merge main

.github/ISSUE_TEMPLATE/bug_report.yml

       label: Self Checks
       description: "To make sure we get to you in time, please check the following :)"
       options:
+        - label: I have read the [Contributing Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) and [Language Policy](https://github.com/langgenius/dify/issues/1542).
+          required: true
         - label: This is only for bug report, if you would like to ask a question, please head to [Discussions](https://github.com/langgenius/dify/discussions/categories/general).
           required: true
         - 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)).
+        - label: I confirm that I am using English to submit this report, otherwise it will be closed.
           required: true
-        - label: "[FOR CHINESE USERS] 请务必使用英文提交 Issue，否则会被关闭。谢谢！:)"
+        - label: 【中文用户 & Non English User】请使用英语提交，否则会被关闭 ：）
           required: true
         - label: "Please do not modify this template :) and fill in all the required fields."
           required: true
     attributes:
       label: Steps to reproduce
       description: We highly suggest including screenshots and a bug report log. Please use the right markdown syntax for code blocks.
-      placeholder: Having detailed steps helps us reproduce the bug.
+      placeholder: Having detailed steps helps us reproduce the bug. If you have logs, please use fenced code blocks (triple backticks ```) to format them.
     validations:
       required: true
 
   - type: textarea
     attributes:
       label: ✔️ Expected Behavior
-      placeholder: What were you expecting?
+      description: Describe what you expected to happen.
+      placeholder: What were you expecting? Please do not copy and paste the steps to reproduce here.
     validations:
-      required: false
+      required: true
 
   - type: textarea
     attributes:
       label: ❌ Actual Behavior
-      placeholder: What happened instead?
+      description: Describe what actually happened.
+      placeholder: What happened instead? Please do not copy and paste the steps to reproduce here.
     validations:
       required: false

.github/ISSUE_TEMPLATE/config.yml

 blank_issues_enabled: false
 contact_links:
+  - name: "\U0001F4A1 Model Providers & Plugins"
+    url: "https://github.com/langgenius/dify-official-plugins/issues/new/choose"
+    about: Report issues with official plugins or model providers, you will need to provide the plugin version and other relevant details.
+  - name: "\U0001F4AC Documentation Issues"
+    url: "https://github.com/langgenius/dify-docs/issues/new"
+    about: Report issues with the documentation, such as typos, outdated information, or missing content. Please provide the specific section and details of the issue.
   - name: "\U0001F4E7 Discussions"
     url: https://github.com/langgenius/dify/discussions/categories/general
-    about: General discussions and request help from the community
+    about: General discussions and seek help from the community

.github/ISSUE_TEMPLATE/document_issue.yml

-name: "📚 Documentation Issue"
-description: Report issues in our documentation
-labels:
-  - documentation
-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 report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
-          required: true
-        - label: "[FOR CHINESE USERS] 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: Provide a description of requested docs changes
-      placeholder: Briefly describe which document needs to be corrected and why.
-    validations:
-      required: true

.github/ISSUE_TEMPLATE/feature_request.yml

       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.
+        - label: I have read the [Contributing Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) and [Language Policy](https://github.com/langgenius/dify/issues/1542).
           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)).
+        - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
           required: true
-        - label: "[FOR CHINESE USERS] 请务必使用英文提交 Issue，否则会被关闭。谢谢！:)"
+        - label: I confirm that I am using English to submit this report, otherwise it will be closed.
           required: true
         - label: "Please do not modify this template :) and fill in all the required fields."
           required: true

.github/ISSUE_TEMPLATE/translation_issue.yml

-name: "🌐 Localization/Translation issue"
-description: Report incorrect translations. [please use English :)]
-labels:
-  - translation
-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: "[FOR CHINESE USERS] 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: input
-    attributes:
-      label: Dify version
-      description: Hover over system tray icon or look at Settings
-    validations:
-      required: true
-  - type: input
-    attributes:
-      label: Utility with translation issue
-      placeholder: Some area
-      description: Please input here the utility with the translation issue
-    validations:
-      required: true
-  - type: input
-    attributes:
-      label: 🌐 Language affected
-      placeholder: "German"
-    validations:
-      required: true
-  - type: textarea
-    attributes:
-      label: ❌ Actual phrase(s)
-      placeholder: What is there? Please include a screenshot as that is extremely helpful.
-    validations:
-      required: true
-  - type: textarea
-    attributes:
-      label: ✔️ Expected phrase(s)
-      placeholder: What was expected?
-    validations:
-      required: true
-  - type: textarea
-    attributes:
-      label: ℹ Why is the current translation wrong
-      placeholder: Why do you feel this is incorrect?
-    validations:
-      required: true

