77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
"""核心数据模型（纯标准库，无外部依赖）"""
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from __future__ import annotations
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import time
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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@dataclass
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class Classification:
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"""分类器输出：领域 + 置信度 + 难度"""
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domain: str
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confidence: float
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difficulty: str # easy | medium | hard
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difficulty_score: float = 0.5
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raw_scores: Dict[str, float] = field(default_factory=dict)
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matched_rules: List[str] = field(default_factory=list)
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@dataclass
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class ExpertResponse:
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"""专家模型输出"""
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text: str
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model_used: str
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latency_ms: float = 0.0
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tokens: int = 0
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cost_est: float = 0.0 # 相对成本估计（美元，近似）
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@dataclass
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class RouterResult:
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"""一次路由的完整结果"""
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query: str
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response: str
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domain: str
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difficulty: str
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confidence: float
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upgraded: bool # 是否升级到大模型
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quality_score: float
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model_used: str
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route: List[str] = field(default_factory=list) # 路由决策轨迹
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latency_ms: float = 0.0
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cache_hit: bool = False
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cache_level: Optional[str] = None # exact | semantic
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cost_est: float = 0.0
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error: Optional[str] = None
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subdomain: Optional[str] = None # 二级子领域（如 investing/labor）
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subdomain2: Optional[str] = None # 三级子领域（如 fund/overtime）
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domain_group: Optional[str] = None # 大领域组（两级路由第一级：tech/professional/...）
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request_id: Optional[str] = None # 请求 ID（配合 /traces/{id} 查询完整推理链）
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def to_dict(self) -> Dict[str, Any]:
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return {
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"query": self.query,
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"response": self.response,
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"domain": self.domain,
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"difficulty": self.difficulty,
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"confidence": round(self.confidence, 4),
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"upgraded": self.upgraded,
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"quality_score": round(self.quality_score, 4),
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"model_used": self.model_used,
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"route": self.route,
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"latency_ms": round(self.latency_ms, 2),
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"cache_hit": self.cache_hit,
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"cache_level": self.cache_level,
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"cost_est": round(self.cost_est, 6),
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"error": self.error,
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"subdomain": self.subdomain,
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"subdomain2": self.subdomain2,
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"domain_group": self.domain_group,
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"request_id": self.request_id,
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}
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def now_ms() -> float:
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return time.perf_counter() * 1000.0
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