Vector DB

Overview

Vector databases store and efficiently search embedding vectors, enabling fast semantic similarity search over millions of documents.

What is it?

Specialized databases optimized for storing and querying vector embeddings.

Why it matters

Vector DBs make RAG practical at scale. They enable sub-second semantic search over massive knowledge bases.

How it works

Documents are converted to embeddings and stored with metadata. Queries are also embedded, and the DB finds the most similar vectors using algorithms like HNSW.

Real-World Examples

Tools & Libraries