fix bug

.gitignore

@@ -174,5 +174,6 @@ sdks/python-client/dify_client.egg-info
 .vscode/*
 !.vscode/launch.json
 pyrightconfig.json
+api/.vscode
 
 .idea/

CONTRIBUTING.md

@@ -81,7 +81,7 @@ Dify requires the following dependencies to build, make sure they're installed o
 
 Dify is composed of a backend and a frontend. Navigate to the backend directory by `cd api/`, then follow the [Backend README](api/README.md) to install it. In a separate terminal, navigate to the frontend directory by `cd web/`, then follow the [Frontend README](web/README.md) to install.
 
-Check the [installation FAQ](https://docs.dify.ai/getting-started/faq/install-faq) for a list of common issues and steps to troubleshoot.
+Check the [installation FAQ](https://docs.dify.ai/learn-more/faq/self-host-faq) for a list of common issues and steps to troubleshoot.
 
 ### 5. Visit dify in your browser
 

CONTRIBUTING_CN.md

@@ -77,7 +77,7 @@ Dify 依赖以下工具和库：
 
 Dify 由后端和前端组成。通过 `cd api/` 导航到后端目录，然后按照 [后端 README](api/README.md) 进行安装。在另一个终端中，通过 `cd web/` 导航到前端目录，然后按照 [前端 README](web/README.md) 进行安装。
 
-查看 [安装常见问题解答](https://docs.dify.ai/getting-started/faq/install-faq) 以获取常见问题列表和故障排除步骤。
+查看 [安装常见问题解答](https://docs.dify.ai/v/zh-hans/learn-more/faq/install-faq) 以获取常见问题列表和故障排除步骤。
 
 ### 5. 在浏览器中访问 Dify
 

CONTRIBUTING_JA.md

@@ -82,7 +82,7 @@ Dify はバックエンドとフロントエンドから構成されています
 まず`cd api/`でバックエンドのディレクトリに移動し、[Backend README](api/README.md)に従ってインストールします。
 次に別のターミナルで、`cd web/`でフロントエンドのディレクトリに移動し、[Frontend README](web/README.md)に従ってインストールしてください。
 
-よくある問題とトラブルシューティングの手順については、[installation FAQ](https://docs.dify.ai/getting-started/faq/install-faq) を確認してください。
+よくある問題とトラブルシューティングの手順については、[installation FAQ](https://docs.dify.ai/v/japanese/learn-more/faq/install-faq) を確認してください。
 
 ### 5. ブラウザで dify にアクセスする
 

api/.env.example

@@ -256,3 +256,7 @@ WORKFLOW_CALL_MAX_DEPTH=5
 # App configuration
 APP_MAX_EXECUTION_TIME=1200
 APP_MAX_ACTIVE_REQUESTS=0
+
+
+# Celery beat configuration
+CELERY_BEAT_SCHEDULER_TIME=1

api/app.py

@@ -1,7 +1,5 @@
 import os
 
-from configs import dify_config
-
 if os.environ.get("DEBUG", "false").lower() != 'true':
     from gevent import monkey
 
@@ -23,7 +21,9 @@ from flask import Flask, Response, request
 from flask_cors import CORS
 from werkzeug.exceptions import Unauthorized
 
+import contexts
 from commands import register_commands
+from configs import dify_config
 
 # DO NOT REMOVE BELOW
 from events import event_handlers
@@ -181,7 +181,10 @@ def load_user_from_request(request_from_flask_login):
     decoded = PassportService().verify(auth_token)
     user_id = decoded.get('user_id')
 
-    return AccountService.load_logged_in_account(account_id=user_id, token=auth_token)
+    account = AccountService.load_logged_in_account(account_id=user_id, token=auth_token)
+    if account:
+        contexts.tenant_id.set(account.current_tenant_id)
+    return account
 
 
 @login_manager.unauthorized_handler

api/configs/feature/__init__.py

@@ -23,6 +23,7 @@ class SecurityConfig(BaseSettings):
         default=24,
     )
 
+
 class AppExecutionConfig(BaseSettings):
     """
     App Execution configs
@@ -405,7 +406,6 @@ class DataSetConfig(BaseSettings):
         default=False,
     )
 
-
 class WorkspaceConfig(BaseSettings):
     """
     Workspace configs
@@ -435,6 +435,13 @@ class ImageFormatConfig(BaseSettings):
     )
 
 
+class CeleryBeatConfig(BaseSettings):
+    CELERY_BEAT_SCHEDULER_TIME: int = Field(
+        description='the time of the celery scheduler, default to 1 day',
+        default=1,
+    )
+
+
 class FeatureConfig(
     # place the configs in alphabet order
     AppExecutionConfig,
@@ -462,5 +469,6 @@ class FeatureConfig(
 
     # hosted services config
     HostedServiceConfig,
+    CeleryBeatConfig,
 ):
     pass

api/configs/feature/hosted_service/__init__.py

@@ -79,7 +79,7 @@ class HostedAzureOpenAiConfig(BaseSettings):
         default=False,
     )
 
-    HOSTED_OPENAI_API_KEY: Optional[str] = Field(
+    HOSTED_AZURE_OPENAI_API_KEY: Optional[str] = Field(
         description='',
         default=None,
     )

api/configs/middleware/vdb/myscale_config.py

@@ -1,4 +1,3 @@
-from typing import Optional
 
 from pydantic import BaseModel, Field, PositiveInt
 
@@ -8,32 +7,32 @@ class MyScaleConfig(BaseModel):
     MyScale configs
     """
 
-    MYSCALE_HOST: Optional[str] = Field(
+    MYSCALE_HOST: str = Field(
         description='MyScale host',
-        default=None,
+        default='localhost',
     )
 
-    MYSCALE_PORT: Optional[PositiveInt] = Field(
+    MYSCALE_PORT: PositiveInt = Field(
         description='MyScale port',
         default=8123,
     )
 
-    MYSCALE_USER: Optional[str] = Field(
+    MYSCALE_USER: str = Field(
         description='MyScale user',
-        default=None,
+        default='default',
     )
 
-    MYSCALE_PASSWORD: Optional[str] = Field(
+    MYSCALE_PASSWORD: str = Field(
         description='MyScale password',
-        default=None,
+        default='',
     )
 
-    MYSCALE_DATABASE: Optional[str] = Field(
+    MYSCALE_DATABASE: str = Field(
         description='MyScale database name',
-        default=None,
+        default='default',
     )
 
-    MYSCALE_FTS_PARAMS: Optional[str] = Field(
+    MYSCALE_FTS_PARAMS: str = Field(
         description='MyScale fts index parameters',
-        default=None,
+        default='',
     )

api/constants/__init__.py

@@ -0,0 +1,2 @@
+# TODO: Update all string in code to use this constant
+HIDDEN_VALUE = '[__HIDDEN__]'

api/contexts/__init__.py

@@ -0,0 +1,3 @@
+from contextvars import ContextVar
+
+tenant_id: ContextVar[str] = ContextVar('tenant_id')

api/controllers/console/app/app.py

@@ -212,7 +212,7 @@ class AppCopyApi(Resource):
         parser.add_argument('icon_background', type=str, location='json')
         args = parser.parse_args()
 
-        data = AppDslService.export_dsl(app_model=app_model)
+        data = AppDslService.export_dsl(app_model=app_model, include_secret=True)
         app = AppDslService.import_and_create_new_app(
             tenant_id=current_user.current_tenant_id,
             data=data,
@@ -234,8 +234,13 @@ class AppExportApi(Resource):
         if not current_user.is_editor:
             raise Forbidden()
 
+        # Add include_secret params
+        parser = reqparse.RequestParser()
+        parser.add_argument('include_secret', type=inputs.boolean, default=False, location='args')
+        args = parser.parse_args()
+
         return {
-            "data": AppDslService.export_dsl(app_model=app_model)
+            "data": AppDslService.export_dsl(app_model=app_model, include_secret=args['include_secret'])
         }
 
 

api/controllers/console/app/workflow.py

@@ -13,6 +13,7 @@ from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
+from core.app.segments import factory
 from core.errors.error import AppInvokeQuotaExceededError
 from fields.workflow_fields import workflow_fields
 from fields.workflow_run_fields import workflow_run_node_execution_fields
@@ -41,7 +42,7 @@ class DraftWorkflowApi(Resource):
         # The role of the current user in the ta table must be admin, owner, or editor
         if not current_user.is_editor:
             raise Forbidden()
-        
+
         # fetch draft workflow by app_model
         workflow_service = WorkflowService()
         workflow = workflow_service.get_draft_workflow(app_model=app_model)
@@ -64,13 +65,15 @@ class DraftWorkflowApi(Resource):
         if not current_user.is_editor:
             raise Forbidden()
         
-        content_type = request.headers.get('Content-Type')
+        content_type = request.headers.get('Content-Type', '')
 
         if 'application/json' in content_type:
             parser = reqparse.RequestParser()
             parser.add_argument('graph', type=dict, required=True, nullable=False, location='json')
             parser.add_argument('features', type=dict, required=True, nullable=False, location='json')
             parser.add_argument('hash', type=str, required=False, location='json')
+            # TODO: set this to required=True after frontend is updated
+            parser.add_argument('environment_variables', type=list, required=False, location='json')
             args = parser.parse_args()
         elif 'text/plain' in content_type:
             try:
@@ -84,7 +87,8 @@ class DraftWorkflowApi(Resource):
                 args = {
                     'graph': data.get('graph'),
                     'features': data.get('features'),
-                    'hash': data.get('hash')
+                    'hash': data.get('hash'),
+                    'environment_variables': data.get('environment_variables')
                 }
             except json.JSONDecodeError:
                 return {'message': 'Invalid JSON data'}, 400
@@ -94,12 +98,15 @@ class DraftWorkflowApi(Resource):
         workflow_service = WorkflowService()
 
         try:
+            environment_variables_list = args.get('environment_variables') or []
+            environment_variables = [factory.build_variable_from_mapping(obj) for obj in environment_variables_list]
             workflow = workflow_service.sync_draft_workflow(
                 app_model=app_model,
-                graph=args.get('graph'),
-                features=args.get('features'),
+                graph=args['graph'],
+                features=args['features'],
                 unique_hash=args.get('hash'),
-                account=current_user
+                account=current_user,
+                environment_variables=environment_variables,
             )
         except WorkflowHashNotEqualError:
             raise DraftWorkflowNotSync()

api/controllers/console/datasets/datasets.py

@@ -1,10 +1,11 @@
 import flask_restful
-from flask import current_app, request
+from flask import request
 from flask_login import current_user
 from flask_restful import Resource, marshal, marshal_with, reqparse
 from werkzeug.exceptions import Forbidden, NotFound
 
 import services
+from configs import dify_config
 from controllers.console import api
 from controllers.console.apikey import api_key_fields, api_key_list
 from controllers.console.app.error import ProviderNotInitializeError
@@ -530,7 +531,7 @@ class DatasetApiBaseUrlApi(Resource):
     @account_initialization_required
     def get(self):
         return {
-            'api_base_url': (current_app.config['SERVICE_API_URL'] if current_app.config['SERVICE_API_URL']
+            'api_base_url': (dify_config.SERVICE_API_URL if dify_config.SERVICE_API_URL
                              else request.host_url.rstrip('/')) + '/v1'
         }
 
