The openai-functions Python project simplifies the usage of OpenAI's ChatGPT function calling feature. It abstracts away the complexity of parsing function signatures and docstrings by providing developers with a clean and intuitive interface.

You can install openai-functions from PyPI using pip:

pip install openai-functions 

1. Import the necessary modules and provide your API key:

import enum import openai from openai_functions import Conversation openai.api_key = "<YOUR_API_KEY>"

2. Create a Conversation instance:

conversation = Conversation()

3. Define your functions using the @conversation.add_function decorator:

class Unit(enum.Enum): FAHRENHEIT = "fahrenheit" CELSIUS = "celsius" @conversation.add_function() def get_current_weather(location: str, unit: Unit = Unit.FAHRENHEIT) -> dict: """Get the current weather in a given location.   Args:  location (str): The city and state, e.g., San Francisco, CA  unit (Unit): The unit to use, e.g., fahrenheit or celsius  """ return { "location": location, "temperature": "72", "unit": unit.value, "forecast": ["sunny", "windy"], }

4. Ask the AI a question:

response = conversation.ask("What's the weather in San Francisco?") # Should return something like: # The current weather in San Francisco is 72 degrees Fahrenheit and it is sunny and windy.

You can read more about how to use Conversation here.

from openai_functions import FunctionWrapper wrapper = FunctionWrapper(get_current_weather) schema = wrapper.schema result = wrapper({"location": "San Francisco, CA"})

Or you could use skills.

1. Import the necessary modules and provide your API key:

from dataclasses import dataclass import openai from openai_functions import nlp openai.api_key = "<YOUR_API_KEY>"

3. Define your data container using the @nlp decorator:

@nlp @dataclass class Person: """Extract personal info""" name: str age: int

4. Ask the AI for the extracted data:

person = Person.from_natural_language("I'm Jack and I'm 20 years old.")

You can read more about @nlp here.

Note: mypy does not parse class decorators (#3135), so you might have trouble getting type checking when using it like this. Consider using something like nlp(Person).from_natural_language to get proper type support.

openai-functions takes care of the following tasks:

• Parsing the function signatures (with type annotations) and docstrings.
• Sending the conversation and function descriptions to the OpenAI model.
• Deciding whether to call a function based on the model's response.
• Calling the appropriate function with the provided arguments.
• Updating the conversation with the function response.
• Repeating the process until the model generates a user-facing message.

This abstraction allows developers to focus on defining their functions and adding user messages without worrying about the details of function calling.

Please note that openai-functions is an unofficial project not maintained by OpenAI. Use it at your discretion.