Milvus is a specialized vector database engine designed for the indexing, management, and high-speed similarity retrieval of high-dimensional vector embeddings. It functions as a similarity search engine capable of identifying nearest neighbors within large-scale vector spaces, supporting the storage and retrieval of billions of data points while maintaining consistent performance.
The system utilizes a distributed architecture that decouples storage, query, and coordination into independent services, allowing for horizontal scaling across clusters. It employs a global indexing mechanism that builds specialized data structures across immutable, independently indexed segments. This design, combined with a shared-storage decoupled model, enables compute and storage resources to scale independently in cloud environments, while a log-based persistence layer ensures data durability and state recovery.
The platform supports a wide range of data retrieval patterns, including retrieval-augmented generation, hybrid search, and multimodal data retrieval for text, images, and graphs. Deployment options range from lightweight local instances for rapid prototyping to robust standalone setups and fully managed distributed clusters. Documentation includes sizing tools to assist in estimating hardware requirements based on specific data volumes and operational patterns.
