Q&A and LLM Caching

[srinisubramanian]

Assuming LLM Caching using GPTCache or others is turned on. How does on ensure that wrong answers dont end up getting cached? For. e.g a question might be answered unsatisfactorily. If this is cached, potentially the same answer is returned later also.

In other words, can user feedback be used to control if a response has to be cached or not?

[dosubot[bot]]

🤖

Based on the information available in the LangChain repository, it seems that there isn't a built-in mechanism for handling user feedback in the caching process. However, you can certainly implement this feature in your application using LangChain.

As mentioned in the context, you can introduce a feedback mechanism where users can flag unsatisfactory responses. Once a response is flagged, you can use the delete_by_document_id method to remove the unsatisfactory response from the cache. Here's a simple way to implement this:

def handle_feedback(self, feedback: dict):
    """
    Handle user feedback. If the feedback indicates the response is unsatisfactory,
    delete the response from the cache.
    """
    if feedback['satisfactory'] == False:
        document_id = feedback['document_id']
        self.delete_by_document_id(document_id)

In this code, feedback is a dictionary that contains a satisfactory key indicating whether the user found the response satisfactory, and a document_id key that corresponds to the ID of the cached response. If the response is unsatisfactory, the delete_by_document_id method is called to remove the response from the cache.

Please note that this is a simple implementation and you might need to adjust it according to your specific needs and application design. For example, you might want to add additional checks or logging, or handle feedback in a different way.

You can find the delete_by_document_id method in the libs/langchain/langchain/cache.py file.

If you need further assistance or have more specific questions about implementing this feature, feel free to ask.

Sources

• libs/langchain/langchain/cache.py

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