Milvus is an open-source vector database built to power AI applications and embedding similarity search. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.
Both Milvus Standalone and Milvus Cluster are available.
Milvus was released under the [open-source Apache License 2.0](https://github.com/milvus-io/milvus/blob/master/LICENSE) in October 2019. It is currently a graduate project under [LF AI & Data Foundation](https://lfaidata.foundation/).
<summary><b>Simplified unstructured data management</b></summary>
<li>Rich APIs designed for data science workflows.</li><li>Consistent user experience across laptop, local cluster, and cloud.</li><li>Embed real-time search and analytics into virtually any application.</li>
<summary><b>Reliable, always on vector database</b></summary>
Milvus’ built-in replication and failover/failback features ensure data and applications can maintain business continuity in the event of a disruption.
<summary><b>Highly scalable and elastic</b></summary>
Component-level scalability makes it possible to scale up and down on demand. Milvus can autoscale at a component level according to the load type, making resource scheduling much more efficient.
In addition to vectors, Milvus supports data types such as Boolean, integers, floating-point numbers, and more. A collection in Milvus can hold multiple fields for accommodating different data features or properties. Milvus pairs scalar filtering with powerful vector similarity search to offer a modern, flexible platform for analyzing unstructured data.
Milvus combines stream and batch processing for data storage to balance timeliness and efficiency. Its unified interface makes vector similarity search a breeze.
<summary><b>Community supported, industry recognized</b></summary>
With over 1,000 enterprise users, 6,000+ stars on GitHub, and an active open-source community, you’re not alone when you use Milvus. As a graduate project under the <ahref="https://lfaidata.foundation/">LF AI & Data Foundation</a>, Milvus has institutional support.
> **IMPORTANT** The master branch is for the development of Milvus v2.0. On March 9th, 2021, we released Milvus v1.0, the first stable version of Milvus with long-term support. To use Milvus v1.0, switch to [branch 1.0](https://github.com/milvus-io/milvus/tree/1.0).
Milvus [bootcamp](https://github.com/milvus-io/bootcamp/tree/new-bootcamp) are designed to expose users to both the simplicity and depth of the vector database. Discover how to run benchmark tests as well as build similarity search applications spanning chatbots, recommendation systems, reverse image search, molecular search, and much more.
Contributions to Milvus are welcome from everyone. See [Guidelines for Contributing](https://github.com/milvus-io/milvus/blob/master/CONTRIBUTING.md) for details on submitting patches and the contribution workflow. See our [community repository](https://github.com/milvus-io/community) to learn about our governance and access more community resources.
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Zilliz, the company behind Milvus, is [actively hiring](https://app.mokahr.com/apply/zilliz/37974#/) full-stack developers and solution engineers to build the next-generation open-source data fabric.
Reference to cite when you use Milvus in a research paper:
```json
@inproceedings{2021milvus,
title={Milvus: A Purpose-Built Vector Data Management System},
author={Wang, Jianguo and Yi, Xiaomeng and Guo, Rentong and Jin, Hai and Xu, Peng and Li, Shengjun and Wang, Xiangyu and Guo, Xiangzhou and Li, Chengming and Xu, Xiaohai and others},
booktitle={Proceedings of the 2021 International Conference on Management of Data},