localbrain-rag 0.1.1

Local-first general-purpose RAG with an MCP server, CLI, and web console

localbrain

Local-first general-purpose RAG — point it at folders/files, index them, and search by meaning through an MCP server (for Claude Code etc.), a CLI, and a web console. Everything runs on your machine; generation is done by your MCP client (e.g. Claude), so localbrain only needs a small embedding model — no local LLM, no Ollama daemon required.

• 🔎 Semantic search + Cross-Encoder reranking
• 🧩 MCP tools (search, add_path, reindex, query_insights, …)
• 🖥️ Web console: source management · manual indexing (live progress) · search test · model swap
• ♻️ Incremental indexing (only changed files), swappable embedding model
• 📈 Query clustering insights (FAQs & knowledge gaps) — a self-improving loop
• 🔒 Fully local; pluggable providers (fastembed ONNX / sentence-transformers / Ollama)

Install

Installed as localbrain-rag on PyPI; the command and import stay localbrain.

Default (CPU, no extra setup)

pip install localbrain-rag

Uses fastembed (ONNX, multilingual e5) — works on CPU with no PyTorch. Good enough to start.

Best quality (GPU + bge-m3) — recommended

1. Install a CUDA build of PyTorch matching your GPU (example: CUDA 12.6):

   pip install torch --index-url https://download.pytorch.org/whl/cu126
   

2. Install localbrain with sentence-transformers:

   pip install "localbrain-rag[st]"
   

3. Point the config at bge-m3 (see Configuration). Models auto-download on first use.

No NVIDIA GPU? Skip step 1 — pip install "localbrain-rag[st]" installs a CPU PyTorch and still works (slower).

Quick start

# CLI
localbrain add-source "C:\Users\me\notes" --globs "*.md,*.txt"
localbrain index
localbrain search "what did we decide about delivery delays"
localbrain insights          # FAQ clusters + knowledge gaps
localbrain stats
localbrain --version

# Web console  →  http://127.0.0.1:8765
localbrain-web

# MCP server (stdio) — register with Claude Code
localbrain-mcp

Configuration

Config lives at ~/.localbrain/config.json (override the dir with LOCALBRAIN_HOME). Data (SQLite + Chroma vectors + model-by-model collections) also lives under ~/.localbrain.

{
  "embedding": { "provider": "sentence-transformers", "model": "BAAI/bge-m3", "fp16": false },
  "chunk": { "size": 1000, "overlap": 150 },
  "rerank": { "enabled": true, "provider": "cross-encoder",
              "model": "BAAI/bge-reranker-v2-m3", "candidate_k": 30, "fp16": false },
  "search_k": 5
}

• Swap models freely — change embedding.model, then localbrain index --rebuild (text is kept, so it re-embeds without re-reading files). Each model uses its own vector collection (cosine distance).
• fp16: true halves VRAM and speeds up inference on GPU (ignored on CPU). Handy for ~6 GB cards.
• Reranking improves accuracy; scores become Cross-Encoder relevance (≈0.8+ strong match, ≈0 none).

Models & first run

First search/index downloads models from Hugging Face into the HF cache (HF_HOME): bge-m3 (~2 GB) + bge-reranker-v2-m3 (~2 GB). Subsequent runs are cached/offline. fastembed default models are much smaller.

⚠️ One process owns writes

The web server and CLI share the same on-disk vector store. ChromaDB does not reflect writes made by another process while a server is running. So:

• Index from the web console (Indexing tab), or
• stop localbrain-web → run localbrain index → restart the server.

Don't run localbrain index while localbrain-web is up — the running server won't see the new docs.

Docker (optional, server scenario)

A container only sees mounted volumes, so the "browse & index any local folder" UX is limited — use Docker to serve a mounted documents folder. GPU works via NVIDIA Container Toolkit (Windows: Docker Desktop + WSL2). See Dockerfile / docker-compose.yml:

DOCS_DIR=/path/to/docs docker compose up --build   # http://localhost:8765 ; add /docs as a source

Architecture

core/        pure library (single-responsibility modules: ingest, embed, rerank, store, search, insights)
services/    orchestration (indexing / search / insights / model)
adapters/    thin entry points: cli · mcp_server · web   (all share core via context.py)

License

MIT — see LICENSE. Design notes in docs/spec/.