jevcal

fits a per-question confidence threshold to a target accuracy on your own labeled data, verifies it on a held-out split, reports how much traffic still has to escalate to an LLM, and fails CI when a model update breaks the locked thresholds.

SCORE 呼び出しの形

この質問型の一般的な骨格であって、このプロジェクトの実際のコードではない。

from typesafe import TypeSafe

ts = TypeSafe()
result = ts.evaluate(
    state=candidate,
    questions={
        "relevance": {
            "type": "score",
            "levels": ["low", "medium", "high"],
            "instructions": "How relevant is this to the query?",
        }
    },
)
if result["relevance"] >= 0.7:
    keep(candidate)