- 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
- Move MemoryClientFactory from app.core.memory.client_factory to app.core.memory.utils.llm.llm_utils
- Update all evaluation modules to import MemoryClientFactory from new location (locomo, longmemeval, memsciqa)
- Move GenerateCacheRequest from memory_storage_service to memory_storage_schema
- Update memory_storage_controller imports to reflect schema reorganization
- Add analytics_user_summary import to memory_storage_controller from user_memory_service
- Consolidate utility imports across evaluation test files for consistency
- Improve code organization by centralizing LLM utilities in dedicated utils module
- 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
- Replace the system prompt of the prompt optimization model with a built-in prompt.
- Remove system prompt entries from the database.
- Remove the API endpoint for managing system prompt configuration.
- Added API endpoints for prompt optimization:
* POST /prompt/sessions: Create a new prompt optimization session
* GET /prompt/sessions/{session_id}: Retrieve session message history
* POST /prompt/sessions/{session_id}/messages: Send message and get optimized prompt
* PUT /prompt/model: Create or update system prompt model configuration
- Added database models for prompt optimization:
* prompt_opt_session: Stores session metadata
* prompt_opt_session_history: Stores session message history
* prompt_opt_message: Stores user and assistant messages
* prompt_opt_model_config: Stores system prompt model configurations
- Updated service layer to handle message creation, prompt optimization, and variable parsing
- Added corresponding Pydantic schemas for request and response validation
