tzt
c072a1a237
- 新增 router_system/llm_client.py：OpenAICompatClient 统一 experts/judge/fallback
三处复制的懒建 AsyncClient + /chat/completions + choices/usage 解析（~60 行去重）；
密钥解析统一走 config.get_api_key（激活原死代码，顺带消除 experts 默认环境名不一致）
- 语义缓存 L2：条目容器 list→OrderedDict（提升/淘汰 O(n)→O(1)），按 query 天然去重；
n-gram 向量 lru_cache 复用（同一次 miss 的 get/put 免重复分词）；
A/B：淘汰路径 0.040→0.034s，miss→put 往返 9.41→8.57s（-9%）
- RuleJudge 覆盖度：response.lower() 提出逐词循环（原 O(terms×len) 重复复制）
- extract_content_terms 纯函数 lru_cache 化（专家与 Judge 对同一查询免重复分词），返回 tuple
- RuleClassifier：_score 去掉败者领域白建的命中词 list（胜出后单独收集）；
修复 code 规则 ("api",0.7) 重复登记（原命中计 1.4 分）
- difficulty：正则模块级预编译
- tests：恢复上一轮引入的乱码中文 docstring；网关测试输入串恢复为可判 code 的中文查询
210 lines
9.4 KiB
Python
210 lines
9.4 KiB
Python
"""意图分类器：识别查询领域（code/math/legal/medical/general）与难度。
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- RuleClassifier：关键词/正则规则打分，纯标准库，零依赖，可离线运行。
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- HuggingFaceClassifier：可选，基于 transformers 的分类模型（需安装 ML 依赖）。
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置信度设计：每个领域有一组 (关键词, 权重)。命中权重求和得原始分 s，
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confidence = 1 - exp(-s)，保证 s=1 -> 0.63，s=2 -> 0.86，s=3 -> 0.95。
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无领域命中（或最高分领域为 general）时置信度低，触发 should_fallback。
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"""
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from __future__ import annotations
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import math
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from typing import Dict, List, Tuple
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from .difficulty import estimate_difficulty
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from .models import Classification
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# ---------------------------------------------------------------
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# 领域关键词规则： (关键词, 权重)
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# ---------------------------------------------------------------
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DOMAIN_RULES: Dict[str, List[Tuple[str, float]]] = {
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"code": [
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# 中文
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("python", 1.2), ("java", 1.2), ("javascript", 1.2), ("typescript", 1.2),
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("代码", 1.2), ("编程", 1.2), ("函数", 0.9), ("接口", 0.8), ("报错", 0.9),
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("调试", 0.9), ("部署", 0.8), ("算法", 0.8), ("数组", 0.8), ("排序", 0.9),
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("正则", 0.8), ("数据库", 0.7), ("sql", 0.8), ("git", 0.7), ("api", 0.7),
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("变量", 0.7), ("循环", 0.7), ("递归", 0.8), ("重构", 0.8), ("编译", 0.9),
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("测试", 0.6), ("前端", 0.8), ("后端", 0.8), ("爬虫", 0.8), ("脚本", 0.7),
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# 英文
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("function", 0.9), ("class", 0.8), ("bug", 0.9), ("debug", 0.9),
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("compile", 0.9), ("error", 0.6), ("code", 0.7), ("script", 0.7),
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("algorithm", 0.8), ("sort", 0.7), ("array", 0.7), ("regex", 0.8),
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("import", 0.7), ("loop", 0.7), ("recursion", 0.8), ("refactor", 0.8),
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("deploy", 0.8), ("docker", 0.8), ("kubernetes", 0.8),
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("async", 0.7), ("flask", 0.7), ("django", 0.7),
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("索引", 0.8), ("优化", 0.7), ("查询", 0.6),
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],
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"math": [
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("数学", 1.2), ("方程", 1.0), ("求解", 0.8), ("导数", 1.0), ("积分", 1.0),
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("矩阵", 0.9), ("概率", 0.9), ("统计", 0.8), ("证明", 0.8), ("定理", 0.9),
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("微积分", 1.1), ("代数", 0.9), ("几何", 0.9), ("不等式", 0.9),
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("equation", 1.0), ("derivative", 1.0), ("integral", 1.0), ("calculus", 1.1),
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("matrix", 0.9), ("probability", 0.9), ("statistics", 0.8), ("proof", 0.8),
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("theorem", 0.9), ("algebra", 0.9), ("geometry", 0.9), ("sqrt", 0.8),
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("gcd", 0.8), ("lim", 0.8), ("polynomial", 0.9), ("summation", 0.7),
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("math", 0.7), ("解", 0.6), ("计算", 0.8), ("等于", 0.6), ("求值", 0.7), ("函数", 0.6),
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],
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"legal": [
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("法律", 1.2), ("合同", 1.0), ("法条", 1.0), ("合规", 1.0), ("诉讼", 1.0),
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("知识产权", 1.1), ("版权", 0.9), ("专利", 0.9), ("违约", 0.9), ("赔偿", 0.8),
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("仲裁", 0.9), ("劳动法", 1.0), ("刑法", 1.0), ("民法典", 1.0),
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("法规", 0.8), ("条款", 0.7), ("律师", 0.8), ("起诉", 0.9), ("判决", 0.9),
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("law", 1.0), ("legal", 1.1), ("contract", 1.0), ("compliance", 1.0),
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("litigation", 1.0), ("copyright", 0.9), ("patent", 0.9), ("trademark", 0.9),
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("liability", 0.9), ("regulatory", 0.8), ("jurisdiction", 0.9),
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("clause", 0.8), ("agreement", 0.7), ("申请", 0.6),
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],
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"medical": [
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("医疗", 1.2), ("药物", 1.0), ("症状", 1.0), ("诊断", 1.0), ("治疗", 0.9),
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("医生", 0.9), ("血压", 0.9), ("高血压", 1.0), ("糖尿病", 1.0), ("感冒", 0.9),
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("剂量", 0.9), ("副作用", 0.9), ("手术", 0.9), ("患者", 0.9),
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("吃药", 0.9), ("发烧", 1.0), ("疫苗", 0.9), ("感染", 0.9), ("体检", 0.7),
