From fe49a59dcbbc81831506eaea5a7d19e9060dfed0 Mon Sep 17 00:00:00 2001
From: Jai <jaichopra@gmail.com>
Date: Sun, 19 Mar 2023 13:03:49 -0700
Subject: [PATCH] Update README.md

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+<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">
+
+**Serverless, low-latency vector database for AI applications**
+
+<a href="">Documentation</a> •
+<a href="https://blog.eto.ai/">Blog</a> •
+<a href="https://discord.gg/zMM32dvNtd">Discord</a> •
+<a href="https://twitter.com/etodotai">Twitter</a>
+
+</p>
+</div>
+
+<hr />
+
+LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies storage, retrieval and filtering for vectors and more.
+
+The key features of Lance include:
+
+* Scale to billion+ vector-search all without managing servers.
+
+* Combine attribute-based information with vectors and store them as a single source-of-truth.
+
+* Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
+
+* Ecosystem integrations: Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
+
+Lance'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.
+
+## Quick Start
+
+**Installation**
+
+```shell
+pip install lancedb
+```
+
+**Quickstart**
+```python
+import 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]).where("price < 15").limit(1).to_df()
+```