Introduce x-logging anchor with json-file driver, size/file caps, and
container name tagging so Alloy on puck can reliably tail every service
through the Docker socket. Apply to all services and inject
MNEMOSYNE_COMPONENT env vars (init/app/mcp/worker) for consistent log
attribution both
Update deployment documentation to reflect that the MCPSigningKey is
persisted in Mnemosyne's database and used directly for minting team
JWTs, rather than being shared with Daedalus via vault. Remove the
obsolete vault variable reference and document the key rotation
procedure.
Rework README and docker-compose comments to document the deliberate
chicken-and-egg escape: the `init` sidecar now only runs `migrate` and
`load_library_types`, leaving `setup_neo4j_indexes` as a manual step
after the system embedding model is configured in `/admin/`. This
avoids making `app` unreachable on first boot when no embedding model
row exists yet, while preserving loud failure on dimension mismatch.
Refine the phase-2 integration spec to reflect implementation details:
- Change `resolved_libraries` from `set[str]` to ordered `list[str]`
- Document `MCPToken.allowed_libraries` as JSONField (not M2M) since
Library lives in Neo4j, not Django's ORM
- Clarify that `Library.workspace_id` is a content-routing attribute,
not an authorization axis
- Describe retirement of the three-branch `_WORKSPACE_SCOPE_CLAUSE` in
favor of a single `lib.uid IN $resolved_libraries` check
- Specify team JWT resolution via `TeamWorkspaceAssignment` DB join
- Note admin UI materializes full Library UID list explicitly
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.
