fix: remove unused fields

.github/actions/setup-poetry/action.yml

@@ -8,7 +8,7 @@ inputs:
   poetry-version:
     description: Poetry version to set up
     required: true
-    default: '2.0.1'
+    default: '1.8.4'
   poetry-lockfile:
     description: Path to the Poetry lockfile to restore cache from
     required: true

.github/workflows/api-tests.yml

@@ -42,23 +42,25 @@ jobs:
         run: poetry install -C api --with dev
 
       - name: Check dependencies in pyproject.toml
-        run: poetry run -P api bash dev/pytest/pytest_artifacts.sh
+        run: poetry run -C api bash dev/pytest/pytest_artifacts.sh
 
       - name: Run Unit tests
-        run: poetry run -P api bash dev/pytest/pytest_unit_tests.sh
+        run: poetry run -C api bash dev/pytest/pytest_unit_tests.sh
 
       - name: Run ModelRuntime
-        run: poetry run -P api bash dev/pytest/pytest_model_runtime.sh
+        run: poetry run -C api bash dev/pytest/pytest_model_runtime.sh
 
       - name: Run dify config tests
-        run: poetry run -P api python dev/pytest/pytest_config_tests.py
+        run: poetry run -C api python dev/pytest/pytest_config_tests.py
 
       - name: Run Tool
-        run: poetry run -P api bash dev/pytest/pytest_tools.sh
+        run: poetry run -C api bash dev/pytest/pytest_tools.sh
 
       - name: Run mypy
         run: |
-          poetry run -C api python -m mypy --install-types --non-interactive .
+          pushd api
+          poetry run python -m mypy --install-types --non-interactive .
+          popd
 
       - name: Set up dotenvs
         run: |
@@ -78,4 +80,4 @@ jobs:
             ssrf_proxy
 
       - name: Run Workflow
-        run: poetry run -P api bash dev/pytest/pytest_workflow.sh
+        run: poetry run -C api bash dev/pytest/pytest_workflow.sh

.github/workflows/style.yml

@@ -38,12 +38,12 @@ jobs:
         if: steps.changed-files.outputs.any_changed == 'true'
         run: |
           poetry run -C api ruff --version
-          poetry run -C api ruff check ./
-          poetry run -C api ruff format --check ./
+          poetry run -C api ruff check ./api
+          poetry run -C api ruff format --check ./api
 
       - name: Dotenv check
         if: steps.changed-files.outputs.any_changed == 'true'
-        run: poetry run -P api dotenv-linter ./api/.env.example ./web/.env.example
+        run: poetry run -C api dotenv-linter ./api/.env.example ./web/.env.example
 
       - name: Lint hints
         if: failure()
@@ -82,33 +82,6 @@ jobs:
         if: steps.changed-files.outputs.any_changed == 'true'
         run: yarn run lint
 
-  docker-compose-template:
-    name: Docker Compose Template
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Check changed files
-        id: changed-files
-        uses: tj-actions/changed-files@v45
-        with:
-          files: |
-            docker/generate_docker_compose
-            docker/.env.example
-            docker/docker-compose-template.yaml
-            docker/docker-compose.yaml
-
-      - name: Generate Docker Compose
-        if: steps.changed-files.outputs.any_changed == 'true'
-        run: |
-          cd docker
-          ./generate_docker_compose
-
-      - name: Check for changes
-        if: steps.changed-files.outputs.any_changed == 'true'
-        run: git diff --exit-code
 
   superlinter:
     name: SuperLinter

.github/workflows/vdb-tests.yml

@@ -70,4 +70,4 @@ jobs:
             tidb
 
       - name: Test Vector Stores
-        run: poetry run -P api bash dev/pytest/pytest_vdb.sh
+        run: poetry run -C api bash dev/pytest/pytest_vdb.sh

api/.env.example

@@ -23,9 +23,6 @@ FILES_ACCESS_TIMEOUT=300
 # 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/.ruff.toml

@@ -53,12 +53,10 @@ ignore = [
     "FURB152", # math-constant
     "UP007", # non-pep604-annotation
     "UP032", # f-string
-    "UP045", # non-pep604-annotation-optional
     "B005", # strip-with-multi-characters
     "B006", # mutable-argument-default
     "B007", # unused-loop-control-variable
     "B026", # star-arg-unpacking-after-keyword-arg
-    "B903", # class-as-data-structure
     "B904", # raise-without-from-inside-except
     "B905", # zip-without-explicit-strict
     "N806", # non-lowercase-variable-in-function
@@ -87,11 +85,11 @@ ignore = [
 ]
 "tests/*" = [
     "F811", # redefined-while-unused
+    "F401", # unused-import
 ]
 
 [lint.pyflakes]
-allowed-unused-imports = [
+extend-generics = [
     "_pytest.monkeypatch",
     "tests.integration_tests",
-    "tests.unit_tests",
 ]

api/Dockerfile

@@ -4,7 +4,7 @@ FROM python:3.12-slim-bookworm AS base
 WORKDIR /app/api
 
 # Install Poetry
-ENV POETRY_VERSION=2.0.1
+ENV POETRY_VERSION=1.8.4
 
 # if you located in China, you can use aliyun mirror to speed up
 # RUN pip install --no-cache-dir poetry==${POETRY_VERSION} -i https://mirrors.aliyun.com/pypi/simple/
@@ -52,14 +52,12 @@ RUN apt-get update \
     && apt-get install -y --no-install-recommends curl nodejs libgmp-dev libmpfr-dev libmpc-dev \
     # if you located in China, you can use aliyun mirror to speed up
     # && echo "deb http://mirrors.aliyun.com/debian testing main" > /etc/apt/sources.list \
-    && echo "deb http://deb.debian.org/debian bookworm main" > /etc/apt/sources.list \
+    && echo "deb http://deb.debian.org/debian testing main" > /etc/apt/sources.list \
     && apt-get update \
     # For Security
-    && apt-get install -y --no-install-recommends expat libldap-2.5-0 perl libsqlite3-0 zlib1g \
+    && apt-get install -y --no-install-recommends expat=2.6.4-1 libldap-2.5-0=2.5.18+dfsg-3+b1 perl=5.40.0-8 libsqlite3-0=3.46.1-1 zlib1g=1:1.3.dfsg+really1.3.1-1+b1 \
     # install a chinese font to support the use of tools like matplotlib
     && apt-get install -y fonts-noto-cjk \
-    # install libmagic to support the use of python-magic guess MIMETYPE
-    && apt-get install -y libmagic1 \
     && apt-get autoremove -y \
     && rm -rf /var/lib/apt/lists/*
 

api/README.md

@@ -79,5 +79,5 @@
 2. Run the tests locally with mocked system environment variables in `tool.pytest_env` section in `pyproject.toml`
 
    ```bash
-   poetry run -P api bash dev/pytest/pytest_all_tests.sh
+   poetry run -C api bash dev/pytest/pytest_all_tests.sh
    ```

api/app.py

@@ -1,8 +1,12 @@
-import os
-import sys
+from libs import version_utils
+
+# preparation before creating app
+version_utils.check_supported_python_version()
 
 
 def is_db_command():
+    import sys
+
     if len(sys.argv) > 1 and sys.argv[0].endswith("flask") and sys.argv[1] == "db":
         return True
     return False
@@ -14,25 +18,10 @@ if is_db_command():
 
     app = create_migrations_app()
 else:
-    # 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
-        monkey.patch_all()
-
-        from grpc.experimental import gevent as grpc_gevent  # type: ignore
-
-        # grpc gevent
-        grpc_gevent.init_gevent()
-
-        import psycogreen.gevent  # type: ignore
-
-        psycogreen.gevent.patch_psycopg()
-
     from app_factory import create_app
+    from libs import threadings_utils
+
+    threadings_utils.apply_gevent_threading_patch()
 
     app = create_app()
     celery = app.extensions["celery"]

api/configs/feature/__init__.py

@@ -146,7 +146,7 @@ class EndpointConfig(BaseSettings):
     )
 
     CONSOLE_WEB_URL: str = Field(
-        description="Base URL for the console web interface,used for frontend references and CORS configuration",
+        description="Base URL for the console web interface," "used for frontend references and CORS configuration",
         default="",
     )
 
@@ -488,11 +488,6 @@ class AuthConfig(BaseSettings):
         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,
@@ -606,7 +601,7 @@ class RagEtlConfig(BaseSettings):
 
     UNSTRUCTURED_API_KEY: Optional[str] = Field(
         description="API key for Unstructured.io service",
-        default="",
+        default=None,
     )
 
     SCARF_NO_ANALYTICS: Optional[str] = Field(
@@ -672,11 +667,6 @@ class IndexingConfig(BaseSettings):
         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(
@@ -775,13 +765,6 @@ class LoginConfig(BaseSettings):
     )
 
 
-class AccountConfig(BaseSettings):
-    ACCOUNT_DELETION_TOKEN_EXPIRY_MINUTES: PositiveInt = Field(
-        description="Duration in minutes for which a account deletion token remains valid",
-        default=5,
-    )
-
-
 class FeatureConfig(
     # place the configs in alphabet order
     AppExecutionConfig,
@@ -809,7 +792,6 @@ class FeatureConfig(
     WorkflowNodeExecutionConfig,
     WorkspaceConfig,
     LoginConfig,
-    AccountConfig,
     # hosted services config
     HostedServiceConfig,
     CeleryBeatConfig,

api/configs/feature/hosted_service/__init__.py

@@ -181,7 +181,7 @@ class HostedFetchAppTemplateConfig(BaseSettings):
     """
 
     HOSTED_FETCH_APP_TEMPLATES_MODE: str = Field(
-        description="Mode for fetching app templates: remote, db, or builtin default to remote,",
+        description="Mode for fetching app templates: remote, db, or builtin" " default to remote,",
         default="remote",
     )
 

api/configs/middleware/vdb/milvus_config.py

@@ -33,9 +33,3 @@ class MilvusConfig(BaseSettings):
         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

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.15.2",
+        default="0.14.2",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/common/helpers.py

@@ -1,32 +1,12 @@
 import mimetypes
 import os
-import platform
 import re
 import urllib.parse
-import warnings
 from collections.abc import Mapping
 from typing import Any
 from uuid import uuid4
 
 import httpx
-
-try:
-    import magic
-except ImportError:
-    if platform.system() == "Windows":
-        warnings.warn(
-            "To use python-magic guess MIMETYPE, you need to run `pip install python-magic-bin`", stacklevel=2
-        )
-    elif platform.system() == "Darwin":
-        warnings.warn("To use python-magic guess MIMETYPE, you need to run `brew install libmagic`", stacklevel=2)
-    elif platform.system() == "Linux":
-        warnings.warn(
-            "To use python-magic guess MIMETYPE, you need to run `sudo apt-get install libmagic1`", stacklevel=2
-        )
-    else:
-        warnings.warn("To use python-magic guess MIMETYPE, you need to install `libmagic`", stacklevel=2)
-    magic = None  # type: ignore
-
 from pydantic import BaseModel
 
 from configs import dify_config
@@ -67,13 +47,6 @@ def guess_file_info_from_response(response: httpx.Response):
         # If guessing fails, use Content-Type from response headers
         mimetype = response.headers.get("Content-Type", "application/octet-stream")
 
-    # Use python-magic to guess MIME type if still unknown or generic
-    if mimetype == "application/octet-stream" and magic is not None:
-        try:
-            mimetype = magic.from_buffer(response.content[:1024], mime=True)
-        except magic.MagicException:
-            pass
-
     extension = os.path.splitext(filename)[1]
 
     # Ensure filename has an extension

api/controllers/console/admin.py

@@ -56,7 +56,7 @@ class InsertExploreAppListApi(Resource):
 
         app = App.query.filter(App.id == args["app_id"]).first()
         if not app:
-            raise NotFound(f"App '{args['app_id']}' is not found")
+            raise NotFound(f'App \'{args["app_id"]}\' is not found')
 
         site = app.site
         if not site:

api/controllers/console/app/app.py

@@ -57,13 +57,12 @@ class AppListApi(Resource):
         )
         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.id, current_user.current_tenant_id, args)
+        app_pagination = app_service.get_paginate_apps(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/audio.py

@@ -22,7 +22,7 @@ from controllers.console.wraps import account_initialization_required, setup_req
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
 from libs.login import login_required
-from models import App, AppMode
+from models.model import AppMode
 from services.audio_service import AudioService
 from services.errors.audio import (
     AudioTooLargeServiceError,
@@ -79,7 +79,7 @@ class ChatMessageTextApi(Resource):
     @login_required
     @account_initialization_required
     @get_app_model
-    def post(self, app_model: App):
+    def post(self, app_model):
         from werkzeug.exceptions import InternalServerError
 
         try:
@@ -98,13 +98,9 @@ class ChatMessageTextApi(Resource):
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
-                if text_to_speech is None:
-                    raise ValueError("TTS is not enabled")
                 voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
-                    if app_model.app_model_config is None:
-                        raise ValueError("AppModelConfig not found")
                     voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None

api/controllers/console/app/completion.py

@@ -20,6 +20,7 @@ from controllers.web.error import InvokeRateLimitError as InvokeRateLimitHttpErr
 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,
@@ -75,7 +76,7 @@ class CompletionMessageApi(Resource):
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
             raise CompletionRequestError(e.description)
-        except ValueError as e:
+        except (ValueError, AppInvokeQuotaExceededError) as e:
             raise e
         except Exception as e:
             logging.exception("internal server error.")
@@ -140,7 +141,7 @@ class ChatMessageApi(Resource):
             raise InvokeRateLimitHttpError(ex.description)
         except InvokeError as e:
             raise CompletionRequestError(e.description)
-        except ValueError as e:
+        except (ValueError, AppInvokeQuotaExceededError) as e:
             raise e
         except Exception as e:
             logging.exception("internal server error.")

