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Description:

openaifunctions 1.0.2

OpenAI functions
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.

Installation
You can install openai-functions from PyPI using pip:
pip install openai-functions

Usage

Import the necessary modules and provide your API key:

import enum
import openai
from openai_functions import Conversation

openai.api_key = "<YOUR_API_KEY>"

Create a Conversation instance:

conversation = Conversation()

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"],
}

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.
More barebones use - just schema generation and result parsing:
from openai_functions import FunctionWrapper

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

Or you could use skills.
Another use case: data extraction

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>"

Define your data container using the @nlp decorator:

@nlp
@dataclass
class Person:
"""Extract personal info"""

name: str
age: int

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