AI Glossary Term

What does “Vector Database” mean?

Definition

A database optimized for storing and querying high-dimensional embedding vectors.

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How Vector Database works

Example

A support bot stores embeddings of every help article in a vector database. When a customer asks a question, the database returns the article vectors closest to the question's vector in milliseconds.

Why it matters

Vector databases make similarity search fast at scale, which underlies retrieval for chatbots and recommendations.

Common questions

Do I always need one?

Not for small collections, where a simple in-memory search may do.

How is it fast?

Approximate nearest-neighbor indexes, such as HNSW, avoid comparing the query with every vector.

Is it a replacement for a regular database?

Usually it complements one, storing vectors alongside or linked to the main data.

HNSWA graph-based index structure for fast approximate nearest-neighbor search over high-dimensional vectors.EmbeddingA numeric vector representation that captures semantic meaning of text, images, or other data.Word EmbeddingA dense vector representation of words capturing semantic relationships.Embedding ModelA model specialized for converting data into vectors used for semantic search, clustering, and retrieval.Convolutional Neural Network (CNN)A neural architecture optimized for processing grid-like data such as images.GradientA vector showing how much each parameter should change to reduce loss.

Learn more in our free guides

Vector Databases Residual Vector Quantization X-Vector Speaker Embeddings Function Vectors and Task Representations

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Vector Databases Quiz Residual Vector Quantization Quiz X-Vector Speaker Embeddings Quiz