As an open source vector similarity search engine, Milvus is easy-to-use, highly reliable, scalable, robust, and blazing fast. Adopted by over 100 organizations and institutions worldwide, Milvus empowers applications in a variety of fields, including image processing, computer vision, natural language processing, voice recognition, recommender systems, drug discovery, etc.
Milvus has the following architecture:
For more detailed introduction of Milvus and its architecture, see Milvus overview. Keep up-to-date with newest releases and latest updates by reading Milvus release notes.
Milvus is an LF AI Foundation incubation project. Learn more at lfai.foundation.
See the Milvus install guide to install Milvus using Docker. To install Milvus from source code, see build from source.
Try an example program with Milvus using Python, Java, Go, or C++ example code.
You can use Milvus to build intelligent systems in a variety of AI application scenarios. Refer to Milvus Scenarios for live demos. You can also refer to Milvus Bootcamp for detailed solutions and application scenarios.
See our test reports for more information about performance benchmarking of different indexes in Milvus.
To learn what's coming up soon in Milvus, read our Roadmap.
It is a Work in Progress, and is subject to reasonable adjustments when necessary. And we greatly welcome any comments/requirements/suggestions regarding Milvus roadmap.:clap:
Contributions are welcomed and greatly appreciated. Please read our contribution guidelines for detailed contribution workflow. This project adheres to the code of conduct of Milvus. By participating, you are expected to uphold this code.
We use GitHub issues to track issues and bugs. For general questions and public discussions, please join our community.
❤️To connect with other users and contributors, welcome to join our Slack channel.
See our community repository to learn about our governance and access more community resources.