Structured Outputs

Structured outputs are a way to ensure that language model responses follow a predefined format. Instead of returning free-form text, the model is guided to generate responses in a consistent, machine-readable structure—making it easier to parse, validate, and integrate into applications.

Python

from openai import OpenAI
from pydantic import BaseModel

client = OpenAI(
  base_url="https://cortecs.ai/api/v1/models/serverless",
  api_key="<API_KEY>",
)

class CalendarEvent(BaseModel):
    name: str
    date: str
    participants: list[str]

completion = client.chat.completions.parse(
  model="<MODEL_NAME>",
  messages=[
        {"role": "system", "content": "Extract the event information."},
        {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
    ],
  response_format=CalendarEvent,
)

print(completion.choices[0].message.content)

Node.js

import OpenAI from "openai";
import { zodResponseFormat } from "openai/helpers/zod";
import { z } from "zod";

const openai = new OpenAI({
    apiKey: '<API_KEY>',
    baseURL: '<MODEL_URL>'
});

const CalendarEvent = z.object({
  name: z.string(),
  date: z.string(),
  participants: z.array(z.string()),
});

const completion = await openai.chat.completions.parse({
  model: "<MODEL_NAME>",
  messages: [
    { role: "system", content: "Extract the event information." },
    { role: "user", content: "Alice and Bob are going to a science fair on Friday." },
  ],
  response_format: zodResponseFormat(CalendarEvent, "event"),
});

const event = completion.choices[0].message.parsed;