Merge branch 'main' into fix/chore-fix

api/.env.example

 # Access token expiration time in minutes
 ACCESS_TOKEN_EXPIRE_MINUTES=60
 
+# Refresh token expiration time in days
+REFRESH_TOKEN_EXPIRE_DAYS=30
+
 # celery configuration
 CELERY_BROKER_URL=redis://:difyai123456@localhost:6379/1
 

api/app.py

 
     app = create_migrations_app()
 else:
-    if os.environ.get("FLASK_DEBUG", "False") != "True":
+    # It seems that JetBrains Python debugger does not work well with gevent,
+    # so we need to disable gevent in debug mode.
+    # If you are using debugpy and set GEVENT_SUPPORT=True, you can debug with gevent.
+    if (flask_debug := os.environ.get("FLASK_DEBUG", "0")) and flask_debug.lower() in {"false", "0", "no"}:
         from gevent import monkey  # type: ignore
 
         # gevent

api/configs/feature/__init__.py

         default=60,
     )
 
+    REFRESH_TOKEN_EXPIRE_DAYS: PositiveFloat = Field(
+        description="Expiration time for refresh tokens in days",
+        default=30,
+    )
+
     LOGIN_LOCKOUT_DURATION: PositiveInt = Field(
         description="Time (in seconds) a user must wait before retrying login after exceeding the rate limit.",
         default=86400,
         default=4000,
     )
 
+    CHILD_CHUNKS_PREVIEW_NUMBER: PositiveInt = Field(
+        description="Maximum number of child chunks to preview",
+        default=50,
+    )
+
 
 class MultiModalTransferConfig(BaseSettings):
     MULTIMODAL_SEND_FORMAT: Literal["base64", "url"] = Field(

api/configs/middleware/vdb/milvus_config.py

         description="Name of the Milvus database to connect to (default is 'default')",
         default="default",
     )
+
+    MILVUS_ENABLE_HYBRID_SEARCH: bool = Field(
+        description="Enable hybrid search features (requires Milvus >= 2.5.0). Set to false for compatibility with "
+        "older versions",
+        default=True,
+    )

api/configs/packaging/__init__.py

 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.14.2",
+        default="0.15.0",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/console/app/app.py

         )
         parser.add_argument("name", type=str, location="args", required=False)
         parser.add_argument("tag_ids", type=uuid_list, location="args", required=False)
+        parser.add_argument("is_created_by_me", type=inputs.boolean, location="args", required=False)
 
         args = parser.parse_args()
 
         # get app list
         app_service = AppService()
-        app_pagination = app_service.get_paginate_apps(current_user.current_tenant_id, args)
+        app_pagination = app_service.get_paginate_apps(current_user.id, current_user.current_tenant_id, args)
         if not app_pagination:
             return {"data": [], "total": 0, "page": 1, "limit": 20, "has_more": False}
 

api/controllers/console/app/completion.py

 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import (
-    AppInvokeQuotaExceededError,
     ModelCurrentlyNotSupportError,
     ProviderTokenNotInitError,
     QuotaExceededError,
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
             raise CompletionRequestError(e.description)
-        except (ValueError, AppInvokeQuotaExceededError) as e:
+        except ValueError as e:
             raise e
         except Exception as e:
             logging.exception("internal server error.")
             raise InvokeRateLimitHttpError(ex.description)
         except InvokeError as e:
             raise CompletionRequestError(e.description)
-        except (ValueError, AppInvokeQuotaExceededError) as e:
+        except ValueError as e:
             raise e
         except Exception as e:
             logging.exception("internal server error.")

api/controllers/console/app/statistic.py

             messages m
             ON c.id = m.conversation_id
         WHERE
-            c.override_model_configs IS NULL
-            AND c.app_id = :app_id"""
+            c.app_id = :app_id"""
         arg_dict = {"tz": account.timezone, "app_id": app_model.id}
 
         timezone = pytz.timezone(account.timezone)

api/controllers/console/datasets/datasets.py

                 | VectorType.MYSCALE
                 | VectorType.ORACLE
                 | VectorType.ELASTICSEARCH
+                | VectorType.ELASTICSEARCH_JA
                 | VectorType.PGVECTOR
                 | VectorType.TIDB_ON_QDRANT
                 | VectorType.LINDORM
                 | VectorType.MYSCALE
                 | VectorType.ORACLE
                 | VectorType.ELASTICSEARCH
+                | VectorType.ELASTICSEARCH_JA
                 | VectorType.COUCHBASE
                 | VectorType.PGVECTOR
                 | VectorType.LINDORM

api/controllers/console/datasets/datasets_document.py

         parser.add_argument("original_document_id", type=str, required=False, location="json")
         parser.add_argument("doc_form", type=str, default="text_model", required=False, nullable=False, location="json")
         parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
-
+        parser.add_argument("embedding_model", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("embedding_model_provider", type=str, required=False, nullable=True, location="json")
         parser.add_argument(
             "doc_language", type=str, default="English", required=False, nullable=False, location="json"
         )

api/controllers/console/explore/completion.py

 from controllers.console.explore.wraps import InstalledAppResource
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.errors.error import (
+    ModelCurrentlyNotSupportError,
+    ProviderTokenNotInitError,
+    QuotaExceededError,
+)
 from core.model_runtime.errors.invoke import InvokeError
 from extensions.ext_database import db
 from libs import helper

api/controllers/console/explore/workflow.py

 from controllers.console.explore.wraps import InstalledAppResource
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.errors.error import (
+    ModelCurrentlyNotSupportError,
+    ProviderTokenNotInitError,
+    QuotaExceededError,
+)
 from core.model_runtime.errors.invoke import InvokeError
 from libs import helper
 from libs.login import current_user

api/controllers/service_api/app/completion.py

 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import (
-    AppInvokeQuotaExceededError,
     ModelCurrentlyNotSupportError,
     ProviderTokenNotInitError,
     QuotaExceededError,
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
             raise CompletionRequestError(e.description)
-        except (ValueError, AppInvokeQuotaExceededError) as e:
+        except ValueError as e:
             raise e
         except Exception as e:
             logging.exception("internal server error.")
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
             raise CompletionRequestError(e.description)
-        except (ValueError, AppInvokeQuotaExceededError) as e:
+        except ValueError as e:
             raise e
         except Exception as e:
             logging.exception("internal server error.")

api/controllers/service_api/app/workflow.py

 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import (
-    AppInvokeQuotaExceededError,
     ModelCurrentlyNotSupportError,
     ProviderTokenNotInitError,
     QuotaExceededError,
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
             raise CompletionRequestError(e.description)
-        except (ValueError, AppInvokeQuotaExceededError) as e:
+        except ValueError as e:
             raise e
         except Exception as e:
             logging.exception("internal server error.")

api/controllers/service_api/dataset/document.py

             user=current_user,
             source="datasets",
         )
-        data_source = {"type": "upload_file", "info_list": {"file_info_list": {"file_ids": [upload_file.id]}}}
+        data_source = {
+            "type": "upload_file",
+            "info_list": {"data_source_type": "upload_file", "file_info_list": {"file_ids": [upload_file.id]}},
+        }
         args["data_source"] = data_source
         # validate args
         knowledge_config = KnowledgeConfig(**args)
                 raise FileTooLargeError(file_too_large_error.description)
             except services.errors.file.UnsupportedFileTypeError:
                 raise UnsupportedFileTypeError()
-            data_source = {"type": "upload_file", "info_list": {"file_info_list": {"file_ids": [upload_file.id]}}}
+            data_source = {
+                "type": "upload_file",
+                "info_list": {"data_source_type": "upload_file", "file_info_list": {"file_ids": [upload_file.id]}},
+            }
             args["data_source"] = data_source
         # validate args
         args["original_document_id"] = str(document_id)

api/controllers/service_api/wraps.py

 from collections.abc import Callable
-from datetime import UTC, datetime
+from datetime import UTC, datetime, timedelta
 from enum import Enum
 from functools import wraps
 from typing import Optional
 from flask_login import user_logged_in  # type: ignore
 from flask_restful import Resource  # type: ignore
 from pydantic import BaseModel
+from sqlalchemy import select, update
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import Forbidden, Unauthorized
 
 from extensions.ext_database import db
     return decorator
 
 
-def validate_and_get_api_token(scope=None):
+def validate_and_get_api_token(scope: str | None = None):
     """
     Validate and get API token.
     """
     if auth_scheme != "bearer":
         raise Unauthorized("Authorization scheme must be 'Bearer'")
 
-    api_token = (
-        db.session.query(ApiToken)
-        .filter(
-            ApiToken.token == auth_token,
-            ApiToken.type == scope,
+    current_time = datetime.now(UTC).replace(tzinfo=None)
+    cutoff_time = current_time - timedelta(minutes=1)
+    with Session(db.engine, expire_on_commit=False) as session:
+        update_stmt = (
+            update(ApiToken)
+            .where(ApiToken.token == auth_token, ApiToken.last_used_at < cutoff_time, ApiToken.type == scope)
+            .values(last_used_at=current_time)
+            .returning(ApiToken)
         )
-        .first()
-    )
-
-    if not api_token:
-        raise Unauthorized("Access token is invalid")
-
-    api_token.last_used_at = datetime.now(UTC).replace(tzinfo=None)
-    db.session.commit()
+        result = session.execute(update_stmt)
+        api_token = result.scalar_one_or_none()
+
+        if not api_token:
+            stmt = select(ApiToken).where(ApiToken.token == auth_token, ApiToken.type == scope)
+            api_token = session.scalar(stmt)
+            if not api_token:
+                raise Unauthorized("Access token is invalid")
+        else:
+            session.commit()
 
     return api_token
 

api/controllers/web/completion.py

 from controllers.web.wraps import WebApiResource
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.errors.error import (
+    ModelCurrentlyNotSupportError,
+    ProviderTokenNotInitError,
+    QuotaExceededError,
+)
 from core.model_runtime.errors.invoke import InvokeError
 from libs import helper
 from libs.helper import uuid_value

api/controllers/web/workflow.py

 from controllers.web.wraps import WebApiResource
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.errors.error import (
+    ModelCurrentlyNotSupportError,
+    ProviderTokenNotInitError,
+    QuotaExceededError,
+)
 from core.model_runtime.errors.invoke import InvokeError
 from libs import helper
 from models.model import App, AppMode, EndUser

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

 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
 from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom
 from core.app.entities.task_entities import ChatbotAppBlockingResponse, ChatbotAppStreamResponse
-from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
+from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.prompt.utils.get_thread_messages_length import get_thread_messages_length
 from extensions.ext_database import db
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except (ValueError, InvokeError) as e:
+            except ValueError as e:
                 if dify_config.DEBUG:
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)

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

 from models.enums import CreatedByRole
 from models.workflow import (
     Workflow,
-    WorkflowNodeExecution,
     WorkflowRunStatus,
 )
 
 logger = logging.getLogger(__name__)
 
 
-class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleManage, MessageCycleManage):
+class AdvancedChatAppGenerateTaskPipeline:
     """
     AdvancedChatAppGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
 
-    _task_state: WorkflowTaskState
-    _application_generate_entity: AdvancedChatAppGenerateEntity
-    _workflow_system_variables: dict[SystemVariableKey, Any]
-    _wip_workflow_node_executions: dict[str, WorkflowNodeExecution]
-    _conversation_name_generate_thread: Optional[Thread] = None
-
     def __init__(
         self,
         application_generate_entity: AdvancedChatAppGenerateEntity,
         stream: bool,
         dialogue_count: int,
     ) -> None:
-        super().__init__(
+        self._base_task_pipeline = BasedGenerateTaskPipeline(
             application_generate_entity=application_generate_entity,
             queue_manager=queue_manager,
             stream=stream,
         else:
             raise NotImplementedError(f"User type not supported: {type(user)}")
 
+        self._workflow_cycle_manager = WorkflowCycleManage(
+            application_generate_entity=application_generate_entity,
+            workflow_system_variables={
+                SystemVariableKey.QUERY: message.query,
+                SystemVariableKey.FILES: application_generate_entity.files,
+                SystemVariableKey.CONVERSATION_ID: conversation.id,
+                SystemVariableKey.USER_ID: user_session_id,
+                SystemVariableKey.DIALOGUE_COUNT: dialogue_count,
+                SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
+                SystemVariableKey.WORKFLOW_ID: workflow.id,
+                SystemVariableKey.WORKFLOW_RUN_ID: application_generate_entity.workflow_run_id,
+            },
+        )
+
+        self._task_state = WorkflowTaskState()
+        self._message_cycle_manager = MessageCycleManage(
+            application_generate_entity=application_generate_entity, task_state=self._task_state
+        )
+
+        self._application_generate_entity = application_generate_entity
         self._workflow_id = workflow.id
         self._workflow_features_dict = workflow.features_dict
-
         self._conversation_id = conversation.id
         self._conversation_mode = conversation.mode
-
         self._message_id = message.id
         self._message_created_at = int(message.created_at.timestamp())
-
-        self._workflow_system_variables = {
-            SystemVariableKey.QUERY: message.query,
-            SystemVariableKey.FILES: application_generate_entity.files,
-            SystemVariableKey.CONVERSATION_ID: conversation.id,
-            SystemVariableKey.USER_ID: user_session_id,
-            SystemVariableKey.DIALOGUE_COUNT: dialogue_count,
-            SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
-            SystemVariableKey.WORKFLOW_ID: workflow.id,
-            SystemVariableKey.WORKFLOW_RUN_ID: application_generate_entity.workflow_run_id,
-        }
-
-        self._task_state = WorkflowTaskState()
-        self._wip_workflow_node_executions = {}
-        self._wip_workflow_agent_logs = {}
-
-        self._conversation_name_generate_thread = None
+        self._conversation_name_generate_thread: Thread | None = None
         self._recorded_files: list[Mapping[str, Any]] = []
-        self._workflow_run_id = ""
+        self._workflow_run_id: str = ""
 
     def process(self) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
         """
         :return:
         """
         # start generate conversation name thread
-        self._conversation_name_generate_thread = self._generate_conversation_name(
+        self._conversation_name_generate_thread = self._message_cycle_manager._generate_conversation_name(
             conversation_id=self._conversation_id, query=self._application_generate_entity.query
         )
 
         generator = self._wrapper_process_stream_response(trace_manager=self._application_generate_entity.trace_manager)
 
-        if self._stream:
+        if self._base_task_pipeline._stream:
             return self._to_stream_response(generator)
         else:
             return self._to_blocking_response(generator)
         # init fake graph runtime state
         graph_runtime_state: Optional[GraphRuntimeState] = None
 
-        for queue_message in self._queue_manager.listen():
+        for queue_message in self._base_task_pipeline._queue_manager.listen():
             event = queue_message.event
 
             if isinstance(event, QueuePingEvent):
-                yield self._ping_stream_response()
+                yield self._base_task_pipeline._ping_stream_response()
             elif isinstance(event, QueueErrorEvent):
-                with Session(db.engine) as session:
-                    err = self._handle_error(event=event, session=session, message_id=self._message_id)
+                with Session(db.engine, expire_on_commit=False) as session:
+                    err = self._base_task_pipeline._handle_error(
+                        event=event, session=session, message_id=self._message_id
+                    )
                     session.commit()
-                yield self._error_to_stream_response(err)
+                yield self._base_task_pipeline._error_to_stream_response(err)
                 break
             elif isinstance(event, QueueWorkflowStartedEvent):
                 # override graph runtime state
                 graph_runtime_state = event.graph_runtime_state
 
-                with Session(db.engine) as session:
+                with Session(db.engine, expire_on_commit=False) as session:
                     # init workflow run
-                    workflow_run = self._handle_workflow_run_start(
+                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_start(
                         session=session,
                         workflow_id=self._workflow_id,
                         user_id=self._user_id,
                     if not message:
                         raise ValueError(f"Message not found: {self._message_id}")
                     message.workflow_run_id = workflow_run.id
-                    workflow_start_resp = self._workflow_start_to_stream_response(
+                    workflow_start_resp = self._workflow_cycle_manager._workflow_start_to_stream_response(
                         session=session, task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
                     session.commit()
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    workflow_node_execution = self._handle_workflow_node_execution_retried(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    workflow_node_execution = self._workflow_cycle_manager._handle_workflow_node_execution_retried(
                         session=session, workflow_run=workflow_run, event=event
                     )
-                    node_retry_resp = self._workflow_node_retry_to_stream_response(
+                    node_retry_resp = self._workflow_cycle_manager._workflow_node_retry_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    workflow_node_execution = self._handle_node_execution_start(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    workflow_node_execution = self._workflow_cycle_manager._handle_node_execution_start(
                         session=session, workflow_run=workflow_run, event=event
                     )
 
-                    node_start_resp = self._workflow_node_start_to_stream_response(
+                    node_start_resp = self._workflow_cycle_manager._workflow_node_start_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
             elif isinstance(event, QueueNodeSucceededEvent):
                 # Record files if it's an answer node or end node
                 if event.node_type in [NodeType.ANSWER, NodeType.END]:
-                    self._recorded_files.extend(self._fetch_files_from_node_outputs(event.outputs or {}))
+                    self._recorded_files.extend(
+                        self._workflow_cycle_manager._fetch_files_from_node_outputs(event.outputs or {})
+                    )
 
-                with Session(db.engine) as session:
-                    workflow_node_execution = self._handle_workflow_node_execution_success(session=session, event=event)
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_node_execution = self._workflow_cycle_manager._handle_workflow_node_execution_success(
+                        session=session, event=event
+                    )
 
-                    node_finish_resp = self._workflow_node_finish_to_stream_response(
+                    node_finish_resp = self._workflow_cycle_manager._workflow_node_finish_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
                 if node_finish_resp:
                     yield node_finish_resp
             elif isinstance(event, QueueNodeFailedEvent | QueueNodeInIterationFailedEvent | QueueNodeExceptionEvent):
-                with Session(db.engine) as session:
-                    workflow_node_execution = self._handle_workflow_node_execution_failed(session=session, event=event)
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_node_execution = self._workflow_cycle_manager._handle_workflow_node_execution_failed(
+                        session=session, event=event
+                    )
 
-                    node_finish_resp = self._workflow_node_finish_to_stream_response(
+                    node_finish_resp = self._workflow_cycle_manager._workflow_node_finish_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    parallel_start_resp = self._workflow_parallel_branch_start_to_stream_response(
-                        session=session,
-                        task_id=self._application_generate_entity.task_id,
-                        workflow_run=workflow_run,
-                        event=event,
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    parallel_start_resp = (
+                        self._workflow_cycle_manager._workflow_parallel_branch_start_to_stream_response(
+                            session=session,
+                            task_id=self._application_generate_entity.task_id,
+                            workflow_run=workflow_run,
+                            event=event,
+                        )
                     )
 
                 yield parallel_start_resp
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    parallel_finish_resp = self._workflow_parallel_branch_finished_to_stream_response(
-                        session=session,
-                        task_id=self._application_generate_entity.task_id,
-                        workflow_run=workflow_run,
-                        event=event,
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    parallel_finish_resp = (
+                        self._workflow_cycle_manager._workflow_parallel_branch_finished_to_stream_response(
+                            session=session,
+                            task_id=self._application_generate_entity.task_id,
+                            workflow_run=workflow_run,
+                            event=event,
+                        )
                     )
 
                 yield parallel_finish_resp
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    iter_start_resp = self._workflow_iteration_start_to_stream_response(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    iter_start_resp = self._workflow_cycle_manager._workflow_iteration_start_to_stream_response(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    iter_next_resp = self._workflow_iteration_next_to_stream_response(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    iter_next_resp = self._workflow_cycle_manager._workflow_iteration_next_to_stream_response(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    iter_finish_resp = self._workflow_iteration_completed_to_stream_response(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    iter_finish_resp = self._workflow_cycle_manager._workflow_iteration_completed_to_stream_response(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
                 if not graph_runtime_state:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._handle_workflow_run_success(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_success(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
                         trace_manager=trace_manager,
                     )
 
-                    workflow_finish_resp = self._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
                         session=session, task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
                     session.commit()
 
                 yield workflow_finish_resp
-                self._queue_manager.publish(QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE)
+                self._base_task_pipeline._queue_manager.publish(
+                    QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE
+                )
             elif isinstance(event, QueueWorkflowPartialSuccessEvent):
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
                 if not graph_runtime_state:
                     raise ValueError("graph runtime state not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._handle_workflow_run_partial_success(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_partial_success(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
                         conversation_id=None,
                         trace_manager=trace_manager,
                     )
-                    workflow_finish_resp = self._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
                         session=session, task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
                     session.commit()
 
                 yield workflow_finish_resp
-                self._queue_manager.publish(QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE)
+                self._base_task_pipeline._queue_manager.publish(
+                    QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE
+                )
             elif isinstance(event, QueueWorkflowFailedEvent):
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
                 if not graph_runtime_state:
                     raise ValueError("graph runtime state not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._handle_workflow_run_failed(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_failed(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
                         trace_manager=trace_manager,
                         exceptions_count=event.exceptions_count,
                     )
-                    workflow_finish_resp = self._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
                         session=session, task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
                     err_event = QueueErrorEvent(error=ValueError(f"Run failed: {workflow_run.error}"))
-                    err = self._handle_error(event=err_event, session=session, message_id=self._message_id)
+                    err = self._base_task_pipeline._handle_error(
+                        event=err_event, session=session, message_id=self._message_id
+                    )
                     session.commit()
 
                 yield workflow_finish_resp
-                yield self._error_to_stream_response(err)
+                yield self._base_task_pipeline._error_to_stream_response(err)
                 break
             elif isinstance(event, QueueStopEvent):
                 if self._workflow_run_id and graph_runtime_state:
-                    with Session(db.engine) as session:
-                        workflow_run = self._handle_workflow_run_failed(
+                    with Session(db.engine, expire_on_commit=False) as session:
+                        workflow_run = self._workflow_cycle_manager._handle_workflow_run_failed(
                             session=session,
                             workflow_run_id=self._workflow_run_id,
                             start_at=graph_runtime_state.start_at,
                             conversation_id=self._conversation_id,
                             trace_manager=trace_manager,
                         )
-                        workflow_finish_resp = self._workflow_finish_to_stream_response(
+                        workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
                             session=session,
                             task_id=self._application_generate_entity.task_id,
                             workflow_run=workflow_run,
                 yield self._message_end_to_stream_response()
                 break
             elif isinstance(event, QueueRetrieverResourcesEvent):
-                self._handle_retriever_resources(event)
+                self._message_cycle_manager._handle_retriever_resources(event)
 
-                with Session(db.engine) as session:
+                with Session(db.engine, expire_on_commit=False) as session:
                     message = self._get_message(session=session)
                     message.message_metadata = (
                         json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
                     )
                     session.commit()
             elif isinstance(event, QueueAnnotationReplyEvent):
-                self._handle_annotation_reply(event)
+                self._message_cycle_manager._handle_annotation_reply(event)
 
-                with Session(db.engine) as session:
+                with Session(db.engine, expire_on_commit=False) as session:
                     message = self._get_message(session=session)
                     message.message_metadata = (
                         json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
                     tts_publisher.publish(queue_message)
 
                 self._task_state.answer += delta_text
-                yield self._message_to_stream_response(
+                yield self._message_cycle_manager._message_to_stream_response(
                     answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
                 )
             elif isinstance(event, QueueMessageReplaceEvent):
                 # published by moderation
-                yield self._message_replace_to_stream_response(answer=event.text)
+                yield self._message_cycle_manager._message_replace_to_stream_response(answer=event.text)
             elif isinstance(event, QueueAdvancedChatMessageEndEvent):
                 if not graph_runtime_state:
                     raise ValueError("graph runtime state not initialized.")
 
