merge main

.github/workflows/api-tests.yml

@@ -50,6 +50,9 @@ jobs:
       - name: Run ModelRuntime
         run: poetry run -C api bash dev/pytest/pytest_model_runtime.sh
 
+      - name: Run dify config tests
+        run: poetry run -C api python dev/pytest/pytest_config_tests.py
+
       - name: Run Tool
         run: poetry run -C api bash dev/pytest/pytest_tools.sh
 

.github/workflows/expose_service_ports.sh

@@ -9,5 +9,6 @@ yq eval '.services["pgvecto-rs"].ports += ["5431:5432"]' -i docker/docker-compos
 yq eval '.services["elasticsearch"].ports += ["9200:9200"]' -i docker/docker-compose.yaml
 yq eval '.services.couchbase-server.ports += ["8091-8096:8091-8096"]' -i docker/docker-compose.yaml
 yq eval '.services.couchbase-server.ports += ["11210:11210"]' -i docker/docker-compose.yaml
+yq eval '.services.tidb.ports += ["4000:4000"]' -i docker/docker-compose.yaml
 
-echo "Ports exposed for sandbox, weaviate, qdrant, chroma, milvus, pgvector, pgvecto-rs, elasticsearch, couchbase"
+echo "Ports exposed for sandbox, weaviate, tidb, qdrant, chroma, milvus, pgvector, pgvecto-rs, elasticsearch, couchbase"

api/.env.example

@@ -60,17 +60,8 @@ DB_DATABASE=dify
 STORAGE_TYPE=opendal
 
 # Apache OpenDAL storage configuration, refer to https://github.com/apache/opendal
-STORAGE_OPENDAL_SCHEME=fs
-# OpenDAL FS
+OPENDAL_SCHEME=fs
 OPENDAL_FS_ROOT=storage
-# OpenDAL S3
-OPENDAL_S3_ROOT=/
-OPENDAL_S3_BUCKET=your-bucket-name
-OPENDAL_S3_ENDPOINT=https://s3.amazonaws.com
-OPENDAL_S3_ACCESS_KEY_ID=your-access-key
-OPENDAL_S3_SECRET_ACCESS_KEY=your-secret-key
-OPENDAL_S3_REGION=your-region
-OPENDAL_S3_SERVER_SIDE_ENCRYPTION=
 
 # S3 Storage configuration
 S3_USE_AWS_MANAGED_IAM=false
@@ -313,8 +304,7 @@ UPLOAD_VIDEO_FILE_SIZE_LIMIT=100
 UPLOAD_AUDIO_FILE_SIZE_LIMIT=50
 
 # Model configuration
-MULTIMODAL_SEND_IMAGE_FORMAT=base64
-MULTIMODAL_SEND_VIDEO_FORMAT=base64
+MULTIMODAL_SEND_FORMAT=base64
 PROMPT_GENERATION_MAX_TOKENS=512
 CODE_GENERATION_MAX_TOKENS=1024
 
@@ -409,6 +399,7 @@ INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH=4000
 WORKFLOW_MAX_EXECUTION_STEPS=500
 WORKFLOW_MAX_EXECUTION_TIME=1200
 WORKFLOW_CALL_MAX_DEPTH=5
+WORKFLOW_PARALLEL_DEPTH_LIMIT=3
 MAX_VARIABLE_SIZE=204800
 
 # App configuration
@@ -435,3 +426,5 @@ CREATE_TIDB_SERVICE_JOB_ENABLED=false
 
 # Maximum number of submitted thread count in a ThreadPool for parallel node execution
 MAX_SUBMIT_COUNT=100
+# Lockout duration in seconds
+LOGIN_LOCKOUT_DURATION=86400

api/.ruff.toml

@@ -70,7 +70,6 @@ ignore = [
     "SIM113", # eumerate-for-loop
     "SIM117", # multiple-with-statements
     "SIM210", # if-expr-with-true-false
-    "SIM300", # yoda-conditions,
 ]
 
 [lint.per-file-ignores]

api/app.py

@@ -1,13 +1,30 @@
-from app_factory import create_app
-from libs import threadings_utils, version_utils
+from libs import version_utils
 
 # preparation before creating app
 version_utils.check_supported_python_version()
-threadings_utils.apply_gevent_threading_patch()
+
+
+def is_db_command():
+    import sys
+
+    if len(sys.argv) > 1 and sys.argv[0].endswith("flask") and sys.argv[1] == "db":
+        return True
+    return False
+
 
 # create app
-app = create_app()
-celery = app.extensions["celery"]
+if is_db_command():
+    from app_factory import create_migrations_app
+
+    app = create_migrations_app()
+else:
+    from app_factory import create_app
+    from libs import threadings_utils
+
+    threadings_utils.apply_gevent_threading_patch()
+
+    app = create_app()
+    celery = app.extensions["celery"]
 
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=5001)

api/app_factory.py

@@ -1,5 +1,4 @@
 import logging
-import os
 import time
 
 from configs import dify_config
@@ -17,15 +16,6 @@ def create_flask_app_with_configs() -> DifyApp:
     dify_app = DifyApp(__name__)
     dify_app.config.from_mapping(dify_config.model_dump())
 
-    # populate configs into system environment variables
-    for key, value in dify_app.config.items():
-        if isinstance(value, str):
-            os.environ[key] = value
-        elif isinstance(value, int | float | bool):
-            os.environ[key] = str(value)
-        elif value is None:
-            os.environ[key] = ""
-
     return dify_app
 
 
@@ -98,3 +88,14 @@ def initialize_extensions(app: DifyApp):
         end_time = time.perf_counter()
         if dify_config.DEBUG:
             logging.info(f"Loaded {short_name} ({round((end_time - start_time) * 1000, 2)} ms)")
+
+
+def create_migrations_app():
+    app = create_flask_app_with_configs()
+    from extensions import ext_database, ext_migrate
+
+    # Initialize only required extensions
+    ext_database.init_app(app)
+    ext_migrate.init_app(app)
+
+    return app

api/commands.py

@@ -555,7 +555,8 @@ def create_tenant(email: str, language: Optional[str] = None, name: Optional[str
     if language not in languages:
         language = "en-US"
 
-    name = name.strip()
+    # Validates name encoding for non-Latin characters.
+    name = name.strip().encode("utf-8").decode("utf-8") if name else None
 
     # generate random password
     new_password = secrets.token_urlsafe(16)

api/configs/feature/__init__.py

@@ -433,6 +433,11 @@ class WorkflowConfig(BaseSettings):
         default=5,
     )
 
+    WORKFLOW_PARALLEL_DEPTH_LIMIT: PositiveInt = Field(
+        description="Maximum allowed depth for nested parallel executions",
+        default=3,
+    )
+
     MAX_VARIABLE_SIZE: PositiveInt = Field(
         description="Maximum size in bytes for a single variable in workflows. Default to 200 KB.",
         default=200 * 1024,
@@ -485,6 +490,11 @@ class AuthConfig(BaseSettings):
         default=60,
     )
 
+    LOGIN_LOCKOUT_DURATION: PositiveInt = Field(
+        description="Time (in seconds) a user must wait before retrying login after exceeding the rate limit.",
+        default=86400,
+    )
+
 
 class ModerationConfig(BaseSettings):
     """
@@ -660,14 +670,9 @@ class IndexingConfig(BaseSettings):
     )
 
 
-class VisionFormatConfig(BaseSettings):
-    MULTIMODAL_SEND_IMAGE_FORMAT: Literal["base64", "url"] = Field(
-        description="Format for sending images in multimodal contexts ('base64' or 'url'), default is base64",
-        default="base64",
-    )
-
-    MULTIMODAL_SEND_VIDEO_FORMAT: Literal["base64", "url"] = Field(
-        description="Format for sending videos in multimodal contexts ('base64' or 'url'), default is base64",
+class MultiModalTransferConfig(BaseSettings):
+    MULTIMODAL_SEND_FORMAT: Literal["base64", "url"] = Field(
+        description="Format for sending files in multimodal contexts ('base64' or 'url'), default is base64",
         default="base64",
     )
 
@@ -773,13 +778,13 @@ class FeatureConfig(
     FileAccessConfig,
     FileUploadConfig,
     HttpConfig,
-    VisionFormatConfig,
     InnerAPIConfig,
     IndexingConfig,
     LoggingConfig,
     MailConfig,
     ModelLoadBalanceConfig,
     ModerationConfig,
+    MultiModalTransferConfig,
     PositionConfig,
     RagEtlConfig,
     SecurityConfig,

api/configs/middleware/storage/opendal_storage_config.py

@@ -1,51 +1,9 @@
-from enum import StrEnum
-from typing import Literal
-
 from pydantic import Field
 from pydantic_settings import BaseSettings
 
 
-class OpenDALScheme(StrEnum):
-    FS = "fs"
-    S3 = "s3"
-
-
 class OpenDALStorageConfig(BaseSettings):
-    STORAGE_OPENDAL_SCHEME: str = Field(
-        default=OpenDALScheme.FS.value,
+    OPENDAL_SCHEME: str = Field(
+        default="fs",
         description="OpenDAL scheme.",
     )
-    # FS
-    OPENDAL_FS_ROOT: str = Field(
-        default="storage",
-        description="Root path for local storage.",
-    )
-    # S3
-    OPENDAL_S3_ROOT: str = Field(
-        default="/",
-        description="Root path for S3 storage.",
-    )
-    OPENDAL_S3_BUCKET: str = Field(
-        default="",
-        description="S3 bucket name.",
-    )
-    OPENDAL_S3_ENDPOINT: str = Field(
-        default="https://s3.amazonaws.com",
-        description="S3 endpoint URL.",
-    )
-    OPENDAL_S3_ACCESS_KEY_ID: str = Field(
-        default="",
-        description="S3 access key ID.",
-    )
-    OPENDAL_S3_SECRET_ACCESS_KEY: str = Field(
-        default="",
-        description="S3 secret access key.",
-    )
-    OPENDAL_S3_REGION: str = Field(
-        default="",
-        description="S3 region.",
-    )
-    OPENDAL_S3_SERVER_SIDE_ENCRYPTION: Literal["aws:kms", ""] = Field(
-        default="",
-        description="S3 server-side encryption.",
-    )

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.14.0",
+        default="0.14.1",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/common/errors.py

@@ -4,3 +4,8 @@ from werkzeug.exceptions import HTTPException
 class FilenameNotExistsError(HTTPException):
     code = 400
     description = "The specified filename does not exist."
+
+
+class RemoteFileUploadError(HTTPException):
+    code = 400
+    description = "Error uploading remote file."

api/controllers/console/admin.py

@@ -31,7 +31,7 @@ def admin_required(view):
         if auth_scheme != "bearer":
             raise Unauthorized("Invalid Authorization header format. Expected 'Bearer <api-key>' format.")
 
-        if dify_config.ADMIN_API_KEY != auth_token:
+        if auth_token != dify_config.ADMIN_API_KEY:
             raise Unauthorized("API key is invalid.")
 
         return view(*args, **kwargs)

api/controllers/console/app/model_config.py

@@ -65,7 +65,7 @@ class ModelConfigResource(Resource):
                         provider_type=agent_tool_entity.provider_type,
                         identity_id=f"AGENT.{app_model.id}",
                     )
-                except Exception as e:
+                except Exception:
                     continue
 
                 # get decrypted parameters
@@ -97,7 +97,7 @@ class ModelConfigResource(Resource):
                             app_id=app_model.id,
                             agent_tool=agent_tool_entity,
                         )
-                    except Exception as e:
+                    except Exception:
                         continue
 
                 manager = ToolParameterConfigurationManager(

api/controllers/console/app/ops_trace.py

@@ -1,4 +1,5 @@
 from flask_restful import Resource, reqparse
+from werkzeug.exceptions import BadRequest
 
 from controllers.console import api
 from controllers.console.app.error import TracingConfigCheckError, TracingConfigIsExist, TracingConfigNotExist
@@ -26,7 +27,7 @@ class TraceAppConfigApi(Resource):
                 return {"has_not_configured": True}
             return trace_config
         except Exception as e:
-            raise e
+            raise BadRequest(str(e))
 
     @setup_required
     @login_required
@@ -48,7 +49,7 @@ class TraceAppConfigApi(Resource):
                 raise TracingConfigCheckError()
             return result
         except Exception as e:
-            raise e
+            raise BadRequest(str(e))
 
     @setup_required
     @login_required
@@ -68,7 +69,7 @@ class TraceAppConfigApi(Resource):
                 raise TracingConfigNotExist()
             return {"result": "success"}
         except Exception as e:
-            raise e
+            raise BadRequest(str(e))
 
