errors import ToolInvokeError, ToolParameterValidationError, ToolProviderCredentialValidationError
 from core.tools.tool.tool import Tool
 
 API_TOOL_DEFAULT_TIMEOUT = (
-    int(getenv('API_TOOL_DEFAULT_CONNECT_TIMEOUT', '10')),
-    int(getenv('API_TOOL_DEFAULT_READ_TIMEOUT', '60'))
+    int(getenv("API_TOOL_DEFAULT_CONNECT_TIMEOUT", "10")),
+    int(getenv("API_TOOL_DEFAULT_READ_TIMEOUT", "60")),
 )
 
 
@@ -24,31 +24,32 @@ class ApiTool(Tool):
     Api tool
     """
 
-    def fork_tool_runtime(self, runtime: dict[str, Any]) -> 'Tool':
+    def fork_tool_runtime(self, runtime: dict[str, Any]) -> "Tool":
         """
-            fork a new tool with meta data
+        fork a new tool with meta data
 
-            :param meta: the meta data of a tool call processing, tenant_id is required
-            :return: the new tool
+        :param meta: the meta data of a tool call processing, tenant_id is required
+        :return: the new tool
         """
         return self.__class__(
             identity=self.identity.model_copy() if self.identity else None,
             parameters=self.parameters.copy() if self.parameters else None,
             description=self.description.model_copy() if self.description else None,
             api_bundle=self.api_bundle.model_copy() if self.api_bundle else None,
-            runtime=Tool.Runtime(**runtime)
+            runtime=Tool.Runtime(**runtime),
         )
 
-    def validate_credentials(self, credentials: dict[str, Any], parameters: dict[str, Any],
-                             format_only: bool = False) -> str:
+    def validate_credentials(
+        self, credentials: dict[str, Any], parameters: dict[str, Any], format_only: bool = False
+    ) -> str:
         """
-            validate the credentials for Api tool
+        validate the credentials for Api tool
         """
-        # assemble validate request and request parameters 
+        # assemble validate request and request parameters
         headers = self.assembling_request(parameters)
 
         if format_only:
-            return ''
+            return ""
 
         response = self.do_http_request(self.api_bundle.server_url, self.api_bundle.method, headers, parameters)
         # validate response
@@ -61,30 +62,30 @@ class ApiTool(Tool):
         headers = {}
         credentials = self.runtime.credentials or {}
 
-        if 'auth_type' not in credentials:
-            raise ToolProviderCredentialValidationError('Missing auth_type')
+        if "auth_type" not in credentials:
+            raise ToolProviderCredentialValidationError("Missing auth_type")
 
-        if credentials['auth_type'] == 'api_key':
-            api_key_header = 'api_key'
+        if credentials["auth_type"] == "api_key":
+            api_key_header = "api_key"
 
-            if 'api_key_header' in credentials:
-                api_key_header = credentials['api_key_header']
+            if "api_key_header" in credentials:
+                api_key_header = credentials["api_key_header"]
 
-            if 'api_key_value' not in credentials:
-                raise ToolProviderCredentialValidationError('Missing api_key_value')
-            elif not isinstance(credentials['api_key_value'], str):
-                raise ToolProviderCredentialValidationError('api_key_value must be a string')
+            if "api_key_value" not in credentials:
+                raise ToolProviderCredentialValidationError("Missing api_key_value")
+            elif not isinstance(credentials["api_key_value"], str):
+                raise ToolProviderCredentialValidationError("api_key_value must be a string")
 
-            if 'api_key_header_prefix' in credentials:
-                api_key_header_prefix = credentials['api_key_header_prefix']
-                if api_key_header_prefix == 'basic' and credentials['api_key_value']:
-                    credentials['api_key_value'] = f'Basic {credentials["api_key_value"]}'
-                elif api_key_header_prefix == 'bearer' and credentials['api_key_value']:
-                    credentials['api_key_value'] = f'Bearer {credentials["api_key_value"]}'
-                elif api_key_header_prefix == 'custom':
+            if "api_key_header_prefix" in credentials:
+                api_key_header_prefix = credentials["api_key_header_prefix"]
+                if api_key_header_prefix == "basic" and credentials["api_key_value"]:
+                    credentials["api_key_value"] = f'Basic {credentials["api_key_value"]}'
+                elif api_key_header_prefix == "bearer" and credentials["api_key_value"]:
+                    credentials["api_key_value"] = f'Bearer {credentials["api_key_value"]}'
+                elif api_key_header_prefix == "custom":
                     pass
 
-            headers[api_key_header] = credentials['api_key_value']
+            headers[api_key_header] = credentials["api_key_value"]
 
         needed_parameters = [parameter for parameter in self.api_bundle.parameters if parameter.required]
         for parameter in needed_parameters:
@@ -98,13 +99,13 @@ class ApiTool(Tool):
 
     def validate_and_parse_response(self, response: httpx.Response) -> str:
         """
-            validate the response
+        validate the response
         """
         if isinstance(response, httpx.Response):
             if response.status_code >= 400:
                 raise ToolInvokeError(f"Request failed with status code {response.status_code} and {response.text}")
             if not response.content:
-                return 'Empty response from the tool, please check your parameters and try again.'
+                return "Empty response from the tool, please check your parameters and try again."
             try:
                 response = response.json()
                 try:
@@ -114,21 +115,22 @@ class ApiTool(Tool):
             except Exception as e:
                 return response.text
         else:
-            raise ValueError(f'Invalid response type {type(response)}')
+            raise ValueError(f"Invalid response type {type(response)}")
 
     @staticmethod
     def get_parameter_value(parameter, parameters):
-        if parameter['name'] in parameters:
-            return parameters[parameter['name']]
-        elif parameter.get('required', False):
+        if parameter["name"] in parameters:
+            return parameters[parameter["name"]]
+        elif parameter.get("required", False):
             raise ToolParameterValidationError(f"Missing required parameter {parameter['name']}")
         else:
-            return (parameter.get('schema', {}) or {}).get('default', '')
+            return (parameter.get("schema", {}) or {}).get("default", "")
 
-    def do_http_request(self, url: str, method: str, headers: dict[str, Any],
-                        parameters: dict[str, Any]) -> httpx.Response:
+    def do_http_request(
+        self, url: str, method: str, headers: dict[str, Any], parameters: dict[str, Any]
+    ) -> httpx.Response:
         """
-            do http request depending on api bundle
+        do http request depending on api bundle
         """
         method = method.lower()
 
@@ -138,29 +140,30 @@ class ApiTool(Tool):
         cookies = {}
 
         # check parameters
-        for parameter in self.api_bundle.openapi.get('parameters', []):
+        for parameter in self.api_bundle.openapi.get("parameters", []):
             value = self.get_parameter_value(parameter, parameters)
-            if parameter['in'] == 'path':
-                path_params[parameter['name']] = value
+            if parameter["in"] == "path":
+                path_params[parameter["name"]] = value
 
-            elif parameter['in'] == 'query':
-                if value !='': params[parameter['name']] = value
+            elif parameter["in"] == "query":
+                if value != "":
+                    params[parameter["name"]] = value
 
-            elif parameter['in'] == 'cookie':
-                cookies[parameter['name']] = value
+            elif parameter["in"] == "cookie":
+                cookies[parameter["name"]] = value
 
-            elif parameter['in'] == 'header':
-                headers[parameter['name']] = value
+            elif parameter["in"] == "header":
+                headers[parameter["name"]] = value
 
         # check if there is a request body and handle it
-        if 'requestBody' in self.api_bundle.openapi and self.api_bundle.openapi['requestBody'] is not None:
+        if "requestBody" in self.api_bundle.openapi and self.api_bundle.openapi["requestBody"] is not None:
             # handle json request body
-            if 'content' in self.api_bundle.openapi['requestBody']:
-                for content_type in self.api_bundle.openapi['requestBody']['content']:
-                    headers['Content-Type'] = content_type
-                    body_schema = self.api_bundle.openapi['requestBody']['content'][content_type]['schema']
-                    required = body_schema.get('required', [])
-                    properties = body_schema.get('properties', {})
+            if "content" in self.api_bundle.openapi["requestBody"]:
+                for content_type in self.api_bundle.openapi["requestBody"]["content"]:
+                    headers["Content-Type"] = content_type
+                    body_schema = self.api_bundle.openapi["requestBody"]["content"][content_type]["schema"]
+                    required = body_schema.get("required", [])
+                    properties = body_schema.get("properties", {})
                     for name, property in properties.items():
                         if name in parameters:
                             # convert type
@@ -169,63 +172,71 @@ class ApiTool(Tool):
                             raise ToolParameterValidationError(
                                 f"Missing required parameter {name} in operation {self.api_bundle.operation_id}"
                             )
-                        elif 'default' in property:
-                            body[name] = property['default']
+                        elif "default" in property:
+                            body[name] = property["default"]
                         else:
                             body[name] = None
                     break
 
         # replace path parameters
         for name, value in path_params.items():
-            url = url.replace(f'{{{name}}}', f'{value}')
+            url = url.replace(f"{{{name}}}", f"{value}")
 
         # parse http body data if needed, for GET/HEAD/OPTIONS/TRACE, the body is ignored
-        if 'Content-Type' in headers:
-            if headers['Content-Type'] == 'application/json':
+        if "Content-Type" in headers:
+            if headers["Content-Type"] == "application/json":
                 body = json.dumps(body)
-            elif headers['Content-Type'] == 'application/x-www-form-urlencoded':
+            elif headers["Content-Type"] == "application/x-www-form-urlencoded":
                 body = urlencode(body)
             else:
                 body = body
 
-        if method in ('get', 'head', 'post', 'put', 'delete', 'patch'):
-            response = getattr(ssrf_proxy, method)(url, params=params, headers=headers, cookies=cookies, data=body,
-                                                   timeout=API_TOOL_DEFAULT_TIMEOUT, follow_redirects=True)
+        if method in {"get", "head", "post", "put", "delete", "patch"}:
+            response = getattr(ssrf_proxy, method)(
+                url,
+                params=params,
+                headers=headers,
+                cookies=cookies,
+                data=body,
+                timeout=API_TOOL_DEFAULT_TIMEOUT,
+                follow_redirects=True,
+            )
             return response
         else:
-            raise ValueError(f'Invalid http method {self.method}')
+            raise ValueError(f"Invalid http method {self.method}")
 
-    def _convert_body_property_any_of(self, property: dict[str, Any], value: Any, any_of: list[dict[str, Any]],
-                                      max_recursive=10) -> Any:
+    def _convert_body_property_any_of(
+        self, property: dict[str, Any], value: Any, any_of: list[dict[str, Any]], max_recursive=10
+    ) -> Any:
         if max_recursive <= 0:
             raise Exception("Max recursion depth reached")
         for option in any_of or []:
             try:
-                if 'type' in option:
+                if "type" in option:
                     # Attempt to convert the value based on the type.
-                    if option['type'] == 'integer' or option['type'] == 'int':
+                    if option["type"] == "integer" or option["type"] == "int":
                         return int(value)
-                    elif option['type'] == 'number':
-                        if '.' in str(value):
+                    elif option["type"] == "number":
+                        if "." in str(value):
                             return float(value)
                         else:
                             return int(value)
-                    elif option['type'] == 'string':
+                    elif option["type"] == "string":
                         return str(value)
-                    elif option['type'] == 'boolean':
-                        if str(value).lower() in ['true', '1']:
+                    elif option["type"] == "boolean":
+                        if str(value).lower() in {"true", "1"}:
                             return True
-                        elif str(value).lower() in ['false', '0']:
+                        elif str(value).lower() in {"false", "0"}:
                             return False
                         else:
                             continue  # Not a boolean, try next option
-                    elif option['type'] == 'null' and not value:
+                    elif option["type"] == "null" and not value:
                         return None
                     else:
                         continue  # Unsupported type, try next option
-                elif 'anyOf' in option and isinstance(option['anyOf'], list):
+                elif "anyOf" in option and isinstance(option["anyOf"], list):
                     # Recursive call to handle nested anyOf
-                    return self._convert_body_property_any_of(property, value, option['anyOf'], max_recursive - 1)
+                    return self._convert_body_property_any_of(property, value, option["anyOf"], max_recursive - 1)
             except ValueError:
                 continue  # Conversion failed, try next option
         # If no option succeeded, you might want to return the value as is or raise an error
@@ -233,23 +244,23 @@ class ApiTool(Tool):
 
     def _convert_body_property_type(self, property: dict[str, Any], value: Any) -> Any:
         try:
-            if 'type' in property:
-                if property['type'] == 'integer' or property['type'] == 'int':
+            if "type" in property:
+                if property["type"] == "integer" or property["type"] == "int":
                     return int(value)
-                elif property['type'] == 'number':
+                elif property["type"] == "number":
                     # check if it is a float
-                    if '.' in str(value):
+                    if "." in str(value):
                         return float(value)
                     else:
                         return int(value)
-                elif property['type'] == 'string':
+                elif property["type"] == "string":
                     return str(value)
-                elif property['type'] == 'boolean':
+                elif property["type"] == "boolean":
                     return bool(value)
-                elif property['type'] == 'null':
+                elif property["type"] == "null":
                     if value is None:
                         return None
-                elif property['type'] == 'object' or property['type'] == 'array':
+                elif property["type"] == "object" or property["type"] == "array":
                     if isinstance(value, str):
                         try:
                             # an array str like '[1,2]' also can convert to list [1,2] through json.loads
@@ -264,8 +275,8 @@ class ApiTool(Tool):
                         return value
                 else:
                     raise ValueError(f"Invalid type {property['type']} for property {property}")
-            elif 'anyOf' in property and isinstance(property['anyOf'], list):
-                return self._convert_body_property_any_of(property, value, property['anyOf'])
+            elif "anyOf" in property and isinstance(property["anyOf"], list):
+                return self._convert_body_property_any_of(property, value, property["anyOf"])
         except ValueError as e:
             return value
 
diff --git a/api/core/tools/tool/builtin_tool.py b/api/core/tools/tool/builtin_tool.py
index ad7a88838b..8edaf7c0e6 100644
--- a/api/core/tools/tool/builtin_tool.py
+++ b/api/core/tools/tool/builtin_tool.py
@@ -1,4 +1,3 @@
-
 from core.model_runtime.entities.llm_entities import LLMResult
 from core.model_runtime.entities.message_entities import PromptMessage, SystemPromptMessage, UserPromptMessage
 from core.tools.entities.tool_entities import ToolProviderType
@@ -16,40 +15,38 @@ Please summarize the text you got.
 
 class BuiltinTool(Tool):
     """
-        Builtin tool
+    Builtin tool
 
-        :param meta: the meta data of a tool call processing
+    :param meta: the meta data of a tool call processing
     """
 
-    def invoke_model(
-        self, user_id: str, prompt_messages: list[PromptMessage], stop: list[str]
-    ) -> LLMResult:
+    def invoke_model(self, user_id: str, prompt_messages: list[PromptMessage], stop: list[str]) -> LLMResult:
         """
-            invoke model
+        invoke model
 
-            :param model_config: the model config
-            :param prompt_messages: the prompt messages
-            :param stop: the stop words
-            :return: the model result
+        :param model_config: the model config
+        :param prompt_messages: the prompt messages
+        :param stop: the stop words
+        :return: the model result
         """
         # invoke model
         return ModelInvocationUtils.invoke(
             user_id=user_id,
             tenant_id=self.runtime.tenant_id,
-            tool_type='builtin',
+            tool_type="builtin",
             tool_name=self.identity.name,
             prompt_messages=prompt_messages,
         )
-    
+
     def tool_provider_type(self) -> ToolProviderType:
         return ToolProviderType.BUILT_IN
-    
+
     def get_max_tokens(self) -> int:
         """
-            get max tokens
+        get max tokens
 
-            :param model_config: the model config
-            :return: the max tokens
+        :param model_config: the model config
+        :return: the max tokens
         """
         return ModelInvocationUtils.get_max_llm_context_tokens(
             tenant_id=self.runtime.tenant_id,
@@ -57,39 +54,34 @@ class BuiltinTool(Tool):
 
     def get_prompt_tokens(self, prompt_messages: list[PromptMessage]) -> int:
         """
-            get prompt tokens
+        get prompt tokens
 
-            :param prompt_messages: the prompt messages
-            :return: the tokens
+        :param prompt_messages: the prompt messages
+        :return: the tokens
         """
-        return ModelInvocationUtils.calculate_tokens(
-            tenant_id=self.runtime.tenant_id,
-            prompt_messages=prompt_messages
-        )
+        return ModelInvocationUtils.calculate_tokens(tenant_id=self.runtime.tenant_id, prompt_messages=prompt_messages)
 
     def summary(self, user_id: str, content: str) -> str:
         max_tokens = self.get_max_tokens()
 
-        if self.get_prompt_tokens(prompt_messages=[
-            UserPromptMessage(content=content)
-        ]) < max_tokens * 0.6:
+        if self.get_prompt_tokens(prompt_messages=[UserPromptMessage(content=content)]) < max_tokens * 0.6:
             return content
-        
+
         def get_prompt_tokens(content: str) -> int:
-            return self.get_prompt_tokens(prompt_messages=[
-                SystemPromptMessage(content=_SUMMARY_PROMPT),
-                UserPromptMessage(content=content)
-            ])
-        
+            return self.get_prompt_tokens(
+                prompt_messages=[SystemPromptMessage(content=_SUMMARY_PROMPT), UserPromptMessage(content=content)]
+            )
+
         def summarize(content: str) -> str:
-            summary = self.invoke_model(user_id=user_id, prompt_messages=[
-                SystemPromptMessage(content=_SUMMARY_PROMPT),
-                UserPromptMessage(content=content)
-            ], stop=[])
+            summary = self.invoke_model(
+                user_id=user_id,
+                prompt_messages=[SystemPromptMessage(content=_SUMMARY_PROMPT), UserPromptMessage(content=content)],
+                stop=[],
+            )
 
             return summary.message.content
 
-        lines = content.split('\n')
+        lines = content.split("\n")
         new_lines = []
         # split long line into multiple lines
         for i in range(len(lines)):
@@ -100,8 +92,8 @@ class BuiltinTool(Tool):
                 new_lines.append(line)
             elif get_prompt_tokens(line) > max_tokens * 0.7:
                 while get_prompt_tokens(line) > max_tokens * 0.7:
-                    new_lines.append(line[:int(max_tokens * 0.5)])
-                    line = line[int(max_tokens * 0.5):]
+                    new_lines.append(line[: int(max_tokens * 0.5)])
+                    line = line[int(max_tokens * 0.5) :]
                 new_lines.append(line)
             else:
                 new_lines.append(line)
@@ -125,17 +117,15 @@ class BuiltinTool(Tool):
             summary = summarize(message)
             summaries.append(summary)
 
-        result = '\n'.join(summaries)
+        result = "\n".join(summaries)
 
-        if self.get_prompt_tokens(prompt_messages=[
-            UserPromptMessage(content=result)
-        ]) > max_tokens * 0.7:
+        if self.get_prompt_tokens(prompt_messages=[UserPromptMessage(content=result)]) > max_tokens * 0.7:
             return self.summary(user_id=user_id, content=result)
-        
+
         return result
-    
+
     def get_url(self, url: str, user_agent: str = None) -> str:
         """
-            get url
+        get url
         """
-        return get_url(url, user_agent=user_agent)
\ No newline at end of file
+        return get_url(url, user_agent=user_agent)
diff --git a/api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py b/api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py
index 7cb7c033bb..ab7b40a253 100644
--- a/api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py
+++ b/api/core/tools/tool/dataset_retriever/dataset_multi_retriever_tool.py
@@ -8,20 +8,17 @@ from core.model_manager import ModelManager
 from core.model_runtime.entities.model_entities import ModelType
 from core.rag.datasource.retrieval_service import RetrievalService
 from core.rag.rerank.rerank_model import RerankModelRunner
-from core.rag.retrieval.retrival_methods import RetrievalMethod
+from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.tool.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
 from extensions.ext_database import db
 from models.dataset import Dataset, Document, DocumentSegment
 
 default_retrieval_model = {
-    'search_method': RetrievalMethod.SEMANTIC_SEARCH.value,
-    'reranking_enable': False,
-    'reranking_model': {
-        'reranking_provider_name': '',
-        'reranking_model_name': ''
-    },
-    'top_k': 2,
-    'score_threshold_enabled': False
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "reranking_enable": False,
+    "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
+    "top_k": 2,
+    "score_threshold_enabled": False,
 }
 
 
@@ -31,6 +28,7 @@ class DatasetMultiRetrieverToolInput(BaseModel):
 
 class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
     """Tool for querying multi dataset."""
+
     name: str = "dataset_"
     args_schema: type[BaseModel] = DatasetMultiRetrieverToolInput
     description: str = "dataset multi retriever and rerank. "
@@ -38,27 +36,26 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
     reranking_provider_name: str
     reranking_model_name: str
 
-
     @classmethod
     def from_dataset(cls, dataset_ids: list[str], tenant_id: str, **kwargs):
         return cls(
-            name=f"dataset_{tenant_id.replace('-', '_')}",
-            tenant_id=tenant_id,
-            dataset_ids=dataset_ids,
-            **kwargs
+            name=f"dataset_{tenant_id.replace('-', '_')}", tenant_id=tenant_id, dataset_ids=dataset_ids, **kwargs
         )
 
     def _run(self, query: str) -> str:
         threads = []
         all_documents = []
         for dataset_id in self.dataset_ids:
-            retrieval_thread = threading.Thread(target=self._retriever, kwargs={
-                'flask_app': current_app._get_current_object(),
-                'dataset_id': dataset_id,
-                'query': query,
-                'all_documents': all_documents,
-                'hit_callbacks': self.hit_callbacks
-            })
+            retrieval_thread = threading.Thread(
+                target=self._retriever,
+                kwargs={
+                    "flask_app": current_app._get_current_object(),
+                    "dataset_id": dataset_id,
+                    "query": query,
+                    "all_documents": all_documents,
+                    "hit_callbacks": self.hit_callbacks,
+                },
+            )
             threads.append(retrieval_thread)
             retrieval_thread.start()
         for thread in threads:
@@ -69,7 +66,7 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
             tenant_id=self.tenant_id,
             provider=self.reranking_provider_name,
             model_type=ModelType.RERANK,
-            model=self.reranking_model_name
+            model=self.reranking_model_name,
         )
 
         rerank_runner = RerankModelRunner(rerank_model_instance)
@@ -80,62 +77,61 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
 
         document_score_list = {}
         for item in all_documents:
-            if item.metadata.get('score'):
-                document_score_list[item.metadata['doc_id']] = item.metadata['score']
+            if item.metadata.get("score"):
+                document_score_list[item.metadata["doc_id"]] = item.metadata["score"]
 
         document_context_list = []
-        index_node_ids = [document.metadata['doc_id'] for document in all_documents]
+        index_node_ids = [document.metadata["doc_id"] for document in all_documents]
         segments = DocumentSegment.query.filter(
             DocumentSegment.dataset_id.in_(self.dataset_ids),
             DocumentSegment.completed_at.isnot(None),
-            DocumentSegment.status == 'completed',
+            DocumentSegment.status == "completed",
             DocumentSegment.enabled == True,
-            DocumentSegment.index_node_id.in_(index_node_ids)
+            DocumentSegment.index_node_id.in_(index_node_ids),
         ).all()
 
         if segments:
             index_node_id_to_position = {id: position for position, id in enumerate(index_node_ids)}
-            sorted_segments = sorted(segments,
-                                     key=lambda segment: index_node_id_to_position.get(segment.index_node_id,
-                                                                                       float('inf')))
+            sorted_segments = sorted(
+                segments, key=lambda segment: index_node_id_to_position.get(segment.index_node_id, float("inf"))
+            )
             for segment in sorted_segments:
                 if segment.answer:
-                    document_context_list.append(f'question:{segment.get_sign_content()} answer:{segment.answer}')
+                    document_context_list.append(f"question:{segment.get_sign_content()} answer:{segment.answer}")
                 else:
                     document_context_list.append(segment.get_sign_content())
             if self.return_resource:
                 context_list = []
                 resource_number = 1
                 for segment in sorted_segments:
-                    dataset = Dataset.query.filter_by(
-                        id=segment.dataset_id
+                    dataset = Dataset.query.filter_by(id=segment.dataset_id).first()
+                    document = Document.query.filter(
+                        Document.id == segment.document_id,
+                        Document.enabled == True,
+                        Document.archived == False,
                     ).first()
-                    document = Document.query.filter(Document.id == segment.document_id,
-                                                     Document.enabled == True,
-                                                     Document.archived == False,
-                                                     ).first()
                     if dataset and document:
                         source = {
-                            'position': resource_number,
-                            'dataset_id': dataset.id,
-                            'dataset_name': dataset.name,
-                            'document_id': document.id,
-                            'document_name': document.name,
-                            'data_source_type': document.data_source_type,
-                            'segment_id': segment.id,
-                            'retriever_from': self.retriever_from,
-                            'score': document_score_list.get(segment.index_node_id, None)
+                            "position": resource_number,
+                            "dataset_id": dataset.id,
+                            "dataset_name": dataset.name,
+                            "document_id": document.id,
+                            "document_name": document.name,
+                            "data_source_type": document.data_source_type,
+                            "segment_id": segment.id,
+                            "retriever_from": self.retriever_from,
+                            "score": document_score_list.get(segment.index_node_id, None),
                         }
 
-                        if self.retriever_from == 'dev':
-                            source['hit_count'] = segment.hit_count
-                            source['word_count'] = segment.word_count
-                            source['segment_position'] = segment.position
-                            source['index_node_hash'] = segment.index_node_hash
+                        if self.retriever_from == "dev":
+                            source["hit_count"] = segment.hit_count
+                            source["word_count"] = segment.word_count
+                            source["segment_position"] = segment.position
+                            source["index_node_hash"] = segment.index_node_hash
                         if segment.answer:
-                            source['content'] = f'question:{segment.content} \nanswer:{segment.answer}'
+                            source["content"] = f"question:{segment.content} \nanswer:{segment.answer}"
                         else:
-                            source['content'] = segment.content
+                            source["content"] = segment.content
                         context_list.append(source)
                     resource_number += 1
 
@@ -144,13 +140,18 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
 
             return str("\n".join(document_context_list))
 
-    def _retriever(self, flask_app: Flask, dataset_id: str, query: str, all_documents: list,
-                   hit_callbacks: list[DatasetIndexToolCallbackHandler]):
+    def _retriever(
+        self,
+        flask_app: Flask,
+        dataset_id: str,
+        query: str,
+        all_documents: list,
+        hit_callbacks: list[DatasetIndexToolCallbackHandler],
+    ):
         with flask_app.app_context():
-            dataset = db.session.query(Dataset).filter(
-                Dataset.tenant_id == self.tenant_id,
-                Dataset.id == dataset_id
-            ).first()
+            dataset = (
+                db.session.query(Dataset).filter(Dataset.tenant_id == self.tenant_id, Dataset.id == dataset_id).first()
+            )
 
             if not dataset:
                 return []
@@ -159,31 +160,34 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
                 hit_callback.on_query(query, dataset.id)
 
             # get retrieval model , if the model is not setting , using default
-            retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+            retrieval_model = dataset.retrieval_model or default_retrieval_model
 
             if dataset.indexing_technique == "economy":
                 # use keyword table query
-                documents = RetrievalService.retrieve(retrival_method='keyword_search',
-                                                      dataset_id=dataset.id,
-                                                      query=query,
-                                                      top_k=self.top_k
-                                                      )
+                documents = RetrievalService.retrieve(
+                    retrieval_method="keyword_search",
+                    dataset_id=dataset.id,
+                    query=query,
+                    top_k=retrieval_model.get("top_k") or 2,
+                )
                 if documents:
                     all_documents.extend(documents)
             else:
                 if self.top_k > 0:
                     # retrieval source
-                    documents = RetrievalService.retrieve(retrival_method=retrieval_model['search_method'],
-                                                          dataset_id=dataset.id,
-                                                          query=query,
-                                                          top_k=self.top_k,
-                                                          score_threshold=retrieval_model.get('score_threshold', .0)
-                                                          if retrieval_model['score_threshold_enabled'] else None,
-                                                          reranking_model=retrieval_model.get('reranking_model', None)
-                                                          if retrieval_model['reranking_enable'] else None,
-                                                          reranking_mode=retrieval_model.get('reranking_mode')
-                                                          if retrieval_model.get('reranking_mode') else 'reranking_model',
-                                                          weights=retrieval_model.get('weights', None),
-                                                          )
+                    documents = RetrievalService.retrieve(
+                        retrieval_method=retrieval_model["search_method"],
+                        dataset_id=dataset.id,
+                        query=query,
+                        top_k=retrieval_model.get("top_k") or 2,
+                        score_threshold=retrieval_model.get("score_threshold", 0.0)
+                        if retrieval_model["score_threshold_enabled"]
+                        else 0.0,
+                        reranking_model=retrieval_model.get("reranking_model", None)
+                        if retrieval_model["reranking_enable"]
+                        else None,
+                        reranking_mode=retrieval_model.get("reranking_mode") or "reranking_model",
+                        weights=retrieval_model.get("weights", None),
+                    )
 
-                    all_documents.extend(documents)
\ No newline at end of file
+                    all_documents.extend(documents)
diff --git a/api/core/tools/tool/dataset_retriever/dataset_retriever_base_tool.py b/api/core/tools/tool/dataset_retriever/dataset_retriever_base_tool.py
index 62e97a0230..dad8c77357 100644
--- a/api/core/tools/tool/dataset_retriever/dataset_retriever_base_tool.py
+++ b/api/core/tools/tool/dataset_retriever/dataset_retriever_base_tool.py
@@ -9,6 +9,7 @@ from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCa
 
 class DatasetRetrieverBaseTool(BaseModel, ABC):
     """Tool for querying a Dataset."""
+
     name: str = "dataset"
     description: str = "use this to retrieve a dataset. "
     tenant_id: str
diff --git a/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py b/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py
index a7e70af628..8dc60408c9 100644
--- a/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py
+++ b/api/core/tools/tool/dataset_retriever/dataset_retriever_tool.py
@@ -1,22 +1,18 @@
-
 from pydantic import BaseModel, Field
 
 from core.rag.datasource.retrieval_service import RetrievalService
-from core.rag.retrieval.retrival_methods import RetrievalMethod
+from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.tool.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
 from extensions.ext_database import db
 from models.dataset import Dataset, Document, DocumentSegment
 
 default_retrieval_model = {
-    'search_method': RetrievalMethod.SEMANTIC_SEARCH.value,
-    'reranking_enable': False,
-    'reranking_model': {
-        'reranking_provider_name': '',
-        'reranking_model_name': ''
-    },
-    'reranking_mode': 'reranking_model',
-    'top_k': 2,
-    'score_threshold_enabled': False
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "reranking_enable": False,
+    "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
+    "reranking_mode": "reranking_model",
+    "top_k": 2,
+    "score_threshold_enabled": False,
 }
 
 
@@ -26,64 +22,63 @@ class DatasetRetrieverToolInput(BaseModel):
 
 class DatasetRetrieverTool(DatasetRetrieverBaseTool):
     """Tool for querying a Dataset."""
+
     name: str = "dataset"
     args_schema: type[BaseModel] = DatasetRetrieverToolInput
     description: str = "use this to retrieve a dataset. "
     dataset_id: str
 
-
     @classmethod
     def from_dataset(cls, dataset: Dataset, **kwargs):
         description = dataset.description
         if not description:
-            description = 'useful for when you want to answer queries about the ' + dataset.name
+            description = "useful for when you want to answer queries about the " + dataset.name
 
-        description = description.replace('\n', '').replace('\r', '')
+        description = description.replace("\n", "").replace("\r", "")
         return cls(
             name=f"dataset_{dataset.id.replace('-', '_')}",
             tenant_id=dataset.tenant_id,
             dataset_id=dataset.id,
             description=description,
-            **kwargs
+            **kwargs,
         )
 
     def _run(self, query: str) -> str:
-        dataset = db.session.query(Dataset).filter(
-            Dataset.tenant_id == self.tenant_id,
-            Dataset.id == self.dataset_id
-        ).first()
+        dataset = (
+            db.session.query(Dataset).filter(Dataset.tenant_id == self.tenant_id, Dataset.id == self.dataset_id).first()
+        )
 
         if not dataset:
-            return ''
+            return ""
 
         for hit_callback in self.hit_callbacks:
             hit_callback.on_query(query, dataset.id)
 
         # get retrieval model , if the model is not setting , using default
-        retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+        retrieval_model = dataset.retrieval_model or default_retrieval_model
         if dataset.indexing_technique == "economy":
             # use keyword table query
-            documents = RetrievalService.retrieve(retrival_method='keyword_search',
-                                                  dataset_id=dataset.id,
-                                                  query=query,
-                                                  top_k=self.top_k
-                                                  )
+            documents = RetrievalService.retrieve(
+                retrieval_method="keyword_search", dataset_id=dataset.id, query=query, top_k=self.top_k
+            )
             return str("\n".join([document.page_content for document in documents]))
         else:
             if self.top_k > 0:
                 # retrieval source
-                documents = RetrievalService.retrieve(retrival_method=retrieval_model.get('search_method', 'semantic_search'),
-                                                      dataset_id=dataset.id,
-                                                      query=query,
-                                                      top_k=self.top_k,
-                                                      score_threshold=retrieval_model.get('score_threshold', .0)
-                                                      if retrieval_model['score_threshold_enabled'] else None,
-                                                      reranking_model=retrieval_model.get('reranking_model', None)
-                                                      if retrieval_model['reranking_enable'] else None,
-                                                      reranking_mode=retrieval_model.get('reranking_mode')
-                                                      if retrieval_model.get('reranking_mode') else 'reranking_model',
-                                                      weights=retrieval_model.get('weights', None),
-                                                      )
+                documents = RetrievalService.retrieve(
+                    retrieval_method=retrieval_model.get("search_method", "semantic_search"),
+                    dataset_id=dataset.id,
+                    query=query,
+                    top_k=self.top_k,
+                    score_threshold=retrieval_model.get("score_threshold", 0.0)
+                    if retrieval_model["score_threshold_enabled"]
+                    else 0.0,
+                    reranking_model=retrieval_model.get("reranking_model", None)
+                    if retrieval_model["reranking_enable"]
+                    else None,
+                    reranking_mode=retrieval_model.get("reranking_mode") or "reranking_model",
+                    weights=retrieval_model.get("weights", None),
+                )
             else:
                 documents = []
 
@@ -92,25 +87,26 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
             document_score_list = {}
             if dataset.indexing_technique != "economy":
                 for item in documents:
-                    if item.metadata.get('score'):
-                        document_score_list[item.metadata['doc_id']] = item.metadata['score']
+                    if item.metadata.get("score"):
+                        document_score_list[item.metadata["doc_id"]] = item.metadata["score"]
             document_context_list = []
-            index_node_ids = [document.metadata['doc_id'] for document in documents]
-            segments = DocumentSegment.query.filter(DocumentSegment.dataset_id == self.dataset_id,
-                                                    DocumentSegment.completed_at.isnot(None),
-                                                    DocumentSegment.status == 'completed',
-                                                    DocumentSegment.enabled == True,
-                                                    DocumentSegment.index_node_id.in_(index_node_ids)
-                                                    ).all()
+            index_node_ids = [document.metadata["doc_id"] for document in documents]
+            segments = DocumentSegment.query.filter(
+                DocumentSegment.dataset_id == self.dataset_id,
+                DocumentSegment.completed_at.isnot(None),
+                DocumentSegment.status == "completed",
+                DocumentSegment.enabled == True,
+                DocumentSegment.index_node_id.in_(index_node_ids),
+            ).all()
 
             if segments:
                 index_node_id_to_position = {id: position for position, id in enumerate(index_node_ids)}
-                sorted_segments = sorted(segments,
-                                         key=lambda segment: index_node_id_to_position.get(segment.index_node_id,
-                                                                                           float('inf')))
+                sorted_segments = sorted(
+                    segments, key=lambda segment: index_node_id_to_position.get(segment.index_node_id, float("inf"))
+                )
                 for segment in sorted_segments:
                     if segment.answer:
-                        document_context_list.append(f'question:{segment.get_sign_content()} answer:{segment.answer}')
+                        document_context_list.append(f"question:{segment.get_sign_content()} answer:{segment.answer}")
                     else:
                         document_context_list.append(segment.get_sign_content())
                 if self.return_resource:
@@ -118,36 +114,36 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                     resource_number = 1
                     for segment in sorted_segments:
                         context = {}
-                        document = Document.query.filter(Document.id == segment.document_id,
-                                                         Document.enabled == True,
-                                                         Document.archived == False,
-                                                         ).first()
+                        document = Document.query.filter(
+                            Document.id == segment.document_id,
+                            Document.enabled == True,
+                            Document.archived == False,
+                        ).first()
                         if dataset and document:
                             source = {
-                                'position': resource_number,
-                                'dataset_id': dataset.id,
-                                'dataset_name': dataset.name,
-                                'document_id': document.id,
-                                'document_name': document.name,
-                                'data_source_type': document.data_source_type,
-                                'segment_id': segment.id,
-                                'retriever_from': self.retriever_from,
-                                'score': document_score_list.get(segment.index_node_id, None)
-
+                                "position": resource_number,
+                                "dataset_id": dataset.id,
+                                "dataset_name": dataset.name,
+                                "document_id": document.id,
+                                "document_name": document.name,
+                                "data_source_type": document.data_source_type,
+                                "segment_id": segment.id,
+                                "retriever_from": self.retriever_from,
+                                "score": document_score_list.get(segment.index_node_id, None),
                             }
-                            if self.retriever_from == 'dev':
-                                source['hit_count'] = segment.hit_count
-                                source['word_count'] = segment.word_count
-                                source['segment_position'] = segment.position
-                                source['index_node_hash'] = segment.index_node_hash
+                            if self.retriever_from == "dev":
+                                source["hit_count"] = segment.hit_count
+                                source["word_count"] = segment.word_count
+                                source["segment_position"] = segment.position
+                                source["index_node_hash"] = segment.index_node_hash
                             if segment.answer:
-                                source['content'] = f'question:{segment.content} \nanswer:{segment.answer}'
+                                source["content"] = f"question:{segment.content} \nanswer:{segment.answer}"
                             else:
-                                source['content'] = segment.content
+                                source["content"] = segment.content
                             context_list.append(source)
                         resource_number += 1
 
                     for hit_callback in self.hit_callbacks:
                         hit_callback.return_retriever_resource_info(context_list)
 
-            return str("\n".join(document_context_list))
\ No newline at end of file
+            return str("\n".join(document_context_list))
diff --git a/api/core/tools/tool/dataset_retriever_tool.py b/api/core/tools/tool/dataset_retriever_tool.py
index 1170e1b7a5..3c9295c493 100644
--- a/api/core/tools/tool/dataset_retriever_tool.py
+++ b/api/core/tools/tool/dataset_retriever_tool.py
@@ -17,16 +17,17 @@ from core.tools.tool.tool import Tool
 
 
 class DatasetRetrieverTool(Tool):
-    retrival_tool: DatasetRetrieverBaseTool
+    retrieval_tool: DatasetRetrieverBaseTool
 
     @staticmethod
-    def get_dataset_tools(tenant_id: str,
-                          dataset_ids: list[str],
-                          retrieve_config: DatasetRetrieveConfigEntity,
-                          return_resource: bool,
-                          invoke_from: InvokeFrom,
-                          hit_callback: DatasetIndexToolCallbackHandler
-                          ) -> list['DatasetRetrieverTool']:
+    def get_dataset_tools(
+        tenant_id: str,
+        dataset_ids: list[str],
+        retrieve_config: DatasetRetrieveConfigEntity,
+        return_resource: bool,
+        invoke_from: InvokeFrom,
+        hit_callback: DatasetIndexToolCallbackHandler,
+    ) -> list["DatasetRetrieverTool"]:
         """
         get dataset tool
         """
@@ -42,29 +43,29 @@ class DatasetRetrieverTool(Tool):
         # Agent only support SINGLE mode
         original_retriever_mode = retrieve_config.retrieve_strategy
         retrieve_config.retrieve_strategy = DatasetRetrieveConfigEntity.RetrieveStrategy.SINGLE
-        retrival_tools = feature.to_dataset_retriever_tool(
+        retrieval_tools = feature.to_dataset_retriever_tool(
             tenant_id=tenant_id,
             dataset_ids=dataset_ids,
             retrieve_config=retrieve_config,
             return_resource=return_resource,
             invoke_from=invoke_from,
-            hit_callback=hit_callback
+            hit_callback=hit_callback,
         )
         # restore retrieve strategy
         retrieve_config.retrieve_strategy = original_retriever_mode
 
-        # convert retrival tools to Tools
+        # convert retrieval tools to Tools
         tools = []
-        for retrival_tool in retrival_tools:
+        for retrieval_tool in retrieval_tools:
             tool = DatasetRetrieverTool(
-                retrival_tool=retrival_tool,
-                identity=ToolIdentity(provider='', author='', name=retrival_tool.name, label=I18nObject(en_US='', zh_Hans='')),
+                retrieval_tool=retrieval_tool,
+                identity=ToolIdentity(
+                    provider="", author="", name=retrieval_tool.name, label=I18nObject(en_US="", zh_Hans="")
+                ),
                 parameters=[],
                 is_team_authorization=True,
-                description=ToolDescription(
-                    human=I18nObject(en_US='', zh_Hans=''),
-                    llm=retrival_tool.description),
-                runtime=DatasetRetrieverTool.Runtime()
+                description=ToolDescription(human=I18nObject(en_US="", zh_Hans=""), llm=retrieval_tool.description),
+                runtime=DatasetRetrieverTool.Runtime(),
             )
 
             tools.append(tool)
@@ -73,16 +74,18 @@ class DatasetRetrieverTool(Tool):
 
     def get_runtime_parameters(self) -> list[ToolParameter]:
         return [
-            ToolParameter(name='query',
-                          label=I18nObject(en_US='', zh_Hans=''),
-                          human_description=I18nObject(en_US='', zh_Hans=''),
-                          type=ToolParameter.ToolParameterType.STRING,
-                          form=ToolParameter.ToolParameterForm.LLM,
-                          llm_description='Query for the dataset to be used to retrieve the dataset.',
-                          required=True,
-                          default=''),
+            ToolParameter(
+                name="query",
+                label=I18nObject(en_US="", zh_Hans=""),
+                human_description=I18nObject(en_US="", zh_Hans=""),
+                type=ToolParameter.ToolParameterType.STRING,
+                form=ToolParameter.ToolParameterForm.LLM,
+                llm_description="Query for the dataset to be used to retrieve the dataset.",
+                required=True,
+                default="",
+            ),
         ]
-    
+
     def tool_provider_type(self) -> ToolProviderType:
         return ToolProviderType.DATASET_RETRIEVAL
 
@@ -90,12 +93,12 @@ class DatasetRetrieverTool(Tool):
         """
         invoke dataset retriever tool
         """
-        query = tool_parameters.get('query')
+        query = tool_parameters.get("query")
         if not query:
-            return self.create_text_message(text='please input query')
+            return self.create_text_message(text="please input query")
 
         # invoke dataset retriever tool
-        result = self.retrival_tool._run(query=query)
+        result = self.retrieval_tool._run(query=query)
 
         return self.create_text_message(text=result)
 
diff --git a/api/core/tools/tool/tool.py b/api/core/tools/tool/tool.py
index d990131b5f..d9e9a0faad 100644
--- a/api/core/tools/tool/tool.py
+++ b/api/core/tools/tool/tool.py
@@ -35,15 +35,16 @@ class Tool(BaseModel, ABC):
     # pydantic configs
     model_config = ConfigDict(protected_namespaces=())
 
-    @field_validator('parameters', mode='before')
+    @field_validator("parameters", mode="before")
     @classmethod
     def set_parameters(cls, v, validation_info: ValidationInfo) -> list[ToolParameter]:
         return v or []
 
     class Runtime(BaseModel):
         """
-            Meta data of a tool call processing
+        Meta data of a tool call processing
         """
+
         def __init__(self, **data: Any):
             super().__init__(**data)
             if not self.runtime_parameters:
@@ -62,15 +63,15 @@ class Tool(BaseModel, ABC):
     def __init__(self, **data: Any):
         super().__init__(**data)
 
-    class VARIABLE_KEY(Enum):
-        IMAGE = 'image'
+    class VariableKey(Enum):
+        IMAGE = "image"
 
-    def fork_tool_runtime(self, runtime: dict[str, Any]) -> 'Tool':
+    def fork_tool_runtime(self, runtime: dict[str, Any]) -> "Tool":
         """
-            fork a new tool with meta data
+        fork a new tool with meta data
 
-            :param meta: the meta data of a tool call processing, tenant_id is required
-            :return: the new tool
+        :param meta: the meta data of a tool call processing, tenant_id is required
+        :return: the new tool
         """
         return self.__class__(
             identity=self.identity.model_copy() if self.identity else None,
@@ -82,22 +83,22 @@ class Tool(BaseModel, ABC):
     @abstractmethod
     def tool_provider_type(self) -> ToolProviderType:
         """
-            get the tool provider type
+        get the tool provider type
 
-            :return: the tool provider type
+        :return: the tool provider type
         """
 
     def load_variables(self, variables: ToolRuntimeVariablePool):
         """
-            load variables from database
+        load variables from database
 
-            :param conversation_id: the conversation id
+        :param conversation_id: the conversation id
         """
         self.variables = variables
 
     def set_image_variable(self, variable_name: str, image_key: str) -> None:
         """
-            set an image variable
+        set an image variable
         """
         if not self.variables:
             return
@@ -106,7 +107,7 @@ class Tool(BaseModel, ABC):
 
     def set_text_variable(self, variable_name: str, text: str) -> None:
         """
-            set a text variable
+        set a text variable
         """
         if not self.variables:
             return
@@ -115,10 +116,10 @@ class Tool(BaseModel, ABC):
 
     def get_variable(self, name: Union[str, Enum]) -> Optional[ToolRuntimeVariable]:
         """
-            get a variable
+        get a variable
 
-            :param name: the name of the variable
-            :return: the variable
+        :param name: the name of the variable
+        :return: the variable
         """
         if not self.variables:
             return None
@@ -134,21 +135,21 @@ class Tool(BaseModel, ABC):
 
     def get_default_image_variable(self) -> Optional[ToolRuntimeVariable]:
         """
-            get the default image variable
+        get the default image variable
 
-            :return: the image variable
+        :return: the image variable
         """
         if not self.variables:
             return None
 
-        return self.get_variable(self.VARIABLE_KEY.IMAGE)
+        return self.get_variable(self.VariableKey.IMAGE)
 
     def get_variable_file(self, name: Union[str, Enum]) -> Optional[bytes]:
         """
-            get a variable file
+        get a variable file
 
-            :param name: the name of the variable
-            :return: the variable file
+        :param name: the name of the variable
+        :return: the variable file
         """
         variable = self.get_variable(name)
         if not variable:
@@ -167,9 +168,9 @@ class Tool(BaseModel, ABC):
 
     def list_variables(self) -> list[ToolRuntimeVariable]:
         """
-            list all variables
+        list all variables
 
-            :return: the variables
+        :return: the variables
         """
         if not self.variables:
             return []
@@ -178,9 +179,9 @@ class Tool(BaseModel, ABC):
 
     def list_default_image_variables(self) -> list[ToolRuntimeVariable]:
         """
-            list all image variables
+        list all image variables
 
-            :return: the image variables
+        :return: the image variables
         """
         if not self.variables:
             return []
@@ -188,7 +189,7 @@ class Tool(BaseModel, ABC):
         result = []
 
         for variable in self.variables.pool:
-            if variable.name.startswith(self.VARIABLE_KEY.IMAGE.value):
+            if variable.name.startswith(self.VariableKey.IMAGE.value):
                 result.append(variable)
 
         return result
@@ -220,38 +221,42 @@ class Tool(BaseModel, ABC):
         result = deepcopy(tool_parameters)
         for parameter in self.parameters or []:
             if parameter.name in tool_parameters:
-                result[parameter.name] = ToolParameterConverter.cast_parameter_by_type(tool_parameters[parameter.name], parameter.type)
+                result[parameter.name] = ToolParameterConverter.cast_parameter_by_type(
+                    tool_parameters[parameter.name], parameter.type
+                )
 
         return result
 
     @abstractmethod
-    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         pass
 
     def validate_credentials(self, credentials: dict[str, Any], parameters: dict[str, Any]) -> None:
         """
-            validate the credentials
+        validate the credentials
 
-            :param credentials: the credentials
-            :param parameters: the parameters
+        :param credentials: the credentials
+        :param parameters: the parameters
         """
         pass
 
     def get_runtime_parameters(self) -> list[ToolParameter]:
         """
-            get the runtime parameters
+        get the runtime parameters
 
-            interface for developer to dynamic change the parameters of a tool depends on the variables pool
+        interface for developer to dynamic change the parameters of a tool depends on the variables pool
 
-            :return: the runtime parameters
+        :return: the runtime parameters
         """
         return self.parameters or []
 
     def get_all_runtime_parameters(self) -> list[ToolParameter]:
         """
-            get all runtime parameters
+        get all runtime parameters
 
-            :return: all runtime parameters
+        :return: all runtime parameters
         """
         parameters = self.parameters or []
         parameters = parameters.copy()
@@ -281,67 +286,49 @@ class Tool(BaseModel, ABC):
 
         return parameters
 
-    def create_image_message(self, image: str, save_as: str = '') -> ToolInvokeMessage:
+    def create_image_message(self, image: str, save_as: str = "") -> ToolInvokeMessage:
         """
-            create an image message
+        create an image message
 
-            :param image: the url of the image
-            :return: the image message
+        :param image: the url of the image
+        :return: the image message
         """
-        return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.IMAGE,
-                                 message=image,
-                                 save_as=save_as)
+        return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.IMAGE, message=image, save_as=save_as)
 
     def create_file_var_message(self, file_var: "FileVar") -> ToolInvokeMessage:
-        return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.FILE_VAR,
-                                 message='',
-                                 meta={
-                                     'file_var': file_var
-                                 },
-                                 save_as='')
-
-    def create_link_message(self, link: str, save_as: str = '') -> ToolInvokeMessage:
-        """
-            create a link message
-
-            :param link: the url of the link
-            :return: the link message
-        """
-        return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.LINK,
-                                 message=link,
-                                 save_as=save_as)
-
-    def create_text_message(self, text: str, save_as: str = '') -> ToolInvokeMessage:
-        """
-            create a text message
-
-            :param text: the text
-            :return: the text message
-        """
         return ToolInvokeMessage(
-            type=ToolInvokeMessage.MessageType.TEXT,
-            message=text,
-            save_as=save_as
+            type=ToolInvokeMessage.MessageType.FILE_VAR, message="", meta={"file_var": file_var}, save_as=""
         )
 
-    def create_blob_message(self, blob: bytes, meta: dict = None, save_as: str = '') -> ToolInvokeMessage:
+    def create_link_message(self, link: str, save_as: str = "") -> ToolInvokeMessage:
         """
-            create a blob message
+        create a link message
 
-            :param blob: the blob
-            :return: the blob message
+        :param link: the url of the link
+        :return: the link message
         """
-        return ToolInvokeMessage(
-            type=ToolInvokeMessage.MessageType.BLOB,
-            message=blob, meta=meta,
-            save_as=save_as
-        )
+        return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.LINK, message=link, save_as=save_as)
+
+    def create_text_message(self, text: str, save_as: str = "") -> ToolInvokeMessage:
+        """
+        create a text message
+
+        :param text: the text
+        :return: the text message
+        """
+        return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.TEXT, message=text, save_as=save_as)
+
+    def create_blob_message(self, blob: bytes, meta: dict = None, save_as: str = "") -> ToolInvokeMessage:
+        """
+        create a blob message
+
+        :param blob: the blob
+        :return: the blob message
+        """
+        return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.BLOB, message=blob, meta=meta, save_as=save_as)
 
     def create_json_message(self, object: dict) -> ToolInvokeMessage:
         """
-            create a json message
+        create a json message
         """
-        return ToolInvokeMessage(
-            type=ToolInvokeMessage.MessageType.JSON,
-            message=object
-        )
+        return ToolInvokeMessage(type=ToolInvokeMessage.MessageType.JSON, message=object)
diff --git a/api/core/tools/tool/workflow_tool.py b/api/core/tools/tool/workflow_tool.py
index 12e498e76d..ad0c7fc631 100644
--- a/api/core/tools/tool/workflow_tool.py
+++ b/api/core/tools/tool/workflow_tool.py
@@ -1,7 +1,7 @@
 import json
 import logging
 from copy import deepcopy
-from typing import Any, Union
+from typing import Any, Optional, Union
 
 from core.file.file_obj import FileTransferMethod, FileVar
 from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter, ToolProviderType
@@ -13,22 +13,25 @@ from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
+
 class WorkflowTool(Tool):
     workflow_app_id: str
     version: str
     workflow_entities: dict[str, Any]
     workflow_call_depth: int
+    thread_pool_id: Optional[str] = None
 
     label: str
 
     """
     Workflow tool.
     """
+
     def tool_provider_type(self) -> ToolProviderType:
         """
-            get the tool provider type
+        get the tool provider type
 
-            :return: the tool provider type
+        :return: the tool provider type
         """
         return ToolProviderType.WORKFLOW
 
@@ -36,7 +39,7 @@ class WorkflowTool(Tool):
         self, user_id: str, tool_parameters: dict[str, Any]
     ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
         """
-            invoke the tool
+        invoke the tool
         """
         app = self._get_app(app_id=self.workflow_app_id)
         workflow = self._get_workflow(app_id=self.workflow_app_id, version=self.version)
@@ -45,32 +48,31 @@ class WorkflowTool(Tool):
         tool_parameters, files = self._transform_args(tool_parameters)
 
         from core.app.apps.workflow.app_generator import WorkflowAppGenerator
+
         generator = WorkflowAppGenerator()
         result = generator.generate(
-            app_model=app, 
-            workflow=workflow, 
-            user=self._get_user(user_id), 
-            args={
-                'inputs': tool_parameters,
-                'files': files
-            }, 
+            app_model=app,
+            workflow=workflow,
+            user=self._get_user(user_id),
+            args={"inputs": tool_parameters, "files": files},
             invoke_from=self.runtime.invoke_from,
             stream=False,
             call_depth=self.workflow_call_depth + 1,
+            workflow_thread_pool_id=self.thread_pool_id,
         )
 
-        data = result.get('data', {})
+        data = result.get("data", {})
+
+        if data.get("error"):
+            raise Exception(data.get("error"))
 
-        if data.get('error'):
-            raise Exception(data.get('error'))
-        
         result = []
 
-        outputs = data.get('outputs', {})
+        outputs = data.get("outputs", {})
         outputs, files = self._extract_files(outputs)
         for file in files:
             result.append(self.create_file_var_message(file))
-        
+
         result.append(self.create_text_message(json.dumps(outputs, ensure_ascii=False)))
         result.append(self.create_json_message(outputs))
 
@@ -78,7 +80,7 @@ class WorkflowTool(Tool):
 
     def _get_user(self, user_id: str) -> Union[EndUser, Account]:
         """
-            get the user by user id
+        get the user by user id
         """
 
         user = db.session.query(EndUser).filter(EndUser.id == user_id).first()
@@ -86,16 +88,16 @@ class WorkflowTool(Tool):
             user = db.session.query(Account).filter(Account.id == user_id).first()
 
         if not user:
-            raise ValueError('user not found')
+            raise ValueError("user not found")
 
         return user
 
-    def fork_tool_runtime(self, runtime: dict[str, Any]) -> 'WorkflowTool':
+    def fork_tool_runtime(self, runtime: dict[str, Any]) -> "WorkflowTool":
         """
-            fork a new tool with meta data
+        fork a new tool with meta data
 
-            :param meta: the meta data of a tool call processing, tenant_id is required
-            :return: the new tool
+        :param meta: the meta data of a tool call processing, tenant_id is required
+        :return: the new tool
         """
         return self.__class__(
             identity=deepcopy(self.identity),
@@ -106,45 +108,44 @@ class WorkflowTool(Tool):
             workflow_entities=self.workflow_entities,
             workflow_call_depth=self.workflow_call_depth,
             version=self.version,
-            label=self.label
+            label=self.label,
         )
-    
+
     def _get_workflow(self, app_id: str, version: str) -> Workflow:
         """
-            get the workflow by app id and version
+        get the workflow by app id and version
         """
         if not version:
-            workflow = db.session.query(Workflow).filter(
-                Workflow.app_id == app_id, 
-                Workflow.version != 'draft'
-            ).order_by(Workflow.created_at.desc()).first()
+            workflow = (
+                db.session.query(Workflow)
+                .filter(Workflow.app_id == app_id, Workflow.version != "draft")
+                .order_by(Workflow.created_at.desc())
+                .first()
+            )
         else:
-            workflow = db.session.query(Workflow).filter(
-                Workflow.app_id == app_id, 
-                Workflow.version == version
-            ).first()
+            workflow = db.session.query(Workflow).filter(Workflow.app_id == app_id, Workflow.version == version).first()
 
         if not workflow:
-            raise ValueError('workflow not found or not published')
+            raise ValueError("workflow not found or not published")
 
         return workflow
-    
+
     def _get_app(self, app_id: str) -> App:
         """
-            get the app by app id
+        get the app by app id
         """
         app = db.session.query(App).filter(App.id == app_id).first()
         if not app:
-            raise ValueError('app not found')
+            raise ValueError("app not found")
 
         return app
-    
+
     def _transform_args(self, tool_parameters: dict) -> tuple[dict, list[dict]]:
         """
-            transform the tool parameters
+        transform the tool parameters
 
-            :param tool_parameters: the tool parameters
-            :return: tool_parameters, files
+        :param tool_parameters: the tool parameters
+        :return: tool_parameters, files
         """
         parameter_rules = self.get_all_runtime_parameters()
         parameters_result = {}
@@ -157,15 +158,15 @@ class WorkflowTool(Tool):
                         file_var_list = [FileVar(**f) for f in file]
                         for file_var in file_var_list:
                             file_dict = {
-                                'transfer_method': file_var.transfer_method.value,
-                                'type': file_var.type.value,
+                                "transfer_method": file_var.transfer_method.value,
+                                "type": file_var.type.value,
                             }
                             if file_var.transfer_method == FileTransferMethod.TOOL_FILE:
-                                file_dict['tool_file_id'] = file_var.related_id
+                                file_dict["tool_file_id"] = file_var.related_id
                             elif file_var.transfer_method == FileTransferMethod.LOCAL_FILE:
-                                file_dict['upload_file_id'] = file_var.related_id
+                                file_dict["upload_file_id"] = file_var.related_id
                             elif file_var.transfer_method == FileTransferMethod.REMOTE_URL:
-                                file_dict['url'] = file_var.preview_url
+                                file_dict["url"] = file_var.preview_url
 
                             files.append(file_dict)
                     except Exception as e:
@@ -174,13 +175,13 @@ class WorkflowTool(Tool):
                 parameters_result[parameter.name] = tool_parameters.get(parameter.name)
 
         return parameters_result, files
-    
+
     def _extract_files(self, outputs: dict) -> tuple[dict, list[FileVar]]:
         """
-            extract files from the result
+        extract files from the result
 
-            :param result: the result
-            :return: the result, files
+        :param result: the result
+        :return: the result, files
         """
         files = []
         result = {}
@@ -188,7 +189,7 @@ class WorkflowTool(Tool):
             if isinstance(value, list):
                 has_file = False
                 for item in value:
-                    if isinstance(item, dict) and item.get('__variant') == 'FileVar':
+                    if isinstance(item, dict) and item.get("__variant") == "FileVar":
                         try:
                             files.append(FileVar(**item))
                             has_file = True
@@ -199,4 +200,4 @@ class WorkflowTool(Tool):
 
             result[key] = value
 
-        return result, files
\ No newline at end of file
+        return result, files
diff --git a/api/core/tools/tool_engine.py b/api/core/tools/tool_engine.py
index 0e15151aa4..9912114dd6 100644
--- a/api/core/tools/tool_engine.py
+++ b/api/core/tools/tool_engine.py
@@ -33,12 +33,17 @@ class ToolEngine:
     """
     Tool runtime engine take care of the tool executions.
     """
+
     @staticmethod
     def agent_invoke(
-        tool: Tool, tool_parameters: Union[str, dict],
-        user_id: str, tenant_id: str, message: Message, invoke_from: InvokeFrom,
+        tool: Tool,
+        tool_parameters: Union[str, dict],
+        user_id: str,
+        tenant_id: str,
+        message: Message,
+        invoke_from: InvokeFrom,
         agent_tool_callback: DifyAgentCallbackHandler,
-        trace_manager: Optional[TraceQueueManager] = None
+        trace_manager: Optional[TraceQueueManager] = None,
     ) -> tuple[str, list[tuple[MessageFile, bool]], ToolInvokeMeta]:
         """
         Agent invokes the tool with the given arguments.
@@ -47,40 +52,30 @@ class ToolEngine:
         if isinstance(tool_parameters, str):
             # check if this tool has only one parameter
             parameters = [
-                parameter for parameter in tool.get_runtime_parameters() or []
+                parameter
+                for parameter in tool.get_runtime_parameters() or []
                 if parameter.form == ToolParameter.ToolParameterForm.LLM
             ]
             if parameters and len(parameters) == 1:
-                tool_parameters = {
-                    parameters[0].name: tool_parameters
-                }
+                tool_parameters = {parameters[0].name: tool_parameters}
             else:
                 raise ValueError(f"tool_parameters should be a dict, but got a string: {tool_parameters}")
 
         # invoke the tool
         try:
             # hit the callback handler
-            agent_tool_callback.on_tool_start(
-                tool_name=tool.identity.name, 
-                tool_inputs=tool_parameters
-            )
+            agent_tool_callback.on_tool_start(tool_name=tool.identity.name, tool_inputs=tool_parameters)
 
             meta, response = ToolEngine._invoke(tool, tool_parameters, user_id)
             response = ToolFileMessageTransformer.transform_tool_invoke_messages(
-                messages=response, 
-                user_id=user_id, 
-                tenant_id=tenant_id, 
-                conversation_id=message.conversation_id
+                messages=response, user_id=user_id, tenant_id=tenant_id, conversation_id=message.conversation_id
             )
 
             # extract binary data from tool invoke message
             binary_files = ToolEngine._extract_tool_response_binary(response)
             # create message file
             message_files = ToolEngine._create_message_files(
-                tool_messages=binary_files,
-                agent_message=message,
-                invoke_from=invoke_from,
-                user_id=user_id
+                tool_messages=binary_files, agent_message=message, invoke_from=invoke_from, user_id=user_id
             )
 
             plain_text = ToolEngine._convert_tool_response_to_str(response)
@@ -91,7 +86,7 @@ class ToolEngine:
                 tool_inputs=tool_parameters,
                 tool_outputs=plain_text,
                 message_id=message.id,
-                trace_manager=trace_manager
+                trace_manager=trace_manager,
             )
 
             # transform tool invoke message to get LLM friendly message
@@ -99,14 +94,10 @@ class ToolEngine:
         except ToolProviderCredentialValidationError as e:
             error_response = "Please check your tool provider credentials"
             agent_tool_callback.on_tool_error(e)
-        except (
-            ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
-        ) as e:
+        except (ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError) as e:
             error_response = f"there is not a tool named {tool.identity.name}"
             agent_tool_callback.on_tool_error(e)
-        except (
-            ToolParameterValidationError
-        ) as e:
+        except ToolParameterValidationError as e:
             error_response = f"tool parameters validation error: {e}, please check your tool parameters"
             agent_tool_callback.on_tool_error(e)
         except ToolInvokeError as e:
@@ -124,23 +115,24 @@ class ToolEngine:
         return error_response, [], ToolInvokeMeta.error_instance(error_response)
 
     @staticmethod
-    def workflow_invoke(tool: Tool, tool_parameters: Mapping[str, Any],
-                        user_id: str,
-                        workflow_tool_callback: DifyWorkflowCallbackHandler,
-                        workflow_call_depth: int,
-                        ) -> list[ToolInvokeMessage]:
+    def workflow_invoke(
+        tool: Tool,
+        tool_parameters: Mapping[str, Any],
+        user_id: str,
+        workflow_tool_callback: DifyWorkflowCallbackHandler,
+        workflow_call_depth: int,
+        thread_pool_id: Optional[str] = None,
+    ) -> list[ToolInvokeMessage]:
         """
         Workflow invokes the tool with the given arguments.
         """
         try:
             # hit the callback handler
-            workflow_tool_callback.on_tool_start(
-                tool_name=tool.identity.name, 
-                tool_inputs=tool_parameters
-            )
+            workflow_tool_callback.on_tool_start(tool_name=tool.identity.name, tool_inputs=tool_parameters)
 
             if isinstance(tool, WorkflowTool):
                 tool.workflow_call_depth = workflow_call_depth + 1
+                tool.thread_pool_id = thread_pool_id
 
             if tool.runtime and tool.runtime.runtime_parameters:
                 tool_parameters = {**tool.runtime.runtime_parameters, **tool_parameters}
@@ -157,21 +149,24 @@ class ToolEngine:
         except Exception as e:
             workflow_tool_callback.on_tool_error(e)
             raise e
-        
+
     @staticmethod
-    def _invoke(tool: Tool, tool_parameters: dict, user_id: str) \
-          -> tuple[ToolInvokeMeta, list[ToolInvokeMessage]]:
+    def _invoke(tool: Tool, tool_parameters: dict, user_id: str) -> tuple[ToolInvokeMeta, list[ToolInvokeMessage]]:
         """
         Invoke the tool with the given arguments.
         """
         started_at = datetime.now(timezone.utc)
-        meta = ToolInvokeMeta(time_cost=0.0, error=None, tool_config={
-            'tool_name': tool.identity.name,
-            'tool_provider': tool.identity.provider,
-            'tool_provider_type': tool.tool_provider_type().value,
-            'tool_parameters': deepcopy(tool.runtime.runtime_parameters),
-            'tool_icon': tool.identity.icon
-        })
+        meta = ToolInvokeMeta(
+            time_cost=0.0,
+            error=None,
+            tool_config={
+                "tool_name": tool.identity.name,
+                "tool_provider": tool.identity.provider,
+                "tool_provider_type": tool.tool_provider_type().value,
+                "tool_parameters": deepcopy(tool.runtime.runtime_parameters),
+                "tool_icon": tool.identity.icon,
+            },
+        )
         try:
             response = tool.invoke(user_id, tool_parameters)
         except Exception as e:
@@ -182,28 +177,30 @@ class ToolEngine:
             meta.time_cost = (ended_at - started_at).total_seconds()
 
         return meta, response
-    
+
     @staticmethod
     def _convert_tool_response_to_str(tool_response: list[ToolInvokeMessage]) -> str:
         """
         Handle tool response
         """
-        result = ''
+        result = ""
         for response in tool_response:
             if response.type == ToolInvokeMessage.MessageType.TEXT:
                 result += response.message
             elif response.type == ToolInvokeMessage.MessageType.LINK:
                 result += f"result link: {response.message}. please tell user to check it."
-            elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
-                 response.type == ToolInvokeMessage.MessageType.IMAGE:
-                result += "image has been created and sent to user already, you do not need to create it, just tell the user to check it now."
+            elif response.type in {ToolInvokeMessage.MessageType.IMAGE_LINK, ToolInvokeMessage.MessageType.IMAGE}:
+                result += (
+                    "image has been created and sent to user already, you do not need to create it,"
+                    " just tell the user to check it now."
+                )
             elif response.type == ToolInvokeMessage.MessageType.JSON:
                 result += f"tool response: {json.dumps(response.message, ensure_ascii=False)}."
             else:
                 result += f"tool response: {response.message}."
 
         return result
-    
+
     @staticmethod
     def _extract_tool_response_binary(tool_response: list[ToolInvokeMessage]) -> list[ToolInvokeMessageBinary]:
         """
@@ -212,52 +209,56 @@ class ToolEngine:
         result = []
 
         for response in tool_response:
-            if response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
-                response.type == ToolInvokeMessage.MessageType.IMAGE:
+            if response.type in {ToolInvokeMessage.MessageType.IMAGE_LINK, ToolInvokeMessage.MessageType.IMAGE}:
                 mimetype = None
-                if response.meta.get('mime_type'):
-                    mimetype = response.meta.get('mime_type')
+                if response.meta.get("mime_type"):
+                    mimetype = response.meta.get("mime_type")
                 else:
                     try:
                         url = URL(response.message)
                         extension = url.suffix
-                        guess_type_result, _ = guess_type(f'a{extension}')
+                        guess_type_result, _ = guess_type(f"a{extension}")
                         if guess_type_result:
                             mimetype = guess_type_result
                     except Exception:
                         pass
-                
+
                 if not mimetype:
-                    mimetype = 'image/jpeg'
-                    
-                result.append(ToolInvokeMessageBinary(
-                    mimetype=response.meta.get('mime_type', 'image/jpeg'),
-                    url=response.message,
-                    save_as=response.save_as,
-                ))
-            elif response.type == ToolInvokeMessage.MessageType.BLOB:
-                result.append(ToolInvokeMessageBinary(
-                    mimetype=response.meta.get('mime_type', 'octet/stream'),
-                    url=response.message,
-                    save_as=response.save_as,
-                ))
-            elif response.type == ToolInvokeMessage.MessageType.LINK:
-                # check if there is a mime type in meta
-                if response.meta and 'mime_type' in response.meta:
-                    result.append(ToolInvokeMessageBinary(
-                        mimetype=response.meta.get('mime_type', 'octet/stream') if response.meta else 'octet/stream',
+                    mimetype = "image/jpeg"
+
+                result.append(
+                    ToolInvokeMessageBinary(
+                        mimetype=response.meta.get("mime_type", "image/jpeg"),
                         url=response.message,
                         save_as=response.save_as,
-                    ))
+                    )
+                )
+            elif response.type == ToolInvokeMessage.MessageType.BLOB:
+                result.append(
+                    ToolInvokeMessageBinary(
+                        mimetype=response.meta.get("mime_type", "octet/stream"),
+                        url=response.message,
+                        save_as=response.save_as,
+                    )
+                )
+            elif response.type == ToolInvokeMessage.MessageType.LINK:
+                # check if there is a mime type in meta
+                if response.meta and "mime_type" in response.meta:
+                    result.append(
+                        ToolInvokeMessageBinary(
+                            mimetype=response.meta.get("mime_type", "octet/stream")
+                            if response.meta
+                            else "octet/stream",
+                            url=response.message,
+                            save_as=response.save_as,
+                        )
+                    )
 
         return result
-    
+
     @staticmethod
     def _create_message_files(
-        tool_messages: list[ToolInvokeMessageBinary],
-        agent_message: Message,
-        invoke_from: InvokeFrom,
-        user_id: str
+        tool_messages: list[ToolInvokeMessageBinary], agent_message: Message, invoke_from: InvokeFrom, user_id: str
     ) -> list[tuple[Any, str]]:
         """
         Create message file
@@ -268,29 +269,29 @@ class ToolEngine:
         result = []
 
         for message in tool_messages:
-            file_type = 'bin'
-            if 'image' in message.mimetype:
-                file_type = 'image'
-            elif 'video' in message.mimetype:
-                file_type = 'video'
-            elif 'audio' in message.mimetype:
-                file_type = 'audio'
-            elif 'text' in message.mimetype:
-                file_type = 'text'
-            elif 'pdf' in message.mimetype:
-                file_type = 'pdf'
-            elif 'zip' in message.mimetype:
-                file_type = 'archive'
+            file_type = "bin"
+            if "image" in message.mimetype:
+                file_type = "image"
+            elif "video" in message.mimetype:
+                file_type = "video"
+            elif "audio" in message.mimetype:
+                file_type = "audio"
+            elif "text" in message.mimetype:
+                file_type = "text"
+            elif "pdf" in message.mimetype:
+                file_type = "pdf"
+            elif "zip" in message.mimetype:
+                file_type = "archive"
             # ...
 
             message_file = MessageFile(
                 message_id=agent_message.id,
                 type=file_type,
                 transfer_method=FileTransferMethod.TOOL_FILE.value,
-                belongs_to='assistant',
+                belongs_to="assistant",
                 url=message.url,
                 upload_file_id=None,
-                created_by_role=('account'if invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end_user'),
+                created_by_role=("account" if invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER} else "end_user"),
                 created_by=user_id,
             )
 
@@ -298,11 +299,8 @@ class ToolEngine:
             db.session.commit()
             db.session.refresh(message_file)
 
-            result.append((
-                message_file.id,
-                message.save_as
-            ))
+            result.append((message_file.id, message.save_as))
 
         db.session.close()
 
-        return result
\ No newline at end of file
+        return result
diff --git a/api/core/tools/tool_file_manager.py b/api/core/tools/tool_file_manager.py
index f9f7c7d78a..ad3b9c7328 100644
--- a/api/core/tools/tool_file_manager.py
+++ b/api/core/tools/tool_file_manager.py
@@ -27,24 +27,24 @@ class ToolFileManager:
         sign file to get a temporary url
         """
         base_url = dify_config.FILES_URL
-        file_preview_url = f'{base_url}/files/tools/{tool_file_id}{extension}'
+        file_preview_url = f"{base_url}/files/tools/{tool_file_id}{extension}"
 
         timestamp = str(int(time.time()))
         nonce = os.urandom(16).hex()
-        data_to_sign = f'file-preview|{tool_file_id}|{timestamp}|{nonce}'
-        secret_key = dify_config.SECRET_KEY.encode() if dify_config.SECRET_KEY else b''
+        data_to_sign = f"file-preview|{tool_file_id}|{timestamp}|{nonce}"
+        secret_key = dify_config.SECRET_KEY.encode() if dify_config.SECRET_KEY else b""
         sign = hmac.new(secret_key, data_to_sign.encode(), hashlib.sha256).digest()
         encoded_sign = base64.urlsafe_b64encode(sign).decode()
 
-        return f'{file_preview_url}?timestamp={timestamp}&nonce={nonce}&sign={encoded_sign}'
+        return f"{file_preview_url}?timestamp={timestamp}&nonce={nonce}&sign={encoded_sign}"
 
     @staticmethod
     def verify_file(file_id: str, timestamp: str, nonce: str, sign: str) -> bool:
         """
         verify signature
         """
-        data_to_sign = f'file-preview|{file_id}|{timestamp}|{nonce}'
-        secret_key = dify_config.SECRET_KEY.encode() if dify_config.SECRET_KEY else b''
+        data_to_sign = f"file-preview|{file_id}|{timestamp}|{nonce}"
+        secret_key = dify_config.SECRET_KEY.encode() if dify_config.SECRET_KEY else b""
         recalculated_sign = hmac.new(secret_key, data_to_sign.encode(), hashlib.sha256).digest()
         recalculated_encoded_sign = base64.urlsafe_b64encode(recalculated_sign).decode()
 
@@ -62,9 +62,9 @@ class ToolFileManager:
         """
         create file
         """
-        extension = guess_extension(mimetype) or '.bin'
+        extension = guess_extension(mimetype) or ".bin"
         unique_name = uuid4().hex
-        filename = f'tools/{tenant_id}/{unique_name}{extension}'
+        filename = f"tools/{tenant_id}/{unique_name}{extension}"
         storage.save(filename, file_binary)
 
         tool_file = ToolFile(
@@ -90,10 +90,10 @@ class ToolFileManager:
         response = get(file_url)
         response.raise_for_status()
         blob = response.content
-        mimetype = guess_type(file_url)[0] or 'octet/stream'
-        extension = guess_extension(mimetype) or '.bin'
+        mimetype = guess_type(file_url)[0] or "octet/stream"
+        extension = guess_extension(mimetype) or ".bin"
         unique_name = uuid4().hex
-        filename = f'tools/{tenant_id}/{unique_name}{extension}'
+        filename = f"tools/{tenant_id}/{unique_name}{extension}"
         storage.save(filename, blob)
 
         tool_file = ToolFile(
@@ -166,13 +166,12 @@ class ToolFileManager:
         # Check if message_file is not None
         if message_file is not None:
             # get tool file id
-            tool_file_id = message_file.url.split('/')[-1]
+            tool_file_id = message_file.url.split("/")[-1]
             # trim extension
-            tool_file_id = tool_file_id.split('.')[0]
+            tool_file_id = tool_file_id.split(".")[0]
         else:
             tool_file_id = None
 
-
         tool_file: ToolFile = (
             db.session.query(ToolFile)
             .filter(
@@ -216,4 +215,4 @@ class ToolFileManager:
 # init tool_file_parser
 from core.file.tool_file_parser import tool_file_manager
 
-tool_file_manager['manager'] = ToolFileManager
+tool_file_manager["manager"] = ToolFileManager
diff --git a/api/core/tools/tool_label_manager.py b/api/core/tools/tool_label_manager.py
index 97788a7a07..2a5a2944ef 100644
--- a/api/core/tools/tool_label_manager.py
+++ b/api/core/tools/tool_label_manager.py
@@ -15,7 +15,7 @@ class ToolLabelManager:
         """
         tool_labels = [label for label in tool_labels if label in default_tool_label_name_list]
         return list(set(tool_labels))
-    
+
     @classmethod
     def update_tool_labels(cls, controller: ToolProviderController, labels: list[str]):
         """
@@ -26,20 +26,20 @@ class ToolLabelManager:
         if isinstance(controller, ApiToolProviderController | WorkflowToolProviderController):
             provider_id = controller.provider_id
         else:
-            raise ValueError('Unsupported tool type')
+            raise ValueError("Unsupported tool type")
 
         # delete old labels
-        db.session.query(ToolLabelBinding).filter(
-            ToolLabelBinding.tool_id == provider_id
-        ).delete()
+        db.session.query(ToolLabelBinding).filter(ToolLabelBinding.tool_id == provider_id).delete()
 
         # insert new labels
         for label in labels:
-            db.session.add(ToolLabelBinding(
-                tool_id=provider_id,
-                tool_type=controller.provider_type.value,
-                label_name=label,
-            ))
+            db.session.add(
+                ToolLabelBinding(
+                    tool_id=provider_id,
+                    tool_type=controller.provider_type.value,
+                    label_name=label,
+                )
+            )
 
         db.session.commit()
 
@@ -53,12 +53,16 @@ class ToolLabelManager:
         elif isinstance(controller, BuiltinToolProviderController):
             return controller.tool_labels
         else:
-            raise ValueError('Unsupported tool type')
+            raise ValueError("Unsupported tool type")
 
-        labels: list[ToolLabelBinding] = db.session.query(ToolLabelBinding.label_name).filter(
-            ToolLabelBinding.tool_id == provider_id,
-            ToolLabelBinding.tool_type == controller.provider_type.value,
-        ).all()
+        labels: list[ToolLabelBinding] = (
+            db.session.query(ToolLabelBinding.label_name)
+            .filter(
+                ToolLabelBinding.tool_id == provider_id,
+                ToolLabelBinding.tool_type == controller.provider_type.value,
+            )
+            .all()
+        )
 
         return [label.label_name for label in labels]
 
@@ -75,22 +79,20 @@ class ToolLabelManager:
         """
         if not tool_providers:
             return {}
-        
+
         for controller in tool_providers:
             if not isinstance(controller, ApiToolProviderController | WorkflowToolProviderController):
-                raise ValueError('Unsupported tool type')
-            
+                raise ValueError("Unsupported tool type")
+
         provider_ids = [controller.provider_id for controller in tool_providers]
 
-        labels: list[ToolLabelBinding] = db.session.query(ToolLabelBinding).filter(
-            ToolLabelBinding.tool_id.in_(provider_ids)
-        ).all()
+        labels: list[ToolLabelBinding] = (
+            db.session.query(ToolLabelBinding).filter(ToolLabelBinding.tool_id.in_(provider_ids)).all()
+        )
 
-        tool_labels = {
-            label.tool_id: [] for label in labels
-        }
+        tool_labels = {label.tool_id: [] for label in labels}
 
         for label in labels:
             tool_labels[label.tool_id].append(label.label_name)
 
-        return tool_labels
\ No newline at end of file
+        return tool_labels
diff --git a/api/core/tools/tool_manager.py b/api/core/tools/tool_manager.py
index 4a0188af49..a3303797e1 100644
--- a/api/core/tools/tool_manager.py
+++ b/api/core/tools/tool_manager.py
@@ -25,7 +25,6 @@ from core.tools.tool.tool import Tool
 from core.tools.tool_label_manager import ToolLabelManager
 from core.tools.utils.configuration import ToolConfigurationManager, ToolParameterConfigurationManager
 from core.tools.utils.tool_parameter_converter import ToolParameterConverter
-from core.workflow.nodes.tool.entities import ToolEntity
 from extensions.ext_database import db
 from models.tools import ApiToolProvider, BuiltinToolProvider, WorkflowToolProvider
 from services.tools.tools_transform_service import ToolTransformService
@@ -42,29 +41,29 @@ class ToolManager:
     @classmethod
     def get_builtin_provider(cls, provider: str) -> BuiltinToolProviderController:
         """
-            get the builtin provider
+        get the builtin provider
 
-            :param provider: the name of the provider
-            :return: the provider
+        :param provider: the name of the provider
+        :return: the provider
         """
         if len(cls._builtin_providers) == 0:
             # init the builtin providers
             cls.load_builtin_providers_cache()
 
         if provider not in cls._builtin_providers:
-            raise ToolProviderNotFoundError(f'builtin provider {provider} not found')
+            raise ToolProviderNotFoundError(f"builtin provider {provider} not found")
 
         return cls._builtin_providers[provider]
 
     @classmethod
     def get_builtin_tool(cls, provider: str, tool_name: str) -> BuiltinTool:
         """
-            get the builtin tool
+        get the builtin tool
 
-            :param provider: the name of the provider
-            :param tool_name: the name of the tool
+        :param provider: the name of the provider
+        :param tool_name: the name of the tool
 
-            :return: the provider, the tool
+        :return: the provider, the tool
         """
         provider_controller = cls.get_builtin_provider(provider)
         tool = provider_controller.get_tool(tool_name)
@@ -72,67 +71,76 @@ class ToolManager:
         return tool
 
     @classmethod
-    def get_tool(cls, provider_type: str, provider_id: str, tool_name: str, tenant_id: str = None) \
-            -> Union[BuiltinTool, ApiTool]:
+    def get_tool(
+        cls, provider_type: str, provider_id: str, tool_name: str, tenant_id: str = None
+    ) -> Union[BuiltinTool, ApiTool]:
         """
-            get the tool
+        get the tool
 
-            :param provider_type: the type of the provider
-            :param provider_name: the name of the provider
-            :param tool_name: the name of the tool
+        :param provider_type: the type of the provider
+        :param provider_name: the name of the provider
+        :param tool_name: the name of the tool
 
-            :return: the tool
+        :return: the tool
         """
-        if provider_type == 'builtin':
+        if provider_type == "builtin":
             return cls.get_builtin_tool(provider_id, tool_name)
-        elif provider_type == 'api':
+        elif provider_type == "api":
             if tenant_id is None:
-                raise ValueError('tenant id is required for api provider')
+                raise ValueError("tenant id is required for api provider")
             api_provider, _ = cls.get_api_provider_controller(tenant_id, provider_id)
             return api_provider.get_tool(tool_name)
-        elif provider_type == 'app':
-            raise NotImplementedError('app provider not implemented')
+        elif provider_type == "app":
+            raise NotImplementedError("app provider not implemented")
         else:
-            raise ToolProviderNotFoundError(f'provider type {provider_type} not found')
+            raise ToolProviderNotFoundError(f"provider type {provider_type} not found")
 
     @classmethod
-    def get_tool_runtime(cls, provider_type: str,
-                         provider_id: str,
-                         tool_name: str,
-                         tenant_id: str,
-                         invoke_from: InvokeFrom = InvokeFrom.DEBUGGER,
-                         tool_invoke_from: ToolInvokeFrom = ToolInvokeFrom.AGENT) \
-            -> Union[BuiltinTool, ApiTool]:
+    def get_tool_runtime(
+        cls,
+        provider_type: str,
+        provider_id: str,
+        tool_name: str,
+        tenant_id: str,
+        invoke_from: InvokeFrom = InvokeFrom.DEBUGGER,
+        tool_invoke_from: ToolInvokeFrom = ToolInvokeFrom.AGENT,
+    ) -> Union[BuiltinTool, ApiTool]:
         """
-            get the tool runtime
+        get the tool runtime
 
-            :param provider_type: the type of the provider
-            :param provider_name: the name of the provider
-            :param tool_name: the name of the tool
+        :param provider_type: the type of the provider
+        :param provider_name: the name of the provider
+        :param tool_name: the name of the tool
 
-            :return: the tool
+        :return: the tool
         """
-        if provider_type == 'builtin':
+        if provider_type == "builtin":
             builtin_tool = cls.get_builtin_tool(provider_id, tool_name)
 
             # check if the builtin tool need credentials
             provider_controller = cls.get_builtin_provider(provider_id)
             if not provider_controller.need_credentials:
-                return builtin_tool.fork_tool_runtime(runtime={
-                    'tenant_id': tenant_id,
-                    'credentials': {},
-                    'invoke_from': invoke_from,
-                    'tool_invoke_from': tool_invoke_from,
-                })
+                return builtin_tool.fork_tool_runtime(
+                    runtime={
+                        "tenant_id": tenant_id,
+                        "credentials": {},
+                        "invoke_from": invoke_from,
+                        "tool_invoke_from": tool_invoke_from,
+                    }
+                )
 
             # get credentials
-            builtin_provider: BuiltinToolProvider = db.session.query(BuiltinToolProvider).filter(
-                BuiltinToolProvider.tenant_id == tenant_id,
-                BuiltinToolProvider.provider == provider_id,
-            ).first()
+            builtin_provider: BuiltinToolProvider = (
+                db.session.query(BuiltinToolProvider)
+                .filter(
+                    BuiltinToolProvider.tenant_id == tenant_id,
+                    BuiltinToolProvider.provider == provider_id,
+                )
+                .first()
+            )
 
             if builtin_provider is None:
-                raise ToolProviderNotFoundError(f'builtin provider {provider_id} not found')
+                raise ToolProviderNotFoundError(f"builtin provider {provider_id} not found")
 
             # decrypt the credentials
             credentials = builtin_provider.credentials
@@ -141,17 +149,19 @@ class ToolManager:
 
             decrypted_credentials = tool_configuration.decrypt_tool_credentials(credentials)
 
-            return builtin_tool.fork_tool_runtime(runtime={
-                'tenant_id': tenant_id,
-                'credentials': decrypted_credentials,
-                'runtime_parameters': {},
-                'invoke_from': invoke_from,
-                'tool_invoke_from': tool_invoke_from,
-            })
+            return builtin_tool.fork_tool_runtime(
+                runtime={
+                    "tenant_id": tenant_id,
+                    "credentials": decrypted_credentials,
+                    "runtime_parameters": {},
+                    "invoke_from": invoke_from,
+                    "tool_invoke_from": tool_invoke_from,
+                }
+            )
 
-        elif provider_type == 'api':
+        elif provider_type == "api":
             if tenant_id is None:
-                raise ValueError('tenant id is required for api provider')
+                raise ValueError("tenant id is required for api provider")
 
             api_provider, credentials = cls.get_api_provider_controller(tenant_id, provider_id)
 
@@ -159,40 +169,43 @@ class ToolManager:
             tool_configuration = ToolConfigurationManager(tenant_id=tenant_id, provider_controller=api_provider)
             decrypted_credentials = tool_configuration.decrypt_tool_credentials(credentials)
 
-            return api_provider.get_tool(tool_name).fork_tool_runtime(runtime={
-                'tenant_id': tenant_id,
-                'credentials': decrypted_credentials,
-                'invoke_from': invoke_from,
-                'tool_invoke_from': tool_invoke_from,
-            })
-        elif provider_type == 'workflow':
-            workflow_provider = db.session.query(WorkflowToolProvider).filter(
-                WorkflowToolProvider.tenant_id == tenant_id,
-                WorkflowToolProvider.id == provider_id
-            ).first()
-
-            if workflow_provider is None:
-                raise ToolProviderNotFoundError(f'workflow provider {provider_id} not found')
-
-            controller = ToolTransformService.workflow_provider_to_controller(
-                db_provider=workflow_provider
+            return api_provider.get_tool(tool_name).fork_tool_runtime(
+                runtime={
+                    "tenant_id": tenant_id,
+                    "credentials": decrypted_credentials,
+                    "invoke_from": invoke_from,
+                    "tool_invoke_from": tool_invoke_from,
+                }
+            )
+        elif provider_type == "workflow":
+            workflow_provider = (
+                db.session.query(WorkflowToolProvider)
+                .filter(WorkflowToolProvider.tenant_id == tenant_id, WorkflowToolProvider.id == provider_id)
+                .first()
             )
 
-            return controller.get_tools(user_id=None, tenant_id=workflow_provider.tenant_id)[0].fork_tool_runtime(runtime={
-                'tenant_id': tenant_id,
-                'credentials': {},
-                'invoke_from': invoke_from,
-                'tool_invoke_from': tool_invoke_from,
-            })
-        elif provider_type == 'app':
-            raise NotImplementedError('app provider not implemented')
+            if workflow_provider is None:
+                raise ToolProviderNotFoundError(f"workflow provider {provider_id} not found")
+
+            controller = ToolTransformService.workflow_provider_to_controller(db_provider=workflow_provider)
+
+            return controller.get_tools(user_id=None, tenant_id=workflow_provider.tenant_id)[0].fork_tool_runtime(
+                runtime={
+                    "tenant_id": tenant_id,
+                    "credentials": {},
+                    "invoke_from": invoke_from,
+                    "tool_invoke_from": tool_invoke_from,
+                }
+            )
+        elif provider_type == "app":
+            raise NotImplementedError("app provider not implemented")
         else:
-            raise ToolProviderNotFoundError(f'provider type {provider_type} not found')
+            raise ToolProviderNotFoundError(f"provider type {provider_type} not found")
 
     @classmethod
     def _init_runtime_parameter(cls, parameter_rule: ToolParameter, parameters: dict) -> Union[str, int, float, bool]:
         """
-            init runtime parameter
+        init runtime parameter
         """
         parameter_value = parameters.get(parameter_rule.name)
         if not parameter_value and parameter_value != 0:
@@ -206,14 +219,17 @@ class ToolManager:
             options = [x.value for x in parameter_rule.options]
             if parameter_value is not None and parameter_value not in options:
                 raise ValueError(
-                    f"tool parameter {parameter_rule.name} value {parameter_value} not in options {options}")
+                    f"tool parameter {parameter_rule.name} value {parameter_value} not in options {options}"
+                )
 
         return ToolParameterConverter.cast_parameter_by_type(parameter_value, parameter_rule.type)
 
     @classmethod
-    def get_agent_tool_runtime(cls, tenant_id: str, app_id: str, agent_tool: AgentToolEntity, invoke_from: InvokeFrom = InvokeFrom.DEBUGGER) -> Tool:
+    def get_agent_tool_runtime(
+        cls, tenant_id: str, app_id: str, agent_tool: AgentToolEntity, invoke_from: InvokeFrom = InvokeFrom.DEBUGGER
+    ) -> Tool:
         """
-            get the agent tool runtime
+        get the agent tool runtime
         """
         tool_entity = cls.get_tool_runtime(
             provider_type=agent_tool.provider_type,
@@ -221,7 +237,7 @@ class ToolManager:
             tool_name=agent_tool.tool_name,
             tenant_id=tenant_id,
             invoke_from=invoke_from,
-            tool_invoke_from=ToolInvokeFrom.AGENT
+            tool_invoke_from=ToolInvokeFrom.AGENT,
         )
         runtime_parameters = {}
         parameters = tool_entity.get_all_runtime_parameters()
@@ -241,7 +257,7 @@ class ToolManager:
             tool_runtime=tool_entity,
             provider_name=agent_tool.provider_id,
             provider_type=agent_tool.provider_type,
-            identity_id=f'AGENT.{app_id}'
+            identity_id=f"AGENT.{app_id}",
         )
         runtime_parameters = encryption_manager.decrypt_tool_parameters(runtime_parameters)
 
@@ -249,9 +265,16 @@ class ToolManager:
         return tool_entity
 
     @classmethod
-    def get_workflow_tool_runtime(cls, tenant_id: str, app_id: str, node_id: str, workflow_tool: ToolEntity, invoke_from: InvokeFrom = InvokeFrom.DEBUGGER) -> Tool:
+    def get_workflow_tool_runtime(
+        cls,
+        tenant_id: str,
+        app_id: str,
+        node_id: str,
+        workflow_tool: "ToolEntity",
+        invoke_from: InvokeFrom = InvokeFrom.DEBUGGER,
+    ) -> Tool:
         """
-            get the workflow tool runtime
+        get the workflow tool runtime
         """
         tool_entity = cls.get_tool_runtime(
             provider_type=workflow_tool.provider_type,
@@ -259,7 +282,7 @@ class ToolManager:
             tool_name=workflow_tool.tool_name,
             tenant_id=tenant_id,
             invoke_from=invoke_from,
-            tool_invoke_from=ToolInvokeFrom.WORKFLOW
+            tool_invoke_from=ToolInvokeFrom.WORKFLOW,
         )
         runtime_parameters = {}
         parameters = tool_entity.get_all_runtime_parameters()
@@ -276,7 +299,7 @@ class ToolManager:
             tool_runtime=tool_entity,
             provider_name=workflow_tool.provider_id,
             provider_type=workflow_tool.provider_type,
-            identity_id=f'WORKFLOW.{app_id}.{node_id}'
+            identity_id=f"WORKFLOW.{app_id}.{node_id}",
         )
 
         if runtime_parameters:
@@ -288,24 +311,30 @@ class ToolManager:
     @classmethod
     def get_builtin_provider_icon(cls, provider: str) -> tuple[str, str]:
         """
-            get the absolute path of the icon of the builtin provider
+        get the absolute path of the icon of the builtin provider
 
-            :param provider: the name of the provider
+        :param provider: the name of the provider
 
-            :return: the absolute path of the icon, the mime type of the icon
+        :return: the absolute path of the icon, the mime type of the icon
         """
         # get provider
         provider_controller = cls.get_builtin_provider(provider)
 
-        absolute_path = path.join(path.dirname(path.realpath(__file__)), 'provider', 'builtin', provider, '_assets',
-                                  provider_controller.identity.icon)
+        absolute_path = path.join(
+            path.dirname(path.realpath(__file__)),
+            "provider",
+            "builtin",
+            provider,
+            "_assets",
+            provider_controller.identity.icon,
+        )
         # check if the icon exists
         if not path.exists(absolute_path):
-            raise ToolProviderNotFoundError(f'builtin provider {provider} icon not found')
+            raise ToolProviderNotFoundError(f"builtin provider {provider} icon not found")
 
         # get the mime type
         mime_type, _ = mimetypes.guess_type(absolute_path)
-        mime_type = mime_type or 'application/octet-stream'
+        mime_type = mime_type or "application/octet-stream"
 
         return absolute_path, mime_type
 
@@ -326,23 +355,25 @@ class ToolManager:
     @classmethod
     def _list_builtin_providers(cls) -> Generator[BuiltinToolProviderController, None, None]:
         """
-            list all the builtin providers
+        list all the builtin providers
         """
-        for provider in listdir(path.join(path.dirname(path.realpath(__file__)), 'provider', 'builtin')):
-            if provider.startswith('__'):
+        for provider in listdir(path.join(path.dirname(path.realpath(__file__)), "provider", "builtin")):
+            if provider.startswith("__"):
                 continue
 
-            if path.isdir(path.join(path.dirname(path.realpath(__file__)), 'provider', 'builtin', provider)):
-                if provider.startswith('__'):
+            if path.isdir(path.join(path.dirname(path.realpath(__file__)), "provider", "builtin", provider)):
+                if provider.startswith("__"):
                     continue
 
                 # init provider
                 try:
                     provider_class = load_single_subclass_from_source(
-                        module_name=f'core.tools.provider.builtin.{provider}.{provider}',
-                        script_path=path.join(path.dirname(path.realpath(__file__)),
-                                              'provider', 'builtin', provider, f'{provider}.py'),
-                        parent_type=BuiltinToolProviderController)
+                        module_name=f"core.tools.provider.builtin.{provider}.{provider}",
+                        script_path=path.join(
+                            path.dirname(path.realpath(__file__)), "provider", "builtin", provider, f"{provider}.py"
+                        ),
+                        parent_type=BuiltinToolProviderController,
+                    )
                     provider: BuiltinToolProviderController = provider_class()
                     cls._builtin_providers[provider.identity.name] = provider
                     for tool in provider.get_tools():
@@ -350,7 +381,7 @@ class ToolManager:
                     yield provider
 
                 except Exception as e:
-                    logger.error(f'load builtin provider {provider} error: {e}')
+                    logger.error(f"load builtin provider {provider} error: {e}")
                     continue
         # set builtin providers loaded
         cls._builtin_providers_loaded = True
@@ -368,11 +399,11 @@ class ToolManager:
     @classmethod
     def get_tool_label(cls, tool_name: str) -> Union[I18nObject, None]:
         """
-            get the tool label
+        get the tool label
 
-            :param tool_name: the name of the tool
+        :param tool_name: the name of the tool
 
-            :return: the label of the tool
+        :return: the label of the tool
         """
         if len(cls._builtin_tools_labels) == 0:
             # init the builtin providers
@@ -384,75 +415,78 @@ class ToolManager:
         return cls._builtin_tools_labels[tool_name]
 
     @classmethod
-    def user_list_providers(cls, user_id: str, tenant_id: str, typ: UserToolProviderTypeLiteral) -> list[UserToolProvider]:
+    def user_list_providers(
+        cls, user_id: str, tenant_id: str, typ: UserToolProviderTypeLiteral
+    ) -> list[UserToolProvider]:
         result_providers: dict[str, UserToolProvider] = {}
 
         filters = []
         if not typ:
-            filters.extend(['builtin', 'api', 'workflow'])
+            filters.extend(["builtin", "api", "workflow"])
         else:
             filters.append(typ)
 
-        if 'builtin' in filters:
-
+        if "builtin" in filters:
             # get builtin providers
             builtin_providers = cls.list_builtin_providers()
 
             # get db builtin providers
-            db_builtin_providers: list[BuiltinToolProvider] = db.session.query(BuiltinToolProvider). \
-                filter(BuiltinToolProvider.tenant_id == tenant_id).all()
+            db_builtin_providers: list[BuiltinToolProvider] = (
+                db.session.query(BuiltinToolProvider).filter(BuiltinToolProvider.tenant_id == tenant_id).all()
+            )
 
             find_db_builtin_provider = lambda provider: next(
-                (x for x in db_builtin_providers if x.provider == provider),
-                None
+                (x for x in db_builtin_providers if x.provider == provider), None
             )
 
             # append builtin providers
             for provider in builtin_providers:
                 # handle include, exclude
                 if is_filtered(
-                        include_set=dify_config.POSITION_TOOL_INCLUDES_SET,
-                        exclude_set=dify_config.POSITION_TOOL_EXCLUDES_SET,
-                        data=provider,
-                        name_func=lambda x: x.identity.name
+                    include_set=dify_config.POSITION_TOOL_INCLUDES_SET,
+                    exclude_set=dify_config.POSITION_TOOL_EXCLUDES_SET,
+                    data=provider,
+                    name_func=lambda x: x.identity.name,
                 ):
                     continue
 
                 user_provider = ToolTransformService.builtin_provider_to_user_provider(
                     provider_controller=provider,
                     db_provider=find_db_builtin_provider(provider.identity.name),
-                    decrypt_credentials=False
+                    decrypt_credentials=False,
                 )
 
                 result_providers[provider.identity.name] = user_provider
 
         # get db api providers
 
-        if 'api' in filters:
-            db_api_providers: list[ApiToolProvider] = db.session.query(ApiToolProvider). \
-                filter(ApiToolProvider.tenant_id == tenant_id).all()
+        if "api" in filters:
+            db_api_providers: list[ApiToolProvider] = (
+                db.session.query(ApiToolProvider).filter(ApiToolProvider.tenant_id == tenant_id).all()
+            )
 
-            api_provider_controllers = [{
-                'provider': provider,
-                'controller': ToolTransformService.api_provider_to_controller(provider)
-            } for provider in db_api_providers]
+            api_provider_controllers = [
+                {"provider": provider, "controller": ToolTransformService.api_provider_to_controller(provider)}
+                for provider in db_api_providers
+            ]
 
             # get labels
-            labels = ToolLabelManager.get_tools_labels([x['controller'] for x in api_provider_controllers])
+            labels = ToolLabelManager.get_tools_labels([x["controller"] for x in api_provider_controllers])
 
             for api_provider_controller in api_provider_controllers:
                 user_provider = ToolTransformService.api_provider_to_user_provider(
-                    provider_controller=api_provider_controller['controller'],
-                    db_provider=api_provider_controller['provider'],
+                    provider_controller=api_provider_controller["controller"],
+                    db_provider=api_provider_controller["provider"],
                     decrypt_credentials=False,
-                    labels=labels.get(api_provider_controller['controller'].provider_id, [])
+                    labels=labels.get(api_provider_controller["controller"].provider_id, []),
                 )
-                result_providers[f'api_provider.{user_provider.name}'] = user_provider
+                result_providers[f"api_provider.{user_provider.name}"] = user_provider
 
-        if 'workflow' in filters:
+        if "workflow" in filters:
             # get workflow providers
-            workflow_providers: list[WorkflowToolProvider] = db.session.query(WorkflowToolProvider). \
-                filter(WorkflowToolProvider.tenant_id == tenant_id).all()
+            workflow_providers: list[WorkflowToolProvider] = (
+                db.session.query(WorkflowToolProvider).filter(WorkflowToolProvider.tenant_id == tenant_id).all()
+            )
 
             workflow_provider_controllers = []
             for provider in workflow_providers:
@@ -471,32 +505,36 @@ class ToolManager:
                     provider_controller=provider_controller,
                     labels=labels.get(provider_controller.provider_id, []),
                 )
-                result_providers[f'workflow_provider.{user_provider.name}'] = user_provider
+                result_providers[f"workflow_provider.{user_provider.name}"] = user_provider
 
         return BuiltinToolProviderSort.sort(list(result_providers.values()))
 
     @classmethod
-    def get_api_provider_controller(cls, tenant_id: str, provider_id: str) -> tuple[
-            ApiToolProviderController, dict[str, Any]]:
+    def get_api_provider_controller(
+        cls, tenant_id: str, provider_id: str
+    ) -> tuple[ApiToolProviderController, dict[str, Any]]:
         """
-            get the api provider
+        get the api provider
 
-            :param provider_name: the name of the provider
+        :param provider_name: the name of the provider
 
-            :return: the provider controller, the credentials
+        :return: the provider controller, the credentials
         """
-        provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
-            ApiToolProvider.id == provider_id,
-            ApiToolProvider.tenant_id == tenant_id,
-        ).first()
+        provider: ApiToolProvider = (
+            db.session.query(ApiToolProvider)
+            .filter(
+                ApiToolProvider.id == provider_id,
+                ApiToolProvider.tenant_id == tenant_id,
+            )
+            .first()
+        )
 
         if provider is None:
-            raise ToolProviderNotFoundError(f'api provider {provider_id} not found')
+            raise ToolProviderNotFoundError(f"api provider {provider_id} not found")
 
         controller = ApiToolProviderController.from_db(
             provider,
-            ApiProviderAuthType.API_KEY if provider.credentials['auth_type'] == 'api_key' else
-            ApiProviderAuthType.NONE
+            ApiProviderAuthType.API_KEY if provider.credentials["auth_type"] == "api_key" else ApiProviderAuthType.NONE,
         )
         controller.load_bundled_tools(provider.tools)
 
@@ -505,18 +543,22 @@ class ToolManager:
     @classmethod
     def user_get_api_provider(cls, provider: str, tenant_id: str) -> dict:
         """
-            get api provider
+        get api provider
         """
         """
             get tool provider
         """
-        provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
-            ApiToolProvider.tenant_id == tenant_id,
-            ApiToolProvider.name == provider,
-        ).first()
+        provider: ApiToolProvider = (
+            db.session.query(ApiToolProvider)
+            .filter(
+                ApiToolProvider.tenant_id == tenant_id,
+                ApiToolProvider.name == provider,
+            )
+            .first()
+        )
 
         if provider is None:
-            raise ValueError(f'you have not added provider {provider}')
+            raise ValueError(f"you have not added provider {provider}")
 
         try:
             credentials = json.loads(provider.credentials_str) or {}
@@ -525,7 +567,7 @@ class ToolManager:
 
         # package tool provider controller
         controller = ApiToolProviderController.from_db(
-            provider, ApiProviderAuthType.API_KEY if credentials['auth_type'] == 'api_key' else ApiProviderAuthType.NONE
+            provider, ApiProviderAuthType.API_KEY if credentials["auth_type"] == "api_key" else ApiProviderAuthType.NONE
         )
         # init tool configuration
         tool_configuration = ToolConfigurationManager(tenant_id=tenant_id, provider_controller=controller)
@@ -536,62 +578,62 @@ class ToolManager:
         try:
             icon = json.loads(provider.icon)
         except:
-            icon = {
-                "background": "#252525",
-                "content": "\ud83d\ude01"
-            }
+            icon = {"background": "#252525", "content": "\ud83d\ude01"}
 
         # add tool labels
         labels = ToolLabelManager.get_tool_labels(controller)
 
-        return jsonable_encoder({
-            'schema_type': provider.schema_type,
-            'schema': provider.schema,
-            'tools': provider.tools,
-            'icon': icon,
-            'description': provider.description,
-            'credentials': masked_credentials,
-            'privacy_policy': provider.privacy_policy,
-            'custom_disclaimer': provider.custom_disclaimer,
-            'labels': labels,
-        })
+        return jsonable_encoder(
+            {
+                "schema_type": provider.schema_type,
+                "schema": provider.schema,
+                "tools": provider.tools,
+                "icon": icon,
+                "description": provider.description,
+                "credentials": masked_credentials,
+                "privacy_policy": provider.privacy_policy,
+                "custom_disclaimer": provider.custom_disclaimer,
+                "labels": labels,
+            }
+        )
 
     @classmethod
     def get_tool_icon(cls, tenant_id: str, provider_type: str, provider_id: str) -> Union[str, dict]:
         """
-            get the tool icon
+        get the tool icon
 
-            :param tenant_id: the id of the tenant
-            :param provider_type: the type of the provider
-            :param provider_id: the id of the provider
-            :return:
+        :param tenant_id: the id of the tenant
+        :param provider_type: the type of the provider
+        :param provider_id: the id of the provider
+        :return:
         """
         provider_type = provider_type
         provider_id = provider_id
-        if provider_type == 'builtin':
-            return (dify_config.CONSOLE_API_URL
-                    + "/console/api/workspaces/current/tool-provider/builtin/"
-                    + provider_id
-                    + "/icon")
-        elif provider_type == 'api':
+        if provider_type == "builtin":
+            return (
+                dify_config.CONSOLE_API_URL
+                + "/console/api/workspaces/current/tool-provider/builtin/"
+                + provider_id
+                + "/icon"
+            )
+        elif provider_type == "api":
             try:
-                provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
-                    ApiToolProvider.tenant_id == tenant_id,
-                    ApiToolProvider.id == provider_id
-                ).first()
+                provider: ApiToolProvider = (
+                    db.session.query(ApiToolProvider)
+                    .filter(ApiToolProvider.tenant_id == tenant_id, ApiToolProvider.id == provider_id)
+                    .first()
+                )
                 return json.loads(provider.icon)
             except:
-                return {
-                    "background": "#252525",
-                    "content": "\ud83d\ude01"
-                }
-        elif provider_type == 'workflow':
-            provider: WorkflowToolProvider = db.session.query(WorkflowToolProvider).filter(
-                WorkflowToolProvider.tenant_id == tenant_id,
-                WorkflowToolProvider.id == provider_id
-            ).first()
+                return {"background": "#252525", "content": "\ud83d\ude01"}
+        elif provider_type == "workflow":
+            provider: WorkflowToolProvider = (
+                db.session.query(WorkflowToolProvider)
+                .filter(WorkflowToolProvider.tenant_id == tenant_id, WorkflowToolProvider.id == provider_id)
+                .first()
+            )
             if provider is None:
-                raise ToolProviderNotFoundError(f'workflow provider {provider_id} not found')
+                raise ToolProviderNotFoundError(f"workflow provider {provider_id} not found")
 
             return json.loads(provider.icon)
         else:
diff --git a/api/core/tools/utils/configuration.py b/api/core/tools/utils/configuration.py
index b213879e96..83600d21c1 100644
--- a/api/core/tools/utils/configuration.py
+++ b/api/core/tools/utils/configuration.py
@@ -56,12 +56,13 @@ class ToolConfigurationManager(BaseModel):
             if field.type == ToolProviderCredentials.CredentialsType.SECRET_INPUT:
                 if field_name in credentials:
                     if len(credentials[field_name]) > 6:
-                        credentials[field_name] = \
-                            credentials[field_name][:2] + \
-                            '*' * (len(credentials[field_name]) - 4) + \
-                            credentials[field_name][-2:]
+                        credentials[field_name] = (
+                            credentials[field_name][:2]
+                            + "*" * (len(credentials[field_name]) - 4)
+                            + credentials[field_name][-2:]
+                        )
                     else:
-                        credentials[field_name] = '*' * len(credentials[field_name])
+                        credentials[field_name] = "*" * len(credentials[field_name])
 
         return credentials
 
@@ -72,9 +73,9 @@ class ToolConfigurationManager(BaseModel):
         return a deep copy of credentials with decrypted values
         """
         cache = ToolProviderCredentialsCache(
-            tenant_id=self.tenant_id, 
-            identity_id=f'{self.provider_controller.provider_type.value}.{self.provider_controller.identity.name}',
-            cache_type=ToolProviderCredentialsCacheType.PROVIDER
+            tenant_id=self.tenant_id,
+            identity_id=f"{self.provider_controller.provider_type.value}.{self.provider_controller.identity.name}",
+            cache_type=ToolProviderCredentialsCacheType.PROVIDER,
         )
         cached_credentials = cache.get()
         if cached_credentials:
@@ -95,16 +96,18 @@ class ToolConfigurationManager(BaseModel):
 
     def delete_tool_credentials_cache(self):
         cache = ToolProviderCredentialsCache(
-            tenant_id=self.tenant_id, 
-            identity_id=f'{self.provider_controller.provider_type.value}.{self.provider_controller.identity.name}',
-            cache_type=ToolProviderCredentialsCacheType.PROVIDER
+            tenant_id=self.tenant_id,
+            identity_id=f"{self.provider_controller.provider_type.value}.{self.provider_controller.identity.name}",
+            cache_type=ToolProviderCredentialsCacheType.PROVIDER,
         )
         cache.delete()
 
+
 class ToolParameterConfigurationManager(BaseModel):
     """
     Tool parameter configuration manager
     """
+
     tenant_id: str
     tool_runtime: Tool
     provider_name: str
@@ -152,15 +155,19 @@ class ToolParameterConfigurationManager(BaseModel):
         current_parameters = self._merge_parameters()
 
         for parameter in current_parameters:
-            if parameter.form == ToolParameter.ToolParameterForm.FORM and parameter.type == ToolParameter.ToolParameterType.SECRET_INPUT:
+            if (
+                parameter.form == ToolParameter.ToolParameterForm.FORM
+                and parameter.type == ToolParameter.ToolParameterType.SECRET_INPUT
+            ):
                 if parameter.name in parameters:
                     if len(parameters[parameter.name]) > 6:
-                        parameters[parameter.name] = \
-                            parameters[parameter.name][:2] + \
-                            '*' * (len(parameters[parameter.name]) - 4) + \
-                            parameters[parameter.name][-2:]
+                        parameters[parameter.name] = (
+                            parameters[parameter.name][:2]
+                            + "*" * (len(parameters[parameter.name]) - 4)
+                            + parameters[parameter.name][-2:]
+                        )
                     else:
-                        parameters[parameter.name] = '*' * len(parameters[parameter.name])
+                        parameters[parameter.name] = "*" * len(parameters[parameter.name])
 
         return parameters
 
@@ -176,7 +183,10 @@ class ToolParameterConfigurationManager(BaseModel):
         parameters = self._deep_copy(parameters)
 
         for parameter in current_parameters:
-            if parameter.form == ToolParameter.ToolParameterForm.FORM and parameter.type == ToolParameter.ToolParameterType.SECRET_INPUT:
+            if (
+                parameter.form == ToolParameter.ToolParameterForm.FORM
+                and parameter.type == ToolParameter.ToolParameterType.SECRET_INPUT
+            ):
                 if parameter.name in parameters:
                     encrypted = encrypter.encrypt_token(self.tenant_id, parameters[parameter.name])
                     parameters[parameter.name] = encrypted
@@ -191,10 +201,10 @@ class ToolParameterConfigurationManager(BaseModel):
         """
         cache = ToolParameterCache(
             tenant_id=self.tenant_id,
-            provider=f'{self.provider_type}.{self.provider_name}',
+            provider=f"{self.provider_type}.{self.provider_name}",
             tool_name=self.tool_runtime.identity.name,
             cache_type=ToolParameterCacheType.PARAMETER,
-            identity_id=self.identity_id
+            identity_id=self.identity_id,
         )
         cached_parameters = cache.get()
         if cached_parameters:
@@ -205,7 +215,10 @@ class ToolParameterConfigurationManager(BaseModel):
         has_secret_input = False
 
         for parameter in current_parameters:
-            if parameter.form == ToolParameter.ToolParameterForm.FORM and parameter.type == ToolParameter.ToolParameterType.SECRET_INPUT:
+            if (
+                parameter.form == ToolParameter.ToolParameterForm.FORM
+                and parameter.type == ToolParameter.ToolParameterType.SECRET_INPUT
+            ):
                 if parameter.name in parameters:
                     try:
                         has_secret_input = True
@@ -221,9 +234,9 @@ class ToolParameterConfigurationManager(BaseModel):
     def delete_tool_parameters_cache(self):
         cache = ToolParameterCache(
             tenant_id=self.tenant_id,
-            provider=f'{self.provider_type}.{self.provider_name}',
+            provider=f"{self.provider_type}.{self.provider_name}",
             tool_name=self.tool_runtime.identity.name,
             cache_type=ToolParameterCacheType.PARAMETER,
-            identity_id=self.identity_id
+            identity_id=self.identity_id,
         )
         cache.delete()
diff --git a/api/core/tools/utils/feishu_api_utils.py b/api/core/tools/utils/feishu_api_utils.py
index e6b288868f..ffdb06498f 100644
--- a/api/core/tools/utils/feishu_api_utils.py
+++ b/api/core/tools/utils/feishu_api_utils.py
@@ -17,8 +17,9 @@ class FeishuRequest:
         redis_client.setex(feishu_tenant_access_token, res.get("expire"), res.get("tenant_access_token"))
         return res.get("tenant_access_token")
 
-    def _send_request(self, url: str, method: str = "post", require_token: bool = True, payload: dict = None,
-                      params: dict = None):
+    def _send_request(
+        self, url: str, method: str = "post", require_token: bool = True, payload: dict = None, params: dict = None
+    ):
         headers = {
             "Content-Type": "application/json",
             "user-agent": "Dify",
@@ -42,10 +43,7 @@ class FeishuRequest:
         }
         """
         url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/access_token/get_tenant_access_token"
-        payload = {
-            "app_id": app_id,
-            "app_secret": app_secret
-        }
+        payload = {"app_id": app_id, "app_secret": app_secret}
         res = self._send_request(url, require_token=False, payload=payload)
         return res
 
@@ -76,15 +74,11 @@ class FeishuRequest:
 
     def write_document(self, document_id: str, content: str, position: str = "start") -> dict:
         url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/write_document"
-        payload = {
-            "document_id": document_id,
-            "content": content,
-            "position": position
-        }
+        payload = {"document_id": document_id, "content": content, "position": position}
         res = self._send_request(url, payload=payload)
-        return res.get("data")
+        return res
 
-    def get_document_raw_content(self, document_id: str) -> dict:
+    def get_document_content(self, document_id: str, mode: str, lang: int = 0) -> dict:
         """
         API url: https://open.larkoffice.com/document/server-docs/docs/docs/docx-v1/document/raw_content
         Example Response:
@@ -95,19 +89,21 @@ class FeishuRequest:
                 "content": "云文档\n多人实时协同，插入一切元素。不仅是在线文档，更是强大的创作和互动工具\n云文档：专为协作而生\n"
             }
         }
-        """
+        """  # noqa: E501
         params = {
             "document_id": document_id,
+            "mode": mode,
+            "lang": lang,
         }
-        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/get_document_raw_content"
+        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/get_document_content"
         res = self._send_request(url, method="get", params=params)
         return res.get("data").get("content")
 
-    def list_document_block(self, document_id: str, page_token: str, page_size: int = 500) -> dict:
+    def list_document_blocks(self, document_id: str, page_token: str, page_size: int = 500) -> dict:
         """
         API url: https://open.larkoffice.com/document/server-docs/docs/docs/docx-v1/document/list
         """
-        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/list_document_block"
+        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/list_document_blocks"
         params = {
             "document_id": document_id,
             "page_size": page_size,
diff --git a/api/core/tools/utils/message_transformer.py b/api/core/tools/utils/message_transformer.py
index 564b9d3e14..3cfab207ba 100644
--- a/api/core/tools/utils/message_transformer.py
+++ b/api/core/tools/utils/message_transformer.py
@@ -7,98 +7,106 @@ from core.tools.tool_file_manager import ToolFileManager
 
 logger = logging.getLogger(__name__)
 
+
 class ToolFileMessageTransformer:
     @classmethod
-    def transform_tool_invoke_messages(cls, messages: list[ToolInvokeMessage],
-                                       user_id: str,
-                                       tenant_id: str,
-                                       conversation_id: str) -> list[ToolInvokeMessage]:
+    def transform_tool_invoke_messages(
+        cls, messages: list[ToolInvokeMessage], user_id: str, tenant_id: str, conversation_id: str
+    ) -> list[ToolInvokeMessage]:
         """
         Transform tool message and handle file download
         """
         result = []
 
         for message in messages:
-            if message.type == ToolInvokeMessage.MessageType.TEXT:
-                result.append(message)
-            elif message.type == ToolInvokeMessage.MessageType.LINK:
+            if message.type in {ToolInvokeMessage.MessageType.TEXT, ToolInvokeMessage.MessageType.LINK}:
                 result.append(message)
             elif message.type == ToolInvokeMessage.MessageType.IMAGE:
                 # try to download image
                 try:
                     file = ToolFileManager.create_file_by_url(
-                        user_id=user_id,
-                        tenant_id=tenant_id,
-                        conversation_id=conversation_id,
-                        file_url=message.message
+                        user_id=user_id, tenant_id=tenant_id, conversation_id=conversation_id, file_url=message.message
                     )
 
                     url = f'/files/tools/{file.id}{guess_extension(file.mimetype) or ".png"}'
 
-                    result.append(ToolInvokeMessage(
-                        type=ToolInvokeMessage.MessageType.IMAGE_LINK,
-                        message=url,
-                        save_as=message.save_as,
-                        meta=message.meta.copy() if message.meta is not None else {},
-                    ))
+                    result.append(
+                        ToolInvokeMessage(
+                            type=ToolInvokeMessage.MessageType.IMAGE_LINK,
+                            message=url,
+                            save_as=message.save_as,
+                            meta=message.meta.copy() if message.meta is not None else {},
+                        )
+                    )
                 except Exception as e:
                     logger.exception(e)
-                    result.append(ToolInvokeMessage(
-                        type=ToolInvokeMessage.MessageType.TEXT,
-                        message=f"Failed to download image: {message.message}, you can try to download it yourself.",
-                        meta=message.meta.copy() if message.meta is not None else {},
-                        save_as=message.save_as,
-                    ))
+                    result.append(
+                        ToolInvokeMessage(
+                            type=ToolInvokeMessage.MessageType.TEXT,
+                            message=f"Failed to download image: {message.message}, please try to download it manually.",
+                            meta=message.meta.copy() if message.meta is not None else {},
+                            save_as=message.save_as,
+                        )
+                    )
             elif message.type == ToolInvokeMessage.MessageType.BLOB:
                 # get mime type and save blob to storage
-                mimetype = message.meta.get('mime_type', 'octet/stream')
+                mimetype = message.meta.get("mime_type", "octet/stream")
                 # if message is str, encode it to bytes
                 if isinstance(message.message, str):
-                    message.message = message.message.encode('utf-8')
+                    message.message = message.message.encode("utf-8")
 
                 file = ToolFileManager.create_file_by_raw(
-                    user_id=user_id, tenant_id=tenant_id,
+                    user_id=user_id,
+                    tenant_id=tenant_id,
                     conversation_id=conversation_id,
                     file_binary=message.message,
-                    mimetype=mimetype
+                    mimetype=mimetype,
                 )
 
                 url = cls.get_tool_file_url(file.id, guess_extension(file.mimetype))
 
                 # check if file is image
-                if 'image' in mimetype:
-                    result.append(ToolInvokeMessage(
-                        type=ToolInvokeMessage.MessageType.IMAGE_LINK,
-                        message=url,
-                        save_as=message.save_as,
-                        meta=message.meta.copy() if message.meta is not None else {},
-                    ))
+                if "image" in mimetype:
+                    result.append(
+                        ToolInvokeMessage(
+                            type=ToolInvokeMessage.MessageType.IMAGE_LINK,
+                            message=url,
+                            save_as=message.save_as,
+                            meta=message.meta.copy() if message.meta is not None else {},
+                        )
+                    )
                 else:
-                    result.append(ToolInvokeMessage(
-                        type=ToolInvokeMessage.MessageType.LINK,
-                        message=url,
-                        save_as=message.save_as,
-                        meta=message.meta.copy() if message.meta is not None else {},
-                    ))
+                    result.append(
+                        ToolInvokeMessage(
+                            type=ToolInvokeMessage.MessageType.LINK,
+                            message=url,
+                            save_as=message.save_as,
+                            meta=message.meta.copy() if message.meta is not None else {},
+                        )
+                    )
             elif message.type == ToolInvokeMessage.MessageType.FILE_VAR:
-                file_var = message.meta.get('file_var')
+                file_var = message.meta.get("file_var")
                 if file_var:
                     if file_var.transfer_method == FileTransferMethod.TOOL_FILE:
                         url = cls.get_tool_file_url(file_var.related_id, file_var.extension)
                         if file_var.type == FileType.IMAGE:
-                            result.append(ToolInvokeMessage(
-                                type=ToolInvokeMessage.MessageType.IMAGE_LINK,
-                                message=url,
-                                save_as=message.save_as,
-                                meta=message.meta.copy() if message.meta is not None else {},
-                            ))
+                            result.append(
+                                ToolInvokeMessage(
+                                    type=ToolInvokeMessage.MessageType.IMAGE_LINK,
+                                    message=url,
+                                    save_as=message.save_as,
+                                    meta=message.meta.copy() if message.meta is not None else {},
+                                )
+                            )
                         else:
-                            result.append(ToolInvokeMessage(
-                                type=ToolInvokeMessage.MessageType.LINK,
-                                message=url,
-                                save_as=message.save_as,
-                                meta=message.meta.copy() if message.meta is not None else {},
-                            ))
+                            result.append(
+                                ToolInvokeMessage(
+                                    type=ToolInvokeMessage.MessageType.LINK,
+                                    message=url,
+                                    save_as=message.save_as,
+                                    meta=message.meta.copy() if message.meta is not None else {},
+                                )
+                            )
             else:
                 result.append(message)
 
diff --git a/api/core/tools/utils/model_invocation_utils.py b/api/core/tools/utils/model_invocation_utils.py
index 9e8ef47823..4e226810d6 100644
--- a/api/core/tools/utils/model_invocation_utils.py
+++ b/api/core/tools/utils/model_invocation_utils.py
@@ -1,7 +1,7 @@
 """
-    For some reason, model will be used in tools like WebScraperTool, WikipediaSearchTool etc.
+For some reason, model will be used in tools like WebScraperTool, WikipediaSearchTool etc.
 
-    Therefore, a model manager is needed to list/invoke/validate models.
+Therefore, a model manager is needed to list/invoke/validate models.
 """
 
 import json
@@ -27,52 +27,49 @@ from models.tools import ToolModelInvoke
 class InvokeModelError(Exception):
     pass
 
+
 class ModelInvocationUtils:
     @staticmethod
     def get_max_llm_context_tokens(
         tenant_id: str,
     ) -> int:
         """
-            get max llm context tokens of the model
+        get max llm context tokens of the model
         """
         model_manager = ModelManager()
         model_instance = model_manager.get_default_model_instance(
-            tenant_id=tenant_id, model_type=ModelType.LLM,
+            tenant_id=tenant_id,
+            model_type=ModelType.LLM,
         )
 
         if not model_instance:
-            raise InvokeModelError('Model not found')
-        
+            raise InvokeModelError("Model not found")
+
         llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
         schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
 
         if not schema:
-            raise InvokeModelError('No model schema found')
+            raise InvokeModelError("No model schema found")
 
         max_tokens = schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE, None)
         if max_tokens is None:
             return 2048
-        
+
         return max_tokens
 
     @staticmethod
-    def calculate_tokens(
-        tenant_id: str,
-        prompt_messages: list[PromptMessage]
-    ) -> int:
+    def calculate_tokens(tenant_id: str, prompt_messages: list[PromptMessage]) -> int:
         """
-            calculate tokens from prompt messages and model parameters
+        calculate tokens from prompt messages and model parameters
         """
 
         # get model instance
         model_manager = ModelManager()
-        model_instance = model_manager.get_default_model_instance(
-            tenant_id=tenant_id, model_type=ModelType.LLM
-        )
+        model_instance = model_manager.get_default_model_instance(tenant_id=tenant_id, model_type=ModelType.LLM)
 
         if not model_instance:
-            raise InvokeModelError('Model not found')
-        
+            raise InvokeModelError("Model not found")
+
         # get tokens
         tokens = model_instance.get_llm_num_tokens(prompt_messages)
 
@@ -80,9 +77,7 @@ class ModelInvocationUtils:
 
     @staticmethod
     def invoke(
-        user_id: str, tenant_id: str,
-        tool_type: str, tool_name: str,
-        prompt_messages: list[PromptMessage]
+        user_id: str, tenant_id: str, tool_type: str, tool_name: str, prompt_messages: list[PromptMessage]
     ) -> LLMResult:
         """
         invoke model with parameters in user's own context
@@ -103,15 +98,16 @@ class ModelInvocationUtils:
         model_manager = ModelManager()
         # get model instance
         model_instance = model_manager.get_default_model_instance(
-            tenant_id=tenant_id, model_type=ModelType.LLM,
+            tenant_id=tenant_id,
+            model_type=ModelType.LLM,
         )
 
         # get prompt tokens
         prompt_tokens = model_instance.get_llm_num_tokens(prompt_messages)
 
         model_parameters = {
-            'temperature': 0.8,
-            'top_p': 0.8,
+            "temperature": 0.8,
+            "top_p": 0.8,
         }
 
         # create tool model invoke
@@ -123,14 +119,14 @@ class ModelInvocationUtils:
             tool_name=tool_name,
             model_parameters=json.dumps(model_parameters),
             prompt_messages=json.dumps(jsonable_encoder(prompt_messages)),
-            model_response='',
+            model_response="",
             prompt_tokens=prompt_tokens,
             answer_tokens=0,
             answer_unit_price=0,
             answer_price_unit=0,
             provider_response_latency=0,
             total_price=0,
-            currency='USD',
+            currency="USD",
         )
 
         db.session.add(tool_model_invoke)
@@ -140,20 +136,24 @@ class ModelInvocationUtils:
             response: LLMResult = model_instance.invoke_llm(
                 prompt_messages=prompt_messages,
                 model_parameters=model_parameters,
-                tools=[], stop=[], stream=False, user=user_id, callbacks=[]
+                tools=[],
+                stop=[],
+                stream=False,
+                user=user_id,
+                callbacks=[],
             )
         except InvokeRateLimitError as e:
-            raise InvokeModelError(f'Invoke rate limit error: {e}')
+            raise InvokeModelError(f"Invoke rate limit error: {e}")
         except InvokeBadRequestError as e:
-            raise InvokeModelError(f'Invoke bad request error: {e}')
+            raise InvokeModelError(f"Invoke bad request error: {e}")
         except InvokeConnectionError as e:
-            raise InvokeModelError(f'Invoke connection error: {e}')
+            raise InvokeModelError(f"Invoke connection error: {e}")
         except InvokeAuthorizationError as e:
-            raise InvokeModelError('Invoke authorization error')
+            raise InvokeModelError("Invoke authorization error")
         except InvokeServerUnavailableError as e:
-            raise InvokeModelError(f'Invoke server unavailable error: {e}')
+            raise InvokeModelError(f"Invoke server unavailable error: {e}")
         except Exception as e:
-            raise InvokeModelError(f'Invoke error: {e}')
+            raise InvokeModelError(f"Invoke error: {e}")
 
         # update tool model invoke
         tool_model_invoke.model_response = response.message.content
diff --git a/api/core/tools/utils/parser.py b/api/core/tools/utils/parser.py
index f711f7c9f3..9ead4f8e5c 100644
--- a/api/core/tools/utils/parser.py
+++ b/api/core/tools/utils/parser.py
@@ -1,4 +1,3 @@
-
 import re
 import uuid
 from json import dumps as json_dumps
@@ -16,54 +15,56 @@ from core.tools.errors import ToolApiSchemaError, ToolNotSupportedError, ToolPro
 
 class ApiBasedToolSchemaParser:
     @staticmethod
-    def parse_openapi_to_tool_bundle(openapi: dict, extra_info: dict = None, warning: dict = None) -> list[ApiToolBundle]:
+    def parse_openapi_to_tool_bundle(
+        openapi: dict, extra_info: dict = None, warning: dict = None
+    ) -> list[ApiToolBundle]:
         warning = warning if warning is not None else {}
         extra_info = extra_info if extra_info is not None else {}
 
         # set description to extra_info
-        extra_info['description'] = openapi['info'].get('description', '')
+        extra_info["description"] = openapi["info"].get("description", "")
 
-        if len(openapi['servers']) == 0:
-            raise ToolProviderNotFoundError('No server found in the openapi yaml.')
+        if len(openapi["servers"]) == 0:
+            raise ToolProviderNotFoundError("No server found in the openapi yaml.")
 
-        server_url = openapi['servers'][0]['url']
+        server_url = openapi["servers"][0]["url"]
 
         # list all interfaces
         interfaces = []
-        for path, path_item in openapi['paths'].items():
-            methods = ['get', 'post', 'put', 'delete', 'patch', 'head', 'options', 'trace']
+        for path, path_item in openapi["paths"].items():
+            methods = ["get", "post", "put", "delete", "patch", "head", "options", "trace"]
             for method in methods:
                 if method in path_item:
-                    interfaces.append({
-                        'path': path,
-                        'method': method,
-                        'operation': path_item[method],
-                    })
+                    interfaces.append(
+                        {
+                            "path": path,
+                            "method": method,
+                            "operation": path_item[method],
+                        }
+                    )
 
         # get all parameters
         bundles = []
         for interface in interfaces:
             # convert parameters
             parameters = []
-            if 'parameters' in interface['operation']:
-                for parameter in interface['operation']['parameters']:
+            if "parameters" in interface["operation"]:
+                for parameter in interface["operation"]["parameters"]:
                     tool_parameter = ToolParameter(
-                        name=parameter['name'],
-                        label=I18nObject(
-                            en_US=parameter['name'],
-                            zh_Hans=parameter['name']
-                        ),
+                        name=parameter["name"],
+                        label=I18nObject(en_US=parameter["name"], zh_Hans=parameter["name"]),
                         human_description=I18nObject(
-                            en_US=parameter.get('description', ''),
-                            zh_Hans=parameter.get('description', '')
+                            en_US=parameter.get("description", ""), zh_Hans=parameter.get("description", "")
                         ),
                         type=ToolParameter.ToolParameterType.STRING,
-                        required=parameter.get('required', False),
+                        required=parameter.get("required", False),
                         form=ToolParameter.ToolParameterForm.LLM,
-                        llm_description=parameter.get('description'),
-                        default=parameter['schema']['default'] if 'schema' in parameter and 'default' in parameter['schema'] else None,
+                        llm_description=parameter.get("description"),
+                        default=parameter["schema"]["default"]
+                        if "schema" in parameter and "default" in parameter["schema"]
+                        else None,
                     )
-                   
+
                     # check if there is a type
                     typ = ApiBasedToolSchemaParser._get_tool_parameter_type(parameter)
                     if typ:
@@ -72,44 +73,40 @@ class ApiBasedToolSchemaParser:
                     parameters.append(tool_parameter)
             # create tool bundle
             # check if there is a request body
-            if 'requestBody' in interface['operation']:
-                request_body = interface['operation']['requestBody']
-                if 'content' in request_body:
-                    for content_type, content in request_body['content'].items():
+            if "requestBody" in interface["operation"]:
+                request_body = interface["operation"]["requestBody"]
+                if "content" in request_body:
+                    for content_type, content in request_body["content"].items():
                         # if there is a reference, get the reference and overwrite the content
-                        if 'schema' not in content:
+                        if "schema" not in content:
                             continue
 
-                        if '$ref' in content['schema']:
+                        if "$ref" in content["schema"]:
                             # get the reference
                             root = openapi
-                            reference = content['schema']['$ref'].split('/')[1:]
+                            reference = content["schema"]["$ref"].split("/")[1:]
                             for ref in reference:
                                 root = root[ref]
                             # overwrite the content
-                            interface['operation']['requestBody']['content'][content_type]['schema'] = root
+                            interface["operation"]["requestBody"]["content"][content_type]["schema"] = root
 
                     # parse body parameters
-                    if 'schema' in interface['operation']['requestBody']['content'][content_type]:
-                        body_schema = interface['operation']['requestBody']['content'][content_type]['schema']
-                        required = body_schema.get('required', [])
-                        properties = body_schema.get('properties', {})
+                    if "schema" in interface["operation"]["requestBody"]["content"][content_type]:
+                        body_schema = interface["operation"]["requestBody"]["content"][content_type]["schema"]
+                        required = body_schema.get("required", [])
+                        properties = body_schema.get("properties", {})
                         for name, property in properties.items():
                             tool = ToolParameter(
                                 name=name,
-                                label=I18nObject(
-                                    en_US=name,
-                                    zh_Hans=name
-                                ),
+                                label=I18nObject(en_US=name, zh_Hans=name),
                                 human_description=I18nObject(
-                                    en_US=property.get('description', ''),
-                                    zh_Hans=property.get('description', '')
+                                    en_US=property.get("description", ""), zh_Hans=property.get("description", "")
                                 ),
                                 type=ToolParameter.ToolParameterType.STRING,
                                 required=name in required,
                                 form=ToolParameter.ToolParameterForm.LLM,
-                                llm_description=property.get('description', ''),
-                                default=property.get('default', None),
+                                llm_description=property.get("description", ""),
+                                default=property.get("default", None),
                             )
 
                             # check if there is a type
@@ -127,172 +124,176 @@ class ApiBasedToolSchemaParser:
                 parameters_count[parameter.name] += 1
             for name, count in parameters_count.items():
                 if count > 1:
-                    warning['duplicated_parameter'] = f'Parameter {name} is duplicated.'
+                    warning["duplicated_parameter"] = f"Parameter {name} is duplicated."
 
             # check if there is a operation id, use $path_$method as operation id if not
-            if 'operationId' not in interface['operation']:
+            if "operationId" not in interface["operation"]:
                 # remove special characters like / to ensure the operation id is valid ^[a-zA-Z0-9_-]{1,64}$
-                path = interface['path']
-                if interface['path'].startswith('/'):
-                    path = interface['path'][1:]
+                path = interface["path"]
+                if interface["path"].startswith("/"):
+                    path = interface["path"][1:]
                 # remove special characters like / to ensure the operation id is valid ^[a-zA-Z0-9_-]{1,64}$
-                path = re.sub(r'[^a-zA-Z0-9_-]', '', path)
+                path = re.sub(r"[^a-zA-Z0-9_-]", "", path)
                 if not path:
                     path = str(uuid.uuid4())
-                    
-                interface['operation']['operationId'] = f'{path}_{interface["method"]}'
 
-            bundles.append(ApiToolBundle(
-                server_url=server_url + interface['path'],
-                method=interface['method'],
-                summary=interface['operation']['description'] if 'description' in interface['operation'] else 
-                        interface['operation'].get('summary', None),
-                operation_id=interface['operation']['operationId'],
-                parameters=parameters,
-                author='',
-                icon=None,
-                openapi=interface['operation'],
-            ))
+                interface["operation"]["operationId"] = f'{path}_{interface["method"]}'
+
+            bundles.append(
+                ApiToolBundle(
+                    server_url=server_url + interface["path"],
+                    method=interface["method"],
+                    summary=interface["operation"]["description"]
+                    if "description" in interface["operation"]
+                    else interface["operation"].get("summary", None),
+                    operation_id=interface["operation"]["operationId"],
+                    parameters=parameters,
+                    author="",
+                    icon=None,
+                    openapi=interface["operation"],
+                )
+            )
 
         return bundles
-    
+
     @staticmethod
     def _get_tool_parameter_type(parameter: dict) -> ToolParameter.ToolParameterType:
         parameter = parameter or {}
         typ = None
-        if 'type' in parameter:
-            typ = parameter['type']
-        elif 'schema' in parameter and 'type' in parameter['schema']:
-            typ = parameter['schema']['type']
-        
-        if typ == 'integer' or typ == 'number':
+        if "type" in parameter:
+            typ = parameter["type"]
+        elif "schema" in parameter and "type" in parameter["schema"]:
+            typ = parameter["schema"]["type"]
+
+        if typ in {"integer", "number"}:
             return ToolParameter.ToolParameterType.NUMBER
-        elif typ == 'boolean':
+        elif typ == "boolean":
             return ToolParameter.ToolParameterType.BOOLEAN
-        elif typ == 'string':
+        elif typ == "string":
             return ToolParameter.ToolParameterType.STRING
 
     @staticmethod
-    def parse_openapi_yaml_to_tool_bundle(yaml: str, extra_info: dict = None, warning: dict = None) -> list[ApiToolBundle]:
+    def parse_openapi_yaml_to_tool_bundle(
+        yaml: str, extra_info: dict = None, warning: dict = None
+    ) -> list[ApiToolBundle]:
         """
-            parse openapi yaml to tool bundle
+        parse openapi yaml to tool bundle
 
-            :param yaml: the yaml string
-            :return: the tool bundle
+        :param yaml: the yaml string
+        :return: the tool bundle
         """
         warning = warning if warning is not None else {}
         extra_info = extra_info if extra_info is not None else {}
 
         openapi: dict = safe_load(yaml)
         if openapi is None:
-            raise ToolApiSchemaError('Invalid openapi yaml.')
+            raise ToolApiSchemaError("Invalid openapi yaml.")
         return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(openapi, extra_info=extra_info, warning=warning)
-    
+
     @staticmethod
     def parse_swagger_to_openapi(swagger: dict, extra_info: dict = None, warning: dict = None) -> dict:
         """
-            parse swagger to openapi
+        parse swagger to openapi
 
-            :param swagger: the swagger dict
-            :return: the openapi dict
+        :param swagger: the swagger dict
+        :return: the openapi dict
         """
         # convert swagger to openapi
-        info = swagger.get('info', {
-            'title': 'Swagger',
-            'description': 'Swagger',
-            'version': '1.0.0'
-        })
+        info = swagger.get("info", {"title": "Swagger", "description": "Swagger", "version": "1.0.0"})
 
-        servers = swagger.get('servers', [])
+        servers = swagger.get("servers", [])
 
         if len(servers) == 0:
-            raise ToolApiSchemaError('No server found in the swagger yaml.')
+            raise ToolApiSchemaError("No server found in the swagger yaml.")
 
         openapi = {
-            'openapi': '3.0.0',
-            'info': {
-                'title': info.get('title', 'Swagger'),
-                'description': info.get('description', 'Swagger'),
-                'version': info.get('version', '1.0.0')
+            "openapi": "3.0.0",
+            "info": {
+                "title": info.get("title", "Swagger"),
+                "description": info.get("description", "Swagger"),
+                "version": info.get("version", "1.0.0"),
             },
-            'servers': swagger['servers'],
-            'paths': {},
-            'components': {
-                'schemas': {}
-            }
+            "servers": swagger["servers"],
+            "paths": {},
+            "components": {"schemas": {}},
         }
 
         # check paths
-        if 'paths' not in swagger or len(swagger['paths']) == 0:
-            raise ToolApiSchemaError('No paths found in the swagger yaml.')
+        if "paths" not in swagger or len(swagger["paths"]) == 0:
+            raise ToolApiSchemaError("No paths found in the swagger yaml.")
 
         # convert paths
-        for path, path_item in swagger['paths'].items():
-            openapi['paths'][path] = {}
+        for path, path_item in swagger["paths"].items():
+            openapi["paths"][path] = {}
             for method, operation in path_item.items():
-                if 'operationId' not in operation:
-                    raise ToolApiSchemaError(f'No operationId found in operation {method} {path}.')
-                
-                if ('summary' not in operation or len(operation['summary']) == 0) and \
-                    ('description' not in operation or len(operation['description']) == 0):
-                    warning['missing_summary'] = f'No summary or description found in operation {method} {path}.'
-                
-                openapi['paths'][path][method] = {
-                    'operationId': operation['operationId'],
-                    'summary': operation.get('summary', ''),
-                    'description': operation.get('description', ''),
-                    'parameters': operation.get('parameters', []),
-                    'responses': operation.get('responses', {}),
+                if "operationId" not in operation:
+                    raise ToolApiSchemaError(f"No operationId found in operation {method} {path}.")
+
+                if ("summary" not in operation or len(operation["summary"]) == 0) and (
+                    "description" not in operation or len(operation["description"]) == 0
+                ):
+                    warning["missing_summary"] = f"No summary or description found in operation {method} {path}."
+
+                openapi["paths"][path][method] = {
+                    "operationId": operation["operationId"],
+                    "summary": operation.get("summary", ""),
+                    "description": operation.get("description", ""),
+                    "parameters": operation.get("parameters", []),
+                    "responses": operation.get("responses", {}),
                 }
 
-                if 'requestBody' in operation:
-                    openapi['paths'][path][method]['requestBody'] = operation['requestBody']
+                if "requestBody" in operation:
+                    openapi["paths"][path][method]["requestBody"] = operation["requestBody"]
 
         # convert definitions
-        for name, definition in swagger['definitions'].items():
-            openapi['components']['schemas'][name] = definition
+        for name, definition in swagger["definitions"].items():
+            openapi["components"]["schemas"][name] = definition
 
         return openapi
 
     @staticmethod
-    def parse_openai_plugin_json_to_tool_bundle(json: str, extra_info: dict = None, warning: dict = None) -> list[ApiToolBundle]:
+    def parse_openai_plugin_json_to_tool_bundle(
+        json: str, extra_info: dict = None, warning: dict = None
+    ) -> list[ApiToolBundle]:
         """
-            parse openapi plugin yaml to tool bundle
+        parse openapi plugin yaml to tool bundle
 
-            :param json: the json string
-            :return: the tool bundle
+        :param json: the json string
+        :return: the tool bundle
         """
         warning = warning if warning is not None else {}
         extra_info = extra_info if extra_info is not None else {}
 
         try:
             openai_plugin = json_loads(json)
-            api = openai_plugin['api']
-            api_url = api['url']
-            api_type = api['type']
+            api = openai_plugin["api"]
+            api_url = api["url"]
+            api_type = api["type"]
         except:
-            raise ToolProviderNotFoundError('Invalid openai plugin json.')
-        
-        if api_type != 'openapi':
-            raise ToolNotSupportedError('Only openapi is supported now.')
-        
+            raise ToolProviderNotFoundError("Invalid openai plugin json.")
+
+        if api_type != "openapi":
+            raise ToolNotSupportedError("Only openapi is supported now.")
+
         # get openapi yaml
-        response = get(api_url, headers={
-            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) '
-        }, timeout=5)
+        response = get(api_url, headers={"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "}, timeout=5)
 
         if response.status_code != 200:
-            raise ToolProviderNotFoundError('cannot get openapi yaml from url.')
-        
-        return ApiBasedToolSchemaParser.parse_openapi_yaml_to_tool_bundle(response.text, extra_info=extra_info, warning=warning)
-    
-    @staticmethod
-    def auto_parse_to_tool_bundle(content: str, extra_info: dict = None, warning: dict = None) -> tuple[list[ApiToolBundle], str]:
-        """
-            auto parse to tool bundle
+            raise ToolProviderNotFoundError("cannot get openapi yaml from url.")
 
-            :param content: the content
-            :return: tools bundle, schema_type
+        return ApiBasedToolSchemaParser.parse_openapi_yaml_to_tool_bundle(
+            response.text, extra_info=extra_info, warning=warning
+        )
+
+    @staticmethod
+    def auto_parse_to_tool_bundle(
+        content: str, extra_info: dict = None, warning: dict = None
+    ) -> tuple[list[ApiToolBundle], str]:
+        """
+        auto parse to tool bundle
+
+        :param content: the content
+        :return: tools bundle, schema_type
         """
         warning = warning if warning is not None else {}
         extra_info = extra_info if extra_info is not None else {}
@@ -301,7 +302,7 @@ class ApiBasedToolSchemaParser:
         loaded_content = None
         json_error = None
         yaml_error = None
-        
+
         try:
             loaded_content = json_loads(content)
         except JSONDecodeError as e:
@@ -313,34 +314,48 @@ class ApiBasedToolSchemaParser:
             except YAMLError as e:
                 yaml_error = e
         if loaded_content is None:
-            raise ToolApiSchemaError(f'Invalid api schema, schema is neither json nor yaml. json error: {str(json_error)}, yaml error: {str(yaml_error)}')
+            raise ToolApiSchemaError(
+                f"Invalid api schema, schema is neither json nor yaml. json error: {str(json_error)},"
+                f" yaml error: {str(yaml_error)}"
+            )
 
         swagger_error = None
         openapi_error = None
         openapi_plugin_error = None
         schema_type = None
-        
+
         try:
-            openapi = ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(loaded_content, extra_info=extra_info, warning=warning)
+            openapi = ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(
+                loaded_content, extra_info=extra_info, warning=warning
+            )
             schema_type = ApiProviderSchemaType.OPENAPI.value
             return openapi, schema_type
         except ToolApiSchemaError as e:
             openapi_error = e
-        
+
         # openai parse error, fallback to swagger
         try:
-            converted_swagger = ApiBasedToolSchemaParser.parse_swagger_to_openapi(loaded_content, extra_info=extra_info, warning=warning)
+            converted_swagger = ApiBasedToolSchemaParser.parse_swagger_to_openapi(
+                loaded_content, extra_info=extra_info, warning=warning
+            )
             schema_type = ApiProviderSchemaType.SWAGGER.value
-            return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(converted_swagger, extra_info=extra_info, warning=warning), schema_type
+            return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(
+                converted_swagger, extra_info=extra_info, warning=warning
+            ), schema_type
         except ToolApiSchemaError as e:
             swagger_error = e
-        
+
         # swagger parse error, fallback to openai plugin
         try:
-            openapi_plugin = ApiBasedToolSchemaParser.parse_openai_plugin_json_to_tool_bundle(json_dumps(loaded_content), extra_info=extra_info, warning=warning)
+            openapi_plugin = ApiBasedToolSchemaParser.parse_openai_plugin_json_to_tool_bundle(
+                json_dumps(loaded_content), extra_info=extra_info, warning=warning
+            )
             return openapi_plugin, ApiProviderSchemaType.OPENAI_PLUGIN.value
         except ToolNotSupportedError as e:
             # maybe it's not plugin at all
             openapi_plugin_error = e
 
-        raise ToolApiSchemaError(f'Invalid api schema, openapi error: {str(openapi_error)}, swagger error: {str(swagger_error)}, openapi plugin error: {str(openapi_plugin_error)}')
+        raise ToolApiSchemaError(
+            f"Invalid api schema, openapi error: {str(openapi_error)}, swagger error: {str(swagger_error)},"
+            f" openapi plugin error: {str(openapi_plugin_error)}"
+        )
diff --git a/api/core/tools/utils/tool_parameter_converter.py b/api/core/tools/utils/tool_parameter_converter.py
index 6f88eeaa0a..6f7610651c 100644
--- a/api/core/tools/utils/tool_parameter_converter.py
+++ b/api/core/tools/utils/tool_parameter_converter.py
@@ -7,16 +7,18 @@ class ToolParameterConverter:
     @staticmethod
     def get_parameter_type(parameter_type: str | ToolParameter.ToolParameterType) -> str:
         match parameter_type:
-            case ToolParameter.ToolParameterType.STRING \
-                 | ToolParameter.ToolParameterType.SECRET_INPUT \
-                 | ToolParameter.ToolParameterType.SELECT:
-                return 'string'
+            case (
+                ToolParameter.ToolParameterType.STRING
+                | ToolParameter.ToolParameterType.SECRET_INPUT
+                | ToolParameter.ToolParameterType.SELECT
+            ):
+                return "string"
 
             case ToolParameter.ToolParameterType.BOOLEAN:
-                return 'boolean'
+                return "boolean"
 
             case ToolParameter.ToolParameterType.NUMBER:
-                return 'number'
+                return "number"
 
             case _:
                 raise ValueError(f"Unsupported parameter type {parameter_type}")
@@ -26,11 +28,13 @@ class ToolParameterConverter:
         # convert tool parameter config to correct type
         try:
             match parameter_type:
-                case ToolParameter.ToolParameterType.STRING \
-                     | ToolParameter.ToolParameterType.SECRET_INPUT \
-                     | ToolParameter.ToolParameterType.SELECT:
+                case (
+                    ToolParameter.ToolParameterType.STRING
+                    | ToolParameter.ToolParameterType.SECRET_INPUT
+                    | ToolParameter.ToolParameterType.SELECT
+                ):
                     if value is None:
-                        return ''
+                        return ""
                     else:
                         return value if isinstance(value, str) else str(value)
 
@@ -41,9 +45,9 @@ class ToolParameterConverter:
                         # Allowed YAML boolean value strings: https://yaml.org/type/bool.html
                         # and also '0' for False and '1' for True
                         match value.lower():
-                            case 'true' | 'yes' | 'y' | '1':
+                            case "true" | "yes" | "y" | "1":
                                 return True
-                            case 'false' | 'no' | 'n' | '0':
+                            case "false" | "no" | "n" | "0":
                                 return False
                             case _:
                                 return bool(value)
@@ -53,8 +57,8 @@ class ToolParameterConverter:
                 case ToolParameter.ToolParameterType.NUMBER:
                     if isinstance(value, int) | isinstance(value, float):
                         return value
-                    elif isinstance(value, str) and value != '':
-                        if '.' in value:
+                    elif isinstance(value, str) and value != "":
+                        if "." in value:
                             return float(value)
                         else:
                             return int(value)
diff --git a/api/core/tools/utils/web_reader_tool.py b/api/core/tools/utils/web_reader_tool.py
index a461328ae6..1ced7d0488 100644
--- a/api/core/tools/utils/web_reader_tool.py
+++ b/api/core/tools/utils/web_reader_tool.py
@@ -8,6 +8,7 @@ import subprocess
 import tempfile
 import unicodedata
 from contextlib import contextmanager
+from pathlib import Path
 from urllib.parse import unquote
 
 import chardet
@@ -32,13 +33,14 @@ TEXT:
 
 def page_result(text: str, cursor: int, max_length: int) -> str:
     """Page through `text` and return a substring of `max_length` characters starting from `cursor`."""
-    return text[cursor: cursor + max_length]
+    return text[cursor : cursor + max_length]
 
 
 def get_url(url: str, user_agent: str = None) -> str:
     """Fetch URL and return the contents as a string."""
     headers = {
-        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)"
+        " Chrome/91.0.4472.124 Safari/537.36"
     }
     if user_agent:
         headers["User-Agent"] = user_agent
@@ -49,15 +51,15 @@ def get_url(url: str, user_agent: str = None) -> str:
 
     if response.status_code == 200:
         # check content-type
-        content_type = response.headers.get('Content-Type')
+        content_type = response.headers.get("Content-Type")
         if content_type:
-            main_content_type = response.headers.get('Content-Type').split(';')[0].strip()
+            main_content_type = response.headers.get("Content-Type").split(";")[0].strip()
         else:
-            content_disposition = response.headers.get('Content-Disposition', '')
+            content_disposition = response.headers.get("Content-Disposition", "")
             filename_match = re.search(r'filename="([^"]+)"', content_disposition)
             if filename_match:
                 filename = unquote(filename_match.group(1))
-                extension = re.search(r'\.(\w+)$', filename)
+                extension = re.search(r"\.(\w+)$", filename)
                 if extension:
                     main_content_type = mimetypes.guess_type(filename)[0]
 
@@ -78,7 +80,7 @@ def get_url(url: str, user_agent: str = None) -> str:
 
     # Detect encoding using chardet
     detected_encoding = chardet.detect(response.content)
-    encoding = detected_encoding['encoding']
+    encoding = detected_encoding["encoding"]
     if encoding:
         try:
             content = response.content.decode(encoding)
@@ -89,35 +91,34 @@ def get_url(url: str, user_agent: str = None) -> str:
 
     a = extract_using_readabilipy(content)
 
-    if not a['plain_text'] or not a['plain_text'].strip():
-        return ''
+    if not a["plain_text"] or not a["plain_text"].strip():
+        return ""
 
     res = FULL_TEMPLATE.format(
-        title=a['title'],
-        authors=a['byline'],
-        publish_date=a['date'],
+        title=a["title"],
+        authors=a["byline"],
+        publish_date=a["date"],
         top_image="",
-        text=a['plain_text'] if a['plain_text'] else "",
+        text=a["plain_text"] or "",
     )
 
     return res
 
 
 def extract_using_readabilipy(html):
-    with tempfile.NamedTemporaryFile(delete=False, mode='w+') as f_html:
+    with tempfile.NamedTemporaryFile(delete=False, mode="w+") as f_html:
         f_html.write(html)
         f_html.close()
     html_path = f_html.name
 
     # Call Mozilla's Readability.js Readability.parse() function via node, writing output to a temporary file
     article_json_path = html_path + ".json"
-    jsdir = os.path.join(find_module_path('readabilipy'), 'javascript')
+    jsdir = os.path.join(find_module_path("readabilipy"), "javascript")
     with chdir(jsdir):
         subprocess.check_call(["node", "ExtractArticle.js", "-i", html_path, "-o", article_json_path])
 
     # Read output of call to Readability.parse() from JSON file and return as Python dictionary
-    with open(article_json_path, encoding="utf-8") as json_file:
-        input_json = json.loads(json_file.read())
+    input_json = json.loads(Path(article_json_path).read_text(encoding="utf-8"))
 
     # Deleting files after processing
     os.unlink(article_json_path)
@@ -129,7 +130,7 @@ def extract_using_readabilipy(html):
         "date": None,
         "content": None,
         "plain_content": None,
-        "plain_text": None
+        "plain_text": None,
     }
     # Populate article fields from readability fields where present
     if input_json:
@@ -145,7 +146,7 @@ def extract_using_readabilipy(html):
             article_json["plain_text"] = extract_text_blocks_as_plain_text(article_json["plain_content"])
         if input_json.get("textContent"):
             article_json["plain_text"] = input_json["textContent"]
-            article_json["plain_text"] = re.sub(r'\n\s*\n', '\n', article_json["plain_text"])
+            article_json["plain_text"] = re.sub(r"\n\s*\n", "\n", article_json["plain_text"])
 
     return article_json
 
@@ -158,6 +159,7 @@ def find_module_path(module_name):
 
     return None
 
+
 @contextmanager
 def chdir(path):
     """Change directory in context and return to original on exit"""
@@ -172,12 +174,14 @@ def chdir(path):
 
 def extract_text_blocks_as_plain_text(paragraph_html):
     # Load article as DOM
-    soup = BeautifulSoup(paragraph_html, 'html.parser')
+    soup = BeautifulSoup(paragraph_html, "html.parser")
     # Select all lists
-    list_elements = soup.find_all(['ul', 'ol'])
+    list_elements = soup.find_all(["ul", "ol"])
     # Prefix text in all list items with "* " and make lists paragraphs
     for list_element in list_elements:
-        plain_items = "".join(list(filter(None, [plain_text_leaf_node(li)["text"] for li in list_element.find_all('li')])))
+        plain_items = "".join(
+            list(filter(None, [plain_text_leaf_node(li)["text"] for li in list_element.find_all("li")]))
+        )
         list_element.string = plain_items
         list_element.name = "p"
     # Select all text blocks
@@ -189,8 +193,8 @@ def extract_text_blocks_as_plain_text(paragraph_html):
 
 
 def plain_text_leaf_node(element):
-    # Extract all text, stripped of any child HTML elements and normalise it
-    plain_text = normalise_text(element.get_text())
+    # Extract all text, stripped of any child HTML elements and normalize it
+    plain_text = normalize_text(element.get_text())
     if plain_text != "" and element.name == "li":
         plain_text = "* {}, ".format(plain_text)
     if plain_text == "":
@@ -204,7 +208,7 @@ def plain_text_leaf_node(element):
 
 def plain_content(readability_content, content_digests, node_indexes):
     # Load article as DOM
-    soup = BeautifulSoup(readability_content, 'html.parser')
+    soup = BeautifulSoup(readability_content, "html.parser")
     # Make all elements plain
     elements = plain_elements(soup.contents, content_digests, node_indexes)
     if node_indexes:
@@ -217,8 +221,7 @@ def plain_content(readability_content, content_digests, node_indexes):
 
 def plain_elements(elements, content_digests, node_indexes):
     # Get plain content versions of all elements
-    elements = [plain_element(element, content_digests, node_indexes)
-                for element in elements]
+    elements = [plain_element(element, content_digests, node_indexes) for element in elements]
     if content_digests:
         # Add content digest attribute to nodes
         elements = [add_content_digest(element) for element in elements]
@@ -231,8 +234,8 @@ def plain_element(element, content_digests, node_indexes):
         # For leaf node elements, extract the text content, discarding any HTML tags
         # 1. Get element contents as text
         plain_text = element.get_text()
-        # 2. Normalise the extracted text string to a canonical representation
-        plain_text = normalise_text(plain_text)
+        # 2. Normalize the extracted text string to a canonical representation
+        plain_text = normalize_text(plain_text)
         # 3. Update element content to be plain text
         element.string = plain_text
     elif is_text(element):
@@ -243,7 +246,7 @@ def plain_element(element, content_digests, node_indexes):
             element = type(element)("")
         else:
             plain_text = element.string
-            plain_text = normalise_text(plain_text)
+            plain_text = normalize_text(plain_text)
             element = type(element)(plain_text)
     else:
         # If not a leaf node or leaf type call recursively on child nodes, replacing
@@ -258,21 +261,19 @@ def add_node_indexes(element, node_index="0"):
     # Add index to current element
     element["data-node-index"] = node_index
     # Add index to child elements
-    for local_idx, child in enumerate(
-            [c for c in element.contents if not is_text(c)], start=1):
+    for local_idx, child in enumerate([c for c in element.contents if not is_text(c)], start=1):
         # Can't add attributes to leaf string types
-        child_index = "{stem}.{local}".format(
-            stem=node_index, local=local_idx)
+        child_index = "{stem}.{local}".format(stem=node_index, local=local_idx)
         add_node_indexes(child, node_index=child_index)
     return element
 
 
-def normalise_text(text):
-    """Normalise unicode and whitespace."""
-    # Normalise unicode first to try and standardise whitespace characters as much as possible before normalising them
+def normalize_text(text):
+    """Normalize unicode and whitespace."""
+    # Normalize unicode first to try and standardize whitespace characters as much as possible before normalizing them
     text = strip_control_characters(text)
-    text = normalise_unicode(text)
-    text = normalise_whitespace(text)
+    text = normalize_unicode(text)
+    text = normalize_whitespace(text)
     return text
 
 
@@ -284,29 +285,35 @@ def strip_control_characters(text):
     #   [Cn]: Other, Not Assigned
     #   [Co]: Other, Private Use
     #   [Cs]: Other, Surrogate
-    control_chars = {'Cc', 'Cf', 'Cn', 'Co', 'Cs'}
-    retained_chars = ['\t', '\n', '\r', '\f']
+    control_chars = {"Cc", "Cf", "Cn", "Co", "Cs"}
+    retained_chars = ["\t", "\n", "\r", "\f"]
 
     # Remove non-printing control characters
-    return "".join(["" if (unicodedata.category(char) in control_chars) and (char not in retained_chars) else char for char in text])
+    return "".join(
+        [
+            "" if (unicodedata.category(char) in control_chars) and (char not in retained_chars) else char
+            for char in text
+        ]
+    )
 
 
-def normalise_unicode(text):
-    """Normalise unicode such that things that are visually equivalent map to the same unicode string where possible."""
+def normalize_unicode(text):
+    """Normalize unicode such that things that are visually equivalent map to the same unicode string where possible."""
     normal_form = "NFKC"
     text = unicodedata.normalize(normal_form, text)
     return text
 
 
-def normalise_whitespace(text):
+def normalize_whitespace(text):
     """Replace runs of whitespace characters with a single space as this is what happens when HTML text is displayed."""
     text = regex.sub(r"\s+", " ", text)
     # Remove leading and trailing whitespace
     text = text.strip()
     return text
 
+
 def is_leaf(element):
-    return (element.name in ['p', 'li'])
+    return element.name in {"p", "li"}
 
 
 def is_text(element):
@@ -330,7 +337,7 @@ def content_digest(element):
         if trimmed_string == "":
             digest = ""
         else:
-            digest = hashlib.sha256(trimmed_string.encode('utf-8')).hexdigest()
+            digest = hashlib.sha256(trimmed_string.encode("utf-8")).hexdigest()
     else:
         contents = element.contents
         num_contents = len(contents)
@@ -343,9 +350,8 @@ def content_digest(element):
         else:
             # Build content digest from the "non-empty" digests of child nodes
             digest = hashlib.sha256()
-            child_digests = list(
-                filter(lambda x: x != "", [content_digest(content) for content in contents]))
+            child_digests = list(filter(lambda x: x != "", [content_digest(content) for content in contents]))
             for child in child_digests:
-                digest.update(child.encode('utf-8'))
+                digest.update(child.encode("utf-8"))
             digest = digest.hexdigest()
     return digest
diff --git a/api/core/tools/utils/workflow_configuration_sync.py b/api/core/tools/utils/workflow_configuration_sync.py
index ff5505bbbf..94d9fd9eb9 100644
--- a/api/core/tools/utils/workflow_configuration_sync.py
+++ b/api/core/tools/utils/workflow_configuration_sync.py
@@ -10,27 +10,25 @@ class WorkflowToolConfigurationUtils:
         """
         for configuration in configurations:
             if not WorkflowToolParameterConfiguration(**configuration):
-                raise ValueError('invalid parameter configuration')
+                raise ValueError("invalid parameter configuration")
 
     @classmethod
     def get_workflow_graph_variables(cls, graph: dict) -> list[VariableEntity]:
         """
         get workflow graph variables
         """
-        nodes = graph.get('nodes', [])
-        start_node = next(filter(lambda x: x.get('data', {}).get('type') == 'start', nodes), None)
+        nodes = graph.get("nodes", [])
+        start_node = next(filter(lambda x: x.get("data", {}).get("type") == "start", nodes), None)
 
         if not start_node:
             return []
 
-        return [
-            VariableEntity(**variable) for variable in start_node.get('data', {}).get('variables', [])
-        ]
-    
+        return [VariableEntity(**variable) for variable in start_node.get("data", {}).get("variables", [])]
+
     @classmethod
-    def check_is_synced(cls, 
-                        variables: list[VariableEntity], 
-                        tool_configurations: list[WorkflowToolParameterConfiguration]) -> None:
+    def check_is_synced(
+        cls, variables: list[VariableEntity], tool_configurations: list[WorkflowToolParameterConfiguration]
+    ) -> None:
         """
         check is synced
 
@@ -39,10 +37,10 @@ class WorkflowToolConfigurationUtils:
         variable_names = [variable.variable for variable in variables]
 
         if len(tool_configurations) != len(variables):
-            raise ValueError('parameter configuration mismatch, please republish the tool to update')
-        
+            raise ValueError("parameter configuration mismatch, please republish the tool to update")
+
         for parameter in tool_configurations:
             if parameter.name not in variable_names:
-                raise ValueError('parameter configuration mismatch, please republish the tool to update')
+                raise ValueError("parameter configuration mismatch, please republish the tool to update")
 
-        return True
\ No newline at end of file
+        return True
diff --git a/api/core/tools/utils/yaml_utils.py b/api/core/tools/utils/yaml_utils.py
index f751c43096..99b9f80499 100644
--- a/api/core/tools/utils/yaml_utils.py
+++ b/api/core/tools/utils/yaml_utils.py
@@ -18,12 +18,12 @@ def load_yaml_file(file_path: str, ignore_error: bool = True, default_value: Any
     :return: an object of the YAML content
     """
     try:
-        with open(file_path, encoding='utf-8') as yaml_file:
+        with open(file_path, encoding="utf-8") as yaml_file:
             try:
                 yaml_content = yaml.safe_load(yaml_file)
-                return yaml_content if yaml_content else default_value
+                return yaml_content or default_value
             except Exception as e:
-                raise YAMLError(f'Failed to load YAML file {file_path}: {e}')
+                raise YAMLError(f"Failed to load YAML file {file_path}: {e}")
     except Exception as e:
         if ignore_error:
             return default_value
diff --git a/api/core/workflow/callbacks/base_workflow_callback.py b/api/core/workflow/callbacks/base_workflow_callback.py
index 6db8adf4c2..83086d1afc 100644
--- a/api/core/workflow/callbacks/base_workflow_callback.py
+++ b/api/core/workflow/callbacks/base_workflow_callback.py
@@ -1,116 +1,12 @@
 from abc import ABC, abstractmethod
-from typing import Any, Optional
 
-from core.app.entities.queue_entities import AppQueueEvent
-from core.workflow.entities.base_node_data_entities import BaseNodeData
-from core.workflow.entities.node_entities import NodeType
+from core.workflow.graph_engine.entities.event import GraphEngineEvent
 
 
 class WorkflowCallback(ABC):
     @abstractmethod
-    def on_workflow_run_started(self) -> None:
+    def on_event(self, event: GraphEngineEvent) -> None:
         """
-        Workflow run started
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def on_workflow_run_succeeded(self) -> None:
-        """
-        Workflow run succeeded
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def on_workflow_run_failed(self, error: str) -> None:
-        """
-        Workflow run failed
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def on_workflow_node_execute_started(self, node_id: str,
-                                         node_type: NodeType,
-                                         node_data: BaseNodeData,
-                                         node_run_index: int = 1,
-                                         predecessor_node_id: Optional[str] = None) -> None:
-        """
-        Workflow node execute started
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def on_workflow_node_execute_succeeded(self, node_id: str,
-                                           node_type: NodeType,
-                                           node_data: BaseNodeData,
-                                           inputs: Optional[dict] = None,
-                                           process_data: Optional[dict] = None,
-                                           outputs: Optional[dict] = None,
-                                           execution_metadata: Optional[dict] = None) -> None:
-        """
-        Workflow node execute succeeded
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def on_workflow_node_execute_failed(self, node_id: str,
-                                        node_type: NodeType,
-                                        node_data: BaseNodeData,
-                                        error: str,
-                                        inputs: Optional[dict] = None,
-                                        outputs: Optional[dict] = None,
-                                        process_data: Optional[dict] = None) -> None:
-        """
-        Workflow node execute failed
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def on_node_text_chunk(self, node_id: str, text: str, metadata: Optional[dict] = None) -> None:
-        """
-        Publish text chunk
-        """
-        raise NotImplementedError
-    
-    @abstractmethod
-    def on_workflow_iteration_started(self, 
-                                      node_id: str,
-                                      node_type: NodeType,
-                                      node_run_index: int = 1,
-                                      node_data: Optional[BaseNodeData] = None,
-                                      inputs: Optional[dict] = None,
-                                      predecessor_node_id: Optional[str] = None,
-                                      metadata: Optional[dict] = None) -> None:
-        """
-        Publish iteration started
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def on_workflow_iteration_next(self, node_id: str, 
-                                   node_type: NodeType,
-                                   index: int, 
-                                   node_run_index: int,
-                                   output: Optional[Any],
-                                   ) -> None:
-        """
-        Publish iteration next
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def on_workflow_iteration_completed(self, node_id: str, 
-                                        node_type: NodeType,
-                                        node_run_index: int,
-                                        outputs: dict) -> None:
-        """
-        Publish iteration completed
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def on_event(self, event: AppQueueEvent) -> None:
-        """
-        Publish event
+        Published event
         """
         raise NotImplementedError
diff --git a/api/core/workflow/entities/base_node_data_entities.py b/api/core/workflow/entities/base_node_data_entities.py
index 6bf0c11c7d..2a864dd7a8 100644
--- a/api/core/workflow/entities/base_node_data_entities.py
+++ b/api/core/workflow/entities/base_node_data_entities.py
@@ -8,8 +8,10 @@ class BaseNodeData(ABC, BaseModel):
     title: str
     desc: Optional[str] = None
 
+
 class BaseIterationNodeData(BaseNodeData):
-    start_node_id: str
+    start_node_id: Optional[str] = None
+
 
 class BaseIterationState(BaseModel):
     iteration_node_id: str
@@ -19,4 +21,4 @@ class BaseIterationState(BaseModel):
     class MetaData(BaseModel):
         pass
 
-    metadata: MetaData
\ No newline at end of file
+    metadata: MetaData
diff --git a/api/core/workflow/entities/node_entities.py b/api/core/workflow/entities/node_entities.py
index 025453567b..5353b99ed3 100644
--- a/api/core/workflow/entities/node_entities.py
+++ b/api/core/workflow/entities/node_entities.py
@@ -1,9 +1,9 @@
-from collections.abc import Mapping
 from enum import Enum
 from typing import Any, Optional
 
 from pydantic import BaseModel
 
+from core.model_runtime.entities.llm_entities import LLMUsage
 from models import WorkflowNodeExecutionStatus
 
 
@@ -12,27 +12,28 @@ class NodeType(Enum):
     Node Types.
     """
 
-    START = 'start'
-    END = 'end'
-    ANSWER = 'answer'
-    LLM = 'llm'
-    KNOWLEDGE_RETRIEVAL = 'knowledge-retrieval'
-    IF_ELSE = 'if-else'
-    CODE = 'code'
-    TEMPLATE_TRANSFORM = 'template-transform'
-    QUESTION_CLASSIFIER = 'question-classifier'
-    HTTP_REQUEST = 'http-request'
-    TOOL = 'tool'
-    VARIABLE_AGGREGATOR = 'variable-aggregator'
+    START = "start"
+    END = "end"
+    ANSWER = "answer"
+    LLM = "llm"
+    KNOWLEDGE_RETRIEVAL = "knowledge-retrieval"
+    IF_ELSE = "if-else"
+    CODE = "code"
+    TEMPLATE_TRANSFORM = "template-transform"
+    QUESTION_CLASSIFIER = "question-classifier"
+    HTTP_REQUEST = "http-request"
+    TOOL = "tool"
+    VARIABLE_AGGREGATOR = "variable-aggregator"
     # TODO: merge this into VARIABLE_AGGREGATOR
-    VARIABLE_ASSIGNER = 'variable-assigner'
-    LOOP = 'loop'
-    ITERATION = 'iteration'
-    PARAMETER_EXTRACTOR = 'parameter-extractor'
-    CONVERSATION_VARIABLE_ASSIGNER = 'assigner'
+    VARIABLE_ASSIGNER = "variable-assigner"
+    LOOP = "loop"
+    ITERATION = "iteration"
+    ITERATION_START = "iteration-start"  # fake start node for iteration
+    PARAMETER_EXTRACTOR = "parameter-extractor"
+    CONVERSATION_VARIABLE_ASSIGNER = "assigner"
 
     @classmethod
-    def value_of(cls, value: str) -> 'NodeType':
+    def value_of(cls, value: str) -> "NodeType":
         """
         Get value of given node type.
 
@@ -42,7 +43,7 @@ class NodeType(Enum):
         for node_type in cls:
             if node_type.value == value:
                 return node_type
-        raise ValueError(f'invalid node type value {value}')
+        raise ValueError(f"invalid node type value {value}")
 
 
 class NodeRunMetadataKey(Enum):
@@ -50,12 +51,16 @@ class NodeRunMetadataKey(Enum):
     Node Run Metadata Key.
     """
 
-    TOTAL_TOKENS = 'total_tokens'
-    TOTAL_PRICE = 'total_price'
-    CURRENCY = 'currency'
-    TOOL_INFO = 'tool_info'
-    ITERATION_ID = 'iteration_id'
-    ITERATION_INDEX = 'iteration_index'
+    TOTAL_TOKENS = "total_tokens"
+    TOTAL_PRICE = "total_price"
+    CURRENCY = "currency"
+    TOOL_INFO = "tool_info"
+    ITERATION_ID = "iteration_id"
+    ITERATION_INDEX = "iteration_index"
+    PARALLEL_ID = "parallel_id"
+    PARALLEL_START_NODE_ID = "parallel_start_node_id"
+    PARENT_PARALLEL_ID = "parent_parallel_id"
+    PARENT_PARALLEL_START_NODE_ID = "parent_parallel_start_node_id"
 
 
 class NodeRunResult(BaseModel):
@@ -65,11 +70,33 @@ class NodeRunResult(BaseModel):
 
     status: WorkflowNodeExecutionStatus = WorkflowNodeExecutionStatus.RUNNING
 
-    inputs: Optional[Mapping[str, Any]] = None  # node inputs
-    process_data: Optional[dict] = None  # process data
-    outputs: Optional[Mapping[str, Any]] = None  # node outputs
+    inputs: Optional[dict[str, Any]] = None  # node inputs
+    process_data: Optional[dict[str, Any]] = None  # process data
+    outputs: Optional[dict[str, Any]] = None  # node outputs
     metadata: Optional[dict[NodeRunMetadataKey, Any]] = None  # node metadata
+    llm_usage: Optional[LLMUsage] = None  # llm usage
 
     edge_source_handle: Optional[str] = None  # source handle id of node with multiple branches
 
     error: Optional[str] = None  # error message if status is failed
+
+
+class UserFrom(Enum):
+    """
+    User from
+    """
+
+    ACCOUNT = "account"
+    END_USER = "end-user"
+
+    @classmethod
+    def value_of(cls, value: str) -> "UserFrom":
+        """
+        Value of
+        :param value: value
+        :return:
+        """
+        for item in cls:
+            if item.value == value:
+                return item
+        raise ValueError(f"Invalid value: {value}")
diff --git a/api/core/workflow/entities/variable_entities.py b/api/core/workflow/entities/variable_entities.py
index 19d9af2a61..1dfb1852f8 100644
--- a/api/core/workflow/entities/variable_entities.py
+++ b/api/core/workflow/entities/variable_entities.py
@@ -5,5 +5,6 @@ class VariableSelector(BaseModel):
     """
     Variable Selector.
     """
+
     variable: str
     value_selector: list[str]
diff --git a/api/core/workflow/entities/variable_pool.py b/api/core/workflow/entities/variable_pool.py
index 8120b2ac78..b94b7f7198 100644
--- a/api/core/workflow/entities/variable_pool.py
+++ b/api/core/workflow/entities/variable_pool.py
@@ -2,6 +2,7 @@ from collections import defaultdict
 from collections.abc import Mapping, Sequence
 from typing import Any, Union
 
+from pydantic import BaseModel, Field, model_validator
 from typing_extensions import deprecated
 
 from core.app.segments import Segment, Variable, factory
@@ -16,43 +17,48 @@ ENVIRONMENT_VARIABLE_NODE_ID = "env"
 CONVERSATION_VARIABLE_NODE_ID = "conversation"
 
 
-class VariablePool:
-    def __init__(
-        self,
-        system_variables: Mapping[SystemVariableKey, Any],
-        user_inputs: Mapping[str, Any],
-        environment_variables: Sequence[Variable],
-        conversation_variables: Sequence[Variable] | None = None,
-    ) -> None:
-        # system variables
-        # for example:
-        # {
-        #     'query': 'abc',
-        #     'files': []
-        # }
+class VariablePool(BaseModel):
+    # Variable dictionary is a dictionary for looking up variables by their selector.
+    # The first element of the selector is the node id, it's the first-level key in the dictionary.
+    # Other elements of the selector are the keys in the second-level dictionary. To get the key, we hash the
+    # elements of the selector except the first one.
+    variable_dictionary: dict[str, dict[int, Segment]] = Field(
+        description="Variables mapping", default=defaultdict(dict)
+    )
 
-        # Varaible dictionary is a dictionary for looking up variables by their selector.
-        # The first element of the selector is the node id, it's the first-level key in the dictionary.
-        # Other elements of the selector are the keys in the second-level dictionary. To get the key, we hash the
-        # elements of the selector except the first one.
-        self._variable_dictionary: dict[str, dict[int, Segment]] = defaultdict(dict)
+    # TODO: This user inputs is not used for pool.
+    user_inputs: Mapping[str, Any] = Field(
+        description="User inputs",
+    )
 
-        # TODO: This user inputs is not used for pool.
-        self.user_inputs = user_inputs
+    system_variables: Mapping[SystemVariableKey, Any] = Field(
+        description="System variables",
+    )
 
+    environment_variables: Sequence[Variable] = Field(description="Environment variables.", default_factory=list)
+
+    conversation_variables: Sequence[Variable] | None = None
+
+    @model_validator(mode="after")
+    def val_model_after(self):
+        """
+        Append system variables
+        :return:
+        """
         # Add system variables to the variable pool
-        self.system_variables = system_variables
-        for key, value in system_variables.items():
+        for key, value in self.system_variables.items():
             self.add((SYSTEM_VARIABLE_NODE_ID, key.value), value)
 
         # Add environment variables to the variable pool
-        for var in environment_variables:
+        for var in self.environment_variables or []:
             self.add((ENVIRONMENT_VARIABLE_NODE_ID, var.name), var)
 
         # Add conversation variables to the variable pool
-        for var in conversation_variables or []:
+        for var in self.conversation_variables or []:
             self.add((CONVERSATION_VARIABLE_NODE_ID, var.name), var)
 
+        return self
+
     def add(self, selector: Sequence[str], value: Any, /) -> None:
         """
         Adds a variable to the variable pool.
@@ -79,7 +85,7 @@ class VariablePool:
             v = factory.build_segment(value)
 
         hash_key = hash(tuple(selector[1:]))
-        self._variable_dictionary[selector[0]][hash_key] = v
+        self.variable_dictionary[selector[0]][hash_key] = v
 
     def get(self, selector: Sequence[str], /) -> Segment | None:
         """
@@ -97,7 +103,7 @@ class VariablePool:
         if len(selector) < 2:
             raise ValueError("Invalid selector")
         hash_key = hash(tuple(selector[1:]))
-        value = self._variable_dictionary[selector[0]].get(hash_key)
+        value = self.variable_dictionary[selector[0]].get(hash_key)
 
         return value
 
@@ -118,7 +124,7 @@ class VariablePool:
         if len(selector) < 2:
             raise ValueError("Invalid selector")
         hash_key = hash(tuple(selector[1:]))
-        value = self._variable_dictionary[selector[0]].get(hash_key)
+        value = self.variable_dictionary[selector[0]].get(hash_key)
         return value.to_object() if value else None
 
     def remove(self, selector: Sequence[str], /):
@@ -134,7 +140,19 @@ class VariablePool:
         if not selector:
             return
         if len(selector) == 1:
-            self._variable_dictionary[selector[0]] = {}
+            self.variable_dictionary[selector[0]] = {}
             return
         hash_key = hash(tuple(selector[1:]))
-        self._variable_dictionary[selector[0]].pop(hash_key, None)
+        self.variable_dictionary[selector[0]].pop(hash_key, None)
+
+    def remove_node(self, node_id: str, /):
+        """
+        Remove all variables associated with a given node id.
+
+        Args:
+            node_id (str): The node id to remove.
+
+        Returns:
+            None
+        """
+        self.variable_dictionary.pop(node_id, None)
diff --git a/api/core/workflow/entities/workflow_entities.py b/api/core/workflow/entities/workflow_entities.py
index 9b35b8df8a..0a1eb57de4 100644
--- a/api/core/workflow/entities/workflow_entities.py
+++ b/api/core/workflow/entities/workflow_entities.py
@@ -46,13 +46,16 @@ class WorkflowRunState:
 
     current_iteration_state: Optional[BaseIterationState]
 
-    def __init__(self, workflow: Workflow,
-                 start_at: float,
-                 variable_pool: VariablePool,
-                 user_id: str,
-                 user_from: UserFrom,
-                 invoke_from: InvokeFrom,
-                 workflow_call_depth: int):
+    def __init__(
+        self,
+        workflow: Workflow,
+        start_at: float,
+        variable_pool: VariablePool,
+        user_id: str,
+        user_from: UserFrom,
+        invoke_from: InvokeFrom,
+        workflow_call_depth: int,
+    ):
         self.workflow_id = workflow.id
         self.tenant_id = workflow.tenant_id
         self.app_id = workflow.app_id
@@ -66,8 +69,7 @@ class WorkflowRunState:
         self.variable_pool = variable_pool
 
         self.total_tokens = 0
-        self.workflow_nodes_and_results = []
 
-        self.current_iteration_state = None
         self.workflow_node_steps = 1
-        self.workflow_node_runs = []
\ No newline at end of file
+        self.workflow_node_runs = []
+        self.current_iteration_state = None
diff --git a/api/core/workflow/errors.py b/api/core/workflow/errors.py
index fe79fadf66..07cbcd981e 100644
--- a/api/core/workflow/errors.py
+++ b/api/core/workflow/errors.py
@@ -1,10 +1,8 @@
-from core.workflow.entities.node_entities import NodeType
+from core.workflow.nodes.base_node import BaseNode
 
 
 class WorkflowNodeRunFailedError(Exception):
-    def __init__(self, node_id: str, node_type: NodeType, node_title: str, error: str):
-        self.node_id = node_id
-        self.node_type = node_type
-        self.node_title = node_title
+    def __init__(self, node_instance: BaseNode, error: str):
+        self.node_instance = node_instance
         self.error = error
-        super().__init__(f"Node {node_title} run failed: {error}")
+        super().__init__(f"Node {node_instance.node_data.title} run failed: {error}")
diff --git a/api/core/workflow/graph_engine/__init__.py b/api/core/workflow/graph_engine/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/workflow/graph_engine/condition_handlers/__init__.py b/api/core/workflow/graph_engine/condition_handlers/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/workflow/graph_engine/condition_handlers/base_handler.py b/api/core/workflow/graph_engine/condition_handlers/base_handler.py
new file mode 100644
index 0000000000..697392b2a3
--- /dev/null
+++ b/api/core/workflow/graph_engine/condition_handlers/base_handler.py
@@ -0,0 +1,25 @@
+from abc import ABC, abstractmethod
+
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.graph_engine.entities.run_condition import RunCondition
+from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
+
+
+class RunConditionHandler(ABC):
+    def __init__(self, init_params: GraphInitParams, graph: Graph, condition: RunCondition):
+        self.init_params = init_params
+        self.graph = graph
+        self.condition = condition
+
+    @abstractmethod
+    def check(self, graph_runtime_state: GraphRuntimeState, previous_route_node_state: RouteNodeState) -> bool:
+        """
+        Check if the condition can be executed
+
+        :param graph_runtime_state: graph runtime state
+        :param previous_route_node_state: previous route node state
+        :return: bool
+        """
+        raise NotImplementedError
diff --git a/api/core/workflow/graph_engine/condition_handlers/branch_identify_handler.py b/api/core/workflow/graph_engine/condition_handlers/branch_identify_handler.py
new file mode 100644
index 0000000000..af695df7d8
--- /dev/null
+++ b/api/core/workflow/graph_engine/condition_handlers/branch_identify_handler.py
@@ -0,0 +1,25 @@
+from core.workflow.graph_engine.condition_handlers.base_handler import RunConditionHandler
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
+
+
+class BranchIdentifyRunConditionHandler(RunConditionHandler):
+    def check(self, graph_runtime_state: GraphRuntimeState, previous_route_node_state: RouteNodeState) -> bool:
+        """
+        Check if the condition can be executed
+
+        :param graph_runtime_state: graph runtime state
+        :param previous_route_node_state: previous route node state
+        :return: bool
+        """
+        if not self.condition.branch_identify:
+            raise Exception("Branch identify is required")
+
+        run_result = previous_route_node_state.node_run_result
+        if not run_result:
+            return False
+
+        if not run_result.edge_source_handle:
+            return False
+
+        return self.condition.branch_identify == run_result.edge_source_handle
diff --git a/api/core/workflow/graph_engine/condition_handlers/condition_handler.py b/api/core/workflow/graph_engine/condition_handlers/condition_handler.py
new file mode 100644
index 0000000000..eda5fe079c
--- /dev/null
+++ b/api/core/workflow/graph_engine/condition_handlers/condition_handler.py
@@ -0,0 +1,28 @@
+from core.workflow.graph_engine.condition_handlers.base_handler import RunConditionHandler
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
+from core.workflow.utils.condition.processor import ConditionProcessor
+
+
+class ConditionRunConditionHandlerHandler(RunConditionHandler):
+    def check(self, graph_runtime_state: GraphRuntimeState, previous_route_node_state: RouteNodeState) -> bool:
+        """
+        Check if the condition can be executed
+
+        :param graph_runtime_state: graph runtime state
+        :param previous_route_node_state: previous route node state
+        :return: bool
+        """
+        if not self.condition.conditions:
+            return True
+
+        # process condition
+        condition_processor = ConditionProcessor()
+        input_conditions, group_result = condition_processor.process_conditions(
+            variable_pool=graph_runtime_state.variable_pool, conditions=self.condition.conditions
+        )
+
+        # Apply the logical operator for the current case
+        compare_result = all(group_result)
+
+        return compare_result
diff --git a/api/core/workflow/graph_engine/condition_handlers/condition_manager.py b/api/core/workflow/graph_engine/condition_handlers/condition_manager.py
new file mode 100644
index 0000000000..1c9237d82f
--- /dev/null
+++ b/api/core/workflow/graph_engine/condition_handlers/condition_manager.py
@@ -0,0 +1,25 @@
+from core.workflow.graph_engine.condition_handlers.base_handler import RunConditionHandler
+from core.workflow.graph_engine.condition_handlers.branch_identify_handler import BranchIdentifyRunConditionHandler
+from core.workflow.graph_engine.condition_handlers.condition_handler import ConditionRunConditionHandlerHandler
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.run_condition import RunCondition
+
+
+class ConditionManager:
+    @staticmethod
+    def get_condition_handler(
+        init_params: GraphInitParams, graph: Graph, run_condition: RunCondition
+    ) -> RunConditionHandler:
+        """
+        Get condition handler
+
+        :param init_params: init params
+        :param graph: graph
+        :param run_condition: run condition
+        :return: condition handler
+        """
+        if run_condition.type == "branch_identify":
+            return BranchIdentifyRunConditionHandler(init_params=init_params, graph=graph, condition=run_condition)
+        else:
+            return ConditionRunConditionHandlerHandler(init_params=init_params, graph=graph, condition=run_condition)
diff --git a/api/core/workflow/graph_engine/entities/__init__.py b/api/core/workflow/graph_engine/entities/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/workflow/graph_engine/entities/event.py b/api/core/workflow/graph_engine/entities/event.py
new file mode 100644
index 0000000000..06dc4cb8f4
--- /dev/null
+++ b/api/core/workflow/graph_engine/entities/event.py
@@ -0,0 +1,163 @@
+from datetime import datetime
+from typing import Any, Optional
+
+from pydantic import BaseModel, Field
+
+from core.workflow.entities.base_node_data_entities import BaseNodeData
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
+
+
+class GraphEngineEvent(BaseModel):
+    pass
+
+
+###########################################
+# Graph Events
+###########################################
+
+
+class BaseGraphEvent(GraphEngineEvent):
+    pass
+
+
+class GraphRunStartedEvent(BaseGraphEvent):
+    pass
+
+
+class GraphRunSucceededEvent(BaseGraphEvent):
+    outputs: Optional[dict[str, Any]] = None
+    """outputs"""
+
+
+class GraphRunFailedEvent(BaseGraphEvent):
+    error: str = Field(..., description="failed reason")
+
+
+###########################################
+# Node Events
+###########################################
+
+
+class BaseNodeEvent(GraphEngineEvent):
+    id: str = Field(..., description="node execution id")
+    node_id: str = Field(..., description="node id")
+    node_type: NodeType = Field(..., description="node type")
+    node_data: BaseNodeData = Field(..., description="node data")
+    route_node_state: RouteNodeState = Field(..., description="route node state")
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
+
+
+class NodeRunStartedEvent(BaseNodeEvent):
+    predecessor_node_id: Optional[str] = None
+    """predecessor node id"""
+
+
+class NodeRunStreamChunkEvent(BaseNodeEvent):
+    chunk_content: str = Field(..., description="chunk content")
+    from_variable_selector: Optional[list[str]] = None
+    """from variable selector"""
+
+
+class NodeRunRetrieverResourceEvent(BaseNodeEvent):
+    retriever_resources: list[dict] = Field(..., description="retriever resources")
+    context: str = Field(..., description="context")
+
+
+class NodeRunSucceededEvent(BaseNodeEvent):
+    pass
+
+
+class NodeRunFailedEvent(BaseNodeEvent):
+    error: str = Field(..., description="error")
+
+
+###########################################
+# Parallel Branch Events
+###########################################
+
+
+class BaseParallelBranchEvent(GraphEngineEvent):
+    parallel_id: str = Field(..., description="parallel id")
+    """parallel id"""
+    parallel_start_node_id: str = Field(..., description="parallel start node id")
+    """parallel start node id"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
+
+
+class ParallelBranchRunStartedEvent(BaseParallelBranchEvent):
+    pass
+
+
+class ParallelBranchRunSucceededEvent(BaseParallelBranchEvent):
+    pass
+
+
+class ParallelBranchRunFailedEvent(BaseParallelBranchEvent):
+    error: str = Field(..., description="failed reason")
+
+
+###########################################
+# Iteration Events
+###########################################
+
+
+class BaseIterationEvent(GraphEngineEvent):
+    iteration_id: str = Field(..., description="iteration node execution id")
+    iteration_node_id: str = Field(..., description="iteration node id")
+    iteration_node_type: NodeType = Field(..., description="node type, iteration or loop")
+    iteration_node_data: BaseNodeData = Field(..., description="node data")
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+
+
+class IterationRunStartedEvent(BaseIterationEvent):
+    start_at: datetime = Field(..., description="start at")
+    inputs: Optional[dict[str, Any]] = None
+    metadata: Optional[dict[str, Any]] = None
+    predecessor_node_id: Optional[str] = None
+
+
+class IterationRunNextEvent(BaseIterationEvent):
+    index: int = Field(..., description="index")
+    pre_iteration_output: Optional[Any] = Field(None, description="pre iteration output")
+
+
+class IterationRunSucceededEvent(BaseIterationEvent):
+    start_at: datetime = Field(..., description="start at")
+    inputs: Optional[dict[str, Any]] = None
+    outputs: Optional[dict[str, Any]] = None
+    metadata: Optional[dict[str, Any]] = None
+    steps: int = 0
+
+
+class IterationRunFailedEvent(BaseIterationEvent):
+    start_at: datetime = Field(..., description="start at")
+    inputs: Optional[dict[str, Any]] = None
+    outputs: Optional[dict[str, Any]] = None
+    metadata: Optional[dict[str, Any]] = None
+    steps: int = 0
+    error: str = Field(..., description="failed reason")
+
+
+InNodeEvent = BaseNodeEvent | BaseParallelBranchEvent | BaseIterationEvent
diff --git a/api/core/workflow/graph_engine/entities/graph.py b/api/core/workflow/graph_engine/entities/graph.py
new file mode 100644
index 0000000000..1175f4af2a
--- /dev/null
+++ b/api/core/workflow/graph_engine/entities/graph.py
@@ -0,0 +1,715 @@
+import uuid
+from collections.abc import Mapping
+from typing import Any, Optional, cast
+
+from pydantic import BaseModel, Field
+
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.graph_engine.entities.run_condition import RunCondition
+from core.workflow.nodes.answer.answer_stream_generate_router import AnswerStreamGeneratorRouter
+from core.workflow.nodes.answer.entities import AnswerStreamGenerateRoute
+from core.workflow.nodes.end.end_stream_generate_router import EndStreamGeneratorRouter
+from core.workflow.nodes.end.entities import EndStreamParam
+
+
+class GraphEdge(BaseModel):
+    source_node_id: str = Field(..., description="source node id")
+    target_node_id: str = Field(..., description="target node id")
+    run_condition: Optional[RunCondition] = None
+    """run condition"""
+
+
+class GraphParallel(BaseModel):
+    id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="random uuid parallel id")
+    start_from_node_id: str = Field(..., description="start from node id")
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id"""
+    end_to_node_id: Optional[str] = None
+    """end to node id"""
+
+
+class Graph(BaseModel):
+    root_node_id: str = Field(..., description="root node id of the graph")
+    node_ids: list[str] = Field(default_factory=list, description="graph node ids")
+    node_id_config_mapping: dict[str, dict] = Field(
+        default_factory=list, description="node configs mapping (node id: node config)"
+    )
+    edge_mapping: dict[str, list[GraphEdge]] = Field(
+        default_factory=dict, description="graph edge mapping (source node id: edges)"
+    )
+    reverse_edge_mapping: dict[str, list[GraphEdge]] = Field(
+        default_factory=dict, description="reverse graph edge mapping (target node id: edges)"
+    )
+    parallel_mapping: dict[str, GraphParallel] = Field(
+        default_factory=dict, description="graph parallel mapping (parallel id: parallel)"
+    )
+    node_parallel_mapping: dict[str, str] = Field(
+        default_factory=dict, description="graph node parallel mapping (node id: parallel id)"
+    )
+    answer_stream_generate_routes: AnswerStreamGenerateRoute = Field(..., description="answer stream generate routes")
+    end_stream_param: EndStreamParam = Field(..., description="end stream param")
+
+    @classmethod
+    def init(cls, graph_config: Mapping[str, Any], root_node_id: Optional[str] = None) -> "Graph":
+        """
+        Init graph
+
+        :param graph_config: graph config
+        :param root_node_id: root node id
+        :return: graph
+        """
+        # edge configs
+        edge_configs = graph_config.get("edges")
+        if edge_configs is None:
+            edge_configs = []
+
+        edge_configs = cast(list, edge_configs)
+
+        # reorganize edges mapping
+        edge_mapping: dict[str, list[GraphEdge]] = {}
+        reverse_edge_mapping: dict[str, list[GraphEdge]] = {}
+        target_edge_ids = set()
+        for edge_config in edge_configs:
+            source_node_id = edge_config.get("source")
+            if not source_node_id:
+                continue
+
+            if source_node_id not in edge_mapping:
+                edge_mapping[source_node_id] = []
+
+            target_node_id = edge_config.get("target")
+            if not target_node_id:
+                continue
+
+            if target_node_id not in reverse_edge_mapping:
+                reverse_edge_mapping[target_node_id] = []
+
+            target_edge_ids.add(target_node_id)
+
+            # parse run condition
+            run_condition = None
+            if edge_config.get("sourceHandle") and edge_config.get("sourceHandle") != "source":
+                run_condition = RunCondition(type="branch_identify", branch_identify=edge_config.get("sourceHandle"))
+
+            graph_edge = GraphEdge(
+                source_node_id=source_node_id, target_node_id=target_node_id, run_condition=run_condition
+            )
+
+            edge_mapping[source_node_id].append(graph_edge)
+            reverse_edge_mapping[target_node_id].append(graph_edge)
+
+        # node configs
+        node_configs = graph_config.get("nodes")
+        if not node_configs:
+            raise ValueError("Graph must have at least one node")
+
+        node_configs = cast(list, node_configs)
+
+        # fetch nodes that have no predecessor node
+        root_node_configs = []
+        all_node_id_config_mapping: dict[str, dict] = {}
+        for node_config in node_configs:
+            node_id = node_config.get("id")
+            if not node_id:
+                continue
+
+            if node_id not in target_edge_ids:
+                root_node_configs.append(node_config)
+
+            all_node_id_config_mapping[node_id] = node_config
+
+        root_node_ids = [node_config.get("id") for node_config in root_node_configs]
+
+        # fetch root node
+        if not root_node_id:
+            # if no root node id, use the START type node as root node
+            root_node_id = next(
+                (
+                    node_config.get("id")
+                    for node_config in root_node_configs
+                    if node_config.get("data", {}).get("type", "") == NodeType.START.value
+                ),
+                None,
+            )
+
+        if not root_node_id or root_node_id not in root_node_ids:
+            raise ValueError(f"Root node id {root_node_id} not found in the graph")
+
+        # Check whether it is connected to the previous node
+        cls._check_connected_to_previous_node(route=[root_node_id], edge_mapping=edge_mapping)
+
+        # fetch all node ids from root node
+        node_ids = [root_node_id]
+        cls._recursively_add_node_ids(node_ids=node_ids, edge_mapping=edge_mapping, node_id=root_node_id)
+
+        node_id_config_mapping = {node_id: all_node_id_config_mapping[node_id] for node_id in node_ids}
+
+        # init parallel mapping
+        parallel_mapping: dict[str, GraphParallel] = {}
+        node_parallel_mapping: dict[str, str] = {}
+        cls._recursively_add_parallels(
+            edge_mapping=edge_mapping,
+            reverse_edge_mapping=reverse_edge_mapping,
+            start_node_id=root_node_id,
+            parallel_mapping=parallel_mapping,
+            node_parallel_mapping=node_parallel_mapping,
+        )
+
+        # Check if it exceeds N layers of parallel
+        for parallel in parallel_mapping.values():
+            if parallel.parent_parallel_id:
+                cls._check_exceed_parallel_limit(
+                    parallel_mapping=parallel_mapping, level_limit=3, parent_parallel_id=parallel.parent_parallel_id
+                )
+
+        # init answer stream generate routes
+        answer_stream_generate_routes = AnswerStreamGeneratorRouter.init(
+            node_id_config_mapping=node_id_config_mapping, reverse_edge_mapping=reverse_edge_mapping
+        )
+
+        # init end stream param
+        end_stream_param = EndStreamGeneratorRouter.init(
+            node_id_config_mapping=node_id_config_mapping,
+            reverse_edge_mapping=reverse_edge_mapping,
+            node_parallel_mapping=node_parallel_mapping,
+        )
+
+        # init graph
+        graph = cls(
+            root_node_id=root_node_id,
+            node_ids=node_ids,
+            node_id_config_mapping=node_id_config_mapping,
+            edge_mapping=edge_mapping,
+            reverse_edge_mapping=reverse_edge_mapping,
+            parallel_mapping=parallel_mapping,
+            node_parallel_mapping=node_parallel_mapping,
+            answer_stream_generate_routes=answer_stream_generate_routes,
+            end_stream_param=end_stream_param,
+        )
+
+        return graph
+
+    def add_extra_edge(
+        self, source_node_id: str, target_node_id: str, run_condition: Optional[RunCondition] = None
+    ) -> None:
+        """
+        Add extra edge to the graph
+
+        :param source_node_id: source node id
+        :param target_node_id: target node id
+        :param run_condition: run condition
+        """
+        if source_node_id not in self.node_ids or target_node_id not in self.node_ids:
+            return
+
+        if source_node_id not in self.edge_mapping:
+            self.edge_mapping[source_node_id] = []
+
+        if target_node_id in [graph_edge.target_node_id for graph_edge in self.edge_mapping[source_node_id]]:
+            return
+
+        graph_edge = GraphEdge(
+            source_node_id=source_node_id, target_node_id=target_node_id, run_condition=run_condition
+        )
+
+        self.edge_mapping[source_node_id].append(graph_edge)
+
+    def get_leaf_node_ids(self) -> list[str]:
+        """
+        Get leaf node ids of the graph
+
+        :return: leaf node ids
+        """
+        leaf_node_ids = []
+        for node_id in self.node_ids:
+            if node_id not in self.edge_mapping or (
+                len(self.edge_mapping[node_id]) == 1
+                and self.edge_mapping[node_id][0].target_node_id == self.root_node_id
+            ):
+                leaf_node_ids.append(node_id)
+
+        return leaf_node_ids
+
+    @classmethod
+    def _recursively_add_node_ids(
+        cls, node_ids: list[str], edge_mapping: dict[str, list[GraphEdge]], node_id: str
+    ) -> None:
+        """
+        Recursively add node ids
+
+        :param node_ids: node ids
+        :param edge_mapping: edge mapping
+        :param node_id: node id
+        """
+        for graph_edge in edge_mapping.get(node_id, []):
+            if graph_edge.target_node_id in node_ids:
+                continue
+
+            node_ids.append(graph_edge.target_node_id)
+            cls._recursively_add_node_ids(
+                node_ids=node_ids, edge_mapping=edge_mapping, node_id=graph_edge.target_node_id
+            )
+
+    @classmethod
+    def _check_connected_to_previous_node(cls, route: list[str], edge_mapping: dict[str, list[GraphEdge]]) -> None:
+        """
+        Check whether it is connected to the previous node
+        """
+        last_node_id = route[-1]
+
+        for graph_edge in edge_mapping.get(last_node_id, []):
+            if not graph_edge.target_node_id:
+                continue
+
+            if graph_edge.target_node_id in route:
+                raise ValueError(
+                    f"Node {graph_edge.source_node_id} is connected to the previous node, please check the graph."
+                )
+
+            new_route = route.copy()
+            new_route.append(graph_edge.target_node_id)
+            cls._check_connected_to_previous_node(
+                route=new_route,
+                edge_mapping=edge_mapping,
+            )
+
+    @classmethod
+    def _recursively_add_parallels(
+        cls,
+        edge_mapping: dict[str, list[GraphEdge]],
+        reverse_edge_mapping: dict[str, list[GraphEdge]],
+        start_node_id: str,
+        parallel_mapping: dict[str, GraphParallel],
+        node_parallel_mapping: dict[str, str],
+        parent_parallel: Optional[GraphParallel] = None,
+    ) -> None:
+        """
+        Recursively add parallel ids
+
+        :param edge_mapping: edge mapping
+        :param start_node_id: start from node id
+        :param parallel_mapping: parallel mapping
+        :param node_parallel_mapping: node parallel mapping
+        :param parent_parallel: parent parallel
+        """
+        target_node_edges = edge_mapping.get(start_node_id, [])
+        parallel = None
+        if len(target_node_edges) > 1:
+            # fetch all node ids in current parallels
+            parallel_branch_node_ids = {}
+            condition_edge_mappings = {}
+            for graph_edge in target_node_edges:
+                if graph_edge.run_condition is None:
+                    if "default" not in parallel_branch_node_ids:
+                        parallel_branch_node_ids["default"] = []
+
+                    parallel_branch_node_ids["default"].append(graph_edge.target_node_id)
+                else:
+                    condition_hash = graph_edge.run_condition.hash
+                    if condition_hash not in condition_edge_mappings:
+                        condition_edge_mappings[condition_hash] = []
+
+                    condition_edge_mappings[condition_hash].append(graph_edge)
+
+            for condition_hash, graph_edges in condition_edge_mappings.items():
+                if len(graph_edges) > 1:
+                    if condition_hash not in parallel_branch_node_ids:
+                        parallel_branch_node_ids[condition_hash] = []
+
+                    for graph_edge in graph_edges:
+                        parallel_branch_node_ids[condition_hash].append(graph_edge.target_node_id)
+
+            condition_parallels = {}
+            for condition_hash, condition_parallel_branch_node_ids in parallel_branch_node_ids.items():
+                # any target node id in node_parallel_mapping
+                parallel = None
+                if condition_parallel_branch_node_ids:
+                    parent_parallel_id = parent_parallel.id if parent_parallel else None
+
+                    parallel = GraphParallel(
+                        start_from_node_id=start_node_id,
+                        parent_parallel_id=parent_parallel.id if parent_parallel else None,
+                        parent_parallel_start_node_id=parent_parallel.start_from_node_id if parent_parallel else None,
+                    )
+                    parallel_mapping[parallel.id] = parallel
+                    condition_parallels[condition_hash] = parallel
+
+                    in_branch_node_ids = cls._fetch_all_node_ids_in_parallels(
+                        edge_mapping=edge_mapping,
+                        reverse_edge_mapping=reverse_edge_mapping,
+                        parallel_branch_node_ids=condition_parallel_branch_node_ids,
+                    )
+
+                    # collect all branches node ids
+                    parallel_node_ids = []
+                    for _, node_ids in in_branch_node_ids.items():
+                        for node_id in node_ids:
+                            in_parent_parallel = True
+                            if parent_parallel_id:
+                                in_parent_parallel = False
+                                for parallel_node_id, parallel_id in node_parallel_mapping.items():
+                                    if parallel_id == parent_parallel_id and parallel_node_id == node_id:
+                                        in_parent_parallel = True
+                                        break
+
+                            if in_parent_parallel:
+                                parallel_node_ids.append(node_id)
+                                node_parallel_mapping[node_id] = parallel.id
+
+                    outside_parallel_target_node_ids = set()
+                    for node_id in parallel_node_ids:
+                        if node_id == parallel.start_from_node_id:
+                            continue
+
+                        node_edges = edge_mapping.get(node_id)
+                        if not node_edges:
+                            continue
+
+                        if len(node_edges) > 1:
+                            continue
+
+                        target_node_id = node_edges[0].target_node_id
+                        if target_node_id in parallel_node_ids:
+                            continue
+
+                        if parent_parallel_id:
+                            parent_parallel = parallel_mapping.get(parent_parallel_id)
+                            if not parent_parallel:
+                                continue
+
+                        if (
+                            (
+                                node_parallel_mapping.get(target_node_id)
+                                and node_parallel_mapping.get(target_node_id) == parent_parallel_id
+                            )
+                            or (
+                                parent_parallel
+                                and parent_parallel.end_to_node_id
+                                and target_node_id == parent_parallel.end_to_node_id
+                            )
+                            or (not node_parallel_mapping.get(target_node_id) and not parent_parallel)
+                        ):
+                            outside_parallel_target_node_ids.add(target_node_id)
+
+                    if len(outside_parallel_target_node_ids) == 1:
+                        if (
+                            parent_parallel
+                            and parent_parallel.end_to_node_id
+                            and parallel.end_to_node_id == parent_parallel.end_to_node_id
+                        ):
+                            parallel.end_to_node_id = None
+                        else:
+                            parallel.end_to_node_id = outside_parallel_target_node_ids.pop()
+
+            if condition_edge_mappings:
+                for condition_hash, graph_edges in condition_edge_mappings.items():
+                    for graph_edge in graph_edges:
+                        current_parallel: GraphParallel | None = cls._get_current_parallel(
+                            parallel_mapping=parallel_mapping,
+                            graph_edge=graph_edge,
+                            parallel=condition_parallels.get(condition_hash),
+                            parent_parallel=parent_parallel,
+                        )
+
+                        cls._recursively_add_parallels(
+                            edge_mapping=edge_mapping,
+                            reverse_edge_mapping=reverse_edge_mapping,
+                            start_node_id=graph_edge.target_node_id,
+                            parallel_mapping=parallel_mapping,
+                            node_parallel_mapping=node_parallel_mapping,
+                            parent_parallel=current_parallel,
+                        )
+            else:
+                for graph_edge in target_node_edges:
+                    current_parallel = cls._get_current_parallel(
+                        parallel_mapping=parallel_mapping,
+                        graph_edge=graph_edge,
+                        parallel=parallel,
+                        parent_parallel=parent_parallel,
+                    )
+
+                    cls._recursively_add_parallels(
+                        edge_mapping=edge_mapping,
+                        reverse_edge_mapping=reverse_edge_mapping,
+                        start_node_id=graph_edge.target_node_id,
+                        parallel_mapping=parallel_mapping,
+                        node_parallel_mapping=node_parallel_mapping,
+                        parent_parallel=current_parallel,
+                    )
+        else:
+            for graph_edge in target_node_edges:
+                current_parallel = cls._get_current_parallel(
+                    parallel_mapping=parallel_mapping,
+                    graph_edge=graph_edge,
+                    parallel=parallel,
+                    parent_parallel=parent_parallel,
+                )
+
+                cls._recursively_add_parallels(
+                    edge_mapping=edge_mapping,
+                    reverse_edge_mapping=reverse_edge_mapping,
+                    start_node_id=graph_edge.target_node_id,
+                    parallel_mapping=parallel_mapping,
+                    node_parallel_mapping=node_parallel_mapping,
+                    parent_parallel=current_parallel,
+                )
+
+    @classmethod
+    def _get_current_parallel(
+        cls,
+        parallel_mapping: dict[str, GraphParallel],
+        graph_edge: GraphEdge,
+        parallel: Optional[GraphParallel] = None,
+        parent_parallel: Optional[GraphParallel] = None,
+    ) -> Optional[GraphParallel]:
+        """
+        Get current parallel
+        """
+        current_parallel = None
+        if parallel:
+            current_parallel = parallel
+        elif parent_parallel:
+            if not parent_parallel.end_to_node_id or (
+                parent_parallel.end_to_node_id and graph_edge.target_node_id != parent_parallel.end_to_node_id
+            ):
+                current_parallel = parent_parallel
+            else:
+                # fetch parent parallel's parent parallel
+                parent_parallel_parent_parallel_id = parent_parallel.parent_parallel_id
+                if parent_parallel_parent_parallel_id:
+                    parent_parallel_parent_parallel = parallel_mapping.get(parent_parallel_parent_parallel_id)
+                    if parent_parallel_parent_parallel and (
+                        not parent_parallel_parent_parallel.end_to_node_id
+                        or (
+                            parent_parallel_parent_parallel.end_to_node_id
+                            and graph_edge.target_node_id != parent_parallel_parent_parallel.end_to_node_id
+                        )
+                    ):
+                        current_parallel = parent_parallel_parent_parallel
+
+        return current_parallel
+
+    @classmethod
+    def _check_exceed_parallel_limit(
+        cls,
+        parallel_mapping: dict[str, GraphParallel],
+        level_limit: int,
+        parent_parallel_id: str,
+        current_level: int = 1,
+    ) -> None:
+        """
+        Check if it exceeds N layers of parallel
+        """
+        parent_parallel = parallel_mapping.get(parent_parallel_id)
+        if not parent_parallel:
+            return
+
+        current_level += 1
+        if current_level > level_limit:
+            raise ValueError(f"Exceeds {level_limit} layers of parallel")
+
+        if parent_parallel.parent_parallel_id:
+            cls._check_exceed_parallel_limit(
+                parallel_mapping=parallel_mapping,
+                level_limit=level_limit,
+                parent_parallel_id=parent_parallel.parent_parallel_id,
+                current_level=current_level,
+            )
+
+    @classmethod
+    def _recursively_add_parallel_node_ids(
+        cls,
+        branch_node_ids: list[str],
+        edge_mapping: dict[str, list[GraphEdge]],
+        merge_node_id: str,
+        start_node_id: str,
+    ) -> None:
+        """
+        Recursively add node ids
+
+        :param branch_node_ids: in branch node ids
+        :param edge_mapping: edge mapping
+        :param merge_node_id: merge node id
+        :param start_node_id: start node id
+        """
+        for graph_edge in edge_mapping.get(start_node_id, []):
+            if graph_edge.target_node_id != merge_node_id and graph_edge.target_node_id not in branch_node_ids:
+                branch_node_ids.append(graph_edge.target_node_id)
+                cls._recursively_add_parallel_node_ids(
+                    branch_node_ids=branch_node_ids,
+                    edge_mapping=edge_mapping,
+                    merge_node_id=merge_node_id,
+                    start_node_id=graph_edge.target_node_id,
+                )
+
+    @classmethod
+    def _fetch_all_node_ids_in_parallels(
+        cls,
+        edge_mapping: dict[str, list[GraphEdge]],
+        reverse_edge_mapping: dict[str, list[GraphEdge]],
+        parallel_branch_node_ids: list[str],
+    ) -> dict[str, list[str]]:
+        """
+        Fetch all node ids in parallels
+        """
+        routes_node_ids: dict[str, list[str]] = {}
+        for parallel_branch_node_id in parallel_branch_node_ids:
+            routes_node_ids[parallel_branch_node_id] = [parallel_branch_node_id]
+
+            # fetch routes node ids
+            cls._recursively_fetch_routes(
+                edge_mapping=edge_mapping,
+                start_node_id=parallel_branch_node_id,
+                routes_node_ids=routes_node_ids[parallel_branch_node_id],
+            )
+
+        # fetch leaf node ids from routes node ids
+        leaf_node_ids: dict[str, list[str]] = {}
+        merge_branch_node_ids: dict[str, list[str]] = {}
+        for branch_node_id, node_ids in routes_node_ids.items():
+            for node_id in node_ids:
+                if node_id not in edge_mapping or len(edge_mapping[node_id]) == 0:
+                    if branch_node_id not in leaf_node_ids:
+                        leaf_node_ids[branch_node_id] = []
+
+                    leaf_node_ids[branch_node_id].append(node_id)
+
+                for branch_node_id2, inner_route2 in routes_node_ids.items():
+                    if (
+                        branch_node_id != branch_node_id2
+                        and node_id in inner_route2
+                        and len(reverse_edge_mapping.get(node_id, [])) > 1
+                        and cls._is_node_in_routes(
+                            reverse_edge_mapping=reverse_edge_mapping,
+                            start_node_id=node_id,
+                            routes_node_ids=routes_node_ids,
+                        )
+                    ):
+                        if node_id not in merge_branch_node_ids:
+                            merge_branch_node_ids[node_id] = []
+
+                        if branch_node_id2 not in merge_branch_node_ids[node_id]:
+                            merge_branch_node_ids[node_id].append(branch_node_id2)
+
+        # sorted merge_branch_node_ids by branch_node_ids length desc
+        merge_branch_node_ids = dict(sorted(merge_branch_node_ids.items(), key=lambda x: len(x[1]), reverse=True))
+
+        duplicate_end_node_ids = {}
+        for node_id, branch_node_ids in merge_branch_node_ids.items():
+            for node_id2, branch_node_ids2 in merge_branch_node_ids.items():
+                if node_id != node_id2 and set(branch_node_ids) == set(branch_node_ids2):
+                    if (node_id, node_id2) not in duplicate_end_node_ids and (
+                        node_id2,
+                        node_id,
+                    ) not in duplicate_end_node_ids:
+                        duplicate_end_node_ids[(node_id, node_id2)] = branch_node_ids
+
+        for (node_id, node_id2), branch_node_ids in duplicate_end_node_ids.items():
+            # check which node is after
+            if cls._is_node2_after_node1(node1_id=node_id, node2_id=node_id2, edge_mapping=edge_mapping):
+                if node_id in merge_branch_node_ids:
+                    del merge_branch_node_ids[node_id2]
+            elif cls._is_node2_after_node1(node1_id=node_id2, node2_id=node_id, edge_mapping=edge_mapping):
+                if node_id2 in merge_branch_node_ids:
+                    del merge_branch_node_ids[node_id]
+
+        branches_merge_node_ids: dict[str, str] = {}
+        for node_id, branch_node_ids in merge_branch_node_ids.items():
+            if len(branch_node_ids) <= 1:
+                continue
+
+            for branch_node_id in branch_node_ids:
+                if branch_node_id in branches_merge_node_ids:
+                    continue
+
+                branches_merge_node_ids[branch_node_id] = node_id
+
+        in_branch_node_ids: dict[str, list[str]] = {}
+        for branch_node_id, node_ids in routes_node_ids.items():
+            in_branch_node_ids[branch_node_id] = []
+            if branch_node_id not in branches_merge_node_ids:
+                # all node ids in current branch is in this thread
+                in_branch_node_ids[branch_node_id].append(branch_node_id)
+                in_branch_node_ids[branch_node_id].extend(node_ids)
+            else:
+                merge_node_id = branches_merge_node_ids[branch_node_id]
+                if merge_node_id != branch_node_id:
+                    in_branch_node_ids[branch_node_id].append(branch_node_id)
+
+                # fetch all node ids from branch_node_id and merge_node_id
+                cls._recursively_add_parallel_node_ids(
+                    branch_node_ids=in_branch_node_ids[branch_node_id],
+                    edge_mapping=edge_mapping,
+                    merge_node_id=merge_node_id,
+                    start_node_id=branch_node_id,
+                )
+
+        return in_branch_node_ids
+
+    @classmethod
+    def _recursively_fetch_routes(
+        cls, edge_mapping: dict[str, list[GraphEdge]], start_node_id: str, routes_node_ids: list[str]
+    ) -> None:
+        """
+        Recursively fetch route
+        """
+        if start_node_id not in edge_mapping:
+            return
+
+        for graph_edge in edge_mapping[start_node_id]:
+            # find next node ids
+            if graph_edge.target_node_id not in routes_node_ids:
+                routes_node_ids.append(graph_edge.target_node_id)
+
+                cls._recursively_fetch_routes(
+                    edge_mapping=edge_mapping, start_node_id=graph_edge.target_node_id, routes_node_ids=routes_node_ids
+                )
+
+    @classmethod
+    def _is_node_in_routes(
+        cls, reverse_edge_mapping: dict[str, list[GraphEdge]], start_node_id: str, routes_node_ids: dict[str, list[str]]
+    ) -> bool:
+        """
+        Recursively check if the node is in the routes
+        """
+        if start_node_id not in reverse_edge_mapping:
+            return False
+
+        all_routes_node_ids = set()
+        parallel_start_node_ids: dict[str, list[str]] = {}
+        for branch_node_id, node_ids in routes_node_ids.items():
+            all_routes_node_ids.update(node_ids)
+
+            if branch_node_id in reverse_edge_mapping:
+                for graph_edge in reverse_edge_mapping[branch_node_id]:
+                    if graph_edge.source_node_id not in parallel_start_node_ids:
+                        parallel_start_node_ids[graph_edge.source_node_id] = []
+
+                    parallel_start_node_ids[graph_edge.source_node_id].append(branch_node_id)
+
+        for _, branch_node_ids in parallel_start_node_ids.items():
+            if set(branch_node_ids) == set(routes_node_ids.keys()):
+                return True
+
+        return False
+
+    @classmethod
+    def _is_node2_after_node1(cls, node1_id: str, node2_id: str, edge_mapping: dict[str, list[GraphEdge]]) -> bool:
+        """
+        is node2 after node1
+        """
+        if node1_id not in edge_mapping:
+            return False
+
+        for graph_edge in edge_mapping[node1_id]:
+            if graph_edge.target_node_id == node2_id:
+                return True
+
+            if cls._is_node2_after_node1(
+                node1_id=graph_edge.target_node_id, node2_id=node2_id, edge_mapping=edge_mapping
+            ):
+                return True
+
+        return False
diff --git a/api/core/workflow/graph_engine/entities/graph_init_params.py b/api/core/workflow/graph_engine/entities/graph_init_params.py
new file mode 100644
index 0000000000..1a403f3e49
--- /dev/null
+++ b/api/core/workflow/graph_engine/entities/graph_init_params.py
@@ -0,0 +1,21 @@
+from collections.abc import Mapping
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import UserFrom
+from models.workflow import WorkflowType
+
+
+class GraphInitParams(BaseModel):
+    # init params
+    tenant_id: str = Field(..., description="tenant / workspace id")
+    app_id: str = Field(..., description="app id")
+    workflow_type: WorkflowType = Field(..., description="workflow type")
+    workflow_id: str = Field(..., description="workflow id")
+    graph_config: Mapping[str, Any] = Field(..., description="graph config")
+    user_id: str = Field(..., description="user id")
+    user_from: UserFrom = Field(..., description="user from, account or end-user")
+    invoke_from: InvokeFrom = Field(..., description="invoke from, service-api, web-app, explore or debugger")
+    call_depth: int = Field(..., description="call depth")
diff --git a/api/core/workflow/graph_engine/entities/graph_runtime_state.py b/api/core/workflow/graph_engine/entities/graph_runtime_state.py
new file mode 100644
index 0000000000..afc09bfac5
--- /dev/null
+++ b/api/core/workflow/graph_engine/entities/graph_runtime_state.py
@@ -0,0 +1,27 @@
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+from core.model_runtime.entities.llm_entities import LLMUsage
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.graph_engine.entities.runtime_route_state import RuntimeRouteState
+
+
+class GraphRuntimeState(BaseModel):
+    variable_pool: VariablePool = Field(..., description="variable pool")
+    """variable pool"""
+
+    start_at: float = Field(..., description="start time")
+    """start time"""
+    total_tokens: int = 0
+    """total tokens"""
+    llm_usage: LLMUsage = LLMUsage.empty_usage()
+    """llm usage info"""
+    outputs: dict[str, Any] = {}
+    """outputs"""
+
+    node_run_steps: int = 0
+    """node run steps"""
+
+    node_run_state: RuntimeRouteState = RuntimeRouteState()
+    """node run state"""
diff --git a/api/core/workflow/graph_engine/entities/next_graph_node.py b/api/core/workflow/graph_engine/entities/next_graph_node.py
new file mode 100644
index 0000000000..6aa4341ddf
--- /dev/null
+++ b/api/core/workflow/graph_engine/entities/next_graph_node.py
@@ -0,0 +1,13 @@
+from typing import Optional
+
+from pydantic import BaseModel
+
+from core.workflow.graph_engine.entities.graph import GraphParallel
+
+
+class NextGraphNode(BaseModel):
+    node_id: str
+    """next node id"""
+
+    parallel: Optional[GraphParallel] = None
+    """parallel"""
diff --git a/api/core/workflow/graph_engine/entities/run_condition.py b/api/core/workflow/graph_engine/entities/run_condition.py
new file mode 100644
index 0000000000..eedce8842b
--- /dev/null
+++ b/api/core/workflow/graph_engine/entities/run_condition.py
@@ -0,0 +1,21 @@
+import hashlib
+from typing import Literal, Optional
+
+from pydantic import BaseModel
+
+from core.workflow.utils.condition.entities import Condition
+
+
+class RunCondition(BaseModel):
+    type: Literal["branch_identify", "condition"]
+    """condition type"""
+
+    branch_identify: Optional[str] = None
+    """branch identify like: sourceHandle, required when type is branch_identify"""
+
+    conditions: Optional[list[Condition]] = None
+    """conditions to run the node, required when type is condition"""
+
+    @property
+    def hash(self) -> str:
+        return hashlib.sha256(self.model_dump_json().encode()).hexdigest()
diff --git a/api/core/workflow/graph_engine/entities/runtime_route_state.py b/api/core/workflow/graph_engine/entities/runtime_route_state.py
new file mode 100644
index 0000000000..bb24b51112
--- /dev/null
+++ b/api/core/workflow/graph_engine/entities/runtime_route_state.py
@@ -0,0 +1,109 @@
+import uuid
+from datetime import datetime, timezone
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+from core.workflow.entities.node_entities import NodeRunResult
+from models.workflow import WorkflowNodeExecutionStatus
+
+
+class RouteNodeState(BaseModel):
+    class Status(Enum):
+        RUNNING = "running"
+        SUCCESS = "success"
+        FAILED = "failed"
+        PAUSED = "paused"
+
+    id: str = Field(default_factory=lambda: str(uuid.uuid4()))
+    """node state id"""
+
+    node_id: str
+    """node id"""
+
+    node_run_result: Optional[NodeRunResult] = None
+    """node run result"""
+
+    status: Status = Status.RUNNING
+    """node status"""
+
+    start_at: datetime
+    """start time"""
+
+    paused_at: Optional[datetime] = None
+    """paused time"""
+
+    finished_at: Optional[datetime] = None
+    """finished time"""
+
+    failed_reason: Optional[str] = None
+    """failed reason"""
+
+    paused_by: Optional[str] = None
+    """paused by"""
+
+    index: int = 1
+
+    def set_finished(self, run_result: NodeRunResult) -> None:
+        """
+        Node finished
+
+        :param run_result: run result
+        """
+        if self.status in {RouteNodeState.Status.SUCCESS, RouteNodeState.Status.FAILED}:
+            raise Exception(f"Route state {self.id} already finished")
+
+        if run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED:
+            self.status = RouteNodeState.Status.SUCCESS
+        elif run_result.status == WorkflowNodeExecutionStatus.FAILED:
+            self.status = RouteNodeState.Status.FAILED
+            self.failed_reason = run_result.error
+        else:
+            raise Exception(f"Invalid route status {run_result.status}")
+
+        self.node_run_result = run_result
+        self.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
+
+
+class RuntimeRouteState(BaseModel):
+    routes: dict[str, list[str]] = Field(
+        default_factory=dict, description="graph state routes (source_node_state_id: target_node_state_id)"
+    )
+
+    node_state_mapping: dict[str, RouteNodeState] = Field(
+        default_factory=dict, description="node state mapping (route_node_state_id: route_node_state)"
+    )
+
+    def create_node_state(self, node_id: str) -> RouteNodeState:
+        """
+        Create node state
+
+        :param node_id: node id
+        """
+        state = RouteNodeState(node_id=node_id, start_at=datetime.now(timezone.utc).replace(tzinfo=None))
+        self.node_state_mapping[state.id] = state
+        return state
+
+    def add_route(self, source_node_state_id: str, target_node_state_id: str) -> None:
+        """
+        Add route to the graph state
+
+        :param source_node_state_id: source node state id
+        :param target_node_state_id: target node state id
+        """
+        if source_node_state_id not in self.routes:
+            self.routes[source_node_state_id] = []
+
+        self.routes[source_node_state_id].append(target_node_state_id)
+
+    def get_routes_with_node_state_by_source_node_state_id(self, source_node_state_id: str) -> list[RouteNodeState]:
+        """
+        Get routes with node state by source node id
+
+        :param source_node_state_id: source node state id
+        :return: routes with node state
+        """
+        return [
+            self.node_state_mapping[target_state_id] for target_state_id in self.routes.get(source_node_state_id, [])
+        ]
diff --git a/api/core/workflow/graph_engine/graph_engine.py b/api/core/workflow/graph_engine/graph_engine.py
new file mode 100644
index 0000000000..1db9b690ab
--- /dev/null
+++ b/api/core/workflow/graph_engine/graph_engine.py
@@ -0,0 +1,726 @@
+import logging
+import queue
+import time
+import uuid
+from collections.abc import Generator, Mapping
+from concurrent.futures import ThreadPoolExecutor, wait
+from typing import Any, Optional
+
+from flask import Flask, current_app
+
+from core.app.apps.base_app_queue_manager import GenerateTaskStoppedError
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import (
+    NodeRunMetadataKey,
+    NodeType,
+    UserFrom,
+)
+from core.workflow.entities.variable_pool import VariablePool, VariableValue
+from core.workflow.graph_engine.condition_handlers.condition_manager import ConditionManager
+from core.workflow.graph_engine.entities.event import (
+    BaseIterationEvent,
+    GraphEngineEvent,
+    GraphRunFailedEvent,
+    GraphRunStartedEvent,
+    GraphRunSucceededEvent,
+    NodeRunFailedEvent,
+    NodeRunRetrieverResourceEvent,
+    NodeRunStartedEvent,
+    NodeRunStreamChunkEvent,
+    NodeRunSucceededEvent,
+    ParallelBranchRunFailedEvent,
+    ParallelBranchRunStartedEvent,
+    ParallelBranchRunSucceededEvent,
+)
+from core.workflow.graph_engine.entities.graph import Graph, GraphEdge
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
+from core.workflow.nodes.answer.answer_stream_processor import AnswerStreamProcessor
+from core.workflow.nodes.base_node import BaseNode
+from core.workflow.nodes.end.end_stream_processor import EndStreamProcessor
+from core.workflow.nodes.event import RunCompletedEvent, RunRetrieverResourceEvent, RunStreamChunkEvent
+from core.workflow.nodes.node_mapping import node_classes
+from extensions.ext_database import db
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
+
+logger = logging.getLogger(__name__)
+
+
+class GraphEngineThreadPool(ThreadPoolExecutor):
+    def __init__(
+        self, max_workers=None, thread_name_prefix="", initializer=None, initargs=(), max_submit_count=100
+    ) -> None:
+        super().__init__(max_workers, thread_name_prefix, initializer, initargs)
+        self.max_submit_count = max_submit_count
+        self.submit_count = 0
+
+    def submit(self, fn, *args, **kwargs):
+        self.submit_count += 1
+        self.check_is_full()
+
+        return super().submit(fn, *args, **kwargs)
+
+    def check_is_full(self) -> None:
+        print(f"submit_count: {self.submit_count}, max_submit_count: {self.max_submit_count}")
+        if self.submit_count > self.max_submit_count:
+            raise ValueError(f"Max submit count {self.max_submit_count} of workflow thread pool reached.")
+
+
+class GraphEngine:
+    workflow_thread_pool_mapping: dict[str, GraphEngineThreadPool] = {}
+
+    def __init__(
+        self,
+        tenant_id: str,
+        app_id: str,
+        workflow_type: WorkflowType,
+        workflow_id: str,
+        user_id: str,
+        user_from: UserFrom,
+        invoke_from: InvokeFrom,
+        call_depth: int,
+        graph: Graph,
+        graph_config: Mapping[str, Any],
+        variable_pool: VariablePool,
+        max_execution_steps: int,
+        max_execution_time: int,
+        thread_pool_id: Optional[str] = None,
+    ) -> None:
+        thread_pool_max_submit_count = 100
+        thread_pool_max_workers = 10
+
+        # init thread pool
+        if thread_pool_id:
+            if thread_pool_id not in GraphEngine.workflow_thread_pool_mapping:
+                raise ValueError(f"Max submit count {thread_pool_max_submit_count} of workflow thread pool reached.")
+
+            self.thread_pool_id = thread_pool_id
+            self.thread_pool = GraphEngine.workflow_thread_pool_mapping[thread_pool_id]
+            self.is_main_thread_pool = False
+        else:
+            self.thread_pool = GraphEngineThreadPool(
+                max_workers=thread_pool_max_workers, max_submit_count=thread_pool_max_submit_count
+            )
+            self.thread_pool_id = str(uuid.uuid4())
+            self.is_main_thread_pool = True
+            GraphEngine.workflow_thread_pool_mapping[self.thread_pool_id] = self.thread_pool
+
+        self.graph = graph
+        self.init_params = GraphInitParams(
+            tenant_id=tenant_id,
+            app_id=app_id,
+            workflow_type=workflow_type,
+            workflow_id=workflow_id,
+            graph_config=graph_config,
+            user_id=user_id,
+            user_from=user_from,
+            invoke_from=invoke_from,
+            call_depth=call_depth,
+        )
+
+        self.graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
+
+        self.max_execution_steps = max_execution_steps
+        self.max_execution_time = max_execution_time
+
+    def run(self) -> Generator[GraphEngineEvent, None, None]:
+        # trigger graph run start event
+        yield GraphRunStartedEvent()
+
+        try:
+            stream_processor_cls: type[AnswerStreamProcessor | EndStreamProcessor]
+            if self.init_params.workflow_type == WorkflowType.CHAT:
+                stream_processor_cls = AnswerStreamProcessor
+            else:
+                stream_processor_cls = EndStreamProcessor
+
+            stream_processor = stream_processor_cls(
+                graph=self.graph, variable_pool=self.graph_runtime_state.variable_pool
+            )
+
+            # run graph
+            generator = stream_processor.process(self._run(start_node_id=self.graph.root_node_id))
+
+            for item in generator:
+                try:
+                    yield item
+                    if isinstance(item, NodeRunFailedEvent):
+                        yield GraphRunFailedEvent(error=item.route_node_state.failed_reason or "Unknown error.")
+                        return
+                    elif isinstance(item, NodeRunSucceededEvent):
+                        if item.node_type == NodeType.END:
+                            self.graph_runtime_state.outputs = (
+                                item.route_node_state.node_run_result.outputs
+                                if item.route_node_state.node_run_result
+                                and item.route_node_state.node_run_result.outputs
+                                else {}
+                            )
+                        elif item.node_type == NodeType.ANSWER:
+                            if "answer" not in self.graph_runtime_state.outputs:
+                                self.graph_runtime_state.outputs["answer"] = ""
+
+                            self.graph_runtime_state.outputs["answer"] += "\n" + (
+                                item.route_node_state.node_run_result.outputs.get("answer", "")
+                                if item.route_node_state.node_run_result
+                                and item.route_node_state.node_run_result.outputs
+                                else ""
+                            )
+
+                            self.graph_runtime_state.outputs["answer"] = self.graph_runtime_state.outputs[
+                                "answer"
+                            ].strip()
+                except Exception as e:
+                    logger.exception(f"Graph run failed: {str(e)}")
+                    yield GraphRunFailedEvent(error=str(e))
+                    return
+
+            # trigger graph run success event
+            yield GraphRunSucceededEvent(outputs=self.graph_runtime_state.outputs)
+        except GraphRunFailedError as e:
+            yield GraphRunFailedEvent(error=e.error)
+            return
+        except Exception as e:
+            logger.exception("Unknown Error when graph running")
+            yield GraphRunFailedEvent(error=str(e))
+            raise e
+        finally:
+            if self.is_main_thread_pool and self.thread_pool_id in GraphEngine.workflow_thread_pool_mapping:
+                del GraphEngine.workflow_thread_pool_mapping[self.thread_pool_id]
+
+    def _run(
+        self,
+        start_node_id: str,
+        in_parallel_id: Optional[str] = None,
+        parent_parallel_id: Optional[str] = None,
+        parent_parallel_start_node_id: Optional[str] = None,
+    ) -> Generator[GraphEngineEvent, None, None]:
+        parallel_start_node_id = None
+        if in_parallel_id:
+            parallel_start_node_id = start_node_id
+
+        next_node_id = start_node_id
+        previous_route_node_state: Optional[RouteNodeState] = None
+        while True:
+            # max steps reached
+            if self.graph_runtime_state.node_run_steps > self.max_execution_steps:
+                raise GraphRunFailedError("Max steps {} reached.".format(self.max_execution_steps))
+
+            # or max execution time reached
+            if self._is_timed_out(
+                start_at=self.graph_runtime_state.start_at, max_execution_time=self.max_execution_time
+            ):
+                raise GraphRunFailedError("Max execution time {}s reached.".format(self.max_execution_time))
+
+            # init route node state
+            route_node_state = self.graph_runtime_state.node_run_state.create_node_state(node_id=next_node_id)
+
+            # get node config
+            node_id = route_node_state.node_id
+            node_config = self.graph.node_id_config_mapping.get(node_id)
+            if not node_config:
+                raise GraphRunFailedError(f"Node {node_id} config not found.")
+
+            # convert to specific node
+            node_type = NodeType.value_of(node_config.get("data", {}).get("type"))
+            node_cls = node_classes.get(node_type)
+            if not node_cls:
+                raise GraphRunFailedError(f"Node {node_id} type {node_type} not found.")
+
+            previous_node_id = previous_route_node_state.node_id if previous_route_node_state else None
+
+            # init workflow run state
+            node_instance = node_cls(  # type: ignore
+                id=route_node_state.id,
+                config=node_config,
+                graph_init_params=self.init_params,
+                graph=self.graph,
+                graph_runtime_state=self.graph_runtime_state,
+                previous_node_id=previous_node_id,
+                thread_pool_id=self.thread_pool_id,
+            )
+
+            try:
+                # run node
+                generator = self._run_node(
+                    node_instance=node_instance,
+                    route_node_state=route_node_state,
+                    parallel_id=in_parallel_id,
+                    parallel_start_node_id=parallel_start_node_id,
+                    parent_parallel_id=parent_parallel_id,
+                    parent_parallel_start_node_id=parent_parallel_start_node_id,
+                )
+
+                for item in generator:
+                    if isinstance(item, NodeRunStartedEvent):
+                        self.graph_runtime_state.node_run_steps += 1
+                        item.route_node_state.index = self.graph_runtime_state.node_run_steps
+
+                    yield item
+
+                self.graph_runtime_state.node_run_state.node_state_mapping[route_node_state.id] = route_node_state
+
+                # append route
+                if previous_route_node_state:
+                    self.graph_runtime_state.node_run_state.add_route(
+                        source_node_state_id=previous_route_node_state.id, target_node_state_id=route_node_state.id
+                    )
+            except Exception as e:
+                route_node_state.status = RouteNodeState.Status.FAILED
+                route_node_state.failed_reason = str(e)
+                yield NodeRunFailedEvent(
+                    error=str(e),
+                    id=node_instance.id,
+                    node_id=next_node_id,
+                    node_type=node_type,
+                    node_data=node_instance.node_data,
+                    route_node_state=route_node_state,
+                    parallel_id=in_parallel_id,
+                    parallel_start_node_id=parallel_start_node_id,
+                    parent_parallel_id=parent_parallel_id,
+                    parent_parallel_start_node_id=parent_parallel_start_node_id,
+                )
+                raise e
+
+            # It may not be necessary, but it is necessary. :)
+            if (
+                self.graph.node_id_config_mapping[next_node_id].get("data", {}).get("type", "").lower()
+                == NodeType.END.value
+            ):
+                break
+
+            previous_route_node_state = route_node_state
+
+            # get next node ids
+            edge_mappings = self.graph.edge_mapping.get(next_node_id)
+            if not edge_mappings:
+                break
+
+            if len(edge_mappings) == 1:
+                edge = edge_mappings[0]
+
+                if edge.run_condition:
+                    result = ConditionManager.get_condition_handler(
+                        init_params=self.init_params,
+                        graph=self.graph,
+                        run_condition=edge.run_condition,
+                    ).check(
+                        graph_runtime_state=self.graph_runtime_state,
+                        previous_route_node_state=previous_route_node_state,
+                    )
+
+                    if not result:
+                        break
+
+                next_node_id = edge.target_node_id
+            else:
+                final_node_id = None
+
+                if any(edge.run_condition for edge in edge_mappings):
+                    # if nodes has run conditions, get node id which branch to take based on the run condition results
+                    condition_edge_mappings = {}
+                    for edge in edge_mappings:
+                        if edge.run_condition:
+                            run_condition_hash = edge.run_condition.hash
+                            if run_condition_hash not in condition_edge_mappings:
+                                condition_edge_mappings[run_condition_hash] = []
+
+                            condition_edge_mappings[run_condition_hash].append(edge)
+
+                    for _, sub_edge_mappings in condition_edge_mappings.items():
+                        if len(sub_edge_mappings) == 0:
+                            continue
+
+                        edge = sub_edge_mappings[0]
+
+                        result = ConditionManager.get_condition_handler(
+                            init_params=self.init_params,
+                            graph=self.graph,
+                            run_condition=edge.run_condition,
+                        ).check(
+                            graph_runtime_state=self.graph_runtime_state,
+                            previous_route_node_state=previous_route_node_state,
+                        )
+
+                        if not result:
+                            continue
+
+                        if len(sub_edge_mappings) == 1:
+                            final_node_id = edge.target_node_id
+                        else:
+                            parallel_generator = self._run_parallel_branches(
+                                edge_mappings=sub_edge_mappings,
+                                in_parallel_id=in_parallel_id,
+                                parallel_start_node_id=parallel_start_node_id,
+                            )
+
+                            for item in parallel_generator:
+                                if isinstance(item, str):
+                                    final_node_id = item
+                                else:
+                                    yield item
+
+                        break
+
+                    if not final_node_id:
+                        break
+
+                    next_node_id = final_node_id
+                else:
+                    parallel_generator = self._run_parallel_branches(
+                        edge_mappings=edge_mappings,
+                        in_parallel_id=in_parallel_id,
+                        parallel_start_node_id=parallel_start_node_id,
+                    )
+
+                    for item in parallel_generator:
+                        if isinstance(item, str):
+                            final_node_id = item
+                        else:
+                            yield item
+
+                    if not final_node_id:
+                        break
+
+                    next_node_id = final_node_id
+
+            if in_parallel_id and self.graph.node_parallel_mapping.get(next_node_id, "") != in_parallel_id:
+                break
+
+    def _run_parallel_branches(
+        self,
+        edge_mappings: list[GraphEdge],
+        in_parallel_id: Optional[str] = None,
+        parallel_start_node_id: Optional[str] = None,
+    ) -> Generator[GraphEngineEvent | str, None, None]:
+        # if nodes has no run conditions, parallel run all nodes
+        parallel_id = self.graph.node_parallel_mapping.get(edge_mappings[0].target_node_id)
+        if not parallel_id:
+            node_id = edge_mappings[0].target_node_id
+            node_config = self.graph.node_id_config_mapping.get(node_id)
+            if not node_config:
+                raise GraphRunFailedError(
+                    f"Node {node_id} related parallel not found or incorrectly connected to multiple parallel branches."
+                )
+
+            node_title = node_config.get("data", {}).get("title")
+            raise GraphRunFailedError(
+                f"Node {node_title} related parallel not found or incorrectly connected to multiple parallel branches."
+            )
+
+        parallel = self.graph.parallel_mapping.get(parallel_id)
+        if not parallel:
+            raise GraphRunFailedError(f"Parallel {parallel_id} not found.")
+
+        # run parallel nodes, run in new thread and use queue to get results
+        q: queue.Queue = queue.Queue()
+
+        # Create a list to store the threads
+        futures = []
+
+        # new thread
+        for edge in edge_mappings:
+            if (
+                edge.target_node_id not in self.graph.node_parallel_mapping
+                or self.graph.node_parallel_mapping.get(edge.target_node_id, "") != parallel_id
+            ):
+                continue
+
+            futures.append(
+                self.thread_pool.submit(
+                    self._run_parallel_node,
+                    **{
+                        "flask_app": current_app._get_current_object(),  # type: ignore[attr-defined]
+                        "q": q,
+                        "parallel_id": parallel_id,
+                        "parallel_start_node_id": edge.target_node_id,
+                        "parent_parallel_id": in_parallel_id,
+                        "parent_parallel_start_node_id": parallel_start_node_id,
+                    },
+                )
+            )
+
+        succeeded_count = 0
+        while True:
+            try:
+                event = q.get(timeout=1)
+                if event is None:
+                    break
+
+                yield event
+                if event.parallel_id == parallel_id:
+                    if isinstance(event, ParallelBranchRunSucceededEvent):
+                        succeeded_count += 1
+                        if succeeded_count == len(futures):
+                            q.put(None)
+
+                        continue
+                    elif isinstance(event, ParallelBranchRunFailedEvent):
+                        raise GraphRunFailedError(event.error)
+            except queue.Empty:
+                continue
+
+        # wait all threads
+        wait(futures)
+
+        # get final node id
+        final_node_id = parallel.end_to_node_id
+        if final_node_id:
+            yield final_node_id
+
+    def _run_parallel_node(
+        self,
+        flask_app: Flask,
+        q: queue.Queue,
+        parallel_id: str,
+        parallel_start_node_id: str,
+        parent_parallel_id: Optional[str] = None,
+        parent_parallel_start_node_id: Optional[str] = None,
+    ) -> None:
+        """
+        Run parallel nodes
+        """
+        with flask_app.app_context():
+            try:
+                q.put(
+                    ParallelBranchRunStartedEvent(
+                        parallel_id=parallel_id,
+                        parallel_start_node_id=parallel_start_node_id,
+                        parent_parallel_id=parent_parallel_id,
+                        parent_parallel_start_node_id=parent_parallel_start_node_id,
+                    )
+                )
+
+                # run node
+                generator = self._run(
+                    start_node_id=parallel_start_node_id,
+                    in_parallel_id=parallel_id,
+                    parent_parallel_id=parent_parallel_id,
+                    parent_parallel_start_node_id=parent_parallel_start_node_id,
+                )
+
+                for item in generator:
+                    q.put(item)
+
+                # trigger graph run success event
+                q.put(
+                    ParallelBranchRunSucceededEvent(
+                        parallel_id=parallel_id,
+                        parallel_start_node_id=parallel_start_node_id,
+                        parent_parallel_id=parent_parallel_id,
+                        parent_parallel_start_node_id=parent_parallel_start_node_id,
+                    )
+                )
+            except GraphRunFailedError as e:
+                q.put(
+                    ParallelBranchRunFailedEvent(
+                        parallel_id=parallel_id,
+                        parallel_start_node_id=parallel_start_node_id,
+                        parent_parallel_id=parent_parallel_id,
+                        parent_parallel_start_node_id=parent_parallel_start_node_id,
+                        error=e.error,
+                    )
+                )
+            except Exception as e:
+                logger.exception("Unknown Error when generating in parallel")
+                q.put(
+                    ParallelBranchRunFailedEvent(
+                        parallel_id=parallel_id,
+                        parallel_start_node_id=parallel_start_node_id,
+                        parent_parallel_id=parent_parallel_id,
+                        parent_parallel_start_node_id=parent_parallel_start_node_id,
+                        error=str(e),
+                    )
+                )
+            finally:
+                db.session.remove()
+
+    def _run_node(
+        self,
+        node_instance: BaseNode,
+        route_node_state: RouteNodeState,
+        parallel_id: Optional[str] = None,
+        parallel_start_node_id: Optional[str] = None,
+        parent_parallel_id: Optional[str] = None,
+        parent_parallel_start_node_id: Optional[str] = None,
+    ) -> Generator[GraphEngineEvent, None, None]:
+        """
+        Run node
+        """
+        # trigger node run start event
+        yield NodeRunStartedEvent(
+            id=node_instance.id,
+            node_id=node_instance.node_id,
+            node_type=node_instance.node_type,
+            node_data=node_instance.node_data,
+            route_node_state=route_node_state,
+            predecessor_node_id=node_instance.previous_node_id,
+            parallel_id=parallel_id,
+            parallel_start_node_id=parallel_start_node_id,
+            parent_parallel_id=parent_parallel_id,
+            parent_parallel_start_node_id=parent_parallel_start_node_id,
+        )
+
+        db.session.close()
+
+        try:
+            # run node
+            generator = node_instance.run()
+            for item in generator:
+                if isinstance(item, GraphEngineEvent):
+                    if isinstance(item, BaseIterationEvent):
+                        # add parallel info to iteration event
+                        item.parallel_id = parallel_id
+                        item.parallel_start_node_id = parallel_start_node_id
+                        item.parent_parallel_id = parent_parallel_id
+                        item.parent_parallel_start_node_id = parent_parallel_start_node_id
+
+                    yield item
+                else:
+                    if isinstance(item, RunCompletedEvent):
+                        run_result = item.run_result
+                        route_node_state.set_finished(run_result=run_result)
+
+                        if run_result.status == WorkflowNodeExecutionStatus.FAILED:
+                            yield NodeRunFailedEvent(
+                                error=route_node_state.failed_reason or "Unknown error.",
+                                id=node_instance.id,
+                                node_id=node_instance.node_id,
+                                node_type=node_instance.node_type,
+                                node_data=node_instance.node_data,
+                                route_node_state=route_node_state,
+                                parallel_id=parallel_id,
+                                parallel_start_node_id=parallel_start_node_id,
+                                parent_parallel_id=parent_parallel_id,
+                                parent_parallel_start_node_id=parent_parallel_start_node_id,
+                            )
+                        elif run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED:
+                            if run_result.metadata and run_result.metadata.get(NodeRunMetadataKey.TOTAL_TOKENS):
+                                # plus state total_tokens
+                                self.graph_runtime_state.total_tokens += int(
+                                    run_result.metadata.get(NodeRunMetadataKey.TOTAL_TOKENS)  # type: ignore[arg-type]
+                                )
+
+                            if run_result.llm_usage:
+                                # use the latest usage
+                                self.graph_runtime_state.llm_usage += run_result.llm_usage
+
+                            # append node output variables to variable pool
+                            if run_result.outputs:
+                                for variable_key, variable_value in run_result.outputs.items():
+                                    # append variables to variable pool recursively
+                                    self._append_variables_recursively(
+                                        node_id=node_instance.node_id,
+                                        variable_key_list=[variable_key],
+                                        variable_value=variable_value,
+                                    )
+
+                            # add parallel info to run result metadata
+                            if parallel_id and parallel_start_node_id:
+                                if not run_result.metadata:
+                                    run_result.metadata = {}
+
+                                run_result.metadata[NodeRunMetadataKey.PARALLEL_ID] = parallel_id
+                                run_result.metadata[NodeRunMetadataKey.PARALLEL_START_NODE_ID] = parallel_start_node_id
+                                if parent_parallel_id and parent_parallel_start_node_id:
+                                    run_result.metadata[NodeRunMetadataKey.PARENT_PARALLEL_ID] = parent_parallel_id
+                                    run_result.metadata[NodeRunMetadataKey.PARENT_PARALLEL_START_NODE_ID] = (
+                                        parent_parallel_start_node_id
+                                    )
+
+                            yield NodeRunSucceededEvent(
+                                id=node_instance.id,
+                                node_id=node_instance.node_id,
+                                node_type=node_instance.node_type,
+                                node_data=node_instance.node_data,
+                                route_node_state=route_node_state,
+                                parallel_id=parallel_id,
+                                parallel_start_node_id=parallel_start_node_id,
+                                parent_parallel_id=parent_parallel_id,
+                                parent_parallel_start_node_id=parent_parallel_start_node_id,
+                            )
+
+                        break
+                    elif isinstance(item, RunStreamChunkEvent):
+                        yield NodeRunStreamChunkEvent(
+                            id=node_instance.id,
+                            node_id=node_instance.node_id,
+                            node_type=node_instance.node_type,
+                            node_data=node_instance.node_data,
+                            chunk_content=item.chunk_content,
+                            from_variable_selector=item.from_variable_selector,
+                            route_node_state=route_node_state,
+                            parallel_id=parallel_id,
+                            parallel_start_node_id=parallel_start_node_id,
+                            parent_parallel_id=parent_parallel_id,
+                            parent_parallel_start_node_id=parent_parallel_start_node_id,
+                        )
+                    elif isinstance(item, RunRetrieverResourceEvent):
+                        yield NodeRunRetrieverResourceEvent(
+                            id=node_instance.id,
+                            node_id=node_instance.node_id,
+                            node_type=node_instance.node_type,
+                            node_data=node_instance.node_data,
+                            retriever_resources=item.retriever_resources,
+                            context=item.context,
+                            route_node_state=route_node_state,
+                            parallel_id=parallel_id,
+                            parallel_start_node_id=parallel_start_node_id,
+                            parent_parallel_id=parent_parallel_id,
+                            parent_parallel_start_node_id=parent_parallel_start_node_id,
+                        )
+        except GenerateTaskStoppedError:
+            # trigger node run failed event
+            route_node_state.status = RouteNodeState.Status.FAILED
+            route_node_state.failed_reason = "Workflow stopped."
+            yield NodeRunFailedEvent(
+                error="Workflow stopped.",
+                id=node_instance.id,
+                node_id=node_instance.node_id,
+                node_type=node_instance.node_type,
+                node_data=node_instance.node_data,
+                route_node_state=route_node_state,
+                parallel_id=parallel_id,
+                parallel_start_node_id=parallel_start_node_id,
+                parent_parallel_id=parent_parallel_id,
+                parent_parallel_start_node_id=parent_parallel_start_node_id,
+            )
+            return
+        except Exception as e:
+            logger.exception(f"Node {node_instance.node_data.title} run failed: {str(e)}")
+            raise e
+        finally:
+            db.session.close()
+
+    def _append_variables_recursively(self, node_id: str, variable_key_list: list[str], variable_value: VariableValue):
+        """
+        Append variables recursively
+        :param node_id: node id
+        :param variable_key_list: variable key list
+        :param variable_value: variable value
+        :return:
+        """
+        self.graph_runtime_state.variable_pool.add([node_id] + variable_key_list, variable_value)
+
+        # if variable_value is a dict, then recursively append variables
+        if isinstance(variable_value, dict):
+            for key, value in variable_value.items():
+                # construct new key list
+                new_key_list = variable_key_list + [key]
+                self._append_variables_recursively(
+                    node_id=node_id, variable_key_list=new_key_list, variable_value=value
+                )
+
+    def _is_timed_out(self, start_at: float, max_execution_time: int) -> bool:
+        """
+        Check timeout
+        :param start_at: start time
+        :param max_execution_time: max execution time
+        :return:
+        """
+        return time.perf_counter() - start_at > max_execution_time
+
+
+class GraphRunFailedError(Exception):
+    def __init__(self, error: str):
+        self.error = error
diff --git a/api/core/workflow/nodes/answer/answer_node.py b/api/core/workflow/nodes/answer/answer_node.py
index 5bae27092f..deacbbbbb0 100644
--- a/api/core/workflow/nodes/answer/answer_node.py
+++ b/api/core/workflow/nodes/answer/answer_node.py
@@ -1,9 +1,8 @@
-from typing import cast
+from collections.abc import Mapping, Sequence
+from typing import Any, cast
 