@@ -540,20 +541,20 @@ class DatasetRetrievalSettingApi(Resource):
     @login_required
     @account_initialization_required
     def get(self):
-        vector_type = current_app.config['VECTOR_STORE']
+        vector_type = dify_config.VECTOR_STORE
         match vector_type:
             case VectorType.MILVUS | VectorType.RELYT | VectorType.PGVECTOR | VectorType.TIDB_VECTOR | VectorType.CHROMA | VectorType.TENCENT | VectorType.ORACLE:
                 return {
                     'retrieval_method': [
-                        RetrievalMethod.SEMANTIC_SEARCH
+                        RetrievalMethod.SEMANTIC_SEARCH.value
                     ]
                 }
             case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH | VectorType.ANALYTICDB | VectorType.MYSCALE:
                 return {
                     'retrieval_method': [
-                        RetrievalMethod.SEMANTIC_SEARCH,
-                        RetrievalMethod.FULL_TEXT_SEARCH,
-                        RetrievalMethod.HYBRID_SEARCH,
+                        RetrievalMethod.SEMANTIC_SEARCH.value,
+                        RetrievalMethod.FULL_TEXT_SEARCH.value,
+                        RetrievalMethod.HYBRID_SEARCH.value,
                     ]
                 }
             case _:
@@ -569,15 +570,15 @@ class DatasetRetrievalSettingMockApi(Resource):
             case VectorType.MILVUS | VectorType.RELYT | VectorType.PGVECTOR | VectorType.TIDB_VECTOR | VectorType.CHROMA | VectorType.TENCENT | VectorType.ORACLE:
                 return {
                     'retrieval_method': [
-                        RetrievalMethod.SEMANTIC_SEARCH
+                        RetrievalMethod.SEMANTIC_SEARCH.value
                     ]
                 }
             case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH| VectorType.ANALYTICDB | VectorType.MYSCALE:
                 return {
                     'retrieval_method': [
-                        RetrievalMethod.SEMANTIC_SEARCH,
-                        RetrievalMethod.FULL_TEXT_SEARCH,
-                        RetrievalMethod.HYBRID_SEARCH,
+                        RetrievalMethod.SEMANTIC_SEARCH.value,
+                        RetrievalMethod.FULL_TEXT_SEARCH.value,
+                        RetrievalMethod.HYBRID_SEARCH.value,
                     ]
                 }
             case _:

api/controllers/console/datasets/datasets_segments.py

@@ -75,7 +75,7 @@ class DatasetDocumentSegmentListApi(Resource):
         )
 
         if last_id is not None:
-            last_segment = DocumentSegment.query.get(str(last_id))
+            last_segment = db.session.get(DocumentSegment, str(last_id))
             if last_segment:
                 query = query.filter(
                     DocumentSegment.position > last_segment.position)

api/controllers/console/datasets/file.py

@@ -1,8 +1,9 @@
-from flask import current_app, request
+from flask import request
 from flask_login import current_user
 from flask_restful import Resource, marshal_with
 
 import services
+from configs import dify_config
 from controllers.console import api
 from controllers.console.datasets.error import (
     FileTooLargeError,
@@ -26,9 +27,9 @@ class FileApi(Resource):
     @account_initialization_required
     @marshal_with(upload_config_fields)
     def get(self):
-        file_size_limit = current_app.config.get("UPLOAD_FILE_SIZE_LIMIT")
-        batch_count_limit = current_app.config.get("UPLOAD_FILE_BATCH_LIMIT")
-        image_file_size_limit = current_app.config.get("UPLOAD_IMAGE_FILE_SIZE_LIMIT")
+        file_size_limit = dify_config.UPLOAD_FILE_SIZE_LIMIT
+        batch_count_limit = dify_config.UPLOAD_FILE_BATCH_LIMIT
+        image_file_size_limit = dify_config.UPLOAD_IMAGE_FILE_SIZE_LIMIT
         return {
             'file_size_limit': file_size_limit,
             'batch_count_limit': batch_count_limit,
@@ -76,7 +77,7 @@ class FileSupportTypeApi(Resource):
     @login_required
     @account_initialization_required
     def get(self):
-        etl_type = current_app.config['ETL_TYPE']
+        etl_type = dify_config.ETL_TYPE
         allowed_extensions = UNSTRUCTURED_ALLOWED_EXTENSIONS if etl_type == 'Unstructured' else ALLOWED_EXTENSIONS
         return {'allowed_extensions': allowed_extensions}
 

api/controllers/console/explore/audio.py

@@ -78,10 +78,12 @@ class ChatTextApi(InstalledAppResource):
             parser = reqparse.RequestParser()
             parser.add_argument('message_id', type=str, required=False, location='json')
             parser.add_argument('voice', type=str, location='json')
+            parser.add_argument('text', type=str, location='json')
             parser.add_argument('streaming', type=bool, location='json')
             args = parser.parse_args()
 
-            message_id = args.get('message_id')
+            message_id = args.get('message_id', None)
+            text = args.get('text', None)
             if (app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]
                     and app_model.workflow
                     and app_model.workflow.features_dict):
@@ -95,7 +97,8 @@ class ChatTextApi(InstalledAppResource):
             response = AudioService.transcript_tts(
                 app_model=app_model,
                 message_id=message_id,
-                voice=voice
+                voice=voice,
+                text=text
             )
             return response
         except services.errors.app_model_config.AppModelConfigBrokenError:

api/controllers/console/explore/parameter.py

@@ -1,7 +1,7 @@
 
-from flask import current_app
 from flask_restful import fields, marshal_with
 
+from configs import dify_config
 from controllers.console import api
 from controllers.console.app.error import AppUnavailableError
 from controllers.console.explore.wraps import InstalledAppResource
@@ -78,7 +78,7 @@ class AppParameterApi(InstalledAppResource):
                                                      "transfer_methods": ["remote_url", "local_file"]
                                                  }}),
             'system_parameters': {
-                'image_file_size_limit': current_app.config.get('UPLOAD_IMAGE_FILE_SIZE_LIMIT')
+                'image_file_size_limit': dify_config.UPLOAD_IMAGE_FILE_SIZE_LIMIT
             }
         }
 

api/controllers/console/init_validate.py

@@ -1,8 +1,9 @@
 import os
 
-from flask import current_app, session
+from flask import session
 from flask_restful import Resource, reqparse
 
+from configs import dify_config
 from libs.helper import str_len
 from models.model import DifySetup
 from services.account_service import TenantService
@@ -40,7 +41,7 @@ class InitValidateAPI(Resource):
         return {'result': 'success'}, 201
 
 def get_init_validate_status():
-    if current_app.config['EDITION'] == 'SELF_HOSTED':
+    if dify_config.EDITION == 'SELF_HOSTED':
         if os.environ.get('INIT_PASSWORD'):
             return session.get('is_init_validated') or DifySetup.query.first()
     

api/controllers/console/setup.py

@@ -1,8 +1,9 @@
 from functools import wraps
 
-from flask import current_app, request
+from flask import request
 from flask_restful import Resource, reqparse
 
+from configs import dify_config
 from libs.helper import email, get_remote_ip, str_len
 from libs.password import valid_password
 from models.model import DifySetup
@@ -17,7 +18,7 @@ from .wraps import only_edition_self_hosted
 class SetupApi(Resource):
 
     def get(self):
-        if current_app.config['EDITION'] == 'SELF_HOSTED':
+        if dify_config.EDITION == 'SELF_HOSTED':
             setup_status = get_setup_status()
             if setup_status:
                 return {
@@ -77,7 +78,7 @@ def setup_required(view):
 
 
 def get_setup_status():
-    if current_app.config['EDITION'] == 'SELF_HOSTED':
+    if dify_config.EDITION == 'SELF_HOSTED':
         return DifySetup.query.first()
     else:
         return True

api/controllers/console/version.py

@@ -3,9 +3,10 @@ import json
 import logging
 
 import requests
-from flask import current_app
 from flask_restful import Resource, reqparse
 
+from configs import dify_config
+
 from . import api
 
 
@@ -15,16 +16,16 @@ class VersionApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument('current_version', type=str, required=True, location='args')
         args = parser.parse_args()
-        check_update_url = current_app.config['CHECK_UPDATE_URL']
+        check_update_url = dify_config.CHECK_UPDATE_URL
 
         result = {
-            'version': current_app.config['CURRENT_VERSION'],
+            'version': dify_config.CURRENT_VERSION,
             'release_date': '',
             'release_notes': '',
             'can_auto_update': False,
             'features': {
-                'can_replace_logo': current_app.config['CAN_REPLACE_LOGO'],
-                'model_load_balancing_enabled': current_app.config['MODEL_LB_ENABLED']
+                'can_replace_logo': dify_config.CAN_REPLACE_LOGO,
+                'model_load_balancing_enabled': dify_config.MODEL_LB_ENABLED
             }
         }
 

api/controllers/console/workspace/account.py

@@ -1,10 +1,11 @@
 import datetime
 
 import pytz
-from flask import current_app, request
+from flask import request
 from flask_login import current_user
 from flask_restful import Resource, fields, marshal_with, reqparse
 
+from configs import dify_config
 from constants.languages import supported_language
 from controllers.console import api
 from controllers.console.setup import setup_required
@@ -36,7 +37,7 @@ class AccountInitApi(Resource):
 
         parser = reqparse.RequestParser()
 
-        if current_app.config['EDITION'] == 'CLOUD':
+        if dify_config.EDITION == 'CLOUD':
             parser.add_argument('invitation_code', type=str, location='json')
 
         parser.add_argument(
@@ -45,7 +46,7 @@ class AccountInitApi(Resource):
                             required=True, location='json')
         args = parser.parse_args()
 
-        if current_app.config['EDITION'] == 'CLOUD':
+        if dify_config.EDITION == 'CLOUD':
             if not args['invitation_code']:
                 raise ValueError('invitation_code is required')
 

api/controllers/console/workspace/members.py

@@ -1,8 +1,8 @@
-from flask import current_app
 from flask_login import current_user
 from flask_restful import Resource, abort, marshal_with, reqparse
 
 import services
+from configs import dify_config
 from controllers.console import api
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check
@@ -48,7 +48,7 @@ class MemberInviteEmailApi(Resource):
 
         inviter = current_user
         invitation_results = []
-        console_web_url = current_app.config.get("CONSOLE_WEB_URL")
+        console_web_url = dify_config.CONSOLE_WEB_URL
         for invitee_email in invitee_emails:
             try:
                 token = RegisterService.invite_new_member(inviter.current_tenant, invitee_email, interface_language, role=invitee_role, inviter=inviter)
@@ -117,7 +117,7 @@ class MemberUpdateRoleApi(Resource):
         if not TenantAccountRole.is_valid_role(new_role):
             return {'code': 'invalid-role', 'message': 'Invalid role'}, 400
 
-        member = Account.query.get(str(member_id))
+        member = db.session.get(Account, str(member_id))
         if not member:
             abort(404)
 

api/controllers/console/workspace/tool_providers.py

@@ -1,10 +1,11 @@
 import io
 
-from flask import current_app, send_file
+from flask import send_file
 from flask_login import current_user
 from flask_restful import Resource, reqparse
 from werkzeug.exceptions import Forbidden
 
+from configs import dify_config
 from controllers.console import api
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
@@ -104,7 +105,7 @@ class ToolBuiltinProviderIconApi(Resource):
     @setup_required
     def get(self, provider):
         icon_bytes, mimetype = BuiltinToolManageService.get_builtin_tool_provider_icon(provider)
-        icon_cache_max_age = current_app.config.get('TOOL_ICON_CACHE_MAX_AGE')
+        icon_cache_max_age = dify_config.TOOL_ICON_CACHE_MAX_AGE
         return send_file(io.BytesIO(icon_bytes), mimetype=mimetype, max_age=icon_cache_max_age)
 
 class ToolApiProviderAddApi(Resource):

api/controllers/console/wraps.py

@@ -1,9 +1,10 @@
 import json
 from functools import wraps
 
-from flask import abort, current_app, request
+from flask import abort, request
 from flask_login import current_user
 
+from configs import dify_config
 from controllers.console.workspace.error import AccountNotInitializedError
 from services.feature_service import FeatureService
 from services.operation_service import OperationService
@@ -26,7 +27,7 @@ def account_initialization_required(view):
 def only_edition_cloud(view):
     @wraps(view)
     def decorated(*args, **kwargs):
-        if current_app.config['EDITION'] != 'CLOUD':
+        if dify_config.EDITION != 'CLOUD':
             abort(404)
 
         return view(*args, **kwargs)
@@ -37,7 +38,7 @@ def only_edition_cloud(view):
 def only_edition_self_hosted(view):
     @wraps(view)
     def decorated(*args, **kwargs):
-        if current_app.config['EDITION'] != 'SELF_HOSTED':
+        if dify_config.EDITION != 'SELF_HOSTED':
             abort(404)
 
         return view(*args, **kwargs)

api/controllers/service_api/app/audio.py

@@ -76,10 +76,12 @@ class TextApi(Resource):
             parser = reqparse.RequestParser()
             parser.add_argument('message_id', type=str, required=False, location='json')
             parser.add_argument('voice', type=str, location='json')
+            parser.add_argument('text', type=str, location='json')
             parser.add_argument('streaming', type=bool, location='json')
             args = parser.parse_args()
 
