Embedding models

Embedding models convert complex, high-dimensional data (text, images, audio) into dense, low-dimensional numerical vectors, enabling machines to process semantic meaning and relationships.

Embedding models are the core engine for modern AI applications: they transform unstructured data—like a document or an image—into a fixed-length, N-dimensional vector (a list of numbers). This vector is an 'embedding,' a numerical representation where semantic similarity is encoded by spatial proximity (closer vectors mean more related concepts). For example, models like Word2Vec and BERT generate these vectors, typically in dimensions such as 768 or 1536. This process is critical for tasks like semantic search, clustering, and Retrieval-Augmented Generation (RAG), allowing systems to accurately find the most relevant information based on meaning, not just keyword matching.

https://en.wikipedia.org/wiki/Embedding_(machine_learning)