Merge branch 'feat/collaboration' into deploy/dev

.github/workflows/autofix.yml

@@ -30,6 +30,8 @@ jobs:
         run: |
           uvx --from ast-grep-cli sg --pattern 'db.session.query($WHATEVER).filter($HERE)' --rewrite 'db.session.query($WHATEVER).where($HERE)' -l py --update-all
           uvx --from ast-grep-cli sg --pattern 'session.query($WHATEVER).filter($HERE)' --rewrite 'session.query($WHATEVER).where($HERE)' -l py --update-all
+          uvx --from ast-grep-cli sg -p '$A = db.Column($$$B)' -r '$A = mapped_column($$$B)' -l py --update-all
+          uvx --from ast-grep-cli sg -p '$A : $T = db.Column($$$B)' -r '$A : $T = mapped_column($$$B)' -l py --update-all
           # Convert Optional[T] to T | None (ignoring quoted types)
           cat > /tmp/optional-rule.yml << 'EOF'
           id: convert-optional-to-union

.github/workflows/build-push.yml

@@ -4,8 +4,7 @@ on:
   push:
     branches:
       - "main"
-      - "deploy/dev"
-      - "deploy/enterprise"
+      - "deploy/**"
       - "build/**"
       - "release/e-*"
       - "hotfix/**"

.github/workflows/deploy-trigger-dev.yml

@@ -1,4 +1,4 @@
-name: Deploy RAG Dev
+name: Deploy Trigger Dev
 
 permissions:
   contents: read
@@ -7,7 +7,7 @@ on:
   workflow_run:
     workflows: ["Build and Push API & Web"]
     branches:
-      - "deploy/rag-dev"
+      - "deploy/trigger-dev"
     types:
       - completed
 
@@ -16,12 +16,12 @@ jobs:
     runs-on: ubuntu-latest
     if: |
       github.event.workflow_run.conclusion == 'success' &&
-      github.event.workflow_run.head_branch == 'deploy/rag-dev'
+      github.event.workflow_run.head_branch == 'deploy/trigger-dev'
     steps:
       - name: Deploy to server
         uses: appleboy/ssh-action@v0.1.8
         with:
-          host: ${{ secrets.RAG_SSH_HOST }}
+          host: ${{ secrets.TRIGGER_SSH_HOST }}
           username: ${{ secrets.SSH_USER }}
           key: ${{ secrets.SSH_PRIVATE_KEY }}
           script: |

api/.env.example

@@ -343,6 +343,15 @@ OCEANBASE_VECTOR_DATABASE=test
 OCEANBASE_MEMORY_LIMIT=6G
 OCEANBASE_ENABLE_HYBRID_SEARCH=false
 
+# AlibabaCloud MySQL Vector configuration
+ALIBABACLOUD_MYSQL_HOST=127.0.0.1
+ALIBABACLOUD_MYSQL_PORT=3306
+ALIBABACLOUD_MYSQL_USER=root
+ALIBABACLOUD_MYSQL_PASSWORD=root
+ALIBABACLOUD_MYSQL_DATABASE=dify
+ALIBABACLOUD_MYSQL_MAX_CONNECTION=5
+ALIBABACLOUD_MYSQL_HNSW_M=6
+
 # openGauss configuration
 OPENGAUSS_HOST=127.0.0.1
 OPENGAUSS_PORT=6600

api/configs/middleware/__init__.py

@@ -18,6 +18,7 @@ from .storage.opendal_storage_config import OpenDALStorageConfig
 from .storage.supabase_storage_config import SupabaseStorageConfig
 from .storage.tencent_cos_storage_config import TencentCloudCOSStorageConfig
 from .storage.volcengine_tos_storage_config import VolcengineTOSStorageConfig
+from .vdb.alibabacloud_mysql_config import AlibabaCloudMySQLConfig
 from .vdb.analyticdb_config import AnalyticdbConfig
 from .vdb.baidu_vector_config import BaiduVectorDBConfig
 from .vdb.chroma_config import ChromaConfig
@@ -330,6 +331,7 @@ class MiddlewareConfig(
     ClickzettaConfig,
     HuaweiCloudConfig,
     MilvusConfig,
+    AlibabaCloudMySQLConfig,
     MyScaleConfig,
     OpenSearchConfig,
     OracleConfig,

api/configs/middleware/vdb/alibabacloud_mysql_config.py

@@ -0,0 +1,54 @@
+from pydantic import Field, PositiveInt
+from pydantic_settings import BaseSettings
+
+
+class AlibabaCloudMySQLConfig(BaseSettings):
+    """
+    Configuration settings for AlibabaCloud MySQL vector database
+    """
+
+    ALIBABACLOUD_MYSQL_HOST: str = Field(
+        description="Hostname or IP address of the AlibabaCloud MySQL server (e.g., 'localhost' or 'mysql.aliyun.com')",
+        default="localhost",
+    )
+
+    ALIBABACLOUD_MYSQL_PORT: PositiveInt = Field(
+        description="Port number on which the AlibabaCloud MySQL server is listening (default is 3306)",
+        default=3306,
+    )
+
+    ALIBABACLOUD_MYSQL_USER: str = Field(
+        description="Username for authenticating with AlibabaCloud MySQL (default is 'root')",
+        default="root",
+    )
+
+    ALIBABACLOUD_MYSQL_PASSWORD: str = Field(
+        description="Password for authenticating with AlibabaCloud MySQL (default is an empty string)",
+        default="",
+    )
+
+    ALIBABACLOUD_MYSQL_DATABASE: str = Field(
+        description="Name of the AlibabaCloud MySQL database to connect to (default is 'dify')",
+        default="dify",
+    )
+
+    ALIBABACLOUD_MYSQL_MAX_CONNECTION: PositiveInt = Field(
+        description="Maximum number of connections in the connection pool",
+        default=5,
+    )
+
+    ALIBABACLOUD_MYSQL_CHARSET: str = Field(
+        description="Character set for AlibabaCloud MySQL connection (default is 'utf8mb4')",
+        default="utf8mb4",
+    )
+
+    ALIBABACLOUD_MYSQL_DISTANCE_FUNCTION: str = Field(
+        description="Distance function used for vector similarity search in AlibabaCloud MySQL "
+        "(e.g., 'cosine', 'euclidean')",
+        default="cosine",
+    )
+
+    ALIBABACLOUD_MYSQL_HNSW_M: PositiveInt = Field(
+        description="Maximum number of connections per layer for HNSW vector index (default is 6, range: 3-200)",
+        default=6,
+    )

api/configs/middleware/vdb/opensearch_config.py

@@ -1,23 +1,24 @@
-from enum import Enum
+from enum import StrEnum
 from typing import Literal
 
 from pydantic import Field, PositiveInt
 from pydantic_settings import BaseSettings
 
 
+class AuthMethod(StrEnum):
+    """
+    Authentication method for OpenSearch
+    """
+
+    BASIC = "basic"
+    AWS_MANAGED_IAM = "aws_managed_iam"
+
+
 class OpenSearchConfig(BaseSettings):
     """
     Configuration settings for OpenSearch
     """
 
-    class AuthMethod(Enum):
-        """
-        Authentication method for OpenSearch
-        """
-
-        BASIC = "basic"
-        AWS_MANAGED_IAM = "aws_managed_iam"
-
     OPENSEARCH_HOST: str | None = Field(
         description="Hostname or IP address of the OpenSearch server (e.g., 'localhost' or 'opensearch.example.com')",
         default=None,

api/controllers/console/app/app.py

@@ -304,7 +304,7 @@ class AppCopyApi(Resource):
             account = cast(Account, current_user)
             result = import_service.import_app(
                 account=account,
-                import_mode=ImportMode.YAML_CONTENT.value,
+                import_mode=ImportMode.YAML_CONTENT,
                 yaml_content=yaml_content,
                 name=args.get("name"),
                 description=args.get("description"),

api/controllers/console/app/app_import.py

@@ -70,9 +70,9 @@ class AppImportApi(Resource):
             EnterpriseService.WebAppAuth.update_app_access_mode(result.app_id, "private")
         # Return appropriate status code based on result
         status = result.status
-        if status == ImportStatus.FAILED.value:
+        if status == ImportStatus.FAILED:
             return result.model_dump(mode="json"), 400
-        elif status == ImportStatus.PENDING.value:
+        elif status == ImportStatus.PENDING:
             return result.model_dump(mode="json"), 202
         return result.model_dump(mode="json"), 200
 
@@ -97,7 +97,7 @@ class AppImportConfirmApi(Resource):
             session.commit()
 
         # Return appropriate status code based on result
-        if result.status == ImportStatus.FAILED.value:
+        if result.status == ImportStatus.FAILED:
             return result.model_dump(mode="json"), 400
         return result.model_dump(mode="json"), 200
 

api/controllers/console/app/conversation.py

@@ -309,7 +309,7 @@ class ChatConversationApi(Resource):
             )
 
         if app_model.mode == AppMode.ADVANCED_CHAT:
-            query = query.where(Conversation.invoke_from != InvokeFrom.DEBUGGER.value)
+            query = query.where(Conversation.invoke_from != InvokeFrom.DEBUGGER)
 
         match args["sort_by"]:
             case "created_at":

api/controllers/console/app/model_config.py

@@ -14,6 +14,7 @@ from core.tools.tool_manager import ToolManager
 from core.tools.utils.configuration import ToolParameterConfigurationManager
 from events.app_event import app_model_config_was_updated
 from extensions.ext_database import db
+from libs.datetime_utils import naive_utc_now
 from libs.login import login_required
 from models.account import Account
 from models.model import AppMode, AppModelConfig
@@ -172,6 +173,8 @@ class ModelConfigResource(Resource):
         db.session.flush()
 
         app_model.app_model_config_id = new_app_model_config.id
+        app_model.updated_by = current_user.id
+        app_model.updated_at = naive_utc_now()
         db.session.commit()
 
         app_model_config_was_updated.send(app_model, app_model_config=new_app_model_config)

api/controllers/console/app/statistic.py

@@ -52,7 +52,7 @@ FROM
 WHERE
     app_id = :app_id
     AND invoke_from != :invoke_from"""
-        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER.value}
+        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER}
 
         timezone = pytz.timezone(account.timezone)
         utc_timezone = pytz.utc
@@ -127,7 +127,7 @@ class DailyConversationStatistic(Resource):
                 sa.func.count(sa.distinct(Message.conversation_id)).label("conversation_count"),
             )
             .select_from(Message)
-            .where(Message.app_id == app_model.id, Message.invoke_from != InvokeFrom.DEBUGGER.value)
+            .where(Message.app_id == app_model.id, Message.invoke_from != InvokeFrom.DEBUGGER)
         )
 
         if args["start"]:
@@ -190,7 +190,7 @@ FROM
 WHERE
     app_id = :app_id
     AND invoke_from != :invoke_from"""
-        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER.value}
+        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER}
 
         timezone = pytz.timezone(account.timezone)
         utc_timezone = pytz.utc
@@ -263,7 +263,7 @@ FROM
 WHERE
     app_id = :app_id
     AND invoke_from != :invoke_from"""
-        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER.value}
+        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER}
 
         timezone = pytz.timezone(account.timezone)
         utc_timezone = pytz.utc
@@ -345,7 +345,7 @@ FROM
         WHERE
             c.app_id = :app_id
             AND m.invoke_from != :invoke_from"""
-        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER.value}
+        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER}
 
         timezone = pytz.timezone(account.timezone)
         utc_timezone = pytz.utc
@@ -432,7 +432,7 @@ LEFT JOIN
 WHERE
     m.app_id = :app_id
     AND m.invoke_from != :invoke_from"""
-        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER.value}
+        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER}
 
         timezone = pytz.timezone(account.timezone)
         utc_timezone = pytz.utc
@@ -509,7 +509,7 @@ FROM
 WHERE
     app_id = :app_id
     AND invoke_from != :invoke_from"""
-        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER.value}
+        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER}
 
         timezone = pytz.timezone(account.timezone)
         utc_timezone = pytz.utc
@@ -584,7 +584,7 @@ FROM
 WHERE
     app_id = :app_id
     AND invoke_from != :invoke_from"""
-        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER.value}
+        arg_dict = {"tz": account.timezone, "app_id": app_model.id, "invoke_from": InvokeFrom.DEBUGGER}
 
         timezone = pytz.timezone(account.timezone)
         utc_timezone = pytz.utc

api/controllers/console/app/workflow.py

@@ -27,6 +27,7 @@ from fields.online_user_fields import online_user_list_fields
 from fields.workflow_fields import workflow_fields, workflow_pagination_fields
 from fields.workflow_run_fields import workflow_run_node_execution_fields
 from libs import helper
+from libs.datetime_utils import naive_utc_now
 from libs.helper import TimestampField, uuid_value
 from libs.login import current_user, login_required
 from models import App
@@ -679,8 +680,12 @@ class PublishedWorkflowApi(Resource):
                 marked_comment=args.marked_comment or "",
             )
 
-            app_model.workflow_id = workflow.id
-            db.session.commit()  # NOTE: this is necessary for update app_model.workflow_id
+            # Update app_model within the same session to ensure atomicity
+            app_model_in_session = session.get(App, app_model.id)
+            if app_model_in_session:
+                app_model_in_session.workflow_id = workflow.id
+                app_model_in_session.updated_by = current_user.id
+                app_model_in_session.updated_at = naive_utc_now()
 
             workflow_created_at = TimestampField().format(workflow.created_at)
 

api/controllers/console/app/workflow_statistic.py

@@ -47,7 +47,7 @@ WHERE
         arg_dict = {
             "tz": account.timezone,
             "app_id": app_model.id,
-            "triggered_from": WorkflowRunTriggeredFrom.APP_RUN.value,
+            "triggered_from": WorkflowRunTriggeredFrom.APP_RUN,
         }
 
         timezone = pytz.timezone(account.timezone)
@@ -115,7 +115,7 @@ WHERE
         arg_dict = {
             "tz": account.timezone,
             "app_id": app_model.id,
-            "triggered_from": WorkflowRunTriggeredFrom.APP_RUN.value,
+            "triggered_from": WorkflowRunTriggeredFrom.APP_RUN,
         }
 
         timezone = pytz.timezone(account.timezone)
@@ -183,7 +183,7 @@ WHERE
         arg_dict = {
             "tz": account.timezone,
             "app_id": app_model.id,
-            "triggered_from": WorkflowRunTriggeredFrom.APP_RUN.value,
+            "triggered_from": WorkflowRunTriggeredFrom.APP_RUN,
         }
 
         timezone = pytz.timezone(account.timezone)
@@ -269,7 +269,7 @@ GROUP BY
         arg_dict = {
             "tz": account.timezone,
             "app_id": app_model.id,
-            "triggered_from": WorkflowRunTriggeredFrom.APP_RUN.value,
+            "triggered_from": WorkflowRunTriggeredFrom.APP_RUN,
         }
 
         timezone = pytz.timezone(account.timezone)

api/controllers/console/auth/activate.py

@@ -103,7 +103,7 @@ class ActivateApi(Resource):
         account.interface_language = args["interface_language"]
         account.timezone = args["timezone"]
         account.interface_theme = "light"
-        account.status = AccountStatus.ACTIVE.value
+        account.status = AccountStatus.ACTIVE
         account.initialized_at = naive_utc_now()
         db.session.commit()
 

api/controllers/console/auth/oauth.py

@@ -130,11 +130,11 @@ class OAuthCallback(Resource):
             return redirect(f"{dify_config.CONSOLE_WEB_URL}/signin?message={e.description}")
 
         # Check account status
-        if account.status == AccountStatus.BANNED.value:
+        if account.status == AccountStatus.BANNED:
             return redirect(f"{dify_config.CONSOLE_WEB_URL}/signin?message=Account is banned.")
 
-        if account.status == AccountStatus.PENDING.value:
-            account.status = AccountStatus.ACTIVE.value
+        if account.status == AccountStatus.PENDING:
+            account.status = AccountStatus.ACTIVE
             account.initialized_at = naive_utc_now()
             db.session.commit()
 

api/controllers/console/datasets/data_source.py

@@ -256,7 +256,7 @@ class DataSourceNotionApi(Resource):
             credential_id = notion_info.get("credential_id")
             for page in notion_info["pages"]:
                 extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.NOTION.value,
+                    datasource_type=DatasourceType.NOTION,
                     notion_info=NotionInfo.model_validate(
                         {
                             "credential_id": credential_id,

api/controllers/console/datasets/datasets.py

@@ -500,7 +500,7 @@ class DatasetIndexingEstimateApi(Resource):
             if file_details:
                 for file_detail in file_details:
                     extract_setting = ExtractSetting(
-                        datasource_type=DatasourceType.FILE.value,
+                        datasource_type=DatasourceType.FILE,
                         upload_file=file_detail,
                         document_model=args["doc_form"],
                     )
@@ -512,7 +512,7 @@ class DatasetIndexingEstimateApi(Resource):
                 credential_id = notion_info.get("credential_id")
                 for page in notion_info["pages"]:
                     extract_setting = ExtractSetting(
-                        datasource_type=DatasourceType.NOTION.value,
+                        datasource_type=DatasourceType.NOTION,
                         notion_info=NotionInfo.model_validate(
                             {
                                 "credential_id": credential_id,
@@ -529,7 +529,7 @@ class DatasetIndexingEstimateApi(Resource):
             website_info_list = args["info_list"]["website_info_list"]
             for url in website_info_list["urls"]:
                 extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.WEBSITE.value,
+                    datasource_type=DatasourceType.WEBSITE,
                     website_info=WebsiteInfo.model_validate(
                         {
                             "provider": website_info_list["provider"],
@@ -786,7 +786,7 @@ class DatasetRetrievalSettingApi(Resource):
                 | VectorType.VIKINGDB
                 | VectorType.UPSTASH
             ):
-                return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}
+                return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH]}
             case (
                 VectorType.QDRANT
                 | VectorType.WEAVIATE
@@ -810,12 +810,13 @@ class DatasetRetrievalSettingApi(Resource):
                 | VectorType.MATRIXONE
                 | VectorType.CLICKZETTA
                 | VectorType.BAIDU
+                | VectorType.ALIBABACLOUD_MYSQL
             ):
                 return {
                     "retrieval_method": [
-                        RetrievalMethod.SEMANTIC_SEARCH.value,
-                        RetrievalMethod.FULL_TEXT_SEARCH.value,
-                        RetrievalMethod.HYBRID_SEARCH.value,
+                        RetrievalMethod.SEMANTIC_SEARCH,
+                        RetrievalMethod.FULL_TEXT_SEARCH,
+                        RetrievalMethod.HYBRID_SEARCH,
                     ]
                 }
             case _:
@@ -842,7 +843,7 @@ class DatasetRetrievalSettingMockApi(Resource):
                 | VectorType.VIKINGDB
                 | VectorType.UPSTASH
             ):
-                return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}
+                return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH]}
             case (
                 VectorType.QDRANT
                 | VectorType.WEAVIATE
@@ -864,12 +865,13 @@ class DatasetRetrievalSettingMockApi(Resource):
                 | VectorType.MATRIXONE
                 | VectorType.CLICKZETTA
                 | VectorType.BAIDU
+                | VectorType.ALIBABACLOUD_MYSQL
             ):
                 return {
                     "retrieval_method": [
-                        RetrievalMethod.SEMANTIC_SEARCH.value,
-                        RetrievalMethod.FULL_TEXT_SEARCH.value,
-                        RetrievalMethod.HYBRID_SEARCH.value,
+                        RetrievalMethod.SEMANTIC_SEARCH,
+                        RetrievalMethod.FULL_TEXT_SEARCH,
+                        RetrievalMethod.HYBRID_SEARCH,
                     ]
                 }
             case _:

api/controllers/console/datasets/datasets_document.py

@@ -475,7 +475,7 @@ class DocumentIndexingEstimateApi(DocumentResource):
                     raise NotFound("File not found.")
 
                 extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.FILE.value, upload_file=file, document_model=document.doc_form
+                    datasource_type=DatasourceType.FILE, upload_file=file, document_model=document.doc_form
                 )
 
                 indexing_runner = IndexingRunner()
@@ -538,7 +538,7 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
                     raise NotFound("File not found.")
 
                 extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.FILE.value, upload_file=file_detail, document_model=document.doc_form
+                    datasource_type=DatasourceType.FILE, upload_file=file_detail, document_model=document.doc_form
                 )
                 extract_settings.append(extract_setting)
 
@@ -546,7 +546,7 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
                 if not data_source_info:
                     continue
                 extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.NOTION.value,
+                    datasource_type=DatasourceType.NOTION,
                     notion_info=NotionInfo.model_validate(
                         {
                             "credential_id": data_source_info["credential_id"],
@@ -563,7 +563,7 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
                 if not data_source_info:
                     continue
                 extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.WEBSITE.value,
+                    datasource_type=DatasourceType.WEBSITE,
                     website_info=WebsiteInfo.model_validate(
                         {
                             "provider": data_source_info["provider"],

api/controllers/console/datasets/rag_pipeline/rag_pipeline_import.py

@@ -60,9 +60,9 @@ class RagPipelineImportApi(Resource):
 
         # Return appropriate status code based on result
         status = result.status
-        if status == ImportStatus.FAILED.value:
+        if status == ImportStatus.FAILED:
             return result.model_dump(mode="json"), 400
-        elif status == ImportStatus.PENDING.value:
+        elif status == ImportStatus.PENDING:
             return result.model_dump(mode="json"), 202
         return result.model_dump(mode="json"), 200
 
@@ -87,7 +87,7 @@ class RagPipelineImportConfirmApi(Resource):
             session.commit()
 
         # Return appropriate status code based on result
-        if result.status == ImportStatus.FAILED.value:
+        if result.status == ImportStatus.FAILED:
             return result.model_dump(mode="json"), 400
         return result.model_dump(mode="json"), 200
 

api/controllers/inner_api/plugin/wraps.py

@@ -25,8 +25,8 @@ def get_user(tenant_id: str, user_id: str | None) -> EndUser:
     As a result, it could only be considered as an end user id.
     """
     if not user_id:
-        user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID.value
-    is_anonymous = user_id == DefaultEndUserSessionID.DEFAULT_SESSION_ID.value
+        user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
+    is_anonymous = user_id == DefaultEndUserSessionID.DEFAULT_SESSION_ID
     try:
         with Session(db.engine) as session:
             user_model = None
@@ -85,7 +85,7 @@ def get_user_tenant(view: Callable[P, R] | None = None):
                 raise ValueError("tenant_id is required")
 
             if not user_id:
-                user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID.value
+                user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
 
             try:
                 tenant_model = (

api/controllers/service_api/wraps.py

@@ -313,7 +313,7 @@ def create_or_update_end_user_for_user_id(app_model: App, user_id: str | None =
     Create or update session terminal based on user ID.
     """
     if not user_id:
-        user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID.value
+        user_id = DefaultEndUserSessionID.DEFAULT_SESSION_ID
 
     with Session(db.engine, expire_on_commit=False) as session:
         end_user = (
@@ -332,7 +332,7 @@ def create_or_update_end_user_for_user_id(app_model: App, user_id: str | None =
                 tenant_id=app_model.tenant_id,
                 app_id=app_model.id,
                 type="service_api",
-                is_anonymous=user_id == DefaultEndUserSessionID.DEFAULT_SESSION_ID.value,
+                is_anonymous=user_id == DefaultEndUserSessionID.DEFAULT_SESSION_ID,
                 session_id=user_id,
             )
             session.add(end_user)

api/core/app/app_config/easy_ui_based_app/dataset/manager.py

@@ -197,12 +197,12 @@ class DatasetConfigManager:
 
