optimize the unused dataset clean logic

.github/DISCUSSION_TEMPLATE/general.yml

@@ -1,24 +0,0 @@
-title: "General Discussion"
-body:
-  - type: checkboxes
-    attributes:
-      label: Self Checks
-      description: "To make sure we get to you in time, please check the following :)"
-      options:
-        - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
-          required: true
-        - label: I confirm that I am using English to submit this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
-          required: true
-        - label: "请务必使用英文提交 Issue，否则会被关闭。谢谢！:）"
-          required: true
-        - label: "Please do not modify this template :) and fill in all the required fields."
-          required: true
-  - type: textarea
-    attributes:
-      label: Content
-      placeholder: Please describe the content you would like to discuss.
-    validations:
-      required: true
-  - type: markdown
-    attributes:
-      value: Please limit one request per issue.

.github/DISCUSSION_TEMPLATE/help.yml

@@ -1,30 +0,0 @@
-title: "Help"
-body:
-  - type: checkboxes
-    attributes:
-      label: Self Checks
-      description: "To make sure we get to you in time, please check the following :)"
-      options:
-        - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
-          required: true
-        - label: I confirm that I am using English to submit this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
-          required: true
-        - label: "请务必使用英文提交 Issue，否则会被关闭。谢谢！:）"
-          required: true
-        - label: "Please do not modify this template :) and fill in all the required fields."
-          required: true
-  - type: textarea
-    attributes:
-      label: 1. Is this request related to a challenge you're experiencing? Tell me about your story.
-      placeholder: Please describe the specific scenario or problem you're facing as clearly as possible. For instance "I was trying to use [feature] for [specific task], and [what happened]... It was frustrating because...."
-    validations:
-      required: true
-  - type: textarea
-    attributes:
-      label: 2. Additional context or comments
-      placeholder: (Any other information, comments, documentations, links, or screenshots that would provide more clarity. This is the place to add anything else not covered above.)
-    validations:
-      required: false
-  - type: markdown
-    attributes:
-      value: Please limit one request per issue.

.github/DISCUSSION_TEMPLATE/suggestion.yml

@@ -1,37 +0,0 @@
-title: Suggestions for New Features
-body:
-  - type: checkboxes
-    attributes:
-      label: Self Checks
-      description: "To make sure we get to you in time, please check the following :)"
-      options:
-        - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
-          required: true
-        - label: I confirm that I am using English to submit this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
-          required: true
-        - label: "请务必使用英文提交 Issue，否则会被关闭。谢谢！:）"
-          required: true
-        - label: "Please do not modify this template :) and fill in all the required fields."
-          required: true
-  - type: textarea
-    attributes:
-      label: 1. Is this request related to a challenge you're experiencing? Tell me about your story.
-      placeholder: Please describe the specific scenario or problem you're facing as clearly as possible. For instance "I was trying to use [feature] for [specific task], and [what happened]... It was frustrating because...."
-    validations:
-      required: true
-  - type: textarea
-    attributes:
-      label: 2. Additional context or comments
-      placeholder: (Any other information, comments, documentations, links, or screenshots that would provide more clarity. This is the place to add anything else not covered above.)
-    validations:
-      required: false
-  - type: checkboxes
-    attributes:
-      label: 3. Can you help us with this feature?
-      description: Let us know! This is not a commitment, but a starting point for collaboration.
-      options:
-        - label: I am interested in contributing to this feature.
-          required: false
-  - type: markdown
-    attributes:
-      value: Please limit one request per issue.

.github/workflows/api-tests.yml

@@ -89,5 +89,6 @@ jobs:
             pgvecto-rs
             pgvector
             chroma
+            myscale
       - name: Test Vector Stores
         run: poetry run -C api bash dev/pytest/pytest_vdb.sh

.gitignore

@@ -174,6 +174,5 @@ 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/learn-more/faq/self-host-faq) for a list of common issues and steps to troubleshoot.
+Check the [installation FAQ](https://docs.dify.ai/getting-started/faq/install-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/v/zh-hans/learn-more/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/getting-started/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/v/japanese/learn-more/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/getting-started/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
 

README.md

@@ -216,6 +216,7 @@ At the same time, please consider supporting Dify by sharing it on social media
 
 * [Github Discussion](https://github.com/langgenius/dify/discussions). Best for: sharing feedback and asking questions.
 * [GitHub Issues](https://github.com/langgenius/dify/issues). Best for: bugs you encounter using Dify.AI, and feature proposals. See our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
+* [Email](mailto:support@dify.ai?subject=[GitHub]Questions%20About%20Dify). Best for: questions you have about using Dify.AI.
 * [Discord](https://discord.gg/FngNHpbcY7). Best for: sharing your applications and hanging out with the community.
 * [Twitter](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.
 

README_AR.md

@@ -199,6 +199,7 @@ docker compose up -d
 ## المجتمع والاتصال
 * [مناقشة Github](https://github.com/langgenius/dify/discussions). الأفضل لـ: مشاركة التعليقات وطرح الأسئلة.
 * [المشكلات على GitHub](https://github.com/langgenius/dify/issues). الأفضل لـ: الأخطاء التي تواجهها في استخدام Dify.AI، واقتراحات الميزات. انظر [دليل المساهمة](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
+* [البريد الإلكتروني](mailto:support@dify.ai?subject=[GitHub]Questions%20About%20Dify). الأفضل لـ: الأسئلة التي تتعلق باستخدام Dify.AI.
 * [Discord](https://discord.gg/FngNHpbcY7). الأفضل لـ: مشاركة تطبيقاتك والترفيه مع المجتمع.
 * [تويتر](https://twitter.com/dify_ai). الأفضل لـ: مشاركة تطبيقاتك والترفيه مع المجتمع.
 

README_ES.md

@@ -224,6 +224,7 @@ Al mismo tiempo, considera apoyar a Dify compartiéndolo en redes sociales y en
 
 * [Discusión en GitHub](https://github.com/langgenius/dify/discussions). Lo mejor para: compartir comentarios y hacer preguntas.
 * [Reporte de problemas en GitHub](https://github.com/langgenius/dify/issues). Lo mejor para: errores que encuentres usando Dify.AI y propuestas de características. Consulta nuestra [Guía de contribución](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
+* [Correo electrónico](mailto:support@dify.ai?subject=[GitHub]Questions%20About%20Dify). Lo mejor para: preguntas que tengas sobre el uso de Dify.AI.
 * [Discord](https://discord.gg/FngNHpbcY7). Lo mejor para: compartir tus aplicaciones y pasar el rato con la comunidad.
 * [Twitter](https://twitter.com/dify_ai). Lo mejor para: compartir tus aplicaciones y pasar el rato con la comunidad.
 