README.md

 
 </br>
 
-The easiest way to start the Dify server is through [docker compose](docker/docker-compose.yaml). Before running Dify with the following commands, make sure that [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed on your machine:
+The easiest way to start the Dify server is through [Docker Compose](docker/docker-compose.yaml). Before running Dify with the following commands, make sure that [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed on your machine:
 
 ```bash
 cd dify
 - [Helm Chart by @magicsong](https://github.com/magicsong/ai-charts)
 - [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Using Terraform for Deployment
 
 
 ## Security disclosure
 
-To protect your privacy, please avoid posting security issues on GitHub. Instead, send your questions to security@dify.ai and we will provide you with a more detailed answer.
+To protect your privacy, please avoid posting security issues on GitHub. Instead, report issues to security@dify.ai, and our team will respond with detailed answer.
 
 ## License
 
-This repository is available under the [Dify Open Source License](LICENSE), which is essentially Apache 2.0 with a few additional restrictions.
+This repository is licensed under the [Dify Open Source License](LICENSE), based on Apache 2.0 with additional conditions.

README_AR.md

 - [رسم بياني Helm من قبل @magicsong](https://github.com/magicsong/ai-charts)
 - [ملف YAML من قبل @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [ملف YAML من قبل @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 جديد! ملفات YAML (تدعم Dify v1.6.0) بواسطة @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### استخدام Terraform للتوزيع
 

README_BN.md

 - [Helm Chart by @magicsong](https://github.com/magicsong/ai-charts)
 - [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 নতুন! YAML ফাইলসমূহ (Dify v1.6.0 সমর্থিত) তৈরি করেছেন @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
+
 
 #### টেরাফর্ম ব্যবহার করে ডিপ্লয়
 

README_CN.md

 
 如果您需要自定义配置，请参考 [.env.example](docker/.env.example) 文件中的注释，并更新 `.env` 文件中对应的值。此外，您可能需要根据您的具体部署环境和需求对 `docker-compose.yaml` 文件本身进行调整，例如更改镜像版本、端口映射或卷挂载。完成任何更改后，请重新运行 `docker-compose up -d`。您可以在[此处](https://docs.dify.ai/getting-started/install-self-hosted/environments)找到可用环境变量的完整列表。
 
-#### 使用 Helm Chart 部署
+#### 使用 Helm Chart 或 Kubernetes 资源清单（YAML）部署
 
-使用 [Helm Chart](https://helm.sh/) 版本或者 YAML 文件，可以在 Kubernetes 上部署 Dify。
+使用 [Helm Chart](https://helm.sh/) 版本或者 Kubernetes 资源清单（YAML），可以在 Kubernetes 上部署 Dify。
 
 - [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
 - [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
 - [YAML 文件 by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
 
+- [🚀 NEW! YAML 文件 (支持 Dify v1.6.0) by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
+
+
+
 #### 使用 Terraform 部署
 
 使用 [terraform](https://www.terraform.io/) 一键将 Dify 部署到云平台

README_DE.md

 - [Helm Chart by @magicsong](https://github.com/magicsong/ai-charts)
 - [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Terraform für die Bereitstellung verwenden
 

README_ES.md

 - [Gráfico Helm por @magicsong](https://github.com/magicsong/ai-charts)
 - [Ficheros YAML por @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [Ficheros YAML por @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 ¡NUEVO! Archivos YAML (compatible con Dify v1.6.0) por @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Uso de Terraform para el despliegue
 

README_FR.md

 - [Helm Chart par @magicsong](https://github.com/magicsong/ai-charts)
 - [Fichier YAML par @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [Fichier YAML par @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 NOUVEAU ! Fichiers YAML (compatible avec Dify v1.6.0) par @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Utilisation de Terraform pour le déploiement
 

README_JA.md

 - [Helm Chart by @magicsong](https://github.com/magicsong/ai-charts)
 - [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 新着！YAML ファイル（Dify v1.6.0 対応）by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Terraformを使用したデプロイ
 

README_KL.md

 - [Helm Chart by @magicsong](https://github.com/magicsong/ai-charts)
 - [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Terraform atorlugu pilersitsineq
 

README_KR.md

 - [Helm Chart by @magicsong](https://github.com/magicsong/ai-charts)
 - [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Terraform을 사용한 배포
 

README_PT.md

 - [Helm Chart de @magicsong](https://github.com/magicsong/ai-charts)
 - [Arquivo YAML por @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [Arquivo YAML por @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 NOVO! Arquivos YAML (Compatível com Dify v1.6.0) por @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Usando o Terraform para Implantação
 

README_SI.md

 - [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
 - [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Uporaba Terraform za uvajanje
 

README_TR.md

 - [@BorisPolonsky tarafından Helm Chart](https://github.com/BorisPolonsky/dify-helm)
 - [@Winson-030 tarafından YAML dosyası](https://github.com/Winson-030/dify-kubernetes)
 - [@wyy-holding tarafından YAML dosyası](https://github.com/wyy-holding/dify-k8s)
+- [🚀 YENİ! YAML dosyaları (Dify v1.6.0 destekli) @Zhoneym tarafından](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Dağıtım için Terraform Kullanımı
 

README_TW.md

 
 如果您需要自定義配置，請參考我們的 [.env.example](docker/.env.example) 文件中的註釋，並在您的 `.env` 文件中更新相應的值。此外，根據您特定的部署環境和需求，您可能需要調整 `docker-compose.yaml` 文件本身，例如更改映像版本、端口映射或卷掛載。進行任何更改後，請重新運行 `docker-compose up -d`。您可以在[這裡](https://docs.dify.ai/getting-started/install-self-hosted/environments)找到可用環境變數的完整列表。
 
-如果您想配置高可用性設置，社區貢獻的 [Helm Charts](https://helm.sh/) 和 YAML 文件允許在 Kubernetes 上部署 Dify。
+如果您想配置高可用性設置，社區貢獻的 [Helm Charts](https://helm.sh/) 和 Kubernetes 資源清單（YAML）允許在 Kubernetes 上部署 Dify。
 
 - [由 @LeoQuote 提供的 Helm Chart](https://github.com/douban/charts/tree/master/charts/dify)
 - [由 @BorisPolonsky 提供的 Helm Chart](https://github.com/BorisPolonsky/dify-helm)
 - [由 @Winson-030 提供的 YAML 文件](https://github.com/Winson-030/dify-kubernetes)
 - [由 @wyy-holding 提供的 YAML 文件](https://github.com/wyy-holding/dify-k8s)
+- [🚀 NEW! YAML 檔案（支援 Dify v1.6.0）by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 ### 使用 Terraform 進行部署
 

README_VI.md

 - [Helm Chart bởi @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
 - [Tệp YAML bởi @Winson-030](https://github.com/Winson-030/dify-kubernetes)
 - [Tệp YAML bởi @wyy-holding](https://github.com/wyy-holding/dify-k8s)
+- [🚀 MỚI! Tệp YAML (Hỗ trợ Dify v1.6.0) bởi @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
 
 #### Sử dụng Terraform để Triển khai
 

api/.env.example

 # hybrid: Save new data to object storage, read from both object storage and RDBMS
 WORKFLOW_NODE_EXECUTION_STORAGE=rdbms
 
+# Repository configuration
+# Core workflow execution repository implementation
+CORE_WORKFLOW_EXECUTION_REPOSITORY=core.repositories.sqlalchemy_workflow_execution_repository.SQLAlchemyWorkflowExecutionRepository
+
+# Core workflow node execution repository implementation
+CORE_WORKFLOW_NODE_EXECUTION_REPOSITORY=core.repositories.sqlalchemy_workflow_node_execution_repository.SQLAlchemyWorkflowNodeExecutionRepository
+
+# API workflow node execution repository implementation
+API_WORKFLOW_NODE_EXECUTION_REPOSITORY=repositories.sqlalchemy_api_workflow_node_execution_repository.DifyAPISQLAlchemyWorkflowNodeExecutionRepository
+
+# API workflow run repository implementation
+API_WORKFLOW_RUN_REPOSITORY=repositories.sqlalchemy_api_workflow_run_repository.DifyAPISQLAlchemyWorkflowRunRepository
+
 # App configuration
 APP_MAX_EXECUTION_TIME=1200
 APP_MAX_ACTIVE_REQUESTS=0
 
 # Reset password token expiry minutes
 RESET_PASSWORD_TOKEN_EXPIRY_MINUTES=5
+CHANGE_EMAIL_TOKEN_EXPIRY_MINUTES=5
+OWNER_TRANSFER_TOKEN_EXPIRY_MINUTES=5
 
 CREATE_TIDB_SERVICE_JOB_ENABLED=false
 
 
 # Enable OpenTelemetry
 ENABLE_OTEL=false
+OTLP_TRACE_ENDPOINT=
+OTLP_METRIC_ENDPOINT=
 OTLP_BASE_ENDPOINT=http://localhost:4318
 OTLP_API_KEY=
 OTEL_EXPORTER_OTLP_PROTOCOL=

api/configs/feature/__init__.py

         description="Duration in minutes for which a password reset token remains valid",
         default=5,
     )
+    CHANGE_EMAIL_TOKEN_EXPIRY_MINUTES: PositiveInt = Field(
+        description="Duration in minutes for which a change email token remains valid",
+        default=5,
+    )
+
+    OWNER_TRANSFER_TOKEN_EXPIRY_MINUTES: PositiveInt = Field(
+        description="Duration in minutes for which a owner transfer token remains valid",
+        default=5,
+    )
 
     LOGIN_DISABLED: bool = Field(
         description="Whether to disable login checks",
     )
 
 
+class RepositoryConfig(BaseSettings):
+    """
+    Configuration for repository implementations
+    """
+
+    CORE_WORKFLOW_EXECUTION_REPOSITORY: str = Field(
+        description="Repository implementation for WorkflowExecution. Specify as a module path",
+        default="core.repositories.sqlalchemy_workflow_execution_repository.SQLAlchemyWorkflowExecutionRepository",
+    )
+
+    CORE_WORKFLOW_NODE_EXECUTION_REPOSITORY: str = Field(
+        description="Repository implementation for WorkflowNodeExecution. Specify as a module path",
+        default="core.repositories.sqlalchemy_workflow_node_execution_repository.SQLAlchemyWorkflowNodeExecutionRepository",
+    )
+
+    API_WORKFLOW_NODE_EXECUTION_REPOSITORY: str = Field(
+        description="Service-layer repository implementation for WorkflowNodeExecutionModel operations. "
+        "Specify as a module path",
+        default="repositories.sqlalchemy_api_workflow_node_execution_repository.DifyAPISQLAlchemyWorkflowNodeExecutionRepository",
+    )
+
+    API_WORKFLOW_RUN_REPOSITORY: str = Field(
+        description="Service-layer repository implementation for WorkflowRun operations. Specify as a module path",
+        default="repositories.sqlalchemy_api_workflow_run_repository.DifyAPISQLAlchemyWorkflowRunRepository",
+    )
+
+
 class AuthConfig(BaseSettings):
     """
     Configuration for authentication and OAuth
         default=86400,
     )
 
+    CHANGE_EMAIL_LOCKOUT_DURATION: PositiveInt = Field(
+        description="Time (in seconds) a user must wait before retrying change email after exceeding the rate limit.",
+        default=86400,
+    )
+
+    OWNER_TRANSFER_LOCKOUT_DURATION: PositiveInt = Field(
+        description="Time (in seconds) a user must wait before retrying owner transfer after exceeding the rate limit.",
+        default=86400,
+    )
+
 
 class ModerationConfig(BaseSettings):
     """
     MultiModalTransferConfig,
     PositionConfig,
     RagEtlConfig,
+    RepositoryConfig,
     SecurityConfig,
     ToolConfig,
     UpdateConfig,

api/configs/middleware/__init__.py

         default=3600,
     )
 
+    SQLALCHEMY_POOL_USE_LIFO: bool = Field(
+        description="If True, SQLAlchemy will use last-in-first-out way to retrieve connections from pool.",
+        default=False,
+    )
+
     SQLALCHEMY_POOL_PRE_PING: bool = Field(
         description="If True, enables connection pool pre-ping feature to check connections.",
         default=False,
             "pool_recycle": self.SQLALCHEMY_POOL_RECYCLE,
             "pool_pre_ping": self.SQLALCHEMY_POOL_PRE_PING,
             "connect_args": connect_args,
+            "pool_use_lifo": self.SQLALCHEMY_POOL_USE_LIFO,
         }
 
 

api/configs/observability/otel/otel_config.py

         default=False,
     )
 
+    OTLP_TRACE_ENDPOINT: str = Field(
+        description="OTLP trace endpoint",
+        default="",
+    )
+
+    OTLP_METRIC_ENDPOINT: str = Field(
+        description="OTLP metric endpoint",
+        default="",
+    )
+
     OTLP_BASE_ENDPOINT: str = Field(
         description="OTLP base endpoint",
         default="http://localhost:4318",

api/controllers/console/app/app.py

         parser.add_argument("icon", type=str, location="json")
         parser.add_argument("icon_background", type=str, location="json")
         parser.add_argument("use_icon_as_answer_icon", type=bool, location="json")
+        parser.add_argument("max_active_requests", type=int, location="json")
         args = parser.parse_args()
 
         app_service = AppService()

api/controllers/console/app/mcp_server.py

     @get_app_model
     @marshal_with(app_server_fields)
     def post(self, app_model):
-        # The role of the current user in the ta table must be editor, admin, or owner
         if not current_user.is_editor:
             raise NotFound()
         parser = reqparse.RequestParser()
-        parser.add_argument("description", type=str, required=True, location="json")
+        parser.add_argument("description", type=str, required=False, location="json")
         parser.add_argument("parameters", type=dict, required=True, location="json")
         args = parser.parse_args()
+
+        description = args.get("description")
+        if not description:
+            description = app_model.description or ""
+
         server = AppMCPServer(
             name=app_model.name,
-            description=args["description"],
+            description=description,
             parameters=json.dumps(args["parameters"], ensure_ascii=False),
             status=AppMCPServerStatus.ACTIVE,
             app_id=app_model.id,
             raise NotFound()
         parser = reqparse.RequestParser()
         parser.add_argument("id", type=str, required=True, location="json")
-        parser.add_argument("description", type=str, required=True, location="json")
+        parser.add_argument("description", type=str, required=False, location="json")
         parser.add_argument("parameters", type=dict, required=True, location="json")
         parser.add_argument("status", type=str, required=False, location="json")
         args = parser.parse_args()
         server = db.session.query(AppMCPServer).filter(AppMCPServer.id == args["id"]).first()
         if not server:
             raise NotFound()
-        server.description = args["description"]
+
+        description = args.get("description")
+        if description is None:
+            pass
+        elif not description:
+            server.description = app_model.description or ""
+        else:
+            server.description = description
+
         server.parameters = json.dumps(args["parameters"], ensure_ascii=False)
         if args["status"]:
             if args["status"] not in [status.value for status in AppMCPServerStatus]:

api/controllers/console/app/statistic.py

 from decimal import Decimal
 
 import pytz
+import sqlalchemy as sa
 from flask import jsonify
 from flask_login import current_user
 from flask_restful import Resource, reqparse
 from controllers.console import api
 from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import account_initialization_required, setup_required
+from core.app.entities.app_invoke_entities import InvokeFrom
 from extensions.ext_database import db
 from libs.helper import DatetimeString
 from libs.login import login_required
-from models.model import AppMode
+from models import AppMode, Message
 
 
 class DailyMessageStatistic(Resource):
         parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
         args = parser.parse_args()
 
-        sql_query = """SELECT
-    DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
-    COUNT(DISTINCT messages.conversation_id) AS conversation_count
-FROM
-    messages
-WHERE
-    app_id = :app_id"""
-        arg_dict = {"tz": account.timezone, "app_id": app_model.id}
-
         timezone = pytz.timezone(account.timezone)
         utc_timezone = pytz.utc
 
+        stmt = (
+            sa.select(
+                sa.func.date(
+                    sa.func.date_trunc("day", sa.text("created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz"))
+                ).label("date"),
+                sa.func.count(sa.distinct(Message.conversation_id)).label("conversation_count"),
+            )
+            .select_from(Message)
+            .where(Message.app_id == app_model.id, Message.invoke_from != InvokeFrom.DEBUGGER.value)
+        )
+
         if args["start"]:
             start_datetime = datetime.strptime(args["start"], "%Y-%m-%d %H:%M")
             start_datetime = start_datetime.replace(second=0)
-
             start_datetime_timezone = timezone.localize(start_datetime)
             start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
-
-            sql_query += " AND created_at >= :start"
-            arg_dict["start"] = start_datetime_utc
+            stmt = stmt.where(Message.created_at >= start_datetime_utc)
 
         if args["end"]:
             end_datetime = datetime.strptime(args["end"], "%Y-%m-%d %H:%M")
             end_datetime = end_datetime.replace(second=0)
-
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
+            stmt = stmt.where(Message.created_at < end_datetime_utc)
 
-            sql_query += " AND created_at < :end"
-            arg_dict["end"] = end_datetime_utc
-
-        sql_query += " GROUP BY date ORDER BY date"
+        stmt = stmt.group_by("date").order_by("date")
 
         response_data = []
-
         with db.engine.begin() as conn:
-            rs = conn.execute(db.text(sql_query), arg_dict)
-            for i in rs:
-                response_data.append({"date": str(i.date), "conversation_count": i.conversation_count})
+            rs = conn.execute(stmt, {"tz": account.timezone})
+            for row in rs:
+                response_data.append({"date": str(row.date), "conversation_count": row.conversation_count})
 
         return jsonify({"data": response_data})
 

api/controllers/console/app/workflow_draft_variable.py

     return parser
 
 
+def _serialize_variable_type(workflow_draft_var: WorkflowDraftVariable) -> str:
+    value_type = workflow_draft_var.value_type
+    return value_type.exposed_type().value
+
+
 _WORKFLOW_DRAFT_VARIABLE_WITHOUT_VALUE_FIELDS = {
     "id": fields.String,
     "type": fields.String(attribute=lambda model: model.get_variable_type()),
     "name": fields.String,
     "description": fields.String,
     "selector": fields.List(fields.String, attribute=lambda model: model.get_selector()),
-    "value_type": fields.String,
+    "value_type": fields.String(attribute=_serialize_variable_type),
     "edited": fields.Boolean(attribute=lambda model: model.edited),
     "visible": fields.Boolean,
 }
     "name": fields.String,
     "description": fields.String,
     "selector": fields.List(fields.String, attribute=lambda model: model.get_selector()),
-    "value_type": fields.String,
+    "value_type": fields.String(attribute=_serialize_variable_type),
     "edited": fields.Boolean(attribute=lambda model: model.edited),
     "visible": fields.Boolean,
 }
                     "name": v.name,
                     "description": v.description,
                     "selector": v.selector,
-                    "value_type": v.value_type.value,
+                    "value_type": v.value_type.exposed_type().value,
                     "value": v.value,
                     # Do not track edited for env vars.
                     "edited": False,

api/controllers/console/app/wraps.py

                 raise AppNotFoundError()
 
             app_mode = AppMode.value_of(app_model.mode)
-            if app_mode == AppMode.CHANNEL:
-                raise AppNotFoundError()
 
             if mode is not None:
                 if isinstance(mode, list):

api/controllers/console/auth/error.py

 
 class PasswordResetRateLimitExceededError(BaseHTTPException):
     error_code = "password_reset_rate_limit_exceeded"
