What are the prerequisites to run LanceDB?
Node.js v14 or later, Ollama running locally with the `nomic-embed-text` model, and a LanceDB storage location with read/write permissions.
@vurtnec
基本信息
LanceDB MCP Server nodejs
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"lanceDB": {
"command": "node",
"args": [
"/path/to/lancedb-node/dist/index.js",
"--db-path",
"/path/to/your/lancedb/storage"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
LanceDB is a Node.js MCP server that connects to a LanceDB database and performs vector similarity search using Ollama’s embedding model. It is intended for developers who want to integrate vector search into AI workflows or build retrieval-augmented generation (RAG) applications.
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.
nomic-embed-text modelNode.js v14 or later, Ollama running locally with the `nomic-embed-text` model, and a LanceDB storage location with read/write permissions.
Add the following to your MCP configuration JSON, replacing the paths with your actual locations: ```json { "mcpServers": { "lanceDB": { "command": "node", "args": ["/path/to/lancedb-node/dist/index.js", "--db-path", "/path/to/your/lancedb/storage"] } } } ```
It uses Ollama’s `nomic-embed-text` model, which produces embeddings with 768 dimensions.
Data is stored in the LanceDB directory specified by the `--db-path` argument in the MCP configuration (or the default path used in the test script).
The project depends on `@lancedb/lancedb` (LanceDB client), `apache-arrow`, and `node-fetch` for making API calls to Ollama.
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