Is Numeric (is_numeric)
Contents
Metric description
Is numeric checks whether the output parses as a numeric value (integer or real) according to the implementation’s rules.
How to interpret the score
- 100: the output is numeric.
- 0: not numeric or invalid for this check.
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_numeric
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): Text or representation 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": "3.14159"}
]
payload = {
"threshold": 100,
"model_slug": "o4-mini",
"is_blocking": True,
"data_collection_id": None,
"evaluations": [
{
"metrics": ["is_numeric"],
"threshold": 100,
"model_slug": "o4-mini",
"data": data,
}
],
}
response = post_custom_run(payload)
response.raise_for_status()
print(json.dumps(response.json(), indent=2))