How do embeddings support vector search?

Embeddings enable vector search by converting complex data like text, images, or user behavior into numerical vectors that capture semantic meaning. These vectors represent data points in a high-dimensional space, where similar items are positioned closer together. For example, the sentence “a cat sits on a mat” and “a kitten rests on a rug” would generate embeddings near each other, reflecting their shared meaning. Vector search engines compare these numerical representations using distance metrics like cosine similarity or Euclidean distance to find the closest matches. Without embeddings, directly comparing raw text or images would be computationally impractical or impossible at scale.

A key example is natural language processing (NLP): tools like Word2Vec or BERT convert words or sentences into vectors that preserve contextual relationships. The word “car” might have a vector closer to “vehicle” than to “banana,” allowing search systems to understand synonyms or related concepts. In image search, convolutional neural networks (CNNs) generate embeddings where photos of beaches cluster together, distinct from images of mountains. Platforms like Spotify use embeddings to represent songs based on audio features and user listening patterns, enabling recommendations by finding tracks with similar vector profiles. These embeddings abstract away unstructured data into a format optimized for mathematical comparison.

Vector search systems leverage approximate nearest neighbor (ANN) algorithms, such as HNSW or IVF, to efficiently query embeddings in large datasets. Traditional exact search methods become impractical when dealing with millions of high-dimensional vectors, but ANN techniques balance speed and accuracy by organizing vectors into search-friendly structures. For instance, an e-commerce site might index product embeddings to enable “find similar items” features—searching a jacket’s embedding returns other jackets with comparable styles. Embeddings also support hybrid search systems, combining vector similarity with structured filters (e.g., price ranges). By transforming data into a unified numerical format, embeddings make it possible to search across diverse data types using a single, scalable framework.