Milvus is an open-source vector database engine developed by Zilliz, a company specializing in artificial intelligence and machine learning. Milvus is designed to efficiently store, manage, and search large-scale vector data, making it particularly well-suited for applications in the fields of machine learning, data mining, computer vision, and other areas where vectors play a central role.
Key features of Milvus include:
Vector Storage: Milvus is optimized for the storage of vector data, making it suitable for handling embeddings, feature vectors, and other high-dimensional data commonly used in machine learning tasks.
Scalability: Milvus is designed to be highly scalable and can handle large datasets with millions or even billions of vectors. It can be deployed on clusters of machines to distribute the data and queries efficiently.
Vector Indexing: Milvus supports various indexing methods and algorithms to speed up vector similarity searches. This is crucial for applications like recommendation systems, image retrieval, and more.
Versatile Query Support: Milvus enables complex queries for similarity search, range searches, and more. It supports both vector-to-vector and vector-to-collection (e.g., finding similar vectors within a collection) search operations.
GPU Acceleration: Milvus can take advantage of GPUs (Graphics Processing Units) to accelerate vector search operations, significantly improving query performance for large datasets.
Integrations: Milvus can be integrated with various programming languages and frameworks, making it accessible for developers working with different technologies.
Open Source: Milvus is open-source and freely available for developers, which has contributed to its growing popularity in the machine learning and AI communities.
Milvus is often used in applications where similarity search is a critical component, such as recommendation systems, content-based image retrieval, text search, and more. Its performance and scalability make it a valuable tool for managing and querying large sets of high-dimensional vectors efficiently.