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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

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Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

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README 

        





LanceDB Logo



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



LanceDB

lancdb

[![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 Multimodal Search









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 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

* 📈 2000x better performance with Lance over Parquet

* 🤖 Build a question and answer bot with LanceDB