- 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
* [feature]A set of information for role recognition writing
* [feature]A set of information for role recognition writing
* [fix]Fix the code after rebasing.
* [feature]A set of information for role recognition writing
* [fix]Fix the code after rebasing.
* [fix]Based on the AI review to fix the code
* [changes]Disable the function of batch writing multiple groups of conversations in a cumulative manner
* [fix]Addressing vulnerability risks
* [fix]Fixing short-term memory writing
* [feature]A set of information for role recognition writing
* [fix]Fix the code after rebasing.
* [feature]A set of information for role recognition writing
* [fix]Fix the code after rebasing.
* [fix]Based on the AI review to fix the code
* [fix]Fixing short-term memory writing
* [feature]A set of information for role recognition writing
* [feature]A set of information for role recognition writing
* [fix]Fix the code after rebasing.
* [feature]A set of information for role recognition writing
* [fix]Fix the code after rebasing.
* [fix]Based on the AI review to fix the code
* [changes]Disable the function of batch writing multiple groups of conversations in a cumulative manner
* [fix]Addressing vulnerability risks
* 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
---------
Co-authored-by: Ke Sun <kesun5@illinois.edu>
- Add [PERF] prefixed logging throughout hybrid search pipeline for better performance visibility
- Break down latency metrics with separate timing for config loading, embedder initialization, and rerank computation
- Format latency breakdown as JSON in performance summary logs
- Optimize batch_record_access to process node access records in parallel using asyncio.gather instead of sequential processing
- Add performance timing instrumentation for forgetting config loading and rerank computation stages
- Reorganize imports in access_history_manager for consistency
- Improve observability of search performance bottlenecks through structured logging
* [add]Newly added: Memory configuration for returning results
* [add]Newly added: Memory configuration for returning results
* [changes]Based on the improvement of AI review
- Make config_id optional in UserInput and Write_UserInput schemas
- Update write_memory and read_memory method signatures to accept Optional[str] for config_id
- Implement automatic config retrieval from end_user connection when config_id is not provided
- Add explicit error handling for missing memory configurations with descriptive error messages
- Improve emotion_controller to validate config_id using MemoryConfigService when provided
- Fallback to get_end_user_connected_config when config_id is None
- Distinguish between specific "no configuration found" errors and other exceptions for better debugging
- Ensures users can operate without explicitly providing config_id if they have a connected configuration
- Remove deprecated main.py entry point from memory module
- Reorganize imports across controllers and services for consistency
- Update emotion controller to pass db session instead of config_id to services
- Enhance memory agent controller with db session parameter for status_type and user_profile endpoints
- Refactor memory agent service to accept db parameter in classify_message_type method
- Improve configuration handling in celery_app by removing automatic database reload
- Update all memory-related services to use centralized config management
- Standardize import ordering and remove unused imports across 50+ files
- Add pilot_run_service for new pilot execution workflow
- Refactor extraction engine, reflection engine, and search services for better modularity
- Update LLM utilities and embedder configuration for improved flexibility
- Enhance type classifier and verification tools with better error handling
- Improve memory evaluation modules (LOCOMO, LongMemEval, MemSciQA) with consistent patterns
- Reorganize imports and remove unused dependencies across memory agent controllers
- Extract config validation logic into dedicated validators module
- Create new memory_config_model and memory_config_schema for configuration management
- Implement memory_config_service for centralized config handling
- Add embedder_utils module for embedding model utilities
- Refactor memory agent service to use new config validation framework
- Clean up configuration files (remove config.json, testdata.json, dbrun.json)
- Remove deprecated hybrid_chatbot.py and config overrides
- Update logging configuration and error handling across memory modules
- Consolidate LLM and embedding model validation into validators
- Improve code organization and reduce duplication in memory storage services
- Enhance type classification and verification tools with better error handling
