Intermediate · Systems

Similarity Search

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Finding the items in a dataset most similar to a given query, usually by vector distance.

Technical Definition

Finding the items in a dataset most similar to a given query, usually by vector distance.

How it works

Powers semantic search, recommendation, deduplication, and RAG. Approximate Nearest Neighbor (ANN) algorithms trade tiny recall losses for huge speedups, making real-time search over billions of vectors feasible.

Related Concepts

• Embedding — A dense vector representation that captures semantic meaning, mapping discrete items like words into continuous mathematical space.
• Vector Database — A specialized database optimized for storing, indexing, and querying high-dimensional embedding vectors using similarity search.
• Indexing — Organizing data so that lookups, searches, or similarity queries run quickly.