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With LLMs, the inputs are highly variable so exact match caching is generally less useful. Semantic caching groups similar inputs and returns relevant results accordingly. So {"dish":"spaghetti bolognese"} and {"dish":"spaghetti with meat sauce"} could return the same cached result.


Or store as sentence embedding and calculate the vector distance, but creates many edge cases




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