- Sync memory_count after Neo4j write and forgetting cycle
- Filter Neo4j end user list by memory_count > 0
- Filter RAG end user list by Memory knowledge chunk count
The default thinking budget tokens value was changed from 10000 to 1024 in base.py, and the minimum validation constraint was updated from 1024 to 1 in app_schema.py to allow smaller budgets while maintaining backward compatibility.
- Replace truthiness checks with 'is not None' for data.message in graph_data and community_graph endpoints to handle empty string correctly
- Remove Optional wrapper from GraphStatistics.edge_types since it already has a default_factory
- Add user_memory_schema.py with typed Pydantic models for all user memory
API responses: MemoryInsightReportData, UserSummaryData, GraphData,
MemoryTypeStatItem, cache result models, and RelationshipEvolutionData
- Refactor user_memory_controllers.py to construct schema instances and
return model_dump() instead of raw dicts
- Remove unused imports (datetime, timestamp_to_datetime, EndUserInfoResponse,
EndUserInfoCreate, EndUser)
* release/v0.3.2: (245 commits)
fix(conversation_schema): refine citations field type to Dict[str, Any]
fix(tool_controller): re-raise HTTPException to preserve original status codes
fix(workflow): add reasoning content, suggested questions, citations and audio status support
feat(workflow): augment logging queries and ameliorate error handling
fix(api_key): bypass publication check for SERVICE type API keys
fix(multimodal_service): add '文档内容：' prefix to document text and simplify image placeholder text
fix(api): convert config_id to string in write_router
fix(api): convert end_user_id to string in write_router
fix(multimodal_service): refactor image processing to use intermediate list before extending result
fix(web): node status ui
fix(api): correct import paths in memory_read and celery task command
fix(api): correct import paths in memory_read and celery task command
refactor(tool): flatten request body parameters for model exposure
fix(api): correct import paths in memory_read and celery task command
refactor(workflow): streamline node execution handling and log service logic
feat(web): http request add process
feat(web): workflow app logs
fix(app_chat_service,draft_run_service): move system_prompt augmentation before LangChainAgent instantiation
fix(app_chat_service,draft_run_service): move system_prompt augmentation before LangChainAgent instantiation
refactor(http_request): simplify request handling and remove unused fields
...
# Conflicts:
# api/app/controllers/file_controller.py
# api/app/tasks.py
- Introduce `reasoning_content`, `suggested_questions`, `citations`, and `audio_status` fields in conversation and app response schemas
- Conditionally set `audio_status` to `"pending"` only when `audio_url` is present
- Replace `model_dump` override with `@model_serializer(mode="wrap")` for cleaner serialization logic
- Change knowledge base validation failure from `RuntimeError` to warning + `continue` to avoid halting retrieval on invalid KB
- Augment workflow logs with execution status fields and loop node information.
- Refactor log service to handle distinct processing logic for workflows and agents.
- Construct message and node logs derived from workflow_executions data.
Added `allow_download` flag to citation config and `download_url` field to citation output. Implemented `/citations/{document_id}/download` endpoint to serve original files when enabled. Removed unused `files` field and `HttpRequestDataProcessing` model from HTTP request node config.
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(http_request): augment debugging capabilities with raw request generation and improved error handling.
- feat(app_log): extend session filtering logic to support retrieving all session types.
- feat(log): add 'process' field to node execution records for better data tracking.
- Augment HTTP request node capabilities and add generated curl commands for easier debugging.
feat(log): implement workflow execution logs and search functionality
- Add detailed logging for workflow node execution and enable search capabilities within application logs.
feat(auth): introduce middleware to verify application publication status
- Add a check to ensure the application is published before allowing access.
fix(converter): rectify variable handling logic in Dify converter
- Correct issues related to processing variables within the Dify converter module.
refactor(model): remove quota check decorator from model update operations
- Decouple quota validation from the model update process to streamline the logic.
- Fix write_router to use actual_end_user_id instead of end_user_id
- Add task status tracking via Redis in scheduler
- Expose task_id in memory write response
- Fix logging import path in scheduler
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
- Rename endpoints from write_api_service/read_api_service to write/read for clarity
- Add async task-based endpoints (/write, /read) that dispatch to Celery with fair locking
- Add task status polling endpoints (/write/status, /read/status) to check async operation results
- Add synchronous endpoints (/write/sync, /read/sync) for blocking operations with direct results
- Introduce TaskStatusResponse schema for task status polling responses
- Add MemoryWriteSyncResponse and MemoryReadSyncResponse schemas for sync operations
- Implement write_memory_sync and read_memory_sync methods in MemoryAPIService
- Remove await from async service calls in task-based endpoints (now handled by Celery)
- Add Query parameter import for task_id in status endpoints
- Update docstrings to clarify async vs sync behavior and task polling workflow
- Integrate task_service for retrieving Celery task results
- Create new memory_config_api_controller.py for dedicated memory configuration management
- Add /end_user/info GET endpoint to retrieve end user information (aliases, metadata)
- Add /end_user/info/update POST endpoint to update end user details
- Move /memory/configs endpoint from memory_api_controller to memory_config_api_controller
- Extract _get_current_user helper function to build user context from API key auth
- Support optional app_id parameter in end user creation with UUID validation
- Update service controller imports with alphabetical ordering and multi-line formatting
- Register memory_config_api_controller router in service module initialization
- Refactor memory_api_controller imports for consistency and clarity
- Should be merged after v0.2.9
- Create new end_user_api_controller.py with POST /end_user/create endpoint
- Implement API Key authentication requirement with memory scope
- Add support for optional memory_config_id parameter with workspace default fallback
- Update memory_api_schema.py to remove workspace_id from request (now derived from API key auth)
- Add memory_config_id field to CreateEndUserResponse schema
- Register end_user_api_controller router in service module
- Migrate end user creation from unauthenticated to authenticated API flow