-                output_moderation_answer = self._handle_output_moderation_when_task_finished(self._task_state.answer)
+                output_moderation_answer = self._base_task_pipeline._handle_output_moderation_when_task_finished(
+                    self._task_state.answer
+                )
                 if output_moderation_answer:
                     self._task_state.answer = output_moderation_answer
-                    yield self._message_replace_to_stream_response(answer=output_moderation_answer)
+                    yield self._message_cycle_manager._message_replace_to_stream_response(
+                        answer=output_moderation_answer
+                    )
 
                 # Save message
-                with Session(db.engine) as session:
+                with Session(db.engine, expire_on_commit=False) as session:
                     self._save_message(session=session, graph_runtime_state=graph_runtime_state)
                     session.commit()
 
     def _save_message(self, *, session: Session, graph_runtime_state: Optional[GraphRuntimeState] = None) -> None:
         message = self._get_message(session=session)
         message.answer = self._task_state.answer
-        message.provider_response_latency = time.perf_counter() - self._start_at
+        message.provider_response_latency = time.perf_counter() - self._base_task_pipeline._start_at
         message.message_metadata = (
             json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
         )
         :param text: text
         :return: True if output moderation should direct output, otherwise False
         """
-        if self._output_moderation_handler:
-            if self._output_moderation_handler.should_direct_output():
+        if self._base_task_pipeline._output_moderation_handler:
+            if self._base_task_pipeline._output_moderation_handler.should_direct_output():
                 # stop subscribe new token when output moderation should direct output
-                self._task_state.answer = self._output_moderation_handler.get_final_output()
-                self._queue_manager.publish(
+                self._task_state.answer = self._base_task_pipeline._output_moderation_handler.get_final_output()
+                self._base_task_pipeline._queue_manager.publish(
                     QueueTextChunkEvent(text=self._task_state.answer), PublishFrom.TASK_PIPELINE
                 )
 
-                self._queue_manager.publish(
+                self._base_task_pipeline._queue_manager.publish(
                     QueueStopEvent(stopped_by=QueueStopEvent.StopBy.OUTPUT_MODERATION), PublishFrom.TASK_PIPELINE
                 )
                 return True
             else:
-                self._output_moderation_handler.append_new_token(text)
+                self._base_task_pipeline._output_moderation_handler.append_new_token(text)
 
         return False
 

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

 from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
 from core.app.entities.app_invoke_entities import AgentChatAppGenerateEntity, InvokeFrom
-from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
+from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from extensions.ext_database import db
 from factories import file_factory
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except (ValueError, InvokeError) as e:
+            except ValueError as e:
                 if dify_config.DEBUG:
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)

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

 from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
 from core.app.entities.app_invoke_entities import ChatAppGenerateEntity, InvokeFrom
-from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
+from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from extensions.ext_database import db
 from factories import file_factory
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except (ValueError, InvokeError) as e:
+            except ValueError as e:
                 if dify_config.DEBUG:
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)

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

 from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
 from core.app.entities.app_invoke_entities import CompletionAppGenerateEntity, InvokeFrom
-from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
+from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from extensions.ext_database import db
 from factories import file_factory
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except (ValueError, InvokeError) as e:
+            except ValueError as e:
                 if dify_config.DEBUG:
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)

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

 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.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
+from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from extensions.ext_database import db
 from factories import file_factory
             single_iteration_run=WorkflowAppGenerateEntity.SingleIterationRunEntity(
                 node_id=node_id, inputs=args["inputs"]
             ),
+            workflow_run_id=str(uuid.uuid4()),
         )
         contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
         contexts.plugin_tool_providers.set({})
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except (ValueError, InvokeError) as e:
+            except ValueError as e:
                 if dify_config.DEBUG:
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)

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

 import logging
 import time
 from collections.abc import Generator
-from typing import Any, Optional, Union
+from typing import Optional, Union
 
 from sqlalchemy.orm import Session
 
     Workflow,
     WorkflowAppLog,
     WorkflowAppLogCreatedFrom,
-    WorkflowNodeExecution,
     WorkflowRun,
     WorkflowRunStatus,
 )
 logger = logging.getLogger(__name__)
 
 
-class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleManage):
+class WorkflowAppGenerateTaskPipeline:
     """
     WorkflowAppGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
 
-    _task_state: WorkflowTaskState
-    _application_generate_entity: WorkflowAppGenerateEntity
-    _workflow_system_variables: dict[SystemVariableKey, Any]
-    _wip_workflow_node_executions: dict[str, WorkflowNodeExecution]
-
     def __init__(
         self,
         application_generate_entity: WorkflowAppGenerateEntity,
         user: Union[Account, EndUser],
         stream: bool,
     ) -> None:
-        super().__init__(
+        self._base_task_pipeline = BasedGenerateTaskPipeline(
             application_generate_entity=application_generate_entity,
             queue_manager=queue_manager,
             stream=stream,
         else:
             raise ValueError(f"Invalid user type: {type(user)}")
 
+        self._workflow_cycle_manager = WorkflowCycleManage(
+            application_generate_entity=application_generate_entity,
+            workflow_system_variables={
+                SystemVariableKey.FILES: application_generate_entity.files,
+                SystemVariableKey.USER_ID: user_session_id,
+                SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
+                SystemVariableKey.WORKFLOW_ID: workflow.id,
+                SystemVariableKey.WORKFLOW_RUN_ID: application_generate_entity.workflow_run_id,
+            },
+        )
+
+        self._application_generate_entity = application_generate_entity
         self._workflow_id = workflow.id
         self._workflow_features_dict = workflow.features_dict
-
-        self._workflow_system_variables = {
-            SystemVariableKey.FILES: application_generate_entity.files,
-            SystemVariableKey.USER_ID: user_session_id,
-            SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
-            SystemVariableKey.WORKFLOW_ID: workflow.id,
-            SystemVariableKey.WORKFLOW_RUN_ID: application_generate_entity.workflow_run_id,
-        }
-
         self._task_state = WorkflowTaskState()
         self._workflow_run_id = ""
 
         :return:
         """
         generator = self._wrapper_process_stream_response(trace_manager=self._application_generate_entity.trace_manager)
-        if self._stream:
+        if self._base_task_pipeline._stream:
             return self._to_stream_response(generator)
         else:
             return self._to_blocking_response(generator)
         """
         graph_runtime_state = None
 
-        for queue_message in self._queue_manager.listen():
+        for queue_message in self._base_task_pipeline._queue_manager.listen():
             event = queue_message.event
 
             if isinstance(event, QueuePingEvent):
-                yield self._ping_stream_response()
+                yield self._base_task_pipeline._ping_stream_response()
             elif isinstance(event, QueueErrorEvent):
-                err = self._handle_error(event=event)
-                yield self._error_to_stream_response(err)
+                err = self._base_task_pipeline._handle_error(event=event)
+                yield self._base_task_pipeline._error_to_stream_response(err)
                 break
             elif isinstance(event, QueueWorkflowStartedEvent):
                 # override graph runtime state
                 graph_runtime_state = event.graph_runtime_state
 
-                with Session(db.engine) as session:
+                with Session(db.engine, expire_on_commit=False) as session:
                     # init workflow run
-                    workflow_run = self._handle_workflow_run_start(
+                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_start(
                         session=session,
                         workflow_id=self._workflow_id,
                         user_id=self._user_id,
                         created_by_role=self._created_by_role,
                     )
                     self._workflow_run_id = workflow_run.id
-                    start_resp = self._workflow_start_to_stream_response(
+                    start_resp = self._workflow_cycle_manager._workflow_start_to_stream_response(
                         session=session, task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
                     session.commit()
             ):
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    workflow_node_execution = self._handle_workflow_node_execution_retried(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    workflow_node_execution = self._workflow_cycle_manager._handle_workflow_node_execution_retried(
                         session=session, workflow_run=workflow_run, event=event
                     )
-                    response = self._workflow_node_retry_to_stream_response(
+                    response = self._workflow_cycle_manager._workflow_node_retry_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    workflow_node_execution = self._handle_node_execution_start(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    workflow_node_execution = self._workflow_cycle_manager._handle_node_execution_start(
                         session=session, workflow_run=workflow_run, event=event
                     )
-                    node_start_response = self._workflow_node_start_to_stream_response(
+                    node_start_response = self._workflow_cycle_manager._workflow_node_start_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
                 if node_start_response:
                     yield node_start_response
             elif isinstance(event, QueueNodeSucceededEvent):
-                with Session(db.engine) as session:
-                    workflow_node_execution = self._handle_workflow_node_execution_success(session=session, event=event)
-                    node_success_response = self._workflow_node_finish_to_stream_response(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_node_execution = self._workflow_cycle_manager._handle_workflow_node_execution_success(
+                        session=session, event=event
+                    )
+                    node_success_response = self._workflow_cycle_manager._workflow_node_finish_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
                 if node_success_response:
                     yield node_success_response
             elif isinstance(event, QueueNodeFailedEvent | QueueNodeInIterationFailedEvent | QueueNodeExceptionEvent):
-                with Session(db.engine) as session:
-                    workflow_node_execution = self._handle_workflow_node_execution_failed(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_node_execution = self._workflow_cycle_manager._handle_workflow_node_execution_failed(
                         session=session,
                         event=event,
                     )
-                    node_failed_response = self._workflow_node_finish_to_stream_response(
+                    node_failed_response = self._workflow_cycle_manager._workflow_node_finish_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    parallel_start_resp = self._workflow_parallel_branch_start_to_stream_response(
-                        session=session,
-                        task_id=self._application_generate_entity.task_id,
-                        workflow_run=workflow_run,
-                        event=event,
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    parallel_start_resp = (
+                        self._workflow_cycle_manager._workflow_parallel_branch_start_to_stream_response(
+                            session=session,
+                            task_id=self._application_generate_entity.task_id,
+                            workflow_run=workflow_run,
+                            event=event,
+                        )
                     )
 
                 yield parallel_start_resp
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    parallel_finish_resp = self._workflow_parallel_branch_finished_to_stream_response(
-                        session=session,
-                        task_id=self._application_generate_entity.task_id,
-                        workflow_run=workflow_run,
-                        event=event,
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    parallel_finish_resp = (
+                        self._workflow_cycle_manager._workflow_parallel_branch_finished_to_stream_response(
+                            session=session,
+                            task_id=self._application_generate_entity.task_id,
+                            workflow_run=workflow_run,
+                            event=event,
+                        )
                     )
 
                 yield parallel_finish_resp
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    iter_start_resp = self._workflow_iteration_start_to_stream_response(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    iter_start_resp = self._workflow_cycle_manager._workflow_iteration_start_to_stream_response(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    iter_next_resp = self._workflow_iteration_next_to_stream_response(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    iter_next_resp = self._workflow_cycle_manager._workflow_iteration_next_to_stream_response(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._get_workflow_run(session=session, workflow_run_id=self._workflow_run_id)
-                    iter_finish_resp = self._workflow_iteration_completed_to_stream_response(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    iter_finish_resp = self._workflow_cycle_manager._workflow_iteration_completed_to_stream_response(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
                 if not graph_runtime_state:
                     raise ValueError("graph runtime state not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._handle_workflow_run_success(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_success(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
                     # save workflow app log
                     self._save_workflow_app_log(session=session, workflow_run=workflow_run)
 
-                    workflow_finish_resp = self._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
                 if not graph_runtime_state:
                     raise ValueError("graph runtime state not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._handle_workflow_run_partial_success(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_partial_success(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
                     # save workflow app log
                     self._save_workflow_app_log(session=session, workflow_run=workflow_run)
 
-                    workflow_finish_resp = self._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
                         session=session, task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
                     session.commit()
                 if not graph_runtime_state:
                     raise ValueError("graph runtime state not initialized.")
 
-                with Session(db.engine) as session:
-                    workflow_run = self._handle_workflow_run_failed(
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_failed(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
                     # save workflow app log
                     self._save_workflow_app_log(session=session, workflow_run=workflow_run)
 
-                    workflow_finish_resp = self._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
                         session=session, task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
                     session.commit()

api/core/app/entities/app_invoke_entities.py

 
     # app config
     app_config: WorkflowUIBasedAppConfig
-    workflow_run_id: Optional[str] = None
+    workflow_run_id: str
 
     class SingleIterationRunEntity(BaseModel):
         """

api/core/app/task_pipeline/based_generate_task_pipeline.py

 from core.app.entities.task_entities import (
     ErrorStreamResponse,
     PingStreamResponse,
-    TaskState,
 )
 from core.errors.error import QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
     BasedGenerateTaskPipeline is a class that generate stream output and state management for Application.
     """
 
-    _task_state: TaskState
-    _application_generate_entity: AppGenerateEntity
-
     def __init__(
         self,
         application_generate_entity: AppGenerateEntity,
         queue_manager: AppQueueManager,
         stream: bool,
     ) -> None:
-        """
-        Initialize GenerateTaskPipeline.
-        :param application_generate_entity: application generate entity
-        :param queue_manager: queue manager
-        :param user: user
-        :param stream: stream
-        """
         self._application_generate_entity = application_generate_entity
         self._queue_manager = queue_manager
         self._start_at = time.perf_counter()

api/core/app/task_pipeline/message_cycle_manage.py

 
 
 class MessageCycleManage:
-    _application_generate_entity: Union[
-        ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity, AdvancedChatAppGenerateEntity
-    ]
-    _task_state: Union[EasyUITaskState, WorkflowTaskState]
+    def __init__(
+        self,
+        *,
+        application_generate_entity: Union[
+            ChatAppGenerateEntity,
+            CompletionAppGenerateEntity,
+            AgentChatAppGenerateEntity,
+            AdvancedChatAppGenerateEntity,
+        ],
+        task_state: Union[EasyUITaskState, WorkflowTaskState],
+    ) -> None:
+        self._application_generate_entity = application_generate_entity
+        self._task_state = task_state
 
     def _generate_conversation_name(self, *, conversation_id: str, query: str) -> Optional[Thread]:
         """

api/core/app/task_pipeline/workflow_cycle_manage.py

     ParallelBranchStartStreamResponse,
     WorkflowFinishStreamResponse,
     WorkflowStartStreamResponse,
-    WorkflowTaskState,
 )
 from core.file import FILE_MODEL_IDENTITY, File
 from core.model_runtime.utils.encoders import jsonable_encoder
     WorkflowRunStatus,
 )
 
-from .exc import WorkflowNodeExecutionNotFoundError, WorkflowRunNotFoundError
+from .exc import WorkflowRunNotFoundError
 
 
 class WorkflowCycleManage:
-    _application_generate_entity: Union[AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity]
-    _task_state: WorkflowTaskState
-    _workflow_system_variables: dict[SystemVariableKey, Any]
+    def __init__(
+        self,
+        *,
+        application_generate_entity: Union[AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity],
+        workflow_system_variables: dict[SystemVariableKey, Any],
+    ) -> None:
+        self._workflow_run: WorkflowRun | None = None
+        self._workflow_node_executions: dict[str, WorkflowNodeExecution] = {}
+        self._application_generate_entity = application_generate_entity
+        self._workflow_system_variables = workflow_system_variables
 
     def _handle_workflow_run_start(
         self,
         inputs = dict(WorkflowEntry.handle_special_values(inputs) or {})
 
         # init workflow run
-        workflow_run_id = str(self._workflow_system_variables.get(SystemVariableKey.WORKFLOW_RUN_ID, uuid4()))
+        # TODO: This workflow_run_id should always not be None, maybe we can use a more elegant way to handle this
+        workflow_run_id = str(self._workflow_system_variables.get(SystemVariableKey.WORKFLOW_RUN_ID) or uuid4())
 
         workflow_run = WorkflowRun()
         workflow_run.id = workflow_run_id
         workflow_run.finished_at = datetime.now(UTC).replace(tzinfo=None)
         workflow_run.exceptions_count = exceptions_count
 
-        stmt = select(WorkflowNodeExecution).where(
+        stmt = select(WorkflowNodeExecution.node_execution_id).where(
             WorkflowNodeExecution.tenant_id == workflow_run.tenant_id,
             WorkflowNodeExecution.app_id == workflow_run.app_id,
             WorkflowNodeExecution.workflow_id == workflow_run.workflow_id,
             WorkflowNodeExecution.workflow_run_id == workflow_run.id,
             WorkflowNodeExecution.status == WorkflowNodeExecutionStatus.RUNNING.value,
         )
-
-        running_workflow_node_executions = session.scalars(stmt).all()
+        ids = session.scalars(stmt).all()
+        # Use self._get_workflow_node_execution here to make sure the cache is updated
+        running_workflow_node_executions = [
+            self._get_workflow_node_execution(session=session, node_execution_id=id) for id in ids if id
+        ]
 
         for workflow_node_execution in running_workflow_node_executions:
+            now = datetime.now(UTC).replace(tzinfo=None)
             workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
             workflow_node_execution.error = error
-            finish_at = datetime.now(UTC).replace(tzinfo=None)
-            workflow_node_execution.finished_at = finish_at
-            workflow_node_execution.elapsed_time = (finish_at - workflow_node_execution.created_at).total_seconds()
+            workflow_node_execution.finished_at = now
+            workflow_node_execution.elapsed_time = (now - workflow_node_execution.created_at).total_seconds()
 
         if trace_manager:
             trace_manager.add_trace_task(
         workflow_node_execution.created_at = datetime.now(UTC).replace(tzinfo=None)
 
         session.add(workflow_node_execution)
+
+        self._workflow_node_executions[event.node_execution_id] = workflow_node_execution
         return workflow_node_execution
 
     def _handle_workflow_node_execution_success(
         workflow_node_execution.finished_at = finished_at
         workflow_node_execution.elapsed_time = elapsed_time
 
+        workflow_node_execution = session.merge(workflow_node_execution)
         return workflow_node_execution
 
     def _handle_workflow_node_execution_failed(
         workflow_node_execution.elapsed_time = elapsed_time
         workflow_node_execution.execution_metadata = execution_metadata
 
+        workflow_node_execution = session.merge(workflow_node_execution)
         return workflow_node_execution
 
     def _handle_workflow_node_execution_retried(
         workflow_node_execution.index = event.node_run_index
 
         session.add(workflow_node_execution)
+
+        self._workflow_node_executions[event.node_execution_id] = workflow_node_execution
         return workflow_node_execution
 
     #################################################
         return None
 
     def _get_workflow_run(self, *, session: Session, workflow_run_id: str) -> WorkflowRun:
-        """
-        Refetch workflow run
-        :param workflow_run_id: workflow run id
-        :return:
-        """
+        if self._workflow_run and self._workflow_run.id == workflow_run_id:
+            cached_workflow_run = self._workflow_run
+            cached_workflow_run = session.merge(cached_workflow_run)
+            return cached_workflow_run
         stmt = select(WorkflowRun).where(WorkflowRun.id == workflow_run_id)
         workflow_run = session.scalar(stmt)
         if not workflow_run:
             raise WorkflowRunNotFoundError(workflow_run_id)
+        self._workflow_run = workflow_run
 
         return workflow_run
 
     def _get_workflow_node_execution(self, session: Session, node_execution_id: str) -> WorkflowNodeExecution:
-        stmt = select(WorkflowNodeExecution).where(WorkflowNodeExecution.node_execution_id == node_execution_id)
-        workflow_node_execution = session.scalar(stmt)
-        if not workflow_node_execution:
-            raise WorkflowNodeExecutionNotFoundError(node_execution_id)
-
-        return workflow_node_execution
+        if node_execution_id not in self._workflow_node_executions:
+            raise ValueError(f"Workflow node execution not found: {node_execution_id}")
+        cached_workflow_node_execution = self._workflow_node_executions[node_execution_id]
+        return cached_workflow_node_execution
 
     def _handle_agent_log(self, task_id: str, event: QueueAgentLogEvent) -> AgentLogStreamResponse:
         """

api/core/model_runtime/model_providers/__base/tokenizers/gpt2_tokenzier.py

-import tiktoken
+from threading import Lock
+from typing import Any
+
+_tokenizer: Any = None
+_lock = Lock()
 
 
 class GPT2Tokenizer:
+    @staticmethod
+    def _get_num_tokens_by_gpt2(text: str) -> int:
+        """
+        use gpt2 tokenizer to get num tokens
+        """
+        _tokenizer = GPT2Tokenizer.get_encoder()
+        tokens = _tokenizer.encode(text)
+        return len(tokens)
+
     @staticmethod
     def get_num_tokens(text: str) -> int:
-        encoding = tiktoken.encoding_for_model("gpt2")
-        tiktoken_vec = encoding.encode(text)
-        return len(tiktoken_vec)
+        # Because this process needs more cpu resource, we turn this back before we find a better way to handle it.
+        #
+        # future = _executor.submit(GPT2Tokenizer._get_num_tokens_by_gpt2, text)
+        # result = future.result()
+        # return cast(int, result)
+        return GPT2Tokenizer._get_num_tokens_by_gpt2(text)
+
+    @staticmethod
+    def get_encoder() -> Any:
+        global _tokenizer, _lock
+        with _lock:
+            if _tokenizer is None:
+                # Try to use tiktoken to get the tokenizer because it is faster
+                #
+                try:
+                    import tiktoken
+
+                    _tokenizer = tiktoken.get_encoding("gpt2")
+                except Exception:
+                    from os.path import abspath, dirname, join
+
+                    from transformers import GPT2Tokenizer as TransformerGPT2Tokenizer  # type: ignore
+
+                    base_path = abspath(__file__)
+                    gpt2_tokenizer_path = join(dirname(base_path), "gpt2")
+                    _tokenizer = TransformerGPT2Tokenizer.from_pretrained(gpt2_tokenizer_path)
+
+            return _tokenizer