README_FR.md

@@ -222,6 +222,7 @@ Dans le même temps, veuillez envisager de soutenir Dify en le partageant sur le
 
 * [Discussion GitHub](https://github.com/langgenius/dify/discussions). Meilleur pour: partager des commentaires et poser des questions.
 * [Problèmes GitHub](https://github.com/langgenius/dify/issues). Meilleur pour: les bogues que vous rencontrez en utilisant Dify.AI et les propositions de fonctionnalités. Consultez notre [Guide de contribution](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
+* [E-mail](mailto:support@dify.ai?subject=[GitHub]Questions%20About%20Dify). Meilleur pour: les questions que vous avez sur l'utilisation de Dify.AI.
 * [Discord](https://discord.gg/FngNHpbcY7). Meilleur pour: partager vos applications et passer du temps avec la communauté.
 * [Twitter](https://twitter.com/dify_ai). Meilleur pour: partager vos applications et passer du temps avec la communauté.
 

README_JA.md

@@ -221,6 +221,7 @@ docker compose up -d
 
 * [Github Discussion](https://github.com/langgenius/dify/discussions). 主に: フィードバックの共有や質問。
 * [GitHub Issues](https://github.com/langgenius/dify/issues). 主に: Dify.AIを使用する際に発生するエラーや問題については、[貢献ガイド](CONTRIBUTING_JA.md)を参照してください
+* [Email](mailto:support@dify.ai?subject=[GitHub]Questions%20About%20Dify). 主に: Dify.AIの使用に関する質問。
 * [Discord](https://discord.gg/FngNHpbcY7). 主に: アプリケーションの共有やコミュニティとの交流。
 * [Twitter](https://twitter.com/dify_ai). 主に: アプリケーションの共有やコミュニティとの交流。
 
@@ -238,7 +239,7 @@ docker compose up -d
     <td>無料の30分間のミーティングをスケジュール</td>
   </tr>
   <tr>
-    <td><a href='https://github.com/langgenius/dify/issues'>技術サポート</a></td>
+    <td><a href='mailto:support@dify.ai?subject=[GitHub]Technical%20Support'>技術サポート</a></td>
     <td>技術的な問題やサポートに関する質問</td>
   </tr>
   <tr>

README_KL.md

@@ -224,6 +224,7 @@ At the same time, please consider supporting Dify by sharing it on social media
 
 ). Best for: sharing feedback and asking questions.
 * [GitHub Issues](https://github.com/langgenius/dify/issues). Best for: bugs you encounter using Dify.AI, and feature proposals. See our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
+* [Email](mailto:support@dify.ai?subject=[GitHub]Questions%20About%20Dify). Best for: questions you have about using Dify.AI.
 * [Discord](https://discord.gg/FngNHpbcY7). Best for: sharing your applications and hanging out with the community.
 * [Twitter](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.
 

README_KR.md

@@ -214,6 +214,7 @@ Dify를 Kubernetes에 배포하고 프리미엄 스케일링 설정을 구성했
 
 * [Github 토론](https://github.com/langgenius/dify/discussions). 피드백 공유 및 질문하기에 적합합니다.
 * [GitHub 이슈](https://github.com/langgenius/dify/issues). Dify.AI 사용 중 발견한 버그와 기능 제안에 적합합니다. [기여 가이드](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)를 참조하세요. 
+* [이메일](mailto:support@dify.ai?subject=[GitHub]Questions%20About%20Dify). Dify.AI 사용에 대한 질문하기에 적합합니다.
 * [디스코드](https://discord.gg/FngNHpbcY7). 애플리케이션 공유 및 커뮤니티와 소통하기에 적합합니다.
 * [트위터](https://twitter.com/dify_ai). 애플리케이션 공유 및 커뮤니티와 소통하기에 적합합니다.
 

api/app.py

@@ -1,5 +1,7 @@
 import os
 
+from configs import dify_config
+
 if os.environ.get("DEBUG", "false").lower() != 'true':
     from gevent import monkey
 
@@ -21,9 +23,7 @@ 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
@@ -181,10 +181,7 @@ def load_user_from_request(request_from_flask_login):
     decoded = PassportService().verify(auth_token)
     user_id = decoded.get('user_id')
 
-    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
+    return AccountService.load_logged_in_account(account_id=user_id, token=auth_token)
 
 
 @login_manager.unauthorized_handler

api/configs/feature/__init__.py

@@ -406,6 +406,7 @@ class DataSetConfig(BaseSettings):
         default=False,
     )
 
+
 class WorkspaceConfig(BaseSettings):
     """
     Workspace configs

api/configs/feature/hosted_service/__init__.py

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

api/configs/middleware/vdb/myscale_config.py

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

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description='Dify version',
-        default='0.6.15',
+        default='0.6.14',
     )
 
     COMMIT_SHA: str = Field(

api/constants/__init__.py

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

api/contexts/__init__.py

@@ -1,3 +0,0 @@
-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, include_secret=True)
+        data = AppDslService.export_dsl(app_model=app_model)
         app = AppDslService.import_and_create_new_app(
             tenant_id=current_user.current_tenant_id,
             data=data,
@@ -234,13 +234,8 @@ 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, include_secret=args['include_secret'])
+            "data": AppDslService.export_dsl(app_model=app_model)
         }
 
 

api/controllers/console/app/generator.py

@@ -22,19 +22,17 @@ class RuleGenerateApi(Resource):
     @account_initialization_required
     def post(self):
         parser = reqparse.RequestParser()
-        parser.add_argument('instruction', type=str, required=True, nullable=False, location='json')
-        parser.add_argument('model_config', type=dict, required=True, nullable=False, location='json')
-        parser.add_argument('no_variable', type=bool, required=True, default=False, location='json')
+        parser.add_argument('audiences', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('hoping_to_solve', type=str, required=True, nullable=False, location='json')
         args = parser.parse_args()
 
         account = current_user
 
         try:
             rules = LLMGenerator.generate_rule_config(
-                tenant_id=account.current_tenant_id,
-                instruction=args['instruction'],
-                model_config=args['model_config'],
-                no_variable=args['no_variable']
+                account.current_tenant_id,
+                args['audiences'],
+                args['hoping_to_solve']
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)

api/controllers/console/app/statistic.py

@@ -281,7 +281,7 @@ class UserSatisfactionRateStatistic(Resource):
                         SELECT date(DATE_TRUNC('day', m.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
                             COUNT(m.id) as message_count, COUNT(mf.id) as feedback_count 
                             FROM messages m
-                            LEFT JOIN message_feedbacks mf on mf.message_id=m.id and mf.rating='like'
+                            LEFT JOIN message_feedbacks mf on mf.message_id=m.id
                             WHERE m.app_id = :app_id 
                         '''
         arg_dict = {'tz': account.timezone, 'app_id': app_model.id}

api/controllers/console/app/workflow.py

@@ -13,7 +13,6 @@ 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
@@ -42,7 +41,7 @@ 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)
@@ -65,15 +64,13 @@ 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:
@@ -87,8 +84,7 @@ class DraftWorkflowApi(Resource):
                 args = {
                     'graph': data.get('graph'),
                     'features': data.get('features'),
-                    'hash': data.get('hash'),
-                    'environment_variables': data.get('environment_variables')
+                    'hash': data.get('hash')
                 }
             except json.JSONDecodeError:
                 return {'message': 'Invalid JSON data'}, 400
@@ -98,15 +94,12 @@ 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['graph'],
-                features=args['features'],
+                graph=args.get('graph'),
+                features=args.get('features'),
                 unique_hash=args.get('hash'),
-                account=current_user,
-                environment_variables=environment_variables,
+                account=current_user
             )
         except WorkflowHashNotEqualError:
             raise DraftWorkflowNotSync()

api/controllers/console/datasets/datasets.py

@@ -1,11 +1,10 @@
 import flask_restful
-from flask import request
+from flask import current_app, 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
@@ -531,7 +530,7 @@ class DatasetApiBaseUrlApi(Resource):
     @account_initialization_required
     def get(self):
         return {
-            'api_base_url': (dify_config.SERVICE_API_URL if dify_config.SERVICE_API_URL
+            'api_base_url': (current_app.config['SERVICE_API_URL'] if current_app.config['SERVICE_API_URL']
                              else request.host_url.rstrip('/')) + '/v1'
         }
 