api/controllers/console/app/statistic.py

@@ -273,7 +273,8 @@ FROM
             messages m
             ON c.id = m.conversation_id
         WHERE
-            c.app_id = :app_id"""
+            c.override_model_configs IS NULL
+            AND c.app_id = :app_id"""
         arg_dict = {"tz": account.timezone, "app_id": app_model.id}
 
         timezone = pytz.timezone(account.timezone)

api/controllers/console/app/workflow.py

@@ -2,7 +2,7 @@ import json
 import logging
 
 from flask import abort, request
-from flask_restful import Resource, inputs, marshal_with, reqparse  # type: ignore
+from flask_restful import Resource, marshal_with, reqparse  # type: ignore
 from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
 
 import services
@@ -14,7 +14,7 @@ from controllers.console.wraps import account_initialization_required, setup_req
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
 from factories import variable_factory
-from fields.workflow_fields import workflow_fields, workflow_pagination_fields
+from fields.workflow_fields import workflow_fields
 from fields.workflow_run_fields import workflow_run_node_execution_fields
 from libs import helper
 from libs.helper import TimestampField, uuid_value
@@ -440,29 +440,29 @@ class WorkflowConfigApi(Resource):
         }
 
 
-class PublishedAllWorkflowApi(Resource):
+class DraftWorkflowNodeRetriableApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
     @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
-    @marshal_with(workflow_pagination_fields)
-    def get(self, app_model: App):
+    @marshal_with(workflow_run_node_execution_fields)
+    def post(self, app_model: App, node_id: str):
         """
-        Get published workflows
+        Run draft workflow node
         """
+        # The role of the current user in the ta table must be admin, owner, or editor
         if not current_user.is_editor:
             raise Forbidden()
 
         parser = reqparse.RequestParser()
-        parser.add_argument("page", type=inputs.int_range(1, 99999), required=False, default=1, location="args")
-        parser.add_argument("limit", type=inputs.int_range(1, 100), required=False, default=20, location="args")
+        parser.add_argument("inputs", type=dict, required=True, nullable=False, location="json")
         args = parser.parse_args()
-        page = args.get("page")
-        limit = args.get("limit")
         workflow_service = WorkflowService()
-        workflows, has_more = workflow_service.get_all_published_workflow(app_model=app_model, page=page, limit=limit)
+        workflow_node_execution = workflow_service.run_retriable_draft_workflow_node(
+            app_model=app_model, node_id=node_id, user_inputs=args.get("inputs", {}), account=current_user
+        )
 
-        return {"items": workflows, "page": page, "limit": limit, "has_more": has_more}
+        return workflow_node_execution
 
 
 api.add_resource(DraftWorkflowApi, "/apps/<uuid:app_id>/workflows/draft")
@@ -479,9 +479,9 @@ api.add_resource(
     WorkflowDraftRunIterationNodeApi, "/apps/<uuid:app_id>/workflows/draft/iteration/nodes/<string:node_id>/run"
 )
 api.add_resource(PublishedWorkflowApi, "/apps/<uuid:app_id>/workflows/publish")
-api.add_resource(PublishedAllWorkflowApi, "/apps/<uuid:app_id>/workflows")
 api.add_resource(DefaultBlockConfigsApi, "/apps/<uuid:app_id>/workflows/default-workflow-block-configs")
 api.add_resource(
     DefaultBlockConfigApi, "/apps/<uuid:app_id>/workflows/default-workflow-block-configs/<string:block_type>"
 )
 api.add_resource(ConvertToWorkflowApi, "/apps/<uuid:app_id>/convert-to-workflow")
+api.add_resource(DraftWorkflowNodeRetriableApi, "/apps/<uuid:app_id>/workflows/draft/retry/nodes/<string:node_id>/run")

api/controllers/console/auth/error.py

@@ -53,9 +53,3 @@ class EmailCodeLoginRateLimitExceededError(BaseHTTPException):
     error_code = "email_code_login_rate_limit_exceeded"
     description = "Too many login emails have been sent. Please try again in 5 minutes."
     code = 429
-
-
-class EmailCodeAccountDeletionRateLimitExceededError(BaseHTTPException):
-    error_code = "email_code_account_deletion_rate_limit_exceeded"
-    description = "Too many account deletion emails have been sent. Please try again in 5 minutes."
-    code = 429

api/controllers/console/auth/forgot_password.py

@@ -6,8 +6,13 @@ from flask_restful import Resource, reqparse  # type: ignore
 
 from constants.languages import languages
 from controllers.console import api
-from controllers.console.auth.error import EmailCodeError, InvalidEmailError, InvalidTokenError, PasswordMismatchError
-from controllers.console.error import AccountInFreezeError, AccountNotFound, EmailSendIpLimitError
+from controllers.console.auth.error import (
+    EmailCodeError,
+    InvalidEmailError,
+    InvalidTokenError,
+    PasswordMismatchError,
+)
+from controllers.console.error import AccountNotFound, EmailSendIpLimitError
 from controllers.console.wraps import setup_required
 from events.tenant_event import tenant_was_created
 from extensions.ext_database import db
@@ -15,7 +20,6 @@ from libs.helper import email, extract_remote_ip
 from libs.password import hash_password, valid_password
 from models.account import Account
 from services.account_service import AccountService, TenantService
-from services.errors.account import AccountRegisterError
 from services.errors.workspace import WorkSpaceNotAllowedCreateError
 from services.feature_service import FeatureService
 
@@ -125,8 +129,6 @@ class ForgotPasswordResetApi(Resource):
                 )
             except WorkSpaceNotAllowedCreateError:
                 pass
-            except AccountRegisterError as are:
-                raise AccountInFreezeError()
 
         return {"result": "success"}
 

api/controllers/console/auth/login.py

@@ -5,7 +5,6 @@ from flask import request
 from flask_restful import Resource, reqparse  # type: ignore
 
 import services
-from configs import dify_config
 from constants.languages import languages
 from controllers.console import api
 from controllers.console.auth.error import (
@@ -17,7 +16,6 @@ from controllers.console.auth.error import (
 )
 from controllers.console.error import (
     AccountBannedError,
-    AccountInFreezeError,
     AccountNotFound,
     EmailSendIpLimitError,
     NotAllowedCreateWorkspace,
@@ -28,8 +26,6 @@ from libs.helper import email, extract_remote_ip
 from libs.password import valid_password
 from models.account import Account
 from services.account_service import AccountService, RegisterService, TenantService
-from services.billing_service import BillingService
-from services.errors.account import AccountRegisterError
 from services.errors.workspace import WorkSpaceNotAllowedCreateError
 from services.feature_service import FeatureService
 
@@ -48,9 +44,6 @@ class LoginApi(Resource):
         parser.add_argument("language", type=str, required=False, default="en-US", location="json")
         args = parser.parse_args()
 
-        if dify_config.BILLING_ENABLED and BillingService.is_email_in_freeze(args["email"]):
-            raise AccountInFreezeError()
-
         is_login_error_rate_limit = AccountService.is_login_error_rate_limit(args["email"])
         if is_login_error_rate_limit:
             raise EmailPasswordLoginLimitError()
@@ -120,10 +113,8 @@ class ResetPasswordSendEmailApi(Resource):
             language = "zh-Hans"
         else:
             language = "en-US"
-        try:
-            account = AccountService.get_user_through_email(args["email"])
-        except AccountRegisterError as are:
-            raise AccountInFreezeError()
+
+        account = AccountService.get_user_through_email(args["email"])
         if account is None:
             if FeatureService.get_system_features().is_allow_register:
                 token = AccountService.send_reset_password_email(email=args["email"], language=language)
@@ -151,11 +142,8 @@ class EmailCodeLoginSendEmailApi(Resource):
             language = "zh-Hans"
         else:
             language = "en-US"
-        try:
-            account = AccountService.get_user_through_email(args["email"])
-        except AccountRegisterError as are:
-            raise AccountInFreezeError()
 
+        account = AccountService.get_user_through_email(args["email"])
         if account is None:
             if FeatureService.get_system_features().is_allow_register:
                 token = AccountService.send_email_code_login_email(email=args["email"], language=language)
@@ -189,10 +177,7 @@ class EmailCodeLoginApi(Resource):
             raise EmailCodeError()
 
         AccountService.revoke_email_code_login_token(args["token"])
-        try:
-            account = AccountService.get_user_through_email(user_email)
-        except AccountRegisterError as are:
-            raise AccountInFreezeError()
+        account = AccountService.get_user_through_email(user_email)
         if account:
             tenant = TenantService.get_join_tenants(account)
             if not tenant:
@@ -211,8 +196,6 @@ class EmailCodeLoginApi(Resource):
                 )
             except WorkSpaceNotAllowedCreateError:
                 return NotAllowedCreateWorkspace()
-            except AccountRegisterError as are:
-                raise AccountInFreezeError()
         token_pair = AccountService.login(account, ip_address=extract_remote_ip(request))
         AccountService.reset_login_error_rate_limit(args["email"])
         return {"result": "success", "data": token_pair.model_dump()}

api/controllers/console/auth/oauth.py

@@ -16,7 +16,7 @@ from libs.oauth import GitHubOAuth, GoogleOAuth, OAuthUserInfo
 from models import Account
 from models.account import AccountStatus
 from services.account_service import AccountService, RegisterService, TenantService
-from services.errors.account import AccountNotFoundError, AccountRegisterError
+from services.errors.account import AccountNotFoundError
 from services.errors.workspace import WorkSpaceNotAllowedCreateError, WorkSpaceNotFoundError
 from services.feature_service import FeatureService
 
@@ -99,8 +99,6 @@ class OAuthCallback(Resource):
                 f"{dify_config.CONSOLE_WEB_URL}/signin"
                 "?message=Workspace not found, please contact system admin to invite you to join in a workspace."
             )
-        except AccountRegisterError as e:
-            return redirect(f"{dify_config.CONSOLE_WEB_URL}/signin?message={e.description}")
 
         # Check account status
         if account.status == AccountStatus.BANNED.value:

api/controllers/console/datasets/datasets.py

@@ -52,12 +52,12 @@ class DatasetListApi(Resource):
         # provider = request.args.get("provider", default="vendor")
         search = request.args.get("keyword", default=None, type=str)
         tag_ids = request.args.getlist("tag_ids")
-        include_all = request.args.get("include_all", default="false").lower() == "true"
+
         if ids:
             datasets, total = DatasetService.get_datasets_by_ids(ids, current_user.current_tenant_id)
         else:
             datasets, total = DatasetService.get_datasets(
-                page, limit, current_user.current_tenant_id, current_user, search, tag_ids, include_all
+                page, limit, current_user.current_tenant_id, current_user, search, tag_ids
             )
 
         # check embedding setting
@@ -457,7 +457,7 @@ class DatasetIndexingEstimateApi(Resource):
             )
         except LLMBadRequestError:
             raise ProviderNotInitializeError(
-                "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                "No Embedding Model available. Please configure a valid provider " "in the Settings -> Model Provider."
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
@@ -619,7 +619,9 @@ class DatasetRetrievalSettingApi(Resource):
         vector_type = dify_config.VECTOR_STORE
         match vector_type:
             case (
-                VectorType.RELYT
+                VectorType.MILVUS
+                | VectorType.RELYT
+                | VectorType.PGVECTOR
                 | VectorType.TIDB_VECTOR
                 | VectorType.CHROMA
                 | VectorType.TENCENT
@@ -638,12 +640,10 @@ class DatasetRetrievalSettingApi(Resource):
                 | VectorType.MYSCALE
                 | VectorType.ORACLE
                 | VectorType.ELASTICSEARCH
-                | VectorType.ELASTICSEARCH_JA
                 | VectorType.PGVECTOR
                 | VectorType.TIDB_ON_QDRANT
                 | VectorType.LINDORM
                 | VectorType.COUCHBASE
-                | VectorType.MILVUS
             ):
                 return {
                     "retrieval_method": [
@@ -683,7 +683,6 @@ class DatasetRetrievalSettingMockApi(Resource):
                 | VectorType.MYSCALE
                 | VectorType.ORACLE
                 | VectorType.ELASTICSEARCH
-                | VectorType.ELASTICSEARCH_JA
                 | VectorType.COUCHBASE
                 | VectorType.PGVECTOR
                 | VectorType.LINDORM

api/controllers/console/datasets/datasets_document.py

@@ -257,8 +257,7 @@ class DatasetDocumentListApi(Resource):
         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"
         )
@@ -350,7 +349,8 @@ class DatasetInitApi(Resource):
                 )
             except InvokeAuthorizationError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -525,7 +525,8 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
                 return response.model_dump(), 200
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)

api/controllers/console/datasets/datasets_segments.py

@@ -168,7 +168,8 @@ class DatasetDocumentSegmentApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -216,7 +217,8 @@ class DatasetDocumentSegmentAddApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -265,7 +267,8 @@ class DatasetDocumentSegmentUpdateApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -365,9 +368,9 @@ class DatasetDocumentSegmentBatchImportApi(Resource):
             result = []
             for index, row in df.iterrows():
                 if document.doc_form == "qa_model":
-                    data = {"content": row.iloc[0], "answer": row.iloc[1]}
+                    data = {"content": row[0], "answer": row[1]}
                 else:
-                    data = {"content": row.iloc[0]}
+                    data = {"content": row[0]}
                 result.append(data)
             if len(result) == 0:
                 raise ValueError("The CSV file is empty.")
@@ -434,7 +437,8 @@ class ChildChunkAddApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)

api/controllers/console/error.py

@@ -92,12 +92,3 @@ class UnauthorizedAndForceLogout(BaseHTTPException):
     error_code = "unauthorized_and_force_logout"
     description = "Unauthorized and force logout."
     code = 401
-
-
-class AccountInFreezeError(BaseHTTPException):
-    error_code = "account_in_freeze"
-    code = 400
-    description = (
-        "This email account has been deleted within the past 30 days"
-        "and is temporarily unavailable for new account registration."
-    )

api/controllers/console/explore/completion.py

@@ -18,11 +18,7 @@ from controllers.console.explore.error import NotChatAppError, NotCompletionAppE
 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/conversation.py

