# Structured Outputs

Structured outputs allow you to control and define the format of a model’s response using a schema. This makes it easy to enforce consistency, guarantee valid JSON, and parse results programmatically. Below we show two approaches: simple JSON output using `json_object` and fully validated structured output using Pydantic classes.

***

#### Simple JSON  Example

JSON output is ideal for simple structured responses where you only need the model to return well-formed JSON.

{% hint style="info" %}
To enable JSON Output, users should:

1. Set the `response_format` parameter to `{'type': 'json_object'}`.
2. Include the word "json" in the system or user prompt, and provide an example of the desired JSON format to guide the model in outputting valid JSON.
3. Set the `max_tokens` parameter reasonably to prevent the JSON string from being truncated midway.
   {% endhint %}

```python
import json
from openai import OpenAI

client = OpenAI(
    api_key="<YOUR_API_KEY>", 
    base_url="https://api.arcee.ai/api/v1"
)

system_prompt = """
The user will provide some exam text. Please parse the "question" and "answer" and output them in JSON format. 

EXAMPLE INPUT: 
Which is the highest mountain in the world? Mount Everest.

EXAMPLE JSON OUTPUT:
{
    "question": "What is the capital of France?",
    "answer": "Paris"
}
"""

user_prompt = "What is the capital of Italy?"

messages = [{"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}]

response = client.chat.completions.create(
    model="MODEL_NAME",
    messages=messages,
    response_format={
        'type': 'json_object'
    }
)

print(json.loads(response.choices[0].message.content))
```