Grade Quality Across
Every Dimension.

Represent dimensions, scales, weights, and critical gates as data.

from __future__ import annotations
from dataclasses import dataclass, field


@dataclass(frozen=True)
class Dimension:
    """One scored quality dimension within a rubric.

    Args:
        id: Machine-readable dimension name.
        weight: Relative importance in the overall score.
        levels: Mapping of score value to its concrete description.
        critical: If true, a zero score fails the entire item.
    """
    id: str
    weight: float
    levels: dict[int, str]
    critical: bool = False

    @property
    def max_score(self) -> int:
        return max(self.levels)


@dataclass(frozen=True)
class Rubric:
    """A weighted collection of scoring dimensions."""
    name: str
    dimensions: list[Dimension]

Ask for structured, per-dimension scores with rationales — never a single opaque number.

import json
from .models import Rubric

JUDGE_PROMPT = """You are a strict, consistent grader.
Score the RESPONSE against each rubric dimension.
Return JSON: {{"scores": {{"": }}, "rationale": {{"": ""}}}}.

RUBRIC:
{rubric}

QUESTION: {question}
RESPONSE: {response}"""


def render_rubric(rubric: Rubric) -> str:
    """Render a rubric into a compact, judge-friendly description."""
    lines = []
    for dim in rubric.dimensions:
        levels = "; ".join(f"{k}={v}" for k, v in sorted(dim.levels.items()))
        lines.append(f"- {dim.id} (0..{dim.max_score}): {levels}")
    return "\n".join(lines)


def judge_response(rubric: Rubric, question: str, response: str, llm) -> dict:
    """Grade a single response with an LLM judge.

    Args:
        rubric: The rubric to apply.
        question: The original user question for context.
        response: The agent output being graded.
        llm: Callable mapping a prompt to a JSON string response.

    Returns:
        A dict with ``scores`` and ``rationale`` per dimension.

    Raises:
        ValueError: If the judge returns malformed JSON.
    """
    raw = llm(JUDGE_PROMPT.format(
        rubric=render_rubric(rubric), question=question, response=response,
    ))
    try:
        return json.loads(raw)
    except json.JSONDecodeError as exc:
        raise ValueError(f"Judge returned invalid JSON: {raw!r}") from exc

Normalize each dimension, apply weights, and enforce critical gates.

from .models import Rubric


def aggregate(rubric: Rubric, scores: dict[str, int]) -> dict:
    """Combine per-dimension scores into a single normalized result.

    Args:
        rubric: The rubric that produced the scores.
        scores: Raw integer score per dimension id.

    Returns:
        A dict with the ``overall`` score (0.0–1.0) and a ``passed`` flag
        that is false when any critical dimension scored zero.
    """
    total_weight = sum(d.weight for d in rubric.dimensions)
    overall = 0.0
    passed = True
    for dim in rubric.dimensions:
        value = scores.get(dim.id, 0)
        if dim.critical and value == 0:
            passed = False
        overall += (value / dim.max_score) * (dim.weight / total_weight)
    return {"overall": round(overall, 3), "passed": passed}

Trust the judge only after measuring its agreement with human scores on a labeled sample.

def test_judge_agrees_with_humans():
    """The LLM judge must correlate strongly with human anchor scores."""
    human, model = [], []
    for anchor in load_anchor_set("rubrics/anchors.jsonl"):
        result = judge_response(RUBRIC, anchor.question, anchor.response, llm)
        model.append(aggregate(RUBRIC, result["scores"])["overall"])
        human.append(anchor.human_overall)
    assert pearson(human, model) >= 0.8, "Judge is not calibrated to humans"