Merge branch 'feat/add-search-for-firecrawl' of github.com:ftonato/dify into feat/add-search-for-firecrawl

.devcontainer/post_create_command.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-npm add -g pnpm@10.8.0
+npm add -g pnpm@10.11.1
 cd web && pnpm install
 pipx install uv
 

.gitignore

@@ -192,12 +192,12 @@ sdks/python-client/dist
 sdks/python-client/dify_client.egg-info
 
 .vscode/*
-!.vscode/launch.json
+!.vscode/launch.json.template
+!.vscode/README.md
 pyrightconfig.json
 api/.vscode
 
 .idea/
-.vscode
 
 # pnpm
 /.pnpm-store
@@ -207,3 +207,6 @@ plugins.jsonl
 
 # mise
 mise.toml
+
+# Next.js build output
+.next/

.vscode/README.md

@@ -0,0 +1,14 @@
+# Debugging with VS Code
+
+This `launch.json.template` file provides various debug configurations for the Dify project within VS Code / Cursor. To use these configurations, you should copy the contents of this file into a new file named `launch.json` in the same `.vscode` directory.
+
+## How to Use
+
+1.  **Create `launch.json`**: If you don't have one, create a file named `launch.json` inside the `.vscode` directory.
+2.  **Copy Content**: Copy the entire content from `launch.json.template` into your newly created `launch.json` file.
+3.  **Select Debug Configuration**: Go to the Run and Debug view in VS Code / Cursor (Ctrl+Shift+D or Cmd+Shift+D).
+4.  **Start Debugging**: Select the desired configuration from the dropdown menu and click the green play button.
+
+## Tips
+
+- If you need to debug with Edge browser instead of Chrome, modify the `serverReadyAction` configuration in the "Next.js: debug full stack" section, change `"debugWithChrome"` to `"debugWithEdge"` to use Microsoft Edge for debugging.

.vscode/launch.json.template

@@ -0,0 +1,68 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Flask API",
+            "type": "debugpy",
+            "request": "launch",
+            "module": "flask",
+            "env": {
+                "FLASK_APP": "app.py",
+                "FLASK_ENV": "development",
+                "GEVENT_SUPPORT": "True"
+            },
+            "args": [
+                "run",
+                "--host=0.0.0.0",
+                "--port=5001",
+                "--no-debugger",
+                "--no-reload"
+            ],
+            "jinja": true,
+            "justMyCode": true,
+            "cwd": "${workspaceFolder}/api",
+            "python": "${workspaceFolder}/api/.venv/bin/python"
+        },
+        {
+            "name": "Python: Celery Worker (Solo)",
+            "type": "debugpy",
+            "request": "launch",
+            "module": "celery",
+            "env": {
+                "GEVENT_SUPPORT": "True"
+            },
+            "args": [
+                "-A",
+                "app.celery",
+                "worker",
+                "-P",
+                "solo",
+                "-c",
+                "1",
+                "-Q",
+                "dataset,generation,mail,ops_trace",
+                "--loglevel",
+                "INFO"
+            ],
+            "justMyCode": false,
+            "cwd": "${workspaceFolder}/api",
+            "python": "${workspaceFolder}/api/.venv/bin/python"
+        },
+        {
+            "name": "Next.js: debug full stack",
+            "type": "node",
+            "request": "launch",
+            "program": "${workspaceFolder}/web/node_modules/next/dist/bin/next",
+            "runtimeArgs": ["--inspect"],
+            "skipFiles": ["<node_internals>/**"],
+            "serverReadyAction": {
+              "action": "debugWithChrome",
+              "killOnServerStop": true,
+              "pattern": "- Local:.+(https?://.+)",
+              "uriFormat": "%s",
+              "webRoot": "${workspaceFolder}/web"
+            },
+            "cwd": "${workspaceFolder}/web"
+          }
+    ]
+}

api/.env.example

@@ -491,3 +491,10 @@ OTEL_METRIC_EXPORT_TIMEOUT=30000
 
 # Prevent Clickjacking
 ALLOW_EMBED=false
+
+# Dataset queue monitor configuration
+QUEUE_MONITOR_THRESHOLD=200
+# You can configure multiple ones, separated by commas. eg: test1@dify.ai,test2@dify.ai
+QUEUE_MONITOR_ALERT_EMAILS=
+# Monitor interval in minutes, default is 30 minutes
+QUEUE_MONITOR_INTERVAL=30

api/commands.py

@@ -846,6 +846,9 @@ def clear_orphaned_file_records(force: bool):
         {"type": "text", "table": "workflow_node_executions", "column": "outputs"},
         {"type": "text", "table": "conversations", "column": "introduction"},
         {"type": "text", "table": "conversations", "column": "system_instruction"},
+        {"type": "text", "table": "accounts", "column": "avatar"},
+        {"type": "text", "table": "apps", "column": "icon"},
+        {"type": "text", "table": "sites", "column": "icon"},
         {"type": "json", "table": "messages", "column": "inputs"},
         {"type": "json", "table": "messages", "column": "message"},
     ]

api/configs/middleware/__init__.py

@@ -2,7 +2,7 @@ import os
 from typing import Any, Literal, Optional
 from urllib.parse import parse_qsl, quote_plus
 
-from pydantic import Field, NonNegativeInt, PositiveFloat, PositiveInt, computed_field
+from pydantic import Field, NonNegativeFloat, NonNegativeInt, PositiveFloat, PositiveInt, computed_field
 from pydantic_settings import BaseSettings
 
 from .cache.redis_config import RedisConfig
@@ -256,6 +256,25 @@ class InternalTestConfig(BaseSettings):
     )
 
 
+class DatasetQueueMonitorConfig(BaseSettings):
+    """
+    Configuration settings for Dataset Queue Monitor
+    """
+
+    QUEUE_MONITOR_THRESHOLD: Optional[NonNegativeInt] = Field(
+        description="Threshold for dataset queue monitor",
+        default=200,
+    )
+    QUEUE_MONITOR_ALERT_EMAILS: Optional[str] = Field(
+        description="Emails for dataset queue monitor alert, separated by commas",
+        default=None,
+    )
+    QUEUE_MONITOR_INTERVAL: Optional[NonNegativeFloat] = Field(
+        description="Interval for dataset queue monitor in minutes",
+        default=30,
+    )
+
+
 class MiddlewareConfig(
     # place the configs in alphabet order
     CeleryConfig,
@@ -303,5 +322,6 @@ class MiddlewareConfig(
     BaiduVectorDBConfig,
     OpenGaussConfig,
     TableStoreConfig,
+    DatasetQueueMonitorConfig,
 ):
     pass

api/configs/remote_settings_sources/nacos/http_request.py

@@ -60,8 +60,7 @@ class NacosHttpClient:
             sign_str = tenant + "+"
         if group:
             sign_str = sign_str + group + "+"
-        if sign_str:
-            sign_str += ts
+        sign_str += ts  # Directly concatenate ts without conditional checks, because the nacos auth header forced it.
         return sign_str
 
     def get_access_token(self, force_refresh=False):

api/controllers/console/app/workflow_app_log.py

@@ -6,12 +6,12 @@ from sqlalchemy.orm import Session
 from controllers.console import api
 from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import account_initialization_required, setup_required
+from core.workflow.entities.workflow_execution import WorkflowExecutionStatus
 from extensions.ext_database import db
 from fields.workflow_app_log_fields import workflow_app_log_pagination_fields
 from libs.login import login_required
 from models import App
 from models.model import AppMode
-from models.workflow import WorkflowRunStatus
 from services.workflow_app_service import WorkflowAppService
 
 
@@ -38,7 +38,7 @@ class WorkflowAppLogApi(Resource):
         parser.add_argument("limit", type=int_range(1, 100), default=20, location="args")
         args = parser.parse_args()
 
-        args.status = WorkflowRunStatus(args.status) if args.status else None
+        args.status = WorkflowExecutionStatus(args.status) if args.status else None
         if args.created_at__before:
             args.created_at__before = isoparse(args.created_at__before)
 

api/controllers/service_api/app/workflow.py

@@ -24,12 +24,13 @@ from core.errors.error import (
     QuotaExceededError,
 )
 from core.model_runtime.errors.invoke import InvokeError
+from core.workflow.entities.workflow_execution import WorkflowExecutionStatus
 from extensions.ext_database import db
 from fields.workflow_app_log_fields import workflow_app_log_pagination_fields
 from libs import helper
 from libs.helper import TimestampField
 from models.model import App, AppMode, EndUser
-from models.workflow import WorkflowRun, WorkflowRunStatus
+from models.workflow import WorkflowRun
 from services.app_generate_service import AppGenerateService
 from services.errors.llm import InvokeRateLimitError
 from services.workflow_app_service import WorkflowAppService
@@ -138,7 +139,7 @@ class WorkflowAppLogApi(Resource):
         parser.add_argument("limit", type=int_range(1, 100), default=20, location="args")
         args = parser.parse_args()
 
-        args.status = WorkflowRunStatus(args.status) if args.status else None
+        args.status = WorkflowExecutionStatus(args.status) if args.status else None
         if args.created_at__before:
             args.created_at__before = isoparse(args.created_at__before)
 

api/controllers/service_api/dataset/dataset.py

@@ -1,19 +1,21 @@
 from flask import request
-from flask_restful import marshal, reqparse
+from flask_restful import marshal, marshal_with, reqparse
 from werkzeug.exceptions import Forbidden, NotFound
 
 import services.dataset_service
 from controllers.service_api import api
 from controllers.service_api.dataset.error import DatasetInUseError, DatasetNameDuplicateError
-from controllers.service_api.wraps import DatasetApiResource
+from controllers.service_api.wraps import DatasetApiResource, validate_dataset_token
 from core.model_runtime.entities.model_entities import ModelType
 from core.plugin.entities.plugin import ModelProviderID
 from core.provider_manager import ProviderManager
 from fields.dataset_fields import dataset_detail_fields
+from fields.tag_fields import tag_fields
 from libs.login import current_user
 from models.dataset import Dataset, DatasetPermissionEnum
 from services.dataset_service import DatasetPermissionService, DatasetService
 from services.entities.knowledge_entities.knowledge_entities import RetrievalModel
+from services.tag_service import TagService
 
 
 def _validate_name(name):
@@ -320,5 +322,135 @@ class DatasetApi(DatasetApiResource):
             raise DatasetInUseError()
 
 
+class DatasetTagsApi(DatasetApiResource):
+    @validate_dataset_token
+    @marshal_with(tag_fields)
+    def get(self, _, dataset_id):
+        """Get all knowledge type tags."""
+        tags = TagService.get_tags("knowledge", current_user.current_tenant_id)
+
+        return tags, 200
+
+    @validate_dataset_token
+    def post(self, _, dataset_id):
+        """Add a knowledge type tag."""
+        if not (current_user.is_editor or current_user.is_dataset_editor):
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument(
+            "name",
+            nullable=False,
+            required=True,
+            help="Name must be between 1 to 50 characters.",
+            type=DatasetTagsApi._validate_tag_name,
+        )
+
+        args = parser.parse_args()
+        args["type"] = "knowledge"
+        tag = TagService.save_tags(args)
+
+        response = {"id": tag.id, "name": tag.name, "type": tag.type, "binding_count": 0}
+
+        return response, 200
+
+    @validate_dataset_token
+    def patch(self, _, dataset_id):
+        if not (current_user.is_editor or current_user.is_dataset_editor):
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument(
+            "name",
+            nullable=False,
+            required=True,
+            help="Name must be between 1 to 50 characters.",
+            type=DatasetTagsApi._validate_tag_name,
+        )
+        parser.add_argument("tag_id", nullable=False, required=True, help="Id of a tag.", type=str)
+        args = parser.parse_args()
+        args["type"] = "knowledge"
+        tag = TagService.update_tags(args, args.get("tag_id"))
+
+        binding_count = TagService.get_tag_binding_count(args.get("tag_id"))
+
+        response = {"id": tag.id, "name": tag.name, "type": tag.type, "binding_count": binding_count}
+
+        return response, 200
+
+    @validate_dataset_token
+    def delete(self, _, dataset_id):
+        """Delete a knowledge type tag."""
+        if not current_user.is_editor:
+            raise Forbidden()
+        parser = reqparse.RequestParser()
+        parser.add_argument("tag_id", nullable=False, required=True, help="Id of a tag.", type=str)
+        args = parser.parse_args()
+        TagService.delete_tag(args.get("tag_id"))
+
+        return 204
+
+    @staticmethod
+    def _validate_tag_name(name):
+        if not name or len(name) < 1 or len(name) > 50:
+            raise ValueError("Name must be between 1 to 50 characters.")
+        return name
+
+
+class DatasetTagBindingApi(DatasetApiResource):
+    @validate_dataset_token
+    def post(self, _, dataset_id):
+        # The role of the current user in the ta table must be admin, owner, editor, or dataset_operator
+        if not (current_user.is_editor or current_user.is_dataset_editor):
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument(
+            "tag_ids", type=list, nullable=False, required=True, location="json", help="Tag IDs is required."
+        )
+        parser.add_argument(
+            "target_id", type=str, nullable=False, required=True, location="json", help="Target Dataset ID is required."
+        )
+
+        args = parser.parse_args()
+        args["type"] = "knowledge"
+        TagService.save_tag_binding(args)
+
+        return 204
+
+
+class DatasetTagUnbindingApi(DatasetApiResource):
+    @validate_dataset_token
+    def post(self, _, dataset_id):
+        # The role of the current user in the ta table must be admin, owner, editor, or dataset_operator
+        if not (current_user.is_editor or current_user.is_dataset_editor):
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("tag_id", type=str, nullable=False, required=True, help="Tag ID is required.")
+        parser.add_argument("target_id", type=str, nullable=False, required=True, help="Target ID is required.")
+
+        args = parser.parse_args()
+        args["type"] = "knowledge"
+        TagService.delete_tag_binding(args)
+
+        return 204
+
+
+class DatasetTagsBindingStatusApi(DatasetApiResource):
+    @validate_dataset_token
+    def get(self, _, *args, **kwargs):
+        """Get all knowledge type tags."""
+        dataset_id = kwargs.get("dataset_id")
+        tags = TagService.get_tags_by_target_id("knowledge", current_user.current_tenant_id, str(dataset_id))
+        tags_list = [{"id": tag.id, "name": tag.name} for tag in tags]
+        response = {"data": tags_list, "total": len(tags)}
+        return response, 200
+
+
 api.add_resource(DatasetListApi, "/datasets")
 api.add_resource(DatasetApi, "/datasets/<uuid:dataset_id>")
+api.add_resource(DatasetTagsApi, "/datasets/tags")
+api.add_resource(DatasetTagBindingApi, "/datasets/tags/binding")
+api.add_resource(DatasetTagUnbindingApi, "/datasets/tags/unbinding")
+api.add_resource(DatasetTagsBindingStatusApi, "/datasets/<uuid:dataset_id>/tags")

api/controllers/service_api/dataset/document.py

@@ -175,8 +175,11 @@ class DocumentAddByFileApi(DatasetApiResource):
 
         if not dataset:
             raise ValueError("Dataset does not exist.")
-        if not dataset.indexing_technique and not args.get("indexing_technique"):
+
+        indexing_technique = args.get("indexing_technique") or dataset.indexing_technique
+        if not indexing_technique:
             raise ValueError("indexing_technique is required.")
+        args["indexing_technique"] = indexing_technique
 
         # save file info
         file = request.files["file"]
@@ -206,12 +209,16 @@ class DocumentAddByFileApi(DatasetApiResource):
         knowledge_config = KnowledgeConfig(**args)
         DocumentService.document_create_args_validate(knowledge_config)
 
+        dataset_process_rule = dataset.latest_process_rule if "process_rule" not in args else None
+        if not knowledge_config.original_document_id and not dataset_process_rule and not knowledge_config.process_rule:
+            raise ValueError("process_rule is required.")
+
         try:
             documents, batch = DocumentService.save_document_with_dataset_id(
                 dataset=dataset,
                 knowledge_config=knowledge_config,
                 account=dataset.created_by_account,
-                dataset_process_rule=dataset.latest_process_rule if "process_rule" not in args else None,
+                dataset_process_rule=dataset_process_rule,
                 created_from="api",
             )
         except ProviderTokenNotInitError as ex:

api/controllers/service_api/dataset/segment.py

@@ -208,6 +208,28 @@ class DatasetSegmentApi(DatasetApiResource):
         )
         return {"data": marshal(updated_segment, segment_fields), "doc_form": document.doc_form}, 200
 
+    def get(self, tenant_id, dataset_id, document_id, segment_id):
+        # check dataset
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        # check user's model setting
+        DatasetService.check_dataset_model_setting(dataset)
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset_id, document_id)
+        if not document:
+            raise NotFound("Document not found.")
+        # check segment
+        segment_id = str(segment_id)
+        segment = SegmentService.get_segment_by_id(segment_id=segment_id, tenant_id=current_user.current_tenant_id)
+        if not segment:
+            raise NotFound("Segment not found.")
+
+        return {"data": marshal(segment, segment_fields), "doc_form": document.doc_form}, 200
+
 
 class ChildChunkApi(DatasetApiResource):
     """Resource for child chunks."""

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

@@ -70,7 +70,7 @@ class ModelConfigConverter:
         if not model_mode:
             model_mode = LLMMode.CHAT.value
             if model_schema and model_schema.model_properties.get(ModelPropertyKey.MODE):
-                model_mode = LLMMode.value_of(model_schema.model_properties[ModelPropertyKey.MODE]).value
+                model_mode = LLMMode(model_schema.model_properties[ModelPropertyKey.MODE]).value
 
         if not model_schema:
             raise ValueError(f"Model {model_name} not exist.")

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

@@ -27,8 +27,8 @@ from core.ops.ops_trace_manager import TraceQueueManager
 from core.prompt.utils.get_thread_messages_length import get_thread_messages_length
 from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
 from core.repositories.sqlalchemy_workflow_execution_repository import SQLAlchemyWorkflowExecutionRepository
-from core.workflow.repository.workflow_execution_repository import WorkflowExecutionRepository
-from core.workflow.repository.workflow_node_execution_repository import WorkflowNodeExecutionRepository
+from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
+from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
 from extensions.ext_database import db
 from factories import file_factory
 from models import Account, App, Conversation, EndUser, Message, Workflow, WorkflowNodeExecutionTriggeredFrom

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

@@ -140,7 +140,7 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
                 SystemVariableKey.DIALOGUE_COUNT: self._dialogue_count,
                 SystemVariableKey.APP_ID: app_config.app_id,
                 SystemVariableKey.WORKFLOW_ID: app_config.workflow_id,
-                SystemVariableKey.WORKFLOW_RUN_ID: self.application_generate_entity.workflow_run_id,
+                SystemVariableKey.WORKFLOW_EXECUTION_ID: self.application_generate_entity.workflow_run_id,
             }
 
             # init variable pool

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

@@ -1,4 +1,3 @@
-import json
 import logging
 import time
 from collections.abc import Generator, Mapping
@@ -57,26 +56,23 @@ from core.app.entities.task_entities import (
     WorkflowTaskState,
 )
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
-from core.app.task_pipeline.message_cycle_manage import MessageCycleManage
+from core.app.task_pipeline.message_cycle_manager import MessageCycleManager
 from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
 from core.model_runtime.entities.llm_entities import LLMUsage
-from core.model_runtime.utils.encoders import jsonable_encoder
 from core.ops.ops_trace_manager import TraceQueueManager
+from core.workflow.entities.workflow_execution import WorkflowExecutionStatus, WorkflowType
 from core.workflow.enums import SystemVariableKey
 from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes import NodeType
-from core.workflow.repository.workflow_execution_repository import WorkflowExecutionRepository
-from core.workflow.repository.workflow_node_execution_repository import WorkflowNodeExecutionRepository
-from core.workflow.workflow_cycle_manager import WorkflowCycleManager
+from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
+from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
+from core.workflow.workflow_cycle_manager import CycleManagerWorkflowInfo, WorkflowCycleManager
 from events.message_event import message_was_created
 from extensions.ext_database import db
 from models import Conversation, EndUser, Message, MessageFile
 from models.account import Account
 from models.enums import CreatorUserRole
-from models.workflow import (
-    Workflow,
-    WorkflowRunStatus,
-)
+from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
@@ -126,8 +122,14 @@ class AdvancedChatAppGenerateTaskPipeline:
                 SystemVariableKey.DIALOGUE_COUNT: dialogue_count,
                 SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
                 SystemVariableKey.WORKFLOW_ID: workflow.id,
-                SystemVariableKey.WORKFLOW_RUN_ID: application_generate_entity.workflow_run_id,
+                SystemVariableKey.WORKFLOW_EXECUTION_ID: application_generate_entity.workflow_run_id,
             },
+            workflow_info=CycleManagerWorkflowInfo(
+                workflow_id=workflow.id,
+                workflow_type=WorkflowType(workflow.type),
+                version=workflow.version,
+                graph_data=workflow.graph_dict,
+            ),
             workflow_execution_repository=workflow_execution_repository,
             workflow_node_execution_repository=workflow_node_execution_repository,
         )
@@ -137,7 +139,7 @@ class AdvancedChatAppGenerateTaskPipeline:
         )
 
         self._task_state = WorkflowTaskState()
-        self._message_cycle_manager = MessageCycleManage(
+        self._message_cycle_manager = MessageCycleManager(
             application_generate_entity=application_generate_entity, task_state=self._task_state
         )
 
@@ -158,7 +160,7 @@ class AdvancedChatAppGenerateTaskPipeline:
         :return:
         """
         # start generate conversation name thread
-        self._conversation_name_generate_thread = self._message_cycle_manager._generate_conversation_name(
+        self._conversation_name_generate_thread = self._message_cycle_manager.generate_conversation_name(
             conversation_id=self._conversation_id, query=self._application_generate_entity.query
         )
 