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

         return False
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        if not ids:
+            return
         quoted_ids = [f"'{id}'" for id in ids]
         self._db.table(self._collection_name).delete(filter=f"id IN({', '.join(quoted_ids)})")
 

api/core/rag/datasource/vdb/chroma/chroma_vector.py

         self._client.delete_collection(self._collection_name)
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        if not ids:
+            return
         collection = self._client.get_or_create_collection(self._collection_name)
         collection.delete(ids=ids)
 

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

+import json
+import logging
+from typing import Any, Optional
+
+from flask import current_app
+
+from core.rag.datasource.vdb.elasticsearch.elasticsearch_vector import (
+    ElasticSearchConfig,
+    ElasticSearchVector,
+    ElasticSearchVectorFactory,
+)
+from core.rag.datasource.vdb.field import Field
+from core.rag.datasource.vdb.vector_type import VectorType
+from core.rag.embedding.embedding_base import Embeddings
+from extensions.ext_redis import redis_client
+from models.dataset import Dataset
+
+logger = logging.getLogger(__name__)
+
+
+class ElasticSearchJaVector(ElasticSearchVector):
+    def create_collection(
+        self,
+        embeddings: list[list[float]],
+        metadatas: Optional[list[dict[Any, Any]]] = None,
+        index_params: Optional[dict] = None,
+    ):
+        lock_name = f"vector_indexing_lock_{self._collection_name}"
+        with redis_client.lock(lock_name, timeout=20):
+            collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
+            if redis_client.get(collection_exist_cache_key):
+                logger.info(f"Collection {self._collection_name} already exists.")
+                return
+
+            if not self._client.indices.exists(index=self._collection_name):
+                dim = len(embeddings[0])
+                settings = {
+                    "analysis": {
+                        "analyzer": {
+                            "ja_analyzer": {
+                                "type": "custom",
+                                "char_filter": [
+                                    "icu_normalizer",
+                                    "kuromoji_iteration_mark",
+                                ],
+                                "tokenizer": "kuromoji_tokenizer",
+                                "filter": [
+                                    "kuromoji_baseform",
+                                    "kuromoji_part_of_speech",
+                                    "ja_stop",
+                                    "kuromoji_number",
+                                    "kuromoji_stemmer",
+                                ],
+                            }
+                        }
+                    }
+                }
+                mappings = {
+                    "properties": {
+                        Field.CONTENT_KEY.value: {
+                            "type": "text",
+                            "analyzer": "ja_analyzer",
+                            "search_analyzer": "ja_analyzer",
+                        },
+                        Field.VECTOR.value: {  # Make sure the dimension is correct here
+                            "type": "dense_vector",
+                            "dims": dim,
+                            "index": True,
+                            "similarity": "cosine",
+                        },
+                        Field.METADATA_KEY.value: {
+                            "type": "object",
+                            "properties": {
+                                "doc_id": {"type": "keyword"}  # Map doc_id to keyword type
+                            },
+                        },
+                    }
+                }
+                self._client.indices.create(index=self._collection_name, settings=settings, mappings=mappings)
+
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+
+
+class ElasticSearchJaVectorFactory(ElasticSearchVectorFactory):
+    def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> ElasticSearchJaVector:
+        if dataset.index_struct_dict:
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
+            collection_name = class_prefix
+        else:
+            dataset_id = dataset.id
+            collection_name = Dataset.gen_collection_name_by_id(dataset_id)
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.ELASTICSEARCH, collection_name))
+
+        config = current_app.config
+        return ElasticSearchJaVector(
+            index_name=collection_name,
+            config=ElasticSearchConfig(
+                host=config.get("ELASTICSEARCH_HOST", "localhost"),
+                port=config.get("ELASTICSEARCH_PORT", 9200),
+                username=config.get("ELASTICSEARCH_USERNAME", ""),
+                password=config.get("ELASTICSEARCH_PASSWORD", ""),
+            ),
+            attributes=[],
+        )

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

         return bool(self._client.exists(index=self._collection_name, id=id))
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        if not ids:
+            return
         for id in ids:
             self._client.delete(index=self._collection_name, id=id)
 

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

     METADATA_KEY = "metadata"
     GROUP_KEY = "group_id"
     VECTOR = "vector"
+    # Sparse Vector aims to support full text search
+    SPARSE_VECTOR = "sparse_vector"
     TEXT_KEY = "text"
     PRIMARY_KEY = "id"
     DOC_ID = "metadata.doc_id"

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

 import logging
 from typing import Any, Optional
 
+from packaging import version
 from pydantic import BaseModel, model_validator
 from pymilvus import MilvusClient, MilvusException  # type: ignore
 from pymilvus.milvus_client import IndexParams  # type: ignore
 
 
 class MilvusConfig(BaseModel):
-    uri: str
-    token: Optional[str] = None
-    user: str
-    password: str
-    batch_size: int = 100
-    database: str = "default"
+    """
+    Configuration class for Milvus connection.
+    """
+
+    uri: str  # Milvus server URI
+    token: Optional[str] = None  # Optional token for authentication
+    user: str  # Username for authentication
+    password: str  # Password for authentication
+    batch_size: int = 100  # Batch size for operations
+    database: str = "default"  # Database name
+    enable_hybrid_search: bool = False  # Flag to enable hybrid search
 
     @model_validator(mode="before")
     @classmethod
     def validate_config(cls, values: dict) -> dict:
+        """
+        Validate the configuration values.
+        Raises ValueError if required fields are missing.
+        """
         if not values.get("uri"):
             raise ValueError("config MILVUS_URI is required")
         if not values.get("user"):
         return values
 
     def to_milvus_params(self):
+        """
+        Convert the configuration to a dictionary of Milvus connection parameters.
+        """
         return {
             "uri": self.uri,
             "token": self.token,
 
 
 class MilvusVector(BaseVector):
+    """
+    Milvus vector storage implementation.
+    """
+
     def __init__(self, collection_name: str, config: MilvusConfig):
         super().__init__(collection_name)
         self._client_config = config
         self._client = self._init_client(config)
-        self._consistency_level = "Session"
-        self._fields: list[str] = []
+        self._consistency_level = "Session"  # Consistency level for Milvus operations
+        self._fields: list[str] = []  # List of fields in the collection
+        self._hybrid_search_enabled = self._check_hybrid_search_support()  # Check if hybrid search is supported
+
+    def _check_hybrid_search_support(self) -> bool:
+        """
+        Check if the current Milvus version supports hybrid search.
+        Returns True if the version is >= 2.5.0, otherwise False.
+        """
+        if not self._client_config.enable_hybrid_search:
+            return False
+
+        try:
+            milvus_version = self._client.get_server_version()
+            return version.parse(milvus_version).base_version >= version.parse("2.5.0").base_version
+        except Exception as e:
+            logger.warning(f"Failed to check Milvus version: {str(e)}. Disabling hybrid search.")
+            return False
 
     def get_type(self) -> str:
+        """
+        Get the type of vector storage (Milvus).
+        """
         return VectorType.MILVUS
 
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
+        """
+        Create a collection and add texts with embeddings.
+        """
         index_params = {"metric_type": "IP", "index_type": "HNSW", "params": {"M": 8, "efConstruction": 64}}
         metadatas = [d.metadata if d.metadata is not None else {} for d in texts]
         self.create_collection(embeddings, metadatas, index_params)
         self.add_texts(texts, embeddings)
 
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        """
+        Add texts and their embeddings to the collection.
+        """
         insert_dict_list = []
         for i in range(len(documents)):
             insert_dict = {
+                # Do not need to insert the sparse_vector field separately, as the text_bm25_emb
+                # function will automatically convert the native text into a sparse vector for us.
                 Field.CONTENT_KEY.value: documents[i].page_content,
                 Field.VECTOR.value: embeddings[i],
                 Field.METADATA_KEY.value: documents[i].metadata,
             insert_dict_list.append(insert_dict)
         # Total insert count
         total_count = len(insert_dict_list)
-
         pks: list[str] = []
 
         for i in range(0, total_count, 1000):
-            batch_insert_list = insert_dict_list[i : i + 1000]
             # Insert into the collection.
+            batch_insert_list = insert_dict_list[i : i + 1000]
             try:
                 ids = self._client.insert(collection_name=self._collection_name, data=batch_insert_list)
                 pks.extend(ids)
         return pks
 
     def get_ids_by_metadata_field(self, key: str, value: str):
+        """
+        Get document IDs by metadata field key and value.
+        """
         result = self._client.query(
             collection_name=self._collection_name, filter=f'metadata["{key}"] == "{value}"', output_fields=["id"]
         )
             return None
 
     def delete_by_metadata_field(self, key: str, value: str):
+        """
+        Delete documents by metadata field key and value.
+        """
         if self._client.has_collection(self._collection_name):
             ids = self.get_ids_by_metadata_field(key, value)
             if ids:
                 self._client.delete(collection_name=self._collection_name, pks=ids)
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        """
+        Delete documents by their IDs.
+        """
         if self._client.has_collection(self._collection_name):
             result = self._client.query(
                 collection_name=self._collection_name, filter=f'metadata["doc_id"] in {ids}', output_fields=["id"]
                 self._client.delete(collection_name=self._collection_name, pks=ids)
 
     def delete(self) -> None:
+        """
+        Delete the entire collection.
+        """
         if self._client.has_collection(self._collection_name):
             self._client.drop_collection(self._collection_name, None)
 
     def text_exists(self, id: str) -> bool:
+        """
+        Check if a text with the given ID exists in the collection.
+        """
         if not self._client.has_collection(self._collection_name):
             return False
 
 
         return len(result) > 0
 
+    def field_exists(self, field: str) -> bool:
+        """
+        Check if a field exists in the collection.
+        """
+        return field in self._fields
+
+    def _process_search_results(
+        self, results: list[Any], output_fields: list[str], score_threshold: float = 0.0
+    ) -> list[Document]:
+        """
+        Common method to process search results
+
+        :param results: Search results
+        :param output_fields: Fields to be output
+        :param score_threshold: Score threshold for filtering
+        :return: List of documents
+        """
+        docs = []
+        for result in results[0]:
+            metadata = result["entity"].get(output_fields[1], {})
+            metadata["score"] = result["distance"]
+
+            if result["distance"] > score_threshold:
+                doc = Document(page_content=result["entity"].get(output_fields[0], ""), metadata=metadata)
+                docs.append(doc)
+
+        return docs
+
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
-        # Set search parameters.
+        """
+        Search for documents by vector similarity.
+        """
         results = self._client.search(
             collection_name=self._collection_name,
             data=[query_vector],
+            anns_field=Field.VECTOR.value,
             limit=kwargs.get("top_k", 4),
             output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
         )
-        # Organize results.
-        docs = []
-        for result in results[0]:
-            metadata = result["entity"].get(Field.METADATA_KEY.value)
-            metadata["score"] = result["distance"]
-            score_threshold = float(kwargs.get("score_threshold") or 0.0)
-            if result["distance"] > score_threshold:
-                doc = Document(page_content=result["entity"].get(Field.CONTENT_KEY.value), metadata=metadata)
-                docs.append(doc)
-        return docs
+
+        return self._process_search_results(
+            results,
+            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+            score_threshold=float(kwargs.get("score_threshold") or 0.0),
+        )
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        # milvus/zilliz doesn't support bm25 search
-        return []
+        """
+        Search for documents by full-text search (if hybrid search is enabled).
+        """
+        if not self._hybrid_search_enabled or not self.field_exists(Field.SPARSE_VECTOR.value):
+            logger.warning("Full-text search is not supported in current Milvus version (requires >= 2.5.0)")
+            return []
+
+        results = self._client.search(
+            collection_name=self._collection_name,
+            data=[query],
+            anns_field=Field.SPARSE_VECTOR.value,
+            limit=kwargs.get("top_k", 4),
+            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+        )
+
+        return self._process_search_results(
+            results,
+            output_fields=[Field.CONTENT_KEY.value, Field.METADATA_KEY.value],
+            score_threshold=float(kwargs.get("score_threshold") or 0.0),
+        )
 
     def create_collection(
         self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
     ):
+        """
+        Create a new collection in Milvus with the specified schema and index parameters.
+        """
         lock_name = "vector_indexing_lock_{}".format(self._collection_name)
         with redis_client.lock(lock_name, timeout=20):
             collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
                 return
             # Grab the existing collection if it exists
             if not self._client.has_collection(self._collection_name):
-                from pymilvus import CollectionSchema, DataType, FieldSchema  # type: ignore
+                from pymilvus import CollectionSchema, DataType, FieldSchema, Function, FunctionType  # type: ignore
                 from pymilvus.orm.types import infer_dtype_bydata  # type: ignore
 
                 # Determine embedding dim
                 if metadatas:
                     fields.append(FieldSchema(Field.METADATA_KEY.value, DataType.JSON, max_length=65_535))
 
-                # Create the text field
-                fields.append(FieldSchema(Field.CONTENT_KEY.value, DataType.VARCHAR, max_length=65_535))
+                # Create the text field, enable_analyzer will be set True to support milvus automatically
+                # transfer text to sparse_vector, reference: https://milvus.io/docs/full-text-search.md
+                fields.append(
+                    FieldSchema(
+                        Field.CONTENT_KEY.value,
+                        DataType.VARCHAR,
+                        max_length=65_535,
+                        enable_analyzer=self._hybrid_search_enabled,
+                    )
+                )
                 # Create the primary key field
                 fields.append(FieldSchema(Field.PRIMARY_KEY.value, DataType.INT64, is_primary=True, auto_id=True))
                 # Create the vector field, supports binary or float vectors
                 fields.append(FieldSchema(Field.VECTOR.value, infer_dtype_bydata(embeddings[0]), dim=dim))
+                # Create Sparse Vector Index for the collection
+                if self._hybrid_search_enabled:
+                    fields.append(FieldSchema(Field.SPARSE_VECTOR.value, DataType.SPARSE_FLOAT_VECTOR))
 
-                # Create the schema for the collection
                 schema = CollectionSchema(fields)
 
+                # Create custom function to support text to sparse vector by BM25
+                if self._hybrid_search_enabled:
+                    bm25_function = Function(
+                        name="text_bm25_emb",
+                        input_field_names=[Field.CONTENT_KEY.value],
+                        output_field_names=[Field.SPARSE_VECTOR.value],
+                        function_type=FunctionType.BM25,
+                    )
+                    schema.add_function(bm25_function)
+
                 for x in schema.fields:
                     self._fields.append(x.name)
                 # Since primary field is auto-id, no need to track it
                 index_params_obj = IndexParams()
                 index_params_obj.add_index(field_name=Field.VECTOR.value, **index_params)
 
+                # Create Sparse Vector Index for the collection
+                if self._hybrid_search_enabled:
+                    index_params_obj.add_index(
+                        field_name=Field.SPARSE_VECTOR.value, index_type="AUTOINDEX", metric_type="BM25"
+                    )
+
                 # Create the collection
-                collection_name = self._collection_name
                 self._client.create_collection(
-                    collection_name=collection_name,
+                    collection_name=self._collection_name,
                     schema=schema,
                     index_params=index_params_obj,
                     consistency_level=self._consistency_level,
             redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
     def _init_client(self, config) -> MilvusClient:
+        """
+        Initialize and return a Milvus client.
+        """
         client = MilvusClient(uri=config.uri, user=config.user, password=config.password, db_name=config.database)
         return client
 
 
 class MilvusVectorFactory(AbstractVectorFactory):
+    """
+    Factory class for creating MilvusVector instances.
+    """
+
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> MilvusVector:
+        """
+        Initialize a MilvusVector instance for the given dataset.
+        """
         if dataset.index_struct_dict:
             class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix
                 user=dify_config.MILVUS_USER or "",
                 password=dify_config.MILVUS_PASSWORD or "",
                 database=dify_config.MILVUS_DATABASE or "",
+                enable_hybrid_search=dify_config.MILVUS_ENABLE_HYBRID_SEARCH or False,
             ),
         )

api/core/rag/datasource/vdb/myscale/myscale_vector.py

         return results.row_count > 0
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        if not ids:
+            return
         self._client.command(
             f"DELETE FROM {self._config.database}.{self._collection_name} WHERE id IN {str(tuple(ids))}"
         )

api/core/rag/datasource/vdb/oceanbase/oceanbase_vector.py

         return bool(cur.rowcount != 0)
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        if not ids:
+            return
         self._client.delete(table_name=self._collection_name, ids=ids)
 
     def get_ids_by_metadata_field(self, key: str, value: str) -> list[str]:

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

         return docs
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        if not ids:
+            return
         with self._get_cursor() as cur:
             cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s" % (tuple(ids),))
 

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

         return docs
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        # Avoiding crashes caused by performing delete operations on empty lists in certain scenarios
+        # Scenario 1: extract a document fails, resulting in a table not being created.
+        # Then clicking the retry button triggers a delete operation on an empty list.
+        if not ids:
+            return
         with self._get_cursor() as cur:
             cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
 

api/core/rag/datasource/vdb/tencent/tencent_vector.py

         return False
 
     def delete_by_ids(self, ids: list[str]) -> None:
+        if not ids:
+            return
         self._db.collection(self._collection_name).delete(document_ids=ids)
 
     def delete_by_metadata_field(self, key: str, value: str) -> None:

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

             db.session.query(TidbAuthBinding).filter(TidbAuthBinding.tenant_id == dataset.tenant_id).one_or_none()
         )
         if not tidb_auth_binding:
-            idle_tidb_auth_binding = (
-                db.session.query(TidbAuthBinding)
-                .filter(TidbAuthBinding.active == False, TidbAuthBinding.status == "ACTIVE")
-                .limit(1)
-                .one_or_none()
-            )
-            if idle_tidb_auth_binding:
-                idle_tidb_auth_binding.active = True
-                idle_tidb_auth_binding.tenant_id = dataset.tenant_id
-                db.session.commit()
-                TIDB_ON_QDRANT_API_KEY = f"{idle_tidb_auth_binding.account}:{idle_tidb_auth_binding.password}"
-            else:
-                with redis_client.lock("create_tidb_serverless_cluster_lock", timeout=900):
-                    tidb_auth_binding = (
+            with redis_client.lock("create_tidb_serverless_cluster_lock", timeout=900):
+                tidb_auth_binding = (
+                    db.session.query(TidbAuthBinding)
+                    .filter(TidbAuthBinding.tenant_id == dataset.tenant_id)
+                    .one_or_none()
+                )
+                if tidb_auth_binding:
+                    TIDB_ON_QDRANT_API_KEY = f"{tidb_auth_binding.account}:{tidb_auth_binding.password}"
+
+                else:
+                    idle_tidb_auth_binding = (
                         db.session.query(TidbAuthBinding)
-                        .filter(TidbAuthBinding.tenant_id == dataset.tenant_id)
+                        .filter(TidbAuthBinding.active == False, TidbAuthBinding.status == "ACTIVE")
+                        .limit(1)
                         .one_or_none()
                     )
-                    if tidb_auth_binding:
-                        TIDB_ON_QDRANT_API_KEY = f"{tidb_auth_binding.account}:{tidb_auth_binding.password}"
-
+                    if idle_tidb_auth_binding:
+                        idle_tidb_auth_binding.active = True
+                        idle_tidb_auth_binding.tenant_id = dataset.tenant_id
+                        db.session.commit()
+                        TIDB_ON_QDRANT_API_KEY = f"{idle_tidb_auth_binding.account}:{idle_tidb_auth_binding.password}"
                     else:
                         new_cluster = TidbService.create_tidb_serverless_cluster(
                             dify_config.TIDB_PROJECT_ID or "",
                         db.session.add(new_tidb_auth_binding)
                         db.session.commit()
                         TIDB_ON_QDRANT_API_KEY = f"{new_tidb_auth_binding.account}:{new_tidb_auth_binding.password}"
-
         else:
             TIDB_ON_QDRANT_API_KEY = f"{tidb_auth_binding.account}:{tidb_auth_binding.password}"
 

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

                 from core.rag.datasource.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
 
                 return ElasticSearchVectorFactory
+            case VectorType.ELASTICSEARCH_JA:
+                from core.rag.datasource.vdb.elasticsearch.elasticsearch_ja_vector import (
+                    ElasticSearchJaVectorFactory,
+                )
+
+                return ElasticSearchJaVectorFactory
             case VectorType.TIDB_VECTOR:
                 from core.rag.datasource.vdb.tidb_vector.tidb_vector import TiDBVectorFactory
 

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

     TENCENT = "tencent"
     ORACLE = "oracle"
     ELASTICSEARCH = "elasticsearch"
+    ELASTICSEARCH_JA = "elasticsearch-ja"
     LINDORM = "lindorm"
     COUCHBASE = "couchbase"
     BAIDU = "baidu"

api/core/rag/extractor/pdf_extractor.py

         self._file_cache_key = file_cache_key
 
     def extract(self) -> list[Document]:
-        plaintext_file_key = ""
         plaintext_file_exists = False
         if self._file_cache_key:
             try:
         text = "\n\n".join(text_list)
 