-    description = "Too many password reset emails have been sent. Please try again in 1 minutes."
+    description = "Too many password reset emails have been sent. Please try again in 1 minute."
+    code = 429
+
+
+class EmailChangeRateLimitExceededError(BaseHTTPException):
+    error_code = "email_change_rate_limit_exceeded"
+    description = "Too many email change emails have been sent. Please try again in 1 minute."
+    code = 429
+
+
+class OwnerTransferRateLimitExceededError(BaseHTTPException):
+    error_code = "owner_transfer_rate_limit_exceeded"
+    description = "Too many owner transfer emails have been sent. Please try again in 1 minute."
     code = 429
 
 
     error_code = "email_password_reset_limit"
     description = "Too many failed password reset attempts. Please try again in 24 hours."
     code = 429
+
+
+class EmailChangeLimitError(BaseHTTPException):
+    error_code = "email_change_limit"
+    description = "Too many failed email change attempts. Please try again in 24 hours."
+    code = 429
+
+
+class EmailAlreadyInUseError(BaseHTTPException):
+    error_code = "email_already_in_use"
+    description = "A user with this email already exists."
+    code = 400
+
+
+class OwnerTransferLimitError(BaseHTTPException):
+    error_code = "owner_transfer_limit"
+    description = "Too many failed owner transfer attempts. Please try again in 24 hours."
+    code = 429
+
+
+class NotOwnerError(BaseHTTPException):
+    error_code = "not_owner"
+    description = "You are not the owner of the workspace."
+    code = 400
+
+
+class CannotTransferOwnerToSelfError(BaseHTTPException):
+    error_code = "cannot_transfer_owner_to_self"
+    description = "You cannot transfer ownership to yourself."
+    code = 400
+
+
+class MemberNotInTenantError(BaseHTTPException):
+    error_code = "member_not_in_tenant"
+    description = "The member is not in the workspace."
+    code = 400

api/controllers/console/datasets/error.py

     code = 415
 
 
-class HighQualityDatasetOnlyError(BaseHTTPException):
-    error_code = "high_quality_dataset_only"
-    description = "Current operation only supports 'high-quality' datasets."
-    code = 400
-
-
 class DatasetNotInitializedError(BaseHTTPException):
     error_code = "dataset_not_initialized"
     description = "The dataset is still being initialized or indexing. Please wait a moment."

api/controllers/console/workspace/account.py

 from flask import request
 from flask_login import current_user
 from flask_restful import Resource, fields, marshal_with, reqparse
+from sqlalchemy import select
+from sqlalchemy.orm import Session
 
 from configs import dify_config
 from constants.languages import supported_language
 from controllers.console import api
+from controllers.console.auth.error import (
+    EmailAlreadyInUseError,
+    EmailChangeLimitError,
+    EmailCodeError,
+    InvalidEmailError,
+    InvalidTokenError,
+)
+from controllers.console.error import AccountNotFound, EmailSendIpLimitError
 from controllers.console.workspace.error import (
     AccountAlreadyInitedError,
     CurrentPasswordIncorrectError,
 from controllers.console.wraps import (
     account_initialization_required,
     cloud_edition_billing_enabled,
+    enable_change_email,
     enterprise_license_required,
     only_edition_cloud,
     setup_required,
 )
 from extensions.ext_database import db
 from fields.member_fields import account_fields
-from libs.helper import TimestampField, timezone
+from libs.helper import TimestampField, email, extract_remote_ip, timezone
 from libs.login import login_required
 from models import AccountIntegrate, InvitationCode
+from models.account import Account
 from services.account_service import AccountService
 from services.billing_service import BillingService
 from services.errors.account import CurrentPasswordIncorrectError as ServiceCurrentPasswordIncorrectError
         return BillingService.EducationIdentity.autocomplete(args["keywords"], args["page"], args["limit"])
 
 
+class ChangeEmailSendEmailApi(Resource):
+    @enable_change_email
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("email", type=email, required=True, location="json")
+        parser.add_argument("language", type=str, required=False, location="json")
+        parser.add_argument("phase", type=str, required=False, location="json")
+        parser.add_argument("token", type=str, required=False, location="json")
+        args = parser.parse_args()
+
+        ip_address = extract_remote_ip(request)
+        if AccountService.is_email_send_ip_limit(ip_address):
+            raise EmailSendIpLimitError()
+
+        if args["language"] is not None and args["language"] == "zh-Hans":
+            language = "zh-Hans"
+        else:
+            language = "en-US"
+        account = None
+        user_email = args["email"]
+        if args["phase"] is not None and args["phase"] == "new_email":
+            if args["token"] is None:
+                raise InvalidTokenError()
+
+            reset_data = AccountService.get_change_email_data(args["token"])
+            if reset_data is None:
+                raise InvalidTokenError()
+            user_email = reset_data.get("email", "")
+
+            if user_email != current_user.email:
+                raise InvalidEmailError()
+        else:
+            with Session(db.engine) as session:
+                account = session.execute(select(Account).filter_by(email=args["email"])).scalar_one_or_none()
+            if account is None:
+                raise AccountNotFound()
+
+        token = AccountService.send_change_email_email(
+            account=account, email=args["email"], old_email=user_email, language=language, phase=args["phase"]
+        )
+        return {"result": "success", "data": token}
+
+
+class ChangeEmailCheckApi(Resource):
+    @enable_change_email
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("email", type=email, required=True, location="json")
+        parser.add_argument("code", type=str, required=True, location="json")
+        parser.add_argument("token", type=str, required=True, nullable=False, location="json")
+        args = parser.parse_args()
+
+        user_email = args["email"]
+
+        is_change_email_error_rate_limit = AccountService.is_change_email_error_rate_limit(args["email"])
+        if is_change_email_error_rate_limit:
+            raise EmailChangeLimitError()
+
+        token_data = AccountService.get_change_email_data(args["token"])
+        if token_data is None:
+            raise InvalidTokenError()
+
+        if user_email != token_data.get("email"):
+            raise InvalidEmailError()
+
+        if args["code"] != token_data.get("code"):
+            AccountService.add_change_email_error_rate_limit(args["email"])
+            raise EmailCodeError()
+
+        # Verified, revoke the first token
+        AccountService.revoke_change_email_token(args["token"])
+
+        # Refresh token data by generating a new token
+        _, new_token = AccountService.generate_change_email_token(
+            user_email, code=args["code"], old_email=token_data.get("old_email"), additional_data={}
+        )
+
+        AccountService.reset_change_email_error_rate_limit(args["email"])
+        return {"is_valid": True, "email": token_data.get("email"), "token": new_token}
+
+
+class ChangeEmailResetApi(Resource):
+    @enable_change_email
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @marshal_with(account_fields)
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("new_email", type=email, required=True, location="json")
+        parser.add_argument("token", type=str, required=True, nullable=False, location="json")
+        args = parser.parse_args()
+
+        reset_data = AccountService.get_change_email_data(args["token"])
+        if not reset_data:
+            raise InvalidTokenError()
+
+        AccountService.revoke_change_email_token(args["token"])
+
+        if not AccountService.check_email_unique(args["new_email"]):
+            raise EmailAlreadyInUseError()
+
+        old_email = reset_data.get("old_email", "")
+        if current_user.email != old_email:
+            raise AccountNotFound()
+
+        updated_account = AccountService.update_account(current_user, email=args["new_email"])
+
+        return updated_account
+
+
+class CheckEmailUnique(Resource):
+    @setup_required
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("email", type=email, required=True, location="json")
+        args = parser.parse_args()
+        if not AccountService.check_email_unique(args["email"]):
+            raise EmailAlreadyInUseError()
+        return {"result": "success"}
+
+
 # Register API resources
 api.add_resource(AccountInitApi, "/account/init")
 api.add_resource(AccountProfileApi, "/account/profile")
 api.add_resource(EducationVerifyApi, "/account/education/verify")
 api.add_resource(EducationApi, "/account/education")
 api.add_resource(EducationAutoCompleteApi, "/account/education/autocomplete")
+# Change email
+api.add_resource(ChangeEmailSendEmailApi, "/account/change-email")
+api.add_resource(ChangeEmailCheckApi, "/account/change-email/validity")
+api.add_resource(ChangeEmailResetApi, "/account/change-email/reset")
+api.add_resource(CheckEmailUnique, "/account/change-email/check-email-unique")
 # api.add_resource(AccountEmailApi, '/account/email')
 # api.add_resource(AccountEmailVerifyApi, '/account/email-verify')

api/controllers/console/workspace/error.py

     code = 400
 
 
-class ProviderRequestFailedError(BaseHTTPException):
-    error_code = "provider_request_failed"
-    description = None
-    code = 400
-
-
 class InvalidInvitationCodeError(BaseHTTPException):
     error_code = "invalid_invitation_code"
     description = "Invalid invitation code."

api/controllers/console/workspace/members.py

 from urllib import parse
 
+from flask import request
 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.error import WorkspaceMembersLimitExceeded
+from controllers.console.auth.error import (
+    CannotTransferOwnerToSelfError,
+    EmailCodeError,
+    InvalidEmailError,
+    InvalidTokenError,
+    MemberNotInTenantError,
+    NotOwnerError,
+    OwnerTransferLimitError,
+)
+from controllers.console.error import EmailSendIpLimitError, WorkspaceMembersLimitExceeded
 from controllers.console.wraps import (
     account_initialization_required,
     cloud_edition_billing_resource_check,
+    is_allow_transfer_owner,
     setup_required,
 )
 from extensions.ext_database import db
 from fields.member_fields import account_with_role_list_fields
+from libs.helper import extract_remote_ip
 from libs.login import login_required
 from models.account import Account, TenantAccountRole
-from services.account_service import RegisterService, TenantService
+from services.account_service import AccountService, RegisterService, TenantService
 from services.errors.account import AccountAlreadyInTenantError
 from services.feature_service import FeatureService
 
         return {"result": "success", "accounts": members}, 200
 
 
+class SendOwnerTransferEmailApi(Resource):
+    """Send owner transfer email."""
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @is_allow_transfer_owner
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("language", type=str, required=False, location="json")
+        args = parser.parse_args()
+        ip_address = extract_remote_ip(request)
+        if AccountService.is_email_send_ip_limit(ip_address):
+            raise EmailSendIpLimitError()
+
+        # check if the current user is the owner of the workspace
+        if not TenantService.is_owner(current_user, current_user.current_tenant):
+            raise NotOwnerError()
+
+        if args["language"] is not None and args["language"] == "zh-Hans":
+            language = "zh-Hans"
+        else:
+            language = "en-US"
+
+        email = current_user.email
+
+        token = AccountService.send_owner_transfer_email(
+            account=current_user,
+            email=email,
+            language=language,
+            workspace_name=current_user.current_tenant.name,
+        )
+
+        return {"result": "success", "data": token}
+
+
+class OwnerTransferCheckApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @is_allow_transfer_owner
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("code", type=str, required=True, location="json")
+        parser.add_argument("token", type=str, required=True, nullable=False, location="json")
+        args = parser.parse_args()
+        # check if the current user is the owner of the workspace
+        if not TenantService.is_owner(current_user, current_user.current_tenant):
+            raise NotOwnerError()
+
+        user_email = current_user.email
+
+        is_owner_transfer_error_rate_limit = AccountService.is_owner_transfer_error_rate_limit(user_email)
+        if is_owner_transfer_error_rate_limit:
+            raise OwnerTransferLimitError()
+
+        token_data = AccountService.get_owner_transfer_data(args["token"])
+        if token_data is None:
+            raise InvalidTokenError()
+
+        if user_email != token_data.get("email"):
+            raise InvalidEmailError()
+
+        if args["code"] != token_data.get("code"):
+            AccountService.add_owner_transfer_error_rate_limit(user_email)
+            raise EmailCodeError()
+
+        # Verified, revoke the first token
+        AccountService.revoke_owner_transfer_token(args["token"])
+
+        # Refresh token data by generating a new token
+        _, new_token = AccountService.generate_owner_transfer_token(user_email, code=args["code"], additional_data={})
+
+        AccountService.reset_owner_transfer_error_rate_limit(user_email)
+        return {"is_valid": True, "email": token_data.get("email"), "token": new_token}
+
+
+class OwnerTransfer(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @is_allow_transfer_owner
+    def post(self, member_id):
+        parser = reqparse.RequestParser()
+        parser.add_argument("token", type=str, required=True, nullable=False, location="json")
+        args = parser.parse_args()
+
+        # check if the current user is the owner of the workspace
+        if not TenantService.is_owner(current_user, current_user.current_tenant):
+            raise NotOwnerError()
+
+        if current_user.id == str(member_id):
+            raise CannotTransferOwnerToSelfError()
+
+        transfer_token_data = AccountService.get_owner_transfer_data(args["token"])
+        if not transfer_token_data:
+            raise InvalidTokenError()
+
+        if transfer_token_data.get("email") != current_user.email:
+            raise InvalidEmailError()
+
+        AccountService.revoke_owner_transfer_token(args["token"])
+
+        member = db.session.get(Account, str(member_id))
+        if not member:
+            abort(404)
+        else:
+            member_account = member
+        if not TenantService.is_member(member_account, current_user.current_tenant):
+            raise MemberNotInTenantError()
+
+        try:
+            assert member is not None, "Member not found"
+            TenantService.update_member_role(current_user.current_tenant, member, "owner", current_user)
+
+            AccountService.send_new_owner_transfer_notify_email(
+                account=member,
+                email=member.email,
+                workspace_name=current_user.current_tenant.name,
+            )
+
+            AccountService.send_old_owner_transfer_notify_email(
+                account=current_user,
+                email=current_user.email,
+                workspace_name=current_user.current_tenant.name,
+                new_owner_email=member.email,
+            )
+
+        except Exception as e:
+            raise ValueError(str(e))
+
+        return {"result": "success"}
+
+
 api.add_resource(MemberListApi, "/workspaces/current/members")
 api.add_resource(MemberInviteEmailApi, "/workspaces/current/members/invite-email")
 api.add_resource(MemberCancelInviteApi, "/workspaces/current/members/<uuid:member_id>")
 api.add_resource(MemberUpdateRoleApi, "/workspaces/current/members/<uuid:member_id>/update-role")
 api.add_resource(DatasetOperatorMemberListApi, "/workspaces/current/dataset-operators")
+# owner transfer
+api.add_resource(SendOwnerTransferEmailApi, "/workspaces/current/members/send-owner-transfer-confirm-email")
+api.add_resource(OwnerTransferCheckApi, "/workspaces/current/members/owner-transfer-check")
+api.add_resource(OwnerTransfer, "/workspaces/current/members/<uuid:member_id>/owner-transfer")

api/controllers/console/wraps.py

         abort(403)
 
     return decorated
+
+
+def enable_change_email(view):
+    @wraps(view)
+    def decorated(*args, **kwargs):
+        features = FeatureService.get_system_features()
+        if features.enable_change_email:
+            return view(*args, **kwargs)
+
+        # otherwise, return 403
+        abort(403)
+
+    return decorated
+
+
+def is_allow_transfer_owner(view):
+    @wraps(view)
+    def decorated(*args, **kwargs):
+        features = FeatureService.get_features(current_user.current_tenant_id)
+        if features.is_allow_transfer_workspace:
+            return view(*args, **kwargs)
+
+        # otherwise, return 403
+        abort(403)
+
+    return decorated

api/controllers/service_api/app/workflow.py

 from dateutil.parser import isoparse
 from flask_restful import Resource, fields, marshal_with, reqparse
 from flask_restful.inputs import int_range
-from sqlalchemy.orm import Session
+from sqlalchemy.orm import Session, sessionmaker
 from werkzeug.exceptions import InternalServerError
 
 from controllers.service_api import api
 from libs import helper
 from libs.helper import TimestampField
 from models.model import App, AppMode, EndUser
-from models.workflow import WorkflowRun
+from repositories.factory import DifyAPIRepositoryFactory
 from services.app_generate_service import AppGenerateService
 from services.errors.llm import InvokeRateLimitError
 from services.workflow_app_service import WorkflowAppService
         if app_mode not in [AppMode.WORKFLOW, AppMode.ADVANCED_CHAT]:
             raise NotWorkflowAppError()
 
-        workflow_run = db.session.query(WorkflowRun).filter(WorkflowRun.id == workflow_run_id).first()
+        # Use repository to get workflow run
+        session_maker = sessionmaker(bind=db.engine, expire_on_commit=False)
+        workflow_run_repo = DifyAPIRepositoryFactory.create_api_workflow_run_repository(session_maker)
+
+        workflow_run = workflow_run_repo.get_workflow_run_by_id(
+            tenant_id=app_model.tenant_id,
+            app_id=app_model.id,
+            run_id=workflow_run_id,
+        )
         return workflow_run
 
 

api/controllers/service_api/dataset/error.py

     code = 415
 
 
-class HighQualityDatasetOnlyError(BaseHTTPException):
-    error_code = "high_quality_dataset_only"
-    description = "Current operation only supports 'high-quality' datasets."
-    code = 400
-
-
 class DatasetNotInitializedError(BaseHTTPException):
     error_code = "dataset_not_initialized"
     description = "The dataset is still being initialized or indexing. Please wait a moment."

api/core/agent/base_agent_runner.py

 import uuid
 from typing import Optional, Union, cast
 
+from sqlalchemy import select
+
 from core.agent.entities import AgentEntity, AgentToolEntity
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
             if isinstance(prompt_message, SystemPromptMessage):
                 result.append(prompt_message)
 
-        messages: list[Message] = (
-            db.session.query(Message)
