- Fix relative imports in memory_read.py to use absolute app paths
- Change celery scheduler command from `python app/celery_task_scheduler.py` to `python -m app.celery_task_scheduler`
- Fix relative imports in memory_read.py to use absolute app paths
- Change celery scheduler command from `python app/celery_task_scheduler.py` to `python -m app.celery_task_scheduler`
- Fix relative imports in memory_read.py to use absolute app paths
- Change celery scheduler command from `python app/celery_task_scheduler.py` to `python -m app.celery_task_scheduler`
- Removed `last_request` field and related logic for storing raw request string
- Replaced `_extract_output` and `_extract_extra_fields` to use `process_data` instead of `request`
- Updated `_build_content` to directly parse JSON body without intermediate rendering step
- Modified `execute` to generate `process_data` from actual HTTP request object instead of manual string building
- Added `process_data` field to `HttpRequestNodeOutput` model for consistent debugging info
- Pass workspace_id to multimodal_service.process_files across app_chat_service, draft_run_service
- Fetch tenant_id from workspace in multimodal_service for proper file storage scoping
- Update image placeholder format from "[第N页 第M张图片]" to "[图片 第N页 第M张图片]" for clarity
- Add strict URL preservation rules to system prompt for agents handling document images
- Refactor _save_doc_image_to_storage to accept explicit tenant_id and workspace_id instead of inferring from FileMetadata
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.
- Fix episodic memory time filter to use UTC (datetime.fromtimestamp with tz=timezone.utc)
to match Neo4j stored UTC timestamps
- Add POST /v1/memory/analytics/generate_cache endpoint for cache generation via API Key
Modified files:
- api/app/services/memory_explicit_service.py
- api/app/controllers/service/user_memory_api_controller.py
- Parameterize SKIP/LIMIT in Cypher query instead of f-string interpolation
- Add UUID format validation in validate_end_user_in_workspace before DB query
- Update limit/depth Query descriptions to clarify auto-cap behavior in service layer
- Move uuid import to module level in api_key_utils.py
Modified files:
- api/app/services/memory_explicit_service.py
- api/app/core/api_key_utils.py
- api/app/controllers/service/user_memory_api_controller.py
- 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.
Add external service APIs for memory detail queries
Provides memory data access endpoints for external service integration
Add utility functions for API key user resolution and end_user validation
Modified files:
- api/app/controllers/service/user_memory_api_controller.py
- api/app/core/api_key_utils.py
- api/app/controllers/service/__init__.py
Split explicit memory overview into two independent endpoints:
- GET /memory/explicit-memory/episodics: episodic memory paginated query
with date range filter (millisecond timestamp) and episodic type filter
using Neo4j datetime() for precise time comparison
- GET /memory/explicit-memory/semantics: semantic memory full list query
returns data as array directly
Modified files:
- api/app/controllers/memory_explicit_controller.py
- api/app/services/memory_explicit_service.py
- Refactor quota management logic to support usage checks scoped by workspace.
- Update quota statistics API to return granular quota details for each workspace.
- Revise default configuration settings for terminal user and model limits.
- Remove quota check decorators from the model controller.
- Refactor quota management logic to support usage checks scoped by workspace.
- Update quota statistics API to return granular quota details for each workspace.
- Revise default configuration settings for terminal user and model limits.
- Remove quota check decorators from the model controller.
- Refactor quota management logic to support usage checks scoped by workspace.
- Update quota statistics API to return granular quota details for each workspace.
- Revise default configuration settings for terminal user and model limits.
- Remove quota check decorators from the model controller.
- 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
* 'release/v0.3.1' of github.com:SuanmoSuanyangTechnology/MemoryBear:
fix(web): stream add default error message
fix(quota): restrict quota check to new terminal user creation only
fix(api): fix API Key rate limiting and terminal user quota checks
feat(exception): enhance I18nException response format and add error code mapping
feat(quota): add quota checks during app duplication and import operations
fix(知识服务): 添加工作空间模型配置的校验
refactor(knowledge_service): 简化模型绑定逻辑，直接使用工作区配置
fix(知识服务): 修复创建知识库时未检查视觉模型存在的错误
refactor(knowledge_service): 优化模型绑定逻辑，使用ID查询并简化回退机制
