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A database that stores and retrieves data by semantic similarity using embeddings.
Vector databases index high-dimensional numerical representations of text (embeddings) and support fast approximate nearest-neighbor search. When a user asks a question, their query is embedded and matched against stored document chunks to find semantically related content. Popular options include Pinecone, Qdrant, and Supabase pgvector.
Plainly
Think of Vector DB as a labeled box where an app keeps important things. A database that stores and retrieves data by semantic similarity using embeddings.
In practice
Use it when a feature reads, writes, migrates, validates, or audits stored information. In practice, define the owner, input, output, and failure mode before you rely on it.