LanceDB is an open-source database for vector search, built with persistent storage, which greatly simplifies retrieval, filtering, and management of embeddings. Designed for multimodal AI, LanceDB supports various types of data including text, images, videos, and more.

Key Features

Production-Scale Vector Search

LanceDB offers production-scale vector search capabilities with no servers to manage, making it easy to deploy and scale.

Multimodal Data Support

Store, query, and filter vectors, metadata, and multimodal data such as text, images, videos, and point clouds.

Versatile Query Capabilities

Supports vector similarity search, full-text search, and SQL, providing a flexible and powerful query interface.

Native Language Support

LanceDB offers native support for Python and JavaScript/TypeScript, enabling seamless integration into existing projects.

Zero-Copy and Automatic Versioning

Manage versions of your data without needing extra infrastructure. LanceDB supports zero-copy and automatic versioning, making data management efficient and straightforward.

GPU Support

Leverage GPU support for building vector indexes, enhancing performance for large-scale operations.

Ecosystem Integrations

LanceDB integrates with popular tools and frameworks such as LangChain 🦜️🔗, LlamaIndex 🦙, Apache Arrow, Pandas, Polars, DuckDB, and more.

Built with Rust

The core of LanceDB is written in Rust, providing high performance and safety. It is built using Lance, an open-source columnar format designed for performant ML workloads.

Quick Start


Install the vectordb package:

npm install vectordb

Example usage:

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 =[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
  .where("price >= 10")


Install the lancedb package:

pip install lancedb

Example usage:

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 =[100, 100]).limit(2).to_pandas()

Blogs, Tutorials & Videos

Explore various resources to learn more about LanceDB and its capabilities:

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