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'.

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:

It's important to note that AI Functions are not suited for certain tasks, particularly those involving mathematical calculations and precision. As observed in the case of calculating the area of a triangle and finding the nth prime number, GPT models can struggle with providing accurate results. The limitations of GPT models in such cases are mainly due to their inherent inability to perform precise arithmetic and the ambiguity in understanding user inputs.

In conclusion, while AI Functions can be helpful in various scenarios, they may not be the optimal choice for tasks requiring mathematical accuracy or specific domain knowledge. For such use-cases, utilizing traditional algorithms and libraries would yield better results.

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.