How does vector search rank results?

Vector search ranks results by measuring the similarity between a query vector and vectors representing stored data. Each piece of data (e.g., text, images) is converted into a high-dimensional vector using machine learning models like word2vec, BERT, or CLIP. When a user submits a query, it is also transformed into a vector. The system then calculates the distance or similarity between the query vector and all stored vectors, returning items with the smallest distances or highest similarity scores. Common metrics include cosine similarity (measuring the angle between vectors), Euclidean distance (straight-line distance in space), and dot product (magnitude and direction alignment). The choice of metric depends on the use case and how the vectors are normalized.

Several factors influence how accurately and efficiently results are ranked. First, the quality of the embeddings (vectors) is critical. Models trained on domain-specific data (e.g., medical texts versus product descriptions) capture nuances better, leading to more relevant matches. Second, indexing methods like approximate nearest neighbor (ANN) algorithms (e.g., HNSW, FAISS) balance speed and accuracy. These algorithms organize vectors into structures like graphs or trees to quickly find candidates without exhaustive comparisons. For example, HNSW (Hierarchical Navigable Small World) builds layered graphs to prioritize local similarities, reducing search time. Third, preprocessing steps like dimensionality reduction (e.g., PCA) or normalization (scaling vectors to unit length) can improve performance. For instance, normalizing vectors ensures cosine similarity and dot product produce consistent rankings.

A practical example is a recommendation system for movies. Suppose each movie is embedded into a vector based on genres, synopses, and user ratings. A query for “action films with strong female leads” is converted into a vector, and the system retrieves movies whose vectors are closest to the query vector using cosine similarity. Another example is image search: a user uploads a photo of a red sneaker, and the system matches it against shoe product vectors. Developers can fine-tune ranking by adjusting the similarity metric, experimenting with embedding models, or tweaking ANN parameters like the number of candidates considered. For instance, increasing the “ef” parameter in HNSW trades speed for higher recall. These choices directly impact the balance between latency, computational cost, and result relevance.