Vector Database
In plain terms
A database optimized for similarity search over embeddings.
Going deeper
A vector database stores high-dimensional embeddings and finds nearest neighbors quickly, powering semantic search and AI retrieval. It's central to RAG systems.
Under the hood
Uses approximate-nearest-neighbor indexes (HNSW) for speed; the storage layer behind much of modern AI search.
Examples
RAG, semantic search, recommendations, Pinecone, Weaviate, pgvector.
Think of it like...
Like a librarian who finds books by meaning and similarity, not exact title match.