-            .filter(
-                Message.conversation_id == self.message.conversation_id,
+        messages = (
+            (
+                db.session.execute(
+                    select(Message)
+                    .where(Message.conversation_id == self.message.conversation_id)
+                    .order_by(Message.created_at.desc())
+                )
             )
-            .order_by(Message.created_at.desc())
+            .scalars()
             .all()
         )
 

api/core/agent/plugin_entities.py

         APP_SELECTOR = CommonParameterType.APP_SELECTOR.value
         MODEL_SELECTOR = CommonParameterType.MODEL_SELECTOR.value
         TOOLS_SELECTOR = CommonParameterType.TOOLS_SELECTOR.value
+        ANY = CommonParameterType.ANY.value
 
         # deprecated, should not use.
         SYSTEM_FILES = CommonParameterType.SYSTEM_FILES.value

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

 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.prompt.utils.get_thread_messages_length import get_thread_messages_length
-from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
-from core.repositories.sqlalchemy_workflow_execution_repository import SQLAlchemyWorkflowExecutionRepository
+from core.repositories import DifyCoreRepositoryFactory
 from core.workflow.repositories.draft_variable_repository import (
     DraftVariableSaverFactory,
 )
             workflow_triggered_from = WorkflowRunTriggeredFrom.DEBUGGING
         else:
             workflow_triggered_from = WorkflowRunTriggeredFrom.APP_RUN
-        workflow_execution_repository = SQLAlchemyWorkflowExecutionRepository(
+        workflow_execution_repository = DifyCoreRepositoryFactory.create_workflow_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,
             triggered_from=workflow_triggered_from,
         )
         # Create workflow node execution repository
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,
         # Create session factory
         session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
         # Create workflow execution(aka workflow run) repository
-        workflow_execution_repository = SQLAlchemyWorkflowExecutionRepository(
+        workflow_execution_repository = DifyCoreRepositoryFactory.create_workflow_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,
             triggered_from=WorkflowRunTriggeredFrom.DEBUGGING,
         )
         # Create workflow node execution repository
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,
         # Create session factory
         session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
         # Create workflow execution(aka workflow run) repository
-        workflow_execution_repository = SQLAlchemyWorkflowExecutionRepository(
+        workflow_execution_repository = DifyCoreRepositoryFactory.create_workflow_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,
             triggered_from=WorkflowRunTriggeredFrom.DEBUGGING,
         )
         # Create workflow node execution repository
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,

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

     QueueTextChunkEvent,
 )
 from core.moderation.base import ModerationError
+from core.variables.variables import VariableUnion
 from core.workflow.callbacks import WorkflowCallback, WorkflowLoggingCallback
 from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.enums import SystemVariableKey
+from core.workflow.system_variable import SystemVariable
 from core.workflow.variable_loader import VariableLoader
 from core.workflow.workflow_entry import WorkflowEntry
 from extensions.ext_database import db
         if not workflow:
             raise ValueError("Workflow not initialized")
 
-        user_id = None
+        user_id: str | None = None
         if self.application_generate_entity.invoke_from in {InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API}:
             end_user = db.session.query(EndUser).filter(EndUser.id == self.application_generate_entity.user_id).first()
             if end_user:
                 session.commit()
 
             # Create a variable pool.
-            system_inputs = {
-                SystemVariableKey.QUERY: query,
-                SystemVariableKey.FILES: files,
-                SystemVariableKey.CONVERSATION_ID: self.conversation.id,
-                SystemVariableKey.USER_ID: user_id,
-                SystemVariableKey.DIALOGUE_COUNT: self._dialogue_count,
-                SystemVariableKey.APP_ID: app_config.app_id,
-                SystemVariableKey.WORKFLOW_ID: app_config.workflow_id,
-                SystemVariableKey.WORKFLOW_EXECUTION_ID: self.application_generate_entity.workflow_run_id,
-            }
+            system_inputs = SystemVariable(
+                query=query,
+                files=files,
+                conversation_id=self.conversation.id,
+                user_id=user_id,
+                dialogue_count=self._dialogue_count,
+                app_id=app_config.app_id,
+                workflow_id=app_config.workflow_id,
+                workflow_execution_id=self.application_generate_entity.workflow_run_id,
+            )
 
             # init variable pool
             variable_pool = VariablePool(
                 system_variables=system_inputs,
                 user_inputs=inputs,
                 environment_variables=workflow.environment_variables,
-                conversation_variables=conversation_variables,
+                # Based on the definition of `VariableUnion`,
+                # `list[Variable]` can be safely used as `list[VariableUnion]` since they are compatible.
+                conversation_variables=cast(list[VariableUnion], conversation_variables),
             )
 
             # init graph

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

 from core.model_runtime.entities.llm_entities import LLMUsage
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.workflow.entities.workflow_execution import WorkflowExecutionStatus, WorkflowType
-from core.workflow.enums import SystemVariableKey
 from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.nodes import NodeType
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
 from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
+from core.workflow.system_variable import SystemVariable
 from core.workflow.workflow_cycle_manager import CycleManagerWorkflowInfo, WorkflowCycleManager
 from events.message_event import message_was_created
 from extensions.ext_database import db
 
         self._workflow_cycle_manager = WorkflowCycleManager(
             application_generate_entity=application_generate_entity,
-            workflow_system_variables={
-                SystemVariableKey.QUERY: message.query,
-                SystemVariableKey.FILES: application_generate_entity.files,
-                SystemVariableKey.CONVERSATION_ID: conversation.id,
-                SystemVariableKey.USER_ID: user_session_id,
-                SystemVariableKey.DIALOGUE_COUNT: dialogue_count,
-                SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
-                SystemVariableKey.WORKFLOW_ID: workflow.id,
-                SystemVariableKey.WORKFLOW_EXECUTION_ID: application_generate_entity.workflow_run_id,
-            },
+            workflow_system_variables=SystemVariable(
+                query=message.query,
+                files=application_generate_entity.files,
+                conversation_id=conversation.id,
+                user_id=user_session_id,
+                dialogue_count=dialogue_count,
+                app_id=application_generate_entity.app_config.app_id,
+                workflow_id=workflow.id,
+                workflow_execution_id=application_generate_entity.workflow_run_id,
+            ),
             workflow_info=CycleManagerWorkflowInfo(
                 workflow_id=workflow.id,
                 workflow_type=WorkflowType(workflow.type),

api/core/app/apps/base_app_runner.py

 
 
 class AppRunner:
-    def get_pre_calculate_rest_tokens(
-        self,
-        app_record: App,
-        model_config: ModelConfigWithCredentialsEntity,
-        prompt_template_entity: PromptTemplateEntity,
-        inputs: Mapping[str, str],
-        files: Sequence["File"],
-        query: Optional[str] = None,
-    ) -> int:
-        """
-        Get pre calculate rest tokens
-        :param app_record: app record
-        :param model_config: model config entity
-        :param prompt_template_entity: prompt template entity
-        :param inputs: inputs
-        :param files: files
-        :param query: query
-        :return:
-        """
-        # Invoke model
-        model_instance = ModelInstance(
-            provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
-        )
-
-        model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
-
-        max_tokens = 0
-        for parameter_rule in model_config.model_schema.parameter_rules:
-            if parameter_rule.name == "max_tokens" or (
-                parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
-            ):
-                max_tokens = (
-                    model_config.parameters.get(parameter_rule.name)
-                    or model_config.parameters.get(parameter_rule.use_template or "")
-                ) or 0
-
-        if model_context_tokens is None:
-            return -1
-
-        if max_tokens is None:
-            max_tokens = 0
-
-        # get prompt messages without memory and context
-        prompt_messages, stop = self.organize_prompt_messages(
-            app_record=app_record,
-            model_config=model_config,
-            prompt_template_entity=prompt_template_entity,
-            inputs=inputs,
-            files=files,
-            query=query,
-        )
-
-        prompt_tokens = model_instance.get_llm_num_tokens(prompt_messages)
-
-        rest_tokens: int = model_context_tokens - max_tokens - prompt_tokens
-        if rest_tokens < 0:
-            raise InvokeBadRequestError(
-                "Query or prefix prompt is too long, you can reduce the prefix prompt, "
-                "or shrink the max token, or switch to a llm with a larger token limit size."
-            )
-
-        return rest_tokens
-
     def recalc_llm_max_tokens(
         self, model_config: ModelConfigWithCredentialsEntity, prompt_messages: list[PromptMessage]
     ):

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

 from core.app.entities.task_entities import WorkflowAppBlockingResponse, WorkflowAppStreamResponse
 from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
-from core.repositories.sqlalchemy_workflow_execution_repository import SQLAlchemyWorkflowExecutionRepository
+from core.repositories import DifyCoreRepositoryFactory
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
 from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
             workflow_triggered_from = WorkflowRunTriggeredFrom.DEBUGGING
         else:
             workflow_triggered_from = WorkflowRunTriggeredFrom.APP_RUN
-        workflow_execution_repository = SQLAlchemyWorkflowExecutionRepository(
+        workflow_execution_repository = DifyCoreRepositoryFactory.create_workflow_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,
             triggered_from=workflow_triggered_from,
         )
         # Create workflow node execution repository
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,
         # Create session factory
         session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
         # Create workflow execution(aka workflow run) repository
-        workflow_execution_repository = SQLAlchemyWorkflowExecutionRepository(
+        workflow_execution_repository = DifyCoreRepositoryFactory.create_workflow_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,
             triggered_from=WorkflowRunTriggeredFrom.DEBUGGING,
         )
         # Create workflow node execution repository
-        session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
-
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,
         # Create session factory
         session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
         # Create workflow execution(aka workflow run) repository
-        workflow_execution_repository = SQLAlchemyWorkflowExecutionRepository(
+        workflow_execution_repository = DifyCoreRepositoryFactory.create_workflow_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,
             triggered_from=WorkflowRunTriggeredFrom.DEBUGGING,
         )
         # Create workflow node execution repository
-        session_factory = sessionmaker(bind=db.engine, expire_on_commit=False)
-
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=user,
             app_id=application_generate_entity.app_config.app_id,

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

 )
 from core.workflow.callbacks import WorkflowCallback, WorkflowLoggingCallback
 from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.enums import SystemVariableKey
+from core.workflow.system_variable import SystemVariable
 from core.workflow.variable_loader import VariableLoader
 from core.workflow.workflow_entry import WorkflowEntry
 from extensions.ext_database import db
             files = self.application_generate_entity.files
 
             # Create a variable pool.
-            system_inputs = {
-                SystemVariableKey.FILES: files,
-                SystemVariableKey.USER_ID: user_id,
-                SystemVariableKey.APP_ID: app_config.app_id,
-                SystemVariableKey.WORKFLOW_ID: app_config.workflow_id,
-                SystemVariableKey.WORKFLOW_EXECUTION_ID: self.application_generate_entity.workflow_execution_id,
-            }
+
+            system_inputs = SystemVariable(
+                files=files,
+                user_id=user_id,
+                app_id=app_config.app_id,
+                workflow_id=app_config.workflow_id,
+                workflow_execution_id=self.application_generate_entity.workflow_execution_id,
+            )
 
             variable_pool = VariablePool(
                 system_variables=system_inputs,

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

 from collections.abc import Generator
 from typing import Optional, Union
 
-from sqlalchemy import select
 from sqlalchemy.orm import Session
 
 from constants.tts_auto_play_timeout import TTS_AUTO_PLAY_TIMEOUT, TTS_AUTO_PLAY_YIELD_CPU_TIME
 from core.base.tts import AppGeneratorTTSPublisher, AudioTrunk
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.workflow.entities.workflow_execution import WorkflowExecution, WorkflowExecutionStatus, WorkflowType
-from core.workflow.enums import SystemVariableKey
 from core.workflow.repositories.draft_variable_repository import DraftVariableSaverFactory
 from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
 from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
+from core.workflow.system_variable import SystemVariable
 from core.workflow.workflow_cycle_manager import CycleManagerWorkflowInfo, WorkflowCycleManager
 from extensions.ext_database import db
 from models.account import Account
     Workflow,
     WorkflowAppLog,
     WorkflowAppLogCreatedFrom,
-    WorkflowRun,
 )
 
 logger = logging.getLogger(__name__)
 
         self._workflow_cycle_manager = WorkflowCycleManager(
             application_generate_entity=application_generate_entity,
-            workflow_system_variables={
-                SystemVariableKey.FILES: application_generate_entity.files,
-                SystemVariableKey.USER_ID: user_session_id,
-                SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
-                SystemVariableKey.WORKFLOW_ID: workflow.id,
-                SystemVariableKey.WORKFLOW_EXECUTION_ID: application_generate_entity.workflow_execution_id,
-            },
+            workflow_system_variables=SystemVariable(
+                files=application_generate_entity.files,
+                user_id=user_session_id,
+                app_id=application_generate_entity.app_config.app_id,
+                workflow_id=workflow.id,
+                workflow_execution_id=application_generate_entity.workflow_execution_id,
+            ),
             workflow_info=CycleManagerWorkflowInfo(
                 workflow_id=workflow.id,
                 workflow_type=WorkflowType(workflow.type),
             tts_publisher.publish(None)
 
     def _save_workflow_app_log(self, *, session: Session, workflow_execution: WorkflowExecution) -> None:
-        workflow_run = session.scalar(select(WorkflowRun).where(WorkflowRun.id == workflow_execution.id_))
-        assert workflow_run is not None
         invoke_from = self._application_generate_entity.invoke_from
         if invoke_from == InvokeFrom.SERVICE_API:
             created_from = WorkflowAppLogCreatedFrom.SERVICE_API
             return
 
         workflow_app_log = WorkflowAppLog()
-        workflow_app_log.tenant_id = workflow_run.tenant_id
-        workflow_app_log.app_id = workflow_run.app_id
-        workflow_app_log.workflow_id = workflow_run.workflow_id
-        workflow_app_log.workflow_run_id = workflow_run.id
+        workflow_app_log.tenant_id = self._application_generate_entity.app_config.tenant_id
+        workflow_app_log.app_id = self._application_generate_entity.app_config.app_id
+        workflow_app_log.workflow_id = workflow_execution.workflow_id
+        workflow_app_log.workflow_run_id = workflow_execution.id_
         workflow_app_log.created_from = created_from.value
         workflow_app_log.created_by_role = self._created_by_role
         workflow_app_log.created_by = self._user_id

api/core/app/apps/workflow_app_runner.py

 from core.workflow.graph_engine.entities.graph import Graph
 from core.workflow.nodes import NodeType
 from core.workflow.nodes.node_mapping import NODE_TYPE_CLASSES_MAPPING
+from core.workflow.system_variable import SystemVariable
 from core.workflow.variable_loader import DUMMY_VARIABLE_LOADER, VariableLoader, load_into_variable_pool
 from core.workflow.workflow_entry import WorkflowEntry
 from extensions.ext_database import db
 
         # init variable pool
         variable_pool = VariablePool(
-            system_variables={},
+            system_variables=SystemVariable.empty(),
             user_inputs={},
             environment_variables=workflow.environment_variables,
         )
 
         # init variable pool
         variable_pool = VariablePool(
-            system_variables={},
+            system_variables=SystemVariable.empty(),
             user_inputs={},
             environment_variables=workflow.environment_variables,
         )

api/core/app/task_pipeline/exc.py

 class WorkflowRunNotFoundError(RecordNotFoundError):
     def __init__(self, workflow_run_id: str):
         super().__init__("WorkflowRun", workflow_run_id)
-
-
-class WorkflowNodeExecutionNotFoundError(RecordNotFoundError):
-    def __init__(self, workflow_node_execution_id: str):
-        super().__init__("WorkflowNodeExecution", workflow_node_execution_id)

api/core/entities/parameter_entities.py

     APP_SELECTOR = "app-selector"
     MODEL_SELECTOR = "model-selector"
     TOOLS_SELECTOR = "array[tools]"
+    ANY = "any"
 
     # Dynamic select parameter
     # Once you are not sure about the available options until authorization is done

api/core/file/tool_file_parser.py

 _tool_file_manager_factory: Callable[[], "ToolFileManager"] | None = None
 
 
-class ToolFileParser:
-    @staticmethod
-    def get_tool_file_manager() -> "ToolFileManager":
-        assert _tool_file_manager_factory is not None