     @setup_required
     @login_required
@@ -85,7 +86,7 @@ class TraceAppConfigApi(Resource):
                 raise TracingConfigNotExist()
             return {"result": "success"}
         except Exception as e:
-            raise e
+            raise BadRequest(str(e))
 
 
 api.add_resource(TraceAppConfigApi, "/apps/<uuid:app_id>/trace-config")

api/controllers/console/app/workflow.py

@@ -6,6 +6,7 @@ from flask_restful import Resource, marshal_with, reqparse
 from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
 
 import services
+from configs import dify_config
 from controllers.console import api
 from controllers.console.app.error import ConversationCompletedError, DraftWorkflowNotExist, DraftWorkflowNotSync
 from controllers.console.app.wraps import get_app_model
@@ -426,7 +427,21 @@ class ConvertToWorkflowApi(Resource):
         }
 
 
+class WorkflowConfigApi(Resource):
+    """Resource for workflow configuration."""
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App):
+        return {
+            "parallel_depth_limit": dify_config.WORKFLOW_PARALLEL_DEPTH_LIMIT,
+        }
+
+
 api.add_resource(DraftWorkflowApi, "/apps/<uuid:app_id>/workflows/draft")
+api.add_resource(WorkflowConfigApi, "/apps/<uuid:app_id>/workflows/draft/config")
 api.add_resource(AdvancedChatDraftWorkflowRunApi, "/apps/<uuid:app_id>/advanced-chat/workflows/draft/run")
 api.add_resource(DraftWorkflowRunApi, "/apps/<uuid:app_id>/workflows/draft/run")
 api.add_resource(WorkflowTaskStopApi, "/apps/<uuid:app_id>/workflow-runs/tasks/<string:task_id>/stop")

api/controllers/console/app/wraps.py

@@ -5,8 +5,7 @@ from typing import Optional, Union
 from controllers.console.app.error import AppNotFoundError
 from extensions.ext_database import db
 from libs.login import current_user
-from models import App
-from models.model import AppMode
+from models import App, AppMode
 
 
 def get_app_model(view: Optional[Callable] = None, *, mode: Union[AppMode, list[AppMode]] = None):

api/controllers/console/datasets/datasets_document.py

@@ -948,7 +948,7 @@ class DocumentRetryApi(DocumentResource):
                 if document.indexing_status == "completed":
                     raise DocumentAlreadyFinishedError()
                 retry_documents.append(document)
-            except Exception as e:
+            except Exception:
                 logging.exception(f"Failed to retry document, document id: {document_id}")
                 continue
         # retry document

api/controllers/console/explore/conversation.py

@@ -1,12 +1,14 @@
 from flask_login import current_user
 from flask_restful import marshal_with, reqparse
 from flask_restful.inputs import int_range
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import NotFound
 
 from controllers.console import api
 from controllers.console.explore.error import NotChatAppError
 from controllers.console.explore.wraps import InstalledAppResource
 from core.app.entities.app_invoke_entities import InvokeFrom
+from extensions.ext_database import db
 from fields.conversation_fields import conversation_infinite_scroll_pagination_fields, simple_conversation_fields
 from libs.helper import uuid_value
 from models.model import AppMode
@@ -34,14 +36,16 @@ class ConversationListApi(InstalledAppResource):
             pinned = True if args["pinned"] == "true" else False
 
         try:
-            return WebConversationService.pagination_by_last_id(
-                app_model=app_model,
-                user=current_user,
-                last_id=args["last_id"],
-                limit=args["limit"],
-                invoke_from=InvokeFrom.EXPLORE,
-                pinned=pinned,
-            )
+            with Session(db.engine) as session:
+                return WebConversationService.pagination_by_last_id(
+                    session=session,
+                    app_model=app_model,
+                    user=current_user,
+                    last_id=args["last_id"],
+                    limit=args["limit"],
+                    invoke_from=InvokeFrom.EXPLORE,
+                    pinned=pinned,
+                )
         except LastConversationNotExistsError:
             raise NotFound("Last Conversation Not Exists.")
 

api/controllers/console/explore/message.py

@@ -70,7 +70,7 @@ class MessageFeedbackApi(InstalledAppResource):
         args = parser.parse_args()
 
         try:
-            MessageService.create_feedback(app_model, message_id, current_user, args["rating"])
+            MessageService.create_feedback(app_model, message_id, current_user, args["rating"], args["content"])
         except services.errors.message.MessageNotExistsError:
             raise NotFound("Message Not Exists.")
 

api/controllers/console/explore/recommended_app.py

@@ -4,6 +4,7 @@ from flask_restful import Resource, fields, marshal_with, reqparse
 from constants.languages import languages
 from controllers.console import api
 from controllers.console.wraps import account_initialization_required
+from libs.helper import AppIconUrlField
 from libs.login import login_required
 from services.recommended_app_service import RecommendedAppService
 
@@ -12,6 +13,8 @@ app_fields = {
     "name": fields.String,
     "mode": fields.String,
     "icon": fields.String,
+    "icon_type": fields.String,
+    "icon_url": AppIconUrlField,
     "icon_background": fields.String,
 }
 

api/controllers/console/files.py

@@ -1,6 +1,7 @@
 from flask import request
 from flask_login import current_user
 from flask_restful import Resource, marshal_with
+from werkzeug.exceptions import Forbidden
 
 import services
 from configs import dify_config
@@ -58,6 +59,9 @@ class FileApi(Resource):
         if not file.filename:
             raise FilenameNotExistsError
 
+        if source == "datasets" and not current_user.is_dataset_editor:
+            raise Forbidden()
+
         if source not in ("datasets", None):
             source = None
 

api/controllers/console/remote_files.py

@@ -7,6 +7,7 @@ from flask_restful import Resource, marshal_with, reqparse
 
 import services
 from controllers.common import helpers
+from controllers.common.errors import RemoteFileUploadError
 from core.file import helpers as file_helpers
 from core.helper import ssrf_proxy
 from fields.file_fields import file_fields_with_signed_url, remote_file_info_fields
@@ -43,10 +44,14 @@ class RemoteFileUploadApi(Resource):
 
         url = args["url"]
 
-        resp = ssrf_proxy.head(url=url)
-        if resp.status_code != httpx.codes.OK:
-            resp = ssrf_proxy.get(url=url, timeout=3, follow_redirects=True)
-        resp.raise_for_status()
+        try:
+            resp = ssrf_proxy.head(url=url)
+            if resp.status_code != httpx.codes.OK:
+                resp = ssrf_proxy.get(url=url, timeout=3, follow_redirects=True)
+            if resp.status_code != httpx.codes.OK:
+                raise RemoteFileUploadError(f"Failed to fetch file from {url}: {resp.text}")
+        except httpx.RequestError as e:
+            raise RemoteFileUploadError(f"Failed to fetch file from {url}: {str(e)}")
 
         file_info = helpers.guess_file_info_from_response(resp)
 

api/controllers/console/workspace/tool_providers.py

@@ -3,12 +3,14 @@ import io
 from flask import send_file
 from flask_login import current_user
 from flask_restful import Resource, reqparse
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import Forbidden
 
 from configs import dify_config
 from controllers.console import api
 from controllers.console.wraps import account_initialization_required, enterprise_license_required, setup_required
 from core.model_runtime.utils.encoders import jsonable_encoder
+from extensions.ext_database import db
 from libs.helper import alphanumeric, uuid_value
 from libs.login import login_required
 from services.tools.api_tools_manage_service import ApiToolManageService
@@ -91,12 +93,16 @@ class ToolBuiltinProviderUpdateApi(Resource):
 
         args = parser.parse_args()
 
-        return BuiltinToolManageService.update_builtin_tool_provider(
-            user_id,
-            tenant_id,
-            provider,
-            args["credentials"],
-        )
+        with Session(db.engine) as session:
+            result = BuiltinToolManageService.update_builtin_tool_provider(
+                session=session,
+                user_id=user_id,
+                tenant_id=tenant_id,
+                provider_name=provider,
+                credentials=args["credentials"],
+            )
+            session.commit()
+        return result
 
 
 class ToolBuiltinProviderGetCredentialsApi(Resource):
@@ -104,13 +110,11 @@ class ToolBuiltinProviderGetCredentialsApi(Resource):
     @login_required
     @account_initialization_required
     def get(self, provider):
-        user_id = current_user.id
         tenant_id = current_user.current_tenant_id
 
         return BuiltinToolManageService.get_builtin_tool_provider_credentials(
-            user_id,
-            tenant_id,
-            provider,
+            tenant_id=tenant_id,
+            provider_name=provider,
         )
 
 

api/controllers/service_api/app/conversation.py

@@ -1,5 +1,6 @@
 from flask_restful import Resource, marshal_with, reqparse
 from flask_restful.inputs import int_range
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import NotFound
 
 import services
@@ -7,6 +8,7 @@ from controllers.service_api import api
 from controllers.service_api.app.error import NotChatAppError
 from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
 from core.app.entities.app_invoke_entities import InvokeFrom
+from extensions.ext_database import db
 from fields.conversation_fields import (
     conversation_delete_fields,
     conversation_infinite_scroll_pagination_fields,
@@ -39,14 +41,16 @@ class ConversationApi(Resource):
         args = parser.parse_args()
 
         try:
-            return ConversationService.pagination_by_last_id(
-                app_model=app_model,
-                user=end_user,
-                last_id=args["last_id"],
-                limit=args["limit"],
-                invoke_from=InvokeFrom.SERVICE_API,
-                sort_by=args["sort_by"],
-            )
+            with Session(db.engine) as session:
+                return ConversationService.pagination_by_last_id(
+                    session=session,
+                    app_model=app_model,
+                    user=end_user,
+                    last_id=args["last_id"],
+                    limit=args["limit"],
+                    invoke_from=InvokeFrom.SERVICE_API,
+                    sort_by=args["sort_by"],
+                )
         except services.errors.conversation.LastConversationNotExistsError:
             raise NotFound("Last Conversation Not Exists.")
 

api/controllers/service_api/app/message.py

@@ -104,10 +104,11 @@ class MessageFeedbackApi(Resource):
 
         parser = reqparse.RequestParser()
         parser.add_argument("rating", type=str, choices=["like", "dislike", None], location="json")
+        parser.add_argument("content", type=str, location="json")
         args = parser.parse_args()
 
         try:
-            MessageService.create_feedback(app_model, message_id, end_user, args["rating"])
+            MessageService.create_feedback(app_model, message_id, end_user, args["rating"], args["content"])
         except services.errors.message.MessageNotExistsError:
             raise NotFound("Message Not Exists.")
 

api/controllers/web/conversation.py

@@ -1,11 +1,13 @@
 from flask_restful import marshal_with, reqparse
 from flask_restful.inputs import int_range
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import NotFound
 
 from controllers.web import api
 from controllers.web.error import NotChatAppError
 from controllers.web.wraps import WebApiResource
 from core.app.entities.app_invoke_entities import InvokeFrom
+from extensions.ext_database import db
 from fields.conversation_fields import conversation_infinite_scroll_pagination_fields, simple_conversation_fields
 from libs.helper import uuid_value
 from models.model import AppMode
@@ -40,15 +42,17 @@ class ConversationListApi(WebApiResource):
             pinned = True if args["pinned"] == "true" else False
 
         try:
-            return WebConversationService.pagination_by_last_id(
-                app_model=app_model,
-                user=end_user,
-                last_id=args["last_id"],
-                limit=args["limit"],
-                invoke_from=InvokeFrom.WEB_APP,
-                pinned=pinned,
-                sort_by=args["sort_by"],
-            )
+            with Session(db.engine) as session:
+                return WebConversationService.pagination_by_last_id(
+                    session=session,
+                    app_model=app_model,
+                    user=end_user,
+                    last_id=args["last_id"],
+                    limit=args["limit"],
+                    invoke_from=InvokeFrom.WEB_APP,
+                    pinned=pinned,
+                    sort_by=args["sort_by"],
+                )
         except LastConversationNotExistsError:
             raise NotFound("Last Conversation Not Exists.")
 

api/controllers/web/message.py

@@ -108,7 +108,7 @@ class MessageFeedbackApi(WebApiResource):
         args = parser.parse_args()
 
         try:
-            MessageService.create_feedback(app_model, message_id, end_user, args["rating"])
+            MessageService.create_feedback(app_model, message_id, end_user, args["rating"], args["content"])
         except services.errors.message.MessageNotExistsError:
             raise NotFound("Message Not Exists.")
 

api/controllers/web/remote_files.py

@@ -5,6 +5,7 @@ from flask_restful import marshal_with, reqparse
 
 import services
 from controllers.common import helpers
+from controllers.common.errors import RemoteFileUploadError
 from controllers.web.wraps import WebApiResource
 from core.file import helpers as file_helpers
 from core.helper import ssrf_proxy
@@ -38,10 +39,14 @@ class RemoteFileUploadApi(WebApiResource):
 
         url = args["url"]
 
