# lancedb

**Repository Path**: liunix61/lancedb

## Basic Information

- **Project Name**: lancedb
- **Description**: LanceDB 是一个用于矢量搜索的开源数据库，采用持久存储构建，极大地简化了嵌入的检索、过滤和管理
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: https://www.oschina.net/p/lancedb
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2023-07-26
- **Last Updated**: 2023-07-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="center">
<p align="center">

<img width="275" alt="LanceDB Logo" src="https://user-images.githubusercontent.com/917119/226205734-6063d87a-1ecc-45fe-85be-1dea6383a3d8.png">

**Developer-friendly, serverless vector database for AI applications**

<a href="https://lancedb.github.io/lancedb/">Documentation</a> •
<a href="https://blog.lancedb.com/">Blog</a> •
<a href="https://discord.gg/zMM32dvNtd">Discord</a> •
<a href="https://twitter.com/lancedb">Twitter</a>

</p>

<img max-width="750px" alt="LanceDB Multimodal Search" src="https://github.com/lancedb/lancedb/assets/917119/09c5afc5-7816-4687-bae4-f2ca194426ec">

</p>
</div>

<hr />

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 🦜️🔗](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lanecdb.html), [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 <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.

## Quick Start

**Javascript**
```shell
npm install vectordb
```

```javascript
const lancedb = require('vectordb');
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 }])

const query = table.search([0.1, 0.3]);
query.limit = 20;
const results = await query.execute();
```

**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()
```

## Blogs, Tutorials & Videos
* 📈 <a href="https://blog.eto.ai/benchmarking-random-access-in-lance-ed690757a826">2000x better performance with Lance over Parquet</a>
* 🤖 <a href="https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb">Build a question and answer bot with LanceDB</a>