         # save plaintext file for caching
-        if not plaintext_file_exists and plaintext_file_key:
-            storage.save(plaintext_file_key, text.encode("utf-8"))
+        if not plaintext_file_exists and self._file_cache_key:
+            storage.save(self._file_cache_key, text.encode("utf-8"))
 
         return documents
 

api/core/rag/index_processor/processor/parent_child_index_processor.py

 import uuid
 from typing import Optional
 
+from configs import dify_config
 from core.model_manager import ModelInstance
 from core.rag.cleaner.clean_processor import CleanProcessor
 from core.rag.datasource.retrieval_service import RetrievalService
             child_nodes = self._split_child_nodes(
                 document, rules, process_rule.get("mode"), kwargs.get("embedding_model_instance")
             )
+            if kwargs.get("preview"):
+                if len(child_nodes) > dify_config.CHILD_CHUNKS_PREVIEW_NUMBER:
+                    child_nodes = child_nodes[: dify_config.CHILD_CHUNKS_PREVIEW_NUMBER]
+
             document.children = child_nodes
             doc_id = str(uuid.uuid4())
             hash = helper.generate_text_hash(document.page_content)

api/core/tools/custom_tool/tool.py

             else:
                 body = body
 
-        if method in {"get", "head", "post", "put", "delete", "patch"}:
-            response: httpx.Response = getattr(ssrf_proxy, method)(
+        if method in {
+            "get",
+            "head",
+            "post",
+            "put",
+            "delete",
+            "patch",
+            "options",
+            "GET",
+            "POST",
+            "PUT",
+            "PATCH",
+            "DELETE",
+            "HEAD",
+            "OPTIONS",
+        }:
+            response: httpx.Response = getattr(ssrf_proxy, method.lower())(
                 url,
                 params=params,
                 headers=headers,

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

 import io
 import json
 import logging
+import operator
 import os
 import tempfile
-from typing import cast
+from collections.abc import Mapping, Sequence
+from typing import Any, cast
 
 import docx
 import pandas as pd
 import pypdfium2  # type: ignore
 import yaml  # type: ignore
+from docx.table import Table
+from docx.text.paragraph import Paragraph
 
 from configs import dify_config
 from core.file import File, FileTransferMethod, file_manager
                 process_data=process_data,
             )
 
+    @classmethod
+    def _extract_variable_selector_to_variable_mapping(
+        cls,
+        *,
+        graph_config: Mapping[str, Any],
+        node_id: str,
+        node_data: DocumentExtractorNodeData,
+    ) -> Mapping[str, Sequence[str]]:
+        """
+        Extract variable selector to variable mapping
+        :param graph_config: graph config
+        :param node_id: node id
+        :param node_data: node data
+        :return:
+        """
+        return {node_id + ".files": node_data.variable_selector}
+
 
 def _extract_text_by_mime_type(*, file_content: bytes, mime_type: str) -> str:
     """Extract text from a file based on its MIME type."""
         doc_file = io.BytesIO(file_content)
         doc = docx.Document(doc_file)
         text = []
-        # Process paragraphs
-        for paragraph in doc.paragraphs:
-            if paragraph.text.strip():
-                text.append(paragraph.text)
 
-        # Process tables
-        for table in doc.tables:
-            # Table header
-            try:
-                # table maybe cause errors so ignore it.
-                if len(table.rows) > 0 and table.rows[0].cells is not None:
+        # Keep track of paragraph and table positions
+        content_items: list[tuple[int, str, Table | Paragraph]] = []
+
+        # Process paragraphs and tables
+        for i, paragraph in enumerate(doc.paragraphs):
+            if paragraph.text.strip():
+                content_items.append((i, "paragraph", paragraph))
+
+        for i, table in enumerate(doc.tables):
+            content_items.append((i, "table", table))
+
+        # Sort content items based on their original position
+        content_items.sort(key=operator.itemgetter(0))
+
+        # Process sorted content
+        for _, item_type, item in content_items:
+            if item_type == "paragraph":
+                if isinstance(item, Table):
+                    continue
+                text.append(item.text)
+            elif item_type == "table":
+                # Process tables
+                if not isinstance(item, Table):
+                    continue
+                try:
                     # Check if any cell in the table has text
                     has_content = False
-                    for row in table.rows:
+                    for row in item.rows:
                         if any(cell.text.strip() for cell in row.cells):
                             has_content = True
                             break
 
                     if has_content:
-                        markdown_table = "| " + " | ".join(cell.text for cell in table.rows[0].cells) + " |\n"
-                        markdown_table += "| " + " | ".join(["---"] * len(table.rows[0].cells)) + " |\n"
-                        for row in table.rows[1:]:
-                            markdown_table += "| " + " | ".join(cell.text for cell in row.cells) + " |\n"
+                        cell_texts = [cell.text.replace("\n", "<br>") for cell in item.rows[0].cells]
+                        markdown_table = f"| {' | '.join(cell_texts)} |\n"
+                        markdown_table += f"| {' | '.join(['---'] * len(item.rows[0].cells))} |\n"
+
+                        for row in item.rows[1:]:
+                            # Replace newlines with <br> in each cell
+                            row_cells = [cell.text.replace("\n", "<br>") for cell in row.cells]
+                            markdown_table += "| " + " | ".join(row_cells) + " |\n"
+
                         text.append(markdown_table)
-            except Exception as e:
-                logger.warning(f"Failed to extract table from DOC/DOCX: {e}")
-                continue
+                except Exception as e:
+                    logger.warning(f"Failed to extract table from DOC/DOCX: {e}")
+                    continue
 
         return "\n".join(text)
+
     except Exception as e:
         raise TextExtractionError(f"Failed to extract text from DOC/DOCX: {str(e)}") from e
 

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

     Code Node Data.
     """
 
-    method: Literal["get", "post", "put", "patch", "delete", "head"]
+    method: Literal[
+        "get",
+        "post",
+        "put",
+        "patch",
+        "delete",
+        "head",
+        "options",
+        "GET",
+        "POST",
+        "PUT",
+        "PATCH",
+        "DELETE",
+        "HEAD",
+        "OPTIONS",
+    ]
     url: str
     authorization: HttpRequestNodeAuthorization
     headers: str

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

 
 
 class Executor:
-    method: Literal["get", "head", "post", "put", "delete", "patch"]
+    method: Literal[
+        "get",
+        "head",
+        "post",
+        "put",
+        "delete",
+        "patch",
+        "options",
+        "GET",
+        "POST",
+        "PUT",
+        "PATCH",
+        "DELETE",
+        "HEAD",
+        "OPTIONS",
+    ]
     url: str
     params: list[tuple[str, str]] | None
     content: str | bytes | None
                 node_data.authorization.config.api_key
             ).text
 
-        # check if node_data.url is a valid URL
-        if not node_data.url:
-            raise InvalidURLError("url is required")
-        if not node_data.url.startswith(("http://", "https://")):
-            raise InvalidURLError("url should start with http:// or https://")
-
         self.url: str = node_data.url
         self.method = node_data.method
         self.auth = node_data.authorization
     def _init_url(self):
         self.url = self.variable_pool.convert_template(self.node_data.url).text
 
+        # check if url is a valid URL
+        if not self.url:
+            raise InvalidURLError("url is required")
+        if not self.url.startswith(("http://", "https://")):
+            raise InvalidURLError("url should start with http:// or https://")
+
     def _init_params(self):
         """
         Almost same as _init_headers(), difference:
                     if len(data) != 1:
                         raise RequestBodyError("json body type should have exactly one item")
                     json_string = self.variable_pool.convert_template(data[0].value).text
-                    json_object = json.loads(json_string, strict=False)
+                    try:
+                        json_object = json.loads(json_string, strict=False)
+                    except json.JSONDecodeError as e:
+                        raise RequestBodyError(f"Failed to parse JSON: {json_string}") from e
                     self.json = json_object
                     # self.json = self._parse_object_contains_variables(json_object)
                 case "binary":
         """
         do http request depending on api bundle
         """
-        if self.method not in {"get", "head", "post", "put", "delete", "patch"}:
+        if self.method not in {
+            "get",
+            "head",
+            "post",
+            "put",
+            "delete",
+            "patch",
+            "options",
+            "GET",
+            "POST",
+            "PUT",
+            "PATCH",
+            "DELETE",
+            "HEAD",
+            "OPTIONS",
+        }:
             raise InvalidHttpMethodError(f"Invalid http method {self.method}")
 
         request_args = {
         }
         # request_args = {k: v for k, v in request_args.items() if v is not None}
         try:
-            response = getattr(ssrf_proxy, self.method)(**request_args)
+            response = getattr(ssrf_proxy, self.method.lower())(**request_args)
         except (ssrf_proxy.MaxRetriesExceededError, httpx.RequestError) as e:
             raise HttpRequestNodeError(str(e))
         # FIXME: fix type ignore, this maybe httpx type issue

api/core/workflow/workflow_entry.py

             ):
                 raise ValueError(f"Variable key {node_variable} not found in user inputs.")
 
+            # environment variable already exist in variable pool, not from user inputs
+            if variable_pool.get(variable_selector):
+                continue
+
             # fetch variable node id from variable selector
             variable_node_id = variable_selector[0]
             variable_key_list = variable_selector[1:]

api/docker/entrypoint.sh

       --bind "${DIFY_BIND_ADDRESS:-0.0.0.0}:${DIFY_PORT:-5001}" \
       --workers ${SERVER_WORKER_AMOUNT:-1} \
       --worker-class ${SERVER_WORKER_CLASS:-gevent} \
+      --worker-connections ${SERVER_WORKER_CONNECTIONS:-10} \
       --timeout ${GUNICORN_TIMEOUT:-200} \
       app:app
   fi

api/extensions/ext_logging.py

         timezone = pytz.timezone(log_tz)
 
         def time_converter(seconds):
-            return datetime.utcfromtimestamp(seconds).astimezone(timezone).timetuple()
+            return datetime.fromtimestamp(seconds, tz=timezone).timetuple()
 
         for handler in logging.root.handlers:
             if handler.formatter:

api/factories/file_factory.py

     tenant_id: str,
     transfer_method: FileTransferMethod,
 ) -> File:
-    url = mapping.get("url")
+    url = mapping.get("url") or mapping.get("remote_url")
     if not url:
         raise ValueError("Invalid file url")
 

api/libs/oauth_data_source.py

         response = requests.get(url=f"{self._NOTION_BLOCK_SEARCH}/{block_id}", headers=headers)
         response_json = response.json()
         if response.status_code != 200:
-            raise ValueError(f"Error fetching block parent page ID: {response_json.message}")
+            message = response_json.get("message", "unknown error")
+            raise ValueError(f"Error fetching block parent page ID: {message}")
         parent = response_json["parent"]
         parent_type = parent["type"]
         if parent_type == "block_id":

api/migrations/versions/2025_01_01_2000-a91b476a53de_change_workflow_runs_total_tokens_to_.py

+"""change workflow_runs.total_tokens to bigint
+
+Revision ID: a91b476a53de
+Revises: 923752d42eb6
+Create Date: 2025-01-01 20:00:01.207369
+
+"""
+from alembic import op
+import models as models
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = 'a91b476a53de'
+down_revision = '923752d42eb6'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('workflow_runs', schema=None) as batch_op:
+        batch_op.alter_column('total_tokens',
+               existing_type=sa.INTEGER(),
+               type_=sa.BigInteger(),
+               existing_nullable=False,
+               existing_server_default=sa.text('0'))
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('workflow_runs', schema=None) as batch_op:
+        batch_op.alter_column('total_tokens',
+               existing_type=sa.BigInteger(),
+               type_=sa.INTEGER(),
+               existing_nullable=False,
+               existing_server_default=sa.text('0'))
+
+    # ### end Alembic commands ###

api/models/workflow.py

     status: Mapped[str] = mapped_column(db.String(255))  # running, succeeded, failed, stopped, partial-succeeded
     outputs: Mapped[Optional[str]] = mapped_column(sa.Text, default="{}")
     error: Mapped[Optional[str]] = mapped_column(db.Text)
-    elapsed_time = db.Column(db.Float, nullable=False, server_default=db.text("0"))
-    total_tokens: Mapped[int] = mapped_column(server_default=db.text("0"))
+    elapsed_time = db.Column(db.Float, nullable=False, server_default=sa.text("0"))
+    total_tokens: Mapped[int] = mapped_column(sa.BigInteger, server_default=sa.text("0"))
     total_steps = db.Column(db.Integer, server_default=db.text("0"))
     created_by_role: Mapped[str] = mapped_column(db.String(255))  # account, end_user
     created_by = db.Column(StringUUID, nullable=False)

api/pyproject.toml

 pypdfium2 = "~4.30.0"
 python = ">=3.11,<3.13"
 python-docx = "~1.1.0"
-python-dotenv = "1.0.0"
+python-dotenv = "1.0.1"
 pyyaml = "~6.0.1"
 readabilipy = "0.2.0"
 redis = { version = "~5.0.3", extras = ["hiredis"] }
 sentry-sdk = { version = "~1.44.1", extras = ["flask"] }
 sqlalchemy = "~2.0.29"
 starlette = "0.41.0"
-tencentcloud-sdk-python-hunyuan = "~3.0.1158"
+tencentcloud-sdk-python-hunyuan = "~3.0.1294"
 tiktoken = "~0.8.0"
 tokenizers = "~0.15.0"
 transformers = "~4.35.0"
 volcengine-python-sdk = {extras = ["ark"], version = "~1.0.98"}
 websocket-client = "~1.7.0"
 xinference-client = "0.15.2"
-yarl = "~1.9.4"
+yarl = "~1.18.3"
 youtube-transcript-api = "~0.6.2"
 zhipuai = "~2.1.5"
 # Before adding new dependency, consider place it in alphabet order (a-z) and suitable group.
 oracledb = "~2.2.1"
 pgvecto-rs = { version = "~0.2.1", extras = ['sqlalchemy'] }
 pgvector = "0.2.5"
-pymilvus = "~2.4.4"
+pymilvus = "~2.5.0"
 pymochow = "1.3.1"
 pyobvector = "~0.1.6"
 qdrant-client = "1.7.3"

api/schedule/clean_unused_datasets_task.py

                     else:
                         plan = plan_cache.decode()
                     if plan == "sandbox":
-                        # add auto disable log
-                        documents = (
-                            db.session.query(Document)
-                            .filter(
-                                Document.dataset_id == dataset.id,
-                                Document.enabled == True,
-                                Document.archived == False,
-                            )
-                            .all()
-                        )
-                        for document in documents:
-                            dataset_auto_disable_log = DatasetAutoDisableLog(
-                                tenant_id=dataset.tenant_id,
-                                dataset_id=dataset.id,
-                                document_id=document.id,
-                            )
-                            db.session.add(dataset_auto_disable_log)
                         # remove index
                         index_processor = IndexProcessorFactory(dataset.doc_form).init_index_processor()
                         index_processor.clean(dataset, None)

api/services/account_service.py

 
 REFRESH_TOKEN_PREFIX = "refresh_token:"
 ACCOUNT_REFRESH_TOKEN_PREFIX = "account_refresh_token:"
-REFRESH_TOKEN_EXPIRY = timedelta(days=30)
+REFRESH_TOKEN_EXPIRY = timedelta(days=dify_config.REFRESH_TOKEN_EXPIRE_DAYS)
 
 
 class AccountService:

api/services/app_dsl_service.py

 import uuid
 from enum import StrEnum
 from typing import Optional, cast
+from urllib.parse import urlparse
 from uuid import uuid4
 
 import yaml  # type: ignore
             raise ValueError(f"Invalid import_mode: {import_mode}")
 
         # Get YAML content
-        content: bytes | str = b""
+        content: str = ""
         if mode == ImportMode.YAML_URL:
             if not yaml_url:
                 return Import(
                     error="yaml_url is required when import_mode is yaml-url",
                 )
             try:
-                # tricky way to handle url from github to github raw url
-                if yaml_url.startswith("https://github.com") and yaml_url.endswith((".yml", ".yaml")):
+                parsed_url = urlparse(yaml_url)
+                if (
+                    parsed_url.scheme == "https"
+                    and parsed_url.netloc == "github.com"
+                    and parsed_url.path.endswith((".yml", ".yaml"))
+                ):
                     yaml_url = yaml_url.replace("https://github.com", "https://raw.githubusercontent.com")
                     yaml_url = yaml_url.replace("/blob/", "/")
                 response = ssrf_proxy.get(yaml_url.strip(), follow_redirects=True, timeout=(10, 10))
                 response.raise_for_status()
-                content = response.content
+                content = response.content.decode()
 
                 if len(content) > DSL_MAX_SIZE:
                     return Import(

api/services/app_service.py

 
 
 class AppService:
-    def get_paginate_apps(self, tenant_id: str, args: dict) -> Pagination | None:
+    def get_paginate_apps(self, user_id: str, tenant_id: str, args: dict) -> Pagination | None:
         """
         Get app list with pagination
+        :param user_id: user id
         :param tenant_id: tenant id
         :param args: request args
         :return:
         elif args["mode"] == "channel":
             filters.append(App.mode == AppMode.CHANNEL.value)
 
+        if args.get("is_created_by_me", False):
+            filters.append(App.created_by == user_id)
         if args.get("name"):
             name = args["name"][:30]
             filters.append(App.name.ilike(f"%{name}%"))

api/services/billing_service.py

 import os
-from typing import Optional
+from typing import Literal, Optional
 
 import httpx
 from tenacity import retry, retry_if_exception_type, stop_before_delay, wait_fixed
         params = {"tenant_id": tenant_id}
 
         billing_info = cls._send_request("GET", "/subscription/info", params=params)
-
         return billing_info
 
     @classmethod
         retry=retry_if_exception_type(httpx.RequestError),
         reraise=True,
     )
-    def _send_request(cls, method, endpoint, json=None, params=None):
+    def _send_request(cls, method: Literal["GET", "POST", "DELETE"], endpoint: str, json=None, params=None):
         headers = {"Content-Type": "application/json", "Billing-Api-Secret-Key": cls.secret_key}
 
         url = f"{cls.base_url}{endpoint}"
         response = httpx.request(method, url, json=json, params=params, headers=headers)
-
+        if method == "GET" and response.status_code != httpx.codes.OK:
+            raise ValueError("Unable to retrieve billing information. Please try again later or contact support.")
         return response.json()
 
     @staticmethod

api/services/dataset_service.py

                 else:
                     return [], 0
             else:
-                if user.current_role not in (TenantAccountRole.OWNER, TenantAccountRole.ADMIN):
+                if user.current_role != TenantAccountRole.OWNER:
                     # show all datasets that the user has permission to access
                     if permitted_dataset_ids:
                         query = query.filter(
         if dataset.tenant_id != user.current_tenant_id:
             logging.debug(f"User {user.id} does not have permission to access dataset {dataset.id}")
             raise NoPermissionError("You do not have permission to access this dataset.")
-        if user.current_role not in (TenantAccountRole.OWNER, TenantAccountRole.ADMIN):
+        if user.current_role != TenantAccountRole.OWNER:
             if dataset.permission == DatasetPermissionEnum.ONLY_ME and dataset.created_by != user.id:
                 logging.debug(f"User {user.id} does not have permission to access dataset {dataset.id}")
                 raise NoPermissionError("You do not have permission to access this dataset.")
         if not user:
             raise ValueError("User not found")
 
-        if user.current_role not in (TenantAccountRole.OWNER, TenantAccountRole.ADMIN):
+        if user.current_role != TenantAccountRole.OWNER:
             if dataset.permission == DatasetPermissionEnum.ONLY_ME:
                 if dataset.created_by != user.id:
                     raise NoPermissionError("You do not have permission to access this dataset.")
 