@@ -302,15 +304,12 @@ class AdvancedChatAppGenerateTaskPipeline:
 
                 with Session(db.engine, expire_on_commit=False) as session:
                     # init workflow run
-                    workflow_execution = self._workflow_cycle_manager.handle_workflow_run_start(
-                        session=session,
-                        workflow_id=self._workflow_id,
-                    )
-                    self._workflow_run_id = workflow_execution.id
+                    workflow_execution = self._workflow_cycle_manager.handle_workflow_run_start()
+                    self._workflow_run_id = workflow_execution.id_
                     message = self._get_message(session=session)
                     if not message:
                         raise ValueError(f"Message not found: {self._message_id}")
-                    message.workflow_run_id = workflow_execution.id
+                    message.workflow_run_id = workflow_execution.id_
                     workflow_start_resp = self._workflow_response_converter.workflow_start_to_stream_response(
                         task_id=self._application_generate_entity.task_id,
                         workflow_execution=workflow_execution,
@@ -550,7 +549,7 @@ class AdvancedChatAppGenerateTaskPipeline:
                         workflow_run_id=self._workflow_run_id,
                         total_tokens=graph_runtime_state.total_tokens,
                         total_steps=graph_runtime_state.node_run_steps,
-                        status=WorkflowRunStatus.FAILED,
+                        status=WorkflowExecutionStatus.FAILED,
                         error_message=event.error,
                         conversation_id=self._conversation_id,
                         trace_manager=trace_manager,
@@ -576,7 +575,7 @@ class AdvancedChatAppGenerateTaskPipeline:
                             workflow_run_id=self._workflow_run_id,
                             total_tokens=graph_runtime_state.total_tokens,
                             total_steps=graph_runtime_state.node_run_steps,
-                            status=WorkflowRunStatus.STOPPED,
+                            status=WorkflowExecutionStatus.STOPPED,
                             error_message=event.get_stop_reason(),
                             conversation_id=self._conversation_id,
                             trace_manager=trace_manager,
@@ -604,22 +603,18 @@ class AdvancedChatAppGenerateTaskPipeline:
                 yield self._message_end_to_stream_response()
                 break
             elif isinstance(event, QueueRetrieverResourcesEvent):
-                self._message_cycle_manager._handle_retriever_resources(event)
+                self._message_cycle_manager.handle_retriever_resources(event)
 
                 with Session(db.engine, expire_on_commit=False) as session:
                     message = self._get_message(session=session)
-                    message.message_metadata = (
-                        json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
-                    )
+                    message.message_metadata = self._task_state.metadata.model_dump_json()
                     session.commit()
             elif isinstance(event, QueueAnnotationReplyEvent):
-                self._message_cycle_manager._handle_annotation_reply(event)
+                self._message_cycle_manager.handle_annotation_reply(event)
 
                 with Session(db.engine, expire_on_commit=False) as session:
                     message = self._get_message(session=session)
-                    message.message_metadata = (
-                        json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
-                    )
+                    message.message_metadata = self._task_state.metadata.model_dump_json()
                     session.commit()
             elif isinstance(event, QueueTextChunkEvent):
                 delta_text = event.text
@@ -636,12 +631,12 @@ class AdvancedChatAppGenerateTaskPipeline:
                     tts_publisher.publish(queue_message)
 
                 self._task_state.answer += delta_text
-                yield self._message_cycle_manager._message_to_stream_response(
+                yield self._message_cycle_manager.message_to_stream_response(
                     answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
                 )
             elif isinstance(event, QueueMessageReplaceEvent):
                 # published by moderation
-                yield self._message_cycle_manager._message_replace_to_stream_response(
+                yield self._message_cycle_manager.message_replace_to_stream_response(
                     answer=event.text, reason=event.reason
                 )
             elif isinstance(event, QueueAdvancedChatMessageEndEvent):
@@ -653,7 +648,7 @@ class AdvancedChatAppGenerateTaskPipeline:
                 )
                 if output_moderation_answer:
                     self._task_state.answer = output_moderation_answer
-                    yield self._message_cycle_manager._message_replace_to_stream_response(
+                    yield self._message_cycle_manager.message_replace_to_stream_response(
                         answer=output_moderation_answer,
                         reason=QueueMessageReplaceEvent.MessageReplaceReason.OUTPUT_MODERATION,
                     )
@@ -682,9 +677,7 @@ class AdvancedChatAppGenerateTaskPipeline:
         message = self._get_message(session=session)
         message.answer = self._task_state.answer
         message.provider_response_latency = time.perf_counter() - self._base_task_pipeline._start_at
-        message.message_metadata = (
-            json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
-        )
+        message.message_metadata = self._task_state.metadata.model_dump_json()
         message_files = [
             MessageFile(
                 message_id=message.id,
@@ -712,9 +705,9 @@ class AdvancedChatAppGenerateTaskPipeline:
             message.answer_price_unit = usage.completion_price_unit
             message.total_price = usage.total_price
             message.currency = usage.currency
-            self._task_state.metadata["usage"] = jsonable_encoder(usage)
+            self._task_state.metadata.usage = usage
         else:
-            self._task_state.metadata["usage"] = jsonable_encoder(LLMUsage.empty_usage())
+            self._task_state.metadata.usage = LLMUsage.empty_usage()
         message_was_created.send(
             message,
             application_generate_entity=self._application_generate_entity,
@@ -725,18 +718,16 @@ class AdvancedChatAppGenerateTaskPipeline:
         Message end to stream response.
         :return:
         """
-        extras = {}
-        if self._task_state.metadata:
-            extras["metadata"] = self._task_state.metadata.copy()
+        extras = self._task_state.metadata.model_dump()
 
-            if "annotation_reply" in extras["metadata"]:
-                del extras["metadata"]["annotation_reply"]
+        if self._task_state.metadata.annotation_reply:
+            del extras["annotation_reply"]
 
         return MessageEndStreamResponse(
             task_id=self._application_generate_entity.task_id,
             id=self._message_id,
             files=self._recorded_files,
-            metadata=extras.get("metadata", {}),
+            metadata=extras,
         )
 
     def _handle_output_moderation_chunk(self, text: str) -> bool:

api/core/app/apps/common/workflow_response_converter.py

@@ -44,15 +44,14 @@ from core.app.entities.task_entities import (
 )
 from core.file import FILE_MODEL_IDENTITY, File
 from core.tools.tool_manager import ToolManager
-from core.workflow.entities.node_execution_entities import NodeExecution
-from core.workflow.entities.workflow_execution_entities import WorkflowExecution
+from core.workflow.entities.workflow_execution import WorkflowExecution
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecution, WorkflowNodeExecutionStatus
 from core.workflow.nodes import NodeType
 from core.workflow.nodes.tool.entities import ToolNodeData
 from models import (
     Account,
     CreatorUserRole,
     EndUser,
-    WorkflowNodeExecutionStatus,
     WorkflowRun,
 )
 
@@ -73,11 +72,10 @@ class WorkflowResponseConverter:
     ) -> WorkflowStartStreamResponse:
         return WorkflowStartStreamResponse(
             task_id=task_id,
-            workflow_run_id=workflow_execution.id,
+            workflow_run_id=workflow_execution.id_,
             data=WorkflowStartStreamResponse.Data(
-                id=workflow_execution.id,
+                id=workflow_execution.id_,
                 workflow_id=workflow_execution.workflow_id,
-                sequence_number=workflow_execution.sequence_number,
                 inputs=workflow_execution.inputs,
                 created_at=int(workflow_execution.started_at.timestamp()),
             ),
@@ -91,7 +89,7 @@ class WorkflowResponseConverter:
         workflow_execution: WorkflowExecution,
     ) -> WorkflowFinishStreamResponse:
         created_by = None
-        workflow_run = session.scalar(select(WorkflowRun).where(WorkflowRun.id == workflow_execution.id))
+        workflow_run = session.scalar(select(WorkflowRun).where(WorkflowRun.id == workflow_execution.id_))
         assert workflow_run is not None
         if workflow_run.created_by_role == CreatorUserRole.ACCOUNT:
             stmt = select(Account).where(Account.id == workflow_run.created_by)
@@ -122,11 +120,10 @@ class WorkflowResponseConverter:
 
         return WorkflowFinishStreamResponse(
             task_id=task_id,
-            workflow_run_id=workflow_execution.id,
+            workflow_run_id=workflow_execution.id_,
             data=WorkflowFinishStreamResponse.Data(
-                id=workflow_execution.id,
+                id=workflow_execution.id_,
                 workflow_id=workflow_execution.workflow_id,
-                sequence_number=workflow_execution.sequence_number,
                 status=workflow_execution.status,
                 outputs=workflow_execution.outputs,
                 error=workflow_execution.error_message,
@@ -146,16 +143,16 @@ class WorkflowResponseConverter:
         *,
         event: QueueNodeStartedEvent,
         task_id: str,
-        workflow_node_execution: NodeExecution,
+        workflow_node_execution: WorkflowNodeExecution,
     ) -> Optional[NodeStartStreamResponse]:
         if workflow_node_execution.node_type in {NodeType.ITERATION, NodeType.LOOP}:
             return None
-        if not workflow_node_execution.workflow_run_id:
+        if not workflow_node_execution.workflow_execution_id:
             return None
 
         response = NodeStartStreamResponse(
             task_id=task_id,
-            workflow_run_id=workflow_node_execution.workflow_run_id,
+            workflow_run_id=workflow_node_execution.workflow_execution_id,
             data=NodeStartStreamResponse.Data(
                 id=workflow_node_execution.id,
                 node_id=workflow_node_execution.node_id,
@@ -196,18 +193,18 @@ class WorkflowResponseConverter:
         | QueueNodeInLoopFailedEvent
         | QueueNodeExceptionEvent,
         task_id: str,
-        workflow_node_execution: NodeExecution,
+        workflow_node_execution: WorkflowNodeExecution,
     ) -> Optional[NodeFinishStreamResponse]:
         if workflow_node_execution.node_type in {NodeType.ITERATION, NodeType.LOOP}:
             return None
-        if not workflow_node_execution.workflow_run_id:
+        if not workflow_node_execution.workflow_execution_id:
             return None
         if not workflow_node_execution.finished_at:
             return None
 
         return NodeFinishStreamResponse(
             task_id=task_id,
-            workflow_run_id=workflow_node_execution.workflow_run_id,
+            workflow_run_id=workflow_node_execution.workflow_execution_id,
             data=NodeFinishStreamResponse.Data(
                 id=workflow_node_execution.id,
                 node_id=workflow_node_execution.node_id,
@@ -239,18 +236,18 @@ class WorkflowResponseConverter:
         *,
         event: QueueNodeRetryEvent,
         task_id: str,
-        workflow_node_execution: NodeExecution,
+        workflow_node_execution: WorkflowNodeExecution,
     ) -> Optional[Union[NodeRetryStreamResponse, NodeFinishStreamResponse]]:
         if workflow_node_execution.node_type in {NodeType.ITERATION, NodeType.LOOP}:
             return None
-        if not workflow_node_execution.workflow_run_id:
+        if not workflow_node_execution.workflow_execution_id:
             return None
         if not workflow_node_execution.finished_at:
             return None
 
         return NodeRetryStreamResponse(
             task_id=task_id,
-            workflow_run_id=workflow_node_execution.workflow_run_id,
+            workflow_run_id=workflow_node_execution.workflow_execution_id,
             data=NodeRetryStreamResponse.Data(
                 id=workflow_node_execution.id,
                 node_id=workflow_node_execution.node_id,

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

@@ -25,8 +25,8 @@ from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
 from core.repositories.sqlalchemy_workflow_execution_repository import SQLAlchemyWorkflowExecutionRepository
-from core.workflow.repository.workflow_execution_repository import WorkflowExecutionRepository
-from core.workflow.repository.workflow_node_execution_repository import WorkflowNodeExecutionRepository
+from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
+from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
 from extensions.ext_database import db
 from factories import file_factory
 from models import Account, App, EndUser, Workflow, WorkflowNodeExecutionTriggeredFrom
@@ -132,7 +132,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             invoke_from=invoke_from,
             call_depth=call_depth,
             trace_manager=trace_manager,
-            workflow_run_id=workflow_run_id,
+            workflow_execution_id=workflow_run_id,
         )
 
         contexts.plugin_tool_providers.set({})
@@ -279,7 +279,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             single_iteration_run=WorkflowAppGenerateEntity.SingleIterationRunEntity(
                 node_id=node_id, inputs=args["inputs"]
             ),
-            workflow_run_id=str(uuid.uuid4()),
+            workflow_execution_id=str(uuid.uuid4()),
         )
         contexts.plugin_tool_providers.set({})
         contexts.plugin_tool_providers_lock.set(threading.Lock())
@@ -355,7 +355,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             invoke_from=InvokeFrom.DEBUGGER,
             extras={"auto_generate_conversation_name": False},
             single_loop_run=WorkflowAppGenerateEntity.SingleLoopRunEntity(node_id=node_id, inputs=args["inputs"]),
-            workflow_run_id=str(uuid.uuid4()),
+            workflow_execution_id=str(uuid.uuid4()),
         )
         contexts.plugin_tool_providers.set({})
         contexts.plugin_tool_providers_lock.set(threading.Lock())

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

@@ -95,7 +95,7 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
                 SystemVariableKey.USER_ID: user_id,
                 SystemVariableKey.APP_ID: app_config.app_id,
                 SystemVariableKey.WORKFLOW_ID: app_config.workflow_id,
-                SystemVariableKey.WORKFLOW_RUN_ID: self.application_generate_entity.workflow_run_id,
+                SystemVariableKey.WORKFLOW_EXECUTION_ID: self.application_generate_entity.workflow_execution_id,
             }
 
             variable_pool = VariablePool(

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

@@ -50,16 +50,15 @@ from core.app.entities.task_entities import (
     WorkflowAppStreamResponse,
     WorkflowFinishStreamResponse,
     WorkflowStartStreamResponse,
-    WorkflowTaskState,
 )
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
 from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.workflow_execution_entities import WorkflowExecution
+from core.workflow.entities.workflow_execution import WorkflowExecution, WorkflowExecutionStatus, WorkflowType
 from core.workflow.enums import SystemVariableKey
-from core.workflow.repository.workflow_execution_repository import WorkflowExecutionRepository
-from core.workflow.repository.workflow_node_execution_repository import WorkflowNodeExecutionRepository
-from core.workflow.workflow_cycle_manager import WorkflowCycleManager
+from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
+from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
+from core.workflow.workflow_cycle_manager import CycleManagerWorkflowInfo, WorkflowCycleManager
 from extensions.ext_database import db
 from models.account import Account
 from models.enums import CreatorUserRole
@@ -69,7 +68,6 @@ from models.workflow import (
     WorkflowAppLog,
     WorkflowAppLogCreatedFrom,
     WorkflowRun,
-    WorkflowRunStatus,
 )
 
 logger = logging.getLogger(__name__)
@@ -114,8 +112,14 @@ class WorkflowAppGenerateTaskPipeline:
                 SystemVariableKey.USER_ID: user_session_id,
                 SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
                 SystemVariableKey.WORKFLOW_ID: workflow.id,
-                SystemVariableKey.WORKFLOW_RUN_ID: application_generate_entity.workflow_run_id,
+                SystemVariableKey.WORKFLOW_EXECUTION_ID: application_generate_entity.workflow_execution_id,
             },
+            workflow_info=CycleManagerWorkflowInfo(
+                workflow_id=workflow.id,
+                workflow_type=WorkflowType(workflow.type),
+                version=workflow.version,
+                graph_data=workflow.graph_dict,
+            ),
             workflow_execution_repository=workflow_execution_repository,
             workflow_node_execution_repository=workflow_node_execution_repository,
         )
@@ -125,9 +129,7 @@ class WorkflowAppGenerateTaskPipeline:
         )
 
         self._application_generate_entity = application_generate_entity
-        self._workflow_id = workflow.id
         self._workflow_features_dict = workflow.features_dict
-        self._task_state = WorkflowTaskState()
         self._workflow_run_id = ""
 
     def process(self) -> Union[WorkflowAppBlockingResponse, Generator[WorkflowAppStreamResponse, None, None]]:
@@ -266,17 +268,13 @@ class WorkflowAppGenerateTaskPipeline:
                 # override graph runtime state
                 graph_runtime_state = event.graph_runtime_state
 
-                with Session(db.engine, expire_on_commit=False) as session:
-                    # init workflow run
-                    workflow_execution = self._workflow_cycle_manager.handle_workflow_run_start(
-                        session=session,
-                        workflow_id=self._workflow_id,
-                    )
-                    self._workflow_run_id = workflow_execution.id
-                    start_resp = self._workflow_response_converter.workflow_start_to_stream_response(
-                        task_id=self._application_generate_entity.task_id,
-                        workflow_execution=workflow_execution,
-                    )
+                # init workflow run
+                workflow_execution = self._workflow_cycle_manager.handle_workflow_run_start()
+                self._workflow_run_id = workflow_execution.id_
+                start_resp = self._workflow_response_converter.workflow_start_to_stream_response(
+                    task_id=self._application_generate_entity.task_id,
+                    workflow_execution=workflow_execution,
+                )
 
                 yield start_resp
             elif isinstance(
@@ -511,9 +509,9 @@ class WorkflowAppGenerateTaskPipeline:
                         workflow_run_id=self._workflow_run_id,
                         total_tokens=graph_runtime_state.total_tokens,
                         total_steps=graph_runtime_state.node_run_steps,
-                        status=WorkflowRunStatus.FAILED
+                        status=WorkflowExecutionStatus.FAILED
                         if isinstance(event, QueueWorkflowFailedEvent)
-                        else WorkflowRunStatus.STOPPED,
+                        else WorkflowExecutionStatus.STOPPED,
                         error_message=event.error
                         if isinstance(event, QueueWorkflowFailedEvent)
                         else event.get_stop_reason(),
@@ -542,7 +540,6 @@ class WorkflowAppGenerateTaskPipeline:
                 if tts_publisher:
                     tts_publisher.publish(queue_message)
 
-                self._task_state.answer += delta_text
                 yield self._text_chunk_to_stream_response(
                     delta_text, from_variable_selector=event.from_variable_selector
                 )
@@ -557,7 +554,7 @@ class WorkflowAppGenerateTaskPipeline:
             tts_publisher.publish(None)
 
     def _save_workflow_app_log(self, *, session: Session, workflow_execution: WorkflowExecution) -> None:
-        workflow_run = session.scalar(select(WorkflowRun).where(WorkflowRun.id == workflow_execution.id))
+        workflow_run = session.scalar(select(WorkflowRun).where(WorkflowRun.id == workflow_execution.id_))
         assert workflow_run is not None
         invoke_from = self._application_generate_entity.invoke_from
         if invoke_from == InvokeFrom.SERVICE_API:

api/core/app/apps/workflow_app_runner.py

@@ -29,8 +29,8 @@ from core.app.entities.queue_entities import (
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
-from core.workflow.entities.node_entities import NodeRunMetadataKey
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
 from core.workflow.graph_engine.entities.event import (
     AgentLogEvent,
     GraphEngineEvent,
@@ -295,7 +295,7 @@ class WorkflowBasedAppRunner(AppRunner):
             inputs: Mapping[str, Any] | None = {}
             process_data: Mapping[str, Any] | None = {}
             outputs: Mapping[str, Any] | None = {}
-            execution_metadata: Mapping[NodeRunMetadataKey, Any] | None = {}
+            execution_metadata: Mapping[WorkflowNodeExecutionMetadataKey, Any] | None = {}
             if node_run_result:
                 inputs = node_run_result.inputs
                 process_data = node_run_result.process_data

api/core/app/entities/app_invoke_entities.py

@@ -76,6 +76,8 @@ class AppGenerateEntity(BaseModel):
     App Generate Entity.
     """
 
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
     task_id: str
 
     # app config
@@ -99,9 +101,6 @@ class AppGenerateEntity(BaseModel):
     # tracing instance
     trace_manager: Optional[TraceQueueManager] = None
 
-    class Config:
-        arbitrary_types_allowed = True
-
 
 class EasyUIBasedAppGenerateEntity(AppGenerateEntity):
     """
@@ -205,7 +204,7 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):
 
     # app config
     app_config: WorkflowUIBasedAppConfig
-    workflow_run_id: str
+    workflow_execution_id: str
 
     class SingleIterationRunEntity(BaseModel):
         """

api/core/app/entities/queue_entities.py

@@ -1,4 +1,4 @@
-from collections.abc import Mapping
+from collections.abc import Mapping, Sequence
 from datetime import datetime
 from enum import Enum, StrEnum
 from typing import Any, Optional
@@ -6,7 +6,9 @@ from typing import Any, Optional
 from pydantic import BaseModel
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
-from core.workflow.entities.node_entities import AgentNodeStrategyInit, NodeRunMetadataKey
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
+from core.workflow.entities.node_entities import AgentNodeStrategyInit
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
 from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes import NodeType
 from core.workflow.nodes.base import BaseNodeData
@@ -282,7 +284,7 @@ class QueueRetrieverResourcesEvent(AppQueueEvent):
     """
 
     event: QueueEvent = QueueEvent.RETRIEVER_RESOURCES
-    retriever_resources: list[dict]
+    retriever_resources: Sequence[RetrievalSourceMetadata]
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
     in_loop_id: Optional[str] = None
@@ -412,7 +414,7 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     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
+    execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
 
     error: Optional[str] = None
     """single iteration duration map"""
@@ -446,7 +448,7 @@ class QueueNodeRetryEvent(QueueNodeStartedEvent):
     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
+    execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
 
     error: str
     retry_index: int  # retry index
@@ -480,7 +482,7 @@ class QueueNodeInIterationFailedEvent(AppQueueEvent):
     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
+    execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
 
     error: str
 
@@ -513,7 +515,7 @@ class QueueNodeInLoopFailedEvent(AppQueueEvent):
     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
+    execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
 
     error: str
 
@@ -546,7 +548,7 @@ class QueueNodeExceptionEvent(AppQueueEvent):
     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
+    execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
 
     error: str
 
@@ -579,7 +581,7 @@ class QueueNodeFailedEvent(AppQueueEvent):
     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
+    execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
 
     error: str
 

api/core/app/entities/task_entities.py

@@ -2,12 +2,29 @@ from collections.abc import Mapping, Sequence
 from enum import Enum
 from typing import Any, Optional
 
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel, ConfigDict, Field
 
-from core.model_runtime.entities.llm_entities import LLMResult
+from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.workflow.entities.node_entities import AgentNodeStrategyInit, NodeRunMetadataKey
-from models.workflow import WorkflowNodeExecutionStatus
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
+from core.workflow.entities.node_entities import AgentNodeStrategyInit
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+
+
+class AnnotationReplyAccount(BaseModel):
+    id: str
+    name: str
+
+
+class AnnotationReply(BaseModel):
+    id: str
+    account: AnnotationReplyAccount
+
+
+class TaskStateMetadata(BaseModel):
+    annotation_reply: AnnotationReply | None = None
+    retriever_resources: Sequence[RetrievalSourceMetadata] = Field(default_factory=list)
+    usage: LLMUsage | None = None
 
 
 class TaskState(BaseModel):
@@ -15,7 +32,7 @@ class TaskState(BaseModel):
     TaskState entity
     """
 
-    metadata: dict = {}
+    metadata: TaskStateMetadata = Field(default_factory=TaskStateMetadata)
 
 
 class EasyUITaskState(TaskState):
@@ -189,7 +206,6 @@ class WorkflowStartStreamResponse(StreamResponse):
 
         id: str
         workflow_id: str
-        sequence_number: int
         inputs: Mapping[str, Any]
         created_at: int
 
@@ -210,7 +226,6 @@ class WorkflowFinishStreamResponse(StreamResponse):
 
         id: str
         workflow_id: str
-        sequence_number: int
         status: str
         outputs: Optional[Mapping[str, Any]] = None
         error: Optional[str] = None
@@ -307,7 +322,7 @@ class NodeFinishStreamResponse(StreamResponse):
         status: str
         error: Optional[str] = None
         elapsed_time: float
-        execution_metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None
+        execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
         created_at: int
         finished_at: int
         files: Optional[Sequence[Mapping[str, Any]]] = []
@@ -376,7 +391,7 @@ class NodeRetryStreamResponse(StreamResponse):
         status: str
         error: Optional[str] = None
         elapsed_time: float
-        execution_metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None
+        execution_metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None
         created_at: int
         finished_at: int
         files: Optional[Sequence[Mapping[str, Any]]] = []

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -1,4 +1,3 @@
-import json
 import logging
 import time
 from collections.abc import Generator
@@ -43,7 +42,7 @@ from core.app.entities.task_entities import (
     StreamResponse,
 )
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
-from core.app.task_pipeline.message_cycle_manage import MessageCycleManage
+from core.app.task_pipeline.message_cycle_manager import MessageCycleManager
 from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
@@ -51,7 +50,6 @@ from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
 )
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.model_runtime.utils.encoders import jsonable_encoder
 from core.ops.entities.trace_entity import TraceTaskName
 from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
@@ -63,7 +61,7 @@ from models.model import AppMode, Conversation, Message, MessageAgentThought
 logger = logging.getLogger(__name__)
 
 
-class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleManage):
+class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline):
     """
     EasyUIBasedGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
@@ -104,6 +102,11 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
             )
         )
 
