77Fit score
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Model details →Best embedding models
Embedding models output dense vectors. Use them for retrieval, clustering, classification, and reranking.
77Fit score
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Model details →77Fit score
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Model details →Top 25 models for semantic search
Frequently asked questions
What's the difference between an embedding model and an LLM?
An embedding model returns a fixed-size vector representing a piece of text — used for similarity. An LLM generates text. They serve completely different roles in a RAG pipeline.
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