- 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