+        self._message_cycle_manager = MessageCycleManager(
+            application_generate_entity=application_generate_entity,
+            task_state=self._task_state,
+        )
+
         self._conversation_name_generate_thread: Optional[Thread] = None
 
     def process(
@@ -115,7 +118,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
     ]:
         if self._application_generate_entity.app_config.app_mode != AppMode.COMPLETION:
             # start generate conversation name thread
-            self._conversation_name_generate_thread = self._generate_conversation_name(
+            self._conversation_name_generate_thread = self._message_cycle_manager.generate_conversation_name(
                 conversation_id=self._conversation_id, query=self._application_generate_entity.query or ""
             )
 
@@ -136,9 +139,9 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
             if isinstance(stream_response, ErrorStreamResponse):
                 raise stream_response.err
             elif isinstance(stream_response, MessageEndStreamResponse):
-                extras = {"usage": jsonable_encoder(self._task_state.llm_result.usage)}
+                extras = {"usage": self._task_state.llm_result.usage.model_dump()}
                 if self._task_state.metadata:
-                    extras["metadata"] = self._task_state.metadata
+                    extras["metadata"] = self._task_state.metadata.model_dump()
                 response: Union[ChatbotAppBlockingResponse, CompletionAppBlockingResponse]
                 if self._conversation_mode == AppMode.COMPLETION.value:
                     response = CompletionAppBlockingResponse(
@@ -277,7 +280,9 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                 )
                 if output_moderation_answer:
                     self._task_state.llm_result.message.content = output_moderation_answer
-                    yield self._message_replace_to_stream_response(answer=output_moderation_answer)
+                    yield self._message_cycle_manager.message_replace_to_stream_response(
+                        answer=output_moderation_answer
+                    )
 
                 with Session(db.engine) as session:
                     # Save message
@@ -286,9 +291,9 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                 message_end_resp = self._message_end_to_stream_response()
                 yield message_end_resp
             elif isinstance(event, QueueRetrieverResourcesEvent):
-                self._handle_retriever_resources(event)
+                self._message_cycle_manager.handle_retriever_resources(event)
             elif isinstance(event, QueueAnnotationReplyEvent):
-                annotation = self._handle_annotation_reply(event)
+                annotation = self._message_cycle_manager.handle_annotation_reply(event)
                 if annotation:
                     self._task_state.llm_result.message.content = annotation.content
             elif isinstance(event, QueueAgentThoughtEvent):
@@ -296,7 +301,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                 if agent_thought_response is not None:
                     yield agent_thought_response
             elif isinstance(event, QueueMessageFileEvent):
-                response = self._message_file_to_stream_response(event)
+                response = self._message_cycle_manager.message_file_to_stream_response(event)
                 if response:
                     yield response
             elif isinstance(event, QueueLLMChunkEvent | QueueAgentMessageEvent):
@@ -318,7 +323,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                 self._task_state.llm_result.message.content = current_content
 
                 if isinstance(event, QueueLLMChunkEvent):
-                    yield self._message_to_stream_response(
+                    yield self._message_cycle_manager.message_to_stream_response(
                         answer=cast(str, delta_text),
                         message_id=self._message_id,
                     )
@@ -328,7 +333,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
                         message_id=self._message_id,
                     )
             elif isinstance(event, QueueMessageReplaceEvent):
-                yield self._message_replace_to_stream_response(answer=event.text)
+                yield self._message_cycle_manager.message_replace_to_stream_response(answer=event.text)
             elif isinstance(event, QueuePingEvent):
                 yield self._ping_stream_response()
             else:
@@ -372,9 +377,7 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         message.provider_response_latency = time.perf_counter() - self._start_at
         message.total_price = usage.total_price
         message.currency = usage.currency
-        message.message_metadata = (
-            json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
-        )
+        message.message_metadata = self._task_state.metadata.model_dump_json()
 
         if trace_manager:
             trace_manager.add_trace_task(
@@ -423,16 +426,12 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         Message end to stream response.
         :return:
         """
-        self._task_state.metadata["usage"] = jsonable_encoder(self._task_state.llm_result.usage)
-
-        extras = {}
-        if self._task_state.metadata:
-            extras["metadata"] = self._task_state.metadata
-
+        self._task_state.metadata.usage = self._task_state.llm_result.usage
+        metadata_dict = self._task_state.metadata.model_dump()
         return MessageEndStreamResponse(
             task_id=self._application_generate_entity.task_id,
             id=self._message_id,
-            metadata=extras.get("metadata", {}),
+            metadata=metadata_dict,
         )
 
     def _agent_message_to_stream_response(self, answer: str, message_id: str) -> AgentMessageStreamResponse:

api/core/app/task_pipeline/message_cycle_manager.py

@@ -17,6 +17,8 @@ from core.app.entities.queue_entities import (
     QueueRetrieverResourcesEvent,
 )
 from core.app.entities.task_entities import (
+    AnnotationReply,
+    AnnotationReplyAccount,
     EasyUITaskState,
     MessageFileStreamResponse,
     MessageReplaceStreamResponse,
@@ -30,7 +32,7 @@ from models.model import AppMode, Conversation, MessageAnnotation, MessageFile
 from services.annotation_service import AppAnnotationService
 
 
-class MessageCycleManage:
+class MessageCycleManager:
     def __init__(
         self,
         *,
@@ -45,7 +47,7 @@ class MessageCycleManage:
         self._application_generate_entity = application_generate_entity
         self._task_state = task_state
 
-    def _generate_conversation_name(self, *, conversation_id: str, query: str) -> Optional[Thread]:
+    def generate_conversation_name(self, *, conversation_id: str, query: str) -> Optional[Thread]:
         """
         Generate conversation name.
         :param conversation_id: conversation id
@@ -102,7 +104,7 @@ class MessageCycleManage:
                 db.session.commit()
                 db.session.close()
 
-    def _handle_annotation_reply(self, event: QueueAnnotationReplyEvent) -> Optional[MessageAnnotation]:
+    def handle_annotation_reply(self, event: QueueAnnotationReplyEvent) -> Optional[MessageAnnotation]:
         """
         Handle annotation reply.
         :param event: event
@@ -111,25 +113,28 @@ class MessageCycleManage:
         annotation = AppAnnotationService.get_annotation_by_id(event.message_annotation_id)
         if annotation:
             account = annotation.account
-            self._task_state.metadata["annotation_reply"] = {
-                "id": annotation.id,
-                "account": {"id": annotation.account_id, "name": account.name if account else "Dify user"},
-            }
+            self._task_state.metadata.annotation_reply = AnnotationReply(
+                id=annotation.id,
+                account=AnnotationReplyAccount(
+                    id=annotation.account_id,
+                    name=account.name if account else "Dify user",
+                ),
+            )
 
             return annotation
 
         return None
 
-    def _handle_retriever_resources(self, event: QueueRetrieverResourcesEvent) -> None:
+    def handle_retriever_resources(self, event: QueueRetrieverResourcesEvent) -> None:
         """
         Handle retriever resources.
         :param event: event
         :return:
         """
         if self._application_generate_entity.app_config.additional_features.show_retrieve_source:
-            self._task_state.metadata["retriever_resources"] = event.retriever_resources
+            self._task_state.metadata.retriever_resources = event.retriever_resources
 
-    def _message_file_to_stream_response(self, event: QueueMessageFileEvent) -> Optional[MessageFileStreamResponse]:
+    def message_file_to_stream_response(self, event: QueueMessageFileEvent) -> Optional[MessageFileStreamResponse]:
         """
         Message file to stream response.
         :param event: event
@@ -166,7 +171,7 @@ class MessageCycleManage:
 
         return None
 
-    def _message_to_stream_response(
+    def message_to_stream_response(
         self, answer: str, message_id: str, from_variable_selector: Optional[list[str]] = None
     ) -> MessageStreamResponse:
         """
@@ -182,7 +187,7 @@ class MessageCycleManage:
             from_variable_selector=from_variable_selector,
         )
 
-    def _message_replace_to_stream_response(self, answer: str, reason: str = "") -> MessageReplaceStreamResponse:
+    def message_replace_to_stream_response(self, answer: str, reason: str = "") -> MessageReplaceStreamResponse:
         """
         Message replace to stream response.
         :param answer: answer

api/core/callback_handler/index_tool_callback_handler.py

@@ -1,8 +1,10 @@
 import logging
+from collections.abc import Sequence
 
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.rag.index_processor.constant.index_type import IndexType
 from core.rag.models.document import Document
 from extensions.ext_database import db
@@ -85,7 +87,8 @@ class DatasetIndexToolCallbackHandler:
 
                 db.session.commit()
 
-    def return_retriever_resource_info(self, resource: list):
+    # TODO(-LAN-): Improve type check
+    def return_retriever_resource_info(self, resource: Sequence[RetrievalSourceMetadata]):
         """Handle return_retriever_resource_info."""
         self._queue_manager.publish(
             QueueRetrieverResourcesEvent(retriever_resources=resource), PublishFrom.APPLICATION_MANAGER

api/core/entities/model_entities.py

@@ -55,6 +55,25 @@ class ProviderModelWithStatusEntity(ProviderModel):
     status: ModelStatus
     load_balancing_enabled: bool = False
 
+    def raise_for_status(self) -> None:
+        """
+        Check model status and raise ValueError if not active.
+
+        :raises ValueError: When model status is not active, with a descriptive message
+        """
+        if self.status == ModelStatus.ACTIVE:
+            return
+
+        error_messages = {
+            ModelStatus.NO_CONFIGURE: "Model is not configured",
+            ModelStatus.QUOTA_EXCEEDED: "Model quota has been exceeded",
+            ModelStatus.NO_PERMISSION: "No permission to use this model",
+            ModelStatus.DISABLED: "Model is disabled",
+        }
+
+        if self.status in error_messages:
+            raise ValueError(error_messages[self.status])
+
 
 class ModelWithProviderEntity(ProviderModelWithStatusEntity):
     """

api/core/extension/extensible.py

@@ -41,45 +41,53 @@ class Extensible:
         extensions = []
         position_map: dict[str, int] = {}
 
-        # get the path of the current class
-        current_path = os.path.abspath(cls.__module__.replace(".", os.path.sep) + ".py")
-        current_dir_path = os.path.dirname(current_path)
-
-        # traverse subdirectories
-        for subdir_name in os.listdir(current_dir_path):
-            if subdir_name.startswith("__"):
-                continue
-
-            subdir_path = os.path.join(current_dir_path, subdir_name)
-            extension_name = subdir_name
-            if os.path.isdir(subdir_path):
+        # Get the package name from the module path
+        package_name = ".".join(cls.__module__.split(".")[:-1])
+
+        try:
+            # Get package directory path
+            package_spec = importlib.util.find_spec(package_name)
+            if not package_spec or not package_spec.origin:
+                raise ImportError(f"Could not find package {package_name}")
+
+            package_dir = os.path.dirname(package_spec.origin)
+
+            # Traverse subdirectories
+            for subdir_name in os.listdir(package_dir):
+                if subdir_name.startswith("__"):
+                    continue
+
+                subdir_path = os.path.join(package_dir, subdir_name)
+                if not os.path.isdir(subdir_path):
+                    continue
+
+                extension_name = subdir_name
                 file_names = os.listdir(subdir_path)
 
-                # is builtin extension, builtin extension
-                # in the front-end page and business logic, there are special treatments.
+                # Check for extension module file
+                if (extension_name + ".py") not in file_names:
+                    logging.warning(f"Missing {extension_name}.py file in {subdir_path}, Skip.")
+                    continue
+
+                # Check for builtin flag and position
                 builtin = False
-                # default position is 0 can not be None for sort_to_dict_by_position_map
                 position = 0
                 if "__builtin__" in file_names:
                     builtin = True
-
                     builtin_file_path = os.path.join(subdir_path, "__builtin__")
                     if os.path.exists(builtin_file_path):
                         position = int(Path(builtin_file_path).read_text(encoding="utf-8").strip())
                     position_map[extension_name] = position
 
-                if (extension_name + ".py") not in file_names:
-                    logging.warning(f"Missing {extension_name}.py file in {subdir_path}, Skip.")
-                    continue
-
-                # Dynamic loading {subdir_name}.py file and find the subclass of Extensible
-                py_path = os.path.join(subdir_path, extension_name + ".py")
-                spec = importlib.util.spec_from_file_location(extension_name, py_path)
+                # Import the extension module
+                module_name = f"{package_name}.{extension_name}.{extension_name}"
+                spec = importlib.util.find_spec(module_name)
                 if not spec or not spec.loader:
-                    raise Exception(f"Failed to load module {extension_name} from {py_path}")
+                    raise ImportError(f"Failed to load module {module_name}")
                 mod = importlib.util.module_from_spec(spec)
                 spec.loader.exec_module(mod)
 
+                # Find extension class
                 extension_class = None
                 for name, obj in vars(mod).items():
                     if isinstance(obj, type) and issubclass(obj, cls) and obj != cls:
@@ -87,21 +95,21 @@ class Extensible:
                         break
 
                 if not extension_class:
-                    logging.warning(f"Missing subclass of {cls.__name__} in {py_path}, Skip.")
+                    logging.warning(f"Missing subclass of {cls.__name__} in {module_name}, Skip.")
                     continue
 
+                # Load schema if not builtin
                 json_data: dict[str, Any] = {}
                 if not builtin:
-                    if "schema.json" not in file_names:
+                    json_path = os.path.join(subdir_path, "schema.json")
+                    if not os.path.exists(json_path):
                         logging.warning(f"Missing schema.json file in {subdir_path}, Skip.")
                         continue
 
-                    json_path = os.path.join(subdir_path, "schema.json")
-                    json_data = {}
-                    if os.path.exists(json_path):
-                        with open(json_path, encoding="utf-8") as f:
-                            json_data = json.load(f)
+                    with open(json_path, encoding="utf-8") as f:
+                        json_data = json.load(f)
 
+                # Create extension
                 extensions.append(
                     ModuleExtension(
                         extension_class=extension_class,
@@ -113,6 +121,11 @@ class Extensible:
                     )
                 )
 
+        except Exception as e:
+            logging.exception("Error scanning extensions")
+            raise
+
+        # Sort extensions by position
         sorted_extensions = sort_to_dict_by_position_map(
             position_map=position_map, data=extensions, name_func=lambda x: x.name
         )

api/core/helper/code_executor/code_executor.py

@@ -15,6 +15,7 @@ from core.helper.code_executor.python3.python3_transformer import Python3Templat
 from core.helper.code_executor.template_transformer import TemplateTransformer
 
 logger = logging.getLogger(__name__)
+code_execution_endpoint_url = URL(str(dify_config.CODE_EXECUTION_ENDPOINT))
 
 
 class CodeExecutionError(Exception):
@@ -64,7 +65,7 @@ class CodeExecutor:
         :param code: code
         :return:
         """
-        url = URL(str(dify_config.CODE_EXECUTION_ENDPOINT)) / "v1" / "sandbox" / "run"
+        url = code_execution_endpoint_url / "v1" / "sandbox" / "run"
 
         headers = {"X-Api-Key": dify_config.CODE_EXECUTION_API_KEY}
 

api/core/helper/marketplace.py

@@ -7,29 +7,28 @@ from configs import dify_config
 from core.helper.download import download_with_size_limit
 from core.plugin.entities.marketplace import MarketplacePluginDeclaration
 
+marketplace_api_url = URL(str(dify_config.MARKETPLACE_API_URL))
 
-def get_plugin_pkg_url(plugin_unique_identifier: str):
-    return (URL(str(dify_config.MARKETPLACE_API_URL)) / "api/v1/plugins/download").with_query(
-        unique_identifier=plugin_unique_identifier
-    )
+
+def get_plugin_pkg_url(plugin_unique_identifier: str) -> str:
+    return str((marketplace_api_url / "api/v1/plugins/download").with_query(unique_identifier=plugin_unique_identifier))
 
 
 def download_plugin_pkg(plugin_unique_identifier: str):
-    url = str(get_plugin_pkg_url(plugin_unique_identifier))
-    return download_with_size_limit(url, dify_config.PLUGIN_MAX_PACKAGE_SIZE)
+    return download_with_size_limit(get_plugin_pkg_url(plugin_unique_identifier), dify_config.PLUGIN_MAX_PACKAGE_SIZE)
 
 
 def batch_fetch_plugin_manifests(plugin_ids: list[str]) -> Sequence[MarketplacePluginDeclaration]:
     if len(plugin_ids) == 0:
         return []
 
-    url = str(URL(str(dify_config.MARKETPLACE_API_URL)) / "api/v1/plugins/batch")
+    url = str(marketplace_api_url / "api/v1/plugins/batch")
     response = requests.post(url, json={"plugin_ids": plugin_ids})
     response.raise_for_status()
     return [MarketplacePluginDeclaration(**plugin) for plugin in response.json()["data"]["plugins"]]
 
 
 def record_install_plugin_event(plugin_unique_identifier: str):
-    url = str(URL(str(dify_config.MARKETPLACE_API_URL)) / "api/v1/stats/plugins/install_count")
+    url = str(marketplace_api_url / "api/v1/stats/plugins/install_count")
     response = requests.post(url, json={"unique_identifier": plugin_unique_identifier})
     response.raise_for_status()

api/core/llm_generator/prompts.py

@@ -1,61 +1,20 @@
-# Written by YORKI MINAKO🤡, Edited by Xiaoyi
-CONVERSATION_TITLE_PROMPT = """You need to decompose the user's input into "subject" and "intention" in order to accurately figure out what the user's input language actually is.
-Notice: the language type user uses could be diverse, which can be English, Chinese, Italian, Español, Arabic, Japanese, French, and etc.
-ENSURE your output is in the SAME language as the user's input!
-Your output is restricted only to: (Input language) Intention + Subject(short as possible)
-Your output MUST be a valid JSON.
+# Written by YORKI MINAKO🤡, Edited by Xiaoyi, Edited by yasu-oh
+CONVERSATION_TITLE_PROMPT = """You are asked to generate a concise chat title by decomposing the user’s input into two parts: “Intention” and “Subject”.
 
-Tip: When the user's question is directed at you (the language model), you can add an emoji to make it more fun.
+1. Detect Input Language
+Automatically identify the language of the user’s input (e.g. English, Chinese, Italian, Español, Arabic, Japanese, French, and etc.).
 
+2. Generate Title
+- Combine Intention + Subject into a single, as-short-as-possible phrase.
+- The title must be natural, friendly, and in the same language as the input.
+- If the input is a direct question to the model, you may add an emoji at the end.
 
-example 1:
-User Input: hi, yesterday i had some burgers.
+3. Output Format
+Return **only** a valid JSON object with these exact keys and no additional text:
 {
-  "Language Type": "The user's input is pure English",
-  "Your Reasoning": "The language of my output must be pure English.",
-  "Your Output": "sharing yesterday's food"
-}
-
-example 2:
-User Input: hello
-{
-  "Language Type": "The user's input is pure English",
-  "Your Reasoning": "The language of my output must be pure English.",
-  "Your Output": "Greeting myself☺️"
-}
-
-
-example 3:
-User Input: why mmap file: oom
-{
-  "Language Type": "The user's input is written in pure English",
-  "Your Reasoning": "The language of my output must be pure English.",
-  "Your Output": "Asking about the reason for mmap file: oom"
-}
-
-
-example 4:
-User Input: www.convinceme.yesterday-you-ate-seafood.tv讲了什么？
-{
-  "Language Type": "The user's input English-Chinese mixed",
-  "Your Reasoning": "The English-part is an URL, the main intention is still written in Chinese, so the language of my output must be using Chinese.",
-  "Your Output": "询问网站www.convinceme.yesterday-you-ate-seafood.tv"
-}
-
-example 5:
-User Input: why小红的年龄is老than小明？
-{
-  "Language Type": "The user's input is English-Chinese mixed",
-  "Your Reasoning": "The English parts are filler words, the main intention is written in Chinese, besides, Chinese occupies a greater \"actual meaning\" than English, so the language of my output must be using Chinese.",
-  "Your Output": "询问小红和小明的年龄"
-}
-
-example 6:
-User Input: yo, 你今天咋样？
-{
-  "Language Type": "The user's input is English-Chinese mixed",
-  "Your Reasoning": "The English-part is a subjective particle, the main intention is written in Chinese, so the language of my output must be using Chinese.",
-  "Your Output": "查询今日我的状态☺️"
+  "Language Type": "<Detected language>",
+  "Your Reasoning": "<Brief explanation in that language>",
+  "Your Output": "<Intention + Subject>"
 }
 
 User Input:

api/core/model_runtime/entities/llm_entities.py

@@ -17,19 +17,6 @@ class LLMMode(StrEnum):
     COMPLETION = "completion"
     CHAT = "chat"
 
-    @classmethod
-    def value_of(cls, value: str) -> "LLMMode":
-        """
-        Get value of given mode.
-
-        :param value: mode value
-        :return: mode
-        """
-        for mode in cls:
-            if mode.value == value:
-                return mode
-        raise ValueError(f"invalid mode value {value}")
-
 
 class LLMUsage(ModelUsage):
     """

api/core/model_runtime/entities/model_entities.py

@@ -160,6 +160,10 @@ class ProviderModel(BaseModel):
     deprecated: bool = False
     model_config = ConfigDict(protected_namespaces=())
 
+    @property
+    def support_structure_output(self) -> bool:
+        return self.features is not None and ModelFeature.STRUCTURED_OUTPUT in self.features
+
 
 class ParameterRule(BaseModel):
     """

api/core/model_runtime/utils/encoders.py

@@ -129,17 +129,18 @@ def jsonable_encoder(
             sqlalchemy_safe=sqlalchemy_safe,
         )
     if dataclasses.is_dataclass(obj):
-        # FIXME: mypy error, try to fix it instead of using type: ignore
-        obj_dict = dataclasses.asdict(obj)  # type: ignore
-        return jsonable_encoder(
-            obj_dict,
-            by_alias=by_alias,
-            exclude_unset=exclude_unset,
-            exclude_defaults=exclude_defaults,
-            exclude_none=exclude_none,
-            custom_encoder=custom_encoder,
-            sqlalchemy_safe=sqlalchemy_safe,
-        )
+        # Ensure obj is a dataclass instance, not a dataclass type
+        if not isinstance(obj, type):
+            obj_dict = dataclasses.asdict(obj)
+            return jsonable_encoder(
+                obj_dict,
+                by_alias=by_alias,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                exclude_none=exclude_none,
+                custom_encoder=custom_encoder,
+                sqlalchemy_safe=sqlalchemy_safe,
+            )
     if isinstance(obj, Enum):
         return obj.value
     if isinstance(obj, PurePath):

api/core/ops/base_trace_instance.py

@@ -1,7 +1,11 @@
 from abc import ABC, abstractmethod
 
+from sqlalchemy.orm import Session
+
 from core.ops.entities.config_entity import BaseTracingConfig
 from core.ops.entities.trace_entity import BaseTraceInfo
+from extensions.ext_database import db
+from models import Account, App, TenantAccountJoin
 
 
 class BaseTraceInstance(ABC):
@@ -24,3 +28,38 @@ class BaseTraceInstance(ABC):
         Subclasses must implement specific tracing logic for activities.
         """
         ...
+
+    def get_service_account_with_tenant(self, app_id: str) -> Account:
+        """
+        Get service account for an app and set up its tenant.
+
+        Args:
+            app_id: The ID of the app
+
+        Returns:
+            Account: The service account with tenant set up
+
+        Raises:
+            ValueError: If app, creator account or tenant cannot be found
+        """
+        with Session(db.engine, expire_on_commit=False) as session:
+            # Get the app to find its creator
+            app = session.query(App).filter(App.id == app_id).first()
+            if not app:
+                raise ValueError(f"App with id {app_id} not found")
+
+            if not app.created_by:
+                raise ValueError(f"App with id {app_id} has no creator (created_by is None)")
+
+            service_account = session.query(Account).filter(Account.id == app.created_by).first()
+            if not service_account:
+                raise ValueError(f"Creator account with id {app.created_by} not found for app {app_id}")
+
+            current_tenant = (
+                session.query(TenantAccountJoin).filter_by(account_id=service_account.id, current=True).first()
+            )
+            if not current_tenant:
+                raise ValueError(f"Current tenant not found for account {service_account.id}")
+            service_account.set_tenant_id(current_tenant.tenant_id)
+
+            return service_account

api/core/ops/entities/trace_entity.py

@@ -3,7 +3,7 @@ from datetime import datetime
 from enum import StrEnum
 from typing import Any, Optional, Union
 
