Embeddings

Embeddings are numerical vector representations of text, images, audio, or other data used by AI models to capture the semantic meaning of content. An embedding converts a piece of text—such as a sentence or document—into a vector of hundreds or thousands of decimal numbers. Semantically similar content receives similar vectors; related concepts are positioned close together in the vector space. Embedding models like OpenAI's text-embedding-ada-002, Voyage AI, or Google's text-embedding-004 are specifically trained for this purpose. They allow machines to compare texts without relying on explicit rules or keyword lists—a system can therefore understand that 'buy a car' and 'purchase a vehicle' are semantically equivalent, even though they share no common words. In enterprise contexts, embeddings are most commonly used for Retrieval-Augmented Generation (RAG): documents are embedded and stored in a vector database. When a user submits a query, it is also embedded and compared against document vectors to find the most relevant sources, which are then provided as context to the language model. Additional applications include semantic search, recommendation systems, duplicate detection, content classification, and clustering.