How does vector search retrieve relevant results?

Vector search retrieves relevant results by comparing numerical representations of data (vectors) to find items that are semantically or contextually similar to a query. It works by first converting data—such as text, images, or user behavior—into high-dimensional vectors using machine learning models like word embeddings (e.g., BERT) or image encoders (e.g., CNNs). These vectors capture meaningful features of the data, like the semantic meaning of a sentence or visual patterns in an image. When a user submits a query, the system converts it into a vector using the same model and then searches for the closest matching vectors in a precomputed index using similarity metrics like cosine similarity or Euclidean distance.

The retrieval process relies on efficient algorithms to handle large-scale vector datasets. Instead of comparing the query vector to every stored vector (which would be computationally expensive), approximate nearest neighbor (ANN) algorithms like HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), or LSH (Locality-Sensitive Hashing) are used. These methods organize vectors into structures that allow the system to quickly narrow down candidates. For example, HNSW builds a layered graph where nodes represent vectors, enabling fast traversal to find neighbors. IVF partitions vectors into clusters and only searches the most relevant clusters for a query. These optimizations balance speed and accuracy, making vector search practical for real-time applications.

The effectiveness of vector search depends on the quality of the embedding model and the choice of similarity metric. For instance, in a product recommendation system, embeddings might capture user preferences and product attributes. If a user searches for “comfortable running shoes,” the embedding model maps this query to a vector close to products tagged with terms like “cushioned,” “lightweight,” or “athletic.” Developers must also tune parameters like the number of clusters in IVF or the graph layers in HNSW to optimize performance. Tools like FAISS or Elasticsearch’s vector search capabilities provide configurable implementations, allowing developers to adapt the system to their specific data and latency requirements. By focusing on these components, vector search enables applications like semantic text search, image retrieval, or personalized recommendations with high accuracy.