-        resp = ssrf_proxy.head(url=url)
-        if resp.status_code != httpx.codes.OK:
-            resp = ssrf_proxy.get(url=url, timeout=3)
-        resp.raise_for_status()
+        try:
+            resp = ssrf_proxy.head(url=url)
+            if resp.status_code != httpx.codes.OK:
+                resp = ssrf_proxy.get(url=url, timeout=3, follow_redirects=True)
+            if resp.status_code != httpx.codes.OK:
+                raise RemoteFileUploadError(f"Failed to fetch file from {url}: {resp.text}")
+        except httpx.RequestError as e:
+            raise RemoteFileUploadError(f"Failed to fetch file from {url}: {str(e)}")
 
         file_info = helpers.guess_file_info_from_response(resp)
 

api/core/app/apps/advanced_chat/app_generator_tts_publisher.py

@@ -4,14 +4,17 @@ import logging
 import queue
 import re
 import threading
+from collections.abc import Iterable
 
 from core.app.entities.queue_entities import (
+    MessageQueueMessage,
     QueueAgentMessageEvent,
     QueueLLMChunkEvent,
     QueueNodeSucceededEvent,
     QueueTextChunkEvent,
+    WorkflowQueueMessage,
 )
-from core.model_manager import ModelManager
+from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities.model_entities import ModelType
 
 
@@ -21,7 +24,7 @@ class AudioTrunk:
         self.status = status
 
 
-def _invoice_tts(text_content: str, model_instance, tenant_id: str, voice: str):
+def _invoice_tts(text_content: str, model_instance: ModelInstance, tenant_id: str, voice: str):
     if not text_content or text_content.isspace():
         return
     return model_instance.invoke_tts(
@@ -29,13 +32,19 @@ def _invoice_tts(text_content: str, model_instance, tenant_id: str, voice: str):
     )
 
 
-def _process_future(future_queue, audio_queue):
+def _process_future(
+    future_queue: queue.Queue[concurrent.futures.Future[Iterable[bytes] | None] | None],
+    audio_queue: queue.Queue[AudioTrunk],
+):
     while True:
         try:
             future = future_queue.get()
             if future is None:
                 break
-            for audio in future.result():
+            invoke_result = future.result()
+            if not invoke_result:
+                continue
+            for audio in invoke_result:
                 audio_base64 = base64.b64encode(bytes(audio))
                 audio_queue.put(AudioTrunk("responding", audio=audio_base64))
         except Exception as e:
@@ -49,8 +58,8 @@ class AppGeneratorTTSPublisher:
         self.logger = logging.getLogger(__name__)
         self.tenant_id = tenant_id
         self.msg_text = ""
-        self._audio_queue = queue.Queue()
-        self._msg_queue = queue.Queue()
+        self._audio_queue: queue.Queue[AudioTrunk] = queue.Queue()
+        self._msg_queue: queue.Queue[WorkflowQueueMessage | MessageQueueMessage | None] = queue.Queue()
         self.match = re.compile(r"[。.!?]")
         self.model_manager = ModelManager()
         self.model_instance = self.model_manager.get_default_model_instance(
@@ -66,14 +75,11 @@ class AppGeneratorTTSPublisher:
         self._runtime_thread = threading.Thread(target=self._runtime).start()
         self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=3)
 
-    def publish(self, message):
-        try:
-            self._msg_queue.put(message)
-        except Exception as e:
-            self.logger.warning(e)
+    def publish(self, message: WorkflowQueueMessage | MessageQueueMessage | None, /):
+        self._msg_queue.put(message)
 
     def _runtime(self):
-        future_queue = queue.Queue()
+        future_queue: queue.Queue[concurrent.futures.Future[Iterable[bytes] | None] | None] = queue.Queue()
         threading.Thread(target=_process_future, args=(future_queue, self._audio_queue)).start()
         while True:
             try:
@@ -110,7 +116,7 @@ class AppGeneratorTTSPublisher:
                 break
         future_queue.put(None)
 
-    def check_and_get_audio(self) -> AudioTrunk | None:
+    def check_and_get_audio(self):
         try:
             if self._last_audio_event and self._last_audio_event.status == "finish":
                 if self.executor:

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -22,6 +22,7 @@ from core.app.entities.queue_entities import (
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
+    QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
     QueueParallelBranchRunFailedEvent,
@@ -179,7 +180,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             else:
                 continue
 
-        raise Exception("Queue listening stopped unexpectedly.")
+        raise ValueError("queue listening stopped unexpectedly.")
 
     def _to_stream_response(
         self, generator: Generator[StreamResponse, None, None]
@@ -196,11 +197,11 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 stream_response=stream_response,
             )
 
-    def _listen_audio_msg(self, publisher, task_id: str):
+    def _listen_audio_msg(self, publisher: AppGeneratorTTSPublisher | None, task_id: str):
         if not publisher:
             return None
-        audio_msg: AudioTrunk = publisher.check_and_get_audio()
-        if audio_msg and audio_msg.status != "finish":
+        audio_msg = publisher.check_and_get_audio()
+        if audio_msg and isinstance(audio_msg, AudioTrunk) and audio_msg.status != "finish":
             return MessageAudioStreamResponse(audio=audio_msg.audio, task_id=task_id)
         return None
 
@@ -221,7 +222,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
         for response in self._process_stream_response(tts_publisher=tts_publisher, trace_manager=trace_manager):
             while True:
-                audio_response = self._listen_audio_msg(tts_publisher, task_id=task_id)
+                audio_response = self._listen_audio_msg(publisher=tts_publisher, task_id=task_id)
                 if audio_response:
                     yield audio_response
                 else:
@@ -290,9 +291,27 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 yield self._workflow_start_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                 )
+            elif isinstance(
+                event,
+                QueueNodeRetryEvent,
+            ):
+                if not workflow_run:
+                    raise ValueError("workflow run not initialized.")
+                workflow_node_execution = self._handle_workflow_node_execution_retried(
+                    workflow_run=workflow_run, event=event
+                )
+
+                response = self._workflow_node_retry_to_stream_response(
+                    event=event,
+                    task_id=self._application_generate_entity.task_id,
+                    workflow_node_execution=workflow_node_execution,
+                )
+
+                if response:
+                    yield response
             elif isinstance(event, QueueNodeStartedEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 workflow_node_execution = self._handle_node_execution_start(workflow_run=workflow_run, event=event)
 
@@ -330,47 +349,48 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
                 if response:
                     yield response
+
             elif isinstance(event, QueueParallelBranchRunStartedEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 yield self._workflow_parallel_branch_start_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
             elif isinstance(event, QueueParallelBranchRunSucceededEvent | QueueParallelBranchRunFailedEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 yield self._workflow_parallel_branch_finished_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
             elif isinstance(event, QueueIterationStartEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 yield self._workflow_iteration_start_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
             elif isinstance(event, QueueIterationNextEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 yield self._workflow_iteration_next_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
             elif isinstance(event, QueueIterationCompletedEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 yield self._workflow_iteration_completed_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
             elif isinstance(event, QueueWorkflowSucceededEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 if not graph_runtime_state:
-                    raise Exception("Graph runtime state not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 workflow_run = self._handle_workflow_run_success(
                     workflow_run=workflow_run,
@@ -389,10 +409,10 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 self._queue_manager.publish(QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE)
             elif isinstance(event, QueueWorkflowPartialSuccessEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 if not graph_runtime_state:
-                    raise Exception("Graph runtime state not initialized.")
+                    raise ValueError("graph runtime state not initialized.")
 
                 workflow_run = self._handle_workflow_run_partial_success(
                     workflow_run=workflow_run,
@@ -412,10 +432,10 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 self._queue_manager.publish(QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE)
             elif isinstance(event, QueueWorkflowFailedEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 if not graph_runtime_state:
-                    raise Exception("Graph runtime state not initialized.")
+                    raise ValueError("graph runtime state not initialized.")
 
                 workflow_run = self._handle_workflow_run_failed(
                     workflow_run=workflow_run,
@@ -494,7 +514,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
                 # only publish tts message at text chunk streaming
                 if tts_publisher:
-                    tts_publisher.publish(message=queue_message)
+                    tts_publisher.publish(queue_message)
 
                 self._task_state.answer += delta_text
                 yield self._message_to_stream_response(
@@ -505,7 +525,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 yield self._message_replace_to_stream_response(answer=event.text)
             elif isinstance(event, QueueAdvancedChatMessageEndEvent):
                 if not graph_runtime_state:
-                    raise Exception("Graph runtime state not initialized.")
+                    raise ValueError("graph runtime state not initialized.")
 
                 output_moderation_answer = self._handle_output_moderation_when_task_finished(self._task_state.answer)
                 if output_moderation_answer:

api/core/app/apps/base_app_queue_manager.py

@@ -1,7 +1,6 @@
 import queue
 import time
 from abc import abstractmethod
-from collections.abc import Generator
 from enum import Enum
 from typing import Any
 
@@ -11,9 +10,11 @@ from configs import dify_config
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.queue_entities import (
     AppQueueEvent,
+    MessageQueueMessage,
     QueueErrorEvent,
     QueuePingEvent,
     QueueStopEvent,
+    WorkflowQueueMessage,
 )
 from extensions.ext_redis import redis_client
 
@@ -37,11 +38,11 @@ class AppQueueManager:
             AppQueueManager._generate_task_belong_cache_key(self._task_id), 1800, f"{user_prefix}-{self._user_id}"
         )
 
-        q = queue.Queue()
+        q: queue.Queue[WorkflowQueueMessage | MessageQueueMessage | None] = queue.Queue()
 
         self._q = q
 
-    def listen(self) -> Generator:
+    def listen(self):
         """
         Listen to queue
         :return:

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -18,6 +18,7 @@ from core.app.entities.queue_entities import (
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
+    QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
     QueueParallelBranchRunFailedEvent,
@@ -154,7 +155,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
             else:
                 continue
 
-        raise Exception("Queue listening stopped unexpectedly.")
+        raise ValueError("queue listening stopped unexpectedly.")
 
     def _to_stream_response(
         self, generator: Generator[StreamResponse, None, None]
@@ -170,11 +171,11 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
 
             yield WorkflowAppStreamResponse(workflow_run_id=workflow_run_id, stream_response=stream_response)
 
-    def _listen_audio_msg(self, publisher, task_id: str):
+    def _listen_audio_msg(self, publisher: AppGeneratorTTSPublisher | None, task_id: str):
         if not publisher:
             return None
-        audio_msg: AudioTrunk = publisher.check_and_get_audio()
-        if audio_msg and audio_msg.status != "finish":
+        audio_msg = publisher.check_and_get_audio()
+        if audio_msg and isinstance(audio_msg, AudioTrunk) and audio_msg.status != "finish":
             return MessageAudioStreamResponse(audio=audio_msg.audio, task_id=task_id)
         return None
 
@@ -195,7 +196,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
 
         for response in self._process_stream_response(tts_publisher=tts_publisher, trace_manager=trace_manager):
             while True:
-                audio_response = self._listen_audio_msg(tts_publisher, task_id=task_id)
+                audio_response = self._listen_audio_msg(publisher=tts_publisher, task_id=task_id)
                 if audio_response:
                     yield audio_response
                 else:
@@ -217,7 +218,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                     break
                 else:
                     yield MessageAudioStreamResponse(audio=audio_trunk.audio, task_id=task_id)
-            except Exception as e:
+            except Exception:
                 logger.exception(f"Fails to get audio trunk, task_id: {task_id}")
                 break
         if tts_publisher:
@@ -253,9 +254,27 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 yield self._workflow_start_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                 )
+            elif isinstance(
+                event,
+                QueueNodeRetryEvent,
+            ):
+                if not workflow_run:
+                    raise ValueError("workflow run not initialized.")
+                workflow_node_execution = self._handle_workflow_node_execution_retried(
+                    workflow_run=workflow_run, event=event
+                )
+
+                response = self._workflow_node_retry_to_stream_response(
+                    event=event,
+                    task_id=self._application_generate_entity.task_id,
+                    workflow_node_execution=workflow_node_execution,
+                )
+
+                if response:
+                    yield response
             elif isinstance(event, QueueNodeStartedEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 workflow_node_execution = self._handle_node_execution_start(workflow_run=workflow_run, event=event)
 
@@ -286,50 +305,50 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                     task_id=self._application_generate_entity.task_id,
                     workflow_node_execution=workflow_node_execution,
                 )
-
                 if node_failed_response:
                     yield node_failed_response
+
             elif isinstance(event, QueueParallelBranchRunStartedEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 yield self._workflow_parallel_branch_start_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
             elif isinstance(event, QueueParallelBranchRunSucceededEvent | QueueParallelBranchRunFailedEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 yield self._workflow_parallel_branch_finished_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
             elif isinstance(event, QueueIterationStartEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 yield self._workflow_iteration_start_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
             elif isinstance(event, QueueIterationNextEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 yield self._workflow_iteration_next_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
             elif isinstance(event, QueueIterationCompletedEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 yield self._workflow_iteration_completed_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
             elif isinstance(event, QueueWorkflowSucceededEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 if not graph_runtime_state:
-                    raise Exception("Graph runtime state not initialized.")
+                    raise ValueError("graph runtime state not initialized.")
 
                 workflow_run = self._handle_workflow_run_success(
                     workflow_run=workflow_run,
@@ -349,10 +368,10 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 )
             elif isinstance(event, QueueWorkflowPartialSuccessEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 if not graph_runtime_state:
-                    raise Exception("Graph runtime state not initialized.")
+                    raise ValueError("graph runtime state not initialized.")
 
