rag-vault

Local-first RAG over my Obsidian vault — ask my second brain questions in the terminal, get grounded answers with citations that deep-link back into Obsidian. The retrieval internals are hand-rolled (BM25 + embeddings + reciprocal rank fusion), and the whole engine doubles as an MCP server so AI agents on my machine can search my notes as a tool.

Why hand-rolled?

At vault scale (dozens–hundreds of notes), a vector database is overkill — brute-force cosine over a numpy matrix answers in under a millisecond. So this repo implements the interesting parts itself, in plain Python I can defend line by line:

• Vector store → SQLite + a numpy matrix (unit-normalized float32; cosine = dot product)

• BM25 → ~40 lines of term-frequency math (k1=1.5, b=0.75)

• Hybrid fusion → Reciprocal Rank Fusion: score = Σ 1/(60 + rank) — no weights to tune

• Chunking → markdown-aware: splits on headings, keeps a Note > Heading breadcrumb, extracts [[wikilinks]]

Frameworks (LangChain, LlamaIndex) would hide exactly the parts this project exists to understand.

Local-first by design. My vault contains journal entries and career notes. Embeddings (nomic-embed-text) and default answer generation (qwen2.5:3b) run on my Mac via Ollama. Nothing leaves the machine unless I explicitly pass --provider claude — and even then, only the question plus the retrieved excerpts are sent, never the vault.

How it works

flowchart LR
    V[Obsidian vault\n*.md] --> C[chunker\nheading-aware]
    C --> E[embeddings\nnomic-embed-text]
    E --> S[(SQLite\nincremental sync)]
    Q[question] --> H{hybrid search}
    S --> H
    H -->|cosine| F[RRF fusion]
    H -->|BM25| F
    F --> A[grounded answer\nqwen local / claude opt-in]
    A --> T[cited answer\nobsidian:// links]

Retrieval quality, measured

vault eval scores retrieval against 12 real questions with known source notes (hit@k: was the right note in the top k; MRR: mean reciprocal rank of the first hit):

(numbers from my vault — rerun with python -m ragvault eval)

On this vault, plain vector search actually beats hybrid on hit@3/hit@6/MRR — my questions phrase concepts close to how the notes word them, so dense embeddings alone do well, and RRF's rank-based blend lets BM25's misses drag hybrid down a bit. Hybrid still beats BM25 alone across the board, and I'd expect it to pull ahead on a vault with more exact-keyword lookups (IDs, code, jargon).

Quickstart

Works on any Obsidian vault (or any folder of markdown):

git clone https://github.com/maxrotemberg04-spec/rag-vault && cd rag-vault
python3 -m venv .venv && .venv/bin/pip install -r requirements.txt
ollama pull nomic-embed-text   # embeddings (local)
ollama pull qwen2.5:3b         # answers (local)

export RAGVAULT_VAULT=~/path/to/your/vault
.venv/bin/python -m ragvault index
.venv/bin/python -m ragvault ask "what did I decide about X?"
.venv/bin/python -m ragvault search "keyword hunt" --mode bm25   # no LLM needed

--provider claude uses the Anthropic API for answers if ANTHROPIC_API_KEY is set (retrieved excerpts only — the vault itself never uploads).

Agents can use it too (MCP)

The same engine runs as an MCP server:

claude mcp add ragvault -e RAGVAULT_VAULT=$HOME/Documents/FOCUS -- \
    $PWD/.venv/bin/python $PWD/mcp_server.py

Claude Code sessions then get two tools — search_vault (hybrid retrieval; the agent synthesizes) and ask_vault (fully local answer). My "Educator" Claude session uses this instead of grepping the vault.

Design decisions

• No vector DB — at this scale the honest engineering answer is a numpy dot product. At ~100k documents I'd reach for HNSW indexes (or pgvector) and this section would change.

• RRF over weighted score fusion — rank-based fusion is scale-free, so BM25 and cosine scores never need calibrating against each other.

• Two texts per chunk — verbatim display_text for humans, cleaned embed_text (wikilinks resolved, callout markers stripped, breadcrumb prepended) for the models.

• Grounding contract — the answer prompt allows only the retrieved excerpts, requires [n] citations, and must say "That's not in the vault" rather than guess.

• Evals are part of the product — same philosophy as my eval-harness: if you can't measure retrieval, you can't improve it.

Limitations & roadmap

Single-user, single-vault by design. No re-ranker; no stemming (BM25 is exact-token). Roadmap: vault ui (local web page), link-graph ranking boost using the [[wikilink]] graph, --watch auto-reindex, semantic query cache.

Full design doc: docs/design.md

MIT © Max Rotemberg