Robert Helewka
2a8a3d75b4
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>
174 lines
9.7 KiB
Markdown
174 lines
9.7 KiB
Markdown
# Mnemosyne
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*"The electric light did not come from the continuous improvement of candles."* — Oren Harari
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**The memory of everything you know.**
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Mnemosyne is a content-type-aware, multimodal personal knowledge management system built on Neo4j knowledge graphs and Qwen3-VL multimodal AI models. Named after the Titan goddess of memory and mother of the nine Muses, Mnemosyne doesn't just store your knowledge — it understands what kind of knowledge it is, connects it through relationships, and makes it all searchable through text, images, and natural language.
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## What Makes This Different
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Every existing knowledge base tool treats all documents identically: text in, chunks out, vectors stored. A novel and a PostgreSQL manual get the same treatment.
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Mnemosyne knows the difference:
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- **A textbook** has chapters, an index, technical terminology, and pedagogical structure. It's chunked accordingly, and when an LLM retrieves results, it knows this is instructional content.
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- **A novel** has narrative flow, characters, plot arcs, dialogue. The LLM knows to interpret results as creative fiction.
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- **Album artwork** is a visual asset tied to an artist, genre, and era. It's embedded multimodally — searchable by both image similarity and text description.
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- **A journal entry** is personal, temporal, reflective. The LLM treats it differently than a reference manual.
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This **content-type awareness** flows through every layer: chunking strategy, embedding instructions, re-ranking, and the final LLM prompt.
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## Core Architecture
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| Component | Technology | Purpose |
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|-----------|-----------|---------|
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| **Knowledge Graph** | Neo4j 5.x | Relationships + vector storage (no dimension limits) |
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| **Multimodal Embeddings** | Qwen3-VL-Embedding-8B | Text + image + video in unified vector space (4096d) |
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| **Multimodal Re-ranking** | Synesis (Qwen3-VL-Reranker-2B) | Cross-attention precision scoring via `/v1/rerank` |
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| **Web Framework** | Django 5.x + DRF | Auth, admin, API, content management |
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| **Object Storage** | S3/MinIO | Original content + chunk text storage |
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| **Async Processing** | Celery + RabbitMQ | Document embedding, graph construction |
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| **LLM Interface** | MCP Server | Primary interface for Claude, Copilot, etc. |
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| **GPU Serving** | vLLM + llama.cpp | Local model inference |
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## Library Types
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| Library | Example Content | Multimodal? | Graph Relationships |
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|---------|----------------|-------------|-------------------|
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| **Fiction** | Novels, short stories | Cover art | Author → Book → Character → Theme |
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| **Nonfiction** | History, biography, science writing | Photos, charts | Author → Work → Topic → Person/Place |
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| **Technical** | Textbooks, manuals, docs | Diagrams, screenshots | Product → Manual → Section → Procedure |
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| **Music** | Lyrics, liner notes | Album artwork | Artist → Album → Track → Genre |
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| **Film** | Scripts, synopses | Stills, posters | Director → Film → Scene → Actor |
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| **Art** | Descriptions, catalogs | The artwork itself | Artist → Piece → Style → Movement |
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| **Journal** | Personal entries, plans, observations | Photos | Date → Entry → Topic → Person/Place |
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| **Business** | Proposals, marketing, strategy | Logos, charts | Client → Engagement → Deliverable |
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| **Finance** | Statements, tax, market commentary | Charts, statement scans | Account → Instrument → Period |
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## Search Pipeline
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```
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Query → Vector Search (Neo4j) + Graph Traversal (Cypher) + Full-Text Search
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→ Candidate Fusion → Qwen3-VL Re-ranking → Ranked Chunks + Metadata
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→ MCP tool result (the calling LLM does its own synthesis)
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```
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## Heritage
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Mnemosyne's RAG pipeline architecture is inspired by [Spelunker](https://git.helu.ca/r/spelunker), an enterprise RFP response platform. The proven patterns — hybrid search, two-stage RAG (responder + reviewer), citation-based retrieval, and async document processing — are carried forward and enhanced with multimodal capabilities and knowledge graph relationships.
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## Running Mnemosyne
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Mnemosyne runs as three cooperating processes: the Django web app (REST API + admin), the MCP server (LLM-facing tools), and one or more Celery workers (async embedding + ingest). All three read configuration from `mnemosyne/.env` (copy from `mnemosyne/.env example` and fill in secrets).
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Hosts in the Ouranos lab:
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- **Postgres** — `portia.incus:5432` (Django ORM: users, IngestJob)
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- **Neo4j** — `ariel.incus:25554` (knowledge graph + vectors)
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- **RabbitMQ** — `oberon.incus:5672` (Celery broker)
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- **MinIO** — `nyx.helu.ca:8555` (S3-compatible; `mnemosyne-content` and `daedalus` buckets)
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- **Memcached** — `127.0.0.1:11211` (task progress)
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### One-time setup
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```bash
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cd mnemosyne/
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python manage.py migrate # Apply Django ORM migrations
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python manage.py setup_neo4j_indexes # Create Neo4j vector + full-text indexes
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python manage.py load_library_types # Load LIBRARY_TYPE_DEFAULTS into Neo4j
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```
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### Start the web app
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The Django REST API serves `/library/api/*` (libraries, collections, items, search, workspaces, ingest) and Django admin. Use Gunicorn in production; `runserver` for dev.