-            message_id = args.get('message_id')
+            message_id = args.get('message_id', None)
+            text = args.get('text', None)
             if (app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]
                     and app_model.workflow
                     and app_model.workflow.features_dict):
@@ -87,15 +89,15 @@ class TextApi(Resource):
                 voice = args.get('voice') if args.get('voice') else text_to_speech.get('voice')
             else:
                 try:
-                    voice = args.get('voice') if args.get('voice') else app_model.app_model_config.text_to_speech_dict.get(
-                        'voice')
+                    voice = args.get('voice') if args.get('voice') else app_model.app_model_config.text_to_speech_dict.get('voice')
                 except Exception:
                     voice = None
             response = AudioService.transcript_tts(
                 app_model=app_model,
                 message_id=message_id,
                 end_user=end_user.external_user_id,
-                voice=voice
+                voice=voice,
+                text=text
             )
 
             return response

api/controllers/service_api/app/workflow.py

@@ -1,6 +1,6 @@
 import logging
 
-from flask_restful import Resource, reqparse
+from flask_restful import Resource, fields, marshal_with, reqparse
 from werkzeug.exceptions import InternalServerError
 
 from controllers.service_api import api
@@ -21,14 +21,43 @@ from core.errors.error import (
     QuotaExceededError,
 )
 from core.model_runtime.errors.invoke import InvokeError
+from extensions.ext_database import db
 from libs import helper
 from models.model import App, AppMode, EndUser
+from models.workflow import WorkflowRun
 from services.app_generate_service import AppGenerateService
 
 logger = logging.getLogger(__name__)
 
 
 class WorkflowRunApi(Resource):
+    workflow_run_fields = {
+        'id': fields.String,
+        'workflow_id': fields.String,
+        'status': fields.String,
+        'inputs': fields.Raw,
+        'outputs': fields.Raw,
+        'error': fields.String,
+        'total_steps': fields.Integer,
+        'total_tokens': fields.Integer,
+        'created_at': fields.DateTime,
+        'finished_at': fields.DateTime,
+        'elapsed_time': fields.Float,
+    }
+
+    @validate_app_token
+    @marshal_with(workflow_run_fields)
+    def get(self, app_model: App, workflow_id: str):
+        """
+        Get a workflow task running detail
+        """
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        workflow_run = db.session.query(WorkflowRun).filter(WorkflowRun.id == workflow_id).first()
+        return workflow_run
+
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
     def post(self, app_model: App, end_user: EndUser):
         """
@@ -88,5 +117,5 @@ class WorkflowTaskStopApi(Resource):
         }
 
 
-api.add_resource(WorkflowRunApi, '/workflows/run')
+api.add_resource(WorkflowRunApi, '/workflows/run/<string:workflow_id>', '/workflows/run')
 api.add_resource(WorkflowTaskStopApi, '/workflows/tasks/<string:task_id>/stop')

api/controllers/web/audio.py

@@ -74,10 +74,12 @@ class TextApi(WebApiResource):
             parser = reqparse.RequestParser()
             parser.add_argument('message_id', type=str, required=False, location='json')
             parser.add_argument('voice', type=str, location='json')
+            parser.add_argument('text', type=str, location='json')
             parser.add_argument('streaming', type=bool, location='json')
             args = parser.parse_args()
 
-            message_id = args.get('message_id')
+            message_id = args.get('message_id', None)
+            text = args.get('text', None)
             if (app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]
                     and app_model.workflow
                     and app_model.workflow.features_dict):
@@ -94,7 +96,8 @@ class TextApi(WebApiResource):
                 app_model=app_model,
                 message_id=message_id,
                 end_user=end_user.external_user_id,
-                voice=voice
+                voice=voice,
+                text=text
             )
 
             return response

api/core/agent/fc_agent_runner.py

@@ -342,10 +342,14 @@ class FunctionCallAgentRunner(BaseAgentRunner):
         """
         tool_calls = []
         for prompt_message in llm_result_chunk.delta.message.tool_calls:
+            args = {}
+            if prompt_message.function.arguments != '':
+                args = json.loads(prompt_message.function.arguments)
+
             tool_calls.append((
                 prompt_message.id,
                 prompt_message.function.name,
-                json.loads(prompt_message.function.arguments),
+                args,
             ))
 
         return tool_calls
@@ -359,10 +363,14 @@ class FunctionCallAgentRunner(BaseAgentRunner):
         """
         tool_calls = []
         for prompt_message in llm_result.message.tool_calls:
+            args = {}
+            if prompt_message.function.arguments != '':
+                args = json.loads(prompt_message.function.arguments)
+
             tool_calls.append((
                 prompt_message.id,
                 prompt_message.function.name,
-                json.loads(prompt_message.function.arguments),
+                args,
             ))
 
         return tool_calls

api/core/app/app_config/base_app_config_manager.py

@@ -1,6 +1,7 @@
-from typing import Optional, Union
+from collections.abc import Mapping
+from typing import Any
 
-from core.app.app_config.entities import AppAdditionalFeatures, EasyUIBasedAppModelConfigFrom
+from core.app.app_config.entities import AppAdditionalFeatures
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.app_config.features.more_like_this.manager import MoreLikeThisConfigManager
 from core.app.app_config.features.opening_statement.manager import OpeningStatementConfigManager
@@ -10,37 +11,19 @@ from core.app.app_config.features.suggested_questions_after_answer.manager impor
     SuggestedQuestionsAfterAnswerConfigManager,
 )
 from core.app.app_config.features.text_to_speech.manager import TextToSpeechConfigManager
-from models.model import AppMode, AppModelConfig
+from models.model import AppMode
 
 
 class BaseAppConfigManager:
-
     @classmethod
-    def convert_to_config_dict(cls, config_from: EasyUIBasedAppModelConfigFrom,
-                               app_model_config: Union[AppModelConfig, dict],
-                               config_dict: Optional[dict] = None) -> dict:
-        """
-        Convert app model config to config dict
-        :param config_from: app model config from
-        :param app_model_config: app model config
-        :param config_dict: app model config dict
-        :return:
-        """
-        if config_from != EasyUIBasedAppModelConfigFrom.ARGS:
-            app_model_config_dict = app_model_config.to_dict()
-            config_dict = app_model_config_dict.copy()
-
-        return config_dict
-
-    @classmethod
-    def convert_features(cls, config_dict: dict, app_mode: AppMode) -> AppAdditionalFeatures:
+    def convert_features(cls, config_dict: Mapping[str, Any], app_mode: AppMode) -> AppAdditionalFeatures:
         """
         Convert app config to app model config
 
         :param config_dict: app config
         :param app_mode: app mode
         """
-        config_dict = config_dict.copy()
+        config_dict = dict(config_dict.items())
 
         additional_features = AppAdditionalFeatures()
         additional_features.show_retrieve_source = RetrievalResourceConfigManager.convert(

api/core/app/app_config/features/file_upload/manager.py

@@ -1,11 +1,12 @@
-from typing import Optional
+from collections.abc import Mapping
+from typing import Any, Optional
 
 from core.app.app_config.entities import FileExtraConfig
 
 
 class FileUploadConfigManager:
     @classmethod
-    def convert(cls, config: dict, is_vision: bool = True) -> Optional[FileExtraConfig]:
+    def convert(cls, config: Mapping[str, Any], is_vision: bool = True) -> Optional[FileExtraConfig]:
         """
         Convert model config to model config
 

api/core/app/app_config/features/text_to_speech/manager.py

@@ -3,13 +3,13 @@ from core.app.app_config.entities import TextToSpeechEntity
 
 class TextToSpeechConfigManager:
     @classmethod
-    def convert(cls, config: dict) -> bool:
+    def convert(cls, config: dict):
         """
         Convert model config to model config
 
         :param config: model config args
         """
-        text_to_speech = False
+        text_to_speech = None
         text_to_speech_dict = config.get('text_to_speech')
         if text_to_speech_dict:
             if text_to_speech_dict.get('enabled'):

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

@@ -1,3 +1,4 @@
+import contextvars
 import logging
 import os
 import threading
@@ -8,6 +9,7 @@ from typing import Union
 from flask import Flask, current_app
 from pydantic import ValidationError
 
+import contexts
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfigManager
 from core.app.apps.advanced_chat.app_runner import AdvancedChatAppRunner
@@ -107,6 +109,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             extras=extras,
             trace_manager=trace_manager
         )
+        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
         return self._generate(
             app_model=app_model,
@@ -173,6 +176,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 inputs=args['inputs']
             )
         )
+        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
         return self._generate(
             app_model=app_model,
@@ -225,6 +229,8 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             'queue_manager': queue_manager,
             'conversation_id': conversation.id,
             'message_id': message.id,
+            'user': user,
+            'context': contextvars.copy_context()
         })
 
         worker_thread.start()
@@ -249,7 +255,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                          application_generate_entity: AdvancedChatAppGenerateEntity,
                          queue_manager: AppQueueManager,
                          conversation_id: str,
-                         message_id: str) -> None:
+                         message_id: str,
+                         user: Account,
+                         context: contextvars.Context) -> None:
         """
         Generate worker in a new thread.
         :param flask_app: Flask app
@@ -259,6 +267,8 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         :param message_id: message ID
         :return:
         """
+        for var, val in context.items():
+            var.set(val)
         with flask_app.app_context():
             try:
                 runner = AdvancedChatAppRunner()

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

@@ -1,7 +1,8 @@
 import logging
 import os
 import time
-from typing import Optional, cast
+from collections.abc import Mapping
+from typing import Any, Optional, cast
 
 from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfig
 from core.app.apps.advanced_chat.workflow_event_trigger_callback import WorkflowEventTriggerCallback
@@ -14,6 +15,7 @@ from core.app.entities.app_invoke_entities import (
 )
 from core.app.entities.queue_entities import QueueAnnotationReplyEvent, QueueStopEvent, QueueTextChunkEvent
 from core.moderation.base import ModerationException
+from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
 from core.workflow.entities.node_entities import SystemVariable, UserFrom
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
 from extensions.ext_database import db
@@ -86,7 +88,7 @@ class AdvancedChatAppRunner(AppRunner):
 
         db.session.close()
 
-        workflow_callbacks = [WorkflowEventTriggerCallback(
+        workflow_callbacks: list[WorkflowCallback] = [WorkflowEventTriggerCallback(
             queue_manager=queue_manager,
             workflow=workflow
         )]
@@ -160,7 +162,7 @@ class AdvancedChatAppRunner(AppRunner):
             self, queue_manager: AppQueueManager,
             app_record: App,
             app_generate_entity: AdvancedChatAppGenerateEntity,
-            inputs: dict,
+            inputs: Mapping[str, Any],
             query: str,
             message_id: str
     ) -> bool:

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

@@ -1,9 +1,11 @@
 import json
 from collections.abc import Generator
-from typing import cast
+from typing import Any, cast
 
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
 from core.app.entities.task_entities import (
+    AppBlockingResponse,
+    AppStreamResponse,
     ChatbotAppBlockingResponse,
     ChatbotAppStreamResponse,
     ErrorStreamResponse,
@@ -18,12 +20,13 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
     _blocking_response_type = ChatbotAppBlockingResponse
 
     @classmethod
-    def convert_blocking_full_response(cls, blocking_response: ChatbotAppBlockingResponse) -> dict:
+    def convert_blocking_full_response(cls, blocking_response: AppBlockingResponse) -> dict[str, Any]:
         """
         Convert blocking full response.
         :param blocking_response: blocking response
         :return:
         """
+        blocking_response = cast(ChatbotAppBlockingResponse, blocking_response)
         response = {
             'event': 'message',
             'task_id': blocking_response.task_id,
@@ -39,7 +42,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
         return response
 
     @classmethod
-    def convert_blocking_simple_response(cls, blocking_response: ChatbotAppBlockingResponse) -> dict:
+    def convert_blocking_simple_response(cls, blocking_response: AppBlockingResponse) -> dict[str, Any]:
         """
         Convert blocking simple response.
         :param blocking_response: blocking response
@@ -53,8 +56,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
         return response
 
     @classmethod
-    def convert_stream_full_response(cls, stream_response: Generator[ChatbotAppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_full_response(cls, stream_response: Generator[AppStreamResponse, None, None]) -> Generator[str, Any, None]:
         """
         Convert stream full response.
         :param stream_response: stream response
@@ -83,8 +85,7 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             yield json.dumps(response_chunk)
 
     @classmethod
-    def convert_stream_simple_response(cls, stream_response: Generator[ChatbotAppStreamResponse, None, None]) \
-            -> Generator[str, None, None]:
+    def convert_stream_simple_response(cls, stream_response: Generator[AppStreamResponse, None, None]) -> Generator[str, Any, None]:
         """
         Convert stream simple response.
         :param stream_response: stream response

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

@@ -113,7 +113,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         self._stream_generate_routes = self._get_stream_generate_routes()
         self._conversation_name_generate_thread = None
 
-    def process(self) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
+    def process(self):
         """
         Process generate task pipeline.
         :return:
@@ -136,8 +136,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         else:
             return self._to_blocking_response(generator)
 
-    def _to_blocking_response(self, generator: Generator[StreamResponse, None, None]) \
-            -> ChatbotAppBlockingResponse:
+    def _to_blocking_response(self, generator: Generator[StreamResponse, None, None]) -> ChatbotAppBlockingResponse:
         """
         Process blocking response.
         :return:
@@ -167,8 +166,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
         raise Exception('Queue listening stopped unexpectedly.')
 