-from core.prompt.utils.prompt_template_parser import PromptTemplateParser
-from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.nodes.answer.answer_stream_generate_router import AnswerStreamGeneratorRouter
 from core.workflow.nodes.answer.entities import (
     AnswerNodeData,
     GenerateRouteChunk,
@@ -19,102 +18,40 @@ class AnswerNode(BaseNode):
     _node_data_cls = AnswerNodeData
     _node_type: NodeType = NodeType.ANSWER
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         """
         Run node
-        :param variable_pool: variable pool
         :return:
         """
         node_data = self.node_data
         node_data = cast(AnswerNodeData, node_data)
 
         # generate routes
-        generate_routes = self.extract_generate_route_from_node_data(node_data)
+        generate_routes = AnswerStreamGeneratorRouter.extract_generate_route_from_node_data(node_data)
 
-        answer = ''
+        answer = ""
         for part in generate_routes:
-            if part.type == "var":
+            if part.type == GenerateRouteChunk.ChunkType.VAR:
                 part = cast(VarGenerateRouteChunk, part)
                 value_selector = part.value_selector
-                value = variable_pool.get(value_selector)
+                value = self.graph_runtime_state.variable_pool.get(value_selector)
+
                 if value:
                     answer += value.markdown
             else:
                 part = cast(TextGenerateRouteChunk, part)
                 answer += part.text
 
-        return NodeRunResult(
-            status=WorkflowNodeExecutionStatus.SUCCEEDED,
-            outputs={
-                "answer": answer
-            }
-        )
+        return NodeRunResult(status=WorkflowNodeExecutionStatus.SUCCEEDED, outputs={"answer": answer})
 
     @classmethod
-    def extract_generate_route_selectors(cls, config: dict) -> list[GenerateRouteChunk]:
-        """
-        Extract generate route selectors
-        :param config: node config
-        :return:
-        """
-        node_data = cls._node_data_cls(**config.get("data", {}))
-        node_data = cast(AnswerNodeData, node_data)
-
-        return cls.extract_generate_route_from_node_data(node_data)
-
-    @classmethod
-    def extract_generate_route_from_node_data(cls, node_data: AnswerNodeData) -> list[GenerateRouteChunk]:
-        """
-        Extract generate route from node data
-        :param node_data: node data object
-        :return:
-        """
-        variable_template_parser = VariableTemplateParser(template=node_data.answer)
-        variable_selectors = variable_template_parser.extract_variable_selectors()
-
-        value_selector_mapping = {
-            variable_selector.variable: variable_selector.value_selector
-            for variable_selector in variable_selectors
-        }
-
-        variable_keys = list(value_selector_mapping.keys())
-
-        # format answer template
-        template_parser = PromptTemplateParser(template=node_data.answer, with_variable_tmpl=True)
-        template_variable_keys = template_parser.variable_keys
-
-        # Take the intersection of variable_keys and template_variable_keys
-        variable_keys = list(set(variable_keys) & set(template_variable_keys))
-
-        template = node_data.answer
-        for var in variable_keys:
-            template = template.replace(f'{{{{{var}}}}}', f'Ω{{{{{var}}}}}Ω')
-
-        generate_routes = []
-        for part in template.split('Ω'):
-            if part:
-                if cls._is_variable(part, variable_keys):
-                    var_key = part.replace('Ω', '').replace('{{', '').replace('}}', '')
-                    value_selector = value_selector_mapping[var_key]
-                    generate_routes.append(VarGenerateRouteChunk(
-                        value_selector=value_selector
-                    ))
-                else:
-                    generate_routes.append(TextGenerateRouteChunk(
-                        text=part
-                    ))
-
-        return generate_routes
-
-    @classmethod
-    def _is_variable(cls, part, variable_keys):
-        cleaned_part = part.replace('{{', '').replace('}}', '')
-        return part.startswith('{{') and cleaned_part in variable_keys
-
-    @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: AnswerNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
@@ -126,6 +63,6 @@ class AnswerNode(BaseNode):
 
         variable_mapping = {}
         for variable_selector in variable_selectors:
-            variable_mapping[variable_selector.variable] = variable_selector.value_selector
+            variable_mapping[node_id + "." + variable_selector.variable] = variable_selector.value_selector
 
         return variable_mapping
diff --git a/api/core/workflow/nodes/answer/answer_stream_generate_router.py b/api/core/workflow/nodes/answer/answer_stream_generate_router.py
new file mode 100644
index 0000000000..bbd1f88867
--- /dev/null
+++ b/api/core/workflow/nodes/answer/answer_stream_generate_router.py
@@ -0,0 +1,165 @@
+from core.prompt.utils.prompt_template_parser import PromptTemplateParser
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.nodes.answer.entities import (
+    AnswerNodeData,
+    AnswerStreamGenerateRoute,
+    GenerateRouteChunk,
+    TextGenerateRouteChunk,
+    VarGenerateRouteChunk,
+)
+from core.workflow.utils.variable_template_parser import VariableTemplateParser
+
+
+class AnswerStreamGeneratorRouter:
+    @classmethod
+    def init(
+        cls,
+        node_id_config_mapping: dict[str, dict],
+        reverse_edge_mapping: dict[str, list["GraphEdge"]],  # type: ignore[name-defined]
+    ) -> AnswerStreamGenerateRoute:
+        """
+        Get stream generate routes.
+        :return:
+        """
+        # parse stream output node value selectors of answer nodes
+        answer_generate_route: dict[str, list[GenerateRouteChunk]] = {}
+        for answer_node_id, node_config in node_id_config_mapping.items():
+            if node_config.get("data", {}).get("type") != NodeType.ANSWER.value:
+                continue
+
+            # get generate route for stream output
+            generate_route = cls._extract_generate_route_selectors(node_config)
+            answer_generate_route[answer_node_id] = generate_route
+
+        # fetch answer dependencies
+        answer_node_ids = list(answer_generate_route.keys())
+        answer_dependencies = cls._fetch_answers_dependencies(
+            answer_node_ids=answer_node_ids,
+            reverse_edge_mapping=reverse_edge_mapping,
+            node_id_config_mapping=node_id_config_mapping,
+        )
+
+        return AnswerStreamGenerateRoute(
+            answer_generate_route=answer_generate_route, answer_dependencies=answer_dependencies
+        )
+
+    @classmethod
+    def extract_generate_route_from_node_data(cls, node_data: AnswerNodeData) -> list[GenerateRouteChunk]:
+        """
+        Extract generate route from node data
+        :param node_data: node data object
+        :return:
+        """
+        variable_template_parser = VariableTemplateParser(template=node_data.answer)
+        variable_selectors = variable_template_parser.extract_variable_selectors()
+
+        value_selector_mapping = {
+            variable_selector.variable: variable_selector.value_selector for variable_selector in variable_selectors
+        }
+
+        variable_keys = list(value_selector_mapping.keys())
+
+        # format answer template
+        template_parser = PromptTemplateParser(template=node_data.answer, with_variable_tmpl=True)
+        template_variable_keys = template_parser.variable_keys
+
+        # Take the intersection of variable_keys and template_variable_keys
+        variable_keys = list(set(variable_keys) & set(template_variable_keys))
+
+        template = node_data.answer
+        for var in variable_keys:
+            template = template.replace(f"{{{{{var}}}}}", f"Ω{{{{{var}}}}}Ω")
+
+        generate_routes: list[GenerateRouteChunk] = []
+        for part in template.split("Ω"):
+            if part:
+                if cls._is_variable(part, variable_keys):
+                    var_key = part.replace("Ω", "").replace("{{", "").replace("}}", "")
+                    value_selector = value_selector_mapping[var_key]
+                    generate_routes.append(VarGenerateRouteChunk(value_selector=value_selector))
+                else:
+                    generate_routes.append(TextGenerateRouteChunk(text=part))
+
+        return generate_routes
+
+    @classmethod
+    def _extract_generate_route_selectors(cls, config: dict) -> list[GenerateRouteChunk]:
+        """
+        Extract generate route selectors
+        :param config: node config
+        :return:
+        """
+        node_data = AnswerNodeData(**config.get("data", {}))
+        return cls.extract_generate_route_from_node_data(node_data)
+
+    @classmethod
+    def _is_variable(cls, part, variable_keys):
+        cleaned_part = part.replace("{{", "").replace("}}", "")
+        return part.startswith("{{") and cleaned_part in variable_keys
+
+    @classmethod
+    def _fetch_answers_dependencies(
+        cls,
+        answer_node_ids: list[str],
+        reverse_edge_mapping: dict[str, list["GraphEdge"]],  # type: ignore[name-defined]
+        node_id_config_mapping: dict[str, dict],
+    ) -> dict[str, list[str]]:
+        """
+        Fetch answer dependencies
+        :param answer_node_ids: answer node ids
+        :param reverse_edge_mapping: reverse edge mapping
+        :param node_id_config_mapping: node id config mapping
+        :return:
+        """
+        answer_dependencies: dict[str, list[str]] = {}
+        for answer_node_id in answer_node_ids:
+            if answer_dependencies.get(answer_node_id) is None:
+                answer_dependencies[answer_node_id] = []
+
+            cls._recursive_fetch_answer_dependencies(
+                current_node_id=answer_node_id,
+                answer_node_id=answer_node_id,
+                node_id_config_mapping=node_id_config_mapping,
+                reverse_edge_mapping=reverse_edge_mapping,
+                answer_dependencies=answer_dependencies,
+            )
+
+        return answer_dependencies
+
+    @classmethod
+    def _recursive_fetch_answer_dependencies(
+        cls,
+        current_node_id: str,
+        answer_node_id: str,
+        node_id_config_mapping: dict[str, dict],
+        reverse_edge_mapping: dict[str, list["GraphEdge"]],  # type: ignore[name-defined]
+        answer_dependencies: dict[str, list[str]],
+    ) -> None:
+        """
+        Recursive fetch answer dependencies
+        :param current_node_id: current node id
+        :param answer_node_id: answer node id
+        :param node_id_config_mapping: node id config mapping
+        :param reverse_edge_mapping: reverse edge mapping
+        :param answer_dependencies: answer dependencies
+        :return:
+        """
+        reverse_edges = reverse_edge_mapping.get(current_node_id, [])
+        for edge in reverse_edges:
+            source_node_id = edge.source_node_id
+            source_node_type = node_id_config_mapping[source_node_id].get("data", {}).get("type")
+            if source_node_type in {
+                NodeType.ANSWER.value,
+                NodeType.IF_ELSE.value,
+                NodeType.QUESTION_CLASSIFIER.value,
+                NodeType.ITERATION.value,
+            }:
+                answer_dependencies[answer_node_id].append(source_node_id)
+            else:
+                cls._recursive_fetch_answer_dependencies(
+                    current_node_id=source_node_id,
+                    answer_node_id=answer_node_id,
+                    node_id_config_mapping=node_id_config_mapping,
+                    reverse_edge_mapping=reverse_edge_mapping,
+                    answer_dependencies=answer_dependencies,
+                )
diff --git a/api/core/workflow/nodes/answer/answer_stream_processor.py b/api/core/workflow/nodes/answer/answer_stream_processor.py
new file mode 100644
index 0000000000..32dbf436ec
--- /dev/null
+++ b/api/core/workflow/nodes/answer/answer_stream_processor.py
@@ -0,0 +1,221 @@
+import logging
+from collections.abc import Generator
+from typing import Optional, cast
+
+from core.file.file_obj import FileVar
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.graph_engine.entities.event import (
+    GraphEngineEvent,
+    NodeRunStartedEvent,
+    NodeRunStreamChunkEvent,
+    NodeRunSucceededEvent,
+)
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.nodes.answer.base_stream_processor import StreamProcessor
+from core.workflow.nodes.answer.entities import GenerateRouteChunk, TextGenerateRouteChunk, VarGenerateRouteChunk
+
+logger = logging.getLogger(__name__)
+
+
+class AnswerStreamProcessor(StreamProcessor):
+    def __init__(self, graph: Graph, variable_pool: VariablePool) -> None:
+        super().__init__(graph, variable_pool)
+        self.generate_routes = graph.answer_stream_generate_routes
+        self.route_position = {}
+        for answer_node_id, route_chunks in self.generate_routes.answer_generate_route.items():
+            self.route_position[answer_node_id] = 0
+        self.current_stream_chunk_generating_node_ids: dict[str, list[str]] = {}
+
+    def process(self, generator: Generator[GraphEngineEvent, None, None]) -> Generator[GraphEngineEvent, None, None]:
+        for event in generator:
+            if isinstance(event, NodeRunStartedEvent):
+                if event.route_node_state.node_id == self.graph.root_node_id and not self.rest_node_ids:
+                    self.reset()
+
+                yield event
+            elif isinstance(event, NodeRunStreamChunkEvent):
+                if event.in_iteration_id:
+                    yield event
+                    continue
+
+                if event.route_node_state.node_id in self.current_stream_chunk_generating_node_ids:
+                    stream_out_answer_node_ids = self.current_stream_chunk_generating_node_ids[
+                        event.route_node_state.node_id
+                    ]
+                else:
+                    stream_out_answer_node_ids = self._get_stream_out_answer_node_ids(event)
+                    self.current_stream_chunk_generating_node_ids[event.route_node_state.node_id] = (
+                        stream_out_answer_node_ids
+                    )
+
+                for _ in stream_out_answer_node_ids:
+                    yield event
+            elif isinstance(event, NodeRunSucceededEvent):
+                yield event
+                if event.route_node_state.node_id in self.current_stream_chunk_generating_node_ids:
+                    # update self.route_position after all stream event finished
+                    for answer_node_id in self.current_stream_chunk_generating_node_ids[event.route_node_state.node_id]:
+                        self.route_position[answer_node_id] += 1
+
+                    del self.current_stream_chunk_generating_node_ids[event.route_node_state.node_id]
+
+                # remove unreachable nodes
+                self._remove_unreachable_nodes(event)
+
+                # generate stream outputs
+                yield from self._generate_stream_outputs_when_node_finished(event)
+            else:
+                yield event
+
+    def reset(self) -> None:
+        self.route_position = {}
+        for answer_node_id, route_chunks in self.generate_routes.answer_generate_route.items():
+            self.route_position[answer_node_id] = 0
+        self.rest_node_ids = self.graph.node_ids.copy()
+        self.current_stream_chunk_generating_node_ids = {}
+
+    def _generate_stream_outputs_when_node_finished(
+        self, event: NodeRunSucceededEvent
+    ) -> Generator[GraphEngineEvent, None, None]:
+        """
+        Generate stream outputs.
+        :param event: node run succeeded event
+        :return:
+        """
+        for answer_node_id, position in self.route_position.items():
+            # all depends on answer node id not in rest node ids
+            if event.route_node_state.node_id != answer_node_id and (
+                answer_node_id not in self.rest_node_ids
+                or not all(
+                    dep_id not in self.rest_node_ids
+                    for dep_id in self.generate_routes.answer_dependencies[answer_node_id]
+                )
+            ):
+                continue
+
+            route_position = self.route_position[answer_node_id]
+            route_chunks = self.generate_routes.answer_generate_route[answer_node_id][route_position:]
+
+            for route_chunk in route_chunks:
+                if route_chunk.type == GenerateRouteChunk.ChunkType.TEXT:
+                    route_chunk = cast(TextGenerateRouteChunk, route_chunk)
+                    yield NodeRunStreamChunkEvent(
+                        id=event.id,
+                        node_id=event.node_id,
+                        node_type=event.node_type,
+                        node_data=event.node_data,
+                        chunk_content=route_chunk.text,
+                        route_node_state=event.route_node_state,
+                        parallel_id=event.parallel_id,
+                        parallel_start_node_id=event.parallel_start_node_id,
+                        from_variable_selector=[answer_node_id, "answer"],
+                    )
+                else:
+                    route_chunk = cast(VarGenerateRouteChunk, route_chunk)
+                    value_selector = route_chunk.value_selector
+                    if not value_selector:
+                        break
+
+                    value = self.variable_pool.get(value_selector)
+
+                    if value is None:
+                        break
+
+                    text = value.markdown
+
+                    if text:
+                        yield NodeRunStreamChunkEvent(
+                            id=event.id,
+                            node_id=event.node_id,
+                            node_type=event.node_type,
+                            node_data=event.node_data,
+                            chunk_content=text,
+                            from_variable_selector=value_selector,
+                            route_node_state=event.route_node_state,
+                            parallel_id=event.parallel_id,
+                            parallel_start_node_id=event.parallel_start_node_id,
+                        )
+
+                self.route_position[answer_node_id] += 1
+
+    def _get_stream_out_answer_node_ids(self, event: NodeRunStreamChunkEvent) -> list[str]:
+        """
+        Is stream out support
+        :param event: queue text chunk event
+        :return:
+        """
+        if not event.from_variable_selector:
+            return []
+
+        stream_output_value_selector = event.from_variable_selector
+        if not stream_output_value_selector:
+            return []
+
+        stream_out_answer_node_ids = []
+        for answer_node_id, route_position in self.route_position.items():
+            if answer_node_id not in self.rest_node_ids:
+                continue
+
+            # all depends on answer node id not in rest node ids
+            if all(
+                dep_id not in self.rest_node_ids for dep_id in self.generate_routes.answer_dependencies[answer_node_id]
+            ):
+                if route_position >= len(self.generate_routes.answer_generate_route[answer_node_id]):
+                    continue
+
+                route_chunk = self.generate_routes.answer_generate_route[answer_node_id][route_position]
+
+                if route_chunk.type != GenerateRouteChunk.ChunkType.VAR:
+                    continue
+
+                route_chunk = cast(VarGenerateRouteChunk, route_chunk)
+                value_selector = route_chunk.value_selector
+
+                # check chunk node id is before current node id or equal to current node id
+                if value_selector != stream_output_value_selector:
+                    continue
+
+                stream_out_answer_node_ids.append(answer_node_id)
+
+        return stream_out_answer_node_ids
+
+    @classmethod
+    def _fetch_files_from_variable_value(cls, value: dict | list) -> list[dict]:
+        """
+        Fetch files from variable value
+        :param value: variable value
+        :return:
+        """
+        if not value:
+            return []
+
+        files = []
+        if isinstance(value, list):
+            for item in value:
+                file_var = cls._get_file_var_from_value(item)
+                if file_var:
+                    files.append(file_var)
+        elif isinstance(value, dict):
+            file_var = cls._get_file_var_from_value(value)
+            if file_var:
+                files.append(file_var)
+
+        return files
+
+    @classmethod
+    def _get_file_var_from_value(cls, value: dict | list) -> Optional[dict]:
+        """
+        Get file var from value
+        :param value: variable value
+        :return:
+        """
+        if not value:
+            return None
+
+        if isinstance(value, dict):
+            if "__variant" in value and value["__variant"] == FileVar.__name__:
+                return value
+        elif isinstance(value, FileVar):
+            return value.to_dict()
+
+        return None
diff --git a/api/core/workflow/nodes/answer/base_stream_processor.py b/api/core/workflow/nodes/answer/base_stream_processor.py
new file mode 100644
index 0000000000..36c3fe180a
--- /dev/null
+++ b/api/core/workflow/nodes/answer/base_stream_processor.py
@@ -0,0 +1,70 @@
+from abc import ABC, abstractmethod
+from collections.abc import Generator
+
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.graph_engine.entities.event import GraphEngineEvent, NodeRunSucceededEvent
+from core.workflow.graph_engine.entities.graph import Graph
+
+
+class StreamProcessor(ABC):
+    def __init__(self, graph: Graph, variable_pool: VariablePool) -> None:
+        self.graph = graph
+        self.variable_pool = variable_pool
+        self.rest_node_ids = graph.node_ids.copy()
+
+    @abstractmethod
+    def process(self, generator: Generator[GraphEngineEvent, None, None]) -> Generator[GraphEngineEvent, None, None]:
+        raise NotImplementedError
+
+    def _remove_unreachable_nodes(self, event: NodeRunSucceededEvent) -> None:
+        finished_node_id = event.route_node_state.node_id
+        if finished_node_id not in self.rest_node_ids:
+            return
+
+        # remove finished node id
+        self.rest_node_ids.remove(finished_node_id)
+
+        run_result = event.route_node_state.node_run_result
+        if not run_result:
+            return
+
+        if run_result.edge_source_handle:
+            reachable_node_ids = []
+            unreachable_first_node_ids = []
+            for edge in self.graph.edge_mapping[finished_node_id]:
+                if (
+                    edge.run_condition
+                    and edge.run_condition.branch_identify
+                    and run_result.edge_source_handle == edge.run_condition.branch_identify
+                ):
+                    reachable_node_ids.extend(self._fetch_node_ids_in_reachable_branch(edge.target_node_id))
+                    continue
+                else:
+                    unreachable_first_node_ids.append(edge.target_node_id)
+
+            for node_id in unreachable_first_node_ids:
+                self._remove_node_ids_in_unreachable_branch(node_id, reachable_node_ids)
+
+    def _fetch_node_ids_in_reachable_branch(self, node_id: str) -> list[str]:
+        node_ids = []
+        for edge in self.graph.edge_mapping.get(node_id, []):
+            if edge.target_node_id == self.graph.root_node_id:
+                continue
+
+            node_ids.append(edge.target_node_id)
+            node_ids.extend(self._fetch_node_ids_in_reachable_branch(edge.target_node_id))
+        return node_ids
+
+    def _remove_node_ids_in_unreachable_branch(self, node_id: str, reachable_node_ids: list[str]) -> None:
+        """
+        remove target node ids until merge
+        """
+        if node_id not in self.rest_node_ids:
+            return
+
+        self.rest_node_ids.remove(node_id)
+        for edge in self.graph.edge_mapping.get(node_id, []):
+            if edge.target_node_id in reachable_node_ids:
+                continue
+
+            self._remove_node_ids_in_unreachable_branch(edge.target_node_id, reachable_node_ids)
diff --git a/api/core/workflow/nodes/answer/entities.py b/api/core/workflow/nodes/answer/entities.py
index 9effbbbe67..e356e7fd70 100644
--- a/api/core/workflow/nodes/answer/entities.py
+++ b/api/core/workflow/nodes/answer/entities.py
@@ -1,5 +1,6 @@
+from enum import Enum
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from core.workflow.entities.base_node_data_entities import BaseNodeData
 
@@ -8,27 +9,56 @@ class AnswerNodeData(BaseNodeData):
     """
     Answer Node Data.
     """
-    answer: str
+
+    answer: str = Field(..., description="answer template string")
 
 
 class GenerateRouteChunk(BaseModel):
     """
     Generate Route Chunk.
     """
-    type: str
+
+    class ChunkType(Enum):
+        VAR = "var"
+        TEXT = "text"
+
+    type: ChunkType = Field(..., description="generate route chunk type")
 
 
 class VarGenerateRouteChunk(GenerateRouteChunk):
     """
     Var Generate Route Chunk.
     """
-    type: str = "var"
-    value_selector: list[str]
+
+    type: GenerateRouteChunk.ChunkType = GenerateRouteChunk.ChunkType.VAR
+    """generate route chunk type"""
+    value_selector: list[str] = Field(..., description="value selector")
 
 
 class TextGenerateRouteChunk(GenerateRouteChunk):
     """
     Text Generate Route Chunk.
     """
-    type: str = "text"
-    text: str
+
+    type: GenerateRouteChunk.ChunkType = GenerateRouteChunk.ChunkType.TEXT
+    """generate route chunk type"""
+    text: str = Field(..., description="text")
+
+
+class AnswerNodeDoubleLink(BaseModel):
+    node_id: str = Field(..., description="node id")
+    source_node_ids: list[str] = Field(..., description="source node ids")
+    target_node_ids: list[str] = Field(..., description="target node ids")
+
+
+class AnswerStreamGenerateRoute(BaseModel):
+    """
+    AnswerStreamGenerateRoute entity
+    """
+
+    answer_dependencies: dict[str, list[str]] = Field(
+        ..., description="answer dependencies (answer node id -> dependent answer node ids)"
+    )
+    answer_generate_route: dict[str, list[GenerateRouteChunk]] = Field(
+        ..., description="answer generate route (answer node id -> generate route chunks)"
+    )
diff --git a/api/core/workflow/nodes/base_node.py b/api/core/workflow/nodes/base_node.py
index 3d9cf52771..7bfe45a13c 100644
--- a/api/core/workflow/nodes/base_node.py
+++ b/api/core/workflow/nodes/base_node.py
@@ -1,142 +1,99 @@
 from abc import ABC, abstractmethod
-from collections.abc import Mapping, Sequence
-from enum import Enum
+from collections.abc import Generator, Mapping, Sequence
 from typing import Any, Optional
 
-from core.app.entities.app_invoke_entities import InvokeFrom
-from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
-from core.workflow.entities.base_node_data_entities import BaseIterationState, BaseNodeData
+from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
-from models import WorkflowNodeExecutionStatus
-
-
-class UserFrom(Enum):
-    """
-    User from
-    """
-    ACCOUNT = "account"
-    END_USER = "end-user"
-
-    @classmethod
-    def value_of(cls, value: str) -> "UserFrom":
-        """
-        Value of
-        :param value: value
-        :return:
-        """
-        for item in cls:
-            if item.value == value:
-                return item
-        raise ValueError(f"Invalid value: {value}")
+from core.workflow.graph_engine.entities.event import InNodeEvent
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.nodes.event import RunCompletedEvent, RunEvent
 
 
 class BaseNode(ABC):
     _node_data_cls: type[BaseNodeData]
     _node_type: NodeType
 
-    tenant_id: str
-    app_id: str
-    workflow_id: str
-    user_id: str
-    user_from: UserFrom
-    invoke_from: InvokeFrom
-    
-    workflow_call_depth: int
+    def __init__(
+        self,
+        id: str,
+        config: Mapping[str, Any],
+        graph_init_params: GraphInitParams,
+        graph: Graph,
+        graph_runtime_state: GraphRuntimeState,
+        previous_node_id: Optional[str] = None,
+        thread_pool_id: Optional[str] = None,
+    ) -> None:
+        self.id = id
+        self.tenant_id = graph_init_params.tenant_id
+        self.app_id = graph_init_params.app_id
+        self.workflow_type = graph_init_params.workflow_type
+        self.workflow_id = graph_init_params.workflow_id
+        self.graph_config = graph_init_params.graph_config
+        self.user_id = graph_init_params.user_id
+        self.user_from = graph_init_params.user_from
+        self.invoke_from = graph_init_params.invoke_from
+        self.workflow_call_depth = graph_init_params.call_depth
+        self.graph = graph
+        self.graph_runtime_state = graph_runtime_state
+        self.previous_node_id = previous_node_id
+        self.thread_pool_id = thread_pool_id
 
-    node_id: str
-    node_data: BaseNodeData
-    node_run_result: Optional[NodeRunResult] = None
-
-    callbacks: Sequence[WorkflowCallback]
-
-    is_answer_previous_node: bool = False
-
-    def __init__(self, tenant_id: str,
-                 app_id: str,
-                 workflow_id: str,
-                 user_id: str,
-                 user_from: UserFrom,
-                 invoke_from: InvokeFrom,
-                 config: Mapping[str, Any],
-                 callbacks: Sequence[WorkflowCallback] | None = None,
-                 workflow_call_depth: int = 0) -> None:
-        self.tenant_id = tenant_id
-        self.app_id = app_id
-        self.workflow_id = workflow_id
-        self.user_id = user_id
-        self.user_from = user_from
-        self.invoke_from = invoke_from
-        self.workflow_call_depth = workflow_call_depth
-
-        # TODO: May need to check if key exists.
-        self.node_id = config["id"]
-        if not self.node_id:
+        node_id = config.get("id")
+        if not node_id:
             raise ValueError("Node ID is required.")
 
+        self.node_id = node_id
         self.node_data = self._node_data_cls(**config.get("data", {}))
-        self.callbacks = callbacks or []
 
     @abstractmethod
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult | Generator[RunEvent | InNodeEvent, None, None]:
         """
         Run node
-        :param variable_pool: variable pool
         :return:
         """
         raise NotImplementedError
 
-    def run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def run(self) -> Generator[RunEvent | InNodeEvent, None, None]:
         """
         Run node entry
-        :param variable_pool: variable pool
         :return:
         """
-        try:
-            result = self._run(
-                variable_pool=variable_pool
-            )
-            self.node_run_result = result
-            return result
-        except Exception as e:
-            return NodeRunResult(
-                status=WorkflowNodeExecutionStatus.FAILED,
-                error=str(e),
-            )
+        result = self._run()
 
-    def publish_text_chunk(self, text: str, value_selector: list[str] | None = None) -> None:
-        """
-        Publish text chunk
-        :param text: chunk text
-        :param value_selector: value selector
-        :return:
-        """
-        if self.callbacks:
-            for callback in self.callbacks:
-                callback.on_node_text_chunk(
-                    node_id=self.node_id,
-                    text=text,
-                    metadata={
-                        "node_type": self.node_type,
-                        "is_answer_previous_node": self.is_answer_previous_node,
-                        "value_selector": value_selector
-                    }
-                )
+        if isinstance(result, NodeRunResult):
+            yield RunCompletedEvent(run_result=result)
+        else:
+            yield from result
 
     @classmethod
-    def extract_variable_selector_to_variable_mapping(cls, config: dict):
+    def extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], config: dict
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
         :param config: node config
         :return:
         """
+        node_id = config.get("id")
+        if not node_id:
+            raise ValueError("Node ID is required when extracting variable selector to variable mapping.")
+
         node_data = cls._node_data_cls(**config.get("data", {}))
-        return cls._extract_variable_selector_to_variable_mapping(node_data)
+        return cls._extract_variable_selector_to_variable_mapping(
+            graph_config=graph_config, node_id=node_id, node_data=node_data
+        )
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> Mapping[str, Sequence[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: BaseNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
@@ -158,38 +115,3 @@ class BaseNode(ABC):
         :return:
         """
         return self._node_type
-
-class BaseIterationNode(BaseNode):
-    @abstractmethod
-    def _run(self, variable_pool: VariablePool) -> BaseIterationState:
-        """
-        Run node
-        :param variable_pool: variable pool
-        :return:
-        """
-        raise NotImplementedError
-
-    def run(self, variable_pool: VariablePool) -> BaseIterationState:
-        """
-        Run node entry
-        :param variable_pool: variable pool
-        :return:
-        """
-        return self._run(variable_pool=variable_pool)
-
-    def get_next_iteration(self, variable_pool: VariablePool, state: BaseIterationState) -> NodeRunResult | str:
-        """
-        Get next iteration start node id based on the graph.
-        :param graph: graph
-        :return: next node id
-        """
-        return self._get_next_iteration(variable_pool, state)
-    
-    @abstractmethod
-    def _get_next_iteration(self, variable_pool: VariablePool, state: BaseIterationState) -> NodeRunResult | str:
-        """
-        Get next iteration start node id based on the graph.
-        :param graph: graph
-        :return: next node id
-        """
-        raise NotImplementedError
diff --git a/api/core/workflow/nodes/code/code_node.py b/api/core/workflow/nodes/code/code_node.py
index 335991ae87..9da7ad99f3 100644
--- a/api/core/workflow/nodes/code/code_node.py
+++ b/api/core/workflow/nodes/code/code_node.py
@@ -1,12 +1,12 @@
-from typing import Optional, Union, cast
+from collections.abc import Mapping, Sequence
+from typing import Any, Optional, Union, cast
 
 from configs import dify_config
-from core.helper.code_executor.code_executor import CodeExecutionException, CodeExecutor, CodeLanguage
+from core.helper.code_executor.code_executor import CodeExecutionError, CodeExecutor, CodeLanguage
 from core.helper.code_executor.code_node_provider import CodeNodeProvider
 from core.helper.code_executor.javascript.javascript_code_provider import JavascriptCodeProvider
 from core.helper.code_executor.python3.python3_code_provider import Python3CodeProvider
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.code.entities import CodeNodeData
 from models.workflow import WorkflowNodeExecutionStatus
@@ -25,21 +25,20 @@ class CodeNode(BaseNode):
         """
         code_language = CodeLanguage.PYTHON3
         if filters:
-            code_language = (filters.get("code_language", CodeLanguage.PYTHON3))
+            code_language = filters.get("code_language", CodeLanguage.PYTHON3)
 
         providers: list[type[CodeNodeProvider]] = [Python3CodeProvider, JavascriptCodeProvider]
-        code_provider: type[CodeNodeProvider] = next(p for p in providers
-                                                     if p.is_accept_language(code_language))
+        code_provider: type[CodeNodeProvider] = next(p for p in providers if p.is_accept_language(code_language))
 
         return code_provider.get_default_config()
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         """
         Run code
-        :param variable_pool: variable pool
         :return:
         """
-        node_data = cast(CodeNodeData, self.node_data)
+        node_data = self.node_data
+        node_data = cast(CodeNodeData, node_data)
 
         # Get code language
         code_language = node_data.code_language
@@ -49,7 +48,7 @@ class CodeNode(BaseNode):
         variables = {}
         for variable_selector in node_data.variables:
             variable = variable_selector.variable
-            value = variable_pool.get_any(variable_selector.value_selector)
+            value = self.graph_runtime_state.variable_pool.get_any(variable_selector.value_selector)
 
             variables[variable] = value
         # Run code
@@ -62,18 +61,10 @@ class CodeNode(BaseNode):
 
             # Transform result
             result = self._transform_result(result, node_data.outputs)
-        except (CodeExecutionException, ValueError) as e:
-            return NodeRunResult(
-                status=WorkflowNodeExecutionStatus.FAILED,
-                inputs=variables,
-                error=str(e)
-            )
+        except (CodeExecutionError, ValueError) as e:
+            return NodeRunResult(status=WorkflowNodeExecutionStatus.FAILED, inputs=variables, error=str(e))
 
-        return NodeRunResult(
-            status=WorkflowNodeExecutionStatus.SUCCEEDED,
-            inputs=variables,
-            outputs=result
-        )
+        return NodeRunResult(status=WorkflowNodeExecutionStatus.SUCCEEDED, inputs=variables, outputs=result)
 
     def _check_string(self, value: str, variable: str) -> str:
         """
@@ -83,16 +74,18 @@ class CodeNode(BaseNode):
         :return:
         """
         if not isinstance(value, str):
-            if isinstance(value, type(None)):
+            if value is None:
                 return None
             else:
                 raise ValueError(f"Output variable `{variable}` must be a string")
-        
-        if len(value) > dify_config.CODE_MAX_STRING_LENGTH:
-            raise ValueError(f'The length of output variable `{variable}` must be'
-                             f' less than {dify_config.CODE_MAX_STRING_LENGTH} characters')
 
-        return value.replace('\x00', '')
+        if len(value) > dify_config.CODE_MAX_STRING_LENGTH:
+            raise ValueError(
+                f"The length of output variable `{variable}` must be"
+                f" less than {dify_config.CODE_MAX_STRING_LENGTH} characters"
+            )
+
+        return value.replace("\x00", "")
 
     def _check_number(self, value: Union[int, float], variable: str) -> Union[int, float]:
         """
@@ -102,26 +95,30 @@ class CodeNode(BaseNode):
         :return:
         """
         if not isinstance(value, int | float):
-            if isinstance(value, type(None)):
+            if value is None:
                 return None
             else:
                 raise ValueError(f"Output variable `{variable}` must be a number")
 
         if value > dify_config.CODE_MAX_NUMBER or value < dify_config.CODE_MIN_NUMBER:
-            raise ValueError(f'Output variable `{variable}` is out of range,'
-                             f' it must be between {dify_config.CODE_MIN_NUMBER} and {dify_config.CODE_MAX_NUMBER}.')
+            raise ValueError(
+                f"Output variable `{variable}` is out of range,"
+                f" it must be between {dify_config.CODE_MIN_NUMBER} and {dify_config.CODE_MAX_NUMBER}."
+            )
 
         if isinstance(value, float):
             # raise error if precision is too high
-            if len(str(value).split('.')[1]) > dify_config.CODE_MAX_PRECISION:
-                raise ValueError(f'Output variable `{variable}` has too high precision,'
-                                 f' it must be less than {dify_config.CODE_MAX_PRECISION} digits.')
+            if len(str(value).split(".")[1]) > dify_config.CODE_MAX_PRECISION:
+                raise ValueError(
+                    f"Output variable `{variable}` has too high precision,"
+                    f" it must be less than {dify_config.CODE_MAX_PRECISION} digits."
+                )
 
         return value
 
-    def _transform_result(self, result: dict, output_schema: Optional[dict[str, CodeNodeData.Output]],
-                          prefix: str = '',
-                          depth: int = 1) -> dict:
+    def _transform_result(
+        self, result: dict, output_schema: Optional[dict[str, CodeNodeData.Output]], prefix: str = "", depth: int = 1
+    ) -> dict:
         """
         Transform result
         :param result: result
@@ -139,185 +136,196 @@ class CodeNode(BaseNode):
                     self._transform_result(
                         result=output_value,
                         output_schema=None,
-                        prefix=f'{prefix}.{output_name}' if prefix else output_name,
-                        depth=depth + 1
+                        prefix=f"{prefix}.{output_name}" if prefix else output_name,
+                        depth=depth + 1,
                     )
                 elif isinstance(output_value, int | float):
                     self._check_number(
-                        value=output_value,
-                        variable=f'{prefix}.{output_name}' if prefix else output_name
+                        value=output_value, variable=f"{prefix}.{output_name}" if prefix else output_name
                     )
                 elif isinstance(output_value, str):
                     self._check_string(
-                        value=output_value,
-                        variable=f'{prefix}.{output_name}' if prefix else output_name
+                        value=output_value, variable=f"{prefix}.{output_name}" if prefix else output_name
                     )
                 elif isinstance(output_value, list):
                     first_element = output_value[0] if len(output_value) > 0 else None
                     if first_element is not None:
-                        if isinstance(first_element, int | float) and all(value is None or isinstance(value, int | float) for value in output_value):
+                        if isinstance(first_element, int | float) and all(
+                            value is None or isinstance(value, int | float) for value in output_value
+                        ):
                             for i, value in enumerate(output_value):
                                 self._check_number(
                                     value=value,
-                                    variable=f'{prefix}.{output_name}[{i}]' if prefix else f'{output_name}[{i}]'
+                                    variable=f"{prefix}.{output_name}[{i}]" if prefix else f"{output_name}[{i}]",
                                 )
-                        elif isinstance(first_element, str) and all(value is None or isinstance(value, str) for value in output_value):
+                        elif isinstance(first_element, str) and all(
+                            value is None or isinstance(value, str) for value in output_value
+                        ):
                             for i, value in enumerate(output_value):
                                 self._check_string(
                                     value=value,
-                                    variable=f'{prefix}.{output_name}[{i}]' if prefix else f'{output_name}[{i}]'
+                                    variable=f"{prefix}.{output_name}[{i}]" if prefix else f"{output_name}[{i}]",
                                 )
-                        elif isinstance(first_element, dict) and all(value is None or isinstance(value, dict) for value in output_value):
+                        elif isinstance(first_element, dict) and all(
+                            value is None or isinstance(value, dict) for value in output_value
+                        ):
                             for i, value in enumerate(output_value):
                                 if value is not None:
                                     self._transform_result(
                                         result=value,
                                         output_schema=None,
-                                        prefix=f'{prefix}.{output_name}[{i}]' if prefix else f'{output_name}[{i}]',
-                                        depth=depth + 1
+                                        prefix=f"{prefix}.{output_name}[{i}]" if prefix else f"{output_name}[{i}]",
+                                        depth=depth + 1,
                                     )
                         else:
-                            raise ValueError(f'Output {prefix}.{output_name} is not a valid array. make sure all elements are of the same type.')
-                elif isinstance(output_value, type(None)):
+                            raise ValueError(
+                                f"Output {prefix}.{output_name} is not a valid array."
+                                f" make sure all elements are of the same type."
+                            )
+                elif output_value is None:
                     pass
                 else:
-                    raise ValueError(f'Output {prefix}.{output_name} is not a valid type.')
-                
+                    raise ValueError(f"Output {prefix}.{output_name} is not a valid type.")
+
             return result
 
         parameters_validated = {}
         for output_name, output_config in output_schema.items():
-            dot = '.' if prefix else ''
+            dot = "." if prefix else ""
             if output_name not in result:
-                raise ValueError(f'Output {prefix}{dot}{output_name} is missing.')
-            
-            if output_config.type == 'object':
+                raise ValueError(f"Output {prefix}{dot}{output_name} is missing.")
+
+            if output_config.type == "object":
                 # check if output is object
                 if not isinstance(result.get(output_name), dict):
                     if isinstance(result.get(output_name), type(None)):
                         transformed_result[output_name] = None
                     else:
                         raise ValueError(
-                            f'Output {prefix}{dot}{output_name} is not an object, got {type(result.get(output_name))} instead.'
+                            f"Output {prefix}{dot}{output_name} is not an object,"
+                            f" got {type(result.get(output_name))} instead."
                         )
                 else:
                     transformed_result[output_name] = self._transform_result(
                         result=result[output_name],
                         output_schema=output_config.children,
-                        prefix=f'{prefix}.{output_name}',
-                        depth=depth + 1
+                        prefix=f"{prefix}.{output_name}",
+                        depth=depth + 1,
                     )
-            elif output_config.type == 'number':
+            elif output_config.type == "number":
                 # check if number available
                 transformed_result[output_name] = self._check_number(
-                    value=result[output_name],
-                    variable=f'{prefix}{dot}{output_name}'
+                    value=result[output_name], variable=f"{prefix}{dot}{output_name}"
                 )
-            elif output_config.type == 'string':
+            elif output_config.type == "string":
                 # check if string available
                 transformed_result[output_name] = self._check_string(
                     value=result[output_name],
-                    variable=f'{prefix}{dot}{output_name}',
+                    variable=f"{prefix}{dot}{output_name}",
                 )
-            elif output_config.type == 'array[number]':
+            elif output_config.type == "array[number]":
                 # check if array of number available
                 if not isinstance(result[output_name], list):
                     if isinstance(result[output_name], type(None)):
                         transformed_result[output_name] = None
                     else:
                         raise ValueError(
-                            f'Output {prefix}{dot}{output_name} is not an array, got {type(result.get(output_name))} instead.'
+                            f"Output {prefix}{dot}{output_name} is not an array,"
+                            f" got {type(result.get(output_name))} instead."
                         )
                 else:
                     if len(result[output_name]) > dify_config.CODE_MAX_NUMBER_ARRAY_LENGTH:
                         raise ValueError(
-                            f'The length of output variable `{prefix}{dot}{output_name}` must be'
-                            f' less than {dify_config.CODE_MAX_NUMBER_ARRAY_LENGTH} elements.'
+                            f"The length of output variable `{prefix}{dot}{output_name}` must be"
+                            f" less than {dify_config.CODE_MAX_NUMBER_ARRAY_LENGTH} elements."
                         )
 
                     transformed_result[output_name] = [
-                        self._check_number(
-                            value=value,
-                            variable=f'{prefix}{dot}{output_name}[{i}]'
-                        )
+                        self._check_number(value=value, variable=f"{prefix}{dot}{output_name}[{i}]")
                         for i, value in enumerate(result[output_name])
                     ]
-            elif output_config.type == 'array[string]':
+            elif output_config.type == "array[string]":
                 # check if array of string available
                 if not isinstance(result[output_name], list):
                     if isinstance(result[output_name], type(None)):
                         transformed_result[output_name] = None
                     else:
                         raise ValueError(
-                            f'Output {prefix}{dot}{output_name} is not an array, got {type(result.get(output_name))} instead.'
+                            f"Output {prefix}{dot}{output_name} is not an array,"
+                            f" got {type(result.get(output_name))} instead."
                         )
                 else:
                     if len(result[output_name]) > dify_config.CODE_MAX_STRING_ARRAY_LENGTH:
                         raise ValueError(
-                            f'The length of output variable `{prefix}{dot}{output_name}` must be'
-                            f' less than {dify_config.CODE_MAX_STRING_ARRAY_LENGTH} elements.'
+                            f"The length of output variable `{prefix}{dot}{output_name}` must be"
+                            f" less than {dify_config.CODE_MAX_STRING_ARRAY_LENGTH} elements."
                         )
 
                     transformed_result[output_name] = [
-                        self._check_string(
-                            value=value,
-                            variable=f'{prefix}{dot}{output_name}[{i}]'
-                        )
+                        self._check_string(value=value, variable=f"{prefix}{dot}{output_name}[{i}]")
                         for i, value in enumerate(result[output_name])
                     ]
-            elif output_config.type == 'array[object]':
+            elif output_config.type == "array[object]":
                 # check if array of object available
                 if not isinstance(result[output_name], list):
                     if isinstance(result[output_name], type(None)):
                         transformed_result[output_name] = None
                     else:
                         raise ValueError(
-                            f'Output {prefix}{dot}{output_name} is not an array, got {type(result.get(output_name))} instead.'
+                            f"Output {prefix}{dot}{output_name} is not an array,"
+                            f" got {type(result.get(output_name))} instead."
                         )
                 else:
                     if len(result[output_name]) > dify_config.CODE_MAX_OBJECT_ARRAY_LENGTH:
                         raise ValueError(
-                            f'The length of output variable `{prefix}{dot}{output_name}` must be'
-                            f' less than {dify_config.CODE_MAX_OBJECT_ARRAY_LENGTH} elements.'
+                            f"The length of output variable `{prefix}{dot}{output_name}` must be"
+                            f" less than {dify_config.CODE_MAX_OBJECT_ARRAY_LENGTH} elements."
                         )
-                    
+
                     for i, value in enumerate(result[output_name]):
                         if not isinstance(value, dict):
-                            if isinstance(value, type(None)):
+                            if value is None:
                                 pass
                             else:
                                 raise ValueError(
-                                    f'Output {prefix}{dot}{output_name}[{i}] is not an object, got {type(value)} instead at index {i}.'
+                                    f"Output {prefix}{dot}{output_name}[{i}] is not an object,"
+                                    f" got {type(value)} instead at index {i}."
                                 )
 
                     transformed_result[output_name] = [
-                        None if value is None else self._transform_result(
+                        None
+                        if value is None
+                        else self._transform_result(
                             result=value,
                             output_schema=output_config.children,
-                            prefix=f'{prefix}{dot}{output_name}[{i}]',
-                            depth=depth + 1
+                            prefix=f"{prefix}{dot}{output_name}[{i}]",
+                            depth=depth + 1,
                         )
                         for i, value in enumerate(result[output_name])
                     ]
             else:
-                raise ValueError(f'Output type {output_config.type} is not supported.')
-            
+                raise ValueError(f"Output type {output_config.type} is not supported.")
+
             parameters_validated[output_name] = True
 
         # check if all output parameters are validated
         if len(parameters_validated) != len(result):
-            raise ValueError('Not all output parameters are validated.')
+            raise ValueError("Not all output parameters are validated.")
 
         return transformed_result
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: CodeNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: CodeNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
-
         return {
-            variable_selector.variable: variable_selector.value_selector for variable_selector in node_data.variables
+            node_id + "." + variable_selector.variable: variable_selector.value_selector
+            for variable_selector in node_data.variables
         }
diff --git a/api/core/workflow/nodes/code/entities.py b/api/core/workflow/nodes/code/entities.py
index c0701ecccd..5eb0e0f63f 100644
--- a/api/core/workflow/nodes/code/entities.py
+++ b/api/core/workflow/nodes/code/entities.py
@@ -11,9 +11,10 @@ class CodeNodeData(BaseNodeData):
     """
     Code Node Data.
     """
+
     class Output(BaseModel):
-        type: Literal['string', 'number', 'object', 'array[string]', 'array[number]', 'array[object]']
-        children: Optional[dict[str, 'Output']] = None
+        type: Literal["string", "number", "object", "array[string]", "array[number]", "array[object]"]
+        children: Optional[dict[str, "Output"]] = None
 
     class Dependency(BaseModel):
         name: str
@@ -23,4 +24,4 @@ class CodeNodeData(BaseNodeData):
     code_language: Literal[CodeLanguage.PYTHON3, CodeLanguage.JAVASCRIPT]
     code: str
     outputs: dict[str, Output]
-    dependencies: Optional[list[Dependency]] = None
\ No newline at end of file
+    dependencies: Optional[list[Dependency]] = None
diff --git a/api/core/workflow/nodes/end/end_node.py b/api/core/workflow/nodes/end/end_node.py
index 440dfa2f27..7b78d67be8 100644
--- a/api/core/workflow/nodes/end/end_node.py
+++ b/api/core/workflow/nodes/end/end_node.py
@@ -1,8 +1,7 @@
-from typing import cast
+from collections.abc import Mapping, Sequence
+from typing import Any, cast
 
-from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.end.entities import EndNodeData
 from models.workflow import WorkflowNodeExecutionStatus
@@ -12,10 +11,9 @@ class EndNode(BaseNode):
     _node_data_cls = EndNodeData
     _node_type = NodeType.END
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         """
         Run node
-        :param variable_pool: variable pool
         :return:
         """
         node_data = self.node_data
@@ -24,62 +22,19 @@ class EndNode(BaseNode):
 
         outputs = {}
         for variable_selector in output_variables:
-            value = variable_pool.get_any(variable_selector.value_selector)
+            value = self.graph_runtime_state.variable_pool.get_any(variable_selector.value_selector)
             outputs[variable_selector.variable] = value
 
-        return NodeRunResult(
-            status=WorkflowNodeExecutionStatus.SUCCEEDED,
-            inputs=outputs,
-            outputs=outputs
-        )
+        return NodeRunResult(status=WorkflowNodeExecutionStatus.SUCCEEDED, inputs=outputs, outputs=outputs)
 
     @classmethod
-    def extract_generate_nodes(cls, graph: dict, config: dict) -> list[str]:
-        """
-        Extract generate nodes
-        :param graph: graph
-        :param config: node config
-        :return:
-        """
-        node_data = cls._node_data_cls(**config.get("data", {}))
-        node_data = cast(EndNodeData, node_data)
-
-        return cls.extract_generate_nodes_from_node_data(graph, node_data)
-
-    @classmethod
-    def extract_generate_nodes_from_node_data(cls, graph: dict, node_data: EndNodeData) -> list[str]:
-        """
-        Extract generate nodes from node data
-        :param graph: graph
-        :param node_data: node data object
-        :return:
-        """
-        nodes = graph.get('nodes', [])
-        node_mapping = {node.get('id'): node for node in nodes}
-
-        variable_selectors = node_data.outputs
-
-        generate_nodes = []
-        for variable_selector in variable_selectors:
-            if not variable_selector.value_selector:
-                continue
-
-            node_id = variable_selector.value_selector[0]
-            if node_id != 'sys' and node_id in node_mapping:
-                node = node_mapping[node_id]
-                node_type = node.get('data', {}).get('type')
-                if node_type == NodeType.LLM.value and variable_selector.value_selector[1] == 'text':
-                    generate_nodes.append(node_id)
-
-        # remove duplicates
-        generate_nodes = list(set(generate_nodes))
-
-        return generate_nodes
-
-    @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: EndNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
diff --git a/api/core/workflow/nodes/end/end_stream_generate_router.py b/api/core/workflow/nodes/end/end_stream_generate_router.py
new file mode 100644
index 0000000000..9a7d2ecde3
--- /dev/null
+++ b/api/core/workflow/nodes/end/end_stream_generate_router.py
@@ -0,0 +1,151 @@
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.nodes.end.entities import EndNodeData, EndStreamParam
+
+
+class EndStreamGeneratorRouter:
+    @classmethod
+    def init(
+        cls,
+        node_id_config_mapping: dict[str, dict],
+        reverse_edge_mapping: dict[str, list["GraphEdge"]],  # type: ignore[name-defined]
+        node_parallel_mapping: dict[str, str],
+    ) -> EndStreamParam:
+        """
+        Get stream generate routes.
+        :return:
+        """
+        # parse stream output node value selector of end nodes
+        end_stream_variable_selectors_mapping: dict[str, list[list[str]]] = {}
+        for end_node_id, node_config in node_id_config_mapping.items():
+            if node_config.get("data", {}).get("type") != NodeType.END.value:
+                continue
+
+            # skip end node in parallel
+            if end_node_id in node_parallel_mapping:
+                continue
+
+            # get generate route for stream output
+            stream_variable_selectors = cls._extract_stream_variable_selector(node_id_config_mapping, node_config)
+            end_stream_variable_selectors_mapping[end_node_id] = stream_variable_selectors
+
+        # fetch end dependencies
+        end_node_ids = list(end_stream_variable_selectors_mapping.keys())
+        end_dependencies = cls._fetch_ends_dependencies(
+            end_node_ids=end_node_ids,
+            reverse_edge_mapping=reverse_edge_mapping,
+            node_id_config_mapping=node_id_config_mapping,
+        )
+
+        return EndStreamParam(
+            end_stream_variable_selector_mapping=end_stream_variable_selectors_mapping,
+            end_dependencies=end_dependencies,
+        )
+
+    @classmethod
+    def extract_stream_variable_selector_from_node_data(
+        cls, node_id_config_mapping: dict[str, dict], node_data: EndNodeData
+    ) -> list[list[str]]:
+        """
+        Extract stream variable selector from node data
+        :param node_id_config_mapping: node id config mapping
+        :param node_data: node data object
+        :return:
+        """
+        variable_selectors = node_data.outputs
+
+        value_selectors = []
+        for variable_selector in variable_selectors:
+            if not variable_selector.value_selector:
+                continue
+
+            node_id = variable_selector.value_selector[0]
+            if node_id != "sys" and node_id in node_id_config_mapping:
+                node = node_id_config_mapping[node_id]
+                node_type = node.get("data", {}).get("type")
+                if (
+                    variable_selector.value_selector not in value_selectors
+                    and node_type == NodeType.LLM.value
+                    and variable_selector.value_selector[1] == "text"
+                ):
+                    value_selectors.append(variable_selector.value_selector)
+
+        return value_selectors
+
+    @classmethod
+    def _extract_stream_variable_selector(
+        cls, node_id_config_mapping: dict[str, dict], config: dict
+    ) -> list[list[str]]:
+        """
+        Extract stream variable selector from node config
+        :param node_id_config_mapping: node id config mapping
+        :param config: node config
+        :return:
+        """
+        node_data = EndNodeData(**config.get("data", {}))
+        return cls.extract_stream_variable_selector_from_node_data(node_id_config_mapping, node_data)
+
+    @classmethod
+    def _fetch_ends_dependencies(
+        cls,
+        end_node_ids: list[str],
+        reverse_edge_mapping: dict[str, list["GraphEdge"]],  # type: ignore[name-defined]
+        node_id_config_mapping: dict[str, dict],
+    ) -> dict[str, list[str]]:
+        """
+        Fetch end dependencies
+        :param end_node_ids: end node ids
+        :param reverse_edge_mapping: reverse edge mapping
+        :param node_id_config_mapping: node id config mapping
+        :return:
+        """
+        end_dependencies: dict[str, list[str]] = {}
+        for end_node_id in end_node_ids:
+            if end_dependencies.get(end_node_id) is None:
+                end_dependencies[end_node_id] = []
+
+            cls._recursive_fetch_end_dependencies(
+                current_node_id=end_node_id,
+                end_node_id=end_node_id,
+                node_id_config_mapping=node_id_config_mapping,
+                reverse_edge_mapping=reverse_edge_mapping,
+                end_dependencies=end_dependencies,
+            )
+
+        return end_dependencies
+
+    @classmethod
+    def _recursive_fetch_end_dependencies(
+        cls,
+        current_node_id: str,
+        end_node_id: str,
+        node_id_config_mapping: dict[str, dict],
+        reverse_edge_mapping: dict[str, list["GraphEdge"]],
+        # type: ignore[name-defined]
+        end_dependencies: dict[str, list[str]],
+    ) -> None:
+        """
+        Recursive fetch end dependencies
+        :param current_node_id: current node id
+        :param end_node_id: end node id
+        :param node_id_config_mapping: node id config mapping
+        :param reverse_edge_mapping: reverse edge mapping
+        :param end_dependencies: end dependencies
+        :return:
+        """
+        reverse_edges = reverse_edge_mapping.get(current_node_id, [])
+        for edge in reverse_edges:
+            source_node_id = edge.source_node_id
+            source_node_type = node_id_config_mapping[source_node_id].get("data", {}).get("type")
+            if source_node_type in {
+                NodeType.IF_ELSE.value,
+                NodeType.QUESTION_CLASSIFIER,
+            }:
+                end_dependencies[end_node_id].append(source_node_id)
+            else:
+                cls._recursive_fetch_end_dependencies(
+                    current_node_id=source_node_id,
+                    end_node_id=end_node_id,
+                    node_id_config_mapping=node_id_config_mapping,
+                    reverse_edge_mapping=reverse_edge_mapping,
+                    end_dependencies=end_dependencies,
+                )
diff --git a/api/core/workflow/nodes/end/end_stream_processor.py b/api/core/workflow/nodes/end/end_stream_processor.py
new file mode 100644
index 0000000000..0366d7965d
--- /dev/null
+++ b/api/core/workflow/nodes/end/end_stream_processor.py
@@ -0,0 +1,187 @@
+import logging
+from collections.abc import Generator
+
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.graph_engine.entities.event import (
+    GraphEngineEvent,
+    NodeRunStartedEvent,
+    NodeRunStreamChunkEvent,
+    NodeRunSucceededEvent,
+)
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.nodes.answer.base_stream_processor import StreamProcessor
+
+logger = logging.getLogger(__name__)
+
+
+class EndStreamProcessor(StreamProcessor):
+    def __init__(self, graph: Graph, variable_pool: VariablePool) -> None:
+        super().__init__(graph, variable_pool)
+        self.end_stream_param = graph.end_stream_param
+        self.route_position = {}
+        for end_node_id, _ in self.end_stream_param.end_stream_variable_selector_mapping.items():
+            self.route_position[end_node_id] = 0
+        self.current_stream_chunk_generating_node_ids: dict[str, list[str]] = {}
+        self.has_outputed = False
+        self.outputed_node_ids = set()
+
+    def process(self, generator: Generator[GraphEngineEvent, None, None]) -> Generator[GraphEngineEvent, None, None]:
+        for event in generator:
+            if isinstance(event, NodeRunStartedEvent):
+                if event.route_node_state.node_id == self.graph.root_node_id and not self.rest_node_ids:
+                    self.reset()
+
+                yield event
+            elif isinstance(event, NodeRunStreamChunkEvent):
+                if event.in_iteration_id:
+                    if self.has_outputed and event.node_id not in self.outputed_node_ids:
+                        event.chunk_content = "\n" + event.chunk_content
+
+                    self.outputed_node_ids.add(event.node_id)
+                    self.has_outputed = True
+                    yield event
+                    continue
+
+                if event.route_node_state.node_id in self.current_stream_chunk_generating_node_ids:
+                    stream_out_end_node_ids = self.current_stream_chunk_generating_node_ids[
+                        event.route_node_state.node_id
+                    ]
+                else:
+                    stream_out_end_node_ids = self._get_stream_out_end_node_ids(event)
+                    self.current_stream_chunk_generating_node_ids[event.route_node_state.node_id] = (
+                        stream_out_end_node_ids
+                    )
+
+                if stream_out_end_node_ids:
+                    if self.has_outputed and event.node_id not in self.outputed_node_ids:
+                        event.chunk_content = "\n" + event.chunk_content
+
+                    self.outputed_node_ids.add(event.node_id)
+                    self.has_outputed = True
+                    yield event
+            elif isinstance(event, NodeRunSucceededEvent):
+                yield event
+                if event.route_node_state.node_id in self.current_stream_chunk_generating_node_ids:
+                    # update self.route_position after all stream event finished
+                    for end_node_id in self.current_stream_chunk_generating_node_ids[event.route_node_state.node_id]:
+                        self.route_position[end_node_id] += 1
+
+                    del self.current_stream_chunk_generating_node_ids[event.route_node_state.node_id]
+
+                # remove unreachable nodes
+                self._remove_unreachable_nodes(event)
+
+                # generate stream outputs
+                yield from self._generate_stream_outputs_when_node_finished(event)
+            else:
+                yield event
+
+    def reset(self) -> None:
+        self.route_position = {}
+        for end_node_id, _ in self.end_stream_param.end_stream_variable_selector_mapping.items():
+            self.route_position[end_node_id] = 0
+        self.rest_node_ids = self.graph.node_ids.copy()
+        self.current_stream_chunk_generating_node_ids = {}
+
+    def _generate_stream_outputs_when_node_finished(
+        self, event: NodeRunSucceededEvent
+    ) -> Generator[GraphEngineEvent, None, None]:
+        """
+        Generate stream outputs.
+        :param event: node run succeeded event
+        :return:
+        """
+        for end_node_id, position in self.route_position.items():
+            # all depends on end node id not in rest node ids
+            if event.route_node_state.node_id != end_node_id and (
+                end_node_id not in self.rest_node_ids
+                or not all(
+                    dep_id not in self.rest_node_ids for dep_id in self.end_stream_param.end_dependencies[end_node_id]
+                )
+            ):
+                continue
+
+            route_position = self.route_position[end_node_id]
+
+            position = 0
+            value_selectors = []
+            for current_value_selectors in self.end_stream_param.end_stream_variable_selector_mapping[end_node_id]:
+                if position >= route_position:
+                    value_selectors.append(current_value_selectors)
+
+                position += 1
+
+            for value_selector in value_selectors:
+                if not value_selector:
+                    continue
+
+                value = self.variable_pool.get(value_selector)
+
+                if value is None:
+                    break
+
+                text = value.markdown
+
+                if text:
+                    current_node_id = value_selector[0]
+                    if self.has_outputed and current_node_id not in self.outputed_node_ids:
+                        text = "\n" + text
+
+                    self.outputed_node_ids.add(current_node_id)
+                    self.has_outputed = True
+                    yield NodeRunStreamChunkEvent(
+                        id=event.id,
+                        node_id=event.node_id,
+                        node_type=event.node_type,
+                        node_data=event.node_data,
+                        chunk_content=text,
+                        from_variable_selector=value_selector,
+                        route_node_state=event.route_node_state,
+                        parallel_id=event.parallel_id,
+                        parallel_start_node_id=event.parallel_start_node_id,
+                    )
+
+                self.route_position[end_node_id] += 1
+
+    def _get_stream_out_end_node_ids(self, event: NodeRunStreamChunkEvent) -> list[str]:
+        """
+        Is stream out support
+        :param event: queue text chunk event
+        :return:
+        """
+        if not event.from_variable_selector:
+            return []
+
+        stream_output_value_selector = event.from_variable_selector
+        if not stream_output_value_selector:
+            return []
+
+        stream_out_end_node_ids = []
+        for end_node_id, route_position in self.route_position.items():
+            if end_node_id not in self.rest_node_ids:
+                continue
+
+            # all depends on end node id not in rest node ids
+            if all(dep_id not in self.rest_node_ids for dep_id in self.end_stream_param.end_dependencies[end_node_id]):
+                if route_position >= len(self.end_stream_param.end_stream_variable_selector_mapping[end_node_id]):
+                    continue
+
+                position = 0
+                value_selector = None
+                for current_value_selectors in self.end_stream_param.end_stream_variable_selector_mapping[end_node_id]:
+                    if position == route_position:
+                        value_selector = current_value_selectors
+                        break
+
+                    position += 1
+
+                if not value_selector:
+                    continue
+
+                # check chunk node id is before current node id or equal to current node id
+                if value_selector != stream_output_value_selector:
+                    continue
+
+                stream_out_end_node_ids.append(end_node_id)
+
+        return stream_out_end_node_ids
diff --git a/api/core/workflow/nodes/end/entities.py b/api/core/workflow/nodes/end/entities.py
index ad4fc8f04f..c3270ac22a 100644
--- a/api/core/workflow/nodes/end/entities.py
+++ b/api/core/workflow/nodes/end/entities.py
@@ -1,3 +1,5 @@
+from pydantic import BaseModel, Field
+
 from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.variable_entities import VariableSelector
 
@@ -6,4 +8,18 @@ class EndNodeData(BaseNodeData):
     """
     END Node Data.
     """
+
     outputs: list[VariableSelector]
+
+
+class EndStreamParam(BaseModel):
+    """
+    EndStreamParam entity
+    """
+
+    end_dependencies: dict[str, list[str]] = Field(
+        ..., description="end dependencies (end node id -> dependent node ids)"
+    )
+    end_stream_variable_selector_mapping: dict[str, list[list[str]]] = Field(
+        ..., description="end stream variable selector mapping (end node id -> stream variable selectors)"
+    )
diff --git a/api/core/workflow/nodes/event.py b/api/core/workflow/nodes/event.py
new file mode 100644
index 0000000000..276c13a6d4
--- /dev/null
+++ b/api/core/workflow/nodes/event.py
@@ -0,0 +1,20 @@
+from pydantic import BaseModel, Field
+
+from core.workflow.entities.node_entities import NodeRunResult
+
+
+class RunCompletedEvent(BaseModel):
+    run_result: NodeRunResult = Field(..., description="run result")
+
+
+class RunStreamChunkEvent(BaseModel):
+    chunk_content: str = Field(..., description="chunk content")
+    from_variable_selector: list[str] = Field(..., description="from variable selector")
+
+
+class RunRetrieverResourceEvent(BaseModel):
+    retriever_resources: list[dict] = Field(..., description="retriever resources")
+    context: str = Field(..., description="context")
+
+
+RunEvent = RunCompletedEvent | RunStreamChunkEvent | RunRetrieverResourceEvent
diff --git a/api/core/workflow/nodes/http_request/entities.py b/api/core/workflow/nodes/http_request/entities.py
index c066d469d8..66dd1f2dc6 100644
--- a/api/core/workflow/nodes/http_request/entities.py
+++ b/api/core/workflow/nodes/http_request/entities.py
@@ -7,32 +7,32 @@ from core.workflow.entities.base_node_data_entities import BaseNodeData
 
 
 class HttpRequestNodeAuthorizationConfig(BaseModel):
-    type: Literal[None, 'basic', 'bearer', 'custom']
+    type: Literal[None, "basic", "bearer", "custom"]
     api_key: Union[None, str] = None
     header: Union[None, str] = None
 
 
 class HttpRequestNodeAuthorization(BaseModel):
-    type: Literal['no-auth', 'api-key']
+    type: Literal["no-auth", "api-key"]
     config: Optional[HttpRequestNodeAuthorizationConfig] = None
 
-    @field_validator('config', mode='before')
+    @field_validator("config", mode="before")
     @classmethod
     def check_config(cls, v: HttpRequestNodeAuthorizationConfig, values: ValidationInfo):
         """
         Check config, if type is no-auth, config should be None, otherwise it should be a dict.
         """
-        if values.data['type'] == 'no-auth':
+        if values.data["type"] == "no-auth":
             return None
         else:
             if not v or not isinstance(v, dict):
-                raise ValueError('config should be a dict')
+                raise ValueError("config should be a dict")
 
             return v
 
 
 class HttpRequestNodeBody(BaseModel):
-    type: Literal['none', 'form-data', 'x-www-form-urlencoded', 'raw-text', 'json']
+    type: Literal["none", "form-data", "x-www-form-urlencoded", "raw-text", "json"]
     data: Union[None, str] = None
 
 
@@ -47,7 +47,7 @@ class HttpRequestNodeData(BaseNodeData):
     Code Node Data.
     """
 
-    method: Literal['get', 'post', 'put', 'patch', 'delete', 'head']
+    method: Literal["get", "post", "put", "patch", "delete", "head"]
     url: str
     authorization: HttpRequestNodeAuthorization
     headers: str
diff --git a/api/core/workflow/nodes/http_request/http_executor.py b/api/core/workflow/nodes/http_request/http_executor.py
index d16bff58bd..f8ab4e3132 100644
--- a/api/core/workflow/nodes/http_request/http_executor.py
+++ b/api/core/workflow/nodes/http_request/http_executor.py
@@ -6,8 +6,8 @@ from urllib.parse import urlencode
 
 import httpx
 
-import core.helper.ssrf_proxy as ssrf_proxy
 from configs import dify_config
+from core.helper import ssrf_proxy
 from core.workflow.entities.variable_entities import VariableSelector
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.http_request.entities import (
@@ -33,12 +33,12 @@ class HttpExecutorResponse:
         check if response is file
         """
         content_type = self.get_content_type()
-        file_content_types = ['image', 'audio', 'video']
+        file_content_types = ["image", "audio", "video"]
 
         return any(v in content_type for v in file_content_types)
 
     def get_content_type(self) -> str:
-        return self.headers.get('content-type', '')
+        return self.headers.get("content-type", "")
 
     def extract_file(self) -> tuple[str, bytes]:
         """
@@ -47,28 +47,28 @@ class HttpExecutorResponse:
         if self.is_file:
             return self.get_content_type(), self.body
 
-        return '', b''
+        return "", b""
 
     @property
     def content(self) -> str:
         if isinstance(self.response, httpx.Response):
             return self.response.text
         else:
-            raise ValueError(f'Invalid response type {type(self.response)}')
+            raise ValueError(f"Invalid response type {type(self.response)}")
 
     @property
     def body(self) -> bytes:
         if isinstance(self.response, httpx.Response):
             return self.response.content
         else:
-            raise ValueError(f'Invalid response type {type(self.response)}')
+            raise ValueError(f"Invalid response type {type(self.response)}")
 
     @property
     def status_code(self) -> int:
         if isinstance(self.response, httpx.Response):
             return self.response.status_code
         else:
-            raise ValueError(f'Invalid response type {type(self.response)}')
+            raise ValueError(f"Invalid response type {type(self.response)}")
 
     @property
     def size(self) -> int:
@@ -77,11 +77,11 @@ class HttpExecutorResponse:
     @property
     def readable_size(self) -> str:
         if self.size < 1024:
-            return f'{self.size} bytes'
+            return f"{self.size} bytes"
         elif self.size < 1024 * 1024:
-            return f'{(self.size / 1024):.2f} KB'
+            return f"{(self.size / 1024):.2f} KB"
         else:
-            return f'{(self.size / 1024 / 1024):.2f} MB'
+            return f"{(self.size / 1024 / 1024):.2f} MB"
 
 
 class HttpExecutor:
@@ -120,7 +120,7 @@ class HttpExecutor:
         """
         check if body is json
         """
-        if body and body.type == 'json' and body.data:
+        if body and body.type == "json" and body.data:
             try:
                 json.loads(body.data)
                 return True
@@ -134,15 +134,15 @@ class HttpExecutor:
         """
         Convert the string like `aa:bb\n cc:dd` to dict `{aa:bb, cc:dd}`
         """
-        kv_paris = convert_text.split('\n')
+        kv_paris = convert_text.split("\n")
         result = {}
         for kv in kv_paris:
             if not kv.strip():
                 continue
 
-            kv = kv.split(':', maxsplit=1)
+            kv = kv.split(":", maxsplit=1)
             if len(kv) == 1:
-                k, v = kv[0], ''
+                k, v = kv[0], ""
             else:
                 k, v = kv
             result[k.strip()] = v
@@ -166,31 +166,31 @@ class HttpExecutor:
             # check if it's a valid JSON
             is_valid_json = self._is_json_body(node_data.body)
 
-            body_data = node_data.body.data or ''
+            body_data = node_data.body.data or ""
             if body_data:
                 body_data, body_data_variable_selectors = self._format_template(body_data, variable_pool, is_valid_json)
 
-            content_type_is_set = any(key.lower() == 'content-type' for key in self.headers)
-            if node_data.body.type == 'json' and not content_type_is_set:
-                self.headers['Content-Type'] = 'application/json'
-            elif node_data.body.type == 'x-www-form-urlencoded' and not content_type_is_set:
-                self.headers['Content-Type'] = 'application/x-www-form-urlencoded'
+            content_type_is_set = any(key.lower() == "content-type" for key in self.headers)
+            if node_data.body.type == "json" and not content_type_is_set:
+                self.headers["Content-Type"] = "application/json"
+            elif node_data.body.type == "x-www-form-urlencoded" and not content_type_is_set:
+                self.headers["Content-Type"] = "application/x-www-form-urlencoded"
 
-            if node_data.body.type in ['form-data', 'x-www-form-urlencoded']:
+            if node_data.body.type in {"form-data", "x-www-form-urlencoded"}:
                 body = self._to_dict(body_data)
 
-                if node_data.body.type == 'form-data':
-                    self.files = {k: ('', v) for k, v in body.items()}
-                    random_str = lambda n: ''.join([chr(randint(97, 122)) for _ in range(n)])
-                    self.boundary = f'----WebKitFormBoundary{random_str(16)}'
+                if node_data.body.type == "form-data":
+                    self.files = {k: ("", v) for k, v in body.items()}
+                    random_str = lambda n: "".join([chr(randint(97, 122)) for _ in range(n)])
+                    self.boundary = f"----WebKitFormBoundary{random_str(16)}"
 
-                    self.headers['Content-Type'] = f'multipart/form-data; boundary={self.boundary}'
+                    self.headers["Content-Type"] = f"multipart/form-data; boundary={self.boundary}"
                 else:
                     self.body = urlencode(body)
-            elif node_data.body.type in ['json', 'raw-text']:
+            elif node_data.body.type in {"json", "raw-text"}:
                 self.body = body_data
-            elif node_data.body.type == 'none':
-                self.body = ''
+            elif node_data.body.type == "none":
+                self.body = ""
 
         self.variable_selectors = (
             server_url_variable_selectors
@@ -202,23 +202,23 @@ class HttpExecutor:
     def _assembling_headers(self) -> dict[str, Any]:
         authorization = deepcopy(self.authorization)
         headers = deepcopy(self.headers) or {}
-        if self.authorization.type == 'api-key':
+        if self.authorization.type == "api-key":
             if self.authorization.config is None:
-                raise ValueError('self.authorization config is required')
+                raise ValueError("self.authorization config is required")
             if authorization.config is None:
-                raise ValueError('authorization config is required')
+                raise ValueError("authorization config is required")
 
             if self.authorization.config.api_key is None:
-                raise ValueError('api_key is required')
+                raise ValueError("api_key is required")
 
             if not authorization.config.header:
-                authorization.config.header = 'Authorization'
+                authorization.config.header = "Authorization"
 
-            if self.authorization.config.type == 'bearer':
-                headers[authorization.config.header] = f'Bearer {authorization.config.api_key}'
-            elif self.authorization.config.type == 'basic':
-                headers[authorization.config.header] = f'Basic {authorization.config.api_key}'
-            elif self.authorization.config.type == 'custom':
+            if self.authorization.config.type == "bearer":
+                headers[authorization.config.header] = f"Bearer {authorization.config.api_key}"
+            elif self.authorization.config.type == "basic":
+                headers[authorization.config.header] = f"Basic {authorization.config.api_key}"
+            elif self.authorization.config.type == "custom":
                 headers[authorization.config.header] = authorization.config.api_key
 
         return headers
@@ -230,10 +230,13 @@ class HttpExecutor:
         if isinstance(response, httpx.Response):
             executor_response = HttpExecutorResponse(response)
         else:
-            raise ValueError(f'Invalid response type {type(response)}')
+            raise ValueError(f"Invalid response type {type(response)}")
 
-        threshold_size = dify_config.HTTP_REQUEST_NODE_MAX_BINARY_SIZE if executor_response.is_file \
+        threshold_size = (
+            dify_config.HTTP_REQUEST_NODE_MAX_BINARY_SIZE
+            if executor_response.is_file
             else dify_config.HTTP_REQUEST_NODE_MAX_TEXT_SIZE
+        )
         if executor_response.size > threshold_size:
             raise ValueError(
                 f'{"File" if executor_response.is_file else "Text"} size is too large,'
@@ -248,17 +251,17 @@ class HttpExecutor:
         do http request depending on api bundle
         """
         kwargs = {
-            'url': self.server_url,
-            'headers': headers,
-            'params': self.params,
-            'timeout': (self.timeout.connect, self.timeout.read, self.timeout.write),
-            'follow_redirects': True,
+            "url": self.server_url,
+            "headers": headers,
+            "params": self.params,
+            "timeout": (self.timeout.connect, self.timeout.read, self.timeout.write),
+            "follow_redirects": True,
         }
 
-        if self.method in ('get', 'head', 'post', 'put', 'delete', 'patch'):
+        if self.method in {"get", "head", "post", "put", "delete", "patch"}:
             response = getattr(ssrf_proxy, self.method)(data=self.body, files=self.files, **kwargs)
         else:
-            raise ValueError(f'Invalid http method {self.method}')
+            raise ValueError(f"Invalid http method {self.method}")
         return response
 
     def invoke(self) -> HttpExecutorResponse:
@@ -280,15 +283,15 @@ class HttpExecutor:
         """
         server_url = self.server_url
         if self.params:
-            server_url += f'?{urlencode(self.params)}'
+            server_url += f"?{urlencode(self.params)}"
 
-        raw_request = f'{self.method.upper()} {server_url} HTTP/1.1\n'
+        raw_request = f"{self.method.upper()} {server_url} HTTP/1.1\n"
 
         headers = self._assembling_headers()
         for k, v in headers.items():
             # get authorization header
-            if self.authorization.type == 'api-key':
-                authorization_header = 'Authorization'
+            if self.authorization.type == "api-key":
+                authorization_header = "Authorization"
                 if self.authorization.config and self.authorization.config.header:
                     authorization_header = self.authorization.config.header
 
@@ -296,21 +299,21 @@ class HttpExecutor:
                     raw_request += f'{k}: {"*" * len(v)}\n'
                     continue
 
-            raw_request += f'{k}: {v}\n'
+            raw_request += f"{k}: {v}\n"
 
-        raw_request += '\n'
+        raw_request += "\n"
 
         # if files, use multipart/form-data with boundary
         if self.files:
             boundary = self.boundary
-            raw_request += f'--{boundary}'
+            raw_request += f"--{boundary}"
             for k, v in self.files.items():
                 raw_request += f'\nContent-Disposition: form-data; name="{k}"\n\n'
-                raw_request += f'{v[1]}\n'
-                raw_request += f'--{boundary}'
-            raw_request += '--'
+                raw_request += f"{v[1]}\n"
+                raw_request += f"--{boundary}"
+            raw_request += "--"
         else:
-            raw_request += self.body or ''
+            raw_request += self.body or ""
 
         return raw_request
 
@@ -328,9 +331,9 @@ class HttpExecutor:
             for variable_selector in variable_selectors:
                 variable = variable_pool.get_any(variable_selector.value_selector)
                 if variable is None:
-                    raise ValueError(f'Variable {variable_selector.variable} not found')
+                    raise ValueError(f"Variable {variable_selector.variable} not found")
                 if escape_quotes and isinstance(variable, str):
-                    value = variable.replace('"', '\\"').replace('\n', '\\n')
+                    value = variable.replace('"', '\\"').replace("\n", "\\n")
                 else:
                     value = variable
                 variable_value_mapping[variable_selector.variable] = value
diff --git a/api/core/workflow/nodes/http_request/http_request_node.py b/api/core/workflow/nodes/http_request/http_request_node.py
index 037a7a1848..cd40819126 100644
--- a/api/core/workflow/nodes/http_request/http_request_node.py
+++ b/api/core/workflow/nodes/http_request/http_request_node.py
@@ -1,15 +1,14 @@
 import logging
+from collections.abc import Mapping, Sequence
 from mimetypes import guess_extension
 from os import path
-from typing import cast
+from typing import Any, cast
 
 from configs import dify_config
 from core.app.segments import parser
 from core.file.file_obj import FileTransferMethod, FileType, FileVar
 from core.tools.tool_file_manager import ToolFileManager
-from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.http_request.entities import (
     HttpRequestNodeData,
@@ -32,33 +31,37 @@ class HttpRequestNode(BaseNode):
     @classmethod
     def get_default_config(cls, filters: dict | None = None) -> dict:
         return {
-            'type': 'http-request',
-            'config': {
-                'method': 'get',
-                'authorization': {
-                    'type': 'no-auth',
+            "type": "http-request",
+            "config": {
+                "method": "get",
+                "authorization": {
+                    "type": "no-auth",
                 },
-                'body': {'type': 'none'},
-                'timeout': {
+                "body": {"type": "none"},
+                "timeout": {
                     **HTTP_REQUEST_DEFAULT_TIMEOUT.model_dump(),
-                    'max_connect_timeout': dify_config.HTTP_REQUEST_MAX_CONNECT_TIMEOUT,
-                    'max_read_timeout': dify_config.HTTP_REQUEST_MAX_READ_TIMEOUT,
-                    'max_write_timeout': dify_config.HTTP_REQUEST_MAX_WRITE_TIMEOUT,
+                    "max_connect_timeout": dify_config.HTTP_REQUEST_MAX_CONNECT_TIMEOUT,
+                    "max_read_timeout": dify_config.HTTP_REQUEST_MAX_READ_TIMEOUT,
+                    "max_write_timeout": dify_config.HTTP_REQUEST_MAX_WRITE_TIMEOUT,
                 },
             },
         }
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         node_data: HttpRequestNodeData = cast(HttpRequestNodeData, self.node_data)
         # TODO: Switch to use segment directly
         if node_data.authorization.config and node_data.authorization.config.api_key:
-            node_data.authorization.config.api_key = parser.convert_template(template=node_data.authorization.config.api_key, variable_pool=variable_pool).text
+            node_data.authorization.config.api_key = parser.convert_template(
+                template=node_data.authorization.config.api_key, variable_pool=self.graph_runtime_state.variable_pool
+            ).text
 
         # init http executor
         http_executor = None
         try:
             http_executor = HttpExecutor(
-                node_data=node_data, timeout=self._get_request_timeout(node_data), variable_pool=variable_pool
+                node_data=node_data,
+                timeout=self._get_request_timeout(node_data),
+                variable_pool=self.graph_runtime_state.variable_pool,
             )
 
             # invoke http executor
@@ -67,7 +70,7 @@ class HttpRequestNode(BaseNode):
             process_data = {}
             if http_executor:
                 process_data = {
-                    'request': http_executor.to_raw_request(),
+                    "request": http_executor.to_raw_request(),
                 }
             return NodeRunResult(
                 status=WorkflowNodeExecutionStatus.FAILED,
@@ -80,13 +83,13 @@ class HttpRequestNode(BaseNode):
         return NodeRunResult(
             status=WorkflowNodeExecutionStatus.SUCCEEDED,
             outputs={
-                'status_code': response.status_code,
-                'body': response.content if not files else '',
-                'headers': response.headers,
-                'files': files,
+                "status_code": response.status_code,
+                "body": response.content if not files else "",
+                "headers": response.headers,
+                "files": files,
             },
             process_data={
-                'request': http_executor.to_raw_request(),
+                "request": http_executor.to_raw_request(),
             },
         )
 
@@ -102,13 +105,16 @@ class HttpRequestNode(BaseNode):
         return timeout
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: HttpRequestNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
-        node_data = cast(HttpRequestNodeData, node_data)
         try:
             http_executor = HttpExecutor(node_data=node_data, timeout=HTTP_REQUEST_DEFAULT_TIMEOUT)
 
@@ -116,11 +122,11 @@ class HttpRequestNode(BaseNode):
 
             variable_mapping = {}
             for variable_selector in variable_selectors:
-                variable_mapping[variable_selector.variable] = variable_selector.value_selector
+                variable_mapping[node_id + "." + variable_selector.variable] = variable_selector.value_selector
 
             return variable_mapping
         except Exception as e:
-            logging.exception(f'Failed to extract variable selector to variable mapping: {e}')
+            logging.exception(f"Failed to extract variable selector to variable mapping: {e}")
             return {}
 
     def extract_files(self, url: str, response: HttpExecutorResponse) -> list[FileVar]:
@@ -134,7 +140,7 @@ class HttpRequestNode(BaseNode):
             # extract filename from url
             filename = path.basename(url)
             # extract extension if possible
-            extension = guess_extension(mimetype) or '.bin'
+            extension = guess_extension(mimetype) or ".bin"
 
             tool_file = ToolFileManager.create_file_by_raw(
                 user_id=self.user_id,
diff --git a/api/core/workflow/nodes/if_else/entities.py b/api/core/workflow/nodes/if_else/entities.py
index bc6dce0d3b..54c1081fd3 100644
--- a/api/core/workflow/nodes/if_else/entities.py
+++ b/api/core/workflow/nodes/if_else/entities.py
@@ -3,20 +3,7 @@ from typing import Literal, Optional
 from pydantic import BaseModel
 
 from core.workflow.entities.base_node_data_entities import BaseNodeData
-
-
-class Condition(BaseModel):
-    """
-    Condition entity
-    """
-    variable_selector: list[str]
-    comparison_operator: Literal[
-        # for string or array
-        "contains", "not contains", "start with", "end with", "is", "is not", "empty", "not empty",
-            # for number
-        "=", "≠", ">", "<", "≥", "≤", "null", "not null"
-    ]
-    value: Optional[str] = None
+from core.workflow.utils.condition.entities import Condition
 
 
 class IfElseNodeData(BaseNodeData):
@@ -28,6 +15,7 @@ class IfElseNodeData(BaseNodeData):
         """
         Case entity representing a single logical condition group
         """
+
         case_id: str
         logical_operator: Literal["and", "or"]
         conditions: list[Condition]
diff --git a/api/core/workflow/nodes/if_else/if_else_node.py b/api/core/workflow/nodes/if_else/if_else_node.py
index c6d235627f..37384202d8 100644
--- a/api/core/workflow/nodes/if_else/if_else_node.py
+++ b/api/core/workflow/nodes/if_else/if_else_node.py
@@ -1,12 +1,10 @@
-from collections.abc import Sequence
-from typing import Optional, cast
+from collections.abc import Mapping, Sequence
+from typing import Any, cast
 
-from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import BaseNode
-from core.workflow.nodes.if_else.entities import Condition, IfElseNodeData
-from core.workflow.utils.variable_template_parser import VariableTemplateParser
+from core.workflow.nodes.if_else.entities import IfElseNodeData
+from core.workflow.utils.condition.processor import ConditionProcessor
 from models.workflow import WorkflowNodeExecutionStatus
 
 
@@ -14,31 +12,30 @@ class IfElseNode(BaseNode):
     _node_data_cls = IfElseNodeData
     _node_type = NodeType.IF_ELSE
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         """
         Run node
-        :param variable_pool: variable pool
         :return:
         """
         node_data = self.node_data
         node_data = cast(IfElseNodeData, node_data)
 
-        node_inputs = {
-            "conditions": []
-        }
+        node_inputs: dict[str, list] = {"conditions": []}
 
-        process_datas = {
-            "condition_results": []
-        }
+        process_datas: dict[str, list] = {"condition_results": []}
 
         input_conditions = []
         final_result = False
         selected_case_id = None
+        condition_processor = ConditionProcessor()
         try:
             # Check if the new cases structure is used
             if node_data.cases:
                 for case in node_data.cases:
-                    input_conditions, group_result = self.process_conditions(variable_pool, case.conditions)
+                    input_conditions, group_result = condition_processor.process_conditions(
+                        variable_pool=self.graph_runtime_state.variable_pool, conditions=case.conditions
+                    )
+
                     # Apply the logical operator for the current case
                     final_result = all(group_result) if case.logical_operator == "and" else any(group_result)
 
@@ -57,28 +54,23 @@ class IfElseNode(BaseNode):
 
             else:
                 # Fallback to old structure if cases are not defined
-                input_conditions, group_result = self.process_conditions(variable_pool, node_data.conditions)
+                input_conditions, group_result = condition_processor.process_conditions(
+                    variable_pool=self.graph_runtime_state.variable_pool, conditions=node_data.conditions
+                )
 
                 final_result = all(group_result) if node_data.logical_operator == "and" else any(group_result)
 
                 selected_case_id = "true" if final_result else "false"
 
                 process_datas["condition_results"].append(
-                    {
-                        "group": "default",
-                        "results": group_result,
-                        "final_result": final_result
-                    }
+                    {"group": "default", "results": group_result, "final_result": final_result}
                 )
 
             node_inputs["conditions"] = input_conditions
 
         except Exception as e:
             return NodeRunResult(
-                status=WorkflowNodeExecutionStatus.FAILED,
-                inputs=node_inputs,
-                process_data=process_datas,
-                error=str(e)
+                status=WorkflowNodeExecutionStatus.FAILED, inputs=node_inputs, process_data=process_datas, error=str(e)
             )
 
         outputs = {"result": final_result, "selected_case_id": selected_case_id}
@@ -87,370 +79,20 @@ class IfElseNode(BaseNode):
             status=WorkflowNodeExecutionStatus.SUCCEEDED,
             inputs=node_inputs,
             process_data=process_datas,
-            edge_source_handle=selected_case_id if selected_case_id else "false",  # Use case ID or 'default'
-            outputs=outputs
+            edge_source_handle=selected_case_id or "false",  # Use case ID or 'default'
+            outputs=outputs,
         )
 
         return data
 
-    def evaluate_condition(
-        self, actual_value: Optional[str | list], expected_value: str, comparison_operator: str
-    ) -> bool:
-        """
-        Evaluate condition
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :param comparison_operator: comparison operator
-
-        :return: bool
-        """
-        if comparison_operator == "contains":
-            return self._assert_contains(actual_value, expected_value)
-        elif comparison_operator == "not contains":
-            return self._assert_not_contains(actual_value, expected_value)
-        elif comparison_operator == "start with":
-            return self._assert_start_with(actual_value, expected_value)
-        elif comparison_operator == "end with":
-            return self._assert_end_with(actual_value, expected_value)
-        elif comparison_operator == "is":
-            return self._assert_is(actual_value, expected_value)
-        elif comparison_operator == "is not":
-            return self._assert_is_not(actual_value, expected_value)
-        elif comparison_operator == "empty":
-            return self._assert_empty(actual_value)
-        elif comparison_operator == "not empty":
-            return self._assert_not_empty(actual_value)
-        elif comparison_operator == "=":
-            return self._assert_equal(actual_value, expected_value)
-        elif comparison_operator == "≠":
-            return self._assert_not_equal(actual_value, expected_value)
-        elif comparison_operator == ">":
-            return self._assert_greater_than(actual_value, expected_value)
-        elif comparison_operator == "<":
-            return self._assert_less_than(actual_value, expected_value)
-        elif comparison_operator == "≥":
-            return self._assert_greater_than_or_equal(actual_value, expected_value)
-        elif comparison_operator == "≤":
-            return self._assert_less_than_or_equal(actual_value, expected_value)
-        elif comparison_operator == "null":
-            return self._assert_null(actual_value)
-        elif comparison_operator == "not null":
-            return self._assert_not_null(actual_value)
-        else:
-            raise ValueError(f"Invalid comparison operator: {comparison_operator}")
-
-    def process_conditions(self, variable_pool: VariablePool, conditions: Sequence[Condition]):
-        input_conditions = []
-        group_result = []
-
-        for condition in conditions:
-            actual_variable = variable_pool.get_any(condition.variable_selector)
-
-            if condition.value is not None:
-                variable_template_parser = VariableTemplateParser(template=condition.value)
-                expected_value = variable_template_parser.extract_variable_selectors()
-                variable_selectors = variable_template_parser.extract_variable_selectors()
-                if variable_selectors:
-                    for variable_selector in variable_selectors:
-                        value = variable_pool.get_any(variable_selector.value_selector)
-                        expected_value = variable_template_parser.format({variable_selector.variable: value})
-                else:
-                    expected_value = condition.value
-            else:
-                expected_value = None
-
-            comparison_operator = condition.comparison_operator
-            input_conditions.append(
-                {
-                    "actual_value": actual_variable,
-                    "expected_value": expected_value,
-                    "comparison_operator": comparison_operator
-                }
-            )
-
-            result = self.evaluate_condition(actual_variable, expected_value, comparison_operator)
-            group_result.append(result)
-
-        return input_conditions, group_result
-
-    def _assert_contains(self, actual_value: Optional[str | list], expected_value: str) -> bool:
-        """
-        Assert contains
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if not actual_value:
-            return False
-
-        if not isinstance(actual_value, str | list):
-            raise ValueError('Invalid actual value type: string or array')
-
-        if expected_value not in actual_value:
-            return False
-        return True
-
-    def _assert_not_contains(self, actual_value: Optional[str | list], expected_value: str) -> bool:
-        """
-        Assert not contains
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if not actual_value:
-            return True
-
-        if not isinstance(actual_value, str | list):
-            raise ValueError('Invalid actual value type: string or array')
-
-        if expected_value in actual_value:
-            return False
-        return True
-
-    def _assert_start_with(self, actual_value: Optional[str], expected_value: str) -> bool:
-        """
-        Assert start with
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if not actual_value:
-            return False
-
-        if not isinstance(actual_value, str):
-            raise ValueError('Invalid actual value type: string')
-
-        if not actual_value.startswith(expected_value):
-            return False
-        return True
-
-    def _assert_end_with(self, actual_value: Optional[str], expected_value: str) -> bool:
-        """
-        Assert end with
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if not actual_value:
-            return False
-
-        if not isinstance(actual_value, str):
-            raise ValueError('Invalid actual value type: string')
-
-        if not actual_value.endswith(expected_value):
-            return False
-        return True
-
-    def _assert_is(self, actual_value: Optional[str], expected_value: str) -> bool:
-        """
-        Assert is
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if actual_value is None:
-            return False
-
-        if not isinstance(actual_value, str):
-            raise ValueError('Invalid actual value type: string')
-
-        if actual_value != expected_value:
-            return False
-        return True
-
-    def _assert_is_not(self, actual_value: Optional[str], expected_value: str) -> bool:
-        """
-        Assert is not
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if actual_value is None:
-            return False
-
-        if not isinstance(actual_value, str):
-            raise ValueError('Invalid actual value type: string')
-
-        if actual_value == expected_value:
-            return False
-        return True
-
-    def _assert_empty(self, actual_value: Optional[str]) -> bool:
-        """
-        Assert empty
-        :param actual_value: actual value
-        :return:
-        """
-        if not actual_value:
-            return True
-        return False
-
-    def _assert_not_empty(self, actual_value: Optional[str]) -> bool:
-        """
-        Assert not empty
-        :param actual_value: actual value
-        :return:
-        """
-        if actual_value:
-            return True
-        return False
-
-    def _assert_equal(self, actual_value: Optional[int | float], expected_value: str) -> bool:
-        """
-        Assert equal
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if actual_value is None:
-            return False
-
-        if not isinstance(actual_value, int | float):
-            raise ValueError('Invalid actual value type: number')
-
-        if isinstance(actual_value, int):
-            expected_value = int(expected_value)
-        else:
-            expected_value = float(expected_value)
-
-        if actual_value != expected_value:
-            return False
-        return True
-
-    def _assert_not_equal(self, actual_value: Optional[int | float], expected_value: str) -> bool:
-        """
-        Assert not equal
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if actual_value is None:
-            return False
-
-        if not isinstance(actual_value, int | float):
-            raise ValueError('Invalid actual value type: number')
-
-        if isinstance(actual_value, int):
-            expected_value = int(expected_value)
-        else:
-            expected_value = float(expected_value)
-
-        if actual_value == expected_value:
-            return False
-        return True
-
-    def _assert_greater_than(self, actual_value: Optional[int | float], expected_value: str) -> bool:
-        """
-        Assert greater than
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if actual_value is None:
-            return False
-
-        if not isinstance(actual_value, int | float):
-            raise ValueError('Invalid actual value type: number')
-
-        if isinstance(actual_value, int):
-            expected_value = int(expected_value)
-        else:
-            expected_value = float(expected_value)
-
-        if actual_value <= expected_value:
-            return False
-        return True
-
-    def _assert_less_than(self, actual_value: Optional[int | float], expected_value: str) -> bool:
-        """
-        Assert less than
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if actual_value is None:
-            return False
-
-        if not isinstance(actual_value, int | float):
-            raise ValueError('Invalid actual value type: number')
-
-        if isinstance(actual_value, int):
-            expected_value = int(expected_value)
-        else:
-            expected_value = float(expected_value)
-
-        if actual_value >= expected_value:
-            return False
-        return True
-
-    def _assert_greater_than_or_equal(self, actual_value: Optional[int | float], expected_value: str) -> bool:
-        """
-        Assert greater than or equal
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if actual_value is None:
-            return False
-
-        if not isinstance(actual_value, int | float):
-            raise ValueError('Invalid actual value type: number')
-
-        if isinstance(actual_value, int):
-            expected_value = int(expected_value)
-        else:
-            expected_value = float(expected_value)
-
-        if actual_value < expected_value:
-            return False
-        return True
-
-    def _assert_less_than_or_equal(self, actual_value: Optional[int | float], expected_value: str) -> bool:
-        """
-        Assert less than or equal
-        :param actual_value: actual value
-        :param expected_value: expected value
-        :return:
-        """
-        if actual_value is None:
-            return False
-
-        if not isinstance(actual_value, int | float):
-            raise ValueError('Invalid actual value type: number')
-
-        if isinstance(actual_value, int):
-            expected_value = int(expected_value)
-        else:
-            expected_value = float(expected_value)
-
-        if actual_value > expected_value:
-            return False
-        return True
-
-    def _assert_null(self, actual_value: Optional[int | float]) -> bool:
-        """
-        Assert null
-        :param actual_value: actual value
-        :return:
-        """
-        if actual_value is None:
-            return True
-        return False
-
-    def _assert_not_null(self, actual_value: Optional[int | float]) -> bool:
-        """
-        Assert not null
-        :param actual_value: actual value
-        :return:
-        """
-        if actual_value is not None:
-            return True
-        return False
-
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: IfElseNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
diff --git a/api/core/workflow/nodes/iteration/entities.py b/api/core/workflow/nodes/iteration/entities.py
index 177b47b951..3c2c189159 100644
--- a/api/core/workflow/nodes/iteration/entities.py
+++ b/api/core/workflow/nodes/iteration/entities.py
@@ -1,20 +1,31 @@
 from typing import Any, Optional
 
-from core.workflow.entities.base_node_data_entities import BaseIterationNodeData, BaseIterationState
+from core.workflow.entities.base_node_data_entities import BaseIterationNodeData, BaseIterationState, BaseNodeData
 
 
 class IterationNodeData(BaseIterationNodeData):
     """
     Iteration Node Data.
     """
-    parent_loop_id: Optional[str] = None # redundant field, not used currently
-    iterator_selector: list[str] # variable selector
-    output_selector: list[str] # output selector
+
+    parent_loop_id: Optional[str] = None  # redundant field, not used currently
+    iterator_selector: list[str]  # variable selector
+    output_selector: list[str]  # output selector
+
+
+class IterationStartNodeData(BaseNodeData):
+    """
+    Iteration Start Node Data.
+    """
+
+    pass
+
 
 class IterationState(BaseIterationState):
     """
     Iteration State.
     """
+
     outputs: list[Any] = None
     current_output: Optional[Any] = None
 
@@ -22,6 +33,7 @@ class IterationState(BaseIterationState):
         """
         Data.
         """
+
         iterator_length: int
 
     def get_last_output(self) -> Optional[Any]:
@@ -31,9 +43,9 @@ class IterationState(BaseIterationState):
         if self.outputs:
             return self.outputs[-1]
         return None
-    
+
     def get_current_output(self) -> Optional[Any]:
         """
         Get current output.
         """
-        return self.current_output
\ No newline at end of file
+        return self.current_output
diff --git a/api/core/workflow/nodes/iteration/iteration_node.py b/api/core/workflow/nodes/iteration/iteration_node.py
index 54dfe8b7f4..6f20745daf 100644
--- a/api/core/workflow/nodes/iteration/iteration_node.py
+++ b/api/core/workflow/nodes/iteration/iteration_node.py
@@ -1,124 +1,305 @@
-from typing import cast
+import logging
+from collections.abc import Generator, Mapping, Sequence
+from datetime import datetime, timezone
+from typing import Any, cast
 
+from configs import dify_config
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.workflow.entities.base_node_data_entities import BaseIterationState
-from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.nodes.base_node import BaseIterationNode
-from core.workflow.nodes.iteration.entities import IterationNodeData, IterationState
+from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType
+from core.workflow.graph_engine.entities.event import (
+    BaseGraphEvent,
+    BaseNodeEvent,
+    BaseParallelBranchEvent,
+    GraphRunFailedEvent,
+    InNodeEvent,
+    IterationRunFailedEvent,
+    IterationRunNextEvent,
+    IterationRunStartedEvent,
+    IterationRunSucceededEvent,
+    NodeRunStreamChunkEvent,
+    NodeRunSucceededEvent,
+)
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.nodes.base_node import BaseNode
+from core.workflow.nodes.event import RunCompletedEvent, RunEvent
+from core.workflow.nodes.iteration.entities import IterationNodeData
 from models.workflow import WorkflowNodeExecutionStatus
 
+logger = logging.getLogger(__name__)
 
-class IterationNode(BaseIterationNode):
+
+class IterationNode(BaseNode):
     """
     Iteration Node.
     """
+
     _node_data_cls = IterationNodeData
     _node_type = NodeType.ITERATION
 
-    def _run(self, variable_pool: VariablePool) -> BaseIterationState:
+    def _run(self) -> Generator[RunEvent | InNodeEvent, None, None]:
         """
         Run the node.
         """
         self.node_data = cast(IterationNodeData, self.node_data)
-        iterator = variable_pool.get_any(self.node_data.iterator_selector)
+        iterator_list_segment = self.graph_runtime_state.variable_pool.get(self.node_data.iterator_selector)
 
-        if not isinstance(iterator, list):
-            raise ValueError(f"Invalid iterator value: {iterator}, please provide a list.")
+        if not iterator_list_segment:
+            raise ValueError(f"Iterator variable {self.node_data.iterator_selector} not found")
 
-        state = IterationState(iteration_node_id=self.node_id, index=-1, inputs={
-            'iterator_selector': iterator
-        }, outputs=[], metadata=IterationState.MetaData(
-            iterator_length=len(iterator) if iterator is not None else 0
-        ))
-        
-        self._set_current_iteration_variable(variable_pool, state)
-        return state
+        iterator_list_value = iterator_list_segment.to_object()
 
-    def _get_next_iteration(self, variable_pool: VariablePool, state: IterationState) -> NodeRunResult | str:
-        """
-        Get next iteration start node id based on the graph.
-        :param graph: graph
-        :return: next node id
-        """
-        # resolve current output
-        self._resolve_current_output(variable_pool, state)
-        # move to next iteration
-        self._next_iteration(variable_pool, state)
+        if not isinstance(iterator_list_value, list):
+            raise ValueError(f"Invalid iterator value: {iterator_list_value}, please provide a list.")
 
-        node_data = cast(IterationNodeData, self.node_data)
-        if self._reached_iteration_limit(variable_pool, state):
-            return NodeRunResult(
-                status=WorkflowNodeExecutionStatus.SUCCEEDED,
-                outputs={
-                    'output': jsonable_encoder(state.outputs)
-                }
+        inputs = {"iterator_selector": iterator_list_value}
+
+        graph_config = self.graph_config
+
+        if not self.node_data.start_node_id:
+            raise ValueError(f"field start_node_id in iteration {self.node_id} not found")
+
+        root_node_id = self.node_data.start_node_id
+
+        # init graph
+        iteration_graph = Graph.init(graph_config=graph_config, root_node_id=root_node_id)
+
+        if not iteration_graph:
+            raise ValueError("iteration graph not found")
+
+        variable_pool = self.graph_runtime_state.variable_pool
+
+        # append iteration variable (item, index) to variable pool
+        variable_pool.add([self.node_id, "index"], 0)
+        variable_pool.add([self.node_id, "item"], iterator_list_value[0])
+
+        # init graph engine
+        from core.workflow.graph_engine.graph_engine import GraphEngine
+
+        graph_engine = GraphEngine(
+            tenant_id=self.tenant_id,
+            app_id=self.app_id,
+            workflow_type=self.workflow_type,
+            workflow_id=self.workflow_id,
+            user_id=self.user_id,
+            user_from=self.user_from,
+            invoke_from=self.invoke_from,
+            call_depth=self.workflow_call_depth,
+            graph=iteration_graph,
+            graph_config=graph_config,
+            variable_pool=variable_pool,
+            max_execution_steps=dify_config.WORKFLOW_MAX_EXECUTION_STEPS,
+            max_execution_time=dify_config.WORKFLOW_MAX_EXECUTION_TIME,
+        )
+
+        start_at = datetime.now(timezone.utc).replace(tzinfo=None)
+
+        yield IterationRunStartedEvent(
+            iteration_id=self.id,
+            iteration_node_id=self.node_id,
+            iteration_node_type=self.node_type,
+            iteration_node_data=self.node_data,
+            start_at=start_at,
+            inputs=inputs,
+            metadata={"iterator_length": len(iterator_list_value)},
+            predecessor_node_id=self.previous_node_id,
+        )
+
+        yield IterationRunNextEvent(
+            iteration_id=self.id,
+            iteration_node_id=self.node_id,
+            iteration_node_type=self.node_type,
+            iteration_node_data=self.node_data,
+            index=0,
+            pre_iteration_output=None,
+        )
+
+        outputs: list[Any] = []
+        try:
+            for _ in range(len(iterator_list_value)):
+                # run workflow
+                rst = graph_engine.run()
+                for event in rst:
+                    if isinstance(event, (BaseNodeEvent | BaseParallelBranchEvent)) and not event.in_iteration_id:
+                        event.in_iteration_id = self.node_id
+
+                    if (
+                        isinstance(event, BaseNodeEvent)
+                        and event.node_type == NodeType.ITERATION_START
+                        and not isinstance(event, NodeRunStreamChunkEvent)
+                    ):
+                        continue
+
+                    if isinstance(event, NodeRunSucceededEvent):
+                        if event.route_node_state.node_run_result:
+                            metadata = event.route_node_state.node_run_result.metadata
+                            if not metadata:
+                                metadata = {}
+
+                            if NodeRunMetadataKey.ITERATION_ID not in metadata:
+                                metadata[NodeRunMetadataKey.ITERATION_ID] = self.node_id
+                                metadata[NodeRunMetadataKey.ITERATION_INDEX] = variable_pool.get_any(
+                                    [self.node_id, "index"]
+                                )
+                                event.route_node_state.node_run_result.metadata = metadata
+
+                        yield event
+                    elif isinstance(event, BaseGraphEvent):
+                        if isinstance(event, GraphRunFailedEvent):
+                            # iteration run failed
+                            yield IterationRunFailedEvent(
+                                iteration_id=self.id,
+                                iteration_node_id=self.node_id,
+                                iteration_node_type=self.node_type,
+                                iteration_node_data=self.node_data,
+                                start_at=start_at,
+                                inputs=inputs,
+                                outputs={"output": jsonable_encoder(outputs)},
+                                steps=len(iterator_list_value),
+                                metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
+                                error=event.error,
+                            )
+
+                            yield RunCompletedEvent(
+                                run_result=NodeRunResult(
+                                    status=WorkflowNodeExecutionStatus.FAILED,
+                                    error=event.error,
+                                )
+                            )
+                            return
+                    else:
+                        event = cast(InNodeEvent, event)
+                        yield event
+
+                # append to iteration output variable list
+                current_iteration_output = variable_pool.get_any(self.node_data.output_selector)
+                outputs.append(current_iteration_output)
+
+                # remove all nodes outputs from variable pool
+                for node_id in iteration_graph.node_ids:
+                    variable_pool.remove_node(node_id)
+
+                # move to next iteration
+                current_index = variable_pool.get([self.node_id, "index"])
+                if current_index is None:
+                    raise ValueError(f"iteration {self.node_id} current index not found")
+
+                next_index = int(current_index.to_object()) + 1
+                variable_pool.add([self.node_id, "index"], next_index)
+
+                if next_index < len(iterator_list_value):
+                    variable_pool.add([self.node_id, "item"], iterator_list_value[next_index])
+
+                yield IterationRunNextEvent(
+                    iteration_id=self.id,
+                    iteration_node_id=self.node_id,
+                    iteration_node_type=self.node_type,
+                    iteration_node_data=self.node_data,
+                    index=next_index,
+                    pre_iteration_output=jsonable_encoder(current_iteration_output)
+                    if current_iteration_output
+                    else None,
+                )
+
+            yield IterationRunSucceededEvent(
+                iteration_id=self.id,
+                iteration_node_id=self.node_id,
+                iteration_node_type=self.node_type,
+                iteration_node_data=self.node_data,
+                start_at=start_at,
+                inputs=inputs,
+                outputs={"output": jsonable_encoder(outputs)},
+                steps=len(iterator_list_value),
+                metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
             )
-        
-        return node_data.start_node_id
-    
-    def _set_current_iteration_variable(self, variable_pool: VariablePool, state: IterationState):
-        """
-        Set current iteration variable.
-        :variable_pool: variable pool
-        """
-        node_data = cast(IterationNodeData, self.node_data)
 
-        variable_pool.add((self.node_id, 'index'), state.index)
-        # get the iterator value
-        iterator = variable_pool.get_any(node_data.iterator_selector)
+            yield RunCompletedEvent(
+                run_result=NodeRunResult(
+                    status=WorkflowNodeExecutionStatus.SUCCEEDED, outputs={"output": jsonable_encoder(outputs)}
+                )
+            )
+        except Exception as e:
+            # iteration run failed
+            logger.exception("Iteration run failed")
+            yield IterationRunFailedEvent(
+                iteration_id=self.id,
+                iteration_node_id=self.node_id,
+                iteration_node_type=self.node_type,
+                iteration_node_data=self.node_data,
+                start_at=start_at,
+                inputs=inputs,
+                outputs={"output": jsonable_encoder(outputs)},
+                steps=len(iterator_list_value),
+                metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
+                error=str(e),
+            )
 
-        if iterator is None or not isinstance(iterator, list):
-            return
-        
-        if state.index < len(iterator):
-            variable_pool.add((self.node_id, 'item'), iterator[state.index])
-
-    def _next_iteration(self, variable_pool: VariablePool, state: IterationState):
-        """
-        Move to next iteration.
-        :param variable_pool: variable pool
-        """
-        state.index += 1
-        self._set_current_iteration_variable(variable_pool, state)
-
-    def _reached_iteration_limit(self, variable_pool: VariablePool, state: IterationState):
-        """
-        Check if iteration limit is reached.
-        :return: True if iteration limit is reached, False otherwise
-        """
-        node_data = cast(IterationNodeData, self.node_data)
-        iterator =  variable_pool.get_any(node_data.iterator_selector)
-
-        if iterator is None or not isinstance(iterator, list):
-            return True
-
-        return state.index >= len(iterator)
-    
-    def _resolve_current_output(self, variable_pool: VariablePool, state: IterationState):
-        """
-        Resolve current output.
-        :param variable_pool: variable pool
-        """
-        output_selector = cast(IterationNodeData, self.node_data).output_selector
-        output = variable_pool.get_any(output_selector)
-        # clear the output for this iteration
-        variable_pool.remove([self.node_id] + output_selector[1:])
-        state.current_output = output
-        if output is not None:
-            # NOTE: This is a temporary patch to process double nested list (for example, DALL-E output in iteration).
-            if isinstance(output, list):
-                state.outputs.extend(output)
-            else:
-                state.outputs.append(output)
+            yield RunCompletedEvent(
+                run_result=NodeRunResult(
+                    status=WorkflowNodeExecutionStatus.FAILED,
+                    error=str(e),
+                )
+            )
+        finally:
+            # remove iteration variable (item, index) from variable pool after iteration run completed
+            variable_pool.remove([self.node_id, "index"])
+            variable_pool.remove([self.node_id, "item"])
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: IterationNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: IterationNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
-        return {
-            'input_selector': node_data.iterator_selector,
-        }
\ No newline at end of file
+        variable_mapping = {
+            f"{node_id}.input_selector": node_data.iterator_selector,
+        }
+
+        # init graph
+        iteration_graph = Graph.init(graph_config=graph_config, root_node_id=node_data.start_node_id)
+
+        if not iteration_graph:
+            raise ValueError("iteration graph not found")
+
+        for sub_node_id, sub_node_config in iteration_graph.node_id_config_mapping.items():
+            if sub_node_config.get("data", {}).get("iteration_id") != node_id:
+                continue
+
+            # variable selector to variable mapping
+            try:
+                # Get node class
+                from core.workflow.nodes.node_mapping import node_classes
+
+                node_type = NodeType.value_of(sub_node_config.get("data", {}).get("type"))
+                node_cls = node_classes.get(node_type)
+                if not node_cls:
+                    continue
+
+                node_cls = cast(BaseNode, node_cls)
+
+                sub_node_variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
+                    graph_config=graph_config, config=sub_node_config
+                )
+                sub_node_variable_mapping = cast(dict[str, list[str]], sub_node_variable_mapping)
+            except NotImplementedError:
+                sub_node_variable_mapping = {}
+
+            # remove iteration variables
+            sub_node_variable_mapping = {
+                sub_node_id + "." + key: value
+                for key, value in sub_node_variable_mapping.items()
+                if value[0] != node_id
+            }
+
+            variable_mapping.update(sub_node_variable_mapping)
+
+        # remove variable out from iteration
+        variable_mapping = {
+            key: value for key, value in variable_mapping.items() if value[0] not in iteration_graph.node_ids
+        }
+
+        return variable_mapping
diff --git a/api/core/workflow/nodes/iteration/iteration_start_node.py b/api/core/workflow/nodes/iteration/iteration_start_node.py
new file mode 100644
index 0000000000..88b9665ac6
--- /dev/null
+++ b/api/core/workflow/nodes/iteration/iteration_start_node.py
@@ -0,0 +1,35 @@
+from collections.abc import Mapping, Sequence
+from typing import Any
+
+from core.workflow.entities.node_entities import NodeRunResult, NodeType
+from core.workflow.nodes.base_node import BaseNode
+from core.workflow.nodes.iteration.entities import IterationNodeData, IterationStartNodeData
+from models.workflow import WorkflowNodeExecutionStatus
+
+
+class IterationStartNode(BaseNode):
+    """
+    Iteration Start Node.
+    """
+
+    _node_data_cls = IterationStartNodeData
+    _node_type = NodeType.ITERATION_START
+
+    def _run(self) -> NodeRunResult:
+        """
+        Run the node.
+        """
+        return NodeRunResult(status=WorkflowNodeExecutionStatus.SUCCEEDED)
+
+    @classmethod
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: IterationNodeData
+    ) -> Mapping[str, Sequence[str]]:
+        """
+        Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
+        :param node_data: node data
+        :return:
+        """
+        return {}
diff --git a/api/core/workflow/nodes/knowledge_retrieval/entities.py b/api/core/workflow/nodes/knowledge_retrieval/entities.py
index 7cf392277c..1cd88039b1 100644
--- a/api/core/workflow/nodes/knowledge_retrieval/entities.py
+++ b/api/core/workflow/nodes/knowledge_retrieval/entities.py
@@ -9,6 +9,7 @@ class RerankingModelConfig(BaseModel):
     """
     Reranking Model Config.
     """
+
     provider: str
     model: str
 
@@ -17,6 +18,7 @@ class VectorSetting(BaseModel):
     """
     Vector Setting.
     """
+
     vector_weight: float
     embedding_provider_name: str
     embedding_model_name: str
@@ -26,6 +28,7 @@ class KeywordSetting(BaseModel):
     """
     Keyword Setting.
     """
+
     keyword_weight: float
 
 
@@ -33,6 +36,7 @@ class WeightedScoreConfig(BaseModel):
     """
     Weighted score Config.
     """
+
     vector_setting: VectorSetting
     keyword_setting: KeywordSetting
 
@@ -41,17 +45,20 @@ class MultipleRetrievalConfig(BaseModel):
     """
     Multiple Retrieval Config.
     """
+
     top_k: int
     score_threshold: Optional[float] = None
-    reranking_mode: str = 'reranking_model'
+    reranking_mode: str = "reranking_model"
     reranking_enable: bool = True
     reranking_model: Optional[RerankingModelConfig] = None
     weights: Optional[WeightedScoreConfig] = None
 
+
 class ModelConfig(BaseModel):
     """
-     Model Config.
+    Model Config.
     """
+
     provider: str
     name: str
     mode: str
@@ -62,6 +69,7 @@ class SingleRetrievalConfig(BaseModel):
     """
     Single Retrieval Config.
     """
+
     model: ModelConfig
 
 
@@ -69,9 +77,10 @@ class KnowledgeRetrievalNodeData(BaseNodeData):
     """
     Knowledge retrieval Node Data.
     """
-    type: str = 'knowledge-retrieval'
+
+    type: str = "knowledge-retrieval"
     query_variable_selector: list[str]
     dataset_ids: list[str]
-    retrieval_mode: Literal['single', 'multiple']
+    retrieval_mode: Literal["single", "multiple"]
     multiple_retrieval_config: Optional[MultipleRetrievalConfig] = None
     single_retrieval_config: Optional[SingleRetrievalConfig] = None
diff --git a/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py b/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
index 01bf6e16e6..af55688a52 100644
--- a/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
+++ b/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py
@@ -1,3 +1,5 @@
+import logging
+from collections.abc import Mapping, Sequence
 from typing import Any, cast
 
 from sqlalchemy import func
@@ -11,25 +13,22 @@ from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities.model_entities import ModelFeature, ModelType
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
-from core.rag.retrieval.retrival_methods import RetrievalMethod
-from core.workflow.entities.base_node_data_entities import BaseNodeData
+from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.knowledge_retrieval.entities import KnowledgeRetrievalNodeData
 from extensions.ext_database import db
 from models.dataset import Dataset, Document, DocumentSegment
 from models.workflow import WorkflowNodeExecutionStatus
 
+logger = logging.getLogger(__name__)
+
 default_retrieval_model = {
-    'search_method': RetrievalMethod.SEMANTIC_SEARCH.value,
-    'reranking_enable': False,
-    'reranking_model': {
-        'reranking_provider_name': '',
-        'reranking_model_name': ''
-    },
-    'top_k': 2,
-    'score_threshold_enabled': False
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "reranking_enable": False,
+    "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
+    "top_k": 2,
+    "score_threshold_enabled": False,
 }
 
 
@@ -37,68 +36,53 @@ class KnowledgeRetrievalNode(BaseNode):
     _node_data_cls = KnowledgeRetrievalNodeData
     node_type = NodeType.KNOWLEDGE_RETRIEVAL
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
-        node_data: KnowledgeRetrievalNodeData = cast(self._node_data_cls, self.node_data)
+    def _run(self) -> NodeRunResult:
+        node_data = cast(KnowledgeRetrievalNodeData, self.node_data)
 
         # extract variables
-        variable = variable_pool.get_any(node_data.query_variable_selector)
+        variable = self.graph_runtime_state.variable_pool.get_any(node_data.query_variable_selector)
         query = variable
-        variables = {
-            'query': query
-        }
+        variables = {"query": query}
         if not query:
             return NodeRunResult(
-                status=WorkflowNodeExecutionStatus.FAILED,
-                inputs=variables,
-                error="Query is required."
+                status=WorkflowNodeExecutionStatus.FAILED, inputs=variables, error="Query is required."
             )
         # retrieve knowledge
         try:
-            results = self._fetch_dataset_retriever(
-                node_data=node_data, query=query
-            )
-            outputs = {
-                'result': results
-            }
+            results = self._fetch_dataset_retriever(node_data=node_data, query=query)
+            outputs = {"result": results}
             return NodeRunResult(
-                status=WorkflowNodeExecutionStatus.SUCCEEDED,
-                inputs=variables,
-                process_data=None,
-                outputs=outputs
+                status=WorkflowNodeExecutionStatus.SUCCEEDED, inputs=variables, process_data=None, outputs=outputs
             )
 
         except Exception as e:
+            logger.exception("Error when running knowledge retrieval node")
+            return NodeRunResult(status=WorkflowNodeExecutionStatus.FAILED, inputs=variables, error=str(e))
 
-            return NodeRunResult(
-                status=WorkflowNodeExecutionStatus.FAILED,
-                inputs=variables,
-                error=str(e)
-            )
-
-    def _fetch_dataset_retriever(self, node_data: KnowledgeRetrievalNodeData, query: str) -> list[
-        dict[str, Any]]:
+    def _fetch_dataset_retriever(self, node_data: KnowledgeRetrievalNodeData, query: str) -> list[dict[str, Any]]:
         available_datasets = []
         dataset_ids = node_data.dataset_ids
 
         # Subquery: Count the number of available documents for each dataset
-        subquery = db.session.query(
-            Document.dataset_id,
-            func.count(Document.id).label('available_document_count')
-        ).filter(
-            Document.indexing_status == 'completed',
-            Document.enabled == True,
-            Document.archived == False,
-            Document.dataset_id.in_(dataset_ids)
-        ).group_by(Document.dataset_id).having(
-            func.count(Document.id) > 0
-        ).subquery()
+        subquery = (
+            db.session.query(Document.dataset_id, func.count(Document.id).label("available_document_count"))
+            .filter(
+                Document.indexing_status == "completed",
+                Document.enabled == True,
+                Document.archived == False,
+                Document.dataset_id.in_(dataset_ids),
+            )
+            .group_by(Document.dataset_id)
+            .having(func.count(Document.id) > 0)
+            .subquery()
+        )
 
-        results = db.session.query(Dataset).join(
-            subquery, Dataset.id == subquery.c.dataset_id
-        ).filter(
-            Dataset.tenant_id == self.tenant_id,
-            Dataset.id.in_(dataset_ids)
-        ).all()
+        results = (
+            db.session.query(Dataset)
+            .join(subquery, Dataset.id == subquery.c.dataset_id)
+            .filter(Dataset.tenant_id == self.tenant_id, Dataset.id.in_(dataset_ids))
+            .all()
+        )
 
         for dataset in results:
             # pass if dataset is not available
@@ -115,16 +99,14 @@ class KnowledgeRetrievalNode(BaseNode):
             model_type_instance = cast(LargeLanguageModel, model_type_instance)
             # get model schema
             model_schema = model_type_instance.get_model_schema(
-                model=model_config.model,
-                credentials=model_config.credentials
+                model=model_config.model, credentials=model_config.credentials
             )
 
             if model_schema:
                 planning_strategy = PlanningStrategy.REACT_ROUTER
                 features = model_schema.features
                 if features:
-                    if ModelFeature.TOOL_CALL in features \
-                            or ModelFeature.MULTI_TOOL_CALL in features:
+                    if ModelFeature.TOOL_CALL in features or ModelFeature.MULTI_TOOL_CALL in features:
                         planning_strategy = PlanningStrategy.ROUTER
                 all_documents = dataset_retrieval.single_retrieve(
                     available_datasets=available_datasets,
@@ -135,110 +117,123 @@ class KnowledgeRetrievalNode(BaseNode):
                     query=query,
                     model_config=model_config,
                     model_instance=model_instance,
-                    planning_strategy=planning_strategy
+                    planning_strategy=planning_strategy,
                 )
         elif node_data.retrieval_mode == DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE.value:
-            if node_data.multiple_retrieval_config.reranking_mode == 'reranking_model':
+            if node_data.multiple_retrieval_config.reranking_mode == "reranking_model":
                 reranking_model = {
-                    'reranking_provider_name': node_data.multiple_retrieval_config.reranking_model.provider,
-                    'reranking_model_name': node_data.multiple_retrieval_config.reranking_model.model
+                    "reranking_provider_name": node_data.multiple_retrieval_config.reranking_model.provider,
+                    "reranking_model_name": node_data.multiple_retrieval_config.reranking_model.model,
                 }
                 weights = None
-            elif node_data.multiple_retrieval_config.reranking_mode == 'weighted_score':
+            elif node_data.multiple_retrieval_config.reranking_mode == "weighted_score":
                 reranking_model = None
+                vector_setting = node_data.multiple_retrieval_config.weights.vector_setting
                 weights = {
-                    'vector_setting': {
-                        "vector_weight": node_data.multiple_retrieval_config.weights.vector_setting.vector_weight,
-                        "embedding_provider_name": node_data.multiple_retrieval_config.weights.vector_setting.embedding_provider_name,
-                        "embedding_model_name": node_data.multiple_retrieval_config.weights.vector_setting.embedding_model_name,
+                    "vector_setting": {
+                        "vector_weight": vector_setting.vector_weight,
+                        "embedding_provider_name": vector_setting.embedding_provider_name,
+                        "embedding_model_name": vector_setting.embedding_model_name,
                     },
-                    'keyword_setting': {
+                    "keyword_setting": {
                         "keyword_weight": node_data.multiple_retrieval_config.weights.keyword_setting.keyword_weight
-                    }
+                    },
                 }
             else:
                 reranking_model = None
                 weights = None
-            all_documents = dataset_retrieval.multiple_retrieve(self.app_id, self.tenant_id, self.user_id,
-                                                                self.user_from.value,
-                                                                available_datasets, query,
-                                                                node_data.multiple_retrieval_config.top_k,
-                                                                node_data.multiple_retrieval_config.score_threshold,
-                                                                node_data.multiple_retrieval_config.reranking_mode,
-                                                                reranking_model,
-                                                                weights,
-                                                                node_data.multiple_retrieval_config.reranking_enable,
-                                                                )
+            all_documents = dataset_retrieval.multiple_retrieve(
+                self.app_id,
+                self.tenant_id,
+                self.user_id,
+                self.user_from.value,
+                available_datasets,
+                query,
+                node_data.multiple_retrieval_config.top_k,
+                node_data.multiple_retrieval_config.score_threshold,
+                node_data.multiple_retrieval_config.reranking_mode,
+                reranking_model,
+                weights,
+                node_data.multiple_retrieval_config.reranking_enable,
+            )
 
         context_list = []
         if all_documents:
             document_score_list = {}
+            page_number_list = {}
             for item in all_documents:
-                if item.metadata.get('score'):
-                    document_score_list[item.metadata['doc_id']] = item.metadata['score']
+                if item.metadata.get("score"):
+                    document_score_list[item.metadata["doc_id"]] = item.metadata["score"]
 
-            index_node_ids = [document.metadata['doc_id'] for document in all_documents]
+            index_node_ids = [document.metadata["doc_id"] for document in all_documents]
             segments = DocumentSegment.query.filter(
                 DocumentSegment.dataset_id.in_(dataset_ids),
                 DocumentSegment.completed_at.isnot(None),
-                DocumentSegment.status == 'completed',
+                DocumentSegment.status == "completed",
                 DocumentSegment.enabled == True,
-                DocumentSegment.index_node_id.in_(index_node_ids)
+                DocumentSegment.index_node_id.in_(index_node_ids),
             ).all()
             if segments:
                 index_node_id_to_position = {id: position for position, id in enumerate(index_node_ids)}
-                sorted_segments = sorted(segments,
-                                         key=lambda segment: index_node_id_to_position.get(segment.index_node_id,
-                                                                                           float('inf')))
+                sorted_segments = sorted(
+                    segments, key=lambda segment: index_node_id_to_position.get(segment.index_node_id, float("inf"))
+                )
 
                 for segment in sorted_segments:
-                    dataset = Dataset.query.filter_by(
-                        id=segment.dataset_id
+                    dataset = Dataset.query.filter_by(id=segment.dataset_id).first()
+                    document = Document.query.filter(
+                        Document.id == segment.document_id,
+                        Document.enabled == True,
+                        Document.archived == False,
                     ).first()
-                    document = Document.query.filter(Document.id == segment.document_id,
-                                                     Document.enabled == True,
-                                                     Document.archived == False,
-                                                     ).first()
+
                     resource_number = 1
                     if dataset and document:
-
                         source = {
-                            'metadata': {
-                                '_source': 'knowledge',
-                                'position': resource_number,
-                                'dataset_id': dataset.id,
-                                'dataset_name': dataset.name,
-                                'document_id': document.id,
-                                'document_name': document.name,
-                                'document_data_source_type': document.data_source_type,
-                                'segment_id': segment.id,
-                                'retriever_from': 'workflow',
-                                'score': document_score_list.get(segment.index_node_id, None),
-                                'segment_hit_count': segment.hit_count,
-                                'segment_word_count': segment.word_count,
-                                'segment_position': segment.position,
-                                'segment_index_node_hash': segment.index_node_hash,
+                            "metadata": {
+                                "_source": "knowledge",
+                                "position": resource_number,
+                                "dataset_id": dataset.id,
+                                "dataset_name": dataset.name,
+                                "document_id": document.id,
+                                "document_name": document.name,
+                                "document_data_source_type": document.data_source_type,
+                                "segment_id": segment.id,
+                                "retriever_from": "workflow",
+                                "score": document_score_list.get(segment.index_node_id, None),
+                                "segment_hit_count": segment.hit_count,
+                                "segment_word_count": segment.word_count,
+                                "segment_position": segment.position,
+                                "segment_index_node_hash": segment.index_node_hash,
                             },
-                            'title': document.name
+                            "title": document.name,
                         }
                         if segment.answer:
-                            source['content'] = f'question:{segment.get_sign_content()} \nanswer:{segment.answer}'
+                            source["content"] = f"question:{segment.get_sign_content()} \nanswer:{segment.answer}"
                         else:
-                            source['content'] = segment.get_sign_content()
+                            source["content"] = segment.get_sign_content()
                         context_list.append(source)
                         resource_number += 1
         return context_list
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
-        node_data = node_data
-        node_data = cast(cls._node_data_cls, node_data)
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: KnowledgeRetrievalNodeData
+    ) -> Mapping[str, Sequence[str]]:
+        """
+        Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
+        :param node_data: node data
+        :return:
+        """
         variable_mapping = {}
-        variable_mapping['query'] = node_data.query_variable_selector
+        variable_mapping[node_id + ".query"] = node_data.query_variable_selector
         return variable_mapping
 
-    def _fetch_model_config(self, node_data: KnowledgeRetrievalNodeData) -> tuple[
-        ModelInstance, ModelConfigWithCredentialsEntity]:
+    def _fetch_model_config(
+        self, node_data: KnowledgeRetrievalNodeData
+    ) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
         """
         Fetch model config
         :param node_data: node data
@@ -249,10 +244,7 @@ class KnowledgeRetrievalNode(BaseNode):
 
         model_manager = ModelManager()
         model_instance = model_manager.get_model_instance(
-            tenant_id=self.tenant_id,
-            model_type=ModelType.LLM,
-            provider=provider_name,
-            model=model_name
+            tenant_id=self.tenant_id, model_type=ModelType.LLM, provider=provider_name, model=model_name
         )
 
         provider_model_bundle = model_instance.provider_model_bundle
@@ -263,8 +255,7 @@ class KnowledgeRetrievalNode(BaseNode):
 
         # check model
         provider_model = provider_model_bundle.configuration.get_provider_model(
-            model=model_name,
-            model_type=ModelType.LLM
+            model=model_name, model_type=ModelType.LLM
         )
 
         if provider_model is None:
@@ -280,19 +271,16 @@ class KnowledgeRetrievalNode(BaseNode):
         # model config
         completion_params = node_data.single_retrieval_config.model.completion_params
         stop = []
-        if 'stop' in completion_params:
-            stop = completion_params['stop']
-            del completion_params['stop']
+        if "stop" in completion_params:
+            stop = completion_params["stop"]
+            del completion_params["stop"]
 
         # get model mode
         model_mode = node_data.single_retrieval_config.model.mode
         if not model_mode:
             raise ValueError("LLM mode is required.")
 
-        model_schema = model_type_instance.get_model_schema(
-            model_name,
-            model_credentials
-        )
+        model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
 
         if not model_schema:
             raise ValueError(f"Model {model_name} not exist.")
diff --git a/api/core/workflow/nodes/llm/entities.py b/api/core/workflow/nodes/llm/entities.py
index 1e48a10bc7..93ee0ac250 100644
--- a/api/core/workflow/nodes/llm/entities.py
+++ b/api/core/workflow/nodes/llm/entities.py
@@ -11,6 +11,7 @@ class ModelConfig(BaseModel):
     """
     Model Config.
     """
+
     provider: str
     name: str
     mode: str
@@ -21,6 +22,7 @@ class ContextConfig(BaseModel):
     """
     Context Config.
     """
+
     enabled: bool
     variable_selector: Optional[list[str]] = None
 
@@ -29,37 +31,47 @@ class VisionConfig(BaseModel):
     """
     Vision Config.
     """
+
     class Configs(BaseModel):
         """
         Configs.
         """
-        detail: Literal['low', 'high']
+
+        detail: Literal["low", "high"]
 
     enabled: bool
     configs: Optional[Configs] = None
 
+
 class PromptConfig(BaseModel):
     """
     Prompt Config.
     """
+
     jinja2_variables: Optional[list[VariableSelector]] = None
 
+
 class LLMNodeChatModelMessage(ChatModelMessage):
     """
     LLM Node Chat Model Message.
     """
+
     jinja2_text: Optional[str] = None
 
+
 class LLMNodeCompletionModelPromptTemplate(CompletionModelPromptTemplate):
     """
     LLM Node Chat Model Prompt Template.
     """
+
     jinja2_text: Optional[str] = None
 
+
 class LLMNodeData(BaseNodeData):
     """
     LLM Node Data.
     """
+
     model: ModelConfig
     prompt_template: Union[list[LLMNodeChatModelMessage], LLMNodeCompletionModelPromptTemplate]
     prompt_config: Optional[PromptConfig] = None
diff --git a/api/core/workflow/nodes/llm/llm_node.py b/api/core/workflow/nodes/llm/llm_node.py
index 49f61bd597..3d336b0b0b 100644
--- a/api/core/workflow/nodes/llm/llm_node.py
+++ b/api/core/workflow/nodes/llm/llm_node.py
@@ -1,16 +1,17 @@
 import json
-from collections.abc import Generator
+from collections.abc import Generator, Mapping, Sequence
 from copy import deepcopy
-from typing import TYPE_CHECKING, Optional, cast
+from typing import TYPE_CHECKING, Any, Optional, cast
+
+from pydantic import BaseModel
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
-from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
 from core.entities.model_entities import ModelStatus
 from core.entities.provider_entities import QuotaUnit
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
-from core.model_runtime.entities.llm_entities import LLMUsage
+from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.model_runtime.entities.message_entities import (
     ImagePromptMessageContent,
     PromptMessage,
@@ -25,7 +26,9 @@ from core.prompt.utils.prompt_message_util import PromptMessageUtil
 from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
+from core.workflow.graph_engine.entities.event import InNodeEvent
 from core.workflow.nodes.base_node import BaseNode
+from core.workflow.nodes.event import RunCompletedEvent, RunEvent, RunRetrieverResourceEvent, RunStreamChunkEvent
 from core.workflow.nodes.llm.entities import (
     LLMNodeChatModelMessage,
     LLMNodeCompletionModelPromptTemplate,
@@ -42,18 +45,27 @@ if TYPE_CHECKING:
     from core.file.file_obj import FileVar
 
 
+class ModelInvokeCompleted(BaseModel):
+    """
+    Model invoke completed
+    """
+
+    text: str
+    usage: LLMUsage
+    finish_reason: Optional[str] = None
+
 
 class LLMNode(BaseNode):
     _node_data_cls = LLMNodeData
     _node_type = NodeType.LLM
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> Generator[RunEvent | InNodeEvent, None, None]:
         """
         Run node
-        :param variable_pool: variable pool
         :return:
         """
         node_data = cast(LLMNodeData, deepcopy(self.node_data))
+        variable_pool = self.graph_runtime_state.variable_pool
 
         node_inputs = None
         process_data = None
@@ -77,13 +89,18 @@ class LLMNode(BaseNode):
             files = self._fetch_files(node_data, variable_pool)
 
             if files:
-                node_inputs['#files#'] = [file.to_dict() for file in files]
+                node_inputs["#files#"] = [file.to_dict() for file in files]
 
             # fetch context value
-            context = self._fetch_context(node_data, variable_pool)
+            generator = self._fetch_context(node_data, variable_pool)
+            context = None
+            for event in generator:
+                if isinstance(event, RunRetrieverResourceEvent):
+                    context = event.context
+                    yield event
 
             if context:
-                node_inputs['#context#'] = context
+                node_inputs["#context#"] = context  # type: ignore
 
             # fetch model config
             model_instance, model_config = self._fetch_model_config(node_data.model)
@@ -94,63 +111,78 @@ class LLMNode(BaseNode):
             # fetch prompt messages
             prompt_messages, stop = self._fetch_prompt_messages(
                 node_data=node_data,
-                query=variable_pool.get_any(['sys', SystemVariableKey.QUERY.value])
-                if node_data.memory else None,
+                query=variable_pool.get_any(["sys", SystemVariableKey.QUERY.value]) if node_data.memory else None,
                 query_prompt_template=node_data.memory.query_prompt_template if node_data.memory else None,
                 inputs=inputs,
                 files=files,
                 context=context,
                 memory=memory,
-                model_config=model_config
+                model_config=model_config,
             )
 
             process_data = {
-                'model_mode': model_config.mode,
-                'prompts': PromptMessageUtil.prompt_messages_to_prompt_for_saving(
-                    model_mode=model_config.mode,
-                    prompt_messages=prompt_messages
+                "model_mode": model_config.mode,
+                "prompts": PromptMessageUtil.prompt_messages_to_prompt_for_saving(
+                    model_mode=model_config.mode, prompt_messages=prompt_messages
                 ),
-                'model_provider': model_config.provider,
-                'model_name': model_config.model,      
+                "model_provider": model_config.provider,
+                "model_name": model_config.model,
             }
 
             # handle invoke result
-            result_text, usage, finish_reason = self._invoke_llm(
+            generator = self._invoke_llm(
                 node_data_model=node_data.model,
                 model_instance=model_instance,
                 prompt_messages=prompt_messages,
-                stop=stop
+                stop=stop,
             )
+
+            result_text = ""
+            usage = LLMUsage.empty_usage()
+            finish_reason = None
+            for event in generator:
+                if isinstance(event, RunStreamChunkEvent):
+                    yield event
+                elif isinstance(event, ModelInvokeCompleted):
+                    result_text = event.text
+                    usage = event.usage
+                    finish_reason = event.finish_reason
+                    break
         except Exception as e:
-            return NodeRunResult(
-                status=WorkflowNodeExecutionStatus.FAILED,
-                error=str(e),
-                inputs=node_inputs,
-                process_data=process_data
+            yield RunCompletedEvent(
+                run_result=NodeRunResult(
+                    status=WorkflowNodeExecutionStatus.FAILED,
+                    error=str(e),
+                    inputs=node_inputs,
+                    process_data=process_data,
+                )
             )
+            return
 
-        outputs = {
-            'text': result_text,
-            'usage': jsonable_encoder(usage),
-            'finish_reason': finish_reason
-        }
+        outputs = {"text": result_text, "usage": jsonable_encoder(usage), "finish_reason": finish_reason}
 
-        return NodeRunResult(
-            status=WorkflowNodeExecutionStatus.SUCCEEDED,
-            inputs=node_inputs,
-            process_data=process_data,
-            outputs=outputs,
-            metadata={
-                NodeRunMetadataKey.TOTAL_TOKENS: usage.total_tokens,
-                NodeRunMetadataKey.TOTAL_PRICE: usage.total_price,
-                NodeRunMetadataKey.CURRENCY: usage.currency
-            }
+        yield RunCompletedEvent(
+            run_result=NodeRunResult(
+                status=WorkflowNodeExecutionStatus.SUCCEEDED,
+                inputs=node_inputs,
+                process_data=process_data,
+                outputs=outputs,
+                metadata={
+                    NodeRunMetadataKey.TOTAL_TOKENS: usage.total_tokens,
+                    NodeRunMetadataKey.TOTAL_PRICE: usage.total_price,
+                    NodeRunMetadataKey.CURRENCY: usage.currency,
+                },
+                llm_usage=usage,
+            )
         )
 
-    def _invoke_llm(self, node_data_model: ModelConfig,
-                    model_instance: ModelInstance,
-                    prompt_messages: list[PromptMessage],
-                    stop: list[str]) -> tuple[str, LLMUsage]:
+    def _invoke_llm(
+        self,
+        node_data_model: ModelConfig,
+        model_instance: ModelInstance,
+        prompt_messages: list[PromptMessage],
+        stop: Optional[list[str]] = None,
+    ) -> Generator[RunEvent | ModelInvokeCompleted, None, None]:
         """
         Invoke large language model
         :param node_data_model: node data model
@@ -170,31 +202,38 @@ class LLMNode(BaseNode):
         )
 
         # handle invoke result
-        text, usage, finish_reason = self._handle_invoke_result(
-            invoke_result=invoke_result
-        )
+        generator = self._handle_invoke_result(invoke_result=invoke_result)
+
+        usage = LLMUsage.empty_usage()
+        for event in generator:
+            yield event
+            if isinstance(event, ModelInvokeCompleted):
+                usage = event.usage
 
         # deduct quota
         self.deduct_llm_quota(tenant_id=self.tenant_id, model_instance=model_instance, usage=usage)
 
-        return text, usage, finish_reason
-
-    def _handle_invoke_result(self, invoke_result: Generator) -> tuple[str, LLMUsage]:
+    def _handle_invoke_result(
+        self, invoke_result: LLMResult | Generator
+    ) -> Generator[RunEvent | ModelInvokeCompleted, None, None]:
         """
         Handle invoke result
         :param invoke_result: invoke result
         :return:
         """
+        if isinstance(invoke_result, LLMResult):
+            return
+
         model = None
-        prompt_messages = []
-        full_text = ''
+        prompt_messages: list[PromptMessage] = []
+        full_text = ""
         usage = None
         finish_reason = None
         for result in invoke_result:
             text = result.delta.message.content
             full_text += text
 
-            self.publish_text_chunk(text=text, value_selector=[self.node_id, 'text'])
+            yield RunStreamChunkEvent(chunk_content=text, from_variable_selector=[self.node_id, "text"])
 
             if not model:
                 model = result.model
@@ -211,10 +250,10 @@ class LLMNode(BaseNode):
         if not usage:
             usage = LLMUsage.empty_usage()
 
-        return full_text, usage, finish_reason
+        yield ModelInvokeCompleted(text=full_text, usage=usage, finish_reason=finish_reason)
 
-    def _transform_chat_messages(self,
-        messages: list[LLMNodeChatModelMessage] | LLMNodeCompletionModelPromptTemplate
+    def _transform_chat_messages(
+        self, messages: list[LLMNodeChatModelMessage] | LLMNodeCompletionModelPromptTemplate
     ) -> list[LLMNodeChatModelMessage] | LLMNodeCompletionModelPromptTemplate:
         """
         Transform chat messages
@@ -224,13 +263,13 @@ class LLMNode(BaseNode):
         """
 
         if isinstance(messages, LLMNodeCompletionModelPromptTemplate):
-            if messages.edition_type == 'jinja2':
+            if messages.edition_type == "jinja2" and messages.jinja2_text:
                 messages.text = messages.jinja2_text
 
             return messages
 
         for message in messages:
-            if message.edition_type == 'jinja2':
+            if message.edition_type == "jinja2" and message.jinja2_text:
                 message.text = message.jinja2_text
 
         return messages
@@ -249,17 +288,15 @@ class LLMNode(BaseNode):
 
         for variable_selector in node_data.prompt_config.jinja2_variables or []:
             variable = variable_selector.variable
-            value = variable_pool.get_any(
-                variable_selector.value_selector
-            )
+            value = variable_pool.get_any(variable_selector.value_selector)
 
             def parse_dict(d: dict) -> str:
                 """
                 Parse dict into string
                 """
                 # check if it's a context structure
-                if 'metadata' in d and '_source' in d['metadata'] and 'content' in d:
-                    return d['content']
+                if "metadata" in d and "_source" in d["metadata"] and "content" in d:
+                    return d["content"]
 
                 # else, parse the dict
                 try:
@@ -270,7 +307,7 @@ class LLMNode(BaseNode):
             if isinstance(value, str):
                 value = value
             elif isinstance(value, list):
-                result = ''
+                result = ""
                 for item in value:
                     if isinstance(item, dict):
                         result += parse_dict(item)
@@ -280,7 +317,7 @@ class LLMNode(BaseNode):
                         result += str(item)
                     else:
                         result += str(item)
-                    result += '\n'
+                    result += "\n"
                 value = result.strip()
             elif isinstance(value, dict):
                 value = parse_dict(value)
@@ -315,18 +352,19 @@ class LLMNode(BaseNode):
         for variable_selector in variable_selectors:
             variable_value = variable_pool.get_any(variable_selector.value_selector)
             if variable_value is None:
-                raise ValueError(f'Variable {variable_selector.variable} not found')
+                raise ValueError(f"Variable {variable_selector.variable} not found")
 
             inputs[variable_selector.variable] = variable_value
 
         memory = node_data.memory
         if memory and memory.query_prompt_template:
-            query_variable_selectors = (VariableTemplateParser(template=memory.query_prompt_template)
-                                        .extract_variable_selectors())
+            query_variable_selectors = VariableTemplateParser(
+                template=memory.query_prompt_template
+            ).extract_variable_selectors()
             for variable_selector in query_variable_selectors:
                 variable_value = variable_pool.get_any(variable_selector.value_selector)
                 if variable_value is None:
-                    raise ValueError(f'Variable {variable_selector.variable} not found')
+                    raise ValueError(f"Variable {variable_selector.variable} not found")
 
                 inputs[variable_selector.variable] = variable_value
 
@@ -342,13 +380,13 @@ class LLMNode(BaseNode):
         if not node_data.vision.enabled:
             return []
 
-        files = variable_pool.get_any(['sys', SystemVariableKey.FILES.value])
+        files = variable_pool.get_any(["sys", SystemVariableKey.FILES.value])
         if not files:
             return []
 
         return files
 
-    def _fetch_context(self, node_data: LLMNodeData, variable_pool: VariablePool) -> Optional[str]:
+    def _fetch_context(self, node_data: LLMNodeData, variable_pool: VariablePool) -> Generator[RunEvent, None, None]:
         """
         Fetch context
         :param node_data: node data
@@ -356,42 +394,34 @@ class LLMNode(BaseNode):
         :return:
         """
         if not node_data.context.enabled:
-            return None
+            return
 
         if not node_data.context.variable_selector:
-            return None
+            return
 
         context_value = variable_pool.get_any(node_data.context.variable_selector)
         if context_value:
             if isinstance(context_value, str):
-                return context_value
+                yield RunRetrieverResourceEvent(retriever_resources=[], context=context_value)
             elif isinstance(context_value, list):
-                context_str = ''
+                context_str = ""
                 original_retriever_resource = []
                 for item in context_value:
                     if isinstance(item, str):
-                        context_str += item + '\n'
+                        context_str += item + "\n"
                     else:
-                        if 'content' not in item:
-                            raise ValueError(f'Invalid context structure: {item}')
+                        if "content" not in item:
+                            raise ValueError(f"Invalid context structure: {item}")
 
-                        context_str += item['content'] + '\n'
+                        context_str += item["content"] + "\n"
 
                         retriever_resource = self._convert_to_original_retriever_resource(item)
                         if retriever_resource:
                             original_retriever_resource.append(retriever_resource)
 
-                if self.callbacks and original_retriever_resource:
-                    for callback in self.callbacks:
-                        callback.on_event(
-                            event=QueueRetrieverResourcesEvent(
-                                retriever_resources=original_retriever_resource
-                            )
-                        )
-
-                return context_str.strip()
-
-        return None
+                yield RunRetrieverResourceEvent(
+                    retriever_resources=original_retriever_resource, context=context_str.strip()
+                )
 
     def _convert_to_original_retriever_resource(self, context_dict: dict) -> Optional[dict]:
         """
@@ -399,32 +429,37 @@ class LLMNode(BaseNode):
         :param context_dict: context dict
         :return:
         """
-        if ('metadata' in context_dict and '_source' in context_dict['metadata']
-                and context_dict['metadata']['_source'] == 'knowledge'):
-            metadata = context_dict.get('metadata', {})
+        if (
+            "metadata" in context_dict
+            and "_source" in context_dict["metadata"]
+            and context_dict["metadata"]["_source"] == "knowledge"
+        ):
+            metadata = context_dict.get("metadata", {})
+
             source = {
-                'position': metadata.get('position'),
-                'dataset_id': metadata.get('dataset_id'),
-                'dataset_name': metadata.get('dataset_name'),
-                'document_id': metadata.get('document_id'),
-                'document_name': metadata.get('document_name'),
-                'data_source_type': metadata.get('document_data_source_type'),
-                'segment_id': metadata.get('segment_id'),
-                'retriever_from': metadata.get('retriever_from'),
-                'score': metadata.get('score'),
-                'hit_count': metadata.get('segment_hit_count'),
-                'word_count': metadata.get('segment_word_count'),
-                'segment_position': metadata.get('segment_position'),
-                'index_node_hash': metadata.get('segment_index_node_hash'),
-                'content': context_dict.get('content'),
+                "position": metadata.get("position"),
+                "dataset_id": metadata.get("dataset_id"),
+                "dataset_name": metadata.get("dataset_name"),
+                "document_id": metadata.get("document_id"),
+                "document_name": metadata.get("document_name"),
+                "data_source_type": metadata.get("document_data_source_type"),
+                "segment_id": metadata.get("segment_id"),
+                "retriever_from": metadata.get("retriever_from"),
+                "score": metadata.get("score"),
+                "hit_count": metadata.get("segment_hit_count"),
+                "word_count": metadata.get("segment_word_count"),
+                "segment_position": metadata.get("segment_position"),
+                "index_node_hash": metadata.get("segment_index_node_hash"),
+                "content": context_dict.get("content"),
             }
 
             return source
 
         return None
 
-    def _fetch_model_config(self, node_data_model: ModelConfig) -> tuple[
-        ModelInstance, ModelConfigWithCredentialsEntity]:
+    def _fetch_model_config(
+        self, node_data_model: ModelConfig
+    ) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
         """
         Fetch model config
         :param node_data_model: node data model
@@ -435,10 +470,7 @@ class LLMNode(BaseNode):
 
         model_manager = ModelManager()
         model_instance = model_manager.get_model_instance(
-            tenant_id=self.tenant_id,
-            model_type=ModelType.LLM,
-            provider=provider_name,
-            model=model_name
+            tenant_id=self.tenant_id, model_type=ModelType.LLM, provider=provider_name, model=model_name
         )
 
         provider_model_bundle = model_instance.provider_model_bundle
@@ -449,8 +481,7 @@ class LLMNode(BaseNode):
 
         # check model
         provider_model = provider_model_bundle.configuration.get_provider_model(
-            model=model_name,
-            model_type=ModelType.LLM
+            model=model_name, model_type=ModelType.LLM
         )
 
         if provider_model is None:
@@ -466,19 +497,16 @@ class LLMNode(BaseNode):
         # model config
         completion_params = node_data_model.completion_params
         stop = []
-        if 'stop' in completion_params:
-            stop = completion_params['stop']
-            del completion_params['stop']
+        if "stop" in completion_params:
+            stop = completion_params["stop"]
+            del completion_params["stop"]
 
         # get model mode
         model_mode = node_data_model.mode
         if not model_mode:
             raise ValueError("LLM mode is required.")
 
-        model_schema = model_type_instance.get_model_schema(
-            model_name,
-            model_credentials
-        )
+        model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
 
         if not model_schema:
             raise ValueError(f"Model {model_name} not exist.")
@@ -494,9 +522,9 @@ class LLMNode(BaseNode):
             stop=stop,
         )
 
-    def _fetch_memory(self, node_data_memory: Optional[MemoryConfig],
-                      variable_pool: VariablePool,
-                      model_instance: ModelInstance) -> Optional[TokenBufferMemory]:
+    def _fetch_memory(
+        self, node_data_memory: Optional[MemoryConfig], variable_pool: VariablePool, model_instance: ModelInstance
+    ) -> Optional[TokenBufferMemory]:
         """
         Fetch memory
         :param node_data_memory: node data memory
@@ -507,35 +535,35 @@ class LLMNode(BaseNode):
             return None
 
         # get conversation id
-        conversation_id = variable_pool.get_any(['sys', SystemVariableKey.CONVERSATION_ID.value])
+        conversation_id = variable_pool.get_any(["sys", SystemVariableKey.CONVERSATION_ID.value])
         if conversation_id is None:
             return None
 
         # get conversation
-        conversation = db.session.query(Conversation).filter(
-            Conversation.app_id == self.app_id,
-            Conversation.id == conversation_id
-        ).first()
+        conversation = (
+            db.session.query(Conversation)
+            .filter(Conversation.app_id == self.app_id, Conversation.id == conversation_id)
+            .first()
+        )
 
         if not conversation:
             return None
 
-        memory = TokenBufferMemory(
-            conversation=conversation,
-            model_instance=model_instance
-        )
+        memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
 
         return memory
 
-    def _fetch_prompt_messages(self, node_data: LLMNodeData,
-                               query: Optional[str],
-                               query_prompt_template: Optional[str],
-                               inputs: dict[str, str],
-                               files: list["FileVar"],
-                               context: Optional[str],
-                               memory: Optional[TokenBufferMemory],
-                               model_config: ModelConfigWithCredentialsEntity) \
-            -> tuple[list[PromptMessage], Optional[list[str]]]:
+    def _fetch_prompt_messages(
+        self,
+        node_data: LLMNodeData,
+        query: Optional[str],
+        query_prompt_template: Optional[str],
+        inputs: dict[str, str],
+        files: list["FileVar"],
+        context: Optional[str],
+        memory: Optional[TokenBufferMemory],
+        model_config: ModelConfigWithCredentialsEntity,
+    ) -> tuple[list[PromptMessage], Optional[list[str]]]:
         """
         Fetch prompt messages
         :param node_data: node data
@@ -552,7 +580,7 @@ class LLMNode(BaseNode):
         prompt_messages = prompt_transform.get_prompt(
             prompt_template=node_data.prompt_template,
             inputs=inputs,
-            query=query if query else '',
+            query=query or "",
             files=files,
             context=context,
             memory_config=node_data.memory,
@@ -572,7 +600,11 @@ class LLMNode(BaseNode):
             if not isinstance(prompt_message.content, str):
                 prompt_message_content = []
                 for content_item in prompt_message.content:
-                    if vision_enabled and content_item.type == PromptMessageContentType.IMAGE and isinstance(content_item, ImagePromptMessageContent):
+                    if (
+                        vision_enabled
+                        and content_item.type == PromptMessageContentType.IMAGE
+                        and isinstance(content_item, ImagePromptMessageContent)
+                    ):
                         # Override vision config if LLM node has vision config
                         if vision_detail:
                             content_item.detail = ImagePromptMessageContent.DETAIL(vision_detail)
@@ -582,15 +614,18 @@ class LLMNode(BaseNode):
 
                 if len(prompt_message_content) > 1:
                     prompt_message.content = prompt_message_content
-                elif (len(prompt_message_content) == 1
-                      and prompt_message_content[0].type == PromptMessageContentType.TEXT):
+                elif (
+                    len(prompt_message_content) == 1 and prompt_message_content[0].type == PromptMessageContentType.TEXT
+                ):
                     prompt_message.content = prompt_message_content[0].data
 
             filtered_prompt_messages.append(prompt_message)
 
         if not filtered_prompt_messages:
-            raise ValueError("No prompt found in the LLM configuration. "
-                             "Please ensure a prompt is properly configured before proceeding.")
+            raise ValueError(
+                "No prompt found in the LLM configuration. "
+                "Please ensure a prompt is properly configured before proceeding."
+            )
 
         return filtered_prompt_messages, stop
 
@@ -628,7 +663,7 @@ class LLMNode(BaseNode):
             elif quota_unit == QuotaUnit.CREDITS:
                 used_quota = 1
 
-                if 'gpt-4' in model_instance.model:
+                if "gpt-4" in model_instance.model:
                     used_quota = 20
             else:
                 used_quota = 1
@@ -639,28 +674,31 @@ class LLMNode(BaseNode):
                 Provider.provider_name == model_instance.provider,
                 Provider.provider_type == ProviderType.SYSTEM.value,
                 Provider.quota_type == system_configuration.current_quota_type.value,
-                Provider.quota_limit > Provider.quota_used
-            ).update({'quota_used': Provider.quota_used + used_quota})
+                Provider.quota_limit > Provider.quota_used,
+            ).update({"quota_used": Provider.quota_used + used_quota})
             db.session.commit()
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: LLMNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: LLMNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
-
         prompt_template = node_data.prompt_template
 
         variable_selectors = []
         if isinstance(prompt_template, list):
             for prompt in prompt_template:
-                if prompt.edition_type != 'jinja2':
+                if prompt.edition_type != "jinja2":
                     variable_template_parser = VariableTemplateParser(template=prompt.text)
                     variable_selectors.extend(variable_template_parser.extract_variable_selectors())
         else:
-            if prompt_template.edition_type != 'jinja2':
+            if prompt_template.edition_type != "jinja2":
                 variable_template_parser = VariableTemplateParser(template=prompt_template.text)
                 variable_selectors = variable_template_parser.extract_variable_selectors()
 
@@ -670,36 +708,39 @@ class LLMNode(BaseNode):
 
         memory = node_data.memory
         if memory and memory.query_prompt_template:
-            query_variable_selectors = (VariableTemplateParser(template=memory.query_prompt_template)
-                                        .extract_variable_selectors())
+            query_variable_selectors = VariableTemplateParser(
+                template=memory.query_prompt_template
+            ).extract_variable_selectors()
             for variable_selector in query_variable_selectors:
                 variable_mapping[variable_selector.variable] = variable_selector.value_selector
 
         if node_data.context.enabled:
-            variable_mapping['#context#'] = node_data.context.variable_selector
+            variable_mapping["#context#"] = node_data.context.variable_selector
 
         if node_data.vision.enabled:
-            variable_mapping['#files#'] = ['sys', SystemVariableKey.FILES.value]
+            variable_mapping["#files#"] = ["sys", SystemVariableKey.FILES.value]
 
         if node_data.memory:
-            variable_mapping['#sys.query#'] = ['sys', SystemVariableKey.QUERY.value]
+            variable_mapping["#sys.query#"] = ["sys", SystemVariableKey.QUERY.value]
 
         if node_data.prompt_config:
             enable_jinja = False
 
             if isinstance(prompt_template, list):
                 for prompt in prompt_template:
-                    if prompt.edition_type == 'jinja2':
+                    if prompt.edition_type == "jinja2":
                         enable_jinja = True
                         break
             else:
-                if prompt_template.edition_type == 'jinja2':
+                if prompt_template.edition_type == "jinja2":
                     enable_jinja = True
 
             if enable_jinja:
                 for variable_selector in node_data.prompt_config.jinja2_variables or []:
                     variable_mapping[variable_selector.variable] = variable_selector.value_selector
 
+        variable_mapping = {node_id + "." + key: value for key, value in variable_mapping.items()}
+
         return variable_mapping
 
     @classmethod
@@ -715,26 +756,19 @@ class LLMNode(BaseNode):
                 "prompt_templates": {
                     "chat_model": {
                         "prompts": [
-                            {
-                                "role": "system",
-                                "text": "You are a helpful AI assistant.",
-                                "edition_type": "basic"
-                            }
+                            {"role": "system", "text": "You are a helpful AI assistant.", "edition_type": "basic"}
                         ]
                     },
                     "completion_model": {
-                        "conversation_histories_role": {
-                            "user_prefix": "Human",
-                            "assistant_prefix": "Assistant"
-                        },
+                        "conversation_histories_role": {"user_prefix": "Human", "assistant_prefix": "Assistant"},
                         "prompt": {
                             "text": "Here is the chat histories between human and assistant, inside "
-                                    "<histories></histories> XML tags.\n\n<histories>\n{{"
-                                    "#histories#}}\n</histories>\n\n\nHuman: {{#sys.query#}}\n\nAssistant:",
-                            "edition_type": "basic"
+                            "<histories></histories> XML tags.\n\n<histories>\n{{"
+                            "#histories#}}\n</histories>\n\n\nHuman: {{#sys.query#}}\n\nAssistant:",
+                            "edition_type": "basic",
                         },
-                        "stop": ["Human:"]
-                    }
+                        "stop": ["Human:"],
+                    },
                 }
-            }
+            },
         }
diff --git a/api/core/workflow/nodes/loop/entities.py b/api/core/workflow/nodes/loop/entities.py
index 8a5684551e..a8a0debe64 100644
--- a/api/core/workflow/nodes/loop/entities.py
+++ b/api/core/workflow/nodes/loop/entities.py
@@ -1,4 +1,3 @@
-
 from core.workflow.entities.base_node_data_entities import BaseIterationNodeData, BaseIterationState
 
 
@@ -7,7 +6,8 @@ class LoopNodeData(BaseIterationNodeData):
     Loop Node Data.
     """
 
+
 class LoopState(BaseIterationState):
     """
     Loop State.
-    """
\ No newline at end of file
+    """
diff --git a/api/core/workflow/nodes/loop/loop_node.py b/api/core/workflow/nodes/loop/loop_node.py
index 7d53c6f5f2..fbc68b79cb 100644
--- a/api/core/workflow/nodes/loop/loop_node.py
+++ b/api/core/workflow/nodes/loop/loop_node.py
@@ -1,20 +1,37 @@
-from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.nodes.base_node import BaseIterationNode
+from typing import Any
+
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.loop.entities import LoopNodeData, LoopState
+from core.workflow.utils.condition.entities import Condition
 
 
-class LoopNode(BaseIterationNode):
+class LoopNode(BaseNode):
     """
     Loop Node.
     """
+
     _node_data_cls = LoopNodeData
     _node_type = NodeType.LOOP
 
-    def _run(self, variable_pool: VariablePool) -> LoopState:
-        return super()._run(variable_pool)
+    def _run(self) -> LoopState:
+        return super()._run()
 
-    def _get_next_iteration(self, variable_loop: VariablePool) -> NodeRunResult | str:
+    @classmethod
+    def get_conditions(cls, node_config: dict[str, Any]) -> list[Condition]:
         """
-        Get next iteration start node id based on the graph.
+        Get conditions.
         """
+        node_id = node_config.get("id")
+        if not node_id:
+            return []
+
+        # TODO waiting for implementation
+        return [
+            Condition(
+                variable_selector=[node_id, "index"],
+                comparison_operator="≤",
+                value_type="value_selector",
+                value_selector=[],
+            )
+        ]
diff --git a/api/core/workflow/nodes/node_mapping.py b/api/core/workflow/nodes/node_mapping.py
new file mode 100644
index 0000000000..b98525e86e
--- /dev/null
+++ b/api/core/workflow/nodes/node_mapping.py
@@ -0,0 +1,37 @@
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.nodes.answer.answer_node import AnswerNode
+from core.workflow.nodes.code.code_node import CodeNode
+from core.workflow.nodes.end.end_node import EndNode
+from core.workflow.nodes.http_request.http_request_node import HttpRequestNode
+from core.workflow.nodes.if_else.if_else_node import IfElseNode
+from core.workflow.nodes.iteration.iteration_node import IterationNode
+from core.workflow.nodes.iteration.iteration_start_node import IterationStartNode
+from core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node import KnowledgeRetrievalNode
+from core.workflow.nodes.llm.llm_node import LLMNode
+from core.workflow.nodes.parameter_extractor.parameter_extractor_node import ParameterExtractorNode
+from core.workflow.nodes.question_classifier.question_classifier_node import QuestionClassifierNode
+from core.workflow.nodes.start.start_node import StartNode
+from core.workflow.nodes.template_transform.template_transform_node import TemplateTransformNode
+from core.workflow.nodes.tool.tool_node import ToolNode
+from core.workflow.nodes.variable_aggregator.variable_aggregator_node import VariableAggregatorNode
+from core.workflow.nodes.variable_assigner import VariableAssignerNode
+
+node_classes = {
+    NodeType.START: StartNode,
+    NodeType.END: EndNode,
+    NodeType.ANSWER: AnswerNode,
+    NodeType.LLM: LLMNode,
+    NodeType.KNOWLEDGE_RETRIEVAL: KnowledgeRetrievalNode,
+    NodeType.IF_ELSE: IfElseNode,
+    NodeType.CODE: CodeNode,
+    NodeType.TEMPLATE_TRANSFORM: TemplateTransformNode,
+    NodeType.QUESTION_CLASSIFIER: QuestionClassifierNode,
+    NodeType.HTTP_REQUEST: HttpRequestNode,
+    NodeType.TOOL: ToolNode,
+    NodeType.VARIABLE_AGGREGATOR: VariableAggregatorNode,
+    NodeType.VARIABLE_ASSIGNER: VariableAggregatorNode,  # original name of VARIABLE_AGGREGATOR
+    NodeType.ITERATION: IterationNode,
+    NodeType.ITERATION_START: IterationStartNode,
+    NodeType.PARAMETER_EXTRACTOR: ParameterExtractorNode,
+    NodeType.CONVERSATION_VARIABLE_ASSIGNER: VariableAssignerNode,
+}
diff --git a/api/core/workflow/nodes/parameter_extractor/entities.py b/api/core/workflow/nodes/parameter_extractor/entities.py
index 7bb123b126..5697d7c049 100644
--- a/api/core/workflow/nodes/parameter_extractor/entities.py
+++ b/api/core/workflow/nodes/parameter_extractor/entities.py
@@ -8,47 +8,52 @@ from core.workflow.entities.base_node_data_entities import BaseNodeData
 
 class ModelConfig(BaseModel):
     """
-     Model Config.
+    Model Config.
     """
+
     provider: str
     name: str
     mode: str
     completion_params: dict[str, Any] = {}
 
+
 class ParameterConfig(BaseModel):
     """
     Parameter Config.
     """
+
     name: str
-    type: Literal['string', 'number', 'bool', 'select', 'array[string]', 'array[number]', 'array[object]']
+    type: Literal["string", "number", "bool", "select", "array[string]", "array[number]", "array[object]"]
     options: Optional[list[str]] = None
     description: str
     required: bool
 
-    @field_validator('name', mode='before')
+    @field_validator("name", mode="before")
     @classmethod
     def validate_name(cls, value) -> str:
         if not value:
-            raise ValueError('Parameter name is required')
-        if value in ['__reason', '__is_success']:
-            raise ValueError('Invalid parameter name, __reason and __is_success are reserved')
+            raise ValueError("Parameter name is required")
+        if value in {"__reason", "__is_success"}:
+            raise ValueError("Invalid parameter name, __reason and __is_success are reserved")
         return value
 
+
 class ParameterExtractorNodeData(BaseNodeData):
     """
     Parameter Extractor Node Data.
     """
+
     model: ModelConfig
     query: list[str]
     parameters: list[ParameterConfig]
     instruction: Optional[str] = None
     memory: Optional[MemoryConfig] = None
-    reasoning_mode: Literal['function_call', 'prompt']
+    reasoning_mode: Literal["function_call", "prompt"]
 
-    @field_validator('reasoning_mode', mode='before')
+    @field_validator("reasoning_mode", mode="before")
     @classmethod
     def set_reasoning_mode(cls, v) -> str:
-        return v or 'function_call'
+        return v or "function_call"
 
     def get_parameter_json_schema(self) -> dict:
         """
@@ -56,32 +61,26 @@ class ParameterExtractorNodeData(BaseNodeData):
 
         :return: parameter json schema
         """
-        parameters = {
-            'type': 'object',
-            'properties': {},
-            'required': []
-        }
+        parameters = {"type": "object", "properties": {}, "required": []}
 
         for parameter in self.parameters:
-            parameter_schema = {
-                'description': parameter.description
-            }
+            parameter_schema = {"description": parameter.description}
 
-            if parameter.type in ['string', 'select']:
-                parameter_schema['type'] = 'string'
-            elif parameter.type.startswith('array'):
-                parameter_schema['type'] = 'array'
+            if parameter.type in {"string", "select"}:
+                parameter_schema["type"] = "string"
+            elif parameter.type.startswith("array"):
+                parameter_schema["type"] = "array"
                 nested_type = parameter.type[6:-1]
-                parameter_schema['items'] = {'type': nested_type}
+                parameter_schema["items"] = {"type": nested_type}
             else:
-                parameter_schema['type'] = parameter.type
+                parameter_schema["type"] = parameter.type
 
-            if parameter.type == 'select':
-                parameter_schema['enum'] = parameter.options
+            if parameter.type == "select":
+                parameter_schema["enum"] = parameter.options
+
+            parameters["properties"][parameter.name] = parameter_schema
 
-            parameters['properties'][parameter.name] = parameter_schema
-            
             if parameter.required:
-                parameters['required'].append(parameter.name)
+                parameters["required"].append(parameter.name)
 
-        return parameters
\ No newline at end of file
+        return parameters
diff --git a/api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py b/api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py
index 2876695a82..a6454bd1cd 100644
--- a/api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py
+++ b/api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py
@@ -1,6 +1,7 @@
 import json
 import uuid
-from typing import Optional, cast
+from collections.abc import Mapping, Sequence
+from typing import Any, Optional, cast
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.memory.token_buffer_memory import TokenBufferMemory
@@ -44,6 +45,7 @@ class ParameterExtractorNode(LLMNode):
     """
     Parameter Extractor Node.
     """
+
     _node_data_cls = ParameterExtractorNodeData
     _node_type = NodeType.PARAMETER_EXTRACTOR
 
@@ -56,30 +58,27 @@ class ParameterExtractorNode(LLMNode):
             "model": {
                 "prompt_templates": {
                     "completion_model": {
-                        "conversation_histories_role": {
-                            "user_prefix": "Human",
-                            "assistant_prefix": "Assistant"
-                        },
-                        "stop": ["Human:"]
+                        "conversation_histories_role": {"user_prefix": "Human", "assistant_prefix": "Assistant"},
+                        "stop": ["Human:"],
                     }
                 }
             }
         }
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         """
         Run the node.
         """
         node_data = cast(ParameterExtractorNodeData, self.node_data)
-        variable = variable_pool.get_any(node_data.query)
+        variable = self.graph_runtime_state.variable_pool.get_any(node_data.query)
         if not variable:
             raise ValueError("Input variable content not found or is empty")
         query = variable
 
         inputs = {
-            'query': query,
-            'parameters': jsonable_encoder(node_data.parameters),
-            'instruction': jsonable_encoder(node_data.instruction),
+            "query": query,
+            "parameters": jsonable_encoder(node_data.parameters),
+            "instruction": jsonable_encoder(node_data.instruction),
         }
 
         model_instance, model_config = self._fetch_model_config(node_data.model)
@@ -92,29 +91,31 @@ class ParameterExtractorNode(LLMNode):
             raise ValueError("Model schema not found")
 
         # fetch memory
-        memory = self._fetch_memory(node_data.memory, variable_pool, model_instance)
+        memory = self._fetch_memory(node_data.memory, self.graph_runtime_state.variable_pool, model_instance)
 
-        if set(model_schema.features or []) & {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL} \
-            and node_data.reasoning_mode == 'function_call':
-            # use function call 
+        if (
+            set(model_schema.features or []) & {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL}
+            and node_data.reasoning_mode == "function_call"
+        ):
+            # use function call
             prompt_messages, prompt_message_tools = self._generate_function_call_prompt(
-                node_data, query, variable_pool, model_config, memory
+                node_data, query, self.graph_runtime_state.variable_pool, model_config, memory
             )
         else:
             # use prompt engineering
-            prompt_messages = self._generate_prompt_engineering_prompt(node_data, query, variable_pool, model_config,
-                                                                       memory)
+            prompt_messages = self._generate_prompt_engineering_prompt(
+                node_data, query, self.graph_runtime_state.variable_pool, model_config, memory
+            )
             prompt_message_tools = []
 
         process_data = {
-            'model_mode': model_config.mode,
-            'prompts': PromptMessageUtil.prompt_messages_to_prompt_for_saving(
-                model_mode=model_config.mode,
-                prompt_messages=prompt_messages
+            "model_mode": model_config.mode,
+            "prompts": PromptMessageUtil.prompt_messages_to_prompt_for_saving(
+                model_mode=model_config.mode, prompt_messages=prompt_messages
             ),
-            'usage': None,
-            'function': {} if not prompt_message_tools else jsonable_encoder(prompt_message_tools[0]),
-            'tool_call': None,
+            "usage": None,
+            "function": {} if not prompt_message_tools else jsonable_encoder(prompt_message_tools[0]),
+            "tool_call": None,
         }
 
         try:
@@ -125,20 +126,17 @@ class ParameterExtractorNode(LLMNode):
                 tools=prompt_message_tools,
                 stop=model_config.stop,
             )
-            process_data['usage'] = jsonable_encoder(usage)
-            process_data['tool_call'] = jsonable_encoder(tool_call)
-            process_data['llm_text'] = text
+            process_data["usage"] = jsonable_encoder(usage)
+            process_data["tool_call"] = jsonable_encoder(tool_call)
+            process_data["llm_text"] = text
         except Exception as e:
             return NodeRunResult(
                 status=WorkflowNodeExecutionStatus.FAILED,
                 inputs=inputs,
                 process_data=process_data,
-                outputs={
-                    '__is_success': 0,
-                    '__reason': str(e)
-                },
+                outputs={"__is_success": 0, "__reason": str(e)},
                 error=str(e),
-                metadata={}
+                metadata={},
             )
 
         error = None
@@ -163,23 +161,23 @@ class ParameterExtractorNode(LLMNode):
             status=WorkflowNodeExecutionStatus.SUCCEEDED,
             inputs=inputs,
             process_data=process_data,
-            outputs={
-                '__is_success': 1 if not error else 0,
-                '__reason': error,
-                **result
-            },
+            outputs={"__is_success": 1 if not error else 0, "__reason": error, **result},
             metadata={
                 NodeRunMetadataKey.TOTAL_TOKENS: usage.total_tokens,
                 NodeRunMetadataKey.TOTAL_PRICE: usage.total_price,
-                NodeRunMetadataKey.CURRENCY: usage.currency
-            }
+                NodeRunMetadataKey.CURRENCY: usage.currency,
+            },
+            llm_usage=usage,
         )
 
-    def _invoke_llm(self, node_data_model: ModelConfig,
-                    model_instance: ModelInstance,
-                    prompt_messages: list[PromptMessage],
-                    tools: list[PromptMessageTool],
-                    stop: list[str]) -> tuple[str, LLMUsage, Optional[AssistantPromptMessage.ToolCall]]:
+    def _invoke_llm(
+        self,
+        node_data_model: ModelConfig,
+        model_instance: ModelInstance,
+        prompt_messages: list[PromptMessage],
+        tools: list[PromptMessageTool],
+        stop: list[str],
+    ) -> tuple[str, LLMUsage, Optional[AssistantPromptMessage.ToolCall]]:
         """
         Invoke large language model
         :param node_data_model: node data model
@@ -212,32 +210,35 @@ class ParameterExtractorNode(LLMNode):
 
         return text, usage, tool_call
 
-    def _generate_function_call_prompt(self,
-                                       node_data: ParameterExtractorNodeData,
-                                       query: str,
-                                       variable_pool: VariablePool,
-                                       model_config: ModelConfigWithCredentialsEntity,
-                                       memory: Optional[TokenBufferMemory],
-                                       ) -> tuple[list[PromptMessage], list[PromptMessageTool]]:
+    def _generate_function_call_prompt(
+        self,
+        node_data: ParameterExtractorNodeData,
+        query: str,
+        variable_pool: VariablePool,
+        model_config: ModelConfigWithCredentialsEntity,
+        memory: Optional[TokenBufferMemory],
+    ) -> tuple[list[PromptMessage], list[PromptMessageTool]]:
         """
         Generate function call prompt.
         """
-        query = FUNCTION_CALLING_EXTRACTOR_USER_TEMPLATE.format(content=query, structure=json.dumps(
-            node_data.get_parameter_json_schema()))
+        query = FUNCTION_CALLING_EXTRACTOR_USER_TEMPLATE.format(
+            content=query, structure=json.dumps(node_data.get_parameter_json_schema())
+        )
 
         prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
-        rest_token = self._calculate_rest_token(node_data, query, variable_pool, model_config, '')
-        prompt_template = self._get_function_calling_prompt_template(node_data, query, variable_pool, memory,
-                                                                     rest_token)
+        rest_token = self._calculate_rest_token(node_data, query, variable_pool, model_config, "")
+        prompt_template = self._get_function_calling_prompt_template(
+            node_data, query, variable_pool, memory, rest_token
+        )
         prompt_messages = prompt_transform.get_prompt(
             prompt_template=prompt_template,
             inputs={},
-            query='',
+            query="",
             files=[],
-            context='',
+            context="",
             memory_config=node_data.memory,
             memory=None,
-            model_config=model_config
+            model_config=model_config,
         )
 
         # find last user message
@@ -250,124 +251,125 @@ class ParameterExtractorNode(LLMNode):
         example_messages = []
         for example in FUNCTION_CALLING_EXTRACTOR_EXAMPLE:
             id = uuid.uuid4().hex
-            example_messages.extend([
-                UserPromptMessage(content=example['user']['query']),
-                AssistantPromptMessage(
-                    content=example['assistant']['text'],
-                    tool_calls=[
-                        AssistantPromptMessage.ToolCall(
-                            id=id,
-                            type='function',
-                            function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                                name=example['assistant']['function_call']['name'],
-                                arguments=json.dumps(example['assistant']['function_call']['parameters']
-                                                     )
-                            ))
-                    ]
-                ),
-                ToolPromptMessage(
-                    content='Great! You have called the function with the correct parameters.',
-                    tool_call_id=id
-                ),
-                AssistantPromptMessage(
-                    content='I have extracted the parameters, let\'s move on.',
-                )
-            ])
+            example_messages.extend(
+                [
+                    UserPromptMessage(content=example["user"]["query"]),
+                    AssistantPromptMessage(
+                        content=example["assistant"]["text"],
+                        tool_calls=[
+                            AssistantPromptMessage.ToolCall(
+                                id=id,
+                                type="function",
+                                function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+                                    name=example["assistant"]["function_call"]["name"],
+                                    arguments=json.dumps(example["assistant"]["function_call"]["parameters"]),
+                                ),
+                            )
+                        ],
+                    ),
+                    ToolPromptMessage(
+                        content="Great! You have called the function with the correct parameters.", tool_call_id=id
+                    ),
+                    AssistantPromptMessage(
+                        content="I have extracted the parameters, let's move on.",
+                    ),
+                ]
+            )
 
-        prompt_messages = prompt_messages[:last_user_message_idx] + \
-                          example_messages + prompt_messages[last_user_message_idx:]
+        prompt_messages = (
+            prompt_messages[:last_user_message_idx] + example_messages + prompt_messages[last_user_message_idx:]
+        )
 
         # generate tool
         tool = PromptMessageTool(
             name=FUNCTION_CALLING_EXTRACTOR_NAME,
-            description='Extract parameters from the natural language text',
+            description="Extract parameters from the natural language text",
             parameters=node_data.get_parameter_json_schema(),
         )
 
         return prompt_messages, [tool]
 
-    def _generate_prompt_engineering_prompt(self,
-                                            data: ParameterExtractorNodeData,
-                                            query: str,
-                                            variable_pool: VariablePool,
-                                            model_config: ModelConfigWithCredentialsEntity,
-                                            memory: Optional[TokenBufferMemory],
-                                            ) -> list[PromptMessage]:
+    def _generate_prompt_engineering_prompt(
+        self,
+        data: ParameterExtractorNodeData,
+        query: str,
+        variable_pool: VariablePool,
+        model_config: ModelConfigWithCredentialsEntity,
+        memory: Optional[TokenBufferMemory],
+    ) -> list[PromptMessage]:
         """
         Generate prompt engineering prompt.
         """
         model_mode = ModelMode.value_of(data.model.mode)
 
         if model_mode == ModelMode.COMPLETION:
-            return self._generate_prompt_engineering_completion_prompt(
-                data, query, variable_pool, model_config, memory
-            )
+            return self._generate_prompt_engineering_completion_prompt(data, query, variable_pool, model_config, memory)
         elif model_mode == ModelMode.CHAT:
-            return self._generate_prompt_engineering_chat_prompt(
-                data, query, variable_pool, model_config, memory
-            )
+            return self._generate_prompt_engineering_chat_prompt(data, query, variable_pool, model_config, memory)
         else:
             raise ValueError(f"Invalid model mode: {model_mode}")
 
-    def _generate_prompt_engineering_completion_prompt(self,
-                                                       node_data: ParameterExtractorNodeData,
-                                                       query: str,
-                                                       variable_pool: VariablePool,
-                                                       model_config: ModelConfigWithCredentialsEntity,
-                                                       memory: Optional[TokenBufferMemory],
-                                                       ) -> list[PromptMessage]:
+    def _generate_prompt_engineering_completion_prompt(
+        self,
+        node_data: ParameterExtractorNodeData,
+        query: str,
+        variable_pool: VariablePool,
+        model_config: ModelConfigWithCredentialsEntity,
+        memory: Optional[TokenBufferMemory],
+    ) -> list[PromptMessage]:
         """
         Generate completion prompt.
         """
         prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
-        rest_token = self._calculate_rest_token(node_data, query, variable_pool, model_config, '')
-        prompt_template = self._get_prompt_engineering_prompt_template(node_data, query, variable_pool, memory,
-                                                                       rest_token)
+        rest_token = self._calculate_rest_token(node_data, query, variable_pool, model_config, "")
+        prompt_template = self._get_prompt_engineering_prompt_template(
+            node_data, query, variable_pool, memory, rest_token
+        )
         prompt_messages = prompt_transform.get_prompt(
             prompt_template=prompt_template,
-            inputs={
-                'structure': json.dumps(node_data.get_parameter_json_schema())
-            },
-            query='',
+            inputs={"structure": json.dumps(node_data.get_parameter_json_schema())},
+            query="",
             files=[],
-            context='',
+            context="",
             memory_config=node_data.memory,
             memory=memory,
-            model_config=model_config
+            model_config=model_config,
         )
 
         return prompt_messages
 
-    def _generate_prompt_engineering_chat_prompt(self,
-                                                 node_data: ParameterExtractorNodeData,
-                                                 query: str,
-                                                 variable_pool: VariablePool,
-                                                 model_config: ModelConfigWithCredentialsEntity,
-                                                 memory: Optional[TokenBufferMemory],
-                                                 ) -> list[PromptMessage]:
+    def _generate_prompt_engineering_chat_prompt(
+        self,
+        node_data: ParameterExtractorNodeData,
+        query: str,
+        variable_pool: VariablePool,
+        model_config: ModelConfigWithCredentialsEntity,
+        memory: Optional[TokenBufferMemory],
+    ) -> list[PromptMessage]:
         """
         Generate chat prompt.
         """
         prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
-        rest_token = self._calculate_rest_token(node_data, query, variable_pool, model_config, '')
+        rest_token = self._calculate_rest_token(node_data, query, variable_pool, model_config, "")
         prompt_template = self._get_prompt_engineering_prompt_template(
             node_data,
             CHAT_GENERATE_JSON_USER_MESSAGE_TEMPLATE.format(
-                structure=json.dumps(node_data.get_parameter_json_schema()),
-                text=query
+                structure=json.dumps(node_data.get_parameter_json_schema()), text=query
             ),
-            variable_pool, memory, rest_token
+            variable_pool,
+            memory,
+            rest_token,
         )
 
         prompt_messages = prompt_transform.get_prompt(
             prompt_template=prompt_template,
             inputs={},
-            query='',
+            query="",
             files=[],
-            context='',
+            context="",
             memory_config=node_data.memory,
             memory=None,
-            model_config=model_config
+            model_config=model_config,
         )
 
         # find last user message
@@ -379,18 +381,23 @@ class ParameterExtractorNode(LLMNode):
         # add example messages before last user message
         example_messages = []
         for example in CHAT_EXAMPLE:
-            example_messages.extend([
-                UserPromptMessage(content=CHAT_GENERATE_JSON_USER_MESSAGE_TEMPLATE.format(
-                    structure=json.dumps(example['user']['json']),
-                    text=example['user']['query'],
-                )),
-                AssistantPromptMessage(
-                    content=json.dumps(example['assistant']['json']),
-                )
-            ])
+            example_messages.extend(
+                [
+                    UserPromptMessage(
+                        content=CHAT_GENERATE_JSON_USER_MESSAGE_TEMPLATE.format(
+                            structure=json.dumps(example["user"]["json"]),
+                            text=example["user"]["query"],
+                        )
+                    ),
+                    AssistantPromptMessage(
+                        content=json.dumps(example["assistant"]["json"]),
+                    ),
+                ]
+            )
 
-        prompt_messages = prompt_messages[:last_user_message_idx] + \
-                          example_messages + prompt_messages[last_user_message_idx:]
+        prompt_messages = (
+            prompt_messages[:last_user_message_idx] + example_messages + prompt_messages[last_user_message_idx:]
+        )
 
         return prompt_messages
 
@@ -405,28 +412,28 @@ class ParameterExtractorNode(LLMNode):
             if parameter.required and parameter.name not in result:
                 raise ValueError(f"Parameter {parameter.name} is required")
 
-            if parameter.type == 'select' and parameter.options and result.get(parameter.name) not in parameter.options:
+            if parameter.type == "select" and parameter.options and result.get(parameter.name) not in parameter.options:
                 raise ValueError(f"Invalid `select` value for parameter {parameter.name}")
 
-            if parameter.type == 'number' and not isinstance(result.get(parameter.name), int | float):
+            if parameter.type == "number" and not isinstance(result.get(parameter.name), int | float):
                 raise ValueError(f"Invalid `number` value for parameter {parameter.name}")
 
-            if parameter.type == 'bool' and not isinstance(result.get(parameter.name), bool):
+            if parameter.type == "bool" and not isinstance(result.get(parameter.name), bool):
                 raise ValueError(f"Invalid `bool` value for parameter {parameter.name}")
 
-            if parameter.type == 'string' and not isinstance(result.get(parameter.name), str):
+            if parameter.type == "string" and not isinstance(result.get(parameter.name), str):
                 raise ValueError(f"Invalid `string` value for parameter {parameter.name}")
 
-            if parameter.type.startswith('array'):
+            if parameter.type.startswith("array"):
                 if not isinstance(result.get(parameter.name), list):
                     raise ValueError(f"Invalid `array` value for parameter {parameter.name}")
                 nested_type = parameter.type[6:-1]
                 for item in result.get(parameter.name):
-                    if nested_type == 'number' and not isinstance(item, int | float):
+                    if nested_type == "number" and not isinstance(item, int | float):
                         raise ValueError(f"Invalid `array[number]` value for parameter {parameter.name}")
-                    if nested_type == 'string' and not isinstance(item, str):
+                    if nested_type == "string" and not isinstance(item, str):
                         raise ValueError(f"Invalid `array[string]` value for parameter {parameter.name}")
-                    if nested_type == 'object' and not isinstance(item, dict):
+                    if nested_type == "object" and not isinstance(item, dict):
                         raise ValueError(f"Invalid `array[object]` value for parameter {parameter.name}")
         return result
 
@@ -438,12 +445,12 @@ class ParameterExtractorNode(LLMNode):
         for parameter in data.parameters:
             if parameter.name in result:
                 # transform value
-                if parameter.type == 'number':
+                if parameter.type == "number":
                     if isinstance(result[parameter.name], int | float):
                         transformed_result[parameter.name] = result[parameter.name]
                     elif isinstance(result[parameter.name], str):
                         try:
-                            if '.' in result[parameter.name]:
+                            if "." in result[parameter.name]:
                                 result[parameter.name] = float(result[parameter.name])
                             else:
                                 result[parameter.name] = int(result[parameter.name])
@@ -460,40 +467,40 @@ class ParameterExtractorNode(LLMNode):
                 #             transformed_result[parameter.name] = bool(result[parameter.name].lower() == 'true')
                 #     elif isinstance(result[parameter.name], int):
                 #         transformed_result[parameter.name] = bool(result[parameter.name])
-                elif parameter.type in ['string', 'select']:
+                elif parameter.type in {"string", "select"}:
                     if isinstance(result[parameter.name], str):
                         transformed_result[parameter.name] = result[parameter.name]
-                elif parameter.type.startswith('array'):
+                elif parameter.type.startswith("array"):
                     if isinstance(result[parameter.name], list):
                         nested_type = parameter.type[6:-1]
                         transformed_result[parameter.name] = []
                         for item in result[parameter.name]:
-                            if nested_type == 'number':
+                            if nested_type == "number":
                                 if isinstance(item, int | float):
                                     transformed_result[parameter.name].append(item)
                                 elif isinstance(item, str):
                                     try:
-                                        if '.' in item:
+                                        if "." in item:
                                             transformed_result[parameter.name].append(float(item))
                                         else:
                                             transformed_result[parameter.name].append(int(item))
                                     except ValueError:
                                         pass
-                            elif nested_type == 'string':
+                            elif nested_type == "string":
                                 if isinstance(item, str):
                                     transformed_result[parameter.name].append(item)
-                            elif nested_type == 'object':
+                            elif nested_type == "object":
                                 if isinstance(item, dict):
                                     transformed_result[parameter.name].append(item)
 
             if parameter.name not in transformed_result:
-                if parameter.type == 'number':
+                if parameter.type == "number":
                     transformed_result[parameter.name] = 0
-                elif parameter.type == 'bool':
+                elif parameter.type == "bool":
                     transformed_result[parameter.name] = False
-                elif parameter.type in ['string', 'select']:
-                    transformed_result[parameter.name] = ''
-                elif parameter.type.startswith('array'):
+                elif parameter.type in {"string", "select"}:
+                    transformed_result[parameter.name] = ""
+                elif parameter.type.startswith("array"):
                     transformed_result[parameter.name] = []
 
         return transformed_result
@@ -509,24 +516,24 @@ class ParameterExtractorNode(LLMNode):
             """
             stack = []
             for i, c in enumerate(text):
-                if c == '{' or c == '[':
+                if c in {"{", "["}:
                     stack.append(c)
-                elif c == '}' or c == ']':
+                elif c in {"}", "]"}:
                     # check if stack is empty
                     if not stack:
                         return text[:i]
                     # check if the last element in stack is matching
-                    if (c == '}' and stack[-1] == '{') or (c == ']' and stack[-1] == '['):
+                    if (c == "}" and stack[-1] == "{") or (c == "]" and stack[-1] == "["):
                         stack.pop()
                         if not stack:
-                            return text[:i + 1]
+                            return text[: i + 1]
                     else:
                         return text[:i]
             return None
 
         # extract json from the text
         for idx in range(len(result)):
-            if result[idx] == '{' or result[idx] == '[':
+            if result[idx] == "{" or result[idx] == "[":
                 json_str = extract_json(result[idx:])
                 if json_str:
                     try:
@@ -549,12 +556,12 @@ class ParameterExtractorNode(LLMNode):
         """
         result = {}
         for parameter in data.parameters:
-            if parameter.type == 'number':
+            if parameter.type == "number":
                 result[parameter.name] = 0
-            elif parameter.type == 'bool':
+            elif parameter.type == "bool":
                 result[parameter.name] = False
-            elif parameter.type in ['string', 'select']:
-                result[parameter.name] = ''
+            elif parameter.type in {"string", "select"}:
+                result[parameter.name] = ""
 
         return result
 
@@ -570,71 +577,76 @@ class ParameterExtractorNode(LLMNode):
 
         return variable_template_parser.format(inputs)
 
-    def _get_function_calling_prompt_template(self, node_data: ParameterExtractorNodeData, query: str,
-                                              variable_pool: VariablePool,
-                                              memory: Optional[TokenBufferMemory],
-                                              max_token_limit: int = 2000) \
-            -> list[ChatModelMessage]:
+    def _get_function_calling_prompt_template(
+        self,
+        node_data: ParameterExtractorNodeData,
+        query: str,
+        variable_pool: VariablePool,
+        memory: Optional[TokenBufferMemory],
+        max_token_limit: int = 2000,
+    ) -> list[ChatModelMessage]:
         model_mode = ModelMode.value_of(node_data.model.mode)
         input_text = query
-        memory_str = ''
-        instruction = self._render_instruction(node_data.instruction or '', variable_pool)
+        memory_str = ""
+        instruction = self._render_instruction(node_data.instruction or "", variable_pool)
 
         if memory:
-            memory_str = memory.get_history_prompt_text(max_token_limit=max_token_limit,
-                                                        message_limit=node_data.memory.window.size)
+            memory_str = memory.get_history_prompt_text(
+                max_token_limit=max_token_limit, message_limit=node_data.memory.window.size
+            )
         if model_mode == ModelMode.CHAT:
             system_prompt_messages = ChatModelMessage(
                 role=PromptMessageRole.SYSTEM,
-                text=FUNCTION_CALLING_EXTRACTOR_SYSTEM_PROMPT.format(histories=memory_str, instruction=instruction)
-            )
-            user_prompt_message = ChatModelMessage(
-                role=PromptMessageRole.USER,
-                text=input_text
+                text=FUNCTION_CALLING_EXTRACTOR_SYSTEM_PROMPT.format(histories=memory_str, instruction=instruction),
             )
+            user_prompt_message = ChatModelMessage(role=PromptMessageRole.USER, text=input_text)
             return [system_prompt_messages, user_prompt_message]
         else:
             raise ValueError(f"Model mode {model_mode} not support.")
 
-    def _get_prompt_engineering_prompt_template(self, node_data: ParameterExtractorNodeData, query: str,
-                                                variable_pool: VariablePool,
-                                                memory: Optional[TokenBufferMemory],
-                                                max_token_limit: int = 2000) \
-            -> list[ChatModelMessage]:
-
+    def _get_prompt_engineering_prompt_template(
+        self,
+        node_data: ParameterExtractorNodeData,
+        query: str,
+        variable_pool: VariablePool,
+        memory: Optional[TokenBufferMemory],
+        max_token_limit: int = 2000,
+    ) -> list[ChatModelMessage]:
         model_mode = ModelMode.value_of(node_data.model.mode)
         input_text = query
-        memory_str = ''
-        instruction = self._render_instruction(node_data.instruction or '', variable_pool)
+        memory_str = ""
+        instruction = self._render_instruction(node_data.instruction or "", variable_pool)
 
         if memory:
-            memory_str = memory.get_history_prompt_text(max_token_limit=max_token_limit,
-                                                        message_limit=node_data.memory.window.size)
+            memory_str = memory.get_history_prompt_text(
+                max_token_limit=max_token_limit, message_limit=node_data.memory.window.size
+            )
         if model_mode == ModelMode.CHAT:
             system_prompt_messages = ChatModelMessage(
                 role=PromptMessageRole.SYSTEM,
-                text=FUNCTION_CALLING_EXTRACTOR_SYSTEM_PROMPT.format(histories=memory_str, instruction=instruction)
-            )
-            user_prompt_message = ChatModelMessage(
-                role=PromptMessageRole.USER,
-                text=input_text
+                text=FUNCTION_CALLING_EXTRACTOR_SYSTEM_PROMPT.format(histories=memory_str, instruction=instruction),
             )
+            user_prompt_message = ChatModelMessage(role=PromptMessageRole.USER, text=input_text)
             return [system_prompt_messages, user_prompt_message]
         elif model_mode == ModelMode.COMPLETION:
             return CompletionModelPromptTemplate(
-                text=COMPLETION_GENERATE_JSON_PROMPT.format(histories=memory_str,
-                                                            text=input_text,
-                                                            instruction=instruction)
-                .replace('{γγγ', '')
-                .replace('}γγγ', '')
+                text=COMPLETION_GENERATE_JSON_PROMPT.format(
+                    histories=memory_str, text=input_text, instruction=instruction
+                )
+                .replace("{γγγ", "")
+                .replace("}γγγ", "")
             )
         else:
             raise ValueError(f"Model mode {model_mode} not support.")
 
