# 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="afm-13cf46d35fd48a6aa2da4c8d62424de8", 
    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="arcee-ai/trinity-mini-thinking",
    messages=messages,
    response_format={
        'type': 'json_object'
    }
)

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

#### Pydantic Example

For strict validation and predictable structure, you can define a schema using a Pydantic class. The class is converted to a JSON Schema that the model must follow.

```python
from openai import OpenAI
from pydantic import BaseModel
import json

# Define your structured output model
class QAResponse(BaseModel):
    question: str
    answer: str

# Convert the Pydantic class to a JSON schema
qa_schema = QAResponse.model_json_schema()

client = OpenAI(
    api_key="afm-13cf46d35fd48a6aa2da4c8d62424de8",
    base_url="https://api.arcee.ai/api/v1"
)

system_prompt = "Extract the question and answer. Only return valid JSON."

user_prompt = "Who discovered penicillin? Alexander Fleming."

response = client.chat.completions.create(
    model="arcee-ai/trinity-mini-thinking",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt}
    ],
    temperature=0,
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "QAResponse",
            "schema": qa_schema
        }
    }
)

# Validate and parse the model output into a Pydantic object
parsed = QAResponse(**json.loads(response.choices[0].message.content))

print(parsed)

```