What is Milvus?

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.

Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility. For more architecture details, see Milvus Architecture Overview.

Milvus was released under the open-source Apache License 2.0 in October 2019. It is currently a graduate project under LF AI & Data Foundation.

Key features

Millisecond search on trillion vector datasets

Average latency measured in milliseconds on trillion vector datasets.

Simplified unstructured data management

Rich APIs designed for data science workflows.
Consistent user experience across laptop, local cluster, and cloud.
Embed real-time search and analytics into virtually any application.

Reliable, always on vector database

Milvus’ built-in replication and failover/failback features ensure data and applications can maintain business continuity in the event of a disruption.

Highly scalable and elastic

Component-level scalability makes it possible to scale up and down on demand. Milvus can autoscale at a component level according to the load type, making resource scheduling much more efficient.

Hybrid search

Since Milvus 2.4, we introduced multi-vector support and a hybrid search framework, which means users can bring in several vector fields (up to 10) into a single collection. These vectors in different columns represent diverse facets of data, originating from different embedding models or undergoing distinct processing methods. The results of hybrid searches are integrated using reranking strategies, such as Reciprocal Rank Fusion (RRF) and Weighted Scoring.

This feature is particularly useful in comprehensive search scenarios, such as identifying the most similar person in a vector library based on various attributes like pictures, voice, fingerprints, etc. For details, refer to Hybrid Search for more.

Unified Lambda structure

Milvus combines stream and batch processing for data storage to balance timeliness and efficiency. Its unified interface makes vector similarity search a breeze.

Community supported, industry recognized

With over 1,000 enterprise users, 27,000+ stars on GitHub, and an active open-source community, you’re not alone when you use Milvus. As a graduate project under the LF AI & Data Foundation, Milvus has institutional support.

Quick start

Start with Zilliz Cloud

Zilliz Cloud is a fully managed service on cloud and the simplest way to deploy LF AI Milvus®, See Zilliz Cloud and start your free trial.

Install Milvus

Build Milvus from source code

Check the requirements first.

Linux systems (Ubuntu 20.04 or later recommended):

go: >= 1.21
cmake: >= 3.26.4
gcc: 9.5
python: > 3.8 and  <= 3.11

MacOS systems with x86_64 (Big Sur 11.5 or later recommended):

go: >= 1.21
cmake: >= 3.26.4
llvm: >= 15
python: > 3.8 and  <= 3.11

MacOS systems with Apple Silicon (Monterey 12.0.1 or later recommended):

go: >= 1.21 (Arch=ARM64)
cmake: >= 3.26.4
llvm: >= 15
python: > 3.8 and  <= 3.11

Clone Milvus repo and build.

# Clone github repository.
$ git clone https://github.com/milvus-io/milvus.git

# Install third-party dependencies.
$ cd milvus/
$ ./scripts/install_deps.sh

# Compile Milvus.
$ make

For the full story, see developer's documentation.

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.