     @staticmethod
     def get_dataset_auto_disable_logs(dataset_id: str) -> dict:
+        features = FeatureService.get_features(current_user.current_tenant_id)
+        if not features.billing.enabled or features.billing.subscription.plan == "sandbox":
+            return {
+                "document_ids": [],
+                "count": 0,
+            }
         # get recent 30 days auto disable logs
         start_date = datetime.datetime.now() - datetime.timedelta(days=30)
         dataset_auto_disable_logs = DatasetAutoDisableLog.query.filter(
             dataset.indexing_technique = knowledge_config.indexing_technique
             if knowledge_config.indexing_technique == "high_quality":
                 model_manager = ModelManager()
-                embedding_model = model_manager.get_default_model_instance(
-                    tenant_id=current_user.current_tenant_id, model_type=ModelType.TEXT_EMBEDDING
-                )
-                dataset.embedding_model = embedding_model.model
-                dataset.embedding_model_provider = embedding_model.provider
+                if knowledge_config.embedding_model and knowledge_config.embedding_model_provider:
+                    dataset_embedding_model = knowledge_config.embedding_model
+                    dataset_embedding_model_provider = knowledge_config.embedding_model_provider
+                else:
+                    embedding_model = model_manager.get_default_model_instance(
+                        tenant_id=current_user.current_tenant_id, model_type=ModelType.TEXT_EMBEDDING
+                    )
+                    dataset_embedding_model = embedding_model.model
+                    dataset_embedding_model_provider = embedding_model.provider
+                dataset.embedding_model = dataset_embedding_model
+                dataset.embedding_model_provider = dataset_embedding_model_provider
                 dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
-                    embedding_model.provider, embedding_model.model
+                    dataset_embedding_model_provider, dataset_embedding_model
                 )
                 dataset.collection_binding_id = dataset_collection_binding.id
                 if not dataset.retrieval_model:
                         "score_threshold_enabled": False,
                     }
 
-                    dataset.retrieval_model = knowledge_config.retrieval_model.model_dump() or default_retrieval_model  # type: ignore
+                    dataset.retrieval_model = (
+                        knowledge_config.retrieval_model.model_dump()
+                        if knowledge_config.retrieval_model
+                        else default_retrieval_model
+                    )  # type: ignore
 
         documents = []
         if knowledge_config.original_document_id:

api/services/workflow_app_service.py

             query = query.join(WorkflowRun, WorkflowRun.id == WorkflowAppLog.workflow_run_id)
 
         if keyword:
-            keyword_like_val = f"%{args['keyword'][:30]}%"
+            keyword_like_val = f"%{keyword[:30].encode('unicode_escape').decode('utf-8')}%".replace(r"\u", r"\\u")
             keyword_conditions = [
                 WorkflowRun.inputs.ilike(keyword_like_val),
                 WorkflowRun.outputs.ilike(keyword_like_val),

api/tasks/deal_dataset_vector_index_task.py

 
         if not dataset:
             raise Exception("Dataset not found")
-        index_type = dataset.doc_form
+        index_type = dataset.doc_form or IndexType.PARAGRAPH_INDEX
         index_processor = IndexProcessorFactory(index_type).init_index_processor()
         if action == "remove":
             index_processor.clean(dataset, None, with_keywords=False)
                             {"indexing_status": "error", "error": str(e)}, synchronize_session=False
                         )
                         db.session.commit()
+            else:
+                # clean collection
+                index_processor.clean(dataset, None, with_keywords=False, delete_child_chunks=False)
 
         end_at = time.perf_counter()
         logging.info(

api/tests/integration_tests/model_runtime/gpustack/test_speech2text.py

+import os
+from pathlib import Path
+
+import pytest
+
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.gpustack.speech2text.speech2text import GPUStackSpeech2TextModel
+
+
+def test_validate_credentials():
+    model = GPUStackSpeech2TextModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model="faster-whisper-medium",
+            credentials={
+                "endpoint_url": "invalid_url",
+                "api_key": "invalid_api_key",
+            },
+        )
+
+    model.validate_credentials(
+        model="faster-whisper-medium",
+        credentials={
+            "endpoint_url": os.environ.get("GPUSTACK_SERVER_URL"),
+            "api_key": os.environ.get("GPUSTACK_API_KEY"),
+        },
+    )
+
+
+def test_invoke_model():
+    model = GPUStackSpeech2TextModel()
+
+    # Get the directory of the current file
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+
+    # Get assets directory
+    assets_dir = os.path.join(os.path.dirname(current_dir), "assets")
+
+    # Construct the path to the audio file
+    audio_file_path = os.path.join(assets_dir, "audio.mp3")
+
+    file = Path(audio_file_path).read_bytes()
+
+    result = model.invoke(
+        model="faster-whisper-medium",
+        credentials={
+            "endpoint_url": os.environ.get("GPUSTACK_SERVER_URL"),
+            "api_key": os.environ.get("GPUSTACK_API_KEY"),
+        },
+        file=file,
+    )
+
+    assert isinstance(result, str)
+    assert result == "1, 2, 3, 4, 5, 6, 7, 8, 9, 10"

api/tests/integration_tests/model_runtime/gpustack/test_tts.py

+import os
+
+from core.model_runtime.model_providers.gpustack.tts.tts import GPUStackText2SpeechModel
+
+
+def test_invoke_model():
+    model = GPUStackText2SpeechModel()
+
+    result = model.invoke(
+        model="cosyvoice-300m-sft",
+        tenant_id="test",
+        credentials={
+            "endpoint_url": os.environ.get("GPUSTACK_SERVER_URL"),
+            "api_key": os.environ.get("GPUSTACK_API_KEY"),
+        },
+        content_text="Hello world",
+        voice="Chinese Female",
+    )
+
+    content = b""
+    for chunk in result:
+        content += chunk
+
+    assert content != b""

api/tests/integration_tests/vdb/milvus/test_milvus.py

         )
 
     def search_by_full_text(self):
-        # milvus dos not support full text searching yet in < 2.3.x
+        # milvus support BM25 full text search after version 2.5.0-beta
         hits_by_full_text = self.vector.search_by_full_text(query=get_example_text())
-        assert len(hits_by_full_text) == 0
+        assert len(hits_by_full_text) >= 0
 
     def get_ids_by_metadata_field(self):
         ids = self.vector.get_ids_by_metadata_field(key="document_id", value=self.example_doc_id)

docker-legacy/docker-compose.yaml

 services:
   # API service
   api:
-    image: langgenius/dify-api:0.14.2
+    image: langgenius/dify-api:0.15.0
     restart: always
     environment:
       # Startup mode, 'api' starts the API server.
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.14.2
+    image: langgenius/dify-api:0.15.0
     restart: always
     environment:
       CONSOLE_WEB_URL: ''
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.14.2
+    image: langgenius/dify-web:0.15.0
     restart: always
     environment:
       # The base URL of console application api server, refers to the Console base URL of WEB service if console domain is

docker/.env.example

 # Access token expiration time in minutes
 ACCESS_TOKEN_EXPIRE_MINUTES=60
 
+# Refresh token expiration time in days
+REFRESH_TOKEN_EXPIRE_DAYS=30
+
 # The maximum number of active requests for the application, where 0 means unlimited, should be a non-negative integer.
 APP_MAX_ACTIVE_REQUESTS=0
 APP_MAX_EXECUTION_TIME=1200
 # The number of API server workers, i.e., the number of workers.
 # Formula: number of cpu cores x 2 + 1 for sync, 1 for Gevent
 # Reference: https://docs.gunicorn.org/en/stable/design.html#how-many-workers
-SERVER_WORKER_AMOUNT=
+SERVER_WORKER_AMOUNT=1
 
 # Defaults to gevent. If using windows, it can be switched to sync or solo.
-SERVER_WORKER_CLASS=
+SERVER_WORKER_CLASS=gevent
+
+# Default number of worker connections, the default is 10.
+SERVER_WORKER_CONNECTIONS=10
 
 # Similar to SERVER_WORKER_CLASS.
 # If using windows, it can be switched to sync or solo.
 # ------------------------------
 
 # The type of vector store to use.
-# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `tidb_vector`, `oracle`, `tencent`, `elasticsearch`, `analyticdb`, `couchbase`, `vikingdb`, `oceanbase`.
+# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `tidb_vector`, `oracle`, `tencent`, `elasticsearch`, `elasticsearch-ja`, `analyticdb`, `couchbase`, `vikingdb`, `oceanbase`.
 VECTOR_STORE=weaviate
 
 # The Weaviate endpoint URL. Only available when VECTOR_STORE is `weaviate`.
 MILVUS_TOKEN=
 MILVUS_USER=root
 MILVUS_PASSWORD=Milvus
+MILVUS_ENABLE_HYBRID_SEARCH=False
 
 # MyScale configuration, only available when VECTOR_STORE is `myscale`
 # For multi-language support, please set MYSCALE_FTS_PARAMS with referring to:
 TENCENT_VECTOR_DB_REPLICAS=2
 
 # ElasticSearch configuration, only available when VECTOR_STORE is `elasticsearch`
-ELASTICSEARCH_HOST=0.0.0.0
+ELASTICSEARCH_HOST=elasticsearch
 ELASTICSEARCH_PORT=9200
 ELASTICSEARCH_USERNAME=elastic
 ELASTICSEARCH_PASSWORD=elastic
 # Maximum number of submitted thread count in a ThreadPool for parallel node execution
 MAX_SUBMIT_COUNT=100
 
+# The maximum number of top-k value for RAG.
+TOP_K_MAX_VALUE=10
+
 # ------------------------------
 # Plugin Daemon Configuration
 # ------------------------------
 
 MARKETPLACE_ENABLED=true
 MARKETPLACE_API_URL=https://marketplace-plugin.dify.dev
+

docker/docker-compose-template.yaml

       CSP_WHITELIST: ${CSP_WHITELIST:-}
       MARKETPLACE_API_URL: ${MARKETPLACE_API_URL:-https://marketplace-plugin.dify.dev}
       MARKETPLACE_URL: ${MARKETPLACE_URL:-https://marketplace-plugin.dify.dev}
+      TOP_K_MAX_VALUE: ${TOP_K_MAX_VALUE:-}
 
   # The postgres database.
   db:
     volumes:
       - ./volumes/db/data:/var/lib/postgresql/data
     healthcheck:
-      test: ['CMD', 'pg_isready']
+      test: [ 'CMD', 'pg_isready' ]
       interval: 1s
       timeout: 3s
       retries: 30
     # Set the redis password when startup redis server.
     command: redis-server --requirepass ${REDIS_PASSWORD:-difyai123456}
     healthcheck:
-      test: ['CMD', 'redis-cli', 'ping']
+      test: [ 'CMD', 'redis-cli', 'ping' ]
 
   # The DifySandbox
   sandbox:
     volumes:
       - ./volumes/sandbox/dependencies:/dependencies
     healthcheck:
-      test: ['CMD', 'curl', '-f', 'http://localhost:8194/health']
+      test: [ 'CMD', 'curl', '-f', 'http://localhost:8194/health' ]
     networks:
       - ssrf_proxy_network
 
     volumes:
       - ./ssrf_proxy/squid.conf.template:/etc/squid/squid.conf.template
       - ./ssrf_proxy/docker-entrypoint.sh:/docker-entrypoint-mount.sh
-    entrypoint:
-      [
-        'sh',
-        '-c',
-        "cp /docker-entrypoint-mount.sh /docker-entrypoint.sh && sed -i 's/\r$$//' /docker-entrypoint.sh && chmod +x /docker-entrypoint.sh && /docker-entrypoint.sh",
-      ]
+    entrypoint: [ 'sh', '-c', "cp /docker-entrypoint-mount.sh /docker-entrypoint.sh && sed -i 's/\r$$//' /docker-entrypoint.sh && chmod +x /docker-entrypoint.sh && /docker-entrypoint.sh" ]
     environment:
       # pls clearly modify the squid env vars to fit your network environment.
       HTTP_PORT: ${SSRF_HTTP_PORT:-3128}
       - CERTBOT_EMAIL=${CERTBOT_EMAIL}
       - CERTBOT_DOMAIN=${CERTBOT_DOMAIN}
       - CERTBOT_OPTIONS=${CERTBOT_OPTIONS:-}
-    entrypoint: ['/docker-entrypoint.sh']
-    command: ['tail', '-f', '/dev/null']
+    entrypoint: [ '/docker-entrypoint.sh' ]
+    command: [ 'tail', '-f', '/dev/null' ]
 
   # The nginx reverse proxy.
   # used for reverse proxying the API service and Web service.
       - ./volumes/certbot/conf/live:/etc/letsencrypt/live # cert dir (with certbot container)
       - ./volumes/certbot/conf:/etc/letsencrypt
       - ./volumes/certbot/www:/var/www/html
-    entrypoint:
-      [
-        'sh',
-        '-c',
-        "cp /docker-entrypoint-mount.sh /docker-entrypoint.sh && sed -i 's/\r$$//' /docker-entrypoint.sh && chmod +x /docker-entrypoint.sh && /docker-entrypoint.sh",
-      ]
+    entrypoint: [ 'sh', '-c', "cp /docker-entrypoint-mount.sh /docker-entrypoint.sh && sed -i 's/\r$$//' /docker-entrypoint.sh && chmod +x /docker-entrypoint.sh && /docker-entrypoint.sh" ]
     environment:
       NGINX_SERVER_NAME: ${NGINX_SERVER_NAME:-_}
       NGINX_HTTPS_ENABLED: ${NGINX_HTTPS_ENABLED:-false}
     working_dir: /opt/couchbase
     stdin_open: true
     tty: true
-    entrypoint: [""]
+    entrypoint: [ "" ]
     command: sh -c "/opt/couchbase/init/init-cbserver.sh"
     volumes:
       - ./volumes/couchbase/data:/opt/couchbase/var/lib/couchbase/data
     volumes:
       - ./volumes/pgvector/data:/var/lib/postgresql/data
     healthcheck:
-      test: ['CMD', 'pg_isready']
+      test: [ 'CMD', 'pg_isready' ]
       interval: 1s
       timeout: 3s
       retries: 30
     volumes:
       - ./volumes/pgvecto_rs/data:/var/lib/postgresql/data
     healthcheck:
-      test: ['CMD', 'pg_isready']
+      test: [ 'CMD', 'pg_isready' ]
       interval: 1s
       timeout: 3s
       retries: 30
       - ./volumes/milvus/etcd:/etcd
     command: etcd -advertise-client-urls=http://127.0.0.1:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd
     healthcheck:
-      test: ['CMD', 'etcdctl', 'endpoint', 'health']
+      test: [ 'CMD', 'etcdctl', 'endpoint', 'health' ]
       interval: 30s
       timeout: 20s
       retries: 3
       - ./volumes/milvus/minio:/minio_data
     command: minio server /minio_data --console-address ":9001"
     healthcheck:
-      test: ['CMD', 'curl', '-f', 'http://localhost:9000/minio/health/live']
+      test: [ 'CMD', 'curl', '-f', 'http://localhost:9000/minio/health/live' ]
       interval: 30s
       timeout: 20s
       retries: 3
     image: milvusdb/milvus:v2.3.1
     profiles:
       - milvus
-    command: ['milvus', 'run', 'standalone']
+    command: [ 'milvus', 'run', 'standalone' ]
     environment:
       ETCD_ENDPOINTS: ${ETCD_ENDPOINTS:-etcd:2379}
       MINIO_ADDRESS: ${MINIO_ADDRESS:-minio:9000}
     volumes:
       - ./volumes/milvus/milvus:/var/lib/milvus
     healthcheck:
-      test: ['CMD', 'curl', '-f', 'http://localhost:9091/healthz']
+      test: [ 'CMD', 'curl', '-f', 'http://localhost:9091/healthz' ]
       interval: 30s
       start_period: 90s
       timeout: 20s
     ports:
       - ${ELASTICSEARCH_PORT:-9200}:9200
     healthcheck:
-      test: ['CMD', 'curl', '-s', 'http://localhost:9200/_cluster/health?pretty']
+      test: [ 'CMD', 'curl', '-s', 'http://localhost:9200/_cluster/health?pretty' ]
       interval: 30s
       timeout: 10s
       retries: 50
     ports:
       - ${KIBANA_PORT:-5601}:5601
     healthcheck:
-      test: ['CMD-SHELL', 'curl -s http://localhost:5601 >/dev/null || exit 1']
+      test: [ 'CMD-SHELL', 'curl -s http://localhost:5601 >/dev/null || exit 1' ]
       interval: 30s
       timeout: 10s
       retries: 3

docker/docker-compose.yaml

   MIGRATION_ENABLED: ${MIGRATION_ENABLED:-true}
   FILES_ACCESS_TIMEOUT: ${FILES_ACCESS_TIMEOUT:-300}
   ACCESS_TOKEN_EXPIRE_MINUTES: ${ACCESS_TOKEN_EXPIRE_MINUTES:-60}
+  REFRESH_TOKEN_EXPIRE_DAYS: ${REFRESH_TOKEN_EXPIRE_DAYS:-30}
   APP_MAX_ACTIVE_REQUESTS: ${APP_MAX_ACTIVE_REQUESTS:-0}
   APP_MAX_EXECUTION_TIME: ${APP_MAX_EXECUTION_TIME:-1200}
   DIFY_BIND_ADDRESS: ${DIFY_BIND_ADDRESS:-0.0.0.0}
   DIFY_PORT: ${DIFY_PORT:-5001}
-  SERVER_WORKER_AMOUNT: ${SERVER_WORKER_AMOUNT:-}
-  SERVER_WORKER_CLASS: ${SERVER_WORKER_CLASS:-}
+  SERVER_WORKER_AMOUNT: ${SERVER_WORKER_AMOUNT:-1}
+  SERVER_WORKER_CLASS: ${SERVER_WORKER_CLASS:-gevent}
+  SERVER_WORKER_CONNECTIONS: ${SERVER_WORKER_CONNECTIONS:-10}
   CELERY_WORKER_CLASS: ${CELERY_WORKER_CLASS:-}
   GUNICORN_TIMEOUT: ${GUNICORN_TIMEOUT:-360}
   CELERY_WORKER_AMOUNT: ${CELERY_WORKER_AMOUNT:-}
   MILVUS_TOKEN: ${MILVUS_TOKEN:-}
   MILVUS_USER: ${MILVUS_USER:-root}
   MILVUS_PASSWORD: ${MILVUS_PASSWORD:-Milvus}
+  MILVUS_ENABLE_HYBRID_SEARCH: ${MILVUS_ENABLE_HYBRID_SEARCH:-False}
   MYSCALE_HOST: ${MYSCALE_HOST:-myscale}
   MYSCALE_PORT: ${MYSCALE_PORT:-8123}
   MYSCALE_USER: ${MYSCALE_USER:-default}
   ENDPOINT_URL_TEMPLATE: ${ENDPOINT_URL_TEMPLATE:-http://localhost/e/{hook_id}}
   MARKETPLACE_ENABLED: ${MARKETPLACE_ENABLED:-true}
   MARKETPLACE_API_URL: ${MARKETPLACE_API_URL:-https://marketplace-plugin.dify.dev}
+  TOP_K_MAX_VALUE: ${TOP_K_MAX_VALUE:-10}
 
 services:
   # API service
       CSP_WHITELIST: ${CSP_WHITELIST:-}
       MARKETPLACE_API_URL: ${MARKETPLACE_API_URL:-https://marketplace-plugin.dify.dev}
       MARKETPLACE_URL: ${MARKETPLACE_URL:-https://marketplace-plugin.dify.dev}
+      TOP_K_MAX_VALUE: ${TOP_K_MAX_VALUE:-}
 
   # The postgres database.
   db:
     volumes:
       - ./volumes/db/data:/var/lib/postgresql/data
     healthcheck:
-      test: ['CMD', 'pg_isready']
+      test: [ 'CMD', 'pg_isready' ]
       interval: 1s
       timeout: 3s
       retries: 30
     # Set the redis password when startup redis server.
     command: redis-server --requirepass ${REDIS_PASSWORD:-difyai123456}
     healthcheck:
-      test: ['CMD', 'redis-cli', 'ping']
+      test: [ 'CMD', 'redis-cli', 'ping' ]
 
   # The DifySandbox
   sandbox:
     volumes:
       - ./volumes/sandbox/dependencies:/dependencies
     healthcheck:
-      test: ['CMD', 'curl', '-f', 'http://localhost:8194/health']
+      test: [ 'CMD', 'curl', '-f', 'http://localhost:8194/health' ]
     networks:
       - ssrf_proxy_network
 
     volumes:
       - ./ssrf_proxy/squid.conf.template:/etc/squid/squid.conf.template
       - ./ssrf_proxy/docker-entrypoint.sh:/docker-entrypoint-mount.sh
-    entrypoint:
-      [
-        'sh',
-        '-c',
-        "cp /docker-entrypoint-mount.sh /docker-entrypoint.sh && sed -i 's/\r$$//' /docker-entrypoint.sh && chmod +x /docker-entrypoint.sh && /docker-entrypoint.sh",
-      ]
+    entrypoint: [ 'sh', '-c', "cp /docker-entrypoint-mount.sh /docker-entrypoint.sh && sed -i 's/\r$$//' /docker-entrypoint.sh && chmod +x /docker-entrypoint.sh && /docker-entrypoint.sh" ]
     environment:
       # pls clearly modify the squid env vars to fit your network environment.
       HTTP_PORT: ${SSRF_HTTP_PORT:-3128}
       - CERTBOT_EMAIL=${CERTBOT_EMAIL}
       - CERTBOT_DOMAIN=${CERTBOT_DOMAIN}
       - CERTBOT_OPTIONS=${CERTBOT_OPTIONS:-}
-    entrypoint: ['/docker-entrypoint.sh']
-    command: ['tail', '-f', '/dev/null']
+    entrypoint: [ '/docker-entrypoint.sh' ]
+    command: [ 'tail', '-f', '/dev/null' ]
 
