- Removed try-except block for importing `audit_logger` and directly imported it.
- Removed redundant checks for `audit_logger` being `None` before logging operations.
- Added a check in `MemoryPerceptualService` to return `None` if `model_config` or `llm` is `None`.
- Adjust multi-modal memory write behavior for text and visual data
- Mask API keys in model list response to prevent exposure
- Add capability-based filtering to the model list API
- Add end_user_controller with unauthenticated endpoint for creating end users
- Implement get_or_create_end_user logic to handle duplicate end users by other_id
- Register end_user_controller router in main controller initialization
- Add list_memory_configs endpoint to retrieve all workspace memory configurations
- Update MemoryWriteRequest and MemoryReadRequest to make config_id required field
- Refactor memory API endpoints to parse request body directly instead of using Body parameter
- Add CreateEndUserRequest and CreateEndUserResponse schemas for end user creation
- Add ListConfigsResponse schema for configs listing endpoint
- Remove unused config_id and llm_model_id parameters from Neo4j write operation
- Update .gitignore to exclude redbear-mem-metrics and pitch-deck directories
- Add TODO comment to verify end_user sources (chat, draft, apikey)
- Comment out release validation check to support draft run mode
- Add TODO note explaining temporary fix for draft execution
- Handle null current_release_id in result by returning None instead of failing
- Improve import formatting for MemoryConfig model import statement
- Allow configuration retrieval when app has no published release
* [add]Create a workspace and initialize the default ontology engineering scenario
* [add]The language parameters for creating the workspace determine the default language for switching in the ontology project.
* [changes]Standardized return format
* [add]The default ontology is associated with the default configuration.
* [add]Create a workspace and initialize the default ontology engineering scenario
* [add]The language parameters for creating the workspace determine the default language for switching in the ontology project.
* [changes]Standardized return format
* [add]The default ontology is associated with the default configuration.
- Add workspace_id fallback parameter to memory config loading across all services
- Update hot_memory_tags.py to pass workspace_id when resolving memory configuration
- Enhance emotion_analytics_service.py to support workspace_id as fallback for config resolution
- Improve implicit_memory_service.py with workspace_id fallback in config loading
- Update memory_agent_service.py to handle workspace_id resolution and add refactoring TODO
- Enhance preference_analysis.jinja2 prompt with critical guidance on supporting_evidence extraction
- Add validation to check both config_id and workspace_id before raising configuration errors
- Improve error handling and logging for memory configuration resolution across services
- This enables more flexible memory configuration resolution when config_id is unavailable
- Add null check for actual_config_id before calling term_memory_save in langchain_agent.py to prevent errors when memory config is unavailable
- Add warning log when skipping term_memory_save due to missing memory config
- Fix incorrect attribute reference from memory_config.id to memory_config.config_id in memory_agent_service.py
- Fix method call from private _get_workspace_default_config to public get_workspace_default_config in memory_config_service.py
- Ensures graceful handling of missing memory configurations and prevents runtime errors
* [changes]add user_summary language unification
* [add]Entity extraction, user memory, emotion suggestions, unified language type for writing
* [add]Complete the switch between Chinese and English for the emotion labels and emotion suggestions fields.
* [changes]add user_summary language unification
* [add]Entity extraction, user memory, emotion suggestions, unified language type for writing
* [add]Complete the switch between Chinese and English for the emotion labels and emotion suggestions fields.