-from pydantic import BaseModel, ConfigDict, field_validator
+from pydantic import BaseModel, ConfigDict, field_serializer, field_validator
 
 
 class BaseTraceInfo(BaseModel):
@@ -24,10 +24,13 @@ class BaseTraceInfo(BaseModel):
             return v
         return ""
 
-    class Config:
-        json_encoders = {
-            datetime: lambda v: v.isoformat(),
-        }
+    model_config = ConfigDict(protected_namespaces=())
+
+    @field_serializer("start_time", "end_time")
+    def serialize_datetime(self, dt: datetime | None) -> str | None:
+        if dt is None:
+            return None
+        return dt.isoformat()
 
 
 class WorkflowTraceInfo(BaseTraceInfo):

api/core/ops/langfuse_trace/langfuse_trace.py

@@ -4,7 +4,7 @@ from datetime import datetime, timedelta
 from typing import Optional
 
 from langfuse import Langfuse  # type: ignore
-from sqlalchemy.orm import Session, sessionmaker
+from sqlalchemy.orm import sessionmaker
 
 from core.ops.base_trace_instance import BaseTraceInstance
 from core.ops.entities.config_entity import LangfuseConfig
@@ -31,7 +31,7 @@ from core.ops.utils import filter_none_values
 from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
 from core.workflow.nodes.enums import NodeType
 from extensions.ext_database import db
-from models import Account, App, EndUser, WorkflowNodeExecutionTriggeredFrom
+from models import EndUser, WorkflowNodeExecutionTriggeredFrom
 
 logger = logging.getLogger(__name__)
 
@@ -114,22 +114,11 @@ class LangFuseDataTrace(BaseTraceInstance):
         # through workflow_run_id get all_nodes_execution using repository
         session_factory = sessionmaker(bind=db.engine)
         # Find the app's creator account
-        with Session(db.engine, expire_on_commit=False) as session:
-            # Get the app to find its creator
-            app_id = trace_info.metadata.get("app_id")
-            if not app_id:
-                raise ValueError("No app_id found in trace_info metadata")
-
-            app = session.query(App).filter(App.id == app_id).first()
-            if not app:
-                raise ValueError(f"App with id {app_id} not found")
-
-            if not app.created_by:
-                raise ValueError(f"App with id {app_id} has no creator (created_by is None)")
-
-            service_account = session.query(Account).filter(Account.id == app.created_by).first()
-            if not service_account:
-                raise ValueError(f"Creator account with id {app.created_by} not found for app {app_id}")
+        app_id = trace_info.metadata.get("app_id")
+        if not app_id:
+            raise ValueError("No app_id found in trace_info metadata")
+
+        service_account = self.get_service_account_with_tenant(app_id)
 
         workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
             session_factory=session_factory,

api/core/ops/langsmith_trace/langsmith_trace.py

@@ -6,7 +6,7 @@ from typing import Optional, cast
 
 from langsmith import Client
 from langsmith.schemas import RunBase
-from sqlalchemy.orm import Session, sessionmaker
+from sqlalchemy.orm import sessionmaker
 
 from core.ops.base_trace_instance import BaseTraceInstance
 from core.ops.entities.config_entity import LangSmithConfig
@@ -28,10 +28,10 @@ from core.ops.langsmith_trace.entities.langsmith_trace_entity import (
 )
 from core.ops.utils import filter_none_values, generate_dotted_order
 from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
-from core.workflow.entities.node_entities import NodeRunMetadataKey
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes.enums import NodeType
 from extensions.ext_database import db
-from models import Account, App, EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
+from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 
 logger = logging.getLogger(__name__)
 
@@ -139,22 +139,11 @@ class LangSmithDataTrace(BaseTraceInstance):
         # through workflow_run_id get all_nodes_execution using repository
         session_factory = sessionmaker(bind=db.engine)
         # Find the app's creator account
-        with Session(db.engine, expire_on_commit=False) as session:
-            # Get the app to find its creator
-            app_id = trace_info.metadata.get("app_id")
-            if not app_id:
-                raise ValueError("No app_id found in trace_info metadata")
+        app_id = trace_info.metadata.get("app_id")
+        if not app_id:
+            raise ValueError("No app_id found in trace_info metadata")
 
-            app = session.query(App).filter(App.id == app_id).first()
-            if not app:
-                raise ValueError(f"App with id {app_id} not found")
-
-            if not app.created_by:
-                raise ValueError(f"App with id {app_id} has no creator (created_by is None)")
-
-            service_account = session.query(Account).filter(Account.id == app.created_by).first()
-            if not service_account:
-                raise ValueError(f"Creator account with id {app.created_by} not found for app {app_id}")
+        service_account = self.get_service_account_with_tenant(app_id)
 
         workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
             session_factory=session_factory,
@@ -185,7 +174,7 @@ class LangSmithDataTrace(BaseTraceInstance):
             finished_at = created_at + timedelta(seconds=elapsed_time)
 
             execution_metadata = node_execution.metadata if node_execution.metadata else {}
-            node_total_tokens = execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS) or 0
+            node_total_tokens = execution_metadata.get(WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS) or 0
             metadata = {str(key): value for key, value in execution_metadata.items()}
             metadata.update(
                 {

api/core/ops/opik_trace/opik_trace.py

@@ -6,7 +6,7 @@ from typing import Optional, cast
 
 from opik import Opik, Trace
 from opik.id_helpers import uuid4_to_uuid7
-from sqlalchemy.orm import Session, sessionmaker
+from sqlalchemy.orm import sessionmaker
 
 from core.ops.base_trace_instance import BaseTraceInstance
 from core.ops.entities.config_entity import OpikConfig
@@ -22,10 +22,10 @@ from core.ops.entities.trace_entity import (
     WorkflowTraceInfo,
 )
 from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
-from core.workflow.entities.node_entities import NodeRunMetadataKey
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes.enums import NodeType
 from extensions.ext_database import db
-from models import Account, App, EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
+from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 
 logger = logging.getLogger(__name__)
 
@@ -154,22 +154,11 @@ class OpikDataTrace(BaseTraceInstance):
         # through workflow_run_id get all_nodes_execution using repository
         session_factory = sessionmaker(bind=db.engine)
         # Find the app's creator account
-        with Session(db.engine, expire_on_commit=False) as session:
-            # Get the app to find its creator
-            app_id = trace_info.metadata.get("app_id")
-            if not app_id:
-                raise ValueError("No app_id found in trace_info metadata")
+        app_id = trace_info.metadata.get("app_id")
+        if not app_id:
+            raise ValueError("No app_id found in trace_info metadata")
 
-            app = session.query(App).filter(App.id == app_id).first()
-            if not app:
-                raise ValueError(f"App with id {app_id} not found")
-
-            if not app.created_by:
-                raise ValueError(f"App with id {app_id} has no creator (created_by is None)")
-
-            service_account = session.query(Account).filter(Account.id == app.created_by).first()
-            if not service_account:
-                raise ValueError(f"Creator account with id {app.created_by} not found for app {app_id}")
+        service_account = self.get_service_account_with_tenant(app_id)
 
         workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
             session_factory=session_factory,
@@ -246,7 +235,7 @@ class OpikDataTrace(BaseTraceInstance):
             parent_span_id = trace_info.workflow_app_log_id or trace_info.workflow_run_id
 
             if not total_tokens:
-                total_tokens = execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS) or 0
+                total_tokens = execution_metadata.get(WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS) or 0
 
             span_data = {
                 "trace_id": opik_trace_id,

api/core/ops/ops_trace_manager.py

@@ -30,7 +30,7 @@ from core.ops.entities.trace_entity import (
     WorkflowTraceInfo,
 )
 from core.ops.utils import get_message_data
-from core.workflow.entities.workflow_execution_entities import WorkflowExecution
+from core.workflow.entities.workflow_execution import WorkflowExecution
 from extensions.ext_database import db
 from extensions.ext_storage import storage
 from models.model import App, AppModelConfig, Conversation, Message, MessageFile, TraceAppConfig
@@ -386,7 +386,7 @@ class TraceTask:
     ):
         self.trace_type = trace_type
         self.message_id = message_id
-        self.workflow_run_id = workflow_execution.id if workflow_execution else None
+        self.workflow_run_id = workflow_execution.id_ if workflow_execution else None
         self.conversation_id = conversation_id
         self.user_id = user_id
         self.timer = timer
@@ -487,6 +487,7 @@ class TraceTask:
                 "file_list": file_list,
                 "triggered_from": workflow_run.triggered_from,
                 "user_id": user_id,
+                "app_id": workflow_run.app_id,
             }
 
             workflow_trace_info = WorkflowTraceInfo(

api/core/ops/weave_trace/weave_trace.py

@@ -6,7 +6,7 @@ from typing import Any, Optional, cast
 
 import wandb
 import weave
-from sqlalchemy.orm import Session, sessionmaker
+from sqlalchemy.orm import sessionmaker
 
 from core.ops.base_trace_instance import BaseTraceInstance
 from core.ops.entities.config_entity import WeaveConfig
@@ -23,10 +23,10 @@ from core.ops.entities.trace_entity import (
 )
 from core.ops.weave_trace.entities.weave_trace_entity import WeaveTraceModel
 from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
-from core.workflow.entities.node_entities import NodeRunMetadataKey
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes.enums import NodeType
 from extensions.ext_database import db
-from models import Account, App, EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
+from models import EndUser, MessageFile, WorkflowNodeExecutionTriggeredFrom
 
 logger = logging.getLogger(__name__)
 
@@ -133,22 +133,11 @@ class WeaveDataTrace(BaseTraceInstance):
         # through workflow_run_id get all_nodes_execution using repository
         session_factory = sessionmaker(bind=db.engine)
         # Find the app's creator account
-        with Session(db.engine, expire_on_commit=False) as session:
-            # Get the app to find its creator
-            app_id = trace_info.metadata.get("app_id")
-            if not app_id:
-                raise ValueError("No app_id found in trace_info metadata")
+        app_id = trace_info.metadata.get("app_id")
+        if not app_id:
+            raise ValueError("No app_id found in trace_info metadata")
 
-            app = session.query(App).filter(App.id == app_id).first()
-            if not app:
-                raise ValueError(f"App with id {app_id} not found")
-
-            if not app.created_by:
-                raise ValueError(f"App with id {app_id} has no creator (created_by is None)")
-
-            service_account = session.query(Account).filter(Account.id == app.created_by).first()
-            if not service_account:
-                raise ValueError(f"Creator account with id {app.created_by} not found for app {app_id}")
+        service_account = self.get_service_account_with_tenant(app_id)
 
         workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
             session_factory=session_factory,
@@ -179,7 +168,7 @@ class WeaveDataTrace(BaseTraceInstance):
             finished_at = created_at + timedelta(seconds=elapsed_time)
 
             execution_metadata = node_execution.metadata if node_execution.metadata else {}
-            node_total_tokens = execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS) or 0
+            node_total_tokens = execution_metadata.get(WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS) or 0
             attributes = {str(k): v for k, v in execution_metadata.items()}
             attributes.update(
                 {

api/core/plugin/impl/base.py

@@ -31,8 +31,7 @@ from core.plugin.impl.exc import (
     PluginUniqueIdentifierError,
 )
 
-plugin_daemon_inner_api_baseurl = dify_config.PLUGIN_DAEMON_URL
-plugin_daemon_inner_api_key = dify_config.PLUGIN_DAEMON_KEY
+plugin_daemon_inner_api_baseurl = URL(str(dify_config.PLUGIN_DAEMON_URL))
 
 T = TypeVar("T", bound=(BaseModel | dict | list | bool | str))
 
@@ -53,9 +52,9 @@ class BasePluginClient:
         """
         Make a request to the plugin daemon inner API.
         """
-        url = URL(str(plugin_daemon_inner_api_baseurl)) / path
+        url = plugin_daemon_inner_api_baseurl / path
         headers = headers or {}
-        headers["X-Api-Key"] = plugin_daemon_inner_api_key
+        headers["X-Api-Key"] = dify_config.PLUGIN_DAEMON_KEY
         headers["Accept-Encoding"] = "gzip, deflate, br"
 
         if headers.get("Content-Type") == "application/json" and isinstance(data, dict):

api/core/provider_manager.py

@@ -3,7 +3,9 @@ from collections import defaultdict
 from json import JSONDecodeError
 from typing import Any, Optional, cast
 
+from sqlalchemy import select
 from sqlalchemy.exc import IntegrityError
+from sqlalchemy.orm import Session
 
 from configs import dify_config
 from core.entities.model_entities import DefaultModelEntity, DefaultModelProviderEntity
@@ -393,19 +395,13 @@ class ProviderManager:
 
     @staticmethod
     def _get_all_providers(tenant_id: str) -> dict[str, list[Provider]]:
-        """
-        Get all provider records of the workspace.
-
-        :param tenant_id: workspace id
-        :return:
-        """
-        providers = db.session.query(Provider).filter(Provider.tenant_id == tenant_id, Provider.is_valid == True).all()
-
         provider_name_to_provider_records_dict = defaultdict(list)
-        for provider in providers:
-            # TODO: Use provider name with prefix after the data migration
-            provider_name_to_provider_records_dict[str(ModelProviderID(provider.provider_name))].append(provider)
-
+        with Session(db.engine, expire_on_commit=False) as session:
+            stmt = select(Provider).where(Provider.tenant_id == tenant_id, Provider.is_valid == True)
+            providers = session.scalars(stmt)
+            for provider in providers:
+                # Use provider name with prefix after the data migration
+                provider_name_to_provider_records_dict[str(ModelProviderID(provider.provider_name))].append(provider)
         return provider_name_to_provider_records_dict
 
     @staticmethod
@@ -416,17 +412,12 @@ class ProviderManager:
         :param tenant_id: workspace id
         :return:
         """
-        # Get all provider model records of the workspace
-        provider_models = (
-            db.session.query(ProviderModel)
-            .filter(ProviderModel.tenant_id == tenant_id, ProviderModel.is_valid == True)
-            .all()
-        )
-
         provider_name_to_provider_model_records_dict = defaultdict(list)
-        for provider_model in provider_models:
-            provider_name_to_provider_model_records_dict[provider_model.provider_name].append(provider_model)
-
+        with Session(db.engine, expire_on_commit=False) as session:
+            stmt = select(ProviderModel).where(ProviderModel.tenant_id == tenant_id, ProviderModel.is_valid == True)
+            provider_models = session.scalars(stmt)
+            for provider_model in provider_models:
+                provider_name_to_provider_model_records_dict[provider_model.provider_name].append(provider_model)
         return provider_name_to_provider_model_records_dict
 
     @staticmethod
@@ -437,17 +428,14 @@ class ProviderManager:
         :param tenant_id: workspace id
         :return:
         """
-        preferred_provider_types = (
-            db.session.query(TenantPreferredModelProvider)
-            .filter(TenantPreferredModelProvider.tenant_id == tenant_id)
-            .all()
-        )
-
-        provider_name_to_preferred_provider_type_records_dict = {
-            preferred_provider_type.provider_name: preferred_provider_type
-            for preferred_provider_type in preferred_provider_types
-        }
-
+        provider_name_to_preferred_provider_type_records_dict = {}
+        with Session(db.engine, expire_on_commit=False) as session:
+            stmt = select(TenantPreferredModelProvider).where(TenantPreferredModelProvider.tenant_id == tenant_id)
+            preferred_provider_types = session.scalars(stmt)
+            provider_name_to_preferred_provider_type_records_dict = {
+                preferred_provider_type.provider_name: preferred_provider_type
+                for preferred_provider_type in preferred_provider_types
+            }
         return provider_name_to_preferred_provider_type_records_dict
 
     @staticmethod
@@ -458,18 +446,14 @@ class ProviderManager:
         :param tenant_id: workspace id
         :return:
         """
-        provider_model_settings = (
-            db.session.query(ProviderModelSetting).filter(ProviderModelSetting.tenant_id == tenant_id).all()
-        )
-
         provider_name_to_provider_model_settings_dict = defaultdict(list)
-        for provider_model_setting in provider_model_settings:
-            (
+        with Session(db.engine, expire_on_commit=False) as session:
+            stmt = select(ProviderModelSetting).where(ProviderModelSetting.tenant_id == tenant_id)
+            provider_model_settings = session.scalars(stmt)
+            for provider_model_setting in provider_model_settings:
                 provider_name_to_provider_model_settings_dict[provider_model_setting.provider_name].append(
                     provider_model_setting
                 )
-            )
-
         return provider_name_to_provider_model_settings_dict
 
     @staticmethod
@@ -492,15 +476,14 @@ class ProviderManager:
         if not model_load_balancing_enabled:
             return {}
 
-        provider_load_balancing_configs = (
-            db.session.query(LoadBalancingModelConfig).filter(LoadBalancingModelConfig.tenant_id == tenant_id).all()
-        )
-
         provider_name_to_provider_load_balancing_model_configs_dict = defaultdict(list)
-        for provider_load_balancing_config in provider_load_balancing_configs:
-            provider_name_to_provider_load_balancing_model_configs_dict[
-                provider_load_balancing_config.provider_name
-            ].append(provider_load_balancing_config)
+        with Session(db.engine, expire_on_commit=False) as session:
+            stmt = select(LoadBalancingModelConfig).where(LoadBalancingModelConfig.tenant_id == tenant_id)
+            provider_load_balancing_configs = session.scalars(stmt)
+            for provider_load_balancing_config in provider_load_balancing_configs:
+                provider_name_to_provider_load_balancing_model_configs_dict[
+                    provider_load_balancing_config.provider_name
+                ].append(provider_load_balancing_config)
 
         return provider_name_to_provider_load_balancing_model_configs_dict
 
@@ -626,10 +609,9 @@ class ProviderManager:
             if not cached_provider_credentials:
                 try:
                     # fix origin data
-                    if (
-                        custom_provider_record.encrypted_config
-                        and not custom_provider_record.encrypted_config.startswith("{")
-                    ):
+                    if custom_provider_record.encrypted_config is None:
+                        raise ValueError("No credentials found")
+                    if not custom_provider_record.encrypted_config.startswith("{"):
                         provider_credentials = {"openai_api_key": custom_provider_record.encrypted_config}
                     else:
                         provider_credentials = json.loads(custom_provider_record.encrypted_config)
@@ -733,7 +715,7 @@ class ProviderManager:
             return SystemConfiguration(enabled=False)
 
         # Convert provider_records to dict
-        quota_type_to_provider_records_dict = {}
+        quota_type_to_provider_records_dict: dict[ProviderQuotaType, Provider] = {}
         for provider_record in provider_records:
             if provider_record.provider_type != ProviderType.SYSTEM.value:
                 continue
@@ -758,6 +740,11 @@ class ProviderManager:
             else:
                 provider_record = quota_type_to_provider_records_dict[provider_quota.quota_type]
 
+                if provider_record.quota_used is None:
+                    raise ValueError("quota_used is None")
+                if provider_record.quota_limit is None:
+                    raise ValueError("quota_limit is None")
+
                 quota_configuration = QuotaConfiguration(
                     quota_type=provider_quota.quota_type,
                     quota_unit=provider_hosting_configuration.quota_unit or QuotaUnit.TOKENS,
@@ -791,10 +778,9 @@ class ProviderManager:
                 cached_provider_credentials = provider_credentials_cache.get()
 
                 if not cached_provider_credentials:
-                    try:
-                        provider_credentials: dict[str, Any] = json.loads(provider_record.encrypted_config)
-                    except JSONDecodeError:
-                        provider_credentials = {}
+                    provider_credentials: dict[str, Any] = {}
+                    if provider_records and provider_records[0].encrypted_config:
+                        provider_credentials = json.loads(provider_records[0].encrypted_config)
 
                     # Get provider credential secret variables
                     provider_credential_secret_variables = self._extract_secret_variables(

api/core/rag/datasource/keyword/jieba/stopwords.py

@@ -720,7 +720,7 @@ STOPWORDS = {
     "〉",
     "〈",
     "…",
-    "　",
+    " ",
     "0",
     "1",
     "2",
@@ -731,16 +731,6 @@ STOPWORDS = {
     "7",
     "8",
     "9",
-    "０",
-    "１",
-    "２",
-    "３",
-    "４",
-    "５",
-    "６",
-    "７",
-    "８",
-    "９",
     "二",
     "三",
     "四",

api/core/rag/datasource/vdb/baidu/baidu_vector.py

@@ -85,7 +85,6 @@ class BaiduVector(BaseVector):
             end = min(start + batch_size, total_count)
             rows = []
             assert len(metadatas) == total_count, "metadatas length should be equal to total_count"
-            # FIXME do you need this assert?
             for i in range(start, end, 1):
                 row = Row(
                     id=metadatas[i].get("doc_id", str(uuid.uuid4())),

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

@@ -142,7 +142,7 @@ class ElasticSearchVector(BaseVector):
             if score > score_threshold:
                 if doc.metadata is not None:
                     doc.metadata["score"] = score
-            docs.append(doc)
+                    docs.append(doc)
 
         return docs
 

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

@@ -97,6 +97,10 @@ class MilvusVector(BaseVector):
 
         try:
             milvus_version = self._client.get_server_version()
+            # Check if it's Zilliz Cloud - it supports full-text search with Milvus 2.5 compatibility
+            if "Zilliz Cloud" in milvus_version:
+                return True
+            # For standard Milvus installations, check version number
             return version.parse(milvus_version).base_version >= version.parse("2.5.0").base_version
         except Exception as e:
             logger.warning(f"Failed to check Milvus version: {str(e)}. Disabling hybrid search.")

