feat: add task type

.gitignore

@@ -209,7 +209,6 @@ api/.vscode
 .history
 
 .idea/
-web/migration/
 
 # pnpm
 /.pnpm-store

api/app_factory.py

@@ -71,8 +71,6 @@ def create_app() -> DifyApp:
 
 
 def initialize_extensions(app: DifyApp):
-    # Initialize Flask context capture for workflow execution
-    from context.flask_app_context import init_flask_context
     from extensions import (
         ext_app_metrics,
         ext_blueprints,
@@ -102,8 +100,6 @@ def initialize_extensions(app: DifyApp):
         ext_warnings,
     )
 
-    init_flask_context()
-
     extensions = [
         ext_timezone,
         ext_logging,

api/context/__init__.py

@@ -1,74 +0,0 @@
-"""
-Core Context - Framework-agnostic context management.
-
-This module provides context management that is independent of any specific
-web framework. Framework-specific implementations register their context
-capture functions at application initialization time.
-
-This ensures the workflow layer remains completely decoupled from Flask
-or any other web framework.
-"""
-
-import contextvars
-from collections.abc import Callable
-
-from core.workflow.context.execution_context import (
-    ExecutionContext,
-    IExecutionContext,
-    NullAppContext,
-)
-
-# Global capturer function - set by framework-specific modules
-_capturer: Callable[[], IExecutionContext] | None = None
-
-
-def register_context_capturer(capturer: Callable[[], IExecutionContext]) -> None:
-    """
-    Register a context capture function.
-
-    This should be called by framework-specific modules (e.g., Flask)
-    during application initialization.
-
-    Args:
-        capturer: Function that captures current context and returns IExecutionContext
-    """
-    global _capturer
-    _capturer = capturer
-
-
-def capture_current_context() -> IExecutionContext:
-    """
-    Capture current execution context.
-
-    This function uses the registered context capturer. If no capturer
-    is registered, it returns a minimal context with only contextvars
-    (suitable for non-framework environments like tests or standalone scripts).
-
-    Returns:
-        IExecutionContext with captured context
-    """
-    if _capturer is None:
-        # No framework registered - return minimal context
-        return ExecutionContext(
-            app_context=NullAppContext(),
-            context_vars=contextvars.copy_context(),
-        )
-
-    return _capturer()
-
-
-def reset_context_provider() -> None:
-    """
-    Reset the context capturer.
-
-    This is primarily useful for testing to ensure a clean state.
-    """
-    global _capturer
-    _capturer = None
-
-
-__all__ = [
-    "capture_current_context",
-    "register_context_capturer",
-    "reset_context_provider",
-]

api/context/flask_app_context.py

@@ -1,198 +0,0 @@
-"""
-Flask App Context - Flask implementation of AppContext interface.
-"""
-
-import contextvars
-from collections.abc import Generator
-from contextlib import contextmanager
-from typing import Any, final
-
-from flask import Flask, current_app, g
-
-from context import register_context_capturer
-from core.workflow.context.execution_context import (
-    AppContext,
-    IExecutionContext,
-)
-
-
-@final
-class FlaskAppContext(AppContext):
-    """
-    Flask implementation of AppContext.
-
-    This adapts Flask's app context to the AppContext interface.
-    """
-
-    def __init__(self, flask_app: Flask) -> None:
-        """
-        Initialize Flask app context.
-
-        Args:
-            flask_app: The Flask application instance
-        """
-        self._flask_app = flask_app
-
-    def get_config(self, key: str, default: Any = None) -> Any:
-        """Get configuration value from Flask app config."""
-        return self._flask_app.config.get(key, default)
-
-    def get_extension(self, name: str) -> Any:
-        """Get Flask extension by name."""
-        return self._flask_app.extensions.get(name)
-
-    @contextmanager
-    def enter(self) -> Generator[None, None, None]:
-        """Enter Flask app context."""
-        with self._flask_app.app_context():
-            yield
-
-    @property
-    def flask_app(self) -> Flask:
-        """Get the underlying Flask app instance."""
-        return self._flask_app
-
-
-def capture_flask_context(user: Any = None) -> IExecutionContext:
-    """
-    Capture current Flask execution context.
-
-    This function captures the Flask app context and contextvars from the
-    current environment. It should be called from within a Flask request or
-    app context.
-
-    Args:
-        user: Optional user object to include in context
-
-    Returns:
-        IExecutionContext with captured Flask context
-
-    Raises:
-        RuntimeError: If called outside Flask context
-    """
-    # Get Flask app instance
-    flask_app = current_app._get_current_object()  # type: ignore
-
-    # Save current user if available
-    saved_user = user
-    if saved_user is None:
-        # Check for user in g (flask-login)
-        if hasattr(g, "_login_user"):
-            saved_user = g._login_user
-
-    # Capture contextvars
-    context_vars = contextvars.copy_context()
-
-    return FlaskExecutionContext(
-        flask_app=flask_app,
-        context_vars=context_vars,
-        user=saved_user,
-    )
-
-
-@final
-class FlaskExecutionContext:
-    """
-    Flask-specific execution context.
-
-    This is a specialized version of ExecutionContext that includes Flask app
-    context. It provides the same interface as ExecutionContext but with
-    Flask-specific implementation.
-    """
-
-    def __init__(
-        self,
-        flask_app: Flask,
-        context_vars: contextvars.Context,
-        user: Any = None,
-    ) -> None:
-        """
-        Initialize Flask execution context.
-
-        Args:
-            flask_app: Flask application instance
-            context_vars: Python contextvars
-            user: Optional user object
-        """
-        self._app_context = FlaskAppContext(flask_app)
-        self._context_vars = context_vars
-        self._user = user
-        self._flask_app = flask_app
-
-    @property
-    def app_context(self) -> FlaskAppContext:
-        """Get Flask app context."""
-        return self._app_context
-
-    @property
-    def context_vars(self) -> contextvars.Context:
-        """Get context variables."""
-        return self._context_vars
-
-    @property
-    def user(self) -> Any:
-        """Get user object."""
-        return self._user
-
-    def __enter__(self) -> "FlaskExecutionContext":
-        """Enter the Flask execution context."""
-        # Restore context variables
-        for var, val in self._context_vars.items():
-            var.set(val)
-
-        # Save current user from g if available
-        saved_user = None
-        if hasattr(g, "_login_user"):
-            saved_user = g._login_user
-
-        # Enter Flask app context
-        self._cm = self._app_context.enter()
-        self._cm.__enter__()
-
-        # Restore user in new app context
-        if saved_user is not None:
-            g._login_user = saved_user
-
-        return self
-
-    def __exit__(self, *args: Any) -> None:
-        """Exit the Flask execution context."""
-        if hasattr(self, "_cm"):
-            self._cm.__exit__(*args)
-
-    @contextmanager
-    def enter(self) -> Generator[None, None, None]:
-        """Enter Flask execution context as context manager."""
-        # Restore context variables
-        for var, val in self._context_vars.items():
-            var.set(val)
-
-        # Save current user from g if available
-        saved_user = None
-        if hasattr(g, "_login_user"):
-            saved_user = g._login_user
-
-        # Enter Flask app context
-        with self._flask_app.app_context():
-            # Restore user in new app context
-            if saved_user is not None:
-                g._login_user = saved_user
-            yield
-
-
-def init_flask_context() -> None:
-    """
-    Initialize Flask context capture by registering the capturer.
-
-    This function should be called during Flask application initialization
-    to register the Flask-specific context capturer with the core context module.
-
-    Example:
-        app = Flask(__name__)
-        init_flask_context()  # Register Flask context capturer
-
-    Note:
-        This function does not need the app instance as it uses Flask's
-        `current_app` to get the app when capturing context.
-    """
-    register_context_capturer(capture_flask_context)

api/controllers/console/app/generator.py

@@ -55,35 +55,6 @@ class InstructionTemplatePayload(BaseModel):
     type: str = Field(..., description="Instruction template type")
 
 
-class ContextGeneratePayload(BaseModel):
-    """Payload for generating extractor code node."""
-
-    workflow_id: str = Field(..., description="Workflow ID")
-    node_id: str = Field(..., description="Current tool/llm node ID")
-    parameter_name: str = Field(..., description="Parameter name to generate code for")
-    language: str = Field(default="python3", description="Code language (python3/javascript)")
-    prompt_messages: list[dict[str, Any]] = Field(
-        ..., description="Multi-turn conversation history, last message is the current instruction"
-    )
-    model_config_data: dict[str, Any] = Field(..., alias="model_config", description="Model configuration")
-
-
-class SuggestedQuestionsPayload(BaseModel):
-    """Payload for generating suggested questions."""
-
-    workflow_id: str = Field(..., description="Workflow ID")
-    node_id: str = Field(..., description="Current tool/llm node ID")
-    parameter_name: str = Field(..., description="Parameter name")
-    language: str = Field(
-        default="English", description="Language for generated questions (e.g. English, Chinese, Japanese)"
-    )
-    model_config_data: dict[str, Any] | None = Field(
-        default=None,
-        alias="model_config",
-        description="Model configuration (optional, uses system default if not provided)",
-    )
-
-
 def reg(cls: type[BaseModel]):
     console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
 
@@ -93,8 +64,6 @@ reg(RuleCodeGeneratePayload)
 reg(RuleStructuredOutputPayload)
 reg(InstructionGeneratePayload)
 reg(InstructionTemplatePayload)
-reg(ContextGeneratePayload)
-reg(SuggestedQuestionsPayload)
 
 
 @console_ns.route("/rule-generate")
@@ -309,74 +278,3 @@ class InstructionGenerationTemplateApi(Resource):
                 return {"data": INSTRUCTION_GENERATE_TEMPLATE_CODE}
             case _:
                 raise ValueError(f"Invalid type: {args.type}")
-
-
-@console_ns.route("/context-generate")
-class ContextGenerateApi(Resource):
-    @console_ns.doc("generate_with_context")
-    @console_ns.doc(description="Generate with multi-turn conversation context")
-    @console_ns.expect(console_ns.models[ContextGeneratePayload.__name__])
-    @console_ns.response(200, "Content generated successfully")
-    @console_ns.response(400, "Invalid request parameters or workflow not found")
-    @console_ns.response(402, "Provider quota exceeded")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self):
-        from core.llm_generator.utils import deserialize_prompt_messages
-
-        args = ContextGeneratePayload.model_validate(console_ns.payload)
-        _, current_tenant_id = current_account_with_tenant()
-
-        prompt_messages = deserialize_prompt_messages(args.prompt_messages)
-
-        try:
-            return LLMGenerator.generate_with_context(
-                tenant_id=current_tenant_id,
-                workflow_id=args.workflow_id,
-                node_id=args.node_id,
-                parameter_name=args.parameter_name,
-                language=args.language,
-                prompt_messages=prompt_messages,
-                model_config=args.model_config_data,
-            )
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
-        except QuotaExceededError:
-            raise ProviderQuotaExceededError()
-        except ModelCurrentlyNotSupportError:
-            raise ProviderModelCurrentlyNotSupportError()
-        except InvokeError as e:
-            raise CompletionRequestError(e.description)
-
-
-@console_ns.route("/context-generate/suggested-questions")
-class SuggestedQuestionsApi(Resource):
-    @console_ns.doc("generate_suggested_questions")
-    @console_ns.doc(description="Generate suggested questions for context generation")
-    @console_ns.expect(console_ns.models[SuggestedQuestionsPayload.__name__])
-    @console_ns.response(200, "Questions generated successfully")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self):
-        args = SuggestedQuestionsPayload.model_validate(console_ns.payload)
-        _, current_tenant_id = current_account_with_tenant()
-
-        try:
-            return LLMGenerator.generate_suggested_questions(
-                tenant_id=current_tenant_id,
-                workflow_id=args.workflow_id,
-                node_id=args.node_id,
-                parameter_name=args.parameter_name,
-                language=args.language,
-                model_config=args.model_config_data,
-            )
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
-        except QuotaExceededError:
-            raise ProviderQuotaExceededError()
-        except ModelCurrentlyNotSupportError:
-            raise ProviderModelCurrentlyNotSupportError()
-        except InvokeError as e:
-            raise CompletionRequestError(e.description)

api/controllers/console/app/workflow.py

@@ -46,8 +46,6 @@ from models.workflow import Workflow
 from services.app_generate_service import AppGenerateService
 from services.errors.app import WorkflowHashNotEqualError
 from services.errors.llm import InvokeRateLimitError
-from services.workflow.entities import MentionGraphRequest, MentionParameterSchema
-from services.workflow.mention_graph_service import MentionGraphService
 from services.workflow_service import DraftWorkflowDeletionError, WorkflowInUseError, WorkflowService
 
 logger = logging.getLogger(__name__)
@@ -190,15 +188,6 @@ class DraftWorkflowTriggerRunAllPayload(BaseModel):
     node_ids: list[str]
 
 
-class MentionGraphPayload(BaseModel):
-    """Request payload for generating mention graph."""
-
-    parent_node_id: str = Field(description="ID of the parent node that uses the extracted value")
-    parameter_key: str = Field(description="Key of the parameter being extracted")
-    context_source: list[str] = Field(description="Variable selector for the context source")
-    parameter_schema: dict[str, Any] = Field(description="Schema of the parameter to extract")
-
-
 def reg(cls: type[BaseModel]):
     console_ns.schema_model(cls.__name__, cls.model_json_schema(ref_template=DEFAULT_REF_TEMPLATE_SWAGGER_2_0))
 
@@ -216,7 +205,6 @@ reg(WorkflowListQuery)
 reg(WorkflowUpdatePayload)
 reg(DraftWorkflowTriggerRunPayload)
 reg(DraftWorkflowTriggerRunAllPayload)
-reg(MentionGraphPayload)
 
 
 # TODO(QuantumGhost): Refactor existing node run API to handle file parameter parsing
@@ -1178,54 +1166,3 @@ class DraftWorkflowTriggerRunAllApi(Resource):
                     "status": "error",
                 }
             ), 400
-
-
-@console_ns.route("/apps/<uuid:app_id>/workflows/draft/mention-graph")
-class MentionGraphApi(Resource):
-    """
-    API for generating Mention LLM node graph structures.
-
-    This endpoint creates a complete graph structure containing an LLM node
-    configured to extract values from list[PromptMessage] variables.
-    """
-
-    @console_ns.doc("generate_mention_graph")
-    @console_ns.doc(description="Generate a Mention LLM node graph structure")
-    @console_ns.doc(params={"app_id": "Application ID"})
-    @console_ns.expect(console_ns.models[MentionGraphPayload.__name__])
-    @console_ns.response(200, "Mention graph generated successfully")
-    @console_ns.response(400, "Invalid request parameters")
-    @console_ns.response(403, "Permission denied")
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    @edit_permission_required
-    def post(self, app_model: App):
-        """
-        Generate a Mention LLM node graph structure.
-
-        Returns a complete graph structure containing a single LLM node
-        configured for extracting values from list[PromptMessage] context.
-        """
-
-        payload = MentionGraphPayload.model_validate(console_ns.payload or {})
-
-        parameter_schema = MentionParameterSchema(
-            name=payload.parameter_schema.get("name", payload.parameter_key),
-            type=payload.parameter_schema.get("type", "string"),
-            description=payload.parameter_schema.get("description", ""),
-        )
-
-        request = MentionGraphRequest(
-            parent_node_id=payload.parent_node_id,
-            parameter_key=payload.parameter_key,
-            context_source=payload.context_source,
-            parameter_schema=parameter_schema,
-        )
-
-        with Session(db.engine) as session:
-            service = MentionGraphService(session)
-            response = service.generate_mention_graph(tenant_id=app_model.tenant_id, request=request)
-
-        return response.model_dump()

api/controllers/console/app/workflow_draft_variable.py

@@ -17,7 +17,7 @@ from controllers.console.wraps import account_initialization_required, edit_perm
 from controllers.web.error import InvalidArgumentError, NotFoundError
 from core.file import helpers as file_helpers
 from core.variables.segment_group import SegmentGroup
-from core.variables.segments import ArrayFileSegment, ArrayPromptMessageSegment, FileSegment, Segment
+from core.variables.segments import ArrayFileSegment, FileSegment, Segment
 from core.variables.types import SegmentType
 from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID, SYSTEM_VARIABLE_NODE_ID
 from extensions.ext_database import db
@@ -58,8 +58,6 @@ def _convert_values_to_json_serializable_object(value: Segment):
         return value.value.model_dump()
     elif isinstance(value, ArrayFileSegment):
         return [i.model_dump() for i in value.value]
-    elif isinstance(value, ArrayPromptMessageSegment):
-        return value.to_object()
     elif isinstance(value, SegmentGroup):
         return [_convert_values_to_json_serializable_object(i) for i in value.value]
     else:

api/controllers/console/auth/activate.py

@@ -69,13 +69,6 @@ class ActivateCheckApi(Resource):
         if invitation:
             data = invitation.get("data", {})
             tenant = invitation.get("tenant", None)
-
-            # Check workspace permission
-            if tenant:
-                from libs.workspace_permission import check_workspace_member_invite_permission
-
-                check_workspace_member_invite_permission(tenant.id)
-
             workspace_name = tenant.name if tenant else None
             workspace_id = tenant.id if tenant else None
             invitee_email = data.get("email") if data else None

api/controllers/console/workspace/members.py

@@ -107,12 +107,6 @@ class MemberInviteEmailApi(Resource):
         inviter = current_user
         if not inviter.current_tenant:
             raise ValueError("No current tenant")
-
-        # Check workspace permission for member invitations
-        from libs.workspace_permission import check_workspace_member_invite_permission
-
-        check_workspace_member_invite_permission(inviter.current_tenant.id)
-
         invitation_results = []
         console_web_url = dify_config.CONSOLE_WEB_URL
 

api/controllers/console/workspace/workspace.py

@@ -20,7 +20,6 @@ from controllers.console.error import AccountNotLinkTenantError
 from controllers.console.wraps import (
     account_initialization_required,
     cloud_edition_billing_resource_check,
-    only_edition_enterprise,
     setup_required,
 )
 from enums.cloud_plan import CloudPlan
@@ -29,7 +28,6 @@ from libs.helper import TimestampField
 from libs.login import current_account_with_tenant, login_required
 from models.account import Tenant, TenantStatus
 from services.account_service import TenantService
-from services.enterprise.enterprise_service import EnterpriseService
 from services.feature_service import FeatureService
 from services.file_service import FileService
 from services.workspace_service import WorkspaceService
@@ -290,31 +288,3 @@ class WorkspaceInfoApi(Resource):
         db.session.commit()
 
         return {"result": "success", "tenant": marshal(WorkspaceService.get_tenant_info(tenant), tenant_fields)}
-
-
-@console_ns.route("/workspaces/current/permission")
-class WorkspacePermissionApi(Resource):
-    """Get workspace permissions for the current workspace."""
-
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @only_edition_enterprise
-    def get(self):
-        """
-        Get workspace permission settings.
-        Returns permission flags that control workspace features like member invitations and owner transfer.
-        """
-        _, current_tenant_id = current_account_with_tenant()
-
-        if not current_tenant_id:
-            raise ValueError("No current tenant")
-
-        # Get workspace permissions from enterprise service
-        permission = EnterpriseService.WorkspacePermissionService.get_permission(current_tenant_id)
-
-        return {
-            "workspace_id": permission.workspace_id,
-            "allow_member_invite": permission.allow_member_invite,
-            "allow_owner_transfer": permission.allow_owner_transfer,
-        }, 200

api/controllers/console/wraps.py

@@ -286,12 +286,13 @@ def enable_change_email(view: Callable[P, R]):
 def is_allow_transfer_owner(view: Callable[P, R]):
     @wraps(view)
     def decorated(*args: P.args, **kwargs: P.kwargs):
-        from libs.workspace_permission import check_workspace_owner_transfer_permission
-
         _, current_tenant_id = current_account_with_tenant()
-        # Check both billing/plan level and workspace policy level permissions
-        check_workspace_owner_transfer_permission(current_tenant_id)
-        return view(*args, **kwargs)
+        features = FeatureService.get_features(current_tenant_id)
+        if features.is_allow_transfer_workspace:
+            return view(*args, **kwargs)
+
+        # otherwise, return 403
+        abort(403)
 
     return decorated
 

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

@@ -82,7 +82,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk
 
     @classmethod
@@ -110,7 +110,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -120,6 +120,6 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
 
             yield response_chunk

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

@@ -81,7 +81,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk
 
     @classmethod
@@ -109,7 +109,7 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -117,6 +117,6 @@ class AgentChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
 
             yield response_chunk

api/core/app/apps/chat/generate_response_converter.py

@@ -81,7 +81,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk
 
     @classmethod
@@ -109,7 +109,7 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
                 metadata = sub_stream_response_dict.get("metadata", {})
                 sub_stream_response_dict["metadata"] = cls._get_simple_metadata(metadata)
                 response_chunk.update(sub_stream_response_dict)
@@ -117,6 +117,6 @@ class ChatAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
 
             yield response_chunk

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

@@ -70,8 +70,6 @@ class _NodeSnapshot:
     """Empty string means the node is not executing inside an iteration."""
     loop_id: str = ""
     """Empty string means the node is not executing inside a loop."""
-    mention_parent_id: str = ""
-    """Empty string means the node is not an extractor node."""
 
 
 class WorkflowResponseConverter:
@@ -133,7 +131,6 @@ class WorkflowResponseConverter:
             start_at=event.start_at,
             iteration_id=event.in_iteration_id or "",
             loop_id=event.in_loop_id or "",
-            mention_parent_id=event.in_mention_parent_id or "",
         )
         node_execution_id = NodeExecutionId(event.node_execution_id)
         self._node_snapshots[node_execution_id] = snapshot
@@ -290,7 +287,6 @@ class WorkflowResponseConverter:
                 created_at=int(snapshot.start_at.timestamp()),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
-                mention_parent_id=event.in_mention_parent_id,
                 agent_strategy=event.agent_strategy,
             ),
         )
@@ -377,7 +373,6 @@ class WorkflowResponseConverter:
                 files=self.fetch_files_from_node_outputs(event.outputs or {}),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
-                mention_parent_id=event.in_mention_parent_id,
             ),
         )
 
@@ -427,7 +422,6 @@ class WorkflowResponseConverter:
                 files=self.fetch_files_from_node_outputs(event.outputs or {}),
                 iteration_id=event.in_iteration_id,
                 loop_id=event.in_loop_id,
-                mention_parent_id=event.in_mention_parent_id,
                 retry_index=event.retry_index,
             ),
         )

api/core/app/apps/completion/generate_response_converter.py

@@ -79,7 +79,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk
 
     @classmethod
@@ -106,7 +106,7 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
             }
 
             if isinstance(sub_stream_response, MessageEndStreamResponse):
-                sub_stream_response_dict = sub_stream_response.model_dump(mode="json", exclude_none=True)
+                sub_stream_response_dict = sub_stream_response.model_dump(mode="json")
                 metadata = sub_stream_response_dict.get("metadata", {})
                 if not isinstance(metadata, dict):
                     metadata = {}
@@ -116,6 +116,6 @@ class CompletionAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
 
             yield response_chunk

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

@@ -60,7 +60,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(cast(dict, data))
             else:
-                response_chunk.update(sub_stream_response.model_dump(exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump())
             yield response_chunk
 
     @classmethod
@@ -91,5 +91,5 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(cast(dict, sub_stream_response.to_ignore_detail_dict()))
             else:
-                response_chunk.update(sub_stream_response.model_dump(exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump())
             yield response_chunk

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

@@ -8,7 +8,7 @@ from typing import Any, Literal, Union, overload
 from flask import Flask, current_app
 from pydantic import ValidationError
 from sqlalchemy import select
-from sqlalchemy.orm import sessionmaker
+from sqlalchemy.orm import Session, sessionmaker
 
 import contexts
 from configs import dify_config
@@ -23,7 +23,6 @@ from core.app.apps.workflow.generate_response_converter import WorkflowAppGenera
 from core.app.apps.workflow.generate_task_pipeline import WorkflowAppGenerateTaskPipeline
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.task_entities import WorkflowAppBlockingResponse, WorkflowAppStreamResponse
-from core.db.session_factory import session_factory
 from core.helper.trace_id_helper import extract_external_trace_id_from_args
 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
@@ -477,7 +476,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
         :return:
         """
         with preserve_flask_contexts(flask_app, context_vars=context):
-            with session_factory.create_session() as session:
+            with Session(db.engine, expire_on_commit=False) as session:
                 workflow = session.scalar(
                     select(Workflow).where(
                         Workflow.tenant_id == application_generate_entity.app_config.tenant_id,

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

@@ -60,7 +60,7 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk
 
     @classmethod
@@ -91,5 +91,5 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
             elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
                 response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
-                response_chunk.update(sub_stream_response.model_dump(mode="json", exclude_none=True))
+                response_chunk.update(sub_stream_response.model_dump(mode="json"))
             yield response_chunk

api/core/app/apps/workflow_app_runner.py

@@ -385,7 +385,6 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_mention_parent_id=event.in_mention_parent_id,
                     inputs=inputs,
                     process_data=process_data,
                     outputs=outputs,
@@ -406,7 +405,6 @@ class WorkflowBasedAppRunner:
                     start_at=event.start_at,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_mention_parent_id=event.in_mention_parent_id,
                     agent_strategy=event.agent_strategy,
                     provider_type=event.provider_type,
                     provider_id=event.provider_id,
@@ -430,7 +428,6 @@ class WorkflowBasedAppRunner:
                     execution_metadata=execution_metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunFailedEvent):
@@ -447,7 +444,6 @@ class WorkflowBasedAppRunner:
                     execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunExceptionEvent):
@@ -464,7 +460,6 @@ class WorkflowBasedAppRunner:
                     execution_metadata=event.node_run_result.metadata,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunStreamChunkEvent):
@@ -474,7 +469,6 @@ class WorkflowBasedAppRunner:
                     from_variable_selector=list(event.selector),
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunRetrieverResourceEvent):
@@ -483,7 +477,6 @@ class WorkflowBasedAppRunner:
                     retriever_resources=event.retriever_resources,
                     in_iteration_id=event.in_iteration_id,
                     in_loop_id=event.in_loop_id,
-                    in_mention_parent_id=event.in_mention_parent_id,
                 )
             )
         elif isinstance(event, NodeRunAgentLogEvent):

api/core/app/entities/queue_entities.py

@@ -190,8 +190,6 @@ class QueueTextChunkEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_mention_parent_id: str | None = None
-    """parent node id if this is an extractor node event"""
 
 
 class QueueAgentMessageEvent(AppQueueEvent):
@@ -231,8 +229,6 @@ class QueueRetrieverResourcesEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_mention_parent_id: str | None = None
-    """parent node id if this is an extractor node event"""
 
 
 class QueueAnnotationReplyEvent(AppQueueEvent):
@@ -310,8 +306,6 @@ class QueueNodeStartedEvent(AppQueueEvent):
     node_run_index: int = 1  # FIXME(-LAN-): may not used
     in_iteration_id: str | None = None
     in_loop_id: str | None = None
-    in_mention_parent_id: str | None = None
-    """parent node id if this is an extractor node event"""
     start_at: datetime
     agent_strategy: AgentNodeStrategyInit | None = None
 
@@ -334,8 +328,6 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_mention_parent_id: str | None = None
-    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)
@@ -391,8 +383,6 @@ class QueueNodeExceptionEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_mention_parent_id: str | None = None
-    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)
@@ -417,8 +407,6 @@ class QueueNodeFailedEvent(AppQueueEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_mention_parent_id: str | None = None
-    """parent node id if this is an extractor node event"""
     start_at: datetime
 
     inputs: Mapping[str, object] = Field(default_factory=dict)

api/core/app/entities/task_entities.py

@@ -262,7 +262,6 @@ class NodeStartStreamResponse(StreamResponse):
         extras: dict[str, object] = Field(default_factory=dict)
         iteration_id: str | None = None
         loop_id: str | None = None
-        mention_parent_id: str | None = None
         agent_strategy: AgentNodeStrategyInit | None = None
 
     event: StreamEvent = StreamEvent.NODE_STARTED
@@ -286,7 +285,6 @@ class NodeStartStreamResponse(StreamResponse):
                 "extras": {},
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
-                "mention_parent_id": self.data.mention_parent_id,
             },
         }
 
@@ -322,7 +320,6 @@ class NodeFinishStreamResponse(StreamResponse):
         files: Sequence[Mapping[str, Any]] | None = []
         iteration_id: str | None = None
         loop_id: str | None = None
-        mention_parent_id: str | None = None
 
     event: StreamEvent = StreamEvent.NODE_FINISHED
     workflow_run_id: str
@@ -352,7 +349,6 @@ class NodeFinishStreamResponse(StreamResponse):
                 "files": [],
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
-                "mention_parent_id": self.data.mention_parent_id,
             },
         }
 
@@ -388,7 +384,6 @@ class NodeRetryStreamResponse(StreamResponse):
         files: Sequence[Mapping[str, Any]] | None = []
         iteration_id: str | None = None
         loop_id: str | None = None
-        mention_parent_id: str | None = None
         retry_index: int = 0
 
     event: StreamEvent = StreamEvent.NODE_RETRY
@@ -419,7 +414,6 @@ class NodeRetryStreamResponse(StreamResponse):
                 "files": [],
                 "iteration_id": self.data.iteration_id,
                 "loop_id": self.data.loop_id,
-                "mention_parent_id": self.data.mention_parent_id,
                 "retry_index": self.data.retry_index,
             },
         }

api/core/file/file_manager.py

@@ -1,5 +1,4 @@
 import base64
-import logging
 from collections.abc import Mapping
 
 from configs import dify_config
@@ -11,10 +10,7 @@ from core.model_runtime.entities import (
     TextPromptMessageContent,
     VideoPromptMessageContent,
 )
-from core.model_runtime.entities.message_entities import (
-    MultiModalPromptMessageContent,
-    PromptMessageContentUnionTypes,
-)
+from core.model_runtime.entities.message_entities import PromptMessageContentUnionTypes
 from core.tools.signature import sign_tool_file
 from extensions.ext_storage import storage
 
@@ -22,8 +18,6 @@ from . import helpers
 from .enums import FileAttribute
 from .models import File, FileTransferMethod, FileType
 
-logger = logging.getLogger(__name__)
-
 
 def get_attr(*, file: File, attr: FileAttribute):
     match attr:
@@ -95,8 +89,6 @@ def to_prompt_message_content(
         "format": f.extension.removeprefix("."),
         "mime_type": f.mime_type,
         "filename": f.filename or "",
-        # Encoded file reference for context restoration: "transfer_method:related_id" or "remote:url"
-        "file_ref": _encode_file_ref(f),
     }
     if f.type == FileType.IMAGE:
         params["detail"] = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
@@ -104,17 +96,6 @@ def to_prompt_message_content(
     return prompt_class_map[f.type].model_validate(params)
 
