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[doc] Add New Readme Page (#2405)
Added a new readme for better navigation, updated language and more detail <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Updated the README with a modernized header, improved structure, and clearer descriptions of features and architecture. - Expanded and reorganized key features and product offerings for better clarity. - Simplified installation instructions and added a table of SDK interfaces with documentation links. - Enhanced community and contributor sections with new visuals and links to social and support channels. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
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David Myriel
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README.md
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README.md
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<a href="https://cloud.lancedb.com" target="_blank">
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<img src="https://github.com/user-attachments/assets/92dad0a2-2a37-4ce1-b783-0d1b4f30a00c" alt="LanceDB Cloud Public Beta" width="100%" style="max-width: 100%;">
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</a>
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<div align="center">
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<p align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="https://github.com/user-attachments/assets/ac270358-333e-4bea-a132-acefaa94040e">
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<source media="(prefers-color-scheme: light)" srcset="https://github.com/user-attachments/assets/b864d814-0d29-4784-8fd9-807297c758c0">
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<img alt="LanceDB Logo" src="https://github.com/user-attachments/assets/b864d814-0d29-4784-8fd9-807297c758c0" width=300>
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</picture>
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[](https://lancedb.com)
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[](https://lancedb.com/)
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[](https://blog.lancedb.com/)
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[](https://discord.gg/zMM32dvNtd)
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[](https://twitter.com/lancedb)
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[](https://www.linkedin.com/company/lancedb/)
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**Search More, Manage Less**
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<a href='https://github.com/lancedb/vectordb-recipes/tree/main' target="_blank"><img alt='LanceDB' src='https://img.shields.io/badge/VectorDB_Recipes-100000?style=for-the-badge&logo=LanceDB&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
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<a href='https://lancedb.github.io/lancedb/' target="_blank"><img alt='lancdb' src='https://img.shields.io/badge/DOCS-100000?style=for-the-badge&logo=lancdb&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
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[](https://blog.lancedb.com/)
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[](https://discord.gg/zMM32dvNtd)
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[](https://twitter.com/lancedb)
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[](https://gurubase.io/g/lancedb)
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<img src="docs/src/assets/lancedb.png" alt="LanceDB" width="50%">
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</p>
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# **The Multimodal AI Lakehouse**
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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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[**How to Install** ](#how-to-install) ✦ [**Detailed Documentation**](https://lancedb.github.io/lancedb/) ✦ [**Tutorials and Recipes**](https://github.com/lancedb/vectordb-recipes/tree/main) ✦ [**Contributors**](#contributors)
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**The ultimate multimodal data platform for AI/ML applications.**
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LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease.
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LanceDB is a central location where developers can build, train and analyze their AI workloads.
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</p>
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</div>
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<hr />
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<br>
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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.
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## **Demo: Multimodal Search by Keyword, Vector or with SQL**
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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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The key features of LanceDB include:
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## **Star LanceDB to get updates!**
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* Production-scale vector search with no servers to manage.
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<details>
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<summary>⭐ Click here ⭐ to see how fast we're growing!</summary>
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=lancedb/lancedb&theme=dark&type=Date">
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<img width="100%" src="https://api.star-history.com/svg?repos=lancedb/lancedb&theme=dark&type=Date">
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</picture>
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</details>
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* Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
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## **Key Features**:
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* Support for vector similarity search, full-text search and SQL.
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- **Fast Vector Search**: Search billions of vectors in milliseconds with state-of-the-art indexing.
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- **Comprehensive Search**: Support for vector similarity search, full-text search and SQL.
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- **Multimodal Support**: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
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- **Advanced Features**: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.
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* Native Python and Javascript/Typescript support.
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### **Products**:
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- **Open Source & Local**: 100% open source, runs locally or in your cloud. No vendor lock-in.
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- **Cloud and Enterprise**: Production-scale vector search with no servers to manage. Complete data sovereignty and security.
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* Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
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### **Ecosystem**:
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- **Columnar Storage**: Built on the Lance columnar format for efficient storage and analytics.
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- **Seamless Integration**: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
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- **Rich 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.
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* GPU support in building vector index(*).
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## **How to Install**:
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* 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.
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Follow the [Quickstart](https://lancedb.github.io/lancedb/basic/) doc to set up LanceDB locally.
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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.
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**API & SDK:** We also support Python, Typescript and Rust SDKs
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## Quick Start
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| Interface | Documentation |
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|-----------|---------------|
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| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
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| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
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| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
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| REST API | https://docs.lancedb.com/api-reference/introduction |
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**Javascript**
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```shell
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npm install @lancedb/lancedb
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```
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## **Join Us and Contribute**
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```javascript
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import * as lancedb from "@lancedb/lancedb";
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We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.
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const db = await lancedb.connect("data/sample-lancedb");
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const table = await db.createTable("vectors", [
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{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
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{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },
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], {mode: 'overwrite'});
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If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our [**Discord**](https://discord.gg/G5DcmnZWKB) server.
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[**Check out the GitHub Issues**](https://github.com/lancedb/lancedb/issues) if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.
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## **Contributors**
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<a href="https://github.com/lancedb/lancedb/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=lancedb/lancedb" />
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</a>
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const query = table.vectorSearch([0.1, 0.3]).limit(2);
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const results = await query.toArray();
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## **Stay in Touch With Us**
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<div align="center">
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// You can also search for rows by specific criteria without involving a vector search.
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const rowsByCriteria = await table.query().where("price >= 10").toArray();
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```
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</br>
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**Python**
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```shell
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pip install lancedb
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```
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[](https://lancedb.com/)
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[](https://blog.lancedb.com/)
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[](https://discord.gg/zMM32dvNtd)
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[](https://twitter.com/lancedb)
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[](https://www.linkedin.com/company/lancedb/)
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```python
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import lancedb
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uri = "data/sample-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]).limit(2).to_pandas()
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```
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## Blogs, Tutorials & Videos
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* 📈 <a href="https://blog.lancedb.com/benchmarking-random-access-in-lance/">2000x better performance with Lance over Parquet</a>
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* 🤖 <a href="https://github.com/lancedb/vectordb-recipes/tree/main/examples/Youtube-Search-QA-Bot">Build a question and answer bot with LanceDB</a>
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</div>
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