从模型调用到应用链路：把不确定的模型放进确定的程序边界

用户输入 → 输入校验 → 上下文组装 → 模型调用 → 结果校验 → 业务输出
               ↓             ↓           ↓
             拒绝请求      超时/重试    降级/人工复核

from dataclasses import asdict, dataclass
from time import perf_counter
from uuid import uuid4


@dataclass
class Result:
    request_id: str
    status: str
    summary: str | None
    error: str | None
    elapsed_ms: int


def fake_model(text: str) -> str:
    # 只模拟模型边界，便于离线验证链路。
    return text.strip().replace("；", "，")[:60]


def handle_event(payload: dict) -> dict:
    started = perf_counter()
    request_id = str(uuid4())
    try:
        text = payload.get("text")
        if not isinstance(text, str) or not text.strip():
            raise ValueError("text_required")
        if len(text) > 500:
            raise ValueError("text_too_long")

        summary = fake_model(text)
        if not summary or len(summary) > 60:
            raise ValueError("invalid_model_output")
        result = Result(request_id, "ok", summary, None, 0)
    except ValueError as error:
        result = Result(request_id, "rejected", None, str(error), 0)

    result.elapsed_ms = round((perf_counter() - started) * 1000)
    return asdict(result)


print(handle_event({"text": "节点 AX-3 温度偏高；复核后确认为传感器漂移。"}))
print(handle_event({"text": ""}))

{'status': 'ok', 'summary': '节点 AX-3 温度偏高，复核后确认为传感器漂移。', ...}
{'status': 'rejected', 'summary': None, 'error': 'text_required', ...}