 
-def _encode_file_ref(f: File) -> str | None:
-    """Encode file reference as 'transfer_method:id_or_url' string."""
-    if f.transfer_method == FileTransferMethod.REMOTE_URL:
-        return f"remote:{f.remote_url}" if f.remote_url else None
-    elif f.transfer_method == FileTransferMethod.LOCAL_FILE:
-        return f"local:{f.related_id}" if f.related_id else None
-    elif f.transfer_method == FileTransferMethod.TOOL_FILE:
-        return f"tool:{f.related_id}" if f.related_id else None
-    return None
-
-
 def download(f: File, /):
     if f.transfer_method in (
         FileTransferMethod.TOOL_FILE,
@@ -183,128 +164,3 @@ def _to_url(f: File, /):
         return sign_tool_file(tool_file_id=f.related_id, extension=f.extension)
     else:
         raise ValueError(f"Unsupported transfer method: {f.transfer_method}")
-
-
-def restore_multimodal_content(
-    content: MultiModalPromptMessageContent,
-) -> MultiModalPromptMessageContent:
-    """
-    Restore base64_data or url for multimodal content from file_ref.
-
-    file_ref format: "transfer_method:id_or_url" (e.g., "local:abc123", "remote:https://...")
-
-    Args:
-        content: MultiModalPromptMessageContent with file_ref field
-
-    Returns:
-        MultiModalPromptMessageContent with restored base64_data or url
-    """
-    # Skip if no file reference or content already has data
-    if not content.file_ref:
-        return content
-    if content.base64_data or content.url:
-        return content
-
-    try:
-        file = _build_file_from_ref(
-            file_ref=content.file_ref,
-            file_format=content.format,
-            mime_type=content.mime_type,
-            filename=content.filename,
-        )
-        if not file:
-            return content
-
-        # Restore content based on config
-        if dify_config.MULTIMODAL_SEND_FORMAT == "base64":
-            restored_base64 = _get_encoded_string(file)
-            return content.model_copy(update={"base64_data": restored_base64})
-        else:
-            restored_url = _to_url(file)
-            return content.model_copy(update={"url": restored_url})
-
-    except Exception as e:
-        logger.warning("Failed to restore multimodal content: %s", e)
-        return content
-
-
-def _build_file_from_ref(
-    file_ref: str,
-    file_format: str | None,
-    mime_type: str | None,
-    filename: str | None,
-) -> File | None:
-    """
-    Build a File object from encoded file_ref string.
-
-    Args:
-        file_ref: Encoded reference "transfer_method:id_or_url"
-        file_format: The file format/extension (without dot)
-        mime_type: The mime type
-        filename: The filename
-
-    Returns:
-        File object with storage_key loaded, or None if not found
-    """
-    from sqlalchemy import select
-    from sqlalchemy.orm import Session
-
-    from extensions.ext_database import db
-    from models.model import UploadFile
-    from models.tools import ToolFile
-
-    # Parse file_ref: "method:value"
-    if ":" not in file_ref:
-        logger.warning("Invalid file_ref format: %s", file_ref)
-        return None
-
-    method, value = file_ref.split(":", 1)
-    extension = f".{file_format}" if file_format else None
-
-    if method == "remote":
-        return File(
-            tenant_id="",
-            type=FileType.IMAGE,
-            transfer_method=FileTransferMethod.REMOTE_URL,
-            remote_url=value,
-            extension=extension,
-            mime_type=mime_type,
-            filename=filename,
-            storage_key="",
-        )
-
-    # Query database for storage_key
-    with Session(db.engine) as session:
-        if method == "local":
-            stmt = select(UploadFile).where(UploadFile.id == value)
-            upload_file = session.scalar(stmt)
-            if upload_file:
-                return File(
-                    tenant_id=upload_file.tenant_id,
-                    type=FileType(upload_file.extension)
-                    if hasattr(FileType, upload_file.extension.upper())
-                    else FileType.IMAGE,
-                    transfer_method=FileTransferMethod.LOCAL_FILE,
-                    related_id=value,
-                    extension=extension or ("." + upload_file.extension if upload_file.extension else None),
-                    mime_type=mime_type or upload_file.mime_type,
-                    filename=filename or upload_file.name,
-                    storage_key=upload_file.key,
-                )
-        elif method == "tool":
-            stmt = select(ToolFile).where(ToolFile.id == value)
-            tool_file = session.scalar(stmt)
-            if tool_file:
-                return File(
-                    tenant_id=tool_file.tenant_id,
-                    type=FileType.IMAGE,
-                    transfer_method=FileTransferMethod.TOOL_FILE,
-                    related_id=value,
-                    extension=extension,
-                    mime_type=mime_type or tool_file.mimetype,
-                    filename=filename or tool_file.name,
-                    storage_key=tool_file.file_key,
-                )
-
-    logger.warning("File not found for file_ref: %s", file_ref)
-    return None

api/core/llm_generator/llm_generator.py

@@ -1,16 +1,11 @@
 import json
 import logging
 import re
-from collections.abc import Mapping, Sequence
-from typing import Any, Protocol, cast
+from collections.abc import Sequence
+from typing import Protocol, cast
 
 import json_repair
 
-from core.llm_generator.output_models import (
-    CodeNodeStructuredOutput,
-    InstructionModifyOutput,
-    SuggestedQuestionsOutput,
-)
 from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
 from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
 from core.llm_generator.prompts import (
@@ -398,432 +393,6 @@ class LLMGenerator:
             logger.exception("Failed to invoke LLM model, model: %s", model_config.get("name"))
             return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
 
-    @classmethod
-    def generate_with_context(
-        cls,
-        tenant_id: str,
-        workflow_id: str,
-        node_id: str,
-        parameter_name: str,
-        language: str,
-        prompt_messages: list[PromptMessage],
-        model_config: dict,
-    ) -> dict:
-        """
-        Generate extractor code node based on conversation context.
-
-        Args:
-            tenant_id: Tenant/workspace ID
-            workflow_id: Workflow ID
-            node_id: Current tool/llm node ID
-            parameter_name: Parameter name to generate code for
-            language: Code language (python3/javascript)
-            prompt_messages: Multi-turn conversation history (last message is instruction)
-            model_config: Model configuration (provider, name, completion_params)
-
-        Returns:
-            dict with CodeNodeData format:
-            - variables: Input variable selectors
-            - code_language: Code language
-            - code: Generated code
-            - outputs: Output definitions
-            - message: Explanation
-            - error: Error message if any
-        """
-        from sqlalchemy import select
-        from sqlalchemy.orm import Session
-
-        from services.workflow_service import WorkflowService
-
-        # Get workflow
-        with Session(db.engine) as session:
-            stmt = select(App).where(App.id == workflow_id)
-            app = session.scalar(stmt)
-            if not app:
-                return cls._error_response(f"App {workflow_id} not found")
-
-            workflow = WorkflowService().get_draft_workflow(app_model=app)
-            if not workflow:
-                return cls._error_response(f"Workflow for app {workflow_id} not found")
-
-        # Get upstream nodes via edge backtracking
-        upstream_nodes = cls._get_upstream_nodes(workflow.graph_dict, node_id)
-
-        # Get current node info
-        current_node = cls._get_node_by_id(workflow.graph_dict, node_id)
-        if not current_node:
-            return cls._error_response(f"Node {node_id} not found")
-
-        # Get parameter info
-        parameter_info = cls._get_parameter_info(
-            tenant_id=tenant_id,
-            node_data=current_node.get("data", {}),
-            parameter_name=parameter_name,
-        )
-
-        # Build system prompt
-        system_prompt = cls._build_extractor_system_prompt(
-            upstream_nodes=upstream_nodes,
-            current_node=current_node,
-            parameter_info=parameter_info,
-            language=language,
-        )
-
-        # Construct complete prompt_messages with system prompt
-        complete_messages: list[PromptMessage] = [
-            SystemPromptMessage(content=system_prompt),
-            *prompt_messages,
-        ]
-
-        from core.llm_generator.output_parser.structured_output import invoke_llm_with_pydantic_model
-
-        # Get model instance and schema
-        provider = model_config.get("provider", "")
-        model_name = model_config.get("name", "")
-        model_instance = ModelManager().get_model_instance(
-            tenant_id=tenant_id,
-            model_type=ModelType.LLM,
-            provider=provider,
-            model=model_name,
-        )
-
-        model_schema = model_instance.model_type_instance.get_model_schema(model_name, model_instance.credentials)
-        if not model_schema:
-            return cls._error_response(f"Model schema not found for {model_name}")
-
-        model_parameters = model_config.get("completion_params", {})
-        try:
-            response = invoke_llm_with_pydantic_model(
-                provider=provider,
-                model_schema=model_schema,
-                model_instance=model_instance,
-                prompt_messages=complete_messages,
-                output_model=CodeNodeStructuredOutput,
-                model_parameters=model_parameters,
-                stream=False,
-                tenant_id=tenant_id,
-            )
-
-            return cls._parse_code_node_output(
-                response.structured_output, language, parameter_info.get("type", "string")
-            )
-
-        except InvokeError as e:
-            return cls._error_response(str(e))
-        except Exception as e:
-            logger.exception("Failed to generate with context, model: %s", model_config.get("name"))
-            return cls._error_response(f"An unexpected error occurred: {str(e)}")
-
-    @classmethod
-    def _error_response(cls, error: str) -> dict:
-        """Return error response in CodeNodeData format."""
-        return {
-            "variables": [],
-            "code_language": "python3",
-            "code": "",
-            "outputs": {},
-            "message": "",
-            "error": error,
-        }
-
-    @classmethod
-    def generate_suggested_questions(
-        cls,
-        tenant_id: str,
-        workflow_id: str,
-        node_id: str,
-        parameter_name: str,
-        language: str,
-        model_config: dict | None = None,
-    ) -> dict:
-        """
-        Generate suggested questions for context generation.
-
-        Returns dict with questions array and error field.
-        """
-        from sqlalchemy import select
-        from sqlalchemy.orm import Session
-
-        from core.llm_generator.output_parser.structured_output import invoke_llm_with_pydantic_model
-        from services.workflow_service import WorkflowService
-
-        # Get workflow context (reuse existing logic)
-        with Session(db.engine) as session:
-            stmt = select(App).where(App.id == workflow_id)
-            app = session.scalar(stmt)
-            if not app:
-                return {"questions": [], "error": f"App {workflow_id} not found"}
-
-            workflow = WorkflowService().get_draft_workflow(app_model=app)
-            if not workflow:
-                return {"questions": [], "error": f"Workflow for app {workflow_id} not found"}
-
-        upstream_nodes = cls._get_upstream_nodes(workflow.graph_dict, node_id)
-        current_node = cls._get_node_by_id(workflow.graph_dict, node_id)
-        if not current_node:
-            return {"questions": [], "error": f"Node {node_id} not found"}
-
-        parameter_info = cls._get_parameter_info(
-            tenant_id=tenant_id,
-            node_data=current_node.get("data", {}),
-            parameter_name=parameter_name,
-        )
-
-        # Build prompt
-        system_prompt = cls._build_suggested_questions_prompt(
-            upstream_nodes=upstream_nodes,
-            current_node=current_node,
-            parameter_info=parameter_info,
-            language=language,
-        )
-
-        prompt_messages: list[PromptMessage] = [
-            SystemPromptMessage(content=system_prompt),
-        ]
-
-        # Get model instance - use default if model_config not provided
-        model_manager = ModelManager()
-        if model_config:
-            provider = model_config.get("provider", "")
-            model_name = model_config.get("name", "")
-            model_instance = model_manager.get_model_instance(
-                tenant_id=tenant_id,
-                model_type=ModelType.LLM,
-                provider=provider,
-                model=model_name,
-            )
-        else:
-            model_instance = model_manager.get_default_model_instance(
-                tenant_id=tenant_id,
-                model_type=ModelType.LLM,
-            )
-            model_name = model_instance.model
-
-        model_schema = model_instance.model_type_instance.get_model_schema(model_name, model_instance.credentials)
-        if not model_schema:
-            return {"questions": [], "error": f"Model schema not found for {model_name}"}
-
-        completion_params = model_config.get("completion_params", {}) if model_config else {}
-        model_parameters = {**completion_params, "max_tokens": 256}
-        try:
-            response = invoke_llm_with_pydantic_model(
-                provider=model_instance.provider,
-                model_schema=model_schema,
-                model_instance=model_instance,
-                prompt_messages=prompt_messages,
-                output_model=SuggestedQuestionsOutput,
-                model_parameters=model_parameters,
-                stream=False,
-                tenant_id=tenant_id,
-            )
-
-            questions = response.structured_output.get("questions", []) if response.structured_output else []
-            return {"questions": questions, "error": ""}
-
-        except InvokeError as e:
-            return {"questions": [], "error": str(e)}
-        except Exception as e:
-            logger.exception("Failed to generate suggested questions, model: %s", model_name)
-            return {"questions": [], "error": f"An unexpected error occurred: {str(e)}"}
-
-    @classmethod
-    def _build_suggested_questions_prompt(
-        cls,
-        upstream_nodes: list[dict],
-        current_node: dict,
-        parameter_info: dict,
-        language: str = "English",
-    ) -> str:
-        """Build minimal prompt for suggested questions generation."""
-        # Simplify upstream nodes to reduce tokens
-        sources = [f"{n['title']}({','.join(n.get('outputs', {}).keys())})" for n in upstream_nodes[:5]]
-        param_type = parameter_info.get("type", "string")
-        param_desc = parameter_info.get("description", "")[:100]
-
-        return f"""Suggest 3 code generation questions for extracting data.
-Sources: {", ".join(sources)}
-Target: {parameter_info.get("name")}({param_type}) - {param_desc}
-Output 3 short, practical questions in {language}."""
-
-    @classmethod
-    def _get_upstream_nodes(cls, graph_dict: Mapping[str, Any], node_id: str) -> list[dict]:
-        """
-        Get all upstream nodes via edge backtracking.
-
-        Traverses the graph backwards from node_id to collect all reachable nodes.
-        """
-        from collections import defaultdict
-
-        nodes = {n["id"]: n for n in graph_dict.get("nodes", [])}
-        edges = graph_dict.get("edges", [])
-
-        # Build reverse adjacency list
-        reverse_adj: dict[str, list[str]] = defaultdict(list)
-        for edge in edges:
-            reverse_adj[edge["target"]].append(edge["source"])
-
-        # BFS to find all upstream nodes
-        visited: set[str] = set()
-        queue = [node_id]
-        upstream: list[dict] = []
-
-        while queue:
-            current = queue.pop(0)
-            for source in reverse_adj.get(current, []):
-                if source not in visited:
-                    visited.add(source)
-                    queue.append(source)
-                    if source in nodes:
-                        upstream.append(cls._extract_node_info(nodes[source]))
-
-        return upstream
-
-    @classmethod
-    def _get_node_by_id(cls, graph_dict: Mapping[str, Any], node_id: str) -> dict | None:
-        """Get node by ID from graph."""
-        for node in graph_dict.get("nodes", []):
-            if node["id"] == node_id:
-                return node
-        return None
-
-    @classmethod
-    def _extract_node_info(cls, node: dict) -> dict:
-        """Extract minimal node info with outputs based on node type."""
-        node_type = node["data"]["type"]
-        node_data = node.get("data", {})
-
-        # Build outputs based on node type (only type, no description to reduce tokens)
-        outputs: dict[str, str] = {}
-        match node_type:
-            case "start":
-                for var in node_data.get("variables", []):
-                    name = var.get("variable", var.get("name", ""))
-                    outputs[name] = var.get("type", "string")
-            case "llm":
-                outputs["text"] = "string"
-            case "code":
-                for name, output in node_data.get("outputs", {}).items():
-                    outputs[name] = output.get("type", "string")
-            case "http-request":
-                outputs = {"body": "string", "status_code": "number", "headers": "object"}
-            case "knowledge-retrieval":
-                outputs["result"] = "array[object]"
-            case "tool":
-                outputs = {"text": "string", "json": "object"}
-            case _:
-                outputs["output"] = "string"
-
-        info: dict = {
-            "id": node["id"],
-            "title": node_data.get("title", node["id"]),
-            "outputs": outputs,
-        }
-        # Only include description if not empty
-        desc = node_data.get("desc", "")
-        if desc:
-            info["desc"] = desc
-
-        return info
-
-    @classmethod
-    def _get_parameter_info(cls, tenant_id: str, node_data: dict, parameter_name: str) -> dict:
-        """Get parameter info from tool schema using ToolManager."""
-        default_info = {"name": parameter_name, "type": "string", "description": ""}
-
-        if node_data.get("type") != "tool":
-            return default_info
-
-        try:
-            from core.app.entities.app_invoke_entities import InvokeFrom
-            from core.tools.entities.tool_entities import ToolProviderType
-            from core.tools.tool_manager import ToolManager
-
-            provider_type_str = node_data.get("provider_type", "")
-            provider_type = ToolProviderType(provider_type_str) if provider_type_str else ToolProviderType.BUILT_IN
-
-            tool_runtime = ToolManager.get_tool_runtime(
-                provider_type=provider_type,
-                provider_id=node_data.get("provider_id", ""),
-                tool_name=node_data.get("tool_name", ""),
-                tenant_id=tenant_id,
-                invoke_from=InvokeFrom.DEBUGGER,
-            )
-
-            parameters = tool_runtime.get_merged_runtime_parameters()
-            for param in parameters:
-                if param.name == parameter_name:
-                    return {
-                        "name": param.name,
-                        "type": param.type.value if hasattr(param.type, "value") else str(param.type),
-                        "description": param.llm_description
-                        or (param.human_description.en_US if param.human_description else ""),
-                        "required": param.required,
-                    }
-        except Exception as e:
-            logger.debug("Failed to get parameter info from ToolManager: %s", e)
-
-        return default_info
-
-    @classmethod
-    def _build_extractor_system_prompt(
-        cls,
-        upstream_nodes: list[dict],
-        current_node: dict,
-        parameter_info: dict,
-        language: str,
-    ) -> str:
-        """Build system prompt for extractor code generation."""
-        upstream_json = json.dumps(upstream_nodes, indent=2, ensure_ascii=False)
-        param_type = parameter_info.get("type", "string")
-        return f"""You are a code generator for workflow automation.
-
-Generate {language} code to extract/transform upstream node outputs for the target parameter.
-
-## Upstream Nodes
-{upstream_json}
-
-## Target
-Node: {current_node["data"].get("title", current_node["id"])}
-Parameter: {parameter_info.get("name")} ({param_type}) - {parameter_info.get("description", "")}
-
-## Requirements
-- Write a main function that returns type: {param_type}
-- Use value_selector format: ["node_id", "output_name"]
-"""
-
-    @classmethod
-    def _parse_code_node_output(cls, content: Mapping[str, Any] | None, language: str, parameter_type: str) -> dict:
-        """
-        Parse structured output to CodeNodeData format.
-
-        Args:
-            content: Structured output dict from invoke_llm_with_structured_output
-            language: Code language
-            parameter_type: Expected parameter type
-
-        Returns dict with variables, code_language, code, outputs, message, error.
-        """
-        if content is None:
-            return cls._error_response("Empty or invalid response from LLM")
-
-        # Validate and normalize variables
-        variables = [
-            {"variable": v.get("variable", ""), "value_selector": v.get("value_selector", [])}
-            for v in content.get("variables", [])
-            if isinstance(v, dict)
-        ]
-
-        outputs = content.get("outputs", {"result": {"type": parameter_type}})
-
-        return {
-            "variables": variables,
-            "code_language": language,
-            "code": content.get("code", ""),
-            "outputs": outputs,
-            "message": content.get("explanation", ""),
-            "error": "",
-        }
-
     @staticmethod
     def instruction_modify_legacy(
         tenant_id: str, flow_id: str, current: str, instruction: str, model_config: dict, ideal_output: str | None
@@ -960,10 +529,6 @@ Parameter: {parameter_info.get("name")} ({param_type}) - {parameter_info.get("de
             provider=model_config.get("provider", ""),
             model=model_config.get("name", ""),
         )
-        model_name = model_config.get("name", "")
-        model_schema = model_instance.model_type_instance.get_model_schema(model_name, model_instance.credentials)
-        if not model_schema:
-            return {"error": f"Model schema not found for {model_name}"}
         match node_type:
             case "llm" | "agent":
                 system_prompt = LLM_MODIFY_PROMPT_SYSTEM
@@ -987,18 +552,20 @@ Parameter: {parameter_info.get("name")} ({param_type}) - {parameter_info.get("de
         model_parameters = {"temperature": 0.4}
 
         try:
-            from core.llm_generator.output_parser.structured_output import invoke_llm_with_pydantic_model
-
-            response = invoke_llm_with_pydantic_model(
-                provider=model_instance.provider,
-                model_schema=model_schema,
-                model_instance=model_instance,
-                prompt_messages=list(prompt_messages),
-                output_model=InstructionModifyOutput,
-                model_parameters=model_parameters,
-                stream=False,
+            response: LLMResult = model_instance.invoke_llm(
+                prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
             )
-            return response.structured_output or {}
+
+            generated_raw = response.message.get_text_content()
+            first_brace = generated_raw.find("{")
+            last_brace = generated_raw.rfind("}")
+            if first_brace == -1 or last_brace == -1 or last_brace < first_brace:
+                raise ValueError(f"Could not find a valid JSON object in response: {generated_raw}")
+            json_str = generated_raw[first_brace : last_brace + 1]
+            data = json_repair.loads(json_str)
+            if not isinstance(data, dict):
+                raise TypeError(f"Expected a JSON object, but got {type(data).__name__}")
+            return data
         except InvokeError as e:
             error = str(e)
             return {"error": f"Failed to generate code. Error: {error}"}

api/core/llm_generator/output_models.py

@@ -1,34 +0,0 @@
-from __future__ import annotations
-
-from pydantic import BaseModel, ConfigDict, Field
-
-from core.variables.types import SegmentType
-from core.workflow.nodes.base.entities import VariableSelector
-
-
-class SuggestedQuestionsOutput(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    questions: list[str] = Field(min_length=3, max_length=3)
-
-
-class CodeNodeOutput(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    type: SegmentType
-
-
-class CodeNodeStructuredOutput(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    variables: list[VariableSelector]
-    code: str
-    outputs: dict[str, CodeNodeOutput]
-    explanation: str
-
-
-class InstructionModifyOutput(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    modified: str
-    message: str

api/core/llm_generator/output_parser/file_ref.py

@@ -1,188 +0,0 @@
-"""
-File reference detection and conversion for structured output.
-
-This module provides utilities to:
-1. Detect file reference fields in JSON Schema (format: "dify-file-ref")
-2. Convert file ID strings to File objects after LLM returns
-"""
-
-import uuid
-from collections.abc import Mapping
-from typing import Any
-
-from core.file import File
-from core.variables.segments import ArrayFileSegment, FileSegment
-from factories.file_factory import build_from_mapping
-
-FILE_REF_FORMAT = "dify-file-ref"
-
-
-def is_file_ref_property(schema: dict) -> bool:
-    """Check if a schema property is a file reference."""
-    return schema.get("type") == "string" and schema.get("format") == FILE_REF_FORMAT
-
-
-def detect_file_ref_fields(schema: Mapping[str, Any], path: str = "") -> list[str]:
-    """
-    Recursively detect file reference fields in schema.
-
-    Args:
-        schema: JSON Schema to analyze
-        path: Current path in the schema (used for recursion)
-
-    Returns:
-        List of JSON paths containing file refs, e.g., ["image_id", "files[*]"]
-    """
-    file_ref_paths: list[str] = []
-    schema_type = schema.get("type")
-
-    if schema_type == "object":
-        for prop_name, prop_schema in schema.get("properties", {}).items():
-            current_path = f"{path}.{prop_name}" if path else prop_name
-
-            if is_file_ref_property(prop_schema):
-                file_ref_paths.append(current_path)
-            elif isinstance(prop_schema, dict):
-                file_ref_paths.extend(detect_file_ref_fields(prop_schema, current_path))
-
-    elif schema_type == "array":
-        items_schema = schema.get("items", {})
-        array_path = f"{path}[*]" if path else "[*]"
-
-        if is_file_ref_property(items_schema):
-            file_ref_paths.append(array_path)
-        elif isinstance(items_schema, dict):
-            file_ref_paths.extend(detect_file_ref_fields(items_schema, array_path))
-
-    return file_ref_paths
-
-
-def convert_file_refs_in_output(
-    output: Mapping[str, Any],
-    json_schema: Mapping[str, Any],
-    tenant_id: str,
-) -> dict[str, Any]:
-    """
-    Convert file ID strings to File objects based on schema.
-
-    Args:
-        output: The structured_output from LLM result
-        json_schema: The original JSON schema (to detect file ref fields)
-        tenant_id: Tenant ID for file lookup
-
-    Returns:
-        Output with file references converted to File objects
-    """
-    file_ref_paths = detect_file_ref_fields(json_schema)
-    if not file_ref_paths:
-        return dict(output)
-
-    result = _deep_copy_dict(output)
-
-    for path in file_ref_paths:
-        _convert_path_in_place(result, path.split("."), tenant_id)
-
-    return result
-
-
-def _deep_copy_dict(obj: Mapping[str, Any]) -> dict[str, Any]:
-    """Deep copy a mapping to a mutable dict."""
-    result: dict[str, Any] = {}
-    for key, value in obj.items():
-        if isinstance(value, Mapping):
-            result[key] = _deep_copy_dict(value)
-        elif isinstance(value, list):
-            result[key] = [_deep_copy_dict(item) if isinstance(item, Mapping) else item for item in value]
-        else:
-            result[key] = value
-    return result
-
-
-def _convert_path_in_place(obj: dict, path_parts: list[str], tenant_id: str) -> None:
-    """Convert file refs at the given path in place, wrapping in Segment types."""
-    if not path_parts:
-        return
-
-    current = path_parts[0]
-    remaining = path_parts[1:]
-
-    # Handle array notation like "files[*]"
-    if current.endswith("[*]"):
-        key = current[:-3] if current != "[*]" else None
-        target = obj.get(key) if key else obj
-
-        if isinstance(target, list):
-            if remaining:
-                # Nested array with remaining path - recurse into each item
-                for item in target:
-                    if isinstance(item, dict):
-                        _convert_path_in_place(item, remaining, tenant_id)
-            else:
-                # Array of file IDs - convert all and wrap in ArrayFileSegment
-                files: list[File] = []
-                for item in target:
-                    file = _convert_file_id(item, tenant_id)
-                    if file is not None:
-                        files.append(file)
-                # Replace the array with ArrayFileSegment
-                if key:
-                    obj[key] = ArrayFileSegment(value=files)
-        return
-
-    if not remaining:
-        # Leaf node - convert the value and wrap in FileSegment
-        if current in obj:
-            file = _convert_file_id(obj[current], tenant_id)
-            if file is not None:
-                obj[current] = FileSegment(value=file)
-            else:
-                obj[current] = None
-    else:
-        # Recurse into nested object
-        if current in obj and isinstance(obj[current], dict):
-            _convert_path_in_place(obj[current], remaining, tenant_id)
-
-
-def _convert_file_id(file_id: Any, tenant_id: str) -> File | None:
-    """
-    Convert a file ID string to a File object.
-
-    Tries multiple file sources in order:
-    1. ToolFile (files generated by tools/workflows)
-    2. UploadFile (files uploaded by users)
-    """
-    if not isinstance(file_id, str):
-        return None
-
-    # Validate UUID format
-    try:
-        uuid.UUID(file_id)
-    except ValueError:
-        return None
-
-    # Try ToolFile first (files generated by tools/workflows)
-    try:
-        return build_from_mapping(
-            mapping={
-                "transfer_method": "tool_file",
-                "tool_file_id": file_id,
-            },
-            tenant_id=tenant_id,
-        )
-    except ValueError:
-        pass
-
-    # Try UploadFile (files uploaded by users)
-    try:
-        return build_from_mapping(
-            mapping={
-                "transfer_method": "local_file",
-                "upload_file_id": file_id,
-            },
-            tenant_id=tenant_id,
-        )
-    except ValueError:
-        pass
-
-    # File not found in any source
-    return None

api/core/llm_generator/output_parser/structured_output.py

@@ -2,13 +2,12 @@ import json
 from collections.abc import Generator, Mapping, Sequence
 from copy import deepcopy
 from enum import StrEnum
-from typing import Any, Literal, TypeVar, cast, overload
+from typing import Any, Literal, cast, overload
 
 import json_repair
-from pydantic import BaseModel, TypeAdapter, ValidationError
+from pydantic import TypeAdapter, ValidationError
 
 from core.llm_generator.output_parser.errors import OutputParserError
-from core.llm_generator.output_parser.file_ref import convert_file_refs_in_output
 from core.llm_generator.prompts import STRUCTURED_OUTPUT_PROMPT
 from core.model_manager import ModelInstance
 from core.model_runtime.callbacks.base_callback import Callback
@@ -44,9 +43,6 @@ class SpecialModelType(StrEnum):
     OLLAMA = "ollama"
 
 
-T = TypeVar("T", bound=BaseModel)
-
-
 @overload
 def invoke_llm_with_structured_output(
     *,
@@ -61,7 +57,6 @@ def invoke_llm_with_structured_output(
     stream: Literal[True],
     user: str | None = None,
     callbacks: list[Callback] | None = None,
-    tenant_id: str | None = None,
 ) -> Generator[LLMResultChunkWithStructuredOutput, None, None]: ...
 @overload
 def invoke_llm_with_structured_output(
@@ -77,7 +72,6 @@ def invoke_llm_with_structured_output(
     stream: Literal[False],
     user: str | None = None,
     callbacks: list[Callback] | None = None,
-    tenant_id: str | None = None,
 ) -> LLMResultWithStructuredOutput: ...
 @overload
 def invoke_llm_with_structured_output(
@@ -93,7 +87,6 @@ def invoke_llm_with_structured_output(
     stream: bool = True,
     user: str | None = None,
     callbacks: list[Callback] | None = None,
-    tenant_id: str | None = None,
 ) -> LLMResultWithStructuredOutput | Generator[LLMResultChunkWithStructuredOutput, None, None]: ...
 def invoke_llm_with_structured_output(
     *,
@@ -108,30 +101,23 @@ def invoke_llm_with_structured_output(
     stream: bool = True,
     user: str | None = None,
     callbacks: list[Callback] | None = None,
-    tenant_id: str | None = None,
 ) -> LLMResultWithStructuredOutput | Generator[LLMResultChunkWithStructuredOutput, None, None]:
     """
-    Invoke large language model with structured output.
+    Invoke large language model with structured output
+    1. This method invokes model_instance.invoke_llm with json_schema
+    2. Try to parse the result as structured output
 
-    This method invokes model_instance.invoke_llm with json_schema and parses
-    the result as structured output.
-
-    :param provider: model provider name
-    :param model_schema: model schema entity
-    :param model_instance: model instance to invoke
     :param prompt_messages: prompt messages
-    :param json_schema: json schema for structured output
+    :param json_schema: json schema
     :param model_parameters: model parameters
     :param tools: tools for tool calling
     :param stop: stop words
     :param stream: is stream response
     :param user: unique user id
     :param callbacks: callbacks
-    :param tenant_id: tenant ID for file reference conversion. When provided and
-                      json_schema contains file reference fields (format: "dify-file-ref"),
-                      file IDs in the output will be automatically converted to File objects.
     :return: full response or stream response chunk generator result
     """
+
     # handle native json schema
     model_parameters_with_json_schema: dict[str, Any] = {
         **(model_parameters or {}),
@@ -167,18 +153,8 @@ def invoke_llm_with_structured_output(
                 f"Failed to parse structured output, LLM result is not a string: {llm_result.message.content}"
             )
 
-        structured_output = _parse_structured_output(llm_result.message.content)
-
-        # Convert file references if tenant_id is provided
-        if tenant_id is not None:
-            structured_output = convert_file_refs_in_output(
-                output=structured_output,
-                json_schema=json_schema,
-                tenant_id=tenant_id,
-            )
-
         return LLMResultWithStructuredOutput(
-            structured_output=structured_output,
+            structured_output=_parse_structured_output(llm_result.message.content),
             model=llm_result.model,
             message=llm_result.message,
             usage=llm_result.usage,
@@ -210,18 +186,8 @@ def invoke_llm_with_structured_output(
                     delta=event.delta,
                 )
 
-            structured_output = _parse_structured_output(result_text)
-
-            # Convert file references if tenant_id is provided
-            if tenant_id is not None:
-                structured_output = convert_file_refs_in_output(
-                    output=structured_output,
-                    json_schema=json_schema,
-                    tenant_id=tenant_id,
-                )
-
             yield LLMResultChunkWithStructuredOutput(
-                structured_output=structured_output,
+                structured_output=_parse_structured_output(result_text),
                 model=model_schema.model,
                 prompt_messages=prompt_messages,
                 system_fingerprint=system_fingerprint,
@@ -236,87 +202,6 @@ def invoke_llm_with_structured_output(
         return generator()
 