                 workflow_run = self._handle_workflow_run_partial_success(
                     workflow_run=workflow_run,
@@ -373,10 +392,10 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 )
             elif isinstance(event, QueueWorkflowFailedEvent | QueueStopEvent):
                 if not workflow_run:
-                    raise Exception("Workflow run not initialized.")
+                    raise ValueError("workflow run not initialized.")
 
                 if not graph_runtime_state:
-                    raise Exception("Graph runtime state not initialized.")
+                    raise ValueError("graph runtime state not initialized.")
                 workflow_run = self._handle_workflow_run_failed(
                     workflow_run=workflow_run,
                     start_at=graph_runtime_state.start_at,
@@ -404,7 +423,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
 
                 # only publish tts message at text chunk streaming
                 if tts_publisher:
-                    tts_publisher.publish(message=queue_message)
+                    tts_publisher.publish(queue_message)
 
                 self._task_state.answer += delta_text
                 yield self._text_chunk_to_stream_response(

api/core/app/apps/workflow_app_runner.py

@@ -11,6 +11,7 @@ from core.app.entities.queue_entities import (
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
+    QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
     QueueParallelBranchRunFailedEvent,
@@ -38,6 +39,7 @@ from core.workflow.graph_engine.entities.event import (
     NodeRunExceptionEvent,
     NodeRunFailedEvent,
     NodeRunRetrieverResourceEvent,
+    NodeRunRetryEvent,
     NodeRunStartedEvent,
     NodeRunStreamChunkEvent,
     NodeRunSucceededEvent,
@@ -186,6 +188,41 @@ class WorkflowBasedAppRunner(AppRunner):
             )
         elif isinstance(event, GraphRunFailedEvent):
             self._publish_event(QueueWorkflowFailedEvent(error=event.error, exceptions_count=event.exceptions_count))
+        elif isinstance(event, NodeRunRetryEvent):
+            node_run_result = event.route_node_state.node_run_result
+            if node_run_result:
+                inputs = node_run_result.inputs
+                process_data = node_run_result.process_data
+                outputs = node_run_result.outputs
+                execution_metadata = node_run_result.metadata
+            else:
+                inputs = {}
+                process_data = {}
+                outputs = {}
+                execution_metadata = {}
+            self._publish_event(
+                QueueNodeRetryEvent(
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_data=event.node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    start_at=event.start_at,
+                    node_run_index=event.route_node_state.index,
+                    predecessor_node_id=event.predecessor_node_id,
+                    in_iteration_id=event.in_iteration_id,
+                    parallel_mode_run_id=event.parallel_mode_run_id,
+                    inputs=inputs,
+                    process_data=process_data,
+                    outputs=outputs,
+                    error=event.error,
+                    execution_metadata=execution_metadata,
+                    retry_index=event.retry_index,
+                )
+            )
         elif isinstance(event, NodeRunStartedEvent):
             self._publish_event(
                 QueueNodeStartedEvent(
@@ -205,6 +242,17 @@ class WorkflowBasedAppRunner(AppRunner):
                 )
             )
         elif isinstance(event, NodeRunSucceededEvent):
+            node_run_result = event.route_node_state.node_run_result
+            if node_run_result:
+                inputs = node_run_result.inputs
+                process_data = node_run_result.process_data
+                outputs = node_run_result.outputs
+                execution_metadata = node_run_result.metadata
+            else:
+                inputs = {}
+                process_data = {}
+                outputs = {}
+                execution_metadata = {}
             self._publish_event(
                 QueueNodeSucceededEvent(
                     node_execution_id=event.id,
@@ -216,18 +264,10 @@ class WorkflowBasedAppRunner(AppRunner):
                     parent_parallel_id=event.parent_parallel_id,
                     parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                     start_at=event.route_node_state.start_at,
-                    inputs=event.route_node_state.node_run_result.inputs
-                    if event.route_node_state.node_run_result
-                    else {},
-                    process_data=event.route_node_state.node_run_result.process_data
-                    if event.route_node_state.node_run_result
-                    else {},
-                    outputs=event.route_node_state.node_run_result.outputs
-                    if event.route_node_state.node_run_result
-                    else {},
-                    execution_metadata=event.route_node_state.node_run_result.metadata
-                    if event.route_node_state.node_run_result
-                    else {},
+                    inputs=inputs,
+                    process_data=process_data,
+                    outputs=outputs,
+                    execution_metadata=execution_metadata,
                     in_iteration_id=event.in_iteration_id,
                 )
             )

api/core/app/entities/queue_entities.py

@@ -1,3 +1,4 @@
+from collections.abc import Mapping
 from datetime import datetime
 from enum import Enum, StrEnum
 from typing import Any, Optional
@@ -43,6 +44,7 @@ class QueueEvent(StrEnum):
     ERROR = "error"
     PING = "ping"
     STOP = "stop"
+    RETRY = "retry"
 
 
 class AppQueueEvent(BaseModel):
@@ -84,9 +86,9 @@ class QueueIterationStartEvent(AppQueueEvent):
     start_at: datetime
 
     node_run_index: int
-    inputs: Optional[dict[str, Any]] = None
+    inputs: Optional[Mapping[str, Any]] = None
     predecessor_node_id: Optional[str] = None
-    metadata: Optional[dict[str, Any]] = None
+    metadata: Optional[Mapping[str, Any]] = None
 
 
 class QueueIterationNextEvent(AppQueueEvent):
@@ -138,9 +140,9 @@ class QueueIterationCompletedEvent(AppQueueEvent):
     start_at: datetime
 
     node_run_index: int
-    inputs: Optional[dict[str, Any]] = None
-    outputs: Optional[dict[str, Any]] = None
-    metadata: Optional[dict[str, Any]] = None
+    inputs: Optional[Mapping[str, Any]] = None
+    outputs: Optional[Mapping[str, Any]] = None
+    metadata: Optional[Mapping[str, Any]] = None
     steps: int = 0
 
     error: Optional[str] = None
@@ -303,9 +305,9 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     start_at: datetime
 
-    inputs: Optional[dict[str, Any]] = None
-    process_data: Optional[dict[str, Any]] = None
-    outputs: Optional[dict[str, Any]] = None
+    inputs: Optional[Mapping[str, Any]] = None
+    process_data: Optional[Mapping[str, Any]] = None
+    outputs: Optional[Mapping[str, Any]] = None
     execution_metadata: Optional[dict[NodeRunMetadataKey, Any]] = None
 
     error: Optional[str] = None
@@ -313,6 +315,20 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     iteration_duration_map: Optional[dict[str, float]] = None
 
 
+class QueueNodeRetryEvent(QueueNodeStartedEvent):
+    """QueueNodeRetryEvent entity"""
+
+    event: QueueEvent = QueueEvent.RETRY
+
+    inputs: Optional[Mapping[str, Any]] = None
+    process_data: Optional[Mapping[str, Any]] = None
+    outputs: Optional[Mapping[str, Any]] = None
+    execution_metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None
+
+    error: str
+    retry_index: int  # retry index
+
+
 class QueueNodeInIterationFailedEvent(AppQueueEvent):
     """
     QueueNodeInIterationFailedEvent entity
@@ -336,10 +352,10 @@ class QueueNodeInIterationFailedEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     start_at: datetime
 
-    inputs: Optional[dict[str, Any]] = None
-    process_data: Optional[dict[str, Any]] = None
-    outputs: Optional[dict[str, Any]] = None
-    execution_metadata: Optional[dict[NodeRunMetadataKey, Any]] = None
+    inputs: Optional[Mapping[str, Any]] = None
+    process_data: Optional[Mapping[str, Any]] = None
+    outputs: Optional[Mapping[str, Any]] = None
+    execution_metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None
 
     error: str
 
@@ -367,10 +383,10 @@ class QueueNodeExceptionEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     start_at: datetime
 
-    inputs: Optional[dict[str, Any]] = None
-    process_data: Optional[dict[str, Any]] = None
-    outputs: Optional[dict[str, Any]] = None
-    execution_metadata: Optional[dict[NodeRunMetadataKey, Any]] = None
+    inputs: Optional[Mapping[str, Any]] = None
+    process_data: Optional[Mapping[str, Any]] = None
+    outputs: Optional[Mapping[str, Any]] = None
+    execution_metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None
 
     error: str
 
@@ -398,10 +414,10 @@ class QueueNodeFailedEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     start_at: datetime
 
-    inputs: Optional[dict[str, Any]] = None
-    process_data: Optional[dict[str, Any]] = None
-    outputs: Optional[dict[str, Any]] = None
-    execution_metadata: Optional[dict[NodeRunMetadataKey, Any]] = None
+    inputs: Optional[Mapping[str, Any]] = None
+    process_data: Optional[Mapping[str, Any]] = None
+    outputs: Optional[Mapping[str, Any]] = None
+    execution_metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None
 
     error: str
 

api/core/app/entities/task_entities.py

@@ -52,6 +52,7 @@ class StreamEvent(Enum):
     WORKFLOW_FINISHED = "workflow_finished"
     NODE_STARTED = "node_started"
     NODE_FINISHED = "node_finished"
+    NODE_RETRY = "node_retry"
     PARALLEL_BRANCH_STARTED = "parallel_branch_started"
     PARALLEL_BRANCH_FINISHED = "parallel_branch_finished"
     ITERATION_STARTED = "iteration_started"
@@ -342,6 +343,75 @@ class NodeFinishStreamResponse(StreamResponse):
         }
 
 
+class NodeRetryStreamResponse(StreamResponse):
+    """
+    NodeFinishStreamResponse entity
+    """
+
+    class Data(BaseModel):
+        """
+        Data entity
+        """
+
+        id: str
+        node_id: str
+        node_type: str
+        title: str
+        index: int
+        predecessor_node_id: Optional[str] = None
+        inputs: Optional[dict] = None
+        process_data: Optional[dict] = None
+        outputs: Optional[dict] = None
+        status: str
+        error: Optional[str] = None
+        elapsed_time: float
+        execution_metadata: Optional[dict] = None
+        created_at: int
+        finished_at: int
+        files: Optional[Sequence[Mapping[str, Any]]] = []
+        parallel_id: Optional[str] = None
+        parallel_start_node_id: Optional[str] = None
+        parent_parallel_id: Optional[str] = None
+        parent_parallel_start_node_id: Optional[str] = None
+        iteration_id: Optional[str] = None
+        retry_index: int = 0
+
+    event: StreamEvent = StreamEvent.NODE_RETRY
+    workflow_run_id: str
+    data: Data
+
+    def to_ignore_detail_dict(self):
+        return {
+            "event": self.event.value,
+            "task_id": self.task_id,
+            "workflow_run_id": self.workflow_run_id,
+            "data": {
+                "id": self.data.id,
+                "node_id": self.data.node_id,
+                "node_type": self.data.node_type,
+                "title": self.data.title,
+                "index": self.data.index,
+                "predecessor_node_id": self.data.predecessor_node_id,
+                "inputs": None,
+                "process_data": None,
+                "outputs": None,
+                "status": self.data.status,
+                "error": None,
+                "elapsed_time": self.data.elapsed_time,
+                "execution_metadata": None,
+                "created_at": self.data.created_at,
+                "finished_at": self.data.finished_at,
+                "files": [],
+                "parallel_id": self.data.parallel_id,
+                "parallel_start_node_id": self.data.parallel_start_node_id,
+                "parent_parallel_id": self.data.parent_parallel_id,
+                "parent_parallel_start_node_id": self.data.parent_parallel_start_node_id,
+                "iteration_id": self.data.iteration_id,
+                "retry_index": self.data.retry_index,
+            },
+        }
+
+
 class ParallelBranchStartStreamResponse(StreamResponse):
     """
     ParallelBranchStartStreamResponse entity

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -201,11 +201,11 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                     stream_response=stream_response,
                 )
 
-    def _listen_audio_msg(self, publisher, task_id: str):
+    def _listen_audio_msg(self, publisher: AppGeneratorTTSPublisher | None, task_id: str):
         if publisher is None:
             return None
-        audio_msg: AudioTrunk = publisher.check_and_get_audio()
-        if audio_msg and audio_msg.status != "finish":
+        audio_msg = publisher.check_and_get_audio()
+        if audio_msg and isinstance(audio_msg, AudioTrunk) and audio_msg.status != "finish":
             # audio_str = audio_msg.audio.decode('utf-8', errors='ignore')
             return MessageAudioStreamResponse(audio=audio_msg.audio, task_id=task_id)
         return None

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -15,6 +15,7 @@ from core.app.entities.queue_entities import (
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
+    QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
     QueueParallelBranchRunFailedEvent,
@@ -26,6 +27,7 @@ from core.app.entities.task_entities import (
     IterationNodeNextStreamResponse,
     IterationNodeStartStreamResponse,
     NodeFinishStreamResponse,
+    NodeRetryStreamResponse,
     NodeStartStreamResponse,
     ParallelBranchFinishedStreamResponse,
     ParallelBranchStartStreamResponse,
@@ -271,9 +273,9 @@ class WorkflowCycleManage:
 
         db.session.close()
 
