Vector Database (Vector Store)

A vector store (also called a vector database) is a data management system purpose-built for storing, indexing, and searching vector embeddings — dense numerical arrays that represent the semantic content of text, images, audio, or other data. Vector stores use approximate nearest-neighbor algorithms (such as HNSW or IVF) to retrieve the most semantically similar records to a query embedding with low latency, even across millions of vectors.

Vector stores are a critical infrastructure component in enterprise AI architectures, particularly for knowledge retrieval and personalization systems. In commerce, they serve as the retrieval layer in RAG pipelines (storing product, policy, or knowledge base embeddings), the similarity engine for visual search, and the similarity backbone for recommendation systems. Choosing the right vector store involves tradeoffs between query speed, recall accuracy, metadata filtering capability, scalability, and operational complexity. Many enterprises begin with lightweight embedded options (Chroma, FAISS) and migrate to managed services (Pinecone, Weaviate Cloud) as production scale demands grow.