AI-decision-maker

asks Jev `Choice` questions to classify CSV columns into a 13-code type vocabulary and each dataset into one of six scenes, then executes every write locally; measured Jev at 6.6–12.7× an LLM's token cost on this task because the output is already one character while per-question criteria repeat.

CHOICE 호출은 이렇게 생겼다

이 질문 유형의 일반적인 뼈대이지 이 프로젝트의 실제 코드가 아니다.

from typesafe import TypeSafe

ts = TypeSafe()  # reads TYPESAFE_API_KEY
result = ts.evaluate(
    state=page_state,
    questions={
        "next_action": {
            "type": "choice",
            "options": ["click", "scroll", "type", "done"],
            "instructions": "What should the agent do next?",
        }
    },
)
action = result["next_action"]          # the chosen option
confidence = result["next_action_confidence"]