 
-@overload
-def invoke_llm_with_pydantic_model(
-    *,
-    provider: str,
-    model_schema: AIModelEntity,
-    model_instance: ModelInstance,
-    prompt_messages: Sequence[PromptMessage],
-    output_model: type[T],
-    model_parameters: Mapping | None = None,
-    tools: Sequence[PromptMessageTool] | None = None,
-    stop: list[str] | None = None,
-    stream: Literal[False] = False,
-    user: str | None = None,
-    callbacks: list[Callback] | None = None,
-    tenant_id: str | None = None,
-) -> LLMResultWithStructuredOutput: ...
-
-
-def invoke_llm_with_pydantic_model(
-    *,
-    provider: str,
-    model_schema: AIModelEntity,
-    model_instance: ModelInstance,
-    prompt_messages: Sequence[PromptMessage],
-    output_model: type[T],
-    model_parameters: Mapping | None = None,
-    tools: Sequence[PromptMessageTool] | None = None,
-    stop: list[str] | None = None,
-    stream: bool = False,
-    user: str | None = None,
-    callbacks: list[Callback] | None = None,
-    tenant_id: str | None = None,
-) -> LLMResultWithStructuredOutput:
-    """
-    Invoke large language model with a Pydantic output model.
-
-    This helper generates a JSON schema from the Pydantic model, invokes the
-    structured-output LLM path, and validates the result in non-streaming mode.
-    """
-    if stream:
-        raise ValueError("invoke_llm_with_pydantic_model only supports stream=False")
-
-    json_schema = _schema_from_pydantic(output_model)
-    result = invoke_llm_with_structured_output(
-        provider=provider,
-        model_schema=model_schema,
-        model_instance=model_instance,
-        prompt_messages=prompt_messages,
-        json_schema=json_schema,
-        model_parameters=model_parameters,
-        tools=tools,
-        stop=stop,
-        stream=False,
-        user=user,
-        callbacks=callbacks,
-        tenant_id=tenant_id,
-    )
-
-    structured_output = result.structured_output
-    if structured_output is None:
-        raise OutputParserError("Structured output is empty")
-
-    validated_output = _validate_structured_output(output_model, structured_output)
-    return result.model_copy(update={"structured_output": validated_output})
-
-
-def _schema_from_pydantic(output_model: type[BaseModel]) -> dict[str, Any]:
-    return output_model.model_json_schema()
-
-
-def _validate_structured_output(
-    output_model: type[T],
-    structured_output: Mapping[str, Any],
-) -> dict[str, Any]:
-    try:
-        validated_output = output_model.model_validate(structured_output)
-    except ValidationError as exc:
-        raise OutputParserError(f"Structured output validation failed: {exc}") from exc
-    return validated_output.model_dump(mode="python")
-
-
 def _handle_native_json_schema(
     provider: str,
     model_schema: AIModelEntity,

api/core/llm_generator/utils.py

@@ -1,45 +0,0 @@
-"""Utility functions for LLM generator."""
-
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    PromptMessageRole,
-    SystemPromptMessage,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
-
-
-def deserialize_prompt_messages(messages: list[dict]) -> list[PromptMessage]:
-    """
-    Deserialize list of dicts to list[PromptMessage].
-
-    Expected format:
-        [
-            {"role": "user", "content": "..."},
-            {"role": "assistant", "content": "..."},
-        ]
-    """
-    result: list[PromptMessage] = []
-    for msg in messages:
-        role = PromptMessageRole.value_of(msg["role"])
-        content = msg.get("content", "")
-
-        match role:
-            case PromptMessageRole.USER:
-                result.append(UserPromptMessage(content=content))
-            case PromptMessageRole.ASSISTANT:
-                result.append(AssistantPromptMessage(content=content))
-            case PromptMessageRole.SYSTEM:
-                result.append(SystemPromptMessage(content=content))
-            case PromptMessageRole.TOOL:
-                result.append(ToolPromptMessage(content=content, tool_call_id=msg.get("tool_call_id", "")))
-
-    return result
-
-
-def serialize_prompt_messages(messages: list[PromptMessage]) -> list[dict]:
-    """
-    Serialize list[PromptMessage] to list of dicts.
-    """
-    return [{"role": msg.role.value, "content": msg.content} for msg in messages]

api/core/memory/README.md

@@ -1,267 +0,0 @@
-# Memory Module
-
-This module provides memory management for LLM conversations, enabling context retention across dialogue turns.
-
-## Overview
-
-The memory module contains two types of memory implementations:
-
-1. **TokenBufferMemory** - Conversation-level memory (existing)
-2. **NodeTokenBufferMemory** - Node-level memory (**Chatflow only**)
-
-> **Note**: `NodeTokenBufferMemory` is only available in **Chatflow** (advanced-chat mode).
-> This is because it requires both `conversation_id` and `node_id`, which are only present in Chatflow.
-> Standard Workflow mode does not have `conversation_id` and therefore cannot use node-level memory.
-
-```
-┌─────────────────────────────────────────────────────────────────────────────┐
-│                           Memory Architecture                               │
-├─────────────────────────────────────────────────────────────────────────────┤
-│                                                                             │
-│  ┌─────────────────────────────────────────────────────────────────────-┐   │
-│  │                      TokenBufferMemory                               │   │
-│  │  Scope: Conversation                                                 │   │
-│  │  Storage: Database (Message table)                                   │   │
-│  │  Key: conversation_id                                                │   │
-│  └─────────────────────────────────────────────────────────────────────-┘   │
-│                                                                             │
-│  ┌─────────────────────────────────────────────────────────────────────-┐   │
-│  │                    NodeTokenBufferMemory                             │   │
-│  │  Scope: Node within Conversation                                     │   │
-│  │  Storage: WorkflowNodeExecutionModel.outputs["context"]              │   │
-│  │  Key: (conversation_id, node_id, workflow_run_id)                    │   │
-│  └─────────────────────────────────────────────────────────────────────-┘   │
-│                                                                             │
-└─────────────────────────────────────────────────────────────────────────────┘
-```
-
----
-
-## TokenBufferMemory (Existing)
-
-### Purpose
-
-`TokenBufferMemory` retrieves conversation history from the `Message` table and converts it to `PromptMessage` objects for LLM context.
-
-### Key Features
-
-- **Conversation-scoped**: All messages within a conversation are candidates
-- **Thread-aware**: Uses `parent_message_id` to extract only the current thread (supports regeneration scenarios)
-- **Token-limited**: Truncates history to fit within `max_token_limit`
-- **File support**: Handles `MessageFile` attachments (images, documents, etc.)
-
-### Data Flow
-
-```
-Message Table                    TokenBufferMemory              LLM
-     │                                  │                        │
-     │  SELECT * FROM messages          │                        │
-     │  WHERE conversation_id = ?       │                        │
-     │  ORDER BY created_at DESC        │                        │
-     ├─────────────────────────────────▶│                        │
-     │                                  │                        │
-     │                    extract_thread_messages()              │
-     │                                  │                        │
-     │                    build_prompt_message_with_files()      │
-     │                                  │                        │
-     │                    truncate by max_token_limit            │
-     │                                  │                        │
-     │                                  │  Sequence[PromptMessage]
-     │                                  ├───────────────────────▶│
-     │                                  │                        │
-```
-
-### Thread Extraction
-
-When a user regenerates a response, a new thread is created:
-
-```
-Message A (user)
-    └── Message A' (assistant)
-            └── Message B (user)
-                    └── Message B' (assistant)
-            └── Message A'' (assistant, regenerated)  ← New thread
-                    └── Message C (user)
-                            └── Message C' (assistant)
-```
-
-`extract_thread_messages()` traces back from the latest message using `parent_message_id` to get only the current thread: `[A, A'', C, C']`
-
-### Usage
-
-```python
-from core.memory.token_buffer_memory import TokenBufferMemory
-
-memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
-history = memory.get_history_prompt_messages(max_token_limit=2000, message_limit=100)
-```
-
----
-
-## NodeTokenBufferMemory
-
-### Purpose
-
-`NodeTokenBufferMemory` provides **node-scoped memory** within a conversation. Each LLM node in a workflow can maintain its own independent conversation history.
-
-### Use Cases
-
-1. **Multi-LLM Workflows**: Different LLM nodes need separate context
-2. **Iterative Processing**: An LLM node in a loop needs to accumulate context across iterations
-3. **Specialized Agents**: Each agent node maintains its own dialogue history
-
-### Design: Zero Extra Storage
-
-**Key insight**: LLM node already saves complete context in `outputs["context"]`.
-
-Each LLM node execution outputs:
-```python
-outputs = {
-    "text": clean_text,
-    "context": self._build_context(prompt_messages, clean_text),  # Complete dialogue history!
-    ...
-}
-```
-
-This `outputs["context"]` contains:
-- All previous user/assistant messages (excluding system prompt)
-- The current assistant response
-
-**No separate storage needed** - we just read from the last execution's `outputs["context"]`.
-
-### Benefits
-
-| Aspect | Old Design (Object Storage) | New Design (outputs["context"]) |
-|--------|----------------------------|--------------------------------|
-| Storage | Separate JSON file | Already in WorkflowNodeExecutionModel |
-| Concurrency | Race condition risk | No issue (each execution is INSERT) |
-| Cleanup | Need separate cleanup task | Follows node execution lifecycle |
-| Migration | Required | None |
-| Complexity | High | Low |
-
-### Data Flow
-
-```
-WorkflowNodeExecutionModel        NodeTokenBufferMemory           LLM Node
-     │                                  │                           │
-     │                                  │◀── get_history_prompt_messages()
-     │                                  │                           │
-     │  SELECT outputs FROM             │                           │
-     │  workflow_node_executions        │                           │
-     │  WHERE workflow_run_id = ?       │                           │
-     │  AND node_id = ?                 │                           │
-     │◀─────────────────────────────────┤                           │
-     │                                  │                           │
-     │  outputs["context"]              │                           │
-     ├─────────────────────────────────▶│                           │
-     │                                  │                           │
-     │                    deserialize PromptMessages                │
-     │                                  │                           │
-     │                    truncate by max_token_limit               │
-     │                                  │                           │
-     │                                  │  Sequence[PromptMessage]  │
-     │                                  ├──────────────────────────▶│
-     │                                  │                           │
-```
-
-### Thread Tracking
-
-Thread extraction still uses `Message` table's `parent_message_id` structure:
-
-1. Query `Message` table for conversation → get thread's `workflow_run_ids`
-2. Get the last completed `workflow_run_id` in the thread
-3. Query `WorkflowNodeExecutionModel` for that execution's `outputs["context"]`
-
-### API
-
-```python
-class NodeTokenBufferMemory:
-    def __init__(
-        self,
-        app_id: str,
-        conversation_id: str,
-        node_id: str,
-        tenant_id: str,
-        model_instance: ModelInstance,
-    ):
-        """Initialize node-level memory."""
-        ...
-
-    def get_history_prompt_messages(
-        self,
-        *,
-        max_token_limit: int = 2000,
-        message_limit: int | None = None,
-    ) -> Sequence[PromptMessage]:
-        """
-        Retrieve history as PromptMessage sequence.
-        
-        Reads from last completed execution's outputs["context"].
-        """
-        ...
-
-    # Legacy methods (no-op, kept for compatibility)
-    def add_messages(self, *args, **kwargs) -> None: pass
-    def flush(self) -> None: pass
-    def clear(self) -> None: pass
-```
-
-### Configuration
-
-Add to `MemoryConfig` in `core/workflow/nodes/llm/entities.py`:
-
-```python
-class MemoryMode(StrEnum):
-    CONVERSATION = "conversation"  # Use TokenBufferMemory (default)
-    NODE = "node"                  # Use NodeTokenBufferMemory (Chatflow only)
-
-class MemoryConfig(BaseModel):
-    role_prefix: RolePrefix | None = None
-    window: MemoryWindowConfig | None = None
-    query_prompt_template: str | None = None
-    mode: MemoryMode = MemoryMode.CONVERSATION
-```
-
-**Mode Behavior:**
-
-| Mode           | Memory Class          | Scope                    | Availability  |
-| -------------- | --------------------- | ------------------------ | ------------- |
-| `conversation` | TokenBufferMemory     | Entire conversation      | All app modes |
-| `node`         | NodeTokenBufferMemory | Per-node in conversation | Chatflow only |
-
-> When `mode=node` is used in a non-Chatflow context (no conversation_id), it falls back to no memory.
-
----
-
-## Comparison
-
-| Feature        | TokenBufferMemory        | NodeTokenBufferMemory              |
-| -------------- | ------------------------ | ---------------------------------- |
-| Scope          | Conversation             | Node within Conversation           |
-| Storage        | Database (Message table) | WorkflowNodeExecutionModel.outputs |
-| Thread Support | Yes                      | Yes                                |
-| File Support   | Yes (via MessageFile)    | Yes (via context serialization)    |
-| Token Limit    | Yes                      | Yes                                |
-| Use Case       | Standard chat apps       | Complex workflows                  |
-
----
-
-## Extending to Other Nodes
-
-Currently only **LLM Node** outputs `context` in its outputs. To enable node memory for other nodes:
-
-1. Add `outputs["context"] = self._build_context(prompt_messages, response)` in the node
-2. The `NodeTokenBufferMemory` will automatically pick it up
-
-Nodes that could potentially support this:
-- `question_classifier`
-- `parameter_extractor`
-- `agent`
-
----
-
-## Future Considerations
-
-1. **Cleanup**: Node memory lifecycle follows `WorkflowNodeExecutionModel`, which already has cleanup mechanisms
-2. **Compression**: For very long conversations, consider summarization strategies
-3. **Extension**: Other nodes may benefit from node-level memory

api/core/memory/__init__.py

@@ -1,11 +0,0 @@
-from core.memory.base import BaseMemory
-from core.memory.node_token_buffer_memory import (
-    NodeTokenBufferMemory,
-)
-from core.memory.token_buffer_memory import TokenBufferMemory
-
-__all__ = [
-    "BaseMemory",
-    "NodeTokenBufferMemory",
-    "TokenBufferMemory",
-]

api/core/memory/base.py

@@ -1,83 +0,0 @@
-"""
-Base memory interfaces and types.
-
-This module defines the common protocol for memory implementations.
-"""
-
-from abc import ABC, abstractmethod
-from collections.abc import Sequence
-
-from core.model_runtime.entities import ImagePromptMessageContent, PromptMessage
-
-
-class BaseMemory(ABC):
-    """
-    Abstract base class for memory implementations.
-
-    Provides a common interface for both conversation-level and node-level memory.
-    """
-
-    @abstractmethod
-    def get_history_prompt_messages(
-        self,
-        *,
-        max_token_limit: int = 2000,
-        message_limit: int | None = None,
-    ) -> Sequence[PromptMessage]:
-        """
-        Get history prompt messages.
-
-        :param max_token_limit: Maximum tokens for history
-        :param message_limit: Maximum number of messages
-        :return: Sequence of PromptMessage for LLM context
-        """
-        pass
-
-    def get_history_prompt_text(
-        self,
-        human_prefix: str = "Human",
-        ai_prefix: str = "Assistant",
-        max_token_limit: int = 2000,
-        message_limit: int | None = None,
-    ) -> str:
-        """
-        Get history prompt as formatted text.
-
-        :param human_prefix: Prefix for human messages
-        :param ai_prefix: Prefix for assistant messages
-        :param max_token_limit: Maximum tokens for history
-        :param message_limit: Maximum number of messages
-        :return: Formatted history text
-        """
-        from core.model_runtime.entities import (
-            PromptMessageRole,
-            TextPromptMessageContent,
-        )
-
-        prompt_messages = self.get_history_prompt_messages(
-            max_token_limit=max_token_limit,
-            message_limit=message_limit,
-        )
-
-        string_messages = []
-        for m in prompt_messages:
-            if m.role == PromptMessageRole.USER:
-                role = human_prefix
-            elif m.role == PromptMessageRole.ASSISTANT:
-                role = ai_prefix
-            else:
-                continue
-
-            if isinstance(m.content, list):
-                inner_msg = ""
-                for content in m.content:
-                    if isinstance(content, TextPromptMessageContent):
-                        inner_msg += f"{content.data}\n"
-                    elif isinstance(content, ImagePromptMessageContent):
-                        inner_msg += "[image]\n"
-                string_messages.append(f"{role}: {inner_msg.strip()}")
-            else:
-                message = f"{role}: {m.content}"
-                string_messages.append(message)
-
-        return "\n".join(string_messages)

api/core/memory/node_token_buffer_memory.py

@@ -1,197 +0,0 @@
-"""
-Node-level Token Buffer Memory for Chatflow.
-
-This module provides node-scoped memory within a conversation.
-Each LLM node in a workflow can maintain its own independent conversation history.
-
-Note: This is only available in Chatflow (advanced-chat mode) because it requires
-both conversation_id and node_id.
-
-Design:
-- History is read directly from WorkflowNodeExecutionModel.outputs["context"]
-- No separate storage needed - the context is already saved during node execution
-- Thread tracking leverages Message table's parent_message_id structure
-"""
-
-import logging
-from collections.abc import Sequence
-from typing import cast
-
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-
-from core.file import file_manager
-from core.memory.base import BaseMemory
-from core.model_manager import ModelInstance
-from core.model_runtime.entities import (
-    AssistantPromptMessage,
-    MultiModalPromptMessageContent,
-    PromptMessage,
-    PromptMessageRole,
-    SystemPromptMessage,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
-from core.model_runtime.entities.message_entities import PromptMessageContentUnionTypes
-from core.prompt.utils.extract_thread_messages import extract_thread_messages
-from extensions.ext_database import db
-from models.model import Message
-from models.workflow import WorkflowNodeExecutionModel
-
-logger = logging.getLogger(__name__)
-
-
-class NodeTokenBufferMemory(BaseMemory):
-    """
-    Node-level Token Buffer Memory.
-
-    Provides node-scoped memory within a conversation. Each LLM node can maintain
-    its own independent conversation history.
-
-    Key design: History is read directly from WorkflowNodeExecutionModel.outputs["context"],
-    which is already saved during node execution. No separate storage needed.
-    """
-
-    def __init__(
-        self,
-        app_id: str,
-        conversation_id: str,
-        node_id: str,
-        tenant_id: str,
-        model_instance: ModelInstance,
-    ):
-        self.app_id = app_id
-        self.conversation_id = conversation_id
-        self.node_id = node_id
-        self.tenant_id = tenant_id
-        self.model_instance = model_instance
-
-    def _get_thread_workflow_run_ids(self) -> list[str]:
-        """
-        Get workflow_run_ids for the current thread by querying Message table.
-        Returns workflow_run_ids in chronological order (oldest first).
-        """
-        with Session(db.engine, expire_on_commit=False) as session:
-            stmt = (
-                select(Message)
-                .where(Message.conversation_id == self.conversation_id)
-                .order_by(Message.created_at.desc())
-                .limit(500)
-            )
-            messages = list(session.scalars(stmt).all())
-
-        if not messages:
-            return []
-
-        # Extract thread messages using existing logic
-        thread_messages = extract_thread_messages(messages)
-
-        # For newly created message, its answer is temporarily empty, skip it
-        if thread_messages and not thread_messages[0].answer and thread_messages[0].answer_tokens == 0:
-            thread_messages.pop(0)
-
-        # Reverse to get chronological order, extract workflow_run_ids
-        return [msg.workflow_run_id for msg in reversed(thread_messages) if msg.workflow_run_id]
-
-    def _deserialize_prompt_message(self, msg_dict: dict) -> PromptMessage:
-        """Deserialize a dict to PromptMessage based on role."""
-        role = msg_dict.get("role")
-        if role in (PromptMessageRole.USER, "user"):
-            return UserPromptMessage.model_validate(msg_dict)
-        elif role in (PromptMessageRole.ASSISTANT, "assistant"):
-            return AssistantPromptMessage.model_validate(msg_dict)
-        elif role in (PromptMessageRole.SYSTEM, "system"):
-            return SystemPromptMessage.model_validate(msg_dict)
-        elif role in (PromptMessageRole.TOOL, "tool"):
-            return ToolPromptMessage.model_validate(msg_dict)
-        else:
-            return PromptMessage.model_validate(msg_dict)
-
-    def _deserialize_context(self, context_data: list[dict]) -> list[PromptMessage]:
-        """Deserialize context data from outputs to list of PromptMessage."""
-        messages = []
-        for msg_dict in context_data:
-            try:
-                msg = self._deserialize_prompt_message(msg_dict)
-                msg = self._restore_multimodal_content(msg)
-                messages.append(msg)
-            except Exception as e:
-                logger.warning("Failed to deserialize prompt message: %s", e)
-        return messages
-
-    def _restore_multimodal_content(self, message: PromptMessage) -> PromptMessage:
-        """
-        Restore multimodal content (base64 or url) from file_ref.
-
-        When context is saved, base64_data is cleared to save storage space.
-        This method restores the content by parsing file_ref (format: "method:id_or_url").
-        """
-        content = message.content
-        if content is None or isinstance(content, str):
-            return message
-
-        # Process list content, restoring multimodal data from file references
-        restored_content: list[PromptMessageContentUnionTypes] = []
-        for item in content:
-            if isinstance(item, MultiModalPromptMessageContent):
-                # restore_multimodal_content preserves the concrete subclass type
-                restored_item = file_manager.restore_multimodal_content(item)
-                restored_content.append(cast(PromptMessageContentUnionTypes, restored_item))
-            else:
-                restored_content.append(item)
-
-        return message.model_copy(update={"content": restored_content})
-
-    def get_history_prompt_messages(
-        self,
-        *,
-        max_token_limit: int = 2000,
-        message_limit: int | None = None,
-    ) -> Sequence[PromptMessage]:
-        """
-        Retrieve history as PromptMessage sequence.
-        History is read directly from the last completed node execution's outputs["context"].
-        """
-        _ = message_limit  # unused, kept for interface compatibility
-
-        thread_workflow_run_ids = self._get_thread_workflow_run_ids()
-        if not thread_workflow_run_ids:
-            return []
-
-        # Get the last completed workflow_run_id (contains accumulated context)
-        last_run_id = thread_workflow_run_ids[-1]
-
-        with Session(db.engine, expire_on_commit=False) as session:
-            stmt = select(WorkflowNodeExecutionModel).where(
-                WorkflowNodeExecutionModel.workflow_run_id == last_run_id,
-                WorkflowNodeExecutionModel.node_id == self.node_id,
-                WorkflowNodeExecutionModel.status == "succeeded",
-            )
-            execution = session.scalars(stmt).first()
-
-            if not execution:
-                return []
-
-            outputs = execution.outputs_dict
-            if not outputs:
-                return []
-
-            context_data = outputs.get("context")
-
-        if not context_data or not isinstance(context_data, list):
-            return []
-
-        prompt_messages = self._deserialize_context(context_data)
-        if not prompt_messages:
-            return []
-
-        # Truncate by token limit
-        try:
-            current_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
-            while current_tokens > max_token_limit and len(prompt_messages) > 1:
-                prompt_messages.pop(0)
-                current_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
-        except Exception as e:
-            logger.warning("Failed to count tokens for truncation: %s", e)
-
-        return prompt_messages

api/core/memory/token_buffer_memory.py

@@ -5,12 +5,12 @@ from sqlalchemy.orm import sessionmaker
 
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.file import file_manager
-from core.memory.base import BaseMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities import (
     AssistantPromptMessage,
     ImagePromptMessageContent,
     PromptMessage,
+    PromptMessageRole,
     TextPromptMessageContent,
     UserPromptMessage,
 )
@@ -24,7 +24,7 @@ from repositories.api_workflow_run_repository import APIWorkflowRunRepository
 from repositories.factory import DifyAPIRepositoryFactory
 
 
-class TokenBufferMemory(BaseMemory):
+class TokenBufferMemory:
     def __init__(
         self,
         conversation: Conversation,
@@ -115,14 +115,10 @@ class TokenBufferMemory(BaseMemory):
                 return AssistantPromptMessage(content=prompt_message_contents)
 
     def get_history_prompt_messages(
-        self,
-        *,
-        max_token_limit: int = 2000,
-        message_limit: int | None = None,
+        self, max_token_limit: int = 2000, message_limit: int | None = None
     ) -> Sequence[PromptMessage]:
         """
         Get history prompt messages.
-
         :param max_token_limit: max token limit
         :param message_limit: message limit
         """
@@ -204,3 +200,44 @@ class TokenBufferMemory(BaseMemory):
                 curr_message_tokens = self.model_instance.get_llm_num_tokens(prompt_messages)
 
         return prompt_messages
+
+    def get_history_prompt_text(
+        self,
+        human_prefix: str = "Human",
+        ai_prefix: str = "Assistant",
+        max_token_limit: int = 2000,
+        message_limit: int | None = None,
+    ) -> str:
+        """
+        Get history prompt text.
+        :param human_prefix: human prefix
+        :param ai_prefix: ai prefix
+        :param max_token_limit: max token limit
+        :param message_limit: message limit
+        :return:
+        """
+        prompt_messages = self.get_history_prompt_messages(max_token_limit=max_token_limit, message_limit=message_limit)
+
+        string_messages = []
+        for m in prompt_messages:
+            if m.role == PromptMessageRole.USER:
+                role = human_prefix
+            elif m.role == PromptMessageRole.ASSISTANT:
+                role = ai_prefix
+            else:
+                continue
+
+            if isinstance(m.content, list):
+                inner_msg = ""
+                for content in m.content:
+                    if isinstance(content, TextPromptMessageContent):
+                        inner_msg += f"{content.data}\n"
+                    elif isinstance(content, ImagePromptMessageContent):
+                        inner_msg += "[image]\n"
+
+                string_messages.append(f"{role}: {inner_msg.strip()}")
+            else:
+                message = f"{role}: {m.content}"
+                string_messages.append(message)
+
+        return "\n".join(string_messages)

api/core/model_runtime/entities/message_entities.py

@@ -91,9 +91,6 @@ class MultiModalPromptMessageContent(PromptMessageContent):
     mime_type: str = Field(default=..., description="the mime type of multi-modal file")
     filename: str = Field(default="", description="the filename of multi-modal file")
 
-    # File reference for context restoration, format: "transfer_method:related_id" or "remote:url"
-    file_ref: str | None = Field(default=None, description="Encoded file reference for restoration")
-
     @property
     def data(self):
         return self.url or f"data:{self.mime_type};base64,{self.base64_data}"
@@ -279,5 +276,7 @@ class ToolPromptMessage(PromptMessage):
 
         :return: True if prompt message is empty, False otherwise
         """
-        # ToolPromptMessage is not empty if it has content OR has a tool_call_id
-        return super().is_empty() and not self.tool_call_id
+        if not super().is_empty() and not self.tool_call_id:
+            return False
+
+        return True

api/core/prompt/advanced_prompt_transform.py

@@ -5,7 +5,7 @@ from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEnti
 from core.file import file_manager
 from core.file.models import File
 from core.helper.code_executor.jinja2.jinja2_formatter import Jinja2Formatter
-from core.memory.base import BaseMemory
+from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.entities import (
     AssistantPromptMessage,
     PromptMessage,
@@ -43,7 +43,7 @@ class AdvancedPromptTransform(PromptTransform):
         files: Sequence[File],
         context: str | None,
         memory_config: MemoryConfig | None,
-        memory: BaseMemory | None,
+        memory: TokenBufferMemory | None,
         model_config: ModelConfigWithCredentialsEntity,
         image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -84,7 +84,7 @@ class AdvancedPromptTransform(PromptTransform):
         files: Sequence[File],
         context: str | None,
         memory_config: MemoryConfig | None,
-        memory: BaseMemory | None,
+        memory: TokenBufferMemory | None,
         model_config: ModelConfigWithCredentialsEntity,
         image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -145,7 +145,7 @@ class AdvancedPromptTransform(PromptTransform):
         files: Sequence[File],
         context: str | None,
         memory_config: MemoryConfig | None,
-        memory: BaseMemory | None,
+        memory: TokenBufferMemory | None,
         model_config: ModelConfigWithCredentialsEntity,
         image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -270,7 +270,7 @@ class AdvancedPromptTransform(PromptTransform):
 
     def _set_histories_variable(
         self,
-        memory: BaseMemory,
+        memory: TokenBufferMemory,
         memory_config: MemoryConfig,
         raw_prompt: str,
         role_prefix: MemoryConfig.RolePrefix,

api/core/prompt/entities/advanced_prompt_entities.py

@@ -1,4 +1,3 @@
-from enum import StrEnum
 from typing import Literal
 
 from pydantic import BaseModel
@@ -6,13 +5,6 @@ from pydantic import BaseModel
 from core.model_runtime.entities.message_entities import PromptMessageRole
 
 
-class MemoryMode(StrEnum):
-    """Memory mode for LLM nodes."""
-
-    CONVERSATION = "conversation"  # Use TokenBufferMemory (default, existing behavior)
-    NODE = "node"  # Use NodeTokenBufferMemory (Chatflow only)
-
-
 class ChatModelMessage(BaseModel):
     """
     Chat Message.
@@ -56,4 +48,3 @@ class MemoryConfig(BaseModel):
     role_prefix: RolePrefix | None = None
     window: WindowConfig
     query_prompt_template: str | None = None
-    mode: MemoryMode = MemoryMode.CONVERSATION

api/core/prompt/prompt_transform.py

@@ -1,7 +1,7 @@
 from typing import Any
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
-from core.memory.base import BaseMemory
+from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.message_entities import PromptMessage
 from core.model_runtime.entities.model_entities import ModelPropertyKey
@@ -11,7 +11,7 @@ from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 class PromptTransform:
     def _append_chat_histories(
         self,
-        memory: BaseMemory,
+        memory: TokenBufferMemory,
         memory_config: MemoryConfig,
         prompt_messages: list[PromptMessage],
         model_config: ModelConfigWithCredentialsEntity,
@@ -52,7 +52,7 @@ class PromptTransform:
 
     def _get_history_messages_from_memory(
         self,
-        memory: BaseMemory,
+        memory: TokenBufferMemory,
         memory_config: MemoryConfig,
         max_token_limit: int,
         human_prefix: str | None = None,
@@ -73,7 +73,7 @@ class PromptTransform:
         return memory.get_history_prompt_text(**kwargs)
 
     def _get_history_messages_list_from_memory(
-        self, memory: BaseMemory, memory_config: MemoryConfig, max_token_limit: int
+        self, memory: TokenBufferMemory, memory_config: MemoryConfig, max_token_limit: int
     ) -> list[PromptMessage]:
         """Get memory messages."""
         return list(

api/core/tools/tool_manager.py

@@ -1047,8 +1047,6 @@ class ToolManager:
                         continue
                     tool_input = ToolNodeData.ToolInput.model_validate(tool_configurations.get(parameter.name, {}))
                     if tool_input.type == "variable":
-                        if not isinstance(tool_input.value, list):
-                            raise ToolParameterError(f"Invalid variable selector for {parameter.name}")
                         variable = variable_pool.get(tool_input.value)
                         if variable is None:
                             raise ToolParameterError(f"Variable {tool_input.value} does not exist")
@@ -1058,11 +1056,6 @@ class ToolManager:
                     elif tool_input.type == "mixed":
                         segment_group = variable_pool.convert_template(str(tool_input.value))
                         parameter_value = segment_group.text
-                    elif tool_input.type == "mention":
-                        # Mention type not supported in agent mode
-                        raise ToolParameterError(
-                            f"Mention type not supported in agent for parameter '{parameter.name}'"
-                        )
                     else:
                         raise ToolParameterError(f"Unknown tool input type '{tool_input.type}'")
                     runtime_parameters[parameter.name] = parameter_value

api/core/tools/workflow_as_tool/tool.py

@@ -5,6 +5,7 @@ import logging
 from collections.abc import Generator, Mapping, Sequence
 from typing import Any, cast
 
+from flask import has_request_context
 from sqlalchemy import select
 
 from core.db.session_factory import session_factory
@@ -28,21 +29,6 @@ from models.workflow import Workflow
 logger = logging.getLogger(__name__)
 
 
-def _try_resolve_user_from_request() -> Account | EndUser | None:
-    """
-    Try to resolve user from Flask request context.
-
-    Returns None if not in a request context or if user is not available.
-    """
-    # Note: `current_user` is a LocalProxy. Never compare it with None directly.
-    # Use _get_current_object() to dereference the proxy
-    user = getattr(current_user, "_get_current_object", lambda: current_user)()
-    # Check if we got a valid user object
-    if user is not None and hasattr(user, "id"):
-        return user
-    return None
-
-
 class WorkflowTool(Tool):
     """
     Workflow tool.
@@ -223,13 +209,21 @@ class WorkflowTool(Tool):
         Returns:
             Account | EndUser | None: The resolved user object, or None if resolution fails.
         """
-        # Try to resolve user from request context first
-        user = _try_resolve_user_from_request()
-        if user is not None:
-            return user
+        if has_request_context():
+            return self._resolve_user_from_request()
+        else:
+            return self._resolve_user_from_database(user_id=user_id)
 
-        # Fall back to database resolution
-        return self._resolve_user_from_database(user_id=user_id)
+    def _resolve_user_from_request(self) -> Account | EndUser | None:
+        """
+        Resolve user from Flask request context.
+        """
+        try:
+            # Note: `current_user` is a LocalProxy. Never compare it with None directly.
+            return getattr(current_user, "_get_current_object", lambda: current_user)()
+        except Exception as e:
+            logger.warning("Failed to resolve user from request context: %s", e)
+            return None
 
     def _resolve_user_from_database(self, user_id: str) -> Account | EndUser | None:
         """

api/core/variables/__init__.py

@@ -4,7 +4,6 @@ from .segments import (
     ArrayFileSegment,
     ArrayNumberSegment,
     ArrayObjectSegment,
-    ArrayPromptMessageSegment,
     ArraySegment,
     ArrayStringSegment,
     FileSegment,
@@ -21,7 +20,6 @@ from .variables import (
     ArrayFileVariable,
     ArrayNumberVariable,
     ArrayObjectVariable,
-    ArrayPromptMessageVariable,
     ArrayStringVariable,
     ArrayVariable,
     FileVariable,
@@ -44,8 +42,6 @@ __all__ = [
     "ArrayNumberVariable",
     "ArrayObjectSegment",
     "ArrayObjectVariable",
-    "ArrayPromptMessageSegment",
-    "ArrayPromptMessageVariable",
     "ArraySegment",
     "ArrayStringSegment",
     "ArrayStringVariable",

api/core/variables/segments.py

@@ -6,7 +6,6 @@ from typing import Annotated, Any, TypeAlias
 from pydantic import BaseModel, ConfigDict, Discriminator, Tag, field_validator
 
 from core.file import File
-from core.model_runtime.entities import PromptMessage
 
 from .types import SegmentType
 
@@ -209,15 +208,6 @@ class ArrayBooleanSegment(ArraySegment):
     value: Sequence[bool]
 
 
-class ArrayPromptMessageSegment(ArraySegment):
-    value_type: SegmentType = SegmentType.ARRAY_PROMPT_MESSAGE
-    value: Sequence[PromptMessage]
-
-    def to_object(self):
-        """Convert to JSON-serializable format for database storage and frontend."""
-        return [msg.model_dump() for msg in self.value]
-
-
 def get_segment_discriminator(v: Any) -> SegmentType | None:
     if isinstance(v, Segment):
         return v.value_type
@@ -258,7 +248,6 @@ SegmentUnion: TypeAlias = Annotated[
         | Annotated[ArrayObjectSegment, Tag(SegmentType.ARRAY_OBJECT)]
         | Annotated[ArrayFileSegment, Tag(SegmentType.ARRAY_FILE)]
         | Annotated[ArrayBooleanSegment, Tag(SegmentType.ARRAY_BOOLEAN)]
-        | Annotated[ArrayPromptMessageSegment, Tag(SegmentType.ARRAY_PROMPT_MESSAGE)]
     ),
     Discriminator(get_segment_discriminator),
 ]

api/core/variables/types.py

@@ -45,7 +45,6 @@ class SegmentType(StrEnum):
     ARRAY_OBJECT = "array[object]"
     ARRAY_FILE = "array[file]"
     ARRAY_BOOLEAN = "array[boolean]"
-    ARRAY_PROMPT_MESSAGE = "array[message]"
 
     NONE = "none"
 

api/core/variables/utils.py

@@ -3,10 +3,8 @@ from typing import Any
 
 import orjson
 
-from core.model_runtime.entities import PromptMessage
-
 from .segment_group import SegmentGroup
-from .segments import ArrayFileSegment, ArrayPromptMessageSegment, FileSegment, Segment
+from .segments import ArrayFileSegment, FileSegment, Segment
 
 
 def to_selector(node_id: str, name: str, paths: Iterable[str] = ()) -> Sequence[str]:
@@ -18,7 +16,7 @@ def to_selector(node_id: str, name: str, paths: Iterable[str] = ()) -> Sequence[
 
 def segment_orjson_default(o: Any):
     """Default function for orjson serialization of Segment types"""
-    if isinstance(o, (ArrayFileSegment, ArrayPromptMessageSegment)):
+    if isinstance(o, ArrayFileSegment):
         return [v.model_dump() for v in o.value]
     elif isinstance(o, FileSegment):
         return o.value.model_dump()
@@ -26,8 +24,6 @@ def segment_orjson_default(o: Any):
         return [segment_orjson_default(seg) for seg in o.value]
     elif isinstance(o, Segment):
         return o.value
-    elif isinstance(o, PromptMessage):
-        return o.model_dump()
     raise TypeError(f"Object of type {type(o).__name__} is not JSON serializable")
 
 

api/core/variables/variables.py

@@ -12,7 +12,6 @@ from .segments import (
     ArrayFileSegment,
     ArrayNumberSegment,
     ArrayObjectSegment,
-    ArrayPromptMessageSegment,
     ArraySegment,
     ArrayStringSegment,
     BooleanSegment,
@@ -111,10 +110,6 @@ class ArrayBooleanVariable(ArrayBooleanSegment, ArrayVariable):
     pass
 
 
-class ArrayPromptMessageVariable(ArrayPromptMessageSegment, ArrayVariable):
-    pass
-
-
 class RAGPipelineVariable(BaseModel):
     belong_to_node_id: str = Field(description="belong to which node id, shared means public")
     type: str = Field(description="variable type, text-input, paragraph, select, number,  file, file-list")
@@ -165,7 +160,6 @@ Variable: TypeAlias = Annotated[
         | Annotated[ArrayObjectVariable, Tag(SegmentType.ARRAY_OBJECT)]
         | Annotated[ArrayFileVariable, Tag(SegmentType.ARRAY_FILE)]
         | Annotated[ArrayBooleanVariable, Tag(SegmentType.ARRAY_BOOLEAN)]
-        | Annotated[ArrayPromptMessageVariable, Tag(SegmentType.ARRAY_PROMPT_MESSAGE)]
         | Annotated[SecretVariable, Tag(SegmentType.SECRET)]
     ),
     Discriminator(get_segment_discriminator),

api/core/workflow/context/__init__.py

@@ -1,22 +0,0 @@
-"""
-Execution Context - Context management for workflow execution.
-
-This package provides Flask-independent context management for workflow
-execution in multi-threaded environments.
-"""
-
-from core.workflow.context.execution_context import (
-    AppContext,
-    ExecutionContext,
-    IExecutionContext,
-    NullAppContext,
-    capture_current_context,
-)
-
-__all__ = [
-    "AppContext",
-    "ExecutionContext",
-    "IExecutionContext",
-    "NullAppContext",
-    "capture_current_context",
-]

api/core/workflow/context/execution_context.py

@@ -1,216 +0,0 @@
-"""
-Execution Context - Abstracted context management for workflow execution.
-"""
-
-import contextvars
-from abc import ABC, abstractmethod
-from collections.abc import Generator
-from contextlib import AbstractContextManager, contextmanager
-from typing import Any, Protocol, final, runtime_checkable
-
-
-class AppContext(ABC):
-    """
-    Abstract application context interface.
-
-    This abstraction allows workflow execution to work with or without Flask
-    by providing a common interface for application context management.
-    """
-
-    @abstractmethod
-    def get_config(self, key: str, default: Any = None) -> Any:
-        """Get configuration value by key."""
-        pass
-
-    @abstractmethod
-    def get_extension(self, name: str) -> Any:
-        """Get Flask extension by name (e.g., 'db', 'cache')."""
-        pass
-
-    @abstractmethod
-    def enter(self) -> AbstractContextManager[None]:
-        """Enter the application context."""
-        pass
-
-
-@runtime_checkable
-class IExecutionContext(Protocol):
-    """
-    Protocol for execution context.
-
-    This protocol defines the interface that all execution contexts must implement,
-    allowing both ExecutionContext and FlaskExecutionContext to be used interchangeably.
-    """
-
-    def __enter__(self) -> "IExecutionContext":
-        """Enter the execution context."""
-        ...
-
-    def __exit__(self, *args: Any) -> None:
-        """Exit the execution context."""
-        ...
-
-    @property
-    def user(self) -> Any:
-        """Get user object."""
-        ...
-
-
-@final
-class ExecutionContext:
-    """
-    Execution context for workflow execution in worker threads.
-
-    This class encapsulates all context needed for workflow execution:
-    - Application context (Flask app or standalone)
-    - Context variables for Python contextvars
-    - User information (optional)
-
-    It is designed to be serializable and passable to worker threads.
-    """
-
-    def __init__(
-        self,
-        app_context: AppContext | None = None,
-        context_vars: contextvars.Context | None = None,
-        user: Any = None,
-    ) -> None:
-        """
-        Initialize execution context.
-
-        Args:
-            app_context: Application context (Flask or standalone)
-            context_vars: Python contextvars to preserve
-            user: User object (optional)
-        """
-        self._app_context = app_context
-        self._context_vars = context_vars
-        self._user = user
-
-    @property
-    def app_context(self) -> AppContext | None:
-        """Get application context."""
-        return self._app_context
-
-    @property
-    def context_vars(self) -> contextvars.Context | None:
-        """Get context variables."""
-        return self._context_vars
-
-    @property
-    def user(self) -> Any:
-        """Get user object."""
-        return self._user
-
-    @contextmanager
-    def enter(self) -> Generator[None, None, None]:
-        """
-        Enter this execution context.
-
-        This is a convenience method that creates a context manager.
-        """
-        # Restore context variables if provided
-        if self._context_vars:
-            for var, val in self._context_vars.items():
-                var.set(val)
-
-        # Enter app context if available
-        if self._app_context is not None:
-            with self._app_context.enter():
-                yield
-        else:
-            yield
-
-    def __enter__(self) -> "ExecutionContext":
-        """Enter the execution context."""
-        self._cm = self.enter()
-        self._cm.__enter__()
-        return self
-
-    def __exit__(self, *args: Any) -> None:
-        """Exit the execution context."""
-        if hasattr(self, "_cm"):
-            self._cm.__exit__(*args)
-
-
-class NullAppContext(AppContext):
-    """
-    Null implementation of AppContext for non-Flask environments.
-
-    This is used when running without Flask (e.g., in tests or standalone mode).
-    """
-
-    def __init__(self, config: dict[str, Any] | None = None) -> None:
-        """
-        Initialize null app context.
-
-        Args:
-            config: Optional configuration dictionary
-        """
-        self._config = config or {}
-        self._extensions: dict[str, Any] = {}
-
-    def get_config(self, key: str, default: Any = None) -> Any:
-        """Get configuration value by key."""
-        return self._config.get(key, default)
-
-    def get_extension(self, name: str) -> Any:
-        """Get extension by name."""
-        return self._extensions.get(name)
-
-    def set_extension(self, name: str, extension: Any) -> None:
-        """Set extension by name."""
-        self._extensions[name] = extension
-
-    @contextmanager
-    def enter(self) -> Generator[None, None, None]:
-        """Enter null context (no-op)."""
-        yield
-
-
-class ExecutionContextBuilder:
-    """
-    Builder for creating ExecutionContext instances.
-
-    This provides a fluent API for building execution contexts.
-    """
-
-    def __init__(self) -> None:
-        self._app_context: AppContext | None = None
-        self._context_vars: contextvars.Context | None = None
-        self._user: Any = None
-
-    def with_app_context(self, app_context: AppContext) -> "ExecutionContextBuilder":
-        """Set application context."""
-        self._app_context = app_context
-        return self
-
-    def with_context_vars(self, context_vars: contextvars.Context) -> "ExecutionContextBuilder":
-        """Set context variables."""
-        self._context_vars = context_vars
-        return self
-
-    def with_user(self, user: Any) -> "ExecutionContextBuilder":
-        """Set user."""
-        self._user = user
-        return self
-
-    def build(self) -> ExecutionContext:
-        """Build the execution context."""
-        return ExecutionContext(
-            app_context=self._app_context,
-            context_vars=self._context_vars,
-            user=self._user,
-        )
-
-
-def capture_current_context() -> IExecutionContext:
-    """
-    Capture current execution context from the calling environment.
-
-    Returns:
-        IExecutionContext with captured context
-    """
-    from context import capture_current_context
-
-    return capture_current_context()

api/core/workflow/enums.py

@@ -63,7 +63,6 @@ class NodeType(StrEnum):
     TRIGGER_SCHEDULE = "trigger-schedule"
     TRIGGER_PLUGIN = "trigger-plugin"
     HUMAN_INPUT = "human-input"
-    GROUP = "group"
 
     @property
     def is_trigger_node(self) -> bool:
@@ -253,7 +252,6 @@ class WorkflowNodeExecutionMetadataKey(StrEnum):
     LOOP_VARIABLE_MAP = "loop_variable_map"  # single loop variable output
     DATASOURCE_INFO = "datasource_info"
     COMPLETED_REASON = "completed_reason"  # completed reason for loop node
-    MENTION_PARENT_ID = "mention_parent_id"  # parent node id for extractor nodes
 
 
 class WorkflowNodeExecutionStatus(StrEnum):

api/core/workflow/graph/graph.py

@@ -307,14 +307,7 @@ class Graph:
         if not node_configs:
             raise ValueError("Graph must have at least one node")
 
-        # Filter out UI-only node types:
-        # - custom-note: top-level type (node_config.type == "custom-note")
-        # - group: data-level type (node_config.data.type == "group")
-        node_configs = [
-            node_config
-            for node_config in node_configs
-            if node_config.get("type", "") != "custom-note" and node_config.get("data", {}).get("type", "") != "group"
-        ]
+        node_configs = [node_config for node_config in node_configs if node_config.get("type", "") != "custom-note"]
 
         # Parse node configurations
         node_configs_map = cls._parse_node_configs(node_configs)

api/core/workflow/graph_engine/event_management/event_handlers.py

@@ -93,8 +93,8 @@ class EventHandler:
         Args:
             event: The event to handle
         """
-        # Events in loops, iterations, or extractor groups are always collected
-        if event.in_loop_id or event.in_iteration_id or event.in_mention_parent_id:
+        # Events in loops or iterations are always collected
+        if event.in_loop_id or event.in_iteration_id:
             self._event_collector.collect(event)
             return
         return self._dispatch(event)
@@ -125,11 +125,6 @@ class EventHandler:
         Args:
             event: The node started event
         """
-        # Check if this is an extractor node (has parent_node_id)
-        if self._is_extractor_node(event.node_id):
-            self._handle_extractor_node_started(event)
-            return
-
         # Track execution in domain model
         node_execution = self._graph_execution.get_or_create_node_execution(event.node_id)
         is_initial_attempt = node_execution.retry_count == 0
@@ -169,11 +164,6 @@ class EventHandler:
         Args:
             event: The node succeeded event
         """
-        # Check if this is an extractor node (has parent_node_id)
-        if self._is_extractor_node(event.node_id):
-            self._handle_extractor_node_success(event)
-            return
-
         # Update domain model
         node_execution = self._graph_execution.get_or_create_node_execution(event.node_id)
         node_execution.mark_taken()
@@ -236,11 +226,6 @@ class EventHandler:
         Args:
             event: The node failed event
         """
-        # Check if this is an extractor node (has parent_node_id)
-        if self._is_extractor_node(event.node_id):
-            self._handle_extractor_node_failed(event)
-            return
-
         # Update domain model
         node_execution = self._graph_execution.get_or_create_node_execution(event.node_id)
         node_execution.mark_failed(event.error)
@@ -360,57 +345,3 @@ class EventHandler:
                     self._graph_runtime_state.set_output("answer", value)
             else:
                 self._graph_runtime_state.set_output(key, value)
-
-    def _is_extractor_node(self, node_id: str) -> bool:
-        """
-        Check if node_id represents an extractor node (has parent_node_id).
-
-        Extractor nodes extract values from list[PromptMessage] for their parent node.
-        They have a parent_node_id field pointing to their parent node.
-        """
-        node = self._graph.nodes.get(node_id)
-        if node is None:
-            return False
-        return node.node_data.is_extractor_node
-
-    def _handle_extractor_node_started(self, event: NodeRunStartedEvent) -> None:
-        """
-        Handle extractor node started event.
-
-        Extractor nodes don't need full execution tracking, just collect the event.
-        """
-        # Track in response coordinator for stream ordering
-        self._response_coordinator.track_node_execution(event.node_id, event.id)
-
-        # Collect the event
-        self._event_collector.collect(event)
-
-    def _handle_extractor_node_success(self, event: NodeRunSucceededEvent) -> None:
-        """
-        Handle extractor node success event.
-
-        Extractor nodes need special handling:
-        - Store outputs in variable pool (for reference by other nodes)
-        - Accumulate token usage
-        - Collect the event for logging
-        - Do NOT process edges or enqueue next nodes (parent node handles that)
-        """
-        self._accumulate_node_usage(event.node_run_result.llm_usage)
-
-        # Store outputs in variable pool
-        self._store_node_outputs(event.node_id, event.node_run_result.outputs)
-
-        # Collect the event
-        self._event_collector.collect(event)
-
-    def _handle_extractor_node_failed(self, event: NodeRunFailedEvent) -> None:
-        """
-        Handle extractor node failed event.
-
-        Extractor node failures are collected for logging,
-        but the parent node is responsible for handling the error.
-        """
-        self._accumulate_node_usage(event.node_run_result.llm_usage)
-
-        # Collect the event for logging
-        self._event_collector.collect(event)

api/core/workflow/graph_engine/graph_engine.py

@@ -7,13 +7,15 @@ Domain-Driven Design principles for improved maintainability and testability.
 
 from __future__ import annotations
 
+import contextvars
 import logging
 import queue
 import threading
 from collections.abc import Generator
 from typing import TYPE_CHECKING, cast, final
 
-from core.workflow.context import capture_current_context
+from flask import Flask, current_app
+
 from core.workflow.enums import NodeExecutionType
 from core.workflow.graph import Graph
 from core.workflow.graph_events import (
@@ -157,8 +159,17 @@ class GraphEngine:
         self._layers: list[GraphEngineLayer] = []
 
         # === Worker Pool Setup ===
-        # Capture execution context for worker threads
-        execution_context = capture_current_context()
+        # Capture Flask app context for worker threads
+        flask_app: Flask | None = None
+        try:
+            app = current_app._get_current_object()  # type: ignore
+            if isinstance(app, Flask):
+                flask_app = app
+        except RuntimeError:
+            pass
+
+        # Capture context variables for worker threads
+        context_vars = contextvars.copy_context()
 
         # Create worker pool for parallel node execution
         self._worker_pool = WorkerPool(
@@ -166,7 +177,8 @@ class GraphEngine:
             event_queue=self._event_queue,
             graph=self._graph,
             layers=self._layers,
-            execution_context=execution_context,
+            flask_app=flask_app,
+            context_vars=context_vars,
             min_workers=self._min_workers,
             max_workers=self._max_workers,
             scale_up_threshold=self._scale_up_threshold,

api/core/workflow/graph_engine/layers/persistence.py

@@ -68,7 +68,6 @@ class _NodeRuntimeSnapshot:
     predecessor_node_id: str | None
     iteration_id: str | None
     loop_id: str | None
-    mention_parent_id: str | None
     created_at: datetime
 
 
@@ -231,7 +230,6 @@ class WorkflowPersistenceLayer(GraphEngineLayer):
         metadata = {
             WorkflowNodeExecutionMetadataKey.ITERATION_ID: event.in_iteration_id,
             WorkflowNodeExecutionMetadataKey.LOOP_ID: event.in_loop_id,
-            WorkflowNodeExecutionMetadataKey.MENTION_PARENT_ID: event.in_mention_parent_id,
         }
 
         domain_execution = WorkflowNodeExecution(
@@ -258,7 +256,6 @@ class WorkflowPersistenceLayer(GraphEngineLayer):
             predecessor_node_id=event.predecessor_node_id,
             iteration_id=event.in_iteration_id,
             loop_id=event.in_loop_id,
-            mention_parent_id=event.in_mention_parent_id,
             created_at=event.start_at,
         )
         self._node_snapshots[event.id] = snapshot

api/core/workflow/graph_engine/worker.py

@@ -5,27 +5,26 @@ Workers pull node IDs from the ready_queue, execute nodes, and push events
 to the event_queue for the dispatcher to process.
 """
 
+import contextvars
 import queue
 import threading
 import time
 from collections.abc import Sequence
 from datetime import datetime
-from typing import TYPE_CHECKING, final
+from typing import final
 from uuid import uuid4
 
+from flask import Flask
 from typing_extensions import override
 
-from core.workflow.context import IExecutionContext
 from core.workflow.graph import Graph
 from core.workflow.graph_engine.layers.base import GraphEngineLayer
 from core.workflow.graph_events import GraphNodeEventBase, NodeRunFailedEvent
 from core.workflow.nodes.base.node import Node
+from libs.flask_utils import preserve_flask_contexts
 
 from .ready_queue import ReadyQueue
 
-if TYPE_CHECKING:
-    pass
-
 
 @final
 class Worker(threading.Thread):
@@ -45,7 +44,8 @@ class Worker(threading.Thread):
         layers: Sequence[GraphEngineLayer],
         stop_event: threading.Event,
         worker_id: int = 0,
-        execution_context: IExecutionContext | None = None,
+        flask_app: Flask | None = None,
+        context_vars: contextvars.Context | None = None,
     ) -> None:
         """
         Initialize worker thread.
@@ -56,17 +56,19 @@ class Worker(threading.Thread):
             graph: Graph containing nodes to execute
             layers: Graph engine layers for node execution hooks
             worker_id: Unique identifier for this worker
-            execution_context: Optional execution context for context preservation
+            flask_app: Optional Flask application for context preservation
+            context_vars: Optional context variables to preserve in worker thread
         """
         super().__init__(name=f"GraphWorker-{worker_id}", daemon=True)
         self._ready_queue = ready_queue
         self._event_queue = event_queue
         self._graph = graph
         self._worker_id = worker_id
-        self._execution_context = execution_context
+        self._flask_app = flask_app
+        self._context_vars = context_vars
+        self._last_task_time = time.time()
         self._stop_event = stop_event
         self._layers = layers if layers is not None else []
-        self._last_task_time = time.time()
 
     def stop(self) -> None:
         """Worker is controlled via shared stop_event from GraphEngine.
@@ -133,9 +135,11 @@ class Worker(threading.Thread):
 
         error: Exception | None = None
 
-        # Execute the node with preserved context if execution context is provided
-        if self._execution_context is not None:
-            with self._execution_context:
+        if self._flask_app and self._context_vars:
+            with preserve_flask_contexts(
+                flask_app=self._flask_app,
+                context_vars=self._context_vars,
+            ):
                 self._invoke_node_run_start_hooks(node)
                 try:
                     node_events = node.run()

api/core/workflow/graph_engine/worker_management/worker_pool.py

@@ -8,10 +8,9 @@ DynamicScaler, and WorkerFactory into a single class.
 import logging
 import queue
 import threading
-from typing import final
+from typing import TYPE_CHECKING, final
 
 from configs import dify_config
-from core.workflow.context import IExecutionContext
 from core.workflow.graph import Graph
 from core.workflow.graph_events import GraphNodeEventBase
 
@@ -21,6 +20,11 @@ from ..worker import Worker
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from contextvars import Context
+
+    from flask import Flask
+
 
 @final
 class WorkerPool:
@@ -38,7 +42,8 @@ class WorkerPool:
         graph: Graph,
         layers: list[GraphEngineLayer],
         stop_event: threading.Event,
-        execution_context: IExecutionContext | None = None,
+        flask_app: "Flask | None" = None,
+        context_vars: "Context | None" = None,
         min_workers: int | None = None,
         max_workers: int | None = None,
         scale_up_threshold: int | None = None,
@@ -52,7 +57,8 @@ class WorkerPool:
             event_queue: Queue for worker events
             graph: The workflow graph
             layers: Graph engine layers for node execution hooks
-            execution_context: Optional execution context for context preservation
+            flask_app: Optional Flask app for context preservation
+            context_vars: Optional context variables
             min_workers: Minimum number of workers
             max_workers: Maximum number of workers
             scale_up_threshold: Queue depth to trigger scale up
@@ -61,7 +67,8 @@ class WorkerPool:
         self._ready_queue = ready_queue
         self._event_queue = event_queue
         self._graph = graph
-        self._execution_context = execution_context
+        self._flask_app = flask_app
+        self._context_vars = context_vars
         self._layers = layers
 
         # Scaling parameters with defaults
@@ -145,7 +152,8 @@ class WorkerPool:
             graph=self._graph,
             layers=self._layers,
             worker_id=worker_id,
-            execution_context=self._execution_context,
+            flask_app=self._flask_app,
+            context_vars=self._context_vars,
             stop_event=self._stop_event,
         )
 

api/core/workflow/graph_events/base.py

@@ -21,12 +21,6 @@ class GraphNodeEventBase(GraphEngineEvent):
     """iteration id if node is in iteration"""
     in_loop_id: str | None = None
     """loop id if node is in loop"""
-    in_mention_parent_id: str | None = None
-    """Parent node id if this is an extractor node event.
-    
-    When set, indicates this event belongs to an extractor node that
-    is extracting values for the specified parent node.
-    """
 
     # The version of the node, or "1" if not specified.
     node_version: str = "1"

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

@@ -12,20 +12,11 @@ from sqlalchemy.orm import Session
 from core.agent.entities import AgentToolEntity
 from core.agent.plugin_entities import AgentStrategyParameter
 from core.file import File, FileTransferMethod
-from core.memory.base import BaseMemory
-from core.memory.node_token_buffer_memory import NodeTokenBufferMemory
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities.llm_entities import LLMUsage, LLMUsageMetadata
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    PromptMessage,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
 from core.model_runtime.entities.model_entities import AIModelEntity, ModelType
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.prompt.entities.advanced_prompt_entities import MemoryMode
 from core.provider_manager import ProviderManager
 from core.tools.entities.tool_entities import (
     ToolIdentity,
@@ -145,9 +136,6 @@ class AgentNode(Node[AgentNodeData]):
             )
             return
 
-        # Fetch memory for node memory saving
-        memory = self._fetch_memory_for_save()
-
         try:
             yield from self._transform_message(
                 messages=message_stream,
@@ -161,7 +149,6 @@ class AgentNode(Node[AgentNodeData]):
                 node_type=self.node_type,
                 node_id=self._node_id,
                 node_execution_id=self.id,
-                memory=memory,
             )
         except PluginDaemonClientSideError as e:
             transform_error = AgentMessageTransformError(
@@ -408,20 +395,8 @@ class AgentNode(Node[AgentNodeData]):
             icon = None
         return icon
 
-    def _fetch_memory(self, model_instance: ModelInstance) -> BaseMemory | None:
-        """
-        Fetch memory based on configuration mode.
-
-        Returns TokenBufferMemory for conversation mode (default),
-        or NodeTokenBufferMemory for node mode (Chatflow only).
-        """
-        node_data = self.node_data
-        memory_config = node_data.memory
-
-        if not memory_config:
-            return None
-
-        # get conversation id (required for both modes in Chatflow)
+    def _fetch_memory(self, model_instance: ModelInstance) -> TokenBufferMemory | None:
+        # get conversation id
         conversation_id_variable = self.graph_runtime_state.variable_pool.get(
             ["sys", SystemVariableKey.CONVERSATION_ID]
         )
@@ -429,26 +404,16 @@ class AgentNode(Node[AgentNodeData]):
             return None
         conversation_id = conversation_id_variable.value
 
-        # Return appropriate memory type based on mode
-        if memory_config.mode == MemoryMode.NODE:
-            # Node-level memory (Chatflow only)
-            return NodeTokenBufferMemory(
-                app_id=self.app_id,
-                conversation_id=conversation_id,
-                node_id=self._node_id,
-                tenant_id=self.tenant_id,
-                model_instance=model_instance,
-            )
-        else:
-            # Conversation-level memory (default)
-            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
-            return TokenBufferMemory(conversation=conversation, model_instance=model_instance)
+        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)
+
+        return memory
 
     def _fetch_model(self, value: dict[str, Any]) -> tuple[ModelInstance, AIModelEntity | None]:
         provider_manager = ProviderManager()
@@ -492,136 +457,6 @@ class AgentNode(Node[AgentNodeData]):
         else:
             return [tool for tool in tools if tool.get("type") != ToolProviderType.MCP]
 
-    def _fetch_memory_for_save(self) -> BaseMemory | None:
-        """
-        Fetch memory instance for saving node memory.
-        This is a simplified version that doesn't require model_instance.
-        """
-        from core.model_manager import ModelManager
-        from core.model_runtime.entities.model_entities import ModelType
-
-        node_data = self.node_data
-        if not node_data.memory:
-            return None
-
-        # Get conversation_id
-        conversation_id_var = self.graph_runtime_state.variable_pool.get(["sys", SystemVariableKey.CONVERSATION_ID])
-        if not isinstance(conversation_id_var, StringSegment):
-            return None
-        conversation_id = conversation_id_var.value
-
-        # Return appropriate memory type based on mode
-        if node_data.memory.mode == MemoryMode.NODE:
-            # For node memory, we need a model_instance for token counting
-            # Use a simple default model for this purpose
-            try:
-                model_instance = ModelManager().get_default_model_instance(
-                    tenant_id=self.tenant_id,
-                    model_type=ModelType.LLM,
-                )
-            except Exception:
-                return None
-
-            return NodeTokenBufferMemory(
-                app_id=self.app_id,
-                conversation_id=conversation_id,
-                node_id=self._node_id,
-                tenant_id=self.tenant_id,
-                model_instance=model_instance,
-            )
-        else:
-            # Conversation-level memory doesn't need saving here
-            return None
-
-    def _build_context(
-        self,
-        parameters_for_log: dict[str, Any],
-        user_query: str,
-        assistant_response: str,
-        agent_logs: list[AgentLogEvent],
-    ) -> list[PromptMessage]:
-        """
-        Build context from user query, tool calls, and assistant response.
-        Format: user -> assistant(with tool_calls) -> tool -> assistant
-
-        The context includes:
-        - Current user query (always present, may be empty)
-        - Assistant message with tool_calls (if tools were called)
-        - Tool results
-        - Assistant's final response
-        """
-        context_messages: list[PromptMessage] = []
-
-        # Always add user query (even if empty, to maintain conversation structure)
-        context_messages.append(UserPromptMessage(content=user_query or ""))
-
-        # Extract actual tool calls from agent logs
-        # Only include logs with label starting with "CALL " - these are real tool invocations
-        tool_calls: list[AssistantPromptMessage.ToolCall] = []
-        tool_results: list[tuple[str, str, str]] = []  # (tool_call_id, tool_name, result)
-
-        for log in agent_logs:
-            if log.status == "success" and log.label and log.label.startswith("CALL "):
-                # Extract tool name from label (format: "CALL tool_name")
-                tool_name = log.label[5:]  # Remove "CALL " prefix
-                tool_call_id = log.message_id
-
-                # Parse tool response from data
-                data = log.data or {}
-                tool_response = ""
-
-                # Try to extract the actual tool response
-                if "tool_response" in data:
-                    tool_response = data["tool_response"]
-                elif "output" in data:
-                    tool_response = data["output"]
-                elif "result" in data:
-                    tool_response = data["result"]
-
-                if isinstance(tool_response, dict):
-                    tool_response = str(tool_response)
-
-                # Get tool input for arguments
-                tool_input = data.get("tool_call_input", {}) or data.get("input", {})
-                if isinstance(tool_input, dict):
-                    import json
-
-                    tool_input_str = json.dumps(tool_input, ensure_ascii=False)
-                else:
-                    tool_input_str = str(tool_input) if tool_input else ""
-
-                if tool_response:
-                    tool_calls.append(
-                        AssistantPromptMessage.ToolCall(
-                            id=tool_call_id,
-                            type="function",
-                            function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                                name=tool_name,
-                                arguments=tool_input_str,
-                            ),
-                        )
-                    )
-                    tool_results.append((tool_call_id, tool_name, str(tool_response)))
-
-        # Add assistant message with tool_calls if there were tool calls
-        if tool_calls:
-            context_messages.append(AssistantPromptMessage(content="", tool_calls=tool_calls))
-
-            # Add tool result messages
-            for tool_call_id, tool_name, result in tool_results:
-                context_messages.append(
-                    ToolPromptMessage(
-                        content=result,
-                        tool_call_id=tool_call_id,
-                        name=tool_name,
-                    )
-                )
-
-        # Add final assistant response
-        context_messages.append(AssistantPromptMessage(content=assistant_response))
-
-        return context_messages
-
     def _transform_message(
         self,
         messages: Generator[ToolInvokeMessage, None, None],
@@ -632,7 +467,6 @@ class AgentNode(Node[AgentNodeData]):
         node_type: NodeType,
         node_id: str,
         node_execution_id: str,
-        memory: BaseMemory | None = None,
     ) -> Generator[NodeEventBase, None, None]:
         """
         Convert ToolInvokeMessages into tuple[plain_text, files]
@@ -877,12 +711,6 @@ class AgentNode(Node[AgentNodeData]):
                 is_final=True,
             )
 
-        # Get user query from parameters for building context
-        user_query = parameters_for_log.get("query", "")
-
-        # Build context from history, user query, tool calls and assistant response
-        context = self._build_context(parameters_for_log, user_query, text, agent_logs)
-
         yield StreamCompletedEvent(
             node_run_result=NodeRunResult(
                 status=WorkflowNodeExecutionStatus.SUCCEEDED,
@@ -891,7 +719,6 @@ class AgentNode(Node[AgentNodeData]):
                     "usage": jsonable_encoder(llm_usage),
                     "files": ArrayFileSegment(value=files),
                     "json": json_output,
-                    "context": context,
                     **variables,
                 },
                 metadata={

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

@@ -1,10 +1,4 @@
-from .entities import (
-    BaseIterationNodeData,
-    BaseIterationState,
-    BaseLoopNodeData,
-    BaseLoopState,
-    BaseNodeData,
-)
+from .entities import BaseIterationNodeData, BaseIterationState, BaseLoopNodeData, BaseLoopState, BaseNodeData
 from .usage_tracking_mixin import LLMUsageTrackingMixin
 
 __all__ = [

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

@@ -175,16 +175,6 @@ class BaseNodeData(ABC, BaseModel):
     default_value: list[DefaultValue] | None = None
     retry_config: RetryConfig = RetryConfig()
 
-    # Parent node ID when this node is used as an extractor.
-    # If set, this node is an "attached" extractor node that extracts values
-    # from list[PromptMessage] for the parent node's parameters.
-    parent_node_id: str | None = None
-
-    @property
-    def is_extractor_node(self) -> bool:
-        """Check if this node is an extractor node (has parent_node_id)."""
-        return self.parent_node_id is not None
-
     @property
     def default_value_dict(self) -> dict[str, Any]:
         if self.default_value:

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

@@ -270,87 +270,10 @@ class Node(Generic[NodeDataT]):
         """Check if execution should be stopped."""
         return self.graph_runtime_state.stop_event.is_set()
 