-    def _to_stream_response(self, generator: Generator[StreamResponse, None, None]) \
-            -> Generator[ChatbotAppStreamResponse, None, None]:
+    def _to_stream_response(self, generator: Generator[StreamResponse, None, None]) -> Generator[ChatbotAppStreamResponse, Any, None]:
         """
         To stream response.
         :return:

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

@@ -14,13 +14,13 @@ from core.app.entities.queue_entities import (
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
-from core.workflow.callbacks.base_workflow_callback import BaseWorkflowCallback
+from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
 from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeType
 from models.workflow import Workflow
 
 
-class WorkflowEventTriggerCallback(BaseWorkflowCallback):
+class WorkflowEventTriggerCallback(WorkflowCallback):
 
     def __init__(self, queue_manager: AppQueueManager, workflow: Workflow):
         self._queue_manager = queue_manager

api/core/app/apps/base_app_generate_response_converter.py

@@ -1,7 +1,7 @@
 import logging
 from abc import ABC, abstractmethod
 from collections.abc import Generator
-from typing import Union
+from typing import Any, Union
 
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.task_entities import AppBlockingResponse, AppStreamResponse
@@ -15,44 +15,41 @@ class AppGenerateResponseConverter(ABC):
     @classmethod
     def convert(cls, response: Union[
         AppBlockingResponse,
-        Generator[AppStreamResponse, None, None]
-    ], invoke_from: InvokeFrom) -> Union[
-        dict,
-        Generator[str, None, None]
-    ]:
+        Generator[AppStreamResponse, Any, None]
+    ], invoke_from: InvokeFrom):
         if invoke_from in [InvokeFrom.DEBUGGER, InvokeFrom.SERVICE_API]:
-            if isinstance(response, cls._blocking_response_type):
+            if isinstance(response, AppBlockingResponse):
                 return cls.convert_blocking_full_response(response)
             else:
-                def _generate():
+                def _generate_full_response() -> Generator[str, Any, None]:
                     for chunk in cls.convert_stream_full_response(response):
                         if chunk == 'ping':
                             yield f'event: {chunk}\n\n'
                         else:
                             yield f'data: {chunk}\n\n'
 
-                return _generate()
+                return _generate_full_response()
         else:
-            if isinstance(response, cls._blocking_response_type):
+            if isinstance(response, AppBlockingResponse):
                 return cls.convert_blocking_simple_response(response)
             else:
-                def _generate():
+                def _generate_simple_response() -> Generator[str, Any, None]:
                     for chunk in cls.convert_stream_simple_response(response):
                         if chunk == 'ping':
                             yield f'event: {chunk}\n\n'
                         else:
                             yield f'data: {chunk}\n\n'
 
-                return _generate()
+                return _generate_simple_response()
 
     @classmethod
     @abstractmethod
-    def convert_blocking_full_response(cls, blocking_response: AppBlockingResponse) -> dict:
+    def convert_blocking_full_response(cls, blocking_response: AppBlockingResponse) -> dict[str, Any]:
         raise NotImplementedError
 
     @classmethod
     @abstractmethod
-    def convert_blocking_simple_response(cls, blocking_response: AppBlockingResponse) -> dict:
+    def convert_blocking_simple_response(cls, blocking_response: AppBlockingResponse) -> dict[str, Any]:
         raise NotImplementedError
 
     @classmethod
@@ -68,7 +65,7 @@ class AppGenerateResponseConverter(ABC):
         raise NotImplementedError
 
     @classmethod
-    def _get_simple_metadata(cls, metadata: dict) -> dict:
+    def _get_simple_metadata(cls, metadata: dict[str, Any]):
         """
         Get simple metadata.
         :param metadata: metadata

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

@@ -1,3 +1,4 @@
+import contextvars
 import logging
 import os
 import threading
@@ -8,6 +9,7 @@ from typing import Union
 from flask import Flask, current_app
 from pydantic import ValidationError
 
+import contexts
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.base_app_generator import BaseAppGenerator
 from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException, PublishFrom
@@ -38,7 +40,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
         invoke_from: InvokeFrom,
         stream: bool = True,
         call_depth: int = 0,
-    ) -> Union[dict, Generator[dict, None, None]]:
+    ):
         """
         Generate App response.
 
@@ -86,6 +88,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             call_depth=call_depth,
             trace_manager=trace_manager
         )
+        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
         return self._generate(
             app_model=app_model,
@@ -126,7 +129,8 @@ class WorkflowAppGenerator(BaseAppGenerator):
         worker_thread = threading.Thread(target=self._generate_worker, kwargs={
             'flask_app': current_app._get_current_object(),
             'application_generate_entity': application_generate_entity,
-            'queue_manager': queue_manager
+            'queue_manager': queue_manager,
+            'context': contextvars.copy_context()
         })
 
         worker_thread.start()
@@ -150,8 +154,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
                                   node_id: str,
                                   user: Account,
                                   args: dict,
-                                  stream: bool = True) \
-            -> Union[dict, Generator[dict, None, None]]:
+                                  stream: bool = True):
         """
         Generate App response.
 
@@ -193,6 +196,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 inputs=args['inputs']
             )
         )
+        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
         return self._generate(
             app_model=app_model,
@@ -205,7 +209,8 @@ class WorkflowAppGenerator(BaseAppGenerator):
 
     def _generate_worker(self, flask_app: Flask,
                          application_generate_entity: WorkflowAppGenerateEntity,
-                         queue_manager: AppQueueManager) -> None:
+                         queue_manager: AppQueueManager,
+                         context: contextvars.Context) -> None:
         """
         Generate worker in a new thread.
         :param flask_app: Flask app
@@ -213,6 +218,8 @@ class WorkflowAppGenerator(BaseAppGenerator):
         :param queue_manager: queue manager
         :return:
         """
+        for var, val in context.items():
+            var.set(val)
         with flask_app.app_context():
             try:
                 # workflow app

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

@@ -10,6 +10,7 @@ from core.app.entities.app_invoke_entities import (
     InvokeFrom,
     WorkflowAppGenerateEntity,
 )
+from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
 from core.workflow.entities.node_entities import SystemVariable, UserFrom
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
 from extensions.ext_database import db
@@ -56,7 +57,7 @@ class WorkflowAppRunner:
 
         db.session.close()
 
-        workflow_callbacks = [WorkflowEventTriggerCallback(
+        workflow_callbacks: list[WorkflowCallback] = [WorkflowEventTriggerCallback(
             queue_manager=queue_manager,
             workflow=workflow
         )]

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

@@ -14,13 +14,13 @@ from core.app.entities.queue_entities import (
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
 )
-from core.workflow.callbacks.base_workflow_callback import BaseWorkflowCallback
+from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
 from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeType
 from models.workflow import Workflow
 
 
-class WorkflowEventTriggerCallback(BaseWorkflowCallback):
+class WorkflowEventTriggerCallback(WorkflowCallback):
 
     def __init__(self, queue_manager: AppQueueManager, workflow: Workflow):
         self._queue_manager = queue_manager

api/core/app/apps/workflow_logging_callback.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from core.app.entities.queue_entities import AppQueueEvent
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.workflow.callbacks.base_workflow_callback import BaseWorkflowCallback
+from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
 from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeType
 
@@ -15,7 +15,7 @@ _TEXT_COLOR_MAPPING = {
 }
 
 
-class WorkflowLoggingCallback(BaseWorkflowCallback):
+class WorkflowLoggingCallback(WorkflowCallback):
 
     def __init__(self) -> None:
         self.current_node_id = None

api/core/app/entities/app_invoke_entities.py

@@ -1,3 +1,4 @@
+from collections.abc import Mapping
 from enum import Enum
 from typing import Any, Optional
 
@@ -76,7 +77,7 @@ class AppGenerateEntity(BaseModel):
     # app config
     app_config: AppConfig
 
-    inputs: dict[str, Any]
+    inputs: Mapping[str, Any]
     files: list[FileVar] = []
     user_id: str
 
@@ -140,7 +141,7 @@ class AdvancedChatAppGenerateEntity(AppGenerateEntity):
     app_config: WorkflowUIBasedAppConfig
 
     conversation_id: Optional[str] = None
-    query: Optional[str] = None
+    query: str
 
     class SingleIterationRunEntity(BaseModel):
         """

api/core/app/segments/__init__.py

@@ -0,0 +1,27 @@
+from .segment_group import SegmentGroup
+from .segments import Segment
+from .types import SegmentType
+from .variables import (
+    ArrayVariable,
+    FileVariable,
+    FloatVariable,
+    IntegerVariable,
+    ObjectVariable,
+    SecretVariable,
+    StringVariable,
+    Variable,
+)
+
+__all__ = [
+    'IntegerVariable',
+    'FloatVariable',
+    'ObjectVariable',
+    'SecretVariable',
+    'FileVariable',
+    'StringVariable',
+    'ArrayVariable',
+    'Variable',
+    'SegmentType',
+    'SegmentGroup',
+    'Segment'
+]

api/core/app/segments/factory.py

@@ -0,0 +1,64 @@
+from collections.abc import Mapping
+from typing import Any
+
+from core.file.file_obj import FileVar
+
+from .segments import Segment, StringSegment
+from .types import SegmentType
+from .variables import (
+    ArrayVariable,
+    FileVariable,
+    FloatVariable,
+    IntegerVariable,
+    ObjectVariable,
+    SecretVariable,
+    StringVariable,
+    Variable,
+)
+
+
+def build_variable_from_mapping(m: Mapping[str, Any], /) -> Variable:
+    if (value_type := m.get('value_type')) is None:
+        raise ValueError('missing value type')
+    if not m.get('name'):
+        raise ValueError('missing name')
+    if (value := m.get('value')) is None:
+        raise ValueError('missing value')
+    match value_type:
+        case SegmentType.STRING:
+            return StringVariable.model_validate(m)
+        case SegmentType.NUMBER if isinstance(value, int):
+            return IntegerVariable.model_validate(m)
+        case SegmentType.NUMBER if isinstance(value, float):
+            return FloatVariable.model_validate(m)
+        case SegmentType.SECRET:
+            return SecretVariable.model_validate(m)
+        case SegmentType.NUMBER if not isinstance(value, float | int):
+            raise ValueError(f'invalid number value {value}')
+    raise ValueError(f'not supported value type {value_type}')
+
+
+def build_anonymous_variable(value: Any, /) -> Variable:
+    if isinstance(value, str):
+        return StringVariable(name='anonymous', value=value)
+    if isinstance(value, int):
+        return IntegerVariable(name='anonymous', value=value)
+    if isinstance(value, float):
+        return FloatVariable(name='anonymous', value=value)
+    if isinstance(value, dict):
+        # TODO: Limit the depth of the object
+        obj = {k: build_anonymous_variable(v) for k, v in value.items()}
+        return ObjectVariable(name='anonymous', value=obj)
+    if isinstance(value, list):
+        # TODO: Limit the depth of the array
+        elements = [build_anonymous_variable(v) for v in value]
+        return ArrayVariable(name='anonymous', value=elements)
+    if isinstance(value, FileVar):
+        return FileVariable(name='anonymous', value=value)
+    raise ValueError(f'not supported value {value}')
+
+
+def build_segment(value: Any, /) -> Segment:
+    if isinstance(value, str):
+        return StringSegment(value=value)
+    raise ValueError(f'not supported value {value}')

