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.
Document that the system embedding model must be seeded before running
`setup_neo4j_indexes`, since vector index dimensions are read from the
`llm_manager_llmmodel` row. Update Docker instructions to reflect the
`init` sidecar behavior, which now runs migrations and library_type
defaults automatically while deferring vector index creation.
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.
Reorganize Docker Compose services: the Django/gunicorn container is now
`app` and nginx is `web`, better reflecting their roles. Add a dedicated
gunicorn configuration and install curl in the runtime image for health
checks.
Update documentation to reflect:
- Neo4j migration from ariel.incus to a dedicated umbriel.incus instance
- Rationale for requiring a dedicated Neo4j instance (single-tenancy
assumptions, label/index isolation, schema ownership)
- New service naming in compose commands and log tailing examples
Adds a complete deployment surface for production:
Dockerfile multi-stage 3.12-slim build, collectstatic
baked into the image, runs as non-root mnemosyne
uid/gid 1000.
docker/entrypoint.sh dispatches `web | mcp | worker | beat | migrate
| setup | shell` from a single image, so every
service in compose runs the same artifact.
docker-compose.yaml five services: static-init (one-shot copies
statics into the shared volume on every up),
web (gunicorn), mcp (uvicorn), worker (celery),
nginx. External services (Postgres, Neo4j,
RabbitMQ, S3, Memcached, embedder, reranker)
reached over the 10.10.0.0/24 internal network
and configured via mnemosyne/.env.
nginx/mnemosyne.conf reverse proxy: /library/* and /admin/* → web,
/mcp/* → mcp, /static/* → volume, /metrics
internal-network-only (127/8 + RFC1918), /healthz
proxies to /mcp/health for liveness probes.
.gitea/workflows/ CVE scan + image build, image pushed to
git.helu.ca/r/mnemosyne. Trivy scans pyproject
extras (dev/test/lint/docs) and the built image.
pyproject.toml adds [test], [lint], [docs] extras so the CI
pip-compile step has something to resolve.
README documents the bring-up flow (`docker compose run --rm web migrate`,
then `setup`, then `up -d`), day-to-day commands, and the env-var values
that need adjusting for production (DEBUG=False, KVDB_LOCATION pointing
at the external memcached, AWS keys filled in, etc.).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds a "Running Mnemosyne" section with the three commands needed to
operate the system: Django web app (gunicorn), MCP server (uvicorn on
:22091), and Celery worker — with notes on the embedding queue that
the Daedalus ingest task depends on.
Adds the Ouranos host map (Portia / Ariel / Oberon / Nyx / Memcached),
one-time setup commands (migrate, setup_neo4j_indexes, load_library_types),
the Daedalus integration endpoints table, and the two new library types
(business, finance) in the existing Library Types table.
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
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.