   # The nginx reverse proxy.
   # used for reverse proxying the API service and Web service.
       - ./volumes/certbot/conf/live:/etc/letsencrypt/live # cert dir (with certbot container)
       - ./volumes/certbot/conf:/etc/letsencrypt
       - ./volumes/certbot/www:/var/www/html
-    entrypoint:
-      [
-        'sh',
-        '-c',
-        "cp /docker-entrypoint-mount.sh /docker-entrypoint.sh && sed -i 's/\r$$//' /docker-entrypoint.sh && chmod +x /docker-entrypoint.sh && /docker-entrypoint.sh",
-      ]
+    entrypoint: [ 'sh', '-c', "cp /docker-entrypoint-mount.sh /docker-entrypoint.sh && sed -i 's/\r$$//' /docker-entrypoint.sh && chmod +x /docker-entrypoint.sh && /docker-entrypoint.sh" ]
     environment:
       NGINX_SERVER_NAME: ${NGINX_SERVER_NAME:-_}
       NGINX_HTTPS_ENABLED: ${NGINX_HTTPS_ENABLED:-false}
     working_dir: /opt/couchbase
     stdin_open: true
     tty: true
-    entrypoint: [""]
+    entrypoint: [ "" ]
     command: sh -c "/opt/couchbase/init/init-cbserver.sh"
     volumes:
       - ./volumes/couchbase/data:/opt/couchbase/var/lib/couchbase/data
     volumes:
       - ./volumes/pgvector/data:/var/lib/postgresql/data
     healthcheck:
-      test: ['CMD', 'pg_isready']
+      test: [ 'CMD', 'pg_isready' ]
       interval: 1s
       timeout: 3s
       retries: 30
     volumes:
       - ./volumes/pgvecto_rs/data:/var/lib/postgresql/data
     healthcheck:
-      test: ['CMD', 'pg_isready']
+      test: [ 'CMD', 'pg_isready' ]
       interval: 1s
       timeout: 3s
       retries: 30
       - ./volumes/milvus/etcd:/etcd
     command: etcd -advertise-client-urls=http://127.0.0.1:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd
     healthcheck:
-      test: ['CMD', 'etcdctl', 'endpoint', 'health']
+      test: [ 'CMD', 'etcdctl', 'endpoint', 'health' ]
       interval: 30s
       timeout: 20s
       retries: 3
       - ./volumes/milvus/minio:/minio_data
     command: minio server /minio_data --console-address ":9001"
     healthcheck:
-      test: ['CMD', 'curl', '-f', 'http://localhost:9000/minio/health/live']
+      test: [ 'CMD', 'curl', '-f', 'http://localhost:9000/minio/health/live' ]
       interval: 30s
       timeout: 20s
       retries: 3
 
   milvus-standalone:
     container_name: milvus-standalone
-    image: milvusdb/milvus:v2.3.1
+    image: milvusdb/milvus:v2.5.0-beta
     profiles:
       - milvus
-    command: ['milvus', 'run', 'standalone']
+    command: [ 'milvus', 'run', 'standalone' ]
     environment:
       ETCD_ENDPOINTS: ${ETCD_ENDPOINTS:-etcd:2379}
       MINIO_ADDRESS: ${MINIO_ADDRESS:-minio:9000}
     volumes:
       - ./volumes/milvus/milvus:/var/lib/milvus
     healthcheck:
-      test: ['CMD', 'curl', '-f', 'http://localhost:9091/healthz']
+      test: [ 'CMD', 'curl', '-f', 'http://localhost:9091/healthz' ]
       interval: 30s
       start_period: 90s
       timeout: 20s
     container_name: elasticsearch
     profiles:
       - elasticsearch
+      - elasticsearch-ja
     restart: always
     volumes:
+      - ./elasticsearch/docker-entrypoint.sh:/docker-entrypoint-mount.sh
       - dify_es01_data:/usr/share/elasticsearch/data
     environment:
       ELASTIC_PASSWORD: ${ELASTICSEARCH_PASSWORD:-elastic}
+      VECTOR_STORE: ${VECTOR_STORE:-}
       cluster.name: dify-es-cluster
       node.name: dify-es0
       discovery.type: single-node
-      xpack.license.self_generated.type: trial
+      xpack.license.self_generated.type: basic
       xpack.security.enabled: 'true'
       xpack.security.enrollment.enabled: 'false'
       xpack.security.http.ssl.enabled: 'false'
     ports:
       - ${ELASTICSEARCH_PORT:-9200}:9200
+    deploy:
+      resources:
+        limits:
+          memory: 2g
+    entrypoint: [ 'sh', '-c', "sh /docker-entrypoint-mount.sh" ]
     healthcheck:
-      test: ['CMD', 'curl', '-s', 'http://localhost:9200/_cluster/health?pretty']
+      test: [ 'CMD', 'curl', '-s', 'http://localhost:9200/_cluster/health?pretty' ]
       interval: 30s
       timeout: 10s
       retries: 50
     ports:
       - ${KIBANA_PORT:-5601}:5601
     healthcheck:
-      test: ['CMD-SHELL', 'curl -s http://localhost:5601 >/dev/null || exit 1']
+      test: [ 'CMD-SHELL', 'curl -s http://localhost:5601 >/dev/null || exit 1' ]
       interval: 30s
       timeout: 10s
       retries: 3

docker/elasticsearch/docker-entrypoint.sh

+#!/bin/bash
+
+set -e
+
+if [ "${VECTOR_STORE}" = "elasticsearch-ja" ]; then
+    # Check if the ICU tokenizer plugin is installed
+    if ! /usr/share/elasticsearch/bin/elasticsearch-plugin list | grep -q analysis-icu; then
+        printf '%s\n' "Installing the ICU tokenizer plugin"
+        if ! /usr/share/elasticsearch/bin/elasticsearch-plugin install analysis-icu; then
+            printf '%s\n' "Failed to install the ICU tokenizer plugin"
+            exit 1
+        fi
+    fi
+    # Check if the Japanese language analyzer plugin is installed
+    if ! /usr/share/elasticsearch/bin/elasticsearch-plugin list | grep -q analysis-kuromoji; then
+        printf '%s\n' "Installing the Japanese language analyzer plugin"
+        if ! /usr/share/elasticsearch/bin/elasticsearch-plugin install analysis-kuromoji; then
+            printf '%s\n' "Failed to install the Japanese language analyzer plugin"
+            exit 1
+        fi
+    fi
+fi
+
+# Run the original entrypoint script
+exec /bin/tini -- /usr/local/bin/docker-entrypoint.sh

web/.env.example

 
 # CSP https://developer.mozilla.org/en-US/docs/Web/HTTP/CSP
 NEXT_PUBLIC_CSP_WHITELIST=
+
+# The maximum number of top-k value for RAG.
+NEXT_PUBLIC_TOP_K_MAX_VALUE=10

web/app/(commonLayout)/apps/Apps.tsx

 import { useStore as useTagStore } from '@/app/components/base/tag-management/store'
 import TagManagementModal from '@/app/components/base/tag-management'
 import TagFilter from '@/app/components/base/tag-management/filter'
+import CheckboxWithLabel from '@/app/components/datasets/create/website/base/checkbox-with-label'
 
 const getKey = (
   pageIndex: number,
   previousPageData: AppListResponse,
   activeTab: string,
+  isCreatedByMe: boolean,
   tags: string[],
   keywords: string,
 ) => {
   if (!pageIndex || previousPageData.has_more) {
-    const params: any = { url: 'apps', params: { page: pageIndex + 1, limit: 30, name: keywords } }
+    const params: any = { url: 'apps', params: { page: pageIndex + 1, limit: 30, name: keywords, is_created_by_me: isCreatedByMe } }
 
     if (activeTab !== 'all')
       params.params.mode = activeTab
     defaultTab: 'all',
   })
   const { query: { tagIDs = [], keywords = '' }, setQuery } = useAppsQueryState()
+  const [isCreatedByMe, setIsCreatedByMe] = useState(false)
   const [tagFilterValue, setTagFilterValue] = useState<string[]>(tagIDs)
   const [searchKeywords, setSearchKeywords] = useState(keywords)
   const setKeywords = useCallback((keywords: string) => {
   }, [setQuery])
 
   const { data, isLoading, setSize, mutate } = useSWRInfinite(
-    (pageIndex: number, previousPageData: AppListResponse) => getKey(pageIndex, previousPageData, activeTab, tagIDs, searchKeywords),
+    (pageIndex: number, previousPageData: AppListResponse) => getKey(pageIndex, previousPageData, activeTab, isCreatedByMe, tagIDs, searchKeywords),
     fetchAppList,
     { revalidateFirstPage: true },
   )
           options={options}
         />
         <div className='flex items-center gap-2'>
+          <CheckboxWithLabel
+            className='mr-2'
+            label={t('app.showMyCreatedAppsOnly')}
+            isChecked={isCreatedByMe}
+            onChange={() => setIsCreatedByMe(!isCreatedByMe)}
+          />
           <TagFilter type='app' value={tagFilterValue} onChange={handleTagsChange} />
           <Input
             showLeftIcon

web/app/(commonLayout)/datasets/template/template.en.mdx

           - <code>high_quality</code> High quality: embedding using embedding model, built as vector database index
           - <code>economy</code> Economy: Build using inverted index of keyword table index
       </Property>
+      <Property name='doc_form' type='string' key='doc_form'>
+        Format of indexed content
+          - <code>text_model</code> Text documents are directly embedded; `economy` mode defaults to using this form
+          - <code>hierarchical_model</code> Parent-child mode
+          - <code>qa_model</code> Q&A Mode: Generates Q&A pairs for segmented documents and then embeds the questions
+      </Property>
+      <Property name='doc_language' type='string' key='doc_language'>
+        In Q&A mode, specify the language of the document, for example: <code>English</code>, <code>Chinese</code>
+      </Property>
       <Property name='process_rule' type='object' key='process_rule'>
         Processing rules
           - <code>mode</code> (string) Cleaning, segmentation mode, automatic / custom
             - <code>segmentation</code> (object) Segmentation rules
               - <code>separator</code> Custom segment identifier, currently only allows one delimiter to be set. Default is \n
               - <code>max_tokens</code> Maximum length (token) defaults to 1000
+            - <code>parent_mode</code> Retrieval mode of parent chunks: <code>full-doc</code> full text retrieval / <code>paragraph</code> paragraph retrieval
+            - <code>subchunk_segmentation</code> (object) Child chunk rules
+              - <code>separator</code> Segmentation identifier. Currently, only one delimiter is allowed. The default is <code>***</code>
+              - <code>max_tokens</code> The maximum length (tokens) must be validated to be shorter than the length of the parent chunk
+              - <code>chunk_overlap</code> Define the overlap between adjacent chunks (optional)
       </Property>
     </Properties>
   </Col>
           - <code>high_quality</code> High quality: embedding using embedding model, built as vector database index
           - <code>economy</code> Economy: Build using inverted index of keyword table index
 
+        - <code>doc_form</code> Format of indexed content
+          - <code>text_model</code> Text documents are directly embedded; `economy` mode defaults to using this form
+          - <code>hierarchical_model</code> Parent-child mode
+          - <code>qa_model</code> Q&A Mode: Generates Q&A pairs for segmented documents and then embeds the questions
+
+        - <code>doc_language</code> In Q&A mode, specify the language of the document, for example: <code>English</code>, <code>Chinese</code>
+
         - <code>process_rule</code> Processing rules
           - <code>mode</code> (string) Cleaning, segmentation mode, automatic / custom
           - <code>rules</code> (object) Custom rules (in automatic mode, this field is empty)
             - <code>segmentation</code> (object) Segmentation rules
               - <code>separator</code> Custom segment identifier, currently only allows one delimiter to be set. Default is \n
               - <code>max_tokens</code> Maximum length (token) defaults to 1000
+            - <code>parent_mode</code> Retrieval mode of parent chunks: <code>full-doc</code> full text retrieval / <code>paragraph</code> paragraph retrieval
+            - <code>subchunk_segmentation</code> (object) Child chunk rules
+              - <code>separator</code> Segmentation identifier. Currently, only one delimiter is allowed. The default is <code>***</code>
+              - <code>max_tokens</code> The maximum length (tokens) must be validated to be shorter than the length of the parent chunk
+              - <code>chunk_overlap</code> Define the overlap between adjacent chunks (optional)
       </Property>
       <Property name='file' type='multipart/form-data' key='file'>
         Files that need to be uploaded.
             - <code>segmentation</code> (object) Segmentation rules
               - <code>separator</code> Custom segment identifier, currently only allows one delimiter to be set. Default is \n
               - <code>max_tokens</code> Maximum length (token) defaults to 1000
+            - <code>parent_mode</code> Retrieval mode of parent chunks: <code>full-doc</code> full text retrieval / <code>paragraph</code> paragraph retrieval
+            - <code>subchunk_segmentation</code> (object) Child chunk rules
+              - <code>separator</code> Segmentation identifier. Currently, only one delimiter is allowed. The default is <code>***</code>
+              - <code>max_tokens</code> The maximum length (tokens) must be validated to be shorter than the length of the parent chunk
+              - <code>chunk_overlap</code> Define the overlap between adjacent chunks (optional)
       </Property>
     </Properties>
   </Col>
             - <code>segmentation</code> (object) Segmentation rules
               - <code>separator</code> Custom segment identifier, currently only allows one delimiter to be set. Default is \n
               - <code>max_tokens</code> Maximum length (token) defaults to 1000
+            - <code>parent_mode</code> Retrieval mode of parent chunks: <code>full-doc</code> full text retrieval / <code>paragraph</code> paragraph retrieval
+            - <code>subchunk_segmentation</code> (object) Child chunk rules
+              - <code>separator</code> Segmentation identifier. Currently, only one delimiter is allowed. The default is <code>***</code>
+              - <code>max_tokens</code> The maximum length (tokens) must be validated to be shorter than the length of the parent chunk
+              - <code>chunk_overlap</code> Define the overlap between adjacent chunks (optional)
       </Property>
     </Properties>
   </Col>
 <Heading
   url='/datasets/{dataset_id}/documents/{document_id}/segments/{segment_id}'
   method='POST'
-  title='Update a Chunk in a Document '
+  title='Update a Chunk in a Document'
   name='#update_segment'
 />
 <Row>
         - <code>answer</code> (text) Answer content, passed if the knowledge is in Q&A mode (optional)
         - <code>keywords</code> (list) Keyword (optional)
         - <code>enabled</code> (bool) False / true (optional)
+        - <code>regenerate_child_chunks</code> (bool) Whether to regenerate child chunks (optional)
       </Property>
     </Properties>
   </Col>

web/app/(commonLayout)/datasets/template/template.zh.mdx

           - <code>high_quality</code> 高质量：使用  embedding 模型进行嵌入，构建为向量数据库索引
           - <code>economy</code> 经济：使用 keyword table index 的倒排索引进行构建
       </Property>
+      <Property name='doc_form' type='string' key='doc_form'>
+        索引内容的形式
+          - <code>text_model</code> text 文档直接 embedding，经济模式默认为该模式
+          - <code>hierarchical_model</code> parent-child 模式
+          - <code>qa_model</code> Q&A 模式：为分片文档生成 Q&A 对，然后对问题进行 embedding
+      </Property>
+      <Property name='doc_language' type='string' key='doc_language'>
+        在 Q&A 模式下，指定文档的语言，例如：<code>English</code>、<code>Chinese</code>
+      </Property>
       <Property name='process_rule' type='object' key='process_rule'>
         处理规则
           - <code>mode</code> (string) 清洗、分段模式 ，automatic 自动 / custom 自定义
                   - <code>remove_urls_emails</code> 删除 URL、电子邮件地址
               - <code>enabled</code> (bool) 是否选中该规则，不传入文档 ID 时代表默认值
             - <code>segmentation</code> (object) 分段规则
-              - <code>separator</code> 自定义分段标识符，目前仅允许设置一个分隔符。默认为 \n
+              - <code>separator</code> 自定义分段标识符，目前仅允许设置一个分隔符。默认为 <code>\n</code>
               - <code>max_tokens</code> 最大长度（token）默认为 1000
+            - <code>parent_mode</code> 父分段的召回模式 <code>full-doc</code> 全文召回 / <code>paragraph</code> 段落召回
+            - <code>subchunk_segmentation</code> (object) 子分段规则
+              - <code>separator</code> 分段标识符，目前仅允许设置一个分隔符。默认为 <code>***</code>
+              - <code>max_tokens</code> 最大长度 (token) 需要校验小于父级的长度
+              - <code>chunk_overlap</code> 分段重叠指的是在对数据进行分段时，段与段之间存在一定的重叠部分（选填）
       </Property>
     </Properties>
   </Col>
           - <code>high_quality</code> 高质量：使用  embedding 模型进行嵌入，构建为向量数据库索引
           - <code>economy</code> 经济：使用 keyword table index 的倒排索引进行构建
 
+        - <code>doc_form</code> 索引内容的形式
+          - <code>text_model</code> text 文档直接 embedding，经济模式默认为该模式
+          - <code>hierarchical_model</code> parent-child 模式
+          - <code>qa_model</code> Q&A 模式：为分片文档生成 Q&A 对，然后对问题进行 embedding
+
+        - <code>doc_language</code> 在 Q&A 模式下，指定文档的语言，例如：<code>English</code>、<code>Chinese</code>
+
         - <code>process_rule</code> 处理规则
           - <code>mode</code> (string) 清洗、分段模式 ，automatic 自动 / custom 自定义
           - <code>rules</code> (object) 自定义规则（自动模式下，该字段为空）
             - <code>segmentation</code> (object) 分段规则
               - <code>separator</code> 自定义分段标识符，目前仅允许设置一个分隔符。默认为 \n
               - <code>max_tokens</code> 最大长度（token）默认为 1000
+            - <code>parent_mode</code> 父分段的召回模式 <code>full-doc</code> 全文召回 / <code>paragraph</code> 段落召回
+            - <code>subchunk_segmentation</code> (object) 子分段规则
+              - <code>separator</code> 分段标识符，目前仅允许设置一个分隔符。默认为 <code>***</code>
+              - <code>max_tokens</code> 最大长度 (token) 需要校验小于父级的长度
+              - <code>chunk_overlap</code> 分段重叠指的是在对数据进行分段时，段与段之间存在一定的重叠部分（选填）
       </Property>
       <Property name='file' type='multipart/form-data' key='file'>
         需要上传的文件。
 <Heading
   url='/datasets/{dataset_id}/documents/{document_id}/update-by-text'
   method='POST'
-  title='通过文本更新文档 '
+  title='通过文本更新文档'
   name='#update-by-text'
 />
 <Row>
             - <code>segmentation</code> (object) 分段规则
               - <code>separator</code> 自定义分段标识符，目前仅允许设置一个分隔符。默认为 \n
               - <code>max_tokens</code> 最大长度（token）默认为 1000
+            - <code>parent_mode</code> 父分段的召回模式 <code>full-doc</code> 全文召回 / <code>paragraph</code> 段落召回
+            - <code>subchunk_segmentation</code> (object) 子分段规则
+              - <code>separator</code> 分段标识符，目前仅允许设置一个分隔符。默认为 <code>***</code>
+              - <code>max_tokens</code> 最大长度 (token) 需要校验小于父级的长度
+              - <code>chunk_overlap</code> 分段重叠指的是在对数据进行分段时，段与段之间存在一定的重叠部分（选填）
       </Property>
     </Properties>
   </Col>
 <Heading
   url='/datasets/{dataset_id}/documents/{document_id}/update-by-file'
   method='POST'
-  title='通过文件更新文档  '
+  title='通过文件更新文档'
   name='#update-by-file'
 />
 <Row>
             - <code>segmentation</code> (object) 分段规则
               - <code>separator</code> 自定义分段标识符，目前仅允许设置一个分隔符。默认为 \n
               - <code>max_tokens</code> 最大长度（token）默认为 1000
+            - <code>parent_mode</code> 父分段的召回模式 <code>full-doc</code> 全文召回 / <code>paragraph</code> 段落召回
+            - <code>subchunk_segmentation</code> (object) 子分段规则
+              - <code>separator</code> 分段标识符，目前仅允许设置一个分隔符。默认为 <code>***</code>
+              - <code>max_tokens</code> 最大长度 (token) 需要校验小于父级的长度
+              - <code>chunk_overlap</code> 分段重叠指的是在对数据进行分段时，段与段之间存在一定的重叠部分（选填）
       </Property>
     </Properties>
   </Col>
         - <code>answer</code> (text) 答案内容，非必填，如果知识库的模式为 Q&A 模式则传值
         - <code>keywords</code> (list) 关键字，非必填
         - <code>enabled</code> (bool) false/true，非必填
+        - <code>regenerate_child_chunks</code> (bool) 是否重新生成子分段，非必填
       </Property>
     </Properties>
   </Col>

web/app/components/app/configuration/config-prompt/prompt-editor-height-resize-wrap.tsx

   const [clientY, setClientY] = useState(0)
   const [isResizing, setIsResizing] = useState(false)
   const [prevUserSelectStyle, setPrevUserSelectStyle] = useState(getComputedStyle(document.body).userSelect)
+  const [oldHeight, setOldHeight] = useState(height)
 
   const handleStartResize = useCallback((e: React.MouseEvent<HTMLElement>) => {
     setClientY(e.clientY)
     setIsResizing(true)
+    setOldHeight(height)
     setPrevUserSelectStyle(getComputedStyle(document.body).userSelect)
     document.body.style.userSelect = 'none'
-  }, [])
+  }, [height])
 
   const handleStopResize = useCallback(() => {
     setIsResizing(false)
       return
 
     const offset = e.clientY - clientY
-    let newHeight = height + offset
-    setClientY(e.clientY)
+    let newHeight = oldHeight + offset
     if (newHeight < minHeight)
       newHeight = minHeight
     onHeightChange(newHeight)

web/app/components/app/configuration/config-prompt/simple-prompt-input.tsx

 import { INSERT_VARIABLE_VALUE_BLOCK_COMMAND } from '@/app/components/base/prompt-editor/plugins/variable-block'
 import { PROMPT_EDITOR_UPDATE_VALUE_BY_EVENT_EMITTER } from '@/app/components/base/prompt-editor/plugins/update-block'
 import useBreakpoints, { MediaType } from '@/hooks/use-breakpoints'
+import { useFeaturesStore } from '@/app/components/base/features/hooks'
 
 export type ISimplePromptInput = {
   mode: AppType
   const { t } = useTranslation()
   const media = useBreakpoints()
   const isMobile = media === MediaType.mobile
+  const featuresStore = useFeaturesStore()
+  const {
+    features,
+    setFeatures,
+  } = featuresStore!.getState()
 
   const { eventEmitter } = useEventEmitterContextContext()
   const {
     })
     setModelConfig(newModelConfig)
     setPrevPromptConfig(modelConfig.configs)
-    if (mode !== AppType.completion)
+
+    if (mode !== AppType.completion) {
       setIntroduction(res.opening_statement)
+      const newFeatures = produce(features, (draft) => {
+        draft.opening = {
+          ...draft.opening,
+          enabled: !!res.opening_statement,
+          opening_statement: res.opening_statement,
+        }
+      })
+      setFeatures(newFeatures)
+    }
     showAutomaticFalse()
   }
   const minHeight = initEditorHeight || 228

web/app/components/app/configuration/dataset-config/params-config/config-content.tsx

 
   const {
     modelList: rerankModelList,
-    defaultModel: rerankDefaultModel,
-    currentModel: isRerankDefaultModelValid,
   } = useModelListAndDefaultModelAndCurrentProviderAndModel(ModelTypeEnum.rerank)
 
   const {
     currentModel: currentRerankModel,
   } = useCurrentProviderAndModel(
     rerankModelList,
-    rerankDefaultModel
-      ? {
-        ...rerankDefaultModel,
-        provider: rerankDefaultModel.provider.provider,
-      }
-      : undefined,
+    {
+      provider: datasetConfigs.reranking_model?.reranking_provider_name,
+      model: datasetConfigs.reranking_model?.reranking_model_name,
+    },
   )
 