-        return _tool_file_manager_factory()
-
-
 def set_tool_file_manager_factory(factory: Callable[[], "ToolFileManager"]) -> None:
     global _tool_file_manager_factory
     _tool_file_manager_factory = factory

api/core/helper/code_executor/template_transformer.py

 from collections.abc import Mapping
 from typing import Any
 
+from core.variables.utils import SegmentJSONEncoder
+
 
 class TemplateTransformer(ABC):
     _code_placeholder: str = "{{code}}"
             result_str = cls.extract_result_str_from_response(response)
             result = json.loads(result_str)
         except json.JSONDecodeError as e:
-            raise ValueError(f"Failed to parse JSON response: {str(e)}. Response content: {result_str[:200]}...")
+            raise ValueError(f"Failed to parse JSON response: {str(e)}.")
         except ValueError as e:
             # Re-raise ValueError from extract_result_str_from_response
             raise e
         except Exception as e:
             raise ValueError(f"Unexpected error during response transformation: {str(e)}")
 
-        # Check if the result contains an error
-        if isinstance(result, dict) and "error" in result:
-            raise ValueError(f"JavaScript execution error: {result['error']}")
-
         if not isinstance(result, dict):
             raise ValueError(f"Result must be a dict, got {type(result).__name__}")
         if not all(isinstance(k, str) for k in result):
 
     @classmethod
     def serialize_inputs(cls, inputs: Mapping[str, Any]) -> str:
-        inputs_json_str = json.dumps(inputs, ensure_ascii=False).encode()
+        inputs_json_str = json.dumps(inputs, ensure_ascii=False, cls=SegmentJSONEncoder).encode()
         input_base64_encoded = b64encode(inputs_json_str).decode("utf-8")
         return input_base64_encoded
 

api/core/helper/url_signer.py

-import base64
-import hashlib
-import hmac
-import os
-import time
-
-from pydantic import BaseModel, Field
-
-from configs import dify_config
-
-
-class SignedUrlParams(BaseModel):
-    sign_key: str = Field(..., description="The sign key")
-    timestamp: str = Field(..., description="Timestamp")
-    nonce: str = Field(..., description="Nonce")
-    sign: str = Field(..., description="Signature")
-
-
-class UrlSigner:
-    @classmethod
-    def get_signed_url(cls, url: str, sign_key: str, prefix: str) -> str:
-        signed_url_params = cls.get_signed_url_params(sign_key, prefix)
-        return (
-            f"{url}?timestamp={signed_url_params.timestamp}"
-            f"&nonce={signed_url_params.nonce}&sign={signed_url_params.sign}"
-        )
-
-    @classmethod
-    def get_signed_url_params(cls, sign_key: str, prefix: str) -> SignedUrlParams:
-        timestamp = str(int(time.time()))
-        nonce = os.urandom(16).hex()
-        sign = cls._sign(sign_key, timestamp, nonce, prefix)
-
-        return SignedUrlParams(sign_key=sign_key, timestamp=timestamp, nonce=nonce, sign=sign)
-
-    @classmethod
-    def verify(cls, sign_key: str, timestamp: str, nonce: str, sign: str, prefix: str) -> bool:
-        recalculated_sign = cls._sign(sign_key, timestamp, nonce, prefix)
-
-        return sign == recalculated_sign
-
-    @classmethod
-    def _sign(cls, sign_key: str, timestamp: str, nonce: str, prefix: str) -> str:
-        if not dify_config.SECRET_KEY:
-            raise Exception("SECRET_KEY is not set")
-
-        data_to_sign = f"{prefix}|{sign_key}|{timestamp}|{nonce}"
-        secret_key = dify_config.SECRET_KEY.encode()
-        sign = hmac.new(secret_key, data_to_sign.encode(), hashlib.sha256).digest()
-        encoded_sign = base64.urlsafe_b64encode(sign).decode()
-
-        return encoded_sign

api/core/llm_generator/llm_generator.py

 
             model_manager = ModelManager()
 
-            model_instance = model_manager.get_default_model_instance(
+            model_instance = model_manager.get_model_instance(
                 tenant_id=tenant_id,
                 model_type=ModelType.LLM,
+                provider=model_config.get("provider", ""),
+                model=model_config.get("name", ""),
             )
 
             try:

api/core/mcp/auth/auth_flow.py

     response = requests.post(token_url, data=params)
     if not response.ok:
         raise ValueError(f"Token refresh failed: HTTP {response.status_code}")
-    return OAuthTokens.parse_obj(response.json())
+    return OAuthTokens.model_validate(response.json())
 
 
 def register_client(

api/core/mcp/server/streamable_http.py

         if not self.end_user:
             raise ValueError("User not found")
         request = cast(types.CallToolRequest, self.request.root)
-        args = request.params.arguments
-        if not args:
-            raise ValueError("No arguments provided")
+        args = request.params.arguments or {}
         if self.app.mode in {AppMode.WORKFLOW.value}:
             args = {"inputs": args}
         elif self.app.mode in {AppMode.COMPLETION.value}:

api/core/mcp/session/base_session.py

 import logging
 import queue
 from collections.abc import Callable
-from concurrent.futures import ThreadPoolExecutor
+from concurrent.futures import Future, ThreadPoolExecutor, TimeoutError
 from contextlib import ExitStack
 from datetime import timedelta
 from types import TracebackType
         self._session_read_timeout_seconds = read_timeout_seconds
         self._in_flight = {}
         self._exit_stack = ExitStack()
+        # Initialize executor and future to None for proper cleanup checks
+        self._executor: ThreadPoolExecutor | None = None
+        self._receiver_future: Future | None = None
 
     def __enter__(self) -> Self:
-        self._executor = ThreadPoolExecutor()
+        # The thread pool is dedicated to running `_receive_loop`. Setting `max_workers` to 1
+        # ensures no unnecessary threads are created.
+        self._executor = ThreadPoolExecutor(max_workers=1)
         self._receiver_future = self._executor.submit(self._receive_loop)
         return self
 
     def check_receiver_status(self) -> None:
-        if self._receiver_future.done():
+        """`check_receiver_status` ensures that any exceptions raised during the
+        execution of `_receive_loop` are retrieved and propagated."""
+        if self._receiver_future and self._receiver_future.done():
             self._receiver_future.result()
 
     def __exit__(
         self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: TracebackType | None
     ) -> None:
-        self._exit_stack.close()
         self._read_stream.put(None)
         self._write_stream.put(None)
 
+        # Wait for the receiver loop to finish
+        if self._receiver_future:
+            try:
+                self._receiver_future.result(timeout=5.0)  # Wait up to 5 seconds
+            except TimeoutError:
+                # If the receiver loop is still running after timeout, we'll force shutdown
+                pass
+
+        # Shutdown the executor
+        if self._executor:
+            self._executor.shutdown(wait=True)
+
     def send_request(
         self,
         request: SendRequestT,

api/core/memory/token_buffer_memory.py

 from collections.abc import Sequence
 from typing import Optional
 
+from sqlalchemy import select
+
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.file import file_manager
 from core.model_manager import ModelInstance
 from extensions.ext_database import db
 from factories import file_factory
 from models.model import AppMode, Conversation, Message, MessageFile
-from models.workflow import WorkflowRun
+from models.workflow import Workflow, WorkflowRun
 
 
 class TokenBufferMemory:
-    def __init__(self, conversation: Conversation, model_instance: ModelInstance) -> None:
+    def __init__(
+        self,
+        conversation: Conversation,
+        model_instance: ModelInstance,
+    ) -> None:
         self.conversation = conversation
         self.model_instance = model_instance
 
         app_record = self.conversation.app
 
         # fetch limited messages, and return reversed
-        query = (
-            db.session.query(
-                Message.id,
-                Message.query,
-                Message.answer,
-                Message.created_at,
-                Message.workflow_run_id,
-                Message.parent_message_id,
-                Message.answer_tokens,
-            )
-            .filter(
-                Message.conversation_id == self.conversation.id,
-            )
-            .order_by(Message.created_at.desc())
+        stmt = (
+            select(Message).where(Message.conversation_id == self.conversation.id).order_by(Message.created_at.desc())
         )
 
         if message_limit and message_limit > 0:
         else:
             message_limit = 500
 
-        messages = query.limit(message_limit).all()
+        stmt = stmt.limit(message_limit)
+
+        messages = db.session.scalars(stmt).all()
 
         # instead of all messages from the conversation, we only need to extract messages
         # that belong to the thread of last message
             files = db.session.query(MessageFile).filter(MessageFile.message_id == message.id).all()
             if files:
                 file_extra_config = None
-                if self.conversation.mode not in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
+                if self.conversation.mode in {AppMode.AGENT_CHAT, AppMode.COMPLETION, AppMode.CHAT}:
                     file_extra_config = FileUploadConfigManager.convert(self.conversation.model_config)
+                elif self.conversation.mode in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
+                    workflow_run = db.session.scalar(
+                        select(WorkflowRun).where(WorkflowRun.id == message.workflow_run_id)
+                    )
+                    if not workflow_run:
+                        raise ValueError(f"Workflow run not found: {message.workflow_run_id}")
+                    workflow = db.session.scalar(select(Workflow).where(Workflow.id == workflow_run.workflow_id))
+                    if not workflow:
+                        raise ValueError(f"Workflow not found: {workflow_run.workflow_id}")
+                    file_extra_config = FileUploadConfigManager.convert(workflow.features_dict, is_vision=False)
                 else:
-                    if message.workflow_run_id:
-                        workflow_run = (
-                            db.session.query(WorkflowRun).filter(WorkflowRun.id == message.workflow_run_id).first()
-                        )
-
-                        if workflow_run and workflow_run.workflow:
-                            file_extra_config = FileUploadConfigManager.convert(
-                                workflow_run.workflow.features_dict, is_vision=False
-                            )
+                    raise AssertionError(f"Invalid app mode: {self.conversation.mode}")
 
                 detail = ImagePromptMessageContent.DETAIL.LOW
                 if file_extra_config and app_record:

api/core/ops/aliyun_trace/aliyun_trace.py

             else:
                 node_span = self.build_workflow_task_span(trace_id, workflow_span_id, trace_info, node_execution)
             return node_span
-        except Exception:
+        except Exception as e:
+            logging.debug(f"Error occurred in build_workflow_node_span: {e}", exc_info=True)
             return None
 
     def get_workflow_node_status(self, node_execution: WorkflowNodeExecution) -> Status:
             start_time=convert_datetime_to_nanoseconds(node_execution.created_at),
             end_time=convert_datetime_to_nanoseconds(node_execution.finished_at),
             attributes={
-                GEN_AI_SESSION_ID: trace_info.metadata.get("conversation_id", ""),
+                GEN_AI_SESSION_ID: trace_info.metadata.get("conversation_id") or "",
                 GEN_AI_SPAN_KIND: GenAISpanKind.TASK.value,
                 GEN_AI_FRAMEWORK: "dify",
                 INPUT_VALUE: json.dumps(node_execution.inputs, ensure_ascii=False),
             start_time=convert_datetime_to_nanoseconds(node_execution.created_at),
             end_time=convert_datetime_to_nanoseconds(node_execution.finished_at),
             attributes={
-                GEN_AI_SESSION_ID: trace_info.metadata.get("conversation_id", ""),
+                GEN_AI_SESSION_ID: trace_info.metadata.get("conversation_id") or "",
                 GEN_AI_SPAN_KIND: GenAISpanKind.LLM.value,
                 GEN_AI_FRAMEWORK: "dify",
                 GEN_AI_MODEL_NAME: process_data.get("model_name", ""),
                 start_time=convert_datetime_to_nanoseconds(trace_info.start_time),
                 end_time=convert_datetime_to_nanoseconds(trace_info.end_time),
                 attributes={
-                    GEN_AI_SESSION_ID: trace_info.metadata.get("conversation_id", ""),
+                    GEN_AI_SESSION_ID: trace_info.metadata.get("conversation_id") or "",
                     GEN_AI_USER_ID: str(user_id),
                     GEN_AI_SPAN_KIND: GenAISpanKind.CHAIN.value,
                     GEN_AI_FRAMEWORK: "dify",

api/core/ops/langfuse_trace/langfuse_trace.py

     UnitEnum,
 )
 from core.ops.utils import filter_none_values
-from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
+from core.repositories import DifyCoreRepositoryFactory
 from core.workflow.nodes.enums import NodeType
 from extensions.ext_database import db
 from models import EndUser, WorkflowNodeExecutionTriggeredFrom
 
         service_account = self.get_service_account_with_tenant(app_id)
 
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=service_account,
-            app_id=trace_info.metadata.get("app_id"),
+            app_id=app_id,
             triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
         )
 

api/core/ops/langsmith_trace/langsmith_trace.py

     LangSmithRunUpdateModel,
 )
 from core.ops.utils import filter_none_values, generate_dotted_order
-from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
+from core.repositories import DifyCoreRepositoryFactory
 from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes.enums import NodeType
 from extensions.ext_database import db
 
         service_account = self.get_service_account_with_tenant(app_id)
 
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=service_account,
-            app_id=trace_info.metadata.get("app_id"),
+            app_id=app_id,
             triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
         )
 

api/core/ops/opik_trace/opik_trace.py

     TraceTaskName,
     WorkflowTraceInfo,
 )
-from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
+from core.repositories import DifyCoreRepositoryFactory
 from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes.enums import NodeType
 from extensions.ext_database import db
 
         service_account = self.get_service_account_with_tenant(app_id)
 
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=service_account,
-            app_id=trace_info.metadata.get("app_id"),
+            app_id=app_id,
             triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
         )
 
                 "trace_id": opik_trace_id,
                 "id": prepare_opik_uuid(created_at, node_execution_id),
                 "parent_span_id": prepare_opik_uuid(trace_info.start_time, parent_span_id),
-                "name": node_type,
+                "name": node_name,
                 "type": run_type,
                 "start_time": created_at,
                 "end_time": finished_at,

api/core/ops/weave_trace/weave_trace.py

     WorkflowTraceInfo,
 )
 from core.ops.weave_trace.entities.weave_trace_entity import WeaveTraceModel
-from core.repositories import SQLAlchemyWorkflowNodeExecutionRepository
+from core.repositories import DifyCoreRepositoryFactory
 from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey
 from core.workflow.nodes.enums import NodeType
 from extensions.ext_database import db
 
         service_account = self.get_service_account_with_tenant(app_id)
 
-        workflow_node_execution_repository = SQLAlchemyWorkflowNodeExecutionRepository(
+        workflow_node_execution_repository = DifyCoreRepositoryFactory.create_workflow_node_execution_repository(
             session_factory=session_factory,
             user=service_account,
-            app_id=trace_info.metadata.get("app_id"),
+            app_id=app_id,
             triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN,
         )
 

api/core/plugin/entities/parameters.py

 
 from core.entities.parameter_entities import CommonParameterType
 from core.tools.entities.common_entities import I18nObject
+from core.workflow.nodes.base.entities import NumberType
 
 
 class PluginParameterOption(BaseModel):
     APP_SELECTOR = CommonParameterType.APP_SELECTOR.value
     MODEL_SELECTOR = CommonParameterType.MODEL_SELECTOR.value
     TOOLS_SELECTOR = CommonParameterType.TOOLS_SELECTOR.value
+    ANY = CommonParameterType.ANY.value
     DYNAMIC_SELECT = CommonParameterType.DYNAMIC_SELECT.value
 
     # deprecated, should not use.
                 if value and not isinstance(value, list):
                     raise ValueError("The tools selector must be a list.")
                 return value
+            case PluginParameterType.ANY:
+                if value and not isinstance(value, str | dict | list | NumberType):
+                    raise ValueError("The var selector must be a string, dictionary, list or number.")
+                return value
             case PluginParameterType.ARRAY:
                 if not isinstance(value, list):
                     # Try to parse JSON string for arrays

api/core/plugin/entities/plugin.py

         return self
 
 
-class GithubPackage(BaseModel):
-    repo: str
-    version: str
-    package: str
-
-
-class GithubVersion(BaseModel):
-    repo: str
-    version: str
-
-
 class GenericProviderID:
     organization: str
     plugin_name: str

api/core/plugin/impl/plugin.py

             "GET",
             f"plugin/{tenant_id}/management/list",
             PluginListResponse,
-            params={"page": 1, "page_size": 256},
+            params={"page": 1, "page_size": 256, "response_type": "paged"},
         )
         return result.list
 
             "GET",
             f"plugin/{tenant_id}/management/list",
             PluginListResponse,
-            params={"page": page, "page_size": page_size},
+            params={"page": page, "page_size": page_size, "response_type": "paged"},
         )
 
     def upload_pkg(

api/core/prompt/advanced_prompt_transform.py

 
             if prompt_item.edition_type == "basic" or not prompt_item.edition_type:
                 if self.with_variable_tmpl:
-                    vp = VariablePool()
+                    vp = VariablePool.empty()
                     for k, v in inputs.items():
                         if k.startswith("#"):
                             vp.add(k[1:-1].split("."), v)

api/core/prompt/utils/extract_thread_messages.py

-from typing import Any
+from collections.abc import Sequence
 
 from constants import UUID_NIL
+from models import Message
 
 
-def extract_thread_messages(messages: list[Any]):
-    thread_messages = []
+def extract_thread_messages(messages: Sequence[Message]):
+    thread_messages: list[Message] = []
     next_message = None
 
     for message in messages:

api/core/prompt/utils/get_thread_messages_length.py

+from sqlalchemy import select
+
 from core.prompt.utils.extract_thread_messages import extract_thread_messages
 from extensions.ext_database import db
 from models.model import Message
