Vector Store

A vector store is a data storage and retrieval system optimized for managing vector embeddings — high-dimensional numerical arrays that represent the semantic content of text, images, or other data. Vector stores index these embeddings using algorithms designed for approximate nearest-neighbor (ANN) search, enabling fast retrieval of semantically similar items without exhaustive comparison of all stored vectors.

In enterprise AI systems, vector stores function as the memory layer for knowledge retrieval. They are commonly used in RAG architectures to store embedded chunks of documentation, product data, or domain knowledge that an LLM can query at inference time. For commerce platforms, this translates to capabilities such as semantic product search, contextual customer service responses grounded in policy documents, and similarity-based recommendations. The operational considerations for vector stores — including index management, embedding versioning, and update latency — are important architectural concerns as these systems move from prototypes to production scale.