Vector database

A vector database is a specialized system: it stores, indexes, and queries high-dimensional data embeddings for rapid, large-scale semantic similarity search.

This technology is purpose-built to manage unstructured data (text, images, audio) by converting it into numerical arrays called vector embeddings (often 100 to 1,000+ dimensions). Unlike traditional databases, a vector database uses algorithms like HNSW (Hierarchical Navigable Small World) to index these vectors, enabling lightning-fast Approximate Nearest Neighbor (ANN) searches based on distance metrics (e.g., cosine similarity). This capability is critical for modern AI: it powers Retrieval-Augmented Generation (RAG) to provide contextual memory for Large Language Models (LLMs), drives semantic search engines, and delivers real-time, personalized recommendations with high recall accuracy.

https://www.ibm.com/topics/vector-database