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<img width="275" alt="LanceDB Logo" src="https://github.com/lancedb/lancedb/assets/5846846/37d7c7ad-c2fd-4f56-9f16-fffb0d17c73a">

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

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

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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 <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({
  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 = table.search([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.search(undefined).where("price >= 10").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_pandas()
```

## Blogs, Tutorials & Videos
* 📈 <a href="https://blog.lancedb.com/benchmarking-random-access-in-lance/">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>