-    def _calculate_rest_token(self, node_data: ParameterExtractorNodeData, query: str,
-                              variable_pool: VariablePool,
-                              model_config: ModelConfigWithCredentialsEntity,
-                              context: Optional[str]) -> int:
+    def _calculate_rest_token(
+        self,
+        node_data: ParameterExtractorNodeData,
+        query: str,
+        variable_pool: VariablePool,
+        model_config: ModelConfigWithCredentialsEntity,
+        context: Optional[str],
+    ) -> int:
         prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
 
         model_instance, model_config = self._fetch_model_config(node_data.model)
@@ -654,12 +666,12 @@ class ParameterExtractorNode(LLMNode):
         prompt_messages = prompt_transform.get_prompt(
             prompt_template=prompt_template,
             inputs={},
-            query='',
+            query="",
             files=[],
             context=context,
             memory_config=node_data.memory,
             memory=None,
-            model_config=model_config
+            model_config=model_config,
         )
         rest_tokens = 2000
 
@@ -668,26 +680,28 @@ class ParameterExtractorNode(LLMNode):
             model_type_instance = model_config.provider_model_bundle.model_type_instance
             model_type_instance = cast(LargeLanguageModel, model_type_instance)
 
-            curr_message_tokens = model_type_instance.get_num_tokens(
-                model_config.model,
-                model_config.credentials,
-                prompt_messages
-            ) + 1000  # add 1000 to ensure tool call messages
+            curr_message_tokens = (
+                model_type_instance.get_num_tokens(model_config.model, model_config.credentials, prompt_messages) + 1000
+            )  # add 1000 to ensure tool call messages
 
             max_tokens = 0
             for parameter_rule in model_config.model_schema.parameter_rules:
-                if (parameter_rule.name == 'max_tokens'
-                        or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
-                    max_tokens = (model_config.parameters.get(parameter_rule.name)
-                                  or model_config.parameters.get(parameter_rule.use_template)) or 0
+                if parameter_rule.name == "max_tokens" or (
+                    parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+                ):
+                    max_tokens = (
+                        model_config.parameters.get(parameter_rule.name)
+                        or model_config.parameters.get(parameter_rule.use_template)
+                    ) or 0
 
             rest_tokens = model_context_tokens - max_tokens - curr_message_tokens
             rest_tokens = max(rest_tokens, 0)
 
         return rest_tokens
 
-    def _fetch_model_config(self, node_data_model: ModelConfig) -> tuple[
-        ModelInstance, ModelConfigWithCredentialsEntity]:
+    def _fetch_model_config(
+        self, node_data_model: ModelConfig
+    ) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
         """
         Fetch model config.
         """
@@ -697,22 +711,23 @@ class ParameterExtractorNode(LLMNode):
         return self._model_instance, self._model_config
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: ParameterExtractorNodeData) -> dict[
-        str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: ParameterExtractorNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
-        node_data = node_data
-
-        variable_mapping = {
-            'query': node_data.query
-        }
+        variable_mapping = {"query": node_data.query}
 
         if node_data.instruction:
             variable_template_parser = VariableTemplateParser(template=node_data.instruction)
             for selector in variable_template_parser.extract_variable_selectors():
                 variable_mapping[selector.variable] = selector.value_selector
 
+        variable_mapping = {node_id + "." + key: value for key, value in variable_mapping.items()}
+
         return variable_mapping
diff --git a/api/core/workflow/nodes/parameter_extractor/prompts.py b/api/core/workflow/nodes/parameter_extractor/prompts.py
index 499c58d505..58fcecc53b 100644
--- a/api/core/workflow/nodes/parameter_extractor/prompts.py
+++ b/api/core/workflow/nodes/parameter_extractor/prompts.py
@@ -1,4 +1,4 @@
-FUNCTION_CALLING_EXTRACTOR_NAME = 'extract_parameters'
+FUNCTION_CALLING_EXTRACTOR_NAME = "extract_parameters"
 
 FUNCTION_CALLING_EXTRACTOR_SYSTEM_PROMPT = f"""You are a helpful assistant tasked with extracting structured information based on specific criteria provided. Follow the guidelines below to ensure consistency and accuracy.
 ### Task
@@ -23,7 +23,7 @@ Steps:
 To illustrate, if the task involves extracting a user's name and their request, your function call might look like this: Ensure your output follows a similar structure to examples.
 ### Final Output
 Produce well-formatted function calls in json without XML tags, as shown in the example.
-"""
+"""  # noqa: E501
 
 FUNCTION_CALLING_EXTRACTOR_USER_TEMPLATE = f"""extract structured information from context inside <context></context> XML tags by calling the function {FUNCTION_CALLING_EXTRACTOR_NAME} with the correct parameters with structure inside <structure></structure> XML tags.
 <context>
@@ -33,63 +33,52 @@ FUNCTION_CALLING_EXTRACTOR_USER_TEMPLATE = f"""extract structured information fr
 <structure>
 \x7bstructure\x7d
 </structure>
-"""
+"""  # noqa: E501
 
-FUNCTION_CALLING_EXTRACTOR_EXAMPLE = [{
-    'user': {
-        'query': 'What is the weather today in SF?',
-        'function': {
-            'name': FUNCTION_CALLING_EXTRACTOR_NAME,
-            'parameters': {
-                'type': 'object',
-                'properties': {
-                    'location': {
-                        'type': 'string',
-                        'description': 'The location to get the weather information',
-                        'required': True
+FUNCTION_CALLING_EXTRACTOR_EXAMPLE = [
+    {
+        "user": {
+            "query": "What is the weather today in SF?",
+            "function": {
+                "name": FUNCTION_CALLING_EXTRACTOR_NAME,
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The location to get the weather information",
+                            "required": True,
+                        },
                     },
+                    "required": ["location"],
                 },
-                'required': ['location']
-            }
-        }
+            },
+        },
+        "assistant": {
+            "text": "I need always call the function with the correct parameters."
+            " in this case, I need to call the function with the location parameter.",
+            "function_call": {"name": FUNCTION_CALLING_EXTRACTOR_NAME, "parameters": {"location": "San Francisco"}},
+        },
     },
-    'assistant': {
-        'text': 'I need always call the function with the correct parameters. in this case, I need to call the function with the location parameter.',
-        'function_call' : {
-            'name': FUNCTION_CALLING_EXTRACTOR_NAME,
-            'parameters': {
-                'location': 'San Francisco'
-            }
-        }
-    }
-}, {
-    'user': {
-        'query': 'I want to eat some apple pie.',
-        'function': {
-            'name': FUNCTION_CALLING_EXTRACTOR_NAME,
-            'parameters': {
-                'type': 'object',
-                'properties': {
-                    'food': {
-                        'type': 'string',
-                        'description': 'The food to eat',
-                        'required': True
-                    }
+    {
+        "user": {
+            "query": "I want to eat some apple pie.",
+            "function": {
+                "name": FUNCTION_CALLING_EXTRACTOR_NAME,
+                "parameters": {
+                    "type": "object",
+                    "properties": {"food": {"type": "string", "description": "The food to eat", "required": True}},
+                    "required": ["food"],
                 },
-                'required': ['food']
-            }
-        }
+            },
+        },
+        "assistant": {
+            "text": "I need always call the function with the correct parameters."
+            " in this case, I need to call the function with the food parameter.",
+            "function_call": {"name": FUNCTION_CALLING_EXTRACTOR_NAME, "parameters": {"food": "apple pie"}},
+        },
     },
-    'assistant': {
-        'text': 'I need always call the function with the correct parameters. in this case, I need to call the function with the food parameter.',
-        'function_call' : {
-            'name': FUNCTION_CALLING_EXTRACTOR_NAME,
-            'parameters': {
-                'food': 'apple pie'
-            }
-        }
-    }
-}]
+]
 
 COMPLETION_GENERATE_JSON_PROMPT = """### Instructions:
 Some extra information are provided below, I should always follow the instructions as possible as I can.
@@ -130,7 +119,7 @@ Inside <text></text> XML tags, there is a text that I should extract parameters
 ### Answer
 I should always output a valid JSON object. Output nothing other than the JSON object. 
 ```JSON
-"""
+"""  # noqa: E501
 
 CHAT_GENERATE_JSON_PROMPT = """You should always follow the instructions and output a valid JSON object.
 The structure of the JSON object you can found in the instructions.
@@ -161,46 +150,33 @@ Inside <text></text> XML tags, there is a text that you should convert to a JSON
 </text>
 """
 
-CHAT_EXAMPLE = [{
-    'user': {
-        'query': 'What is the weather today in SF?',
-        'json': {
-            'type': 'object',
-            'properties': {
-                'location': {
-                    'type': 'string',
-                    'description': 'The location to get the weather information',
-                    'required': True
-                }
+CHAT_EXAMPLE = [
+    {
+        "user": {
+            "query": "What is the weather today in SF?",
+            "json": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The location to get the weather information",
+                        "required": True,
+                    }
+                },
+                "required": ["location"],
             },
-            'required': ['location']
-        }
+        },
+        "assistant": {"text": "I need to output a valid JSON object.", "json": {"location": "San Francisco"}},
     },
-    'assistant': {
-        'text': 'I need to output a valid JSON object.',
-        'json': {
-            'location': 'San Francisco'
-        }
-    }
-}, {
-    'user': {
-        'query': 'I want to eat some apple pie.',
-        'json': {
-            'type': 'object',
-            'properties': {
-                'food': {
-                    'type': 'string',
-                    'description': 'The food to eat',
-                    'required': True
-                }
+    {
+        "user": {
+            "query": "I want to eat some apple pie.",
+            "json": {
+                "type": "object",
+                "properties": {"food": {"type": "string", "description": "The food to eat", "required": True}},
+                "required": ["food"],
             },
-            'required': ['food']
-        }
+        },
+        "assistant": {"text": "I need to output a valid JSON object.", "json": {"result": "apple pie"}},
     },
-    'assistant': {
-        'text': 'I need to output a valid JSON object.',
-        'json': {
-            'result': 'apple pie'
-        }
-    }
-}]
\ No newline at end of file
+]
diff --git a/api/core/workflow/nodes/question_classifier/entities.py b/api/core/workflow/nodes/question_classifier/entities.py
index c0b0a8b696..40f7ce7582 100644
--- a/api/core/workflow/nodes/question_classifier/entities.py
+++ b/api/core/workflow/nodes/question_classifier/entities.py
@@ -8,8 +8,9 @@ from core.workflow.entities.base_node_data_entities import BaseNodeData
 
 class ModelConfig(BaseModel):
     """
-     Model Config.
+    Model Config.
     """
+
     provider: str
     name: str
     mode: str
@@ -20,6 +21,7 @@ class ClassConfig(BaseModel):
     """
     Class Config.
     """
+
     id: str
     name: str
 
@@ -28,8 +30,9 @@ class QuestionClassifierNodeData(BaseNodeData):
     """
     Knowledge retrieval Node Data.
     """
+
     query_variable_selector: list[str]
-    type: str = 'question-classifier'
+    type: str = "question-classifier"
     model: ModelConfig
     classes: list[ClassConfig]
     instruction: Optional[str] = None
diff --git a/api/core/workflow/nodes/question_classifier/question_classifier_node.py b/api/core/workflow/nodes/question_classifier/question_classifier_node.py
index f4057d50f3..2ae58bc5f7 100644
--- a/api/core/workflow/nodes/question_classifier/question_classifier_node.py
+++ b/api/core/workflow/nodes/question_classifier/question_classifier_node.py
@@ -1,10 +1,12 @@
 import json
 import logging
-from typing import Optional, Union, cast
+from collections.abc import Mapping, Sequence
+from typing import Any, Optional, Union, cast
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
+from core.model_runtime.entities.llm_entities import LLMUsage
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole
 from core.model_runtime.entities.model_entities import ModelPropertyKey
 from core.model_runtime.utils.encoders import jsonable_encoder
@@ -13,10 +15,9 @@ from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, Comp
 from core.prompt.simple_prompt_transform import ModelMode
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
 from core.prompt.utils.prompt_template_parser import PromptTemplateParser
-from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType
 from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.nodes.llm.llm_node import LLMNode
+from core.workflow.nodes.llm.llm_node import LLMNode, ModelInvokeCompleted
 from core.workflow.nodes.question_classifier.entities import QuestionClassifierNodeData
 from core.workflow.nodes.question_classifier.template_prompts import (
     QUESTION_CLASSIFIER_ASSISTANT_PROMPT_1,
@@ -36,46 +37,49 @@ class QuestionClassifierNode(LLMNode):
     _node_data_cls = QuestionClassifierNodeData
     node_type = NodeType.QUESTION_CLASSIFIER
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         node_data: QuestionClassifierNodeData = cast(self._node_data_cls, self.node_data)
         node_data = cast(QuestionClassifierNodeData, node_data)
+        variable_pool = self.graph_runtime_state.variable_pool
 
         # extract variables
         variable = variable_pool.get(node_data.query_variable_selector)
         query = variable.value if variable else None
-        variables = {
-            'query': query
-        }
+        variables = {"query": query}
         # fetch model config
         model_instance, model_config = self._fetch_model_config(node_data.model)
         # fetch memory
         memory = self._fetch_memory(node_data.memory, variable_pool, model_instance)
         # fetch instruction
-        instruction = self._format_instruction(node_data.instruction, variable_pool) if node_data.instruction else ''
+        instruction = self._format_instruction(node_data.instruction, variable_pool) if node_data.instruction else ""
         node_data.instruction = instruction
         # fetch prompt messages
         prompt_messages, stop = self._fetch_prompt(
-            node_data=node_data,
-            context='',
-            query=query,
-            memory=memory,
-            model_config=model_config
+            node_data=node_data, context="", query=query, memory=memory, model_config=model_config
         )
 
         # handle invoke result
-        result_text, usage, finish_reason = self._invoke_llm(
-            node_data_model=node_data.model,
-            model_instance=model_instance,
-            prompt_messages=prompt_messages,
-            stop=stop
+        generator = self._invoke_llm(
+            node_data_model=node_data.model, model_instance=model_instance, prompt_messages=prompt_messages, stop=stop
         )
+
+        result_text = ""
+        usage = LLMUsage.empty_usage()
+        finish_reason = None
+        for event in generator:
+            if isinstance(event, ModelInvokeCompleted):
+                result_text = event.text
+                usage = event.usage
+                finish_reason = event.finish_reason
+                break
+
         category_name = node_data.classes[0].name
         category_id = node_data.classes[0].id
         try:
             result_text_json = parse_and_check_json_markdown(result_text, [])
             # result_text_json = json.loads(result_text.strip('```JSON\n'))
-            if 'category_name' in result_text_json and 'category_id' in result_text_json:
-                category_id_result = result_text_json['category_id']
+            if "category_name" in result_text_json and "category_id" in result_text_json:
+                category_id_result = result_text_json["category_id"]
                 classes = node_data.classes
                 classes_map = {class_.id: class_.name for class_ in classes}
                 category_ids = [_class.id for _class in classes]
@@ -87,17 +91,14 @@ class QuestionClassifierNode(LLMNode):
             logging.error(f"Failed to parse result text: {result_text}")
         try:
             process_data = {
-                'model_mode': model_config.mode,
-                'prompts': PromptMessageUtil.prompt_messages_to_prompt_for_saving(
-                    model_mode=model_config.mode,
-                    prompt_messages=prompt_messages
+                "model_mode": model_config.mode,
+                "prompts": PromptMessageUtil.prompt_messages_to_prompt_for_saving(
+                    model_mode=model_config.mode, prompt_messages=prompt_messages
                 ),
-                'usage': jsonable_encoder(usage),
-                'finish_reason': finish_reason
-            }
-            outputs = {
-                'class_name': category_name
+                "usage": jsonable_encoder(usage),
+                "finish_reason": finish_reason,
             }
+            outputs = {"class_name": category_name}
 
             return NodeRunResult(
                 status=WorkflowNodeExecutionStatus.SUCCEEDED,
@@ -108,8 +109,9 @@ class QuestionClassifierNode(LLMNode):
                 metadata={
                     NodeRunMetadataKey.TOTAL_TOKENS: usage.total_tokens,
                     NodeRunMetadataKey.TOTAL_PRICE: usage.total_price,
-                    NodeRunMetadataKey.CURRENCY: usage.currency
-                }
+                    NodeRunMetadataKey.CURRENCY: usage.currency,
+                },
+                llm_usage=usage,
             )
 
         except ValueError as e:
@@ -120,21 +122,32 @@ class QuestionClassifierNode(LLMNode):
                 metadata={
                     NodeRunMetadataKey.TOTAL_TOKENS: usage.total_tokens,
                     NodeRunMetadataKey.TOTAL_PRICE: usage.total_price,
-                    NodeRunMetadataKey.CURRENCY: usage.currency
-                }
+                    NodeRunMetadataKey.CURRENCY: usage.currency,
+                },
+                llm_usage=usage,
             )
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
-        node_data = node_data
-        node_data = cast(cls._node_data_cls, node_data)
-        variable_mapping = {'query': node_data.query_variable_selector}
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: QuestionClassifierNodeData
+    ) -> Mapping[str, Sequence[str]]:
+        """
+        Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
+        :param node_data: node data
+        :return:
+        """
+        variable_mapping = {"query": node_data.query_variable_selector}
         variable_selectors = []
         if node_data.instruction:
             variable_template_parser = VariableTemplateParser(template=node_data.instruction)
             variable_selectors.extend(variable_template_parser.extract_variable_selectors())
         for variable_selector in variable_selectors:
             variable_mapping[variable_selector.variable] = variable_selector.value_selector
+
+        variable_mapping = {node_id + "." + key: value for key, value in variable_mapping.items()}
+
         return variable_mapping
 
     @classmethod
@@ -144,19 +157,16 @@ class QuestionClassifierNode(LLMNode):
         :param filters: filter by node config parameters.
         :return:
         """
-        return {
-            "type": "question-classifier",
-            "config": {
-                "instructions": ""
-            }
-        }
+        return {"type": "question-classifier", "config": {"instructions": ""}}
 
-    def _fetch_prompt(self, node_data: QuestionClassifierNodeData,
-                      query: str,
-                      context: Optional[str],
-                      memory: Optional[TokenBufferMemory],
-                      model_config: ModelConfigWithCredentialsEntity) \
-            -> tuple[list[PromptMessage], Optional[list[str]]]:
+    def _fetch_prompt(
+        self,
+        node_data: QuestionClassifierNodeData,
+        query: str,
+        context: Optional[str],
+        memory: Optional[TokenBufferMemory],
+        model_config: ModelConfigWithCredentialsEntity,
+    ) -> tuple[list[PromptMessage], Optional[list[str]]]:
         """
         Fetch prompt
         :param node_data: node data
@@ -172,118 +182,122 @@ class QuestionClassifierNode(LLMNode):
         prompt_messages = prompt_transform.get_prompt(
             prompt_template=prompt_template,
             inputs={},
-            query='',
+            query="",
             files=[],
             context=context,
             memory_config=node_data.memory,
             memory=None,
-            model_config=model_config
+            model_config=model_config,
         )
         stop = model_config.stop
 
         return prompt_messages, stop
 
-    def _calculate_rest_token(self, node_data: QuestionClassifierNodeData, query: str,
-                              model_config: ModelConfigWithCredentialsEntity,
-                              context: Optional[str]) -> int:
+    def _calculate_rest_token(
+        self,
+        node_data: QuestionClassifierNodeData,
+        query: str,
+        model_config: ModelConfigWithCredentialsEntity,
+        context: Optional[str],
+    ) -> int:
         prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
         prompt_template = self._get_prompt_template(node_data, query, None, 2000)
         prompt_messages = prompt_transform.get_prompt(
             prompt_template=prompt_template,
             inputs={},
-            query='',
+            query="",
             files=[],
             context=context,
             memory_config=node_data.memory,
             memory=None,
-            model_config=model_config
+            model_config=model_config,
         )
         rest_tokens = 2000
 
         model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
         if model_context_tokens:
             model_instance = ModelInstance(
-                provider_model_bundle=model_config.provider_model_bundle,
-                model=model_config.model
+                provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
             )
 
-            curr_message_tokens = model_instance.get_llm_num_tokens(
-                prompt_messages
-            )
+            curr_message_tokens = model_instance.get_llm_num_tokens(prompt_messages)
 
             max_tokens = 0
             for parameter_rule in model_config.model_schema.parameter_rules:
-                if (parameter_rule.name == 'max_tokens'
-                        or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
-                    max_tokens = (model_config.parameters.get(parameter_rule.name)
-                                  or model_config.parameters.get(parameter_rule.use_template)) or 0
+                if parameter_rule.name == "max_tokens" or (
+                    parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+                ):
+                    max_tokens = (
+                        model_config.parameters.get(parameter_rule.name)
+                        or model_config.parameters.get(parameter_rule.use_template)
+                    ) or 0
 
             rest_tokens = model_context_tokens - max_tokens - curr_message_tokens
             rest_tokens = max(rest_tokens, 0)
 
         return rest_tokens
 
-    def _get_prompt_template(self, node_data: QuestionClassifierNodeData, query: str,
-                             memory: Optional[TokenBufferMemory],
-                             max_token_limit: int = 2000) \
-            -> Union[list[ChatModelMessage], CompletionModelPromptTemplate]:
+    def _get_prompt_template(
+        self,
+        node_data: QuestionClassifierNodeData,
+        query: str,
+        memory: Optional[TokenBufferMemory],
+        max_token_limit: int = 2000,
+    ) -> Union[list[ChatModelMessage], CompletionModelPromptTemplate]:
         model_mode = ModelMode.value_of(node_data.model.mode)
         classes = node_data.classes
         categories = []
         for class_ in classes:
-            category = {
-                'category_id': class_.id,
-                'category_name': class_.name
-            }
+            category = {"category_id": class_.id, "category_name": class_.name}
             categories.append(category)
-        instruction = node_data.instruction if node_data.instruction else ''
+        instruction = node_data.instruction or ""
         input_text = query
-        memory_str = ''
+        memory_str = ""
         if memory:
-            memory_str = memory.get_history_prompt_text(max_token_limit=max_token_limit,
-                                                        message_limit=node_data.memory.window.size)
+            memory_str = memory.get_history_prompt_text(
+                max_token_limit=max_token_limit, message_limit=node_data.memory.window.size
+            )
         prompt_messages = []
         if model_mode == ModelMode.CHAT:
             system_prompt_messages = ChatModelMessage(
-                role=PromptMessageRole.SYSTEM,
-                text=QUESTION_CLASSIFIER_SYSTEM_PROMPT.format(histories=memory_str)
+                role=PromptMessageRole.SYSTEM, text=QUESTION_CLASSIFIER_SYSTEM_PROMPT.format(histories=memory_str)
             )
             prompt_messages.append(system_prompt_messages)
             user_prompt_message_1 = ChatModelMessage(
-                role=PromptMessageRole.USER,
-                text=QUESTION_CLASSIFIER_USER_PROMPT_1
+                role=PromptMessageRole.USER, text=QUESTION_CLASSIFIER_USER_PROMPT_1
             )
             prompt_messages.append(user_prompt_message_1)
             assistant_prompt_message_1 = ChatModelMessage(
-                role=PromptMessageRole.ASSISTANT,
-                text=QUESTION_CLASSIFIER_ASSISTANT_PROMPT_1
+                role=PromptMessageRole.ASSISTANT, text=QUESTION_CLASSIFIER_ASSISTANT_PROMPT_1
             )
             prompt_messages.append(assistant_prompt_message_1)
             user_prompt_message_2 = ChatModelMessage(
-                role=PromptMessageRole.USER,
-                text=QUESTION_CLASSIFIER_USER_PROMPT_2
+                role=PromptMessageRole.USER, text=QUESTION_CLASSIFIER_USER_PROMPT_2
             )
             prompt_messages.append(user_prompt_message_2)
             assistant_prompt_message_2 = ChatModelMessage(
-                role=PromptMessageRole.ASSISTANT,
-                text=QUESTION_CLASSIFIER_ASSISTANT_PROMPT_2
+                role=PromptMessageRole.ASSISTANT, text=QUESTION_CLASSIFIER_ASSISTANT_PROMPT_2
             )
             prompt_messages.append(assistant_prompt_message_2)
             user_prompt_message_3 = ChatModelMessage(
                 role=PromptMessageRole.USER,
-                text=QUESTION_CLASSIFIER_USER_PROMPT_3.format(input_text=input_text,
-                                                              categories=json.dumps(categories, ensure_ascii=False),
-                                                              classification_instructions=instruction)
+                text=QUESTION_CLASSIFIER_USER_PROMPT_3.format(
+                    input_text=input_text,
+                    categories=json.dumps(categories, ensure_ascii=False),
+                    classification_instructions=instruction,
+                ),
             )
             prompt_messages.append(user_prompt_message_3)
             return prompt_messages
         elif model_mode == ModelMode.COMPLETION:
             return CompletionModelPromptTemplate(
-                text=QUESTION_CLASSIFIER_COMPLETION_PROMPT.format(histories=memory_str,
-                                                                  input_text=input_text,
-                                                                  categories=json.dumps(categories),
-                                                                  classification_instructions=instruction,
-                                                                  ensure_ascii=False)
+                text=QUESTION_CLASSIFIER_COMPLETION_PROMPT.format(
+                    histories=memory_str,
+                    input_text=input_text,
+                    categories=json.dumps(categories),
+                    classification_instructions=instruction,
+                    ensure_ascii=False,
+                )
             )
 
         else:
@@ -299,14 +313,12 @@ class QuestionClassifierNode(LLMNode):
             variable = variable_pool.get(variable_selector.value_selector)
             variable_value = variable.value if variable else None
             if variable_value is None:
-                raise ValueError(f'Variable {variable_selector.variable} not found')
+                raise ValueError(f"Variable {variable_selector.variable} not found")
 
             inputs[variable_selector.variable] = variable_value
 
         prompt_template = PromptTemplateParser(template=instruction, with_variable_tmpl=True)
         prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
 
-        instruction = prompt_template.format(
-            prompt_inputs
-        )
+        instruction = prompt_template.format(prompt_inputs)
         return instruction
diff --git a/api/core/workflow/nodes/question_classifier/template_prompts.py b/api/core/workflow/nodes/question_classifier/template_prompts.py
index e0de148cc2..ce32b01aa4 100644
--- a/api/core/workflow/nodes/question_classifier/template_prompts.py
+++ b/api/core/workflow/nodes/question_classifier/template_prompts.py
@@ -1,5 +1,3 @@
-
-
 QUESTION_CLASSIFIER_SYSTEM_PROMPT = """
     ### Job Description',
     You are a text classification engine that analyzes text data and assigns categories based on user input or automatically determined categories.
@@ -14,13 +12,13 @@ QUESTION_CLASSIFIER_SYSTEM_PROMPT = """
     <histories>
     {histories}
     </histories>
-"""
+"""  # noqa: E501
 
 QUESTION_CLASSIFIER_USER_PROMPT_1 = """
     { "input_text": ["I recently had a great experience with your company. The service was prompt and the staff was very friendly."],
     "categories": [{"category_id":"f5660049-284f-41a7-b301-fd24176a711c","category_name":"Customer Service"},{"category_id":"8d007d06-f2c9-4be5-8ff6-cd4381c13c60","category_name":"Satisfaction"},{"category_id":"5fbbbb18-9843-466d-9b8e-b9bfbb9482c8","category_name":"Sales"},{"category_id":"23623c75-7184-4a2e-8226-466c2e4631e4","category_name":"Product"}],
     "classification_instructions": ["classify the text based on the feedback provided by customer"]}
-"""
+"""  # noqa: E501
 
 QUESTION_CLASSIFIER_ASSISTANT_PROMPT_1 = """
 ```json
@@ -34,7 +32,7 @@ QUESTION_CLASSIFIER_USER_PROMPT_2 = """
     {"input_text": ["bad service, slow to bring the food"],
     "categories": [{"category_id":"80fb86a0-4454-4bf5-924c-f253fdd83c02","category_name":"Food Quality"},{"category_id":"f6ff5bc3-aca0-4e4a-8627-e760d0aca78f","category_name":"Experience"},{"category_id":"cc771f63-74e7-4c61-882e-3eda9d8ba5d7","category_name":"Price"}],
     "classification_instructions": []}
-"""
+"""  # noqa: E501
 