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```bash
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cd mnemosyne/
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# Development
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python manage.py runserver 0.0.0.0:8000
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# Production
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gunicorn --bind 0.0.0.0:8000 --workers 3 mnemosyne.wsgi:application
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```
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### Start the MCP server
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The MCP server exposes the LLM-facing tools (`search`, `get_chunk`, `list_libraries`, `list_collections`, `list_items`, `get_health`) over Streamable HTTP at `/mcp` and SSE at `/mcp/sse`. Run as a separate Uvicorn process, on its own port, so it can be reverse-proxied or scaled independently of the Django app.
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```bash
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cd mnemosyne/
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# Single command: ASGI server hosting the FastMCP app
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uvicorn mnemosyne.asgi:app --host 0.0.0.0 --port 22091 --workers 1
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```
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The `mcp_server/asgi.py` mounts FastMCP at `/mcp` (Streamable HTTP) and `/mcp/sse` (SSE), with a `/mcp/health` JSON probe for HAProxy/Pallas.
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### Start a Celery worker
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A single worker that handles all queues (development) plus the focused command Daedalus depends on (the `embedding` queue, where the Daedalus ingest task lives).
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```bash
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cd mnemosyne/
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# Development — one worker, all queues
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celery -A mnemosyne worker -l info -Q celery,embedding,batch
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# Production — embedding queue (handles Daedalus ingest + embed_item)
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celery -A mnemosyne worker -l info -Q embedding -c 1 -n embedding@%h
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# Production — batch queue (collection/library bulk operations)
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celery -A mnemosyne worker -l info -Q batch -c 2 -n batch@%h
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# Production — default queue (LLM validation, misc)
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celery -A mnemosyne worker -l info -Q celery -c 2 -n default@%h
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```
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Daedalus's `POST /library/api/ingest/` dispatches `library.tasks.ingest_from_daedalus` to the **embedding** queue. If you only run one worker, make sure it consumes `embedding` or that task will sit in the broker.
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To bypass workers in dev/test, set `CELERY_TASK_ALWAYS_EAGER=True` in `.env`.
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**Scheduler & monitoring (optional):**
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```bash
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celery -A mnemosyne beat -l info # Periodic task scheduler
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celery -A mnemosyne flower --port=5555 # Web monitoring UI
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```
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See [Phase 2: Celery Workers & Scheduler](docs/PHASE_2_EMBEDDING_PIPELINE.md#celery-workers--scheduler) for queue tuning, reliability settings, and task progress tracking.
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### Daedalus integration endpoints
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These endpoints are used by the Daedalus FastAPI backend (HTTP Basic auth). All under `/library/api/`:
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| Method | Route | Purpose |
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|--------|-------|---------|
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| POST | `/workspaces/` | Create a workspace (idempotent on `workspace_id`); body: `{workspace_id, name, library_type, description?}` |
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| GET | `/workspaces/{workspace_id}/` | Workspace status (item/chunk counts) |
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| DELETE | `/workspaces/{workspace_id}/` | Delete workspace + reachable content; preserves shared concepts |
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| POST | `/ingest/` | Queue a file for ingestion + embedding |
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| GET | `/jobs/{job_id}/` | Poll ingest job status |
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| POST | `/jobs/{job_id}/retry/` | Re-dispatch a failed job |
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| GET | `/jobs/?status=&library_uid=` | List recent jobs |
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See [docs/mnemosyne_integration.md](docs/mnemosyne_integration.md) for the full Daedalus contract.
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## Architecture Note: Retrieval, Not Synthesis
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Mnemosyne is a **retrieval engine**, not a RAG pipeline. It stores, embeds, and ranks — it does not synthesize answers.
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The earlier roadmap had a server-side RAG layer that took a query and returned a written answer with citations. That layer has been removed. Calling LLMs (Claude via MCP, principally) are perfectly capable of driving iterative retrieval themselves when given the right primitives, and a server-side synthesis hop adds latency, cost, and a place where errors are harder to debug. Letting the calling LLM see chunks directly — and follow citations, pivot mid-search, or call `get_chunk` for full text — beats pre-digesting them.
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If a "knowledge subagent" is ever wanted (a wrapper that takes a question and returns a written answer), it lives **outside** Mnemosyne as a thin client over the MCP tools, with its own system prompt. No coupling, no extra inference hop inside the server, and the subagent's behavior can iterate independently.
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## Documentation
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- **[Architecture Documentation](docs/mnemosyne.html)** — Full system architecture with diagrams
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- **[Phase 1: Foundation](docs/PHASE_1_FOUNDATION.md)** — Project skeleton, Neo4j data model, content-type system
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- **[Phase 2: Embedding Pipeline](docs/PHASE_2_EMBEDDING_PIPELINE.md)** — Qwen3-VL multimodal embedding
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- **[Phase 3: Search & Re-ranking](docs/PHASE_3_SEARCH_AND_RERANKING.md)** — Hybrid search + re-ranker
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- **[Phase 5: MCP Server](docs/PHASE_5_MCP_SERVER.md)** — Retrieval primitives for LLMs (`search`, `get_chunk`, `list_libraries`, …)
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- **[Phase 6: Backport to Spelunker](docs/PHASE_6_BACKPORT_TO_SPELUNKER.md)** — Proven patterns flowing back
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