Sample project to demonstrate feeding OpenAI a set of API Documentation, LLM queries and then getting back (or even executing) the API Payload best suited to the request.
This project uses pipenv to manage the virtual environment and dependencies. Make sure to have install pipenv first!
- Install:
pipenv install - Activate Shell:
pipenv shell - Run a Command:
pipenv run <SCIPRT>
This will take the user input, call to GPT to parse it and propose a function call, make the function call, and then handle the response. Future improvements could be to keep a running message history so there is context aware message chains
> I need to start a print of cat.gcode
// will suggest that the `send_printer_command` function be called (and result in a call to the api)
> What is the status of my print?
// will suggest that the `printer_status` function be called (maybe?)
> The API responded with the folloing payload, can you describe what this means in human readable description? {some_payload_here}
// will suggest that the `handle_printer_response` function be called (which then parses and presents the human readable description!)