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Milvus (vector database)

Open-source distributed vector database From Wikipedia, the free encyclopedia

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Milvus is a distributed vector database developed by Zilliz. It is available as both open-source software and a cloud service called Zilliz Cloud.

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Milvus is an open-source project under the LF AI & Data Foundation[2] and is distributed under the Apache License 2.0.

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History

Milvus has been developed by Zilliz since 2017.[3]

Milvus joined Linux Foundation as an incubation project in January 2020 and became a graduate in June 2021.[2] The details about its architecture and possible applications were presented at ACM SIGMOD Conference in 2021.[4]

Milvus 2.0, a major redesign of the whole product with a new architecture,[5] was released in January 2022.

Features

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Various similarity search-related features are available in Milvus:[6]

Milvus' similarity search engine relies on heavily-modified forks of third-party open-source similarity search libraries, such as Faiss,[7][8] DiskANN[9][10] and hnswlib.[11]

Milvus includes optimizations for I/O data layout, specific to graph search indices.[12]

Database

As a database, Milvus provides the following features:[6]

Deployment options

Milvus can be deployed as an embedded database, standalone server, or distributed cluster. Zilliz Cloud offers a fully managed version.[16]

GPU support

Milvus provides GPU accelerated index building and search using Nvidia CUDA technology[17][18] via the Nvidia RAFT library,[19] including a recent GPU-based graph indexing algorithm known as CAGRA.[20]

Integration

Milvus provides official SDK clients for Java, NodeJS, Python and Go.[21] An additional C# SDK client was contributed by Microsoft.[6][22] The database can integrate with DataDog,[23] Prometheus and Grafana for monitoring and alerts, as well as generative AI frameworks Haystack,[24] LangChain,[25] IBM Watsonx,[26] and those provided by OpenAI.[27][28]

Several storage providers have built integrations with Milvus to support AI workloads and large-scale vector search. These integrations aim to optimize performance, simplify inferencing workflows, and enhance data management capabilities:

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See also

References

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