What is Vector Search?

Vector search is a search method that uses numerical vector embeddings to find semantically similar results, allowing systems to understand context and intent rather than relying on exact keyword matches.

A vector search engine converts data and queries into vectors, measures similarity using distance metrics like cosine similarity, and retrieves the nearest neighbors that best match the user’s intent.

Traditional search depends on exact keyword overlap. Vector search understands meaning, enabling more accurate and relevant retrieval across unstructured data, natural language, and varied phrasing.

Vector databases store and index vector embeddings at scale using specialized index types such as HNSW, IVF, and PQ to support fast approximate nearest neighbor search.

Vector search powers product recommendations, media discovery, customer support, semantic document retrieval, and retrieval-augmented generation (RAG) for large language models.