     Get the number of thread messages based on the parent message id.
     """
     # Fetch all messages related to the conversation
-    query = (
-        db.session.query(
-            Message.id,
-            Message.parent_message_id,
-            Message.answer,
-        )
-        .filter(
-            Message.conversation_id == conversation_id,
-        )
-        .order_by(Message.created_at.desc())
-    )
+    stmt = select(Message).where(Message.conversation_id == conversation_id).order_by(Message.created_at.desc())
 
-    messages = query.all()
+    messages = db.session.scalars(stmt).all()
 
     # Extract thread messages
     thread_messages = extract_thread_messages(messages)

api/core/rag/cleaner/unstructured/unstructured_extra_whitespace_cleaner.py

-"""Abstract interface for document clean implementations."""
-
-from core.rag.cleaner.cleaner_base import BaseCleaner
-
-
-class UnstructuredNonAsciiCharsCleaner(BaseCleaner):
-    def clean(self, content) -> str:
-        """clean document content."""
-        from unstructured.cleaners.core import clean_extra_whitespace
-
-        # Returns "ITEM 1A: RISK FACTORS"
-        return clean_extra_whitespace(content)

api/core/rag/cleaner/unstructured/unstructured_group_broken_paragraphs_cleaner.py

-"""Abstract interface for document clean implementations."""
-
-from core.rag.cleaner.cleaner_base import BaseCleaner
-
-
-class UnstructuredGroupBrokenParagraphsCleaner(BaseCleaner):
-    def clean(self, content) -> str:
-        """clean document content."""
-        import re
-
-        from unstructured.cleaners.core import group_broken_paragraphs
-
-        para_split_re = re.compile(r"(\s*\n\s*){3}")
-
-        return group_broken_paragraphs(content, paragraph_split=para_split_re)

api/core/rag/cleaner/unstructured/unstructured_non_ascii_chars_cleaner.py

-"""Abstract interface for document clean implementations."""
-
-from core.rag.cleaner.cleaner_base import BaseCleaner
-
-
-class UnstructuredNonAsciiCharsCleaner(BaseCleaner):
-    def clean(self, content) -> str:
-        """clean document content."""
-        from unstructured.cleaners.core import clean_non_ascii_chars
-
-        # Returns "This text contains non-ascii characters!"
-        return clean_non_ascii_chars(content)

api/core/rag/cleaner/unstructured/unstructured_replace_unicode_quotes_cleaner.py

-"""Abstract interface for document clean implementations."""
-
-from core.rag.cleaner.cleaner_base import BaseCleaner
-
-
-class UnstructuredNonAsciiCharsCleaner(BaseCleaner):
-    def clean(self, content) -> str:
-        """Replaces unicode quote characters, such as the \x91 character in a string."""
-
-        from unstructured.cleaners.core import replace_unicode_quotes
-
-        return replace_unicode_quotes(content)

api/core/rag/cleaner/unstructured/unstructured_translate_text_cleaner.py

-"""Abstract interface for document clean implementations."""
-
-from core.rag.cleaner.cleaner_base import BaseCleaner
-
-
-class UnstructuredTranslateTextCleaner(BaseCleaner):
-    def clean(self, content) -> str:
-        """clean document content."""
-        from unstructured.cleaners.translate import translate_text
-
-        return translate_text(content)

api/core/rag/datasource/retrieval_service.py

 from typing import Optional
 
 from flask import Flask, current_app
-from sqlalchemy.orm import load_only
+from sqlalchemy.orm import Session, load_only
 
 from configs import dify_config
 from core.rag.data_post_processor.data_post_processor import DataPostProcessor
 
     @classmethod
     def _get_dataset(cls, dataset_id: str) -> Optional[Dataset]:
-        return db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
+        with Session(db.engine) as session:
+            return session.query(Dataset).filter(Dataset.id == dataset_id).first()
 
     @classmethod
     def keyword_search(

api/core/rag/datasource/vdb/tablestore/tablestore_vector.py

 
 import tablestore  # type: ignore
 from pydantic import BaseModel, model_validator
+from tablestore import BatchGetRowRequest, TableInBatchGetRowItem
 
 from configs import dify_config
 from core.rag.datasource.vdb.field import Field
         self._index_name = f"{collection_name}_idx"
         self._tags_field = f"{Field.METADATA_KEY.value}_tags"
 
+    def create_collection(self, embeddings: list[list[float]], **kwargs):
+        dimension = len(embeddings[0])
+        self._create_collection(dimension)
+
+    def get_by_ids(self, ids: list[str]) -> list[Document]:
+        docs = []
+        request = BatchGetRowRequest()
+        columns_to_get = [Field.METADATA_KEY.value, Field.CONTENT_KEY.value]
+        rows_to_get = [[("id", _id)] for _id in ids]
+        request.add(TableInBatchGetRowItem(self._table_name, rows_to_get, columns_to_get, None, 1))
+
+        result = self._tablestore_client.batch_get_row(request)
+        table_result = result.get_result_by_table(self._table_name)
+        for item in table_result:
+            if item.is_ok and item.row:
+                kv = {k: v for k, v, t in item.row.attribute_columns}
+                docs.append(
+                    Document(
+                        page_content=kv[Field.CONTENT_KEY.value], metadata=json.loads(kv[Field.METADATA_KEY.value])
+                    )
+                )
+        return docs
+
     def get_type(self) -> str:
         return VectorType.TABLESTORE
 

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

-from typing import Optional
-
-from pydantic import BaseModel
-
-
-class ClusterEntity(BaseModel):
-    """
-    Model Config Entity.
-    """
-
-    name: str
-    cluster_id: str
-    displayName: str
-    region: str
-    spendingLimit: Optional[int] = 1000
-    version: str
-    createdBy: str

api/core/rag/extractor/blob/blob.py

 
 import contextlib
 import mimetypes
-from abc import ABC, abstractmethod
-from collections.abc import Generator, Iterable, Mapping
+from collections.abc import Generator, Mapping
 from io import BufferedReader, BytesIO
 from pathlib import Path, PurePath
 from typing import Any, Optional, Union
         if self.source:
             str_repr += f" {self.source}"
         return str_repr
-
-
-class BlobLoader(ABC):
-    """Abstract interface for blob loaders implementation.
-
-    Implementer should be able to load raw content from a datasource system according
-    to some criteria and return the raw content lazily as a stream of blobs.
-    """
-
-    @abstractmethod
-    def yield_blobs(
-        self,
-    ) -> Iterable[Blob]:
-        """A lazy loader for raw data represented by Blob object.
-
-        Returns:
-            A generator over blobs
-        """

api/core/rag/extractor/unstructured/unstructured_pdf_extractor.py

-import logging
-
-from core.rag.extractor.extractor_base import BaseExtractor
-from core.rag.models.document import Document
-
-logger = logging.getLogger(__name__)
-
-
-class UnstructuredPDFExtractor(BaseExtractor):
-    """Load pdf files.
-
-
-    Args:
-        file_path: Path to the file to load.
-
-        api_url: Unstructured API URL
-
-        api_key: Unstructured API Key
-    """
-
-    def __init__(self, file_path: str, api_url: str, api_key: str):
-        """Initialize with file path."""
-        self._file_path = file_path
-        self._api_url = api_url
-        self._api_key = api_key
-
-    def extract(self) -> list[Document]:
-        if self._api_url:
-            from unstructured.partition.api import partition_via_api
-
-            elements = partition_via_api(
-                filename=self._file_path, api_url=self._api_url, api_key=self._api_key, strategy="auto"
-            )
-        else:
-            from unstructured.partition.pdf import partition_pdf
-
-            elements = partition_pdf(filename=self._file_path, strategy="auto")
-
-        from unstructured.chunking.title import chunk_by_title
-
-        chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
-        documents = []
-        for chunk in chunks:
-            text = chunk.text.strip()
-            documents.append(Document(page_content=text))
-
-        return documents

api/core/rag/extractor/unstructured/unstructured_text_extractor.py

-import logging
-
-from core.rag.extractor.extractor_base import BaseExtractor
-from core.rag.models.document import Document
-
-logger = logging.getLogger(__name__)
-
-
-class UnstructuredTextExtractor(BaseExtractor):
-    """Load msg files.
-
-
-    Args:
-        file_path: Path to the file to load.
-    """
-
-    def __init__(self, file_path: str, api_url: str):
-        """Initialize with file path."""
-        self._file_path = file_path
-        self._api_url = api_url
-
-    def extract(self) -> list[Document]:
-        from unstructured.partition.text import partition_text
-
-        elements = partition_text(filename=self._file_path)
-        from unstructured.chunking.title import chunk_by_title
-
-        chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
-        documents = []
-        for chunk in chunks:
-            text = chunk.text.strip()
-            documents.append(Document(page_content=text))
-
-        return documents

api/core/rag/retrieval/dataset_retrieval.py

 from flask import Flask, current_app
 from sqlalchemy import Float, and_, or_, text
 from sqlalchemy import cast as sqlalchemy_cast
+from sqlalchemy.orm import Session
 
 from core.app.app_config.entities import (
     DatasetEntity,
         metadata_condition: Optional[MetadataCondition] = None,
     ):
         with flask_app.app_context():
-            dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
+            with Session(db.engine) as session:
+                dataset = session.query(Dataset).filter(Dataset.id == dataset_id).first()
 
             if not dataset:
                 return []

api/core/rag/splitter/text_splitter.py

     Any,
     Literal,
     Optional,
-    TypedDict,
     TypeVar,
     Union,
 )
         raise NotImplementedError
 
 
-class CharacterTextSplitter(TextSplitter):
-    """Splitting text that looks at characters."""
-
-    def __init__(self, separator: str = "\n\n", **kwargs: Any) -> None:
-        """Create a new TextSplitter."""
-        super().__init__(**kwargs)
-        self._separator = separator
-
-    def split_text(self, text: str) -> list[str]:
-        """Split incoming text and return chunks."""
-        # First we naively split the large input into a bunch of smaller ones.
-        splits = _split_text_with_regex(text, self._separator, self._keep_separator)
-        _separator = "" if self._keep_separator else self._separator
-        _good_splits_lengths = []  # cache the lengths of the splits
-        if splits:
-            _good_splits_lengths.extend(self._length_function(splits))
-        return self._merge_splits(splits, _separator, _good_splits_lengths)
-
-
-class LineType(TypedDict):
-    """Line type as typed dict."""
-
-    metadata: dict[str, str]
-    content: str
-
-
-class HeaderType(TypedDict):
-    """Header type as typed dict."""
-
-    level: int
-    name: str
-    data: str
-
-
-class MarkdownHeaderTextSplitter:
-    """Splitting markdown files based on specified headers."""
-
-    def __init__(self, headers_to_split_on: list[tuple[str, str]], return_each_line: bool = False):
-        """Create a new MarkdownHeaderTextSplitter.
-
-        Args:
-            headers_to_split_on: Headers we want to track
-            return_each_line: Return each line w/ associated headers
-        """
-        # Output line-by-line or aggregated into chunks w/ common headers
-        self.return_each_line = return_each_line
-        # Given the headers we want to split on,
-        # (e.g., "#, ##, etc") order by length
-        self.headers_to_split_on = sorted(headers_to_split_on, key=lambda split: len(split[0]), reverse=True)
-
-    def aggregate_lines_to_chunks(self, lines: list[LineType]) -> list[Document]:
-        """Combine lines with common metadata into chunks
-        Args:
-            lines: Line of text / associated header metadata
-        """
-        aggregated_chunks: list[LineType] = []
-
-        for line in lines:
-            if aggregated_chunks and aggregated_chunks[-1]["metadata"] == line["metadata"]:
-                # If the last line in the aggregated list
-                # has the same metadata as the current line,
-                # append the current content to the last lines's content
-                aggregated_chunks[-1]["content"] += "  \n" + line["content"]
-            else:
-                # Otherwise, append the current line to the aggregated list
-                aggregated_chunks.append(line)
-
-        return [Document(page_content=chunk["content"], metadata=chunk["metadata"]) for chunk in aggregated_chunks]
-
-    def split_text(self, text: str) -> list[Document]:
-        """Split markdown file
-        Args:
-            text: Markdown file"""
-
-        # Split the input text by newline character ("\n").
-        lines = text.split("\n")
-        # Final output
-        lines_with_metadata: list[LineType] = []
-        # Content and metadata of the chunk currently being processed
-        current_content: list[str] = []
-        current_metadata: dict[str, str] = {}
-        # Keep track of the nested header structure
-        # header_stack: List[Dict[str, Union[int, str]]] = []
-        header_stack: list[HeaderType] = []
-        initial_metadata: dict[str, str] = {}
-
-        for line in lines:
-            stripped_line = line.strip()
-            # Check each line against each of the header types (e.g., #, ##)
-            for sep, name in self.headers_to_split_on:
-                # Check if line starts with a header that we intend to split on
-                if stripped_line.startswith(sep) and (
-                    # Header with no text OR header is followed by space
-                    # Both are valid conditions that sep is being used a header
-                    len(stripped_line) == len(sep) or stripped_line[len(sep)] == " "
-                ):
-                    # Ensure we are tracking the header as metadata
-                    if name is not None:
-                        # Get the current header level
-                        current_header_level = sep.count("#")
-
-                        # Pop out headers of lower or same level from the stack
-                        while header_stack and header_stack[-1]["level"] >= current_header_level:
-                            # We have encountered a new header
-                            # at the same or higher level
-                            popped_header = header_stack.pop()
-                            # Clear the metadata for the
-                            # popped header in initial_metadata
-                            if popped_header["name"] in initial_metadata:
-                                initial_metadata.pop(popped_header["name"])
-
-                        # Push the current header to the stack
-                        header: HeaderType = {
-                            "level": current_header_level,
-                            "name": name,
-                            "data": stripped_line[len(sep) :].strip(),
-                        }
-                        header_stack.append(header)
-                        # Update initial_metadata with the current header
-                        initial_metadata[name] = header["data"]
-
-                    # Add the previous line to the lines_with_metadata
-                    # only if current_content is not empty
-                    if current_content:
-                        lines_with_metadata.append(
-                            {
-                                "content": "\n".join(current_content),
-                                "metadata": current_metadata.copy(),
-                            }
-                        )
-                        current_content.clear()
-
-                    break
-            else:
-                if stripped_line:
-                    current_content.append(stripped_line)
-                elif current_content:
-                    lines_with_metadata.append(
-                        {
-                            "content": "\n".join(current_content),
-                            "metadata": current_metadata.copy(),
-                        }
-                    )
-                    current_content.clear()
-
-            current_metadata = initial_metadata.copy()
-
-        if current_content:
-            lines_with_metadata.append({"content": "\n".join(current_content), "metadata": current_metadata})
-
-        # lines_with_metadata has each line with associated header metadata
-        # aggregate these into chunks based on common metadata
-        if not self.return_each_line:
-            return self.aggregate_lines_to_chunks(lines_with_metadata)
-        else:
-            return [
-                Document(page_content=chunk["content"], metadata=chunk["metadata"]) for chunk in lines_with_metadata
-            ]
-
-
-# should be in newer Python versions (3.10+)
 # @dataclass(frozen=True, kw_only=True, slots=True)
 @dataclass(frozen=True)
 class Tokenizer:

api/core/repositories/__init__.py

 defined in the core.workflow.repository package.
 """
 
+from core.repositories.factory import DifyCoreRepositoryFactory, RepositoryImportError
 from core.repositories.sqlalchemy_workflow_node_execution_repository import SQLAlchemyWorkflowNodeExecutionRepository
 
 __all__ = [
+    "DifyCoreRepositoryFactory",
+    "RepositoryImportError",
     "SQLAlchemyWorkflowNodeExecutionRepository",
 ]

api/core/repositories/factory.py

+"""
+Repository factory for dynamically creating repository instances based on configuration.
+
+This module provides a Django-like settings system for repository implementations,
+allowing users to configure different repository backends through string paths.
+"""
+
+import importlib
+import inspect
+import logging
+from typing import Protocol, Union
+
+from sqlalchemy.engine import Engine
+from sqlalchemy.orm import sessionmaker
+
+from configs import dify_config
+from core.workflow.repositories.workflow_execution_repository import WorkflowExecutionRepository
+from core.workflow.repositories.workflow_node_execution_repository import WorkflowNodeExecutionRepository
+from models import Account, EndUser
+from models.enums import WorkflowRunTriggeredFrom
+from models.workflow import WorkflowNodeExecutionTriggeredFrom
+
+logger = logging.getLogger(__name__)
+
+
+class RepositoryImportError(Exception):
+    """Raised when a repository implementation cannot be imported or instantiated."""
+
+    pass
+
+
+class DifyCoreRepositoryFactory:
+    """
+    Factory for creating repository instances based on configuration.
+
+    This factory supports Django-like settings where repository implementations
+    are specified as module paths (e.g., 'module.submodule.ClassName').
+    """
+
+    @staticmethod
+    def _import_class(class_path: str) -> type:
+        """
+        Import a class from a module path string.
+
+        Args:
+            class_path: Full module path to the class (e.g., 'module.submodule.ClassName')
+
+        Returns:
+            The imported class
+
+        Raises:
+            RepositoryImportError: If the class cannot be imported
+        """
+        try:
+            module_path, class_name = class_path.rsplit(".", 1)
+            module = importlib.import_module(module_path)
+            repo_class = getattr(module, class_name)
+            assert isinstance(repo_class, type)
+            return repo_class