@@ -32,7 +32,7 @@ class ConversationListApi(InstalledAppResource):
 
         pinned = None
         if "pinned" in args and args["pinned"] is not None:
-            pinned = args["pinned"] == "true"
+            pinned = True if args["pinned"] == "true" else False
 
         try:
             with Session(db.engine) as session:

api/controllers/console/explore/message.py

@@ -50,7 +50,7 @@ class MessageListApi(InstalledAppResource):
 
         try:
             return MessageService.pagination_by_first_id(
-                app_model, current_user, args["conversation_id"], args["first_id"], args["limit"]
+                app_model, current_user, args["conversation_id"], args["first_id"], args["limit"], "desc"
             )
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
@@ -66,17 +66,10 @@ class MessageFeedbackApi(InstalledAppResource):
 
         parser = reqparse.RequestParser()
         parser.add_argument("rating", type=str, choices=["like", "dislike", None], location="json")
-        parser.add_argument("content", type=str, location="json")
         args = parser.parse_args()
 
         try:
-            MessageService.create_feedback(
-                app_model=app_model,
-                message_id=message_id,
-                user=current_user,
-                rating=args.get("rating"),
-                content=args.get("content"),
-            )
+            MessageService.create_feedback(app_model, message_id, current_user, args.get("rating"), args.get("content"))
         except services.errors.message.MessageNotExistsError:
             raise NotFound("Message Not Exists.")
 

api/controllers/console/explore/workflow.py

@@ -13,11 +13,7 @@ from controllers.console.explore.error import NotWorkflowAppError
 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/console/workspace/account.py

@@ -11,7 +11,6 @@ from controllers.console import api
 from controllers.console.workspace.error import (
     AccountAlreadyInitedError,
     CurrentPasswordIncorrectError,
-    InvalidAccountDeletionCodeError,
     InvalidInvitationCodeError,
     RepeatPasswordNotMatchError,
 )
@@ -22,7 +21,6 @@ from libs.helper import TimestampField, timezone
 from libs.login import login_required
 from models import AccountIntegrate, InvitationCode
 from services.account_service import AccountService
-from services.billing_service import BillingService
 from services.errors.account import CurrentPasswordIncorrectError as ServiceCurrentPasswordIncorrectError
 
 
@@ -244,54 +242,6 @@ class AccountIntegrateApi(Resource):
         return {"data": integrate_data}
 
 
-class AccountDeleteVerifyApi(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def get(self):
-        account = current_user
-
-        token, code = AccountService.generate_account_deletion_verification_code(account)
-        AccountService.send_account_deletion_verification_email(account, code)
-
-        return {"result": "success", "data": token}
-
-
-class AccountDeleteApi(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self):
-        account = current_user
-
-        parser = reqparse.RequestParser()
-        parser.add_argument("token", type=str, required=True, location="json")
-        parser.add_argument("code", type=str, required=True, location="json")
-        args = parser.parse_args()
-
-        if not AccountService.verify_account_deletion_code(args["token"], args["code"]):
-            raise InvalidAccountDeletionCodeError()
-
-        AccountService.delete_account(account)
-
-        return {"result": "success"}
-
-
-class AccountDeleteUpdateFeedbackApi(Resource):
-    @setup_required
-    def post(self):
-        account = current_user
-
-        parser = reqparse.RequestParser()
-        parser.add_argument("email", type=str, required=True, location="json")
-        parser.add_argument("feedback", type=str, required=True, location="json")
-        args = parser.parse_args()
-
-        BillingService.update_account_deletion_feedback(args["email"], args["feedback"])
-
-        return {"result": "success"}
-
-
 # Register API resources
 api.add_resource(AccountInitApi, "/account/init")
 api.add_resource(AccountProfileApi, "/account/profile")
@@ -302,8 +252,5 @@ api.add_resource(AccountInterfaceThemeApi, "/account/interface-theme")
 api.add_resource(AccountTimezoneApi, "/account/timezone")
 api.add_resource(AccountPasswordApi, "/account/password")
 api.add_resource(AccountIntegrateApi, "/account/integrates")
-api.add_resource(AccountDeleteVerifyApi, "/account/delete/verify")
-api.add_resource(AccountDeleteApi, "/account/delete")
-api.add_resource(AccountDeleteUpdateFeedbackApi, "/account/delete/feedback")
 # api.add_resource(AccountEmailApi, '/account/email')
 # api.add_resource(AccountEmailVerifyApi, '/account/email-verify')

api/controllers/console/workspace/error.py

@@ -35,9 +35,3 @@ class AccountNotInitializedError(BaseHTTPException):
     error_code = "account_not_initialized"
     description = "The account has not been initialized yet. Please proceed with the initialization process first."
     code = 400
-
-
-class InvalidAccountDeletionCodeError(BaseHTTPException):
-    error_code = "invalid_account_deletion_code"
-    description = "Invalid account deletion code."
-    code = 400

api/controllers/console/workspace/members.py

@@ -122,7 +122,7 @@ class MemberUpdateRoleApi(Resource):
             return {"code": "invalid-role", "message": "Invalid role"}, 400
 
         member = db.session.get(Account, str(member_id))
-        if not member:
+        if member:
             abort(404)
 
         try:

api/controllers/inner_api/workspace/workspace.py

@@ -1,5 +1,3 @@
-import json
-
 from flask_restful import Resource, reqparse  # type: ignore
 
 from controllers.console.wraps import setup_required
@@ -31,34 +29,4 @@ class EnterpriseWorkspace(Resource):
         return {"message": "enterprise workspace created."}
 
 
-class EnterpriseWorkspaceNoOwnerEmail(Resource):
-    @setup_required
-    @inner_api_only
-    def post(self):
-        parser = reqparse.RequestParser()
-        parser.add_argument("name", type=str, required=True, location="json")
-        args = parser.parse_args()
-
-        tenant = TenantService.create_tenant(args["name"], is_from_dashboard=True)
-
-        tenant_was_created.send(tenant)
-
-        resp = {
-            "id": tenant.id,
-            "name": tenant.name,
-            "encrypt_public_key": tenant.encrypt_public_key,
-            "plan": tenant.plan,
-            "status": tenant.status,
-            "custom_config": json.loads(tenant.custom_config) if tenant.custom_config else {},
-            "created_at": tenant.created_at.isoformat() if tenant.created_at else None,
-            "updated_at": tenant.updated_at.isoformat() if tenant.updated_at else None,
-        }
-
-        return {
-            "message": "enterprise workspace created.",
-            "tenant": resp,
-        }
-
-
 api.add_resource(EnterpriseWorkspace, "/enterprise/workspace")
-api.add_resource(EnterpriseWorkspaceNoOwnerEmail, "/enterprise/workspace/ownerless")

api/controllers/service_api/__init__.py

@@ -7,4 +7,4 @@ api = ExternalApi(bp)
 
 from . import index
 from .app import app, audio, completion, conversation, file, message, workflow
-from .dataset import dataset, document, hit_testing, segment, upload_file
+from .dataset import dataset, document, hit_testing, segment

api/controllers/service_api/app/completion.py

@@ -18,6 +18,7 @@ from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate
 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,
@@ -73,7 +74,7 @@ class CompletionApi(Resource):
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
             raise CompletionRequestError(e.description)
-        except ValueError as e:
+        except (ValueError, AppInvokeQuotaExceededError) as e:
             raise e
         except Exception as e:
             logging.exception("internal server error.")
@@ -132,7 +133,7 @@ class ChatApi(Resource):
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
             raise CompletionRequestError(e.description)
-        except ValueError as e:
+        except (ValueError, AppInvokeQuotaExceededError) as e:
             raise e
         except Exception as e:
             logging.exception("internal server error.")

api/controllers/service_api/app/message.py

@@ -108,13 +108,7 @@ class MessageFeedbackApi(Resource):
         args = parser.parse_args()
 
         try:
-            MessageService.create_feedback(
-                app_model=app_model,
-                message_id=message_id,
-                user=end_user,
-                rating=args.get("rating"),
-                content=args.get("content"),
-            )
+            MessageService.create_feedback(app_model, message_id, end_user, args.get("rating"), args.get("content"))
         except services.errors.message.MessageNotExistsError:
             raise NotFound("Message Not Exists.")
 

api/controllers/service_api/app/workflow.py

@@ -16,6 +16,7 @@ from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate
 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,
@@ -93,7 +94,7 @@ class WorkflowRunApi(Resource):
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
             raise CompletionRequestError(e.description)
-        except ValueError as e:
+        except (ValueError, AppInvokeQuotaExceededError) as e:
             raise e
         except Exception as e:
             logging.exception("internal server error.")

api/controllers/service_api/dataset/dataset.py

@@ -31,11 +31,8 @@ class DatasetListApi(DatasetApiResource):
         # provider = request.args.get("provider", default="vendor")
         search = request.args.get("keyword", default=None, type=str)
         tag_ids = request.args.getlist("tag_ids")
-        include_all = request.args.get("include_all", default="false").lower() == "true"
 
-        datasets, total = DatasetService.get_datasets(
-            page, limit, tenant_id, current_user, search, tag_ids, include_all
-        )
+        datasets, total = DatasetService.get_datasets(page, limit, tenant_id, current_user, search, tag_ids)
         # check embedding setting
         provider_manager = ProviderManager()
         configurations = provider_manager.get_configurations(tenant_id=current_user.current_tenant_id)

api/controllers/service_api/dataset/document.py

@@ -8,17 +8,12 @@ from werkzeug.exceptions import NotFound
 import services.dataset_service
 from controllers.common.errors import FilenameNotExistsError
 from controllers.service_api import api
-from controllers.service_api.app.error import (
-    FileTooLargeError,
-    NoFileUploadedError,
-    ProviderNotInitializeError,
-    TooManyFilesError,
-    UnsupportedFileTypeError,
-)
+from controllers.service_api.app.error import ProviderNotInitializeError
 from controllers.service_api.dataset.error import (
     ArchivedDocumentImmutableError,
     DocumentIndexingError,
-    InvalidMetadataError,
+    NoFileUploadedError,
+    TooManyFilesError,
 )
 from controllers.service_api.wraps import DatasetApiResource, cloud_edition_billing_resource_check
 from core.errors.error import ProviderTokenNotInitError
@@ -51,9 +46,6 @@ class DocumentAddByTextApi(DatasetApiResource):
             "indexing_technique", type=str, choices=Dataset.INDEXING_TECHNIQUE_LIST, nullable=False, location="json"
         )
         parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
-        parser.add_argument("doc_type", type=str, required=False, nullable=True, location="json")
-        parser.add_argument("doc_metadata", type=dict, required=False, nullable=True, location="json")
-
         args = parser.parse_args()
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -65,28 +57,6 @@ class DocumentAddByTextApi(DatasetApiResource):
         if not dataset.indexing_technique and not args["indexing_technique"]:
             raise ValueError("indexing_technique is required.")
 
-        # Validate metadata if provided
-        if args.get("doc_type") or args.get("doc_metadata"):
-            if not args.get("doc_type") or not args.get("doc_metadata"):
-                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
-
-            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
-                raise InvalidMetadataError(
-                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
-                )
-
-            if not isinstance(args["doc_metadata"], dict):
-                raise InvalidMetadataError("doc_metadata must be a dictionary")
-
-            # Validate metadata schema based on doc_type
-            if args["doc_type"] != "others":
-                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
-                for key, value in args["doc_metadata"].items():
-                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
-                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
-            # set to MetaDataConfig
-            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
-
         text = args.get("text")
         name = args.get("name")
         if text is None or name is None:
@@ -133,8 +103,6 @@ class DocumentUpdateByTextApi(DatasetApiResource):
             "doc_language", type=str, default="English", required=False, nullable=False, location="json"
         )
         parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
-        parser.add_argument("doc_type", type=str, required=False, nullable=True, location="json")
-        parser.add_argument("doc_metadata", type=dict, required=False, nullable=True, location="json")
         args = parser.parse_args()
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -143,32 +111,6 @@ class DocumentUpdateByTextApi(DatasetApiResource):
         if not dataset:
             raise ValueError("Dataset is not exist.")
 
