What is Similarity Search?
Similarity search is the process of finding items in a collection that are most similar to a query item, typically based on vector representations in a high-dimensional embedding space. The search computes similarity or distance between the query vector and stored vectors, returning the k most similar items (k-nearest neighbors). This fundamental operation underlies semantic search, recommendation systems, and retrieval in RAG applications.
The basic approach involves encoding both the query and stored items as embeddings, selecting a distance metric (cosine similarity, Euclidean distance, dot product), and computing distances between the query and all stored items to find the nearest neighbors. For large collections, brute-force comparison becomes impractically slow, so approximate nearest neighbor (ANN) algorithms and specialized index structures enable fast similarity search at the cost of some accuracy. Vector databases implement these optimizations transparently.
Similarity search has become central to modern AI applications, enabling semantic retrieval that finds content based on meaning rather than keywords. The quality of similarity search depends on the embedding model (which determines what "similar" means), the distance metric (which defines how similarity is measured), and the indexing strategy (which affects speed-accuracy tradeoffs). Understanding similarity search principles is essential for building effective RAG systems, agent memory, and any application requiring semantic matching or content recommendation.