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

@@ -261,7 +261,7 @@ class OracleVector(BaseVector):
                 words = pseg.cut(query)
                 current_entity = ""
                 for word, pos in words:
-                    if pos in {"nr", "Ng", "eng", "nz", "n", "ORG", "v"}:  # nr: 人名, ns: 地名, nt: 机构名
+                    if pos in {"nr", "Ng", "eng", "nz", "n", "ORG", "v"}:  # nr: 人名，ns: 地名，nt: 机构名
                         current_entity += word
                     else:
                         if current_entity:

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

@@ -245,4 +245,4 @@ class TidbService:
             return cluster_infos
         else:
             response.raise_for_status()
-            return []  # FIXME for mypy, This line will not be reached as raise_for_status() will raise an exception
+            return []

api/core/rag/entities/citation_metadata.py

@@ -0,0 +1,23 @@
+from typing import Any, Optional
+
+from pydantic import BaseModel
+
+
+class RetrievalSourceMetadata(BaseModel):
+    position: Optional[int] = None
+    dataset_id: Optional[str] = None
+    dataset_name: Optional[str] = None
+    document_id: Optional[str] = None
+    document_name: Optional[str] = None
+    data_source_type: Optional[str] = None
+    segment_id: Optional[str] = None
+    retriever_from: Optional[str] = None
+    score: Optional[float] = None
+    hit_count: Optional[int] = None
+    word_count: Optional[int] = None
+    segment_position: Optional[int] = None
+    index_node_hash: Optional[str] = None
+    content: Optional[str] = None
+    page: Optional[int] = None
+    doc_metadata: Optional[dict[str, Any]] = None
+    title: Optional[str] = None

api/core/rag/extractor/entity/extract_setting.py

@@ -27,6 +27,8 @@ class WebsiteInfo(BaseModel):
     website import info.
     """
 
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
     provider: str
     job_id: str
     url: str
@@ -34,12 +36,6 @@ class WebsiteInfo(BaseModel):
     tenant_id: str
     only_main_content: bool = False
 
-    class Config:
-        arbitrary_types_allowed = True
-
-    def __init__(self, **data) -> None:
-        super().__init__(**data)
-
 
 class ExtractSetting(BaseModel):
     """

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

@@ -22,6 +22,7 @@ class FirecrawlApp:
             "formats": ["markdown"],
             "onlyMainContent": True,
             "timeout": 30000,
+            "integration": "dify"
         }
         if params:
             json_data.update(params)
@@ -39,7 +40,7 @@ class FirecrawlApp:
     def crawl_url(self, url, params=None) -> str:
         # Documentation: https://docs.firecrawl.dev/api-reference/endpoint/crawl-post
         headers = self._prepare_headers()
-        json_data = {"url": url}
+        json_data = {"url": url, "integration": "dify"}
         if params:
             json_data.update(params)
         response = self._post_request(f"{self.base_url}/v1/crawl", json_data, headers)
@@ -49,7 +50,6 @@ class FirecrawlApp:
             return cast(str, job_id)
         else:
             self._handle_error(response, "start crawl job")
-            # FIXME: unreachable code for mypy
             return ""  # unreachable
 
     def check_crawl_status(self, job_id) -> dict[str, Any]:
@@ -82,7 +82,6 @@ class FirecrawlApp:
                 )
         else:
             self._handle_error(response, "check crawl status")
-            # FIXME: unreachable code for mypy
             return {}  # unreachable
 
     def _format_crawl_status_response(
@@ -127,6 +126,7 @@ class FirecrawlApp:
     def _handle_error(self, response, action) -> None:
         error_message = response.json().get("error", "Unknown error occurred")
         raise Exception(f"Failed to {action}. Status code: {response.status_code}. Error: {error_message}")
+        return  # Explicit return for mypy compliance
 
     def search(self, query: str, params: dict[str, Any] | None = None) -> dict[str, Any]:
         # Documentation: https://docs.firecrawl.dev/api-reference/endpoint/search
@@ -139,6 +139,7 @@ class FirecrawlApp:
             "timeout": 60000,
             "ignoreInvalidURLs": False,
             "scrapeOptions": {},
+            "integration": "dify"
         }
         if params:
             json_data.update(params)
@@ -147,7 +148,7 @@ class FirecrawlApp:
             response_data = response.json()
             if not response_data.get("success"):
                 raise Exception(f"Search failed. Error: {response_data.get('warning', 'Unknown error')}")
-            return response_data
+            return cast(dict[str, Any], response_data)
         elif response.status_code in {402, 409, 500, 429, 408}:
             self._handle_error(response, "perform search")
             return {}  # Avoid additional exception after handling error

api/core/rag/models/document.py

@@ -45,13 +45,12 @@ class BaseDocumentTransformer(ABC):
         .. code-block:: python
 
             class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel):
+                model_config = ConfigDict(arbitrary_types_allowed=True)
+
                 embeddings: Embeddings
                 similarity_fn: Callable = cosine_similarity
                 similarity_threshold: float = 0.95
 
-                class Config:
-                    arbitrary_types_allowed = True
-
                 def transform_documents(
                     self, documents: Sequence[Document], **kwargs: Any
                 ) -> Sequence[Document]:

api/core/rag/retrieval/dataset_retrieval.py

@@ -35,6 +35,7 @@ from core.prompt.simple_prompt_transform import ModelMode
 from core.rag.data_post_processor.data_post_processor import DataPostProcessor
 from core.rag.datasource.keyword.jieba.jieba_keyword_table_handler import JiebaKeywordTableHandler
 from core.rag.datasource.retrieval_service import RetrievalService
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.rag.entities.context_entities import DocumentContext
 from core.rag.entities.metadata_entities import Condition, MetadataCondition
 from core.rag.index_processor.constant.index_type import IndexType
@@ -198,21 +199,21 @@ class DatasetRetrieval:
 
         dify_documents = [item for item in all_documents if item.provider == "dify"]
         external_documents = [item for item in all_documents if item.provider == "external"]
-        document_context_list = []
-        retrieval_resource_list = []
+        document_context_list: list[DocumentContext] = []
+        retrieval_resource_list: list[RetrievalSourceMetadata] = []
         # deal with external documents
         for item in external_documents:
             document_context_list.append(DocumentContext(content=item.page_content, score=item.metadata.get("score")))
-            source = {
-                "dataset_id": item.metadata.get("dataset_id"),
-                "dataset_name": item.metadata.get("dataset_name"),
-                "document_id": item.metadata.get("document_id") or item.metadata.get("title"),
-                "document_name": item.metadata.get("title"),
-                "data_source_type": "external",
-                "retriever_from": invoke_from.to_source(),
-                "score": item.metadata.get("score"),
-                "content": item.page_content,
-            }
+            source = RetrievalSourceMetadata(
+                dataset_id=item.metadata.get("dataset_id"),
+                dataset_name=item.metadata.get("dataset_name"),
+                document_id=item.metadata.get("document_id") or item.metadata.get("title"),
+                document_name=item.metadata.get("title"),
+                data_source_type="external",
+                retriever_from=invoke_from.to_source(),
+                score=item.metadata.get("score"),
+                content=item.page_content,
+            )
             retrieval_resource_list.append(source)
         # deal with dify documents
         if dify_documents:
@@ -248,32 +249,32 @@ class DatasetRetrieval:
                             .first()
                         )
                         if dataset and document:
-                            source = {
-                                "dataset_id": dataset.id,
-                                "dataset_name": dataset.name,
-                                "document_id": document.id,
-                                "document_name": document.name,
-                                "data_source_type": document.data_source_type,
-                                "segment_id": segment.id,
-                                "retriever_from": invoke_from.to_source(),
-                                "score": record.score or 0.0,
-                                "doc_metadata": document.doc_metadata,
-                            }
+                            source = RetrievalSourceMetadata(
+                                dataset_id=dataset.id,
+                                dataset_name=dataset.name,
+                                document_id=document.id,
+                                document_name=document.name,
+                                data_source_type=document.data_source_type,
+                                segment_id=segment.id,
+                                retriever_from=invoke_from.to_source(),
+                                score=record.score or 0.0,
+                                doc_metadata=document.doc_metadata,
+                            )
 
                             if invoke_from.to_source() == "dev":
-                                source["hit_count"] = segment.hit_count
-                                source["word_count"] = segment.word_count
-                                source["segment_position"] = segment.position
-                                source["index_node_hash"] = segment.index_node_hash
+                                source.hit_count = segment.hit_count
+                                source.word_count = segment.word_count
+                                source.segment_position = segment.position
+                                source.index_node_hash = segment.index_node_hash
                             if segment.answer:
-                                source["content"] = f"question:{segment.content} \nanswer:{segment.answer}"
+                                source.content = f"question:{segment.content} \nanswer:{segment.answer}"
                             else:
-                                source["content"] = segment.content
+                                source.content = segment.content
                             retrieval_resource_list.append(source)
         if hit_callback and retrieval_resource_list:
-            retrieval_resource_list = sorted(retrieval_resource_list, key=lambda x: x.get("score") or 0.0, reverse=True)
+            retrieval_resource_list = sorted(retrieval_resource_list, key=lambda x: x.score or 0.0, reverse=True)
             for position, item in enumerate(retrieval_resource_list, start=1):
-                item["position"] = position
+                item.position = position
             hit_callback.return_retriever_resource_info(retrieval_resource_list)
         if document_context_list:
             document_context_list = sorted(document_context_list, key=lambda x: x.score or 0.0, reverse=True)
@@ -936,6 +937,9 @@ class DatasetRetrieval:
         return metadata_filter_document_ids, metadata_condition
 
     def _replace_metadata_filter_value(self, text: str, inputs: dict) -> str:
+        if not inputs:
+            return text
+
         def replacer(match):
             key = match.group(1)
             return str(inputs.get(key, f"{{{{{key}}}}}"))

api/core/repositories/sqlalchemy_workflow_execution_repository.py

@@ -10,12 +10,12 @@ from sqlalchemy import select
 from sqlalchemy.engine import Engine
 from sqlalchemy.orm import sessionmaker
 
-from core.workflow.entities.workflow_execution_entities import (
+from core.workflow.entities.workflow_execution import (
     WorkflowExecution,
     WorkflowExecutionStatus,
     WorkflowType,
 )
-from core.workflow.repository.workflow_execution_repository import WorkflowExecutionRepository
+from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
 from models import (
     Account,
     CreatorUserRole,
@@ -104,10 +104,9 @@ class SQLAlchemyWorkflowExecutionRepository(WorkflowExecutionRepository):
         status = WorkflowExecutionStatus(db_model.status)
 
         return WorkflowExecution(
-            id=db_model.id,
+            id_=db_model.id,
             workflow_id=db_model.workflow_id,
-            sequence_number=db_model.sequence_number,
-            type=WorkflowType(db_model.type),
+            workflow_type=WorkflowType(db_model.type),
             workflow_version=db_model.version,
             graph=graph,
             inputs=inputs,
@@ -140,14 +139,29 @@ class SQLAlchemyWorkflowExecutionRepository(WorkflowExecutionRepository):
             raise ValueError("created_by_role is required in repository constructor")
 
         db_model = WorkflowRun()
-        db_model.id = domain_model.id
+        db_model.id = domain_model.id_
         db_model.tenant_id = self._tenant_id
         if self._app_id is not None:
             db_model.app_id = self._app_id
         db_model.workflow_id = domain_model.workflow_id
         db_model.triggered_from = self._triggered_from
-        db_model.sequence_number = domain_model.sequence_number
-        db_model.type = domain_model.type
+
+        # Check if this is a new record
+        with self._session_factory() as session:
+            existing = session.scalar(select(WorkflowRun).where(WorkflowRun.id == domain_model.id_))
+            if not existing:
+                # For new records, get the next sequence number
+                stmt = select(WorkflowRun.sequence_number).where(
+                    WorkflowRun.app_id == self._app_id,
+                    WorkflowRun.tenant_id == self._tenant_id,
+                )
+                max_sequence = session.scalar(stmt.order_by(WorkflowRun.sequence_number.desc()))
+                db_model.sequence_number = (max_sequence or 0) + 1
+            else:
+                # For updates, keep the existing sequence number
+                db_model.sequence_number = existing.sequence_number
+
+        db_model.type = domain_model.workflow_type
         db_model.version = domain_model.workflow_version
         db_model.graph = json.dumps(domain_model.graph) if domain_model.graph else None
         db_model.inputs = json.dumps(domain_model.inputs) if domain_model.inputs else None

api/core/repositories/sqlalchemy_workflow_node_execution_repository.py

@@ -12,19 +12,18 @@ from sqlalchemy.engine import Engine
 from sqlalchemy.orm import sessionmaker
 
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.workflow.entities.node_entities import NodeRunMetadataKey
-from core.workflow.entities.node_execution_entities import (
-    NodeExecution,
-    NodeExecutionStatus,
+from core.workflow.entities.workflow_node_execution import (
+    WorkflowNodeExecution,
+    WorkflowNodeExecutionMetadataKey,
+    WorkflowNodeExecutionStatus,
 )
 from core.workflow.nodes.enums import NodeType
-from core.workflow.repository.workflow_node_execution_repository import OrderConfig, WorkflowNodeExecutionRepository
+from core.workflow.repositories.workflow_node_execution_repository import OrderConfig, WorkflowNodeExecutionRepository
 from models import (
     Account,
     CreatorUserRole,
     EndUser,
-    WorkflowNodeExecution,
-    WorkflowNodeExecutionStatus,
+    WorkflowNodeExecutionModel,
     WorkflowNodeExecutionTriggeredFrom,
 )
 
@@ -87,9 +86,9 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
 
         # Initialize in-memory cache for node executions
         # Key: node_execution_id, Value: WorkflowNodeExecution (DB model)
-        self._node_execution_cache: dict[str, WorkflowNodeExecution] = {}
+        self._node_execution_cache: dict[str, WorkflowNodeExecutionModel] = {}
 
-    def _to_domain_model(self, db_model: WorkflowNodeExecution) -> NodeExecution:
+    def _to_domain_model(self, db_model: WorkflowNodeExecutionModel) -> WorkflowNodeExecution:
         """
         Convert a database model to a domain model.
 
@@ -103,16 +102,16 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
         inputs = db_model.inputs_dict
         process_data = db_model.process_data_dict
         outputs = db_model.outputs_dict
-        metadata = {NodeRunMetadataKey(k): v for k, v in db_model.execution_metadata_dict.items()}
+        metadata = {WorkflowNodeExecutionMetadataKey(k): v for k, v in db_model.execution_metadata_dict.items()}
 
         # Convert status to domain enum
-        status = NodeExecutionStatus(db_model.status)
+        status = WorkflowNodeExecutionStatus(db_model.status)
 
-        return NodeExecution(
+        return WorkflowNodeExecution(
             id=db_model.id,
             node_execution_id=db_model.node_execution_id,
             workflow_id=db_model.workflow_id,
-            workflow_run_id=db_model.workflow_run_id,
+            workflow_execution_id=db_model.workflow_run_id,
             index=db_model.index,
             predecessor_node_id=db_model.predecessor_node_id,
             node_id=db_model.node_id,
@@ -129,7 +128,7 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
             finished_at=db_model.finished_at,
         )
 
-    def to_db_model(self, domain_model: NodeExecution) -> WorkflowNodeExecution:
+    def to_db_model(self, domain_model: WorkflowNodeExecution) -> WorkflowNodeExecutionModel:
         """
         Convert a domain model to a database model.
 
@@ -147,14 +146,14 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
         if not self._creator_user_role:
             raise ValueError("created_by_role is required in repository constructor")
 
-        db_model = WorkflowNodeExecution()
+        db_model = WorkflowNodeExecutionModel()
         db_model.id = domain_model.id
         db_model.tenant_id = self._tenant_id
         if self._app_id is not None:
             db_model.app_id = self._app_id
         db_model.workflow_id = domain_model.workflow_id
         db_model.triggered_from = self._triggered_from
-        db_model.workflow_run_id = domain_model.workflow_run_id
+        db_model.workflow_run_id = domain_model.workflow_execution_id
         db_model.index = domain_model.index
         db_model.predecessor_node_id = domain_model.predecessor_node_id
         db_model.node_execution_id = domain_model.node_execution_id
@@ -176,7 +175,7 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
         db_model.finished_at = domain_model.finished_at
         return db_model
 
-    def save(self, execution: NodeExecution) -> None:
+    def save(self, execution: WorkflowNodeExecution) -> None:
         """
         Save or update a NodeExecution domain entity to the database.
 
@@ -208,7 +207,7 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
                 logger.debug(f"Updating cache for node_execution_id: {db_model.node_execution_id}")
                 self._node_execution_cache[db_model.node_execution_id] = db_model
 
-    def get_by_node_execution_id(self, node_execution_id: str) -> Optional[NodeExecution]:
+    def get_by_node_execution_id(self, node_execution_id: str) -> Optional[WorkflowNodeExecution]:
         """
         Retrieve a NodeExecution by its node_execution_id.
 
@@ -231,13 +230,13 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
         # If not in cache, query the database
         logger.debug(f"Cache miss for node_execution_id: {node_execution_id}, querying database")
         with self._session_factory() as session:
-            stmt = select(WorkflowNodeExecution).where(
-                WorkflowNodeExecution.node_execution_id == node_execution_id,
-                WorkflowNodeExecution.tenant_id == self._tenant_id,
+            stmt = select(WorkflowNodeExecutionModel).where(
+                WorkflowNodeExecutionModel.node_execution_id == node_execution_id,
+                WorkflowNodeExecutionModel.tenant_id == self._tenant_id,
             )
 
             if self._app_id:
-                stmt = stmt.where(WorkflowNodeExecution.app_id == self._app_id)
+                stmt = stmt.where(WorkflowNodeExecutionModel.app_id == self._app_id)
 
             db_model = session.scalar(stmt)
             if db_model:
@@ -253,7 +252,7 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
         self,
         workflow_run_id: str,
         order_config: Optional[OrderConfig] = None,
-    ) -> Sequence[WorkflowNodeExecution]:
+    ) -> Sequence[WorkflowNodeExecutionModel]:
         """
         Retrieve all WorkflowNodeExecution database models for a specific workflow run.
 
@@ -271,20 +270,20 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
             A list of WorkflowNodeExecution database models
         """
         with self._session_factory() as session:
-            stmt = select(WorkflowNodeExecution).where(
-                WorkflowNodeExecution.workflow_run_id == workflow_run_id,
-                WorkflowNodeExecution.tenant_id == self._tenant_id,
-                WorkflowNodeExecution.triggered_from == WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
+            stmt = select(WorkflowNodeExecutionModel).where(
+                WorkflowNodeExecutionModel.workflow_run_id == workflow_run_id,
+                WorkflowNodeExecutionModel.tenant_id == self._tenant_id,
+                WorkflowNodeExecutionModel.triggered_from == WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
             )
 
             if self._app_id:
-                stmt = stmt.where(WorkflowNodeExecution.app_id == self._app_id)
+                stmt = stmt.where(WorkflowNodeExecutionModel.app_id == self._app_id)
 
             # Apply ordering if provided
             if order_config and order_config.order_by:
                 order_columns: list[UnaryExpression] = []
                 for field in order_config.order_by:
-                    column = getattr(WorkflowNodeExecution, field, None)
+                    column = getattr(WorkflowNodeExecutionModel, field, None)
                     if not column:
                         continue
                     if order_config.order_direction == "desc":
@@ -308,7 +307,7 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
         self,
         workflow_run_id: str,
         order_config: Optional[OrderConfig] = None,
-    ) -> Sequence[NodeExecution]:
+    ) -> Sequence[WorkflowNodeExecution]:
         """
         Retrieve all NodeExecution instances for a specific workflow run.
 
@@ -335,7 +334,7 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
 
         return domain_models
 
-    def get_running_executions(self, workflow_run_id: str) -> Sequence[NodeExecution]:
+    def get_running_executions(self, workflow_run_id: str) -> Sequence[WorkflowNodeExecution]:
         """
         Retrieve all running NodeExecution instances for a specific workflow run.
 
@@ -349,15 +348,15 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
             A list of running NodeExecution instances
         """
         with self._session_factory() as session:
-            stmt = select(WorkflowNodeExecution).where(
-                WorkflowNodeExecution.workflow_run_id == workflow_run_id,
-                WorkflowNodeExecution.tenant_id == self._tenant_id,
-                WorkflowNodeExecution.status == WorkflowNodeExecutionStatus.RUNNING,
-                WorkflowNodeExecution.triggered_from == WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
+            stmt = select(WorkflowNodeExecutionModel).where(
+                WorkflowNodeExecutionModel.workflow_run_id == workflow_run_id,
+                WorkflowNodeExecutionModel.tenant_id == self._tenant_id,
+                WorkflowNodeExecutionModel.status == WorkflowNodeExecutionStatus.RUNNING,
+                WorkflowNodeExecutionModel.triggered_from == WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
             )
 
             if self._app_id:
-                stmt = stmt.where(WorkflowNodeExecution.app_id == self._app_id)
+                stmt = stmt.where(WorkflowNodeExecutionModel.app_id == self._app_id)
 
             db_models = session.scalars(stmt).all()
             domain_models = []
@@ -382,10 +381,10 @@ class SQLAlchemyWorkflowNodeExecutionRepository(WorkflowNodeExecutionRepository)
         It also clears the in-memory cache.
         """
         with self._session_factory() as session:
-            stmt = delete(WorkflowNodeExecution).where(WorkflowNodeExecution.tenant_id == self._tenant_id)
+            stmt = delete(WorkflowNodeExecutionModel).where(WorkflowNodeExecutionModel.tenant_id == self._tenant_id)
 
             if self._app_id:
-                stmt = stmt.where(WorkflowNodeExecution.app_id == self._app_id)
+                stmt = stmt.where(WorkflowNodeExecutionModel.app_id == self._app_id)
 
             result = session.execute(stmt)
             session.commit()

api/core/tools/custom_tool/tool.py

@@ -168,7 +168,7 @@ class ApiTool(Tool):
                 cookies[parameter["name"]] = value
 
             elif parameter["in"] == "header":
-                headers[parameter["name"]] = value
+                headers[parameter["name"]] = str(value)
 
         # check if there is a request body and handle it
         if "requestBody" in self.api_bundle.openapi and self.api_bundle.openapi["requestBody"] is not None:

api/core/tools/entities/tool_entities.py

@@ -279,7 +279,6 @@ class ToolParameter(PluginParameter):
         :param options: the options of the parameter
         """
         # convert options to ToolParameterOption
-        # FIXME fix the type error
         if options:
             option_objs = [
                 PluginParameterOption(value=option, label=I18nObject(en_US=option, zh_Hans=option))

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

@@ -8,6 +8,7 @@ from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCa
 from core.model_manager import ModelManager
 from core.model_runtime.entities.model_entities import ModelType
 from core.rag.datasource.retrieval_service import RetrievalService
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.rag.models.document import Document as RagDocument
 from core.rag.rerank.rerank_model import RerankModelRunner
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
@@ -107,7 +108,7 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
                 else:
                     document_context_list.append(segment.get_sign_content())
             if self.return_resource:
-                context_list = []
+                context_list: list[RetrievalSourceMetadata] = []
                 resource_number = 1
                 for segment in sorted_segments:
                     dataset = db.session.query(Dataset).filter_by(id=segment.dataset_id).first()
@@ -121,28 +122,28 @@ class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
                         .first()
                     )
                     if dataset and document:
-                        source = {
-                            "position": resource_number,
-                            "dataset_id": dataset.id,
-                            "dataset_name": dataset.name,
-                            "document_id": document.id,
-                            "document_name": document.name,
-                            "data_source_type": document.data_source_type,
-                            "segment_id": segment.id,
-                            "retriever_from": self.retriever_from,
-                            "score": document_score_list.get(segment.index_node_id, None),
-                            "doc_metadata": document.doc_metadata,
-                        }
+                        source = RetrievalSourceMetadata(
+                            position=resource_number,
+                            dataset_id=dataset.id,
+                            dataset_name=dataset.name,
+                            document_id=document.id,
+                            document_name=document.name,
+                            data_source_type=document.data_source_type,
+                            segment_id=segment.id,
+                            retriever_from=self.retriever_from,
+                            score=document_score_list.get(segment.index_node_id, None),
+                            doc_metadata=document.doc_metadata,
+                        )
 
                         if self.retriever_from == "dev":
-                            source["hit_count"] = segment.hit_count
-                            source["word_count"] = segment.word_count
-                            source["segment_position"] = segment.position
-                            source["index_node_hash"] = segment.index_node_hash
+                            source.hit_count = segment.hit_count
+                            source.word_count = segment.word_count
+                            source.segment_position = segment.position
+                            source.index_node_hash = segment.index_node_hash
                         if segment.answer:
-                            source["content"] = f"question:{segment.content} \nanswer:{segment.answer}"
+                            source.content = f"question:{segment.content} \nanswer:{segment.answer}"
                         else:
-                            source["content"] = segment.content
+                            source.content = segment.content
                         context_list.append(source)
                     resource_number += 1
 