         # strategy
         if "strategy" not in config["agent_mode"] or not config["agent_mode"].get("strategy"):
-            config["agent_mode"]["strategy"] = PlanningStrategy.ROUTER.value
+            config["agent_mode"]["strategy"] = PlanningStrategy.ROUTER
 
         has_datasets = False
         if config.get("agent_mode", {}).get("strategy") in {
-            PlanningStrategy.ROUTER.value,
-            PlanningStrategy.REACT_ROUTER.value,
+            PlanningStrategy.ROUTER,
+            PlanningStrategy.REACT_ROUTER,
         }:
             for tool in config.get("agent_mode", {}).get("tools", []):
                 key = list(tool.keys())[0]

api/core/app/app_config/easy_ui_based_app/model_config/converter.py

@@ -68,9 +68,13 @@ class ModelConfigConverter:
         # get model mode
         model_mode = model_config.mode
         if not model_mode:
-            model_mode = LLMMode.CHAT.value
+            model_mode = LLMMode.CHAT
             if model_schema and model_schema.model_properties.get(ModelPropertyKey.MODE):
-                model_mode = LLMMode(model_schema.model_properties[ModelPropertyKey.MODE]).value
+                try:
+                    model_mode = LLMMode(model_schema.model_properties[ModelPropertyKey.MODE])
+                except ValueError:
+                    # Fall back to CHAT mode if the stored value is invalid
+                    model_mode = LLMMode.CHAT
 
         if not model_schema:
             raise ValueError(f"Model {model_name} not exist.")

api/core/app/app_config/easy_ui_based_app/prompt_template/manager.py

@@ -100,7 +100,7 @@ class PromptTemplateConfigManager:
             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:
+            if app_mode == AppMode.CHAT and config["model"]["mode"] == ModelMode.COMPLETION:
                 user_prefix = config["completion_prompt_config"]["conversation_histories_role"]["user_prefix"]
                 assistant_prefix = config["completion_prompt_config"]["conversation_histories_role"]["assistant_prefix"]
 
@@ -110,7 +110,7 @@ class PromptTemplateConfigManager:
                 if not assistant_prefix:
                     config["completion_prompt_config"]["conversation_histories_role"]["assistant_prefix"] = "Assistant"
 
-            if config["model"]["mode"] == ModelMode.CHAT.value:
+            if config["model"]["mode"] == ModelMode.CHAT:
                 prompt_list = config["chat_prompt_config"]["prompt"]
 
                 if len(prompt_list) > 10:

api/core/app/apps/agent_chat/app_config_manager.py

@@ -186,7 +186,7 @@ class AgentChatAppConfigManager(BaseAppConfigManager):
             raise ValueError("enabled in agent_mode must be of boolean type")
 
         if not agent_mode.get("strategy"):
-            agent_mode["strategy"] = PlanningStrategy.ROUTER.value
+            agent_mode["strategy"] = PlanningStrategy.ROUTER
 
         if 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")

api/core/app/apps/agent_chat/app_runner.py

@@ -198,9 +198,9 @@ class AgentChatAppRunner(AppRunner):
         # start agent runner
         if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
             # check LLM mode
-            if model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT.value:
+            if model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT:
                 runner_cls = CotChatAgentRunner
-            elif model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.COMPLETION.value:
+            elif model_schema.model_properties.get(ModelPropertyKey.MODE) == LLMMode.COMPLETION:
                 runner_cls = CotCompletionAgentRunner
             else:
                 raise ValueError(f"Invalid LLM mode: {model_schema.model_properties.get(ModelPropertyKey.MODE)}")

api/core/app/apps/pipeline/pipeline_runner.py

@@ -229,8 +229,8 @@ class PipelineRunner(WorkflowBasedAppRunner):
             workflow_id=workflow.id,
             graph_config=graph_config,
             user_id=self.application_generate_entity.user_id,
-            user_from=UserFrom.ACCOUNT.value,
-            invoke_from=InvokeFrom.SERVICE_API.value,
+            user_from=UserFrom.ACCOUNT,
+            invoke_from=InvokeFrom.SERVICE_API,
             call_depth=0,
         )
 

api/core/app/apps/workflow_app_runner.py

@@ -100,8 +100,8 @@ class WorkflowBasedAppRunner:
             workflow_id=workflow_id,
             graph_config=graph_config,
             user_id=user_id,
-            user_from=UserFrom.ACCOUNT.value,
-            invoke_from=InvokeFrom.SERVICE_API.value,
+            user_from=UserFrom.ACCOUNT,
+            invoke_from=InvokeFrom.SERVICE_API,
             call_depth=0,
         )
 
@@ -244,8 +244,8 @@ class WorkflowBasedAppRunner:
             workflow_id=workflow.id,
             graph_config=graph_config,
             user_id="",
-            user_from=UserFrom.ACCOUNT.value,
-            invoke_from=InvokeFrom.SERVICE_API.value,
+            user_from=UserFrom.ACCOUNT,
+            invoke_from=InvokeFrom.SERVICE_API,
             call_depth=0,
         )
 

api/core/datasource/entities/api_entities.py

@@ -49,7 +49,7 @@ class DatasourceProviderApiEntity(BaseModel):
         for datasource in datasources:
             if datasource.get("parameters"):
                 for parameter in datasource.get("parameters"):
-                    if parameter.get("type") == DatasourceParameter.DatasourceParameterType.SYSTEM_FILES.value:
+                    if parameter.get("type") == DatasourceParameter.DatasourceParameterType.SYSTEM_FILES:
                         parameter["type"] = "files"
         # -------------
 

api/core/datasource/entities/datasource_entities.py

@@ -54,16 +54,16 @@ class DatasourceParameter(PluginParameter):
         removes TOOLS_SELECTOR from PluginParameterType
         """
 
-        STRING = PluginParameterType.STRING.value
-        NUMBER = PluginParameterType.NUMBER.value
-        BOOLEAN = PluginParameterType.BOOLEAN.value
-        SELECT = PluginParameterType.SELECT.value
-        SECRET_INPUT = PluginParameterType.SECRET_INPUT.value
-        FILE = PluginParameterType.FILE.value
-        FILES = PluginParameterType.FILES.value
+        STRING = PluginParameterType.STRING
+        NUMBER = PluginParameterType.NUMBER
+        BOOLEAN = PluginParameterType.BOOLEAN
+        SELECT = PluginParameterType.SELECT
+        SECRET_INPUT = PluginParameterType.SECRET_INPUT
+        FILE = PluginParameterType.FILE
+        FILES = PluginParameterType.FILES
 
         # deprecated, should not use.
-        SYSTEM_FILES = PluginParameterType.SYSTEM_FILES.value
+        SYSTEM_FILES = PluginParameterType.SYSTEM_FILES
 
         def as_normal_type(self):
             return as_normal_type(self)

api/core/entities/provider_configuration.py

@@ -207,7 +207,7 @@ class ProviderConfiguration(BaseModel):
         """
         stmt = select(Provider).where(
             Provider.tenant_id == self.tenant_id,
-            Provider.provider_type == ProviderType.CUSTOM.value,
+            Provider.provider_type == ProviderType.CUSTOM,
             Provider.provider_name.in_(self._get_provider_names()),
         )
 
@@ -458,7 +458,7 @@ class ProviderConfiguration(BaseModel):
                     provider_record = Provider(
                         tenant_id=self.tenant_id,
                         provider_name=self.provider.provider,
-                        provider_type=ProviderType.CUSTOM.value,
+                        provider_type=ProviderType.CUSTOM,
                         is_valid=True,
                         credential_id=new_record.id,
                     )
@@ -1414,7 +1414,7 @@ class ProviderConfiguration(BaseModel):
         """
         secret_input_form_variables = []
         for credential_form_schema in credential_form_schemas:
-            if credential_form_schema.type.value == FormType.SECRET_INPUT.value:
+            if credential_form_schema.type == FormType.SECRET_INPUT:
                 secret_input_form_variables.append(credential_form_schema.variable)
 
         return secret_input_form_variables

api/core/extension/api_based_extension_requestor.py

@@ -1,13 +1,13 @@
 from typing import cast
 
-import requests
+import httpx
 
 from configs import dify_config
 from models.api_based_extension import APIBasedExtensionPoint
 
 
 class APIBasedExtensionRequestor:
-    timeout: tuple[int, int] = (5, 60)
+    timeout: httpx.Timeout = httpx.Timeout(60.0, connect=5.0)
     """timeout for request connect and read"""
 
     def __init__(self, api_endpoint: str, api_key: str):
@@ -27,25 +27,23 @@ class APIBasedExtensionRequestor:
         url = self.api_endpoint
 
         try:
-            # proxy support for security
-            proxies = None
+            mounts: dict[str, httpx.BaseTransport] | None = 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,
+                mounts = {
+                    "http://": httpx.HTTPTransport(proxy=dify_config.SSRF_PROXY_HTTP_URL),
+                    "https://": httpx.HTTPTransport(proxy=dify_config.SSRF_PROXY_HTTPS_URL),
                 }
 
-            response = requests.request(
-                method="POST",
-                url=url,
-                json={"point": point.value, "params": params},
-                headers=headers,
-                timeout=self.timeout,
-                proxies=proxies,
-            )
-        except requests.Timeout:
+            with httpx.Client(mounts=mounts, timeout=self.timeout) as client:
+                response = client.request(
+                    method="POST",
+                    url=url,
+                    json={"point": point.value, "params": params},
+                    headers=headers,
+                )
+        except httpx.TimeoutException:
             raise ValueError("request timeout")
-        except requests.ConnectionError:
+        except httpx.RequestError:
             raise ValueError("request connection error")
 
         if response.status_code != 200:

api/core/indexing_runner.py

@@ -343,7 +343,7 @@ class IndexingRunner:
 
             if file_detail:
                 extract_setting = ExtractSetting(
-                    datasource_type=DatasourceType.FILE.value,
+                    datasource_type=DatasourceType.FILE,
                     upload_file=file_detail,
                     document_model=dataset_document.doc_form,
                 )
@@ -356,7 +356,7 @@ class IndexingRunner:
             ):
                 raise ValueError("no notion import info found")
             extract_setting = ExtractSetting(
-                datasource_type=DatasourceType.NOTION.value,
+                datasource_type=DatasourceType.NOTION,
                 notion_info=NotionInfo.model_validate(
                     {
                         "credential_id": data_source_info["credential_id"],
@@ -379,7 +379,7 @@ class IndexingRunner:
             ):
                 raise ValueError("no website import info found")
             extract_setting = ExtractSetting(
-                datasource_type=DatasourceType.WEBSITE.value,
+                datasource_type=DatasourceType.WEBSITE,
                 website_info=WebsiteInfo.model_validate(
                     {
                         "provider": data_source_info["provider"],

api/core/llm_generator/output_parser/structured_output.py

@@ -224,8 +224,8 @@ def _handle_native_json_schema(
 
     # Set appropriate response format if required by the model
     for rule in rules:
-        if rule.name == "response_format" and ResponseFormat.JSON_SCHEMA.value in rule.options:
-            model_parameters["response_format"] = ResponseFormat.JSON_SCHEMA.value
+        if rule.name == "response_format" and ResponseFormat.JSON_SCHEMA in rule.options:
+            model_parameters["response_format"] = ResponseFormat.JSON_SCHEMA
 
     return model_parameters
 
@@ -239,10 +239,10 @@ def _set_response_format(model_parameters: dict, rules: list):
     """
     for rule in rules:
         if rule.name == "response_format":
-            if ResponseFormat.JSON.value in rule.options:
-                model_parameters["response_format"] = ResponseFormat.JSON.value
-            elif ResponseFormat.JSON_OBJECT.value in rule.options:
-                model_parameters["response_format"] = ResponseFormat.JSON_OBJECT.value
+            if ResponseFormat.JSON in rule.options:
+                model_parameters["response_format"] = ResponseFormat.JSON
+            elif ResponseFormat.JSON_OBJECT in rule.options:
+                model_parameters["response_format"] = ResponseFormat.JSON_OBJECT
 
 
 def _handle_prompt_based_schema(

api/core/ops/arize_phoenix_trace/arize_phoenix_trace.py

@@ -213,9 +213,9 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
                     node_metadata.update(json.loads(node_execution.execution_metadata))
 
                 # Determine the correct span kind based on node type
-                span_kind = OpenInferenceSpanKindValues.CHAIN.value
+                span_kind = OpenInferenceSpanKindValues.CHAIN
                 if node_execution.node_type == "llm":
-                    span_kind = OpenInferenceSpanKindValues.LLM.value
+                    span_kind = OpenInferenceSpanKindValues.LLM
                     provider = process_data.get("model_provider")
                     model = process_data.get("model_name")
                     if provider:
@@ -230,18 +230,18 @@ class ArizePhoenixDataTrace(BaseTraceInstance):
                         node_metadata["prompt_tokens"] = usage_data.get("prompt_tokens", 0)
                         node_metadata["completion_tokens"] = usage_data.get("completion_tokens", 0)
                 elif node_execution.node_type == "dataset_retrieval":
-                    span_kind = OpenInferenceSpanKindValues.RETRIEVER.value
+                    span_kind = OpenInferenceSpanKindValues.RETRIEVER
                 elif node_execution.node_type == "tool":
-                    span_kind = OpenInferenceSpanKindValues.TOOL.value
+                    span_kind = OpenInferenceSpanKindValues.TOOL
                 else:
-                    span_kind = OpenInferenceSpanKindValues.CHAIN.value
+                    span_kind = OpenInferenceSpanKindValues.CHAIN
 
                 node_span = self.tracer.start_span(
                     name=node_execution.node_type,
                     attributes={
                         SpanAttributes.INPUT_VALUE: node_execution.inputs or "{}",
                         SpanAttributes.OUTPUT_VALUE: node_execution.outputs or "{}",
-                        SpanAttributes.OPENINFERENCE_SPAN_KIND: span_kind,
+                        SpanAttributes.OPENINFERENCE_SPAN_KIND: span_kind.value,
                         SpanAttributes.METADATA: json.dumps(node_metadata, ensure_ascii=False),
                         SpanAttributes.SESSION_ID: trace_info.conversation_id or "",
                     },

api/core/ops/langfuse_trace/langfuse_trace.py

@@ -73,7 +73,7 @@ class LangFuseDataTrace(BaseTraceInstance):
 
         if trace_info.message_id:
             trace_id = trace_info.trace_id or trace_info.message_id
-            name = TraceTaskName.MESSAGE_TRACE.value
+            name = TraceTaskName.MESSAGE_TRACE
             trace_data = LangfuseTrace(
                 id=trace_id,
                 user_id=user_id,
@@ -88,7 +88,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             self.add_trace(langfuse_trace_data=trace_data)
             workflow_span_data = LangfuseSpan(
                 id=trace_info.workflow_run_id,
-                name=TraceTaskName.WORKFLOW_TRACE.value,
+                name=TraceTaskName.WORKFLOW_TRACE,
                 input=dict(trace_info.workflow_run_inputs),
                 output=dict(trace_info.workflow_run_outputs),
                 trace_id=trace_id,
@@ -103,7 +103,7 @@ class LangFuseDataTrace(BaseTraceInstance):
             trace_data = LangfuseTrace(
                 id=trace_id,
                 user_id=user_id,
-                name=TraceTaskName.WORKFLOW_TRACE.value,
+                name=TraceTaskName.WORKFLOW_TRACE,
                 input=dict(trace_info.workflow_run_inputs),
                 output=dict(trace_info.workflow_run_outputs),
                 metadata=metadata,
@@ -253,7 +253,7 @@ class LangFuseDataTrace(BaseTraceInstance):
         trace_data = LangfuseTrace(
             id=trace_id,
             user_id=user_id,
-            name=TraceTaskName.MESSAGE_TRACE.value,
+            name=TraceTaskName.MESSAGE_TRACE,
             input={
                 "message": trace_info.inputs,
                 "files": file_list,
@@ -303,7 +303,7 @@ class LangFuseDataTrace(BaseTraceInstance):
         if trace_info.message_data is None:
             return
         span_data = LangfuseSpan(
-            name=TraceTaskName.MODERATION_TRACE.value,
+            name=TraceTaskName.MODERATION_TRACE,
             input=trace_info.inputs,
             output={
                 "action": trace_info.action,
@@ -331,7 +331,7 @@ class LangFuseDataTrace(BaseTraceInstance):
         )
 
         generation_data = LangfuseGeneration(
-            name=TraceTaskName.SUGGESTED_QUESTION_TRACE.value,
+            name=TraceTaskName.SUGGESTED_QUESTION_TRACE,
             input=trace_info.inputs,
             output=str(trace_info.suggested_question),
             trace_id=trace_info.trace_id or trace_info.message_id,
@@ -349,7 +349,7 @@ class LangFuseDataTrace(BaseTraceInstance):
         if trace_info.message_data is None:
             return
         dataset_retrieval_span_data = LangfuseSpan(
-            name=TraceTaskName.DATASET_RETRIEVAL_TRACE.value,
+            name=TraceTaskName.DATASET_RETRIEVAL_TRACE,
             input=trace_info.inputs,
             output={"documents": trace_info.documents},
             trace_id=trace_info.trace_id or trace_info.message_id,
@@ -377,7 +377,7 @@ class LangFuseDataTrace(BaseTraceInstance):
 
     def generate_name_trace(self, trace_info: GenerateNameTraceInfo):
         name_generation_trace_data = LangfuseTrace(
-            name=TraceTaskName.GENERATE_NAME_TRACE.value,
+            name=TraceTaskName.GENERATE_NAME_TRACE,
             input=trace_info.inputs,
             output=trace_info.outputs,
             user_id=trace_info.tenant_id,
@@ -388,7 +388,7 @@ class LangFuseDataTrace(BaseTraceInstance):
         self.add_trace(langfuse_trace_data=name_generation_trace_data)
 
         name_generation_span_data = LangfuseSpan(
-            name=TraceTaskName.GENERATE_NAME_TRACE.value,
+            name=TraceTaskName.GENERATE_NAME_TRACE,
             input=trace_info.inputs,
             output=trace_info.outputs,
             trace_id=trace_info.conversation_id,

api/core/ops/langsmith_trace/langsmith_trace.py

@@ -81,7 +81,7 @@ class LangSmithDataTrace(BaseTraceInstance):
         if trace_info.message_id:
             message_run = LangSmithRunModel(
                 id=trace_info.message_id,
-                name=TraceTaskName.MESSAGE_TRACE.value,
+                name=TraceTaskName.MESSAGE_TRACE,
                 inputs=dict(trace_info.workflow_run_inputs),
                 outputs=dict(trace_info.workflow_run_outputs),
                 run_type=LangSmithRunType.chain,
@@ -110,7 +110,7 @@ class LangSmithDataTrace(BaseTraceInstance):
             file_list=trace_info.file_list,
             total_tokens=trace_info.total_tokens,
             id=trace_info.workflow_run_id,
-            name=TraceTaskName.WORKFLOW_TRACE.value,
+            name=TraceTaskName.WORKFLOW_TRACE,
             inputs=dict(trace_info.workflow_run_inputs),
             run_type=LangSmithRunType.tool,
             start_time=trace_info.workflow_data.created_at,
@@ -271,7 +271,7 @@ class LangSmithDataTrace(BaseTraceInstance):
             output_tokens=trace_info.answer_tokens,
             total_tokens=trace_info.total_tokens,
             id=message_id,
-            name=TraceTaskName.MESSAGE_TRACE.value,
+            name=TraceTaskName.MESSAGE_TRACE,
             inputs=trace_info.inputs,
             run_type=LangSmithRunType.chain,
             start_time=trace_info.start_time,
@@ -327,7 +327,7 @@ class LangSmithDataTrace(BaseTraceInstance):
         if trace_info.message_data is None:
             return
         langsmith_run = LangSmithRunModel(
-            name=TraceTaskName.MODERATION_TRACE.value,
+            name=TraceTaskName.MODERATION_TRACE,
             inputs=trace_info.inputs,
             outputs={
                 "action": trace_info.action,
@@ -362,7 +362,7 @@ class LangSmithDataTrace(BaseTraceInstance):
         if message_data is None:
             return
         suggested_question_run = LangSmithRunModel(
-            name=TraceTaskName.SUGGESTED_QUESTION_TRACE.value,
+            name=TraceTaskName.SUGGESTED_QUESTION_TRACE,
             inputs=trace_info.inputs,
             outputs=trace_info.suggested_question,
             run_type=LangSmithRunType.tool,
@@ -391,7 +391,7 @@ class LangSmithDataTrace(BaseTraceInstance):
         if trace_info.message_data is None:
             return
         dataset_retrieval_run = LangSmithRunModel(
-            name=TraceTaskName.DATASET_RETRIEVAL_TRACE.value,
+            name=TraceTaskName.DATASET_RETRIEVAL_TRACE,
             inputs=trace_info.inputs,
             outputs={"documents": trace_info.documents},
             run_type=LangSmithRunType.retriever,
@@ -447,7 +447,7 @@ class LangSmithDataTrace(BaseTraceInstance):
 
     def generate_name_trace(self, trace_info: GenerateNameTraceInfo):
         name_run = LangSmithRunModel(
-            name=TraceTaskName.GENERATE_NAME_TRACE.value,
+            name=TraceTaskName.GENERATE_NAME_TRACE,
             inputs=trace_info.inputs,
             outputs=trace_info.outputs,
             run_type=LangSmithRunType.tool,

api/core/ops/opik_trace/opik_trace.py

@@ -108,7 +108,7 @@ class OpikDataTrace(BaseTraceInstance):
 