 QUESTION_CLASSIFIER_ASSISTANT_PROMPT_2 = """
 ```json
@@ -75,4 +73,4 @@ Here is the chat histories between human and assistant, inside <histories></hist
 ### User Input
 {{"input_text" : ["{input_text}"], "categories" : {categories},"classification_instruction" : ["{classification_instructions}"]}}
 ### Assistant Output
-"""
+"""  # noqa: E501
diff --git a/api/core/workflow/nodes/start/entities.py b/api/core/workflow/nodes/start/entities.py
index b81ce15bd7..11d2ebe5dd 100644
--- a/api/core/workflow/nodes/start/entities.py
+++ b/api/core/workflow/nodes/start/entities.py
@@ -10,4 +10,5 @@ class StartNodeData(BaseNodeData):
     """
     Start Node Data
     """
+
     variables: Sequence[VariableEntity] = Field(default_factory=list)
diff --git a/api/core/workflow/nodes/start/start_node.py b/api/core/workflow/nodes/start/start_node.py
index 54e66bd671..96c887c58d 100644
--- a/api/core/workflow/nodes/start/start_node.py
+++ b/api/core/workflow/nodes/start/start_node.py
@@ -1,7 +1,8 @@
+from collections.abc import Mapping, Sequence
+from typing import Any
 
-from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import SYSTEM_VARIABLE_NODE_ID, VariablePool
+from core.workflow.entities.variable_pool import SYSTEM_VARIABLE_NODE_ID
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.start.entities import StartNodeData
 from models.workflow import WorkflowNodeExecutionStatus
@@ -11,28 +12,27 @@ class StartNode(BaseNode):
     _node_data_cls = StartNodeData
     _node_type = NodeType.START
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         """
         Run node
-        :param variable_pool: variable pool
         :return:
         """
-        node_inputs = dict(variable_pool.user_inputs)
-        system_inputs = variable_pool.system_variables
+        node_inputs = dict(self.graph_runtime_state.variable_pool.user_inputs)
+        system_inputs = self.graph_runtime_state.variable_pool.system_variables
 
         for var in system_inputs:
-            node_inputs[SYSTEM_VARIABLE_NODE_ID + '.' + var] = system_inputs[var]
+            node_inputs[SYSTEM_VARIABLE_NODE_ID + "." + var] = system_inputs[var]
 
-        return NodeRunResult(
-            status=WorkflowNodeExecutionStatus.SUCCEEDED,
-            inputs=node_inputs,
-            outputs=node_inputs
-        )
+        return NodeRunResult(status=WorkflowNodeExecutionStatus.SUCCEEDED, inputs=node_inputs, outputs=node_inputs)
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: StartNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
diff --git a/api/core/workflow/nodes/template_transform/entities.py b/api/core/workflow/nodes/template_transform/entities.py
index d9099a8118..e934d69fa3 100644
--- a/api/core/workflow/nodes/template_transform/entities.py
+++ b/api/core/workflow/nodes/template_transform/entities.py
@@ -1,5 +1,3 @@
-
-
 from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.variable_entities import VariableSelector
 
@@ -8,5 +6,6 @@ class TemplateTransformNodeData(BaseNodeData):
     """
     Code Node Data.
     """
+
     variables: list[VariableSelector]
-    template: str
\ No newline at end of file
+    template: str
diff --git a/api/core/workflow/nodes/template_transform/template_transform_node.py b/api/core/workflow/nodes/template_transform/template_transform_node.py
index 21f71db6c5..32c99e0d1c 100644
--- a/api/core/workflow/nodes/template_transform/template_transform_node.py
+++ b/api/core/workflow/nodes/template_transform/template_transform_node.py
@@ -1,14 +1,15 @@
 import os
-from typing import Optional, cast
+from collections.abc import Mapping, Sequence
+from typing import Any, Optional, cast
 
-from core.helper.code_executor.code_executor import CodeExecutionException, CodeExecutor, CodeLanguage
+from core.helper.code_executor.code_executor import CodeExecutionError, CodeExecutor, CodeLanguage
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.template_transform.entities import TemplateTransformNodeData
 from models.workflow import WorkflowNodeExecutionStatus
 
-MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH = int(os.environ.get('TEMPLATE_TRANSFORM_MAX_LENGTH', '80000'))
+MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH = int(os.environ.get("TEMPLATE_TRANSFORM_MAX_LENGTH", "80000"))
+
 
 class TemplateTransformNode(BaseNode):
     _node_data_cls = TemplateTransformNodeData
@@ -23,18 +24,10 @@ class TemplateTransformNode(BaseNode):
         """
         return {
             "type": "template-transform",
-            "config": {
-                "variables": [
-                    {
-                        "variable": "arg1",
-                        "value_selector": []
-                    }
-                ],
-                "template": "{{ arg1 }}"
-            }
+            "config": {"variables": [{"variable": "arg1", "value_selector": []}], "template": "{{ arg1 }}"},
         }
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         """
         Run node
         """
@@ -45,44 +38,39 @@ class TemplateTransformNode(BaseNode):
         variables = {}
         for variable_selector in node_data.variables:
             variable_name = variable_selector.variable
-            value = variable_pool.get_any(variable_selector.value_selector)
+            value = self.graph_runtime_state.variable_pool.get_any(variable_selector.value_selector)
             variables[variable_name] = value
         # Run code
         try:
             result = CodeExecutor.execute_workflow_code_template(
-                language=CodeLanguage.JINJA2,
-                code=node_data.template,
-                inputs=variables
+                language=CodeLanguage.JINJA2, code=node_data.template, inputs=variables
             )
-        except CodeExecutionException as e:
+        except CodeExecutionError as e:
+            return NodeRunResult(inputs=variables, status=WorkflowNodeExecutionStatus.FAILED, error=str(e))
+
+        if len(result["result"]) > MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH:
             return NodeRunResult(
                 inputs=variables,
                 status=WorkflowNodeExecutionStatus.FAILED,
-                error=str(e)
-            )
-        
-        if len(result['result']) > MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH:
-            return NodeRunResult(
-                inputs=variables,
-                status=WorkflowNodeExecutionStatus.FAILED,
-                error=f"Output length exceeds {MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH} characters"
+                error=f"Output length exceeds {MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH} characters",
             )
 
         return NodeRunResult(
-            status=WorkflowNodeExecutionStatus.SUCCEEDED,
-            inputs=variables,
-            outputs={
-                'output': result['result']
-            }
+            status=WorkflowNodeExecutionStatus.SUCCEEDED, inputs=variables, outputs={"output": result["result"]}
         )
-    
+
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: TemplateTransformNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: TemplateTransformNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
         return {
-            variable_selector.variable: variable_selector.value_selector for variable_selector in node_data.variables
-        }
\ No newline at end of file
+            node_id + "." + variable_selector.variable: variable_selector.value_selector
+            for variable_selector in node_data.variables
+        }
diff --git a/api/core/workflow/nodes/tool/entities.py b/api/core/workflow/nodes/tool/entities.py
index 5da5cd0727..9d222b10b9 100644
--- a/api/core/workflow/nodes/tool/entities.py
+++ b/api/core/workflow/nodes/tool/entities.py
@@ -8,46 +8,47 @@ from core.workflow.entities.base_node_data_entities import BaseNodeData
 
 class ToolEntity(BaseModel):
     provider_id: str
-    provider_type: Literal['builtin', 'api', 'workflow']
-    provider_name: str # redundancy
+    provider_type: Literal["builtin", "api", "workflow"]
+    provider_name: str  # redundancy
     tool_name: str
-    tool_label: str # redundancy
+    tool_label: str  # redundancy
     tool_configurations: dict[str, Any]
 
-    @field_validator('tool_configurations', mode='before')
+    @field_validator("tool_configurations", mode="before")
     @classmethod
     def validate_tool_configurations(cls, value, values: ValidationInfo):
         if not isinstance(value, dict):
-            raise ValueError('tool_configurations must be a dictionary')
-        
-        for key in values.data.get('tool_configurations', {}).keys():
-            value = values.data.get('tool_configurations', {}).get(key)
+            raise ValueError("tool_configurations must be a dictionary")
+
+        for key in values.data.get("tool_configurations", {}):
+            value = values.data.get("tool_configurations", {}).get(key)
             if not isinstance(value, str | int | float | bool):
-                raise ValueError(f'{key} must be a string')
-            
+                raise ValueError(f"{key} must be a string")
+
         return value
 
+
 class ToolNodeData(BaseNodeData, ToolEntity):
     class ToolInput(BaseModel):
         # TODO: check this type
         value: Union[Any, list[str]]
-        type: Literal['mixed', 'variable', 'constant']
+        type: Literal["mixed", "variable", "constant"]
 
-        @field_validator('type', mode='before')
+        @field_validator("type", mode="before")
         @classmethod
         def check_type(cls, value, validation_info: ValidationInfo):
             typ = value
-            value = validation_info.data.get('value')
-            if typ == 'mixed' and not isinstance(value, str):
-                raise ValueError('value must be a string')
-            elif typ == 'variable':
+            value = validation_info.data.get("value")
+            if typ == "mixed" and not isinstance(value, str):
+                raise ValueError("value must be a string")
+            elif typ == "variable":
                 if not isinstance(value, list):
-                    raise ValueError('value must be a list')
+                    raise ValueError("value must be a list")
                 for val in value:
                     if not isinstance(val, str):
-                        raise ValueError('value must be a list of strings')
-            elif typ == 'constant' and not isinstance(value, str | int | float | bool):
-                raise ValueError('value must be a string, int, float, or bool')
+                        raise ValueError("value must be a list of strings")
+            elif typ == "constant" and not isinstance(value, str | int | float | bool):
+                raise ValueError("value must be a string, int, float, or bool")
             return typ
 
     """
diff --git a/api/core/workflow/nodes/tool/tool_node.py b/api/core/workflow/nodes/tool/tool_node.py
index ccce9ef360..3b86b29cf8 100644
--- a/api/core/workflow/nodes/tool/tool_node.py
+++ b/api/core/workflow/nodes/tool/tool_node.py
@@ -26,7 +26,7 @@ class ToolNode(BaseNode):
     _node_data_cls = ToolNodeData
     _node_type = NodeType.TOOL
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         """
         Run the tool node
         """
@@ -34,10 +34,7 @@ class ToolNode(BaseNode):
         node_data = cast(ToolNodeData, self.node_data)
 
         # fetch tool icon
-        tool_info = {
-            'provider_type': node_data.provider_type,
-            'provider_id': node_data.provider_id
-        }
+        tool_info = {"provider_type": node_data.provider_type, "provider_id": node_data.provider_id}
 
         # get tool runtime
         try:
@@ -48,16 +45,21 @@ class ToolNode(BaseNode):
             return NodeRunResult(
                 status=WorkflowNodeExecutionStatus.FAILED,
                 inputs={},
-                metadata={
-                    NodeRunMetadataKey.TOOL_INFO: tool_info
-                },
-                error=f'Failed to get tool runtime: {str(e)}'
+                metadata={NodeRunMetadataKey.TOOL_INFO: tool_info},
+                error=f"Failed to get tool runtime: {str(e)}",
             )
 
         # get parameters
         tool_parameters = tool_runtime.get_runtime_parameters() or []
-        parameters = self._generate_parameters(tool_parameters=tool_parameters, variable_pool=variable_pool, node_data=node_data)
-        parameters_for_log = self._generate_parameters(tool_parameters=tool_parameters, variable_pool=variable_pool, node_data=node_data, for_log=True)
+        parameters = self._generate_parameters(
+            tool_parameters=tool_parameters, variable_pool=self.graph_runtime_state.variable_pool, node_data=node_data
+        )
+        parameters_for_log = self._generate_parameters(
+            tool_parameters=tool_parameters,
+            variable_pool=self.graph_runtime_state.variable_pool,
+            node_data=node_data,
+            for_log=True,
+        )
 
         try:
             messages = ToolEngine.workflow_invoke(
@@ -66,15 +68,14 @@ class ToolNode(BaseNode):
                 user_id=self.user_id,
                 workflow_tool_callback=DifyWorkflowCallbackHandler(),
                 workflow_call_depth=self.workflow_call_depth,
+                thread_pool_id=self.thread_pool_id,
             )
         except Exception as e:
             return NodeRunResult(
                 status=WorkflowNodeExecutionStatus.FAILED,
                 inputs=parameters_for_log,
-                metadata={
-                    NodeRunMetadataKey.TOOL_INFO: tool_info
-                },
-                error=f'Failed to invoke tool: {str(e)}',
+                metadata={NodeRunMetadataKey.TOOL_INFO: tool_info},
+                error=f"Failed to invoke tool: {str(e)}",
             )
 
         # convert tool messages
@@ -82,15 +83,9 @@ class ToolNode(BaseNode):
 
         return NodeRunResult(
             status=WorkflowNodeExecutionStatus.SUCCEEDED,
-            outputs={
-                'text': plain_text,
-                'files': files,
-                'json': json
-            },
-            metadata={
-                NodeRunMetadataKey.TOOL_INFO: tool_info
-            },
-            inputs=parameters_for_log
+            outputs={"text": plain_text, "files": files, "json": json},
+            metadata={NodeRunMetadataKey.TOOL_INFO: tool_info},
+            inputs=parameters_for_log,
         )
 
     def _generate_parameters(
@@ -122,12 +117,10 @@ class ToolNode(BaseNode):
                 result[parameter_name] = None
                 continue
             if parameter.type == ToolParameter.ToolParameterType.FILE:
-                result[parameter_name] = [
-                    v.to_dict() for v in self._fetch_files(variable_pool)
-                ]
+                result[parameter_name] = [v.to_dict() for v in self._fetch_files(variable_pool)]
             else:
                 tool_input = node_data.tool_parameters[parameter_name]
-                if tool_input.type == 'variable':
+                if tool_input.type == "variable":
                     # TODO: check if the variable exists in the variable pool
                     parameter_value = variable_pool.get(tool_input.value).value
                 else:
@@ -141,11 +134,11 @@ class ToolNode(BaseNode):
         return result
 
     def _fetch_files(self, variable_pool: VariablePool) -> list[FileVar]:
-        variable = variable_pool.get(['sys', SystemVariableKey.FILES.value])
+        variable = variable_pool.get(["sys", SystemVariableKey.FILES.value])
         assert isinstance(variable, ArrayAnyVariable | ArrayAnySegment)
         return list(variable.value) if variable else []
 
-    def _convert_tool_messages(self, messages: list[ToolInvokeMessage]):
+    def _convert_tool_messages(self, messages: list[ToolInvokeMessage]) -> tuple[str, list[FileVar], list[dict]]:
         """
         Convert ToolInvokeMessages into tuple[plain_text, files]
         """
@@ -170,38 +163,41 @@ class ToolNode(BaseNode):
         result = []
 
         for response in tool_response:
-            if response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
-                    response.type == ToolInvokeMessage.MessageType.IMAGE:
+            if response.type in {ToolInvokeMessage.MessageType.IMAGE_LINK, ToolInvokeMessage.MessageType.IMAGE}:
                 url = response.message
                 ext = path.splitext(url)[1]
-                mimetype = response.meta.get('mime_type', 'image/jpeg')
-                filename = response.save_as or url.split('/')[-1]
-                transfer_method = response.meta.get('transfer_method', FileTransferMethod.TOOL_FILE)
+                mimetype = response.meta.get("mime_type", "image/jpeg")
+                filename = response.save_as or url.split("/")[-1]
+                transfer_method = response.meta.get("transfer_method", FileTransferMethod.TOOL_FILE)
 
                 # get tool file id
-                tool_file_id = url.split('/')[-1].split('.')[0]
-                result.append(FileVar(
-                    tenant_id=self.tenant_id,
-                    type=FileType.IMAGE,
-                    transfer_method=transfer_method,
-                    url=url,
-                    related_id=tool_file_id,
-                    filename=filename,
-                    extension=ext,
-                    mime_type=mimetype,
-                ))
+                tool_file_id = url.split("/")[-1].split(".")[0]
+                result.append(
+                    FileVar(
+                        tenant_id=self.tenant_id,
+                        type=FileType.IMAGE,
+                        transfer_method=transfer_method,
+                        url=url,
+                        related_id=tool_file_id,
+                        filename=filename,
+                        extension=ext,
+                        mime_type=mimetype,
+                    )
+                )
             elif response.type == ToolInvokeMessage.MessageType.BLOB:
                 # get tool file id
-                tool_file_id = response.message.split('/')[-1].split('.')[0]
-                result.append(FileVar(
-                    tenant_id=self.tenant_id,
-                    type=FileType.IMAGE,
-                    transfer_method=FileTransferMethod.TOOL_FILE,
-                    related_id=tool_file_id,
-                    filename=response.save_as,
-                    extension=path.splitext(response.save_as)[1],
-                    mime_type=response.meta.get('mime_type', 'application/octet-stream'),
-                ))
+                tool_file_id = response.message.split("/")[-1].split(".")[0]
+                result.append(
+                    FileVar(
+                        tenant_id=self.tenant_id,
+                        type=FileType.IMAGE,
+                        transfer_method=FileTransferMethod.TOOL_FILE,
+                        related_id=tool_file_id,
+                        filename=response.save_as,
+                        extension=path.splitext(response.save_as)[1],
+                        mime_type=response.meta.get("mime_type", "application/octet-stream"),
+                    )
+                )
             elif response.type == ToolInvokeMessage.MessageType.LINK:
                 pass  # TODO:
 
@@ -211,32 +207,43 @@ class ToolNode(BaseNode):
         """
         Extract tool response text
         """
-        return '\n'.join([
-            f'{message.message}' if message.type == ToolInvokeMessage.MessageType.TEXT else
-            f'Link: {message.message}' if message.type == ToolInvokeMessage.MessageType.LINK else ''
-            for message in tool_response
-        ])
+        return "\n".join(
+            [
+                f"{message.message}"
+                if message.type == ToolInvokeMessage.MessageType.TEXT
+                else f"Link: {message.message}"
+                if message.type == ToolInvokeMessage.MessageType.LINK
+                else ""
+                for message in tool_response
+            ]
+        )
 
     def _extract_tool_response_json(self, tool_response: list[ToolInvokeMessage]) -> list[dict]:
         return [message.message for message in tool_response if message.type == ToolInvokeMessage.MessageType.JSON]
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: ToolNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: ToolNodeData
+    ) -> Mapping[str, Sequence[str]]:
         """
         Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
         :param node_data: node data
         :return:
         """
         result = {}
         for parameter_name in node_data.tool_parameters:
             input = node_data.tool_parameters[parameter_name]
-            if input.type == 'mixed':
+            if input.type == "mixed":
                 selectors = VariableTemplateParser(input.value).extract_variable_selectors()
                 for selector in selectors:
                     result[selector.variable] = selector.value_selector
-            elif input.type == 'variable':
+            elif input.type == "variable":
                 result[parameter_name] = input.value
-            elif input.type == 'constant':
+            elif input.type == "constant":
                 pass
 
+        result = {node_id + "." + key: value for key, value in result.items()}
+
         return result
diff --git a/api/core/workflow/nodes/variable_aggregator/entities.py b/api/core/workflow/nodes/variable_aggregator/entities.py
index e5de38dc0f..eb893a04e3 100644
--- a/api/core/workflow/nodes/variable_aggregator/entities.py
+++ b/api/core/workflow/nodes/variable_aggregator/entities.py
@@ -1,5 +1,3 @@
-
-
 from typing import Literal, Optional
 
 from pydantic import BaseModel
@@ -11,23 +9,27 @@ class AdvancedSettings(BaseModel):
     """
     Advanced setting.
     """
+
     group_enabled: bool
 
     class Group(BaseModel):
         """
         Group.
         """
-        output_type: Literal['string', 'number', 'object', 'array[string]', 'array[number]', 'array[object]']
+
+        output_type: Literal["string", "number", "object", "array[string]", "array[number]", "array[object]"]
         variables: list[list[str]]
         group_name: str
 
     groups: list[Group]
 
+
 class VariableAssignerNodeData(BaseNodeData):
     """
     Knowledge retrieval Node Data.
     """
-    type: str = 'variable-assigner'
+
+    type: str = "variable-assigner"
     output_type: str
     variables: list[list[str]]
     advanced_settings: Optional[AdvancedSettings] = None
diff --git a/api/core/workflow/nodes/variable_aggregator/variable_aggregator_node.py b/api/core/workflow/nodes/variable_aggregator/variable_aggregator_node.py
index 885f7d7617..f03eae257a 100644
--- a/api/core/workflow/nodes/variable_aggregator/variable_aggregator_node.py
+++ b/api/core/workflow/nodes/variable_aggregator/variable_aggregator_node.py
@@ -1,8 +1,7 @@
-from typing import cast
+from collections.abc import Mapping, Sequence
+from typing import Any, cast
 
-from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.variable_aggregator.entities import VariableAssignerNodeData
 from models.workflow import WorkflowNodeExecutionStatus
@@ -12,7 +11,7 @@ class VariableAggregatorNode(BaseNode):
     _node_data_cls = VariableAssignerNodeData
     _node_type = NodeType.VARIABLE_AGGREGATOR
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         node_data = cast(VariableAssignerNodeData, self.node_data)
         # Get variables
         outputs = {}
@@ -20,34 +19,33 @@ class VariableAggregatorNode(BaseNode):
 
         if not node_data.advanced_settings or not node_data.advanced_settings.group_enabled:
             for selector in node_data.variables:
-                variable = variable_pool.get_any(selector)
+                variable = self.graph_runtime_state.variable_pool.get_any(selector)
                 if variable is not None:
-                    outputs = {
-                        "output": variable
-                    }
+                    outputs = {"output": variable}
 
-                    inputs = {
-                        '.'.join(selector[1:]): variable
-                    }
+                    inputs = {".".join(selector[1:]): variable}
                     break
         else:
             for group in node_data.advanced_settings.groups:
                 for selector in group.variables:
-                    variable = variable_pool.get_any(selector)
+                    variable = self.graph_runtime_state.variable_pool.get_any(selector)
 
                     if variable is not None:
-                        outputs[group.group_name] = {
-                            'output': variable
-                        }
-                        inputs['.'.join(selector[1:])] = variable
+                        outputs[group.group_name] = {"output": variable}
+                        inputs[".".join(selector[1:])] = variable
                         break
 
-        return NodeRunResult(
-            status=WorkflowNodeExecutionStatus.SUCCEEDED,
-            outputs=outputs,
-            inputs=inputs
-        )
+        return NodeRunResult(status=WorkflowNodeExecutionStatus.SUCCEEDED, outputs=outputs, inputs=inputs)
 
     @classmethod
-    def _extract_variable_selector_to_variable_mapping(cls, node_data: BaseNodeData) -> dict[str, list[str]]:
+    def _extract_variable_selector_to_variable_mapping(
+        cls, graph_config: Mapping[str, Any], node_id: str, node_data: VariableAssignerNodeData
+    ) -> Mapping[str, Sequence[str]]:
+        """
+        Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
+        :param node_data: node data
+        :return:
+        """
         return {}
diff --git a/api/core/workflow/nodes/variable_assigner/__init__.py b/api/core/workflow/nodes/variable_assigner/__init__.py
index d791d51523..83da4bdc79 100644
--- a/api/core/workflow/nodes/variable_assigner/__init__.py
+++ b/api/core/workflow/nodes/variable_assigner/__init__.py
@@ -2,7 +2,7 @@ from .node import VariableAssignerNode
 from .node_data import VariableAssignerData, WriteMode
 
 __all__ = [
-    'VariableAssignerNode',
-    'VariableAssignerData',
-    'WriteMode',
+    "VariableAssignerNode",
+    "VariableAssignerData",
+    "WriteMode",
 ]
diff --git a/api/core/workflow/nodes/variable_assigner/node.py b/api/core/workflow/nodes/variable_assigner/node.py
index 8c2adcabb9..3969299795 100644
--- a/api/core/workflow/nodes/variable_assigner/node.py
+++ b/api/core/workflow/nodes/variable_assigner/node.py
@@ -6,7 +6,6 @@ from sqlalchemy.orm import Session
 from core.app.segments import SegmentType, Variable, factory
 from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import BaseNode
 from extensions.ext_database import db
 from models import ConversationVariable, WorkflowNodeExecutionStatus
@@ -19,49 +18,49 @@ class VariableAssignerNode(BaseNode):
     _node_data_cls: type[BaseNodeData] = VariableAssignerData
     _node_type: NodeType = NodeType.CONVERSATION_VARIABLE_ASSIGNER
 
-    def _run(self, variable_pool: VariablePool) -> NodeRunResult:
+    def _run(self) -> NodeRunResult:
         data = cast(VariableAssignerData, self.node_data)
 
         # Should be String, Number, Object, ArrayString, ArrayNumber, ArrayObject
-        original_variable = variable_pool.get(data.assigned_variable_selector)
+        original_variable = self.graph_runtime_state.variable_pool.get(data.assigned_variable_selector)
         if not isinstance(original_variable, Variable):
-            raise VariableAssignerNodeError('assigned variable not found')
+            raise VariableAssignerNodeError("assigned variable not found")
 
         match data.write_mode:
             case WriteMode.OVER_WRITE:
-                income_value = variable_pool.get(data.input_variable_selector)
+                income_value = self.graph_runtime_state.variable_pool.get(data.input_variable_selector)
                 if not income_value:
-                    raise VariableAssignerNodeError('input value not found')
-                updated_variable = original_variable.model_copy(update={'value': income_value.value})
+                    raise VariableAssignerNodeError("input value not found")
+                updated_variable = original_variable.model_copy(update={"value": income_value.value})
 
             case WriteMode.APPEND:
-                income_value = variable_pool.get(data.input_variable_selector)
+                income_value = self.graph_runtime_state.variable_pool.get(data.input_variable_selector)
                 if not income_value:
-                    raise VariableAssignerNodeError('input value not found')
+                    raise VariableAssignerNodeError("input value not found")
                 updated_value = original_variable.value + [income_value.value]
-                updated_variable = original_variable.model_copy(update={'value': updated_value})
+                updated_variable = original_variable.model_copy(update={"value": updated_value})
 
             case WriteMode.CLEAR:
                 income_value = get_zero_value(original_variable.value_type)
-                updated_variable = original_variable.model_copy(update={'value': income_value.to_object()})
+                updated_variable = original_variable.model_copy(update={"value": income_value.to_object()})
 
             case _:
-                raise VariableAssignerNodeError(f'unsupported write mode: {data.write_mode}')
+                raise VariableAssignerNodeError(f"unsupported write mode: {data.write_mode}")
 
         # Over write the variable.
-        variable_pool.add(data.assigned_variable_selector, updated_variable)
+        self.graph_runtime_state.variable_pool.add(data.assigned_variable_selector, updated_variable)
 
         # TODO: Move database operation to the pipeline.
         # Update conversation variable.
-        conversation_id = variable_pool.get(['sys', 'conversation_id'])
+        conversation_id = self.graph_runtime_state.variable_pool.get(["sys", "conversation_id"])
         if not conversation_id:
-            raise VariableAssignerNodeError('conversation_id not found')
+            raise VariableAssignerNodeError("conversation_id not found")
         update_conversation_variable(conversation_id=conversation_id.text, variable=updated_variable)
 
         return NodeRunResult(
             status=WorkflowNodeExecutionStatus.SUCCEEDED,
             inputs={
-                'value': income_value.to_object(),
+                "value": income_value.to_object(),
             },
         )
 
@@ -73,7 +72,7 @@ def update_conversation_variable(conversation_id: str, variable: Variable):
     with Session(db.engine) as session:
         row = session.scalar(stmt)
         if not row:
-            raise VariableAssignerNodeError('conversation variable not found in the database')
+            raise VariableAssignerNodeError("conversation variable not found in the database")
         row.data = variable.model_dump_json()
         session.commit()
 
@@ -85,8 +84,8 @@ def get_zero_value(t: SegmentType):
         case SegmentType.OBJECT:
             return factory.build_segment({})
         case SegmentType.STRING:
-            return factory.build_segment('')
+            return factory.build_segment("")
         case SegmentType.NUMBER:
             return factory.build_segment(0)
         case _:
-            raise VariableAssignerNodeError(f'unsupported variable type: {t}')
+            raise VariableAssignerNodeError(f"unsupported variable type: {t}")
diff --git a/api/core/workflow/nodes/variable_assigner/node_data.py b/api/core/workflow/nodes/variable_assigner/node_data.py
index b3652b6802..8ac8eadf7c 100644
--- a/api/core/workflow/nodes/variable_assigner/node_data.py
+++ b/api/core/workflow/nodes/variable_assigner/node_data.py
@@ -6,14 +6,14 @@ from core.workflow.entities.base_node_data_entities import BaseNodeData
 
 
 class WriteMode(str, Enum):
-    OVER_WRITE = 'over-write'
-    APPEND = 'append'
-    CLEAR = 'clear'
+    OVER_WRITE = "over-write"
+    APPEND = "append"
+    CLEAR = "clear"
 
 
 class VariableAssignerData(BaseNodeData):
-    title: str = 'Variable Assigner'
-    desc: Optional[str] = 'Assign a value to a variable'
+    title: str = "Variable Assigner"
+    desc: Optional[str] = "Assign a value to a variable"
     assigned_variable_selector: Sequence[str]
     write_mode: WriteMode
     input_variable_selector: Sequence[str]
diff --git a/api/core/workflow/utils/condition/__init__.py b/api/core/workflow/utils/condition/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/workflow/utils/condition/entities.py b/api/core/workflow/utils/condition/entities.py
new file mode 100644
index 0000000000..b8e8b881a5
--- /dev/null
+++ b/api/core/workflow/utils/condition/entities.py
@@ -0,0 +1,32 @@
+from typing import Literal, Optional
+
+from pydantic import BaseModel
+
+
+class Condition(BaseModel):
+    """
+    Condition entity
+    """
+
+    variable_selector: list[str]
+    comparison_operator: Literal[
+        # for string or array
+        "contains",
+        "not contains",
+        "start with",
+        "end with",
+        "is",
+        "is not",
+        "empty",
+        "not empty",
+        # for number
+        "=",
+        "≠",
+        ">",
+        "<",
+        "≥",
+        "≤",
+        "null",
+        "not null",
+    ]
+    value: Optional[str] = None
diff --git a/api/core/workflow/utils/condition/processor.py b/api/core/workflow/utils/condition/processor.py
new file mode 100644
index 0000000000..395ee82478
--- /dev/null
+++ b/api/core/workflow/utils/condition/processor.py
@@ -0,0 +1,381 @@
+from collections.abc import Sequence
+from typing import Any, Optional
+
+from core.file.file_obj import FileVar
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.utils.condition.entities import Condition
+from core.workflow.utils.variable_template_parser import VariableTemplateParser
+
+
+class ConditionProcessor:
+    def process_conditions(self, variable_pool: VariablePool, conditions: Sequence[Condition]):
+        input_conditions = []
+        group_result = []
+
+        index = 0
+        for condition in conditions:
+            index += 1
+            actual_value = variable_pool.get_any(condition.variable_selector)
+
+            expected_value = None
+            if condition.value is not None:
+                variable_template_parser = VariableTemplateParser(template=condition.value)
+                variable_selectors = variable_template_parser.extract_variable_selectors()
+                if variable_selectors:
+                    for variable_selector in variable_selectors:
+                        value = variable_pool.get_any(variable_selector.value_selector)
+                        expected_value = variable_template_parser.format({variable_selector.variable: value})
+
+                    if expected_value is None:
+                        expected_value = condition.value
+                else:
+                    expected_value = condition.value
+
+            comparison_operator = condition.comparison_operator
+            input_conditions.append(
+                {
+                    "actual_value": actual_value,
+                    "expected_value": expected_value,
+                    "comparison_operator": comparison_operator,
+                }
+            )
+
+            result = self.evaluate_condition(actual_value, comparison_operator, expected_value)
+            group_result.append(result)
+
+        return input_conditions, group_result
+
+    def evaluate_condition(
+        self,
+        actual_value: Optional[str | int | float | dict[Any, Any] | list[Any] | FileVar | None],
+        comparison_operator: str,
+        expected_value: Optional[str] = None,
+    ) -> bool:
+        """
+        Evaluate condition
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :param comparison_operator: comparison operator
+
+        :return: bool
+        """
+        if comparison_operator == "contains":
+            return self._assert_contains(actual_value, expected_value)
+        elif comparison_operator == "not contains":
+            return self._assert_not_contains(actual_value, expected_value)
+        elif comparison_operator == "start with":
+            return self._assert_start_with(actual_value, expected_value)
+        elif comparison_operator == "end with":
+            return self._assert_end_with(actual_value, expected_value)
+        elif comparison_operator == "is":
+            return self._assert_is(actual_value, expected_value)
+        elif comparison_operator == "is not":
+            return self._assert_is_not(actual_value, expected_value)
+        elif comparison_operator == "empty":
+            return self._assert_empty(actual_value)
+        elif comparison_operator == "not empty":
+            return self._assert_not_empty(actual_value)
+        elif comparison_operator == "=":
+            return self._assert_equal(actual_value, expected_value)
+        elif comparison_operator == "≠":
+            return self._assert_not_equal(actual_value, expected_value)
+        elif comparison_operator == ">":
+            return self._assert_greater_than(actual_value, expected_value)
+        elif comparison_operator == "<":
+            return self._assert_less_than(actual_value, expected_value)
+        elif comparison_operator == "≥":
+            return self._assert_greater_than_or_equal(actual_value, expected_value)
+        elif comparison_operator == "≤":
+            return self._assert_less_than_or_equal(actual_value, expected_value)
+        elif comparison_operator == "null":
+            return self._assert_null(actual_value)
+        elif comparison_operator == "not null":
+            return self._assert_not_null(actual_value)
+        else:
+            raise ValueError(f"Invalid comparison operator: {comparison_operator}")
+
+    def _assert_contains(self, actual_value: Optional[str | list], expected_value: str) -> bool:
+        """
+        Assert contains
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if not actual_value:
+            return False
+
+        if not isinstance(actual_value, str | list):
+            raise ValueError("Invalid actual value type: string or array")
+
+        if expected_value not in actual_value:
+            return False
+        return True
+
+    def _assert_not_contains(self, actual_value: Optional[str | list], expected_value: str) -> bool:
+        """
+        Assert not contains
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if not actual_value:
+            return True
+
+        if not isinstance(actual_value, str | list):
+            raise ValueError("Invalid actual value type: string or array")
+
+        if expected_value in actual_value:
+            return False
+        return True
+
+    def _assert_start_with(self, actual_value: Optional[str], expected_value: str) -> bool:
+        """
+        Assert start with
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if not actual_value:
+            return False
+
+        if not isinstance(actual_value, str):
+            raise ValueError("Invalid actual value type: string")
+
+        if not actual_value.startswith(expected_value):
+            return False
+        return True
+
+    def _assert_end_with(self, actual_value: Optional[str], expected_value: str) -> bool:
+        """
+        Assert end with
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if not actual_value:
+            return False
+
+        if not isinstance(actual_value, str):
+            raise ValueError("Invalid actual value type: string")
+
+        if not actual_value.endswith(expected_value):
+            return False
+        return True
+
+    def _assert_is(self, actual_value: Optional[str], expected_value: str) -> bool:
+        """
+        Assert is
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if actual_value is None:
+            return False
+
+        if not isinstance(actual_value, str):
+            raise ValueError("Invalid actual value type: string")
+
+        if actual_value != expected_value:
+            return False
+        return True
+
+    def _assert_is_not(self, actual_value: Optional[str], expected_value: str) -> bool:
+        """
+        Assert is not
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if actual_value is None:
+            return False
+
+        if not isinstance(actual_value, str):
+            raise ValueError("Invalid actual value type: string")
+
+        if actual_value == expected_value:
+            return False
+        return True
+
+    def _assert_empty(self, actual_value: Optional[str]) -> bool:
+        """
+        Assert empty
+        :param actual_value: actual value
+        :return:
+        """
+        if not actual_value:
+            return True
+        return False
+
+    def _assert_not_empty(self, actual_value: Optional[str]) -> bool:
+        """
+        Assert not empty
+        :param actual_value: actual value
+        :return:
+        """
+        if actual_value:
+            return True
+        return False
+
+    def _assert_equal(self, actual_value: Optional[int | float], expected_value: str | int | float) -> bool:
+        """
+        Assert equal
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if actual_value is None:
+            return False
+
+        if not isinstance(actual_value, int | float):
+            raise ValueError("Invalid actual value type: number")
+
+        if isinstance(actual_value, int):
+            expected_value = int(expected_value)
+        else:
+            expected_value = float(expected_value)
+
+        if actual_value != expected_value:
+            return False
+        return True
+
+    def _assert_not_equal(self, actual_value: Optional[int | float], expected_value: str | int | float) -> bool:
+        """
+        Assert not equal
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if actual_value is None:
+            return False
+
+        if not isinstance(actual_value, int | float):
+            raise ValueError("Invalid actual value type: number")
+
+        if isinstance(actual_value, int):
+            expected_value = int(expected_value)
+        else:
+            expected_value = float(expected_value)
+
+        if actual_value == expected_value:
+            return False
+        return True
+
+    def _assert_greater_than(self, actual_value: Optional[int | float], expected_value: str | int | float) -> bool:
+        """
+        Assert greater than
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if actual_value is None:
+            return False
+
+        if not isinstance(actual_value, int | float):
+            raise ValueError("Invalid actual value type: number")
+
+        if isinstance(actual_value, int):
+            expected_value = int(expected_value)
+        else:
+            expected_value = float(expected_value)
+
+        if actual_value <= expected_value:
+            return False
+        return True
+
+    def _assert_less_than(self, actual_value: Optional[int | float], expected_value: str | int | float) -> bool:
+        """
+        Assert less than
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if actual_value is None:
+            return False
+
+        if not isinstance(actual_value, int | float):
+            raise ValueError("Invalid actual value type: number")
+
+        if isinstance(actual_value, int):
+            expected_value = int(expected_value)
+        else:
+            expected_value = float(expected_value)
+
+        if actual_value >= expected_value:
+            return False
+        return True
+
+    def _assert_greater_than_or_equal(
+        self, actual_value: Optional[int | float], expected_value: str | int | float
+    ) -> bool:
+        """
+        Assert greater than or equal
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if actual_value is None:
+            return False
+
+        if not isinstance(actual_value, int | float):
+            raise ValueError("Invalid actual value type: number")
+
+        if isinstance(actual_value, int):
+            expected_value = int(expected_value)
+        else:
+            expected_value = float(expected_value)
+
+        if actual_value < expected_value:
+            return False
+        return True
+
+    def _assert_less_than_or_equal(
+        self, actual_value: Optional[int | float], expected_value: str | int | float
+    ) -> bool:
+        """
+        Assert less than or equal
+        :param actual_value: actual value
+        :param expected_value: expected value
+        :return:
+        """
+        if actual_value is None:
+            return False
+
+        if not isinstance(actual_value, int | float):
+            raise ValueError("Invalid actual value type: number")
+
+        if isinstance(actual_value, int):
+            expected_value = int(expected_value)
+        else:
+            expected_value = float(expected_value)
+
+        if actual_value > expected_value:
+            return False
+        return True
+
+    def _assert_null(self, actual_value: Optional[int | float]) -> bool:
+        """
+        Assert null
+        :param actual_value: actual value
+        :return:
+        """
+        if actual_value is None:
+            return True
+        return False
+
+    def _assert_not_null(self, actual_value: Optional[int | float]) -> bool:
+        """
+        Assert not null
+        :param actual_value: actual value
+        :return:
+        """
+        if actual_value is not None:
+            return True
+        return False
+
+
+class ConditionAssertionError(Exception):
+    def __init__(self, message: str, conditions: list[dict], sub_condition_compare_results: list[dict]) -> None:
+        self.message = message
+        self.conditions = conditions
+        self.sub_condition_compare_results = sub_condition_compare_results
+        super().__init__(self.message)
diff --git a/api/core/workflow/utils/variable_template_parser.py b/api/core/workflow/utils/variable_template_parser.py
index c43fde172c..fd0e48b862 100644
--- a/api/core/workflow/utils/variable_template_parser.py
+++ b/api/core/workflow/utils/variable_template_parser.py
@@ -5,7 +5,7 @@ from typing import Any
 from core.workflow.entities.variable_entities import VariableSelector
 from core.workflow.entities.variable_pool import VariablePool
 
-REGEX = re.compile(r'\{\{(#[a-zA-Z0-9_]{1,50}(\.[a-zA-Z_][a-zA-Z0-9_]{0,29}){1,10}#)\}\}')
+REGEX = re.compile(r"\{\{(#[a-zA-Z0-9_]{1,50}(\.[a-zA-Z_][a-zA-Z0-9_]{0,29}){1,10}#)\}\}")
 
 
 def parse_mixed_template(*, template: str, variable_pool: VariablePool) -> str:
@@ -20,7 +20,7 @@ def parse_mixed_template(*, template: str, variable_pool: VariablePool) -> str:
     # e.g. ('#node_id.query.name#', ['node_id', 'query', 'name'])
     key_selectors = filter(
         lambda t: len(t[1]) >= 2,
-        ((key, selector.replace('#', '').split('.')) for key, selector in zip(variable_keys, variable_keys)),
+        ((key, selector.replace("#", "").split(".")) for key, selector in zip(variable_keys, variable_keys)),
     )
     inputs = {key: variable_pool.get_any(selector) for key, selector in key_selectors}
 
@@ -29,13 +29,13 @@ def parse_mixed_template(*, template: str, variable_pool: VariablePool) -> str:
         # return original matched string if key not found
         value = inputs.get(key, match.group(0))
         if value is None:
-            value = ''
+            value = ""
         value = str(value)
         # remove template variables if required
-        return re.sub(REGEX, r'{\1}', value)
+        return re.sub(REGEX, r"{\1}", value)
 
     result = re.sub(REGEX, replacer, template)
-    result = re.sub(r'<\|.*?\|>', '', result)
+    result = re.sub(r"<\|.*?\|>", "", result)
     return result
 
 
@@ -101,8 +101,8 @@ class VariableTemplateParser:
         """
         variable_selectors = []
         for variable_key in self.variable_keys:
-            remove_hash = variable_key.replace('#', '')
-            split_result = remove_hash.split('.')
+            remove_hash = variable_key.replace("#", "")
+            split_result = remove_hash.split(".")
             if len(split_result) < 2:
                 continue
 
@@ -127,7 +127,7 @@ class VariableTemplateParser:
             value = inputs.get(key, match.group(0))  # return original matched string if key not found
 
             if value is None:
-                value = ''
+                value = ""
             # convert the value to string
             if isinstance(value, list | dict | bool | int | float):
                 value = str(value)
@@ -136,7 +136,7 @@ class VariableTemplateParser:
             return VariableTemplateParser.remove_template_variables(value)
 
         prompt = re.sub(REGEX, replacer, self.template)
-        return re.sub(r'<\|.*?\|>', '', prompt)
+        return re.sub(r"<\|.*?\|>", "", prompt)
 
     @classmethod
     def remove_template_variables(cls, text: str):
@@ -149,4 +149,4 @@ class VariableTemplateParser:
         Returns:
             The text with template variables removed.
         """
-        return re.sub(REGEX, r'{\1}', text)
+        return re.sub(REGEX, r"{\1}", text)
diff --git a/api/core/workflow/workflow_engine_manager.py b/api/core/workflow/workflow_engine_manager.py
index 3157eedfee..e69de29bb2 100644
--- a/api/core/workflow/workflow_engine_manager.py
+++ b/api/core/workflow/workflow_engine_manager.py
@@ -1,1005 +0,0 @@
-import logging
-import time
-from collections.abc import Mapping, Sequence
-from typing import Any, Optional, cast
-
-import contexts
-from configs import dify_config
-from core.app.apps.base_app_queue_manager import GenerateTaskStoppedException
-from core.app.entities.app_invoke_entities import InvokeFrom
-from core.file.file_obj import FileExtraConfig, FileTransferMethod, FileType, FileVar
-from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
-from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType
-from core.workflow.entities.variable_pool import VariablePool, VariableValue
-from core.workflow.entities.workflow_entities import WorkflowNodeAndResult, WorkflowRunState
-from core.workflow.errors import WorkflowNodeRunFailedError
-from core.workflow.nodes.answer.answer_node import AnswerNode
-from core.workflow.nodes.base_node import BaseIterationNode, BaseNode, UserFrom
-from core.workflow.nodes.code.code_node import CodeNode
-from core.workflow.nodes.end.end_node import EndNode
-from core.workflow.nodes.http_request.http_request_node import HttpRequestNode
-from core.workflow.nodes.if_else.if_else_node import IfElseNode
-from core.workflow.nodes.iteration.entities import IterationState
-from core.workflow.nodes.iteration.iteration_node import IterationNode
-from core.workflow.nodes.knowledge_retrieval.knowledge_retrieval_node import KnowledgeRetrievalNode
-from core.workflow.nodes.llm.entities import LLMNodeData
-from core.workflow.nodes.llm.llm_node import LLMNode
-from core.workflow.nodes.parameter_extractor.parameter_extractor_node import ParameterExtractorNode
-from core.workflow.nodes.question_classifier.question_classifier_node import QuestionClassifierNode
-from core.workflow.nodes.start.start_node import StartNode
-from core.workflow.nodes.template_transform.template_transform_node import TemplateTransformNode
-from core.workflow.nodes.tool.tool_node import ToolNode
-from core.workflow.nodes.variable_aggregator.variable_aggregator_node import VariableAggregatorNode
-from core.workflow.nodes.variable_assigner import VariableAssignerNode
-from extensions.ext_database import db
-from models.workflow import (
-    Workflow,
-    WorkflowNodeExecutionStatus,
-)
-
-node_classes: Mapping[NodeType, type[BaseNode]] = {
-    NodeType.START: StartNode,
-    NodeType.END: EndNode,
-    NodeType.ANSWER: AnswerNode,
-    NodeType.LLM: LLMNode,
-    NodeType.KNOWLEDGE_RETRIEVAL: KnowledgeRetrievalNode,
-    NodeType.IF_ELSE: IfElseNode,
-    NodeType.CODE: CodeNode,
-    NodeType.TEMPLATE_TRANSFORM: TemplateTransformNode,
-    NodeType.QUESTION_CLASSIFIER: QuestionClassifierNode,
-    NodeType.HTTP_REQUEST: HttpRequestNode,
-    NodeType.TOOL: ToolNode,
-    NodeType.VARIABLE_AGGREGATOR: VariableAggregatorNode,
-    NodeType.VARIABLE_ASSIGNER: VariableAggregatorNode,
-    NodeType.ITERATION: IterationNode,
-    NodeType.PARAMETER_EXTRACTOR: ParameterExtractorNode,
-    NodeType.CONVERSATION_VARIABLE_ASSIGNER: VariableAssignerNode,
-}
-
-logger = logging.getLogger(__name__)
-
-
-class WorkflowEngineManager:
-    def get_default_configs(self) -> list[dict]:
-        """
-        Get default block configs
-        """
-        default_block_configs = []
-        for node_type, node_class in node_classes.items():
-            default_config = node_class.get_default_config()
-            if default_config:
-                default_block_configs.append(default_config)
-
-        return default_block_configs
-
-    def get_default_config(self, node_type: NodeType, filters: Optional[dict] = None) -> Optional[dict]:
-        """
-        Get default config of node.
-        :param node_type: node type
-        :param filters: filter by node config parameters.
-        :return:
-        """
-        node_class = node_classes.get(node_type)
-        if not node_class:
-            return None
-
-        default_config = node_class.get_default_config(filters=filters)
-        if not default_config:
-            return None
-
-        return default_config
-
-    def run_workflow(
-        self,
-        *,
-        workflow: Workflow,
-        user_id: str,
-        user_from: UserFrom,
-        invoke_from: InvokeFrom,
-        callbacks: Sequence[WorkflowCallback],
-        call_depth: int = 0,
-        variable_pool: VariablePool | None = None,
-    ) -> None:
-        """
-        :param workflow: Workflow instance
-        :param user_id: user id
-        :param user_from: user from
-        :param invoke_from: invoke from
-        :param callbacks: workflow callbacks
-        :param call_depth: call depth
-        :param variable_pool: variable pool
-        """
-        # fetch workflow graph
-        graph = workflow.graph_dict
-        if not graph:
-            raise ValueError('workflow graph not found')
-
-        if 'nodes' not in graph or 'edges' not in graph:
-            raise ValueError('nodes or edges not found in workflow graph')
-
-        if not isinstance(graph.get('nodes'), list):
-            raise ValueError('nodes in workflow graph must be a list')
-
-        if not isinstance(graph.get('edges'), list):
-            raise ValueError('edges in workflow graph must be a list')
-
-
-        workflow_call_max_depth = dify_config.WORKFLOW_CALL_MAX_DEPTH
-        if call_depth > workflow_call_max_depth:
-            raise ValueError('Max workflow call depth {} reached.'.format(workflow_call_max_depth))
-
-        # init workflow run state
-        if not variable_pool:
-            variable_pool = contexts.workflow_variable_pool.get()
-        workflow_run_state = WorkflowRunState(
-            workflow=workflow,
-            start_at=time.perf_counter(),
-            variable_pool=variable_pool,
-            user_id=user_id,
-            user_from=user_from,
-            invoke_from=invoke_from,
-            workflow_call_depth=call_depth
-        )
-
-        # init workflow run
-        if callbacks:
-            for callback in callbacks:
-                callback.on_workflow_run_started()
-
-        # run workflow
-        self._run_workflow(
-            workflow=workflow,
-            workflow_run_state=workflow_run_state,
-            callbacks=callbacks,
-        )
-
-    def _run_workflow(self, workflow: Workflow,
-                     workflow_run_state: WorkflowRunState,
-                     callbacks: Sequence[WorkflowCallback],
-                     start_at: Optional[str] = None,
-                     end_at: Optional[str] = None) -> None:
-        """
-        Run workflow
-        :param workflow: Workflow instance
-        :param user_id: user id
-        :param user_from: user from
-        :param user_inputs: user variables inputs
-        :param system_inputs: system inputs, like: query, files
-        :param callbacks: workflow callbacks
-        :param call_depth: call depth
-        :param start_at: force specific start node
-        :param end_at: force specific end node
-        :return:
-        """
-        graph = workflow.graph_dict
-
-        try:
-            answer_prov_node_ids = []
-            for node in graph.get('nodes', []):
-                if node.get('id', '') == 'answer':
-                    try:
-                        answer_prov_node_ids.append(node.get('data', {})
-                                                    .get('answer', '')
-                                                    .replace('#', '')
-                                                    .replace('.text', '')
-                                                    .replace('{{', '')
-                                                    .replace('}}', '').split('.')[0])
-                    except Exception as e:
-                        logger.error(e)
-
-            predecessor_node: BaseNode | None = None
-            current_iteration_node: BaseIterationNode | None = None
-            has_entry_node = False
-            max_execution_steps = dify_config.WORKFLOW_MAX_EXECUTION_STEPS
-            max_execution_time = dify_config.WORKFLOW_MAX_EXECUTION_TIME
-            while True:
-                # get next node, multiple target nodes in the future
-                next_node = self._get_next_overall_node(
-                    workflow_run_state=workflow_run_state,
-                    graph=graph,
-                    predecessor_node=predecessor_node,
-                    callbacks=callbacks,
-                    start_at=start_at,
-                    end_at=end_at
-                )
-
-                if not next_node:
-                    # reached loop/iteration end or overall end
-                    if current_iteration_node and workflow_run_state.current_iteration_state:
-                        # reached loop/iteration end
-                        # get next iteration
-                        next_iteration = current_iteration_node.get_next_iteration(
-                            variable_pool=workflow_run_state.variable_pool,
-                            state=workflow_run_state.current_iteration_state
-                        )
-                        self._workflow_iteration_next(
-                            graph=graph,
-                            current_iteration_node=current_iteration_node,
-                            workflow_run_state=workflow_run_state,
-                            callbacks=callbacks
-                        )
-                        if isinstance(next_iteration, NodeRunResult):
-                            if next_iteration.outputs:
-                                for variable_key, variable_value in next_iteration.outputs.items():
-                                    # append variables to variable pool recursively
-                                    self._append_variables_recursively(
-                                        variable_pool=workflow_run_state.variable_pool,
-                                        node_id=current_iteration_node.node_id,
-                                        variable_key_list=[variable_key],
-                                        variable_value=variable_value
-                                    )
-                            self._workflow_iteration_completed(
-                                current_iteration_node=current_iteration_node,
-                                workflow_run_state=workflow_run_state,
-                                callbacks=callbacks
-                            )
-                            # iteration has ended
-                            next_node = self._get_next_overall_node(
-                                workflow_run_state=workflow_run_state,
-                                graph=graph,
-                                predecessor_node=current_iteration_node,
-                                callbacks=callbacks,
-                                start_at=start_at,
-                                end_at=end_at
-                            )
-                            current_iteration_node = None
-                            workflow_run_state.current_iteration_state = None
-                            # continue overall process
-                        elif isinstance(next_iteration, str):
-                            # move to next iteration
-                            next_node_id = next_iteration
-                            # get next id
-                            next_node = self._get_node(workflow_run_state=workflow_run_state, graph=graph, node_id=next_node_id, callbacks=callbacks)
-
-                if not next_node:
-                    break
-
-                # check is already ran
-                if self._check_node_has_ran(workflow_run_state, next_node.node_id):
-                    predecessor_node = next_node
-                    continue
-
-                has_entry_node = True
-
-                # max steps reached
-                if workflow_run_state.workflow_node_steps > max_execution_steps:
-                    raise ValueError('Max steps {} reached.'.format(max_execution_steps))
-
-                # or max execution time reached
-                if self._is_timed_out(start_at=workflow_run_state.start_at, max_execution_time=max_execution_time):
-                    raise ValueError('Max execution time {}s reached.'.format(max_execution_time))
-
-                # handle iteration nodes
-                if isinstance(next_node, BaseIterationNode):
-                    current_iteration_node = next_node
-                    workflow_run_state.current_iteration_state = next_node.run(
-                        variable_pool=workflow_run_state.variable_pool
-                    )
-                    self._workflow_iteration_started(
-                        graph=graph,
-                        current_iteration_node=current_iteration_node,
-                        workflow_run_state=workflow_run_state,
-                        predecessor_node_id=predecessor_node.node_id if predecessor_node else None,
-                        callbacks=callbacks
-                    )
-                    predecessor_node = next_node
-                    # move to start node of iteration
-                    next_node_id = next_node.get_next_iteration(
-                        variable_pool=workflow_run_state.variable_pool,
-                        state=workflow_run_state.current_iteration_state
-                    )
-                    self._workflow_iteration_next(
-                        graph=graph,
-                        current_iteration_node=current_iteration_node,
-                        workflow_run_state=workflow_run_state,
-                        callbacks=callbacks
-                    )
-                    if isinstance(next_node_id, NodeRunResult):
-                        # iteration has ended
-                        current_iteration_node.set_output(
-                            variable_pool=workflow_run_state.variable_pool,
-                            state=workflow_run_state.current_iteration_state
-                        )
-                        self._workflow_iteration_completed(
-                            current_iteration_node=current_iteration_node,
-                            workflow_run_state=workflow_run_state,
-                            callbacks=callbacks
-                        )
-                        current_iteration_node = None
-                        workflow_run_state.current_iteration_state = None
-                        continue
-                    else:
-                        next_node = self._get_node(workflow_run_state=workflow_run_state, graph=graph, node_id=next_node_id, callbacks=callbacks)
-
-                if next_node and next_node.node_id in answer_prov_node_ids:
-                    next_node.is_answer_previous_node = True
-
-                # run workflow, run multiple target nodes in the future
-                self._run_workflow_node(
-                    workflow_run_state=workflow_run_state,
-                    node=next_node,
-                    predecessor_node=predecessor_node,
-                    callbacks=callbacks
-                )
-
-                if next_node.node_type in [NodeType.END]:
-                    break
-
-                predecessor_node = next_node
-
-            if not has_entry_node:
-                self._workflow_run_failed(
-                    error='Start node not found in workflow graph.',
-                    callbacks=callbacks
-                )
-                return
-        except GenerateTaskStoppedException as e:
-            return
-        except Exception as e:
-            self._workflow_run_failed(
-                error=str(e),
-                callbacks=callbacks
-            )
-            return
-
-        # workflow run success
-        self._workflow_run_success(
-            callbacks=callbacks
-        )
-
-    def single_step_run_workflow_node(self, workflow: Workflow,
-                                      node_id: str,
-                                      user_id: str,
-                                      user_inputs: dict) -> tuple[BaseNode, NodeRunResult]:
-        """
-        Single step run workflow node
-        :param workflow: Workflow instance
-        :param node_id: node id
-        :param user_id: user id
-        :param user_inputs: user inputs
-        :return:
-        """
-        # fetch node info from workflow graph
-        graph = workflow.graph_dict
-        if not graph:
-            raise ValueError('workflow graph not found')
-
-        nodes = graph.get('nodes')
-        if not nodes:
-            raise ValueError('nodes not found in workflow graph')
-
-        # fetch node config from node id
-        node_config = None
-        for node in nodes:
-            if node.get('id') == node_id:
-                node_config = node
-                break
-
-        if not node_config:
-            raise ValueError('node id not found in workflow graph')
-
-        # Get node class
-        node_type = NodeType.value_of(node_config.get('data', {}).get('type'))
-        node_cls = node_classes.get(node_type)
-
-        # init workflow run state
-        node_instance = node_cls(
-            tenant_id=workflow.tenant_id,
-            app_id=workflow.app_id,
-            workflow_id=workflow.id,
-            user_id=user_id,
-            user_from=UserFrom.ACCOUNT,
-            invoke_from=InvokeFrom.DEBUGGER,
-            config=node_config,
-            workflow_call_depth=0
-        )
-
-        try:
-            # init variable pool
-            variable_pool = VariablePool(
-                system_variables={},
-                user_inputs={},
-                environment_variables=workflow.environment_variables,
-                conversation_variables=workflow.conversation_variables,
-            )
-
-            if node_cls is None:
-                raise ValueError('Node class not found')
-            # variable selector to variable mapping
-            variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(node_config)
-
-            self._mapping_user_inputs_to_variable_pool(
-                variable_mapping=variable_mapping,
-                user_inputs=user_inputs,
-                variable_pool=variable_pool,
-                tenant_id=workflow.tenant_id,
-                node_instance=node_instance
-            )
-
-            # run node
-            node_run_result = node_instance.run(
-                variable_pool=variable_pool
-            )
-
-            # sign output files
-            node_run_result.outputs = self.handle_special_values(node_run_result.outputs)
-        except Exception as e:
-            raise WorkflowNodeRunFailedError(
-                node_id=node_instance.node_id,
-                node_type=node_instance.node_type,
-                node_title=node_instance.node_data.title,
-                error=str(e)
-            )
-
-        return node_instance, node_run_result
-
-    def single_step_run_iteration_workflow_node(self, workflow: Workflow,
-                                            node_id: str,
-                                            user_id: str,
-                                            user_inputs: dict,
-                                            callbacks: Sequence[WorkflowCallback],
-    ) -> None:
-        """
-        Single iteration run workflow node
-        """
-        # fetch node info from workflow graph
-        graph = workflow.graph_dict
-        if not graph:
-            raise ValueError('workflow graph not found')
-
-        nodes = graph.get('nodes')
-        if not nodes:
-            raise ValueError('nodes not found in workflow graph')
-
-        for node in nodes:
-            if node.get('id') == node_id:
-                if node.get('data', {}).get('type') in [
-                    NodeType.ITERATION.value,
-                    NodeType.LOOP.value,
-                ]:
-                    node_config = node
-                else:
-                    raise ValueError('node id is not an iteration node')
-
-        # init variable pool
-        variable_pool = VariablePool(
-            system_variables={},
-            user_inputs={},
-            environment_variables=workflow.environment_variables,
-            conversation_variables=workflow.conversation_variables,
-        )
-
-        # variable selector to variable mapping
-        iteration_nested_nodes = [
-            node for node in nodes
-            if node.get('data', {}).get('iteration_id') == node_id or node.get('id') == node_id
-        ]
-        iteration_nested_node_ids = [node.get('id') for node in iteration_nested_nodes]
-
-        if not iteration_nested_nodes:
-            raise ValueError('iteration has no nested nodes')
-
-        # init workflow run
-        if callbacks:
-            for callback in callbacks:
-                callback.on_workflow_run_started()
-
-        for node_config in iteration_nested_nodes:
-            # mapping user inputs to variable pool
-            node_cls = node_classes.get(NodeType.value_of(node_config.get('data', {}).get('type')))
-            if node_cls is None:
-                raise ValueError('Node class not found')
-            variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(node_config)
-
-            # remove iteration variables
-            variable_mapping = {
-                f'{node_config.get("id")}.{key}': value for key, value in variable_mapping.items()
-                if value[0] != node_id
-            }
-
-            # remove variable out from iteration
-            variable_mapping = {
-                key: value for key, value in variable_mapping.items()
-                if value[0] not in iteration_nested_node_ids
-            }
-
-            # append variables to variable pool
-            node_instance = node_cls(
-                tenant_id=workflow.tenant_id,
-                app_id=workflow.app_id,
-                workflow_id=workflow.id,
-                user_id=user_id,
-                user_from=UserFrom.ACCOUNT,
-                invoke_from=InvokeFrom.DEBUGGER,
-                config=node_config,
-                callbacks=callbacks,
-                workflow_call_depth=0
-            )
-
-            self._mapping_user_inputs_to_variable_pool(
-                variable_mapping=variable_mapping,
-                user_inputs=user_inputs,
-                variable_pool=variable_pool,
-                tenant_id=workflow.tenant_id,
-                node_instance=node_instance
-            )
-
-        # fetch end node of iteration
-        end_node_id = None
-        for edge in graph.get('edges'):
-            if edge.get('source') == node_id:
-                end_node_id = edge.get('target')
-                break
-
-        if not end_node_id:
-            raise ValueError('end node of iteration not found')
-
-        # init workflow run state
-        workflow_run_state = WorkflowRunState(
-            workflow=workflow,
-            start_at=time.perf_counter(),
-            variable_pool=variable_pool,
-            user_id=user_id,
-            user_from=UserFrom.ACCOUNT,
-            invoke_from=InvokeFrom.DEBUGGER,
-            workflow_call_depth=0
-        )
-
-        # run workflow
-        self._run_workflow(
-            workflow=workflow,
-            workflow_run_state=workflow_run_state,
-            callbacks=callbacks,
-            start_at=node_id,
-            end_at=end_node_id
-        )
-
-    def _workflow_run_success(self, callbacks: Sequence[WorkflowCallback]) -> None:
-        """
-        Workflow run success
-        :param callbacks: workflow callbacks
-        :return:
-        """
-
-        if callbacks:
-            for callback in callbacks:
-                callback.on_workflow_run_succeeded()
-
-    def _workflow_run_failed(self, error: str,
-                             callbacks: Sequence[WorkflowCallback]) -> None:
-        """
-        Workflow run failed
-        :param error: error message
-        :param callbacks: workflow callbacks
-        :return:
-        """
-        if callbacks:
-            for callback in callbacks:
-                callback.on_workflow_run_failed(
-                    error=error
-                )
-
-    def _workflow_iteration_started(self, *, graph: Mapping[str, Any],
-                                    current_iteration_node: BaseIterationNode,
-                                    workflow_run_state: WorkflowRunState,
-                                    predecessor_node_id: Optional[str] = None,
-                                    callbacks: Sequence[WorkflowCallback]) -> None:
-        """
-        Workflow iteration started
-        :param current_iteration_node: current iteration node
-        :param workflow_run_state: workflow run state
-        :param callbacks: workflow callbacks
-        :return:
-        """
-        # get nested nodes
-        iteration_nested_nodes = [
-            node for node in graph.get('nodes')
-            if node.get('data', {}).get('iteration_id') == current_iteration_node.node_id
-        ]
-
-        if not iteration_nested_nodes:
-            raise ValueError('iteration has no nested nodes')
-
-        if callbacks:
-            if isinstance(workflow_run_state.current_iteration_state, IterationState):
-                for callback in callbacks:
-                    callback.on_workflow_iteration_started(
-                        node_id=current_iteration_node.node_id,
-                        node_type=NodeType.ITERATION,
-                        node_run_index=workflow_run_state.workflow_node_steps,
-                        node_data=current_iteration_node.node_data,
-                        inputs=workflow_run_state.current_iteration_state.inputs,
-                        predecessor_node_id=predecessor_node_id,
-                        metadata=workflow_run_state.current_iteration_state.metadata.model_dump()
-                    )
-
-        # add steps
-        workflow_run_state.workflow_node_steps += 1
-
-    def _workflow_iteration_next(self, *, graph: Mapping[str, Any],
-                                 current_iteration_node: BaseIterationNode,
-                                 workflow_run_state: WorkflowRunState,
-                                 callbacks: Sequence[WorkflowCallback]) -> None:
-        """
-        Workflow iteration next
-        :param workflow_run_state: workflow run state
-        :return:
-        """
-        if callbacks:
-            if isinstance(workflow_run_state.current_iteration_state, IterationState):
-                for callback in callbacks:
-                    callback.on_workflow_iteration_next(
-                        node_id=current_iteration_node.node_id,
-                        node_type=NodeType.ITERATION,
-                        index=workflow_run_state.current_iteration_state.index,
-                        node_run_index=workflow_run_state.workflow_node_steps,
-                        output=workflow_run_state.current_iteration_state.get_current_output()
-                    )
-        # clear ran nodes
-        workflow_run_state.workflow_node_runs = [
-            node_run for node_run in workflow_run_state.workflow_node_runs
-            if node_run.iteration_node_id != current_iteration_node.node_id
-        ]
-
-        # clear variables in current iteration
-        nodes = graph.get('nodes')
-        nodes = [node for node in nodes if node.get('data', {}).get('iteration_id') == current_iteration_node.node_id]
-
-        for node in nodes:
-            workflow_run_state.variable_pool.remove((node.get('id'),))
-
-    def _workflow_iteration_completed(self, *, current_iteration_node: BaseIterationNode,
-                                        workflow_run_state: WorkflowRunState,
-                                        callbacks: Sequence[WorkflowCallback]) -> None:
-        if callbacks:
-            if isinstance(workflow_run_state.current_iteration_state, IterationState):
-                for callback in callbacks:
-                    callback.on_workflow_iteration_completed(
-                        node_id=current_iteration_node.node_id,
-                        node_type=NodeType.ITERATION,
-                        node_run_index=workflow_run_state.workflow_node_steps,
-                        outputs={
-                            'output': workflow_run_state.current_iteration_state.outputs
-                        }
-                    )
-
-    def _get_next_overall_node(self, *, workflow_run_state: WorkflowRunState,
-                       graph: Mapping[str, Any],
-                       predecessor_node: Optional[BaseNode] = None,
-                       callbacks: Sequence[WorkflowCallback],
-                       start_at: Optional[str] = None,
-                       end_at: Optional[str] = None) -> Optional[BaseNode]:
-        """
-        Get next node
-        multiple target nodes in the future.
-        :param graph: workflow graph
-        :param predecessor_node: predecessor node
-        :param callbacks: workflow callbacks
-        :return:
-        """
-        nodes = graph.get('nodes')
-        if not nodes:
-            return None
-
-        if not predecessor_node:
-            for node_config in nodes:
-                node_cls = None
-                if start_at:
-                    if node_config.get('id') == start_at:
-                        node_cls = node_classes.get(NodeType.value_of(node_config.get('data', {}).get('type')))
-                else:
-                    if node_config.get('data', {}).get('type', '') == NodeType.START.value:
-                        node_cls = StartNode
-                if node_cls:
-                    return node_cls(
-                        tenant_id=workflow_run_state.tenant_id,
-                        app_id=workflow_run_state.app_id,
-                        workflow_id=workflow_run_state.workflow_id,
-                        user_id=workflow_run_state.user_id,
-                        user_from=workflow_run_state.user_from,
-                        invoke_from=workflow_run_state.invoke_from,
-                        config=node_config,
-                        callbacks=callbacks,
-                        workflow_call_depth=workflow_run_state.workflow_call_depth
-                    )
-
-        else:
-            edges = graph.get('edges')
-            source_node_id = predecessor_node.node_id
-
-            # fetch all outgoing edges from source node
-            outgoing_edges = [edge for edge in edges if edge.get('source') == source_node_id]
-            if not outgoing_edges:
-                return None
-
-            # fetch target node id from outgoing edges
-            outgoing_edge = None
-            source_handle = predecessor_node.node_run_result.edge_source_handle \
-                if predecessor_node.node_run_result else None
-            if source_handle:
-                for edge in outgoing_edges:
-                    if edge.get('sourceHandle') and edge.get('sourceHandle') == source_handle:
-                        outgoing_edge = edge
-                        break
-            else:
-                outgoing_edge = outgoing_edges[0]
-
-            if not outgoing_edge:
-                return None
-
-            target_node_id = outgoing_edge.get('target')
-
-            if end_at and target_node_id == end_at:
-                return None
-
-            # fetch target node from target node id
-            target_node_config = None
-            for node in nodes:
-                if node.get('id') == target_node_id:
-                    target_node_config = node
-                    break
-
-            if not target_node_config:
-                return None
-
-            # get next node
-            target_node = node_classes.get(NodeType.value_of(target_node_config.get('data', {}).get('type')))
-
-            return target_node(
-                tenant_id=workflow_run_state.tenant_id,
-                app_id=workflow_run_state.app_id,
-                workflow_id=workflow_run_state.workflow_id,
-                user_id=workflow_run_state.user_id,
-                user_from=workflow_run_state.user_from,
-                invoke_from=workflow_run_state.invoke_from,
-                config=target_node_config,
-                callbacks=callbacks,
-                workflow_call_depth=workflow_run_state.workflow_call_depth
-            )
-
-    def _get_node(self, workflow_run_state: WorkflowRunState,
-                  graph: Mapping[str, Any],
-                  node_id: str,
-                  callbacks: Sequence[WorkflowCallback]):
-        """
-        Get node from graph by node id
-        """
-        nodes = graph.get('nodes')
-        if not nodes:
-            return None
-
-        for node_config in nodes:
-            if node_config.get('id') == node_id:
-                node_type = NodeType.value_of(node_config.get('data', {}).get('type'))
-                node_cls = node_classes[node_type]
-                return node_cls(
-                    tenant_id=workflow_run_state.tenant_id,
-                    app_id=workflow_run_state.app_id,
-                    workflow_id=workflow_run_state.workflow_id,
-                    user_id=workflow_run_state.user_id,
-                    user_from=workflow_run_state.user_from,
-                    invoke_from=workflow_run_state.invoke_from,
-                    config=node_config,
-                    callbacks=callbacks,
-                    workflow_call_depth=workflow_run_state.workflow_call_depth
-                )
-
-    def _is_timed_out(self, start_at: float, max_execution_time: int) -> bool:
-        """
-        Check timeout
-        :param start_at: start time
-        :param max_execution_time: max execution time
-        :return:
-        """
-        return time.perf_counter() - start_at > max_execution_time
-
-    def _check_node_has_ran(self, workflow_run_state: WorkflowRunState, node_id: str) -> bool:
-        """
-        Check node has ran
-        """
-        return bool([
-            node_and_result for node_and_result in workflow_run_state.workflow_node_runs
-            if node_and_result.node_id == node_id
-        ])
-
-    def _run_workflow_node(self, *, workflow_run_state: WorkflowRunState,
-                           node: BaseNode,
-                           predecessor_node: Optional[BaseNode] = None,
-                           callbacks: Sequence[WorkflowCallback]) -> None:
-        if callbacks:
-            for callback in callbacks:
-                callback.on_workflow_node_execute_started(
-                    node_id=node.node_id,
-                    node_type=node.node_type,
-                    node_data=node.node_data,
-                    node_run_index=workflow_run_state.workflow_node_steps,
-                    predecessor_node_id=predecessor_node.node_id if predecessor_node else None
-                )
-
-        db.session.close()
-
-        workflow_nodes_and_result = WorkflowNodeAndResult(
-            node=node,
-            result=None
-        )
-
-        # add to workflow_nodes_and_results
-        workflow_run_state.workflow_nodes_and_results.append(workflow_nodes_and_result)
-
-        # add steps
-        workflow_run_state.workflow_node_steps += 1
-
-        # mark node as running
-        if workflow_run_state.current_iteration_state:
-            workflow_run_state.workflow_node_runs.append(WorkflowRunState.NodeRun(
-                node_id=node.node_id,
-                iteration_node_id=workflow_run_state.current_iteration_state.iteration_node_id
-            ))
-
-        try:
-            # run node, result must have inputs, process_data, outputs, execution_metadata
-            node_run_result = node.run(
-                variable_pool=workflow_run_state.variable_pool
-            )
-        except GenerateTaskStoppedException as e:
-            node_run_result = NodeRunResult(
-                status=WorkflowNodeExecutionStatus.FAILED,
-                error='Workflow stopped.'
-            )
-        except Exception as e:
-            logger.exception(f"Node {node.node_data.title} run failed: {str(e)}")
-            node_run_result = NodeRunResult(
-                status=WorkflowNodeExecutionStatus.FAILED,
-                error=str(e)
-            )
-
-        if node_run_result.status == WorkflowNodeExecutionStatus.FAILED:
-            # node run failed
-            if callbacks:
-                for callback in callbacks:
-                    callback.on_workflow_node_execute_failed(
-                        node_id=node.node_id,
-                        node_type=node.node_type,
-                        node_data=node.node_data,
-                        error=node_run_result.error,
-                        inputs=node_run_result.inputs,
-                        outputs=node_run_result.outputs,
-                        process_data=node_run_result.process_data,
-                    )
-
-            raise ValueError(f"Node {node.node_data.title} run failed: {node_run_result.error}")
-
-        if node.is_answer_previous_node and not isinstance(node, LLMNode):
-            if not node_run_result.metadata:
-                node_run_result.metadata = {}
-            node_run_result.metadata["is_answer_previous_node"]=True
-        workflow_nodes_and_result.result = node_run_result
-
-        # node run success
-        if callbacks:
-            for callback in callbacks:
-                callback.on_workflow_node_execute_succeeded(
-                    node_id=node.node_id,
-                    node_type=node.node_type,
-                    node_data=node.node_data,
-                    inputs=node_run_result.inputs,
-                    process_data=node_run_result.process_data,
-                    outputs=node_run_result.outputs,
-                    execution_metadata=node_run_result.metadata
-                )
-
-        if node_run_result.outputs:
-            for variable_key, variable_value in node_run_result.outputs.items():
-                # append variables to variable pool recursively
-                self._append_variables_recursively(
-                    variable_pool=workflow_run_state.variable_pool,
-                    node_id=node.node_id,
-                    variable_key_list=[variable_key],
-                    variable_value=variable_value
-                )
-
-        if node_run_result.metadata and node_run_result.metadata.get(NodeRunMetadataKey.TOTAL_TOKENS):
-            workflow_run_state.total_tokens += int(node_run_result.metadata.get(NodeRunMetadataKey.TOTAL_TOKENS))
-
-        db.session.close()
-
-    def _append_variables_recursively(self, variable_pool: VariablePool,
-                                      node_id: str,
-                                      variable_key_list: list[str],
-                                      variable_value: VariableValue):
-        """
-        Append variables recursively
-        :param variable_pool: variable pool
-        :param node_id: node id
-        :param variable_key_list: variable key list
-        :param variable_value: variable value
-        :return:
-        """
-        variable_pool.add(
-            [node_id] + variable_key_list, variable_value
-        )
-
-        # if variable_value is a dict, then recursively append variables
-        if isinstance(variable_value, dict):
-            for key, value in variable_value.items():
-                # construct new key list
-                new_key_list = variable_key_list + [key]
-                self._append_variables_recursively(
-                    variable_pool=variable_pool,
-                    node_id=node_id,
-                    variable_key_list=new_key_list,
-                    variable_value=value
-                )
-
-    @classmethod
-    def handle_special_values(cls, value: Optional[dict]) -> Optional[dict]:
-        """
-        Handle special values
-        :param value: value
-        :return:
-        """
-        if not value:
-            return None
-
-        new_value = value.copy()
-        if isinstance(new_value, dict):
-            for key, val in new_value.items():
-                if isinstance(val, FileVar):
-                    new_value[key] = val.to_dict()
-                elif isinstance(val, list):
-                    new_val = []
-                    for v in val:
-                        if isinstance(v, FileVar):
-                            new_val.append(v.to_dict())
-                        else:
-                            new_val.append(v)
-
-                    new_value[key] = new_val
-
-        return new_value
-
-    def _mapping_user_inputs_to_variable_pool(self,
-                                              variable_mapping: Mapping[str, Sequence[str]],
-                                              user_inputs: dict,
-                                              variable_pool: VariablePool,
-                                              tenant_id: str,
-                                              node_instance: BaseNode):
-        for variable_key, variable_selector in variable_mapping.items():
-            if variable_key not in user_inputs and not variable_pool.get(variable_selector):
-                raise ValueError(f'Variable key {variable_key} not found in user inputs.')
-
-            # fetch variable node id from variable selector
-            variable_node_id = variable_selector[0]
-            variable_key_list = variable_selector[1:]
-
-            # get value
-            value = user_inputs.get(variable_key)
-
-            # FIXME: temp fix for image type
-            if node_instance.node_type == NodeType.LLM:
-                new_value = []
-                if isinstance(value, list):
-                    node_data = node_instance.node_data
-                    node_data = cast(LLMNodeData, node_data)
-
-                    detail = node_data.vision.configs.detail if node_data.vision.configs else None
-
-                    for item in value:
-                        if isinstance(item, dict) and 'type' in item and item['type'] == 'image':
-                            transfer_method = FileTransferMethod.value_of(item.get('transfer_method'))
-                            file = FileVar(
-                                tenant_id=tenant_id,
-                                type=FileType.IMAGE,
-                                transfer_method=transfer_method,
-                                url=item.get('url') if transfer_method == FileTransferMethod.REMOTE_URL else None,
-                                related_id=item.get(
-                                    'upload_file_id') if transfer_method == FileTransferMethod.LOCAL_FILE else None,
-                                extra_config=FileExtraConfig(image_config={'detail': detail} if detail else None),
-                            )
-                            new_value.append(file)
-
-                if new_value:
-                    value = new_value
-
-            # append variable and value to variable pool
-            variable_pool.add([variable_node_id]+variable_key_list, value)
diff --git a/api/core/workflow/workflow_entry.py b/api/core/workflow/workflow_entry.py
new file mode 100644
index 0000000000..74a598ada5
--- /dev/null
+++ b/api/core/workflow/workflow_entry.py
@@ -0,0 +1,295 @@
+import logging
+import time
+import uuid
+from collections.abc import Generator, Mapping, Sequence
+from typing import Any, Optional, cast
+
+from configs import dify_config
+from core.app.app_config.entities import FileExtraConfig
+from core.app.apps.base_app_queue_manager import GenerateTaskStoppedError
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.file.file_obj import FileTransferMethod, FileType, FileVar
+from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
+from core.workflow.entities.base_node_data_entities import BaseNodeData
+from core.workflow.entities.node_entities import NodeType, UserFrom
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.errors import WorkflowNodeRunFailedError
+from core.workflow.graph_engine.entities.event import GraphEngineEvent, GraphRunFailedEvent, InNodeEvent
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.graph_engine.graph_engine import GraphEngine
+from core.workflow.nodes.base_node import BaseNode
+from core.workflow.nodes.event import RunEvent
+from core.workflow.nodes.llm.entities import LLMNodeData
+from core.workflow.nodes.node_mapping import node_classes
+from models.workflow import (
+    Workflow,
+    WorkflowType,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class WorkflowEntry:
+    def __init__(
+        self,
+        tenant_id: str,
+        app_id: str,
+        workflow_id: str,
+        workflow_type: WorkflowType,
+        graph_config: Mapping[str, Any],
+        graph: Graph,
+        user_id: str,
+        user_from: UserFrom,
+        invoke_from: InvokeFrom,
+        call_depth: int,
+        variable_pool: VariablePool,
+        thread_pool_id: Optional[str] = None,
+    ) -> None:
+        """
+        Init workflow entry
+        :param tenant_id: tenant id
+        :param app_id: app id
+        :param workflow_id: workflow id
+        :param workflow_type: workflow type
+        :param graph_config: workflow graph config
+        :param graph: workflow graph
+        :param user_id: user id
+        :param user_from: user from
+        :param invoke_from: invoke from
+        :param call_depth: call depth
+        :param variable_pool: variable pool
+        :param thread_pool_id: thread pool id
+        """
+        # check call depth
+        workflow_call_max_depth = dify_config.WORKFLOW_CALL_MAX_DEPTH
+        if call_depth > workflow_call_max_depth:
+            raise ValueError("Max workflow call depth {} reached.".format(workflow_call_max_depth))
+
+        # init workflow run state
+        self.graph_engine = GraphEngine(
+            tenant_id=tenant_id,
+            app_id=app_id,
+            workflow_type=workflow_type,
+            workflow_id=workflow_id,
+            user_id=user_id,
+            user_from=user_from,
+            invoke_from=invoke_from,
+            call_depth=call_depth,
+            graph=graph,
+            graph_config=graph_config,
+            variable_pool=variable_pool,
+            max_execution_steps=dify_config.WORKFLOW_MAX_EXECUTION_STEPS,
+            max_execution_time=dify_config.WORKFLOW_MAX_EXECUTION_TIME,
+            thread_pool_id=thread_pool_id,
+        )
+
+    def run(
+        self,
+        *,
+        callbacks: Sequence[WorkflowCallback],
+    ) -> Generator[GraphEngineEvent, None, None]:
+        """
+        :param callbacks: workflow callbacks
+        """
+        graph_engine = self.graph_engine
+
+        try:
+            # run workflow
+            generator = graph_engine.run()
+            for event in generator:
+                if callbacks:
+                    for callback in callbacks:
+                        callback.on_event(event=event)
+                yield event
+        except GenerateTaskStoppedError:
+            pass
+        except Exception as e:
+            logger.exception("Unknown Error when workflow entry running")
+            if callbacks:
+                for callback in callbacks:
+                    callback.on_event(event=GraphRunFailedEvent(error=str(e)))
+            return
+
+    @classmethod
+    def single_step_run(
+        cls, workflow: Workflow, node_id: str, user_id: str, user_inputs: dict
+    ) -> tuple[BaseNode, Generator[RunEvent | InNodeEvent, None, None]]:
+        """
+        Single step run workflow node
+        :param workflow: Workflow instance
+        :param node_id: node id
+        :param user_id: user id
+        :param user_inputs: user inputs
+        :return:
+        """
+        # fetch node info from workflow graph
+        graph = workflow.graph_dict
+        if not graph:
+            raise ValueError("workflow graph not found")
+
+        nodes = graph.get("nodes")
+        if not nodes:
+            raise ValueError("nodes not found in workflow graph")
+
+        # fetch node config from node id
+        node_config = None
+        for node in nodes:
+            if node.get("id") == node_id:
+                node_config = node
+                break
+
+        if not node_config:
+            raise ValueError("node id not found in workflow graph")
+
+        # Get node class
+        node_type = NodeType.value_of(node_config.get("data", {}).get("type"))
+        node_cls = node_classes.get(node_type)
+        node_cls = cast(type[BaseNode], node_cls)
+
+        if not node_cls:
+            raise ValueError(f"Node class not found for node type {node_type}")
+
+        # init variable pool
+        variable_pool = VariablePool(
+            system_variables={},
+            user_inputs={},
+            environment_variables=workflow.environment_variables,
+        )
+
+        # init graph
+        graph = Graph.init(graph_config=workflow.graph_dict)
+
+        # init workflow run state
+        node_instance: BaseNode = node_cls(
+            id=str(uuid.uuid4()),
+            config=node_config,
+            graph_init_params=GraphInitParams(
+                tenant_id=workflow.tenant_id,
+                app_id=workflow.app_id,
+                workflow_type=WorkflowType.value_of(workflow.type),
+                workflow_id=workflow.id,
+                graph_config=workflow.graph_dict,
+                user_id=user_id,
+                user_from=UserFrom.ACCOUNT,
+                invoke_from=InvokeFrom.DEBUGGER,
+                call_depth=0,
+            ),
+            graph=graph,
+            graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
+        )
+
+        try:
+            # variable selector to variable mapping
+            try:
+                variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
+                    graph_config=workflow.graph_dict, config=node_config
+                )
+            except NotImplementedError:
+                variable_mapping = {}
+
+            cls.mapping_user_inputs_to_variable_pool(
+                variable_mapping=variable_mapping,
+                user_inputs=user_inputs,
+                variable_pool=variable_pool,
+                tenant_id=workflow.tenant_id,
+                node_type=node_type,
+                node_data=node_instance.node_data,
+            )
+
+            # run node
+            generator = node_instance.run()
+
+            return node_instance, generator
+        except Exception as e:
+            raise WorkflowNodeRunFailedError(node_instance=node_instance, error=str(e))
+
+    @classmethod
+    def handle_special_values(cls, value: Optional[Mapping[str, Any]]) -> Optional[dict]:
+        """
+        Handle special values
+        :param value: value
+        :return:
+        """
+        if not value:
+            return None
+
+        new_value = dict(value) if value else {}
+        if isinstance(new_value, dict):
+            for key, val in new_value.items():
+                if isinstance(val, FileVar):
+                    new_value[key] = val.to_dict()
+                elif isinstance(val, list):
+                    new_val = []
+                    for v in val:
+                        if isinstance(v, FileVar):
+                            new_val.append(v.to_dict())
+                        else:
+                            new_val.append(v)
+
+                    new_value[key] = new_val
+
+        return new_value
+
+    @classmethod
+    def mapping_user_inputs_to_variable_pool(
+        cls,
+        variable_mapping: Mapping[str, Sequence[str]],
+        user_inputs: dict,
+        variable_pool: VariablePool,
+        tenant_id: str,
+        node_type: NodeType,
+        node_data: BaseNodeData,
+    ) -> None:
+        for node_variable, variable_selector in variable_mapping.items():
+            # fetch node id and variable key from node_variable
+            node_variable_list = node_variable.split(".")
+            if len(node_variable_list) < 1:
+                raise ValueError(f"Invalid node variable {node_variable}")
+
+            node_variable_key = ".".join(node_variable_list[1:])
+
+            if (node_variable_key not in user_inputs and node_variable not in user_inputs) and not variable_pool.get(
+                variable_selector
+            ):
+                raise ValueError(f"Variable key {node_variable} not found in user inputs.")
+
+            # fetch variable node id from variable selector
+            variable_node_id = variable_selector[0]
+            variable_key_list = variable_selector[1:]
+            variable_key_list = cast(list[str], variable_key_list)
+
+            # get input value
+            input_value = user_inputs.get(node_variable)
+            if not input_value:
+                input_value = user_inputs.get(node_variable_key)
+
+            # FIXME: temp fix for image type
+            if node_type == NodeType.LLM:
+                new_value = []
+                if isinstance(input_value, list):
+                    node_data = cast(LLMNodeData, node_data)
+
+                    detail = node_data.vision.configs.detail if node_data.vision.configs else None
+
+                    for item in input_value:
+                        if isinstance(item, dict) and "type" in item and item["type"] == "image":
+                            transfer_method = FileTransferMethod.value_of(item.get("transfer_method"))
+                            file = FileVar(
+                                tenant_id=tenant_id,
+                                type=FileType.IMAGE,
+                                transfer_method=transfer_method,
+                                url=item.get("url") if transfer_method == FileTransferMethod.REMOTE_URL else None,
+                                related_id=item.get("upload_file_id")
+                                if transfer_method == FileTransferMethod.LOCAL_FILE
+                                else None,
+                                extra_config=FileExtraConfig(image_config={"detail": detail} if detail else None),
+                            )
+                            new_value.append(file)
+
+                if new_value:
+                    value = new_value
+
+            # append variable and value to variable pool
+            variable_pool.add([variable_node_id] + variable_key_list, input_value)
diff --git a/api/events/event_handlers/__init__.py b/api/events/event_handlers/__init__.py
index 7ee7146d09..1d6ad35333 100644
--- a/api/events/event_handlers/__init__.py
+++ b/api/events/event_handlers/__init__.py
@@ -3,8 +3,8 @@ from .clean_when_document_deleted import handle
 from .create_document_index import handle
 from .create_installed_app_when_app_created import handle
 from .create_site_record_when_app_created import handle
-from .deduct_quota_when_messaeg_created import handle
+from .deduct_quota_when_message_created import handle
 from .delete_tool_parameters_cache_when_sync_draft_workflow import handle
 from .update_app_dataset_join_when_app_model_config_updated import handle
 from .update_app_dataset_join_when_app_published_workflow_updated import handle
-from .update_provider_last_used_at_when_messaeg_created import handle
+from .update_provider_last_used_at_when_message_created import handle
diff --git a/api/events/event_handlers/create_document_index.py b/api/events/event_handlers/create_document_index.py
index 72a135e73d..54f6a76e16 100644
--- a/api/events/event_handlers/create_document_index.py
+++ b/api/events/event_handlers/create_document_index.py
@@ -5,7 +5,7 @@ import time
 import click
 from werkzeug.exceptions import NotFound
 
-from core.indexing_runner import DocumentIsPausedException, IndexingRunner
+from core.indexing_runner import DocumentIsPausedError, IndexingRunner
 from events.event_handlers.document_index_event import document_index_created
 from extensions.ext_database import db
 from models.dataset import Document
@@ -43,7 +43,7 @@ def handle(sender, **kwargs):
         indexing_runner.run(documents)
         end_at = time.perf_counter()
         logging.info(click.style("Processed dataset: {} latency: {}".format(dataset_id, end_at - start_at), fg="green"))
-    except DocumentIsPausedException as ex:
+    except DocumentIsPausedError as ex:
         logging.info(click.style(str(ex), fg="yellow"))
     except Exception:
         pass
diff --git a/api/events/event_handlers/deduct_quota_when_messaeg_created.py b/api/events/event_handlers/deduct_quota_when_message_created.py
similarity index 100%
rename from api/events/event_handlers/deduct_quota_when_messaeg_created.py
rename to api/events/event_handlers/deduct_quota_when_message_created.py
diff --git a/api/events/event_handlers/update_app_dataset_join_when_app_model_config_updated.py b/api/events/event_handlers/update_app_dataset_join_when_app_model_config_updated.py
index 59375b1a0b..de7c0f4dfe 100644
--- a/api/events/event_handlers/update_app_dataset_join_when_app_model_config_updated.py
+++ b/api/events/event_handlers/update_app_dataset_join_when_app_model_config_updated.py
@@ -18,8 +18,7 @@ def handle(sender, **kwargs):
         added_dataset_ids = dataset_ids
     else:
         old_dataset_ids = set()
-        for app_dataset_join in app_dataset_joins:
-            old_dataset_ids.add(app_dataset_join.dataset_id)
+        old_dataset_ids.update(app_dataset_join.dataset_id for app_dataset_join in app_dataset_joins)
 
         added_dataset_ids = dataset_ids - old_dataset_ids
         removed_dataset_ids = old_dataset_ids - dataset_ids
diff --git a/api/events/event_handlers/update_app_dataset_join_when_app_published_workflow_updated.py b/api/events/event_handlers/update_app_dataset_join_when_app_published_workflow_updated.py
index 333b85ecb2..c5e98e263f 100644
--- a/api/events/event_handlers/update_app_dataset_join_when_app_published_workflow_updated.py
+++ b/api/events/event_handlers/update_app_dataset_join_when_app_published_workflow_updated.py
@@ -22,8 +22,7 @@ def handle(sender, **kwargs):
         added_dataset_ids = dataset_ids
     else:
         old_dataset_ids = set()
-        for app_dataset_join in app_dataset_joins:
-            old_dataset_ids.add(app_dataset_join.dataset_id)
+        old_dataset_ids.update(app_dataset_join.dataset_id for app_dataset_join in app_dataset_joins)
 
         added_dataset_ids = dataset_ids - old_dataset_ids
         removed_dataset_ids = old_dataset_ids - dataset_ids
diff --git a/api/events/event_handlers/update_provider_last_used_at_when_messaeg_created.py b/api/events/event_handlers/update_provider_last_used_at_when_message_created.py
similarity index 100%
rename from api/events/event_handlers/update_provider_last_used_at_when_messaeg_created.py
rename to api/events/event_handlers/update_provider_last_used_at_when_message_created.py
diff --git a/api/extensions/ext_celery.py b/api/extensions/ext_celery.py
index f5ec7c1759..0ff9f90847 100644
--- a/api/extensions/ext_celery.py
+++ b/api/extensions/ext_celery.py
@@ -10,11 +10,21 @@ def init_app(app: Flask) -> Celery:
             with app.app_context():
                 return self.run(*args, **kwargs)
 
+    broker_transport_options = {}
+
+    if app.config.get("CELERY_USE_SENTINEL"):
+        broker_transport_options = {
+            "master_name": app.config.get("CELERY_SENTINEL_MASTER_NAME"),
+            "sentinel_kwargs": {
+                "socket_timeout": app.config.get("CELERY_SENTINEL_SOCKET_TIMEOUT", 0.1),
+            },
+        }
+
     celery_app = Celery(
         app.name,
         task_cls=FlaskTask,
-        broker=app.config["CELERY_BROKER_URL"],
-        backend=app.config["CELERY_BACKEND"],
+        broker=app.config.get("CELERY_BROKER_URL"),
+        backend=app.config.get("CELERY_BACKEND"),
         task_ignore_result=True,
     )
 
@@ -27,11 +37,12 @@ def init_app(app: Flask) -> Celery:
     }
 
     celery_app.conf.update(
-        result_backend=app.config["CELERY_RESULT_BACKEND"],
+        result_backend=app.config.get("CELERY_RESULT_BACKEND"),
+        broker_transport_options=broker_transport_options,
         broker_connection_retry_on_startup=True,
     )
 
-    if app.config["BROKER_USE_SSL"]:
+    if app.config.get("BROKER_USE_SSL"):
         celery_app.conf.update(
             broker_use_ssl=ssl_options,  # Add the SSL options to the broker configuration
         )
@@ -43,7 +54,7 @@ def init_app(app: Flask) -> Celery:
         "schedule.clean_embedding_cache_task",
         "schedule.clean_unused_datasets_task",
     ]
-    day = app.config["CELERY_BEAT_SCHEDULER_TIME"]
+    day = app.config.get("CELERY_BEAT_SCHEDULER_TIME")
     beat_schedule = {
         "clean_embedding_cache_task": {
             "task": "schedule.clean_embedding_cache_task.clean_embedding_cache_task",
diff --git a/api/extensions/ext_redis.py b/api/extensions/ext_redis.py
index d5fb162fd8..054769e7ff 100644
--- a/api/extensions/ext_redis.py
+++ b/api/extensions/ext_redis.py
@@ -1,26 +1,83 @@
 import redis
 from redis.connection import Connection, SSLConnection
+from redis.sentinel import Sentinel
 
-redis_client = redis.Redis()
+
+class RedisClientWrapper(redis.Redis):
+    """
+    A wrapper class for the Redis client that addresses the issue where the global
+    `redis_client` variable cannot be updated when a new Redis instance is returned
+    by Sentinel.
+
+    This class allows for deferred initialization of the Redis client, enabling the
+    client to be re-initialized with a new instance when necessary. This is particularly
+    useful in scenarios where the Redis instance may change dynamically, such as during
+    a failover in a Sentinel-managed Redis setup.
+
+    Attributes:
+        _client (redis.Redis): The actual Redis client instance. It remains None until
+                               initialized with the `initialize` method.
+
+    Methods:
+        initialize(client): Initializes the Redis client if it hasn't been initialized already.
+        __getattr__(item): Delegates attribute access to the Redis client, raising an error
+                           if the client is not initialized.
+    """
+
+    def __init__(self):
+        self._client = None
+
+    def initialize(self, client):
+        if self._client is None:
+            self._client = client
+
+    def __getattr__(self, item):
+        if self._client is None:
+            raise RuntimeError("Redis client is not initialized. Call init_app first.")
+        return getattr(self._client, item)
+
+
+redis_client = RedisClientWrapper()
 
 
 def init_app(app):
+    global redis_client
     connection_class = Connection
     if app.config.get("REDIS_USE_SSL"):
         connection_class = SSLConnection
 
-    redis_client.connection_pool = redis.ConnectionPool(
-        **{
-            "host": app.config.get("REDIS_HOST"),
-            "port": app.config.get("REDIS_PORT"),
-            "username": app.config.get("REDIS_USERNAME"),
-            "password": app.config.get("REDIS_PASSWORD"),
-            "db": app.config.get("REDIS_DB"),
-            "encoding": "utf-8",
-            "encoding_errors": "strict",
-            "decode_responses": False,
-        },
-        connection_class=connection_class,
-    )
+    redis_params = {
+        "username": app.config.get("REDIS_USERNAME"),
+        "password": app.config.get("REDIS_PASSWORD"),
+        "db": app.config.get("REDIS_DB"),
+        "encoding": "utf-8",
+        "encoding_errors": "strict",
+        "decode_responses": False,
+    }
+
+    if app.config.get("REDIS_USE_SENTINEL"):
+        sentinel_hosts = [
+            (node.split(":")[0], int(node.split(":")[1])) for node in app.config.get("REDIS_SENTINELS").split(",")
+        ]
+        sentinel = Sentinel(
+            sentinel_hosts,
+            sentinel_kwargs={
+                "socket_timeout": app.config.get("REDIS_SENTINEL_SOCKET_TIMEOUT", 0.1),
+                "username": app.config.get("REDIS_SENTINEL_USERNAME"),
+                "password": app.config.get("REDIS_SENTINEL_PASSWORD"),
+            },
+        )
+        master = sentinel.master_for(app.config.get("REDIS_SENTINEL_SERVICE_NAME"), **redis_params)
+        redis_client.initialize(master)
+    else:
+        redis_params.update(
+            {
+                "host": app.config.get("REDIS_HOST"),
+                "port": app.config.get("REDIS_PORT"),
+                "connection_class": connection_class,
+            }
+        )
+        pool = redis.ConnectionPool(**redis_params)
+        redis_client.initialize(redis.Redis(connection_pool=pool))
 
     app.extensions["redis"] = redis_client
diff --git a/api/extensions/ext_sentry.py b/api/extensions/ext_sentry.py
index 3b7b0a37f4..c2dc736038 100644
--- a/api/extensions/ext_sentry.py
+++ b/api/extensions/ext_sentry.py
@@ -1,18 +1,29 @@
 import openai
 import sentry_sdk
+from langfuse import parse_error
 from sentry_sdk.integrations.celery import CeleryIntegration
 from sentry_sdk.integrations.flask import FlaskIntegration
 from werkzeug.exceptions import HTTPException
 
 
+def before_send(event, hint):
+    if "exc_info" in hint:
+        exc_type, exc_value, tb = hint["exc_info"]
+        if parse_error.defaultErrorResponse in str(exc_value):
+            return None
+
+    return event
+
+
 def init_app(app):
     if app.config.get("SENTRY_DSN"):
         sentry_sdk.init(
             dsn=app.config.get("SENTRY_DSN"),
             integrations=[FlaskIntegration(), CeleryIntegration()],
-            ignore_errors=[HTTPException, ValueError, openai.APIStatusError],
+            ignore_errors=[HTTPException, ValueError, openai.APIStatusError, parse_error.defaultErrorResponse],
             traces_sample_rate=app.config.get("SENTRY_TRACES_SAMPLE_RATE", 1.0),
             profiles_sample_rate=app.config.get("SENTRY_PROFILES_SAMPLE_RATE", 1.0),
             environment=app.config.get("DEPLOY_ENV"),
             release=f"dify-{app.config.get('CURRENT_VERSION')}-{app.config.get('COMMIT_SHA')}",
+            before_send=before_send,
         )
diff --git a/api/extensions/ext_storage.py b/api/extensions/ext_storage.py
index e6c4352577..5ce18b7292 100644
--- a/api/extensions/ext_storage.py
+++ b/api/extensions/ext_storage.py
@@ -6,10 +6,12 @@ from flask import Flask
 from extensions.storage.aliyun_storage import AliyunStorage
 from extensions.storage.azure_storage import AzureStorage
 from extensions.storage.google_storage import GoogleStorage
+from extensions.storage.huawei_storage import HuaweiStorage
 from extensions.storage.local_storage import LocalStorage
 from extensions.storage.oci_storage import OCIStorage
 from extensions.storage.s3_storage import S3Storage
 from extensions.storage.tencent_storage import TencentStorage
+from extensions.storage.volcengine_storage import VolcengineStorage
 
 
 class Storage:
@@ -30,6 +32,10 @@ class Storage:
             self.storage_runner = TencentStorage(app=app)
         elif storage_type == "oci-storage":
             self.storage_runner = OCIStorage(app=app)
+        elif storage_type == "huawei-obs":
+            self.storage_runner = HuaweiStorage(app=app)
+        elif storage_type == "volcengine-tos":
+            self.storage_runner = VolcengineStorage(app=app)
         else:
             self.storage_runner = LocalStorage(app=app)
 
diff --git a/api/extensions/storage/aliyun_storage.py b/api/extensions/storage/aliyun_storage.py
index bee237fc17..2677912aa9 100644
--- a/api/extensions/storage/aliyun_storage.py
+++ b/api/extensions/storage/aliyun_storage.py
@@ -31,54 +31,34 @@ class AliyunStorage(BaseStorage):
         )
 
     def save(self, filename, data):
-        if not self.folder or self.folder.endswith("/"):
-            filename = self.folder + filename
-        else:
-            filename = self.folder + "/" + filename
-        self.client.put_object(filename, data)
+        self.client.put_object(self.__wrapper_folder_filename(filename), data)
 
     def load_once(self, filename: str) -> bytes:
-        if not self.folder or self.folder.endswith("/"):
-            filename = self.folder + filename
-        else:
-            filename = self.folder + "/" + filename
-
-        with closing(self.client.get_object(filename)) as obj:
+        with closing(self.client.get_object(self.__wrapper_folder_filename(filename))) as obj:
             data = obj.read()
         return data
 
     def load_stream(self, filename: str) -> Generator:
         def generate(filename: str = filename) -> Generator:
-            if not self.folder or self.folder.endswith("/"):
-                filename = self.folder + filename
-            else:
-                filename = self.folder + "/" + filename
-
-            with closing(self.client.get_object(filename)) as obj:
+            with closing(self.client.get_object(self.__wrapper_folder_filename(filename))) as obj:
                 while chunk := obj.read(4096):
                     yield chunk
 
         return generate()
 
     def download(self, filename, target_filepath):
-        if not self.folder or self.folder.endswith("/"):
-            filename = self.folder + filename
-        else:
-            filename = self.folder + "/" + filename
-
-        self.client.get_object_to_file(filename, target_filepath)
+        self.client.get_object_to_file(self.__wrapper_folder_filename(filename), target_filepath)
 
     def exists(self, filename):
-        if not self.folder or self.folder.endswith("/"):
-            filename = self.folder + filename
-        else:
-            filename = self.folder + "/" + filename
-
-        return self.client.object_exists(filename)
+        return self.client.object_exists(self.__wrapper_folder_filename(filename))
 
     def delete(self, filename):
-        if not self.folder or self.folder.endswith("/"):
-            filename = self.folder + filename
-        else:
-            filename = self.folder + "/" + filename
-        self.client.delete_object(filename)
+        self.client.delete_object(self.__wrapper_folder_filename(filename))
+
+    def __wrapper_folder_filename(self, filename) -> str:
+        if self.folder:
+            if self.folder.endswith("/"):
+                filename = self.folder + filename
+            else:
+                filename = self.folder + "/" + filename
+        return filename
diff --git a/api/extensions/storage/google_storage.py b/api/extensions/storage/google_storage.py
index 9ed1fcf0b4..c42f946fa8 100644
--- a/api/extensions/storage/google_storage.py
+++ b/api/extensions/storage/google_storage.py
@@ -5,7 +5,7 @@ from collections.abc import Generator
 from contextlib import closing
 
 from flask import Flask
-from google.cloud import storage as GoogleCloudStorage
+from google.cloud import storage as google_cloud_storage
 
 from extensions.storage.base_storage import BaseStorage
 
@@ -23,9 +23,9 @@ class GoogleStorage(BaseStorage):
             service_account_json = base64.b64decode(service_account_json_str).decode("utf-8")
             # convert str to object
             service_account_obj = json.loads(service_account_json)
-            self.client = GoogleCloudStorage.Client.from_service_account_info(service_account_obj)
+            self.client = google_cloud_storage.Client.from_service_account_info(service_account_obj)
         else:
-            self.client = GoogleCloudStorage.Client()
+            self.client = google_cloud_storage.Client()
 
     def save(self, filename, data):
         bucket = self.client.get_bucket(self.bucket_name)
diff --git a/api/extensions/storage/huawei_storage.py b/api/extensions/storage/huawei_storage.py
new file mode 100644
index 0000000000..269a008fba
--- /dev/null
+++ b/api/extensions/storage/huawei_storage.py
@@ -0,0 +1,53 @@
+from collections.abc import Generator
+
+from flask import Flask
+from obs import ObsClient
+
+from extensions.storage.base_storage import BaseStorage
+
+
+class HuaweiStorage(BaseStorage):
+    """Implementation for huawei obs storage."""
+
+    def __init__(self, app: Flask):
+        super().__init__(app)
+        app_config = self.app.config
+        self.bucket_name = app_config.get("HUAWEI_OBS_BUCKET_NAME")
+        self.client = ObsClient(
+            access_key_id=app_config.get("HUAWEI_OBS_ACCESS_KEY"),
+            secret_access_key=app_config.get("HUAWEI_OBS_SECRET_KEY"),
+            server=app_config.get("HUAWEI_OBS_SERVER"),
+        )
+
+    def save(self, filename, data):
+        self.client.putObject(bucketName=self.bucket_name, objectKey=filename, content=data)
+
+    def load_once(self, filename: str) -> bytes:
+        data = self.client.getObject(bucketName=self.bucket_name, objectKey=filename)["body"].response.read()
+        return data
+
+    def load_stream(self, filename: str) -> Generator:
+        def generate(filename: str = filename) -> Generator:
+            response = self.client.getObject(bucketName=self.bucket_name, objectKey=filename)["body"].response
+            yield from response.read(4096)
+
+        return generate()
+
+    def download(self, filename, target_filepath):
+        self.client.getObject(bucketName=self.bucket_name, objectKey=filename, downloadPath=target_filepath)
+
+    def exists(self, filename):
+        res = self._get_meta(filename)
+        if res is None:
+            return False
+        return True
+
+    def delete(self, filename):
+        self.client.deleteObject(bucketName=self.bucket_name, objectKey=filename)
+
+    def _get_meta(self, filename):
+        res = self.client.getObjectMetadata(bucketName=self.bucket_name, objectKey=filename)
+        if res.status < 300:
+            return res
+        else:
+            return None
diff --git a/api/extensions/storage/local_storage.py b/api/extensions/storage/local_storage.py
index 46ee4bf80f..f833ae85dc 100644
--- a/api/extensions/storage/local_storage.py
+++ b/api/extensions/storage/local_storage.py
@@ -1,6 +1,7 @@
 import os
 import shutil
 from collections.abc import Generator
+from pathlib import Path
 
 from flask import Flask
 
@@ -26,8 +27,7 @@ class LocalStorage(BaseStorage):
         folder = os.path.dirname(filename)
         os.makedirs(folder, exist_ok=True)
 
-        with open(os.path.join(os.getcwd(), filename), "wb") as f:
-            f.write(data)
+        Path(os.path.join(os.getcwd(), filename)).write_bytes(data)
 
     def load_once(self, filename: str) -> bytes:
         if not self.folder or self.folder.endswith("/"):
@@ -38,9 +38,7 @@ class LocalStorage(BaseStorage):
         if not os.path.exists(filename):
             raise FileNotFoundError("File not found")
 
-        with open(filename, "rb") as f:
-            data = f.read()
-
+        data = Path(filename).read_bytes()
         return data
 
     def load_stream(self, filename: str) -> Generator:
diff --git a/api/extensions/storage/volcengine_storage.py b/api/extensions/storage/volcengine_storage.py
new file mode 100644
index 0000000000..f74ad2ee6d
--- /dev/null
+++ b/api/extensions/storage/volcengine_storage.py
@@ -0,0 +1,48 @@
+from collections.abc import Generator
+
+import tos
+from flask import Flask
+
+from extensions.storage.base_storage import BaseStorage
+
+
+class VolcengineStorage(BaseStorage):
+    """Implementation for Volcengine TOS storage."""
+
+    def __init__(self, app: Flask):
+        super().__init__(app)
+        app_config = self.app.config
+        self.bucket_name = app_config.get("VOLCENGINE_TOS_BUCKET_NAME")
+        self.client = tos.TosClientV2(
+            ak=app_config.get("VOLCENGINE_TOS_ACCESS_KEY"),
+            sk=app_config.get("VOLCENGINE_TOS_SECRET_KEY"),
+            endpoint=app_config.get("VOLCENGINE_TOS_ENDPOINT"),
+            region=app_config.get("VOLCENGINE_TOS_REGION"),
+        )
+
+    def save(self, filename, data):
+        self.client.put_object(bucket=self.bucket_name, key=filename, content=data)
+
+    def load_once(self, filename: str) -> bytes:
+        data = self.client.get_object(bucket=self.bucket_name, key=filename).read()
+        return data
+
+    def load_stream(self, filename: str) -> Generator:
+        def generate(filename: str = filename) -> Generator:
+            response = self.client.get_object(bucket=self.bucket_name, key=filename)
+            while chunk := response.read(4096):
+                yield chunk
+
+        return generate()
+
+    def download(self, filename, target_filepath):
+        self.client.get_object_to_file(bucket=self.bucket_name, key=filename, file_path=target_filepath)
+
+    def exists(self, filename):
+        res = self.client.head_object(bucket=self.bucket_name, key=filename)
+        if res.status_code != 200:
+            return False
+        return True
+
+    def delete(self, filename):
+        self.client.delete_object(bucket=self.bucket_name, key=filename)
diff --git a/api/libs/gmpy2_pkcs10aep_cipher.py b/api/libs/gmpy2_pkcs10aep_cipher.py
index 2d306edb40..83f9c74e33 100644
--- a/api/libs/gmpy2_pkcs10aep_cipher.py
+++ b/api/libs/gmpy2_pkcs10aep_cipher.py
@@ -31,7 +31,7 @@ from Crypto.Util.py3compat import _copy_bytes, bord
 from Crypto.Util.strxor import strxor
 
 
-class PKCS1OAEP_Cipher:
+class PKCS1OAepCipher:
     """Cipher object for PKCS#1 v1.5 OAEP.
     Do not create directly: use :func:`new` instead."""
 
@@ -204,7 +204,8 @@ class PKCS1OAEP_Cipher:
 
 
 def new(key, hashAlgo=None, mgfunc=None, label=b"", randfunc=None):
-    """Return a cipher object :class:`PKCS1OAEP_Cipher` that can be used to perform PKCS#1 OAEP encryption or decryption.
+    """Return a cipher object :class:`PKCS1OAEP_Cipher`
+     that can be used to perform PKCS#1 OAEP encryption or decryption.
 
     :param key:
       The key object to use to encrypt or decrypt the message.
@@ -237,4 +238,4 @@ def new(key, hashAlgo=None, mgfunc=None, label=b"", randfunc=None):
 
     if randfunc is None:
         randfunc = Random.get_random_bytes
-    return PKCS1OAEP_Cipher(key, hashAlgo, mgfunc, label, randfunc)
+    return PKCS1OAepCipher(key, hashAlgo, mgfunc, label, randfunc)
diff --git a/api/libs/helper.py b/api/libs/helper.py
index af0c2dace1..d664ef1ae7 100644
--- a/api/libs/helper.py
+++ b/api/libs/helper.py
@@ -84,7 +84,7 @@ def timestamp_value(timestamp):
         raise ValueError(error)
 
 
-class str_len:
+class StrLen:
     """Restrict input to an integer in a range (inclusive)"""
 
     def __init__(self, max_length, argument="argument"):
@@ -102,7 +102,7 @@ class str_len:
         return value
 
 
-class float_range:
+class FloatRange:
     """Restrict input to an float in a range (inclusive)"""
 
     def __init__(self, low, high, argument="argument"):
@@ -121,7 +121,7 @@ class float_range:
         return value
 
 
-class datetime_string:
+class DatetimeString:
     def __init__(self, format, argument="argument"):
         self.format = format
         self.argument = argument
diff --git a/api/libs/json_in_md_parser.py b/api/libs/json_in_md_parser.py
index 41d6905899..185ff3f95e 100644
--- a/api/libs/json_in_md_parser.py
+++ b/api/libs/json_in_md_parser.py
@@ -1,6 +1,6 @@
 import json
 
-from core.llm_generator.output_parser.errors import OutputParserException
+from core.llm_generator.output_parser.errors import OutputParserError
 
 
 def parse_json_markdown(json_string: str) -> dict:
@@ -33,10 +33,10 @@ def parse_and_check_json_markdown(text: str, expected_keys: list[str]) -> dict:
     try:
         json_obj = parse_json_markdown(text)
     except json.JSONDecodeError as e:
-        raise OutputParserException(f"Got invalid JSON object. Error: {e}")
+        raise OutputParserError(f"Got invalid JSON object. Error: {e}")
     for key in expected_keys:
         if key not in json_obj:
-            raise OutputParserException(
-                f"Got invalid return object. Expected key `{key}` " f"to be present, but got {json_obj}"
+            raise OutputParserError(
+                f"Got invalid return object. Expected key `{key}` to be present, but got {json_obj}"
             )
     return json_obj
diff --git a/api/libs/oauth_data_source.py b/api/libs/oauth_data_source.py
index 6da1a6d39b..05a73b09b7 100644
--- a/api/libs/oauth_data_source.py
+++ b/api/libs/oauth_data_source.py
@@ -158,7 +158,7 @@ class NotionOAuth(OAuthDataSource):
             page_icon = page_result["icon"]
             if page_icon:
                 icon_type = page_icon["type"]
-                if icon_type == "external" or icon_type == "file":
+                if icon_type in {"external", "file"}:
                     url = page_icon[icon_type]["url"]
                     icon = {"type": "url", "url": url if url.startswith("http") else f"https://www.notion.so{url}"}
                 else:
@@ -191,7 +191,7 @@ class NotionOAuth(OAuthDataSource):
             page_icon = database_result["icon"]
             if page_icon:
                 icon_type = page_icon["type"]
-                if icon_type == "external" or icon_type == "file":
+                if icon_type in {"external", "file"}:
                     url = page_icon[icon_type]["url"]
                     icon = {"type": "url", "url": url if url.startswith("http") else f"https://www.notion.so{url}"}
                 else:
diff --git a/api/libs/rsa.py b/api/libs/rsa.py
index a578bf3e56..637bcc4a1d 100644
--- a/api/libs/rsa.py
+++ b/api/libs/rsa.py
@@ -4,9 +4,9 @@ from Crypto.Cipher import AES
 from Crypto.PublicKey import RSA
 from Crypto.Random import get_random_bytes
 
-import libs.gmpy2_pkcs10aep_cipher as gmpy2_pkcs10aep_cipher
 from extensions.ext_redis import redis_client
 from extensions.ext_storage import storage
+from libs import gmpy2_pkcs10aep_cipher
 
 
 def generate_key_pair(tenant_id):
diff --git a/api/migrations/versions/03f98355ba0e_add_workflow_tool_label_and_tool_.py b/api/migrations/versions/03f98355ba0e_add_workflow_tool_label_and_tool_.py
index 0fba6a87eb..8cd4ec552b 100644
--- a/api/migrations/versions/03f98355ba0e_add_workflow_tool_label_and_tool_.py
+++ b/api/migrations/versions/03f98355ba0e_add_workflow_tool_label_and_tool_.py
@@ -24,6 +24,7 @@ def upgrade():
     with op.batch_alter_table('tool_workflow_providers', schema=None) as batch_op:
         batch_op.add_column(sa.Column('label', sa.String(length=255), server_default='', nullable=False))
 
+
 def downgrade():
     with op.batch_alter_table('tool_workflow_providers', schema=None) as batch_op:
         batch_op.drop_column('label')
diff --git a/api/migrations/versions/675b5321501b_add_node_execution_id_into_node_.py b/api/migrations/versions/675b5321501b_add_node_execution_id_into_node_.py
new file mode 100644
index 0000000000..55824945da
--- /dev/null
+++ b/api/migrations/versions/675b5321501b_add_node_execution_id_into_node_.py
@@ -0,0 +1,35 @@
+"""add node_execution_id into node_executions
+
+Revision ID: 675b5321501b
+Revises: 030f4915f36a
+Create Date: 2024-08-12 10:54:02.259331
+
+"""
+import sqlalchemy as sa
+from alembic import op
+
+import models as models
+
+# revision identifiers, used by Alembic.
+revision = '675b5321501b'
+down_revision = '030f4915f36a'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('workflow_node_executions', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('node_execution_id', sa.String(length=255), nullable=True))
+        batch_op.create_index('workflow_node_execution_id_idx', ['tenant_id', 'app_id', 'workflow_id', 'triggered_from', 'node_execution_id'], unique=False)
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('workflow_node_executions', schema=None) as batch_op:
+        batch_op.drop_index('workflow_node_execution_id_idx')
+        batch_op.drop_column('node_execution_id')
+
+    # ### end Alembic commands ###
diff --git a/api/migrations/versions/9e98fbaffb88_add_workflow_tool_version.py b/api/migrations/versions/9e98fbaffb88_add_workflow_tool_version.py
index bfda7d619c..92f41f0abd 100644
--- a/api/migrations/versions/9e98fbaffb88_add_workflow_tool_version.py
+++ b/api/migrations/versions/9e98fbaffb88_add_workflow_tool_version.py
@@ -21,6 +21,7 @@ def upgrade():
     with op.batch_alter_table('tool_workflow_providers', schema=None) as batch_op:
         batch_op.add_column(sa.Column('version', sa.String(length=255), server_default='', nullable=False))
 
+
 def downgrade():
     with op.batch_alter_table('tool_workflow_providers', schema=None) as batch_op:
         batch_op.drop_column('version')
diff --git a/api/migrations/versions/de95f5c77138_migration_serpapi_api_key.py b/api/migrations/versions/de95f5c77138_migration_serpapi_api_key.py
index 2365766837..fcca705d21 100644
--- a/api/migrations/versions/de95f5c77138_migration_serpapi_api_key.py
+++ b/api/migrations/versions/de95f5c77138_migration_serpapi_api_key.py
@@ -99,7 +99,7 @@ def upgrade():
             id=id,
             tenant_id=tenant_id,
             user_id=user_id,
-            provider='google',            
+            provider='google', 
             encrypted_credentials=encrypted_credentials,
             created_at=created_at,
             updated_at=updated_at
diff --git a/api/models/__init__.py b/api/models/__init__.py
index 4012611471..30ceef057e 100644
--- a/api/models/__init__.py
+++ b/api/models/__init__.py
@@ -4,7 +4,7 @@ from .model import App, AppMode, Message
 from .types import StringUUID
 from .workflow import ConversationVariable, Workflow, WorkflowNodeExecutionStatus
 
-__all__ = ['ConversationVariable', 'StringUUID', 'AppMode', 'WorkflowNodeExecutionStatus', 'Workflow', 'App', 'Message']
+__all__ = ["ConversationVariable", "StringUUID", "AppMode", "WorkflowNodeExecutionStatus", "Workflow", "App", "Message"]
 
 
 class CreatedByRole(Enum):
@@ -12,11 +12,11 @@ class CreatedByRole(Enum):
     Enum class for createdByRole
     """
 
-    ACCOUNT = 'account'
-    END_USER = 'end_user'
+    ACCOUNT = "account"
+    END_USER = "end_user"
 
     @classmethod
-    def value_of(cls, value: str) -> 'CreatedByRole':
+    def value_of(cls, value: str) -> "CreatedByRole":
         """
         Get value of given mode.
 
@@ -26,4 +26,4 @@ class CreatedByRole(Enum):
         for role in cls:
             if role.value == value:
                 return role
-        raise ValueError(f'invalid createdByRole value {value}')
+        raise ValueError(f"invalid createdByRole value {value}")
diff --git a/api/models/account.py b/api/models/account.py
index 67d940b7b7..60b4f11aad 100644
--- a/api/models/account.py
+++ b/api/models/account.py
@@ -9,21 +9,18 @@ from .types import StringUUID
 
 
 class AccountStatus(str, enum.Enum):
-    PENDING = 'pending'
-    UNINITIALIZED = 'uninitialized'
-    ACTIVE = 'active'
-    BANNED = 'banned'
-    CLOSED = 'closed'
+    PENDING = "pending"
+    UNINITIALIZED = "uninitialized"
+    ACTIVE = "active"
+    BANNED = "banned"
+    CLOSED = "closed"
 
 
 class Account(UserMixin, db.Model):
-    __tablename__ = 'accounts'
-    __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='account_pkey'),
-        db.Index('account_email_idx', 'email')
-    )
+    __tablename__ = "accounts"
+    __table_args__ = (db.PrimaryKeyConstraint("id", name="account_pkey"), db.Index("account_email_idx", "email"))
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     name = db.Column(db.String(255), nullable=False)
     email = db.Column(db.String(255), nullable=False)
     password = db.Column(db.String(255), nullable=True)
@@ -34,11 +31,11 @@ class Account(UserMixin, db.Model):
     timezone = db.Column(db.String(255))
     last_login_at = db.Column(db.DateTime)
     last_login_ip = db.Column(db.String(255))
-    last_active_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    last_active_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     status = db.Column(db.String(16), nullable=False, server_default=db.text("'active'::character varying"))
     initialized_at = db.Column(db.DateTime)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def is_password_set(self):
@@ -65,11 +62,13 @@ class Account(UserMixin, db.Model):
     @current_tenant_id.setter
     def current_tenant_id(self, value: str):
         try:
-            tenant_account_join = db.session.query(Tenant, TenantAccountJoin) \
-                .filter(Tenant.id == value) \
-                .filter(TenantAccountJoin.tenant_id == Tenant.id) \
-                .filter(TenantAccountJoin.account_id == self.id) \
+            tenant_account_join = (
+                db.session.query(Tenant, TenantAccountJoin)
+                .filter(Tenant.id == value)
+                .filter(TenantAccountJoin.tenant_id == Tenant.id)
+                .filter(TenantAccountJoin.account_id == self.id)
                 .one_or_none()
+            )
 
             if tenant_account_join:
                 tenant, ta = tenant_account_join
@@ -91,20 +90,18 @@ class Account(UserMixin, db.Model):
 
     @classmethod
     def get_by_openid(cls, provider: str, open_id: str) -> db.Model:
-        account_integrate = db.session.query(AccountIntegrate). \
-            filter(AccountIntegrate.provider == provider, AccountIntegrate.open_id == open_id). \
-            one_or_none()
+        account_integrate = (
+            db.session.query(AccountIntegrate)
+            .filter(AccountIntegrate.provider == provider, AccountIntegrate.open_id == open_id)
+            .one_or_none()
+        )
         if account_integrate:
-            return db.session.query(Account). \
-                filter(Account.id == account_integrate.account_id). \
-                one_or_none()
+            return db.session.query(Account).filter(Account.id == account_integrate.account_id).one_or_none()
         return None
 
     def get_integrates(self) -> list[db.Model]:
         ai = db.Model
-        return db.session.query(ai).filter(
-            ai.account_id == self.id
-        ).all()
+        return db.session.query(ai).filter(ai.account_id == self.id).all()
 
     # check current_user.current_tenant.current_role in ['admin', 'owner']
     @property
@@ -123,61 +120,75 @@ class Account(UserMixin, db.Model):
     def is_dataset_operator(self):
         return self._current_tenant.current_role == TenantAccountRole.DATASET_OPERATOR
 
+
 class TenantStatus(str, enum.Enum):
-    NORMAL = 'normal'
-    ARCHIVE = 'archive'
+    NORMAL = "normal"
+    ARCHIVE = "archive"
 
 
 class TenantAccountRole(str, enum.Enum):
-    OWNER = 'owner'
-    ADMIN = 'admin'
-    EDITOR = 'editor'
-    NORMAL = 'normal'
-    DATASET_OPERATOR = 'dataset_operator'
+    OWNER = "owner"
+    ADMIN = "admin"
+    EDITOR = "editor"
+    NORMAL = "normal"
+    DATASET_OPERATOR = "dataset_operator"
 
     @staticmethod
     def is_valid_role(role: str) -> bool:
-        return role and role in {TenantAccountRole.OWNER, TenantAccountRole.ADMIN, TenantAccountRole.EDITOR,
-                                 TenantAccountRole.NORMAL, TenantAccountRole.DATASET_OPERATOR}
+        return role and role in {
+            TenantAccountRole.OWNER,
+            TenantAccountRole.ADMIN,
+            TenantAccountRole.EDITOR,
+            TenantAccountRole.NORMAL,
+            TenantAccountRole.DATASET_OPERATOR,
+        }
 
     @staticmethod
     def is_privileged_role(role: str) -> bool:
         return role and role in {TenantAccountRole.OWNER, TenantAccountRole.ADMIN}
-    
+
     @staticmethod
     def is_non_owner_role(role: str) -> bool:
-        return role and role in {TenantAccountRole.ADMIN, TenantAccountRole.EDITOR, TenantAccountRole.NORMAL,
-                                 TenantAccountRole.DATASET_OPERATOR}
-    
+        return role and role in {
+            TenantAccountRole.ADMIN,
+            TenantAccountRole.EDITOR,
+            TenantAccountRole.NORMAL,
+            TenantAccountRole.DATASET_OPERATOR,
+        }
+
     @staticmethod
     def is_editing_role(role: str) -> bool:
         return role and role in {TenantAccountRole.OWNER, TenantAccountRole.ADMIN, TenantAccountRole.EDITOR}
 
     @staticmethod
     def is_dataset_edit_role(role: str) -> bool:
-        return role and role in {TenantAccountRole.OWNER, TenantAccountRole.ADMIN, TenantAccountRole.EDITOR,
-                                 TenantAccountRole.DATASET_OPERATOR}
+        return role and role in {
+            TenantAccountRole.OWNER,
+            TenantAccountRole.ADMIN,
+            TenantAccountRole.EDITOR,
+            TenantAccountRole.DATASET_OPERATOR,
+        }
+
 
 class Tenant(db.Model):
-    __tablename__ = 'tenants'
-    __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tenant_pkey'),
-    )
+    __tablename__ = "tenants"
+    __table_args__ = (db.PrimaryKeyConstraint("id", name="tenant_pkey"),)
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     name = db.Column(db.String(255), nullable=False)
     encrypt_public_key = db.Column(db.Text)
     plan = db.Column(db.String(255), nullable=False, server_default=db.text("'basic'::character varying"))
     status = db.Column(db.String(255), nullable=False, server_default=db.text("'normal'::character varying"))
     custom_config = db.Column(db.Text)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     def get_accounts(self) -> list[Account]:
-        return db.session.query(Account).filter(
-            Account.id == TenantAccountJoin.account_id,
-            TenantAccountJoin.tenant_id == self.id
-        ).all()
+        return (
+            db.session.query(Account)
+            .filter(Account.id == TenantAccountJoin.account_id, TenantAccountJoin.tenant_id == self.id)
+            .all()
+        )
 
     @property
     def custom_config_dict(self) -> dict:
@@ -189,54 +200,54 @@ class Tenant(db.Model):
 
 
 class TenantAccountJoinRole(enum.Enum):
-    OWNER = 'owner'
-    ADMIN = 'admin'
-    NORMAL = 'normal'
-    DATASET_OPERATOR = 'dataset_operator'
+    OWNER = "owner"
+    ADMIN = "admin"
+    NORMAL = "normal"
+    DATASET_OPERATOR = "dataset_operator"
 
 
 class TenantAccountJoin(db.Model):
-    __tablename__ = 'tenant_account_joins'
+    __tablename__ = "tenant_account_joins"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tenant_account_join_pkey'),
-        db.Index('tenant_account_join_account_id_idx', 'account_id'),
-        db.Index('tenant_account_join_tenant_id_idx', 'tenant_id'),
-        db.UniqueConstraint('tenant_id', 'account_id', name='unique_tenant_account_join')
+        db.PrimaryKeyConstraint("id", name="tenant_account_join_pkey"),
+        db.Index("tenant_account_join_account_id_idx", "account_id"),
+        db.Index("tenant_account_join_tenant_id_idx", "tenant_id"),
+        db.UniqueConstraint("tenant_id", "account_id", name="unique_tenant_account_join"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     account_id = db.Column(StringUUID, nullable=False)
-    current = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
-    role = db.Column(db.String(16), nullable=False, server_default='normal')
+    current = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
+    role = db.Column(db.String(16), nullable=False, server_default="normal")
     invited_by = db.Column(StringUUID, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class AccountIntegrate(db.Model):
-    __tablename__ = 'account_integrates'
+    __tablename__ = "account_integrates"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='account_integrate_pkey'),
-        db.UniqueConstraint('account_id', 'provider', name='unique_account_provider'),
-        db.UniqueConstraint('provider', 'open_id', name='unique_provider_open_id')
+        db.PrimaryKeyConstraint("id", name="account_integrate_pkey"),
+        db.UniqueConstraint("account_id", "provider", name="unique_account_provider"),
+        db.UniqueConstraint("provider", "open_id", name="unique_provider_open_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     account_id = db.Column(StringUUID, nullable=False)
     provider = db.Column(db.String(16), nullable=False)
     open_id = db.Column(db.String(255), nullable=False)
     encrypted_token = db.Column(db.String(255), nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class InvitationCode(db.Model):
-    __tablename__ = 'invitation_codes'
+    __tablename__ = "invitation_codes"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='invitation_code_pkey'),
-        db.Index('invitation_codes_batch_idx', 'batch'),
-        db.Index('invitation_codes_code_idx', 'code', 'status')
+        db.PrimaryKeyConstraint("id", name="invitation_code_pkey"),
+        db.Index("invitation_codes_batch_idx", "batch"),
+        db.Index("invitation_codes_code_idx", "code", "status"),
     )
 
     id = db.Column(db.Integer, nullable=False)
@@ -247,4 +258,4 @@ class InvitationCode(db.Model):
     used_by_tenant_id = db.Column(StringUUID)
     used_by_account_id = db.Column(StringUUID)
     deprecated_at = db.Column(db.DateTime)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
diff --git a/api/models/api_based_extension.py b/api/models/api_based_extension.py
index 7f69323628..97173747af 100644
--- a/api/models/api_based_extension.py
+++ b/api/models/api_based_extension.py
@@ -6,22 +6,22 @@ from .types import StringUUID
 
 
 class APIBasedExtensionPoint(enum.Enum):
-    APP_EXTERNAL_DATA_TOOL_QUERY = 'app.external_data_tool.query'
-    PING = 'ping'
-    APP_MODERATION_INPUT = 'app.moderation.input'
-    APP_MODERATION_OUTPUT = 'app.moderation.output'
+    APP_EXTERNAL_DATA_TOOL_QUERY = "app.external_data_tool.query"
+    PING = "ping"
+    APP_MODERATION_INPUT = "app.moderation.input"
+    APP_MODERATION_OUTPUT = "app.moderation.output"
 
 
 class APIBasedExtension(db.Model):
-    __tablename__ = 'api_based_extensions'
+    __tablename__ = "api_based_extensions"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='api_based_extension_pkey'),
-        db.Index('api_based_extension_tenant_idx', 'tenant_id'),
+        db.PrimaryKeyConstraint("id", name="api_based_extension_pkey"),
+        db.Index("api_based_extension_tenant_idx", "tenant_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     name = db.Column(db.String(255), nullable=False)
     api_endpoint = db.Column(db.String(255), nullable=False)
     api_key = db.Column(db.Text, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
diff --git a/api/models/dataset.py b/api/models/dataset.py
index 0289e18ea4..4221aa4bb1 100644
--- a/api/models/dataset.py
+++ b/api/models/dataset.py
@@ -14,7 +14,7 @@ from sqlalchemy import func
 from sqlalchemy.dialects.postgresql import JSONB
 
 from configs import dify_config
-from core.rag.retrieval.retrival_methods import RetrievalMethod
+from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from extensions.ext_database import db
 from extensions.ext_storage import storage
 
@@ -24,38 +24,35 @@ from .types import StringUUID
 
 
 class DatasetPermissionEnum(str, enum.Enum):
-    ONLY_ME = 'only_me'
-    ALL_TEAM = 'all_team_members'
-    PARTIAL_TEAM = 'partial_members'
+    ONLY_ME = "only_me"
+    ALL_TEAM = "all_team_members"
+    PARTIAL_TEAM = "partial_members"
+
 
 class Dataset(db.Model):
-    __tablename__ = 'datasets'
+    __tablename__ = "datasets"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='dataset_pkey'),
-        db.Index('dataset_tenant_idx', 'tenant_id'),
-        db.Index('retrieval_model_idx', "retrieval_model", postgresql_using='gin')
+        db.PrimaryKeyConstraint("id", name="dataset_pkey"),
+        db.Index("dataset_tenant_idx", "tenant_id"),
+        db.Index("retrieval_model_idx", "retrieval_model", postgresql_using="gin"),
     )
 
     INDEXING_TECHNIQUE_LIST = ['high_quality', 'economy', None]
     PROVIDER_LIST = ['vendor', 'external', None]
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     name = db.Column(db.String(255), nullable=False)
     description = db.Column(db.Text, nullable=True)
-    provider = db.Column(db.String(255), nullable=False,
-                         server_default=db.text("'vendor'::character varying"))
-    permission = db.Column(db.String(255), nullable=False,
-                           server_default=db.text("'only_me'::character varying"))
+    provider = db.Column(db.String(255), nullable=False, server_default=db.text("'vendor'::character varying"))
+    permission = db.Column(db.String(255), nullable=False, server_default=db.text("'only_me'::character varying"))
     data_source_type = db.Column(db.String(255))
     indexing_technique = db.Column(db.String(255), nullable=True)
     index_struct = db.Column(db.Text, nullable=True)
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False,
-                           server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     updated_by = db.Column(StringUUID, nullable=True)
-    updated_at = db.Column(db.DateTime, nullable=False,
-                           server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     embedding_model = db.Column(db.String(255), nullable=True)
     embedding_model_provider = db.Column(db.String(255), nullable=True)
     collection_binding_id = db.Column(StringUUID, nullable=True)
@@ -63,8 +60,9 @@ class Dataset(db.Model):
 
     @property
     def dataset_keyword_table(self):
-        dataset_keyword_table = db.session.query(DatasetKeywordTable).filter(
-            DatasetKeywordTable.dataset_id == self.id).first()
+        dataset_keyword_table = (
+            db.session.query(DatasetKeywordTable).filter(DatasetKeywordTable.dataset_id == self.id).first()
+        )
         if dataset_keyword_table:
             return dataset_keyword_table
 
@@ -80,13 +78,19 @@ class Dataset(db.Model):
 
     @property
     def latest_process_rule(self):
-        return DatasetProcessRule.query.filter(DatasetProcessRule.dataset_id == self.id) \
-            .order_by(DatasetProcessRule.created_at.desc()).first()
+        return (
+            DatasetProcessRule.query.filter(DatasetProcessRule.dataset_id == self.id)
+            .order_by(DatasetProcessRule.created_at.desc())
+            .first()
+        )
 
     @property
     def app_count(self):
-        return db.session.query(func.count(AppDatasetJoin.id)).filter(AppDatasetJoin.dataset_id == self.id,
-                                                                      App.id == AppDatasetJoin.app_id).scalar()
+        return (
+            db.session.query(func.count(AppDatasetJoin.id))
+            .filter(AppDatasetJoin.dataset_id == self.id, App.id == AppDatasetJoin.app_id)
+            .scalar()
+        )
 
     @property
     def document_count(self):
@@ -94,30 +98,40 @@ class Dataset(db.Model):
 
     @property
     def available_document_count(self):
-        return db.session.query(func.count(Document.id)).filter(
-            Document.dataset_id == self.id,
-            Document.indexing_status == 'completed',
-            Document.enabled == True,
-            Document.archived == False
-        ).scalar()
+        return (
+            db.session.query(func.count(Document.id))
+            .filter(
+                Document.dataset_id == self.id,
+                Document.indexing_status == "completed",
+                Document.enabled == True,
+                Document.archived == False,
+            )
+            .scalar()
+        )
 
     @property
     def available_segment_count(self):
-        return db.session.query(func.count(DocumentSegment.id)).filter(
-            DocumentSegment.dataset_id == self.id,
-            DocumentSegment.status == 'completed',
-            DocumentSegment.enabled == True
-        ).scalar()
+        return (
+            db.session.query(func.count(DocumentSegment.id))
+            .filter(
+                DocumentSegment.dataset_id == self.id,
+                DocumentSegment.status == "completed",
+                DocumentSegment.enabled == True,
+            )
+            .scalar()
+        )
 
     @property
     def word_count(self):
-        return Document.query.with_entities(func.coalesce(func.sum(Document.word_count))) \
-            .filter(Document.dataset_id == self.id).scalar()
+        return (
+            Document.query.with_entities(func.coalesce(func.sum(Document.word_count)))
+            .filter(Document.dataset_id == self.id)
+            .scalar()
+        )
 
     @property
     def doc_form(self):
-        document = db.session.query(Document).filter(
-            Document.dataset_id == self.id).first()
+        document = db.session.query(Document).filter(Document.dataset_id == self.id).first()
         if document:
             return document.doc_form
         return None
@@ -125,76 +139,68 @@ class Dataset(db.Model):
     @property
     def retrieval_model_dict(self):
         default_retrieval_model = {
-            'search_method': RetrievalMethod.SEMANTIC_SEARCH.value,
-            'reranking_enable': False,
-            'reranking_model': {
-                'reranking_provider_name': '',
-                'reranking_model_name': ''
-            },
-            'top_k': 2,
-            'score_threshold_enabled': False
+            "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+            "reranking_enable": False,
+            "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
+            "top_k": 2,
+            "score_threshold_enabled": False,
         }
-        return self.retrieval_model if self.retrieval_model else default_retrieval_model
+        return self.retrieval_model or default_retrieval_model
 
     @property
     def tags(self):
-        tags = db.session.query(Tag).join(
-            TagBinding,
-            Tag.id == TagBinding.tag_id
-        ).filter(
-            TagBinding.target_id == self.id,
-            TagBinding.tenant_id == self.tenant_id,
-            Tag.tenant_id == self.tenant_id,
-            Tag.type == 'knowledge'
-        ).all()
+        tags = (
+            db.session.query(Tag)
+            .join(TagBinding, Tag.id == TagBinding.tag_id)
+            .filter(
+                TagBinding.target_id == self.id,
+                TagBinding.tenant_id == self.tenant_id,
+                Tag.tenant_id == self.tenant_id,
+                Tag.type == "knowledge",
+            )
+            .all()
+        )
 
-        return tags if tags else []
+        return tags or []
 
     @staticmethod
     def gen_collection_name_by_id(dataset_id: str) -> str:
         normalized_dataset_id = dataset_id.replace("-", "_")
-        return f'Vector_index_{normalized_dataset_id}_Node'
+        return f"Vector_index_{normalized_dataset_id}_Node"
 
 
 class DatasetProcessRule(db.Model):
-    __tablename__ = 'dataset_process_rules'
+    __tablename__ = "dataset_process_rules"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='dataset_process_rule_pkey'),
-        db.Index('dataset_process_rule_dataset_id_idx', 'dataset_id'),
+        db.PrimaryKeyConstraint("id", name="dataset_process_rule_pkey"),
+        db.Index("dataset_process_rule_dataset_id_idx", "dataset_id"),
     )
 
-    id = db.Column(StringUUID, nullable=False,
-                   server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, nullable=False, server_default=db.text("uuid_generate_v4()"))
     dataset_id = db.Column(StringUUID, nullable=False)
-    mode = db.Column(db.String(255), nullable=False,
-                     server_default=db.text("'automatic'::character varying"))
+    mode = db.Column(db.String(255), nullable=False, server_default=db.text("'automatic'::character varying"))
     rules = db.Column(db.Text, nullable=True)
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False,
-                           server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
-    MODES = ['automatic', 'custom']
-    PRE_PROCESSING_RULES = ['remove_stopwords', 'remove_extra_spaces', 'remove_urls_emails']
+    MODES = ["automatic", "custom"]
+    PRE_PROCESSING_RULES = ["remove_stopwords", "remove_extra_spaces", "remove_urls_emails"]
     AUTOMATIC_RULES = {
-        'pre_processing_rules': [
-            {'id': 'remove_extra_spaces', 'enabled': True},
-            {'id': 'remove_urls_emails', 'enabled': False}
+        "pre_processing_rules": [
+            {"id": "remove_extra_spaces", "enabled": True},
+            {"id": "remove_urls_emails", "enabled": False},
         ],
-        'segmentation': {
-            'delimiter': '\n',
-            'max_tokens': 500,
-            'chunk_overlap': 50
-        }
+        "segmentation": {"delimiter": "\n", "max_tokens": 500, "chunk_overlap": 50},
     }
 
     def to_dict(self):
         return {
-            'id': self.id,
-            'dataset_id': self.dataset_id,
-            'mode': self.mode,
-            'rules': self.rules_dict,
-            'created_by': self.created_by,
-            'created_at': self.created_at,
+            "id": self.id,
+            "dataset_id": self.dataset_id,
+            "mode": self.mode,
+            "rules": self.rules_dict,
+            "created_by": self.created_by,
+            "created_at": self.created_at,
         }
 
     @property
@@ -206,17 +212,16 @@ class DatasetProcessRule(db.Model):
 
 
 class Document(db.Model):
-    __tablename__ = 'documents'
+    __tablename__ = "documents"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='document_pkey'),
-        db.Index('document_dataset_id_idx', 'dataset_id'),
-        db.Index('document_is_paused_idx', 'is_paused'),
-        db.Index('document_tenant_idx', 'tenant_id'),
+        db.PrimaryKeyConstraint("id", name="document_pkey"),
+        db.Index("document_dataset_id_idx", "dataset_id"),
+        db.Index("document_is_paused_idx", "is_paused"),
+        db.Index("document_tenant_idx", "tenant_id"),
     )
 
     # initial fields
-    id = db.Column(StringUUID, nullable=False,
-                   server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, nullable=False, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     dataset_id = db.Column(StringUUID, nullable=False)
     position = db.Column(db.Integer, nullable=False)
@@ -228,8 +233,7 @@ class Document(db.Model):
     created_from = db.Column(db.String(255), nullable=False)
     created_by = db.Column(StringUUID, nullable=False)
     created_api_request_id = db.Column(StringUUID, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False,
-                           server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     # start processing
     processing_started_at = db.Column(db.DateTime, nullable=True)
@@ -251,7 +255,7 @@ class Document(db.Model):
     completed_at = db.Column(db.DateTime, nullable=True)
 
     # pause
-    is_paused = db.Column(db.Boolean, nullable=True, server_default=db.text('false'))
+    is_paused = db.Column(db.Boolean, nullable=True, server_default=db.text("false"))
     paused_by = db.Column(StringUUID, nullable=True)
     paused_at = db.Column(db.DateTime, nullable=True)
 
@@ -260,44 +264,39 @@ class Document(db.Model):
     stopped_at = db.Column(db.DateTime, nullable=True)
 
     # basic fields
-    indexing_status = db.Column(db.String(
-        255), nullable=False, server_default=db.text("'waiting'::character varying"))
-    enabled = db.Column(db.Boolean, nullable=False,
-                        server_default=db.text('true'))
+    indexing_status = db.Column(db.String(255), nullable=False, server_default=db.text("'waiting'::character varying"))
+    enabled = db.Column(db.Boolean, nullable=False, server_default=db.text("true"))
     disabled_at = db.Column(db.DateTime, nullable=True)
     disabled_by = db.Column(StringUUID, nullable=True)
-    archived = db.Column(db.Boolean, nullable=False,
-                         server_default=db.text('false'))
+    archived = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
     archived_reason = db.Column(db.String(255), nullable=True)
     archived_by = db.Column(StringUUID, nullable=True)
     archived_at = db.Column(db.DateTime, nullable=True)
-    updated_at = db.Column(db.DateTime, nullable=False,
-                           server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     doc_type = db.Column(db.String(40), nullable=True)
     doc_metadata = db.Column(db.JSON, nullable=True)
-    doc_form = db.Column(db.String(
-        255), nullable=False, server_default=db.text("'text_model'::character varying"))
+    doc_form = db.Column(db.String(255), nullable=False, server_default=db.text("'text_model'::character varying"))
     doc_language = db.Column(db.String(255), nullable=True)
 
-    DATA_SOURCES = ['upload_file', 'notion_import', 'website_crawl']
+    DATA_SOURCES = ["upload_file", "notion_import", "website_crawl"]
 
     @property
     def display_status(self):
         status = None
-        if self.indexing_status == 'waiting':
-            status = 'queuing'
-        elif self.indexing_status not in ['completed', 'error', 'waiting'] and self.is_paused:
-            status = 'paused'
-        elif self.indexing_status in ['parsing', 'cleaning', 'splitting', 'indexing']:
-            status = 'indexing'
-        elif self.indexing_status == 'error':
-            status = 'error'
-        elif self.indexing_status == 'completed' and not self.archived and self.enabled:
-            status = 'available'
-        elif self.indexing_status == 'completed' and not self.archived and not self.enabled:
-            status = 'disabled'
-        elif self.indexing_status == 'completed' and self.archived:
-            status = 'archived'
+        if self.indexing_status == "waiting":
+            status = "queuing"
+        elif self.indexing_status not in {"completed", "error", "waiting"} and self.is_paused:
+            status = "paused"
+        elif self.indexing_status in {"parsing", "cleaning", "splitting", "indexing"}:
+            status = "indexing"
+        elif self.indexing_status == "error":
+            status = "error"
+        elif self.indexing_status == "completed" and not self.archived and self.enabled:
+            status = "available"
+        elif self.indexing_status == "completed" and not self.archived and not self.enabled:
+            status = "disabled"
+        elif self.indexing_status == "completed" and self.archived:
+            status = "archived"
         return status
 
     @property
@@ -314,24 +313,26 @@ class Document(db.Model):
     @property
     def data_source_detail_dict(self):
         if self.data_source_info:
-            if self.data_source_type == 'upload_file':
+            if self.data_source_type == "upload_file":
                 data_source_info_dict = json.loads(self.data_source_info)
-                file_detail = db.session.query(UploadFile). \
-                    filter(UploadFile.id == data_source_info_dict['upload_file_id']). \
-                    one_or_none()
+                file_detail = (
+                    db.session.query(UploadFile)
+                    .filter(UploadFile.id == data_source_info_dict["upload_file_id"])
+                    .one_or_none()
+                )
                 if file_detail:
                     return {
-                        'upload_file': {
-                            'id': file_detail.id,
-                            'name': file_detail.name,
-                            'size': file_detail.size,
-                            'extension': file_detail.extension,
-                            'mime_type': file_detail.mime_type,
-                            'created_by': file_detail.created_by,
-                            'created_at': file_detail.created_at.timestamp()
+                        "upload_file": {
+                            "id": file_detail.id,
+                            "name": file_detail.name,
+                            "size": file_detail.size,
+                            "extension": file_detail.extension,
+                            "mime_type": file_detail.mime_type,
+                            "created_by": file_detail.created_by,
+                            "created_at": file_detail.created_at.timestamp(),
                         }
                     }
-            elif self.data_source_type == 'notion_import' or self.data_source_type == 'website_crawl':
+            elif self.data_source_type in {"notion_import", "website_crawl"}:
                 return json.loads(self.data_source_info)
         return {}
 
@@ -357,120 +358,123 @@ class Document(db.Model):
 
     @property
     def hit_count(self):
-        return DocumentSegment.query.with_entities(func.coalesce(func.sum(DocumentSegment.hit_count))) \
-            .filter(DocumentSegment.document_id == self.id).scalar()
+        return (
+            DocumentSegment.query.with_entities(func.coalesce(func.sum(DocumentSegment.hit_count)))
+            .filter(DocumentSegment.document_id == self.id)
+            .scalar()
+        )
 
     def to_dict(self):
         return {
-            'id': self.id,
-            'tenant_id': self.tenant_id,
-            'dataset_id': self.dataset_id,
-            'position': self.position,
-            'data_source_type': self.data_source_type,
-            'data_source_info': self.data_source_info,
-            'dataset_process_rule_id': self.dataset_process_rule_id,
-            'batch': self.batch,
-            'name': self.name,
-            'created_from': self.created_from,
-            'created_by': self.created_by,
-            'created_api_request_id': self.created_api_request_id,
-            'created_at': self.created_at,
-            'processing_started_at': self.processing_started_at,
-            'file_id': self.file_id,
-            'word_count': self.word_count,
-            'parsing_completed_at': self.parsing_completed_at,
-            'cleaning_completed_at': self.cleaning_completed_at,
-            'splitting_completed_at': self.splitting_completed_at,
-            'tokens': self.tokens,
-            'indexing_latency': self.indexing_latency,
-            'completed_at': self.completed_at,
-            'is_paused': self.is_paused,
-            'paused_by': self.paused_by,
-            'paused_at': self.paused_at,
-            'error': self.error,
-            'stopped_at': self.stopped_at,
-            'indexing_status': self.indexing_status,
-            'enabled': self.enabled,
-            'disabled_at': self.disabled_at,
-            'disabled_by': self.disabled_by,
-            'archived': self.archived,
-            'archived_reason': self.archived_reason,
-            'archived_by': self.archived_by,
-            'archived_at': self.archived_at,
-            'updated_at': self.updated_at,
-            'doc_type': self.doc_type,
-            'doc_metadata': self.doc_metadata,
-            'doc_form': self.doc_form,
-            'doc_language': self.doc_language,
-            'display_status': self.display_status,
-            'data_source_info_dict': self.data_source_info_dict,
-            'average_segment_length': self.average_segment_length,
-            'dataset_process_rule': self.dataset_process_rule.to_dict() if self.dataset_process_rule else None,
-            'dataset': self.dataset.to_dict() if self.dataset else None,
-            'segment_count': self.segment_count,
-            'hit_count': self.hit_count
+            "id": self.id,
+            "tenant_id": self.tenant_id,
+            "dataset_id": self.dataset_id,
+            "position": self.position,
+            "data_source_type": self.data_source_type,
+            "data_source_info": self.data_source_info,
+            "dataset_process_rule_id": self.dataset_process_rule_id,
+            "batch": self.batch,
+            "name": self.name,
+            "created_from": self.created_from,
+            "created_by": self.created_by,
+            "created_api_request_id": self.created_api_request_id,
+            "created_at": self.created_at,
+            "processing_started_at": self.processing_started_at,
+            "file_id": self.file_id,
+            "word_count": self.word_count,
+            "parsing_completed_at": self.parsing_completed_at,
+            "cleaning_completed_at": self.cleaning_completed_at,
+            "splitting_completed_at": self.splitting_completed_at,
+            "tokens": self.tokens,
+            "indexing_latency": self.indexing_latency,
+            "completed_at": self.completed_at,
+            "is_paused": self.is_paused,
+            "paused_by": self.paused_by,
+            "paused_at": self.paused_at,
+            "error": self.error,
+            "stopped_at": self.stopped_at,
+            "indexing_status": self.indexing_status,
+            "enabled": self.enabled,
+            "disabled_at": self.disabled_at,
+            "disabled_by": self.disabled_by,
+            "archived": self.archived,
+            "archived_reason": self.archived_reason,
+            "archived_by": self.archived_by,
+            "archived_at": self.archived_at,
+            "updated_at": self.updated_at,
+            "doc_type": self.doc_type,
+            "doc_metadata": self.doc_metadata,
+            "doc_form": self.doc_form,
+            "doc_language": self.doc_language,
+            "display_status": self.display_status,
+            "data_source_info_dict": self.data_source_info_dict,
+            "average_segment_length": self.average_segment_length,
+            "dataset_process_rule": self.dataset_process_rule.to_dict() if self.dataset_process_rule else None,
+            "dataset": self.dataset.to_dict() if self.dataset else None,
+            "segment_count": self.segment_count,
+            "hit_count": self.hit_count,
         }
 
     @classmethod
     def from_dict(cls, data: dict):
         return cls(
-            id=data.get('id'),
-            tenant_id=data.get('tenant_id'),
-            dataset_id=data.get('dataset_id'),
-            position=data.get('position'),
-            data_source_type=data.get('data_source_type'),
-            data_source_info=data.get('data_source_info'),
-            dataset_process_rule_id=data.get('dataset_process_rule_id'),
-            batch=data.get('batch'),
-            name=data.get('name'),
-            created_from=data.get('created_from'),
-            created_by=data.get('created_by'),
-            created_api_request_id=data.get('created_api_request_id'),
-            created_at=data.get('created_at'),
-            processing_started_at=data.get('processing_started_at'),
-            file_id=data.get('file_id'),
-            word_count=data.get('word_count'),
-            parsing_completed_at=data.get('parsing_completed_at'),
-            cleaning_completed_at=data.get('cleaning_completed_at'),
-            splitting_completed_at=data.get('splitting_completed_at'),
-            tokens=data.get('tokens'),
-            indexing_latency=data.get('indexing_latency'),
-            completed_at=data.get('completed_at'),
-            is_paused=data.get('is_paused'),
-            paused_by=data.get('paused_by'),
-            paused_at=data.get('paused_at'),
-            error=data.get('error'),
-            stopped_at=data.get('stopped_at'),
-            indexing_status=data.get('indexing_status'),
-            enabled=data.get('enabled'),
-            disabled_at=data.get('disabled_at'),
-            disabled_by=data.get('disabled_by'),
-            archived=data.get('archived'),
-            archived_reason=data.get('archived_reason'),
-            archived_by=data.get('archived_by'),
-            archived_at=data.get('archived_at'),
-            updated_at=data.get('updated_at'),
-            doc_type=data.get('doc_type'),
-            doc_metadata=data.get('doc_metadata'),
-            doc_form=data.get('doc_form'),
-            doc_language=data.get('doc_language')
+            id=data.get("id"),
+            tenant_id=data.get("tenant_id"),
+            dataset_id=data.get("dataset_id"),
+            position=data.get("position"),
+            data_source_type=data.get("data_source_type"),
+            data_source_info=data.get("data_source_info"),
+            dataset_process_rule_id=data.get("dataset_process_rule_id"),
+            batch=data.get("batch"),
+            name=data.get("name"),
+            created_from=data.get("created_from"),
+            created_by=data.get("created_by"),
+            created_api_request_id=data.get("created_api_request_id"),
+            created_at=data.get("created_at"),
+            processing_started_at=data.get("processing_started_at"),
+            file_id=data.get("file_id"),
+            word_count=data.get("word_count"),
+            parsing_completed_at=data.get("parsing_completed_at"),
+            cleaning_completed_at=data.get("cleaning_completed_at"),
+            splitting_completed_at=data.get("splitting_completed_at"),
+            tokens=data.get("tokens"),
+            indexing_latency=data.get("indexing_latency"),
+            completed_at=data.get("completed_at"),
+            is_paused=data.get("is_paused"),
+            paused_by=data.get("paused_by"),
+            paused_at=data.get("paused_at"),
+            error=data.get("error"),
+            stopped_at=data.get("stopped_at"),
+            indexing_status=data.get("indexing_status"),
+            enabled=data.get("enabled"),
+            disabled_at=data.get("disabled_at"),
+            disabled_by=data.get("disabled_by"),
+            archived=data.get("archived"),
+            archived_reason=data.get("archived_reason"),
+            archived_by=data.get("archived_by"),
+            archived_at=data.get("archived_at"),
+            updated_at=data.get("updated_at"),
+            doc_type=data.get("doc_type"),
+            doc_metadata=data.get("doc_metadata"),
+            doc_form=data.get("doc_form"),
+            doc_language=data.get("doc_language"),
         )
 
+
 class DocumentSegment(db.Model):
-    __tablename__ = 'document_segments'
+    __tablename__ = "document_segments"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='document_segment_pkey'),
-        db.Index('document_segment_dataset_id_idx', 'dataset_id'),
-        db.Index('document_segment_document_id_idx', 'document_id'),
-        db.Index('document_segment_tenant_dataset_idx', 'dataset_id', 'tenant_id'),
-        db.Index('document_segment_tenant_document_idx', 'document_id', 'tenant_id'),
-        db.Index('document_segment_dataset_node_idx', 'dataset_id', 'index_node_id'),
-        db.Index('document_segment_tenant_idx', 'tenant_id'),
+        db.PrimaryKeyConstraint("id", name="document_segment_pkey"),
+        db.Index("document_segment_dataset_id_idx", "dataset_id"),
+        db.Index("document_segment_document_id_idx", "document_id"),
+        db.Index("document_segment_tenant_dataset_idx", "dataset_id", "tenant_id"),
+        db.Index("document_segment_tenant_document_idx", "document_id", "tenant_id"),
+        db.Index("document_segment_dataset_node_idx", "dataset_id", "index_node_id"),
+        db.Index("document_segment_tenant_idx", "tenant_id"),
     )
 
     # initial fields
-    id = db.Column(StringUUID, nullable=False,
-                   server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, nullable=False, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     dataset_id = db.Column(StringUUID, nullable=False)
     document_id = db.Column(StringUUID, nullable=False)
@@ -487,18 +491,14 @@ class DocumentSegment(db.Model):
 
     # basic fields
     hit_count = db.Column(db.Integer, nullable=False, default=0)
-    enabled = db.Column(db.Boolean, nullable=False,
-                        server_default=db.text('true'))
+    enabled = db.Column(db.Boolean, nullable=False, server_default=db.text("true"))
     disabled_at = db.Column(db.DateTime, nullable=True)
     disabled_by = db.Column(StringUUID, nullable=True)
-    status = db.Column(db.String(255), nullable=False,
-                       server_default=db.text("'waiting'::character varying"))
+    status = db.Column(db.String(255), nullable=False, server_default=db.text("'waiting'::character varying"))
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False,
-                           server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     updated_by = db.Column(StringUUID, nullable=True)
-    updated_at = db.Column(db.DateTime, nullable=False,
-                           server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     indexing_at = db.Column(db.DateTime, nullable=True)
     completed_at = db.Column(db.DateTime, nullable=True)
     error = db.Column(db.Text, nullable=True)
@@ -514,17 +514,19 @@ class DocumentSegment(db.Model):
 
     @property
     def previous_segment(self):
-        return db.session.query(DocumentSegment).filter(
-            DocumentSegment.document_id == self.document_id,
-            DocumentSegment.position == self.position - 1
-        ).first()
+        return (
+            db.session.query(DocumentSegment)
+            .filter(DocumentSegment.document_id == self.document_id, DocumentSegment.position == self.position - 1)
+            .first()
+        )
 
     @property
     def next_segment(self):
-        return db.session.query(DocumentSegment).filter(
-            DocumentSegment.document_id == self.document_id,
-            DocumentSegment.position == self.position + 1
-        ).first()
+        return (
+            db.session.query(DocumentSegment)
+            .filter(DocumentSegment.document_id == self.document_id, DocumentSegment.position == self.position + 1)
+            .first()
+        )
 
     def get_sign_content(self):
         pattern = r"/files/([a-f0-9\-]+)/image-preview"
@@ -536,7 +538,7 @@ class DocumentSegment(db.Model):
             nonce = os.urandom(16).hex()
             timestamp = str(int(time.time()))
             data_to_sign = f"image-preview|{upload_file_id}|{timestamp}|{nonce}"
-            secret_key = dify_config.SECRET_KEY.encode() if dify_config.SECRET_KEY else b''
+            secret_key = dify_config.SECRET_KEY.encode() if dify_config.SECRET_KEY else b""
             sign = hmac.new(secret_key, data_to_sign.encode(), hashlib.sha256).digest()
             encoded_sign = base64.urlsafe_b64encode(sign).decode()
 
@@ -547,21 +549,20 @@ class DocumentSegment(db.Model):
         # Reconstruct the text with signed URLs
         offset = 0
         for start, end, signed_url in signed_urls:
-            text = text[:start + offset] + signed_url + text[end + offset:]
+            text = text[: start + offset] + signed_url + text[end + offset :]
             offset += len(signed_url) - (end - start)
 
         return text
 
 
-
 class AppDatasetJoin(db.Model):
-    __tablename__ = 'app_dataset_joins'
+    __tablename__ = "app_dataset_joins"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='app_dataset_join_pkey'),
-        db.Index('app_dataset_join_app_dataset_idx', 'dataset_id', 'app_id'),
+        db.PrimaryKeyConstraint("id", name="app_dataset_join_pkey"),
+        db.Index("app_dataset_join_app_dataset_idx", "dataset_id", "app_id"),
     )
 
-    id = db.Column(StringUUID, primary_key=True, nullable=False, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, primary_key=True, nullable=False, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     dataset_id = db.Column(StringUUID, nullable=False)
     created_at = db.Column(db.DateTime, nullable=False, server_default=db.func.current_timestamp())
@@ -572,13 +573,13 @@ class AppDatasetJoin(db.Model):
 
 
 class DatasetQuery(db.Model):
-    __tablename__ = 'dataset_queries'
+    __tablename__ = "dataset_queries"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='dataset_query_pkey'),
-        db.Index('dataset_query_dataset_id_idx', 'dataset_id'),
+        db.PrimaryKeyConstraint("id", name="dataset_query_pkey"),
+        db.Index("dataset_query_dataset_id_idx", "dataset_id"),
     )
 
-    id = db.Column(StringUUID, primary_key=True, nullable=False, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, primary_key=True, nullable=False, server_default=db.text("uuid_generate_v4()"))
     dataset_id = db.Column(StringUUID, nullable=False)
     content = db.Column(db.Text, nullable=False)
     source = db.Column(db.String(255), nullable=False)
@@ -589,17 +590,18 @@ class DatasetQuery(db.Model):
 
 
 class DatasetKeywordTable(db.Model):
-    __tablename__ = 'dataset_keyword_tables'
+    __tablename__ = "dataset_keyword_tables"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='dataset_keyword_table_pkey'),
-        db.Index('dataset_keyword_table_dataset_id_idx', 'dataset_id'),
+        db.PrimaryKeyConstraint("id", name="dataset_keyword_table_pkey"),
+        db.Index("dataset_keyword_table_dataset_id_idx", "dataset_id"),
     )
 
-    id = db.Column(StringUUID, primary_key=True, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, primary_key=True, server_default=db.text("uuid_generate_v4()"))
     dataset_id = db.Column(StringUUID, nullable=False, unique=True)
     keyword_table = db.Column(db.Text, nullable=False)
-    data_source_type = db.Column(db.String(255), nullable=False,
-                                 server_default=db.text("'database'::character varying"))
+    data_source_type = db.Column(
+        db.String(255), nullable=False, server_default=db.text("'database'::character varying")
+    )
 
     @property
     def keyword_table_dict(self):
@@ -615,19 +617,17 @@ class DatasetKeywordTable(db.Model):
                 return dct
 
         # get dataset
-        dataset = Dataset.query.filter_by(
-            id=self.dataset_id
-        ).first()
+        dataset = Dataset.query.filter_by(id=self.dataset_id).first()
         if not dataset:
             return None
-        if self.data_source_type == 'database':
+        if self.data_source_type == "database":
             return json.loads(self.keyword_table, cls=SetDecoder) if self.keyword_table else None
         else:
-            file_key = 'keyword_files/' + dataset.tenant_id + '/' + self.dataset_id + '.txt'
+            file_key = "keyword_files/" + dataset.tenant_id + "/" + self.dataset_id + ".txt"
             try:
                 keyword_table_text = storage.load_once(file_key)
                 if keyword_table_text:
-                    return json.loads(keyword_table_text.decode('utf-8'), cls=SetDecoder)
+                    return json.loads(keyword_table_text.decode("utf-8"), cls=SetDecoder)
                 return None
             except Exception as e:
                 logging.exception(str(e))
@@ -635,21 +635,21 @@ class DatasetKeywordTable(db.Model):
 
 
 class Embedding(db.Model):
-    __tablename__ = 'embeddings'
+    __tablename__ = "embeddings"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='embedding_pkey'),
-        db.UniqueConstraint('model_name', 'hash', 'provider_name', name='embedding_hash_idx'),
-        db.Index('created_at_idx', 'created_at')
+        db.PrimaryKeyConstraint("id", name="embedding_pkey"),
+        db.UniqueConstraint("model_name", "hash", "provider_name", name="embedding_hash_idx"),
+        db.Index("created_at_idx", "created_at"),
     )
 
-    id = db.Column(StringUUID, primary_key=True, server_default=db.text('uuid_generate_v4()'))
-    model_name = db.Column(db.String(255), nullable=False,
-                           server_default=db.text("'text-embedding-ada-002'::character varying"))
+    id = db.Column(StringUUID, primary_key=True, server_default=db.text("uuid_generate_v4()"))
+    model_name = db.Column(
+        db.String(255), nullable=False, server_default=db.text("'text-embedding-ada-002'::character varying")
+    )
     hash = db.Column(db.String(64), nullable=False)
     embedding = db.Column(db.LargeBinary, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    provider_name = db.Column(db.String(255), nullable=False,
-                              server_default=db.text("''::character varying"))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    provider_name = db.Column(db.String(255), nullable=False, server_default=db.text("''::character varying"))
 
     def set_embedding(self, embedding_data: list[float]):
         self.embedding = pickle.dumps(embedding_data, protocol=pickle.HIGHEST_PROTOCOL)
@@ -659,36 +659,35 @@ class Embedding(db.Model):
 
 
 class DatasetCollectionBinding(db.Model):
-    __tablename__ = 'dataset_collection_bindings'
+    __tablename__ = "dataset_collection_bindings"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='dataset_collection_bindings_pkey'),
-        db.Index('provider_model_name_idx', 'provider_name', 'model_name')
-
+        db.PrimaryKeyConstraint("id", name="dataset_collection_bindings_pkey"),
+        db.Index("provider_model_name_idx", "provider_name", "model_name"),
     )
 
-    id = db.Column(StringUUID, primary_key=True, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, primary_key=True, server_default=db.text("uuid_generate_v4()"))
     provider_name = db.Column(db.String(40), nullable=False)
     model_name = db.Column(db.String(255), nullable=False)
     type = db.Column(db.String(40), server_default=db.text("'dataset'::character varying"), nullable=False)
     collection_name = db.Column(db.String(64), nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class DatasetPermission(db.Model):
-    __tablename__ = 'dataset_permissions'
+    __tablename__ = "dataset_permissions"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='dataset_permission_pkey'),
-        db.Index('idx_dataset_permissions_dataset_id', 'dataset_id'),
-        db.Index('idx_dataset_permissions_account_id', 'account_id'),
-        db.Index('idx_dataset_permissions_tenant_id', 'tenant_id')
+        db.PrimaryKeyConstraint("id", name="dataset_permission_pkey"),
+        db.Index("idx_dataset_permissions_dataset_id", "dataset_id"),
+        db.Index("idx_dataset_permissions_account_id", "account_id"),
+        db.Index("idx_dataset_permissions_tenant_id", "tenant_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'), primary_key=True)
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"), primary_key=True)
     dataset_id = db.Column(StringUUID, nullable=False)
     account_id = db.Column(StringUUID, nullable=False)
     tenant_id = db.Column(StringUUID, nullable=False)
-    has_permission = db.Column(db.Boolean, nullable=False, server_default=db.text('true'))
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    has_permission = db.Column(db.Boolean, nullable=False, server_default=db.text("true"))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class ExternalApiTemplates(db.Model):
diff --git a/api/models/model.py b/api/models/model.py
index e81d25fbc9..ae0bc3210b 100644
--- a/api/models/model.py
+++ b/api/models/model.py
@@ -20,25 +20,23 @@ from .types import StringUUID
 
 
 class DifySetup(db.Model):
-    __tablename__ = 'dify_setups'
-    __table_args__ = (
-        db.PrimaryKeyConstraint('version', name='dify_setup_pkey'),
-    )
+    __tablename__ = "dify_setups"
+    __table_args__ = (db.PrimaryKeyConstraint("version", name="dify_setup_pkey"),)
 
     version = db.Column(db.String(255), nullable=False)
-    setup_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    setup_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class AppMode(Enum):
-    COMPLETION = 'completion'
-    WORKFLOW = 'workflow'
-    CHAT = 'chat'
-    ADVANCED_CHAT = 'advanced-chat'
-    AGENT_CHAT = 'agent-chat'
-    CHANNEL = 'channel'
+    COMPLETION = "completion"
+    WORKFLOW = "workflow"
+    CHAT = "chat"
+    ADVANCED_CHAT = "advanced-chat"
+    AGENT_CHAT = "agent-chat"
+    CHANNEL = "channel"
 
     @classmethod
-    def value_of(cls, value: str) -> 'AppMode':
+    def value_of(cls, value: str) -> "AppMode":
         """
         Get value of given mode.
 
@@ -48,21 +46,19 @@ class AppMode(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid mode value {value}')
+        raise ValueError(f"invalid mode value {value}")
 
 
 class IconType(Enum):
     IMAGE = "image"
     EMOJI = "emoji"
 
-class App(db.Model):
-    __tablename__ = 'apps'
-    __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='app_pkey'),
-        db.Index('app_tenant_id_idx', 'tenant_id')
-    )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+class App(db.Model):
+    __tablename__ = "apps"
+    __table_args__ = (db.PrimaryKeyConstraint("id", name="app_pkey"), db.Index("app_tenant_id_idx", "tenant_id"))
+
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     name = db.Column(db.String(255), nullable=False)
     description = db.Column(db.Text, nullable=False, server_default=db.text("''::character varying"))
@@ -75,17 +71,17 @@ class App(db.Model):
     status = db.Column(db.String(255), nullable=False, server_default=db.text("'normal'::character varying"))
     enable_site = db.Column(db.Boolean, nullable=False)
     enable_api = db.Column(db.Boolean, nullable=False)
-    api_rpm = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
-    api_rph = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
-    is_demo = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
-    is_public = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
-    is_universal = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
+    api_rpm = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
+    api_rph = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
+    is_demo = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
+    is_public = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
+    is_universal = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
     tracing = db.Column(db.Text, nullable=True)
     max_active_requests = db.Column(db.Integer, nullable=True)
     created_by = db.Column(StringUUID, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     updated_by = db.Column(StringUUID, nullable=True)
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     use_icon_as_answer_icon = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
 
     @property
@@ -97,7 +93,7 @@ class App(db.Model):
             if app_model_config:
                 return app_model_config.pre_prompt
             else:
-                return ''
+                return ""
 
     @property
     def site(self):
@@ -105,24 +101,24 @@ class App(db.Model):
         return site
 
     @property
-    def app_model_config(self) -> Optional['AppModelConfig']:
+    def app_model_config(self) -> Optional["AppModelConfig"]:
         if self.app_model_config_id:
             return db.session.query(AppModelConfig).filter(AppModelConfig.id == self.app_model_config_id).first()
 
         return None
 
     @property
-    def workflow(self) -> Optional['Workflow']:
+    def workflow(self) -> Optional["Workflow"]:
         if self.workflow_id:
             from .workflow import Workflow
+
             return db.session.query(Workflow).filter(Workflow.id == self.workflow_id).first()
 
         return None
 
     @property
     def api_base_url(self):
-        return (dify_config.SERVICE_API_URL if dify_config.SERVICE_API_URL
-                else request.host_url.rstrip('/')) + '/v1'
+        return (dify_config.SERVICE_API_URL or request.host_url.rstrip("/")) + "/v1"
 
     @property
     def tenant(self):
@@ -136,8 +132,9 @@ class App(db.Model):
             return False
         if not app_model_config.agent_mode:
             return False
-        if self.app_model_config.agent_mode_dict.get('enabled', False) \
-                and self.app_model_config.agent_mode_dict.get('strategy', '') in ['function_call', 'react']:
+        if self.app_model_config.agent_mode_dict.get("enabled", False) and self.app_model_config.agent_mode_dict.get(
+            "strategy", ""
+        ) in {"function_call", "react"}:
             self.mode = AppMode.AGENT_CHAT.value
             db.session.commit()
             return True
@@ -159,16 +156,16 @@ class App(db.Model):
         if not app_model_config.agent_mode:
             return []
         agent_mode = app_model_config.agent_mode_dict
-        tools = agent_mode.get('tools', [])
+        tools = agent_mode.get("tools", [])
 
         provider_ids = []
 
         for tool in tools:
             keys = list(tool.keys())
             if len(keys) >= 4:
-                provider_type = tool.get('provider_type', '')
-                provider_id = tool.get('provider_id', '')
-                if provider_type == 'api':
+                provider_type = tool.get("provider_type", "")
+                provider_id = tool.get("provider_id", "")
+                if provider_type == "api":
                     # check if provider id is a uuid string, if not, skip
                     try:
                         uuid.UUID(provider_id)
@@ -180,8 +177,7 @@ class App(db.Model):
             return []
 
         api_providers = db.session.execute(
-            text('SELECT id FROM tool_api_providers WHERE id IN :provider_ids'),
-            {'provider_ids': tuple(provider_ids)}
+            text("SELECT id FROM tool_api_providers WHERE id IN :provider_ids"), {"provider_ids": tuple(provider_ids)}
         ).fetchall()
 
         deleted_tools = []
@@ -190,44 +186,43 @@ class App(db.Model):
         for tool in tools:
             keys = list(tool.keys())
             if len(keys) >= 4:
-                provider_type = tool.get('provider_type', '')
-                provider_id = tool.get('provider_id', '')
-                if provider_type == 'api' and provider_id not in current_api_provider_ids:
-                    deleted_tools.append(tool['tool_name'])
+                provider_type = tool.get("provider_type", "")
+                provider_id = tool.get("provider_id", "")
+                if provider_type == "api" and provider_id not in current_api_provider_ids:
+                    deleted_tools.append(tool["tool_name"])
 
         return deleted_tools
 
     @property
     def tags(self):
-        tags = db.session.query(Tag).join(
-            TagBinding,
-            Tag.id == TagBinding.tag_id
-        ).filter(
-            TagBinding.target_id == self.id,
-            TagBinding.tenant_id == self.tenant_id,
-            Tag.tenant_id == self.tenant_id,
-            Tag.type == 'app'
-        ).all()
+        tags = (
+            db.session.query(Tag)
+            .join(TagBinding, Tag.id == TagBinding.tag_id)
+            .filter(
+                TagBinding.target_id == self.id,
+                TagBinding.tenant_id == self.tenant_id,
+                Tag.tenant_id == self.tenant_id,
+                Tag.type == "app",
+            )
+            .all()
+        )
 
-        return tags if tags else []
+        return tags or []
 
 
 class AppModelConfig(db.Model):
-    __tablename__ = 'app_model_configs'
-    __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='app_model_config_pkey'),
-        db.Index('app_app_id_idx', 'app_id')
-    )
+    __tablename__ = "app_model_configs"
+    __table_args__ = (db.PrimaryKeyConstraint("id", name="app_model_config_pkey"), db.Index("app_app_id_idx", "app_id"))
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     provider = db.Column(db.String(255), nullable=True)
     model_id = db.Column(db.String(255), nullable=True)
     configs = db.Column(db.JSON, nullable=True)
     created_by = db.Column(StringUUID, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     updated_by = db.Column(StringUUID, nullable=True)
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     opening_statement = db.Column(db.Text)
     suggested_questions = db.Column(db.Text)
     suggested_questions_after_answer = db.Column(db.Text)
@@ -263,28 +258,29 @@ class AppModelConfig(db.Model):
 
     @property
     def suggested_questions_after_answer_dict(self) -> dict:
-        return json.loads(self.suggested_questions_after_answer) if self.suggested_questions_after_answer \
+        return (
+            json.loads(self.suggested_questions_after_answer)
+            if self.suggested_questions_after_answer
             else {"enabled": False}
+        )
 
     @property
     def speech_to_text_dict(self) -> dict:
-        return json.loads(self.speech_to_text) if self.speech_to_text \
-            else {"enabled": False}
+        return json.loads(self.speech_to_text) if self.speech_to_text else {"enabled": False}
 
     @property
     def text_to_speech_dict(self) -> dict:
-        return json.loads(self.text_to_speech) if self.text_to_speech \
-            else {"enabled": False}
+        return json.loads(self.text_to_speech) if self.text_to_speech else {"enabled": False}
 
     @property
     def retriever_resource_dict(self) -> dict:
-        return json.loads(self.retriever_resource) if self.retriever_resource \
-            else {"enabled": True}
+        return json.loads(self.retriever_resource) if self.retriever_resource else {"enabled": True}
 
     @property
     def annotation_reply_dict(self) -> dict:
-        annotation_setting = db.session.query(AppAnnotationSetting).filter(
-            AppAnnotationSetting.app_id == self.app_id).first()
+        annotation_setting = (
+            db.session.query(AppAnnotationSetting).filter(AppAnnotationSetting.app_id == self.app_id).first()
+        )
         if annotation_setting:
             collection_binding_detail = annotation_setting.collection_binding_detail
             return {
@@ -293,8 +289,8 @@ class AppModelConfig(db.Model):
                 "score_threshold": annotation_setting.score_threshold,
                 "embedding_model": {
                     "embedding_provider_name": collection_binding_detail.provider_name,
-                    "embedding_model_name": collection_binding_detail.model_name
-                }
+                    "embedding_model_name": collection_binding_detail.model_name,
+                },
             }
 
         else:
@@ -306,13 +302,15 @@ class AppModelConfig(db.Model):
 
     @property
     def sensitive_word_avoidance_dict(self) -> dict:
-        return json.loads(self.sensitive_word_avoidance) if self.sensitive_word_avoidance \
+        return (
+            json.loads(self.sensitive_word_avoidance)
+            if self.sensitive_word_avoidance
             else {"enabled": False, "type": "", "configs": []}
+        )
 
     @property
     def external_data_tools_list(self) -> list[dict]:
-        return json.loads(self.external_data_tools) if self.external_data_tools \
-            else []
+        return json.loads(self.external_data_tools) if self.external_data_tools else []
 
     @property
     def user_input_form_list(self) -> dict:
@@ -320,8 +318,11 @@ class AppModelConfig(db.Model):
 
     @property
     def agent_mode_dict(self) -> dict:
-        return json.loads(self.agent_mode) if self.agent_mode else {"enabled": False, "strategy": None, "tools": [],
-                                                                    "prompt": None}
+        return (
+            json.loads(self.agent_mode)
+            if self.agent_mode
+            else {"enabled": False, "strategy": None, "tools": [], "prompt": None}
+        )
 
     @property
     def chat_prompt_config_dict(self) -> dict:
@@ -335,19 +336,28 @@ class AppModelConfig(db.Model):
     def dataset_configs_dict(self) -> dict:
         if self.dataset_configs:
             dataset_configs = json.loads(self.dataset_configs)
-            if 'retrieval_model' not in dataset_configs:
-                return {'retrieval_model': 'single'}
+            if "retrieval_model" not in dataset_configs:
+                return {"retrieval_model": "single"}
             else:
                 return dataset_configs
         return {
-                'retrieval_model': 'multiple',
-            }
+            "retrieval_model": "multiple",
+        }
 
     @property
     def file_upload_dict(self) -> dict:
-        return json.loads(self.file_upload) if self.file_upload else {
-            "image": {"enabled": False, "number_limits": 3, "detail": "high",
-                      "transfer_methods": ["remote_url", "local_file"]}}
+        return (
+            json.loads(self.file_upload)
+            if self.file_upload
+            else {
+                "image": {
+                    "enabled": False,
+                    "number_limits": 3,
+                    "detail": "high",
+                    "transfer_methods": ["remote_url", "local_file"],
+                }
+            }
+        )
 
     def to_dict(self) -> dict:
         return {
@@ -370,44 +380,53 @@ class AppModelConfig(db.Model):
             "chat_prompt_config": self.chat_prompt_config_dict,
             "completion_prompt_config": self.completion_prompt_config_dict,
             "dataset_configs": self.dataset_configs_dict,
-            "file_upload": self.file_upload_dict
+            "file_upload": self.file_upload_dict,
         }
 
     def from_model_config_dict(self, model_config: dict):
-        self.opening_statement = model_config.get('opening_statement')
-        self.suggested_questions = json.dumps(model_config['suggested_questions']) \
-            if model_config.get('suggested_questions') else None
-        self.suggested_questions_after_answer = json.dumps(model_config['suggested_questions_after_answer']) \
-            if model_config.get('suggested_questions_after_answer') else None
-        self.speech_to_text = json.dumps(model_config['speech_to_text']) \
-            if model_config.get('speech_to_text') else None
-        self.text_to_speech = json.dumps(model_config['text_to_speech']) \
-            if model_config.get('text_to_speech') else None
-        self.more_like_this = json.dumps(model_config['more_like_this']) \
-            if model_config.get('more_like_this') else None
-        self.sensitive_word_avoidance = json.dumps(model_config['sensitive_word_avoidance']) \
-            if model_config.get('sensitive_word_avoidance') else None
-        self.external_data_tools = json.dumps(model_config['external_data_tools']) \
-            if model_config.get('external_data_tools') else None
-        self.model = json.dumps(model_config['model']) \
-            if model_config.get('model') else None
-        self.user_input_form = json.dumps(model_config['user_input_form']) \
-            if model_config.get('user_input_form') else None
-        self.dataset_query_variable = model_config.get('dataset_query_variable')
-        self.pre_prompt = model_config['pre_prompt']
-        self.agent_mode = json.dumps(model_config['agent_mode']) \
-            if model_config.get('agent_mode') else None
-        self.retriever_resource = json.dumps(model_config['retriever_resource']) \
-            if model_config.get('retriever_resource') else None
-        self.prompt_type = model_config.get('prompt_type', 'simple')
-        self.chat_prompt_config = json.dumps(model_config.get('chat_prompt_config')) \
-            if model_config.get('chat_prompt_config') else None
-        self.completion_prompt_config = json.dumps(model_config.get('completion_prompt_config')) \
-            if model_config.get('completion_prompt_config') else None
-        self.dataset_configs = json.dumps(model_config.get('dataset_configs')) \
-            if model_config.get('dataset_configs') else None
-        self.file_upload = json.dumps(model_config.get('file_upload')) \
-            if model_config.get('file_upload') else None
+        self.opening_statement = model_config.get("opening_statement")
+        self.suggested_questions = (
+            json.dumps(model_config["suggested_questions"]) if model_config.get("suggested_questions") else None
+        )
+        self.suggested_questions_after_answer = (
+            json.dumps(model_config["suggested_questions_after_answer"])
+            if model_config.get("suggested_questions_after_answer")
+            else None
+        )
+        self.speech_to_text = json.dumps(model_config["speech_to_text"]) if model_config.get("speech_to_text") else None
+        self.text_to_speech = json.dumps(model_config["text_to_speech"]) if model_config.get("text_to_speech") else None
+        self.more_like_this = json.dumps(model_config["more_like_this"]) if model_config.get("more_like_this") else None
+        self.sensitive_word_avoidance = (
+            json.dumps(model_config["sensitive_word_avoidance"])
+            if model_config.get("sensitive_word_avoidance")
+            else None
+        )
+        self.external_data_tools = (
+            json.dumps(model_config["external_data_tools"]) if model_config.get("external_data_tools") else None
+        )
+        self.model = json.dumps(model_config["model"]) if model_config.get("model") else None
+        self.user_input_form = (
+            json.dumps(model_config["user_input_form"]) if model_config.get("user_input_form") else None
+        )
+        self.dataset_query_variable = model_config.get("dataset_query_variable")
+        self.pre_prompt = model_config["pre_prompt"]
+        self.agent_mode = json.dumps(model_config["agent_mode"]) if model_config.get("agent_mode") else None
+        self.retriever_resource = (
+            json.dumps(model_config["retriever_resource"]) if model_config.get("retriever_resource") else None
+        )
+        self.prompt_type = model_config.get("prompt_type", "simple")
+        self.chat_prompt_config = (
+            json.dumps(model_config.get("chat_prompt_config")) if model_config.get("chat_prompt_config") else None
+        )
+        self.completion_prompt_config = (
+            json.dumps(model_config.get("completion_prompt_config"))
+            if model_config.get("completion_prompt_config")
+            else None
+        )
+        self.dataset_configs = (
+            json.dumps(model_config.get("dataset_configs")) if model_config.get("dataset_configs") else None
+        )
+        self.file_upload = json.dumps(model_config.get("file_upload")) if model_config.get("file_upload") else None
         return self
 
     def copy(self):
@@ -432,21 +451,21 @@ class AppModelConfig(db.Model):
             chat_prompt_config=self.chat_prompt_config,
             completion_prompt_config=self.completion_prompt_config,
             dataset_configs=self.dataset_configs,
-            file_upload=self.file_upload
+            file_upload=self.file_upload,
         )
 
         return new_app_model_config
 
 
 class RecommendedApp(db.Model):
-    __tablename__ = 'recommended_apps'
+    __tablename__ = "recommended_apps"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='recommended_app_pkey'),
-        db.Index('recommended_app_app_id_idx', 'app_id'),
-        db.Index('recommended_app_is_listed_idx', 'is_listed', 'language')
+        db.PrimaryKeyConstraint("id", name="recommended_app_pkey"),
+        db.Index("recommended_app_app_id_idx", "app_id"),
+        db.Index("recommended_app_is_listed_idx", "is_listed", "language"),
     )
 
-    id = db.Column(StringUUID, primary_key=True, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, primary_key=True, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     description = db.Column(db.JSON, nullable=False)
     copyright = db.Column(db.String(255), nullable=False)
@@ -457,8 +476,8 @@ class RecommendedApp(db.Model):
     is_listed = db.Column(db.Boolean, nullable=False, default=True)
     install_count = db.Column(db.Integer, nullable=False, default=0)
     language = db.Column(db.String(255), nullable=False, server_default=db.text("'en-US'::character varying"))
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def app(self):
@@ -467,22 +486,22 @@ class RecommendedApp(db.Model):
 
 
 class InstalledApp(db.Model):
-    __tablename__ = 'installed_apps'
+    __tablename__ = "installed_apps"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='installed_app_pkey'),
-        db.Index('installed_app_tenant_id_idx', 'tenant_id'),
-        db.Index('installed_app_app_id_idx', 'app_id'),
-        db.UniqueConstraint('tenant_id', 'app_id', name='unique_tenant_app')
+        db.PrimaryKeyConstraint("id", name="installed_app_pkey"),
+        db.Index("installed_app_tenant_id_idx", "tenant_id"),
+        db.Index("installed_app_app_id_idx", "app_id"),
+        db.UniqueConstraint("tenant_id", "app_id", name="unique_tenant_app"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     app_id = db.Column(StringUUID, nullable=False)
     app_owner_tenant_id = db.Column(StringUUID, nullable=False)
     position = db.Column(db.Integer, nullable=False, default=0)
-    is_pinned = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
+    is_pinned = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
     last_used_at = db.Column(db.DateTime, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def app(self):
@@ -496,13 +515,13 @@ class InstalledApp(db.Model):
 
 
 class Conversation(db.Model):
-    __tablename__ = 'conversations'
+    __tablename__ = "conversations"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='conversation_pkey'),
-        db.Index('conversation_app_from_user_idx', 'app_id', 'from_source', 'from_end_user_id')
+        db.PrimaryKeyConstraint("id", name="conversation_pkey"),
+        db.Index("conversation_app_from_user_idx", "app_id", "from_source", "from_end_user_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     app_model_config_id = db.Column(StringUUID, nullable=True)
     model_provider = db.Column(db.String(255), nullable=True)
@@ -514,7 +533,7 @@ class Conversation(db.Model):
     inputs = db.Column(db.JSON)
     introduction = db.Column(db.Text)
     system_instruction = db.Column(db.Text)
-    system_instruction_tokens = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
+    system_instruction_tokens = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
     status = db.Column(db.String(255), nullable=False)
     invoke_from = db.Column(db.String(255), nullable=True)
     from_source = db.Column(db.String(255), nullable=False)
@@ -523,13 +542,15 @@ class Conversation(db.Model):
     read_at = db.Column(db.DateTime)
     read_account_id = db.Column(StringUUID)
     dialogue_count: Mapped[int] = mapped_column(default=0)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
-    messages = db.relationship("Message", backref="conversation", lazy='select', passive_deletes="all")
-    message_annotations = db.relationship("MessageAnnotation", backref="conversation", lazy='select', passive_deletes="all")
+    messages = db.relationship("Message", backref="conversation", lazy="select", passive_deletes="all")
+    message_annotations = db.relationship(
+        "MessageAnnotation", backref="conversation", lazy="select", passive_deletes="all"
+    )
 
-    is_deleted = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
+    is_deleted = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
 
     @property
     def model_config(self):
@@ -542,20 +563,21 @@ class Conversation(db.Model):
             if self.override_model_configs:
                 override_model_configs = json.loads(self.override_model_configs)
 
-                if 'model' in override_model_configs:
+                if "model" in override_model_configs:
                     app_model_config = AppModelConfig()
                     app_model_config = app_model_config.from_model_config_dict(override_model_configs)
                     model_config = app_model_config.to_dict()
                 else:
-                    model_config['configs'] = override_model_configs
+                    model_config["configs"] = override_model_configs
             else:
-                app_model_config = db.session.query(AppModelConfig).filter(
-                    AppModelConfig.id == self.app_model_config_id).first()
+                app_model_config = (
+                    db.session.query(AppModelConfig).filter(AppModelConfig.id == self.app_model_config_id).first()
+                )
 
                 model_config = app_model_config.to_dict()
 
-        model_config['model_id'] = self.model_id
-        model_config['provider'] = self.model_provider
+        model_config["model_id"] = self.model_id
+        model_config["provider"] = self.model_provider
 
         return model_config
 
@@ -568,7 +590,7 @@ class Conversation(db.Model):
             if first_message:
                 return first_message.query
             else:
-                return ''
+                return ""
 
     @property
     def annotated(self):
@@ -584,31 +606,51 @@ class Conversation(db.Model):
 
     @property
     def user_feedback_stats(self):
-        like = db.session.query(MessageFeedback) \
-            .filter(MessageFeedback.conversation_id == self.id,
-                    MessageFeedback.from_source == 'user',
-                    MessageFeedback.rating == 'like').count()
+        like = (
+            db.session.query(MessageFeedback)
+            .filter(
+                MessageFeedback.conversation_id == self.id,
+                MessageFeedback.from_source == "user",
+                MessageFeedback.rating == "like",
+            )
+            .count()
+        )
 
-        dislike = db.session.query(MessageFeedback) \
-            .filter(MessageFeedback.conversation_id == self.id,
-                    MessageFeedback.from_source == 'user',
-                    MessageFeedback.rating == 'dislike').count()
+        dislike = (
+            db.session.query(MessageFeedback)
+            .filter(
+                MessageFeedback.conversation_id == self.id,
+                MessageFeedback.from_source == "user",
+                MessageFeedback.rating == "dislike",
+            )
+            .count()
+        )
 
-        return {'like': like, 'dislike': dislike}
+        return {"like": like, "dislike": dislike}
 
     @property
     def admin_feedback_stats(self):
-        like = db.session.query(MessageFeedback) \
-            .filter(MessageFeedback.conversation_id == self.id,
-                    MessageFeedback.from_source == 'admin',
-                    MessageFeedback.rating == 'like').count()
+        like = (
+            db.session.query(MessageFeedback)
+            .filter(
+                MessageFeedback.conversation_id == self.id,
+                MessageFeedback.from_source == "admin",
+                MessageFeedback.rating == "like",
+            )
+            .count()
+        )
 
-        dislike = db.session.query(MessageFeedback) \
-            .filter(MessageFeedback.conversation_id == self.id,
-                    MessageFeedback.from_source == 'admin',
-                    MessageFeedback.rating == 'dislike').count()
+        dislike = (
+            db.session.query(MessageFeedback)
+            .filter(
+                MessageFeedback.conversation_id == self.id,
+                MessageFeedback.from_source == "admin",
+                MessageFeedback.rating == "dislike",
+            )
+            .count()
+        )
 
-        return {'like': like, 'dislike': dislike}
+        return {"like": like, "dislike": dislike}
 
     @property
     def first_message(self):
@@ -642,33 +684,33 @@ class Conversation(db.Model):
 
 
 class Message(db.Model):
-    __tablename__ = 'messages'
+    __tablename__ = "messages"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='message_pkey'),
-        db.Index('message_app_id_idx', 'app_id', 'created_at'),
-        db.Index('message_conversation_id_idx', 'conversation_id'),
-        db.Index('message_end_user_idx', 'app_id', 'from_source', 'from_end_user_id'),
-        db.Index('message_account_idx', 'app_id', 'from_source', 'from_account_id'),
-        db.Index('message_workflow_run_id_idx', 'conversation_id', 'workflow_run_id')
+        db.PrimaryKeyConstraint("id", name="message_pkey"),
+        db.Index("message_app_id_idx", "app_id", "created_at"),
+        db.Index("message_conversation_id_idx", "conversation_id"),
+        db.Index("message_end_user_idx", "app_id", "from_source", "from_end_user_id"),
+        db.Index("message_account_idx", "app_id", "from_source", "from_account_id"),
+        db.Index("message_workflow_run_id_idx", "conversation_id", "workflow_run_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     model_provider = db.Column(db.String(255), nullable=True)
     model_id = db.Column(db.String(255), nullable=True)
     override_model_configs = db.Column(db.Text)
-    conversation_id = db.Column(StringUUID, db.ForeignKey('conversations.id'), nullable=False)
+    conversation_id = db.Column(StringUUID, db.ForeignKey("conversations.id"), nullable=False)
     inputs = db.Column(db.JSON)
     query = db.Column(db.Text, nullable=False)
     message = db.Column(db.JSON, nullable=False)
-    message_tokens = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
+    message_tokens = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
     message_unit_price = db.Column(db.Numeric(10, 4), nullable=False)
-    message_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
+    message_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text("0.001"))
     answer = db.Column(db.Text, nullable=False)
-    answer_tokens = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
+    answer_tokens = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
     answer_unit_price = db.Column(db.Numeric(10, 4), nullable=False)
-    answer_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
-    provider_response_latency = db.Column(db.Float, nullable=False, server_default=db.text('0'))
+    answer_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text("0.001"))
+    provider_response_latency = db.Column(db.Float, nullable=False, server_default=db.text("0"))
     total_price = db.Column(db.Numeric(10, 7))
     currency = db.Column(db.String(255), nullable=False)
     status = db.Column(db.String(255), nullable=False, server_default=db.text("'normal'::character varying"))
@@ -678,9 +720,9 @@ class Message(db.Model):
     from_source = db.Column(db.String(255), nullable=False)
     from_end_user_id = db.Column(StringUUID)
     from_account_id = db.Column(StringUUID)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    agent_based = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    agent_based = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
     workflow_run_id = db.Column(StringUUID)
 
     @property
@@ -688,7 +730,7 @@ class Message(db.Model):
         if not self.answer:
             return self.answer
 
-        pattern = r'\[!?.*?\]\((((http|https):\/\/.+)?\/files\/(tools\/)?[\w-]+.*?timestamp=.*&nonce=.*&sign=.*)\)'
+        pattern = r"\[!?.*?\]\((((http|https):\/\/.+)?\/files\/(tools\/)?[\w-]+.*?timestamp=.*&nonce=.*&sign=.*)\)"
         matches = re.findall(pattern, self.answer)
 
         if not matches:
@@ -704,9 +746,9 @@ class Message(db.Model):
 
         re_sign_file_url_answer = self.answer
         for url in urls:
-            if 'files/tools' in url:
+            if "files/tools" in url:
                 # get tool file id
-                tool_file_id_pattern = r'\/files\/tools\/([\.\w-]+)?\?timestamp='
+                tool_file_id_pattern = r"\/files\/tools\/([\.\w-]+)?\?timestamp="
                 result = re.search(tool_file_id_pattern, url)
                 if not result:
                     continue
@@ -714,25 +756,24 @@ class Message(db.Model):
                 tool_file_id = result.group(1)
 
                 # get extension
-                if '.' in tool_file_id:
-                    split_result = tool_file_id.split('.')
-                    extension = f'.{split_result[-1]}'
+                if "." in tool_file_id:
+                    split_result = tool_file_id.split(".")
+                    extension = f".{split_result[-1]}"
                     if len(extension) > 10:
-                        extension = '.bin'
+                        extension = ".bin"
                     tool_file_id = split_result[0]
                 else:
-                    extension = '.bin'
+                    extension = ".bin"
 
                 if not tool_file_id:
                     continue
 
                 sign_url = ToolFileParser.get_tool_file_manager().sign_file(
-                    tool_file_id=tool_file_id,
-                    extension=extension
+                    tool_file_id=tool_file_id, extension=extension
                 )
             else:
                 # get upload file id
-                upload_file_id_pattern = r'\/files\/([\w-]+)\/image-preview?\?timestamp='
+                upload_file_id_pattern = r"\/files\/([\w-]+)\/image-preview?\?timestamp="
                 result = re.search(upload_file_id_pattern, url)
                 if not result:
                     continue
@@ -750,14 +791,20 @@ class Message(db.Model):
 
     @property
     def user_feedback(self):
-        feedback = db.session.query(MessageFeedback).filter(MessageFeedback.message_id == self.id,
-                                                            MessageFeedback.from_source == 'user').first()
+        feedback = (
+            db.session.query(MessageFeedback)
+            .filter(MessageFeedback.message_id == self.id, MessageFeedback.from_source == "user")
+            .first()
+        )
         return feedback
 
     @property
     def admin_feedback(self):
-        feedback = db.session.query(MessageFeedback).filter(MessageFeedback.message_id == self.id,
-                                                            MessageFeedback.from_source == 'admin').first()
+        feedback = (
+            db.session.query(MessageFeedback)
+            .filter(MessageFeedback.message_id == self.id, MessageFeedback.from_source == "admin")
+            .first()
+        )
         return feedback
 
     @property
@@ -772,11 +819,15 @@ class Message(db.Model):
 
     @property
     def annotation_hit_history(self):
-        annotation_history = (db.session.query(AppAnnotationHitHistory)
-                              .filter(AppAnnotationHitHistory.message_id == self.id).first())
+        annotation_history = (
+            db.session.query(AppAnnotationHitHistory).filter(AppAnnotationHitHistory.message_id == self.id).first()
+        )
         if annotation_history:
-            annotation = (db.session.query(MessageAnnotation).
-                          filter(MessageAnnotation.id == annotation_history.annotation_id).first())
+            annotation = (
+                db.session.query(MessageAnnotation)
+                .filter(MessageAnnotation.id == annotation_history.annotation_id)
+                .first()
+            )
             return annotation
         return None
 
@@ -784,8 +835,9 @@ class Message(db.Model):
     def app_model_config(self):
         conversation = db.session.query(Conversation).filter(Conversation.id == self.conversation_id).first()
         if conversation:
-            return db.session.query(AppModelConfig).filter(
-                AppModelConfig.id == conversation.app_model_config_id).first()
+            return (
+                db.session.query(AppModelConfig).filter(AppModelConfig.id == conversation.app_model_config_id).first()
+            )
 
         return None
 
@@ -799,13 +851,21 @@ class Message(db.Model):
 
     @property
     def agent_thoughts(self):
-        return db.session.query(MessageAgentThought).filter(MessageAgentThought.message_id == self.id) \
-            .order_by(MessageAgentThought.position.asc()).all()
+        return (
+            db.session.query(MessageAgentThought)
+            .filter(MessageAgentThought.message_id == self.id)
+            .order_by(MessageAgentThought.position.asc())
+            .all()
+        )
 
     @property
     def retriever_resources(self):
-        return db.session.query(DatasetRetrieverResource).filter(DatasetRetrieverResource.message_id == self.id) \
-            .order_by(DatasetRetrieverResource.position.asc()).all()
+        return (
+            db.session.query(DatasetRetrieverResource)
+            .filter(DatasetRetrieverResource.message_id == self.id)
+            .order_by(DatasetRetrieverResource.position.asc())
+            .all()
+        )
 
     @property
     def message_files(self):
@@ -818,39 +878,39 @@ class Message(db.Model):
         files = []
         for message_file in message_files:
             url = message_file.url
-            if message_file.type == 'image':
-                if message_file.transfer_method == 'local_file':
-                    upload_file = (db.session.query(UploadFile)
-                                   .filter(
-                        UploadFile.id == message_file.upload_file_id
-                    ).first())
-
-                    url = UploadFileParser.get_image_data(
-                        upload_file=upload_file,
-                        force_url=True
+            if message_file.type == "image":
+                if message_file.transfer_method == "local_file":
+                    upload_file = (
+                        db.session.query(UploadFile).filter(UploadFile.id == message_file.upload_file_id).first()
                     )
-                if message_file.transfer_method == 'tool_file':
+
+                    url = UploadFileParser.get_image_data(upload_file=upload_file, force_url=True)
+                if message_file.transfer_method == "tool_file":
                     # get tool file id
-                    tool_file_id = message_file.url.split('/')[-1]
+                    tool_file_id = message_file.url.split("/")[-1]
                     # trim extension
-                    tool_file_id = tool_file_id.split('.')[0]
+                    tool_file_id = tool_file_id.split(".")[0]
 
                     # get extension
-                    if '.' in message_file.url:
+                    if "." in message_file.url:
                         extension = f'.{message_file.url.split(".")[-1]}'
                         if len(extension) > 10:
-                            extension = '.bin'
+                            extension = ".bin"
                     else:
-                        extension = '.bin'
+                        extension = ".bin"
                     # add sign url
-                    url = ToolFileParser.get_tool_file_manager().sign_file(tool_file_id=tool_file_id, extension=extension)
+                    url = ToolFileParser.get_tool_file_manager().sign_file(
+                        tool_file_id=tool_file_id, extension=extension
+                    )
 
-            files.append({
-                'id': message_file.id,
-                'type': message_file.type,
-                'url': url,
-                'belongs_to': message_file.belongs_to if message_file.belongs_to else 'user'
-            })
+            files.append(
+                {
+                    "id": message_file.id,
+                    "type": message_file.type,
+                    "url": url,
+                    "belongs_to": message_file.belongs_to or "user",
+                }
+            )
 
         return files
 
@@ -858,64 +918,65 @@ class Message(db.Model):
     def workflow_run(self):
         if self.workflow_run_id:
             from .workflow import WorkflowRun
+
             return db.session.query(WorkflowRun).filter(WorkflowRun.id == self.workflow_run_id).first()
 
         return None
 
     def to_dict(self) -> dict:
         return {
-            'id': self.id,
-            'app_id': self.app_id,
-            'conversation_id': self.conversation_id,
-            'inputs': self.inputs,
-            'query': self.query,
-            'message': self.message,
-            'answer': self.answer,
-            'status': self.status,
-            'error': self.error,
-            'message_metadata': self.message_metadata_dict,
-            'from_source': self.from_source,
-            'from_end_user_id': self.from_end_user_id,
-            'from_account_id': self.from_account_id,
-            'created_at': self.created_at.isoformat(),
-            'updated_at': self.updated_at.isoformat(),
-            'agent_based': self.agent_based,
-            'workflow_run_id': self.workflow_run_id
+            "id": self.id,
+            "app_id": self.app_id,
+            "conversation_id": self.conversation_id,
+            "inputs": self.inputs,
+            "query": self.query,
+            "message": self.message,
+            "answer": self.answer,
+            "status": self.status,
+            "error": self.error,
+            "message_metadata": self.message_metadata_dict,
+            "from_source": self.from_source,
+            "from_end_user_id": self.from_end_user_id,
+            "from_account_id": self.from_account_id,
+            "created_at": self.created_at.isoformat(),
+            "updated_at": self.updated_at.isoformat(),
+            "agent_based": self.agent_based,
+            "workflow_run_id": self.workflow_run_id,
         }
 
     @classmethod
     def from_dict(cls, data: dict):
         return cls(
-            id=data['id'],
-            app_id=data['app_id'],
-            conversation_id=data['conversation_id'],
-            inputs=data['inputs'],
-            query=data['query'],
-            message=data['message'],
-            answer=data['answer'],
-            status=data['status'],
-            error=data['error'],
-            message_metadata=json.dumps(data['message_metadata']),
-            from_source=data['from_source'],
-            from_end_user_id=data['from_end_user_id'],
-            from_account_id=data['from_account_id'],
-            created_at=data['created_at'],
-            updated_at=data['updated_at'],
-            agent_based=data['agent_based'],
-            workflow_run_id=data['workflow_run_id']
+            id=data["id"],
+            app_id=data["app_id"],
+            conversation_id=data["conversation_id"],
+            inputs=data["inputs"],
+            query=data["query"],
+            message=data["message"],
+            answer=data["answer"],
+            status=data["status"],
+            error=data["error"],
+            message_metadata=json.dumps(data["message_metadata"]),
+            from_source=data["from_source"],
+            from_end_user_id=data["from_end_user_id"],
+            from_account_id=data["from_account_id"],
+            created_at=data["created_at"],
+            updated_at=data["updated_at"],
+            agent_based=data["agent_based"],
+            workflow_run_id=data["workflow_run_id"],
         )
 
 
 class MessageFeedback(db.Model):
-    __tablename__ = 'message_feedbacks'
+    __tablename__ = "message_feedbacks"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='message_feedback_pkey'),
-        db.Index('message_feedback_app_idx', 'app_id'),
-        db.Index('message_feedback_message_idx', 'message_id', 'from_source'),
-        db.Index('message_feedback_conversation_idx', 'conversation_id', 'from_source', 'rating')
+        db.PrimaryKeyConstraint("id", name="message_feedback_pkey"),
+        db.Index("message_feedback_app_idx", "app_id"),
+        db.Index("message_feedback_message_idx", "message_id", "from_source"),
+        db.Index("message_feedback_conversation_idx", "conversation_id", "from_source", "rating"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     conversation_id = db.Column(StringUUID, nullable=False)
     message_id = db.Column(StringUUID, nullable=False)
@@ -924,8 +985,8 @@ class MessageFeedback(db.Model):
     from_source = db.Column(db.String(255), nullable=False)
     from_end_user_id = db.Column(StringUUID)
     from_account_id = db.Column(StringUUID)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def from_account(self):
@@ -934,14 +995,14 @@ class MessageFeedback(db.Model):
 
 
 class MessageFile(db.Model):
-    __tablename__ = 'message_files'
+    __tablename__ = "message_files"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='message_file_pkey'),
-        db.Index('message_file_message_idx', 'message_id'),
-        db.Index('message_file_created_by_idx', 'created_by')
+        db.PrimaryKeyConstraint("id", name="message_file_pkey"),
+        db.Index("message_file_message_idx", "message_id"),
+        db.Index("message_file_created_by_idx", "created_by"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     message_id = db.Column(StringUUID, nullable=False)
     type = db.Column(db.String(255), nullable=False)
     transfer_method = db.Column(db.String(255), nullable=False)
@@ -950,28 +1011,28 @@ class MessageFile(db.Model):
     upload_file_id = db.Column(StringUUID, nullable=True)
     created_by_role = db.Column(db.String(255), nullable=False)
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class MessageAnnotation(db.Model):
-    __tablename__ = 'message_annotations'
+    __tablename__ = "message_annotations"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='message_annotation_pkey'),
-        db.Index('message_annotation_app_idx', 'app_id'),
-        db.Index('message_annotation_conversation_idx', 'conversation_id'),
-        db.Index('message_annotation_message_idx', 'message_id')
+        db.PrimaryKeyConstraint("id", name="message_annotation_pkey"),
+        db.Index("message_annotation_app_idx", "app_id"),
+        db.Index("message_annotation_conversation_idx", "conversation_id"),
+        db.Index("message_annotation_message_idx", "message_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
-    conversation_id = db.Column(StringUUID, db.ForeignKey('conversations.id'), nullable=True)
+    conversation_id = db.Column(StringUUID, db.ForeignKey("conversations.id"), nullable=True)
     message_id = db.Column(StringUUID, nullable=True)
     question = db.Column(db.Text, nullable=True)
     content = db.Column(db.Text, nullable=False)
-    hit_count = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
+    hit_count = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
     account_id = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def account(self):
@@ -985,32 +1046,35 @@ class MessageAnnotation(db.Model):
 
 
 class AppAnnotationHitHistory(db.Model):
-    __tablename__ = 'app_annotation_hit_histories'
+    __tablename__ = "app_annotation_hit_histories"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='app_annotation_hit_histories_pkey'),
-        db.Index('app_annotation_hit_histories_app_idx', 'app_id'),
-        db.Index('app_annotation_hit_histories_account_idx', 'account_id'),
-        db.Index('app_annotation_hit_histories_annotation_idx', 'annotation_id'),
-        db.Index('app_annotation_hit_histories_message_idx', 'message_id'),
+        db.PrimaryKeyConstraint("id", name="app_annotation_hit_histories_pkey"),
+        db.Index("app_annotation_hit_histories_app_idx", "app_id"),
+        db.Index("app_annotation_hit_histories_account_idx", "account_id"),
+        db.Index("app_annotation_hit_histories_annotation_idx", "annotation_id"),
+        db.Index("app_annotation_hit_histories_message_idx", "message_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     annotation_id = db.Column(StringUUID, nullable=False)
     source = db.Column(db.Text, nullable=False)
     question = db.Column(db.Text, nullable=False)
     account_id = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    score = db.Column(Float, nullable=False, server_default=db.text('0'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    score = db.Column(Float, nullable=False, server_default=db.text("0"))
     message_id = db.Column(StringUUID, nullable=False)
     annotation_question = db.Column(db.Text, nullable=False)
     annotation_content = db.Column(db.Text, nullable=False)
 
     @property
     def account(self):
-        account = (db.session.query(Account)
-                   .join(MessageAnnotation, MessageAnnotation.account_id == Account.id)
-                   .filter(MessageAnnotation.id == self.annotation_id).first())
+        account = (
+            db.session.query(Account)
+            .join(MessageAnnotation, MessageAnnotation.account_id == Account.id)
+            .filter(MessageAnnotation.id == self.annotation_id)
+            .first()
+        )
         return account
 
     @property
@@ -1020,89 +1084,99 @@ class AppAnnotationHitHistory(db.Model):
 
 
 class AppAnnotationSetting(db.Model):
-    __tablename__ = 'app_annotation_settings'
+    __tablename__ = "app_annotation_settings"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='app_annotation_settings_pkey'),
-        db.Index('app_annotation_settings_app_idx', 'app_id')
+        db.PrimaryKeyConstraint("id", name="app_annotation_settings_pkey"),
+        db.Index("app_annotation_settings_app_idx", "app_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
-    score_threshold = db.Column(Float, nullable=False, server_default=db.text('0'))
+    score_threshold = db.Column(Float, nullable=False, server_default=db.text("0"))
     collection_binding_id = db.Column(StringUUID, nullable=False)
     created_user_id = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     updated_user_id = db.Column(StringUUID, nullable=False)
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def created_account(self):
-        account = (db.session.query(Account)
-                   .join(AppAnnotationSetting, AppAnnotationSetting.created_user_id == Account.id)
-                   .filter(AppAnnotationSetting.id == self.annotation_id).first())
+        account = (
+            db.session.query(Account)
+            .join(AppAnnotationSetting, AppAnnotationSetting.created_user_id == Account.id)
+            .filter(AppAnnotationSetting.id == self.annotation_id)
+            .first()
+        )
         return account
 
     @property
     def updated_account(self):
-        account = (db.session.query(Account)
-                   .join(AppAnnotationSetting, AppAnnotationSetting.updated_user_id == Account.id)
-                   .filter(AppAnnotationSetting.id == self.annotation_id).first())
+        account = (
+            db.session.query(Account)
+            .join(AppAnnotationSetting, AppAnnotationSetting.updated_user_id == Account.id)
+            .filter(AppAnnotationSetting.id == self.annotation_id)
+            .first()
+        )
         return account
 
     @property
     def collection_binding_detail(self):
         from .dataset import DatasetCollectionBinding
-        collection_binding_detail = (db.session.query(DatasetCollectionBinding)
-                                     .filter(DatasetCollectionBinding.id == self.collection_binding_id).first())
+
+        collection_binding_detail = (
+            db.session.query(DatasetCollectionBinding)
+            .filter(DatasetCollectionBinding.id == self.collection_binding_id)
+            .first()
+        )
         return collection_binding_detail
 
 
 class OperationLog(db.Model):
-    __tablename__ = 'operation_logs'
+    __tablename__ = "operation_logs"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='operation_log_pkey'),
-        db.Index('operation_log_account_action_idx', 'tenant_id', 'account_id', 'action')
+        db.PrimaryKeyConstraint("id", name="operation_log_pkey"),
+        db.Index("operation_log_account_action_idx", "tenant_id", "account_id", "action"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     account_id = db.Column(StringUUID, nullable=False)
     action = db.Column(db.String(255), nullable=False)
     content = db.Column(db.JSON)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     created_ip = db.Column(db.String(255), nullable=False)
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class EndUser(UserMixin, db.Model):
-    __tablename__ = 'end_users'
+    __tablename__ = "end_users"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='end_user_pkey'),
-        db.Index('end_user_session_id_idx', 'session_id', 'type'),
-        db.Index('end_user_tenant_session_id_idx', 'tenant_id', 'session_id', 'type'),
+        db.PrimaryKeyConstraint("id", name="end_user_pkey"),
+        db.Index("end_user_session_id_idx", "session_id", "type"),
+        db.Index("end_user_tenant_session_id_idx", "tenant_id", "session_id", "type"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     app_id = db.Column(StringUUID, nullable=True)
     type = db.Column(db.String(255), nullable=False)
     external_user_id = db.Column(db.String(255), nullable=True)
     name = db.Column(db.String(255))
-    is_anonymous = db.Column(db.Boolean, nullable=False, server_default=db.text('true'))
+    is_anonymous = db.Column(db.Boolean, nullable=False, server_default=db.text("true"))
     session_id = db.Column(db.String(255), nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class Site(db.Model):
-    __tablename__ = 'sites'
+    __tablename__ = "sites"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='site_pkey'),
-        db.Index('site_app_id_idx', 'app_id'),
-        db.Index('site_code_idx', 'code', 'status')
+        db.PrimaryKeyConstraint("id", name="site_pkey"),
+        db.Index("site_app_id_idx", "app_id"),
+        db.Index("site_code_idx", "code", "status"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     title = db.Column(db.String(255), nullable=False)
     icon_type = db.Column(db.String(255), nullable=True)
@@ -1111,20 +1185,20 @@ class Site(db.Model):
     description = db.Column(db.Text)
     default_language = db.Column(db.String(255), nullable=False)
     chat_color_theme = db.Column(db.String(255))
-    chat_color_theme_inverted = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
+    chat_color_theme_inverted = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
     copyright = db.Column(db.String(255))
     privacy_policy = db.Column(db.String(255))
-    show_workflow_steps = db.Column(db.Boolean, nullable=False, server_default=db.text('true'))
+    show_workflow_steps = db.Column(db.Boolean, nullable=False, server_default=db.text("true"))
     use_icon_as_answer_icon = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
     custom_disclaimer = db.Column(db.String(255), nullable=True)
     customize_domain = db.Column(db.String(255))
     customize_token_strategy = db.Column(db.String(255), nullable=False)
-    prompt_public = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
+    prompt_public = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
     status = db.Column(db.String(255), nullable=False, server_default=db.text("'normal'::character varying"))
     created_by = db.Column(StringUUID, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     updated_by = db.Column(StringUUID, nullable=True)
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     code = db.Column(db.String(255))
 
     @staticmethod
@@ -1138,26 +1212,25 @@ class Site(db.Model):
 
     @property
     def app_base_url(self):
-        return (
-            dify_config.APP_WEB_URL if  dify_config.APP_WEB_URL else request.url_root.rstrip('/'))
+        return dify_config.APP_WEB_URL or request.url_root.rstrip("/")
 
 
 class ApiToken(db.Model):
-    __tablename__ = 'api_tokens'
+    __tablename__ = "api_tokens"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='api_token_pkey'),
-        db.Index('api_token_app_id_type_idx', 'app_id', 'type'),
-        db.Index('api_token_token_idx', 'token', 'type'),
-        db.Index('api_token_tenant_idx', 'tenant_id', 'type')
+        db.PrimaryKeyConstraint("id", name="api_token_pkey"),
+        db.Index("api_token_app_id_type_idx", "app_id", "type"),
+        db.Index("api_token_token_idx", "token", "type"),
+        db.Index("api_token_tenant_idx", "tenant_id", "type"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=True)
     tenant_id = db.Column(StringUUID, nullable=True)
     type = db.Column(db.String(16), nullable=False)
     token = db.Column(db.String(255), nullable=False)
     last_used_at = db.Column(db.DateTime, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @staticmethod
     def generate_api_key(prefix, n):
@@ -1170,13 +1243,13 @@ class ApiToken(db.Model):
 
 
 class UploadFile(db.Model):
-    __tablename__ = 'upload_files'
+    __tablename__ = "upload_files"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='upload_file_pkey'),
-        db.Index('upload_file_tenant_idx', 'tenant_id')
+        db.PrimaryKeyConstraint("id", name="upload_file_pkey"),
+        db.Index("upload_file_tenant_idx", "tenant_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     storage_type = db.Column(db.String(255), nullable=False)
     key = db.Column(db.String(255), nullable=False)
@@ -1186,38 +1259,38 @@ class UploadFile(db.Model):
     mime_type = db.Column(db.String(255), nullable=True)
     created_by_role = db.Column(db.String(255), nullable=False, server_default=db.text("'account'::character varying"))
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    used = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    used = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
     used_by = db.Column(StringUUID, nullable=True)
     used_at = db.Column(db.DateTime, nullable=True)
     hash = db.Column(db.String(255), nullable=True)
 
 
 class ApiRequest(db.Model):
-    __tablename__ = 'api_requests'
+    __tablename__ = "api_requests"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='api_request_pkey'),
-        db.Index('api_request_token_idx', 'tenant_id', 'api_token_id')
+        db.PrimaryKeyConstraint("id", name="api_request_pkey"),
+        db.Index("api_request_token_idx", "tenant_id", "api_token_id"),
     )
 
-    id = db.Column(StringUUID, nullable=False, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, nullable=False, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     api_token_id = db.Column(StringUUID, nullable=False)
     path = db.Column(db.String(255), nullable=False)
     request = db.Column(db.Text, nullable=True)
     response = db.Column(db.Text, nullable=True)
     ip = db.Column(db.String(255), nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class MessageChain(db.Model):
-    __tablename__ = 'message_chains'
+    __tablename__ = "message_chains"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='message_chain_pkey'),
-        db.Index('message_chain_message_id_idx', 'message_id')
+        db.PrimaryKeyConstraint("id", name="message_chain_pkey"),
+        db.Index("message_chain_message_id_idx", "message_id"),
     )
 
-    id = db.Column(StringUUID, nullable=False, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, nullable=False, server_default=db.text("uuid_generate_v4()"))
     message_id = db.Column(StringUUID, nullable=False)
     type = db.Column(db.String(255), nullable=False)
     input = db.Column(db.Text, nullable=True)
@@ -1226,14 +1299,14 @@ class MessageChain(db.Model):
 
 
 class MessageAgentThought(db.Model):
-    __tablename__ = 'message_agent_thoughts'
+    __tablename__ = "message_agent_thoughts"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='message_agent_thought_pkey'),
-        db.Index('message_agent_thought_message_id_idx', 'message_id'),
-        db.Index('message_agent_thought_message_chain_id_idx', 'message_chain_id'),
+        db.PrimaryKeyConstraint("id", name="message_agent_thought_pkey"),
+        db.Index("message_agent_thought_message_id_idx", "message_id"),
+        db.Index("message_agent_thought_message_chain_id_idx", "message_chain_id"),
     )
 
-    id = db.Column(StringUUID, nullable=False, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, nullable=False, server_default=db.text("uuid_generate_v4()"))
     message_id = db.Column(StringUUID, nullable=False)
     message_chain_id = db.Column(StringUUID, nullable=True)
     position = db.Column(db.Integer, nullable=False)
@@ -1248,12 +1321,12 @@ class MessageAgentThought(db.Model):
     message = db.Column(db.Text, nullable=True)
     message_token = db.Column(db.Integer, nullable=True)
     message_unit_price = db.Column(db.Numeric, nullable=True)
-    message_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
+    message_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text("0.001"))
     message_files = db.Column(db.Text, nullable=True)
     answer = db.Column(db.Text, nullable=True)
     answer_token = db.Column(db.Integer, nullable=True)
     answer_unit_price = db.Column(db.Numeric, nullable=True)
-    answer_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
+    answer_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text("0.001"))
     tokens = db.Column(db.Integer, nullable=True)
     total_price = db.Column(db.Numeric, nullable=True)
     currency = db.Column(db.String, nullable=True)
@@ -1310,9 +1383,7 @@ class MessageAgentThought(db.Model):
                             result[tool] = {}
                 return result
             else:
-                return {
-                    tool: {} for tool in tools
-                }
+                return {tool: {} for tool in tools}
         except Exception as e:
             return {}
 
@@ -1333,22 +1404,20 @@ class MessageAgentThought(db.Model):
                             result[tool] = {}
                 return result
             else:
-                return {
-                    tool: {} for tool in tools
-                }
+                return {tool: {} for tool in tools}
         except Exception as e:
             if self.observation:
                 return dict.fromkeys(tools, self.observation)
 
 
 class DatasetRetrieverResource(db.Model):
-    __tablename__ = 'dataset_retriever_resources'
+    __tablename__ = "dataset_retriever_resources"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='dataset_retriever_resource_pkey'),
-        db.Index('dataset_retriever_resource_message_id_idx', 'message_id'),
+        db.PrimaryKeyConstraint("id", name="dataset_retriever_resource_pkey"),
+        db.Index("dataset_retriever_resource_message_id_idx", "message_id"),
     )
 
-    id = db.Column(StringUUID, nullable=False, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, nullable=False, server_default=db.text("uuid_generate_v4()"))
     message_id = db.Column(StringUUID, nullable=False)
     position = db.Column(db.Integer, nullable=False)
     dataset_id = db.Column(StringUUID, nullable=False)
@@ -1369,57 +1438,57 @@ class DatasetRetrieverResource(db.Model):
 
 
 class Tag(db.Model):
-    __tablename__ = 'tags'
+    __tablename__ = "tags"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tag_pkey'),
-        db.Index('tag_type_idx', 'type'),
-        db.Index('tag_name_idx', 'name'),
+        db.PrimaryKeyConstraint("id", name="tag_pkey"),
+        db.Index("tag_type_idx", "type"),
+        db.Index("tag_name_idx", "name"),
     )
 
-    TAG_TYPE_LIST = ['knowledge', 'app']
+    TAG_TYPE_LIST = ["knowledge", "app"]
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=True)
     type = db.Column(db.String(16), nullable=False)
     name = db.Column(db.String(255), nullable=False)
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class TagBinding(db.Model):
-    __tablename__ = 'tag_bindings'
+    __tablename__ = "tag_bindings"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tag_binding_pkey'),
-        db.Index('tag_bind_target_id_idx', 'target_id'),
-        db.Index('tag_bind_tag_id_idx', 'tag_id'),
+        db.PrimaryKeyConstraint("id", name="tag_binding_pkey"),
+        db.Index("tag_bind_target_id_idx", "target_id"),
+        db.Index("tag_bind_tag_id_idx", "tag_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=True)
     tag_id = db.Column(StringUUID, nullable=True)
     target_id = db.Column(StringUUID, nullable=True)
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class TraceAppConfig(db.Model):
-    __tablename__ = 'trace_app_config'
+    __tablename__ = "trace_app_config"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tracing_app_config_pkey'),
-        db.Index('trace_app_config_app_id_idx', 'app_id'),
+        db.PrimaryKeyConstraint("id", name="tracing_app_config_pkey"),
+        db.Index("trace_app_config_app_id_idx", "app_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     tracing_provider = db.Column(db.String(255), nullable=True)
     tracing_config = db.Column(db.JSON, nullable=True)
     created_at = db.Column(db.DateTime, nullable=False, server_default=func.now())
     updated_at = db.Column(db.DateTime, nullable=False, server_default=func.now(), onupdate=func.now())
-    is_active = db.Column(db.Boolean, nullable=False, server_default=db.text('true'))
+    is_active = db.Column(db.Boolean, nullable=False, server_default=db.text("true"))
 
     @property
     def tracing_config_dict(self):
-        return self.tracing_config if self.tracing_config else {}
+        return self.tracing_config or {}
 
     @property
     def tracing_config_str(self):
@@ -1427,11 +1496,11 @@ class TraceAppConfig(db.Model):
 
     def to_dict(self):
         return {
-            'id': self.id,
-            'app_id': self.app_id,
-            'tracing_provider': self.tracing_provider,
-            'tracing_config': self.tracing_config_dict,
+            "id": self.id,
+            "app_id": self.app_id,
+            "tracing_provider": self.tracing_provider,
+            "tracing_config": self.tracing_config_dict,
             "is_active": self.is_active,
-            "created_at": self.created_at.__str__() if self.created_at else None,
-            'updated_at': self.updated_at.__str__() if self.updated_at else None,
+            "created_at": str(self.created_at) if self.created_at else None,
+            "updated_at": str(self.updated_at) if self.updated_at else None,
         }
diff --git a/api/models/provider.py b/api/models/provider.py
index 5d92ee6eb6..644915e781 100644
--- a/api/models/provider.py
+++ b/api/models/provider.py
@@ -6,8 +6,8 @@ from .types import StringUUID
 
 
 class ProviderType(Enum):
-    CUSTOM = 'custom'
-    SYSTEM = 'system'
+    CUSTOM = "custom"
+    SYSTEM = "system"
 
     @staticmethod
     def value_of(value):
@@ -18,13 +18,13 @@ class ProviderType(Enum):
 
 
 class ProviderQuotaType(Enum):
-    PAID = 'paid'
+    PAID = "paid"
     """hosted paid quota"""
 
-    FREE = 'free'
+    FREE = "free"
     """third-party free quota"""
 
-    TRIAL = 'trial'
+    TRIAL = "trial"
     """hosted trial quota"""
 
     @staticmethod
@@ -39,36 +39,42 @@ class Provider(db.Model):
     """
     Provider model representing the API providers and their configurations.
     """
-    __tablename__ = 'providers'
+
+    __tablename__ = "providers"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='provider_pkey'),
-        db.Index('provider_tenant_id_provider_idx', 'tenant_id', 'provider_name'),
-        db.UniqueConstraint('tenant_id', 'provider_name', 'provider_type', 'quota_type', name='unique_provider_name_type_quota')
+        db.PrimaryKeyConstraint("id", name="provider_pkey"),
+        db.Index("provider_tenant_id_provider_idx", "tenant_id", "provider_name"),
+        db.UniqueConstraint(
+            "tenant_id", "provider_name", "provider_type", "quota_type", name="unique_provider_name_type_quota"
+        ),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     provider_name = db.Column(db.String(255), nullable=False)
     provider_type = db.Column(db.String(40), nullable=False, server_default=db.text("'custom'::character varying"))
     encrypted_config = db.Column(db.Text, nullable=True)
-    is_valid = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
+    is_valid = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
     last_used = db.Column(db.DateTime, nullable=True)
 
     quota_type = db.Column(db.String(40), nullable=True, server_default=db.text("''::character varying"))
     quota_limit = db.Column(db.BigInteger, nullable=True)
     quota_used = db.Column(db.BigInteger, default=0)
 
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     def __repr__(self):
-        return f"<Provider(id={self.id}, tenant_id={self.tenant_id}, provider_name='{self.provider_name}', provider_type='{self.provider_type}')>"
+        return (
+            f"<Provider(id={self.id}, tenant_id={self.tenant_id}, provider_name='{self.provider_name}',"
+            f" provider_type='{self.provider_type}')>"
+        )
 
     @property
     def token_is_set(self):
         """
-         Returns True if the encrypted_config is not None, indicating that the token is set.
-         """
+        Returns True if the encrypted_config is not None, indicating that the token is set.
+        """
         return self.encrypted_config is not None
 
     @property
@@ -86,118 +92,123 @@ class ProviderModel(db.Model):
     """
     Provider model representing the API provider_models and their configurations.
     """
-    __tablename__ = 'provider_models'
+
+    __tablename__ = "provider_models"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='provider_model_pkey'),
-        db.Index('provider_model_tenant_id_provider_idx', 'tenant_id', 'provider_name'),
-        db.UniqueConstraint('tenant_id', 'provider_name', 'model_name', 'model_type', name='unique_provider_model_name')
+        db.PrimaryKeyConstraint("id", name="provider_model_pkey"),
+        db.Index("provider_model_tenant_id_provider_idx", "tenant_id", "provider_name"),
+        db.UniqueConstraint(
+            "tenant_id", "provider_name", "model_name", "model_type", name="unique_provider_model_name"
+        ),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     provider_name = db.Column(db.String(255), nullable=False)
     model_name = db.Column(db.String(255), nullable=False)
     model_type = db.Column(db.String(40), nullable=False)
     encrypted_config = db.Column(db.Text, nullable=True)
-    is_valid = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    is_valid = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class TenantDefaultModel(db.Model):
-    __tablename__ = 'tenant_default_models'
+    __tablename__ = "tenant_default_models"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tenant_default_model_pkey'),
-        db.Index('tenant_default_model_tenant_id_provider_type_idx', 'tenant_id', 'provider_name', 'model_type'),
+        db.PrimaryKeyConstraint("id", name="tenant_default_model_pkey"),
+        db.Index("tenant_default_model_tenant_id_provider_type_idx", "tenant_id", "provider_name", "model_type"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     provider_name = db.Column(db.String(255), nullable=False)
     model_name = db.Column(db.String(255), nullable=False)
     model_type = db.Column(db.String(40), nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class TenantPreferredModelProvider(db.Model):
-    __tablename__ = 'tenant_preferred_model_providers'
+    __tablename__ = "tenant_preferred_model_providers"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tenant_preferred_model_provider_pkey'),
-        db.Index('tenant_preferred_model_provider_tenant_provider_idx', 'tenant_id', 'provider_name'),
+        db.PrimaryKeyConstraint("id", name="tenant_preferred_model_provider_pkey"),
+        db.Index("tenant_preferred_model_provider_tenant_provider_idx", "tenant_id", "provider_name"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     provider_name = db.Column(db.String(255), nullable=False)
     preferred_provider_type = db.Column(db.String(40), nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class ProviderOrder(db.Model):
-    __tablename__ = 'provider_orders'
+    __tablename__ = "provider_orders"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='provider_order_pkey'),
-        db.Index('provider_order_tenant_provider_idx', 'tenant_id', 'provider_name'),
+        db.PrimaryKeyConstraint("id", name="provider_order_pkey"),
+        db.Index("provider_order_tenant_provider_idx", "tenant_id", "provider_name"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     provider_name = db.Column(db.String(255), nullable=False)
     account_id = db.Column(StringUUID, nullable=False)
     payment_product_id = db.Column(db.String(191), nullable=False)
     payment_id = db.Column(db.String(191))
     transaction_id = db.Column(db.String(191))
-    quantity = db.Column(db.Integer, nullable=False, server_default=db.text('1'))
+    quantity = db.Column(db.Integer, nullable=False, server_default=db.text("1"))
     currency = db.Column(db.String(40))
     total_amount = db.Column(db.Integer)
     payment_status = db.Column(db.String(40), nullable=False, server_default=db.text("'wait_pay'::character varying"))
     paid_at = db.Column(db.DateTime)
     pay_failed_at = db.Column(db.DateTime)
     refunded_at = db.Column(db.DateTime)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class ProviderModelSetting(db.Model):
     """
     Provider model settings for record the model enabled status and load balancing status.
     """
-    __tablename__ = 'provider_model_settings'
+
+    __tablename__ = "provider_model_settings"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='provider_model_setting_pkey'),
-        db.Index('provider_model_setting_tenant_provider_model_idx', 'tenant_id', 'provider_name', 'model_type'),
+        db.PrimaryKeyConstraint("id", name="provider_model_setting_pkey"),
+        db.Index("provider_model_setting_tenant_provider_model_idx", "tenant_id", "provider_name", "model_type"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     provider_name = db.Column(db.String(255), nullable=False)
     model_name = db.Column(db.String(255), nullable=False)
     model_type = db.Column(db.String(40), nullable=False)
-    enabled = db.Column(db.Boolean, nullable=False, server_default=db.text('true'))
-    load_balancing_enabled = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    enabled = db.Column(db.Boolean, nullable=False, server_default=db.text("true"))
+    load_balancing_enabled = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
 
 class LoadBalancingModelConfig(db.Model):
     """
     Configurations for load balancing models.
     """
-    __tablename__ = 'load_balancing_model_configs'
+
+    __tablename__ = "load_balancing_model_configs"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='load_balancing_model_config_pkey'),
-        db.Index('load_balancing_model_config_tenant_provider_model_idx', 'tenant_id', 'provider_name', 'model_type'),
+        db.PrimaryKeyConstraint("id", name="load_balancing_model_config_pkey"),
+        db.Index("load_balancing_model_config_tenant_provider_model_idx", "tenant_id", "provider_name", "model_type"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     provider_name = db.Column(db.String(255), nullable=False)
     model_name = db.Column(db.String(255), nullable=False)
     model_type = db.Column(db.String(40), nullable=False)
     name = db.Column(db.String(255), nullable=False)
     encrypted_config = db.Column(db.Text, nullable=True)
-    enabled = db.Column(db.Boolean, nullable=False, server_default=db.text('true'))
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    enabled = db.Column(db.Boolean, nullable=False, server_default=db.text("true"))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
diff --git a/api/models/source.py b/api/models/source.py
index adc00028be..07695f06e6 100644
--- a/api/models/source.py
+++ b/api/models/source.py
@@ -8,48 +8,48 @@ from .types import StringUUID
 
 
 class DataSourceOauthBinding(db.Model):
-    __tablename__ = 'data_source_oauth_bindings'
+    __tablename__ = "data_source_oauth_bindings"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='source_binding_pkey'),
-        db.Index('source_binding_tenant_id_idx', 'tenant_id'),
-        db.Index('source_info_idx', "source_info", postgresql_using='gin')
+        db.PrimaryKeyConstraint("id", name="source_binding_pkey"),
+        db.Index("source_binding_tenant_id_idx", "tenant_id"),
+        db.Index("source_info_idx", "source_info", postgresql_using="gin"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     access_token = db.Column(db.String(255), nullable=False)
     provider = db.Column(db.String(255), nullable=False)
     source_info = db.Column(JSONB, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    disabled = db.Column(db.Boolean, nullable=True, server_default=db.text('false'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    disabled = db.Column(db.Boolean, nullable=True, server_default=db.text("false"))
 
 
 class DataSourceApiKeyAuthBinding(db.Model):
-    __tablename__ = 'data_source_api_key_auth_bindings'
+    __tablename__ = "data_source_api_key_auth_bindings"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='data_source_api_key_auth_binding_pkey'),
-        db.Index('data_source_api_key_auth_binding_tenant_id_idx', 'tenant_id'),
-        db.Index('data_source_api_key_auth_binding_provider_idx', 'provider'),
+        db.PrimaryKeyConstraint("id", name="data_source_api_key_auth_binding_pkey"),
+        db.Index("data_source_api_key_auth_binding_tenant_id_idx", "tenant_id"),
+        db.Index("data_source_api_key_auth_binding_provider_idx", "provider"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     category = db.Column(db.String(255), nullable=False)
     provider = db.Column(db.String(255), nullable=False)
     credentials = db.Column(db.Text, nullable=True)  # JSON
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    disabled = db.Column(db.Boolean, nullable=True, server_default=db.text('false'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    disabled = db.Column(db.Boolean, nullable=True, server_default=db.text("false"))
 
     def to_dict(self):
         return {
-            'id': self.id,
-            'tenant_id': self.tenant_id,
-            'category': self.category,
-            'provider': self.provider,
-            'credentials': json.loads(self.credentials),
-            'created_at': self.created_at.timestamp(),
-            'updated_at': self.updated_at.timestamp(),
-            'disabled': self.disabled
+            "id": self.id,
+            "tenant_id": self.tenant_id,
+            "category": self.category,
+            "provider": self.provider,
+            "credentials": json.loads(self.credentials),
+            "created_at": self.created_at.timestamp(),
+            "updated_at": self.updated_at.timestamp(),
+            "disabled": self.disabled,
         }
diff --git a/api/models/task.py b/api/models/task.py
index 618d831d8e..57b147c78d 100644
--- a/api/models/task.py
+++ b/api/models/task.py
@@ -8,15 +8,18 @@ from extensions.ext_database import db
 class CeleryTask(db.Model):
     """Task result/status."""
 
-    __tablename__ = 'celery_taskmeta'
+    __tablename__ = "celery_taskmeta"
 
-    id = db.Column(db.Integer, db.Sequence('task_id_sequence'),
-                   primary_key=True, autoincrement=True)
+    id = db.Column(db.Integer, db.Sequence("task_id_sequence"), primary_key=True, autoincrement=True)
     task_id = db.Column(db.String(155), unique=True)
     status = db.Column(db.String(50), default=states.PENDING)
     result = db.Column(db.PickleType, nullable=True)
-    date_done = db.Column(db.DateTime, default=lambda: datetime.now(timezone.utc).replace(tzinfo=None),
-                          onupdate=lambda: datetime.now(timezone.utc).replace(tzinfo=None), nullable=True)
+    date_done = db.Column(
+        db.DateTime,
+        default=lambda: datetime.now(timezone.utc).replace(tzinfo=None),
+        onupdate=lambda: datetime.now(timezone.utc).replace(tzinfo=None),
+        nullable=True,
+    )
     traceback = db.Column(db.Text, nullable=True)
     name = db.Column(db.String(155), nullable=True)
     args = db.Column(db.LargeBinary, nullable=True)
@@ -29,11 +32,9 @@ class CeleryTask(db.Model):
 class CeleryTaskSet(db.Model):
     """TaskSet result."""
 
-    __tablename__ = 'celery_tasksetmeta'
+    __tablename__ = "celery_tasksetmeta"
 
-    id = db.Column(db.Integer, db.Sequence('taskset_id_sequence'),
-                   autoincrement=True, primary_key=True)
+    id = db.Column(db.Integer, db.Sequence("taskset_id_sequence"), autoincrement=True, primary_key=True)
     taskset_id = db.Column(db.String(155), unique=True)
     result = db.Column(db.PickleType, nullable=True)
-    date_done = db.Column(db.DateTime, default=lambda: datetime.now(timezone.utc).replace(tzinfo=None),
-                          nullable=True)
+    date_done = db.Column(db.DateTime, default=lambda: datetime.now(timezone.utc).replace(tzinfo=None), nullable=True)
diff --git a/api/models/tool.py b/api/models/tool.py
index 79a70c6b1f..a81bb65174 100644
--- a/api/models/tool.py
+++ b/api/models/tool.py
@@ -7,7 +7,7 @@ from .types import StringUUID
 
 
 class ToolProviderName(Enum):
-    SERPAPI = 'serpapi'
+    SERPAPI = "serpapi"
 
     @staticmethod
     def value_of(value):
@@ -18,25 +18,25 @@ class ToolProviderName(Enum):
 
 
 class ToolProvider(db.Model):
-    __tablename__ = 'tool_providers'
+    __tablename__ = "tool_providers"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tool_provider_pkey'),
-        db.UniqueConstraint('tenant_id', 'tool_name', name='unique_tool_provider_tool_name')
+        db.PrimaryKeyConstraint("id", name="tool_provider_pkey"),
+        db.UniqueConstraint("tenant_id", "tool_name", name="unique_tool_provider_tool_name"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     tool_name = db.Column(db.String(40), nullable=False)
     encrypted_credentials = db.Column(db.Text, nullable=True)
-    is_enabled = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    is_enabled = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def credentials_is_set(self):
         """
-         Returns True if the encrypted_config is not None, indicating that the token is set.
-         """
+        Returns True if the encrypted_config is not None, indicating that the token is set.
+        """
         return self.encrypted_credentials is not None
 
     @property
diff --git a/api/models/tools.py b/api/models/tools.py
index 069dc5bad0..861066a2d5 100644
--- a/api/models/tools.py
+++ b/api/models/tools.py
@@ -15,15 +15,16 @@ class BuiltinToolProvider(db.Model):
     """
     This table stores the tool provider information for built-in tools for each tenant.
     """
-    __tablename__ = 'tool_builtin_providers'
+
+    __tablename__ = "tool_builtin_providers"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tool_builtin_provider_pkey'),
+        db.PrimaryKeyConstraint("id", name="tool_builtin_provider_pkey"),
         # one tenant can only have one tool provider with the same name
-        db.UniqueConstraint('tenant_id', 'provider', name='unique_builtin_tool_provider')
+        db.UniqueConstraint("tenant_id", "provider", name="unique_builtin_tool_provider"),
     )
 
     # id of the tool provider
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     # id of the tenant
     tenant_id = db.Column(StringUUID, nullable=True)
     # who created this tool provider
@@ -32,34 +33,37 @@ class BuiltinToolProvider(db.Model):
     provider = db.Column(db.String(40), nullable=False)
     # credential of the tool provider
     encrypted_credentials = db.Column(db.Text, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def credentials(self) -> dict:
         return json.loads(self.encrypted_credentials)
 
+
 class PublishedAppTool(db.Model):
     """
     The table stores the apps published as a tool for each person.
     """
-    __tablename__ = 'tool_published_apps'
+
+    __tablename__ = "tool_published_apps"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='published_app_tool_pkey'),
-        db.UniqueConstraint('app_id', 'user_id', name='unique_published_app_tool')
+        db.PrimaryKeyConstraint("id", name="published_app_tool_pkey"),
+        db.UniqueConstraint("app_id", "user_id", name="unique_published_app_tool"),
     )
 
     # id of the tool provider
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     # id of the app
-    app_id = db.Column(StringUUID, ForeignKey('apps.id'), nullable=False)
+    app_id = db.Column(StringUUID, ForeignKey("apps.id"), nullable=False)
     # who published this tool
     user_id = db.Column(StringUUID, nullable=False)
     # description of the tool, stored in i18n format, for human
     description = db.Column(db.Text, nullable=False)
     # llm_description of the tool, for LLM
     llm_description = db.Column(db.Text, nullable=False)
-    # query description, query will be seem as a parameter of the tool, to describe this parameter to llm, we need this field
+    # query description, query will be seem as a parameter of the tool,
+    # to describe this parameter to llm, we need this field
     query_description = db.Column(db.Text, nullable=False)
     # query name, the name of the query parameter
     query_name = db.Column(db.String(40), nullable=False)
@@ -67,28 +71,30 @@ class PublishedAppTool(db.Model):
     tool_name = db.Column(db.String(40), nullable=False)
     # author
     author = db.Column(db.String(40), nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def description_i18n(self) -> I18nObject:
         return I18nObject(**json.loads(self.description))
-    
+
     @property
     def app(self) -> App:
         return db.session.query(App).filter(App.id == self.app_id).first()
 
+
 class ApiToolProvider(db.Model):
     """
     The table stores the api providers.
     """
-    __tablename__ = 'tool_api_providers'
+
+    __tablename__ = "tool_api_providers"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tool_api_provider_pkey'),
-        db.UniqueConstraint('name', 'tenant_id', name='unique_api_tool_provider')
+        db.PrimaryKeyConstraint("id", name="tool_api_provider_pkey"),
+        db.UniqueConstraint("name", "tenant_id", name="unique_api_tool_provider"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     # name of the api provider
     name = db.Column(db.String(40), nullable=False)
     # icon
@@ -111,21 +117,21 @@ class ApiToolProvider(db.Model):
     # custom_disclaimer
     custom_disclaimer = db.Column(db.String(255), nullable=True)
 
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def schema_type(self) -> ApiProviderSchemaType:
         return ApiProviderSchemaType.value_of(self.schema_type_str)
-    
+
     @property
     def tools(self) -> list[ApiToolBundle]:
         return [ApiToolBundle(**tool) for tool in json.loads(self.tools_str)]
-    
+
     @property
     def credentials(self) -> dict:
         return json.loads(self.credentials_str)
-    
+
     @property
     def user(self) -> Account:
         return db.session.query(Account).filter(Account.id == self.user_id).first()
@@ -134,17 +140,19 @@ class ApiToolProvider(db.Model):
     def tenant(self) -> Tenant:
         return db.session.query(Tenant).filter(Tenant.id == self.tenant_id).first()
 
+
 class ToolLabelBinding(db.Model):
     """
     The table stores the labels for tools.
     """
-    __tablename__ = 'tool_label_bindings'
+
+    __tablename__ = "tool_label_bindings"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tool_label_bind_pkey'),
-        db.UniqueConstraint('tool_id', 'label_name', name='unique_tool_label_bind'),
+        db.PrimaryKeyConstraint("id", name="tool_label_bind_pkey"),
+        db.UniqueConstraint("tool_id", "label_name", name="unique_tool_label_bind"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     # tool id
     tool_id = db.Column(db.String(64), nullable=False)
     # tool type
@@ -152,28 +160,30 @@ class ToolLabelBinding(db.Model):
     # label name
     label_name = db.Column(db.String(40), nullable=False)
 
+
 class WorkflowToolProvider(db.Model):
     """
     The table stores the workflow providers.
     """
-    __tablename__ = 'tool_workflow_providers'
+
+    __tablename__ = "tool_workflow_providers"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tool_workflow_provider_pkey'),
-        db.UniqueConstraint('name', 'tenant_id', name='unique_workflow_tool_provider'),
-        db.UniqueConstraint('tenant_id', 'app_id', name='unique_workflow_tool_provider_app_id'),
+        db.PrimaryKeyConstraint("id", name="tool_workflow_provider_pkey"),
+        db.UniqueConstraint("name", "tenant_id", name="unique_workflow_tool_provider"),
+        db.UniqueConstraint("tenant_id", "app_id", name="unique_workflow_tool_provider_app_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     # name of the workflow provider
     name = db.Column(db.String(40), nullable=False)
     # label of the workflow provider
-    label = db.Column(db.String(255), nullable=False, server_default='')
+    label = db.Column(db.String(255), nullable=False, server_default="")
     # icon
     icon = db.Column(db.String(255), nullable=False)
     # app id of the workflow provider
     app_id = db.Column(StringUUID, nullable=False)
     # version of the workflow provider
-    version = db.Column(db.String(255), nullable=False, server_default='')
+    version = db.Column(db.String(255), nullable=False, server_default="")
     # who created this tool
     user_id = db.Column(StringUUID, nullable=False)
     # tenant id
@@ -181,17 +191,17 @@ class WorkflowToolProvider(db.Model):
     # description of the provider
     description = db.Column(db.Text, nullable=False)
     # parameter configuration
-    parameter_configuration = db.Column(db.Text, nullable=False, server_default='[]')
+    parameter_configuration = db.Column(db.Text, nullable=False, server_default="[]")
     # privacy policy
-    privacy_policy = db.Column(db.String(255), nullable=True, server_default='')
+    privacy_policy = db.Column(db.String(255), nullable=True, server_default="")
 
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def schema_type(self) -> ApiProviderSchemaType:
         return ApiProviderSchemaType.value_of(self.schema_type_str)
-    
+
     @property
     def user(self) -> Account:
         return db.session.query(Account).filter(Account.id == self.user_id).first()
@@ -199,28 +209,25 @@ class WorkflowToolProvider(db.Model):
     @property
     def tenant(self) -> Tenant:
         return db.session.query(Tenant).filter(Tenant.id == self.tenant_id).first()
-    
+
     @property
     def parameter_configurations(self) -> list[WorkflowToolParameterConfiguration]:
-        return [
-            WorkflowToolParameterConfiguration(**config)
-            for config in json.loads(self.parameter_configuration)
-        ]
-    
+        return [WorkflowToolParameterConfiguration(**config) for config in json.loads(self.parameter_configuration)]
+
     @property
     def app(self) -> App:
         return db.session.query(App).filter(App.id == self.app_id).first()
 
+
 class ToolModelInvoke(db.Model):
     """
     store the invoke logs from tool invoke
     """
-    __tablename__ = "tool_model_invokes"
-    __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tool_model_invoke_pkey'),
-    )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    __tablename__ = "tool_model_invokes"
+    __table_args__ = (db.PrimaryKeyConstraint("id", name="tool_model_invoke_pkey"),)
+
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     # who invoke this tool
     user_id = db.Column(StringUUID, nullable=False)
     # tenant id
@@ -238,29 +245,31 @@ class ToolModelInvoke(db.Model):
     # invoke response
     model_response = db.Column(db.Text, nullable=False)
 
-    prompt_tokens = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
-    answer_tokens = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
+    prompt_tokens = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
+    answer_tokens = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
     answer_unit_price = db.Column(db.Numeric(10, 4), nullable=False)
-    answer_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text('0.001'))
-    provider_response_latency = db.Column(db.Float, nullable=False, server_default=db.text('0'))
+    answer_price_unit = db.Column(db.Numeric(10, 7), nullable=False, server_default=db.text("0.001"))
+    provider_response_latency = db.Column(db.Float, nullable=False, server_default=db.text("0"))
     total_price = db.Column(db.Numeric(10, 7))
     currency = db.Column(db.String(255), nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+
 
 class ToolConversationVariables(db.Model):
     """
     store the conversation variables from tool invoke
     """
+
     __tablename__ = "tool_conversation_variables"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tool_conversation_variables_pkey'),
+        db.PrimaryKeyConstraint("id", name="tool_conversation_variables_pkey"),
         # add index for user_id and conversation_id
-        db.Index('user_id_idx', 'user_id'),
-        db.Index('conversation_id_idx', 'conversation_id'),
+        db.Index("user_id_idx", "user_id"),
+        db.Index("conversation_id_idx", "conversation_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     # conversation user id
     user_id = db.Column(StringUUID, nullable=False)
     # tenant id
@@ -270,25 +279,27 @@ class ToolConversationVariables(db.Model):
     # variables pool
     variables_str = db.Column(db.Text, nullable=False)
 
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def variables(self) -> dict:
         return json.loads(self.variables_str)
-    
+
+
 class ToolFile(db.Model):
     """
     store the file created by agent
     """
+
     __tablename__ = "tool_files"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='tool_file_pkey'),
+        db.PrimaryKeyConstraint("id", name="tool_file_pkey"),
         # add index for conversation_id
-        db.Index('tool_file_conversation_id_idx', 'conversation_id'),
+        db.Index("tool_file_conversation_id_idx", "conversation_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     # conversation user id
     user_id = db.Column(StringUUID, nullable=False)
     # tenant id
@@ -300,4 +311,4 @@ class ToolFile(db.Model):
     # mime type
     mimetype = db.Column(db.String(255), nullable=False)
     # original url
-    original_url = db.Column(db.String(2048), nullable=True)
\ No newline at end of file
+    original_url = db.Column(db.String(2048), nullable=True)
diff --git a/api/models/types.py b/api/models/types.py
index 1614ec2018..cb6773e70c 100644
--- a/api/models/types.py
+++ b/api/models/types.py
@@ -9,13 +9,13 @@ class StringUUID(TypeDecorator):
     def process_bind_param(self, value, dialect):
         if value is None:
             return value
-        elif dialect.name == 'postgresql':
+        elif dialect.name == "postgresql":
             return str(value)
         else:
             return value.hex
 
     def load_dialect_impl(self, dialect):
-        if dialect.name == 'postgresql':
+        if dialect.name == "postgresql":
             return dialect.type_descriptor(UUID())
         else:
             return dialect.type_descriptor(CHAR(36))
@@ -23,4 +23,4 @@ class StringUUID(TypeDecorator):
     def process_result_value(self, value, dialect):
         if value is None:
             return value
-        return str(value)
\ No newline at end of file
+        return str(value)
diff --git a/api/models/web.py b/api/models/web.py
index 0e901d5f84..bc088c185d 100644
--- a/api/models/web.py
+++ b/api/models/web.py
@@ -1,4 +1,3 @@
-
 from extensions.ext_database import db
 
 from .model import Message
@@ -6,18 +5,18 @@ from .types import StringUUID
 
 
 class SavedMessage(db.Model):
-    __tablename__ = 'saved_messages'
+    __tablename__ = "saved_messages"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='saved_message_pkey'),
-        db.Index('saved_message_message_idx', 'app_id', 'message_id', 'created_by_role', 'created_by'),
+        db.PrimaryKeyConstraint("id", name="saved_message_pkey"),
+        db.Index("saved_message_message_idx", "app_id", "message_id", "created_by_role", "created_by"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     message_id = db.Column(StringUUID, nullable=False)
     created_by_role = db.Column(db.String(255), nullable=False, server_default=db.text("'end_user'::character varying"))
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def message(self):
@@ -25,15 +24,15 @@ class SavedMessage(db.Model):
 
 
 class PinnedConversation(db.Model):
-    __tablename__ = 'pinned_conversations'
+    __tablename__ = "pinned_conversations"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='pinned_conversation_pkey'),
-        db.Index('pinned_conversation_conversation_idx', 'app_id', 'conversation_id', 'created_by_role', 'created_by'),
+        db.PrimaryKeyConstraint("id", name="pinned_conversation_pkey"),
+        db.Index("pinned_conversation_conversation_idx", "app_id", "conversation_id", "created_by_role", "created_by"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     app_id = db.Column(StringUUID, nullable=False)
     conversation_id = db.Column(StringUUID, nullable=False)
     created_by_role = db.Column(db.String(255), nullable=False, server_default=db.text("'end_user'::character varying"))
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
diff --git a/api/models/workflow.py b/api/models/workflow.py
index cdd5e1992d..9c93ea4cea 100644
--- a/api/models/workflow.py
+++ b/api/models/workflow.py
@@ -22,11 +22,12 @@ class CreatedByRole(Enum):
     """
     Created By Role Enum
     """
-    ACCOUNT = 'account'
-    END_USER = 'end_user'
+
+    ACCOUNT = "account"
+    END_USER = "end_user"
 
     @classmethod
-    def value_of(cls, value: str) -> 'CreatedByRole':
+    def value_of(cls, value: str) -> "CreatedByRole":
         """
         Get value of given mode.
 
@@ -36,18 +37,19 @@ class CreatedByRole(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid created by role value {value}')
+        raise ValueError(f"invalid created by role value {value}")
 
 
 class WorkflowType(Enum):
     """
     Workflow Type Enum
     """
-    WORKFLOW = 'workflow'
-    CHAT = 'chat'
+
+    WORKFLOW = "workflow"
+    CHAT = "chat"
 
     @classmethod
-    def value_of(cls, value: str) -> 'WorkflowType':
+    def value_of(cls, value: str) -> "WorkflowType":
         """
         Get value of given mode.
 
@@ -57,10 +59,10 @@ class WorkflowType(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid workflow type value {value}')
+        raise ValueError(f"invalid workflow type value {value}")
 
     @classmethod
-    def from_app_mode(cls, app_mode: Union[str, 'AppMode']) -> 'WorkflowType':
+    def from_app_mode(cls, app_mode: Union[str, "AppMode"]) -> "WorkflowType":
         """
         Get workflow type from app mode.
 
@@ -68,6 +70,7 @@ class WorkflowType(Enum):
         :return: workflow type
         """
         from models.model import AppMode
+
         app_mode = app_mode if isinstance(app_mode, AppMode) else AppMode.value_of(app_mode)
         return cls.WORKFLOW if app_mode == AppMode.WORKFLOW else cls.CHAT
 
@@ -105,13 +108,13 @@ class Workflow(db.Model):
     - updated_at (timestamp) `optional` Last update time
     """
 
-    __tablename__ = 'workflows'
+    __tablename__ = "workflows"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='workflow_pkey'),
-        db.Index('workflow_version_idx', 'tenant_id', 'app_id', 'version'),
+        db.PrimaryKeyConstraint("id", name="workflow_pkey"),
+        db.Index("workflow_version_idx", "tenant_id", "app_id", "version"),
     )
 
-    id: Mapped[str] = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id: Mapped[str] = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id: Mapped[str] = db.Column(StringUUID, nullable=False)
     app_id: Mapped[str] = db.Column(StringUUID, nullable=False)
     type: Mapped[str] = db.Column(db.String(255), nullable=False)
@@ -119,15 +122,31 @@ class Workflow(db.Model):
     graph: Mapped[str] = db.Column(db.Text)
     features: Mapped[str] = db.Column(db.Text)
     created_by: Mapped[str] = db.Column(StringUUID, nullable=False)
-    created_at: Mapped[datetime] = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at: Mapped[datetime] = db.Column(
+        db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)")
+    )
     updated_by: Mapped[str] = db.Column(StringUUID)
     updated_at: Mapped[datetime] = db.Column(db.DateTime)
-    _environment_variables: Mapped[str] = db.Column('environment_variables', db.Text, nullable=False, server_default='{}')
-    _conversation_variables: Mapped[str] = db.Column('conversation_variables', db.Text, nullable=False, server_default='{}')
+    _environment_variables: Mapped[str] = db.Column(
+        "environment_variables", db.Text, nullable=False, server_default="{}"
+    )
+    _conversation_variables: Mapped[str] = db.Column(
+        "conversation_variables", db.Text, nullable=False, server_default="{}"
+    )
 
-    def __init__(self, *, tenant_id: str, app_id: str, type: str, version: str, graph: str,
-                 features: str, created_by: str, environment_variables: Sequence[Variable],
-                 conversation_variables: Sequence[Variable]):
+    def __init__(
+        self,
+        *,
+        tenant_id: str,
+        app_id: str,
+        type: str,
+        version: str,
+        graph: str,
+        features: str,
+        created_by: str,
+        environment_variables: Sequence[Variable],
+        conversation_variables: Sequence[Variable],
+    ):
         self.tenant_id = tenant_id
         self.app_id = app_id
         self.type = type
@@ -160,22 +179,20 @@ class Workflow(db.Model):
             return []
 
         graph_dict = self.graph_dict
-        if 'nodes' not in graph_dict:
+        if "nodes" not in graph_dict:
             return []
 
-        start_node = next((node for node in graph_dict['nodes'] if node['data']['type'] == 'start'), None)
+        start_node = next((node for node in graph_dict["nodes"] if node["data"]["type"] == "start"), None)
         if not start_node:
             return []
 
         # get user_input_form from start node
-        variables = start_node.get('data', {}).get('variables', [])
+        variables = start_node.get("data", {}).get("variables", [])
 
         if to_old_structure:
             old_structure_variables = []
             for variable in variables:
-                old_structure_variables.append({
-                    variable['type']: variable
-                })
+                old_structure_variables.append({variable["type"]: variable})
 
             return old_structure_variables
 
@@ -188,25 +205,24 @@ class Workflow(db.Model):
 
         :return: hash
         """
-        entity = {
-            'graph': self.graph_dict,
-            'features': self.features_dict
-        }
+        entity = {"graph": self.graph_dict, "features": self.features_dict}
 
         return helper.generate_text_hash(json.dumps(entity, sort_keys=True))
 
     @property
     def tool_published(self) -> bool:
         from models.tools import WorkflowToolProvider
-        return db.session.query(WorkflowToolProvider).filter(
-            WorkflowToolProvider.app_id == self.app_id
-        ).first() is not None
+
+        return (
+            db.session.query(WorkflowToolProvider).filter(WorkflowToolProvider.app_id == self.app_id).first()
+            is not None
+        )
 
     @property
     def environment_variables(self) -> Sequence[Variable]:
         # TODO: find some way to init `self._environment_variables` when instance created.
         if self._environment_variables is None:
-            self._environment_variables = '{}'
+            self._environment_variables = "{}"
 
         tenant_id = contexts.tenant_id.get()
 
@@ -215,9 +231,7 @@ class Workflow(db.Model):
 
         # decrypt secret variables value
         decrypt_func = (
-            lambda var: var.model_copy(
-                update={'value': encrypter.decrypt_token(tenant_id=tenant_id, token=var.value)}