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

@@ -4,6 +4,7 @@ from pydantic import BaseModel, Field
 
 from core.app.app_config.entities import DatasetRetrieveConfigEntity, ModelConfig
 from core.rag.datasource.retrieval_service import RetrievalService
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.rag.entities.context_entities import DocumentContext
 from core.rag.models.document import Document as RetrievalDocument
 from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
@@ -14,7 +15,7 @@ from models.dataset import Dataset
 from models.dataset import Document as DatasetDocument
 from services.external_knowledge_service import ExternalDatasetService
 
-default_retrieval_model = {
+default_retrieval_model: dict[str, Any] = {
     "search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
     "reranking_enable": False,
     "reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
@@ -79,7 +80,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
         else:
             document_ids_filter = None
         if dataset.provider == "external":
-            results = []
+            results: list[RetrievalDocument] = []
             external_documents = ExternalDatasetService.fetch_external_knowledge_retrieval(
                 tenant_id=dataset.tenant_id,
                 dataset_id=dataset.id,
@@ -100,21 +101,21 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                     document.metadata["dataset_name"] = dataset.name
                     results.append(document)
             # deal with external documents
-            context_list = []
+            context_list: list[RetrievalSourceMetadata] = []
             for position, item in enumerate(results, start=1):
                 if item.metadata is not None:
-                    source = {
-                        "position": position,
-                        "dataset_id": item.metadata.get("dataset_id"),
-                        "dataset_name": item.metadata.get("dataset_name"),
-                        "document_id": item.metadata.get("document_id") or item.metadata.get("title"),
-                        "document_name": item.metadata.get("title"),
-                        "data_source_type": "external",
-                        "retriever_from": self.retriever_from,
-                        "score": item.metadata.get("score"),
-                        "title": item.metadata.get("title"),
-                        "content": item.page_content,
-                    }
+                    source = RetrievalSourceMetadata(
+                        position=position,
+                        dataset_id=item.metadata.get("dataset_id"),
+                        dataset_name=item.metadata.get("dataset_name"),
+                        document_id=item.metadata.get("document_id") or item.metadata.get("title"),
+                        document_name=item.metadata.get("title"),
+                        data_source_type="external",
+                        retriever_from=self.retriever_from,
+                        score=item.metadata.get("score"),
+                        title=item.metadata.get("title"),
+                        content=item.page_content,
+                    )
                     context_list.append(source)
             for hit_callback in self.hit_callbacks:
                 hit_callback.return_retriever_resource_info(context_list)
@@ -125,7 +126,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                 return ""
             # get retrieval model , if the model is not setting , using default
             retrieval_model: dict[str, Any] = dataset.retrieval_model or default_retrieval_model
-            retrieval_resource_list = []
+            retrieval_resource_list: list[RetrievalSourceMetadata] = []
             if dataset.indexing_technique == "economy":
                 # use keyword table query
                 documents = RetrievalService.retrieve(
@@ -163,7 +164,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                     for item in documents:
                         if item.metadata is not None and item.metadata.get("score"):
                             document_score_list[item.metadata["doc_id"]] = item.metadata["score"]
-                document_context_list = []
+                document_context_list: list[DocumentContext] = []
                 records = RetrievalService.format_retrieval_documents(documents)
                 if records:
                     for record in records:
@@ -197,37 +198,37 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                                 .first()
                             )
                             if dataset and document:
-                                source = {
-                                    "dataset_id": dataset.id,
-                                    "dataset_name": dataset.name,
-                                    "document_id": document.id,  # type: ignore
-                                    "document_name": document.name,  # type: ignore
-                                    "data_source_type": document.data_source_type,  # type: ignore
-                                    "segment_id": segment.id,
-                                    "retriever_from": self.retriever_from,
-                                    "score": record.score or 0.0,
-                                    "doc_metadata": document.doc_metadata,  # type: ignore
-                                }
+                                source = RetrievalSourceMetadata(
+                                    dataset_id=dataset.id,
+                                    dataset_name=dataset.name,
+                                    document_id=document.id,  # type: ignore
+                                    document_name=document.name,  # type: ignore
+                                    data_source_type=document.data_source_type,  # type: ignore
+                                    segment_id=segment.id,
+                                    retriever_from=self.retriever_from,
+                                    score=record.score or 0.0,
+                                    doc_metadata=document.doc_metadata,  # type: ignore
+                                )
 
                                 if self.retriever_from == "dev":
-                                    source["hit_count"] = segment.hit_count
-                                    source["word_count"] = segment.word_count
-                                    source["segment_position"] = segment.position
-                                    source["index_node_hash"] = segment.index_node_hash
+                                    source.hit_count = segment.hit_count
+                                    source.word_count = segment.word_count
+                                    source.segment_position = segment.position
+                                    source.index_node_hash = segment.index_node_hash
                                 if segment.answer:
-                                    source["content"] = f"question:{segment.content} \nanswer:{segment.answer}"
+                                    source.content = f"question:{segment.content} \nanswer:{segment.answer}"
                                 else:
-                                    source["content"] = segment.content
+                                    source.content = segment.content
                                 retrieval_resource_list.append(source)
 
             if self.return_resource and retrieval_resource_list:
                 retrieval_resource_list = sorted(
                     retrieval_resource_list,
-                    key=lambda x: x.get("score") or 0.0,
+                    key=lambda x: x.score or 0.0,
                     reverse=True,
                 )
                 for position, item in enumerate(retrieval_resource_list, start=1):  # type: ignore
-                    item["position"] = position  # type: ignore
+                    item.position = position  # type: ignore
                 for hit_callback in self.hit_callbacks:
                     hit_callback.return_retriever_resource_info(retrieval_resource_list)
             if document_context_list:

api/core/tools/utils/message_transformer.py

@@ -66,7 +66,6 @@ class ToolFileMessageTransformer:
                 if not isinstance(message.message, ToolInvokeMessage.BlobMessage):
                     raise ValueError("unexpected message type")
 
-                # FIXME: should do a type check here.
                 assert isinstance(message.message.blob, bytes)
                 tool_file_manager = ToolFileManager()
                 file = tool_file_manager.create_file_by_raw(

api/core/tools/utils/parser.py

@@ -55,6 +55,13 @@ class ApiBasedToolSchemaParser:
             # convert parameters
             parameters = []
             if "parameters" in interface["operation"]:
+                for i, parameter in enumerate(interface["operation"]["parameters"]):
+                    if "$ref" in parameter:
+                        root = openapi
+                        reference = parameter["$ref"].split("/")[1:]
+                        for ref in reference:
+                            root = root[ref]
+                        interface["operation"]["parameters"][i] = root
                 for parameter in interface["operation"]["parameters"]:
                     tool_parameter = ToolParameter(
                         name=parameter["name"],

api/core/workflow/entities/node_entities.py

@@ -1,36 +1,10 @@
 from collections.abc import Mapping
-from enum import StrEnum
 from typing import Any, Optional
 
 from pydantic import BaseModel
 
 from core.model_runtime.entities.llm_entities import LLMUsage
-from models.workflow import WorkflowNodeExecutionStatus
-
-
-class NodeRunMetadataKey(StrEnum):
-    """
-    Node Run Metadata Key.
-    """
-
-    TOTAL_TOKENS = "total_tokens"
-    TOTAL_PRICE = "total_price"
-    CURRENCY = "currency"
-    TOOL_INFO = "tool_info"
-    AGENT_LOG = "agent_log"
-    ITERATION_ID = "iteration_id"
-    ITERATION_INDEX = "iteration_index"
-    LOOP_ID = "loop_id"
-    LOOP_INDEX = "loop_index"
-    PARALLEL_ID = "parallel_id"
-    PARALLEL_START_NODE_ID = "parallel_start_node_id"
-    PARENT_PARALLEL_ID = "parent_parallel_id"
-    PARENT_PARALLEL_START_NODE_ID = "parent_parallel_start_node_id"
-    PARALLEL_MODE_RUN_ID = "parallel_mode_run_id"
-    ITERATION_DURATION_MAP = "iteration_duration_map"  # single iteration duration if iteration node runs
-    LOOP_DURATION_MAP = "loop_duration_map"  # single loop duration if loop node runs
-    ERROR_STRATEGY = "error_strategy"  # node in continue on error mode return the field
-    LOOP_VARIABLE_MAP = "loop_variable_map"  # single loop variable output
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 
 
 class NodeRunResult(BaseModel):
@@ -43,7 +17,7 @@ class NodeRunResult(BaseModel):
     inputs: Optional[Mapping[str, Any]] = None  # node inputs
     process_data: Optional[Mapping[str, Any]] = None  # process data
     outputs: Optional[Mapping[str, Any]] = None  # node outputs
-    metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None  # node metadata
+    metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None  # node metadata
     llm_usage: Optional[LLMUsage] = None  # llm usage
 
     edge_source_handle: Optional[str] = None  # source handle id of node with multiple branches

api/core/workflow/entities/workflow_execution.py

@@ -36,12 +36,10 @@ class WorkflowExecution(BaseModel):
     user, tenant, and app attributes.
     """
 
-    id: str = Field(...)
+    id_: str = Field(...)
     workflow_id: str = Field(...)
     workflow_version: str = Field(...)
-    sequence_number: int = Field(...)
-
-    type: WorkflowType = Field(...)
+    workflow_type: WorkflowType = Field(...)
     graph: Mapping[str, Any] = Field(...)
 
     inputs: Mapping[str, Any] = Field(...)
@@ -69,20 +67,18 @@ class WorkflowExecution(BaseModel):
     def new(
         cls,
         *,
-        id: str,
+        id_: str,
         workflow_id: str,
-        sequence_number: int,
-        type: WorkflowType,
+        workflow_type: WorkflowType,
         workflow_version: str,
         graph: Mapping[str, Any],
         inputs: Mapping[str, Any],
         started_at: datetime,
     ) -> "WorkflowExecution":
         return WorkflowExecution(
-            id=id,
+            id_=id_,
             workflow_id=workflow_id,
-            sequence_number=sequence_number,
-            type=type,
+            workflow_type=workflow_type,
             workflow_version=workflow_version,
             graph=graph,
             inputs=inputs,

api/core/workflow/entities/workflow_node_execution.py

@@ -13,11 +13,35 @@ from typing import Any, Optional
 
 from pydantic import BaseModel, Field
 
-from core.workflow.entities.node_entities import NodeRunMetadataKey
 from core.workflow.nodes.enums import NodeType
 
 
-class NodeExecutionStatus(StrEnum):
+class WorkflowNodeExecutionMetadataKey(StrEnum):
+    """
+    Node Run Metadata Key.
+    """
+
+    TOTAL_TOKENS = "total_tokens"
+    TOTAL_PRICE = "total_price"
+    CURRENCY = "currency"
+    TOOL_INFO = "tool_info"
+    AGENT_LOG = "agent_log"
+    ITERATION_ID = "iteration_id"
+    ITERATION_INDEX = "iteration_index"
+    LOOP_ID = "loop_id"
+    LOOP_INDEX = "loop_index"
+    PARALLEL_ID = "parallel_id"
+    PARALLEL_START_NODE_ID = "parallel_start_node_id"
+    PARENT_PARALLEL_ID = "parent_parallel_id"
+    PARENT_PARALLEL_START_NODE_ID = "parent_parallel_start_node_id"
+    PARALLEL_MODE_RUN_ID = "parallel_mode_run_id"
+    ITERATION_DURATION_MAP = "iteration_duration_map"  # single iteration duration if iteration node runs
+    LOOP_DURATION_MAP = "loop_duration_map"  # single loop duration if loop node runs
+    ERROR_STRATEGY = "error_strategy"  # node in continue on error mode return the field
+    LOOP_VARIABLE_MAP = "loop_variable_map"  # single loop variable output
+
+
+class WorkflowNodeExecutionStatus(StrEnum):
     """
     Node Execution Status Enum.
     """
@@ -29,7 +53,7 @@ class NodeExecutionStatus(StrEnum):
     RETRY = "retry"
 
 
-class NodeExecution(BaseModel):
+class WorkflowNodeExecution(BaseModel):
     """
     Domain model for workflow node execution.
 
@@ -46,7 +70,7 @@ class NodeExecution(BaseModel):
     id: str  # Unique identifier for this execution record
     node_execution_id: Optional[str] = None  # Optional secondary ID for cross-referencing
     workflow_id: str  # ID of the workflow this node belongs to
-    workflow_run_id: Optional[str] = None  # ID of the specific workflow run (null for single-step debugging)
+    workflow_execution_id: Optional[str] = None  # ID of the specific workflow run (null for single-step debugging)
 
     # Execution positioning and flow
     index: int  # Sequence number for ordering in trace visualization
@@ -61,12 +85,12 @@ class NodeExecution(BaseModel):
     outputs: Optional[Mapping[str, Any]] = None  # Output variables produced by this node
 
     # Execution state
-    status: NodeExecutionStatus = NodeExecutionStatus.RUNNING  # Current execution status
+    status: WorkflowNodeExecutionStatus = WorkflowNodeExecutionStatus.RUNNING  # Current execution status
     error: Optional[str] = None  # Error message if execution failed
     elapsed_time: float = Field(default=0.0)  # Time taken for execution in seconds
 
     # Additional metadata
-    metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None  # Execution metadata (tokens, cost, etc.)
+    metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None  # Execution metadata (tokens, cost, etc.)
 
     # Timing information
     created_at: datetime  # When execution started
@@ -77,7 +101,7 @@ class NodeExecution(BaseModel):
         inputs: Optional[Mapping[str, Any]] = None,
         process_data: Optional[Mapping[str, Any]] = None,
         outputs: Optional[Mapping[str, Any]] = None,
-        metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None,
+        metadata: Optional[Mapping[WorkflowNodeExecutionMetadataKey, Any]] = None,
     ) -> None:
         """
         Update the model from mappings.

api/core/workflow/enums.py

@@ -13,4 +13,4 @@ class SystemVariableKey(StrEnum):
     DIALOGUE_COUNT = "dialogue_count"
     APP_ID = "app_id"
     WORKFLOW_ID = "workflow_id"
-    WORKFLOW_RUN_ID = "workflow_run_id"
+    WORKFLOW_EXECUTION_ID = "workflow_run_id"

api/core/workflow/graph_engine/entities/event.py

@@ -1,9 +1,10 @@
-from collections.abc import Mapping
+from collections.abc import Mapping, Sequence
 from datetime import datetime
 from typing import Any, Optional
 
 from pydantic import BaseModel, Field
 
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.workflow.entities.node_entities import AgentNodeStrategyInit
 from core.workflow.graph_engine.entities.runtime_route_state import RouteNodeState
 from core.workflow.nodes import NodeType
@@ -82,7 +83,7 @@ class NodeRunStreamChunkEvent(BaseNodeEvent):
 
 
 class NodeRunRetrieverResourceEvent(BaseNodeEvent):
-    retriever_resources: list[dict] = Field(..., description="retriever resources")
+    retriever_resources: Sequence[RetrievalSourceMetadata] = Field(..., description="retriever resources")
     context: str = Field(..., description="context")
 
 

api/core/workflow/graph_engine/entities/runtime_route_state.py

@@ -6,7 +6,7 @@ from typing import Optional
 from pydantic import BaseModel, Field
 
 from core.workflow.entities.node_entities import NodeRunResult
-from models.workflow import WorkflowNodeExecutionStatus
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 
 
 class RouteNodeState(BaseModel):

api/core/workflow/graph_engine/graph_engine.py

@@ -14,8 +14,9 @@ from flask import Flask, current_app, has_request_context
 from configs import dify_config
 from core.app.apps.base_app_queue_manager import GenerateTaskStoppedError
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.workflow.entities.node_entities import AgentNodeStrategyInit, NodeRunMetadataKey, NodeRunResult
+from core.workflow.entities.node_entities import AgentNodeStrategyInit, NodeRunResult
 from core.workflow.entities.variable_pool import VariablePool, VariableValue
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from core.workflow.graph_engine.condition_handlers.condition_manager import ConditionManager
 from core.workflow.graph_engine.entities.event import (
     BaseAgentEvent,
@@ -52,9 +53,8 @@ from core.workflow.nodes.end.end_stream_processor import EndStreamProcessor
 from core.workflow.nodes.enums import ErrorStrategy, FailBranchSourceHandle
 from core.workflow.nodes.event import RunCompletedEvent, RunRetrieverResourceEvent, RunStreamChunkEvent
 from core.workflow.nodes.node_mapping import NODE_TYPE_CLASSES_MAPPING
-from extensions.ext_database import db
 from models.enums import UserFrom
-from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
+from models.workflow import WorkflowType
 
 logger = logging.getLogger(__name__)
 
@@ -606,8 +606,6 @@ class GraphEngine:
                         error=str(e),
                     )
                 )
-            finally:
-                db.session.remove()
 
     def _run_node(
         self,
@@ -645,7 +643,6 @@ class GraphEngine:
             agent_strategy=agent_strategy,
         )
 
-        db.session.close()
         max_retries = node_instance.node_data.retry_config.max_retries
         retry_interval = node_instance.node_data.retry_config.retry_interval_seconds
         retries = 0
@@ -759,10 +756,12 @@ class GraphEngine:
                                     and node_instance.node_data.error_strategy is ErrorStrategy.FAIL_BRANCH
                                 ):
                                     run_result.edge_source_handle = FailBranchSourceHandle.SUCCESS
-                                if run_result.metadata and run_result.metadata.get(NodeRunMetadataKey.TOTAL_TOKENS):
+                                if run_result.metadata and run_result.metadata.get(
+                                    WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS
+                                ):
                                     # plus state total_tokens
                                     self.graph_runtime_state.total_tokens += int(
-                                        run_result.metadata.get(NodeRunMetadataKey.TOTAL_TOKENS)  # type: ignore[arg-type]
+                                        run_result.metadata.get(WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS)  # type: ignore[arg-type]
                                     )
 
                                 if run_result.llm_usage:
@@ -785,13 +784,17 @@ class GraphEngine:
 
                                 if parallel_id and parallel_start_node_id:
                                     metadata_dict = dict(run_result.metadata)
-                                    metadata_dict[NodeRunMetadataKey.PARALLEL_ID] = parallel_id
-                                    metadata_dict[NodeRunMetadataKey.PARALLEL_START_NODE_ID] = parallel_start_node_id
+                                    metadata_dict[WorkflowNodeExecutionMetadataKey.PARALLEL_ID] = parallel_id
+                                    metadata_dict[WorkflowNodeExecutionMetadataKey.PARALLEL_START_NODE_ID] = (
+                                        parallel_start_node_id
+                                    )
                                     if parent_parallel_id and parent_parallel_start_node_id:
-                                        metadata_dict[NodeRunMetadataKey.PARENT_PARALLEL_ID] = parent_parallel_id
-                                        metadata_dict[NodeRunMetadataKey.PARENT_PARALLEL_START_NODE_ID] = (
-                                            parent_parallel_start_node_id
+                                        metadata_dict[WorkflowNodeExecutionMetadataKey.PARENT_PARALLEL_ID] = (
+                                            parent_parallel_id
                                         )
+                                        metadata_dict[
+                                            WorkflowNodeExecutionMetadataKey.PARENT_PARALLEL_START_NODE_ID
+                                        ] = parent_parallel_start_node_id
                                     run_result.metadata = metadata_dict
 
                                 yield NodeRunSucceededEvent(
@@ -856,8 +859,6 @@ class GraphEngine:
             except Exception as e:
                 logger.exception(f"Node {node_instance.node_data.title} run failed")
                 raise e
-            finally:
-                db.session.close()
 
     def _append_variables_recursively(self, node_id: str, variable_key_list: list[str], variable_value: VariableValue):
         """
@@ -923,7 +924,7 @@ class GraphEngine:
             "error": error_result.error,
             "inputs": error_result.inputs,
             "metadata": {
-                NodeRunMetadataKey.ERROR_STRATEGY: node_instance.node_data.error_strategy,
+                WorkflowNodeExecutionMetadataKey.ERROR_STRATEGY: node_instance.node_data.error_strategy,
             },
         }
 

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

@@ -2,6 +2,9 @@ import json
 from collections.abc import Generator, Mapping, Sequence
 from typing import Any, Optional, cast
 
+from sqlalchemy import select
+from sqlalchemy.orm import Session
+
 from core.agent.entities import AgentToolEntity
 from core.agent.plugin_entities import AgentStrategyParameter
 from core.memory.token_buffer_memory import TokenBufferMemory
@@ -15,6 +18,7 @@ from core.tools.tool_manager import ToolManager
 from core.variables.segments import StringSegment
 from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.enums import SystemVariableKey
 from core.workflow.nodes.agent.entities import AgentNodeData, AgentOldVersionModelFeatures, ParamsAutoGenerated
 from core.workflow.nodes.base.entities import BaseNodeData
@@ -25,7 +29,6 @@ from core.workflow.utils.variable_template_parser import VariableTemplateParser
 from extensions.ext_database import db
 from factories.agent_factory import get_plugin_agent_strategy
 from models.model import Conversation
-from models.workflow import WorkflowNodeExecutionStatus
 
 
 class AgentNode(ToolNode):
@@ -320,15 +323,12 @@ class AgentNode(ToolNode):
             return None
         conversation_id = conversation_id_variable.value
 
-        # get conversation
-        conversation = (
-            db.session.query(Conversation)
-            .filter(Conversation.app_id == self.app_id, Conversation.id == conversation_id)
-            .first()
-        )
+        with Session(db.engine, expire_on_commit=False) as session:
+            stmt = select(Conversation).where(Conversation.app_id == self.app_id, Conversation.id == conversation_id)
+            conversation = session.scalar(stmt)
 
-        if not conversation:
-            return None
+            if not conversation:
+                return None
 
         memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
 

api/core/workflow/nodes/answer/answer_node.py

@@ -3,6 +3,7 @@ from typing import Any, cast
 
 from core.variables import ArrayFileSegment, FileSegment
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.answer.answer_stream_generate_router import AnswerStreamGeneratorRouter
 from core.workflow.nodes.answer.entities import (
     AnswerNodeData,
@@ -13,7 +14,6 @@ from core.workflow.nodes.answer.entities import (
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.utils.variable_template_parser import VariableTemplateParser
-from models.workflow import WorkflowNodeExecutionStatus
 
 
 class AnswerNode(BaseNode[AnswerNodeData]):

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

@@ -4,9 +4,9 @@ from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Generic, Optional, TypeVar, Union, cast
 
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.enums import CONTINUE_ON_ERROR_NODE_TYPE, RETRY_ON_ERROR_NODE_TYPE, NodeType
 from core.workflow.nodes.event import NodeEvent, RunCompletedEvent
-from models.workflow import WorkflowNodeExecutionStatus
 
 from .entities import BaseNodeData
 

api/core/workflow/nodes/code/code_node.py

@@ -8,10 +8,10 @@ from core.helper.code_executor.javascript.javascript_code_provider import Javasc
 from core.helper.code_executor.python3.python3_code_provider import Python3CodeProvider
 from core.variables.segments import ArrayFileSegment
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.code.entities import CodeNodeData
 from core.workflow.nodes.enums import NodeType
-from models.workflow import WorkflowNodeExecutionStatus
 
 from .exc import (
     CodeNodeError,

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

@@ -26,9 +26,9 @@ from core.helper import ssrf_proxy
 from core.variables import ArrayFileSegment
 from core.variables.segments import FileSegment
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
-from models.workflow import WorkflowNodeExecutionStatus
 
 from .entities import DocumentExtractorNodeData
 from .exc import DocumentExtractorError, FileDownloadError, TextExtractionError, UnsupportedFileTypeError
@@ -397,19 +397,44 @@ def _extract_text_from_csv(file_content: bytes) -> str:
         if not rows:
             return ""
 