-    def _find_extractor_node_configs(self) -> list[dict[str, Any]]:
-        """
-        Find all extractor node configurations that have parent_node_id == self._node_id.
-
-        Returns:
-            List of node configuration dicts for extractor nodes
-        """
-        nodes = self.graph_config.get("nodes", [])
-        extractor_configs = []
-        for node_config in nodes:
-            node_data = node_config.get("data", {})
-            if node_data.get("parent_node_id") == self._node_id:
-                extractor_configs.append(node_config)
-        return extractor_configs
-
-    def _execute_mention_nodes(self) -> Generator[GraphNodeEventBase, None, None]:
-        """
-        Execute all extractor nodes associated with this node.
-
-        Extractor nodes are nodes with parent_node_id == self._node_id.
-        They are executed before the main node to extract values from list[PromptMessage].
-        """
-        from core.workflow.nodes.node_mapping import LATEST_VERSION, NODE_TYPE_CLASSES_MAPPING
-
-        extractor_configs = self._find_extractor_node_configs()
-        logger.debug("[Extractor] Found %d extractor nodes for parent '%s'", len(extractor_configs), self._node_id)
-        if not extractor_configs:
-            return
-
-        for config in extractor_configs:
-            node_id = config.get("id")
-            node_data = config.get("data", {})
-            node_type_str = node_data.get("type")
-
-            if not node_id or not node_type_str:
-                continue
-
-            # Get node class
-            try:
-                node_type = NodeType(node_type_str)
-            except ValueError:
-                continue
-
-            node_mapping = NODE_TYPE_CLASSES_MAPPING.get(node_type)
-            if not node_mapping:
-                continue
-
-            node_version = str(node_data.get("version", "1"))
-            node_cls = node_mapping.get(node_version) or node_mapping.get(LATEST_VERSION)
-            if not node_cls:
-                continue
-
-            # Instantiate and execute the extractor node
-            extractor_node = node_cls(
-                id=node_id,
-                config=config,
-                graph_init_params=self._graph_init_params,
-                graph_runtime_state=self.graph_runtime_state,
-            )
-
-            # Execute and process extractor node events
-            for event in extractor_node.run():
-                # Tag event with parent node id for stream ordering and history tracking
-                if isinstance(event, GraphNodeEventBase):
-                    event.in_mention_parent_id = self._node_id
-
-                if isinstance(event, NodeRunSucceededEvent):
-                    # Store extractor node outputs in variable pool
-                    outputs: Mapping[str, Any] = event.node_run_result.outputs
-                    for variable_name, variable_value in outputs.items():
-                        self.graph_runtime_state.variable_pool.add((node_id, variable_name), variable_value)
-                if not isinstance(event, NodeRunStreamChunkEvent):
-                    yield event
-
     def run(self) -> Generator[GraphNodeEventBase, None, None]:
         execution_id = self.ensure_execution_id()
         self._start_at = naive_utc_now()
 
-        # Step 1: Execute associated extractor nodes before main node execution
-        yield from self._execute_mention_nodes()
-
         # Create and push start event with required fields
         start_event = NodeRunStartedEvent(
             id=execution_id,

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

@@ -1,9 +1,11 @@
+import contextvars
 import logging
 from collections.abc import Generator, Mapping, Sequence
 from concurrent.futures import Future, ThreadPoolExecutor, as_completed
 from datetime import UTC, datetime
 from typing import TYPE_CHECKING, Any, NewType, cast
 
+from flask import Flask, current_app
 from typing_extensions import TypeIs
 
 from core.model_runtime.entities.llm_entities import LLMUsage
@@ -37,6 +39,7 @@ from core.workflow.nodes.base.node import Node
 from core.workflow.nodes.iteration.entities import ErrorHandleMode, IterationNodeData
 from core.workflow.runtime import VariablePool
 from libs.datetime_utils import naive_utc_now
+from libs.flask_utils import preserve_flask_contexts
 
 from .exc import (
     InvalidIteratorValueError,
@@ -48,7 +51,6 @@ from .exc import (
 )
 
 if TYPE_CHECKING:
-    from core.workflow.context import IExecutionContext
     from core.workflow.graph_engine import GraphEngine
 
 logger = logging.getLogger(__name__)
@@ -250,7 +252,8 @@ class IterationNode(LLMUsageTrackingMixin, Node[IterationNodeData]):
                     self._execute_single_iteration_parallel,
                     index=index,
                     item=item,
-                    execution_context=self._capture_execution_context(),
+                    flask_app=current_app._get_current_object(),  # type: ignore
+                    context_vars=contextvars.copy_context(),
                 )
                 future_to_index[future] = index
 
@@ -303,10 +306,11 @@ class IterationNode(LLMUsageTrackingMixin, Node[IterationNodeData]):
         self,
         index: int,
         item: object,
-        execution_context: "IExecutionContext",
+        flask_app: Flask,
+        context_vars: contextvars.Context,
     ) -> tuple[datetime, list[GraphNodeEventBase], object | None, dict[str, Variable], LLMUsage]:
         """Execute a single iteration in parallel mode and return results."""
-        with execution_context:
+        with preserve_flask_contexts(flask_app=flask_app, context_vars=context_vars):
             iter_start_at = datetime.now(UTC).replace(tzinfo=None)
             events: list[GraphNodeEventBase] = []
             outputs_temp: list[object] = []
@@ -335,12 +339,6 @@ class IterationNode(LLMUsageTrackingMixin, Node[IterationNodeData]):
                 graph_engine.graph_runtime_state.llm_usage,
             )
 
-    def _capture_execution_context(self) -> "IExecutionContext":
-        """Capture current execution context for parallel iterations."""
-        from core.workflow.context import capture_current_context
-
-        return capture_current_context()
-
     def _handle_iteration_success(
         self,
         started_at: datetime,

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

@@ -1,7 +1,7 @@
 from collections.abc import Mapping, Sequence
-from typing import Annotated, Any, Literal, TypeAlias
+from typing import Any, Literal
 
-from pydantic import BaseModel, ConfigDict, Field, field_validator
+from pydantic import BaseModel, Field, field_validator
 
 from core.model_runtime.entities import ImagePromptMessageContent, LLMMode
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
@@ -58,28 +58,9 @@ class LLMNodeCompletionModelPromptTemplate(CompletionModelPromptTemplate):
     jinja2_text: str | None = None
 
 
-class PromptMessageContext(BaseModel):
-    """Context variable reference in prompt template.
-
-    YAML/JSON format: { "$context": ["node_id", "variable_name"] }
-    This will be expanded to list[PromptMessage] at runtime.
-    """
-
-    model_config = ConfigDict(populate_by_name=True)
-
-    value_selector: Sequence[str] = Field(alias="$context")
-
-
-# Union type for prompt template items (static message or context variable reference)
-PromptTemplateItem: TypeAlias = Annotated[
-    LLMNodeChatModelMessage | PromptMessageContext,
-    Field(discriminator=None),
-]
-
-
 class LLMNodeData(BaseNodeData):
     model: ModelConfig
-    prompt_template: Sequence[PromptTemplateItem] | LLMNodeCompletionModelPromptTemplate
+    prompt_template: Sequence[LLMNodeChatModelMessage] | LLMNodeCompletionModelPromptTemplate
     prompt_config: PromptConfig = Field(default_factory=PromptConfig)
     memory: MemoryConfig | None = None
     context: ContextConfig

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

@@ -8,20 +8,12 @@ from configs import dify_config
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.entities.provider_entities import ProviderQuotaType, QuotaUnit
 from core.file.models import File
-from core.memory import NodeTokenBufferMemory, TokenBufferMemory
-from core.memory.base import BaseMemory
+from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities.llm_entities import LLMUsage
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    MultiModalPromptMessageContent,
-    PromptMessage,
-    PromptMessageContentUnionTypes,
-    PromptMessageRole,
-)
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.prompt.entities.advanced_prompt_entities import MemoryConfig, MemoryMode
+from core.prompt.entities.advanced_prompt_entities import MemoryConfig
 from core.variables.segments import ArrayAnySegment, ArrayFileSegment, FileSegment, NoneSegment, StringSegment
 from core.workflow.enums import SystemVariableKey
 from core.workflow.nodes.llm.entities import ModelConfig
@@ -94,56 +86,25 @@ def fetch_files(variable_pool: VariablePool, selector: Sequence[str]) -> Sequenc
 
 
 def fetch_memory(
-    variable_pool: VariablePool,
-    app_id: str,
-    tenant_id: str,
-    node_data_memory: MemoryConfig | None,
-    model_instance: ModelInstance,
-    node_id: str = "",
-) -> BaseMemory | None:
-    """
-    Fetch memory based on configuration mode.
-
-    Returns TokenBufferMemory for conversation mode (default),
-    or NodeTokenBufferMemory for node mode (Chatflow only).
-
-    :param variable_pool: Variable pool containing system variables
-    :param app_id: Application ID
-    :param tenant_id: Tenant ID
-    :param node_data_memory: Memory configuration
-    :param model_instance: Model instance for token counting
-    :param node_id: Node ID in the workflow (required for node mode)
-    :return: Memory instance or None if not applicable
-    """
+    variable_pool: VariablePool, app_id: str, node_data_memory: MemoryConfig | None, model_instance: ModelInstance
+) -> TokenBufferMemory | None:
     if not node_data_memory:
         return None
 
-    # Get conversation_id from variable pool (required for both modes in Chatflow)
+    # get conversation id
     conversation_id_variable = variable_pool.get(["sys", SystemVariableKey.CONVERSATION_ID])
     if not isinstance(conversation_id_variable, StringSegment):
         return None
     conversation_id = conversation_id_variable.value
 
-    # Return appropriate memory type based on mode
-    if node_data_memory.mode == MemoryMode.NODE:
-        # Node-level memory (Chatflow only)
-        if not node_id:
+    with Session(db.engine, expire_on_commit=False) as session:
+        stmt = select(Conversation).where(Conversation.app_id == app_id, Conversation.id == conversation_id)
+        conversation = session.scalar(stmt)
+        if not conversation:
             return None
-        return NodeTokenBufferMemory(
-            app_id=app_id,
-            conversation_id=conversation_id,
-            node_id=node_id,
-            tenant_id=tenant_id,
-            model_instance=model_instance,
-        )
-    else:
-        # Conversation-level memory (default)
-        with Session(db.engine, expire_on_commit=False) as session:
-            stmt = select(Conversation).where(Conversation.app_id == app_id, Conversation.id == conversation_id)
-            conversation = session.scalar(stmt)
-            if not conversation:
-                return None
-        return TokenBufferMemory(conversation=conversation, model_instance=model_instance)
+
+    memory = TokenBufferMemory(conversation=conversation, model_instance=model_instance)
+    return memory
 
 
 def deduct_llm_quota(tenant_id: str, model_instance: ModelInstance, usage: LLMUsage):
@@ -209,87 +170,3 @@ def deduct_llm_quota(tenant_id: str, model_instance: ModelInstance, usage: LLMUs
                 )
                 session.execute(stmt)
                 session.commit()
-
-
-def build_context(
-    prompt_messages: Sequence[PromptMessage],
-    assistant_response: str,
-) -> list[PromptMessage]:
-    """
-    Build context from prompt messages and assistant response.
-    Excludes system messages and includes the current LLM response.
-    Returns list[PromptMessage] for use with ArrayPromptMessageSegment.
-
-    Note: Multi-modal content base64 data is truncated to avoid storing large data in context.
-    """
-    context_messages: list[PromptMessage] = [
-        _truncate_multimodal_content(m) for m in prompt_messages if m.role != PromptMessageRole.SYSTEM
-    ]
-    context_messages.append(AssistantPromptMessage(content=assistant_response))
-    return context_messages
-
-
-def _truncate_multimodal_content(message: PromptMessage) -> PromptMessage:
-    """
-    Truncate multi-modal content base64 data in a message to avoid storing large data.
-    Preserves the PromptMessage structure for ArrayPromptMessageSegment compatibility.
-
-    If file_ref is present, clears base64_data and url (they can be restored later).
-    Otherwise, truncates base64_data as fallback for legacy data.
-    """
-    content = message.content
-    if content is None or isinstance(content, str):
-        return message
-
-    # Process list content, handling multi-modal data based on file_ref availability
-    new_content: list[PromptMessageContentUnionTypes] = []
-    for item in content:
-        if isinstance(item, MultiModalPromptMessageContent):
-            if item.file_ref:
-                # Clear base64 and url, keep file_ref for later restoration
-                new_content.append(item.model_copy(update={"base64_data": "", "url": ""}))
-            else:
-                # Fallback: truncate base64_data if no file_ref (legacy data)
-                truncated_base64 = ""
-                if item.base64_data:
-                    truncated_base64 = item.base64_data[:10] + "...[TRUNCATED]..." + item.base64_data[-10:]
-                new_content.append(item.model_copy(update={"base64_data": truncated_base64}))
-        else:
-            new_content.append(item)
-
-    return message.model_copy(update={"content": new_content})
-
-
-def restore_multimodal_content_in_messages(messages: Sequence[PromptMessage]) -> list[PromptMessage]:
-    """
-    Restore multimodal content (base64 or url) in a list of PromptMessages.
-
-    When context is saved, base64_data is cleared to save storage space.
-    This function restores the content by parsing file_ref in each MultiModalPromptMessageContent.
-
-    Args:
-        messages: List of PromptMessages that may contain truncated multimodal content
-
-    Returns:
-        List of PromptMessages with restored multimodal content
-    """
-    from core.file import file_manager
-
-    return [_restore_message_content(msg, file_manager) for msg in messages]
-
-
-def _restore_message_content(message: PromptMessage, file_manager) -> PromptMessage:
-    """Restore multimodal content in a single PromptMessage."""
-    content = message.content
-    if content is None or isinstance(content, str):
-        return message
-
-    restored_content: list[PromptMessageContentUnionTypes] = []
-    for item in content:
-        if isinstance(item, MultiModalPromptMessageContent):
-            restored_item = file_manager.restore_multimodal_content(item)
-            restored_content.append(cast(PromptMessageContentUnionTypes, restored_item))
-        else:
-            restored_content.append(item)
-
-    return message.model_copy(update={"content": restored_content})

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

@@ -7,7 +7,7 @@ import logging
 import re
 import time
 from collections.abc import Generator, Mapping, Sequence
-from typing import TYPE_CHECKING, Any, Literal, cast
+from typing import TYPE_CHECKING, Any, Literal
 
 from sqlalchemy import select
 
@@ -16,7 +16,7 @@ from core.file import File, FileTransferMethod, FileType, file_manager
 from core.helper.code_executor import CodeExecutor, CodeLanguage
 from core.llm_generator.output_parser.errors import OutputParserError
 from core.llm_generator.output_parser.structured_output import invoke_llm_with_structured_output
-from core.memory.base import BaseMemory
+from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities import (
     ImagePromptMessageContent,
@@ -51,7 +51,6 @@ from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.tools.signature import sign_upload_file
 from core.variables import (
     ArrayFileSegment,
-    ArrayPromptMessageSegment,
     ArraySegment,
     FileSegment,
     NoneSegment,
@@ -88,7 +87,6 @@ from .entities import (
     LLMNodeCompletionModelPromptTemplate,
     LLMNodeData,
     ModelConfig,
-    PromptMessageContext,
 )
 from .exc import (
     InvalidContextStructureError,
@@ -161,9 +159,8 @@ class LLMNode(Node[LLMNodeData]):
         variable_pool = self.graph_runtime_state.variable_pool
 
         try:
-            # Parse prompt template to separate static messages and context references
-            prompt_template = self.node_data.prompt_template
-            static_messages, context_refs, template_order = self._parse_prompt_template()
+            # init messages template
+            self.node_data.prompt_template = self._transform_chat_messages(self.node_data.prompt_template)
 
             # fetch variables and fetch values from variable pool
             inputs = self._fetch_inputs(node_data=self.node_data)
@@ -211,10 +208,8 @@ class LLMNode(Node[LLMNodeData]):
             memory = llm_utils.fetch_memory(
                 variable_pool=variable_pool,
                 app_id=self.app_id,
-                tenant_id=self.tenant_id,
                 node_data_memory=self.node_data.memory,
                 model_instance=model_instance,
-                node_id=self._node_id,
             )
 
             query: str | None = None
@@ -225,40 +220,21 @@ class LLMNode(Node[LLMNodeData]):
                 ):
                     query = query_variable.text
 
-            # Get prompt messages
-            prompt_messages: Sequence[PromptMessage]
-            stop: Sequence[str] | None
-            if isinstance(prompt_template, list) and context_refs:
-                prompt_messages, stop = self._build_prompt_messages_with_context(
-                    context_refs=context_refs,
-                    template_order=template_order,
-                    static_messages=static_messages,
-                    query=query,
-                    files=files,
-                    context=context,
-                    memory=memory,
-                    model_config=model_config,
-                    context_files=context_files,
-                )
-            else:
-                prompt_messages, stop = LLMNode.fetch_prompt_messages(
-                    sys_query=query,
-                    sys_files=files,
-                    context=context,
-                    memory=memory,
-                    model_config=model_config,
-                    prompt_template=cast(
-                        Sequence[LLMNodeChatModelMessage] | LLMNodeCompletionModelPromptTemplate,
-                        self.node_data.prompt_template,
-                    ),
-                    memory_config=self.node_data.memory,
-                    vision_enabled=self.node_data.vision.enabled,
-                    vision_detail=self.node_data.vision.configs.detail,
-                    variable_pool=variable_pool,
-                    jinja2_variables=self.node_data.prompt_config.jinja2_variables,
-                    tenant_id=self.tenant_id,
-                    context_files=context_files,
-                )
+            prompt_messages, stop = LLMNode.fetch_prompt_messages(
+                sys_query=query,
+                sys_files=files,
+                context=context,
+                memory=memory,
+                model_config=model_config,
+                prompt_template=self.node_data.prompt_template,
+                memory_config=self.node_data.memory,
+                vision_enabled=self.node_data.vision.enabled,
+                vision_detail=self.node_data.vision.configs.detail,
+                variable_pool=variable_pool,
+                jinja2_variables=self.node_data.prompt_config.jinja2_variables,
+                tenant_id=self.tenant_id,
+                context_files=context_files,
+            )
 
             # handle invoke result
             generator = LLMNode.invoke_llm(
@@ -274,7 +250,6 @@ class LLMNode(Node[LLMNodeData]):
                 node_id=self._node_id,
                 node_type=self.node_type,
                 reasoning_format=self.node_data.reasoning_format,
-                tenant_id=self.tenant_id,
             )
 
             structured_output: LLMStructuredOutput | None = None
@@ -326,7 +301,6 @@ class LLMNode(Node[LLMNodeData]):
                 "reasoning_content": reasoning_content,
                 "usage": jsonable_encoder(usage),
                 "finish_reason": finish_reason,
-                "context": llm_utils.build_context(prompt_messages, clean_text),
             }
             if structured_output:
                 outputs["structured_output"] = structured_output.structured_output
@@ -393,7 +367,6 @@ class LLMNode(Node[LLMNodeData]):
         node_id: str,
         node_type: NodeType,
         reasoning_format: Literal["separated", "tagged"] = "tagged",
-        tenant_id: str | None = None,
     ) -> Generator[NodeEventBase | LLMStructuredOutput, None, None]:
         model_schema = model_instance.model_type_instance.get_model_schema(
             node_data_model.name, model_instance.credentials
@@ -417,7 +390,6 @@ class LLMNode(Node[LLMNodeData]):
                 stop=list(stop or []),
                 stream=True,
                 user=user_id,
-                tenant_id=tenant_id,
             )
         else:
             request_start_time = time.perf_counter()
@@ -609,212 +581,6 @@ class LLMNode(Node[LLMNodeData]):
 
         return messages
 
-    def _parse_prompt_template(
-        self,
-    ) -> tuple[list[LLMNodeChatModelMessage], list[PromptMessageContext], list[tuple[int, str]]]:
-        """
-        Parse prompt_template to separate static messages and context references.
-
-        Returns:
-            Tuple of (static_messages, context_refs, template_order)
-            - static_messages: list of LLMNodeChatModelMessage
-            - context_refs: list of PromptMessageContext
-            - template_order: list of (index, type) tuples preserving original order
-        """
-        prompt_template = self.node_data.prompt_template
-        static_messages: list[LLMNodeChatModelMessage] = []
-        context_refs: list[PromptMessageContext] = []
-        template_order: list[tuple[int, str]] = []
-
-        if isinstance(prompt_template, list):
-            for idx, item in enumerate(prompt_template):
-                if isinstance(item, PromptMessageContext):
-                    context_refs.append(item)
-                    template_order.append((idx, "context"))
-                else:
-                    static_messages.append(item)
-                    template_order.append((idx, "static"))
-            # Transform static messages for jinja2
-            if static_messages:
-                self.node_data.prompt_template = self._transform_chat_messages(static_messages)
-
-        return static_messages, context_refs, template_order
-
-    def _build_prompt_messages_with_context(
-        self,
-        *,
-        context_refs: list[PromptMessageContext],
-        template_order: list[tuple[int, str]],
-        static_messages: list[LLMNodeChatModelMessage],
-        query: str | None,
-        files: Sequence[File],
-        context: str | None,
-        memory: BaseMemory | None,
-        model_config: ModelConfigWithCredentialsEntity,
-        context_files: list[File],
-    ) -> tuple[list[PromptMessage], Sequence[str] | None]:
-        """
-        Build prompt messages by combining static messages and context references in DSL order.
-
-        Returns:
-            Tuple of (prompt_messages, stop_sequences)
-        """
-        variable_pool = self.graph_runtime_state.variable_pool
-
-        # Process messages in DSL order: iterate once and handle each type directly
-        combined_messages: list[PromptMessage] = []
-        context_idx = 0
-        static_idx = 0
-
-        for _, type_ in template_order:
-            if type_ == "context":
-                # Handle context reference
-                ctx_ref = context_refs[context_idx]
-                ctx_var = variable_pool.get(ctx_ref.value_selector)
-                if ctx_var is None:
-                    raise VariableNotFoundError(f"Variable {'.'.join(ctx_ref.value_selector)} not found")
-                if not isinstance(ctx_var, ArrayPromptMessageSegment):
-                    raise InvalidVariableTypeError(f"Variable {'.'.join(ctx_ref.value_selector)} is not array[message]")
-                # Restore multimodal content (base64/url) that was truncated when saving context
-                restored_messages = llm_utils.restore_multimodal_content_in_messages(ctx_var.value)
-                combined_messages.extend(restored_messages)
-                context_idx += 1
-            else:
-                # Handle static message
-                static_msg = static_messages[static_idx]
-                processed_msgs = LLMNode.handle_list_messages(
-                    messages=[static_msg],
-                    context=context,
-                    jinja2_variables=self.node_data.prompt_config.jinja2_variables or [],
-                    variable_pool=variable_pool,
-                    vision_detail_config=self.node_data.vision.configs.detail,
-                )
-                combined_messages.extend(processed_msgs)
-                static_idx += 1
-
-        # Append memory messages
-        memory_messages = _handle_memory_chat_mode(
-            memory=memory,
-            memory_config=self.node_data.memory,
-            model_config=model_config,
-        )
-        combined_messages.extend(memory_messages)
-
-        # Append current query if provided
-        if query:
-            query_message = LLMNodeChatModelMessage(
-                text=query,
-                role=PromptMessageRole.USER,
-                edition_type="basic",
-            )
-            query_msgs = LLMNode.handle_list_messages(
-                messages=[query_message],
-                context="",
-                jinja2_variables=[],
-                variable_pool=variable_pool,
-                vision_detail_config=self.node_data.vision.configs.detail,
-            )
-            combined_messages.extend(query_msgs)
-
-        # Handle files (sys_files and context_files)
-        combined_messages = self._append_files_to_messages(
-            messages=combined_messages,
-            sys_files=files,
-            context_files=context_files,
-            model_config=model_config,
-        )
-
-        # Filter empty messages and get stop sequences
-        combined_messages = self._filter_messages(combined_messages, model_config)
-        stop = self._get_stop_sequences(model_config)
-
-        return combined_messages, stop
-
-    def _append_files_to_messages(
-        self,
-        *,
-        messages: list[PromptMessage],
-        sys_files: Sequence[File],
-        context_files: list[File],
-        model_config: ModelConfigWithCredentialsEntity,
-    ) -> list[PromptMessage]:
-        """Append sys_files and context_files to messages."""
-        vision_enabled = self.node_data.vision.enabled
-        vision_detail = self.node_data.vision.configs.detail
-
-        # Handle sys_files (will be deprecated later)
-        if vision_enabled and sys_files:
-            file_prompts = [
-                file_manager.to_prompt_message_content(file, image_detail_config=vision_detail) for file in sys_files
-            ]
-            if messages and isinstance(messages[-1], UserPromptMessage) and isinstance(messages[-1].content, list):
-                messages[-1] = UserPromptMessage(content=file_prompts + messages[-1].content)
-            else:
-                messages.append(UserPromptMessage(content=file_prompts))
-
-        # Handle context_files
-        if vision_enabled and context_files:
-            file_prompts = [
-                file_manager.to_prompt_message_content(file, image_detail_config=vision_detail)
-                for file in context_files
-            ]
-            if messages and isinstance(messages[-1], UserPromptMessage) and isinstance(messages[-1].content, list):
-                messages[-1] = UserPromptMessage(content=file_prompts + messages[-1].content)
-            else:
-                messages.append(UserPromptMessage(content=file_prompts))
-
-        return messages
-
-    def _filter_messages(
-        self, messages: list[PromptMessage], model_config: ModelConfigWithCredentialsEntity
-    ) -> list[PromptMessage]:
-        """Filter empty messages and unsupported content types."""
-        filtered_messages: list[PromptMessage] = []
-
-        for message in messages:
-            if isinstance(message.content, list):
-                filtered_content: list[PromptMessageContentUnionTypes] = []
-                for content_item in message.content:
-                    # Skip non-text content if features are not defined
-                    if not model_config.model_schema.features:
-                        if content_item.type != PromptMessageContentType.TEXT:
-                            continue
-                        filtered_content.append(content_item)
-                        continue
-
-                    # Skip content if corresponding feature is not supported
-                    feature_map = {
-                        PromptMessageContentType.IMAGE: ModelFeature.VISION,
-                        PromptMessageContentType.DOCUMENT: ModelFeature.DOCUMENT,
-                        PromptMessageContentType.VIDEO: ModelFeature.VIDEO,
-                        PromptMessageContentType.AUDIO: ModelFeature.AUDIO,
-                    }
-                    required_feature = feature_map.get(content_item.type)
-                    if required_feature and required_feature not in model_config.model_schema.features:
-                        continue
-                    filtered_content.append(content_item)
-
-                # Simplify single text content
-                if len(filtered_content) == 1 and filtered_content[0].type == PromptMessageContentType.TEXT:
-                    message.content = filtered_content[0].data
-                else:
-                    message.content = filtered_content
-
-            if not message.is_empty():
-                filtered_messages.append(message)
-
-        if not filtered_messages:
-            raise NoPromptFoundError(
-                "No prompt found in the LLM configuration. "
-                "Please ensure a prompt is properly configured before proceeding."
-            )
-
-        return filtered_messages
-
-    def _get_stop_sequences(self, model_config: ModelConfigWithCredentialsEntity) -> Sequence[str] | None:
-        """Get stop sequences from model config."""
-        return model_config.stop
-
     def _fetch_jinja_inputs(self, node_data: LLMNodeData) -> dict[str, str]:
         variables: dict[str, Any] = {}
 
@@ -1012,7 +778,7 @@ class LLMNode(Node[LLMNodeData]):
         sys_query: str | None = None,
         sys_files: Sequence[File],
         context: str | None = None,
-        memory: BaseMemory | None = None,
+        memory: TokenBufferMemory | None = None,
         model_config: ModelConfigWithCredentialsEntity,
         prompt_template: Sequence[LLMNodeChatModelMessage] | LLMNodeCompletionModelPromptTemplate,
         memory_config: MemoryConfig | None = None,
@@ -1571,7 +1337,7 @@ def _calculate_rest_token(
 
 def _handle_memory_chat_mode(
     *,
-    memory: BaseMemory | None,
+    memory: TokenBufferMemory | None,
     memory_config: MemoryConfig | None,
     model_config: ModelConfigWithCredentialsEntity,
 ) -> Sequence[PromptMessage]:
@@ -1588,7 +1354,7 @@ def _handle_memory_chat_mode(
 
 def _handle_memory_completion_mode(
     *,
-    memory: BaseMemory | None,
+    memory: TokenBufferMemory | None,
     memory_config: MemoryConfig | None,
     model_config: ModelConfigWithCredentialsEntity,
 ) -> str:

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

@@ -7,7 +7,7 @@ from typing import Any, cast
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.file import File
-from core.memory.base import BaseMemory
+from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities import ImagePromptMessageContent
 from core.model_runtime.entities.llm_entities import LLMUsage
@@ -145,10 +145,8 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         memory = llm_utils.fetch_memory(
             variable_pool=variable_pool,
             app_id=self.app_id,
-            tenant_id=self.tenant_id,
             node_data_memory=node_data.memory,
             model_instance=model_instance,
-            node_id=self._node_id,
         )
 
         if (
@@ -246,10 +244,6 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         # transform result into standard format
         result = self._transform_result(data=node_data, result=result or {})
 
-        # Build context from prompt messages and response
-        assistant_response = json.dumps(result, ensure_ascii=False)
-        context = llm_utils.build_context(prompt_messages, assistant_response)
-
         return NodeRunResult(
             status=WorkflowNodeExecutionStatus.SUCCEEDED,
             inputs=inputs,
@@ -258,7 +252,6 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
                 "__is_success": 1 if not error else 0,
                 "__reason": error,
                 "__usage": jsonable_encoder(usage),
-                "context": context,
                 **result,
             },
             metadata={
@@ -306,7 +299,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_config: ModelConfigWithCredentialsEntity,
-        memory: BaseMemory | None,
+        memory: TokenBufferMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> tuple[list[PromptMessage], list[PromptMessageTool]]:
@@ -388,7 +381,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_config: ModelConfigWithCredentialsEntity,
-        memory: BaseMemory | None,
+        memory: TokenBufferMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -426,7 +419,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_config: ModelConfigWithCredentialsEntity,
-        memory: BaseMemory | None,
+        memory: TokenBufferMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -460,7 +453,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         query: str,
         variable_pool: VariablePool,
         model_config: ModelConfigWithCredentialsEntity,
-        memory: BaseMemory | None,
+        memory: TokenBufferMemory | None,
         files: Sequence[File],
         vision_detail: ImagePromptMessageContent.DETAIL | None = None,
     ) -> list[PromptMessage]:
@@ -688,7 +681,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         node_data: ParameterExtractorNodeData,
         query: str,
         variable_pool: VariablePool,
-        memory: BaseMemory | None,
+        memory: TokenBufferMemory | None,
         max_token_limit: int = 2000,
     ) -> list[ChatModelMessage]:
         model_mode = ModelMode(node_data.model.mode)
@@ -715,7 +708,7 @@ class ParameterExtractorNode(Node[ParameterExtractorNodeData]):
         node_data: ParameterExtractorNodeData,
         query: str,
         variable_pool: VariablePool,
-        memory: BaseMemory | None,
+        memory: TokenBufferMemory | None,
         max_token_limit: int = 2000,
     ):
         model_mode = ModelMode(node_data.model.mode)

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

@@ -4,7 +4,7 @@ from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
-from core.memory.base import BaseMemory
+from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities import LLMUsage, ModelPropertyKey, PromptMessageRole
 from core.model_runtime.utils.encoders import jsonable_encoder
@@ -96,10 +96,8 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
         memory = llm_utils.fetch_memory(
             variable_pool=variable_pool,
             app_id=self.app_id,
-            tenant_id=self.tenant_id,
             node_data_memory=node_data.memory,
             model_instance=model_instance,
-            node_id=self._node_id,
         )
         # fetch instruction
         node_data.instruction = node_data.instruction or ""
@@ -199,15 +197,10 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
                 "model_provider": model_config.provider,
                 "model_name": model_config.model,
             }
-            # Build context from prompt messages and response
-            assistant_response = f"class_name: {category_name}, class_id: {category_id}"
-            context = llm_utils.build_context(prompt_messages, assistant_response)
-
             outputs = {
                 "class_name": category_name,
                 "class_id": category_id,
                 "usage": jsonable_encoder(usage),
-                "context": context,
             }
 
             return NodeRunResult(
@@ -319,7 +312,7 @@ class QuestionClassifierNode(Node[QuestionClassifierNodeData]):
         self,
         node_data: QuestionClassifierNodeData,
         query: str,
-        memory: BaseMemory | None,
+        memory: TokenBufferMemory | None,
         max_token_limit: int = 2000,
     ):
         model_mode = ModelMode(node_data.model.mode)

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

@@ -1,63 +1,11 @@
-import re
-from collections.abc import Sequence
-from typing import Any, Literal, Self, Union
+from typing import Any, Literal, Union
 
-from pydantic import BaseModel, field_validator, model_validator
+from pydantic import BaseModel, field_validator
 from pydantic_core.core_schema import ValidationInfo
 
 from core.tools.entities.tool_entities import ToolProviderType
 from core.workflow.nodes.base.entities import BaseNodeData
 
-# Pattern to match mention value format: {{@node.context@}}instruction
-# The placeholder {{@node.context@}} must appear at the beginning
-# Format: {{@agent_node_id.context@}} where agent_node_id is dynamic, context is fixed
-MENTION_VALUE_PATTERN = re.compile(r"^\{\{@([a-zA-Z0-9_]+)\.context@\}\}(.*)$", re.DOTALL)
-
-
-def parse_mention_value(value: str) -> tuple[str, str]:
-    """Parse mention value into (node_id, instruction).
-
-    Args:
-        value: The mention value string like "{{@llm.context@}}extract keywords"
-
-    Returns:
-        Tuple of (node_id, instruction)
-
-    Raises:
-        ValueError: If value format is invalid
-    """
-    match = MENTION_VALUE_PATTERN.match(value)
-    if not match:
-        raise ValueError(
-            "For mention type, value must start with {{@node.context@}} placeholder, "
-            "e.g., '{{@llm.context@}}extract keywords'"
-        )
-    return match.group(1), match.group(2)
-
-
-class MentionConfig(BaseModel):
-    """Configuration for extracting value from context variable.
-
-    Used when a tool parameter needs to be extracted from list[PromptMessage]
-    context using an extractor LLM node.
-
-    Note: instruction is embedded in the value field as "{{@node.context@}}instruction"
-    """
-
-    # ID of the extractor LLM node
-    extractor_node_id: str
-
-    # Output variable selector from extractor node
-    # e.g., ["text"], ["structured_output", "query"]
-    output_selector: Sequence[str]
-
-    # Strategy when output is None
-    null_strategy: Literal["raise_error", "use_default"] = "raise_error"
-
-    # Default value when null_strategy is "use_default"
-    # Type should match the parameter's expected type
-    default_value: Any = None
-
 
 class ToolEntity(BaseModel):
     provider_id: str
@@ -87,9 +35,7 @@ class ToolNodeData(BaseNodeData, ToolEntity):
     class ToolInput(BaseModel):
         # TODO: check this type
         value: Union[Any, list[str]]
-        type: Literal["mixed", "variable", "constant", "mention"]
-        # Required config for mention type, extracting value from context variable
-        mention_config: MentionConfig | None = None
+        type: Literal["mixed", "variable", "constant"]
 
         @field_validator("type", mode="before")
         @classmethod
@@ -102,9 +48,6 @@ class ToolNodeData(BaseNodeData, ToolEntity):
 
             if typ == "mixed" and not isinstance(value, str):
                 raise ValueError("value must be a string")
-            elif typ == "mention":
-                # Skip here, will be validated in model_validator
-                pass
             elif typ == "variable":
                 if not isinstance(value, list):
                     raise ValueError("value must be a list")
@@ -115,26 +58,6 @@ class ToolNodeData(BaseNodeData, ToolEntity):
                 raise ValueError("value must be a string, int, float, bool or dict")
             return typ
 
-        @model_validator(mode="after")
-        def check_mention_type(self) -> Self:
-            """Validate mention type with mention_config."""
-            if self.type != "mention":
-                return self
-
-            value = self.value
-            if value is None:
-                return self
-
-            if not isinstance(value, str):
-                raise ValueError("value must be a string for mention type")
-            # For mention type, value must match format: {{@node.context@}}instruction
-            # This will raise ValueError if format is invalid
-            parse_mention_value(value)
-            # mention_config is required for mention type
-            if self.mention_config is None:
-                raise ValueError("mention_config is required for mention type")
-            return self
-
     tool_parameters: dict[str, ToolInput]
     # The version of the tool parameter.
     # If this value is None, it indicates this is a previous version

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

@@ -1,10 +1,7 @@
-import logging
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any
 
 from sqlalchemy import select
-
-logger = logging.getLogger(__name__)
 from sqlalchemy.orm import Session
 
 from core.callback_handler.workflow_tool_callback_handler import DifyWorkflowCallbackHandler
@@ -187,7 +184,6 @@ class ToolNode(Node[ToolNodeData]):
             tool_parameters (Sequence[ToolParameter]): The list of tool parameters.
             variable_pool (VariablePool): The variable pool containing the variables.
             node_data (ToolNodeData): The data associated with the tool node.
-            for_log (bool): Whether to generate parameters for logging.
 
