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Structured Outputs

Sutro’s SDK allows you to enforce a JSON schema for the outputs of your inference job. This can be useful if you want to ensure that the outputs of your inference job are in a specific format, or if you want to extract specific information from the outputs. To specify a schema for the outputs of your inference job, you can pass a Pydantic Model or a JSON schema to the output_schema parameter in the SDK. If you pass a JSON schema, it must follow the json-schema.org specification. On rare occasions, the model may not adhere to the provided schema and will produce invalid JSON; in this case, Sutro will return the raw, unparsed text. If you need help improving JSON adherence, please contact us.

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Example with Pydantic Model

import sutro as so
from pydantic import BaseModel

texts = [
    "The airplane is flying at an altitude of 30,000 feet.",
    "The capital of France is Paris.",
    "The best way to cook a steak is on a grill."
]

class AviationOutput(BaseModel):
    is_aviation_related: bool
    answer_justification: str

results = so.infer(texts, system_prompt="Is the following sentence aviation related? Please provide a justification for your answer.", output_schema=AviationOutput)

print(results)

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Example with JSON Schema

import sutro as so

texts = [
    "The airplane is flying at an altitude of 30,000 feet.",
    "The capital of France is Paris.",
    "The best way to cook a steak is on a grill."
]

json_schema = {
    "type": "object",
    "properties": {
        "is_aviation_related": {
            "type": "boolean"
        },
        "answer_justification": {
            "type": "string"
        }
    },
    "required": ["is_aviation_related", "answer_justification"]
}

results = so.infer(texts, system_prompt="Is the following sentence aviation related? Please provide a justification for your answer.", output_schema=json_schema)

print(results)