-        # indexing_technique is already set in dataset since this is an update
-        args["indexing_technique"] = dataset.indexing_technique
-
-        # Validate metadata if provided
-        if args.get("doc_type") or args.get("doc_metadata"):
-            if not args.get("doc_type") or not args.get("doc_metadata"):
-                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
-
-            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
-                raise InvalidMetadataError(
-                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
-                )
-
-            if not isinstance(args["doc_metadata"], dict):
-                raise InvalidMetadataError("doc_metadata must be a dictionary")
-
-            # Validate metadata schema based on doc_type
-            if args["doc_type"] != "others":
-                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
-                for key, value in args["doc_metadata"].items():
-                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
-                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
-
-            # set to MetaDataConfig
-            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
-
         if args["text"]:
             text = args.get("text")
             name = args.get("name")
@@ -215,30 +157,6 @@ class DocumentAddByFileApi(DatasetApiResource):
             args["doc_form"] = "text_model"
         if "doc_language" not in args:
             args["doc_language"] = "English"
-
-        # Validate metadata if provided
-        if args.get("doc_type") or args.get("doc_metadata"):
-            if not args.get("doc_type") or not args.get("doc_metadata"):
-                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
-
-            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
-                raise InvalidMetadataError(
-                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
-                )
-
-            if not isinstance(args["doc_metadata"], dict):
-                raise InvalidMetadataError("doc_metadata must be a dictionary")
-
-            # Validate metadata schema based on doc_type
-            if args["doc_type"] != "others":
-                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
-                for key, value in args["doc_metadata"].items():
-                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
-                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
-
-            # set to MetaDataConfig
-            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
-
         # get dataset info
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -268,10 +186,7 @@ class DocumentAddByFileApi(DatasetApiResource):
             user=current_user,
             source="datasets",
         )
-        data_source = {
-            "type": "upload_file",
-            "info_list": {"data_source_type": "upload_file", "file_info_list": {"file_ids": [upload_file.id]}},
-        }
+        data_source = {"type": "upload_file", "info_list": {"file_info_list": {"file_ids": [upload_file.id]}}}
         args["data_source"] = data_source
         # validate args
         knowledge_config = KnowledgeConfig(**args)
@@ -306,29 +221,6 @@ class DocumentUpdateByFileApi(DatasetApiResource):
         if "doc_language" not in args:
             args["doc_language"] = "English"
 
-        # Validate metadata if provided
-        if args.get("doc_type") or args.get("doc_metadata"):
-            if not args.get("doc_type") or not args.get("doc_metadata"):
-                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
-
-            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
-                raise InvalidMetadataError(
-                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
-                )
-
-            if not isinstance(args["doc_metadata"], dict):
-                raise InvalidMetadataError("doc_metadata must be a dictionary")
-
-            # Validate metadata schema based on doc_type
-            if args["doc_type"] != "others":
-                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
-                for key, value in args["doc_metadata"].items():
-                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
-                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
-
-            # set to MetaDataConfig
-            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
-
         # get dataset info
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -346,22 +238,14 @@ class DocumentUpdateByFileApi(DatasetApiResource):
             if not file.filename:
                 raise FilenameNotExistsError
 
-            try:
-                upload_file = FileService.upload_file(
-                    filename=file.filename,
-                    content=file.read(),
-                    mimetype=file.mimetype,
-                    user=current_user,
-                    source="datasets",
-                )
-            except services.errors.file.FileTooLargeError as file_too_large_error:
-                raise FileTooLargeError(file_too_large_error.description)
-            except services.errors.file.UnsupportedFileTypeError:
-                raise UnsupportedFileTypeError()
-            data_source = {
-                "type": "upload_file",
-                "info_list": {"data_source_type": "upload_file", "file_info_list": {"file_ids": [upload_file.id]}},
-            }
+            upload_file = FileService.upload_file(
+                filename=file.filename,
+                content=file.read(),
+                mimetype=file.mimetype,
+                user=current_user,
+                source="datasets",
+            )
+            data_source = {"type": "upload_file", "info_list": {"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/dataset/segment.py

@@ -53,7 +53,8 @@ class SegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -94,7 +95,8 @@ class SegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -173,7 +175,8 @@ class DatasetSegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider "
+                    "in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)

api/controllers/service_api/dataset/upload_file.py

@@ -1,54 +0,0 @@
-from werkzeug.exceptions import NotFound
-
-from controllers.service_api import api
-from controllers.service_api.wraps import (
-    DatasetApiResource,
-)
-from core.file import helpers as file_helpers
-from extensions.ext_database import db
-from models.dataset import Dataset
-from models.model import UploadFile
-from services.dataset_service import DocumentService
-
-
-class UploadFileApi(DatasetApiResource):
-    def get(self, tenant_id, dataset_id, document_id):
-        """Get upload file."""
-        # check dataset
-        dataset_id = str(dataset_id)
-        tenant_id = str(tenant_id)
-        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
-        if not dataset:
-            raise NotFound("Dataset not found.")
-        # check document
-        document_id = str(document_id)
-        document = DocumentService.get_document(dataset.id, document_id)
-        if not document:
-            raise NotFound("Document not found.")
-        # check upload file
-        if document.data_source_type != "upload_file":
-            raise ValueError(f"Document data source type ({document.data_source_type}) is not upload_file.")
-        data_source_info = document.data_source_info_dict
-        if data_source_info and "upload_file_id" in data_source_info:
-            file_id = data_source_info["upload_file_id"]
-            upload_file = db.session.query(UploadFile).filter(UploadFile.id == file_id).first()
-            if not upload_file:
-                raise NotFound("UploadFile not found.")
-        else:
-            raise ValueError("Upload file id not found in document data source info.")
-
-        url = file_helpers.get_signed_file_url(upload_file_id=upload_file.id)
-        return {
-            "id": upload_file.id,
-            "name": upload_file.name,
-            "size": upload_file.size,
-            "extension": upload_file.extension,
-            "url": url,
-            "download_url": f"{url}&as_attachment=true",
-            "mime_type": upload_file.mime_type,
-            "created_by": upload_file.created_by,
-            "created_at": upload_file.created_at.timestamp(),
-        }, 200
-
-
-api.add_resource(UploadFileApi, "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/upload-file")

api/controllers/service_api/wraps.py

@@ -1,5 +1,5 @@
 from collections.abc import Callable
-from datetime import UTC, datetime, timedelta
+from datetime import UTC, datetime
 from enum import Enum
 from functools import wraps
 from typing import Optional
@@ -8,8 +8,6 @@ from flask import current_app, request
 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
@@ -176,7 +174,7 @@ def validate_dataset_token(view=None):
     return decorator
 
 
-def validate_and_get_api_token(scope: str | None = None):
+def validate_and_get_api_token(scope=None):
     """
     Validate and get API token.
     """
@@ -190,29 +188,20 @@ def validate_and_get_api_token(scope: str | None = None):
     if auth_scheme != "bearer":
         raise Unauthorized("Authorization scheme must be 'Bearer'")
 
-    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.is_(None) | (ApiToken.last_used_at < cutoff_time)),
-                ApiToken.type == scope,
-            )
-            .values(last_used_at=current_time)
-            .returning(ApiToken)
+    api_token = (
+        db.session.query(ApiToken)
+        .filter(
+            ApiToken.token == auth_token,
+            ApiToken.type == scope,
         )
-        result = session.execute(update_stmt)
-        api_token = result.scalar_one_or_none()
+        .first()
+    )
 
-        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()
+    if not api_token:
+        raise Unauthorized("Access token is invalid")
+
+    api_token.last_used_at = datetime.now(UTC).replace(tzinfo=None)
+    db.session.commit()
 
     return api_token
 
@@ -240,7 +229,7 @@ def create_or_update_end_user_for_user_id(app_model: App, user_id: Optional[str]
             tenant_id=app_model.tenant_id,
             app_id=app_model.id,
             type="service_api",
-            is_anonymous=user_id == "DEFAULT-USER",
+            is_anonymous=True if user_id == "DEFAULT-USER" else False,
             session_id=user_id,
         )
         db.session.add(end_user)

api/controllers/web/completion.py

@@ -19,11 +19,7 @@ from controllers.web.error import InvokeRateLimitError as InvokeRateLimitHttpErr
 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/conversation.py

@@ -39,7 +39,7 @@ class ConversationListApi(WebApiResource):
 
         pinned = None
         if "pinned" in args and args["pinned"] is not None:
-            pinned = args["pinned"] == "true"
+            pinned = True if args["pinned"] == "true" else False
 
         try:
             with Session(db.engine) as session:

api/controllers/web/message.py

@@ -91,7 +91,7 @@ class MessageListApi(WebApiResource):
 
         try:
             return MessageService.pagination_by_first_id(
-                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"]
+                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"], "desc"
             )
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")

api/controllers/web/workflow.py

@@ -14,11 +14,7 @@ from controllers.web.error import (
 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/agent/base_agent_runner.py

@@ -339,13 +339,13 @@ class BaseAgentRunner(AppRunner):
             raise ValueError(f"Agent thought {agent_thought.id} not found")
         agent_thought = queried_thought
 
-        if thought:
+        if thought is not None:
             agent_thought.thought = thought
 
-        if tool_name:
+        if tool_name is not None:
             agent_thought.tool = tool_name
 
-        if tool_input:
+        if tool_input is not None:
             if isinstance(tool_input, dict):
                 try:
                     tool_input = json.dumps(tool_input, ensure_ascii=False)
@@ -354,7 +354,7 @@ class BaseAgentRunner(AppRunner):
 
             agent_thought.tool_input = tool_input
 
-        if observation:
+        if observation is not None:
             if isinstance(observation, dict):
                 try:
                     observation = json.dumps(observation, ensure_ascii=False)
@@ -363,7 +363,7 @@ class BaseAgentRunner(AppRunner):
 
             agent_thought.observation = observation
 
-        if answer:
+        if answer is not None:
             agent_thought.answer = answer
 
         if messages_ids is not None and len(messages_ids) > 0:

api/core/agent/cot_agent_runner.py

@@ -172,7 +172,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
 
             self.save_agent_thought(
                 agent_thought=agent_thought,
-                tool_name=(scratchpad.action.action_name if scratchpad.action and not scratchpad.is_final() else ""),
+                tool_name=scratchpad.action.action_name if scratchpad.action else "",
                 tool_input={scratchpad.action.action_name: scratchpad.action.action_input} if scratchpad.action else {},
                 tool_invoke_meta={},
                 thought=scratchpad.thought or "",

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

@@ -21,7 +21,7 @@ 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 AdvancedChatAppGenerateEntity, InvokeFrom
 from core.app.entities.task_entities import ChatbotAppBlockingResponse, ChatbotAppStreamResponse
-from core.model_runtime.errors.invoke import InvokeAuthorizationError
+from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 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
@@ -336,7 +336,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except ValueError as e:
+            except (ValueError, InvokeError) 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

@@ -67,17 +67,24 @@ from models.account import Account
 from models.enums import CreatedByRole
 from models.workflow import (
     Workflow,
+    WorkflowNodeExecution,
     WorkflowRunStatus,
 )
 
 logger = logging.getLogger(__name__)
 
 
-class AdvancedChatAppGenerateTaskPipeline:
+class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleManage, MessageCycleManage):
     """
     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,
@@ -89,7 +96,7 @@ class AdvancedChatAppGenerateTaskPipeline:
         stream: bool,
         dialogue_count: int,
     ) -> None:
-        self._base_task_pipeline = BasedGenerateTaskPipeline(
+        super().__init__(
             application_generate_entity=application_generate_entity,
             queue_manager=queue_manager,
             stream=stream,
@@ -106,35 +113,32 @@ class AdvancedChatAppGenerateTaskPipeline:
         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._conversation_name_generate_thread: Thread | None = None
+
+        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._conversation_name_generate_thread = None
         self._recorded_files: list[Mapping[str, Any]] = []
-        self._workflow_run_id: str = ""
+        self._workflow_run_id = ""
 
     def process(self) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
         """
@@ -142,13 +146,13 @@ class AdvancedChatAppGenerateTaskPipeline:
         :return:
         """
         # start generate conversation name thread
-        self._conversation_name_generate_thread = self._message_cycle_manager._generate_conversation_name(
+        self._conversation_name_generate_thread = self._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._base_task_pipeline._stream:
+        if self._stream:
             return self._to_stream_response(generator)
         else:
             return self._to_blocking_response(generator)
@@ -265,26 +269,24 @@ class AdvancedChatAppGenerateTaskPipeline:
         # init fake graph runtime state
         graph_runtime_state: Optional[GraphRuntimeState] = None
 
-        for queue_message in self._base_task_pipeline._queue_manager.listen():
+        for queue_message in self._queue_manager.listen():
             event = queue_message.event
 
             if isinstance(event, QueuePingEvent):
-                yield self._base_task_pipeline._ping_stream_response()
+                yield self._ping_stream_response()
             elif isinstance(event, QueueErrorEvent):
-                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
-                    )
+                with Session(db.engine) as session:
+                    err = self._handle_error(event=event, session=session, message_id=self._message_id)
                     session.commit()
-                yield self._base_task_pipeline._error_to_stream_response(err)
+                yield self._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, expire_on_commit=False) as session:
+                with Session(db.engine) as session:
                     # init workflow run
-                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_start(
+                    workflow_run = self._handle_workflow_run_start(
                         session=session,
                         workflow_id=self._workflow_id,
                         user_id=self._user_id,
@@ -295,7 +297,7 @@ class AdvancedChatAppGenerateTaskPipeline:
                     if not message:
                         raise ValueError(f"Message not found: {self._message_id}")
                     message.workflow_run_id = workflow_run.id
-                    workflow_start_resp = self._workflow_cycle_manager._workflow_start_to_stream_response(
+                    workflow_start_resp = self._workflow_start_to_stream_response(
                         session=session, task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
                     session.commit()
@@ -308,14 +310,12 @@ class AdvancedChatAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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(
+                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(
                         session=session, workflow_run=workflow_run, event=event
                     )
-                    node_retry_resp = self._workflow_cycle_manager._workflow_node_retry_to_stream_response(
+                    node_retry_resp = self._workflow_node_retry_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
@@ -329,15 +329,13 @@ class AdvancedChatAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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(
+                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(
                         session=session, workflow_run=workflow_run, event=event
                     )
 
-                    node_start_resp = self._workflow_cycle_manager._workflow_node_start_to_stream_response(
+                    node_start_resp = self._workflow_node_start_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
@@ -350,16 +348,12 @@ class AdvancedChatAppGenerateTaskPipeline:
             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._workflow_cycle_manager._fetch_files_from_node_outputs(event.outputs or {})
-                    )
+                    self._recorded_files.extend(self._fetch_files_from_node_outputs(event.outputs or {}))
 
-                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
-                    )
+                with Session(db.engine) as session:
+                    workflow_node_execution = self._handle_workflow_node_execution_success(session=session, event=event)
 
-                    node_finish_resp = self._workflow_cycle_manager._workflow_node_finish_to_stream_response(
+                    node_finish_resp = self._workflow_node_finish_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
@@ -370,12 +364,10 @@ class AdvancedChatAppGenerateTaskPipeline:
                 if node_finish_resp:
                     yield node_finish_resp
             elif isinstance(event, QueueNodeFailedEvent | QueueNodeInIterationFailedEvent | QueueNodeExceptionEvent):
-                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
-                    )
+                with Session(db.engine) as session:
+                    workflow_node_execution = self._handle_workflow_node_execution_failed(session=session, event=event)
 
-                    node_finish_resp = self._workflow_cycle_manager._workflow_node_finish_to_stream_response(
+                    node_finish_resp = self._workflow_node_finish_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
@@ -389,17 +381,13 @@ class AdvancedChatAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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,
-                        )
+                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,
                     )
 
                 yield parallel_start_resp
@@ -407,17 +395,13 @@ class AdvancedChatAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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,
-                        )
+                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,
                     )
 
                 yield parallel_finish_resp
@@ -425,11 +409,9 @@ class AdvancedChatAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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(
+                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(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
@@ -441,11 +423,9 @@ class AdvancedChatAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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(
+                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(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
@@ -457,11 +437,9 @@ class AdvancedChatAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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(
+                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(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
@@ -476,8 +454,8 @@ class AdvancedChatAppGenerateTaskPipeline:
                 if not graph_runtime_state:
                     raise ValueError("workflow run not initialized.")
 
