The AI doesn’t need another prompt or instruction. You just provide the return with the name of the function after the user’s question and identical chat history. AI sees that it must have called the function, got the return, and now decides if it can answer.
Ah, I get it now. Your content was hidden in a collapsible section in my browser. Also I didn’t know the final format for how to send back the data as “function” role. I’d read the docs, which didn’t quite make sense either until your example, so thanks!
From what I gather OP wants to do two things:
Explore the data and then create regular reports.
I suppose one could do the first step with the Code Interpreter and write the actual functions to pass the data from the function calls to.
I’ll assume you’ll build it on Python.
LangChain is an awesome library that can help you create literal chains where AI agents work together to solve a task.
Here’s an article by Sami Maameri which explains how to create an app that reads documents and feeds the info extracted to GPT.
I recommend using FLAML to automatically select the best model(s) and finding accurate hyperparamenters.
Correct, but the code interpreter (which I believe is now called data analysis) is not available from the API yet.
I assumed that reports are created once and then reused several times before adjustments are made. So, you use Code Interpreter to explore the data and write the actual functions to create the reports.
From there you follow _j’s example to create the reports via API.
I use ChatWith.tools and it’s great
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