Skip to main content

Is Boolean (is_boolean)

Contents

Metric description

Is boolean checks whether the output represents a boolean value (for example true or false in common string or typed forms supported by the implementation).

How to interpret the score

  • 100: the value is accepted as boolean.
  • 0: otherwise.

API usage

Prerequisites

After the environment variables are configured, the next step is to create a JSON payload for the custom-runs request. For a field-by-field description of the payload (top-level keys, evaluations, and each row in data), see Custom run request body.

Shortname: is_boolean

Default threshold: 100

Structural metrics run without an LLM (deterministic checks). Your run may still include model_slug where the API expects it; scoring does not depend on it for this category.

Inputs (each object in data)

  • output (str, required): Value to validate.

Eval metadata

Structural metrics do not populate eval_metadata; the field is omitted or ull on the result object.

Example

import json
import os

import requests
from dotenv import load_dotenv

load_dotenv(override=True)

_API_KEY = os.getenv("AEGIS_API_KEY")
_BASE_URL = os.getenv("AEGIS_API_BASE_URL")
_CUSTOM_RUN_URL = f"{_BASE_URL}/runs/custom"


def post_custom_run(payload: dict) -> requests.Response:
"""POST JSON payload to Aegis custom runs; returns the raw response."""
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {_API_KEY}",
}
return requests.post(
_CUSTOM_RUN_URL,
headers=headers,
data=json.dumps(payload),
)


if __name__ == "__main__":
data = [
{"output": "true"}
]

payload = {
"threshold": 100,
"model_slug": "o4-mini",
"is_blocking": True,
"data_collection_id": None,
"evaluations": [
{
"metrics": ["is_boolean"],
"threshold": 100,
"model_slug": "o4-mini",
"data": data,
}
],
}

response = post_custom_run(payload)
response.raise_for_status()
print(json.dumps(response.json(), indent=2))