4dee7dfac6
* Optimize config test. Dir src/config 99% lines covered * add unittest coverage * optimize cache&config unittest * code format * format * format code * fix merge conflict * cover src/utils unittest * '#831 fix exe_path judge error' * #831 fix exe_path judge error * add some unittest coverage * add some unittest coverage * improve coverage of src/wrapper * improve src/wrapper coverage * *test optimize db/meta unittest * fix bug * *test optimize mysqlMetaImpl unittest * *style: format code * import server& scheduler unittest coverage * handover next work * *test: add some test_meta test case * *format code * *fix: fix typo * feat(codecov): improve code coverage for src/db(#872) * feat(codecov): improve code coverage for src/db/engine(#872) * feat(codecov): improve code coverage(#872) * fix config unittest bug * feat(codecov): improve code coverage core/db/engine(#872) * feat(codecov): improve code coverage core/knowhere * feat(codecov): improve code coverage core/knowhere * feat(codecov): improve code coverage * feat(codecov): fix cpu test some error * feat(codecov): improve code coverage * feat(codecov): rename some fiu * fix(db/meta): fix switch/case default action * feat(codecov): improve code coverage(#872) * fix error caused by merge code * format code * feat(codecov): improve code coverage & format code(#872) * feat(codecov): fix test error(#872) * feat(codecov): fix unittest test_mem(#872) * feat(codecov): fix unittest(#872) * feat(codecov): fix unittest for resource manager(#872) * feat(codecov): code format (#872) * feat(codecov): trigger ci(#872) * fix(RequestScheduler): remove a wrong sleep statement * test(test_rpc): fix rpc test * Fix format issue * Remove unused comments * Fix unit test error Co-authored-by: ABNER-1 <ABNER-1@users.noreply.github.com> Co-authored-by: Jin Hai <hai.jin@zilliz.com>
What is Milvus
Milvus is the world's fastest similarity search engine for massive-scale feature vectors. Built with heterogeneous computing architecture for the best cost efficiency. Searches over billion-scale vectors take only milliseconds with minimum computing resources.
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.
Roadmap
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.👏
Application scenarios
Milvus is broadly applicable to a variety of areas. Below screenshot showcases our content-based image retrieval demo system built based on Milvus and VGG.
To explore more Milvus solutions and application scenarios, see our bootcamp repository.
Test reports
See our test reports for more information about performance benchmarking of different indexes in Milvus.
Supported clients
Get started
See the Milvus install guide for using Docker containers. To install Milvus from source code, see build from source.
To edit Milvus settings, read Milvus configuration.
Try your first Milvus program
Try running a program with Milvus using Python, Java, or C++ example code.
Contribution guidelines
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.
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.
Contributors
Below is a list of Milvus contributors. We greatly appreciate your contributions!
 zerowe-seven 💻 |
 erdustiggen 💻 |
 gaolizhou 💻 |
 Sijie Zhang 📖 |
 PizzaL 💻 |
 levylll 💻 |
 aaronjin2010 💻 |