Structured output by pydantic class in OpenAi

structured_output_openai.py

from dotenv import load_dotenv

load_dotenv()


from openai import OpenAI
from pydantic import BaseModel
from typing import List
import openai

# initialize client
client = OpenAI()


# pydantic classes
class Songs(BaseModel):
    name: str


class Album(BaseModel):
    name: str
    songs: List[Songs]
    producer: str
    release_date: str


# two methods to access the response: using tools or response_format
# 1) using tools: It comes from the tool_calls attribute as function=ParsedFunction(arguments=....
completion = client.beta.chat.completions.parse(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "please provide random famous album details"}
    ],
    tools=[openai.pydantic_function_tool(Album)],
)

print(completion)
print(f"\n{completion.choices[0].message.tool_calls[0].function.parsed_arguments}\n")

tool_calls = completion.choices[0].message.tool_calls
for index, tool_call in enumerate(tool_calls):
    response = tool_call.function.arguments
    print(response)


# 2) using response_format. Set a model parameter 'response_format'  to the desired pydantic class eg. response_format=Album

completion = client.beta.chat.completions.parse(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "user",
            "content": "please provide random famous album details",
        }
    ],
    response_format=Album,
)

message = completion.choices[0].message

if message.parsed:
    print(completion.choices[0].message.content)
else:
    print(message.refusal)