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teach-ChatGPT-your-R-library

Fine tune ChatGPT on your R functions!

A good practice of R programming is to document all the functions using a roxygen2-like flavour, commenting them using the #' chars before the definition. For example:

#' This is an example of how to document functions
#' @param function_name character. Name of the function we are documenting now
#' @param function_usage character. description of the function
#' @outputs description of how to use the function
function_example <- function(function_name, function_usage){
  description <- paste0(function_name, ' is a function that is used for ', function_usage)
  return(description)
}

In big projects, many of this utility functions can be created, and eventually be grouped in a library.

In this repo I fine tune ChatGPT over a set of functions having that standard format, to 'teach' to the model the work I've done so far and leverage on its help in the future.

Prerequisites

  • An OpenAI API key is required to use this program. Visit https://beta.openai.com/signup/ to sign up for an API key if you don't have one already
  • Python 3.6 or later installed on your system
  • A conda virtual environment

To install anaconda on your IOS system follow the guide at Medium. Once it is installed, create the conda virtual env and activate it running:

conda create --name myenv
conda activate myenv

Install the openai Python package by running:

conda install -c conda-forge openai

Setup

  • Clone the repository or download the it running 
    git clone https://github.com/ferrazzipietro/teach-ChatGPT-your-R-library.git
  • Modify the a CONFIG.json file in the root directory of the project adding your information, included you API KEY
  • Copy your .R file containing the functions in the roxygen2 format. Make sure that each function has its comment section and that the file contains only the functions of interest and nothing else.

Usage

  • Open a terminal/command prompt and navigate to the directory containing the source code.
  • Fine tune the choosen model running: 
    python main.py
    The idea is that the comments to the function are given to the model has they were the prompts, while the function itself is the completion.
  • if the queue of Openai is crowded, you might recive a message saying run openai api fine_tunes.follow -i YOUR-SESSION-CODE to relaunch the session. If it happens, make sure to add your openai API KEY: 
    openai -k YOUR-API-KEY api fine_tunes.follow -i YOUR-SESSION-CODE

Costs

The cost of the fine tuning depends on the model. Now (18/06/2023) the available ones are the following:

image

the prices can be find at the 'Fine tuning models' section of OpenAi Pricing.

For me, the fine tuning of davinci costed around 4$ for 100 imputed functions, while ada costed me 0.05$ for the same amount of input data.

Acknowledgements

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