Vector similarity search in AWS S3 Vector works by mathematically comparing a query vector against all stored vectors in a vector index using distance calculations to find the most semantically similar matches. When you submit a search request through the QueryVectors API, you provide a query vector (typically generated using the same embedding model used for your stored data), specify how many results you want (topK parameter), and optionally include metadata filters to narrow your search scope. The service calculates distances between your query vector and every vector in the index using the distance metric configured during index creation, then returns the closest matches ranked by similarity score.
The mathematical foundation relies on distance metrics that measure similarity in multi-dimensional space. Cosine similarity, commonly used for text embeddings, measures the angle between vectors regardless of their magnitude, making it ideal for semantic search where direction matters more than scale. For example, the vectors for “car” and “automobile” would have a small cosine distance despite potentially different magnitudes. Euclidean distance calculates straight-line distance in vector space and works well for embeddings where magnitude is significant. The search process involves computing these distances across potentially millions of vectors, which S3 Vector optimizes through automatic indexing algorithms that maintain sub-second query performance without requiring manual tuning.
Advanced search capabilities include metadata filtering and result customization options that enhance search relevance. You can apply filters based on metadata attributes attached to vectors, such as restricting searches to specific document categories, time periods, or content types. For instance, when searching a knowledge base, you might filter results to only include documents from the last year or specific departments. The service returns not only the most similar vectors but also their similarity scores, unique keys, and associated metadata, enabling rich search experiences. You can also configure whether to return the actual vector data or just the metadata and scores, optimizing for your application’s needs. The search results maintain consistency with recent updates, and the service handles concurrent queries efficiently while scaling automatically based on demand.