https://github.com/lancedb/lancedb

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

approximate-nearest-neighbor-search image-search nearest-neighbor-search recommender-system search-engine semantic-search similarity-search vector-database

Last synced: 11 days ago
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Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

• Host: GitHub
• URL: https://github.com/lancedb/lancedb
• Owner: lancedb
• License: apache-2.0
• Created: 2023-02-28T01:15:17.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-25T17:25:15.000Z (14 days ago)
• Last Synced: 2024-10-27T20:05:44.331Z (12 days ago)
• Topics: approximate-nearest-neighbor-search, image-search, nearest-neighbor-search, recommender-system, search-engine, semantic-search, similarity-search, vector-database
• Language: Rust
• Homepage: https://lancedb.github.io/lancedb/
• Size: 14.2 MB
• Stars: 4,515
• Watchers: 30
• Forks: 316
• Open Issues: 345
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        






**Developer-friendly, database for multimodal AI**





[![Blog](https://img.shields.io/badge/Blog-12100E?style=for-the-badge&logoColor=white)](https://blog.lancedb.com/)

[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/zMM32dvNtd)

[![Twitter](https://img.shields.io/badge/Twitter-%231DA1F2.svg?style=for-the-badge&logo=Twitter&logoColor=white)](https://twitter.com/lancedb)









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 🦜️🔗](https://python.langchain.com/docs/integrations/vectorstores/lancedb/), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), 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**

```shell

npm install @lancedb/lancedb

```

```javascript

import * as lancedb from "@lancedb/lancedb";

const db = await lancedb.connect("data/sample-lancedb");

const table = await db.createTable("vectors", [

	{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },

	{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },

], {mode: 'overwrite'});

const query = table.vectorSearch([0.1, 0.3]).limit(2);

const results = await query.toArray();

// You can also search for rows by specific criteria without involving a vector search.

const rowsByCriteria = await table.query().where("price >= 10").toArray();

```

**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_pandas()

```

## Blogs, Tutorials & Videos

* 📈 2000x better performance with Lance over Parquet

* 🤖 Build a question and answer bot with LanceDB