pip install openai-function-call

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],
        messages=[
            {
                "role": "system",
                "content": "You must use the `sum` function instead of adding yourself.",
            },
            {
                "role": "user",
                "content": "What is 6+3 use the `sum` function",
            },
        ],
    )

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

import openai
from openai_function_call import OpenAISchema

from pydantic import Field

class UserDetails(OpenAISchema):
    """User Details"""
    name: str = Field(..., description="User's name")
    age: int = Field(..., description="User's age")

completion = openai.ChatCompletion.create(
    model="gpt-3.5-turbo-0613",
    functions=[UserDetails.openai_schema],
    messages=[
        {"role": "system", "content": "I'm going to ask for user details. Use UserDetails to parse this data."},
        {"role": "user", "content": "My name is John Doe and I'm 30 years old."},
    ],
)

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

from openai_function_call import openai_schema

@openai_schema
class UserDetails(BaseModel):
    """User Details"""
    name: str = Field(..., description="User's name")
    age: int = Field(..., description="User's age")

from pprint import pprint

from openai_function_call import OpenAISchema
from openai_function_call.dsl import ChatCompletion, MultiTask, messages as m
from openai_function_call.dsl.messages import SystemIdentity, SystemTask, SystemStyle, SystemGuidelines, SystemTips

# Define a subtask you'd like to extract from then,
# We'll use MultTask to easily map it to a List[Search]
# so we can extract more than one
class Search(OpenAISchema):
    id: int
    query: str

tasks = (
    ChatCompletion(name="Acme Inc Email Segmentation", model="gpt-3.5-turbo-0613")
    | SystemIdentity(identity="World class state of the art agent") # if no identity is provided, this is the default one
    | SystemTask(task="Segment emails into search queries")
    | SystemStyle(style="Professional, clear and concise")
    | SystemGuidelines(guidelines=[
        'You never swear',
        'You are polite',
        'You say please and thank you often.'
    ])
    | SystemTips(tips=[
        "When unsure about the correct segmentation, try to think about the task as a whole",
        "If acronyms are used expand them to their full form",
        "Use multiple phrases to describe the same thing"]
                  )
    | MultiTask(subtask_class=Search)
    | m.TaggedMessage(
        tag="email",
        content="Can you find the video I sent last week and also the post about dogs",
    )
    | m.ChainOfThought()
)
# Its important that this just builds you request,
# all these | operators are overloaded and all we do is compile
# it to the openai kwargs
# Also note that the System components are combined sequentially
# so the order matters!
assert isinstance(tasks, ChatCompletion)
pprint(tasks.kwargs, indent=3)
"""
{
    "messages": [
        {
            "role": "system",
            "content": "You are a world class state of the art agent.\n\nYour purpose is to correctly complete this task:
                        `Segment emails into search queries`.\n\nYour style when answering is professional, clear and concise\n\n
                        These are the guidelines you consider when completing your task:\n\n* You never swear\n* You are polite\n* You say please and thank you often.\n\nHere are some tips to help you complete the task:\n\n* When unsure about the correct segmentation, try to think about the task as a whole\n* If acronyms are used expand them to their full form\n* Use multiple phrases to describe the same thing"
        },
        ...
        {
            "role": "user",
            "content": "<email>Can you find the video I sent last week and also the post about dogs</email>"
        },
        {
            "role": "assistant",
            "content": "Lets think step by step to get the correct answer:"
        }
    ],
    "functions": [
        {
            "name": "MultiSearch",
            "description": "Correct segmentation of `Search` tasks",
            "parameters": {
                "type": "object",
                "properties": {
                    "tasks": {
                        "description": "Correctly segmented list of `Search` tasks",
                        "type": "array",
                        "items": {"$ref": "#/definitions/Search"}
                    }
                },
                "definitions": {
                    "Search": {
                        "type": "object",
                        "properties": {
                            "id": {"type": "integer"},
                            "query": {"type": "string"}
                        },
                        "required": ["id", "query"]
                    }
                },
                "required": ["tasks"]
            }
        }
    ],
    "function_call": {"name": "MultiSearch"},
    "max_tokens": 1000,
    "temperature": 0.1,
    "model": "gpt-3.5-turbo-0613"
"""

# Once we call .create we'll be returned with a multitask object that contains our list of task
result = tasks.create()

for task in result.tasks:
    # We can now extract the list of tasks as we could normally
    assert isinstance(task, Search)