+        # Combine multi-line text in the header row
+        header_row = [cell.replace("\n", " ").replace("\r", "") for cell in rows[0]]
+
         # Create Markdown table
-        markdown_table = "| " + " | ".join(rows[0]) + " |\n"
-        markdown_table += "| " + " | ".join(["---"] * len(rows[0])) + " |\n"
+        markdown_table = "| " + " | ".join(header_row) + " |\n"
+        markdown_table += "| " + " | ".join(["-" * len(col) for col in rows[0]]) + " |\n"
+
+        # Process each data row and combine multi-line text in each cell
         for row in rows[1:]:
-            markdown_table += "| " + " | ".join(row) + " |\n"
+            processed_row = [cell.replace("\n", " ").replace("\r", "") for cell in row]
+            markdown_table += "| " + " | ".join(processed_row) + " |\n"
 
-        return markdown_table.strip()
+        return markdown_table
     except Exception as e:
         raise TextExtractionError(f"Failed to extract text from CSV: {str(e)}") from e
 
 
 def _extract_text_from_excel(file_content: bytes) -> str:
     """Extract text from an Excel file using pandas."""
+
+    def _construct_markdown_table(df: pd.DataFrame) -> str:
+        """Manually construct a Markdown table from a DataFrame."""
+        # Construct the header row
+        header_row = "| " + " | ".join(df.columns) + " |"
+
+        # Construct the separator row
+        separator_row = "| " + " | ".join(["-" * len(col) for col in df.columns]) + " |"
+
+        # Construct the data rows
+        data_rows = []
+        for _, row in df.iterrows():
+            data_row = "| " + " | ".join(map(str, row)) + " |"
+            data_rows.append(data_row)
+
+        # Combine all rows into a single string
+        markdown_table = "\n".join([header_row, separator_row] + data_rows)
+        return markdown_table
+
     try:
         excel_file = pd.ExcelFile(io.BytesIO(file_content))
         markdown_table = ""
@@ -417,8 +442,15 @@ def _extract_text_from_excel(file_content: bytes) -> str:
             try:
                 df = excel_file.parse(sheet_name=sheet_name)
                 df.dropna(how="all", inplace=True)
-                # Create Markdown table two times to separate tables with a newline
-                markdown_table += df.to_markdown(index=False, floatfmt="") + "\n\n"
+
+                # Combine multi-line text in each cell into a single line
+                df = df.applymap(lambda x: " ".join(str(x).splitlines()) if isinstance(x, str) else x)  # type: ignore
+
+                # Combine multi-line text in column names into a single line
+                df.columns = pd.Index([" ".join(col.splitlines()) for col in df.columns])
+
+                # Manually construct the Markdown table
+                markdown_table += _construct_markdown_table(df) + "\n\n"
             except Exception as e:
                 continue
         return markdown_table

api/core/workflow/nodes/end/end_node.py

@@ -1,8 +1,8 @@
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.end.entities import EndNodeData
 from core.workflow.nodes.enums import NodeType
-from models.workflow import WorkflowNodeExecutionStatus
 
 
 class EndNode(BaseNode[EndNodeData]):

api/core/workflow/nodes/event/event.py

@@ -1,10 +1,12 @@
+from collections.abc import Sequence
 from datetime import datetime
 
 from pydantic import BaseModel, Field
 
 from core.model_runtime.entities.llm_entities import LLMUsage
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.workflow.entities.node_entities import NodeRunResult
-from models.workflow import WorkflowNodeExecutionStatus
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 
 
 class RunCompletedEvent(BaseModel):
@@ -17,7 +19,7 @@ class RunStreamChunkEvent(BaseModel):
 
 
 class RunRetrieverResourceEvent(BaseModel):
-    retriever_resources: list[dict] = Field(..., description="retriever resources")
+    retriever_resources: Sequence[RetrievalSourceMetadata] = Field(..., description="retriever resources")
     context: str = Field(..., description="context")
 
 

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

@@ -8,12 +8,12 @@ from core.file import File, FileTransferMethod
 from core.tools.tool_file_manager import ToolFileManager
 from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.variable_entities import VariableSelector
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.http_request.executor import Executor
 from core.workflow.utils import variable_template_parser
 from factories import file_factory
-from models.workflow import WorkflowNodeExecutionStatus
 
 from .entities import (
     HttpRequestNodeData,

api/core/workflow/nodes/if_else/if_else_node.py

@@ -4,12 +4,12 @@ from typing_extensions import deprecated
 
 from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.if_else.entities import IfElseNodeData
 from core.workflow.utils.condition.entities import Condition
 from core.workflow.utils.condition.processor import ConditionProcessor
-from models.workflow import WorkflowNodeExecutionStatus
 
 
 class IfElseNode(BaseNode[IfElseNodeData]):

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

@@ -12,10 +12,10 @@ from flask import Flask, current_app, has_request_context
 from configs import dify_config
 from core.variables import ArrayVariable, IntegerVariable, NoneVariable
 from core.workflow.entities.node_entities import (
-    NodeRunMetadataKey,
     NodeRunResult,
 )
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from core.workflow.graph_engine.entities.event import (
     BaseGraphEvent,
     BaseNodeEvent,
@@ -37,7 +37,6 @@ from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.event import NodeEvent, RunCompletedEvent
 from core.workflow.nodes.iteration.entities import ErrorHandleMode, IterationNodeData
-from models.workflow import WorkflowNodeExecutionStatus
 
 from .exc import (
     InvalidIteratorValueError,
@@ -249,8 +248,8 @@ class IterationNode(BaseNode[IterationNodeData]):
                     status=WorkflowNodeExecutionStatus.SUCCEEDED,
                     outputs={"output": outputs},
                     metadata={
-                        NodeRunMetadataKey.ITERATION_DURATION_MAP: iter_run_map,
-                        NodeRunMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
+                        WorkflowNodeExecutionMetadataKey.ITERATION_DURATION_MAP: iter_run_map,
+                        WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
                     },
                 )
             )
@@ -361,16 +360,16 @@ class IterationNode(BaseNode[IterationNodeData]):
             event.parallel_mode_run_id = parallel_mode_run_id
 
         iter_metadata = {
-            NodeRunMetadataKey.ITERATION_ID: self.node_id,
-            NodeRunMetadataKey.ITERATION_INDEX: iter_run_index,
+            WorkflowNodeExecutionMetadataKey.ITERATION_ID: self.node_id,
+            WorkflowNodeExecutionMetadataKey.ITERATION_INDEX: iter_run_index,
         }
         if parallel_mode_run_id:
             # for parallel, the specific branch ID is more important than the sequential index
-            iter_metadata[NodeRunMetadataKey.PARALLEL_MODE_RUN_ID] = parallel_mode_run_id
+            iter_metadata[WorkflowNodeExecutionMetadataKey.PARALLEL_MODE_RUN_ID] = parallel_mode_run_id
 
         if event.route_node_state.node_run_result:
             current_metadata = event.route_node_state.node_run_result.metadata or {}
-            if NodeRunMetadataKey.ITERATION_ID not in current_metadata:
+            if WorkflowNodeExecutionMetadataKey.ITERATION_ID not in current_metadata:
                 event.route_node_state.node_run_result.metadata = {**current_metadata, **iter_metadata}
 
         return event

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

@@ -1,8 +1,8 @@
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.iteration.entities import IterationStartNodeData
-from models.workflow import WorkflowNodeExecutionStatus
 
 
 class IterationStartNode(BaseNode[IterationStartNodeData]):

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

@@ -8,6 +8,7 @@ from typing import Any, Optional, cast
 
 from sqlalchemy import Float, and_, func, or_, text
 from sqlalchemy import cast as sqlalchemy_cast
+from sqlalchemy.orm import Session
 
 from core.app.app_config.entities import DatasetRetrieveConfigEntity
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
@@ -24,6 +25,7 @@ from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.variables import StringSegment
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.event.event import ModelInvokeCompletedEvent
 from core.workflow.nodes.knowledge_retrieval.template_prompts import (
@@ -41,7 +43,6 @@ from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from libs.json_in_md_parser import parse_and_check_json_markdown
 from models.dataset import Dataset, DatasetMetadata, Document, RateLimitLog
-from models.workflow import WorkflowNodeExecutionStatus
 from services.feature_service import FeatureService
 
 from .entities import KnowledgeRetrievalNodeData, ModelConfig
@@ -85,30 +86,31 @@ class KnowledgeRetrievalNode(LLMNode):
             return NodeRunResult(
                 status=WorkflowNodeExecutionStatus.FAILED, inputs=variables, error="Query is required."
             )
+        # TODO(-LAN-): Move this check outside.
         # check rate limit
-        if self.tenant_id:
-            knowledge_rate_limit = FeatureService.get_knowledge_rate_limit(self.tenant_id)
-            if knowledge_rate_limit.enabled:
-                current_time = int(time.time() * 1000)
-                key = f"rate_limit_{self.tenant_id}"
-                redis_client.zadd(key, {current_time: current_time})
-                redis_client.zremrangebyscore(key, 0, current_time - 60000)
-                request_count = redis_client.zcard(key)
-                if request_count > knowledge_rate_limit.limit:
+        knowledge_rate_limit = FeatureService.get_knowledge_rate_limit(self.tenant_id)
+        if knowledge_rate_limit.enabled:
+            current_time = int(time.time() * 1000)
+            key = f"rate_limit_{self.tenant_id}"
+            redis_client.zadd(key, {current_time: current_time})
+            redis_client.zremrangebyscore(key, 0, current_time - 60000)
+            request_count = redis_client.zcard(key)
+            if request_count > knowledge_rate_limit.limit:
+                with Session(db.engine) as session:
                     # add ratelimit record
                     rate_limit_log = RateLimitLog(
                         tenant_id=self.tenant_id,
                         subscription_plan=knowledge_rate_limit.subscription_plan,
                         operation="knowledge",
                     )
-                    db.session.add(rate_limit_log)
-                    db.session.commit()
-                    return NodeRunResult(
-                        status=WorkflowNodeExecutionStatus.FAILED,
-                        inputs=variables,
-                        error="Sorry, you have reached the knowledge base request rate limit of your subscription.",
-                        error_type="RateLimitExceeded",
-                    )
+                    session.add(rate_limit_log)
+                    session.commit()
+                return NodeRunResult(
+                    status=WorkflowNodeExecutionStatus.FAILED,
+                    inputs=variables,
+                    error="Sorry, you have reached the knowledge base request rate limit of your subscription.",
+                    error_type="RateLimitExceeded",
+                )
 
         # retrieve knowledge
         try:
@@ -173,7 +175,9 @@ class KnowledgeRetrievalNode(LLMNode):
         dataset_retrieval = DatasetRetrieval()
         if node_data.retrieval_mode == DatasetRetrieveConfigEntity.RetrieveStrategy.SINGLE.value:
             # fetch model config
-            model_instance, model_config = self._fetch_model_config(node_data.single_retrieval_config.model)  # type: ignore
+            if node_data.single_retrieval_config is None:
+                raise ValueError("single_retrieval_config is required")
+            model_instance, model_config = self.get_model_config(node_data.single_retrieval_config.model)
             # check model is support tool calling
             model_type_instance = model_config.provider_model_bundle.model_type_instance
             model_type_instance = cast(LargeLanguageModel, model_type_instance)
@@ -424,7 +428,7 @@ class KnowledgeRetrievalNode(LLMNode):
             raise ValueError("metadata_model_config is required")
         # get metadata model instance
         # fetch model config
-        model_instance, model_config = self._fetch_model_config(node_data.metadata_model_config)  # type: ignore
+        model_instance, model_config = self.get_model_config(metadata_model_config)
         # fetch prompt messages
         prompt_template = self._get_prompt_template(
             node_data=node_data,
@@ -550,14 +554,7 @@ class KnowledgeRetrievalNode(LLMNode):
         variable_mapping[node_id + ".query"] = node_data.query_variable_selector
         return variable_mapping
 
-    def _fetch_model_config(self, model: ModelConfig) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:  # type: ignore
-        """
-        Fetch model config
-        :param model: model
-        :return:
-        """
-        if model is None:
-            raise ValueError("model is required")
+    def get_model_config(self, model: ModelConfig) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
         model_name = model.name
         provider_name = model.provider
 

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

@@ -4,9 +4,9 @@ from typing import Any, Literal, Union
 from core.file import File
 from core.variables import ArrayFileSegment, ArrayNumberSegment, ArrayStringSegment
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
-from models.workflow import WorkflowNodeExecutionStatus
 
 from .entities import ListOperatorNodeData
 from .exc import InvalidConditionError, InvalidFilterValueError, InvalidKeyError, ListOperatorError

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

@@ -66,7 +66,8 @@ class LLMNodeData(BaseNodeData):
     context: ContextConfig
     vision: VisionConfig = Field(default_factory=VisionConfig)
     structured_output: dict | None = None
-    structured_output_enabled: bool = False
+    # We used 'structured_output_enabled' in the past, but it's not a good name.
+    structured_output_switch_on: bool = Field(False, alias="structured_output_enabled")
 
     @field_validator("prompt_config", mode="before")
     @classmethod
@@ -74,3 +75,7 @@ class LLMNodeData(BaseNodeData):
         if v is None:
             return PromptConfig()
         return v
+
+    @property
+    def structured_output_enabled(self) -> bool:
+        return self.structured_output_switch_on and self.structured_output is not None

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

@@ -7,12 +7,12 @@ from datetime import UTC, datetime
 from typing import TYPE_CHECKING, Any, Optional, cast
 
 import json_repair
+from sqlalchemy import select, update
+from sqlalchemy.orm import Session
 
 from configs import dify_config
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
-from core.entities.model_entities import ModelStatus
 from core.entities.provider_entities import QuotaUnit
-from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.file import FileType, file_manager
 from core.helper.code_executor import CodeExecutor, CodeLanguage
 from core.memory.token_buffer_memory import TokenBufferMemory
@@ -43,6 +43,7 @@ from core.model_runtime.utils.encoders import jsonable_encoder
 from core.plugin.entities.plugin import ModelProviderID
 from core.prompt.entities.advanced_prompt_entities import CompletionModelPromptTemplate, MemoryConfig
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.variables import (
     ArrayAnySegment,
     ArrayFileSegment,
@@ -53,9 +54,10 @@ from core.variables import (
     StringSegment,
 )
 from core.workflow.constants import SYSTEM_VARIABLE_NODE_ID
-from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult
+from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.variable_entities import VariableSelector
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from core.workflow.enums import SystemVariableKey
 from core.workflow.graph_engine.entities.event import InNodeEvent
 from core.workflow.nodes.base import BaseNode
@@ -70,14 +72,12 @@ from core.workflow.nodes.event import (
 from core.workflow.utils.structured_output.entities import (
     ResponseFormat,
     SpecialModelType,
-    SupportStructuredOutputStatus,
 )
 from core.workflow.utils.structured_output.prompt import STRUCTURED_OUTPUT_PROMPT
 from core.workflow.utils.variable_template_parser import VariableTemplateParser
 from extensions.ext_database import db
 from models.model import Conversation
 from models.provider import Provider, ProviderType
-from models.workflow import WorkflowNodeExecutionStatus
 
 from .entities import (
     LLMNodeChatModelMessage,
@@ -267,14 +267,14 @@ class LLMNode(BaseNode[LLMNodeData]):
                     process_data=process_data,
                     outputs=outputs,
                     metadata={
-                        NodeRunMetadataKey.TOTAL_TOKENS: usage.total_tokens,
-                        NodeRunMetadataKey.TOTAL_PRICE: usage.total_price,
-                        NodeRunMetadataKey.CURRENCY: usage.currency,
+                        WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: usage.total_tokens,
+                        WorkflowNodeExecutionMetadataKey.TOTAL_PRICE: usage.total_price,
+                        WorkflowNodeExecutionMetadataKey.CURRENCY: usage.currency,
                     },
                     llm_usage=usage,
                 )
             )
-        except LLMNodeError as e:
+        except ValueError as e:
             yield RunCompletedEvent(
                 run_result=NodeRunResult(
                     status=WorkflowNodeExecutionStatus.FAILED,
@@ -302,8 +302,6 @@ class LLMNode(BaseNode[LLMNodeData]):
         prompt_messages: Sequence[PromptMessage],
         stop: Optional[Sequence[str]] = None,
     ) -> Generator[NodeEvent, None, None]:
-        db.session.close()
-
         invoke_result = model_instance.invoke_llm(
             prompt_messages=list(prompt_messages),
             model_parameters=node_data_model.completion_params,
@@ -474,7 +472,7 @@ class LLMNode(BaseNode[LLMNodeData]):
                 yield RunRetrieverResourceEvent(retriever_resources=[], context=context_value_variable.value)
             elif isinstance(context_value_variable, ArraySegment):
                 context_str = ""
-                original_retriever_resource = []
+                original_retriever_resource: list[RetrievalSourceMetadata] = []
                 for item in context_value_variable.value:
                     if isinstance(item, str):
                         context_str += item + "\n"
@@ -492,7 +490,7 @@ class LLMNode(BaseNode[LLMNodeData]):
                     retriever_resources=original_retriever_resource, context=context_str.strip()
                 )
 
-    def _convert_to_original_retriever_resource(self, context_dict: dict) -> Optional[dict]:
+    def _convert_to_original_retriever_resource(self, context_dict: dict):
         if (
             "metadata" in context_dict
             and "_source" in context_dict["metadata"]
@@ -500,24 +498,24 @@ class LLMNode(BaseNode[LLMNodeData]):
         ):
             metadata = context_dict.get("metadata", {})
 
-            source = {
-                "position": metadata.get("position"),
-                "dataset_id": metadata.get("dataset_id"),
-                "dataset_name": metadata.get("dataset_name"),
-                "document_id": metadata.get("document_id"),
-                "document_name": metadata.get("document_name"),
-                "data_source_type": metadata.get("data_source_type"),
-                "segment_id": metadata.get("segment_id"),
-                "retriever_from": metadata.get("retriever_from"),
-                "score": metadata.get("score"),
-                "hit_count": metadata.get("segment_hit_count"),
-                "word_count": metadata.get("segment_word_count"),
-                "segment_position": metadata.get("segment_position"),
-                "index_node_hash": metadata.get("segment_index_node_hash"),
-                "content": context_dict.get("content"),
-                "page": metadata.get("page"),
-                "doc_metadata": metadata.get("doc_metadata"),
-            }
+            source = RetrievalSourceMetadata(
+                position=metadata.get("position"),
+                dataset_id=metadata.get("dataset_id"),
+                dataset_name=metadata.get("dataset_name"),
+                document_id=metadata.get("document_id"),
+                document_name=metadata.get("document_name"),
+                data_source_type=metadata.get("data_source_type"),
+                segment_id=metadata.get("segment_id"),
+                retriever_from=metadata.get("retriever_from"),
+                score=metadata.get("score"),
+                hit_count=metadata.get("segment_hit_count"),
+                word_count=metadata.get("segment_word_count"),
+                segment_position=metadata.get("segment_position"),
+                index_node_hash=metadata.get("segment_index_node_hash"),
+                content=context_dict.get("content"),
+                page=metadata.get("page"),
+                doc_metadata=metadata.get("doc_metadata"),
+            )
 
             return source
 
@@ -526,65 +524,53 @@ class LLMNode(BaseNode[LLMNodeData]):
     def _fetch_model_config(
         self, node_data_model: ModelConfig
     ) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
-        model_name = node_data_model.name
-        provider_name = node_data_model.provider
+        if not node_data_model.mode:
+            raise LLMModeRequiredError("LLM mode is required.")
 
-        model_manager = ModelManager()
-        model_instance = model_manager.get_model_instance(
-            tenant_id=self.tenant_id, model_type=ModelType.LLM, provider=provider_name, model=model_name
+        model = ModelManager().get_model_instance(
+            tenant_id=self.tenant_id,
+            model_type=ModelType.LLM,
+            provider=node_data_model.provider,
+            model=node_data_model.name,
         )
 
-        provider_model_bundle = model_instance.provider_model_bundle
-        model_type_instance = model_instance.model_type_instance
-        model_type_instance = cast(LargeLanguageModel, model_type_instance)
-
-        model_credentials = model_instance.credentials
+        model.model_type_instance = cast(LargeLanguageModel, model.model_type_instance)
 
         # check model
-        provider_model = provider_model_bundle.configuration.get_provider_model(
-            model=model_name, model_type=ModelType.LLM
+        provider_model = model.provider_model_bundle.configuration.get_provider_model(
+            model=node_data_model.name, model_type=ModelType.LLM
         )
 
         if provider_model is None:
-            raise ModelNotExistError(f"Model {model_name} not exist.")
-
-        if provider_model.status == ModelStatus.NO_CONFIGURE:
-            raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
-        elif provider_model.status == ModelStatus.NO_PERMISSION:
-            raise ModelCurrentlyNotSupportError(f"Dify Hosted OpenAI {model_name} currently not support.")
-        elif provider_model.status == ModelStatus.QUOTA_EXCEEDED:
-            raise QuotaExceededError(f"Model provider {provider_name} quota exceeded.")
+            raise ModelNotExistError(f"Model {node_data_model.name} not exist.")
+        provider_model.raise_for_status()
 
         # model config
-        completion_params = node_data_model.completion_params
-        stop = []
-        if "stop" in completion_params:
-            stop = completion_params["stop"]
-            del completion_params["stop"]
-
-        # get model mode
-        model_mode = node_data_model.mode
-        if not model_mode:
-            raise LLMModeRequiredError("LLM mode is required.")
-
-        model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
+        stop: list[str] = []
+        if "stop" in node_data_model.completion_params:
+            stop = node_data_model.completion_params.pop("stop")
 
+        model_schema = model.model_type_instance.get_model_schema(node_data_model.name, model.credentials)
         if not model_schema:
-            raise ModelNotExistError(f"Model {model_name} not exist.")
-        support_structured_output = self._check_model_structured_output_support()
-        if support_structured_output == SupportStructuredOutputStatus.SUPPORTED:
-            completion_params = self._handle_native_json_schema(completion_params, model_schema.parameter_rules)
-        elif support_structured_output == SupportStructuredOutputStatus.UNSUPPORTED:
-            # Set appropriate response format based on model capabilities
-            self._set_response_format(completion_params, model_schema.parameter_rules)
-        return model_instance, ModelConfigWithCredentialsEntity(
-            provider=provider_name,
-            model=model_name,
+            raise ModelNotExistError(f"Model {node_data_model.name} not exist.")
+
+        if self.node_data.structured_output_enabled:
+            if model_schema.support_structure_output:
+                node_data_model.completion_params = self._handle_native_json_schema(
+                    node_data_model.completion_params, model_schema.parameter_rules
+                )
+            else:
+                # Set appropriate response format based on model capabilities
+                self._set_response_format(node_data_model.completion_params, model_schema.parameter_rules)
+
+        return model, ModelConfigWithCredentialsEntity(
+            provider=node_data_model.provider,
+            model=node_data_model.name,
             model_schema=model_schema,
-            mode=model_mode,
-            provider_model_bundle=provider_model_bundle,
-            credentials=model_credentials,
-            parameters=completion_params,
+            mode=node_data_model.mode,
+            provider_model_bundle=model.provider_model_bundle,
+            credentials=model.credentials,
+            parameters=node_data_model.completion_params,
             stop=stop,
         )
 
@@ -602,15 +588,11 @@ class LLMNode(BaseNode[LLMNodeData]):
             return None
         conversation_id = conversation_id_variable.value
 