-            )
+            lambda var: var.model_copy(update={"value": encrypter.decrypt_token(tenant_id=tenant_id, token=var.value)})
             if isinstance(var, SecretVariable)
             else var
         )
@@ -230,19 +244,18 @@ class Workflow(db.Model):
 
         value = list(value)
         if any(var for var in value if not var.id):
-            raise ValueError('environment variable require a unique id')
+            raise ValueError("environment variable require a unique id")
 
-        # Compare inputs and origin variables, if the value is HIDDEN_VALUE, use the origin variable value (only update `name`).
+        # Compare inputs and origin variables,
+        # if the value is HIDDEN_VALUE, use the origin variable value (only update `name`).
         origin_variables_dictionary = {var.id: var for var in self.environment_variables}
         for i, variable in enumerate(value):
             if variable.id in origin_variables_dictionary and variable.value == HIDDEN_VALUE:
-                value[i] = origin_variables_dictionary[variable.id].model_copy(update={'name': variable.name})
+                value[i] = origin_variables_dictionary[variable.id].model_copy(update={"name": variable.name})
 
         # encrypt secret variables value
         encrypt_func = (
-            lambda var: var.model_copy(
-                update={'value': encrypter.encrypt_token(tenant_id=tenant_id, token=var.value)}
-            )
+            lambda var: var.model_copy(update={"value": encrypter.encrypt_token(tenant_id=tenant_id, token=var.value)})
             if isinstance(var, SecretVariable)
             else var
         )
@@ -256,15 +269,15 @@ class Workflow(db.Model):
     def to_dict(self, *, include_secret: bool = False) -> Mapping[str, Any]:
         environment_variables = list(self.environment_variables)
         environment_variables = [
-            v if not isinstance(v, SecretVariable) or include_secret else v.model_copy(update={'value': ''})
+            v if not isinstance(v, SecretVariable) or include_secret else v.model_copy(update={"value": ""})
             for v in environment_variables
         ]
 
         result = {
-            'graph': self.graph_dict,
-            'features': self.features_dict,
-            'environment_variables': [var.model_dump(mode='json') for var in environment_variables],
-            'conversation_variables': [var.model_dump(mode='json') for var in self.conversation_variables],
+            "graph": self.graph_dict,
+            "features": self.features_dict,
+            "environment_variables": [var.model_dump(mode="json") for var in environment_variables],
+            "conversation_variables": [var.model_dump(mode="json") for var in self.conversation_variables],
         }
         return result
 
@@ -272,7 +285,7 @@ class Workflow(db.Model):
     def conversation_variables(self) -> Sequence[Variable]:
         # TODO: find some way to init `self._conversation_variables` when instance created.
         if self._conversation_variables is None:
-            self._conversation_variables = '{}'
+            self._conversation_variables = "{}"
 
         variables_dict: dict[str, Any] = json.loads(self._conversation_variables)
         results = [factory.build_variable_from_mapping(v) for v in variables_dict.values()]
@@ -290,11 +303,12 @@ class WorkflowRunTriggeredFrom(Enum):
     """
     Workflow Run Triggered From Enum
     """
-    DEBUGGING = 'debugging'
-    APP_RUN = 'app-run'
+
+    DEBUGGING = "debugging"
+    APP_RUN = "app-run"
 
     @classmethod
-    def value_of(cls, value: str) -> 'WorkflowRunTriggeredFrom':
+    def value_of(cls, value: str) -> "WorkflowRunTriggeredFrom":
         """
         Get value of given mode.
 
@@ -304,20 +318,21 @@ class WorkflowRunTriggeredFrom(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid workflow run triggered from value {value}')
+        raise ValueError(f"invalid workflow run triggered from value {value}")
 
 
 class WorkflowRunStatus(Enum):
     """
     Workflow Run Status Enum
     """
-    RUNNING = 'running'
-    SUCCEEDED = 'succeeded'
-    FAILED = 'failed'
-    STOPPED = 'stopped'
+
+    RUNNING = "running"
+    SUCCEEDED = "succeeded"
+    FAILED = "failed"
+    STOPPED = "stopped"
 
     @classmethod
-    def value_of(cls, value: str) -> 'WorkflowRunStatus':
+    def value_of(cls, value: str) -> "WorkflowRunStatus":
         """
         Get value of given mode.
 
@@ -327,7 +342,7 @@ class WorkflowRunStatus(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid workflow run status value {value}')
+        raise ValueError(f"invalid workflow run status value {value}")
 
 
 class WorkflowRun(db.Model):
@@ -368,14 +383,14 @@ class WorkflowRun(db.Model):
     - finished_at (timestamp) End time
     """
 
-    __tablename__ = 'workflow_runs'
+    __tablename__ = "workflow_runs"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='workflow_run_pkey'),
-        db.Index('workflow_run_triggerd_from_idx', 'tenant_id', 'app_id', 'triggered_from'),
-        db.Index('workflow_run_tenant_app_sequence_idx', 'tenant_id', 'app_id', 'sequence_number'),
+        db.PrimaryKeyConstraint("id", name="workflow_run_pkey"),
+        db.Index("workflow_run_triggerd_from_idx", "tenant_id", "app_id", "triggered_from"),
+        db.Index("workflow_run_tenant_app_sequence_idx", "tenant_id", "app_id", "sequence_number"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     app_id = db.Column(StringUUID, nullable=False)
     sequence_number = db.Column(db.Integer, nullable=False)
@@ -388,26 +403,25 @@ class WorkflowRun(db.Model):
     status = db.Column(db.String(255), nullable=False)
     outputs = db.Column(db.Text)
     error = db.Column(db.Text)
-    elapsed_time = db.Column(db.Float, nullable=False, server_default=db.text('0'))
-    total_tokens = db.Column(db.Integer, nullable=False, server_default=db.text('0'))
-    total_steps = db.Column(db.Integer, server_default=db.text('0'))
+    elapsed_time = db.Column(db.Float, nullable=False, server_default=db.text("0"))
+    total_tokens = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
+    total_steps = db.Column(db.Integer, server_default=db.text("0"))
     created_by_role = db.Column(db.String(255), nullable=False)
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     finished_at = db.Column(db.DateTime)
 
     @property
     def created_by_account(self):
         created_by_role = CreatedByRole.value_of(self.created_by_role)
-        return db.session.get(Account, self.created_by) \
-            if created_by_role == CreatedByRole.ACCOUNT else None
+        return db.session.get(Account, self.created_by) if created_by_role == CreatedByRole.ACCOUNT else None
 
     @property
     def created_by_end_user(self):
         from models.model import EndUser
+
         created_by_role = CreatedByRole.value_of(self.created_by_role)
-        return db.session.get(EndUser, self.created_by) \
-            if created_by_role == CreatedByRole.END_USER else None
+        return db.session.get(EndUser, self.created_by) if created_by_role == CreatedByRole.END_USER else None
 
     @property
     def graph_dict(self):
@@ -422,12 +436,12 @@ class WorkflowRun(db.Model):
         return json.loads(self.outputs) if self.outputs else None
 
     @property
-    def message(self) -> Optional['Message']:
+    def message(self) -> Optional["Message"]:
         from models.model import Message
-        return db.session.query(Message).filter(
-            Message.app_id == self.app_id,
-            Message.workflow_run_id == self.id
-        ).first()
+
+        return (
+            db.session.query(Message).filter(Message.app_id == self.app_id, Message.workflow_run_id == self.id).first()
+        )
 
     @property
     def workflow(self):
@@ -435,51 +449,51 @@ class WorkflowRun(db.Model):
 
     def to_dict(self):
         return {
-            'id': self.id,
-            'tenant_id': self.tenant_id,
-            'app_id': self.app_id,
-            'sequence_number': self.sequence_number,
-            'workflow_id': self.workflow_id,
-            'type': self.type,
-            'triggered_from': self.triggered_from,
-            'version': self.version,
-            'graph': self.graph_dict,
-            'inputs': self.inputs_dict,
-            'status': self.status,
-            'outputs': self.outputs_dict,
-            'error': self.error,
-            'elapsed_time': self.elapsed_time,
-            'total_tokens': self.total_tokens,
-            'total_steps': self.total_steps,
-            'created_by_role': self.created_by_role,
-            'created_by': self.created_by,
-            'created_at': self.created_at,
-            'finished_at': self.finished_at,
+            "id": self.id,
+            "tenant_id": self.tenant_id,
+            "app_id": self.app_id,
+            "sequence_number": self.sequence_number,
+            "workflow_id": self.workflow_id,
+            "type": self.type,
+            "triggered_from": self.triggered_from,
+            "version": self.version,
+            "graph": self.graph_dict,
+            "inputs": self.inputs_dict,
+            "status": self.status,
+            "outputs": self.outputs_dict,
+            "error": self.error,
+            "elapsed_time": self.elapsed_time,
+            "total_tokens": self.total_tokens,
+            "total_steps": self.total_steps,
+            "created_by_role": self.created_by_role,
+            "created_by": self.created_by,
+            "created_at": self.created_at,
+            "finished_at": self.finished_at,
         }
 
     @classmethod
-    def from_dict(cls, data: dict) -> 'WorkflowRun':
+    def from_dict(cls, data: dict) -> "WorkflowRun":
         return cls(
-            id=data.get('id'),
-            tenant_id=data.get('tenant_id'),
-            app_id=data.get('app_id'),
-            sequence_number=data.get('sequence_number'),
-            workflow_id=data.get('workflow_id'),
-            type=data.get('type'),
-            triggered_from=data.get('triggered_from'),
-            version=data.get('version'),
-            graph=json.dumps(data.get('graph')),
-            inputs=json.dumps(data.get('inputs')),
-            status=data.get('status'),
-            outputs=json.dumps(data.get('outputs')),
-            error=data.get('error'),
-            elapsed_time=data.get('elapsed_time'),
-            total_tokens=data.get('total_tokens'),
-            total_steps=data.get('total_steps'),
-            created_by_role=data.get('created_by_role'),
-            created_by=data.get('created_by'),
-            created_at=data.get('created_at'),
-            finished_at=data.get('finished_at'),
+            id=data.get("id"),
+            tenant_id=data.get("tenant_id"),
+            app_id=data.get("app_id"),
+            sequence_number=data.get("sequence_number"),
+            workflow_id=data.get("workflow_id"),
+            type=data.get("type"),
+            triggered_from=data.get("triggered_from"),
+            version=data.get("version"),
+            graph=json.dumps(data.get("graph")),
+            inputs=json.dumps(data.get("inputs")),
+            status=data.get("status"),
+            outputs=json.dumps(data.get("outputs")),
+            error=data.get("error"),
+            elapsed_time=data.get("elapsed_time"),
+            total_tokens=data.get("total_tokens"),
+            total_steps=data.get("total_steps"),
+            created_by_role=data.get("created_by_role"),
+            created_by=data.get("created_by"),
+            created_at=data.get("created_at"),
+            finished_at=data.get("finished_at"),
         )
 
 
@@ -487,11 +501,12 @@ class WorkflowNodeExecutionTriggeredFrom(Enum):
     """
     Workflow Node Execution Triggered From Enum
     """
-    SINGLE_STEP = 'single-step'
-    WORKFLOW_RUN = 'workflow-run'
+
+    SINGLE_STEP = "single-step"
+    WORKFLOW_RUN = "workflow-run"
 
     @classmethod
-    def value_of(cls, value: str) -> 'WorkflowNodeExecutionTriggeredFrom':
+    def value_of(cls, value: str) -> "WorkflowNodeExecutionTriggeredFrom":
         """
         Get value of given mode.
 
@@ -501,19 +516,20 @@ class WorkflowNodeExecutionTriggeredFrom(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid workflow node execution triggered from value {value}')
+        raise ValueError(f"invalid workflow node execution triggered from value {value}")
 
 
 class WorkflowNodeExecutionStatus(Enum):
     """
     Workflow Node Execution Status Enum
     """
-    RUNNING = 'running'
-    SUCCEEDED = 'succeeded'
-    FAILED = 'failed'
+
+    RUNNING = "running"
+    SUCCEEDED = "succeeded"
+    FAILED = "failed"
 
     @classmethod
-    def value_of(cls, value: str) -> 'WorkflowNodeExecutionStatus':
+    def value_of(cls, value: str) -> "WorkflowNodeExecutionStatus":
         """
         Get value of given mode.
 
@@ -523,7 +539,7 @@ class WorkflowNodeExecutionStatus(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid workflow node execution status value {value}')
+        raise ValueError(f"invalid workflow node execution status value {value}")
 
 
 class WorkflowNodeExecution(db.Model):
@@ -574,16 +590,31 @@ class WorkflowNodeExecution(db.Model):
     - finished_at (timestamp) End time
     """
 
-    __tablename__ = 'workflow_node_executions'
+    __tablename__ = "workflow_node_executions"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='workflow_node_execution_pkey'),
-        db.Index('workflow_node_execution_workflow_run_idx', 'tenant_id', 'app_id', 'workflow_id',
-                 'triggered_from', 'workflow_run_id'),
-        db.Index('workflow_node_execution_node_run_idx', 'tenant_id', 'app_id', 'workflow_id',
-                 'triggered_from', 'node_id'),
+        db.PrimaryKeyConstraint("id", name="workflow_node_execution_pkey"),
+        db.Index(
+            "workflow_node_execution_workflow_run_idx",
+            "tenant_id",
+            "app_id",
+            "workflow_id",
+            "triggered_from",
+            "workflow_run_id",
+        ),
+        db.Index(
+            "workflow_node_execution_node_run_idx", "tenant_id", "app_id", "workflow_id", "triggered_from", "node_id"
+        ),
+        db.Index(
+            "workflow_node_execution_id_idx",
+            "tenant_id",
+            "app_id",
+            "workflow_id",
+            "triggered_from",
+            "node_execution_id",
+        ),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     app_id = db.Column(StringUUID, nullable=False)
     workflow_id = db.Column(StringUUID, nullable=False)
@@ -591,6 +622,7 @@ class WorkflowNodeExecution(db.Model):
     workflow_run_id = db.Column(StringUUID)
     index = db.Column(db.Integer, nullable=False)
     predecessor_node_id = db.Column(db.String(255))
+    node_execution_id = db.Column(db.String(255), nullable=True)
     node_id = db.Column(db.String(255), nullable=False)
     node_type = db.Column(db.String(255), nullable=False)
     title = db.Column(db.String(255), nullable=False)
@@ -599,9 +631,9 @@ class WorkflowNodeExecution(db.Model):
     outputs = db.Column(db.Text)
     status = db.Column(db.String(255), nullable=False)
     error = db.Column(db.Text)
-    elapsed_time = db.Column(db.Float, nullable=False, server_default=db.text('0'))
+    elapsed_time = db.Column(db.Float, nullable=False, server_default=db.text("0"))
     execution_metadata = db.Column(db.Text)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     created_by_role = db.Column(db.String(255), nullable=False)
     created_by = db.Column(StringUUID, nullable=False)
     finished_at = db.Column(db.DateTime)
@@ -609,15 +641,14 @@ class WorkflowNodeExecution(db.Model):
     @property
     def created_by_account(self):
         created_by_role = CreatedByRole.value_of(self.created_by_role)
-        return db.session.get(Account, self.created_by) \
-            if created_by_role == CreatedByRole.ACCOUNT else None
+        return db.session.get(Account, self.created_by) if created_by_role == CreatedByRole.ACCOUNT else None
 
     @property
     def created_by_end_user(self):
         from models.model import EndUser
+
         created_by_role = CreatedByRole.value_of(self.created_by_role)
-        return db.session.get(EndUser, self.created_by) \
-            if created_by_role == CreatedByRole.END_USER else None
+        return db.session.get(EndUser, self.created_by) if created_by_role == CreatedByRole.END_USER else None
 
     @property
     def inputs_dict(self):
@@ -638,15 +669,17 @@ class WorkflowNodeExecution(db.Model):
     @property
     def extras(self):
         from core.tools.tool_manager import ToolManager
+
         extras = {}
         if self.execution_metadata_dict:
             from core.workflow.entities.node_entities import NodeType
-            if self.node_type == NodeType.TOOL.value and 'tool_info' in self.execution_metadata_dict:
-                tool_info = self.execution_metadata_dict['tool_info']
-                extras['icon'] = ToolManager.get_tool_icon(
+
+            if self.node_type == NodeType.TOOL.value and "tool_info" in self.execution_metadata_dict:
+                tool_info = self.execution_metadata_dict["tool_info"]
+                extras["icon"] = ToolManager.get_tool_icon(
                     tenant_id=self.tenant_id,
-                    provider_type=tool_info['provider_type'],
-                    provider_id=tool_info['provider_id']
+                    provider_type=tool_info["provider_type"],
+                    provider_id=tool_info["provider_id"],
                 )
 
         return extras
@@ -656,12 +689,13 @@ class WorkflowAppLogCreatedFrom(Enum):
     """
     Workflow App Log Created From Enum
     """
-    SERVICE_API = 'service-api'
-    WEB_APP = 'web-app'
-    INSTALLED_APP = 'installed-app'
+
+    SERVICE_API = "service-api"
+    WEB_APP = "web-app"
+    INSTALLED_APP = "installed-app"
 
     @classmethod
-    def value_of(cls, value: str) -> 'WorkflowAppLogCreatedFrom':
+    def value_of(cls, value: str) -> "WorkflowAppLogCreatedFrom":
         """
         Get value of given mode.
 
@@ -671,7 +705,7 @@ class WorkflowAppLogCreatedFrom(Enum):
         for mode in cls:
             if mode.value == value:
                 return mode
-        raise ValueError(f'invalid workflow app log created from value {value}')
+        raise ValueError(f"invalid workflow app log created from value {value}")
 
 
 class WorkflowAppLog(db.Model):
@@ -703,13 +737,13 @@ class WorkflowAppLog(db.Model):
     - created_at (timestamp) Creation time
     """
 
-    __tablename__ = 'workflow_app_logs'
+    __tablename__ = "workflow_app_logs"
     __table_args__ = (
-        db.PrimaryKeyConstraint('id', name='workflow_app_log_pkey'),
-        db.Index('workflow_app_log_app_idx', 'tenant_id', 'app_id'),
+        db.PrimaryKeyConstraint("id", name="workflow_app_log_pkey"),
+        db.Index("workflow_app_log_app_idx", "tenant_id", "app_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text('uuid_generate_v4()'))
+    id = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
     tenant_id = db.Column(StringUUID, nullable=False)
     app_id = db.Column(StringUUID, nullable=False)
     workflow_id = db.Column(StringUUID, nullable=False)
@@ -717,7 +751,7 @@ class WorkflowAppLog(db.Model):
     created_from = db.Column(db.String(255), nullable=False)
     created_by_role = db.Column(db.String(255), nullable=False)
     created_by = db.Column(StringUUID, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
 
     @property
     def workflow_run(self):
@@ -726,26 +760,27 @@ class WorkflowAppLog(db.Model):
     @property
     def created_by_account(self):
         created_by_role = CreatedByRole.value_of(self.created_by_role)
-        return db.session.get(Account, self.created_by) \
-            if created_by_role == CreatedByRole.ACCOUNT else None
+        return db.session.get(Account, self.created_by) if created_by_role == CreatedByRole.ACCOUNT else None
 
     @property
     def created_by_end_user(self):
         from models.model import EndUser
+
         created_by_role = CreatedByRole.value_of(self.created_by_role)
-        return db.session.get(EndUser, self.created_by) \
-            if created_by_role == CreatedByRole.END_USER else None
+        return db.session.get(EndUser, self.created_by) if created_by_role == CreatedByRole.END_USER else None
 
 
 class ConversationVariable(db.Model):
-    __tablename__ = 'workflow_conversation_variables'
+    __tablename__ = "workflow_conversation_variables"
 
     id: Mapped[str] = db.Column(StringUUID, primary_key=True)
     conversation_id: Mapped[str] = db.Column(StringUUID, nullable=False, primary_key=True)
     app_id: Mapped[str] = db.Column(StringUUID, nullable=False, index=True)
     data = db.Column(db.Text, nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, index=True, server_default=db.text('CURRENT_TIMESTAMP(0)'))
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=func.current_timestamp(), onupdate=func.current_timestamp())
+    created_at = db.Column(db.DateTime, nullable=False, index=True, server_default=db.text("CURRENT_TIMESTAMP(0)"))
+    updated_at = db.Column(
+        db.DateTime, nullable=False, server_default=func.current_timestamp(), onupdate=func.current_timestamp()
+    )
 
     def __init__(self, *, id: str, app_id: str, conversation_id: str, data: str) -> None:
         self.id = id
@@ -754,7 +789,7 @@ class ConversationVariable(db.Model):
         self.data = data
 
     @classmethod
-    def from_variable(cls, *, app_id: str, conversation_id: str, variable: Variable) -> 'ConversationVariable':
+    def from_variable(cls, *, app_id: str, conversation_id: str, variable: Variable) -> "ConversationVariable":
         obj = cls(
             id=variable.id,
             app_id=app_id,
diff --git a/api/poetry.lock b/api/poetry.lock
index 7d26dbdc57..191db600e4 100644
--- a/api/poetry.lock
+++ b/api/poetry.lock
@@ -364,27 +364,27 @@ alibabacloud-tea = ">=0.0.1"
 
 [[package]]
 name = "aliyun-python-sdk-core"
-version = "2.15.1"
+version = "2.15.2"
 description = "The core module of Aliyun Python SDK."
 optional = false
 python-versions = "*"
 files = [
-    {file = "aliyun-python-sdk-core-2.15.1.tar.gz", hash = "sha256:518550d07f537cd3afac3b6c93b5c997ce3440e4d0c054e3acbdaa8261e90adf"},
+    {file = "aliyun-python-sdk-core-2.15.2.tar.gz", hash = "sha256:54f66a53e193c61c5e16ea4505a0cab43543f8ad2ef22833f69c4d5e5151c17d"},
 ]
 
 [package.dependencies]
-cryptography = ">=2.6.0"
+cryptography = ">=3.0.0"
 jmespath = ">=0.9.3,<1.0.0"
 
 [[package]]
 name = "aliyun-python-sdk-kms"
-version = "2.16.4"
+version = "2.16.5"
 description = "The kms module of Aliyun Python sdk."
 optional = false
 python-versions = "*"
 files = [
-    {file = "aliyun-python-sdk-kms-2.16.4.tar.gz", hash = "sha256:0d5bb165c07b6a972939753a128507393f48011792ee0ec4f59b6021eabd9752"},
-    {file = "aliyun_python_sdk_kms-2.16.4-py2.py3-none-any.whl", hash = "sha256:6d412663ef8c35dc3bb42be6a3ee76a9bc07acdadca6dd26815131062bedf4c5"},
+    {file = "aliyun-python-sdk-kms-2.16.5.tar.gz", hash = "sha256:f328a8a19d83ecbb965ffce0ec1e9930755216d104638cd95ecd362753b813b3"},
+    {file = "aliyun_python_sdk_kms-2.16.5-py2.py3-none-any.whl", hash = "sha256:24b6cdc4fd161d2942619479c8d050c63ea9cd22b044fe33b60bbb60153786f0"},
 ]
 
 [package.dependencies]
@@ -520,22 +520,22 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "24.2.0"
+version = "23.2.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
-    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
+    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
+    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
 ]
 
 [package.extras]
-benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
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-dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
-tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "authlib"
@@ -553,13 +553,13 @@ cryptography = "*"
 
 [[package]]
 name = "azure-ai-inference"
-version = "1.0.0b3"
+version = "1.0.0b4"
 description = "Microsoft Azure Ai Inference Client Library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -569,13 +569,13 @@ typing-extensions = ">=4.6.0"
 
 [[package]]
 name = "azure-ai-ml"
-version = "1.19.0"
+version = "1.20.0"
 description = "Microsoft Azure Machine Learning Client Library for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -809,17 +809,17 @@ files = [
 
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 name = "boto3"
-version = "1.34.148"
+version = "1.35.17"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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+botocore = ">=1.35.17,<1.36.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -828,13 +828,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.162"
+version = "1.35.17"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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 urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}
 
 [package.extras]
-crt = ["awscrt (==0.21.2)"]
+crt = ["awscrt (==0.21.5)"]
 
 [[package]]
 name = "bottleneck"
@@ -1049,13 +1049,13 @@ beautifulsoup4 = "*"
 
 [[package]]
 name = "build"
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+version = "1.2.2"
 description = "A simple, correct Python build frontend"
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 [package.dependencies]
@@ -1140,89 +1140,89 @@ zstd = ["zstandard (==0.22.0)"]
 
 [[package]]
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+version = "2024.8.30"
 description = "Python package for providing Mozilla's CA Bundle."
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 [[package]]
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 ]
 
 [package.dependencies]
@@ -1416,6 +1416,17 @@ typer = ">=0.9.0"
 typing-extensions = ">=4.5.0"
 uvicorn = {version = ">=0.18.3", extras = ["standard"]}
 
+[[package]]
+name = "circuitbreaker"
+version = "2.0.0"
+description = "Python Circuit Breaker pattern implementation"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "click"
 version = "8.1.7"
@@ -1708,6 +1719,17 @@ lz4 = ["clickhouse-cityhash (>=1.0.2.1)", "lz4", "lz4 (<=3.0.1)"]
 numpy = ["numpy (>=1.12.0)", "pandas (>=0.24.0)"]
 zstd = ["clickhouse-cityhash (>=1.0.2.1)", "zstd"]
 
+[[package]]
+name = "cloudpickle"
+version = "2.2.1"
+description = "Extended pickling support for Python objects"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
 [[package]]
 name = "cloudscraper"
 version = "1.2.71"
@@ -1774,66 +1796,87 @@ cron = ["capturer (>=2.4)"]
 
 [[package]]
 name = "contourpy"
-version = "1.2.1"
+version = "1.3.0"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
-numpy = ">=1.20"
+numpy = ">=1.23"
 
 [package.extras]
 bokeh = ["bokeh", "selenium"]
 docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
-mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.8.0)", "types-Pillow"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.11.1)", "types-Pillow"]
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
-test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
+test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
 
 [[package]]
 name = "cos-python-sdk-v5"
@@ -1936,38 +1979,43 @@ files = [
 
 [[package]]
 name = "cryptography"
-version = "43.0.0"
+version = "42.0.8"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "cryptography-42.0.8.tar.gz", hash = "sha256:8d09d05439ce7baa8e9e95b07ec5b6c886f548deb7e0f69ef25f64b3bce842f2"},
 ]
 
 [package.dependencies]
@@ -1980,7 +2028,7 @@ nox = ["nox"]
 pep8test = ["check-sdist", "click", "mypy", "ruff"]
 sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["certifi", "cryptography-vectors (==43.0.0)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -2114,6 +2162,21 @@ wrapt = ">=1.10,<2"
 [package.extras]
 dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
 
+[[package]]
+name = "dill"
+version = "0.3.8"
+description = "serialize all of Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
+    {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
+]
+
+[package.extras]
+graph = ["objgraph (>=1.7.2)"]
+profile = ["gprof2dot (>=2022.7.29)"]
+
 [[package]]
 name = "distro"
 version = "1.9.0"
@@ -2125,6 +2188,28 @@ files = [
     {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
 ]
 
+[[package]]
+name = "docker"
+version = "7.1.0"
+description = "A Python library for the Docker Engine API."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "docker-7.1.0-py3-none-any.whl", hash = "sha256:c96b93b7f0a746f9e77d325bcfb87422a3d8bd4f03136ae8a85b37f1898d5fc0"},
+    {file = "docker-7.1.0.tar.gz", hash = "sha256:ad8c70e6e3f8926cb8a92619b832b4ea5299e2831c14284663184e200546fa6c"},
+]
+
+[package.dependencies]
+pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""}
+requests = ">=2.26.0"
+urllib3 = ">=1.26.0"
+
+[package.extras]
+dev = ["coverage (==7.2.7)", "pytest (==7.4.2)", "pytest-cov (==4.1.0)", "pytest-timeout (==2.1.0)", "ruff (==0.1.8)"]
+docs = ["myst-parser (==0.18.0)", "sphinx (==5.1.1)"]
+ssh = ["paramiko (>=2.4.3)"]
+websockets = ["websocket-client (>=1.3.0)"]
+
 [[package]]
 name = "docstring-parser"
 version = "0.16"
@@ -2156,68 +2241,68 @@ typing_extensions = ">=4.0,<5.0"
 
 [[package]]
 name = "duckdb"
-version = "1.0.0"
+version = "1.1.0"
 description = "DuckDB in-process database"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2304,6 +2389,19 @@ django = ["dj-database-url", "dj-email-url", "django-cache-url"]
 lint = ["flake8 (==4.0.1)", "flake8-bugbear (==21.9.2)", "mypy (==0.910)", "pre-commit (>=2.4,<3.0)"]
 tests = ["dj-database-url", "dj-email-url", "django-cache-url", "pytest"]
 
+[[package]]
+name = "esdk-obs-python"
+version = "3.24.6.1"
+description = "OBS Python SDK"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+pycryptodome = ">=3.10.1"
+
 [[package]]
 name = "et-xmlfile"
 version = "1.1.0"
@@ -2331,13 +2429,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastapi"
-version = "0.112.1"
+version = "0.114.1"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "fastapi-0.114.1-py3-none-any.whl", hash = "sha256:5d4746f6e4b7dff0b4f6b6c6d5445645285f662fe75886e99af7ee2d6b58bb3e"},
+    {file = "fastapi-0.114.1.tar.gz", hash = "sha256:1d7bbbeabbaae0acb0c22f0ab0b040f642d3093ca3645f8c876b6f91391861d8"},
 ]
 
 [package.dependencies]
@@ -2346,47 +2444,47 @@ starlette = ">=0.37.2,<0.39.0"
 typing-extensions = ">=4.8.0"
 
 [package.extras]
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+all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=2.11.2)", "python-multipart (>=0.0.7)", "uvicorn[standard] (>=0.12.0)"]
 
 [[package]]
 name = "fastavro"
-version = "1.9.5"
+version = "1.9.7"
 description = "Fast read/write of AVRO files"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -2426,19 +2524,19 @@ sgmllib3k = "*"
 
 [[package]]
 name = "filelock"
-version = "3.15.4"
+version = "3.16.0"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"]
-typing = ["typing-extensions (>=4.8)"]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.1.1)", "pytest (>=8.3.2)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.3)"]
+typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "filetype"
@@ -2491,13 +2589,13 @@ flask = "*"
 
 [[package]]
 name = "flask-cors"
-version = "4.0.1"
+version = "4.0.2"
 description = "A Flask extension adding a decorator for CORS support"
 optional = false
 python-versions = "*"
 files = [
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 [package.extras]
@@ -3300,6 +3357,21 @@ typing-extensions = "*"
 [package.extras]
 dev = ["Pillow", "absl-py", "black", "ipython", "nose2", "pandas", "pytype", "pyyaml"]
 
+[[package]]
+name = "google-pasta"
+version = "0.2.0"
+description = "pasta is an AST-based Python refactoring library"
+optional = false
+python-versions = "*"
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+]
+
+[package.dependencies]
+six = "*"
+
 [[package]]
 name = "google-resumable-media"
 version = "2.7.2"
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 [package.extras]
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 [[package]]
 name = "grpcio-status"
@@ -3826,13 +3906,13 @@ test = ["Cython (>=0.29.24,<0.30.0)"]
 
 [[package]]
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@@ -3849,6 +3929,7 @@ brotli = ["brotli", "brotlicffi"]
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 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
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 [[package]]
 name = "huggingface-hub"
@@ -3909,43 +3990,43 @@ files = [
 
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 ]
 
 [[package]]
 name = "kombu"
-version = "5.4.0"
+version = "5.4.1"
 description = "Messaging library for Python."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4250,7 +4341,7 @@ confluentkafka = ["confluent-kafka (>=2.2.0)"]
 consul = ["python-consul2 (==0.1.5)"]
 librabbitmq = ["librabbitmq (>=2.0.0)"]
 mongodb = ["pymongo (>=4.1.1)"]
-msgpack = ["msgpack (==1.0.8)"]
+msgpack = ["msgpack (==1.1.0)"]
 pyro = ["pyro4 (==4.82)"]
 qpid = ["qpid-python (>=0.26)", "qpid-tools (>=0.26)"]
 redis = ["redis (>=4.5.2,!=4.5.5,!=5.0.2)"]
@@ -4302,13 +4393,13 @@ six = "*"
 
 [[package]]
 name = "langfuse"
-version = "2.44.0"
+version = "2.48.0"
 description = "A client library for accessing langfuse"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -4316,7 +4407,7 @@ anyio = ">=4.4.0,<5.0.0"
 backoff = ">=1.10.0"
 httpx = ">=0.15.4,<1.0"
 idna = ">=3.7,<4.0"
-packaging = ">=23.2,<24.0"
+packaging = ">=23.2,<25.0"
 pydantic = ">=1.10.7,<3.0"
 wrapt = ">=1.14,<2.0"
 
@@ -4327,13 +4418,13 @@ openai = ["openai (>=0.27.8)"]
 
 [[package]]
 name = "langsmith"
-version = "0.1.101"
+version = "0.1.118"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -4803,15 +4894,15 @@ files = [
 
 [[package]]
 name = "milvus-lite"
-version = "2.4.9"
+version = "2.4.10"
 description = "A lightweight version of Milvus wrapped with Python."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -4909,6 +5000,22 @@ files = [
 [package.extras]
 test = ["mypy (>=1.0)", "pytest (>=7.0.0)"]
 
+[[package]]
+name = "mock"
+version = "4.0.3"
+description = "Rolling backport of unittest.mock for all Pythons"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
+[package.extras]
+build = ["blurb", "twine", "wheel"]
+docs = ["sphinx"]
+test = ["pytest (<5.4)", "pytest-cov"]
+
 [[package]]
 name = "monotonic"
 version = "1.6"
@@ -4939,22 +5046,22 @@ tests = ["pytest (>=4.6)"]
 
 [[package]]
 name = "msal"
-version = "1.30.0"
+version = "1.31.0"
 description = "The Microsoft Authentication Library (MSAL) for Python library enables your app to access the Microsoft Cloud by supporting authentication of users with Microsoft Azure Active Directory accounts (AAD) and Microsoft Accounts (MSA) using industry standard OAuth2 and OpenID Connect."
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 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
-cryptography = ">=2.5,<45"
+cryptography = ">=2.5,<46"
 PyJWT = {version = ">=1.0.0,<3", extras = ["crypto"]}
 requests = ">=2.0.0,<3"
 
 [package.extras]
-broker = ["pymsalruntime (>=0.13.2,<0.17)"]
+broker = ["pymsalruntime (>=0.14,<0.18)", "pymsalruntime (>=0.17,<0.18)"]
 
 [[package]]
 name = "msal-extensions"
@@ -5011,103 +5118,132 @@ async = ["aiodns", "aiohttp (>=3.0)"]
 
 [[package]]
 name = "multidict"
-version = "6.0.5"
+version = "6.1.0"
 description = "multidict implementation"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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 signals = ["blinker (>=1.4.0)"]
 signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
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 ]
 
 [package.dependencies]
@@ -5524,42 +5679,42 @@ kerberos = ["requests-kerberos"]
 
 [[package]]
 name = "opentelemetry-api"
-version = "1.26.0"
+version = "1.27.0"
 description = "OpenTelemetry Python API"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_api-1.26.0-py3-none-any.whl", hash = "sha256:7d7ea33adf2ceda2dd680b18b1677e4152000b37ca76e679da71ff103b943064"},
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+    {file = "opentelemetry_api-1.27.0-py3-none-any.whl", hash = "sha256:953d5871815e7c30c81b56d910c707588000fff7a3ca1c73e6531911d53065e7"},
+    {file = "opentelemetry_api-1.27.0.tar.gz", hash = "sha256:ed673583eaa5f81b5ce5e86ef7cdaf622f88ef65f0b9aab40b843dcae5bef342"},
 ]
 
 [package.dependencies]
 deprecated = ">=1.2.6"
-importlib-metadata = ">=6.0,<=8.0.0"
+importlib-metadata = ">=6.0,<=8.4.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-common"
-version = "1.26.0"
+version = "1.27.0"
 description = "OpenTelemetry Protobuf encoding"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_common-1.26.0-py3-none-any.whl", hash = "sha256:ee4d8f8891a1b9c372abf8d109409e5b81947cf66423fd998e56880057afbc71"},
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+    {file = "opentelemetry_exporter_otlp_proto_common-1.27.0-py3-none-any.whl", hash = "sha256:675db7fffcb60946f3a5c43e17d1168a3307a94a930ecf8d2ea1f286f3d4f79a"},
+    {file = "opentelemetry_exporter_otlp_proto_common-1.27.0.tar.gz", hash = "sha256:159d27cf49f359e3798c4c3eb8da6ef4020e292571bd8c5604a2a573231dd5c8"},
 ]
 
 [package.dependencies]
-opentelemetry-proto = "1.26.0"
+opentelemetry-proto = "1.27.0"
 
 [[package]]
 name = "opentelemetry-exporter-otlp-proto-grpc"
-version = "1.26.0"
+version = "1.27.0"
 description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_exporter_otlp_proto_grpc-1.26.0-py3-none-any.whl", hash = "sha256:e2be5eff72ebcb010675b818e8d7c2e7d61ec451755b8de67a140bc49b9b0280"},
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+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.27.0-py3-none-any.whl", hash = "sha256:56b5bbd5d61aab05e300d9d62a6b3c134827bbd28d0b12f2649c2da368006c9e"},
+    {file = "opentelemetry_exporter_otlp_proto_grpc-1.27.0.tar.gz", hash = "sha256:af6f72f76bcf425dfb5ad11c1a6d6eca2863b91e63575f89bb7b4b55099d968f"},
 ]
 
 [package.dependencies]
@@ -5567,19 +5722,19 @@ deprecated = ">=1.2.6"
 googleapis-common-protos = ">=1.52,<2.0"
 grpcio = ">=1.0.0,<2.0.0"
 opentelemetry-api = ">=1.15,<2.0"
-opentelemetry-exporter-otlp-proto-common = "1.26.0"
-opentelemetry-proto = "1.26.0"
-opentelemetry-sdk = ">=1.26.0,<1.27.0"
+opentelemetry-exporter-otlp-proto-common = "1.27.0"
+opentelemetry-proto = "1.27.0"
+opentelemetry-sdk = ">=1.27.0,<1.28.0"
 
 [[package]]
 name = "opentelemetry-instrumentation"
-version = "0.47b0"
+version = "0.48b0"
 description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation-0.47b0-py3-none-any.whl", hash = "sha256:88974ee52b1db08fc298334b51c19d47e53099c33740e48c4f084bd1afd052d5"},
-    {file = "opentelemetry_instrumentation-0.47b0.tar.gz", hash = "sha256:96f9885e450c35e3f16a4f33145f2ebf620aea910c9fd74a392bbc0f807a350f"},
+    {file = "opentelemetry_instrumentation-0.48b0-py3-none-any.whl", hash = "sha256:a69750dc4ba6a5c3eb67986a337185a25b739966d80479befe37b546fc870b44"},
+    {file = "opentelemetry_instrumentation-0.48b0.tar.gz", hash = "sha256:94929685d906380743a71c3970f76b5f07476eea1834abd5dd9d17abfe23cc35"},
 ]
 
 [package.dependencies]
@@ -5589,55 +5744,55 @@ wrapt = ">=1.0.0,<2.0.0"
 
 [[package]]
 name = "opentelemetry-instrumentation-asgi"
-version = "0.47b0"
+version = "0.48b0"
 description = "ASGI instrumentation for OpenTelemetry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation_asgi-0.47b0-py3-none-any.whl", hash = "sha256:b798dc4957b3edc9dfecb47a4c05809036a4b762234c5071212fda39ead80ade"},
-    {file = "opentelemetry_instrumentation_asgi-0.47b0.tar.gz", hash = "sha256:e78b7822c1bca0511e5e9610ec484b8994a81670375e570c76f06f69af7c506a"},
+    {file = "opentelemetry_instrumentation_asgi-0.48b0-py3-none-any.whl", hash = "sha256:ddb1b5fc800ae66e85a4e2eca4d9ecd66367a8c7b556169d9e7b57e10676e44d"},
+    {file = "opentelemetry_instrumentation_asgi-0.48b0.tar.gz", hash = "sha256:04c32174b23c7fa72ddfe192dad874954968a6a924608079af9952964ecdf785"},
 ]
 
 [package.dependencies]
 asgiref = ">=3.0,<4.0"
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.47b0"
-opentelemetry-semantic-conventions = "0.47b0"
-opentelemetry-util-http = "0.47b0"
+opentelemetry-instrumentation = "0.48b0"
+opentelemetry-semantic-conventions = "0.48b0"
+opentelemetry-util-http = "0.48b0"
 
 [package.extras]
 instruments = ["asgiref (>=3.0,<4.0)"]
 
 [[package]]
 name = "opentelemetry-instrumentation-fastapi"
-version = "0.47b0"
+version = "0.48b0"
 description = "OpenTelemetry FastAPI Instrumentation"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_instrumentation_fastapi-0.47b0-py3-none-any.whl", hash = "sha256:5ac28dd401160b02e4f544a85a9e4f61a8cbe5b077ea0379d411615376a2bd21"},
-    {file = "opentelemetry_instrumentation_fastapi-0.47b0.tar.gz", hash = "sha256:0c7c10b5d971e99a420678ffd16c5b1ea4f0db3b31b62faf305fbb03b4ebee36"},
+    {file = "opentelemetry_instrumentation_fastapi-0.48b0-py3-none-any.whl", hash = "sha256:afeb820a59e139d3e5d96619600f11ce0187658b8ae9e3480857dd790bc024f2"},
+    {file = "opentelemetry_instrumentation_fastapi-0.48b0.tar.gz", hash = "sha256:21a72563ea412c0b535815aeed75fc580240f1f02ebc72381cfab672648637a2"},
 ]
 
 [package.dependencies]
 opentelemetry-api = ">=1.12,<2.0"
-opentelemetry-instrumentation = "0.47b0"
-opentelemetry-instrumentation-asgi = "0.47b0"
-opentelemetry-semantic-conventions = "0.47b0"
-opentelemetry-util-http = "0.47b0"
+opentelemetry-instrumentation = "0.48b0"
+opentelemetry-instrumentation-asgi = "0.48b0"
+opentelemetry-semantic-conventions = "0.48b0"
+opentelemetry-util-http = "0.48b0"
 
 [package.extras]
-instruments = ["fastapi (>=0.58,<1.0)", "fastapi-slim (>=0.111.0,<0.112.0)"]
+instruments = ["fastapi (>=0.58,<1.0)"]
 
 [[package]]
 name = "opentelemetry-proto"
-version = "1.26.0"
+version = "1.27.0"
 description = "OpenTelemetry Python Proto"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_proto-1.26.0-py3-none-any.whl", hash = "sha256:6c4d7b4d4d9c88543bcf8c28ae3f8f0448a753dc291c18c5390444c90b76a725"},
-    {file = "opentelemetry_proto-1.26.0.tar.gz", hash = "sha256:c5c18796c0cab3751fc3b98dee53855835e90c0422924b484432ac852d93dc1e"},
+    {file = "opentelemetry_proto-1.27.0-py3-none-any.whl", hash = "sha256:b133873de5581a50063e1e4b29cdcf0c5e253a8c2d8dc1229add20a4c3830ace"},
+    {file = "opentelemetry_proto-1.27.0.tar.gz", hash = "sha256:33c9345d91dafd8a74fc3d7576c5a38f18b7fdf8d02983ac67485386132aedd6"},
 ]
 
 [package.dependencies]
@@ -5645,44 +5800,44 @@ protobuf = ">=3.19,<5.0"
 
 [[package]]
 name = "opentelemetry-sdk"
-version = "1.26.0"
+version = "1.27.0"
 description = "OpenTelemetry Python SDK"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_sdk-1.26.0-py3-none-any.whl", hash = "sha256:feb5056a84a88670c041ea0ded9921fca559efec03905dddeb3885525e0af897"},
-    {file = "opentelemetry_sdk-1.26.0.tar.gz", hash = "sha256:c90d2868f8805619535c05562d699e2f4fb1f00dbd55a86dcefca4da6fa02f85"},
+    {file = "opentelemetry_sdk-1.27.0-py3-none-any.whl", hash = "sha256:365f5e32f920faf0fd9e14fdfd92c086e317eaa5f860edba9cdc17a380d9197d"},
+    {file = "opentelemetry_sdk-1.27.0.tar.gz", hash = "sha256:d525017dea0ccce9ba4e0245100ec46ecdc043f2d7b8315d56b19aff0904fa6f"},
 ]
 
 [package.dependencies]
-opentelemetry-api = "1.26.0"
-opentelemetry-semantic-conventions = "0.47b0"
+opentelemetry-api = "1.27.0"
+opentelemetry-semantic-conventions = "0.48b0"
 typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "opentelemetry-semantic-conventions"
-version = "0.47b0"
+version = "0.48b0"
 description = "OpenTelemetry Semantic Conventions"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_semantic_conventions-0.47b0-py3-none-any.whl", hash = "sha256:4ff9d595b85a59c1c1413f02bba320ce7ea6bf9e2ead2b0913c4395c7bbc1063"},
-    {file = "opentelemetry_semantic_conventions-0.47b0.tar.gz", hash = "sha256:a8d57999bbe3495ffd4d510de26a97dadc1dace53e0275001b2c1b2f67992a7e"},
+    {file = "opentelemetry_semantic_conventions-0.48b0-py3-none-any.whl", hash = "sha256:a0de9f45c413a8669788a38569c7e0a11ce6ce97861a628cca785deecdc32a1f"},
+    {file = "opentelemetry_semantic_conventions-0.48b0.tar.gz", hash = "sha256:12d74983783b6878162208be57c9effcb89dc88691c64992d70bb89dc00daa1a"},
 ]
 
 [package.dependencies]
 deprecated = ">=1.2.6"
-opentelemetry-api = "1.26.0"
+opentelemetry-api = "1.27.0"
 
 [[package]]
 name = "opentelemetry-util-http"
-version = "0.47b0"
+version = "0.48b0"
 description = "Web util for OpenTelemetry"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "opentelemetry_util_http-0.47b0-py3-none-any.whl", hash = "sha256:3d3215e09c4a723b12da6d0233a31395aeb2bb33a64d7b15a1500690ba250f19"},
-    {file = "opentelemetry_util_http-0.47b0.tar.gz", hash = "sha256:352a07664c18eef827eb8ddcbd64c64a7284a39dd1655e2f16f577eb046ccb32"},
+    {file = "opentelemetry_util_http-0.48b0-py3-none-any.whl", hash = "sha256:76f598af93aab50328d2a69c786beaedc8b6a7770f7a818cc307eb353debfffb"},
+    {file = "opentelemetry_util_http-0.48b0.tar.gz", hash = "sha256:60312015153580cc20f322e5cdc3d3ecad80a71743235bdb77716e742814623c"},
 ]
 
 [[package]]
@@ -5825,13 +5980,13 @@ files = [
 
 [[package]]
 name = "packaging"
-version = "23.2"
+version = "24.1"
 description = "Core utilities for Python packages"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "packaging-23.2-py3-none-any.whl", hash = "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"},
-    {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
+    {file = "packaging-24.1-py3-none-any.whl", hash = "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124"},
+    {file = "packaging-24.1.tar.gz", hash = "sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002"},
 ]
 
 [[package]]
@@ -5916,6 +6071,23 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
 test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.9.2)"]
 
+[[package]]
+name = "pathos"
+version = "0.3.2"
+description = "parallel graph management and execution in heterogeneous computing"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pathos-0.3.2-py3-none-any.whl", hash = "sha256:d669275e6eb4b3fbcd2846d7a6d1bba315fe23add0c614445ba1408d8b38bafe"},
+    {file = "pathos-0.3.2.tar.gz", hash = "sha256:4f2a42bc1e10ccf0fe71961e7145fc1437018b6b21bd93b2446abc3983e49a7a"},
+]
+
+[package.dependencies]
+dill = ">=0.3.8"
+multiprocess = ">=0.70.16"
+pox = ">=0.3.4"
+ppft = ">=1.7.6.8"
+
 [[package]]
 name = "peewee"
 version = "3.17.6"
@@ -6060,29 +6232,29 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "4.2.2"
+version = "4.3.2"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "platformdirs-4.2.2-py3-none-any.whl", hash = "sha256:2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee"},
-    {file = "platformdirs-4.2.2.tar.gz", hash = "sha256:38b7b51f512eed9e84a22788b4bce1de17c0adb134d6becb09836e37d8654cd3"},
+    {file = "platformdirs-4.3.2-py3-none-any.whl", hash = "sha256:eb1c8582560b34ed4ba105009a4badf7f6f85768b30126f351328507b2beb617"},
+    {file = "platformdirs-4.3.2.tar.gz", hash = "sha256:9e5e27a08aa095dd127b9f2e764d74254f482fef22b0970773bfba79d091ab8c"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
-type = ["mypy (>=1.8)"]
+docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
+type = ["mypy (>=1.11.2)"]
 
 [[package]]
 name = "plotly"
-version = "5.23.0"
+version = "5.24.0"
 description = "An open-source, interactive data visualization library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "plotly-5.23.0-py3-none-any.whl", hash = "sha256:76cbe78f75eddc10c56f5a4ee3e7ccaade7c0a57465546f02098c0caed6c2d1a"},
-    {file = "plotly-5.23.0.tar.gz", hash = "sha256:89e57d003a116303a34de6700862391367dd564222ab71f8531df70279fc0193"},
+    {file = "plotly-5.24.0-py3-none-any.whl", hash = "sha256:0e54efe52c8cef899f7daa41be9ed97dfb6be622613a2a8f56a86a0634b2b67e"},
+    {file = "plotly-5.24.0.tar.gz", hash = "sha256:eae9f4f54448682442c92c1e97148e3ad0c52f0cf86306e1b76daba24add554a"},
 ]
 
 [package.dependencies]
@@ -6136,13 +6308,13 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
 
 [[package]]
 name = "posthog"
-version = "3.5.2"
+version = "3.6.5"
 description = "Integrate PostHog into any python application."
 optional = false
 python-versions = "*"
 files = [
-    {file = "posthog-3.5.2-py2.py3-none-any.whl", hash = "sha256:605b3d92369971cc99290b1fcc8534cbddac3726ef7972caa993454a5ecfb644"},
-    {file = "posthog-3.5.2.tar.gz", hash = "sha256:a383a80c1f47e0243f5ce359e81e06e2e7b37eb39d1d6f8d01c3e64ed29df2ee"},
+    {file = "posthog-3.6.5-py2.py3-none-any.whl", hash = "sha256:f8b7c573826b061a1d22c9495169c38ebe83a1df2729f49c7129a9c23a02acf6"},
+    {file = "posthog-3.6.5.tar.gz", hash = "sha256:7fd3ca809e15476c35f75d18cd6bba31395daf0a17b75242965c469fb6292510"},
 ]
 
 [package.dependencies]
@@ -6155,7 +6327,32 @@ six = ">=1.5"
 [package.extras]
 dev = ["black", "flake8", "flake8-print", "isort", "pre-commit"]
 sentry = ["django", "sentry-sdk"]
-test = ["coverage", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint", "pytest", "pytest-timeout"]
+test = ["coverage", "django", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint", "pytest", "pytest-timeout"]
+
+[[package]]
+name = "pox"
+version = "0.3.4"
+description = "utilities for filesystem exploration and automated builds"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pox-0.3.4-py3-none-any.whl", hash = "sha256:651b8ae8a7b341b7bfd267f67f63106daeb9805f1ac11f323d5280d2da93fdb6"},
+    {file = "pox-0.3.4.tar.gz", hash = "sha256:16e6eca84f1bec3828210b06b052adf04cf2ab20c22fd6fbef5f78320c9a6fed"},
+]
+
+[[package]]
+name = "ppft"
+version = "1.7.6.8"
+description = "distributed and parallel Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "ppft-1.7.6.8-py3-none-any.whl", hash = "sha256:de2dd4b1b080923dd9627fbdea52649fd741c752fce4f3cf37e26f785df23d9b"},
+    {file = "ppft-1.7.6.8.tar.gz", hash = "sha256:76a429a7d7b74c4d743f6dba8351e58d62b6432ed65df9fe204790160dab996d"},
+]
+
+[package.extras]
+dill = ["dill (>=0.3.8)"]
 
 [[package]]
 name = "primp"
@@ -6402,24 +6599,24 @@ test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
 
 [[package]]
 name = "pyasn1"
-version = "0.6.0"
+version = "0.6.1"
 description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pyasn1-0.6.0-py2.py3-none-any.whl", hash = "sha256:cca4bb0f2df5504f02f6f8a775b6e416ff9b0b3b16f7ee80b5a3153d9b804473"},
-    {file = "pyasn1-0.6.0.tar.gz", hash = "sha256:3a35ab2c4b5ef98e17dfdec8ab074046fbda76e281c5a706ccd82328cfc8f64c"},
+    {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
+    {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
 ]
 
 [[package]]
 name = "pyasn1-modules"
-version = "0.4.0"
+version = "0.4.1"
 description = "A collection of ASN.1-based protocols modules"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pyasn1_modules-0.4.0-py3-none-any.whl", hash = "sha256:be04f15b66c206eed667e0bb5ab27e2b1855ea54a842e5037738099e8ca4ae0b"},
-    {file = "pyasn1_modules-0.4.0.tar.gz", hash = "sha256:831dbcea1b177b28c9baddf4c6d1013c24c3accd14a1873fffaa6a2e905f17b6"},
+    {file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
+    {file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
 ]
 
 [package.dependencies]
@@ -6692,18 +6889,18 @@ tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
 [[package]]
 name = "pymilvus"
-version = "2.4.5"
+version = "2.4.6"
 description = "Python Sdk for Milvus"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pymilvus-2.4.5-py3-none-any.whl", hash = "sha256:dc4f2d1eac8db9cf3951de39566a1a244695760bb94d8310fbfc73d6d62bb267"},
-    {file = "pymilvus-2.4.5.tar.gz", hash = "sha256:1a497fe9b41d6bf62b1d5e1c412960922dde1598576fcbb8818040c8af11149f"},
+    {file = "pymilvus-2.4.6-py3-none-any.whl", hash = "sha256:b4c43472edc313b845d313be50610e19054e6954b2c5c3b515565c596c2d3d97"},
+    {file = "pymilvus-2.4.6.tar.gz", hash = "sha256:6ac3eb91c92cc01bbe444fe83f895f02d7b2546d96ac67998630bf31ac074d66"},
 ]
 
 [package.dependencies]
 environs = "<=9.5.0"
-grpcio = ">=1.49.1,<=1.63.0"
+grpcio = ">=1.49.1"
 milvus-lite = {version = ">=2.4.0,<2.5.0", markers = "sys_platform != \"win32\""}
 pandas = ">=1.2.4"
 protobuf = ">=3.20.0"
@@ -6730,6 +6927,24 @@ files = [
 ed25519 = ["PyNaCl (>=1.4.0)"]
 rsa = ["cryptography"]
 
+[[package]]
+name = "pyopenssl"
+version = "24.2.1"
+description = "Python wrapper module around the OpenSSL library"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pyOpenSSL-24.2.1-py3-none-any.whl", hash = "sha256:967d5719b12b243588573f39b0c677637145c7a1ffedcd495a487e58177fbb8d"},
+    {file = "pyopenssl-24.2.1.tar.gz", hash = "sha256:4247f0dbe3748d560dcbb2ff3ea01af0f9a1a001ef5f7c4c647956ed8cbf0e95"},
+]
+
+[package.dependencies]
+cryptography = ">=41.0.5,<44"
+
+[package.extras]
+docs = ["sphinx (!=5.2.0,!=5.2.0.post0,!=7.2.5)", "sphinx-rtd-theme"]
+test = ["pretend", "pytest (>=3.0.1)", "pytest-rerunfailures"]
+
 [[package]]
 name = "pypandoc"
 version = "1.13"
@@ -6743,13 +6958,13 @@ files = [
 
 [[package]]
 name = "pyparsing"
-version = "3.1.2"
+version = "3.1.4"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
 files = [
-    {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
-    {file = "pyparsing-3.1.2.tar.gz", hash = "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad"},
+    {file = "pyparsing-3.1.4-py3-none-any.whl", hash = "sha256:a6a7ee4235a3f944aa1fa2249307708f893fe5717dc603503c6c7969c070fb7c"},
+    {file = "pyparsing-3.1.4.tar.gz", hash = "sha256:f86ec8d1a83f11977c9a6ea7598e8c27fc5cddfa5b07ea2241edbbde1d7bc032"},
 ]
 
 [package.extras]
@@ -6810,24 +7025,24 @@ files = [
 
 [[package]]
 name = "pyreadline3"
-version = "3.4.1"
+version = "3.4.3"
 description = "A python implementation of GNU readline."
 optional = false
 python-versions = "*"
 files = [
-    {file = "pyreadline3-3.4.1-py3-none-any.whl", hash = "sha256:b0efb6516fd4fb07b45949053826a62fa4cb353db5be2bbb4a7aa1fdd1e345fb"},
-    {file = "pyreadline3-3.4.1.tar.gz", hash = "sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae"},
+    {file = "pyreadline3-3.4.3-py3-none-any.whl", hash = "sha256:f832c5898f4f9a0f81d48a8c499b39d0179de1a465ea3def1a7e7231840b4ed6"},
+    {file = "pyreadline3-3.4.3.tar.gz", hash = "sha256:ebab0baca37f50e2faa1dd99a6da1c75de60e0d68a3b229c134bbd12786250e2"},
 ]
 
 [[package]]
 name = "pytest"
-version = "8.3.2"
+version = "8.3.3"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.3.2-py3-none-any.whl", hash = "sha256:4ba08f9ae7dcf84ded419494d229b48d0903ea6407b030eaec46df5e6a73bba5"},
-    {file = "pytest-8.3.2.tar.gz", hash = "sha256:c132345d12ce551242c87269de812483f5bcc87cdbb4722e48487ba194f9fdce"},
+    {file = "pytest-8.3.3-py3-none-any.whl", hash = "sha256:a6853c7375b2663155079443d2e45de913a911a11d669df02a50814944db57b2"},
+    {file = "pytest-8.3.3.tar.gz", hash = "sha256:70b98107bd648308a7952b06e6ca9a50bc660be218d53c257cc1fc94fda10181"},
 ]
 
 [package.dependencies]
@@ -6863,21 +7078,21 @@ histogram = ["pygal", "pygaljs"]
 
 [[package]]
 name = "pytest-env"
-version = "1.1.3"
+version = "1.1.4"
 description = "pytest plugin that allows you to add environment variables."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest_env-1.1.3-py3-none-any.whl", hash = "sha256:aada77e6d09fcfb04540a6e462c58533c37df35fa853da78707b17ec04d17dfc"},
-    {file = "pytest_env-1.1.3.tar.gz", hash = "sha256:fcd7dc23bb71efd3d35632bde1bbe5ee8c8dc4489d6617fb010674880d96216b"},
+    {file = "pytest_env-1.1.4-py3-none-any.whl", hash = "sha256:a4212056d4d440febef311a98fdca56c31256d58fb453d103cba4e8a532b721d"},
+    {file = "pytest_env-1.1.4.tar.gz", hash = "sha256:86653658da8f11c6844975db955746c458a9c09f1e64957603161e2ff93f5133"},
 ]
 
 [package.dependencies]
-pytest = ">=7.4.3"
+pytest = ">=8.3.2"
 tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-test = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "pytest-mock (>=3.12)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "pytest-mock (>=3.14)"]
 
 [[package]]
 name = "pytest-mock"
@@ -7091,13 +7306,13 @@ XlsxWriter = ">=0.5.7"
 
 [[package]]
 name = "pytz"
-version = "2024.1"
+version = "2024.2"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
-    {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
-    {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
+    {file = "pytz-2024.2-py2.py3-none-any.whl", hash = "sha256:31c7c1817eb7fae7ca4b8c7ee50c72f93aa2dd863de768e1ef4245d426aa0725"},
+    {file = "pytz-2024.2.tar.gz", hash = "sha256:2aa355083c50a0f93fa581709deac0c9ad65cca8a9e9beac660adcbd493c798a"},
 ]
 
 [[package]]
@@ -7264,119 +7479,119 @@ dev = ["pytest"]
 
 [[package]]
 name = "rapidfuzz"
-version = "3.9.6"
+version = "3.9.7"
 description = "rapid fuzzy string matching"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "rapidfuzz-3.9.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a7ed0d0b9c85720f0ae33ac5efc8dc3f60c1489dad5c29d735fbdf2f66f0431f"},
-    {file = "rapidfuzz-3.9.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f3deff6ab7017ed21b9aec5874a07ad13e6b2a688af055837f88b743c7bfd947"},
-    {file = "rapidfuzz-3.9.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c3f9fc060160507b2704f7d1491bd58453d69689b580cbc85289335b14fe8ca"},
-    {file = "rapidfuzz-3.9.6-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c4e86c2b3827fa6169ad6e7d4b790ce02a20acefb8b78d92fa4249589bbc7a2c"},
-    {file = "rapidfuzz-3.9.6-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f982e1aafb4bd8207a5e073b1efef9e68a984e91330e1bbf364f9ed157ed83f0"},
-    {file = "rapidfuzz-3.9.6-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9196a51d0ec5eaaaf5bca54a85b7b1e666fc944c332f68e6427503af9fb8c49e"},
-    {file = "rapidfuzz-3.9.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fb5a514064e02585b1cc09da2fe406a6dc1a7e5f3e92dd4f27c53e5f1465ec81"},
-    {file = "rapidfuzz-3.9.6-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:e3a4244f65dbc3580b1275480118c3763f9dc29fc3dd96610560cb5e140a4d4a"},
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 [package.extras]
@@ -7440,90 +7655,105 @@ rpds-py = ">=0.7.0"
 
 [[package]]
 name = "regex"
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 description = "Alternative regular expression module, to replace re."
 optional = false
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 [[package]]
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 [[package]]
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 ]
 
 [package.dependencies]
@@ -8352,13 +8675,13 @@ doc = ["sphinx"]
 
 [[package]]
 name = "starlette"
-version = "0.38.2"
+version = "0.38.5"
 description = "The little ASGI library that shines."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -8412,6 +8735,17 @@ files = [
 [package.extras]
 widechars = ["wcwidth"]
 
+[[package]]
+name = "tblib"
+version = "3.0.0"
+description = "Traceback serialization library."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
 [[package]]
 name = "tcvectordb"
 version = "1.3.2"
@@ -8444,13 +8778,13 @@ test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
 [[package]]
 name = "tencentcloud-sdk-python-common"
-version = "3.0.1216"
+version = "3.0.1230"
 description = "Tencent Cloud Common SDK for Python"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -8458,17 +8792,17 @@ requests = ">=2.16.0"
 
 [[package]]
 name = "tencentcloud-sdk-python-hunyuan"
-version = "3.0.1216"
+version = "3.0.1230"
 description = "Tencent Cloud Hunyuan SDK for Python"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
-tencentcloud-sdk-python-common = "3.0.1216"
+tencentcloud-sdk-python-common = "3.0.1230"
 
 [[package]]
 name = "threadpoolctl"
@@ -8730,6 +9064,23 @@ files = [
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 ]
 
+[[package]]
+name = "tos"
+version = "2.7.1"
+description = "Volc TOS (Tinder Object Storage) SDK"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+crcmod = ">=1.7"
+Deprecated = ">=1.2.13,<2.0.0"
+pytz = "*"
+requests = ">=2.19.1,<3.dev0"
+six = "*"
+
 [[package]]
 name = "tqdm"
 version = "4.66.5"
@@ -8837,13 +9188,13 @@ requests = ">=2.0.0"
 
 [[package]]
 name = "typer"
-version = "0.12.4"
+version = "0.12.5"
 description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -8854,13 +9205,13 @@ typing-extensions = ">=3.7.4.3"
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20240712"
+version = "2.32.0.20240907"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -9263,12 +9614,12 @@ files = [
 
 [[package]]
 name = "volcengine-python-sdk"
-version = "1.0.98"
+version = "1.0.100"
 description = "Volcengine SDK for Python"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -9285,98 +9636,94 @@ ark = ["anyio (>=3.5.0,<5)", "cached-property", "httpx (>=0.23.0,<1)", "pydantic
 
 [[package]]
 name = "watchfiles"
-version = "0.23.0"
+version = "0.24.0"
 description = "Simple, modern and high performance file watching and code reload in python."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -9442,97 +9789,97 @@ test = ["websockets"]
 
 [[package]]
 name = "websockets"
-version = "13.0"
+version = "13.0.1"
 description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
 optional = false
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 [[package]]
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 [package.dependencies]
@@ -9821,13 +10170,13 @@ multidict = ">=4.0"
 
 [[package]]
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+version = "0.2.43"
 description = "Download market data from Yahoo! Finance API"
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 [package.dependencies]
@@ -9866,18 +10215,22 @@ requests = "*"
 
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+type = ["pytest-mypy"]
 
 [[package]]
 name = "zope-event"
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+    {file = "zope.interface-7.0.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:01e6e58078ad2799130c14a1d34ec89044ada0e1495329d72ee0407b9ae5100d"},
+    {file = "zope.interface-7.0.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:799ef7a444aebbad5a145c3b34bff012b54453cddbde3332d47ca07225792ea4"},
+    {file = "zope.interface-7.0.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d3b7ce6d46fb0e60897d62d1ff370790ce50a57d40a651db91a3dde74f73b738"},
+    {file = "zope.interface-7.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:f418c88f09c3ba159b95a9d1cfcdbe58f208443abb1f3109f4b9b12fd60b187c"},
+    {file = "zope.interface-7.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:84f8794bd59ca7d09d8fce43ae1b571be22f52748169d01a13d3ece8394d8b5b"},
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+    {file = "zope.interface-7.0.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1eeeb92cb7d95c45e726e3c1afe7707919370addae7ed14f614e22217a536958"},
+    {file = "zope.interface-7.0.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ecd32f30f40bfd8511b17666895831a51b532e93fc106bfa97f366589d3e4e0e"},
+    {file = "zope.interface-7.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:5112c530fa8aa2108a3196b9c2f078f5738c1c37cfc716970edc0df0414acda8"},
+    {file = "zope.interface-7.0.3.tar.gz", hash = "sha256:cd2690d4b08ec9eaf47a85914fe513062b20da78d10d6d789a792c0b20307fb1"},
 ]
 
 [package.dependencies]
@@ -10063,4 +10416,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "e4c00268514d26bd07c6b72925e0e3b4558ec972895d252e60e9571e3ac38895"
+content-hash = "9173a56b2efea12804c980511e1465fba43c7a3d83b1ad284ee149851ed67fc5"
diff --git a/api/pyproject.toml b/api/pyproject.toml
index 6a46ab95f3..8c10f1dad9 100644
--- a/api/pyproject.toml
+++ b/api/pyproject.toml
@@ -6,6 +6,9 @@ requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.ruff]
+exclude=[
+    "migrations/*",
+]
 line-length = 120
 
 [tool.ruff.lint]
@@ -13,43 +16,61 @@ preview = true
 select = [
     "B", # flake8-bugbear rules
     "C4", # flake8-comprehensions
+    "E", # pycodestyle E rules
     "F", # pyflakes rules
+    "FURB", # refurb rules
     "I", # isort rules
-    "UP", # pyupgrade rules
-    "B035", # static-key-dict-comprehension
-    "E101", # mixed-spaces-and-tabs
-    "E111", # indentation-with-invalid-multiple
-    "E112", # no-indented-block
-    "E113", # unexpected-indentation
-    "E115", # no-indented-block-comment
-    "E116", # unexpected-indentation-comment
-    "E117", # over-indented
+    "N", # pep8-naming
+    "PT", # flake8-pytest-style rules
+    "PLC0208", # iteration-over-set
+    "PLC2801", # unnecessary-dunder-call
+    "PLC0414", # useless-import-alias
+    "PLR0402", # manual-from-import
+    "PLR1711", # useless-return
+    "PLR1714", # repeated-equality-comparison
+    "PLR6201", # literal-membership
     "RUF019", # unnecessary-key-check
     "RUF100", # unused-noqa
     "RUF101", # redirected-noqa
     "S506", # unsafe-yaml-load
-    "SIM116", # if-else-block-instead-of-dict-lookup
-    "SIM401", # if-else-block-instead-of-dict-get
-    "SIM910", # dict-get-with-none-default
+    "SIM", # flake8-simplify rules
+    "UP", # pyupgrade rules
     "W191", # tab-indentation
     "W605", # invalid-escape-sequence
-    "F601", # multi-value-repeated-key-literal
-    "F602", # multi-value-repeated-key-variable
 ]
 ignore = [
+    "E402", # module-import-not-at-top-of-file
+    "E711", # none-comparison
+    "E712", # true-false-comparison
+    "E721", # type-comparison
+    "E722", # bare-except
+    "E731", # lambda-assignment
     "F403", # undefined-local-with-import-star
     "F405", # undefined-local-with-import-star-usage
     "F821", # undefined-name
     "F841", # unused-variable
+    "FURB113", # repeated-append
+    "FURB152", # math-constant
     "UP007", # non-pep604-annotation
     "UP032", # f-string
     "B005", # strip-with-multi-characters
     "B006", # mutable-argument-default
     "B007", # unused-loop-control-variable
     "B026", # star-arg-unpacking-after-keyword-arg
-#    "B901", # return-in-generator
     "B904", # raise-without-from-inside-except
     "B905", # zip-without-explicit-strict
+    "N806", # non-lowercase-variable-in-function
+    "N815", # mixed-case-variable-in-class-scope
+    "PT011", # pytest-raises-too-broad
+    "SIM102", # collapsible-if
+    "SIM103", # needless-bool
+    "SIM105", # suppressible-exception
+    "SIM107", # return-in-try-except-finally
+    "SIM108", # if-else-block-instead-of-if-exp
+    "SIM113", # eumerate-for-loop
+    "SIM117", # multiple-with-statements
+    "SIM210", # if-expr-with-true-false
+    "SIM300", # yoda-conditions,
 ]
 
 [tool.ruff.lint.per-file-ignores]
@@ -61,6 +82,12 @@ ignore = [
     "F401", # unused-import
     "F811", # redefined-while-unused
 ]
+"configs/*" = [
+    "N802", # invalid-function-name
+]
+"libs/gmpy2_pkcs10aep_cipher.py" = [
+    "N803", # invalid-argument-name
+]
 "tests/*" = [
     "F401", # unused-import
     "F811", # redefined-while-unused
@@ -68,9 +95,6 @@ ignore = [
 
 [tool.ruff.format]
 exclude = [
-    "core/**/*.py",
-    "models/**/*.py",
-    "migrations/**/*",
 ]
 
 [tool.pytest_env]
@@ -112,13 +136,15 @@ authlib = "1.3.1"
 azure-identity = "1.16.1"
 azure-storage-blob = "12.13.0"
 beautifulsoup4 = "4.12.2"
-boto3 = "1.34.148"
+boto3 = "1.35.17"
+sagemaker = "2.231.0"
 bs4 = "~0.0.1"
 cachetools = "~5.3.0"
 celery = "~5.3.6"
 chardet = "~5.1.0"
 cohere = "~5.2.4"
 cos-python-sdk-v5 = "1.9.30"
+esdk-obs-python = "3.24.6.1"
 dashscope = { version = "~1.17.0", extras = ["tokenizer"] }
 flask = "~3.0.1"
 flask-compress = "~1.14"
@@ -183,12 +209,14 @@ zhipuai = "1.0.7"
 # Before adding new dependency, consider place it in alphabet order (a-z) and suitable group.
 
 ############################################################
-# Related transparent dependencies with pinned verion
+# Related transparent dependencies with pinned version
 # required by main implementations
 ############################################################
 azure-ai-ml = "^1.19.0"
 azure-ai-inference = "^1.0.0b3"
 volcengine-python-sdk = {extras = ["ark"], version = "^1.0.98"}
+oci = "^2.133.0"
+tos = "^2.7.1"
 [tool.poetry.group.indriect.dependencies]
 kaleido = "0.2.1"
 rank-bm25 = "~0.2.2"
@@ -255,4 +283,4 @@ optional = true
 
 [tool.poetry.group.lint.dependencies]
 dotenv-linter = "~0.5.0"
-ruff = "~0.6.1"
+ruff = "~0.6.5"
diff --git a/api/services/account_service.py b/api/services/account_service.py
index e1b70fc9ed..66ff5d2b7c 100644
--- a/api/services/account_service.py
+++ b/api/services/account_service.py
@@ -32,7 +32,7 @@ from services.errors.account import (
     NoPermissionError,
     RateLimitExceededError,
     RoleAlreadyAssignedError,
-    TenantNotFound,
+    TenantNotFoundError,
 )
 from tasks.mail_invite_member_task import send_invite_member_mail_task
 from tasks.mail_reset_password_task import send_reset_password_mail_task
@@ -47,7 +47,7 @@ class AccountService:
         if not account:
             return None
 
-        if account.status in [AccountStatus.BANNED.value, AccountStatus.CLOSED.value]:
+        if account.status in {AccountStatus.BANNED.value, AccountStatus.CLOSED.value}:
             raise Unauthorized("Account is banned or closed.")
 
         current_tenant: TenantAccountJoin = TenantAccountJoin.query.filter_by(
@@ -92,7 +92,7 @@ class AccountService:
         if not account:
             raise AccountLoginError("Invalid email or password.")
 
-        if account.status == AccountStatus.BANNED.value or account.status == AccountStatus.CLOSED.value:
+        if account.status in {AccountStatus.BANNED.value, AccountStatus.CLOSED.value}:
             raise AccountLoginError("Account is banned or closed.")
 
         if account.status == AccountStatus.PENDING.value:
@@ -311,13 +311,13 @@ class TenantService:
         """Get tenant by account and add the role"""
         tenant = account.current_tenant
         if not tenant:
-            raise TenantNotFound("Tenant not found.")
+            raise TenantNotFoundError("Tenant not found.")
 
         ta = TenantAccountJoin.query.filter_by(tenant_id=tenant.id, account_id=account.id).first()
         if ta:
             tenant.role = ta.role
         else:
-            raise TenantNotFound("Tenant not found for the account.")
+            raise TenantNotFoundError("Tenant not found for the account.")
         return tenant
 
     @staticmethod
@@ -427,7 +427,7 @@ class TenantService:
             "remove": [TenantAccountRole.OWNER],
             "update": [TenantAccountRole.OWNER],
         }
-        if action not in ["add", "remove", "update"]:
+        if action not in {"add", "remove", "update"}:
             raise InvalidActionError("Invalid action.")
 
         if member:
@@ -544,7 +544,7 @@ class RegisterService:
         """Register account"""
         try:
             account = AccountService.create_account(
-                email=email, name=name, interface_language=language if language else languages[0], password=password
+                email=email, name=name, interface_language=language or languages[0], password=password
             )
             account.status = AccountStatus.ACTIVE.value if not status else status.value
             account.initialized_at = datetime.now(timezone.utc).replace(tzinfo=None)
@@ -614,8 +614,8 @@ class RegisterService:
             "email": account.email,
             "workspace_id": tenant.id,
         }
-        expiryHours = dify_config.INVITE_EXPIRY_HOURS
-        redis_client.setex(cls._get_invitation_token_key(token), expiryHours * 60 * 60, json.dumps(invitation_data))
+        expiry_hours = dify_config.INVITE_EXPIRY_HOURS
+        redis_client.setex(cls._get_invitation_token_key(token), expiry_hours * 60 * 60, json.dumps(invitation_data))
         return token
 
     @classmethod
diff --git a/api/services/app_dsl_service.py b/api/services/app_dsl_service.py
index 895855a9c8..54594e1175 100644
--- a/api/services/app_dsl_service.py
+++ b/api/services/app_dsl_service.py
@@ -13,8 +13,9 @@ from services.workflow_service import WorkflowService
 
 logger = logging.getLogger(__name__)
 
-current_dsl_version = "0.1.1"
+current_dsl_version = "0.1.2"
 dsl_to_dify_version_mapping: dict[str, str] = {
+    "0.1.2": "0.8.0",
     "0.1.1": "0.6.0",  # dsl version -> from dify version
 }
 
@@ -80,18 +81,16 @@ class AppDslService:
             raise ValueError("Missing app in data argument")
 
         # get app basic info
-        name = args.get("name") if args.get("name") else app_data.get("name")
-        description = args.get("description") if args.get("description") else app_data.get("description", "")
-        icon_type = args.get("icon_type") if args.get("icon_type") else app_data.get("icon_type")
-        icon = args.get("icon") if args.get("icon") else app_data.get("icon")
-        icon_background = (
-            args.get("icon_background") if args.get("icon_background") else app_data.get("icon_background")
-        )
+        name = args.get("name") or app_data.get("name")
+        description = args.get("description") or app_data.get("description", "")
+        icon_type = args.get("icon_type") or app_data.get("icon_type")
+        icon = args.get("icon") or app_data.get("icon")
+        icon_background = args.get("icon_background") or app_data.get("icon_background")
         use_icon_as_answer_icon = app_data.get("use_icon_as_answer_icon", False)
 
         # import dsl and create app
         app_mode = AppMode.value_of(app_data.get("mode"))
-        if app_mode in [AppMode.ADVANCED_CHAT, AppMode.WORKFLOW]:
+        if app_mode in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
             app = cls._import_and_create_new_workflow_based_app(
                 tenant_id=tenant_id,
                 app_mode=app_mode,
@@ -104,7 +103,7 @@ class AppDslService:
                 icon_background=icon_background,
                 use_icon_as_answer_icon=use_icon_as_answer_icon,
             )
-        elif app_mode in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.COMPLETION]:
+        elif app_mode in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.COMPLETION}:
             app = cls._import_and_create_new_model_config_based_app(
                 tenant_id=tenant_id,
                 app_mode=app_mode,
@@ -144,7 +143,7 @@ class AppDslService:
 
         # import dsl and overwrite app
         app_mode = AppMode.value_of(app_data.get("mode"))
-        if app_mode not in [AppMode.ADVANCED_CHAT, AppMode.WORKFLOW]:
+        if app_mode not in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
             raise ValueError("Only support import workflow in advanced-chat or workflow app.")
 
         if app_data.get("mode") != app_model.mode:
@@ -178,7 +177,7 @@ class AppDslService:
             },
         }
 
-        if app_mode in [AppMode.ADVANCED_CHAT, AppMode.WORKFLOW]:
+        if app_mode in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
             cls._append_workflow_export_data(
                 export_data=export_data, app_model=app_model, include_secret=include_secret
             )
@@ -239,7 +238,7 @@ class AppDslService:
         :param use_icon_as_answer_icon: use app icon as answer icon
         """
         if not workflow_data:
-            raise ValueError("Missing workflow in data argument " "when app mode is advanced-chat or workflow")
+            raise ValueError("Missing workflow in data argument when app mode is advanced-chat or workflow")
 
         app = cls._create_app(
             tenant_id=tenant_id,
@@ -284,7 +283,7 @@ class AppDslService:
         :param account: Account instance
         """
         if not workflow_data:
-            raise ValueError("Missing workflow in data argument " "when app mode is advanced-chat or workflow")
+            raise ValueError("Missing workflow in data argument when app mode is advanced-chat or workflow")
 
         # fetch draft workflow by app_model
         workflow_service = WorkflowService()
@@ -338,7 +337,7 @@ class AppDslService:
         :param icon_background: app icon background
         """
         if not model_config_data:
-            raise ValueError("Missing model_config in data argument " "when app mode is chat, agent-chat or completion")
+            raise ValueError("Missing model_config in data argument when app mode is chat, agent-chat or completion")
 
         app = cls._create_app(
             tenant_id=tenant_id,
diff --git a/api/services/app_generate_service.py b/api/services/app_generate_service.py
index 747505977f..26517a05fb 100644
--- a/api/services/app_generate_service.py
+++ b/api/services/app_generate_service.py
@@ -12,6 +12,7 @@ from core.app.apps.workflow.app_generator import WorkflowAppGenerator
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.features.rate_limiting import RateLimit
 from models.model import Account, App, AppMode, EndUser
+from models.workflow import Workflow
 from services.errors.llm import InvokeRateLimitError
 from services.workflow_service import WorkflowService
 
@@ -103,9 +104,7 @@ class AppGenerateService:
         return max_active_requests
 
     @classmethod
-    def generate_single_iteration(
-        cls, app_model: App, user: Union[Account, EndUser], node_id: str, args: Any, streaming: bool = True
-    ):
+    def generate_single_iteration(cls, app_model: App, user: Account, node_id: str, args: Any, streaming: bool = True):
         if app_model.mode == AppMode.ADVANCED_CHAT.value:
             workflow = cls._get_workflow(app_model, InvokeFrom.DEBUGGER)
             return AdvancedChatAppGenerator().single_iteration_generate(
@@ -142,7 +141,7 @@ class AppGenerateService:
         )
 
     @classmethod
-    def _get_workflow(cls, app_model: App, invoke_from: InvokeFrom) -> Any:
+    def _get_workflow(cls, app_model: App, invoke_from: InvokeFrom) -> Workflow:
         """
         Get workflow
         :param app_model: app model
diff --git a/api/services/app_service.py b/api/services/app_service.py
index 1dacfea246..ac45d623e8 100644
--- a/api/services/app_service.py
+++ b/api/services/app_service.py
@@ -316,7 +316,7 @@ class AppService:
 
         meta = {"tool_icons": {}}
 
-        if app_mode in [AppMode.ADVANCED_CHAT, AppMode.WORKFLOW]:
+        if app_mode in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
             workflow = app_model.workflow
             if workflow is None:
                 return meta
diff --git a/api/services/audio_service.py b/api/services/audio_service.py
index 05cd1c96a1..7a0cd5725b 100644
--- a/api/services/audio_service.py
+++ b/api/services/audio_service.py
@@ -25,7 +25,7 @@ logger = logging.getLogger(__name__)
 class AudioService:
     @classmethod
     def transcript_asr(cls, app_model: App, file: FileStorage, end_user: Optional[str] = None):
-        if app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]:
+        if app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}:
             workflow = app_model.workflow
             if workflow is None:
                 raise ValueError("Speech to text is not enabled")
@@ -83,7 +83,7 @@ class AudioService:
 
         def invoke_tts(text_content: str, app_model, voice: Optional[str] = None):
             with app.app_context():
-                if app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]:
+                if app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}:
                     workflow = app_model.workflow
                     if workflow is None:
                         raise ValueError("TTS is not enabled")
diff --git a/api/services/auth/firecrawl.py b/api/services/auth/firecrawl.py
index 30e4ee57c0..afc491398f 100644
--- a/api/services/auth/firecrawl.py
+++ b/api/services/auth/firecrawl.py
@@ -37,7 +37,7 @@ class FirecrawlAuth(ApiKeyAuthBase):
         return requests.post(url, headers=headers, json=data)
 
     def _handle_error(self, response):
-        if response.status_code in [402, 409, 500]:
+        if response.status_code in {402, 409, 500}:
             error_message = response.json().get("error", "Unknown error occurred")
             raise Exception(f"Failed to authorize. Status code: {response.status_code}. Error: {error_message}")
         else:
diff --git a/api/services/dataset_service.py b/api/services/dataset_service.py
index e02efe03a2..1c0f4de897 100644
--- a/api/services/dataset_service.py
+++ b/api/services/dataset_service.py
@@ -15,7 +15,7 @@ from core.model_manager import ModelManager
 from core.model_runtime.entities.model_entities import ModelType
 from core.rag.datasource.keyword.keyword_factory import Keyword
 from core.rag.models.document import Document as RAGDocument
-from core.rag.retrieval.retrival_methods import RetrievalMethod
+from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from events.dataset_event import dataset_was_deleted
 from events.document_event import document_was_deleted
 from extensions.ext_database import db
@@ -164,7 +164,7 @@ class DatasetService:
         dataset.tenant_id = tenant_id
         dataset.embedding_model_provider = embedding_model.provider if embedding_model else None
         dataset.embedding_model = embedding_model.model if embedding_model else None
-        dataset.permission = permission if permission else DatasetPermissionEnum.ONLY_ME
+        dataset.permission = permission or DatasetPermissionEnum.ONLY_ME
         dataset.provider = provider
         db.session.add(dataset)
         db.session.flush()
@@ -206,7 +206,7 @@ class DatasetService:
                     "in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
-                raise ValueError(f"The dataset in unavailable, due to: " f"{ex.description}")
+                raise ValueError(f"The dataset in unavailable, due to: {ex.description}")
 
     @staticmethod
     def check_embedding_model_setting(tenant_id: str, embedding_model_provider: str, embedding_model: str):
@@ -220,10 +220,10 @@ class DatasetService:
             )
         except LLMBadRequestError:
             raise ValueError(
-                "No Embedding Model available. Please configure a valid provider " "in the Settings -> Model Provider."
+                "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
             )
         except ProviderTokenNotInitError as ex:
-            raise ValueError(f"The dataset in unavailable, due to: " f"{ex.description}")
+            raise ValueError(f"The dataset in unavailable, due to: {ex.description}")
 
     @staticmethod
     def update_dataset(dataset_id, data, user):
@@ -569,7 +569,7 @@ class DocumentService:
 
     @staticmethod
     def pause_document(document):
-        if document.indexing_status not in ["waiting", "parsing", "cleaning", "splitting", "indexing"]:
+        if document.indexing_status not in {"waiting", "parsing", "cleaning", "splitting", "indexing"}:
             raise DocumentIndexingError()
         # update document to be paused
         document.is_paused = True
@@ -706,11 +706,7 @@ class DocumentService:
                         "score_threshold_enabled": False,
                     }
 
-                    dataset.retrieval_model = (
-                        document_data.get("retrieval_model")
-                        if document_data.get("retrieval_model")
-                        else default_retrieval_model
-                    )
+                    dataset.retrieval_model = document_data.get("retrieval_model") or default_retrieval_model
 
         documents = []
         batch = time.strftime("%Y%m%d%H%M%S") + str(random.randint(100000, 999999))
@@ -1082,12 +1078,8 @@ class DocumentService:
         dataset_collection_binding_id = None
         retrieval_model = None
         if document_data["indexing_technique"] == "high_quality":
-            model_manager = ModelManager()
-            embedding_model = model_manager.get_default_model_instance(
-                tenant_id=current_user.current_tenant_id, model_type=ModelType.TEXT_EMBEDDING
-            )
             dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
-                embedding_model.provider, embedding_model.model
+                document_data["embedding_model_provider"], document_data["embedding_model"]
             )
             dataset_collection_binding_id = dataset_collection_binding.id
             if document_data.get("retrieval_model"):
diff --git a/api/services/errors/account.py b/api/services/errors/account.py
index cae31c5066..82dd9f944a 100644
--- a/api/services/errors/account.py
+++ b/api/services/errors/account.py
@@ -1,7 +1,7 @@
 from services.errors.base import BaseServiceError
 
 
-class AccountNotFound(BaseServiceError):
+class AccountNotFoundError(BaseServiceError):
     pass
 
 
@@ -25,7 +25,7 @@ class LinkAccountIntegrateError(BaseServiceError):
     pass
 
 
-class TenantNotFound(BaseServiceError):
+class TenantNotFoundError(BaseServiceError):
     pass
 
 
diff --git a/api/services/file_service.py b/api/services/file_service.py
index 5780abb2be..bedec76334 100644
--- a/api/services/file_service.py
+++ b/api/services/file_service.py
@@ -56,9 +56,7 @@ class FileService:
             if etl_type == "Unstructured"
             else ALLOWED_EXTENSIONS + IMAGE_EXTENSIONS
         )
-        if extension.lower() not in allowed_extensions:
-            raise UnsupportedFileTypeError()
-        elif only_image and extension.lower() not in IMAGE_EXTENSIONS:
+        if extension.lower() not in allowed_extensions or only_image and extension.lower() not in IMAGE_EXTENSIONS:
             raise UnsupportedFileTypeError()
 
         # read file content
diff --git a/api/services/hit_testing_service.py b/api/services/hit_testing_service.py
index 350f693d64..1f85d92e8c 100644
--- a/api/services/hit_testing_service.py
+++ b/api/services/hit_testing_service.py
@@ -3,7 +3,7 @@ import time
 
 from core.rag.datasource.retrieval_service import RetrievalService
 from core.rag.models.document import Document
-from core.rag.retrieval.retrival_methods import RetrievalMethod
+from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from extensions.ext_database import db
 from models.account import Account
 from models.dataset import Dataset, DatasetQuery, DocumentSegment
@@ -41,22 +41,20 @@ class HitTestingService:
 
         # get retrieval model , if the model is not setting , using default
         if not retrieval_model:
-            retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
+            retrieval_model = dataset.retrieval_model or default_retrieval_model
 
         all_documents = RetrievalService.retrieve(
-            retrival_method=retrieval_model.get("search_method", "semantic_search"),
+            retrieval_method=retrieval_model.get("search_method", "semantic_search"),
             dataset_id=dataset.id,
             query=cls.escape_query_for_search(query),
             top_k=retrieval_model.get("top_k", 2),
             score_threshold=retrieval_model.get("score_threshold", 0.0)
             if retrieval_model["score_threshold_enabled"]
-            else None,
+            else 0.0,
             reranking_model=retrieval_model.get("reranking_model", None)
             if retrieval_model["reranking_enable"]
             else None,
-            reranking_mode=retrieval_model.get("reranking_mode")
-            if retrieval_model.get("reranking_mode")
-            else "reranking_model",
+            reranking_mode=retrieval_model.get("reranking_mode") or "reranking_model",
             weights=retrieval_model.get("weights", None),
             provider=dataset.provider,
             external_retrieval_model=external_retrieval_model,
diff --git a/api/services/model_provider_service.py b/api/services/model_provider_service.py
index c0f3c40762..384a072b37 100644
--- a/api/services/model_provider_service.py
+++ b/api/services/model_provider_service.py
@@ -1,6 +1,7 @@
 import logging
 import mimetypes
 import os
+from pathlib import Path
 from typing import Optional, cast
 
 import requests
@@ -453,9 +454,8 @@ class ModelProviderService:
         mimetype = mimetype or "application/octet-stream"
 
         # read binary from file
-        with open(file_path, "rb") as f:
-            byte_data = f.read()
-            return byte_data, mimetype
+        byte_data = Path(file_path).read_bytes()
+        return byte_data, mimetype
 
     def switch_preferred_provider(self, tenant_id: str, provider: str, preferred_provider_type: str) -> None:
         """
diff --git a/api/services/ops_service.py b/api/services/ops_service.py
index 35aa6817e1..1160a1f275 100644
--- a/api/services/ops_service.py
+++ b/api/services/ops_service.py
@@ -31,8 +31,28 @@ class OpsService:
         if tracing_provider == "langfuse" and (
             "project_key" not in decrypt_tracing_config or not decrypt_tracing_config.get("project_key")
         ):
-            project_key = OpsTraceManager.get_trace_config_project_key(decrypt_tracing_config, tracing_provider)
-            new_decrypt_tracing_config.update({"project_key": project_key})
+            try:
+                project_key = OpsTraceManager.get_trace_config_project_key(decrypt_tracing_config, tracing_provider)
+                new_decrypt_tracing_config.update(
+                    {
+                        "project_url": "{host}/project/{key}".format(
+                            host=decrypt_tracing_config.get("host"), key=project_key
+                        )
+                    }
+                )
+            except Exception:
+                new_decrypt_tracing_config.update(
+                    {"project_url": "{host}/".format(host=decrypt_tracing_config.get("host"))}
+                )
+
+        if tracing_provider == "langsmith" and (
+            "project_url" not in decrypt_tracing_config or not decrypt_tracing_config.get("project_url")
+        ):
+            try:
+                project_url = OpsTraceManager.get_trace_config_project_url(decrypt_tracing_config, tracing_provider)
+                new_decrypt_tracing_config.update({"project_url": project_url})
+            except Exception:
+                new_decrypt_tracing_config.update({"project_url": "https://smith.langchain.com/"})
 
         trace_config_data.tracing_config = new_decrypt_tracing_config
         return trace_config_data.to_dict()
@@ -46,7 +66,7 @@ class OpsService:
         :param tracing_config: tracing config
         :return:
         """
-        if tracing_provider not in provider_config_map.keys() and tracing_provider:
+        if tracing_provider not in provider_config_map and tracing_provider:
             return {"error": f"Invalid tracing provider: {tracing_provider}"}
 
         config_class, other_keys = (
@@ -62,8 +82,14 @@ class OpsService:
         if not OpsTraceManager.check_trace_config_is_effective(tracing_config, tracing_provider):
             return {"error": "Invalid Credentials"}
 
-        # get project key
-        project_key = OpsTraceManager.get_trace_config_project_key(tracing_config, tracing_provider)
+        # get project url
+        if tracing_provider == "langfuse":
+            project_key = OpsTraceManager.get_trace_config_project_key(tracing_config, tracing_provider)
+            project_url = "{host}/project/{key}".format(host=tracing_config.get("host"), key=project_key)
+        elif tracing_provider == "langsmith":
+            project_url = OpsTraceManager.get_trace_config_project_url(tracing_config, tracing_provider)
+        else:
+            project_url = None
 
         # check if trace config already exists
         trace_config_data: TraceAppConfig = (
@@ -78,8 +104,8 @@ class OpsService:
         # get tenant id
         tenant_id = db.session.query(App).filter(App.id == app_id).first().tenant_id
         tracing_config = OpsTraceManager.encrypt_tracing_config(tenant_id, tracing_provider, tracing_config)
-        if tracing_provider == "langfuse" and project_key:
-            tracing_config["project_key"] = project_key
+        if project_url:
+            tracing_config["project_url"] = project_url
         trace_config_data = TraceAppConfig(
             app_id=app_id,
             tracing_provider=tracing_provider,
@@ -99,7 +125,7 @@ class OpsService:
         :param tracing_config: tracing config
         :return:
         """
-        if tracing_provider not in provider_config_map.keys():
+        if tracing_provider not in provider_config_map:
             raise ValueError(f"Invalid tracing provider: {tracing_provider}")
 
         # check if trace config already exists
diff --git a/api/services/recommended_app_service.py b/api/services/recommended_app_service.py
index 10abf0a764..daec8393d0 100644
--- a/api/services/recommended_app_service.py
+++ b/api/services/recommended_app_service.py
@@ -1,6 +1,7 @@
 import json
 import logging
 from os import path
+from pathlib import Path
 from typing import Optional
 
 import requests
@@ -218,10 +219,9 @@ class RecommendedAppService:
             return cls.builtin_data
 
         root_path = current_app.root_path
-        with open(path.join(root_path, "constants", "recommended_apps.json"), encoding="utf-8") as f:
-            json_data = f.read()
-            data = json.loads(json_data)
-            cls.builtin_data = data
+        cls.builtin_data = json.loads(
+            Path(path.join(root_path, "constants", "recommended_apps.json")).read_text(encoding="utf-8")
+        )
 
         return cls.builtin_data
 
diff --git a/api/services/tag_service.py b/api/services/tag_service.py
index 0c17485a9f..5e2851cd8f 100644
--- a/api/services/tag_service.py
+++ b/api/services/tag_service.py
@@ -57,7 +57,7 @@ class TagService:
             .all()
         )
 
-        return tags if tags else []
+        return tags or []
 
     @staticmethod
     def save_tags(args: dict) -> Tag:
diff --git a/api/services/tools/api_tools_manage_service.py b/api/services/tools/api_tools_manage_service.py
index 3ded9c0989..6f6074f596 100644
--- a/api/services/tools/api_tools_manage_service.py
+++ b/api/services/tools/api_tools_manage_service.py
@@ -176,7 +176,8 @@ class ApiToolManageService:
         get api tool provider remote schema
         """
         headers = {
-            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36 Edg/120.0.0.0",
+            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko)"
+            " Chrome/120.0.0.0 Safari/537.36 Edg/120.0.0.0",
             "Accept": "*/*",
         }
 
diff --git a/api/services/tools/builtin_tools_manage_service.py b/api/services/tools/builtin_tools_manage_service.py
index dc8cebb587..e2e49d017e 100644
--- a/api/services/tools/builtin_tools_manage_service.py
+++ b/api/services/tools/builtin_tools_manage_service.py
@@ -1,5 +1,6 @@
 import json
 import logging
+from pathlib import Path
 
 from configs import dify_config
 from core.helper.position_helper import is_filtered
@@ -183,8 +184,7 @@ class BuiltinToolManageService:
         get tool provider icon and it's mimetype
         """
         icon_path, mime_type = ToolManager.get_builtin_provider_icon(provider)
-        with open(icon_path, "rb") as f:
-            icon_bytes = f.read()
+        icon_bytes = Path(icon_path).read_bytes()
 
         return icon_bytes, mime_type
 
diff --git a/api/services/tools/tools_transform_service.py b/api/services/tools/tools_transform_service.py
index 6fb0f2f517..7ae1b9f231 100644
--- a/api/services/tools/tools_transform_service.py
+++ b/api/services/tools/tools_transform_service.py
@@ -33,7 +33,7 @@ class ToolTransformService:
 
         if provider_type == ToolProviderType.BUILT_IN.value:
             return url_prefix + "builtin/" + provider_name + "/icon"
-        elif provider_type in [ToolProviderType.API.value, ToolProviderType.WORKFLOW.value]:
+        elif provider_type in {ToolProviderType.API.value, ToolProviderType.WORKFLOW.value}:
             try:
                 return json.loads(icon)
             except:
diff --git a/api/services/website_service.py b/api/services/website_service.py
index 6dff35d63f..fea605cf30 100644
--- a/api/services/website_service.py
+++ b/api/services/website_service.py
@@ -50,8 +50,8 @@ class WebsiteService:
                 excludes = options.get("excludes").split(",") if options.get("excludes") else []
                 params = {
                     "crawlerOptions": {
-                        "includes": includes if includes else [],
-                        "excludes": excludes if excludes else [],
+                        "includes": includes or [],
+                        "excludes": excludes or [],
                         "generateImgAltText": True,
                         "limit": options.get("limit", 1),
                         "returnOnlyUrls": False,
diff --git a/api/services/workflow/workflow_converter.py b/api/services/workflow/workflow_converter.py
index 4b845be2f4..db1a036e68 100644
--- a/api/services/workflow/workflow_converter.py
+++ b/api/services/workflow/workflow_converter.py
@@ -63,11 +63,11 @@ class WorkflowConverter:
         # create new app
         new_app = App()
         new_app.tenant_id = app_model.tenant_id
-        new_app.name = name if name else app_model.name + "(workflow)"
+        new_app.name = name or app_model.name + "(workflow)"
         new_app.mode = AppMode.ADVANCED_CHAT.value if app_model.mode == AppMode.CHAT.value else AppMode.WORKFLOW.value
-        new_app.icon_type = icon_type if icon_type else app_model.icon_type
-        new_app.icon = icon if icon else app_model.icon
-        new_app.icon_background = icon_background if icon_background else app_model.icon_background
+        new_app.icon_type = icon_type or app_model.icon_type
+        new_app.icon = icon or app_model.icon
+        new_app.icon_background = icon_background or app_model.icon_background
         new_app.enable_site = app_model.enable_site
         new_app.enable_api = app_model.enable_api
         new_app.api_rpm = app_model.api_rpm
diff --git a/api/services/workflow_service.py b/api/services/workflow_service.py
index 4c3ded14ad..0ff81f1f7e 100644
--- a/api/services/workflow_service.py
+++ b/api/services/workflow_service.py
@@ -8,9 +8,11 @@ from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfig
 from core.app.apps.workflow.app_config_manager import WorkflowAppConfigManager
 from core.app.segments import Variable
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.workflow.entities.node_entities import NodeType
+from core.workflow.entities.node_entities import NodeRunResult, NodeType
 from core.workflow.errors import WorkflowNodeRunFailedError
-from core.workflow.workflow_engine_manager import WorkflowEngineManager
+from core.workflow.nodes.event import RunCompletedEvent
+from core.workflow.nodes.node_mapping import node_classes
+from core.workflow.workflow_entry import WorkflowEntry
 from events.app_event import app_draft_workflow_was_synced, app_published_workflow_was_updated
 from extensions.ext_database import db
 from models.account import Account
@@ -172,8 +174,13 @@ class WorkflowService:
         Get default block configs
         """
         # return default block config
-        workflow_engine_manager = WorkflowEngineManager()
-        return workflow_engine_manager.get_default_configs()
+        default_block_configs = []
+        for node_type, node_class in node_classes.items():
+            default_config = node_class.get_default_config()
+            if default_config:
+                default_block_configs.append(default_config)
+
+        return default_block_configs
 
     def get_default_block_config(self, node_type: str, filters: Optional[dict] = None) -> Optional[dict]:
         """
@@ -182,11 +189,18 @@ class WorkflowService:
         :param filters: filter by node config parameters.
         :return:
         """
-        node_type = NodeType.value_of(node_type)
+        node_type_enum: NodeType = NodeType.value_of(node_type)
 
         # return default block config
-        workflow_engine_manager = WorkflowEngineManager()
-        return workflow_engine_manager.get_default_config(node_type, filters)
+        node_class = node_classes.get(node_type_enum)
+        if not node_class:
+            return None
+
+        default_config = node_class.get_default_config(filters=filters)
+        if not default_config:
+            return None
+
+        return default_config
 
     def run_draft_workflow_node(
         self, app_model: App, node_id: str, user_inputs: dict, account: Account
@@ -200,82 +214,68 @@ class WorkflowService:
             raise ValueError("Workflow not initialized")
 
         # run draft workflow node
-        workflow_engine_manager = WorkflowEngineManager()
         start_at = time.perf_counter()
 
         try:
-            node_instance, node_run_result = workflow_engine_manager.single_step_run_workflow_node(
+            node_instance, generator = WorkflowEntry.single_step_run(
                 workflow=draft_workflow,
                 node_id=node_id,
                 user_inputs=user_inputs,
                 user_id=account.id,
             )
+
+            node_run_result: NodeRunResult | None = None
+            for event in generator:
+                if isinstance(event, RunCompletedEvent):
+                    node_run_result = event.run_result
+
+                    # sign output files
+                    node_run_result.outputs = WorkflowEntry.handle_special_values(node_run_result.outputs)
+                    break
+
+            if not node_run_result:
+                raise ValueError("Node run failed with no run result")
+
+            run_succeeded = True if node_run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED else False
+            error = node_run_result.error if not run_succeeded else None
         except WorkflowNodeRunFailedError as e:
-            workflow_node_execution = WorkflowNodeExecution(
-                tenant_id=app_model.tenant_id,
-                app_id=app_model.id,
-                workflow_id=draft_workflow.id,
-                triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value,
-                index=1,
-                node_id=e.node_id,
-                node_type=e.node_type.value,
-                title=e.node_title,
-                status=WorkflowNodeExecutionStatus.FAILED.value,
-                error=e.error,
-                elapsed_time=time.perf_counter() - start_at,
-                created_by_role=CreatedByRole.ACCOUNT.value,
-                created_by=account.id,
-                created_at=datetime.now(timezone.utc).replace(tzinfo=None),
-                finished_at=datetime.now(timezone.utc).replace(tzinfo=None),
-            )
-            db.session.add(workflow_node_execution)
-            db.session.commit()
+            node_instance = e.node_instance
+            run_succeeded = False
+            node_run_result = None
+            error = e.error
 
-            return workflow_node_execution
+        workflow_node_execution = WorkflowNodeExecution()
+        workflow_node_execution.tenant_id = app_model.tenant_id
+        workflow_node_execution.app_id = app_model.id
+        workflow_node_execution.workflow_id = draft_workflow.id
+        workflow_node_execution.triggered_from = WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value
+        workflow_node_execution.index = 1
+        workflow_node_execution.node_id = node_id
+        workflow_node_execution.node_type = node_instance.node_type.value
+        workflow_node_execution.title = node_instance.node_data.title
+        workflow_node_execution.elapsed_time = time.perf_counter() - start_at
+        workflow_node_execution.created_by_role = CreatedByRole.ACCOUNT.value
+        workflow_node_execution.created_by = account.id
+        workflow_node_execution.created_at = datetime.now(timezone.utc).replace(tzinfo=None)
+        workflow_node_execution.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
 
-        if node_run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED:
+        if run_succeeded and node_run_result:
             # create workflow node execution
-            workflow_node_execution = WorkflowNodeExecution(
-                tenant_id=app_model.tenant_id,
-                app_id=app_model.id,
-                workflow_id=draft_workflow.id,
-                triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value,
-                index=1,
-                node_id=node_id,
-                node_type=node_instance.node_type.value,
-                title=node_instance.node_data.title,
-                inputs=json.dumps(node_run_result.inputs) if node_run_result.inputs else None,
-                process_data=json.dumps(node_run_result.process_data) if node_run_result.process_data else None,
-                outputs=json.dumps(jsonable_encoder(node_run_result.outputs)) if node_run_result.outputs else None,
-                execution_metadata=(
-                    json.dumps(jsonable_encoder(node_run_result.metadata)) if node_run_result.metadata else None
-                ),
-                status=WorkflowNodeExecutionStatus.SUCCEEDED.value,
-                elapsed_time=time.perf_counter() - start_at,
-                created_by_role=CreatedByRole.ACCOUNT.value,
-                created_by=account.id,
-                created_at=datetime.now(timezone.utc).replace(tzinfo=None),
-                finished_at=datetime.now(timezone.utc).replace(tzinfo=None),
+            workflow_node_execution.inputs = json.dumps(node_run_result.inputs) if node_run_result.inputs else None
+            workflow_node_execution.process_data = (
+                json.dumps(node_run_result.process_data) if node_run_result.process_data else None
             )
+            workflow_node_execution.outputs = (
+                json.dumps(jsonable_encoder(node_run_result.outputs)) if node_run_result.outputs else None
+            )
+            workflow_node_execution.execution_metadata = (
+                json.dumps(jsonable_encoder(node_run_result.metadata)) if node_run_result.metadata else None
+            )
+            workflow_node_execution.status = WorkflowNodeExecutionStatus.SUCCEEDED.value
         else:
             # create workflow node execution
-            workflow_node_execution = WorkflowNodeExecution(
-                tenant_id=app_model.tenant_id,
-                app_id=app_model.id,
-                workflow_id=draft_workflow.id,
-                triggered_from=WorkflowNodeExecutionTriggeredFrom.SINGLE_STEP.value,
-                index=1,
-                node_id=node_id,
-                node_type=node_instance.node_type.value,
-                title=node_instance.node_data.title,
-                status=node_run_result.status.value,
-                error=node_run_result.error,
-                elapsed_time=time.perf_counter() - start_at,
-                created_by_role=CreatedByRole.ACCOUNT.value,
-                created_by=account.id,
-                created_at=datetime.now(timezone.utc).replace(tzinfo=None),
-                finished_at=datetime.now(timezone.utc).replace(tzinfo=None),
-            )
+            workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
+            workflow_node_execution.error = error
 
         db.session.add(workflow_node_execution)
         db.session.commit()
@@ -295,7 +295,7 @@ class WorkflowService:
         # chatbot convert to workflow mode
         workflow_converter = WorkflowConverter()
 
-        if app_model.mode not in [AppMode.CHAT.value, AppMode.COMPLETION.value]:
+        if app_model.mode not in {AppMode.CHAT.value, AppMode.COMPLETION.value}:
             raise ValueError(f"Current App mode: {app_model.mode} is not supported convert to workflow.")
 
         # convert to workflow
@@ -321,25 +321,3 @@ class WorkflowService:
             )
         else:
             raise ValueError(f"Invalid app mode: {app_model.mode}")
-
-    @classmethod
-    def get_elapsed_time(cls, workflow_run_id: str) -> float:
-        """
-        Get elapsed time
-        """
-        elapsed_time = 0.0
-
-        # fetch workflow node execution by workflow_run_id
-        workflow_nodes = (
-            db.session.query(WorkflowNodeExecution)
-            .filter(WorkflowNodeExecution.workflow_run_id == workflow_run_id)
-            .order_by(WorkflowNodeExecution.created_at.asc())
-            .all()
-        )
-        if not workflow_nodes:
-            return elapsed_time
-
-        for node in workflow_nodes:
-            elapsed_time += node.elapsed_time
-
-        return elapsed_time
diff --git a/api/tasks/document_indexing_sync_task.py b/api/tasks/document_indexing_sync_task.py
index 9ea4c99649..6dd755ab03 100644
--- a/api/tasks/document_indexing_sync_task.py
+++ b/api/tasks/document_indexing_sync_task.py
@@ -6,7 +6,7 @@ import click
 from celery import shared_task
 from werkzeug.exceptions import NotFound
 
-from core.indexing_runner import DocumentIsPausedException, IndexingRunner
+from core.indexing_runner import DocumentIsPausedError, IndexingRunner
 from core.rag.extractor.notion_extractor import NotionExtractor
 from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
 from extensions.ext_database import db
@@ -106,7 +106,7 @@ def document_indexing_sync_task(dataset_id: str, document_id: str):
                 logging.info(
                     click.style("update document: {} latency: {}".format(document.id, end_at - start_at), fg="green")
                 )
-            except DocumentIsPausedException as ex:
+            except DocumentIsPausedError as ex:
                 logging.info(click.style(str(ex), fg="yellow"))
             except Exception:
                 pass
diff --git a/api/tasks/document_indexing_task.py b/api/tasks/document_indexing_task.py
index e0da5f9ed0..72c4674e0f 100644
--- a/api/tasks/document_indexing_task.py
+++ b/api/tasks/document_indexing_task.py
@@ -6,7 +6,7 @@ import click
 from celery import shared_task
 
 from configs import dify_config
-from core.indexing_runner import DocumentIsPausedException, IndexingRunner
+from core.indexing_runner import DocumentIsPausedError, IndexingRunner
 from extensions.ext_database import db
 from models.dataset import Dataset, Document
 from services.feature_service import FeatureService
@@ -72,7 +72,7 @@ def document_indexing_task(dataset_id: str, document_ids: list):
         indexing_runner.run(documents)
         end_at = time.perf_counter()
         logging.info(click.style("Processed dataset: {} latency: {}".format(dataset_id, end_at - start_at), fg="green"))
-    except DocumentIsPausedException as ex:
+    except DocumentIsPausedError as ex:
         logging.info(click.style(str(ex), fg="yellow"))
     except Exception:
         pass
diff --git a/api/tasks/document_indexing_update_task.py b/api/tasks/document_indexing_update_task.py
index 6e681bcf4f..cb38bc668d 100644
--- a/api/tasks/document_indexing_update_task.py
+++ b/api/tasks/document_indexing_update_task.py
@@ -6,7 +6,7 @@ import click
 from celery import shared_task
 from werkzeug.exceptions import NotFound
 
-from core.indexing_runner import DocumentIsPausedException, IndexingRunner
+from core.indexing_runner import DocumentIsPausedError, IndexingRunner
 from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
 from extensions.ext_database import db
 from models.dataset import Dataset, Document, DocumentSegment
@@ -69,7 +69,7 @@ def document_indexing_update_task(dataset_id: str, document_id: str):
         indexing_runner.run([document])
         end_at = time.perf_counter()
         logging.info(click.style("update document: {} latency: {}".format(document.id, end_at - start_at), fg="green"))
-    except DocumentIsPausedException as ex:
+    except DocumentIsPausedError as ex:
         logging.info(click.style(str(ex), fg="yellow"))
     except Exception:
         pass
diff --git a/api/tasks/duplicate_document_indexing_task.py b/api/tasks/duplicate_document_indexing_task.py
index 0a7568c385..f4c3dbd2e2 100644
--- a/api/tasks/duplicate_document_indexing_task.py
+++ b/api/tasks/duplicate_document_indexing_task.py
@@ -6,7 +6,7 @@ import click
 from celery import shared_task
 
 from configs import dify_config
-from core.indexing_runner import DocumentIsPausedException, IndexingRunner
+from core.indexing_runner import DocumentIsPausedError, IndexingRunner
 from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
 from extensions.ext_database import db
 from models.dataset import Dataset, Document, DocumentSegment
@@ -88,7 +88,7 @@ def duplicate_document_indexing_task(dataset_id: str, document_ids: list):
         indexing_runner.run(documents)
         end_at = time.perf_counter()
         logging.info(click.style("Processed dataset: {} latency: {}".format(dataset_id, end_at - start_at), fg="green"))
-    except DocumentIsPausedException as ex:
+    except DocumentIsPausedError as ex:
         logging.info(click.style(str(ex), fg="yellow"))
     except Exception:
         pass
diff --git a/api/tasks/recover_document_indexing_task.py b/api/tasks/recover_document_indexing_task.py
index 18bae14ffa..934eb7430c 100644
--- a/api/tasks/recover_document_indexing_task.py
+++ b/api/tasks/recover_document_indexing_task.py
@@ -5,7 +5,7 @@ import click
 from celery import shared_task
 from werkzeug.exceptions import NotFound
 
-from core.indexing_runner import DocumentIsPausedException, IndexingRunner
+from core.indexing_runner import DocumentIsPausedError, IndexingRunner
 from extensions.ext_database import db
 from models.dataset import Document
 
@@ -29,7 +29,7 @@ def recover_document_indexing_task(dataset_id: str, document_id: str):
 
     try:
         indexing_runner = IndexingRunner()
-        if document.indexing_status in ["waiting", "parsing", "cleaning"]:
+        if document.indexing_status in {"waiting", "parsing", "cleaning"}:
             indexing_runner.run([document])
         elif document.indexing_status == "splitting":
             indexing_runner.run_in_splitting_status(document)
@@ -39,7 +39,7 @@ def recover_document_indexing_task(dataset_id: str, document_id: str):
         logging.info(
             click.style("Processed document: {} latency: {}".format(document.id, end_at - start_at), fg="green")
         )
-    except DocumentIsPausedException as ex:
+    except DocumentIsPausedError as ex:
         logging.info(click.style(str(ex), fg="yellow"))
     except Exception:
         pass
diff --git a/api/tasks/sync_website_document_indexing_task.py b/api/tasks/sync_website_document_indexing_task.py
index 99fb66e1f3..1d2a338c83 100644
--- a/api/tasks/sync_website_document_indexing_task.py
+++ b/api/tasks/sync_website_document_indexing_task.py
@@ -20,7 +20,7 @@ def sync_website_document_indexing_task(dataset_id: str, document_id: str):
     :param dataset_id:
     :param document_id:
 
-    Usage: sunc_website_document_indexing_task.delay(dataset_id, document_id)
+    Usage: sync_website_document_indexing_task.delay(dataset_id, document_id)
     """
     start_at = time.perf_counter()
 
diff --git a/api/tests/integration_tests/model_runtime/__mock/fishaudio.py b/api/tests/integration_tests/model_runtime/__mock/fishaudio.py
new file mode 100644
index 0000000000..bec3babeaf
--- /dev/null
+++ b/api/tests/integration_tests/model_runtime/__mock/fishaudio.py
@@ -0,0 +1,82 @@
+import os
+from collections.abc import Callable
+from typing import Literal
+
+import httpx
+import pytest
+from _pytest.monkeypatch import MonkeyPatch
+
+
+def mock_get(*args, **kwargs):
+    if kwargs.get("headers", {}).get("Authorization") != "Bearer test":
+        raise httpx.HTTPStatusError(
+            "Invalid API key",
+            request=httpx.Request("GET", ""),
+            response=httpx.Response(401),
+        )
+
+    return httpx.Response(
+        200,
+        json={
+            "items": [
+                {"title": "Model 1", "_id": "model1"},
+                {"title": "Model 2", "_id": "model2"},
+            ]
+        },
+        request=httpx.Request("GET", ""),
+    )
+
+
+def mock_stream(*args, **kwargs):
+    class MockStreamResponse:
+        def __init__(self):
+            self.status_code = 200
+
+        def __enter__(self):
+            return self
+
+        def __exit__(self, exc_type, exc_val, exc_tb):
+            pass
+
+        def iter_bytes(self):
+            yield b"Mocked audio data"
+
+    return MockStreamResponse()
+
+
+def mock_fishaudio(
+    monkeypatch: MonkeyPatch,
+    methods: list[Literal["list-models", "tts"]],
+) -> Callable[[], None]:
+    """
+    mock fishaudio module
+
+    :param monkeypatch: pytest monkeypatch fixture
+    :return: unpatch function
+    """
+
+    def unpatch() -> None:
+        monkeypatch.undo()
+
+    if "list-models" in methods:
+        monkeypatch.setattr(httpx, "get", mock_get)
+
+    if "tts" in methods:
+        monkeypatch.setattr(httpx, "stream", mock_stream)
+
+    return unpatch
+
+
+MOCK = os.getenv("MOCK_SWITCH", "false").lower() == "true"
+
+
+@pytest.fixture
+def setup_fishaudio_mock(request, monkeypatch):
+    methods = request.param if hasattr(request, "param") else []
+    if MOCK:
+        unpatch = mock_fishaudio(monkeypatch, methods=methods)
+
+    yield
+
+    if MOCK:
+        unpatch()
diff --git a/api/tests/integration_tests/model_runtime/__mock/google.py b/api/tests/integration_tests/model_runtime/__mock/google.py
index bc0684086f..402bd9c2c2 100644
--- a/api/tests/integration_tests/model_runtime/__mock/google.py
+++ b/api/tests/integration_tests/model_runtime/__mock/google.py
@@ -1,15 +1,13 @@
 from collections.abc import Generator
 
-import google.generativeai.types.content_types as content_types
 import google.generativeai.types.generation_types as generation_config_types
-import google.generativeai.types.safety_types as safety_types
 import pytest
 from _pytest.monkeypatch import MonkeyPatch
 from google.ai import generativelanguage as glm
 from google.ai.generativelanguage_v1beta.types import content as gag_content
 from google.generativeai import GenerativeModel
 from google.generativeai.client import _ClientManager, configure
-from google.generativeai.types import GenerateContentResponse
+from google.generativeai.types import GenerateContentResponse, content_types, safety_types
 from google.generativeai.types.generation_types import BaseGenerateContentResponse
 
 current_api_key = ""
diff --git a/api/tests/integration_tests/model_runtime/__mock/huggingface_tei.py b/api/tests/integration_tests/model_runtime/__mock/huggingface_tei.py
index b37b109eba..b9a721c803 100644
--- a/api/tests/integration_tests/model_runtime/__mock/huggingface_tei.py
+++ b/api/tests/integration_tests/model_runtime/__mock/huggingface_tei.py
@@ -51,7 +51,7 @@ class MockTEIClass:
         #     }
         # }
         embeddings = []
-        for idx, text in enumerate(texts):
+        for idx in range(len(texts)):
             embedding = [0.1] * 768
             embeddings.append(
                 {
@@ -70,6 +70,7 @@ class MockTEIClass:
             },
         }
 
+    @staticmethod
     def invoke_rerank(server_url: str, query: str, texts: list[str]) -> list[dict]:
         #         Example response:
         # [
diff --git a/api/tests/integration_tests/model_runtime/__mock/openai_chat.py b/api/tests/integration_tests/model_runtime/__mock/openai_chat.py
index d9cd7b046e..439f7d56e9 100644
--- a/api/tests/integration_tests/model_runtime/__mock/openai_chat.py
+++ b/api/tests/integration_tests/model_runtime/__mock/openai_chat.py
@@ -6,7 +6,6 @@ from time import time
 # import monkeypatch
 from typing import Any, Literal, Optional, Union
 
-import openai.types.chat.completion_create_params as completion_create_params
 from openai import AzureOpenAI, OpenAI
 from openai._types import NOT_GIVEN, NotGiven
 from openai.resources.chat.completions import Completions
@@ -18,6 +17,7 @@ from openai.types.chat import (
     ChatCompletionMessageToolCall,
     ChatCompletionToolChoiceOptionParam,
     ChatCompletionToolParam,
+    completion_create_params,
 )
 from openai.types.chat.chat_completion import ChatCompletion as _ChatCompletion
 from openai.types.chat.chat_completion import Choice as _ChatCompletionChoice
@@ -254,7 +254,7 @@ class MockChatClass:
             "gpt-3.5-turbo-16k-0613",
         ]
         azure_openai_models = ["gpt35", "gpt-4v", "gpt-35-turbo"]
-        if not re.match(r"^(https?):\/\/[^\s\/$.?#].[^\s]*$", self._client.base_url.__str__()):
+        if not re.match(r"^(https?):\/\/[^\s\/$.?#].[^\s]*$", str(self._client.base_url)):
             raise InvokeAuthorizationError("Invalid base url")
         if model in openai_models + azure_openai_models:
             if not re.match(r"sk-[a-zA-Z0-9]{24,}$", self._client.api_key) and type(self._client) == OpenAI:
diff --git a/api/tests/integration_tests/model_runtime/__mock/openai_completion.py b/api/tests/integration_tests/model_runtime/__mock/openai_completion.py
index c27e89248f..14223668e0 100644
--- a/api/tests/integration_tests/model_runtime/__mock/openai_completion.py
+++ b/api/tests/integration_tests/model_runtime/__mock/openai_completion.py
@@ -112,7 +112,7 @@ class MockCompletionsClass:
         ]
         azure_openai_models = ["gpt-35-turbo-instruct"]
 
-        if not re.match(r"^(https?):\/\/[^\s\/$.?#].[^\s]*$", self._client.base_url.__str__()):
+        if not re.match(r"^(https?):\/\/[^\s\/$.?#].[^\s]*$", str(self._client.base_url)):
             raise InvokeAuthorizationError("Invalid base url")
         if model in openai_models + azure_openai_models:
             if not re.match(r"sk-[a-zA-Z0-9]{24,}$", self._client.api_key) and type(self._client) == OpenAI:
diff --git a/api/tests/integration_tests/model_runtime/__mock/openai_embeddings.py b/api/tests/integration_tests/model_runtime/__mock/openai_embeddings.py
index 4138cdd40d..e27b9891f5 100644
--- a/api/tests/integration_tests/model_runtime/__mock/openai_embeddings.py
+++ b/api/tests/integration_tests/model_runtime/__mock/openai_embeddings.py
@@ -22,7 +22,7 @@ class MockEmbeddingsClass:
         if isinstance(input, str):
             input = [input]
 
-        if not re.match(r"^(https?):\/\/[^\s\/$.?#].[^\s]*$", self._client.base_url.__str__()):
+        if not re.match(r"^(https?):\/\/[^\s\/$.?#].[^\s]*$", str(self._client.base_url)):
             raise InvokeAuthorizationError("Invalid base url")
 
         if len(self._client.api_key) < 18:
@@ -40,7 +40,7 @@ class MockEmbeddingsClass:
                 usage=Usage(prompt_tokens=2, total_tokens=2),
             )
 
-        embeddings = 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+        embeddings = "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"  # noqa: E501
 
         data = []
         for i, text in enumerate(input):
diff --git a/api/tests/integration_tests/model_runtime/__mock/openai_moderation.py b/api/tests/integration_tests/model_runtime/__mock/openai_moderation.py
index 270a88e85f..4262d40f3e 100644
--- a/api/tests/integration_tests/model_runtime/__mock/openai_moderation.py
+++ b/api/tests/integration_tests/model_runtime/__mock/openai_moderation.py
@@ -20,7 +20,7 @@ class MockModerationClass:
         if isinstance(input, str):
             input = [input]
 
-        if not re.match(r"^(https?):\/\/[^\s\/$.?#].[^\s]*$", self._client.base_url.__str__()):
+        if not re.match(r"^(https?):\/\/[^\s\/$.?#].[^\s]*$", str(self._client.base_url)):
             raise InvokeAuthorizationError("Invalid base url")
 
         if len(self._client.api_key) < 18:
diff --git a/api/tests/integration_tests/model_runtime/__mock/openai_speech2text.py b/api/tests/integration_tests/model_runtime/__mock/openai_speech2text.py
index ef361e8613..a51dcab4be 100644
--- a/api/tests/integration_tests/model_runtime/__mock/openai_speech2text.py
+++ b/api/tests/integration_tests/model_runtime/__mock/openai_speech2text.py
@@ -20,7 +20,7 @@ class MockSpeech2TextClass:
         temperature: float | NotGiven = NOT_GIVEN,
         **kwargs: Any,
     ) -> Transcription:
-        if not re.match(r"^(https?):\/\/[^\s\/$.?#].[^\s]*$", self._client.base_url.__str__()):
+        if not re.match(r"^(https?):\/\/[^\s\/$.?#].[^\s]*$", str(self._client.base_url)):
             raise InvokeAuthorizationError("Invalid base url")
 
         if len(self._client.api_key) < 18:
diff --git a/api/tests/integration_tests/model_runtime/__mock/xinference.py b/api/tests/integration_tests/model_runtime/__mock/xinference.py
index 777737187e..299523f4f5 100644
--- a/api/tests/integration_tests/model_runtime/__mock/xinference.py
+++ b/api/tests/integration_tests/model_runtime/__mock/xinference.py
@@ -42,7 +42,7 @@ class MockXinferenceClass:
             model_uid = url.split("/")[-1] or ""
             if not re.match(
                 r"[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{12}", model_uid
-            ) and model_uid not in ["generate", "chat", "embedding", "rerank"]:
+            ) and model_uid not in {"generate", "chat", "embedding", "rerank"}:
                 response.status_code = 404
                 response._content = b"{}"
                 return response
@@ -53,7 +53,7 @@ class MockXinferenceClass:
                 response._content = b"{}"
                 return response
 
-            if model_uid in ["generate", "chat"]:
+            if model_uid in {"generate", "chat"}:
                 response.status_code = 200
                 response._content = b"""{
                     "model_type": "LLM",
diff --git a/api/tests/integration_tests/model_runtime/baichuan/test_llm.py b/api/tests/integration_tests/model_runtime/baichuan/test_llm.py
index ad58610287..fe7fe96891 100644
--- a/api/tests/integration_tests/model_runtime/baichuan/test_llm.py
+++ b/api/tests/integration_tests/model_runtime/baichuan/test_llm.py
@@ -8,11 +8,11 @@ from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk,
 from core.model_runtime.entities.message_entities import AssistantPromptMessage, SystemPromptMessage, UserPromptMessage
 from core.model_runtime.entities.model_entities import AIModelEntity
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.baichuan.llm.llm import BaichuanLarguageModel
+from core.model_runtime.model_providers.baichuan.llm.llm import BaichuanLanguageModel
 
 
 def test_predefined_models():
-    model = BaichuanLarguageModel()
+    model = BaichuanLanguageModel()
     model_schemas = model.predefined_models()
     assert len(model_schemas) >= 1
     assert isinstance(model_schemas[0], AIModelEntity)
@@ -20,7 +20,7 @@ def test_predefined_models():
 
 def test_validate_credentials_for_chat_model():
     sleep(3)
-    model = BaichuanLarguageModel()
+    model = BaichuanLanguageModel()
 
     with pytest.raises(CredentialsValidateFailedError):
         model.validate_credentials(
@@ -38,7 +38,7 @@ def test_validate_credentials_for_chat_model():
 
 def test_invoke_model():
     sleep(3)
-    model = BaichuanLarguageModel()
+    model = BaichuanLanguageModel()
 
     response = model.invoke(
         model="baichuan2-turbo",
@@ -64,7 +64,7 @@ def test_invoke_model():
 
 def test_invoke_model_with_system_message():
     sleep(3)
-    model = BaichuanLarguageModel()
+    model = BaichuanLanguageModel()
 
     response = model.invoke(
         model="baichuan2-turbo",
@@ -93,7 +93,7 @@ def test_invoke_model_with_system_message():
 
 def test_invoke_stream_model():
     sleep(3)
-    model = BaichuanLarguageModel()
+    model = BaichuanLanguageModel()
 
     response = model.invoke(
         model="baichuan2-turbo",
@@ -122,7 +122,7 @@ def test_invoke_stream_model():
 
 def test_invoke_with_search():
     sleep(3)
-    model = BaichuanLarguageModel()
+    model = BaichuanLanguageModel()
 
     response = model.invoke(
         model="baichuan2-turbo",
@@ -156,7 +156,7 @@ def test_invoke_with_search():
 
 def test_get_num_tokens():
     sleep(3)
-    model = BaichuanLarguageModel()
+    model = BaichuanLanguageModel()
 
     response = model.get_num_tokens(
         model="baichuan2-turbo",
diff --git a/api/tests/integration_tests/model_runtime/fishaudio/__init__.py b/api/tests/integration_tests/model_runtime/fishaudio/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/tests/integration_tests/model_runtime/fishaudio/test_provider.py b/api/tests/integration_tests/model_runtime/fishaudio/test_provider.py
new file mode 100644
index 0000000000..3526574b61
--- /dev/null
+++ b/api/tests/integration_tests/model_runtime/fishaudio/test_provider.py
@@ -0,0 +1,33 @@
+import os
+
+import httpx
+import pytest
+
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.fishaudio.fishaudio import FishAudioProvider
+from tests.integration_tests.model_runtime.__mock.fishaudio import setup_fishaudio_mock
+
+
+@pytest.mark.parametrize("setup_fishaudio_mock", [["list-models"]], indirect=True)
+def test_validate_provider_credentials(setup_fishaudio_mock):
+    print("-----", httpx.get)
+    provider = FishAudioProvider()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        provider.validate_provider_credentials(
+            credentials={
+                "api_key": "bad_api_key",
+                "api_base": os.environ.get("FISH_AUDIO_API_BASE", "https://api.fish.audio"),
+                "use_public_models": "false",
+                "latency": "normal",
+            }
+        )
+
+    provider.validate_provider_credentials(
+        credentials={
+            "api_key": os.environ.get("FISH_AUDIO_API_KEY", "test"),
+            "api_base": os.environ.get("FISH_AUDIO_API_BASE", "https://api.fish.audio"),
+            "use_public_models": "false",
+            "latency": "normal",
+        }
+    )
diff --git a/api/tests/integration_tests/model_runtime/fishaudio/test_tts.py b/api/tests/integration_tests/model_runtime/fishaudio/test_tts.py
new file mode 100644
index 0000000000..f61fee28b9
--- /dev/null
+++ b/api/tests/integration_tests/model_runtime/fishaudio/test_tts.py
@@ -0,0 +1,32 @@
+import os
+
+import pytest
+
+from core.model_runtime.model_providers.fishaudio.tts.tts import (
+    FishAudioText2SpeechModel,
+)
+from tests.integration_tests.model_runtime.__mock.fishaudio import setup_fishaudio_mock
+
+
+@pytest.mark.parametrize("setup_fishaudio_mock", [["tts"]], indirect=True)
+def test_invoke_model(setup_fishaudio_mock):
+    model = FishAudioText2SpeechModel()
+
+    result = model.invoke(
+        model="tts-default",
+        tenant_id="test",
+        credentials={
+            "api_key": os.environ.get("FISH_AUDIO_API_KEY", "test"),
+            "api_base": os.environ.get("FISH_AUDIO_API_BASE", "https://api.fish.audio"),
+            "use_public_models": "false",
+            "latency": "normal",
+        },
+        content_text="Hello, world!",
+        voice="03397b4c4be74759b72533b663fbd001",
+    )
+
+    content = b""
+    for chunk in result:
+        content += chunk
+
+    assert content != b""
diff --git a/api/tests/integration_tests/model_runtime/google/test_llm.py b/api/tests/integration_tests/model_runtime/google/test_llm.py
index 4d9d490a87..34d08f270a 100644
--- a/api/tests/integration_tests/model_runtime/google/test_llm.py
+++ b/api/tests/integration_tests/model_runtime/google/test_llm.py
@@ -155,7 +155,7 @@ def test_invoke_chat_model_with_vision_multi_pics(setup_google_mock):
         user="abc-123",
     )
 
-    print(f"resultz: {result.message.content}")
+    print(f"result: {result.message.content}")
     assert isinstance(result, LLMResult)
     assert len(result.message.content) > 0
 
diff --git a/api/tests/integration_tests/model_runtime/oci/__init__.py b/api/tests/integration_tests/model_runtime/oci/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/tests/integration_tests/model_runtime/oci/test_llm.py b/api/tests/integration_tests/model_runtime/oci/test_llm.py
new file mode 100644
index 0000000000..531f26a32e
--- /dev/null
+++ b/api/tests/integration_tests/model_runtime/oci/test_llm.py
@@ -0,0 +1,130 @@
+import os
+from collections.abc import Generator
+
+import pytest
+
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessageTool,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.oci.llm.llm import OCILargeLanguageModel
+
+
+def test_validate_credentials():
+    model = OCILargeLanguageModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model="cohere.command-r-plus",
+            credentials={"oci_config_content": "invalid_key", "oci_key_content": "invalid_key"},
+        )
+
+    model.validate_credentials(
+        model="cohere.command-r-plus",
+        credentials={
+            "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+            "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+        },
+    )
+
+
+def test_invoke_model():
+    model = OCILargeLanguageModel()
+
+    response = model.invoke(
+        model="cohere.command-r-plus",
+        credentials={
+            "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+            "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+        },
+        prompt_messages=[UserPromptMessage(content="Hi")],
+        model_parameters={"temperature": 0.5, "max_tokens": 10},
+        stop=["How"],
+        stream=False,
+        user="abc-123",
+    )
+
+    assert isinstance(response, LLMResult)
+    assert len(response.message.content) > 0
+
+
+def test_invoke_stream_model():
+    model = OCILargeLanguageModel()
+
+    response = model.invoke(
+        model="meta.llama-3-70b-instruct",
+        credentials={
+            "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+            "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+        },
+        prompt_messages=[UserPromptMessage(content="Hi")],
+        model_parameters={"temperature": 0.5, "max_tokens": 100, "seed": 1234},
+        stream=True,
+        user="abc-123",
+    )
+
+    assert isinstance(response, Generator)
+
+    for chunk in response:
+        assert isinstance(chunk, LLMResultChunk)
+        assert isinstance(chunk.delta, LLMResultChunkDelta)
+        assert isinstance(chunk.delta.message, AssistantPromptMessage)
+        assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True
+
+
+def test_invoke_model_with_function():
+    model = OCILargeLanguageModel()
+
+    response = model.invoke(
+        model="cohere.command-r-plus",
+        credentials={
+            "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+            "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+        },
+        prompt_messages=[UserPromptMessage(content="Hi")],
+        model_parameters={"temperature": 0.5, "max_tokens": 100, "seed": 1234},
+        stream=False,
+        user="abc-123",
+        tools=[
+            PromptMessageTool(
+                name="get_current_weather",
+                description="Get the current weather in a given location",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
+                        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                    },
+                    "required": ["location"],
+                },
+            )
+        ],
+    )
+
+    assert isinstance(response, LLMResult)
+    assert len(response.message.content) > 0
+
+
+def test_get_num_tokens():
+    model = OCILargeLanguageModel()
+
+    num_tokens = model.get_num_tokens(
+        model="cohere.command-r-plus",
+        credentials={
+            "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+            "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+    )
+
+    assert num_tokens == 18
diff --git a/api/tests/integration_tests/model_runtime/oci/test_provider.py b/api/tests/integration_tests/model_runtime/oci/test_provider.py
new file mode 100644
index 0000000000..2c7107c7cc
--- /dev/null
+++ b/api/tests/integration_tests/model_runtime/oci/test_provider.py
@@ -0,0 +1,20 @@
+import os
+
+import pytest
+
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.oci.oci import OCIGENAIProvider
+
+
+def test_validate_provider_credentials():
+    provider = OCIGENAIProvider()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        provider.validate_provider_credentials(credentials={})
+
+    provider.validate_provider_credentials(
+        credentials={
+            "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+            "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+        }
+    )
diff --git a/api/tests/integration_tests/model_runtime/oci/test_text_embedding.py b/api/tests/integration_tests/model_runtime/oci/test_text_embedding.py
new file mode 100644
index 0000000000..032c5c681a
--- /dev/null
+++ b/api/tests/integration_tests/model_runtime/oci/test_text_embedding.py
@@ -0,0 +1,58 @@
+import os
+
+import pytest
+
+from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.oci.text_embedding.text_embedding import OCITextEmbeddingModel
+
+
+def test_validate_credentials():
+    model = OCITextEmbeddingModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model="cohere.embed-multilingual-v3.0",
+            credentials={"oci_config_content": "invalid_key", "oci_key_content": "invalid_key"},
+        )
+
+    model.validate_credentials(
+        model="cohere.embed-multilingual-v3.0",
+        credentials={
+            "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+            "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+        },
+    )
+
+
+def test_invoke_model():
+    model = OCITextEmbeddingModel()
+
+    result = model.invoke(
+        model="cohere.embed-multilingual-v3.0",
+        credentials={
+            "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+            "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+        },
+        texts=["hello", "world", " ".join(["long_text"] * 100), " ".join(["another_long_text"] * 100)],
+        user="abc-123",
+    )
+
+    assert isinstance(result, TextEmbeddingResult)
+    assert len(result.embeddings) == 4
+    # assert result.usage.total_tokens == 811
+
+
+def test_get_num_tokens():
+    model = OCITextEmbeddingModel()
+
+    num_tokens = model.get_num_tokens(
+        model="cohere.embed-multilingual-v3.0",
+        credentials={
+            "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+            "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+        },
+        texts=["hello", "world"],
+    )
+
+    assert num_tokens == 2
diff --git a/api/tests/integration_tests/model_runtime/xinference/test_llm.py b/api/tests/integration_tests/model_runtime/xinference/test_llm.py
index 48d1ae323d..fb5e03855d 100644
--- a/api/tests/integration_tests/model_runtime/xinference/test_llm.py
+++ b/api/tests/integration_tests/model_runtime/xinference/test_llm.py
@@ -20,7 +20,7 @@ from tests.integration_tests.model_runtime.__mock.openai import setup_openai_moc
 from tests.integration_tests.model_runtime.__mock.xinference import setup_xinference_mock
 
 
-@pytest.mark.parametrize("setup_openai_mock, setup_xinference_mock", [["chat", "none"]], indirect=True)
+@pytest.mark.parametrize(("setup_openai_mock", "setup_xinference_mock"), [("chat", "none")], indirect=True)
 def test_validate_credentials_for_chat_model(setup_openai_mock, setup_xinference_mock):
     model = XinferenceAILargeLanguageModel()
 
@@ -45,7 +45,7 @@ def test_validate_credentials_for_chat_model(setup_openai_mock, setup_xinference
     )
 
 
-@pytest.mark.parametrize("setup_openai_mock, setup_xinference_mock", [["chat", "none"]], indirect=True)
+@pytest.mark.parametrize(("setup_openai_mock", "setup_xinference_mock"), [("chat", "none")], indirect=True)
 def test_invoke_chat_model(setup_openai_mock, setup_xinference_mock):
     model = XinferenceAILargeLanguageModel()
 
@@ -75,7 +75,7 @@ def test_invoke_chat_model(setup_openai_mock, setup_xinference_mock):
     assert response.usage.total_tokens > 0
 
 
-@pytest.mark.parametrize("setup_openai_mock, setup_xinference_mock", [["chat", "none"]], indirect=True)
+@pytest.mark.parametrize(("setup_openai_mock", "setup_xinference_mock"), [("chat", "none")], indirect=True)
 def test_invoke_stream_chat_model(setup_openai_mock, setup_xinference_mock):
     model = XinferenceAILargeLanguageModel()
 
@@ -109,7 +109,7 @@ def test_invoke_stream_chat_model(setup_openai_mock, setup_xinference_mock):
 
 
 """
-    Funtion calling of xinference does not support stream mode currently
+    Function calling of xinference does not support stream mode currently
 """
 # def test_invoke_stream_chat_model_with_functions():
 #     model = XinferenceAILargeLanguageModel()
@@ -236,7 +236,7 @@ def test_invoke_stream_chat_model(setup_openai_mock, setup_xinference_mock):
 #     assert response.message.tool_calls[0].function.name == 'get_current_weather'
 
 
-@pytest.mark.parametrize("setup_openai_mock, setup_xinference_mock", [["completion", "none"]], indirect=True)
+@pytest.mark.parametrize(("setup_openai_mock", "setup_xinference_mock"), [("completion", "none")], indirect=True)
 def test_validate_credentials_for_generation_model(setup_openai_mock, setup_xinference_mock):
     model = XinferenceAILargeLanguageModel()
 
@@ -261,7 +261,7 @@ def test_validate_credentials_for_generation_model(setup_openai_mock, setup_xinf
     )
 
 
-@pytest.mark.parametrize("setup_openai_mock, setup_xinference_mock", [["completion", "none"]], indirect=True)
+@pytest.mark.parametrize(("setup_openai_mock", "setup_xinference_mock"), [("completion", "none")], indirect=True)
 def test_invoke_generation_model(setup_openai_mock, setup_xinference_mock):
     model = XinferenceAILargeLanguageModel()
 
@@ -286,7 +286,7 @@ def test_invoke_generation_model(setup_openai_mock, setup_xinference_mock):
     assert response.usage.total_tokens > 0
 
 
-@pytest.mark.parametrize("setup_openai_mock, setup_xinference_mock", [["completion", "none"]], indirect=True)
+@pytest.mark.parametrize(("setup_openai_mock", "setup_xinference_mock"), [("completion", "none")], indirect=True)
 def test_invoke_stream_generation_model(setup_openai_mock, setup_xinference_mock):
     model = XinferenceAILargeLanguageModel()
 
diff --git a/api/tests/integration_tests/tools/__mock/http.py b/api/tests/integration_tests/tools/__mock/http.py
index 4dfc530010..d3c1f3101c 100644
--- a/api/tests/integration_tests/tools/__mock/http.py
+++ b/api/tests/integration_tests/tools/__mock/http.py
@@ -7,6 +7,7 @@ from _pytest.monkeypatch import MonkeyPatch
 
 
 class MockedHttp:
+    @staticmethod
     def httpx_request(
         method: Literal["GET", "POST", "PUT", "DELETE", "PATCH", "HEAD"], url: str, **kwargs
     ) -> httpx.Response:
diff --git a/api/tests/integration_tests/utils/test_module_import_helper.py b/api/tests/integration_tests/utils/test_module_import_helper.py
index 7d32f5ae66..50725415e4 100644
--- a/api/tests/integration_tests/utils/test_module_import_helper.py
+++ b/api/tests/integration_tests/utils/test_module_import_helper.py
@@ -9,7 +9,8 @@ def test_loading_subclass_from_source():
     module = load_single_subclass_from_source(
         module_name="ChildClass", script_path=os.path.join(current_path, "child_class.py"), parent_type=ParentClass
     )
-    assert module and module.__name__ == "ChildClass"
+    assert module
+    assert module.__name__ == "ChildClass"
 
 
 def test_load_import_module_from_source():
@@ -17,7 +18,8 @@ def test_load_import_module_from_source():
     module = import_module_from_source(
         module_name="ChildClass", py_file_path=os.path.join(current_path, "child_class.py")
     )
-    assert module and module.__name__ == "ChildClass"
+    assert module
+    assert module.__name__ == "ChildClass"
 
 
 def test_lazy_loading_subclass_from_source():
diff --git a/api/tests/integration_tests/vdb/__mock/tcvectordb.py b/api/tests/integration_tests/vdb/__mock/tcvectordb.py
index 571c1e3d44..53c9b3cae3 100644
--- a/api/tests/integration_tests/vdb/__mock/tcvectordb.py
+++ b/api/tests/integration_tests/vdb/__mock/tcvectordb.py
@@ -13,7 +13,7 @@ from xinference_client.types import Embedding
 
 
 class MockTcvectordbClass:
-    def VectorDBClient(
+    def mock_vector_db_client(
         self,
         url=None,
         username="",
@@ -110,7 +110,7 @@ MOCK = os.getenv("MOCK_SWITCH", "false").lower() == "true"
 @pytest.fixture
 def setup_tcvectordb_mock(request, monkeypatch: MonkeyPatch):
     if MOCK:
-        monkeypatch.setattr(VectorDBClient, "__init__", MockTcvectordbClass.VectorDBClient)
+        monkeypatch.setattr(VectorDBClient, "__init__", MockTcvectordbClass.mock_vector_db_client)
         monkeypatch.setattr(VectorDBClient, "list_databases", MockTcvectordbClass.list_databases)
         monkeypatch.setattr(Database, "collection", MockTcvectordbClass.describe_collection)
         monkeypatch.setattr(Database, "list_collections", MockTcvectordbClass.list_collections)
diff --git a/api/tests/integration_tests/vdb/milvus/test_milvus.py b/api/tests/integration_tests/vdb/milvus/test_milvus.py
index 7b5f19ea62..c99739a863 100644
--- a/api/tests/integration_tests/vdb/milvus/test_milvus.py
+++ b/api/tests/integration_tests/vdb/milvus/test_milvus.py
@@ -12,8 +12,7 @@ class MilvusVectorTest(AbstractVectorTest):
         self.vector = MilvusVector(
             collection_name=self.collection_name,
             config=MilvusConfig(
-                host="localhost",
-                port=19530,
+                uri="http://localhost:19530",
                 user="root",
                 password="Milvus",
             ),
diff --git a/api/tests/integration_tests/vdb/opensearch/test_opensearch.py b/api/tests/integration_tests/vdb/opensearch/test_opensearch.py
index a99b81d41e..2666ce2e1e 100644
--- a/api/tests/integration_tests/vdb/opensearch/test_opensearch.py
+++ b/api/tests/integration_tests/vdb/opensearch/test_opensearch.py
@@ -34,7 +34,7 @@ class TestOpenSearchVector:
         self.vector._client = MagicMock()
 
     @pytest.mark.parametrize(
-        "search_response, expected_length, expected_doc_id",
+        ("search_response", "expected_length", "expected_doc_id"),
         [
             (
                 {
diff --git a/api/tests/integration_tests/vdb/pgvecto_rs/test_pgvecto_rs.py b/api/tests/integration_tests/vdb/pgvecto_rs/test_pgvecto_rs.py
index 6b33217d15..6497f47deb 100644
--- a/api/tests/integration_tests/vdb/pgvecto_rs/test_pgvecto_rs.py
+++ b/api/tests/integration_tests/vdb/pgvecto_rs/test_pgvecto_rs.py
@@ -31,5 +31,5 @@ class PGVectoRSVectorTest(AbstractVectorTest):
         assert len(ids) == 1
 
 
-def test_pgvecot_rs(setup_mock_redis):
+def test_pgvecto_rs(setup_mock_redis):
     PGVectoRSVectorTest().run_all_tests()
diff --git a/api/tests/integration_tests/workflow/nodes/__mock/http.py b/api/tests/integration_tests/workflow/nodes/__mock/http.py
index cfc47bcad4..f1ab23b002 100644
--- a/api/tests/integration_tests/workflow/nodes/__mock/http.py
+++ b/api/tests/integration_tests/workflow/nodes/__mock/http.py
@@ -10,6 +10,7 @@ MOCK = os.getenv("MOCK_SWITCH", "false") == "true"
 
 
 class MockedHttp:
+    @staticmethod
     def httpx_request(
         method: Literal["GET", "POST", "PUT", "DELETE", "PATCH", "HEAD"], url: str, **kwargs
     ) -> httpx.Response:
diff --git a/api/tests/integration_tests/workflow/nodes/code_executor/test_code_executor.py b/api/tests/integration_tests/workflow/nodes/code_executor/test_code_executor.py
index 44dcf9a10f..487178ff58 100644
--- a/api/tests/integration_tests/workflow/nodes/code_executor/test_code_executor.py
+++ b/api/tests/integration_tests/workflow/nodes/code_executor/test_code_executor.py
@@ -1,11 +1,11 @@
 import pytest
 
-from core.helper.code_executor.code_executor import CodeExecutionException, CodeExecutor
+from core.helper.code_executor.code_executor import CodeExecutionError, CodeExecutor
 
 CODE_LANGUAGE = "unsupported_language"
 
 
 def test_unsupported_with_code_template():
-    with pytest.raises(CodeExecutionException) as e:
+    with pytest.raises(CodeExecutionError) as e:
         CodeExecutor.execute_workflow_code_template(language=CODE_LANGUAGE, code="", inputs={})
     assert str(e.value) == f"Unsupported language {CODE_LANGUAGE}"
diff --git a/api/tests/integration_tests/workflow/nodes/code_executor/test_code_python3.py b/api/tests/integration_tests/workflow/nodes/code_executor/test_code_python3.py
index cbe4a5d335..25af312afa 100644
--- a/api/tests/integration_tests/workflow/nodes/code_executor/test_code_python3.py
+++ b/api/tests/integration_tests/workflow/nodes/code_executor/test_code_python3.py
@@ -1,4 +1,3 @@
-import json
 from textwrap import dedent
 
 from core.helper.code_executor.code_executor import CodeExecutor, CodeLanguage
diff --git a/api/tests/integration_tests/workflow/nodes/test_code.py b/api/tests/integration_tests/workflow/nodes/test_code.py
index 6f5421e108..952c90674d 100644
--- a/api/tests/integration_tests/workflow/nodes/test_code.py
+++ b/api/tests/integration_tests/workflow/nodes/test_code.py
@@ -1,17 +1,72 @@
+import time
+import uuid
 from os import getenv
+from typing import cast
 
 import pytest
 
 from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import NodeRunResult, UserFrom
 from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.nodes.base_node import UserFrom
+from core.workflow.enums import SystemVariableKey
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes.code.code_node import CodeNode
-from models.workflow import WorkflowNodeExecutionStatus
+from core.workflow.nodes.code.entities import CodeNodeData
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
 from tests.integration_tests.workflow.nodes.__mock.code_executor import setup_code_executor_mock
 
 CODE_MAX_STRING_LENGTH = int(getenv("CODE_MAX_STRING_LENGTH", "10000"))
 
 
+def init_code_node(code_config: dict):
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-code-target",
+                "source": "start",
+                "target": "code",
+            },
+        ],
+        "nodes": [{"data": {"type": "start"}, "id": "start"}, code_config],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
+        tenant_id="1",
+        app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
+        workflow_id="1",
+        graph_config=graph_config,
+        user_id="1",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    variable_pool = VariablePool(
+        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"},
+        user_inputs={},
+        environment_variables=[],
+        conversation_variables=[],
+    )
+    variable_pool.add(["code", "123", "args1"], 1)
+    variable_pool.add(["code", "123", "args2"], 2)
+
+    node = CodeNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
+        config=code_config,
+    )
+
+    return node
+
+
 @pytest.mark.parametrize("setup_code_executor_mock", [["none"]], indirect=True)
 def test_execute_code(setup_code_executor_mock):
     code = """
@@ -22,44 +77,36 @@ def test_execute_code(setup_code_executor_mock):
     """
     # trim first 4 spaces at the beginning of each line
     code = "\n".join([line[4:] for line in code.split("\n")])
-    node = CodeNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        user_from=UserFrom.ACCOUNT,
-        invoke_from=InvokeFrom.WEB_APP,
-        config={
-            "id": "1",
-            "data": {
-                "outputs": {
-                    "result": {
-                        "type": "number",
-                    },
-                },
-                "title": "123",
-                "variables": [
-                    {
-                        "variable": "args1",
-                        "value_selector": ["1", "123", "args1"],
-                    },
-                    {"variable": "args2", "value_selector": ["1", "123", "args2"]},
-                ],
-                "answer": "123",
-                "code_language": "python3",
-                "code": code,
-            },
-        },
-    )
 
-    # construct variable pool
-    pool = VariablePool(system_variables={}, user_inputs={}, environment_variables=[])
-    pool.add(["1", "123", "args1"], 1)
-    pool.add(["1", "123", "args2"], 2)
+    code_config = {
+        "id": "code",
+        "data": {
+            "outputs": {
+                "result": {
+                    "type": "number",
+                },
+            },
+            "title": "123",
+            "variables": [
+                {
+                    "variable": "args1",
+                    "value_selector": ["1", "123", "args1"],
+                },
+                {"variable": "args2", "value_selector": ["1", "123", "args2"]},
+            ],
+            "answer": "123",
+            "code_language": "python3",
+            "code": code,
+        },
+    }
+
+    node = init_code_node(code_config)
 
     # execute node
-    result = node.run(pool)
+    result = node._run()
+    assert isinstance(result, NodeRunResult)
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+    assert result.outputs is not None
     assert result.outputs["result"] == 3
     assert result.error is None
 
@@ -74,44 +121,34 @@ def test_execute_code_output_validator(setup_code_executor_mock):
     """
     # trim first 4 spaces at the beginning of each line
     code = "\n".join([line[4:] for line in code.split("\n")])
-    node = CodeNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        user_from=UserFrom.ACCOUNT,
-        invoke_from=InvokeFrom.WEB_APP,
-        config={
-            "id": "1",
-            "data": {
-                "outputs": {
-                    "result": {
-                        "type": "string",
-                    },
-                },
-                "title": "123",
-                "variables": [
-                    {
-                        "variable": "args1",
-                        "value_selector": ["1", "123", "args1"],
-                    },
-                    {"variable": "args2", "value_selector": ["1", "123", "args2"]},
-                ],
-                "answer": "123",
-                "code_language": "python3",
-                "code": code,
-            },
-        },
-    )
 
-    # construct variable pool
-    pool = VariablePool(system_variables={}, user_inputs={}, environment_variables=[])
-    pool.add(["1", "123", "args1"], 1)
-    pool.add(["1", "123", "args2"], 2)
+    code_config = {
+        "id": "code",
+        "data": {
+            "outputs": {
+                "result": {
+                    "type": "string",
+                },
+            },
+            "title": "123",
+            "variables": [
+                {
+                    "variable": "args1",
+                    "value_selector": ["1", "123", "args1"],
+                },
+                {"variable": "args2", "value_selector": ["1", "123", "args2"]},
+            ],
+            "answer": "123",
+            "code_language": "python3",
+            "code": code,
+        },
+    }
+
+    node = init_code_node(code_config)
 
     # execute node
-    result = node.run(pool)
-
+    result = node._run()
+    assert isinstance(result, NodeRunResult)
     assert result.status == WorkflowNodeExecutionStatus.FAILED
     assert result.error == "Output variable `result` must be a string"
 
@@ -127,65 +164,60 @@ def test_execute_code_output_validator_depth():
     """
     # trim first 4 spaces at the beginning of each line
     code = "\n".join([line[4:] for line in code.split("\n")])
-    node = CodeNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        user_from=UserFrom.ACCOUNT,
-        invoke_from=InvokeFrom.WEB_APP,
-        config={
-            "id": "1",
-            "data": {
-                "outputs": {
-                    "string_validator": {
-                        "type": "string",
-                    },
-                    "number_validator": {
-                        "type": "number",
-                    },
-                    "number_array_validator": {
-                        "type": "array[number]",
-                    },
-                    "string_array_validator": {
-                        "type": "array[string]",
-                    },
-                    "object_validator": {
-                        "type": "object",
-                        "children": {
-                            "result": {
-                                "type": "number",
-                            },
-                            "depth": {
-                                "type": "object",
-                                "children": {
-                                    "depth": {
-                                        "type": "object",
-                                        "children": {
-                                            "depth": {
-                                                "type": "number",
-                                            }
-                                        },
-                                    }
-                                },
+
+    code_config = {
+        "id": "code",
+        "data": {
+            "outputs": {
+                "string_validator": {
+                    "type": "string",
+                },
+                "number_validator": {
+                    "type": "number",
+                },
+                "number_array_validator": {
+                    "type": "array[number]",
+                },
+                "string_array_validator": {
+                    "type": "array[string]",
+                },
+                "object_validator": {
+                    "type": "object",
+                    "children": {
+                        "result": {
+                            "type": "number",
+                        },
+                        "depth": {
+                            "type": "object",
+                            "children": {
+                                "depth": {
+                                    "type": "object",
+                                    "children": {
+                                        "depth": {
+                                            "type": "number",
+                                        }
+                                    },
+                                }
                             },
                         },
                     },
                 },
-                "title": "123",
-                "variables": [
-                    {
-                        "variable": "args1",
-                        "value_selector": ["1", "123", "args1"],
-                    },
-                    {"variable": "args2", "value_selector": ["1", "123", "args2"]},
-                ],
-                "answer": "123",
-                "code_language": "python3",
-                "code": code,
             },
+            "title": "123",
+            "variables": [
+                {
+                    "variable": "args1",
+                    "value_selector": ["1", "123", "args1"],
+                },
+                {"variable": "args2", "value_selector": ["1", "123", "args2"]},
+            ],
+            "answer": "123",
+            "code_language": "python3",
+            "code": code,
         },
-    )
+    }
+
+    node = init_code_node(code_config)
 
     # construct result
     result = {
@@ -196,6 +228,8 @@ def test_execute_code_output_validator_depth():
         "object_validator": {"result": 1, "depth": {"depth": {"depth": 1}}},
     }
 
+    node.node_data = cast(CodeNodeData, node.node_data)
+
     # validate
     node._transform_result(result, node.node_data.outputs)
 
@@ -250,35 +284,30 @@ def test_execute_code_output_object_list():
     """
     # trim first 4 spaces at the beginning of each line
     code = "\n".join([line[4:] for line in code.split("\n")])
-    node = CodeNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.ACCOUNT,
-        config={
-            "id": "1",
-            "data": {
-                "outputs": {
-                    "object_list": {
-                        "type": "array[object]",
-                    },
+
+    code_config = {
+        "id": "code",
+        "data": {
+            "outputs": {
+                "object_list": {
+                    "type": "array[object]",
                 },
-                "title": "123",
-                "variables": [
-                    {
-                        "variable": "args1",
-                        "value_selector": ["1", "123", "args1"],
-                    },
-                    {"variable": "args2", "value_selector": ["1", "123", "args2"]},
-                ],
-                "answer": "123",
-                "code_language": "python3",
-                "code": code,
             },
+            "title": "123",
+            "variables": [
+                {
+                    "variable": "args1",
+                    "value_selector": ["1", "123", "args1"],
+                },
+                {"variable": "args2", "value_selector": ["1", "123", "args2"]},
+            ],
+            "answer": "123",
+            "code_language": "python3",
+            "code": code,
         },
-    )
+    }
+
+    node = init_code_node(code_config)
 
     # construct result
     result = {
@@ -295,6 +324,8 @@ def test_execute_code_output_object_list():
         ]
     }
 
+    node.node_data = cast(CodeNodeData, node.node_data)
+
     # validate
     node._transform_result(result, node.node_data.outputs)
 
diff --git a/api/tests/integration_tests/workflow/nodes/test_http.py b/api/tests/integration_tests/workflow/nodes/test_http.py
index acb616b325..65aaa0bddd 100644
--- a/api/tests/integration_tests/workflow/nodes/test_http.py
+++ b/api/tests/integration_tests/workflow/nodes/test_http.py
@@ -1,31 +1,69 @@
+import time
+import uuid
 from urllib.parse import urlencode
 
 import pytest
 
 from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import UserFrom
 from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.nodes.base_node import UserFrom
+from core.workflow.enums import SystemVariableKey
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes.http_request.http_request_node import HttpRequestNode
+from models.workflow import WorkflowType
 from tests.integration_tests.workflow.nodes.__mock.http import setup_http_mock
 
-BASIC_NODE_DATA = {
-    "tenant_id": "1",
-    "app_id": "1",
-    "workflow_id": "1",
-    "user_id": "1",
-    "user_from": UserFrom.ACCOUNT,
-    "invoke_from": InvokeFrom.WEB_APP,
-}
 
-# construct variable pool
-pool = VariablePool(system_variables={}, user_inputs={}, environment_variables=[])
-pool.add(["a", "b123", "args1"], 1)
-pool.add(["a", "b123", "args2"], 2)
+def init_http_node(config: dict):
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-next-target",
+                "source": "start",
+                "target": "1",
+            },
+        ],
+        "nodes": [{"data": {"type": "start"}, "id": "start"}, config],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
+        tenant_id="1",
+        app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
+        workflow_id="1",
+        graph_config=graph_config,
+        user_id="1",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    variable_pool = VariablePool(
+        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"},
+        user_inputs={},
+        environment_variables=[],
+        conversation_variables=[],
+    )
+    variable_pool.add(["a", "b123", "args1"], 1)
+    variable_pool.add(["a", "b123", "args2"], 2)
+
+    return HttpRequestNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
+        config=config,
+    )
 
 
 @pytest.mark.parametrize("setup_http_mock", [["none"]], indirect=True)
 def test_get(setup_http_mock):
-    node = HttpRequestNode(
+    node = init_http_node(
         config={
             "id": "1",
             "data": {
@@ -45,12 +83,11 @@ def test_get(setup_http_mock):
                 "params": "A:b",
                 "body": None,
             },
-        },
-        **BASIC_NODE_DATA,
+        }
     )
 
-    result = node.run(pool)
-
+    result = node._run()
+    assert result.process_data is not None
     data = result.process_data.get("request", "")
 
     assert "?A=b" in data
@@ -59,7 +96,7 @@ def test_get(setup_http_mock):
 
 @pytest.mark.parametrize("setup_http_mock", [["none"]], indirect=True)
 def test_no_auth(setup_http_mock):
-    node = HttpRequestNode(
+    node = init_http_node(
         config={
             "id": "1",
             "data": {
@@ -75,12 +112,11 @@ def test_no_auth(setup_http_mock):
                 "params": "A:b",
                 "body": None,
             },
-        },
-        **BASIC_NODE_DATA,
+        }
     )
 
-    result = node.run(pool)
-
+    result = node._run()
+    assert result.process_data is not None
     data = result.process_data.get("request", "")
 
     assert "?A=b" in data
@@ -89,7 +125,7 @@ def test_no_auth(setup_http_mock):
 
 @pytest.mark.parametrize("setup_http_mock", [["none"]], indirect=True)
 def test_custom_authorization_header(setup_http_mock):
-    node = HttpRequestNode(
+    node = init_http_node(
         config={
             "id": "1",
             "data": {
@@ -109,12 +145,11 @@ def test_custom_authorization_header(setup_http_mock):
                 "params": "A:b",
                 "body": None,
             },
-        },
-        **BASIC_NODE_DATA,
+        }
     )
 
-    result = node.run(pool)
-
+    result = node._run()
+    assert result.process_data is not None
     data = result.process_data.get("request", "")
 
     assert "?A=b" in data
@@ -123,7 +158,7 @@ def test_custom_authorization_header(setup_http_mock):
 
 @pytest.mark.parametrize("setup_http_mock", [["none"]], indirect=True)
 def test_template(setup_http_mock):
-    node = HttpRequestNode(
+    node = init_http_node(
         config={
             "id": "1",
             "data": {
@@ -143,11 +178,11 @@ def test_template(setup_http_mock):
                 "params": "A:b\nTemplate:{{#a.b123.args2#}}",
                 "body": None,
             },
-        },
-        **BASIC_NODE_DATA,
+        }
     )
 
-    result = node.run(pool)
+    result = node._run()
+    assert result.process_data is not None
     data = result.process_data.get("request", "")
 
     assert "?A=b" in data
@@ -158,7 +193,7 @@ def test_template(setup_http_mock):
 
 @pytest.mark.parametrize("setup_http_mock", [["none"]], indirect=True)
 def test_json(setup_http_mock):
-    node = HttpRequestNode(
+    node = init_http_node(
         config={
             "id": "1",
             "data": {
@@ -178,11 +213,11 @@ def test_json(setup_http_mock):
                 "params": "A:b",
                 "body": {"type": "json", "data": '{"a": "{{#a.b123.args1#}}"}'},
             },
-        },
-        **BASIC_NODE_DATA,
+        }
     )
 
-    result = node.run(pool)
+    result = node._run()
+    assert result.process_data is not None
     data = result.process_data.get("request", "")
 
     assert '{"a": "1"}' in data
@@ -190,7 +225,7 @@ def test_json(setup_http_mock):
 
 
 def test_x_www_form_urlencoded(setup_http_mock):
-    node = HttpRequestNode(
+    node = init_http_node(
         config={
             "id": "1",
             "data": {
@@ -210,11 +245,11 @@ def test_x_www_form_urlencoded(setup_http_mock):
                 "params": "A:b",
                 "body": {"type": "x-www-form-urlencoded", "data": "a:{{#a.b123.args1#}}\nb:{{#a.b123.args2#}}"},
             },
-        },
-        **BASIC_NODE_DATA,
+        }
     )
 
-    result = node.run(pool)
+    result = node._run()
+    assert result.process_data is not None
     data = result.process_data.get("request", "")
 
     assert "a=1&b=2" in data
@@ -222,7 +257,7 @@ def test_x_www_form_urlencoded(setup_http_mock):
 
 
 def test_form_data(setup_http_mock):
-    node = HttpRequestNode(
+    node = init_http_node(
         config={
             "id": "1",
             "data": {
@@ -242,11 +277,11 @@ def test_form_data(setup_http_mock):
                 "params": "A:b",
                 "body": {"type": "form-data", "data": "a:{{#a.b123.args1#}}\nb:{{#a.b123.args2#}}"},
             },
-        },
-        **BASIC_NODE_DATA,
+        }
     )
 
-    result = node.run(pool)
+    result = node._run()
+    assert result.process_data is not None
     data = result.process_data.get("request", "")
 
     assert 'form-data; name="a"' in data
@@ -257,7 +292,7 @@ def test_form_data(setup_http_mock):
 
 
 def test_none_data(setup_http_mock):
-    node = HttpRequestNode(
+    node = init_http_node(
         config={
             "id": "1",
             "data": {
@@ -277,11 +312,11 @@ def test_none_data(setup_http_mock):
                 "params": "A:b",
                 "body": {"type": "none", "data": "123123123"},
             },
-        },
-        **BASIC_NODE_DATA,
+        }
     )
 
-    result = node.run(pool)
+    result = node._run()
+    assert result.process_data is not None
     data = result.process_data.get("request", "")
 
     assert "X-Header: 123" in data
@@ -289,7 +324,7 @@ def test_none_data(setup_http_mock):
 
 
 def test_mock_404(setup_http_mock):
-    node = HttpRequestNode(
+    node = init_http_node(
         config={
             "id": "1",
             "data": {
@@ -305,19 +340,19 @@ def test_mock_404(setup_http_mock):
                 "params": "",
                 "headers": "X-Header:123",
             },
-        },
-        **BASIC_NODE_DATA,
+        }
     )
 
-    result = node.run(pool)
+    result = node._run()
+    assert result.outputs is not None
     resp = result.outputs
 
     assert 404 == resp.get("status_code")
-    assert "Not Found" in resp.get("body")
+    assert "Not Found" in resp.get("body", "")
 
 
 def test_multi_colons_parse(setup_http_mock):
-    node = HttpRequestNode(
+    node = init_http_node(
         config={
             "id": "1",
             "data": {
@@ -333,13 +368,14 @@ def test_multi_colons_parse(setup_http_mock):
                 "headers": "Referer:http://example3.com\nRedirect:http://example4.com",
                 "body": {"type": "form-data", "data": "Referer:http://example5.com\nRedirect:http://example6.com"},
             },
-        },
-        **BASIC_NODE_DATA,
+        }
     )
 
-    result = node.run(pool)
+    result = node._run()
+    assert result.process_data is not None
+    assert result.outputs is not None
     resp = result.outputs
 
-    assert urlencode({"Redirect": "http://example2.com"}) in result.process_data.get("request")
-    assert 'form-data; name="Redirect"\n\nhttp://example6.com' in result.process_data.get("request")
-    assert "http://example3.com" == resp.get("headers").get("referer")
+    assert urlencode({"Redirect": "http://example2.com"}) in result.process_data.get("request", "")
+    assert 'form-data; name="Redirect"\n\nhttp://example6.com' in result.process_data.get("request", "")
+    assert "http://example3.com" == resp.get("headers", {}).get("referer")
diff --git a/api/tests/integration_tests/workflow/nodes/test_llm.py b/api/tests/integration_tests/workflow/nodes/test_llm.py
index 6bab83a019..dfb43650d2 100644
--- a/api/tests/integration_tests/workflow/nodes/test_llm.py
+++ b/api/tests/integration_tests/workflow/nodes/test_llm.py
@@ -1,5 +1,8 @@
 import json
 import os
+import time
+import uuid
+from collections.abc import Generator
 from unittest.mock import MagicMock
 
 import pytest
@@ -10,28 +13,77 @@ from core.entities.provider_entities import CustomConfiguration, CustomProviderC
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.model_providers import ModelProviderFactory
+from core.workflow.entities.node_entities import UserFrom
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
-from core.workflow.nodes.base_node import UserFrom
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.nodes.event import RunCompletedEvent
 from core.workflow.nodes.llm.llm_node import LLMNode
 from extensions.ext_database import db
 from models.provider import ProviderType
-from models.workflow import WorkflowNodeExecutionStatus
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
 
 """FOR MOCK FIXTURES, DO NOT REMOVE"""
 from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
 from tests.integration_tests.workflow.nodes.__mock.code_executor import setup_code_executor_mock
 
 
-@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
-def test_execute_llm(setup_openai_mock):
-    node = LLMNode(
+def init_llm_node(config: dict) -> LLMNode:
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-next-target",
+                "source": "start",
+                "target": "llm",
+            },
+        ],
+        "nodes": [{"data": {"type": "start"}, "id": "start"}, config],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
         tenant_id="1",
         app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
         workflow_id="1",
+        graph_config=graph_config,
         user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
         user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    variable_pool = VariablePool(
+        system_variables={
+            SystemVariableKey.QUERY: "what's the weather today?",
+            SystemVariableKey.FILES: [],
+            SystemVariableKey.CONVERSATION_ID: "abababa",
+            SystemVariableKey.USER_ID: "aaa",
+        },
+        user_inputs={},
+        environment_variables=[],
+        conversation_variables=[],
+    )
+    variable_pool.add(["abc", "output"], "sunny")
+
+    node = LLMNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
+        config=config,
+    )
+
+    return node
+
+
+@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
+def test_execute_llm(setup_openai_mock):
+    node = init_llm_node(
         config={
             "id": "llm",
             "data": {
@@ -49,19 +101,6 @@ def test_execute_llm(setup_openai_mock):
         },
     )
 
-    # construct variable pool
-    pool = VariablePool(
-        system_variables={
-            SystemVariableKey.QUERY: "what's the weather today?",
-            SystemVariableKey.FILES: [],
-            SystemVariableKey.CONVERSATION_ID: "abababa",
-            SystemVariableKey.USER_ID: "aaa",
-        },
-        user_inputs={},
-        environment_variables=[],
-    )
-    pool.add(["abc", "output"], "sunny")
-
     credentials = {"openai_api_key": os.environ.get("OPENAI_API_KEY")}
 
     provider_instance = ModelProviderFactory().get_provider_instance("openai")
@@ -80,13 +119,15 @@ def test_execute_llm(setup_openai_mock):
         model_type_instance=model_type_instance,
     )
     model_instance = ModelInstance(provider_model_bundle=provider_model_bundle, model="gpt-3.5-turbo")
+    model_schema = model_type_instance.get_model_schema("gpt-3.5-turbo")
+    assert model_schema is not None
     model_config = ModelConfigWithCredentialsEntity(
         model="gpt-3.5-turbo",
         provider="openai",
         mode="chat",
         credentials=credentials,
         parameters={},
-        model_schema=model_type_instance.get_model_schema("gpt-3.5-turbo"),
+        model_schema=model_schema,
         provider_model_bundle=provider_model_bundle,
     )
 
@@ -96,11 +137,16 @@ def test_execute_llm(setup_openai_mock):
     node._fetch_model_config = MagicMock(return_value=(model_instance, model_config))
 
     # execute node
-    result = node.run(pool)
+    result = node._run()
+    assert isinstance(result, Generator)
 
-    assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
-    assert result.outputs["text"] is not None
-    assert result.outputs["usage"]["total_tokens"] > 0
+    for item in result:
+        if isinstance(item, RunCompletedEvent):
+            assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+            assert item.run_result.process_data is not None
+            assert item.run_result.outputs is not None
+            assert item.run_result.outputs.get("text") is not None
+            assert item.run_result.outputs.get("usage", {})["total_tokens"] > 0
 
 
 @pytest.mark.parametrize("setup_code_executor_mock", [["none"]], indirect=True)
@@ -109,13 +155,7 @@ def test_execute_llm_with_jinja2(setup_code_executor_mock, setup_openai_mock):
     """
     Test execute LLM node with jinja2
     """
-    node = LLMNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.ACCOUNT,
+    node = init_llm_node(
         config={
             "id": "llm",
             "data": {
@@ -149,19 +189,6 @@ def test_execute_llm_with_jinja2(setup_code_executor_mock, setup_openai_mock):
         },
     )
 
-    # construct variable pool
-    pool = VariablePool(
-        system_variables={
-            SystemVariableKey.QUERY: "what's the weather today?",
-            SystemVariableKey.FILES: [],
-            SystemVariableKey.CONVERSATION_ID: "abababa",
-            SystemVariableKey.USER_ID: "aaa",
-        },
-        user_inputs={},
-        environment_variables=[],
-    )
-    pool.add(["abc", "output"], "sunny")
-
     credentials = {"openai_api_key": os.environ.get("OPENAI_API_KEY")}
 
     provider_instance = ModelProviderFactory().get_provider_instance("openai")
@@ -181,14 +208,15 @@ def test_execute_llm_with_jinja2(setup_code_executor_mock, setup_openai_mock):
     )
 
     model_instance = ModelInstance(provider_model_bundle=provider_model_bundle, model="gpt-3.5-turbo")
-
+    model_schema = model_type_instance.get_model_schema("gpt-3.5-turbo")
+    assert model_schema is not None
     model_config = ModelConfigWithCredentialsEntity(
         model="gpt-3.5-turbo",
         provider="openai",
         mode="chat",
         credentials=credentials,
         parameters={},
-        model_schema=model_type_instance.get_model_schema("gpt-3.5-turbo"),
+        model_schema=model_schema,
         provider_model_bundle=provider_model_bundle,
     )
 
@@ -198,8 +226,11 @@ def test_execute_llm_with_jinja2(setup_code_executor_mock, setup_openai_mock):
     node._fetch_model_config = MagicMock(return_value=(model_instance, model_config))
 
     # execute node
-    result = node.run(pool)
+    result = node._run()
 
-    assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
-    assert "sunny" in json.dumps(result.process_data)
-    assert "what's the weather today?" in json.dumps(result.process_data)
+    for item in result:
+        if isinstance(item, RunCompletedEvent):
+            assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+            assert item.run_result.process_data is not None
+            assert "sunny" in json.dumps(item.run_result.process_data)
+            assert "what's the weather today?" in json.dumps(item.run_result.process_data)
diff --git a/api/tests/integration_tests/workflow/nodes/test_parameter_extractor.py b/api/tests/integration_tests/workflow/nodes/test_parameter_extractor.py
index ca2bae5c53..88435c4022 100644
--- a/api/tests/integration_tests/workflow/nodes/test_parameter_extractor.py
+++ b/api/tests/integration_tests/workflow/nodes/test_parameter_extractor.py
@@ -1,5 +1,7 @@
 import json
 import os
+import time
+import uuid
 from typing import Optional
 from unittest.mock import MagicMock
 
@@ -8,19 +10,21 @@ import pytest
 from core.app.entities.app_invoke_entities import InvokeFrom, ModelConfigWithCredentialsEntity
 from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
 from core.entities.provider_entities import CustomConfiguration, CustomProviderConfiguration, SystemConfiguration
-from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
+from core.workflow.entities.node_entities import UserFrom
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
-from core.workflow.nodes.base_node import UserFrom
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes.parameter_extractor.parameter_extractor_node import ParameterExtractorNode
 from extensions.ext_database import db
 from models.provider import ProviderType
 
 """FOR MOCK FIXTURES, DO NOT REMOVE"""
-from models.workflow import WorkflowNodeExecutionStatus
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
 from tests.integration_tests.model_runtime.__mock.anthropic import setup_anthropic_mock
 from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
 
@@ -47,13 +51,15 @@ def get_mocked_fetch_model_config(
         model_type_instance=model_type_instance,
     )
     model_instance = ModelInstance(provider_model_bundle=provider_model_bundle, model=model)
+    model_schema = model_type_instance.get_model_schema(model)
+    assert model_schema is not None
     model_config = ModelConfigWithCredentialsEntity(
         model=model,
         provider=provider,
         mode=mode,
         credentials=credentials,
         parameters={},
-        model_schema=model_type_instance.get_model_schema(model),
+        model_schema=model_schema,
         provider_model_bundle=provider_model_bundle,
     )
 
@@ -74,18 +80,62 @@ def get_mocked_fetch_memory(memory_text: str):
     return MagicMock(return_value=MemoryMock())
 
 
+def init_parameter_extractor_node(config: dict):
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-next-target",
+                "source": "start",
+                "target": "llm",
+            },
+        ],
+        "nodes": [{"data": {"type": "start"}, "id": "start"}, config],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
+        tenant_id="1",
+        app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
+        workflow_id="1",
+        graph_config=graph_config,
+        user_id="1",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    variable_pool = VariablePool(
+        system_variables={
+            SystemVariableKey.QUERY: "what's the weather in SF",
+            SystemVariableKey.FILES: [],
+            SystemVariableKey.CONVERSATION_ID: "abababa",
+            SystemVariableKey.USER_ID: "aaa",
+        },
+        user_inputs={},
+        environment_variables=[],
+        conversation_variables=[],
+    )
+    variable_pool.add(["a", "b123", "args1"], 1)
+    variable_pool.add(["a", "b123", "args2"], 2)
+
+    return ParameterExtractorNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
+        config=config,
+    )
+
+
 @pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
 def test_function_calling_parameter_extractor(setup_openai_mock):
     """
     Test function calling for parameter extractor.
     """
-    node = ParameterExtractorNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.ACCOUNT,
+    node = init_parameter_extractor_node(
         config={
             "id": "llm",
             "data": {
@@ -98,7 +148,7 @@ def test_function_calling_parameter_extractor(setup_openai_mock):
                 "reasoning_mode": "function_call",
                 "memory": None,
             },
-        },
+        }
     )
 
     node._fetch_model_config = get_mocked_fetch_model_config(
@@ -121,9 +171,10 @@ def test_function_calling_parameter_extractor(setup_openai_mock):
         environment_variables=[],
     )
 
-    result = node.run(pool)
+    result = node._run()
 
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+    assert result.outputs is not None
     assert result.outputs.get("location") == "kawaii"
     assert result.outputs.get("__reason") == None
 
@@ -133,13 +184,7 @@ def test_instructions(setup_openai_mock):
     """
     Test chat parameter extractor.
     """
-    node = ParameterExtractorNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.ACCOUNT,
+    node = init_parameter_extractor_node(
         config={
             "id": "llm",
             "data": {
@@ -163,29 +208,19 @@ def test_instructions(setup_openai_mock):
     )
     db.session.close = MagicMock()
 
-    # construct variable pool
-    pool = VariablePool(
-        system_variables={
-            SystemVariableKey.QUERY: "what's the weather in SF",
-            SystemVariableKey.FILES: [],
-            SystemVariableKey.CONVERSATION_ID: "abababa",
-            SystemVariableKey.USER_ID: "aaa",
-        },
-        user_inputs={},
-        environment_variables=[],
-    )
-
-    result = node.run(pool)
+    result = node._run()
 
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+    assert result.outputs is not None
     assert result.outputs.get("location") == "kawaii"
     assert result.outputs.get("__reason") == None
 
     process_data = result.process_data
 
+    assert process_data is not None
     process_data.get("prompts")
 
-    for prompt in process_data.get("prompts"):
+    for prompt in process_data.get("prompts", []):
         if prompt.get("role") == "system":
             assert "what's the weather in SF" in prompt.get("text")
 
@@ -195,13 +230,7 @@ def test_chat_parameter_extractor(setup_anthropic_mock):
     """
     Test chat parameter extractor.
     """
-    node = ParameterExtractorNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.ACCOUNT,
+    node = init_parameter_extractor_node(
         config={
             "id": "llm",
             "data": {
@@ -225,27 +254,17 @@ def test_chat_parameter_extractor(setup_anthropic_mock):
     )
     db.session.close = MagicMock()
 
-    # construct variable pool
-    pool = VariablePool(
-        system_variables={
-            SystemVariableKey.QUERY: "what's the weather in SF",
-            SystemVariableKey.FILES: [],
-            SystemVariableKey.CONVERSATION_ID: "abababa",
-            SystemVariableKey.USER_ID: "aaa",
-        },
-        user_inputs={},
-        environment_variables=[],
-    )
-
-    result = node.run(pool)
+    result = node._run()
 
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+    assert result.outputs is not None
     assert result.outputs.get("location") == ""
     assert (
         result.outputs.get("__reason")
         == "Failed to extract result from function call or text response, using empty result."
     )
-    prompts = result.process_data.get("prompts")
+    assert result.process_data is not None
+    prompts = result.process_data.get("prompts", [])
 
     for prompt in prompts:
         if prompt.get("role") == "user":
@@ -258,13 +277,7 @@ def test_completion_parameter_extractor(setup_openai_mock):
     """
     Test completion parameter extractor.
     """
-    node = ParameterExtractorNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.ACCOUNT,
+    node = init_parameter_extractor_node(
         config={
             "id": "llm",
             "data": {
@@ -293,28 +306,18 @@ def test_completion_parameter_extractor(setup_openai_mock):
     )
     db.session.close = MagicMock()
 
-    # construct variable pool
-    pool = VariablePool(
-        system_variables={
-            SystemVariableKey.QUERY: "what's the weather in SF",
-            SystemVariableKey.FILES: [],
-            SystemVariableKey.CONVERSATION_ID: "abababa",
-            SystemVariableKey.USER_ID: "aaa",
-        },
-        user_inputs={},
-        environment_variables=[],
-    )
-
-    result = node.run(pool)
+    result = node._run()
 
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+    assert result.outputs is not None
     assert result.outputs.get("location") == ""
     assert (
         result.outputs.get("__reason")
         == "Failed to extract result from function call or text response, using empty result."
     )
-    assert len(result.process_data.get("prompts")) == 1
-    assert "SF" in result.process_data.get("prompts")[0].get("text")
+    assert result.process_data is not None
+    assert len(result.process_data.get("prompts", [])) == 1
+    assert "SF" in result.process_data.get("prompts", [])[0].get("text")
 
 
 def test_extract_json_response():
@@ -322,13 +325,7 @@ def test_extract_json_response():
     Test extract json response.
     """
 
-    node = ParameterExtractorNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.ACCOUNT,
+    node = init_parameter_extractor_node(
         config={
             "id": "llm",
             "data": {
@@ -357,6 +354,7 @@ def test_extract_json_response():
         hello world.
     """)
 
+    assert result is not None
     assert result["location"] == "kawaii"
 
 
@@ -365,13 +363,7 @@ def test_chat_parameter_extractor_with_memory(setup_anthropic_mock):
     """
     Test chat parameter extractor with memory.
     """
-    node = ParameterExtractorNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.ACCOUNT,
+    node = init_parameter_extractor_node(
         config={
             "id": "llm",
             "data": {
@@ -396,27 +388,17 @@ def test_chat_parameter_extractor_with_memory(setup_anthropic_mock):
     node._fetch_memory = get_mocked_fetch_memory("customized memory")
     db.session.close = MagicMock()
 
-    # construct variable pool
-    pool = VariablePool(
-        system_variables={
-            SystemVariableKey.QUERY: "what's the weather in SF",
-            SystemVariableKey.FILES: [],
-            SystemVariableKey.CONVERSATION_ID: "abababa",
-            SystemVariableKey.USER_ID: "aaa",
-        },
-        user_inputs={},
-        environment_variables=[],
-    )
-
-    result = node.run(pool)
+    result = node._run()
 
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+    assert result.outputs is not None
     assert result.outputs.get("location") == ""
     assert (
         result.outputs.get("__reason")
         == "Failed to extract result from function call or text response, using empty result."
     )
-    prompts = result.process_data.get("prompts")
+    assert result.process_data is not None
+    prompts = result.process_data.get("prompts", [])
 
     latest_role = None
     for prompt in prompts:
@@ -429,5 +411,5 @@ def test_chat_parameter_extractor_with_memory(setup_anthropic_mock):
         if latest_role is not None:
             assert latest_role != prompt.get("role")
 
-        if prompt.get("role") in ["user", "assistant"]:
+        if prompt.get("role") in {"user", "assistant"}:
             latest_role = prompt.get("role")
diff --git a/api/tests/integration_tests/workflow/nodes/test_template_transform.py b/api/tests/integration_tests/workflow/nodes/test_template_transform.py
index 617b6370c9..073c4bb799 100644
--- a/api/tests/integration_tests/workflow/nodes/test_template_transform.py
+++ b/api/tests/integration_tests/workflow/nodes/test_template_transform.py
@@ -1,46 +1,84 @@
+import time
+import uuid
+
 import pytest
 
 from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import UserFrom
 from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.nodes.base_node import UserFrom
+from core.workflow.enums import SystemVariableKey
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes.template_transform.template_transform_node import TemplateTransformNode
-from models.workflow import WorkflowNodeExecutionStatus
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
 from tests.integration_tests.workflow.nodes.__mock.code_executor import setup_code_executor_mock
 
 
 @pytest.mark.parametrize("setup_code_executor_mock", [["none"]], indirect=True)
 def test_execute_code(setup_code_executor_mock):
     code = """{{args2}}"""
-    node = TemplateTransformNode(
+    config = {
+        "id": "1",
+        "data": {
+            "title": "123",
+            "variables": [
+                {
+                    "variable": "args1",
+                    "value_selector": ["1", "123", "args1"],
+                },
+                {"variable": "args2", "value_selector": ["1", "123", "args2"]},
+            ],
+            "template": code,
+        },
+    }
+
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-next-target",
+                "source": "start",
+                "target": "1",
+            },
+        ],
+        "nodes": [{"data": {"type": "start"}, "id": "start"}, config],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
         tenant_id="1",
         app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
         workflow_id="1",
+        graph_config=graph_config,
         user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.END_USER,
-        config={
-            "id": "1",
-            "data": {
-                "title": "123",
-                "variables": [
-                    {
-                        "variable": "args1",
-                        "value_selector": ["1", "123", "args1"],
-                    },
-                    {"variable": "args2", "value_selector": ["1", "123", "args2"]},
-                ],
-                "template": code,
-            },
-        },
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
     )
 
     # construct variable pool
-    pool = VariablePool(system_variables={}, user_inputs={}, environment_variables=[])
-    pool.add(["1", "123", "args1"], 1)
-    pool.add(["1", "123", "args2"], 3)
+    variable_pool = VariablePool(
+        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"},
+        user_inputs={},
+        environment_variables=[],
+        conversation_variables=[],
+    )
+    variable_pool.add(["1", "123", "args1"], 1)
+    variable_pool.add(["1", "123", "args2"], 3)
+
+    node = TemplateTransformNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
+        config=config,
+    )
 
     # execute node
-    result = node.run(pool)
+    result = node._run()
 
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+    assert result.outputs is not None
     assert result.outputs["output"] == "3"
diff --git a/api/tests/integration_tests/workflow/nodes/test_tool.py b/api/tests/integration_tests/workflow/nodes/test_tool.py
index 29c1efa8e7..4d94cdb28a 100644
--- a/api/tests/integration_tests/workflow/nodes/test_tool.py
+++ b/api/tests/integration_tests/workflow/nodes/test_tool.py
@@ -1,21 +1,62 @@
+import time
+import uuid
+
 from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import NodeRunResult, UserFrom
 from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.nodes.base_node import UserFrom
+from core.workflow.enums import SystemVariableKey
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes.tool.tool_node import ToolNode
-from models.workflow import WorkflowNodeExecutionStatus
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
+
+
+def init_tool_node(config: dict):
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-next-target",
+                "source": "start",
+                "target": "1",
+            },
+        ],
+        "nodes": [{"data": {"type": "start"}, "id": "start"}, config],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
+        tenant_id="1",
+        app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
+        workflow_id="1",
+        graph_config=graph_config,
+        user_id="1",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    variable_pool = VariablePool(
+        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"},
+        user_inputs={},
+        environment_variables=[],
+        conversation_variables=[],
+    )
+
+    return ToolNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
+        config=config,
+    )
 
 
 def test_tool_variable_invoke():
-    pool = VariablePool(system_variables={}, user_inputs={}, environment_variables=[])
-    pool.add(["1", "123", "args1"], "1+1")
-
-    node = ToolNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.ACCOUNT,
+    node = init_tool_node(
         config={
             "id": "1",
             "data": {
@@ -34,28 +75,22 @@ def test_tool_variable_invoke():
                     }
                 },
             },
-        },
+        }
     )
 
-    # execute node
-    result = node.run(pool)
+    node.graph_runtime_state.variable_pool.add(["1", "123", "args1"], "1+1")
 
+    # execute node
+    result = node._run()
+    assert isinstance(result, NodeRunResult)
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+    assert result.outputs is not None
     assert "2" in result.outputs["text"]
     assert result.outputs["files"] == []
 
 
 def test_tool_mixed_invoke():
-    pool = VariablePool(system_variables={}, user_inputs={}, environment_variables=[])
-    pool.add(["1", "args1"], "1+1")
-
-    node = ToolNode(
-        tenant_id="1",
-        app_id="1",
-        workflow_id="1",
-        user_id="1",
-        invoke_from=InvokeFrom.WEB_APP,
-        user_from=UserFrom.ACCOUNT,
+    node = init_tool_node(
         config={
             "id": "1",
             "data": {
@@ -74,12 +109,15 @@ def test_tool_mixed_invoke():
                     }
                 },
             },
-        },
+        }
     )
 
-    # execute node
-    result = node.run(pool)
+    node.graph_runtime_state.variable_pool.add(["1", "args1"], "1+1")
 
+    # execute node
+    result = node._run()
+    assert isinstance(result, NodeRunResult)
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+    assert result.outputs is not None
     assert "2" in result.outputs["text"]
     assert result.outputs["files"] == []
diff --git a/api/tests/unit_tests/conftest.py b/api/tests/unit_tests/conftest.py
index afc9802cf1..621c995a4b 100644
--- a/api/tests/unit_tests/conftest.py
+++ b/api/tests/unit_tests/conftest.py
@@ -1,7 +1,24 @@
 import os
 
+import pytest
+from flask import Flask
+
 # Getting the absolute path of the current file's directory
 ABS_PATH = os.path.dirname(os.path.abspath(__file__))
 
 # Getting the absolute path of the project's root directory
 PROJECT_DIR = os.path.abspath(os.path.join(ABS_PATH, os.pardir, os.pardir))
+
+CACHED_APP = Flask(__name__)
+CACHED_APP.config.update({"TESTING": True})
+
+
+@pytest.fixture
+def app() -> Flask:
+    return CACHED_APP
+
+
+@pytest.fixture(autouse=True)
+def _provide_app_context(app: Flask):
+    with app.app_context():
+        yield
diff --git a/api/tests/unit_tests/core/app/segments/test_segment.py b/api/tests/unit_tests/core/app/segments/test_segment.py
index 7cc339d212..73002623f0 100644
--- a/api/tests/unit_tests/core/app/segments/test_segment.py
+++ b/api/tests/unit_tests/core/app/segments/test_segment.py
@@ -21,9 +21,9 @@ def test_segment_group_to_text():
     segments_group = parser.convert_template(template=template, variable_pool=variable_pool)
 
     assert segments_group.text == "Hello, fake-user-id! Your query is fake-user-query. And your key is fake-secret-key."
-    assert (
-        segments_group.log
-        == f"Hello, fake-user-id! Your query is fake-user-query. And your key is {encrypter.obfuscated_token('fake-secret-key')}."
+    assert segments_group.log == (
+        f"Hello, fake-user-id! Your query is fake-user-query."
+        f" And your key is {encrypter.obfuscated_token('fake-secret-key')}."
     )
 
 
diff --git a/api/tests/unit_tests/core/helper/test_ssrf_proxy.py b/api/tests/unit_tests/core/helper/test_ssrf_proxy.py
index 7a0bc70c63..d6e6b0b79c 100644
--- a/api/tests/unit_tests/core/helper/test_ssrf_proxy.py
+++ b/api/tests/unit_tests/core/helper/test_ssrf_proxy.py
@@ -1,6 +1,8 @@
 import random
 from unittest.mock import MagicMock, patch
 
+import pytest
+
 from core.helper.ssrf_proxy import SSRF_DEFAULT_MAX_RETRIES, STATUS_FORCELIST, make_request
 
 
@@ -22,11 +24,9 @@ def test_retry_exceed_max_retries(mock_request):
     side_effects = [mock_response] * SSRF_DEFAULT_MAX_RETRIES
     mock_request.side_effect = side_effects
 
-    try:
+    with pytest.raises(Exception) as e:
         make_request("GET", "http://example.com", max_retries=SSRF_DEFAULT_MAX_RETRIES - 1)
-        raise AssertionError("Expected Exception not raised")
-    except Exception as e:
-        assert str(e) == f"Reached maximum retries ({SSRF_DEFAULT_MAX_RETRIES - 1}) for URL http://example.com"
+    assert str(e.value) == f"Reached maximum retries ({SSRF_DEFAULT_MAX_RETRIES - 1}) for URL http://example.com"
 
 
 @patch("httpx.request")
diff --git a/api/tests/unit_tests/core/prompt/test_advanced_prompt_transform.py b/api/tests/unit_tests/core/prompt/test_advanced_prompt_transform.py
index 24bbde6d4e..24b338601d 100644
--- a/api/tests/unit_tests/core/prompt/test_advanced_prompt_transform.py
+++ b/api/tests/unit_tests/core/prompt/test_advanced_prompt_transform.py
@@ -53,7 +53,7 @@ def test__get_completion_model_prompt_messages():
             "#context#": context,
             "#histories#": "\n".join(
                 [
-                    f"{'Human' if prompt.role.value == 'user' else 'Assistant'}: " f"{prompt.content}"
+                    f"{'Human' if prompt.role.value == 'user' else 'Assistant'}: {prompt.content}"
                     for prompt in history_prompt_messages
                 ]
             ),
diff --git a/api/tests/unit_tests/core/rag/datasource/vdb/milvus/test_milvus.py b/api/tests/unit_tests/core/rag/datasource/vdb/milvus/test_milvus.py
index 8d735cae86..bd414c88f4 100644
--- a/api/tests/unit_tests/core/rag/datasource/vdb/milvus/test_milvus.py
+++ b/api/tests/unit_tests/core/rag/datasource/vdb/milvus/test_milvus.py
@@ -5,7 +5,7 @@ from core.rag.datasource.vdb.milvus.milvus_vector import MilvusConfig
 
 
 def test_default_value():
-    valid_config = {"host": "localhost", "port": 19530, "user": "root", "password": "Milvus"}
+    valid_config = {"uri": "http://localhost:19530", "user": "root", "password": "Milvus"}
 
     for key in valid_config:
         config = valid_config.copy()
@@ -15,5 +15,4 @@ def test_default_value():
         assert e.value.errors()[0]["msg"] == f"Value error, config MILVUS_{key.upper()} is required"
 
     config = MilvusConfig(**valid_config)
-    assert config.secure is False
     assert config.database == "default"
diff --git a/api/tests/unit_tests/core/workflow/graph_engine/__init__.py b/api/tests/unit_tests/core/workflow/graph_engine/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/tests/unit_tests/core/workflow/graph_engine/test_graph.py b/api/tests/unit_tests/core/workflow/graph_engine/test_graph.py
new file mode 100644
index 0000000000..13ba11016a
--- /dev/null
+++ b/api/tests/unit_tests/core/workflow/graph_engine/test_graph.py
@@ -0,0 +1,791 @@
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.run_condition import RunCondition
+from core.workflow.utils.condition.entities import Condition
+
+
+def test_init():
+    graph_config = {
+        "edges": [
+            {
+                "id": "llm-source-answer-target",
+                "source": "llm",
+                "target": "answer",
+            },
+            {
+                "id": "start-source-qc-target",
+                "source": "start",
+                "target": "qc",
+            },
+            {
+                "id": "qc-1-llm-target",
+                "source": "qc",
+                "sourceHandle": "1",
+                "target": "llm",
+            },
+            {
+                "id": "qc-2-http-target",
+                "source": "qc",
+                "sourceHandle": "2",
+                "target": "http",
+            },
+            {
+                "id": "http-source-answer2-target",
+                "source": "http",
+                "target": "answer2",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1"},
+                "id": "answer",
+            },
+            {
+                "data": {"type": "question-classifier"},
+                "id": "qc",
+            },
+            {
+                "data": {
+                    "type": "http-request",
+                },
+                "id": "http",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1"},
+                "id": "answer2",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    start_node_id = "start"
+
+    assert graph.root_node_id == start_node_id
+    assert graph.edge_mapping.get(start_node_id)[0].target_node_id == "qc"
+    assert {"llm", "http"} == {node.target_node_id for node in graph.edge_mapping.get("qc")}
+
+
+def test__init_iteration_graph():
+    graph_config = {
+        "edges": [
+            {
+                "id": "llm-answer",
+                "source": "llm",
+                "sourceHandle": "source",
+                "target": "answer",
+            },
+            {
+                "id": "iteration-source-llm-target",
+                "source": "iteration",
+                "sourceHandle": "source",
+                "target": "llm",
+            },
+            {
+                "id": "template-transform-in-iteration-source-llm-in-iteration-target",
+                "source": "template-transform-in-iteration",
+                "sourceHandle": "source",
+                "target": "llm-in-iteration",
+            },
+            {
+                "id": "llm-in-iteration-source-answer-in-iteration-target",
+                "source": "llm-in-iteration",
+                "sourceHandle": "source",
+                "target": "answer-in-iteration",
+            },
+            {
+                "id": "start-source-code-target",
+                "source": "start",
+                "sourceHandle": "source",
+                "target": "code",
+            },
+            {
+                "id": "code-source-iteration-target",
+                "source": "code",
+                "sourceHandle": "source",
+                "target": "iteration",
+            },
+        ],
+        "nodes": [
+            {
+                "data": {
+                    "type": "start",
+                },
+                "id": "start",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1"},
+                "id": "answer",
+            },
+            {
+                "data": {"type": "iteration"},
+                "id": "iteration",
+            },
+            {
+                "data": {
+                    "type": "template-transform",
+                },
+                "id": "template-transform-in-iteration",
+                "parentId": "iteration",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm-in-iteration",
+                "parentId": "iteration",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1"},
+                "id": "answer-in-iteration",
+                "parentId": "iteration",
+            },
+            {
+                "data": {
+                    "type": "code",
+                },
+                "id": "code",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config, root_node_id="template-transform-in-iteration")
+    graph.add_extra_edge(
+        source_node_id="answer-in-iteration",
+        target_node_id="template-transform-in-iteration",
+        run_condition=RunCondition(
+            type="condition",
+            conditions=[Condition(variable_selector=["iteration", "index"], comparison_operator="≤", value="5")],
+        ),
+    )
+
+    # iteration:
+    #   [template-transform-in-iteration -> llm-in-iteration -> answer-in-iteration]
+
+    assert graph.root_node_id == "template-transform-in-iteration"
+    assert graph.edge_mapping.get("template-transform-in-iteration")[0].target_node_id == "llm-in-iteration"
+    assert graph.edge_mapping.get("llm-in-iteration")[0].target_node_id == "answer-in-iteration"
+    assert graph.edge_mapping.get("answer-in-iteration")[0].target_node_id == "template-transform-in-iteration"
+
+
+def test_parallels_graph():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-llm1-target",
+                "source": "start",
+                "target": "llm1",
+            },
+            {
+                "id": "start-source-llm2-target",
+                "source": "start",
+                "target": "llm2",
+            },
+            {
+                "id": "start-source-llm3-target",
+                "source": "start",
+                "target": "llm3",
+            },
+            {
+                "id": "llm1-source-answer-target",
+                "source": "llm1",
+                "target": "answer",
+            },
+            {
+                "id": "llm2-source-answer-target",
+                "source": "llm2",
+                "target": "answer",
+            },
+            {
+                "id": "llm3-source-answer-target",
+                "source": "llm3",
+                "target": "answer",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm1",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm2",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm3",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1"},
+                "id": "answer",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    assert graph.root_node_id == "start"
+    for i in range(3):
+        start_edges = graph.edge_mapping.get("start")
+        assert start_edges is not None
+        assert start_edges[i].target_node_id == f"llm{i + 1}"
+
+        llm_edges = graph.edge_mapping.get(f"llm{i + 1}")
+        assert llm_edges is not None
+        assert llm_edges[0].target_node_id == "answer"
+
+    assert len(graph.parallel_mapping) == 1
+    assert len(graph.node_parallel_mapping) == 3
+
+    for node_id in ["llm1", "llm2", "llm3"]:
+        assert node_id in graph.node_parallel_mapping
+
+
+def test_parallels_graph2():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-llm1-target",
+                "source": "start",
+                "target": "llm1",
+            },
+            {
+                "id": "start-source-llm2-target",
+                "source": "start",
+                "target": "llm2",
+            },
+            {
+                "id": "start-source-llm3-target",
+                "source": "start",
+                "target": "llm3",
+            },
+            {
+                "id": "llm1-source-answer-target",
+                "source": "llm1",
+                "target": "answer",
+            },
+            {
+                "id": "llm2-source-answer-target",
+                "source": "llm2",
+                "target": "answer",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm1",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm2",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm3",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1"},
+                "id": "answer",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    assert graph.root_node_id == "start"
+    for i in range(3):
+        assert graph.edge_mapping.get("start")[i].target_node_id == f"llm{i + 1}"
+
+        if i < 2:
+            assert graph.edge_mapping.get(f"llm{i + 1}") is not None
+            assert graph.edge_mapping.get(f"llm{i + 1}")[0].target_node_id == "answer"
+
+    assert len(graph.parallel_mapping) == 1
+    assert len(graph.node_parallel_mapping) == 3
+
+    for node_id in ["llm1", "llm2", "llm3"]:
+        assert node_id in graph.node_parallel_mapping
+
+
+def test_parallels_graph3():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-llm1-target",
+                "source": "start",
+                "target": "llm1",
+            },
+            {
+                "id": "start-source-llm2-target",
+                "source": "start",
+                "target": "llm2",
+            },
+            {
+                "id": "start-source-llm3-target",
+                "source": "start",
+                "target": "llm3",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm1",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm2",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm3",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1"},
+                "id": "answer",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    assert graph.root_node_id == "start"
+    for i in range(3):
+        assert graph.edge_mapping.get("start")[i].target_node_id == f"llm{i + 1}"
+
+    assert len(graph.parallel_mapping) == 1
+    assert len(graph.node_parallel_mapping) == 3
+
+    for node_id in ["llm1", "llm2", "llm3"]:
+        assert node_id in graph.node_parallel_mapping
+
+
+def test_parallels_graph4():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-llm1-target",
+                "source": "start",
+                "target": "llm1",
+            },
+            {
+                "id": "start-source-llm2-target",
+                "source": "start",
+                "target": "llm2",
+            },
+            {
+                "id": "start-source-llm3-target",
+                "source": "start",
+                "target": "llm3",
+            },
+            {
+                "id": "llm1-source-answer-target",
+                "source": "llm1",
+                "target": "code1",
+            },
+            {
+                "id": "llm2-source-answer-target",
+                "source": "llm2",
+                "target": "code2",
+            },
+            {
+                "id": "llm3-source-code3-target",
+                "source": "llm3",
+                "target": "code3",
+            },
+            {
+                "id": "code1-source-answer-target",
+                "source": "code1",
+                "target": "answer",
+            },
+            {
+                "id": "code2-source-answer-target",
+                "source": "code2",
+                "target": "answer",
+            },
+            {
+                "id": "code3-source-answer-target",
+                "source": "code3",
+                "target": "answer",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm1",
+            },
+            {
+                "data": {
+                    "type": "code",
+                },
+                "id": "code1",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm2",
+            },
+            {
+                "data": {
+                    "type": "code",
+                },
+                "id": "code2",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm3",
+            },
+            {
+                "data": {
+                    "type": "code",
+                },
+                "id": "code3",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1"},
+                "id": "answer",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    assert graph.root_node_id == "start"
+    for i in range(3):
+        assert graph.edge_mapping.get("start")[i].target_node_id == f"llm{i + 1}"
+        assert graph.edge_mapping.get(f"llm{i + 1}") is not None
+        assert graph.edge_mapping.get(f"llm{i + 1}")[0].target_node_id == f"code{i + 1}"
+        assert graph.edge_mapping.get(f"code{i + 1}") is not None
+        assert graph.edge_mapping.get(f"code{i + 1}")[0].target_node_id == "answer"
+
+    assert len(graph.parallel_mapping) == 1
+    assert len(graph.node_parallel_mapping) == 6
+
+    for node_id in ["llm1", "llm2", "llm3", "code1", "code2", "code3"]:
+        assert node_id in graph.node_parallel_mapping
+
+
+def test_parallels_graph5():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-llm1-target",
+                "source": "start",
+                "target": "llm1",
+            },
+            {
+                "id": "start-source-llm2-target",
+                "source": "start",
+                "target": "llm2",
+            },
+            {
+                "id": "start-source-llm3-target",
+                "source": "start",
+                "target": "llm3",
+            },
+            {
+                "id": "start-source-llm3-target",
+                "source": "start",
+                "target": "llm4",
+            },
+            {
+                "id": "start-source-llm3-target",
+                "source": "start",
+                "target": "llm5",
+            },
+            {
+                "id": "llm1-source-code1-target",
+                "source": "llm1",
+                "target": "code1",
+            },
+            {
+                "id": "llm2-source-code1-target",
+                "source": "llm2",
+                "target": "code1",
+            },
+            {
+                "id": "llm3-source-code2-target",
+                "source": "llm3",
+                "target": "code2",
+            },
+            {
+                "id": "llm4-source-code2-target",
+                "source": "llm4",
+                "target": "code2",
+            },
+            {
+                "id": "llm5-source-code3-target",
+                "source": "llm5",
+                "target": "code3",
+            },
+            {
+                "id": "code1-source-answer-target",
+                "source": "code1",
+                "target": "answer",
+            },
+            {
+                "id": "code2-source-answer-target",
+                "source": "code2",
+                "target": "answer",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm1",
+            },
+            {
+                "data": {
+                    "type": "code",
+                },
+                "id": "code1",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm2",
+            },
+            {
+                "data": {
+                    "type": "code",
+                },
+                "id": "code2",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm3",
+            },
+            {
+                "data": {
+                    "type": "code",
+                },
+                "id": "code3",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1"},
+                "id": "answer",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm4",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm5",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    assert graph.root_node_id == "start"
+    for i in range(5):
+        assert graph.edge_mapping.get("start")[i].target_node_id == f"llm{i + 1}"
+
+    assert graph.edge_mapping.get("llm1") is not None
+    assert graph.edge_mapping.get("llm1")[0].target_node_id == "code1"
+    assert graph.edge_mapping.get("llm2") is not None
+    assert graph.edge_mapping.get("llm2")[0].target_node_id == "code1"
+    assert graph.edge_mapping.get("llm3") is not None
+    assert graph.edge_mapping.get("llm3")[0].target_node_id == "code2"
+    assert graph.edge_mapping.get("llm4") is not None
+    assert graph.edge_mapping.get("llm4")[0].target_node_id == "code2"
+    assert graph.edge_mapping.get("llm5") is not None
+    assert graph.edge_mapping.get("llm5")[0].target_node_id == "code3"
+    assert graph.edge_mapping.get("code1") is not None
+    assert graph.edge_mapping.get("code1")[0].target_node_id == "answer"
+    assert graph.edge_mapping.get("code2") is not None
+    assert graph.edge_mapping.get("code2")[0].target_node_id == "answer"
+
+    assert len(graph.parallel_mapping) == 1
+    assert len(graph.node_parallel_mapping) == 8
+
+    for node_id in ["llm1", "llm2", "llm3", "llm4", "llm5", "code1", "code2", "code3"]:
+        assert node_id in graph.node_parallel_mapping
+
+
+def test_parallels_graph6():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-llm1-target",
+                "source": "start",
+                "target": "llm1",
+            },
+            {
+                "id": "start-source-llm2-target",
+                "source": "start",
+                "target": "llm2",
+            },
+            {
+                "id": "start-source-llm3-target",
+                "source": "start",
+                "target": "llm3",
+            },
+            {
+                "id": "llm1-source-code1-target",
+                "source": "llm1",
+                "target": "code1",
+            },
+            {
+                "id": "llm1-source-code2-target",
+                "source": "llm1",
+                "target": "code2",
+            },
+            {
+                "id": "llm2-source-code3-target",
+                "source": "llm2",
+                "target": "code3",
+            },
+            {
+                "id": "code1-source-answer-target",
+                "source": "code1",
+                "target": "answer",
+            },
+            {
+                "id": "code2-source-answer-target",
+                "source": "code2",
+                "target": "answer",
+            },
+            {
+                "id": "code3-source-answer-target",
+                "source": "code3",
+                "target": "answer",
+            },
+            {
+                "id": "llm3-source-answer-target",
+                "source": "llm3",
+                "target": "answer",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm1",
+            },
+            {
+                "data": {
+                    "type": "code",
+                },
+                "id": "code1",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm2",
+            },
+            {
+                "data": {
+                    "type": "code",
+                },
+                "id": "code2",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm3",
+            },
+            {
+                "data": {
+                    "type": "code",
+                },
+                "id": "code3",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1"},
+                "id": "answer",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    assert graph.root_node_id == "start"
+    for i in range(3):
+        assert graph.edge_mapping.get("start")[i].target_node_id == f"llm{i + 1}"
+
+    assert graph.edge_mapping.get("llm1") is not None
+    assert graph.edge_mapping.get("llm1")[0].target_node_id == "code1"
+    assert graph.edge_mapping.get("llm1") is not None
+    assert graph.edge_mapping.get("llm1")[1].target_node_id == "code2"
+    assert graph.edge_mapping.get("llm2") is not None
+    assert graph.edge_mapping.get("llm2")[0].target_node_id == "code3"
+    assert graph.edge_mapping.get("code1") is not None
+    assert graph.edge_mapping.get("code1")[0].target_node_id == "answer"
+    assert graph.edge_mapping.get("code2") is not None
+    assert graph.edge_mapping.get("code2")[0].target_node_id == "answer"
+    assert graph.edge_mapping.get("code3") is not None
+    assert graph.edge_mapping.get("code3")[0].target_node_id == "answer"
+
+    assert len(graph.parallel_mapping) == 2
+    assert len(graph.node_parallel_mapping) == 6
+
+    for node_id in ["llm1", "llm2", "llm3", "code1", "code2", "code3"]:
+        assert node_id in graph.node_parallel_mapping
+
+    parent_parallel = None
+    child_parallel = None
+    for p_id, parallel in graph.parallel_mapping.items():
+        if parallel.parent_parallel_id is None:
+            parent_parallel = parallel
+        else:
+            child_parallel = parallel
+
+    for node_id in ["llm1", "llm2", "llm3", "code3"]:
+        assert graph.node_parallel_mapping[node_id] == parent_parallel.id
+
+    for node_id in ["code1", "code2"]:
+        assert graph.node_parallel_mapping[node_id] == child_parallel.id
diff --git a/api/tests/unit_tests/core/workflow/graph_engine/test_graph_engine.py b/api/tests/unit_tests/core/workflow/graph_engine/test_graph_engine.py
new file mode 100644
index 0000000000..197288adba
--- /dev/null
+++ b/api/tests/unit_tests/core/workflow/graph_engine/test_graph_engine.py
@@ -0,0 +1,505 @@
+from unittest.mock import patch
+
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, UserFrom
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariableKey
+from core.workflow.graph_engine.entities.event import (
+    BaseNodeEvent,
+    GraphRunFailedEvent,
+    GraphRunStartedEvent,
+    GraphRunSucceededEvent,
+    NodeRunFailedEvent,
+    NodeRunStartedEvent,
+    NodeRunStreamChunkEvent,
+    NodeRunSucceededEvent,
+)
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
+from core.workflow.graph_engine.graph_engine import GraphEngine
+from core.workflow.nodes.event import RunCompletedEvent, RunStreamChunkEvent
+from core.workflow.nodes.llm.llm_node import LLMNode
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
+
+
+@patch("extensions.ext_database.db.session.remove")
+@patch("extensions.ext_database.db.session.close")
+def test_run_parallel_in_workflow(mock_close, mock_remove):
+    graph_config = {
+        "edges": [
+            {
+                "id": "1",
+                "source": "start",
+                "target": "llm1",
+            },
+            {
+                "id": "2",
+                "source": "llm1",
+                "target": "llm2",
+            },
+            {
+                "id": "3",
+                "source": "llm1",
+                "target": "llm3",
+            },
+            {
+                "id": "4",
+                "source": "llm2",
+                "target": "end1",
+            },
+            {
+                "id": "5",
+                "source": "llm3",
+                "target": "end2",
+            },
+        ],
+        "nodes": [
+            {
+                "data": {
+                    "type": "start",
+                    "title": "start",
+                    "variables": [
+                        {
+                            "label": "query",
+                            "max_length": 48,
+                            "options": [],
+                            "required": True,
+                            "type": "text-input",
+                            "variable": "query",
+                        }
+                    ],
+                },
+                "id": "start",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                    "title": "llm1",
+                    "context": {"enabled": False, "variable_selector": []},
+                    "model": {
+                        "completion_params": {"temperature": 0.7},
+                        "mode": "chat",
+                        "name": "gpt-4o",
+                        "provider": "openai",
+                    },
+                    "prompt_template": [
+                        {"role": "system", "text": "say hi"},
+                        {"role": "user", "text": "{{#start.query#}}"},
+                    ],
+                    "vision": {"configs": {"detail": "high"}, "enabled": False},
+                },
+                "id": "llm1",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                    "title": "llm2",
+                    "context": {"enabled": False, "variable_selector": []},
+                    "model": {
+                        "completion_params": {"temperature": 0.7},
+                        "mode": "chat",
+                        "name": "gpt-4o",
+                        "provider": "openai",
+                    },
+                    "prompt_template": [
+                        {"role": "system", "text": "say bye"},
+                        {"role": "user", "text": "{{#start.query#}}"},
+                    ],
+                    "vision": {"configs": {"detail": "high"}, "enabled": False},
+                },
+                "id": "llm2",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                    "title": "llm3",
+                    "context": {"enabled": False, "variable_selector": []},
+                    "model": {
+                        "completion_params": {"temperature": 0.7},
+                        "mode": "chat",
+                        "name": "gpt-4o",
+                        "provider": "openai",
+                    },
+                    "prompt_template": [
+                        {"role": "system", "text": "say good morning"},
+                        {"role": "user", "text": "{{#start.query#}}"},
+                    ],
+                    "vision": {"configs": {"detail": "high"}, "enabled": False},
+                },
+                "id": "llm3",
+            },
+            {
+                "data": {
+                    "type": "end",
+                    "title": "end1",
+                    "outputs": [
+                        {"value_selector": ["llm2", "text"], "variable": "result2"},
+                        {"value_selector": ["start", "query"], "variable": "query"},
+                    ],
+                },
+                "id": "end1",
+            },
+            {
+                "data": {
+                    "type": "end",
+                    "title": "end2",
+                    "outputs": [
+                        {"value_selector": ["llm1", "text"], "variable": "result1"},
+                        {"value_selector": ["llm3", "text"], "variable": "result3"},
+                    ],
+                },
+                "id": "end2",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    variable_pool = VariablePool(
+        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"}, user_inputs={"query": "hi"}
+    )
+
+    graph_engine = GraphEngine(
+        tenant_id="111",
+        app_id="222",
+        workflow_type=WorkflowType.WORKFLOW,
+        workflow_id="333",
+        graph_config=graph_config,
+        user_id="444",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.WEB_APP,
+        call_depth=0,
+        graph=graph,
+        variable_pool=variable_pool,
+        max_execution_steps=500,
+        max_execution_time=1200,
+    )
+
+    def llm_generator(self):
+        contents = ["hi", "bye", "good morning"]
+
+        yield RunStreamChunkEvent(
+            chunk_content=contents[int(self.node_id[-1]) - 1], from_variable_selector=[self.node_id, "text"]
+        )
+
+        yield RunCompletedEvent(
+            run_result=NodeRunResult(
+                status=WorkflowNodeExecutionStatus.SUCCEEDED,
+                inputs={},
+                process_data={},
+                outputs={},
+                metadata={
+                    NodeRunMetadataKey.TOTAL_TOKENS: 1,
+                    NodeRunMetadataKey.TOTAL_PRICE: 1,
+                    NodeRunMetadataKey.CURRENCY: "USD",
+                },
+            )
+        )
+
+    # print("")
+
+    with patch.object(LLMNode, "_run", new=llm_generator):
+        items = []
+        generator = graph_engine.run()
+        for item in generator:
+            # print(type(item), item)
+            items.append(item)
+            if isinstance(item, NodeRunSucceededEvent):
+                assert item.route_node_state.status == RouteNodeState.Status.SUCCESS
+
+            assert not isinstance(item, NodeRunFailedEvent)
+            assert not isinstance(item, GraphRunFailedEvent)
+
+            if isinstance(item, BaseNodeEvent) and item.route_node_state.node_id in {"llm2", "llm3", "end1", "end2"}:
+                assert item.parallel_id is not None
+
+        assert len(items) == 18
+        assert isinstance(items[0], GraphRunStartedEvent)
+        assert isinstance(items[1], NodeRunStartedEvent)
+        assert items[1].route_node_state.node_id == "start"
+        assert isinstance(items[2], NodeRunSucceededEvent)
+        assert items[2].route_node_state.node_id == "start"
+
+
+@patch("extensions.ext_database.db.session.remove")
+@patch("extensions.ext_database.db.session.close")
+def test_run_parallel_in_chatflow(mock_close, mock_remove):
+    graph_config = {
+        "edges": [
+            {
+                "id": "1",
+                "source": "start",
+                "target": "answer1",
+            },
+            {
+                "id": "2",
+                "source": "answer1",
+                "target": "answer2",
+            },
+            {
+                "id": "3",
+                "source": "answer1",
+                "target": "answer3",
+            },
+            {
+                "id": "4",
+                "source": "answer2",
+                "target": "answer4",
+            },
+            {
+                "id": "5",
+                "source": "answer3",
+                "target": "answer5",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start", "title": "start"}, "id": "start"},
+            {"data": {"type": "answer", "title": "answer1", "answer": "1"}, "id": "answer1"},
+            {
+                "data": {"type": "answer", "title": "answer2", "answer": "2"},
+                "id": "answer2",
+            },
+            {
+                "data": {"type": "answer", "title": "answer3", "answer": "3"},
+                "id": "answer3",
+            },
+            {
+                "data": {"type": "answer", "title": "answer4", "answer": "4"},
+                "id": "answer4",
+            },
+            {
+                "data": {"type": "answer", "title": "answer5", "answer": "5"},
+                "id": "answer5",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    variable_pool = VariablePool(
+        system_variables={
+            SystemVariableKey.QUERY: "what's the weather in SF",
+            SystemVariableKey.FILES: [],
+            SystemVariableKey.CONVERSATION_ID: "abababa",
+            SystemVariableKey.USER_ID: "aaa",
+        },
+        user_inputs={},
+    )
+
+    graph_engine = GraphEngine(
+        tenant_id="111",
+        app_id="222",
+        workflow_type=WorkflowType.CHAT,
+        workflow_id="333",
+        graph_config=graph_config,
+        user_id="444",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.WEB_APP,
+        call_depth=0,
+        graph=graph,
+        variable_pool=variable_pool,
+        max_execution_steps=500,
+        max_execution_time=1200,
+    )
+
+    # print("")
+
+    items = []
+    generator = graph_engine.run()
+    for item in generator:
+        # print(type(item), item)
+        items.append(item)
+        if isinstance(item, NodeRunSucceededEvent):
+            assert item.route_node_state.status == RouteNodeState.Status.SUCCESS
+
+        assert not isinstance(item, NodeRunFailedEvent)
+        assert not isinstance(item, GraphRunFailedEvent)
+
+        if isinstance(item, BaseNodeEvent) and item.route_node_state.node_id in {
+            "answer2",
+            "answer3",
+            "answer4",
+            "answer5",
+        }:
+            assert item.parallel_id is not None
+
+    assert len(items) == 23
+    assert isinstance(items[0], GraphRunStartedEvent)
+    assert isinstance(items[1], NodeRunStartedEvent)
+    assert items[1].route_node_state.node_id == "start"
+    assert isinstance(items[2], NodeRunSucceededEvent)
+    assert items[2].route_node_state.node_id == "start"
+
+
+@patch("extensions.ext_database.db.session.remove")
+@patch("extensions.ext_database.db.session.close")
+def test_run_branch(mock_close, mock_remove):
+    graph_config = {
+        "edges": [
+            {
+                "id": "1",
+                "source": "start",
+                "target": "if-else-1",
+            },
+            {
+                "id": "2",
+                "source": "if-else-1",
+                "sourceHandle": "true",
+                "target": "answer-1",
+            },
+            {
+                "id": "3",
+                "source": "if-else-1",
+                "sourceHandle": "false",
+                "target": "if-else-2",
+            },
+            {
+                "id": "4",
+                "source": "if-else-2",
+                "sourceHandle": "true",
+                "target": "answer-2",
+            },
+            {
+                "id": "5",
+                "source": "if-else-2",
+                "sourceHandle": "false",
+                "target": "answer-3",
+            },
+        ],
+        "nodes": [
+            {
+                "data": {
+                    "title": "Start",
+                    "type": "start",
+                    "variables": [
+                        {
+                            "label": "uid",
+                            "max_length": 48,
+                            "options": [],
+                            "required": True,
+                            "type": "text-input",
+                            "variable": "uid",
+                        }
+                    ],
+                },
+                "id": "start",
+            },
+            {
+                "data": {"answer": "1 {{#start.uid#}}", "title": "Answer", "type": "answer", "variables": []},
+                "id": "answer-1",
+            },
+            {
+                "data": {
+                    "cases": [
+                        {
+                            "case_id": "true",
+                            "conditions": [
+                                {
+                                    "comparison_operator": "contains",
+                                    "id": "b0f02473-08b6-4a81-af91-15345dcb2ec8",
+                                    "value": "hi",
+                                    "varType": "string",
+                                    "variable_selector": ["sys", "query"],
+                                }
+                            ],
+                            "id": "true",
+                            "logical_operator": "and",
+                        }
+                    ],
+                    "desc": "",
+                    "title": "IF/ELSE",
+                    "type": "if-else",
+                },
+                "id": "if-else-1",
+            },
+            {
+                "data": {
+                    "cases": [
+                        {
+                            "case_id": "true",
+                            "conditions": [
+                                {
+                                    "comparison_operator": "contains",
+                                    "id": "ae895199-5608-433b-b5f0-0997ae1431e4",
+                                    "value": "takatost",
+                                    "varType": "string",
+                                    "variable_selector": ["sys", "query"],
+                                }
+                            ],
+                            "id": "true",
+                            "logical_operator": "and",
+                        }
+                    ],
+                    "title": "IF/ELSE 2",
+                    "type": "if-else",
+                },
+                "id": "if-else-2",
+            },
+            {
+                "data": {
+                    "answer": "2",
+                    "title": "Answer 2",
+                    "type": "answer",
+                },
+                "id": "answer-2",
+            },
+            {
+                "data": {
+                    "answer": "3",
+                    "title": "Answer 3",
+                    "type": "answer",
+                },
+                "id": "answer-3",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    variable_pool = VariablePool(
+        system_variables={
+            SystemVariableKey.QUERY: "hi",
+            SystemVariableKey.FILES: [],
+            SystemVariableKey.CONVERSATION_ID: "abababa",
+            SystemVariableKey.USER_ID: "aaa",
+        },
+        user_inputs={"uid": "takato"},
+    )
+
+    graph_engine = GraphEngine(
+        tenant_id="111",
+        app_id="222",
+        workflow_type=WorkflowType.CHAT,
+        workflow_id="333",
+        graph_config=graph_config,
+        user_id="444",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.WEB_APP,
+        call_depth=0,
+        graph=graph,
+        variable_pool=variable_pool,
+        max_execution_steps=500,
+        max_execution_time=1200,
+    )
+
+    # print("")
+
+    items = []
+    generator = graph_engine.run()
+    for item in generator:
+        # print(type(item), item)
+        items.append(item)
+
+    assert len(items) == 10
+    assert items[3].route_node_state.node_id == "if-else-1"
+    assert items[4].route_node_state.node_id == "if-else-1"
+    assert isinstance(items[5], NodeRunStreamChunkEvent)
+    assert items[5].chunk_content == "1 "
+    assert isinstance(items[6], NodeRunStreamChunkEvent)
+    assert items[6].chunk_content == "takato"
+    assert items[7].route_node_state.node_id == "answer-1"
+    assert items[8].route_node_state.node_id == "answer-1"
+    assert items[8].route_node_state.node_run_result.outputs["answer"] == "1 takato"
+    assert isinstance(items[9], GraphRunSucceededEvent)
+
+    # print(graph_engine.graph_runtime_state.model_dump_json(indent=2))
diff --git a/api/tests/unit_tests/core/workflow/nodes/answer/__init__.py b/api/tests/unit_tests/core/workflow/nodes/answer/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/tests/unit_tests/core/workflow/nodes/answer/test_answer.py b/api/tests/unit_tests/core/workflow/nodes/answer/test_answer.py
new file mode 100644
index 0000000000..fe4ede6335
--- /dev/null
+++ b/api/tests/unit_tests/core/workflow/nodes/answer/test_answer.py
@@ -0,0 +1,82 @@
+import time
+import uuid
+from unittest.mock import MagicMock
+
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import UserFrom
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariableKey
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.nodes.answer.answer_node import AnswerNode
+from extensions.ext_database import db
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
+
+
+def test_execute_answer():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-llm-target",
+                "source": "start",
+                "target": "llm",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
+        tenant_id="1",
+        app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
+        workflow_id="1",
+        graph_config=graph_config,
+        user_id="1",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    pool = VariablePool(
+        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"},
+        user_inputs={},
+        environment_variables=[],
+    )
+    pool.add(["start", "weather"], "sunny")
+    pool.add(["llm", "text"], "You are a helpful AI.")
+
+    node = AnswerNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=pool, start_at=time.perf_counter()),
+        config={
+            "id": "answer",
+            "data": {
+                "title": "123",
+                "type": "answer",
+                "answer": "Today's weather is {{#start.weather#}}\n{{#llm.text#}}\n{{img}}\nFin.",
+            },
+        },
+    )
+
+    # Mock db.session.close()
+    db.session.close = MagicMock()
+
+    # execute node
+    result = node._run()
+
+    assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+    assert result.outputs["answer"] == "Today's weather is sunny\nYou are a helpful AI.\n{{img}}\nFin."
diff --git a/api/tests/unit_tests/core/workflow/nodes/answer/test_answer_stream_generate_router.py b/api/tests/unit_tests/core/workflow/nodes/answer/test_answer_stream_generate_router.py
new file mode 100644
index 0000000000..bce87536d8
--- /dev/null
+++ b/api/tests/unit_tests/core/workflow/nodes/answer/test_answer_stream_generate_router.py
@@ -0,0 +1,109 @@
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.nodes.answer.answer_stream_generate_router import AnswerStreamGeneratorRouter
+
+
+def test_init():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-llm1-target",
+                "source": "start",
+                "target": "llm1",
+            },
+            {
+                "id": "start-source-llm2-target",
+                "source": "start",
+                "target": "llm2",
+            },
+            {
+                "id": "start-source-llm3-target",
+                "source": "start",
+                "target": "llm3",
+            },
+            {
+                "id": "llm3-source-llm4-target",
+                "source": "llm3",
+                "target": "llm4",
+            },
+            {
+                "id": "llm3-source-llm5-target",
+                "source": "llm3",
+                "target": "llm5",
+            },
+            {
+                "id": "llm4-source-answer2-target",
+                "source": "llm4",
+                "target": "answer2",
+            },
+            {
+                "id": "llm5-source-answer-target",
+                "source": "llm5",
+                "target": "answer",
+            },
+            {
+                "id": "answer2-source-answer-target",
+                "source": "answer2",
+                "target": "answer",
+            },
+            {
+                "id": "llm2-source-answer-target",
+                "source": "llm2",
+                "target": "answer",
+            },
+            {
+                "id": "llm1-source-answer-target",
+                "source": "llm1",
+                "target": "answer",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm1",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm2",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm3",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm4",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm5",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "1{{#llm2.text#}}2"},
+                "id": "answer",
+            },
+            {
+                "data": {"type": "answer", "title": "answer2", "answer": "1{{#llm3.text#}}2"},
+                "id": "answer2",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    answer_stream_generate_route = AnswerStreamGeneratorRouter.init(
+        node_id_config_mapping=graph.node_id_config_mapping, reverse_edge_mapping=graph.reverse_edge_mapping
+    )
+
+    assert answer_stream_generate_route.answer_dependencies["answer"] == ["answer2"]
+    assert answer_stream_generate_route.answer_dependencies["answer2"] == []
diff --git a/api/tests/unit_tests/core/workflow/nodes/answer/test_answer_stream_processor.py b/api/tests/unit_tests/core/workflow/nodes/answer/test_answer_stream_processor.py
new file mode 100644
index 0000000000..6b1d1e9070
--- /dev/null
+++ b/api/tests/unit_tests/core/workflow/nodes/answer/test_answer_stream_processor.py
@@ -0,0 +1,216 @@
+import uuid
+from collections.abc import Generator
+from datetime import datetime, timezone
+
+from core.workflow.entities.node_entities import NodeType
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariableKey
+from core.workflow.graph_engine.entities.event import (
+    GraphEngineEvent,
+    NodeRunStartedEvent,
+    NodeRunStreamChunkEvent,
+    NodeRunSucceededEvent,
+)
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
+from core.workflow.nodes.answer.answer_stream_processor import AnswerStreamProcessor
+from core.workflow.nodes.start.entities import StartNodeData
+
+
+def _recursive_process(graph: Graph, next_node_id: str) -> Generator[GraphEngineEvent, None, None]:
+    if next_node_id == "start":
+        yield from _publish_events(graph, next_node_id)
+
+    for edge in graph.edge_mapping.get(next_node_id, []):
+        yield from _publish_events(graph, edge.target_node_id)
+
+    for edge in graph.edge_mapping.get(next_node_id, []):
+        yield from _recursive_process(graph, edge.target_node_id)
+
+
+def _publish_events(graph: Graph, next_node_id: str) -> Generator[GraphEngineEvent, None, None]:
+    route_node_state = RouteNodeState(node_id=next_node_id, start_at=datetime.now(timezone.utc).replace(tzinfo=None))
+
+    parallel_id = graph.node_parallel_mapping.get(next_node_id)
+    parallel_start_node_id = None
+    if parallel_id:
+        parallel = graph.parallel_mapping.get(parallel_id)
+        parallel_start_node_id = parallel.start_from_node_id if parallel else None
+
+    node_execution_id = str(uuid.uuid4())
+    node_config = graph.node_id_config_mapping[next_node_id]
+    node_type = NodeType.value_of(node_config.get("data", {}).get("type"))
+    mock_node_data = StartNodeData(**{"title": "demo", "variables": []})
+
+    yield NodeRunStartedEvent(
+        id=node_execution_id,
+        node_id=next_node_id,
+        node_type=node_type,
+        node_data=mock_node_data,
+        route_node_state=route_node_state,
+        parallel_id=graph.node_parallel_mapping.get(next_node_id),
+        parallel_start_node_id=parallel_start_node_id,
+    )
+
+    if "llm" in next_node_id:
+        length = int(next_node_id[-1])
+        for i in range(0, length):
+            yield NodeRunStreamChunkEvent(
+                id=node_execution_id,
+                node_id=next_node_id,
+                node_type=node_type,
+                node_data=mock_node_data,
+                chunk_content=str(i),
+                route_node_state=route_node_state,
+                from_variable_selector=[next_node_id, "text"],
+                parallel_id=parallel_id,
+                parallel_start_node_id=parallel_start_node_id,
+            )
+
+    route_node_state.status = RouteNodeState.Status.SUCCESS
+    route_node_state.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
+    yield NodeRunSucceededEvent(
+        id=node_execution_id,
+        node_id=next_node_id,
+        node_type=node_type,
+        node_data=mock_node_data,
+        route_node_state=route_node_state,
+        parallel_id=parallel_id,
+        parallel_start_node_id=parallel_start_node_id,
+    )
+
+
+def test_process():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-llm1-target",
+                "source": "start",
+                "target": "llm1",
+            },
+            {
+                "id": "start-source-llm2-target",
+                "source": "start",
+                "target": "llm2",
+            },
+            {
+                "id": "start-source-llm3-target",
+                "source": "start",
+                "target": "llm3",
+            },
+            {
+                "id": "llm3-source-llm4-target",
+                "source": "llm3",
+                "target": "llm4",
+            },
+            {
+                "id": "llm3-source-llm5-target",
+                "source": "llm3",
+                "target": "llm5",
+            },
+            {
+                "id": "llm4-source-answer2-target",
+                "source": "llm4",
+                "target": "answer2",
+            },
+            {
+                "id": "llm5-source-answer-target",
+                "source": "llm5",
+                "target": "answer",
+            },
+            {
+                "id": "answer2-source-answer-target",
+                "source": "answer2",
+                "target": "answer",
+            },
+            {
+                "id": "llm2-source-answer-target",
+                "source": "llm2",
+                "target": "answer",
+            },
+            {
+                "id": "llm1-source-answer-target",
+                "source": "llm1",
+                "target": "answer",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm1",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm2",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm3",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm4",
+            },
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm5",
+            },
+            {
+                "data": {"type": "answer", "title": "answer", "answer": "a{{#llm2.text#}}b"},
+                "id": "answer",
+            },
+            {
+                "data": {"type": "answer", "title": "answer2", "answer": "c{{#llm3.text#}}d"},
+                "id": "answer2",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    variable_pool = VariablePool(
+        system_variables={
+            SystemVariableKey.QUERY: "what's the weather in SF",
+            SystemVariableKey.FILES: [],
+            SystemVariableKey.CONVERSATION_ID: "abababa",
+            SystemVariableKey.USER_ID: "aaa",
+        },
+        user_inputs={},
+    )
+
+    answer_stream_processor = AnswerStreamProcessor(graph=graph, variable_pool=variable_pool)
+
+    def graph_generator() -> Generator[GraphEngineEvent, None, None]:
+        # print("")
+        for event in _recursive_process(graph, "start"):
+            # print("[ORIGIN]", event.__class__.__name__ + ":", event.route_node_state.node_id,
+            #       " " + (event.chunk_content if isinstance(event, NodeRunStreamChunkEvent) else ""))
+            if isinstance(event, NodeRunSucceededEvent):
+                if "llm" in event.route_node_state.node_id:
+                    variable_pool.add(
+                        [event.route_node_state.node_id, "text"],
+                        "".join(str(i) for i in range(0, int(event.route_node_state.node_id[-1]))),
+                    )
+            yield event
+
+    result_generator = answer_stream_processor.process(graph_generator())
+    stream_contents = ""
+    for event in result_generator:
+        # print("[ANSWER]", event.__class__.__name__ + ":", event.route_node_state.node_id,
+        #       " " + (event.chunk_content if isinstance(event, NodeRunStreamChunkEvent) else ""))
+        if isinstance(event, NodeRunStreamChunkEvent):
+            stream_contents += event.chunk_content
+        pass
+
+    assert stream_contents == "c012da01b"
diff --git a/api/tests/unit_tests/core/workflow/nodes/iteration/__init__.py b/api/tests/unit_tests/core/workflow/nodes/iteration/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/tests/unit_tests/core/workflow/nodes/iteration/test_iteration.py b/api/tests/unit_tests/core/workflow/nodes/iteration/test_iteration.py
new file mode 100644
index 0000000000..b3a89061b2
--- /dev/null
+++ b/api/tests/unit_tests/core/workflow/nodes/iteration/test_iteration.py
@@ -0,0 +1,420 @@
+import time
+import uuid
+from unittest.mock import patch
+
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import NodeRunResult, UserFrom
+from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.enums import SystemVariableKey
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
+from core.workflow.nodes.event import RunCompletedEvent
+from core.workflow.nodes.iteration.iteration_node import IterationNode
+from core.workflow.nodes.template_transform.template_transform_node import TemplateTransformNode
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
+
+
+def test_run():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-pe-target",
+                "source": "start",
+                "target": "pe",
+            },
+            {
+                "id": "iteration-1-source-answer-3-target",
+                "source": "iteration-1",
+                "target": "answer-3",
+            },
+            {
+                "id": "tt-source-if-else-target",
+                "source": "tt",
+                "target": "if-else",
+            },
+            {
+                "id": "if-else-true-answer-2-target",
+                "source": "if-else",
+                "sourceHandle": "true",
+                "target": "answer-2",
+            },
+            {
+                "id": "if-else-false-answer-4-target",
+                "source": "if-else",
+                "sourceHandle": "false",
+                "target": "answer-4",
+            },
+            {
+                "id": "pe-source-iteration-1-target",
+                "source": "pe",
+                "target": "iteration-1",
+            },
+        ],
+        "nodes": [
+            {"data": {"title": "Start", "type": "start", "variables": []}, "id": "start"},
+            {
+                "data": {
+                    "iterator_selector": ["pe", "list_output"],
+                    "output_selector": ["tt", "output"],
+                    "output_type": "array[string]",
+                    "startNodeType": "template-transform",
+                    "start_node_id": "tt",
+                    "title": "iteration",
+                    "type": "iteration",
+                },
+                "id": "iteration-1",
+            },
+            {
+                "data": {
+                    "answer": "{{#tt.output#}}",
+                    "iteration_id": "iteration-1",
+                    "title": "answer 2",
+                    "type": "answer",
+                },
+                "id": "answer-2",
+            },
+            {
+                "data": {
+                    "iteration_id": "iteration-1",
+                    "template": "{{ arg1 }} 123",
+                    "title": "template transform",
+                    "type": "template-transform",
+                    "variables": [{"value_selector": ["sys", "query"], "variable": "arg1"}],
+                },
+                "id": "tt",
+            },
+            {
+                "data": {"answer": "{{#iteration-1.output#}}88888", "title": "answer 3", "type": "answer"},
+                "id": "answer-3",
+            },
+            {
+                "data": {
+                    "conditions": [
+                        {
+                            "comparison_operator": "is",
+                            "id": "1721916275284",
+                            "value": "hi",
+                            "variable_selector": ["sys", "query"],
+                        }
+                    ],
+                    "iteration_id": "iteration-1",
+                    "logical_operator": "and",
+                    "title": "if",
+                    "type": "if-else",
+                },
+                "id": "if-else",
+            },
+            {
+                "data": {"answer": "no hi", "iteration_id": "iteration-1", "title": "answer 4", "type": "answer"},
+                "id": "answer-4",
+            },
+            {
+                "data": {
+                    "instruction": "test1",
+                    "model": {
+                        "completion_params": {"temperature": 0.7},
+                        "mode": "chat",
+                        "name": "gpt-4o",
+                        "provider": "openai",
+                    },
+                    "parameters": [
+                        {"description": "test", "name": "list_output", "required": False, "type": "array[string]"}
+                    ],
+                    "query": ["sys", "query"],
+                    "reasoning_mode": "prompt",
+                    "title": "pe",
+                    "type": "parameter-extractor",
+                },
+                "id": "pe",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
+        tenant_id="1",
+        app_id="1",
+        workflow_type=WorkflowType.CHAT,
+        workflow_id="1",
+        graph_config=graph_config,
+        user_id="1",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    pool = VariablePool(
+        system_variables={
+            SystemVariableKey.QUERY: "dify",
+            SystemVariableKey.FILES: [],
+            SystemVariableKey.CONVERSATION_ID: "abababa",
+            SystemVariableKey.USER_ID: "1",
+        },
+        user_inputs={},
+        environment_variables=[],
+    )
+    pool.add(["pe", "list_output"], ["dify-1", "dify-2"])
+
+    iteration_node = IterationNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=pool, start_at=time.perf_counter()),
+        config={
+            "data": {
+                "iterator_selector": ["pe", "list_output"],
+                "output_selector": ["tt", "output"],
+                "output_type": "array[string]",
+                "startNodeType": "template-transform",
+                "start_node_id": "tt",
+                "title": "迭代",
+                "type": "iteration",
+            },
+            "id": "iteration-1",
+        },
+    )
+
+    def tt_generator(self):
+        return NodeRunResult(
+            status=WorkflowNodeExecutionStatus.SUCCEEDED,
+            inputs={"iterator_selector": "dify"},
+            outputs={"output": "dify 123"},
+        )
+
+    # print("")
+
+    with patch.object(TemplateTransformNode, "_run", new=tt_generator):
+        # execute node
+        result = iteration_node._run()
+
+        count = 0
+        for item in result:
+            # print(type(item), item)
+            count += 1
+            if isinstance(item, RunCompletedEvent):
+                assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+                assert item.run_result.outputs == {"output": ["dify 123", "dify 123"]}
+
+        assert count == 20
+
+
+def test_run_parallel():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-pe-target",
+                "source": "start",
+                "target": "pe",
+            },
+            {
+                "id": "iteration-1-source-answer-3-target",
+                "source": "iteration-1",
+                "target": "answer-3",
+            },
+            {
+                "id": "iteration-start-source-tt-target",
+                "source": "iteration-start",
+                "target": "tt",
+            },
+            {
+                "id": "iteration-start-source-tt-2-target",
+                "source": "iteration-start",
+                "target": "tt-2",
+            },
+            {
+                "id": "tt-source-if-else-target",
+                "source": "tt",
+                "target": "if-else",
+            },
+            {
+                "id": "tt-2-source-if-else-target",
+                "source": "tt-2",
+                "target": "if-else",
+            },
+            {
+                "id": "if-else-true-answer-2-target",
+                "source": "if-else",
+                "sourceHandle": "true",
+                "target": "answer-2",
+            },
+            {
+                "id": "if-else-false-answer-4-target",
+                "source": "if-else",
+                "sourceHandle": "false",
+                "target": "answer-4",
+            },
+            {
+                "id": "pe-source-iteration-1-target",
+                "source": "pe",
+                "target": "iteration-1",
+            },
+        ],
+        "nodes": [
+            {"data": {"title": "Start", "type": "start", "variables": []}, "id": "start"},
+            {
+                "data": {
+                    "iterator_selector": ["pe", "list_output"],
+                    "output_selector": ["tt", "output"],
+                    "output_type": "array[string]",
+                    "startNodeType": "template-transform",
+                    "start_node_id": "iteration-start",
+                    "title": "iteration",
+                    "type": "iteration",
+                },
+                "id": "iteration-1",
+            },
+            {
+                "data": {
+                    "answer": "{{#tt.output#}}",
+                    "iteration_id": "iteration-1",
+                    "title": "answer 2",
+                    "type": "answer",
+                },
+                "id": "answer-2",
+            },
+            {
+                "data": {
+                    "iteration_id": "iteration-1",
+                    "title": "iteration-start",
+                    "type": "iteration-start",
+                },
+                "id": "iteration-start",
+            },
+            {
+                "data": {
+                    "iteration_id": "iteration-1",
+                    "template": "{{ arg1 }} 123",
+                    "title": "template transform",
+                    "type": "template-transform",
+                    "variables": [{"value_selector": ["sys", "query"], "variable": "arg1"}],
+                },
+                "id": "tt",
+            },
+            {
+                "data": {
+                    "iteration_id": "iteration-1",
+                    "template": "{{ arg1 }} 321",
+                    "title": "template transform",
+                    "type": "template-transform",
+                    "variables": [{"value_selector": ["sys", "query"], "variable": "arg1"}],
+                },
+                "id": "tt-2",
+            },
+            {
+                "data": {"answer": "{{#iteration-1.output#}}88888", "title": "answer 3", "type": "answer"},
+                "id": "answer-3",
+            },
+            {
+                "data": {
+                    "conditions": [
+                        {
+                            "comparison_operator": "is",
+                            "id": "1721916275284",
+                            "value": "hi",
+                            "variable_selector": ["sys", "query"],
+                        }
+                    ],
+                    "iteration_id": "iteration-1",
+                    "logical_operator": "and",
+                    "title": "if",
+                    "type": "if-else",
+                },
+                "id": "if-else",
+            },
+            {
+                "data": {"answer": "no hi", "iteration_id": "iteration-1", "title": "answer 4", "type": "answer"},
+                "id": "answer-4",
+            },
+            {
+                "data": {
+                    "instruction": "test1",
+                    "model": {
+                        "completion_params": {"temperature": 0.7},
+                        "mode": "chat",
+                        "name": "gpt-4o",
+                        "provider": "openai",
+                    },
+                    "parameters": [
+                        {"description": "test", "name": "list_output", "required": False, "type": "array[string]"}
+                    ],
+                    "query": ["sys", "query"],
+                    "reasoning_mode": "prompt",
+                    "title": "pe",
+                    "type": "parameter-extractor",
+                },
+                "id": "pe",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
+        tenant_id="1",
+        app_id="1",
+        workflow_type=WorkflowType.CHAT,
+        workflow_id="1",
+        graph_config=graph_config,
+        user_id="1",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    pool = VariablePool(
+        system_variables={
+            SystemVariableKey.QUERY: "dify",
+            SystemVariableKey.FILES: [],
+            SystemVariableKey.CONVERSATION_ID: "abababa",
+            SystemVariableKey.USER_ID: "1",
+        },
+        user_inputs={},
+        environment_variables=[],
+    )
+    pool.add(["pe", "list_output"], ["dify-1", "dify-2"])
+
+    iteration_node = IterationNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=pool, start_at=time.perf_counter()),
+        config={
+            "data": {
+                "iterator_selector": ["pe", "list_output"],
+                "output_selector": ["tt", "output"],
+                "output_type": "array[string]",
+                "startNodeType": "template-transform",
+                "start_node_id": "iteration-start",
+                "title": "迭代",
+                "type": "iteration",
+            },
+            "id": "iteration-1",
+        },
+    )
+
+    def tt_generator(self):
+        return NodeRunResult(
+            status=WorkflowNodeExecutionStatus.SUCCEEDED,
+            inputs={"iterator_selector": "dify"},
+            outputs={"output": "dify 123"},
+        )
+
+    # print("")
+
+    with patch.object(TemplateTransformNode, "_run", new=tt_generator):
+        # execute node
+        result = iteration_node._run()
+
+        count = 0
+        for item in result:
+            # print(type(item), item)
+            count += 1
+            if isinstance(item, RunCompletedEvent):
+                assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+                assert item.run_result.outputs == {"output": ["dify 123", "dify 123"]}
+
+        assert count == 32
diff --git a/api/tests/unit_tests/core/workflow/nodes/test_answer.py b/api/tests/unit_tests/core/workflow/nodes/test_answer.py
index 8020674ee6..cb2e99a854 100644
--- a/api/tests/unit_tests/core/workflow/nodes/test_answer.py
+++ b/api/tests/unit_tests/core/workflow/nodes/test_answer.py
@@ -1,22 +1,70 @@
+import time
+import uuid
 from unittest.mock import MagicMock
 
 from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import UserFrom
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes.answer.answer_node import AnswerNode
-from core.workflow.nodes.base_node import UserFrom
 from extensions.ext_database import db
-from models.workflow import WorkflowNodeExecutionStatus
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
 
 
 def test_execute_answer():
-    node = AnswerNode(
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-answer-target",
+                "source": "start",
+                "target": "answer",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "title": "123",
+                    "type": "answer",
+                    "answer": "Today's weather is {{#start.weather#}}\n{{#llm.text#}}\n{{img}}\nFin.",
+                },
+                "id": "answer",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
         tenant_id="1",
         app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
         workflow_id="1",
+        graph_config=graph_config,
         user_id="1",
         user_from=UserFrom.ACCOUNT,
         invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    variable_pool = VariablePool(
+        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"},
+        user_inputs={},
+        environment_variables=[],
+        conversation_variables=[],
+    )
+    variable_pool.add(["start", "weather"], "sunny")
+    variable_pool.add(["llm", "text"], "You are a helpful AI.")
+
+    node = AnswerNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
         config={
             "id": "answer",
             "data": {
@@ -27,20 +75,11 @@ def test_execute_answer():
         },
     )
 
-    # construct variable pool
-    pool = VariablePool(
-        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"},
-        user_inputs={},
-        environment_variables=[],
-    )
-    pool.add(["start", "weather"], "sunny")
-    pool.add(["llm", "text"], "You are a helpful AI.")
-
     # Mock db.session.close()
     db.session.close = MagicMock()
 
     # execute node
-    result = node._run(pool)
+    result = node._run()
 
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
     assert result.outputs["answer"] == "Today's weather is sunny\nYou are a helpful AI.\n{{img}}\nFin."
diff --git a/api/tests/unit_tests/core/workflow/nodes/test_if_else.py b/api/tests/unit_tests/core/workflow/nodes/test_if_else.py
index 9535bc2186..0795f134d0 100644
--- a/api/tests/unit_tests/core/workflow/nodes/test_if_else.py
+++ b/api/tests/unit_tests/core/workflow/nodes/test_if_else.py
@@ -1,22 +1,63 @@
+import time
+import uuid
 from unittest.mock import MagicMock
 
 from core.app.entities.app_invoke_entities import InvokeFrom
+from core.workflow.entities.node_entities import UserFrom
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
-from core.workflow.nodes.base_node import UserFrom
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes.if_else.if_else_node import IfElseNode
 from extensions.ext_database import db
-from models.workflow import WorkflowNodeExecutionStatus
+from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
 
 
 def test_execute_if_else_result_true():
-    node = IfElseNode(
+    graph_config = {"edges": [], "nodes": [{"data": {"type": "start"}, "id": "start"}]}
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
         tenant_id="1",
         app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
         workflow_id="1",
+        graph_config=graph_config,
         user_id="1",
         user_from=UserFrom.ACCOUNT,
         invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    pool = VariablePool(
+        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"}, user_inputs={}
+    )
+    pool.add(["start", "array_contains"], ["ab", "def"])
+    pool.add(["start", "array_not_contains"], ["ac", "def"])
+    pool.add(["start", "contains"], "cabcde")
+    pool.add(["start", "not_contains"], "zacde")
+    pool.add(["start", "start_with"], "abc")
+    pool.add(["start", "end_with"], "zzab")
+    pool.add(["start", "is"], "ab")
+    pool.add(["start", "is_not"], "aab")
+    pool.add(["start", "empty"], "")
+    pool.add(["start", "not_empty"], "aaa")
+    pool.add(["start", "equals"], 22)
+    pool.add(["start", "not_equals"], 23)
+    pool.add(["start", "greater_than"], 23)
+    pool.add(["start", "less_than"], 21)
+    pool.add(["start", "greater_than_or_equal"], 22)
+    pool.add(["start", "less_than_or_equal"], 21)
+    pool.add(["start", "not_null"], "1212")
+
+    node = IfElseNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=pool, start_at=time.perf_counter()),
         config={
             "id": "if-else",
             "data": {
@@ -63,48 +104,64 @@ def test_execute_if_else_result_true():
         },
     )
 
-    # construct variable pool
-    pool = VariablePool(
-        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"},
-        user_inputs={},
-        environment_variables=[],
-    )
-    pool.add(["start", "array_contains"], ["ab", "def"])
-    pool.add(["start", "array_not_contains"], ["ac", "def"])
-    pool.add(["start", "contains"], "cabcde")
-    pool.add(["start", "not_contains"], "zacde")
-    pool.add(["start", "start_with"], "abc")
-    pool.add(["start", "end_with"], "zzab")
-    pool.add(["start", "is"], "ab")
-    pool.add(["start", "is_not"], "aab")
-    pool.add(["start", "empty"], "")
-    pool.add(["start", "not_empty"], "aaa")
-    pool.add(["start", "equals"], 22)
-    pool.add(["start", "not_equals"], 23)
-    pool.add(["start", "greater_than"], 23)
-    pool.add(["start", "less_than"], 21)
-    pool.add(["start", "greater_than_or_equal"], 22)
-    pool.add(["start", "less_than_or_equal"], 21)
-    pool.add(["start", "not_null"], "1212")
-
     # Mock db.session.close()
     db.session.close = MagicMock()
 
     # execute node
-    result = node._run(pool)
+    result = node._run()
 
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
     assert result.outputs["result"] is True
 
 
 def test_execute_if_else_result_false():
-    node = IfElseNode(
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-llm-target",
+                "source": "start",
+                "target": "llm",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "llm",
+                },
+                "id": "llm",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
         tenant_id="1",
         app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
         workflow_id="1",
+        graph_config=graph_config,
         user_id="1",
         user_from=UserFrom.ACCOUNT,
         invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
+    # construct variable pool
+    pool = VariablePool(
+        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"},
+        user_inputs={},
+        environment_variables=[],
+    )
+    pool.add(["start", "array_contains"], ["1ab", "def"])
+    pool.add(["start", "array_not_contains"], ["ab", "def"])
+
+    node = IfElseNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=pool, start_at=time.perf_counter()),
         config={
             "id": "if-else",
             "data": {
@@ -127,20 +184,11 @@ def test_execute_if_else_result_false():
         },
     )
 
-    # construct variable pool
-    pool = VariablePool(
-        system_variables={SystemVariableKey.FILES: [], SystemVariableKey.USER_ID: "aaa"},
-        user_inputs={},
-        environment_variables=[],
-    )
-    pool.add(["start", "array_contains"], ["1ab", "def"])
-    pool.add(["start", "array_not_contains"], ["ab", "def"])
-
     # Mock db.session.close()
     db.session.close = MagicMock()
 
     # execute node
-    result = node._run(pool)
+    result = node._run()
 
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
     assert result.outputs["result"] is False
diff --git a/api/tests/unit_tests/core/workflow/nodes/test_variable_assigner.py b/api/tests/unit_tests/core/workflow/nodes/test_variable_assigner.py
index e26c7df642..f45a93f1be 100644
--- a/api/tests/unit_tests/core/workflow/nodes/test_variable_assigner.py
+++ b/api/tests/unit_tests/core/workflow/nodes/test_variable_assigner.py
@@ -1,17 +1,56 @@
+import time
+import uuid
 from unittest import mock
 from uuid import uuid4
 
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.segments import ArrayStringVariable, StringVariable
+from core.workflow.entities.node_entities import UserFrom
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
-from core.workflow.nodes.base_node import UserFrom
+from core.workflow.graph_engine.entities.graph import Graph
+from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams
+from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes.variable_assigner import VariableAssignerNode, WriteMode
+from models.workflow import WorkflowType
 
 DEFAULT_NODE_ID = "node_id"
 
 
 def test_overwrite_string_variable():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-assigner-target",
+                "source": "start",
+                "target": "assigner",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "assigner",
+                },
+                "id": "assigner",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
+        tenant_id="1",
+        app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
+        workflow_id="1",
+        graph_config=graph_config,
+        user_id="1",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
     conversation_variable = StringVariable(
         id=str(uuid4()),
         name="test_conversation_variable",
@@ -24,13 +63,24 @@ def test_overwrite_string_variable():
         value="the second value",
     )
 
+    # construct variable pool
+    variable_pool = VariablePool(
+        system_variables={SystemVariableKey.CONVERSATION_ID: "conversation_id"},
+        user_inputs={},
+        environment_variables=[],
+        conversation_variables=[conversation_variable],
+    )
+
+    variable_pool.add(
+        [DEFAULT_NODE_ID, input_variable.name],
+        input_variable,
+    )
+
     node = VariableAssignerNode(
-        tenant_id="tenant_id",
-        app_id="app_id",
-        workflow_id="workflow_id",
-        user_id="user_id",
-        user_from=UserFrom.ACCOUNT,
-        invoke_from=InvokeFrom.DEBUGGER,
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
         config={
             "id": "node_id",
             "data": {
@@ -41,19 +91,8 @@ def test_overwrite_string_variable():
         },
     )
 
-    variable_pool = VariablePool(
-        system_variables={SystemVariableKey.CONVERSATION_ID: "conversation_id"},
-        user_inputs={},
-        environment_variables=[],
-        conversation_variables=[conversation_variable],
-    )
-    variable_pool.add(
-        [DEFAULT_NODE_ID, input_variable.name],
-        input_variable,
-    )
-
     with mock.patch("core.workflow.nodes.variable_assigner.node.update_conversation_variable") as mock_run:
-        node.run(variable_pool)
+        list(node.run())
         mock_run.assert_called_once()
 
     got = variable_pool.get(["conversation", conversation_variable.name])
@@ -63,6 +102,39 @@ def test_overwrite_string_variable():
 
 
 def test_append_variable_to_array():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-assigner-target",
+                "source": "start",
+                "target": "assigner",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "assigner",
+                },
+                "id": "assigner",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
+        tenant_id="1",
+        app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
+        workflow_id="1",
+        graph_config=graph_config,
+        user_id="1",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
     conversation_variable = ArrayStringVariable(
         id=str(uuid4()),
         name="test_conversation_variable",
@@ -75,23 +147,6 @@ def test_append_variable_to_array():
         value="the second value",
     )
 
-    node = VariableAssignerNode(
-        tenant_id="tenant_id",
-        app_id="app_id",
-        workflow_id="workflow_id",
-        user_id="user_id",
-        user_from=UserFrom.ACCOUNT,
-        invoke_from=InvokeFrom.DEBUGGER,
-        config={
-            "id": "node_id",
-            "data": {
-                "assigned_variable_selector": ["conversation", conversation_variable.name],
-                "write_mode": WriteMode.APPEND.value,
-                "input_variable_selector": [DEFAULT_NODE_ID, input_variable.name],
-            },
-        },
-    )
-
     variable_pool = VariablePool(
         system_variables={SystemVariableKey.CONVERSATION_ID: "conversation_id"},
         user_inputs={},
@@ -103,8 +158,23 @@ def test_append_variable_to_array():
         input_variable,
     )
 
+    node = VariableAssignerNode(
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
+        config={
+            "id": "node_id",
+            "data": {
+                "assigned_variable_selector": ["conversation", conversation_variable.name],
+                "write_mode": WriteMode.APPEND.value,
+                "input_variable_selector": [DEFAULT_NODE_ID, input_variable.name],
+            },
+        },
+    )
+
     with mock.patch("core.workflow.nodes.variable_assigner.node.update_conversation_variable") as mock_run:
-        node.run(variable_pool)
+        list(node.run())
         mock_run.assert_called_once()
 
     got = variable_pool.get(["conversation", conversation_variable.name])
@@ -113,19 +183,57 @@ def test_append_variable_to_array():
 
 
 def test_clear_array():
+    graph_config = {
+        "edges": [
+            {
+                "id": "start-source-assigner-target",
+                "source": "start",
+                "target": "assigner",
+            },
+        ],
+        "nodes": [
+            {"data": {"type": "start"}, "id": "start"},
+            {
+                "data": {
+                    "type": "assigner",
+                },
+                "id": "assigner",
+            },
+        ],
+    }
+
+    graph = Graph.init(graph_config=graph_config)
+
+    init_params = GraphInitParams(
+        tenant_id="1",
+        app_id="1",
+        workflow_type=WorkflowType.WORKFLOW,
+        workflow_id="1",
+        graph_config=graph_config,
+        user_id="1",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+    )
+
     conversation_variable = ArrayStringVariable(
         id=str(uuid4()),
         name="test_conversation_variable",
         value=["the first value"],
     )
 
+    variable_pool = VariablePool(
+        system_variables={SystemVariableKey.CONVERSATION_ID: "conversation_id"},
+        user_inputs={},
+        environment_variables=[],
+        conversation_variables=[conversation_variable],
+    )
+
     node = VariableAssignerNode(
-        tenant_id="tenant_id",
-        app_id="app_id",
-        workflow_id="workflow_id",
-        user_id="user_id",
-        user_from=UserFrom.ACCOUNT,
-        invoke_from=InvokeFrom.DEBUGGER,
+        id=str(uuid.uuid4()),
+        graph_init_params=init_params,
+        graph=graph,
+        graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()),
         config={
             "id": "node_id",
             "data": {
@@ -136,14 +244,9 @@ def test_clear_array():
         },
     )
 
-    variable_pool = VariablePool(
-        system_variables={SystemVariableKey.CONVERSATION_ID: "conversation_id"},
-        user_inputs={},
-        environment_variables=[],
-        conversation_variables=[conversation_variable],
-    )
-
-    node.run(variable_pool)
+    with mock.patch("core.workflow.nodes.variable_assigner.node.update_conversation_variable") as mock_run:
+        list(node.run())
+        mock_run.assert_called_once()
 
     got = variable_pool.get(["conversation", conversation_variable.name])
     assert got is not None
diff --git a/api/tests/unit_tests/libs/test_email.py b/api/tests/unit_tests/libs/test_email.py
index f8234f3f3b..ae0177791b 100644
--- a/api/tests/unit_tests/libs/test_email.py
+++ b/api/tests/unit_tests/libs/test_email.py
@@ -1,3 +1,5 @@
+import pytest
+
 from libs.helper import email
 
 
@@ -9,17 +11,11 @@ def test_email_with_valid_email():
 
 
 def test_email_with_invalid_email():
-    try:
+    with pytest.raises(ValueError, match="invalid_email is not a valid email."):
         email("invalid_email")
-    except ValueError as e:
-        assert str(e) == "invalid_email is not a valid email."
 
-    try:
+    with pytest.raises(ValueError, match="@example.com is not a valid email."):
         email("@example.com")
-    except ValueError as e:
-        assert str(e) == "@example.com is not a valid email."
 
-    try:
+    with pytest.raises(ValueError, match="()@example.com is not a valid email."):
         email("()@example.com")
-    except ValueError as e:
-        assert str(e) == "()@example.com is not a valid email."
diff --git a/docker-legacy/docker-compose.yaml b/docker-legacy/docker-compose.yaml
index 548a1c004e..f8c5700cd9 100644
--- a/docker-legacy/docker-compose.yaml
+++ b/docker-legacy/docker-compose.yaml
@@ -2,7 +2,7 @@ version: '3'
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.7.3
+    image: langgenius/dify-api:0.8.2
     restart: always
     environment:
       # Startup mode, 'api' starts the API server.
@@ -128,16 +128,14 @@ services:
       # The Qdrant server gRPC mode PORT.
       QDRANT_GRPC_PORT: 6334
       # Milvus configuration Only available when VECTOR_STORE is `milvus`.
-      # The milvus host.
-      MILVUS_HOST: 127.0.0.1
-      # The milvus host.
-      MILVUS_PORT: 19530
+      # The milvus uri.
+      MILVUS_URI: http://127.0.0.1:19530
+      # The milvus token.
+      MILVUS_TOKEN: ''
       # The milvus username.
       MILVUS_USER: root
       # The milvus password.
       MILVUS_PASSWORD: Milvus
-      # The milvus tls switch.
-      MILVUS_SECURE: 'false'
       # relyt configurations
       RELYT_HOST: db
       RELYT_PORT: 5432
@@ -229,7 +227,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.7.3
+    image: langgenius/dify-api:0.8.2
     restart: always
     environment:
       CONSOLE_WEB_URL: ''
@@ -308,16 +306,14 @@ services:
       # The Qdrant server gRPC mode PORT.
       QDRANT_GRPC_PORT: 6334
       # Milvus configuration Only available when VECTOR_STORE is `milvus`.
-      # The milvus host.
-      MILVUS_HOST: 127.0.0.1
-      # The milvus host.
-      MILVUS_PORT: 19530
+      # The milvus uri.
+      MILVUS_URI: http://127.0.0.1:19530
+      # The milvus token.
+      MILVUS_PORT: ''
       # The milvus username.
       MILVUS_USER: root
       # The milvus password.
       MILVUS_PASSWORD: Milvus
-      # The milvus tls switch.
-      MILVUS_SECURE: 'false'
       # Mail configuration, support: resend
       MAIL_TYPE: ''
       # default send from email address, if not specified
@@ -400,7 +396,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.7.3
+    image: langgenius/dify-web:0.8.2
     restart: always
     environment:
       # The base URL of console application api server, refers to the Console base URL of WEB service if console domain is
diff --git a/docker/.env.example b/docker/.env.example
index d8fa14f7c0..c892c15636 100644
--- a/docker/.env.example
+++ b/docker/.env.example
@@ -75,7 +75,7 @@ INIT_PASSWORD=
 DEPLOY_ENV=PRODUCTION
 
 # Whether to enable the version check policy.
-# If set to empty, https://updates.dify.ai will not be called for version check.
+# If set to empty, https://updates.dify.ai will be called for version check.
 CHECK_UPDATE_URL=https://updates.dify.ai
 
 # Used to change the OpenAI base address, default is https://api.openai.com/v1.
@@ -214,6 +214,18 @@ REDIS_USERNAME=
 REDIS_PASSWORD=difyai123456
 REDIS_USE_SSL=false
 
+# Whether to use Redis Sentinel mode.
+# If set to true, the application will automatically discover and connect to the master node through Sentinel.
+REDIS_USE_SENTINEL=false
+
+# List of Redis Sentinel nodes. If Sentinel mode is enabled, provide at least one Sentinel IP and port.
+# Format: `<sentinel1_ip>:<sentinel1_port>,<sentinel2_ip>:<sentinel2_port>,<sentinel3_ip>:<sentinel3_port>`
+REDIS_SENTINELS=
+REDIS_SENTINEL_SERVICE_NAME=
+REDIS_SENTINEL_USERNAME=
+REDIS_SENTINEL_PASSWORD=
+REDIS_SENTINEL_SOCKET_TIMEOUT=0.1
+
 # ------------------------------
 # Celery Configuration
 # ------------------------------
@@ -221,9 +233,16 @@ REDIS_USE_SSL=false
 # Use redis as the broker, and redis db 1 for celery broker.
 # Format as follows: `redis://<redis_username>:<redis_password>@<redis_host>:<redis_port>/<redis_database>`
 # Example: redis://:difyai123456@redis:6379/1
+# If use Redis Sentinel, format as follows: `sentinel://<sentinel_username>:<sentinel_password>@<sentinel_host>:<sentinel_port>/<redis_database>`
+# Example: sentinel://localhost:26379/1;sentinel://localhost:26380/1;sentinel://localhost:26381/1
 CELERY_BROKER_URL=redis://:difyai123456@redis:6379/1
 BROKER_USE_SSL=false
 
+# If you are using Redis Sentinel for high availability, configure the following settings.
+CELERY_USE_SENTINEL=false
+CELERY_SENTINEL_MASTER_NAME=
+CELERY_SENTINEL_SOCKET_TIMEOUT=0.1
+
 # ------------------------------
 # CORS Configuration
 # Used to set the front-end cross-domain access policy.
@@ -242,7 +261,7 @@ CONSOLE_CORS_ALLOW_ORIGINS=*
 # ------------------------------
 
 # The type of storage to use for storing user files.
-# Supported values are `local` and `s3` and `azure-blob` and `google-storage` and `tencent-cos`,
+# Supported values are `local` and `s3` and `azure-blob` and `google-storage` and `tencent-cos` and `huawei-obs`
 # Default: `local`
 STORAGE_TYPE=local
 
@@ -300,6 +319,28 @@ TENCENT_COS_REGION=your-region
 # The scheme of the Tencent COS service.
 TENCENT_COS_SCHEME=your-scheme
 
+# Huawei OBS Configuration
+# The name of the Huawei OBS bucket to use for storing files.
+HUAWEI_OBS_BUCKET_NAME=your-bucket-name
+# The secret key to use for authenticating with the Huawei OBS service.
+HUAWEI_OBS_SECRET_KEY=your-secret-key
+# The access key to use for authenticating with the Huawei OBS service.
+HUAWEI_OBS_ACCESS_KEY=your-access-key
+# The server url of the HUAWEI OBS service.
+HUAWEI_OBS_SERVER=your-server-url
+
+# Volcengine TOS Configuration
+# The name of the Volcengine TOS bucket to use for storing files.
+VOLCENGINE_TOS_BUCKET_NAME=your-bucket-name
+# The secret key to use for authenticating with the Volcengine TOS service.
+VOLCENGINE_TOS_SECRET_KEY=your-secret-key
+# The access key to use for authenticating with the Volcengine TOS service.
+VOLCENGINE_TOS_ACCESS_KEY=your-access-key
+# The endpoint of the Volcengine TOS service.
+VOLCENGINE_TOS_ENDPOINT=your-server-url
+# The region of the Volcengine TOS service.
+VOLCENGINE_TOS_REGION=your-region
+
 # ------------------------------
 # Vector Database Configuration
 # ------------------------------
@@ -325,16 +366,14 @@ QDRANT_GRPC_ENABLED=false
 QDRANT_GRPC_PORT=6334
 
 # Milvus configuration Only available when VECTOR_STORE is `milvus`.
-# The milvus host.
-MILVUS_HOST=127.0.0.1
-# The milvus host.
-MILVUS_PORT=19530
+# The milvus uri.
+MILVUS_URI=http://127.0.0.1:19530
+# The milvus token.
+MILVUS_TOKEN=
 # The milvus username.
 MILVUS_USER=root
 # The milvus password.
 MILVUS_PASSWORD=Milvus
-# The milvus tls switch.
-MILVUS_SECURE=false
 
 # MyScale configuration, only available when VECTOR_STORE is `myscale`
 # For multi-language support, please set MYSCALE_FTS_PARAMS with referring to:
@@ -529,6 +568,13 @@ SSRF_PROXY_HTTP_URL=http://ssrf_proxy:3128
 # SSRF Proxy server HTTPS URL
 SSRF_PROXY_HTTPS_URL=http://ssrf_proxy:3128
 
+# ------------------------------
+# Environment Variables for web Service
+# ------------------------------
+
+# The timeout for the text generation in millisecond
+TEXT_GENERATION_TIMEOUT_MS=60000
+
 # ------------------------------
 # Environment Variables for db Service
 # ------------------------------
diff --git a/docker/README.md b/docker/README.md
index 1223a58024..7ce3f9bd75 100644
--- a/docker/README.md
+++ b/docker/README.md
@@ -83,7 +83,7 @@ The `.env.example` file provided in the Docker setup is extensive and covers a w
 
 7. **Vector Database Configuration**:
     - `VECTOR_STORE`: Type of vector database (e.g., `weaviate`, `milvus`).
-    - Specific settings for each vector store like `WEAVIATE_ENDPOINT`, `MILVUS_HOST`.
+    - Specific settings for each vector store like `WEAVIATE_ENDPOINT`, `MILVUS_URI`.
 
 8. **CORS Configuration**:
     - `WEB_API_CORS_ALLOW_ORIGINS`, `CONSOLE_CORS_ALLOW_ORIGINS`: Settings for cross-origin resource sharing.
diff --git a/docker/certbot/README.md b/docker/certbot/README.md
index c6f73ae699..21be34b33a 100644
--- a/docker/certbot/README.md
+++ b/docker/certbot/README.md
@@ -2,8 +2,8 @@
 
 ## Short description
 
-Docker-compose certbot configurations with Backward compatibility (without certbot container).  
-Use `docker-compose --profile certbot up` to use this features.
+docker compose certbot configurations with Backward compatibility (without certbot container).  
+Use `docker compose --profile certbot up` to use this features.
 
 ## The simplest way for launching new servers with SSL certificates
 
@@ -18,21 +18,21 @@ Use `docker-compose --profile certbot up` to use this features.
    ```
    execute command:
    ```shell
-   sudo docker network prune
-   sudo docker-compose --profile certbot up --force-recreate -d
+   docker network prune
+   docker compose --profile certbot up --force-recreate -d
    ```
    then after the containers launched:
    ```shell
-   sudo docker-compose exec -it certbot /bin/sh /update-cert.sh
+   docker compose exec -it certbot /bin/sh /update-cert.sh
    ```
-2. Edit `.env` file and `sudo docker-compose --profile certbot up` again.  
+2. Edit `.env` file and `docker compose --profile certbot up` again.  
    set `.env` value additionally
    ```properties
    NGINX_HTTPS_ENABLED=true
    ```
    execute command:
    ```shell
-   sudo docker-compose --profile certbot up -d --no-deps --force-recreate nginx
+   docker compose --profile certbot up -d --no-deps --force-recreate nginx
    ```
    Then you can access your serve with HTTPS.  
    [https://your_domain.com](https://your_domain.com)
@@ -42,8 +42,8 @@ Use `docker-compose --profile certbot up` to use this features.
 For SSL certificates renewal, execute commands below:
 
