lancedb a developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

[irthomasthomas]

• lancedb/lancedb: Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

TITLE

lancedb/lancedb: Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

DESCRIPTION

"LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering and management of embeddings.

The key features of LanceDB include:

• Production-scale vector search with no servers to manage.
• Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
• Support for vector similarity search, full-text search and SQL.
• Native Python and Javascript/Typescript support.
• Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
• GPU support in building vector index(*).
• Ecosystem integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
• LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.

Quick Start

Javascript

npm install vectordb
const lancedb = require('vectordb');
const db = await lancedb.connect('data/sample-lancedb');

const table = await db.createTable({
  name: 'vectors',
  data:  [
    { id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
    { id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }
  ]
})

const query = table.search([0.1, 0.3]).limit(2);
const results = await query.execute();

// You can also search for rows by specific criteria without involving a vector search.
const rowsByCriteria = await table.search(undefined).where("price >= 10").execute();

Python

pip install lancedb
import lancedb

uri = "data/sample-lancedb"
db = lancedb.connect(uri)
table = db.create_table("my_table",
                         data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
                               {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
result = table.search([100, 100]).limit(2).to_pandas()

URL

GitHub Repository

Suggested labels

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