Get scalable vector ANN search with Milvus, a cloud-native database for founders, backed by 45k+ GitHub stars
44,588 stars4,035 forksGoQuality 9/10Updated 6/2/2026100% free ยท open source
What it does
Milvus is a cloud-native database that enables scalable vector Approximate Nearest Neighbors (ANN) search for founders to efficiently manage and query large datasets of vectors.
โขBuilding a recommendation system that requires fast and accurate similarity searches
โขDeveloping a computer vision application that needs to efficiently query large datasets of image embeddings
โขCreating a natural language processing model that requires scalable semantic search capabilities
Quick start
1Create a new Milvus server instance by running the command: `docker run -d --name milvus-db -p 19530:19530 -v /tmp/milvus:/var/lib/milvus milvusdb/milvus:latest`
2Use the Milvus Python SDK to connect to the server: `from milvus import Milvus, Client`
3Create a new index and insert data into the Milvus database: `client.create_index('test', 'ivf_sq8')` and `vectors = [[1.0, 2.0], [3.0, 4.0]]`
4Perform a vector search query: `client.search('test', 'ivf_sq8', vectors)`
Heads up: Make sure to allocate sufficient GPU resources when running Milvus, as it requires significant GPU memory to perform efficient vector searches, and ensure that the Docker container has the necessary permissions to access the host machine's GPU
Saves to your device
Topics
anns
cloud-native
diskann
distributed
embedding-database
embedding-similarity
embedding-store
faiss
golang
hnsw
image-search
llm
nearest-neighbor-search
rag
vector-database
vector-search
vector-similarity
vector-store
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