OpenAI Decisions API vs TypeSafe Jev: Full Guide

Simple Code Example

Below is a complete Python example using Pydantic and the `responses.parse()` method to enforce a strict decision schema. You can learn more about how schema enforcement works in the official OpenAI Structured Outputs documentation 

python

from openai import OpenAI

from pydantic import BaseModel

from typing import Literal

client = OpenAI()

# 1. Define the strictly bounded decision schema

class RoutingDecision(BaseModel):

    selected_team: Literal[“billing_team”, “tech_support”, “fraud_team”]

    confidence_score: float

# 2. Call the API with strict schema enforcement

response = client.responses.parse(

    model=”gpt-6-astra”,

    input=[

        {“role”: “system”, “content”: “Analyze the ticket and route to the correct team.”},

        {“role”: “user”, “content”: “Customer was charged twice on invoice #9948.”}

    ],

    text_format=RoutingDecision,

)

# 3. Access the typed decision output safely

decision = response.output_parsed

print(decision.selected_team)  # Guaranteed to be one of the Literal values

Handling Edge Cases & Refusals

When working with user-generated text, always verify that the model did not issue a safety refusal before processing the result:

Python

for output in response.output:

    if output.type == “message”:

        for item in output.content:

            if item.type == “refusal”:

                print(f”Request refused: {item.refusal}”)