Speaking about observability for RAG Apps.

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Not evey search should be a vector search even In RAG use cases.

At its most basic, this turns your RAG system into pure vector search. At its most advanced, it becomes full caching without querying the model.

Semantic caching can be a powerful tool to optimize your LLM calls by using your embedding model to find similarities with previously generated answers. The way it works: you set a similarity threshold between your current question and past questions that already have answers.

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Interesting talk in french about vectorsearch in @weaviate_io .

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