lancedb/docs/src/index.md

Welcome to LanceDB's Documentation

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

The key features of LanceDB include:

• Production-scale vector search with no servers to manage.

• Combine attribute-based information with vectors and store them as a single source-of-truth.

• Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.

• Ecosystem integrations: 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.

Installation

pip install lancedb

Quickstart

import lancedb

db = lancedb.connect(".")
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_df()

Complete Demos

We will be adding completed demo apps built using LanceDB.

• YouTube Transcript Search

Documentation Quick Links

• Basic Operations - basic functionality of LanceDB.
• Embedding Functions - functions for working with embeddings.
• Indexing - create vector indexes to speed up queries.
• Ecosystem Integrations - integrating LanceDB with python data tooling ecosystem.
• API Reference - detailed documentation for the LanceDB Python SDK.