api/core/app/segments/parser.py

@@ -0,0 +1,17 @@
+import re
+
+from core.app.segments import SegmentGroup, factory
+from core.workflow.entities.variable_pool import VariablePool
+
+VARIABLE_PATTERN = re.compile(r'\{\{#([a-zA-Z0-9_]{1,50}(?:\.[a-zA-Z_][a-zA-Z0-9_]{0,29}){1,10})#\}\}')
+
+
+def convert_template(*, template: str, variable_pool: VariablePool):
+    parts = re.split(VARIABLE_PATTERN, template)
+    segments = []
+    for part in parts:
+        if '.' in part and (value := variable_pool.get(part.split('.'))):
+            segments.append(value)
+        else:
+            segments.append(factory.build_segment(part))
+    return SegmentGroup(segments=segments)

api/core/app/segments/segment_group.py

@@ -0,0 +1,19 @@
+from pydantic import BaseModel
+
+from .segments import Segment
+
+
+class SegmentGroup(BaseModel):
+    segments: list[Segment]
+
+    @property
+    def text(self):
+        return ''.join([segment.text for segment in self.segments])
+
+    @property
+    def log(self):
+        return ''.join([segment.log for segment in self.segments])
+
+    @property
+    def markdown(self):
+        return ''.join([segment.markdown for segment in self.segments])

api/core/app/segments/segments.py

@@ -0,0 +1,39 @@
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, field_validator
+
+from .types import SegmentType
+
+
+class Segment(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    value_type: SegmentType
+    value: Any
+
+    @field_validator('value_type')
+    def validate_value_type(cls, value):
+        """
+        This validator checks if the provided value is equal to the default value of the 'value_type' field.
+        If the value is different, a ValueError is raised.
+        """
+        if value != cls.model_fields['value_type'].default:
+            raise ValueError("Cannot modify 'value_type'")
+        return value
+
+    @property
+    def text(self) -> str:
+        return str(self.value)
+
+    @property
+    def log(self) -> str:
+        return str(self.value)
+
+    @property
+    def markdown(self) -> str:
+        return str(self.value)
+
+
+class StringSegment(Segment):
+    value_type: SegmentType = SegmentType.STRING
+    value: str

api/core/app/segments/types.py

@@ -0,0 +1,17 @@
+from enum import Enum
+
+
+class SegmentType(str, Enum):
+    STRING = 'string'
+    NUMBER = 'number'
+    FILE = 'file'
+
+    SECRET = 'secret'
+
+    OBJECT = 'object'
+
+    ARRAY = 'array'
+    ARRAY_STRING = 'array[string]'
+    ARRAY_NUMBER = 'array[number]'
+    ARRAY_OBJECT = 'array[object]'
+    ARRAY_FILE = 'array[file]'

api/core/app/segments/variables.py

@@ -0,0 +1,83 @@
+import json
+from collections.abc import Mapping, Sequence
+
+from pydantic import Field
+
+from core.file.file_obj import FileVar
+from core.helper import encrypter
+
+from .segments import Segment, StringSegment
+from .types import SegmentType
+
+
+class Variable(Segment):
+    """
+    A variable is a segment that has a name.
+    """
+
+    id: str = Field(
+        default='',
+        description="Unique identity for variable. It's only used by environment variables now.",
+    )
+    name: str
+
+
+class StringVariable(StringSegment, Variable):
+    pass
+
+
+class FloatVariable(Variable):
+    value_type: SegmentType = SegmentType.NUMBER
+    value: float
+
+
+class IntegerVariable(Variable):
+    value_type: SegmentType = SegmentType.NUMBER
+    value: int
+
+
+class ObjectVariable(Variable):
+    value_type: SegmentType = SegmentType.OBJECT
+    value: Mapping[str, Variable]
+
+    @property
+    def text(self) -> str:
+        # TODO: Process variables.
+        return json.dumps(self.model_dump()['value'], ensure_ascii=False)
+
+    @property
+    def log(self) -> str:
+        # TODO: Process variables.
+        return json.dumps(self.model_dump()['value'], ensure_ascii=False, indent=2)
+
+    @property
+    def markdown(self) -> str:
+        # TODO: Use markdown code block
+        return json.dumps(self.model_dump()['value'], ensure_ascii=False, indent=2)
+
+
+class ArrayVariable(Variable):
+    value_type: SegmentType = SegmentType.ARRAY
+    value: Sequence[Variable]
+
+    @property
+    def markdown(self) -> str:
+        return '\n'.join(['- ' + item.markdown for item in self.value])
+
+
+class FileVariable(Variable):
+    value_type: SegmentType = SegmentType.FILE
+    # TODO: embed FileVar in this model.
+    value: FileVar
+
+    @property
+    def markdown(self) -> str:
+        return self.value.to_markdown()
+
+
+class SecretVariable(StringVariable):
+    value_type: SegmentType = SegmentType.SECRET
+
+    @property
+    def log(self) -> str:
+        return encrypter.obfuscated_token(self.value)

api/core/callback_handler/agent_tool_callback_handler.py

@@ -1,9 +1,11 @@
 import os
+from collections.abc import Mapping, Sequence
 from typing import Any, Optional, TextIO, Union
 
 from pydantic import BaseModel
 
 from core.ops.ops_trace_manager import TraceQueueManager, TraceTask, TraceTaskName
+from core.tools.entities.tool_entities import ToolInvokeMessage
 
 _TEXT_COLOR_MAPPING = {
     "blue": "36;1",
@@ -43,7 +45,7 @@ class DifyAgentCallbackHandler(BaseModel):
     def on_tool_start(
         self,
         tool_name: str,
-        tool_inputs: dict[str, Any],
+        tool_inputs: Mapping[str, Any],
     ) -> None:
         """Do nothing."""
         print_text("\n[on_tool_start] ToolCall:" + tool_name + "\n" + str(tool_inputs) + "\n", color=self.color)
@@ -51,8 +53,8 @@ class DifyAgentCallbackHandler(BaseModel):
     def on_tool_end(
         self,
         tool_name: str,
-        tool_inputs: dict[str, Any],
-        tool_outputs: str,
+        tool_inputs: Mapping[str, Any],
+        tool_outputs: Sequence[ToolInvokeMessage],
         message_id: Optional[str] = None,
         timer: Optional[Any] = None,
         trace_manager: Optional[TraceQueueManager] = None

api/core/file/message_file_parser.py

@@ -1,4 +1,5 @@
-from typing import Union
+from collections.abc import Mapping, Sequence
+from typing import Any, Union
 
 import requests
 
@@ -16,7 +17,7 @@ class MessageFileParser:
         self.tenant_id = tenant_id
         self.app_id = app_id
 
-    def validate_and_transform_files_arg(self, files: list[dict], file_extra_config: FileExtraConfig,
+    def validate_and_transform_files_arg(self, files: Sequence[Mapping[str, Any]], file_extra_config: FileExtraConfig,
                                          user: Union[Account, EndUser]) -> list[FileVar]:
         """
         validate and transform files arg

api/core/helper/code_executor/code_executor.py

@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
 CODE_EXECUTION_ENDPOINT = dify_config.CODE_EXECUTION_ENDPOINT
 CODE_EXECUTION_API_KEY = dify_config.CODE_EXECUTION_API_KEY
 
-CODE_EXECUTION_TIMEOUT= (10, 60)
+CODE_EXECUTION_TIMEOUT = (10, 60)
 
 class CodeExecutionException(Exception):
     pass
@@ -64,7 +64,7 @@ class CodeExecutor:
 
     @classmethod
     def execute_code(cls, 
-                     language: Literal['python3', 'javascript', 'jinja2'], 
+                     language: CodeLanguage, 
                      preload: str, 
                      code: str, 
                      dependencies: Optional[list[CodeDependency]] = None) -> str:
@@ -119,7 +119,7 @@ class CodeExecutor:
         return response.data.stdout
 
     @classmethod
-    def execute_workflow_code_template(cls, language: Literal['python3', 'javascript', 'jinja2'], code: str, inputs: dict, dependencies: Optional[list[CodeDependency]] = None) -> dict:
+    def execute_workflow_code_template(cls, language: CodeLanguage, code: str, inputs: dict, dependencies: Optional[list[CodeDependency]] = None) -> dict:
         """
         Execute code
         :param language: code language

api/core/helper/encrypter.py

@@ -6,11 +6,16 @@ from models.account import Tenant
 
 
 def obfuscated_token(token: str):
-    return token[:6] + '*' * (len(token) - 8) + token[-2:]
+    if not token:
+        return token
+    if len(token) <= 8:
+        return '*' * 20
+    return token[:6] + '*' * 12 + token[-2:]
 
 
 def encrypt_token(tenant_id: str, token: str):
-    tenant = db.session.query(Tenant).filter(Tenant.id == tenant_id).first()
+    if not (tenant := db.session.query(Tenant).filter(Tenant.id == tenant_id).first()):
+        raise ValueError(f'Tenant with id {tenant_id} not found')
     encrypted_token = rsa.encrypt(token, tenant.encrypt_public_key)
     return base64.b64encode(encrypted_token).decode()
 

api/core/helper/position_helper.py

@@ -14,6 +14,9 @@ def get_position_map(folder_path: str, *, file_name: str = "_position.yaml") ->
     :return: a dict with name as key and index as value
     """
     position_file_name = os.path.join(folder_path, file_name)
+    if not position_file_name or not os.path.exists(position_file_name):
+        return {}
+    
     positions = load_yaml_file(position_file_name, ignore_error=True)
     position_map = {}
     index = 0

api/core/llm_generator/prompts.py

@@ -64,6 +64,7 @@ User Input:
 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(if the main response is written in Chinese, then the language of your output must be using Chinese.)!\n"
     "The output must be an array in JSON format following the specified schema:\n"
     "[\"question1\",\"question2\",\"question3\"]\n"
 )

api/core/memory/token_buffer_memory.py

@@ -103,7 +103,7 @@ class TokenBufferMemory:
 
         if curr_message_tokens > max_token_limit:
             pruned_memory = []
-            while curr_message_tokens > max_token_limit and prompt_messages:
+            while curr_message_tokens > max_token_limit and len(prompt_messages)>1:
                 pruned_memory.append(prompt_messages.pop(0))
                 curr_message_tokens = self.model_instance.get_llm_num_tokens(
                     prompt_messages

api/core/model_manager.py

@@ -413,6 +413,7 @@ class LBModelManager:
         for load_balancing_config in self._load_balancing_configs:
             if load_balancing_config.name == "__inherit__":
                 if not managed_credentials:
+                    # FIXME: Mutation to loop iterable `self._load_balancing_configs` during iteration
                     # remove __inherit__ if managed credentials is not provided
                     self._load_balancing_configs.remove(load_balancing_config)
                 else:

api/core/model_runtime/model_providers/anthropic/llm/claude-3-5-sonnet-20240620.yaml

@@ -27,9 +27,9 @@ parameter_rules:
   - name: max_tokens
     use_template: max_tokens
     required: true
-    default: 4096
+    default: 8192
     min: 1
-    max: 4096
+    max: 8192
   - name: response_format
     use_template: response_format
 pricing:

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

@@ -113,6 +113,11 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         if system:
             extra_model_kwargs['system'] = system
 