             trace_data = {
                 "id": opik_trace_id,
-                "name": TraceTaskName.MESSAGE_TRACE.value,
+                "name": TraceTaskName.MESSAGE_TRACE,
                 "start_time": trace_info.start_time,
                 "end_time": trace_info.end_time,
                 "metadata": workflow_metadata,
@@ -125,7 +125,7 @@ class OpikDataTrace(BaseTraceInstance):
                 "id": root_span_id,
                 "parent_span_id": None,
                 "trace_id": opik_trace_id,
-                "name": TraceTaskName.WORKFLOW_TRACE.value,
+                "name": TraceTaskName.WORKFLOW_TRACE,
                 "input": wrap_dict("input", trace_info.workflow_run_inputs),
                 "output": wrap_dict("output", trace_info.workflow_run_outputs),
                 "start_time": trace_info.start_time,
@@ -138,7 +138,7 @@ class OpikDataTrace(BaseTraceInstance):
         else:
             trace_data = {
                 "id": opik_trace_id,
-                "name": TraceTaskName.MESSAGE_TRACE.value,
+                "name": TraceTaskName.MESSAGE_TRACE,
                 "start_time": trace_info.start_time,
                 "end_time": trace_info.end_time,
                 "metadata": workflow_metadata,
@@ -290,7 +290,7 @@ class OpikDataTrace(BaseTraceInstance):
 
         trace_data = {
             "id": prepare_opik_uuid(trace_info.start_time, dify_trace_id),
-            "name": TraceTaskName.MESSAGE_TRACE.value,
+            "name": TraceTaskName.MESSAGE_TRACE,
             "start_time": trace_info.start_time,
             "end_time": trace_info.end_time,
             "metadata": wrap_metadata(metadata),
@@ -329,7 +329,7 @@ class OpikDataTrace(BaseTraceInstance):
 
         span_data = {
             "trace_id": prepare_opik_uuid(start_time, trace_info.trace_id or trace_info.message_id),
-            "name": TraceTaskName.MODERATION_TRACE.value,
+            "name": TraceTaskName.MODERATION_TRACE,
             "type": "tool",
             "start_time": start_time,
             "end_time": trace_info.end_time or trace_info.message_data.updated_at,
@@ -355,7 +355,7 @@ class OpikDataTrace(BaseTraceInstance):
 
         span_data = {
             "trace_id": prepare_opik_uuid(start_time, trace_info.trace_id or trace_info.message_id),
-            "name": TraceTaskName.SUGGESTED_QUESTION_TRACE.value,
+            "name": TraceTaskName.SUGGESTED_QUESTION_TRACE,
             "type": "tool",
             "start_time": start_time,
             "end_time": trace_info.end_time or message_data.updated_at,
@@ -375,7 +375,7 @@ class OpikDataTrace(BaseTraceInstance):
 
         span_data = {
             "trace_id": prepare_opik_uuid(start_time, trace_info.trace_id or trace_info.message_id),
-            "name": TraceTaskName.DATASET_RETRIEVAL_TRACE.value,
+            "name": TraceTaskName.DATASET_RETRIEVAL_TRACE,
             "type": "tool",
             "start_time": start_time,
             "end_time": trace_info.end_time or trace_info.message_data.updated_at,
@@ -405,7 +405,7 @@ class OpikDataTrace(BaseTraceInstance):
     def generate_name_trace(self, trace_info: GenerateNameTraceInfo):
         trace_data = {
             "id": prepare_opik_uuid(trace_info.start_time, trace_info.trace_id or trace_info.message_id),
-            "name": TraceTaskName.GENERATE_NAME_TRACE.value,
+            "name": TraceTaskName.GENERATE_NAME_TRACE,
             "start_time": trace_info.start_time,
             "end_time": trace_info.end_time,
             "metadata": wrap_metadata(trace_info.metadata),
@@ -420,7 +420,7 @@ class OpikDataTrace(BaseTraceInstance):
 
         span_data = {
             "trace_id": trace.id,
-            "name": TraceTaskName.GENERATE_NAME_TRACE.value,
+            "name": TraceTaskName.GENERATE_NAME_TRACE,
             "start_time": trace_info.start_time,
             "end_time": trace_info.end_time,
             "metadata": wrap_metadata(trace_info.metadata),

api/core/ops/weave_trace/weave_trace.py

@@ -104,7 +104,7 @@ class WeaveDataTrace(BaseTraceInstance):
 
             message_run = WeaveTraceModel(
                 id=trace_info.message_id,
-                op=str(TraceTaskName.MESSAGE_TRACE.value),
+                op=str(TraceTaskName.MESSAGE_TRACE),
                 inputs=dict(trace_info.workflow_run_inputs),
                 outputs=dict(trace_info.workflow_run_outputs),
                 total_tokens=trace_info.total_tokens,
@@ -126,7 +126,7 @@ class WeaveDataTrace(BaseTraceInstance):
             file_list=trace_info.file_list,
             total_tokens=trace_info.total_tokens,
             id=trace_info.workflow_run_id,
-            op=str(TraceTaskName.WORKFLOW_TRACE.value),
+            op=str(TraceTaskName.WORKFLOW_TRACE),
             inputs=dict(trace_info.workflow_run_inputs),
             outputs=dict(trace_info.workflow_run_outputs),
             attributes=workflow_attributes,
@@ -253,7 +253,7 @@ class WeaveDataTrace(BaseTraceInstance):
 
         message_run = WeaveTraceModel(
             id=trace_id,
-            op=str(TraceTaskName.MESSAGE_TRACE.value),
+            op=str(TraceTaskName.MESSAGE_TRACE),
             input_tokens=trace_info.message_tokens,
             output_tokens=trace_info.answer_tokens,
             total_tokens=trace_info.total_tokens,
@@ -300,7 +300,7 @@ class WeaveDataTrace(BaseTraceInstance):
 
         moderation_run = WeaveTraceModel(
             id=str(uuid.uuid4()),
-            op=str(TraceTaskName.MODERATION_TRACE.value),
+            op=str(TraceTaskName.MODERATION_TRACE),
             inputs=trace_info.inputs,
             outputs={
                 "action": trace_info.action,
@@ -330,7 +330,7 @@ class WeaveDataTrace(BaseTraceInstance):
 
         suggested_question_run = WeaveTraceModel(
             id=str(uuid.uuid4()),
-            op=str(TraceTaskName.SUGGESTED_QUESTION_TRACE.value),
+            op=str(TraceTaskName.SUGGESTED_QUESTION_TRACE),
             inputs=trace_info.inputs,
             outputs=trace_info.suggested_question,
             attributes=attributes,
@@ -355,7 +355,7 @@ class WeaveDataTrace(BaseTraceInstance):
 
         dataset_retrieval_run = WeaveTraceModel(
             id=str(uuid.uuid4()),
-            op=str(TraceTaskName.DATASET_RETRIEVAL_TRACE.value),
+            op=str(TraceTaskName.DATASET_RETRIEVAL_TRACE),
             inputs=trace_info.inputs,
             outputs={"documents": trace_info.documents},
             attributes=attributes,
@@ -397,7 +397,7 @@ class WeaveDataTrace(BaseTraceInstance):
 
         name_run = WeaveTraceModel(
             id=str(uuid.uuid4()),
-            op=str(TraceTaskName.GENERATE_NAME_TRACE.value),
+            op=str(TraceTaskName.GENERATE_NAME_TRACE),
             inputs=trace_info.inputs,
             outputs=trace_info.outputs,
             attributes=attributes,

api/core/plugin/backwards_invocation/node.py

@@ -52,7 +52,7 @@ class PluginNodeBackwardsInvocation(BaseBackwardsInvocation):
             instruction=instruction,  # instruct with variables are not supported
         )
         node_data_dict = node_data.model_dump()
-        node_data_dict["type"] = NodeType.PARAMETER_EXTRACTOR.value
+        node_data_dict["type"] = NodeType.PARAMETER_EXTRACTOR
         execution = workflow_service.run_free_workflow_node(
             node_data_dict,
             tenant_id=tenant_id,

api/core/plugin/entities/request.py

@@ -83,13 +83,13 @@ class RequestInvokeLLM(BaseRequestInvokeModel):
             raise ValueError("prompt_messages must be a list")
 
         for i in range(len(v)):
-            if v[i]["role"] == PromptMessageRole.USER.value:
+            if v[i]["role"] == PromptMessageRole.USER:
                 v[i] = UserPromptMessage.model_validate(v[i])
-            elif v[i]["role"] == PromptMessageRole.ASSISTANT.value:
+            elif v[i]["role"] == PromptMessageRole.ASSISTANT:
                 v[i] = AssistantPromptMessage.model_validate(v[i])
-            elif v[i]["role"] == PromptMessageRole.SYSTEM.value:
+            elif v[i]["role"] == PromptMessageRole.SYSTEM:
                 v[i] = SystemPromptMessage.model_validate(v[i])
-            elif v[i]["role"] == PromptMessageRole.TOOL.value:
+            elif v[i]["role"] == PromptMessageRole.TOOL:
                 v[i] = ToolPromptMessage.model_validate(v[i])
             else:
                 v[i] = PromptMessage.model_validate(v[i])

api/core/plugin/impl/base.py

@@ -2,11 +2,10 @@ import inspect
 import json
 import logging
 from collections.abc import Callable, Generator
-from typing import TypeVar
+from typing import Any, TypeVar
 
-import requests
+import httpx
 from pydantic import BaseModel
-from requests.exceptions import HTTPError
 from yarl import URL
 
 from configs import dify_config
@@ -47,29 +46,56 @@ class BasePluginClient:
         data: bytes | dict | str | None = None,
         params: dict | None = None,
         files: dict | None = None,
-        stream: bool = False,
-    ) -> requests.Response:
+    ) -> httpx.Response:
         """
         Make a request to the plugin daemon inner API.
         """
-        url = plugin_daemon_inner_api_baseurl / path
-        headers = headers or {}
-        headers["X-Api-Key"] = dify_config.PLUGIN_DAEMON_KEY
-        headers["Accept-Encoding"] = "gzip, deflate, br"
+        url, headers, prepared_data, params, files = self._prepare_request(path, headers, data, params, files)
 
-        if headers.get("Content-Type") == "application/json" and isinstance(data, dict):
-            data = json.dumps(data)
+        request_kwargs: dict[str, Any] = {
+            "method": method,
+            "url": url,
+            "headers": headers,
+            "params": params,
+            "files": files,
+        }
+        if isinstance(prepared_data, dict):
+            request_kwargs["data"] = prepared_data
+        elif prepared_data is not None:
+            request_kwargs["content"] = prepared_data
 
         try:
-            response = requests.request(
-                method=method, url=str(url), headers=headers, data=data, params=params, stream=stream, files=files
-            )
-        except requests.ConnectionError:
+            response = httpx.request(**request_kwargs)
+        except httpx.RequestError:
             logger.exception("Request to Plugin Daemon Service failed")
             raise PluginDaemonInnerError(code=-500, message="Request to Plugin Daemon Service failed")
 
         return response
 
+    def _prepare_request(
+        self,
+        path: str,
+        headers: dict | None,
+        data: bytes | dict | str | None,
+        params: dict | None,
+        files: dict | None,
+    ) -> tuple[str, dict, bytes | dict | str | None, dict | None, dict | None]:
+        url = plugin_daemon_inner_api_baseurl / path
+        prepared_headers = dict(headers or {})
+        prepared_headers["X-Api-Key"] = dify_config.PLUGIN_DAEMON_KEY
+        prepared_headers.setdefault("Accept-Encoding", "gzip, deflate, br")
+
+        prepared_data: bytes | dict | str | None = (
+            data if isinstance(data, (bytes, str, dict)) or data is None else None
+        )
+        if isinstance(data, dict):
+            if prepared_headers.get("Content-Type") == "application/json":
+                prepared_data = json.dumps(data)
+            else:
+                prepared_data = data
+
+        return str(url), prepared_headers, prepared_data, params, files
+
     def _stream_request(
         self,
         method: str,
@@ -78,17 +104,38 @@ class BasePluginClient:
         headers: dict | None = None,
         data: bytes | dict | None = None,
         files: dict | None = None,
-    ) -> Generator[bytes, None, None]:
+    ) -> Generator[str, None, None]:
         """
         Make a stream request to the plugin daemon inner API
         """
-        response = self._request(method, path, headers, data, params, files, stream=True)
-        for line in response.iter_lines(chunk_size=1024 * 8):
-            line = line.decode("utf-8").strip()
-            if line.startswith("data:"):
-                line = line[5:].strip()
-            if line:
-                yield line
+        url, headers, prepared_data, params, files = self._prepare_request(path, headers, data, params, files)
+
+        stream_kwargs: dict[str, Any] = {
+            "method": method,
+            "url": url,
+            "headers": headers,
+            "params": params,
+            "files": files,
+        }
+        if isinstance(prepared_data, dict):
+            stream_kwargs["data"] = prepared_data
+        elif prepared_data is not None:
+            stream_kwargs["content"] = prepared_data
+
+        try:
+            with httpx.stream(**stream_kwargs) as response:
+                for raw_line in response.iter_lines():
+                    if raw_line is None:
+                        continue
+                    line = raw_line.decode("utf-8") if isinstance(raw_line, bytes) else raw_line
+                    line = line.strip()
+                    if line.startswith("data:"):
+                        line = line[5:].strip()
+                    if line:
+                        yield line
+        except httpx.RequestError:
+            logger.exception("Stream request to Plugin Daemon Service failed")
+            raise PluginDaemonInnerError(code=-500, message="Request to Plugin Daemon Service failed")
 
     def _stream_request_with_model(
         self,
@@ -139,7 +186,7 @@ class BasePluginClient:
         try:
             response = self._request(method, path, headers, data, params, files)
             response.raise_for_status()
-        except HTTPError as e:
+        except httpx.HTTPStatusError as e:
             logger.exception("Failed to request plugin daemon, status: %s, url: %s", e.response.status_code, path)
             raise e
         except Exception as e:

api/core/provider_manager.py

@@ -610,7 +610,7 @@ class ProviderManager:
 
             provider_quota_to_provider_record_dict = {}
             for provider_record in provider_records:
-                if provider_record.provider_type != ProviderType.SYSTEM.value:
+                if provider_record.provider_type != ProviderType.SYSTEM:
                     continue
 
                 provider_quota_to_provider_record_dict[ProviderQuotaType.value_of(provider_record.quota_type)] = (
@@ -702,7 +702,7 @@ class ProviderManager:
         """Get custom provider configuration."""
         # Find custom provider record (non-system)
         custom_provider_record = next(
-            (record for record in provider_records if record.provider_type != ProviderType.SYSTEM.value), None
+            (record for record in provider_records if record.provider_type != ProviderType.SYSTEM), None
         )
 
         if not custom_provider_record:
@@ -905,7 +905,7 @@ class ProviderManager:
         # Convert provider_records to dict
         quota_type_to_provider_records_dict: dict[ProviderQuotaType, Provider] = {}
         for provider_record in provider_records:
-            if provider_record.provider_type != ProviderType.SYSTEM.value:
+            if provider_record.provider_type != ProviderType.SYSTEM:
                 continue
 
             quota_type_to_provider_records_dict[ProviderQuotaType.value_of(provider_record.quota_type)] = (
@@ -1082,7 +1082,7 @@ class ProviderManager:
         """
         secret_input_form_variables = []
         for credential_form_schema in credential_form_schemas:
-            if credential_form_schema.type.value == FormType.SECRET_INPUT.value:
+            if credential_form_schema.type == FormType.SECRET_INPUT:
                 secret_input_form_variables.append(credential_form_schema.variable)
 
         return secret_input_form_variables

api/core/rag/data_post_processor/data_post_processor.py

@@ -46,7 +46,7 @@ class DataPostProcessor:
         reranking_model: dict | None = None,
         weights: dict | None = None,
     ) -> BaseRerankRunner | None:
-        if reranking_mode == RerankMode.WEIGHTED_SCORE.value and weights:
+        if reranking_mode == RerankMode.WEIGHTED_SCORE and weights:
             runner = RerankRunnerFactory.create_rerank_runner(
                 runner_type=reranking_mode,
                 tenant_id=tenant_id,
@@ -62,7 +62,7 @@ class DataPostProcessor:
                 ),
             )
             return runner
-        elif reranking_mode == RerankMode.RERANKING_MODEL.value:
+        elif reranking_mode == RerankMode.RERANKING_MODEL:
             rerank_model_instance = self._get_rerank_model_instance(tenant_id, reranking_model)
             if rerank_model_instance is None:
                 return None

api/core/rag/datasource/retrieval_service.py

@@ -21,7 +21,7 @@ from models.dataset import Document as DatasetDocument
 from services.external_knowledge_service import ExternalDatasetService
 
 default_retrieval_model = {
-    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH,
     "reranking_enable": False,
     "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
     "top_k": 4,
@@ -107,7 +107,7 @@ class RetrievalService:
             raise ValueError(";\n".join(exceptions))
 
         # Deduplicate documents for hybrid search to avoid duplicate chunks
-        if retrieval_method == RetrievalMethod.HYBRID_SEARCH.value:
+        if retrieval_method == RetrievalMethod.HYBRID_SEARCH:
             all_documents = cls._deduplicate_documents(all_documents)
             data_post_processor = DataPostProcessor(
                 str(dataset.tenant_id), reranking_mode, reranking_model, weights, False
@@ -245,10 +245,10 @@ class RetrievalService:
                         reranking_model
                         and reranking_model.get("reranking_model_name")
                         and reranking_model.get("reranking_provider_name")
-                        and retrieval_method == RetrievalMethod.SEMANTIC_SEARCH.value
+                        and retrieval_method == RetrievalMethod.SEMANTIC_SEARCH
                     ):
                         data_post_processor = DataPostProcessor(
-                            str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL.value), reranking_model, None, False
+                            str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL), reranking_model, None, False
                         )
                         all_documents.extend(
                             data_post_processor.invoke(
@@ -293,10 +293,10 @@ class RetrievalService:
                         reranking_model
                         and reranking_model.get("reranking_model_name")
                         and reranking_model.get("reranking_provider_name")
-                        and retrieval_method == RetrievalMethod.FULL_TEXT_SEARCH.value
+                        and retrieval_method == RetrievalMethod.FULL_TEXT_SEARCH
                     ):
                         data_post_processor = DataPostProcessor(
-                            str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL.value), reranking_model, None, False
+                            str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL), reranking_model, None, False
                         )
                         all_documents.extend(
                             data_post_processor.invoke(

