milvus/README.md

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<details>
<summary>Click to take a quick look at our demos!</summary>
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      <a href="https://zilliz.com/solutions">Image search</a>
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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.

Milvus was released under the open-source Apache License 2.0 in October 2019. It is currently an incubation-stage project under [LF AI & Data Foundation](https://lfaidata.foundation/). 

- **Blazing Fast**

Average latency measured in milliseconds on ten million vector datasets.

Supports CPU SIMD, GPU, and FPGA accelerations, fully utilizing available hardware resources to achieve cost efficiency.

- **Easy to Use**

Rich APIs designed for data science workflows.

Consistent cross-platform UX from laptop, to local cluster, to cloud.

Embed real-time search and analytics into virtually any application. 

- **Stable and Resilient**

Milvus’ built-in replication and failover/failback features ensure data and applications can maintain business continuity in the event of a disruption.

-  **High Elasticity**

Component-level scalability makes it possible to only scale where necessary.

- **Community Backed**

With over 1,000 enterprise users, 5,000+ stars on GitHub, and an active open-source community, you’re not alone when you use Milvus.

> **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).

## Getting Started


### Demos

- [Image Search](https://zilliz.com/milvus-demos): Images made searchable. Instantaneously return the most similar images from a massive database.
- [Chatbots](https://zilliz.com/milvus-demos): Interactive digital customer service that saves users time and businesses money.
- [Chemical Structure Search](https://zilliz.com/milvus-demos): Blazing fast similarity search, substructure search, or superstructure search for a specified molecule.

## Contributing

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.

## Documentation

### Milvus Docs

For documentation about Milvus, see [Milvus Docs](https://milvus.io/docs/overview.md).

### SDK

The implemented SDK and its API documentatation are listed below:

- [Python](https://github.com/milvus-io/pymilvus/tree/1.x)

### Recommended Articles

- [What is an embedding vector? Why and how does it contribute to the development of Machine Learning?](https://milvus.io/docs/v1.0.0/vector.md)
- [Which vector indexes does Milvus support? Which should I choose?](https://milvus.io/docs/v1.0.0/index.md)
- [How does Milvus compare the distance between vectors?](https://milvus.io/docs/v1.0.0/metric.md)
- You can learn more in [Milvus Server Configurations](https://milvus.io/docs/v1.0.0/milvus_config.md).

## Contact

Join the Milvus community on [Slack Channel](https://join.slack.com/t/milvusio/shared_invite/zt-e0u4qu3k-bI2GDNys3ZqX1YCJ9OM~GQ) to share your suggestions, advice, and questions with our engineering team. You can also submit questions to our [FAQ page](https://milvus.io/docs/v1.0.0/performance_faq.md).

Subscribe to our mailing lists:

- [Milvus Technical Steering Committee](https://lists.lfai.foundation/g/milvus-tsc)
- [Milvus Technical Discussions](https://lists.lfai.foundation/g/milvus-technical-discuss)
- [Milvus Announcement](https://lists.lfai.foundation/g/milvus-announce)

Follow us on social media:

- [Milvus Medium](https://medium.com/@milvusio)
- [Milvus CSDN](https://zilliz.blog.csdn.net/)
- [Milvus Twitter](https://twitter.com/milvusio)
- [Milvus Facebook](https://www.facebook.com/io.milvus.5)

## License

Milvus is licensed under the Apache License, Version 2.0. View a copy of the [License file](https://github.com/milvus-io/milvus/blob/master/LICENSE).

## Acknowledgments

Milvus adopts dependencies from the following:

- Thank [FAISS](https://github.com/facebookresearch/faiss) for the excellent search library.
- Thank [etcd](https://github.com/coreos/etcd) for providing some great open-source tools.
- Thank [Pulsar](https://github.com/apache/pulsar) for its great distributed information pub/sub platform.
- Thank [RocksDB](https://github.com/facebook/rocksdb) for the powerful storage engines.