feat(v1): T-R1 采纳 ai-model-router 可解释路由评分——五维加权+硬过滤带拒绝理由+置信度分差推导
- 新增 router_system/explain.py：CandidateProfile（capability/cost/latency/ reliability/difficulty 画像）+ explain_routing（硬过滤先行、逐条人话拒绝理由、 五维归一评分、同分按领域字典序、confidence=0.8+分差推导封顶 0.99） - Router._explain 纯解释层装配（解释层任何异常不影响主链路，D-G4 同款纪律）； 路由胜负与既有决策完全等价，/chat 响应新增 route_explanation 字段（缓存命中为 None） - experts：Expert.nominal_latency_ms 标称延迟元数据（mock 1/hf 300/api 800） - stats：upgraded_by_domain 计数 + domain_reliability()（无数据给 0.9 中性先验） - .gitignore 登记 extra/（参考项目目录不入库） - 新增 tests/test_explain.py 8 项；全量 33 passed（基线 25）
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tzt
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@@ -45,6 +45,8 @@ class Router:
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self.low_confidence_threshold = rcfg.get("low_confidence_threshold", 0.60)
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self.judge_fallback_threshold = rcfg.get("judge_fallback_threshold", 0.70)
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self.cache_enabled = cfg.get("cache", {}).get("enabled", True)
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# T-R1：可解释路由评分层（纯解释，不影响决策；默认开启）
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self.explain_enabled = bool(rcfg.get("explain_enabled", True))
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# ---------------------------------------------------------------
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async def route(self, query: str) -> RouterResult:
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@@ -80,10 +82,16 @@ class Router:
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classification = self.classifier.classify(query)
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route.append(f"classify:{classification.domain}@{classification.confidence:.2f}/{classification.difficulty}")
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# T-R1：候选装配与可解释评分（纯解释层，不改变后续任何决策分支）
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explanation = self._explain(classification) if self.explain_enabled else None
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if explanation is not None:
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route.append(f"explain:{explanation.winner}@{explanation.confidence:.2f}")
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# 低置信度 -> 直接走大模型
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if self.classifier.should_fallback(classification, self.low_confidence_threshold):
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route.append("direct_fallback")
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return await self._fallback_result(query, classification, route, start)
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return await self._fallback_result(query, classification, route, start,
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explanation=explanation)
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# ---- Step 3: 选择专家 ----
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domain = classification.domain
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@@ -101,7 +109,7 @@ class Router:
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self.stats.record_error()
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route.append(f"expert_error:{type(e).__name__}")
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return await self._fallback_result(query, classification, route, start,
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error=str(e))
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error=str(e), explanation=explanation)
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# ---- Step 5: Judge 评估 ----
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try:
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@@ -123,19 +131,46 @@ class Router:
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latency = now_ms() - start
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result = self._finalize(query, classification, final_resp, quality_score=quality_score,
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upgraded=upgraded, route=route, latency_ms=latency,
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model_used=final_resp.model_used, cost_est=final_resp.cost_est)
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model_used=final_resp.model_used, cost_est=final_resp.cost_est,
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explanation=explanation)
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self._record(result, latency)
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# 未升级的结果写缓存
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# 未升级的结果写缓存（解释不随缓存复用——缓存命中路径 explanation=None）
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if self.cache_enabled and not upgraded and result.response:
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self.cache.put(query, result.to_dict())
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return result
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# ---------------------------------------------------------------
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def _explain(self, classification: Classification):
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"""装配候选画像并生成可解释评分（T-R1；任何异常不影响主链路）。"""
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try:
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from .explain import CandidateProfile, explain_routing
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from .experts import _cost_for
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raw = classification.raw_scores or {}
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raw_max = max(raw.values()) if raw else 0.0
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candidates = []
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for dom, expert in self.experts.items():
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cap = (raw.get(dom, 0.0) / raw_max) if raw_max > 0 else 0.0
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reason = "无规则命中（capability=0）" if cap <= 0.0 else None
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candidates.append(CandidateProfile(
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domain=dom, model=getattr(expert, "model", expert.name),
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capability=cap,
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cost_per_mtok=_cost_for(getattr(expert, "model", "mock")),
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nominal_latency_ms=getattr(expert, "nominal_latency_ms", 100.0),
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reliability=self.stats.domain_reliability(dom),
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difficulty_score=classification.difficulty_score,
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hard_fail_reason=reason))
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return explain_routing(candidates)
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except Exception: # noqa: BLE001 解释层故障不影响路由（D-G4 同款纪律）
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return None
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# ---------------------------------------------------------------
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async def _fallback_result(self, query: str, classification: Classification,
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route: list, start: float,
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error: Optional[str] = None) -> RouterResult:
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error: Optional[str] = None,
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explanation=None) -> RouterResult:
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"""兜底路径的公共收尾：调用大模型回退 -> finalize -> 记录指标。
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低置信度直连、专家异常两条路径共用，避免收尾逻辑三处重复。
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@@ -145,7 +180,7 @@ class Router:
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result = self._finalize(query, classification, fb, quality_score=0.0,
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upgraded=True, route=route, latency_ms=latency,
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model_used=fb.model_used, cost_est=fb.cost_est,
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error=error)
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error=error, explanation=explanation)
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self._record(result, latency)
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return result
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@@ -164,7 +199,7 @@ class Router:
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def _finalize(query: str, classification: Classification, resp: ExpertResponse,
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quality_score: float, upgraded: bool, route: list,
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latency_ms: float, model_used: str, cost_est: float,
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error: Optional[str] = None) -> RouterResult:
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error: Optional[str] = None, explanation=None) -> RouterResult:
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return RouterResult(
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query=query,
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response=resp.text,
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@@ -179,6 +214,7 @@ class Router:
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cache_hit=False,
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cost_est=cost_est,
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error=error,
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route_explanation=explanation.to_dict() if explanation is not None else None,
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)
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def _record(self, result: RouterResult, latency_ms: float):
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