api/core/rag/datasource/vdb/alibabacloud_mysql/alibabacloud_mysql_vector.py

@@ -0,0 +1,388 @@
+import hashlib
+import json
+import logging
+import uuid
+from contextlib import contextmanager
+from typing import Any, Literal, cast
+
+import mysql.connector
+from mysql.connector import Error as MySQLError
+from pydantic import BaseModel, model_validator
+
+from configs import dify_config
+from core.rag.datasource.vdb.vector_base import BaseVector
+from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
+from core.rag.datasource.vdb.vector_type import VectorType
+from core.rag.embedding.embedding_base import Embeddings
+from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
+from models.dataset import Dataset
+
+logger = logging.getLogger(__name__)
+
+
+class AlibabaCloudMySQLVectorConfig(BaseModel):
+    host: str
+    port: int
+    user: str
+    password: str
+    database: str
+    max_connection: int
+    charset: str = "utf8mb4"
+    distance_function: Literal["cosine", "euclidean"] = "cosine"
+    hnsw_m: int = 6
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_config(cls, values: dict):
+        if not values.get("host"):
+            raise ValueError("config ALIBABACLOUD_MYSQL_HOST is required")
+        if not values.get("port"):
+            raise ValueError("config ALIBABACLOUD_MYSQL_PORT is required")
+        if not values.get("user"):
+            raise ValueError("config ALIBABACLOUD_MYSQL_USER is required")
+        if values.get("password") is None:
+            raise ValueError("config ALIBABACLOUD_MYSQL_PASSWORD is required")
+        if not values.get("database"):
+            raise ValueError("config ALIBABACLOUD_MYSQL_DATABASE is required")
+        if not values.get("max_connection"):
+            raise ValueError("config ALIBABACLOUD_MYSQL_MAX_CONNECTION is required")
+        return values
+
+
+SQL_CREATE_TABLE = """
+CREATE TABLE IF NOT EXISTS {table_name} (
+    id VARCHAR(36) PRIMARY KEY,
+    text LONGTEXT NOT NULL,
+    meta JSON NOT NULL,
+    embedding VECTOR({dimension}) NOT NULL,
+    VECTOR INDEX (embedding) M={hnsw_m} DISTANCE={distance_function}
+) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
+"""
+
+SQL_CREATE_META_INDEX = """
+CREATE INDEX idx_{index_hash}_meta ON {table_name}
+    ((CAST(JSON_UNQUOTE(JSON_EXTRACT(meta, '$.document_id')) AS CHAR(36))));
+"""
+
+SQL_CREATE_FULLTEXT_INDEX = """
+CREATE FULLTEXT INDEX idx_{index_hash}_text ON {table_name} (text) WITH PARSER ngram;
+"""
+
+
+class AlibabaCloudMySQLVector(BaseVector):
+    def __init__(self, collection_name: str, config: AlibabaCloudMySQLVectorConfig):
+        super().__init__(collection_name)
+        self.pool = self._create_connection_pool(config)
+        self.table_name = collection_name.lower()
+        self.index_hash = hashlib.md5(self.table_name.encode()).hexdigest()[:8]
+        self.distance_function = config.distance_function.lower()
+        self.hnsw_m = config.hnsw_m
+        self._check_vector_support()
+
+    def get_type(self) -> str:
+        return VectorType.ALIBABACLOUD_MYSQL
+
+    def _create_connection_pool(self, config: AlibabaCloudMySQLVectorConfig):
+        # Create connection pool using mysql-connector-python pooling
+        pool_config: dict[str, Any] = {
+            "host": config.host,
+            "port": config.port,
+            "user": config.user,
+            "password": config.password,
+            "database": config.database,
+            "charset": config.charset,
+            "autocommit": True,
+            "pool_name": f"pool_{self.collection_name}",
+            "pool_size": config.max_connection,
+            "pool_reset_session": True,
+        }
+        return mysql.connector.pooling.MySQLConnectionPool(**pool_config)
+
+    def _check_vector_support(self):
+        """Check if the MySQL server supports vector operations."""
+        try:
+            with self._get_cursor() as cur:
+                # Check MySQL version and vector support
+                cur.execute("SELECT VERSION()")
+                version = cur.fetchone()["VERSION()"]
+                logger.debug("Connected to MySQL version: %s", version)
+                # Try to execute a simple vector function to verify support
+                cur.execute("SELECT VEC_FromText('[1,2,3]') IS NOT NULL as vector_support")
+                result = cur.fetchone()
+                if not result or not result.get("vector_support"):
+                    raise ValueError(
+                        "RDS MySQL Vector functions are not available."
+                        " Please ensure you're using RDS MySQL 8.0.36+ with Vector support."
+                    )
+
+        except MySQLError as e:
+            if "FUNCTION" in str(e) and "VEC_FromText" in str(e):
+                raise ValueError(
+                    "RDS MySQL Vector functions are not available."
+                    " Please ensure you're using RDS MySQL 8.0.36+ with Vector support."
+                ) from e
+            raise e
+
+    @contextmanager
+    def _get_cursor(self):
+        conn = self.pool.get_connection()
+        cur = conn.cursor(dictionary=True)
+        try:
+            yield cur
+        finally:
+            cur.close()
+            conn.close()
+
+    def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
+        dimension = len(embeddings[0])
+        self._create_collection(dimension)
+        return self.add_texts(texts, embeddings)
+
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        values = []
+        pks = []
+        for i, doc in enumerate(documents):
+            if doc.metadata is not None:
+                doc_id = doc.metadata.get("doc_id", str(uuid.uuid4()))
+                pks.append(doc_id)
+                # Convert embedding list to Aliyun MySQL vector format
+                vector_str = "[" + ",".join(map(str, embeddings[i])) + "]"
+                values.append(
+                    (
+                        doc_id,
+                        doc.page_content,
+                        json.dumps(doc.metadata),
+                        vector_str,
+                    )
+                )
+
+        with self._get_cursor() as cur:
+            insert_sql = (
+                f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (%s, %s, %s, VEC_FromText(%s))"
+            )
+            cur.executemany(insert_sql, values)
+        return pks
+
+    def text_exists(self, id: str) -> bool:
+        with self._get_cursor() as cur:
+            cur.execute(f"SELECT id FROM {self.table_name} WHERE id = %s", (id,))
+            return cur.fetchone() is not None
+
+    def get_by_ids(self, ids: list[str]) -> list[Document]:
+        if not ids:
+            return []
+
+        with self._get_cursor() as cur:
+            placeholders = ",".join(["%s"] * len(ids))
+            cur.execute(f"SELECT meta, text FROM {self.table_name} WHERE id IN ({placeholders})", ids)
+            docs = []
+            for record in cur:
+                metadata = record["meta"]
+                if isinstance(metadata, str):
+                    metadata = json.loads(metadata)
+                docs.append(Document(page_content=record["text"], metadata=metadata))
+        return docs
+
+    def delete_by_ids(self, ids: list[str]):
+        # Avoiding crashes caused by performing delete operations on empty lists
+        if not ids:
+            return
+
+        with self._get_cursor() as cur:
+            try:
+                placeholders = ",".join(["%s"] * len(ids))
+                cur.execute(f"DELETE FROM {self.table_name} WHERE id IN ({placeholders})", ids)
+            except MySQLError as e:
+                if e.errno == 1146:  # Table doesn't exist
+                    logger.warning("Table %s not found, skipping delete operation.", self.table_name)
+                    return
+                else:
+                    raise e
+
+    def delete_by_metadata_field(self, key: str, value: str):
+        with self._get_cursor() as cur:
+            cur.execute(
+                f"DELETE FROM {self.table_name} WHERE JSON_UNQUOTE(JSON_EXTRACT(meta, %s)) = %s", (f"$.{key}", value)
+            )
+
+    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
+        """
+        Search the nearest neighbors to a vector using RDS MySQL vector distance functions.
+
+        :param query_vector: The input vector to search for similar items.
+        :return: List of Documents that are nearest to the query vector.
+        """
+        top_k = kwargs.get("top_k", 4)
+        if not isinstance(top_k, int) or top_k <= 0:
+            raise ValueError("top_k must be a positive integer")
+
+        document_ids_filter = kwargs.get("document_ids_filter")
+        where_clause = ""
+        params = []
+
+        if document_ids_filter:
+            placeholders = ",".join(["%s"] * len(document_ids_filter))
+            where_clause = f" WHERE JSON_UNQUOTE(JSON_EXTRACT(meta, '$.document_id')) IN ({placeholders}) "
+            params.extend(document_ids_filter)
+
+        # Convert query vector to RDS MySQL vector format
+        query_vector_str = "[" + ",".join(map(str, query_vector)) + "]"
+
+        # Use RSD MySQL's native vector distance functions
+        with self._get_cursor() as cur:
+            # Choose distance function based on configuration
+            distance_func = "VEC_DISTANCE_COSINE" if self.distance_function == "cosine" else "VEC_DISTANCE_EUCLIDEAN"
+
+            # Note: RDS MySQL optimizer will use vector index when ORDER BY + LIMIT are present
+            # Use column alias in ORDER BY to avoid calculating distance twice
+            sql = f"""
+            SELECT meta, text,
+                   {distance_func}(embedding, VEC_FromText(%s)) AS distance
+            FROM {self.table_name}
+            {where_clause}
+            ORDER BY distance
+            LIMIT %s
+            """
+            query_params = [query_vector_str] + params + [top_k]
+
+            cur.execute(sql, query_params)
+
+            docs = []
+            score_threshold = float(kwargs.get("score_threshold") or 0.0)
+
+            for record in cur:
+                try:
+                    distance = float(record["distance"])
+                    # Convert distance to similarity score
+                    if self.distance_function == "cosine":
+                        # For cosine distance: similarity = 1 - distance
+                        similarity = 1.0 - distance
+                    else:
+                        # For euclidean distance: use inverse relationship
+                        # similarity = 1 / (1 + distance)
+                        similarity = 1.0 / (1.0 + distance)
+
+                    metadata = record["meta"]
+                    if isinstance(metadata, str):
+                        metadata = json.loads(metadata)
+                    metadata["score"] = similarity
+                    metadata["distance"] = distance
+
+                    if similarity >= score_threshold:
+                        docs.append(Document(page_content=record["text"], metadata=metadata))
+                except (ValueError, json.JSONDecodeError) as e:
+                    logger.warning("Error processing search result: %s", e)
+                    continue
+
+            return docs
+
+    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
+        top_k = kwargs.get("top_k", 5)
+        if not isinstance(top_k, int) or top_k <= 0:
+            raise ValueError("top_k must be a positive integer")
+
+        document_ids_filter = kwargs.get("document_ids_filter")
+        where_clause = ""
+        params = []
+
+        if document_ids_filter:
+            placeholders = ",".join(["%s"] * len(document_ids_filter))
+            where_clause = f" AND JSON_UNQUOTE(JSON_EXTRACT(meta, '$.document_id')) IN ({placeholders}) "
+            params.extend(document_ids_filter)
+
+        with self._get_cursor() as cur:
+            # Build query parameters: query (twice for MATCH clauses), document_ids_filter (if any), top_k
+            query_params = [query, query] + params + [top_k]
+            cur.execute(
+                f"""SELECT meta, text,
+                    MATCH(text) AGAINST(%s IN NATURAL LANGUAGE MODE) AS score
+                    FROM {self.table_name}
+                    WHERE MATCH(text) AGAINST(%s IN NATURAL LANGUAGE MODE)
+                    {where_clause}
+                    ORDER BY score DESC
+                    LIMIT %s""",
+                query_params,
+            )
+            docs = []
+            for record in cur:
+                metadata = record["meta"]
+                if isinstance(metadata, str):
+                    metadata = json.loads(metadata)
+                metadata["score"] = float(record["score"])
+                docs.append(Document(page_content=record["text"], metadata=metadata))
+        return docs
+
+    def delete(self):
+        with self._get_cursor() as cur:
+            cur.execute(f"DROP TABLE IF EXISTS {self.table_name}")
+
+    def _create_collection(self, dimension: int):
+        collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
+        lock_name = f"{collection_exist_cache_key}_lock"
+        with redis_client.lock(lock_name, timeout=20):
+            if redis_client.get(collection_exist_cache_key):
+                return
+
+            with self._get_cursor() as cur:
+                # Create table with vector column and vector index
+                cur.execute(
+                    SQL_CREATE_TABLE.format(
+                        table_name=self.table_name,
+                        dimension=dimension,
+                        distance_function=self.distance_function,
+                        hnsw_m=self.hnsw_m,
+                    )
+                )
+                # Create metadata index (check if exists first)
+                try:
+                    cur.execute(SQL_CREATE_META_INDEX.format(table_name=self.table_name, index_hash=self.index_hash))
+                except MySQLError as e:
+                    if e.errno != 1061:  # Duplicate key name
+                        logger.warning("Could not create meta index: %s", e)
+
+                # Create full-text index for text search
+                try:
+                    cur.execute(
+                        SQL_CREATE_FULLTEXT_INDEX.format(table_name=self.table_name, index_hash=self.index_hash)
+                    )
+                except MySQLError as e:
+                    if e.errno != 1061:  # Duplicate key name
+                        logger.warning("Could not create fulltext index: %s", e)
+
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+
+
+class AlibabaCloudMySQLVectorFactory(AbstractVectorFactory):
+    def _validate_distance_function(self, distance_function: str) -> Literal["cosine", "euclidean"]:
+        """Validate and return the distance function as a proper Literal type."""
+        if distance_function not in ["cosine", "euclidean"]:
+            raise ValueError(f"Invalid distance function: {distance_function}. Must be 'cosine' or 'euclidean'")
+        return cast(Literal["cosine", "euclidean"], distance_function)
+
+    def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> AlibabaCloudMySQLVector:
+        if dataset.index_struct_dict:
+            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.ALIBABACLOUD_MYSQL, collection_name)
+            )
+        return AlibabaCloudMySQLVector(
+            collection_name=collection_name,
+            config=AlibabaCloudMySQLVectorConfig(
+                host=dify_config.ALIBABACLOUD_MYSQL_HOST or "localhost",
+                port=dify_config.ALIBABACLOUD_MYSQL_PORT,
+                user=dify_config.ALIBABACLOUD_MYSQL_USER or "root",
+                password=dify_config.ALIBABACLOUD_MYSQL_PASSWORD or "",
+                database=dify_config.ALIBABACLOUD_MYSQL_DATABASE or "dify",
+                max_connection=dify_config.ALIBABACLOUD_MYSQL_MAX_CONNECTION,
+                charset=dify_config.ALIBABACLOUD_MYSQL_CHARSET or "utf8mb4",
+                distance_function=self._validate_distance_function(
+                    dify_config.ALIBABACLOUD_MYSQL_DISTANCE_FUNCTION or "cosine"
+                ),
+                hnsw_m=dify_config.ALIBABACLOUD_MYSQL_HNSW_M or 6,
+            ),
+        )

api/core/rag/datasource/vdb/clickzetta/clickzetta_vector.py

@@ -488,9 +488,9 @@ class ClickzettaVector(BaseVector):
         create_table_sql = f"""
         CREATE TABLE IF NOT EXISTS {self._config.schema_name}.{self._table_name} (
             id STRING NOT NULL COMMENT 'Unique document identifier',
-            {Field.CONTENT_KEY.value} STRING NOT NULL COMMENT 'Document text content for search and retrieval',
-            {Field.METADATA_KEY.value} JSON COMMENT 'Document metadata including source, type, and other attributes',
-            {Field.VECTOR.value} VECTOR(FLOAT, {dimension}) NOT NULL COMMENT
+            {Field.CONTENT_KEY} STRING NOT NULL COMMENT 'Document text content for search and retrieval',
+            {Field.METADATA_KEY} JSON COMMENT 'Document metadata including source, type, and other attributes',
+            {Field.VECTOR} VECTOR(FLOAT, {dimension}) NOT NULL COMMENT
                 'High-dimensional embedding vector for semantic similarity search',
             PRIMARY KEY (id)
         ) COMMENT 'Dify RAG knowledge base vector storage table for document embeddings and content'
@@ -519,15 +519,15 @@ class ClickzettaVector(BaseVector):
             existing_indexes = cursor.fetchall()
             for idx in existing_indexes:
                 # Check if vector index already exists on the embedding column
-                if Field.VECTOR.value in str(idx).lower():
-                    logger.info("Vector index already exists on column %s", Field.VECTOR.value)
+                if Field.VECTOR in str(idx).lower():
+                    logger.info("Vector index already exists on column %s", Field.VECTOR)
                     return
         except (RuntimeError, ValueError) as e:
             logger.warning("Failed to check existing indexes: %s", e)
 
         index_sql = f"""
         CREATE VECTOR INDEX IF NOT EXISTS {index_name}
-        ON TABLE {self._config.schema_name}.{self._table_name}({Field.VECTOR.value})
+        ON TABLE {self._config.schema_name}.{self._table_name}({Field.VECTOR})
         PROPERTIES (
             "distance.function" = "{self._config.vector_distance_function}",
             "scalar.type" = "f32",
@@ -560,17 +560,17 @@ class ClickzettaVector(BaseVector):
                 # More precise check: look for inverted index specifically on the content column
                 if (
                     "inverted" in idx_str
-                    and Field.CONTENT_KEY.value.lower() in idx_str
+                    and Field.CONTENT_KEY.lower() in idx_str
                     and (index_name.lower() in idx_str or f"idx_{self._table_name}_text" in idx_str)
                 ):
-                    logger.info("Inverted index already exists on column %s: %s", Field.CONTENT_KEY.value, idx)
+                    logger.info("Inverted index already exists on column %s: %s", Field.CONTENT_KEY, idx)
                     return
         except (RuntimeError, ValueError) as e:
             logger.warning("Failed to check existing indexes: %s", e)
 
         index_sql = f"""
         CREATE INVERTED INDEX IF NOT EXISTS {index_name}
-        ON TABLE {self._config.schema_name}.{self._table_name} ({Field.CONTENT_KEY.value})
+        ON TABLE {self._config.schema_name}.{self._table_name} ({Field.CONTENT_KEY})
         PROPERTIES (
             "analyzer" = "{self._config.analyzer_type}",
             "mode" = "{self._config.analyzer_mode}"
@@ -588,13 +588,13 @@ class ClickzettaVector(BaseVector):
                 or "with the same type" in error_msg
                 or "cannot create inverted index" in error_msg
             ) and "already has index" in error_msg:
-                logger.info("Inverted index already exists on column %s", Field.CONTENT_KEY.value)
+                logger.info("Inverted index already exists on column %s", Field.CONTENT_KEY)
                 # Try to get the existing index name for logging
                 try:
                     cursor.execute(f"SHOW INDEX FROM {self._config.schema_name}.{self._table_name}")
                     existing_indexes = cursor.fetchall()
                     for idx in existing_indexes:
-                        if "inverted" in str(idx).lower() and Field.CONTENT_KEY.value.lower() in str(idx).lower():
+                        if "inverted" in str(idx).lower() and Field.CONTENT_KEY.lower() in str(idx).lower():
                             logger.info("Found existing inverted index: %s", idx)
                             break
                 except (RuntimeError, ValueError):
@@ -669,7 +669,7 @@ class ClickzettaVector(BaseVector):
 
         # Use parameterized INSERT with executemany for better performance and security
         # Cast JSON and VECTOR in SQL, pass raw data as parameters
-        columns = f"id, {Field.CONTENT_KEY.value}, {Field.METADATA_KEY.value}, {Field.VECTOR.value}"
+        columns = f"id, {Field.CONTENT_KEY}, {Field.METADATA_KEY}, {Field.VECTOR}"
         insert_sql = (
             f"INSERT INTO {self._config.schema_name}.{self._table_name} ({columns}) "
             f"VALUES (?, ?, CAST(? AS JSON), CAST(? AS VECTOR({vector_dimension})))"
@@ -767,7 +767,7 @@ class ClickzettaVector(BaseVector):
                 # Use json_extract_string function for ClickZetta compatibility
                 sql = (
                     f"DELETE FROM {self._config.schema_name}.{self._table_name} "
-                    f"WHERE json_extract_string({Field.METADATA_KEY.value}, '$.{key}') = ?"
+                    f"WHERE json_extract_string({Field.METADATA_KEY}, '$.{key}') = ?"
                 )
                 cursor.execute(sql, binding_params=[value])
 
@@ -795,9 +795,7 @@ class ClickzettaVector(BaseVector):
             safe_doc_ids = [str(id).replace("'", "''") for id in document_ids_filter]
             doc_ids_str = ",".join(f"'{id}'" for id in safe_doc_ids)
             # Use json_extract_string function for ClickZetta compatibility
-            filter_clauses.append(
-                f"json_extract_string({Field.METADATA_KEY.value}, '$.document_id') IN ({doc_ids_str})"
-            )
+            filter_clauses.append(f"json_extract_string({Field.METADATA_KEY}, '$.document_id') IN ({doc_ids_str})")
 
         # No need for dataset_id filter since each dataset has its own table
 
@@ -808,23 +806,21 @@ class ClickzettaVector(BaseVector):
             distance_func = "COSINE_DISTANCE"
             if score_threshold > 0:
                 query_vector_str = f"CAST('[{self._format_vector_simple(query_vector)}]' AS VECTOR({vector_dimension}))"
-                filter_clauses.append(
-                    f"{distance_func}({Field.VECTOR.value}, {query_vector_str}) < {2 - score_threshold}"
-                )
+                filter_clauses.append(f"{distance_func}({Field.VECTOR}, {query_vector_str}) < {2 - score_threshold}")
         else:
             # For L2 distance, smaller is better
             distance_func = "L2_DISTANCE"
             if score_threshold > 0:
                 query_vector_str = f"CAST('[{self._format_vector_simple(query_vector)}]' AS VECTOR({vector_dimension}))"
-                filter_clauses.append(f"{distance_func}({Field.VECTOR.value}, {query_vector_str}) < {score_threshold}")
+                filter_clauses.append(f"{distance_func}({Field.VECTOR}, {query_vector_str}) < {score_threshold}")
 
         where_clause = " AND ".join(filter_clauses) if filter_clauses else "1=1"
 
         # Execute vector search query
         query_vector_str = f"CAST('[{self._format_vector_simple(query_vector)}]' AS VECTOR({vector_dimension}))"
         search_sql = f"""
-        SELECT id, {Field.CONTENT_KEY.value}, {Field.METADATA_KEY.value},
-               {distance_func}({Field.VECTOR.value}, {query_vector_str}) AS distance
+        SELECT id, {Field.CONTENT_KEY}, {Field.METADATA_KEY},
+               {distance_func}({Field.VECTOR}, {query_vector_str}) AS distance
         FROM {self._config.schema_name}.{self._table_name}
         WHERE {where_clause}
         ORDER BY distance
@@ -887,9 +883,7 @@ class ClickzettaVector(BaseVector):
             safe_doc_ids = [str(id).replace("'", "''") for id in document_ids_filter]
             doc_ids_str = ",".join(f"'{id}'" for id in safe_doc_ids)
             # Use json_extract_string function for ClickZetta compatibility
-            filter_clauses.append(
-                f"json_extract_string({Field.METADATA_KEY.value}, '$.document_id') IN ({doc_ids_str})"
-            )
+            filter_clauses.append(f"json_extract_string({Field.METADATA_KEY}, '$.document_id') IN ({doc_ids_str})")
 
         # No need for dataset_id filter since each dataset has its own table
 
@@ -897,13 +891,13 @@ class ClickzettaVector(BaseVector):
         # match_all requires all terms to be present
         # Use simple quote escaping for MATCH_ALL since it needs to be in the WHERE clause
         escaped_query = query.replace("'", "''")
-        filter_clauses.append(f"MATCH_ALL({Field.CONTENT_KEY.value}, '{escaped_query}')")
+        filter_clauses.append(f"MATCH_ALL({Field.CONTENT_KEY}, '{escaped_query}')")
 
         where_clause = " AND ".join(filter_clauses)
 
         # Execute full-text search query
         search_sql = f"""
-        SELECT id, {Field.CONTENT_KEY.value}, {Field.METADATA_KEY.value}
+        SELECT id, {Field.CONTENT_KEY}, {Field.METADATA_KEY}
         FROM {self._config.schema_name}.{self._table_name}
         WHERE {where_clause}
         LIMIT {top_k}
@@ -986,19 +980,17 @@ class ClickzettaVector(BaseVector):
             safe_doc_ids = [str(id).replace("'", "''") for id in document_ids_filter]
             doc_ids_str = ",".join(f"'{id}'" for id in safe_doc_ids)
             # Use json_extract_string function for ClickZetta compatibility
-            filter_clauses.append(
-                f"json_extract_string({Field.METADATA_KEY.value}, '$.document_id') IN ({doc_ids_str})"
-            )
+            filter_clauses.append(f"json_extract_string({Field.METADATA_KEY}, '$.document_id') IN ({doc_ids_str})")
 
         # No need for dataset_id filter since each dataset has its own table
 
         # Use simple quote escaping for LIKE clause
         escaped_query = query.replace("'", "''")
-        filter_clauses.append(f"{Field.CONTENT_KEY.value} LIKE '%{escaped_query}%'")
+        filter_clauses.append(f"{Field.CONTENT_KEY} LIKE '%{escaped_query}%'")
         where_clause = " AND ".join(filter_clauses)
 
         search_sql = f"""
-        SELECT id, {Field.CONTENT_KEY.value}, {Field.METADATA_KEY.value}
+        SELECT id, {Field.CONTENT_KEY}, {Field.METADATA_KEY}
         FROM {self._config.schema_name}.{self._table_name}
         WHERE {where_clause}
         LIMIT {top_k}

api/core/rag/datasource/vdb/elasticsearch/elasticsearch_ja_vector.py

@@ -57,18 +57,18 @@ class ElasticSearchJaVector(ElasticSearchVector):
                 }
                 mappings = {
                     "properties": {
-                        Field.CONTENT_KEY.value: {
+                        Field.CONTENT_KEY: {
                             "type": "text",
                             "analyzer": "ja_analyzer",
                             "search_analyzer": "ja_analyzer",
                         },
-                        Field.VECTOR.value: {  # Make sure the dimension is correct here
+                        Field.VECTOR: {  # Make sure the dimension is correct here
                             "type": "dense_vector",
                             "dims": dim,
                             "index": True,
                             "similarity": "cosine",
                         },
-                        Field.METADATA_KEY.value: {
+                        Field.METADATA_KEY: {
                             "type": "object",
                             "properties": {
                                 "doc_id": {"type": "keyword"}  # Map doc_id to keyword type

api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py

@@ -4,7 +4,7 @@ import math
 from typing import Any, cast
 from urllib.parse import urlparse
 
-import requests
+from elasticsearch import ConnectionError as ElasticsearchConnectionError
 from elasticsearch import Elasticsearch
 from flask import current_app
 from packaging.version import parse as parse_version
@@ -138,7 +138,7 @@ class ElasticSearchVector(BaseVector):
             if not client.ping():
                 raise ConnectionError("Failed to connect to Elasticsearch")
 
