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50 lines
1.6 KiB
Markdown
50 lines
1.6 KiB
Markdown
<div align="center">
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<p align="center">
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<img width="275" alt="LanceDB Logo" src="https://user-images.githubusercontent.com/917119/226205734-6063d87a-1ecc-45fe-85be-1dea6383a3d8.png">
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**Serverless, low-latency vector database for AI applications**
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<a href="">Documentation</a> •
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<a href="https://blog.eto.ai/">Blog</a> •
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<a href="https://discord.gg/zMM32dvNtd">Discord</a> •
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<a href="https://twitter.com/etodotai">Twitter</a>
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</p>
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</div>
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<hr />
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LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
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The key features of LanceDB include:
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* Production-scale vector search with no servers to manage.
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* Combine attribute-based information with vectors and store them as a single source-of-truth.
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* Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
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* Ecosystem integrations: Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
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LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/eto-ai/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
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## Quick Start
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**Installation**
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```shell
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pip install lancedb
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```
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**Quickstart**
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```python
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import lancedb
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db = lancedb.connect(uri)
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table = db.create_table("my_table",
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data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
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{"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
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result = table.search([100, 100]).where("price < 15").limit(1).to_df()
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```
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