-        with Session(db.engine, expire_on_commit=False) as session:
-            session.add(workflow_run)
-            session.refresh(workflow_run)
+        # with Session(db.engine, expire_on_commit=False) as session:
+        #     session.add(workflow_run)
+        #     session.refresh(workflow_run)
 
         if trace_manager:
             trace_manager.add_trace_task(
@@ -423,6 +425,59 @@ class WorkflowCycleManage:
 
         return workflow_node_execution
 
+    def _handle_workflow_node_execution_retried(
+        self, workflow_run: WorkflowRun, event: QueueNodeRetryEvent
+    ) -> WorkflowNodeExecution:
+        """
+        Workflow node execution failed
+        :param event: queue node failed event
+        :return:
+        """
+        created_at = event.start_at
+        finished_at = datetime.now(UTC).replace(tzinfo=None)
+        elapsed_time = (finished_at - created_at).total_seconds()
+        inputs = WorkflowEntry.handle_special_values(event.inputs)
+        outputs = WorkflowEntry.handle_special_values(event.outputs)
+        origin_metadata = {
+            NodeRunMetadataKey.ITERATION_ID: event.in_iteration_id,
+            NodeRunMetadataKey.PARALLEL_MODE_RUN_ID: event.parallel_mode_run_id,
+        }
+        merged_metadata = (
+            {**jsonable_encoder(event.execution_metadata), **origin_metadata}
+            if event.execution_metadata is not None
+            else origin_metadata
+        )
+        execution_metadata = json.dumps(merged_metadata)
+
+        workflow_node_execution = WorkflowNodeExecution()
+        workflow_node_execution.tenant_id = workflow_run.tenant_id
+        workflow_node_execution.app_id = workflow_run.app_id
+        workflow_node_execution.workflow_id = workflow_run.workflow_id
+        workflow_node_execution.triggered_from = WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value
+        workflow_node_execution.workflow_run_id = workflow_run.id
+        workflow_node_execution.predecessor_node_id = event.predecessor_node_id
+        workflow_node_execution.node_execution_id = event.node_execution_id
+        workflow_node_execution.node_id = event.node_id
+        workflow_node_execution.node_type = event.node_type.value
+        workflow_node_execution.title = event.node_data.title
+        workflow_node_execution.status = WorkflowNodeExecutionStatus.RETRY.value
+        workflow_node_execution.created_by_role = workflow_run.created_by_role
+        workflow_node_execution.created_by = workflow_run.created_by
+        workflow_node_execution.created_at = created_at
+        workflow_node_execution.finished_at = finished_at
+        workflow_node_execution.elapsed_time = elapsed_time
+        workflow_node_execution.error = event.error
+        workflow_node_execution.inputs = json.dumps(inputs) if inputs else None
+        workflow_node_execution.outputs = json.dumps(outputs) if outputs else None
+        workflow_node_execution.execution_metadata = execution_metadata
+        workflow_node_execution.index = event.node_run_index
+
+        db.session.add(workflow_node_execution)
+        db.session.commit()
+        db.session.refresh(workflow_node_execution)
+
+        return workflow_node_execution
+
     #################################################
     #             to stream responses               #
     #################################################
@@ -457,6 +512,12 @@ class WorkflowCycleManage:
         :param workflow_run: workflow run
         :return:
         """
+        # Attach WorkflowRun to an active session so "created_by_role" can be accessed.
+        workflow_run = db.session.merge(workflow_run)
+
+        # Refresh to ensure any expired attributes are fully loaded
+        db.session.refresh(workflow_run)
+
         created_by = None
         if workflow_run.created_by_role == CreatedByRole.ACCOUNT.value:
             created_by_account = workflow_run.created_by_account
@@ -587,6 +648,51 @@ class WorkflowCycleManage:
             ),
         )
 
+    def _workflow_node_retry_to_stream_response(
+        self,
+        event: QueueNodeRetryEvent,
+        task_id: str,
+        workflow_node_execution: WorkflowNodeExecution,
+    ) -> Optional[NodeFinishStreamResponse]:
+        """
+        Workflow node finish to stream response.
+        :param event: queue node succeeded or failed event
+        :param task_id: task id
+        :param workflow_node_execution: workflow node execution
+        :return:
+        """
+        if workflow_node_execution.node_type in {NodeType.ITERATION.value, NodeType.LOOP.value}:
+            return None
+
+        return NodeRetryStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_node_execution.workflow_run_id,
+            data=NodeRetryStreamResponse.Data(
+                id=workflow_node_execution.id,
+                node_id=workflow_node_execution.node_id,
+                node_type=workflow_node_execution.node_type,
+                index=workflow_node_execution.index,
+                title=workflow_node_execution.title,
+                predecessor_node_id=workflow_node_execution.predecessor_node_id,
+                inputs=workflow_node_execution.inputs_dict,
+                process_data=workflow_node_execution.process_data_dict,
+                outputs=workflow_node_execution.outputs_dict,
+                status=workflow_node_execution.status,
+                error=workflow_node_execution.error,
+                elapsed_time=workflow_node_execution.elapsed_time,
+                execution_metadata=workflow_node_execution.execution_metadata_dict,
+                created_at=int(workflow_node_execution.created_at.timestamp()),
+                finished_at=int(workflow_node_execution.finished_at.timestamp()),
+                files=self._fetch_files_from_node_outputs(workflow_node_execution.outputs_dict or {}),
+                parallel_id=event.parallel_id,
+                parallel_start_node_id=event.parallel_start_node_id,
+                parent_parallel_id=event.parent_parallel_id,
+                parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                iteration_id=event.in_iteration_id,
+                retry_index=event.retry_index,
+            ),
+        )
+
     def _workflow_parallel_branch_start_to_stream_response(
         self, task_id: str, workflow_run: WorkflowRun, event: QueueParallelBranchRunStartedEvent
     ) -> ParallelBranchStartStreamResponse:

api/core/errors/error.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 
-class LLMError(Exception):
+class LLMError(ValueError):
     """Base class for all LLM exceptions."""
 
     description: Optional[str] = None
@@ -16,7 +16,7 @@ class LLMBadRequestError(LLMError):
     description = "Bad Request"
 
 
-class ProviderTokenNotInitError(Exception):
+class ProviderTokenNotInitError(ValueError):
     """
     Custom exception raised when the provider token is not initialized.
     """
@@ -27,7 +27,7 @@ class ProviderTokenNotInitError(Exception):
         self.description = args[0] if args else self.description
 
 
-class QuotaExceededError(Exception):
+class QuotaExceededError(ValueError):
     """
     Custom exception raised when the quota for a provider has been exceeded.
     """
@@ -35,7 +35,7 @@ class QuotaExceededError(Exception):
     description = "Quota Exceeded"
 
 
-class AppInvokeQuotaExceededError(Exception):
+class AppInvokeQuotaExceededError(ValueError):
     """
     Custom exception raised when the quota for an app has been exceeded.
     """
@@ -43,7 +43,7 @@ class AppInvokeQuotaExceededError(Exception):
     description = "App Invoke Quota Exceeded"
 
 
-class ModelCurrentlyNotSupportError(Exception):
+class ModelCurrentlyNotSupportError(ValueError):
     """
     Custom exception raised when the model not support
     """
@@ -51,7 +51,7 @@ class ModelCurrentlyNotSupportError(Exception):
     description = "Model Currently Not Support"
 
 
-class InvokeRateLimitError(Exception):
+class InvokeRateLimitError(ValueError):
     """Raised when the Invoke returns rate limit error."""
 
     description = "Rate Limit Error"

api/core/file/file_manager.py

@@ -1,15 +1,14 @@
 import base64
 
 from configs import dify_config
-from core.file import file_repository
 from core.helper import ssrf_proxy
 from core.model_runtime.entities import (
     AudioPromptMessageContent,
     DocumentPromptMessageContent,
     ImagePromptMessageContent,
+    MultiModalPromptMessageContent,
     VideoPromptMessageContent,
 )
-from extensions.ext_database import db
 from extensions.ext_storage import storage
 
 from . import helpers
@@ -41,53 +40,42 @@ def to_prompt_message_content(
     /,
     *,
     image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
-):
-    match f.type:
-        case FileType.IMAGE:
-            image_detail_config = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
-            if dify_config.MULTIMODAL_SEND_IMAGE_FORMAT == "url":
-                data = _to_url(f)
-            else:
-                data = _to_base64_data_string(f)
+) -> MultiModalPromptMessageContent:
+    if f.extension is None:
+        raise ValueError("Missing file extension")
+    if f.mime_type is None:
+        raise ValueError("Missing file mime_type")
 
-            return ImagePromptMessageContent(data=data, detail=image_detail_config)
-        case FileType.AUDIO:
-            encoded_string = _get_encoded_string(f)
-            if f.extension is None:
-                raise ValueError("Missing file extension")
-            return AudioPromptMessageContent(data=encoded_string, format=f.extension.lstrip("."))
-        case FileType.VIDEO:
-            if dify_config.MULTIMODAL_SEND_VIDEO_FORMAT == "url":
-                data = _to_url(f)
-            else:
-                data = _to_base64_data_string(f)
-            if f.extension is None:
-                raise ValueError("Missing file extension")
-            return VideoPromptMessageContent(data=data, format=f.extension.lstrip("."))
-        case FileType.DOCUMENT:
-            data = _get_encoded_string(f)
-            if f.mime_type is None:
-                raise ValueError("Missing file mime_type")
-            return DocumentPromptMessageContent(
-                encode_format="base64",
-                mime_type=f.mime_type,
-                data=data,
-            )
-        case _:
-            raise ValueError(f"file type {f.type} is not supported")
+    params = {
+        "base64_data": _get_encoded_string(f) if dify_config.MULTIMODAL_SEND_FORMAT == "base64" else "",
+        "url": _to_url(f) if dify_config.MULTIMODAL_SEND_FORMAT == "url" else "",
+        "format": f.extension.removeprefix("."),
+        "mime_type": f.mime_type,
+    }
+    if f.type == FileType.IMAGE:
+        params["detail"] = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
+
+    prompt_class_map = {
+        FileType.IMAGE: ImagePromptMessageContent,
+        FileType.AUDIO: AudioPromptMessageContent,
+        FileType.VIDEO: VideoPromptMessageContent,
+        FileType.DOCUMENT: DocumentPromptMessageContent,
+    }
+
+    try:
+        return prompt_class_map[f.type](**params)
+    except KeyError:
+        raise ValueError(f"file type {f.type} is not supported")
 
 
 def download(f: File, /):
-    if f.transfer_method == FileTransferMethod.TOOL_FILE:
-        tool_file = file_repository.get_tool_file(session=db.session(), file=f)
-        return _download_file_content(tool_file.file_key)
-    elif f.transfer_method == FileTransferMethod.LOCAL_FILE:
-        upload_file = file_repository.get_upload_file(session=db.session(), file=f)
-        return _download_file_content(upload_file.key)
-    # remote file
-    response = ssrf_proxy.get(f.remote_url, follow_redirects=True)
-    response.raise_for_status()
-    return response.content
+    if f.transfer_method in (FileTransferMethod.TOOL_FILE, FileTransferMethod.LOCAL_FILE):
+        return _download_file_content(f._storage_key)
+    elif f.transfer_method == FileTransferMethod.REMOTE_URL:
+        response = ssrf_proxy.get(f.remote_url, follow_redirects=True)
+        response.raise_for_status()
+        return response.content
+    raise ValueError(f"unsupported transfer method: {f.transfer_method}")
 
 
 def _download_file_content(path: str, /):
@@ -118,21 +106,14 @@ def _get_encoded_string(f: File, /):
             response.raise_for_status()
             data = response.content
         case FileTransferMethod.LOCAL_FILE:
-            upload_file = file_repository.get_upload_file(session=db.session(), file=f)
-            data = _download_file_content(upload_file.key)
+            data = _download_file_content(f._storage_key)
         case FileTransferMethod.TOOL_FILE:
-            tool_file = file_repository.get_tool_file(session=db.session(), file=f)
-            data = _download_file_content(tool_file.file_key)
+            data = _download_file_content(f._storage_key)
 
     encoded_string = base64.b64encode(data).decode("utf-8")
     return encoded_string
 