+        except (ValueError, ImportError, AttributeError) as e:
+            raise RepositoryImportError(f"Cannot import repository class '{class_path}': {e}") from e
+
+    @staticmethod
+    def _validate_repository_interface(repository_class: type, expected_interface: type[Protocol]) -> None:  # type: ignore
+        """
+        Validate that a class implements the expected repository interface.
+
+        Args:
+            repository_class: The class to validate
+            expected_interface: The expected interface/protocol
+
+        Raises:
+            RepositoryImportError: If the class doesn't implement the interface
+        """
+        # Check if the class has all required methods from the protocol
+        required_methods = [
+            method
+            for method in dir(expected_interface)
+            if not method.startswith("_") and callable(getattr(expected_interface, method, None))
+        ]
+
+        missing_methods = []
+        for method_name in required_methods:
+            if not hasattr(repository_class, method_name):
+                missing_methods.append(method_name)
+
+        if missing_methods:
+            raise RepositoryImportError(
+                f"Repository class '{repository_class.__name__}' does not implement required methods "
+                f"{missing_methods} from interface '{expected_interface.__name__}'"
+            )
+
+    @staticmethod
+    def _validate_constructor_signature(repository_class: type, required_params: list[str]) -> None:
+        """
+        Validate that a repository class constructor accepts required parameters.
+
+        Args:
+            repository_class: The class to validate
+            required_params: List of required parameter names
+
+        Raises:
+            RepositoryImportError: If the constructor doesn't accept required parameters
+        """
+
+        try:
+            # MyPy may flag the line below with the following error:
+            #
+            # > Accessing "__init__" on an instance is unsound, since
+            # > instance.__init__ could be from an incompatible subclass.
+            #
+            # Despite this, we need to ensure that the constructor of `repository_class`
+            # has a compatible signature.
+            signature = inspect.signature(repository_class.__init__)  # type: ignore[misc]
+            param_names = list(signature.parameters.keys())
+
+            # Remove 'self' parameter
+            if "self" in param_names:
+                param_names.remove("self")
+
+            missing_params = [param for param in required_params if param not in param_names]
+            if missing_params:
+                raise RepositoryImportError(
+                    f"Repository class '{repository_class.__name__}' constructor does not accept required parameters: "
+                    f"{missing_params}. Expected parameters: {required_params}"
+                )
+        except Exception as e:
+            raise RepositoryImportError(
+                f"Failed to validate constructor signature for '{repository_class.__name__}': {e}"
+            ) from e
+
+    @classmethod
+    def create_workflow_execution_repository(
+        cls,
+        session_factory: Union[sessionmaker, Engine],
+        user: Union[Account, EndUser],
+        app_id: str,
+        triggered_from: WorkflowRunTriggeredFrom,
+    ) -> WorkflowExecutionRepository:
+        """
+        Create a WorkflowExecutionRepository instance based on configuration.
+
+        Args:
+            session_factory: SQLAlchemy sessionmaker or engine
+            user: Account or EndUser object
+            app_id: Application ID
+            triggered_from: Source of the execution trigger
+
+        Returns:
+            Configured WorkflowExecutionRepository instance
+
+        Raises:
+            RepositoryImportError: If the configured repository cannot be created
+        """
+        class_path = dify_config.CORE_WORKFLOW_EXECUTION_REPOSITORY
+        logger.debug(f"Creating WorkflowExecutionRepository from: {class_path}")
+
+        try:
+            repository_class = cls._import_class(class_path)
+            cls._validate_repository_interface(repository_class, WorkflowExecutionRepository)
+            cls._validate_constructor_signature(
+                repository_class, ["session_factory", "user", "app_id", "triggered_from"]
+            )
+
+            return repository_class(  # type: ignore[no-any-return]
+                session_factory=session_factory,
+                user=user,
+                app_id=app_id,
+                triggered_from=triggered_from,
+            )
+        except RepositoryImportError:
+            # Re-raise our custom errors as-is
+            raise
+        except Exception as e:
+            logger.exception("Failed to create WorkflowExecutionRepository")
+            raise RepositoryImportError(f"Failed to create WorkflowExecutionRepository from '{class_path}': {e}") from e
+
+    @classmethod
+    def create_workflow_node_execution_repository(
+        cls,
+        session_factory: Union[sessionmaker, Engine],
+        user: Union[Account, EndUser],
+        app_id: str,
+        triggered_from: WorkflowNodeExecutionTriggeredFrom,
+    ) -> WorkflowNodeExecutionRepository:
+        """
+        Create a WorkflowNodeExecutionRepository instance based on configuration.
+
+        Args:
+            session_factory: SQLAlchemy sessionmaker or engine
+            user: Account or EndUser object
+            app_id: Application ID
+            triggered_from: Source of the execution trigger
+
+        Returns:
+            Configured WorkflowNodeExecutionRepository instance
+
+        Raises:
+            RepositoryImportError: If the configured repository cannot be created
+        """
+        class_path = dify_config.CORE_WORKFLOW_NODE_EXECUTION_REPOSITORY
+        logger.debug(f"Creating WorkflowNodeExecutionRepository from: {class_path}")
+
+        try:
+            repository_class = cls._import_class(class_path)
+            cls._validate_repository_interface(repository_class, WorkflowNodeExecutionRepository)
+            cls._validate_constructor_signature(
+                repository_class, ["session_factory", "user", "app_id", "triggered_from"]
+            )
+
+            return repository_class(  # type: ignore[no-any-return]
+                session_factory=session_factory,
+                user=user,
+                app_id=app_id,
+                triggered_from=triggered_from,
+            )
+        except RepositoryImportError:
+            # Re-raise our custom errors as-is
+            raise
+        except Exception as e:
+            logger.exception("Failed to create WorkflowNodeExecutionRepository")
+            raise RepositoryImportError(
+                f"Failed to create WorkflowNodeExecutionRepository from '{class_path}': {e}"
+            ) from e

api/core/tools/entities/tool_entities.py

     cast_parameter_value,
     init_frontend_parameter,
 )
+from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.tools.entities.common_entities import I18nObject
 from core.tools.entities.constants import TOOL_SELECTOR_MODEL_IDENTITY
 
         data: Mapping[str, Any] = Field(..., description="Detailed log data")
         metadata: Optional[Mapping[str, Any]] = Field(default=None, description="The metadata of the log")
 
+    class RetrieverResourceMessage(BaseModel):
+        retriever_resources: list[RetrievalSourceMetadata] = Field(..., description="retriever resources")
+        context: str = Field(..., description="context")
+
     class MessageType(Enum):
         TEXT = "text"
         IMAGE = "image"
         FILE = "file"
         LOG = "log"
         BLOB_CHUNK = "blob_chunk"
+        RETRIEVER_RESOURCES = "retriever_resources"
 
     type: MessageType = MessageType.TEXT
     """
         plain text, image url or link url
     """
     message: (
-        JsonMessage | TextMessage | BlobChunkMessage | BlobMessage | LogMessage | FileMessage | None | VariableMessage
+        JsonMessage
+        | TextMessage
+        | BlobChunkMessage
+        | BlobMessage
+        | LogMessage
+        | FileMessage
+        | None
+        | VariableMessage
+        | RetrieverResourceMessage
     )
     meta: dict[str, Any] | None = None
 
         FILES = PluginParameterType.FILES.value
         APP_SELECTOR = PluginParameterType.APP_SELECTOR.value
         MODEL_SELECTOR = PluginParameterType.MODEL_SELECTOR.value
+        ANY = PluginParameterType.ANY.value
         DYNAMIC_SELECT = PluginParameterType.DYNAMIC_SELECT.value
 
         # MCP object and array type parameters

api/core/tools/utils/parser.py

 import re
-import uuid
 from json import dumps as json_dumps
 from json import loads as json_loads
 from json.decoder import JSONDecodeError
                 # remove special characters like / to ensure the operation id is valid ^[a-zA-Z0-9_-]{1,64}$
                 path = re.sub(r"[^a-zA-Z0-9_-]", "", path)
                 if not path:
-                    path = str(uuid.uuid4())
+                    path = "<root>"
 
                 interface["operation"]["operationId"] = f"{path}_{interface['method']}"
 

api/core/variables/segments.py

 import json
 import sys
 from collections.abc import Mapping, Sequence
-from typing import Any
+from typing import Annotated, Any, TypeAlias
 
-from pydantic import BaseModel, ConfigDict, field_validator
+from pydantic import BaseModel, ConfigDict, Discriminator, Tag, field_validator
 
 from core.file import File
 
 
 
 class Segment(BaseModel):
+    """Segment is runtime type used during the execution of workflow.
+
+    Note: this class is abstract, you should use subclasses of this class instead.
+    """
+
     model_config = ConfigDict(frozen=True)
 
     value_type: SegmentType
 
 
 class FloatSegment(Segment):
-    value_type: SegmentType = SegmentType.NUMBER
+    value_type: SegmentType = SegmentType.FLOAT
     value: float
     # NOTE(QuantumGhost): seems that the equality for FloatSegment with `NaN` value has some problems.
     # The following tests cannot pass.
 
 
 class IntegerSegment(Segment):
-    value_type: SegmentType = SegmentType.NUMBER
+    value_type: SegmentType = SegmentType.INTEGER
     value: int
 
 
     @property
     def text(self) -> str:
         return ""
+
+
+def get_segment_discriminator(v: Any) -> SegmentType | None:
+    if isinstance(v, Segment):
+        return v.value_type
+    elif isinstance(v, dict):
+        value_type = v.get("value_type")
+        if value_type is None:
+            return None
+        try:
+            seg_type = SegmentType(value_type)
+        except ValueError:
+            return None
+        return seg_type
+    else:
+        # return None if the discriminator value isn't found
+        return None
+
+
+# The `SegmentUnion`` type is used to enable serialization and deserialization with Pydantic.
+# Use `Segment` for type hinting when serialization is not required.
+#
+# Note:
+# - All variants in `SegmentUnion` must inherit from the `Segment` class.
+# - The union must include all non-abstract subclasses of `Segment`, except:
+#   - `SegmentGroup`, which is not added to the variable pool.
+#   - `Variable` and its subclasses, which are handled by `VariableUnion`.
+SegmentUnion: TypeAlias = Annotated[
+    (
+        Annotated[NoneSegment, Tag(SegmentType.NONE)]
+        | Annotated[StringSegment, Tag(SegmentType.STRING)]
+        | Annotated[FloatSegment, Tag(SegmentType.FLOAT)]
+        | Annotated[IntegerSegment, Tag(SegmentType.INTEGER)]
+        | Annotated[ObjectSegment, Tag(SegmentType.OBJECT)]
+        | Annotated[FileSegment, Tag(SegmentType.FILE)]
+        | Annotated[ArrayAnySegment, Tag(SegmentType.ARRAY_ANY)]
+        | Annotated[ArrayStringSegment, Tag(SegmentType.ARRAY_STRING)]
+        | Annotated[ArrayNumberSegment, Tag(SegmentType.ARRAY_NUMBER)]
+        | Annotated[ArrayObjectSegment, Tag(SegmentType.ARRAY_OBJECT)]
+        | Annotated[ArrayFileSegment, Tag(SegmentType.ARRAY_FILE)]
+    ),
+    Discriminator(get_segment_discriminator),
+]

api/core/variables/types.py

+from collections.abc import Mapping
 from enum import StrEnum
+from typing import Any, Optional
+
+from core.file.models import File
+
+
+class ArrayValidation(StrEnum):
+    """Strategy for validating array elements"""
+
+    # Skip element validation (only check array container)
+    NONE = "none"
+
+    # Validate the first element (if array is non-empty)
+    FIRST = "first"
+
+    # Validate all elements in the array.
+    ALL = "all"
 
 
 class SegmentType(StrEnum):
     NUMBER = "number"
+    INTEGER = "integer"
+    FLOAT = "float"
     STRING = "string"
     OBJECT = "object"
     SECRET = "secret"
 
     GROUP = "group"
 
-    def is_array_type(self):
+    def is_array_type(self) -> bool:
         return self in _ARRAY_TYPES
 
+    @classmethod
+    def infer_segment_type(cls, value: Any) -> Optional["SegmentType"]:
+        """
+        Attempt to infer the `SegmentType` based on the Python type of the `value` parameter.
+
+        Returns `None` if no appropriate `SegmentType` can be determined for the given `value`.
+        For example, this may occur if the input is a generic Python object of type `object`.
+        """
+
+        if isinstance(value, list):
+            elem_types: set[SegmentType] = set()
+            for i in value:
+                segment_type = cls.infer_segment_type(i)
+                if segment_type is None:
+                    return None
+
+                elem_types.add(segment_type)
+
+            if len(elem_types) != 1:
+                if elem_types.issubset(_NUMERICAL_TYPES):
+                    return SegmentType.ARRAY_NUMBER
+                return SegmentType.ARRAY_ANY
+            elif all(i.is_array_type() for i in elem_types):
+                return SegmentType.ARRAY_ANY
+            match elem_types.pop():
+                case SegmentType.STRING:
+                    return SegmentType.ARRAY_STRING
+                case SegmentType.NUMBER | SegmentType.INTEGER | SegmentType.FLOAT:
+                    return SegmentType.ARRAY_NUMBER
+                case SegmentType.OBJECT:
+                    return SegmentType.ARRAY_OBJECT
+                case SegmentType.FILE:
+                    return SegmentType.ARRAY_FILE
+                case SegmentType.NONE:
+                    return SegmentType.ARRAY_ANY
+                case _:
+                    # This should be unreachable.
+                    raise ValueError(f"not supported value {value}")
+        if value is None:
+            return SegmentType.NONE
+        elif isinstance(value, int) and not isinstance(value, bool):
+            return SegmentType.INTEGER
+        elif isinstance(value, float):
+            return SegmentType.FLOAT
+        elif isinstance(value, str):
+            return SegmentType.STRING
+        elif isinstance(value, dict):
+            return SegmentType.OBJECT
+        elif isinstance(value, File):
+            return SegmentType.FILE
+        else:
+            return None
+
+    def _validate_array(self, value: Any, array_validation: ArrayValidation) -> bool:
+        if not isinstance(value, list):
+            return False
+        # Skip element validation if array is empty
+        if len(value) == 0:
+            return True
+        if self == SegmentType.ARRAY_ANY:
+            return True
+        element_type = _ARRAY_ELEMENT_TYPES_MAPPING[self]
+
+        if array_validation == ArrayValidation.NONE:
+            return True
+        elif array_validation == ArrayValidation.FIRST:
+            return element_type.is_valid(value[0])
+        else:
+            return all([element_type.is_valid(i, array_validation=ArrayValidation.NONE)] for i in value)
+
+    def is_valid(self, value: Any, array_validation: ArrayValidation = ArrayValidation.FIRST) -> bool:
+        """
+        Check if a value matches the segment type.
+        Users of `SegmentType` should call this method, instead of using
+        `isinstance` manually.
+
+        Args:
+            value: The value to validate
+            array_validation: Validation strategy for array types (ignored for non-array types)
+
+        Returns:
+            True if the value matches the type under the given validation strategy
+        """
+        if self.is_array_type():
+            return self._validate_array(value, array_validation)
+        elif self == SegmentType.NUMBER:
+            return isinstance(value, (int, float))
+        elif self == SegmentType.STRING:
+            return isinstance(value, str)
+        elif self == SegmentType.OBJECT:
+            return isinstance(value, dict)
+        elif self == SegmentType.SECRET:
+            return isinstance(value, str)
+        elif self == SegmentType.FILE:
+            return isinstance(value, File)
+        elif self == SegmentType.NONE:
+            return value is None
+        else:
+            raise AssertionError("this statement should be unreachable.")
+
+    def exposed_type(self) -> "SegmentType":
+        """Returns the type exposed to the frontend.
+
+        The frontend treats `INTEGER` and `FLOAT` as `NUMBER`, so these are returned as `NUMBER` here.
+        """
+        if self in (SegmentType.INTEGER, SegmentType.FLOAT):
+            return SegmentType.NUMBER
+        return self
+
+
+_ARRAY_ELEMENT_TYPES_MAPPING: Mapping[SegmentType, SegmentType] = {
+    # ARRAY_ANY does not have correpond element type.
+    SegmentType.ARRAY_STRING: SegmentType.STRING,
+    SegmentType.ARRAY_NUMBER: SegmentType.NUMBER,
+    SegmentType.ARRAY_OBJECT: SegmentType.OBJECT,
+    SegmentType.ARRAY_FILE: SegmentType.FILE,
+}
 
 _ARRAY_TYPES = frozenset(
-    [
+    list(_ARRAY_ELEMENT_TYPES_MAPPING.keys())
+    + [
         SegmentType.ARRAY_ANY,
-        SegmentType.ARRAY_STRING,
-        SegmentType.ARRAY_NUMBER,
-        SegmentType.ARRAY_OBJECT,
-        SegmentType.ARRAY_FILE,
+    ]
+)
+
+
+_NUMERICAL_TYPES = frozenset(
+    [
+        SegmentType.NUMBER,
+        SegmentType.INTEGER,
+        SegmentType.FLOAT,
     ]
 )

api/core/variables/variables.py

 from uuid import uuid4
 
 from pydantic import BaseModel, Field
+from typing import Annotated, TypeAlias, cast
+from uuid import uuid4
+
+from pydantic import Discriminator, Field, Tag
 
 from core.helper import encrypter
 
     ObjectSegment,
     Segment,
     StringSegment,
+    get_segment_discriminator,
 )
 from .types import SegmentType
 
 class Variable(Segment):
     """
     A variable is a segment that has a name.
+
+    It is mainly used to store segments and their selector in VariablePool.
+
+    Note: this class is abstract, you should use subclasses of this class instead.
     """
 
     id: str = Field(
 class RAGPipelineVariableInput(BaseModel):
     variable: RAGPipelineVariable
     value: Any
+# The `VariableUnion`` type is used to enable serialization and deserialization with Pydantic.
+# Use `Variable` for type hinting when serialization is not required.
+#
+# Note:
+# - All variants in `VariableUnion` must inherit from the `Variable` class.
+# - The union must include all non-abstract subclasses of `Segment`, except:
+VariableUnion: TypeAlias = Annotated[
+    (
+        Annotated[NoneVariable, Tag(SegmentType.NONE)]
+        | Annotated[StringVariable, Tag(SegmentType.STRING)]
+        | Annotated[FloatVariable, Tag(SegmentType.FLOAT)]
+        | Annotated[IntegerVariable, Tag(SegmentType.INTEGER)]
+        | Annotated[ObjectVariable, Tag(SegmentType.OBJECT)]
+        | Annotated[FileVariable, Tag(SegmentType.FILE)]