         Returns:
             Mapping[str, Any]: A dictionary containing the generated parameters.
@@ -203,37 +199,14 @@ class ToolNode(Node[ToolNodeData]):
                 continue
             tool_input = node_data.tool_parameters[parameter_name]
             if tool_input.type == "variable":
-                if not isinstance(tool_input.value, list):
-                    raise ToolParameterError(f"Invalid variable selector for parameter '{parameter_name}'")
-                selector = tool_input.value
-                variable = variable_pool.get(selector)
+                variable = variable_pool.get(tool_input.value)
                 if variable is None:
                     if parameter.required:
-                        raise ToolParameterError(f"Variable {selector} does not exist")
+                        raise ToolParameterError(f"Variable {tool_input.value} does not exist")
                     continue
                 parameter_value = variable.value
-            elif tool_input.type == "mention":
-                # Mention type: get value from extractor node's output
-                if tool_input.mention_config is None:
-                    raise ToolParameterError(
-                        f"mention_config is required for mention type parameter '{parameter_name}'"
-                    )
-                mention_config = tool_input.mention_config.model_dump()
-                try:
-                    parameter_value, found = variable_pool.resolve_mention(
-                        mention_config, parameter_name=parameter_name
-                    )
-                    if not found and parameter.required:
-                        raise ToolParameterError(
-                            f"Extractor output not found for required parameter '{parameter_name}'"
-                        )
-                    if not found:
-                        continue
-                except ValueError as e:
-                    raise ToolParameterError(str(e)) from e
             elif tool_input.type in {"mixed", "constant"}:
-                template = str(tool_input.value)
-                segment_group = variable_pool.convert_template(template)
+                segment_group = variable_pool.convert_template(str(tool_input.value))
                 parameter_value = segment_group.log if for_log else segment_group.text
             else:
                 raise ToolParameterError(f"Unknown tool input type '{tool_input.type}'")
@@ -515,12 +488,8 @@ class ToolNode(Node[ToolNodeData]):
                 for selector in selectors:
                     result[selector.variable] = selector.value_selector
             elif input.type == "variable":
-                if isinstance(input.value, list):
-                    selector_key = ".".join(input.value)
-                    result[f"#{selector_key}#"] = input.value
-            elif input.type == "mention":
-                # Mention type: value is handled by extractor node, no direct variable reference
-                pass
+                selector_key = ".".join(input.value)
+                result[f"#{selector_key}#"] = input.value
             elif input.type == "constant":
                 pass
 

api/core/workflow/runtime/variable_pool.py

@@ -268,58 +268,6 @@ class VariablePool(BaseModel):
                 continue
             self.add(selector, value)
 
-    def resolve_mention(
-        self,
-        mention_config: Mapping[str, Any],
-        /,
-        *,
-        parameter_name: str = "",
-    ) -> tuple[Any, bool]:
-        """
-        Resolve a mention parameter value from an extractor node's output.
-
-        Mention parameters reference values extracted by an extractor LLM node
-        from list[PromptMessage] context.
-
-        Args:
-            mention_config: A dict containing:
-                - extractor_node_id: ID of the extractor LLM node
-                - output_selector: Selector path for the output variable (e.g., ["text"])
-                - null_strategy: "raise_error" or "use_default"
-                - default_value: Value to use when null_strategy is "use_default"
-            parameter_name: Name of the parameter being resolved (for error messages)
-
-        Returns:
-            Tuple of (resolved_value, found):
-                - resolved_value: The extracted value, or default_value if not found
-                - found: True if value was found, False if using default
-
-        Raises:
-            ValueError: If extractor_node_id is missing, or if null_strategy is
-                       "raise_error" and the value is not found
-        """
-        extractor_node_id = mention_config.get("extractor_node_id")
-        if not extractor_node_id:
-            raise ValueError(f"Missing extractor_node_id for mention parameter '{parameter_name}'")
-
-        output_selector = list(mention_config.get("output_selector", []))
-        null_strategy = mention_config.get("null_strategy", "raise_error")
-        default_value = mention_config.get("default_value")
-
-        # Build full selector: [extractor_node_id, ...output_selector]
-        full_selector = [extractor_node_id] + output_selector
-        variable = self.get(full_selector)
-
-        if variable is None:
-            if null_strategy == "use_default":
-                return default_value, False
-            raise ValueError(
-                f"Extractor node '{extractor_node_id}' output '{'.'.join(output_selector)}' "
-                f"not found for parameter '{parameter_name}'"
-            )
-
-        return variable.value, True
-
     @classmethod
     def empty(cls) -> VariablePool:
         """Create an empty variable pool."""

api/factories/variable_factory.py

@@ -4,7 +4,6 @@ from uuid import uuid4
 
 from configs import dify_config
 from core.file import File
-from core.model_runtime.entities import PromptMessage
 from core.variables.exc import VariableError
 from core.variables.segments import (
     ArrayAnySegment,
@@ -12,7 +11,6 @@ from core.variables.segments import (
     ArrayFileSegment,
     ArrayNumberSegment,
     ArrayObjectSegment,
-    ArrayPromptMessageSegment,
     ArraySegment,
     ArrayStringSegment,
     BooleanSegment,
@@ -31,7 +29,6 @@ from core.variables.variables import (
     ArrayFileVariable,
     ArrayNumberVariable,
     ArrayObjectVariable,
-    ArrayPromptMessageVariable,
     ArrayStringVariable,
     BooleanVariable,
     FileVariable,
@@ -64,7 +61,6 @@ SEGMENT_TO_VARIABLE_MAP = {
     ArrayFileSegment: ArrayFileVariable,
     ArrayNumberSegment: ArrayNumberVariable,
     ArrayObjectSegment: ArrayObjectVariable,
-    ArrayPromptMessageSegment: ArrayPromptMessageVariable,
     ArrayStringSegment: ArrayStringVariable,
     BooleanSegment: BooleanVariable,
     FileSegment: FileVariable,
@@ -160,13 +156,7 @@ def build_segment(value: Any, /) -> Segment:
         return ObjectSegment(value=value)
     if isinstance(value, File):
         return FileSegment(value=value)
-    if isinstance(value, PromptMessage):
-        # Single PromptMessage should be wrapped in a list
-        return ArrayPromptMessageSegment(value=[value])
     if isinstance(value, list):
-        # Check if all items are PromptMessage
-        if value and all(isinstance(item, PromptMessage) for item in value):
-            return ArrayPromptMessageSegment(value=value)
         items = [build_segment(item) for item in value]
         types = {item.value_type for item in items}
         if all(isinstance(item, ArraySegment) for item in items):
@@ -210,7 +200,6 @@ _segment_factory: Mapping[SegmentType, type[Segment]] = {
     SegmentType.ARRAY_OBJECT: ArrayObjectSegment,
     SegmentType.ARRAY_FILE: ArrayFileSegment,
     SegmentType.ARRAY_BOOLEAN: ArrayBooleanSegment,
-    SegmentType.ARRAY_PROMPT_MESSAGE: ArrayPromptMessageSegment,
 }
 
 
@@ -285,10 +274,6 @@ def build_segment_with_type(segment_type: SegmentType, value: Any) -> Segment:
     ):
         segment_class = _segment_factory[inferred_type]
         return segment_class(value_type=inferred_type, value=value)
-    elif segment_type == SegmentType.ARRAY_PROMPT_MESSAGE and inferred_type == SegmentType.ARRAY_OBJECT:
-        # PromptMessage serializes to dict, so ARRAY_OBJECT is compatible with ARRAY_PROMPT_MESSAGE
-        segment_class = _segment_factory[segment_type]
-        return segment_class(value_type=segment_type, value=value)
     else:
         raise TypeMismatchError(f"Type mismatch: expected {segment_type}, but got {inferred_type}, value={value}")
 

api/libs/workspace_permission.py

@@ -1,74 +0,0 @@
-"""
-Workspace permission helper functions.
-
-These helpers check both billing/plan level and workspace-specific policy level permissions.
-Checks are performed at two levels:
-1. Billing/plan level - via FeatureService (e.g., SANDBOX plan restrictions)
-2. Workspace policy level - via EnterpriseService (admin-configured per workspace)
-"""
-
-import logging
-
-from werkzeug.exceptions import Forbidden
-
-from configs import dify_config
-from services.enterprise.enterprise_service import EnterpriseService
-from services.feature_service import FeatureService
-
-logger = logging.getLogger(__name__)
-
-
-def check_workspace_member_invite_permission(workspace_id: str) -> None:
-    """
-    Check if workspace allows member invitations at both billing and policy levels.
-
-    Checks performed:
-    1. Billing/plan level - For future expansion (currently no plan-level restriction)
-    2. Enterprise policy level - Admin-configured workspace permission
-
-    Args:
-        workspace_id: The workspace ID to check permissions for
-
-    Raises:
-        Forbidden: If either billing plan or workspace policy prohibits member invitations
-    """
-    # Check enterprise workspace policy level (only if enterprise enabled)
-    if dify_config.ENTERPRISE_ENABLED:
-        try:
-            permission = EnterpriseService.WorkspacePermissionService.get_permission(workspace_id)
-            if not permission.allow_member_invite:
-                raise Forbidden("Workspace policy prohibits member invitations")
-        except Forbidden:
-            raise
-        except Exception:
-            logger.exception("Failed to check workspace invite permission for %s", workspace_id)
-
-
-def check_workspace_owner_transfer_permission(workspace_id: str) -> None:
-    """
-    Check if workspace allows owner transfer at both billing and policy levels.
-
-    Checks performed:
-    1. Billing/plan level - SANDBOX plan blocks owner transfer
-    2. Enterprise policy level - Admin-configured workspace permission
-
-    Args:
-        workspace_id: The workspace ID to check permissions for
-
-    Raises:
-        Forbidden: If either billing plan or workspace policy prohibits ownership transfer
-    """
-    features = FeatureService.get_features(workspace_id)
-    if not features.is_allow_transfer_workspace:
-        raise Forbidden("Your current plan does not allow workspace ownership transfer")
-
-    # Check enterprise workspace policy level (only if enterprise enabled)
-    if dify_config.ENTERPRISE_ENABLED:
-        try:
-            permission = EnterpriseService.WorkspacePermissionService.get_permission(workspace_id)
-            if not permission.allow_owner_transfer:
-                raise Forbidden("Workspace policy prohibits ownership transfer")
-        except Forbidden:
-            raise
-        except Exception:
-            logger.exception("Failed to check workspace transfer permission for %s", workspace_id)

api/models/workflow.py

@@ -1285,7 +1285,7 @@ class WorkflowDraftVariable(Base):
     # which may differ from the original value's type. Typically, they are the same,
     # but in cases where the structurally truncated  value still exceeds the size limit,
     # text slicing is applied, and the `value_type` is converted to `STRING`.
-    value_type: Mapped[SegmentType] = mapped_column(EnumText(SegmentType, length=21))
+    value_type: Mapped[SegmentType] = mapped_column(EnumText(SegmentType, length=20))
 
     # The variable's value serialized as a JSON string
     #
@@ -1659,7 +1659,7 @@ class WorkflowDraftVariableFile(Base):
 
     # The `value_type` field records the type of the original value.
     value_type: Mapped[SegmentType] = mapped_column(
-        EnumText(SegmentType, length=21),
+        EnumText(SegmentType, length=20),
         nullable=False,
     )
 

api/services/account_service.py

@@ -1381,11 +1381,6 @@ class RegisterService:
         normalized_email = email.lower()
 
         """Invite new member"""
-        # Check workspace permission for member invitations
-        from libs.workspace_permission import check_workspace_member_invite_permission
-
-        check_workspace_member_invite_permission(tenant.id)
-
         with Session(db.engine) as session:
             account = AccountService.get_account_by_email_with_case_fallback(email, session=session)
 

api/services/enterprise/enterprise_service.py

@@ -13,23 +13,6 @@ class WebAppSettings(BaseModel):
     )
 
 
-class WorkspacePermission(BaseModel):
-    workspace_id: str = Field(
-        description="The ID of the workspace.",
-        alias="workspaceId",
-    )
-    allow_member_invite: bool = Field(
-        description="Whether to allow members to invite new members to the workspace.",
-        default=False,
-        alias="allowMemberInvite",
-    )
-    allow_owner_transfer: bool = Field(
-        description="Whether to allow owners to transfer ownership of the workspace.",
-        default=False,
-        alias="allowOwnerTransfer",
-    )
-
-
 class EnterpriseService:
     @classmethod
     def get_info(cls):
@@ -61,16 +44,6 @@ class EnterpriseService:
         except ValueError as e:
             raise ValueError(f"Invalid date format: {data}") from e
 
-    class WorkspacePermissionService:
-        @classmethod
-        def get_permission(cls, workspace_id: str):
-            if not workspace_id:
-                raise ValueError("workspace_id must be provided.")
-            data = EnterpriseRequest.send_request("GET", f"/workspaces/{workspace_id}/permission")
-            if not data or "permission" not in data:
-                raise ValueError("No data found.")
-            return WorkspacePermission.model_validate(data["permission"])
-
     class WebAppAuth:
         @classmethod
         def is_user_allowed_to_access_webapp(cls, user_id: str, app_id: str):

api/services/enterprise/workspace_sync.py

@@ -12,6 +12,8 @@ logger = logging.getLogger(__name__)
 WORKSPACE_SYNC_QUEUE = "enterprise:workspace:sync:queue"
 WORKSPACE_SYNC_PROCESSING = "enterprise:workspace:sync:processing"
 
+TASK_TYPE_SYNC_TO_WORKSPACE = "sync_to_workspace"
+
 
 class WorkspaceSyncService:
     """Service to publish workspace sync tasks to Redis queue for enterprise backend consumption"""
@@ -38,6 +40,7 @@ class WorkspaceSyncService:
                 "retry_count": 0,
                 "created_at": datetime.now(UTC).isoformat(),
                 "source": source,
+                "type": TASK_TYPE_SYNC_TO_WORKSPACE,
             }
 
             # Push to Redis list (queue) - LPUSH adds to the head, worker consumes from tail with RPOP

api/services/variable_truncator.py

@@ -7,7 +7,6 @@ from typing import Any, Generic, TypeAlias, TypeVar, overload
 
 from configs import dify_config
 from core.file.models import File
-from core.model_runtime.entities import PromptMessage
 from core.variables.segments import (
     ArrayFileSegment,
     ArraySegment,
@@ -288,10 +287,6 @@ class VariableTruncator(BaseTruncator):
             if isinstance(item, File):
                 truncated_value.append(item)
                 continue
-            # Handle PromptMessage types - convert to dict for truncation
-            if isinstance(item, PromptMessage):
-                truncated_value.append(item)
-                continue
             if i >= target_length:
                 return _PartResult(truncated_value, used_size, True)
             if i > 0:

api/services/workflow/entities.py

@@ -163,29 +163,3 @@ class WorkflowScheduleCFSPlanEntity(BaseModel):
 
     schedule_strategy: Strategy
     granularity: int = Field(default=-1)  # -1 means infinite
-
-
-# ========== Mention Graph Entities ==========
-
-
-class MentionParameterSchema(BaseModel):
-    """Schema for the parameter to be extracted from mention context."""
-
-    name: str = Field(description="Parameter name (e.g., 'query')")
-    type: str = Field(default="string", description="Parameter type (e.g., 'string', 'number')")
-    description: str = Field(default="", description="Parameter description for LLM")
-
-
-class MentionGraphRequest(BaseModel):
-    """Request payload for generating mention graph."""
-
-    parent_node_id: str = Field(description="ID of the parent node that uses the extracted value")
-    parameter_key: str = Field(description="Key of the parameter being extracted")
-    context_source: list[str] = Field(description="Variable selector for the context source")
-    parameter_schema: MentionParameterSchema = Field(description="Schema of the parameter to extract")
-
-
-class MentionGraphResponse(BaseModel):
-    """Response containing the generated mention graph."""
-
-    graph: Mapping[str, Any] = Field(description="Complete graph structure with nodes, edges, viewport")

api/services/workflow/mention_graph_service.py

@@ -1,143 +0,0 @@
-"""
-Service for generating Mention LLM node graph structures.
-
-This service creates graph structures containing LLM nodes configured for
-extracting values from list[PromptMessage] variables.
-"""
-
-from typing import Any
-
-from sqlalchemy.orm import Session
-
-from core.model_runtime.entities import LLMMode
-from core.workflow.enums import NodeType
-from services.model_provider_service import ModelProviderService
-from services.workflow.entities import MentionGraphRequest, MentionGraphResponse, MentionParameterSchema
-
-
-class MentionGraphService:
-    """Service for generating Mention LLM node graph structures."""
-
-    def __init__(self, session: Session):
-        self._session = session
-
-    def generate_mention_node_id(self, node_id: str, parameter_name: str) -> str:
-        """Generate mention node ID following the naming convention.
-
-        Format: {node_id}_ext_{parameter_name}
-        """
-        return f"{node_id}_ext_{parameter_name}"
-
-    def generate_mention_graph(self, tenant_id: str, request: MentionGraphRequest) -> MentionGraphResponse:
-        """Generate a complete graph structure containing a Mention LLM node.
-
-        Args:
-            tenant_id: The tenant ID for fetching default model config
-            request: The mention graph generation request
-
-        Returns:
-            Complete graph structure with nodes, edges, and viewport
-        """
-        node_id = self.generate_mention_node_id(request.parent_node_id, request.parameter_key)
-        model_config = self._get_default_model_config(tenant_id)
-        node = self._build_mention_llm_node(
-            node_id=node_id,
-            parent_node_id=request.parent_node_id,
-            context_source=request.context_source,
-            parameter_schema=request.parameter_schema,
-            model_config=model_config,
-        )
-
-        graph = {
-            "nodes": [node],
-            "edges": [],
-            "viewport": {},
-        }
-
-        return MentionGraphResponse(graph=graph)
-
-    def _get_default_model_config(self, tenant_id: str) -> dict[str, Any]:
-        """Get the default LLM model configuration for the tenant."""
-        model_provider_service = ModelProviderService()
-        default_model = model_provider_service.get_default_model_of_model_type(
-            tenant_id=tenant_id,
-            model_type="llm",
-        )
-
-        if default_model:
-            return {
-                "provider": default_model.provider.provider,
-                "name": default_model.model,
-                "mode": LLMMode.CHAT.value,
-                "completion_params": {},
-            }
-
-        # Fallback to empty config if no default model is configured
-        return {
-            "provider": "",
-            "name": "",
-            "mode": LLMMode.CHAT.value,
-            "completion_params": {},
-        }
-
-    def _build_mention_llm_node(
-        self,
-        *,
-        node_id: str,
-        parent_node_id: str,
-        context_source: list[str],
-        parameter_schema: MentionParameterSchema,
-        model_config: dict[str, Any],
-    ) -> dict[str, Any]:
-        """Build the Mention LLM node structure.
-
-        The node uses:
-        - $context in prompt_template to reference the PromptMessage list
-        - structured_output for extracting the specific parameter
-        - parent_node_id to associate with the parent node
-        """
-        prompt_template = [
-            {
-                "role": "system",
-                "text": "Extract the required parameter value from the conversation context above.",
-            },
-            {"$context": context_source},
-            {"role": "user", "text": ""},
-        ]
-
-        structured_output = {
-            "schema": {
-                "type": "object",
-                "properties": {
-                    parameter_schema.name: {
-                        "type": parameter_schema.type,
-                        "description": parameter_schema.description,
-                    }
-                },
-                "required": [parameter_schema.name],
-                "additionalProperties": False,
-            }
-        }
-
-        return {
-            "id": node_id,
-            "position": {"x": 0, "y": 0},
-            "data": {
-                "type": NodeType.LLM.value,
-                "title": f"Mention: {parameter_schema.name}",
-                "desc": f"Extract {parameter_schema.name} from conversation context",
-                "parent_node_id": parent_node_id,
-                "model": model_config,
-                "prompt_template": prompt_template,
-                "context": {
-                    "enabled": False,
-                    "variable_selector": None,
-                },
-                "vision": {
-                    "enabled": False,
-                },
-                "memory": None,
-                "structured_output_enabled": True,
-                "structured_output": structured_output,
-            },
-        }

api/templates/invite_member_mail_template_en-US.html

@@ -83,30 +83,7 @@
       <p class="content1">Dear {{ to }},</p>
       <p class="content2">{{ inviter_name }} is pleased to invite you to join our workspace on Dify, a platform specifically designed for LLM application development. On Dify, you can explore, create, and collaborate to build and operate AI applications.</p>
       <p class="content2">Click the button below to log in to Dify and join the workspace.</p>
-      <div style="text-align: center; margin-bottom: 32px;">
-        <a href="{{ url }}"
-           style="background-color:#2563eb;
-                  color:#ffffff !important;
-                  text-decoration:none;
-                  display:inline-block;
-                  font-weight:600;
-                  border-radius:4px;
-                  font-size:14px;
-                  line-height:18px;
-                  font-family: Helvetica, Arial, sans-serif;
-                  text-align:center;
-                  border-top: 10px solid #2563eb;
-                  border-bottom: 10px solid #2563eb;
-                  border-left: 20px solid #2563eb;
-                  border-right: 20px solid #2563eb;
-                  ">Login Here</a>
-        <p style="font-size: 12px; color: #666666; margin-top: 20px; margin-bottom: 0;">
-          If the button doesn't work, copy and paste this link into your browser:<br>
-          <a href="{{ url }}" style="color: #2563eb; text-decoration: underline; word-break: break-all;">
-            {{ url }}
-          </a>
-        </p>
-      </div>
+      <p style="text-align: center; margin: 0; margin-bottom: 32px;"><a style="color: #fff; text-decoration: none" class="button" href="{{ url }}">Login Here</a></p>
       <p class="content2">Best regards,</p>
       <p class="content2">Dify Team</p>
     </div>

api/templates/invite_member_mail_template_zh-CN.html

@@ -83,30 +83,7 @@
       <p class="content1">尊敬的 {{ to }}，</p>
       <p class="content2">{{ inviter_name }} 现邀请您加入我们在 Dify 的工作区，这是一个专为 LLM 应用开发而设计的平台。在 Dify 上，您可以探索、创造和合作，构建和运营 AI 应用。</p>
       <p class="content2">点击下方按钮即可登录 Dify 并且加入空间。</p>
-      <div style="text-align: center; margin-bottom: 32px;">
-        <a href="{{ url }}"
-           style="background-color:#2563eb;
-                  color:#ffffff !important;
-                  text-decoration:none;
-                  display:inline-block;
-                  font-weight:600;
-                  border-radius:4px;
-                  font-size:14px;
-                  line-height:18px;
-                  font-family: Helvetica, Arial, sans-serif;
-                  text-align:center;
-                  border-top: 10px solid #2563eb;
-                  border-bottom: 10px solid #2563eb;
-                  border-left: 20px solid #2563eb;
-                  border-right: 20px solid #2563eb;
-                  ">在此登录</a>
-        <p style="font-size: 12px; color: #666666; margin-top: 20px; margin-bottom: 0;">
-          如果按钮无法使用，请将以下链接复制到浏览器打开：<br>
-          <a href="{{ url }}" style="color: #2563eb; text-decoration: underline; word-break: break-all;">
-            {{ url }}
-          </a>
-        </p>
-      </div>
+      <p style="text-align: center; margin: 0; margin-bottom: 32px;"><a style="color: #fff; text-decoration: none" class="button" href="{{ url }}">在此登录</a></p>
       <p class="content2">此致，</p>
       <p class="content2">Dify 团队</p>
     </div>

api/templates/register_email_when_account_exist_template_en-US.html

@@ -115,30 +115,7 @@
             We noticed you tried to sign up, but this email is already registered with an existing account.
 
             Please log in here: </p>
-        <div style="text-align: center; margin-bottom: 20px;">
-          <a href="{{ login_url }}"
-             style="background-color:#2563eb;
-                    color:#ffffff !important;
-                    text-decoration:none;
-                    display:inline-block;
-                    font-weight:600;
-                    border-radius:4px;
-                    font-size:14px;
-                    line-height:18px;
-                    font-family: Helvetica, Arial, sans-serif;
-                    text-align:center;
-                    border-top: 10px solid #2563eb;
-                    border-bottom: 10px solid #2563eb;
-                    border-left: 20px solid #2563eb;
-                    border-right: 20px solid #2563eb;
-                    ">Log In</a>
-          <p style="font-size: 12px; color: #666666; margin-top: 20px; margin-bottom: 0;">
-              If the button doesn't work, copy and paste this link into your browser:<br>
-              <a href="{{ login_url }}" style="color: #2563eb; text-decoration: underline; word-break: break-all;">
-                  {{ login_url }}
-              </a>
-          </p>
-        </div>
+        <a href="{{ login_url }}" class="button">Log In</a>
         <p class="description">
             If you forgot your password, you can reset it here: <a href="{{ reset_password_url }}"
                 class="reset-btn">Reset Password</a>

api/templates/register_email_when_account_exist_template_zh-CN.html

@@ -115,30 +115,7 @@
             我们注意到您尝试注册，但此电子邮件已注册。
 
             请在此登录: </p>
-        <div style="text-align: center; margin-bottom: 20px;">
-          <a href="{{ login_url }}"
-             style="background-color:#2563eb;
-                    color:#ffffff !important;
-                    text-decoration:none;
-                    display:inline-block;
-                    font-weight:600;
-                    border-radius:4px;
-                    font-size:14px;
-                    line-height:18px;
-                    font-family: Helvetica, Arial, sans-serif;
-                    text-align:center;
-                    border-top: 10px solid #2563eb;
-                    border-bottom: 10px solid #2563eb;
-                    border-left: 20px solid #2563eb;
-                    border-right: 20px solid #2563eb;
-                    ">登录</a>
-          <p style="font-size: 12px; color: #666666; margin-top: 20px; margin-bottom: 0;">
-              如果按钮无法使用，请将以下链接复制到浏览器打开：<br>
-              <a href="{{ login_url }}" style="color: #2563eb; text-decoration: underline; word-break: break-all;">
-                  {{ login_url }}
-              </a>
-          </p>
-        </div>
+        <a href="{{ login_url }}" class="button">登录</a>
         <p class="description">
             如果您忘记了密码，可以在此重置: <a href="{{ reset_password_url }}" class="reset-btn">重置密码</a>
         </p>

api/templates/without-brand/invite_member_mail_template_en-US.html

@@ -92,34 +92,12 @@
         platform specifically designed for LLM application development. On {{application_title}}, you can explore,
         create, and collaborate to build and operate AI applications.</p>
       <p class="content2">Click the button below to log in to {{application_title}} and join the workspace.</p>
-      <div style="text-align: center; margin-bottom: 32px;">
-        <a href="{{ url }}"
-           style="background-color:#2563eb;
-                  color:#ffffff !important;
-                  text-decoration:none;
-                  display:inline-block;
-                  font-weight:600;
-                  border-radius:4px;
-                  font-size:14px;
-                  line-height:18px;
-                  font-family: Helvetica, Arial, sans-serif;
-                  text-align:center;
-                  border-top: 10px solid #2563eb;
-                  border-bottom: 10px solid #2563eb;
-                  border-left: 20px solid #2563eb;
-                  border-right: 20px solid #2563eb;
-                  ">Login Here</a>
-        <p style="font-size: 12px; color: #666666; margin-top: 20px; margin-bottom: 0;">
-          If the button doesn't work, copy and paste this link into your browser:<br>
-          <a href="{{ url }}" style="color: #2563eb; text-decoration: underline; word-break: break-all;">
-            {{ url }}
-          </a>
-        </p>
-      </div>
+      <p style="text-align: center; margin: 0; margin-bottom: 32px;"><a style="color: #fff; text-decoration: none"
+          class="button" href="{{ url }}">Login Here</a></p>
       <p class="content2">Best regards,</p>
       <p class="content2">{{application_title}} Team</p>
     </div>
   </div>
 </body>
 
-</html>
+</html>

api/templates/without-brand/invite_member_mail_template_zh-CN.html

@@ -81,30 +81,7 @@
       <p class="content1">尊敬的 {{ to }}，</p>
       <p class="content2">{{ inviter_name }} 现邀请您加入我们在 {{application_title}} 的工作区，这是一个专为 LLM 应用开发而设计的平台。在 {{application_title}} 上，您可以探索、创造和合作，构建和运营 AI 应用。</p>
       <p class="content2">点击下方按钮即可登录 {{application_title}} 并且加入空间。</p>
-      <div style="text-align: center; margin-bottom: 32px;">
-        <a href="{{ url }}"
-           style="background-color:#2563eb;
-                  color:#ffffff !important;
-                  text-decoration:none;
-                  display:inline-block;
-                  font-weight:600;
-                  border-radius:4px;
-                  font-size:14px;
-                  line-height:18px;
-                  font-family: Helvetica, Arial, sans-serif;
-                  text-align:center;
-                  border-top: 10px solid #2563eb;
-                  border-bottom: 10px solid #2563eb;
-                  border-left: 20px solid #2563eb;
-                  border-right: 20px solid #2563eb;
-                  ">在此登录</a>
-        <p style="font-size: 12px; color: #666666; margin-top: 20px; margin-bottom: 0;">
-          如果按钮无法使用，请将以下链接复制到浏览器打开：<br>
-          <a href="{{ url }}" style="color: #2563eb; text-decoration: underline; word-break: break-all;">
-            {{ url }}
-          </a>
-        </p>
-      </div>
+      <p style="text-align: center; margin: 0; margin-bottom: 32px;"><a style="color: #fff; text-decoration: none" class="button" href="{{ url }}">在此登录</a></p>
       <p class="content2">此致，</p>
       <p class="content2">{{application_title}} 团队</p>
     </div>

api/templates/without-brand/register_email_when_account_exist_template_en-US.html

@@ -111,30 +111,7 @@
             We noticed you tried to sign up, but this email is already registered with an existing account.
 