-                with Session(db.engine, expire_on_commit=False) as session:
-                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_success(
+                with Session(db.engine) as session:
+                    workflow_run = self._handle_workflow_run_success(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
@@ -488,23 +466,21 @@ class AdvancedChatAppGenerateTaskPipeline:
                         trace_manager=trace_manager,
                     )
 
-                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._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._base_task_pipeline._queue_manager.publish(
-                    QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE
-                )
+                self._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, expire_on_commit=False) as session:
-                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_partial_success(
+                with Session(db.engine) as session:
+                    workflow_run = self._handle_workflow_run_partial_success(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
@@ -515,23 +491,21 @@ class AdvancedChatAppGenerateTaskPipeline:
                         conversation_id=None,
                         trace_manager=trace_manager,
                     )
-                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._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._base_task_pipeline._queue_manager.publish(
-                    QueueAdvancedChatMessageEndEvent(), PublishFrom.TASK_PIPELINE
-                )
+                self._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, expire_on_commit=False) as session:
-                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_failed(
+                with Session(db.engine) as session:
+                    workflow_run = self._handle_workflow_run_failed(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
@@ -543,22 +517,20 @@ class AdvancedChatAppGenerateTaskPipeline:
                         trace_manager=trace_manager,
                         exceptions_count=event.exceptions_count,
                     )
-                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._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._base_task_pipeline._handle_error(
-                        event=err_event, session=session, message_id=self._message_id
-                    )
+                    err = self._handle_error(event=err_event, session=session, message_id=self._message_id)
                     session.commit()
 
                 yield workflow_finish_resp
-                yield self._base_task_pipeline._error_to_stream_response(err)
+                yield self._error_to_stream_response(err)
                 break
             elif isinstance(event, QueueStopEvent):
                 if self._workflow_run_id and graph_runtime_state:
-                    with Session(db.engine, expire_on_commit=False) as session:
-                        workflow_run = self._workflow_cycle_manager._handle_workflow_run_failed(
+                    with Session(db.engine) as session:
+                        workflow_run = self._handle_workflow_run_failed(
                             session=session,
                             workflow_run_id=self._workflow_run_id,
                             start_at=graph_runtime_state.start_at,
@@ -569,7 +541,7 @@ class AdvancedChatAppGenerateTaskPipeline:
                             conversation_id=self._conversation_id,
                             trace_manager=trace_manager,
                         )
-                        workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
+                        workflow_finish_resp = self._workflow_finish_to_stream_response(
                             session=session,
                             task_id=self._application_generate_entity.task_id,
                             workflow_run=workflow_run,
@@ -583,18 +555,18 @@ class AdvancedChatAppGenerateTaskPipeline:
                 yield self._message_end_to_stream_response()
                 break
             elif isinstance(event, QueueRetrieverResourcesEvent):
-                self._message_cycle_manager._handle_retriever_resources(event)
+                self._handle_retriever_resources(event)
 
-                with Session(db.engine, expire_on_commit=False) as session:
+                with Session(db.engine) 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._message_cycle_manager._handle_annotation_reply(event)
+                self._handle_annotation_reply(event)
 
-                with Session(db.engine, expire_on_commit=False) as session:
+                with Session(db.engine) 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
@@ -615,27 +587,23 @@ class AdvancedChatAppGenerateTaskPipeline:
                     tts_publisher.publish(queue_message)
 
                 self._task_state.answer += delta_text
-                yield self._message_cycle_manager._message_to_stream_response(
+                yield self._message_to_stream_response(
                     answer=delta_text, message_id=self._message_id, from_variable_selector=event.from_variable_selector
                 )
             elif isinstance(event, QueueMessageReplaceEvent):
                 # published by moderation
-                yield self._message_cycle_manager._message_replace_to_stream_response(answer=event.text)
+                yield self._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._base_task_pipeline._handle_output_moderation_when_task_finished(
-                    self._task_state.answer
-                )
+                output_moderation_answer = self._handle_output_moderation_when_task_finished(self._task_state.answer)
                 if output_moderation_answer:
                     self._task_state.answer = output_moderation_answer
-                    yield self._message_cycle_manager._message_replace_to_stream_response(
-                        answer=output_moderation_answer
-                    )
+                    yield self._message_replace_to_stream_response(answer=output_moderation_answer)
 
                 # Save message
-                with Session(db.engine, expire_on_commit=False) as session:
+                with Session(db.engine) as session:
                     self._save_message(session=session, graph_runtime_state=graph_runtime_state)
                     session.commit()
 
@@ -653,7 +621,7 @@ class AdvancedChatAppGenerateTaskPipeline:
     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._base_task_pipeline._start_at
+        message.provider_response_latency = time.perf_counter() - self._start_at
         message.message_metadata = (
             json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
         )
@@ -717,20 +685,20 @@ class AdvancedChatAppGenerateTaskPipeline:
         :param text: text
         :return: True if output moderation should direct output, otherwise False
         """
-        if self._base_task_pipeline._output_moderation_handler:
-            if self._base_task_pipeline._output_moderation_handler.should_direct_output():
+        if self._output_moderation_handler:
+            if self._output_moderation_handler.should_direct_output():
                 # stop subscribe new token when output moderation should direct output
-                self._task_state.answer = self._base_task_pipeline._output_moderation_handler.get_final_output()
-                self._base_task_pipeline._queue_manager.publish(
+                self._task_state.answer = self._output_moderation_handler.get_final_output()
+                self._queue_manager.publish(
                     QueueTextChunkEvent(text=self._task_state.answer), PublishFrom.TASK_PIPELINE
                 )
 
-                self._base_task_pipeline._queue_manager.publish(
+                self._queue_manager.publish(
                     QueueStopEvent(stopped_by=QueueStopEvent.StopBy.OUTPUT_MODERATION), PublishFrom.TASK_PIPELINE
                 )
                 return True
             else:
-                self._base_task_pipeline._output_moderation_handler.append_new_token(text)
+                self._output_moderation_handler.append_new_token(text)
 
         return False
 

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

@@ -18,7 +18,7 @@ from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskSt
 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
+from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.ops.ops_trace_manager import TraceQueueManager
 from extensions.ext_database import db
 from factories import file_factory
@@ -245,7 +245,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except ValueError as e:
+            except (ValueError, InvokeError) as e:
                 if dify_config.DEBUG:
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)

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

@@ -202,7 +202,7 @@ class AgentChatAppRunner(AppRunner):
         # change function call strategy based on LLM model
         llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
         model_schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
-        if not model_schema:
+        if not model_schema or not model_schema.features:
             raise ValueError("Model schema not found")
 
         if {ModelFeature.MULTI_TOOL_CALL, ModelFeature.TOOL_CALL}.intersection(model_schema.features or []):

api/core/app/apps/base_app_queue_manager.py

@@ -167,7 +167,8 @@ class AppQueueManager:
         else:
             if isinstance(data, DeclarativeMeta) or hasattr(data, "_sa_instance_state"):
                 raise TypeError(
-                    "Critical Error: Passing SQLAlchemy Model instances that cause thread safety issues is not allowed."
+                    "Critical Error: Passing SQLAlchemy Model instances "
+                    "that cause thread safety issues is not allowed."
                 )
 
 

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

@@ -18,7 +18,7 @@ from core.app.apps.chat.generate_response_converter import ChatAppGenerateRespon
 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
+from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.ops.ops_trace_manager import TraceQueueManager
 from extensions.ext_database import db
 from factories import file_factory
@@ -237,7 +237,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except ValueError as e:
+            except (ValueError, InvokeError) 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

@@ -17,7 +17,7 @@ from core.app.apps.completion.generate_response_converter import CompletionAppGe
 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
+from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.ops.ops_trace_manager import TraceQueueManager
 from extensions.ext_database import db
 from factories import file_factory
@@ -214,7 +214,7 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except ValueError as e:
+            except (ValueError, InvokeError) as e:
                 if dify_config.DEBUG:
                     logger.exception("Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)

api/core/app/apps/message_based_app_generator.py

@@ -89,7 +89,6 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             Conversation.id == conversation_id,
             Conversation.app_id == app_model.id,
             Conversation.status == "normal",
-            Conversation.is_deleted.is_(False),
         ]
 
         if isinstance(user, Account):

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

@@ -20,7 +20,7 @@ from core.app.apps.workflow.generate_response_converter import WorkflowAppGenera
 from core.app.apps.workflow.generate_task_pipeline import WorkflowAppGenerateTaskPipeline
 from core.app.entities.app_invoke_entities import InvokeFrom, WorkflowAppGenerateEntity
 from core.app.entities.task_entities import WorkflowAppBlockingResponse, WorkflowAppStreamResponse
-from core.model_runtime.errors.invoke import InvokeAuthorizationError
+from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.ops.ops_trace_manager import TraceQueueManager
 from extensions.ext_database import db
 from factories import file_factory
@@ -221,7 +221,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
             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)
 
@@ -271,7 +270,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except ValueError as e:
+            except (ValueError, InvokeError) 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

@@ -1,7 +1,7 @@
 import logging
 import time
 from collections.abc import Generator
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 from sqlalchemy.orm import Session
 
@@ -58,6 +58,7 @@ from models.workflow import (
     Workflow,
     WorkflowAppLog,
     WorkflowAppLogCreatedFrom,
+    WorkflowNodeExecution,
     WorkflowRun,
     WorkflowRunStatus,
 )
@@ -65,11 +66,16 @@ from models.workflow import (
 logger = logging.getLogger(__name__)
 
 
-class WorkflowAppGenerateTaskPipeline:
+class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleManage):
     """
     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,
@@ -78,7 +84,7 @@ class WorkflowAppGenerateTaskPipeline:
         user: Union[Account, EndUser],
         stream: bool,
     ) -> None:
-        self._base_task_pipeline = BasedGenerateTaskPipeline(
+        super().__init__(
             application_generate_entity=application_generate_entity,
             queue_manager=queue_manager,
             stream=stream,
@@ -95,21 +101,19 @@ class WorkflowAppGenerateTaskPipeline:
         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._wip_workflow_node_executions = {}
         self._workflow_run_id = ""
 
     def process(self) -> Union[WorkflowAppBlockingResponse, Generator[WorkflowAppStreamResponse, None, None]]:
@@ -118,7 +122,7 @@ class WorkflowAppGenerateTaskPipeline:
         :return:
         """
         generator = self._wrapper_process_stream_response(trace_manager=self._application_generate_entity.trace_manager)
-        if self._base_task_pipeline._stream:
+        if self._stream:
             return self._to_stream_response(generator)
         else:
             return self._to_blocking_response(generator)
@@ -233,29 +237,29 @@ class WorkflowAppGenerateTaskPipeline:
         """
         graph_runtime_state = None
 
-        for queue_message in self._base_task_pipeline._queue_manager.listen():
+        for queue_message in self._queue_manager.listen():
             event = queue_message.event
 
             if isinstance(event, QueuePingEvent):
-                yield self._base_task_pipeline._ping_stream_response()
+                yield self._ping_stream_response()
             elif isinstance(event, QueueErrorEvent):
-                err = self._base_task_pipeline._handle_error(event=event)
-                yield self._base_task_pipeline._error_to_stream_response(err)
+                err = self._handle_error(event=event)
+                yield self._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, expire_on_commit=False) as session:
+                with Session(db.engine) as session:
                     # init workflow run
-                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_start(
+                    workflow_run = self._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_cycle_manager._workflow_start_to_stream_response(
+                    start_resp = self._workflow_start_to_stream_response(
                         session=session, task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
                     session.commit()
@@ -267,14 +271,12 @@ class WorkflowAppGenerateTaskPipeline:
             ):
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
-                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(
+                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(
                         session=session, workflow_run=workflow_run, event=event
                     )
-                    response = self._workflow_cycle_manager._workflow_node_retry_to_stream_response(
+                    response = self._workflow_node_retry_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
@@ -288,14 +290,12 @@ class WorkflowAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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(
+                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(
                         session=session, workflow_run=workflow_run, event=event
                     )
-                    node_start_response = self._workflow_cycle_manager._workflow_node_start_to_stream_response(
+                    node_start_response = self._workflow_node_start_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
@@ -306,11 +306,9 @@ class WorkflowAppGenerateTaskPipeline:
                 if node_start_response:
                     yield node_start_response
             elif isinstance(event, QueueNodeSucceededEvent):
-                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(
+                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(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
@@ -321,12 +319,12 @@ class WorkflowAppGenerateTaskPipeline:
                 if node_success_response:
                     yield node_success_response
             elif isinstance(event, QueueNodeFailedEvent | QueueNodeInIterationFailedEvent | QueueNodeExceptionEvent):
-                with Session(db.engine, expire_on_commit=False) as session:
-                    workflow_node_execution = self._workflow_cycle_manager._handle_workflow_node_execution_failed(
+                with Session(db.engine) as session:
+                    workflow_node_execution = self._handle_workflow_node_execution_failed(
                         session=session,
                         event=event,
                     )
-                    node_failed_response = self._workflow_cycle_manager._workflow_node_finish_to_stream_response(
+                    node_failed_response = self._workflow_node_finish_to_stream_response(
                         session=session,
                         event=event,
                         task_id=self._application_generate_entity.task_id,
@@ -341,17 +339,13 @@ class WorkflowAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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,
-                        )
+                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,
                     )
 
                 yield parallel_start_resp
@@ -360,17 +354,13 @@ class WorkflowAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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,
-                        )
+                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,
                     )
 
                 yield parallel_finish_resp
@@ -379,11 +369,9 @@ class WorkflowAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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(
+                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(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
@@ -396,11 +384,9 @@ class WorkflowAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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(
+                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(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
@@ -413,11 +399,9 @@ class WorkflowAppGenerateTaskPipeline:
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
 
-                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(
+                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(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
@@ -432,8 +416,8 @@ class WorkflowAppGenerateTaskPipeline:
                 if not graph_runtime_state:
                     raise ValueError("graph runtime state not initialized.")
 