-        except requests.ConnectionError as e:
+        except ElasticsearchConnectionError as e:
             raise ConnectionError(f"Vector database connection error: {str(e)}")
         except Exception as e:
             raise ConnectionError(f"Elasticsearch client initialization failed: {str(e)}")
@@ -163,9 +163,9 @@ class ElasticSearchVector(BaseVector):
                 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 {},
+                    Field.CONTENT_KEY: documents[i].page_content,
+                    Field.VECTOR: embeddings[i] or None,
+                    Field.METADATA_KEY: documents[i].metadata or {},
                 },
             )
         self._client.indices.refresh(index=self._collection_name)
@@ -193,7 +193,7 @@ class ElasticSearchVector(BaseVector):
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 4)
         num_candidates = math.ceil(top_k * 1.5)
-        knn = {"field": Field.VECTOR.value, "query_vector": query_vector, "k": top_k, "num_candidates": num_candidates}
+        knn = {"field": Field.VECTOR, "query_vector": query_vector, "k": top_k, "num_candidates": num_candidates}
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
             knn["filter"] = {"terms": {"metadata.document_id": document_ids_filter}}
@@ -205,9 +205,9 @@ class ElasticSearchVector(BaseVector):
             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],
+                        page_content=hit["_source"][Field.CONTENT_KEY],
+                        vector=hit["_source"][Field.VECTOR],
+                        metadata=hit["_source"][Field.METADATA_KEY],
                     ),
                     hit["_score"],
                 )
@@ -224,13 +224,13 @@ class ElasticSearchVector(BaseVector):
         return docs
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        query_str: dict[str, Any] = {"match": {Field.CONTENT_KEY.value: query}}
+        query_str: dict[str, Any] = {"match": {Field.CONTENT_KEY: query}}
         document_ids_filter = kwargs.get("document_ids_filter")
 
         if document_ids_filter:
             query_str = {
                 "bool": {
-                    "must": {"match": {Field.CONTENT_KEY.value: query}},
+                    "must": {"match": {Field.CONTENT_KEY: query}},
                     "filter": {"terms": {"metadata.document_id": document_ids_filter}},
                 }
             }
@@ -240,9 +240,9 @@ class ElasticSearchVector(BaseVector):
         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],
+                    page_content=hit["_source"][Field.CONTENT_KEY],
+                    vector=hit["_source"][Field.VECTOR],
+                    metadata=hit["_source"][Field.METADATA_KEY],
                 )
             )
 
@@ -270,14 +270,14 @@ class ElasticSearchVector(BaseVector):
                 dim = len(embeddings[0])
                 mappings = {
                     "properties": {
-                        Field.CONTENT_KEY.value: {"type": "text"},
-                        Field.VECTOR.value: {  # Make sure the dimension is correct here
+                        Field.CONTENT_KEY: {"type": "text"},
+                        Field.VECTOR: {  # Make sure the dimension is correct here
                             "type": "dense_vector",
                             "dims": dim,
                             "index": True,
                             "similarity": "cosine",
                         },
-                        Field.METADATA_KEY.value: {
+                        Field.METADATA_KEY: {
                             "type": "object",
                             "properties": {
                                 "doc_id": {"type": "keyword"},  # Map doc_id to keyword type

api/core/rag/datasource/vdb/huawei/huawei_cloud_vector.py

@@ -67,9 +67,9 @@ class HuaweiCloudVector(BaseVector):
                 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 {},
+                    Field.CONTENT_KEY: documents[i].page_content,
+                    Field.VECTOR: embeddings[i] or None,
+                    Field.METADATA_KEY: documents[i].metadata or {},
                 },
             )
         self._client.indices.refresh(index=self._collection_name)
@@ -101,7 +101,7 @@ class HuaweiCloudVector(BaseVector):
             "size": top_k,
             "query": {
                 "vector": {
-                    Field.VECTOR.value: {
+                    Field.VECTOR: {
                         "vector": query_vector,
                         "topk": top_k,
                     }
@@ -116,9 +116,9 @@ class HuaweiCloudVector(BaseVector):
             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],
+                        page_content=hit["_source"][Field.CONTENT_KEY],
+                        vector=hit["_source"][Field.VECTOR],
+                        metadata=hit["_source"][Field.METADATA_KEY],
                     ),
                     hit["_score"],
                 )
@@ -135,15 +135,15 @@ class HuaweiCloudVector(BaseVector):
         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: query}}
         results = self._client.search(index=self._collection_name, query=query_str, size=kwargs.get("top_k", 4))
         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],
+                    page_content=hit["_source"][Field.CONTENT_KEY],
+                    vector=hit["_source"][Field.VECTOR],
+                    metadata=hit["_source"][Field.METADATA_KEY],
                 )
             )
 
@@ -171,8 +171,8 @@ class HuaweiCloudVector(BaseVector):
                 dim = len(embeddings[0])
                 mappings = {
                     "properties": {
-                        Field.CONTENT_KEY.value: {"type": "text"},
-                        Field.VECTOR.value: {  # Make sure the dimension is correct here
+                        Field.CONTENT_KEY: {"type": "text"},
+                        Field.VECTOR: {  # Make sure the dimension is correct here
                             "type": "vector",
                             "dimension": dim,
                             "indexing": True,
@@ -181,7 +181,7 @@ class HuaweiCloudVector(BaseVector):
                             "neighbors": 32,
                             "efc": 128,
                         },
-                        Field.METADATA_KEY.value: {
+                        Field.METADATA_KEY: {
                             "type": "object",
                             "properties": {
                                 "doc_id": {"type": "keyword"}  # Map doc_id to keyword type

api/core/rag/datasource/vdb/lindorm/lindorm_vector.py

@@ -125,9 +125,9 @@ class LindormVectorStore(BaseVector):
                     }
                 }
                 action_values: dict[str, Any] = {
-                    Field.CONTENT_KEY.value: documents[i].page_content,
-                    Field.VECTOR.value: embeddings[i],
-                    Field.METADATA_KEY.value: documents[i].metadata,
+                    Field.CONTENT_KEY: documents[i].page_content,
+                    Field.VECTOR: embeddings[i],
+                    Field.METADATA_KEY: documents[i].metadata,
                 }
                 if self._using_ugc:
                     action_header["index"]["routing"] = self._routing
@@ -149,7 +149,7 @@ class LindormVectorStore(BaseVector):
 
     def get_ids_by_metadata_field(self, key: str, value: str):
         query: dict[str, Any] = {
-            "query": {"bool": {"must": [{"term": {f"{Field.METADATA_KEY.value}.{key}.keyword": value}}]}}
+            "query": {"bool": {"must": [{"term": {f"{Field.METADATA_KEY}.{key}.keyword": value}}]}}
         }
         if self._using_ugc:
             query["query"]["bool"]["must"].append({"term": {f"{ROUTING_FIELD}.keyword": self._routing}})
@@ -252,14 +252,14 @@ class LindormVectorStore(BaseVector):
         search_query: dict[str, Any] = {
             "size": top_k,
             "_source": True,
-            "query": {"knn": {Field.VECTOR.value: {"vector": query_vector, "k": top_k}}},
+            "query": {"knn": {Field.VECTOR: {"vector": query_vector, "k": top_k}}},
         }
 
         final_ext: dict[str, Any] = {"lvector": {}}
         if filters is not None and len(filters) > 0:
             # when using filter, transform filter from List[Dict] to Dict as valid format
             filter_dict = {"bool": {"must": filters}} if len(filters) > 1 else filters[0]
-            search_query["query"]["knn"][Field.VECTOR.value]["filter"] = filter_dict  # filter should be Dict
+            search_query["query"]["knn"][Field.VECTOR]["filter"] = filter_dict  # filter should be Dict
             final_ext["lvector"]["filter_type"] = "pre_filter"
 
         if final_ext != {"lvector": {}}:
@@ -279,9 +279,9 @@ class LindormVectorStore(BaseVector):
             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],
+                        page_content=hit["_source"][Field.CONTENT_KEY],
+                        vector=hit["_source"][Field.VECTOR],
+                        metadata=hit["_source"][Field.METADATA_KEY],
                     ),
                     hit["_score"],
                 )
@@ -318,9 +318,9 @@ class LindormVectorStore(BaseVector):
 
         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)
+            metadata = hit["_source"].get(Field.METADATA_KEY)
+            vector = hit["_source"].get(Field.VECTOR)
+            page_content = hit["_source"].get(Field.CONTENT_KEY)
             doc = Document(page_content=page_content, vector=vector, metadata=metadata)
             docs.append(doc)
 
@@ -342,8 +342,8 @@ class LindormVectorStore(BaseVector):
                     "settings": {"index": {"knn": True, "knn_routing": self._using_ugc}},
                     "mappings": {
                         "properties": {
-                            Field.CONTENT_KEY.value: {"type": "text"},
-                            Field.VECTOR.value: {
+                            Field.CONTENT_KEY: {"type": "text"},
+                            Field.VECTOR: {
                                 "type": "knn_vector",
                                 "dimension": len(embeddings[0]),  # Make sure the dimension is correct here
                                 "method": {

api/core/rag/datasource/vdb/milvus/milvus_vector.py

@@ -85,7 +85,7 @@ class MilvusVector(BaseVector):
             collection_info = self._client.describe_collection(self._collection_name)
             fields = [field["name"] for field in collection_info["fields"]]
         # Since primary field is auto-id, no need to track it
-        self._fields = [f for f in fields if f != Field.PRIMARY_KEY.value]
+        self._fields = [f for f in fields if f != Field.PRIMARY_KEY]
 
     def _check_hybrid_search_support(self) -> bool:
         """
@@ -130,9 +130,9 @@ class MilvusVector(BaseVector):
             insert_dict = {
                 # Do not need to insert the sparse_vector field separately, as the text_bm25_emb
                 # function will automatically convert the native text into a sparse vector for us.
-                Field.CONTENT_KEY.value: documents[i].page_content,
-                Field.VECTOR.value: embeddings[i],
-                Field.METADATA_KEY.value: documents[i].metadata,
+                Field.CONTENT_KEY: documents[i].page_content,
+                Field.VECTOR: embeddings[i],
+                Field.METADATA_KEY: documents[i].metadata,
             }
             insert_dict_list.append(insert_dict)
         # Total insert count
@@ -243,15 +243,15 @@ class MilvusVector(BaseVector):
         results = self._client.search(
             collection_name=self._collection_name,
             data=[query_vector],
-            anns_field=Field.VECTOR.value,
+            anns_field=Field.VECTOR,
             limit=kwargs.get("top_k", 4),
-            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+            output_fields=[Field.CONTENT_KEY, Field.METADATA_KEY],
             filter=filter,
         )
 
         return self._process_search_results(
             results,
-            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+            output_fields=[Field.CONTENT_KEY, Field.METADATA_KEY],
             score_threshold=float(kwargs.get("score_threshold") or 0.0),
         )
 
@@ -264,7 +264,7 @@ class MilvusVector(BaseVector):
                 "Full-text search is disabled: set MILVUS_ENABLE_HYBRID_SEARCH=true (requires Milvus >= 2.5.0)."
             )
             return []
-        if not self.field_exists(Field.SPARSE_VECTOR.value):
+        if not self.field_exists(Field.SPARSE_VECTOR):
             logger.warning(
                 "Full-text search unavailable: collection missing 'sparse_vector' field; "
                 "recreate the collection after enabling MILVUS_ENABLE_HYBRID_SEARCH to add BM25 sparse index."
@@ -279,15 +279,15 @@ class MilvusVector(BaseVector):
         results = self._client.search(
             collection_name=self._collection_name,
             data=[query],
-            anns_field=Field.SPARSE_VECTOR.value,
+            anns_field=Field.SPARSE_VECTOR,
             limit=kwargs.get("top_k", 4),
-            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+            output_fields=[Field.CONTENT_KEY, Field.METADATA_KEY],
             filter=filter,
         )
 
         return self._process_search_results(
             results,
-            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+            output_fields=[Field.CONTENT_KEY, Field.METADATA_KEY],
             score_threshold=float(kwargs.get("score_threshold") or 0.0),
         )
 
@@ -311,7 +311,7 @@ class MilvusVector(BaseVector):
                 dim = len(embeddings[0])
                 fields = []
                 if metadatas:
-                    fields.append(FieldSchema(Field.METADATA_KEY.value, DataType.JSON, max_length=65_535))
+                    fields.append(FieldSchema(Field.METADATA_KEY, DataType.JSON, max_length=65_535))
 
                 # Create the text field, enable_analyzer will be set True to support milvus automatically
                 # transfer text to sparse_vector, reference: https://milvus.io/docs/full-text-search.md
@@ -326,15 +326,15 @@ class MilvusVector(BaseVector):
                 ):
                     content_field_kwargs["analyzer_params"] = self._client_config.analyzer_params
 
-                fields.append(FieldSchema(Field.CONTENT_KEY.value, DataType.VARCHAR, **content_field_kwargs))
+                fields.append(FieldSchema(Field.CONTENT_KEY, DataType.VARCHAR, **content_field_kwargs))
 
                 # 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, 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, infer_dtype_bydata(embeddings[0]), dim=dim))
                 # Create Sparse Vector Index for the collection
                 if self._hybrid_search_enabled:
-                    fields.append(FieldSchema(Field.SPARSE_VECTOR.value, DataType.SPARSE_FLOAT_VECTOR))
+                    fields.append(FieldSchema(Field.SPARSE_VECTOR, DataType.SPARSE_FLOAT_VECTOR))
 
                 schema = CollectionSchema(fields)
 
@@ -342,8 +342,8 @@ class MilvusVector(BaseVector):
                 if self._hybrid_search_enabled:
                     bm25_function = Function(
                         name="text_bm25_emb",
-                        input_field_names=[Field.CONTENT_KEY.value],
-                        output_field_names=[Field.SPARSE_VECTOR.value],
+                        input_field_names=[Field.CONTENT_KEY],
+                        output_field_names=[Field.SPARSE_VECTOR],
                         function_type=FunctionType.BM25,
                     )
                     schema.add_function(bm25_function)
@@ -352,12 +352,12 @@ class MilvusVector(BaseVector):
 
                 # Create Index params for the collection
                 index_params_obj = IndexParams()
-                index_params_obj.add_index(field_name=Field.VECTOR.value, **index_params)
+                index_params_obj.add_index(field_name=Field.VECTOR, **index_params)
 
                 # Create Sparse Vector Index for the collection
                 if self._hybrid_search_enabled:
                     index_params_obj.add_index(
-                        field_name=Field.SPARSE_VECTOR.value, index_type="AUTOINDEX", metric_type="BM25"
+                        field_name=Field.SPARSE_VECTOR, index_type="AUTOINDEX", metric_type="BM25"
                     )
 
                 # Create the collection

api/core/rag/datasource/vdb/opensearch/opensearch_vector.py

@@ -1,6 +1,6 @@
 import json
 import logging
-from typing import Any, Literal
+from typing import Any
 from uuid import uuid4
 
 from opensearchpy import OpenSearch, Urllib3AWSV4SignerAuth, Urllib3HttpConnection, helpers
@@ -8,6 +8,7 @@ from opensearchpy.helpers import BulkIndexError
 from pydantic import BaseModel, model_validator
 
 from configs import dify_config
+from configs.middleware.vdb.opensearch_config import AuthMethod
 from core.rag.datasource.vdb.field import Field
 from core.rag.datasource.vdb.vector_base import BaseVector
 from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
@@ -25,7 +26,7 @@ class OpenSearchConfig(BaseModel):
     port: int
     secure: bool = False  # use_ssl
     verify_certs: bool = True
-    auth_method: Literal["basic", "aws_managed_iam"] = "basic"
+    auth_method: AuthMethod = AuthMethod.BASIC
     user: str | None = None
     password: str | None = None
     aws_region: str | None = None
@@ -98,9 +99,9 @@ class OpenSearchVector(BaseVector):
                 "_op_type": "index",
                 "_index": self._collection_name.lower(),
                 "_source": {
-                    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,
+                    Field.CONTENT_KEY: documents[i].page_content,
+                    Field.VECTOR: embeddings[i],  # Make sure you pass an array here
+                    Field.METADATA_KEY: documents[i].metadata,
                 },
             }
             # See https://github.com/langchain-ai/langchainjs/issues/4346#issuecomment-1935123377
@@ -116,7 +117,7 @@ class OpenSearchVector(BaseVector):
         )
 
     def get_ids_by_metadata_field(self, key: str, value: str):
-        query = {"query": {"term": {f"{Field.METADATA_KEY.value}.{key}": value}}}
+        query = {"query": {"term": {f"{Field.METADATA_KEY}.{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"]]
@@ -180,17 +181,17 @@ class OpenSearchVector(BaseVector):
 
         query = {
             "size": kwargs.get("top_k", 4),
-            "query": {"knn": {Field.VECTOR.value: {Field.VECTOR.value: query_vector, "k": kwargs.get("top_k", 4)}}},
+            "query": {"knn": {Field.VECTOR: {Field.VECTOR: query_vector, "k": kwargs.get("top_k", 4)}}},
         }
         document_ids_filter = kwargs.get("document_ids_filter")
         if document_ids_filter:
             query["query"] = {
                 "script_score": {
-                    "query": {"bool": {"filter": [{"terms": {Field.DOCUMENT_ID.value: document_ids_filter}}]}},
+                    "query": {"bool": {"filter": [{"terms": {Field.DOCUMENT_ID: document_ids_filter}}]}},
                     "script": {
                         "source": "knn_score",
                         "lang": "knn",
-                        "params": {"field": Field.VECTOR.value, "query_value": query_vector, "space_type": "l2"},
+                        "params": {"field": Field.VECTOR, "query_value": query_vector, "space_type": "l2"},
                     },
                 }
             }
@@ -203,7 +204,7 @@ class OpenSearchVector(BaseVector):
 
         docs = []
         for hit in response["hits"]["hits"]:
-            metadata = hit["_source"].get(Field.METADATA_KEY.value, {})
+            metadata = hit["_source"].get(Field.METADATA_KEY, {})
 
             # Make sure metadata is a dictionary
             if metadata is None:
@@ -212,7 +213,7 @@ class OpenSearchVector(BaseVector):
             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)
+                doc = Document(page_content=hit["_source"].get(Field.CONTENT_KEY), metadata=metadata)
                 docs.append(doc)
 
         return docs
@@ -227,9 +228,9 @@ class OpenSearchVector(BaseVector):
 
         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)
+            metadata = hit["_source"].get(Field.METADATA_KEY)
+            vector = hit["_source"].get(Field.VECTOR)
+            page_content = hit["_source"].get(Field.CONTENT_KEY)
             doc = Document(page_content=page_content, vector=vector, metadata=metadata)
             docs.append(doc)
 
@@ -250,8 +251,8 @@ class OpenSearchVector(BaseVector):
                     "settings": {"index": {"knn": True}},
                     "mappings": {
                         "properties": {
-                            Field.CONTENT_KEY.value: {"type": "text"},
-                            Field.VECTOR.value: {
+                            Field.CONTENT_KEY: {"type": "text"},
+                            Field.VECTOR: {
                                 "type": "knn_vector",
                                 "dimension": len(embeddings[0]),  # Make sure the dimension is correct here
                                 "method": {
@@ -261,7 +262,7 @@ class OpenSearchVector(BaseVector):
                                     "parameters": {"ef_construction": 64, "m": 8},
                                 },
                             },
-                            Field.METADATA_KEY.value: {
+                            Field.METADATA_KEY: {
                                 "type": "object",
                                 "properties": {
                                     "doc_id": {"type": "keyword"},  # Map doc_id to keyword type
@@ -293,7 +294,7 @@ class OpenSearchVectorFactory(AbstractVectorFactory):
             port=dify_config.OPENSEARCH_PORT,
             secure=dify_config.OPENSEARCH_SECURE,
             verify_certs=dify_config.OPENSEARCH_VERIFY_CERTS,
-            auth_method=dify_config.OPENSEARCH_AUTH_METHOD.value,
+            auth_method=dify_config.OPENSEARCH_AUTH_METHOD,
             user=dify_config.OPENSEARCH_USER,
             password=dify_config.OPENSEARCH_PASSWORD,
             aws_region=dify_config.OPENSEARCH_AWS_REGION,

api/core/rag/datasource/vdb/oracle/oraclevector.py

@@ -289,7 +289,8 @@ class OracleVector(BaseVector):
                 words = pseg.cut(query)
                 current_entity = ""
                 for word, pos in words:
-                    if pos in {"nr", "Ng", "eng", "nz", "n", "ORG", "v"}:  # nr: 人名，ns: 地名，nt: 机构名
+                    # nr: person name, ns: place name, nt: organization name
+                    if pos in {"nr", "Ng", "eng", "nz", "n", "ORG", "v"}:
                         current_entity += word
                     else:
                         if current_entity:

api/core/rag/datasource/vdb/pyvastbase/vastbase_vector.py

@@ -213,7 +213,7 @@ class VastbaseVector(BaseVector):
 
             with self._get_cursor() as cur:
                 cur.execute(SQL_CREATE_TABLE.format(table_name=self.table_name, dimension=dimension))
-                # Vastbase 支持的向量维度取值范围为 [1,16000]
+                # Vastbase supports vector dimensions in range [1, 16000]
                 if dimension <= 16000:
                     cur.execute(SQL_CREATE_INDEX.format(table_name=self.table_name))
             redis_client.set(collection_exist_cache_key, 1, ex=3600)

api/core/rag/datasource/vdb/qdrant/qdrant_vector.py

@@ -147,15 +147,13 @@ class QdrantVector(BaseVector):
 
                 # create group_id payload index
                 self._client.create_payload_index(
-                    collection_name, Field.GROUP_KEY.value, field_schema=PayloadSchemaType.KEYWORD
+                    collection_name, Field.GROUP_KEY, 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, field_schema=PayloadSchemaType.KEYWORD)
                 # create document_id payload index
                 self._client.create_payload_index(
-                    collection_name, Field.DOCUMENT_ID.value, field_schema=PayloadSchemaType.KEYWORD
+                    collection_name, Field.DOCUMENT_ID, field_schema=PayloadSchemaType.KEYWORD
                 )
                 # create full text index
                 text_index_params = TextIndexParams(
@@ -165,9 +163,7 @@ class QdrantVector(BaseVector):
                     max_token_len=20,
                     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, 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):
@@ -220,10 +216,10 @@ class QdrantVector(BaseVector):
                     self._build_payloads(
                         batch_texts,
                         batch_metadatas,
-                        Field.CONTENT_KEY.value,
-                        Field.METADATA_KEY.value,
+                        Field.CONTENT_KEY,
+                        Field.METADATA_KEY,
                         group_id or "",  # Ensure group_id is never None
-                        Field.GROUP_KEY.value,
+                        Field.GROUP_KEY,
                     ),
                 )
             ]
@@ -381,12 +377,12 @@ class QdrantVector(BaseVector):
         for result in results:
             if result.payload is None:
                 continue
-            metadata = result.payload.get(Field.METADATA_KEY.value) or {}
+            metadata = result.payload.get(Field.METADATA_KEY) or {}
             # duplicate check score threshold
             if result.score >= score_threshold:
                 metadata["score"] = result.score
                 doc = Document(
-                    page_content=result.payload.get(Field.CONTENT_KEY.value, ""),
+                    page_content=result.payload.get(Field.CONTENT_KEY, ""),
                     metadata=metadata,
                 )
                 docs.append(doc)
@@ -433,7 +429,7 @@ class QdrantVector(BaseVector):
         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, Field.METADATA_KEY)
                 documents.append(document)
 
         return documents

api/core/rag/datasource/vdb/tablestore/tablestore_vector.py

@@ -55,7 +55,7 @@ class TableStoreVector(BaseVector):
         self._normalize_full_text_bm25_score = config.normalize_full_text_bm25_score
         self._table_name = f"{collection_name}"
         self._index_name = f"{collection_name}_idx"
-        self._tags_field = f"{Field.METADATA_KEY.value}_tags"
+        self._tags_field = f"{Field.METADATA_KEY}_tags"
 
     def create_collection(self, embeddings: list[list[float]], **kwargs):
         dimension = len(embeddings[0])
@@ -64,7 +64,7 @@ class TableStoreVector(BaseVector):
     def get_by_ids(self, ids: list[str]) -> list[Document]:
         docs = []
         request = BatchGetRowRequest()
-        columns_to_get = [Field.METADATA_KEY.value, Field.CONTENT_KEY.value]
+        columns_to_get = [Field.METADATA_KEY, Field.CONTENT_KEY]
         rows_to_get = [[("id", _id)] for _id in ids]
         request.add(TableInBatchGetRowItem(self._table_name, rows_to_get, columns_to_get, None, 1))
 
