Embedding

An embedding maps text into a dense vector so semantic similarity becomes distance — the backbone of RAG and semantic search. Choosing an embedding model is a separate decision from your generation model: weigh retrieval quality on your domain, vector dimensionality (which drives storage and query cost), max input length, and per-token or per-request price across providers.