As an open source vector database, Milvus is easy-to-use, highly reliable, robust, and blazing fast. Adopted by over 600 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, and more.
The following is Milvus architecture:
For more detailed introduction of Milvus and its architecture, see Milvus overview. See Milvus release notes to keep up-to-date with its releases and updates.
Milvus is an incubation-stage project at LF AI & Data Foundation.
See 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. See Milvus Scenarios for live demos. You can also see 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 appreciate 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. You must abide by this code to participate.
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, you can join our Slack channel.
See our community repository to learn more about our governance and access more community resources.