Example:

function = "def fake_people(n: int) -> list[dict]:"
args = ["4"]
description_string = """Generates n examples of fake data representing people, each with a name and an age."""

result = ai_functions.ai_function(function_string, args, description_string, model)

""" Output: [
  {"name": "John Doe", "age": 35},
  {"name": "Jane Smith", "age": 28},
  {"name": "Alice Johnson", "age": 42},
  {"name": "Bob Brown", "age": 23}
]"""

An easy-to-use implementation of AI functions using OpenAI's GPT-4 (or any other model version) to perform various tasks. This project is heavily inspired by Ask Marvin.

1. Clone the repository:

git clone https://github.com/YourUsername/SuperSimpleAIFunctions.git

2. Install the required dependencies:

pip install -r requirements.txt

3. Obtain an OpenAI API key and store it in a keys.py file in the same directory as the scripts or set it as an environment variable.

ai_functions.py contains the following function:

def ai_function(function, args, description, model="gpt-4"):

The ai_function takes the following parameters:

• function: A string describing the function signature.
• args: A list of arguments for the function.
• description: A string describing the purpose of the function.
• model: (Optional) A string specifying the GPT model to use. Default is 'gpt-4'.
• error_correction: (Optional) A boolean specifying if mulitple additional attempts are being made to auto-correct errors. Default is 'false'.

Example usage:

import ai_functions

function = "def add(a: int, b: int) -> int:"
args = ["5", "7"]
description = "Adds two integers."

result = ai_functions.ai_function(function, args, description)
print(result)  # Output: 12

The table below shows the success rate of the AI functions with different GPT models without using error correction:

We are looking for more test cases that will push the boundaries of AI Functions.

test_ai_functions.py contains test cases for the ai_function. To run the tests, execute the script with Python:

python test_ai_functions.py

The test script will output the results of each test case and provide a success rate.

Contributions are welcome! If you would like to add more test cases or improve the existing code, please feel free to submit a pull request.