@@ -73,11 +73,7 @@ class TableStoreVector(BaseVector):
         for item in table_result:
             if item.is_ok and item.row:
                 kv = {k: v for k, v, _ in item.row.attribute_columns}
-                docs.append(
-                    Document(
-                        page_content=kv[Field.CONTENT_KEY.value], metadata=json.loads(kv[Field.METADATA_KEY.value])
-                    )
-                )
+                docs.append(Document(page_content=kv[Field.CONTENT_KEY], metadata=json.loads(kv[Field.METADATA_KEY])))
         return docs
 
     def get_type(self) -> str:
@@ -95,9 +91,9 @@ class TableStoreVector(BaseVector):
             self._write_row(
                 primary_key=uuids[i],
                 attributes={
-                    Field.CONTENT_KEY.value: documents[i].page_content,
-                    Field.VECTOR.value: embeddings[i],
-                    Field.METADATA_KEY.value: documents[i].metadata,
+                    Field.CONTENT_KEY: documents[i].page_content,
+                    Field.VECTOR: embeddings[i],
+                    Field.METADATA_KEY: documents[i].metadata,
                 },
             )
         return uuids
@@ -180,7 +176,7 @@ class TableStoreVector(BaseVector):
 
         field_schemas = [
             tablestore.FieldSchema(
-                Field.CONTENT_KEY.value,
+                Field.CONTENT_KEY,
                 tablestore.FieldType.TEXT,
                 analyzer=tablestore.AnalyzerType.MAXWORD,
                 index=True,
@@ -188,7 +184,7 @@ class TableStoreVector(BaseVector):
                 store=False,
             ),
             tablestore.FieldSchema(
-                Field.VECTOR.value,
+                Field.VECTOR,
                 tablestore.FieldType.VECTOR,
                 vector_options=tablestore.VectorOptions(
                     data_type=tablestore.VectorDataType.VD_FLOAT_32,
@@ -197,7 +193,7 @@ class TableStoreVector(BaseVector):
                 ),
             ),
             tablestore.FieldSchema(
-                Field.METADATA_KEY.value,
+                Field.METADATA_KEY,
                 tablestore.FieldType.KEYWORD,
                 index=True,
                 store=False,
@@ -233,15 +229,15 @@ class TableStoreVector(BaseVector):
         pk = [("id", primary_key)]
 
         tags = []
-        for key, value in attributes[Field.METADATA_KEY.value].items():
+        for key, value in attributes[Field.METADATA_KEY].items():
             tags.append(str(key) + "=" + str(value))
 
         attribute_columns = [
-            (Field.CONTENT_KEY.value, attributes[Field.CONTENT_KEY.value]),
-            (Field.VECTOR.value, json.dumps(attributes[Field.VECTOR.value])),
+            (Field.CONTENT_KEY, attributes[Field.CONTENT_KEY]),
+            (Field.VECTOR, json.dumps(attributes[Field.VECTOR])),
             (
-                Field.METADATA_KEY.value,
-                json.dumps(attributes[Field.METADATA_KEY.value]),
+                Field.METADATA_KEY,
+                json.dumps(attributes[Field.METADATA_KEY]),
             ),
             (self._tags_field, json.dumps(tags)),
         ]
@@ -270,7 +266,7 @@ class TableStoreVector(BaseVector):
                 index_name=self._index_name,
                 search_query=query,
                 columns_to_get=tablestore.ColumnsToGet(
-                    column_names=[Field.PRIMARY_KEY.value], return_type=tablestore.ColumnReturnType.SPECIFIED
+                    column_names=[Field.PRIMARY_KEY], return_type=tablestore.ColumnReturnType.SPECIFIED
                 ),
             )
 
@@ -288,7 +284,7 @@ class TableStoreVector(BaseVector):
         self, query_vector: list[float], document_ids_filter: list[str] | None, top_k: int, score_threshold: float
     ) -> list[Document]:
         knn_vector_query = tablestore.KnnVectorQuery(
-            field_name=Field.VECTOR.value,
+            field_name=Field.VECTOR,
             top_k=top_k,
             float32_query_vector=query_vector,
         )
@@ -311,8 +307,8 @@ class TableStoreVector(BaseVector):
                 for col in search_hit.row[1]:
                     ots_column_map[col[0]] = col[1]
 
-                vector_str = ots_column_map.get(Field.VECTOR.value)
-                metadata_str = ots_column_map.get(Field.METADATA_KEY.value)
+                vector_str = ots_column_map.get(Field.VECTOR)
+                metadata_str = ots_column_map.get(Field.METADATA_KEY)
 
                 vector = json.loads(vector_str) if vector_str else None
                 metadata = json.loads(metadata_str) if metadata_str else {}
@@ -321,7 +317,7 @@ class TableStoreVector(BaseVector):
 
                 documents.append(
                     Document(
-                        page_content=ots_column_map.get(Field.CONTENT_KEY.value) or "",
+                        page_content=ots_column_map.get(Field.CONTENT_KEY) or "",
                         vector=vector,
                         metadata=metadata,
                     )
@@ -343,7 +339,7 @@ class TableStoreVector(BaseVector):
         self, query: str, document_ids_filter: list[str] | None, top_k: int, score_threshold: float
     ) -> list[Document]:
         bool_query = tablestore.BoolQuery(must_queries=[], filter_queries=[], should_queries=[], must_not_queries=[])
-        bool_query.must_queries.append(tablestore.MatchQuery(text=query, field_name=Field.CONTENT_KEY.value))
+        bool_query.must_queries.append(tablestore.MatchQuery(text=query, field_name=Field.CONTENT_KEY))
 
         if document_ids_filter:
             bool_query.filter_queries.append(tablestore.TermsQuery(self._tags_field, document_ids_filter))
@@ -374,10 +370,10 @@ class TableStoreVector(BaseVector):
             for col in search_hit.row[1]:
                 ots_column_map[col[0]] = col[1]
 
-            metadata_str = ots_column_map.get(Field.METADATA_KEY.value)
+            metadata_str = ots_column_map.get(Field.METADATA_KEY)
             metadata = json.loads(metadata_str) if metadata_str else {}
 
-            vector_str = ots_column_map.get(Field.VECTOR.value)
+            vector_str = ots_column_map.get(Field.VECTOR)
             vector = json.loads(vector_str) if vector_str else None
 
             if score:
@@ -385,7 +381,7 @@ class TableStoreVector(BaseVector):
 
             documents.append(
                 Document(
-                    page_content=ots_column_map.get(Field.CONTENT_KEY.value) or "",
+                    page_content=ots_column_map.get(Field.CONTENT_KEY) or "",
                     vector=vector,
                     metadata=metadata,
                 )

api/core/rag/datasource/vdb/tidb_on_qdrant/tidb_on_qdrant_vector.py

@@ -5,9 +5,10 @@ from collections.abc import Generator, Iterable, Sequence
 from itertools import islice
 from typing import TYPE_CHECKING, Any, Union
 
+import httpx
 import qdrant_client
-import requests
 from flask import current_app
+from httpx import DigestAuth
 from pydantic import BaseModel
 from qdrant_client.http import models as rest
 from qdrant_client.http.models import (
@@ -19,7 +20,6 @@ from qdrant_client.http.models import (
     TokenizerType,
 )
 from qdrant_client.local.qdrant_local import QdrantLocal
-from requests.auth import HTTPDigestAuth
 from sqlalchemy import select
 
 from configs import dify_config
@@ -141,15 +141,13 @@ class TidbOnQdrantVector(BaseVector):
 
                 # create group_id payload index
                 self._client.create_payload_index(
-                    collection_name, Field.GROUP_KEY.value, field_schema=PayloadSchemaType.KEYWORD
+                    collection_name, Field.GROUP_KEY, 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, field_schema=PayloadSchemaType.KEYWORD)
                 # create document_id payload index
                 self._client.create_payload_index(
-                    collection_name, Field.DOCUMENT_ID.value, field_schema=PayloadSchemaType.KEYWORD
+                    collection_name, Field.DOCUMENT_ID, field_schema=PayloadSchemaType.KEYWORD
                 )
                 # create full text index
                 text_index_params = TextIndexParams(
@@ -159,9 +157,7 @@ class TidbOnQdrantVector(BaseVector):
                     max_token_len=20,
                     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, 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):
@@ -211,10 +207,10 @@ class TidbOnQdrantVector(BaseVector):
                     self._build_payloads(
                         batch_texts,
                         batch_metadatas,
-                        Field.CONTENT_KEY.value,
-                        Field.METADATA_KEY.value,
+                        Field.CONTENT_KEY,
+                        Field.METADATA_KEY,
                         group_id or "",
-                        Field.GROUP_KEY.value,
+                        Field.GROUP_KEY,
                     ),
                 )
             ]
@@ -349,13 +345,13 @@ class TidbOnQdrantVector(BaseVector):
         for result in results:
             if result.payload is None:
                 continue
-            metadata = result.payload.get(Field.METADATA_KEY.value) or {}
+            metadata = result.payload.get(Field.METADATA_KEY) or {}
             # duplicate check score threshold
             score_threshold = kwargs.get("score_threshold") or 0.0
             if result.score >= score_threshold:
                 metadata["score"] = result.score
                 doc = Document(
-                    page_content=result.payload.get(Field.CONTENT_KEY.value, ""),
+                    page_content=result.payload.get(Field.CONTENT_KEY, ""),
                     metadata=metadata,
                 )
                 docs.append(doc)
@@ -392,7 +388,7 @@ class TidbOnQdrantVector(BaseVector):
         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, Field.METADATA_KEY)
                 documents.append(document)
 
         return documents
@@ -504,10 +500,10 @@ class TidbOnQdrantVectorFactory(AbstractVectorFactory):
         }
         cluster_data = {"displayName": display_name, "region": region_object, "labels": labels}
 
-        response = requests.post(
+        response = httpx.post(
             f"{tidb_config.api_url}/clusters",
             json=cluster_data,
-            auth=HTTPDigestAuth(tidb_config.public_key, tidb_config.private_key),
+            auth=DigestAuth(tidb_config.public_key, tidb_config.private_key),
         )
 
         if response.status_code == 200:
@@ -527,10 +523,10 @@ class TidbOnQdrantVectorFactory(AbstractVectorFactory):
 
         body = {"password": new_password}
 
-        response = requests.put(
+        response = httpx.put(
             f"{tidb_config.api_url}/clusters/{cluster_id}/password",
             json=body,
-            auth=HTTPDigestAuth(tidb_config.public_key, tidb_config.private_key),
+            auth=DigestAuth(tidb_config.public_key, tidb_config.private_key),
         )
 
         if response.status_code == 200:

api/core/rag/datasource/vdb/tidb_on_qdrant/tidb_service.py

@@ -2,8 +2,8 @@ import time
 import uuid
 from collections.abc import Sequence
 
-import requests
-from requests.auth import HTTPDigestAuth
+import httpx
+from httpx import DigestAuth
 
 from configs import dify_config
 from extensions.ext_database import db
@@ -49,7 +49,7 @@ class TidbService:
             "rootPassword": password,
         }
 
-        response = requests.post(f"{api_url}/clusters", json=cluster_data, auth=HTTPDigestAuth(public_key, private_key))
+        response = httpx.post(f"{api_url}/clusters", json=cluster_data, auth=DigestAuth(public_key, private_key))
 
         if response.status_code == 200:
             response_data = response.json()
@@ -83,7 +83,7 @@ class TidbService:
         :return: The response from the API.
         """
 
-        response = requests.delete(f"{api_url}/clusters/{cluster_id}", auth=HTTPDigestAuth(public_key, private_key))
+        response = httpx.delete(f"{api_url}/clusters/{cluster_id}", auth=DigestAuth(public_key, private_key))
 
         if response.status_code == 200:
             return response.json()
@@ -102,7 +102,7 @@ class TidbService:
         :return: The response from the API.
         """
 
-        response = requests.get(f"{api_url}/clusters/{cluster_id}", auth=HTTPDigestAuth(public_key, private_key))
+        response = httpx.get(f"{api_url}/clusters/{cluster_id}", auth=DigestAuth(public_key, private_key))
 
         if response.status_code == 200:
             return response.json()
@@ -127,10 +127,10 @@ class TidbService:
 
         body = {"password": new_password, "builtinRole": "role_admin", "customRoles": []}
 
-        response = requests.patch(
+        response = httpx.patch(
             f"{api_url}/clusters/{cluster_id}/sqlUsers/{account}",
             json=body,
-            auth=HTTPDigestAuth(public_key, private_key),
+            auth=DigestAuth(public_key, private_key),
         )
 
         if response.status_code == 200:
@@ -161,9 +161,7 @@ class TidbService:
         tidb_serverless_list_map = {item.cluster_id: item for item in tidb_serverless_list}
         cluster_ids = [item.cluster_id for item in tidb_serverless_list]
         params = {"clusterIds": cluster_ids, "view": "BASIC"}
-        response = requests.get(
-            f"{api_url}/clusters:batchGet", params=params, auth=HTTPDigestAuth(public_key, private_key)
-        )
+        response = httpx.get(f"{api_url}/clusters:batchGet", params=params, auth=DigestAuth(public_key, private_key))
 
         if response.status_code == 200:
             response_data = response.json()
@@ -224,8 +222,8 @@ class TidbService:
             clusters.append(cluster_data)
 
         request_body = {"requests": clusters}
-        response = requests.post(
-            f"{api_url}/clusters:batchCreate", json=request_body, auth=HTTPDigestAuth(public_key, private_key)
+        response = httpx.post(
+            f"{api_url}/clusters:batchCreate", json=request_body, auth=DigestAuth(public_key, private_key)
         )
 
         if response.status_code == 200:

api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py

@@ -55,13 +55,13 @@ class TiDBVector(BaseVector):
         return Table(
             self._collection_name,
             self._orm_base.metadata,
-            Column(Field.PRIMARY_KEY.value, String(36), primary_key=True, nullable=False),
+            Column(Field.PRIMARY_KEY, String(36), primary_key=True, nullable=False),
             Column(
-                Field.VECTOR.value,
+                Field.VECTOR,
                 VectorType(dim),
                 nullable=False,
             ),
-            Column(Field.TEXT_KEY.value, TEXT, nullable=False),
+            Column(Field.TEXT_KEY, TEXT, nullable=False),
             Column("meta", JSON, nullable=False),
             Column("create_time", DateTime, server_default=sqlalchemy.text("CURRENT_TIMESTAMP")),
             Column(

api/core/rag/datasource/vdb/vector_factory.py

@@ -71,6 +71,12 @@ class Vector:
                 from core.rag.datasource.vdb.milvus.milvus_vector import MilvusVectorFactory
 
                 return MilvusVectorFactory
+            case VectorType.ALIBABACLOUD_MYSQL:
+                from core.rag.datasource.vdb.alibabacloud_mysql.alibabacloud_mysql_vector import (
+                    AlibabaCloudMySQLVectorFactory,
+                )
+
+                return AlibabaCloudMySQLVectorFactory
             case VectorType.MYSCALE:
                 from core.rag.datasource.vdb.myscale.myscale_vector import MyScaleVectorFactory
 

api/core/rag/datasource/vdb/vector_type.py

@@ -2,6 +2,7 @@ from enum import StrEnum
 
 
 class VectorType(StrEnum):
+    ALIBABACLOUD_MYSQL = "alibabacloud_mysql"
     ANALYTICDB = "analyticdb"
     CHROMA = "chroma"
     MILVUS = "milvus"

api/core/rag/datasource/vdb/vikingdb/vikingdb_vector.py

@@ -76,11 +76,11 @@ class VikingDBVector(BaseVector):
 
             if not self._has_collection():
                 fields = [
-                    Field(field_name=vdb_Field.PRIMARY_KEY.value, field_type=FieldType.String, is_primary_key=True),
-                    Field(field_name=vdb_Field.METADATA_KEY.value, field_type=FieldType.String),
-                    Field(field_name=vdb_Field.GROUP_KEY.value, field_type=FieldType.String),
-                    Field(field_name=vdb_Field.CONTENT_KEY.value, field_type=FieldType.Text),
-                    Field(field_name=vdb_Field.VECTOR.value, field_type=FieldType.Vector, dim=dimension),
+                    Field(field_name=vdb_Field.PRIMARY_KEY, field_type=FieldType.String, is_primary_key=True),
+                    Field(field_name=vdb_Field.METADATA_KEY, field_type=FieldType.String),
+                    Field(field_name=vdb_Field.GROUP_KEY, field_type=FieldType.String),
+                    Field(field_name=vdb_Field.CONTENT_KEY, field_type=FieldType.Text),
+                    Field(field_name=vdb_Field.VECTOR, field_type=FieldType.Vector, dim=dimension),
                 ]
 
                 self._client.create_collection(
@@ -100,7 +100,7 @@ class VikingDBVector(BaseVector):
                     collection_name=self._collection_name,
                     index_name=self._index_name,
                     vector_index=vector_index,
-                    partition_by=vdb_Field.GROUP_KEY.value,
+                    partition_by=vdb_Field.GROUP_KEY,
                     description="Index For Dify",
                 )
             redis_client.set(collection_exist_cache_key, 1, ex=3600)
@@ -126,11 +126,11 @@ class VikingDBVector(BaseVector):
             # FIXME: fix the type of metadata later
             doc = Data(
                 {
-                    vdb_Field.PRIMARY_KEY.value: metadatas[i]["doc_id"],  # type: ignore
-                    vdb_Field.VECTOR.value: embeddings[i] if embeddings else None,
-                    vdb_Field.CONTENT_KEY.value: page_content,
-                    vdb_Field.METADATA_KEY.value: json.dumps(metadata),
-                    vdb_Field.GROUP_KEY.value: self._group_id,
+                    vdb_Field.PRIMARY_KEY: metadatas[i]["doc_id"],  # type: ignore
+                    vdb_Field.VECTOR: embeddings[i] if embeddings else None,
+                    vdb_Field.CONTENT_KEY: page_content,
+                    vdb_Field.METADATA_KEY: json.dumps(metadata),
+                    vdb_Field.GROUP_KEY: self._group_id,
                 }
             )
             docs.append(doc)
@@ -151,7 +151,7 @@ class VikingDBVector(BaseVector):
         # Note: Metadata field value is an dict, but vikingdb field
         # not support json type
         results = self._client.get_index(self._collection_name, self._index_name).search(
-            filter={"op": "must", "field": vdb_Field.GROUP_KEY.value, "conds": [self._group_id]},
+            filter={"op": "must", "field": vdb_Field.GROUP_KEY, "conds": [self._group_id]},
             # max value is 5000
             limit=5000,
         )
@@ -161,7 +161,7 @@ class VikingDBVector(BaseVector):
 
         ids = []
         for result in results:
-            metadata = result.fields.get(vdb_Field.METADATA_KEY.value)
+            metadata = result.fields.get(vdb_Field.METADATA_KEY)
             if metadata is not None:
                 metadata = json.loads(metadata)
                 if metadata.get(key) == value:
@@ -189,12 +189,12 @@ class VikingDBVector(BaseVector):
 
         docs = []
         for result in results:
-            metadata = result.fields.get(vdb_Field.METADATA_KEY.value)
+            metadata = result.fields.get(vdb_Field.METADATA_KEY)
             if metadata is not None:
                 metadata = json.loads(metadata)
             if result.score >= score_threshold:
                 metadata["score"] = result.score
-                doc = Document(page_content=result.fields.get(vdb_Field.CONTENT_KEY.value), metadata=metadata)
+                doc = Document(page_content=result.fields.get(vdb_Field.CONTENT_KEY), metadata=metadata)
                 docs.append(doc)
         docs = sorted(docs, key=lambda x: x.metadata.get("score", 0) if x.metadata else 0, reverse=True)
         return docs

api/core/rag/datasource/vdb/weaviate/weaviate_vector.py

@@ -2,7 +2,6 @@ import datetime
 import json
 from typing import Any
 
-import requests
 import weaviate  # type: ignore
 from pydantic import BaseModel, model_validator
 
@@ -45,8 +44,8 @@ class WeaviateVector(BaseVector):
             client = weaviate.Client(
                 url=config.endpoint, auth_client_secret=auth_config, timeout_config=(5, 60), startup_period=None
             )
-        except requests.ConnectionError:
-            raise ConnectionError("Vector database connection error")
+        except Exception as exc:
+            raise ConnectionError("Vector database connection error") from exc
 
         client.batch.configure(
             # `batch_size` takes an `int` value to enable auto-batching
@@ -105,7 +104,7 @@ class WeaviateVector(BaseVector):
 
         with self._client.batch as batch:
             for i, text in enumerate(texts):
-                data_properties = {Field.TEXT_KEY.value: text}
+                data_properties = {Field.TEXT_KEY: text}
                 if metadatas is not None:
                     # metadata maybe None
                     for key, val in (metadatas[i] or {}).items():
@@ -183,7 +182,7 @@ class WeaviateVector(BaseVector):
         """Look up similar documents by embedding vector in Weaviate."""
         collection_name = self._collection_name
         properties = self._attributes
-        properties.append(Field.TEXT_KEY.value)
+        properties.append(Field.TEXT_KEY)
         query_obj = self._client.query.get(collection_name, properties)
 
         vector = {"vector": query_vector}
@@ -205,7 +204,7 @@ class WeaviateVector(BaseVector):
 
         docs_and_scores = []
         for res in result["data"]["Get"][collection_name]:
-            text = res.pop(Field.TEXT_KEY.value)
+            text = res.pop(Field.TEXT_KEY)
             score = 1 - res["_additional"]["distance"]
             docs_and_scores.append((Document(page_content=text, metadata=res), score))
 
