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Text Readability (text_readability)

Contents​

Metric description​

Text readability produces a composite score from several established readability formulas (including Flesch Reading Ease, Dale–Chall, Gunning Fog, and SMOG). Higher values generally mean easier reading for typical English text.

How to interpret the score​

  • Closer to 100: easier reading by the combined formulas (relative to the metric’s internal scaling).
  • Closer to 0: harder reading or text that failed readability analysis.

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: text_readability

Default threshold: 70

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): Prose to score.

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": "The quick brown fox jumps over the lazy dog. " * 5}
    ]

    payload = {
        "threshold": 70,
        "model_slug": "o4-mini",
        "is_blocking": True,
        "data_collection_id": None,
        "evaluations": [
            {
                "metrics": ["text_readability"],
                "threshold": 70,
                "model_slug": "o4-mini",
                "data": data,
            }
        ],
    }

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