Vector Database

A vector database is a specialized data store designed to efficiently index and query high-dimensional vector embeddings — numerical representations of data such as text, images, or audio that capture semantic meaning. Unlike traditional databases that retrieve records by exact field matches, vector databases perform approximate nearest-neighbor (ANN) search, finding records whose vectors are most similar to a query vector in high-dimensional space.

Vector databases are the storage backbone of RAG (Retrieval-Augmented Generation) systems and semantic search applications. In commerce, they enable capabilities such as finding products semantically similar to a natural language query, identifying customers with similar purchase histories, clustering support tickets by topic, or retrieving the most relevant policy documents for an AI assistant. Popular vector database options include Pinecone, Weaviate, Qdrant, Chroma, and pgvector (a Postgres extension). Selection criteria include query latency, scalability, filtering capabilities, and integration with the model stack.