- Replace plain image URLs with `<img src="..." data-url="...">` HTML tags in multimodal and document extractor services
- Propagate citations from workflow end events to client responses
- Update system prompts to instruct LLMs to render images using Markdown `` with strict UUID-preserving URL copying
- Consolidate node data retrieval from workflow_executions.output_data to unify storage access.
- Optimize the construction of messages and execution records to support opening suggestions.
- Eliminate redundant queries and storage logic to simplify the overall codebase structure.
- Rectify exception propagation during node execution failures to ensure errors are correctly raised.
- Bolster workflow logging to support failed status records and persist node execution data, including loop nodes.
Added document image extraction capability for PDF and DOCX files, including page/index metadata and storage integration. Extended `process_files` with `document_image_recognition` flag to conditionally enable vision-based image processing when model supports it. Updated knowledge repository and workflow node logic to enforce status=1 checks. Added PyMuPDF dependency.
feat(app): support file metadata in chat messages and DSL app overwrite
- Extended chat message file objects with `name`, `size`, and `file_type` fields across app_chat_service and workflow_service
- Added ability to overwrite existing app configurations via DSL import in app_dsl_service, including type validation and config update logic for AgentConfig, MultiAgentConfig, and WorkflowConfig
- Trigger opening statement on new conversation in run/run_stream
- Fix /opening endpoint to support workflow app type
- Fix features field missing in workflow config release snapshot
- Knowledge node returns citations alongside chunks
- Aggregate citations from all knowledge nodes in result builder
- Filter citations based on features.citation.enabled switch
- Fix WorkflowConfigCreate circular import in app_schema
- Migrate historical workflow queries from legacy ORM Query API to SQLAlchemy 2.0 select() + execute()
- Limit query fields and use pagination to reduce returned data, improving performance
- Preserve original ordering and filtering logic
* [fix]Fix the interface for statistics of recent activities and applications
* [changes]Modify the code based on the AI review
1.Use the boolean auxiliary methods provided by SQLAlchemy instead of using == True in the is_active filter.
2.The calculation of the "PROJECT_ROOT" has now been hardcoded with five levels of nested os.path.dirname calls.
* [fix]Fix the interface for statistics of recent activities and applications
* [changes]Modify the code based on the AI review
1.Use the boolean auxiliary methods provided by SQLAlchemy instead of using == True in the is_active filter.
2.The calculation of the "PROJECT_ROOT" has now been hardcoded with five levels of nested os.path.dirname calls.