 
-def _to_base64_data_string(f: File, /):
-    encoded_string = _get_encoded_string(f)
-    return f"data:{f.mime_type};base64,{encoded_string}"
-
-
 def _to_url(f: File, /):
     if f.transfer_method == FileTransferMethod.REMOTE_URL:
         if f.remote_url is None:

api/core/file/file_repository.py

@@ -1,32 +0,0 @@
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-
-from models import ToolFile, UploadFile
-
-from .models import File
-
-
-def get_upload_file(*, session: Session, file: File):
-    if file.related_id is None:
-        raise ValueError("Missing file related_id")
-    stmt = select(UploadFile).filter(
-        UploadFile.id == file.related_id,
-        UploadFile.tenant_id == file.tenant_id,
-    )
-    record = session.scalar(stmt)
-    if not record:
-        raise ValueError(f"upload file {file.related_id} not found")
-    return record
-
-
-def get_tool_file(*, session: Session, file: File):
-    if file.related_id is None:
-        raise ValueError("Missing file related_id")
-    stmt = select(ToolFile).filter(
-        ToolFile.id == file.related_id,
-        ToolFile.tenant_id == file.tenant_id,
-    )
-    record = session.scalar(stmt)
-    if not record:
-        raise ValueError(f"tool file {file.related_id} not found")
-    return record

api/core/file/models.py

@@ -47,6 +47,38 @@ class File(BaseModel):
     mime_type: Optional[str] = None
     size: int = -1
 
+    # Those properties are private, should not be exposed to the outside.
+    _storage_key: str
+
+    def __init__(
+        self,
+        *,
+        id: Optional[str] = None,
+        tenant_id: str,
+        type: FileType,
+        transfer_method: FileTransferMethod,
+        remote_url: Optional[str] = None,
+        related_id: Optional[str] = None,
+        filename: Optional[str] = None,
+        extension: Optional[str] = None,
+        mime_type: Optional[str] = None,
+        size: int = -1,
+        storage_key: str,
+    ):
+        super().__init__(
+            id=id,
+            tenant_id=tenant_id,
+            type=type,
+            transfer_method=transfer_method,
+            remote_url=remote_url,
+            related_id=related_id,
+            filename=filename,
+            extension=extension,
+            mime_type=mime_type,
+            size=size,
+        )
+        self._storage_key = storage_key
+
     def to_dict(self) -> Mapping[str, str | int | None]:
         data = self.model_dump(mode="json")
         return {

api/core/helper/code_executor/code_executor.py

@@ -118,7 +118,7 @@ class CodeExecutor:
         return response.data.stdout or ""
 
     @classmethod
-    def execute_workflow_code_template(cls, language: CodeLanguage, code: str, inputs: Mapping[str, Any]) -> dict:
+    def execute_workflow_code_template(cls, language: CodeLanguage, code: str, inputs: Mapping[str, Any]):
         """
         Execute code
         :param language: code language

api/core/helper/code_executor/template_transformer.py

@@ -25,7 +25,7 @@ class TemplateTransformer(ABC):
         return runner_script, preload_script
 
     @classmethod
-    def extract_result_str_from_response(cls, response: str) -> str:
+    def extract_result_str_from_response(cls, response: str):
         result = re.search(rf"{cls._result_tag}(.*){cls._result_tag}", response, re.DOTALL)
         if not result:
             raise ValueError("Failed to parse result")
@@ -33,13 +33,21 @@ class TemplateTransformer(ABC):
         return result
 
     @classmethod
-    def transform_response(cls, response: str) -> dict:
+    def transform_response(cls, response: str) -> Mapping[str, Any]:
         """
         Transform response to dict
         :param response: response
         :return:
         """
-        return json.loads(cls.extract_result_str_from_response(response))
+        try:
+            result = json.loads(cls.extract_result_str_from_response(response))
+        except json.JSONDecodeError:
+            raise ValueError("failed to parse response")
+        if not isinstance(result, dict):
+            raise ValueError("result must be a dict")
+        if not all(isinstance(k, str) for k in result):
+            raise ValueError("result keys must be strings")
+        return result
 
     @classmethod
     @abstractmethod

api/core/helper/encrypter.py

@@ -1,6 +1,5 @@
 import base64
 
-from extensions.ext_database import db
 from libs import rsa
 
 
@@ -14,6 +13,7 @@ def obfuscated_token(token: str):
 
 def encrypt_token(tenant_id: str, token: str):
     from models.account import Tenant
+    from models.engine import db
 
     if not (tenant := db.session.query(Tenant).filter(Tenant.id == tenant_id).first()):
         raise ValueError(f"Tenant with id {tenant_id} not found")

api/core/helper/ssrf_proxy.py

@@ -24,7 +24,7 @@ BACKOFF_FACTOR = 0.5
 STATUS_FORCELIST = [429, 500, 502, 503, 504]
 
 
-class MaxRetriesExceededError(Exception):
+class MaxRetriesExceededError(ValueError):
     """Raised when the maximum number of retries is exceeded."""
 
     pass
@@ -65,14 +65,16 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
                     f"Received status code {response.status_code} for URL {url} which is in the force list")
 
         except httpx.RequestError as e:
-            logging.warning(
-                f"Request to URL {url} failed on attempt {retries + 1}: {e}")
+            logging.warning(f"Request to URL {url} failed on attempt {
+                            retries + 1}: {e}")
+            if max_retries == 0:
+                raise
 
         retries += 1
         if retries <= max_retries:
             time.sleep(BACKOFF_FACTOR * (2 ** (retries - 1)))
-
-    raise MaxRetriesExceededError(f"Reached maximum retries ({max_retries}) for URL {url}")
+    raise MaxRetriesExceededError(
+        f"Reached maximum retries ({max_retries}) for URL {url}")
 
 
 def get(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):

api/core/llm_generator/output_parser/errors.py

@@ -1,2 +1,2 @@
-class OutputParserError(Exception):
+class OutputParserError(ValueError):
     pass

api/core/model_runtime/entities/__init__.py

@@ -4,6 +4,7 @@ from .message_entities import (
     AudioPromptMessageContent,
     DocumentPromptMessageContent,
     ImagePromptMessageContent,
+    MultiModalPromptMessageContent,
     PromptMessage,
     PromptMessageContent,
     PromptMessageContentType,
@@ -27,6 +28,7 @@ __all__ = [
     "LLMResultChunkDelta",
     "LLMUsage",
     "ModelPropertyKey",
+    "MultiModalPromptMessageContent",
     "PromptMessage",
     "PromptMessage",
     "PromptMessageContent",

api/core/model_runtime/entities/message_entities.py

@@ -1,9 +1,9 @@
 from abc import ABC
 from collections.abc import Sequence
 from enum import Enum, StrEnum
-from typing import Literal, Optional
+from typing import Optional
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field, computed_field, field_validator
 
 
 class PromptMessageRole(Enum):
@@ -67,7 +67,6 @@ class PromptMessageContent(BaseModel):
     """
 
     type: PromptMessageContentType
-    data: str
 
 
 class TextPromptMessageContent(PromptMessageContent):
@@ -76,21 +75,35 @@ class TextPromptMessageContent(PromptMessageContent):
     """
 
     type: PromptMessageContentType = PromptMessageContentType.TEXT
+    data: str
 
 
-class VideoPromptMessageContent(PromptMessageContent):
+class MultiModalPromptMessageContent(PromptMessageContent):
+    """
+    Model class for multi-modal prompt message content.
+    """
+
+    type: PromptMessageContentType
+    format: str = Field(default=..., description="the format of multi-modal file")
+    base64_data: str = Field(default="", description="the base64 data of multi-modal file")
+    url: str = Field(default="", description="the url of multi-modal file")
+    mime_type: str = Field(default=..., description="the mime type of multi-modal file")
+
+    @computed_field(return_type=str)
+    @property
+    def data(self):
+        return self.url or f"data:{self.mime_type};base64,{self.base64_data}"
+
+
+class VideoPromptMessageContent(MultiModalPromptMessageContent):
     type: PromptMessageContentType = PromptMessageContentType.VIDEO
-    data: str = Field(..., description="Base64 encoded video data")
-    format: str = Field(..., description="Video format")
 
 
-class AudioPromptMessageContent(PromptMessageContent):
+class AudioPromptMessageContent(MultiModalPromptMessageContent):
     type: PromptMessageContentType = PromptMessageContentType.AUDIO
-    data: str = Field(..., description="Base64 encoded audio data")
-    format: str = Field(..., description="Audio format")
 
 
-class ImagePromptMessageContent(PromptMessageContent):
+class ImagePromptMessageContent(MultiModalPromptMessageContent):
     """
     Model class for image prompt message content.
     """
@@ -103,11 +116,8 @@ class ImagePromptMessageContent(PromptMessageContent):
     detail: DETAIL = DETAIL.LOW
 
 
-class DocumentPromptMessageContent(PromptMessageContent):
+class DocumentPromptMessageContent(MultiModalPromptMessageContent):
     type: PromptMessageContentType = PromptMessageContentType.DOCUMENT
-    encode_format: Literal["base64"]
-    mime_type: str
-    data: str
 
 
 class PromptMessage(ABC, BaseModel):

api/core/model_runtime/errors/invoke.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 
-class InvokeError(Exception):
+class InvokeError(ValueError):
     """Base class for all LLM exceptions."""
 
     description: Optional[str] = None

api/core/model_runtime/errors/validate.py

@@ -1,4 +1,4 @@
-class CredentialsValidateFailedError(Exception):
+class CredentialsValidateFailedError(ValueError):
     """
     Credentials validate failed error
     """

api/core/model_runtime/model_providers/anthropic/llm/llm.py

@@ -1,5 +1,4 @@
 import base64
-import io
 import json
 from collections.abc import Generator, Sequence
 from typing import Optional, Union, cast
@@ -18,7 +17,6 @@ from anthropic.types import (
 )
 from anthropic.types.beta.tools import ToolsBetaMessage
 from httpx import Timeout
-from PIL import Image
 
 from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities import (
@@ -498,22 +496,19 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                                 sub_messages.append(sub_message_dict)
                             elif message_content.type == PromptMessageContentType.IMAGE:
                                 message_content = cast(ImagePromptMessageContent, message_content)
-                                if not message_content.data.startswith("data:"):
+                                if not message_content.base64_data:
                                     # fetch image data from url
                                     try:
-                                        image_content = requests.get(message_content.data).content
-                                        with Image.open(io.BytesIO(image_content)) as img:
-                                            mime_type = f"image/{img.format.lower()}"
+                                        image_content = requests.get(message_content.url).content
                                         base64_data = base64.b64encode(image_content).decode("utf-8")
                                     except Exception as ex:
                                         raise ValueError(
                                             f"Failed to fetch image data from url {message_content.data}, {ex}"
                                         )
                                 else:
-                                    data_split = message_content.data.split(";base64,")
-                                    mime_type = data_split[0].replace("data:", "")
-                                    base64_data = data_split[1]
+                                    base64_data = message_content.base64_data
 
+                                mime_type = message_content.mime_type
                                 if mime_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}:
                                     raise ValueError(
                                         f"Unsupported image type {mime_type}, "
@@ -534,9 +529,9 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                                 sub_message_dict = {
                                     "type": "document",
                                     "source": {
-                                        "type": message_content.encode_format,
+                                        "type": "base64",
                                         "media_type": message_content.mime_type,
-                                        "data": message_content.data,
+                                        "data": message_content.base64_data,
                                     },
                                 }
                                 sub_messages.append(sub_message_dict)

api/core/model_runtime/model_providers/azure_openai/_constant.py

@@ -819,6 +819,82 @@ LLM_BASE_MODELS = [
             ),
         ),
     ),
+    AzureBaseModel(
+        base_model_name="gpt-4o-2024-11-20",
+        entity=AIModelEntity(
+            model="fake-deployment-name",
+            label=I18nObject(
+                en_US="fake-deployment-name-label",
+            ),
+            model_type=ModelType.LLM,
+            features=[
+                ModelFeature.AGENT_THOUGHT,
+                ModelFeature.VISION,
+                ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
+            ],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 128000,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name="temperature",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
+                ),
+                ParameterRule(
+                    name="top_p",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
+                ),
+                ParameterRule(
+                    name="presence_penalty",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
+                ),
+                ParameterRule(
+                    name="frequency_penalty",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
+                ),
+                _get_max_tokens(default=512, min_val=1, max_val=16384),
+                ParameterRule(
+                    name="seed",
+                    label=I18nObject(zh_Hans="种子", en_US="Seed"),
+                    type="int",
+                    help=AZURE_DEFAULT_PARAM_SEED_HELP,
+                    required=False,
+                    precision=2,
+                    min=0,
+                    max=1,
+                ),
+                ParameterRule(
+                    name="response_format",
+                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
+                    type="string",
+                    help=I18nObject(
+                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
+                    ),
+                    required=False,
+                    options=["text", "json_object", "json_schema"],
+                ),
+                ParameterRule(
+                    name="json_schema",
+                    label=I18nObject(en_US="JSON Schema"),
+                    type="text",
+                    help=I18nObject(
+                        zh_Hans="设置返回的json schema，llm将按照它返回",
+                        en_US="Set a response json schema will ensure LLM to adhere it.",
+                    ),
+                    required=False,
+                ),
+            ],
+            pricing=PriceConfig(
+                input=5.00,
+                output=15.00,
+                unit=0.000001,
+                currency="USD",
+            ),
+        ),
+    ),
     AzureBaseModel(
         base_model_name="gpt-4-turbo",
         entity=AIModelEntity(

api/core/model_runtime/model_providers/azure_openai/azure_openai.yaml

@@ -86,6 +86,9 @@ model_credential_schema:
         - label:
             en_US: '2024-06-01'
           value: '2024-06-01'
+        - label:
+            en_US: '2024-10-21'
+          value: '2024-10-21'
       placeholder:
         zh_Hans: 在此选择您的 API 版本
         en_US: Select your API Version here
@@ -168,6 +171,12 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: llm
+        - label:
+            en_US: gpt-4o-2024-11-20
+          value: gpt-4o-2024-11-20
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: gpt-4-turbo
           value: gpt-4-turbo

api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py

@@ -92,7 +92,10 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
                 average = embeddings_batch[0]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
-            embeddings[i] = (average / np.linalg.norm(average)).tolist()
+            embedding = (average / np.linalg.norm(average)).tolist()
+            if np.isnan(embedding).any():
+                raise ValueError("Normalized embedding is nan please try again")
+            embeddings[i] = embedding
 
