AI Glossary

Vector Database

A vector database stores and retrieves embedding vectors efficiently, enabling similarity search for RAG and recommendation workflows.

Definition

A vector database stores and retrieves embedding vectors efficiently, enabling similarity search for RAG and recommendation workflows.

Plain English explanation

It is a specialized store for finding nearest-neighbor vectors at scale.

Technical explanation

Vector databases index embedding vectors for approximate nearest-neighbor search, often alongside metadata filters for permissions and hybrid retrieval.

Why it matters

They are infrastructure for semantic search and RAG at scale.

Real-world applications

  • RAG corpora
  • Similar-item search
  • Multimodal retrieval stores

Benefits

  • Fast similarity search
  • Scales dense retrieval

Limitations

  • Index quality depends on embedding model choice
  • Not a substitute for access control design

Common misconceptions

  • A vector DB alone is not an AI product strategy

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FAQ

Do I always need a specialized vector DB?

Not always—some teams start with existing search stacks that add vector fields. Choice depends on scale and ops constraints.

Last reviewed

Sources

  • ANN search literature (HNSW and related methods)

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