175 lines
6.4 KiB
Python
175 lines
6.4 KiB
Python
"""质量控制器（Judge）：评估专家输出，决定是否升级大模型。
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- RuleJudge：零依赖启发式（内容覆盖度 / 长度充分性 / 领域格式 / 安全提示），
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稳定可测，适合 MVP 与离线演示。
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- LLMJudge：可选，基于 transformers 小模型或 API 的 LLM-as-Judge。
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设计对齐实现方案：overall_score < judge_fallback_threshold -> 升级大模型。
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Dict, List
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from .experts import extract_content_terms
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# 各领域期望的响应长度范围（字符数）
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_EXPECTED_LEN = {
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"code": (60, 2000),
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"math": (60, 2000),
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"legal": (80, 3000),
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"medical": (80, 3000),
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"general": (40, 2000),
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}
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# 领域格式检查：响应应包含的标记
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_DOMAIN_FORMAT_HINTS = {
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"code": ["```", "def ", "function", "class "],
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"math": ["步骤", "推导", "=", "解", "step"],
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"legal": ["⚠", "法律", "意见", "合规", "contract", "law"],
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"medical": ["⚠", "就医", "医生", "症状", "诊断", "symptom"],
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"general": [],
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}
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@dataclass
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class QualityEvaluation:
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overall_score: float
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scores: Dict[str, float] = field(default_factory=dict)
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needs_fallback: bool = False
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reasons: List[str] = field(default_factory=list)
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def to_dict(self) -> Dict:
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return {
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"overall_score": round(self.overall_score, 4),
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"scores": {k: round(v, 4) for k, v in self.scores.items()},
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"needs_fallback": self.needs_fallback,
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"reasons": self.reasons,
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}
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class BaseJudge:
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async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation:
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raise NotImplementedError
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class RuleJudge(BaseJudge):
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"""启发式质量评估（零依赖）。"""
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def __init__(self, fallback_threshold: float = 0.70):
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self.fallback_threshold = fallback_threshold
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async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation:
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scores: Dict[str, float] = {}
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reasons: List[str] = []
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# 1) 内容覆盖度：查询中的内容词有多少出现在响应里
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terms = extract_content_terms(query)
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if terms:
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hit = sum(1 for t in terms if t in response.lower())
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coverage = hit / len(terms)
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scores["coverage"] = coverage
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if coverage < 0.4:
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reasons.append(f"内容覆盖度低 ({coverage:.0%})")
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else:
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scores["coverage"] = 1.0
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# 2) 长度充分性
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lo, hi = _EXPECTED_LEN.get(domain, (40, 2000))
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n = len(response)
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if n < lo:
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scores["length"] = max(0.0, n / lo)
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reasons.append(f"响应过短 ({n} 字符)")
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elif n > hi:
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scores["length"] = 0.8
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reasons.append(f"响应过长 ({n} 字符)")
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else:
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scores["length"] = 1.0
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# 3) 领域格式检查
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hints = _DOMAIN_FORMAT_HINTS.get(domain, [])
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if hints:
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hit_hints = sum(1 for h in hints if h in response)
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scores["format"] = min(1.0, 0.4 + 0.2 * hit_hints)
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if hit_hints == 0:
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reasons.append("缺少领域格式特征")
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else:
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scores["format"] = 1.0
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# 4) 安全/免责提示（法律、医疗领域应有警示语）
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if domain in ("legal", "medical") and ("⚠" not in response and "提示" not in response):
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scores["safety"] = 0.6
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reasons.append("缺少免责提示")
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else:
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scores["safety"] = 1.0
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weights = {"coverage": 0.4, "length": 0.2, "format": 0.2, "safety": 0.2}
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overall = sum(scores.get(k, 0.0) * w for k, w in weights.items())
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needs = overall < self.fallback_threshold
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if needs:
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reasons.append("质量分低于阈值，建议升级大模型")
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return QualityEvaluation(
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overall_score=round(overall, 4),
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scores=scores,
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needs_fallback=needs,
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reasons=reasons,
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)
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class LLMJudge(BaseJudge):
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"""可选：LLM-as-Judge（API 后端）。"""
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def __init__(self, model: str, base_url: str, api_key: str, fallback_threshold: float = 0.70):
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self.model = model
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self.base_url = base_url.rstrip("/")
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self.api_key = api_key
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self.fallback_threshold = fallback_threshold
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self._client = None
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def _get_client(self):
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if self._client is None:
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import httpx
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self._client = httpx.AsyncClient(timeout=60.0)
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return self._client
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async def evaluate(self, query: str, response: str, domain: str) -> QualityEvaluation:
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client = self._get_client()
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prompt = (
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f"你是质量评审员。评估以下回答对查询的满足程度，输出 0-1 分（相关性/正确性/完整性）。\n"
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f"查询: {query}\n领域: {domain}\n回答: {response[:2000]}\n"
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f"只输出一个 0 到 1 之间的数字。"
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)
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resp = await client.post(
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f"{self.base_url}/chat/completions",
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headers={"Authorization": f"Bearer {self.api_key}"},
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json={"model": self.model, "messages": [{"role": "user", "content": prompt}]},
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)
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resp.raise_for_status()
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try:
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score = float(resp.json()["choices"][0]["message"]["content"].strip())
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score = max(0.0, min(1.0, score))
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except Exception:
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score = 0.5
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return QualityEvaluation(
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overall_score=score,
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scores={"llm_judge": score},
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needs_fallback=score < self.fallback_threshold,
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)
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def build_judge(cfg: Dict, fallback_threshold: float = 0.70) -> BaseJudge:
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"""cfg 为 judge 段配置。"""
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jtype = cfg.get("type", "rule")
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if jtype == "rule":
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return RuleJudge(fallback_threshold=fallback_threshold)
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if jtype == "llm":
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import os
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api_key = cfg.get("api_key") or os.environ.get(cfg.get("api_key_env", "API_KEY"))
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return LLMJudge(
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cfg.get("model", "deepseek-chat"),
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cfg.get("base_url", "https://api.deepseek.com/v1"),
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api_key or "",
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fallback_threshold=fallback_threshold,
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)
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raise ValueError(f"未知 judge 类型: {jtype}（支持 rule | llm）")
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