-                with Session(db.engine, expire_on_commit=False) as session:
-                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_success(
+                with Session(db.engine) as session:
+                    workflow_run = self._handle_workflow_run_success(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
@@ -447,7 +431,7 @@ class WorkflowAppGenerateTaskPipeline:
                     # save workflow app log
                     self._save_workflow_app_log(session=session, workflow_run=workflow_run)
 
-                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._workflow_finish_to_stream_response(
                         session=session,
                         task_id=self._application_generate_entity.task_id,
                         workflow_run=workflow_run,
@@ -461,8 +445,8 @@ class WorkflowAppGenerateTaskPipeline:
                 if not graph_runtime_state:
                     raise ValueError("graph runtime state not initialized.")
 
-                with Session(db.engine, expire_on_commit=False) as session:
-                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_partial_success(
+                with Session(db.engine) as session:
+                    workflow_run = self._handle_workflow_run_partial_success(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
@@ -477,7 +461,7 @@ class WorkflowAppGenerateTaskPipeline:
                     # save workflow app log
                     self._save_workflow_app_log(session=session, workflow_run=workflow_run)
 
-                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._workflow_finish_to_stream_response(
                         session=session, task_id=self._application_generate_entity.task_id, workflow_run=workflow_run
                     )
                     session.commit()
@@ -489,8 +473,8 @@ class WorkflowAppGenerateTaskPipeline:
                 if not graph_runtime_state:
                     raise ValueError("graph runtime state not initialized.")
 
-                with Session(db.engine, expire_on_commit=False) as session:
-                    workflow_run = self._workflow_cycle_manager._handle_workflow_run_failed(
+                with Session(db.engine) as session:
+                    workflow_run = self._handle_workflow_run_failed(
                         session=session,
                         workflow_run_id=self._workflow_run_id,
                         start_at=graph_runtime_state.start_at,
@@ -508,7 +492,7 @@ class WorkflowAppGenerateTaskPipeline:
                     # save workflow app log
                     self._save_workflow_app_log(session=session, workflow_run=workflow_run)
 
-                    workflow_finish_resp = self._workflow_cycle_manager._workflow_finish_to_stream_response(
+                    workflow_finish_resp = self._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

@@ -195,7 +195,7 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):
 
     # app config
     app_config: WorkflowUIBasedAppConfig
-    workflow_run_id: str
+    workflow_run_id: Optional[str] = None
 
     class SingleIterationRunEntity(BaseModel):
         """

api/core/app/task_pipeline/based_generate_task_pipeline.py

@@ -15,6 +15,7 @@ from core.app.entities.queue_entities import (
 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
@@ -29,12 +30,22 @@ class BasedGenerateTaskPipeline:
     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

@@ -31,19 +31,10 @@ from services.annotation_service import AppAnnotationService
 
 
 class MessageCycleManage:
-    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
+    _application_generate_entity: Union[
+        ChatAppGenerateEntity, CompletionAppGenerateEntity, AgentChatAppGenerateEntity, AdvancedChatAppGenerateEntity
+    ]
+    _task_state: Union[EasyUITaskState, WorkflowTaskState]
 
     def _generate_conversation_name(self, *, conversation_id: str, query: str) -> Optional[Thread]:
         """
@@ -145,7 +136,7 @@ class MessageCycleManage:
 
             # get extension
             if "." in message_file.url:
-                extension = f".{message_file.url.split('.')[-1]}"
+                extension = f'.{message_file.url.split(".")[-1]}'
                 if len(extension) > 10:
                     extension = ".bin"
             else:

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -34,6 +34,7 @@ from core.app.entities.task_entities import (
     ParallelBranchStartStreamResponse,
     WorkflowFinishStreamResponse,
     WorkflowStartStreamResponse,
+    WorkflowTaskState,
 )
 from core.file import FILE_MODEL_IDENTITY, File
 from core.model_runtime.utils.encoders import jsonable_encoder
@@ -57,20 +58,13 @@ from models.workflow import (
     WorkflowRunStatus,
 )
 
-from .exc import WorkflowRunNotFoundError
+from .exc import WorkflowNodeExecutionNotFoundError, WorkflowRunNotFoundError
 
 
 class WorkflowCycleManage:
-    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
+    _application_generate_entity: Union[AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity]
+    _task_state: WorkflowTaskState
+    _workflow_system_variables: dict[SystemVariableKey, Any]
 
     def _handle_workflow_run_start(
         self,
@@ -108,8 +102,7 @@ class WorkflowCycleManage:
         inputs = dict(WorkflowEntry.handle_special_values(inputs) or {})
 
         # init workflow run
-        # 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_id = str(self._workflow_system_variables.get(SystemVariableKey.WORKFLOW_RUN_ID, uuid4()))
 
         workflow_run = WorkflowRun()
         workflow_run.id = workflow_run_id
@@ -246,7 +239,7 @@ class WorkflowCycleManage:
         workflow_run.finished_at = datetime.now(UTC).replace(tzinfo=None)
         workflow_run.exceptions_count = exceptions_count
 
-        stmt = select(WorkflowNodeExecution.node_execution_id).where(
+        stmt = select(WorkflowNodeExecution).where(
             WorkflowNodeExecution.tenant_id == workflow_run.tenant_id,
             WorkflowNodeExecution.app_id == workflow_run.app_id,
             WorkflowNodeExecution.workflow_id == workflow_run.workflow_id,
@@ -254,18 +247,16 @@ class WorkflowCycleManage:
             WorkflowNodeExecution.workflow_run_id == workflow_run.id,
             WorkflowNodeExecution.status == WorkflowNodeExecutionStatus.RUNNING.value,
         )
-        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
-        ]
+
+        running_workflow_node_executions = session.scalars(stmt).all()
 
         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
-            workflow_node_execution.finished_at = now
-            workflow_node_execution.elapsed_time = (now - workflow_node_execution.created_at).total_seconds()
+            workflow_node_execution.finished_at = datetime.now(UTC).replace(tzinfo=None)
+            workflow_node_execution.elapsed_time = (
+                workflow_node_execution.finished_at - workflow_node_execution.created_at
+            ).total_seconds()
 
         if trace_manager:
             trace_manager.add_trace_task(
@@ -283,7 +274,7 @@ class WorkflowCycleManage:
         self, *, session: Session, workflow_run: WorkflowRun, event: QueueNodeStartedEvent
     ) -> WorkflowNodeExecution:
         workflow_node_execution = WorkflowNodeExecution()
-        workflow_node_execution.id = str(uuid4())
+        workflow_node_execution.id = event.node_execution_id
         workflow_node_execution.tenant_id = workflow_run.tenant_id
         workflow_node_execution.app_id = workflow_run.app_id
         workflow_node_execution.workflow_id = workflow_run.workflow_id
@@ -307,8 +298,6 @@ class WorkflowCycleManage:
         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(
@@ -336,7 +325,6 @@ class WorkflowCycleManage:
         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(
@@ -376,7 +364,6 @@ class WorkflowCycleManage:
         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(
@@ -404,7 +391,7 @@ class WorkflowCycleManage:
         execution_metadata = json.dumps(merged_metadata)
 
         workflow_node_execution = WorkflowNodeExecution()
-        workflow_node_execution.id = str(uuid4())
+        workflow_node_execution.id = event.node_execution_id
         workflow_node_execution.tenant_id = workflow_run.tenant_id
         workflow_node_execution.app_id = workflow_run.app_id
         workflow_node_execution.workflow_id = workflow_run.workflow_id
@@ -428,8 +415,6 @@ class WorkflowCycleManage:
         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
 
     #################################################
@@ -826,20 +811,22 @@ class WorkflowCycleManage:
         return None
 
     def _get_workflow_run(self, *, session: Session, workflow_run_id: str) -> WorkflowRun:
-        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
+        """
+        Refetch workflow run
+        :param workflow_run_id: workflow run id
+        :return:
+        """
         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:
-        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
+        stmt = select(WorkflowNodeExecution).where(WorkflowNodeExecution.id == node_execution_id)
+        workflow_node_execution = session.scalar(stmt)
+        if not workflow_node_execution:
+            raise WorkflowNodeExecutionNotFoundError(node_execution_id)
+
+        return workflow_node_execution

api/core/external_data_tool/api/api.py

@@ -62,9 +62,8 @@ class ApiExternalDataTool(ExternalDataTool):
 
         if not api_based_extension:
             raise ValueError(
-                "[External data tool] API query failed, variable: {}, error: api_based_extension_id is invalid".format(
-                    self.variable
-                )
+                "[External data tool] API query failed, variable: {}, "
+                "error: api_based_extension_id is invalid".format(self.variable)
             )
 
         # decrypt api_key

api/core/file/models.py

@@ -90,7 +90,7 @@ class File(BaseModel):
     def markdown(self) -> str:
         url = self.generate_url()
         if self.type == FileType.IMAGE:
-            text = f"![{self.filename or ''}]({url})"
+            text = f'![{self.filename or ""}]({url})'
         else:
             text = f"[{self.filename or url}]({url})"
 

api/core/indexing_runner.py

@@ -530,6 +530,7 @@ class IndexingRunner:
         # chunk nodes by chunk size
         indexing_start_at = time.perf_counter()
         tokens = 0
+        chunk_size = 10
         if dataset_document.doc_form != IndexType.PARENT_CHILD_INDEX:
             # create keyword index
             create_keyword_thread = threading.Thread(
@@ -538,22 +539,11 @@ class IndexingRunner:
             )
             create_keyword_thread.start()
 
-        max_workers = 10
         if dataset.indexing_technique == "high_quality":
-            with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+            with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
                 futures = []
-
-                # Distribute documents into multiple groups based on the hash values of page_content
-                # This is done to prevent multiple threads from processing the same document,
-                # Thereby avoiding potential database insertion deadlocks
-                document_groups: list[list[Document]] = [[] for _ in range(max_workers)]
-                for document in documents:
-                    hash = helper.generate_text_hash(document.page_content)
-                    group_index = int(hash, 16) % max_workers
-                    document_groups[group_index].append(document)
-                for chunk_documents in document_groups:
-                    if len(chunk_documents) == 0:
-                        continue
+                for i in range(0, len(documents), chunk_size):
+                    chunk_documents = documents[i : i + chunk_size]
                     futures.append(
                         executor.submit(
                             self._process_chunk,

api/core/llm_generator/prompts.py

@@ -131,7 +131,7 @@ JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE = (
 SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
     "Please help me predict the three most likely questions that human would ask, "
     "and keeping each question under 20 characters.\n"
-    "MAKE SURE your output is the SAME language as the Assistant's latest response. "
+    "MAKE SURE your output is the SAME language as the Assistant's latest response"
     "The output must be an array in JSON format following the specified schema:\n"
     '["question1","question2","question3"]\n'
 )

api/core/model_runtime/model_providers/__base/ai_model.py

@@ -221,12 +221,13 @@ class AIModel(ABC):
         :param credentials: model credentials
         :return: model schema
         """
-        # Try to get model schema from predefined models
-        for predefined_model in self.predefined_models():
-            if model == predefined_model.model:
-                return predefined_model
+        # get predefined models (predefined_models)
+        models = self.predefined_models()
+
+        model_map = {model.model: model for model in models}
+        if model in model_map:
+            return model_map[model]
 
-        # Try to get model schema from credentials
         if credentials:
             model_schema = self.get_customizable_model_schema_from_credentials(model, credentials)
             if model_schema:

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

@@ -1,8 +1,8 @@
-import logging
+from os.path import abspath, dirname, join
 from threading import Lock
 from typing import Any
 
-logger = logging.getLogger(__name__)
+from transformers import GPT2Tokenizer as TransformerGPT2Tokenizer  # type: ignore
 
 _tokenizer: Any = None
 _lock = Lock()
@@ -15,16 +15,11 @@ class GPT2Tokenizer:
         use gpt2 tokenizer to get num tokens
         """
         _tokenizer = GPT2Tokenizer.get_encoder()
-        tokens = _tokenizer.encode(text)
+        tokens = _tokenizer.encode(text, verbose=False)
         return len(tokens)
 
