A vector database is a specialized data store optimized for indexing, storing, and querying high‑dimensional vector embeddings produced by machine learning models. It enables efficient similarity search (e.g., nearest neighbors) over millions or billions of vectors, which is critical for modern AI applications like semantic search, recommendation, and retrieval‑augmented generation (RAG). Vector DBs matter because they provide the infrastructure layer that makes unstructured data—text, images, audio, code—searchable and usable in real time by AI systems.