Vector Embeddings

Vector embeddings are dense numerical representations of data — text, images, audio, user behavior, or other inputs — generated by trained neural networks such that semantically similar inputs produce numerically similar vectors in a high-dimensional space. The distance between two embedding vectors (measured by cosine similarity or Euclidean distance) reflects semantic relatedness: the embeddings for "running shoes" and "athletic footwear" will be close together, even though they share no literal words.

Embeddings are the enabling technology behind semantic search, recommendation, and retrieval in modern commerce AI. By converting product titles, customer queries, review text, and behavioral data into a shared embedding space, systems can perform semantic matching that far surpasses traditional keyword search. Embedding models from providers like OpenAI, Cohere, and open-source projects (Sentence Transformers, BGE) can be fine-tuned on domain-specific data to improve relevance for particular product categories or customer vocabularies. The quality of embeddings is a fundamental determinant of downstream retrieval system performance.