local-only server

The server can only run on the client’s local machine because it depends on local resources.

Integrations

Provides a vector search implementation using Node.js, enabling semantic search capabilities for documents stored in a LanceDB database.
Leverages Ollama's embedding model (nomic-embed-text) to create custom embedding functions for converting text into vector representations that can be searched.
Supports package management for the MCP server installation and dependency management using pnpm.

LanceDB Node.js Vector Search

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

Clone the repository
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.

This server cannot be installed

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security - not tested

F

license - not found

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quality - not tested