An embedding model is a specialized model trained to map text to a fixed-dimensional vector such that similar text produces similar vectors. They're cheaper, faster, and smaller than chat models — and you only need one for your whole RAG system.
Production picks: OpenAI's text-embedding-3-large (3072 dim, strong quality), Cohere's embed-v3 (multilingual), open-source BGE-M3 or Nomic Embed for self-hosted. Don't switch embedding models lightly — your entire vector index has to be re-embedded.
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