1. Add the "Skills" module;
2. The loading of the model square has been modified to be controlled through environment variables;
3. Dynamic scheduling of the skill binding tool;
4. Agent Integration Skills
* [changes]Add 'id' as the secondary sorting key, and 'scene_id' now returns a UUID object
* [fix]Fix the "end_user" return to be sorted by update time.
* [fix]Set the default values of the memory configuration model based on the spatial model.
* [fix]Remove the entity extraction check combination model, read the configuration list, and add the return of scene_id
* [fix]Fix the "end_user" return to be sorted by update time.
* [fix]
* [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
- 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
* [changes]Optimize the time consumption of the "/end_users" interface
* [fix]Optimize the time consumption of the "/hot_memory_tags" interface
* [changes]Optimize the time consumption of the "/end_users" interface
* [fix]Optimize the time consumption of the "/hot_memory_tags" interface
* [changes]Improve the code based on AI review
* [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>
