Vector Databases

What Vector Databases Do

Vector databases store high-dimensional numerical representations (embeddings) and enable fast similarity search. Unlike traditional databases that match exact values, vector databases find the closest vectors to a query vector, enabling semantic matching.

Core capabilities:

• Store embeddings alongside metadata and original content
• Perform approximate nearest neighbor (ANN) search at scale
• Filter results by metadata combined with vector similarity
• Handle millions to billions of vectors with sub-second query times

Embedding Basics

An embedding is a fixed-length array of floats capturing semantic meaning. Text with similar meaning produces vectors that are close together in the embedding space.

• Dimensions: Vector length. Common sizes: 384, 768, 1536, 3072. Higher = more nuance, more cost.
• Embedding model: Converts raw data into vectors. Different models produce different dimensions.
• Distance metric: How similarity between two vectors is measured.