LanceDB
About
Vector search using the LanceDB vector database and Ollama embedding models.
Details
- Author
- vurtnec
- Downloads
- 429
- Categories
- Other, Database
Jump to
- Connects to a LanceDB database for vector storage
- Custom embedding function using Ollama’s nomic-embed-text model
- Performs vector similarity search against stored documents
- Processes and displays search results with similarity scores
- Integrates with Claude Desktop as an MCP service
- Uses 768‑dimensional embeddings
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
LanceDBCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install dependencies with pnpm install, then run the vector search test script using pnpm test-vector-search or node test-vector-search.js. To use as an MCP service with Claude Desktop, add the provided JSON configuration to your MCP settings, replacing the paths with your actual installation and storage directories.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"lancedb": {
"lanceDB": {
"command": "node",
"args": [
"/path/to/lancedb-node/dist/index.js",
"--db-path",
"/path/to/your/lancedb/storage"
]
}
}
}
}McpServers
{
"lanceDB": {
"command": "node",
"args": [
"/path/to/lancedb-node/dist/index.js",
"--db-path",
"/path/to/your/lancedb/storage"
]
}
}Vector search using the LanceDB vector database and Ollama embedding models.
A Node.js implementation for vector search using LanceDB and Ollama's embedding model.
- Connect to a LanceDB database
- Create custom embedding functions using Ollama
- Perform vector similarity search against stored documents
- Process and display search results
- Node.js (v14 or later)
- Ollama running locally with thenomic-embed-textmodel
- LanceDB storage location with read/write permissions
- Clone the repository
- Install dependencies:
- @lancedb/lancedb: LanceDB client for Node.js
- apache-arrow: For handling columnar data
- node-fetch: For making API calls to Ollama
- LanceDB at the configured path
- Ollama API athttp://localhost:11434/api/embeddings
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
The project includes a customOllamaEmbeddingFunctionthat:
- Sends text to the Ollama API
- Receives embeddings with 768 dimensions
- Formats them for use with LanceDB
The example searches for "how to define success criteria" in the "ai-rag" table, displaying results with their similarity scores.
Contributions are welcome! Please feel free to submit a Pull Request.
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