A specialized database designed to store and query high-dimensional vector embeddings, used for AI search and memory.
A Vector Database is a data storage system built to handle vector embeddings: mathematical representations of unstructured data (such as text, images, or audio) generated by machine learning models.
Unlike traditional relational databases that search for exact keyword matches, vector databases perform "similarity searches." They can rapidly find the vectors that are mathematically closest to a given query vector.
Retrieval-Augmented Generation (RAG) is built on this capability. When an enterprise Large Language Model needs to look up corporate documents to answer a question, it queries a vector database for the most semantically relevant information.
See also Machine Learning, Retrieval-Augmented Generation, Large Language Model, Data Mobilization, Data Lakehouse and Data Sovereignty.