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Oh the embedding LLMs are usually lightweight BERT models with few layers and <<1B weights, while LLMs are easily 10-100x larger. The idea is to ingest the text
by visarga 3y ago
Oh the embedding LLMs are usually lightweight BERT models with few layers and <<1B weights, while LLMs are easily 10-100x larger. The idea is to ingest the text in a LLM to extract the facets you are going to search and add those extra tokens to the original text. Then you do regular RAG.
- sroussey 3y agoHow?
- wodenokoto 3y agoYou want to enrich the base text before embedding? With what kind of prompt? It’s a pretty simple thing to add to a pipeline. Have you tried?