-  const rerankModel = (() => {
-    if (datasetConfigs.reranking_model?.reranking_provider_name) {
-      return {
-        provider_name: datasetConfigs.reranking_model.reranking_provider_name,
-        model_name: datasetConfigs.reranking_model.reranking_model_name,
-      }
+  const rerankModel = useMemo(() => {
+    return {
+      provider_name: datasetConfigs?.reranking_model?.reranking_provider_name ?? '',
+      model_name: datasetConfigs?.reranking_model?.reranking_model_name ?? '',
     }
-    else if (rerankDefaultModel) {
-      return {
-        provider_name: rerankDefaultModel.provider.provider,
-        model_name: rerankDefaultModel.model,
-      }
-    }
-  })()
+  }, [datasetConfigs.reranking_model])
 
   const handleParamChange = (key: string, value: number) => {
     if (key === 'top_k') {
   }
 
   const handleRerankModeChange = (mode: RerankingModeEnum) => {
+    if (mode === datasetConfigs.reranking_mode)
+      return
+
+    if (mode === RerankingModeEnum.RerankingModel && !currentRerankModel)
+      Toast.notify({ type: 'error', message: t('workflow.errorMsg.rerankModelRequired') })
+
     onChange({
       ...datasetConfigs,
       reranking_mode: mode,
 
   const canManuallyToggleRerank = useMemo(() => {
     return (selectedDatasetsMode.allInternal && selectedDatasetsMode.allEconomic)
-      || selectedDatasetsMode.allExternal
+    || selectedDatasetsMode.allExternal
   }, [selectedDatasetsMode.allEconomic, selectedDatasetsMode.allExternal, selectedDatasetsMode.allInternal])
 
   const showRerankModel = useMemo(() => {
     if (!canManuallyToggleRerank)
       return true
-    else if (canManuallyToggleRerank && !isRerankDefaultModelValid)
-      return false
 
     return datasetConfigs.reranking_enable
-  }, [canManuallyToggleRerank, datasetConfigs.reranking_enable, isRerankDefaultModelValid])
+  }, [datasetConfigs.reranking_enable, canManuallyToggleRerank])
 
-  const handleDisabledSwitchClick = useCallback(() => {
-    if (!currentRerankModel && !showRerankModel)
+  const handleDisabledSwitchClick = useCallback((enable: boolean) => {
+    if (!currentRerankModel && enable)
       Toast.notify({ type: 'error', message: t('workflow.errorMsg.rerankModelRequired') })
-  }, [currentRerankModel, showRerankModel, t])
-
-  useEffect(() => {
-    if (canManuallyToggleRerank && showRerankModel !== datasetConfigs.reranking_enable) {
-      onChange({
-        ...datasetConfigs,
-        reranking_enable: showRerankModel,
-      })
-    }
-  }, [canManuallyToggleRerank, showRerankModel, datasetConfigs, onChange])
+    onChange({
+      ...datasetConfigs,
+      reranking_enable: enable,
+    })
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [currentRerankModel, datasetConfigs, onChange])
 
   return (
     <div>
                 <div className='flex items-center'>
                   {
                     selectedDatasetsMode.allEconomic && !selectedDatasetsMode.mixtureInternalAndExternal && (
-                      <div
-                        className='flex items-center'
-                        onClick={handleDisabledSwitchClick}
-                      >
-                        <Switch
-                          size='md'
-                          defaultValue={showRerankModel}
-                          disabled={!currentRerankModel || !canManuallyToggleRerank}
-                          onChange={(v) => {
-                            if (canManuallyToggleRerank) {
-                              onChange({
-                                ...datasetConfigs,
-                                reranking_enable: v,
-                              })
-                            }
-                          }}
-                        />
-                      </div>
+                      <Switch
+                        size='md'
+                        defaultValue={showRerankModel}
+                        disabled={!canManuallyToggleRerank}
+                        onChange={handleDisabledSwitchClick}
+                      />
                     )
                   }
                   <div className='leading-[32px] ml-1 text-text-secondary system-sm-semibold'>{t('common.modelProvider.rerankModel.key')}</div>
                     triggerClassName='ml-1 w-4 h-4'
                   />
                 </div>
-                <div>
-                  <ModelSelector
-                    defaultModel={rerankModel && { provider: rerankModel?.provider_name, model: rerankModel?.model_name }}
-                    onSelect={(v) => {
-                      onChange({
-                        ...datasetConfigs,
-                        reranking_model: {
-                          reranking_provider_name: v.provider,
-                          reranking_model_name: v.model,
-                        },
-                      })
-                    }}
-                    modelList={rerankModelList}
-                  />
-                </div>
+                {
+                  showRerankModel && (
+                    <div>
+                      <ModelSelector
+                        defaultModel={rerankModel && { provider: rerankModel?.provider_name, model: rerankModel?.model_name }}
+                        onSelect={(v) => {
+                          onChange({
+                            ...datasetConfigs,
+                            reranking_model: {
+                              reranking_provider_name: v.provider,
+                              reranking_model_name: v.model,
+                            },
+                          })
+                        }}
+                        modelList={rerankModelList}
+                      />
+                    </div>
+                  )}
               </div>
             )
           }

web/app/components/app/configuration/dataset-config/params-config/index.tsx

 import Button from '@/app/components/base/button'
 import { RETRIEVE_TYPE } from '@/types/app'
 import Toast from '@/app/components/base/toast'
-import { useModelListAndDefaultModelAndCurrentProviderAndModel } from '@/app/components/header/account-setting/model-provider-page/hooks'
+import { useCurrentProviderAndModel, useModelListAndDefaultModelAndCurrentProviderAndModel } from '@/app/components/header/account-setting/model-provider-page/hooks'
 import { ModelTypeEnum } from '@/app/components/header/account-setting/model-provider-page/declarations'
 import { RerankingModeEnum } from '@/models/datasets'
 import type { DataSet } from '@/models/datasets'
   }, [datasetConfigs])
 
   const {
-    defaultModel: rerankDefaultModel,
-    currentModel: isRerankDefaultModelValid,
+    modelList: rerankModelList,
+    currentModel: rerankDefaultModel,
     currentProvider: rerankDefaultProvider,
   } = useModelListAndDefaultModelAndCurrentProviderAndModel(ModelTypeEnum.rerank)
 
+  const {
+    currentModel: isCurrentRerankModelValid,
+  } = useCurrentProviderAndModel(
+    rerankModelList,
+    {
+      provider: tempDataSetConfigs.reranking_model?.reranking_provider_name ?? '',
+      model: tempDataSetConfigs.reranking_model?.reranking_model_name ?? '',
+    },
+  )
+
   const isValid = () => {
     let errMsg = ''
     if (tempDataSetConfigs.retrieval_model === RETRIEVE_TYPE.multiWay) {
       if (tempDataSetConfigs.reranking_enable
         && tempDataSetConfigs.reranking_mode === RerankingModeEnum.RerankingModel
-        && !isRerankDefaultModelValid
+        && !isCurrentRerankModelValid
       )
         errMsg = t('appDebug.datasetConfig.rerankModelRequired')
     }
   const handleSave = () => {
     if (!isValid())
       return
-    const config = { ...tempDataSetConfigs }
-    if (config.retrieval_model === RETRIEVE_TYPE.multiWay
-      && config.reranking_mode === RerankingModeEnum.RerankingModel
-      && !config.reranking_model) {
-      config.reranking_model = {
-        reranking_provider_name: rerankDefaultModel?.provider?.provider,
-        reranking_model_name: rerankDefaultModel?.model,
-      } as any
-    }
-    setDatasetConfigs(config)
+    setDatasetConfigs(tempDataSetConfigs)
     setRerankSettingModalOpen(false)
   }
 
       reranking_enable: restConfigs.reranking_enable,
     }, selectedDatasets, selectedDatasets, {
       provider: rerankDefaultProvider?.provider,
-      model: isRerankDefaultModelValid?.model,
+      model: rerankDefaultModel?.model,
     })
 
     setTempDataSetConfigs({
       ...retrievalConfig,
-      reranking_model: restConfigs.reranking_model && {
-        reranking_provider_name: restConfigs.reranking_model.reranking_provider_name,
-        reranking_model_name: restConfigs.reranking_model.reranking_model_name,
+      reranking_model: {
+        reranking_provider_name: retrievalConfig.reranking_model?.provider || '',
+        reranking_model_name: retrievalConfig.reranking_model?.model || '',
       },
       retrieval_model,
       score_threshold_enabled,

web/app/components/app/configuration/dataset-config/params-config/weighted-score.tsx

 
   return (
     <div>
-      <div className='px-3 pt-5 h-[52px] space-x-3 rounded-lg border border-components-panel-border'>
+      <div className='px-3 pt-5 pb-2 space-x-3 rounded-lg border border-components-panel-border'>
         <Slider
           className={cn('grow h-0.5 !bg-util-colors-teal-teal-500 rounded-full')}
           max={1.0}
           onChange={v => onChange({ value: [v, (10 - v * 10) / 10] })}
           trackClassName='weightedScoreSliderTrack'
         />
-        <div className='flex justify-between mt-1'>
+        <div className='flex justify-between mt-3'>
           <div className='shrink-0 flex items-center w-[90px] system-xs-semibold-uppercase text-util-colors-blue-light-blue-light-500'>
             <div className='mr-1 truncate uppercase' title={t('dataset.weightedScore.semantic') || ''}>
               {t('dataset.weightedScore.semantic')}

web/app/components/app/configuration/dataset-config/settings-modal/index.tsx

 import Button from '@/app/components/base/button'
 import Input from '@/app/components/base/input'
 import Textarea from '@/app/components/base/textarea'
-import { type DataSet, RerankingModeEnum } from '@/models/datasets'
+import { type DataSet } from '@/models/datasets'
 import { useToastContext } from '@/app/components/base/toast'
 import { updateDatasetSetting } from '@/service/datasets'
 import { useAppContext } from '@/context/app-context'
 import RetrievalSettings from '@/app/components/datasets/external-knowledge-base/create/RetrievalSettings'
 import RetrievalMethodConfig from '@/app/components/datasets/common/retrieval-method-config'
 import EconomicalRetrievalMethodConfig from '@/app/components/datasets/common/economical-retrieval-method-config'
-import { ensureRerankModelSelected, isReRankModelSelected } from '@/app/components/datasets/common/check-rerank-model'
+import { isReRankModelSelected } from '@/app/components/datasets/common/check-rerank-model'
 import { AlertTriangle } from '@/app/components/base/icons/src/vender/solid/alertsAndFeedback'
 import PermissionSelector from '@/app/components/datasets/settings/permission-selector'
 import ModelSelector from '@/app/components/header/account-setting/model-provider-page/model-selector'
     }
     if (
       !isReRankModelSelected({
-        rerankDefaultModel,
-        isRerankDefaultModelValid: !!isRerankDefaultModelValid,
         rerankModelList,
         retrievalConfig,
         indexMethod,
       notify({ type: 'error', message: t('appDebug.datasetConfig.rerankModelRequired') })
       return
     }
-    const postRetrievalConfig = ensureRerankModelSelected({
-      rerankDefaultModel: rerankDefaultModel!,
-      retrievalConfig: {
-        ...retrievalConfig,
-        reranking_enable: retrievalConfig.reranking_mode === RerankingModeEnum.RerankingModel,
-      },
-      indexMethod,
-    })
     try {
       setLoading(true)
       const { id, name, description, permission } = localeCurrentDataset
           permission,
           indexing_technique: indexMethod,
           retrieval_model: {
-            ...postRetrievalConfig,
-            score_threshold: postRetrievalConfig.score_threshold_enabled ? postRetrievalConfig.score_threshold : 0,
+            ...retrievalConfig,
+            score_threshold: retrievalConfig.score_threshold_enabled ? retrievalConfig.score_threshold : 0,
           },
           embedding_model: localeCurrentDataset.embedding_model,
           embedding_model_provider: localeCurrentDataset.embedding_model_provider,
       onSave({
         ...localeCurrentDataset,
         indexing_technique: indexMethod,
-        retrieval_model_dict: postRetrievalConfig,
+        retrieval_model_dict: retrievalConfig,
       })
     }
     catch (e) {

web/app/components/app/configuration/index.tsx

 
     setDatasetConfigs({
       ...retrievalConfig,
-      reranking_model: restConfigs.reranking_model && {
-        reranking_provider_name: restConfigs.reranking_model.reranking_provider_name,
-        reranking_model_name: restConfigs.reranking_model.reranking_model_name,
+      reranking_model: {
+        reranking_provider_name: retrievalConfig?.reranking_model?.provider || '',
+        reranking_model_name: retrievalConfig?.reranking_model?.model || '',
       },
       retrieval_model,
       score_threshold_enabled,

web/app/components/base/chat/chat/chat-input-area/index.tsx

   inputs?: Record<string, any>
   inputsForm?: InputForm[]
   theme?: Theme | null
+  isResponding?: boolean
 }
 const ChatInputArea = ({
   showFeatureBar,
   inputs = {},
   inputsForm = [],
   theme,
+  isResponding,
 }: ChatInputAreaProps) => {
   const { t } = useTranslation()
   const { notify } = useToastContext()
   const historyRef = useRef([''])
   const [currentIndex, setCurrentIndex] = useState(-1)
   const handleSend = () => {
+    if (isResponding) {
+      notify({ type: 'info', message: t('appDebug.errorMessage.waitForResponse') })
+      return
+    }
+
     if (onSend) {
       const { files, setFiles } = filesStore.getState()
       if (files.find(item => item.transferMethod === TransferMethod.local_file && !item.uploadedId)) {
         setQuery(historyRef.current[currentIndex + 1])
       }
       else if (currentIndex === historyRef.current.length - 1) {
-      // If it is the last element, clear the input box
+        // If it is the last element, clear the input box
         setCurrentIndex(historyRef.current.length)
         setQuery('')
       }
                   'p-1 w-full leading-6 body-lg-regular text-text-tertiary outline-none',
                 )}
                 placeholder={t('common.chat.inputPlaceholder') || ''}
+                autoFocus
                 autoSize={{ minRows: 1 }}
                 onResize={handleTextareaResize}
                 value={query}

web/app/components/base/chat/chat/index.tsx

                   inputs={inputs}
                   inputsForm={inputsForm}
                   theme={themeBuilder?.theme}
+                  isResponding={isResponding}
                 />
               )
             }

web/app/components/base/chat/chat/question.tsx

   } = item
 
   return (
-    <div className='flex justify-end mb-2 last:mb-0 pl-10'>
-      <div className='group relative mr-4'>
+    <div className='flex justify-end mb-2 last:mb-0 pl-14'>
+      <div className='group relative mr-4 max-w-full'>
         <div
           className='px-4 py-3 bg-[#D1E9FF]/50 rounded-2xl text-sm text-gray-900'
           style={theme?.chatBubbleColorStyle ? CssTransform(theme.chatBubbleColorStyle) : {}}

web/app/components/base/markdown.tsx

     }
     else if (language === 'echarts') {
       return (
-        <div style={{ minHeight: '350px', minWidth: '700px' }}>
+        <div style={{ minHeight: '350px', minWidth: '100%', overflowX: 'scroll' }}>
           <ErrorBoundary>
-            <ReactEcharts option={chartData} />
+            <ReactEcharts option={chartData} style={{ minWidth: '700px' }} />
           </ErrorBoundary>
         </div>
       )

web/app/components/base/param-item/top-k-item.tsx

   enable: boolean
 }
 
+const maxTopK = (() => {
+  const configValue = parseInt(globalThis.document?.body?.getAttribute('data-public-top-k-max-value') || '', 10)
+  if (configValue && !isNaN(configValue))
+    return configValue
+  return 10
+})()
 const VALUE_LIMIT = {
   default: 2,
   step: 1,
   min: 1,
-  max: 10,
+  max: maxTopK,
 }
 
 const key = 'top_k'

web/app/components/datasets/common/check-rerank-model.ts

 import { RerankingModeEnum } from '@/models/datasets'
 
 export const isReRankModelSelected = ({
-  rerankDefaultModel,
-  isRerankDefaultModelValid,
   retrievalConfig,
   rerankModelList,
   indexMethod,
 }: {
-  rerankDefaultModel?: DefaultModelResponse
-  isRerankDefaultModelValid: boolean
   retrievalConfig: RetrievalConfig
   rerankModelList: Model[]
   indexMethod?: string
       return provider?.models.find(({ model }) => model === retrievalConfig.reranking_model?.reranking_model_name)
     }
 
-    if (isRerankDefaultModelValid)
-      return !!rerankDefaultModel
-
     return false
   })()
 
+  if (
+    indexMethod === 'high_quality'
+    && ([RETRIEVE_METHOD.semantic, RETRIEVE_METHOD.fullText].includes(retrievalConfig.search_method))
+    && retrievalConfig.reranking_enable
+    && !rerankModelSelected
+  )
+    return false
+
   if (
     indexMethod === 'high_quality'
     && (retrievalConfig.search_method === RETRIEVE_METHOD.hybrid && retrievalConfig.reranking_mode !== RerankingModeEnum.WeightedScore)

web/app/components/datasets/common/economical-retrieval-method-config/index.tsx

 import type { RetrievalConfig } from '@/types/app'
 
 type Props = {
+  disabled?: boolean
   value: RetrievalConfig
   onChange: (value: RetrievalConfig) => void
 }
 
 const EconomicalRetrievalMethodConfig: FC<Props> = ({
+  disabled = false,
   value,
   onChange,
 }) => {
 
   return (
     <div className='space-y-2'>
-      <OptionCard icon={<Image className='w-4 h-4' src={retrievalIcon.vector} alt='' />}
+      <OptionCard
+        disabled={disabled} icon={<Image className='w-4 h-4' src={retrievalIcon.vector} alt='' />}
         title={t('dataset.retrieval.invertedIndex.title')}
         description={t('dataset.retrieval.invertedIndex.description')} isActive
         activeHeaderClassName='bg-dataset-option-card-purple-gradient'

web/app/components/datasets/common/retrieval-method-config/index.tsx

 'use client'
 import type { FC } from 'react'
-import React from 'react'
+import React, { useCallback } from 'react'
 import { useTranslation } from 'react-i18next'
 import Image from 'next/image'
 import RetrievalParamConfig from '../retrieval-param-config'
 import type { RetrievalConfig } from '@/types/app'
 import { RETRIEVE_METHOD } from '@/types/app'
 import { useProviderContext } from '@/context/provider-context'
-import { useDefaultModel } from '@/app/components/header/account-setting/model-provider-page/hooks'
+import { useModelListAndDefaultModelAndCurrentProviderAndModel } from '@/app/components/header/account-setting/model-provider-page/hooks'
 import { ModelTypeEnum } from '@/app/components/header/account-setting/model-provider-page/declarations'
 import {
   DEFAULT_WEIGHTED_SCORE,
 import Badge from '@/app/components/base/badge'
 
 type Props = {
+  disabled?: boolean
   value: RetrievalConfig
   onChange: (value: RetrievalConfig) => void
 }
 
 const RetrievalMethodConfig: FC<Props> = ({
-  value: passValue,
+  disabled = false,
+  value,
   onChange,
 }) => {
   const { t } = useTranslation()
   const { supportRetrievalMethods } = useProviderContext()
-  const { data: rerankDefaultModel } = useDefaultModel(ModelTypeEnum.rerank)
-  const value = (() => {
-    if (!passValue.reranking_model.reranking_model_name) {
-      return {
-        ...passValue,
-        reranking_model: {
-          reranking_provider_name: rerankDefaultModel?.provider.provider || '',
-          reranking_model_name: rerankDefaultModel?.model || '',
-        },
-        reranking_mode: passValue.reranking_mode || (rerankDefaultModel ? RerankingModeEnum.RerankingModel : RerankingModeEnum.WeightedScore),
-        weights: passValue.weights || {
-          weight_type: WeightedScoreEnum.Customized,
-          vector_setting: {
-            vector_weight: DEFAULT_WEIGHTED_SCORE.other.semantic,
-            embedding_provider_name: '',
-            embedding_model_name: '',
-          },
-          keyword_setting: {
-            keyword_weight: DEFAULT_WEIGHTED_SCORE.other.keyword,
-          },
-        },
-      }
+  const {
+    defaultModel: rerankDefaultModel,
+    currentModel: isRerankDefaultModelValid,
+  } = useModelListAndDefaultModelAndCurrentProviderAndModel(ModelTypeEnum.rerank)
+
+  const onSwitch = useCallback((retrieveMethod: RETRIEVE_METHOD) => {
+    if ([RETRIEVE_METHOD.semantic, RETRIEVE_METHOD.fullText].includes(retrieveMethod)) {
+      onChange({
+        ...value,
+        search_method: retrieveMethod,
+        ...(!value.reranking_model.reranking_model_name
+          ? {
+            reranking_model: {
+              reranking_provider_name: isRerankDefaultModelValid ? rerankDefaultModel?.provider?.provider ?? '' : '',
+              reranking_model_name: isRerankDefaultModelValid ? rerankDefaultModel?.model ?? '' : '',
+            },
+            reranking_enable: !!isRerankDefaultModelValid,
+          }
+          : {
+            reranking_enable: true,
+          }),
+      })
     }
-    return passValue
-  })()
+    if (retrieveMethod === RETRIEVE_METHOD.hybrid) {
+      onChange({
+        ...value,
+        search_method: retrieveMethod,
+        ...(!value.reranking_model.reranking_model_name
+          ? {
+            reranking_model: {
+              reranking_provider_name: isRerankDefaultModelValid ? rerankDefaultModel?.provider?.provider ?? '' : '',
+              reranking_model_name: isRerankDefaultModelValid ? rerankDefaultModel?.model ?? '' : '',
+            },
+            reranking_enable: !!isRerankDefaultModelValid,
+            reranking_mode: isRerankDefaultModelValid ? RerankingModeEnum.RerankingModel : RerankingModeEnum.WeightedScore,
+          }
+          : {
+            reranking_enable: true,
+            reranking_mode: RerankingModeEnum.RerankingModel,
+          }),
+        ...(!value.weights
+          ? {
+            weights: {
+              weight_type: WeightedScoreEnum.Customized,
+              vector_setting: {
+                vector_weight: DEFAULT_WEIGHTED_SCORE.other.semantic,
+                embedding_provider_name: '',
+                embedding_model_name: '',
+              },
+              keyword_setting: {
+                keyword_weight: DEFAULT_WEIGHTED_SCORE.other.keyword,
+              },
+            },
+          }
+          : {}),
+      })
+    }
+  }, [value, rerankDefaultModel, isRerankDefaultModelValid, onChange])
+
   return (
     <div className='space-y-2'>
       {supportRetrievalMethods.includes(RETRIEVE_METHOD.semantic) && (
-        <OptionCard icon={<Image className='w-4 h-4' src={retrievalIcon.vector} alt='' />}
+        <OptionCard disabled={disabled} icon={<Image className='w-4 h-4' src={retrievalIcon.vector} alt='' />}
           title={t('dataset.retrieval.semantic_search.title')}
           description={t('dataset.retrieval.semantic_search.description')}
           isActive={
             value.search_method === RETRIEVE_METHOD.semantic
           }
-          onSwitched={() => onChange({
-            ...value,
-            search_method: RETRIEVE_METHOD.semantic,
-          })}
+          onSwitched={() => onSwitch(RETRIEVE_METHOD.semantic)}
           effectImg={Effect.src}
           activeHeaderClassName='bg-dataset-option-card-purple-gradient'
         >
           />
         </OptionCard>
       )}
-      {supportRetrievalMethods.includes(RETRIEVE_METHOD.semantic) && (
-        <OptionCard icon={<Image className='w-4 h-4' src={retrievalIcon.fullText} alt='' />}
+      {supportRetrievalMethods.includes(RETRIEVE_METHOD.fullText) && (
+        <OptionCard disabled={disabled} icon={<Image className='w-4 h-4' src={retrievalIcon.fullText} alt='' />}
           title={t('dataset.retrieval.full_text_search.title')}
           description={t('dataset.retrieval.full_text_search.description')}
           isActive={
             value.search_method === RETRIEVE_METHOD.fullText
           }
-          onSwitched={() => onChange({
-            ...value,
-            search_method: RETRIEVE_METHOD.fullText,
-          })}
+          onSwitched={() => onSwitch(RETRIEVE_METHOD.fullText)}
           effectImg={Effect.src}
           activeHeaderClassName='bg-dataset-option-card-purple-gradient'
         >
           />
         </OptionCard>
       )}
-      {supportRetrievalMethods.includes(RETRIEVE_METHOD.semantic) && (
-        <OptionCard icon={<Image className='w-4 h-4' src={retrievalIcon.hybrid} alt='' />}
+      {supportRetrievalMethods.includes(RETRIEVE_METHOD.hybrid) && (
+        <OptionCard disabled={disabled} icon={<Image className='w-4 h-4' src={retrievalIcon.hybrid} alt='' />}
           title={
             <div className='flex items-center space-x-1'>
               <div>{t('dataset.retrieval.hybrid_search.title')}</div>
           description={t('dataset.retrieval.hybrid_search.description')} isActive={
             value.search_method === RETRIEVE_METHOD.hybrid
           }
-          onSwitched={() => onChange({
-            ...value,
-            search_method: RETRIEVE_METHOD.hybrid,
-            reranking_enable: true,
-          })}
+          onSwitched={() => onSwitch(RETRIEVE_METHOD.hybrid)}
           effectImg={Effect.src}
           activeHeaderClassName='bg-dataset-option-card-purple-gradient'
         >