-        # get conversation
-        conversation = (
-            db.session.query(Conversation)
-            .filter(Conversation.app_id == self.app_id, Conversation.id == conversation_id)
-            .first()
-        )
-
-        if not conversation:
-            return None
+        with Session(db.engine, expire_on_commit=False) as session:
+            stmt = select(Conversation).where(Conversation.app_id == self.app_id, Conversation.id == conversation_id)
+            conversation = session.scalar(stmt)
+            if not conversation:
+                return None
 
         memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
 
@@ -789,13 +771,25 @@ class LLMNode(BaseNode[LLMNodeData]):
                 "No prompt found in the LLM configuration. "
                 "Please ensure a prompt is properly configured before proceeding."
             )
-        support_structured_output = self._check_model_structured_output_support()
-        if support_structured_output == SupportStructuredOutputStatus.UNSUPPORTED:
-            filtered_prompt_messages = self._handle_prompt_based_schema(
-                prompt_messages=filtered_prompt_messages,
-            )
-        stop = model_config.stop
-        return filtered_prompt_messages, stop
+
+        model = ModelManager().get_model_instance(
+            tenant_id=self.tenant_id,
+            model_type=ModelType.LLM,
+            provider=self.node_data.model.provider,
+            model=self.node_data.model.name,
+        )
+        model_schema = model.model_type_instance.get_model_schema(
+            model=self.node_data.model.name,
+            credentials=model.credentials,
+        )
+        if not model_schema:
+            raise ModelNotExistError(f"Model {self.node_data.model.name} not exist.")
+        if self.node_data.structured_output_enabled:
+            if not model_schema.support_structure_output:
+                filtered_prompt_messages = self._handle_prompt_based_schema(
+                    prompt_messages=filtered_prompt_messages,
+                )
+        return filtered_prompt_messages, model_config.stop
 
     def _parse_structured_output(self, result_text: str) -> dict[str, Any]:
         structured_output: dict[str, Any] = {}
@@ -846,20 +840,24 @@ class LLMNode(BaseNode[LLMNodeData]):
                 used_quota = 1
 
         if used_quota is not None and system_configuration.current_quota_type is not None:
-            db.session.query(Provider).filter(
-                Provider.tenant_id == tenant_id,
-                # TODO: Use provider name with prefix after the data migration.
-                Provider.provider_name == ModelProviderID(model_instance.provider).provider_name,
-                Provider.provider_type == ProviderType.SYSTEM.value,
-                Provider.quota_type == system_configuration.current_quota_type.value,
-                Provider.quota_limit > Provider.quota_used,
-            ).update(
-                {
-                    "quota_used": Provider.quota_used + used_quota,
-                    "last_used": datetime.now(tz=UTC).replace(tzinfo=None),
-                }
-            )
-            db.session.commit()
+            with Session(db.engine) as session:
+                stmt = (
+                    update(Provider)
+                    .where(
+                        Provider.tenant_id == tenant_id,
+                        # TODO: Use provider name with prefix after the data migration.
+                        Provider.provider_name == ModelProviderID(model_instance.provider).provider_name,
+                        Provider.provider_type == ProviderType.SYSTEM.value,
+                        Provider.quota_type == system_configuration.current_quota_type.value,
+                        Provider.quota_limit > Provider.quota_used,
+                    )
+                    .values(
+                        quota_used=Provider.quota_used + used_quota,
+                        last_used=datetime.now(tz=UTC).replace(tzinfo=None),
+                    )
+                )
+                session.execute(stmt)
+                session.commit()
 
     @classmethod
     def _extract_variable_selector_to_variable_mapping(
@@ -902,7 +900,7 @@ class LLMNode(BaseNode[LLMNodeData]):
             variable_mapping["#context#"] = node_data.context.variable_selector
 
         if node_data.vision.enabled:
-            variable_mapping["#files#"] = ["sys", SystemVariableKey.FILES.value]
+            variable_mapping["#files#"] = node_data.vision.configs.variable_selector
 
         if node_data.memory:
             variable_mapping["#sys.query#"] = ["sys", SystemVariableKey.QUERY.value]
@@ -1184,32 +1182,6 @@ class LLMNode(BaseNode[LLMNodeData]):
         except json.JSONDecodeError:
             raise LLMNodeError("structured_output_schema is not valid JSON format")
 
-    def _check_model_structured_output_support(self) -> SupportStructuredOutputStatus:
-        """
-        Check if the current model supports structured output.
-
-        Returns:
-            SupportStructuredOutput: The support status of structured output
-        """
-        # Early return if structured output is disabled
-        if (
-            not isinstance(self.node_data, LLMNodeData)
-            or not self.node_data.structured_output_enabled
-            or not self.node_data.structured_output
-        ):
-            return SupportStructuredOutputStatus.DISABLED
-        # Get model schema and check if it exists
-        model_schema = self._fetch_model_schema(self.node_data.model.provider)
-        if not model_schema:
-            return SupportStructuredOutputStatus.DISABLED
-
-        # Check if model supports structured output feature
-        return (
-            SupportStructuredOutputStatus.SUPPORTED
-            if bool(model_schema.features and ModelFeature.STRUCTURED_OUTPUT in model_schema.features)
-            else SupportStructuredOutputStatus.UNSUPPORTED
-        )
-
     def _save_multimodal_output_and_convert_result_to_markdown(
         self,
         contents: str | list[PromptMessageContentUnionTypes] | None,

api/core/workflow/nodes/loop/loop_end_node.py

@@ -1,8 +1,8 @@
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.loop.entities import LoopEndNodeData
-from models.workflow import WorkflowNodeExecutionStatus
 
 
 class LoopEndNode(BaseNode[LoopEndNodeData]):

api/core/workflow/nodes/loop/loop_node.py

@@ -15,7 +15,8 @@ from core.variables import (
     SegmentType,
     StringSegment,
 )
-from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult
+from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from core.workflow.graph_engine.entities.event import (
     BaseGraphEvent,
     BaseNodeEvent,
@@ -37,7 +38,6 @@ from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.event import NodeEvent, RunCompletedEvent
 from core.workflow.nodes.loop.entities import LoopNodeData
 from core.workflow.utils.condition.processor import ConditionProcessor
-from models.workflow import WorkflowNodeExecutionStatus
 
 if TYPE_CHECKING:
     from core.workflow.entities.variable_pool import VariablePool
@@ -187,10 +187,10 @@ class LoopNode(BaseNode[LoopNodeData]):
                 outputs=self.node_data.outputs,
                 steps=loop_count,
                 metadata={
-                    NodeRunMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
+                    WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
                     "completed_reason": "loop_break" if check_break_result else "loop_completed",
-                    NodeRunMetadataKey.LOOP_DURATION_MAP: loop_duration_map,
-                    NodeRunMetadataKey.LOOP_VARIABLE_MAP: single_loop_variable_map,
+                    WorkflowNodeExecutionMetadataKey.LOOP_DURATION_MAP: loop_duration_map,
+                    WorkflowNodeExecutionMetadataKey.LOOP_VARIABLE_MAP: single_loop_variable_map,
                 },
             )
 
@@ -198,9 +198,9 @@ class LoopNode(BaseNode[LoopNodeData]):
                 run_result=NodeRunResult(
                     status=WorkflowNodeExecutionStatus.SUCCEEDED,
                     metadata={
-                        NodeRunMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
-                        NodeRunMetadataKey.LOOP_DURATION_MAP: loop_duration_map,
-                        NodeRunMetadataKey.LOOP_VARIABLE_MAP: single_loop_variable_map,
+                        WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
+                        WorkflowNodeExecutionMetadataKey.LOOP_DURATION_MAP: loop_duration_map,
+                        WorkflowNodeExecutionMetadataKey.LOOP_VARIABLE_MAP: single_loop_variable_map,
                     },
                     outputs=self.node_data.outputs,
                     inputs=inputs,
@@ -221,8 +221,8 @@ class LoopNode(BaseNode[LoopNodeData]):
                 metadata={
                     "total_tokens": graph_engine.graph_runtime_state.total_tokens,
                     "completed_reason": "error",
-                    NodeRunMetadataKey.LOOP_DURATION_MAP: loop_duration_map,
-                    NodeRunMetadataKey.LOOP_VARIABLE_MAP: single_loop_variable_map,
+                    WorkflowNodeExecutionMetadataKey.LOOP_DURATION_MAP: loop_duration_map,
+                    WorkflowNodeExecutionMetadataKey.LOOP_VARIABLE_MAP: single_loop_variable_map,
                 },
                 error=str(e),
             )
@@ -232,9 +232,9 @@ class LoopNode(BaseNode[LoopNodeData]):
                     status=WorkflowNodeExecutionStatus.FAILED,
                     error=str(e),
                     metadata={
-                        NodeRunMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
-                        NodeRunMetadataKey.LOOP_DURATION_MAP: loop_duration_map,
-                        NodeRunMetadataKey.LOOP_VARIABLE_MAP: single_loop_variable_map,
+                        WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
+                        WorkflowNodeExecutionMetadataKey.LOOP_DURATION_MAP: loop_duration_map,
+                        WorkflowNodeExecutionMetadataKey.LOOP_VARIABLE_MAP: single_loop_variable_map,
                     },
                 )
             )
@@ -322,7 +322,9 @@ class LoopNode(BaseNode[LoopNodeData]):
                         inputs=inputs,
                         steps=current_index,
                         metadata={
-                            NodeRunMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
+                            WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: (
+                                graph_engine.graph_runtime_state.total_tokens
+                            ),
                             "completed_reason": "error",
                         },
                         error=event.error,
@@ -331,7 +333,11 @@ class LoopNode(BaseNode[LoopNodeData]):
                         run_result=NodeRunResult(
                             status=WorkflowNodeExecutionStatus.FAILED,
                             error=event.error,
-                            metadata={NodeRunMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens},
+                            metadata={
+                                WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: (
+                                    graph_engine.graph_runtime_state.total_tokens
+                                )
+                            },
                         )
                     )
                     return {"check_break_result": True}
@@ -347,7 +353,7 @@ class LoopNode(BaseNode[LoopNodeData]):
                     inputs=inputs,
                     steps=current_index,
                     metadata={
-                        NodeRunMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
+                        WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens,
                         "completed_reason": "error",
                     },
                     error=event.error,
@@ -356,7 +362,9 @@ class LoopNode(BaseNode[LoopNodeData]):
                     run_result=NodeRunResult(
                         status=WorkflowNodeExecutionStatus.FAILED,
                         error=event.error,
-                        metadata={NodeRunMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens},
+                        metadata={
+                            WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: graph_engine.graph_runtime_state.total_tokens
+                        },
                     )
                 )
                 return {"check_break_result": True}
@@ -411,11 +419,11 @@ class LoopNode(BaseNode[LoopNodeData]):
             metadata = event.route_node_state.node_run_result.metadata
             if not metadata:
                 metadata = {}
-            if NodeRunMetadataKey.LOOP_ID not in metadata:
+            if WorkflowNodeExecutionMetadataKey.LOOP_ID not in metadata:
                 metadata = {
                     **metadata,
-                    NodeRunMetadataKey.LOOP_ID: self.node_id,
-                    NodeRunMetadataKey.LOOP_INDEX: iter_run_index,
+                    WorkflowNodeExecutionMetadataKey.LOOP_ID: self.node_id,
+                    WorkflowNodeExecutionMetadataKey.LOOP_INDEX: iter_run_index,
                 }
                 event.route_node_state.node_run_result.metadata = metadata
         return event

api/core/workflow/nodes/loop/loop_start_node.py

@@ -1,8 +1,8 @@
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.loop.entities import LoopStartNodeData
-from models.workflow import WorkflowNodeExecutionStatus
 
 
 class LoopStartNode(BaseNode[LoopStartNodeData]):

api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py

@@ -25,13 +25,12 @@ from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate
 from core.prompt.simple_prompt_transform import ModelMode
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
-from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult
+from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.llm import LLMNode, ModelConfig
 from core.workflow.utils import variable_template_parser
-from extensions.ext_database import db
-from models.workflow import WorkflowNodeExecutionStatus
 
 from .entities import ParameterExtractorNodeData
 from .exc import (
@@ -244,9 +243,9 @@ class ParameterExtractorNode(LLMNode):
             process_data=process_data,
             outputs={"__is_success": 1 if not error else 0, "__reason": error, **result},
             metadata={
-                NodeRunMetadataKey.TOTAL_TOKENS: usage.total_tokens,
-                NodeRunMetadataKey.TOTAL_PRICE: usage.total_price,
-                NodeRunMetadataKey.CURRENCY: usage.currency,
+                WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: usage.total_tokens,
+                WorkflowNodeExecutionMetadataKey.TOTAL_PRICE: usage.total_price,
+                WorkflowNodeExecutionMetadataKey.CURRENCY: usage.currency,
             },
             llm_usage=usage,
         )
@@ -259,8 +258,6 @@ class ParameterExtractorNode(LLMNode):
         tools: list[PromptMessageTool],
         stop: list[str],
     ) -> tuple[str, LLMUsage, Optional[AssistantPromptMessage.ToolCall]]:
-        db.session.close()
-
         invoke_result = model_instance.invoke_llm(
             prompt_messages=prompt_messages,
             model_parameters=node_data_model.completion_params,
@@ -816,7 +813,6 @@ class ParameterExtractorNode(LLMNode):
         :param node_data: node data
         :return:
         """
-        # FIXME: fix the type error later
         variable_mapping: dict[str, Sequence[str]] = {"query": node_data.query}
 
         if node_data.instruction:

api/core/workflow/nodes/question_classifier/question_classifier_node.py

@@ -10,7 +10,8 @@ from core.model_runtime.utils.encoders import jsonable_encoder
 from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.simple_prompt_transform import ModelMode
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
-from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult
+from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.event import ModelInvokeCompletedEvent
 from core.workflow.nodes.llm import (
@@ -20,7 +21,6 @@ from core.workflow.nodes.llm import (
 )
 from core.workflow.utils.variable_template_parser import VariableTemplateParser
 from libs.json_in_md_parser import parse_and_check_json_markdown
-from models.workflow import WorkflowNodeExecutionStatus
 
 from .entities import QuestionClassifierNodeData
 from .exc import InvalidModelTypeError
@@ -79,9 +79,13 @@ class QuestionClassifierNode(LLMNode):
             memory=memory,
             max_token_limit=rest_token,
         )
+        # Some models (e.g. Gemma, Mistral) force roles alternation (user/assistant/user/assistant...).
+        # If both self._get_prompt_template and self._fetch_prompt_messages append a user prompt,
+        # two consecutive user prompts will be generated, causing model's error.
+        # To avoid this, set sys_query to an empty string so that only one user prompt is appended at the end.
         prompt_messages, stop = self._fetch_prompt_messages(
             prompt_template=prompt_template,
-            sys_query=query,
+            sys_query="",
             memory=memory,
             model_config=model_config,
             sys_files=files,
@@ -142,9 +146,9 @@ class QuestionClassifierNode(LLMNode):
                 outputs=outputs,
                 edge_source_handle=category_id,
                 metadata={
-                    NodeRunMetadataKey.TOTAL_TOKENS: usage.total_tokens,
-                    NodeRunMetadataKey.TOTAL_PRICE: usage.total_price,
-                    NodeRunMetadataKey.CURRENCY: usage.currency,
+                    WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: usage.total_tokens,
+                    WorkflowNodeExecutionMetadataKey.TOTAL_PRICE: usage.total_price,
+                    WorkflowNodeExecutionMetadataKey.CURRENCY: usage.currency,
                 },
                 llm_usage=usage,
             )
@@ -154,9 +158,9 @@ class QuestionClassifierNode(LLMNode):
                 inputs=variables,
                 error=str(e),
                 metadata={
-                    NodeRunMetadataKey.TOTAL_TOKENS: usage.total_tokens,
-                    NodeRunMetadataKey.TOTAL_PRICE: usage.total_price,
-                    NodeRunMetadataKey.CURRENCY: usage.currency,
+                    WorkflowNodeExecutionMetadataKey.TOTAL_TOKENS: usage.total_tokens,
+                    WorkflowNodeExecutionMetadataKey.TOTAL_PRICE: usage.total_price,
+                    WorkflowNodeExecutionMetadataKey.CURRENCY: usage.currency,
                 },
                 llm_usage=usage,
             )

api/core/workflow/nodes/start/start_node.py

@@ -1,9 +1,9 @@
 from core.workflow.constants import SYSTEM_VARIABLE_NODE_ID
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.start.entities import StartNodeData
-from models.workflow import WorkflowNodeExecutionStatus
 
 
 class StartNode(BaseNode[StartNodeData]):

api/core/workflow/nodes/template_transform/template_transform_node.py

@@ -4,10 +4,10 @@ from typing import Any, Optional
 
 from core.helper.code_executor.code_executor import CodeExecutionError, CodeExecutor, CodeLanguage
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.template_transform.entities import TemplateTransformNodeData
-from models.workflow import WorkflowNodeExecutionStatus
 
 MAX_TEMPLATE_TRANSFORM_OUTPUT_LENGTH = int(os.environ.get("TEMPLATE_TRANSFORM_MAX_LENGTH", "80000"))
 

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

@@ -14,8 +14,9 @@ from core.tools.tool_engine import ToolEngine
 from core.tools.utils.message_transformer import ToolFileMessageTransformer
 from core.variables.segments import ArrayAnySegment
 from core.variables.variables import ArrayAnyVariable
-from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult
+from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from core.workflow.enums import SystemVariableKey
 from core.workflow.graph_engine.entities.event import AgentLogEvent
 from core.workflow.nodes.base import BaseNode
@@ -25,7 +26,6 @@ from core.workflow.utils.variable_template_parser import VariableTemplateParser
 from extensions.ext_database import db
 from factories import file_factory
 from models import ToolFile
-from models.workflow import WorkflowNodeExecutionStatus
 from services.tools.builtin_tools_manage_service import BuiltinToolManageService
 
 from .entities import ToolNodeData
@@ -70,7 +70,7 @@ class ToolNode(BaseNode[ToolNodeData]):
                 run_result=NodeRunResult(
                     status=WorkflowNodeExecutionStatus.FAILED,
                     inputs={},
-                    metadata={NodeRunMetadataKey.TOOL_INFO: tool_info},
+                    metadata={WorkflowNodeExecutionMetadataKey.TOOL_INFO: tool_info},
                     error=f"Failed to get tool runtime: {str(e)}",
                     error_type=type(e).__name__,
                 )
@@ -110,7 +110,7 @@ class ToolNode(BaseNode[ToolNodeData]):
                 run_result=NodeRunResult(
                     status=WorkflowNodeExecutionStatus.FAILED,
                     inputs=parameters_for_log,
-                    metadata={NodeRunMetadataKey.TOOL_INFO: tool_info},
+                    metadata={WorkflowNodeExecutionMetadataKey.TOOL_INFO: tool_info},
                     error=f"Failed to invoke tool: {str(e)}",
                     error_type=type(e).__name__,
                 )
@@ -125,7 +125,7 @@ class ToolNode(BaseNode[ToolNodeData]):
                 run_result=NodeRunResult(
                     status=WorkflowNodeExecutionStatus.FAILED,
                     inputs=parameters_for_log,
-                    metadata={NodeRunMetadataKey.TOOL_INFO: tool_info},
+                    metadata={WorkflowNodeExecutionMetadataKey.TOOL_INFO: tool_info},
                     error=f"Failed to transform tool message: {str(e)}",
                     error_type=type(e).__name__,
                 )
@@ -201,7 +201,7 @@ class ToolNode(BaseNode[ToolNodeData]):
         json: list[dict] = []
 
         agent_logs: list[AgentLogEvent] = []
-        agent_execution_metadata: Mapping[NodeRunMetadataKey, Any] = {}
+        agent_execution_metadata: Mapping[WorkflowNodeExecutionMetadataKey, Any] = {}
 
         variables: dict[str, Any] = {}
 
@@ -274,7 +274,7 @@ class ToolNode(BaseNode[ToolNodeData]):
                     agent_execution_metadata = {
                         key: value
                         for key, value in msg_metadata.items()
-                        if key in NodeRunMetadataKey.__members__.values()
+                        if key in WorkflowNodeExecutionMetadataKey.__members__.values()
                     }
                 json.append(message.message.json_object)
             elif message.type == ToolInvokeMessage.MessageType.LINK:
@@ -366,8 +366,8 @@ class ToolNode(BaseNode[ToolNodeData]):
                 outputs={"text": text, "files": files, "json": json, **variables},
                 metadata={
                     **agent_execution_metadata,
-                    NodeRunMetadataKey.TOOL_INFO: tool_info,
-                    NodeRunMetadataKey.AGENT_LOG: agent_logs,
+                    WorkflowNodeExecutionMetadataKey.TOOL_INFO: tool_info,
+                    WorkflowNodeExecutionMetadataKey.AGENT_LOG: agent_logs,
                 },
                 inputs=parameters_for_log,
             )

api/core/workflow/nodes/variable_aggregator/entities.py

@@ -1,7 +1,8 @@
-from typing import Literal, Optional
+from typing import Optional
 
 from pydantic import BaseModel
 
+from core.variables.types import SegmentType
 from core.workflow.nodes.base import BaseNodeData
 
 
@@ -17,7 +18,7 @@ class AdvancedSettings(BaseModel):
         Group.
         """
 
-        output_type: Literal["string", "number", "object", "array[string]", "array[number]", "array[object]"]
+        output_type: SegmentType
         variables: list[list[str]]
         group_name: str
 

api/core/workflow/nodes/variable_aggregator/variable_aggregator_node.py

@@ -1,8 +1,8 @@
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.variable_aggregator.entities import VariableAssignerNodeData
-from models.workflow import WorkflowNodeExecutionStatus
 
 
 class VariableAggregatorNode(BaseNode[VariableAssignerNodeData]):

api/core/workflow/nodes/variable_assigner/v1/node.py

@@ -1,11 +1,11 @@
 from core.variables import SegmentType, Variable
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.variable_assigner.common import helpers as common_helpers
 from core.workflow.nodes.variable_assigner.common.exc import VariableOperatorNodeError
 from factories import variable_factory
-from models.workflow import WorkflowNodeExecutionStatus
 
 from .node_data import VariableAssignerData, WriteMode
 

api/core/workflow/nodes/variable_assigner/v2/node.py

@@ -6,11 +6,11 @@ from core.app.entities.app_invoke_entities import InvokeFrom
 from core.variables import SegmentType, Variable
 from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID
 from core.workflow.entities.node_entities import NodeRunResult
+from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.variable_assigner.common import helpers as common_helpers
 from core.workflow.nodes.variable_assigner.common.exc import VariableOperatorNodeError
-from models.workflow import WorkflowNodeExecutionStatus
 
 from . import helpers
 from .constants import EMPTY_VALUE_MAPPING

api/core/workflow/repositories/__init__.py

@@ -6,7 +6,7 @@ for accessing and manipulating data, regardless of the underlying
 storage mechanism.
 """
 
-from core.workflow.repository.workflow_node_execution_repository import OrderConfig, WorkflowNodeExecutionRepository
+from core.workflow.repositories.workflow_node_execution_repository import OrderConfig, WorkflowNodeExecutionRepository
 
 __all__ = [
     "OrderConfig",

api/core/workflow/repositories/workflow_execution_repository.py

@@ -1,6 +1,6 @@
 from typing import Optional, Protocol
 
-from core.workflow.entities.workflow_execution_entities import WorkflowExecution
+from core.workflow.entities.workflow_execution import WorkflowExecution
 
 
 class WorkflowExecutionRepository(Protocol):