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enesjakozh

LanceDB Node

by vurtnec
GitHub
JavaScript
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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.

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.

This server cannot be installed

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local-only server

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

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

  1. Overview
    1. Prerequisites
      1. Installation
        1. Dependencies
          1. Usage
            1. Configuration
              1. MCP Configuration
                1. Custom Embedding Function
                  1. Vector Search Example
                    1. License
                      1. Contributing

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