         # calc usage
         usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)

api/core/model_runtime/model_providers/bedrock/get_bedrock_client.py

@@ -1,11 +1,19 @@
+from collections.abc import Mapping
+
 import boto3
 from botocore.config import Config
 
+from core.model_runtime.errors.invoke import InvokeBadRequestError
+
+
+def get_bedrock_client(service_name: str, credentials: Mapping[str, str]):
+    region_name = credentials.get("aws_region")
+    if not region_name:
+        raise InvokeBadRequestError("aws_region is required")
+    client_config = Config(region_name=region_name)
+    aws_access_key_id = credentials.get("aws_access_key_id")
+    aws_secret_access_key = credentials.get("aws_secret_access_key")
 
-def get_bedrock_client(service_name, credentials=None):
-    client_config = Config(region_name=credentials["aws_region"])
-    aws_access_key_id = credentials["aws_access_key_id"]
-    aws_secret_access_key = credentials["aws_secret_access_key"]
     if aws_access_key_id and aws_secret_access_key:
         # use aksk to call bedrock
         client = boto3.client(

api/core/model_runtime/model_providers/bedrock/rerank/rerank.py

@@ -62,7 +62,10 @@ class BedrockRerankModel(RerankModel):
                 }
             )
         modelId = model
-        region = credentials["aws_region"]
+        region = credentials.get("aws_region")
+        # region is a required field
+        if not region:
+            raise InvokeBadRequestError("aws_region is required in credentials")
         model_package_arn = f"arn:aws:bedrock:{region}::foundation-model/{modelId}"
         rerankingConfiguration = {
             "type": "BEDROCK_RERANKING_MODEL",

api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py

@@ -88,7 +88,10 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
                 average = embeddings_batch[0]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
-            embeddings[i] = (average / np.linalg.norm(average)).tolist()
+            embedding = (average / np.linalg.norm(average)).tolist()
+            if np.isnan(embedding).any():
+                raise ValueError("Normalized embedding is nan please try again")
+            embeddings[i] = embedding
 
         # calc usage
         usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)

api/core/model_runtime/model_providers/gitee_ai/llm/InternVL2-8B.yaml

@@ -0,0 +1,93 @@
+model: InternVL2-8B
+label:
+  en_US: InternVL2-8B
+model_type: llm
+features:
+  - vision
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_k
+    use_template: top_k
+    label:
+      en_US: "Top K"
+      zh_Hans: "Top K"
+    type: int
+    default: 50
+    min: 0
+    max: 100
+    required: true
+    help:
+      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
+      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
+      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/InternVL2.5-26B.yaml

@@ -0,0 +1,93 @@
+model: InternVL2.5-26B
+label:
+  en_US: InternVL2.5-26B
+model_type: llm
+features:
+  - vision
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    label:
+      en_US: "Max Tokens"
+      zh_Hans: "最大Token数"
+    type: int
+    default: 512
+    min: 1
+    required: true
+    help:
+      en_US: "The maximum number of tokens that can be generated by the model varies depending on the model."
+      zh_Hans: "模型可生成的最大 token 个数，不同模型上限不同。"
+
+  - name: temperature
+    use_template: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_p
+    use_template: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_k
+    use_template: top_k
+    label:
+      en_US: "Top K"
+      zh_Hans: "Top K"
+    type: int
+    default: 50
+    min: 0
+    max: 100
+    required: true
+    help:
+      en_US: "The value range is [0,100], which limits the model to only select from the top k words with the highest probability when choosing the next word at each step. The larger the value, the more diverse text generation will be."
+      zh_Hans: "取值范围为 [0,100]，限制模型在每一步选择下一个词时，只从概率最高的前 k 个词中选取。数值越大，文本生成越多样。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: -1.0
+    max: 1.0
+    precision: 1
+    required: false
+    help:
+      en_US: "Used to adjust the frequency of repeated content in automatically generated text. Positive numbers reduce repetition, while negative numbers increase repetition. After setting this parameter, if a word has already appeared in the text, the model will decrease the probability of choosing that word for subsequent generation."
+      zh_Hans: "用于调整自动生成文本中重复内容的频率。正数减少重复，负数增加重复。设置此参数后，如果一个词在文本中已经出现过，模型在后续生成中选择该词的概率会降低。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/gitee_ai/llm/_position.yaml

@@ -6,3 +6,5 @@
 - deepseek-coder-33B-instruct-chat
 - deepseek-coder-33B-instruct-completions
 - codegeex4-all-9b
+- InternVL2.5-26B
+- InternVL2-8B

api/core/model_runtime/model_providers/gitee_ai/llm/llm.py

@@ -29,18 +29,26 @@ class GiteeAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials, model, model_parameters)
-        return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+        return super()._invoke(
+            GiteeAILargeLanguageModel.MODEL_TO_IDENTITY.get(model, model),
+            credentials,
+            prompt_messages,
+            model_parameters,
+            tools,
+            stop,
+            stream,
+            user,
+        )
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials, None)
-        super().validate_credentials(model, credentials)
+        self._add_custom_parameters(credentials, model, None)
+        super().validate_credentials(GiteeAILargeLanguageModel.MODEL_TO_IDENTITY.get(model, model), credentials)
 
-    def _add_custom_parameters(self, credentials: dict, model: Optional[str]) -> None:
+    def _add_custom_parameters(self, credentials: dict, model: Optional[str], model_parameters: dict) -> None:
         if model is None:
             model = "Qwen2-72B-Instruct"
 
-        model_identity = GiteeAILargeLanguageModel.MODEL_TO_IDENTITY.get(model, model)
-        credentials["endpoint_url"] = f"https://ai.gitee.com/api/serverless/{model_identity}/"
+        credentials["endpoint_url"] = "https://ai.gitee.com/v1"
         if model.endswith("completions"):
             credentials["mode"] = LLMMode.COMPLETION.value
         else:

api/core/model_runtime/model_providers/google/llm/_position.yaml

@@ -1,3 +1,5 @@
+- gemini-2.0-flash-exp
+- gemini-2.0-flash-thinking-exp-1219
 - gemini-1.5-pro
 - gemini-1.5-pro-latest
 - gemini-1.5-pro-001
@@ -11,6 +13,8 @@
 - gemini-1.5-flash-exp-0827
 - gemini-1.5-flash-8b-exp-0827
 - gemini-1.5-flash-8b-exp-0924
+- gemini-exp-1206
+- gemini-exp-1121
 - gemini-exp-1114
 - gemini-pro
 - gemini-pro-vision

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-001.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-002.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0924.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-001.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-002.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-2.0-flash-exp.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-2.0-flash-thinking-exp-1219.yaml

@@ -0,0 +1,39 @@
+model: gemini-2.0-flash-thinking-exp-1219
+label:
+  en_US: Gemini 2.0 Flash Thinking Exp 1219
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 32767
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/google/llm/gemini-exp-1114.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 32767

api/core/model_runtime/model_providers/google/llm/gemini-exp-1121.yaml

@@ -7,6 +7,9 @@ features:
   - vision
   - tool-call
   - stream-tool-call
+  - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 32767

api/core/model_runtime/model_providers/google/llm/gemini-exp-1206.yaml

@@ -7,6 +7,9 @@ features:
   - vision
   - tool-call
   - stream-tool-call
+  - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/learnlm-1.5-pro-experimental.yaml

@@ -7,6 +7,9 @@ features:
   - vision
   - tool-call
   - stream-tool-call
+  - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 32767

api/core/model_runtime/model_providers/google/llm/llm.py

@@ -1,27 +1,27 @@
 import base64
-import io
 import json
+import os
+import tempfile
+import time
 from collections.abc import Generator
-from typing import Optional, Union, cast
+from typing import Optional, Union
 
 import google.ai.generativelanguage as glm
 import google.generativeai as genai
 import requests
 from google.api_core import exceptions
-from google.generativeai.client import _ClientManager
-from google.generativeai.types import ContentType, GenerateContentResponse
+from google.generativeai.types import ContentType, File, GenerateContentResponse
 from google.generativeai.types.content_types import to_part
-from PIL import Image
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
-    DocumentPromptMessageContent,
-    ImagePromptMessageContent,
     PromptMessage,
+    PromptMessageContent,
     PromptMessageContentType,
     PromptMessageTool,
     SystemPromptMessage,
+    TextPromptMessageContent,
     ToolPromptMessage,
     UserPromptMessage,
 )
@@ -35,21 +35,7 @@ from core.model_runtime.errors.invoke import (
 )
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-
-GOOGLE_AVAILABLE_MIMETYPE = [
-    "application/pdf",
-    "application/x-javascript",
-    "text/javascript",
-    "application/x-python",
-    "text/x-python",
-    "text/plain",
-    "text/html",
-    "text/css",
-    "text/md",
-    "text/csv",
-    "text/xml",
-    "text/rtf",
-]
+from extensions.ext_redis import redis_client
 
 
 class GoogleLargeLanguageModel(LargeLanguageModel):
@@ -158,7 +144,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         """
 
         try:
-            ping_message = SystemPromptMessage(content="ping")
+            ping_message = UserPromptMessage(content="ping")
             self._generate(model, credentials, [ping_message], {"max_output_tokens": 5})
 
         except Exception as ex:
@@ -201,30 +187,24 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         if stop:
             config_kwargs["stop_sequences"] = stop
 
-        google_model = genai.GenerativeModel(model_name=model)
+        genai.configure(api_key=credentials["google_api_key"])
 
         history = []
+        system_instruction = None
 
-        # hack for gemini-pro-vision, which currently does not support multi-turn chat
-        if model == "gemini-pro-vision":
-            last_msg = prompt_messages[-1]
-            content = self._format_message_to_glm_content(last_msg)
-            history.append(content)
-        else:
-            for msg in prompt_messages:  # makes message roles strictly alternating
-                content = self._format_message_to_glm_content(msg)
-                if history and history[-1]["role"] == content["role"]:
-                    history[-1]["parts"].extend(content["parts"])
-                else:
-                    history.append(content)
+        for msg in prompt_messages:  # makes message roles strictly alternating
+            content = self._format_message_to_glm_content(msg)
+            if history and history[-1]["role"] == content["role"]:
+                history[-1]["parts"].extend(content["parts"])
+            elif content["role"] == "system":
+                system_instruction = content["parts"][0]
+            else:
+                history.append(content)
 
-        # Create a new ClientManager with tenant's API key
-        new_client_manager = _ClientManager()
-        new_client_manager.configure(api_key=credentials["google_api_key"])
-        new_custom_client = new_client_manager.make_client("generative")
-
-        google_model._client = new_custom_client
+        if not history:
+            raise InvokeError("The user prompt message is required. You only add a system prompt message.")
 
