lore-mcp

LORE — Local Offline Retrieval Engine for MCP

An MCP server for semantic search over your local technical documents. No cloud, no external database — just a single .db file on your workstation.

What it does

• Indexes a directory of Markdown/text files into a portable SQLite database using vector embeddings

• Exposes two MCP tools (search_docs, list_sources) for any MCP client (Claude Code, Claude Desktop, Cursor, etc.)

• Runs locally with automatic GPU/API/CPU fallback for embedding generation

Related MCP server: docs-mcp

Status

Early development. See the backlog for planned features.

Quickstart

Prerequisites

• Python ≥ 3.10

• (Optional) NVIDIA GPU with CUDA for faster embeddings

Installation

pip install lore-mcp

Note: not yet published on PyPI. For now, install from source:

git clone https://github.com/rchanter/lore-mcp.git
cd lore-mcp
pip install -e .

Configure your MCP client

Add to your MCP client configuration (e.g. .claude/settings.json):

{
  "mcpServers": {
    "lore": {
      "command": "lore-mcp",
      "args": []
    }
  }
}

Index your documents

lore-mcp index /path/to/your/docs/

Search

Once configured, your MCP client can use:

• search_docs("your query", top_k=5) — semantic search over indexed documents

• list_sources() — list all indexed files with chunk counts

Environment variables

Architecture

lore-mcp uses BAAI/bge-m3 for embeddings (1024 dimensions, multilingual) and sqlite-vec for vector storage in a single .db file.

Embedding generation falls back automatically: local GPU (CUDA) → remote API (OpenAI-compatible) → local CPU.

See docs/ for detailed documentation (coming with MVP).

Roadmap

MVP (v0.1.0)

• SQLite + sqlite-vec storage backend

• Embedding with GPU/API/CPU fallback

• MCP server (search_docs, list_sources)

• CLI ingestion tool

• Tests (TDD)

Post-MVP

• Example corpus and sample database

• Incremental re-indexing

• Metadata filtering

• Hybrid search (vector + keyword)

• Image captioning during ingestion

• Docker image

AI-assisted development

This project is developed with AI assistance (Claude, Anthropic). All AI-assisted content is marked with Co-Authored-By trailers in commits. Every contribution — human or AI-assisted — is reviewed, tested, and validated by a human before being committed.

See docs/ai-guidelines.md for the full guidelines.

License

GPL-3.0-or-later — see docs/adr/001-license-gpl-v3.md for the rationale.

Copyright (C) 2026 Romain Chantereau