             Please log in here: </p>
-        <div style="text-align: center; margin-bottom: 20px;">
-          <a href="{{ login_url }}"
-             style="background-color:#2563eb;
-                    color:#ffffff !important;
-                    text-decoration:none;
-                    display:inline-block;
-                    font-weight:600;
-                    border-radius:4px;
-                    font-size:14px;
-                    line-height:18px;
-                    font-family: Helvetica, Arial, sans-serif;
-                    text-align:center;
-                    border-top: 10px solid #2563eb;
-                    border-bottom: 10px solid #2563eb;
-                    border-left: 20px solid #2563eb;
-                    border-right: 20px solid #2563eb;
-                    ">Log In</a>
-          <p style="font-size: 12px; color: #666666; margin-top: 20px; margin-bottom: 0;">
-              If the button doesn't work, copy and paste this link into your browser:<br>
-              <a href="{{ login_url }}" style="color: #2563eb; text-decoration: underline; word-break: break-all;">
-                  {{ login_url }}
-              </a>
-          </p>
-        </div>
+        <a href="{{ login_url }}" class="button">Log In</a>
         <p class="description">
             If you forgot your password, you can reset it here: <a href="{{ reset_password_url }}"
                 class="reset-btn">Reset Password</a>

api/templates/without-brand/register_email_when_account_exist_template_zh-CN.html

@@ -111,30 +111,7 @@
             我们注意到您尝试注册，但此电子邮件已注册。
 
             请在此登录: </p>
-        <div style="text-align: center; margin-bottom: 20px;">
-          <a href="{{ login_url }}"
-             style="background-color:#2563eb;
-                    color:#ffffff !important;
-                    text-decoration:none;
-                    display:inline-block;
-                    font-weight:600;
-                    border-radius:4px;
-                    font-size:14px;
-                    line-height:18px;
-                    font-family: Helvetica, Arial, sans-serif;
-                    text-align:center;
-                    border-top: 10px solid #2563eb;
-                    border-bottom: 10px solid #2563eb;
-                    border-left: 20px solid #2563eb;
-                    border-right: 20px solid #2563eb;
-                    ">登录</a>
-          <p style="font-size: 12px; color: #666666; margin-top: 20px; margin-bottom: 0;">
-              如果按钮无法使用，请将以下链接复制到浏览器打开：<br>
-              <a href="{{ login_url }}" style="color: #2563eb; text-decoration: underline; word-break: break-all;">
-                  {{ login_url }}
-              </a>
-          </p>
-        </div>
+        <a href="{{ login_url }}" class="button">登录</a>
         <p class="description">
             如果您忘记了密码，可以在此重置: <a href="{{ reset_password_url }}" class="reset-btn">重置密码</a>
         </p>

api/tests/fixtures/file output schema.yml

@@ -1,181 +0,0 @@
-app:
-  description: ''
-  icon: 🤖
-  icon_background: '#FFEAD5'
-  mode: advanced-chat
-  name: file output schema
-  use_icon_as_answer_icon: false
-dependencies:
-- current_identifier: null
-  type: marketplace
-  value:
-    marketplace_plugin_unique_identifier: langgenius/openai:0.2.3@5a7f82fa86e28332ad51941d0b491c1e8a38ead539656442f7bf4c6129cd15fa
-    version: null
-kind: app
-version: 0.5.0
-workflow:
-  conversation_variables: []
-  environment_variables: []
-  features:
-    file_upload:
-      allowed_file_extensions:
-      - .JPG
-      - .JPEG
-      - .PNG
-      - .GIF
-      - .WEBP
-      - .SVG
-      allowed_file_types:
-      - image
-      allowed_file_upload_methods:
-      - remote_url
-      - local_file
-      enabled: true
-      fileUploadConfig:
-        attachment_image_file_size_limit: 2
-        audio_file_size_limit: 50
-        batch_count_limit: 5
-        file_size_limit: 15
-        file_upload_limit: 10
-        image_file_batch_limit: 10
-        image_file_size_limit: 10
-        single_chunk_attachment_limit: 10
-        video_file_size_limit: 100
-        workflow_file_upload_limit: 10
-      number_limits: 3
-    opening_statement: ''
-    retriever_resource:
-      enabled: true
-    sensitive_word_avoidance:
-      enabled: false
-    speech_to_text:
-      enabled: false
-    suggested_questions: []
-    suggested_questions_after_answer:
-      enabled: false
-    text_to_speech:
-      enabled: false
-      language: ''
-      voice: ''
-  graph:
-    edges:
-    - data:
-        sourceType: start
-        targetType: llm
-      id: 1768292241666-llm
-      source: '1768292241666'
-      sourceHandle: source
-      target: llm
-      targetHandle: target
-      type: custom
-    - data:
-        sourceType: llm
-        targetType: answer
-      id: llm-answer
-      source: llm
-      sourceHandle: source
-      target: answer
-      targetHandle: target
-      type: custom
-    nodes:
-    - data:
-        selected: false
-        title: User Input
-        type: start
-        variables: []
-      height: 73
-      id: '1768292241666'
-      position:
-        x: 80
-        y: 282
-      positionAbsolute:
-        x: 80
-        y: 282
-      sourcePosition: right
-      targetPosition: left
-      type: custom
-      width: 242
-    - data:
-        context:
-          enabled: false
-          variable_selector: []
-        memory:
-          query_prompt_template: '{{#sys.query#}}
-
-
-            {{#sys.files#}}'
-          role_prefix:
-            assistant: ''
-            user: ''
-          window:
-            enabled: false
-            size: 10
-        model:
-          completion_params:
-            temperature: 0.7
-          mode: chat
-          name: gpt-4o-mini
-          provider: langgenius/openai/openai
-        prompt_template:
-        - id: e30d75d7-7d85-49ec-be3c-3baf7f6d3c5a
-          role: system
-          text: ''
-        selected: false
-        structured_output:
-          schema:
-            additionalProperties: false
-            properties:
-              image:
-                description: File ID (UUID) of the selected image
-                format: dify-file-ref
-                type: string
-            required:
-            - image
-            type: object
-        structured_output_enabled: true
-        title: LLM
-        type: llm
-        vision:
-          configs:
-            detail: high
-            variable_selector:
-            - sys
-            - files
-          enabled: true
-      height: 88
-      id: llm
-      position:
-        x: 380
-        y: 282
-      positionAbsolute:
-        x: 380
-        y: 282
-      selected: false
-      sourcePosition: right
-      targetPosition: left
-      type: custom
-      width: 242
-    - data:
-        answer: '{{#llm.structured_output.image#}}'
-        selected: false
-        title: Answer
-        type: answer
-        variables: []
-      height: 103
-      id: answer
-      position:
-        x: 680
-        y: 282
-      positionAbsolute:
-        x: 680
-        y: 282
-      selected: true
-      sourcePosition: right
-      targetPosition: left
-      type: custom
-      width: 242
-    viewport:
-      x: -149
-      y: 97.5
-      zoom: 1
-  rag_pipeline_variables: []

api/tests/fixtures/pav-test-extraction.yml

@@ -1,307 +0,0 @@
-app:
-  description: Test for variable extraction feature
-  icon: 🤖
-  icon_background: '#FFEAD5'
-  mode: advanced-chat
-  name: pav-test-extraction
-  use_icon_as_answer_icon: false
-dependencies:
-- current_identifier: null
-  type: marketplace
-  value:
-    marketplace_plugin_unique_identifier: langgenius/google:0.0.8@3efcf55ffeef9d0f77715e0afb23534952ae0cb385c051d0637e86d71199d1a6
-    version: null
-- current_identifier: null
-  type: marketplace
-  value:
-    marketplace_plugin_unique_identifier: langgenius/openai:0.2.3@5a7f82fa86e28332ad51941d0b491c1e8a38ead539656442f7bf4c6129cd15fa
-    version: null
-- current_identifier: null
-  type: marketplace
-  value:
-    marketplace_plugin_unique_identifier: langgenius/tongyi:0.1.16@d8bffbe45418f0c117fb3393e5e40e61faee98f9a2183f062e5a280e74b15d21
-    version: null
-kind: app
-version: 0.5.0
-workflow:
-  conversation_variables: []
-  environment_variables: []
-  features:
-    file_upload:
-      allowed_file_extensions:
-      - .JPG
-      - .JPEG
-      - .PNG
-      - .GIF
-      - .WEBP
-      - .SVG
-      allowed_file_types:
-      - image
-      allowed_file_upload_methods:
-      - local_file
-      - remote_url
-      enabled: false
-      image:
-        enabled: false
-        number_limits: 3
-        transfer_methods:
-        - local_file
-        - remote_url
-      number_limits: 3
-    opening_statement: 你好！我是一个搜索助手，请告诉我你想搜索什么内容。
-    retriever_resource:
-      enabled: true
-    sensitive_word_avoidance:
-      enabled: false
-    speech_to_text:
-      enabled: false
-    suggested_questions: []
-    suggested_questions_after_answer:
-      enabled: false
-    text_to_speech:
-      enabled: false
-      language: ''
-      voice: ''
-  graph:
-    edges:
-    - data:
-        sourceType: start
-        targetType: llm
-      id: 1767773675796-llm
-      source: '1767773675796'
-      sourceHandle: source
-      target: llm
-      targetHandle: target
-      type: custom
-    - data:
-        isInIteration: false
-        isInLoop: false
-        sourceType: llm
-        targetType: tool
-      id: llm-source-1767773709491-target
-      source: llm
-      sourceHandle: source
-      target: '1767773709491'
-      targetHandle: target
-      type: custom
-      zIndex: 0
-    - data:
-        isInIteration: false
-        isInLoop: false
-        sourceType: tool
-        targetType: answer
-      id: tool-source-answer-target
-      source: '1767773709491'
-      sourceHandle: source
-      target: answer
-      targetHandle: target
-      type: custom
-      zIndex: 0
-    nodes:
-    - data:
-        selected: false
-        title: User Input
-        type: start
-        variables: []
-      height: 73
-      id: '1767773675796'
-      position:
-        x: 80
-        y: 282
-      positionAbsolute:
-        x: 80
-        y: 282
-      sourcePosition: right
-      targetPosition: left
-      type: custom
-      width: 242
-    - data:
-        context:
-          enabled: false
-          variable_selector: []
-        memory:
-          mode: node
-          query_prompt_template: '{{#sys.query#}}'
-          role_prefix:
-            assistant: ''
-            user: ''
-          window:
-            enabled: true
-            size: 10
-        model:
-          completion_params:
-            temperature: 0.7
-          mode: chat
-          name: qwen-max
-          provider: langgenius/tongyi/tongyi
-        prompt_template:
-        - id: 11d06d15-914a-4915-a5b1-0e35ab4fba51
-          role: system
-          text: '你是一个智能搜索助手。用户会告诉你他们想搜索的内容。
-
-            请与用户进行对话，了解他们的搜索需求。
-
-            当用户明确表达了想要搜索的内容后，你可以回复"好的，我来帮你搜索"。
-
-            '
-        selected: false
-        title: LLM
-        type: llm
-        vision:
-          enabled: false
-      height: 88
-      id: llm
-      position:
-        x: 380
-        y: 282
-      positionAbsolute:
-        x: 380
-        y: 282
-      selected: false
-      sourcePosition: right
-      targetPosition: left
-      type: custom
-      width: 242
-    - data:
-        is_team_authorization: true
-        paramSchemas:
-        - auto_generate: null
-          default: null
-          form: llm
-          human_description:
-            en_US: used for searching
-            ja_JP: used for searching
-            pt_BR: used for searching
-            zh_Hans: 用于搜索网页内容
-          label:
-            en_US: Query string
-            ja_JP: Query string
-            pt_BR: Query string
-            zh_Hans: 查询语句
-          llm_description: key words for searching
-          max: null
-          min: null
-          name: query
-          options: []
-          placeholder: null
-          precision: null
-          required: true
-          scope: null
-          template: null
-          type: string
-        params:
-          query: ''
-        plugin_id: langgenius/google
-        plugin_unique_identifier: langgenius/google:0.0.8@3efcf55ffeef9d0f77715e0afb23534952ae0cb385c051d0637e86d71199d1a6
-        provider_icon: http://localhost:5001/console/api/workspaces/current/plugin/icon?tenant_id=7217e801-f6f5-49ec-8103-d7de97a4b98f&filename=1c5871163478957bac64c3fe33d72d003f767497d921c74b742aad27a8344a74.svg
-        provider_id: langgenius/google/google
-        provider_name: langgenius/google/google
-        provider_type: builtin
-        selected: false
-        title: GoogleSearch
-        tool_configurations: {}
-        tool_description: A tool for performing a Google SERP search and extracting
-          snippets and webpages.Input should be a search query.
-        tool_label: GoogleSearch
-        tool_name: google_search
-        tool_node_version: '2'
-        tool_parameters:
-          query:
-            type: mention
-            value: '{{@llm.context@}}请从对话历史中提取用户想要搜索的关键词，只返回关键词本身'
-            mention_config:
-              extractor_node_id: 1767773709491_ext_query
-              output_selector:
-              - structured_output
-              - query
-              null_strategy: use_default
-              default_value: ''
-        type: tool
-      height: 52
-      id: '1767773709491'
-      position:
-        x: 682
-        y: 282
-      positionAbsolute:
-        x: 682
-        y: 282
-      selected: false
-      sourcePosition: right
-      targetPosition: left
-      type: custom
-      width: 242
-    - data:
-        context:
-          enabled: false
-          variable_selector: []
-        model:
-          completion_params:
-            temperature: 0.7
-          mode: chat
-          name: gpt-4o-mini
-          provider: langgenius/openai/openai
-        parent_node_id: '1767773709491'
-        prompt_template:
-        - $context:
-          - llm
-          - context
-          id: 75d58e22-dc59-40c8-ba6f-aeb28f4f305a
-        - id: 18ba6710-77f5-47f4-b144-9191833bb547
-          role: user
-          text: 请从对话历史中提取用户想要搜索的关键词，只返回关键词本身，不要返回其他内容
-        selected: false
-        structured_output:
-          schema:
-            additionalProperties: false
-            properties:
-              query:
-                description: 搜索的关键词
-                type: string
-            required:
-            - query
-            type: object
-        structured_output_enabled: true
-        title: 提取搜索关键词
-        type: llm
-        vision:
-          enabled: false
-      height: 88
-      id: 1767773709491_ext_query
-      position:
-        x: 531
-        y: 382
-      positionAbsolute:
-        x: 531
-        y: 382
-      selected: true
-      sourcePosition: right
-      targetPosition: left
-      type: custom
-      width: 242
-    - data:
-        answer: '搜索结果：
-
-          {{#1767773709491.text#}}
-
-          '
-        selected: false
-        title: Answer
-        type: answer
-      height: 103
-      id: answer
-      position:
-        x: 984
-        y: 282
-      positionAbsolute:
-        x: 984
-        y: 282
-      selected: false
-      sourcePosition: right
-      targetPosition: left
-      type: custom
-      width: 242
-    viewport:
-      x: -151
-      y: 123
-      zoom: 1
-  rag_pipeline_variables: []

api/tests/unit_tests/core/file/test_file_manager.py

@@ -1,182 +0,0 @@
-"""Tests for file_manager module, specifically multimodal content handling."""
-
-from unittest.mock import patch
-
-from core.file import File, FileTransferMethod, FileType
-from core.file.file_manager import (
-    _encode_file_ref,
-    restore_multimodal_content,
-    to_prompt_message_content,
-)
-from core.model_runtime.entities.message_entities import ImagePromptMessageContent
-
-
-class TestEncodeFileRef:
-    """Tests for _encode_file_ref function."""
-
-    def test_encodes_local_file(self):
-        """Local file should be encoded as 'local:id'."""
-        file = File(
-            tenant_id="t",
-            type=FileType.IMAGE,
-            transfer_method=FileTransferMethod.LOCAL_FILE,
-            related_id="abc123",
-            storage_key="key",
-        )
-        assert _encode_file_ref(file) == "local:abc123"
-
-    def test_encodes_tool_file(self):
-        """Tool file should be encoded as 'tool:id'."""
-        file = File(
-            tenant_id="t",
-            type=FileType.IMAGE,
-            transfer_method=FileTransferMethod.TOOL_FILE,
-            related_id="xyz789",
-            storage_key="key",
-        )
-        assert _encode_file_ref(file) == "tool:xyz789"
-
-    def test_encodes_remote_url(self):
-        """Remote URL should be encoded as 'remote:url'."""
-        file = File(
-            tenant_id="t",
-            type=FileType.IMAGE,
-            transfer_method=FileTransferMethod.REMOTE_URL,
-            remote_url="https://example.com/image.png",
-            storage_key="",
-        )
-        assert _encode_file_ref(file) == "remote:https://example.com/image.png"
-
-
-class TestToPromptMessageContent:
-    """Tests for to_prompt_message_content function with file_ref field."""
-
-    @patch("core.file.file_manager.dify_config")
-    @patch("core.file.file_manager._get_encoded_string")
-    def test_includes_file_ref(self, mock_get_encoded, mock_config):
-        """Generated content should include file_ref field."""
-        mock_config.MULTIMODAL_SEND_FORMAT = "base64"
-        mock_get_encoded.return_value = "base64data"
-
-        file = File(
-            id="test-message-file-id",
-            tenant_id="test-tenant",
-            type=FileType.IMAGE,
-            transfer_method=FileTransferMethod.LOCAL_FILE,
-            related_id="test-related-id",
-            remote_url=None,
-            extension=".png",
-            mime_type="image/png",
-            filename="test.png",
-            storage_key="test-key",
-        )
-
-        result = to_prompt_message_content(file)
-
-        assert isinstance(result, ImagePromptMessageContent)
-        assert result.file_ref == "local:test-related-id"
-        assert result.base64_data == "base64data"
-
-
-class TestRestoreMultimodalContent:
-    """Tests for restore_multimodal_content function."""
-
-    def test_returns_content_unchanged_when_no_file_ref(self):
-        """Content without file_ref should pass through unchanged."""
-        content = ImagePromptMessageContent(
-            format="png",
-            base64_data="existing-data",
-            mime_type="image/png",
-            file_ref=None,
-        )
-
-        result = restore_multimodal_content(content)
-
-        assert result.base64_data == "existing-data"
-
-    def test_returns_content_unchanged_when_already_has_data(self):
-        """Content that already has base64_data should not be reloaded."""
-        content = ImagePromptMessageContent(
-            format="png",
-            base64_data="existing-data",
-            mime_type="image/png",
-            file_ref="local:file-id",
-        )
-
-        result = restore_multimodal_content(content)
-
-        assert result.base64_data == "existing-data"
-
-    def test_returns_content_unchanged_when_already_has_url(self):
-        """Content that already has url should not be reloaded."""
-        content = ImagePromptMessageContent(
-            format="png",
-            url="https://example.com/image.png",
-            mime_type="image/png",
-            file_ref="local:file-id",
-        )
-
-        result = restore_multimodal_content(content)
-
-        assert result.url == "https://example.com/image.png"
-
-    @patch("core.file.file_manager.dify_config")
-    @patch("core.file.file_manager._build_file_from_ref")
-    @patch("core.file.file_manager._to_url")
-    def test_restores_url_from_file_ref(self, mock_to_url, mock_build_file, mock_config):
-        """Content should be restored from file_ref when url is empty (url mode)."""
-        mock_config.MULTIMODAL_SEND_FORMAT = "url"
-        mock_build_file.return_value = "mock_file"
-        mock_to_url.return_value = "https://restored-url.com/image.png"
-
-        content = ImagePromptMessageContent(
-            format="png",
-            base64_data="",
-            url="",
-            mime_type="image/png",
-            filename="test.png",
-            file_ref="local:test-file-id",
-        )
-
-        result = restore_multimodal_content(content)
-
-        assert result.url == "https://restored-url.com/image.png"
-        mock_build_file.assert_called_once()
-
-    @patch("core.file.file_manager.dify_config")
-    @patch("core.file.file_manager._build_file_from_ref")
-    @patch("core.file.file_manager._get_encoded_string")
-    def test_restores_base64_from_file_ref(self, mock_get_encoded, mock_build_file, mock_config):
-        """Content should be restored as base64 when in base64 mode."""
-        mock_config.MULTIMODAL_SEND_FORMAT = "base64"
-        mock_build_file.return_value = "mock_file"
-        mock_get_encoded.return_value = "restored-base64-data"
-
-        content = ImagePromptMessageContent(
-            format="png",
-            base64_data="",
-            url="",
-            mime_type="image/png",
-            filename="test.png",
-            file_ref="local:test-file-id",
-        )
-
-        result = restore_multimodal_content(content)
-
-        assert result.base64_data == "restored-base64-data"
-        mock_build_file.assert_called_once()
-
-    def test_handles_invalid_file_ref_gracefully(self):
-        """Invalid file_ref format should be handled gracefully."""
-        content = ImagePromptMessageContent(
-            format="png",
-            base64_data="",
-            url="",
-            mime_type="image/png",
-            file_ref="invalid_format_no_colon",
-        )
-
-        result = restore_multimodal_content(content)
-
-        # Should return unchanged on error
-        assert result.base64_data == ""

api/tests/unit_tests/core/llm_generator/output_parser/test_file_ref.py

@@ -1,269 +0,0 @@
-"""
-Unit tests for file reference detection and conversion.
-"""
-
-import uuid
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from core.file import File, FileTransferMethod, FileType
-from core.llm_generator.output_parser.file_ref import (
-    FILE_REF_FORMAT,
-    convert_file_refs_in_output,
-    detect_file_ref_fields,
-    is_file_ref_property,
-)
-from core.variables.segments import ArrayFileSegment, FileSegment
-
-
-class TestIsFileRefProperty:
-    """Tests for is_file_ref_property function."""
-
-    def test_valid_file_ref(self):
-        schema = {"type": "string", "format": FILE_REF_FORMAT}
-        assert is_file_ref_property(schema) is True
-
-    def test_invalid_type(self):
-        schema = {"type": "number", "format": FILE_REF_FORMAT}
-        assert is_file_ref_property(schema) is False
-
-    def test_missing_format(self):
-        schema = {"type": "string"}
-        assert is_file_ref_property(schema) is False
-
-    def test_wrong_format(self):
-        schema = {"type": "string", "format": "uuid"}
-        assert is_file_ref_property(schema) is False
-
-
-class TestDetectFileRefFields:
-    """Tests for detect_file_ref_fields function."""
-
-    def test_simple_file_ref(self):
-        schema = {
-            "type": "object",
-            "properties": {
-                "image": {"type": "string", "format": FILE_REF_FORMAT},
-            },
-        }
-        paths = detect_file_ref_fields(schema)
-        assert paths == ["image"]
-
-    def test_multiple_file_refs(self):
-        schema = {
-            "type": "object",
-            "properties": {
-                "image": {"type": "string", "format": FILE_REF_FORMAT},
-                "document": {"type": "string", "format": FILE_REF_FORMAT},
-                "name": {"type": "string"},
-            },
-        }
-        paths = detect_file_ref_fields(schema)
-        assert set(paths) == {"image", "document"}
-
-    def test_array_of_file_refs(self):
-        schema = {
-            "type": "object",
-            "properties": {
-                "files": {
-                    "type": "array",
-                    "items": {"type": "string", "format": FILE_REF_FORMAT},
-                },
-            },
-        }
-        paths = detect_file_ref_fields(schema)
-        assert paths == ["files[*]"]
-
-    def test_nested_file_ref(self):
-        schema = {
-            "type": "object",
-            "properties": {
-                "data": {
-                    "type": "object",
-                    "properties": {
-                        "image": {"type": "string", "format": FILE_REF_FORMAT},
-                    },
-                },
-            },
-        }
-        paths = detect_file_ref_fields(schema)
-        assert paths == ["data.image"]
-
-    def test_no_file_refs(self):
-        schema = {
-            "type": "object",
-            "properties": {
-                "name": {"type": "string"},
-                "count": {"type": "number"},
-            },
-        }
-        paths = detect_file_ref_fields(schema)
-        assert paths == []
-
-    def test_empty_schema(self):
-        schema = {}
-        paths = detect_file_ref_fields(schema)
-        assert paths == []
-
-    def test_mixed_schema(self):
-        schema = {
-            "type": "object",
-            "properties": {
-                "query": {"type": "string"},
-                "image": {"type": "string", "format": FILE_REF_FORMAT},
-                "documents": {
-                    "type": "array",
-                    "items": {"type": "string", "format": FILE_REF_FORMAT},
-                },
-            },
-        }
-        paths = detect_file_ref_fields(schema)
-        assert set(paths) == {"image", "documents[*]"}
-
-
-class TestConvertFileRefsInOutput:
-    """Tests for convert_file_refs_in_output function."""
-
-    @pytest.fixture
-    def mock_file(self):
-        """Create a mock File object with all required attributes."""
-        file = MagicMock(spec=File)
-        file.type = FileType.IMAGE
-        file.transfer_method = FileTransferMethod.TOOL_FILE
-        file.related_id = "test-related-id"
-        file.remote_url = None
-        file.tenant_id = "tenant_123"
-        file.id = None
-        file.filename = "test.png"
-        file.extension = ".png"
-        file.mime_type = "image/png"
-        file.size = 1024
-        file.dify_model_identity = "__dify__file__"
-        return file
-
-    @pytest.fixture
-    def mock_build_from_mapping(self, mock_file):
-        """Mock the build_from_mapping function."""
-        with patch("core.llm_generator.output_parser.file_ref.build_from_mapping") as mock:
-            mock.return_value = mock_file
-            yield mock
-
-    def test_convert_simple_file_ref(self, mock_build_from_mapping, mock_file):
-        file_id = str(uuid.uuid4())
-        output = {"image": file_id}
-        schema = {
-            "type": "object",
-            "properties": {
-                "image": {"type": "string", "format": FILE_REF_FORMAT},
-            },
-        }
-
-        result = convert_file_refs_in_output(output, schema, "tenant_123")
-
-        # Result should be wrapped in FileSegment
-        assert isinstance(result["image"], FileSegment)
-        assert result["image"].value == mock_file
-        mock_build_from_mapping.assert_called_once_with(
-            mapping={"transfer_method": "tool_file", "tool_file_id": file_id},
-            tenant_id="tenant_123",
-        )
-
-    def test_convert_array_of_file_refs(self, mock_build_from_mapping, mock_file):
-        file_id1 = str(uuid.uuid4())
-        file_id2 = str(uuid.uuid4())
-        output = {"files": [file_id1, file_id2]}
-        schema = {
-            "type": "object",
-            "properties": {
-                "files": {
-                    "type": "array",
-                    "items": {"type": "string", "format": FILE_REF_FORMAT},
-                },
-            },
-        }
-
-        result = convert_file_refs_in_output(output, schema, "tenant_123")
-
-        # Result should be wrapped in ArrayFileSegment
-        assert isinstance(result["files"], ArrayFileSegment)
-        assert list(result["files"].value) == [mock_file, mock_file]
-        assert mock_build_from_mapping.call_count == 2
-
-    def test_no_conversion_without_file_refs(self):
-        output = {"name": "test", "count": 5}
-        schema = {
-            "type": "object",
-            "properties": {
-                "name": {"type": "string"},
-                "count": {"type": "number"},
-            },
-        }
-
-        result = convert_file_refs_in_output(output, schema, "tenant_123")
-
-        assert result == {"name": "test", "count": 5}
-
-    def test_invalid_uuid_returns_none(self):
-        output = {"image": "not-a-valid-uuid"}
-        schema = {
-            "type": "object",
-            "properties": {
-                "image": {"type": "string", "format": FILE_REF_FORMAT},
-            },
-        }
-
-        result = convert_file_refs_in_output(output, schema, "tenant_123")
-
-        assert result["image"] is None
-
-    def test_file_not_found_returns_none(self):
-        file_id = str(uuid.uuid4())
-        output = {"image": file_id}
-        schema = {
-            "type": "object",
-            "properties": {
-                "image": {"type": "string", "format": FILE_REF_FORMAT},
-            },
-        }
-
-        with patch("core.llm_generator.output_parser.file_ref.build_from_mapping") as mock:
-            mock.side_effect = ValueError("File not found")
-            result = convert_file_refs_in_output(output, schema, "tenant_123")
-
-        assert result["image"] is None
-
-    def test_preserves_non_file_fields(self, mock_build_from_mapping, mock_file):
-        file_id = str(uuid.uuid4())
-        output = {"query": "search term", "image": file_id, "count": 10}
-        schema = {
-            "type": "object",
-            "properties": {
-                "query": {"type": "string"},
-                "image": {"type": "string", "format": FILE_REF_FORMAT},
-                "count": {"type": "number"},
-            },
-        }
-
-        result = convert_file_refs_in_output(output, schema, "tenant_123")
-
-        assert result["query"] == "search term"
-        assert isinstance(result["image"], FileSegment)
-        assert result["image"].value == mock_file
-        assert result["count"] == 10
-
-    def test_does_not_modify_original_output(self, mock_build_from_mapping, mock_file):
-        file_id = str(uuid.uuid4())
-        original = {"image": file_id}
-        output = dict(original)
-        schema = {
-            "type": "object",
-            "properties": {
-                "image": {"type": "string", "format": FILE_REF_FORMAT},
-            },
-        }
-
-        convert_file_refs_in_output(output, schema, "tenant_123")
-
-        # Original should still contain the string ID
-        assert original["image"] == file_id

api/tests/unit_tests/core/workflow/context/__init__.py

@@ -1 +0,0 @@
-"""Tests for workflow context management."""

