- Rename StatementExtractionStep → StatementTemporalExtractionStep and
extract_statement.jinja2 → extract_statement_temporal.jinja2 to reflect
merged temporal extraction logic
- Move extraction_pipeline_orchestrator.py out of steps/ to engine root
- Move dedup_step.py into steps/ directory
- Introduce WriteMemoryRequest schema to replace positional args in write_memory()
- Extract _resolve_and_load_config, _preprocess_files, _write_neo4j, and
_invalidate_interest_cache as private helpers in MemoryAgentService
- Remove shadow pipeline and simplify NEW_PIPELINE_ENABLED branch
- Merge 类型归属/成员隶属/任职服务 relation types into single 归属身份关系 in triplet prompt
- Add alias merge logic (别名属于) in deduplication and MERGE_ALIAS_BELONGS_TO Cypher query
- Add StorageType, Language, MessageItem enums/models to memory_agent_schema
- Reduce AgentMemory_Long_Term.DEFAULT_SCOPE from 6 to 1
- Delete standalone extract_temporal.jinja2 (logic merged into statement step)
- Fix write_router to use actual_end_user_id instead of end_user_id
- Add task status tracking via Redis in scheduler
- Expose task_id in memory write response
- Fix logging import path in scheduler
- Rename endpoints from write_api_service/read_api_service to write/read for clarity
- Add async task-based endpoints (/write, /read) that dispatch to Celery with fair locking
- Add task status polling endpoints (/write/status, /read/status) to check async operation results
- Add synchronous endpoints (/write/sync, /read/sync) for blocking operations with direct results
- Introduce TaskStatusResponse schema for task status polling responses
- Add MemoryWriteSyncResponse and MemoryReadSyncResponse schemas for sync operations
- Implement write_memory_sync and read_memory_sync methods in MemoryAPIService
- Remove await from async service calls in task-based endpoints (now handled by Celery)
- Add Query parameter import for task_id in status endpoints
- Update docstrings to clarify async vs sync behavior and task polling workflow
- Integrate task_service for retrieving Celery task results
- Create new memory_config_api_controller.py for dedicated memory configuration management
- Add /end_user/info GET endpoint to retrieve end user information (aliases, metadata)
- Add /end_user/info/update POST endpoint to update end user details
- Move /memory/configs endpoint from memory_api_controller to memory_config_api_controller
- Extract _get_current_user helper function to build user context from API key auth
- Support optional app_id parameter in end user creation with UUID validation
- Update service controller imports with alphabetical ordering and multi-line formatting
- Register memory_config_api_controller router in service module initialization
- Refactor memory_api_controller imports for consistency and clarity
- Remove unauthenticated end_user_controller and its router registration
- Move end user creation logic to authenticated memory_api_controller endpoint
- Add create_end_user method to MemoryAPIService with workspace authorization
- Fix retrieve_nodes import in read_graph to use correct function reference
- Consolidate end user management under authenticated memory API with API key scoping
- 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
* [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.
- Add MemoryAPIService with read_memory and write_memory methods for managing user memories
- Create memory_api_schema.py with request/response schemas for read and write operations
- Implement write_memory_api_service endpoint for storing memory content with configurable storage backends
- Implement read_memory_api_service endpoint for querying memories with context-aware responses
- Add memory-specific error codes (MEMORY_WRITE_FAILED, MEMORY_READ_FAILED, MEMORY_CONFIG_NOT_FOUND) to error_codes.py