@@ -541,15 +540,15 @@ class DatasetRetrievalSettingApi(Resource):
     @login_required
     @account_initialization_required
     def get(self):
-        vector_type = dify_config.VECTOR_STORE
+        vector_type = current_app.config['VECTOR_STORE']
         match vector_type:
-            case VectorType.MILVUS | VectorType.RELYT | VectorType.PGVECTOR | VectorType.TIDB_VECTOR | VectorType.CHROMA | VectorType.TENCENT:
+            case VectorType.MILVUS | VectorType.RELYT | VectorType.PGVECTOR | VectorType.TIDB_VECTOR | VectorType.CHROMA | VectorType.TENCENT | VectorType.ORACLE:
                 return {
                     'retrieval_method': [
                         RetrievalMethod.SEMANTIC_SEARCH.value
                     ]
                 }
-            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH | VectorType.ANALYTICDB | VectorType.MYSCALE | VectorType.ORACLE:
+            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH | VectorType.ANALYTICDB | VectorType.MYSCALE:
                 return {
                     'retrieval_method': [
                         RetrievalMethod.SEMANTIC_SEARCH.value,
@@ -567,13 +566,13 @@ class DatasetRetrievalSettingMockApi(Resource):
     @account_initialization_required
     def get(self, vector_type):
         match vector_type:
-            case VectorType.MILVUS | VectorType.RELYT | VectorType.PGVECTOR | VectorType.TIDB_VECTOR | VectorType.CHROMA | VectorType.TENCENT:
+            case VectorType.MILVUS | VectorType.RELYT | VectorType.PGVECTOR | VectorType.TIDB_VECTOR | VectorType.CHROMA | VectorType.TENCENT | VectorType.ORACLE:
                 return {
                     'retrieval_method': [
                         RetrievalMethod.SEMANTIC_SEARCH.value
                     ]
                 }
-            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH| VectorType.ANALYTICDB | VectorType.MYSCALE | VectorType.ORACLE:
+            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH| VectorType.ANALYTICDB | VectorType.MYSCALE:
                 return {
                     'retrieval_method': [
                         RetrievalMethod.SEMANTIC_SEARCH.value,

api/controllers/console/datasets/datasets_segments.py

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

api/controllers/console/datasets/file.py

@@ -1,9 +1,8 @@
-from flask import request
+from flask import current_app, 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,
@@ -27,9 +26,9 @@ class FileApi(Resource):
     @account_initialization_required
     @marshal_with(upload_config_fields)
     def get(self):
-        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
+        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")
         return {
             'file_size_limit': file_size_limit,
             'batch_count_limit': batch_count_limit,
@@ -77,7 +76,7 @@ class FileSupportTypeApi(Resource):
     @login_required
     @account_initialization_required
     def get(self):
-        etl_type = dify_config.ETL_TYPE
+        etl_type = current_app.config['ETL_TYPE']
         allowed_extensions = UNSTRUCTURED_ALLOWED_EXTENSIONS if etl_type == 'Unstructured' else ALLOWED_EXTENSIONS
         return {'allowed_extensions': allowed_extensions}
 

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': dify_config.UPLOAD_IMAGE_FILE_SIZE_LIMIT
+                'image_file_size_limit': current_app.config.get('UPLOAD_IMAGE_FILE_SIZE_LIMIT')
             }
         }
 

api/controllers/console/init_validate.py

@@ -1,9 +1,8 @@
 import os
 
-from flask import session
+from flask import current_app, 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
@@ -41,7 +40,7 @@ class InitValidateAPI(Resource):
         return {'result': 'success'}, 201
 
 def get_init_validate_status():
-    if dify_config.EDITION == 'SELF_HOSTED':
+    if current_app.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

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

api/controllers/console/version.py

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

api/controllers/console/workspace/account.py

@@ -1,11 +1,10 @@
 import datetime
 
 import pytz
-from flask import request
+from flask import current_app, 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
@@ -37,7 +36,7 @@ class AccountInitApi(Resource):
 
         parser = reqparse.RequestParser()
 
-        if dify_config.EDITION == 'CLOUD':
+        if current_app.config['EDITION'] == 'CLOUD':
             parser.add_argument('invitation_code', type=str, location='json')
 
         parser.add_argument(
@@ -46,7 +45,7 @@ class AccountInitApi(Resource):
                             required=True, location='json')
         args = parser.parse_args()
 
-        if dify_config.EDITION == 'CLOUD':
+        if current_app.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 = dify_config.CONSOLE_WEB_URL
+        console_web_url = current_app.config.get("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 = db.session.get(Account, str(member_id))
+        member = Account.query.get(str(member_id))
         if not member:
             abort(404)
 

api/controllers/console/workspace/tool_providers.py

@@ -1,11 +1,10 @@
 import io
 
-from flask import send_file
+from flask import current_app, 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
@@ -105,7 +104,7 @@ class ToolBuiltinProviderIconApi(Resource):
     @setup_required
     def get(self, provider):
         icon_bytes, mimetype = BuiltinToolManageService.get_builtin_tool_provider_icon(provider)
-        icon_cache_max_age = dify_config.TOOL_ICON_CACHE_MAX_AGE
+        icon_cache_max_age = current_app.config.get('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

@@ -1,10 +1,9 @@
 import json
 from functools import wraps
 
-from flask import abort, request
+from flask import abort, current_app, 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
@@ -27,7 +26,7 @@ def account_initialization_required(view):
 def only_edition_cloud(view):
     @wraps(view)
     def decorated(*args, **kwargs):
-        if dify_config.EDITION != 'CLOUD':
+        if current_app.config['EDITION'] != 'CLOUD':
             abort(404)
 
         return view(*args, **kwargs)
@@ -38,7 +37,7 @@ def only_edition_cloud(view):
 def only_edition_self_hosted(view):
     @wraps(view)
     def decorated(*args, **kwargs):
-        if dify_config.EDITION != 'SELF_HOSTED':
+        if current_app.config['EDITION'] != 'SELF_HOSTED':
             abort(404)
 
         return view(*args, **kwargs)

api/controllers/service_api/app/workflow.py

@@ -1,6 +1,6 @@
 import logging
 
-from flask_restful import Resource, fields, marshal_with, reqparse
+from flask_restful import Resource, reqparse
 from werkzeug.exceptions import InternalServerError
 
 from controllers.service_api import api
@@ -21,43 +21,14 @@ 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):
         """
@@ -117,5 +88,5 @@ class WorkflowTaskStopApi(Resource):
         }
 
 
-api.add_resource(WorkflowRunApi, '/workflows/run/<string:workflow_id>', '/workflows/run')
+api.add_resource(WorkflowRunApi, '/workflows/run')
 api.add_resource(WorkflowTaskStopApi, '/workflows/tasks/<string:task_id>/stop')

api/core/agent/fc_agent_runner.py

@@ -342,14 +342,10 @@ 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,
-                args,
+                json.loads(prompt_message.function.arguments),
             ))
 
         return tool_calls
@@ -363,14 +359,10 @@ 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,
-                args,
+                json.loads(prompt_message.function.arguments),
             ))
 
         return tool_calls

api/core/app/app_config/base_app_config_manager.py

@@ -1,7 +1,6 @@
-from collections.abc import Mapping
-from typing import Any
+from typing import Optional, Union
 
-from core.app.app_config.entities import AppAdditionalFeatures
+from core.app.app_config.entities import AppAdditionalFeatures, EasyUIBasedAppModelConfigFrom
 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
@@ -11,19 +10,37 @@ 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
+from models.model import AppMode, AppModelConfig
 
 
 class BaseAppConfigManager:
+
     @classmethod
-    def convert_features(cls, config_dict: Mapping[str, Any], app_mode: AppMode) -> AppAdditionalFeatures:
+    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:
         """
         Convert app config to app model config
 
         :param config_dict: app config
         :param app_mode: app mode
         """
-        config_dict = dict(config_dict.items())
+        config_dict = config_dict.copy()
 
         additional_features = AppAdditionalFeatures()
         additional_features.show_retrieve_source = RetrievalResourceConfigManager.convert(

api/core/app/app_config/easy_ui_based_app/dataset/manager.py

@@ -62,12 +62,7 @@ class DatasetConfigManager:
             return None
 
         # dataset configs
-        if 'dataset_configs' in config and config.get('dataset_configs'):
-            dataset_configs = config.get('dataset_configs')
-        else:
-            dataset_configs = {
-                'retrieval_model': 'multiple'
-            }
+        dataset_configs = config.get('dataset_configs', {'retrieval_model': 'single'})
         query_variable = config.get('dataset_query_variable')
 
         if dataset_configs['retrieval_model'] == 'single':
@@ -88,10 +83,9 @@ class DatasetConfigManager:
                     retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
                         dataset_configs['retrieval_model']
                     ),
-                    top_k=dataset_configs.get('top_k', 4),
+                    top_k=dataset_configs.get('top_k'),
                     score_threshold=dataset_configs.get('score_threshold'),
-                    reranking_model=dataset_configs.get('reranking_model'),
-                    weights=dataset_configs.get('weights')
+                    reranking_model=dataset_configs.get('reranking_model')
                 )
             )
 

api/core/app/app_config/entities.py

@@ -159,11 +159,7 @@ class DatasetRetrieveConfigEntity(BaseModel):
     retrieve_strategy: RetrieveStrategy
     top_k: Optional[int] = None
     score_threshold: Optional[float] = None
-    rerank_mode: Optional[str] = 'reranking_model'
     reranking_model: Optional[dict] = None
-    weights: Optional[dict] = None
-
-
 
 
 class DatasetEntity(BaseModel):

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

@@ -1,12 +1,11 @@
-from collections.abc import Mapping
-from typing import Any, Optional
+from typing import Optional
 
 from core.app.app_config.entities import FileExtraConfig
 
 
 class FileUploadConfigManager:
     @classmethod
-    def convert(cls, config: Mapping[str, Any], is_vision: bool = True) -> Optional[FileExtraConfig]:
+    def convert(cls, config: dict, 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):
+    def convert(cls, config: dict) -> bool:
         """
         Convert model config to model config
 
         :param config: model config args
         """
-        text_to_speech = None
+        text_to_speech = False
         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

@@ -1,4 +1,3 @@
-import contextvars
 import logging
 import os
 import threading
@@ -9,7 +8,6 @@ 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
@@ -109,7 +107,6 @@ 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,
@@ -176,7 +173,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                 inputs=args['inputs']
             )
         )
-        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
         return self._generate(
             app_model=app_model,
@@ -229,8 +225,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             'queue_manager': queue_manager,
             'conversation_id': conversation.id,
             'message_id': message.id,
-            'user': user,
-            'context': contextvars.copy_context()
         })
 
         worker_thread.start()
@@ -255,9 +249,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                          application_generate_entity: AdvancedChatAppGenerateEntity,
                          queue_manager: AppQueueManager,
                          conversation_id: str,
-                         message_id: str,
-                         user: Account,
-                         context: contextvars.Context) -> None:
+                         message_id: str) -> None:
         """
         Generate worker in a new thread.
         :param flask_app: Flask app
@@ -267,8 +259,6 @@ 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

@@ -1,8 +1,7 @@
 import logging
 import os
 import time
-from collections.abc import Mapping
-from typing import Any, Optional, cast
+from typing import 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
@@ -15,7 +14,6 @@ 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
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
@@ -89,7 +87,7 @@ class AdvancedChatAppRunner(AppRunner):
 
         db.session.close()
 
-        workflow_callbacks: list[WorkflowCallback] = [WorkflowEventTriggerCallback(
+        workflow_callbacks = [WorkflowEventTriggerCallback(
             queue_manager=queue_manager,
             workflow=workflow
         )]
@@ -163,7 +161,7 @@ class AdvancedChatAppRunner(AppRunner):
             self, queue_manager: AppQueueManager,
             app_record: App,
             app_generate_entity: AdvancedChatAppGenerateEntity,
-            inputs: Mapping[str, Any],
+            inputs: dict,
             query: str,
             message_id: str
     ) -> bool:

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

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

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

@@ -118,7 +118,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         self._stream_generate_routes = self._get_stream_generate_routes()
         self._conversation_name_generate_thread = None
 
-    def process(self):
+    def process(self) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
         """
         Process generate task pipeline.
         :return:
@@ -141,7 +141,8 @@ 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:
@@ -171,7 +172,8 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
         raise Exception('Queue listening stopped unexpectedly.')
 