@@ -233,7 +232,7 @@ class WeaviateVector(BaseVector):
         collection_name = self._collection_name
         content: dict[str, Any] = {"concepts": [query]}
         properties = self._attributes
-        properties.append(Field.TEXT_KEY.value)
+        properties.append(Field.TEXT_KEY)
         if kwargs.get("search_distance"):
             content["certainty"] = kwargs.get("search_distance")
         query_obj = self._client.query.get(collection_name, properties)
@@ -251,7 +250,7 @@ class WeaviateVector(BaseVector):
             raise ValueError(f"Error during query: {result['errors']}")
         docs = []
         for res in result["data"]["Get"][collection_name]:
-            text = res.pop(Field.TEXT_KEY.value)
+            text = res.pop(Field.TEXT_KEY)
             additional = res.pop("_additional")
             docs.append(Document(page_content=text, vector=additional["vector"], metadata=res))
         return docs

api/core/rag/entities/event.py

@@ -20,12 +20,12 @@ class BaseDatasourceEvent(BaseModel):
 
 
 class DatasourceErrorEvent(BaseDatasourceEvent):
-    event: str = DatasourceStreamEvent.ERROR.value
+    event: DatasourceStreamEvent = DatasourceStreamEvent.ERROR
     error: str = Field(..., description="error message")
 
 
 class DatasourceCompletedEvent(BaseDatasourceEvent):
-    event: str = DatasourceStreamEvent.COMPLETED.value
+    event: DatasourceStreamEvent = DatasourceStreamEvent.COMPLETED
     data: Mapping[str, Any] | list = Field(..., description="result")
     total: int | None = Field(default=0, description="total")
     completed: int | None = Field(default=0, description="completed")
@@ -33,6 +33,6 @@ class DatasourceCompletedEvent(BaseDatasourceEvent):
 
 
 class DatasourceProcessingEvent(BaseDatasourceEvent):
-    event: str = DatasourceStreamEvent.PROCESSING.value
+    event: DatasourceStreamEvent = DatasourceStreamEvent.PROCESSING
     total: int | None = Field(..., description="total")
     completed: int | None = Field(..., description="completed")

api/core/rag/extractor/extract_processor.py

@@ -45,7 +45,7 @@ class ExtractProcessor:
         cls, upload_file: UploadFile, return_text: bool = False, is_automatic: bool = False
     ) -> Union[list[Document], str]:
         extract_setting = ExtractSetting(
-            datasource_type=DatasourceType.FILE.value, upload_file=upload_file, document_model="text_model"
+            datasource_type=DatasourceType.FILE, upload_file=upload_file, document_model="text_model"
         )
         if return_text:
             delimiter = "\n"
@@ -76,7 +76,7 @@ class ExtractProcessor:
             # https://stackoverflow.com/questions/26541416/generate-temporary-file-names-without-creating-actual-file-in-python#comment90414256_26541521
             file_path = f"{temp_dir}/{tempfile.gettempdir()}{suffix}"
             Path(file_path).write_bytes(response.content)
-            extract_setting = ExtractSetting(datasource_type=DatasourceType.FILE.value, document_model="text_model")
+            extract_setting = ExtractSetting(datasource_type=DatasourceType.FILE, document_model="text_model")
             if return_text:
                 delimiter = "\n"
                 return delimiter.join(
@@ -92,7 +92,7 @@ class ExtractProcessor:
     def extract(
         cls, extract_setting: ExtractSetting, is_automatic: bool = False, file_path: str | None = None
     ) -> list[Document]:
-        if extract_setting.datasource_type == DatasourceType.FILE.value:
+        if extract_setting.datasource_type == DatasourceType.FILE:
             with tempfile.TemporaryDirectory() as temp_dir:
                 if not file_path:
                     assert extract_setting.upload_file is not None, "upload_file is required"
@@ -163,7 +163,7 @@ class ExtractProcessor:
                         # txt
                         extractor = TextExtractor(file_path, autodetect_encoding=True)
                 return extractor.extract()
-        elif extract_setting.datasource_type == DatasourceType.NOTION.value:
+        elif extract_setting.datasource_type == DatasourceType.NOTION:
             assert extract_setting.notion_info is not None, "notion_info is required"
             extractor = NotionExtractor(
                 notion_workspace_id=extract_setting.notion_info.notion_workspace_id,
@@ -174,7 +174,7 @@ class ExtractProcessor:
                 credential_id=extract_setting.notion_info.credential_id,
             )
             return extractor.extract()
-        elif extract_setting.datasource_type == DatasourceType.WEBSITE.value:
+        elif extract_setting.datasource_type == DatasourceType.WEBSITE:
             assert extract_setting.website_info is not None, "website_info is required"
             if extract_setting.website_info.provider == "firecrawl":
                 extractor = FirecrawlWebExtractor(

api/core/rag/extractor/firecrawl/firecrawl_app.py

@@ -2,7 +2,7 @@ import json
 import time
 from typing import Any, cast
 
-import requests
+import httpx
 
 from extensions.ext_storage import storage
 
@@ -104,18 +104,18 @@ class FirecrawlApp:
     def _prepare_headers(self) -> dict[str, Any]:
         return {"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"}
 
-    def _post_request(self, url, data, headers, retries=3, backoff_factor=0.5) -> requests.Response:
+    def _post_request(self, url, data, headers, retries=3, backoff_factor=0.5) -> httpx.Response:
         for attempt in range(retries):
-            response = requests.post(url, headers=headers, json=data)
+            response = httpx.post(url, headers=headers, json=data)
             if response.status_code == 502:
                 time.sleep(backoff_factor * (2**attempt))
             else:
                 return response
         return response
 
-    def _get_request(self, url, headers, retries=3, backoff_factor=0.5) -> requests.Response:
+    def _get_request(self, url, headers, retries=3, backoff_factor=0.5) -> httpx.Response:
         for attempt in range(retries):
-            response = requests.get(url, headers=headers)
+            response = httpx.get(url, headers=headers)
             if response.status_code == 502:
                 time.sleep(backoff_factor * (2**attempt))
             else:

api/core/rag/extractor/notion_extractor.py

@@ -3,7 +3,7 @@ import logging
 import operator
 from typing import Any, cast
 
-import requests
+import httpx
 
 from configs import dify_config
 from core.rag.extractor.extractor_base import BaseExtractor
@@ -92,7 +92,7 @@ class NotionExtractor(BaseExtractor):
             if next_cursor:
                 current_query["start_cursor"] = next_cursor
 
-            res = requests.post(
+            res = httpx.post(
                 DATABASE_URL_TMPL.format(database_id=database_id),
                 headers={
                     "Authorization": "Bearer " + self._notion_access_token,
@@ -160,7 +160,7 @@ class NotionExtractor(BaseExtractor):
         while True:
             query_dict: dict[str, Any] = {} if not start_cursor else {"start_cursor": start_cursor}
             try:
-                res = requests.request(
+                res = httpx.request(
                     "GET",
                     block_url,
                     headers={
@@ -173,7 +173,7 @@ class NotionExtractor(BaseExtractor):
                 if res.status_code != 200:
                     raise ValueError(f"Error fetching Notion block data: {res.text}")
                 data = res.json()
-            except requests.RequestException as e:
+            except httpx.HTTPError as e:
                 raise ValueError("Error fetching Notion block data") from e
             if "results" not in data or not isinstance(data["results"], list):
                 raise ValueError("Error fetching Notion block data")
@@ -222,7 +222,7 @@ class NotionExtractor(BaseExtractor):
         while True:
             query_dict: dict[str, Any] = {} if not start_cursor else {"start_cursor": start_cursor}
 
-            res = requests.request(
+            res = httpx.request(
                 "GET",
                 block_url,
                 headers={
@@ -282,7 +282,7 @@ class NotionExtractor(BaseExtractor):
         while not done:
             query_dict: dict[str, Any] = {} if not start_cursor else {"start_cursor": start_cursor}
 
-            res = requests.request(
+            res = httpx.request(
                 "GET",
                 block_url,
                 headers={
@@ -354,7 +354,7 @@ class NotionExtractor(BaseExtractor):
 
         query_dict: dict[str, Any] = {}
 
-        res = requests.request(
+        res = httpx.request(
             "GET",
             retrieve_page_url,
             headers={

api/core/rag/extractor/watercrawl/client.py

@@ -3,8 +3,8 @@ from collections.abc import Generator
 from typing import Union
 from urllib.parse import urljoin
 
-import requests
-from requests import Response
+import httpx
+from httpx import Response
 
 from core.rag.extractor.watercrawl.exceptions import (
     WaterCrawlAuthenticationError,
@@ -20,28 +20,45 @@ class BaseAPIClient:
         self.session = self.init_session()
 
     def init_session(self):
-        session = requests.Session()
-        session.headers.update({"X-API-Key": self.api_key})
-        session.headers.update({"Content-Type": "application/json"})
-        session.headers.update({"Accept": "application/json"})
-        session.headers.update({"User-Agent": "WaterCrawl-Plugin"})
-        session.headers.update({"Accept-Language": "en-US"})
-        return session
+        headers = {
+            "X-API-Key": self.api_key,
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+            "User-Agent": "WaterCrawl-Plugin",
+            "Accept-Language": "en-US",
+        }
+        return httpx.Client(headers=headers, timeout=None)
+
+    def _request(
+        self,
+        method: str,
+        endpoint: str,
+        query_params: dict | None = None,
+        data: dict | None = None,
+        **kwargs,
+    ) -> Response:
+        stream = kwargs.pop("stream", False)
+        url = urljoin(self.base_url, endpoint)
+        if stream:
+            request = self.session.build_request(method, url, params=query_params, json=data)
+            return self.session.send(request, stream=True, **kwargs)
+
+        return self.session.request(method, url, params=query_params, json=data, **kwargs)
 
     def _get(self, endpoint: str, query_params: dict | None = None, **kwargs):
-        return self.session.get(urljoin(self.base_url, endpoint), params=query_params, **kwargs)
+        return self._request("GET", endpoint, query_params=query_params, **kwargs)
 
     def _post(self, endpoint: str, query_params: dict | None = None, data: dict | None = None, **kwargs):
-        return self.session.post(urljoin(self.base_url, endpoint), params=query_params, json=data, **kwargs)
+        return self._request("POST", endpoint, query_params=query_params, data=data, **kwargs)
 
     def _put(self, endpoint: str, query_params: dict | None = None, data: dict | None = None, **kwargs):
-        return self.session.put(urljoin(self.base_url, endpoint), params=query_params, json=data, **kwargs)
+        return self._request("PUT", endpoint, query_params=query_params, data=data, **kwargs)
 
     def _delete(self, endpoint: str, query_params: dict | None = None, **kwargs):
-        return self.session.delete(urljoin(self.base_url, endpoint), params=query_params, **kwargs)
+        return self._request("DELETE", endpoint, query_params=query_params, **kwargs)
 
     def _patch(self, endpoint: str, query_params: dict | None = None, data: dict | None = None, **kwargs):
-        return self.session.patch(urljoin(self.base_url, endpoint), params=query_params, json=data, **kwargs)
+        return self._request("PATCH", endpoint, query_params=query_params, data=data, **kwargs)
 
 
 class WaterCrawlAPIClient(BaseAPIClient):
@@ -49,14 +66,17 @@ class WaterCrawlAPIClient(BaseAPIClient):
         super().__init__(api_key, base_url)
 
     def process_eventstream(self, response: Response, download: bool = False) -> Generator:
-        for line in response.iter_lines():
-            line = line.decode("utf-8")
-            if line.startswith("data:"):
-                line = line[5:].strip()
-                data = json.loads(line)
-                if data["type"] == "result" and download:
-                    data["data"] = self.download_result(data["data"])
-                yield data
+        try:
+            for raw_line in response.iter_lines():
+                line = raw_line.decode("utf-8") if isinstance(raw_line, bytes) else raw_line
+                if line.startswith("data:"):
+                    line = line[5:].strip()
+                    data = json.loads(line)
+                    if data["type"] == "result" and download:
+                        data["data"] = self.download_result(data["data"])
+                    yield data
+        finally:
+            response.close()
 
     def process_response(self, response: Response) -> dict | bytes | list | None | Generator:
         if response.status_code == 401:
@@ -170,7 +190,10 @@ class WaterCrawlAPIClient(BaseAPIClient):
                 return event_data["data"]
 
     def download_result(self, result_object: dict):
-        response = requests.get(result_object["result"])
-        response.raise_for_status()
-        result_object["result"] = response.json()
+        response = httpx.get(result_object["result"], timeout=None)
+        try:
+            response.raise_for_status()
+            result_object["result"] = response.json()
+        finally:
+            response.close()
         return result_object

api/core/rag/extractor/word_extractor.py

@@ -9,7 +9,7 @@ import uuid
 from urllib.parse import urlparse
 from xml.etree import ElementTree
 
-import requests
+import httpx
 from docx import Document as DocxDocument
 
 from configs import dify_config
@@ -43,15 +43,19 @@ class WordExtractor(BaseExtractor):
 
         # If the file is a web path, download it to a temporary file, and use that
         if not os.path.isfile(self.file_path) and self._is_valid_url(self.file_path):
-            r = requests.get(self.file_path)
+            response = httpx.get(self.file_path, timeout=None)
 
-            if r.status_code != 200:
-                raise ValueError(f"Check the url of your file; returned status code {r.status_code}")
+            if response.status_code != 200:
+                response.close()
+                raise ValueError(f"Check the url of your file; returned status code {response.status_code}")
 
             self.web_path = self.file_path
             # TODO: use a better way to handle the file
             self.temp_file = tempfile.NamedTemporaryFile()  # noqa SIM115
-            self.temp_file.write(r.content)
+            try:
+                self.temp_file.write(response.content)
+            finally:
+                response.close()
             self.file_path = self.temp_file.name
         elif not os.path.isfile(self.file_path):
             raise ValueError(f"File path {self.file_path} is not a valid file or url")

api/core/rag/rerank/rerank_factory.py

@@ -8,9 +8,9 @@ class RerankRunnerFactory:
     @staticmethod
     def create_rerank_runner(runner_type: str, *args, **kwargs) -> BaseRerankRunner:
         match runner_type:
-            case RerankMode.RERANKING_MODEL.value:
+            case RerankMode.RERANKING_MODEL:
                 return RerankModelRunner(*args, **kwargs)
-            case RerankMode.WEIGHTED_SCORE.value:
+            case RerankMode.WEIGHTED_SCORE:
                 return WeightRerankRunner(*args, **kwargs)
             case _:
                 raise ValueError(f"Unknown runner type: {runner_type}")

api/core/rag/retrieval/dataset_retrieval.py

@@ -61,7 +61,7 @@ from models.dataset import Document as DatasetDocument
 from services.external_knowledge_service import ExternalDatasetService
 
 default_retrieval_model: dict[str, Any] = {
-    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH,
     "reranking_enable": False,
     "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
     "top_k": 4,
@@ -692,7 +692,7 @@ class DatasetRetrieval:
         if retrieve_config.retrieve_strategy == DatasetRetrieveConfigEntity.RetrieveStrategy.SINGLE:
             # get retrieval model config
             default_retrieval_model = {
-                "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+                "search_method": RetrievalMethod.SEMANTIC_SEARCH,
                 "reranking_enable": False,
                 "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
                 "top_k": 2,

api/core/rag/retrieval/retrieval_methods.py

@@ -9,8 +9,8 @@ class RetrievalMethod(Enum):
 
     @staticmethod
     def is_support_semantic_search(retrieval_method: str) -> bool:
-        return retrieval_method in {RetrievalMethod.SEMANTIC_SEARCH.value, RetrievalMethod.HYBRID_SEARCH.value}
+        return retrieval_method in {RetrievalMethod.SEMANTIC_SEARCH, RetrievalMethod.HYBRID_SEARCH}
 
     @staticmethod
     def is_support_fulltext_search(retrieval_method: str) -> bool:
-        return retrieval_method in {RetrievalMethod.FULL_TEXT_SEARCH.value, RetrievalMethod.HYBRID_SEARCH.value}
+        return retrieval_method in {RetrievalMethod.FULL_TEXT_SEARCH, RetrievalMethod.HYBRID_SEARCH}

api/core/tools/builtin_tool/provider.py

@@ -111,7 +111,7 @@ class BuiltinToolProviderController(ToolProviderController):
 
         :return: the credentials schema
         """
-        return self.get_credentials_schema_by_type(CredentialType.API_KEY.value)
+        return self.get_credentials_schema_by_type(CredentialType.API_KEY)
 
     def get_credentials_schema_by_type(self, credential_type: str) -> list[ProviderConfig]:
         """
@@ -122,7 +122,7 @@ class BuiltinToolProviderController(ToolProviderController):
         """
         if credential_type == CredentialType.OAUTH2.value:
             return self.entity.oauth_schema.credentials_schema.copy() if self.entity.oauth_schema else []
-        if credential_type == CredentialType.API_KEY.value:
+        if credential_type == CredentialType.API_KEY:
             return self.entity.credentials_schema.copy() if self.entity.credentials_schema else []
         raise ValueError(f"Invalid credential type: {credential_type}")
 
@@ -134,15 +134,15 @@ class BuiltinToolProviderController(ToolProviderController):
         """
         return self.entity.oauth_schema.client_schema.copy() if self.entity.oauth_schema else []
 
-    def get_supported_credential_types(self) -> list[str]:
+    def get_supported_credential_types(self) -> list[CredentialType]:
         """
         returns the credential support type of the provider
         """
         types = []
         if self.entity.credentials_schema is not None and len(self.entity.credentials_schema) > 0:
-            types.append(CredentialType.API_KEY.value)
+            types.append(CredentialType.API_KEY)
         if self.entity.oauth_schema is not None and len(self.entity.oauth_schema.credentials_schema) > 0:
-            types.append(CredentialType.OAUTH2.value)
+            types.append(CredentialType.OAUTH2)
         return types
 
     def get_tools(self) -> list[BuiltinTool]:

api/core/tools/custom_tool/tool.py

@@ -290,6 +290,7 @@ class ApiTool(Tool):
             method_lc
         ](  # https://discuss.python.org/t/type-inference-for-function-return-types/42926
             url,
+            max_retries=0,
             params=params,
             headers=headers,
             cookies=cookies,

api/core/tools/entities/api_entities.py

@@ -61,7 +61,7 @@ class ToolProviderApiEntity(BaseModel):
         for tool in tools:
             if tool.get("parameters"):
                 for parameter in tool.get("parameters"):
-                    if parameter.get("type") == ToolParameter.ToolParameterType.SYSTEM_FILES.value:
+                    if parameter.get("type") == ToolParameter.ToolParameterType.SYSTEM_FILES:
                         parameter["type"] = "files"
                     if parameter.get("input_schema") is None:
                         parameter.pop("input_schema", None)
@@ -110,7 +110,9 @@ class ToolProviderCredentialApiEntity(BaseModel):
 
 
 class ToolProviderCredentialInfoApiEntity(BaseModel):
-    supported_credential_types: list[str] = Field(description="The supported credential types of the provider")
+    supported_credential_types: list[CredentialType] = Field(
+        description="The supported credential types of the provider"
+    )
     is_oauth_custom_client_enabled: bool = Field(
         default=False, description="Whether the OAuth custom client is enabled for the provider"
     )

api/core/tools/entities/tool_entities.py

@@ -113,7 +113,7 @@ class ApiProviderAuthType(StrEnum):
         # normalize & tiny alias for backward compatibility
         v = (value or "").strip().lower()
         if v == "api_key":
-            v = cls.API_KEY_HEADER.value
+            v = cls.API_KEY_HEADER
 
         for mode in cls:
             if mode.value == v:

api/core/tools/utils/dataset_retriever/dataset_multi_retriever_tool.py

@@ -18,7 +18,7 @@ from extensions.ext_database import db
 from models.dataset import Dataset, Document, DocumentSegment
 
 default_retrieval_model: dict[str, Any] = {
-    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH,
     "reranking_enable": False,
     "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
     "top_k": 2,

api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py

@@ -17,7 +17,7 @@ from models.dataset import Document as DatasetDocument
 from services.external_knowledge_service import ExternalDatasetService
 
 default_retrieval_model: dict[str, Any] = {
-    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH,
     "reranking_enable": False,
     "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
     "reranking_mode": "reranking_model",

api/core/tools/utils/parser.py

@@ -4,8 +4,8 @@ from json import loads as json_loads
 from json.decoder import JSONDecodeError
 from typing import Any
 
+import httpx
 from flask import request
-from requests import get
 from yaml import YAMLError, safe_load
 
 from core.tools.entities.common_entities import I18nObject
@@ -334,15 +334,20 @@ class ApiBasedToolSchemaParser:
             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)
-
-        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
+        response = httpx.get(
+            api_url, headers={"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "}, timeout=5
         )
 