     @staticmethod
     def get_num_tokens(text: str) -> int:
-        # 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
@@ -32,20 +27,8 @@ class GPT2Tokenizer:
         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)
-                    logger.info("Fallback to Transformers' GPT-2 tokenizer from tiktoken")
+                base_path = abspath(__file__)
+                gpt2_tokenizer_path = join(dirname(base_path), "gpt2")
+                _tokenizer = TransformerGPT2Tokenizer.from_pretrained(gpt2_tokenizer_path)
 
             return _tokenizer

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

@@ -53,9 +53,6 @@ model_credential_schema:
       type: select
       required: true
       options:
-        - label:
-            en_US: 2024-12-01-preview
-          value: 2024-12-01-preview
         - label:
             en_US: 2024-10-01-preview
           value: 2024-10-01-preview

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

@@ -108,7 +108,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         ai_model_entity = self._get_ai_model_entity(base_model_name=base_model_name, model=model)
 
         if not ai_model_entity:
-            raise CredentialsValidateFailedError(f"Base Model Name {credentials['base_model_name']} is invalid")
+            raise CredentialsValidateFailedError(f'Base Model Name {credentials["base_model_name"]} is invalid')
 
         try:
             client = AzureOpenAI(**self._to_credential_kwargs(credentials))

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

@@ -130,7 +130,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
             raise CredentialsValidateFailedError("Base Model Name is required")
 
         if not self._get_ai_model_entity(credentials["base_model_name"], model):
-            raise CredentialsValidateFailedError(f"Base Model Name {credentials['base_model_name']} is invalid")
+            raise CredentialsValidateFailedError(f'Base Model Name {credentials["base_model_name"]} is invalid')
 
         try:
             credentials_kwargs = self._to_credential_kwargs(credentials)

api/core/model_runtime/model_providers/bedrock/bedrock.yaml

@@ -44,7 +44,6 @@ provider_credential_schema:
       label:
         en_US: AWS Region
         zh_Hans: AWS 地区
-        ja_JP: AWS リージョン
       type: select
       default: us-east-1
       options:
@@ -52,77 +51,62 @@ provider_credential_schema:
           label:
             en_US: US East (N. Virginia)
             zh_Hans: 美国东部 (弗吉尼亚北部)
-            ja_JP: 米国 (バージニア北部)
         - value: us-east-2
           label:
             en_US: US East (Ohio)
-            zh_Hans: 美国东部 (俄亥俄)
-            ja_JP: 米国 (オハイオ)
+            zh_Hans: 美国东部 (弗吉尼亚北部)
         - value: us-west-2
           label:
             en_US: US West (Oregon)
             zh_Hans: 美国西部 (俄勒冈州)
-            ja_JP: 米国 (オレゴン)
         - value: ap-south-1
           label:
             en_US: Asia Pacific (Mumbai)
             zh_Hans: 亚太地区（孟买）
-            ja_JP: アジアパシフィック (ムンバイ)
         - value: ap-southeast-1
           label:
             en_US: Asia Pacific (Singapore)
             zh_Hans: 亚太地区 (新加坡)
-            ja_JP: アジアパシフィック (シンガポール)
         - value: ap-southeast-2
           label:
             en_US: Asia Pacific (Sydney)
             zh_Hans: 亚太地区 (悉尼)
-            ja_JP: アジアパシフィック (シドニー)
         - value: ap-northeast-1
           label:
             en_US: Asia Pacific (Tokyo)
             zh_Hans: 亚太地区 (东京)
-            ja_JP: アジアパシフィック (東京)
         - value: ap-northeast-2
           label:
             en_US: Asia Pacific (Seoul)
             zh_Hans: 亚太地区（首尔）
-            ja_JP: アジアパシフィック (ソウル)
         - value: ca-central-1
           label:
             en_US: Canada (Central)
             zh_Hans: 加拿大（中部）
-            ja_JP: カナダ (中部)
         - value: eu-central-1
           label:
             en_US: Europe (Frankfurt)
             zh_Hans: 欧洲 (法兰克福)
-            ja_JP: 欧州 (フランクフルト)
         - value: eu-west-1
           label:
             en_US: Europe (Ireland)
             zh_Hans: 欧洲（爱尔兰）
-            ja_JP: 欧州 (アイルランド)
         - value: eu-west-2
           label:
             en_US: Europe (London)
             zh_Hans: 欧洲西部 (伦敦)
-            ja_JP: 欧州 (ロンドン)
         - value: eu-west-3
           label:
             en_US: Europe (Paris)
             zh_Hans: 欧洲（巴黎）
-            ja_JP: 欧州 (パリ)
         - value: sa-east-1
           label:
             en_US: South America (São Paulo)
             zh_Hans: 南美洲（圣保罗）
-            ja_JP: 南米 (サンパウロ)
         - value: us-gov-west-1
           label:
             en_US: AWS GovCloud (US-West)
             zh_Hans: AWS GovCloud (US-West)
-            ja_JP: AWS GovCloud (米国西部)
     - variable: model_for_validation
       required: false
       label:

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

@@ -70,7 +70,7 @@ class BedrockRerankModel(RerankModel):
         rerankingConfiguration = {
             "type": "BEDROCK_RERANKING_MODEL",
             "bedrockRerankingConfiguration": {
-                "numberOfResults": min(top_n, len(text_sources)),
+                "numberOfResults": top_n,
                 "modelConfiguration": {
                     "modelArn": model_package_arn,
                 },

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

@@ -677,17 +677,16 @@ class CohereLargeLanguageModel(LargeLanguageModel):
 
         :return: model schema
         """
-        mode = credentials.get("mode")
-        base_model_schema = None
-        for predefined_model in self.predefined_models():
-            if (
-                mode == "chat" and predefined_model.model == "command-light-chat"
-            ) or predefined_model.model == "command-light":
-                base_model_schema = predefined_model
-                break
+        # get model schema
+        models = self.predefined_models()
+        model_map = {model.model: model for model in models}
 
-        if not base_model_schema:
-            raise ValueError("Model not found")
+        mode = credentials.get("mode")
+
+        if mode == "chat":
+            base_model_schema = model_map["command-light-chat"]
+        else:
+            base_model_schema = model_map["command-light"]
 
         base_model_schema = cast(AIModelEntity, base_model_schema)
 

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

@@ -1,3 +1,2 @@
 - deepseek-chat
 - deepseek-coder
-- deepseek-reasoner

api/core/model_runtime/model_providers/deepseek/llm/deepseek-chat.yaml

@@ -10,7 +10,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
-  context_size: 64000
+  context_size: 128000
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/deepseek/llm/deepseek-coder.yaml

@@ -10,7 +10,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
-  context_size: 64000
+  context_size: 128000
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/deepseek/llm/deepseek-reasoner.yaml

@@ -1,21 +0,0 @@
-model: deepseek-reasoner
-label:
-  zh_Hans: deepseek-reasoner
-  en_US: deepseek-reasoner
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 64000
-parameter_rules:
-  - name: max_tokens
-    use_template: max_tokens
-    min: 1
-    max: 8192
-    default: 4096
-pricing:
-  input: "4"
-  output: "16"
-  unit: "0.000001"
-  currency: RMB

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

@@ -1,13 +1,10 @@
-import json
 from collections.abc import Generator
 from typing import Optional, Union
 
-import requests
 from yarl import URL
 
-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
 from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
     PromptMessage,
     PromptMessageTool,
 )
@@ -27,6 +24,9 @@ class DeepseekLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
+        # {"response_format": "xx"} need convert to {"response_format": {"type": "xx"}}
+        if "response_format" in model_parameters:
+            model_parameters["response_format"] = {"type": model_parameters.get("response_format")}
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
@@ -39,208 +39,3 @@ class DeepseekLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         credentials["mode"] = LLMMode.CHAT.value
         credentials["function_calling_type"] = "tool_call"
         credentials["stream_function_calling"] = "support"
-
-    def _handle_generate_stream_response(
-        self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
-    ) -> Generator:
-        """
-        Handle llm stream response
-
-        :param model: model name
-        :param credentials: model credentials
-        :param response: streamed response
-        :param prompt_messages: prompt messages
-        :return: llm response chunk generator
-        """
-        full_assistant_content = ""
-        chunk_index = 0
-        is_reasoning_started = False  # Add flag to track reasoning state
-
-        def create_final_llm_result_chunk(
-            id: Optional[str], index: int, message: AssistantPromptMessage, finish_reason: str, usage: dict
-        ) -> LLMResultChunk:
-            # calculate num tokens
-            prompt_tokens = usage and usage.get("prompt_tokens")
-            if prompt_tokens is None:
-                prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
-            completion_tokens = usage and usage.get("completion_tokens")
-            if completion_tokens is None:
-                completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
-
-            # transform usage
-            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
-
-            return LLMResultChunk(
-                id=id,
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
-            )
-
-        # delimiter for stream response, need unicode_escape
-        import codecs
-
-        delimiter = credentials.get("stream_mode_delimiter", "\n\n")
-        delimiter = codecs.decode(delimiter, "unicode_escape")
-
-        tools_calls: list[AssistantPromptMessage.ToolCall] = []
-
-        def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
-            def get_tool_call(tool_call_id: str):
-                if not tool_call_id:
-                    return tools_calls[-1]
-
-                tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None)
-                if tool_call is None:
-                    tool_call = AssistantPromptMessage.ToolCall(
-                        id=tool_call_id,
-                        type="function",
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
-                    )
-                    tools_calls.append(tool_call)
-
-                return tool_call
-
-            for new_tool_call in new_tool_calls:
-                # get tool call
-                tool_call = get_tool_call(new_tool_call.function.name)
-                # update tool call
-                if new_tool_call.id:
-                    tool_call.id = new_tool_call.id
-                if new_tool_call.type:
-                    tool_call.type = new_tool_call.type
-                if new_tool_call.function.name:
-                    tool_call.function.name = new_tool_call.function.name
-                if new_tool_call.function.arguments:
-                    tool_call.function.arguments += new_tool_call.function.arguments
-
-        finish_reason = None  # The default value of finish_reason is None
-        message_id, usage = None, None
-        for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
-            chunk = chunk.strip()
-            if chunk:
-                # ignore sse comments
-                if chunk.startswith(":"):
-                    continue
-                decoded_chunk = chunk.strip().removeprefix("data:").lstrip()
-                if decoded_chunk == "[DONE]":  # Some provider returns "data: [DONE]"
-                    continue
-
-                try:
-                    chunk_json: dict = json.loads(decoded_chunk)
-                # stream ended
-                except json.JSONDecodeError as e:
-                    yield create_final_llm_result_chunk(
-                        id=message_id,
-                        index=chunk_index + 1,
-                        message=AssistantPromptMessage(content=""),
-                        finish_reason="Non-JSON encountered.",
-                        usage=usage,
-                    )
-                    break
-                # handle the error here. for issue #11629
-                if chunk_json.get("error") and chunk_json.get("choices") is None:
-                    raise ValueError(chunk_json.get("error"))
-
-                if chunk_json:
-                    if u := chunk_json.get("usage"):
-                        usage = u
-                if not chunk_json or len(chunk_json["choices"]) == 0:
-                    continue
-
-                choice = chunk_json["choices"][0]
-                finish_reason = chunk_json["choices"][0].get("finish_reason")
-                message_id = chunk_json.get("id")
-                chunk_index += 1
-
-                if "delta" in choice:
-                    delta = choice["delta"]
-                    is_reasoning = delta.get("reasoning_content")
-                    delta_content = delta.get("content") or delta.get("reasoning_content")
-
-                    assistant_message_tool_calls = None
-
-                    if "tool_calls" in delta and credentials.get("function_calling_type", "no_call") == "tool_call":
-                        assistant_message_tool_calls = delta.get("tool_calls", None)
-                    elif (
-                        "function_call" in delta
-                        and credentials.get("function_calling_type", "no_call") == "function_call"
-                    ):
-                        assistant_message_tool_calls = [
-                            {"id": "tool_call_id", "type": "function", "function": delta.get("function_call", {})}
-                        ]
-
-                    # assistant_message_function_call = delta.delta.function_call
-
-                    # extract tool calls from response
-                    if assistant_message_tool_calls:
-                        tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
-                        increase_tool_call(tool_calls)
-
-                    if delta_content is None or delta_content == "":
-                        continue
-
-                    # Add markdown quote markers for reasoning content
-                    if is_reasoning:
-                        if not is_reasoning_started:
-                            delta_content = "> 💭 " + delta_content
-                            is_reasoning_started = True
-                        elif "\n\n" in delta_content:
-                            delta_content = delta_content.replace("\n\n", "\n> ")
-                        elif "\n" in delta_content:
-                            delta_content = delta_content.replace("\n", "\n> ")
-                    elif is_reasoning_started:
-                        # If we were in reasoning mode but now getting regular content,
-                        # add \n\n to close the reasoning block
-                        delta_content = "\n\n" + delta_content
-                        is_reasoning_started = False
-
-                    # transform assistant message to prompt message
-                    assistant_prompt_message = AssistantPromptMessage(
-                        content=delta_content,
-                    )
-
-                    # reset tool calls
-                    tool_calls = []
-                    full_assistant_content += delta_content
-                elif "text" in choice:
-                    choice_text = choice.get("text", "")
-                    if choice_text == "":
-                        continue
-
-                    # transform assistant message to prompt message
-                    assistant_prompt_message = AssistantPromptMessage(content=choice_text)
-                    full_assistant_content += choice_text
-                else:
-                    continue
-
-                yield LLMResultChunk(
-                    id=message_id,
-                    model=model,
-                    prompt_messages=prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=chunk_index,
-                        message=assistant_prompt_message,
-                    ),
-                )
-
-            chunk_index += 1
-
-        if tools_calls:
-            yield LLMResultChunk(
-                id=message_id,
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=chunk_index,
-                    message=AssistantPromptMessage(tool_calls=tools_calls, content=""),
-                ),
-            )
-
-        yield create_final_llm_result_chunk(
-            id=message_id,
-            index=chunk_index,
-            message=AssistantPromptMessage(content=""),
-            finish_reason=finish_reason,
-            usage=usage,
-        )

