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 retrevial, 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.

• 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

=== "Python" shell pip install lancedb

  ```python
  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_df()
  ```

=== "Javascript" shell npm install vectordb

  ```javascript
  const lancedb = require("vectordb");

  const uri = "data/sample-lancedb";
  const db = await lancedb.connect(uri);
  const table = await db.createTable("my_table",
        [{ id: 1, vector: [3.1, 4.1], item: "foo", price: 10.0 },
        { id: 2, vector: [5.9, 26.5], item: "bar", price: 20.0 }])
  const results = await table.search([100, 100]).limit(2).execute();
  ```

Complete Demos (Python)

• YouTube Transcript Search
• Documentation QA Bot using LangChain
• Multimodal search using CLIP
• Serverless QA Bot with S3 and Lambda
• Serverless QA Bot with Modal

Complete Demos (JavaScript)

• 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.
• Full text search - [EXPERIMENTAL] full-text search API
• Ecosystem Integrations - integrating LanceDB with python data tooling ecosystem.
• Python API Reference - detailed documentation for the LanceDB Python SDK.
• Node API Reference - detailed documentation for the LanceDB Python SDK.