- Replace direct memory agent service calls with unified MemoryService in read endpoint
- Update query preprocessor to use new prompt format and return structured queries
- Enhance MemorySearchResult model with filtering, merging, and ID tracking capabilities
- Add intermediate outputs display for problem split, perceptual retrieval, and search results
- Fix parameter alignment and remove unused history parameter in memory agent service
- Consolidate memory search services by removing separate content_search.py and perceptual_search.py
- Update model client handling in base_pipeline.py to use ModelApiKeyService for LLM client initialization
- Add new prompt files and modify existing services to support consolidated search architecture
- Refactor memory read pipeline and related services to use updated model client approach
- Consolidate memory search services by removing separate content_search.py and perceptual_search.py
- Update model client handling in base_pipeline.py to use ModelApiKeyService for LLM client initialization
- Add new prompt files and modify existing services to support consolidated search architecture
- Refactor memory read pipeline and related services to use updated model client approach
- Replace storage_services/search with new read_services/memory_search structure
- Implement content_search and perceptual_search strategies
- Add query_preprocessor for search optimization
- Create memory_service as unified interface
- Update celery_app and graph_search for new architecture
- Add enums for memory operations
- Implement base_pipeline and memory_read pipeline patterns
Align update response with get_end_user_info by extracting profile,
knowledge_tags, and behavioral_hints to top-level keys instead of
returning raw meta_data dict.
Change image_url from required to optional in both operation_tool.py and
tool_service.py for image_understand operation, avoiding parameter validation
conflict with uploaded_files priority logic.
Remove unused operation variable from OpenClawTool.execute().
- Make MetadataExtractor language param optional (default None) to
support auto-detection fallback when no language is explicitly set
- Refactor clean_metadata from walrus-operator dict comprehension to
explicit loop for correctness and readability
- Remove _replace_first_person_with_user from StatementExtractor to preserve
original user text for downstream metadata/alias extraction
- Delete metadata_utils.py module, inline clean_metadata into Celery task
- Remove unused imports and commented-out collect_user_raw_messages method
- Apply formatting cleanup across metadata models and extraction orchestrator