 ```shell
-sudo docker-compose exec -it certbot /bin/sh /update-cert.sh
-sudo docker-compose exec nginx nginx -s reload
+docker compose exec -it certbot /bin/sh /update-cert.sh
+docker compose exec nginx nginx -s reload
 ```
 
 ## Options for certbot
@@ -57,14 +57,14 @@ CERTBOT_OPTIONS=--dry-run
 To apply changes to `CERTBOT_OPTIONS`, regenerate the certbot container before updating the certificates.
 
 ```shell
-sudo docker-compose --profile certbot up -d --no-deps --force-recreate certbot
-sudo docker-compose exec -it certbot /bin/sh /update-cert.sh
+docker compose --profile certbot up -d --no-deps --force-recreate certbot
+docker compose exec -it certbot /bin/sh /update-cert.sh
 ```
 
 Then, reload the nginx container if necessary.
 
 ```shell
-sudo docker-compose exec nginx nginx -s reload
+docker compose exec nginx nginx -s reload
 ```
 
 ## For legacy servers
@@ -72,5 +72,5 @@ sudo docker-compose exec nginx nginx -s reload
 To use cert files dir `nginx/ssl` as before, simply launch containers WITHOUT `--profile certbot` option.
 
 ```shell
-sudo docker-compose up -d
+docker compose up -d
 ```
diff --git a/docker/docker-compose.middleware.yaml b/docker/docker-compose.middleware.yaml
index adaecc69ec..251c62fee1 100644
--- a/docker/docker-compose.middleware.yaml
+++ b/docker/docker-compose.middleware.yaml
@@ -41,7 +41,7 @@ services:
 
   # The DifySandbox
   sandbox:
-    image: langgenius/dify-sandbox:0.2.6
+    image: langgenius/dify-sandbox:0.2.9
     restart: always
     environment:
       # The DifySandbox configurations
@@ -56,6 +56,8 @@ services:
       SANDBOX_PORT: ${SANDBOX_PORT:-8194}
     volumes:
       - ./volumes/sandbox/dependencies:/dependencies
+    healthcheck:
+      test: [ "CMD", "curl", "-f", "http://localhost:8194/health" ]
     networks:
       - ssrf_proxy_network
 
@@ -87,6 +89,7 @@ services:
   weaviate:
     image: semitechnologies/weaviate:1.19.0
     profiles:
+      - ""
       - weaviate
     restart: always
     volumes:
diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml
index 9a532eed07..0fbc695177 100644
--- a/docker/docker-compose.yaml
+++ b/docker/docker-compose.yaml
@@ -42,8 +42,17 @@ x-shared-env: &shared-api-worker-env
   REDIS_PASSWORD: ${REDIS_PASSWORD:-difyai123456}
   REDIS_USE_SSL: ${REDIS_USE_SSL:-false}
   REDIS_DB: 0
+  REDIS_USE_SENTINEL: ${REDIS_USE_SENTINEL:-false}
+  REDIS_SENTINELS: ${REDIS_SENTINELS:-}
+  REDIS_SENTINEL_SERVICE_NAME: ${REDIS_SENTINEL_SERVICE_NAME:-}
+  REDIS_SENTINEL_USERNAME: ${REDIS_SENTINEL_USERNAME:-}
+  REDIS_SENTINEL_PASSWORD: ${REDIS_SENTINEL_PASSWORD:-}
+  REDIS_SENTINEL_SOCKET_TIMEOUT: ${REDIS_SENTINEL_SOCKET_TIMEOUT:-0.1}
   CELERY_BROKER_URL: ${CELERY_BROKER_URL:-redis://:difyai123456@redis:6379/1}
   BROKER_USE_SSL: ${BROKER_USE_SSL:-false}
+  CELERY_USE_SENTINEL: ${CELERY_USE_SENTINEL:-false}
+  CELERY_SENTINEL_MASTER_NAME: ${CELERY_SENTINEL_MASTER_NAME:-}
+  CELERY_SENTINEL_SOCKET_TIMEOUT: ${CELERY_SENTINEL_SOCKET_TIMEOUT:-0.1}
   WEB_API_CORS_ALLOW_ORIGINS: ${WEB_API_CORS_ALLOW_ORIGINS:-*}
   CONSOLE_CORS_ALLOW_ORIGINS: ${CONSOLE_CORS_ALLOW_ORIGINS:-*}
   STORAGE_TYPE: ${STORAGE_TYPE:-local}
@@ -66,17 +75,26 @@ x-shared-env: &shared-api-worker-env
   ALIYUN_OSS_ENDPOINT: ${ALIYUN_OSS_ENDPOINT:-}
   ALIYUN_OSS_REGION: ${ALIYUN_OSS_REGION:-}
   ALIYUN_OSS_AUTH_VERSION: ${ALIYUN_OSS_AUTH_VERSION:-v4}
-  ALIYUN_OSS_PATHS: ${ALIYUN_OSS_PATH:-}
+  ALIYUN_OSS_PATH: ${ALIYUN_OSS_PATH:-}
   TENCENT_COS_BUCKET_NAME: ${TENCENT_COS_BUCKET_NAME:-}
   TENCENT_COS_SECRET_KEY: ${TENCENT_COS_SECRET_KEY:-}
   TENCENT_COS_SECRET_ID: ${TENCENT_COS_SECRET_ID:-}
   TENCENT_COS_REGION: ${TENCENT_COS_REGION:-}
   TENCENT_COS_SCHEME: ${TENCENT_COS_SCHEME:-}
+  HUAWEI_OBS_BUCKET_NAME: ${HUAWEI_OBS_BUCKET_NAME:-}
+  HUAWEI_OBS_SECRET_KEY: ${HUAWEI_OBS_SECRET_KEY:-}
+  HUAWEI_OBS_ACCESS_KEY: ${HUAWEI_OBS_ACCESS_KEY:-}
+  HUAWEI_OBS_SERVER: ${HUAWEI_OBS_SERVER:-}
   OCI_ENDPOINT: ${OCI_ENDPOINT:-}
   OCI_BUCKET_NAME: ${OCI_BUCKET_NAME:-}
   OCI_ACCESS_KEY: ${OCI_ACCESS_KEY:-}
   OCI_SECRET_KEY: ${OCI_SECRET_KEY:-}
   OCI_REGION: ${OCI_REGION:-}
+  VOLCENGINE_TOS_BUCKET_NAME: ${VOLCENGINE_TOS_BUCKET_NAME:-}
+  VOLCENGINE_TOS_SECRET_KEY: ${VOLCENGINE_TOS_SECRET_KEY:-}
+  VOLCENGINE_TOS_ACCESS_KEY: ${VOLCENGINE_TOS_ACCESS_KEY:-}
+  VOLCENGINE_TOS_ENDPOINT: ${VOLCENGINE_TOS_ENDPOINT:-}
+  VOLCENGINE_TOS_REGION: ${VOLCENGINE_TOS_REGION:-}
   VECTOR_STORE: ${VECTOR_STORE:-weaviate}
   WEAVIATE_ENDPOINT: ${WEAVIATE_ENDPOINT:-http://weaviate:8080}
   WEAVIATE_API_KEY: ${WEAVIATE_API_KEY:-WVF5YThaHlkYwhGUSmCRgsX3tD5ngdN8pkih}
@@ -85,11 +103,10 @@ x-shared-env: &shared-api-worker-env
   QDRANT_CLIENT_TIMEOUT: ${QDRANT_CLIENT_TIMEOUT:-20}
   QDRANT_GRPC_ENABLED: ${QDRANT_GRPC_ENABLED:-false}
   QDRANT_GRPC_PORT: ${QDRANT_GRPC_PORT:-6334}
-  MILVUS_HOST: ${MILVUS_HOST:-127.0.0.1}
-  MILVUS_PORT: ${MILVUS_PORT:-19530}
+  MILVUS_URI: ${MILVUS_URI:-http://127.0.0.1:19530}
+  MILVUS_TOKEN: ${MILVUS_TOKEN:-}
   MILVUS_USER: ${MILVUS_USER:-root}
   MILVUS_PASSWORD: ${MILVUS_PASSWORD:-Milvus}
-  MILVUS_SECURE: ${MILVUS_SECURE:-false}
   MYSCALE_HOST: ${MYSCALE_HOST:-myscale}
   MYSCALE_PORT: ${MYSCALE_PORT:-8123}
   MYSCALE_USER: ${MYSCALE_USER:-default}
@@ -191,7 +208,7 @@ x-shared-env: &shared-api-worker-env
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.7.3
+    image: langgenius/dify-api:0.8.2
     restart: always
     environment:
       # Use the shared environment variables.
@@ -211,7 +228,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.7.3
+    image: langgenius/dify-api:0.8.2
     restart: always
     environment:
       # Use the shared environment variables.
@@ -230,13 +247,14 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.7.3
+    image: langgenius/dify-web:0.8.2
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}
       APP_API_URL: ${APP_API_URL:-}
       SENTRY_DSN: ${WEB_SENTRY_DSN:-}
       NEXT_TELEMETRY_DISABLED: ${NEXT_TELEMETRY_DISABLED:-0}
+      TEXT_GENERATION_TIMEOUT_MS: ${TEXT_GENERATION_TIMEOUT_MS:-60000}
 
   # The postgres database.
   db:
@@ -275,7 +293,7 @@ services:
 
   # The DifySandbox
   sandbox:
-    image: langgenius/dify-sandbox:0.2.6
+    image: langgenius/dify-sandbox:0.2.9
     restart: always
     environment:
       # The DifySandbox configurations
@@ -290,6 +308,8 @@ services:
       SANDBOX_PORT: ${SANDBOX_PORT:-8194}
     volumes:
       - ./volumes/sandbox/dependencies:/dependencies
+    healthcheck:
+      test: [ "CMD", "curl", "-f", "http://localhost:8194/health" ]
     networks:
       - ssrf_proxy_network
 
@@ -332,7 +352,7 @@ services:
       - CERTBOT_DOMAIN=${CERTBOT_DOMAIN}
       - CERTBOT_OPTIONS=${CERTBOT_OPTIONS:-}
     entrypoint: [ "/docker-entrypoint.sh" ]
-    command: ["tail", "-f", "/dev/null"]
+    command: [ "tail", "-f", "/dev/null" ]
 
   # The nginx reverse proxy.
   # used for reverse proxying the API service and Web service.
@@ -378,7 +398,7 @@ services:
   weaviate:
     image: semitechnologies/weaviate:1.19.0
     profiles:
-      - ''
+      - ""
       - weaviate
     restart: always
     volumes:
@@ -474,13 +494,13 @@ services:
       - oracle
     restart: always
     volumes:
-      - type: volume
-        source: oradata
+      - source: oradata
+        type: volume
         target: /opt/oracle/oradata
       - ./startupscripts:/opt/oracle/scripts/startup
     environment:
-      - ORACLE_PWD=${ORACLE_PWD:-Dify123456}
-      - ORACLE_CHARACTERSET=${ORACLE_CHARACTERSET:-AL32UTF8}
+      ORACLE_PWD: ${ORACLE_PWD:-Dify123456}
+      ORACLE_CHARACTERSET: ${ORACLE_CHARACTERSET:-AL32UTF8}
 
   # Milvus vector database services
   etcd:
@@ -489,10 +509,10 @@ services:
     profiles:
       - milvus
     environment:
-      - ETCD_AUTO_COMPACTION_MODE=${ETCD_AUTO_COMPACTION_MODE:-revision}
-      - ETCD_AUTO_COMPACTION_RETENTION=${ETCD_AUTO_COMPACTION_RETENTION:-1000}
-      - ETCD_QUOTA_BACKEND_BYTES=${ETCD_QUOTA_BACKEND_BYTES:-4294967296}
-      - ETCD_SNAPSHOT_COUNT=${ETCD_SNAPSHOT_COUNT:-50000}
+      ETCD_AUTO_COMPACTION_MODE: ${ETCD_AUTO_COMPACTION_MODE:-revision}
+      ETCD_AUTO_COMPACTION_RETENTION: ${ETCD_AUTO_COMPACTION_RETENTION:-1000}
+      ETCD_QUOTA_BACKEND_BYTES: ${ETCD_QUOTA_BACKEND_BYTES:-4294967296}
+      ETCD_SNAPSHOT_COUNT: ${ETCD_SNAPSHOT_COUNT:-50000}
     volumes:
       - ./volumes/milvus/etcd:/etcd
     command: etcd -advertise-client-urls=http://127.0.0.1:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd
@@ -542,8 +562,11 @@ services:
       timeout: 20s
       retries: 3
     depends_on:
-      - "etcd"
-      - "minio"
+      - etcd
+      - minio
+    ports:
+      - 19530:19530
+      - 9091:9091
     networks:
       - milvus
 
@@ -554,10 +577,10 @@ services:
     profiles:
       - opensearch
     environment:
-      - discovery.type=${OPENSEARCH_DISCOVERY_TYPE:-single-node}
-      - bootstrap.memory_lock=${OPENSEARCH_BOOTSTRAP_MEMORY_LOCK:-true}
-      - OPENSEARCH_JAVA_OPTS=-Xms${OPENSEARCH_JAVA_OPTS_MIN:-512m} -Xmx${OPENSEARCH_JAVA_OPTS_MAX:-1024m}
-      - OPENSEARCH_INITIAL_ADMIN_PASSWORD=${OPENSEARCH_INITIAL_ADMIN_PASSWORD:-Qazwsxedc!@#123}
+      discovery.type: ${OPENSEARCH_DISCOVERY_TYPE:-single-node}
+      bootstrap.memory_lock: ${OPENSEARCH_BOOTSTRAP_MEMORY_LOCK:-true}
+      OPENSEARCH_JAVA_OPTS: -Xms${OPENSEARCH_JAVA_OPTS_MIN:-512m} -Xmx${OPENSEARCH_JAVA_OPTS_MAX:-1024m}
+      OPENSEARCH_INITIAL_ADMIN_PASSWORD: ${OPENSEARCH_INITIAL_ADMIN_PASSWORD:-Qazwsxedc!@#123}
     ulimits:
       memlock:
         soft: ${OPENSEARCH_MEMLOCK_SOFT:--1}
@@ -597,7 +620,7 @@ services:
       - ./volumes/myscale/log:/var/log/clickhouse-server
       - ./volumes/myscale/config/users.d/custom_users_config.xml:/etc/clickhouse-server/users.d/custom_users_config.xml
     ports:
-      - "${MYSCALE_PORT:-8123}:${MYSCALE_PORT:-8123}"
+      - ${MYSCALE_PORT:-8123}:${MYSCALE_PORT:-8123}
 
   # https://www.elastic.co/guide/en/elasticsearch/reference/current/settings.html
   # https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#docker-prod-prerequisites
@@ -610,18 +633,18 @@ services:
     volumes:
       - dify_es01_data:/usr/share/elasticsearch/data
     environment:
-      - ELASTIC_PASSWORD=${ELASTICSEARCH_PASSWORD:-elastic}
-      - cluster.name=dify-es-cluster
-      - node.name=dify-es0
-      - discovery.type=single-node
-      - xpack.license.self_generated.type=trial
-      - xpack.security.enabled=true
-      - xpack.security.enrollment.enabled=false
-      - xpack.security.http.ssl.enabled=false
+      ELASTIC_PASSWORD: ${ELASTICSEARCH_PASSWORD:-elastic}
+      cluster.name: dify-es-cluster
+      node.name: dify-es0
+      discovery.type: single-node
+      xpack.license.self_generated.type: trial
+      xpack.security.enabled: "true"
+      xpack.security.enrollment.enabled: "false"
+      xpack.security.http.ssl.enabled: "false"
     ports:
       - ${ELASTICSEARCH_PORT:-9200}:9200
     healthcheck:
-      test: ["CMD", "curl", "-s", "http://localhost:9200/_cluster/health?pretty"]
+      test: [ "CMD", "curl", "-s", "http://localhost:9200/_cluster/health?pretty" ]
       interval: 30s
       timeout: 10s
       retries: 50
@@ -637,15 +660,15 @@ services:
       - elasticsearch
     restart: always
     environment:
-      - XPACK_ENCRYPTEDSAVEDOBJECTS_ENCRYPTIONKEY=d1a66dfd-c4d3-4a0a-8290-2abcb83ab3aa
-      - NO_PROXY=localhost,127.0.0.1,elasticsearch,kibana
-      - XPACK_SECURITY_ENABLED=true
-      - XPACK_SECURITY_ENROLLMENT_ENABLED=false
-      - XPACK_SECURITY_HTTP_SSL_ENABLED=false
-      - XPACK_FLEET_ISAIRGAPPED=true
-      - I18N_LOCALE=zh-CN
-      - SERVER_PORT=5601
-      - ELASTICSEARCH_HOSTS="http://elasticsearch:9200"
+      XPACK_ENCRYPTEDSAVEDOBJECTS_ENCRYPTIONKEY: d1a66dfd-c4d3-4a0a-8290-2abcb83ab3aa
+      NO_PROXY: localhost,127.0.0.1,elasticsearch,kibana
+      XPACK_SECURITY_ENABLED: "true"
+      XPACK_SECURITY_ENROLLMENT_ENABLED: "false"
+      XPACK_SECURITY_HTTP_SSL_ENABLED: "false"
+      XPACK_FLEET_ISAIRGAPPED: "true"
+      I18N_LOCALE: zh-CN
+      SERVER_PORT: "5601"
+      ELASTICSEARCH_HOSTS: http://elasticsearch:9200
     ports:
       - ${KIBANA_PORT:-5601}:5601
     healthcheck:
diff --git a/docker/startupscripts/init.sh b/docker/startupscripts/init.sh
index ee7600850a..c6e6e1966f 100755
--- a/docker/startupscripts/init.sh
+++ b/docker/startupscripts/init.sh
@@ -1,13 +1,13 @@
 #!/usr/bin/env bash
 
-DB_INITIALISED="/opt/oracle/oradata/dbinit"
-#[ -f ${DB_INITIALISED} ] && exit
-#touch ${DB_INITIALISED}
-if [ -f ${DB_INITIALISED} ]; then
+DB_INITIALIZED="/opt/oracle/oradata/dbinit"
+#[ -f ${DB_INITIALIZED} ] && exit
+#touch ${DB_INITIALIZED}
+if [ -f ${DB_INITIALIZED} ]; then
   echo 'File exists. Standards for have been Init'
   exit
 else
-  echo 'File does not exist. Standards for first time Strart up this DB'
+  echo 'File does not exist. Standards for first time Start up this DB'
   "$ORACLE_HOME"/bin/sqlplus -s "/ as sysdba" @"/opt/oracle/scripts/startup/init_user.script"; 
-  touch ${DB_INITIALISED}
+  touch ${DB_INITIALIZED}
 fi
diff --git a/sdks/nodejs-client/README.md b/sdks/nodejs-client/README.md
index 50303b4867..37b5ca2d0a 100644
--- a/sdks/nodejs-client/README.md
+++ b/sdks/nodejs-client/README.md
@@ -18,7 +18,7 @@ const query = 'Please tell me a short story in 10 words or less.'
 const remote_url_files = [{
     type: 'image',
     transfer_method: 'remote_url',
-    url: 'your_url_addresss'
+    url: 'your_url_address'
 }]
 
 // Create a completion client
diff --git a/sdks/python-client/dify_client/client.py b/sdks/python-client/dify_client/client.py
index a8da1d7cae..2be079bdf3 100644
--- a/sdks/python-client/dify_client/client.py
+++ b/sdks/python-client/dify_client/client.py
@@ -1,3 +1,4 @@
+import json
 import requests
 
 
@@ -131,3 +132,284 @@ class WorkflowClient(DifyClient):
     def stop(self, task_id, user):
         data = {"user": user}
         return self._send_request("POST", f"/workflows/tasks/{task_id}/stop", data)
+
+    def get_result(self, workflow_run_id):
+        return self._send_request("GET", f"/workflows/run/{workflow_run_id}")
+
+
+
+class KnowledgeBaseClient(DifyClient):
+
+    def __init__(self, api_key, base_url: str = 'https://api.dify.ai/v1', dataset_id: str = None):
+        """
+        Construct a KnowledgeBaseClient object.
+
+        Args:
+            api_key (str): API key of Dify.
+            base_url (str, optional): Base URL of Dify API. Defaults to 'https://api.dify.ai/v1'.
+            dataset_id (str, optional): ID of the dataset. Defaults to None. You don't need this if you just want to
+                create a new dataset. or list datasets. otherwise you need to set this.
+        """
+        super().__init__(
+            api_key=api_key,
+            base_url=base_url
+        )
+        self.dataset_id = dataset_id
+
+    def _get_dataset_id(self):
+        if self.dataset_id is None:
+            raise ValueError("dataset_id is not set")
+        return self.dataset_id
+
+    def create_dataset(self, name: str, **kwargs):
+        return self._send_request('POST', '/datasets', {'name': name}, **kwargs)
+
+    def list_datasets(self, page: int = 1, page_size: int = 20, **kwargs):
+        return self._send_request('GET', f'/datasets?page={page}&limit={page_size}', **kwargs)
+
+    def create_document_by_text(self, name, text, extra_params: dict = None, **kwargs):
+        """
+        Create a document by text.
+
+        :param name: Name of the document
+        :param text: Text content of the document
+        :param extra_params: extra parameters pass to the API, such as indexing_technique, process_rule. (optional)
+            e.g.
+            {
+            'indexing_technique': 'high_quality',
+            'process_rule': {
+                'rules': {
+                    'pre_processing_rules': [
+                        {'id': 'remove_extra_spaces', 'enabled': True},
+                        {'id': 'remove_urls_emails', 'enabled': True}
+                    ],
+                    'segmentation': {
+                        'separator': '\n',
+                        'max_tokens': 500
+                    }
+                },
+                'mode': 'custom'
+            }
+        }
+        :return: Response from the API
+        """
+        data = {
+            'indexing_technique': 'high_quality',
+            'process_rule': {
+                'mode': 'automatic'
+            },
+            'name': name,
+            'text': text
+        }
+        if extra_params is not None and isinstance(extra_params, dict):
+            data.update(extra_params)
+        url = f"/datasets/{self._get_dataset_id()}/document/create_by_text"
+        return self._send_request("POST", url, json=data, **kwargs)
+
+    def update_document_by_text(self, document_id, name, text, extra_params: dict = None, **kwargs):
+        """
+        Update a document by text.
+
+        :param document_id: ID of the document
+        :param name: Name of the document
+        :param text: Text content of the document
+        :param extra_params: extra parameters pass to the API, such as indexing_technique, process_rule. (optional)
+            e.g.
+            {
+            'indexing_technique': 'high_quality',
+            'process_rule': {
+                'rules': {
+                    'pre_processing_rules': [
+                        {'id': 'remove_extra_spaces', 'enabled': True},
+                        {'id': 'remove_urls_emails', 'enabled': True}
+                    ],
+                    'segmentation': {
+                        'separator': '\n',
+                        'max_tokens': 500
+                    }
+                },
+                'mode': 'custom'
+            }
+        }
+        :return: Response from the API
+        """
+        data = {
+            'name': name,
+            'text': text
+        }
+        if extra_params is not None and isinstance(extra_params, dict):
+            data.update(extra_params)
+        url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/update_by_text"
+        return self._send_request("POST", url, json=data, **kwargs)
+
+    def create_document_by_file(self, file_path, original_document_id=None, extra_params: dict = None):
+        """
+        Create a document by file.
+
+        :param file_path: Path to the file
+        :param original_document_id: pass this ID if you want to replace the original document (optional)
+        :param extra_params: extra parameters pass to the API, such as indexing_technique, process_rule. (optional)
+            e.g.
+            {
+            'indexing_technique': 'high_quality',
+            'process_rule': {
+                'rules': {
+                    'pre_processing_rules': [
+                        {'id': 'remove_extra_spaces', 'enabled': True},
+                        {'id': 'remove_urls_emails', 'enabled': True}
+                    ],
+                    'segmentation': {
+                        'separator': '\n',
+                        'max_tokens': 500
+                    }
+                },
+                'mode': 'custom'
+            }
+        }
+        :return: Response from the API
+        """
+        files = {"file": open(file_path, "rb")}
+        data = {
+            'process_rule': {
+                'mode': 'automatic'
+            },
+            'indexing_technique': 'high_quality'
+        }
+        if extra_params is not None and isinstance(extra_params, dict):
+            data.update(extra_params)
+        if original_document_id is not None:
+            data['original_document_id'] = original_document_id
+        url = f"/datasets/{self._get_dataset_id()}/document/create_by_file"
+        return self._send_request_with_files("POST", url, {"data": json.dumps(data)}, files)
+
+    def update_document_by_file(self, document_id, file_path, extra_params: dict = None):
+        """
+        Update a document by file.
+
+        :param document_id: ID of the document
+        :param file_path: Path to the file
+        :param extra_params: extra parameters pass to the API, such as indexing_technique, process_rule. (optional)
+            e.g.
+            {
+            'indexing_technique': 'high_quality',
+            'process_rule': {
+                'rules': {
+                    'pre_processing_rules': [
+                        {'id': 'remove_extra_spaces', 'enabled': True},
+                        {'id': 'remove_urls_emails', 'enabled': True}
+                    ],
+                    'segmentation': {
+                        'separator': '\n',
+                        'max_tokens': 500
+                    }
+                },
+                'mode': 'custom'
+            }
+        }
+        :return:
+        """
+        files = {"file": open(file_path, "rb")}
+        data = {}
+        if extra_params is not None and isinstance(extra_params, dict):
+            data.update(extra_params)
+        url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/update_by_file"
+        return self._send_request_with_files("POST", url, {"data": json.dumps(data)}, files)
+
+    def batch_indexing_status(self, batch_id: str, **kwargs):
+        """
+        Get the status of the batch indexing.
+
+        :param batch_id: ID of the batch uploading
+        :return: Response from the API
+        """
+        url = f"/datasets/{self._get_dataset_id()}/documents/{batch_id}/indexing-status"
+        return self._send_request("GET", url, **kwargs)
+
+    def delete_dataset(self):
+        """
+        Delete this dataset.
+
+        :return: Response from the API
+        """
+        url = f"/datasets/{self._get_dataset_id()}"
+        return self._send_request("DELETE", url)
+
+    def delete_document(self, document_id):
+        """
+        Delete a document.
+
+        :param document_id: ID of the document
+        :return: Response from the API
+        """
+        url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}"
+        return self._send_request("DELETE", url)
+
+    def list_documents(self, page: int = None, page_size: int = None, keyword: str = None, **kwargs):
+        """
+        Get a list of documents in this dataset.
+
+        :return: Response from the API
+        """
+        params = {}
+        if page is not None:
+            params['page'] = page
+        if page_size is not None:
+            params['limit'] = page_size
+        if keyword is not None:
+            params['keyword'] = keyword
+        url = f"/datasets/{self._get_dataset_id()}/documents"
+        return self._send_request("GET", url, params=params, **kwargs)
+
+    def add_segments(self, document_id, segments, **kwargs):
+        """
+        Add segments to a document.
+
+        :param document_id: ID of the document
+        :param segments: List of segments to add, example: [{"content": "1", "answer": "1", "keyword": ["a"]}]
+        :return: Response from the API
+        """
+        data = {"segments": segments}
+        url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/segments"
+        return self._send_request("POST", url, json=data, **kwargs)
+
+    def query_segments(self, document_id, keyword: str = None, status: str = None, **kwargs):
+        """
+        Query segments in this document.
+
+        :param document_id: ID of the document
+        :param keyword: query keyword, optional
+        :param status: status of the segment, optional, e.g. completed
+        """
+        url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/segments"
+        params = {}
+        if keyword is not None:
+            params['keyword'] = keyword
+        if status is not None:
+            params['status'] = status
+        if "params" in kwargs:
+            params.update(kwargs["params"])
+        return self._send_request("GET", url, params=params, **kwargs)
+
+    def delete_document_segment(self, document_id, segment_id):
+        """
+        Delete a segment from a document.
+
+        :param document_id: ID of the document
+        :param segment_id: ID of the segment
+        :return: Response from the API
+        """
+        url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/segments/{segment_id}"
+        return self._send_request("DELETE", url)
+
+    def update_document_segment(self, document_id, segment_id, segment_data, **kwargs):
+        """
+        Update a segment in a document.
+
+        :param document_id: ID of the document
+        :param segment_id: ID of the segment
+        :param segment_data: Data of the segment, example: {"content": "1", "answer": "1", "keyword": ["a"], "enabled": True}
+        :return: Response from the API
+        """
+        data = {"segment": segment_data}
+        url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/segments/{segment_id}"
+        return self._send_request("POST", url, json=data, **kwargs)
diff --git a/sdks/python-client/setup.py b/sdks/python-client/setup.py
index e74748377e..bb8ca46d97 100644
--- a/sdks/python-client/setup.py
+++ b/sdks/python-client/setup.py
@@ -5,7 +5,7 @@ with open("README.md", "r", encoding="utf-8") as fh:
 
 setup(
     name="dify-client",
-    version="0.1.10",
+    version="0.1.12",
     author="Dify",
     author_email="hello@dify.ai",
     description="A package for interacting with the Dify Service-API",
diff --git a/sdks/python-client/tests/test_client.py b/sdks/python-client/tests/test_client.py
index 5259d082ca..301e733b6b 100644
--- a/sdks/python-client/tests/test_client.py
+++ b/sdks/python-client/tests/test_client.py
@@ -1,10 +1,157 @@
 import os
+import time
 import unittest
 
-from dify_client.client import ChatClient, CompletionClient, DifyClient
+from dify_client.client import ChatClient, CompletionClient, DifyClient, KnowledgeBaseClient
 
 API_KEY = os.environ.get("API_KEY")
 APP_ID = os.environ.get("APP_ID")
+API_BASE_URL = os.environ.get("API_BASE_URL", "https://api.dify.ai/v1")
+FILE_PATH_BASE = os.path.dirname(__file__)
+
+
+class TestKnowledgeBaseClient(unittest.TestCase):
+    def setUp(self):
+        self.knowledge_base_client = KnowledgeBaseClient(API_KEY, base_url=API_BASE_URL)
+        self.README_FILE_PATH = os.path.abspath(os.path.join(FILE_PATH_BASE, "../README.md"))
+        self.dataset_id = None
+        self.document_id = None
+        self.segment_id = None
+        self.batch_id = None
+
+    def _get_dataset_kb_client(self):
+        self.assertIsNotNone(self.dataset_id)
+        return KnowledgeBaseClient(API_KEY, base_url=API_BASE_URL, dataset_id=self.dataset_id)
+
+    def test_001_create_dataset(self):
+        response = self.knowledge_base_client.create_dataset(name="test_dataset")
+        data = response.json()
+        self.assertIn("id", data)
+        self.dataset_id = data["id"]
+        self.assertEqual("test_dataset", data["name"])
+
+        # the following tests require to be executed in order because they use
+        # the dataset/document/segment ids from the previous test
+        self._test_002_list_datasets()
+        self._test_003_create_document_by_text()
+        time.sleep(1)
+        self._test_004_update_document_by_text()
+        # self._test_005_batch_indexing_status()
+        time.sleep(1)
+        self._test_006_update_document_by_file()
+        time.sleep(1)
+        self._test_007_list_documents()
+        self._test_008_delete_document()
+        self._test_009_create_document_by_file()
+        time.sleep(1)
+        self._test_010_add_segments()
+        self._test_011_query_segments()
+        self._test_012_update_document_segment()
+        self._test_013_delete_document_segment()
+        self._test_014_delete_dataset()
+
+    def _test_002_list_datasets(self):
+        response = self.knowledge_base_client.list_datasets()
+        data = response.json()
+        self.assertIn("data", data)
+        self.assertIn("total", data)
+
+    def _test_003_create_document_by_text(self):
+        client = self._get_dataset_kb_client()
+        response = client.create_document_by_text("test_document", "test_text")
+        data = response.json()
+        self.assertIn("document", data)
+        self.document_id = data["document"]["id"]
+        self.batch_id = data["batch"]
+
+    def _test_004_update_document_by_text(self):
+        client = self._get_dataset_kb_client()
+        self.assertIsNotNone(self.document_id)
+        response = client.update_document_by_text(self.document_id, "test_document_updated", "test_text_updated")
+        data = response.json()
+        self.assertIn("document", data)
+        self.assertIn("batch", data)
+        self.batch_id = data["batch"]
+
+    def _test_005_batch_indexing_status(self):
+        client = self._get_dataset_kb_client()
+        response = client.batch_indexing_status(self.batch_id)
+        data = response.json()
+        self.assertEqual(response.status_code, 200)
+
+    def _test_006_update_document_by_file(self):
+        client = self._get_dataset_kb_client()
+        self.assertIsNotNone(self.document_id)
+        response = client.update_document_by_file(self.document_id, self.README_FILE_PATH)
+        data = response.json()
+        self.assertIn("document", data)
+        self.assertIn("batch", data)
+        self.batch_id = data["batch"]
+
+    def _test_007_list_documents(self):
+        client = self._get_dataset_kb_client()
+        response = client.list_documents()
+        data = response.json()
+        self.assertIn("data", data)
+
+    def _test_008_delete_document(self):
+        client = self._get_dataset_kb_client()
+        self.assertIsNotNone(self.document_id)
+        response = client.delete_document(self.document_id)
+        data = response.json()
+        self.assertIn("result", data)
+        self.assertEqual("success", data["result"])
+
+    def _test_009_create_document_by_file(self):
+        client = self._get_dataset_kb_client()
+        response = client.create_document_by_file(self.README_FILE_PATH)
+        data = response.json()
+        self.assertIn("document", data)
+        self.document_id = data["document"]["id"]
+        self.batch_id = data["batch"]
+
+    def _test_010_add_segments(self):
+        client = self._get_dataset_kb_client()
+        response = client.add_segments(self.document_id, [
+            {"content": "test text segment 1"}
+        ])
+        data = response.json()
+        self.assertIn("data", data)
+        self.assertGreater(len(data["data"]), 0)
+        segment = data["data"][0]
+        self.segment_id = segment["id"]
+
+    def _test_011_query_segments(self):
+        client = self._get_dataset_kb_client()
+        response = client.query_segments(self.document_id)
+        data = response.json()
+        self.assertIn("data", data)
+        self.assertGreater(len(data["data"]), 0)
+
+    def _test_012_update_document_segment(self):
+        client = self._get_dataset_kb_client()
+        self.assertIsNotNone(self.segment_id)
+        response = client.update_document_segment(self.document_id, self.segment_id,
+                                                  {"content": "test text segment 1 updated"}
+                                                  )
+        data = response.json()
+        self.assertIn("data", data)
+        self.assertGreater(len(data["data"]), 0)
+        segment = data["data"]
+        self.assertEqual("test text segment 1 updated", segment["content"])
+
+    def _test_013_delete_document_segment(self):
+        client = self._get_dataset_kb_client()
+        self.assertIsNotNone(self.segment_id)
+        response = client.delete_document_segment(self.document_id, self.segment_id)
+        data = response.json()
+        self.assertIn("result", data)
+        self.assertEqual("success", data["result"])
+
+    def _test_014_delete_dataset(self):
+        client = self._get_dataset_kb_client()
+        response = client.delete_dataset()
+        self.assertEqual(204, response.status_code)
 
 
 class TestChatClient(unittest.TestCase):
diff --git a/web/.env.example b/web/.env.example
index 3045cef2f9..8e254082b3 100644
--- a/web/.env.example
+++ b/web/.env.example
@@ -19,3 +19,6 @@ NEXT_TELEMETRY_DISABLED=1
 
 # Disable Upload Image as WebApp icon default is false
 NEXT_PUBLIC_UPLOAD_IMAGE_AS_ICON=false
+
+# The timeout for the text generation in millisecond
+NEXT_PUBLIC_TEXT_GENERATION_TIMEOUT_MS=60000
diff --git a/web/app/(commonLayout)/app/(appDetailLayout)/[appId]/layout.tsx b/web/app/(commonLayout)/app/(appDetailLayout)/[appId]/layout.tsx
index 8723420d84..e728749b85 100644
--- a/web/app/(commonLayout)/app/(appDetailLayout)/[appId]/layout.tsx
+++ b/web/app/(commonLayout)/app/(appDetailLayout)/[appId]/layout.tsx
@@ -108,7 +108,7 @@ const AppDetailLayout: FC<IAppDetailLayoutProps> = (props) => {
   useEffect(() => {
     setAppDetail()
     fetchAppDetail({ url: '/apps', id: appId }).then((res) => {
-      // redirections
+      // redirection
       if ((res.mode === 'workflow' || res.mode === 'advanced-chat') && (pathname).endsWith('configuration')) {
         router.replace(`/app/${appId}/workflow`)
       }
diff --git a/web/app/(commonLayout)/app/(appDetailLayout)/[appId]/overview/tracing/provider-panel.tsx b/web/app/(commonLayout)/app/(appDetailLayout)/[appId]/overview/tracing/provider-panel.tsx
index b908322a92..6e5046ecf8 100644
--- a/web/app/(commonLayout)/app/(appDetailLayout)/[appId]/overview/tracing/provider-panel.tsx
+++ b/web/app/(commonLayout)/app/(appDetailLayout)/[appId]/overview/tracing/provider-panel.tsx
@@ -48,9 +48,10 @@ const ProviderPanel: FC<Props> = ({
     e.preventDefault()
     e.stopPropagation()
 
-    const url = `${config?.host}/project/${config?.project_key}`
-    window.open(url, '_blank', 'noopener,noreferrer')
-  }, [])
+    const url = config?.project_url
+    if (url)
+      window.open(url, '_blank', 'noopener,noreferrer')
+  }, [config?.project_url])
 
   const handleChosen = useCallback((e: React.MouseEvent) => {
     e.stopPropagation()
diff --git a/web/app/(commonLayout)/apps/AppCard.tsx b/web/app/(commonLayout)/apps/AppCard.tsx
index fb39dee5a3..1ffb132cf8 100644
--- a/web/app/(commonLayout)/apps/AppCard.tsx
+++ b/web/app/(commonLayout)/apps/AppCard.tsx
@@ -21,7 +21,7 @@ import Divider from '@/app/components/base/divider'
 import { getRedirection } from '@/utils/app-redirection'
 import { useProviderContext } from '@/context/provider-context'
 import { NEED_REFRESH_APP_LIST_KEY } from '@/config'
-import { AiText, ChatBot, CuteRobote } from '@/app/components/base/icons/src/vender/solid/communication'
+import { AiText, ChatBot, CuteRobot } from '@/app/components/base/icons/src/vender/solid/communication'
 import { Route } from '@/app/components/base/icons/src/vender/solid/mapsAndTravel'
 import type { CreateAppModalProps } from '@/app/components/explore/create-app-modal'
 import EditAppModal from '@/app/components/explore/create-app-modal'
@@ -273,7 +273,7 @@ const AppCard = ({ app, onRefresh }: AppCardProps) => {
                 <ChatBot className='w-3 h-3 text-[#1570EF]' />
               )}
               {app.mode === 'agent-chat' && (
-                <CuteRobote className='w-3 h-3 text-indigo-600' />
+                <CuteRobot className='w-3 h-3 text-indigo-600' />
               )}
               {app.mode === 'chat' && (
                 <ChatBot className='w-3 h-3 text-[#1570EF]' />
diff --git a/web/app/(commonLayout)/datasets/(datasetDetailLayout)/[datasetId]/layout.tsx b/web/app/(commonLayout)/datasets/(datasetDetailLayout)/[datasetId]/layout.tsx
index 7cd083d11b..e691cc05f6 100644
--- a/web/app/(commonLayout)/datasets/(datasetDetailLayout)/[datasetId]/layout.tsx
+++ b/web/app/(commonLayout)/datasets/(datasetDetailLayout)/[datasetId]/layout.tsx
@@ -35,7 +35,7 @@ import { DataSourceType } from '@/models/datasets'
 import useBreakpoints, { MediaType } from '@/hooks/use-breakpoints'
 import { LanguagesSupported } from '@/i18n/language'
 import { useStore } from '@/app/components/app/store'
-import { AiText, ChatBot, CuteRobote } from '@/app/components/base/icons/src/vender/solid/communication'
+import { AiText, ChatBot, CuteRobot } from '@/app/components/base/icons/src/vender/solid/communication'
 import { Route } from '@/app/components/base/icons/src/vender/solid/mapsAndTravel'
 import { getLocaleOnClient } from '@/i18n'
 import { useAppContext } from '@/context/app-context'
@@ -67,7 +67,7 @@ const LikedItem = ({
               <ChatBot className='w-2.5 h-2.5 text-[#1570EF]' />
             )}
             {detail.mode === 'agent-chat' && (
-              <CuteRobote className='w-2.5 h-2.5 text-indigo-600' />
+              <CuteRobot className='w-2.5 h-2.5 text-indigo-600' />
             )}
             {detail.mode === 'chat' && (
               <ChatBot className='w-2.5 h-2.5 text-[#1570EF]' />
diff --git a/web/app/(commonLayout)/datasets/template/template.en.mdx b/web/app/(commonLayout)/datasets/template/template.en.mdx
index 33451b8a0b..99fab1ed8b 100644
--- a/web/app/(commonLayout)/datasets/template/template.en.mdx
+++ b/web/app/(commonLayout)/datasets/template/template.en.mdx
@@ -148,7 +148,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
         - original_document_id Source document ID (optional)
           - Used to re-upload the document or modify the document cleaning and segmentation configuration. The missing information is copied from the source document
           - The source document cannot be an archived document
-          - When original_document_id is passed in, the update operation is performed on behalf of the document. process_rule is a fillable item. If not filled in, the segmentation method of the source document will be used by defaul
+          - When original_document_id is passed in, the update operation is performed on behalf of the document. process_rule is a fillable item. If not filled in, the segmentation method of the source document will be used by default
           - When original_document_id is not passed in, the new operation is performed on behalf of the document, and process_rule is required
 
         - indexing_technique Index mode
@@ -985,7 +985,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
 
     ### Request Body
     <Properties>
-      <Property name='segment' type='object list' key='segment'>
+      <Property name='segment' type='object' key='segment'>
         - <code>content</code> (text) text content/question content，required
         - <code>answer</code> (text) Answer content, not required, passed if the Knowledge is in qa mode
         - <code>keywords</code> (list) keyword, not required
diff --git a/web/app/(commonLayout)/datasets/template/template.zh.mdx b/web/app/(commonLayout)/datasets/template/template.zh.mdx
index dc48f92f18..2f8c7e99dc 100644
--- a/web/app/(commonLayout)/datasets/template/template.zh.mdx
+++ b/web/app/(commonLayout)/datasets/template/template.zh.mdx
@@ -142,7 +142,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
       </Property>
     </Properties>
 
-    ### Request Bodys
+    ### Request Body
     <Properties>
       <Property name='data' type='multipart/form-data json string' key='data'>
         - original_document_id 源文档 ID （选填）
@@ -985,7 +985,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
 
     ### Request Body
     <Properties>
-      <Property name='segment' type='object list' key='segment'>
+      <Property name='segment' type='object' key='segment'>
         - <code>content</code> (text) 文本内容/问题内容，必填
         - <code>answer</code> (text) 答案内容，非必填，如果知识库的模式为qa模式则传值
         - <code>keywords</code> (list) 关键字，非必填
diff --git a/web/app/components/app-sidebar/app-info.tsx b/web/app/components/app-sidebar/app-info.tsx
index b4a7a8a619..12fe5cba46 100644
--- a/web/app/components/app-sidebar/app-info.tsx
+++ b/web/app/components/app-sidebar/app-info.tsx
@@ -22,7 +22,7 @@ import { copyApp, deleteApp, exportAppConfig, updateAppInfo } from '@/service/ap
 import DuplicateAppModal from '@/app/components/app/duplicate-modal'
 import type { DuplicateAppModalProps } from '@/app/components/app/duplicate-modal'
 import CreateAppModal from '@/app/components/explore/create-app-modal'
-import { AiText, ChatBot, CuteRobote } from '@/app/components/base/icons/src/vender/solid/communication'
+import { AiText, ChatBot, CuteRobot } from '@/app/components/base/icons/src/vender/solid/communication'
 import { Route } from '@/app/components/base/icons/src/vender/solid/mapsAndTravel'
 import type { CreateAppModalProps } from '@/app/components/explore/create-app-modal'
 import { NEED_REFRESH_APP_LIST_KEY } from '@/config'
@@ -214,7 +214,7 @@ const AppInfo = ({ expand }: IAppInfoProps) => {
                   <ChatBot className={cn('w-3 h-3 text-[#1570EF]', !expand && '!w-2.5 !h-2.5')} />
                 )}
                 {appDetail.mode === 'agent-chat' && (
-                  <CuteRobote className={cn('w-3 h-3 text-indigo-600', !expand && '!w-2.5 !h-2.5')} />
+                  <CuteRobot className={cn('w-3 h-3 text-indigo-600', !expand && '!w-2.5 !h-2.5')} />
                 )}
                 {appDetail.mode === 'chat' && (
                   <ChatBot className={cn('w-3 h-3 text-[#1570EF]', !expand && '!w-2.5 !h-2.5')} />
@@ -280,7 +280,7 @@ const AppInfo = ({ expand }: IAppInfoProps) => {
                     <ChatBot className='w-3 h-3 text-[#1570EF]' />
                   )}
                   {appDetail.mode === 'agent-chat' && (
-                    <CuteRobote className='w-3 h-3 text-indigo-600' />
+                    <CuteRobot className='w-3 h-3 text-indigo-600' />
                   )}
                   {appDetail.mode === 'chat' && (
                     <ChatBot className='w-3 h-3 text-[#1570EF]' />
@@ -323,7 +323,7 @@ const AppInfo = ({ expand }: IAppInfoProps) => {
                 </div>
               </div>
             </div>
-            {/* desscription */}
+            {/* description */}
             {appDetail.description && (
               <div className='px-4 py-2 text-gray-500 text-xs leading-[18px]'>{appDetail.description}</div>
             )}
diff --git a/web/app/components/app/annotation/index.tsx b/web/app/components/app/annotation/index.tsx
index 0f54f5bfc3..c66aaef6ce 100644
--- a/web/app/components/app/annotation/index.tsx
+++ b/web/app/components/app/annotation/index.tsx
@@ -232,8 +232,8 @@ const Annotation: FC<Props> = ({
             middlePagesSiblingCount={1}
             setCurrentPage={setCurrPage}
             totalPages={Math.ceil(total / APP_PAGE_LIMIT)}
-            truncableClassName="w-8 px-0.5 text-center"
-            truncableText="..."
+            truncatableClassName="w-8 px-0.5 text-center"
+            truncatableText="..."
           >
             <Pagination.PrevButton
               disabled={currPage === 0}
diff --git a/web/app/components/app/annotation/view-annotation-modal/index.tsx b/web/app/components/app/annotation/view-annotation-modal/index.tsx
index 7b96ad134e..daa8434ff7 100644
--- a/web/app/components/app/annotation/view-annotation-modal/index.tsx
+++ b/web/app/components/app/annotation/view-annotation-modal/index.tsx
@@ -156,8 +156,8 @@ const ViewAnnotationModal: FC<Props> = ({
             middlePagesSiblingCount={1}
             setCurrentPage={setCurrPage}
             totalPages={Math.ceil(total / APP_PAGE_LIMIT)}
-            truncableClassName="w-8 px-0.5 text-center"
-            truncableText="..."
+            truncatableClassName="w-8 px-0.5 text-center"
+            truncatableText="..."
           >
             <Pagination.PrevButton
               disabled={currPage === 0}
diff --git a/web/app/components/app/app-publisher/index.tsx b/web/app/components/app/app-publisher/index.tsx
index 2bcc74ec01..0558e29956 100644
--- a/web/app/components/app/app-publisher/index.tsx
+++ b/web/app/components/app/app-publisher/index.tsx
@@ -63,7 +63,6 @@ const AppPublisher = ({
   const [published, setPublished] = useState(false)
   const [open, setOpen] = useState(false)
   const appDetail = useAppStore(state => state.appDetail)
-  const [publishedTime, setPublishedTime] = useState<number | undefined>(publishedAt)
   const { app_base_url: appBaseURL = '', access_token: accessToken = '' } = appDetail?.site ?? {}
   const appMode = (appDetail?.mode !== 'completion' && appDetail?.mode !== 'workflow') ? 'chat' : appDetail.mode
   const appURL = `${appBaseURL}/${appMode}/${accessToken}`
@@ -77,7 +76,6 @@ const AppPublisher = ({
     try {
       await onPublish?.(modelAndParameter)
       setPublished(true)
-      setPublishedTime(Date.now())
     }
     catch (e) {
       setPublished(false)
@@ -133,13 +131,13 @@ const AppPublisher = ({
         <div className='w-[336px] bg-white rounded-2xl border-[0.5px] border-gray-200 shadow-xl'>
           <div className='p-4 pt-3'>
             <div className='flex items-center h-6 text-xs font-medium text-gray-500 uppercase'>
-              {publishedTime ? t('workflow.common.latestPublished') : t('workflow.common.currentDraftUnpublished')}
+              {publishedAt ? t('workflow.common.latestPublished') : t('workflow.common.currentDraftUnpublished')}
             </div>
-            {publishedTime
+            {publishedAt
               ? (
                 <div className='flex justify-between items-center h-[18px]'>
                   <div className='flex items-center mt-[3px] mb-[3px] leading-[18px] text-[13px] font-medium text-gray-700'>
-                    {t('workflow.common.publishedAt')} {formatTimeFromNow(publishedTime)}
+                    {t('workflow.common.publishedAt')} {formatTimeFromNow(publishedAt)}
                   </div>
                   <Button
                     className={`
@@ -177,18 +175,18 @@ const AppPublisher = ({
                   {
                     published
                       ? t('workflow.common.published')
-                      : publishedTime ? t('workflow.common.update') : t('workflow.common.publish')
+                      : publishedAt ? t('workflow.common.update') : t('workflow.common.publish')
                   }
                 </Button>
               )
             }
           </div>
           <div className='p-4 pt-3 border-t-[0.5px] border-t-black/5'>
-            <SuggestedAction disabled={!publishedTime} link={appURL} icon={<PlayCircle />}>{t('workflow.common.runApp')}</SuggestedAction>
+            <SuggestedAction disabled={!publishedAt} link={appURL} icon={<PlayCircle />}>{t('workflow.common.runApp')}</SuggestedAction>
             {appDetail?.mode === 'workflow'
               ? (
                 <SuggestedAction
-                  disabled={!publishedTime}
+                  disabled={!publishedAt}
                   link={`${appURL}${appURL.includes('?') ? '&' : '?'}mode=batch`}
                   icon={<LeftIndent02 className='w-4 h-4' />}
                 >
@@ -201,16 +199,16 @@ const AppPublisher = ({
                     setEmbeddingModalOpen(true)
                     handleTrigger()
                   }}
-                  disabled={!publishedTime}
+                  disabled={!publishedAt}
                   icon={<CodeBrowser className='w-4 h-4' />}
                 >
                   {t('workflow.common.embedIntoSite')}
                 </SuggestedAction>
               )}
-            <SuggestedAction disabled={!publishedTime} link='./develop' icon={<FileText className='w-4 h-4' />}>{t('workflow.common.accessAPIReference')}</SuggestedAction>
+            <SuggestedAction disabled={!publishedAt} link='./develop' icon={<FileText className='w-4 h-4' />}>{t('workflow.common.accessAPIReference')}</SuggestedAction>
             {appDetail?.mode === 'workflow' && (
               <WorkflowToolConfigureButton
-                disabled={!publishedTime}
+                disabled={!publishedAt}
                 published={!!toolPublished}
                 detailNeedUpdate={!!toolPublished && published}
                 workflowAppId={appDetail?.id}
diff --git a/web/app/components/app/configuration/config-prompt/advanced-prompt-input.tsx b/web/app/components/app/configuration/config-prompt/advanced-prompt-input.tsx
index a07fe69821..afa2bf8e27 100644
--- a/web/app/components/app/configuration/config-prompt/advanced-prompt-input.tsx
+++ b/web/app/components/app/configuration/config-prompt/advanced-prompt-input.tsx
@@ -262,7 +262,7 @@ const AdvancedPromptInput: FC<Props> = ({
       {isShowConfirmAddVar && (
         <ConfirmAddVar
           varNameArr={newPromptVariables.map(v => v.name)}
-          onConfrim={handleAutoAdd(true)}
+          onConfirm={handleAutoAdd(true)}
           onCancel={handleAutoAdd(false)}
           onHide={hideConfirmAddVar}
         />
diff --git a/web/app/components/app/configuration/config-prompt/confirm-add-var/index.tsx b/web/app/components/app/configuration/config-prompt/confirm-add-var/index.tsx
index f08f2ffc69..922f8bb36a 100644
--- a/web/app/components/app/configuration/config-prompt/confirm-add-var/index.tsx
+++ b/web/app/components/app/configuration/config-prompt/confirm-add-var/index.tsx
@@ -7,7 +7,7 @@ import Button from '@/app/components/base/button'
 
 export type IConfirmAddVarProps = {
   varNameArr: string[]
-  onConfrim: () => void
+  onConfirm: () => void
   onCancel: () => void
   onHide: () => void
 }
@@ -22,7 +22,7 @@ const VarIcon = (
 
 const ConfirmAddVar: FC<IConfirmAddVarProps> = ({
   varNameArr,
-  onConfrim,
+  onConfirm,
   onCancel,
   // onHide,
 }) => {
@@ -63,7 +63,7 @@ const ConfirmAddVar: FC<IConfirmAddVarProps> = ({
         </div>
         <div className='mt-7 flex justify-end space-x-2'>
           <Button onClick={onCancel}>{t('common.operation.cancel')}</Button>
-          <Button variant='primary' onClick={onConfrim}>{t('common.operation.add')}</Button>
+          <Button variant='primary' onClick={onConfirm}>{t('common.operation.add')}</Button>
         </div>
       </div>
 
diff --git a/web/app/components/app/configuration/config-prompt/conversation-histroy/edit-modal.tsx b/web/app/components/app/configuration/config-prompt/conversation-history/edit-modal.tsx
similarity index 100%
rename from web/app/components/app/configuration/config-prompt/conversation-histroy/edit-modal.tsx
rename to web/app/components/app/configuration/config-prompt/conversation-history/edit-modal.tsx
diff --git a/web/app/components/app/configuration/config-prompt/conversation-histroy/history-panel.tsx b/web/app/components/app/configuration/config-prompt/conversation-history/history-panel.tsx
similarity index 100%
rename from web/app/components/app/configuration/config-prompt/conversation-histroy/history-panel.tsx
rename to web/app/components/app/configuration/config-prompt/conversation-history/history-panel.tsx
diff --git a/web/app/components/app/configuration/config-prompt/simple-prompt-input.tsx b/web/app/components/app/configuration/config-prompt/simple-prompt-input.tsx
index 69e01a8e22..d7bfe8534e 100644
--- a/web/app/components/app/configuration/config-prompt/simple-prompt-input.tsx
+++ b/web/app/components/app/configuration/config-prompt/simple-prompt-input.tsx
@@ -33,7 +33,7 @@ export type ISimplePromptInput = {
   promptTemplate: string
   promptVariables: PromptVariable[]
   readonly?: boolean
-  onChange?: (promp: string, promptVariables: PromptVariable[]) => void
+  onChange?: (prompt: string, promptVariables: PromptVariable[]) => void
   noTitle?: boolean
   gradientBorder?: boolean
   editorHeight?: number
@@ -239,7 +239,7 @@ const Prompt: FC<ISimplePromptInput> = ({
       {isShowConfirmAddVar && (
         <ConfirmAddVar
           varNameArr={newPromptVariables.map(v => v.name)}
-          onConfrim={handleAutoAdd(true)}
+          onConfirm={handleAutoAdd(true)}
           onCancel={handleAutoAdd(false)}
           onHide={hideConfirmAddVar}
         />
diff --git a/web/app/components/app/configuration/config-var/config-modal/index.tsx b/web/app/components/app/configuration/config-var/config-modal/index.tsx
index 3296c77fb2..606280653e 100644
--- a/web/app/components/app/configuration/config-var/config-modal/index.tsx
+++ b/web/app/components/app/configuration/config-var/config-modal/index.tsx
@@ -47,7 +47,7 @@ const ConfigModal: FC<IConfigModalProps> = ({
     if (!isValid) {
       Toast.notify({
         type: 'error',
-        message: t(`appDebug.varKeyError.${errorMessageKey}`, { key: t('appDebug.variableConig.varName') }),
+        message: t(`appDebug.varKeyError.${errorMessageKey}`, { key: t('appDebug.variableConfig.varName') }),
       })
       return false
     }
@@ -101,7 +101,7 @@ const ConfigModal: FC<IConfigModalProps> = ({
     // }
 
     if (!tempPayload.label) {
-      Toast.notify({ type: 'error', message: t('appDebug.variableConig.errorMsg.labelNameRequired') })
+      Toast.notify({ type: 'error', message: t('appDebug.variableConfig.errorMsg.labelNameRequired') })
       return
     }
     if (isStringInput || type === InputVarType.number) {
@@ -109,7 +109,7 @@ const ConfigModal: FC<IConfigModalProps> = ({
     }
     else {
       if (options?.length === 0) {
-        Toast.notify({ type: 'error', message: t('appDebug.variableConig.errorMsg.atLeastOneOption') })
+        Toast.notify({ type: 'error', message: t('appDebug.variableConfig.errorMsg.atLeastOneOption') })
         return
       }
       const obj: Record<string, boolean> = {}
@@ -122,7 +122,7 @@ const ConfigModal: FC<IConfigModalProps> = ({
         obj[o] = true
       })
       if (hasRepeatedItem) {
-        Toast.notify({ type: 'error', message: t('appDebug.variableConig.errorMsg.optionRepeat') })
+        Toast.notify({ type: 'error', message: t('appDebug.variableConfig.errorMsg.optionRepeat') })
         return
       }
       onConfirm(tempPayload, moreInfo)
@@ -131,14 +131,14 @@ const ConfigModal: FC<IConfigModalProps> = ({
 
   return (
     <Modal
-      title={t(`appDebug.variableConig.${isCreate ? 'addModalTitle' : 'editModalTitle'}`)}
+      title={t(`appDebug.variableConfig.${isCreate ? 'addModalTitle' : 'editModalTitle'}`)}
       isShow={isShow}
       onClose={onClose}
     >
       <div className='mb-8'>
         <div className='space-y-2'>
 
-          <Field title={t('appDebug.variableConig.fieldType')}>
+          <Field title={t('appDebug.variableConfig.fieldType')}>
             <div className='flex space-x-2'>
               <SelectTypeItem type={InputVarType.textInput} selected={type === InputVarType.textInput} onClick={() => handlePayloadChange('type')(InputVarType.textInput)} />
               <SelectTypeItem type={InputVarType.paragraph} selected={type === InputVarType.paragraph} onClick={() => handlePayloadChange('type')(InputVarType.paragraph)} />
@@ -147,39 +147,39 @@ const ConfigModal: FC<IConfigModalProps> = ({
             </div>
           </Field>
 
-          <Field title={t('appDebug.variableConig.varName')}>
+          <Field title={t('appDebug.variableConfig.varName')}>
             <input
               type='text'
               className={inputClassName}
               value={variable}
               onChange={e => handlePayloadChange('variable')(e.target.value)}
               onBlur={handleVarKeyBlur}
-              placeholder={t('appDebug.variableConig.inputPlaceholder')!}
+              placeholder={t('appDebug.variableConfig.inputPlaceholder')!}
             />
           </Field>
-          <Field title={t('appDebug.variableConig.labelName')}>
+          <Field title={t('appDebug.variableConfig.labelName')}>
             <input
               type='text'
               className={inputClassName}
               value={label as string}
               onChange={e => handlePayloadChange('label')(e.target.value)}
-              placeholder={t('appDebug.variableConig.inputPlaceholder')!}
+              placeholder={t('appDebug.variableConfig.inputPlaceholder')!}
             />
           </Field>
 
           {isStringInput && (
-            <Field title={t('appDebug.variableConig.maxLength')}>
+            <Field title={t('appDebug.variableConfig.maxLength')}>
               <ConfigString maxLength={type === InputVarType.textInput ? TEXT_MAX_LENGTH : Infinity} modelId={modelConfig.model_id} value={max_length} onChange={handlePayloadChange('max_length')} />
             </Field>
 
           )}
           {type === InputVarType.select && (
-            <Field title={t('appDebug.variableConig.options')}>
+            <Field title={t('appDebug.variableConfig.options')}>
               <ConfigSelect options={options || []} onChange={handlePayloadChange('options')} />
             </Field>
           )}
 
-          <Field title={t('appDebug.variableConig.required')}>
+          <Field title={t('appDebug.variableConfig.required')}>
             <Switch defaultValue={tempPayload.required} onChange={handlePayloadChange('required')} />
           </Field>
         </div>
diff --git a/web/app/components/app/configuration/config-var/config-select/index.tsx b/web/app/components/app/configuration/config-var/config-select/index.tsx
index e23c7330b1..449cb8b12f 100644
--- a/web/app/components/app/configuration/config-var/config-select/index.tsx
+++ b/web/app/components/app/configuration/config-var/config-select/index.tsx
@@ -77,7 +77,7 @@ const ConfigSelect: FC<IConfigSelectProps> = ({
         onClick={() => { onChange([...options, '']) }}
         className='flex items-center h-9 px-3 gap-2 rounded-lg cursor-pointer text-gray-400  bg-gray-100'>
         <PlusIcon width={16} height={16}></PlusIcon>
-        <div className='text-gray-500 text-[13px]'>{t('appDebug.variableConig.addOption')}</div>
+        <div className='text-gray-500 text-[13px]'>{t('appDebug.variableConfig.addOption')}</div>
       </div>
     </div>
   )
diff --git a/web/app/components/app/configuration/config-var/index.tsx b/web/app/components/app/configuration/config-var/index.tsx
index 802528e0af..fc165571c4 100644
--- a/web/app/components/app/configuration/config-var/index.tsx
+++ b/web/app/components/app/configuration/config-var/index.tsx
@@ -88,7 +88,6 @@ const ConfigVar: FC<IConfigVarProps> = ({ promptVariables, readonly, onPromptVar
     } as InputVar
   })()
   const updatePromptVariableItem = (payload: InputVar) => {
-    console.log(payload)
     const newPromptVariables = produce(promptVariables, (draft) => {
       const { variable, label, type, ...rest } = payload
       draft[currIndex] = {
diff --git a/web/app/components/app/configuration/config-var/select-type-item/index.tsx b/web/app/components/app/configuration/config-var/select-type-item/index.tsx
index bb5e700d11..c76aed1a10 100644
--- a/web/app/components/app/configuration/config-var/select-type-item/index.tsx
+++ b/web/app/components/app/configuration/config-var/select-type-item/index.tsx
@@ -18,7 +18,7 @@ const SelectTypeItem: FC<ISelectTypeItemProps> = ({
   onClick,
 }) => {
   const { t } = useTranslation()
-  const typeName = t(`appDebug.variableConig.${type}`)
+  const typeName = t(`appDebug.variableConfig.${type}`)
 
   return (
     <div
diff --git a/web/app/components/app/configuration/config-var/select-var-type.tsx b/web/app/components/app/configuration/config-var/select-var-type.tsx
index 137f62b2bb..14d4f926ed 100644
--- a/web/app/components/app/configuration/config-var/select-var-type.tsx
+++ b/web/app/components/app/configuration/config-var/select-var-type.tsx
@@ -62,14 +62,14 @@ const SelectVarType: FC<Props> = ({
       <PortalToFollowElemContent style={{ zIndex: 1000 }}>
         <div className='bg-white border border-gray-200 shadow-lg rounded-lg min-w-[192px]'>
           <div className='p-1'>
-            <SelectItem type={InputVarType.textInput} value='string' text={t('appDebug.variableConig.string')} onClick={handleChange}></SelectItem>
-            <SelectItem type={InputVarType.paragraph} value='paragraph' text={t('appDebug.variableConig.paragraph')} onClick={handleChange}></SelectItem>
-            <SelectItem type={InputVarType.select} value='select' text={t('appDebug.variableConig.select')} onClick={handleChange}></SelectItem>
-            <SelectItem type={InputVarType.number} value='number' text={t('appDebug.variableConig.number')} onClick={handleChange}></SelectItem>
+            <SelectItem type={InputVarType.textInput} value='string' text={t('appDebug.variableConfig.string')} onClick={handleChange}></SelectItem>
+            <SelectItem type={InputVarType.paragraph} value='paragraph' text={t('appDebug.variableConfig.paragraph')} onClick={handleChange}></SelectItem>
+            <SelectItem type={InputVarType.select} value='select' text={t('appDebug.variableConfig.select')} onClick={handleChange}></SelectItem>
+            <SelectItem type={InputVarType.number} value='number' text={t('appDebug.variableConfig.number')} onClick={handleChange}></SelectItem>
           </div>
           <div className='h-[1px] bg-gray-100'></div>
           <div className='p-1'>
-            <SelectItem Icon={ApiConnection} value='api' text={t('appDebug.variableConig.apiBasedVar')} onClick={handleChange}></SelectItem>
+            <SelectItem Icon={ApiConnection} value='api' text={t('appDebug.variableConfig.apiBasedVar')} onClick={handleChange}></SelectItem>
           </div>
         </div>
       </PortalToFollowElemContent>
diff --git a/web/app/components/app/configuration/config/agent/agent-setting/index.tsx b/web/app/components/app/configuration/config/agent/agent-setting/index.tsx
index b295a4e709..959336457f 100644
--- a/web/app/components/app/configuration/config/agent/agent-setting/index.tsx
+++ b/web/app/components/app/configuration/config/agent/agent-setting/index.tsx
@@ -5,7 +5,7 @@ import { useTranslation } from 'react-i18next'
 import { RiCloseLine } from '@remixicon/react'
 import ItemPanel from './item-panel'
 import Button from '@/app/components/base/button'
-import { CuteRobote } from '@/app/components/base/icons/src/vender/solid/communication'
+import { CuteRobot } from '@/app/components/base/icons/src/vender/solid/communication'
 import { Unblur } from '@/app/components/base/icons/src/vender/solid/education'
 import Slider from '@/app/components/base/slider'
 import type { AgentConfig } from '@/models/debug'
@@ -65,7 +65,7 @@ const AgentSetting: FC<Props> = ({
           <ItemPanel
             className='mb-4'
             icon={
-              <CuteRobote className='w-4 h-4 text-indigo-600' />
+              <CuteRobot className='w-4 h-4 text-indigo-600' />
             }
             name={t('appDebug.agent.agentMode')}
             description={t('appDebug.agent.agentModeDes')}
diff --git a/web/app/components/app/configuration/config/assistant-type-picker/index.tsx b/web/app/components/app/configuration/config/assistant-type-picker/index.tsx
index 6bdf678f85..336d736e3b 100644
--- a/web/app/components/app/configuration/config/assistant-type-picker/index.tsx
+++ b/web/app/components/app/configuration/config/assistant-type-picker/index.tsx
@@ -12,7 +12,7 @@ import {
 } from '@/app/components/base/portal-to-follow-elem'
 import { BubbleText } from '@/app/components/base/icons/src/vender/solid/education'
 import Radio from '@/app/components/base/radio/ui'
-import { CuteRobote } from '@/app/components/base/icons/src/vender/solid/communication'
+import { CuteRobot } from '@/app/components/base/icons/src/vender/solid/communication'
 import { Settings04 } from '@/app/components/base/icons/src/vender/line/general'
 import { ArrowUpRight } from '@/app/components/base/icons/src/vender/line/arrows'
 import type { AgentConfig } from '@/models/debug'
@@ -117,7 +117,7 @@ const AssistantTypePicker: FC<Props> = ({
       >
         <PortalToFollowElemTrigger onClick={() => setOpen(v => !v)}>
           <div className={cn(open && 'bg-gray-50', 'flex items-center h-8 px-3 border border-black/5 rounded-lg cursor-pointer select-none space-x-1 text-indigo-600')}>
-            {isAgent ? <BubbleText className='w-3 h-3' /> : <CuteRobote className='w-3 h-3' />}
+            {isAgent ? <BubbleText className='w-3 h-3' /> : <CuteRobot className='w-3 h-3' />}
             <div className='text-xs font-medium'>{t(`appDebug.assistantType.${isAgent ? 'agentAssistant' : 'chatAssistant'}.name`)}</div>
             <RiArrowDownSLine className='w-3 h-3' />
           </div>
@@ -135,7 +135,7 @@ const AssistantTypePicker: FC<Props> = ({
               onClick={handleChange}
             />
             <SelectItem
-              Icon={CuteRobote}
+              Icon={CuteRobot}
               value='agent'
               disabled={disabled}
               text={t('appDebug.assistantType.agentAssistant.name')}
diff --git a/web/app/components/app/configuration/config/feature/use-feature.tsx b/web/app/components/app/configuration/config/feature/use-feature.tsx
index 190c50eab5..acc08dd4a4 100644
--- a/web/app/components/app/configuration/config/feature/use-feature.tsx
+++ b/web/app/components/app/configuration/config/feature/use-feature.tsx
@@ -35,7 +35,7 @@ function useFeature({
   moderation: boolean
   setModeration: (moderation: boolean) => void
 }) {
-  const [tempshowOpeningStatement, setTempShowOpeningStatement] = React.useState(!!introduction)
+  const [tempShowOpeningStatement, setTempShowOpeningStatement] = React.useState(!!introduction)
   useEffect(() => {
     // wait to api data back
     if (introduction)
@@ -48,7 +48,7 @@ function useFeature({
   // }, [moreLikeThis])
 
   const featureConfig = {
-    openingStatement: tempshowOpeningStatement,
+    openingStatement: tempShowOpeningStatement,
     moreLikeThis,
     suggestedQuestionsAfterAnswer,
     speechToText,
diff --git a/web/app/components/app/configuration/config/index.tsx b/web/app/components/app/configuration/config/index.tsx
index b8bedba20b..12551f508e 100644
--- a/web/app/components/app/configuration/config/index.tsx
+++ b/web/app/components/app/configuration/config/index.tsx
@@ -7,9 +7,9 @@ import { useBoolean, useScroll } from 'ahooks'
 import { useFormattingChangedDispatcher } from '../debug/hooks'
 import DatasetConfig from '../dataset-config'
 import ChatGroup from '../features/chat-group'
-import ExperienceEnchanceGroup from '../features/experience-enchance-group'
+import ExperienceEnhanceGroup from '../features/experience-enhance-group'
 import Toolbox from '../toolbox'
-import HistoryPanel from '../config-prompt/conversation-histroy/history-panel'
+import HistoryPanel from '../config-prompt/conversation-history/history-panel'
 import ConfigVision from '../config-vision'
 import useAnnotationConfig from '../toolbox/annotation/use-annotation-config'
 import AddFeatureBtn from './feature/add-feature-btn'
@@ -254,7 +254,7 @@ const Config: FC = () => {
           />
         )}
 
-        {/* ChatConifig */}
+        {/* ChatConfig */}
         {
           hasChatConfig && (
             <ChatGroup
@@ -277,7 +277,7 @@ const Config: FC = () => {
 
         {/* Text Generation config */}{
           hasCompletionConfig && (
-            <ExperienceEnchanceGroup
+            <ExperienceEnhanceGroup
               isShowMoreLike={moreLikeThisConfig.enabled}
               isShowTextToSpeech={featureConfig.textToSpeech && !!text2speechDefaultModel}
             />
diff --git a/web/app/components/app/configuration/dataset-config/params-config/config-content.tsx b/web/app/components/app/configuration/dataset-config/params-config/config-content.tsx
index 7f55649dab..91cae54bb8 100644
--- a/web/app/components/app/configuration/dataset-config/params-config/config-content.tsx
+++ b/web/app/components/app/configuration/dataset-config/params-config/config-content.tsx
@@ -1,20 +1,12 @@
 'use client'
 
-import { memo, useMemo } from 'react'
+import { memo, useEffect, useMemo } from 'react'
 import type { FC } from 'react'
 import { useTranslation } from 'react-i18next'
-import {
-  RiAlertFill,
-} from '@remixicon/react'
 import WeightedScore from './weighted-score'
 import TopKItem from '@/app/components/base/param-item/top-k-item'
 import ScoreThresholdItem from '@/app/components/base/param-item/score-threshold-item'
-import RadioCard from '@/app/components/base/radio-card/simple'
 import { RETRIEVE_TYPE } from '@/types/app'
-import {
-  MultiPathRetrieval,
-  NTo1Retrieval,
-} from '@/app/components/base/icons/src/public/common'
 import type {
   DatasetConfigs,
 } from '@/models/debug'
@@ -31,7 +23,6 @@ import { RerankingModeEnum } from '@/models/datasets'
 import cn from '@/utils/classnames'
 import { useSelectedDatasetsMode } from '@/app/components/workflow/nodes/knowledge-retrieval/hooks'
 import Switch from '@/app/components/base/switch'
-import { useGetLanguage } from '@/context/i18n'
 
 type Props = {
   datasetConfigs: DatasetConfigs
@@ -43,11 +34,6 @@ type Props = {
   selectedDatasets?: DataSet[]
 }
 
-const LEGACY_LINK_MAP = {
-  en_US: 'https://docs.dify.ai/guides/knowledge-base/integrate-knowledge-within-application',
-  zh_Hans: 'https://docs.dify.ai/v/zh-hans/guides/knowledge-base/integrate_knowledge_within_application',
-} as Record<string, string>
-
 const ConfigContent: FC<Props> = ({
   datasetConfigs,
   onChange,
@@ -58,15 +44,18 @@ const ConfigContent: FC<Props> = ({
   selectedDatasets = [],
 }) => {
   const { t } = useTranslation()
-  const language = useGetLanguage()
   const selectedDatasetsMode = useSelectedDatasetsMode(selectedDatasets)
   const type = datasetConfigs.retrieval_model
-  const setType = (value: RETRIEVE_TYPE) => {
-    onChange({
-      ...datasetConfigs,
-      retrieval_model: value,
-    }, true)
-  }
+
+  useEffect(() => {
+    if (type === RETRIEVE_TYPE.oneWay) {
+      onChange({
+        ...datasetConfigs,
+        retrieval_model: RETRIEVE_TYPE.multiWay,
+      }, isInWorkflow)
+    }
+  }, [type])
+
   const {
     modelList: rerankModelList,
     defaultModel: rerankDefaultModel,
@@ -166,63 +155,21 @@ const ConfigContent: FC<Props> = ({
   return (
     <div>
       <div className='system-xl-semibold text-text-primary'>{t('dataset.retrievalSettings')}</div>
-      <div className='mt-2 space-y-3'>
-        <RadioCard
-          icon={<NTo1Retrieval className='shrink-0 mr-3 w-9 h-9 rounded-lg' />}
-          title={(
-            <div className='flex items-center'>
-              {t('appDebug.datasetConfig.retrieveOneWay.title')}
-              <Tooltip
-                popupContent={(
-                  <div className='w-[320px]'>
-                    {t('dataset.nTo1RetrievalLegacy')}
-                  </div>
-                )}
-              >
-                <div className='ml-1 flex items-center px-[5px] h-[18px] rounded-[5px] border border-text-accent-secondary system-2xs-medium-uppercase text-text-accent-secondary'>legacy</div>
-              </Tooltip>
-            </div>
-          )}
-          description={t('appDebug.datasetConfig.retrieveOneWay.description')}
-          isChosen={type === RETRIEVE_TYPE.oneWay}
-          onChosen={() => { setType(RETRIEVE_TYPE.oneWay) }}
-          extra={(
-            <div className='flex pl-3 pr-1 py-3 border-t border-divider-subtle bg-state-warning-hover rounded-b-xl'>
-              <RiAlertFill className='shrink-0 mr-1.5 w-4 h-4 text-text-warning-secondary' />
-              <div className='system-xs-medium text-text-primary'>
-                {t('dataset.nTo1RetrievalLegacyLinkText')}
-                <a
-                  className='text-text-accent'
-                  href={LEGACY_LINK_MAP[language]}
-                  target='_blank'
-                  rel='noopener noreferrer'
-                >
-                  {t('dataset.nTo1RetrievalLegacyLink')}
-                </a>
-              </div>
-            </div>
-          )}
-        />
-        <RadioCard
-          icon={<MultiPathRetrieval className='shrink-0 mr-3 w-9 h-9 rounded-lg' />}
-          title={t('appDebug.datasetConfig.retrieveMultiWay.title')}
-          description={t('appDebug.datasetConfig.retrieveMultiWay.description')}
-          isChosen={type === RETRIEVE_TYPE.multiWay}
-          onChosen={() => { setType(RETRIEVE_TYPE.multiWay) }}
-        />
+      <div className='system-xs-regular text-text-tertiary'>
+        {t('dataset.defaultRetrievalTip')}
       </div>
       {type === RETRIEVE_TYPE.multiWay && (
         <>
-          <div className='mb-2 mt-4 h-[1px] bg-divider-subtle'></div>
-          <div
-            className='flex items-center mb-2 h-6 system-md-semibold text-text-secondary'
-          >
-            {t('dataset.rerankSettings')}
+          <div className='flex items-center my-2 py-1 h-6'>
+            <div className='shrink-0 mr-2 system-xs-semibold-uppercase text-text-secondary'>
+              {t('dataset.rerankSettings')}
+            </div>
+            <div className='grow h-[1px] bg-gradient-to-l from-white to-[rgba(16,24,40,0.08)]'></div>
           </div>
           {
             selectedDatasetsMode.inconsistentEmbeddingModel
             && (
-              <div className='mt-4 system-xs-regular text-text-warning'>
+              <div className='mt-4 system-xs-medium text-text-warning'>
                 {t('dataset.inconsistentEmbeddingModelTip')}
               </div>
             )
@@ -230,7 +177,7 @@ const ConfigContent: FC<Props> = ({
           {
             selectedDatasetsMode.mixtureHighQualityAndEconomic
             && (
-              <div className='mt-4 system-xs-regular text-text-warning'>
+              <div className='mt-4 system-xs-medium text-text-warning'>
                 {t('dataset.mixtureHighQualityAndEconomicTip')}
               </div>
             )
diff --git a/web/app/components/app/configuration/dataset-config/params-config/index.tsx b/web/app/components/app/configuration/dataset-config/params-config/index.tsx
index 5cb76e32b2..656cbfea65 100644
--- a/web/app/components/app/configuration/dataset-config/params-config/index.tsx
+++ b/web/app/components/app/configuration/dataset-config/params-config/index.tsx
@@ -70,13 +70,13 @@ const ParamsConfig = ({
 
   const {
     defaultModel: rerankDefaultModel,
-    currentModel: isRerankDefaultModelVaild,
+    currentModel: isRerankDefaultModelValid,
   } = useModelListAndDefaultModelAndCurrentProviderAndModel(ModelTypeEnum.rerank)
 
   const isValid = () => {
     let errMsg = ''
     if (tempDataSetConfigs.retrieval_model === RETRIEVE_TYPE.multiWay) {
-      if (!tempDataSetConfigs.reranking_model?.reranking_model_name && (!rerankDefaultModel && isRerankDefaultModelVaild))
+      if (!tempDataSetConfigs.reranking_model?.reranking_model_name && (!rerankDefaultModel && isRerankDefaultModelValid))
         errMsg = t('appDebug.datasetConfig.rerankModelRequired')
     }
     if (errMsg) {
diff --git a/web/app/components/app/configuration/dataset-config/select-dataset/index.tsx b/web/app/components/app/configuration/dataset-config/select-dataset/index.tsx
index f9117a51c3..4493755ba0 100644
--- a/web/app/components/app/configuration/dataset-config/select-dataset/index.tsx
+++ b/web/app/components/app/configuration/dataset-config/select-dataset/index.tsx
@@ -135,7 +135,7 @@ const SelectDataSet: FC<ISelectDataSetProps> = ({
                   </div>
                   <div className={cn('max-w-[200px] text-[13px] font-medium text-gray-800 overflow-hidden text-ellipsis whitespace-nowrap', !item.embedding_available && 'opacity-50 !max-w-[120px]')}>{item.name}</div>
                   {!item.embedding_available && (
-                    <span className='ml-1 shrink-0 px-1 border boder-gray-200 rounded-md text-gray-500 text-xs font-normal leading-[18px]'>{t('dataset.unavailable')}</span>
+                    <span className='ml-1 shrink-0 px-1 border border-gray-200 rounded-md text-gray-500 text-xs font-normal leading-[18px]'>{t('dataset.unavailable')}</span>
                   )}
                 </div>
                 {
diff --git a/web/app/components/app/configuration/dataset-config/settings-modal/index.tsx b/web/app/components/app/configuration/dataset-config/settings-modal/index.tsx
index eec5979dd5..65858ce8cf 100644
--- a/web/app/components/app/configuration/dataset-config/settings-modal/index.tsx
+++ b/web/app/components/app/configuration/dataset-config/settings-modal/index.tsx
@@ -51,7 +51,7 @@ const SettingsModal: FC<SettingsModalProps> = ({
   const {
     modelList: rerankModelList,
     defaultModel: rerankDefaultModel,
-    currentModel: isRerankDefaultModelVaild,
+    currentModel: isRerankDefaultModelValid,
   } = useModelListAndDefaultModelAndCurrentProviderAndModel(ModelTypeEnum.rerank)
   const { t } = useTranslation()
   const { notify } = useToastContext()
@@ -83,7 +83,7 @@ const SettingsModal: FC<SettingsModalProps> = ({
     if (
       !isReRankModelSelected({
         rerankDefaultModel,
-        isRerankDefaultModelVaild: !!isRerankDefaultModelVaild,
+        isRerankDefaultModelValid: !!isRerankDefaultModelValid,
         rerankModelList,
         retrievalConfig,
         indexMethod,
diff --git a/web/app/components/app/configuration/debug/debug-with-multiple-model/chat-item.tsx b/web/app/components/app/configuration/debug/debug-with-multiple-model/chat-item.tsx
index e79cdf4793..80dfb5c534 100644
--- a/web/app/components/app/configuration/debug/debug-with-multiple-model/chat-item.tsx
+++ b/web/app/components/app/configuration/debug/debug-with-multiple-model/chat-item.tsx
@@ -89,7 +89,7 @@ const ChatItem: FC<ChatItemProps> = ({
       `apps/${appId}/chat-messages`,
       data,
       {
-        onGetConvesationMessages: (conversationId, getAbortController) => fetchConversationMessages(appId, conversationId, getAbortController),
+        onGetConversationMessages: (conversationId, getAbortController) => fetchConversationMessages(appId, conversationId, getAbortController),
         onGetSuggestedQuestions: (responseItemId, getAbortController) => fetchSuggestedQuestions(appId, responseItemId, getAbortController),
       },
     )
@@ -129,6 +129,7 @@ const ChatItem: FC<ChatItemProps> = ({
       questionIcon={<Avatar name={userProfile.name} size={40} />}
       allToolIcons={allToolIcons}
       hideLogModal
+      noSpacing
     />
   )
 }
diff --git a/web/app/components/app/configuration/debug/debug-with-multiple-model/index.tsx b/web/app/components/app/configuration/debug/debug-with-multiple-model/index.tsx
index 892d0cfe8b..3d2f3bca59 100644
--- a/web/app/components/app/configuration/debug/debug-with-multiple-model/index.tsx
+++ b/web/app/components/app/configuration/debug/debug-with-multiple-model/index.tsx
@@ -128,6 +128,7 @@ const DebugWithMultipleModel = () => {
               onSend={handleSend}
               speechToTextConfig={speechToTextConfig}
               visionConfig={visionConfig}
+              noSpacing
             />
           </div>
         )
diff --git a/web/app/components/app/configuration/debug/debug-with-single-model/index.tsx b/web/app/components/app/configuration/debug/debug-with-single-model/index.tsx
index ea15c1a4ce..d93ad00659 100644
--- a/web/app/components/app/configuration/debug/debug-with-single-model/index.tsx
+++ b/web/app/components/app/configuration/debug/debug-with-single-model/index.tsx
@@ -94,7 +94,7 @@ const DebugWithSingleModel = forwardRef<DebugWithSingleModelRefType, DebugWithSi
       `apps/${appId}/chat-messages`,
       data,
       {
-        onGetConvesationMessages: (conversationId, getAbortController) => fetchConversationMessages(appId, conversationId, getAbortController),
+        onGetConversationMessages: (conversationId, getAbortController) => fetchConversationMessages(appId, conversationId, getAbortController),
         onGetSuggestedQuestions: (responseItemId, getAbortController) => fetchSuggestedQuestions(appId, responseItemId, getAbortController),
       },
     )
@@ -130,6 +130,7 @@ const DebugWithSingleModel = forwardRef<DebugWithSingleModelRefType, DebugWithSi
       onAnnotationEdited={handleAnnotationEdited}
       onAnnotationAdded={handleAnnotationAdded}
       onAnnotationRemoved={handleAnnotationRemoved}
+      noSpacing
     />
   )
 })
diff --git a/web/app/components/app/configuration/features/chat-group/opening-statement/index.tsx b/web/app/components/app/configuration/features/chat-group/opening-statement/index.tsx
index d007225bda..e652579cfc 100644
--- a/web/app/components/app/configuration/features/chat-group/opening-statement/index.tsx
+++ b/web/app/components/app/configuration/features/chat-group/opening-statement/index.tsx
@@ -227,7 +227,7 @@ const OpeningStatement: FC<IOpeningStatementProps> = ({
             onClick={() => { setTempSuggestedQuestions([...tempSuggestedQuestions, '']) }}
             className='mt-1 flex items-center h-9 px-3 gap-2 rounded-lg cursor-pointer text-gray-400  bg-gray-100 hover:bg-gray-200'>
             <RiAddLine className='w-4 h-4' />
-            <div className='text-gray-500 text-[13px]'>{t('appDebug.variableConig.addOption')}</div>
+            <div className='text-gray-500 text-[13px]'>{t('appDebug.variableConfig.addOption')}</div>
           </div>
         )}
       </div>
@@ -287,7 +287,7 @@ const OpeningStatement: FC<IOpeningStatementProps> = ({
         {isShowConfirmAddVar && (
           <ConfirmAddVar
             varNameArr={notIncludeKeys}
-            onConfrim={autoAddVar}
+            onConfirm={autoAddVar}
             onCancel={cancelAutoAddVar}
             onHide={hideConfirmAddVar}
           />
diff --git a/web/app/components/app/configuration/features/experience-enchance-group/index.tsx b/web/app/components/app/configuration/features/experience-enhance-group/index.tsx
similarity index 88%
rename from web/app/components/app/configuration/features/experience-enchance-group/index.tsx
rename to web/app/components/app/configuration/features/experience-enhance-group/index.tsx
index 6902a17468..4a629a6b0e 100644
--- a/web/app/components/app/configuration/features/experience-enchance-group/index.tsx
+++ b/web/app/components/app/configuration/features/experience-enhance-group/index.tsx
@@ -16,7 +16,7 @@ type ExperienceGroupProps = {
   isShowMoreLike: boolean
 }
 
-const ExperienceEnchanceGroup: FC<ExperienceGroupProps> = ({
+const ExperienceEnhanceGroup: FC<ExperienceGroupProps> = ({
   isShowTextToSpeech,
   isShowMoreLike,
 }) => {
@@ -40,4 +40,4 @@ const ExperienceEnchanceGroup: FC<ExperienceGroupProps> = ({
     </div>
   )
 }
-export default React.memo(ExperienceEnchanceGroup)
+export default React.memo(ExperienceEnhanceGroup)
diff --git a/web/app/components/app/configuration/features/experience-enchance-group/more-like-this/index.tsx b/web/app/components/app/configuration/features/experience-enhance-group/more-like-this/index.tsx
similarity index 100%
rename from web/app/components/app/configuration/features/experience-enchance-group/more-like-this/index.tsx
rename to web/app/components/app/configuration/features/experience-enhance-group/more-like-this/index.tsx
diff --git a/web/app/components/app/configuration/index.tsx b/web/app/components/app/configuration/index.tsx
index 432accb0d2..357dc84b7a 100644
--- a/web/app/components/app/configuration/index.tsx
+++ b/web/app/components/app/configuration/index.tsx
@@ -14,7 +14,7 @@ import Loading from '../../base/loading'
 import AppPublisher from '../app-publisher'
 import AgentSettingButton from './config/agent-setting-button'
 import useAdvancedPromptConfig from './hooks/use-advanced-prompt-config'
-import EditHistoryModal from './config-prompt/conversation-histroy/edit-modal'
+import EditHistoryModal from './config-prompt/conversation-history/edit-modal'
 import {
   useDebugWithSingleOrMultipleModel,
   useFormattingChangedDispatcher,
diff --git a/web/app/components/app/configuration/toolbox/annotation/annotation-ctrl-btn/index.tsx b/web/app/components/app/configuration/toolbox/annotation/annotation-ctrl-btn/index.tsx
index 111c380afc..809b907d62 100644
--- a/web/app/components/app/configuration/toolbox/annotation/annotation-ctrl-btn/index.tsx
+++ b/web/app/components/app/configuration/toolbox/annotation/annotation-ctrl-btn/index.tsx
@@ -73,7 +73,7 @@ const CacheCtrlBtn: FC<Props> = ({
     setShowModal(false)
   }
   return (
-    <div className={cn(className, 'inline-block')}>
+    <div className={cn('inline-block', className)}>
       <div className='inline-flex p-0.5 space-x-0.5 rounded-lg bg-white border border-gray-100 shadow-md text-gray-500 cursor-pointer'>
         {cached
           ? (
@@ -101,7 +101,6 @@ const CacheCtrlBtn: FC<Props> = ({
             ? (
               <Tooltip
                 popupContent={t('appDebug.feature.annotation.add')}
-                needsDelay
               >
                 <div
                   className='p-1 rounded-md hover:bg-[#EEF4FF] hover:text-[#444CE7] cursor-pointer'
@@ -115,7 +114,6 @@ const CacheCtrlBtn: FC<Props> = ({
         }
         <Tooltip
           popupContent={t('appDebug.feature.annotation.edit')}
-          needsDelay
         >
           <div
             className='p-1 cursor-pointer rounded-md hover:bg-black/5'
diff --git a/web/app/components/app/configuration/toolbox/moderation/moderation-setting-modal.tsx b/web/app/components/app/configuration/toolbox/moderation/moderation-setting-modal.tsx
index 64b2dd222a..589eb42ab3 100644
--- a/web/app/components/app/configuration/toolbox/moderation/moderation-setting-modal.tsx
+++ b/web/app/components/app/configuration/toolbox/moderation/moderation-setting-modal.tsx
@@ -64,7 +64,7 @@ const ModerationSettingModal: FC<ModerationSettingModalProps> = ({
   const systemOpenaiProviderQuota = systemOpenaiProviderEnabled ? openaiProvider?.system_configuration.quota_configurations.find(item => item.quota_type === openaiProvider.system_configuration.current_quota_type) : undefined
   const systemOpenaiProviderCanUse = systemOpenaiProviderQuota?.is_valid
   const customOpenaiProvidersCanUse = openaiProvider?.custom_configuration.status === CustomConfigurationStatusEnum.active
-  const openaiProviderConfiged = customOpenaiProvidersCanUse || systemOpenaiProviderCanUse
+  const isOpenAIProviderConfigured = customOpenaiProvidersCanUse || systemOpenaiProviderCanUse
   const providers: Provider[] = [
     {
       key: 'openai_moderation',
@@ -190,7 +190,7 @@ const ModerationSettingModal: FC<ModerationSettingModalProps> = ({
   }
 
   const handleSave = () => {
-    if (localeData.type === 'openai_moderation' && !openaiProviderConfiged)
+    if (localeData.type === 'openai_moderation' && !isOpenAIProviderConfigured)
       return
 
     if (!localeData.config?.inputs_config?.enabled && !localeData.config?.outputs_config?.enabled) {
@@ -254,7 +254,7 @@ const ModerationSettingModal: FC<ModerationSettingModalProps> = ({
                 className={`
                   flex items-center px-3 py-2 rounded-lg text-sm text-gray-900 cursor-pointer
                   ${localeData.type === provider.key ? 'bg-white border-[1.5px] border-primary-400 shadow-sm' : 'border border-gray-100 bg-gray-25'}
-                  ${localeData.type === 'openai_moderation' && provider.key === 'openai_moderation' && !openaiProviderConfiged && 'opacity-50'}
+                  ${localeData.type === 'openai_moderation' && provider.key === 'openai_moderation' && !isOpenAIProviderConfigured && 'opacity-50'}
                 `}
                 onClick={() => handleDataTypeChange(provider.key)}
               >
@@ -267,7 +267,7 @@ const ModerationSettingModal: FC<ModerationSettingModalProps> = ({
           }
         </div>
         {
-          !isLoading && !openaiProviderConfiged && localeData.type === 'openai_moderation' && (
+          !isLoading && !isOpenAIProviderConfigured && localeData.type === 'openai_moderation' && (
             <div className='flex items-center mt-2 px-3 py-2 bg-[#FFFAEB] rounded-lg border border-[#FEF0C7]'>
               <InfoCircle className='mr-1 w-4 h-4 text-[#F79009]' />
               <div className='flex items-center text-xs font-medium text-gray-700'>
@@ -361,7 +361,7 @@ const ModerationSettingModal: FC<ModerationSettingModalProps> = ({
         <Button
           variant='primary'
           onClick={handleSave}
-          disabled={localeData.type === 'openai_moderation' && !openaiProviderConfiged}
+          disabled={localeData.type === 'openai_moderation' && !isOpenAIProviderConfigured}
         >
           {t('common.operation.save')}
         </Button>
diff --git a/web/app/components/app/configuration/tools/external-data-tool-modal.tsx b/web/app/components/app/configuration/tools/external-data-tool-modal.tsx
index 3187990609..2785f435e4 100644
--- a/web/app/components/app/configuration/tools/external-data-tool-modal.tsx
+++ b/web/app/components/app/configuration/tools/external-data-tool-modal.tsx
@@ -172,12 +172,12 @@ const ExternalDataToolModal: FC<ExternalDataToolModalProps> = ({
       }
     }
 
-    const formatedData = formatData(localeData)
+    const formattedData = formatData(localeData)
 
-    if (onValidateBeforeSave && !onValidateBeforeSave(formatedData))
+    if (onValidateBeforeSave && !onValidateBeforeSave(formattedData))
       return
 
-    onSave(formatData(formatedData))
+    onSave(formatData(formattedData))
   }
 
   const action = data.type ? t('common.operation.edit') : t('common.operation.add')
@@ -189,7 +189,7 @@ const ExternalDataToolModal: FC<ExternalDataToolModalProps> = ({
       className='!p-8 !pb-6 !max-w-none !w-[640px]'
     >
       <div className='mb-2 text-xl font-semibold text-gray-900'>
-        {`${action} ${t('appDebug.variableConig.apiBasedVar')}`}
+        {`${action} ${t('appDebug.variableConfig.apiBasedVar')}`}
       </div>
       <div className='py-2'>
         <div className='leading-9 text-sm font-medium text-gray-900'>
diff --git a/web/app/components/app/create-app-modal/index.tsx b/web/app/components/app/create-app-modal/index.tsx
index 5fb0d7ef3f..d503e71918 100644
--- a/web/app/components/app/create-app-modal/index.tsx
+++ b/web/app/components/app/create-app-modal/index.tsx
@@ -21,7 +21,7 @@ import Modal from '@/app/components/base/modal'
 import Button from '@/app/components/base/button'
 import AppIcon from '@/app/components/base/app-icon'
 import AppsFull from '@/app/components/billing/apps-full-in-dialog'
-import { AiText, ChatBot, CuteRobote } from '@/app/components/base/icons/src/vender/solid/communication'
+import { AiText, ChatBot, CuteRobot } from '@/app/components/base/icons/src/vender/solid/communication'
 import { Route } from '@/app/components/base/icons/src/vender/solid/mapsAndTravel'
 import Tooltip from '@/app/components/base/tooltip'
 import { NEED_REFRESH_APP_LIST_KEY } from '@/config'
@@ -158,7 +158,7 @@ const CreateAppModal = ({ show, onSuccess, onClose }: CreateAppDialogProps) => {
                 setShowChatBotType(false)
               }}
             >
-              <CuteRobote className='w-6 h-6 text-indigo-600' />
+              <CuteRobot className='w-6 h-6 text-indigo-600' />
               <div className='h-5 text-[13px] font-medium leading-[18px]'>{t('app.types.agent')}</div>
             </div>
           </Tooltip>
diff --git a/web/app/components/app/log/index.tsx b/web/app/components/app/log/index.tsx
index dd6ebd08f0..77fcb6fcf9 100644
--- a/web/app/components/app/log/index.tsx
+++ b/web/app/components/app/log/index.tsx
@@ -119,8 +119,8 @@ const Logs: FC<ILogsProps> = ({ appDetail }) => {
             middlePagesSiblingCount={1}
             setCurrentPage={setCurrPage}
             totalPages={Math.ceil(total / APP_PAGE_LIMIT)}
-            truncableClassName="w-8 px-0.5 text-center"
-            truncableText="..."
+            truncatableClassName="w-8 px-0.5 text-center"
+            truncatableText="..."
           >
             <Pagination.PrevButton
               disabled={currPage === 0}
diff --git a/web/app/components/app/log/list.tsx b/web/app/components/app/log/list.tsx
index 0bc118c46f..caec10c4f7 100644
--- a/web/app/components/app/log/list.tsx
+++ b/web/app/components/app/log/list.tsx
@@ -19,7 +19,7 @@ import { useTranslation } from 'react-i18next'
 import s from './style.module.css'
 import VarPanel from './var-panel'
 import cn from '@/utils/classnames'
-import type { FeedbackFunc, Feedbacktype, IChatItem, SubmitAnnotationFunc } from '@/app/components/base/chat/chat/type'
+import type { FeedbackFunc, FeedbackType, IChatItem, SubmitAnnotationFunc } from '@/app/components/base/chat/chat/type'
 import type { Annotation, ChatConversationFullDetailResponse, ChatConversationGeneralDetail, ChatConversationsResponse, ChatMessage, ChatMessagesRequest, CompletionConversationFullDetailResponse, CompletionConversationGeneralDetail, CompletionConversationsResponse, LogAnnotation } from '@/models/log'
 import type { App } from '@/types/app'
 import Loading from '@/app/components/base/loading'
@@ -338,7 +338,7 @@ function DetailPanel<T extends ChatConversationFullDetailResponse | CompletionCo
   return (
     <div ref={ref} className='rounded-xl border-[0.5px] border-gray-200 h-full flex flex-col overflow-auto'>
       {/* Panel Header */}
-      <div className='border-b border-gray-100 py-4 px-6 flex items-center justify-between'>
+      <div className='border-b border-gray-100 py-4 px-6 flex items-center justify-between bg-components-panel-bg'>
         <div>
           <div className='text-gray-500 text-[10px] leading-[14px]'>{isChatMode ? t('appLog.detail.conversationId') : t('appLog.detail.time')}</div>
           {isChatMode && (
@@ -541,7 +541,7 @@ const CompletionConversationDetailComp: FC<{ appId?: string; conversationId?: st
   const { notify } = useContext(ToastContext)
   const { t } = useTranslation()
 
-  const handleFeedback = async (mid: string, { rating }: Feedbacktype): Promise<boolean> => {
+  const handleFeedback = async (mid: string, { rating }: FeedbackType): Promise<boolean> => {
     try {
       await updateLogMessageFeedbacks({ url: `/apps/${appId}/feedbacks`, body: { message_id: mid, rating } })
       conversationDetailMutate()
@@ -586,7 +586,7 @@ const ChatConversationDetailComp: FC<{ appId?: string; conversationId?: string }
   const { notify } = useContext(ToastContext)
   const { t } = useTranslation()
 
-  const handleFeedback = async (mid: string, { rating }: Feedbacktype): Promise<boolean> => {
+  const handleFeedback = async (mid: string, { rating }: FeedbackType): Promise<boolean> => {
     try {
       await updateLogMessageFeedbacks({ url: `/apps/${appId}/feedbacks`, body: { message_id: mid, rating } })
       notify({ type: 'success', message: t('common.actionMsg.modifiedSuccessfully') })
@@ -725,7 +725,7 @@ const ConversationList: FC<IConversationList> = ({ logs, appDetail, onRefresh })
         onClose={onCloseDrawer}
         mask={isMobile}
         footer={null}
-        panelClassname='mt-16 mx-2 sm:mr-2 mb-4 !p-0 !max-w-[640px] rounded-xl'
+        panelClassname='mt-16 mx-2 sm:mr-2 mb-4 !p-0 !max-w-[640px] rounded-xl bg-background-gradient-bg-fill-chat-bg-1'
       >
         <DrawerContext.Provider value={{
           onClose: onCloseDrawer,
diff --git a/web/app/components/app/text-generate/item/index.tsx b/web/app/components/app/text-generate/item/index.tsx
index 9794967d9d..e3bd8eadc5 100644
--- a/web/app/components/app/text-generate/item/index.tsx
+++ b/web/app/components/app/text-generate/item/index.tsx
@@ -16,13 +16,13 @@ import { Markdown } from '@/app/components/base/markdown'
 import Loading from '@/app/components/base/loading'
 import Toast from '@/app/components/base/toast'
 import AudioBtn from '@/app/components/base/audio-btn'
-import type { Feedbacktype } from '@/app/components/base/chat/chat/type'
+import type { FeedbackType } from '@/app/components/base/chat/chat/type'
 import { fetchMoreLikeThis, updateFeedback } from '@/service/share'
 import { File02 } from '@/app/components/base/icons/src/vender/line/files'
 import { Bookmark } from '@/app/components/base/icons/src/vender/line/general'
 import { Stars02 } from '@/app/components/base/icons/src/vender/line/weather'
 import { RefreshCcw01 } from '@/app/components/base/icons/src/vender/line/arrows'
-import { fetchTextGenerationMessge } from '@/service/debug'
+import { fetchTextGenerationMessage } from '@/service/debug'
 import AnnotationCtrlBtn from '@/app/components/app/configuration/toolbox/annotation/annotation-ctrl-btn'
 import EditReplyModal from '@/app/components/app/annotation/edit-annotation-modal'
 import { useStore as useAppStore } from '@/app/components/app/store'
@@ -47,8 +47,8 @@ export type IGenerationItemProps = {
   isInWebApp?: boolean
   moreLikeThis?: boolean
   depth?: number
-  feedback?: Feedbacktype
-  onFeedback?: (feedback: Feedbacktype) => void
+  feedback?: FeedbackType
+  onFeedback?: (feedback: FeedbackType) => void
   onSave?: (messageId: string) => void
   isMobile?: boolean
   isInstalledApp: boolean
@@ -125,7 +125,7 @@ const GenerationItem: FC<IGenerationItemProps> = ({
   const [completionRes, setCompletionRes] = useState('')
   const [childMessageId, setChildMessageId] = useState<string | null>(null)
   const hasChild = !!childMessageId
-  const [childFeedback, setChildFeedback] = useState<Feedbacktype>({
+  const [childFeedback, setChildFeedback] = useState<FeedbackType>({
     rating: null,
   })
   const {
@@ -135,7 +135,7 @@ const GenerationItem: FC<IGenerationItemProps> = ({
   const setCurrentLogItem = useAppStore(s => s.setCurrentLogItem)
   const setShowPromptLogModal = useAppStore(s => s.setShowPromptLogModal)
 
-  const handleFeedback = async (childFeedback: Feedbacktype) => {
+  const handleFeedback = async (childFeedback: FeedbackType) => {
     await updateFeedback({ url: `/messages/${childMessageId}/feedbacks`, body: { rating: childFeedback.rating } }, isInstalledApp, installedAppId)
     setChildFeedback(childFeedback)
   }
@@ -205,7 +205,7 @@ const GenerationItem: FC<IGenerationItemProps> = ({
   }, [isLoading])
 
   const handleOpenLogModal = async () => {
-    const data = await fetchTextGenerationMessge({
+    const data = await fetchTextGenerationMessage({
       appId: params.appId as string,
       messageId: messageId!,
     })
diff --git a/web/app/components/app/type-selector/index.tsx b/web/app/components/app/type-selector/index.tsx
index 2bd4f8d082..a09e189f50 100644
--- a/web/app/components/app/type-selector/index.tsx
+++ b/web/app/components/app/type-selector/index.tsx
@@ -9,7 +9,7 @@ import {
 } from '@/app/components/base/portal-to-follow-elem'
 import { Check, DotsGrid } from '@/app/components/base/icons/src/vender/line/general'
 import { XCircle } from '@/app/components/base/icons/src/vender/solid/general'
-import { ChatBot, CuteRobote } from '@/app/components/base/icons/src/vender/solid/communication'
+import { ChatBot, CuteRobot } from '@/app/components/base/icons/src/vender/solid/communication'
 import { Route } from '@/app/components/base/icons/src/vender/solid/mapsAndTravel'
 export type AppSelectorProps = {
   value: string
@@ -65,7 +65,7 @@ const AppTypeSelector = ({ value, onChange }: AppSelectorProps) => {
             {value === 'agent' && (
               <>
                 <div className='w-4 h-4 p-[1px]'>
-                  <CuteRobote className='w-3.5 h-3.5 text-indigo-600' />
+                  <CuteRobot className='w-3.5 h-3.5 text-indigo-600' />
                 </div>
                 <div className=''>{t('app.typeSelector.agent')}</div>
                 <div className='w-4 h-4 p-[1px]' onClick={(e) => {
@@ -106,7 +106,7 @@ const AppTypeSelector = ({ value, onChange }: AppSelectorProps) => {
               onChange('agent')
               setOpen(false)
             }}>
-              <CuteRobote className='mr-2 w-4 h-4 text-indigo-600' />
+              <CuteRobot className='mr-2 w-4 h-4 text-indigo-600' />
               <div className='grow text-gray-700 text-[13px] font-medium leading-[18px]'>{t('app.typeSelector.agent')}</div>
               {value === 'agent' && <Check className='w-4 h-4 text-primary-600' />}
             </div>
diff --git a/web/app/components/app/workflow-log/index.tsx b/web/app/components/app/workflow-log/index.tsx
index 303c2069b2..b7e20f0a0b 100644
--- a/web/app/components/app/workflow-log/index.tsx
+++ b/web/app/components/app/workflow-log/index.tsx
@@ -93,8 +93,8 @@ const Logs: FC<ILogsProps> = ({ appDetail }) => {
             middlePagesSiblingCount={1}
             setCurrentPage={setCurrPage}
             totalPages={Math.ceil(total / APP_PAGE_LIMIT)}
-            truncableClassName="w-8 px-0.5 text-center"
-            truncableText="..."
+            truncatableClassName="w-8 px-0.5 text-center"
+            truncatableText="..."
           >
             <Pagination.PrevButton
               disabled={currPage === 0}
diff --git a/web/app/components/base/agent-log-modal/detail.tsx b/web/app/components/base/agent-log-modal/detail.tsx
index 5b34d2e464..e90f593ad0 100644
--- a/web/app/components/base/agent-log-modal/detail.tsx
+++ b/web/app/components/base/agent-log-modal/detail.tsx
@@ -97,7 +97,7 @@ const AgentLogDetail: FC<AgentLogDetailProps> = ({
           onClick={() => switchTab('TRACING')}
         >{t('runLog.tracing')}</div>
       </div>
-      {/* panel detal */}
+      {/* panel detail */}
       <div className={cn('grow bg-white h-0 overflow-y-auto rounded-b-2xl', currentTab !== 'DETAIL' && '!bg-gray-50')}>
         {loading && (
           <div className='flex h-full items-center justify-center bg-white'>
diff --git a/web/app/components/base/app-unavailable.tsx b/web/app/components/base/app-unavailable.tsx
index c5ea7be0b9..b8b42108a9 100644
--- a/web/app/components/base/app-unavailable.tsx
+++ b/web/app/components/base/app-unavailable.tsx
@@ -22,7 +22,7 @@ const AppUnavailable: FC<IAppUnavailableProps> = ({
         style={{
           borderRight: '1px solid rgba(0,0,0,.3)',
         }}>{code}</h1>
-      <div className='text-sm'>{unknownReason || (isUnknownReason ? t('share.common.appUnkonwError') : t('share.common.appUnavailable'))}</div>
+      <div className='text-sm'>{unknownReason || (isUnknownReason ? t('share.common.appUnknownError') : t('share.common.appUnavailable'))}</div>
     </div>
   )
 }
diff --git a/web/app/components/base/audio-gallery/AudioPlayer.module.css b/web/app/components/base/audio-gallery/AudioPlayer.module.css
new file mode 100644
index 0000000000..6c070e107c
--- /dev/null
+++ b/web/app/components/base/audio-gallery/AudioPlayer.module.css
@@ -0,0 +1,119 @@
+.audioPlayer {
+  display: flex;
+  flex-direction: row;
+  align-items: center;
+  background-color: #ffffff;
+  border-radius: 10px;
+  padding: 8px;
+  min-width: 240px;
+  max-width: 420px;
+  max-height: 40px;
+  backdrop-filter: blur(5px);
+  border: 1px solid rgba(16, 24, 40, 0.08);
+  box-shadow: 0 1px 2px rgba(9, 9, 11, 0.05);
+  gap: 8px;
+}
+
+.playButton {
+  display: inline-flex;
+  width: 16px;
+  height: 16px;
+  border-radius: 50%;
+  background-color: #296DFF;
+  color: white;
+  border: none;
+  cursor: pointer;
+  align-items: center;
+  justify-content: center;
+  transition: background-color 0.1s;
+  flex-shrink: 0;
+}
+
+.playButton:hover {
+  background-color: #3367d6;
+}
+
+.playButton:disabled {
+  background-color: #bdbdbf;
+}
+
+.audioControls {
+  flex-grow: 1;
+  
+}
+
+.progressBarContainer {
+  height: 32px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.waveform {
+  position: relative;
+  display: flex;
+  cursor: pointer;
+  height: 24px;
+  width: 100%;
+  flex-grow: 1;
+  align-items: center;
+  justify-content: center;
+}
+
+.progressBar {
+  position: absolute;
+  top: 0;
+  left: 0;
+  opacity: 0.5;
+  border-radius: 2px;
+  flex: none;
+  order: 55;
+  flex-grow: 0;
+  height: 100%;
+  background-color: rgba(66, 133, 244, 0.3);
+  pointer-events: none;
+}
+
+.timeDisplay {
+  /* position: absolute; */
+  color: #296DFF;
+  border-radius: 2px;
+  order: 0;
+  height: 100%;
+  width: 50px;
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+}
+
+/* .currentTime {
+    position: absolute;
+    bottom: calc(100% + 5px);
+    transform: translateX(-50%);
+    background-color: rgba(255,255,255,.8);
+    padding: 2px 4px;
+    border-radius:10px;
+    box-shadow: 0 1px 5px rgba(0, 0, 0, 0.08);
+} */
+
+.duration {
+  background-color: rgba(255, 255, 255, 0.8);
+  padding: 2px 4px;
+  border-radius: 10px;
+}
+
+.source_unavailable {
+  border: none;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  width: 100%;
+  height: 100%;
+  position: absolute;
+  color: #bdbdbf;
+}
+
+.playButton svg path,
+.playButton svg rect{
+   fill:currentColor;
+}
diff --git a/web/app/components/base/audio-gallery/AudioPlayer.tsx b/web/app/components/base/audio-gallery/AudioPlayer.tsx
new file mode 100644
index 0000000000..c482981e8a
--- /dev/null
+++ b/web/app/components/base/audio-gallery/AudioPlayer.tsx
@@ -0,0 +1,320 @@
+import React, { useCallback, useEffect, useRef, useState } from 'react'
+import { t } from 'i18next'
+import styles from './AudioPlayer.module.css'
+import Toast from '@/app/components/base/toast'
+
+type AudioPlayerProps = {
+  src: string
+}
+
+const AudioPlayer: React.FC<AudioPlayerProps> = ({ src }) => {
+  const [isPlaying, setIsPlaying] = useState(false)
+  const [currentTime, setCurrentTime] = useState(0)
+  const [duration, setDuration] = useState(0)
+  const [waveformData, setWaveformData] = useState<number[]>([])
+  const [bufferedTime, setBufferedTime] = useState(0)
+  const audioRef = useRef<HTMLAudioElement>(null)
+  const canvasRef = useRef<HTMLCanvasElement>(null)
+  const [hasStartedPlaying, setHasStartedPlaying] = useState(false)
+  const [hoverTime, setHoverTime] = useState(0)
+  const [isAudioAvailable, setIsAudioAvailable] = useState(true)
+
+  useEffect(() => {
+    const audio = audioRef.current
+    if (!audio)
+      return
+
+    const handleError = () => {
+      setIsAudioAvailable(false)
+    }
+
+    const setAudioData = () => {
+      setDuration(audio.duration)
+    }
+
+    const setAudioTime = () => {
+      setCurrentTime(audio.currentTime)
+    }
+
+    const handleProgress = () => {
+      if (audio.buffered.length > 0)
+        setBufferedTime(audio.buffered.end(audio.buffered.length - 1))
+    }
+
+    const handleEnded = () => {
+      setIsPlaying(false)
+    }
+
+    audio.addEventListener('loadedmetadata', setAudioData)
+    audio.addEventListener('timeupdate', setAudioTime)
+    audio.addEventListener('progress', handleProgress)
+    audio.addEventListener('ended', handleEnded)
+    audio.addEventListener('error', handleError)
+
+    // Preload audio metadata
+    audio.load()
+
+    // Delayed generation of waveform data
+    // eslint-disable-next-line @typescript-eslint/no-use-before-define
+    const timer = setTimeout(() => generateWaveformData(src), 1000)
+
+    return () => {
+      audio.removeEventListener('loadedmetadata', setAudioData)
+      audio.removeEventListener('timeupdate', setAudioTime)
+      audio.removeEventListener('progress', handleProgress)
+      audio.removeEventListener('ended', handleEnded)
+      audio.removeEventListener('error', handleError)
+      clearTimeout(timer)
+    }
+  }, [src])
+
+  const generateWaveformData = async (audioSrc: string) => {
+    if (!window.AudioContext && !(window as any).webkitAudioContext) {
+      setIsAudioAvailable(false)
+      Toast.notify({
+        type: 'error',
+        message: 'Web Audio API is not supported in this browser',
+      })
+      return null
+    }
+
+    const url = new URL(src)
+    const isHttp = url.protocol === 'http:' || url.protocol === 'https:'
+    if (!isHttp) {
+      setIsAudioAvailable(false)
+      return null
+    }
+
+    const audioContext = new (window.AudioContext || (window as any).webkitAudioContext)()
+    const samples = 70
+
+    try {
+      const response = await fetch(audioSrc, { mode: 'cors' })
+      if (!response || !response.ok) {
+        setIsAudioAvailable(false)
+        return null
+      }
+
+      const arrayBuffer = await response.arrayBuffer()
+      const audioBuffer = await audioContext.decodeAudioData(arrayBuffer)
+      const channelData = audioBuffer.getChannelData(0)
+      const blockSize = Math.floor(channelData.length / samples)
+      const waveformData: number[] = []
+
+      for (let i = 0; i < samples; i++) {
+        let sum = 0
+        for (let j = 0; j < blockSize; j++)
+          sum += Math.abs(channelData[i * blockSize + j])
+
+        // Apply nonlinear scaling to enhance small amplitudes
+        waveformData.push((sum / blockSize) * 5)
+      }
+
+      // Normalized waveform data
+      const maxAmplitude = Math.max(...waveformData)
+      const normalizedWaveform = waveformData.map(amp => amp / maxAmplitude)
+
+      setWaveformData(normalizedWaveform)
+      setIsAudioAvailable(true)
+    }
+    catch (error) {
+      const waveform: number[] = []
+      let prevValue = Math.random()
+
+      for (let i = 0; i < samples; i++) {
+        const targetValue = Math.random()
+        const interpolatedValue = prevValue + (targetValue - prevValue) * 0.3
+        waveform.push(interpolatedValue)
+        prevValue = interpolatedValue
+      }
+
+      const maxAmplitude = Math.max(...waveform)
+      const randomWaveform = waveform.map(amp => amp / maxAmplitude)
+
+      setWaveformData(randomWaveform)
+      setIsAudioAvailable(true)
+    }
+    finally {
+      await audioContext.close()
+    }
+  }
+
+  const togglePlay = useCallback(() => {
+    const audio = audioRef.current
+    if (audio && isAudioAvailable) {
+      if (isPlaying) {
+        setHasStartedPlaying(false)
+        audio.pause()
+      }
+      else {
+        setHasStartedPlaying(true)
+        audio.play().catch(error => console.error('Error playing audio:', error))
+      }
+
+      setIsPlaying(!isPlaying)
+    }
+    else {
+      Toast.notify({
+        type: 'error',
+        message: 'Audio element not found',
+      })
+      setIsAudioAvailable(false)
+    }
+  }, [isAudioAvailable, isPlaying])
+
+  const handleCanvasInteraction = useCallback((e: React.MouseEvent | React.TouchEvent) => {
+    e.preventDefault()
+
+    const getClientX = (event: React.MouseEvent | React.TouchEvent): number => {
+      if ('touches' in event)
+        return event.touches[0].clientX
+      return event.clientX
+    }
+
+    const updateProgress = (clientX: number) => {
+      const canvas = canvasRef.current
+      const audio = audioRef.current
+      if (!canvas || !audio)
+        return
+
+      const rect = canvas.getBoundingClientRect()
+      const percent = Math.min(Math.max(0, clientX - rect.left), rect.width) / rect.width
+      const newTime = percent * duration
+
+      // Removes the buffer check, allowing drag to any location
+      audio.currentTime = newTime
+      setCurrentTime(newTime)
+
+      if (!isPlaying) {
+        setIsPlaying(true)
+        audio.play().catch((error) => {
+          Toast.notify({
+            type: 'error',
+            message: `Error playing audio: ${error}`,
+          })
+          setIsPlaying(false)
+        })
+      }
+    }
+
+    updateProgress(getClientX(e))
+  }, [duration, isPlaying])
+
+  const formatTime = (time: number) => {
+    const minutes = Math.floor(time / 60)
+    const seconds = Math.floor(time % 60)
+    return `${minutes}:${seconds.toString().padStart(2, '0')}`
+  }
+
+  const drawWaveform = useCallback(() => {
+    const canvas = canvasRef.current
+    if (!canvas)
+      return
+
+    const ctx = canvas.getContext('2d')
+    if (!ctx)
+      return
+
+    const width = canvas.width
+    const height = canvas.height
+    const data = waveformData
+
+    ctx.clearRect(0, 0, width, height)
+
+    const barWidth = width / data.length
+    const playedWidth = (currentTime / duration) * width
+    const cornerRadius = 2
+
+    // Draw waveform bars
+    data.forEach((value, index) => {
+      let color
+
+      if (index * barWidth <= playedWidth)
+        color = '#296DFF'
+      else if ((index * barWidth / width) * duration <= hoverTime)
+        color = 'rgba(21,90,239,.40)'
+      else
+        color = 'rgba(21,90,239,.20)'
+
+      const barHeight = value * height
+      const rectX = index * barWidth
+      const rectY = (height - barHeight) / 2
+      const rectWidth = barWidth * 0.5
+      const rectHeight = barHeight
+
+      ctx.lineWidth = 1
+      ctx.fillStyle = color
+      if (ctx.roundRect) {
+        ctx.beginPath()
+        ctx.roundRect(rectX, rectY, rectWidth, rectHeight, cornerRadius)
+        ctx.fill()
+      }
+      else {
+        ctx.fillRect(rectX, rectY, rectWidth, rectHeight)
+      }
+    })
+  }, [currentTime, duration, hoverTime, waveformData])
+
+  useEffect(() => {
+    drawWaveform()
+  }, [drawWaveform, bufferedTime, hasStartedPlaying])
+
+  const handleMouseMove = useCallback((e: React.MouseEvent) => {
+    const canvas = canvasRef.current
+    const audio = audioRef.current
+    if (!canvas || !audio)
+      return
+
+    const rect = canvas.getBoundingClientRect()
+    const percent = Math.min(Math.max(0, e.clientX - rect.left), rect.width) / rect.width
+    const time = percent * duration
+
+    // Check if the hovered position is within a buffered range before updating hoverTime
+    for (let i = 0; i < audio.buffered.length; i++) {
+      if (time >= audio.buffered.start(i) && time <= audio.buffered.end(i)) {
+        setHoverTime(time)
+        break
+      }
+    }
+  }, [duration])
+
+  return (
+    <div className={styles.audioPlayer}>
+      <audio ref={audioRef} src={src} preload="auto"/>
+      <button className={styles.playButton} onClick={togglePlay} disabled={!isAudioAvailable}>
+        {isPlaying
+          ? (
+            <svg viewBox="0 0 24 24" width="16" height="16">
+              <rect x="7" y="6" width="3" height="12" rx="1.5" ry="1.5"/>
+              <rect x="15" y="6" width="3" height="12" rx="1.5" ry="1.5"/>
+            </svg>
+          )
+          : (
+            <svg viewBox="0 0 24 24" width="16" height="16">
+              <path d="M8 5v14l11-7z" fill="currentColor"/>
+            </svg>
+          )}
+      </button>
+      <div className={isAudioAvailable ? styles.audioControls : styles.audioControls_disabled} hidden={!isAudioAvailable}>
+        <div className={styles.progressBarContainer}>
+          <canvas
+            ref={canvasRef}
+            className={styles.waveform}
+            onClick={handleCanvasInteraction}
+            onMouseMove={handleMouseMove}
+            onMouseDown={handleCanvasInteraction}
+          />
+          {/* <div className={styles.currentTime} style={{ left: `${(currentTime / duration) * 81}%`, bottom: '29px' }}>
+            {formatTime(currentTime)}
+          </div> */}
+          <div className={styles.timeDisplay}>
+            <span className={styles.duration}>{formatTime(duration)}</span>
+          </div>
+        </div>
+      </div>
+      <div className={styles.source_unavailable} hidden={isAudioAvailable}>{t('common.operation.audioSourceUnavailable')}</div>
+    </div>
+  )
+}
+
+export default AudioPlayer
diff --git a/web/app/components/base/audio-gallery/index.tsx b/web/app/components/base/audio-gallery/index.tsx
new file mode 100644
index 0000000000..6e11d43164
--- /dev/null
+++ b/web/app/components/base/audio-gallery/index.tsx
@@ -0,0 +1,12 @@
+import React from 'react'
+import AudioPlayer from './AudioPlayer'
+
+type Props = {
+  srcs: string[]
+}
+
+const AudioGallery: React.FC<Props> = ({ srcs }) => {
+  return (<><br/>{srcs.map((src, index) => (<AudioPlayer key={`audio_${index}`} src={src}/>))}</>)
+}
+
+export default React.memo(AudioGallery)
diff --git a/web/app/components/base/block-input/index.tsx b/web/app/components/base/block-input/index.tsx
index 79ff646bd1..43c14de4c9 100644
--- a/web/app/components/base/block-input/index.tsx
+++ b/web/app/components/base/block-input/index.tsx
@@ -53,7 +53,7 @@ const BlockInput: FC<IBlockInputProps> = ({
   const [isEditing, setIsEditing] = useState<boolean>(false)
   useEffect(() => {
     if (isEditing && contentEditableRef.current) {
-      // TODO: Focus at the click positon
+      // TODO: Focus at the click position
       if (currentValue)
         contentEditableRef.current.setSelectionRange(currentValue.length, currentValue.length)
 
@@ -119,7 +119,7 @@ const BlockInput: FC<IBlockInputProps> = ({
             onBlur={() => {
               blur()
               setIsEditing(false)
-              // click confirm also make blur. Then outter value is change. So below code has problem.
+              // click confirm also make blur. Then outer value is change. So below code has problem.
               // setTimeout(() => {
               //   handleCancel()
               // }, 1000)
diff --git a/web/app/components/base/chat/chat-with-history/chat-wrapper.tsx b/web/app/components/base/chat/chat-with-history/chat-wrapper.tsx
index 1129b18a8d..5a7bf1f17e 100644
--- a/web/app/components/base/chat/chat-with-history/chat-wrapper.tsx
+++ b/web/app/components/base/chat/chat-with-history/chat-wrapper.tsx
@@ -144,9 +144,9 @@ const ChatWrapper = () => {
       config={appConfig}
       chatList={chatList}
       isResponding={isResponding}
-      chatContainerInnerClassName={`mx-auto pt-6 w-full max-w-[720px] ${isMobile && 'px-4'}`}
+      chatContainerInnerClassName={`mx-auto pt-6 w-full max-w-full ${isMobile && 'px-4'}`}
       chatFooterClassName='pb-4'
-      chatFooterInnerClassName={`mx-auto w-full max-w-[720px] ${isMobile && 'px-4'}`}
+      chatFooterInnerClassName={`mx-auto w-full max-w-full ${isMobile && 'px-4'}`}
       onSend={doSend}
       onStopResponding={handleStop}
       chatNode={chatNode}
diff --git a/web/app/components/base/chat/chat-with-history/config-panel/index.tsx b/web/app/components/base/chat/chat-with-history/config-panel/index.tsx
index 05f253290f..c864a3925d 100644
--- a/web/app/components/base/chat/chat-with-history/config-panel/index.tsx
+++ b/web/app/components/base/chat/chat-with-history/config-panel/index.tsx
@@ -152,7 +152,7 @@ const ConfigPanel = () => {
                 : (
                   <div className={`flex items-center justify-end ${isMobile && 'w-full'}`}>
                     <div className='flex items-center pr-3 space-x-3'>
-                      <span className='uppercase'>{t('share.chat.powerBy')}</span>
+                      <span className='uppercase'>{t('share.chat.poweredBy')}</span>
                       {
                         customConfig?.replace_webapp_logo
                           ? <img src={customConfig?.replace_webapp_logo} alt='logo' className='block w-auto h-5' />
diff --git a/web/app/components/base/chat/chat-with-history/hooks.tsx b/web/app/components/base/chat/chat-with-history/hooks.tsx
index fe952efc27..1e05cc39ef 100644
--- a/web/app/components/base/chat/chat-with-history/hooks.tsx
+++ b/web/app/components/base/chat/chat-with-history/hooks.tsx
@@ -217,12 +217,12 @@ export const useChatWithHistory = (installedAppInfo?: InstalledApp) => {
   }, [newConversation])
 
   const currentConversationItem = useMemo(() => {
-    let coversationItem = conversationList.find(item => item.id === currentConversationId)
+    let conversationItem = conversationList.find(item => item.id === currentConversationId)
 
-    if (!coversationItem && pinnedConversationList.length)
-      coversationItem = pinnedConversationList.find(item => item.id === currentConversationId)
+    if (!conversationItem && pinnedConversationList.length)
+      conversationItem = pinnedConversationList.find(item => item.id === currentConversationId)
 
-    return coversationItem
+    return conversationItem
   }, [conversationList, currentConversationId, pinnedConversationList])
 
   const { notify } = useToastContext()
diff --git a/web/app/components/base/chat/chat/answer/index.tsx b/web/app/components/base/chat/chat/answer/index.tsx
index 270cd553c2..5fe2a7bad5 100644
--- a/web/app/components/base/chat/chat/answer/index.tsx
+++ b/web/app/components/base/chat/chat/answer/index.tsx
@@ -8,7 +8,6 @@ import type {
   ChatConfig,
   ChatItem,
 } from '../../types'
-import { useChatContext } from '../context'
 import Operation from './operation'
 import AgentContent from './agent-content'
 import BasicContent from './basic-content'
@@ -16,13 +15,13 @@ import SuggestedQuestions from './suggested-questions'
 import More from './more'
 import WorkflowProcess from './workflow-process'
 import { AnswerTriangle } from '@/app/components/base/icons/src/vender/solid/general'
-import { MessageFast } from '@/app/components/base/icons/src/vender/solid/communication'
 import LoadingAnim from '@/app/components/base/chat/chat/loading-anim'
 import Citation from '@/app/components/base/chat/chat/citation'
 import { EditTitle } from '@/app/components/app/annotation/edit-annotation-modal/edit-item'
 import type { Emoji } from '@/app/components/tools/types'
 import type { AppData } from '@/models/share'
 import AnswerIcon from '@/app/components/base/answer-icon'
+import cn from '@/utils/classnames'
 
 type AnswerProps = {
   item: ChatItem
@@ -61,26 +60,24 @@ const Answer: FC<AnswerProps> = ({
   } = item
   const hasAgentThoughts = !!agent_thoughts?.length
 
-  const [containerWidth] = useState(0)
+  const [containerWidth, setContainerWidth] = useState(0)
   const [contentWidth, setContentWidth] = useState(0)
   const containerRef = useRef<HTMLDivElement>(null)
   const contentRef = useRef<HTMLDivElement>(null)
 
-  const {
-    config: chatContextConfig,
-  } = useChatContext()
+  const getContainerWidth = () => {
+    if (containerRef.current)
+      setContainerWidth(containerRef.current?.clientWidth + 16)
+  }
+  useEffect(() => {
+    getContainerWidth()
+  }, [])
 
-  const voiceRef = useRef(chatContextConfig?.text_to_speech?.voice)
   const getContentWidth = () => {
     if (contentRef.current)
       setContentWidth(contentRef.current?.clientWidth)
   }
 
-  useEffect(() => {
-    voiceRef.current = chatContextConfig?.text_to_speech?.voice
-  }
-  , [chatContextConfig?.text_to_speech?.voice])
-
   useEffect(() => {
     if (!responding)
       getContentWidth()
@@ -89,36 +86,20 @@ const Answer: FC<AnswerProps> = ({
   return (
     <div className='flex mb-2 last:mb-0'>
       <div className='shrink-0 relative w-10 h-10'>
-        {
-          answerIcon || <AnswerIcon />
-        }
-        {
-          responding && (
-            <div className='absolute -top-[3px] -left-[3px] pl-[6px] flex items-center w-4 h-4 bg-white rounded-full shadow-xs border-[0.5px] border-gray-50'>
-              <LoadingAnim type='avatar' />
-            </div>
-          )
-        }
+        {answerIcon || <AnswerIcon />}
+        {responding && (
+          <div className='absolute -top-[3px] -left-[3px] pl-[6px] flex items-center w-4 h-4 bg-white rounded-full shadow-xs border-[0.5px] border-gray-50'>
+            <LoadingAnim type='avatar' />
+          </div>
+        )}
       </div>
       <div className='chat-answer-container group grow w-0 ml-4' ref={containerRef}>
-        <div className={`group relative pr-10 ${chatAnswerContainerInner}`}>
+        <div className={cn('group relative pr-10', chatAnswerContainerInner)}>
           <AnswerTriangle className='absolute -left-2 top-0 w-2 h-3 text-gray-100' />
           <div
             ref={contentRef}
-            className={`
-              relative inline-block px-4 py-3 max-w-full bg-gray-100 rounded-b-2xl rounded-tr-2xl text-sm text-gray-900
-              ${workflowProcess && 'w-full'}
-            `}
+            className={cn('relative inline-block px-4 py-3 max-w-full bg-gray-100 rounded-b-2xl rounded-tr-2xl text-sm text-gray-900', workflowProcess && 'w-full')}
           >
-            {annotation?.id && (
-              <div
-                className='absolute -top-3.5 -right-3.5 box-border flex items-center justify-center h-7 w-7 p-0.5 rounded-lg bg-white cursor-pointer text-[#444CE7] shadow-md group-hover:hidden'
-              >
-                <div className='p-1 rounded-lg bg-[#EEF4FF] '>
-                  <MessageFast className='w-4 h-4' />
-                </div>
-              </div>
-            )}
             {
               !responding && (
                 <Operation
diff --git a/web/app/components/base/chat/chat/answer/operation.tsx b/web/app/components/base/chat/chat/answer/operation.tsx
index f54e736826..08267bb09c 100644
--- a/web/app/components/base/chat/chat/answer/operation.tsx
+++ b/web/app/components/base/chat/chat/answer/operation.tsx
@@ -149,7 +149,7 @@ const Operation: FC<OperationProps> = ({
           />
         )}
         {
-          !positionRight && annotation?.id && (
+          annotation?.id && (
             <div
               className='relative box-border flex items-center justify-center h-7 w-7 p-0.5 rounded-lg bg-white cursor-pointer text-[#444CE7] shadow-md group-hover:hidden'
             >
diff --git a/web/app/components/base/chat/chat/answer/workflow-process.tsx b/web/app/components/base/chat/chat/answer/workflow-process.tsx
index 5f36e40c40..1f17798f83 100644
--- a/web/app/components/base/chat/chat/answer/workflow-process.tsx
+++ b/web/app/components/base/chat/chat/answer/workflow-process.tsx
@@ -11,10 +11,10 @@ import {
 } from '@remixicon/react'
 import { useTranslation } from 'react-i18next'
 import type { ChatItem, WorkflowProcess } from '../../types'
+import TracingPanel from '@/app/components/workflow/run/tracing-panel'
 import cn from '@/utils/classnames'
 import { CheckCircle } from '@/app/components/base/icons/src/vender/solid/general'
 import { WorkflowRunningStatus } from '@/app/components/workflow/types'
-import NodePanel from '@/app/components/workflow/run/node'
 import { useStore as useAppStore } from '@/app/components/app/store'
 
 type WorkflowProcessProps = {
@@ -107,16 +107,12 @@ const WorkflowProcessItem = ({
         !collapse && (
           <div className='mt-1.5'>
             {
-              data.tracing.map(node => (
-                <div key={node.id} className='mb-1 last-of-type:mb-0'>
-                  <NodePanel
-                    nodeInfo={node}
-                    hideInfo={hideInfo}
-                    hideProcessDetail={hideProcessDetail}
-                    onShowIterationDetail={showIterationDetail}
-                  />
-                </div>
-              ))
+              <TracingPanel
+                list={data.tracing}
+                onShowIterationDetail={showIterationDetail}
+                hideNodeInfo={hideInfo}
+                hideNodeProcessDetail={hideProcessDetail}
+              />
             }
           </div>
         )
diff --git a/web/app/components/base/chat/chat/chat-input.tsx b/web/app/components/base/chat/chat/chat-input.tsx
index c4578fab62..fdb09dc3ae 100644
--- a/web/app/components/base/chat/chat/chat-input.tsx
+++ b/web/app/components/base/chat/chat/chat-input.tsx
@@ -32,18 +32,21 @@ import {
   useDraggableUploader,
   useImageFiles,
 } from '@/app/components/base/image-uploader/hooks'
+import cn from '@/utils/classnames'
 
 type ChatInputProps = {
   visionConfig?: VisionConfig
   speechToTextConfig?: EnableType
   onSend?: OnSend
   theme?: Theme | null
+  noSpacing?: boolean
 }
 const ChatInput: FC<ChatInputProps> = ({
   visionConfig,
   speechToTextConfig,
   onSend,
   theme,
+  noSpacing,
 }) => {
   const { appData } = useChatWithHistoryContext()
   const { t } = useTranslation()
@@ -146,7 +149,7 @@ const ChatInput: FC<ChatInputProps> = ({
 
   return (
     <>
-      <div className='relative'>
+      <div className={cn('relative', !noSpacing && 'px-8')}>
         <div
           className={`
             p-[5.5px] max-h-[150px] bg-white border-[1.5px] border-gray-200 rounded-xl overflow-y-auto
@@ -156,7 +159,7 @@ const ChatInput: FC<ChatInputProps> = ({
           {
             visionConfig?.enabled && (
               <>
-                <div className='absolute bottom-2 left-2 flex items-center'>
+                <div className={cn('absolute bottom-2 flex items-center', noSpacing ? 'left-2' : 'left-10')}>
                   <ChatImageUploader
                     settings={visionConfig}
                     onUpload={onUpload}
@@ -193,7 +196,7 @@ const ChatInput: FC<ChatInputProps> = ({
             onDrop={onDrop}
             autoSize
           />
-          <div className='absolute bottom-[7px] right-2 flex items-center h-8'>
+          <div className={cn('absolute bottom-[7px] flex items-center h-8', noSpacing ? 'right-2' : 'right-10')}>
             <div className='flex items-center px-1 h-5 rounded-md bg-gray-100 text-xs font-medium text-gray-500'>
               {query.trim().length}
             </div>
diff --git a/web/app/components/base/chat/chat/citation/index.tsx b/web/app/components/base/chat/chat/citation/index.tsx
index 4bed9638d3..2ca7b80ae7 100644
--- a/web/app/components/base/chat/chat/citation/index.tsx
+++ b/web/app/components/base/chat/chat/citation/index.tsx
@@ -24,7 +24,7 @@ const Citation: FC<CitationProps> = ({
 }) => {
   const { t } = useTranslation()
   const elesRef = useRef<HTMLDivElement[]>([])
-  const [limitNumberInOneLine, setlimitNumberInOneLine] = useState(0)
+  const [limitNumberInOneLine, setLimitNumberInOneLine] = useState(0)
   const [showMore, setShowMore] = useState(false)
   const resources = useMemo(() => data.reduce((prev: Resources[], next) => {
     const documentId = next.document_id
@@ -57,14 +57,14 @@ const Citation: FC<CitationProps> = ({
         totalWidth -= elesRef.current[i].clientWidth
 
         if (totalWidth + 34 > containerWidth!)
-          setlimitNumberInOneLine(i - 1)
+          setLimitNumberInOneLine(i - 1)
         else
-          setlimitNumberInOneLine(i)
+          setLimitNumberInOneLine(i)
 
         break
       }
       else {
-        setlimitNumberInOneLine(i + 1)
+        setLimitNumberInOneLine(i + 1)
       }
     }
   }
diff --git a/web/app/components/base/chat/chat/citation/popup.tsx b/web/app/components/base/chat/chat/citation/popup.tsx
index d039d98e6b..b61bf623fe 100644
--- a/web/app/components/base/chat/chat/citation/popup.tsx
+++ b/web/app/components/base/chat/chat/citation/popup.tsx
@@ -53,72 +53,74 @@ const Popup: FC<PopupProps> = ({
         </div>
       </PortalToFollowElemTrigger>
       <PortalToFollowElemContent style={{ zIndex: 1000 }}>
-        <div className='w-[360px] bg-gray-50 rounded-xl shadow-lg'>
+        <div className='max-w-[360px] bg-gray-50 rounded-xl shadow-lg'>
           <div className='px-4 pt-3 pb-2'>
             <div className='flex items-center h-[18px]'>
               <FileIcon type={fileType} className='shrink-0 mr-1 w-4 h-4' />
               <div className='text-xs font-medium text-gray-600 truncate'>{data.documentName}</div>
             </div>
           </div>
-          <div className='px-4 py-0.5 max-h-[450px] bg-white rounded-lg overflow-auto'>
-            {
-              data.sources.map((source, index) => (
-                <Fragment key={index}>
-                  <div className='group py-3'>
-                    <div className='flex items-center justify-between mb-2'>
-                      <div className='flex items-center px-1.5 h-5 border border-gray-200 rounded-md'>
-                        <Hash02 className='mr-0.5 w-3 h-3 text-gray-400' />
-                        <div className='text-[11px] font-medium text-gray-500'>
-                          {source.segment_position || index + 1}
+          <div className='px-4 py-0.5 max-h-[450px] bg-white rounded-lg overflow-y-auto'>
+            <div className='w-full'>
+              {
+                data.sources.map((source, index) => (
+                  <Fragment key={index}>
+                    <div className='group py-3'>
+                      <div className='flex items-center justify-between mb-2'>
+                        <div className='flex items-center px-1.5 h-5 border border-gray-200 rounded-md'>
+                          <Hash02 className='mr-0.5 w-3 h-3 text-gray-400' />
+                          <div className='text-[11px] font-medium text-gray-500'>
+                            {source.segment_position || index + 1}
+                          </div>
                         </div>
+                        {
+                          showHitInfo && (
+                            <Link
+                              href={`/datasets/${source.dataset_id}/documents/${source.document_id}`}
+                              className='hidden items-center h-[18px] text-xs text-primary-600 group-hover:flex'>
+                              {t('common.chat.citation.linkToDataset')}
+                              <ArrowUpRight className='ml-1 w-3 h-3' />
+                            </Link>
+                          )
+                        }
                       </div>
+                      <div className='text-[13px] text-gray-800 break-words'>{source.content}</div>
                       {
                         showHitInfo && (
-                          <Link
-                            href={`/datasets/${source.dataset_id}/documents/${source.document_id}`}
-                            className='hidden items-center h-[18px] text-xs text-primary-600 group-hover:flex'>
-                            {t('common.chat.citation.linkToDataset')}
-                            <ArrowUpRight className='ml-1 w-3 h-3' />
-                          </Link>
+                          <div className='flex items-center mt-2 text-xs font-medium text-gray-500 flex-wrap'>
+                            <Tooltip
+                              text={t('common.chat.citation.characters')}
+                              data={source.word_count}
+                              icon={<TypeSquare className='mr-1 w-3 h-3' />}
+                            />
+                            <Tooltip
+                              text={t('common.chat.citation.hitCount')}
+                              data={source.hit_count}
+                              icon={<Target04 className='mr-1 w-3 h-3' />}
+                            />
+                            <Tooltip
+                              text={t('common.chat.citation.vectorHash')}
+                              data={source.index_node_hash.substring(0, 7)}
+                              icon={<BezierCurve03 className='mr-1 w-3 h-3' />}
+                            />
+                            {
+                              source.score && (
+                                <ProgressTooltip data={Number(source.score.toFixed(2))} />
+                              )
+                            }
+                          </div>
                         )
                       }
                     </div>
-                    <div className='text-[13px] text-gray-800'>{source.content}</div>
                     {
-                      showHitInfo && (
-                        <div className='flex items-center mt-2 text-xs font-medium text-gray-500'>
-                          <Tooltip
-                            text={t('common.chat.citation.characters')}
-                            data={source.word_count}
-                            icon={<TypeSquare className='mr-1 w-3 h-3' />}
-                          />
-                          <Tooltip
-                            text={t('common.chat.citation.hitCount')}
-                            data={source.hit_count}
-                            icon={<Target04 className='mr-1 w-3 h-3' />}
-                          />
-                          <Tooltip
-                            text={t('common.chat.citation.vectorHash')}
-                            data={source.index_node_hash.substring(0, 7)}
-                            icon={<BezierCurve03 className='mr-1 w-3 h-3' />}
-                          />
-                          {
-                            source.score && (
-                              <ProgressTooltip data={Number(source.score.toFixed(2))} />
-                            )
-                          }
-                        </div>
+                      index !== data.sources.length - 1 && (
+                        <div className='my-1 h-[1px] bg-black/5' />
                       )
                     }
-                  </div>
-                  {
-                    index !== data.sources.length - 1 && (
-                      <div className='my-1 h-[1px] bg-black/5' />
-                    )
-                  }
-                </Fragment>
-              ))
-            }
+                  </Fragment>
+                ))
+              }
+            </div>
           </div>
         </div>
       </PortalToFollowElemContent>
diff --git a/web/app/components/base/chat/chat/hooks.ts b/web/app/components/base/chat/chat/hooks.ts
index 4caa8116df..892f88c4ad 100644
--- a/web/app/components/base/chat/chat/hooks.ts
+++ b/web/app/components/base/chat/chat/hooks.ts
@@ -26,7 +26,7 @@ import { AudioPlayerManager } from '@/app/components/base/audio-btn/audio.player
 
 type GetAbortController = (abortController: AbortController) => void
 type SendCallback = {
-  onGetConvesationMessages?: (conversationId: string, getAbortController: GetAbortController) => Promise<any>
+  onGetConversationMessages?: (conversationId: string, getAbortController: GetAbortController) => Promise<any>
   onGetSuggestedQuestions?: (responseItemId: string, getAbortController: GetAbortController) => Promise<any>
   onConversationComplete?: (conversationId: string) => void
   isPublicAPI?: boolean
@@ -198,7 +198,7 @@ export const useChat = (
     url: string,
     data: any,
     {
-      onGetConvesationMessages,
+      onGetConversationMessages,
       onGetSuggestedQuestions,
       onConversationComplete,
       isPublicAPI,
@@ -241,8 +241,6 @@ export const useChat = (
       isAnswer: true,
     }
 
-    let isInIteration = false
-
     handleResponding(true)
     hasStopResponded.current = false
 
@@ -324,8 +322,8 @@ export const useChat = (
           if (onConversationComplete)
             onConversationComplete(conversationId.current)
 
-          if (conversationId.current && !hasStopResponded.current && onGetConvesationMessages) {
-            const { data }: any = await onGetConvesationMessages(
+          if (conversationId.current && !hasStopResponded.current && onGetConversationMessages) {
+            const { data }: any = await onGetConversationMessages(
               conversationId.current,
               newAbortController => conversationMessagesAbortControllerRef.current = newAbortController,
             )
@@ -503,12 +501,13 @@ export const useChat = (
               ...responseItem,
             }
           }))
-          isInIteration = true
         },
         onIterationFinish: ({ data }) => {
           const tracing = responseItem.workflowProcess!.tracing!
-          tracing[tracing.length - 1] = {
-            ...tracing[tracing.length - 1],
+          const iterationIndex = tracing.findIndex(item => item.node_id === data.node_id
+            && (item.execution_metadata?.parallel_id === data.execution_metadata?.parallel_id || item.parallel_id === data.execution_metadata?.parallel_id))!
+          tracing[iterationIndex] = {
+            ...tracing[iterationIndex],
             ...data,
             status: WorkflowRunningStatus.Succeeded,
           } as any
@@ -520,10 +519,9 @@ export const useChat = (
               ...responseItem,
             }
           }))
-          isInIteration = false
         },
         onNodeStarted: ({ data }) => {
-          if (isInIteration)
+          if (data.iteration_id)
             return
 
           responseItem.workflowProcess!.tracing!.push({
@@ -539,10 +537,15 @@ export const useChat = (
           }))
         },
         onNodeFinished: ({ data }) => {
-          if (isInIteration)
+          if (data.iteration_id)
             return
 
-          const currentIndex = responseItem.workflowProcess!.tracing!.findIndex(item => item.node_id === data.node_id)
+          const currentIndex = responseItem.workflowProcess!.tracing!.findIndex((item) => {
+            if (!item.execution_metadata?.parallel_id)
+              return item.node_id === data.node_id
+
+            return item.node_id === data.node_id && (item.execution_metadata?.parallel_id === data.execution_metadata.parallel_id)
+          })
           responseItem.workflowProcess!.tracing[currentIndex] = data as any
           handleUpdateChatList(produce(chatListRef.current, (draft) => {
             const currentIndex = draft.findIndex(item => item.id === responseItem.id)
diff --git a/web/app/components/base/chat/chat/index.tsx b/web/app/components/base/chat/chat/index.tsx
index c5d9af45c2..68194193c4 100644
--- a/web/app/components/base/chat/chat/index.tsx
+++ b/web/app/components/base/chat/chat/index.tsx
@@ -60,6 +60,7 @@ export type ChatProps = {
   hideProcessDetail?: boolean
   hideLogModal?: boolean
   themeBuilder?: ThemeBuilder
+  noSpacing?: boolean
 }
 
 const Chat: FC<ChatProps> = ({
@@ -89,6 +90,7 @@ const Chat: FC<ChatProps> = ({
   hideProcessDetail,
   hideLogModal,
   themeBuilder,
+  noSpacing,
 }) => {
   const { t } = useTranslation()
   const { currentLogItem, setCurrentLogItem, showPromptLogModal, setShowPromptLogModal, showAgentLogModal, setShowAgentLogModal } = useAppStore(useShallow(state => ({
@@ -106,10 +108,10 @@ const Chat: FC<ChatProps> = ({
   const chatFooterInnerRef = useRef<HTMLDivElement>(null)
   const userScrolledRef = useRef(false)
 
-  const handleScrolltoBottom = useCallback(() => {
-    if (chatContainerRef.current && !userScrolledRef.current)
+  const handleScrollToBottom = useCallback(() => {
+    if (chatList.length > 1 && chatContainerRef.current && !userScrolledRef.current)
       chatContainerRef.current.scrollTop = chatContainerRef.current.scrollHeight
-  }, [])
+  }, [chatList.length])
 
   const handleWindowResize = useCallback(() => {
     if (chatContainerRef.current)
@@ -123,14 +125,14 @@ const Chat: FC<ChatProps> = ({
   }, [])
 
   useEffect(() => {
-    handleScrolltoBottom()
+    handleScrollToBottom()
     handleWindowResize()
-  }, [handleScrolltoBottom, handleWindowResize])
+  }, [handleScrollToBottom, handleWindowResize])
 
   useEffect(() => {
     if (chatContainerRef.current) {
       requestAnimationFrame(() => {
-        handleScrolltoBottom()
+        handleScrollToBottom()
         handleWindowResize()
       })
     }
@@ -148,7 +150,7 @@ const Chat: FC<ChatProps> = ({
           const { blockSize } = entry.borderBoxSize[0]
 
           chatContainerRef.current!.style.paddingBottom = `${blockSize}px`
-          handleScrolltoBottom()
+          handleScrollToBottom()
         }
       })
 
@@ -158,7 +160,7 @@ const Chat: FC<ChatProps> = ({
         resizeObserver.disconnect()
       }
     }
-  }, [handleScrolltoBottom])
+  }, [handleScrollToBottom])
 
   useEffect(() => {
     const chatContainer = chatContainerRef.current
@@ -192,12 +194,12 @@ const Chat: FC<ChatProps> = ({
       <div className='relative h-full'>
         <div
           ref={chatContainerRef}
-          className={classNames('relative h-full overflow-y-auto', chatContainerClassName)}
+          className={classNames('relative h-full overflow-y-auto overflow-x-hidden', chatContainerClassName)}
         >
           {chatNode}
           <div
             ref={chatContainerInnerRef}
-            className={`${chatContainerInnerClassName}`}
+            className={classNames('w-full', !noSpacing && 'px-8', chatContainerInnerClassName)}
           >
             {
               chatList.map((item, index) => {
@@ -268,6 +270,7 @@ const Chat: FC<ChatProps> = ({
                   speechToTextConfig={config?.speech_to_text}
                   onSend={onSend}
                   theme={themeBuilder?.theme}
+                  noSpacing={noSpacing}
                 />
               )
             }
diff --git a/web/app/components/base/chat/chat/loading-anim/index.tsx b/web/app/components/base/chat/chat/loading-anim/index.tsx
index 09f8a54789..dd43ef9c14 100644
--- a/web/app/components/base/chat/chat/loading-anim/index.tsx
+++ b/web/app/components/base/chat/chat/loading-anim/index.tsx
@@ -3,15 +3,15 @@ import type { FC } from 'react'
 import React from 'react'
 import s from './style.module.css'
 
-export type ILoaidingAnimProps = {
+export type ILoadingAnimProps = {
   type: 'text' | 'avatar'
 }
 
-const LoaidingAnim: FC<ILoaidingAnimProps> = ({
+const LoadingAnim: FC<ILoadingAnimProps> = ({
   type,
 }) => {
   return (
     <div className={`${s['dot-flashing']} ${s[type]}`}></div>
   )
 }
-export default React.memo(LoaidingAnim)
+export default React.memo(LoadingAnim)
diff --git a/web/app/components/base/chat/chat/type.ts b/web/app/components/base/chat/chat/type.ts
index 16ccff4d4d..b2cb18011c 100644
--- a/web/app/components/base/chat/chat/type.ts
+++ b/web/app/components/base/chat/chat/type.ts
@@ -8,14 +8,14 @@ export type MessageMore = {
   latency: number | string
 }
 
-export type Feedbacktype = {
+export type FeedbackType = {
   rating: MessageRating
   content?: string | null
 }
 
 export type FeedbackFunc = (
   messageId: string,
-  feedback: Feedbacktype
+  feedback: FeedbackType
 ) => Promise<any>
 export type SubmitAnnotationFunc = (
   messageId: string,
@@ -71,11 +71,11 @@ export type IChatItem = {
   /**
    * The user feedback result of this message
    */
-  feedback?: Feedbacktype
+  feedback?: FeedbackType
   /**
    * The admin feedback result of this message
    */
-  adminFeedback?: Feedbacktype
+  adminFeedback?: FeedbackType
   /**
    * Whether to hide the feedback area
    */
diff --git a/web/app/components/base/chat/embedded-chatbot/chat-wrapper.tsx b/web/app/components/base/chat/embedded-chatbot/chat-wrapper.tsx
index 4934339e87..48ee411058 100644
--- a/web/app/components/base/chat/embedded-chatbot/chat-wrapper.tsx
+++ b/web/app/components/base/chat/embedded-chatbot/chat-wrapper.tsx
@@ -14,7 +14,7 @@ import {
   getUrl,
   stopChatMessageResponding,
 } from '@/service/share'
-import LogoAvatar from '@/app/components/base/logo/logo-embeded-chat-avatar'
+import LogoAvatar from '@/app/components/base/logo/logo-embedded-chat-avatar'
 import AnswerIcon from '@/app/components/base/answer-icon'
 
 const ChatWrapper = () => {
@@ -99,7 +99,7 @@ const ChatWrapper = () => {
       return (
         <>
           {!currentConversationId && (
-            <div className={cn('mx-auto w-full max-w-[720px] tablet:px-4', isMobile && 'px-4')}>
+            <div className={cn('mx-auto w-full max-w-full tablet:px-4', isMobile && 'px-4')}>
               <div className='mb-6' />
               <ConfigPanel />
               <div
@@ -132,9 +132,9 @@ const ChatWrapper = () => {
       config={appConfig}
       chatList={chatList}
       isResponding={isResponding}
-      chatContainerInnerClassName={cn('mx-auto w-full max-w-[720px] tablet:px-4', isMobile && 'px-4')}
+      chatContainerInnerClassName={cn('mx-auto w-full max-w-full tablet:px-4', isMobile && 'px-4')}
       chatFooterClassName='pb-4'
-      chatFooterInnerClassName={cn('mx-auto w-full max-w-[720px] tablet:px-4', isMobile && 'px-4')}
+      chatFooterInnerClassName={cn('mx-auto w-full max-w-full tablet:px-4', isMobile && 'px-4')}
       onSend={doSend}
       onStopResponding={handleStop}
       chatNode={chatNode}
diff --git a/web/app/components/base/chat/embedded-chatbot/config-panel/index.tsx b/web/app/components/base/chat/embedded-chatbot/config-panel/index.tsx
index df5d12ef14..2cc46cadf8 100644
--- a/web/app/components/base/chat/embedded-chatbot/config-panel/index.tsx
+++ b/web/app/components/base/chat/embedded-chatbot/config-panel/index.tsx
@@ -160,7 +160,7 @@ const ConfigPanel = () => {
                 : (
                   <div className={cn('flex items-center justify-end', isMobile && 'w-full')}>
                     <div className='flex items-center pr-3 space-x-3'>
-                      <span className='uppercase'>{t('share.chat.powerBy')}</span>
+                      <span className='uppercase'>{t('share.chat.poweredBy')}</span>
                       {
                         customConfig?.replace_webapp_logo
                           ? <img src={customConfig?.replace_webapp_logo} alt='logo' className='block w-auto h-5' />
diff --git a/web/app/components/base/chat/embedded-chatbot/index.tsx b/web/app/components/base/chat/embedded-chatbot/index.tsx
index 480adaae2d..407c0de6d8 100644
--- a/web/app/components/base/chat/embedded-chatbot/index.tsx
+++ b/web/app/components/base/chat/embedded-chatbot/index.tsx
@@ -17,7 +17,7 @@ import { checkOrSetAccessToken } from '@/app/components/share/utils'
 import AppUnavailable from '@/app/components/base/app-unavailable'
 import useBreakpoints, { MediaType } from '@/hooks/use-breakpoints'
 import Loading from '@/app/components/base/loading'
-import LogoHeader from '@/app/components/base/logo/logo-embeded-chat-header'
+import LogoHeader from '@/app/components/base/logo/logo-embedded-chat-header'
 import Header from '@/app/components/base/chat/embedded-chatbot/header'
 import ConfigPanel from '@/app/components/base/chat/embedded-chatbot/config-panel'
 import ChatWrapper from '@/app/components/base/chat/embedded-chatbot/chat-wrapper'
diff --git a/web/app/components/base/chat/types.ts b/web/app/components/base/chat/types.ts
index baffe42843..21277fec57 100644
--- a/web/app/components/base/chat/types.ts
+++ b/web/app/components/base/chat/types.ts
@@ -22,7 +22,7 @@ export type UserInputForm = {
 }
 
 export type UserInputFormTextInput = {
-  'text-inpput': UserInputForm & {
+  'text-input': UserInputForm & {
     max_length: number
   }
 }
diff --git a/web/app/components/base/features/feature-panel/moderation/moderation-setting-modal.tsx b/web/app/components/base/features/feature-panel/moderation/moderation-setting-modal.tsx
index 9acc51b5f3..635506c053 100644
--- a/web/app/components/base/features/feature-panel/moderation/moderation-setting-modal.tsx
+++ b/web/app/components/base/features/feature-panel/moderation/moderation-setting-modal.tsx
@@ -67,7 +67,7 @@ const ModerationSettingModal: FC<ModerationSettingModalProps> = ({
   const systemOpenaiProviderQuota = systemOpenaiProviderEnabled ? openaiProvider?.system_configuration.quota_configurations.find(item => item.quota_type === openaiProvider.system_configuration.current_quota_type) : undefined
   const systemOpenaiProviderCanUse = systemOpenaiProviderQuota?.is_valid
   const customOpenaiProvidersCanUse = openaiProvider?.custom_configuration.status === CustomConfigurationStatusEnum.active
-  const openaiProviderConfiged = customOpenaiProvidersCanUse || systemOpenaiProviderCanUse
+  const isOpenAIProviderConfigured = customOpenaiProvidersCanUse || systemOpenaiProviderCanUse
   const providers: Provider[] = [
     {
       key: 'openai_moderation',
@@ -193,7 +193,7 @@ const ModerationSettingModal: FC<ModerationSettingModalProps> = ({
   }
 
   const handleSave = () => {
-    if (localeData.type === 'openai_moderation' && !openaiProviderConfiged)
+    if (localeData.type === 'openai_moderation' && !isOpenAIProviderConfigured)
       return
 
     if (!localeData.config?.inputs_config?.enabled && !localeData.config?.outputs_config?.enabled) {
@@ -257,7 +257,7 @@ const ModerationSettingModal: FC<ModerationSettingModalProps> = ({
                 className={`
                   flex items-center px-3 py-2 rounded-lg text-sm text-gray-900 cursor-pointer
                   ${localeData.type === provider.key ? 'bg-white border-[1.5px] border-primary-400 shadow-sm' : 'border border-gray-100 bg-gray-25'}
-                  ${localeData.type === 'openai_moderation' && provider.key === 'openai_moderation' && !openaiProviderConfiged && 'opacity-50'}
+                  ${localeData.type === 'openai_moderation' && provider.key === 'openai_moderation' && !isOpenAIProviderConfigured && 'opacity-50'}
                 `}
                 onClick={() => handleDataTypeChange(provider.key)}
               >
@@ -270,7 +270,7 @@ const ModerationSettingModal: FC<ModerationSettingModalProps> = ({
           }
         </div>
         {
-          !isLoading && !openaiProviderConfiged && localeData.type === 'openai_moderation' && (
+          !isLoading && !isOpenAIProviderConfigured && localeData.type === 'openai_moderation' && (
             <div className='flex items-center mt-2 px-3 py-2 bg-[#FFFAEB] rounded-lg border border-[#FEF0C7]'>
               <InfoCircle className='mr-1 w-4 h-4 text-[#F79009]' />
               <div className='flex items-center text-xs font-medium text-gray-700'>
@@ -364,7 +364,7 @@ const ModerationSettingModal: FC<ModerationSettingModalProps> = ({
         <Button
           variant='primary'
           onClick={handleSave}
-          disabled={localeData.type === 'openai_moderation' && !openaiProviderConfiged}
+          disabled={localeData.type === 'openai_moderation' && !isOpenAIProviderConfigured}
         >
           {t('common.operation.save')}
         </Button>
diff --git a/web/app/components/base/features/feature-panel/opening-statement/index.tsx b/web/app/components/base/features/feature-panel/opening-statement/index.tsx
index 54bf8bd937..b039165c9e 100644
--- a/web/app/components/base/features/feature-panel/opening-statement/index.tsx
+++ b/web/app/components/base/features/feature-panel/opening-statement/index.tsx
@@ -248,7 +248,7 @@ const OpeningStatement: FC<OpeningStatementProps> = ({
             onClick={() => { setTempSuggestedQuestions([...tempSuggestedQuestions, '']) }}
             className='mt-1 flex items-center h-9 px-3 gap-2 rounded-lg cursor-pointer text-gray-400  bg-gray-100 hover:bg-gray-200'>
             <RiAddLine className='w-4 h-4' />
-            <div className='text-gray-500 text-[13px]'>{t('appDebug.variableConig.addOption')}</div>
+            <div className='text-gray-500 text-[13px]'>{t('appDebug.variableConfig.addOption')}</div>
           </div>
         )}
       </div>
@@ -308,7 +308,7 @@ const OpeningStatement: FC<OpeningStatementProps> = ({
         {isShowConfirmAddVar && (
           <ConfirmAddVar
             varNameArr={notIncludeKeys}
-            onConfrim={autoAddVar}
+            onConfirm={autoAddVar}
             onCancel={cancelAutoAddVar}
             onHide={hideConfirmAddVar}
           />
diff --git a/web/app/components/base/file-icon/index.tsx b/web/app/components/base/file-icon/index.tsx
index 874637ca7a..21e48b3dd4 100644
--- a/web/app/components/base/file-icon/index.tsx
+++ b/web/app/components/base/file-icon/index.tsx
@@ -8,7 +8,7 @@ import {
   Md,
   Pdf,
   Txt,
-  Unknow,
+  Unknown,
   Xlsx,
 } from '@/app/components/base/icons/src/public/files'
 import { Notion } from '@/app/components/base/icons/src/public/common'
@@ -47,7 +47,7 @@ const FileIcon: FC<FileIconProps> = ({
     case 'notion':
       return <Notion className={className} />
     default:
-      return <Unknow className={className} />
+      return <Unknown className={className} />
   }
 }
 
diff --git a/web/app/components/base/icons/assets/vender/solid/communication/cute-robote.svg b/web/app/components/base/icons/assets/vender/solid/communication/cute-robote.svg
index 5eb7476085..8fa74ce264 100644
--- a/web/app/components/base/icons/assets/vender/solid/communication/cute-robote.svg
+++ b/web/app/components/base/icons/assets/vender/solid/communication/cute-robote.svg
@@ -1,5 +1,5 @@
 <svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
-<g id="cute-robote">
+<g id="cute-robot">
 <path id="Icon" fill-rule="evenodd" clip-rule="evenodd" d="M12 1C12.5523 1 13 1.44772 13 2V3H17C18.6569 3 20 4.34315 20 6V11C20 11.8885 19.6138 12.6868 19 13.2361V14.5858L20.7071 16.2929C21.0976 16.6834 21.0976 17.3166 20.7071 17.7071C20.3166 18.0976 19.6834 18.0976 19.2929 17.7071L18.681 17.0952C17.7905 19.9377 15.1361 22 12 22C8.8639 22 6.20948 19.9377 5.31897 17.0952L4.70711 17.7071C4.31658 18.0976 3.68342 18.0976 3.29289 17.7071C2.90237 17.3166 2.90237 16.6834 3.29289 16.2929L5 14.5858V13.2361C4.38625 12.6868 4 11.8885 4 11V6C4 4.34315 5.34315 3 7 3H11V2C11 1.44772 11.4477 1 12 1ZM7 5C6.44772 5 6 5.44772 6 6V11C6 11.5523 6.44772 12 7 12H17C17.5523 12 18 11.5523 18 11V6C18 5.44772 17.5523 5 17 5H7ZM9 7C9.55228 7 10 7.44772 10 8V9C10 9.55228 9.55228 10 9 10C8.44772 10 8 9.55228 8 9V8C8 7.44772 8.44772 7 9 7ZM15 7C15.5523 7 16 7.44772 16 8V9C16 9.55228 15.5523 10 15 10C14.4477 10 14 9.55228 14 9V8C14 7.44772 14.4477 7 15 7Z" fill="black"/>
 </g>
 </svg>
diff --git a/web/app/components/base/icons/src/public/files/Unknow.json b/web/app/components/base/icons/src/public/files/Unknown.json
similarity index 99%
rename from web/app/components/base/icons/src/public/files/Unknow.json
rename to web/app/components/base/icons/src/public/files/Unknown.json
index 33067fa96f..c39df990d0 100644
--- a/web/app/components/base/icons/src/public/files/Unknow.json
+++ b/web/app/components/base/icons/src/public/files/Unknown.json
@@ -195,5 +195,5 @@
 			}
 		]
 	},
-	"name": "Unknow"
+	"name": "Unknown"
 }
\ No newline at end of file
diff --git a/web/app/components/base/icons/src/public/files/Unknow.tsx b/web/app/components/base/icons/src/public/files/Unknown.tsx
similarity index 87%
rename from web/app/components/base/icons/src/public/files/Unknow.tsx
rename to web/app/components/base/icons/src/public/files/Unknown.tsx
index ce84d344bf..de909ed65e 100644
--- a/web/app/components/base/icons/src/public/files/Unknow.tsx
+++ b/web/app/components/base/icons/src/public/files/Unknown.tsx
@@ -2,7 +2,7 @@
 // DON NOT EDIT IT MANUALLY
 
 import * as React from 'react'
-import data from './Unknow.json'
+import data from './Unknown.json'
 import IconBase from '@/app/components/base/icons/IconBase'
 import type { IconBaseProps, IconData } from '@/app/components/base/icons/IconBase'
 
@@ -11,6 +11,6 @@ const Icon = React.forwardRef<React.MutableRefObject<SVGElement>, Omit<IconBaseP
   ref,
 ) => <IconBase {...props} ref={ref} data={data as IconData} />)
 
-Icon.displayName = 'Unknow'
+Icon.displayName = 'Unknown'
 
 export default Icon
diff --git a/web/app/components/base/icons/src/public/files/index.ts b/web/app/components/base/icons/src/public/files/index.ts
index 2814c4ae39..f38c28cbdb 100644
--- a/web/app/components/base/icons/src/public/files/index.ts
+++ b/web/app/components/base/icons/src/public/files/index.ts
@@ -6,6 +6,6 @@ export { default as Json } from './Json'
 export { default as Md } from './Md'
 export { default as Pdf } from './Pdf'
 export { default as Txt } from './Txt'
-export { default as Unknow } from './Unknow'
+export { default as Unknown } from './Unknown'
 export { default as Xlsx } from './Xlsx'
 export { default as Yaml } from './Yaml'
diff --git a/web/app/components/base/icons/src/vender/solid/communication/CuteRobote.json b/web/app/components/base/icons/src/vender/solid/communication/CuteRobot.json
similarity index 96%
rename from web/app/components/base/icons/src/vender/solid/communication/CuteRobote.json
rename to web/app/components/base/icons/src/vender/solid/communication/CuteRobot.json
index f8c92c3174..5b36575f56 100644
--- a/web/app/components/base/icons/src/vender/solid/communication/CuteRobote.json
+++ b/web/app/components/base/icons/src/vender/solid/communication/CuteRobot.json
@@ -15,7 +15,7 @@
 				"type": "element",
 				"name": "g",
 				"attributes": {
-					"id": "cute-robote"
+					"id": "cute-robot"
 				},
 				"children": [
 					{
@@ -34,5 +34,5 @@
 			}
 		]
 	},
-	"name": "CuteRobote"
+	"name": "CuteRobot"
 }
\ No newline at end of file
diff --git a/web/app/components/base/icons/src/vender/solid/communication/CuteRobote.tsx b/web/app/components/base/icons/src/vender/solid/communication/CuteRobot.tsx
similarity index 86%
rename from web/app/components/base/icons/src/vender/solid/communication/CuteRobote.tsx
rename to web/app/components/base/icons/src/vender/solid/communication/CuteRobot.tsx
index d416fb5b66..49994048b7 100644
--- a/web/app/components/base/icons/src/vender/solid/communication/CuteRobote.tsx
+++ b/web/app/components/base/icons/src/vender/solid/communication/CuteRobot.tsx
@@ -2,7 +2,7 @@
 // DON NOT EDIT IT MANUALLY
 
 import * as React from 'react'
-import data from './CuteRobote.json'
+import data from './CuteRobot.json'
 import IconBase from '@/app/components/base/icons/IconBase'
 import type { IconBaseProps, IconData } from '@/app/components/base/icons/IconBase'
 
@@ -11,6 +11,6 @@ const Icon = React.forwardRef<React.MutableRefObject<SVGElement>, Omit<IconBaseP
   ref,
 ) => <IconBase {...props} ref={ref} data={data as IconData} />)
 
-Icon.displayName = 'CuteRobote'
+Icon.displayName = 'CuteRobot'
 
 export default Icon
diff --git a/web/app/components/base/icons/src/vender/solid/communication/index.ts b/web/app/components/base/icons/src/vender/solid/communication/index.ts
index 854953c116..673de27463 100644
--- a/web/app/components/base/icons/src/vender/solid/communication/index.ts
+++ b/web/app/components/base/icons/src/vender/solid/communication/index.ts
@@ -1,6 +1,6 @@
 export { default as AiText } from './AiText'
 export { default as ChatBot } from './ChatBot'
-export { default as CuteRobote } from './CuteRobote'
+export { default as CuteRobot } from './CuteRobot'
 export { default as EditList } from './EditList'
 export { default as MessageDotsCircle } from './MessageDotsCircle'
 export { default as MessageFast } from './MessageFast'
diff --git a/web/app/components/base/image-uploader/audio-preview.tsx b/web/app/components/base/image-uploader/audio-preview.tsx
new file mode 100644
index 0000000000..24ede8aa43
--- /dev/null
+++ b/web/app/components/base/image-uploader/audio-preview.tsx
@@ -0,0 +1,38 @@
+import type { FC } from 'react'
+import { createPortal } from 'react-dom'
+import { RiCloseLine } from '@remixicon/react'
+
+type AudioPreviewProps = {
+  url: string
+  title: string
+  onCancel: () => void
+}
+const AudioPreview: FC<AudioPreviewProps> = ({
+  url,
+  title,
+  onCancel,
+}) => {
+  return createPortal(
+    <div className='fixed inset-0 p-8 flex items-center justify-center bg-black/80 z-[1000]' onClick={e => e.stopPropagation()}>
+      <div>
+        <audio controls title={title} autoPlay={false} preload="metadata">
+          <source
+            type="audio/mpeg"
+            src={url}
+            className='max-w-full max-h-full'
+          />
+        </audio>
+      </div>
+      <div
+        className='absolute top-6 right-6 flex items-center justify-center w-8 h-8 bg-white/[0.08] rounded-lg backdrop-blur-[2px] cursor-pointer'
+        onClick={onCancel}
+      >
+        <RiCloseLine className='w-4 h-4 text-gray-500'/>
+      </div>
+    </div>
+    ,
+    document.body,
+  )
+}
+
+export default AudioPreview
diff --git a/web/app/components/base/image-uploader/hooks.ts b/web/app/components/base/image-uploader/hooks.ts
index bb929f4a40..41074000a2 100644
--- a/web/app/components/base/image-uploader/hooks.ts
+++ b/web/app/components/base/image-uploader/hooks.ts
@@ -199,7 +199,7 @@ export const useClipboardUploader = ({ visionConfig, onUpload, files }: useClipb
   const handleClipboardPaste = useCallback((e: ClipboardEvent<HTMLTextAreaElement>) => {
     // reserve native text copy behavior
     const file = e.clipboardData?.files[0]
-    // when copyed file, prevent default action
+    // when copied file, prevent default action
     if (file) {
       e.preventDefault()
       handleLocalFileUpload(file)
diff --git a/web/app/components/base/image-uploader/image-preview.tsx b/web/app/components/base/image-uploader/image-preview.tsx
index 5b33832a14..e5bd4c1bbc 100644
--- a/web/app/components/base/image-uploader/image-preview.tsx
+++ b/web/app/components/base/image-uploader/image-preview.tsx
@@ -1,28 +1,270 @@
 import type { FC } from 'react'
+import React, { useCallback, useEffect, useRef, useState } from 'react'
+import { t } from 'i18next'
 import { createPortal } from 'react-dom'
-import { RiCloseLine } from '@remixicon/react'
+import { RiAddBoxLine, RiCloseLine, RiDownloadCloud2Line, RiFileCopyLine, RiZoomInLine, RiZoomOutLine } from '@remixicon/react'
+import Tooltip from '@/app/components/base/tooltip'
+import Toast from '@/app/components/base/toast'
 
 type ImagePreviewProps = {
   url: string
+  title: string
   onCancel: () => void
 }
+
+const isBase64 = (str: string): boolean => {
+  try {
+    return btoa(atob(str)) === str
+  }
+  catch (err) {
+    return false
+  }
+}
+
 const ImagePreview: FC<ImagePreviewProps> = ({
   url,
+  title,
   onCancel,
 }) => {
+  const [scale, setScale] = useState(1)
+  const [position, setPosition] = useState({ x: 0, y: 0 })
+  const [isDragging, setIsDragging] = useState(false)
+  const imgRef = useRef<HTMLImageElement>(null)
+  const dragStartRef = useRef({ x: 0, y: 0 })
+  const [isCopied, setIsCopied] = useState(false)
+  const containerRef = useRef<HTMLDivElement>(null)
+
+  const openInNewTab = () => {
+    // Open in a new window, considering the case when the page is inside an iframe
+    if (url.startsWith('http') || url.startsWith('https')) {
+      window.open(url, '_blank')
+    }
+    else if (url.startsWith('data:image')) {
+      // Base64 image
+      const win = window.open()
+      win?.document.write(`<img src="${url}" alt="${title}" />`)
+    }
+    else {
+      Toast.notify({
+        type: 'error',
+        message: `Unable to open image: ${url}`,
+      })
+    }
+  }
+  const downloadImage = () => {
+    // Open in a new window, considering the case when the page is inside an iframe
+    if (url.startsWith('http') || url.startsWith('https')) {
+      const a = document.createElement('a')
+      a.href = url
+      a.download = title
+      a.click()
+    }
+    else if (url.startsWith('data:image')) {
+      // Base64 image
+      const a = document.createElement('a')
+      a.href = url
+      a.download = title
+      a.click()
+    }
+    else {
+      Toast.notify({
+        type: 'error',
+        message: `Unable to open image: ${url}`,
+      })
+    }
+  }
+
+  const zoomIn = () => {
+    setScale(prevScale => Math.min(prevScale * 1.2, 15))
+  }
+
+  const zoomOut = () => {
+    setScale((prevScale) => {
+      const newScale = Math.max(prevScale / 1.2, 0.5)
+      if (newScale === 1)
+        setPosition({ x: 0, y: 0 }) // Reset position when fully zoomed out
+
+      return newScale
+    })
+  }
+
+  const imageTobase64ToBlob = (base64: string, type = 'image/png'): Blob => {
+    const byteCharacters = atob(base64)
+    const byteArrays = []
+
+    for (let offset = 0; offset < byteCharacters.length; offset += 512) {
+      const slice = byteCharacters.slice(offset, offset + 512)
+      const byteNumbers = new Array(slice.length)
+      for (let i = 0; i < slice.length; i++)
+        byteNumbers[i] = slice.charCodeAt(i)
+
+      const byteArray = new Uint8Array(byteNumbers)
+      byteArrays.push(byteArray)
+    }
+
+    return new Blob(byteArrays, { type })
+  }
+
+  const imageCopy = useCallback(() => {
+    const shareImage = async () => {
+      try {
+        const base64Data = url.split(',')[1]
+        const blob = imageTobase64ToBlob(base64Data, 'image/png')
+
+        await navigator.clipboard.write([
+          new ClipboardItem({
+            [blob.type]: blob,
+          }),
+        ])
+        setIsCopied(true)
+
+        Toast.notify({
+          type: 'success',
+          message: t('common.operation.imageCopied'),
+        })
+      }
+      catch (err) {
+        console.error('Failed to copy image:', err)
+
+        const link = document.createElement('a')
+        link.href = url
+        link.download = `${title}.png`
+        document.body.appendChild(link)
+        link.click()
+        document.body.removeChild(link)
+
+        Toast.notify({
+          type: 'info',
+          message: t('common.operation.imageDownloaded'),
+        })
+      }
+    }
+    shareImage()
+  }, [title, url])
+
+  const handleWheel = useCallback((e: React.WheelEvent<HTMLDivElement>) => {
+    if (e.deltaY < 0)
+      zoomIn()
+    else
+      zoomOut()
+  }, [])
+
+  const handleMouseDown = useCallback((e: React.MouseEvent<HTMLDivElement>) => {
+    if (scale > 1) {
+      setIsDragging(true)
+      dragStartRef.current = { x: e.clientX - position.x, y: e.clientY - position.y }
+    }
+  }, [scale, position])
+
+  const handleMouseMove = useCallback((e: React.MouseEvent<HTMLDivElement>) => {
+    if (isDragging && scale > 1) {
+      const deltaX = e.clientX - dragStartRef.current.x
+      const deltaY = e.clientY - dragStartRef.current.y
+
+      // Calculate boundaries
+      const imgRect = imgRef.current?.getBoundingClientRect()
+      const containerRect = imgRef.current?.parentElement?.getBoundingClientRect()
+
+      if (imgRect && containerRect) {
+        const maxX = (imgRect.width * scale - containerRect.width) / 2
+        const maxY = (imgRect.height * scale - containerRect.height) / 2
+
+        setPosition({
+          x: Math.max(-maxX, Math.min(maxX, deltaX)),
+          y: Math.max(-maxY, Math.min(maxY, deltaY)),
+        })
+      }
+    }
+  }, [isDragging, scale])
+
+  const handleMouseUp = useCallback(() => {
+    setIsDragging(false)
+  }, [])
+
+  useEffect(() => {
+    document.addEventListener('mouseup', handleMouseUp)
+    return () => {
+      document.removeEventListener('mouseup', handleMouseUp)
+    }
+  }, [handleMouseUp])
+
+  useEffect(() => {
+    const handleKeyDown = (event: KeyboardEvent) => {
+      if (event.key === 'Escape')
+        onCancel()
+    }
+
+    window.addEventListener('keydown', handleKeyDown)
+
+    // Set focus to the container element
+    if (containerRef.current)
+      containerRef.current.focus()
+
+    // Cleanup function
+    return () => {
+      window.removeEventListener('keydown', handleKeyDown)
+    }
+  }, [onCancel])
+
   return createPortal(
-    <div className='fixed inset-0 p-8 flex items-center justify-center bg-black/80 z-[1000]' onClick={e => e.stopPropagation()}>
+    <div className='fixed inset-0 p-8 flex items-center justify-center bg-black/80 z-[1000] image-preview-container'
+      onClick={e => e.stopPropagation()}
+      onWheel={handleWheel}
+      onMouseDown={handleMouseDown}
+      onMouseMove={handleMouseMove}
+      onMouseUp={handleMouseUp}
+      style={{ cursor: scale > 1 ? 'move' : 'default' }}
+      tabIndex={-1}>
+      {/* eslint-disable-next-line @next/next/no-img-element */}
       <img
-        alt='preview image'
-        src={url}
+        ref={imgRef}
+        alt={title}
+        src={isBase64(url) ? `data:image/png;base64,${url}` : url}
         className='max-w-full max-h-full'
+        style={{
+          transform: `scale(${scale}) translate(${position.x}px, ${position.y}px)`,
+          transition: isDragging ? 'none' : 'transform 0.2s ease-in-out',
+        }}
       />
-      <div
-        className='absolute top-6 right-6 flex items-center justify-center w-8 h-8 bg-white/8 rounded-lg backdrop-blur-[2px] cursor-pointer'
-        onClick={onCancel}
-      >
-        <RiCloseLine className='w-4 h-4 text-white' />
-      </div>
+      <Tooltip popupContent={t('common.operation.copyImage')}>
+        <div className='absolute top-6 right-48 flex items-center justify-center w-8 h-8 rounded-lg cursor-pointer'
+          onClick={imageCopy}>
+          {isCopied
+            ? <RiFileCopyLine className='w-4 h-4 text-green-500'/>
+            : <RiFileCopyLine className='w-4 h-4 text-gray-500'/>}
+        </div>
+      </Tooltip>
+      <Tooltip popupContent={t('common.operation.zoomOut')}>
+        <div className='absolute top-6 right-40 flex items-center justify-center w-8 h-8 rounded-lg cursor-pointer'
+          onClick={zoomOut}>
+          <RiZoomOutLine className='w-4 h-4 text-gray-500'/>
+        </div>
+      </Tooltip>
+      <Tooltip popupContent={t('common.operation.zoomIn')}>
+        <div className='absolute top-6 right-32 flex items-center justify-center w-8 h-8 rounded-lg cursor-pointer'
+          onClick={zoomIn}>
+          <RiZoomInLine className='w-4 h-4 text-gray-500'/>
+        </div>
+      </Tooltip>
+      <Tooltip popupContent={t('common.operation.download')}>
+        <div className='absolute top-6 right-24 flex items-center justify-center w-8 h-8 rounded-lg cursor-pointer'
+          onClick={downloadImage}>
+          <RiDownloadCloud2Line className='w-4 h-4 text-gray-500'/>
+        </div>
+      </Tooltip>
+      <Tooltip popupContent={t('common.operation.openInNewTab')}>
+        <div className='absolute top-6 right-16 flex items-center justify-center w-8 h-8 rounded-lg cursor-pointer'
+          onClick={openInNewTab}>
+          <RiAddBoxLine className='w-4 h-4 text-gray-500'/>
+        </div>
+      </Tooltip>
+      <Tooltip popupContent={t('common.operation.close')}>
+        <div
+          className='absolute top-6 right-6 flex items-center justify-center w-8 h-8 bg-white/8 rounded-lg backdrop-blur-[2px] cursor-pointer'
+          onClick={onCancel}>
+          <RiCloseLine className='w-4 h-4 text-gray-500'/>
+        </div>
+      </Tooltip>
     </div>,
     document.body,
   )
diff --git a/web/app/components/base/image-uploader/video-preview.tsx b/web/app/components/base/image-uploader/video-preview.tsx
new file mode 100644
index 0000000000..15291412eb
--- /dev/null
+++ b/web/app/components/base/image-uploader/video-preview.tsx
@@ -0,0 +1,38 @@
+import type { FC } from 'react'
+import { createPortal } from 'react-dom'
+import { RiCloseLine } from '@remixicon/react'
+
+type VideoPreviewProps = {
+  url: string
+  title: string
+  onCancel: () => void
+}
+const VideoPreview: FC<VideoPreviewProps> = ({
+  url,
+  title,
+  onCancel,
+}) => {
+  return createPortal(
+    <div className='fixed inset-0 p-8 flex items-center justify-center bg-black/80 z-[1000]' onClick={e => e.stopPropagation()}>
+      <div>
+        <video controls title={title} autoPlay={false} preload="metadata">
+          <source
+            type="video/mp4"
+            src={url}
+            className='max-w-full max-h-full'
+          />
+        </video>
+      </div>
+      <div
+        className='absolute top-6 right-6 flex items-center justify-center w-8 h-8 bg-white/[0.08] rounded-lg backdrop-blur-[2px] cursor-pointer'
+        onClick={onCancel}
+      >
+        <RiCloseLine className='w-4 h-4 text-gray-500'/>
+      </div>
+    </div>
+    ,
+    document.body,
+  )
+}
+
+export default VideoPreview
diff --git a/web/app/components/base/logo/logo-embedded-chat-avatar.tsx b/web/app/components/base/logo/logo-embedded-chat-avatar.tsx
new file mode 100644
index 0000000000..7fd94827eb
--- /dev/null
+++ b/web/app/components/base/logo/logo-embedded-chat-avatar.tsx
@@ -0,0 +1,18 @@
+import type { FC } from 'react'
+
+type LogoEmbeddedChatAvatarProps = {
+  className?: string
+}
+const LogoEmbeddedChatAvatar: FC<LogoEmbeddedChatAvatarProps> = ({
+  className,
+}) => {
+  return (
+    <img
+      src='/logo/logo-embedded-chat-avatar.png'
+      className={`block w-10 h-10 ${className}`}
+      alt='logo'
+    />
+  )
+}
+
+export default LogoEmbeddedChatAvatar
diff --git a/web/app/components/base/logo/logo-embedded-chat-header.tsx b/web/app/components/base/logo/logo-embedded-chat-header.tsx
new file mode 100644
index 0000000000..976ce0c77a
--- /dev/null
+++ b/web/app/components/base/logo/logo-embedded-chat-header.tsx
@@ -0,0 +1,19 @@
+import type { FC } from 'react'
+
+type LogoEmbeddedChatHeaderProps = {
+  className?: string
+}
+
+const LogoEmbeddedChatHeader: FC<LogoEmbeddedChatHeaderProps> = ({
+  className,
+}) => {
+  return (
+    <img
+      src='/logo/logo-embedded-chat-header.png'
+      className={`block w-auto h-6 ${className}`}
+      alt='logo'
+    />
+  )
+}
+
+export default LogoEmbeddedChatHeader
diff --git a/web/app/components/base/logo/logo-embeded-chat-avatar.tsx b/web/app/components/base/logo/logo-embeded-chat-avatar.tsx
deleted file mode 100644
index c5880e7bf6..0000000000
--- a/web/app/components/base/logo/logo-embeded-chat-avatar.tsx
+++ /dev/null
@@ -1,18 +0,0 @@
-import type { FC } from 'react'
-
-type LogoEmbededChatAvatarProps = {
-  className?: string
-}
-const LogoEmbededChatAvatar: FC<LogoEmbededChatAvatarProps> = ({
-  className,
-}) => {
-  return (
-    <img
-      src='/logo/logo-embeded-chat-avatar.png'
-      className={`block w-10 h-10 ${className}`}
-      alt='logo'
-    />
-  )
-}
-
-export default LogoEmbededChatAvatar
diff --git a/web/app/components/base/logo/logo-embeded-chat-header.tsx b/web/app/components/base/logo/logo-embeded-chat-header.tsx
deleted file mode 100644
index f979d501c2..0000000000
--- a/web/app/components/base/logo/logo-embeded-chat-header.tsx
+++ /dev/null
@@ -1,18 +0,0 @@
-import type { FC } from 'react'
-
-type LogoEmbededChatHeaderProps = {
-  className?: string
-}
-const LogoEmbededChatHeader: FC<LogoEmbededChatHeaderProps> = ({
-  className,
-}) => {
-  return (
-    <img
-      src='/logo/logo-embeded-chat-header.png'
-      className={`block w-auto h-6 ${className}`}
-      alt='logo'
-    />
-  )
-}
-
-export default LogoEmbededChatHeader
diff --git a/web/app/components/base/markdown.tsx b/web/app/components/base/markdown.tsx
index 6e8ae6c9e6..443ee3410c 100644
--- a/web/app/components/base/markdown.tsx
+++ b/web/app/components/base/markdown.tsx
@@ -5,6 +5,7 @@ import RemarkMath from 'remark-math'
 import RemarkBreaks from 'remark-breaks'
 import RehypeKatex from 'rehype-katex'
 import RemarkGfm from 'remark-gfm'
+import RehypeRaw from 'rehype-raw'
 import SyntaxHighlighter from 'react-syntax-highlighter'
 import { atelierHeathLight } from 'react-syntax-highlighter/dist/esm/styles/hljs'
 import type { RefObject } from 'react'
@@ -15,6 +16,10 @@ import CopyBtn from '@/app/components/base/copy-btn'
 import SVGBtn from '@/app/components/base/svg'
 import Flowchart from '@/app/components/base/mermaid'
 import ImageGallery from '@/app/components/base/image-gallery'
+import { useChatContext } from '@/app/components/base/chat/chat/context'
+import VideoGallery from '@/app/components/base/video-gallery'
+import AudioGallery from '@/app/components/base/audio-gallery'
+import SVGRenderer from '@/app/components/base/svg-gallery'
 
 // Available language https://github.com/react-syntax-highlighter/react-syntax-highlighter/blob/master/AVAILABLE_LANGUAGES_HLJS.MD
 const capitalizationLanguageNameMap: Record<string, string> = {
@@ -33,6 +38,11 @@ const capitalizationLanguageNameMap: Record<string, string> = {
   markdown: 'MarkDown',
   makefile: 'MakeFile',
   echarts: 'ECharts',
+  shell: 'Shell',
+  powershell: 'PowerShell',
+  json: 'JSON',
+  latex: 'Latex',
+  svg: 'SVG',
 }
 const getCorrectCapitalizationLanguageName = (language: string) => {
   if (!language)
@@ -65,6 +75,7 @@ export function PreCode(props: { children: any }) {
   )
 }
 
+// eslint-disable-next-line unused-imports/no-unused-vars
 const useLazyLoad = (ref: RefObject<Element>): boolean => {
   const [isIntersecting, setIntersecting] = useState<boolean>(false)
 
@@ -99,6 +110,7 @@ const useLazyLoad = (ref: RefObject<Element>): boolean => {
 // Error: Minified React error 185;
 // visit https://reactjs.org/docs/error-decoder.html?invariant=185 for the full message
 // or use the non-minified dev environment for full errors and additional helpful warnings.
+
 const CodeBlock: CodeComponent = memo(({ inline, className, children, ...props }) => {
   const [isSVG, setIsSVG] = useState(true)
   const match = /language-(\w+)/.exec(className || '')
@@ -126,12 +138,7 @@ const CodeBlock: CodeComponent = memo(({ inline, className, children, ...props }
           >
             <div className='text-[13px] text-gray-500 font-normal'>{languageShowName}</div>
             <div style={{ display: 'flex' }}>
-              {language === 'mermaid'
-                && <SVGBtn
-                  isSVG={isSVG}
-                  setIsSVG={setIsSVG}
-                />
-              }
+              {language === 'mermaid' && <SVGBtn isSVG={isSVG} setIsSVG={setIsSVG}/>}
               <CopyBtn
                 className='mr-1'
                 value={String(children).replace(/\n$/, '')}
@@ -141,12 +148,10 @@ const CodeBlock: CodeComponent = memo(({ inline, className, children, ...props }
           </div>
           {(language === 'mermaid' && isSVG)
             ? (<Flowchart PrimitiveCode={String(children).replace(/\n$/, '')} />)
-            : (
-              (language === 'echarts')
-                ? (<div style={{ minHeight: '250px', minWidth: '250px' }}><ErrorBoundary><ReactEcharts
-                  option={chartData}
-                >
-                </ReactEcharts></ErrorBoundary></div>)
+            : (language === 'echarts'
+              ? (<div style={{ minHeight: '350px', minWidth: '700px' }}><ErrorBoundary><ReactEcharts option={chartData} /></ErrorBoundary></div>)
+              : (language === 'svg'
+                ? (<ErrorBoundary><SVGRenderer content={String(children).replace(/\n$/, '')} /></ErrorBoundary>)
                 : (<SyntaxHighlighter
                   {...props}
                   style={atelierHeathLight}
@@ -159,49 +164,92 @@ const CodeBlock: CodeComponent = memo(({ inline, className, children, ...props }
                   PreTag="div"
                 >
                   {String(children).replace(/\n$/, '')}
-                </SyntaxHighlighter>))}
+                </SyntaxHighlighter>)))}
         </div>
       )
-      : (
-        <code {...props} className={className}>
-          {children}
-        </code>
-      )
+      : (<code {...props} className={className}>{children}</code>)
   }, [chartData, children, className, inline, isSVG, language, languageShowName, match, props])
 })
-
 CodeBlock.displayName = 'CodeBlock'
 
+const VideoBlock: CodeComponent = memo(({ node }) => {
+  const srcs = node.children.filter(child => 'properties' in child).map(child => (child as any).properties.src)
+  if (srcs.length === 0)
+    return null
+  return <VideoGallery key={srcs.join()} srcs={srcs} />
+})
+VideoBlock.displayName = 'VideoBlock'
+
+const AudioBlock: CodeComponent = memo(({ node }) => {
+  const srcs = node.children.filter(child => 'properties' in child).map(child => (child as any).properties.src)
+  if (srcs.length === 0)
+    return null
+  return <AudioGallery key={srcs.join()} srcs={srcs} />
+})
+AudioBlock.displayName = 'AudioBlock'
+
+const Paragraph = (paragraph: any) => {
+  const { node }: any = paragraph
+  const children_node = node.children
+  if (children_node && children_node[0] && 'tagName' in children_node[0] && children_node[0].tagName === 'img') {
+    return (
+      <>
+        <ImageGallery srcs={[children_node[0].properties.src]} />
+        <p>{paragraph.children.slice(1)}</p>
+      </>
+    )
+  }
+  return <p>{paragraph.children}</p>
+}
+
+const Img = ({ src }: any) => {
+  return (<ImageGallery srcs={[src]} />)
+}
+
+const Link = ({ node, ...props }: any) => {
+  if (node.properties?.href && node.properties.href?.toString().startsWith('abbr')) {
+    // eslint-disable-next-line react-hooks/rules-of-hooks
+    const { onSend } = useChatContext()
+    const hidden_text = decodeURIComponent(node.properties.href.toString().split('abbr:')[1])
+
+    return <abbr className="underline decoration-dashed !decoration-primary-700 cursor-pointer" onClick={() => onSend?.(hidden_text)} title={node.children[0]?.value}>{node.children[0]?.value}</abbr>
+  }
+  else {
+    return <a {...props} target="_blank" className="underline decoration-dashed !decoration-primary-700 cursor-pointer">{node.children[0] ? node.children[0]?.value : 'Download'}</a>
+  }
+}
+
 export function Markdown(props: { content: string; className?: string }) {
   const latexContent = preprocessLaTeX(props.content)
   return (
     <div className={cn(props.className, 'markdown-body')}>
       <ReactMarkdown
-        remarkPlugins={[[RemarkMath, { singleDollarTextMath: false }], RemarkGfm, RemarkBreaks]}
+        remarkPlugins={[[RemarkGfm, RemarkMath, { singleDollarTextMath: false }], RemarkBreaks]}
         rehypePlugins={[
-          RehypeKatex as any,
+          RehypeKatex,
+          RehypeRaw as any,
+          // The Rehype plug-in is used to remove the ref attribute of an element
+          () => {
+            return (tree) => {
+              const iterate = (node: any) => {
+                if (node.type === 'element' && !node.properties?.src && node.properties?.ref && node.properties.ref.startsWith('{') && node.properties.ref.endsWith('}'))
+                  delete node.properties.ref
+
+                if (node.children)
+                  node.children.forEach(iterate)
+              }
+              tree.children.forEach(iterate)
+            }
+          },
         ]}
+        disallowedElements={['script', 'iframe', 'head', 'html', 'meta', 'link', 'style', 'body']}
         components={{
           code: CodeBlock,
-          img({ src }) {
-            return (
-              <ImageGallery srcs={[src || '']} />
-            )
-          },
-          p: (paragraph) => {
-            const { node }: any = paragraph
-            if (node.children[0].tagName === 'img') {
-              const image = node.children[0]
-
-              return (
-                <>
-                  <ImageGallery srcs={[image.properties.src]} />
-                  <p>{paragraph.children.slice(1)}</p>
-                </>
-              )
-            }
-            return <p>{paragraph.children}</p>
-          },
+          img: Img,
+          video: VideoBlock,
+          audio: AudioBlock,
+          a: Link,
+          p: Paragraph,
         }}
         linkTarget='_blank'
       >
@@ -217,19 +265,23 @@ export function Markdown(props: { content: string; className?: string }) {
 // This can happen when a component attempts to access an undefined object that references an unregistered map, causing the program to crash.
 
 export default class ErrorBoundary extends Component {
-  constructor(props) {
+  constructor(props: any) {
     super(props)
     this.state = { hasError: false }
   }
 
-  componentDidCatch(error, errorInfo) {
+  componentDidCatch(error: any, errorInfo: any) {
     this.setState({ hasError: true })
     console.error(error, errorInfo)
   }
 
   render() {
+    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
+    // @ts-expect-error
     if (this.state.hasError)
-      return <div>Oops! ECharts reported a runtime error. <br />(see the browser console for more information)</div>
+      return <div>Oops! An error occurred. This could be due to an ECharts runtime error or invalid SVG content. <br />(see the browser console for more information)</div>
+    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
+    // @ts-expect-error
     return this.props.children
   }
 }
diff --git a/web/app/components/base/notion-page-selector/base.tsx b/web/app/components/base/notion-page-selector/base.tsx
index 63aff09a93..e3b321b120 100644
--- a/web/app/components/base/notion-page-selector/base.tsx
+++ b/web/app/components/base/notion-page-selector/base.tsx
@@ -72,7 +72,7 @@ const NotionPageSelector = ({
   const handleSelectWorkspace = useCallback((workspaceId: string) => {
     setCurrentWorkspaceId(workspaceId)
   }, [])
-  const handleSelecPages = (newSelectedPagesId: Set<string>) => {
+  const handleSelectPages = (newSelectedPagesId: Set<string>) => {
     const selectedPages = Array.from(newSelectedPagesId).map(pageId => getPagesMapAndSelectedPagesId[0][pageId])
 
     setSelectedPagesId(new Set(Array.from(newSelectedPagesId)))
@@ -117,7 +117,7 @@ const NotionPageSelector = ({
                   searchValue={searchValue}
                   list={currentWorkspace?.pages || []}
                   pagesMap={getPagesMapAndSelectedPagesId[0]}
-                  onSelect={handleSelecPages}
+                  onSelect={handleSelectPages}
                   canPreview={canPreview}
                   previewPageId={previewPageId}
                   onPreview={handlePreviewPage}
diff --git a/web/app/components/base/notion-page-selector/page-selector/index.tsx b/web/app/components/base/notion-page-selector/page-selector/index.tsx
index b61fa34567..8f398790e7 100644
--- a/web/app/components/base/notion-page-selector/page-selector/index.tsx
+++ b/web/app/components/base/notion-page-selector/page-selector/index.tsx
@@ -22,13 +22,13 @@ type PageSelectorProps = {
 type NotionPageTreeItem = {
   children: Set<string>
   descendants: Set<string>
-  deepth: number
+  depth: number
   ancestors: string[]
 } & DataSourceNotionPage
 type NotionPageTreeMap = Record<string, NotionPageTreeItem>
 type NotionPageItem = {
   expand: boolean
-  deepth: number
+  depth: number
 } & DataSourceNotionPage
 
 const recursivePushInParentDescendants = (
@@ -51,7 +51,7 @@ const recursivePushInParentDescendants = (
         ...pagesMap[parentId],
         children,
         descendants,
-        deepth: 0,
+        depth: 0,
         ancestors: [],
       }
     }
@@ -60,7 +60,7 @@ const recursivePushInParentDescendants = (
       listTreeMap[parentId].descendants.add(pageId)
       listTreeMap[parentId].descendants.add(leafItem.page_id)
     }
-    leafItem.deepth++
+    leafItem.depth++
     leafItem.ancestors.unshift(listTreeMap[parentId].page_name)
 
     if (listTreeMap[parentId].parent_id !== 'root')
@@ -95,7 +95,7 @@ const ItemComponent = ({ index, style, data }: ListChildComponentProps<{
       return (
         <div
           className={cn(s.arrow, current.expand && s['arrow-expand'], 'shrink-0 mr-1 w-5 h-5 hover:bg-gray-200 rounded-md')}
-          style={{ marginLeft: current.deepth * 8 }}
+          style={{ marginLeft: current.depth * 8 }}
           onClick={() => handleToggle(index)}
         />
       )
@@ -106,7 +106,7 @@ const ItemComponent = ({ index, style, data }: ListChildComponentProps<{
       )
     }
     return (
-      <div className='shrink-0 mr-1 w-5 h-5' style={{ marginLeft: current.deepth * 8 }} />
+      <div className='shrink-0 mr-1 w-5 h-5' style={{ marginLeft: current.depth * 8 }} />
     )
   }
 
@@ -185,7 +185,7 @@ const PageSelector = ({
       return {
         ...item,
         expand: false,
-        deepth: 0,
+        depth: 0,
       }
     }))
   }
@@ -195,7 +195,7 @@ const PageSelector = ({
     return {
       ...item,
       expand: false,
-      deepth: 0,
+      depth: 0,
     }
   })
   const currentDataList = searchValue ? searchDataList : dataList
@@ -205,7 +205,7 @@ const PageSelector = ({
     return list.reduce((prev: NotionPageTreeMap, next: DataSourceNotionPage) => {
       const pageId = next.page_id
       if (!prev[pageId])
-        prev[pageId] = { ...next, children: new Set(), descendants: new Set(), deepth: 0, ancestors: [] }
+        prev[pageId] = { ...next, children: new Set(), descendants: new Set(), depth: 0, ancestors: [] }
 
       recursivePushInParentDescendants(pagesMap, prev, prev[pageId], prev[pageId])
       return prev
@@ -233,7 +233,7 @@ const PageSelector = ({
         ...childrenIds.map(item => ({
           ...pagesMap[item],
           expand: false,
-          deepth: listMapWithChildrenAndDescendants[item].deepth,
+          depth: listMapWithChildrenAndDescendants[item].depth,
         })),
         ...dataList.slice(index + 1)]
     }
diff --git a/web/app/components/base/pagination/index.tsx b/web/app/components/base/pagination/index.tsx
index 98b1e266ae..f8c5684b55 100644
--- a/web/app/components/base/pagination/index.tsx
+++ b/web/app/components/base/pagination/index.tsx
@@ -23,8 +23,8 @@ const CustomizedPagination: FC<Props> = ({ current, onChange, total, limit = 10
       middlePagesSiblingCount={1}
       setCurrentPage={onChange}
       totalPages={totalPages}
-      truncableClassName="w-8 px-0.5 text-center"
-      truncableText="..."
+      truncatableClassName="w-8 px-0.5 text-center"
+      truncatableText="..."
     >
       <Pagination.PrevButton
         disabled={current === 0}
diff --git a/web/app/components/base/prompt-editor/hooks.ts b/web/app/components/base/prompt-editor/hooks.ts
index 03074b9b17..c9e4cc129c 100644
--- a/web/app/components/base/prompt-editor/hooks.ts
+++ b/web/app/components/base/prompt-editor/hooks.ts
@@ -35,8 +35,8 @@ import { DELETE_QUERY_BLOCK_COMMAND } from './plugins/query-block'
 import type { CustomTextNode } from './plugins/custom-text/node'
 import { registerLexicalTextEntity } from './utils'
 
-export type UseSelectOrDeleteHanlder = (nodeKey: string, command?: LexicalCommand<undefined>) => [RefObject<HTMLDivElement>, boolean]
-export const useSelectOrDelete: UseSelectOrDeleteHanlder = (nodeKey: string, command?: LexicalCommand<undefined>) => {
+export type UseSelectOrDeleteHandler = (nodeKey: string, command?: LexicalCommand<undefined>) => [RefObject<HTMLDivElement>, boolean]
+export const useSelectOrDelete: UseSelectOrDeleteHandler = (nodeKey: string, command?: LexicalCommand<undefined>) => {
   const ref = useRef<HTMLDivElement>(null)
   const [editor] = useLexicalComposerContext()
   const [isSelected, setSelected, clearSelection] = useLexicalNodeSelection(nodeKey)
diff --git a/web/app/components/base/search-input/index.tsx b/web/app/components/base/search-input/index.tsx
index a85bc2db8a..4b3821da5a 100644
--- a/web/app/components/base/search-input/index.tsx
+++ b/web/app/components/base/search-input/index.tsx
@@ -25,9 +25,9 @@ const SearchInput: FC<SearchInputProps> = ({
   return (
     <div className={cn(
       'group flex items-center px-2 h-8 rounded-lg bg-gray-200 hover:bg-gray-300 border border-transparent overflow-hidden',
-      focus && '!bg-white hover:bg-white shawdow-xs !border-gray-300',
-      !focus && value && 'hover:!bg-gray-200 hover:!shawdow-xs hover:!border-black/5',
-      white && '!bg-white hover:!bg-white shawdow-xs !border-gray-300 hover:!border-gray-300',
+      focus && '!bg-white hover:bg-white shadow-xs !border-gray-300',
+      !focus && value && 'hover:!bg-gray-200 hover:!shadow-xs hover:!border-black/5',
+      white && '!bg-white hover:!bg-white shadow-xs !border-gray-300 hover:!border-gray-300',
       className,
     )}>
       <div className="pointer-events-none shrink-0 flex items-center mr-1.5 justify-center w-4 h-4">
diff --git a/web/app/components/base/select/locale.tsx b/web/app/components/base/select/locale.tsx
index 3066364316..8b89c66950 100644
--- a/web/app/components/base/select/locale.tsx
+++ b/web/app/components/base/select/locale.tsx
@@ -77,7 +77,7 @@ export function InputSelect({
     <div className="w-full">
       <Menu as="div" className="w-full">
         <div>
-          <Menu.Button className="iappearance-none block w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm placeholder-gray-400 sm:text-sm h-[38px] text-left">
+          <Menu.Button className="appearance-none block w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm placeholder-gray-400 sm:text-sm h-[38px] text-left">
             {item?.name}
           </Menu.Button>
         </div>
diff --git a/web/app/components/base/slider/index.tsx b/web/app/components/base/slider/index.tsx
index 18ef3a7a29..2b8f181633 100644
--- a/web/app/components/base/slider/index.tsx
+++ b/web/app/components/base/slider/index.tsx
@@ -32,7 +32,7 @@ const Slider: React.FC<ISliderProps> = ({
     max={max || 100}
     step={step || 1}
     className={cn('relative slider', className)}
-    thumbClassName={cn('absolute top-[-9px] w-2 h-5 border-[0.5px] border-components-silder-knob-border rounded-[3px] bg-components-silder-knob shadow-sm  focus:outline-none', !disabled && 'cursor-pointer', thumbClassName)}
+    thumbClassName={cn('absolute top-[-9px] w-2 h-5 border-[0.5px] border-components-slider-knob-border rounded-[3px] bg-components-slider-knob shadow-sm  focus:outline-none', !disabled && 'cursor-pointer', thumbClassName)}
     trackClassName={cn('h-0.5 rounded-full slider-track', trackClassName)}
     onChange={onChange}
   />
diff --git a/web/app/components/base/slider/style.css b/web/app/components/base/slider/style.css
index 6b4394ed93..e215a9914e 100644
--- a/web/app/components/base/slider/style.css
+++ b/web/app/components/base/slider/style.css
@@ -3,9 +3,9 @@
 }
 
 .slider-track {
-    background-color: var(--color-components-silder-range);
+    background-color: var(--color-components-slider-range);
 }
 
 .slider-track-1 {
-    background-color: var(--color-components-silder-track);
+    background-color: var(--color-components-slider-track);
 }
\ No newline at end of file
diff --git a/web/app/components/base/svg-gallery/index.tsx b/web/app/components/base/svg-gallery/index.tsx
new file mode 100644
index 0000000000..81e8e87655
--- /dev/null
+++ b/web/app/components/base/svg-gallery/index.tsx
@@ -0,0 +1,79 @@
+import { useEffect, useRef, useState } from 'react'
+import { SVG } from '@svgdotjs/svg.js'
+import ImagePreview from '@/app/components/base/image-uploader/image-preview'
+
+export const SVGRenderer = ({ content }: { content: string }) => {
+  const svgRef = useRef<HTMLDivElement>(null)
+  const [imagePreview, setImagePreview] = useState('')
+  const [windowSize, setWindowSize] = useState({
+    width: typeof window !== 'undefined' ? window.innerWidth : 0,
+    height: typeof window !== 'undefined' ? window.innerHeight : 0,
+  })
+
+  const svgToDataURL = (svgElement: Element): string => {
+    const svgString = new XMLSerializer().serializeToString(svgElement)
+    const base64String = Buffer.from(svgString).toString('base64')
+    return `data:image/svg+xml;base64,${base64String}`
+  }
+
+  useEffect(() => {
+    const handleResize = () => {
+      setWindowSize({ width: window.innerWidth, height: window.innerHeight })
+    }
+
+    window.addEventListener('resize', handleResize)
+    return () => window.removeEventListener('resize', handleResize)
+  }, [])
+
+  useEffect(() => {
+    if (svgRef.current) {
+      try {
+        svgRef.current.innerHTML = ''
+        const draw = SVG().addTo(svgRef.current).size('100%', '100%')
+
+        const parser = new DOMParser()
+        const svgDoc = parser.parseFromString(content, 'image/svg+xml')
+        const svgElement = svgDoc.documentElement
+
+        if (!(svgElement instanceof SVGElement))
+          throw new Error('Invalid SVG content')
+
+        const originalWidth = parseInt(svgElement.getAttribute('width') || '400', 10)
+        const originalHeight = parseInt(svgElement.getAttribute('height') || '600', 10)
+        const scale = Math.min(windowSize.width / originalWidth, windowSize.height / originalHeight, 1)
+        const scaledWidth = originalWidth * scale
+        const scaledHeight = originalHeight * scale
+        draw.size(scaledWidth, scaledHeight)
+
+        const rootElement = draw.svg(content)
+        rootElement.scale(scale)
+
+        rootElement.click(() => {
+          setImagePreview(svgToDataURL(svgElement as Element))
+        })
+      }
+      catch (error) {
+        if (svgRef.current)
+          svgRef.current.innerHTML = 'Error rendering SVG. Wait for the image content to complete.'
+      }
+    }
+  }, [content, windowSize])
+
+  return (
+    <>
+      <div ref={svgRef} style={{
+        width: '100%',
+        height: '100%',
+        minHeight: '300px',
+        maxHeight: '80vh',
+        display: 'flex',
+        justifyContent: 'center',
+        alignItems: 'center',
+        cursor: 'pointer',
+      }} />
+      {imagePreview && (<ImagePreview url={imagePreview} title='Preview' onCancel={() => setImagePreview('')} />)}
+    </>
+  )
+}
+
+export default SVGRenderer
diff --git a/web/app/components/base/tag-input/index.tsx b/web/app/components/base/tag-input/index.tsx
index 7eab355a0d..404fd89f38 100644
--- a/web/app/components/base/tag-input/index.tsx
+++ b/web/app/components/base/tag-input/index.tsx
@@ -46,16 +46,16 @@ const TagInput: FC<TagInputProps> = ({
       if (isSpecialMode)
         e.preventDefault()
 
-      const valueTrimed = value.trim()
-      if (!valueTrimed || (items.find(item => item === valueTrimed)))
+      const valueTrimmed = value.trim()
+      if (!valueTrimmed || (items.find(item => item === valueTrimmed)))
         return
 
-      if (valueTrimed.length > 20) {
+      if (valueTrimmed.length > 20) {
         notify({ type: 'error', message: t('datasetDocuments.segment.keywordError') })
         return
       }
 
-      onChange([...items, valueTrimed])
+      onChange([...items, valueTrimmed])
       setTimeout(() => {
         setValue('')
       })
diff --git a/web/app/components/base/text-generation/types.ts b/web/app/components/base/text-generation/types.ts
index 82a4177592..1e2c04f6e8 100644
--- a/web/app/components/base/text-generation/types.ts
+++ b/web/app/components/base/text-generation/types.ts
@@ -15,7 +15,7 @@ export type UserInputForm = {
 }
 
 export type UserInputFormTextInput = {
-  'text-inpput': UserInputForm & {
+  'text-input': UserInputForm & {
     max_length: number
   }
 }
diff --git a/web/app/components/base/video-gallery/VideoPlayer.module.css b/web/app/components/base/video-gallery/VideoPlayer.module.css
new file mode 100644
index 0000000000..04c4a367d6
--- /dev/null
+++ b/web/app/components/base/video-gallery/VideoPlayer.module.css
@@ -0,0 +1,188 @@
+.videoPlayer {
+  position: relative;
+  width: 100%;
+  max-width: 800px;
+  margin: 0 auto;
+  border-radius: 8px;
+  overflow: hidden;
+}
+
+.video {
+  width: 100%;
+  display: block;
+}
+
+.controls {
+  position: absolute;
+  bottom: 0;
+  left: 0;
+  right: 0;
+  width: 100%;
+  height: 100%;
+  display: flex;
+  flex-direction: column;
+  justify-content: flex-end;
+  transition: opacity 0.3s ease;
+}
+
+.controls.hidden {
+  opacity: 0;
+}
+
+.controls.visible {
+  opacity: 1;
+}
+
+.overlay {
+  background: linear-gradient(to top, rgba(0, 0, 0, 0.7) 0%, transparent 100%);
+  padding: 20px;
+  display: flex;
+  flex-direction: column;
+}
+
+.progressBarContainer {
+  width: 100%;
+  margin-bottom: 10px;
+}
+
+.controlsContent {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+}
+
+.leftControls, .rightControls {
+  display: flex;
+  align-items: center;
+}
+
+.playPauseButton, .muteButton, .fullscreenButton {
+  background: none;
+  border: none;
+  color: white;
+  cursor: pointer;
+  padding: 4px;
+  margin-right: 10px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
+
+.playPauseButton:hover, .muteButton:hover, .fullscreenButton:hover {
+  background-color: rgba(255, 255, 255, 0.1);
+  border-radius: 50%;
+}
+
+.time {
+  color: white;
+  font-size: 14px;
+  margin-left: 8px;
+}
+
+.volumeControl {
+  display: flex;
+  align-items: center;
+  margin-right: 16px;
+}
+
+.volumeSlider {
+  width: 60px;
+  height: 4px;
+  background: rgba(255, 255, 255, 0.3);
+  border-radius: 2px;
+  cursor: pointer;
+  margin-left: 12px;
+  position: relative;
+}
+
+.volumeLevel {
+  position: absolute;
+  top: 0;
+  left: 0;
+  height: 100%;
+  background: #ffffff;
+  border-radius: 2px;
+}
+
+.progressBar {
+  position: relative;
+  width: 100%;
+  height: 4px;
+  background: rgba(255, 255, 255, 0.3);
+  cursor: pointer;
+  border-radius: 2px;
+  overflow: visible;
+  transition: height 0.2s ease;
+}
+
+.progressBar:hover {
+  height: 6px;
+}
+
+.progress {
+  height: 100%;
+  background: #ffffff;
+  transition: width 0.1s ease-in-out;
+}
+
+.hoverTimeIndicator {
+  position: absolute;
+  bottom: 100%;
+  transform: translateX(-50%);
+  background-color: rgba(0, 0, 0, 0.7);
+  color: white;
+  padding: 4px 8px;
+  border-radius: 4px;
+  font-size: 12px;
+  pointer-events: none;
+  white-space: nowrap;
+  margin-bottom: 8px;
+}
+
+.hoverTimeIndicator::after {
+  content: '';
+  position: absolute;
+  top: 100%;
+  left: 50%;
+  margin-left: -4px;
+  border-width: 4px;
+  border-style: solid;
+  border-color: rgba(0, 0, 0, 0.7) transparent transparent transparent;
+}
+
+.controls.smallSize .controlsContent {
+  justify-content: space-between;
+}
+
+.controls.smallSize .leftControls,
+.controls.smallSize .rightControls {
+  flex: 0 0 auto;
+  display: flex;
+  align-items: center;
+}
+
+.controls.smallSize .rightControls {
+  justify-content: flex-end;
+}
+
+.controls.smallSize .progressBarContainer {
+  margin-bottom: 4px;
+}
+
+.controls.smallSize .playPauseButton,
+.controls.smallSize .muteButton,
+.controls.smallSize .fullscreenButton {
+  padding: 2px;
+  margin-right: 4px;
+}
+
+.controls.smallSize .playPauseButton svg,
+.controls.smallSize .muteButton svg,
+.controls.smallSize .fullscreenButton svg {
+  width: 16px;
+  height: 16px;
+}
+
+.controls.smallSize .muteButton {
+  order: -1;
+}
diff --git a/web/app/components/base/video-gallery/VideoPlayer.tsx b/web/app/components/base/video-gallery/VideoPlayer.tsx
new file mode 100644
index 0000000000..d7c86a1af9
--- /dev/null
+++ b/web/app/components/base/video-gallery/VideoPlayer.tsx
@@ -0,0 +1,278 @@
+import React, { useCallback, useEffect, useRef, useState } from 'react'
+import styles from './VideoPlayer.module.css'
+
+type VideoPlayerProps = {
+  src: string
+}
+
+const PlayIcon = () => (
+  <svg width="20" height="20" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+    <path d="M8 5V19L19 12L8 5Z" fill="currentColor"/>
+  </svg>
+)
+
+const PauseIcon = () => (
+  <svg width="20" height="20" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+    <path d="M6 19H10V5H6V19ZM14 5V19H18V5H14Z" fill="currentColor"/>
+  </svg>
+)
+
+const MuteIcon = () => (
+  <svg width="20" height="20" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+    <path d="M3 9V15H7L12 20V4L7 9H3ZM16.5 12C16.5 10.23 15.48 8.71 14 7.97V16.02C15.48 15.29 16.5 13.77 16.5 12ZM14 3.23V5.29C16.89 6.15 19 8.83 19 12C19 15.17 16.89 17.85 14 18.71V20.77C18.01 19.86 21 16.28 21 12C21 7.72 18.01 4.14 14 3.23Z" fill="currentColor"/>
+  </svg>
+)
+
+const UnmuteIcon = () => (
+  <svg width="20" height="20" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+    <path d="M4.34 2.93L2.93 4.34L7.29 8.7L7 9H3V15H7L12 20V13.41L16.18 17.59C15.69 17.96 15.16 18.27 14.58 18.5V20.58C15.94 20.22 17.15 19.56 18.13 18.67L19.66 20.2L21.07 18.79L4.34 2.93ZM10 15.17L7.83 13H5V11H7.83L10 8.83V15.17ZM19 12C19 12.82 18.85 13.61 18.59 14.34L20.12 15.87C20.68 14.7 21 13.39 21 12C21 7.72 18.01 4.14 14 3.23V5.29C16.89 6.15 19 8.83 19 12ZM12 4L10.12 5.88L12 7.76V4ZM16.5 12C16.5 10.23 15.48 8.71 14 7.97V10.18L16.45 12.63C16.48 12.43 16.5 12.22 16.5 12Z" fill="currentColor"/>
+  </svg>
+)
+
+const FullscreenIcon = () => (
+  <svg width="20" height="20" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+    <path d="M7 14H5V19H10V17H7V14ZM5 10H7V7H10V5H5V10ZM17 17H14V19H19V14H17V17ZM14 5V7H17V10H19V5H14Z" fill="currentColor"/>
+  </svg>
+)
+
+const VideoPlayer: React.FC<VideoPlayerProps> = ({ src }) => {
+  const [isPlaying, setIsPlaying] = useState(false)
+  const [currentTime, setCurrentTime] = useState(0)
+  const [duration, setDuration] = useState(0)
+  const [isMuted, setIsMuted] = useState(false)
+  const [volume, setVolume] = useState(1)
+  const [isDragging, setIsDragging] = useState(false)
+  const [isControlsVisible, setIsControlsVisible] = useState(true)
+  const [hoverTime, setHoverTime] = useState<number | null>(null)
+  const videoRef = useRef<HTMLVideoElement>(null)
+  const progressRef = useRef<HTMLDivElement>(null)
+  const volumeRef = useRef<HTMLDivElement>(null)
+  const controlsTimeoutRef = useRef<NodeJS.Timeout | null>(null)
+  const [isSmallSize, setIsSmallSize] = useState(false)
+  const containerRef = useRef<HTMLDivElement>(null)
+
+  useEffect(() => {
+    const video = videoRef.current
+    if (!video)
+      return
+
+    const setVideoData = () => {
+      setDuration(video.duration)
+      setVolume(video.volume)
+    }
+
+    const setVideoTime = () => {
+      setCurrentTime(video.currentTime)
+    }
+
+    const handleEnded = () => {
+      setIsPlaying(false)
+    }
+
+    video.addEventListener('loadedmetadata', setVideoData)
+    video.addEventListener('timeupdate', setVideoTime)
+    video.addEventListener('ended', handleEnded)
+
+    return () => {
+      video.removeEventListener('loadedmetadata', setVideoData)
+      video.removeEventListener('timeupdate', setVideoTime)
+      video.removeEventListener('ended', handleEnded)
+    }
+  }, [src])
+
+  useEffect(() => {
+    return () => {
+      if (controlsTimeoutRef.current)
+        clearTimeout(controlsTimeoutRef.current)
+    }
+  }, [])
+
+  const showControls = useCallback(() => {
+    setIsControlsVisible(true)
+    if (controlsTimeoutRef.current)
+      clearTimeout(controlsTimeoutRef.current)
+
+    controlsTimeoutRef.current = setTimeout(() => setIsControlsVisible(false), 3000)
+  }, [])
+
+  const togglePlayPause = useCallback(() => {
+    const video = videoRef.current
+    if (video) {
+      if (isPlaying)
+        video.pause()
+      else video.play().catch(error => console.error('Error playing video:', error))
+      setIsPlaying(!isPlaying)
+    }
+  }, [isPlaying])
+
+  const toggleMute = useCallback(() => {
+    const video = videoRef.current
+    if (video) {
+      const newMutedState = !video.muted
+      video.muted = newMutedState
+      setIsMuted(newMutedState)
+      setVolume(newMutedState ? 0 : (video.volume > 0 ? video.volume : 1))
+      video.volume = newMutedState ? 0 : (video.volume > 0 ? video.volume : 1)
+    }
+  }, [])
+
+  const toggleFullscreen = useCallback(() => {
+    const video = videoRef.current
+    if (video) {
+      if (document.fullscreenElement)
+        document.exitFullscreen()
+      else video.requestFullscreen()
+    }
+  }, [])
+
+  const formatTime = (time: number) => {
+    const minutes = Math.floor(time / 60)
+    const seconds = Math.floor(time % 60)
+    return `${minutes.toString().padStart(2, '0')}:${seconds.toString().padStart(2, '0')}`
+  }
+
+  const updateVideoProgress = useCallback((clientX: number) => {
+    const progressBar = progressRef.current
+    const video = videoRef.current
+    if (progressBar && video) {
+      const rect = progressBar.getBoundingClientRect()
+      const pos = (clientX - rect.left) / rect.width
+      const newTime = pos * video.duration
+      if (newTime >= 0 && newTime <= video.duration) {
+        setHoverTime(newTime)
+        if (isDragging)
+          video.currentTime = newTime
+      }
+    }
+  }, [isDragging])
+
+  const handleMouseMove = useCallback((e: React.MouseEvent<HTMLDivElement>) => {
+    updateVideoProgress(e.clientX)
+  }, [updateVideoProgress])
+
+  const handleMouseLeave = useCallback(() => {
+    if (!isDragging)
+      setHoverTime(null)
+  }, [isDragging])
+
+  const handleMouseDown = useCallback((e: React.MouseEvent<HTMLDivElement>) => {
+    e.preventDefault()
+    setIsDragging(true)
+    updateVideoProgress(e.clientX)
+  }, [updateVideoProgress])
+
+  useEffect(() => {
+    const handleGlobalMouseMove = (e: MouseEvent) => {
+      if (isDragging)
+        updateVideoProgress(e.clientX)
+    }
+
+    const handleGlobalMouseUp = () => {
+      setIsDragging(false)
+      setHoverTime(null)
+    }
+
+    if (isDragging) {
+      document.addEventListener('mousemove', handleGlobalMouseMove)
+      document.addEventListener('mouseup', handleGlobalMouseUp)
+    }
+
+    return () => {
+      document.removeEventListener('mousemove', handleGlobalMouseMove)
+      document.removeEventListener('mouseup', handleGlobalMouseUp)
+    }
+  }, [isDragging, updateVideoProgress])
+
+  const checkSize = useCallback(() => {
+    if (containerRef.current)
+      setIsSmallSize(containerRef.current.offsetWidth < 400)
+  }, [])
+
+  useEffect(() => {
+    checkSize()
+    window.addEventListener('resize', checkSize)
+    return () => window.removeEventListener('resize', checkSize)
+  }, [checkSize])
+
+  const handleVolumeChange = useCallback((e: React.MouseEvent<HTMLDivElement>) => {
+    const volumeBar = volumeRef.current
+    const video = videoRef.current
+    if (volumeBar && video) {
+      const rect = volumeBar.getBoundingClientRect()
+      const newVolume = (e.clientX - rect.left) / rect.width
+      const clampedVolume = Math.max(0, Math.min(1, newVolume))
+      video.volume = clampedVolume
+      setVolume(clampedVolume)
+      setIsMuted(clampedVolume === 0)
+    }
+  }, [])
+
+  return (
+    <div ref={containerRef} className={styles.videoPlayer} onMouseMove={showControls} onMouseEnter={showControls}>
+      <video ref={videoRef} src={src} className={styles.video} />
+      <div className={`${styles.controls} ${isControlsVisible ? styles.visible : styles.hidden} ${isSmallSize ? styles.smallSize : ''}`}>
+        <div className={styles.overlay}>
+          <div className={styles.progressBarContainer}>
+            <div
+              ref={progressRef}
+              className={styles.progressBar}
+              onClick={handleMouseDown}
+              onMouseMove={handleMouseMove}
+              onMouseLeave={handleMouseLeave}
+              onMouseDown={handleMouseDown}
+            >
+              <div className={styles.progress} style={{ width: `${(currentTime / duration) * 100}%` }} />
+              {hoverTime !== null && (
+                <div
+                  className={styles.hoverTimeIndicator}
+                  style={{ left: `${(hoverTime / duration) * 100}%` }}
+                >
+                  {formatTime(hoverTime)}
+                </div>
+              )}
+            </div>
+          </div>
+          <div className={styles.controlsContent}>
+            <div className={styles.leftControls}>
+              <button className={styles.playPauseButton} onClick={togglePlayPause}>
+                {isPlaying ? <PauseIcon /> : <PlayIcon />}
+              </button>
+              {!isSmallSize && (<span className={styles.time}>{formatTime(currentTime)} / {formatTime(duration)}</span>)}
+            </div>
+            <div className={styles.rightControls}>
+              <button className={styles.muteButton} onClick={toggleMute}>
+                {isMuted ? <UnmuteIcon /> : <MuteIcon />}
+              </button>
+              {!isSmallSize && (
+                <div className={styles.volumeControl}>
+                  <div
+                    ref={volumeRef}
+                    className={styles.volumeSlider}
+                    onClick={handleVolumeChange}
+                    onMouseDown={(e) => {
+                      handleVolumeChange(e)
+                      const handleMouseMove = (e: MouseEvent) => handleVolumeChange(e as unknown as React.MouseEvent<HTMLDivElement>)
+                      const handleMouseUp = () => {
+                        document.removeEventListener('mousemove', handleMouseMove)
+                        document.removeEventListener('mouseup', handleMouseUp)
+                      }
+                      document.addEventListener('mousemove', handleMouseMove)
+                      document.addEventListener('mouseup', handleMouseUp)
+                    }}
+                  >
+                    <div className={styles.volumeLevel} style={{ width: `${volume * 100}%` }} />
+                  </div>
+                </div>
+              )}
+              <button className={styles.fullscreenButton} onClick={toggleFullscreen}>
+                <FullscreenIcon />
+              </button>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  )
+}
+
+export default VideoPlayer
diff --git a/web/app/components/base/video-gallery/index.tsx b/web/app/components/base/video-gallery/index.tsx
new file mode 100644
index 0000000000..a41dfe8e0a
--- /dev/null
+++ b/web/app/components/base/video-gallery/index.tsx
@@ -0,0 +1,12 @@
+import React from 'react'
+import VideoPlayer from './VideoPlayer'
+
+type Props = {
+  srcs: string[]
+}
+
+const VideoGallery: React.FC<Props> = ({ srcs }) => {
+  return (<><br/>{srcs.map((src, index) => (<><br/><VideoPlayer key={`video_${index}`} src={src}/></>))}</>)
+}
+
+export default React.memo(VideoGallery)
diff --git a/web/app/components/base/voice-input/index.tsx b/web/app/components/base/voice-input/index.tsx
index 07b3c5ddcf..37ebe3dd7e 100644
--- a/web/app/components/base/voice-input/index.tsx
+++ b/web/app/components/base/voice-input/index.tsx
@@ -16,11 +16,13 @@ import { audioToText } from '@/service/share'
 type VoiceInputTypes = {
   onConverted: (text: string) => void
   onCancel: () => void
+  wordTimestamps?: string
 }
 
 const VoiceInput = ({
   onCancel,
   onConverted,
+  wordTimestamps,
 }: VoiceInputTypes) => {
   const { t } = useTranslation()
   const recorder = useRef(new Recorder({
@@ -88,6 +90,7 @@ const VoiceInput = ({
     const mp3File = new File([mp3Blob], 'temp.mp3', { type: 'audio/mp3' })
     const formData = new FormData()
     formData.append('file', mp3File)
+    formData.append('word_timestamps', wordTimestamps || 'disabled')
 
     let url = ''
     let isPublic = false
@@ -112,7 +115,7 @@ const VoiceInput = ({
       onConverted('')
       onCancel()
     }
-  }, [])
+  }, [clearInterval, onCancel, onConverted, params.appId, params.token, pathname, wordTimestamps])
   const handleStartRecord = async () => {
     try {
       await recorder.current.start()
@@ -146,7 +149,7 @@ const VoiceInput = ({
       }
     }
   }
-  if (originDuration >= 120 && startRecord)
+  if (originDuration >= 600 && startRecord)
     handleStopRecorder()
 
   useEffect(() => {
@@ -204,7 +207,7 @@ const VoiceInput = ({
             </div>
           )
         }
-        <div className={`w-[45px] pl-1 text-xs font-medium ${originDuration > 110 ? 'text-[#F04438]' : 'text-gray-700'}`}>{`0${minutes.toFixed(0)}:${seconds >= 10 ? seconds : `0${seconds}`}`}</div>
+        <div className={`w-[45px] pl-1 text-xs font-medium ${originDuration > 500 ? 'text-[#F04438]' : 'text-gray-700'}`}>{`0${minutes.toFixed(0)}:${seconds >= 10 ? seconds : `0${seconds}`}`}</div>
       </div>
     </div>
   )
diff --git a/web/app/components/browser-initor.tsx b/web/app/components/browser-initor.tsx
index 711ff62a94..939ddd567d 100644
--- a/web/app/components/browser-initor.tsx
+++ b/web/app/components/browser-initor.tsx
@@ -43,10 +43,10 @@ Object.defineProperty(globalThis, 'sessionStorage', {
   value: sessionStorage,
 })
 
-const BrowerInitor = ({
+const BrowserInitor = ({
   children,
 }: { children: React.ReactElement }) => {
   return children
 }
 
-export default BrowerInitor
+export default BrowserInitor
diff --git a/web/app/components/datasets/common/check-rerank-model.ts b/web/app/components/datasets/common/check-rerank-model.ts
index 42810e4bf0..581c2bb69a 100644
--- a/web/app/components/datasets/common/check-rerank-model.ts
+++ b/web/app/components/datasets/common/check-rerank-model.ts
@@ -7,13 +7,13 @@ import { RerankingModeEnum } from '@/models/datasets'
 
 export const isReRankModelSelected = ({
   rerankDefaultModel,
-  isRerankDefaultModelVaild,
+  isRerankDefaultModelValid,
   retrievalConfig,
   rerankModelList,
   indexMethod,
 }: {
   rerankDefaultModel?: DefaultModelResponse
-  isRerankDefaultModelVaild: boolean
+  isRerankDefaultModelValid: boolean
   retrievalConfig: RetrievalConfig
   rerankModelList: Model[]
   indexMethod?: string
@@ -25,7 +25,7 @@ export const isReRankModelSelected = ({
       return provider?.models.find(({ model }) => model === retrievalConfig.reranking_model?.reranking_model_name)
     }
 
-    if (isRerankDefaultModelVaild)
+    if (isRerankDefaultModelValid)
       return !!rerankDefaultModel
 
     return false
diff --git a/web/app/components/datasets/create/assets/unknow.svg b/web/app/components/datasets/create/assets/unknown.svg
similarity index 100%
rename from web/app/components/datasets/create/assets/unknow.svg
rename to web/app/components/datasets/create/assets/unknown.svg
diff --git a/web/app/components/datasets/create/embedding-process/index.module.css b/web/app/components/datasets/create/embedding-process/index.module.css
index a15b1310b4..1ebb006b54 100644
--- a/web/app/components/datasets/create/embedding-process/index.module.css
+++ b/web/app/components/datasets/create/embedding-process/index.module.css
@@ -83,7 +83,7 @@
 
 .fileIcon {
   @apply w-4 h-4 mr-1 bg-center bg-no-repeat;
-  background-image: url(../assets/unknow.svg);
+  background-image: url(../assets/unknown.svg);
   background-size: 16px;
 }
 .fileIcon.csv {
diff --git a/web/app/components/datasets/create/empty-dataset-creation-modal/index.tsx b/web/app/components/datasets/create/empty-dataset-creation-modal/index.tsx
index c490d50b88..23c3f0314a 100644
--- a/web/app/components/datasets/create/empty-dataset-creation-modal/index.tsx
+++ b/web/app/components/datasets/create/empty-dataset-creation-modal/index.tsx
@@ -32,7 +32,7 @@ const EmptyDatasetCreationModal = ({
       return
     }
     if (inputValue.length > 40) {
-      notify({ type: 'error', message: t('datasetCreation.stepOne.modal.nameLengthInvaild') })
+      notify({ type: 'error', message: t('datasetCreation.stepOne.modal.nameLengthInvalid') })
       return
     }
     try {
diff --git a/web/app/components/datasets/create/file-uploader/index.module.css b/web/app/components/datasets/create/file-uploader/index.module.css
index d141815c5a..bf5b7dcaf5 100644
--- a/web/app/components/datasets/create/file-uploader/index.module.css
+++ b/web/app/components/datasets/create/file-uploader/index.module.css
@@ -104,7 +104,7 @@
 
 .fileIcon {
   @apply shrink-0 w-6 h-6 mr-2 bg-center bg-no-repeat;
-  background-image: url(../assets/unknow.svg);
+  background-image: url(../assets/unknown.svg);
   background-size: 24px;
 }
 
diff --git a/web/app/components/datasets/create/step-two/index.tsx b/web/app/components/datasets/create/step-two/index.tsx
index b25ca71311..15332b944d 100644
--- a/web/app/components/datasets/create/step-two/index.tsx
+++ b/web/app/components/datasets/create/step-two/index.tsx
@@ -306,7 +306,7 @@ const StepTwo = ({
   const {
     modelList: rerankModelList,
     defaultModel: rerankDefaultModel,
-    currentModel: isRerankDefaultModelVaild,
+    currentModel: isRerankDefaultModelValid,
   } = useModelListAndDefaultModelAndCurrentProviderAndModel(ModelTypeEnum.rerank)
   const { data: embeddingModelList } = useModelList(ModelTypeEnum.textEmbedding)
   const { data: defaultEmbeddingModel } = useDefaultModel(ModelTypeEnum.textEmbedding)
@@ -344,7 +344,7 @@ const StepTwo = ({
       if (
         !isReRankModelSelected({
           rerankDefaultModel,
-          isRerankDefaultModelVaild: !!isRerankDefaultModelVaild,
+          isRerankDefaultModelValid: !!isRerankDefaultModelValid,
           rerankModelList,
           // eslint-disable-next-line @typescript-eslint/no-use-before-define
           retrievalConfig,
@@ -743,7 +743,7 @@ const StepTwo = ({
             </div>
             {hasSetIndexType && indexType === IndexingType.ECONOMICAL && (
               <div className='mt-2 text-xs text-gray-500 font-medium'>
-                {t('datasetCreation.stepTwo.indexSettedTip')}
+                {t('datasetCreation.stepTwo.indexSettingTip')}
                 <Link className='text-[#155EEF]' href={`/datasets/${datasetId}/settings`}>{t('datasetCreation.stepTwo.datasetSettingLink')}</Link>
               </div>
             )}
@@ -790,7 +790,7 @@ const StepTwo = ({
                 />
                 {!!datasetId && (
                   <div className='mt-2 text-xs text-gray-500 font-medium'>
-                    {t('datasetCreation.stepTwo.indexSettedTip')}
+                    {t('datasetCreation.stepTwo.indexSettingTip')}
                     <Link className='text-[#155EEF]' href={`/datasets/${datasetId}/settings`}>{t('datasetCreation.stepTwo.datasetSettingLink')}</Link>
                   </div>
                 )}
@@ -890,7 +890,7 @@ const StepTwo = ({
               </div>
               <div className={s.divider} />
               <div className={s.segmentCount}>
-                <div className='mb-2 text-xs font-medium text-gray-500'>{t('datasetCreation.stepTwo.emstimateSegment')}</div>
+                <div className='mb-2 text-xs font-medium text-gray-500'>{t('datasetCreation.stepTwo.estimateSegment')}</div>
                 <div className='flex items-center text-sm leading-6 font-medium text-gray-800'>
                   {
                     fileIndexingEstimate
diff --git a/web/app/components/datasets/create/step-two/preview-item/index.tsx b/web/app/components/datasets/create/step-two/preview-item/index.tsx
index fdec6c734d..56102b6540 100644
--- a/web/app/components/datasets/create/step-two/preview-item/index.tsx
+++ b/web/app/components/datasets/create/step-two/preview-item/index.tsx
@@ -41,14 +41,14 @@ const PreviewItem: FC<IPreviewItemProps> = ({
   const charNums = type === PreviewType.TEXT
     ? (content || '').length
     : (qa?.answer || '').length + (qa?.question || '').length
-  const formatedIndex = (() => String(index).padStart(3, '0'))()
+  const formattedIndex = (() => String(index).padStart(3, '0'))()
 
   return (
     <div className='p-4 rounded-xl bg-gray-50'>
       <div className='flex items-center justify-between h-5 text-xs text-gray-500'>
         <div className='flex items-center h-[18px] space-x-1 border border-gray-200 box-border rounded-md italic pl-1 pr-1.5 font-medium'>
           {sharpIcon}
-          <span>{formatedIndex}</span>
+          <span>{formattedIndex}</span>
         </div>
         <div className='flex items-center space-x-1'>
           {textIcon}
diff --git a/web/app/components/datasets/documents/detail/completed/index.tsx b/web/app/components/datasets/documents/detail/completed/index.tsx
index 195b75b0a2..0251dfa54e 100644
--- a/web/app/components/datasets/documents/detail/completed/index.tsx
+++ b/web/app/components/datasets/documents/detail/completed/index.tsx
@@ -24,7 +24,7 @@ import { ToastContext } from '@/app/components/base/toast'
 import type { Item } from '@/app/components/base/select'
 import { SimpleSelect } from '@/app/components/base/select'
 import { deleteSegment, disableSegment, enableSegment, fetchSegments, updateSegment } from '@/service/datasets'
-import type { SegmentDetailModel, SegmentUpdator, SegmentsQuery, SegmentsResponse } from '@/models/datasets'
+import type { SegmentDetailModel, SegmentUpdater, SegmentsQuery, SegmentsResponse } from '@/models/datasets'
 import { asyncRunSafe } from '@/utils'
 import type { CommonResponse } from '@/models/common'
 import AutoHeightTextarea from '@/app/components/base/auto-height-textarea/common'
@@ -322,7 +322,7 @@ const Completed: FC<ICompletedProps> = ({
   }
 
   const handleUpdateSegment = async (segmentId: string, question: string, answer: string, keywords: string[]) => {
-    const params: SegmentUpdator = { content: '' }
+    const params: SegmentUpdater = { content: '' }
     if (docForm === 'qa_model') {
       if (!question.trim())
         return notify({ type: 'error', message: t('datasetDocuments.segment.questionEmpty') })
diff --git a/web/app/components/datasets/documents/detail/new-segment-modal.tsx b/web/app/components/datasets/documents/detail/new-segment-modal.tsx
index 24e0ba3cdc..dae9cf19fb 100644
--- a/web/app/components/datasets/documents/detail/new-segment-modal.tsx
+++ b/web/app/components/datasets/documents/detail/new-segment-modal.tsx
@@ -9,7 +9,7 @@ import Button from '@/app/components/base/button'
 import AutoHeightTextarea from '@/app/components/base/auto-height-textarea/common'
 import { Hash02 } from '@/app/components/base/icons/src/vender/line/general'
 import { ToastContext } from '@/app/components/base/toast'
-import type { SegmentUpdator } from '@/models/datasets'
+import type { SegmentUpdater } from '@/models/datasets'
 import { addSegment } from '@/service/datasets'
 import TagInput from '@/app/components/base/tag-input'
 
@@ -42,7 +42,7 @@ const NewSegmentModal: FC<NewSegmentModalProps> = ({
   }
 
   const handleSave = async () => {
-    const params: SegmentUpdator = { content: '' }
+    const params: SegmentUpdater = { content: '' }
     if (docForm === 'qa_model') {
       if (!question.trim())
         return notify({ type: 'error', message: t('datasetDocuments.segment.questionEmpty') })
diff --git a/web/app/components/datasets/documents/index.tsx b/web/app/components/datasets/documents/index.tsx
index 32c045eccd..81b85c8220 100644
--- a/web/app/components/datasets/documents/index.tsx
+++ b/web/app/components/datasets/documents/index.tsx
@@ -4,8 +4,9 @@ import React, { useMemo, useState } from 'react'
 import useSWR from 'swr'
 import { useTranslation } from 'react-i18next'
 import { useRouter } from 'next/navigation'
-import { debounce, groupBy, omit } from 'lodash-es'
+import { groupBy, omit } from 'lodash-es'
 import { PlusIcon } from '@heroicons/react/24/solid'
+import { useDebounce } from 'ahooks'
 import List from './list'
 import s from './style.module.css'
 import Loading from '@/app/components/base/loading'
@@ -87,9 +88,11 @@ const Documents: FC<IDocumentsProps> = ({ datasetId }) => {
   const isDataSourceFile = dataset?.data_source_type === DataSourceType.FILE
   const embeddingAvailable = !!dataset?.embedding_available
 
+  const debouncedSearchValue = useDebounce(searchValue, { wait: 500 })
+
   const query = useMemo(() => {
-    return { page: currPage + 1, limit, keyword: searchValue, fetch: isDataSourceNotion ? true : '' }
-  }, [searchValue, currPage, isDataSourceNotion])
+    return { page: currPage + 1, limit, keyword: debouncedSearchValue, fetch: isDataSourceNotion ? true : '' }
+  }, [currPage, debouncedSearchValue, isDataSourceNotion])
 
   const { data: documentsRes, error, mutate } = useSWR(
     {
@@ -106,15 +109,15 @@ const Documents: FC<IDocumentsProps> = ({ datasetId }) => {
     let percent = 0
     const documentsData = documentsRes?.data?.map((documentItem) => {
       const { indexing_status, completed_segments, total_segments } = documentItem
-      const isEmbeddinged = indexing_status === 'completed' || indexing_status === 'paused' || indexing_status === 'error'
+      const isEmbedded = indexing_status === 'completed' || indexing_status === 'paused' || indexing_status === 'error'
 
-      if (isEmbeddinged)
+      if (isEmbedded)
         completedNum++
 
       const completedCount = completed_segments || 0
       const totalCount = total_segments || 0
       if (totalCount === 0 && completedCount === 0) {
-        percent = isEmbeddinged ? 100 : 0
+        percent = isEmbedded ? 100 : 0
       }
       else {
         const per = Math.round(completedCount * 100 / totalCount)
@@ -204,7 +207,7 @@ const Documents: FC<IDocumentsProps> = ({ datasetId }) => {
             showLeftIcon
             wrapperClassName='!w-[200px]'
             className='!h-8 !text-[13px]'
-            onChange={debounce(e => setSearchValue(e.target.value), 500)}
+            onChange={e => setSearchValue(e.target.value)}
             value={searchValue}
           />
           <div className='flex gap-2 justify-center items-center !h-8'>
diff --git a/web/app/components/datasets/hit-testing/modify-retrieval-modal.tsx b/web/app/components/datasets/hit-testing/modify-retrieval-modal.tsx
index be5c1be2e7..999f1cdf0d 100644
--- a/web/app/components/datasets/hit-testing/modify-retrieval-modal.tsx
+++ b/web/app/components/datasets/hit-testing/modify-retrieval-modal.tsx
@@ -39,14 +39,14 @@ const ModifyRetrievalModal: FC<Props> = ({
   const {
     modelList: rerankModelList,
     defaultModel: rerankDefaultModel,
-    currentModel: isRerankDefaultModelVaild,
+    currentModel: isRerankDefaultModelValid,
   } = useModelListAndDefaultModelAndCurrentProviderAndModel(ModelTypeEnum.rerank)
 
   const handleSave = () => {
     if (
       !isReRankModelSelected({
         rerankDefaultModel,
-        isRerankDefaultModelVaild: !!isRerankDefaultModelVaild,
+        isRerankDefaultModelValid: !!isRerankDefaultModelValid,
         rerankModelList,
         retrievalConfig,
         indexMethod,
diff --git a/web/app/components/datasets/settings/form/index.tsx b/web/app/components/datasets/settings/form/index.tsx
index 404a8ed6a0..0f6bdd0a59 100644
--- a/web/app/components/datasets/settings/form/index.tsx
+++ b/web/app/components/datasets/settings/form/index.tsx
@@ -73,7 +73,7 @@ const Form = () => {
   const {
     modelList: rerankModelList,
     defaultModel: rerankDefaultModel,
-    currentModel: isRerankDefaultModelVaild,
+    currentModel: isRerankDefaultModelValid,
   } = useModelListAndDefaultModelAndCurrentProviderAndModel(ModelTypeEnum.rerank)
   const { data: embeddingModelList } = useModelList(ModelTypeEnum.textEmbedding)
 
@@ -99,7 +99,7 @@ const Form = () => {
     if (
       !isReRankModelSelected({
         rerankDefaultModel,
-        isRerankDefaultModelVaild: !!isRerankDefaultModelVaild,
+        isRerankDefaultModelValid: !!isRerankDefaultModelValid,
         rerankModelList,
         retrievalConfig,
         indexMethod,
diff --git a/web/app/components/develop/secret-key/secret-key-modal.tsx b/web/app/components/develop/secret-key/secret-key-modal.tsx
index fd28a67e7e..dbb5cc37c7 100644
--- a/web/app/components/develop/secret-key/secret-key-modal.tsx
+++ b/web/app/components/develop/secret-key/secret-key-modal.tsx
@@ -41,7 +41,7 @@ const SecretKeyModal = ({
 }: ISecretKeyModalProps) => {
   const { t } = useTranslation()
   const { formatTime } = useTimestamp()
-  const { currentWorkspace, isCurrentWorkspaceManager } = useAppContext()
+  const { currentWorkspace, isCurrentWorkspaceManager, isCurrentWorkspaceEditor } = useAppContext()
   const [showConfirmDelete, setShowConfirmDelete] = useState(false)
   const [isVisible, setVisible] = useState(false)
   const [newKey, setNewKey] = useState<CreateApiKeyResponse | undefined>(undefined)
@@ -142,7 +142,7 @@ const SecretKeyModal = ({
         )
       }
       <div className='flex'>
-        <Button className={`flex flex-shrink-0 mt-4 ${s.autoWidth}`} onClick={onCreate} disabled={ !currentWorkspace || !isCurrentWorkspaceManager}>
+        <Button className={`flex flex-shrink-0 mt-4 ${s.autoWidth}`} onClick={onCreate} disabled={!currentWorkspace || !isCurrentWorkspaceEditor}>
           <PlusIcon className='flex flex-shrink-0 w-4 h-4' />
           <div className='text-xs font-medium text-gray-800'>{t('appApi.apiKeyModal.createNewSecretKey')}</div>
         </Button>
diff --git a/web/app/components/develop/template/template_advanced_chat.en.mdx b/web/app/components/develop/template/template_advanced_chat.en.mdx
index 6487ac79d7..655b6efce1 100644
--- a/web/app/components/develop/template/template_advanced_chat.en.mdx
+++ b/web/app/components/develop/template/template_advanced_chat.en.mdx
@@ -3,7 +3,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
 
 # Advanced Chat App API
 
-Chat applications support session persistence, allowing previous chat history to be used as context for responses. This can be applicable for chatbots, customer service AI, etc.
+Chat applications support session persistence, allowing previous chat history to be used as context for responses. This can be applicable for chatbot, customer service AI, etc.
 
 <div>
   ### Base URL
@@ -60,7 +60,7 @@ Chat applications support session persistence, allowing previous chat history to
           Should be uniquely defined by the developer within the application.
       </Property>
       <Property name='conversation_id' type='string' key='conversation_id'>
