Embeddings
Vectors of numbers that place texts with similar meaning close together.
An embedding model turns a text into a vector; similar meanings give nearby vectors. That powers semantic search, deduplication, clustering and the retrieval step of RAG. Vectors from different models are not comparable.
Related terms
- RAG (retrieval-augmented generation): Finding relevant documents first and putting them into the prompt, so the model answers from them.
