mcp LanceDB node

A Node.js implementation for vector search using LanceDB and Ollama's embedding model.

Overview

This project demonstrates how to: - Connect to a LanceDB database - Create custom embedding functions using Ollama - Perform vector similarity search against stored documents - Process and display search results

Prerequisites

• Node.js (v14 or later)
• Ollama running locally with the nomic-embed-text model
• LanceDB storage location with read/write permissions

Installation

1. Clone the repository
2. Install dependencies:

pnpm install

Dependencies

• @lancedb/lancedb: LanceDB client for Node.js
• apache-arrow: For handling columnar data
• node-fetch: For making API calls to Ollama

Usage

Run the vector search test script:

pnpm test-vector-search

Or directly execute:

node test-vector-search.js

Configuration

The script connects to: - LanceDB at the configured path - Ollama API at http://localhost:11434/api/embeddings

MCP Configuration

To integrate with Claude Desktop as an MCP service, add the following to your MCP configuration JSON:

{
  "mcpServers": {
    "lanceDB": {
      "command": "node",
      "args": [
        "/path/to/lancedb-node/dist/index.js",
        "--db-path",
        "/path/to/your/lancedb/storage"
      ]
    }
  }
}

Replace the paths with your actual installation paths: - /path/to/lancedb-node/dist/index.js - Path to the compiled index.js file - /path/to/your/lancedb/storage - Path to your LanceDB storage directory

Custom Embedding Function

The project includes a custom OllamaEmbeddingFunction that: - Sends text to the Ollama API - Receives embeddings with 768 dimensions - Formats them for use with LanceDB

Vector Search Example

The example searches for "how to define success criteria" in the "ai-rag" table, displaying results with their similarity scores.

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.