-    def _to_stream_response(self, generator: Generator[StreamResponse, None, None]) -> Generator[ChatbotAppStreamResponse, Any, None]:
+    def _to_stream_response(self, generator: Generator[StreamResponse, None, None]) \
+            -> Generator[ChatbotAppStreamResponse, None, 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 WorkflowCallback
+from core.workflow.callbacks.base_workflow_callback import BaseWorkflowCallback
 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(WorkflowCallback):
+class WorkflowEventTriggerCallback(BaseWorkflowCallback):
 
     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 Any, Union
+from typing import Union
 
 from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.task_entities import AppBlockingResponse, AppStreamResponse
@@ -15,41 +15,44 @@ class AppGenerateResponseConverter(ABC):
     @classmethod
     def convert(cls, response: Union[
         AppBlockingResponse,
-        Generator[AppStreamResponse, Any, None]
-    ], invoke_from: InvokeFrom):
+        Generator[AppStreamResponse, None, None]
+    ], invoke_from: InvokeFrom) -> Union[
+        dict,
+        Generator[str, None, None]
+    ]:
         if invoke_from in [InvokeFrom.DEBUGGER, InvokeFrom.SERVICE_API]:
-            if isinstance(response, AppBlockingResponse):
+            if isinstance(response, cls._blocking_response_type):
                 return cls.convert_blocking_full_response(response)
             else:
-                def _generate_full_response() -> Generator[str, Any, None]:
+                def _generate():
                     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_full_response()
+                return _generate()
         else:
-            if isinstance(response, AppBlockingResponse):
+            if isinstance(response, cls._blocking_response_type):
                 return cls.convert_blocking_simple_response(response)
             else:
-                def _generate_simple_response() -> Generator[str, Any, None]:
+                def _generate():
                     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_simple_response()
+                return _generate()
 
     @classmethod
     @abstractmethod
-    def convert_blocking_full_response(cls, blocking_response: AppBlockingResponse) -> dict[str, Any]:
+    def convert_blocking_full_response(cls, blocking_response: AppBlockingResponse) -> dict:
         raise NotImplementedError
 
     @classmethod
     @abstractmethod
-    def convert_blocking_simple_response(cls, blocking_response: AppBlockingResponse) -> dict[str, Any]:
+    def convert_blocking_simple_response(cls, blocking_response: AppBlockingResponse) -> dict:
         raise NotImplementedError
 
     @classmethod
@@ -65,7 +68,7 @@ class AppGenerateResponseConverter(ABC):
         raise NotImplementedError
 
     @classmethod
-    def _get_simple_metadata(cls, metadata: dict[str, Any]):
+    def _get_simple_metadata(cls, metadata: dict) -> dict:
         """
         Get simple metadata.
         :param metadata: metadata

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

@@ -1,4 +1,3 @@
-import contextvars
 import logging
 import os
 import threading
@@ -9,7 +8,6 @@ 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
@@ -40,7 +38,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
         invoke_from: InvokeFrom,
         stream: bool = True,
         call_depth: int = 0,
-    ):
+    ) -> Union[dict, Generator[dict, None, None]]:
         """
         Generate App response.
 
@@ -88,7 +86,6 @@ 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,
@@ -129,8 +126,7 @@ 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,
-            'context': contextvars.copy_context()
+            'queue_manager': queue_manager
         })
 
         worker_thread.start()
@@ -154,7 +150,8 @@ class WorkflowAppGenerator(BaseAppGenerator):
                                   node_id: str,
                                   user: Account,
                                   args: dict,
-                                  stream: bool = True):
+                                  stream: bool = True) \
+            -> Union[dict, Generator[dict, None, None]]:
         """
         Generate App response.
 
@@ -196,7 +193,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
                 inputs=args['inputs']
             )
         )
-        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
         return self._generate(
             app_model=app_model,
@@ -209,8 +205,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
 
     def _generate_worker(self, flask_app: Flask,
                          application_generate_entity: WorkflowAppGenerateEntity,
-                         queue_manager: AppQueueManager,
-                         context: contextvars.Context) -> None:
+                         queue_manager: AppQueueManager) -> None:
         """
         Generate worker in a new thread.
         :param flask_app: Flask app
@@ -218,8 +213,6 @@ 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

@@ -10,7 +10,6 @@ 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
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
@@ -58,7 +57,7 @@ class WorkflowAppRunner:
 
         db.session.close()
 
-        workflow_callbacks: list[WorkflowCallback] = [WorkflowEventTriggerCallback(
+        workflow_callbacks = [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 WorkflowCallback
+from core.workflow.callbacks.base_workflow_callback import BaseWorkflowCallback
 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(WorkflowCallback):
+class WorkflowEventTriggerCallback(BaseWorkflowCallback):
 
     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 WorkflowCallback
+from core.workflow.callbacks.base_workflow_callback import BaseWorkflowCallback
 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(WorkflowCallback):
+class WorkflowLoggingCallback(BaseWorkflowCallback):
 
     def __init__(self) -> None:
         self.current_node_id = None

api/core/app/entities/app_invoke_entities.py

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

api/core/app/segments/__init__.py

@@ -1,30 +0,0 @@
-from .segment_group import SegmentGroup
-from .segments import NoneSegment, Segment
-from .types import SegmentType
-from .variables import (
-    ArrayVariable,
-    FileVariable,
-    FloatVariable,
-    IntegerVariable,
-    NoneVariable,
-    ObjectVariable,
-    SecretVariable,
-    StringVariable,
-    Variable,
-)
-
-__all__ = [
-    'IntegerVariable',
-    'FloatVariable',
-    'ObjectVariable',
-    'SecretVariable',
-    'FileVariable',
-    'StringVariable',
-    'ArrayVariable',
-    'Variable',
-    'SegmentType',
-    'SegmentGroup',
-    'Segment',
-    'NoneSegment',
-    'NoneVariable',
-]

api/core/app/segments/factory.py

@@ -1,67 +0,0 @@
-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,
-    NoneVariable,
-    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 value is None:
-        return NoneVariable(name='anonymous')
-    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

@@ -1,17 +0,0 @@
-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

@@ -1,19 +0,0 @@
-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

@@ -1,65 +0,0 @@
-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)
-
-    def to_object(self) -> Any:
-        if isinstance(self.value, Segment):
-            return self.value.to_object()
-        if isinstance(self.value, list):
-            return [v.to_object() for v in self.value]
-        if isinstance(self.value, dict):
-            return {k: v.to_object() for k, v in self.value.items()}
-        return self.value
-
-
-class NoneSegment(Segment):
-    value_type: SegmentType = SegmentType.NONE
-    value: None = None
-
-    @property
-    def text(self) -> str:
-        return 'null'
-
-    @property
-    def log(self) -> str:
-        return 'null'
-
-    @property
-    def markdown(self) -> str:
-        return 'null'
-
-
-class StringSegment(Segment):
-    value_type: SegmentType = SegmentType.STRING
-    value: str

api/core/app/segments/types.py

@@ -1,11 +0,0 @@
-from enum import Enum
-
-
-class SegmentType(str, Enum):
-    NONE = 'none'
-    NUMBER = 'number'
-    STRING = 'string'
-    SECRET = 'secret'
-    ARRAY = 'array'
-    OBJECT = 'object'
-    FILE = 'file'

api/core/app/segments/variables.py

@@ -1,88 +0,0 @@
-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 NoneSegment, 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)
-
-
-class NoneVariable(NoneSegment, Variable):
-    value_type: SegmentType = SegmentType.NONE
-    value: None = None

api/core/callback_handler/agent_tool_callback_handler.py

@@ -1,11 +1,9 @@
 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",
@@ -45,7 +43,7 @@ class DifyAgentCallbackHandler(BaseModel):
     def on_tool_start(
         self,
         tool_name: str,
-        tool_inputs: Mapping[str, Any],
+        tool_inputs: dict[str, Any],
     ) -> None:
         """Do nothing."""
         print_text("\n[on_tool_start] ToolCall:" + tool_name + "\n" + str(tool_inputs) + "\n", color=self.color)
@@ -53,8 +51,8 @@ class DifyAgentCallbackHandler(BaseModel):
     def on_tool_end(
         self,
         tool_name: str,
-        tool_inputs: Mapping[str, Any],
-        tool_outputs: Sequence[ToolInvokeMessage],
+        tool_inputs: dict[str, Any],
+        tool_outputs: str,
         message_id: Optional[str] = None,
         timer: Optional[Any] = None,
         trace_manager: Optional[TraceQueueManager] = None

api/core/file/message_file_parser.py

@@ -1,5 +1,4 @@
-from collections.abc import Mapping, Sequence
-from typing import Any, Union
+from typing import Union
 
 import requests
 
@@ -17,7 +16,7 @@ class MessageFileParser:
         self.tenant_id = tenant_id
         self.app_id = app_id
 
