- Remove Phase 4 RAG pipeline in favor of retrieval-only architecture
- Add FastMCP server exposing search, get_chunk, list_libraries tools
- Mount MCP endpoints (streamable HTTP + SSE) via Starlette in ASGI config
- Update README to clarify Mnemosyne is a retrieval engine, not RAG
- Let calling LLMs drive synthesis and iterative retrieval themselves
Implement hybrid search pipeline combining vector, fulltext, and graph
search across Neo4j, with cross-attention reranking via Synesis
(Qwen3-VL-Reranker-2B) `/v1/rerank` endpoint.
- Add SearchService with vector, fulltext, and graph search strategies
- Add SynesisRerankerClient for multimodal reranking via HTTP API
- Add search API endpoint (POST /search/) with filtering by library,
collection, and library_type
- Add SearchRequest/Response serializers and image search results
- Add "nonfiction" to library_type choices
- Consolidate reranker stack from two models to single Synesis service
- Handle image analysis_status as "skipped" when analysis is unavailable
- Add comprehensive tests for search pipeline and reranker client
- Implemented custom form widgets for date, time, and datetime fields with DaisyUI styling.
- Created utility functions for formatting dates, times, and numbers according to user preferences.
- Developed views for profile settings, API key management, and notifications, including health check endpoints.
- Added URL configurations for Themis tests and main application routes.
- Established test cases for custom widgets to ensure proper functionality and integration.
- Defined project metadata and dependencies in pyproject.toml for package management.