+        # Add the new header for claude-3-5-sonnet-20240620 model
+        extra_headers = {}
+        if model == "claude-3-5-sonnet-20240620":
+            extra_headers["anthropic-beta"] = "max-tokens-3-5-sonnet-2024-07-15"
+
         if tools:
             extra_model_kwargs['tools'] = [
                 self._transform_tool_prompt(tool) for tool in tools
@@ -121,6 +126,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 model=model,
                 messages=prompt_message_dicts,
                 stream=stream,
+                extra_headers=extra_headers,
                 **model_parameters,
                 **extra_model_kwargs
             )
@@ -130,6 +136,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 model=model,
                 messages=prompt_message_dicts,
                 stream=stream,
+                extra_headers=extra_headers,
                 **model_parameters,
                 **extra_model_kwargs
             )
@@ -138,7 +145,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
             return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages)
 
         return self._handle_chat_generate_response(model, credentials, response, prompt_messages)
-
+    
     def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                                  model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
                                  stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,

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

@@ -501,7 +501,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                         sub_messages.append(sub_message_dict)
                 message_dict = {"role": "user", "content": sub_messages}
         elif isinstance(message, AssistantPromptMessage):
-            message = cast(AssistantPromptMessage, message)
+            # message = cast(AssistantPromptMessage, message)
             message_dict = {"role": "assistant", "content": message.content}
             if message.tool_calls:
                 message_dict["tool_calls"] = [helper.dump_model(tool_call) for tool_call in message.tool_calls]

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

@@ -1,6 +1,8 @@
 - gpt-4
 - gpt-4o
 - gpt-4o-2024-05-13
+- gpt-4o-mini
+- gpt-4o-mini-2024-07-18
 - gpt-4-turbo
 - gpt-4-turbo-2024-04-09
 - gpt-4-turbo-preview

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

@@ -0,0 +1,44 @@
+model: gpt-4o-mini-2024-07-18
+label:
+  zh_Hans: gpt-4o-mini-2024-07-18
+  en_US: gpt-4o-mini-2024-07-18
+model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 16384
+  - 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: '0.15'
+  output: '0.60'
+  unit: '0.000001'
+  currency: USD

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

@@ -0,0 +1,44 @@
+model: gpt-4o-mini
+label:
+  zh_Hans: gpt-4o-mini
+  en_US: gpt-4o-mini
+model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 16384
+  - 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: '0.15'
+  output: '0.60'
+  unit: '0.000001'
+  currency: USD

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

@@ -1,4 +1,5 @@
 - openai/gpt-4o
+- openai/gpt-4o-mini
 - openai/gpt-4
 - openai/gpt-4-32k
 - openai/gpt-3.5-turbo

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

@@ -0,0 +1,43 @@
+model: openai/gpt-4o-mini
+label:
+  en_US: gpt-4o-mini
+model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 16384
+  - 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: "0.15"
+  output: "0.60"
+  unit: "0.000001"
+  currency: USD

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

@@ -0,0 +1,238 @@
+import json
+import logging
+from collections.abc import Generator
+from typing import Any, Optional, Union
+
+import boto3
+
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    PromptMessageTool,
+)
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, I18nObject, ModelType
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
+
+logger = logging.getLogger(__name__)
+
+
+class SageMakerLargeLanguageModel(LargeLanguageModel):
+    """
+    Model class for Cohere large language model.
+    """
+    sagemaker_client: Any = None
+
+    def _invoke(self, model: str, credentials: dict,
+                prompt_messages: list[PromptMessage], model_parameters: dict,
+                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+                stream: bool = True, user: Optional[str] = None) \
+            -> Union[LLMResult, Generator]:
+        """
+        Invoke large language model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_messages: prompt messages
+        :param model_parameters: model parameters
+        :param tools: tools for tool calling
+        :param stop: stop words
+        :param stream: is stream response
+        :param user: unique user id
+        :return: full response or stream response chunk generator result
+        """
+        # get model mode
+        model_mode = self.get_model_mode(model, credentials)
+
+        if not self.sagemaker_client:
+            access_key = credentials.get('access_key')
+            secret_key = credentials.get('secret_key')
+            aws_region = credentials.get('aws_region')
+            if aws_region:
+                if access_key and secret_key:
+                    self.sagemaker_client = boto3.client("sagemaker-runtime", 
+                        aws_access_key_id=access_key,
+                        aws_secret_access_key=secret_key,
+                        region_name=aws_region)
+                else:
+                    self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
+            else:
+                self.sagemaker_client = boto3.client("sagemaker-runtime")
+
+
+        sagemaker_endpoint = credentials.get('sagemaker_endpoint')
+        response_model = self.sagemaker_client.invoke_endpoint(
+                    EndpointName=sagemaker_endpoint,
+                    Body=json.dumps(
+                    {
+                        "inputs": prompt_messages[0].content,
+                        "parameters": { "stop" : stop},
+                        "history" : []
+                    }
+                    ),
+                    ContentType="application/json",
+                )
+
+        assistant_text = response_model['Body'].read().decode('utf8')
+
+        # transform assistant message to prompt message
+        assistant_prompt_message = AssistantPromptMessage(
+            content=assistant_text
+        )
+
+        usage = self._calc_response_usage(model, credentials, 0, 0)
+
+        response = LLMResult(
+            model=model,
+            prompt_messages=prompt_messages,
+            message=assistant_prompt_message,
+            usage=usage
+        )
+
+        return response
+
+    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+                       tools: Optional[list[PromptMessageTool]] = None) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_messages: prompt messages
+        :param tools: tools for tool calling
+        :return:
+        """
+        # get model mode
+        model_mode = self.get_model_mode(model)
+
+        try:
+            return 0
+        except Exception as e:
+            raise self._transform_invoke_error(e)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            # get model mode
+            model_mode = self.get_model_mode(model)
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [
+                InvokeConnectionError
+            ],
+            InvokeServerUnavailableError: [
+                InvokeServerUnavailableError
+            ],
+            InvokeRateLimitError: [
+                InvokeRateLimitError
+            ],
+            InvokeAuthorizationError: [
+                InvokeAuthorizationError
+            ],
+            InvokeBadRequestError: [
+                InvokeBadRequestError,
+                KeyError,
+                ValueError
+            ]
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+            used to define customizable model schema
+        """
+        rules = [
+            ParameterRule(
+                name='temperature',
+                type=ParameterType.FLOAT,
+                use_template='temperature',
+                label=I18nObject(
+                    zh_Hans='温度',
+                    en_US='Temperature'
+                ),
+            ),
+            ParameterRule(
+                name='top_p',
+                type=ParameterType.FLOAT,
+                use_template='top_p',
+                label=I18nObject(
+                    zh_Hans='Top P',
+                    en_US='Top P'
+                )
+            ),
+            ParameterRule(
+                name='max_tokens',
+                type=ParameterType.INT,
+                use_template='max_tokens',
+                min=1,
+                max=credentials.get('context_length', 2048),
+                default=512,
+                label=I18nObject(
+                    zh_Hans='最大生成长度',
+                    en_US='Max Tokens'
+                )
+            )
+        ]
+
+        completion_type = LLMMode.value_of(credentials["mode"])
+
+        if completion_type == LLMMode.CHAT:
+            print(f"completion_type : {LLMMode.CHAT.value}") 
+
+        if completion_type == LLMMode.COMPLETION:
+            print(f"completion_type : {LLMMode.COMPLETION.value}") 
+
+        features = []
+
+        support_function_call = credentials.get('support_function_call', False)
+        if support_function_call:
+            features.append(ModelFeature.TOOL_CALL)
+
+        support_vision = credentials.get('support_vision', False)
+        if support_vision:
+            features.append(ModelFeature.VISION)
+
+        context_length = credentials.get('context_length', 2048)
+
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(
+                en_US=model
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.LLM,
+            features=features,
+            model_properties={
+                ModelPropertyKey.MODE: completion_type,
+                ModelPropertyKey.CONTEXT_SIZE: context_length
+            },
+            parameter_rules=rules
+        )
+
+        return entity

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

@@ -0,0 +1,190 @@
+import json
+import logging
+from typing import Any, Optional
+
+import boto3
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType
+from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.rerank_model import RerankModel
+
+logger = logging.getLogger(__name__)
+
+class SageMakerRerankModel(RerankModel):
+    """
+    Model class for Cohere rerank model.
+    """
+    sagemaker_client: Any = None
+
+    def _sagemaker_rerank(self, query_input: str, docs: list[str], rerank_endpoint:str):
+        inputs = [query_input]*len(docs)
+        response_model = self.sagemaker_client.invoke_endpoint(
+            EndpointName=rerank_endpoint,
+            Body=json.dumps(
+                {
+                    "inputs": inputs,
+                    "docs": docs
+                }
+            ),
+            ContentType="application/json",
+        )
+        json_str = response_model['Body'].read().decode('utf8')
+        json_obj = json.loads(json_str)
+        scores = json_obj['scores']
+        return scores if isinstance(scores, list) else [scores]
+
+
+    def _invoke(self, model: str, credentials: dict,
+                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
+                user: Optional[str] = None) \
+            -> RerankResult:
+        """
+        Invoke rerank model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n
+        :param user: unique user id
+        :return: rerank result
+        """
+        line = 0
+        try:
+            if len(docs) == 0:
+                return RerankResult(
+                    model=model,
+                    docs=docs
+                )
+
+            line = 1
+            if not self.sagemaker_client:
+                access_key = credentials.get('aws_access_key_id')
+                secret_key = credentials.get('aws_secret_access_key')
+                aws_region = credentials.get('aws_region')
+                if aws_region:
+                    if access_key and secret_key:
+                        self.sagemaker_client = boto3.client("sagemaker-runtime", 
+                            aws_access_key_id=access_key,
+                            aws_secret_access_key=secret_key,
+                            region_name=aws_region)
+                    else:
+                        self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
+                else:
+                    self.sagemaker_client = boto3.client("sagemaker-runtime")
+
+            line = 2
+
+            sagemaker_endpoint = credentials.get('sagemaker_endpoint')
+            candidate_docs = []
+
+            scores = self._sagemaker_rerank(query, docs, sagemaker_endpoint)
+            for idx in range(len(scores)):
+                candidate_docs.append({"content" : docs[idx], "score": scores[idx]})
+
+            sorted(candidate_docs, key=lambda x: x['score'], reverse=True)
+
+            line = 3
+            rerank_documents = []
+            for idx, result in enumerate(candidate_docs):
+                rerank_document = RerankDocument(
+                    index=idx,
+                    text=result.get('content'),
+                    score=result.get('score', -100.0)
+                )
+
+                if score_threshold is not None:
+                    if rerank_document.score >= score_threshold:
+                        rerank_documents.append(rerank_document)
+                else:
+                    rerank_documents.append(rerank_document)
+
+            return RerankResult(
+                model=model,
+                docs=rerank_documents
+            )
+
+        except Exception as e:
+            logger.exception(f'Exception {e}, line : {line}')
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the capital of the United States?",
+                docs=[
+                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                    "Census, Carson City had a population of 55,274.",
+                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                    "are a political division controlled by the United States. Its capital is Saipan.",
+                ],
+                score_threshold=0.8
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [
+                InvokeConnectionError
+            ],
+            InvokeServerUnavailableError: [
+                InvokeServerUnavailableError
+            ],
+            InvokeRateLimitError: [
+                InvokeRateLimitError
+            ],
+            InvokeAuthorizationError: [
+                InvokeAuthorizationError
+            ],
+            InvokeBadRequestError: [
+                InvokeBadRequestError,
+                KeyError,
+                ValueError
+            ]
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+            used to define customizable model schema
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(
+                en_US=model
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.RERANK,
+            model_properties={ },
+            parameter_rules=[]
+        )
+
+        return entity

api/core/model_runtime/model_providers/sagemaker/sagemaker.py

@@ -0,0 +1,17 @@
+import logging
+
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class SageMakerProvider(ModelProvider):
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        pass

