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IronaAI allows you to specify the format of the response from the language model using structured outputs. This feature ensures that the output adheres to a defined structure, such as JSON or a Pydantic model, making it easier to parse and integrate into your application.

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When to use structured outputs

Use structured outputs when you need the language model’s response to follow a specific schema. This is ideal for:
  • Generating structured data for databases
  • Creating consistent API responses
  • Enforcing a predefined output format
  • Structured outputs can be specified as either a JSON object or a Pydantic model.

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Using JSON object as response format

To request a JSON object, pass response_format={"type": "json_object"} to the create method. The response will be a valid JSON string. 
response = client.completions.create(
    response_format={"type": "json_object"},
    messages=[
        {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
        {"role": "user", "content": "Who won the world series in 2020?"}
    ]
)
print(response.choices[0].message.content)
The response will be a JSON string that you can parse with json.loads() in Python or JSON.parse() in TypeScript.

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Using Pydantic model as response format

For more complex structures, define a Pydantic model and pass it as the response_format. The response will be an instance of that model. 
from pydantic import BaseModel

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

class EventsList(BaseModel):
    events: list[CalendarEvent]

messages = [{"role": "user", "content": "List 5 important events in the XIX century"}]

resp = client.completions.create(
    messages=messages,
    response_format=EventsList
)
print(resp)
The response will be an EventsList instance, allowing direct access to its attributes. Note: Pydantic models are Python-specific. In TypeScript, use JSON schema or other validation tools.