A vector database is a database designed to store and query high-dimensional vector embeddings, enabling fast similarity search based on meaning rather than exact keyword matches.
Traditional databases match exact values. Vector databases instead find records whose embeddings are mathematically closest to a query’s embedding, which is what allows semantic search and RAG systems to retrieve conceptually relevant content, not just keyword matches.
Text, images, or structured records are converted into embeddings and stored in a vector database, forming the retrieval layer that AI applications query at inference time.
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