api/tests/unit_tests/core/workflow/context/test_execution_context.py

@@ -1,258 +0,0 @@
-"""Tests for execution context module."""
-
-import contextvars
-from typing import Any
-from unittest.mock import MagicMock
-
-import pytest
-
-from core.workflow.context.execution_context import (
-    AppContext,
-    ExecutionContext,
-    ExecutionContextBuilder,
-    IExecutionContext,
-    NullAppContext,
-)
-
-
-class TestAppContext:
-    """Test AppContext abstract base class."""
-
-    def test_app_context_is_abstract(self):
-        """Test that AppContext cannot be instantiated directly."""
-        with pytest.raises(TypeError):
-            AppContext()  # type: ignore
-
-
-class TestNullAppContext:
-    """Test NullAppContext implementation."""
-
-    def test_null_app_context_get_config(self):
-        """Test get_config returns value from config dict."""
-        config = {"key1": "value1", "key2": "value2"}
-        ctx = NullAppContext(config=config)
-
-        assert ctx.get_config("key1") == "value1"
-        assert ctx.get_config("key2") == "value2"
-
-    def test_null_app_context_get_config_default(self):
-        """Test get_config returns default when key not found."""
-        ctx = NullAppContext()
-
-        assert ctx.get_config("nonexistent", "default") == "default"
-        assert ctx.get_config("nonexistent") is None
-
-    def test_null_app_context_get_extension(self):
-        """Test get_extension returns stored extension."""
-        ctx = NullAppContext()
-        extension = MagicMock()
-        ctx.set_extension("db", extension)
-
-        assert ctx.get_extension("db") == extension
-
-    def test_null_app_context_get_extension_not_found(self):
-        """Test get_extension returns None when extension not found."""
-        ctx = NullAppContext()
-
-        assert ctx.get_extension("nonexistent") is None
-
-    def test_null_app_context_enter_yield(self):
-        """Test enter method yields without any side effects."""
-        ctx = NullAppContext()
-
-        with ctx.enter():
-            # Should not raise any exception
-            pass
-
-
-class TestExecutionContext:
-    """Test ExecutionContext class."""
-
-    def test_initialization_with_all_params(self):
-        """Test ExecutionContext initialization with all parameters."""
-        app_ctx = NullAppContext()
-        context_vars = contextvars.copy_context()
-        user = MagicMock()
-
-        ctx = ExecutionContext(
-            app_context=app_ctx,
-            context_vars=context_vars,
-            user=user,
-        )
-
-        assert ctx.app_context == app_ctx
-        assert ctx.context_vars == context_vars
-        assert ctx.user == user
-
-    def test_initialization_with_minimal_params(self):
-        """Test ExecutionContext initialization with minimal parameters."""
-        ctx = ExecutionContext()
-
-        assert ctx.app_context is None
-        assert ctx.context_vars is None
-        assert ctx.user is None
-
-    def test_enter_with_context_vars(self):
-        """Test enter restores context variables."""
-        test_var = contextvars.ContextVar("test_var")
-        test_var.set("original_value")
-
-        # Copy context with the variable
-        context_vars = contextvars.copy_context()
-
-        # Change the variable
-        test_var.set("new_value")
-
-        # Create execution context and enter it
-        ctx = ExecutionContext(context_vars=context_vars)
-
-        with ctx.enter():
-            # Variable should be restored to original value
-            assert test_var.get() == "original_value"
-
-        # After exiting, variable stays at the value from within the context
-        # (this is expected Python contextvars behavior)
-        assert test_var.get() == "original_value"
-
-    def test_enter_with_app_context(self):
-        """Test enter enters app context if available."""
-        app_ctx = NullAppContext()
-        ctx = ExecutionContext(app_context=app_ctx)
-
-        # Should not raise any exception
-        with ctx.enter():
-            pass
-
-    def test_enter_without_app_context(self):
-        """Test enter works without app context."""
-        ctx = ExecutionContext(app_context=None)
-
-        # Should not raise any exception
-        with ctx.enter():
-            pass
-
-    def test_context_manager_protocol(self):
-        """Test ExecutionContext supports context manager protocol."""
-        ctx = ExecutionContext()
-
-        with ctx:
-            # Should not raise any exception
-            pass
-
-    def test_user_property(self):
-        """Test user property returns set user."""
-        user = MagicMock()
-        ctx = ExecutionContext(user=user)
-
-        assert ctx.user == user
-
-
-class TestIExecutionContextProtocol:
-    """Test IExecutionContext protocol."""
-
-    def test_execution_context_implements_protocol(self):
-        """Test that ExecutionContext implements IExecutionContext protocol."""
-        ctx = ExecutionContext()
-
-        # Should have __enter__ and __exit__ methods
-        assert hasattr(ctx, "__enter__")
-        assert hasattr(ctx, "__exit__")
-        assert hasattr(ctx, "user")
-
-    def test_protocol_compatibility(self):
-        """Test that ExecutionContext can be used where IExecutionContext is expected."""
-
-        def accept_context(context: IExecutionContext) -> Any:
-            """Function that accepts IExecutionContext protocol."""
-            # Just verify it has the required protocol attributes
-            assert hasattr(context, "__enter__")
-            assert hasattr(context, "__exit__")
-            assert hasattr(context, "user")
-            return context.user
-
-        ctx = ExecutionContext(user="test_user")
-        result = accept_context(ctx)
-
-        assert result == "test_user"
-
-    def test_protocol_with_flask_execution_context(self):
-        """Test that IExecutionContext protocol is compatible with different implementations."""
-        # Verify the protocol works with ExecutionContext
-        ctx = ExecutionContext(user="test_user")
-
-        # Should have the required protocol attributes
-        assert hasattr(ctx, "__enter__")
-        assert hasattr(ctx, "__exit__")
-        assert hasattr(ctx, "user")
-        assert ctx.user == "test_user"
-
-        # Should work as context manager
-        with ctx:
-            assert ctx.user == "test_user"
-
-
-class TestExecutionContextBuilder:
-    """Test ExecutionContextBuilder class."""
-
-    def test_builder_with_all_params(self):
-        """Test builder with all parameters set."""
-        app_ctx = NullAppContext()
-        context_vars = contextvars.copy_context()
-        user = MagicMock()
-
-        ctx = (
-            ExecutionContextBuilder().with_app_context(app_ctx).with_context_vars(context_vars).with_user(user).build()
-        )
-
-        assert ctx.app_context == app_ctx
-        assert ctx.context_vars == context_vars
-        assert ctx.user == user
-
-    def test_builder_with_partial_params(self):
-        """Test builder with only some parameters set."""
-        app_ctx = NullAppContext()
-
-        ctx = ExecutionContextBuilder().with_app_context(app_ctx).build()
-
-        assert ctx.app_context == app_ctx
-        assert ctx.context_vars is None
-        assert ctx.user is None
-
-    def test_builder_fluent_interface(self):
-        """Test builder provides fluent interface."""
-        builder = ExecutionContextBuilder()
-
-        # Each method should return the builder
-        assert isinstance(builder.with_app_context(NullAppContext()), ExecutionContextBuilder)
-        assert isinstance(builder.with_context_vars(contextvars.copy_context()), ExecutionContextBuilder)
-        assert isinstance(builder.with_user(None), ExecutionContextBuilder)
-
-
-class TestCaptureCurrentContext:
-    """Test capture_current_context function."""
-
-    def test_capture_current_context_returns_context(self):
-        """Test that capture_current_context returns a valid context."""
-        from core.workflow.context.execution_context import capture_current_context
-
-        result = capture_current_context()
-
-        # Should return an object that implements IExecutionContext
-        assert hasattr(result, "__enter__")
-        assert hasattr(result, "__exit__")
-        assert hasattr(result, "user")
-
-    def test_capture_current_context_captures_contextvars(self):
-        """Test that capture_current_context captures context variables."""
-        # Set a context variable before capturing
-        import contextvars
-
-        test_var = contextvars.ContextVar("capture_test_var")
-        test_var.set("test_value_123")
-
-        from core.workflow.context.execution_context import capture_current_context
-
-        result = capture_current_context()
-
-        # Context variables should be captured
-        assert result.context_vars is not None

api/tests/unit_tests/core/workflow/context/test_flask_app_context.py

@@ -1,316 +0,0 @@
-"""Tests for Flask app context module."""
-
-import contextvars
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-
-class TestFlaskAppContext:
-    """Test FlaskAppContext implementation."""
-
-    @pytest.fixture
-    def mock_flask_app(self):
-        """Create a mock Flask app."""
-        app = MagicMock()
-        app.config = {"TEST_KEY": "test_value"}
-        app.extensions = {"db": MagicMock(), "cache": MagicMock()}
-        app.app_context = MagicMock()
-        app.app_context.return_value.__enter__ = MagicMock(return_value=None)
-        app.app_context.return_value.__exit__ = MagicMock(return_value=None)
-        return app
-
-    def test_flask_app_context_initialization(self, mock_flask_app):
-        """Test FlaskAppContext initialization."""
-        # Import here to avoid Flask dependency in test environment
-        from context.flask_app_context import FlaskAppContext
-
-        ctx = FlaskAppContext(mock_flask_app)
-
-        assert ctx.flask_app == mock_flask_app
-
-    def test_flask_app_context_get_config(self, mock_flask_app):
-        """Test get_config returns Flask app config value."""
-        from context.flask_app_context import FlaskAppContext
-
-        ctx = FlaskAppContext(mock_flask_app)
-
-        assert ctx.get_config("TEST_KEY") == "test_value"
-
-    def test_flask_app_context_get_config_default(self, mock_flask_app):
-        """Test get_config returns default when key not found."""
-        from context.flask_app_context import FlaskAppContext
-
-        ctx = FlaskAppContext(mock_flask_app)
-
-        assert ctx.get_config("NONEXISTENT", "default") == "default"
-
-    def test_flask_app_context_get_extension(self, mock_flask_app):
-        """Test get_extension returns Flask extension."""
-        from context.flask_app_context import FlaskAppContext
-
-        ctx = FlaskAppContext(mock_flask_app)
-        db_ext = mock_flask_app.extensions["db"]
-
-        assert ctx.get_extension("db") == db_ext
-
-    def test_flask_app_context_get_extension_not_found(self, mock_flask_app):
-        """Test get_extension returns None when extension not found."""
-        from context.flask_app_context import FlaskAppContext
-
-        ctx = FlaskAppContext(mock_flask_app)
-
-        assert ctx.get_extension("nonexistent") is None
-
-    def test_flask_app_context_enter(self, mock_flask_app):
-        """Test enter method enters Flask app context."""
-        from context.flask_app_context import FlaskAppContext
-
-        ctx = FlaskAppContext(mock_flask_app)
-
-        with ctx.enter():
-            # Should not raise any exception
-            pass
-
-        # Verify app_context was called
-        mock_flask_app.app_context.assert_called_once()
-
-
-class TestFlaskExecutionContext:
-    """Test FlaskExecutionContext class."""
-
-    @pytest.fixture
-    def mock_flask_app(self):
-        """Create a mock Flask app."""
-        app = MagicMock()
-        app.config = {}
-        app.app_context = MagicMock()
-        app.app_context.return_value.__enter__ = MagicMock(return_value=None)
-        app.app_context.return_value.__exit__ = MagicMock(return_value=None)
-        return app
-
-    def test_initialization(self, mock_flask_app):
-        """Test FlaskExecutionContext initialization."""
-        from context.flask_app_context import FlaskExecutionContext
-
-        context_vars = contextvars.copy_context()
-        user = MagicMock()
-
-        ctx = FlaskExecutionContext(
-            flask_app=mock_flask_app,
-            context_vars=context_vars,
-            user=user,
-        )
-
-        assert ctx.context_vars == context_vars
-        assert ctx.user == user
-
-    def test_app_context_property(self, mock_flask_app):
-        """Test app_context property returns FlaskAppContext."""
-        from context.flask_app_context import FlaskAppContext, FlaskExecutionContext
-
-        ctx = FlaskExecutionContext(
-            flask_app=mock_flask_app,
-            context_vars=contextvars.copy_context(),
-        )
-
-        assert isinstance(ctx.app_context, FlaskAppContext)
-        assert ctx.app_context.flask_app == mock_flask_app
-
-    def test_context_manager_protocol(self, mock_flask_app):
-        """Test FlaskExecutionContext supports context manager protocol."""
-        from context.flask_app_context import FlaskExecutionContext
-
-        ctx = FlaskExecutionContext(
-            flask_app=mock_flask_app,
-            context_vars=contextvars.copy_context(),
-        )
-
-        # Should have __enter__ and __exit__ methods
-        assert hasattr(ctx, "__enter__")
-        assert hasattr(ctx, "__exit__")
-
-        # Should work as context manager
-        with ctx:
-            pass
-
-
-class TestCaptureFlaskContext:
-    """Test capture_flask_context function."""
-
-    @patch("context.flask_app_context.current_app")
-    @patch("context.flask_app_context.g")
-    def test_capture_flask_context_captures_app(self, mock_g, mock_current_app):
-        """Test capture_flask_context captures Flask app."""
-        mock_app = MagicMock()
-        mock_app._get_current_object = MagicMock(return_value=mock_app)
-        mock_current_app._get_current_object = MagicMock(return_value=mock_app)
-
-        from context.flask_app_context import capture_flask_context
-
-        ctx = capture_flask_context()
-
-        assert ctx._flask_app == mock_app
-
-    @patch("context.flask_app_context.current_app")
-    @patch("context.flask_app_context.g")
-    def test_capture_flask_context_captures_user_from_g(self, mock_g, mock_current_app):
-        """Test capture_flask_context captures user from Flask g object."""
-        mock_app = MagicMock()
-        mock_app._get_current_object = MagicMock(return_value=mock_app)
-        mock_current_app._get_current_object = MagicMock(return_value=mock_app)
-
-        mock_user = MagicMock()
-        mock_user.id = "user_123"
-        mock_g._login_user = mock_user
-
-        from context.flask_app_context import capture_flask_context
-
-        ctx = capture_flask_context()
-
-        assert ctx.user == mock_user
-
-    @patch("context.flask_app_context.current_app")
-    def test_capture_flask_context_with_explicit_user(self, mock_current_app):
-        """Test capture_flask_context uses explicit user parameter."""
-        mock_app = MagicMock()
-        mock_app._get_current_object = MagicMock(return_value=mock_app)
-        mock_current_app._get_current_object = MagicMock(return_value=mock_app)
-
-        explicit_user = MagicMock()
-        explicit_user.id = "user_456"
-
-        from context.flask_app_context import capture_flask_context
-
-        ctx = capture_flask_context(user=explicit_user)
-
-        assert ctx.user == explicit_user
-
-    @patch("context.flask_app_context.current_app")
-    def test_capture_flask_context_captures_contextvars(self, mock_current_app):
-        """Test capture_flask_context captures context variables."""
-        mock_app = MagicMock()
-        mock_app._get_current_object = MagicMock(return_value=mock_app)
-        mock_current_app._get_current_object = MagicMock(return_value=mock_app)
-
-        # Set a context variable
-        test_var = contextvars.ContextVar("test_var")
-        test_var.set("test_value")
-
-        from context.flask_app_context import capture_flask_context
-
-        ctx = capture_flask_context()
-
-        # Context variables should be captured
-        assert ctx.context_vars is not None
-        # Verify the variable is in the captured context
-        captured_value = ctx.context_vars[test_var]
-        assert captured_value == "test_value"
-
-
-class TestFlaskExecutionContextIntegration:
-    """Integration tests for FlaskExecutionContext."""
-
-    @pytest.fixture
-    def mock_flask_app(self):
-        """Create a mock Flask app with proper app context."""
-        app = MagicMock()
-        app.config = {"TEST": "value"}
-        app.extensions = {"db": MagicMock()}
-
-        # Mock app context
-        mock_app_context = MagicMock()
-        mock_app_context.__enter__ = MagicMock(return_value=None)
-        mock_app_context.__exit__ = MagicMock(return_value=None)
-        app.app_context.return_value = mock_app_context
-
-        return app
-
-    def test_enter_restores_context_vars(self, mock_flask_app):
-        """Test that enter restores captured context variables."""
-        # Create a context variable and set a value
-        test_var = contextvars.ContextVar("integration_test_var")
-        test_var.set("original_value")
-
-        # Capture the context
-        context_vars = contextvars.copy_context()
-
-        # Change the value
-        test_var.set("new_value")
-
-        # Create FlaskExecutionContext and enter it
-        from context.flask_app_context import FlaskExecutionContext
-
-        ctx = FlaskExecutionContext(
-            flask_app=mock_flask_app,
-            context_vars=context_vars,
-        )
-
-        with ctx:
-            # Value should be restored to original
-            assert test_var.get() == "original_value"
-
-        # After exiting, variable stays at the value from within the context
-        # (this is expected Python contextvars behavior)
-        assert test_var.get() == "original_value"
-
-    def test_enter_enters_flask_app_context(self, mock_flask_app):
-        """Test that enter enters Flask app context."""
-        from context.flask_app_context import FlaskExecutionContext
-
-        ctx = FlaskExecutionContext(
-            flask_app=mock_flask_app,
-            context_vars=contextvars.copy_context(),
-        )
-
-        with ctx:
-            # Verify app context was entered
-            assert mock_flask_app.app_context.called
-
-    @patch("context.flask_app_context.g")
-    def test_enter_restores_user_in_g(self, mock_g, mock_flask_app):
-        """Test that enter restores user in Flask g object."""
-        mock_user = MagicMock()
-        mock_user.id = "test_user"
-
-        # Note: FlaskExecutionContext saves user from g before entering context,
-        # then restores it after entering the app context.
-        # The user passed to constructor is NOT restored to g.
-        # So we need to test the actual behavior.
-
-        # Create FlaskExecutionContext with user in constructor
-        from context.flask_app_context import FlaskExecutionContext
-
-        ctx = FlaskExecutionContext(
-            flask_app=mock_flask_app,
-            context_vars=contextvars.copy_context(),
-            user=mock_user,
-        )
-
-        # Set user in g before entering (simulating existing user in g)
-        mock_g._login_user = mock_user
-
-        with ctx:
-            # After entering, the user from g before entry should be restored
-            assert mock_g._login_user == mock_user
-
-        # The user in constructor is stored but not automatically restored to g
-        # (it's available via ctx.user property)
-        assert ctx.user == mock_user
-
-    def test_enter_method_as_context_manager(self, mock_flask_app):
-        """Test enter method returns a proper context manager."""
-        from context.flask_app_context import FlaskExecutionContext
-
-        ctx = FlaskExecutionContext(
-            flask_app=mock_flask_app,
-            context_vars=contextvars.copy_context(),
-        )
-
-        # enter() should return a generator/context manager
-        with ctx.enter():
-            # Should work without issues
-            pass
-
-        # Verify app context was called
-        assert mock_flask_app.app_context.called

api/tests/unit_tests/core/workflow/graph_engine/event_management/test_event_handlers.py

@@ -25,12 +25,6 @@ class _StubErrorHandler:
     """Minimal error handler stub for tests."""
 
 
-class _StubNodeData:
-    """Simple node data stub with is_extractor_node property."""
-
-    is_extractor_node = False
-
-
 class _StubNode:
     """Simple node stub exposing the attributes needed by the state manager."""
 
@@ -42,7 +36,6 @@ class _StubNode:
         self.error_strategy = None
         self.retry_config = RetryConfig()
         self.retry = False
-        self.node_data = _StubNodeData()
 
 
 def _build_event_handler(node_id: str) -> tuple[EventHandler, EventManager, GraphExecution]:

api/tests/unit_tests/core/workflow/nodes/llm/test_llm_utils.py

@@ -1,174 +0,0 @@
-"""Tests for llm_utils module, specifically multimodal content handling."""
-
-import string
-from unittest.mock import patch
-
-from core.model_runtime.entities.message_entities import (
-    ImagePromptMessageContent,
-    TextPromptMessageContent,
-    UserPromptMessage,
-)
-from core.workflow.nodes.llm.llm_utils import (
-    _truncate_multimodal_content,
-    build_context,
-    restore_multimodal_content_in_messages,
-)
-
-
-class TestTruncateMultimodalContent:
-    """Tests for _truncate_multimodal_content function."""
-
-    def test_returns_message_unchanged_for_string_content(self):
-        """String content should pass through unchanged."""
-        message = UserPromptMessage(content="Hello, world!")
-        result = _truncate_multimodal_content(message)
-        assert result.content == "Hello, world!"
-
-    def test_returns_message_unchanged_for_none_content(self):
-        """None content should pass through unchanged."""
-        message = UserPromptMessage(content=None)
-        result = _truncate_multimodal_content(message)
-        assert result.content is None
-
-    def test_clears_base64_when_file_ref_present(self):
-        """When file_ref is present, base64_data and url should be cleared."""
-        image_content = ImagePromptMessageContent(
-            format="png",
-            base64_data=string.ascii_lowercase,
-            url="https://example.com/image.png",
-            mime_type="image/png",
-            filename="test.png",
-            file_ref="local:test-file-id",
-        )
-        message = UserPromptMessage(content=[image_content])
-
-        result = _truncate_multimodal_content(message)
-
-        assert isinstance(result.content, list)
-        assert len(result.content) == 1
-        result_content = result.content[0]
-        assert isinstance(result_content, ImagePromptMessageContent)
-        assert result_content.base64_data == ""
-        assert result_content.url == ""
-        # file_ref should be preserved
-        assert result_content.file_ref == "local:test-file-id"
-
-    def test_truncates_base64_when_no_file_ref(self):
-        """When file_ref is missing (legacy), base64_data should be truncated."""
-        long_base64 = "a" * 100
-        image_content = ImagePromptMessageContent(
-            format="png",
-            base64_data=long_base64,
-            mime_type="image/png",
-            filename="test.png",
-            file_ref=None,
-        )
-        message = UserPromptMessage(content=[image_content])
-
-        result = _truncate_multimodal_content(message)
-
-        assert isinstance(result.content, list)
-        result_content = result.content[0]
-        assert isinstance(result_content, ImagePromptMessageContent)
-        # Should be truncated with marker
-        assert "...[TRUNCATED]..." in result_content.base64_data
-        assert len(result_content.base64_data) < len(long_base64)
-
-    def test_preserves_text_content(self):
-        """Text content should pass through unchanged."""
-        text_content = TextPromptMessageContent(data="Hello!")
-        image_content = ImagePromptMessageContent(
-            format="png",
-            base64_data="test123",
-            mime_type="image/png",
-            file_ref="local:file-id",
-        )
-        message = UserPromptMessage(content=[text_content, image_content])
-
-        result = _truncate_multimodal_content(message)
-
-        assert isinstance(result.content, list)
-        assert len(result.content) == 2
-        # Text content unchanged
-        assert result.content[0].data == "Hello!"
-        # Image content base64 cleared
-        assert result.content[1].base64_data == ""
-
-
-class TestBuildContext:
-    """Tests for build_context function."""
-
-    def test_excludes_system_messages(self):
-        """System messages should be excluded from context."""
-        from core.model_runtime.entities.message_entities import SystemPromptMessage
-
-        messages = [
-            SystemPromptMessage(content="You are a helpful assistant."),
-            UserPromptMessage(content="Hello!"),
-        ]
-
-        context = build_context(messages, "Hi there!")
-
-        # Should have user message + assistant response, no system message
-        assert len(context) == 2
-        assert context[0].content == "Hello!"
-        assert context[1].content == "Hi there!"
-
-    def test_appends_assistant_response(self):
-        """Assistant response should be appended to context."""
-        messages = [UserPromptMessage(content="What is 2+2?")]
-
-        context = build_context(messages, "The answer is 4.")
-
-        assert len(context) == 2
-        assert context[1].content == "The answer is 4."
-
-
-class TestRestoreMultimodalContentInMessages:
-    """Tests for restore_multimodal_content_in_messages function."""
-
-    @patch("core.file.file_manager.restore_multimodal_content")
-    def test_restores_multimodal_content(self, mock_restore):
-        """Should restore multimodal content in messages."""
-        # Setup mock
-        restored_content = ImagePromptMessageContent(
-            format="png",
-            base64_data="restored-base64",
-            mime_type="image/png",
-            file_ref="local:abc123",
-        )
-        mock_restore.return_value = restored_content
-
-        # Create message with truncated content
-        truncated_content = ImagePromptMessageContent(
-            format="png",
-            base64_data="",
-            mime_type="image/png",
-            file_ref="local:abc123",
-        )
-        message = UserPromptMessage(content=[truncated_content])
-
-        result = restore_multimodal_content_in_messages([message])
-
-        assert len(result) == 1
-        assert result[0].content[0].base64_data == "restored-base64"
-        mock_restore.assert_called_once()
-
-    def test_passes_through_string_content(self):
-        """String content should pass through unchanged."""
-        message = UserPromptMessage(content="Hello!")
-
-        result = restore_multimodal_content_in_messages([message])
-
-        assert len(result) == 1
-        assert result[0].content == "Hello!"
-
-    def test_passes_through_text_content(self):
-        """TextPromptMessageContent should pass through unchanged."""
-        text_content = TextPromptMessageContent(data="Hello!")
-        message = UserPromptMessage(content=[text_content])
-
-        result = restore_multimodal_content_in_messages([message])
-
-        assert len(result) == 1
-        assert result[0].content[0].data == "Hello!"

api/tests/unit_tests/libs/test_workspace_permission.py

@@ -1,142 +0,0 @@
-from unittest.mock import Mock, patch
-
-import pytest
-from werkzeug.exceptions import Forbidden
-
-from libs.workspace_permission import (
-    check_workspace_member_invite_permission,
-    check_workspace_owner_transfer_permission,
-)
-
-
-class TestWorkspacePermissionHelper:
-    """Test workspace permission helper functions."""
-
-    @patch("libs.workspace_permission.dify_config")
-    @patch("libs.workspace_permission.EnterpriseService")
-    def test_community_edition_allows_invite(self, mock_enterprise_service, mock_config):
-        """Community edition should always allow invitations without calling any service."""
-        mock_config.ENTERPRISE_ENABLED = False
-
-        # Should not raise
-        check_workspace_member_invite_permission("test-workspace-id")
-
-        # EnterpriseService should NOT be called in community edition
-        mock_enterprise_service.WorkspacePermissionService.get_permission.assert_not_called()
-
-    @patch("libs.workspace_permission.dify_config")
-    @patch("libs.workspace_permission.FeatureService")
-    def test_community_edition_allows_transfer(self, mock_feature_service, mock_config):
-        """Community edition should check billing plan but not call enterprise service."""
-        mock_config.ENTERPRISE_ENABLED = False
-        mock_features = Mock()
-        mock_features.is_allow_transfer_workspace = True
-        mock_feature_service.get_features.return_value = mock_features
-
-        # Should not raise
-        check_workspace_owner_transfer_permission("test-workspace-id")
-
-        mock_feature_service.get_features.assert_called_once_with("test-workspace-id")
-
-    @patch("libs.workspace_permission.EnterpriseService")
-    @patch("libs.workspace_permission.dify_config")
-    def test_enterprise_blocks_invite_when_disabled(self, mock_config, mock_enterprise_service):
-        """Enterprise edition should block invitations when workspace policy is False."""
-        mock_config.ENTERPRISE_ENABLED = True
-
-        mock_permission = Mock()
-        mock_permission.allow_member_invite = False
-        mock_enterprise_service.WorkspacePermissionService.get_permission.return_value = mock_permission
-
-        with pytest.raises(Forbidden, match="Workspace policy prohibits member invitations"):
-            check_workspace_member_invite_permission("test-workspace-id")
-
-        mock_enterprise_service.WorkspacePermissionService.get_permission.assert_called_once_with("test-workspace-id")
-
-    @patch("libs.workspace_permission.EnterpriseService")
-    @patch("libs.workspace_permission.dify_config")
-    def test_enterprise_allows_invite_when_enabled(self, mock_config, mock_enterprise_service):
-        """Enterprise edition should allow invitations when workspace policy is True."""
-        mock_config.ENTERPRISE_ENABLED = True
-
-        mock_permission = Mock()
-        mock_permission.allow_member_invite = True
-        mock_enterprise_service.WorkspacePermissionService.get_permission.return_value = mock_permission
-
-        # Should not raise
-        check_workspace_member_invite_permission("test-workspace-id")
-
-        mock_enterprise_service.WorkspacePermissionService.get_permission.assert_called_once_with("test-workspace-id")
-
-    @patch("libs.workspace_permission.EnterpriseService")
-    @patch("libs.workspace_permission.dify_config")
-    @patch("libs.workspace_permission.FeatureService")
-    def test_billing_plan_blocks_transfer(self, mock_feature_service, mock_config, mock_enterprise_service):
-        """SANDBOX billing plan should block owner transfer before checking enterprise policy."""
-        mock_config.ENTERPRISE_ENABLED = True
-        mock_features = Mock()
-        mock_features.is_allow_transfer_workspace = False  # SANDBOX plan
-        mock_feature_service.get_features.return_value = mock_features
-
-        with pytest.raises(Forbidden, match="Your current plan does not allow workspace ownership transfer"):
-            check_workspace_owner_transfer_permission("test-workspace-id")
-
-        # Enterprise service should NOT be called since billing plan already blocks
-        mock_enterprise_service.WorkspacePermissionService.get_permission.assert_not_called()
-
-    @patch("libs.workspace_permission.EnterpriseService")
-    @patch("libs.workspace_permission.dify_config")
-    @patch("libs.workspace_permission.FeatureService")
-    def test_enterprise_blocks_transfer_when_disabled(self, mock_feature_service, mock_config, mock_enterprise_service):
-        """Enterprise edition should block transfer when workspace policy is False."""
-        mock_config.ENTERPRISE_ENABLED = True
-        mock_features = Mock()
-        mock_features.is_allow_transfer_workspace = True  # Billing plan allows
-        mock_feature_service.get_features.return_value = mock_features
-
-        mock_permission = Mock()
-        mock_permission.allow_owner_transfer = False  # Workspace policy blocks
-        mock_enterprise_service.WorkspacePermissionService.get_permission.return_value = mock_permission
-
-        with pytest.raises(Forbidden, match="Workspace policy prohibits ownership transfer"):
-            check_workspace_owner_transfer_permission("test-workspace-id")
-
-        mock_enterprise_service.WorkspacePermissionService.get_permission.assert_called_once_with("test-workspace-id")
-
-    @patch("libs.workspace_permission.EnterpriseService")
-    @patch("libs.workspace_permission.dify_config")
-    @patch("libs.workspace_permission.FeatureService")
-    def test_enterprise_allows_transfer_when_both_enabled(
-        self, mock_feature_service, mock_config, mock_enterprise_service
-    ):
-        """Enterprise edition should allow transfer when both billing and workspace policy allow."""
-        mock_config.ENTERPRISE_ENABLED = True
-        mock_features = Mock()
-        mock_features.is_allow_transfer_workspace = True  # Billing plan allows
-        mock_feature_service.get_features.return_value = mock_features
-
-        mock_permission = Mock()
-        mock_permission.allow_owner_transfer = True  # Workspace policy allows
-        mock_enterprise_service.WorkspacePermissionService.get_permission.return_value = mock_permission
-
-        # Should not raise
-        check_workspace_owner_transfer_permission("test-workspace-id")
-
-        mock_enterprise_service.WorkspacePermissionService.get_permission.assert_called_once_with("test-workspace-id")
-
-    @patch("libs.workspace_permission.logger")
-    @patch("libs.workspace_permission.EnterpriseService")
-    @patch("libs.workspace_permission.dify_config")
-    def test_enterprise_service_error_fails_open(self, mock_config, mock_enterprise_service, mock_logger):
-        """On enterprise service error, should fail-open (allow) and log error."""
-        mock_config.ENTERPRISE_ENABLED = True
-
-        # Simulate enterprise service error
-        mock_enterprise_service.WorkspacePermissionService.get_permission.side_effect = Exception("Service unavailable")
-
-        # Should not raise (fail-open)
-        check_workspace_member_invite_permission("test-workspace-id")
-
-        # Should log the error
-        mock_logger.exception.assert_called_once()
-        assert "Failed to check workspace invite permission" in str(mock_logger.exception.call_args)

api/tests/unit_tests/utils/structured_output_parser/test_structured_output_parser.py

@@ -2,13 +2,9 @@ from decimal import Decimal
 from unittest.mock import MagicMock, patch
 
 import pytest
-from pydantic import BaseModel, ConfigDict
 
 from core.llm_generator.output_parser.errors import OutputParserError
-from core.llm_generator.output_parser.structured_output import (
-    invoke_llm_with_pydantic_model,
-    invoke_llm_with_structured_output,
-)
+from core.llm_generator.output_parser.structured_output import invoke_llm_with_structured_output
 from core.model_runtime.entities.llm_entities import (
     LLMResult,
     LLMResultChunk,
@@ -465,68 +461,3 @@ def test_model_specific_schema_preparation():
 
     # For Gemini, the schema should not have additionalProperties and boolean should be converted to string
     assert "json_schema" in call_args.kwargs["model_parameters"]
-
-
-class ExampleOutput(BaseModel):
-    model_config = ConfigDict(extra="forbid")
-
-    name: str
-
-
-def test_structured_output_with_pydantic_model():
-    model_schema = get_model_entity("openai", "gpt-4o", support_structure_output=True)
-    model_instance = get_model_instance()
-    model_instance.invoke_llm.return_value = LLMResult(
-        model="gpt-4o",
-        message=AssistantPromptMessage(content='{"name": "test"}'),
-        usage=create_mock_usage(prompt_tokens=8, completion_tokens=4),
-    )
-
-    prompt_messages = [UserPromptMessage(content="Return a JSON object with name.")]
-
-    result = invoke_llm_with_pydantic_model(
-        provider="openai",
-        model_schema=model_schema,
-        model_instance=model_instance,
-        prompt_messages=prompt_messages,
-        output_model=ExampleOutput,
-        stream=False,
-    )
-
-    assert isinstance(result, LLMResultWithStructuredOutput)
-    assert result.structured_output == {"name": "test"}
-
-
-def test_structured_output_with_pydantic_model_streaming_rejected():
-    model_schema = get_model_entity("openai", "gpt-4o", support_structure_output=True)
-    model_instance = get_model_instance()
-
-    with pytest.raises(ValueError):
-        invoke_llm_with_pydantic_model(
-            provider="openai",
-            model_schema=model_schema,
-            model_instance=model_instance,
-            prompt_messages=[UserPromptMessage(content="test")],
-            output_model=ExampleOutput,
-            stream=True,
-        )
-
-
-def test_structured_output_with_pydantic_model_validation_error():
-    model_schema = get_model_entity("openai", "gpt-4o", support_structure_output=True)
-    model_instance = get_model_instance()
-    model_instance.invoke_llm.return_value = LLMResult(
-        model="gpt-4o",
-        message=AssistantPromptMessage(content='{"name": 123}'),
-        usage=create_mock_usage(prompt_tokens=8, completion_tokens=4),
-    )
-
-    with pytest.raises(OutputParserError):
-        invoke_llm_with_pydantic_model(
-            provider="openai",
-            model_schema=model_schema,
-            model_instance=model_instance,
-            prompt_messages=[UserPromptMessage(content="test")],
-            output_model=ExampleOutput,
-            stream=False,
-        )

web/app/components/app/annotation/index.tsx

@@ -203,7 +203,7 @@ const Annotation: FC<Props> = (props) => {
         </Filter>
         {isLoading
           ? <Loading type="app" />
-
+          // eslint-disable-next-line sonarjs/no-nested-conditional
           : total > 0
             ? (
                 <List

web/app/components/app/configuration/config/automatic/get-automatic-res.tsx

@@ -134,6 +134,7 @@ const GetAutomaticRes: FC<IGetAutomaticResProps> = ({
     },
   ] as const
 
+  // eslint-disable-next-line sonarjs/no-nested-template-literals, sonarjs/no-nested-conditional
   const [instructionFromSessionStorage, setInstruction] = useSessionStorageState<string>(`improve-instruction-${flowId}${isBasicMode ? '' : `-${nodeId}${editorId ? `-${editorId}` : ''}`}`)
   const instruction = instructionFromSessionStorage || ''
   const [ideaOutput, setIdeaOutput] = useState<string>('')

web/app/components/app/overview/settings/index.spec.tsx

@@ -175,7 +175,7 @@ describe('SettingsModal', () => {
     renderSettingsModal()
     fireEvent.click(screen.getByText('appOverview.overview.appInfo.settings.more.entry'))
     const privacyInput = screen.getByPlaceholderText('appOverview.overview.appInfo.settings.more.privacyPolicyPlaceholder')
-
+    // eslint-disable-next-line sonarjs/no-clear-text-protocols
     fireEvent.change(privacyInput, { target: { value: 'ftp://invalid-url' } })
 
     fireEvent.click(screen.getByText('common.operation.save'))

web/app/components/app/overview/trigger-card.tsx

@@ -14,6 +14,7 @@ import { BlockEnum } from '@/app/components/workflow/types'
 import { useAppContext } from '@/context/app-context'
 import { useDocLink } from '@/context/i18n'
 import {
+
   useAppTriggers,
   useInvalidateAppTriggers,
   useUpdateTriggerStatus,