web/app/components/datasets/common/retrieval-param-config/index.tsx

 'use client'
 import type { FC } from 'react'
-import React, { useCallback } from 'react'
+import React, { useCallback, useMemo } from 'react'
 import { useTranslation } from 'react-i18next'
 
 import Image from 'next/image'
   const { t } = useTranslation()
   const canToggleRerankModalEnable = type !== RETRIEVE_METHOD.hybrid
   const isEconomical = type === RETRIEVE_METHOD.invertedIndex
+  const isHybridSearch = type === RETRIEVE_METHOD.hybrid
   const {
-    defaultModel: rerankDefaultModel,
     modelList: rerankModelList,
   } = useModelListAndDefaultModel(ModelTypeEnum.rerank)
 
     currentModel,
   } = useCurrentProviderAndModel(
     rerankModelList,
-    rerankDefaultModel
-      ? {
-        ...rerankDefaultModel,
-        provider: rerankDefaultModel.provider.provider,
-      }
-      : undefined,
+    {
+      provider: value.reranking_model?.reranking_provider_name ?? '',
+      model: value.reranking_model?.reranking_model_name ?? '',
+    },
   )
 
-  const handleDisabledSwitchClick = useCallback(() => {
-    if (!currentModel)
+  const handleDisabledSwitchClick = useCallback((enable: boolean) => {
+    if (enable && !currentModel)
       Toast.notify({ type: 'error', message: t('workflow.errorMsg.rerankModelRequired') })
-  }, [currentModel, rerankDefaultModel, t])
-
-  const isHybridSearch = type === RETRIEVE_METHOD.hybrid
+    onChange({
+      ...value,
+      reranking_enable: enable,
+    })
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [currentModel, onChange, value])
 
-  const rerankModel = (() => {
-    if (value.reranking_model) {
-      return {
-        provider_name: value.reranking_model.reranking_provider_name,
-        model_name: value.reranking_model.reranking_model_name,
-      }
-    }
-    else if (rerankDefaultModel) {
-      return {
-        provider_name: rerankDefaultModel.provider.provider,
-        model_name: rerankDefaultModel.model,
-      }
+  const rerankModel = useMemo(() => {
+    return {
+      provider_name: value.reranking_model.reranking_provider_name,
+      model_name: value.reranking_model.reranking_model_name,
     }
-  })()
+  }, [value.reranking_model])
 
   const handleChangeRerankMode = (v: RerankingModeEnum) => {
     if (v === value.reranking_mode)
         },
       }
     }
+    if (v === RerankingModeEnum.RerankingModel && !currentModel)
+      Toast.notify({ type: 'error', message: t('workflow.errorMsg.rerankModelRequired') })
     onChange(result)
   }
 
         <div>
           <div className='flex items-center space-x-2 mb-2'>
             {canToggleRerankModalEnable && (
-              <div
-                className='flex items-center'
-                onClick={handleDisabledSwitchClick}
-              >
-                <Switch
-                  size='md'
-                  defaultValue={currentModel ? value.reranking_enable : false}
-                  onChange={(v) => {
-                    onChange({
-                      ...value,
-                      reranking_enable: v,
-                    })
-                  }}
-                  disabled={!currentModel}
-                />
-              </div>
+              <Switch
+                size='md'
+                defaultValue={value.reranking_enable}
+                onChange={handleDisabledSwitchClick}
+              />
             )}
             <div className='flex items-center'>
               <span className='mr-0.5 system-sm-semibold text-text-secondary'>{t('common.modelProvider.rerankModel.key')}</span>
               />
             </div>
           </div>
-          <ModelSelector
-            triggerClassName={`${!value.reranking_enable && '!opacity-60 !cursor-not-allowed'}`}
-            defaultModel={rerankModel && { provider: rerankModel.provider_name, model: rerankModel.model_name }}
-            modelList={rerankModelList}
-            readonly={!value.reranking_enable}
-            onSelect={(v) => {
-              onChange({
-                ...value,
-                reranking_model: {
-                  reranking_provider_name: v.provider,
-                  reranking_model_name: v.model,
-                },
-              })
-            }}
-          />
+          {
+            value.reranking_enable && (
+              <ModelSelector
+                defaultModel={rerankModel && { provider: rerankModel.provider_name, model: rerankModel.model_name }}
+                modelList={rerankModelList}
+                onSelect={(v) => {
+                  onChange({
+                    ...value,
+                    reranking_model: {
+                      reranking_provider_name: v.provider,
+                      reranking_model_name: v.model,
+                    },
+                  })
+                }}
+              />
+            )
+          }
         </div>
       )}
       {
             {
               value.reranking_mode !== RerankingModeEnum.WeightedScore && (
                 <ModelSelector
-                  triggerClassName={`${!value.reranking_enable && '!opacity-60 !cursor-not-allowed'}`}
                   defaultModel={rerankModel && { provider: rerankModel.provider_name, model: rerankModel.model_name }}
                   modelList={rerankModelList}
-                  readonly={!value.reranking_enable}
                   onSelect={(v) => {
                     onChange({
                       ...value,

web/app/components/datasets/create/embedding-process/index.tsx

 import { sleep } from '@/utils'
 import { RETRIEVE_METHOD } from '@/types/app'
 import Tooltip from '@/app/components/base/tooltip'
+import { useInvalidDocumentList } from '@/service/knowledge/use-document'
 
 type Props = {
   datasetId: string
   })
 
   const router = useRouter()
+  const invalidDocumentList = useInvalidDocumentList()
   const navToDocumentList = () => {
+    invalidDocumentList()
     router.push(`/datasets/${datasetId}/documents`)
   }
   const navToApiDocs = () => {

web/app/components/datasets/create/step-two/index.tsx

 import { DelimiterInput, MaxLengthInput, OverlapInput } from './inputs'
 import cn from '@/utils/classnames'
 import type { CrawlOptions, CrawlResultItem, CreateDocumentReq, CustomFile, DocumentItem, FullDocumentDetail, ParentMode, PreProcessingRule, ProcessRule, Rules, createDocumentResponse } from '@/models/datasets'
+import { ChunkingMode, DataSourceType, ProcessMode } from '@/models/datasets'
 
 import Button from '@/app/components/base/button'
 import FloatRightContainer from '@/app/components/base/float-right-container'
 import RetrievalMethodConfig from '@/app/components/datasets/common/retrieval-method-config'
 import EconomicalRetrievalMethodConfig from '@/app/components/datasets/common/economical-retrieval-method-config'
 import { type RetrievalConfig } from '@/types/app'
-import { ensureRerankModelSelected, isReRankModelSelected } from '@/app/components/datasets/common/check-rerank-model'
+import { isReRankModelSelected } from '@/app/components/datasets/common/check-rerank-model'
 import Toast from '@/app/components/base/toast'
 import type { NotionPage } from '@/models/common'
 import { DataSourceProvider } from '@/models/common'
-import { ChunkingMode, DataSourceType, RerankingModeEnum } from '@/models/datasets'
 import { useDatasetDetailContext } from '@/context/dataset-detail'
 import I18n from '@/context/i18n'
 import { RETRIEVE_METHOD } from '@/types/app'
 import { ModelTypeEnum } from '@/app/components/header/account-setting/model-provider-page/declarations'
 import Checkbox from '@/app/components/base/checkbox'
 import RadioCard from '@/app/components/base/radio-card'
-import { IS_CE_EDITION } from '@/config'
+import { FULL_DOC_PREVIEW_LENGTH, IS_CE_EDITION } from '@/config'
 import Divider from '@/app/components/base/divider'
 import { getNotionInfo, getWebsiteInfo, useCreateDocument, useCreateFirstDocument, useFetchDefaultProcessRule, useFetchFileIndexingEstimateForFile, useFetchFileIndexingEstimateForNotion, useFetchFileIndexingEstimateForWeb } from '@/service/knowledge/use-create-dataset'
 import Badge from '@/app/components/base/badge'
   onCancel?: () => void
 }
 
-export enum SegmentType {
-  AUTO = 'automatic',
-  CUSTOM = 'custom',
-}
 export enum IndexingType {
   QUALIFIED = 'high_quality',
   ECONOMICAL = 'economy',
 }
 
 const DEFAULT_SEGMENT_IDENTIFIER = '\\n\\n'
-const DEFAULT_MAXMIMUM_CHUNK_LENGTH = 500
+const DEFAULT_MAXIMUM_CHUNK_LENGTH = 500
 const DEFAULT_OVERLAP = 50
 
 type ParentChildConfig = {
   isSetting,
   documentDetail,
   isAPIKeySet,
-  onSetting,
   datasetId,
   indexingType,
   dataSourceType: inCreatePageDataSourceType,
 
   const isInCreatePage = !datasetId || (datasetId && !currentDataset?.data_source_type)
   const dataSourceType = isInCreatePage ? inCreatePageDataSourceType : currentDataset?.data_source_type
-  const [segmentationType, setSegmentationType] = useState<SegmentType>(SegmentType.CUSTOM)
+  const [segmentationType, setSegmentationType] = useState<ProcessMode>(ProcessMode.general)
   const [segmentIdentifier, doSetSegmentIdentifier] = useState(DEFAULT_SEGMENT_IDENTIFIER)
   const setSegmentIdentifier = useCallback((value: string, canEmpty?: boolean) => {
     doSetSegmentIdentifier(value ? escape(value) : (canEmpty ? '' : DEFAULT_SEGMENT_IDENTIFIER))
   }, [])
-  const [maxChunkLength, setMaxChunkLength] = useState(DEFAULT_MAXMIMUM_CHUNK_LENGTH) // default chunk length
+  const [maxChunkLength, setMaxChunkLength] = useState(DEFAULT_MAXIMUM_CHUNK_LENGTH) // default chunk length
   const [limitMaxChunkLength, setLimitMaxChunkLength] = useState(4000)
   const [overlap, setOverlap] = useState(DEFAULT_OVERLAP)
   const [rules, setRules] = useState<PreProcessingRule[]>([])
   )
 
   // QA Related
-  const [isLanguageSelectDisabled, _setIsLanguageSelectDisabled] = useState(false)
   const [isQAConfirmDialogOpen, setIsQAConfirmDialogOpen] = useState(false)
   const [docForm, setDocForm] = useState<ChunkingMode>(
     (datasetId && documentDetail) ? documentDetail.doc_form as ChunkingMode : ChunkingMode.text,
   }
 
   const updatePreview = () => {
-    if (segmentationType === SegmentType.CUSTOM && maxChunkLength > 4000) {
+    if (segmentationType === ProcessMode.general && maxChunkLength > 4000) {
       Toast.notify({ type: 'error', message: t('datasetCreation.stepTwo.maxLengthCheck') })
       return
     }
         model: defaultEmbeddingModel?.model || '',
       },
   )
+  const [retrievalConfig, setRetrievalConfig] = useState(currentDataset?.retrieval_model_dict || {
+    search_method: RETRIEVE_METHOD.semantic,
+    reranking_enable: false,
+    reranking_model: {
+      reranking_provider_name: '',
+      reranking_model_name: '',
+    },
+    top_k: 3,
+    score_threshold_enabled: false,
+    score_threshold: 0.5,
+  } as RetrievalConfig)
+
+  useEffect(() => {
+    if (currentDataset?.retrieval_model_dict)
+      return
+    setRetrievalConfig({
+      search_method: RETRIEVE_METHOD.semantic,
+      reranking_enable: !!isRerankDefaultModelValid,
+      reranking_model: {
+        reranking_provider_name: isRerankDefaultModelValid ? rerankDefaultModel?.provider.provider ?? '' : '',
+        reranking_model_name: isRerankDefaultModelValid ? rerankDefaultModel?.model ?? '' : '',
+      },
+      top_k: 3,
+      score_threshold_enabled: false,
+      score_threshold: 0.5,
+    })
+  // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [rerankDefaultModel, isRerankDefaultModelValid])
+
   const getCreationParams = () => {
     let params
-    if (segmentationType === SegmentType.CUSTOM && overlap > maxChunkLength) {
+    if (segmentationType === ProcessMode.general && overlap > maxChunkLength) {
       Toast.notify({ type: 'error', message: t('datasetCreation.stepTwo.overlapCheck') })
       return
     }
-    if (segmentationType === SegmentType.CUSTOM && maxChunkLength > limitMaxChunkLength) {
+    if (segmentationType === ProcessMode.general && maxChunkLength > limitMaxChunkLength) {
       Toast.notify({ type: 'error', message: t('datasetCreation.stepTwo.maxLengthCheck', { limit: limitMaxChunkLength }) })
       return
     }
         doc_form: currentDocForm,
         doc_language: docLanguage,
         process_rule: getProcessRule(),
-        // eslint-disable-next-line @typescript-eslint/no-use-before-define
         retrieval_model: retrievalConfig, // Readonly. If want to changed, just go to settings page.
         embedding_model: embeddingModel.model, // Readonly
         embedding_model_provider: embeddingModel.provider, // Readonly
       const indexMethod = getIndexing_technique()
       if (
         !isReRankModelSelected({
-          rerankDefaultModel,
-          isRerankDefaultModelValid: !!isRerankDefaultModelValid,
           rerankModelList,
-          // eslint-disable-next-line @typescript-eslint/no-use-before-define
           retrievalConfig,
           indexMethod: indexMethod as string,
         })
         Toast.notify({ type: 'error', message: t('appDebug.datasetConfig.rerankModelRequired') })
         return
       }
-      const postRetrievalConfig = ensureRerankModelSelected({
-        rerankDefaultModel: rerankDefaultModel!,
-        retrievalConfig: {
-          // eslint-disable-next-line @typescript-eslint/no-use-before-define
-          ...retrievalConfig,
-          // eslint-disable-next-line @typescript-eslint/no-use-before-define
-          reranking_enable: retrievalConfig.reranking_mode === RerankingModeEnum.RerankingModel,
-        },
-        indexMethod: indexMethod as string,
-      })
       params = {
         data_source: {
           type: dataSourceType,
         process_rule: getProcessRule(),
         doc_form: currentDocForm,
         doc_language: docLanguage,
-
-        retrieval_model: postRetrievalConfig,
+        retrieval_model: retrievalConfig,
         embedding_model: embeddingModel.model,
         embedding_model_provider: embeddingModel.provider,
       } as CreateDocumentReq
 
   const getDefaultMode = () => {
     if (documentDetail)
-      // @ts-expect-error fix after api refactored
       setSegmentationType(documentDetail.dataset_process_rule.mode)
   }
 
           onSuccess(data) {
             updateIndexingTypeCache && updateIndexingTypeCache(indexType as string)
             updateResultCache && updateResultCache(data)
-            // eslint-disable-next-line @typescript-eslint/no-use-before-define
             updateRetrievalMethodCache && updateRetrievalMethodCache(retrievalConfig.search_method as string)
           },
         },
     isSetting && onSave && onSave()
   }
 
-  const changeToEconomicalType = () => {
-    if (docForm !== ChunkingMode.text)
-      return
-
-    if (!hasSetIndexType)
-      setIndexType(IndexingType.ECONOMICAL)
-  }
-
   useEffect(() => {
     // fetch rules
     if (!isSetting) {
       setIndexType(isAPIKeySet ? IndexingType.QUALIFIED : IndexingType.ECONOMICAL)
   }, [isAPIKeySet, indexingType, datasetId])
 
-  const [retrievalConfig, setRetrievalConfig] = useState(currentDataset?.retrieval_model_dict || {
-    search_method: RETRIEVE_METHOD.semantic,
-    reranking_enable: false,
-    reranking_model: {
-      reranking_provider_name: rerankDefaultModel?.provider.provider,
-      reranking_model_name: rerankDefaultModel?.model,
-    },
-    top_k: 3,
-    score_threshold_enabled: false,
-    score_threshold: 0.5,
-  } as RetrievalConfig)
-
   const economyDomRef = useRef<HTMLDivElement>(null)
   const isHoveringEconomy = useHover(economyDomRef)
 
             <div className={cn('system-md-semibold mb-1', datasetId && 'flex justify-between items-center')}>{t('datasetSettings.form.embeddingModel')}</div>
             <ModelSelector
               readonly={!!datasetId}
+              triggerClassName={datasetId ? 'opacity-50' : ''}
               defaultModel={embeddingModel}
               modelList={embeddingModelList}
               onSelect={(model: DefaultModel) => {
               getIndexing_technique() === IndexingType.QUALIFIED
                 ? (
                   <RetrievalMethodConfig
+                    disabled={!!datasetId}
                     value={retrievalConfig}
                     onChange={setRetrievalConfig}
                   />
                 )
                 : (
                   <EconomicalRetrievalMethodConfig
+                    disabled={!!datasetId}
                     value={retrievalConfig}
                     onChange={setRetrievalConfig}
                   />
           )
           : (
             <div className='flex items-center mt-8 py-2'>
-              <Button loading={isCreating} variant='primary' onClick={createHandle}>{t('datasetCreation.stepTwo.save')}</Button>
+              {!datasetId && <Button loading={isCreating} variant='primary' onClick={createHandle}>{t('datasetCreation.stepTwo.save')}</Button>}
               <Button className='ml-2' onClick={onCancel}>{t('datasetCreation.stepTwo.cancel')}</Button>
             </div>
           )}
               }
               {
                 currentDocForm !== ChunkingMode.qa
-                  && <Badge text={t(
-                    'datasetCreation.stepTwo.previewChunkCount', {
-                      count: estimate?.total_segments || 0,
-                    }) as string}
-                  />
+                && <Badge text={t(
+                  'datasetCreation.stepTwo.previewChunkCount', {
+                    count: estimate?.total_segments || 0,
+                  }) as string}
+                />
               }
             </div>
           </PreviewHeader>}
           {currentDocForm === ChunkingMode.parentChild && currentEstimateMutation.data?.preview && (
             estimate?.preview?.map((item, index) => {
               const indexForLabel = index + 1
+              const childChunks = parentChildConfig.chunkForContext === 'full-doc'
+                ? item.child_chunks.slice(0, FULL_DOC_PREVIEW_LENGTH)
+                : item.child_chunks
               return (
                 <ChunkContainer
                   key={item.content}
                   characterCount={item.content.length}
                 >
                   <FormattedText>
-                    {item.child_chunks.map((child, index) => {
+                    {childChunks.map((child, index) => {
                       const indexForLabel = index + 1
                       return (
                         <PreviewSlice

web/app/components/datasets/create/step-two/option-card.tsx

 
 const TriangleArrow: FC<ComponentProps<'svg'>> = props => (
   <svg xmlns="http://www.w3.org/2000/svg" width="24" height="11" viewBox="0 0 24 11" fill="none" {...props}>
-    <path d="M9.87868 1.12132C11.0503 -0.0502525 12.9497 -0.0502525 14.1213 1.12132L23.3137 10.3137H0.686292L9.87868 1.12132Z" fill="currentColor"/>
+    <path d="M9.87868 1.12132C11.0503 -0.0502525 12.9497 -0.0502525 14.1213 1.12132L23.3137 10.3137H0.686292L9.87868 1.12132Z" fill="currentColor" />
   </svg>
 )
 
       (isActive && !noHighlight)
         ? 'border-[1.5px] border-components-option-card-option-selected-border'
         : 'border border-components-option-card-option-border',
-      disabled && 'opacity-50 cursor-not-allowed',
+      disabled && 'opacity-50 pointer-events-none',
       className,
     )}
     style={{