Embedding Model

Embedding Models convert complex data (text, images, audio) into dense, fixed-length numerical vectors, enabling machines to understand semantic relationships in a high-dimensional space.

Embedding Models are the core engine for modern AI, transforming unstructured data into a numerical vector format that captures contextual meaning: this is vectorization. A model like BERT or Word2Vec maps a word or document into a vector—a sequence of floating-point numbers—in a multi-dimensional vector space. The key is proximity: vectors for semantically similar items, like “dog” and “puppy,” are mathematically closer than those for unrelated items. This vector representation is essential for high-performance applications, specifically powering semantic search, recommendation systems (like Netflix), and Retrieval-Augmented Generation (RAG) architectures by allowing efficient comparison using metrics like Cosine Similarity.

https://www.elastic.co/what-is/vector-embeddings