-    def validate_and_transform_files_arg(self, files: Sequence[Mapping[str, Any]], file_extra_config: FileExtraConfig,
+    def validate_and_transform_files_arg(self, files: list[dict], 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: CodeLanguage, 
+                     language: Literal['python3', 'javascript', 'jinja2'], 
                      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: CodeLanguage, code: str, inputs: dict, dependencies: Optional[list[CodeDependency]] = None) -> dict:
+    def execute_workflow_code_template(cls, language: Literal['python3', 'javascript', 'jinja2'], code: str, inputs: dict, dependencies: Optional[list[CodeDependency]] = None) -> dict:
         """
         Execute code
         :param language: code language

api/core/helper/encrypter.py

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

api/core/helper/position_helper.py

@@ -14,9 +14,6 @@ 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/llm_generator.py

@@ -3,13 +3,10 @@ import logging
 import re
 from typing import Optional
 
+from core.llm_generator.output_parser.errors import OutputParserException
 from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
 from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
-from core.llm_generator.prompts import (
-    CONVERSATION_TITLE_PROMPT,
-    GENERATOR_QA_PROMPT,
-    WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE,
-)
+from core.llm_generator.prompts import CONVERSATION_TITLE_PROMPT, GENERATOR_QA_PROMPT
 from core.model_manager import ModelManager
 from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
 from core.model_runtime.entities.model_entities import ModelType
@@ -118,158 +115,55 @@ class LLMGenerator:
         return questions
 
     @classmethod
-    def generate_rule_config(cls, tenant_id: str, instruction: str, model_config: dict, no_variable: bool) -> dict:
+    def generate_rule_config(cls, tenant_id: str, audiences: str, hoping_to_solve: str) -> dict:
         output_parser = RuleConfigGeneratorOutputParser()
 
-        error = ""
-        error_step = ""
-        rule_config = {
-            "prompt": "",
-            "variables": [],
-            "opening_statement": "",
-            "error": ""
-        }
-        model_parameters = {
-            "max_tokens": 512,
-            "temperature": 0.01
-        }
-
-        if no_variable:
-            prompt_template = PromptTemplateParser(
-                WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE
-            )
-
-            prompt_generate = prompt_template.format(
-                inputs={
-                    "TASK_DESCRIPTION": instruction,
-                },
-                remove_template_variables=False
-            )
-
-            prompt_messages = [UserPromptMessage(content=prompt_generate)]
-
-            model_manager = ModelManager()
-
-            model_instance = model_manager.get_default_model_instance(
-                tenant_id=tenant_id,
-                model_type=ModelType.LLM,
-            )
-
-            try:
-                response = model_instance.invoke_llm(
-                    prompt_messages=prompt_messages,
-                    model_parameters=model_parameters,
-                    stream=False
-                )
-
-                rule_config["prompt"] = response.message.content
-                
-            except InvokeError as e:
-                error = str(e)
-                error_step = "generate rule config"
-            except Exception as e:
-                logging.exception(e)
-                rule_config["error"] = str(e)
-
-            rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
-
-            return rule_config
-
-        # get rule config prompt, parameter and statement
-        prompt_generate, parameter_generate, statement_generate = output_parser.get_format_instructions()
-
         prompt_template = PromptTemplateParser(
-            prompt_generate
+            template=output_parser.get_format_instructions()
         )
 
-        parameter_template = PromptTemplateParser(
-            parameter_generate
-        )
-
-        statement_template = PromptTemplateParser(
-            statement_generate
-        )
-
-        # format the prompt_generate_prompt
-        prompt_generate_prompt = prompt_template.format(
+        prompt = prompt_template.format(
             inputs={
-                "TASK_DESCRIPTION": instruction,
+                "audiences": audiences,
+                "hoping_to_solve": hoping_to_solve,
+                "variable": "{{variable}}",
+                "lanA": "{{lanA}}",
+                "lanB": "{{lanB}}",
+                "topic": "{{topic}}"
             },
             remove_template_variables=False
         )
-        prompt_messages = [UserPromptMessage(content=prompt_generate_prompt)]
 
-        # get model instance
         model_manager = ModelManager()
-        model_instance = model_manager.get_model_instance(
+        model_instance = model_manager.get_default_model_instance(
             tenant_id=tenant_id,
             model_type=ModelType.LLM,
-            provider=model_config.get("provider") if model_config else None,
-            model=model_config.get("name") if model_config else None,
         )
 
+        prompt_messages = [UserPromptMessage(content=prompt)]
+
         try:
-            try:
-                # the first step to generate the task prompt
-                prompt_content = model_instance.invoke_llm(
-                    prompt_messages=prompt_messages,
-                    model_parameters=model_parameters,
-                    stream=False
-                )
-            except InvokeError as e:
-                error = str(e)
-                error_step = "generate prefix prompt"
-                rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
-
-                return rule_config
-
-            rule_config["prompt"] = prompt_content.message.content
-
-            parameter_generate_prompt = parameter_template.format(
-                inputs={
-                    "INPUT_TEXT": prompt_content.message.content,
+            response = model_instance.invoke_llm(
+                prompt_messages=prompt_messages,
+                model_parameters={
+                    "max_tokens": 512,
+                    "temperature": 0
                 },
-                remove_template_variables=False
+                stream=False
             )
-            parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
-
-            # the second step to generate the task_parameter and task_statement
-            statement_generate_prompt = statement_template.format(
-                inputs={
-                    "TASK_DESCRIPTION": instruction,
-                    "INPUT_TEXT": prompt_content.message.content,
-                },
-                remove_template_variables=False
-            )
-            statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
-
-            try:
-                parameter_content = model_instance.invoke_llm(
-                    prompt_messages=parameter_messages,
-                    model_parameters=model_parameters,
-                    stream=False
-                )
-                rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', parameter_content.message.content)
-            except InvokeError as e:
-                error = str(e)
-                error_step = "generate variables"
-
-            try:
-                statement_content = model_instance.invoke_llm(
-                    prompt_messages=statement_messages,
-                    model_parameters=model_parameters,
-                    stream=False
-                )
-                rule_config["opening_statement"] = statement_content.message.content
-            except InvokeError as e:
-                error = str(e)
-                error_step = "generate conversation opener"
 
