Seeking Efficient Method for Debugging Programs Using GPT API Outputs

Hello everyone,

I’m currently working on a Python project that integrates the GPT completion API, but I’m facing a challenge with the debugging process. I’m primarily using a library like Chainlit, the nature of my project makes it unsuitable for a Jupyter Notebook environment,

Issue: Each time I debug the program, I need to start from the beginning, which involves waiting for new outputs from the GPT API. This not only consumes time but also incurs additional costs. A significant concern is the variability in GPT’s outputs, making it difficult to reproduce and pinpoint bugs consistently.

Question: Is there an elegant method or a Python library that allows me to switch between real GPT API outputs and a mock API? Specifically, I’m looking for a way to replay former outputs from the GPT API during the debugging process. This functionality would greatly streamline my debugging workflow and help in consistently reproducing issues.

PS. * What kind of debugging tools or environments are you currently using?

Hi Jacob

I’m actually trying to save exactly this problem with this library: GitHub - TonySimonovsky/AIConversationFlow: AI Conversation Flow provides a framework for managing complex non-linear LLM conversation flows, that are composable, controllable and easily testable.. Though from a slightly different perspective - by being able to test separate steps of the process separately.

Thanks for your help. Although this may not exactly be what I want, it may be very helpful for a well-modulated and well-structured program. I may import this library for a try.

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Right now it is a constructor of macroflows, but within the next few days I plan to add methods to do auto-testing of separate microflows.