api/core/model_runtime/model_providers/sagemaker/sagemaker.yaml

@@ -0,0 +1,125 @@
+provider: sagemaker
+label:
+  zh_Hans: Sagemaker
+  en_US: Sagemaker
+icon_small:
+  en_US: icon_s_en.png
+icon_large:
+  en_US: icon_l_en.png
+description:
+  en_US: Customized model on Sagemaker
+  zh_Hans: Sagemaker上的私有化部署的模型
+background: "#ECE9E3"
+help:
+  title:
+    en_US: How to deploy customized model on Sagemaker
+    zh_Hans: 如何在Sagemaker上的私有化部署的模型
+  url:
+    en_US: https://github.com/aws-samples/dify-aws-tool/blob/main/README.md#how-to-deploy-sagemaker-endpoint
+    zh_Hans: https://github.com/aws-samples/dify-aws-tool/blob/main/README_ZH.md#%E5%A6%82%E4%BD%95%E9%83%A8%E7%BD%B2sagemaker%E6%8E%A8%E7%90%86%E7%AB%AF%E7%82%B9
+supported_model_types:
+  - llm
+  - text-embedding
+  - rerank
+configurate_methods:
+  - customizable-model
+model_credential_schema:
+  model:
+    label:
+      en_US: Model Name
+      zh_Hans: 模型名称
+    placeholder:
+      en_US: Enter your model name
+      zh_Hans: 输入模型名称
+  credential_form_schemas:
+    - variable: mode
+      show_on:
+        - variable: __model_type
+          value: llm
+      label:
+        en_US: Completion mode
+      type: select
+      required: false
+      default: chat
+      placeholder:
+        zh_Hans: 选择对话类型
+        en_US: Select completion mode
+      options:
+        - value: completion
+          label:
+            en_US: Completion
+            zh_Hans: 补全
+        - value: chat
+          label:
+            en_US: Chat
+            zh_Hans: 对话
+    - variable: sagemaker_endpoint
+      label:
+        en_US: sagemaker endpoint
+      type: text-input
+      required: true
+      placeholder:
+        zh_Hans: 请输出你的Sagemaker推理端点
+        en_US: Enter your Sagemaker Inference endpoint
+    - variable: aws_access_key_id
+      required: false
+      label:
+        en_US: Access Key (If not provided, credentials are obtained from the running environment.)
+        zh_Hans: Access Key (如果未提供，凭证将从运行环境中获取。)
+      type: secret-input
+      placeholder:
+        en_US: Enter your Access Key
+        zh_Hans: 在此输入您的 Access Key
+    - variable: aws_secret_access_key
+      required: false
+      label:
+        en_US: Secret Access Key
+        zh_Hans: Secret Access Key
+      type: secret-input
+      placeholder:
+        en_US: Enter your Secret Access Key
+        zh_Hans: 在此输入您的 Secret Access Key
+    - variable: aws_region
+      required: false
+      label:
+        en_US: AWS Region
+        zh_Hans: AWS 地区
+      type: select
+      default: us-east-1
+      options:
+        - value: us-east-1
+          label:
+            en_US: US East (N. Virginia)
+            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)
+        - value: ap-southeast-1
+          label:
+            en_US: Asia Pacific (Singapore)
+            zh_Hans: 亚太地区 (新加坡)
+        - value: ap-northeast-1
+          label:
+            en_US: Asia Pacific (Tokyo)
+            zh_Hans: 亚太地区 (东京)
+        - value: eu-central-1
+          label:
+            en_US: Europe (Frankfurt)
+            zh_Hans: 欧洲 (法兰克福)
+        - value: us-gov-west-1
+          label:
+            en_US: AWS GovCloud (US-West)
+            zh_Hans: AWS GovCloud (US-West)
+        - value: ap-southeast-2
+          label:
+            en_US: Asia Pacific (Sydney)
+            zh_Hans: 亚太地区 (悉尼)
+        - value: cn-north-1
+          label:
+            en_US: AWS Beijing (cn-north-1)
+            zh_Hans: 中国北京 (cn-north-1)
+        - value: cn-northwest-1
+          label:
+            en_US: AWS Ningxia (cn-northwest-1)
+            zh_Hans: 中国宁夏 (cn-northwest-1)

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

@@ -0,0 +1,214 @@
+import itertools
+import json
+import logging
+import time
+from typing import Any, Optional
+
+import boto3
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
+from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
+from core.model_runtime.errors.invoke import (
+        InvokeAuthorizationError,
+        InvokeBadRequestError,
+        InvokeConnectionError,
+        InvokeError,
+        InvokeRateLimitError,
+        InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
+
+BATCH_SIZE = 20
+CONTEXT_SIZE=8192
+
+logger = logging.getLogger(__name__)
+
+def batch_generator(generator, batch_size):
+    while True:
+        batch = list(itertools.islice(generator, batch_size))
+        if not batch:
+            break
+        yield batch
+
+class SageMakerEmbeddingModel(TextEmbeddingModel):
+    """
+    Model class for Cohere text embedding model.
+    """
+    sagemaker_client: Any = None
+
+    def _sagemaker_embedding(self, sm_client, endpoint_name, content_list:list[str]):
+        response_model = sm_client.invoke_endpoint(
+            EndpointName=endpoint_name,
+            Body=json.dumps(
+                {
+                    "inputs": content_list,
+                    "parameters": {},
+                    "is_query" : False,
+                    "instruction" :  ''
+                }
+            ),
+            ContentType="application/json",
+        )
+        json_str = response_model['Body'].read().decode('utf8')
+        json_obj = json.loads(json_str)
+        embeddings = json_obj['embeddings']
+        return embeddings
+
+    def _invoke(self, model: str, credentials: dict,
+                texts: list[str], user: Optional[str] = None) \
+            -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :return: embeddings result
+        """
+        # get model properties
+        try:
+            line = 1
+            if not self.sagemaker_client:
+                access_key = credentials.get('aws_access_key_id')
+                secret_key = credentials.get('aws_secret_access_key')
+                aws_region = credentials.get('aws_region')
+                if aws_region:
+                    if access_key and secret_key:
+                        self.sagemaker_client = boto3.client("sagemaker-runtime", 
+                            aws_access_key_id=access_key,
+                            aws_secret_access_key=secret_key,
+                            region_name=aws_region)
+                    else:
+                        self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
+                else:
+                    self.sagemaker_client = boto3.client("sagemaker-runtime")
+
+            line = 2
+            sagemaker_endpoint = credentials.get('sagemaker_endpoint')
+
+            line = 3
+            truncated_texts = [ item[:CONTEXT_SIZE] for item in texts ]
+
+            batches = batch_generator((text for text in truncated_texts), batch_size=BATCH_SIZE)
+            all_embeddings = []
+
+            line = 4
+            for batch in batches:
+                embeddings = self._sagemaker_embedding(self.sagemaker_client, sagemaker_endpoint, batch)
+                all_embeddings.extend(embeddings)
+
+            line = 5
+            # calc usage
+            usage = self._calc_response_usage(
+                model=model,
+                credentials=credentials,
+                tokens=0 # It's not SAAS API, usage is meaningless
+            )
+            line = 6
+
+            return TextEmbeddingResult(
+                embeddings=all_embeddings,
+                usage=usage,
+                model=model
+            )
+
+        except Exception as e:
+            logger.exception(f'Exception {e}, line : {line}')
+
+    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return:
+        """
+        return 0
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            print("validate_credentials ok....")
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
+        """
+        Calculate response usage
+
+        :param model: model name
+        :param credentials: model credentials
+        :param tokens: input tokens
+        :return: usage
+        """
+        # get input price info
+        input_price_info = self.get_price(
+            model=model,
+            credentials=credentials,
+            price_type=PriceType.INPUT,
+            tokens=tokens
+        )
+
+        # transform usage
+        usage = EmbeddingUsage(
+            tokens=tokens,
+            total_tokens=tokens,
+            unit_price=input_price_info.unit_price,
+            price_unit=input_price_info.unit,
+            total_price=input_price_info.total_amount,
+            currency=input_price_info.currency,
+            latency=time.perf_counter() - self.started_at
+        )
+
+        return usage
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        return {
+            InvokeConnectionError: [
+                InvokeConnectionError
+            ],
+            InvokeServerUnavailableError: [
+                InvokeServerUnavailableError
+            ],
+            InvokeRateLimitError: [
+                InvokeRateLimitError
+            ],
+            InvokeAuthorizationError: [
+                InvokeAuthorizationError
+            ],
+            InvokeBadRequestError: [
+                KeyError
+            ]
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+            used to define customizable model schema
+        """
+        
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(
+                en_US=model
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TEXT_EMBEDDING,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: CONTEXT_SIZE,
+                ModelPropertyKey.MAX_CHUNKS: BATCH_SIZE,
+            },
+            parameter_rules=[]
+        )
+
+        return entity

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

@@ -0,0 +1,6 @@
+- step-1-8k
+- step-1-32k
+- step-1-128k
+- step-1-256k
+- step-1v-8k
+- step-1v-32k