+            rule_config = output_parser.parse(response.message.content)
+        except InvokeError as e:
+            raise e
+        except OutputParserException:
+            raise ValueError('Please give a valid input for intended audience or hoping to solve problems.')
         except Exception as e:
             logging.exception(e)
-            rule_config["error"] = str(e)
-
-        rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
+            rule_config = {
+                "prompt": "",
+                "variables": [],
+                "opening_statement": ""
+            }
 
         return rule_config
 

api/core/llm_generator/output_parser/rule_config_generator.py

@@ -1,18 +1,14 @@
 from typing import Any
 
 from core.llm_generator.output_parser.errors import OutputParserException
-from core.llm_generator.prompts import (
-    RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE,
-    RULE_CONFIG_PROMPT_GENERATE_TEMPLATE,
-    RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE,
-)
+from core.llm_generator.prompts import RULE_CONFIG_GENERATE_TEMPLATE
 from libs.json_in_md_parser import parse_and_check_json_markdown
 
 
 class RuleConfigGeneratorOutputParser:
 
-    def get_format_instructions(self) -> tuple[str, str, str]:
-        return RULE_CONFIG_PROMPT_GENERATE_TEMPLATE, RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE, RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE
+    def get_format_instructions(self) -> str:
+        return RULE_CONFIG_GENERATE_TEMPLATE
 
     def parse(self, text: str) -> Any:
         try:

api/core/llm_generator/prompts.py

@@ -64,7 +64,6 @@ 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"
 )
@@ -81,73 +80,65 @@ GENERATOR_QA_PROMPT = (
     '<QA Pairs>'
 )
 
-WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE = """
-Here is a task description for which I would like you to create a high-quality prompt template for:
-<task_description>
-{{TASK_DESCRIPTION}}
-</task_description>
-Based on task description, please create a well-structured prompt template that another AI could use to consistently complete the task. The prompt template should include:
-- Do not inlcude <input> or <output> section and variables in the prompt, assume user will add them at their own will. 
-- Clear instructions for the AI that will be using this prompt, demarcated with <instructions> tags. The instructions should provide step-by-step directions on how to complete the task using the input variables. Also Specifies in the instructions that the output should not contain any xml tag. 
-- Relevant examples if needed to clarify the task further, demarcated with <example> tags. Do not include variables in the prompt. Give three pairs of input and output examples.   
-- Include other relevant sections demarcated with appropriate XML tags like <examples>, <instructions>.
-- Use the same language as task description. 
-- Output in ``` xml ``` and start with <instruction>
-Please generate the full prompt template with at least 300 words and output only the prompt template.
-"""
+RULE_CONFIG_GENERATE_TEMPLATE = """Given MY INTENDED AUDIENCES and HOPING TO SOLVE using a language model, please select \
+the model prompt that best suits the input. 
+You will be provided with the prompt, variables, and an opening statement. 
+Only the content enclosed in double curly braces, such as {{variable}}, in the prompt can be considered as a variable; \
+otherwise, it cannot exist as a variable in the variables.
+If you believe revising the original input will result in a better response from the language model, you may \
+suggest revisions.
 
-RULE_CONFIG_PROMPT_GENERATE_TEMPLATE = """
-Here is a task description for which I would like you to create a high-quality prompt template for:
-<task_description>
-{{TASK_DESCRIPTION}}
-</task_description>
-Based on task description, please create a well-structured prompt template that another AI could use to consistently complete the task. The prompt template should include:
-- Descriptive variable names surrounded by {{ }} (two curly brackets) to indicate where the actual values will be substituted in. Choose variable names that clearly indicate the type of value expected. Variable names have to be composed of number, english alphabets and underline and nothing else. 
-- Clear instructions for the AI that will be using this prompt, demarcated with <instructions> tags. The instructions should provide step-by-step directions on how to complete the task using the input variables. Also Specifies in the instructions that the output should not contain any xml tag. 
-- Relevant examples if needed to clarify the task further, demarcated with <example> tags. Do not use curly brackets any other than in <instruction> section. 
-- Any other relevant sections demarcated with appropriate XML tags like <input>, <output>, etc.
-- Use the same language as task description. 
-- Output in ``` xml ``` and start with <instruction>
-Please generate the full prompt template and output only the prompt template.
-"""
+<<PRINCIPLES OF GOOD PROMPT>>
+Integrate the intended audience in the prompt e.g. the audience is an expert in the field.
+Break down complex tasks into a sequence of simpler prompts in an interactive conversation.
+Implement example-driven prompting (Use few-shot prompting). 
+When formatting your prompt start with Instruction followed by either Example if relevant. \
+Subsequently present your content. Use one or more line breaks to separate instructions examples questions context and input data.
+Incorporate the following phrases: “Your task is” and “You MUST”.
+Incorporate the following phrases: “You will be penalized”.
+Use leading words like writing “think step by step”.
+Add to your prompt the following phrase “Ensure that your answer is unbiased and does not rely on stereotypes”.
+Assign a role to the large language models.
+Use Delimiters.
+To write an essay /text /paragraph /article or any type of text that should be detailed: “Write a detailed [essay/text/paragraph] for me on [topic] in detail by adding all the information necessary”.
+Clearly state the requirements that the model must follow in order to produce content in the form of the keywords regulations hint or instructions
 
-RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE = """
-I need to extract the following information from the input text. The <information to be extracted> tag specifies the 'type', 'description' and 'required' of the information to be extracted. 
-<information to be extracted>
-variables name bounded two double curly brackets. Variable name has to be composed of number, english alphabets and underline and nothing else. 
-</information to be extracted>
+<< FORMATTING >>
+Return a markdown code snippet with a JSON object formatted to look like, \
+no any other string out of markdown code snippet:
+```json
+{{{{
+    "prompt": string \\ generated prompt
+    "variables": list of string \\ variables
+    "opening_statement": string \\ an opening statement to guide users on how to ask questions with generated prompt \
+and fill in variables, with a welcome sentence, and keep TLDR.
+}}}}
+```
 
-Step 1: Carefully read the input and understand the structure of the expected output.
-Step 2: Extract relevant parameters from the provided text based on the name and description of object. 
-Step 3: Structure the extracted parameters to JSON object as specified in <structure>.
-Step 4: Ensure that the list of variable_names is properly formatted and valid. The output should not contain any XML tags. Output an empty list if there is no valid variable name in input text. 
+<< EXAMPLES >>
+[EXAMPLE A]
+```json
+{
+  "prompt": "I need your help to translate the following {{Input_language}}paper paragraph into {{Target_language}}, in a style similar to a popular science magazine in {{Target_language}}. #### Rules Ensure accurate conveyance of the original text's facts and context during translation. Maintain the original paragraph format and retain technical terms and company abbreviations ",
+  "variables": ["Input_language", "Target_language"],
+  "opening_statement": " Hi. I am your translation assistant. I can help you with any translation and ensure accurate conveyance of information. "
+}
+```
 
