A vector database, also known as a vector store or vector database management system (VDBMS), is a type of database specifically designed to store and manipulate vector data efficiently. Vector data represents spatial or geometric information, often used in applications such as machine learning, computer-aided design (CAD), and computer graphics.
Vector databases find acceptance in realizing some specialized use cases, such as:
Storing embedding in ML: Embeddings are dense, low-dimensional representations of high-dimensional data, often used to capture semantic relationships or patterns in data. These embeddings are commonly used in natural language processing (NLP), computer vision, and other domains of artificial intelligence to encode meaningful information about objects, words, or concepts into a fixed-size vector space.
Recommendation Generation: Vector databases are efficient in storing and managing product recommendations. When a user interacts with the recommendation system (for example, clicking on a product), the system retrieves the feature vector corresponding to the user’s interaction and performs similarity prediction between the user’s vector and the vectors of other items stored in the vector database, to generate a set of recommendations.