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https://lancedb.github.io/lancedb/
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
https://lancedb.github.io/lancedb/
approximate-nearest-neighbor-search image-search nearest-neighbor-search recommender-system search-engine semantic-search similarity-search vector-database
Last synced: 29 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://lancedb.github.io/lancedb/
- Owner: lancedb
- License: apache-2.0
- Created: 2023-02-28T01:15:17.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-11T09:36:26.000Z (about 1 month ago)
- Last Synced: 2024-11-11T10:32:00.129Z (about 1 month 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: 14.3 MB
- Stars: 4,706
- Watchers: 31
- Forks: 325
- Open Issues: 339
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
**Developer-friendly, database for multimodal AI**
[](https://blog.lancedb.com/)
[](https://discord.gg/zMM32dvNtd)
[](https://twitter.com/lancedb)
[](https://gurubase.io/g/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