-### Structure
-Here is the structure of the expected output, I should always follow the output structure. 
-["variable_name_1", "variable_name_2"]
+[EXAMPLE B]
+```json
+{
+  "prompt": "Your task is to review the provided meeting notes and create a concise summary that captures the essential information, focusing on key takeaways and action items assigned to specific individuals or departments during the meeting. Use clear and professional language, and organize the summary in a logical manner using appropriate formatting such as headings, subheadings, and bullet points. Ensure that the summary is easy to understand and provides a comprehensive but succinct overview of the meeting's content, with a particular focus on clearly indicating who is responsible for each action item.",
+  "variables": ["meeting_notes"],
+  "opening_statement": "Hi! I'm your meeting notes summarizer AI. I can help you with any meeting notes and ensure accurate conveyance of information."
+}
+```
 
-### Input Text
-Inside <text></text> XML tags, there is a text that I should extract parameters and convert to a JSON object.
-<text>
-{{INPUT_TEXT}}
-</text>
+<< MY INTENDED AUDIENCES >>
+{{audiences}}
 
-### Answer
-I should always output a valid list. Output nothing other than the list of variable_name. Output an empty list if there is no variable name in input text.
-"""
+<< HOPING TO SOLVE >>
+{{hoping_to_solve}}
 
-RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE = """
-<instruction>
-Step 1: Identify the purpose of the chatbot from the variable {{TASK_DESCRIPTION}} and infer chatbot's tone  (e.g., friendly, professional, etc.) to add personality traits. 
-Step 2: Create a coherent and engaging opening statement.
-Step 3: Ensure the output is welcoming and clearly explains what the chatbot is designed to do. Do not include any XML tags in the output.
-Please use the same language as the user's input language. If user uses chinese then generate opening statement in chinese,  if user uses english then generate opening statement in english. 
-Example Input: 
-Provide customer support for an e-commerce website
-Example Output: 
-Welcome! I'm here to assist you with any questions or issues you might have with your shopping experience. Whether you're looking for product information, need help with your order, or have any other inquiries, feel free to ask. I'm friendly, helpful, and ready to support you in any way I can.
-<Task>
-Here is the task description: {{INPUT_TEXT}}
-
-You just need to generate the output
-"""
+<< OUTPUT >>
+"""

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 len(prompt_messages)>1:
+            while curr_message_tokens > max_token_limit and prompt_messages:
                 pruned_memory.append(prompt_messages.pop(0))
                 curr_message_tokens = self.model_instance.get_llm_num_tokens(
                     prompt_messages

api/core/model_manager.py

@@ -410,7 +410,7 @@ class LBModelManager:
         self._model = model
         self._load_balancing_configs = load_balancing_configs
 
-        for load_balancing_config in self._load_balancing_configs[:]:  # Iterate over a shallow copy of the list
+        for load_balancing_config in self._load_balancing_configs:
             if load_balancing_config.name == "__inherit__":
                 if not managed_credentials:
                     # remove __inherit__ if managed credentials is not provided

api/core/model_runtime/model_providers/_position.yaml

@@ -23,7 +23,6 @@
 - tongyi
 - wenxin
 - moonshot
-- tencent
 - jina
 - chatglm
 - yi

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: 8192
+    default: 4096
     min: 1
-    max: 8192
+    max: 4096
   - name: response_format
     use_template: response_format
 pricing:

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

@@ -113,11 +113,6 @@ 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
@@ -126,7 +121,6 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 model=model,
                 messages=prompt_message_dicts,
                 stream=stream,
-                extra_headers=extra_headers,
                 **model_parameters,
                 **extra_model_kwargs
             )
@@ -136,7 +130,6 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 model=model,
                 messages=prompt_message_dicts,
                 stream=stream,
-                extra_headers=extra_headers,
                 **model_parameters,
                 **extra_model_kwargs
             )
@@ -145,7 +138,7 @@ 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/groq/llm/_position.yaml

@@ -1,7 +0,0 @@
-- llama-3.1-405b-reasoning
-- llama-3.1-70b-versatile
-- llama-3.1-8b-instant
-- llama3-70b-8192
-- llama3-8b-8192
-- mixtral-8x7b-32768
-- llama2-70b-4096

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

@@ -1,25 +0,0 @@
-model: llama-3.1-405b-reasoning
-label:
-  zh_Hans: Llama-3.1-405b-reasoning
-  en_US: Llama-3.1-405b-reasoning
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 131072
-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: '0.05'
-  output: '0.1'
-  unit: '0.000001'
-  currency: USD

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

@@ -1,25 +0,0 @@
-model: llama-3.1-70b-versatile
-label:
-  zh_Hans: Llama-3.1-70b-versatile
-  en_US: Llama-3.1-70b-versatile
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 131072
-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: '0.05'
-  output: '0.1'
-  unit: '0.000001'
-  currency: USD

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

@@ -1,25 +0,0 @@
-model: llama-3.1-8b-instant
-label:
-  zh_Hans: Llama-3.1-8b-instant
-  en_US: Llama-3.1-8b-instant
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 131072
-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: '0.05'
-  output: '0.1'
-  unit: '0.000001'
-  currency: USD

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

@@ -1,8 +1,6 @@
 - 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

@@ -1,44 +0,0 @@
-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

@@ -1,44 +0,0 @@
-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

@@ -1,5 +1,4 @@
 - openai/gpt-4o
-- openai/gpt-4o-mini
 - openai/gpt-4
 - openai/gpt-4-32k
 - openai/gpt-3.5-turbo
@@ -12,9 +11,6 @@
 - google/gemini-pro
 - cohere/command-r-plus
 - cohere/command-r
-- meta-llama/llama-3.1-405b-instruct
-- meta-llama/llama-3.1-70b-instruct
-- meta-llama/llama-3.1-8b-instruct
 - meta-llama/llama-3-70b-instruct
 - meta-llama/llama-3-8b-instruct
 - mistralai/mixtral-8x22b-instruct

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

@@ -1,43 +0,0 @@
-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/openrouter/llm/llama-3.1-405b-instruct.yaml

@@ -1,23 +0,0 @@
-model: meta-llama/llama-3.1-405b-instruct
-label:
-  en_US: llama-3.1-405b-instruct
-model_type: llm
-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
-    required: true
-    default: 512
-    min: 1
-    max: 128000
-pricing:
-  input: "3"
-  output: "3"
-  unit: "0.000001"
-  currency: USD

api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-70b-instruct.yaml

@@ -1,23 +0,0 @@
-model: meta-llama/llama-3.1-70b-instruct
-label:
-  en_US: llama-3.1-70b-instruct
-model_type: llm
-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
-    required: true
-    default: 512
-    min: 1
-    max: 128000
-pricing:
-  input: "0.9"
-  output: "0.9"
-  unit: "0.000001"
-  currency: USD

api/core/model_runtime/model_providers/openrouter/llm/llama-3.1-8b-instruct.yaml

@@ -1,23 +0,0 @@
-model: meta-llama/llama-3.1-8b-instruct
-label:
-  en_US: llama-3.1-8b-instruct
-model_type: llm
-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
-    required: true
-    default: 512
-    min: 1
-    max: 128000
-pricing:
-  input: "0.2"
-  output: "0.2"
-  unit: "0.000001"
-  currency: USD

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

@@ -1,238 +0,0 @@
-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

@@ -1,190 +0,0 @@
-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

@@ -1,17 +0,0 @@
-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

@@ -1,125 +0,0 @@
-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

@@ -1,214 +0,0 @@
-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