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mcp-lancedb-nodo
Nodejs del servidor MCP LancedB
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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
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
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