-      Converation ID, to continue the conversation based on previous chat records, it is necessary to pass the previous message's conversation_id.
+      Conversation ID, to continue the conversation based on previous chat records, it is necessary to pass the previous message's conversation_id.
       </Property>
       <Property name='files' type='array[object]' key='files'>
           File list, suitable for inputting files (images) combined with text understanding and answering questions, available only when the model supports Vision capability.
@@ -239,7 +239,7 @@ Chat applications support session persistence, allowing previous chat history to
         "message_id": "9da23599-e713-473b-982c-4328d4f5c78a",
         "conversation_id": "45701982-8118-4bc5-8e9b-64562b4555f2",
         "mode": "chat",
-        "answer": "iPhone 13 Pro Max specs are listed heere:...",
+        "answer": "iPhone 13 Pro Max specs are listed here:...",
         "metadata": {
             "usage": {
                 "prompt_tokens": 1033,
@@ -732,7 +732,7 @@ Chat applications support session persistence, allowing previous chat history to
     <CodeGroup title="Request" tag="DELETE" label="/conversations/:conversation_id" targetCode={`curl -X DELETE '${props.appDetail.api_base_url}/conversations/:conversation_id' \\\n--header 'Authorization: Bearer {api_key}' \\\n--header 'Content-Type: application/json' \\\n--data-raw '{ \n "user": "abc-123"\n}'`}>
 
     ```bash {{ title: 'cURL' }}
-        curl -X DELETE '${props.appDetail.api_base_url}/conversations/{convsation_id}' \
+        curl -X DELETE '${props.appDetail.api_base_url}/conversations/{conversation_id}' \
         --header 'Content-Type: application/json' \
         --header 'Accept: application/json' \
         --header 'Authorization: Bearer {api_key}' \
diff --git a/web/app/components/develop/template/template_advanced_chat.zh.mdx b/web/app/components/develop/template/template_advanced_chat.zh.mdx
index 33551509e5..2aa42fbb19 100755
--- a/web/app/components/develop/template/template_advanced_chat.zh.mdx
+++ b/web/app/components/develop/template/template_advanced_chat.zh.mdx
@@ -250,7 +250,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty } from '../md.tsx'
         "message_id": "9da23599-e713-473b-982c-4328d4f5c78a",
         "conversation_id": "45701982-8118-4bc5-8e9b-64562b4555f2",
         "mode": "chat",
-        "answer": "iPhone 13 Pro Max specs are listed heere:...",
+        "answer": "iPhone 13 Pro Max specs are listed here:...",
         "metadata": {
             "usage": {
                 "prompt_tokens": 1033,
@@ -767,7 +767,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty } from '../md.tsx'
     <CodeGroup title="Request" tag="DELETE" label="/conversations/:conversation_id" targetCode={`curl -X DELETE '${props.appDetail.api_base_url}/conversations/:conversation_id' \\\n--header 'Authorization: Bearer {api_key}' \\\n--header 'Content-Type: application/json' \\\n--data-raw '{ \n "user": "abc-123"\n}'`}>
 
     ```bash {{ title: 'cURL' }}
-        curl -X DELETE '${props.appDetail.api_base_url}/conversations/{convsation_id}' \
+        curl -X DELETE '${props.appDetail.api_base_url}/conversations/{conversation_id}' \
         --header 'Content-Type: application/json' \
         --header 'Accept: application/json' \
         --header 'Authorization: Bearer {api_key}' \
diff --git a/web/app/components/develop/template/template_chat.en.mdx b/web/app/components/develop/template/template_chat.en.mdx
index 07840640f4..d6dfbaaaf9 100644
--- a/web/app/components/develop/template/template_chat.en.mdx
+++ b/web/app/components/develop/template/template_chat.en.mdx
@@ -3,7 +3,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
 
 # Chat App API
 
-Chat applications support session persistence, allowing previous chat history to be used as context for responses. This can be applicable for chatbots, customer service AI, etc.
+Chat applications support session persistence, allowing previous chat history to be used as context for responses. This can be applicable for chatbot, customer service AI, etc.
 
 <div>
   ### Base URL
@@ -61,7 +61,7 @@ Chat applications support session persistence, allowing previous chat history to
           Should be uniquely defined by the developer within the application.
       </Property>
       <Property name='conversation_id' type='string' key='conversation_id'>
-      Converation ID, to continue the conversation based on previous chat records, it is necessary to pass the previous message's conversation_id.
+      Conversation ID, to continue the conversation based on previous chat records, it is necessary to pass the previous message's conversation_id.
       </Property>
       <Property name='files' type='array[object]' key='files'>
           File list, suitable for inputting files (images) combined with text understanding and answering questions, available only when the model supports Vision capability.
@@ -200,7 +200,7 @@ Chat applications support session persistence, allowing previous chat history to
         "message_id": "9da23599-e713-473b-982c-4328d4f5c78a",
         "conversation_id": "45701982-8118-4bc5-8e9b-64562b4555f2",
         "mode": "chat",
-        "answer": "iPhone 13 Pro Max specs are listed heere:...",
+        "answer": "iPhone 13 Pro Max specs are listed here:...",
         "metadata": {
             "usage": {
                 "prompt_tokens": 1033,
@@ -534,7 +534,7 @@ Chat applications support session persistence, allowing previous chat history to
       - `id` (string) ID
       - `type` (string) File type, image for images
       - `url` (string) Preview image URL
-      - `belongs_to` (string) belongs to，user orassistant
+      - `belongs_to` (string) belongs to，user or assistant
     - `agent_thoughts` (array[object]) Agent thought（Empty if it's a Basic Assistant）
       - `id` (string) Agent thought ID, every iteration has a unique agent thought ID
       - `message_id` (string) Unique message ID
@@ -772,7 +772,7 @@ Chat applications support session persistence, allowing previous chat history to
     <CodeGroup title="Request" tag="DELETE" label="/conversations/:conversation_id" targetCode={`curl -X DELETE '${props.appDetail.api_base_url}/conversations/:conversation_id' \\\n--header 'Authorization: Bearer {api_key}' \\\n--header 'Content-Type: application/json' \\\n--data-raw '{ \n "user": "abc-123"\n}'`}>
 
     ```bash {{ title: 'cURL' }}
-        curl -X DELETE '${props.appDetail.api_base_url}/conversations/{convsation_id}' \
+        curl -X DELETE '${props.appDetail.api_base_url}/conversations/{conversation_id}' \
         --header 'Content-Type: application/json' \
         --header 'Accept: application/json' \
         --header 'Authorization: Bearer {api_key}' \
diff --git a/web/app/components/develop/template/template_chat.zh.mdx b/web/app/components/develop/template/template_chat.zh.mdx
index 727d884c1a..a91da81a1c 100644
--- a/web/app/components/develop/template/template_chat.zh.mdx
+++ b/web/app/components/develop/template/template_chat.zh.mdx
@@ -213,7 +213,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty } from '../md.tsx'
         "message_id": "9da23599-e713-473b-982c-4328d4f5c78a",
         "conversation_id": "45701982-8118-4bc5-8e9b-64562b4555f2",
         "mode": "chat",
-        "answer": "iPhone 13 Pro Max specs are listed heere:...",
+        "answer": "iPhone 13 Pro Max specs are listed here:...",
         "metadata": {
             "usage": {
                 "prompt_tokens": 1033,
@@ -786,7 +786,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty } from '../md.tsx'
     <CodeGroup title="Request" tag="DELETE" label="/conversations/:conversation_id" targetCode={`curl -X DELETE '${props.appDetail.api_base_url}/conversations/:conversation_id' \\\n--header 'Authorization: Bearer {api_key}' \\\n--header 'Content-Type: application/json' \\\n--data-raw '{ \n "user": "abc-123"\n}'`}>
 
     ```bash {{ title: 'cURL' }}
-        curl -X DELETE '${props.appDetail.api_base_url}/conversations/{convsation_id}' \
+        curl -X DELETE '${props.appDetail.api_base_url}/conversations/{conversation_id}' \
         --header 'Content-Type: application/json' \
         --header 'Accept: application/json' \
         --header 'Authorization: Bearer {api_key}' \
diff --git a/web/app/components/develop/template/template_workflow.en.mdx b/web/app/components/develop/template/template_workflow.en.mdx
index 495b051bd0..2bd0fe9daf 100644
--- a/web/app/components/develop/template/template_workflow.en.mdx
+++ b/web/app/components/develop/template/template_workflow.en.mdx
@@ -413,3 +413,109 @@ Workflow applications offers non-session support and is ideal for translation, a
     </CodeGroup>
   </Col>
 </Row>
+
+---
+
+<Heading
+  url='/workflows/logs'
+  method='GET'
+  title='Get workflow logs'
+  name='#Get-Workflow-Logs'
+/>
+<Row>
+  <Col>
+    Returns worklfow logs, with the first page returning the latest `{limit}` messages, i.e., in reverse order.
+
+    ### Query
+
+    <Properties>
+      <Property name='keyword' type='string' key='keyword'>
+        Keyword to search
+      </Property>
+      <Property name='status' type='string' key='status'>
+        succeeded/failed/stopped
+      </Property>
+      <Property name='page' type='int' key='page'>
+        current page, default is 1.
+      </Property>
+      <Property name='limit' type='int' key='limit'>
+          How many chat history messages to return in one request, default is 20.
+      </Property>
+    </Properties>
+
+    ### Response
+  - `page` (int) Current page
+  - `limit` (int) Number of returned items, if input exceeds system limit, returns system limit amount
+  - `total` (int) Number of total items
+  - `has_more` (bool) Whether there is a next page
+  - `data` (array[object]) Log list
+    - `id` (string) ID
+    - `workflow_run` (object) Workflow run
+      - `id` (string) ID
+      - `version` (string) Version
+      - `status` (string) status of execution, `running` / `succeeded` / `failed` / `stopped`
+      - `error` (string) Optional reason of error
+      - `elapsed_time` (float) total seconds to be used
+      - `total_tokens` (int) tokens to be used
+      - `total_steps` (int) default 0
+      - `created_at` (timestamp) start time
+      - `finished_at` (timestamp) end time
+    - `created_from` (string) Created from
+    - `created_by_role` (string) Created by role
+    - `created_by_account` (string) Optional Created by account
+    - `created_by_end_user` (object) Created by end user
+      - `id` (string) ID
+      - `type` (string) Type
+      - `is_anonymous` (bool) Is anonymous
+      - `session_id` (string) Session ID
+    - `created_at` (timestamp) create time
+  </Col>
+  <Col sticky>
+
+    <CodeGroup title="Request" tag="GET" label="/workflows/logs" targetCode={`curl -X GET '${props.appDetail.api_base_url}/workflows/logs'\\\n --header 'Authorization: Bearer {api_key}'`}>
+
+    ```bash {{ title: 'cURL' }}
+    curl -X GET '${props.appDetail.api_base_url}/workflows/logs?limit=1'
+    --header 'Authorization: Bearer {api_key}'
+    ```
+
+    </CodeGroup>
+    ### Response Example
+    <CodeGroup title="Response">
+    ```json {{ title: 'Response' }}
+    {
+        "page": 1,
+        "limit": 1,
+        "total": 7,
+        "has_more": true,
+        "data": [
+            {
+                "id": "e41b93f1-7ca2-40fd-b3a8-999aeb499cc0",
+                "workflow_run": {
+                    "id": "c0640fc8-03ef-4481-a96c-8a13b732a36e",
+                    "version": "2024-08-01 12:17:09.771832",
+                    "status": "succeeded",
+                    "error": null,
+                    "elapsed_time": 1.3588523610014818,
+                    "total_tokens": 0,
+                    "total_steps": 3,
+                    "created_at": 1726139643,
+                    "finished_at": 1726139644
+                },
+                "created_from": "service-api",
+                "created_by_role": "end_user",
+                "created_by_account": null,
+                "created_by_end_user": {
+                    "id": "7f7d9117-dd9d-441d-8970-87e5e7e687a3",
+                    "type": "service_api",
+                    "is_anonymous": false,
+                    "session_id": "abc-123"
+                },
+                "created_at": 1726139644
+            }
+        ]
+    }
+    ```
+    </CodeGroup>
+  </Col>
+</Row>
diff --git a/web/app/components/develop/template/template_workflow.zh.mdx b/web/app/components/develop/template/template_workflow.zh.mdx
index 640a4b3f92..d7d672fbd0 100644
--- a/web/app/components/develop/template/template_workflow.zh.mdx
+++ b/web/app/components/develop/template/template_workflow.zh.mdx
@@ -409,3 +409,109 @@ Workflow 应用无会话支持，适合用于翻译/文章写作/总结 AI 等
     </CodeGroup>
   </Col>
 </Row>
+
+---
+
+<Heading
+  url='/workflows/logs'
+  method='GET'
+  title='获取 workflow 日志'
+  name='#Get-Workflow-Logs'
+/>
+<Row>
+  <Col>
+    倒序返回workflow日志
+
+    ### Query
+
+    <Properties>
+      <Property name='keyword' type='string' key='keyword'>
+        关键字
+      </Property>
+      <Property name='status' type='string' key='status'>
+        执行状态 succeeded/failed/stopped
+      </Property>
+      <Property name='page' type='int' key='page'>
+        当前页码, 默认1.
+      </Property>
+      <Property name='limit' type='int' key='limit'>
+        每页条数, 默认20.
+      </Property>
+    </Properties>
+
+    ### Response
+  - `page` (int) 当前页码
+  - `limit` (int) 每页条数
+  - `total` (int) 总条数
+  - `has_more` (bool) 是否还有更多数据
+  - `data` (array[object]) 当前页码的数据
+    - `id` (string) 标识
+    - `workflow_run` (object) Workflow 执行日志
+      - `id` (string) 标识
+      - `version` (string) 版本
+      - `status` (string) 执行状态, `running` / `succeeded` / `failed` / `stopped`
+      - `error` (string) (可选) 错误
+      - `elapsed_time` (float) 耗时，单位秒
+      - `total_tokens` (int) 消耗的token数量
+      - `total_steps` (int) 执行步骤长度
+      - `created_at` (timestamp) 开始时间
+      - `finished_at` (timestamp) 结束时间
+    - `created_from` (string) 来源
+    - `created_by_role` (string) 角色
+    - `created_by_account` (string) (可选) 帐号
+    - `created_by_end_user` (object) 用户
+      - `id` (string) 标识
+      - `type` (string) 类型
+      - `is_anonymous` (bool) 是否匿名
+      - `session_id` (string) 会话标识
+    - `created_at` (timestamp) 创建时间
+  </Col>
+  <Col sticky>
+
+    <CodeGroup title="Request" tag="GET" label="/workflows/logs" targetCode={`curl -X GET '${props.appDetail.api_base_url}/workflows/logs'\\\n --header 'Authorization: Bearer {api_key}'`}>
+
+    ```bash {{ title: 'cURL' }}
+    curl -X GET '${props.appDetail.api_base_url}/workflows/logs?limit=1'
+    --header 'Authorization: Bearer {api_key}'
+    ```
+
+    </CodeGroup>
+    ### Response Example
+    <CodeGroup title="Response">
+    ```json {{ title: 'Response' }}
+    {
+        "page": 1,
+        "limit": 1,
+        "total": 7,
+        "has_more": true,
+        "data": [
+            {
+                "id": "e41b93f1-7ca2-40fd-b3a8-999aeb499cc0",
+                "workflow_run": {
+                    "id": "c0640fc8-03ef-4481-a96c-8a13b732a36e",
+                    "version": "2024-08-01 12:17:09.771832",
+                    "status": "succeeded",
+                    "error": null,
+                    "elapsed_time": 1.3588523610014818,
+                    "total_tokens": 0,
+                    "total_steps": 3,
+                    "created_at": 1726139643,
+                    "finished_at": 1726139644
+                },
+                "created_from": "service-api",
+                "created_by_role": "end_user",
+                "created_by_account": null,
+                "created_by_end_user": {
+                    "id": "7f7d9117-dd9d-441d-8970-87e5e7e687a3",
+                    "type": "service_api",
+                    "is_anonymous": false,
+                    "session_id": "abc-123"
+                },
+                "created_at": 1726139644
+            }
+        ]
+    }
+    ```
+    </CodeGroup>
+  </Col>
+</Row>
diff --git a/web/app/components/explore/app-card/index.tsx b/web/app/components/explore/app-card/index.tsx
index 96bb714581..b1ea4a95bf 100644
--- a/web/app/components/explore/app-card/index.tsx
+++ b/web/app/components/explore/app-card/index.tsx
@@ -5,7 +5,7 @@ import Button from '../../base/button'
 import cn from '@/utils/classnames'
 import type { App } from '@/models/explore'
 import AppIcon from '@/app/components/base/app-icon'
-import { AiText, ChatBot, CuteRobote } from '@/app/components/base/icons/src/vender/solid/communication'
+import { AiText, ChatBot, CuteRobot } from '@/app/components/base/icons/src/vender/solid/communication'
 import { Route } from '@/app/components/base/icons/src/vender/solid/mapsAndTravel'
 export type AppCardProps = {
   app: App
@@ -38,7 +38,7 @@ const AppCard = ({
               <ChatBot className='w-3 h-3 text-[#1570EF]' />
             )}
             {appBasicInfo.mode === 'agent-chat' && (
-              <CuteRobote className='w-3 h-3 text-indigo-600' />
+              <CuteRobot className='w-3 h-3 text-indigo-600' />
             )}
             {appBasicInfo.mode === 'chat' && (
               <ChatBot className='w-3 h-3 text-[#1570EF]' />
diff --git a/web/app/components/explore/category.tsx b/web/app/components/explore/category.tsx
index cf655c5333..cbf6cd26fe 100644
--- a/web/app/components/explore/category.tsx
+++ b/web/app/components/explore/category.tsx
@@ -15,7 +15,7 @@ export type ICategoryProps = {
   value: string
   onChange: (value: AppCategory | string) => void
   /**
-   * default value for searchparam 'category' in en
+   * default value for search param 'category' in en
    */
   allCategoriesEn: string
 }
diff --git a/web/app/components/header/account-dropdown/index.tsx b/web/app/components/header/account-dropdown/index.tsx
index 1dd03e5290..03157ed7cb 100644
--- a/web/app/components/header/account-dropdown/index.tsx
+++ b/web/app/components/header/account-dropdown/index.tsx
@@ -22,11 +22,11 @@ import { LanguagesSupported } from '@/i18n/language'
 import { useProviderContext } from '@/context/provider-context'
 import { Plan } from '@/app/components/billing/type'
 
-export type IAppSelecotr = {
+export type IAppSelector = {
   isMobile: boolean
 }
 
-export default function AppSelector({ isMobile }: IAppSelecotr) {
+export default function AppSelector({ isMobile }: IAppSelector) {
   const itemClassName = `
     flex items-center w-full h-9 px-3 text-gray-700 text-[14px]
     rounded-lg font-normal hover:bg-gray-50 cursor-pointer
diff --git a/web/app/components/header/account-setting/account-page/index.tsx b/web/app/components/header/account-setting/account-page/index.tsx
index ce7f7e7e22..eecd275b35 100644
--- a/web/app/components/header/account-setting/account-page/index.tsx
+++ b/web/app/components/header/account-setting/account-page/index.tsx
@@ -90,7 +90,7 @@ export default function AccountPage() {
     setPassword('')
     setConfirmPassword('')
   }
-  const handleSavePassowrd = async () => {
+  const handleSavePassword = async () => {
     if (!valid())
       return
     try {
@@ -235,7 +235,7 @@ export default function AccountPage() {
             <Button
               disabled={editing}
               variant='primary'
-              onClick={handleSavePassowrd}
+              onClick={handleSavePassword}
             >
               {userProfile.is_password_set ? t('common.operation.reset') : t('common.operation.save')}
             </Button>
diff --git a/web/app/components/header/account-setting/members-page/index.tsx b/web/app/components/header/account-setting/members-page/index.tsx
index 711e772684..e09e4bbc0d 100644
--- a/web/app/components/header/account-setting/members-page/index.tsx
+++ b/web/app/components/header/account-setting/members-page/index.tsx
@@ -15,7 +15,7 @@ import I18n from '@/context/i18n'
 import { useAppContext } from '@/context/app-context'
 import Avatar from '@/app/components/base/avatar'
 import type { InvitationResult } from '@/models/common'
-import LogoEmbededChatHeader from '@/app/components/base/logo/logo-embeded-chat-header'
+import LogoEmbeddedChatHeader from '@/app/components/base/logo/logo-embedded-chat-header'
 import { useProviderContext } from '@/context/provider-context'
 import { Plan } from '@/app/components/billing/type'
 import UpgradeBtn from '@/app/components/billing/upgrade-btn'
@@ -49,7 +49,7 @@ const MembersPage = () => {
     <>
       <div className='flex flex-col'>
         <div className='flex items-center mb-4 p-3 bg-gray-50 rounded-2xl'>
-          <LogoEmbededChatHeader className='!w-10 !h-10' />
+          <LogoEmbeddedChatHeader className='!w-10 !h-10' />
           <div className='grow mx-2'>
             <div className='text-sm font-medium text-gray-900'>{currentWorkspace?.name}</div>
             {enableBilling && (
diff --git a/web/app/components/header/account-setting/members-page/invited-modal/index.tsx b/web/app/components/header/account-setting/members-page/invited-modal/index.tsx
index 7af19b06c3..fc64d46b06 100644
--- a/web/app/components/header/account-setting/members-page/invited-modal/index.tsx
+++ b/web/app/components/header/account-setting/members-page/invited-modal/index.tsx
@@ -11,8 +11,8 @@ import { IS_CE_EDITION } from '@/config'
 import type { InvitationResult } from '@/models/common'
 import Tooltip from '@/app/components/base/tooltip'
 
-export type SuccessInvationResult = Extract<InvitationResult, { status: 'success' }>
-export type FailedInvationResult = Extract<InvitationResult, { status: 'failed' }>
+export type SuccessInvitationResult = Extract<InvitationResult, { status: 'success' }>
+export type FailedInvitationResult = Extract<InvitationResult, { status: 'failed' }>
 
 type IInvitedModalProps = {
   invitationResults: InvitationResult[]
@@ -24,8 +24,8 @@ const InvitedModal = ({
 }: IInvitedModalProps) => {
   const { t } = useTranslation()
 
-  const successInvationResults = useMemo<SuccessInvationResult[]>(() => invitationResults?.filter(item => item.status === 'success') as SuccessInvationResult[], [invitationResults])
-  const failedInvationResults = useMemo<FailedInvationResult[]>(() => invitationResults?.filter(item => item.status !== 'success') as FailedInvationResult[], [invitationResults])
+  const successInvitationResults = useMemo<SuccessInvitationResult[]>(() => invitationResults?.filter(item => item.status === 'success') as SuccessInvitationResult[], [invitationResults])
+  const failedInvitationResults = useMemo<FailedInvitationResult[]>(() => invitationResults?.filter(item => item.status !== 'success') as FailedInvitationResult[], [invitationResults])
 
   return (
     <div className={s.wrap}>
@@ -49,20 +49,20 @@ const InvitedModal = ({
             <div className='mb-5 text-sm text-gray-500'>{t('common.members.invitationSentTip')}</div>
             <div className='flex flex-col gap-2 mb-9'>
               {
-                !!successInvationResults.length
+                !!successInvitationResults.length
                 && <>
                   <div className='py-2 text-sm font-Medium text-gray-900'>{t('common.members.invitationLink')}</div>
-                  {successInvationResults.map(item =>
+                  {successInvitationResults.map(item =>
                     <InvitationLink key={item.email} value={item} />)}
                 </>
               }
               {
-                !!failedInvationResults.length
+                !!failedInvitationResults.length
                 && <>
-                  <div className='py-2 text-sm font-Medium text-gray-900'>{t('common.members.failedinvitationEmails')}</div>
+                  <div className='py-2 text-sm font-Medium text-gray-900'>{t('common.members.failedInvitationEmails')}</div>
                   <div className='flex flex-wrap justify-between gap-y-1'>
                     {
-                      failedInvationResults.map(item =>
+                      failedInvitationResults.map(item =>
                         <div key={item.email} className='flex justify-center border border-red-300 rounded-md px-1 bg-orange-50'>
                           <Tooltip
                             popupContent={item.message}
diff --git a/web/app/components/header/account-setting/members-page/invited-modal/invitation-link.tsx b/web/app/components/header/account-setting/members-page/invited-modal/invitation-link.tsx
index e912847786..fc0ffdd740 100644
--- a/web/app/components/header/account-setting/members-page/invited-modal/invitation-link.tsx
+++ b/web/app/components/header/account-setting/members-page/invited-modal/invitation-link.tsx
@@ -3,12 +3,12 @@ import React, { useCallback, useEffect, useRef, useState } from 'react'
 import { t } from 'i18next'
 import copy from 'copy-to-clipboard'
 import s from './index.module.css'
-import type { SuccessInvationResult } from '.'
+import type { SuccessInvitationResult } from '.'
 import Tooltip from '@/app/components/base/tooltip'
 import { randomString } from '@/utils'
 
 type IInvitationLinkProps = {
-  value: SuccessInvationResult
+  value: SuccessInvitationResult
 }
 
 const InvitationLink = ({
diff --git a/web/app/components/header/account-setting/model-provider-page/index.tsx b/web/app/components/header/account-setting/model-provider-page/index.tsx
index 656c7b0239..a8a5a0cf42 100644
--- a/web/app/components/header/account-setting/model-provider-page/index.tsx
+++ b/web/app/components/header/account-setting/model-provider-page/index.tsx
@@ -35,9 +35,9 @@ const ModelProviderPage = () => {
   const { modelProviders: providers } = useProviderContext()
   const setShowModelModal = useModalContextSelector(state => state.setShowModelModal)
   const defaultModelNotConfigured = !textGenerationDefaultModel && !embeddingsDefaultModel && !speech2textDefaultModel && !rerankDefaultModel && !ttsDefaultModel
-  const [configedProviders, notConfigedProviders] = useMemo(() => {
-    const configedProviders: ModelProvider[] = []
-    const notConfigedProviders: ModelProvider[] = []
+  const [configuredProviders, notConfiguredProviders] = useMemo(() => {
+    const configuredProviders: ModelProvider[] = []
+    const notConfiguredProviders: ModelProvider[] = []
 
     providers.forEach((provider) => {
       if (
@@ -47,12 +47,12 @@ const ModelProviderPage = () => {
           && provider.system_configuration.quota_configurations.find(item => item.quota_type === provider.system_configuration.current_quota_type)
         )
       )
-        configedProviders.push(provider)
+        configuredProviders.push(provider)
       else
-        notConfigedProviders.push(provider)
+        notConfiguredProviders.push(provider)
     })
 
-    return [configedProviders, notConfigedProviders]
+    return [configuredProviders, notConfiguredProviders]
   }, [providers])
 
   const handleOpenModal = (
@@ -110,10 +110,10 @@ const ModelProviderPage = () => {
         />
       </div>
       {
-        !!configedProviders?.length && (
+        !!configuredProviders?.length && (
           <div className='pb-3'>
             {
-              configedProviders?.map(provider => (
+              configuredProviders?.map(provider => (
                 <ProviderAddedCard
                   key={provider.provider}
                   provider={provider}
@@ -125,7 +125,7 @@ const ModelProviderPage = () => {
         )
       }
       {
-        !!notConfigedProviders?.length && (
+        !!notConfiguredProviders?.length && (
           <>
             <div className='flex items-center mb-2 text-xs font-semibold text-gray-500'>
               + {t('common.modelProvider.addMoreModelProvider')}
@@ -133,7 +133,7 @@ const ModelProviderPage = () => {
             </div>
             <div className='grid grid-cols-3 gap-2'>
               {
-                notConfigedProviders?.map(provider => (
+                notConfiguredProviders?.map(provider => (
                   <ProviderCard
                     key={provider.provider}
                     provider={provider}
diff --git a/web/app/components/header/account-setting/model-provider-page/model-modal/Form.tsx b/web/app/components/header/account-setting/model-provider-page/model-modal/Form.tsx
index afcb4ad50e..c0a7be68a6 100644
--- a/web/app/components/header/account-setting/model-provider-page/model-modal/Form.tsx
+++ b/web/app/components/header/account-setting/model-provider-page/model-modal/Form.tsx
@@ -1,6 +1,5 @@
-import { Fragment, useState } from 'react'
+import { useState } from 'react'
 import type { FC } from 'react'
-import { RiQuestionLine } from '@remixicon/react'
 import { ValidatingTip } from '../../key-validator/ValidateStatus'
 import type {
   CredentialFormSchema,
@@ -68,29 +67,18 @@ const Form: FC<FormProps> = ({
     onChange({ ...value, [key]: val, ...shouldClearVariable })
   }
 
-  // convert tooltip '\n' to <br />
-  const renderTooltipContent = (content: string) => {
-    return content.split('\n').map((line, index, array) => (
-      <Fragment key={index}>
-        {line}
-        {index < array.length - 1 && <br />}
-      </Fragment>
-    ))
-  }
-
   const renderField = (formSchema: CredentialFormSchema) => {
     const tooltip = formSchema.tooltip
     const tooltipContent = (tooltip && (
-      <span className='ml-1 pt-1.5'>
-        <Tooltip popupContent={
-          // w-[100px] caused problem
-          <div className=''>
-            {renderTooltipContent(tooltip[language] || tooltip.en_US)}
-          </div>
-        } >
-          <RiQuestionLine className='w-3 h-3  text-gray-500' />
-        </Tooltip>
-      </span>))
+      <Tooltip
+        popupContent={
+          <div className='w-[200px]'>
+            {tooltip[language] || tooltip.en_US}
+          </div>}
+        triggerClassName='ml-1 w-4 h-4'
+        asChild={false}
+      />
+    ))
     if (formSchema.type === FormTypeEnum.textInput || formSchema.type === FormTypeEnum.secretInput || formSchema.type === FormTypeEnum.textNumber) {
       const {
         variable,
@@ -103,10 +91,10 @@ const Form: FC<FormProps> = ({
       if (show_on.length && !show_on.every(showOnItem => value[showOnItem.variable] === showOnItem.value))
         return null
 
-      const disabed = readonly || (isEditMode && (variable === '__model_type' || variable === '__model_name'))
+      const disabled = readonly || (isEditMode && (variable === '__model_type' || variable === '__model_name'))
       return (
         <div key={variable} className={cn(itemClassName, 'py-3')}>
-          <div className={cn(fieldLabelClassName, 'py-2 text-sm text-gray-900')}>
+          <div className={cn(fieldLabelClassName, 'flex items-center py-2 text-sm text-gray-900')}>
             {label[language] || label.en_US}
             {
               required && (
@@ -116,12 +104,12 @@ const Form: FC<FormProps> = ({
             {tooltipContent}
           </div>
           <Input
-            className={cn(inputClassName, `${disabed && 'cursor-not-allowed opacity-60'}`)}
+            className={cn(inputClassName, `${disabled && 'cursor-not-allowed opacity-60'}`)}
             value={(isShowDefaultValue && ((value[variable] as string) === '' || value[variable] === undefined || value[variable] === null)) ? formSchema.default : value[variable]}
             onChange={val => handleFormChange(variable, val)}
             validated={validatedSuccess}
             placeholder={placeholder?.[language] || placeholder?.en_US}
-            disabled={disabed}
+            disabled={disabled}
             type={formSchema.type === FormTypeEnum.textNumber ? 'number' : 'text'}
             {...(formSchema.type === FormTypeEnum.textNumber ? { min: (formSchema as CredentialFormSchemaNumberInput).min, max: (formSchema as CredentialFormSchemaNumberInput).max } : {})}
           />
@@ -143,11 +131,11 @@ const Form: FC<FormProps> = ({
       if (show_on.length && !show_on.every(showOnItem => value[showOnItem.variable] === showOnItem.value))
         return null
 
-      const disabed = isEditMode && (variable === '__model_type' || variable === '__model_name')
+      const disabled = isEditMode && (variable === '__model_type' || variable === '__model_name')
 
       return (
         <div key={variable} className={cn(itemClassName, 'py-3')}>
-          <div className={cn(fieldLabelClassName, 'py-2 text-sm text-gray-900')}>
+          <div className={cn(fieldLabelClassName, 'flex items-center py-2 text-sm text-gray-900')}>
             {label[language] || label.en_US}
             {
               required && (
@@ -168,7 +156,7 @@ const Form: FC<FormProps> = ({
                   className={`
                     flex items-center px-3 py-2 rounded-lg border border-gray-100 bg-gray-25 cursor-pointer
                     ${value[variable] === option.value && 'bg-white border-[1.5px] border-primary-400 shadow-sm'}
-                    ${disabed && '!cursor-not-allowed opacity-60'}
+                    ${disabled && '!cursor-not-allowed opacity-60'}
                   `}
                   onClick={() => handleFormChange(variable, option.value)}
                   key={`${variable}-${option.value}`}
@@ -203,7 +191,7 @@ const Form: FC<FormProps> = ({
 
       return (
         <div key={variable} className={cn(itemClassName, 'py-3')}>
-          <div className={cn(fieldLabelClassName, 'py-2 text-sm text-gray-900')}>
+          <div className={cn(fieldLabelClassName, 'flex items-center py-2 text-sm text-gray-900')}>
             {label[language] || label.en_US}
 
             {
@@ -247,7 +235,7 @@ const Form: FC<FormProps> = ({
         <div key={variable} className={cn(itemClassName, 'py-3')}>
           <div className='flex items-center justify-between py-2 text-sm text-gray-900'>
             <div className='flex items-center space-x-2'>
-              <span className={cn(fieldLabelClassName, 'py-2 text-sm text-gray-900')}>{label[language] || label.en_US}</span>
+              <span className={cn(fieldLabelClassName, 'flex items-center py-2 text-sm text-gray-900')}>{label[language] || label.en_US}</span>
               {
                 required && (
                   <span className='ml-1 text-red-500'>*</span>
diff --git a/web/app/components/header/account-setting/model-provider-page/model-modal/model-load-balancing-entry-modal.tsx b/web/app/components/header/account-setting/model-provider-page/model-modal/model-load-balancing-entry-modal.tsx
index 86857f1ab2..1c318b9baf 100644
--- a/web/app/components/header/account-setting/model-provider-page/model-modal/model-load-balancing-entry-modal.tsx
+++ b/web/app/components/header/account-setting/model-provider-page/model-modal/model-load-balancing-entry-modal.tsx
@@ -192,12 +192,12 @@ const ModelLoadBalancingEntryModal: FC<ModelModalProps> = ({
   })
   const getSecretValues = useCallback((v: FormValue) => {
     return secretFormSchemas.reduce((prev, next) => {
-      if (v[next.variable] === initialFormSchemasValue[next.variable])
+      if (isEditMode && v[next.variable] && v[next.variable] === initialFormSchemasValue[next.variable])
         prev[next.variable] = '[__HIDDEN__]'
 
       return prev
     }, {} as Record<string, string>)
-  }, [initialFormSchemasValue, secretFormSchemas])
+  }, [initialFormSchemasValue, isEditMode, secretFormSchemas])
 
   // const handleValueChange = ({ __model_type, __model_name, ...v }: FormValue) => {
   const handleValueChange = (v: FormValue) => {
@@ -214,6 +214,7 @@ const ModelLoadBalancingEntryModal: FC<ModelModalProps> = ({
           ...value,
           ...getSecretValues(value),
         },
+        entry?.id,
       )
       if (res.status === ValidatedStatus.Success) {
         // notify({ type: 'success', message: t('common.actionMsg.modifiedSuccessfully') })
diff --git a/web/app/components/header/account-setting/model-provider-page/model-parameter-modal/parameter-item.tsx b/web/app/components/header/account-setting/model-provider-page/model-parameter-modal/parameter-item.tsx
index e60ef418ed..376a08c120 100644
--- a/web/app/components/header/account-setting/model-provider-page/model-parameter-modal/parameter-item.tsx
+++ b/web/app/components/header/account-setting/model-provider-page/model-parameter-modal/parameter-item.tsx
@@ -127,12 +127,10 @@ const ParameterItem: FC<ParameterItemProps> = ({
       && !isNullOrUndefined(parameterRule.min)
       && !isNullOrUndefined(parameterRule.max)
 
-    if (parameterRule.type === 'int' || parameterRule.type === 'float') {
+    if (parameterRule.type === 'int') {
       let step = 100
       if (parameterRule.max) {
-        if (parameterRule.max < 10)
-          step = 0.1
-        else if (parameterRule.max < 100)
+        if (parameterRule.max < 100)
           step = 1
         else if (parameterRule.max < 1000)
           step = 10
@@ -164,6 +162,31 @@ const ParameterItem: FC<ParameterItemProps> = ({
       )
     }
 
+    if (parameterRule.type === 'float') {
+      return (
+        <>
+          {numberInputWithSlide && <Slider
+            className='w-[120px]'
+            value={renderValue as number}
+            min={parameterRule.min}
+            max={parameterRule.max}
+            step={0.1}
+            onChange={handleSlideChange}
+          />}
+          <input
+            ref={numberInputRef}
+            className='shrink-0 block ml-4 pl-3 w-16 h-8 appearance-none outline-none rounded-lg bg-gray-100 text-[13px] text-gra-900'
+            type='number'
+            max={parameterRule.max}
+            min={parameterRule.min}
+            step={numberInputWithSlide ? 0.1 : +`0.${parameterRule.precision || 0}`}
+            onChange={handleNumberInputChange}
+            onBlur={handleNumberInputBlur}
+          />
+        </>
+      )
+    }
+
     if (parameterRule.type === 'boolean') {
       return (
         <Radio.Group
diff --git a/web/app/components/header/account-setting/model-provider-page/provider-added-card/credential-panel.tsx b/web/app/components/header/account-setting/model-provider-page/provider-added-card/credential-panel.tsx
index c25693966e..f141e0018c 100644
--- a/web/app/components/header/account-setting/model-provider-page/provider-added-card/credential-panel.tsx
+++ b/web/app/components/header/account-setting/model-provider-page/provider-added-card/credential-panel.tsx
@@ -36,7 +36,7 @@ const CredentialPanel: FC<CredentialPanelProps> = ({
   const customConfig = provider.custom_configuration
   const systemConfig = provider.system_configuration
   const priorityUseType = provider.preferred_provider_type
-  const customConfiged = customConfig.status === CustomConfigurationStatusEnum.active
+  const isCustomConfigured = customConfig.status === CustomConfigurationStatusEnum.active
   const configurateMethods = provider.configurate_methods
 
   const handleChangePriority = async (key: PreferredProviderTypeEnum) => {
@@ -69,7 +69,7 @@ const CredentialPanel: FC<CredentialPanelProps> = ({
           <div className='shrink-0 relative ml-1 p-1 w-[112px] rounded-lg bg-white/[0.3] border-[0.5px] border-black/5'>
             <div className='flex items-center justify-between mb-1 pt-1 pl-2 pr-[7px] h-5 text-xs font-medium text-gray-500'>
               API-KEY
-              <Indicator color={customConfiged ? 'green' : 'gray'} />
+              <Indicator color={isCustomConfigured ? 'green' : 'gray'} />
             </div>
             <div className='flex items-center gap-0.5'>
               <Button
@@ -81,7 +81,7 @@ const CredentialPanel: FC<CredentialPanelProps> = ({
                 {t('common.operation.setup')}
               </Button>
               {
-                systemConfig.enabled && customConfiged && (
+                systemConfig.enabled && isCustomConfigured && (
                   <PrioritySelector
                     value={priorityUseType}
                     onSelect={handleChangePriority}
@@ -98,7 +98,7 @@ const CredentialPanel: FC<CredentialPanelProps> = ({
         )
       }
       {
-        systemConfig.enabled && customConfiged && !provider.provider_credential_schema && (
+        systemConfig.enabled && isCustomConfigured && !provider.provider_credential_schema && (
           <div className='ml-1'>
             <PrioritySelector
               value={priorityUseType}
diff --git a/web/app/components/header/account-setting/model-provider-page/utils.ts b/web/app/components/header/account-setting/model-provider-page/utils.ts
index 8cad399763..165926b2bb 100644
--- a/web/app/components/header/account-setting/model-provider-page/utils.ts
+++ b/web/app/components/header/account-setting/model-provider-page/utils.ts
@@ -56,14 +56,14 @@ export const validateCredentials = async (predefined: boolean, provider: string,
   }
 }
 
-export const validateLoadBalancingCredentials = async (predefined: boolean, provider: string, v: FormValue): Promise<{
+export const validateLoadBalancingCredentials = async (predefined: boolean, provider: string, v: FormValue, id?: string): Promise<{
   status: ValidatedStatus
   message?: string
 }> => {
   const { __model_name, __model_type, ...credentials } = v
   try {
     const res = await validateModelLoadBalancingCredentials({
-      url: `/workspaces/current/model-providers/${provider}/models/load-balancing-configs/credentials-validate`,
+      url: `/workspaces/current/model-providers/${provider}/models/load-balancing-configs/${id ? `${id}/` : ''}credentials-validate`,
       body: {
         model: __model_name,
         model_type: __model_type,
diff --git a/web/app/components/header/explore-nav/index.tsx b/web/app/components/header/explore-nav/index.tsx
index 032b886613..0046fc293d 100644
--- a/web/app/components/header/explore-nav/index.tsx
+++ b/web/app/components/header/explore-nav/index.tsx
@@ -17,16 +17,16 @@ const ExploreNav = ({
 }: ExploreNavProps) => {
   const { t } = useTranslation()
   const selectedSegment = useSelectedLayoutSegment()
-  const actived = selectedSegment === 'explore'
+  const activated = selectedSegment === 'explore'
 
   return (
     <Link href="/explore/apps" className={classNames(
       className, 'group',
-      actived && 'bg-components-main-nav-nav-button-bg-active shadow-md',
-      actived ? 'text-components-main-nav-nav-button-text-active' : 'text-components-main-nav-nav-button-text hover:bg-components-main-nav-nav-button-bg-hover',
+      activated && 'bg-components-main-nav-nav-button-bg-active shadow-md',
+      activated ? 'text-components-main-nav-nav-button-text-active' : 'text-components-main-nav-nav-button-text hover:bg-components-main-nav-nav-button-bg-hover',
     )}>
       {
-        actived
+        activated
           ? <RiPlanetFill className='mr-2 w-4 h-4' />
           : <RiPlanetLine className='mr-2 w-4 h-4' />
       }
diff --git a/web/app/components/header/nav/index.tsx b/web/app/components/header/nav/index.tsx
index 1ea36c5123..bfb4324320 100644
--- a/web/app/components/header/nav/index.tsx
+++ b/web/app/components/header/nav/index.tsx
@@ -34,21 +34,21 @@ const Nav = ({
   const setAppDetail = useAppStore(state => state.setAppDetail)
   const [hovered, setHovered] = useState(false)
   const segment = useSelectedLayoutSegment()
-  const isActived = Array.isArray(activeSegment) ? activeSegment.includes(segment!) : segment === activeSegment
+  const isActivated = Array.isArray(activeSegment) ? activeSegment.includes(segment!) : segment === activeSegment
 
   return (
     <div className={`
       flex items-center h-8 mr-0 sm:mr-3 px-0.5 rounded-xl text-sm shrink-0 font-medium
-      ${isActived && 'bg-components-main-nav-nav-button-bg-active shadow-md font-semibold'}
-      ${!curNav && !isActived && 'hover:bg-components-main-nav-nav-button-bg-hover'}
+      ${isActivated && 'bg-components-main-nav-nav-button-bg-active shadow-md font-semibold'}
+      ${!curNav && !isActivated && 'hover:bg-components-main-nav-nav-button-bg-hover'}
     `}>
       <Link href={link}>
         <div
           onClick={() => setAppDetail()}
           className={classNames(`
             flex items-center h-7 px-2.5 cursor-pointer rounded-[10px]
-            ${isActived ? 'text-components-main-nav-nav-button-text-active' : 'text-components-main-nav-nav-button-text'}
-            ${curNav && isActived && 'hover:bg-components-main-nav-nav-button-bg-active-hover'}
+            ${isActivated ? 'text-components-main-nav-nav-button-text-active' : 'text-components-main-nav-nav-button-text'}
+            ${curNav && isActivated && 'hover:bg-components-main-nav-nav-button-bg-active-hover'}
           `)}
           onMouseEnter={() => setHovered(true)}
           onMouseLeave={() => setHovered(false)}
@@ -57,7 +57,7 @@ const Nav = ({
             {
               (hovered && curNav)
                 ? <ArrowNarrowLeft className='w-4 h-4' />
-                : isActived
+                : isActivated
                   ? activeIcon
                   : icon
             }
@@ -66,7 +66,7 @@ const Nav = ({
         </div>
       </Link>
       {
-        curNav && isActived && (
+        curNav && isActivated && (
           <>
             <div className='font-light text-gray-300 '>/</div>
             <NavSelector
diff --git a/web/app/components/header/nav/nav-selector/index.tsx b/web/app/components/header/nav/nav-selector/index.tsx
index ab5189bf92..ba9c58fadb 100644
--- a/web/app/components/header/nav/nav-selector/index.tsx
+++ b/web/app/components/header/nav/nav-selector/index.tsx
@@ -11,7 +11,7 @@ import { useRouter } from 'next/navigation'
 import { debounce } from 'lodash-es'
 import cn from '@/utils/classnames'
 import AppIcon from '@/app/components/base/app-icon'
-import { AiText, ChatBot, CuteRobote } from '@/app/components/base/icons/src/vender/solid/communication'
+import { AiText, ChatBot, CuteRobot } from '@/app/components/base/icons/src/vender/solid/communication'
 import { Route } from '@/app/components/base/icons/src/vender/solid/mapsAndTravel'
 import { useAppContext } from '@/context/app-context'
 import { useStore as useAppStore } from '@/app/components/app/store'
@@ -94,7 +94,7 @@ const NavSelector = ({ curNav, navs, createText, isApp, onCreate, onLoadmore }:
                                 <ChatBot className='w-2.5 h-2.5 text-[#1570EF]' />
                               )}
                               {nav.mode === 'agent-chat' && (
-                                <CuteRobote className='w-2.5 h-2.5 text-indigo-600' />
+                                <CuteRobot className='w-2.5 h-2.5 text-indigo-600' />
                               )}
                               {nav.mode === 'chat' && (
                                 <ChatBot className='w-2.5 h-2.5 text-[#1570EF]' />
diff --git a/web/app/components/header/tools-nav/index.tsx b/web/app/components/header/tools-nav/index.tsx
index 5184f5e5ce..096a552229 100644
--- a/web/app/components/header/tools-nav/index.tsx
+++ b/web/app/components/header/tools-nav/index.tsx
@@ -17,16 +17,16 @@ const ToolsNav = ({
 }: ToolsNavProps) => {
   const { t } = useTranslation()
   const selectedSegment = useSelectedLayoutSegment()
-  const actived = selectedSegment === 'tools'
+  const activated = selectedSegment === 'tools'
 
   return (
     <Link href="/tools" className={classNames(
       className, 'group',
-      actived && 'bg-white shadow-md',
-      actived ? 'text-primary-600' : 'text-gray-500 hover:bg-gray-200',
+      activated && 'bg-white shadow-md',
+      activated ? 'text-primary-600' : 'text-gray-500 hover:bg-gray-200',
     )}>
       {
-        actived
+        activated
           ? <RiHammerFill className='mr-2 w-4 h-4' />
           : <RiHammerLine className='mr-2 w-4 h-4' />
       }
diff --git a/web/app/components/share/text-generation/index.tsx b/web/app/components/share/text-generation/index.tsx
index dd6efa86fa..a2f6864242 100644
--- a/web/app/components/share/text-generation/index.tsx
+++ b/web/app/components/share/text-generation/index.tsx
@@ -163,8 +163,8 @@ const TextGeneration: FC<IMainProps> = ({
   }
   const allSuccessTaskList = allTaskList.filter(task => task.status === TaskStatus.completed)
   const allFailedTaskList = allTaskList.filter(task => task.status === TaskStatus.failed)
-  const allTaskFinished = allTaskList.every(task => task.status === TaskStatus.completed)
-  const allTaskRuned = allTaskList.every(task => [TaskStatus.completed, TaskStatus.failed].includes(task.status))
+  const allTasksFinished = allTaskList.every(task => task.status === TaskStatus.completed)
+  const allTasksRun = allTaskList.every(task => [TaskStatus.completed, TaskStatus.failed].includes(task.status))
   const [batchCompletionRes, doSetBatchCompletionRes] = useState<Record<string, string>>({})
   const batchCompletionResRef = useRef<Record<string, string>>({})
   const setBatchCompletionRes = (res: Record<string, string>) => {
@@ -286,7 +286,7 @@ const TextGeneration: FC<IMainProps> = ({
   const handleRunBatch = (data: string[][]) => {
     if (!checkBatchInputs(data))
       return
-    if (!allTaskFinished) {
+    if (!allTasksFinished) {
       notify({ type: 'info', message: t('appDebug.errorMessage.waitForBatchResponse') })
       return
     }
@@ -318,17 +318,17 @@ const TextGeneration: FC<IMainProps> = ({
     showResSidebar()
   }
   const handleCompleted = (completionRes: string, taskId?: number, isSuccess?: boolean) => {
-    const allTasklistLatest = getLatestTaskList()
+    const allTaskListLatest = getLatestTaskList()
     const batchCompletionResLatest = getBatchCompletionRes()
-    const pendingTaskList = allTasklistLatest.filter(task => task.status === TaskStatus.pending)
-    const hadRunedTaskNum = 1 + allTasklistLatest.filter(task => [TaskStatus.completed, TaskStatus.failed].includes(task.status)).length
-    const needToAddNextGroupTask = (getCurrGroupNum() !== hadRunedTaskNum) && pendingTaskList.length > 0 && (hadRunedTaskNum % GROUP_SIZE === 0 || (allTasklistLatest.length - hadRunedTaskNum < GROUP_SIZE))
+    const pendingTaskList = allTaskListLatest.filter(task => task.status === TaskStatus.pending)
+    const runTasksCount = 1 + allTaskListLatest.filter(task => [TaskStatus.completed, TaskStatus.failed].includes(task.status)).length
+    const needToAddNextGroupTask = (getCurrGroupNum() !== runTasksCount) && pendingTaskList.length > 0 && (runTasksCount % GROUP_SIZE === 0 || (allTaskListLatest.length - runTasksCount < GROUP_SIZE))
     // avoid add many task at the same time
     if (needToAddNextGroupTask)
-      setCurrGroupNum(hadRunedTaskNum)
+      setCurrGroupNum(runTasksCount)
 
     const nextPendingTaskIds = needToAddNextGroupTask ? pendingTaskList.slice(0, GROUP_SIZE).map(item => item.id) : []
-    const newAllTaskList = allTasklistLatest.map((item) => {
+    const newAllTaskList = allTaskListLatest.map((item) => {
       if (item.id === taskId) {
         return {
           ...item,
@@ -393,7 +393,7 @@ const TextGeneration: FC<IMainProps> = ({
       })
       const prompt_variables = userInputsFormToPromptVariables(user_input_form)
       setPromptConfig({
-        prompt_template: '', // placeholder for feture
+        prompt_template: '', // placeholder for future
         prompt_variables,
       } as PromptConfig)
       setMoreLikeThisConfig(more_like_this)
@@ -587,7 +587,7 @@ const TextGeneration: FC<IMainProps> = ({
                   isRight: true,
                   extra: savedMessages.length > 0
                     ? (
-                      <div className='ml-1 flext items-center h-5 px-1.5 rounded-md border border-gray-200 text-gray-500 text-xs font-medium'>
+                      <div className='ml-1 flex items-center h-5 px-1.5 rounded-md border border-gray-200 text-gray-500 text-xs font-medium'>
                         {savedMessages.length}
                       </div>
                     )
@@ -614,7 +614,7 @@ const TextGeneration: FC<IMainProps> = ({
               <RunBatch
                 vars={promptConfig.prompt_variables}
                 onSend={handleRunBatch}
-                isAllFinished={allTaskRuned}
+                isAllFinished={allTasksRun}
               />
             </div>
 
diff --git a/web/app/components/share/text-generation/result/content.tsx b/web/app/components/share/text-generation/result/content.tsx
index 17cce0fae5..4e39db42c8 100644
--- a/web/app/components/share/text-generation/result/content.tsx
+++ b/web/app/components/share/text-generation/result/content.tsx
@@ -1,14 +1,14 @@
 import type { FC } from 'react'
 import React from 'react'
 import Header from './header'
-import type { Feedbacktype } from '@/app/components/base/chat/chat/type'
+import type { FeedbackType } from '@/app/components/base/chat/chat/type'
 import { format } from '@/service/base'
 
 export type IResultProps = {
   content: string
   showFeedback: boolean
-  feedback: Feedbacktype
-  onFeedback: (feedback: Feedbacktype) => void
+  feedback: FeedbackType
+  onFeedback: (feedback: FeedbackType) => void
 }
 const Result: FC<IResultProps> = ({
   content,
diff --git a/web/app/components/share/text-generation/result/header.tsx b/web/app/components/share/text-generation/result/header.tsx
index bd5c317153..0233b098d0 100644
--- a/web/app/components/share/text-generation/result/header.tsx
+++ b/web/app/components/share/text-generation/result/header.tsx
@@ -4,7 +4,7 @@ import React from 'react'
 import { useTranslation } from 'react-i18next'
 import { ClipboardDocumentIcon, HandThumbDownIcon, HandThumbUpIcon } from '@heroicons/react/24/outline'
 import copy from 'copy-to-clipboard'
-import type { Feedbacktype } from '@/app/components/base/chat/chat/type'
+import type { FeedbackType } from '@/app/components/base/chat/chat/type'
 import Button from '@/app/components/base/button'
 import Toast from '@/app/components/base/toast'
 import Tooltip from '@/app/components/base/tooltip'
@@ -12,8 +12,8 @@ import Tooltip from '@/app/components/base/tooltip'
 type IResultHeaderProps = {
   result: string
   showFeedback: boolean
-  feedback: Feedbacktype
-  onFeedback: (feedback: Feedbacktype) => void
+  feedback: FeedbackType
+  onFeedback: (feedback: FeedbackType) => void
 }
 
 const Header: FC<IResultHeaderProps> = ({
diff --git a/web/app/components/share/text-generation/result/index.tsx b/web/app/components/share/text-generation/result/index.tsx
index caa2f9183e..a61302fc98 100644
--- a/web/app/components/share/text-generation/result/index.tsx
+++ b/web/app/components/share/text-generation/result/index.tsx
@@ -9,7 +9,7 @@ import TextGenerationRes from '@/app/components/app/text-generate/item'
 import NoData from '@/app/components/share/text-generation/no-data'
 import Toast from '@/app/components/base/toast'
 import { sendCompletionMessage, sendWorkflowMessage, updateFeedback } from '@/service/share'
-import type { Feedbacktype } from '@/app/components/base/chat/chat/type'
+import type { FeedbackType } from '@/app/components/base/chat/chat/type'
 import Loading from '@/app/components/base/loading'
 import type { PromptConfig } from '@/models/debug'
 import type { InstalledApp } from '@/models/explore'
@@ -83,23 +83,23 @@ const Result: FC<IResultProps> = ({
     doSetCompletionRes(res)
   }
   const getCompletionRes = () => completionResRef.current
-  const [workflowProcessData, doSetWorkflowProccessData] = useState<WorkflowProcess>()
+  const [workflowProcessData, doSetWorkflowProcessData] = useState<WorkflowProcess>()
   const workflowProcessDataRef = useRef<WorkflowProcess>()
-  const setWorkflowProccessData = (data: WorkflowProcess) => {
+  const setWorkflowProcessData = (data: WorkflowProcess) => {
     workflowProcessDataRef.current = data
-    doSetWorkflowProccessData(data)
+    doSetWorkflowProcessData(data)
   }
-  const getWorkflowProccessData = () => workflowProcessDataRef.current
+  const getWorkflowProcessData = () => workflowProcessDataRef.current
 
   const { notify } = Toast
   const isNoData = !completionRes
 
   const [messageId, setMessageId] = useState<string | null>(null)
-  const [feedback, setFeedback] = useState<Feedbacktype>({
+  const [feedback, setFeedback] = useState<FeedbackType>({
     rating: null,
   })
 
-  const handleFeedback = async (feedback: Feedbacktype) => {
+  const handleFeedback = async (feedback: FeedbackType) => {
     await updateFeedback({ url: `/messages/${messageId}/feedbacks`, body: { rating: feedback.rating } }, isInstalledApp, installedAppInfo?.id)
     setFeedback(feedback)
   }
@@ -196,14 +196,12 @@ const Result: FC<IResultProps> = ({
     })()
 
     if (isWorkflow) {
-      let isInIteration = false
-
       sendWorkflowMessage(
         data,
         {
           onWorkflowStarted: ({ workflow_run_id }) => {
             tempMessageId = workflow_run_id
-            setWorkflowProccessData({
+            setWorkflowProcessData({
               status: WorkflowRunningStatus.Running,
               tracing: [],
               expand: false,
@@ -211,7 +209,7 @@ const Result: FC<IResultProps> = ({
             })
           },
           onIterationStart: ({ data }) => {
-            setWorkflowProccessData(produce(getWorkflowProccessData()!, (draft) => {
+            setWorkflowProcessData(produce(getWorkflowProcessData()!, (draft) => {
               draft.expand = true
               draft.tracing!.push({
                 ...data,
@@ -219,26 +217,31 @@ const Result: FC<IResultProps> = ({
                 expand: true,
               } as any)
             }))
-            isInIteration = true
           },
           onIterationNext: () => {
+            setWorkflowProcessData(produce(getWorkflowProcessData()!, (draft) => {
+              draft.expand = true
+              const iterations = draft.tracing.find(item => item.node_id === data.node_id
+                && (item.execution_metadata?.parallel_id === data.execution_metadata?.parallel_id || item.parallel_id === data.execution_metadata?.parallel_id))!
+              iterations?.details!.push([])
+            }))
           },
           onIterationFinish: ({ data }) => {
-            setWorkflowProccessData(produce(getWorkflowProccessData()!, (draft) => {
+            setWorkflowProcessData(produce(getWorkflowProcessData()!, (draft) => {
               draft.expand = true
-              // const iteration = draft.tracing![draft.tracing!.length - 1]
-              draft.tracing![draft.tracing!.length - 1] = {
+              const iterationsIndex = draft.tracing.findIndex(item => item.node_id === data.node_id
+                && (item.execution_metadata?.parallel_id === data.execution_metadata?.parallel_id || item.parallel_id === data.execution_metadata?.parallel_id))!
+              draft.tracing[iterationsIndex] = {
                 ...data,
                 expand: !!data.error,
               } as any
             }))
-            isInIteration = false
           },
           onNodeStarted: ({ data }) => {
-            if (isInIteration)
+            if (data.iteration_id)
               return
 
-            setWorkflowProccessData(produce(getWorkflowProccessData()!, (draft) => {
+            setWorkflowProcessData(produce(getWorkflowProcessData()!, (draft) => {
               draft.expand = true
               draft.tracing!.push({
                 ...data,
@@ -248,11 +251,12 @@ const Result: FC<IResultProps> = ({
             }))
           },
           onNodeFinished: ({ data }) => {
-            if (isInIteration)
+            if (data.iteration_id)
               return
 
-            setWorkflowProccessData(produce(getWorkflowProccessData()!, (draft) => {
-              const currentIndex = draft.tracing!.findIndex(trace => trace.node_id === data.node_id)
+            setWorkflowProcessData(produce(getWorkflowProcessData()!, (draft) => {
+              const currentIndex = draft.tracing!.findIndex(trace => trace.node_id === data.node_id
+                && (trace.execution_metadata?.parallel_id === data.execution_metadata?.parallel_id || trace.parallel_id === data.execution_metadata?.parallel_id))
               if (currentIndex > -1 && draft.tracing) {
                 draft.tracing[currentIndex] = {
                   ...(draft.tracing[currentIndex].extras
@@ -265,11 +269,13 @@ const Result: FC<IResultProps> = ({
             }))
           },
           onWorkflowFinished: ({ data }) => {
-            if (isTimeout)
+            if (isTimeout) {
+              notify({ type: 'warning', message: t('appDebug.warningMessage.timeoutExceeded') })
               return
+            }
             if (data.error) {
               notify({ type: 'error', message: data.error })
-              setWorkflowProccessData(produce(getWorkflowProccessData()!, (draft) => {
+              setWorkflowProcessData(produce(getWorkflowProcessData()!, (draft) => {
                 draft.status = WorkflowRunningStatus.Failed
               }))
               setRespondingFalse()
@@ -277,7 +283,7 @@ const Result: FC<IResultProps> = ({
               isEnd = true
               return
             }
-            setWorkflowProccessData(produce(getWorkflowProccessData()!, (draft) => {
+            setWorkflowProcessData(produce(getWorkflowProcessData()!, (draft) => {
               draft.status = WorkflowRunningStatus.Succeeded
             }))
             if (!data.outputs) {
@@ -287,7 +293,7 @@ const Result: FC<IResultProps> = ({
               setCompletionRes(data.outputs)
               const isStringOutput = Object.keys(data.outputs).length === 1 && typeof data.outputs[Object.keys(data.outputs)[0]] === 'string'
               if (isStringOutput) {
-                setWorkflowProccessData(produce(getWorkflowProccessData()!, (draft) => {
+                setWorkflowProcessData(produce(getWorkflowProcessData()!, (draft) => {
                   draft.resultText = data.outputs[Object.keys(data.outputs)[0]]
                 }))
               }
@@ -299,13 +305,13 @@ const Result: FC<IResultProps> = ({
           },
           onTextChunk: (params) => {
             const { data: { text } } = params
-            setWorkflowProccessData(produce(getWorkflowProccessData()!, (draft) => {
+            setWorkflowProcessData(produce(getWorkflowProcessData()!, (draft) => {
               draft.resultText += text
             }))
           },
           onTextReplace: (params) => {
             const { data: { text } } = params
-            setWorkflowProccessData(produce(getWorkflowProccessData()!, (draft) => {
+            setWorkflowProcessData(produce(getWorkflowProcessData()!, (draft) => {
               draft.resultText = text
             }))
           },
@@ -322,8 +328,10 @@ const Result: FC<IResultProps> = ({
           setCompletionRes(res.join(''))
         },
         onCompleted: () => {
-          if (isTimeout)
+          if (isTimeout) {
+            notify({ type: 'warning', message: t('appDebug.warningMessage.timeoutExceeded') })
             return
+          }
           setRespondingFalse()
           setMessageId(tempMessageId)
           onCompleted(getCompletionRes(), taskId, true)
@@ -334,8 +342,10 @@ const Result: FC<IResultProps> = ({
           setCompletionRes(res.join(''))
         },
         onError() {
-          if (isTimeout)
+          if (isTimeout) {
+            notify({ type: 'warning', message: t('appDebug.warningMessage.timeoutExceeded') })
             return
+          }
           setRespondingFalse()
           onCompleted(getCompletionRes(), taskId, false)
           isEnd = true
diff --git a/web/app/components/share/text-generation/run-once/index.tsx b/web/app/components/share/text-generation/run-once/index.tsx
index e3b8d6a0a4..e74616b08d 100644
--- a/web/app/components/share/text-generation/run-once/index.tsx
+++ b/web/app/components/share/text-generation/run-once/index.tsx
@@ -1,4 +1,4 @@
-import type { FC } from 'react'
+import type { FC, FormEvent } from 'react'
 import React from 'react'
 import { useTranslation } from 'react-i18next'
 import {
@@ -39,11 +39,16 @@ const RunOnce: FC<IRunOnceProps> = ({
     onInputsChange(newInputs)
   }
 
+  const onSubmit = (e: FormEvent<HTMLFormElement>) => {
+    e.preventDefault()
+    onSend()
+  }
+
   return (
     <div className="">
       <section>
         {/* input form */}
-        <form>
+        <form onSubmit={onSubmit}>
           {promptConfig.prompt_variables.map(item => (
             <div className='w-full mt-4' key={item.key}>
               <label className='text-gray-900 text-sm font-medium'>{item.name}</label>
@@ -65,12 +70,6 @@ const RunOnce: FC<IRunOnceProps> = ({
                     placeholder={`${item.name}${!item.required ? `(${t('appDebug.variableTable.optional')})` : ''}`}
                     value={inputs[item.key]}
                     onChange={(e) => { onInputsChange({ ...inputs, [item.key]: e.target.value }) }}
-                    onKeyDown={(e) => {
-                      if (e.key === 'Enter') {
-                        e.preventDefault()
-                        onSend()
-                      }
-                    }}
                     maxLength={item.max_length || DEFAULT_VALUE_MAX_LEN}
                   />
                 )}
@@ -124,8 +123,8 @@ const RunOnce: FC<IRunOnceProps> = ({
                 <span className='text-[13px]'>{t('common.operation.clear')}</span>
               </Button>
               <Button
+                type='submit'
                 variant="primary"
-                onClick={onSend}
                 disabled={false}
               >
                 <PlayIcon className="shrink-0 w-4 h-4 mr-1" aria-hidden="true" />
diff --git a/web/app/components/tools/add-tool-modal/category.tsx b/web/app/components/tools/add-tool-modal/category.tsx
index cc2c325410..a18c30ad54 100644
--- a/web/app/components/tools/add-tool-modal/category.tsx
+++ b/web/app/components/tools/add-tool-modal/category.tsx
@@ -17,7 +17,7 @@ type Props = {
 
 const Icon = ({ svgString, active }: { svgString: string; active: boolean }) => {
   const svgRef = useRef<SVGSVGElement | null>(null)
-  const SVGParsor = (svg: string) => {
+  const SVGParser = (svg: string) => {
     if (!svg)
       return null
     const parser = new DOMParser()
@@ -25,7 +25,7 @@ const Icon = ({ svgString, active }: { svgString: string; active: boolean }) =>
     return doc.documentElement
   }
   useMount(() => {
-    const svgElement = SVGParsor(svgString)
+    const svgElement = SVGParser(svgString)
     if (svgRef.current && svgElement)
       svgRef.current.appendChild(svgElement)
   })
diff --git a/web/app/components/tools/add-tool-modal/tools.tsx b/web/app/components/tools/add-tool-modal/tools.tsx
index af26dd3e25..f6080a1c23 100644
--- a/web/app/components/tools/add-tool-modal/tools.tsx
+++ b/web/app/components/tools/add-tool-modal/tools.tsx
@@ -90,7 +90,6 @@ const Blocks = ({
                   )}
                 </div>
               )}
-              needsDelay
             >
               <div className='group/item flex items-center w-full pl-3 pr-1 h-8 rounded-lg hover:bg-gray-50 cursor-pointer'>
                 <BlockIcon
diff --git a/web/app/components/workflow/candidate-node.tsx b/web/app/components/workflow/candidate-node.tsx
index faae545b3b..16d6f852b2 100644
--- a/web/app/components/workflow/candidate-node.tsx
+++ b/web/app/components/workflow/candidate-node.tsx
@@ -14,9 +14,11 @@ import {
 } from './store'
 import { WorkflowHistoryEvent, useNodesInteractions, useWorkflowHistory } from './hooks'
 import { CUSTOM_NODE } from './constants'
+import { getIterationStartNode } from './utils'
 import CustomNode from './nodes'
 import CustomNoteNode from './note-node'
 import { CUSTOM_NOTE_NODE } from './note-node/constants'
+import { BlockEnum } from './types'
 
 const CandidateNode = () => {
   const store = useStoreApi()
@@ -52,6 +54,8 @@ const CandidateNode = () => {
             y,
           },
         })
+        if (candidateNode.data.type === BlockEnum.Iteration)
+          draft.push(getIterationStartNode(candidateNode.id))
       })
       setNodes(newNodes)
       if (candidateNode.type === CUSTOM_NOTE_NODE)
diff --git a/web/app/components/workflow/constants.ts b/web/app/components/workflow/constants.ts
index 070748bab0..6a4629e9c8 100644
--- a/web/app/components/workflow/constants.ts
+++ b/web/app/components/workflow/constants.ts
@@ -15,6 +15,7 @@ import VariableAssignerDefault from './nodes/variable-assigner/default'
 import AssignerDefault from './nodes/assigner/default'
 import EndNodeDefault from './nodes/end/default'
 import IterationDefault from './nodes/iteration/default'
+import IterationStartDefault from './nodes/iteration-start/default'
 
 type NodesExtraData = {
   author: string
@@ -89,6 +90,15 @@ export const NODES_EXTRA_DATA: Record<BlockEnum, NodesExtraData> = {
     getAvailableNextNodes: IterationDefault.getAvailableNextNodes,
     checkValid: IterationDefault.checkValid,
   },
+  [BlockEnum.IterationStart]: {
+    author: 'Dify',
+    about: '',
+    availablePrevNodes: [],
+    availableNextNodes: [],
+    getAvailablePrevNodes: IterationStartDefault.getAvailablePrevNodes,
+    getAvailableNextNodes: IterationStartDefault.getAvailableNextNodes,
+    checkValid: IterationStartDefault.checkValid,
+  },
   [BlockEnum.Code]: {
     author: 'Dify',
     about: '',
@@ -222,6 +232,12 @@ export const NODES_INITIAL_DATA = {
     desc: '',
     ...IterationDefault.defaultValue,
   },
+  [BlockEnum.IterationStart]: {
+    type: BlockEnum.IterationStart,
+    title: '',
+    desc: '',
+    ...IterationStartDefault.defaultValue,
+  },
   [BlockEnum.Code]: {
     type: BlockEnum.Code,
     title: '',
@@ -305,11 +321,13 @@ export const AUTO_LAYOUT_OFFSET = {
 export const ITERATION_NODE_Z_INDEX = 1
 export const ITERATION_CHILDREN_Z_INDEX = 1002
 export const ITERATION_PADDING = {
-  top: 85,
+  top: 65,
   right: 16,
   bottom: 20,
   left: 16,
 }
+export const PARALLEL_LIMIT = 10
+export const PARALLEL_DEPTH_LIMIT = 3
 
 export const RETRIEVAL_OUTPUT_STRUCT = `{
   "content": "",
@@ -412,4 +430,5 @@ export const PARAMETER_EXTRACTOR_COMMON_STRUCT: Var[] = [
 
 export const WORKFLOW_DATA_UPDATE = 'WORKFLOW_DATA_UPDATE'
 export const CUSTOM_NODE = 'custom'
+export const CUSTOM_EDGE = 'custom'
 export const DSL_EXPORT_CHECK = 'DSL_EXPORT_CHECK'
diff --git a/web/app/components/workflow/custom-edge.tsx b/web/app/components/workflow/custom-edge.tsx
index 5e945790d8..68e2ef945e 100644
--- a/web/app/components/workflow/custom-edge.tsx
+++ b/web/app/components/workflow/custom-edge.tsx
@@ -79,7 +79,7 @@ const CustomEdge = ({
         id={id}
         path={edgePath}
         style={{
-          stroke: (selected || data?._connectedNodeIsHovering || data?._runned) ? '#2970FF' : '#D0D5DD',
+          stroke: (selected || data?._connectedNodeIsHovering || data?._run) ? '#2970FF' : '#D0D5DD',
           strokeWidth: 2,
         }}
       />
diff --git a/web/app/components/workflow/header/checklist.tsx b/web/app/components/workflow/header/checklist.tsx
index 7de9cfa2f4..6a9a6a6b9f 100644
--- a/web/app/components/workflow/header/checklist.tsx
+++ b/web/app/components/workflow/header/checklist.tsx
@@ -125,7 +125,7 @@ const WorkflowChecklist = ({
                                 <div className='px-3 py-2 bg-gray-25 rounded-b-lg'>
                                   <div className='flex text-xs leading-[18px] text-gray-500'>
                                     <AlertTriangle className='mt-[3px] mr-2 w-3 h-3 text-[#F79009]' />
-                                    {t('workflow.common.needConnecttip')}
+                                    {t('workflow.common.needConnectTip')}
                                   </div>
                                 </div>
                               )
diff --git a/web/app/components/workflow/header/view-history.tsx b/web/app/components/workflow/header/view-history.tsx
index 06eebfd329..a18ddad65d 100644
--- a/web/app/components/workflow/header/view-history.tsx
+++ b/web/app/components/workflow/header/view-history.tsx
@@ -32,7 +32,7 @@ import {
 } from '@/app/components/base/icons/src/vender/line/time'
 import { AlertTriangle } from '@/app/components/base/icons/src/vender/line/alertsAndFeedback'
 import {
-  fetcChatRunHistory,
+  fetchChatRunHistory,
   fetchWorkflowRunHistory,
 } from '@/service/workflow'
 import Loading from '@/app/components/base/loading'
@@ -67,7 +67,7 @@ const ViewHistory = ({
   const historyWorkflowData = useStore(s => s.historyWorkflowData)
   const { handleBackupDraft } = useWorkflowRun()
   const { data: runList, isLoading: runListLoading } = useSWR((appDetail && !isChatMode && open) ? `/apps/${appDetail.id}/workflow-runs` : null, fetchWorkflowRunHistory)
-  const { data: chatList, isLoading: chatListLoading } = useSWR((appDetail && isChatMode && open) ? `/apps/${appDetail.id}/advanced-chat/workflow-runs` : null, fetcChatRunHistory)
+  const { data: chatList, isLoading: chatListLoading } = useSWR((appDetail && isChatMode && open) ? `/apps/${appDetail.id}/advanced-chat/workflow-runs` : null, fetchChatRunHistory)
 
   const data = isChatMode ? chatList : runList
   const isLoading = isChatMode ? chatListLoading : runListLoading
diff --git a/web/app/components/workflow/hooks/use-checklist.ts b/web/app/components/workflow/hooks/use-checklist.ts
index 7f45769acd..36201ddfef 100644
--- a/web/app/components/workflow/hooks/use-checklist.ts
+++ b/web/app/components/workflow/hooks/use-checklist.ts
@@ -138,7 +138,7 @@ export const useChecklistBeforePublish = () => {
       }
 
       if (!validNodes.find(n => n.id === node.id)) {
-        notify({ type: 'error', message: `[${node.data.title}] ${t('workflow.common.needConnecttip')}` })
+        notify({ type: 'error', message: `[${node.data.title}] ${t('workflow.common.needConnectTip')}` })
         return false
       }
     }
diff --git a/web/app/components/workflow/hooks/use-edges-interactions.ts b/web/app/components/workflow/hooks/use-edges-interactions.ts
index bc3fb0e8bf..a97b65134f 100644
--- a/web/app/components/workflow/hooks/use-edges-interactions.ts
+++ b/web/app/components/workflow/hooks/use-edges-interactions.ts
@@ -155,7 +155,7 @@ export const useEdgesInteractions = () => {
 
     const newEdges = produce(edges, (draft) => {
       draft.forEach((edge) => {
-        edge.data._runned = false
+        edge.data._run = false
       })
     })
     setEdges(newEdges)
diff --git a/web/app/components/workflow/hooks/use-nodes-interactions.ts b/web/app/components/workflow/hooks/use-nodes-interactions.ts
index 3645e18449..af2a1500ba 100644
--- a/web/app/components/workflow/hooks/use-nodes-interactions.ts
+++ b/web/app/components/workflow/hooks/use-nodes-interactions.ts
@@ -16,6 +16,7 @@ import {
   useReactFlow,
   useStoreApi,
 } from 'reactflow'
+import { unionBy } from 'lodash-es'
 import type { ToolDefaultValue } from '../block-selector/types'
 import type {
   Edge,
@@ -25,6 +26,7 @@ import type {
 import { BlockEnum } from '../types'
 import { useWorkflowStore } from '../store'
 import {
+  CUSTOM_EDGE,
   ITERATION_CHILDREN_Z_INDEX,
   ITERATION_PADDING,
   NODES_INITIAL_DATA,
@@ -40,6 +42,7 @@ import {
 } from '../utils'
 import { CUSTOM_NOTE_NODE } from '../note-node/constants'
 import type { IterationNodeType } from '../nodes/iteration/types'
+import { CUSTOM_ITERATION_START_NODE } from '../nodes/iteration-start/constants'
 import type { VariableAssignerNodeType } from '../nodes/variable-assigner/types'
 import { useNodeIterationInteractions } from '../nodes/iteration/use-interactions'
 import { useWorkflowHistoryStore } from '../workflow-history-store'
@@ -60,6 +63,7 @@ export const useNodesInteractions = () => {
   const { store: workflowHistoryStore } = useWorkflowHistoryStore()
   const { handleSyncWorkflowDraft } = useNodesSyncDraft()
   const {
+    checkNestedParallelLimit,
     getAfterNodesInSameBranch,
   } = useWorkflow()
   const { getNodesReadOnly } = useNodesReadOnly()
@@ -79,7 +83,7 @@ export const useNodesInteractions = () => {
     if (getNodesReadOnly())
       return
 
-    if (node.data.isIterationStart || node.type === CUSTOM_NOTE_NODE)
+    if (node.type === CUSTOM_ITERATION_START_NODE || node.type === CUSTOM_NOTE_NODE)
       return
 
     dragNodeStartPosition.current = { x: node.position.x, y: node.position.y }
@@ -89,7 +93,7 @@ export const useNodesInteractions = () => {
     if (getNodesReadOnly())
       return
 
-    if (node.data.isIterationStart)
+    if (node.type === CUSTOM_ITERATION_START_NODE)
       return
 
     const {
@@ -156,7 +160,7 @@ export const useNodesInteractions = () => {
     if (getNodesReadOnly())
       return
 
-    if (node.type === CUSTOM_NOTE_NODE)
+    if (node.type === CUSTOM_NOTE_NODE || node.type === CUSTOM_ITERATION_START_NODE)
       return
 
     const {
@@ -207,13 +211,30 @@ export const useNodesInteractions = () => {
       })
     })
     setEdges(newEdges)
+    const connectedEdges = getConnectedEdges([node], edges).filter(edge => edge.target === node.id)
+
+    const targetNodes: Node[] = []
+    for (let i = 0; i < connectedEdges.length; i++) {
+      const sourceConnectedEdges = getConnectedEdges([{ id: connectedEdges[i].source } as Node], edges).filter(edge => edge.source === connectedEdges[i].source && edge.sourceHandle === connectedEdges[i].sourceHandle)
+      targetNodes.push(...sourceConnectedEdges.map(edge => nodes.find(n => n.id === edge.target)!))
+    }
+    const uniqTargetNodes = unionBy(targetNodes, 'id')
+    if (uniqTargetNodes.length > 1) {
+      const newNodes = produce(nodes, (draft) => {
+        draft.forEach((n) => {
+          if (uniqTargetNodes.some(targetNode => n.id === targetNode.id))
+            n.data._inParallelHovering = true
+        })
+      })
+      setNodes(newNodes)
+    }
   }, [store, workflowStore, getNodesReadOnly])
 
   const handleNodeLeave = useCallback<NodeMouseHandler>((_, node) => {
     if (getNodesReadOnly())
       return
 
-    if (node.type === CUSTOM_NOTE_NODE)
+    if (node.type === CUSTOM_NOTE_NODE || node.type === CUSTOM_ITERATION_START_NODE)
       return
 
     const {
@@ -229,6 +250,7 @@ export const useNodesInteractions = () => {
     const newNodes = produce(getNodes(), (draft) => {
       draft.forEach((node) => {
         node.data._isEntering = false
+        node.data._inParallelHovering = false
       })
     })
     setNodes(newNodes)
@@ -287,6 +309,8 @@ export const useNodesInteractions = () => {
   }, [store, handleSyncWorkflowDraft])
 
   const handleNodeClick = useCallback<NodeMouseHandler>((_, node) => {
+    if (node.type === CUSTOM_ITERATION_START_NODE)
+      return
     handleNodeSelect(node.id)
   }, [handleNodeSelect])
 
@@ -314,25 +338,15 @@ export const useNodesInteractions = () => {
     if (targetNode?.parentId !== sourceNode?.parentId)
       return
 
-    if (targetNode?.data.isIterationStart)
-      return
-
     if (sourceNode?.type === CUSTOM_NOTE_NODE || targetNode?.type === CUSTOM_NOTE_NODE)
       return
 
-    const needDeleteEdges = edges.filter((edge) => {
-      if (
-        (edge.source === source && edge.sourceHandle === sourceHandle)
-        || (edge.target === target && edge.targetHandle === targetHandle && targetNode?.data.type !== BlockEnum.VariableAssigner && targetNode?.data.type !== BlockEnum.VariableAggregator)
-      )
-        return true
+    if (edges.find(edge => edge.source === source && edge.sourceHandle === sourceHandle && edge.target === target && edge.targetHandle === targetHandle))
+      return
 
-      return false
-    })
-    const needDeleteEdgesIds = needDeleteEdges.map(edge => edge.id)
     const newEdge = {
       id: `${source}-${sourceHandle}-${target}-${targetHandle}`,
-      type: 'custom',
+      type: CUSTOM_EDGE,
       source: source!,
       target: target!,
       sourceHandle,
@@ -347,7 +361,6 @@ export const useNodesInteractions = () => {
     }
     const nodesConnectedSourceOrTargetHandleIdsMap = getNodesConnectedSourceOrTargetHandleIdsMap(
       [
-        ...needDeleteEdges.map(edge => ({ type: 'remove', edge })),
         { type: 'add', edge: newEdge },
       ],
       nodes,
@@ -362,19 +375,26 @@ export const useNodesInteractions = () => {
         }
       })
     })
-    setNodes(newNodes)
     const newEdges = produce(edges, (draft) => {
-      const filtered = draft.filter(edge => !needDeleteEdgesIds.includes(edge.id))
-
-      filtered.push(newEdge)
-
-      return filtered
+      draft.push(newEdge)
     })
-    setEdges(newEdges)
 
-    handleSyncWorkflowDraft()
-    saveStateToHistory(WorkflowHistoryEvent.NodeConnect)
-  }, [getNodesReadOnly, store, handleSyncWorkflowDraft, saveStateToHistory])
+    if (checkNestedParallelLimit(newNodes, newEdges, targetNode?.parentId)) {
+      setNodes(newNodes)
+      setEdges(newEdges)
+
+      handleSyncWorkflowDraft()
+      saveStateToHistory(WorkflowHistoryEvent.NodeConnect)
+    }
+    else {
+      const {
+        setConnectingNodePayload,
+        setEnteringNodePayload,
+      } = workflowStore.getState()
+      setConnectingNodePayload(undefined)
+      setEnteringNodePayload(undefined)
+    }
+  }, [getNodesReadOnly, store, workflowStore, handleSyncWorkflowDraft, saveStateToHistory, checkNestedParallelLimit])
 
   const handleNodeConnectStart = useCallback<OnConnectStart>((_, { nodeId, handleType, handleId }) => {
     if (getNodesReadOnly())
@@ -393,14 +413,12 @@ export const useNodesInteractions = () => {
           return
       }
 
-      if (!node.data.isIterationStart) {
-        setConnectingNodePayload({
-          nodeId,
-          nodeType: node.data.type,
-          handleType,
-          handleId,
-        })
-      }
+      setConnectingNodePayload({
+        nodeId,
+        nodeType: node.data.type,
+        handleType,
+        handleId,
+      })
     }
   }, [store, workflowStore, getNodesReadOnly])
 
@@ -510,6 +528,12 @@ export const useNodesInteractions = () => {
           return handleNodeDelete(nodeId)
         }
         else {
+          if (iterationChildren.length === 1) {
+            handleNodeDelete(iterationChildren[0].id)
+            handleNodeDelete(nodeId)
+
+            return
+          }
           const { setShowConfirm, showConfirm } = workflowStore.getState()
 
           if (!showConfirm) {
@@ -541,14 +565,8 @@ export const useNodesInteractions = () => {
           }
         }
 
-        if (node.id === currentNode.parentId) {
+        if (node.id === currentNode.parentId)
           node.data._children = node.data._children?.filter(child => child !== nodeId)
-
-          if (currentNode.id === (node as Node<IterationNodeType>).data.start_node_id) {
-            (node as Node<IterationNodeType>).data.start_node_id = '';
-            (node as Node<IterationNodeType>).data.startNodeType = undefined
-          }
-        }
       })
       draft.splice(currentNodeIndex, 1)
     })
@@ -559,7 +577,7 @@ export const useNodesInteractions = () => {
     setEdges(newEdges)
     handleSyncWorkflowDraft()
 
-    if (currentNode.type === 'custom-note')
+    if (currentNode.type === CUSTOM_NOTE_NODE)
       saveStateToHistory(WorkflowHistoryEvent.NoteDelete)
 
     else
@@ -591,7 +609,10 @@ export const useNodesInteractions = () => {
     } = store.getState()
     const nodes = getNodes()
     const nodesWithSameType = nodes.filter(node => node.data.type === nodeType)
-    const newNode = generateNewNode({
+    const {
+      newNode,
+      newIterationStartNode,
+    } = generateNewNode({
       data: {
         ...NODES_INITIAL_DATA[nodeType],
         title: nodesWithSameType.length > 0 ? `${t(`workflow.blocks.${nodeType}`)} ${nodesWithSameType.length + 1}` : t(`workflow.blocks.${nodeType}`),
@@ -627,7 +648,7 @@ export const useNodesInteractions = () => {
 
       const newEdge: Edge = {
         id: `${prevNodeId}-${prevNodeSourceHandle}-${newNode.id}-${targetHandle}`,
-        type: 'custom',
+        type: CUSTOM_EDGE,
         source: prevNodeId,
         sourceHandle: prevNodeSourceHandle,
         target: newNode.id,
@@ -662,8 +683,10 @@ export const useNodesInteractions = () => {
             node.data._children?.push(newNode.id)
         })
         draft.push(newNode)
+        if (newIterationStartNode)
+          draft.push(newIterationStartNode)
       })
-      setNodes(newNodes)
+
       if (newNode.data.type === BlockEnum.VariableAssigner || newNode.data.type === BlockEnum.VariableAggregator) {
         const { setShowAssignVariablePopup } = workflowStore.getState()
 
@@ -687,7 +710,14 @@ export const useNodesInteractions = () => {
         })
         draft.push(newEdge)
       })
-      setEdges(newEdges)
+
+      if (checkNestedParallelLimit(newNodes, newEdges, prevNode.parentId)) {
+        setNodes(newNodes)
+        setEdges(newEdges)
+      }
+      else {
+        return false
+      }
     }
     if (!prevNodeId && nextNodeId) {
       const nextNodeIndex = nodes.findIndex(node => node.id === nextNodeId)
@@ -706,15 +736,13 @@ export const useNodesInteractions = () => {
         newNode.data.iteration_id = nextNode.parentId
         newNode.zIndex = ITERATION_CHILDREN_Z_INDEX
       }
-      if (nextNode.data.isIterationStart)
-        newNode.data.isIterationStart = true
 
       let newEdge
 
       if ((nodeType !== BlockEnum.IfElse) && (nodeType !== BlockEnum.QuestionClassifier)) {
         newEdge = {
           id: `${newNode.id}-${sourceHandle}-${nextNodeId}-${nextNodeTargetHandle}`,
-          type: 'custom',
+          type: CUSTOM_EDGE,
           source: newNode.id,
           sourceHandle,
           target: nextNodeId,
@@ -763,13 +791,11 @@ export const useNodesInteractions = () => {
             node.data.start_node_id = newNode.id
             node.data.startNodeType = newNode.data.type
           }
-
-          if (node.id === nextNodeId && node.data.isIterationStart)
-            node.data.isIterationStart = false
         })
         draft.push(newNode)
+        if (newIterationStartNode)
+          draft.push(newIterationStartNode)
       })
-      setNodes(newNodes)
       if (newEdge) {
         const newEdges = produce(edges, (draft) => {
           draft.forEach((item) => {
@@ -780,7 +806,21 @@ export const useNodesInteractions = () => {
           })
           draft.push(newEdge)
         })
-        setEdges(newEdges)
+
+        if (checkNestedParallelLimit(newNodes, newEdges, nextNode.parentId)) {
+          setNodes(newNodes)
+          setEdges(newEdges)
+        }
+        else {
+          return false
+        }
+      }
+      else {
+        if (checkNestedParallelLimit(newNodes, edges))
+          setNodes(newNodes)
+
+        else
+          return false
       }
     }
     if (prevNodeId && nextNodeId) {
@@ -804,7 +844,7 @@ export const useNodesInteractions = () => {
       const currentEdgeIndex = edges.findIndex(edge => edge.source === prevNodeId && edge.target === nextNodeId)
       const newPrevEdge = {
         id: `${prevNodeId}-${prevNodeSourceHandle}-${newNode.id}-${targetHandle}`,
-        type: 'custom',
+        type: CUSTOM_EDGE,
         source: prevNodeId,
         sourceHandle: prevNodeSourceHandle,
         target: newNode.id,
@@ -822,7 +862,7 @@ export const useNodesInteractions = () => {
       if (nodeType !== BlockEnum.IfElse && nodeType !== BlockEnum.QuestionClassifier) {
         newNextEdge = {
           id: `${newNode.id}-${sourceHandle}-${nextNodeId}-${nextNodeTargetHandle}`,
-          type: 'custom',
+          type: CUSTOM_EDGE,
           source: newNode.id,
           sourceHandle,
           target: nextNodeId,
@@ -865,6 +905,8 @@ export const useNodesInteractions = () => {
             node.data._children?.push(newNode.id)
         })
         draft.push(newNode)
+        if (newIterationStartNode)
+          draft.push(newIterationStartNode)
       })
       setNodes(newNodes)
       if (newNode.data.type === BlockEnum.VariableAssigner || newNode.data.type === BlockEnum.VariableAggregator) {
@@ -898,7 +940,7 @@ export const useNodesInteractions = () => {
     }
     handleSyncWorkflowDraft()
     saveStateToHistory(WorkflowHistoryEvent.NodeAdd)
-  }, [getNodesReadOnly, store, t, handleSyncWorkflowDraft, saveStateToHistory, workflowStore, getAfterNodesInSameBranch])
+  }, [getNodesReadOnly, store, t, handleSyncWorkflowDraft, saveStateToHistory, workflowStore, getAfterNodesInSameBranch, checkNestedParallelLimit])
 
   const handleNodeChange = useCallback((
     currentNodeId: string,
@@ -919,7 +961,10 @@ export const useNodesInteractions = () => {
     const currentNode = nodes.find(node => node.id === currentNodeId)!
     const connectedEdges = getConnectedEdges([currentNode], edges)
     const nodesWithSameType = nodes.filter(node => node.data.type === nodeType)
-    const newCurrentNode = generateNewNode({
+    const {
+      newNode: newCurrentNode,
+      newIterationStartNode,
+    } = generateNewNode({
       data: {
         ...NODES_INITIAL_DATA[nodeType],
         title: nodesWithSameType.length > 0 ? `${t(`workflow.blocks.${nodeType}`)} ${nodesWithSameType.length + 1}` : t(`workflow.blocks.${nodeType}`),
@@ -929,7 +974,6 @@ export const useNodesInteractions = () => {
         selected: currentNode.data.selected,
         isInIteration: currentNode.data.isInIteration,
         iteration_id: currentNode.data.iteration_id,
-        isIterationStart: currentNode.data.isIterationStart,
       },
       position: {
         x: currentNode.position.x,
@@ -955,18 +999,12 @@ export const useNodesInteractions = () => {
             ...nodesConnectedSourceOrTargetHandleIdsMap[node.id],
           }
         }
-        if (node.id === currentNode.parentId && currentNode.data.isIterationStart) {
-          node.data._children = [
-            newCurrentNode.id,
-            ...(node.data._children || []),
-          ].filter(child => child !== currentNodeId)
-          node.data.start_node_id = newCurrentNode.id
-          node.data.startNodeType = newCurrentNode.data.type
-        }
       })
       const index = draft.findIndex(node => node.id === currentNodeId)
 
       draft.splice(index, 1, newCurrentNode)
+      if (newIterationStartNode)
+        draft.push(newIterationStartNode)
     })
     setNodes(newNodes)
     const newEdges = produce(edges, (draft) => {
@@ -1011,7 +1049,7 @@ export const useNodesInteractions = () => {
   }, [store])
 
   const handleNodeContextMenu = useCallback((e: MouseEvent, node: Node) => {
-    if (node.type === CUSTOM_NOTE_NODE)
+    if (node.type === CUSTOM_NOTE_NODE || node.type === CUSTOM_ITERATION_START_NODE)
       return
 
     e.preventDefault()
@@ -1041,7 +1079,7 @@ export const useNodesInteractions = () => {
 
     if (nodeId) {
       // If nodeId is provided, copy that specific node
-      const nodeToCopy = nodes.find(node => node.id === nodeId && node.data.type !== BlockEnum.Start)
+      const nodeToCopy = nodes.find(node => node.id === nodeId && node.data.type !== BlockEnum.Start && node.type !== CUSTOM_ITERATION_START_NODE)
       if (nodeToCopy)
         setClipboardElements([nodeToCopy])
     }
@@ -1087,7 +1125,10 @@ export const useNodesInteractions = () => {
       clipboardElements.forEach((nodeToPaste, index) => {
         const nodeType = nodeToPaste.data.type
 
-        const newNode = generateNewNode({
+        const {
+          newNode,
+          newIterationStartNode,
+        } = generateNewNode({
           type: nodeToPaste.type,
           data: {
             ...NODES_INITIAL_DATA[nodeType],
@@ -1106,24 +1147,17 @@ export const useNodesInteractions = () => {
           zIndex: nodeToPaste.zIndex,
         })
         newNode.id = newNode.id + index
-
-        // If only the iteration start node is copied, remove the isIterationStart flag
         // This new node is movable and can be placed anywhere
-        if (clipboardElements.length === 1 && newNode.data.isIterationStart)
-          newNode.data.isIterationStart = false
-
         let newChildren: Node[] = []
         if (nodeToPaste.data.type === BlockEnum.Iteration) {
-          newNode.data._children = [];
-          (newNode.data as IterationNodeType).start_node_id = ''
+          newIterationStartNode!.parentId = newNode.id;
+          (newNode.data as IterationNodeType).start_node_id = newIterationStartNode!.id
 
           newChildren = handleNodeIterationChildrenCopy(nodeToPaste.id, newNode.id)
-
           newChildren.forEach((child) => {
             newNode.data._children?.push(child.id)
-            if (child.data.isIterationStart)
-              (newNode.data as IterationNodeType).start_node_id = child.id
           })
+          newChildren.push(newIterationStartNode!)
         }
 
         nodesToPaste.push(newNode)
@@ -1230,6 +1264,42 @@ export const useNodesInteractions = () => {
     saveStateToHistory(WorkflowHistoryEvent.NodeResize)
   }, [getNodesReadOnly, store, handleSyncWorkflowDraft, saveStateToHistory])
 
+  const handleNodeDisconnect = useCallback((nodeId: string) => {
+    if (getNodesReadOnly())
+      return
+
+    const {
+      getNodes,
+      setNodes,
+      edges,
+      setEdges,
+    } = store.getState()
+    const nodes = getNodes()
+    const currentNode = nodes.find(node => node.id === nodeId)!
+    const connectedEdges = getConnectedEdges([currentNode], edges)
+    const nodesConnectedSourceOrTargetHandleIdsMap = getNodesConnectedSourceOrTargetHandleIdsMap(
+      connectedEdges.map(edge => ({ type: 'remove', edge })),
+      nodes,
+    )
+    const newNodes = produce(nodes, (draft: Node[]) => {
+      draft.forEach((node) => {
+        if (nodesConnectedSourceOrTargetHandleIdsMap[node.id]) {
+          node.data = {
+            ...node.data,
+            ...nodesConnectedSourceOrTargetHandleIdsMap[node.id],
+          }
+        }
+      })
+    })
+    setNodes(newNodes)
+    const newEdges = produce(edges, (draft) => {
+      return draft.filter(edge => !connectedEdges.find(connectedEdge => connectedEdge.id === edge.id))
+    })
+    setEdges(newEdges)
+    handleSyncWorkflowDraft()
+    saveStateToHistory(WorkflowHistoryEvent.EdgeDelete)
+  }, [store, getNodesReadOnly, handleSyncWorkflowDraft, saveStateToHistory])
+
   const handleHistoryBack = useCallback(() => {
     if (getNodesReadOnly() || getWorkflowReadOnly())
       return
@@ -1282,6 +1352,7 @@ export const useNodesInteractions = () => {
     handleNodesDuplicate,
     handleNodesDelete,
     handleNodeResize,
+    handleNodeDisconnect,
     handleHistoryBack,
     handleHistoryForward,
   }
diff --git a/web/app/components/workflow/hooks/use-shortcuts.ts b/web/app/components/workflow/hooks/use-shortcuts.ts
index 666c3a45ba..8b1003e89c 100644
--- a/web/app/components/workflow/hooks/use-shortcuts.ts
+++ b/web/app/components/workflow/hooks/use-shortcuts.ts
@@ -70,14 +70,16 @@ export const useShortcuts = (): void => {
   })
 
   useKeyPress(`${getKeyboardKeyCodeBySystem('ctrl')}.c`, (e) => {
-    if (shouldHandleShortcut(e)) {
+    const { showDebugAndPreviewPanel } = workflowStore.getState()
+    if (shouldHandleShortcut(e) && !showDebugAndPreviewPanel) {
       e.preventDefault()
       handleNodesCopy()
     }
   }, { exactMatch: true, useCapture: true })
 
   useKeyPress(`${getKeyboardKeyCodeBySystem('ctrl')}.v`, (e) => {
-    if (shouldHandleShortcut(e)) {
+    const { showDebugAndPreviewPanel } = workflowStore.getState()
+    if (shouldHandleShortcut(e) && !showDebugAndPreviewPanel) {
       e.preventDefault()
       handleNodesPaste()
     }
@@ -98,7 +100,8 @@ export const useShortcuts = (): void => {
   }, { exactMatch: true, useCapture: true })
 
   useKeyPress(`${getKeyboardKeyCodeBySystem('ctrl')}.z`, (e) => {
-    if (shouldHandleShortcut(e)) {
+    const { showDebugAndPreviewPanel } = workflowStore.getState()
+    if (shouldHandleShortcut(e) && !showDebugAndPreviewPanel) {
       e.preventDefault()
       workflowHistoryShortcutsEnabled && handleHistoryBack()
     }
diff --git a/web/app/components/workflow/hooks/use-workflow-interactions.ts b/web/app/components/workflow/hooks/use-workflow-interactions.ts
index 47b8a30a5a..b39a3d8014 100644
--- a/web/app/components/workflow/hooks/use-workflow-interactions.ts
+++ b/web/app/components/workflow/hooks/use-workflow-interactions.ts
@@ -10,7 +10,7 @@ import {
   CUSTOM_NODE, DSL_EXPORT_CHECK,
   WORKFLOW_DATA_UPDATE,
 } from '../constants'
-import type { Node, WorkflowDataUpdator } from '../types'
+import type { Node, WorkflowDataUpdater } from '../types'
 import { ControlMode } from '../types'
 import {
   getLayoutByDagre,
@@ -208,7 +208,7 @@ export const useWorkflowUpdate = () => {
   const workflowStore = useWorkflowStore()
   const { eventEmitter } = useEventEmitterContextContext()
 
-  const handleUpdateWorkflowCanvas = useCallback((payload: WorkflowDataUpdator) => {
+  const handleUpdateWorkflowCanvas = useCallback((payload: WorkflowDataUpdater) => {
     const {
       nodes,
       edges,
@@ -236,7 +236,7 @@ export const useWorkflowUpdate = () => {
     } = workflowStore.getState()
     setIsSyncingWorkflowDraft(true)
     fetchWorkflowDraft(`/apps/${appId}/workflows/draft`).then((response) => {
-      handleUpdateWorkflowCanvas(response.graph as WorkflowDataUpdator)
+      handleUpdateWorkflowCanvas(response.graph as WorkflowDataUpdater)
       setSyncWorkflowDraftHash(response.hash)
       setEnvSecrets((response.environment_variables || []).filter(env => env.value_type === 'secret').reduce((acc, env) => {
         acc[env.id] = env.value
diff --git a/web/app/components/workflow/hooks/use-workflow-run.ts b/web/app/components/workflow/hooks/use-workflow-run.ts
index 96f6557fe0..e1da503f38 100644
--- a/web/app/components/workflow/hooks/use-workflow-run.ts
+++ b/web/app/components/workflow/hooks/use-workflow-run.ts
@@ -1,5 +1,6 @@
 import { useCallback } from 'react'
 import {
+  getIncomers,
   useReactFlow,
   useStoreApi,
 } from 'reactflow'
@@ -8,6 +9,7 @@ import { v4 as uuidV4 } from 'uuid'
 import { usePathname } from 'next/navigation'
 import { useWorkflowStore } from '../store'
 import { useNodesSyncDraft } from '../hooks'
+import type { Node } from '../types'
 import {
   NodeRunningStatus,
   WorkflowRunningStatus,
@@ -140,9 +142,6 @@ export const useWorkflowRun = () => {
       resultText: '',
     })
 
-    let isInIteration = false
-    let iterationLength = 0
-
     let ttsUrl = ''
     let ttsIsPublic = false
     if (params.token) {
@@ -249,19 +248,20 @@ export const useWorkflowRun = () => {
             setEdges,
             transform,
           } = store.getState()
-          if (isInIteration) {
+          const nodes = getNodes()
+          const node = nodes.find(node => node.id === data.node_id)
+          if (node?.parentId) {
             setWorkflowRunningData(produce(workflowRunningData!, (draft) => {
               const tracing = draft.tracing!
-              const iterations = tracing[tracing.length - 1]
-              const currIteration = iterations.details![iterations.details!.length - 1]
-              currIteration.push({
+              const iterations = tracing.find(trace => trace.node_id === node?.parentId)
+              const currIteration = iterations?.details![node.data.iteration_index] || iterations?.details![iterations.details!.length - 1]
+              currIteration?.push({
                 ...data,
                 status: NodeRunningStatus.Running,
               } as any)
             }))
           }
           else {
-            const nodes = getNodes()
             setWorkflowRunningData(produce(workflowRunningData!, (draft) => {
               draft.tracing!.push({
                 ...data,
@@ -288,11 +288,12 @@ export const useWorkflowRun = () => {
               draft[currentNodeIndex].data._runningStatus = NodeRunningStatus.Running
             })
             setNodes(newNodes)
+            const incomeNodesId = getIncomers({ id: data.node_id } as Node, newNodes, edges).filter(node => node.data._runningStatus === NodeRunningStatus.Succeeded).map(node => node.id)
             const newEdges = produce(edges, (draft) => {
-              const edge = draft.find(edge => edge.target === data.node_id && edge.source === prevNodeId)
-
-              if (edge)
-                edge.data = { ...edge.data, _runned: true } as any
+              draft.forEach((edge) => {
+                if (edge.target === data.node_id && incomeNodesId.includes(edge.source))
+                  edge.data = { ...edge.data, _runned: true } as any
+              })
             })
             setEdges(newEdges)
           }
@@ -309,25 +310,46 @@ export const useWorkflowRun = () => {
             getNodes,
             setNodes,
           } = store.getState()
-          if (isInIteration) {
+          const nodes = getNodes()
+          const nodeParentId = nodes.find(node => node.id === data.node_id)!.parentId
+          if (nodeParentId) {
             setWorkflowRunningData(produce(workflowRunningData!, (draft) => {
               const tracing = draft.tracing!
-              const iterations = tracing[tracing.length - 1]
-              const currIteration = iterations.details![iterations.details!.length - 1]
-              const nodeInfo = currIteration[currIteration.length - 1]
+              const iterations = tracing.find(trace => trace.node_id === nodeParentId) // the iteration node
 
-              currIteration[currIteration.length - 1] = {
-                ...nodeInfo,
-                ...data,
-                status: NodeRunningStatus.Succeeded,
-              } as any
+              if (iterations && iterations.details) {
+                const iterationIndex = data.execution_metadata?.iteration_index || 0
+                if (!iterations.details[iterationIndex])
+                  iterations.details[iterationIndex] = []
+
+                const currIteration = iterations.details[iterationIndex]
+                const nodeIndex = currIteration.findIndex(node =>
+                  node.node_id === data.node_id && (
+                    node.execution_metadata?.parallel_id === data.execution_metadata?.parallel_id || node.parallel_id === data.execution_metadata?.parallel_id),
+                )
+                if (data.status === NodeRunningStatus.Succeeded) {
+                  if (nodeIndex !== -1) {
+                    currIteration[nodeIndex] = {
+                      ...currIteration[nodeIndex],
+                      ...data,
+                    } as any
+                  }
+                  else {
+                    currIteration.push({
+                      ...data,
+                    } as any)
+                  }
+                }
+              }
             }))
           }
           else {
-            const nodes = getNodes()
             setWorkflowRunningData(produce(workflowRunningData!, (draft) => {
-              const currentIndex = draft.tracing!.findIndex(trace => trace.node_id === data.node_id)
-
+              const currentIndex = draft.tracing!.findIndex((trace) => {
+                if (!trace.execution_metadata?.parallel_id)
+                  return trace.node_id === data.node_id
+                return trace.node_id === data.node_id && trace.execution_metadata?.parallel_id === data.execution_metadata?.parallel_id
+              })
               if (currentIndex > -1 && draft.tracing) {
                 draft.tracing[currentIndex] = {
                   ...(draft.tracing[currentIndex].extras
@@ -337,16 +359,14 @@ export const useWorkflowRun = () => {
                 } as any
               }
             }))
-
             const newNodes = produce(nodes, (draft) => {
               const currentNode = draft.find(node => node.id === data.node_id)!
-
               currentNode.data._runningStatus = data.status as any
             })
             setNodes(newNodes)
-
             prevNodeId = data.node_id
           }
+
           if (onNodeFinished)
             onNodeFinished(params)
         },
@@ -371,8 +391,6 @@ export const useWorkflowRun = () => {
               details: [],
             } as any)
           }))
-          isInIteration = true
-          iterationLength = data.metadata.iterator_length
 
           const {
             setViewport,
@@ -418,13 +436,13 @@ export const useWorkflowRun = () => {
           } = store.getState()
 
           setWorkflowRunningData(produce(workflowRunningData!, (draft) => {
-            const iteration = draft.tracing![draft.tracing!.length - 1]
-            if (iteration.details!.length >= iterationLength)
-              return
-
-            iteration.details!.push([])
+            const iteration = draft.tracing!.find(trace => trace.node_id === data.node_id)
+            if (iteration) {
+              if (iteration.details!.length >= iteration.metadata.iterator_length!)
+                return
+            }
+            iteration?.details!.push([])
           }))
-
           const nodes = getNodes()
           const newNodes = produce(nodes, (draft) => {
             const currentNode = draft.find(node => node.id === data.node_id)!
@@ -450,13 +468,14 @@ export const useWorkflowRun = () => {
           const nodes = getNodes()
           setWorkflowRunningData(produce(workflowRunningData!, (draft) => {
             const tracing = draft.tracing!
-            tracing[tracing.length - 1] = {
-              ...tracing[tracing.length - 1],
-              ...data,
-              status: NodeRunningStatus.Succeeded,
-            } as any
+            const currIterationNode = tracing.find(trace => trace.node_id === data.node_id)
+            if (currIterationNode) {
+              Object.assign(currIterationNode, {
+                ...data,
+                status: NodeRunningStatus.Succeeded,
+              })
+            }
           }))
-          isInIteration = false
 
           const newNodes = produce(nodes, (draft) => {
             const currentNode = draft.find(node => node.id === data.node_id)!
@@ -470,6 +489,12 @@ export const useWorkflowRun = () => {
           if (onIterationFinish)
             onIterationFinish(params)
         },
+        onParallelBranchStarted: (params) => {
+          // console.log(params, 'parallel start')
+        },
+        onParallelBranchFinished: (params) => {
+          // console.log(params, 'finished')
+        },
         onTextChunk: (params) => {
           const { data: { text } } = params
           const {
diff --git a/web/app/components/workflow/hooks/use-workflow-template.ts b/web/app/components/workflow/hooks/use-workflow-template.ts
index 3af3f733f1..e36f0b61f9 100644
--- a/web/app/components/workflow/hooks/use-workflow-template.ts
+++ b/web/app/components/workflow/hooks/use-workflow-template.ts
@@ -10,13 +10,13 @@ export const useWorkflowTemplate = () => {
   const isChatMode = useIsChatMode()
   const nodesInitialData = useNodesInitialData()
 
-  const startNode = generateNewNode({
+  const { newNode: startNode } = generateNewNode({
     data: nodesInitialData.start,
     position: START_INITIAL_POSITION,
   })
 
   if (isChatMode) {
-    const llmNode = generateNewNode({
+    const { newNode: llmNode } = generateNewNode({
       id: 'llm',
       data: {
         ...nodesInitialData.llm,
@@ -31,7 +31,7 @@ export const useWorkflowTemplate = () => {
       },
     } as any)
 
-    const answerNode = generateNewNode({
+    const { newNode: answerNode } = generateNewNode({
       id: 'answer',
       data: {
         ...nodesInitialData.answer,
diff --git a/web/app/components/workflow/hooks/use-workflow.ts b/web/app/components/workflow/hooks/use-workflow.ts
index cfff4220fa..b201b28b88 100644
--- a/web/app/components/workflow/hooks/use-workflow.ts
+++ b/web/app/components/workflow/hooks/use-workflow.ts
@@ -6,6 +6,7 @@ import {
 } from 'react'
 import dayjs from 'dayjs'
 import { uniqBy } from 'lodash-es'
+import { useTranslation } from 'react-i18next'
 import { useContext } from 'use-context-selector'
 import {
   getIncomers,
@@ -29,6 +30,11 @@ import {
   useWorkflowStore,
 } from '../store'
 import {
+  getParallelInfo,
+} from '../utils'
+import {
+  PARALLEL_DEPTH_LIMIT,
+  PARALLEL_LIMIT,
   SUPPORT_OUTPUT_VARS_NODE,
 } from '../constants'
 import { CUSTOM_NOTE_NODE } from '../note-node/constants'
@@ -50,6 +56,7 @@ import {
 } from '@/service/tools'
 import I18n from '@/context/i18n'
 import { CollectionType } from '@/app/components/tools/types'
+import { CUSTOM_ITERATION_START_NODE } from '@/app/components/workflow/nodes/iteration-start/constants'
 
 export const useIsChatMode = () => {
   const appDetail = useAppStore(s => s.appDetail)
@@ -58,6 +65,7 @@ export const useIsChatMode = () => {
 }
 
 export const useWorkflow = () => {
+  const { t } = useTranslation()
   const { locale } = useContext(I18n)
   const store = useStoreApi()
   const workflowStore = useWorkflowStore()
@@ -77,7 +85,7 @@ export const useWorkflow = () => {
     const currentNode = nodes.find(node => node.id === nodeId)
 
     if (currentNode?.parentId)
-      startNode = nodes.find(node => node.parentId === currentNode.parentId && node.data.isIterationStart)
+      startNode = nodes.find(node => node.parentId === currentNode.parentId && node.type === CUSTOM_ITERATION_START_NODE)
 
     if (!startNode)
       return []
@@ -275,7 +283,43 @@ export const useWorkflow = () => {
     return isUsed
   }, [isVarUsedInNodes])
 
-  const isValidConnection = useCallback(({ source, target }: Connection) => {
+  const checkParallelLimit = useCallback((nodeId: string, nodeHandle = 'source') => {
+    const {
+      edges,
+    } = store.getState()
+    const connectedEdges = edges.filter(edge => edge.source === nodeId && edge.sourceHandle === nodeHandle)
+    if (connectedEdges.length > PARALLEL_LIMIT - 1) {
+      const { setShowTips } = workflowStore.getState()
+      setShowTips(t('workflow.common.parallelTip.limit', { num: PARALLEL_LIMIT }))
+      return false
+    }
+
+    return true
+  }, [store, workflowStore, t])
+
+  const checkNestedParallelLimit = useCallback((nodes: Node[], edges: Edge[], parentNodeId?: string) => {
+    const {
+      parallelList,
+      hasAbnormalEdges,
+    } = getParallelInfo(nodes, edges, parentNodeId)
+
+    if (hasAbnormalEdges)
+      return false
+
+    for (let i = 0; i < parallelList.length; i++) {
+      const parallel = parallelList[i]
+
+      if (parallel.depth > PARALLEL_DEPTH_LIMIT) {
+        const { setShowTips } = workflowStore.getState()
+        setShowTips(t('workflow.common.parallelTip.depthLimit', { num: PARALLEL_DEPTH_LIMIT }))
+        return false
+      }
+    }
+
+    return true
+  }, [t, workflowStore])
+
+  const isValidConnection = useCallback(({ source, sourceHandle, target }: Connection) => {
     const {
       edges,
       getNodes,
@@ -284,12 +328,15 @@ export const useWorkflow = () => {
     const sourceNode: Node = nodes.find(node => node.id === source)!
     const targetNode: Node = nodes.find(node => node.id === target)!
 
-    if (targetNode.data.isIterationStart)
+    if (!checkParallelLimit(source!, sourceHandle || 'source'))
       return false
 
     if (sourceNode.type === CUSTOM_NOTE_NODE || targetNode.type === CUSTOM_NOTE_NODE)
       return false
 
+    if (sourceNode.parentId !== targetNode.parentId)
+      return false
+
     if (sourceNode && targetNode) {
       const sourceNodeAvailableNextNodes = nodesExtraData[sourceNode.data.type].availableNextNodes
       const targetNodeAvailablePrevNodes = [...nodesExtraData[targetNode.data.type].availablePrevNodes, BlockEnum.Start]
@@ -316,7 +363,7 @@ export const useWorkflow = () => {
     }
 
     return !hasCycle(targetNode)
-  }, [store, nodesExtraData])
+  }, [store, nodesExtraData, checkParallelLimit])
 
   const formatTimeFromNow = useCallback((time: number) => {
     return dayjs(time).locale(locale === 'zh-Hans' ? 'zh-cn' : locale).fromNow()
@@ -339,6 +386,8 @@ export const useWorkflow = () => {
     isVarUsedInNodes,
     removeUsedVarInNodes,
     isNodeVarsUsedInNodes,
+    checkParallelLimit,
+    checkNestedParallelLimit,
     isValidConnection,
     formatTimeFromNow,
     getNode,
diff --git a/web/app/components/workflow/index.tsx b/web/app/components/workflow/index.tsx
index d96faa8677..cdccd60a3b 100644
--- a/web/app/components/workflow/index.tsx
+++ b/web/app/components/workflow/index.tsx
@@ -55,6 +55,8 @@ import Header from './header'
 import CustomNode from './nodes'
 import CustomNoteNode from './note-node'
 import { CUSTOM_NOTE_NODE } from './note-node/constants'
+import CustomIterationStartNode from './nodes/iteration-start'
+import { CUSTOM_ITERATION_START_NODE } from './nodes/iteration-start/constants'
 import Operator from './operator'
 import CustomEdge from './custom-edge'
 import CustomConnectionLine from './custom-connection-line'
@@ -67,6 +69,7 @@ import NodeContextmenu from './node-contextmenu'
 import SyncingDataModal from './syncing-data-modal'
 import UpdateDSLModal from './update-dsl-modal'
 import DSLExportConfirmModal from './dsl-export-confirm-modal'
+import LimitTips from './limit-tips'
 import {
   useStore,
   useWorkflowStore,
@@ -92,6 +95,7 @@ import Confirm from '@/app/components/base/confirm'
 const nodeTypes = {
   [CUSTOM_NODE]: CustomNode,
   [CUSTOM_NOTE_NODE]: CustomNoteNode,
+  [CUSTOM_ITERATION_START_NODE]: CustomIterationStartNode,
 }
 const edgeTypes = {
   [CUSTOM_NODE]: CustomEdge,
@@ -317,6 +321,7 @@ const Workflow: FC<WorkflowProps> = memo(({
           />
         )
       }
+      <LimitTips />
       <ReactFlow
         nodeTypes={nodeTypes}
         edgeTypes={edgeTypes}
diff --git a/web/app/components/workflow/limit-tips.tsx b/web/app/components/workflow/limit-tips.tsx
new file mode 100644
index 0000000000..8e90ff280c
--- /dev/null
+++ b/web/app/components/workflow/limit-tips.tsx
@@ -0,0 +1,39 @@
+import {
+  RiAlertFill,
+  RiCloseLine,
+} from '@remixicon/react'
+import { useStore } from './store'
+import ActionButton from '@/app/components/base/action-button'
+
+const LimitTips = () => {
+  const showTips = useStore(s => s.showTips)
+  const setShowTips = useStore(s => s.setShowTips)
+
+  if (!showTips)
+    return null
+
+  return (
+    <div className='absolute bottom-16 left-1/2 -translate-x-1/2 flex items-center rounded-xl p-2 h-10 border border-components-panel-border bg-components-panel-bg-blur shadow-md z-[9]'>
+      <div
+        className='absolute inset-0 opacity-[0.4] rounded-xl'
+        style={{
+          background: 'linear-gradient(92deg, rgba(247, 144, 9, 0.25) 0%, rgba(255, 255, 255, 0.00) 100%)',
+        }}
+      ></div>
+      <div className='flex items-center justify-center w-5 h-5'>
+        <RiAlertFill className='w-4 h-4 text-text-warning-secondary' />
+      </div>
+      <div className='mx-1 px-1 system-xs-medium text-text-primary'>
+        {showTips}
+      </div>
+      <ActionButton
+        className='z-[1]'
+        onClick={() => setShowTips('')}
+      >
+        <RiCloseLine className='w-4 h-4' />
+      </ActionButton>
+    </div>
+  )
+}
+
+export default LimitTips
diff --git a/web/app/components/workflow/nodes/_base/components/before-run-form/form-item.tsx b/web/app/components/workflow/nodes/_base/components/before-run-form/form-item.tsx
index cde437c4c5..603368a4b3 100644
--- a/web/app/components/workflow/nodes/_base/components/before-run-form/form-item.tsx
+++ b/web/app/components/workflow/nodes/_base/components/before-run-form/form-item.tsx
@@ -104,7 +104,7 @@ const FormItem: FC<Props> = ({
               type="text"
               value={value || ''}
               onChange={e => onChange(e.target.value)}
-              placeholder={t('appDebug.variableConig.inputPlaceholder')!}
+              placeholder={t('appDebug.variableConfig.inputPlaceholder')!}
               autoFocus={autoFocus}
             />
           )
@@ -117,7 +117,7 @@ const FormItem: FC<Props> = ({
               type="number"
               value={value || ''}
               onChange={e => onChange(e.target.value)}
-              placeholder={t('appDebug.variableConig.inputPlaceholder')!}
+              placeholder={t('appDebug.variableConfig.inputPlaceholder')!}
               autoFocus={autoFocus}
             />
           )
@@ -129,7 +129,7 @@ const FormItem: FC<Props> = ({
               className="w-full px-3 py-1 text-sm leading-[18px] text-gray-900 border-0 rounded-lg grow h-[120px] bg-gray-50 focus:outline-none focus:ring-1 focus:ring-inset focus:ring-gray-200"
               value={value || ''}
               onChange={e => onChange(e.target.value)}
-              placeholder={t('appDebug.variableConig.inputPlaceholder')!}
+              placeholder={t('appDebug.variableConfig.inputPlaceholder')!}
               autoFocus={autoFocus}
             />
           )
@@ -207,7 +207,7 @@ const FormItem: FC<Props> = ({
                   key={index}
                   isInNode
                   value={item}
-                  title={<span>{t('appDebug.variableConig.content')} {index + 1} </span>}
+                  title={<span>{t('appDebug.variableConfig.content')} {index + 1} </span>}
                   onChange={handleArrayItemChange(index)}
                   headerRight={
                     (value as any).length > 1
diff --git a/web/app/components/workflow/nodes/_base/components/next-step/add.tsx b/web/app/components/workflow/nodes/_base/components/next-step/add.tsx
index 0ab0c8e39e..75694983cd 100644
--- a/web/app/components/workflow/nodes/_base/components/next-step/add.tsx
+++ b/web/app/components/workflow/nodes/_base/components/next-step/add.tsx
@@ -1,6 +1,7 @@
 import {
   memo,
   useCallback,
+  useState,
 } from 'react'
 import { useTranslation } from 'react-i18next'
 import {
@@ -10,6 +11,7 @@ import {
   useAvailableBlocks,
   useNodesInteractions,
   useNodesReadOnly,
+  useWorkflow,
 } from '@/app/components/workflow/hooks'
 import BlockSelector from '@/app/components/workflow/block-selector'
 import type {
@@ -21,18 +23,20 @@ type AddProps = {
   nodeId: string
   nodeData: CommonNodeType
   sourceHandle: string
-  branchName?: string
+  isParallel?: boolean
 }
 const Add = ({
   nodeId,
   nodeData,
   sourceHandle,
-  branchName,
+  isParallel,
 }: AddProps) => {
   const { t } = useTranslation()
+  const [open, setOpen] = useState(false)
   const { handleNodeAdd } = useNodesInteractions()
   const { nodesReadOnly } = useNodesReadOnly()
   const { availableNextBlocks } = useAvailableBlocks(nodeData.type, nodeData.isInIteration)
+  const { checkParallelLimit } = useWorkflow()
 
   const handleSelect = useCallback<OnSelectBlock>((type, toolDefaultValue) => {
     handleNodeAdd(
@@ -47,6 +51,13 @@ const Add = ({
     )
   }, [nodeId, sourceHandle, handleNodeAdd])
 
+  const handleOpenChange = useCallback((newOpen: boolean) => {
+    if (newOpen && !checkParallelLimit(nodeId, sourceHandle))
+      return
+
+    setOpen(newOpen)
+  }, [checkParallelLimit, nodeId, sourceHandle])
+
   const renderTrigger = useCallback((open: boolean) => {
     return (
       <div
@@ -57,26 +68,24 @@ const Add = ({
           ${nodesReadOnly && '!cursor-not-allowed'}
         `}
       >
-        {
-          branchName && (
-            <div
-              className='absolute left-1 right-1 -top-[7.5px] flex items-center h-3 text-[10px] text-text-placeholder font-semibold'
-              title={branchName.toLocaleUpperCase()}
-            >
-              <div className='inline-block px-0.5 rounded-[5px] bg-background-default truncate'>{branchName.toLocaleUpperCase()}</div>
-            </div>
-          )
-        }
         <div className='flex items-center justify-center mr-1.5 w-5 h-5 rounded-[5px] bg-background-default-dimm'>
           <RiAddLine className='w-3 h-3' />
         </div>
-        {t('workflow.panel.selectNextStep')}
+        <div className='flex items-center uppercase'>
+          {
+            isParallel
+              ? t('workflow.common.addParallelNode')
+              : t('workflow.panel.selectNextStep')
+          }
+        </div>
       </div>
     )
-  }, [branchName, t, nodesReadOnly])
+  }, [t, nodesReadOnly, isParallel])
 
   return (
     <BlockSelector
+      open={open}
+      onOpenChange={handleOpenChange}
       disabled={nodesReadOnly}
       onSelect={handleSelect}
       placement='top'
diff --git a/web/app/components/workflow/nodes/_base/components/next-step/container.tsx b/web/app/components/workflow/nodes/_base/components/next-step/container.tsx
new file mode 100644
index 0000000000..4788a93e45
--- /dev/null
+++ b/web/app/components/workflow/nodes/_base/components/next-step/container.tsx
@@ -0,0 +1,55 @@
+import Add from './add'
+import Item from './item'
+import type {
+  CommonNodeType,
+  Node,
+} from '@/app/components/workflow/types'
+
+type ContainerProps = {
+  nodeId: string
+  nodeData: CommonNodeType
+  sourceHandle: string
+  nextNodes: Node[]
+  branchName?: string
+}
+
+const Container = ({
+  nodeId,
+  nodeData,
+  sourceHandle,
+  nextNodes,
+  branchName,
+}: ContainerProps) => {
+  return (
+    <div className='p-0.5 space-y-0.5 rounded-[10px] bg-background-section-burn'>
+      {
+        branchName && (
+          <div
+            className='flex items-center px-2 system-2xs-semibold-uppercase text-text-tertiary truncate'
+            title={branchName}
+          >
+            {branchName}
+          </div>
+        )
+      }
+      {
+        nextNodes.map(nextNode => (
+          <Item
+            key={nextNode.id}
+            nodeId={nextNode.id}
+            data={nextNode.data}
+            sourceHandle='source'
+          />
+        ))
+      }
+      <Add
+        isParallel={!!nextNodes.length}
+        nodeId={nodeId}
+        nodeData={nodeData}
+        sourceHandle={sourceHandle}
+      />
+    </div>
+  )
+}
+
+export default Container
diff --git a/web/app/components/workflow/nodes/_base/components/next-step/index.tsx b/web/app/components/workflow/nodes/_base/components/next-step/index.tsx
index 261eb3fac7..d980eb284e 100644
--- a/web/app/components/workflow/nodes/_base/components/next-step/index.tsx
+++ b/web/app/components/workflow/nodes/_base/components/next-step/index.tsx
@@ -1,4 +1,5 @@
-import { memo } from 'react'
+import { memo, useMemo } from 'react'
+import { useTranslation } from 'react-i18next'
 import {
   getConnectedEdges,
   getOutgoers,
@@ -8,13 +9,11 @@ import {
 import { useToolIcon } from '../../../../hooks'
 import BlockIcon from '../../../../block-icon'
 import type {
-  Branch,
   Node,
 } from '../../../../types'
 import { BlockEnum } from '../../../../types'
-import Add from './add'
-import Item from './item'
 import Line from './line'
+import Container from './container'
 
 type NextStepProps = {
   selectedNode: Node
@@ -22,15 +21,33 @@ type NextStepProps = {
 const NextStep = ({
   selectedNode,
 }: NextStepProps) => {
+  const { t } = useTranslation()
   const data = selectedNode.data
   const toolIcon = useToolIcon(data)
   const store = useStoreApi()
-  const branches = data._targetBranches || []
+  const branches = useMemo(() => {
+    return data._targetBranches || []
+  }, [data])
   const nodeWithBranches = data.type === BlockEnum.IfElse || data.type === BlockEnum.QuestionClassifier
   const edges = useEdges()
   const outgoers = getOutgoers(selectedNode as Node, store.getState().getNodes(), edges)
   const connectedEdges = getConnectedEdges([selectedNode] as Node[], edges).filter(edge => edge.source === selectedNode!.id)
 
+  const branchesOutgoers = useMemo(() => {
+    if (!branches?.length)
+      return []
+
+    return branches.map((branch) => {
+      const connected = connectedEdges.filter(edge => edge.sourceHandle === branch.id)
+      const nextNodes = connected.map(edge => outgoers.find(outgoer => outgoer.id === edge.target)!)
+
+      return {
+        branch,
+        nextNodes,
+      }
+    })
+  }, [branches, connectedEdges, outgoers])
+
   return (
     <div className='flex py-1'>
       <div className='shrink-0 relative flex items-center justify-center w-9 h-9 bg-background-default rounded-lg border-[0.5px] border-divider-regular shadow-xs'>
@@ -39,59 +56,32 @@ const NextStep = ({
           toolIcon={toolIcon}
         />
       </div>
-      <Line linesNumber={nodeWithBranches ? branches.length : 1} />
-      <div className='grow'>
+      <Line
+        list={nodeWithBranches ? branchesOutgoers.map(item => item.nextNodes.length + 1) : [1]}
+      />
+      <div className='grow space-y-2'>
         {
-          !nodeWithBranches && !!outgoers.length && (
-            <Item
-              nodeId={outgoers[0].id}
-              data={outgoers[0].data}
-              sourceHandle='source'
-            />
-          )
-        }
-        {
-          !nodeWithBranches && !outgoers.length && (
-            <Add
+          !nodeWithBranches && (
+            <Container
               nodeId={selectedNode!.id}
               nodeData={selectedNode!.data}
               sourceHandle='source'
+              nextNodes={outgoers}
             />
           )
         }
         {
-          !!branches?.length && nodeWithBranches && (
-            branches.map((branch: Branch) => {
-              const connected = connectedEdges.find(edge => edge.sourceHandle === branch.id)
-              const target = outgoers.find(outgoer => outgoer.id === connected?.target)
-
+          nodeWithBranches && (
+            branchesOutgoers.map((item, index) => {
               return (
-                <div
-                  key={branch.id}
-                  className='mb-3 last-of-type:mb-0'
-                >
-                  {
-                    connected && (
-                      <Item
-                        data={target!.data!}
-                        nodeId={target!.id}
-                        sourceHandle={branch.id}
-                        branchName={branch.name}
-                      />
-                    )
-                  }
-                  {
-                    !connected && (
-                      <Add
-                        key={branch.id}
-                        nodeId={selectedNode!.id}
-                        nodeData={selectedNode!.data}
-                        sourceHandle={branch.id}
-                        branchName={branch.name}
-                      />
-                    )
-                  }
-                </div>
+                <Container
+                  key={item.branch.id}
+                  nodeId={selectedNode!.id}
+                  nodeData={selectedNode!.data}
+                  sourceHandle={item.branch.id}
+                  nextNodes={item.nextNodes}
+                  branchName={item.branch.name || `${t('workflow.nodes.questionClassifiers.class')} ${index + 1}`}
+                />
               )
             })
           )
diff --git a/web/app/components/workflow/nodes/_base/components/next-step/item.tsx b/web/app/components/workflow/nodes/_base/components/next-step/item.tsx
index b806de5684..db3748abd9 100644
--- a/web/app/components/workflow/nodes/_base/components/next-step/item.tsx
+++ b/web/app/components/workflow/nodes/_base/components/next-step/item.tsx
@@ -1,94 +1,82 @@
 import {
   memo,
   useCallback,
+  useState,
 } from 'react'
 import { useTranslation } from 'react-i18next'
-import { intersection } from 'lodash-es'
+import Operator from './operator'
 import type {
   CommonNodeType,
-  OnSelectBlock,
 } from '@/app/components/workflow/types'
 import BlockIcon from '@/app/components/workflow/block-icon'
-import BlockSelector from '@/app/components/workflow/block-selector'
 import {
-  useAvailableBlocks,
   useNodesInteractions,
   useNodesReadOnly,
   useToolIcon,
 } from '@/app/components/workflow/hooks'
 import Button from '@/app/components/base/button'
+import cn from '@/utils/classnames'
 
 type ItemProps = {
   nodeId: string
   sourceHandle: string
-  branchName?: string
   data: CommonNodeType
 }
 const Item = ({
   nodeId,
   sourceHandle,
-  branchName,
   data,
 }: ItemProps) => {
   const { t } = useTranslation()
-  const { handleNodeChange } = useNodesInteractions()
+  const [open, setOpen] = useState(false)
   const { nodesReadOnly } = useNodesReadOnly()
+  const { handleNodeSelect } = useNodesInteractions()
   const toolIcon = useToolIcon(data)
-  const {
-    availablePrevBlocks,
-    availableNextBlocks,
-  } = useAvailableBlocks(data.type, data.isInIteration)
 
-  const handleSelect = useCallback<OnSelectBlock>((type, toolDefaultValue) => {
-    handleNodeChange(nodeId, type, sourceHandle, toolDefaultValue)
-  }, [nodeId, sourceHandle, handleNodeChange])
-  const renderTrigger = useCallback((open: boolean) => {
-    return (
-      <Button
-        size='small'
-        className={`
-          hidden group-hover:flex
-          ${open && '!bg-gray-100 !flex'}
-        `}
-      >
-        {t('workflow.panel.change')}
-      </Button>
-    )
-  }, [t])
+  const handleOpenChange = useCallback((v: boolean) => {
+    setOpen(v)
+  }, [])
 
   return (
     <div
-      className='relative group flex items-center mb-3 last-of-type:mb-0 px-2 h-9 rounded-lg border-[0.5px] border-divider-regular bg-background-default hover:bg-background-default-hover shadow-xs text-xs text-text-secondary cursor-pointer'
+      className='relative group flex items-center last-of-type:mb-0 px-2 h-9 rounded-lg border-[0.5px] border-divider-regular bg-background-default hover:bg-background-default-hover shadow-xs text-xs text-text-secondary cursor-pointer'
     >
-      {
-        branchName && (
-          <div
-            className='absolute left-1 right-1 -top-[7.5px] flex items-center h-3 text-[10px] text-gray-500 font-semibold'
-            title={branchName.toLocaleUpperCase()}
-          >
-            <div className='inline-block px-0.5 rounded-[5px] bg-white truncate'>{branchName.toLocaleUpperCase()}</div>
-          </div>
-        )
-      }
       <BlockIcon
         type={data.type}
         toolIcon={toolIcon}
         className='shrink-0 mr-1.5'
       />
-      <div className='grow system-xs-medium text-text-secondary'>{data.title}</div>
+      <div
+        className='grow system-xs-medium text-text-secondary truncate'
+        title={data.title}
+      >
+        {data.title}
+      </div>
       {
         !nodesReadOnly && (
-          <BlockSelector
-            onSelect={handleSelect}
-            placement='top-end'
-            offset={{
-              mainAxis: 6,
-              crossAxis: 8,
-            }}
-            trigger={renderTrigger}
-            popupClassName='!w-[328px]'
-            availableBlocksTypes={intersection(availablePrevBlocks, availableNextBlocks).filter(item => item !== data.type)}
-          />
+          <>
+            <Button
+              className='hidden group-hover:flex shrink-0 mr-1'
+              size='small'
+              onClick={() => handleNodeSelect(nodeId)}
+            >
+              {t('workflow.common.jumpToNode')}
+            </Button>
+            <div
+              className={cn(
+                'hidden shrink-0 group-hover:flex items-center',
+                open && 'flex',
+              )}
+            >
+              <Operator
+                data={data}
+                nodeId={nodeId}
+                sourceHandle={sourceHandle}
+                open={open}
+                onOpenChange={handleOpenChange}
+              />
+            </div>
+          </>
         )
       }
     </div>
diff --git a/web/app/components/workflow/nodes/_base/components/next-step/line.tsx b/web/app/components/workflow/nodes/_base/components/next-step/line.tsx
index ccf4e8d730..3a4430cb5d 100644
--- a/web/app/components/workflow/nodes/_base/components/next-step/line.tsx
+++ b/web/app/components/workflow/nodes/_base/components/next-step/line.tsx
@@ -1,56 +1,70 @@
 import { memo } from 'react'
 
 type LineProps = {
-  linesNumber: number
+  list: number[]
 }
 const Line = ({
-  linesNumber,
+  list,
 }: LineProps) => {
-  const svgHeight = linesNumber * 36 + (linesNumber - 1) * 12
+  const listHeight = list.map((item) => {
+    return item * 36 + (item - 1) * 2 + 12 + 6
+  })
+  const processedList = listHeight.map((item, index) => {
+    if (index === 0)
+      return item
+
+    return listHeight.slice(0, index).reduce((acc, cur) => acc + cur, 0) + item
+  })
+  const processedListLength = processedList.length
+  const svgHeight = processedList[processedListLength - 1] + (processedListLength - 1) * 8
 
   return (
     <svg className='shrink-0 w-6' style={{ height: svgHeight }}>
       {
-        Array(linesNumber).fill(0).map((_, index) => (
-          <g key={index}>
-            {
-              index === 0 && (
-                <>
+        processedList.map((item, index) => {
+          const prevItem = index > 0 ? processedList[index - 1] : 0
+          const space = prevItem + index * 8 + 16
+          return (
+            <g key={index}>
+              {
+                index === 0 && (
+                  <>
+                    <path
+                      d='M0,18 L24,18'
+                      strokeWidth={1}
+                      fill='none'
+                      className='stroke-divider-solid'
+                    />
+                    <rect
+                      x={0}
+                      y={16}
+                      width={1}
+                      height={4}
+                      className='fill-divider-solid-alt'
+                    />
+                  </>
+                )
+              }
+              {
+                index > 0 && (
                   <path
-                    d='M0,18 L24,18'
+                    d={`M0,18 Q12,18 12,28 L12,${space - 10 + 2} Q12,${space + 2} 24,${space + 2}`}
                     strokeWidth={1}
                     fill='none'
-                    className='stroke-divider-soild'
+                    className='stroke-divider-solid'
                   />
-                  <rect
-                    x={0}
-                    y={16}
-                    width={1}
-                    height={4}
-                    className='fill-divider-soild-alt'
-                  />
-                </>
-              )
-            }
-            {
-              index > 0 && (
-                <path
-                  d={`M0,18 Q12,18 12,28 L12,${index * 48 + 18 - 10} Q12,${index * 48 + 18} 24,${index * 48 + 18}`}
-                  strokeWidth={1}
-                  fill='none'
-                  className='stroke-divider-soild'
-                />
-              )
-            }
-            <rect
-              x={23}
-              y={index * 48 + 18 - 2}
-              width={1}
-              height={4}
-              className='fill-divider-soild-alt'
-            />
-          </g>
-        ))
+                )
+              }
+              <rect
+                x={23}
+                y={space}
+                width={1}
+                height={4}
+                className='fill-divider-solid-alt'
+              />
+            </g>
+          )
+        })
       }
     </svg>
   )
diff --git a/web/app/components/workflow/nodes/_base/components/next-step/operator.tsx b/web/app/components/workflow/nodes/_base/components/next-step/operator.tsx
new file mode 100644
index 0000000000..ad6c7abd0c
--- /dev/null
+++ b/web/app/components/workflow/nodes/_base/components/next-step/operator.tsx
@@ -0,0 +1,129 @@
+import {
+  useCallback,
+} from 'react'
+import { useTranslation } from 'react-i18next'
+import { RiMoreFill } from '@remixicon/react'
+import { intersection } from 'lodash-es'
+import {
+  PortalToFollowElem,
+  PortalToFollowElemContent,
+  PortalToFollowElemTrigger,
+} from '@/app/components/base/portal-to-follow-elem'
+import Button from '@/app/components/base/button'
+import BlockSelector from '@/app/components/workflow/block-selector'
+import {
+  useAvailableBlocks,
+  useNodesInteractions,
+} from '@/app/components/workflow/hooks'
+import type {
+  CommonNodeType,
+  OnSelectBlock,
+} from '@/app/components/workflow/types'
+
+type ChangeItemProps = {
+  data: CommonNodeType
+  nodeId: string
+  sourceHandle: string
+}
+const ChangeItem = ({
+  data,
+  nodeId,
+  sourceHandle,
+}: ChangeItemProps) => {
+  const { t } = useTranslation()
+
+  const { handleNodeChange } = useNodesInteractions()
+  const {
+    availablePrevBlocks,
+    availableNextBlocks,
+  } = useAvailableBlocks(data.type, data.isInIteration)
+
+  const handleSelect = useCallback<OnSelectBlock>((type, toolDefaultValue) => {
+    handleNodeChange(nodeId, type, sourceHandle, toolDefaultValue)
+  }, [nodeId, sourceHandle, handleNodeChange])
+
+  const renderTrigger = useCallback(() => {
+    return (
+      <div className='flex items-center px-2 h-8 rounded-lg cursor-pointer hover:bg-state-base-hover'>
+        {t('workflow.panel.change')}
+      </div>
+    )
+  }, [t])
+
+  return (
+    <BlockSelector
+      onSelect={handleSelect}
+      placement='top-end'
+      offset={{
+        mainAxis: 6,
+        crossAxis: 8,
+      }}
+      trigger={renderTrigger}
+      popupClassName='!w-[328px]'
+      availableBlocksTypes={intersection(availablePrevBlocks, availableNextBlocks).filter(item => item !== data.type)}
+    />
+  )
+}
+
+type OperatorProps = {
+  open: boolean
+  onOpenChange: (v: boolean) => void
+  data: CommonNodeType
+  nodeId: string
+  sourceHandle: string
+}
+const Operator = ({
+  open,
+  onOpenChange,
+  data,
+  nodeId,
+  sourceHandle,
+}: OperatorProps) => {
+  const { t } = useTranslation()
+  const {
+    handleNodeDelete,
+    handleNodeDisconnect,
+  } = useNodesInteractions()
+
+  return (
+    <PortalToFollowElem
+      placement='bottom-end'
+      offset={{ mainAxis: 4, crossAxis: -4 }}
+      open={open}
+      onOpenChange={onOpenChange}
+    >
+      <PortalToFollowElemTrigger onClick={() => onOpenChange(!open)}>
+        <Button className='p-0 w-6 h-6'>
+          <RiMoreFill className='w-4 h-4' />
+        </Button>
+      </PortalToFollowElemTrigger>
+      <PortalToFollowElemContent className='z-10'>
+        <div className='min-w-[120px] rounded-xl border-[0.5px] border-components-panel-border bg-components-panel-bg-blur shadow-lg system-md-regular text-text-secondary'>
+          <div className='p-1'>
+            <ChangeItem
+              data={data}
+              nodeId={nodeId}
+              sourceHandle={sourceHandle}
+            />
+            <div
+              className='flex items-center px-2 h-8 rounded-lg cursor-pointer hover:bg-state-base-hover'
+              onClick={() => handleNodeDisconnect(nodeId)}
+            >
+              {t('workflow.common.disconnect')}
+            </div>
+          </div>
+          <div className='p-1'>
+            <div
+              className='flex items-center px-2 h-8 rounded-lg cursor-pointer hover:bg-state-base-hover'
+              onClick={() => handleNodeDelete(nodeId)}
+            >
+              {t('common.operation.delete')}
+            </div>
+          </div>
+        </div>
+      </PortalToFollowElemContent>
+    </PortalToFollowElem>
+  )
+}
+
+export default Operator
diff --git a/web/app/components/workflow/nodes/_base/components/node-handle.tsx b/web/app/components/workflow/nodes/_base/components/node-handle.tsx
index 56870f79d6..9a662366d4 100644
--- a/web/app/components/workflow/nodes/_base/components/node-handle.tsx
+++ b/web/app/components/workflow/nodes/_base/components/node-handle.tsx
@@ -9,16 +9,22 @@ import {
   Handle,
   Position,
 } from 'reactflow'
+import { useTranslation } from 'react-i18next'
 import { BlockEnum } from '../../../types'
 import type { Node } from '../../../types'
 import BlockSelector from '../../../block-selector'
 import type { ToolDefaultValue } from '../../../block-selector/types'
 import {
   useAvailableBlocks,
+  useIsChatMode,
   useNodesInteractions,
   useNodesReadOnly,
+  useWorkflow,
 } from '../../../hooks'
-import { useStore } from '../../../store'
+import {
+  useStore,
+} from '../../../store'
+import Tooltip from '@/app/components/base/tooltip'
 
 type NodeHandleProps = {
   handleId: string
@@ -38,9 +44,7 @@ export const NodeTargetHandle = memo(({
   const { getNodesReadOnly } = useNodesReadOnly()
   const connected = data._connectedTargetHandleIds?.includes(handleId)
   const { availablePrevBlocks } = useAvailableBlocks(data.type, data.isInIteration)
-  const isConnectable = !!availablePrevBlocks.length && (
-    !data.isIterationStart
-  )
+  const isConnectable = !!availablePrevBlocks.length
 
   const handleOpenChange = useCallback((v: boolean) => {
     setOpen(v)
@@ -112,12 +116,15 @@ export const NodeSourceHandle = memo(({
   handleClassName,
   nodeSelectorClassName,
 }: NodeHandleProps) => {
+  const { t } = useTranslation()
   const notInitialWorkflow = useStore(s => s.notInitialWorkflow)
   const [open, setOpen] = useState(false)
   const { handleNodeAdd } = useNodesInteractions()
   const { getNodesReadOnly } = useNodesReadOnly()
   const { availableNextBlocks } = useAvailableBlocks(data.type, data.isInIteration)
   const isConnectable = !!availableNextBlocks.length
+  const isChatMode = useIsChatMode()
+  const { checkParallelLimit } = useWorkflow()
 
   const connected = data._connectedSourceHandleIds?.includes(handleId)
   const handleOpenChange = useCallback((v: boolean) => {
@@ -125,9 +132,9 @@ export const NodeSourceHandle = memo(({
   }, [])
   const handleHandleClick = useCallback((e: MouseEvent) => {
     e.stopPropagation()
-    if (!connected)
+    if (checkParallelLimit(id, handleId))
       setOpen(v => !v)
-  }, [connected])
+  }, [checkParallelLimit, id, handleId])
   const handleSelect = useCallback((type: BlockEnum, toolDefaultValue?: ToolDefaultValue) => {
     handleNodeAdd(
       {
@@ -142,12 +149,25 @@ export const NodeSourceHandle = memo(({
   }, [handleNodeAdd, id, handleId])
 
   useEffect(() => {
-    if (notInitialWorkflow && data.type === BlockEnum.Start)
+    if (notInitialWorkflow && data.type === BlockEnum.Start && !isChatMode)
       setOpen(true)
-  }, [notInitialWorkflow, data.type])
+  }, [notInitialWorkflow, data.type, isChatMode])
 
   return (
-    <>
+    <Tooltip
+      popupContent={(
+        <div className='system-xs-regular text-text-tertiary'>
+          <div>
+            <span className='system-xs-medium text-text-secondary'>{t('workflow.common.parallelTip.click.title')}</span>
+            {t('workflow.common.parallelTip.click.desc')}
+          </div>
+          <div>
+            <span className='system-xs-medium text-text-secondary'>{t('workflow.common.parallelTip.drag.title')}</span>
+            {t('workflow.common.parallelTip.drag.desc')}
+          </div>
+        </div>
+      )}
+    >
       <Handle
         id={handleId}
         type='source'
@@ -163,7 +183,7 @@ export const NodeSourceHandle = memo(({
         onClick={handleHandleClick}
       >
         {
-          !connected && isConnectable && !getNodesReadOnly() && (
+          isConnectable && !getNodesReadOnly() && (
             <BlockSelector
               open={open}
               onOpenChange={handleOpenChange}
@@ -181,7 +201,7 @@ export const NodeSourceHandle = memo(({
           )
         }
       </Handle>
-    </>
+    </Tooltip>
   )
 })
 NodeSourceHandle.displayName = 'NodeSourceHandle'
diff --git a/web/app/components/workflow/nodes/_base/components/node-resizer.tsx b/web/app/components/workflow/nodes/_base/components/node-resizer.tsx
index 4c83bea8d6..a8e7a9aa11 100644
--- a/web/app/components/workflow/nodes/_base/components/node-resizer.tsx
+++ b/web/app/components/workflow/nodes/_base/components/node-resizer.tsx
@@ -28,8 +28,8 @@ const NodeResizer = ({
   nodeId,
   nodeData,
   icon = <Icon />,
-  minWidth = 272,
-  minHeight = 176,
+  minWidth = 258,
+  minHeight = 152,
   maxWidth,
 }: NodeResizerProps) => {
   const { handleNodeResize } = useNodesInteractions()
diff --git a/web/app/components/workflow/nodes/_base/components/variable-tag.tsx b/web/app/components/workflow/nodes/_base/components/variable-tag.tsx
index 6edda1d7e8..0a80bfe37f 100644
--- a/web/app/components/workflow/nodes/_base/components/variable-tag.tsx
+++ b/web/app/components/workflow/nodes/_base/components/variable-tag.tsx
@@ -1,9 +1,12 @@
 import { useMemo } from 'react'
 import { useNodes } from 'reactflow'
 import { capitalize } from 'lodash-es'
+import { useTranslation } from 'react-i18next'
+import { RiErrorWarningFill } from '@remixicon/react'
 import { VarBlockIcon } from '@/app/components/workflow/block-icon'
 import type {
   CommonNodeType,
+  Node,
   ValueSelector,
   VarType,
 } from '@/app/components/workflow/types'
@@ -11,63 +14,75 @@ import { BlockEnum } from '@/app/components/workflow/types'
 import { Line3 } from '@/app/components/base/icons/src/public/common'
 import { Variable02 } from '@/app/components/base/icons/src/vender/solid/development'
 import { BubbleX, Env } from '@/app/components/base/icons/src/vender/line/others'
-import { isConversationVar, isENV, isSystemVar } from '@/app/components/workflow/nodes/_base/components/variable/utils'
+import { getNodeInfoById, isConversationVar, isENV, isSystemVar } from '@/app/components/workflow/nodes/_base/components/variable/utils'
+import Tooltip from '@/app/components/base/tooltip'
 import cn from '@/utils/classnames'
 
 type VariableTagProps = {
   valueSelector: ValueSelector
   varType: VarType
+  availableNodes?: Node[]
 }
 const VariableTag = ({
   valueSelector,
   varType,
+  availableNodes,
 }: VariableTagProps) => {
   const nodes = useNodes<CommonNodeType>()
   const node = useMemo(() => {
-    if (isSystemVar(valueSelector))
-      return nodes.find(node => node.data.type === BlockEnum.Start)
+    if (isSystemVar(valueSelector)) {
+      const startNode = availableNodes?.find(n => n.data.type === BlockEnum.Start)
+      if (startNode)
+        return startNode
+    }
+    return getNodeInfoById(availableNodes || nodes, valueSelector[0])
+  }, [nodes, valueSelector, availableNodes])
 
-    return nodes.find(node => node.id === valueSelector[0])
-  }, [nodes, valueSelector])
   const isEnv = isENV(valueSelector)
   const isChatVar = isConversationVar(valueSelector)
+  const isValid = Boolean(node) || isEnv || isChatVar
 
   const variableName = isSystemVar(valueSelector) ? valueSelector.slice(0).join('.') : valueSelector.slice(1).join('.')
 
+  const { t } = useTranslation()
   return (
-    <div className='inline-flex items-center px-1.5 max-w-full h-6 text-xs rounded-md border-[0.5px] border-[rgba(16, 2440,0.08)] bg-white shadow-xs'>
-      {!isEnv && !isChatVar && (
-        <>
+    <Tooltip popupContent={!isValid && t('workflow.errorMsg.invalidVariable')}>
+      <div className={cn('inline-flex items-center px-1.5 max-w-full h-6 text-xs rounded-md border-[0.5px] border-[rgba(16, 2440,0.08)] bg-white shadow-xs',
+        !isValid && 'border-red-400 !bg-[#FEF3F2]',
+      )}>
+        {(!isEnv && !isChatVar && <>
           {node && (
-            <VarBlockIcon
-              className='shrink-0 mr-0.5 text-text-secondary'
-              type={node!.data.type}
-            />
+            <>
+              <VarBlockIcon
+                type={BlockEnum.Start}
+              />
+              <div
+                className='max-w-[60px] truncate text-text-secondary font-medium'
+                title={node?.data.title}
+              >
+                {node?.data.title}
+              </div>
+            </>
           )}
-          <div
-            className='max-w-[60px] truncate text-text-secondary font-medium'
-            title={node?.data.title}
-          >
-            {node?.data.title}
-          </div>
           <Line3 className='shrink-0 mx-0.5' />
           <Variable02 className='shrink-0 mr-0.5 w-3.5 h-3.5 text-text-accent' />
-        </>
-      )}
-      {isEnv && <Env className='shrink-0 mr-0.5 w-3.5 h-3.5 text-util-colors-violet-violet-600' />}
-      {isChatVar && <BubbleX className='w-3.5 h-3.5 text-util-colors-teal-teal-700' />}
-      <div
-        className={cn('truncate text-text-accent font-medium', (isEnv || isChatVar) && 'text-text-secondary')}
-        title={variableName}
-      >
-        {variableName}
+        </>)}
+        {isEnv && <Env className='shrink-0 mr-0.5 w-3.5 h-3.5 text-util-colors-violet-violet-600' />}
+        {isChatVar && <BubbleX className='w-3.5 h-3.5 text-util-colors-teal-teal-700' />}
+        <div
+          className={cn('truncate text-text-accent font-medium', (isEnv || isChatVar) && 'text-text-secondary')}
+          title={variableName}
+        >
+          {variableName}
+        </div>
+        {
+          varType && (
+            <div className='shrink-0 ml-0.5 text-text-tertiary'>{capitalize(varType)}</div>
+          )
+        }
+        {!isValid && <RiErrorWarningFill className='ml-0.5 w-3 h-3 text-[#D92D20]' />}
       </div>
-      {
-        varType && (
-          <div className='shrink-0 ml-0.5 text-text-tertiary'>{capitalize(varType)}</div>
-        )
-      }
-    </div>
+    </Tooltip>
   )
 }
 
diff --git a/web/app/components/workflow/nodes/_base/components/variable/var-reference-picker.tsx b/web/app/components/workflow/nodes/_base/components/variable/var-reference-picker.tsx
index e2b1f0a31c..5e51173673 100644
--- a/web/app/components/workflow/nodes/_base/components/variable/var-reference-picker.tsx
+++ b/web/app/components/workflow/nodes/_base/components/variable/var-reference-picker.tsx
@@ -5,9 +5,11 @@ import { useTranslation } from 'react-i18next'
 import {
   RiArrowDownSLine,
   RiCloseLine,
+  RiErrorWarningFill,
 } from '@remixicon/react'
 import produce from 'immer'
-import { useStoreApi } from 'reactflow'
+import { useEdges, useStoreApi } from 'reactflow'
+import useAvailableVarList from '../../hooks/use-available-var-list'
 import VarReferencePopup from './var-reference-popup'
 import { getNodeInfoById, isConversationVar, isENV, isSystemVar } from './utils'
 import ConstantField from './constant-field'
@@ -26,13 +28,14 @@ import {
 } from '@/app/components/base/portal-to-follow-elem'
 import {
   useIsChatMode,
-  useWorkflow,
   useWorkflowVariables,
 } from '@/app/components/workflow/hooks'
 import { VarType as VarKindType } from '@/app/components/workflow/nodes/tool/types'
 import TypeSelector from '@/app/components/workflow/nodes/_base/components/selector'
 import AddButton from '@/app/components/base/button/add-button'
 import Badge from '@/app/components/base/badge'
+import Tooltip from '@/app/components/base/tooltip'
+
 const TRIGGER_DEFAULT_WIDTH = 227
 
 type Props = {
@@ -67,7 +70,7 @@ const VarReferencePicker: FC<Props> = ({
   onlyLeafNodeVar,
   filterVar = () => true,
   availableNodes: passedInAvailableNodes,
-  availableVars,
+  availableVars: passedInAvailableVars,
   isAddBtnTrigger,
   schema,
   valueTypePlaceHolder,
@@ -77,13 +80,15 @@ const VarReferencePicker: FC<Props> = ({
   const {
     getNodes,
   } = store.getState()
+  const edges = useEdges()
   const isChatMode = useIsChatMode()
 
-  const { getTreeLeafNodes, getBeforeNodesInSameBranch } = useWorkflow()
-  const { getCurrentVariableType, getNodeAvailableVars } = useWorkflowVariables()
-  const availableNodes = useMemo(() => {
-    return passedInAvailableNodes || (onlyLeafNodeVar ? getTreeLeafNodes(nodeId) : getBeforeNodesInSameBranch(nodeId))
-  }, [getBeforeNodesInSameBranch, getTreeLeafNodes, nodeId, onlyLeafNodeVar, passedInAvailableNodes])
+  const { getCurrentVariableType } = useWorkflowVariables()
+  const { availableNodes, availableVars } = useAvailableVarList(nodeId, {
+    onlyLeafNodeVar,
+    passedInAvailableNodes,
+    filterVar,
+  })
   const startNode = availableNodes.find((node: any) => {
     return node.data.type === BlockEnum.Start
   })
@@ -102,19 +107,8 @@ const VarReferencePicker: FC<Props> = ({
 
   const [varKindType, setVarKindType] = useState<VarKindType>(defaultVarKindType)
   const isConstant = isSupportConstantValue && varKindType === VarKindType.constant
-  const outputVars = useMemo(() => {
-    if (availableVars)
-      return availableVars
 
-    const vars = getNodeAvailableVars({
-      parentNode: iterationNode,
-      beforeNodes: availableNodes,
-      isChatMode,
-      filterVar,
-    })
-
-    return vars
-  }, [iterationNode, availableNodes, isChatMode, filterVar, availableVars, getNodeAvailableVars])
+  const outputVars = useMemo(() => (passedInAvailableVars || availableVars), [passedInAvailableVars, availableVars])
 
   const [open, setOpen] = useState(false)
   useEffect(() => {
@@ -189,7 +183,7 @@ const VarReferencePicker: FC<Props> = ({
   const handleVarReferenceChange = useCallback((value: ValueSelector, varInfo: Var) => {
     // sys var not passed to backend
     const newValue = produce(value, (draft) => {
-      if (draft[1] && draft[1].startsWith('sys')) {
+      if (draft[1] && draft[1].startsWith('sys.')) {
         draft.shift()
         const paths = draft[0].split('.')
         paths.forEach((p, i) => {
@@ -216,8 +210,16 @@ const VarReferencePicker: FC<Props> = ({
     isConstant: !!isConstant,
   })
 
-  const isEnv = isENV(value as ValueSelector)
-  const isChatVar = isConversationVar(value as ValueSelector)
+  const { isEnv, isChatVar, isValidVar } = useMemo(() => {
+    const isEnv = isENV(value as ValueSelector)
+    const isChatVar = isConversationVar(value as ValueSelector)
+    const isValidVar = Boolean(outputVarNode) || isEnv || isChatVar
+    return {
+      isEnv,
+      isChatVar,
+      isValidVar,
+    }
+  }, [value, edges, outputVarNode])
 
   // 8(left/right-padding) + 14(icon) + 4 + 14 + 2 = 42 + 17 buff
   const availableWidth = triggerWidth - 56
@@ -295,39 +297,44 @@ const VarReferencePicker: FC<Props> = ({
                     className='grow h-full'
                   >
                     <div ref={isSupportConstantValue ? triggerRef : null} className={cn('h-full', isSupportConstantValue && 'flex items-center pl-1 py-1 rounded-lg bg-gray-100')}>
-                      <div className={cn('h-full items-center px-1.5 rounded-[5px]', hasValue ? 'bg-white inline-flex' : 'flex')}>
-                        {hasValue
-                          ? (
-                            <>
-                              {isShowNodeName && !isEnv && !isChatVar && (
-                                <div className='flex items-center'>
-                                  <div className='p-[1px]'>
-                                    <VarBlockIcon
-                                      className='!text-gray-900'
-                                      type={outputVarNode?.type || BlockEnum.Start}
-                                    />
+                      <Tooltip popupContent={!isValidVar && hasValue && t('workflow.errorMsg.invalidVariable')}>
+                        <div className={cn('h-full items-center px-1.5 rounded-[5px] ', hasValue ? 'bg-white inline-flex' : 'flex',
+                          !isValidVar && hasValue && 'border border-red-400 !bg-[#FEF3F2]',
+                        )}>
+                          {hasValue
+                            ? (
+                              <>
+                                {isShowNodeName && !isEnv && !isChatVar && (
+                                  <div className='flex items-center'>
+                                    <div className='px-[1px] h-3'>
+                                      {outputVarNode?.type && <VarBlockIcon
+                                        className='!text-gray-900'
+                                        type={outputVarNode.type}
+                                      />}
+                                    </div>
+                                    <div className='mx-0.5 text-xs font-medium text-gray-700 truncate' title={outputVarNode?.title} style={{
+                                      maxWidth: maxNodeNameWidth,
+                                    }}>{outputVarNode?.title}</div>
+                                    <Line3 className='mr-0.5'></Line3>
                                   </div>
-                                  <div className='mx-0.5 text-xs font-medium text-gray-700 truncate' title={outputVarNode?.title} style={{
-                                    maxWidth: maxNodeNameWidth,
-                                  }}>{outputVarNode?.title}</div>
-                                  <Line3 className='mr-0.5'></Line3>
+                                )}
+                                <div className='flex items-center text-primary-600'>
+                                  {!hasValue && <Variable02 className='w-3.5 h-3.5' />}
+                                  {isEnv && <Env className='w-3.5 h-3.5 text-util-colors-violet-violet-600' />}
+                                  {isChatVar && <BubbleX className='w-3.5 h-3.5 text-util-colors-teal-teal-700' />}
+                                  <div className={cn('ml-0.5 text-xs font-medium truncate', (isEnv || isChatVar) && '!text-text-secondary')} title={varName} style={{
+                                    maxWidth: maxVarNameWidth,
+                                  }}>{varName}</div>
                                 </div>
-                              )}
-                              <div className='flex items-center text-primary-600'>
-                                {!hasValue && <Variable02 className='w-3.5 h-3.5' />}
-                                {isEnv && <Env className='w-3.5 h-3.5 text-util-colors-violet-violet-600' />}
-                                {isChatVar && <BubbleX className='w-3.5 h-3.5 text-util-colors-teal-teal-700' />}
-                                <div className={cn('ml-0.5 text-xs font-medium truncate', (isEnv || isChatVar) && '!text-text-secondary')} title={varName} style={{
-                                  maxWidth: maxVarNameWidth,
-                                }}>{varName}</div>
-                              </div>
-                              <div className='ml-0.5 text-xs font-normal text-gray-500 capitalize truncate' title={type} style={{
-                                maxWidth: maxTypeWidth,
-                              }}>{type}</div>
-                            </>
-                          )
-                          : <div className='text-[13px] font-normal text-gray-400'>{t('workflow.common.setVarValuePlaceholder')}</div>}
-                      </div>
+                                <div className='ml-0.5 text-xs font-normal text-gray-500 capitalize truncate' title={type} style={{
+                                  maxWidth: maxTypeWidth,
+                                }}>{type}</div>
+                                {!isValidVar && <RiErrorWarningFill className='ml-0.5 w-3 h-3 text-[#D92D20]' /> }
+                              </>
+                            )
+                            : <div className='text-[13px] font-normal text-gray-400'>{t('workflow.common.setVarValuePlaceholder')}</div>}
+                        </div>
+                      </Tooltip>
                     </div>
 
                   </VarPickerWrap>
diff --git a/web/app/components/workflow/nodes/_base/hooks/use-available-var-list.ts b/web/app/components/workflow/nodes/_base/hooks/use-available-var-list.ts
index b81feab805..bd17bb1de8 100644
--- a/web/app/components/workflow/nodes/_base/hooks/use-available-var-list.ts
+++ b/web/app/components/workflow/nodes/_base/hooks/use-available-var-list.ts
@@ -4,12 +4,13 @@ import {
   useWorkflow,
   useWorkflowVariables,
 } from '@/app/components/workflow/hooks'
-import type { ValueSelector, Var } from '@/app/components/workflow/types'
+import type { Node, ValueSelector, Var } from '@/app/components/workflow/types'
 type Params = {
   onlyLeafNodeVar?: boolean
   hideEnv?: boolean
   hideChatVar?: boolean
   filterVar: (payload: Var, selector: ValueSelector) => boolean
+  passedInAvailableNodes?: Node[]
 }
 
 const useAvailableVarList = (nodeId: string, {
@@ -17,6 +18,7 @@ const useAvailableVarList = (nodeId: string, {
   filterVar,
   hideEnv,
   hideChatVar,
+  passedInAvailableNodes,
 }: Params = {
   onlyLeafNodeVar: false,
   filterVar: () => true,
@@ -25,7 +27,7 @@ const useAvailableVarList = (nodeId: string, {
   const { getNodeAvailableVars } = useWorkflowVariables()
   const isChatMode = useIsChatMode()
 
-  const availableNodes = onlyLeafNodeVar ? getTreeLeafNodes(nodeId) : getBeforeNodesInSameBranch(nodeId)
+  const availableNodes = passedInAvailableNodes || (onlyLeafNodeVar ? getTreeLeafNodes(nodeId) : getBeforeNodesInSameBranch(nodeId))
 
   const {
     parentNode: iterationNode,
diff --git a/web/app/components/workflow/nodes/_base/hooks/use-resize-panel.ts b/web/app/components/workflow/nodes/_base/hooks/use-resize-panel.ts
index 1a521cd58f..f2259a02cf 100644
--- a/web/app/components/workflow/nodes/_base/hooks/use-resize-panel.ts
+++ b/web/app/components/workflow/nodes/_base/hooks/use-resize-panel.ts
@@ -5,7 +5,7 @@ import {
   useState,
 } from 'react'
 
-export type UseResizePanelPrarams = {
+export type UseResizePanelParams = {
   direction?: 'horizontal' | 'vertical' | 'both'
   triggerDirection?: 'top' | 'right' | 'bottom' | 'left' | 'top-right' | 'top-left' | 'bottom-right' | 'bottom-left'
   minWidth?: number
@@ -15,7 +15,7 @@ export type UseResizePanelPrarams = {
   onResized?: (width: number, height: number) => void
   onResize?: (width: number, height: number) => void
 }
-export const useResizePanel = (params?: UseResizePanelPrarams) => {
+export const useResizePanel = (params?: UseResizePanelParams) => {
   const {
     direction = 'both',
     triggerDirection = 'bottom-right',
diff --git a/web/app/components/workflow/nodes/_base/node.tsx b/web/app/components/workflow/nodes/_base/node.tsx
index 0b45c80888..bd5921c735 100644
--- a/web/app/components/workflow/nodes/_base/node.tsx
+++ b/web/app/components/workflow/nodes/_base/node.tsx
@@ -14,6 +14,7 @@ import {
   RiErrorWarningLine,
   RiLoader2Line,
 } from '@remixicon/react'
+import { useTranslation } from 'react-i18next'
 import type { NodeProps } from '../../types'
 import {
   BlockEnum,
@@ -43,6 +44,7 @@ const BaseNode: FC<BaseNodeProps> = ({
   data,
   children,
 }) => {
+  const { t } = useTranslation()
   const nodeRef = useRef<HTMLDivElement>(null)
   const { nodesReadOnly } = useNodesReadOnly()
   const { handleNodeIterationChildSizeChange } = useNodeIterationInteractions()
@@ -80,6 +82,7 @@ const BaseNode: FC<BaseNodeProps> = ({
       className={cn(
         'flex border-[2px] rounded-2xl',
         showSelectedBorder ? 'border-components-option-card-option-selected-border' : 'border-transparent',
+        !showSelectedBorder && data._inParallelHovering && 'border-workflow-block-border-highlight',
       )}
       ref={nodeRef}
       style={{
@@ -100,6 +103,13 @@ const BaseNode: FC<BaseNodeProps> = ({
           data._isBundled && '!shadow-lg',
         )}
       >
+        {
+          data._inParallelHovering && (
+            <div className='absolute left-2 -top-2.5 top system-2xs-medium-uppercase text-text-tertiary z-10'>
+              {t('workflow.common.parallelRun')}
+            </div>
+          )
+        }
         {
           data._showAddVariablePopup && (
             <AddVariablePopupWithPosition
diff --git a/web/app/components/workflow/nodes/if-else/components/condition-list/condition-item.tsx b/web/app/components/workflow/nodes/if-else/components/condition-list/condition-item.tsx
index c6cb580118..c96f6b3ef6 100644
--- a/web/app/components/workflow/nodes/if-else/components/condition-list/condition-item.tsx
+++ b/web/app/components/workflow/nodes/if-else/components/condition-list/condition-item.tsx
@@ -80,6 +80,7 @@ const ConditionItem = ({
             <VariableTag
               valueSelector={condition.variable_selector}
               varType={condition.varType}
+              availableNodes={availableNodes}
             />
           </div>
           <div className='mx-1 w-[1px] h-3 bg-divider-regular'></div>
diff --git a/web/app/components/workflow/nodes/iteration-start/constants.ts b/web/app/components/workflow/nodes/iteration-start/constants.ts
new file mode 100644
index 0000000000..94e3ccbd90
--- /dev/null
+++ b/web/app/components/workflow/nodes/iteration-start/constants.ts
@@ -0,0 +1 @@
+export const CUSTOM_ITERATION_START_NODE = 'custom-iteration-start'
diff --git a/web/app/components/workflow/nodes/iteration-start/default.ts b/web/app/components/workflow/nodes/iteration-start/default.ts
new file mode 100644
index 0000000000..d98efa7ba2
--- /dev/null
+++ b/web/app/components/workflow/nodes/iteration-start/default.ts
@@ -0,0 +1,21 @@
+import type { NodeDefault } from '../../types'
+import type { IterationStartNodeType } from './types'
+import { ALL_CHAT_AVAILABLE_BLOCKS, ALL_COMPLETION_AVAILABLE_BLOCKS } from '@/app/components/workflow/constants'
+
+const nodeDefault: NodeDefault<IterationStartNodeType> = {
+  defaultValue: {},
+  getAvailablePrevNodes() {
+    return []
+  },
+  getAvailableNextNodes(isChatMode: boolean) {
+    const nodes = isChatMode ? ALL_CHAT_AVAILABLE_BLOCKS : ALL_COMPLETION_AVAILABLE_BLOCKS
+    return nodes
+  },
+  checkValid() {
+    return {
+      isValid: true,
+    }
+  },
+}
+
+export default nodeDefault
diff --git a/web/app/components/workflow/nodes/iteration-start/index.tsx b/web/app/components/workflow/nodes/iteration-start/index.tsx
new file mode 100644
index 0000000000..9d7ac1f905
--- /dev/null
+++ b/web/app/components/workflow/nodes/iteration-start/index.tsx
@@ -0,0 +1,42 @@
+import { memo } from 'react'
+import { useTranslation } from 'react-i18next'
+import type { NodeProps } from 'reactflow'
+import { RiHome5Fill } from '@remixicon/react'
+import Tooltip from '@/app/components/base/tooltip'
+import { NodeSourceHandle } from '@/app/components/workflow/nodes/_base/components/node-handle'
+
+const IterationStartNode = ({ id, data }: NodeProps) => {
+  const { t } = useTranslation()
+
+  return (
+    <div className='group flex nodrag items-center justify-center w-11 h-11 rounded-2xl border border-workflow-block-border bg-white'>
+      <Tooltip popupContent={t('workflow.blocks.iteration-start')} asChild={false}>
+        <div className='flex items-center justify-center w-6 h-6 rounded-full border-[0.5px] border-components-panel-border-subtle bg-util-colors-blue-brand-blue-brand-500'>
+          <RiHome5Fill className='w-3 h-3 text-text-primary-on-surface' />
+        </div>
+      </Tooltip>
+      <NodeSourceHandle
+        id={id}
+        data={data}
+        handleClassName='!top-1/2 !-right-[9px] !-translate-y-1/2'
+        handleId='source'
+      />
+    </div>
+  )
+}
+
+export const IterationStartNodeDumb = () => {
+  const { t } = useTranslation()
+
+  return (
+    <div className='relative left-[17px] top-[21px] flex nodrag items-center justify-center w-11 h-11 rounded-2xl border border-workflow-block-border bg-white z-[11]'>
+      <Tooltip popupContent={t('workflow.blocks.iteration-start')} asChild={false}>
+        <div className='flex items-center justify-center w-6 h-6 rounded-full border-[0.5px] border-components-panel-border-subtle bg-util-colors-blue-brand-blue-brand-500'>
+          <RiHome5Fill className='w-3 h-3 text-text-primary-on-surface' />
+        </div>
+      </Tooltip>
+    </div>
+  )
+}
+
+export default memo(IterationStartNode)
diff --git a/web/app/components/workflow/nodes/iteration-start/types.ts b/web/app/components/workflow/nodes/iteration-start/types.ts
new file mode 100644
index 0000000000..319cce0bc2
--- /dev/null
+++ b/web/app/components/workflow/nodes/iteration-start/types.ts
@@ -0,0 +1,3 @@
+import type { CommonNodeType } from '@/app/components/workflow/types'
+
+export type IterationStartNodeType = CommonNodeType
diff --git a/web/app/components/workflow/nodes/iteration/add-block.tsx b/web/app/components/workflow/nodes/iteration/add-block.tsx
index fd8480b7df..07e2b5daf0 100644
--- a/web/app/components/workflow/nodes/iteration/add-block.tsx
+++ b/web/app/components/workflow/nodes/iteration/add-block.tsx
@@ -2,87 +2,49 @@ import {
   memo,
   useCallback,
 } from 'react'
-import produce from 'immer'
 import {
   RiAddLine,
 } from '@remixicon/react'
-import { useStoreApi } from 'reactflow'
 import { useTranslation } from 'react-i18next'
 import {
-  generateNewNode,
-} from '../../utils'
-import {
-  WorkflowHistoryEvent,
   useAvailableBlocks,
+  useNodesInteractions,
   useNodesReadOnly,
-  useWorkflowHistory,
 } from '../../hooks'
-import { NODES_INITIAL_DATA } from '../../constants'
-import InsertBlock from './insert-block'
 import type { IterationNodeType } from './types'
 import cn from '@/utils/classnames'
 import BlockSelector from '@/app/components/workflow/block-selector'
-import { IterationStart } from '@/app/components/base/icons/src/vender/workflow'
 import type {
   OnSelectBlock,
 } from '@/app/components/workflow/types'
 import {
   BlockEnum,
 } from '@/app/components/workflow/types'
-import Tooltip from '@/app/components/base/tooltip'
 
 type AddBlockProps = {
   iterationNodeId: string
   iterationNodeData: IterationNodeType
 }
 const AddBlock = ({
-  iterationNodeId,
   iterationNodeData,
 }: AddBlockProps) => {
   const { t } = useTranslation()
-  const store = useStoreApi()
   const { nodesReadOnly } = useNodesReadOnly()
+  const { handleNodeAdd } = useNodesInteractions()
   const { availableNextBlocks } = useAvailableBlocks(BlockEnum.Start, true)
-  const { availablePrevBlocks } = useAvailableBlocks(iterationNodeData.startNodeType, true)
-  const { saveStateToHistory } = useWorkflowHistory()
 
   const handleSelect = useCallback<OnSelectBlock>((type, toolDefaultValue) => {
-    const {
-      getNodes,
-      setNodes,
-    } = store.getState()
-    const nodes = getNodes()
-    const nodesWithSameType = nodes.filter(node => node.data.type === type)
-    const newNode = generateNewNode({
-      data: {
-        ...NODES_INITIAL_DATA[type],
-        title: nodesWithSameType.length > 0 ? `${t(`workflow.blocks.${type}`)} ${nodesWithSameType.length + 1}` : t(`workflow.blocks.${type}`),
-        ...(toolDefaultValue || {}),
-        isIterationStart: true,
-        isInIteration: true,
-        iteration_id: iterationNodeId,
+    handleNodeAdd(
+      {
+        nodeType: type,
+        toolDefaultValue,
       },
-      position: {
-        x: 117,
-        y: 85,
+      {
+        prevNodeId: iterationNodeData.start_node_id,
+        prevNodeSourceHandle: 'source',
       },
-      zIndex: 1001,
-      parentId: iterationNodeId,
-      extent: 'parent',
-    })
-    const newNodes = produce(nodes, (draft) => {
-      draft.forEach((node) => {
-        if (node.id === iterationNodeId) {
-          node.data._children = [newNode.id]
-          node.data.start_node_id = newNode.id
-          node.data.startNodeType = newNode.data.type
-        }
-      })
-      draft.push(newNode)
-    })
-    setNodes(newNodes)
-    saveStateToHistory(WorkflowHistoryEvent.NodeAdd)
-  }, [store, t, iterationNodeId, saveStateToHistory])
+    )
+  }, [handleNodeAdd, iterationNodeData.start_node_id])
 
   const renderTriggerElement = useCallback((open: boolean) => {
     return (
@@ -98,35 +60,18 @@ const AddBlock = ({
   }, [nodesReadOnly, t])
 
   return (
-    <div className='absolute top-12 left-6 flex items-center h-8 z-10'>
-      <Tooltip popupContent={t('workflow.blocks.iteration-start')}>
-        <div className='flex items-center justify-center w-6 h-6 rounded-full border-[0.5px] border-black/[0.02] shadow-md bg-primary-500'>
-          <IterationStart className='w-4 h-4 text-white' />
-        </div>
-      </Tooltip>
+    <div className='absolute top-7 left-14 flex items-center h-8 z-10'>
       <div className='group/insert relative w-16 h-0.5 bg-gray-300'>
-        {
-          iterationNodeData.startNodeType && (
-            <InsertBlock
-              startNodeId={iterationNodeData.start_node_id}
-              availableBlocksTypes={availablePrevBlocks}
-            />
-          )
-        }
         <div className='absolute right-0 top-1/2 -translate-y-1/2 w-0.5 h-2 bg-primary-500'></div>
       </div>
-      {
-        !iterationNodeData.startNodeType && (
-          <BlockSelector
-            disabled={nodesReadOnly}
-            onSelect={handleSelect}
-            trigger={renderTriggerElement}
-            triggerInnerClassName='inline-flex'
-            popupClassName='!min-w-[256px]'
-            availableBlocksTypes={availableNextBlocks}
-          />
-        )
-      }
+      <BlockSelector
+        disabled={nodesReadOnly}
+        onSelect={handleSelect}
+        trigger={renderTriggerElement}
+        triggerInnerClassName='inline-flex'
+        popupClassName='!min-w-[256px]'
+        availableBlocksTypes={availableNextBlocks}
+      />
     </div>
   )
 }
diff --git a/web/app/components/workflow/nodes/iteration/default.ts b/web/app/components/workflow/nodes/iteration/default.ts
index 43f8a751ac..3afa52d06e 100644
--- a/web/app/components/workflow/nodes/iteration/default.ts
+++ b/web/app/components/workflow/nodes/iteration/default.ts
@@ -9,6 +9,7 @@ const nodeDefault: NodeDefault<IterationNodeType> = {
     start_node_id: '',
     iterator_selector: [],
     output_selector: [],
+    _children: [],
   },
   getAvailablePrevNodes(isChatMode: boolean) {
     const nodes = isChatMode
diff --git a/web/app/components/workflow/nodes/iteration/insert-block.tsx b/web/app/components/workflow/nodes/iteration/insert-block.tsx
deleted file mode 100644
index d041fe1c74..0000000000
--- a/web/app/components/workflow/nodes/iteration/insert-block.tsx
+++ /dev/null
@@ -1,61 +0,0 @@
-import {
-  memo,
-  useCallback,
-  useState,
-} from 'react'
-import { useNodesInteractions } from '../../hooks'
-import type {
-  BlockEnum,
-  OnSelectBlock,
-} from '../../types'
-import BlockSelector from '../../block-selector'
-import cn from '@/utils/classnames'
-
-type InsertBlockProps = {
-  startNodeId: string
-  availableBlocksTypes: BlockEnum[]
-}
-const InsertBlock = ({
-  startNodeId,
-  availableBlocksTypes,
-}: InsertBlockProps) => {
-  const [open, setOpen] = useState(false)
-  const { handleNodeAdd } = useNodesInteractions()
-
-  const handleOpenChange = useCallback((v: boolean) => {
-    setOpen(v)
-  }, [])
-  const handleInsert = useCallback<OnSelectBlock>((nodeType, toolDefaultValue) => {
-    handleNodeAdd(
-      {
-        nodeType,
-        toolDefaultValue,
-      },
-      {
-        nextNodeId: startNodeId,
-        nextNodeTargetHandle: 'target',
-      },
-    )
-  }, [startNodeId, handleNodeAdd])
-
-  return (
-    <div
-      className={cn(
-        'nopan nodrag',
-        'hidden group-hover/insert:block absolute top-1/2 left-1/2 transform -translate-x-1/2 -translate-y-1/2',
-        open && '!block',
-      )}
-    >
-      <BlockSelector
-        open={open}
-        onOpenChange={handleOpenChange}
-        asChild
-        onSelect={handleInsert}
-        availableBlocksTypes={availableBlocksTypes}
-        triggerClassName={() => 'hover:scale-125 transition-all'}
-      />
-    </div>
-  )
-}
-
-export default memo(InsertBlock)
diff --git a/web/app/components/workflow/nodes/iteration/node.tsx b/web/app/components/workflow/nodes/iteration/node.tsx
index f4520402f3..48a005a261 100644
--- a/web/app/components/workflow/nodes/iteration/node.tsx
+++ b/web/app/components/workflow/nodes/iteration/node.tsx
@@ -8,6 +8,7 @@ import {
   useNodesInitialized,
   useViewport,
 } from 'reactflow'
+import { IterationStartNodeDumb } from '../iteration-start'
 import { useNodeIterationInteractions } from './use-interactions'
 import type { IterationNodeType } from './types'
 import AddBlock from './add-block'
@@ -29,7 +30,7 @@ const Node: FC<NodeProps<IterationNodeType>> = ({
 
   return (
     <div className={cn(
-      'relative min-w-[258px] min-h-[118px] w-full h-full rounded-2xl bg-[#F0F2F7]/90',
+      'relative min-w-[240px] min-h-[90px] w-full h-full rounded-2xl bg-[#F0F2F7]/90',
     )}>
       <Background
         id={`iteration-background-${id}`}
@@ -38,10 +39,19 @@ const Node: FC<NodeProps<IterationNodeType>> = ({
         size={2 / zoom}
         color='#E4E5E7'
       />
-      <AddBlock
-        iterationNodeId={id}
-        iterationNodeData={data}
-      />
+      {
+        data._isCandidate && (
+          <IterationStartNodeDumb />
+        )
+      }
+      {
+        data._children!.length === 1 && (
+          <AddBlock
+            iterationNodeId={id}
+            iterationNodeData={data}
+          />
+        )
+      }
     </div>
   )
 }
diff --git a/web/app/components/workflow/nodes/iteration/use-interactions.ts b/web/app/components/workflow/nodes/iteration/use-interactions.ts
index 219c8e731f..f8e3640cc4 100644
--- a/web/app/components/workflow/nodes/iteration/use-interactions.ts
+++ b/web/app/components/workflow/nodes/iteration/use-interactions.ts
@@ -11,6 +11,7 @@ import {
   ITERATION_PADDING,
   NODES_INITIAL_DATA,
 } from '../../constants'
+import { CUSTOM_ITERATION_START_NODE } from '../iteration-start/constants'
 
 export const useNodeIterationInteractions = () => {
   const { t } = useTranslation()
@@ -107,12 +108,12 @@ export const useNodeIterationInteractions = () => {
   const handleNodeIterationChildrenCopy = useCallback((nodeId: string, newNodeId: string) => {
     const { getNodes } = store.getState()
     const nodes = getNodes()
-    const childrenNodes = nodes.filter(n => n.parentId === nodeId)
+    const childrenNodes = nodes.filter(n => n.parentId === nodeId && n.type !== CUSTOM_ITERATION_START_NODE)
 
     return childrenNodes.map((child, index) => {
       const childNodeType = child.data.type as BlockEnum
       const nodesWithSameType = nodes.filter(node => node.data.type === childNodeType)
-      const newNode = generateNewNode({
+      const { newNode } = generateNewNode({
         data: {
           ...NODES_INITIAL_DATA[childNodeType],
           ...child.data,
@@ -121,6 +122,7 @@ export const useNodeIterationInteractions = () => {
           _connectedSourceHandleIds: [],
           _connectedTargetHandleIds: [],
           title: nodesWithSameType.length > 0 ? `${t(`workflow.blocks.${childNodeType}`)} ${nodesWithSameType.length + 1}` : t(`workflow.blocks.${childNodeType}`),
+          iteration_id: newNodeId,
         },
         position: child.position,
         positionAbsolute: child.positionAbsolute,
diff --git a/web/app/components/workflow/nodes/llm/default.ts b/web/app/components/workflow/nodes/llm/default.ts
index 803add6f00..d7597942e5 100644
--- a/web/app/components/workflow/nodes/llm/default.ts
+++ b/web/app/components/workflow/nodes/llm/default.ts
@@ -44,7 +44,7 @@ const nodeDefault: NodeDefault<LLMNodeType> = {
 
     if (!errorMessages && !payload.memory) {
       const isChatModel = payload.model.mode === 'chat'
-      const isPromptyEmpty = isChatModel
+      const isPromptEmpty = isChatModel
         ? !(payload.prompt_template as PromptItem[]).some((t) => {
           if (t.edition_type === EditionType.jinja2)
             return t.jinja2_text !== ''
@@ -52,7 +52,7 @@ const nodeDefault: NodeDefault<LLMNodeType> = {
           return t.text !== ''
         })
         : ((payload.prompt_template as PromptItem).edition_type === EditionType.jinja2 ? (payload.prompt_template as PromptItem).jinja2_text === '' : (payload.prompt_template as PromptItem).text === '')
-      if (isPromptyEmpty)
+      if (isPromptEmpty)
         errorMessages = t(`${i18nPrefix}.fieldRequired`, { field: t('workflow.nodes.llm.prompt') })
     }
 
diff --git a/web/app/components/workflow/nodes/parameter-extractor/components/extract-parameter/update.tsx b/web/app/components/workflow/nodes/parameter-extractor/components/extract-parameter/update.tsx
index 2ac331558b..081a683234 100644
--- a/web/app/components/workflow/nodes/parameter-extractor/components/extract-parameter/update.tsx
+++ b/web/app/components/workflow/nodes/parameter-extractor/components/extract-parameter/update.tsx
@@ -99,7 +99,7 @@ const AddExtractParameter: FC<Props> = ({
     if (!param.name)
       errMessage = t(`${errorI18nPrefix}.fieldRequired`, { field: t(`${i18nPrefix}.addExtractParameterContent.name`) })
     if (!errMessage && param.type === ParamType.select && (!param.options || param.options.length === 0))
-      errMessage = t(`${errorI18nPrefix}.fieldRequired`, { field: t('appDebug.variableConig.options') })
+      errMessage = t(`${errorI18nPrefix}.fieldRequired`, { field: t('appDebug.variableConfig.options') })
     if (!errMessage && !param.description)
       errMessage = t(`${errorI18nPrefix}.fieldRequired`, { field: t(`${i18nPrefix}.addExtractParameterContent.description`) })
 
@@ -160,7 +160,7 @@ const AddExtractParameter: FC<Props> = ({
                 />
               </Field>
               {param.type === ParamType.select && (
-                <Field title={t('appDebug.variableConig.options')}>
+                <Field title={t('appDebug.variableConfig.options')}>
                   <ConfigSelect options={param.options || []} onChange={handleParamChange('options')} />
                 </Field>
               )}
diff --git a/web/app/components/workflow/operator/add-block.tsx b/web/app/components/workflow/operator/add-block.tsx
index 48222cc528..388fbc053f 100644
--- a/web/app/components/workflow/operator/add-block.tsx
+++ b/web/app/components/workflow/operator/add-block.tsx
@@ -55,7 +55,7 @@ const AddBlock = ({
     } = store.getState()
     const nodes = getNodes()
     const nodesWithSameType = nodes.filter(node => node.data.type === type)
-    const newNode = generateNewNode({
+    const { newNode } = generateNewNode({
       data: {
         ...NODES_INITIAL_DATA[type],
         title: nodesWithSameType.length > 0 ? `${t(`workflow.blocks.${type}`)} ${nodesWithSameType.length + 1}` : t(`workflow.blocks.${type}`),
diff --git a/web/app/components/workflow/operator/hooks.ts b/web/app/components/workflow/operator/hooks.ts
index 5b14211497..edec10bda7 100644
--- a/web/app/components/workflow/operator/hooks.ts
+++ b/web/app/components/workflow/operator/hooks.ts
@@ -11,7 +11,7 @@ export const useOperator = () => {
   const { userProfile } = useAppContext()
 
   const handleAddNote = useCallback(() => {
-    const newNode = generateNewNode({
+    const { newNode } = generateNewNode({
       type: CUSTOM_NOTE_NODE,
       data: {
         title: '',
diff --git a/web/app/components/workflow/panel/chat-record/index.tsx b/web/app/components/workflow/panel/chat-record/index.tsx
index 2ab3165c14..afd20b7358 100644
--- a/web/app/components/workflow/panel/chat-record/index.tsx
+++ b/web/app/components/workflow/panel/chat-record/index.tsx
@@ -103,9 +103,9 @@ const ChatRecord = () => {
               } as any}
               chatList={chatMessageList}
               chatContainerClassName='px-4'
-              chatContainerInnerClassName='pt-6'
+              chatContainerInnerClassName='pt-6 w-full max-w-full mx-auto'
               chatFooterClassName='px-4 rounded-b-2xl'
-              chatFooterInnerClassName='pb-4'
+              chatFooterInnerClassName='pb-4 w-full max-w-full mx-auto'
               chatNode={<UserInput />}
               noChatInput
               allToolIcons={{}}
diff --git a/web/app/components/workflow/panel/debug-and-preview/chat-wrapper.tsx b/web/app/components/workflow/panel/debug-and-preview/chat-wrapper.tsx
index 4655940037..a7dd607e22 100644
--- a/web/app/components/workflow/panel/debug-and-preview/chat-wrapper.tsx
+++ b/web/app/components/workflow/panel/debug-and-preview/chat-wrapper.tsx
@@ -103,9 +103,9 @@ const ChatWrapper = forwardRef<ChatWrapperRefType, ChatWrapperProps>(({ showConv
         chatList={chatList}
         isResponding={isResponding}
         chatContainerClassName='px-3'
-        chatContainerInnerClassName='pt-6'
+        chatContainerInnerClassName='pt-6 w-full max-w-full mx-auto'
         chatFooterClassName='px-4 rounded-bl-2xl'
-        chatFooterInnerClassName='pb-4'
+        chatFooterInnerClassName='pb-4 w-full max-w-full mx-auto'
         onSend={doSend}
         onStopResponding={handleStop}
         chatNode={(
@@ -118,6 +118,7 @@ const ChatWrapper = forwardRef<ChatWrapperRefType, ChatWrapperProps>(({ showConv
             }
           </>
         )}
+        noSpacing
         suggestedQuestions={suggestedQuestions}
         showPromptLog
         chatAnswerContainerInner='!pr-2'
diff --git a/web/app/components/workflow/panel/debug-and-preview/hooks.ts b/web/app/components/workflow/panel/debug-and-preview/hooks.ts
index 54d3915a13..51a018bcb1 100644
--- a/web/app/components/workflow/panel/debug-and-preview/hooks.ts
+++ b/web/app/components/workflow/panel/debug-and-preview/hooks.ts
@@ -180,8 +180,6 @@ export const useChat = (
       isAnswer: true,
     }
 
-    let isInIteration = false
-
     handleResponding(true)
 
     const bodyParams = {
@@ -317,11 +315,11 @@ export const useChat = (
               ...responseItem,
             }
           }))
-          isInIteration = true
         },
-        onIterationNext: () => {
+        onIterationNext: ({ data }) => {
           const tracing = responseItem.workflowProcess!.tracing!
-          const iterations = tracing[tracing.length - 1]
+          const iterations = tracing.find(item => item.node_id === data.node_id
+            && (item.execution_metadata?.parallel_id === data.execution_metadata?.parallel_id || item.parallel_id === data.execution_metadata?.parallel_id))!
           iterations.details!.push([])
 
           handleUpdateChatList(produce(chatListRef.current, (draft) => {
@@ -331,9 +329,10 @@ export const useChat = (
         },
         onIterationFinish: ({ data }) => {
           const tracing = responseItem.workflowProcess!.tracing!
-          const iterations = tracing[tracing.length - 1]
-          tracing[tracing.length - 1] = {
-            ...iterations,
+          const iterationsIndex = tracing.findIndex(item => item.node_id === data.node_id
+            && (item.execution_metadata?.parallel_id === data.execution_metadata?.parallel_id || item.parallel_id === data.execution_metadata?.parallel_id))!
+          tracing[iterationsIndex] = {
+            ...tracing[iterationsIndex],
             ...data,
             status: NodeRunningStatus.Succeeded,
           } as any
@@ -341,67 +340,45 @@ export const useChat = (
             const currentIndex = draft.length - 1
             draft[currentIndex] = responseItem
           }))
-
-          isInIteration = false
         },
         onNodeStarted: ({ data }) => {
-          if (isInIteration) {
-            const tracing = responseItem.workflowProcess!.tracing!
-            const iterations = tracing[tracing.length - 1]
-            const currIteration = iterations.details![iterations.details!.length - 1]
-            currIteration.push({
-              ...data,
-              status: NodeRunningStatus.Running,
-            } as any)
-            handleUpdateChatList(produce(chatListRef.current, (draft) => {
-              const currentIndex = draft.length - 1
-              draft[currentIndex] = responseItem
-            }))
-          }
-          else {
-            responseItem.workflowProcess!.tracing!.push({
-              ...data,
-              status: NodeRunningStatus.Running,
-            } as any)
-            handleUpdateChatList(produce(chatListRef.current, (draft) => {
-              const currentIndex = draft.findIndex(item => item.id === responseItem.id)
-              draft[currentIndex] = {
-                ...draft[currentIndex],
-                ...responseItem,
-              }
-            }))
-          }
+          if (data.iteration_id)
+            return
+
+          responseItem.workflowProcess!.tracing!.push({
+            ...data,
+            status: NodeRunningStatus.Running,
+          } as any)
+          handleUpdateChatList(produce(chatListRef.current, (draft) => {
+            const currentIndex = draft.findIndex(item => item.id === responseItem.id)
+            draft[currentIndex] = {
+              ...draft[currentIndex],
+              ...responseItem,
+            }
+          }))
         },
         onNodeFinished: ({ data }) => {
-          if (isInIteration) {
-            const tracing = responseItem.workflowProcess!.tracing!
-            const iterations = tracing[tracing.length - 1]
-            const currIteration = iterations.details![iterations.details!.length - 1]
-            currIteration[currIteration.length - 1] = {
-              ...data,
-              status: NodeRunningStatus.Succeeded,
-            } as any
-            handleUpdateChatList(produce(chatListRef.current, (draft) => {
-              const currentIndex = draft.length - 1
-              draft[currentIndex] = responseItem
-            }))
-          }
-          else {
-            const currentIndex = responseItem.workflowProcess!.tracing!.findIndex(item => item.node_id === data.node_id)
-            responseItem.workflowProcess!.tracing[currentIndex] = {
-              ...(responseItem.workflowProcess!.tracing[currentIndex].extras
-                ? { extras: responseItem.workflowProcess!.tracing[currentIndex].extras }
-                : {}),
-              ...data,
-            } as any
-            handleUpdateChatList(produce(chatListRef.current, (draft) => {
-              const currentIndex = draft.findIndex(item => item.id === responseItem.id)
-              draft[currentIndex] = {
-                ...draft[currentIndex],
-                ...responseItem,
-              }
-            }))
-          }
+          if (data.iteration_id)
+            return
+
+          const currentIndex = responseItem.workflowProcess!.tracing!.findIndex((item) => {
+            if (!item.execution_metadata?.parallel_id)
+              return item.node_id === data.node_id
+            return item.node_id === data.node_id && (item.execution_metadata?.parallel_id === data.execution_metadata?.parallel_id || item.parallel_id === data.execution_metadata?.parallel_id)
+          })
+          responseItem.workflowProcess!.tracing[currentIndex] = {
+            ...(responseItem.workflowProcess!.tracing[currentIndex]?.extras
+              ? { extras: responseItem.workflowProcess!.tracing[currentIndex].extras }
+              : {}),
+            ...data,
+          } as any
+          handleUpdateChatList(produce(chatListRef.current, (draft) => {
+            const currentIndex = draft.findIndex(item => item.id === responseItem.id)
+            draft[currentIndex] = {
+              ...draft[currentIndex],
+              ...responseItem,
+            }
+          }))
         },
       },
     )
diff --git a/web/app/components/workflow/panel/debug-and-preview/index.tsx b/web/app/components/workflow/panel/debug-and-preview/index.tsx
index 29fc48f896..8e1c489098 100644
--- a/web/app/components/workflow/panel/debug-and-preview/index.tsx
+++ b/web/app/components/workflow/panel/debug-and-preview/index.tsx
@@ -88,7 +88,6 @@ const DebugAndPreview = () => {
                   <RiEqualizer2Line className='w-4 h-4' />
                 </ActionButton>
               </Tooltip>
-              {expanded && <div className='absolute z-10 bottom-[-17px] right-[5px] w-3 h-3 bg-components-panel-on-panel-item-bg border-l-[0.5px] border-t-[0.5px] border-components-panel-border-subtle rotate-45'/>}
             </div>
           )}
           <div className='mx-3 w-[1px] h-3.5 bg-gray-200'></div>
diff --git a/web/app/components/workflow/panel/record.tsx b/web/app/components/workflow/panel/record.tsx
index 079dd2cc86..d79f1a9439 100644
--- a/web/app/components/workflow/panel/record.tsx
+++ b/web/app/components/workflow/panel/record.tsx
@@ -1,5 +1,5 @@
 import { memo, useCallback } from 'react'
-import type { WorkflowDataUpdator } from '../types'
+import type { WorkflowDataUpdater } from '../types'
 import Run from '../run'
 import { useStore } from '../store'
 import { useWorkflowUpdate } from '../hooks'
@@ -9,7 +9,7 @@ const Record = () => {
   const { handleUpdateWorkflowCanvas } = useWorkflowUpdate()
 
   const handleResultCallback = useCallback((res: any) => {
-    const graph: WorkflowDataUpdator = res.graph
+    const graph: WorkflowDataUpdater = res.graph
     handleUpdateWorkflowCanvas({
       nodes: graph.nodes,
       edges: graph.edges,
diff --git a/web/app/components/workflow/run/index.tsx b/web/app/components/workflow/run/index.tsx
index 702ce06e1c..331ef1c2f5 100644
--- a/web/app/components/workflow/run/index.tsx
+++ b/web/app/components/workflow/run/index.tsx
@@ -63,26 +63,22 @@ const RunPanel: FC<RunProps> = ({ hideResult, activeTab = 'RESULT', runID, getRe
   const formatNodeList = useCallback((list: NodeTracing[]) => {
     const allItems = list.reverse()
     const result: NodeTracing[] = []
-    let iterationIndex = 0
     allItems.forEach((item) => {
       const { node_type, execution_metadata } = item
       if (node_type !== BlockEnum.Iteration) {
         const isInIteration = !!execution_metadata?.iteration_id
 
         if (isInIteration) {
-          const iterationDetails = result[result.length - 1].details!
-          const currentIterationIndex = execution_metadata?.iteration_index
-          const isIterationFirstNode = iterationIndex !== currentIterationIndex || iterationDetails.length === 0
+          const iterationNode = result.find(node => node.node_id === execution_metadata?.iteration_id)
+          const iterationDetails = iterationNode?.details
+          const currentIterationIndex = execution_metadata?.iteration_index ?? 0
 
-          if (isIterationFirstNode) {
-            iterationDetails!.push([item])
-            iterationIndex = currentIterationIndex!
+          if (Array.isArray(iterationDetails)) {
+            if (iterationDetails.length === 0 || !iterationDetails[currentIterationIndex])
+              iterationDetails[currentIterationIndex] = [item]
+            else
+              iterationDetails[currentIterationIndex].push(item)
           }
-
-          else {
-            iterationDetails[iterationDetails.length - 1].push(item)
-          }
-
           return
         }
         // not in iteration
@@ -90,7 +86,6 @@ const RunPanel: FC<RunProps> = ({ hideResult, activeTab = 'RESULT', runID, getRe
 
         return
       }
-
       result.push({
         ...item,
         details: [],
@@ -134,12 +129,12 @@ const RunPanel: FC<RunProps> = ({ hideResult, activeTab = 'RESULT', runID, getRe
       getData(appDetail.id, runID)
   }, [appDetail, runID])
 
-  const [height, setHieght] = useState(0)
+  const [height, setHeight] = useState(0)
   const ref = useRef<HTMLDivElement>(null)
 
   const adjustResultHeight = () => {
     if (ref.current)
-      setHieght(ref.current?.clientHeight - 16 - 16 - 2 - 1)
+      setHeight(ref.current?.clientHeight - 16 - 16 - 2 - 1)
   }
 
   useEffect(() => {
@@ -197,7 +192,7 @@ const RunPanel: FC<RunProps> = ({ hideResult, activeTab = 'RESULT', runID, getRe
           onClick={() => switchTab('TRACING')}
         >{t('runLog.tracing')}</div>
       </div>
-      {/* panel detal */}
+      {/* panel detail */}
       <div ref={ref} className={cn('grow bg-white h-0 overflow-y-auto rounded-b-2xl', currentTab !== 'DETAIL' && '!bg-gray-50')}>
         {loading && (
           <div className='flex h-full items-center justify-center bg-white'>
diff --git a/web/app/components/workflow/run/iteration-result-panel.tsx b/web/app/components/workflow/run/iteration-result-panel.tsx
index c833ea0342..4fc30f03df 100644
--- a/web/app/components/workflow/run/iteration-result-panel.tsx
+++ b/web/app/components/workflow/run/iteration-result-panel.tsx
@@ -1,10 +1,14 @@
 'use client'
 import type { FC } from 'react'
-import React, { useCallback } from 'react'
+import React, { useCallback, useState } from 'react'
 import { useTranslation } from 'react-i18next'
-import { RiCloseLine } from '@remixicon/react'
+import {
+  RiArrowRightSLine,
+  RiCloseLine,
+} from '@remixicon/react'
 import { ArrowNarrowLeft } from '../../base/icons/src/vender/line/arrows'
-import NodePanel from './node'
+import TracingPanel from './tracing-panel'
+import { Iteration } from '@/app/components/base/icons/src/vender/workflow'
 import cn from '@/utils/classnames'
 import type { NodeTracing } from '@/types/workflow'
 const i18nPrefix = 'workflow.singleRun'
@@ -23,43 +27,67 @@ const IterationResultPanel: FC<Props> = ({
   noWrap,
 }) => {
   const { t } = useTranslation()
+  const [expandedIterations, setExpandedIterations] = useState<Record<number, boolean>>([])
+
+  const toggleIteration = useCallback((index: number) => {
+    setExpandedIterations(prev => ({
+      ...prev,
+      [index]: !prev[index],
+    }))
+  }, [])
 
   const main = (
     <>
-      <div className={cn(!noWrap && 'shrink-0 ', 'pl-4 pr-3 pt-3')}>
+      <div className={cn(!noWrap && 'shrink-0 ', 'px-4 pt-3')}>
         <div className='shrink-0 flex justify-between items-center h-8'>
-          <div className='text-base font-semibold text-gray-900 truncate'>
+          <div className='system-xl-semibold text-text-primary truncate'>
             {t(`${i18nPrefix}.testRunIteration`)}
           </div>
           <div className='ml-2 shrink-0 p-1 cursor-pointer' onClick={onHide}>
-            <RiCloseLine className='w-4 h-4 text-gray-500 ' />
+            <RiCloseLine className='w-4 h-4 text-text-tertiary' />
           </div>
         </div>
-        <div className='flex items-center py-2 space-x-1 text-primary-600 cursor-pointer' onClick={onBack}>
+        <div className='flex items-center py-2 space-x-1 text-text-accent-secondary cursor-pointer' onClick={onBack}>
           <ArrowNarrowLeft className='w-4 h-4' />
-          <div className='leading-[18px] text-[13px] font-medium'>{t(`${i18nPrefix}.back`)}</div>
+          <div className='system-sm-medium'>{t(`${i18nPrefix}.back`)}</div>
         </div>
       </div>
       {/* List */}
-      <div className={cn(!noWrap ? 'h-0 grow' : 'max-h-full', 'overflow-y-auto px-4 pb-4 bg-gray-50')}>
+      <div className={cn(!noWrap ? 'flex-grow overflow-auto' : 'max-h-full', 'p-2 bg-components-panel-bg')}>
         {list.map((iteration, index) => (
-          <div key={index} className={cn('my-4', index === 0 && 'mt-2')}>
-            <div className='flex items-center'>
-              <div className='shrink-0 leading-[18px] text-xs font-semibold text-gray-500 uppercase'>{t(`${i18nPrefix}.iteration`)} {index + 1}</div>
-              <div
-                className='ml-3 grow w-0 h-px'
-                style={{ background: 'linear-gradient(to right, #F3F4F6, rgba(243, 244, 246, 0))' }}
-              ></div>
+          <div key={index} className={cn('mb-1 overflow-hidden rounded-xl bg-background-section-burn border-none')}>
+            <div
+              className={cn(
+                'flex items-center justify-between w-full px-3 cursor-pointer',
+                expandedIterations[index] ? 'pt-3 pb-2' : 'py-3',
+                'rounded-xl text-left',
+              )}
+              onClick={() => toggleIteration(index)}
+            >
+              <div className={cn('flex items-center gap-2 flex-grow')}>
+                <div className='flex items-center justify-center w-4 h-4 rounded-[5px] border-divider-subtle bg-util-colors-cyan-cyan-500 flex-shrink-0'>
+                  <Iteration className='w-3 h-3 text-text-primary-on-surface' />
+                </div>
+                <span className='system-sm-semibold-uppercase text-text-primary flex-grow'>
+                  {t(`${i18nPrefix}.iteration`)} {index + 1}
+                </span>
+                <RiArrowRightSLine className={cn(
+                  'w-4 h-4 text-text-tertiary transition-transform duration-200 flex-shrink-0',
+                  expandedIterations[index] && 'transform rotate-90',
+                )} />
+              </div>
             </div>
-            <div className='mt-0.5 space-y-1'>
-              {iteration.map(node => (
-                <NodePanel
-                  key={node.id}
-                  className='!px-0 !py-0'
-                  nodeInfo={node}
-                  notShowIterationNav
-                />
-              ))}
+            {expandedIterations[index] && <div
+              className="flex-grow h-px bg-divider-subtle"
+            ></div>}
+            <div className={cn(
+              'overflow-hidden transition-all duration-200',
+              expandedIterations[index] ? 'max-h-[1000px] opacity-100' : 'max-h-0 opacity-0',
+            )}>
+              <TracingPanel
+                list={iteration}
+                className='bg-background-section-burn'
+              />
             </div>
           </div>
         ))}
diff --git a/web/app/components/workflow/run/meta.tsx b/web/app/components/workflow/run/meta.tsx
index 86eb221ad9..b2d7269a51 100644
--- a/web/app/components/workflow/run/meta.tsx
+++ b/web/app/components/workflow/run/meta.tsx
@@ -16,7 +16,7 @@ type Props = {
 const MetaData: FC<Props> = ({
   status,
   executor,
-  startTime = 0,
+  startTime,
   time,
   tokens,
   steps = 1,
@@ -64,7 +64,7 @@ const MetaData: FC<Props> = ({
               <div className='my-[5px] w-[72px] h-2 rounded-sm bg-[rgba(0,0,0,0.05)]'/>
             )}
             {status !== 'running' && (
-              <span>{formatTime(startTime, t('appLog.dateTimeFormat') as string)}</span>
+              <span>{startTime ? formatTime(startTime, t('appLog.dateTimeFormat') as string) : '-'}</span>
             )}
           </div>
         </div>
@@ -75,7 +75,7 @@ const MetaData: FC<Props> = ({
               <div className='my-[5px] w-[72px] h-2 rounded-sm bg-[rgba(0,0,0,0.05)]'/>
             )}
             {status !== 'running' && (
-              <span>{`${time?.toFixed(3)}s`}</span>
+              <span>{time ? `${time.toFixed(3)}s` : '-'}</span>
             )}
           </div>
         </div>
diff --git a/web/app/components/workflow/run/node.tsx b/web/app/components/workflow/run/node.tsx
index 66f996f13b..2e45290ddf 100644
--- a/web/app/components/workflow/run/node.tsx
+++ b/web/app/components/workflow/run/node.tsx
@@ -4,15 +4,17 @@ import type { FC } from 'react'
 import { useCallback, useEffect, useState } from 'react'
 import {
   RiArrowRightSLine,
-  RiCheckboxCircleLine,
+  RiCheckboxCircleFill,
   RiErrorWarningLine,
   RiLoader2Line,
 } from '@remixicon/react'
 import BlockIcon from '../block-icon'
 import { BlockEnum } from '../types'
 import Split from '../nodes/_base/components/split'
+import { Iteration } from '@/app/components/base/icons/src/vender/workflow'
 import cn from '@/utils/classnames'
 import CodeEditor from '@/app/components/workflow/nodes/_base/components/editor/code-editor'
+import Button from '@/app/components/base/button'
 import { CodeLanguage } from '@/app/components/workflow/nodes/code/types'
 import { AlertTriangle } from '@/app/components/base/icons/src/vender/line/alertsAndFeedback'
 import type { NodeTracing } from '@/types/workflow'
@@ -61,31 +63,38 @@ const NodePanel: FC<Props> = ({
       return `${parseFloat((tokens / 1000000).toFixed(3))}M`
   }
 
+  const getCount = (iteration_curr_length: number | undefined, iteration_length: number) => {
+    if ((iteration_curr_length && iteration_curr_length < iteration_length) || !iteration_length)
+      return iteration_curr_length
+
+    return iteration_length
+  }
+
   useEffect(() => {
     setCollapseState(!nodeInfo.expand)
   }, [nodeInfo.expand, setCollapseState])
 
   const isIterationNode = nodeInfo.node_type === BlockEnum.Iteration
-  const handleOnShowIterationDetail = (e: React.MouseEvent<HTMLDivElement>) => {
+  const handleOnShowIterationDetail = (e: React.MouseEvent<HTMLButtonElement>) => {
     e.stopPropagation()
     e.nativeEvent.stopImmediatePropagation()
     onShowIterationDetail?.(nodeInfo.details || [])
   }
   return (
-    <div className={cn('px-4 py-1', className, hideInfo && '!p-0')}>
-      <div className={cn('group transition-all bg-white border border-gray-100 rounded-2xl shadow-xs hover:shadow-md', hideInfo && '!rounded-lg')}>
+    <div className={cn('px-2 py-1', className)}>
+      <div className='group transition-all bg-background-default border border-components-panel-border rounded-[10px] shadows-shadow-xs hover:shadow-md'>
         <div
           className={cn(
-            'flex items-center pl-[6px] pr-3 cursor-pointer',
-            hideInfo ? 'py-2' : 'py-3',
-            !collapseState && (hideInfo ? '!pb-1' : '!pb-2'),
+            'flex items-center pl-1 pr-3 cursor-pointer',
+            hideInfo ? 'py-2' : 'py-1.5',
+            !collapseState && (hideInfo ? '!pb-1' : '!pb-1.5'),
           )}
           onClick={() => setCollapseState(!collapseState)}
         >
           {!hideProcessDetail && (
             <RiArrowRightSLine
               className={cn(
-                'shrink-0 w-3 h-3 mr-1 text-gray-400 transition-all group-hover:text-gray-500',
+                'shrink-0 w-4 h-4 mr-1 text-text-quaternary transition-all group-hover:text-text-tertiary',
                 !collapseState && 'rotate-90',
               )}
             />
@@ -93,23 +102,23 @@ const NodePanel: FC<Props> = ({
 
           <BlockIcon size={hideInfo ? 'xs' : 'sm'} className={cn('shrink-0 mr-2', hideInfo && '!mr-1')} type={nodeInfo.node_type} toolIcon={nodeInfo.extras?.icon || nodeInfo.extras} />
           <div className={cn(
-            'grow text-gray-700 text-[13px] leading-[16px] font-semibold truncate',
+            'grow text-text-secondary system-xs-semibold-uppercase truncate',
             hideInfo && '!text-xs',
           )} title={nodeInfo.title}>{nodeInfo.title}</div>
           {nodeInfo.status !== 'running' && !hideInfo && (
-            <div className='shrink-0 text-gray-500 text-xs leading-[18px]'>{`${getTime(nodeInfo.elapsed_time || 0)} · ${getTokenCount(nodeInfo.execution_metadata?.total_tokens || 0)} tokens`}</div>
+            <div className='shrink-0 text-text-tertiary system-xs-regular'>{nodeInfo.execution_metadata?.total_tokens ? `${getTokenCount(nodeInfo.execution_metadata?.total_tokens || 0)} tokens · ` : ''}{`${getTime(nodeInfo.elapsed_time || 0)}`}</div>
           )}
           {nodeInfo.status === 'succeeded' && (
-            <RiCheckboxCircleLine className='shrink-0 ml-2 w-3.5 h-3.5 text-[#12B76A]' />
+            <RiCheckboxCircleFill className='shrink-0 ml-2 w-3.5 h-3.5 text-text-success' />
           )}
           {nodeInfo.status === 'failed' && (
-            <RiErrorWarningLine className='shrink-0 ml-2 w-3.5 h-3.5 text-[#F04438]' />
+            <RiErrorWarningLine className='shrink-0 ml-2 w-3.5 h-3.5 text-text-warning' />
           )}
           {nodeInfo.status === 'stopped' && (
             <AlertTriangle className='shrink-0 ml-2 w-3.5 h-3.5 text-[#F79009]' />
           )}
           {nodeInfo.status === 'running' && (
-            <div className='shrink-0 flex items-center text-primary-600 text-[13px] leading-[16px] font-medium'>
+            <div className='shrink-0 flex items-center text-text-accent text-[13px] leading-[16px] font-medium'>
               <span className='mr-2 text-xs font-normal'>Running</span>
               <RiLoader2Line className='w-3.5 h-3.5 animate-spin' />
             </div>
@@ -120,25 +129,27 @@ const NodePanel: FC<Props> = ({
             {/* The nav to the iteration detail */}
             {isIterationNode && !notShowIterationNav && (
               <div className='mt-2 mb-1 !px-2'>
-                <div
-                  className='flex items-center h-[34px] justify-between px-3 bg-gray-100 border-[0.5px] border-gray-200 rounded-lg cursor-pointer'
-                  onClick={handleOnShowIterationDetail}>
-                  <div className='leading-[18px] text-[13px] font-medium text-gray-700'>{t('workflow.nodes.iteration.iteration', { count: nodeInfo.metadata?.iterator_length || nodeInfo.details?.length })}</div>
+                <Button
+                  className='flex items-center w-full self-stretch gap-2 px-3 py-2 bg-components-button-tertiary-bg-hover hover:bg-components-button-tertiary-bg-hover rounded-lg cursor-pointer border-none'
+                  onClick={handleOnShowIterationDetail}
+                >
+                  <Iteration className='w-4 h-4 text-components-button-tertiary-text flex-shrink-0' />
+                  <div className='flex-1 text-left system-sm-medium text-components-button-tertiary-text'>{t('workflow.nodes.iteration.iteration', { count: getCount(nodeInfo.details?.length, nodeInfo.metadata?.iterator_length) })}</div>
                   {justShowIterationNavArrow
                     ? (
-                      <RiArrowRightSLine className='w-3.5 h-3.5 text-gray-500' />
+                      <RiArrowRightSLine className='w-4 h-4 text-components-button-tertiary-text flex-shrink-0' />
                     )
                     : (
                       <div className='flex items-center space-x-1 text-[#155EEF]'>
                         <div className='text-[13px] font-normal '>{t('workflow.common.viewDetailInTracingPanel')}</div>
-                        <RiArrowRightSLine className='w-3.5 h-3.5' />
+                        <RiArrowRightSLine className='w-4 h-4 text-components-button-tertiary-text flex-shrink-0' />
                       </div>
                     )}
-                </div>
+                </Button>
                 <Split className='mt-2' />
               </div>
             )}
-            <div className={cn('px-[10px] py-1', hideInfo && '!px-2 !py-0.5')}>
+            <div className={cn('px-[10px]', hideInfo && '!px-2 !py-0.5')}>
               {nodeInfo.status === 'stopped' && (
                 <div className='px-3 py-[10px] bg-[#fffaeb] rounded-lg border-[0.5px] border-[rbga(0,0,0,0.05)] text-xs leading-[18px] text-[#dc6803] shadow-xs'>{t('workflow.tracing.stopBy', { user: nodeInfo.created_by ? nodeInfo.created_by.name : 'N/A' })}</div>
               )}
diff --git a/web/app/components/workflow/run/status.tsx b/web/app/components/workflow/run/status.tsx
index 2eeafca95d..0677e43401 100644
--- a/web/app/components/workflow/run/status.tsx
+++ b/web/app/components/workflow/run/status.tsx
@@ -71,7 +71,7 @@ const StatusPanel: FC<ResultProps> = ({
               <div className='w-16 h-2 rounded-sm bg-[rgba(0,0,0,0.05)]' />
             )}
             {status !== 'running' && (
-              <span>{`${time?.toFixed(3)}s`}</span>
+              <span>{time ? `${time?.toFixed(3)}s` : '-'}</span>
             )}
           </div>
         </div>
diff --git a/web/app/components/workflow/run/tracing-panel.tsx b/web/app/components/workflow/run/tracing-panel.tsx
index c80bcb41db..613c10198d 100644
--- a/web/app/components/workflow/run/tracing-panel.tsx
+++ b/web/app/components/workflow/run/tracing-panel.tsx
@@ -1,24 +1,268 @@
 'use client'
 import type { FC } from 'react'
+import
+React,
+{
+  useCallback,
+  useState,
+} from 'react'
+import cn from 'classnames'
+import {
+  RiArrowDownSLine,
+  RiMenu4Line,
+} from '@remixicon/react'
+import { useTranslation } from 'react-i18next'
 import NodePanel from './node'
+import {
+  BlockEnum,
+} from '@/app/components/workflow/types'
 import type { NodeTracing } from '@/types/workflow'
 
 type TracingPanelProps = {
   list: NodeTracing[]
-  onShowIterationDetail: (detail: NodeTracing[][]) => void
+  onShowIterationDetail?: (detail: NodeTracing[][]) => void
+  className?: string
+  hideNodeInfo?: boolean
+  hideNodeProcessDetail?: boolean
 }
 
-const TracingPanel: FC<TracingPanelProps> = ({ list, onShowIterationDetail }) => {
+type TracingNodeProps = {
+  id: string
+  uniqueId: string
+  isParallel: boolean
+  data: NodeTracing | null
+  children: TracingNodeProps[]
+  parallelTitle?: string
+  branchTitle?: string
+  hideNodeInfo?: boolean
+  hideNodeProcessDetail?: boolean
+}
+
+function buildLogTree(nodes: NodeTracing[], t: (key: string) => string): TracingNodeProps[] {
+  const rootNodes: TracingNodeProps[] = []
+  const parallelStacks: { [key: string]: TracingNodeProps } = {}
+  const levelCounts: { [key: string]: number } = {}
+  const parallelChildCounts: { [key: string]: Set<string> } = {}
+  let uniqueIdCounter = 0
+  const getUniqueId = () => {
+    uniqueIdCounter++
+    return `unique-${uniqueIdCounter}`
+  }
+
+  const getParallelTitle = (parentId: string | null): string => {
+    const levelKey = parentId || 'root'
+    if (!levelCounts[levelKey])
+      levelCounts[levelKey] = 0
+
+    levelCounts[levelKey]++
+
+    const parentTitle = parentId ? parallelStacks[parentId]?.parallelTitle : ''
+    const levelNumber = parentTitle ? parseInt(parentTitle.split('-')[1]) + 1 : 1
+    const letter = parallelChildCounts[levelKey]?.size > 1 ? String.fromCharCode(64 + levelCounts[levelKey]) : ''
+    return `${t('workflow.common.parallel')}-${levelNumber}${letter}`
+  }
+
+  const getBranchTitle = (parentId: string | null, branchNum: number): string => {
+    const levelKey = parentId || 'root'
+    const parentTitle = parentId ? parallelStacks[parentId]?.parallelTitle : ''
+    const levelNumber = parentTitle ? parseInt(parentTitle.split('-')[1]) + 1 : 1
+    const letter = parallelChildCounts[levelKey]?.size > 1 ? String.fromCharCode(64 + levelCounts[levelKey]) : ''
+    const branchLetter = String.fromCharCode(64 + branchNum)
+    return `${t('workflow.common.branch')}-${levelNumber}${letter}-${branchLetter}`
+  }
+
+  // Count parallel children (for figuring out if we need to use letters)
+  for (const node of nodes) {
+    const parent_parallel_id = node.parent_parallel_id ?? node.execution_metadata?.parent_parallel_id ?? null
+    const parallel_id = node.parallel_id ?? node.execution_metadata?.parallel_id ?? null
+
+    if (parallel_id) {
+      const parentKey = parent_parallel_id || 'root'
+      if (!parallelChildCounts[parentKey])
+        parallelChildCounts[parentKey] = new Set()
+
+      parallelChildCounts[parentKey].add(parallel_id)
+    }
+  }
+
+  for (const node of nodes) {
+    const parallel_id = node.parallel_id ?? node.execution_metadata?.parallel_id ?? null
+    const parent_parallel_id = node.parent_parallel_id ?? node.execution_metadata?.parent_parallel_id ?? null
+    const parallel_start_node_id = node.parallel_start_node_id ?? node.execution_metadata?.parallel_start_node_id ?? null
+    const parent_parallel_start_node_id = node.parent_parallel_start_node_id ?? node.execution_metadata?.parent_parallel_start_node_id ?? null
+
+    if (!parallel_id || node.node_type === BlockEnum.End) {
+      rootNodes.push({
+        id: node.id,
+        uniqueId: getUniqueId(),
+        isParallel: false,
+        data: node,
+        children: [],
+      })
+    }
+    else {
+      if (!parallelStacks[parallel_id]) {
+        const newParallelGroup: TracingNodeProps = {
+          id: parallel_id,
+          uniqueId: getUniqueId(),
+          isParallel: true,
+          data: null,
+          children: [],
+          parallelTitle: '',
+        }
+        parallelStacks[parallel_id] = newParallelGroup
+
+        if (parent_parallel_id && parallelStacks[parent_parallel_id]) {
+          const sameBranchIndex = parallelStacks[parent_parallel_id].children.findLastIndex(c =>
+            c.data?.execution_metadata?.parallel_start_node_id === parent_parallel_start_node_id || c.data?.parallel_start_node_id === parent_parallel_start_node_id,
+          )
+          parallelStacks[parent_parallel_id].children.splice(sameBranchIndex + 1, 0, newParallelGroup)
+          newParallelGroup.parallelTitle = getParallelTitle(parent_parallel_id)
+        }
+        else {
+          newParallelGroup.parallelTitle = getParallelTitle(parent_parallel_id)
+          rootNodes.push(newParallelGroup)
+        }
+      }
+      const branchTitle = parallel_start_node_id === node.node_id ? getBranchTitle(parent_parallel_id, parallelStacks[parallel_id].children.length + 1) : ''
+      if (branchTitle) {
+        parallelStacks[parallel_id].children.push({
+          id: node.id,
+          uniqueId: getUniqueId(),
+          isParallel: false,
+          data: node,
+          children: [],
+          branchTitle,
+        })
+      }
+      else {
+        let sameBranchIndex = parallelStacks[parallel_id].children.findLastIndex(c =>
+          c.data?.execution_metadata?.parallel_start_node_id === parallel_start_node_id || c.data?.parallel_start_node_id === parallel_start_node_id,
+        )
+        if (parallelStacks[parallel_id].children[sameBranchIndex + 1]?.isParallel)
+          sameBranchIndex++
+
+        parallelStacks[parallel_id].children.splice(sameBranchIndex + 1, 0, {
+          id: node.id,
+          uniqueId: getUniqueId(),
+          isParallel: false,
+          data: node,
+          children: [],
+          branchTitle,
+        })
+      }
+    }
+  }
+
+  return rootNodes
+}
+
+const TracingPanel: FC<TracingPanelProps> = ({
+  list,
+  onShowIterationDetail,
+  className,
+  hideNodeInfo = false,
+  hideNodeProcessDetail = false,
+}) => {
+  const { t } = useTranslation()
+  const treeNodes = buildLogTree(list, t)
+  const [collapsedNodes, setCollapsedNodes] = useState<Set<string>>(new Set())
+  const [hoveredParallel, setHoveredParallel] = useState<string | null>(null)
+
+  const toggleCollapse = (id: string) => {
+    setCollapsedNodes((prev) => {
+      const newSet = new Set(prev)
+      if (newSet.has(id))
+        newSet.delete(id)
+
+      else
+        newSet.add(id)
+
+      return newSet
+    })
+  }
+
+  const handleParallelMouseEnter = useCallback((id: string) => {
+    setHoveredParallel(id)
+  }, [])
+
+  const handleParallelMouseLeave = useCallback((e: React.MouseEvent) => {
+    const relatedTarget = e.relatedTarget as Element | null
+    if (relatedTarget && 'closest' in relatedTarget) {
+      const closestParallel = relatedTarget.closest('[data-parallel-id]')
+      if (closestParallel)
+        setHoveredParallel(closestParallel.getAttribute('data-parallel-id'))
+
+      else
+        setHoveredParallel(null)
+    }
+    else {
+      setHoveredParallel(null)
+    }
+  }, [])
+
+  const renderNode = (node: TracingNodeProps) => {
+    if (node.isParallel) {
+      const isCollapsed = collapsedNodes.has(node.id)
+      const isHovered = hoveredParallel === node.id
+      return (
+        <div
+          key={node.uniqueId}
+          className="ml-4 mb-2 relative"
+          data-parallel-id={node.id}
+          onMouseEnter={() => handleParallelMouseEnter(node.id)}
+          onMouseLeave={handleParallelMouseLeave}
+        >
+          <div className="flex items-center mb-1">
+            <button
+              onClick={() => toggleCollapse(node.id)}
+              className={cn(
+                'mr-2 transition-colors',
+                isHovered ? 'rounded border-components-button-primary-border bg-components-button-primary-bg text-text-primary-on-surface' : 'text-text-secondary hover:text-text-primary',
+              )}
+            >
+              {isHovered ? <RiArrowDownSLine className="w-3 h-3" /> : <RiMenu4Line className="w-3 h-3 text-text-tertiary" />}
+            </button>
+            <div className="system-xs-semibold-uppercase text-text-secondary flex items-center">
+              <span>{node.parallelTitle}</span>
+            </div>
+            <div
+              className="mx-2 flex-grow h-px bg-divider-subtle"
+              style={{ background: 'linear-gradient(to right, rgba(16, 24, 40, 0.08), rgba(255, 255, 255, 0)' }}
+            ></div>
+          </div>
+          <div className={`pl-2 relative ${isCollapsed ? 'hidden' : ''}`}>
+            <div className={cn(
+              'absolute top-0 bottom-0 left-[5px] w-[2px]',
+              isHovered ? 'bg-text-accent-secondary' : 'bg-divider-subtle',
+            )}></div>
+            {node.children.map(renderNode)}
+          </div>
+        </div>
+      )
+    }
+    else {
+      const isHovered = hoveredParallel === node.id
+      return (
+        <div key={node.uniqueId}>
+          <div className={cn('pl-4 -mb-1.5 system-2xs-medium-uppercase', isHovered ? 'text-text-tertiary' : 'text-text-quaternary')}>
+            {node.branchTitle}
+          </div>
+          <NodePanel
+            nodeInfo={node.data!}
+            onShowIterationDetail={onShowIterationDetail}
+            justShowIterationNavArrow={true}
+            hideInfo={hideNodeInfo}
+            hideProcessDetail={hideNodeProcessDetail}
+          />
+        </div>
+      )
+    }
+  }
+
   return (
-    <div className='bg-gray-50 py-2'>
-      {list.map(node => (
-        <NodePanel
-          key={node.id}
-          nodeInfo={node}
-          onShowIterationDetail={onShowIterationDetail}
-          justShowIterationNavArrow
-        />
-      ))}
+    <div className={cn(className || 'bg-components-panel-bg', 'py-2')}>
+      {treeNodes.map(renderNode)}
     </div>
   )
 }
diff --git a/web/app/components/workflow/store.ts b/web/app/components/workflow/store.ts
index 2e5e774191..853d0c5934 100644
--- a/web/app/components/workflow/store.ts
+++ b/web/app/components/workflow/store.ts
@@ -162,6 +162,8 @@ type Shape = {
   setControlPromptEditorRerenderKey: (controlPromptEditorRerenderKey: number) => void
   showImportDSLModal: boolean
   setShowImportDSLModal: (showImportDSLModal: boolean) => void
+  showTips: string
+  setShowTips: (showTips: string) => void
 }
 
 export const createWorkflowStore = () => {
@@ -262,6 +264,8 @@ export const createWorkflowStore = () => {
     setControlPromptEditorRerenderKey: controlPromptEditorRerenderKey => set(() => ({ controlPromptEditorRerenderKey })),
     showImportDSLModal: false,
     setShowImportDSLModal: showImportDSLModal => set(() => ({ showImportDSLModal })),
+    showTips: '',
+    setShowTips: showTips => set(() => ({ showTips })),
   }))
 }
 
diff --git a/web/app/components/workflow/types.ts b/web/app/components/workflow/types.ts
index 034376fed5..797c2dbd85 100644
--- a/web/app/components/workflow/types.ts
+++ b/web/app/components/workflow/types.ts
@@ -26,6 +26,7 @@ export enum BlockEnum {
   Tool = 'tool',
   ParameterExtractor = 'parameter-extractor',
   Iteration = 'iteration',
+  IterationStart = 'iteration-start',
   Assigner = 'assigner', // is now named as VariableAssigner
 }
 
@@ -54,7 +55,7 @@ export type CommonNodeType<T = {}> = {
   _holdAddVariablePopup?: boolean
   _iterationLength?: number
   _iterationIndex?: number
-  isIterationStart?: boolean
+  _inParallelHovering?: boolean
   isInIteration?: boolean
   iteration_id?: string
   selected?: boolean
@@ -69,7 +70,7 @@ export type CommonEdgeType = {
   _hovering?: boolean
   _connectedNodeIsHovering?: boolean
   _connectedNodeIsSelected?: boolean
-  _runned?: boolean
+  _run?: boolean
   _isBundled?: boolean
   isInIteration?: boolean
   iteration_id?: string
@@ -86,7 +87,7 @@ export type NodePanelProps<T> = {
 }
 export type Edge = ReactFlowEdge<CommonEdgeType>
 
-export type WorkflowDataUpdator = {
+export type WorkflowDataUpdater = {
   nodes: Node[]
   edges: Edge[]
   viewport: Viewport
diff --git a/web/app/components/workflow/utils.ts b/web/app/components/workflow/utils.ts
index 0d07b2e568..91656e3bbc 100644
--- a/web/app/components/workflow/utils.ts
+++ b/web/app/components/workflow/utils.ts
@@ -1,12 +1,15 @@
 import {
   Position,
   getConnectedEdges,
+  getIncomers,
   getOutgoers,
 } from 'reactflow'
 import dagre from '@dagrejs/dagre'
 import { v4 as uuid4 } from 'uuid'
 import {
   cloneDeep,
+  groupBy,
+  isEqual,
   uniqBy,
 } from 'lodash-es'
 import type {
@@ -19,14 +22,17 @@ import type {
 import { BlockEnum } from './types'
 import {
   CUSTOM_NODE,
+  ITERATION_CHILDREN_Z_INDEX,
   ITERATION_NODE_Z_INDEX,
   NODE_WIDTH_X_OFFSET,
   START_INITIAL_POSITION,
 } from './constants'
+import { CUSTOM_ITERATION_START_NODE } from './nodes/iteration-start/constants'
 import type { QuestionClassifierNodeType } from './nodes/question-classifier/types'
 import type { IfElseNodeType } from './nodes/if-else/types'
 import { branchNameCorrect } from './nodes/if-else/utils'
 import type { ToolNodeType } from './nodes/tool/types'
+import type { IterationNodeType } from './nodes/iteration/types'
 import { CollectionType } from '@/app/components/tools/types'
 import { toolParametersToFormSchemas } from '@/app/components/tools/utils/to-form-schema'
 
@@ -34,18 +40,18 @@ const WHITE = 'WHITE'
 const GRAY = 'GRAY'
 const BLACK = 'BLACK'
 
-const isCyclicUtil = (nodeId: string, color: Record<string, string>, adjaList: Record<string, string[]>, stack: string[]) => {
+const isCyclicUtil = (nodeId: string, color: Record<string, string>, adjList: Record<string, string[]>, stack: string[]) => {
   color[nodeId] = GRAY
   stack.push(nodeId)
 
-  for (let i = 0; i < adjaList[nodeId].length; ++i) {
-    const childId = adjaList[nodeId][i]
+  for (let i = 0; i < adjList[nodeId].length; ++i) {
+    const childId = adjList[nodeId][i]
 
     if (color[childId] === GRAY) {
       stack.push(childId)
       return true
     }
-    if (color[childId] === WHITE && isCyclicUtil(childId, color, adjaList, stack))
+    if (color[childId] === WHITE && isCyclicUtil(childId, color, adjList, stack))
       return true
   }
   color[nodeId] = BLACK
@@ -55,21 +61,21 @@ const isCyclicUtil = (nodeId: string, color: Record<string, string>, adjaList: R
 }
 
 const getCycleEdges = (nodes: Node[], edges: Edge[]) => {
-  const adjaList: Record<string, string[]> = {}
+  const adjList: Record<string, string[]> = {}
   const color: Record<string, string> = {}
   const stack: string[] = []
 
   for (const node of nodes) {
     color[node.id] = WHITE
-    adjaList[node.id] = []
+    adjList[node.id] = []
   }
 
   for (const edge of edges)
-    adjaList[edge.source]?.push(edge.target)
+    adjList[edge.source]?.push(edge.target)
 
   for (let i = 0; i < nodes.length; i++) {
     if (color[nodes[i].id] === WHITE)
-      isCyclicUtil(nodes[i].id, color, adjaList, stack)
+      isCyclicUtil(nodes[i].id, color, adjList, stack)
   }
 
   const cycleEdges = []
@@ -84,9 +90,130 @@ const getCycleEdges = (nodes: Node[], edges: Edge[]) => {
   return cycleEdges
 }
 
+export function getIterationStartNode(iterationId: string): Node {
+  return generateNewNode({
+    id: `${iterationId}start`,
+    type: CUSTOM_ITERATION_START_NODE,
+    data: {
+      title: '',
+      desc: '',
+      type: BlockEnum.IterationStart,
+      isInIteration: true,
+    },
+    position: {
+      x: 24,
+      y: 68,
+    },
+    zIndex: ITERATION_CHILDREN_Z_INDEX,
+    parentId: iterationId,
+    selectable: false,
+    draggable: false,
+  }).newNode
+}
+
+export function generateNewNode({ data, position, id, zIndex, type, ...rest }: Omit<Node, 'id'> & { id?: string }): {
+  newNode: Node
+  newIterationStartNode?: Node
+} {
+  const newNode = {
+    id: id || `${Date.now()}`,
+    type: type || CUSTOM_NODE,
+    data,
+    position,
+    targetPosition: Position.Left,
+    sourcePosition: Position.Right,
+    zIndex: data.type === BlockEnum.Iteration ? ITERATION_NODE_Z_INDEX : zIndex,
+    ...rest,
+  } as Node
+
+  if (data.type === BlockEnum.Iteration) {
+    const newIterationStartNode = getIterationStartNode(newNode.id);
+    (newNode.data as IterationNodeType).start_node_id = newIterationStartNode.id;
+    (newNode.data as IterationNodeType)._children = [newIterationStartNode.id]
+    return {
+      newNode,
+      newIterationStartNode,
+    }
+  }
+
+  return {
+    newNode,
+  }
+}
+
+export const preprocessNodesAndEdges = (nodes: Node[], edges: Edge[]) => {
+  const hasIterationNode = nodes.some(node => node.data.type === BlockEnum.Iteration)
+
+  if (!hasIterationNode) {
+    return {
+      nodes,
+      edges,
+    }
+  }
+  const nodesMap = nodes.reduce((prev, next) => {
+    prev[next.id] = next
+    return prev
+  }, {} as Record<string, Node>)
+  const iterationNodesWithStartNode = []
+  const iterationNodesWithoutStartNode = []
+
+  for (let i = 0; i < nodes.length; i++) {
+    const currentNode = nodes[i] as Node<IterationNodeType>
+
+    if (currentNode.data.type === BlockEnum.Iteration) {
+      if (currentNode.data.start_node_id) {
+        if (nodesMap[currentNode.data.start_node_id]?.type !== CUSTOM_ITERATION_START_NODE)
+          iterationNodesWithStartNode.push(currentNode)
+      }
+      else {
+        iterationNodesWithoutStartNode.push(currentNode)
+      }
+    }
+  }
+  const newIterationStartNodesMap = {} as Record<string, Node>
+  const newIterationStartNodes = [...iterationNodesWithStartNode, ...iterationNodesWithoutStartNode].map((iterationNode, index) => {
+    const newNode = getIterationStartNode(iterationNode.id)
+    newNode.id = newNode.id + index
+    newIterationStartNodesMap[iterationNode.id] = newNode
+    return newNode
+  })
+  const newEdges = iterationNodesWithStartNode.map((iterationNode) => {
+    const newNode = newIterationStartNodesMap[iterationNode.id]
+    const startNode = nodesMap[iterationNode.data.start_node_id]
+    const source = newNode.id
+    const sourceHandle = 'source'
+    const target = startNode.id
+    const targetHandle = 'target'
+    return {
+      id: `${source}-${sourceHandle}-${target}-${targetHandle}`,
+      type: 'custom',
+      source,
+      sourceHandle,
+      target,
+      targetHandle,
+      data: {
+        sourceType: newNode.data.type,
+        targetType: startNode.data.type,
+        isInIteration: true,
+        iteration_id: startNode.parentId,
+        _connectedNodeIsSelected: true,
+      },
+      zIndex: ITERATION_CHILDREN_Z_INDEX,
+    }
+  })
+  nodes.forEach((node) => {
+    if (node.data.type === BlockEnum.Iteration && newIterationStartNodesMap[node.id])
+      (node.data as IterationNodeType).start_node_id = newIterationStartNodesMap[node.id].id
+  })
+
+  return {
+    nodes: [...nodes, ...newIterationStartNodes],
+    edges: [...edges, ...newEdges],
+  }
+}
+
 export const initialNodes = (originNodes: Node[], originEdges: Edge[]) => {
-  const nodes = cloneDeep(originNodes)
-  const edges = cloneDeep(originEdges)
+  const { nodes, edges } = preprocessNodesAndEdges(cloneDeep(originNodes), cloneDeep(originEdges))
   const firstNode = nodes[0]
 
   if (!firstNode?.position) {
@@ -148,8 +275,7 @@ export const initialNodes = (originNodes: Node[], originEdges: Edge[]) => {
 }
 
 export const initialEdges = (originEdges: Edge[], originNodes: Node[]) => {
-  const nodes = cloneDeep(originNodes)
-  const edges = cloneDeep(originEdges)
+  const { nodes, edges } = preprocessNodesAndEdges(cloneDeep(originNodes), cloneDeep(originEdges))
   let selectedNode: Node | null = null
   const nodesMap = nodes.reduce((acc, node) => {
     acc[node.id] = node
@@ -291,19 +417,6 @@ export const getNodesConnectedSourceOrTargetHandleIdsMap = (changes: ConnectedSo
   return nodesConnectedSourceOrTargetHandleIdsMap
 }
 
-export const generateNewNode = ({ data, position, id, zIndex, type, ...rest }: Omit<Node, 'id'> & { id?: string }) => {
-  return {
-    id: id || `${Date.now()}`,
-    type: type || CUSTOM_NODE,
-    data,
-    position,
-    targetPosition: Position.Left,
-    sourcePosition: Position.Right,
-    zIndex: data.type === BlockEnum.Iteration ? ITERATION_NODE_Z_INDEX : zIndex,
-    ...rest,
-  } as Node
-}
-
 export const genNewNodeTitleFromOld = (oldTitle: string) => {
   const regex = /^(.+?)\s*\((\d+)\)\s*$/
   const match = oldTitle.match(regex)
@@ -479,3 +592,167 @@ export const variableTransformer = (v: ValueSelector | string) => {
 
   return `{{#${v.join('.')}#}}`
 }
+
+type ParallelInfoItem = {
+  parallelNodeId: string
+  depth: number
+  isBranch?: boolean
+}
+type NodeParallelInfo = {
+  parallelNodeId: string
+  edgeHandleId: string
+  depth: number
+}
+type NodeHandle = {
+  node: Node
+  handle: string
+}
+type NodeStreamInfo = {
+  upstreamNodes: Set<string>
+  downstreamEdges: Set<string>
+}
+export const getParallelInfo = (nodes: Node[], edges: Edge[], parentNodeId?: string) => {
+  let startNode
+
+  if (parentNodeId) {
+    const parentNode = nodes.find(node => node.id === parentNodeId)
+    if (!parentNode)
+      throw new Error('Parent node not found')
+
+    startNode = nodes.find(node => node.id === (parentNode.data as IterationNodeType).start_node_id)
+  }
+  else {
+    startNode = nodes.find(node => node.data.type === BlockEnum.Start)
+  }
+  if (!startNode)
+    throw new Error('Start node not found')
+
+  const parallelList = [] as ParallelInfoItem[]
+  const nextNodeHandles = [{ node: startNode, handle: 'source' }]
+  let hasAbnormalEdges = false
+
+  const traverse = (firstNodeHandle: NodeHandle) => {
+    const nodeEdgesSet = {} as Record<string, Set<string>>
+    const totalEdgesSet = new Set<string>()
+    const nextHandles = [firstNodeHandle]
+    const streamInfo = {} as Record<string, NodeStreamInfo>
+    const parallelListItem = {
+      parallelNodeId: '',
+      depth: 0,
+    } as ParallelInfoItem
+    const nodeParallelInfoMap = {} as Record<string, NodeParallelInfo>
+    nodeParallelInfoMap[firstNodeHandle.node.id] = {
+      parallelNodeId: '',
+      edgeHandleId: '',
+      depth: 0,
+    }
+
+    while (nextHandles.length) {
+      const currentNodeHandle = nextHandles.shift()!
+      const { node: currentNode, handle: currentHandle = 'source' } = currentNodeHandle
+      const currentNodeHandleKey = currentNode.id
+      const connectedEdges = edges.filter(edge => edge.source === currentNode.id && edge.sourceHandle === currentHandle)
+      const connectedEdgesLength = connectedEdges.length
+      const outgoers = nodes.filter(node => connectedEdges.some(edge => edge.target === node.id))
+      const incomers = getIncomers(currentNode, nodes, edges)
+
+      if (!streamInfo[currentNodeHandleKey]) {
+        streamInfo[currentNodeHandleKey] = {
+          upstreamNodes: new Set<string>(),
+          downstreamEdges: new Set<string>(),
+        }
+      }
+
+      if (nodeEdgesSet[currentNodeHandleKey]?.size > 0 && incomers.length > 1) {
+        const newSet = new Set<string>()
+        for (const item of totalEdgesSet) {
+          if (!streamInfo[currentNodeHandleKey].downstreamEdges.has(item))
+            newSet.add(item)
+        }
+        if (isEqual(nodeEdgesSet[currentNodeHandleKey], newSet)) {
+          parallelListItem.depth = nodeParallelInfoMap[currentNode.id].depth
+          nextNodeHandles.push({ node: currentNode, handle: currentHandle })
+          break
+        }
+      }
+
+      if (nodeParallelInfoMap[currentNode.id].depth > parallelListItem.depth)
+        parallelListItem.depth = nodeParallelInfoMap[currentNode.id].depth
+
+      outgoers.forEach((outgoer) => {
+        const outgoerConnectedEdges = getConnectedEdges([outgoer], edges).filter(edge => edge.source === outgoer.id)
+        const sourceEdgesGroup = groupBy(outgoerConnectedEdges, 'sourceHandle')
+        const incomers = getIncomers(outgoer, nodes, edges)
+
+        if (outgoers.length > 1 && incomers.length > 1)
+          hasAbnormalEdges = true
+
+        Object.keys(sourceEdgesGroup).forEach((sourceHandle) => {
+          nextHandles.push({ node: outgoer, handle: sourceHandle })
