Document the end-state auth/authz model unifying the three services
around a bearer → resolved library set abstraction. Replaces the
per-turn JWT forwarding scheme with static team JWTs held by Pallas
deployments, eliminating custom transport code and the monkey-patch
chain that caused opaque failures in agent teams.
Also records the UX shift where Daedalus workspaces attach Teams
(Pallas instances) rather than individual agents.
Introduces a one-shot `init` service in docker-compose that runs Postgres
migrations, Neo4j index setup, and library-type seeding on every `up`.
Long-running services (`app`, `mcp`, `worker`) now depend on its
successful completion via `service_completed_successfully`, blocking the
stack on configuration errors (missing embedding model, dimension
mismatch, unreachable DB) rather than serving silent zero-result
searches.
Also standardizes reranker test fixtures to use the `/v1` OpenAI-style
base URL convention used across other service clients.
Mark per-turn JWT access control as implemented in the Mnemosyne
integration docs. Update Phase 2/3 status tables, replace deferred
language with concrete implementation details, and document the
`MCPSigningKey` model, `resolve_mcp_jwt`, and `_scope_from_claims`
components now live in the MCP server.
- Change app healthcheck from /live/ to /ready/ to verify full
readiness including dependencies (DB, Neo4j, S3)
- Increase healthcheck timeout from 5s to 10s to accommodate
dependency checks
- Add S3 bucket connectivity check to readiness probe
- Update deployment documentation to use /srv/mnemosyne instead
of /opt/mnemosyne as the compose project directory
Replace the minimal placeholder .env.example with a comprehensive template
documenting every variable consumed by docker-compose.yaml, organized by
service (Django core, HTTP, Postgres, Neo4j, Memcached, S3/MinIO, Daedalus,
Celery/RabbitMQ, etc.). Clarifies that this file is rendered from an Ansible
Jinja2 template with vaulted secrets in production, and distinguishes it
from the in-tree mnemosyne/.env used for bare-Python development.
The integration doc was forward-looking spec but most of it now ships:
Phase 1 (REST workspace + ingest API for Daedalus) ✅ implemented
Phase 2 (MCP server: search/get_chunk/list_*/get_health) ✅ implemented
Phase 3 (per-turn signed-token access control) 📋 deferred
Updated:
- Tool table reflects actual implementation (search, get_chunk,
list_libraries, list_collections, list_items, get_health) instead
of the speculative names (search_knowledge, search_by_category, etc.)
- Project structure matches the as-built layout (tools/discovery.py
exists; no separate browse.py).
- REST API table covers both workspace lifecycle endpoints and ingest
endpoints, with correct routes (/library/api/...).
- Ingest request schema includes content_hash and workspace_id
(the actual idempotency key on the Mnemosyne side).
- Celery task description matches library.tasks.ingest_from_daedalus
rather than the placeholder embed_item.
- Phase 6 checklist marks Phases 1+2 done; adds Phase 3 (per-turn
token access control) with a per-Mnemosyne-side TODO list pointing
at the matching Daedalus-side §9 design.
Internal MCP port stays 22091; public access via nginx on 23090.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- 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
Replace OPTIONAL MATCH with MATCH for Library-Collection-Item paths to
ensure results are properly scoped to libraries, and remove per-query
score normalization since RRF fuses results by rank rather than score
magnitude.
Replace the single `DATABASE_URL` connection string with individual
environment variables (`APP_DB_NAME`, `APP_DB_USER`, `APP_DB_PASSWORD`,
`DB_HOST`, `DB_PORT`) for more granular database configuration control.
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
- Introduced a new vision analysis service to classify, describe, and extract text from images.
- Enhanced the Image model with fields for OCR text, vision model name, and analysis status.
- Added a new "nonfiction" library type with specific chunking and embedding configurations.
- Updated content types to include vision prompts for various library types.
- Integrated vision analysis into the embedding pipeline, allowing for image analysis during document processing.
- Implemented metrics to track vision analysis performance and usage.
- Updated UI components to display vision analysis results and statuses in item details and the embedding dashboard.
- Added migration for new vision model fields and usage tracking.
- 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.
