Text, images, or other data are converted into dense vectors by an embedding model; similarity is then measured with a distance metric such as cosine similarity or dot product. It's the retrieval half of most RAG systems and underlies modern semantic search, often combined with traditional keyword (BM25) search in a hybrid setup for better recall.