In a RAG pipeline, what is the role of an embedding model?

AIt summarizes each document chunk into a single sentence before indexing.

BIt converts text chunks into dense numerical vectors that capture semantic meaning, enabling similarity-based retrieval.

CIt compresses LLM output tokens into a latent representation for caching.

DIt translates documents from one language to another before indexing.

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