+        | Annotated[ArrayAnyVariable, Tag(SegmentType.ARRAY_ANY)]
+        | Annotated[ArrayStringVariable, Tag(SegmentType.ARRAY_STRING)]
+        | Annotated[ArrayNumberVariable, Tag(SegmentType.ARRAY_NUMBER)]
+        | Annotated[ArrayObjectVariable, Tag(SegmentType.ARRAY_OBJECT)]
+        | Annotated[ArrayFileVariable, Tag(SegmentType.ARRAY_FILE)]
+        | Annotated[SecretVariable, Tag(SegmentType.SECRET)]
+    ),
+    Discriminator(get_segment_discriminator),
+]

api/core/workflow/entities/variable_pool.py

 import re
 from collections import defaultdict
 from collections.abc import Mapping, Sequence
-from typing import Any, Union
+from typing import Annotated, Any, Union, cast
 
 from pydantic import BaseModel, Field
 
     SYSTEM_VARIABLE_NODE_ID,
 )
 from core.workflow.enums import SystemVariableKey
+from core.variables.variables import VariableUnion
+from core.workflow.constants import CONVERSATION_VARIABLE_NODE_ID, ENVIRONMENT_VARIABLE_NODE_ID, SYSTEM_VARIABLE_NODE_ID
+from core.workflow.system_variable import SystemVariable
 from factories import variable_factory
 
 VariableValue = Union[str, int, float, dict, list, File]
     # The first element of the selector is the node id, it's the first-level key in the dictionary.
     # Other elements of the selector are the keys in the second-level dictionary. To get the key, we hash the
     # elements of the selector except the first one.
-    variable_dictionary: dict[str, dict[int, Segment]] = Field(
+    variable_dictionary: defaultdict[str, Annotated[dict[int, VariableUnion], Field(default_factory=dict)]] = Field(
         description="Variables mapping",
         default=defaultdict(dict),
     )
-    # TODO: This user inputs is not used for pool.
+
+    # The `user_inputs` is used only when constructing the inputs for the `StartNode`. It's not used elsewhere.
     user_inputs: Mapping[str, Any] = Field(
         description="User inputs",
         default_factory=dict,
     )
-    system_variables: Mapping[SystemVariableKey, Any] = Field(
+    system_variables: SystemVariable = Field(
         description="System variables",
-        default_factory=dict,
     )
-    environment_variables: Sequence[Variable] = Field(
+    environment_variables: Sequence[VariableUnion] = Field(
         description="Environment variables.",
         default_factory=list,
     )
-    conversation_variables: Sequence[Variable] = Field(
+    conversation_variables: Sequence[VariableUnion] = Field(
         description="Conversation variables.",
         default_factory=list,
     )
     )
 
     def model_post_init(self, context: Any, /) -> None:
-        for key, value in self.system_variables.items():
-            self.add((SYSTEM_VARIABLE_NODE_ID, key.value), value)
+        # Create a mapping from field names to SystemVariableKey enum values
+        self._add_system_variables(self.system_variables)
         # Add environment variables to the variable pool
         for var in self.environment_variables:
             self.add((ENVIRONMENT_VARIABLE_NODE_ID, var.name), var)
             segment = variable_factory.build_segment(value)
             variable = variable_factory.segment_to_variable(segment=segment, selector=selector)
 
-        hash_key = hash(tuple(selector[1:]))
-        self.variable_dictionary[selector[0]][hash_key] = variable
+        key, hash_key = self._selector_to_keys(selector)
+        # Based on the definition of `VariableUnion`,
+        # `list[Variable]` can be safely used as `list[VariableUnion]` since they are compatible.
+        self.variable_dictionary[key][hash_key] = cast(VariableUnion, variable)
+
+    @classmethod
+    def _selector_to_keys(cls, selector: Sequence[str]) -> tuple[str, int]:
+        return selector[0], hash(tuple(selector[1:]))
+
+    def _has(self, selector: Sequence[str]) -> bool:
+        key, hash_key = self._selector_to_keys(selector)
+        if key not in self.variable_dictionary:
+            return False
+        if hash_key not in self.variable_dictionary[key]:
+            return False
+        return True
 
     def get(self, selector: Sequence[str], /) -> Segment | None:
         """
         if len(selector) < MIN_SELECTORS_LENGTH:
             return None
 
-        hash_key = hash(tuple(selector[1:]))
-        value = self.variable_dictionary[selector[0]].get(hash_key)
+        key, hash_key = self._selector_to_keys(selector)
+        value: Segment | None = self.variable_dictionary[key].get(hash_key)
 
         if value is None:
             selector, attr = selector[:-1], selector[-1]
         if len(selector) == 1:
             self.variable_dictionary[selector[0]] = {}
             return
-        hash_key = hash(tuple(selector[1:]))
-        self.variable_dictionary[selector[0]].pop(hash_key, None)
+        key, hash_key = self._selector_to_keys(selector)
+        self.variable_dictionary[key].pop(hash_key, None)
 
     def convert_template(self, template: str, /):
         parts = VARIABLE_PATTERN.split(template)
         if isinstance(segment, FileSegment):
             return segment
         return None
+
+    def _add_system_variables(self, system_variable: SystemVariable):
+        sys_var_mapping = system_variable.to_dict()
+        for key, value in sys_var_mapping.items():
+            if value is None:
+                continue
+            selector = (SYSTEM_VARIABLE_NODE_ID, key)
+            # If the system variable already exists, do not add it again.
+            # This ensures that we can keep the id of the system variables intact.
+            if self._has(selector):
+                continue
+            self.add(selector, value)  # type: ignore
+
+    @classmethod
+    def empty(cls) -> "VariablePool":
+        """Create an empty variable pool."""
+        return cls(system_variables=SystemVariable.empty())

api/core/workflow/entities/workflow_entities.py

-from typing import Optional
-
-from pydantic import BaseModel
-
-from core.app.entities.app_invoke_entities import InvokeFrom
-from core.workflow.nodes.base import BaseIterationState, BaseLoopState, BaseNode
-from models.enums import UserFrom
-from models.workflow import Workflow, WorkflowType
-
-from .node_entities import NodeRunResult
-from .variable_pool import VariablePool
-
-
-class WorkflowNodeAndResult:
-    node: BaseNode
-    result: Optional[NodeRunResult] = None
-
-    def __init__(self, node: BaseNode, result: Optional[NodeRunResult] = None):
-        self.node = node
-        self.result = result
-
-
-class WorkflowRunState:
-    tenant_id: str
-    app_id: str
-    workflow_id: str
-    workflow_type: WorkflowType
-    user_id: str
-    user_from: UserFrom
-    invoke_from: InvokeFrom
-
-    workflow_call_depth: int
-
-    start_at: float
-    variable_pool: VariablePool
-
-    total_tokens: int = 0
-
-    workflow_nodes_and_results: list[WorkflowNodeAndResult]
-
-    class NodeRun(BaseModel):
-        node_id: str
-        iteration_node_id: str
-        loop_node_id: str
-
-    workflow_node_runs: list[NodeRun]
-    workflow_node_steps: int
-
-    current_iteration_state: Optional[BaseIterationState]
-    current_loop_state: Optional[BaseLoopState]
-
-    def __init__(
-        self,
-        workflow: Workflow,
-        start_at: float,
-        variable_pool: VariablePool,
-        user_id: str,
-        user_from: UserFrom,
-        invoke_from: InvokeFrom,
-        workflow_call_depth: int,
-    ):
-        self.workflow_id = workflow.id
-        self.tenant_id = workflow.tenant_id
-        self.app_id = workflow.app_id
-        self.workflow_type = WorkflowType.value_of(workflow.type)
-        self.user_id = user_id
-        self.user_from = user_from
-        self.invoke_from = invoke_from
-        self.workflow_call_depth = workflow_call_depth
-
-        self.start_at = start_at
-        self.variable_pool = variable_pool
-
-        self.total_tokens = 0
-
-        self.workflow_node_steps = 1
-        self.workflow_node_runs = []
-        self.current_iteration_state = None
-        self.current_loop_state = None

api/core/workflow/graph_engine/entities/graph_runtime_state.py

     """total tokens"""
     llm_usage: LLMUsage = LLMUsage.empty_usage()
     """llm usage info"""
+
+    # The `outputs` field stores the final output values generated by executing workflows or chatflows.
+    #
+    # Note: Since the type of this field is `dict[str, Any]`, its values may not remain consistent
+    # after a serialization and deserialization round trip.
     outputs: dict[str, Any] = {}
-    """outputs"""
 
     node_run_steps: int = 0
     """node run steps"""

api/core/workflow/nodes/code/code_node.py

 from collections.abc import Mapping, Sequence
+from decimal import Decimal
 from typing import Any, Optional
 
 from configs import dify_config
             )
 
         if isinstance(value, float):
+            decimal_value = Decimal(str(value)).normalize()
+            precision = -decimal_value.as_tuple().exponent if decimal_value.as_tuple().exponent < 0 else 0  # type: ignore[operator]
             # raise error if precision is too high
-            if len(str(value).split(".")[1]) > dify_config.CODE_MAX_PRECISION:
+            if precision > dify_config.CODE_MAX_PRECISION:
                 raise OutputValidationError(
                     f"Output variable `{variable}` has too high precision,"
                     f" it must be less than {dify_config.CODE_MAX_PRECISION} digits."

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

                             )
                             return
                         elif self.node_data.error_handle_mode == ErrorHandleMode.TERMINATED:
-                            yield IterationRunFailedEvent(
-                                iteration_id=self.id,
-                                iteration_node_id=self.node_id,
-                                iteration_node_type=self.node_type,
-                                iteration_node_data=self.node_data,
-                                start_at=start_at,
-                                inputs=inputs,
-                                outputs={"output": None},
-                                steps=len(iterator_list_value),
-                                metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
-                                error=event.error,
+                            yield NodeInIterationFailedEvent(
+                                **metadata_event.model_dump(),
                             )
+                            outputs[current_index] = None
+
+                            # clean nodes resources
+                            for node_id in iteration_graph.node_ids:
+                                variable_pool.remove([node_id])
+
+                            # iteration run failed
+                            if self.node_data.is_parallel:
+                                yield IterationRunFailedEvent(
+                                    iteration_id=self.id,
+                                    iteration_node_id=self.node_id,
+                                    iteration_node_type=self.node_type,
+                                    iteration_node_data=self.node_data,
+                                    parallel_mode_run_id=parallel_mode_run_id,
+                                    start_at=start_at,
+                                    inputs=inputs,
+                                    outputs={"output": outputs},
+                                    steps=len(iterator_list_value),
+                                    metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
+                                    error=event.error,
+                                )
+                            else:
+                                yield IterationRunFailedEvent(
+                                    iteration_id=self.id,
+                                    iteration_node_id=self.node_id,
+                                    iteration_node_type=self.node_type,
+                                    iteration_node_data=self.node_data,
+                                    start_at=start_at,
+                                    inputs=inputs,
+                                    outputs={"output": outputs},
+                                    steps=len(iterator_list_value),
+                                    metadata={"total_tokens": graph_engine.graph_runtime_state.total_tokens},
+                                    error=event.error,
+                                )
+
+                            # stop the iterator
+                            yield RunCompletedEvent(
+                                run_result=NodeRunResult(
+                                    status=WorkflowNodeExecutionStatus.FAILED,
+                                    error=event.error,
+                                )
+                            )
+                            return
                     yield metadata_event
 
             current_output_segment = variable_pool.get(self.node_data.output_selector)

api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py

                 error=str(e),
                 error_type=type(e).__name__,
             )
+        finally:
+            db.session.close()
 
     def _fetch_dataset_retriever(self, node_data: KnowledgeRetrievalNodeData, query: str) -> list[dict[str, Any]]:
         available_datasets = []
             .all()
         )
 
+        # avoid blocking at retrieval
+        db.session.close()
+
         for dataset in results:
             # pass if dataset is not available
             if not dataset:

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

 from collections.abc import Mapping
-from typing import Any, Literal, Optional
+from typing import Annotated, Any, Literal, Optional
 
-from pydantic import BaseModel, Field
+from pydantic import AfterValidator, BaseModel, Field
 
+from core.variables.types import SegmentType
 from core.workflow.nodes.base import BaseLoopNodeData, BaseLoopState, BaseNodeData
 from core.workflow.utils.condition.entities import Condition
 
+_VALID_VAR_TYPE = frozenset(
+    [
+        SegmentType.STRING,
+        SegmentType.NUMBER,
+        SegmentType.OBJECT,
+        SegmentType.ARRAY_STRING,
+        SegmentType.ARRAY_NUMBER,
+        SegmentType.ARRAY_OBJECT,
+    ]
+)
+
+
+def _is_valid_var_type(seg_type: SegmentType) -> SegmentType:
+    if seg_type not in _VALID_VAR_TYPE:
+        raise ValueError(...)
+    return seg_type
+
 
 class LoopVariableData(BaseModel):
     """
     """
 
     label: str
-    var_type: Literal["string", "number", "object", "array[string]", "array[number]", "array[object]"]
+    var_type: Annotated[SegmentType, AfterValidator(_is_valid_var_type)]
     value_type: Literal["variable", "constant"]
     value: Optional[Any | list[str]] = None
 

api/core/workflow/nodes/loop/loop_node.py

 
 from configs import dify_config
 from core.variables import (
-    ArrayNumberSegment,
-    ArrayObjectSegment,
-    ArrayStringSegment,
     IntegerSegment,
-    ObjectSegment,
     Segment,
     SegmentType,
-    StringSegment,
 )
 from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
 from core.workflow.nodes.event import NodeEvent, RunCompletedEvent
 from core.workflow.nodes.loop.entities import LoopNodeData
 from core.workflow.utils.condition.processor import ConditionProcessor
+from factories.variable_factory import TypeMismatchError, build_segment_with_type
 
 if TYPE_CHECKING:
     from core.workflow.entities.variable_pool import VariablePool
         return variable_mapping
 
     @staticmethod
-    def _get_segment_for_constant(var_type: str, value: Any) -> Segment:
+    def _get_segment_for_constant(var_type: SegmentType, value: Any) -> Segment:
         """Get the appropriate segment type for a constant value."""
-        segment_mapping: dict[str, tuple[type[Segment], SegmentType]] = {
-            "string": (StringSegment, SegmentType.STRING),
-            "number": (IntegerSegment, SegmentType.NUMBER),
-            "object": (ObjectSegment, SegmentType.OBJECT),
-            "array[string]": (ArrayStringSegment, SegmentType.ARRAY_STRING),
-            "array[number]": (ArrayNumberSegment, SegmentType.ARRAY_NUMBER),
-            "array[object]": (ArrayObjectSegment, SegmentType.ARRAY_OBJECT),
-        }
         if var_type in ["array[string]", "array[number]", "array[object]"]:
-            if value:
+            if value and isinstance(value, str):
                 value = json.loads(value)
             else:
                 value = []
-        segment_info = segment_mapping.get(var_type)
-        if not segment_info:
-            raise ValueError(f"Invalid variable type: {var_type}")
-        segment_class, value_type = segment_info
-        return segment_class(value=value, value_type=value_type)
+        try:
+            return build_segment_with_type(var_type, value)
+        except TypeMismatchError as type_exc:
+            # Attempt to parse the value as a JSON-encoded string, if applicable.
+            if not isinstance(value, str):
+                raise
+            try:
+                value = json.loads(value)
+            except ValueError:
+                raise type_exc
+            return build_segment_with_type(var_type, value)

api/core/workflow/nodes/start/start_node.py

 
     def _run(self) -> NodeRunResult:
         node_inputs = dict(self.graph_runtime_state.variable_pool.user_inputs)
-        system_inputs = self.graph_runtime_state.variable_pool.system_variables
+        system_inputs = self.graph_runtime_state.variable_pool.system_variables.to_dict()
 
         # TODO: System variables should be directly accessible, no need for special handling
         # Set system variables as node outputs.

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

 from core.workflow.graph_engine.entities.event import AgentLogEvent
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
-from core.workflow.nodes.event import RunCompletedEvent, RunStreamChunkEvent
+from core.workflow.nodes.event import RunCompletedEvent, RunRetrieverResourceEvent, RunStreamChunkEvent
 from core.workflow.utils.variable_template_parser import VariableTemplateParser
 from extensions.ext_database import db
 from factories import file_factory
                     agent_logs.append(agent_log)
 
                 yield agent_log
+            elif message.type == ToolInvokeMessage.MessageType.RETRIEVER_RESOURCES:
+                assert isinstance(message.message, ToolInvokeMessage.RetrieverResourceMessage)
+                yield RunRetrieverResourceEvent(
+                    retriever_resources=message.message.retriever_resources,
+                    context=message.message.context,
+                )
 
         # Add agent_logs to outputs['json'] to ensure frontend can access thinking process
         json_output: list[dict[str, Any]] = []

api/core/workflow/nodes/variable_assigner/v1/node.py

 
 
 def get_zero_value(t: SegmentType):
+    # TODO(QuantumGhost): this should be a method of `SegmentType`.
     match t:
         case SegmentType.ARRAY_OBJECT | SegmentType.ARRAY_STRING | SegmentType.ARRAY_NUMBER:
             return variable_factory.build_segment([])
             return variable_factory.build_segment({})
         case SegmentType.STRING:
             return variable_factory.build_segment("")
+        case SegmentType.INTEGER:
+            return variable_factory.build_segment(0)
+        case SegmentType.FLOAT:
+            return variable_factory.build_segment(0.0)
         case SegmentType.NUMBER:
             return variable_factory.build_segment(0)
         case _: