In the world of machine learning and artificial intelligence, efficient storage and retrieval of high-dimensional vector data are crucial. Traditional databases often struggle to handle these complex data structures, leading to performance bottlenecks and inefficient queries. Redis, a popular open-source in-memory data store, has emerged as a powerful solution for building high-performance vector databases capable of handling large-scale machine-learning applications.
What Are Vector Databases?
In the context of machine learning, vectors are arrays of numbers that represent data points in a high-dimensional space. These vectors are commonly used to encode various types of data, such as text, images, and audio, into numerical representations that can be processed by machine learning algorithms. A vector database is a specialized database designed to store, index, and query these high-dimensional vectors efficiently.