+        })
+        if (!outgoerConnectedEdges.length)
+          nextHandles.push({ node: outgoer, handle: 'source' })
+
+        const outgoerKey = outgoer.id
+        if (!nodeEdgesSet[outgoerKey])
+          nodeEdgesSet[outgoerKey] = new Set<string>()
+
+        if (nodeEdgesSet[currentNodeHandleKey]) {
+          for (const item of nodeEdgesSet[currentNodeHandleKey])
+            nodeEdgesSet[outgoerKey].add(item)
+        }
+
+        if (!streamInfo[outgoerKey]) {
+          streamInfo[outgoerKey] = {
+            upstreamNodes: new Set<string>(),
+            downstreamEdges: new Set<string>(),
+          }
+        }
+
+        if (!nodeParallelInfoMap[outgoer.id]) {
+          nodeParallelInfoMap[outgoer.id] = {
+            ...nodeParallelInfoMap[currentNode.id],
+          }
+        }
+
+        if (connectedEdgesLength > 1) {
+          const edge = connectedEdges.find(edge => edge.target === outgoer.id)!
+          nodeEdgesSet[outgoerKey].add(edge.id)
+          totalEdgesSet.add(edge.id)
+
+          streamInfo[currentNodeHandleKey].downstreamEdges.add(edge.id)
+          streamInfo[outgoerKey].upstreamNodes.add(currentNodeHandleKey)
+
+          for (const item of streamInfo[currentNodeHandleKey].upstreamNodes)
+            streamInfo[item].downstreamEdges.add(edge.id)
+
+          if (!parallelListItem.parallelNodeId)
+            parallelListItem.parallelNodeId = currentNode.id
+
+          const prevDepth = nodeParallelInfoMap[currentNode.id].depth + 1
+          const currentDepth = nodeParallelInfoMap[outgoer.id].depth
+
+          nodeParallelInfoMap[outgoer.id].depth = Math.max(prevDepth, currentDepth)
+        }
+        else {
+          for (const item of streamInfo[currentNodeHandleKey].upstreamNodes)
+            streamInfo[outgoerKey].upstreamNodes.add(item)
+
+          nodeParallelInfoMap[outgoer.id].depth = nodeParallelInfoMap[currentNode.id].depth
+        }
+      })
+    }
+
+    parallelList.push(parallelListItem)
+  }
+
+  while (nextNodeHandles.length) {
+    const nodeHandle = nextNodeHandles.shift()!
+    traverse(nodeHandle)
+  }
+
+  return {
+    parallelList,
+    hasAbnormalEdges,
+  }
+}
diff --git a/web/app/layout.tsx b/web/app/layout.tsx
index 9acc131029..e9242edfad 100644
--- a/web/app/layout.tsx
+++ b/web/app/layout.tsx
@@ -1,6 +1,6 @@
 import type { Viewport } from 'next'
 import I18nServer from './components/i18n-server'
-import BrowerInitor from './components/browser-initor'
+import BrowserInitor from './components/browser-initor'
 import SentryInitor from './components/sentry-initor'
 import Topbar from './components/base/topbar'
 import { getLocaleOnServer } from '@/i18n/server'
@@ -43,13 +43,14 @@ const LocaleLayout = ({
         data-public-sentry-dsn={process.env.NEXT_PUBLIC_SENTRY_DSN}
         data-public-maintenance-notice={process.env.NEXT_PUBLIC_MAINTENANCE_NOTICE}
         data-public-site-about={process.env.NEXT_PUBLIC_SITE_ABOUT}
+        data-public-text-generation-timeout-ms={process.env.NEXT_PUBLIC_TEXT_GENERATION_TIMEOUT_MS}
       >
         <Topbar />
-        <BrowerInitor>
+        <BrowserInitor>
           <SentryInitor>
             <I18nServer>{children}</I18nServer>
           </SentryInitor>
-        </BrowerInitor>
+        </BrowserInitor>
       </body>
     </html>
   )
diff --git a/web/app/signin/oneMoreStep.tsx b/web/app/signin/oneMoreStep.tsx
index 42a797b4d6..a4324517a5 100644
--- a/web/app/signin/oneMoreStep.tsx
+++ b/web/app/signin/oneMoreStep.tsx
@@ -97,7 +97,7 @@ const OneMoreStep = () => {
                 }
                 needsDelay
               >
-                <span className='cursor-pointer text-primary-600'>{t('login.donthave')}</span>
+                <span className='cursor-pointer text-primary-600'>{t('login.dontHave')}</span>
               </Tooltip>
             </label>
             <div className="mt-1">
diff --git a/web/app/styles/markdown.scss b/web/app/styles/markdown.scss
index b0a1f60cd2..214d8d2782 100644
--- a/web/app/styles/markdown.scss
+++ b/web/app/styles/markdown.scss
@@ -321,18 +321,12 @@
 .markdown-body h4,
 .markdown-body h5,
 .markdown-body h6 {
-  margin-top: 24px;
-  margin-bottom: 16px;
+  padding-top: 12px;
+  margin-bottom: 12px;
   font-weight: var(--base-text-weight-semibold, 600);
   line-height: 1.25;
 }
 
-
-.markdown-body p {
-  margin-top: 0;
-  margin-bottom: 10px;
-}
-
 .markdown-body blockquote {
   margin: 0;
   padding: 0 8px;
@@ -449,7 +443,7 @@
 .markdown-body pre,
 .markdown-body details {
   margin-top: 0;
-  margin-bottom: 16px;
+  margin-bottom: 12px;
 }
 
 .markdown-body blockquote> :first-child {
diff --git a/web/config/index.ts b/web/config/index.ts
index bb100473b1..21fc2f211c 100644
--- a/web/config/index.ts
+++ b/web/config/index.ts
@@ -103,16 +103,16 @@ export const DEFAULT_PARAGRAPH_VALUE_MAX_LEN = 1000
 export const zhRegex = /^[\u4E00-\u9FA5]$/m
 export const emojiRegex = /^[\uD800-\uDBFF][\uDC00-\uDFFF]$/m
 export const emailRegex = /^[\w.!#$%&'*+\-/=?^{|}~]+@([\w-]+\.)+[\w-]{2,}$/m
-const MAX_ZN_VAR_NAME_LENGHT = 8
-const MAX_EN_VAR_VALUE_LENGHT = 30
+const MAX_ZN_VAR_NAME_LENGTH = 8
+const MAX_EN_VAR_VALUE_LENGTH = 30
 export const getMaxVarNameLength = (value: string) => {
   if (zhRegex.test(value))
-    return MAX_ZN_VAR_NAME_LENGHT
+    return MAX_ZN_VAR_NAME_LENGTH
 
-  return MAX_EN_VAR_VALUE_LENGHT
+  return MAX_EN_VAR_VALUE_LENGTH
 }
 
-export const MAX_VAR_KEY_LENGHT = 30
+export const MAX_VAR_KEY_LENGTH = 30
 
 export const MAX_PROMPT_MESSAGE_LENGTH = 10
 
@@ -246,6 +246,13 @@ Thought: {{agent_scratchpad}}
 
 export const VAR_REGEX = /\{\{(#[a-zA-Z0-9_-]{1,50}(\.[a-zA-Z_][a-zA-Z0-9_]{0,29}){1,10}#)\}\}/gi
 
-export const TEXT_GENERATION_TIMEOUT_MS = 60000
+export let textGenerationTimeoutMs = 60000
+
+if (process.env.NEXT_PUBLIC_TEXT_GENERATION_TIMEOUT_MS && process.env.NEXT_PUBLIC_TEXT_GENERATION_TIMEOUT_MS !== '')
+  textGenerationTimeoutMs = parseInt(process.env.NEXT_PUBLIC_TEXT_GENERATION_TIMEOUT_MS)
+else if (globalThis.document?.body?.getAttribute('data-public-text-generation-timeout-ms') && globalThis.document.body.getAttribute('data-public-text-generation-timeout-ms') !== '')
+  textGenerationTimeoutMs = parseInt(globalThis.document.body.getAttribute('data-public-text-generation-timeout-ms') as string)
+
+export const TEXT_GENERATION_TIMEOUT_MS = textGenerationTimeoutMs
 
 export const DISABLE_UPLOAD_IMAGE_AS_ICON = process.env.NEXT_PUBLIC_DISABLE_UPLOAD_IMAGE_AS_ICON === 'true'
diff --git a/web/docker/entrypoint.sh b/web/docker/entrypoint.sh
index a19c543d68..fc4a8f45bc 100755
--- a/web/docker/entrypoint.sh
+++ b/web/docker/entrypoint.sh
@@ -21,4 +21,6 @@ export NEXT_PUBLIC_SENTRY_DSN=${SENTRY_DSN}
 export NEXT_PUBLIC_SITE_ABOUT=${SITE_ABOUT}
 export NEXT_TELEMETRY_DISABLED=${NEXT_TELEMETRY_DISABLED}
 
+export NEXT_PUBLIC_TEXT_GENERATION_TIMEOUT_MS=${TEXT_GENERATION_TIMEOUT_MS}
+
 pm2 start ./pm2.json --no-daemon
diff --git a/web/hooks/use-moderate.ts b/web/hooks/use-moderate.ts
index 0e2908ff66..11b078fbd9 100644
--- a/web/hooks/use-moderate.ts
+++ b/web/hooks/use-moderate.ts
@@ -13,14 +13,14 @@ export const useModerate = (
   content: string,
   stop: boolean,
   moderationService: (text: string) => ReturnType<ModerationService>,
-  seperateLength = 50,
+  separateLength = 50,
 ) => {
   const moderatedContentMap = useRef<Map<number, string>>(new Map())
   const moderatingIndex = useRef<number[]>([])
   const [contentArr, setContentArr] = useState<string[]>([])
 
   const handleModerate = () => {
-    const stringArr = splitStringByLength(content, seperateLength)
+    const stringArr = splitStringByLength(content, separateLength)
 
     const lastIndex = stringArr.length - 1
     stringArr.forEach((item, index) => {
diff --git a/web/i18n/de-DE/app-api.ts b/web/i18n/de-DE/app-api.ts
index 3704dc8b21..84e0989095 100644
--- a/web/i18n/de-DE/app-api.ts
+++ b/web/i18n/de-DE/app-api.ts
@@ -9,7 +9,7 @@ const translation = {
   play: 'Abspielen',
   pause: 'Pause',
   playing: 'Wiedergabe',
-  merMaind: {
+  merMaid: {
     rerender: 'Neu rendern',
   },
   never: 'Nie',
diff --git a/web/i18n/de-DE/app-debug.ts b/web/i18n/de-DE/app-debug.ts
index acb3f53904..c00c799519 100644
--- a/web/i18n/de-DE/app-debug.ts
+++ b/web/i18n/de-DE/app-debug.ts
@@ -248,7 +248,7 @@ const translation = {
     historyNoBeEmpty: 'Konversationsverlauf muss im Prompt gesetzt sein',
     queryNoBeEmpty: 'Anfrage muss im Prompt gesetzt sein',
   },
-  variableConig: {
+  variableConfig: {
     modalTitle: 'Feldeinstellungen',
     description: 'Einstellung für Variable {{varName}}',
     fieldType: 'Feldtyp',
diff --git a/web/i18n/de-DE/common.ts b/web/i18n/de-DE/common.ts
index 63b3935fb4..6ea06bc8b1 100644
--- a/web/i18n/de-DE/common.ts
+++ b/web/i18n/de-DE/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Parameter',
     duplicate: 'Duplikat',
     rename: 'Umbenennen',
+    audioSourceUnavailable: 'AudioSource ist nicht verfügbar',
   },
   placeholder: {
     input: 'Bitte eingeben',
@@ -191,14 +192,14 @@ const translation = {
     invitationSent: 'Einladung gesendet',
     invitationSentTip: 'Einladung gesendet, und sie können sich bei Dify anmelden, um auf Ihre Teamdaten zuzugreifen.',
     invitationLink: 'Einladungslink',
-    failedinvitationEmails: 'Die folgenden Benutzer wurden nicht erfolgreich eingeladen',
+    failedInvitationEmails: 'Die folgenden Benutzer wurden nicht erfolgreich eingeladen',
     ok: 'OK',
     removeFromTeam: 'Vom Team entfernen',
     removeFromTeamTip: 'Wird den Teamzugang entfernen',
     setAdmin: 'Als Administrator einstellen',
     setMember: 'Als normales Mitglied einstellen',
     setEditor: 'Als Editor einstellen',
-    disinvite: 'Einladung widerrufen',
+    disInvite: 'Einladung widerrufen',
     deleteMember: 'Mitglied löschen',
     you: '(Du)',
     setBuilder: 'Als Builder festlegen',
diff --git a/web/i18n/de-DE/dataset-creation.ts b/web/i18n/de-DE/dataset-creation.ts
index 20ab5cee0f..8b27395049 100644
--- a/web/i18n/de-DE/dataset-creation.ts
+++ b/web/i18n/de-DE/dataset-creation.ts
@@ -45,7 +45,7 @@ const translation = {
       input: 'Wissensname',
       placeholder: 'Bitte eingeben',
       nameNotEmpty: 'Name darf nicht leer sein',
-      nameLengthInvaild: 'Name muss zwischen 1 bis 40 Zeichen lang sein',
+      nameLengthInvalid: 'Name muss zwischen 1 bis 40 Zeichen lang sein',
       cancelButton: 'Abbrechen',
       confirmButton: 'Erstellen',
       failed: 'Erstellung fehlgeschlagen',
@@ -104,8 +104,8 @@ const translation = {
     QATitle: 'Segmentierung im Frage-und-Antwort-Format',
     QATip: 'Diese Option zu aktivieren, wird mehr Tokens verbrauchen',
     QALanguage: 'Segmentierung verwenden',
-    emstimateCost: 'Schätzung',
-    emstimateSegment: 'Geschätzte Chunks',
+    estimateCost: 'Schätzung',
+    estimateSegment: 'Geschätzte Chunks',
     segmentCount: 'Chunks',
     calculating: 'Berechnung...',
     fileSource: 'Dokumente vorverarbeiten',
@@ -128,8 +128,8 @@ const translation = {
     previewSwitchTipStart: 'Die aktuelle Chunk-Vorschau ist im Textformat, ein Wechsel zur Vorschau im Frage-und-Antwort-Format wird',
     previewSwitchTipEnd: ' zusätzliche Tokens verbrauchen',
     characters: 'Zeichen',
-    indexSettedTip: 'Um die Indexmethode zu ändern, bitte gehen Sie zu den ',
-    retrivalSettedTip: 'Um die Indexmethode zu ändern, bitte gehen Sie zu den ',
+    indexSettingTip: 'Um die Indexmethode zu ändern, bitte gehen Sie zu den ',
+    retrievalSettingTip: 'Um die Indexmethode zu ändern, bitte gehen Sie zu den ',
     datasetSettingLink: 'Wissenseinstellungen.',
     websiteSource: 'Preprocess-Website',
     webpageUnit: 'Seiten',
diff --git a/web/i18n/de-DE/dataset.ts b/web/i18n/de-DE/dataset.ts
index 3c288b51b6..6462e56ded 100644
--- a/web/i18n/de-DE/dataset.ts
+++ b/web/i18n/de-DE/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacy: 'N-zu-1-Abruf wird ab September offiziell eingestellt. Es wird empfohlen, den neuesten Multi-Pfad-Abruf zu verwenden, um bessere Ergebnisse zu erzielen.',
   nTo1RetrievalLegacyLink: 'Mehr erfahren',
   nTo1RetrievalLegacyLinkText: 'N-zu-1-Abruf wird im September offiziell eingestellt.',
+  defaultRetrievalTip: 'Standardmäßig wird der Multi-Path-Abruf verwendet. Das Wissen wird aus mehreren Wissensdatenbanken abgerufen und dann neu eingestuft.',
 }
 
 export default translation
diff --git a/web/i18n/de-DE/login.ts b/web/i18n/de-DE/login.ts
index 5bfaa4c6db..4597228463 100644
--- a/web/i18n/de-DE/login.ts
+++ b/web/i18n/de-DE/login.ts
@@ -31,7 +31,7 @@ const translation = {
   pp: 'Datenschutzbestimmungen',
   tosDesc: 'Mit der Anmeldung stimmst du unseren',
   goToInit: 'Wenn du das Konto noch nicht initialisiert hast, gehe bitte zur Initialisierungsseite',
-  donthave: 'Hast du nicht?',
+  dontHave: 'Hast du nicht?',
   invalidInvitationCode: 'Ungültiger Einladungscode',
   accountAlreadyInited: 'Konto bereits initialisiert',
   forgotPassword: 'Passwort vergessen?',
diff --git a/web/i18n/de-DE/share-app.ts b/web/i18n/de-DE/share-app.ts
index 80dc1cecb4..5ea67dd08f 100644
--- a/web/i18n/de-DE/share-app.ts
+++ b/web/i18n/de-DE/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'App ist nicht verfügbar',
-    appUnkonwError: 'App ist nicht verfügbar',
+    appUnknownError: 'App ist nicht verfügbar',
   },
   chat: {
     newChat: 'Neuer Chat',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Chats',
     newChatDefaultName: 'Neues Gespräch',
     resetChat: 'Gespräch zurücksetzen',
-    powerBy: 'Bereitgestellt von',
+    poweredBy: 'Bereitgestellt von',
     prompt: 'Aufforderung',
     privatePromptConfigTitle: 'Konversationseinstellungen',
     publicPromptConfigTitle: 'Anfängliche Aufforderung',
diff --git a/web/i18n/de-DE/workflow.ts b/web/i18n/de-DE/workflow.ts
index 122bca7980..c01d0e6f99 100644
--- a/web/i18n/de-DE/workflow.ts
+++ b/web/i18n/de-DE/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Variable suchen',
     variableNamePlaceholder: 'Variablenname',
     setVarValuePlaceholder: 'Variable setzen',
-    needConnecttip: 'Dieser Schritt ist mit nichts verbunden',
+    needConnectTip: 'Dieser Schritt ist mit nichts verbunden',
     maxTreeDepth: 'Maximales Limit von {{depth}} Knoten pro Ast',
     needEndNode: 'Der Endblock muss hinzugefügt werden',
     needAnswerNode: 'Der Antwortblock muss hinzugefügt werden',
@@ -77,6 +77,24 @@ const translation = {
     importDSLTip: 'Der aktuelle Entwurf wird überschrieben. Exportieren Sie den Workflow vor dem Import als Backup.',
     overwriteAndImport: 'Überschreiben und Importieren',
     backupCurrentDraft: 'Aktuellen Entwurf sichern',
+    parallelTip: {
+      click: {
+        title: 'Klicken',
+        desc: 'hinzuzufügen',
+      },
+      drag: {
+        title: 'Ziehen',
+        desc: 'um eine Verbindung herzustellen',
+      },
+      limit: 'Die Parallelität ist auf {{num}} Zweige beschränkt.',
+      depthLimit: 'Begrenzung der parallelen Verschachtelungsschicht von {{num}} Schichten',
+    },
+    parallelRun: 'Paralleler Lauf',
+    disconnect: 'Trennen',
+    jumpToNode: 'Zu diesem Knoten springen',
+    addParallelNode: 'Parallelen Knoten hinzufügen',
+    parallel: 'PARALLEL',
+    branch: 'ZWEIG',
   },
   env: {
     envPanelTitle: 'Umgebungsvariablen',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': 'ist nicht leer',
         'null': 'ist null',
         'not null': 'ist nicht null',
+        'regex match': 'Regex-Übereinstimmung',
       },
       enterValue: 'Wert eingeben',
       addCondition: 'Bedingung hinzufügen',
diff --git a/web/i18n/en-US/app-api.ts b/web/i18n/en-US/app-api.ts
index f36708c1d0..631faeee9a 100644
--- a/web/i18n/en-US/app-api.ts
+++ b/web/i18n/en-US/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: 'Pause',
   playing: 'Playing',
   loading: 'Loading',
-  merMaind: {
+  merMaid: {
     rerender: 'Redo Rerender',
   },
   never: 'Never',
diff --git a/web/i18n/en-US/app-debug.ts b/web/i18n/en-US/app-debug.ts
index 86c5f720c3..3156c9f6cc 100644
--- a/web/i18n/en-US/app-debug.ts
+++ b/web/i18n/en-US/app-debug.ts
@@ -268,6 +268,9 @@ const translation = {
     notSelectModel: 'Please choose a model',
     waitForImgUpload: 'Please wait for the image to upload',
   },
+  warningMessage: {
+    timeoutExceeded: 'Results are not displayed due to timeout. Please refer to the logs to gather complete results.',
+  },
   chatSubTitle: 'Instructions',
   completionSubTitle: 'Prefix Prompt',
   promptTip:
@@ -301,7 +304,7 @@ const translation = {
     historyNoBeEmpty: 'Conversation history must be set in the prompt',
     queryNoBeEmpty: 'Query must be set in the prompt',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'Add Input Field',
     'editModalTitle': 'Edit Input Field',
     'description': 'Setting for variable {{varName}}',
diff --git a/web/i18n/en-US/app.ts b/web/i18n/en-US/app.ts
index d8cc2ffec0..3377a9b2f3 100644
--- a/web/i18n/en-US/app.ts
+++ b/web/i18n/en-US/app.ts
@@ -88,7 +88,7 @@ const translation = {
   switchTipEnd: ' switching back to Basic Orchestrate.',
   switchLabel: 'The app copy to be created',
   removeOriginal: 'Delete the original app',
-  switchStart: 'Start swtich',
+  switchStart: 'Start switch',
   typeSelector: {
     all: 'ALL Types',
     chatbot: 'Chatbot',
diff --git a/web/i18n/en-US/common.ts b/web/i18n/en-US/common.ts
index 85ac1c0654..23e301485a 100644
--- a/web/i18n/en-US/common.ts
+++ b/web/i18n/en-US/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Params',
     duplicate: 'Duplicate',
     rename: 'Rename',
+    audioSourceUnavailable: 'AudioSource is unavailable',
   },
   errorMsg: {
     fieldRequired: '{{field}} is required',
@@ -199,7 +200,7 @@ const translation = {
     invitationSent: 'Invitation sent',
     invitationSentTip: 'Invitation sent, and they can sign in to Dify to access your team data.',
     invitationLink: 'Invitation Link',
-    failedinvitationEmails: 'Below users were not invited successfully',
+    failedInvitationEmails: 'Below users were not invited successfully',
     ok: 'OK',
     removeFromTeam: 'Remove from team',
     removeFromTeamTip: 'Will remove team access',
@@ -207,7 +208,7 @@ const translation = {
     setMember: 'Set to ordinary member',
     setBuilder: 'Set as builder',
     setEditor: 'Set as editor',
-    disinvite: 'Cancel the invitation',
+    disInvite: 'Cancel the invitation',
     deleteMember: 'Delete Member',
     you: '(You)',
   },
@@ -391,7 +392,7 @@ const translation = {
       selector: {
         pageSelected: 'Pages Selected',
         searchPages: 'Search pages...',
-        noSearchResult: 'No search resluts',
+        noSearchResult: 'No search results',
         addPages: 'Add pages',
         preview: 'PREVIEW',
       },
diff --git a/web/i18n/en-US/dataset-creation.ts b/web/i18n/en-US/dataset-creation.ts
index fa17dce81e..40463593f9 100644
--- a/web/i18n/en-US/dataset-creation.ts
+++ b/web/i18n/en-US/dataset-creation.ts
@@ -50,7 +50,7 @@ const translation = {
       input: 'Knowledge name',
       placeholder: 'Please input',
       nameNotEmpty: 'Name cannot be empty',
-      nameLengthInvaild: 'Name must be between 1 to 40 characters',
+      nameLengthInvalid: 'Name must be between 1 to 40 characters',
       cancelButton: 'Cancel',
       confirmButton: 'Create',
       failed: 'Creation failed',
@@ -91,7 +91,7 @@ const translation = {
     maxLength: 'Maximum chunk length',
     overlap: 'Chunk overlap',
     overlapTip: 'Setting the chunk overlap can maintain the semantic relevance between them, enhancing the retrieve effect. It is recommended to set 10%-25% of the maximum chunk size.',
-    overlapCheck: 'chunk overlap should not bigger than maximun chunk length',
+    overlapCheck: 'chunk overlap should not bigger than maximum chunk length',
     rules: 'Text preprocessing rules',
     removeExtraSpaces: 'Replace consecutive spaces, newlines and tabs',
     removeUrlEmails: 'Delete all URLs and email addresses',
@@ -109,8 +109,8 @@ const translation = {
     QATitle: 'Segmenting in Question & Answer format',
     QATip: 'Enable this option will consume more tokens',
     QALanguage: 'Segment using',
-    emstimateCost: 'Estimation',
-    emstimateSegment: 'Estimated chunks',
+    estimateCost: 'Estimation',
+    estimateSegment: 'Estimated chunks',
     segmentCount: 'chunks',
     calculating: 'Calculating...',
     fileSource: 'Preprocess documents',
@@ -135,8 +135,8 @@ const translation = {
     previewSwitchTipStart: 'The current chunk preview is in text format, switching to a question-and-answer format preview will',
     previewSwitchTipEnd: ' consume additional tokens',
     characters: 'characters',
-    indexSettedTip: 'To change the index method & embedding model, please go to the ',
-    retrivalSettedTip: 'To change the retrieval setting, please go to the ',
+    indexSettingTip: 'To change the index method & embedding model, please go to the ',
+    retrievalSettingTip: 'To change the retrieval setting, please go to the ',
     datasetSettingLink: 'Knowledge settings.',
   },
   stepThree: {
diff --git a/web/i18n/en-US/dataset.ts b/web/i18n/en-US/dataset.ts
index e6914b4a00..a15efe5dc0 100644
--- a/web/i18n/en-US/dataset.ts
+++ b/web/i18n/en-US/dataset.ts
@@ -55,6 +55,7 @@ const translation = {
     hybrid_search: 'HYBRID',
     invertedIndex: 'INVERTED',
   },
+  defaultRetrievalTip: 'Multi-path retrieval is used by default. Knowledge is retrieved from multiple knowledge bases and then re-ranked.',
   mixtureHighQualityAndEconomicTip: 'The Rerank model is required for mixture of high quality and economical knowledge bases.',
   inconsistentEmbeddingModelTip: 'The Rerank model is required if the Embedding models of the selected knowledge bases are inconsistent.',
   retrievalSettings: 'Retrieval Setting',
diff --git a/web/i18n/en-US/login.ts b/web/i18n/en-US/login.ts
index 2cb6ecb785..03b0d27ed5 100644
--- a/web/i18n/en-US/login.ts
+++ b/web/i18n/en-US/login.ts
@@ -32,7 +32,7 @@ const translation = {
   pp: 'Privacy Policy',
   tosDesc: 'By signing up, you agree to our',
   goToInit: 'If you have not initialized the account, please go to the initialization page',
-  donthave: 'Don\'t have?',
+  dontHave: 'Don\'t have?',
   invalidInvitationCode: 'Invalid invitation code',
   accountAlreadyInited: 'Account already initialized',
   forgotPassword: 'Forgot your password?',
diff --git a/web/i18n/en-US/share-app.ts b/web/i18n/en-US/share-app.ts
index f66e923561..b5a219c998 100644
--- a/web/i18n/en-US/share-app.ts
+++ b/web/i18n/en-US/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'App is unavailable',
-    appUnkonwError: 'App is unavailable',
+    appUnknownError: 'App is unavailable',
   },
   chat: {
     newChat: 'New chat',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Chats',
     newChatDefaultName: 'New conversation',
     resetChat: 'Reset conversation',
-    powerBy: 'Powered by',
+    poweredBy: 'Powered by',
     prompt: 'Prompt',
     privatePromptConfigTitle: 'Conversation settings',
     publicPromptConfigTitle: 'Initial Prompt',
diff --git a/web/i18n/en-US/workflow.ts b/web/i18n/en-US/workflow.ts
index e0613a110f..a7e768911f 100644
--- a/web/i18n/en-US/workflow.ts
+++ b/web/i18n/en-US/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Search variable',
     variableNamePlaceholder: 'Variable name',
     setVarValuePlaceholder: 'Set variable',
-    needConnecttip: 'This step is not connected to anything',
+    needConnectTip: 'This step is not connected to anything',
     maxTreeDepth: 'Maximum limit of {{depth}} nodes per branch',
     needEndNode: 'The End block must be added',
     needAnswerNode: 'The Answer block must be added',
@@ -77,6 +77,24 @@ const translation = {
     overwriteAndImport: 'Overwrite and Import',
     importFailure: 'Import failure',
     importSuccess: 'Import success',
+    parallelRun: 'Parallel Run',
+    parallelTip: {
+      click: {
+        title: 'Click',
+        desc: ' to add',
+      },
+      drag: {
+        title: 'Drag',
+        desc: ' to connect',
+      },
+      limit: 'Parallelism is limited to {{num}} branches.',
+      depthLimit: 'Parallel nesting layer limit of {{num}} layers',
+    },
+    disconnect: 'Disconnect',
+    jumpToNode: 'Jump to this node',
+    addParallelNode: 'Add Parallel Node',
+    parallel: 'PARALLEL',
+    branch: 'BRANCH',
   },
   env: {
     envPanelTitle: 'Environment Variables',
diff --git a/web/i18n/es-ES/app-api.ts b/web/i18n/es-ES/app-api.ts
index 5d2bc078e3..1474e5fb1d 100644
--- a/web/i18n/es-ES/app-api.ts
+++ b/web/i18n/es-ES/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: 'Pausa',
   playing: 'Reproduciendo',
   loading: 'Cargando',
-  merMaind: {
+  merMaid: {
     rerender: 'Rehacer Rerender',
   },
   never: 'Nunca',
diff --git a/web/i18n/es-ES/app-debug.ts b/web/i18n/es-ES/app-debug.ts
index 68088c26a6..ab5b82e7d1 100644
--- a/web/i18n/es-ES/app-debug.ts
+++ b/web/i18n/es-ES/app-debug.ts
@@ -259,7 +259,7 @@ const translation = {
     historyNoBeEmpty: 'El historial de conversaciones debe establecerse en la indicación',
     queryNoBeEmpty: 'La consulta debe establecerse en la indicación',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'Agregar Campo de Entrada',
     'editModalTitle': 'Editar Campo de Entrada',
     'description': 'Configuración para la variable {{varName}}',
diff --git a/web/i18n/es-ES/common.ts b/web/i18n/es-ES/common.ts
index 119f465f76..2ba907361f 100644
--- a/web/i18n/es-ES/common.ts
+++ b/web/i18n/es-ES/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Parámetros',
     duplicate: 'Duplicar',
     rename: 'Renombrar',
+    audioSourceUnavailable: 'AudioSource no está disponible',
   },
   errorMsg: {
     fieldRequired: '{{field}} es requerido',
@@ -199,7 +200,7 @@ const translation = {
     invitationSent: 'Invitación enviada',
     invitationSentTip: 'Invitación enviada, y pueden iniciar sesión en Dify para acceder a tus datos del equipo.',
     invitationLink: 'Enlace de invitación',
-    failedinvitationEmails: 'Los siguientes usuarios no fueron invitados exitosamente',
+    failedInvitationEmails: 'Los siguientes usuarios no fueron invitados exitosamente',
     ok: 'OK',
     removeFromTeam: 'Eliminar del equipo',
     removeFromTeamTip: 'Se eliminará el acceso al equipo',
@@ -207,7 +208,7 @@ const translation = {
     setMember: 'Establecer como miembro ordinario',
     setBuilder: 'Establecer como constructor',
     setEditor: 'Establecer como editor',
-    disinvite: 'Cancelar la invitación',
+    disInvite: 'Cancelar la invitación',
     deleteMember: 'Eliminar miembro',
     you: '(Tú)',
   },
diff --git a/web/i18n/es-ES/dataset-creation.ts b/web/i18n/es-ES/dataset-creation.ts
index 66b8e9b302..132c9cbb9b 100644
--- a/web/i18n/es-ES/dataset-creation.ts
+++ b/web/i18n/es-ES/dataset-creation.ts
@@ -50,7 +50,7 @@ const translation = {
       input: 'Nombre del conocimiento',
       placeholder: 'Por favor ingresa',
       nameNotEmpty: 'El nombre no puede estar vacío',
-      nameLengthInvaild: 'El nombre debe tener entre 1 y 40 caracteres',
+      nameLengthInvalid: 'El nombre debe tener entre 1 y 40 caracteres',
       cancelButton: 'Cancelar',
       confirmButton: 'Crear',
       failed: 'Error al crear',
@@ -109,8 +109,8 @@ const translation = {
     QATitle: 'Segmentación en formato de pregunta y respuesta',
     QATip: 'Habilitar esta opción consumirá más tokens',
     QALanguage: 'Segmentar usando',
-    emstimateCost: 'Estimación',
-    emstimateSegment: 'Fragmentos estimados',
+    estimateCost: 'Estimación',
+    estimateSegment: 'Fragmentos estimados',
     segmentCount: 'fragmentos',
     calculating: 'Calculando...',
     fileSource: 'Preprocesar documentos',
@@ -135,8 +135,8 @@ const translation = {
     previewSwitchTipStart: 'La vista previa actual del fragmento está en formato de texto, cambiar a una vista previa en formato de pregunta y respuesta',
     previewSwitchTipEnd: ' consumirá tokens adicionales',
     characters: 'caracteres',
-    indexSettedTip: 'Para cambiar el método de índice, por favor ve a la ',
-    retrivalSettedTip: 'Para cambiar el método de índice, por favor ve a la ',
+    indexSettingTip: 'Para cambiar el método de índice, por favor ve a la ',
+    retrievalSettingTip: 'Para cambiar el método de índice, por favor ve a la ',
     datasetSettingLink: 'configuración del conocimiento.',
   },
   stepThree: {
diff --git a/web/i18n/es-ES/dataset.ts b/web/i18n/es-ES/dataset.ts
index e4fc362efa..4eefb621d2 100644
--- a/web/i18n/es-ES/dataset.ts
+++ b/web/i18n/es-ES/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacy: 'La recuperación N-a-1 será oficialmente obsoleta a partir de septiembre. Se recomienda utilizar la última recuperación de múltiples rutas para obtener mejores resultados.',
   nTo1RetrievalLegacyLink: 'Más información',
   nTo1RetrievalLegacyLinkText: 'La recuperación N-a-1 será oficialmente obsoleta en septiembre.',
+  defaultRetrievalTip: 'De forma predeterminada, se utiliza la recuperación de varias rutas. El conocimiento se recupera de múltiples bases de conocimiento y luego se vuelve a clasificar.',
 }
 
 export default translation
diff --git a/web/i18n/es-ES/login.ts b/web/i18n/es-ES/login.ts
index dc12cfc32f..e56161895e 100644
--- a/web/i18n/es-ES/login.ts
+++ b/web/i18n/es-ES/login.ts
@@ -32,7 +32,7 @@ const translation = {
   pp: 'Política de privacidad',
   tosDesc: 'Al registrarte, aceptas nuestros',
   goToInit: 'Si no has inicializado la cuenta, por favor ve a la página de inicialización',
-  donthave: '¿No tienes?',
+  dontHave: '¿No tienes?',
   invalidInvitationCode: 'Código de invitación inválido',
   accountAlreadyInited: 'La cuenta ya está inicializada',
   forgotPassword: '¿Olvidaste tu contraseña?',
diff --git a/web/i18n/es-ES/share-app.ts b/web/i18n/es-ES/share-app.ts
index 2e436c4327..b1ac171389 100644
--- a/web/i18n/es-ES/share-app.ts
+++ b/web/i18n/es-ES/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'La aplicación no está disponible',
-    appUnkonwError: 'La aplicación no está disponible',
+    appUnknownError: 'La aplicación no está disponible',
   },
   chat: {
     newChat: 'Nuevo chat',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Chats',
     newChatDefaultName: 'Nueva conversación',
     resetChat: 'Reiniciar conversación',
-    powerBy: 'Desarrollado por',
+    poweredBy: 'Desarrollado por',
     prompt: 'Indicación',
     privatePromptConfigTitle: 'Configuración de la conversación',
     publicPromptConfigTitle: 'Indicación inicial',
diff --git a/web/i18n/es-ES/workflow.ts b/web/i18n/es-ES/workflow.ts
index 7b8413018d..2260631d0f 100644
--- a/web/i18n/es-ES/workflow.ts
+++ b/web/i18n/es-ES/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Buscar variable',
     variableNamePlaceholder: 'Nombre de la variable',
     setVarValuePlaceholder: 'Establecer variable',
-    needConnecttip: 'Este paso no está conectado a nada',
+    needConnectTip: 'Este paso no está conectado a nada',
     maxTreeDepth: 'Límite máximo de {{depth}} nodos por rama',
     needEndNode: 'Debe agregarse el bloque de Fin',
     needAnswerNode: 'Debe agregarse el bloque de Respuesta',
@@ -77,6 +77,24 @@ const translation = {
     overwriteAndImport: 'Sobrescribir e importar',
     importFailure: 'Error al importar',
     importSuccess: 'Importación exitosa',
+    parallelTip: {
+      click: {
+        title: 'Clic',
+        desc: 'Para agregar',
+      },
+      drag: {
+        title: 'Arrastrar',
+        desc: 'Para conectarse',
+      },
+      limit: 'El paralelismo se limita a {{num}} ramas.',
+      depthLimit: 'Límite de capa de anidamiento paralelo de capas {{num}}',
+    },
+    parallelRun: 'Ejecución paralela',
+    disconnect: 'Desconectar',
+    jumpToNode: 'Saltar a este nodo',
+    addParallelNode: 'Agregar nodo paralelo',
+    parallel: 'PARALELO',
+    branch: 'RAMA',
   },
   env: {
     envPanelTitle: 'Variables de Entorno',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': 'no está vacío',
         'null': 'es nulo',
         'not null': 'no es nulo',
+        'regex match': 'Coincidencia de expresiones regulares',
       },
       enterValue: 'Ingresa un valor',
       addCondition: 'Agregar condición',
diff --git a/web/i18n/fa-IR/app-api.ts b/web/i18n/fa-IR/app-api.ts
index 0548ef2a2b..7f65481fcf 100644
--- a/web/i18n/fa-IR/app-api.ts
+++ b/web/i18n/fa-IR/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: 'مکث',
   playing: 'در حال پخش',
   loading: 'در حال بارگذاری',
-  merMaind: {
+  merMaid: {
     rerender: 'بازسازی مجدد',
   },
   never: 'هرگز',
diff --git a/web/i18n/fa-IR/app-debug.ts b/web/i18n/fa-IR/app-debug.ts
index 1ce222581d..00891f3b17 100644
--- a/web/i18n/fa-IR/app-debug.ts
+++ b/web/i18n/fa-IR/app-debug.ts
@@ -294,7 +294,7 @@ const translation = {
       historyNoBeEmpty: 'تاریخچه مکالمه باید در پرس و جو تنظیم شود',
       queryNoBeEmpty: 'پرس و جو باید در پرس و جو تنظیم شود',
     },
-    variableConig: {
+    variableConfig: {
       'addModalTitle': 'افزودن فیلد ورودی',
       'editModalTitle': 'ویرایش فیلد ورودی',
       'description': 'تنظیم برای متغیر {{varName}}',
diff --git a/web/i18n/fa-IR/common.ts b/web/i18n/fa-IR/common.ts
index 42c6865a6e..c75ab11a63 100644
--- a/web/i18n/fa-IR/common.ts
+++ b/web/i18n/fa-IR/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'پارامترها',
     duplicate: 'تکرار',
     rename: 'تغییر نام',
+    audioSourceUnavailable: 'منبع صوتی در دسترس نیست',
   },
   errorMsg: {
     fieldRequired: '{{field}} الزامی است',
@@ -199,7 +200,7 @@ const translation = {
     invitationSent: 'دعوت‌نامه ارسال شد',
     invitationSentTip: 'دعوت‌نامه ارسال شد و آنها می‌توانند وارد Dify شوند تا به داده‌های تیم شما دسترسی پیدا کنند.',
     invitationLink: 'لینک دعوت',
-    failedinvitationEmails: 'کاربران زیر با موفقیت دعوت نشدند',
+    failedInvitationEmails: 'کاربران زیر با موفقیت دعوت نشدند',
     ok: 'تایید',
     removeFromTeam: 'حذف از تیم',
     removeFromTeamTip: 'دسترسی تیم را حذف می‌کند',
@@ -207,7 +208,7 @@ const translation = {
     setMember: 'تنظیم به عنوان عضو عادی',
     setBuilder: 'تنظیم به عنوان سازنده',
     setEditor: 'تنظیم به عنوان ویرایشگر',
-    disinvite: 'لغو دعوت',
+    disInvite: 'لغو دعوت',
     deleteMember: 'حذف عضو',
     you: '(شما)',
   },
diff --git a/web/i18n/fa-IR/dataset-creation.ts b/web/i18n/fa-IR/dataset-creation.ts
index f8483af140..e6e6ad5bfb 100644
--- a/web/i18n/fa-IR/dataset-creation.ts
+++ b/web/i18n/fa-IR/dataset-creation.ts
@@ -50,7 +50,7 @@ const translation = {
       input: 'نام دانش',
       placeholder: 'لطفاً وارد کنید',
       nameNotEmpty: 'نام نمیتواند خالی باشد',
-      nameLengthInvaild: 'نام باید بین 1 تا 40 کاراکتر باشد',
+      nameLengthInvalid: 'نام باید بین 1 تا 40 کاراکتر باشد',
       cancelButton: 'لغو',
       confirmButton: 'ایجاد',
       failed: 'ایجاد ناموفق بود',
@@ -109,8 +109,8 @@ const translation = {
     QATitle: 'بخشبندی در قالب پرسش و پاسخ',
     QATip: 'فعال کردن این گزینه توکنهای بیشتری مصرف خواهد کرد',
     QALanguage: 'بخشبندی با استفاده از',
-    emstimateCost: 'برآورد',
-    emstimateSegment: 'بخشهای برآورد شده',
+    estimateCost: 'برآورد',
+    estimateSegment: 'بخشهای برآورد شده',
     segmentCount: 'بخشها',
     calculating: 'در حال محاسبه...',
     fileSource: 'پیشپردازش اسناد',
@@ -135,8 +135,8 @@ const translation = {
     previewSwitchTipStart: 'پیشنمایش بخش فعلی در قالب متن است، تغییر به پیشنمایش قالب پرسش و پاسخ',
     previewSwitchTipEnd: ' توکنهای اضافی مصرف خواهد کرد',
     characters: 'کاراکترها',
-    indexSettedTip: 'برای تغییر روش شاخص، لطفاً به',
-    retrivalSettedTip: 'برای تغییر روش شاخص، لطفاً به',
+    indexSettingTip: 'برای تغییر روش شاخص، لطفاً به',
+    retrievalSettingTip: 'برای تغییر روش شاخص، لطفاً به',
     datasetSettingLink: 'تنظیمات دانش بروید.',
   },
   stepThree: {
diff --git a/web/i18n/fa-IR/dataset.ts b/web/i18n/fa-IR/dataset.ts
index 30036dc68f..e3b9d70e07 100644
--- a/web/i18n/fa-IR/dataset.ts
+++ b/web/i18n/fa-IR/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacy: 'بازیابی N-to-1 از سپتامبر به طور رسمی منسوخ خواهد شد. توصیه می‌شود از بازیابی چند مسیر جدید استفاده کنید تا نتایج بهتری بدست آورید.',
   nTo1RetrievalLegacyLink: 'بیشتر بدانید',
   nTo1RetrievalLegacyLinkText: ' بازیابی N-to-1 از سپتامبر به طور رسمی منسوخ خواهد شد.',
+  defaultRetrievalTip: 'بازیابی چند مسیره به طور پیش فرض استفاده می شود. دانش از چندین پایگاه دانش بازیابی می شود و سپس دوباره رتبه بندی می شود.',
 }
 
 export default translation
diff --git a/web/i18n/fa-IR/login.ts b/web/i18n/fa-IR/login.ts
index 8912561efe..4ac06c866d 100644
--- a/web/i18n/fa-IR/login.ts
+++ b/web/i18n/fa-IR/login.ts
@@ -32,7 +32,7 @@ const translation = {
   pp: 'سیاست حفظ حریم خصوصی',
   tosDesc: 'با ثبت نام، شما با شرایط ما موافقت می‌کنید',
   goToInit: 'اگر حساب را اولیه نکرده‌اید، لطفاً به صفحه اولیه‌سازی بروید',
-  donthave: 'ندارید؟',
+  dontHave: 'ندارید؟',
   invalidInvitationCode: 'کد دعوت نامعتبر است',
   accountAlreadyInited: 'حساب قبلاً اولیه شده است',
   forgotPassword: 'رمز عبور خود را فراموش کرده‌اید؟',
diff --git a/web/i18n/fa-IR/share-app.ts b/web/i18n/fa-IR/share-app.ts
index b74c893e6e..f3f1360a92 100644
--- a/web/i18n/fa-IR/share-app.ts
+++ b/web/i18n/fa-IR/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'اپ در دسترس نیست',
-    appUnkonwError: 'اپ در دسترس نیست',
+    appUnknownError: 'اپ در دسترس نیست',
   },
   chat: {
     newChat: 'چت جدید',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'چت‌ها',
     newChatDefaultName: 'مکالمه جدید',
     resetChat: 'بازنشانی مکالمه',
-    powerBy: 'قدرت‌گرفته از',
+    poweredBy: 'قدرت‌گرفته از',
     prompt: 'پیشنهاد',
     privatePromptConfigTitle: 'تنظیمات مکالمه',
     publicPromptConfigTitle: 'پیشنهاد اولیه',
diff --git a/web/i18n/fa-IR/workflow.ts b/web/i18n/fa-IR/workflow.ts
index 199fd0b7f8..eb36dfdc88 100644
--- a/web/i18n/fa-IR/workflow.ts
+++ b/web/i18n/fa-IR/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'جستجوی متغیر',
     variableNamePlaceholder: 'نام متغیر',
     setVarValuePlaceholder: 'تنظیم متغیر',
-    needConnecttip: 'این مرحله به هیچ چیزی متصل نیست',
+    needConnectTip: 'این مرحله به هیچ چیزی متصل نیست',
     maxTreeDepth: 'حداکثر عمق {{depth}} نود در هر شاخه',
     needEndNode: 'بلوک پایان باید اضافه شود',
     needAnswerNode: 'بلوک پاسخ باید اضافه شود',
@@ -77,6 +77,24 @@ const translation = {
     overwriteAndImport: 'بازنویسی و وارد کردن',
     importFailure: 'خطا در وارد کردن',
     importSuccess: 'وارد کردن موفقیت‌آمیز',
+    parallelTip: {
+      click: {
+        title: 'کلیک کنید',
+        desc: 'اضافه کردن',
+      },
+      drag: {
+        desc: 'برای اتصال',
+        title: 'کشیدن',
+      },
+      depthLimit: 'حد لایه تودرتو موازی لایه های {{num}}',
+      limit: 'موازی سازی به شاخه های {{num}} محدود می شود.',
+    },
+    disconnect: 'قطع',
+    jumpToNode: 'پرش به این گره',
+    parallelRun: 'اجرای موازی',
+    addParallelNode: 'افزودن گره موازی',
+    parallel: 'موازی',
+    branch: 'شاخه',
   },
   env: {
     envPanelTitle: 'متغیرهای محیطی',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': 'خالی نیست',
         'null': 'خالی',
         'not null': 'خالی نیست',
+        'regex match': 'مسابقه regex',
       },
       enterValue: 'مقدار را وارد کنید',
       addCondition: 'افزودن شرط',
diff --git a/web/i18n/fr-FR/app-api.ts b/web/i18n/fr-FR/app-api.ts
index 7ace3ff3ff..af3f752f98 100644
--- a/web/i18n/fr-FR/app-api.ts
+++ b/web/i18n/fr-FR/app-api.ts
@@ -9,7 +9,7 @@ const translation = {
   play: 'Jouer',
   pause: 'Pause',
   playing: 'Jouant',
-  merMaind: {
+  merMaid: {
     rerender: 'Refaire Rerendu',
   },
   never: 'Jamais',
diff --git a/web/i18n/fr-FR/app-debug.ts b/web/i18n/fr-FR/app-debug.ts
index b71d251956..2fd863742b 100644
--- a/web/i18n/fr-FR/app-debug.ts
+++ b/web/i18n/fr-FR/app-debug.ts
@@ -248,7 +248,7 @@ const translation = {
     historyNoBeEmpty: 'L\'historique de la conversation doit être défini dans le prompt',
     queryNoBeEmpty: 'La requête doit être définie dans le prompt',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'Add Input Field',
     'editModalTitle': 'Edit Input Field',
     'description': 'Setting for variable {{varName}}',
diff --git a/web/i18n/fr-FR/common.ts b/web/i18n/fr-FR/common.ts
index 2cd517fd59..c4fed4405d 100644
--- a/web/i18n/fr-FR/common.ts
+++ b/web/i18n/fr-FR/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Paramètres',
     duplicate: 'Dupliquer',
     rename: 'Renommer',
+    audioSourceUnavailable: 'AudioSource n’est pas disponible',
   },
   placeholder: {
     input: 'Veuillez entrer',
@@ -191,14 +192,14 @@ const translation = {
     invitationSent: 'Invitation envoyée',
     invitationSentTip: 'Invitation envoyée, et ils peuvent se connecter à Dify pour accéder aux données de votre équipe.',
     invitationLink: 'Lien d\'invitation',
-    failedinvitationEmails: 'Les utilisateurs ci-dessous n\'ont pas été invités avec succès',
+    failedInvitationEmails: 'Les utilisateurs ci-dessous n\'ont pas été invités avec succès',
     ok: 'D\'accord',
     removeFromTeam: 'Retirer de l\'équipe',
     removeFromTeamTip: 'Supprimera l\'accès de l\'équipe',
     setAdmin: 'Définir comme administrateur',
     setMember: 'Définir en tant que membre ordinaire',
     setEditor: 'Définir en tant qu\'éditeur',
-    disinvite: 'Annuler l\'invitation',
+    disInvite: 'Annuler l\'invitation',
     deleteMember: 'Supprimer Membre',
     you: '(Vous)',
     builder: 'Constructeur',
diff --git a/web/i18n/fr-FR/dataset-creation.ts b/web/i18n/fr-FR/dataset-creation.ts
index 11052c8e64..c08a3e5731 100644
--- a/web/i18n/fr-FR/dataset-creation.ts
+++ b/web/i18n/fr-FR/dataset-creation.ts
@@ -45,7 +45,7 @@ const translation = {
       input: 'Nom de la connaissance',
       placeholder: 'Veuillez entrer',
       nameNotEmpty: 'Le nom ne peut pas être vide',
-      nameLengthInvaild: 'Le nom doit comporter entre 1 et 40 caractères.',
+      nameLengthInvalid: 'Le nom doit comporter entre 1 et 40 caractères.',
       cancelButton: 'Annuler',
       confirmButton: 'Créer',
       failed: 'Création échouée',
@@ -104,8 +104,8 @@ const translation = {
     QATitle: 'Segmentation en format Question & Réponse',
     QATip: 'Activer cette option consommera plus de jetons',
     QALanguage: 'Segmenter en utilisant',
-    emstimateCost: 'Estimation',
-    emstimateSegment: 'Morceaux estimés',
+    estimateCost: 'Estimation',
+    estimateSegment: 'Morceaux estimés',
     segmentCount: 'morceaux',
     calculating: 'En calcul...',
     fileSource: 'Prétraiter les documents',
@@ -128,8 +128,8 @@ const translation = {
     previewSwitchTipStart: 'L\'aperçu actuel du morceau est en format texte, passer à un aperçu en format de questions-réponses va',
     previewSwitchTipEnd: 'consommer des tokens supplémentaires',
     characters: 'personnages',
-    indexSettedTip: 'Pour changer la méthode d\'index, veuillez aller à la',
-    retrivalSettedTip: 'Pour changer la méthode d\'index, veuillez aller à la',
+    indexSettingTip: 'Pour changer la méthode d\'index, veuillez aller à la',
+    retrievalSettingTip: 'Pour changer la méthode d\'index, veuillez aller à la',
     datasetSettingLink: 'Paramètres de connaissance.',
     webpageUnit: 'Pages',
     websiteSource: 'Site web de prétraitement',
diff --git a/web/i18n/fr-FR/dataset.ts b/web/i18n/fr-FR/dataset.ts
index 014168e006..9c8df9f79d 100644
--- a/web/i18n/fr-FR/dataset.ts
+++ b/web/i18n/fr-FR/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacy: 'La récupération N-à-1 sera officiellement obsolète à partir de septembre. Il est recommandé d\'utiliser la dernière récupération multi-chemins pour obtenir de meilleurs résultats.',
   nTo1RetrievalLegacyLink: 'En savoir plus',
   nTo1RetrievalLegacyLinkText: 'La récupération N-à-1 sera officiellement obsolète en septembre.',
+  defaultRetrievalTip: 'La récupération à chemins multiples est utilisée par défaut. Les connaissances sont extraites de plusieurs bases de connaissances, puis reclassées.',
 }
 
 export default translation
diff --git a/web/i18n/fr-FR/login.ts b/web/i18n/fr-FR/login.ts
index 94328bb3ae..cee09cf0e7 100644
--- a/web/i18n/fr-FR/login.ts
+++ b/web/i18n/fr-FR/login.ts
@@ -31,7 +31,7 @@ const translation = {
   pp: 'Politique de Confidentialité',
   tosDesc: 'En vous inscrivant, vous acceptez nos',
   goToInit: 'Si vous n\'avez pas initialisé le compte, veuillez vous rendre sur la page d\'initialisation',
-  donthave: 'Vous n\'avez pas ?',
+  dontHave: 'Vous n\'avez pas ?',
   invalidInvitationCode: 'Code d\'invitation invalide',
   accountAlreadyInited: 'Compte déjà initialisé',
   forgotPassword: 'Mot de passe oublié?',
diff --git a/web/i18n/fr-FR/share-app.ts b/web/i18n/fr-FR/share-app.ts
index 8f9e04e941..44d03b1e35 100644
--- a/web/i18n/fr-FR/share-app.ts
+++ b/web/i18n/fr-FR/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'L\'application n\'est pas disponible',
-    appUnkonwError: 'L\'application n\'est pas disponible',
+    appUnknownError: 'L\'application n\'est pas disponible',
   },
   chat: {
     newChat: 'Nouveau chat',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Discussions',
     newChatDefaultName: 'Nouvelle conversation',
     resetChat: 'Réinitialiser la conversation',
-    powerBy: 'Propulsé par',
+    poweredBy: 'Propulsé par',
     prompt: 'Prompt',
     privatePromptConfigTitle: 'Paramètres de conversation',
     publicPromptConfigTitle: 'Prompt Initial',
diff --git a/web/i18n/fr-FR/workflow.ts b/web/i18n/fr-FR/workflow.ts
index 46f80591d6..878d25804e 100644
--- a/web/i18n/fr-FR/workflow.ts
+++ b/web/i18n/fr-FR/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Rechercher une variable',
     variableNamePlaceholder: 'Nom de la variable',
     setVarValuePlaceholder: 'Définir la valeur de la variable',
-    needConnecttip: 'Cette étape n\'est connectée à rien',
+    needConnectTip: 'Cette étape n\'est connectée à rien',
     maxTreeDepth: 'Limite maximale de {{depth}} nœuds par branche',
     needEndNode: 'Le bloc de fin doit être ajouté',
     needAnswerNode: 'Le bloc de réponse doit être ajouté',
@@ -77,6 +77,24 @@ const translation = {
     overwriteAndImport: 'Écraser et importer',
     importFailure: 'Echec de l\'importation',
     importSuccess: 'Import avec succès',
+    parallelTip: {
+      click: {
+        title: 'Cliquer',
+        desc: 'à ajouter',
+      },
+      drag: {
+        title: 'Traîner',
+        desc: 'pour se connecter',
+      },
+      limit: 'Le parallélisme est limité aux branches {{num}}.',
+      depthLimit: 'Limite de couches d’imbrication parallèle de {{num}} couches',
+    },
+    parallelRun: 'Exécution parallèle',
+    disconnect: 'Déconnecter',
+    jumpToNode: 'Aller à ce nœud',
+    addParallelNode: 'Ajouter un nœud parallèle',
+    parallel: 'PARALLÈLE',
+    branch: 'BRANCHE',
   },
   env: {
     envPanelTitle: 'Variables d\'Environnement',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': 'n\'est pas vide',
         'null': 'est nul',
         'not null': 'n\'est pas nul',
+        'regex match': 'correspondance regex',
       },
       enterValue: 'Entrez la valeur',
       addCondition: 'Ajouter une condition',
diff --git a/web/i18n/hi-IN/app-api.ts b/web/i18n/hi-IN/app-api.ts
index 3c9ceadd29..b983fd0ccb 100644
--- a/web/i18n/hi-IN/app-api.ts
+++ b/web/i18n/hi-IN/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: 'विराम',
   playing: 'चल रहा है',
   loading: 'लोड हो रहा है',
-  merMaind: {
+  merMaid: {
     rerender: 'पुनः रीरेंडर करें',
   },
   never: 'कभी नहीं',
diff --git a/web/i18n/hi-IN/app-debug.ts b/web/i18n/hi-IN/app-debug.ts
index 29944c8d84..1b0633ef32 100644
--- a/web/i18n/hi-IN/app-debug.ts
+++ b/web/i18n/hi-IN/app-debug.ts
@@ -290,7 +290,7 @@ const translation = {
     historyNoBeEmpty: 'संवाद इतिहास प्रॉम्प्ट में सेट होना चाहिए',
     queryNoBeEmpty: 'प्रश्न प्रॉम्प्ट में सेट होना चाहिए',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'इनपुट फ़ील्ड जोड़ें',
     'editModalTitle': 'इनपुट फ़ील्ड संपादित करें',
     'description': 'वेरिएबल {{varName}} के लिए सेटिंग',
diff --git a/web/i18n/hi-IN/common.ts b/web/i18n/hi-IN/common.ts
index 8e9a2e619b..256cb9d426 100644
--- a/web/i18n/hi-IN/common.ts
+++ b/web/i18n/hi-IN/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'पैरामीटर',
     duplicate: 'डुप्लिकेट',
     rename: 'नाम बदलें',
+    audioSourceUnavailable: 'ऑडियो स्रोत अनुपलब्ध है',
   },
   errorMsg: {
     fieldRequired: '{{field}} आवश्यक है',
@@ -205,7 +206,7 @@ const translation = {
     invitationSentTip:
       'आमंत्रण भेजा गया, और वे साइन इन करके आपकी टीम डेटा तक पहुंच सकते हैं।',
     invitationLink: 'आमंत्रण लिंक',
-    failedinvitationEmails:
+    failedInvitationEmails:
       'नीचे दिए गए उपयोगकर्ताओं को सफलतापूर्वक आमंत्रित नहीं किया गया',
     ok: 'ठीक है',
     removeFromTeam: 'टीम से हटाएं',
@@ -214,7 +215,7 @@ const translation = {
     setMember: 'सामान्य सदस्य के रूप में सेट करें',
     setBuilder: 'निर्माता के रूप में सेट करें',
     setEditor: 'संपादक के रूप में सेट करें',
-    disinvite: 'आमंत्रण रद्द करें',
+    disInvite: 'आमंत्रण रद्द करें',
     deleteMember: 'सदस्य को हटाएं',
     you: '(आप)',
     datasetOperator: 'ज्ञान व्यवस्थापक',
diff --git a/web/i18n/hi-IN/dataset-creation.ts b/web/i18n/hi-IN/dataset-creation.ts
index 59913c71ea..0fa71acf4a 100644
--- a/web/i18n/hi-IN/dataset-creation.ts
+++ b/web/i18n/hi-IN/dataset-creation.ts
@@ -51,7 +51,7 @@ const translation = {
       input: 'ज्ञान का नाम',
       placeholder: 'कृपया दर्ज करें',
       nameNotEmpty: 'नाम खाली नहीं हो सकता',
-      nameLengthInvaild: 'नाम 1 से 40 वर्णों के बीच होना चाहिए',
+      nameLengthInvalid: 'नाम 1 से 40 वर्णों के बीच होना चाहिए',
       cancelButton: 'रद्द करें',
       confirmButton: 'बनाएं',
       failed: 'बनाना विफल रहा',
@@ -121,8 +121,8 @@ const translation = {
     QATitle: 'प्रश्न और उत्तर प्रारूप में खंड करना',
     QATip: 'इस विकल्प को सक्षम करने से अधिक टोकन खर्च होंगे',
     QALanguage: 'का उपयोग करके खंड करना',
-    emstimateCost: 'अनुमानित लागत',
-    emstimateSegment: 'अनुमानित खंड',
+    estimateCost: 'अनुमानित लागत',
+    estimateSegment: 'अनुमानित खंड',
     segmentCount: 'खंड',
     calculating: 'गणना कर रहा है...',
     fileSource: 'दस्तावेज़ों को पूर्व-प्रसंस्करण करें',
@@ -152,8 +152,8 @@ const translation = {
       'वर्तमान खंड पूर्वावलोकन पाठ प्रारूप में है, प्रश्न-उत्तर प्रारूप में स्विच करने से',
     previewSwitchTipEnd: ' अतिरिक्त टोकन खर्च होंगे',
     characters: 'वर्ण',
-    indexSettedTip: 'इंडेक्स विधि बदलने के लिए, कृपया जाएं ',
-    retrivalSettedTip: 'इंडेक्स विधि बदलने के लिए, कृपया जाएं ',
+    indexSettingTip: 'इंडेक्स विधि बदलने के लिए, कृपया जाएं ',
+    retrievalSettingTip: 'इंडेक्स विधि बदलने के लिए, कृपया जाएं ',
     datasetSettingLink: 'ज्ञान सेटिंग्स।',
   },
   stepThree: {
diff --git a/web/i18n/hi-IN/dataset.ts b/web/i18n/hi-IN/dataset.ts
index de33113d2b..afd8cd277c 100644
--- a/web/i18n/hi-IN/dataset.ts
+++ b/web/i18n/hi-IN/dataset.ts
@@ -78,6 +78,7 @@ const translation = {
   nTo1RetrievalLegacy: 'N-से-1 पुनर्प्राप्ति सितंबर से आधिकारिक तौर पर बंद कर दी जाएगी। बेहतर परिणाम प्राप्त करने के लिए नवीनतम बहु-मार्ग पुनर्प्राप्ति का उपयोग करने की सिफारिश की जाती है।',
   nTo1RetrievalLegacyLink: 'और जानें',
   nTo1RetrievalLegacyLinkText: 'N-से-1 पुनर्प्राप्ति सितंबर में आधिकारिक तौर पर बंद कर दी जाएगी।',
+  defaultRetrievalTip: 'मल्टी-पाथ रिट्रीवल का उपयोग डिफ़ॉल्ट रूप से किया जाता है। ज्ञान को कई ज्ञान आधारों से पुनर्प्राप्त किया जाता है और फिर फिर से रैंक किया जाता है।',
 }
 
 export default translation
diff --git a/web/i18n/hi-IN/login.ts b/web/i18n/hi-IN/login.ts
index 3ecba9a186..b3ca0b1a52 100644
--- a/web/i18n/hi-IN/login.ts
+++ b/web/i18n/hi-IN/login.ts
@@ -36,7 +36,7 @@ const translation = {
   tosDesc: 'साइन अप करके, आप हमारी सहमति देते हैं',
   goToInit:
     'यदि आपने खाता प्रारंभ नहीं किया है, तो कृपया प्रारंभिक पृष्ठ पर जाएं',
-  donthave: 'नहीं है?',
+  dontHave: 'नहीं है?',
   invalidInvitationCode: 'अवैध निमंत्रण कोड',
   accountAlreadyInited: 'खाता पहले से प्रारंभ किया गया है',
   forgotPassword: 'क्या आपने अपना पासवर्ड भूल गए हैं?',
diff --git a/web/i18n/hi-IN/share-app.ts b/web/i18n/hi-IN/share-app.ts
index 82c17f009d..a5c7816fe2 100644
--- a/web/i18n/hi-IN/share-app.ts
+++ b/web/i18n/hi-IN/share-app.ts
@@ -3,7 +3,7 @@ const translation = {
     welcome: 'आपका स्वागत है',
     appUnavailable: 'ऐप उपलब्ध नहीं है',
     appUnknownError: 'अज्ञात त्रुटि, कृपया पुनः प्रयास करें',
-    appUnkonwError: 'ऐप अनुपलब्ध है',
+    appUnknownError: 'ऐप अनुपलब्ध है',
   },
   chat: {
     newChat: 'नया चैट',
@@ -11,7 +11,7 @@ const translation = {
     unpinnedTitle: 'चैट',
     newChatDefaultName: 'नया संवाद',
     resetChat: 'संवाद रीसेट करें',
-    powerBy: 'संचालित है',
+    poweredBy: 'संचालित है',
     prompt: 'प्रॉम्प्ट',
     privatePromptConfigTitle: 'संवाद सेटिंग्स',
     publicPromptConfigTitle: 'प्रारंभिक प्रॉम्प्ट',
diff --git a/web/i18n/hi-IN/workflow.ts b/web/i18n/hi-IN/workflow.ts
index 95b77956b4..ac356c2067 100644
--- a/web/i18n/hi-IN/workflow.ts
+++ b/web/i18n/hi-IN/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'वेरिएबल खोजें',
     variableNamePlaceholder: 'वेरिएबल नाम',
     setVarValuePlaceholder: 'वेरिएबल सेट करें',
-    needConnecttip: 'यह चरण किसी से जुड़ा नहीं है',
+    needConnectTip: 'यह चरण किसी से जुड़ा नहीं है',
     maxTreeDepth: 'प्रति शाखा अधिकतम {{depth}} नोड्स की सीमा',
     needEndNode: 'अंत ब्लॉक जोड़ा जाना चाहिए',
     needAnswerNode: 'उत्तर ब्लॉक जोड़ा जाना चाहिए',
@@ -80,6 +80,24 @@ const translation = {
     backupCurrentDraft: 'बैकअप वर्तमान ड्राफ्ट',
     importFailure: 'आयात विफलता',
     importDSLTip: 'वर्तमान ड्राफ्ट ओवरराइट हो जाएगा। आयात करने से पहले वर्कफ़्लो को बैकअप के रूप में निर्यात करें.',
+    parallelTip: {
+      click: {
+        title: 'क्लिक करना',
+        desc: 'जोड़ने के लिए',
+      },
+      drag: {
+        title: 'खींचना',
+        desc: 'कनेक्ट करने के लिए',
+      },
+      limit: 'समांतरता {{num}} शाखाओं तक सीमित है।',
+      depthLimit: '{{num}} परतों की समानांतर नेस्टिंग परत सीमा',
+    },
+    disconnect: 'अलग करना',
+    parallelRun: 'समानांतर रन',
+    jumpToNode: 'इस नोड पर जाएं',
+    addParallelNode: 'समानांतर नोड जोड़ें',
+    parallel: 'समानांतर',
+    branch: 'शाखा',
   },
   env: {
     envPanelTitle: 'पर्यावरण चर',
@@ -427,6 +445,7 @@ const translation = {
         'not empty': 'खाली नहीं है',
         'null': 'शून्य है',
         'not null': 'शून्य नहीं है',
+        'regex match': 'रेगेक्स मैच',
       },
       enterValue: 'मान दर्ज करें',
       addCondition: 'शर्त जोड़ें',
diff --git a/web/i18n/it-IT/app-api.ts b/web/i18n/it-IT/app-api.ts
index a31a771a57..48aceb5c48 100644
--- a/web/i18n/it-IT/app-api.ts
+++ b/web/i18n/it-IT/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: 'Pausa',
   playing: 'In Riproduzione',
   loading: 'Caricamento',
-  merMaind: {
+  merMaid: {
     rerender: 'Rifare il rendering',
   },
   never: 'Mai',
diff --git a/web/i18n/it-IT/app-debug.ts b/web/i18n/it-IT/app-debug.ts
index a4cf7bba2d..e4555b973a 100644
--- a/web/i18n/it-IT/app-debug.ts
+++ b/web/i18n/it-IT/app-debug.ts
@@ -293,7 +293,7 @@ const translation = {
       'La cronologia delle conversazioni deve essere impostata nel prompt',
     queryNoBeEmpty: 'La query deve essere impostata nel prompt',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'Aggiungi Campo Input',
     'editModalTitle': 'Modifica Campo Input',
     'description': 'Impostazione per la variabile {{varName}}',
diff --git a/web/i18n/it-IT/common.ts b/web/i18n/it-IT/common.ts
index 01560b9374..aa675bb471 100644
--- a/web/i18n/it-IT/common.ts
+++ b/web/i18n/it-IT/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Parametri',
     duplicate: 'Duplica',
     rename: 'Rinomina',
+    audioSourceUnavailable: 'AudioSource non è disponibile',
   },
   errorMsg: {
     fieldRequired: '{{field}} è obbligatorio',
@@ -209,7 +210,7 @@ const translation = {
     invitationSentTip:
       'Invito inviato, e possono accedere a Dify per accedere ai dati del tuo team.',
     invitationLink: 'Link di Invito',
-    failedinvitationEmails:
+    failedInvitationEmails:
       'Gli utenti seguenti non sono stati invitati con successo',
     ok: 'OK',
     removeFromTeam: 'Rimuovi dal team',
@@ -218,7 +219,7 @@ const translation = {
     setMember: 'Imposta come membro ordinario',
     setBuilder: 'Imposta come builder',
     setEditor: 'Imposta come editor',
-    disinvite: 'Annulla l\'invito',
+    disInvite: 'Annulla l\'invito',
     deleteMember: 'Elimina Membro',
     you: '(Tu)',
   },
diff --git a/web/i18n/it-IT/dataset-creation.ts b/web/i18n/it-IT/dataset-creation.ts
index 553c8218c4..1629776bf3 100644
--- a/web/i18n/it-IT/dataset-creation.ts
+++ b/web/i18n/it-IT/dataset-creation.ts
@@ -52,7 +52,7 @@ const translation = {
       input: 'Nome della Conoscenza',
       placeholder: 'Per favore inserisci',
       nameNotEmpty: 'Il nome non può essere vuoto',
-      nameLengthInvaild: 'Il nome deve essere tra 1 e 40 caratteri',
+      nameLengthInvalid: 'Il nome deve essere tra 1 e 40 caratteri',
       cancelButton: 'Annulla',
       confirmButton: 'Crea',
       failed: 'Creazione fallita',
@@ -124,8 +124,8 @@ const translation = {
     QATitle: 'Segmentazione in formato Domanda & Risposta',
     QATip: 'Abilitare questa opzione consumerà più token',
     QALanguage: 'Segmenta usando',
-    emstimateCost: 'Stima',
-    emstimateSegment: 'Blocchi stimati',
+    estimateCost: 'Stima',
+    estimateSegment: 'Blocchi stimati',
     segmentCount: 'blocchi',
     calculating: 'Calcolo in corso...',
     fileSource: 'Preprocessa documenti',
@@ -155,8 +155,8 @@ const translation = {
       'L\'anteprima del blocco corrente è in formato testo, il passaggio a un\'anteprima in formato domanda e risposta',
     previewSwitchTipEnd: ' consumerà token aggiuntivi',
     characters: 'caratteri',
-    indexSettedTip: 'Per cambiare il metodo di indicizzazione, vai alle ',
-    retrivalSettedTip: 'Per cambiare il metodo di indicizzazione, vai alle ',
+    indexSettingTip: 'Per cambiare il metodo di indicizzazione, vai alle ',
+    retrievalSettingTip: 'Per cambiare il metodo di indicizzazione, vai alle ',
     datasetSettingLink: 'impostazioni della Conoscenza.',
   },
   stepThree: {
diff --git a/web/i18n/it-IT/dataset.ts b/web/i18n/it-IT/dataset.ts
index 9223a3a96d..fc79c9b4c7 100644
--- a/web/i18n/it-IT/dataset.ts
+++ b/web/i18n/it-IT/dataset.ts
@@ -78,6 +78,7 @@ const translation = {
   nTo1RetrievalLegacy: 'Il recupero N-a-1 sarà ufficialmente deprecato da settembre. Si consiglia di utilizzare il più recente recupero multi-percorso per ottenere risultati migliori.',
   nTo1RetrievalLegacyLink: 'Scopri di più',
   nTo1RetrievalLegacyLinkText: 'Il recupero N-a-1 sarà ufficialmente deprecato a settembre.',
+  defaultRetrievalTip: 'Per impostazione predefinita, il recupero a percorsi multipli viene utilizzato. Le informazioni vengono recuperate da più knowledge base e quindi riclassificate.',
 }
 
 export default translation
diff --git a/web/i18n/it-IT/login.ts b/web/i18n/it-IT/login.ts
index 018f9dca46..b46960aa45 100644
--- a/web/i18n/it-IT/login.ts
+++ b/web/i18n/it-IT/login.ts
@@ -38,7 +38,7 @@ const translation = {
   tosDesc: 'Iscrivendoti, accetti i nostri',
   goToInit:
     'Se non hai inizializzato l\'account, vai alla pagina di inizializzazione',
-  donthave: 'Non hai?',
+  dontHave: 'Non hai?',
   invalidInvitationCode: 'Codice di invito non valido',
   accountAlreadyInited: 'Account già inizializzato',
   forgotPassword: 'Hai dimenticato la password?',
diff --git a/web/i18n/it-IT/share-app.ts b/web/i18n/it-IT/share-app.ts
index b1f99d0ba1..772a6e902d 100644
--- a/web/i18n/it-IT/share-app.ts
+++ b/web/i18n/it-IT/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'L\'app non è disponibile',
-    appUnkonwError: 'L\'app non è disponibile',
+    appUnknownError: 'L\'app non è disponibile',
   },
   chat: {
     newChat: 'Nuova chat',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Chat',
     newChatDefaultName: 'Nuova conversazione',
     resetChat: 'Reimposta conversazione',
-    powerBy: 'Powered by',
+    poweredBy: 'Powered by',
     prompt: 'Prompt',
     privatePromptConfigTitle: 'Impostazioni conversazione',
     publicPromptConfigTitle: 'Prompt iniziale',
diff --git a/web/i18n/it-IT/workflow.ts b/web/i18n/it-IT/workflow.ts
index dbaf2d1280..0427a45cd9 100644
--- a/web/i18n/it-IT/workflow.ts
+++ b/web/i18n/it-IT/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Cerca variabile',
     variableNamePlaceholder: 'Nome variabile',
     setVarValuePlaceholder: 'Imposta variabile',
-    needConnecttip: 'Questo passaggio non è collegato a nulla',
+    needConnectTip: 'Questo passaggio non è collegato a nulla',
     maxTreeDepth: 'Limite massimo di {{depth}} nodi per ramo',
     needEndNode: 'Deve essere aggiunto il blocco di Fine',
     needAnswerNode: 'Deve essere aggiunto il blocco di Risposta',
@@ -81,6 +81,24 @@ const translation = {
     overwriteAndImport: 'Sovrascrivi e Importa',
     importFailure: 'Importazione fallita',
     importSuccess: 'Importazione riuscita',
+    parallelTip: {
+      click: {
+        title: 'Clic',
+        desc: 'per aggiungere',
+      },
+      drag: {
+        title: 'Trascinare',
+        desc: 'per collegare',
+      },
+      depthLimit: 'Limite di livelli di annidamento parallelo di {{num}} livelli',
+      limit: 'Il parallelismo è limitato ai rami {{num}}.',
+    },
+    parallelRun: 'Corsa parallela',
+    disconnect: 'Disconnettere',
+    jumpToNode: 'Vai a questo nodo',
+    addParallelNode: 'Aggiungi nodo parallelo',
+    parallel: 'PARALLELO',
+    branch: 'RAMO',
   },
   env: {
     envPanelTitle: 'Variabili d\'Ambiente',
@@ -431,6 +449,7 @@ const translation = {
         'not empty': 'non è vuoto',
         'null': 'è nullo',
         'not null': 'non è nullo',
+        'regex match': 'Corrispondenza regex',
       },
       enterValue: 'Inserisci valore',
       addCondition: 'Aggiungi Condizione',
diff --git a/web/i18n/ja-JP/app-api.ts b/web/i18n/ja-JP/app-api.ts
index 63beaeb45c..721504f9f3 100644
--- a/web/i18n/ja-JP/app-api.ts
+++ b/web/i18n/ja-JP/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: '一時停止',
   playing: '再生中',
   loading: '読み込み中',
-  merMaind: {
+  merMaid: {
     rerender: '再レンダリング',
   },
   never: 'なし',
diff --git a/web/i18n/ja-JP/app-debug.ts b/web/i18n/ja-JP/app-debug.ts
index 6049be2406..e9ee765435 100644
--- a/web/i18n/ja-JP/app-debug.ts
+++ b/web/i18n/ja-JP/app-debug.ts
@@ -295,7 +295,7 @@ const translation = {
     historyNoBeEmpty: 'プロンプトには会話履歴を設定する必要があります',
     queryNoBeEmpty: 'プロンプトにクエリを設定する必要があります',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': '入力フィールドを追加',
     'editModalTitle': '入力フィールドを編集',
     'description': '{{varName}} の変数設定',
diff --git a/web/i18n/ja-JP/common.ts b/web/i18n/ja-JP/common.ts
index 4d756d4d7b..e2517a619d 100644
--- a/web/i18n/ja-JP/common.ts
+++ b/web/i18n/ja-JP/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'パラメータ',
     duplicate: '重複',
     rename: '名前の変更',
+    audioSourceUnavailable: 'AudioSource が利用できません',
   },
   errorMsg: {
     fieldRequired: '{{field}}は必要です',
@@ -199,7 +200,7 @@ const translation = {
     invitationSent: '招待が送信されました',
     invitationSentTip: '招待が送信され、彼らはDifyにサインインしてあなた様のチームデータにアクセスできます。',
     invitationLink: '招待リンク',
-    failedinvitationEmails: '以下のユーザーは正常に招待されませんでした',
+    failedInvitationEmails: '以下のユーザーは正常に招待されませんでした',
     ok: 'OK',
     removeFromTeam: 'チームから削除',
     removeFromTeamTip: 'チームへのアクセスが削除されます',
@@ -207,7 +208,7 @@ const translation = {
     setMember: '通常のメンバーに設定',
     setBuilder: 'ビルダーに設定',
     setEditor: 'エディターに設定',
-    disinvite: '招待をキャンセル',
+    disInvite: '招待をキャンセル',
     deleteMember: 'メンバーを削除',
     you: '(あなた様)',
   },
diff --git a/web/i18n/ja-JP/dataset-creation.ts b/web/i18n/ja-JP/dataset-creation.ts
index 5c90dcf925..e6d204840a 100644
--- a/web/i18n/ja-JP/dataset-creation.ts
+++ b/web/i18n/ja-JP/dataset-creation.ts
@@ -50,7 +50,7 @@ const translation = {
       input: 'ナレッジ名',
       placeholder: '入力してください',
       nameNotEmpty: '名前は空にできません',
-      nameLengthInvaild: '名前は1〜40文字である必要があります',
+      nameLengthInvalid: '名前は1〜40文字である必要があります',
       cancelButton: 'キャンセル',
       confirmButton: '作成',
       failed: '作成に失敗しました',
@@ -109,8 +109,8 @@ const translation = {
     QATitle: '質問と回答形式でセグメント化',
     QATip: 'このオプションを有効にすると、追加のトークンが消費されます',
     QALanguage: '使用言語',
-    emstimateCost: '見積もり',
-    emstimateSegment: '推定チャンク数',
+    estimateCost: '見積もり',
+    estimateSegment: '推定チャンク数',
     segmentCount: 'チャンク',
     calculating: '計算中...',
     fileSource: 'ドキュメントの前処理',
@@ -135,8 +135,8 @@ const translation = {
     previewSwitchTipStart: '現在のチャンクプレビューはテキスト形式です。質問と回答形式のプレビューに切り替えると、',
     previewSwitchTipEnd: ' 追加のトークンが消費されます',
     characters: '文字',
-    indexSettedTip: 'インデックス方法を変更するには、',
-    retrivalSettedTip: '検索方法を変更するには、',
+    indexSettingTip: 'インデックス方法を変更するには、',
+    retrievalSettingTip: '検索方法を変更するには、',
     datasetSettingLink: 'ナレッジ設定',
   },
   stepThree: {
diff --git a/web/i18n/ja-JP/dataset.ts b/web/i18n/ja-JP/dataset.ts
index d86dec0691..a765473b7e 100644
--- a/web/i18n/ja-JP/dataset.ts
+++ b/web/i18n/ja-JP/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacy: '製品計画によると、N-to-1 Retrievalは9月に正式に廃止される予定です。それまでは通常通り使用できます。',
   nTo1RetrievalLegacyLink: '詳細を見る',
   nTo1RetrievalLegacyLinkText: ' N-to-1 retrievalは9月に正式に廃止されます。',
+  defaultRetrievalTip: 'デフォルトでは、マルチパス取得が使用されます。ナレッジは複数のナレッジ ベースから取得され、再ランク付けされます。',
 }
 
 export default translation
diff --git a/web/i18n/ja-JP/login.ts b/web/i18n/ja-JP/login.ts
index dd01e3b96a..72a26df3db 100644
--- a/web/i18n/ja-JP/login.ts
+++ b/web/i18n/ja-JP/login.ts
@@ -32,7 +32,7 @@ const translation = {
   pp: 'プライバシーポリシー',
   tosDesc: 'サインアップすることで、以下に同意するものとします',
   goToInit: 'アカウントを初期化していない場合は、初期化ページに移動してください',
-  donthave: 'お持ちでない場合',
+  dontHave: 'お持ちでない場合',
   invalidInvitationCode: '無効な招待コード',
   accountAlreadyInited: 'アカウントは既に初期化されています',
   forgotPassword: 'パスワードを忘れましたか？',
diff --git a/web/i18n/ja-JP/share-app.ts b/web/i18n/ja-JP/share-app.ts
index 503972dc48..6b7615c408 100644
--- a/web/i18n/ja-JP/share-app.ts
+++ b/web/i18n/ja-JP/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'アプリが利用できません',
-    appUnkonwError: 'アプリが利用できません',
+    appUnknownError: 'アプリが利用できません',
   },
   chat: {
     newChat: '新しいチャット',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'チャット',
     newChatDefaultName: '新しい会話',
     resetChat: '会話をリセット',
-    powerBy: 'Powered by',
+    poweredBy: 'Powered by',
     prompt: 'プロンプト',
     privatePromptConfigTitle: '会話の設定',
     publicPromptConfigTitle: '初期プロンプト',
diff --git a/web/i18n/ja-JP/workflow.ts b/web/i18n/ja-JP/workflow.ts
index 57d008c28d..48c2019601 100644
--- a/web/i18n/ja-JP/workflow.ts
+++ b/web/i18n/ja-JP/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: '変数を検索',
     variableNamePlaceholder: '変数名',
     setVarValuePlaceholder: '変数を設定',
-    needConnecttip: 'このステップは何にも接続されていません',
+    needConnectTip: 'このステップは何にも接続されていません',
     maxTreeDepth: 'ブランチごとの最大制限は{{depth}}ノードです',
     needEndNode: '終了ブロックを追加する必要があります',
     needAnswerNode: '回答ブロックを追加する必要があります',
@@ -77,6 +77,24 @@ const translation = {
     overwriteAndImport: 'オーバライトとインポート',
     importFailure: 'インポート失敗',
     importSuccess: 'インポート成功',
+    parallelTip: {
+      click: {
+        title: 'クリック',
+        desc: '追加する',
+      },
+      drag: {
+        title: 'ドラッグ',
+        desc: '接続するには',
+      },
+      limit: '並列処理は {{num}} ブランチに制限されています。',
+      depthLimit: '{{num}}レイヤーの平行ネストレイヤーの制限',
+    },
+    parallelRun: 'パラレルラン',
+    disconnect: '切る',
+    jumpToNode: 'このノードにジャンプします',
+    addParallelNode: '並列ノードを追加',
+    parallel: '並列',
+    branch: 'ブランチ',
   },
   env: {
     envPanelTitle: '環境変数',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': '空でない',
         'null': 'null',
         'not null': 'nullでない',
+        'regex match': '正規表現マッチ',
       },
       enterValue: '値を入力',
       addCondition: '条件を追加',
diff --git a/web/i18n/ko-KR/app-api.ts b/web/i18n/ko-KR/app-api.ts
index fc978cddf4..4708fac7dd 100644
--- a/web/i18n/ko-KR/app-api.ts
+++ b/web/i18n/ko-KR/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: '일시 정지',
   playing: '실행 중',
   loading: '로드 중',
-  merMaind: {
+  merMaid: {
     rerender: '다시 렌더링',
   },
   never: '없음',
diff --git a/web/i18n/ko-KR/app-debug.ts b/web/i18n/ko-KR/app-debug.ts
index 0a2488b64c..bafe0bf8d8 100644
--- a/web/i18n/ko-KR/app-debug.ts
+++ b/web/i18n/ko-KR/app-debug.ts
@@ -259,7 +259,7 @@ const translation = {
     historyNoBeEmpty: '프롬프트에 대화 기록을 설정해야 합니다',
     queryNoBeEmpty: '프롬프트에 쿼리를 설정해야 합니다',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': '입력 필드 추가',
     'editModalTitle': '입력 필드 편집',
     'description': '{{varName}} 변수 설정',
diff --git a/web/i18n/ko-KR/common.ts b/web/i18n/ko-KR/common.ts
index 383a26210f..8ef55da3f7 100644
--- a/web/i18n/ko-KR/common.ts
+++ b/web/i18n/ko-KR/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: '매개변수',
     duplicate: '중복',
     rename: '이름 바꾸기',
+    audioSourceUnavailable: '오디오 소스를 사용할 수 없습니다.',
   },
   placeholder: {
     input: '입력해주세요',
@@ -187,14 +188,14 @@ const translation = {
     invitationSent: '초대가 전송되었습니다',
     invitationSentTip: '초대가 전송되었으며, 그들은 Dify에 로그인하여 당신의 팀 데이터에 액세스할 수 있습니다.',
     invitationLink: '초대 링크',
-    failedinvitationEmails: '다음 사용자들은 성공적으로 초대되지 않았습니다',
+    failedInvitationEmails: '다음 사용자들은 성공적으로 초대되지 않았습니다',
     ok: '확인',
     removeFromTeam: '팀에서 제거',
     removeFromTeamTip: '팀 액세스가 제거됩니다',
     setAdmin: '관리자 설정',
     setMember: '일반 멤버 설정',
     setEditor: '편집자 설정',
-    disinvite: '초대 취소',
+    disInvite: '초대 취소',
     deleteMember: '멤버 삭제',
     you: '(나)',
     datasetOperator: '지식 관리자',
diff --git a/web/i18n/ko-KR/dataset-creation.ts b/web/i18n/ko-KR/dataset-creation.ts
index 5ca5ae41da..e8851acd2f 100644
--- a/web/i18n/ko-KR/dataset-creation.ts
+++ b/web/i18n/ko-KR/dataset-creation.ts
@@ -45,7 +45,7 @@ const translation = {
       input: '지식 이름',
       placeholder: '입력하세요',
       nameNotEmpty: '이름은 비워둘 수 없습니다',
-      nameLengthInvaild: '이름은 1~40자여야 합니다',
+      nameLengthInvalid: '이름은 1~40자여야 합니다',
       cancelButton: '취소',
       confirmButton: '생성',
       failed: '생성에 실패했습니다',
@@ -104,8 +104,8 @@ const translation = {
     QATitle: '질문과 답변 형식으로 세그먼트화',
     QATip: '이 옵션을 활성화하면 추가 토큰이 소비됩니다',
     QALanguage: '사용 언어',
-    emstimateCost: '예상 비용',
-    emstimateSegment: '예상 청크 수',
+    estimateCost: '예상 비용',
+    estimateSegment: '예상 청크 수',
     segmentCount: '청크',
     calculating: '계산 중...',
     fileSource: '문서 전처리',
@@ -128,8 +128,8 @@ const translation = {
     previewSwitchTipStart: '현재 청크 미리보기는 텍스트 형식입니다. 질문과 답변 형식 미리보기로 전환하면',
     previewSwitchTipEnd: ' 추가 토큰이 소비됩니다',
     characters: '문자',
-    indexSettedTip: '인덱스 방식을 변경하려면,',
-    retrivalSettedTip: '인덱스 방식을 변경하려면,',
+    indexSettingTip: '인덱스 방식을 변경하려면,',
+    retrievalSettingTip: '인덱스 방식을 변경하려면,',
     datasetSettingLink: '지식 설정',
     webpageUnit: '페이지',
     websiteSource: '웹 사이트 전처리',
diff --git a/web/i18n/ko-KR/dataset.ts b/web/i18n/ko-KR/dataset.ts
index 907a1f21b6..9fd0dd16a7 100644
--- a/web/i18n/ko-KR/dataset.ts
+++ b/web/i18n/ko-KR/dataset.ts
@@ -70,6 +70,7 @@ const translation = {
   nTo1RetrievalLegacy: 'N-대-1 검색은 9월부터 공식적으로 더 이상 사용되지 않습니다. 더 나은 결과를 얻으려면 최신 다중 경로 검색을 사용하는 것이 좋습니다.',
   nTo1RetrievalLegacyLink: '자세히 알아보기',
   nTo1RetrievalLegacyLinkText: 'N-대-1 검색은 9월에 공식적으로 더 이상 사용되지 않습니다.',
+  defaultRetrievalTip: '다중 경로 검색이 기본적으로 사용됩니다. 지식은 여러 기술 자료에서 검색된 다음 순위가 다시 매겨집니다.',
 }
 
 export default translation
diff --git a/web/i18n/ko-KR/login.ts b/web/i18n/ko-KR/login.ts
index a4d6dbcd44..ceddeb7b9b 100644
--- a/web/i18n/ko-KR/login.ts
+++ b/web/i18n/ko-KR/login.ts
@@ -31,7 +31,7 @@ const translation = {
   pp: '개인정보 처리 방침',
   tosDesc: '가입함으로써 다음 내용에 동의하게 됩니다.',
   goToInit: '계정이 초기화되지 않았다면 초기화 페이지로 이동하세요.',
-  donthave: '계정이 없으신가요?',
+  dontHave: '계정이 없으신가요?',
   invalidInvitationCode: '유효하지 않은 초대 코드입니다.',
   accountAlreadyInited: '계정은 이미 초기화되었습니다.',
   forgotPassword: '비밀번호를 잊으셨나요?',
diff --git a/web/i18n/ko-KR/share-app.ts b/web/i18n/ko-KR/share-app.ts
index 9c0738b3d7..be2e34a5fc 100644
--- a/web/i18n/ko-KR/share-app.ts
+++ b/web/i18n/ko-KR/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: '앱을 사용할 수 없습니다',
-    appUnkonwError: '앱을 사용할 수 없습니다',
+    appUnknownError: '앱을 사용할 수 없습니다',
   },
   chat: {
     newChat: '새 채팅',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: '채팅',
     newChatDefaultName: '새 대화',
     resetChat: '대화 재설정',
-    powerBy: 'Powered by',
+    poweredBy: 'Powered by',
     prompt: '프롬프트',
     privatePromptConfigTitle: '채팅 설정',
     publicPromptConfigTitle: '초기 프롬프트',
diff --git a/web/i18n/ko-KR/workflow.ts b/web/i18n/ko-KR/workflow.ts
index a511cc1816..4a97943790 100644
--- a/web/i18n/ko-KR/workflow.ts
+++ b/web/i18n/ko-KR/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: '변수 검색',
     variableNamePlaceholder: '변수 이름',
     setVarValuePlaceholder: '변수 값 설정',
-    needConnecttip: '이 단계는 아무것도 연결되어 있지 않습니다',
+    needConnectTip: '이 단계는 아무것도 연결되어 있지 않습니다',
     maxTreeDepth: '분기당 최대 {{depth}} 노드 제한',
     needEndNode: '종료 블록을 추가해야 합니다',
     needAnswerNode: '답변 블록을 추가해야 합니다',
@@ -77,6 +77,24 @@ const translation = {
     importSuccess: '가져오기 성공',
     syncingData: '단 몇 초 만에 데이터를 동기화할 수 있습니다.',
     importDSLTip: '현재 초안을 덮어씁니다. 가져오기 전에 워크플로를 백업으로 내보냅니다.',
+    parallelTip: {
+      click: {
+        title: '클릭',
+        desc: '추가',
+      },
+      drag: {
+        title: '드래그',
+        desc: '연결 방법',
+      },
+      depthLimit: '평행 중첩 레이어 {{num}}개 레이어의 제한',
+      limit: '병렬 처리는 {{num}}개의 분기로 제한됩니다.',
+    },
+    parallelRun: '병렬 실행',
+    disconnect: '분리하다',
+    jumpToNode: '이 노드로 이동',
+    addParallelNode: '병렬 노드 추가',
+    parallel: '병렬',
+    branch: '브랜치',
   },
   env: {
     envPanelTitle: '환경 변수',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': '비어 있지 않음',
         'null': 'null임',
         'not null': 'null이 아님',
+        'regex match': '정규식 일치',
       },
       enterValue: '값 입력',
       addCondition: '조건 추가',
diff --git a/web/i18n/pl-PL/app-api.ts b/web/i18n/pl-PL/app-api.ts
index 46f9cbb454..05cad56fe2 100644
--- a/web/i18n/pl-PL/app-api.ts
+++ b/web/i18n/pl-PL/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: 'Pauza',
   playing: 'Gra',
   loading: 'Ładowanie',
-  merMaind: {
+  merMaid: {
     rerender: 'Przerób Renderowanie',
   },
   never: 'Nigdy',
diff --git a/web/i18n/pl-PL/app-debug.ts b/web/i18n/pl-PL/app-debug.ts
index afb412f264..7cf6c77cb4 100644
--- a/web/i18n/pl-PL/app-debug.ts
+++ b/web/i18n/pl-PL/app-debug.ts
@@ -289,7 +289,7 @@ const translation = {
     historyNoBeEmpty: 'Historia konwersacji musi być ustawiona w monicie',
     queryNoBeEmpty: 'Zapytanie musi być ustawione w monicie',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'Dodaj Pole Wejściowe',
     'editModalTitle': 'Edytuj Pole Wejściowe',
     'description': 'Ustawienia dla zmiennej {{varName}}',
diff --git a/web/i18n/pl-PL/common.ts b/web/i18n/pl-PL/common.ts
index 56f2a31ea3..91f5fb2899 100644
--- a/web/i18n/pl-PL/common.ts
+++ b/web/i18n/pl-PL/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Parametry',
     duplicate: 'Duplikuj',
     rename: 'Zmień nazwę',
+    audioSourceUnavailable: 'AudioSource jest niedostępny',
   },
   placeholder: {
     input: 'Proszę wprowadzić',
@@ -199,14 +200,14 @@ const translation = {
     invitationSentTip:
       'Zaproszenie zostało wysłane, a oni mogą zalogować się do Dify, aby uzyskać dostęp do danych Twojego zespołu.',
     invitationLink: 'Link zaproszenia',
-    failedinvitationEmails: 'Poniższe osoby nie zostały pomyślnie zaproszone',
+    failedInvitationEmails: 'Poniższe osoby nie zostały pomyślnie zaproszone',
     ok: 'OK',
     removeFromTeam: 'Usuń z zespołu',
     removeFromTeamTip: 'Usunie dostęp do zespołu',
     setAdmin: 'Ustaw jako administratora',
     setMember: 'Ustaw jako zwykłego członka',
     setEditor: 'Ustaw jako edytora',
-    disinvite: 'Anuluj zaproszenie',
+    disInvite: 'Anuluj zaproszenie',
     deleteMember: 'Usuń członka',
     you: '(Ty)',
     datasetOperatorTip: 'Może zarządzać tylko bazą wiedzy',
diff --git a/web/i18n/pl-PL/dataset-creation.ts b/web/i18n/pl-PL/dataset-creation.ts
index e751db7f99..64e50c6b33 100644
--- a/web/i18n/pl-PL/dataset-creation.ts
+++ b/web/i18n/pl-PL/dataset-creation.ts
@@ -46,7 +46,7 @@ const translation = {
       input: 'Nazwa Wiedzy',
       placeholder: 'Proszę wpisz',
       nameNotEmpty: 'Nazwa nie może być pusta',
-      nameLengthInvaild: 'Nazwa musi zawierać od 1 do 40 znaków',
+      nameLengthInvalid: 'Nazwa musi zawierać od 1 do 40 znaków',
       cancelButton: 'Anuluj',
       confirmButton: 'Utwórz',
       failed: 'Utworzenie nie powiodło się',
@@ -112,8 +112,8 @@ const translation = {
     QATitle: 'Segmentacja w formacie pytania i odpowiedzi',
     QATip: 'Włączenie tej opcji spowoduje zużycie większej liczby tokenów',
     QALanguage: 'Segmentacja przy użyciu',
-    emstimateCost: 'Oszacowanie',
-    emstimateSegment: 'Oszacowane bloki',
+    estimateCost: 'Oszacowanie',
+    estimateSegment: 'Oszacowane bloki',
     segmentCount: 'bloki',
     calculating: 'Obliczanie...',
     fileSource: 'Przetwarzaj dokumenty',
@@ -141,8 +141,8 @@ const translation = {
       'Aktulany podgląd bloku jest w formacie tekstu, przełączenie na podgląd w formacie pytania i odpowiedzi spowoduje',
     previewSwitchTipEnd: ' dodatkowe zużycie tokenów',
     characters: 'znaki',
-    indexSettedTip: 'Aby zmienić metodę indeksowania, przejdź do ',
-    retrivalSettedTip: 'Aby zmienić metodę indeksowania, przejdź do ',
+    indexSettingTip: 'Aby zmienić metodę indeksowania, przejdź do ',
+    retrievalSettingTip: 'Aby zmienić metodę indeksowania, przejdź do ',
     datasetSettingLink: 'ustawień Wiedzy.',
     webpageUnit: 'Stron',
     websiteSource: 'Witryna internetowa przetwarzania wstępnego',
diff --git a/web/i18n/pl-PL/dataset.ts b/web/i18n/pl-PL/dataset.ts
index 14de4eaf40..efd8b75c95 100644
--- a/web/i18n/pl-PL/dataset.ts
+++ b/web/i18n/pl-PL/dataset.ts
@@ -77,6 +77,7 @@ const translation = {
   nTo1RetrievalLegacy: 'Wyszukiwanie N-do-1 zostanie oficjalnie wycofane od września. Zaleca się korzystanie z najnowszego wyszukiwania wielościeżkowego, aby uzyskać lepsze wyniki.',
   nTo1RetrievalLegacyLink: 'Dowiedz się więcej',
   nTo1RetrievalLegacyLinkText: 'Wyszukiwanie N-do-1 zostanie oficjalnie wycofane we wrześniu.',
+  defaultRetrievalTip: 'Pobieranie wielu ścieżek jest używane domyślnie. Wiedza jest pobierana z wielu baz wiedzy, a następnie ponownie klasyfikowana.',
 }
 
 export default translation
diff --git a/web/i18n/pl-PL/login.ts b/web/i18n/pl-PL/login.ts
index 40ab94f736..be9e74f37d 100644
--- a/web/i18n/pl-PL/login.ts
+++ b/web/i18n/pl-PL/login.ts
@@ -36,7 +36,7 @@ const translation = {
   pp: 'Polityka prywatności',
   tosDesc: 'Założeniem konta zgadzasz się z naszymi',
   goToInit: 'Jeśli nie zainicjowałeś konta, przejdź do strony inicjalizacji',
-  donthave: 'Nie masz?',
+  dontHave: 'Nie masz?',
   invalidInvitationCode: 'Niewłaściwy kod zaproszenia',
   accountAlreadyInited: 'Konto już zainicjowane',
   forgotPassword: 'Zapomniałeś hasła?',
diff --git a/web/i18n/pl-PL/share-app.ts b/web/i18n/pl-PL/share-app.ts
index eb5573c1a1..90b6ca1929 100644
--- a/web/i18n/pl-PL/share-app.ts
+++ b/web/i18n/pl-PL/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'Aplikacja jest niedostępna',
-    appUnkonwError: 'Aplikacja jest niedostępna',
+    appUnknownError: 'Aplikacja jest niedostępna',
   },
   chat: {
     newChat: 'Nowy czat',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Czaty',
     newChatDefaultName: 'Nowa rozmowa',
     resetChat: 'Resetuj rozmowę',
-    powerBy: 'Działany przez',
+    poweredBy: 'Działany przez',
     prompt: 'Podpowiedź',
     privatePromptConfigTitle: 'Ustawienia rozmowy',
     publicPromptConfigTitle: 'Początkowa podpowiedź',
diff --git a/web/i18n/pl-PL/workflow.ts b/web/i18n/pl-PL/workflow.ts
index daa66a7370..41927668f7 100644
--- a/web/i18n/pl-PL/workflow.ts
+++ b/web/i18n/pl-PL/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Szukaj zmiennej',
     variableNamePlaceholder: 'Nazwa zmiennej',
     setVarValuePlaceholder: 'Ustaw zmienną',
-    needConnecttip: 'Ten krok nie jest połączony z niczym',
+    needConnectTip: 'Ten krok nie jest połączony z niczym',
     maxTreeDepth: 'Maksymalny limit {{depth}} węzłów na gałąź',
     needEndNode: 'Należy dodać blok końcowy',
     needAnswerNode: 'Należy dodać blok odpowiedzi',
@@ -77,6 +77,24 @@ const translation = {
     chooseDSL: 'Wybierz plik DSL(yml)',
     backupCurrentDraft: 'Utwórz kopię zapasową bieżącej wersji roboczej',
     importFailure: 'Niepowodzenie importu',
+    parallelTip: {
+      click: {
+        title: 'Klikać',
+        desc: ', aby dodać',
+      },
+      drag: {
+        title: 'Przeciągnąć',
+        desc: 'aby się połączyć',
+      },
+      limit: 'Równoległość jest ograniczona do gałęzi {{num}}.',
+      depthLimit: 'Limit warstw zagnieżdżania równoległego dla warstw {{num}}',
+    },
+    parallelRun: 'Bieg równoległy',
+    jumpToNode: 'Przejdź do tego węzła',
+    disconnect: 'Odłączyć',
+    addParallelNode: 'Dodaj węzeł równoległy',
+    parallel: 'RÓWNOLEGŁY',
+    branch: 'GAŁĄŹ',
   },
   env: {
     envPanelTitle: 'Zmienne Środowiskowe',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': 'nie jest pusty',
         'null': 'jest null',
         'not null': 'nie jest null',
+        'regex match': 'Dopasowanie wyrażenia regularnego',
       },
       enterValue: 'Wpisz wartość',
       addCondition: 'Dodaj warunek',
diff --git a/web/i18n/pt-BR/app-api.ts b/web/i18n/pt-BR/app-api.ts
index 4c6107b069..7bbd25695d 100644
--- a/web/i18n/pt-BR/app-api.ts
+++ b/web/i18n/pt-BR/app-api.ts
@@ -6,7 +6,7 @@ const translation = {
   ok: 'Em Serviço',
   copy: 'Copiar',
   copied: 'Copiado',
-  merMaind: {
+  merMaid: {
     rerender: 'Refazer Rerender',
   },
   never: 'Nunca',
diff --git a/web/i18n/pt-BR/app-debug.ts b/web/i18n/pt-BR/app-debug.ts
index 9605bd5d95..df4312f887 100644
--- a/web/i18n/pt-BR/app-debug.ts
+++ b/web/i18n/pt-BR/app-debug.ts
@@ -265,7 +265,7 @@ const translation = {
     historyNoBeEmpty: 'O histórico da conversa deve ser definido na solicitação',
     queryNoBeEmpty: 'A consulta deve ser definida na solicitação',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'Adicionar Campo de Entrada',
     'editModalTitle': 'Editar Campo de Entrada',
     'description': 'Configuração para a variável {{varName}}',
diff --git a/web/i18n/pt-BR/billing.ts b/web/i18n/pt-BR/billing.ts
index 134c5e82f4..0a7a964376 100644
--- a/web/i18n/pt-BR/billing.ts
+++ b/web/i18n/pt-BR/billing.ts
@@ -1,77 +1,77 @@
 const translation = {
-  currentPlan: 'Current Plan',
+  currentPlan: 'Plano Atual',
   upgradeBtn: {
-    plain: 'Upgrade Plan',
-    encourage: 'Upgrade Now',
+    plain: 'Fazer Upgrade do Plano',
+    encourage: 'Fazer Upgrade Agora',
     encourageShort: 'Upgrade',
   },
-  viewBilling: 'View billing information',
-  buyPermissionDeniedTip: 'Please contact your enterprise administrator to subscribe',
+  viewBilling: 'Ver informações de cobrança',
+  buyPermissionDeniedTip: 'Por favor, entre em contato com o administrador da sua empresa para assinar',
   plansCommon: {
-    title: 'Choose a plan that’s right for you',
-    yearlyTip: 'Get 2 months for free by subscribing yearly!',
-    mostPopular: 'Most Popular',
+    title: 'Escolha o plano que melhor atende você',
+    yearlyTip: 'Receba 2 meses grátis assinando anualmente!',
+    mostPopular: 'Mais Popular',
     planRange: {
-      monthly: 'Monthly',
-      yearly: 'Yearly',
+      monthly: 'Mensalmente',
+      yearly: 'Anualmente',
     },
-    month: 'month',
-    year: 'year',
-    save: 'Save ',
-    free: 'Free',
-    currentPlan: 'Current Plan',
-    contractOwner: 'Contact team manager',
-    startForFree: 'Start for free',
-    getStartedWith: 'Get started with ',
-    contactSales: 'Contact Sales',
-    talkToSales: 'Talk to Sales',
-    modelProviders: 'Model Providers',
-    teamMembers: 'Team Members',
-    buildApps: 'Build Apps',
-    vectorSpace: 'Vector Space',
-    vectorSpaceBillingTooltip: 'Each 1MB can store about 1.2million characters of vectorized data(estimated using OpenAI Embeddings, varies across models).',
-    vectorSpaceTooltip: 'Vector Space is the long-term memory system required for LLMs  to comprehend your data.',
-    documentProcessingPriority: 'Document Processing Priority',
-    documentProcessingPriorityTip: 'For higher document processing priority, please upgrade your plan.',
-    documentProcessingPriorityUpgrade: 'Process more data with higher accuracy at faster speeds.',
+    month: 'mês',
+    year: 'ano',
+    save: 'Economize ',
+    free: 'Grátis',
+    currentPlan: 'Plano Atual',
+    contractOwner: 'Entre em contato com o gerente da equipe',
+    startForFree: 'Comece de graça',
+    getStartedWith: 'Comece com',
+    contactSales: 'Fale com a equipe de Vendas',
+    talkToSales: 'Fale com a equipe de Vendas',
+    modelProviders: 'Fornecedores de Modelos',
+    teamMembers: 'Membros da Equipe',
+    buildApps: 'Construir Aplicações',
+    vectorSpace: 'Espaço Vetorial',
+    vectorSpaceBillingTooltip: 'Cada 1MB pode armazenar cerca de 1,2 milhão de caracteres de dados vetorizados (estimado usando OpenAI Embeddings, varia entre os modelos).',
+    vectorSpaceTooltip: 'O Espaço Vetorial é o sistema de memória de longo prazo necessário para que LLMs compreendam seus dados.',
+    documentProcessingPriority: 'Prioridade no Processamento de Documentos',
+    documentProcessingPriorityTip: 'Para maior prioridade no processamento de documentos, faça o upgrade do seu plano.',
+    documentProcessingPriorityUpgrade: 'Processe mais dados com maior precisão e velocidade.',
     priority: {
-      'standard': 'Standard',
-      'priority': 'Priority',
-      'top-priority': 'Top Priority',
+      'standard': 'Padrão',
+      'priority': 'Prioridade',
+      'top-priority': 'Prioridade Máxima',
     },
-    logsHistory: 'Logs history',
-    days: 'days',
-    unlimited: 'Unlimited',
-    support: 'Support',
+    logsHistory: 'Histórico de Logs',
+    days: 'dias',
+    unlimited: 'Ilimitado',
+    support: 'Suporte',
     supportItems: {
-      communityForums: 'Community forums',
-      emailSupport: 'Email support',
-      priorityEmail: 'Priority email & chat support',
-      logoChange: 'Logo change',
-      SSOAuthentication: 'SSO authentication',
-      personalizedSupport: 'Personalized support',
-      dedicatedAPISupport: 'Dedicated API support',
-      customIntegration: 'Custom integration and support',
-      ragAPIRequest: 'RAG API Requests',
-      agentModel: 'Agent Model',
+      communityForums: 'Fóruns da Comunidade',
+      emailSupport: 'Suporte por E-mail',
+      priorityEmail: 'Suporte prioritário por e-mail e chat',
+      logoChange: 'Mudança de logo',
+      SSOAuthentication: 'Autenticação SSO',
+      personalizedSupport: 'Suporte personalizado',
+      dedicatedAPISupport: 'Suporte dedicado à API',
+      customIntegration: 'Integração e suporte personalizados',
+      ragAPIRequest: 'Solicitações API RAG',
+      agentModel: 'Modelo de Agente',
       workflow: 'Fluxo de trabalho',
       llmLoadingBalancing: 'Balanceamento de carga LLM',
-      bulkUpload: 'Carregar documentos em massa',
-      llmLoadingBalancingTooltip: 'Adicione várias chaves de API aos modelos, ignorando efetivamente os limites de taxa da API.',
+      bulkUpload: 'Upload em massa de documentos',
+      llmLoadingBalancingTooltip: 'Adicione várias chaves de API aos modelos, efetivamente ignorando os limites de taxa da API. ',
       agentMode: 'Modo Agente',
     },
-    comingSoon: 'Coming soon',
-    member: 'Member',
-    memberAfter: 'Member',
+    comingSoon: 'Em breve',
+    member: 'Membro',
+    memberAfter: 'Membro',
     messageRequest: {
-      title: 'Message Credits',
-      tooltip: 'Message invocation quotas for various plans using OpenAI models (except gpt4).Messages over the limit will use your OpenAI API Key.',
+      title: 'Créditos de Mensagem',
+      tooltip: 'Cotas de invocação de mensagens para vários planos usando modelos da OpenAI (exceto gpt4). Mensagens além do limite usarão sua Chave de API da OpenAI.',
     },
     annotatedResponse: {
-      title: 'Annotation Quota Limits',
-      tooltip: 'Manual editing and annotation of responses provides customizable high-quality question-answering abilities for apps. (Applicable only in chat apps)',
+      title: 'Limites de Cota de Anotação',
+      tooltip: 'A edição manual e anotação de respostas oferece habilidades personalizadas de perguntas e respostas de alta qualidade para aplicativos. (Aplicável apenas em aplicativos de chat)',
     },
-    ragAPIRequestTooltip: 'Refers to the number of API calls invoking only the knowledge base processing capabilities of Dify.',
+    ragAPIRequestTooltip: 'Refere-se ao número de chamadas de API que invocam apenas as capacidades de processamento da base de conhecimento do Dify.',
     receiptInfo: 'Somente proprietários e administradores de equipe podem se inscrever e visualizar informações de cobrança',
     customTools: 'Ferramentas personalizadas',
     documentsUploadQuota: 'Cota de upload de documentos',
@@ -82,37 +82,37 @@ const translation = {
   plans: {
     sandbox: {
       name: 'Sandbox',
-      description: '200 times GPT free trial',
-      includesTitle: 'Includes:',
+      description: '200 vezes GPT de teste gratuito',
+      includesTitle: 'Inclui:',
     },
     professional: {
-      name: 'Professional',
-      description: 'For individuals and small teams to unlock more power affordably.',
-      includesTitle: 'Everything in free plan, plus:',
+      name: 'Profissional',
+      description: 'Para indivíduos e pequenas equipes desbloquearem mais poder de forma acessível.',
+      includesTitle: 'Tudo no plano gratuito, além de:',
     },
     team: {
-      name: 'Team',
-      description: 'Collaborate without limits and enjoy top-tier performance.',
-      includesTitle: 'Everything in Professional plan, plus:',
+      name: 'Equipe',
+      description: 'Colabore sem limites e aproveite o desempenho de primeira linha.',
+      includesTitle: 'Tudo no plano Profissional, além de:',
     },
     enterprise: {
-      name: 'Enterprise',
-      description: 'Get full capabilities and support for large-scale mission-critical systems.',
-      includesTitle: 'Everything in Team plan, plus:',
+      name: 'Empresa',
+      description: 'Obtenha capacidades completas e suporte para sistemas críticos em larga escala.',
+      includesTitle: 'Tudo no plano Equipe, além de:',
     },
   },
   vectorSpace: {
-    fullTip: 'Vector Space is full.',
-    fullSolution: 'Upgrade your plan to get more space.',
+    fullTip: 'O Espaço Vetorial está cheio.',
+    fullSolution: 'Faça o upgrade do seu plano para obter mais espaço.',
   },
   apps: {
-    fullTipLine1: 'Upgrade your plan to',
-    fullTipLine2: 'build more apps.',
+    fullTipLine1: 'Faça o upgrade do seu plano para',
+    fullTipLine2: 'construir mais aplicativos.',
   },
   annotatedResponse: {
-    fullTipLine1: 'Upgrade your plan to',
-    fullTipLine2: 'annotate more conversations.',
-    quotaTitle: 'Annotation Reply Quota',
+    fullTipLine1: 'Faça o upgrade do seu plano para',
+    fullTipLine2: 'anotar mais conversas.',
+    quotaTitle: 'Cota de Respostas Anotadas',
   },
 }
 
diff --git a/web/i18n/pt-BR/common.ts b/web/i18n/pt-BR/common.ts
index 6cd62565f1..f9e9eb7888 100644
--- a/web/i18n/pt-BR/common.ts
+++ b/web/i18n/pt-BR/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Parâmetros',
     duplicate: 'Duplicada',
     rename: 'Renomear',
+    audioSourceUnavailable: 'AudioSource não está disponível',
   },
   placeholder: {
     input: 'Por favor, insira',
@@ -191,14 +192,14 @@ const translation = {
     invitationSent: 'Convite enviado',
     invitationSentTip: 'Convite enviado e eles podem fazer login no Dify para acessar os dados da sua equipe.',
     invitationLink: 'Link do Convite',
-    failedinvitationEmails: 'Os seguintes usuários não foram convidados com sucesso',
+    failedInvitationEmails: 'Os seguintes usuários não foram convidados com sucesso',
     ok: 'OK',
     removeFromTeam: 'Remover da equipe',
     removeFromTeamTip: 'Removerá o acesso da equipe',
     setAdmin: 'Definir como administrador',
     setMember: 'Definir como membro comum',
     setEditor: 'Definir como editor',
-    disinvite: 'Cancelar o convite',
+    disInvite: 'Cancelar o convite',
     deleteMember: 'Excluir Membro',
     you: '(Você)',
     datasetOperatorTip: 'Só pode gerenciar a base de dados de conhecimento',
diff --git a/web/i18n/pt-BR/dataset-creation.ts b/web/i18n/pt-BR/dataset-creation.ts
index e7e7fe40fa..4ab78a50c7 100644
--- a/web/i18n/pt-BR/dataset-creation.ts
+++ b/web/i18n/pt-BR/dataset-creation.ts
@@ -45,7 +45,7 @@ const translation = {
       input: 'Nome do Conhecimento',
       placeholder: 'Por favor, insira',
       nameNotEmpty: 'O nome não pode estar vazio',
-      nameLengthInvaild: 'O nome deve ter entre 1 e 40 caracteres',
+      nameLengthInvalid: 'O nome deve ter entre 1 e 40 caracteres',
       cancelButton: 'Cancelar',
       confirmButton: 'Criar',
       failed: 'Falha na criação',
@@ -104,8 +104,8 @@ const translation = {
     QATitle: 'Fragmentação no formato de Perguntas e Respostas',
     QATip: 'Habilitar esta opção consumirá mais tokens',
     QALanguage: 'Fragmentar usando',
-    emstimateCost: 'Estimativa',
-    emstimateSegment: 'Fragmentos estimados',
+    estimateCost: 'Estimativa',
+    estimateSegment: 'Fragmentos estimados',
     segmentCount: 'fragmentos',
     calculating: 'Calculando...',
     fileSource: 'Pré-processar documentos',
@@ -128,8 +128,8 @@ const translation = {
     previewSwitchTipStart: 'A visualização atual do fragmento está no formato de texto, alternar para uma visualização no formato de Perguntas e Respostas irá',
     previewSwitchTipEnd: ' consumir tokens adicionais',
     characters: 'caracteres',
-    indexSettedTip: 'Para alterar o método de índice, por favor vá para as ',
-    retrivalSettedTip: 'Para alterar o método de índice, por favor vá para as ',
+    indexSettingTip: 'Para alterar o método de índice, por favor vá para as ',
+    retrievalSettingTip: 'Para alterar o método de índice, por favor vá para as ',
     datasetSettingLink: 'configurações do Conhecimento.',
     websiteSource: 'Site de pré-processamento',
     webpageUnit: 'Páginas',
diff --git a/web/i18n/pt-BR/dataset.ts b/web/i18n/pt-BR/dataset.ts
index 51d3ad1b44..3feeb99a8b 100644
--- a/web/i18n/pt-BR/dataset.ts
+++ b/web/i18n/pt-BR/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacyLink: 'Saiba mais',
   nTo1RetrievalLegacyLinkText: 'A recuperação N-para-1 será oficialmente descontinuada em setembro.',
   intro6: 'como um plug-in de índice ChatGPT autônomo para publicar',
+  defaultRetrievalTip: 'A recuperação de vários caminhos é usada por padrão. O conhecimento é recuperado de várias bases de dados de conhecimento e, em seguida, reclassificado.',
 }
 
 export default translation
diff --git a/web/i18n/pt-BR/login.ts b/web/i18n/pt-BR/login.ts
index 2c68916334..a1f278dba8 100644
--- a/web/i18n/pt-BR/login.ts
+++ b/web/i18n/pt-BR/login.ts
@@ -31,7 +31,7 @@ const translation = {
   pp: 'Política de Privacidade',
   tosDesc: 'Ao se inscrever, você concorda com nossos',
   goToInit: 'Se você não inicializou a conta, vá para a página de inicialização',
-  donthave: 'Não tem?',
+  dontHave: 'Não tem?',
   invalidInvitationCode: 'Código de convite inválido',
   accountAlreadyInited: 'Conta já iniciada',
   forgotPassword: 'Esqueceu sua senha?',
diff --git a/web/i18n/pt-BR/share-app.ts b/web/i18n/pt-BR/share-app.ts
index 27baf35275..1e1861e01b 100644
--- a/web/i18n/pt-BR/share-app.ts
+++ b/web/i18n/pt-BR/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'O aplicativo não está disponível',
-    appUnkonwError: 'O aplicativo encontrou um erro desconhecido',
+    appUnknownError: 'O aplicativo encontrou um erro desconhecido',
   },
   chat: {
     newChat: 'Nova conversa',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Conversas',
     newChatDefaultName: 'Nova conversa',
     resetChat: 'Redefinir conversa',
-    powerBy: 'Desenvolvido por',
+    poweredBy: 'Desenvolvido por',
     prompt: 'Prompt',
     privatePromptConfigTitle: 'Configurações da conversa',
     publicPromptConfigTitle: 'Prompt inicial',
diff --git a/web/i18n/pt-BR/workflow.ts b/web/i18n/pt-BR/workflow.ts
index 2c44ad1434..222fc788bf 100644
--- a/web/i18n/pt-BR/workflow.ts
+++ b/web/i18n/pt-BR/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Buscar variável',
     variableNamePlaceholder: 'Nome da variável',
     setVarValuePlaceholder: 'Definir valor da variável',
-    needConnecttip: 'Este passo não está conectado a nada',
+    needConnectTip: 'Este passo não está conectado a nada',
     maxTreeDepth: 'Limite máximo de {{depth}} nós por ramo',
     needEndNode: 'O bloco de fim deve ser adicionado',
     needAnswerNode: 'O bloco de resposta deve ser adicionado',
@@ -77,6 +77,24 @@ const translation = {
     importDSLTip: 'O rascunho atual será substituído. Exporte o fluxo de trabalho como backup antes de importar.',
     backupCurrentDraft: 'Fazer backup do rascunho atual',
     importDSL: 'Importar DSL',
+    parallelTip: {
+      click: {
+        title: 'Clique',
+        desc: 'para adicionar',
+      },
+      drag: {
+        title: 'Arrastar',
+        desc: 'para conectar',
+      },
+      limit: 'O paralelismo é limitado a {{num}} ramificações.',
+      depthLimit: 'Limite de camada de aninhamento paralelo de {{num}} camadas',
+    },
+    parallelRun: 'Execução paralela',
+    disconnect: 'Desligar',
+    jumpToNode: 'Ir para este nó',
+    addParallelNode: 'Adicionar nó paralelo',
+    parallel: 'PARALELO',
+    branch: 'RAMIFICAÇÃO',
   },
   env: {
     envPanelTitle: 'Variáveis de Ambiente',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': 'não está vazio',
         'null': 'é nulo',
         'not null': 'não é nulo',
+        'regex match': 'partida regex',
       },
       enterValue: 'Digite o valor',
       addCondition: 'Adicionar condição',
diff --git a/web/i18n/ro-RO/app-api.ts b/web/i18n/ro-RO/app-api.ts
index 0b86ec6976..e6a52ade42 100644
--- a/web/i18n/ro-RO/app-api.ts
+++ b/web/i18n/ro-RO/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: 'Pauză',
   playing: 'În redare',
   loading: 'Se încarcă',
-  merMaind: {
+  merMaid: {
     rerender: 'Reprocesare',
   },
   never: 'Niciodată',
diff --git a/web/i18n/ro-RO/app-debug.ts b/web/i18n/ro-RO/app-debug.ts
index 7363f2954f..bafeee8bb0 100644
--- a/web/i18n/ro-RO/app-debug.ts
+++ b/web/i18n/ro-RO/app-debug.ts
@@ -265,7 +265,7 @@ const translation = {
     historyNoBeEmpty: 'Istoricul conversației trebuie setat în prompt',
     queryNoBeEmpty: 'Interogația trebuie setată în prompt',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'Adăugați câmp de intrare',
     'editModalTitle': 'Editați câmpul de intrare',
     'description': 'Setare pentru variabila {{varName}}',
diff --git a/web/i18n/ro-RO/common.ts b/web/i18n/ro-RO/common.ts
index 6720ec331b..1fd8778106 100644
--- a/web/i18n/ro-RO/common.ts
+++ b/web/i18n/ro-RO/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Parametri',
     duplicate: 'Duplică',
     rename: 'Redenumește',
+    audioSourceUnavailable: 'Sursa audio nu este disponibilă',
   },
   placeholder: {
     input: 'Vă rugăm să introduceți',
@@ -191,14 +192,14 @@ const translation = {
     invitationSent: 'Invitație trimisă',
     invitationSentTip: 'Invitația a fost trimisă și pot să se autentifice în Dify pentru a accesa datele echipei dvs.',
     invitationLink: 'Link de invitație',
-    failedinvitationEmails: 'Următorii utilizatori nu au fost invitați cu succes',
+    failedInvitationEmails: 'Următorii utilizatori nu au fost invitați cu succes',
     ok: 'OK',
     removeFromTeam: 'Elimină din echipă',
     removeFromTeamTip: 'Va elimina accesul la echipă',
     setAdmin: 'Setează ca administrator',
     setMember: 'Setează ca membru obișnuit',
     setEditor: 'Setează ca editor',
-    disinvite: 'Anulează invitația',
+    disInvite: 'Anulează invitația',
     deleteMember: 'Șterge membru',
     you: '(Dvs.)',
     datasetOperatorTip: 'Numai poate gestiona baza de cunoștințe',
diff --git a/web/i18n/ro-RO/dataset-creation.ts b/web/i18n/ro-RO/dataset-creation.ts
index ae1adbc8b2..efe3bb246c 100644
--- a/web/i18n/ro-RO/dataset-creation.ts
+++ b/web/i18n/ro-RO/dataset-creation.ts
@@ -45,7 +45,7 @@ const translation = {
       input: 'Numele Cunoștinței',
       placeholder: 'Vă rugăm să introduceți',
       nameNotEmpty: 'Numele nu poate fi gol',
-      nameLengthInvaild: 'Numele trebuie să fie între 1 și 40 de caractere',
+      nameLengthInvalid: 'Numele trebuie să fie între 1 și 40 de caractere',
       cancelButton: 'Anulează',
       confirmButton: 'Creează',
       failed: 'Crearea a eșuat',
@@ -104,8 +104,8 @@ const translation = {
     QATitle: 'Segmentarea în format Întrebare și Răspuns',
     QATip: 'Activarea acestei opțiuni va consuma mai multe jetoane',
     QALanguage: 'Segmentează folosind',
-    emstimateCost: 'Estimare',
-    emstimateSegment: 'Segmente estimate',
+    estimateCost: 'Estimare',
+    estimateSegment: 'Segmente estimate',
     segmentCount: 'segmente',
     calculating: 'Se calculează...',
     fileSource: 'Prelucrează documente',
@@ -128,8 +128,8 @@ const translation = {
     previewSwitchTipStart: 'Previzualizarea curentă a segmentului este în format text, comutarea la o previzualizare în format întrebare și răspuns va',
     previewSwitchTipEnd: ' consuma jetoane suplimentare',
     characters: 'caractere',
-    indexSettedTip: 'Pentru a modifica metoda de indexare, vă rugăm să mergeți la ',
-    retrivalSettedTip: 'Pentru a modifica metoda de indexare, vă rugăm să mergeți la ',
+    indexSettingTip: 'Pentru a modifica metoda de indexare, vă rugăm să mergeți la ',
+    retrievalSettingTip: 'Pentru a modifica metoda de indexare, vă rugăm să mergeți la ',
     datasetSettingLink: 'setările Cunoștinței.',
     webpageUnit: 'Pagini',
     websiteSource: 'Site-ul web de preprocesare',
diff --git a/web/i18n/ro-RO/dataset.ts b/web/i18n/ro-RO/dataset.ts
index 3e605baf92..baf54f68e6 100644
--- a/web/i18n/ro-RO/dataset.ts
+++ b/web/i18n/ro-RO/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacy: 'Recuperarea N-la-1 va fi oficial depreciată din septembrie. Se recomandă utilizarea celei mai recente recuperări cu căi multiple pentru a obține rezultate mai bune.',
   nTo1RetrievalLegacyLink: 'Află mai multe',
   nTo1RetrievalLegacyLinkText: 'Recuperarea N-la-1 va fi oficial depreciată în septembrie.',
+  defaultRetrievalTip: 'Recuperarea pe mai multe căi este utilizată în mod implicit. Cunoștințele sunt preluate din mai multe baze de cunoștințe și apoi reclasificate.',
 }
 
 export default translation
diff --git a/web/i18n/ro-RO/login.ts b/web/i18n/ro-RO/login.ts
index b692b7c386..6a8d899b33 100644
--- a/web/i18n/ro-RO/login.ts
+++ b/web/i18n/ro-RO/login.ts
@@ -32,7 +32,7 @@ const translation = {
   pp: 'Politica de confidențialitate',
   tosDesc: 'Prin înregistrarea, ești de acord cu',
   goToInit: 'Dacă nu ai inițializat încă contul, te rugăm să mergi la pagina de inițializare',
-  donthave: 'Nu ai?',
+  dontHave: 'Nu ai?',
   invalidInvitationCode: 'Cod de invitație invalid',
   accountAlreadyInited: 'Contul este deja inițializat',
   forgotPassword: 'Ați uitat parola?',
diff --git a/web/i18n/ro-RO/share-app.ts b/web/i18n/ro-RO/share-app.ts
index 06cf083a04..c9ec36ab03 100644
--- a/web/i18n/ro-RO/share-app.ts
+++ b/web/i18n/ro-RO/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'Aplicația nu este disponibilă',
-    appUnkonwError: 'Aplicația nu este disponibilă',
+    appUnknownError: 'Aplicația nu este disponibilă',
   },
   chat: {
     newChat: 'Chat nou',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Conversații',
     newChatDefaultName: 'Conversație nouă',
     resetChat: 'Resetează conversația',
-    powerBy: 'Furnizat de',
+    poweredBy: 'Furnizat de',
     prompt: 'Sugestie',
     privatePromptConfigTitle: 'Setări conversație',
     publicPromptConfigTitle: 'Sugestie inițială',
diff --git a/web/i18n/ro-RO/workflow.ts b/web/i18n/ro-RO/workflow.ts
index 0bfe38c0a5..ac4b718b07 100644
--- a/web/i18n/ro-RO/workflow.ts
+++ b/web/i18n/ro-RO/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Caută variabilă',
     variableNamePlaceholder: 'Nume variabilă',
     setVarValuePlaceholder: 'Setează valoarea variabilei',
-    needConnecttip: 'Acest pas nu este conectat la nimic',
+    needConnectTip: 'Acest pas nu este conectat la nimic',
     maxTreeDepth: 'Limită maximă de {{depth}} noduri pe ramură',
     needEndNode: 'Trebuie adăugat blocul de sfârșit',
     needAnswerNode: 'Trebuie adăugat blocul de răspuns',
@@ -77,6 +77,24 @@ const translation = {
     importSuccess: 'Succesul importului',
     backupCurrentDraft: 'Backup curent draft',
     importDSLTip: 'Proiectul curent va fi suprascris. Exportați fluxul de lucru ca backup înainte de import.',
+    parallelTip: {
+      click: {
+        title: 'Clic',
+        desc: 'pentru a adăuga',
+      },
+      drag: {
+        title: 'Glisa',
+        desc: 'pentru a vă conecta',
+      },
+      depthLimit: 'Limita straturilor de imbricare paralelă a {{num}} straturi',
+      limit: 'Paralelismul este limitat la {{num}} ramuri.',
+    },
+    parallelRun: 'Rulare paralelă',
+    disconnect: 'Deconecta',
+    jumpToNode: 'Sari la acest nod',
+    addParallelNode: 'Adăugare nod paralel',
+    parallel: 'PARALEL',
+    branch: 'RAMURĂ',
   },
   env: {
     envPanelTitle: 'Variabile de Mediu',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': 'nu este gol',
         'null': 'este null',
         'not null': 'nu este null',
+        'regex match': 'potrivire regex',
       },
       enterValue: 'Introduceți valoarea',
       addCondition: 'Adăugați condiție',
diff --git a/web/i18n/ru-RU/app-api.ts b/web/i18n/ru-RU/app-api.ts
index 064621b622..0c56d7a501 100644
--- a/web/i18n/ru-RU/app-api.ts
+++ b/web/i18n/ru-RU/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: 'Приостановить',
   playing: 'Запущено',
   loading: 'Загрузка',
-  merMaind: {
+  merMaid: {
     rerender: 'Перезапустить рендеринг',
   },
   never: 'Никогда',
diff --git a/web/i18n/ru-RU/app-debug.ts b/web/i18n/ru-RU/app-debug.ts
index e976c11aba..038165301e 100644
--- a/web/i18n/ru-RU/app-debug.ts
+++ b/web/i18n/ru-RU/app-debug.ts
@@ -301,7 +301,7 @@ const translation = {
     historyNoBeEmpty: 'История разговоров должна быть установлена в промпте',
     queryNoBeEmpty: 'Запрос должен быть установлен в промпте',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'Добавить поле ввода',
     'editModalTitle': 'Редактировать поле ввода',
     'description': 'Настройка для переменной {{varName}}',
diff --git a/web/i18n/ru-RU/common.ts b/web/i18n/ru-RU/common.ts
index 25a3d1031a..82e3471e60 100644
--- a/web/i18n/ru-RU/common.ts
+++ b/web/i18n/ru-RU/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Параметры',
     duplicate: 'Дублировать',
     rename: 'Переименовать',
+    audioSourceUnavailable: 'AudioSource недоступен',
   },
   errorMsg: {
     fieldRequired: '{{field}} обязательно',
@@ -199,7 +200,7 @@ const translation = {
     invitationSent: 'Приглашение отправлено',
     invitationSentTip: 'Приглашение отправлено, и они могут войти в Dify, чтобы получить доступ к данным вашей команды.',
     invitationLink: 'Ссылка для приглашения',
-    failedinvitationEmails: 'Следующие пользователи не были успешно приглашены',
+    failedInvitationEmails: 'Следующие пользователи не были успешно приглашены',
     ok: 'ОК',
     removeFromTeam: 'Удалить из команды',
     removeFromTeamTip: 'Удалить доступ к команде',
@@ -207,7 +208,7 @@ const translation = {
     setMember: 'Назначить обычным участником',
     setBuilder: 'Назначить разработчиком',
     setEditor: 'Назначить редактором',
-    disinvite: 'Отменить приглашение',
+    disInvite: 'Отменить приглашение',
     deleteMember: 'Удалить участника',
     you: '(Вы)',
   },
diff --git a/web/i18n/ru-RU/dataset-creation.ts b/web/i18n/ru-RU/dataset-creation.ts
index 63c0d89cf9..c4dce774d8 100644
--- a/web/i18n/ru-RU/dataset-creation.ts
+++ b/web/i18n/ru-RU/dataset-creation.ts
@@ -50,7 +50,7 @@ const translation = {
       input: 'Название базы знаний',
       placeholder: 'Пожалуйста, введите',
       nameNotEmpty: 'Название не может быть пустым',
-      nameLengthInvaild: 'Название должно быть от 1 до 40 символов',
+      nameLengthInvalid: 'Название должно быть от 1 до 40 символов',
       cancelButton: 'Отмена',
       confirmButton: 'Создать',
       failed: 'Ошибка создания',
@@ -109,8 +109,8 @@ const translation = {
     QATitle: 'Сегментация в формате вопрос-ответ',
     QATip: 'Включение этой опции приведет к потреблению большего количества токенов',
     QALanguage: 'Сегментировать с помощью',
-    emstimateCost: 'Оценка',
-    emstimateSegment: 'Оценочное количество фрагментов',
+    estimateCost: 'Оценка',
+    estimateSegment: 'Оценочное количество фрагментов',
     segmentCount: 'фрагментов',
     calculating: 'Вычисление...',
     fileSource: 'Предварительная обработка документов',
@@ -135,8 +135,8 @@ const translation = {
     previewSwitchTipStart: 'Текущий предварительный просмотр фрагмента находится в текстовом формате, переключение на предварительный просмотр в формате вопрос-ответ',
     previewSwitchTipEnd: ' потребляет дополнительные токены',
     characters: 'символов',
-    indexSettedTip: 'Чтобы изменить метод индексации, пожалуйста, перейдите в ',
-    retrivalSettedTip: 'Чтобы изменить метод индексации, пожалуйста, перейдите в ',
+    indexSettingTip: 'Чтобы изменить метод индексации, пожалуйста, перейдите в ',
+    retrievalSettingTip: 'Чтобы изменить метод индексации, пожалуйста, перейдите в ',
     datasetSettingLink: 'настройки базы знаний.',
   },
   stepThree: {
diff --git a/web/i18n/ru-RU/dataset.ts b/web/i18n/ru-RU/dataset.ts
index 4e395b5487..4a7590ad60 100644
--- a/web/i18n/ru-RU/dataset.ts
+++ b/web/i18n/ru-RU/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacy: 'Поиск N-к-1 будет официально прекращен с сентября. Рекомендуется использовать новейший многопутный поиск для получения лучших результатов.',
   nTo1RetrievalLegacyLink: 'Узнать больше',
   nTo1RetrievalLegacyLinkText: ' Поиск N-к-1 будет официально прекращен в сентябре.',
+  defaultRetrievalTip: 'По умолчанию используется многоканальная проверка. Знания извлекаются из нескольких баз знаний, а затем повторно ранжируются.',
 }
 
 export default translation
diff --git a/web/i18n/ru-RU/login.ts b/web/i18n/ru-RU/login.ts
index 3d8e3f320e..81918745dd 100644
--- a/web/i18n/ru-RU/login.ts
+++ b/web/i18n/ru-RU/login.ts
@@ -32,7 +32,7 @@ const translation = {
   pp: 'Политика конфиденциальности',
   tosDesc: 'Регистрируясь, вы соглашаетесь с нашими',
   goToInit: 'Если вы не инициализировали учетную запись, перейдите на страницу инициализации',
-  donthave: 'Нет?',
+  dontHave: 'Нет?',
   invalidInvitationCode: 'Неверный пригласительный код',
   accountAlreadyInited: 'Учетная запись уже инициализирована',
   forgotPassword: 'Забыли пароль?',
diff --git a/web/i18n/ru-RU/share-app.ts b/web/i18n/ru-RU/share-app.ts
index 6c06a49cef..f0166b26f1 100644
--- a/web/i18n/ru-RU/share-app.ts
+++ b/web/i18n/ru-RU/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'Приложение недоступно',
-    appUnkonwError: 'Приложение недоступно',
+    appUnknownError: 'Приложение недоступно',
   },
   chat: {
     newChat: 'Новый чат',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Чаты',
     newChatDefaultName: 'Новый разговор',
     resetChat: 'Сбросить разговор',
-    powerBy: 'Работает на',
+    poweredBy: 'Работает на',
     prompt: 'Подсказка',
     privatePromptConfigTitle: 'Настройки разговора',
     publicPromptConfigTitle: 'Начальная подсказка',
diff --git a/web/i18n/ru-RU/workflow.ts b/web/i18n/ru-RU/workflow.ts
index 54a88fd876..1931863895 100644
--- a/web/i18n/ru-RU/workflow.ts
+++ b/web/i18n/ru-RU/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Поиск переменной',
     variableNamePlaceholder: 'Имя переменной',
     setVarValuePlaceholder: 'Установить значение переменной',
-    needConnecttip: 'Этот шаг ни к чему не подключен',
+    needConnectTip: 'Этот шаг ни к чему не подключен',
     maxTreeDepth: 'Максимальный предел {{depth}} узлов на ветку',
     needEndNode: 'Необходимо добавить блок "Конец"',
     needAnswerNode: 'Необходимо добавить блок "Ответ"',
@@ -77,6 +77,24 @@ const translation = {
     overwriteAndImport: 'Перезаписать и импортировать',
     importFailure: 'Ошибка импорта',
     importSuccess: 'Импорт успешно завершен',
+    parallelTip: {
+      click: {
+        title: 'Щелчок',
+        desc: 'добавить',
+      },
+      drag: {
+        title: 'Волочить',
+        desc: 'для подключения',
+      },
+      limit: 'Параллелизм ограничен ветвями {{num}}.',
+      depthLimit: 'Ограничение на количество слоев параллельной вложенности {{num}}',
+    },
+    parallelRun: 'Параллельный прогон',
+    disconnect: 'Разъединять',
+    jumpToNode: 'Перейти к этому узлу',
+    addParallelNode: 'Добавить параллельный узел',
+    parallel: 'ПАРАЛЛЕЛЬНЫЙ',
+    branch: 'ВЕТКА',
   },
   env: {
     envPanelTitle: 'Переменные среды',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': 'не пусто',
         'null': 'null',
         'not null': 'не null',
+        'regex match': 'Совпадение с регулярным выражением',
       },
       enterValue: 'Введите значение',
       addCondition: 'Добавить условие',
diff --git a/web/i18n/tr-TR/app-api.ts b/web/i18n/tr-TR/app-api.ts
index be6466f001..9a64de546b 100644
--- a/web/i18n/tr-TR/app-api.ts
+++ b/web/i18n/tr-TR/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: 'Duraklat',
   playing: 'Oynatılıyor',
   loading: 'Yükleniyor',
-  merMaind: {
+  merMaid: {
     rerender: 'Yeniden İşleme',
   },
   never: 'Asla',
diff --git a/web/i18n/tr-TR/app-debug.ts b/web/i18n/tr-TR/app-debug.ts
index fbf51535fe..f08d221d45 100644
--- a/web/i18n/tr-TR/app-debug.ts
+++ b/web/i18n/tr-TR/app-debug.ts
@@ -301,7 +301,7 @@ const translation = {
     historyNoBeEmpty: 'Konuşma geçmişi prompt\'ta ayarlanmalıdır',
     queryNoBeEmpty: 'Sorgu prompt\'ta ayarlanmalıdır',
   },
-  variableConig: {
+  variableConfig: {
     addModalTitle: 'Giriş Alanı Ekle',
     editModalTitle: 'Giriş Alanı Düzenle',
     description: 'Değişken ayarı {{varName}}',
diff --git a/web/i18n/tr-TR/common.ts b/web/i18n/tr-TR/common.ts
index b0492b557a..a41925cd20 100644
--- a/web/i18n/tr-TR/common.ts
+++ b/web/i18n/tr-TR/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Parametreler',
     duplicate: 'Çoğalt',
     rename: 'Yeniden Adlandır',
+    audioSourceUnavailable: 'AudioSource kullanılamıyor',
   },
   errorMsg: {
     fieldRequired: '{{field}} gereklidir',
@@ -199,7 +200,7 @@ const translation = {
     invitationSent: 'Davet gönderildi',
     invitationSentTip: 'Davet gönderildi, Dify\'ye giriş yaparak takım verilerinize erişebilirler.',
     invitationLink: 'Davet Linki',
-    failedinvitationEmails: 'Aşağıdaki kullanıcılar başarıyla davet edilmedi',
+    failedInvitationEmails: 'Aşağıdaki kullanıcılar başarıyla davet edilmedi',
     ok: 'Tamam',
     removeFromTeam: 'Takımdan Kaldır',
     removeFromTeamTip: 'Takım erişimi kaldırılacak',
@@ -207,7 +208,7 @@ const translation = {
     setMember: 'Normal üye olarak ayarla',
     setBuilder: 'Oluşturucu olarak ayarla',
     setEditor: 'Editör olarak ayarla',
-    disinvite: 'Davetiyeyi iptal et',
+    disInvite: 'Davetiyeyi iptal et',
     deleteMember: 'Üyeyi Sil',
     you: '(Siz)',
   },
diff --git a/web/i18n/tr-TR/dataset-creation.ts b/web/i18n/tr-TR/dataset-creation.ts
index b35cbc26b2..b26608c39f 100644
--- a/web/i18n/tr-TR/dataset-creation.ts
+++ b/web/i18n/tr-TR/dataset-creation.ts
@@ -50,7 +50,7 @@ const translation = {
       input: 'Bilgi adı',
       placeholder: 'Lütfen girin',
       nameNotEmpty: 'Ad boş olamaz',
-      nameLengthInvaild: 'Ad 1 ile 40 karakter arasında olmalıdır',
+      nameLengthInvalid: 'Ad 1 ile 40 karakter arasında olmalıdır',
       cancelButton: 'İptal',
       confirmButton: 'Oluştur',
       failed: 'Oluşturma başarısız',
@@ -109,8 +109,8 @@ const translation = {
     QATitle: 'Soru ve Yanıt formatında parçalama',
     QATip: 'Bu seçeneği etkinleştirmek daha fazla token tüketecektir',
     QALanguage: 'Kullanarak parçalara ayır',
-    emstimateCost: 'Tahmin',
-    emstimateSegment: 'Tahmini parçalar',
+    estimateCost: 'Tahmin',
+    estimateSegment: 'Tahmini parçalar',
     segmentCount: 'parçalar',
     calculating: 'Hesaplanıyor...',
     fileSource: 'Belgeleri ön işleme',
@@ -135,8 +135,8 @@ const translation = {
     previewSwitchTipStart: 'Geçerli parça önizlemesi metin formatındadır, soru ve yanıt formatına geçiş ek tüketir',
     previewSwitchTipEnd: 'token',
     characters: 'karakterler',
-    indexSettedTip: 'Dizin yöntemini değiştirmek için, lütfen',
-    retrivalSettedTip: 'Dizin yöntemini değiştirmek için, lütfen',
+    indexSettingTip: 'Dizin yöntemini değiştirmek için, lütfen',
+    retrievalSettingTip: 'Dizin yöntemini değiştirmek için, lütfen',
     datasetSettingLink: 'Bilgi ayarlarına gidin.',
   },
   stepThree: {
diff --git a/web/i18n/tr-TR/dataset.ts b/web/i18n/tr-TR/dataset.ts
index 4bc16fb4b9..5e55e071c5 100644
--- a/web/i18n/tr-TR/dataset.ts
+++ b/web/i18n/tr-TR/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacy: 'Geri alım stratejisinin optimizasyonu ve yükseltilmesi nedeniyle, N-to-1 geri alımı Eylül ayında resmi olarak kullanım dışı kalacaktır. O zamana kadar normal şekilde kullanabilirsiniz.',
   nTo1RetrievalLegacyLink: 'Daha fazla bilgi edin',
   nTo1RetrievalLegacyLinkText: 'N-1 geri alma Eylül ayında resmi olarak kullanımdan kaldırılacaktır.',
+  defaultRetrievalTip: 'Varsayılan olarak çok alma kullanılır. Bilgi, birden fazla bilgi tabanından alınır ve ardından yeniden sıralanır.',
 }
 
 export default translation
diff --git a/web/i18n/tr-TR/login.ts b/web/i18n/tr-TR/login.ts
index 617b58be36..8f0a9eff89 100644
--- a/web/i18n/tr-TR/login.ts
+++ b/web/i18n/tr-TR/login.ts
@@ -32,7 +32,7 @@ const translation = {
   pp: 'Gizlilik Politikası',
   tosDesc: 'Kaydolarak, Hizmet Şartlarımızı kabul etmiş olursunuz',
   goToInit: 'Hesabı başlatmadıysanız, lütfen başlatma sayfasına gidin',
-  donthave: 'Sahip değil misiniz?',
+  dontHave: 'Sahip değil misiniz?',
   invalidInvitationCode: 'Geçersiz davet kodu',
   accountAlreadyInited: 'Hesap zaten başlatılmış',
   forgotPassword: 'Şifrenizi mi unuttunuz?',
diff --git a/web/i18n/tr-TR/share-app.ts b/web/i18n/tr-TR/share-app.ts
index 4fe58a8b2b..26c6f56fb4 100644
--- a/web/i18n/tr-TR/share-app.ts
+++ b/web/i18n/tr-TR/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'Uygulama kullanılamıyor',
-    appUnkonwError: 'Uygulama kullanılamıyor',
+    appUnknownError: 'Uygulama kullanılamıyor',
   },
   chat: {
     newChat: 'Yeni sohbet',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Sohbetler',
     newChatDefaultName: 'Yeni konuşma',
     resetChat: 'Konuşmayı sıfırla',
-    powerBy: 'Tarafından desteklenmektedir',
+    poweredBy: 'Tarafından desteklenmektedir',
     prompt: 'Prompt',
     privatePromptConfigTitle: 'Konuşma ayarları',
     publicPromptConfigTitle: 'Başlangıç Promptu',
diff --git a/web/i18n/tr-TR/workflow.ts b/web/i18n/tr-TR/workflow.ts
index a0f9c55125..a33a3724ad 100644
--- a/web/i18n/tr-TR/workflow.ts
+++ b/web/i18n/tr-TR/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Değişkeni ara',
     variableNamePlaceholder: 'Değişken adı',
     setVarValuePlaceholder: 'Değişkeni ayarla',
-    needConnecttip: 'Bu adım hiçbir şeye bağlı değil',
+    needConnectTip: 'Bu adım hiçbir şeye bağlı değil',
     maxTreeDepth: 'Her dal için maksimum {{depth}} düğüm limiti',
     needEndNode: 'Son blok eklenmelidir',
     needAnswerNode: 'Yanıt bloğu eklenmelidir',
@@ -77,6 +77,24 @@ const translation = {
     overwriteAndImport: 'Üzerine Yaz ve İçe Aktar',
     importFailure: 'İçe Aktarma Başarısız',
     importSuccess: 'İçe Aktarma Başarılı',
+    parallelTip: {
+      click: {
+        desc: 'Eklemek için',
+        title: 'Tık',
+      },
+      drag: {
+        title: 'Sürükleme',
+        desc: 'Bağlanmak için',
+      },
+      depthLimit: '{{num}} katmanlarının paralel iç içe geçme katmanı sınırı',
+      limit: 'Paralellik {{num}} dallarıyla sınırlıdır.',
+    },
+    jumpToNode: 'Bu düğüme atla',
+    addParallelNode: 'Paralel Düğüm Ekle',
+    disconnect: 'Ayırmak',
+    parallelRun: 'Paralel Koşu',
+    parallel: 'PARALEL',
+    branch: 'DAL',
   },
   env: {
     envPanelTitle: 'Çevre Değişkenleri',
@@ -413,6 +431,7 @@ const translation = {
         'not empty': 'boş değil',
         'null': 'null',
         'not null': 'null değil',
+        'regex match': 'normal ifade maçı',
       },
       enterValue: 'Değer girin',
       addCondition: 'Koşul Ekle',
diff --git a/web/i18n/uk-UA/app-api.ts b/web/i18n/uk-UA/app-api.ts
index 0218a61baf..6f46e9a9b4 100644
--- a/web/i18n/uk-UA/app-api.ts
+++ b/web/i18n/uk-UA/app-api.ts
@@ -9,7 +9,7 @@ const translation = {
   play: 'Відтворити',
   pause: 'Пауза',
   playing: 'Відтворення',
-  merMaind: {
+  merMaid: {
     rerender: 'Повторити рендер',
   },
   never: 'Ніколи',
diff --git a/web/i18n/uk-UA/app-debug.ts b/web/i18n/uk-UA/app-debug.ts
index 7c0ba45b3c..1fc6981122 100644
--- a/web/i18n/uk-UA/app-debug.ts
+++ b/web/i18n/uk-UA/app-debug.ts
@@ -259,7 +259,7 @@ const translation = {
     historyNoBeEmpty: 'Історію розмови необхідно встановити у підказці', // Conversation history must be set in the prompt
     queryNoBeEmpty: 'Запит має бути встановлений у підказці', // Query must be set in the prompt
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'Додати Поле Введення',
     'editModalTitle': 'Редагувати Поле Введення',
     'description': 'Налаштування для змінної {{varName}}',
diff --git a/web/i18n/uk-UA/common.ts b/web/i18n/uk-UA/common.ts
index ed197f9322..cc70772be3 100644
--- a/web/i18n/uk-UA/common.ts
+++ b/web/i18n/uk-UA/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Параметри',
     duplicate: 'дублікат',
     rename: 'Перейменувати',
+    audioSourceUnavailable: 'AudioSource недоступний',
   },
   placeholder: {
     input: 'Будь ласка, введіть текст',
@@ -191,14 +192,14 @@ const translation = {
     invitationSent: 'Запрошення надіслано',
     invitationSentTip: 'Запрошення надіслано, і вони можуть увійти в Dify, щоб отримати доступ до даних вашої команди.',
     invitationLink: 'Посилання на запрошення',
-    failedinvitationEmails: 'Наступних користувачів не було успішно запрошено',
+    failedInvitationEmails: 'Наступних користувачів не було успішно запрошено',
     ok: 'ОК',
     removeFromTeam: 'Видалити з команди',
     removeFromTeamTip: 'Буде видалено доступ до команди',
     setAdmin: 'Призначити адміністратором',
     setMember: 'Встановити як звичайного члена',
     setEditor: 'Встановити як Редактор',
-    disinvite: 'Скасувати запрошення',
+    disInvite: 'Скасувати запрошення',
     deleteMember: 'Видалити учасника',
     you: '(Ви)',
     builder: 'Будівник',
diff --git a/web/i18n/uk-UA/dataset-creation.ts b/web/i18n/uk-UA/dataset-creation.ts
index 5367519fc3..e4a38f41f4 100644
--- a/web/i18n/uk-UA/dataset-creation.ts
+++ b/web/i18n/uk-UA/dataset-creation.ts
@@ -45,7 +45,7 @@ const translation = {
       input: 'Назва Знань',
       placeholder: 'Введіть, будь ласка',
       nameNotEmpty: 'Ім’я не може бути порожнім',
-      nameLengthInvaild: 'Ім’я має бути від 1 до 40 символів',
+      nameLengthInvalid: 'Ім’я має бути від 1 до 40 символів',
       cancelButton: 'Скасувати',
       confirmButton: 'Створити',
       failed: 'Створення не вдалося',
@@ -104,8 +104,8 @@ const translation = {
     QATitle: 'Сегментація у форматі "питання та відповідь"',
     QATip: 'Увімкнення цієї опції споживатиме більше токенів',
     QALanguage: 'Сегментація з використанням',
-    emstimateCost: 'Оцінка',
-    emstimateSegment: 'Орієнтовні фрагменти',
+    estimateCost: 'Оцінка',
+    estimateSegment: 'Орієнтовні фрагменти',
     segmentCount: 'фрагментів',
     calculating: 'Розраховується...',
     fileSource: 'Попередня обробка документа',
@@ -128,8 +128,8 @@ const translation = {
     previewSwitchTipStart: 'Поточний попередній перегляд має текстовий формат, зміна способу подання на формат запитань та відповідей ',
     previewSwitchTipEnd: ' потребує додаткових токенів',
     characters: 'символів',
-    indexSettedTip: 'Щоб змінити метод індексування, будь ласка, перейдіть до ',
-    retrivalSettedTip: 'Щоб змінити метод індексування, будь ласка, перейдіть до ',
+    indexSettingTip: 'Щоб змінити метод індексування, будь ласка, перейдіть до ',
+    retrievalSettingTip: 'Щоб змінити метод індексування, будь ласка, перейдіть до ',
     datasetSettingLink: 'Налаштування знань.',
     webpageUnit: 'Сторінок',
     websiteSource: 'Веб-сайт попередньої обробки',
diff --git a/web/i18n/uk-UA/dataset.ts b/web/i18n/uk-UA/dataset.ts
index 3bf59ed33b..1bf6786976 100644
--- a/web/i18n/uk-UA/dataset.ts
+++ b/web/i18n/uk-UA/dataset.ts
@@ -1,6 +1,7 @@
 const translation = {
   knowledge: 'Знання',
-  documentCount: ' док.', // Скорочення від 'документів'
+  // Скорочення від 'документів'
+  documentCount: ' док.',
   wordCount: ' тис. слів',
   appCount: ' пов\'язаних додатків',
   createDataset: 'Створити Знання',
@@ -71,6 +72,7 @@ const translation = {
   nTo1RetrievalLegacy: 'N-до-1 пошук буде офіційно застарілим з вересня. Рекомендується використовувати найновіший багатошляховий пошук для отримання кращих результатів.',
   nTo1RetrievalLegacyLink: 'Дізнатися більше',
   nTo1RetrievalLegacyLinkText: 'N-до-1 пошук буде офіційно застарілим у вересні.',
+  defaultRetrievalTip: 'За замовчуванням використовується отримання кількома шляхами. Знання витягуються з кількох баз знань, а потім заново ранжуються.',
 }
 
 export default translation
diff --git a/web/i18n/uk-UA/login.ts b/web/i18n/uk-UA/login.ts
index 77cec5ceed..7acc1920fc 100644
--- a/web/i18n/uk-UA/login.ts
+++ b/web/i18n/uk-UA/login.ts
@@ -31,7 +31,7 @@ const translation = {
   pp: 'Політика конфіденційності',
   tosDesc: 'Реєструючись, ви приймаєте наші',
   goToInit: 'Якщо ви ще не ініціалізували обліковий запис, перейдіть на сторінку ініціалізації',
-  donthave: 'Не маєте?',
+  dontHave: 'Не маєте?',
   invalidInvitationCode: 'Недійсний код запрошення',
   accountAlreadyInited: 'Обліковий запис уже ініціалізовано',
   forgotPassword: 'Забули пароль?',
diff --git a/web/i18n/uk-UA/share-app.ts b/web/i18n/uk-UA/share-app.ts
index 4b794ea974..3465a6e5b9 100644
--- a/web/i18n/uk-UA/share-app.ts
+++ b/web/i18n/uk-UA/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'Додаток недоступний',
-    appUnkonwError: 'Додаток недоступний',
+    appUnknownError: 'Додаток недоступний',
   },
   chat: {
     newChat: 'Новий чат',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Чати',
     newChatDefaultName: 'Нова розмова',
     resetChat: 'Очистити розмову',
-    powerBy: 'Забезпечується',
+    poweredBy: 'Забезпечується',
     prompt: 'Підказка',
     privatePromptConfigTitle: 'Налаштування розмови',
     publicPromptConfigTitle: 'Початкова підказка',
diff --git a/web/i18n/uk-UA/workflow.ts b/web/i18n/uk-UA/workflow.ts
index a2db964056..e1bea99bcd 100644
--- a/web/i18n/uk-UA/workflow.ts
+++ b/web/i18n/uk-UA/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Пошук змінної',
     variableNamePlaceholder: 'Назва змінної',
     setVarValuePlaceholder: 'Встановити значення змінної',
-    needConnecttip: 'Цей крок ні до чого не підключений',
+    needConnectTip: 'Цей крок ні до чого не підключений',
     maxTreeDepth: 'Максимальний ліміт {{depth}} вузлів на гілку',
     needEndNode: 'Потрібно додати кінцевий блок',
     needAnswerNode: 'Потрібно додати блок відповіді',
@@ -77,6 +77,24 @@ const translation = {
     chooseDSL: 'Виберіть файл DSL(yml)',
     backupCurrentDraft: 'Резервна поточна чернетка',
     importDSLTip: 'Поточна чернетка буде перезаписана. Експортуйте робочий процес як резервну копію перед імпортом.',
+    parallelTip: {
+      click: {
+        title: 'Натисніть',
+        desc: 'щоб додати',
+      },
+      drag: {
+        title: 'Перетягувати',
+        desc: 'Щоб підключити',
+      },
+      limit: 'Паралелізм обмежується {{num}} гілками.',
+      depthLimit: 'Обмеження рівня паралельного вкладеності шарів {{num}}',
+    },
+    disconnect: 'Відключити',
+    parallelRun: 'Паралельний біг',
+    jumpToNode: 'Перейти до цього вузла',
+    addParallelNode: 'Додати паралельний вузол',
+    parallel: 'ПАРАЛЕЛЬНИЙ',
+    branch: 'ГІЛКА',
   },
   env: {
     envPanelTitle: 'Змінні середовища',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': 'не порожній',
         'null': 'є null',
         'not null': 'не є null',
+        'regex match': 'Регулярний вираз збігу',
       },
       enterValue: 'Введіть значення',
       addCondition: 'Додати умову',
diff --git a/web/i18n/vi-VN/app-api.ts b/web/i18n/vi-VN/app-api.ts
index 384f3a5292..2d4ee90e3c 100644
--- a/web/i18n/vi-VN/app-api.ts
+++ b/web/i18n/vi-VN/app-api.ts
@@ -9,7 +9,7 @@ const translation = {
   play: 'Chạy',
   pause: 'Tạm dừng',
   playing: 'Đang chạy',
-  merMaind: {
+  merMaid: {
     rerender: 'Vẽ lại',
   },
   never: 'Không bao giờ',
diff --git a/web/i18n/vi-VN/app-debug.ts b/web/i18n/vi-VN/app-debug.ts
index 906b39d10a..4e8a1962fe 100644
--- a/web/i18n/vi-VN/app-debug.ts
+++ b/web/i18n/vi-VN/app-debug.ts
@@ -259,7 +259,7 @@ const translation = {
     historyNoBeEmpty: 'Lịch sử cuộc trò chuyện phải được thiết lập trong lời nhắc',
     queryNoBeEmpty: 'Truy vấn phải được thiết lập trong lời nhắc',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': 'Thêm trường nhập',
     'editModalTitle': 'Chỉnh sửa trường nhập',
     'description': 'Cài đặt cho biến {{varName}}',
diff --git a/web/i18n/vi-VN/common.ts b/web/i18n/vi-VN/common.ts
index a3eee07348..252fa7e1df 100644
--- a/web/i18n/vi-VN/common.ts
+++ b/web/i18n/vi-VN/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: 'Tham số',
     duplicate: 'Nhân bản',
     rename: 'Đổi tên',
+    audioSourceUnavailable: 'AudioSource không khả dụng',
   },
   placeholder: {
     input: 'Vui lòng nhập',
@@ -191,14 +192,14 @@ const translation = {
     invitationSent: 'Lời mời đã được gửi',
     invitationSentTip: 'Lời mời đã được gửi, và họ có thể đăng nhập vào Dify để truy cập vào dữ liệu nhóm của bạn.',
     invitationLink: 'Liên kết Lời mời',
-    failedinvitationEmails: 'Dưới đây là danh sách email không gửi được lời mời',
+    failedInvitationEmails: 'Dưới đây là danh sách email không gửi được lời mời',
     ok: 'OK',
     removeFromTeam: 'Xóa khỏi nhóm',
     removeFromTeamTip: 'Sẽ xóa quyền truy cập nhóm',
     setAdmin: 'Đặt làm quản trị viên',
     setMember: 'Đặt thành viên bình thường',
     setEditor: 'Đặt làm biên tập viên',
-    disinvite: 'Hủy lời mời',
+    disInvite: 'Hủy lời mời',
     deleteMember: 'Xóa thành viên',
     you: '(Bạn)',
     datasetOperatorTip: 'Chỉ có thể quản lý cơ sở kiến thức',
diff --git a/web/i18n/vi-VN/dataset-creation.ts b/web/i18n/vi-VN/dataset-creation.ts
index 1c5a24d0bf..da69020287 100644
--- a/web/i18n/vi-VN/dataset-creation.ts
+++ b/web/i18n/vi-VN/dataset-creation.ts
@@ -45,7 +45,7 @@ const translation = {
       input: 'Tên Kiến thức',
       placeholder: 'Vui lòng nhập',
       nameNotEmpty: 'Tên không thể để trống',
-      nameLengthInvaild: 'Tên phải từ 1 đến 40 ký tự',
+      nameLengthInvalid: 'Tên phải từ 1 đến 40 ký tự',
       cancelButton: 'Hủy',
       confirmButton: 'Tạo',
       failed: 'Tạo thất bại',
@@ -104,8 +104,8 @@ const translation = {
     QATitle: 'Phân đoạn theo định dạng Câu hỏi & Trả lời',
     QATip: 'Bật tùy chọn này sẽ tiêu tốn thêm token',
     QALanguage: 'Phân đoạn bằng',
-    emstimateCost: 'Ước tính',
-    emstimateSegment: 'Số đoạn ước tính',
+    estimateCost: 'Ước tính',
+    estimateSegment: 'Số đoạn ước tính',
     segmentCount: 'đoạn',
     calculating: 'Đang tính toán...',
     fileSource: 'Tiền xử lý tài liệu',
@@ -128,8 +128,8 @@ const translation = {
     previewSwitchTipStart: 'Xem trước đoạn hiện tại đang ở định dạng văn bản, chuyển sang xem trước dạng câu hỏi và trả lời sẽ',
     previewSwitchTipEnd: ' tiêu tốn thêm token',
     characters: 'ký tự',
-    indexSettedTip: 'Để thay đổi phương pháp chỉ mục, vui lòng đi tới ',
-    retrivalSettedTip: 'Để thay đổi phương pháp truy xuất, vui lòng đi tới ',
+    indexSettingTip: 'Để thay đổi phương pháp chỉ mục, vui lòng đi tới ',
+    retrievalSettingTip: 'Để thay đổi phương pháp truy xuất, vui lòng đi tới ',
     datasetSettingLink: 'cài đặt Kiến thức.',
     websiteSource: 'Trang web tiền xử lý',
     webpageUnit: 'Trang',
diff --git a/web/i18n/vi-VN/dataset.ts b/web/i18n/vi-VN/dataset.ts
index 81b4597800..a2b9f8d087 100644
--- a/web/i18n/vi-VN/dataset.ts
+++ b/web/i18n/vi-VN/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacy: 'Truy xuất N-đến-1 sẽ chính thức bị loại bỏ từ tháng 9. Khuyến nghị sử dụng truy xuất đa đường dẫn mới nhất để có kết quả tốt hơn.',
   nTo1RetrievalLegacyLink: 'Tìm hiểu thêm',
   nTo1RetrievalLegacyLinkText: 'Truy xuất N-đến-1 sẽ chính thức bị loại bỏ vào tháng 9.',
+  defaultRetrievalTip: 'Truy xuất nhiều đường dẫn được sử dụng theo mặc định. Kiến thức được lấy từ nhiều cơ sở kiến thức và sau đó được xếp hạng lại.',
 }
 
 export default translation
diff --git a/web/i18n/vi-VN/login.ts b/web/i18n/vi-VN/login.ts
index ba729b2dff..0ee39ffe2c 100644
--- a/web/i18n/vi-VN/login.ts
+++ b/web/i18n/vi-VN/login.ts
@@ -31,7 +31,7 @@ const translation = {
   pp: 'Chính sách bảo mật',
   tosDesc: 'Bằng cách đăng ký, bạn đồng ý với',
   goToInit: 'Nếu bạn chưa khởi tạo tài khoản, vui lòng chuyển đến trang khởi tạo',
-  donthave: 'Chưa có tài khoản?',
+  dontHave: 'Chưa có tài khoản?',
   invalidInvitationCode: 'Mã mời không hợp lệ',
   accountAlreadyInited: 'Tài khoản đã được khởi tạo',
   forgotPassword: 'Quên mật khẩu?',
diff --git a/web/i18n/vi-VN/share-app.ts b/web/i18n/vi-VN/share-app.ts
index d440ad55dc..7078ecc299 100644
--- a/web/i18n/vi-VN/share-app.ts
+++ b/web/i18n/vi-VN/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: 'Ứng dụng không khả dụng',
-    appUnkonwError: 'Ứng dụng gặp lỗi không xác định',
+    appUnknownError: 'Ứng dụng gặp lỗi không xác định',
   },
   chat: {
     newChat: 'Cuộc trò chuyện mới',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: 'Trò chuyện',
     newChatDefaultName: 'Cuộc trò chuyện mới',
     resetChat: 'Đặt lại cuộc trò chuyện',
-    powerBy: 'Được cung cấp bởi',
+    poweredBy: 'Được cung cấp bởi',
     prompt: 'Lời nhắc',
     privatePromptConfigTitle: 'Cài đặt cuộc trò chuyện',
     publicPromptConfigTitle: 'Lời nhắc ban đầu',
diff --git a/web/i18n/vi-VN/workflow.ts b/web/i18n/vi-VN/workflow.ts
index fba71190d4..5e6f0e0063 100644
--- a/web/i18n/vi-VN/workflow.ts
+++ b/web/i18n/vi-VN/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     searchVar: 'Tìm kiếm biến',
     variableNamePlaceholder: 'Tên biến',
     setVarValuePlaceholder: 'Đặt giá trị biến',
-    needConnecttip: 'Bước này không được kết nối với bất kỳ điều gì',
+    needConnectTip: 'Bước này không được kết nối với bất kỳ điều gì',
     maxTreeDepth: 'Giới hạn tối đa {{depth}} nút trên mỗi nhánh',
     needEndNode: 'Phải thêm khối Kết thúc',
     needAnswerNode: 'Phải thêm khối Trả lời',
@@ -77,6 +77,24 @@ const translation = {
     overwriteAndImport: 'Ghi đè và nhập',
     importDSL: 'Nhập DSL',
     syncingData: 'Đồng bộ hóa dữ liệu, chỉ vài giây.',
+    parallelTip: {
+      click: {
+        title: 'Bấm',
+        desc: 'để thêm',
+      },
+      drag: {
+        title: 'Kéo',
+        desc: 'Để kết nối',
+      },
+      limit: 'Song song được giới hạn trong các nhánh {{num}}.',
+      depthLimit: 'Giới hạn lớp lồng song song của {{num}} layer',
+    },
+    parallelRun: 'Chạy song song',
+    disconnect: 'Ngắt kết nối',
+    jumpToNode: 'Chuyển đến nút này',
+    addParallelNode: 'Thêm nút song song',
+    parallel: 'SONG SONG',
+    branch: 'NHÁNH',
   },
   env: {
     envPanelTitle: 'Biến Môi Trường',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': 'không trống',
         'null': 'là null',
         'not null': 'không là null',
+        'regex match': 'Trận đấu Regex',
       },
       enterValue: 'Nhập giá trị',
       addCondition: 'Thêm điều kiện',
diff --git a/web/i18n/zh-Hans/app-api.ts b/web/i18n/zh-Hans/app-api.ts
index f8f6ab7083..6b9048b66e 100644
--- a/web/i18n/zh-Hans/app-api.ts
+++ b/web/i18n/zh-Hans/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: '暂停',
   playing: '播放中',
   loading: '加载中',
-  merMaind: {
+  merMaid: {
     rerender: '重新渲染',
   },
   never: '从未',
diff --git a/web/i18n/zh-Hans/app-debug.ts b/web/i18n/zh-Hans/app-debug.ts
index febf80d786..62ef300f4d 100644
--- a/web/i18n/zh-Hans/app-debug.ts
+++ b/web/i18n/zh-Hans/app-debug.ts
@@ -298,7 +298,7 @@ const translation = {
     historyNoBeEmpty: '提示词中必须设置对话历史',
     queryNoBeEmpty: '提示词中必须设置查询内容',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': '添加变量',
     'editModalTitle': '编辑变量',
     'description': '设置变量 {{varName}}',
diff --git a/web/i18n/zh-Hans/common.ts b/web/i18n/zh-Hans/common.ts
index ccbe4a32c6..52ab7d6f02 100644
--- a/web/i18n/zh-Hans/common.ts
+++ b/web/i18n/zh-Hans/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: '参数设置',
     duplicate: '复制',
     rename: '重命名',
+    audioSourceUnavailable: '音源不可用',
   },
   errorMsg: {
     fieldRequired: '{{field}} 为必填项',
@@ -197,14 +198,14 @@ const translation = {
     invitationSent: '邀请已发送',
     invitationSentTip: '邀请已发送，对方登录 Dify 后即可访问你的团队数据。',
     invitationLink: '邀请链接',
-    failedinvitationEmails: '邀请以下邮箱失败',
+    failedInvitationEmails: '邀请以下邮箱失败',
     ok: '好的',
     removeFromTeam: '移除团队',
     removeFromTeamTip: '将取消团队访问',
     setAdmin: '设为管理员',
     setMember: '设为普通成员',
     setEditor: '设为编辑',
-    disinvite: '取消邀请',
+    disInvite: '取消邀请',
     deleteMember: '删除成员',
     you: '（你）',
     builderTip: '可以构建和编辑自己的应用程序',
diff --git a/web/i18n/zh-Hans/dataset-creation.ts b/web/i18n/zh-Hans/dataset-creation.ts
index 53821e71a5..47a15921f7 100644
--- a/web/i18n/zh-Hans/dataset-creation.ts
+++ b/web/i18n/zh-Hans/dataset-creation.ts
@@ -50,7 +50,7 @@ const translation = {
       input: '知识库名称',
       placeholder: '请输入知识库名称',
       nameNotEmpty: '名称不能为空',
-      nameLengthInvaild: '名称长度不能超过 40 个字符',
+      nameLengthInvalid: '名称长度不能超过 40 个字符',
       cancelButton: '取消',
       confirmButton: '创建',
       failed: '创建失败',
@@ -109,8 +109,8 @@ const translation = {
     QATitle: '采用 Q&A 分段模式',
     QATip: '开启后将会消耗额外的 token',
     QALanguage: '分段使用',
-    emstimateCost: '执行嵌入预估消耗',
-    emstimateSegment: '预估分段数',
+    estimateCost: '执行嵌入预估消耗',
+    estimateSegment: '预估分段数',
     segmentCount: '段',
     calculating: '计算中...',
     fileSource: '预处理文档',
@@ -135,8 +135,8 @@ const translation = {
     previewSwitchTipStart: '当前分段预览是文本模式，切换到 Q&A 模式将会',
     previewSwitchTipEnd: '消耗额外的 token',
     characters: '字符',
-    indexSettedTip: '要更改索引方法和 embedding 模型，请转到',
-    retrivalSettedTip: '要更改检索方法，请转到',
+    indexSettingTip: '要更改索引方法和 embedding 模型，请转到',
+    retrievalSettingTip: '要更改检索方法，请转到',
     datasetSettingLink: '知识库设置。',
   },
   stepThree: {
diff --git a/web/i18n/zh-Hans/dataset.ts b/web/i18n/zh-Hans/dataset.ts
index f76be97818..013830af6f 100644
--- a/web/i18n/zh-Hans/dataset.ts
+++ b/web/i18n/zh-Hans/dataset.ts
@@ -55,6 +55,7 @@ const translation = {
     hybrid_search: '混合检索',
     invertedIndex: '倒排索引',
   },
+  defaultRetrievalTip: '默认情况下使用多路召回。从多个知识库中检索知识，然后重新排序。',
   mixtureHighQualityAndEconomicTip: '混合使用高质量和经济型知识库需要配置 Rerank 模型。',
   inconsistentEmbeddingModelTip: '当所选知识库配置的 Embedding 模型不一致时，需要配置 Rerank 模型。',
   retrievalSettings: '召回设置',
diff --git a/web/i18n/zh-Hans/login.ts b/web/i18n/zh-Hans/login.ts
index 53266052f2..f0a6ab76a3 100644
--- a/web/i18n/zh-Hans/login.ts
+++ b/web/i18n/zh-Hans/login.ts
@@ -31,7 +31,7 @@ const translation = {
   pp: '隐私政策',
   tosDesc: '使用即代表你并同意我们的',
   goToInit: '如果您还没有初始化账户，请前往初始化页面',
-  donthave: '还没有邀请码？',
+  dontHave: '还没有邀请码？',
   invalidInvitationCode: '无效的邀请码',
   accountAlreadyInited: '账户已经初始化',
   forgotPassword: '忘记密码？',
diff --git a/web/i18n/zh-Hans/share-app.ts b/web/i18n/zh-Hans/share-app.ts
index 794c5e323d..968381bb37 100644
--- a/web/i18n/zh-Hans/share-app.ts
+++ b/web/i18n/zh-Hans/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: '应用不可用',
-    appUnkonwError: '应用不可用',
+    appUnknownError: '应用不可用',
   },
   chat: {
     newChat: '新对话',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: '对话列表',
     newChatDefaultName: '新的对话',
     resetChat: '重置对话',
-    powerBy: 'Powered by',
+    poweredBy: 'Powered by',
     prompt: '提示词',
     privatePromptConfigTitle: '对话设置',
     publicPromptConfigTitle: '对话前提示词',
diff --git a/web/i18n/zh-Hans/workflow.ts b/web/i18n/zh-Hans/workflow.ts
index 56d1de6ceb..3579ec5df3 100644
--- a/web/i18n/zh-Hans/workflow.ts
+++ b/web/i18n/zh-Hans/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     variableNamePlaceholder: '变量名',
     searchVar: '搜索变量',
     setVarValuePlaceholder: '设置变量值',
-    needConnecttip: '此节点尚未连接到其他节点',
+    needConnectTip: '此节点尚未连接到其他节点',
     maxTreeDepth: '每个分支最大限制 {{depth}} 个节点',
     needEndNode: '必须添加结束节点',
     needAnswerNode: '必须添加直接回复节点',
@@ -77,6 +77,24 @@ const translation = {
     overwriteAndImport: '覆盖并导入',
     importFailure: '导入失败',
     importSuccess: '导入成功',
+    parallelRun: '并行运行',
+    parallelTip: {
+      click: {
+        title: '点击',
+        desc: '添加节点',
+      },
+      drag: {
+        title: '拖拽',
+        desc: '连接节点',
+      },
+      limit: '并行分支限制为 {{num}} 个',
+      depthLimit: '并行嵌套层数限制 {{num}} 层',
+    },
+    disconnect: '断开连接',
+    jumpToNode: '跳转到节点',
+    addParallelNode: '添加并行节点',
+    parallel: '并行',
+    branch: '分支',
   },
   env: {
     envPanelTitle: '环境变量',
diff --git a/web/i18n/zh-Hant/app-api.ts b/web/i18n/zh-Hant/app-api.ts
index 18ae6dcfff..63a9778378 100644
--- a/web/i18n/zh-Hant/app-api.ts
+++ b/web/i18n/zh-Hant/app-api.ts
@@ -10,7 +10,7 @@ const translation = {
   pause: '暫停',
   playing: '播放中',
   loading: '載入中',
-  merMaind: {
+  merMaid: {
     rerender: '重新渲染',
   },
   never: '從未',
diff --git a/web/i18n/zh-Hant/app-debug.ts b/web/i18n/zh-Hant/app-debug.ts
index ca4dfbb0cf..ad29195cb5 100644
--- a/web/i18n/zh-Hant/app-debug.ts
+++ b/web/i18n/zh-Hant/app-debug.ts
@@ -244,7 +244,7 @@ const translation = {
     historyNoBeEmpty: '提示詞中必須設定對話歷史',
     queryNoBeEmpty: '提示詞中必須設定查詢內容',
   },
-  variableConig: {
+  variableConfig: {
     'addModalTitle': '新增變數',
     'editModalTitle': '編輯變數',
     'description': '設定變數 {{varName}}',
diff --git a/web/i18n/zh-Hant/common.ts b/web/i18n/zh-Hant/common.ts
index d6c283079a..c1f3ed2b2b 100644
--- a/web/i18n/zh-Hant/common.ts
+++ b/web/i18n/zh-Hant/common.ts
@@ -37,6 +37,7 @@ const translation = {
     params: '引數設定',
     duplicate: '複製',
     rename: '重新命名',
+    audioSourceUnavailable: '音訊來源不可用',
   },
   placeholder: {
     input: '請輸入',
@@ -191,14 +192,14 @@ const translation = {
     invitationSent: '邀請已傳送',
     invitationSentTip: '邀請已傳送，對方登入 Dify 後即可訪問你的團隊資料。',
     invitationLink: '邀請連結',
-    failedinvitationEmails: '邀請以下郵箱失敗',
+    failedInvitationEmails: '邀請以下郵箱失敗',
     ok: '好的',
     removeFromTeam: '移除團隊',
     removeFromTeamTip: '將取消團隊訪問',
     setAdmin: '設為管理員',
     setMember: '設為普通成員',
     setEditor: '設為編輯',
-    disinvite: '取消邀請',
+    disInvite: '取消邀請',
     deleteMember: '刪除成員',
     you: '（你）',
     setBuilder: 'Set as builder （設置為建構器）',
diff --git a/web/i18n/zh-Hant/dataset-creation.ts b/web/i18n/zh-Hant/dataset-creation.ts
index 9a6f815ca3..fd810d41c1 100644
--- a/web/i18n/zh-Hant/dataset-creation.ts
+++ b/web/i18n/zh-Hant/dataset-creation.ts
@@ -45,7 +45,7 @@ const translation = {
       input: '知識庫名稱',
       placeholder: '請輸入知識庫名稱',
       nameNotEmpty: '名稱不能為空',
-      nameLengthInvaild: '名稱長度不能超過 40 個字元',
+      nameLengthInvalid: '名稱長度不能超過 40 個字元',
       cancelButton: '取消',
       confirmButton: '建立',
       failed: '建立失敗',
@@ -104,8 +104,8 @@ const translation = {
     QATitle: '採用 Q&A 分段模式',
     QATip: '開啟後將會消耗額外的 token',
     QALanguage: '分段使用',
-    emstimateCost: '執行嵌入預估消耗',
-    emstimateSegment: '預估分段數',
+    estimateCost: '執行嵌入預估消耗',
+    estimateSegment: '預估分段數',
     segmentCount: '段',
     calculating: '計算中...',
     fileSource: '預處理文件',
@@ -128,8 +128,8 @@ const translation = {
     previewSwitchTipStart: '當前分段預覽是文字模式，切換到 Q&A 模式將會',
     previewSwitchTipEnd: '消耗額外的 token',
     characters: '字元',
-    indexSettedTip: '要更改索引方法，請轉到',
-    retrivalSettedTip: '要更改檢索方法，請轉到',
+    indexSettingTip: '要更改索引方法，請轉到',
+    retrievalSettingTip: '要更改檢索方法，請轉到',
     datasetSettingLink: '知識庫設定。',
     websiteSource: '預處理網站',
     webpageUnit: '頁面',
diff --git a/web/i18n/zh-Hant/dataset.ts b/web/i18n/zh-Hant/dataset.ts
index 1e011bc987..1888a28631 100644
--- a/web/i18n/zh-Hant/dataset.ts
+++ b/web/i18n/zh-Hant/dataset.ts
@@ -71,6 +71,7 @@ const translation = {
   nTo1RetrievalLegacy: 'N對1檢索將從9月起正式棄用。建議使用最新的多路徑檢索以獲得更好的結果。',
   nTo1RetrievalLegacyLink: '了解更多',
   nTo1RetrievalLegacyLinkText: 'N對1檢索將於9月正式棄用。',
+  defaultRetrievalTip: '默認情況下，使用多路徑檢索。從多個知識庫中檢索知識，然後重新排名。',
 }
 
 export default translation
diff --git a/web/i18n/zh-Hant/login.ts b/web/i18n/zh-Hant/login.ts
index 5b6492ed16..649f618158 100644
--- a/web/i18n/zh-Hant/login.ts
+++ b/web/i18n/zh-Hant/login.ts
@@ -31,7 +31,7 @@ const translation = {
   pp: '隱私政策',
   tosDesc: '使用即代表你並同意我們的',
   goToInit: '如果您還沒有初始化賬戶，請前往初始化頁面',
-  donthave: '還沒有邀請碼？',
+  dontHave: '還沒有邀請碼？',
   invalidInvitationCode: '無效的邀請碼',
   accountAlreadyInited: '賬戶已經初始化',
   forgotPassword: '忘記密碼？',
diff --git a/web/i18n/zh-Hant/share-app.ts b/web/i18n/zh-Hant/share-app.ts
index f14ce68577..ea5f206985 100644
--- a/web/i18n/zh-Hant/share-app.ts
+++ b/web/i18n/zh-Hant/share-app.ts
@@ -2,7 +2,7 @@ const translation = {
   common: {
     welcome: '',
     appUnavailable: '應用不可用',
-    appUnkonwError: '應用不可用',
+    appUnknownError: '應用不可用',
   },
   chat: {
     newChat: '新對話',
@@ -10,7 +10,7 @@ const translation = {
     unpinnedTitle: '對話列表',
     newChatDefaultName: '新的對話',
     resetChat: '重置對話',
-    powerBy: 'Powered by',
+    poweredBy: 'Powered by',
     prompt: '提示詞',
     privatePromptConfigTitle: '對話設定',
     publicPromptConfigTitle: '對話前提示詞',
diff --git a/web/i18n/zh-Hant/workflow.ts b/web/i18n/zh-Hant/workflow.ts
index a5ad2fdb49..8e1b7529fe 100644
--- a/web/i18n/zh-Hant/workflow.ts
+++ b/web/i18n/zh-Hant/workflow.ts
@@ -36,7 +36,7 @@ const translation = {
     variableNamePlaceholder: '變量名',
     searchVar: '搜索變量',
     setVarValuePlaceholder: '設置變量值',
-    needConnecttip: '此節點尚未連接到其他節點',
+    needConnectTip: '此節點尚未連接到其他節點',
     maxTreeDepth: '每個分支最大限制 {{depth}} 個節點',
     needEndNode: '必須添加結束節點',
     needAnswerNode: '必須添加直接回覆節點',
@@ -77,6 +77,24 @@ const translation = {
     syncingData: '同步數據，只需幾秒鐘。',
     importDSLTip: '當前草稿將被覆蓋。在導入之前將工作流匯出為備份。',
     importFailure: '匯入失敗',
+    parallelTip: {
+      click: {
+        title: '點擊',
+        desc: '添加',
+      },
+      drag: {
+        title: '拖动',
+        desc: '連接',
+      },
+      limit: '並行度僅限於 {{num}} 個分支。',
+      depthLimit: '並行嵌套層限制為 {{num}} 個層',
+    },
+    parallelRun: '並行運行',
+    disconnect: '斷開',
+    jumpToNode: '跳轉到此節點',
+    addParallelNode: '添加並行節點',
+    parallel: '並行',
+    branch: '分支',
   },
   env: {
     envPanelTitle: '環境變數',
@@ -412,6 +430,7 @@ const translation = {
         'not empty': '不為空',
         'null': '空',
         'not null': '不為空',
+        'regex match': '正則表達式匹配',
       },
       enterValue: '輸入值',
       addCondition: '添加條件',
diff --git a/web/models/app.ts b/web/models/app.ts
index 82efcd5fa0..e550b82ab6 100644
--- a/web/models/app.ts
+++ b/web/models/app.ts
@@ -4,7 +4,7 @@ import type { App, AppSSO, AppTemplate, SiteConfig } from '@/types/app'
 /* export type App = {
   id: string
   name: string
-  decription: string
+  description: string
   mode: AppMode
   enable_site: boolean
   enable_api: boolean
@@ -103,15 +103,15 @@ export type AppTokenCostsResponse = {
 
 export type UpdateAppModelConfigResponse = { result: string }
 
-export type ApikeyItemResponse = {
+export type ApiKeyItemResponse = {
   id: string
   token: string
   last_used_at: string
   created_at: string
 }
 
-export type ApikeysListResponse = {
-  data: ApikeyItemResponse[]
+export type ApiKeysListResponse = {
+  data: ApiKeyItemResponse[]
 }
 
 export type CreateApiKeyResponse = {
diff --git a/web/models/datasets.ts b/web/models/datasets.ts
index f35de58254..23d1fe6136 100644
--- a/web/models/datasets.ts
+++ b/web/models/datasets.ts
@@ -439,7 +439,7 @@ export type RelatedAppResponse = {
   total: number
 }
 
-export type SegmentUpdator = {
+export type SegmentUpdater = {
   content: string
   answer?: string
   keywords?: string[]
diff --git a/web/models/debug.ts b/web/models/debug.ts
index 2b2af80065..565798e598 100644
--- a/web/models/debug.ts
+++ b/web/models/debug.ts
@@ -215,7 +215,7 @@ export type LogSessionListResponse = {
     query: string // user's query question
     message: string // prompt send to LLM
     answer: string
-    creat_at: string
+    created_at: string
   }[]
   total: number
   page: number
@@ -224,7 +224,7 @@ export type LogSessionListResponse = {
 // log session detail and debug
 export type LogSessionDetailResponse = {
   id: string
-  cnversation_id: string
+  conversation_id: string
   model_provider: string
   query: string
   inputs: Record<string, string | number | object>[]
diff --git a/web/models/log.ts b/web/models/log.ts
index 6f8ebb1a78..8da1c4cf4e 100644
--- a/web/models/log.ts
+++ b/web/models/log.ts
@@ -6,7 +6,7 @@ import type {
 } from '@/app/components/workflow/types'
 import type { Metadata } from '@/app/components/base/chat/chat/type'
 
-// Log type contains key:string conversation_id:string created_at:string quesiton:string answer:string
+// Log type contains key:string conversation_id:string created_at:string question:string answer:string
 export type Conversation = {
   id: string
   key: string
diff --git a/web/package.json b/web/package.json
index 296613b6bb..bc532fb242 100644
--- a/web/package.json
+++ b/web/package.json
@@ -1,6 +1,6 @@
 {
   "name": "dify-web",
-  "version": "0.7.3",
+  "version": "0.8.2",
   "private": true,
   "engines": {
     "node": ">=18.17.0"
@@ -44,6 +44,7 @@
     "classnames": "^2.3.2",
     "copy-to-clipboard": "^3.3.3",
     "crypto-js": "^4.2.0",
+    "@svgdotjs/svg.js": "^3.2.4",
     "dayjs": "^1.11.7",
     "echarts": "^5.4.1",
     "echarts-for-react": "^3.0.2",
@@ -87,6 +88,7 @@
     "reactflow": "^11.11.3",
     "recordrtc": "^5.6.2",
     "rehype-katex": "^6.0.2",
+    "rehype-raw": "^7.0.0",
     "remark-breaks": "^3.0.2",
     "remark-gfm": "^3.0.1",
     "remark-math": "^5.1.1",
diff --git a/web/public/embed.js b/web/public/embed.js
index 14420f0c8c..8ed7a67dc8 100644
--- a/web/public/embed.js
+++ b/web/public/embed.js
@@ -73,7 +73,7 @@
         box-shadow: rgba(150, 150, 150, 0.2) 0px 10px 30px 0px, rgba(150, 150, 150, 0.2) 0px 0px 0px 1px;
         bottom: 5rem; right: 1rem; width: 24rem; max-width: calc(100vw - 2rem); height: 40rem;
         max-height: calc(100vh - 6rem); border-radius: 0.75rem; display: flex; z-index: 2147483647;
-        overflow: hidden; left: unset; background-color: #F3F4F6;
+        overflow: hidden; left: unset; background-color: #F3F4F6;user-select: none;
       `;
 
       document.body.appendChild(iframe);
@@ -255,6 +255,9 @@
     if (!document.getElementById(buttonId)) {
       createButton();
     }
+
+    createIframe();
+    document.getElementById(iframeId).style.display = 'none';
   }
 
   // Add esc Exit keyboard event triggered
diff --git a/web/public/embed.min.js b/web/public/embed.min.js
index ec721a204d..0e023cb5d1 100644
--- a/web/public/embed.min.js
+++ b/web/public/embed.min.js
@@ -1 +1,31 @@
-!function(){const e="difyChatbotConfig",t="dify-chatbot-bubble-button",n="dify-chatbot-bubble-window",o=window[e],i={open:'<svg id="openIcon" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">\n      <path fill-rule="evenodd" clip-rule="evenodd" d="M7.7586 2L16.2412 2C17.0462 1.99999 17.7105 1.99998 18.2517 2.04419C18.8138 2.09012 19.3305 2.18868 19.8159 2.43598C20.5685 2.81947 21.1804 3.43139 21.5639 4.18404C21.8112 4.66937 21.9098 5.18608 21.9557 5.74818C21.9999 6.28937 21.9999 6.95373 21.9999 7.7587L22 14.1376C22.0004 14.933 22.0007 15.5236 21.8636 16.0353C21.4937 17.4156 20.4155 18.4938 19.0352 18.8637C18.7277 18.9461 18.3917 18.9789 17.9999 18.9918L17.9999 20.371C18 20.6062 18 20.846 17.9822 21.0425C17.9651 21.2305 17.9199 21.5852 17.6722 21.8955C17.3872 22.2525 16.9551 22.4602 16.4983 22.4597C16.1013 22.4593 15.7961 22.273 15.6386 22.1689C15.474 22.06 15.2868 21.9102 15.1031 21.7632L12.69 19.8327C12.1714 19.4178 12.0174 19.3007 11.8575 19.219C11.697 19.137 11.5262 19.0771 11.3496 19.0408C11.1737 19.0047 10.9803 19 10.3162 19H7.75858C6.95362 19 6.28927 19 5.74808 18.9558C5.18598 18.9099 4.66928 18.8113 4.18394 18.564C3.43129 18.1805 2.81937 17.5686 2.43588 16.816C2.18859 16.3306 2.09002 15.8139 2.0441 15.2518C1.99988 14.7106 1.99989 14.0463 1.9999 13.2413V7.75868C1.99989 6.95372 1.99988 6.28936 2.0441 5.74818C2.09002 5.18608 2.18859 4.66937 2.43588 4.18404C2.81937 3.43139 3.43129 2.81947 4.18394 2.43598C4.66928 2.18868 5.18598 2.09012 5.74808 2.04419C6.28927 1.99998 6.95364 1.99999 7.7586 2ZM10.5073 7.5C10.5073 6.67157 9.83575 6 9.00732 6C8.1789 6 7.50732 6.67157 7.50732 7.5C7.50732 8.32843 8.1789 9 9.00732 9C9.83575 9 10.5073 8.32843 10.5073 7.5ZM16.6073 11.7001C16.1669 11.3697 15.5426 11.4577 15.2105 11.8959C15.1488 11.9746 15.081 12.0486 15.0119 12.1207C14.8646 12.2744 14.6432 12.4829 14.3566 12.6913C13.7796 13.111 12.9818 13.5001 12.0073 13.5001C11.0328 13.5001 10.235 13.111 9.65799 12.6913C9.37138 12.4829 9.15004 12.2744 9.00274 12.1207C8.93366 12.0486 8.86581 11.9745 8.80418 11.8959C8.472 11.4577 7.84775 11.3697 7.40732 11.7001C6.96549 12.0314 6.87595 12.6582 7.20732 13.1001C7.20479 13.0968 7.21072 13.1043 7.22094 13.1171C7.24532 13.1478 7.29407 13.2091 7.31068 13.2289C7.36932 13.2987 7.45232 13.3934 7.55877 13.5045C7.77084 13.7258 8.08075 14.0172 8.48165 14.3088C9.27958 14.8891 10.4818 15.5001 12.0073 15.5001C13.5328 15.5001 14.735 14.8891 15.533 14.3088C15.9339 14.0172 16.2438 13.7258 16.4559 13.5045C16.5623 13.3934 16.6453 13.2987 16.704 13.2289C16.7333 13.1939 16.7567 13.165 16.7739 13.1432C17.1193 12.6969 17.0729 12.0493 16.6073 11.7001ZM15.0073 6C15.8358 6 16.5073 6.67157 16.5073 7.5C16.5073 8.32843 15.8358 9 15.0073 9C14.1789 9 13.5073 8.32843 13.5073 7.5C13.5073 6.67157 14.1789 6 15.0073 6Z" fill="white"/>\n    </svg>',close:'<svg id="closeIcon" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">\n      <path d="M18 18L6 6M6 18L18 6" stroke="white" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>\n    </svg>'};async function d(){if(!o||!o.token)return void console.error(`${e} is empty or token is not provided`);const d=new URLSearchParams(await async function(){const e=o?.inputs||{},t={};return await Promise.all(Object.entries(e).map((async([e,n])=>{t[e]=await async function(e){const t=(new TextEncoder).encode(e),n=new Response(new Blob([t]).stream().pipeThrough(new CompressionStream("gzip"))).arrayBuffer(),o=new Uint8Array(await n);return btoa(String.fromCharCode(...o))}(n)}))),t}()),s=`${o.baseUrl||`https://${o.isDev?"dev.":""}udify.app`}/chatbot/${o.token}?${d}`;function c(){const e=document.getElementById(n),o=document.getElementById(t);if(e&&o){const t=o.getBoundingClientRect(),n=window.innerHeight-t.bottom,i=window.innerWidth-t.right,d=t.left;e.style.bottom=`${n+t.height+5+e.clientHeight>window.innerHeight?n-e.clientHeight-5:n+t.height+5}px`,e.style.right=`${i+e.clientWidth>window.innerWidth?window.innerWidth-d-e.clientWidth:i}px`}}s.length>2048&&console.error("The URL is too long, please reduce the number of inputs to prevent the bot from failing to load"),document.getElementById(t)||function(){const e=document.createElement("div");Object.entries(o.containerProps||{}).forEach((([t,n])=>{"className"===t?e.classList.add(...n.split(" ")):"style"===t?"object"==typeof n?Object.assign(e.style,n):e.style.cssText=n:"function"==typeof n?e.addEventListener(t.replace(/^on/,"").toLowerCase(),n):e[t]=n})),e.id=t;const d=document.createElement("style");document.head.appendChild(d),d.sheet.insertRule(`\n        #${e.id} {\n          position: fixed;\n          bottom: var(--${e.id}-bottom, 1rem);\n          right: var(--${e.id}-right, 1rem);\n          left: var(--${e.id}-left, unset);\n          top: var(--${e.id}-top, unset);\n          width: var(--${e.id}-width, 50px);\n          height: var(--${e.id}-height, 50px);\n          border-radius: var(--${e.id}-border-radius, 25px);\n          background-color: var(--${e.id}-bg-color, #155EEF);\n          box-shadow: var(--${e.id}-box-shadow, rgba(0, 0, 0, 0.2) 0px 4px 8px 0px);\n          cursor: pointer;\n          z-index: 2147483647;\n          transition: all 0.2s ease-in-out 0s;\n        }\n      `),d.sheet.insertRule(`\n        #${e.id}:hover {\n          transform: var(--${e.id}-hover-transform, scale(1.1));\n        }\n      `);const l=document.createElement("div");l.style.cssText="display: flex; align-items: center; justify-content: center; width: 100%; height: 100%; z-index: 2147483647;",l.innerHTML=i.open,e.appendChild(l),document.body.appendChild(e),e.addEventListener("click",(function(){const e=document.getElementById(n);if(!e)return function(){const e=document.createElement("iframe");e.allow="fullscreen;microphone",e.title="dify chatbot bubble window",e.id=n,e.src=s,e.style.cssText="\n        border: none; position: fixed; flex-direction: column; justify-content: space-between;\n        box-shadow: rgba(150, 150, 150, 0.2) 0px 10px 30px 0px, rgba(150, 150, 150, 0.2) 0px 0px 0px 1px;\n        bottom: 5rem; right: 1rem; width: 24rem; max-width: calc(100vw - 2rem); height: 40rem;\n        max-height: calc(100vh - 6rem); border-radius: 0.75rem; display: flex; z-index: 2147483647;\n        overflow: hidden; left: unset; background-color: #F3F4F6;\n      ",document.body.appendChild(e)}(),c(),this.title="Exit (ESC)",l.innerHTML=i.close,void document.addEventListener("keydown",r);e.style.display="none"===e.style.display?"block":"none",l.innerHTML="none"===e.style.display?i.open:i.close,"none"===e.style.display?document.removeEventListener("keydown",r):document.addEventListener("keydown",r),c()})),o.draggable&&function(e,o){let d,r,s=!1;function c(t){s=!0,d=t.clientX-e.offsetLeft,r=t.clientY-e.offsetTop}function l(c){if(!s)return;e.style.transition="none",e.style.cursor="grabbing";const l=document.getElementById(n);l&&(l.style.display="none",e.querySelector("div").innerHTML=i.open);const a=c.clientX-d,h=window.innerHeight-c.clientY-r,p=e.getBoundingClientRect(),u=window.innerWidth-p.width,m=window.innerHeight-p.height;"x"!==o&&"both"!==o||e.style.setProperty(`--${t}-left`,`${Math.max(0,Math.min(a,u))}px`),"y"!==o&&"both"!==o||e.style.setProperty(`--${t}-bottom`,`${Math.max(0,Math.min(h,m))}px`)}function a(){s=!1,e.style.transition="",e.style.cursor="pointer"}e.addEventListener("mousedown",c),document.addEventListener("mousemove",l),document.addEventListener("mouseup",a)}(e,o.dragAxis||"both")}()}function r(e){if("Escape"===e.key){const e=document.getElementById(n),o=document.getElementById(t);e&&"none"!==e.style.display&&(e.style.display="none",o.querySelector("div").innerHTML=i.open)}}document.addEventListener("keydown",r),o?.dynamicScript?d():document.body.onload=d}();
+(()=>{let t="difyChatbotConfig",a="dify-chatbot-bubble-button",c="dify-chatbot-bubble-window",h=window[t],p={open:`<svg id="openIcon" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+    <path fill-rule="evenodd" clip-rule="evenodd" d="M7.7586 2L16.2412 2C17.0462 1.99999 17.7105 1.99998 18.2517 2.04419C18.8138 2.09012 19.3305 2.18868 19.8159 2.43598C20.5685 2.81947 21.1804 3.43139 21.5639 4.18404C21.8112 4.66937 21.9098 5.18608 21.9557 5.74818C21.9999 6.28937 21.9999 6.95373 21.9999 7.7587L22 14.1376C22.0004 14.933 22.0007 15.5236 21.8636 16.0353C21.4937 17.4156 20.4155 18.4938 19.0352 18.8637C18.7277 18.9461 18.3917 18.9789 17.9999 18.9918L17.9999 20.371C18 20.6062 18 20.846 17.9822 21.0425C17.9651 21.2305 17.9199 21.5852 17.6722 21.8955C17.3872 22.2525 16.9551 22.4602 16.4983 22.4597C16.1013 22.4593 15.7961 22.273 15.6386 22.1689C15.474 22.06 15.2868 21.9102 15.1031 21.7632L12.69 19.8327C12.1714 19.4178 12.0174 19.3007 11.8575 19.219C11.697 19.137 11.5262 19.0771 11.3496 19.0408C11.1737 19.0047 10.9803 19 10.3162 19H7.75858C6.95362 19 6.28927 19 5.74808 18.9558C5.18598 18.9099 4.66928 18.8113 4.18394 18.564C3.43129 18.1805 2.81937 17.5686 2.43588 16.816C2.18859 16.3306 2.09002 15.8139 2.0441 15.2518C1.99988 14.7106 1.99989 14.0463 1.9999 13.2413V7.75868C1.99989 6.95372 1.99988 6.28936 2.0441 5.74818C2.09002 5.18608 2.18859 4.66937 2.43588 4.18404C2.81937 3.43139 3.43129 2.81947 4.18394 2.43598C4.66928 2.18868 5.18598 2.09012 5.74808 2.04419C6.28927 1.99998 6.95364 1.99999 7.7586 2ZM10.5073 7.5C10.5073 6.67157 9.83575 6 9.00732 6C8.1789 6 7.50732 6.67157 7.50732 7.5C7.50732 8.32843 8.1789 9 9.00732 9C9.83575 9 10.5073 8.32843 10.5073 7.5ZM16.6073 11.7001C16.1669 11.3697 15.5426 11.4577 15.2105 11.8959C15.1488 11.9746 15.081 12.0486 15.0119 12.1207C14.8646 12.2744 14.6432 12.4829 14.3566 12.6913C13.7796 13.111 12.9818 13.5001 12.0073 13.5001C11.0328 13.5001 10.235 13.111 9.65799 12.6913C9.37138 12.4829 9.15004 12.2744 9.00274 12.1207C8.93366 12.0486 8.86581 11.9745 8.80418 11.8959C8.472 11.4577 7.84775 11.3697 7.40732 11.7001C6.96549 12.0314 6.87595 12.6582 7.20732 13.1001C7.20479 13.0968 7.21072 13.1043 7.22094 13.1171C7.24532 13.1478 7.29407 13.2091 7.31068 13.2289C7.36932 13.2987 7.45232 13.3934 7.55877 13.5045C7.77084 13.7258 8.08075 14.0172 8.48165 14.3088C9.27958 14.8891 10.4818 15.5001 12.0073 15.5001C13.5328 15.5001 14.735 14.8891 15.533 14.3088C15.9339 14.0172 16.2438 13.7258 16.4559 13.5045C16.5623 13.3934 16.6453 13.2987 16.704 13.2289C16.7333 13.1939 16.7567 13.165 16.7739 13.1432C17.1193 12.6969 17.0729 12.0493 16.6073 11.7001ZM15.0073 6C15.8358 6 16.5073 6.67157 16.5073 7.5C16.5073 8.32843 15.8358 9 15.0073 9C14.1789 9 13.5073 8.32843 13.5073 7.5C13.5073 6.67157 14.1789 6 15.0073 6Z" fill="white"/>
+  </svg>`,close:`<svg id="closeIcon" width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+    <path d="M18 18L6 6M6 18L18 6" stroke="white" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+  </svg>`};async function e(){if(h&&h.token){var e=new URLSearchParams(await(async()=>{var e=h?.inputs||{};let n={};return await Promise.all(Object.entries(e).map(async([e,t])=>{n[e]=(e=t,e=(new TextEncoder).encode(e),e=new Response(new Blob([e]).stream().pipeThrough(new CompressionStream("gzip"))).arrayBuffer(),e=new Uint8Array(await e),await btoa(String.fromCharCode(...e)))})),n})());let t=`${h.baseUrl||`https://${h.isDev?"dev.":""}udify.app`}/chatbot/${h.token}?`+e;function o(){var e=document.createElement("iframe");e.allow="fullscreen;microphone",e.title="dify chatbot bubble window",e.id=c,e.src=t,e.style.cssText=`
+      border: none; position: fixed; flex-direction: column; justify-content: space-between;
+      box-shadow: rgba(150, 150, 150, 0.2) 0px 10px 30px 0px, rgba(150, 150, 150, 0.2) 0px 0px 0px 1px;
+      bottom: 5rem; right: 1rem; width: 24rem; max-width: calc(100vw - 2rem); height: 40rem;
+      max-height: calc(100vh - 6rem); border-radius: 0.75rem; display: flex; z-index: 2147483647;
+      overflow: hidden; left: unset; background-color: #F3F4F6;user-select: none;
+    `,document.body.appendChild(e)}function i(){var e,t,n,o=document.getElementById(c),i=document.getElementById(a);o&&i&&(i=i.getBoundingClientRect(),e=window.innerHeight-i.bottom,t=window.innerWidth-i.right,n=i.left,o.style.bottom=`${e+i.height+5+o.clientHeight>window.innerHeight?e-o.clientHeight-5:e+i.height+5}px`,o.style.right=`${t+o.clientWidth>window.innerWidth?window.innerWidth-n-o.clientWidth:t}px`)}function n(){let n=document.createElement("div");Object.entries(h.containerProps||{}).forEach(([e,t])=>{"className"===e?n.classList.add(...t.split(" ")):"style"===e?"object"==typeof t?Object.assign(n.style,t):n.style.cssText=t:"function"==typeof t?n.addEventListener(e.replace(/^on/,"").toLowerCase(),t):n[e]=t}),n.id=a;var e=document.createElement("style");document.head.appendChild(e),e.sheet.insertRule(`
+      #${n.id} {
+        position: fixed;
+        bottom: var(--${n.id}-bottom, 1rem);
+        right: var(--${n.id}-right, 1rem);
+        left: var(--${n.id}-left, unset);
+        top: var(--${n.id}-top, unset);
+        width: var(--${n.id}-width, 50px);
+        height: var(--${n.id}-height, 50px);
+        border-radius: var(--${n.id}-border-radius, 25px);
+        background-color: var(--${n.id}-bg-color, #155EEF);
+        box-shadow: var(--${n.id}-box-shadow, rgba(0, 0, 0, 0.2) 0px 4px 8px 0px);
+        cursor: pointer;
+        z-index: 2147483647;
+        transition: all 0.2s ease-in-out 0s;
+      }
+    `),e.sheet.insertRule(`
+      #${n.id}:hover {
+        transform: var(--${n.id}-hover-transform, scale(1.1));
+      }
+    `);let t=document.createElement("div");if(t.style.cssText="display: flex; align-items: center; justify-content: center; width: 100%; height: 100%; z-index: 2147483647;",t.innerHTML=p.open,n.appendChild(t),document.body.appendChild(n),n.addEventListener("click",function(){var e=document.getElementById(c);e?(e.style.display="none"===e.style.display?"block":"none",t.innerHTML="none"===e.style.display?p.open:p.close,"none"===e.style.display?document.removeEventListener("keydown",d):document.addEventListener("keydown",d),i()):(o(),i(),this.title="Exit (ESC)",t.innerHTML=p.close,document.addEventListener("keydown",d))}),h.draggable){var s=n;var l=h.dragAxis||"both";let i=!1,d,r;s.addEventListener("mousedown",function(e){i=!0,d=e.clientX-s.offsetLeft,r=e.clientY-s.offsetTop}),document.addEventListener("mousemove",function(e){var t,n,o;i&&(s.style.transition="none",s.style.cursor="grabbing",(t=document.getElementById(c))&&(t.style.display="none",s.querySelector("div").innerHTML=p.open),t=e.clientX-d,e=window.innerHeight-e.clientY-r,o=s.getBoundingClientRect(),n=window.innerWidth-o.width,o=window.innerHeight-o.height,"x"!==l&&"both"!==l||s.style.setProperty(`--${a}-left`,Math.max(0,Math.min(t,n))+"px"),"y"!==l&&"both"!==l||s.style.setProperty(`--${a}-bottom`,Math.max(0,Math.min(e,o))+"px"))}),document.addEventListener("mouseup",function(){i=!1,s.style.transition="",s.style.cursor="pointer"})}}2048<t.length&&console.error("The URL is too long, please reduce the number of inputs to prevent the bot from failing to load"),document.getElementById(a)||n(),o(),document.getElementById(c).style.display="none"}else console.error(t+" is empty or token is not provided")}function d(e){var t;"Escape"===e.key&&(e=document.getElementById(c),t=document.getElementById(a),e)&&"none"!==e.style.display&&(e.style.display="none",t.querySelector("div").innerHTML=p.open)}document.addEventListener("keydown",d),h?.dynamicScript?e():document.body.onload=e})();
\ No newline at end of file
diff --git a/web/public/logo/logo-embeded-chat-avatar.png b/web/public/logo/logo-embedded-chat-avatar.png
similarity index 100%
rename from web/public/logo/logo-embeded-chat-avatar.png
rename to web/public/logo/logo-embedded-chat-avatar.png
diff --git a/web/public/logo/logo-embeded-chat-header.png b/web/public/logo/logo-embedded-chat-header.png
similarity index 100%
rename from web/public/logo/logo-embeded-chat-header.png
rename to web/public/logo/logo-embedded-chat-header.png
diff --git a/web/service/apps.ts b/web/service/apps.ts
index 3cda551474..9dd3b72ade 100644
--- a/web/service/apps.ts
+++ b/web/service/apps.ts
@@ -1,6 +1,6 @@
 import type { Fetcher } from 'swr'
 import { del, get, patch, post, put } from './base'
-import type { ApikeysListResponse, AppDailyConversationsResponse, AppDailyEndUsersResponse, AppDailyMessagesResponse, AppDetailResponse, AppListResponse, AppSSOResponse, AppStatisticsResponse, AppTemplatesResponse, AppTokenCostsResponse, AppVoicesListResponse, CreateApiKeyResponse, GenerationIntroductionResponse, TracingConfig, TracingStatus, UpdateAppModelConfigResponse, UpdateAppSiteCodeResponse, UpdateOpenAIKeyResponse, ValidateOpenAIKeyResponse, WorkflowDailyConversationsResponse } from '@/models/app'
+import type { ApiKeysListResponse, AppDailyConversationsResponse, AppDailyEndUsersResponse, AppDailyMessagesResponse, AppDetailResponse, AppListResponse, AppSSOResponse, AppStatisticsResponse, AppTemplatesResponse, AppTokenCostsResponse, AppVoicesListResponse, CreateApiKeyResponse, GenerationIntroductionResponse, TracingConfig, TracingStatus, UpdateAppModelConfigResponse, UpdateAppSiteCodeResponse, UpdateOpenAIKeyResponse, ValidateOpenAIKeyResponse, WorkflowDailyConversationsResponse } from '@/models/app'
 import type { CommonResponse } from '@/models/common'
 import type { AppIconType, AppMode, ModelConfig } from '@/types/app'
 import type { TracingProvider } from '@/app/(commonLayout)/app/(appDetailLayout)/[appId]/overview/tracing/type'
@@ -110,8 +110,8 @@ export const fetchAppListNoMock: Fetcher<AppListResponse, { url: string; params:
   return get<AppListResponse>(url, params)
 }
 
-export const fetchApiKeysList: Fetcher<ApikeysListResponse, { url: string; params: Record<string, any> }> = ({ url, params }) => {
-  return get<ApikeysListResponse>(url, params)
+export const fetchApiKeysList: Fetcher<ApiKeysListResponse, { url: string; params: Record<string, any> }> = ({ url, params }) => {
+  return get<ApiKeysListResponse>(url, params)
 }
 
 export const delApikey: Fetcher<CommonResponse, { url: string; params: Record<string, any> }> = ({ url, params }) => {
diff --git a/web/service/base.ts b/web/service/base.ts
index bda83f1c8e..83389d8be8 100644
--- a/web/service/base.ts
+++ b/web/service/base.ts
@@ -4,10 +4,12 @@ import type { AnnotationReply, MessageEnd, MessageReplace, ThoughtItem } from '@
 import type { VisionFile } from '@/types/app'
 import type {
   IterationFinishedResponse,
-  IterationNextedResponse,
+  IterationNextResponse,
   IterationStartedResponse,
   NodeFinishedResponse,
   NodeStartedResponse,
+  ParallelBranchFinishedResponse,
+  ParallelBranchStartedResponse,
   TextChunkResponse,
   TextReplaceResponse,
   WorkflowFinishedResponse,
@@ -57,8 +59,10 @@ export type IOnWorkflowFinished = (workflowFinished: WorkflowFinishedResponse) =
 export type IOnNodeStarted = (nodeStarted: NodeStartedResponse) => void
 export type IOnNodeFinished = (nodeFinished: NodeFinishedResponse) => void
 export type IOnIterationStarted = (workflowStarted: IterationStartedResponse) => void
-export type IOnIterationNexted = (workflowStarted: IterationNextedResponse) => void
+export type IOnIterationNext = (workflowStarted: IterationNextResponse) => void
 export type IOnIterationFinished = (workflowFinished: IterationFinishedResponse) => void
+export type IOnParallelBranchStarted = (parallelBranchStarted: ParallelBranchStartedResponse) => void
+export type IOnParallelBranchFinished = (parallelBranchFinished: ParallelBranchFinishedResponse) => void
 export type IOnTextChunk = (textChunk: TextChunkResponse) => void
 export type IOnTTSChunk = (messageId: string, audioStr: string, audioType?: string) => void
 export type IOnTTSEnd = (messageId: string, audioStr: string, audioType?: string) => void
@@ -84,8 +88,10 @@ export type IOtherOptions = {
   onNodeStarted?: IOnNodeStarted
   onNodeFinished?: IOnNodeFinished
   onIterationStart?: IOnIterationStarted
-  onIterationNext?: IOnIterationNexted
+  onIterationNext?: IOnIterationNext
   onIterationFinish?: IOnIterationFinished
+  onParallelBranchStarted?: IOnParallelBranchStarted
+  onParallelBranchFinished?: IOnParallelBranchFinished
   onTextChunk?: IOnTextChunk
   onTTSChunk?: IOnTTSChunk
   onTTSEnd?: IOnTTSEnd
@@ -137,8 +143,10 @@ const handleStream = (
   onNodeStarted?: IOnNodeStarted,
   onNodeFinished?: IOnNodeFinished,
   onIterationStart?: IOnIterationStarted,
-  onIterationNext?: IOnIterationNexted,
+  onIterationNext?: IOnIterationNext,
   onIterationFinish?: IOnIterationFinished,
+  onParallelBranchStarted?: IOnParallelBranchStarted,
+  onParallelBranchFinished?: IOnParallelBranchFinished,
   onTextChunk?: IOnTextChunk,
   onTTSChunk?: IOnTTSChunk,
   onTTSEnd?: IOnTTSEnd,
@@ -187,7 +195,7 @@ const handleStream = (
               return
             }
             if (bufferObj.event === 'message' || bufferObj.event === 'agent_message') {
-              // can not use format here. Because message is splited.
+              // can not use format here. Because message is splitted.
               onData(unicodeToChar(bufferObj.answer), isFirstMessage, {
                 conversationId: bufferObj.conversation_id,
                 taskId: bufferObj.task_id,
@@ -223,11 +231,17 @@ const handleStream = (
               onIterationStart?.(bufferObj as IterationStartedResponse)
             }
             else if (bufferObj.event === 'iteration_next') {
-              onIterationNext?.(bufferObj as IterationNextedResponse)
+              onIterationNext?.(bufferObj as IterationNextResponse)
             }
             else if (bufferObj.event === 'iteration_completed') {
               onIterationFinish?.(bufferObj as IterationFinishedResponse)
             }
+            else if (bufferObj.event === 'parallel_branch_started') {
+              onParallelBranchStarted?.(bufferObj as ParallelBranchStartedResponse)
+            }
+            else if (bufferObj.event === 'parallel_branch_finished') {
+              onParallelBranchFinished?.(bufferObj as ParallelBranchFinishedResponse)
+            }
             else if (bufferObj.event === 'text_chunk') {
               onTextChunk?.(bufferObj as TextChunkResponse)
             }
@@ -488,6 +502,8 @@ export const ssePost = (
     onIterationStart,
     onIterationNext,
     onIterationFinish,
+    onParallelBranchStarted,
+    onParallelBranchFinished,
     onTextChunk,
     onTTSChunk,
     onTTSEnd,
@@ -544,7 +560,7 @@ export const ssePost = (
           return
         }
         onData?.(str, isFirstMessage, moreInfo)
-      }, onCompleted, onThought, onMessageEnd, onMessageReplace, onFile, onWorkflowStarted, onWorkflowFinished, onNodeStarted, onNodeFinished, onIterationStart, onIterationNext, onIterationFinish, onTextChunk, onTTSChunk, onTTSEnd, onTextReplace)
+      }, onCompleted, onThought, onMessageEnd, onMessageReplace, onFile, onWorkflowStarted, onWorkflowFinished, onNodeStarted, onNodeFinished, onIterationStart, onIterationNext, onIterationFinish, onParallelBranchStarted, onParallelBranchFinished, onTextChunk, onTTSChunk, onTTSEnd, onTextReplace)
     }).catch((e) => {
       if (e.toString() !== 'AbortError: The user aborted a request.' && !e.toString().errorMessage.includes('TypeError: Cannot assign to read only property'))
         Toast.notify({ type: 'error', message: e })
diff --git a/web/service/datasets.ts b/web/service/datasets.ts
index c861a73c37..4ca269a7d6 100644
--- a/web/service/datasets.ts
+++ b/web/service/datasets.ts
@@ -18,14 +18,14 @@ import type {
   ProcessRuleResponse,
   RelatedAppResponse,
   SegmentDetailModel,
-  SegmentUpdator,
+  SegmentUpdater,
   SegmentsQuery,
   SegmentsResponse,
   createDocumentResponse,
 } from '@/models/datasets'
 import type { CommonResponse, DataSourceNotionWorkspace } from '@/models/common'
 import type {
-  ApikeysListResponse,
+  ApiKeysListResponse,
   CreateApiKeyResponse,
 } from '@/models/app'
 import type { RetrievalConfig } from '@/types/app'
@@ -184,11 +184,11 @@ export const disableSegment: Fetcher<CommonResponse, { datasetId: string; segmen
   return patch<CommonResponse>(`/datasets/${datasetId}/segments/${segmentId}/disable`)
 }
 
-export const updateSegment: Fetcher<{ data: SegmentDetailModel; doc_form: string }, { datasetId: string; documentId: string; segmentId: string; body: SegmentUpdator }> = ({ datasetId, documentId, segmentId, body }) => {
+export const updateSegment: Fetcher<{ data: SegmentDetailModel; doc_form: string }, { datasetId: string; documentId: string; segmentId: string; body: SegmentUpdater }> = ({ datasetId, documentId, segmentId, body }) => {
   return patch<{ data: SegmentDetailModel; doc_form: string }>(`/datasets/${datasetId}/documents/${documentId}/segments/${segmentId}`, { body })
 }
 
-export const addSegment: Fetcher<{ data: SegmentDetailModel; doc_form: string }, { datasetId: string; documentId: string; body: SegmentUpdator }> = ({ datasetId, documentId, body }) => {
+export const addSegment: Fetcher<{ data: SegmentDetailModel; doc_form: string }, { datasetId: string; documentId: string; body: SegmentUpdater }> = ({ datasetId, documentId, body }) => {
   return post<{ data: SegmentDetailModel; doc_form: string }>(`/datasets/${datasetId}/documents/${documentId}/segment`, { body })
 }
 
@@ -221,8 +221,8 @@ export const fetchNotionPagePreview: Fetcher<{ content: string }, { workspaceID:
   return get<{ content: string }>(`notion/workspaces/${workspaceID}/pages/${pageID}/${pageType}/preview`)
 }
 
-export const fetchApiKeysList: Fetcher<ApikeysListResponse, { url: string; params: Record<string, any> }> = ({ url, params }) => {
-  return get<ApikeysListResponse>(url, params)
+export const fetchApiKeysList: Fetcher<ApiKeysListResponse, { url: string; params: Record<string, any> }> = ({ url, params }) => {
+  return get<ApiKeysListResponse>(url, params)
 }
 
 export const delApikey: Fetcher<CommonResponse, { url: string; params: Record<string, any> }> = ({ url, params }) => {
diff --git a/web/service/debug.ts b/web/service/debug.ts
index 8e90fe565f..38068cad6e 100644
--- a/web/service/debug.ts
+++ b/web/service/debug.ts
@@ -96,7 +96,7 @@ export const fetchPromptTemplate = ({
   })
 }
 
-export const fetchTextGenerationMessge = ({
+export const fetchTextGenerationMessage = ({
   appId,
   messageId,
 }: { appId: string; messageId: string }) => {
diff --git a/web/service/share.ts b/web/service/share.ts
index f5a695f6c3..0e46e30d01 100644
--- a/web/service/share.ts
+++ b/web/service/share.ts
@@ -1,9 +1,9 @@
-import type { IOnCompleted, IOnData, IOnError, IOnFile, IOnIterationFinished, IOnIterationNexted, IOnIterationStarted, IOnMessageEnd, IOnMessageReplace, IOnNodeFinished, IOnNodeStarted, IOnTTSChunk, IOnTTSEnd, IOnTextChunk, IOnTextReplace, IOnThought, IOnWorkflowFinished, IOnWorkflowStarted } from './base'
+import type { IOnCompleted, IOnData, IOnError, IOnFile, IOnIterationFinished, IOnIterationNext, IOnIterationStarted, IOnMessageEnd, IOnMessageReplace, IOnNodeFinished, IOnNodeStarted, IOnTTSChunk, IOnTTSEnd, IOnTextChunk, IOnTextReplace, IOnThought, IOnWorkflowFinished, IOnWorkflowStarted } from './base'
 import {
   del as consoleDel, get as consoleGet, patch as consolePatch, post as consolePost,
   delPublic as del, getPublic as get, patchPublic as patch, postPublic as post, ssePost,
 } from './base'
-import type { Feedbacktype } from '@/app/components/base/chat/chat/type'
+import type { FeedbackType } from '@/app/components/base/chat/chat/type'
 import type {
   AppConversationData,
   AppData,
@@ -86,7 +86,7 @@ export const sendWorkflowMessage = async (
     onNodeFinished: IOnNodeFinished
     onWorkflowFinished: IOnWorkflowFinished
     onIterationStart: IOnIterationStarted
-    onIterationNext: IOnIterationNexted
+    onIterationNext: IOnIterationNext
     onIterationFinish: IOnIterationFinished
     onTextChunk: IOnTextChunk
     onTextReplace: IOnTextReplace
@@ -180,7 +180,7 @@ export const fetchAppMeta = async (isInstalledApp: boolean, installedAppId = '')
   return (getAction('get', isInstalledApp))(getUrl('meta', isInstalledApp, installedAppId)) as Promise<AppMeta>
 }
 
-export const updateFeedback = async ({ url, body }: { url: string; body: Feedbacktype }, isInstalledApp: boolean, installedAppId = '') => {
+export const updateFeedback = async ({ url, body }: { url: string; body: FeedbackType }, isInstalledApp: boolean, installedAppId = '') => {
   return (getAction('post', isInstalledApp))(getUrl(url, isInstalledApp, installedAppId), { body })
 }
 
diff --git a/web/service/workflow.ts b/web/service/workflow.ts
index 93ab0006d4..431beef96c 100644
--- a/web/service/workflow.ts
+++ b/web/service/workflow.ts
@@ -26,7 +26,7 @@ export const fetchWorkflowRunHistory: Fetcher<WorkflowRunHistoryResponse, string
   return get<WorkflowRunHistoryResponse>(url)
 }
 
-export const fetcChatRunHistory: Fetcher<ChatRunHistoryResponse, string> = (url) => {
+export const fetchChatRunHistory: Fetcher<ChatRunHistoryResponse, string> = (url) => {
   return get<ChatRunHistoryResponse>(url)
 }
 
diff --git a/web/themes/dark.css b/web/themes/dark.css
index b94124aad2..8d77329b5a 100644
--- a/web/themes/dark.css
+++ b/web/themes/dark.css
@@ -147,13 +147,13 @@ html[data-theme="dark"] {
 
   --color-components-main-nav-nav-user-border: #FFFFFF0D;
 
-  --color-components-silder-knob: #F4F4F5;
-  --color-components-silder-knob-hover: #FEFEFE;
-  --color-components-silder-knob-disabled: #FFFFFF33;
-  --color-components-silder-range: #296DFF;
-  --color-components-silder-track: #FFFFFF33;
-  --color-components-silder-knob-border-hover: #1018284D;
-  --color-components-silder-knob-border: #10182833;
+  --color-components-slider-knob: #F4F4F5;
+  --color-components-slider-knob-hover: #FEFEFE;
+  --color-components-slider-knob-disabled: #FFFFFF33;
+  --color-components-slider-range: #296DFF;
+  --color-components-slider-track: #FFFFFF33;
+  --color-components-slider-knob-border-hover: #1018284D;
+  --color-components-slider-knob-border: #10182833;
 
   --color-components-segmented-control-item-active-bg: #FFFFFF14;
   --color-components-segmented-control-item-active-border: #C8CEDA14;
@@ -268,7 +268,7 @@ html[data-theme="dark"] {
 
   --color-background-body: #1D1D20;
   --color-background-default-subtle: #222225;
-  --color-background-neurtral-subtle: #1D1D20;
+  --color-background-neutral-subtle: #1D1D20;
   --color-background-sidenav-bg: #27272AEB;
   --color-background-default: #222225;
   --color-background-soft: #18181B40;
@@ -316,6 +316,7 @@ html[data-theme="dark"] {
   --color-workflow-block-border: #FFFFFF14;
   --color-workflow-block-parma-bg: #FFFFFF0D;
   --color-workflow-block-bg: #27272B;
+  --color-workflow-block-border-highlight: #C8CEDA33;
 
   --color-workflow-canvas-workflow-dot-color: #8585AD26;
   --color-workflow-canvas-workflow-bg: #1D1D20;
@@ -324,8 +325,8 @@ html[data-theme="dark"] {
   --color-workflow-link-line-normal: #676F83;
   --color-workflow-link-line-handle: #296DFF;
 
-  --color-workflow-minmap-bg: #27272B;
-  --color-workflow-minmap-block: #C8CEDA14;
+  --color-workflow-minimap-bg: #27272B;
+  --color-workflow-minimap-block: #C8CEDA14;
 
   --color-workflow-display-success-bg: #17B26A33;
   --color-workflow-display-success-border-1: #17B26AE5;
@@ -371,8 +372,8 @@ html[data-theme="dark"] {
   --color-divider-deep: #C8CEDA33;
   --color-divider-burn: #18181BF2;
   --color-divider-intense: #C8CEDA66;
-  --color-divider-soild: #3A3A40;
-  --color-divider-soild-alt: #747481;
+  --color-divider-solid: #3A3A40;
+  --color-divider-solid-alt: #747481;
 
   --color-state-base-hover: #C8CEDA14;
   --color-state-base-active: #C8CEDA33;
@@ -383,24 +384,24 @@ html[data-theme="dark"] {
   --color-state-accent-hover: #155AEF24;
   --color-state-accent-active: #155AEF24;
   --color-state-accent-hover-alt: #155AEF40;
-  --color-state-accent-soild: #5289FF;
+  --color-state-accent-solid: #5289FF;
   --color-state-accent-active-alt: #155AEF33;
 
   --color-state-destructive-hover: #F0443824;
   --color-state-destructive-hover-alt: #F0443840;
   --color-state-destructive-active: #F044384D;
-  --color-state-destructive-soild: #F97066;
+  --color-state-destructive-solid: #F97066;
   --color-state-destructive-border: #F97066;
 
   --color-state-success-hover: #17B26A24;
   --color-state-success-hover-alt: #17B26A40;
   --color-state-success-active: #17B26A4D;
-  --color-state-success-soild: #47CD89;
+  --color-state-success-solid: #47CD89;
 
   --color-state-warning-hover: #F7900924;
   --color-state-warning-hover-alt: #F7900940;
   --color-state-warning-active: #F790094D;
-  --color-state-warning-soild: #F79009;
+  --color-state-warning-solid: #F79009;
 
   --color-effects-highlight: #C8CEDA14;
   --color-effects-highlight-lightmode-off: #C8CEDA14;
diff --git a/web/themes/light.css b/web/themes/light.css
index 80a0fa36f5..89303c250e 100644
--- a/web/themes/light.css
+++ b/web/themes/light.css
@@ -147,13 +147,13 @@ html[data-theme="light"] {
 
   --color-components-main-nav-nav-user-border: #FFFFFF;
 
-  --color-components-silder-knob: #FFFFFF;
-  --color-components-silder-knob-hover: #FFFFFF;
-  --color-components-silder-knob-disabled: #FFFFFFF2;
-  --color-components-silder-range: #296DFF;
-  --color-components-silder-track: #E9EBF0;
-  --color-components-silder-knob-border-hover: #10182833;
-  --color-components-silder-knob-border: #10182824;
+  --color-components-slider-knob: #FFFFFF;
+  --color-components-slider-knob-hover: #FFFFFF;
+  --color-components-slider-knob-disabled: #FFFFFFF2;
+  --color-components-slider-range: #296DFF;
+  --color-components-slider-track: #E9EBF0;
+  --color-components-slider-knob-border-hover: #10182833;
+  --color-components-slider-knob-border: #10182824;
 
   --color-components-segmented-control-item-active-bg: #FFFFFF;
   --color-components-segmented-control-item-active-border: #FFFFFF;
@@ -268,7 +268,7 @@ html[data-theme="light"] {
 
   --color-background-body: #F2F4F7;
   --color-background-default-subtle: #FCFCFD;
-  --color-background-neurtral-subtle: #F9FAFB;
+  --color-background-neutral-subtle: #F9FAFB;
   --color-background-sidenav-bg: #FFFFFFCC;
   --color-background-default: #FFFFFF;
   --color-background-soft: #F9FAFB;
@@ -316,6 +316,7 @@ html[data-theme="light"] {
   --color-workflow-block-border: #FFFFFF;
   --color-workflow-block-parma-bg: #F2F4F7;
   --color-workflow-block-bg: #FCFCFD;
+  --color-workflow-block-border-highlight: #155AEF24;
 
   --color-workflow-canvas-workflow-dot-color: #8585AD26;
   --color-workflow-canvas-workflow-bg: #F2F4F7;
@@ -324,8 +325,8 @@ html[data-theme="light"] {
   --color-workflow-link-line-normal: #D0D5DC;
   --color-workflow-link-line-handle: #296DFF;
 
-  --color-workflow-minmap-bg: #E9EBF0;
-  --color-workflow-minmap-block: #C8CEDA4D;
+  --color-workflow-minimap-bg: #E9EBF0;
+  --color-workflow-minimap-block: #C8CEDA4D;
 
   --color-workflow-display-success-bg: #ECFDF3;
   --color-workflow-display-success-border-1: #17B26ACC;
@@ -371,8 +372,8 @@ html[data-theme="light"] {
   --color-divider-deep: #10182824;
   --color-divider-burn: #1018280A;
   --color-divider-intense: #1018284D;
-  --color-divider-soild: #D0D5DC;
-  --color-divider-soild-alt: #98A2B2;
+  --color-divider-solid: #D0D5DC;
+  --color-divider-solid-alt: #98A2B2;
 
   --color-state-base-hover: #C8CEDA33;
   --color-state-base-active: #C8CEDA66;
@@ -383,24 +384,24 @@ html[data-theme="light"] {
   --color-state-accent-hover: #EFF4FF;
   --color-state-accent-active: #155AEF14;
   --color-state-accent-hover-alt: #D1E0FF;
-  --color-state-accent-soild: #296DFF;
+  --color-state-accent-solid: #296DFF;
   --color-state-accent-active-alt: #155AEF24;
 
   --color-state-destructive-hover: #FEF3F2;
   --color-state-destructive-hover-alt: #FEE4E2;
   --color-state-destructive-active: #FECDCA;
-  --color-state-destructive-soild: #F04438;
+  --color-state-destructive-solid: #F04438;
   --color-state-destructive-border: #FDA29B;
 
   --color-state-success-hover: #ECFDF3;
   --color-state-success-hover-alt: #DCFAE6;
   --color-state-success-active: #ABEFC6;
-  --color-state-success-soild: #17B26A;
+  --color-state-success-solid: #17B26A;
 
   --color-state-warning-hover: #FFFAEB;
   --color-state-warning-hover-alt: #FEF0C7;
   --color-state-warning-active: #FEDF89;
-  --color-state-warning-soild: #F79009;
+  --color-state-warning-solid: #F79009;
 
   --color-effects-highlight: #FFFFFF;
   --color-effects-highlight-lightmode-off: #FFFFFF00;
diff --git a/web/themes/tailwind-theme-var-define.ts b/web/themes/tailwind-theme-var-define.ts
index caeb01b5fa..643c96d1a1 100644
--- a/web/themes/tailwind-theme-var-define.ts
+++ b/web/themes/tailwind-theme-var-define.ts
@@ -147,13 +147,13 @@ const vars = {
 
   'components-main-nav-nav-user-border': 'var(--color-components-main-nav-nav-user-border)',
 
-  'components-silder-knob': 'var(--color-components-silder-knob)',
-  'components-silder-knob-hover': 'var(--color-components-silder-knob-hover)',
-  'components-silder-knob-disabled': 'var(--color-components-silder-knob-disabled)',
-  'components-silder-range': 'var(--color-components-silder-range)',
-  'components-silder-track': 'var(--color-components-silder-track)',
-  'components-silder-knob-border-hover': 'var(--color-components-silder-knob-border-hover)',
-  'components-silder-knob-border': 'var(--color-components-silder-knob-border)',
+  'components-slider-knob': 'var(--color-components-slider-knob)',
+  'components-slider-knob-hover': 'var(--color-components-slider-knob-hover)',
+  'components-slider-knob-disabled': 'var(--color-components-slider-knob-disabled)',
+  'components-slider-range': 'var(--color-components-slider-range)',
+  'components-slider-track': 'var(--color-components-slider-track)',
+  'components-slider-knob-border-hover': 'var(--color-components-slider-knob-border-hover)',
+  'components-slider-knob-border': 'var(--color-components-slider-knob-border)',
 
   'components-segmented-control-item-active-bg': 'var(--color-components-segmented-control-item-active-bg)',
   'components-segmented-control-item-active-border': 'var(--color-components-segmented-control-item-active-border)',
@@ -268,7 +268,7 @@ const vars = {
 
   'background-body': 'var(--color-background-body)',
   'background-default-subtle': 'var(--color-background-default-subtle)',
-  'background-neurtral-subtle': 'var(--color-background-neurtral-subtle)',
+  'background-neutral-subtle': 'var(--color-background-neutral-subtle)',
   'background-sidenav-bg': 'var(--color-background-sidenav-bg)',
   'background-default': 'var(--color-background-default)',
   'background-soft': 'var(--color-background-soft)',
@@ -316,6 +316,7 @@ const vars = {
   'workflow-block-border': 'var(--color-workflow-block-border)',
   'workflow-block-parma-bg': 'var(--color-workflow-block-parma-bg)',
   'workflow-block-bg': 'var(--color-workflow-block-bg)',
+  'workflow-block-border-highlight': 'var(--color-workflow-block-border-highlight)',
 
   'workflow-canvas-workflow-dot-color': 'var(--color-workflow-canvas-workflow-dot-color)',
   'workflow-canvas-workflow-bg': 'var(--color-workflow-canvas-workflow-bg)',
@@ -324,8 +325,8 @@ const vars = {
   'workflow-link-line-normal': 'var(--color-workflow-link-line-normal)',
   'workflow-link-line-handle': 'var(--color-workflow-link-line-handle)',
 
-  'workflow-minmap-bg': 'var(--color-workflow-minmap-bg)',
-  'workflow-minmap-block': 'var(--color-workflow-minmap-block)',
+  'workflow-minimap-bg': 'var(--color-workflow-minimap-bg)',
+  'workflow-minimap-block': 'var(--color-workflow-minimap-block)',
 
   'workflow-display-success-bg': 'var(--color-workflow-display-success-bg)',
   'workflow-display-success-border-1': 'var(--color-workflow-display-success-border-1)',
@@ -371,8 +372,8 @@ const vars = {
   'divider-deep': 'var(--color-divider-deep)',
   'divider-burn': 'var(--color-divider-burn)',
   'divider-intense': 'var(--color-divider-intense)',
-  'divider-soild': 'var(--color-divider-soild)',
-  'divider-soild-alt': 'var(--color-divider-soild-alt)',
+  'divider-solid': 'var(--color-divider-solid)',
+  'divider-solid-alt': 'var(--color-divider-solid-alt)',
 
   'state-base-hover': 'var(--color-state-base-hover)',
   'state-base-active': 'var(--color-state-base-active)',
@@ -383,24 +384,24 @@ const vars = {
   'state-accent-hover': 'var(--color-state-accent-hover)',
   'state-accent-active': 'var(--color-state-accent-active)',
   'state-accent-hover-alt': 'var(--color-state-accent-hover-alt)',
-  'state-accent-soild': 'var(--color-state-accent-soild)',
+  'state-accent-solid': 'var(--color-state-accent-solid)',
   'state-accent-active-alt': 'var(--color-state-accent-active-alt)',
 
   'state-destructive-hover': 'var(--color-state-destructive-hover)',
   'state-destructive-hover-alt': 'var(--color-state-destructive-hover-alt)',
   'state-destructive-active': 'var(--color-state-destructive-active)',
-  'state-destructive-soild': 'var(--color-state-destructive-soild)',
+  'state-destructive-solid': 'var(--color-state-destructive-solid)',
   'state-destructive-border': 'var(--color-state-destructive-border)',
 
   'state-success-hover': 'var(--color-state-success-hover)',
   'state-success-hover-alt': 'var(--color-state-success-hover-alt)',
   'state-success-active': 'var(--color-state-success-active)',
-  'state-success-soild': 'var(--color-state-success-soild)',
+  'state-success-solid': 'var(--color-state-success-solid)',
 
   'state-warning-hover': 'var(--color-state-warning-hover)',
   'state-warning-hover-alt': 'var(--color-state-warning-hover-alt)',
   'state-warning-active': 'var(--color-state-warning-active)',
-  'state-warning-soild': 'var(--color-state-warning-soild)',
+  'state-warning-solid': 'var(--color-state-warning-solid)',
 
   'effects-highlight': 'var(--color-effects-highlight)',
   'effects-highlight-lightmode-off': 'var(--color-effects-highlight-lightmode-off)',
diff --git a/web/types/workflow.ts b/web/types/workflow.ts
index f7991bc4e0..dbf2b3e587 100644
--- a/web/types/workflow.ts
+++ b/web/types/workflow.ts
@@ -26,9 +26,14 @@ export type NodeTracing = {
     currency: string
     iteration_id?: string
     iteration_index?: number
+    parallel_id?: string
+    parallel_start_node_id?: string
+    parent_parallel_id?: string
+    parent_parallel_start_node_id?: string
   }
   metadata: {
     iterator_length: number
+    iterator_index: number
   }
   created_at: number
   created_by: {
@@ -40,6 +45,10 @@ export type NodeTracing = {
   extras?: any
   expand?: boolean // for UI
   details?: NodeTracing[][] // iteration detail
+  parallel_id?: string
+  parallel_start_node_id?: string
+  parent_parallel_id?: string
+  parent_parallel_start_node_id?: string
 }
 
 export type FetchWorkflowDraftResponse = {
@@ -109,6 +118,7 @@ export type NodeStartedResponse = {
   data: {
     id: string
     node_id: string
+    iteration_id?: string
     node_type: string
     index: number
     predecessor_node_id?: string
@@ -125,6 +135,7 @@ export type NodeFinishedResponse = {
   data: {
     id: string
     node_id: string
+    iteration_id?: string
     node_type: string
     index: number
     predecessor_node_id?: string
@@ -138,6 +149,10 @@ export type NodeFinishedResponse = {
       total_tokens: number
       total_price: number
       currency: string
+      parallel_id?: string
+      parallel_start_node_id?: string
+      iteration_index?: number
+      iteration_id?: string
     }
     created_at: number
   }
@@ -152,13 +167,15 @@ export type IterationStartedResponse = {
     node_id: string
     metadata: {
       iterator_length: number
+      iteration_id: string
+      iteration_index: number
     }
     created_at: number
     extras?: any
   }
 }
 
-export type IterationNextedResponse = {
+export type IterationNextResponse = {
   task_id: string
   workflow_run_id: string
   event: string
@@ -169,6 +186,9 @@ export type IterationNextedResponse = {
     output: any
     extras?: any
     created_at: number
+    execution_metadata: {
+      parallel_id?: string
+    }
   }
 }
 
@@ -184,6 +204,39 @@ export type IterationFinishedResponse = {
     status: string
     created_at: number
     error: string
+    execution_metadata: {
+      parallel_id?: string
+    }
+  }
+}
+
+export type ParallelBranchStartedResponse = {
+  task_id: string
+  workflow_run_id: string
+  event: string
+  data: {
+    parallel_id: string
+    parallel_start_node_id: string
+    parent_parallel_id: string
+    parent_parallel_start_node_id: string
+    iteration_id?: string
+    created_at: number
+  }
+}
+
+export type ParallelBranchFinishedResponse = {
+  task_id: string
+  workflow_run_id: string
+  event: string
+  data: {
+    parallel_id: string
+    parallel_start_node_id: string
+    parent_parallel_id: string
+    parent_parallel_start_node_id: string
+    iteration_id?: string
+    status: string
+    created_at: number
+    error: string
   }
 }
 
diff --git a/web/utils/var.ts b/web/utils/var.ts
index 436ebfc70b..236c9debac 100644
--- a/web/utils/var.ts
+++ b/web/utils/var.ts
@@ -1,4 +1,4 @@
-import { MAX_VAR_KEY_LENGHT, VAR_ITEM_TEMPLATE, VAR_ITEM_TEMPLATE_IN_WORKFLOW, getMaxVarNameLength } from '@/config'
+import { MAX_VAR_KEY_LENGTH, VAR_ITEM_TEMPLATE, VAR_ITEM_TEMPLATE_IN_WORKFLOW, getMaxVarNameLength } from '@/config'
 import { CONTEXT_PLACEHOLDER_TEXT, HISTORY_PLACEHOLDER_TEXT, PRE_PROMPT_PLACEHOLDER_TEXT, QUERY_PLACEHOLDER_TEXT } from '@/app/components/base/prompt-editor/constants'
 import { InputVarType } from '@/app/components/workflow/types'
 
@@ -47,7 +47,7 @@ export const checkKey = (key: string, canBeEmpty?: boolean) => {
   if (canBeEmpty && key === '')
     return true
 
-  if (key.length > MAX_VAR_KEY_LENGHT)
+  if (key.length > MAX_VAR_KEY_LENGTH)
     return 'tooLong'
 
   if (otherAllowedRegex.test(key)) {
@@ -86,7 +86,7 @@ export const getVars = (value: string) => {
     return ![CONTEXT_PLACEHOLDER_TEXT, HISTORY_PLACEHOLDER_TEXT, QUERY_PLACEHOLDER_TEXT, PRE_PROMPT_PLACEHOLDER_TEXT].includes(item)
   }).map((item) => {
     return item.replace('{{', '').replace('}}', '')
-  }).filter(key => key.length <= MAX_VAR_KEY_LENGHT) || []
+  }).filter(key => key.length <= MAX_VAR_KEY_LENGTH) || []
   const keyObj: Record<string, boolean> = {}
   // remove duplicate keys
   const res: string[] = []
diff --git a/web/yarn.lock b/web/yarn.lock
index 007b0ac714..bec2059a47 100644
--- a/web/yarn.lock
+++ b/web/yarn.lock
@@ -260,6 +260,13 @@
   resolved "https://registry.npmjs.org/@babel/parser/-/parser-7.24.4.tgz"
   integrity sha512-zTvEBcghmeBma9QIGunWevvBAp4/Qu9Bdq+2k0Ot4fVMD6v3dsC9WOcRSKk7tRRyBM/53yKMJko9xOatGQAwSg==
 
+"@babel/parser@^7.25.4":
+  version "7.25.6"
+  resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.25.6.tgz#85660c5ef388cbbf6e3d2a694ee97a38f18afe2f"
+  integrity sha512-trGdfBdbD0l1ZPmcJ83eNxB9rbEax4ALFTF7fN386TMYbeCQbyme5cOEXQhbGXKebwGaB/J52w1mrklMcbgy6Q==
+  dependencies:
+    "@babel/types" "^7.25.6"
+
 "@babel/plugin-syntax-async-generators@^7.8.4":
   version "7.8.4"
   resolved "https://registry.npmjs.org/@babel/plugin-syntax-async-generators/-/plugin-syntax-async-generators-7.8.4.tgz#a983fb1aeb2ec3f6ed042a210f640e90e786fe0d"
@@ -406,10 +413,10 @@
     "@babel/helper-validator-identifier" "^7.24.7"
     to-fast-properties "^2.0.0"
 
-"@babel/types@^7.24.0":
-  version "7.25.2"
-  resolved "https://registry.npmjs.org/@babel/types/-/types-7.25.2.tgz#55fb231f7dc958cd69ea141a4c2997e819646125"
-  integrity sha512-YTnYtra7W9e6/oAZEHj0bJehPRUlLH9/fbpT5LfB0NhQXyALCRkRs3zH9v07IYhkgpqX6Z78FnuccZr/l4Fs4Q==
+"@babel/types@^7.25.4", "@babel/types@^7.25.6":
+  version "7.25.6"
+  resolved "https://registry.yarnpkg.com/@babel/types/-/types-7.25.6.tgz#893942ddb858f32ae7a004ec9d3a76b3463ef8e6"
+  integrity sha512-/l42B1qxpG6RdfYf343Uw1vmDjeNhneUXtzhojE7pDgfpEypmRhI6j1kr17XCVv4Cgl9HdAiQY2x0GwKm7rWCw==
   dependencies:
     "@babel/helper-string-parser" "^7.24.8"
     "@babel/helper-validator-identifier" "^7.24.7"
@@ -1465,7 +1472,7 @@
 
 "@sindresorhus/is@^4.0.0":
   version "4.6.0"
-  resolved "https://registry.npmjs.org/@sindresorhus/is/-/is-4.6.0.tgz#3c7c9c46e678feefe7a2e5bb609d3dbd665ffb3f"
+  resolved "https://registry.yarnpkg.com/@sindresorhus/is/-/is-4.6.0.tgz#3c7c9c46e678feefe7a2e5bb609d3dbd665ffb3f"
   integrity sha512-t09vSN3MdfsyCHoFcTRCH/iUtG7OJ0CsjzB8cjAmKc/va/kIgeDI/TxsigdncE/4be734m0cvIYwNaV4i2XqAw==
 
 "@sinonjs/commons@^3.0.0":
@@ -1482,6 +1489,11 @@
   dependencies:
     "@sinonjs/commons" "^3.0.0"
 
+"@svgdotjs/svg.js@^3.2.4":
+  version "3.2.4"
+  resolved "https://registry.yarnpkg.com/@svgdotjs/svg.js/-/svg.js-3.2.4.tgz#4716be92a64c66b29921b63f7235fcfb953fb13a"
+  integrity sha512-BjJ/7vWNowlX3Z8O4ywT58DqbNRyYlkk6Yz/D13aB7hGmfQTvGX4Tkgtm/ApYlu9M7lCQi15xUEidqMUmdMYwg==
+
 "@swc/counter@^0.1.3":
   version "0.1.3"
   resolved "https://registry.npmjs.org/@swc/counter/-/counter-0.1.3.tgz"
@@ -1497,7 +1509,7 @@
 
 "@szmarczak/http-timer@^4.0.5":
   version "4.0.6"
-  resolved "https://registry.npmjs.org/@szmarczak/http-timer/-/http-timer-4.0.6.tgz#b4a914bb62e7c272d4e5989fe4440f812ab1d807"
+  resolved "https://registry.yarnpkg.com/@szmarczak/http-timer/-/http-timer-4.0.6.tgz#b4a914bb62e7c272d4e5989fe4440f812ab1d807"
   integrity sha512-4BAffykYOgO+5nzBWYwE3W90sBgLJoUPRWWcL8wlyiM8IB8ipJz3UMJ9KXQd1RKQXpKp8Tutn80HZtWsu2u76w==
   dependencies:
     defer-to-connect "^2.0.0"
@@ -1624,7 +1636,7 @@
 
 "@types/cacheable-request@^6.0.1":
   version "6.0.3"
-  resolved "https://registry.npmjs.org/@types/cacheable-request/-/cacheable-request-6.0.3.tgz#a430b3260466ca7b5ca5bfd735693b36e7a9d183"
+  resolved "https://registry.yarnpkg.com/@types/cacheable-request/-/cacheable-request-6.0.3.tgz#a430b3260466ca7b5ca5bfd735693b36e7a9d183"
   integrity sha512-IQ3EbTzGxIigb1I3qPZc1rWJnH0BmSKv5QYTalEwweFvyBDLSAe24zP0le/hyi7ecGfZVlIVAg4BZqb8WBwKqw==
   dependencies:
     "@types/http-cache-semantics" "*"
@@ -1890,9 +1902,16 @@
   dependencies:
     "@types/unist" "*"
 
+"@types/hast@^3.0.0":
+  version "3.0.4"
+  resolved "https://registry.yarnpkg.com/@types/hast/-/hast-3.0.4.tgz#1d6b39993b82cea6ad783945b0508c25903e15aa"
+  integrity sha512-WPs+bbQw5aCj+x6laNGWLH3wviHtoCv/P3+otBhbOhJgG8qtpdAMlTCxLtsTWA7LH1Oh/bFCHsBn0TPS5m30EQ==
+  dependencies:
+    "@types/unist" "*"
+
 "@types/http-cache-semantics@*":
   version "4.0.4"
-  resolved "https://registry.npmjs.org/@types/http-cache-semantics/-/http-cache-semantics-4.0.4.tgz#b979ebad3919799c979b17c72621c0bc0a31c6c4"
+  resolved "https://registry.yarnpkg.com/@types/http-cache-semantics/-/http-cache-semantics-4.0.4.tgz#b979ebad3919799c979b17c72621c0bc0a31c6c4"
   integrity sha512-1m0bIFVc7eJWyve9S0RnuRgcQqF/Xd5QsUZAZeQFr1Q3/p9JWoQQEqmVy+DPTNpGXwhgIetAoYF8JSc33q29QA==
 
 "@types/istanbul-lib-coverage@*", "@types/istanbul-lib-coverage@^2.0.0", "@types/istanbul-lib-coverage@^2.0.1":
@@ -1963,7 +1982,7 @@
 
 "@types/keyv@^3.1.4":
   version "3.1.4"
-  resolved "https://registry.npmjs.org/@types/keyv/-/keyv-3.1.4.tgz#3ccdb1c6751b0c7e52300bcdacd5bcbf8faa75b6"
+  resolved "https://registry.yarnpkg.com/@types/keyv/-/keyv-3.1.4.tgz#3ccdb1c6751b0c7e52300bcdacd5bcbf8faa75b6"
   integrity sha512-BQ5aZNSCpj7D6K2ksrRCTmKRLEpnPvWDiLPfoGyhZ++8YtiK9d/3DBKPJgry359X/P1PfruyYwvnvwFjuEiEIg==
   dependencies:
     "@types/node" "*"
@@ -1987,6 +2006,13 @@
   dependencies:
     "@types/unist" "*"
 
+"@types/mdast@^4.0.0":
+  version "4.0.4"
+  resolved "https://registry.yarnpkg.com/@types/mdast/-/mdast-4.0.4.tgz#7ccf72edd2f1aa7dd3437e180c64373585804dd6"
+  integrity sha512-kGaNbPh1k7AFzgpud/gMdvIm5xuECykRR+JnWKQno9TAXVa6WIVCGTPvYGekIDL4uwCZQSYbUxNBSb1aUo79oA==
+  dependencies:
+    "@types/unist" "*"
+
 "@types/mdx@^2.0.0":
   version "2.0.5"
   resolved "https://registry.npmjs.org/@types/mdx/-/mdx-2.0.5.tgz"
@@ -2080,7 +2106,7 @@
 
 "@types/responselike@^1.0.0":
   version "1.0.3"
-  resolved "https://registry.npmjs.org/@types/responselike/-/responselike-1.0.3.tgz#cc29706f0a397cfe6df89debfe4bf5cea159db50"
+  resolved "https://registry.yarnpkg.com/@types/responselike/-/responselike-1.0.3.tgz#cc29706f0a397cfe6df89debfe4bf5cea159db50"
   integrity sha512-H/+L+UkTV33uf49PH5pCAUBVPNj2nDBXTN+qS1dOwyyg24l3CcicicCA7ca+HMvJBZcFgl5r8e+RR6elsb4Lyw==
   dependencies:
     "@types/node" "*"
@@ -2110,6 +2136,11 @@
   resolved "https://registry.npmjs.org/@types/unist/-/unist-2.0.6.tgz"
   integrity sha512-PBjIUxZHOuj0R15/xuwJYjFi+KZdNFrehocChv4g5hu6aFroHue8m0lBP0POdK2nKzbw0cgV1mws8+V/JAcEkQ==
 
+"@types/unist@^3.0.0":
+  version "3.0.2"
+  resolved "https://registry.yarnpkg.com/@types/unist/-/unist-3.0.2.tgz#6dd61e43ef60b34086287f83683a5c1b2dc53d20"
+  integrity sha512-dqId9J8K/vGi5Zr7oo212BGii5m3q5Hxlkwy3WpYuKPklmBEvsbMYYyLxAQpSffdLl/gdW0XUpKWFvYmyoWCoQ==
+
 "@types/uuid@^9.0.8":
   version "9.0.8"
   resolved "https://registry.npmjs.org/@types/uuid/-/uuid-9.0.8.tgz"
@@ -2211,6 +2242,11 @@
     "@typescript-eslint/types" "5.59.9"
     eslint-visitor-keys "^3.3.0"
 
+"@ungap/structured-clone@^1.0.0":
+  version "1.2.0"
+  resolved "https://registry.yarnpkg.com/@ungap/structured-clone/-/structured-clone-1.2.0.tgz#756641adb587851b5ccb3e095daf27ae581c8406"
+  integrity sha512-zuVdFrMJiuCDQUMCzQaD6KL28MjnqqN8XnAqiEq9PNm/hCPTSGfrXCOfwj1ow4LFb/tNymJPwsNbVePc1xFqrQ==
+
 "@vue/compiler-core@3.4.25":
   version "3.4.25"
   resolved "https://registry.npmjs.org/@vue/compiler-core/-/compiler-core-3.4.25.tgz"
@@ -2630,7 +2666,7 @@ binary-extensions@^2.0.0:
 
 bing-translate-api@^4.0.2:
   version "4.0.2"
-  resolved "https://registry.npmjs.org/bing-translate-api/-/bing-translate-api-4.0.2.tgz#52807a128e883bf074b4174c5e674ffca60685e7"
+  resolved "https://registry.yarnpkg.com/bing-translate-api/-/bing-translate-api-4.0.2.tgz#52807a128e883bf074b4174c5e674ffca60685e7"
   integrity sha512-JJ8XUehnxzOhHU91oy86xEtp8OOMjVEjCZJX042fKxoO19NNvxJ5omeCcxQNFoPbDqVpBJwqiGVquL0oPdQm1Q==
   dependencies:
     got "^11.8.6"
@@ -2729,12 +2765,12 @@ busboy@1.6.0:
 
 cacheable-lookup@^5.0.3:
   version "5.0.4"
-  resolved "https://registry.npmjs.org/cacheable-lookup/-/cacheable-lookup-5.0.4.tgz#5a6b865b2c44357be3d5ebc2a467b032719a7005"
+  resolved "https://registry.yarnpkg.com/cacheable-lookup/-/cacheable-lookup-5.0.4.tgz#5a6b865b2c44357be3d5ebc2a467b032719a7005"
   integrity sha512-2/kNscPhpcxrOigMZzbiWF7dz8ilhb/nIHU3EyZiXWXpeq/au8qJ8VhdftMkty3n7Gj6HIGalQG8oiBNB3AJgA==
 
 cacheable-request@^7.0.2:
   version "7.0.4"
-  resolved "https://registry.npmjs.org/cacheable-request/-/cacheable-request-7.0.4.tgz#7a33ebf08613178b403635be7b899d3e69bbe817"
+  resolved "https://registry.yarnpkg.com/cacheable-request/-/cacheable-request-7.0.4.tgz#7a33ebf08613178b403635be7b899d3e69bbe817"
   integrity sha512-v+p6ongsrp0yTGbJXjgxPow2+DL93DASP4kXCDKb8/bwRtt9OEF3whggkkDkGNzgcWy2XaF4a8nZglC7uElscg==
   dependencies:
     clone-response "^1.0.2"
@@ -2970,7 +3006,7 @@ cliui@^8.0.1:
 
 clone-response@^1.0.2:
   version "1.0.3"
-  resolved "https://registry.npmjs.org/clone-response/-/clone-response-1.0.3.tgz#af2032aa47816399cf5f0a1d0db902f517abb8c3"
+  resolved "https://registry.yarnpkg.com/clone-response/-/clone-response-1.0.3.tgz#af2032aa47816399cf5f0a1d0db902f517abb8c3"
   integrity sha512-ROoL94jJH2dUVML2Y/5PEDNaSHgeOdSDicUyS7izcF63G6sTc/FTjLub4b8Il9S8S0beOfYt0TaA5qvFK+w0wA==
   dependencies:
     mimic-response "^1.0.0"
@@ -3548,7 +3584,7 @@ decode-named-character-reference@^1.0.0:
 
 decompress-response@^6.0.0:
   version "6.0.0"
-  resolved "https://registry.npmjs.org/decompress-response/-/decompress-response-6.0.0.tgz#ca387612ddb7e104bd16d85aab00d5ecf09c66fc"
+  resolved "https://registry.yarnpkg.com/decompress-response/-/decompress-response-6.0.0.tgz#ca387612ddb7e104bd16d85aab00d5ecf09c66fc"
   integrity sha512-aW35yZM6Bb/4oJlZncMH2LCoZtJXTRxES17vE3hoRiowU2kWHaJKFkSBDnDR+cm9J+9QhXmREyIfv0pji9ejCQ==
   dependencies:
     mimic-response "^3.1.0"
@@ -3612,7 +3648,7 @@ default-browser@^4.0.0:
 
 defer-to-connect@^2.0.0:
   version "2.0.1"
-  resolved "https://registry.npmjs.org/defer-to-connect/-/defer-to-connect-2.0.1.tgz#8016bdb4143e4632b77a3449c6236277de520587"
+  resolved "https://registry.yarnpkg.com/defer-to-connect/-/defer-to-connect-2.0.1.tgz#8016bdb4143e4632b77a3449c6236277de520587"
   integrity sha512-4tvttepXG1VaYGrRibk5EwJd1t4udunSOVMdLSAL6mId1ix438oPwPZMALY41FCijukO1L0twNcGsdzS7dHgDg==
 
 define-data-property@^1.0.1, define-data-property@^1.1.1:
@@ -3665,6 +3701,13 @@ detect-newline@^3.0.0:
   resolved "https://registry.npmjs.org/detect-newline/-/detect-newline-3.1.0.tgz#576f5dfc63ae1a192ff192d8ad3af6308991b651"
   integrity sha512-TLz+x/vEXm/Y7P7wn1EJFNLxYpUD4TgMosxY6fAVJUnJMbupHBOncxyWUG9OpTaH9EBD7uFI5LfEgmMOc54DsA==
 
+devlop@^1.0.0:
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/devlop/-/devlop-1.1.0.tgz#4db7c2ca4dc6e0e834c30be70c94bbc976dc7018"
+  integrity sha512-RWmIqhcFf1lRYBvNmr7qTNuyCt/7/ns2jbpp1+PalgE/rDQcBT0fioSMUpJ93irlUhC5hrg4cYqe6U+0ImW0rA==
+  dependencies:
+    dequal "^2.0.0"
+
 didyoumean@^1.2.2:
   version "1.2.2"
   resolved "https://registry.npmjs.org/didyoumean/-/didyoumean-1.2.2.tgz"
@@ -3816,7 +3859,7 @@ emoji-regex@^9.2.2:
 
 end-of-stream@^1.1.0:
   version "1.4.4"
-  resolved "https://registry.npmjs.org/end-of-stream/-/end-of-stream-1.4.4.tgz#5ae64a5f45057baf3626ec14da0ca5e4b2431eb0"
+  resolved "https://registry.yarnpkg.com/end-of-stream/-/end-of-stream-1.4.4.tgz#5ae64a5f45057baf3626ec14da0ca5e4b2431eb0"
   integrity sha512-+uw1inIHVPQoaVuHzRyXd21icM+cnt4CzD5rW+NC1wjOUSTOs+Te7FOv7AhN7vS9x/oIyhLP5PR1H+phQAHu5Q==
   dependencies:
     once "^1.4.0"
@@ -4660,7 +4703,7 @@ get-package-type@^0.1.0:
 
 get-stream@^5.1.0:
   version "5.2.0"
-  resolved "https://registry.npmjs.org/get-stream/-/get-stream-5.2.0.tgz#4966a1795ee5ace65e706c4b7beb71257d6e22d3"
+  resolved "https://registry.yarnpkg.com/get-stream/-/get-stream-5.2.0.tgz#4966a1795ee5ace65e706c4b7beb71257d6e22d3"
   integrity sha512-nBF+F1rAZVCu/p7rjzgA+Yb4lfYXrpl7a6VmJrU8wF9I1CKvP/QwPNZHnOlwbTkY6dvtFIzFMSyQXbLoTQPRpA==
   dependencies:
     pump "^3.0.0"
@@ -4785,7 +4828,7 @@ gopd@^1.0.1:
 
 got@^11.8.6:
   version "11.8.6"
-  resolved "https://registry.npmjs.org/got/-/got-11.8.6.tgz#276e827ead8772eddbcfc97170590b841823233a"
+  resolved "https://registry.yarnpkg.com/got/-/got-11.8.6.tgz#276e827ead8772eddbcfc97170590b841823233a"
   integrity sha512-6tfZ91bOr7bOXnK7PRDCGBLa1H4U080YHNaAQ2KsMGlLEzRbk44nsZF2E1IeRc3vtJHPVbKCYgdFbaGO2ljd8g==
   dependencies:
     "@sindresorhus/is" "^4.0.0"
@@ -4905,6 +4948,20 @@ hast-util-from-parse5@^7.0.0:
     vfile-location "^4.0.0"
     web-namespaces "^2.0.0"
 
+hast-util-from-parse5@^8.0.0:
+  version "8.0.1"
+  resolved "https://registry.yarnpkg.com/hast-util-from-parse5/-/hast-util-from-parse5-8.0.1.tgz#654a5676a41211e14ee80d1b1758c399a0327651"
+  integrity sha512-Er/Iixbc7IEa7r/XLtuG52zoqn/b3Xng/w6aZQ0xGVxzhw5xUFxcRqdPzP6yFi/4HBYRaifaI5fQ1RH8n0ZeOQ==
+  dependencies:
+    "@types/hast" "^3.0.0"
+    "@types/unist" "^3.0.0"
+    devlop "^1.0.0"
+    hastscript "^8.0.0"
+    property-information "^6.0.0"
+    vfile "^6.0.0"
+    vfile-location "^5.0.0"
+    web-namespaces "^2.0.0"
+
 hast-util-is-element@^2.0.0:
   version "2.1.3"
   resolved "https://registry.npmjs.org/hast-util-is-element/-/hast-util-is-element-2.1.3.tgz"
@@ -4925,6 +4982,32 @@ hast-util-parse-selector@^3.0.0:
   dependencies:
     "@types/hast" "^2.0.0"
 
+hast-util-parse-selector@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/hast-util-parse-selector/-/hast-util-parse-selector-4.0.0.tgz#352879fa86e25616036037dd8931fb5f34cb4a27"
+  integrity sha512-wkQCkSYoOGCRKERFWcxMVMOcYE2K1AaNLU8DXS9arxnLOUEWbOXKXiJUNzEpqZ3JOKpnha3jkFrumEjVliDe7A==
+  dependencies:
+    "@types/hast" "^3.0.0"
+
+hast-util-raw@^9.0.0:
+  version "9.0.4"
+  resolved "https://registry.yarnpkg.com/hast-util-raw/-/hast-util-raw-9.0.4.tgz#2da03e37c46eb1a6f1391f02f9b84ae65818f7ed"
+  integrity sha512-LHE65TD2YiNsHD3YuXcKPHXPLuYh/gjp12mOfU8jxSrm1f/yJpsb0F/KKljS6U9LJoP0Ux+tCe8iJ2AsPzTdgA==
+  dependencies:
+    "@types/hast" "^3.0.0"
+    "@types/unist" "^3.0.0"
+    "@ungap/structured-clone" "^1.0.0"
+    hast-util-from-parse5 "^8.0.0"
+    hast-util-to-parse5 "^8.0.0"
+    html-void-elements "^3.0.0"
+    mdast-util-to-hast "^13.0.0"
+    parse5 "^7.0.0"
+    unist-util-position "^5.0.0"
+    unist-util-visit "^5.0.0"
+    vfile "^6.0.0"
+    web-namespaces "^2.0.0"
+    zwitch "^2.0.0"
+
 hast-util-to-estree@^2.0.0:
   version "2.3.3"
   resolved "https://registry.npmjs.org/hast-util-to-estree/-/hast-util-to-estree-2.3.3.tgz"
@@ -4946,6 +5029,19 @@ hast-util-to-estree@^2.0.0:
     unist-util-position "^4.0.0"
     zwitch "^2.0.0"
 
+hast-util-to-parse5@^8.0.0:
+  version "8.0.0"
+  resolved "https://registry.yarnpkg.com/hast-util-to-parse5/-/hast-util-to-parse5-8.0.0.tgz#477cd42d278d4f036bc2ea58586130f6f39ee6ed"
+  integrity sha512-3KKrV5ZVI8if87DVSi1vDeByYrkGzg4mEfeu4alwgmmIeARiBLKCZS2uw5Gb6nU9x9Yufyj3iudm6i7nl52PFw==
+  dependencies:
+    "@types/hast" "^3.0.0"
+    comma-separated-tokens "^2.0.0"
+    devlop "^1.0.0"
+    property-information "^6.0.0"
+    space-separated-tokens "^2.0.0"
+    web-namespaces "^2.0.0"
+    zwitch "^2.0.0"
+
 hast-util-to-text@^3.1.0:
   version "3.1.2"
   resolved "https://registry.npmjs.org/hast-util-to-text/-/hast-util-to-text-3.1.2.tgz"
@@ -4983,6 +5079,17 @@ hastscript@^7.0.0:
     property-information "^6.0.0"
     space-separated-tokens "^2.0.0"
 
+hastscript@^8.0.0:
+  version "8.0.0"
+  resolved "https://registry.yarnpkg.com/hastscript/-/hastscript-8.0.0.tgz#4ef795ec8dee867101b9f23cc830d4baf4fd781a"
+  integrity sha512-dMOtzCEd3ABUeSIISmrETiKuyydk1w0pa+gE/uormcTpSYuaNJPbX1NU3JLyscSLjwAQM8bWMhhIlnCqnRvDTw==
+  dependencies:
+    "@types/hast" "^3.0.0"
+    comma-separated-tokens "^2.0.0"
+    hast-util-parse-selector "^4.0.0"
+    property-information "^6.0.0"
+    space-separated-tokens "^2.0.0"
+
 heap@^0.2.6:
   version "0.2.7"
   resolved "https://registry.npmjs.org/heap/-/heap-0.2.7.tgz"
@@ -5024,6 +5131,11 @@ html-parse-stringify@^3.0.1:
   dependencies:
     void-elements "3.1.0"
 
+html-void-elements@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/html-void-elements/-/html-void-elements-3.0.0.tgz#fc9dbd84af9e747249034d4d62602def6517f1d7"
+  integrity sha512-bEqo66MRXsUGxWHV5IP0PUiAWwoEjba4VCzg0LjFJBpchPaTfyfCKTG6bc5F8ucKec3q5y6qOdGyYTSBEvhCrg==
+
 htmlparser2@^8.0.1:
   version "8.0.2"
   resolved "https://registry.npmjs.org/htmlparser2/-/htmlparser2-8.0.2.tgz"
@@ -5036,7 +5148,7 @@ htmlparser2@^8.0.1:
 
 http-cache-semantics@^4.0.0:
   version "4.1.1"
-  resolved "https://registry.npmjs.org/http-cache-semantics/-/http-cache-semantics-4.1.1.tgz#abe02fcb2985460bf0323be664436ec3476a6d5a"
+  resolved "https://registry.yarnpkg.com/http-cache-semantics/-/http-cache-semantics-4.1.1.tgz#abe02fcb2985460bf0323be664436ec3476a6d5a"
   integrity sha512-er295DKPVsV82j5kw1Gjt+ADA/XYHsajl82cGNQG2eyoPkvgUhX+nDIyelzhIWbbsXP39EHcI6l5tYs2FYqYXQ==
 
 http-proxy-agent@^5.0.0:
@@ -5050,7 +5162,7 @@ http-proxy-agent@^5.0.0:
 
 http2-wrapper@^1.0.0-beta.5.2:
   version "1.0.3"
-  resolved "https://registry.npmjs.org/http2-wrapper/-/http2-wrapper-1.0.3.tgz#b8f55e0c1f25d4ebd08b3b0c2c079f9590800b3d"
+  resolved "https://registry.yarnpkg.com/http2-wrapper/-/http2-wrapper-1.0.3.tgz#b8f55e0c1f25d4ebd08b3b0c2c079f9590800b3d"
   integrity sha512-V+23sDMr12Wnz7iTcDeJr3O6AIxlnvT/bmaAAAP/Xda35C90p9599p0F1eHR/N1KILWSoWVAiOMFjBBXaXSMxg==
   dependencies:
     quick-lru "^5.1.1"
@@ -6045,7 +6157,7 @@ jsesc@~0.5.0:
 
 json-buffer@3.0.1:
   version "3.0.1"
-  resolved "https://registry.npmjs.org/json-buffer/-/json-buffer-3.0.1.tgz#9338802a30d3b6605fbe0613e094008ca8c05a13"
+  resolved "https://registry.yarnpkg.com/json-buffer/-/json-buffer-3.0.1.tgz#9338802a30d3b6605fbe0613e094008ca8c05a13"
   integrity sha512-4bV5BfR2mqfQTJm+V5tPPdf+ZpuhiIvTuAB5g8kcrXOZpTT/QwwVRWBywX1ozr6lEuPdbHxwaJlm9G6mI2sfSQ==
 
 json-parse-even-better-errors@^2.3.0:
@@ -6102,7 +6214,7 @@ katex@^0.16.0, katex@^0.16.10:
 
 keyv@^4.0.0:
   version "4.5.4"
-  resolved "https://registry.npmjs.org/keyv/-/keyv-4.5.4.tgz#a879a99e29452f942439f2a405e3af8b31d4de93"
+  resolved "https://registry.yarnpkg.com/keyv/-/keyv-4.5.4.tgz#a879a99e29452f942439f2a405e3af8b31d4de93"
   integrity sha512-oxVHkHR/EJf2CNXnWxRLW6mg7JyCCUcG0DtEGmL2ctUo1PNTin1PUil+r/+4r5MpVgC/fn1kjsx7mjSujKqIpw==
   dependencies:
     json-buffer "3.0.1"
@@ -6280,7 +6392,7 @@ loose-envify@^1.1.0, loose-envify@^1.4.0:
 
 lowercase-keys@^2.0.0:
   version "2.0.0"
-  resolved "https://registry.npmjs.org/lowercase-keys/-/lowercase-keys-2.0.0.tgz#2603e78b7b4b0006cbca2fbcc8a3202558ac9479"
+  resolved "https://registry.yarnpkg.com/lowercase-keys/-/lowercase-keys-2.0.0.tgz#2603e78b7b4b0006cbca2fbcc8a3202558ac9479"
   integrity sha512-tqNXrS78oMOE73NMxK4EMLQsQowWf8jKooH9g7xPavRT706R6bkQJ6DY2Te7QukaZsulxa30wQ7bk0pm4XiHmA==
 
 lowlight@^1.17.0:
@@ -6316,12 +6428,12 @@ lz-string@^1.5.0:
   integrity sha512-h5bgJWpxJNswbU7qCrV0tIKQCaS3blPDrqKWx+QxzuzL1zGUzij9XCWLrSLsJPu5t+eWA/ycetzYAO5IOMcWAQ==
 
 magicast@^0.3.4:
-  version "0.3.4"
-  resolved "https://registry.npmjs.org/magicast/-/magicast-0.3.4.tgz#bbda1791d03190a24b00ff3dd18151e7fd381d19"
-  integrity sha512-TyDF/Pn36bBji9rWKHlZe+PZb6Mx5V8IHCSxk7X4aljM4e/vyDvZZYwHewdVaqiA0nb3ghfHU/6AUpDxWoER2Q==
+  version "0.3.5"
+  resolved "https://registry.yarnpkg.com/magicast/-/magicast-0.3.5.tgz#8301c3c7d66704a0771eb1bad74274f0ec036739"
+  integrity sha512-L0WhttDl+2BOsybvEOLK7fW3UA0OQ0IQ2d6Zl2x/a6vVRs3bAY0ECOSHHeL5jD+SbOpOCUEi0y1DgHEn9Qn1AQ==
   dependencies:
-    "@babel/parser" "^7.24.4"
-    "@babel/types" "^7.24.0"
+    "@babel/parser" "^7.25.4"
+    "@babel/types" "^7.25.4"
     source-map-js "^1.2.0"
 
 make-dir@^4.0.0:
@@ -6549,6 +6661,21 @@ mdast-util-to-hast@^12.1.0:
     unist-util-position "^4.0.0"
     unist-util-visit "^4.0.0"
 
+mdast-util-to-hast@^13.0.0:
+  version "13.2.0"
+  resolved "https://registry.yarnpkg.com/mdast-util-to-hast/-/mdast-util-to-hast-13.2.0.tgz#5ca58e5b921cc0a3ded1bc02eed79a4fe4fe41f4"
+  integrity sha512-QGYKEuUsYT9ykKBCMOEDLsU5JRObWQusAolFMeko/tYPufNkRffBAQjIE+99jbA87xv6FgmjLtwjh9wBWajwAA==
+  dependencies:
+    "@types/hast" "^3.0.0"
+    "@types/mdast" "^4.0.0"
+    "@ungap/structured-clone" "^1.0.0"
+    devlop "^1.0.0"
+    micromark-util-sanitize-uri "^2.0.0"
+    trim-lines "^3.0.0"
+    unist-util-position "^5.0.0"
+    unist-util-visit "^5.0.0"
+    vfile "^6.0.0"
+
 mdast-util-to-markdown@^1.0.0, mdast-util-to-markdown@^1.3.0:
   version "1.5.0"
   resolved "https://registry.npmjs.org/mdast-util-to-markdown/-/mdast-util-to-markdown-1.5.0.tgz"
@@ -6865,6 +6992,14 @@ micromark-util-character@^1.0.0:
     micromark-util-symbol "^1.0.0"
     micromark-util-types "^1.0.0"
 
+micromark-util-character@^2.0.0:
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/micromark-util-character/-/micromark-util-character-2.1.0.tgz#31320ace16b4644316f6bf057531689c71e2aee1"
+  integrity sha512-KvOVV+X1yLBfs9dCBSopq/+G1PcgT3lAK07mC4BzXi5E7ahzMAF8oIupDDJ6mievI6F+lAATkbQQlQixJfT3aQ==
+  dependencies:
+    micromark-util-symbol "^2.0.0"
+    micromark-util-types "^2.0.0"
+
 micromark-util-chunked@^1.0.0:
   version "1.1.0"
   resolved "https://registry.npmjs.org/micromark-util-chunked/-/micromark-util-chunked-1.1.0.tgz"
@@ -6911,6 +7046,11 @@ micromark-util-encode@^1.0.0:
   resolved "https://registry.npmjs.org/micromark-util-encode/-/micromark-util-encode-1.1.0.tgz"
   integrity sha512-EuEzTWSTAj9PA5GOAs992GzNh2dGQO52UvAbtSOMvXTxv3Criqb6IOzJUBCmEqrrXSblJIJBbFFv6zPxpreiJw==
 
+micromark-util-encode@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/micromark-util-encode/-/micromark-util-encode-2.0.0.tgz#0921ac7953dc3f1fd281e3d1932decfdb9382ab1"
+  integrity sha512-pS+ROfCXAGLWCOc8egcBvT0kf27GoWMqtdarNfDcjb6YLuV5cM3ioG45Ys2qOVqeqSbjaKg72vU+Wby3eddPsA==
+
 micromark-util-events-to-acorn@^1.0.0:
   version "1.2.3"
   resolved "https://registry.npmjs.org/micromark-util-events-to-acorn/-/micromark-util-events-to-acorn-1.2.3.tgz"
@@ -6953,6 +7093,15 @@ micromark-util-sanitize-uri@^1.0.0, micromark-util-sanitize-uri@^1.1.0:
     micromark-util-encode "^1.0.0"
     micromark-util-symbol "^1.0.0"
 
+micromark-util-sanitize-uri@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/micromark-util-sanitize-uri/-/micromark-util-sanitize-uri-2.0.0.tgz#ec8fbf0258e9e6d8f13d9e4770f9be64342673de"
+  integrity sha512-WhYv5UEcZrbAtlsnPuChHUAsu/iBPOVaEVsntLBIdpibO0ddy8OzavZz3iL2xVvBZOpolujSliP65Kq0/7KIYw==
+  dependencies:
+    micromark-util-character "^2.0.0"
+    micromark-util-encode "^2.0.0"
+    micromark-util-symbol "^2.0.0"
+
 micromark-util-subtokenize@^1.0.0:
   version "1.1.0"
   resolved "https://registry.npmjs.org/micromark-util-subtokenize/-/micromark-util-subtokenize-1.1.0.tgz"
@@ -6968,11 +7117,21 @@ micromark-util-symbol@^1.0.0:
   resolved "https://registry.npmjs.org/micromark-util-symbol/-/micromark-util-symbol-1.1.0.tgz"
   integrity sha512-uEjpEYY6KMs1g7QfJ2eX1SQEV+ZT4rUD3UcF6l57acZvLNK7PBZL+ty82Z1qhK1/yXIY4bdx04FKMgR0g4IAag==
 
+micromark-util-symbol@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/micromark-util-symbol/-/micromark-util-symbol-2.0.0.tgz#12225c8f95edf8b17254e47080ce0862d5db8044"
+  integrity sha512-8JZt9ElZ5kyTnO94muPxIGS8oyElRJaiJO8EzV6ZSyGQ1Is8xwl4Q45qU5UOg+bGH4AikWziz0iN4sFLWs8PGw==
+
 micromark-util-types@^1.0.0, micromark-util-types@^1.0.1:
   version "1.1.0"
   resolved "https://registry.npmjs.org/micromark-util-types/-/micromark-util-types-1.1.0.tgz"
   integrity sha512-ukRBgie8TIAcacscVHSiddHjO4k/q3pnedmzMQ4iwDcK0FtFCohKOlFbaOL/mPgfnPsL3C1ZyxJa4sbWrBl3jg==
 
+micromark-util-types@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/micromark-util-types/-/micromark-util-types-2.0.0.tgz#63b4b7ffeb35d3ecf50d1ca20e68fc7caa36d95e"
+  integrity sha512-oNh6S2WMHWRZrmutsRmDDfkzKtxF+bc2VxLC9dvtrDIRFln627VsFP6fLMgTryGDljgLPjkrzQSDcPrjPyDJ5w==
+
 micromark@^3.0.0:
   version "3.2.0"
   resolved "https://registry.npmjs.org/micromark/-/micromark-3.2.0.tgz"
@@ -7036,12 +7195,12 @@ mimic-fn@^4.0.0:
 
 mimic-response@^1.0.0:
   version "1.0.1"
-  resolved "https://registry.npmjs.org/mimic-response/-/mimic-response-1.0.1.tgz#4923538878eef42063cb8a3e3b0798781487ab1b"
+  resolved "https://registry.yarnpkg.com/mimic-response/-/mimic-response-1.0.1.tgz#4923538878eef42063cb8a3e3b0798781487ab1b"
   integrity sha512-j5EctnkH7amfV/q5Hgmoal1g2QHFJRraOtmx0JpIqkxhBhI/lJSl1nMpQ45hVarwNETOoWEimndZ4QK0RHxuxQ==
 
 mimic-response@^3.1.0:
   version "3.1.0"
-  resolved "https://registry.npmjs.org/mimic-response/-/mimic-response-3.1.0.tgz#2d1d59af9c1b129815accc2c46a022a5ce1fa3c9"
+  resolved "https://registry.yarnpkg.com/mimic-response/-/mimic-response-3.1.0.tgz#2d1d59af9c1b129815accc2c46a022a5ce1fa3c9"
   integrity sha512-z0yWI+4FDrrweS8Zmt4Ej5HdJmky15+L2e6Wgn3+iK5fWzb6T3fhNFq2+MeTRb064c6Wr4N/wv0DzQTjNzHNGQ==
 
 min-indent@^1.0.0:
@@ -7186,8 +7345,9 @@ normalize-range@^0.1.2:
 
 normalize-url@^6.0.1:
   version "6.1.0"
-  resolved "https://registry.npmjs.org/normalize-url/-/normalize-url-6.1.0.tgz#40d0885b535deffe3f3147bec877d05fe4c5668a"
+  resolved "https://registry.yarnpkg.com/normalize-url/-/normalize-url-6.1.0.tgz#40d0885b535deffe3f3147bec877d05fe4c5668a"
   integrity sha512-DlL+XwOy3NxAQ8xuC0okPgK46iuVNAK01YN7RueYBqqFeGsBjV9XmCAzAdgt+667bCl5kPh9EqKKDwnaPG1I7A==
+
 normalize-wheel@^1.0.1:
   version "1.0.1"
   resolved "https://registry.yarnpkg.com/normalize-wheel/-/normalize-wheel-1.0.1.tgz#aec886affdb045070d856447df62ecf86146ec45"
@@ -7309,7 +7469,7 @@ object.values@^1.1.6, object.values@^1.1.7:
 
 once@^1.3.0, once@^1.3.1, once@^1.4.0:
   version "1.4.0"
-  resolved "https://registry.npmjs.org/once/-/once-1.4.0.tgz"
+  resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1"
   integrity sha512-lNaJgI+2Q5URQBkccEKHTQOPaXdUxnZZElQTZY0MFUAuaEqe1E+Nyvgdz/aIyNi6Z9MzO5dv1H8n58/GELp3+w==
   dependencies:
     wrappy "1"
@@ -7352,7 +7512,7 @@ optionator@^0.9.1:
 
 p-cancelable@^2.0.0:
   version "2.1.1"
-  resolved "https://registry.npmjs.org/p-cancelable/-/p-cancelable-2.1.1.tgz#aab7fbd416582fa32a3db49859c122487c5ed2cf"
+  resolved "https://registry.yarnpkg.com/p-cancelable/-/p-cancelable-2.1.1.tgz#aab7fbd416582fa32a3db49859c122487c5ed2cf"
   integrity sha512-BZOr3nRQHOntUjTrH8+Lh54smKHoHyur8We1V8DSMVrl5A2malOOwuJRnKRDjSnkoeBh4at6BwEnb5I7Jl31wg==
 
 p-limit@^2.2.0:
@@ -7683,7 +7843,7 @@ psl@^1.1.33:
 
 pump@^3.0.0:
   version "3.0.0"
-  resolved "https://registry.npmjs.org/pump/-/pump-3.0.0.tgz#b4a2116815bde2f4e1ea602354e8c75565107a64"
+  resolved "https://registry.yarnpkg.com/pump/-/pump-3.0.0.tgz#b4a2116815bde2f4e1ea602354e8c75565107a64"
   integrity sha512-LwZy+p3SFs1Pytd/jYct4wpv49HiYCqd9Rlc5ZVdk0V+8Yzv6jR5Blk3TRmPL1ft69TxP0IMZGJ+WPFU2BFhww==
   dependencies:
     end-of-stream "^1.1.0"
@@ -7728,7 +7888,7 @@ queue-microtask@^1.2.2:
 
 quick-lru@^5.1.1:
   version "5.1.1"
-  resolved "https://registry.npmjs.org/quick-lru/-/quick-lru-5.1.1.tgz#366493e6b3e42a3a6885e2e99d18f80fb7a8c932"
+  resolved "https://registry.yarnpkg.com/quick-lru/-/quick-lru-5.1.1.tgz#366493e6b3e42a3a6885e2e99d18f80fb7a8c932"
   integrity sha512-WuyALRjWPDGtt/wzJiadO5AXY+8hZ80hVpe6MyivgraREW751X3SbhRvG3eLKOYN+8VEvqLcf3wdnt44Z4S4SA==
 
 rc-input@~1.3.5:
@@ -8063,6 +8223,15 @@ rehype-katex@^6.0.2:
     katex "^0.16.0"
     unist-util-visit "^4.0.0"
 
+rehype-raw@^7.0.0:
+  version "7.0.0"
+  resolved "https://registry.yarnpkg.com/rehype-raw/-/rehype-raw-7.0.0.tgz#59d7348fd5dbef3807bbaa1d443efd2dd85ecee4"
+  integrity sha512-/aE8hCfKlQeA8LmyeyQvQF3eBiLRGNlfBJEvWH7ivp9sBqs7TNqBL5X3v157rM4IFETqDnIOO+z5M/biZbo9Ww==
+  dependencies:
+    "@types/hast" "^3.0.0"
+    hast-util-raw "^9.0.0"
+    vfile "^6.0.0"
+
 remark-breaks@^3.0.2:
   version "3.0.3"
   resolved "https://registry.npmjs.org/remark-breaks/-/remark-breaks-3.0.3.tgz"
@@ -8136,7 +8305,7 @@ resize-observer-polyfill@^1.5.1:
 
 resolve-alpn@^1.0.0:
   version "1.2.1"
-  resolved "https://registry.npmjs.org/resolve-alpn/-/resolve-alpn-1.2.1.tgz#b7adbdac3546aaaec20b45e7d8265927072726f9"
+  resolved "https://registry.yarnpkg.com/resolve-alpn/-/resolve-alpn-1.2.1.tgz#b7adbdac3546aaaec20b45e7d8265927072726f9"
   integrity sha512-0a1F4l73/ZFZOakJnQ3FvkJ2+gSTQWz/r2KE5OdDY0TxPm5h4GkqkWWfM47T7HsbnOtcJVEF4epCVy6u7Q3K+g==
 
 resolve-cwd@^3.0.0:
@@ -8186,7 +8355,7 @@ resolve@^2.0.0-next.4:
 
 responselike@^2.0.0:
   version "2.0.1"
-  resolved "https://registry.npmjs.org/responselike/-/responselike-2.0.1.tgz#9a0bc8fdc252f3fb1cca68b016591059ba1422bc"
+  resolved "https://registry.yarnpkg.com/responselike/-/responselike-2.0.1.tgz#9a0bc8fdc252f3fb1cca68b016591059ba1422bc"
   integrity sha512-4gl03wn3hj1HP3yzgdI7d3lCkF95F21Pz4BPGvKHinyQzALR5CapwC8yIi0Rh58DEMQ/SguC03wFj2k0M/mHhw==
   dependencies:
     lowercase-keys "^2.0.0"
@@ -8590,7 +8759,7 @@ string-length@^4.0.1:
 
 string-width@4.2.3, string-width@^4.1.0, string-width@^4.2.0, string-width@^4.2.3, string-width@^5.0.0, string-width@^5.0.1, string-width@^5.1.2:
   version "4.2.3"
-  resolved "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz#269c7117d27b05ad2e536830a8ec895ef9c6d010"
+  resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.3.tgz#269c7117d27b05ad2e536830a8ec895ef9c6d010"
   integrity sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==
   dependencies:
     emoji-regex "^8.0.0"
@@ -8647,14 +8816,7 @@ stringify-entities@^4.0.0:
     character-entities-html4 "^2.0.0"
     character-entities-legacy "^3.0.0"
 
-"strip-ansi-cjs@npm:strip-ansi@^6.0.1":
-  version "6.0.1"
-  resolved "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz"
-  integrity sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==
-  dependencies:
-    ansi-regex "^5.0.1"
-
-strip-ansi@^6.0.0, strip-ansi@^6.0.1:
+"strip-ansi-cjs@npm:strip-ansi@^6.0.1", strip-ansi@^6.0.0, strip-ansi@^6.0.1:
   version "6.0.1"
   resolved "https://registry.npmjs.org/strip-ansi/-/strip-ansi-6.0.1.tgz"
   integrity sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==
@@ -8968,9 +9130,9 @@ tslib@^1.8.1, tslib@^1.9.3:
   integrity sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==
 
 tslib@^2.0.1:
-  version "2.6.3"
-  resolved "https://registry.yarnpkg.com/tslib/-/tslib-2.6.3.tgz#0438f810ad7a9edcde7a241c3d80db693c8cbfe0"
-  integrity sha512-xNvxJEOUiWPGhUuUdQgAJPKOOJfGnIyKySOc09XkKsgdUV/3E2zvwZYdejjmRgPCgcym1juLH3226yA7sEFJKQ==
+  version "2.7.0"
+  resolved "https://registry.yarnpkg.com/tslib/-/tslib-2.7.0.tgz#d9b40c5c40ab59e8738f297df3087bf1a2690c01"
+  integrity sha512-gLXCKdN1/j47AiHiOkJN69hJmcbGTHI0ImLmbYLHykhgeN0jVGola9yVjFgzCUklsZQMW55o+dW7IXv3RCXDzA==
 
 tslib@^2.1.0, tslib@^2.4.0, tslib@^2.4.1, tslib@^2.5.0:
   version "2.5.3"
@@ -9108,6 +9270,13 @@ unist-util-is@^5.0.0:
   dependencies:
     "@types/unist" "^2.0.0"
 
+unist-util-is@^6.0.0:
+  version "6.0.0"
+  resolved "https://registry.yarnpkg.com/unist-util-is/-/unist-util-is-6.0.0.tgz#b775956486aff107a9ded971d996c173374be424"
+  integrity sha512-2qCTHimwdxLfz+YzdGfkqNlH0tLi9xjTnHddPmJwtIG9MGsdbutfTc4P+haPD7l7Cjxf/WZj+we5qfVPvvxfYw==
+  dependencies:
+    "@types/unist" "^3.0.0"
+
 unist-util-position-from-estree@^1.0.0, unist-util-position-from-estree@^1.1.0:
   version "1.1.2"
   resolved "https://registry.npmjs.org/unist-util-position-from-estree/-/unist-util-position-from-estree-1.1.2.tgz"
@@ -9122,6 +9291,13 @@ unist-util-position@^4.0.0:
   dependencies:
     "@types/unist" "^2.0.0"
 
+unist-util-position@^5.0.0:
+  version "5.0.0"
+  resolved "https://registry.yarnpkg.com/unist-util-position/-/unist-util-position-5.0.0.tgz#678f20ab5ca1207a97d7ea8a388373c9cf896be4"
+  integrity sha512-fucsC7HjXvkB5R3kTCO7kUjRdrS0BJt3M/FPxmHMBOm8JQi2BsHAHFsy27E0EolP8rp0NzXsJ+jNPyDWvOJZPA==
+  dependencies:
+    "@types/unist" "^3.0.0"
+
 unist-util-remove-position@^4.0.0:
   version "4.0.2"
   resolved "https://registry.npmjs.org/unist-util-remove-position/-/unist-util-remove-position-4.0.2.tgz"
@@ -9144,6 +9320,13 @@ unist-util-stringify-position@^3.0.0:
   dependencies:
     "@types/unist" "^2.0.0"
 
+unist-util-stringify-position@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/unist-util-stringify-position/-/unist-util-stringify-position-4.0.0.tgz#449c6e21a880e0855bf5aabadeb3a740314abac2"
+  integrity sha512-0ASV06AAoKCDkS2+xw5RXJywruurpbC4JZSm7nr7MOt1ojAzvyyaO+UxZf18j8FCF6kmzCZKcAgN/yu2gm2XgQ==
+  dependencies:
+    "@types/unist" "^3.0.0"
+
 unist-util-visit-parents@^5.0.0, unist-util-visit-parents@^5.1.1:
   version "5.1.3"
   resolved "https://registry.npmjs.org/unist-util-visit-parents/-/unist-util-visit-parents-5.1.3.tgz"
@@ -9152,6 +9335,14 @@ unist-util-visit-parents@^5.0.0, unist-util-visit-parents@^5.1.1:
     "@types/unist" "^2.0.0"
     unist-util-is "^5.0.0"
 
+unist-util-visit-parents@^6.0.0:
+  version "6.0.1"
+  resolved "https://registry.yarnpkg.com/unist-util-visit-parents/-/unist-util-visit-parents-6.0.1.tgz#4d5f85755c3b8f0dc69e21eca5d6d82d22162815"
+  integrity sha512-L/PqWzfTP9lzzEa6CKs0k2nARxTdZduw3zyh8d2NVBnsyvHjSX4TWse388YrrQKbvI8w20fGjGlhgT96WwKykw==
+  dependencies:
+    "@types/unist" "^3.0.0"
+    unist-util-is "^6.0.0"
+
 unist-util-visit@^4.0.0:
   version "4.1.2"
   resolved "https://registry.npmjs.org/unist-util-visit/-/unist-util-visit-4.1.2.tgz"
@@ -9161,6 +9352,15 @@ unist-util-visit@^4.0.0:
     unist-util-is "^5.0.0"
     unist-util-visit-parents "^5.1.1"
 
+unist-util-visit@^5.0.0:
+  version "5.0.0"
+  resolved "https://registry.yarnpkg.com/unist-util-visit/-/unist-util-visit-5.0.0.tgz#a7de1f31f72ffd3519ea71814cccf5fd6a9217d6"
+  integrity sha512-MR04uvD+07cwl/yhVuVWAtw+3GOR/knlL55Nd/wAdblk27GCVt3lqpTivy/tkJcZoNPzTwS1Y+KMojlLDhoTzg==
+  dependencies:
+    "@types/unist" "^3.0.0"
+    unist-util-is "^6.0.0"
+    unist-util-visit-parents "^6.0.0"
+
 universalify@^0.2.0:
   version "0.2.0"
   resolved "https://registry.npmjs.org/universalify/-/universalify-0.2.0.tgz#6451760566fa857534745ab1dde952d1b1761be0"
@@ -9267,6 +9467,14 @@ vfile-location@^4.0.0:
     "@types/unist" "^2.0.0"
     vfile "^5.0.0"
 
+vfile-location@^5.0.0:
+  version "5.0.3"
+  resolved "https://registry.yarnpkg.com/vfile-location/-/vfile-location-5.0.3.tgz#cb9eacd20f2b6426d19451e0eafa3d0a846225c3"
+  integrity sha512-5yXvWDEgqeiYiBe1lbxYF7UMAIm/IcopxMHrMQDq3nvKcjPKIhZklUKL+AE7J7uApI4kwe2snsK+eI6UTj9EHg==
+  dependencies:
+    "@types/unist" "^3.0.0"
+    vfile "^6.0.0"
+
 vfile-message@^3.0.0:
   version "3.1.4"
   resolved "https://registry.npmjs.org/vfile-message/-/vfile-message-3.1.4.tgz"
@@ -9275,6 +9483,14 @@ vfile-message@^3.0.0:
     "@types/unist" "^2.0.0"
     unist-util-stringify-position "^3.0.0"
 
+vfile-message@^4.0.0:
+  version "4.0.2"
+  resolved "https://registry.yarnpkg.com/vfile-message/-/vfile-message-4.0.2.tgz#c883c9f677c72c166362fd635f21fc165a7d1181"
+  integrity sha512-jRDZ1IMLttGj41KcZvlrYAaI3CfqpLpfpf+Mfig13viT6NKvRzWZ+lXz0Y5D60w6uJIBAOGq9mSHf0gktF0duw==
+  dependencies:
+    "@types/unist" "^3.0.0"
+    unist-util-stringify-position "^4.0.0"
+
 vfile@^5.0.0:
   version "5.3.7"
   resolved "https://registry.npmjs.org/vfile/-/vfile-5.3.7.tgz"
@@ -9285,6 +9501,15 @@ vfile@^5.0.0:
     unist-util-stringify-position "^3.0.0"
     vfile-message "^3.0.0"
 
+vfile@^6.0.0:
+  version "6.0.2"
+  resolved "https://registry.yarnpkg.com/vfile/-/vfile-6.0.2.tgz#ef49548ea3d270097a67011921411130ceae7deb"
+  integrity sha512-zND7NlS8rJYb/sPqkb13ZvbbUoExdbi4w3SfRrMq6R3FvnLQmmfpajJNITuuYm6AZ5uao9vy4BAos3EXBPf2rg==
+  dependencies:
+    "@types/unist" "^3.0.0"
+    unist-util-stringify-position "^4.0.0"
+    vfile-message "^4.0.0"
+
 vite-code-inspector-plugin@0.13.0:
   version "0.13.0"
   resolved "https://registry.npmjs.org/vite-code-inspector-plugin/-/vite-code-inspector-plugin-0.13.0.tgz"
@@ -9428,7 +9653,8 @@ word-wrap@^1.2.3:
   resolved "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz"
   integrity sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==
 
-"wrap-ansi-cjs@npm:wrap-ansi@^7.0.0":
+"wrap-ansi-cjs@npm:wrap-ansi@^7.0.0", wrap-ansi@^7.0.0:
+  name wrap-ansi-cjs
   version "7.0.0"
   resolved "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz"
   integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
@@ -9446,15 +9672,6 @@ wrap-ansi@^6.2.0:
     string-width "^4.1.0"
     strip-ansi "^6.0.0"
 
-wrap-ansi@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz"
-  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
-  dependencies:
-    ansi-styles "^4.0.0"
-    string-width "^4.1.0"
-    strip-ansi "^6.0.0"
-
 wrap-ansi@^8.1.0:
   version "8.1.0"
   resolved "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz"
@@ -9581,4 +9798,4 @@ zustand@^4.4.1, zustand@^4.5.2:
 zwitch@^2.0.0:
   version "2.0.4"
   resolved "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz"
-  integrity sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==
\ No newline at end of file
+  integrity sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==