Milvus is an open-source vector database that stores and indexes embeddings to enable high-speed similarity search and retrieval for large-scale AI and machine learning applications.
Milvus is an open-source vector database designed to power high-performance similarity search and retrieval in generative AI and other AI-driven applications. It enables developers to store, index, and query large-scale vector embeddings generated by models for text, images, audio, and more. Built for scalability and low-latency search, Milvus serves as the backbone for applications that require semantic search, recommendation, and real-time AI inference over massive datasets.
Milvus supports multiple indexing methods such as IVF, HNSW, and DiskANN, allowing users to balance recall, latency, and storage based on workload needs. It offers horizontal scalability to tens of billions of vectors via distributed deployment, and provides high-throughput, millisecond-level query performance. Developers can integrate Milvus easily using SDKs for Python, Java, Go, and other languages, or deploy it via Docker, Kubernetes, and cloud-native environments. Features such as hybrid search (combining scalar and vector filters), incremental data ingestion, and automatic sharding and load balancing make it suitable for production-grade systems.
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