+        google_model = genai.GenerativeModel(model_name=model, system_instruction=system_instruction)
         response = google_model.generate_content(
             contents=history,
             generation_config=genai.types.GenerationConfig(**config_kwargs),
@@ -317,8 +297,12 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                     )
                 else:
                     # calculate num tokens
-                    prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-                    completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
+                    if hasattr(response, "usage_metadata") and response.usage_metadata:
+                        prompt_tokens = response.usage_metadata.prompt_token_count
+                        completion_tokens = response.usage_metadata.candidates_token_count
+                    else:
+                        prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
+                        completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
 
                     # transform usage
                     usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
@@ -346,7 +330,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
 
         content = message.content
         if isinstance(content, list):
-            content = "".join(c.data for c in content if c.type != PromptMessageContentType.IMAGE)
+            content = "".join(c.data for c in content if c.type == PromptMessageContentType.TEXT)
 
         if isinstance(message, UserPromptMessage):
             message_text = f"{human_prompt} {content}"
@@ -359,6 +343,40 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
 
         return message_text
 
+    def _upload_file_content_to_google(self, message_content: PromptMessageContent) -> File:
+        key = f"{message_content.type.value}:{hash(message_content.data)}"
+        if redis_client.exists(key):
+            try:
+                return genai.get_file(redis_client.get(key).decode())
+            except:
+                pass
+        with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+            if message_content.base64_data:
+                file_content = base64.b64decode(message_content.base64_data)
+                temp_file.write(file_content)
+            else:
+                try:
+                    response = requests.get(message_content.url)
+                    response.raise_for_status()
+                    temp_file.write(response.content)
+                except Exception as ex:
+                    raise ValueError(f"Failed to fetch data from url {message_content.url}, {ex}")
+            temp_file.flush()
+
+        file = genai.upload_file(path=temp_file.name, mime_type=message_content.mime_type)
+        while file.state.name == "PROCESSING":
+            time.sleep(5)
+            file = genai.get_file(file.name)
+        # google will delete your upload files in 2 days.
+        redis_client.setex(key, 47 * 60 * 60, file.name)
+
+        try:
+            os.unlink(temp_file.name)
+        except PermissionError:
+            # windows may raise permission error
+            pass
+        return file
+
     def _format_message_to_glm_content(self, message: PromptMessage) -> ContentType:
         """
         Format a single message into glm.Content for Google API
@@ -374,28 +392,8 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                 for c in message.content:
                     if c.type == PromptMessageContentType.TEXT:
                         glm_content["parts"].append(to_part(c.data))
-                    elif c.type == PromptMessageContentType.IMAGE:
-                        message_content = cast(ImagePromptMessageContent, c)
-                        if message_content.data.startswith("data:"):
-                            metadata, base64_data = c.data.split(",", 1)
-                            mime_type = metadata.split(";", 1)[0].split(":")[1]
-                        else:
-                            # fetch image data from url
-                            try:
-                                image_content = requests.get(message_content.data).content
-                                with Image.open(io.BytesIO(image_content)) as img:
-                                    mime_type = f"image/{img.format.lower()}"
-                                base64_data = base64.b64encode(image_content).decode("utf-8")
-                            except Exception as ex:
-                                raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")
-                        blob = {"inline_data": {"mime_type": mime_type, "data": base64_data}}
-                        glm_content["parts"].append(blob)
-                    elif c.type == PromptMessageContentType.DOCUMENT:
-                        message_content = cast(DocumentPromptMessageContent, c)
-                        if message_content.mime_type not in GOOGLE_AVAILABLE_MIMETYPE:
-                            raise ValueError(f"Unsupported mime type {message_content.mime_type}")
-                        blob = {"inline_data": {"mime_type": message_content.mime_type, "data": message_content.data}}
-                        glm_content["parts"].append(blob)
+                    else:
+                        glm_content["parts"].append(self._upload_file_content_to_google(c))
 
             return glm_content
         elif isinstance(message, AssistantPromptMessage):
@@ -413,7 +411,10 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                 )
             return glm_content
         elif isinstance(message, SystemPromptMessage):
-            return {"role": "user", "parts": [to_part(message.content)]}
+            if isinstance(message.content, list):
+                text_contents = filter(lambda c: isinstance(c, TextPromptMessageContent), message.content)
+                message.content = "".join(c.data for c in text_contents)
+            return {"role": "system", "parts": [to_part(message.content)]}
         elif isinstance(message, ToolPromptMessage):
             return {
                 "role": "function",

api/core/model_runtime/model_providers/hunyuan/hunyuan.yaml

@@ -3,8 +3,8 @@ label:
   zh_Hans: 腾讯混元
   en_US: Hunyuan
 description:
-  en_US: Models provided by Tencent Hunyuan, such as hunyuan-standard, hunyuan-standard-256k, hunyuan-pro and hunyuan-lite.
-  zh_Hans: 腾讯混元提供的模型，例如 hunyuan-standard、 hunyuan-standard-256k, hunyuan-pro 和 hunyuan-lite。
+  en_US: Models provided by Tencent Hunyuan, such as hunyuan-standard, hunyuan-standard-256k, hunyuan-pro, hunyuan-role, hunyuan-large, hunyuan-large-role, hunyuan-turbo-latest, hunyuan-large-longcontext, hunyuan-turbo, hunyuan-vision, hunyuan-turbo-vision, hunyuan-functioncall and hunyuan-lite.
+  zh_Hans: 腾讯混元提供的模型，例如 hunyuan-standard、 hunyuan-standard-256k, hunyuan-pro, hunyuan-role, hunyuan-large, hunyuan-large-role, hunyuan-turbo-latest, hunyuan-large-longcontext, hunyuan-turbo, hunyuan-vision, hunyuan-turbo-vision, hunyuan-functioncall 和 hunyuan-lite。
 icon_small:
   en_US: icon_s_en.png
 icon_large:

api/core/model_runtime/model_providers/hunyuan/llm/_position.yaml

@@ -4,3 +4,10 @@
 - hunyuan-pro
 - hunyuan-turbo
 - hunyuan-vision
+- hunyuan-role
+- hunyuan-large
+- hunyuan-large-role
+- hunyuan-large-longcontext
+- hunyuan-turbo-latest
+- hunyuan-turbo-vision
+- hunyuan-functioncall

api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-functioncall.yaml

@@ -0,0 +1,38 @@
+model: hunyuan-functioncall
+label:
+  zh_Hans: hunyuan-functioncall
+  en_US: hunyuan-functioncall
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 32000
+  - name: enable_enhance
+    label:
+      zh_Hans: 功能增强
+      en_US: Enable Enhancement
+    type: boolean
+    help:
+      zh_Hans: 功能增强（如搜索）开关，关闭时将直接由主模型生成回复内容，可以降低响应时延（对于流式输出时的首字时延尤为明显）。但在少数场景里，回复效果可能会下降。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false
+    default: true
+pricing:
+  input: '0.004'
+  output: '0.008'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-large-longcontext.yaml

@@ -0,0 +1,38 @@
+model: hunyuan-large-longcontext
+label:
+  zh_Hans: hunyuan-large-longcontext
+  en_US: hunyuan-large-longcontext
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 134000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 134000
+  - name: enable_enhance
+    label:
+      zh_Hans: 功能增强
+      en_US: Enable Enhancement
+    type: boolean
+    help:
+      zh_Hans: 功能增强（如搜索）开关，关闭时将直接由主模型生成回复内容，可以降低响应时延（对于流式输出时的首字时延尤为明显）。但在少数场景里，回复效果可能会下降。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false
+    default: true
+pricing:
+  input: '0.006'
+  output: '0.018'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-large-role.yaml

@@ -0,0 +1,38 @@
+model: hunyuan-large-role
+label:
+  zh_Hans: hunyuan-large-role
+  en_US: hunyuan-large-role
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 32000
+  - name: enable_enhance
+    label:
+      zh_Hans: 功能增强
+      en_US: Enable Enhancement
+    type: boolean
+    help:
+      zh_Hans: 功能增强（如搜索）开关，关闭时将直接由主模型生成回复内容，可以降低响应时延（对于流式输出时的首字时延尤为明显）。但在少数场景里，回复效果可能会下降。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false
+    default: true
+pricing:
+  input: '0.004'
+  output: '0.008'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-large.yaml

@@ -0,0 +1,38 @@
+model: hunyuan-large
+label:
+  zh_Hans: hunyuan-large
+  en_US: hunyuan-large
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 32000
+  - name: enable_enhance
+    label:
+      zh_Hans: 功能增强
+      en_US: Enable Enhancement
+    type: boolean
+    help:
+      zh_Hans: 功能增强（如搜索）开关，关闭时将直接由主模型生成回复内容，可以降低响应时延（对于流式输出时的首字时延尤为明显）。但在少数场景里，回复效果可能会下降。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false
+    default: true
+pricing:
+  input: '0.004'
+  output: '0.012'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-role.yaml

@@ -0,0 +1,38 @@
+model: hunyuan-role
+label:
+  zh_Hans: hunyuan-role
+  en_US: hunyuan-role
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 32000
+  - name: enable_enhance
+    label:
+      zh_Hans: 功能增强
+      en_US: Enable Enhancement
+    type: boolean
+    help:
+      zh_Hans: 功能增强（如搜索）开关，关闭时将直接由主模型生成回复内容，可以降低响应时延（对于流式输出时的首字时延尤为明显）。但在少数场景里，回复效果可能会下降。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false
+    default: true
+pricing:
+  input: '0.004'
+  output: '0.008'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-turbo-latest.yaml

@@ -0,0 +1,38 @@
+model: hunyuan-turbo-latest
+label:
+  zh_Hans: hunyuan-turbo-latest
+  en_US: hunyuan-turbo-latest
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 32000
+  - name: enable_enhance
+    label:
+      zh_Hans: 功能增强
+      en_US: Enable Enhancement
+    type: boolean
+    help:
+      zh_Hans: 功能增强（如搜索）开关，关闭时将直接由主模型生成回复内容，可以降低响应时延（对于流式输出时的首字时延尤为明显）。但在少数场景里，回复效果可能会下降。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false
+    default: true
+pricing:
+  input: '0.015'
+  output: '0.05'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-turbo-vision.yaml

@@ -0,0 +1,39 @@
+model: hunyuan-turbo-vision
+label:
+  zh_Hans: hunyuan-turbo-vision
+  en_US: hunyuan-turbo-vision
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 8000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 8000
+  - name: enable_enhance
+    label:
+      zh_Hans: 功能增强
+      en_US: Enable Enhancement
+    type: boolean
+    help:
+      zh_Hans: 功能增强（如搜索）开关，关闭时将直接由主模型生成回复内容，可以降低响应时延（对于流式输出时的首字时延尤为明显）。但在少数场景里，回复效果可能会下降。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false
+    default: true
+pricing:
+  input: '0.08'
+  output: '0.08'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/openai/llm/_position.yaml

@@ -1,4 +1,7 @@
-- gpt-4o-audio-preview
+- o1
+- o1-2024-12-17
+- o1-mini
+- o1-mini-2024-09-12
 - gpt-4
 - gpt-4o
 - gpt-4o-2024-05-13
@@ -7,10 +10,6 @@
 - chatgpt-4o-latest
 - gpt-4o-mini
 - gpt-4o-mini-2024-07-18
-- o1-preview
-- o1-preview-2024-09-12
-- o1-mini
-- o1-mini-2024-09-12
 - gpt-4-turbo
 - gpt-4-turbo-2024-04-09
 - gpt-4-turbo-preview
@@ -25,4 +24,7 @@
 - gpt-3.5-turbo-1106
 - gpt-3.5-turbo-0613
 - gpt-3.5-turbo-instruct
+- gpt-4o-audio-preview
+- o1-preview
+- o1-preview-2024-09-12
 - text-davinci-003

api/core/model_runtime/model_providers/openai/llm/chatgpt-4o-latest.yaml

@@ -22,7 +22,7 @@ parameter_rules:
     use_template: frequency_penalty
   - name: max_tokens
     use_template: max_tokens
-    default: 512
+    default: 16384
     min: 1
     max: 16384
   - name: response_format

api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-05-13.yaml

@@ -22,9 +22,9 @@ parameter_rules:
     use_template: frequency_penalty
   - name: max_tokens
     use_template: max_tokens
-    default: 512
+    default: 16384
     min: 1
-    max: 4096
+    max: 16384
   - name: response_format
     label:
       zh_Hans: 回复格式

api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-08-06.yaml

@@ -22,7 +22,7 @@ parameter_rules:
     use_template: frequency_penalty
   - name: max_tokens
     use_template: max_tokens
-    default: 512
+    default: 16384
     min: 1
     max: 16384
   - name: response_format

api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-11-20.yaml

@@ -22,7 +22,7 @@ parameter_rules:
     use_template: frequency_penalty
   - name: max_tokens
     use_template: max_tokens
-    default: 512
+    default: 16384
     min: 1
     max: 16384
   - name: response_format

api/core/model_runtime/model_providers/openai/llm/gpt-4o-audio-preview.yaml

@@ -22,9 +22,9 @@ parameter_rules:
     use_template: frequency_penalty
   - name: max_tokens
     use_template: max_tokens
-    default: 512
+    default: 16384
     min: 1
-    max: 4096
+    max: 16384
   - name: response_format
     label:
       zh_Hans: 回复格式

api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini-2024-07-18.yaml

@@ -22,7 +22,7 @@ parameter_rules:
     use_template: frequency_penalty
   - name: max_tokens
     use_template: max_tokens
-    default: 512
+    default: 16384
     min: 1
     max: 16384
   - name: response_format

api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini.yaml

@@ -22,7 +22,7 @@ parameter_rules:
     use_template: frequency_penalty
   - name: max_tokens
     use_template: max_tokens
-    default: 512
+    default: 16384
     min: 1
     max: 16384
   - name: response_format

api/core/model_runtime/model_providers/openai/llm/gpt-4o.yaml

@@ -22,9 +22,9 @@ parameter_rules:
     use_template: frequency_penalty
   - name: max_tokens
     use_template: max_tokens
-    default: 512
+    default: 16384
     min: 1
-    max: 4096
+    max: 16384
   - name: response_format
     label:
       zh_Hans: 回复格式
@@ -38,7 +38,7 @@ parameter_rules:
       - text
       - json_object
 pricing:
-  input: '5.00'
-  output: '15.00'
+  input: '2.50'
+  output: '10.00'
   unit: '0.000001'
   currency: USD