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@flowrag/storage-lancedb

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  • License MIT

🔍 Vector storage implementation based on LanceDB. Manages vector embeddings and provides fast semantic search via similarity search. LanceDB is embedded (no server required), supports millions of vectors, and can also work on S3.

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  • @flowrag/storage-lancedb
  • @flowrag/storage-lancedb/package.json

Readme

@flowrag/storage-lancedb

Vector storage implementation using LanceDB. Embedded, no server required.

Installation

npm install @flowrag/storage-lancedb

Usage

import { LanceDBVectorStorage } from '@flowrag/storage-lancedb';

const vector = new LanceDBVectorStorage({
  path: './data/vectors',
  dimensions: 384,
});

await vector.upsert([{ id: 'chunk:1', vector: [0.1, 0.2, ...], metadata: {} }]);
const results = await vector.search([0.1, 0.2, ...], 10);
const count = await vector.count();

License

MIT