+        try:
+            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
+            )
+        finally:
+            response.close()
+
     @staticmethod
     def auto_parse_to_tool_bundle(
         content: str, extra_info: dict | None = None, warning: dict | None = None
@@ -388,7 +393,7 @@ class ApiBasedToolSchemaParser:
             openapi = ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(
                 loaded_content, extra_info=extra_info, warning=warning
             )
-            schema_type = ApiProviderSchemaType.OPENAPI.value
+            schema_type = ApiProviderSchemaType.OPENAPI
             return openapi, schema_type
         except ToolApiSchemaError as e:
             openapi_error = e
@@ -398,7 +403,7 @@ class ApiBasedToolSchemaParser:
             converted_swagger = ApiBasedToolSchemaParser.parse_swagger_to_openapi(
                 loaded_content, extra_info=extra_info, warning=warning
             )
-            schema_type = ApiProviderSchemaType.SWAGGER.value
+            schema_type = ApiProviderSchemaType.SWAGGER
             return ApiBasedToolSchemaParser.parse_openapi_to_tool_bundle(
                 converted_swagger, extra_info=extra_info, warning=warning
             ), schema_type
@@ -410,7 +415,7 @@ class ApiBasedToolSchemaParser:
             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
+            return openapi_plugin, ApiProviderSchemaType.OPENAI_PLUGIN
         except ToolNotSupportedError as e:
             # maybe it's not plugin at all
             openapi_plugin_error = e

api/core/workflow/nodes/agent/agent_node.py

@@ -252,7 +252,10 @@ class AgentNode(Node):
                     if all(isinstance(v, dict) for _, v in parameters.items()):
                         params = {}
                         for key, param in parameters.items():
-                            if param.get("auto", ParamsAutoGenerated.OPEN.value) == ParamsAutoGenerated.CLOSE.value:
+                            if param.get("auto", ParamsAutoGenerated.OPEN) in (
+                                ParamsAutoGenerated.CLOSE,
+                                0,
+                            ):
                                 value_param = param.get("value", {})
                                 params[key] = value_param.get("value", "") if value_param is not None else None
                             else:
@@ -266,7 +269,7 @@ class AgentNode(Node):
                     value = cast(list[dict[str, Any]], value)
                     tool_value = []
                     for tool in value:
-                        provider_type = ToolProviderType(tool.get("type", ToolProviderType.BUILT_IN.value))
+                        provider_type = ToolProviderType(tool.get("type", ToolProviderType.BUILT_IN))
                         setting_params = tool.get("settings", {})
                         parameters = tool.get("parameters", {})
                         manual_input_params = [key for key, value in parameters.items() if value is not None]
@@ -417,7 +420,7 @@ class AgentNode(Node):
     def _fetch_memory(self, model_instance: ModelInstance) -> TokenBufferMemory | None:
         # get conversation id
         conversation_id_variable = self.graph_runtime_state.variable_pool.get(
-            ["sys", SystemVariableKey.CONVERSATION_ID.value]
+            ["sys", SystemVariableKey.CONVERSATION_ID]
         )
         if not isinstance(conversation_id_variable, StringSegment):
             return None
@@ -476,7 +479,7 @@ class AgentNode(Node):
         if meta_version and Version(meta_version) > Version("0.0.1"):
             return tools
         else:
-            return [tool for tool in tools if tool.get("type") != ToolProviderType.MCP.value]
+            return [tool for tool in tools if tool.get("type") != ToolProviderType.MCP]
 
     def _transform_message(
         self,

api/core/workflow/nodes/datasource/datasource_node.py

@@ -75,11 +75,11 @@ class DatasourceNode(Node):
 
         node_data = self._node_data
         variable_pool = self.graph_runtime_state.variable_pool
-        datasource_type_segement = variable_pool.get(["sys", SystemVariableKey.DATASOURCE_TYPE.value])
+        datasource_type_segement = variable_pool.get(["sys", SystemVariableKey.DATASOURCE_TYPE])
         if not datasource_type_segement:
             raise DatasourceNodeError("Datasource type is not set")
         datasource_type = str(datasource_type_segement.value) if datasource_type_segement.value else None
-        datasource_info_segement = variable_pool.get(["sys", SystemVariableKey.DATASOURCE_INFO.value])
+        datasource_info_segement = variable_pool.get(["sys", SystemVariableKey.DATASOURCE_INFO])
         if not datasource_info_segement:
             raise DatasourceNodeError("Datasource info is not set")
         datasource_info_value = datasource_info_segement.value
@@ -267,7 +267,7 @@ class DatasourceNode(Node):
         return result
 
     def _fetch_files(self, variable_pool: VariablePool) -> list[File]:
-        variable = variable_pool.get(["sys", SystemVariableKey.FILES.value])
+        variable = variable_pool.get(["sys", SystemVariableKey.FILES])
         assert isinstance(variable, ArrayAnyVariable | ArrayAnySegment)
         return list(variable.value) if variable else []
 

api/core/workflow/nodes/http_request/node.py

@@ -234,7 +234,7 @@ class HttpRequestNode(Node):
 
         mapping = {
             "tool_file_id": tool_file.id,
-            "transfer_method": FileTransferMethod.TOOL_FILE.value,
+            "transfer_method": FileTransferMethod.TOOL_FILE,
         }
         file = file_factory.build_from_mapping(
             mapping=mapping,

api/core/workflow/nodes/iteration/iteration_node.py

@@ -95,7 +95,7 @@ class IterationNode(Node):
             "config": {
                 "is_parallel": False,
                 "parallel_nums": 10,
-                "error_handle_mode": ErrorHandleMode.TERMINATED.value,
+                "error_handle_mode": ErrorHandleMode.TERMINATED,
             },
         }
 

api/core/workflow/nodes/knowledge_index/knowledge_index_node.py

@@ -27,7 +27,7 @@ from .exc import (
 logger = logging.getLogger(__name__)
 
 default_retrieval_model = {
-    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH,
     "reranking_enable": False,
     "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
     "top_k": 2,
@@ -77,7 +77,7 @@ class KnowledgeIndexNode(Node):
             raise KnowledgeIndexNodeError("Index chunk variable is required.")
         invoke_from = variable_pool.get(["sys", SystemVariableKey.INVOKE_FROM])
         if invoke_from:
-            is_preview = invoke_from.value == InvokeFrom.DEBUGGER.value
+            is_preview = invoke_from.value == InvokeFrom.DEBUGGER
         else:
             is_preview = False
         chunks = variable.value

api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py

@@ -72,7 +72,7 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 default_retrieval_model = {
-    "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+    "search_method": RetrievalMethod.SEMANTIC_SEARCH,
     "reranking_enable": False,
     "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
     "top_k": 4,

api/core/workflow/nodes/llm/llm_utils.py

@@ -92,7 +92,7 @@ def fetch_memory(
         return None
 
     # get conversation id
-    conversation_id_variable = variable_pool.get(["sys", SystemVariableKey.CONVERSATION_ID.value])
+    conversation_id_variable = variable_pool.get(["sys", SystemVariableKey.CONVERSATION_ID])
     if not isinstance(conversation_id_variable, StringSegment):
         return None
     conversation_id = conversation_id_variable.value

api/core/workflow/nodes/llm/node.py

@@ -956,7 +956,7 @@ class LLMNode(Node):
             variable_mapping["#files#"] = typed_node_data.vision.configs.variable_selector
 
         if typed_node_data.memory:
-            variable_mapping["#sys.query#"] = ["sys", SystemVariableKey.QUERY.value]
+            variable_mapping["#sys.query#"] = ["sys", SystemVariableKey.QUERY]
 
         if typed_node_data.prompt_config:
             enable_jinja = False

api/core/workflow/nodes/tool/tool_node.py

@@ -224,7 +224,7 @@ class ToolNode(Node):
         return result
 
     def _fetch_files(self, variable_pool: "VariablePool") -> list[File]:
-        variable = variable_pool.get(["sys", SystemVariableKey.FILES.value])
+        variable = variable_pool.get(["sys", SystemVariableKey.FILES])
         assert isinstance(variable, ArrayAnyVariable | ArrayAnySegment)
         return list(variable.value) if variable else []
 

api/core/workflow/workflow_entry.py

@@ -227,7 +227,7 @@ class WorkflowEntry:
             "height": node_height,
             "type": "custom",
             "data": {
-                "type": NodeType.START.value,
+                "type": NodeType.START,
                 "title": "Start",
                 "desc": "Start",
             },

api/events/event_handlers/delete_tool_parameters_cache_when_sync_draft_workflow.py

@@ -12,7 +12,7 @@ def handle(sender, **kwargs):
     if synced_draft_workflow is None:
         return
     for node_data in synced_draft_workflow.graph_dict.get("nodes", []):
-        if node_data.get("data", {}).get("type") == NodeType.TOOL.value:
+        if node_data.get("data", {}).get("type") == NodeType.TOOL:
             try:
                 tool_entity = ToolEntity.model_validate(node_data["data"])
                 tool_runtime = ToolManager.get_tool_runtime(

api/events/event_handlers/update_app_dataset_join_when_app_published_workflow_updated.py

@@ -53,7 +53,7 @@ def get_dataset_ids_from_workflow(published_workflow: Workflow) -> set[str]:
 
     # fetch all knowledge retrieval nodes
     knowledge_retrieval_nodes = [
-        node for node in nodes if node.get("data", {}).get("type") == NodeType.KNOWLEDGE_RETRIEVAL.value
+        node for node in nodes if node.get("data", {}).get("type") == NodeType.KNOWLEDGE_RETRIEVAL
     ]
 
     if not knowledge_retrieval_nodes:

api/extensions/ext_otel.py

@@ -138,7 +138,6 @@ def init_app(app: DifyApp):
     from opentelemetry.instrumentation.flask import FlaskInstrumentor
     from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor
     from opentelemetry.instrumentation.redis import RedisInstrumentor
-    from opentelemetry.instrumentation.requests import RequestsInstrumentor
     from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor
     from opentelemetry.metrics import get_meter, get_meter_provider, set_meter_provider
     from opentelemetry.propagate import set_global_textmap
@@ -238,7 +237,6 @@ def init_app(app: DifyApp):
     instrument_exception_logging()
     init_sqlalchemy_instrumentor(app)
     RedisInstrumentor().instrument()
-    RequestsInstrumentor().instrument()
     HTTPXClientInstrumentor().instrument()
     atexit.register(shutdown_tracer)
 

api/extensions/storage/clickzetta_volume/file_lifecycle.py

@@ -264,7 +264,7 @@ class FileLifecycleManager:
                 logger.warning("File %s not found in metadata", filename)
                 return False
 
-            metadata_dict[filename]["status"] = FileStatus.ARCHIVED.value
+            metadata_dict[filename]["status"] = FileStatus.ARCHIVED
             metadata_dict[filename]["modified_at"] = datetime.now().isoformat()
 
             self._save_metadata(metadata_dict)
@@ -309,7 +309,7 @@ class FileLifecycleManager:
             # Update metadata
             metadata_dict = self._load_metadata()
             if filename in metadata_dict:
-                metadata_dict[filename]["status"] = FileStatus.DELETED.value
+                metadata_dict[filename]["status"] = FileStatus.DELETED
                 metadata_dict[filename]["modified_at"] = datetime.now().isoformat()
                 self._save_metadata(metadata_dict)
 

api/factories/file_factory.py

@@ -45,7 +45,7 @@ def build_from_message_file(
     }
 
     # Set the correct ID field based on transfer method
-    if message_file.transfer_method == FileTransferMethod.TOOL_FILE.value:
+    if message_file.transfer_method == FileTransferMethod.TOOL_FILE:
         mapping["tool_file_id"] = message_file.upload_file_id
     else:
         mapping["upload_file_id"] = message_file.upload_file_id
@@ -368,9 +368,7 @@ def _build_from_datasource_file(
     if strict_type_validation and specified_type and detected_file_type.value != specified_type:
         raise ValueError("Detected file type does not match the specified type. Please verify the file.")
 
-    file_type = (
-        FileType(specified_type) if specified_type and specified_type != FileType.CUSTOM.value else detected_file_type
-    )
+    file_type = FileType(specified_type) if specified_type and specified_type != FileType.CUSTOM else detected_file_type
 
     return File(
         id=mapping.get("datasource_file_id"),

api/models/api_based_extension.py

@@ -9,7 +9,7 @@ from .base import Base
 from .types import StringUUID
 
 
-class APIBasedExtensionPoint(enum.Enum):
+class APIBasedExtensionPoint(enum.StrEnum):
     APP_EXTERNAL_DATA_TOOL_QUERY = "app.external_data_tool.query"
     PING = "ping"
     APP_MODERATION_INPUT = "app.moderation.input"

api/models/dataset.py

@@ -61,18 +61,18 @@ class Dataset(Base):
     created_by = mapped_column(StringUUID, nullable=False)
     created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.current_timestamp())
     updated_by = mapped_column(StringUUID, nullable=True)
-    updated_at = mapped_column(db.DateTime, nullable=False, server_default=func.current_timestamp())
+    updated_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
     embedding_model = mapped_column(db.String(255), nullable=True)
     embedding_model_provider = mapped_column(db.String(255), nullable=True)
-    keyword_number = db.Column(db.Integer, nullable=True, server_default=db.text("10"))
+    keyword_number = mapped_column(sa.Integer, nullable=True, server_default=db.text("10"))
     collection_binding_id = mapped_column(StringUUID, nullable=True)
     retrieval_model = mapped_column(JSONB, nullable=True)
-    built_in_field_enabled = mapped_column(db.Boolean, nullable=False, server_default=db.text("false"))
-    icon_info = db.Column(JSONB, nullable=True)
-    runtime_mode = db.Column(db.String(255), nullable=True, server_default=db.text("'general'::character varying"))
-    pipeline_id = db.Column(StringUUID, nullable=True)
-    chunk_structure = db.Column(db.String(255), nullable=True)
-    enable_api = db.Column(db.Boolean, nullable=False, server_default=db.text("true"))
+    built_in_field_enabled = mapped_column(sa.Boolean, nullable=False, server_default=db.text("false"))
+    icon_info = mapped_column(JSONB, nullable=True)
+    runtime_mode = mapped_column(db.String(255), nullable=True, server_default=db.text("'general'::character varying"))
+    pipeline_id = mapped_column(StringUUID, nullable=True)
+    chunk_structure = mapped_column(db.String(255), nullable=True)
+    enable_api = mapped_column(sa.Boolean, nullable=False, server_default=db.text("true"))
 
     @property
     def total_documents(self):
@@ -184,7 +184,7 @@ class Dataset(Base):
     @property
     def retrieval_model_dict(self):
         default_retrieval_model = {
-            "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
+            "search_method": RetrievalMethod.SEMANTIC_SEARCH,
             "reranking_enable": False,
             "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
             "top_k": 2,
@@ -1226,21 +1226,21 @@ class PipelineBuiltInTemplate(Base):  # type: ignore[name-defined]
     __tablename__ = "pipeline_built_in_templates"
     __table_args__ = (db.PrimaryKeyConstraint("id", name="pipeline_built_in_template_pkey"),)
 
-    id = db.Column(StringUUID, server_default=db.text("uuidv7()"))
-    name = db.Column(db.String(255), nullable=False)
-    description = db.Column(db.Text, nullable=False)
-    chunk_structure = db.Column(db.String(255), nullable=False)
-    icon = db.Column(db.JSON, nullable=False)
-    yaml_content = db.Column(db.Text, nullable=False)
-    copyright = db.Column(db.String(255), nullable=False)
-    privacy_policy = db.Column(db.String(255), nullable=False)
-    position = db.Column(db.Integer, nullable=False)
-    install_count = db.Column(db.Integer, nullable=False, default=0)
-    language = db.Column(db.String(255), nullable=False)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=func.current_timestamp())
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=func.current_timestamp())
-    created_by = db.Column(StringUUID, nullable=False)
-    updated_by = db.Column(StringUUID, nullable=True)
+    id = mapped_column(StringUUID, server_default=db.text("uuidv7()"))
+    name = mapped_column(db.String(255), nullable=False)
+    description = mapped_column(sa.Text, nullable=False)
+    chunk_structure = mapped_column(db.String(255), nullable=False)
+    icon = mapped_column(sa.JSON, nullable=False)
+    yaml_content = mapped_column(sa.Text, nullable=False)
+    copyright = mapped_column(db.String(255), nullable=False)
+    privacy_policy = mapped_column(db.String(255), nullable=False)
+    position = mapped_column(sa.Integer, nullable=False)
+    install_count = mapped_column(sa.Integer, nullable=False, default=0)
+    language = mapped_column(db.String(255), nullable=False)
+    created_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
+    updated_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
+    created_by = mapped_column(StringUUID, nullable=False)
+    updated_by = mapped_column(StringUUID, nullable=True)
 
     @property
     def created_user_name(self):
@@ -1257,20 +1257,20 @@ class PipelineCustomizedTemplate(Base):  # type: ignore[name-defined]
         db.Index("pipeline_customized_template_tenant_idx", "tenant_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text("uuidv7()"))
-    tenant_id = db.Column(StringUUID, nullable=False)
-    name = db.Column(db.String(255), nullable=False)
-    description = db.Column(db.Text, nullable=False)
-    chunk_structure = db.Column(db.String(255), nullable=False)
-    icon = db.Column(db.JSON, nullable=False)
-    position = db.Column(db.Integer, nullable=False)
-    yaml_content = db.Column(db.Text, nullable=False)
-    install_count = db.Column(db.Integer, nullable=False, default=0)
-    language = db.Column(db.String(255), nullable=False)
-    created_by = db.Column(StringUUID, nullable=False)
-    updated_by = db.Column(StringUUID, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=func.current_timestamp())
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=func.current_timestamp())
+    id = mapped_column(StringUUID, server_default=db.text("uuidv7()"))
+    tenant_id = mapped_column(StringUUID, nullable=False)
+    name = mapped_column(db.String(255), nullable=False)
+    description = mapped_column(sa.Text, nullable=False)
+    chunk_structure = mapped_column(db.String(255), nullable=False)
+    icon = mapped_column(sa.JSON, nullable=False)
+    position = mapped_column(sa.Integer, nullable=False)
+    yaml_content = mapped_column(sa.Text, nullable=False)
+    install_count = mapped_column(sa.Integer, nullable=False, default=0)
+    language = mapped_column(db.String(255), nullable=False)
+    created_by = mapped_column(StringUUID, nullable=False)
+    updated_by = mapped_column(StringUUID, nullable=True)
+    created_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
+    updated_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
 
     @property
     def created_user_name(self):
@@ -1284,17 +1284,17 @@ class Pipeline(Base):  # type: ignore[name-defined]
     __tablename__ = "pipelines"
     __table_args__ = (db.PrimaryKeyConstraint("id", name="pipeline_pkey"),)
 
-    id = db.Column(StringUUID, server_default=db.text("uuidv7()"))
-    tenant_id: Mapped[str] = 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"))
-    workflow_id = db.Column(StringUUID, nullable=True)
-    is_public = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
-    is_published = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
-    created_by = db.Column(StringUUID, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=func.current_timestamp())
-    updated_by = db.Column(StringUUID, nullable=True)
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=func.current_timestamp())
+    id = mapped_column(StringUUID, server_default=db.text("uuidv7()"))
+    tenant_id: Mapped[str] = mapped_column(StringUUID, nullable=False)
+    name = mapped_column(db.String(255), nullable=False)
+    description = mapped_column(sa.Text, nullable=False, server_default=db.text("''::character varying"))
+    workflow_id = mapped_column(StringUUID, nullable=True)
+    is_public = mapped_column(sa.Boolean, nullable=False, server_default=db.text("false"))
+    is_published = mapped_column(sa.Boolean, nullable=False, server_default=db.text("false"))
+    created_by = mapped_column(StringUUID, nullable=True)
+    created_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
+    updated_by = mapped_column(StringUUID, nullable=True)
+    updated_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
 
     def retrieve_dataset(self, session: Session):
         return session.query(Dataset).where(Dataset.pipeline_id == self.id).first()
@@ -1307,25 +1307,25 @@ class DocumentPipelineExecutionLog(Base):
         db.Index("document_pipeline_execution_logs_document_id_idx", "document_id"),
     )
 
-    id = db.Column(StringUUID, server_default=db.text("uuidv7()"))
-    pipeline_id = db.Column(StringUUID, nullable=False)
-    document_id = db.Column(StringUUID, nullable=False)
-    datasource_type = db.Column(db.String(255), nullable=False)
-    datasource_info = db.Column(db.Text, nullable=False)
-    datasource_node_id = db.Column(db.String(255), nullable=False)
-    input_data = db.Column(db.JSON, nullable=False)
-    created_by = db.Column(StringUUID, nullable=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=func.current_timestamp())
+    id = mapped_column(StringUUID, server_default=db.text("uuidv7()"))
+    pipeline_id = mapped_column(StringUUID, nullable=False)
+    document_id = mapped_column(StringUUID, nullable=False)
+    datasource_type = mapped_column(db.String(255), nullable=False)
+    datasource_info = mapped_column(sa.Text, nullable=False)
+    datasource_node_id = mapped_column(db.String(255), nullable=False)
+    input_data = mapped_column(sa.JSON, nullable=False)
+    created_by = mapped_column(StringUUID, nullable=True)
+    created_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
 
 
 class PipelineRecommendedPlugin(Base):
     __tablename__ = "pipeline_recommended_plugins"
     __table_args__ = (db.PrimaryKeyConstraint("id", name="pipeline_recommended_plugin_pkey"),)
 
-    id = db.Column(StringUUID, server_default=db.text("uuidv7()"))
-    plugin_id = db.Column(db.Text, nullable=False)
-    provider_name = db.Column(db.Text, nullable=False)
-    position = db.Column(db.Integer, nullable=False, default=0)
-    active = db.Column(db.Boolean, nullable=False, default=True)
-    created_at = db.Column(db.DateTime, nullable=False, server_default=func.current_timestamp())
-    updated_at = db.Column(db.DateTime, nullable=False, server_default=func.current_timestamp())
+    id = mapped_column(StringUUID, server_default=db.text("uuidv7()"))
+    plugin_id = mapped_column(sa.Text, nullable=False)
+    provider_name = mapped_column(sa.Text, nullable=False)
+    position = mapped_column(sa.Integer, nullable=False, default=0)
+    active = mapped_column(sa.Boolean, nullable=False, default=True)
+    created_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())
+    updated_at = mapped_column(sa.DateTime, nullable=False, server_default=func.current_timestamp())

api/models/model.py

@@ -186,13 +186,13 @@ class App(Base):
             if len(keys) >= 4:
                 provider_type = tool.get("provider_type", "")
                 provider_id = tool.get("provider_id", "")
-                if provider_type == ToolProviderType.API.value:
+                if provider_type == ToolProviderType.API:
                     try:
                         uuid.UUID(provider_id)
                     except Exception:
                         continue
                     api_provider_ids.append(provider_id)
-                if provider_type == ToolProviderType.BUILT_IN.value:
+                if provider_type == ToolProviderType.BUILT_IN:
                     try:
                         # check if it's hardcoded
                         try:
@@ -251,23 +251,23 @@ class App(Base):
                 provider_type = tool.get("provider_type", "")
                 provider_id = tool.get("provider_id", "")
 
-                if provider_type == ToolProviderType.API.value:
+                if provider_type == ToolProviderType.API:
                     if uuid.UUID(provider_id) not in existing_api_providers:
                         deleted_tools.append(
                             {
-                                "type": ToolProviderType.API.value,
+                                "type": ToolProviderType.API,
                                 "tool_name": tool["tool_name"],
                                 "provider_id": provider_id,
                             }
                         )
 
-                if provider_type == ToolProviderType.BUILT_IN.value:
+                if provider_type == ToolProviderType.BUILT_IN:
                     generic_provider_id = GenericProviderID(provider_id)
 
                     if not existing_builtin_providers[generic_provider_id.provider_name]:
                         deleted_tools.append(
                             {
-                                "type": ToolProviderType.BUILT_IN.value,
+                                "type": ToolProviderType.BUILT_IN,
                                 "tool_name": tool["tool_name"],
                                 "provider_id": provider_id,  # use the original one
                             }
@@ -1212,7 +1212,7 @@ class Message(Base):
 
         files: list[File] = []
         for message_file in message_files:
-            if message_file.transfer_method == FileTransferMethod.LOCAL_FILE.value:
+            if message_file.transfer_method == FileTransferMethod.LOCAL_FILE:
                 if message_file.upload_file_id is None:
                     raise ValueError(f"MessageFile {message_file.id} is a local file but has no upload_file_id")
                 file = file_factory.build_from_mapping(
@@ -1224,7 +1224,7 @@ class Message(Base):
                     },
                     tenant_id=current_app.tenant_id,
                 )
-            elif message_file.transfer_method == FileTransferMethod.REMOTE_URL.value:
+            elif message_file.transfer_method == FileTransferMethod.REMOTE_URL:
                 if message_file.url is None:
                     raise ValueError(f"MessageFile {message_file.id} is a remote url but has no url")
                 file = file_factory.build_from_mapping(
@@ -1237,7 +1237,7 @@ class Message(Base):
                     },
                     tenant_id=current_app.tenant_id,
                 )
-            elif message_file.transfer_method == FileTransferMethod.TOOL_FILE.value:
+            elif message_file.transfer_method == FileTransferMethod.TOOL_FILE:
                 if message_file.upload_file_id is None:
                     assert message_file.url is not None
                     message_file.upload_file_id = message_file.url.split("/")[-1].split(".")[0]