Looks cool and seems like a valuable tool! I really like the idea of LLMs that give you rich answers like this.
I'm curious how you're accurately extracting the data though. Are you prompting to respond in a JSON format, using OpenAI's functions or something else? How do you ensure you have the correct label, dates, values, etc?
Nice. How often do functions fail? I haven't played around with them yet so no idea about reliability.
In regards to extracting the correct data, is that done through the function definitions? You specify all the fields you want to extract in the function and then let GPT go ham?
E.g. operating income, interest income, interest expense, etc.
The functions tend to fail when the prompt is complex and the user asks for a lot of fields, and typically the last field in the json is not closed i.e missing a }, i guess openai is aware of it. It doesn't fail that often to have to write a workaround, atleast not yet.
So like a lot of applications, the problem boils down to being able to serve the right text. You have something that can read and do basic inference .... You need to tell it what to read so that it can answer your question. But it can only read 16k tokens (20k words at best). So that's the basic problem. As it's universal, i.e a problem across applications, its going to get better and information will be a lot easier to get access to...
I'm curious how you're accurately extracting the data though. Are you prompting to respond in a JSON format, using OpenAI's functions or something else? How do you ensure you have the correct label, dates, values, etc?