- Updated API routes to use app_id instead of sandbox_id for file operations, aligning with user-specific sandbox workspaces.
- Enhanced SandboxFileService and related classes to accommodate app_id in file listing and download functionalities.
- Refactored storage key generation for sandbox archives to include app_id, ensuring proper file organization.
- Adjusted frontend contracts and services to reflect the new app_id parameter in API calls.
- Updated the SandboxManager to rename the method for deleting storage to better reflect its purpose.
- Adjusted the WorkflowVariableCollectionApi to utilize the new method name.
- Improved error handling in ArchiveSandboxStorage's delete method to log exceptions during deletion.
- Introduced SandboxManager.delete_storage method for improved storage management.
- Refactored skill loading and tool artifact handling in DifyCliInitializer and SandboxBashSession.
- Updated LLMNode to extract and compile tool artifacts, enhancing integration with skills.
- Improved attribute management in AttrMap for better error handling and retrieval methods.
The `ChatMessageApi` (`POST /console/api/apps/{app_id}/chat-messages`) and
`ModelConfigResource` (`POST /console/api/apps/{app_id}/model-config`)
endpoints do not properly validate user permissions, allowing users without `editor`
permission to access restricted functionality.
This PR addresses this issue by adding proper permission check.
refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025)
This PR addresses serialization issues in the VariablePool model by separating the `value_type` tags for `IntegerSegment`/`FloatSegment` and `IntegerVariable`/`FloatVariable`. Previously, both Integer and Float types shared the same `SegmentType.NUMBER` tag, causing conflicts during serialization.
Key changes:
- Introduce distinct `value_type` tags for Integer and Float segments/variables
- Add `VariableUnion` and `SegmentUnion` types for proper type discrimination
- Leverage Pydantic's discriminated union feature for seamless serialization/deserialization
- Enable accurate serialization of data structures containing these types
Closes#22024.
This pull request introduces a feature aimed at improving the debugging experience during workflow editing. With the addition of variable persistence, the system will automatically retain the output variables from previously executed nodes. These persisted variables can then be reused when debugging subsequent nodes, eliminating the need for repetitive manual input.
By streamlining this aspect of the workflow, the feature minimizes user errors and significantly reduces debugging effort, offering a smoother and more efficient experience.
Key highlights of this change:
- Automatic persistence of output variables for executed nodes.
- Reuse of persisted variables to simplify input steps for nodes requiring them (e.g., `code`, `template`, `variable_assigner`).
- Enhanced debugging experience with reduced friction.
Closes#19735.
