Similarity Search

What Is Similarity Search?

Similarity search is a fundamental operation: given a query item (document, product, image, embedding), find the most similar items from a large collection. Similarity is measured using metrics like Euclidean distance, cosine similarity, or learned distance functions. This simple concept powers many modern applications.

Similarity search on embeddings powers semantic search. Similarity search on product features powers recommendations. Similarity search on user behavior powers content discovery. The key is converting items to a comparable representation (embeddings, features, vectors) and efficiently searching for nearest neighbors in high-dimensional spaces.

Efficient similarity search is non-trivial at scale. Brute-force approaches (comparing to all items) become slow with millions of documents. Specialized indexes (like HNSW used in vector databases) enable searching millions of embeddings in milliseconds. This infrastructure is essential for latency-sensitive applications.