Click to take a quick look at our demos!
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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.
- 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.
- Image Search: Images made searchable. Instantaneously return the most similar images from a massive database.
- Chatbots: Interactive digital customer service that saves users time and businesses money.
- Chemical Structure Search: Blazing fast similarity search, substructure search, or superstructure search for a specified molecule.
Contributions to Milvus are welcome from everyone. See Guidelines for Contributing for details on submitting patches and the contribution workflow. See our community repository to learn about our governance and access more community resources.
For documentation about Milvus, see Milvus Docs.
The implemented SDK and its API documentatation are listed below:
- What is an embedding vector? Why and how does it contribute to the development of Machine Learning?
- Which vector indexes does Milvus support? Which should I choose?
- How does Milvus compare the distance between vectors?
- You can learn more in Milvus Server Configurations.
Join the Milvus community on Slack Channel to share your suggestions, advice, and questions with our engineering team. You can also submit questions to our FAQ page.
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Milvus is licensed under the Apache License, Version 2.0. View a copy of the License file.
Milvus adopts dependencies from the following: