Semantic embeddings

Semantic embeddings translate complex data into high-dimensional vectors where proximity represents shared meaning.

Semantic embeddings map discrete inputs like words, images, or audio into a continuous vector space where geometric distance reflects conceptual similarity. Modern models like Google's Word2Vec or OpenAI's text-embedding-3-small transform raw data into dense arrays of numbers (vectors) across hundreds or thousands of dimensions. This allows machines to perform mathematical operations on meaning: for example, the vector for 'kitten' will cluster near 'cat' rather than 'car' because they share semantic features. By utilizing these numerical representations, developers can build powerful systems for semantic search, recommendation engines, and retrieval-augmented generation (RAG) that understand intent rather than just matching keywords.

https://www.tensorflow.org/text/guide/word_embeddings