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

@@ -0,0 +1,328 @@
+import json
+from collections.abc import Generator
+from typing import Optional, Union, cast
+
+import requests
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    ImagePromptMessageContent,
+    PromptMessage,
+    PromptMessageContent,
+    PromptMessageContentType,
+    PromptMessageTool,
+    SystemPromptMessage,
+    ToolPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.entities.model_entities import (
+    AIModelEntity,
+    FetchFrom,
+    ModelFeature,
+    ModelPropertyKey,
+    ModelType,
+    ParameterRule,
+    ParameterType,
+)
+from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
+
+
+class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
+    def _invoke(self, model: str, credentials: dict,
+                prompt_messages: list[PromptMessage], model_parameters: dict,
+                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+                stream: bool = True, user: Optional[str] = None) \
+            -> Union[LLMResult, Generator]:
+        self._add_custom_parameters(credentials)
+        self._add_function_call(model, credentials)
+        user = user[:32] if user else None
+        return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        self._add_custom_parameters(credentials)
+        super().validate_credentials(model, credentials)
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        return AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model, zh_Hans=model),
+            model_type=ModelType.LLM,
+            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL] 
+                if credentials.get('function_calling_type') == 'tool_call' 
+                else [],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', 8000)),
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name='temperature',
+                    use_template='temperature',
+                    label=I18nObject(en_US='Temperature', zh_Hans='温度'),
+                    type=ParameterType.FLOAT,
+                ),
+                ParameterRule(
+                    name='max_tokens',
+                    use_template='max_tokens',
+                    default=512,
+                    min=1,
+                    max=int(credentials.get('max_tokens', 1024)),
+                    label=I18nObject(en_US='Max Tokens', zh_Hans='最大标记'),
+                    type=ParameterType.INT,
+                ),
+                ParameterRule(
+                    name='top_p',
+                    use_template='top_p',
+                    label=I18nObject(en_US='Top P', zh_Hans='Top P'),
+                    type=ParameterType.FLOAT,
+                ),
+            ]
+        )
+
+    def _add_custom_parameters(self, credentials: dict) -> None:
+        credentials['mode'] = 'chat'
+        credentials['endpoint_url'] = 'https://api.stepfun.com/v1'
+
+    def _add_function_call(self, model: str, credentials: dict) -> None:
+        model_schema = self.get_model_schema(model, credentials)
+        if model_schema and {
+            ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL
+        }.intersection(model_schema.features or []):
+            credentials['function_calling_type'] = 'tool_call'
+
+    def _convert_prompt_message_to_dict(self, message: PromptMessage,credentials: Optional[dict] = None) -> dict:
+        """
+        Convert PromptMessage to dict for OpenAI API format
+        """
+        if isinstance(message, UserPromptMessage):
+            message = cast(UserPromptMessage, message)
+            if isinstance(message.content, str):
+                message_dict = {"role": "user", "content": message.content}
+            else:
+                sub_messages = []
+                for message_content in message.content:
+                    if message_content.type == PromptMessageContentType.TEXT:
+                        message_content = cast(PromptMessageContent, message_content)
+                        sub_message_dict = {
+                            "type": "text",
+                            "text": message_content.data
+                        }
+                        sub_messages.append(sub_message_dict)
+                    elif message_content.type == PromptMessageContentType.IMAGE:
+                        message_content = cast(ImagePromptMessageContent, message_content)
+                        sub_message_dict = {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": message_content.data,
+                            }
+                        }
+                        sub_messages.append(sub_message_dict)
+                message_dict = {"role": "user", "content": sub_messages}
+        elif isinstance(message, AssistantPromptMessage):
+            message = cast(AssistantPromptMessage, message)
+            message_dict = {"role": "assistant", "content": message.content}
+            if message.tool_calls:
+                message_dict["tool_calls"] = []
+                for function_call in message.tool_calls:
+                    message_dict["tool_calls"].append({
+                        "id": function_call.id,
+                        "type": function_call.type,
+                        "function": {
+                            "name": function_call.function.name,
+                            "arguments": function_call.function.arguments
+                        }
+                    })
+        elif isinstance(message, ToolPromptMessage):
+            message = cast(ToolPromptMessage, message)
+            message_dict = {"role": "tool", "content": message.content, "tool_call_id": message.tool_call_id}
+        elif isinstance(message, SystemPromptMessage):
+            message = cast(SystemPromptMessage, message)
+            message_dict = {"role": "system", "content": message.content}
+        else:
+            raise ValueError(f"Got unknown type {message}")
+
+        if message.name:
+            message_dict["name"] = message.name
+
+        return message_dict
+
+    def _extract_response_tool_calls(self, response_tool_calls: list[dict]) -> list[AssistantPromptMessage.ToolCall]:
+        """
+        Extract tool calls from response
+
+        :param response_tool_calls: response tool calls
+        :return: list of tool calls
+        """
+        tool_calls = []
+        if response_tool_calls:
+            for response_tool_call in response_tool_calls:
+                function = AssistantPromptMessage.ToolCall.ToolCallFunction(
+                    name=response_tool_call["function"]["name"] if response_tool_call.get("function", {}).get("name") else "",
+                    arguments=response_tool_call["function"]["arguments"] if response_tool_call.get("function", {}).get("arguments") else ""
+                )
+
+                tool_call = AssistantPromptMessage.ToolCall(
+                    id=response_tool_call["id"] if response_tool_call.get("id") else "",
+                    type=response_tool_call["type"] if response_tool_call.get("type") else "",
+                    function=function
+                )
+                tool_calls.append(tool_call)
+
+        return tool_calls
+
+    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
+
+        def create_final_llm_result_chunk(index: int, message: AssistantPromptMessage, finish_reason: str) \
+                -> LLMResultChunk:
+            # calculate num tokens
+            prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
+            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(
+                model=model,
+                prompt_messages=prompt_messages,
+                delta=LLMResultChunkDelta(
+                    index=index,
+                    message=message,
+                    finish_reason=finish_reason,
+                    usage=usage
+                )
+            )
+
+        tools_calls: list[AssistantPromptMessage.ToolCall] = []
+        finish_reason = "Unknown"
+
+        def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
+            def get_tool_call(tool_name: str):
+                if not tool_name:
+                    return tools_calls[-1]
+
+                tool_call = next((tool_call for tool_call in tools_calls if tool_call.function.name == tool_name), None)
+                if tool_call is None:
+                    tool_call = AssistantPromptMessage.ToolCall(
+                        id='',
+                        type='',
+                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tool_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
+
+        for chunk in response.iter_lines(decode_unicode=True, delimiter="\n\n"):
+            if chunk:
+                # ignore sse comments
+                if chunk.startswith(':'):
+                    continue
+                decoded_chunk = chunk.strip().lstrip('data: ').lstrip()
+                chunk_json = None
+                try:
+                    chunk_json = json.loads(decoded_chunk)
+                # stream ended
+                except json.JSONDecodeError as e:
+                    yield create_final_llm_result_chunk(
+                        index=chunk_index + 1,
+                        message=AssistantPromptMessage(content=""),
+                        finish_reason="Non-JSON encountered."
+                    )
+                    break
+                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')
+                chunk_index += 1
+
+                if 'delta' in choice:
+                    delta = choice['delta']
+                    delta_content = delta.get('content')
+
+                    assistant_message_tool_calls = delta.get('tool_calls', None)
+                    # 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
+
+                    # transform assistant message to prompt message
+                    assistant_prompt_message = AssistantPromptMessage(
+                        content=delta_content,
+                        tool_calls=tool_calls if assistant_message_tool_calls else []
+                    )
+
+                    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
+
+                # check payload indicator for completion
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=chunk_index,
+                        message=assistant_prompt_message,
+                    )
+                )
+
+            chunk_index += 1
+        
+        if tools_calls:
+            yield LLMResultChunk(
+                model=model,
+                prompt_messages=prompt_messages,
+                delta=LLMResultChunkDelta(
+                    index=chunk_index,
+                    message=AssistantPromptMessage(
+                        tool_calls=tools_calls,
+                        content=""
+                    ),
+                )
+            )
+
+        yield create_final_llm_result_chunk(
+            index=chunk_index,
+            message=AssistantPromptMessage(content=""),
+            finish_reason=finish_reason
+        )

api/core/model_runtime/model_providers/stepfun/llm/step-1-128k.yaml

@@ -0,0 +1,25 @@
+model: step-1-128k
+label:
+  zh_Hans: step-1-128k
+  en_US: step-1-128k
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 128000
+pricing:
+  input: '0.04'
+  output: '0.20'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/stepfun/llm/step-1-256k.yaml

@@ -0,0 +1,25 @@
+model: step-1-256k
+label:
+  zh_Hans: step-1-256k
+  en_US: step-1-256k
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 256000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 256000
+pricing:
+  input: '0.095'
+  output: '0.300'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/stepfun/llm/step-1-32k.yaml

@@ -0,0 +1,28 @@
+model: step-1-32k
+label:
+  zh_Hans: step-1-32k
+  en_US: step-1-32k
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 32000
+pricing:
+  input: '0.015'
+  output: '0.070'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/stepfun/llm/step-1-8k.yaml

@@ -0,0 +1,28 @@
+model: step-1-8k
+label:
+  zh_Hans: step-1-8k
+  en_US: step-1-8k
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - multi-tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 8000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8000
+pricing:
+  input: '0.005'
+  output: '0.020'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/stepfun/llm/step-1v-32k.yaml

@@ -0,0 +1,25 @@
+model: step-1v-32k
+label:
+  zh_Hans: step-1v-32k
+  en_US: step-1v-32k
+model_type: llm
+features:
+  - vision
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 32000
+pricing:
+  input: '0.015'
+  output: '0.070'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/stepfun/llm/step-1v-8k.yaml

@@ -0,0 +1,25 @@
+model: step-1v-8k
+label:
+  zh_Hans: step-1v-8k
+  en_US: step-1v-8k
+model_type: llm
+features:
+  - vision
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.005'
+  output: '0.020'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/stepfun/stepfun.py

@@ -0,0 +1,30 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class StepfunProvider(ModelProvider):
+
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.LLM)
+
+            model_instance.validate_credentials(
+                model='step-1-8k',
+                credentials=credentials
+            )
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            raise ex

api/core/model_runtime/model_providers/stepfun/stepfun.yaml

@@ -0,0 +1,81 @@
+provider: stepfun
+label:
+  zh_Hans: 阶跃星辰
+  en_US: Stepfun
+description:
+  en_US: Models provided by stepfun, such as step-1-8k, step-1-32k、step-1v-8k、step-1v-32k, step-1-128k and step-1-256k
+  zh_Hans: 阶跃星辰提供的模型，例如 step-1-8k、step-1-32k、step-1v-8k、step-1v-32k、step-1-128k 和 step-1-256k。
+icon_small:
+  en_US: icon_s_en.png
+icon_large:
+  en_US: icon_l_en.png
+background: "#FFFFFF"
+help:
+  title:
+    en_US: Get your API Key from stepfun
+    zh_Hans: 从 stepfun 获取 API Key
+  url:
+    en_US: https://platform.stepfun.com/interface-key
+supported_model_types:
+  - llm
+configurate_methods:
+  - predefined-model
+  - customizable-model
+provider_credential_schema:
+  credential_form_schemas:
+    - variable: api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key
+model_credential_schema:
+  model:
+    label:
+      en_US: Model Name
+      zh_Hans: 模型名称
+    placeholder:
+      en_US: Enter your model name
+      zh_Hans: 输入模型名称
+  credential_form_schemas:
+    - variable: api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key
+    - variable: context_size
+      label:
+        zh_Hans: 模型上下文长度
+        en_US: Model context size
+      required: true
+      type: text-input
+      default: '8192'
+      placeholder:
+        zh_Hans: 在此输入您的模型上下文长度
+        en_US: Enter your Model context size
+    - variable: max_tokens
+      label:
+        zh_Hans: 最大 token 上限
+        en_US: Upper bound for max tokens
+      default: '8192'
+      type: text-input
+    - variable: function_calling_type
+      label:
+        en_US: Function calling
+      type: select
+      required: false
+      default: no_call
+      options:
+        - value: no_call
+          label:
+            en_US: Not supported
+            zh_Hans: 不支持
+        - value: tool_call
+          label:
+            en_US: Tool Call
+            zh_Hans: Tool Call

api/core/model_runtime/model_providers/wenxin/llm/ernie-3.5-8k-0205.yaml

@@ -35,3 +35,4 @@ parameter_rules:
       zh_Hans: 禁用模型自行进行外部搜索。
       en_US: Disable the model to perform external search.
     required: false
+deprecated: true

api/core/model_runtime/model_providers/wenxin/llm/ernie-4.0-8k-latest.yaml

@@ -1,4 +1,4 @@
-model: ernie-4.0-8k-Latest
+model: ernie-4.0-8k-latest
 label:
   en_US: Ernie-4.0-8K-Latest
 model_type: llm

api/core/model_runtime/model_providers/wenxin/llm/ernie-4.0-turbo-8k.yaml

@@ -0,0 +1,40 @@
+model: ernie-4.0-turbo-8k
+label:
+  en_US: Ernie-4.0-turbo-8K
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0.1
+    max: 1.0
+    default: 0.8
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 2
+    max: 2048
+  - name: presence_penalty
+    use_template: presence_penalty
+    default: 1.0
+    min: 1.0
+    max: 2.0
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: response_format
+    use_template: response_format
+  - name: disable_search
+    label:
+      zh_Hans: 禁用搜索
+      en_US: Disable Search
+    type: boolean
+    help:
+      zh_Hans: 禁用模型自行进行外部搜索。
+      en_US: Disable the model to perform external search.
+    required: false

api/core/model_runtime/model_providers/wenxin/llm/ernie-character-8k-0321.yaml

@@ -28,3 +28,4 @@ parameter_rules:
     default: 1.0
     min: 1.0
     max: 2.0
+deprecated: true

api/core/model_runtime/model_providers/wenxin/llm/ernie-character-8k.yaml

@@ -0,0 +1,30 @@
+model: ernie-character-8k-0321
+label:
+  en_US: ERNIE-Character-8K
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0.1
+    max: 1.0
+    default: 0.95
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1.0
+    default: 0.7
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 2
+    max: 1024
+  - name: presence_penalty
+    use_template: presence_penalty
+    default: 1.0
+    min: 1.0
+    max: 2.0

api/core/model_runtime/model_providers/wenxin/llm/ernie-lite-8k-0308.yaml

@@ -28,3 +28,4 @@ parameter_rules:
     default: 1.0
     min: 1.0
     max: 2.0
+deprecated: true

api/core/model_runtime/model_providers/wenxin/llm/ernie-lite-8k-0922.yaml

@@ -28,3 +28,4 @@ parameter_rules:
     default: 1.0
     min: 1.0
     max: 2.0
+deprecated: true

api/core/model_runtime/model_providers/wenxin/llm/ernie_bot.py

@@ -97,6 +97,7 @@ class BaiduAccessToken:
             baidu_access_tokens_lock.release()
             return token
 
+
 class ErnieMessage:
     class Role(Enum):
         USER = 'user'
@@ -137,7 +138,9 @@ class ErnieBotModel:
         'ernie-speed-appbuilder': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ai_apaas',
         'ernie-lite-8k-0922': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant',
         'ernie-lite-8k-0308': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-lite-8k',
+        'ernie-character-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-char-8k',
         'ernie-character-8k-0321': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-char-8k',
+        'ernie-4.0-tutbo-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k',
         'ernie-4.0-tutbo-8k-preview': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k-preview',
     }
 
@@ -149,7 +152,8 @@ class ErnieBotModel:
         'ernie-3.5-8k-1222',
         'ernie-3.5-4k-0205',
         'ernie-3.5-128k',
-        'ernie-4.0-8k'
+        'ernie-4.0-8k',
+        'ernie-4.0-turbo-8k',
         'ernie-4.0-turbo-8k-preview'
     ]