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

@@ -24,5 +24,8 @@ class GiteeAIEmbeddingModel(OAICompatEmbeddingModel):
         super().validate_credentials(model, credentials)
 
     @staticmethod
-    def _add_custom_parameters(credentials: dict, model: str) -> None:
-        credentials["endpoint_url"] = "https://ai.gitee.com/v1"
+    def _add_custom_parameters(credentials: dict, model: Optional[str]) -> None:
+        if model is None:
+            model = "bge-m3"
+
+        credentials["endpoint_url"] = f"https://ai.gitee.com/api/serverless/{model}/v1/"

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

@@ -1,6 +1,5 @@
 - gemini-2.0-flash-exp
 - gemini-2.0-flash-thinking-exp-1219
-- gemini-2.0-flash-thinking-exp-01-21
 - gemini-1.5-pro
 - gemini-1.5-pro-latest
 - gemini-1.5-pro-001

api/core/model_runtime/model_providers/google/llm/gemini-2.0-flash-thinking-exp-01-21.yaml

@@ -1,39 +0,0 @@
-model: gemini-2.0-flash-thinking-exp-01-21
-label:
-  en_US: Gemini 2.0 Flash Thinking Exp 01-21
-model_type: llm
-features:
-  - agent-thought
-  - vision
-  - document
-  - video
-  - audio
-model_properties:
-  mode: chat
-  context_size: 32767
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-  - name: top_p
-    use_template: top_p
-  - name: top_k
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    type: int
-    help:
-      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-      en_US: Only sample from the top K options for each subsequent token.
-    required: false
-  - name: max_output_tokens
-    use_template: max_tokens
-    default: 8192
-    min: 1
-    max: 8192
-  - name: json_schema
-    use_template: json_schema
-pricing:
-  input: '0.00'
-  output: '0.00'
-  unit: '0.000001'
-  currency: USD

api/core/model_runtime/model_providers/gpustack/gpustack.yaml

@@ -9,8 +9,6 @@ supported_model_types:
   - llm
   - text-embedding
   - rerank
-  - speech2text
-  - tts
 configurate_methods:
   - customizable-model
 model_credential_schema:
@@ -120,19 +118,3 @@ model_credential_schema:
           label:
             en_US: Not Support
             zh_Hans: 不支持
-    - variable: voices
-      show_on:
-        - variable: __model_type
-          value: tts
-      label:
-        en_US: Available Voices (comma-separated)
-        zh_Hans: 可用声音（用英文逗号分隔）
-      type: text-input
-      required: false
-      default: "Chinese Female"
-      placeholder:
-        en_US: "Chinese Female, Chinese Male, Japanese Male, Cantonese Female, English Female, English Male, Korean Female"
-        zh_Hans: "Chinese Female, Chinese Male, Japanese Male, Cantonese Female, English Female, English Male, Korean Female"
-      help:
-        en_US: "List voice names separated by commas. First voice will be used as default."
-        zh_Hans: "用英文逗号分隔的声音列表。第一个声音将作为默认值。"

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

@@ -1,5 +1,7 @@
 from collections.abc import Generator
 
+from yarl import URL
+
 from core.model_runtime.entities.llm_entities import LLMResult
 from core.model_runtime.entities.message_entities import (
     PromptMessage,
@@ -22,10 +24,9 @@ class GPUStackLanguageModel(OAIAPICompatLargeLanguageModel):
         stream: bool = True,
         user: str | None = None,
     ) -> LLMResult | Generator:
-        compatible_credentials = self._get_compatible_credentials(credentials)
         return super()._invoke(
             model,
-            compatible_credentials,
+            credentials,
             prompt_messages,
             model_parameters,
             tools,
@@ -35,15 +36,10 @@ class GPUStackLanguageModel(OAIAPICompatLargeLanguageModel):
         )
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        compatible_credentials = self._get_compatible_credentials(credentials)
-        super().validate_credentials(model, compatible_credentials)
-
-    def _get_compatible_credentials(self, credentials: dict) -> dict:
-        credentials = credentials.copy()
-        base_url = credentials["endpoint_url"].rstrip("/").removesuffix("/v1-openai")
-        credentials["endpoint_url"] = f"{base_url}/v1-openai"
-        return credentials
+        self._add_custom_parameters(credentials)
+        super().validate_credentials(model, credentials)
 
     @staticmethod
     def _add_custom_parameters(credentials: dict) -> None:
+        credentials["endpoint_url"] = str(URL(credentials["endpoint_url"]) / "v1-openai")
         credentials["mode"] = "chat"

api/core/model_runtime/model_providers/gpustack/speech2text/speech2text.py

@@ -1,43 +0,0 @@
-from typing import IO, Optional
-
-from core.model_runtime.model_providers.openai_api_compatible.speech2text.speech2text import OAICompatSpeech2TextModel
-
-
-class GPUStackSpeech2TextModel(OAICompatSpeech2TextModel):
-    """
-    Model class for GPUStack Speech to text model.
-    """
-
-    def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str:
-        """
-        Invoke speech2text model
-        :param model: model name
-        :param credentials: model credentials
-        :param file: audio file
-        :param user: unique user id
-        :return: text for given audio file
-        """
-        compatible_credentials = self._get_compatible_credentials(credentials)
-        return super()._invoke(model, compatible_credentials, file)
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        """
-        Validate model credentials
-
-        :param model: model name
-        :param credentials: model credentials
-        """
-        compatible_credentials = self._get_compatible_credentials(credentials)
-        super().validate_credentials(model, compatible_credentials)
-
-    def _get_compatible_credentials(self, credentials: dict) -> dict:
-        """
-        Get compatible credentials
-
-        :param credentials: model credentials
-        :return: compatible credentials
-        """
-        compatible_credentials = credentials.copy()
-        base_url = credentials["endpoint_url"].rstrip("/").removesuffix("/v1-openai")
-        compatible_credentials["endpoint_url"] = f"{base_url}/v1-openai"
-        return compatible_credentials

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

@@ -1,5 +1,7 @@
 from typing import Optional
 
+from yarl import URL
+
 from core.entities.embedding_type import EmbeddingInputType
 from core.model_runtime.entities.text_embedding_entities import (
     TextEmbeddingResult,
@@ -22,15 +24,12 @@ class GPUStackTextEmbeddingModel(OAICompatEmbeddingModel):
         user: Optional[str] = None,
         input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
-        compatible_credentials = self._get_compatible_credentials(credentials)
-        return super()._invoke(model, compatible_credentials, texts, user, input_type)
+        return super()._invoke(model, credentials, texts, user, input_type)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        compatible_credentials = self._get_compatible_credentials(credentials)
-        super().validate_credentials(model, compatible_credentials)
+        self._add_custom_parameters(credentials)
+        super().validate_credentials(model, credentials)
 
-    def _get_compatible_credentials(self, credentials: dict) -> dict:
-        credentials = credentials.copy()
-        base_url = credentials["endpoint_url"].rstrip("/").removesuffix("/v1-openai")
-        credentials["endpoint_url"] = f"{base_url}/v1-openai"
-        return credentials
+    @staticmethod
+    def _add_custom_parameters(credentials: dict) -> None:
+        credentials["endpoint_url"] = str(URL(credentials["endpoint_url"]) / "v1-openai")

api/core/model_runtime/model_providers/gpustack/tts/tts.py

@@ -1,57 +0,0 @@
-from typing import Any, Optional
-
-from core.model_runtime.model_providers.openai_api_compatible.tts.tts import OAICompatText2SpeechModel
-
-
-class GPUStackText2SpeechModel(OAICompatText2SpeechModel):
-    """
-    Model class for GPUStack Text to Speech model.
-    """
-
-    def _invoke(
-        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
-    ) -> Any:
-        """
-        Invoke text2speech model
-
-        :param model: model name
-        :param tenant_id: user tenant id
-        :param credentials: model credentials
-        :param content_text: text content to be translated
-        :param voice: model timbre
-        :param user: unique user id
-        :return: text translated to audio file
-        """
-        compatible_credentials = self._get_compatible_credentials(credentials)
-        return super()._invoke(
-            model=model,
-            tenant_id=tenant_id,
-            credentials=compatible_credentials,
-            content_text=content_text,
-            voice=voice,
-            user=user,
-        )
-
-    def validate_credentials(self, model: str, credentials: dict, user: Optional[str] = None) -> None:
-        """
-        Validate model credentials
-
-        :param model: model name
-        :param credentials: model credentials
-        :param user: unique user id
-        """
-        compatible_credentials = self._get_compatible_credentials(credentials)
-        super().validate_credentials(model, compatible_credentials)
-
-    def _get_compatible_credentials(self, credentials: dict) -> dict:
-        """
-        Get compatible credentials
-
-        :param credentials: model credentials
-        :return: compatible credentials
-        """
-        compatible_credentials = credentials.copy()
-        base_url = credentials["endpoint_url"].rstrip("/").removesuffix("/v1-openai")
-        compatible_credentials["endpoint_url"] = f"{base_url}/v1-openai"
-
-        return compatible_credentials

api/core/model_runtime/model_providers/groq/llm/gemma-7b-it.yaml

@@ -18,18 +18,6 @@ parameter_rules:
     default: 512
     min: 1
     max: 8192
-  - name: response_format
-    label:
-      zh_Hans: 回复格式
-      en_US: Response Format
-    type: string
-    help:
-      zh_Hans: 指定模型必须输出的格式
-      en_US: specifying the format that the model must output
-    required: false
-    options:
-      - text
-      - json_object
 pricing:
   input: '0.05'
   output: '0.1'

api/core/model_runtime/model_providers/groq/llm/gemma2-9b-it.yaml

@@ -18,18 +18,6 @@ parameter_rules:
     default: 512
     min: 1
     max: 8192
-  - name: response_format
-    label:
-      zh_Hans: 回复格式
-      en_US: Response Format
-    type: string
-    help:
-      zh_Hans: 指定模型必须输出的格式
-      en_US: specifying the format that the model must output
-    required: false
-    options:
-      - text
-      - json_object
 pricing:
   input: '0.05'
   output: '0.1'

api/core/model_runtime/model_providers/groq/llm/llama-3.1-405b-reasoning.yaml

@@ -18,18 +18,6 @@ parameter_rules:
     default: 512
     min: 1
     max: 8192
-  - name: response_format
-    label:
-      zh_Hans: 回复格式
-      en_US: Response Format
-    type: string
-    help:
-      zh_Hans: 指定模型必须输出的格式
-      en_US: specifying the format that the model must output
-    required: false
-    options:
-      - text
-      - json_object
 pricing:
   input: '0.05'
   output: '0.1'

api/core/model_runtime/model_providers/groq/llm/llama-3.1-70b-versatile.yaml

@@ -6,7 +6,6 @@ label:
 model_type: llm
 features:
   - agent-thought
-  - multi-tool-call
 model_properties:
   mode: chat
   context_size: 131072
@@ -20,18 +19,6 @@ parameter_rules:
     default: 512
     min: 1
     max: 8192
-  - name: response_format
-    label:
-      zh_Hans: 回复格式
-      en_US: Response Format
-    type: string
-    help:
-      zh_Hans: 指定模型必须输出的格式
-      en_US: specifying the format that the model must output
-    required: false
-    options:
-      - text
-      - json_object
 pricing:
   input: '0.05'
   output: '0.1'

api/core/model_runtime/model_providers/groq/llm/llama-3.1-8b-instant.yaml

@@ -5,7 +5,6 @@ label:
 model_type: llm
 features:
   - agent-thought
-  - multi-tool-call
 model_properties:
   mode: chat
   context_size: 131072
@@ -19,18 +18,6 @@ parameter_rules:
     default: 512
     min: 1
     max: 8192
-  - name: response_format
-    label:
-      zh_Hans: 回复格式
-      en_US: Response Format
-    type: string
-    help:
-      zh_Hans: 指定模型必须输出的格式
-      en_US: specifying the format that the model must output
-    required: false
-    options:
-      - text
-      - json_object
 pricing:
   input: '0.05'
   output: '0.1'

api/core/model_runtime/model_providers/groq/llm/llama-3.2-11b-text-preview.yaml

@@ -19,18 +19,6 @@ parameter_rules:
     default: 512
     min: 1
     max: 8192
-  - name: response_format
-    label:
-      zh_Hans: 回复格式
-      en_US: Response Format
-    type: string
-    help:
-      zh_Hans: 指定模型必须输出的格式
-      en_US: specifying the format that the model must output
-    required: false
-    options:
-      - text
-      - json_object
 pricing:
   input: '0.05'
   output: '0.1'

api/core/model_runtime/model_providers/groq/llm/llama-3.2-11b-vision-preview.yaml

@@ -19,18 +19,6 @@ parameter_rules:
     default: 512
     min: 1
     max: 8192
-  - name: response_format
-    label:
-      zh_Hans: 回复格式
-      en_US: Response Format
-    type: string
-    help:
-      zh_Hans: 指定模型必须输出的格式
-      en_US: specifying the format that the model must output
-    required: false
-    options:
-      - text
-      - json_object
 pricing:
   input: '0.05'
   output: '0.1'

api/core/model_runtime/model_providers/groq/llm/llama-3.2-1b-preview.yaml

@@ -18,18 +18,6 @@ parameter_rules:
     default: 512
     min: 1
     max: 8192
-  - name: response_format
-    label:
-      zh_Hans: 回复格式
-      en_US: Response Format
-    type: string
-    help:
-      zh_Hans: 指定模型必须输出的格式
-      en_US: specifying the format that the model must output
-    required: false
-    options:
-      - text
-      - json_object
 pricing:
   input: '0.05'
   output: '0.1'