* [changes]Modify the code based on the AI review
- Remove get_memory_config_id function from end_user_repository.py as it's no longer needed
- Remove get_end_user_memory_config_id function from memory_agent_service.py to reduce duplication
- Simplify get_end_user_connected_config to use MemoryConfigService.get_config_with_fallback
- Update get_config_with_fallback signature to accept memory_config_id directly instead of end_user_id
- Remove unnecessary AppRelease query and config parsing logic from get_end_user_connected_config
- Streamline memory config retrieval flow to use service layer abstraction
- Improves code maintainability by centralizing config fallback logic in MemoryConfigService
- Add force parameter to delete_config endpoint for controlled deletion of in-use configs
- Implement MemoryConfigService.delete_config with protection against deleting default configs
- Add validation to prevent deletion of configs with connected end-users unless force=True
- Reorganize controller imports to remove duplicates and improve maintainability
- Clean up unused database connection management code from memory_storage_controller
- Add detailed docstring to delete_config endpoint explaining protection mechanisms
- Update error handling with specific BizCode.RESOURCE_IN_USE for configs in active use
- Add comprehensive logging for deletion attempts, warnings, and affected users
- Refactor ConfigParamsDelete schema usage to use MemoryConfigService directly
- Improve API response structure with affected_users count and force_required flag
- Add memory_config_id field to EndUser model for lazy caching of memory configuration
- Create get_end_user_memory_config_id() function for fast retrieval of cached config ID
- Implement lazy update mechanism in get_end_user_connected_config() to cache memory_config_id
- Optimize memory config lookup by storing config ID directly on end_user record
- Improve import organization and formatting in memory_agent_service.py
- Add indexed foreign key relationship to data_config table for efficient queries
* [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.
* 去掉MCP框架，重构
* 去掉MCP框架，重构
* 去掉MCP框架，重构
* 去掉MCP框架，重构
* 去掉MCP框架，重构
* 去掉MCP框架，重构
* 去掉MCP框架，重构
* feat(celery): add comprehensive logging to worker and write task
- Initialize logging system in Celery worker entry point with LoggingConfig
- Add logger instance and startup message to celery_worker.py
- Reorganize imports in tasks.py for better readability and consistency
- Add detailed logging to write_message_task for debugging and monitoring
- Log task start with group_id, config_id, and storage_type parameters
- Log service execution and completion status with results
- Add exception handling with error logging and stack trace capture
- Log task completion time and Celery task ID for performance tracking
- Improves observability and troubleshooting of async task execution
* 去掉MCP框架，重构
* 去掉MCP框架，重构
* 快速检索，需要在接口部分添加LLM整合
* 快速检索，需要在接口部分添加LLM整合
---------
Co-authored-by: Ke Sun <kesun5@illinois.edu>
* [changes]《Modify the interface》
1.Remove the "/search/entity_graph" interface
2.Reconstruct the "/updated_end_user/profile" interface
3.Remove the "Update Username" interface
4.Fix the batch query of user association memory configuration
* [changes]《Modify the interface》
1.Remove the "/search/entity_graph" interface
2.Reconstruct the "/updated_end_user/profile" interface
3.Remove the "Update Username" interface
4.Fix the batch query of user association memory configuration
* [fix]Fix the error response type
* refactor(celery): optimize task routing and worker configuration
- Simplify Celery queue configuration with single default 'io_tasks' queue
- Implement task routing strategy separating IO-bound and CPU-bound tasks
- Add Flower monitoring support with task event tracking enabled
- Add summary node search optimization to only retrieve summary nodes
- Clean up unused imports and reorganize import statements for consistency
- Update docker-compose configuration to support multi-queue worker setup
* chore(celery): simplify flower configuration and add gevent dependency
* chore(dependencies): add gevent dependency to requirements
- Add gevent==24.11.1 to api/requirements.txt
- Gevent is required for async worker support in Celery
- Complements existing flower and celery configuration
* refactor(celery): simplify async event loop handling and reorganize task queues
- Replace complex nest_asyncio and manual event loop management with asyncio.run() in read_message_task, write_message_task, regenerate_memory_cache, and workspace_reflection_task
- Rename task queues from io_tasks/cpu_tasks to memory_tasks/document_tasks for better semantic clarity
- Update task routing configuration to reflect new queue names for memory agent tasks and document processing tasks
- Remove redundant exception handling comments and simplify error handling logic
- Update README with improved community support section including GitHub Issues, Pull Requests, Discussions, and WeChat community links
- Simplifies event loop management by leveraging asyncio.run() which handles loop creation and cleanup automatically, reducing code complexity and potential race conditions
