Also known as: vector store · vector index · ANN search
A vector database is a data store optimised for indexing and querying high-dimensional embedding vectors, typically using approximate nearest neighbour algorithms to find the most semantically similar items quickly.
Exact nearest-neighbour search over millions of high-dimensional vectors is computationally prohibitive, so these systems use approximate algorithms such as HNSW that trade a small amount of recall for very large speed gains.
Vector search has increasingly become a feature of existing databases rather than a separate product category, with mature relational and search engines adding vector indexing — which for many teams removes the operational cost of running an additional specialised datastore.
Embedding
An embedding is a numerical vector representation of text, images, or other data, positioned in a high-dimensional space such that semantically similar items sit close together.
Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a technique in which relevant documents are retrieved from an external knowledge source and supplied to a language model as context, so that its response is grounded in specific retrieved material rather than parametric memory alone.
Semantic Search
Semantic search retrieves results based on meaning rather than keyword matching, using embeddings to find documents conceptually related to a query even when they share no literal terms.