instructor/docs/index.md

Welcome to OpenAI Function Call

We offer a minially invasive extention of Pydantic.BaseModel named OpenAISchema. It only has two methods, one to generate the correct schema, and one to produce the class from the completion.

This library is more, so a list of examples and a helper class so I'll keep the example as just structured extraction.

If the OpenAI is a chef's knife of code, I hope you sell you a nice handle which comes with a little pamplet of cuttign techniques.

It leverages the data validation capabilities of the Pydantic library to handle output parsing in a more structured and reliable manner.

If you have any feedback, leave an issue or hit me up on twitter.

If you're looking for something more batteries included I strongly recommend MarvinAI which offers a high level api but does not provide as much access to prompting.

!!! tip "Just rip it out!" If you don't want to install dependencies. I recommend literally ripping the function_calls.py into your own codebase. [source code]

Installation

pip install openai_function_call

Usage

Below are examples showcasing the use of function calls and schemas with OpenAI and Pydantic. In later docs we'll go over a wide array of more creative uses.

Example 1: Extraction

!!! Tip Prompt are now sourced from docstrings and descriptions, so write clear and descriptive documentation!

import openai
from openai_function_call import OpenAISchema

from pydantic import Field

class UserDetails(OpenAISchema):
    """Details of a user"""
    name: str = Field(..., description="users's full name")
    age: int

completion = openai.ChatCompletion.create(
    model="gpt-3.5-turbo-0613",
    functions=[UserDetails.openai_schema],
    function_call={"name": UserDetails.openai_schema["name"]},
    messages=[
        {"role": "system", "content": "Extract user details from my requests"},
        {"role": "user", "content": "My name is John Doe and I'm 30 years old."},
    ],
)

user_details = UserDetails.from_response(completion)
print(user_details)  # name="John Doe", age=30

Example 2: Function Calls

import openai
from openai_function_call import openai_function

@openai_function
def sum(a:int, b:int) -> int:
    """Sum description adds a + b"""
    return a + b

completion = openai.ChatCompletion.create(
        model="gpt-3.5-turbo-0613",
        temperature=0,
        functions=[sum.openai_schema],
        function_call={"name": sum.openai_schema["name"]},
        messages=[
            {
                "role": "system",
                "content": "You must use the `sum` function instead of adding yourself.",
            },
            {
                "role": "user",
                "content": "What is 6+3",
            },
        ],
    )

result = sum.from_response(completion)
print(result)  # 9