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("medical", 1.0), ("patient", 0.9), ("symptom", 1.0), ("disease", 0.9),
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("diagnosis", 1.0), ("treatment", 0.8), ("prescription", 1.0),
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("dosage", 0.9), ("side effect", 0.9), ("hypertension", 1.0),
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("diabetes", 1.0), ("surgery", 0.8), ("clinic", 0.7), ("vaccine", 0.9),
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("infection", 0.9),
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],
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"general": [
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("总结", 0.4), ("翻译", 0.4), ("介绍", 0.4), ("解释", 0.3),
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("summarize", 0.4), ("translate", 0.4), ("explain", 0.3),
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("introduce", 0.3), ("what is", 0.3), ("tell me", 0.3),
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("write an essay", 0.4), ("邮件", 0.4), ("email", 0.3),
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("推荐", 0.3), ("评价", 0.3),
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],
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}
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_STOPWORDS = {
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"的", "了", "吗", "呢", "啊", "是", "在", "有", "和", "与", "或", "及", "一个", "如何",
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"the", "a", "an", "is", "are", "to", "of", "in", "on", "for", "with", "and",
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"or", "do", "does", "can", "could", "would", "should", "please", "me", "my",
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}
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class BaseClassifier:
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def classify(self, query: str) -> Classification:
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raise NotImplementedError
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def should_fallback(self, classification: Classification, threshold: float) -> bool:
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return classification.confidence < threshold
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class RuleClassifier(BaseClassifier):
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"""基于关键词规则的分类器（零依赖）。"""
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def __init__(self, confidence_floor: float = 0.55):
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self.confidence_floor = confidence_floor
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self.rules = DOMAIN_RULES
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def _score(self, q: str) -> Dict[str, float]:
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"""对已 lowercase 的查询按领域规则打分，返回有命中的领域分数。
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命中词列表只对最终胜出领域有意义（classify 的唯一消费点），
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故不在各领域上白建 list——胜出后由 _matched_rules 单独收集。
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"""
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scores: Dict[str, float] = {}
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for domain, rules in self.rules.items():
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s = 0.0
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for kw, w in rules:
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if kw in q:
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s += w
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if s > 0:
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scores[domain] = s
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return scores
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def _matched_rules(self, q: str, domain: str) -> List[str]:
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"""收集指定领域命中的关键词（保持登记顺序）。"""
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return [kw for kw, _w in self.rules.get(domain, ()) if kw in q]
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def classify(self, query: str) -> Classification:
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q = query.lower()
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raw = self._score(q)
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if not raw:
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# 完全无命中 -> general，低置信度
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diff, ds = estimate_difficulty(query)
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return Classification(
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domain="general",
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confidence=0.50,
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difficulty=diff,
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difficulty_score=ds,
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raw_scores={},
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matched_rules=[],
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)
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# 同分决胜：按领域名字典序，保证与规则表排列顺序无关的确定性
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best_domain = max(sorted(raw), key=lambda d: raw[d])
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best_score = raw[best_domain]
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confidence = 1.0 - math.exp(-best_score)
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# general 领域天然置信度压低
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if best_domain == "general":
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confidence = min(confidence, self.confidence_floor + 0.05)
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# 与次高分的差距影响置信度（区分度）
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if len(raw) > 1:
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second = max(v for d, v in raw.items() if d != best_domain)
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if second > 0.7 * best_score:
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confidence *= 0.85
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diff, ds = estimate_difficulty(query)
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return Classification(
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domain=best_domain,
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confidence=round(min(0.99, confidence), 4),
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difficulty=diff,
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difficulty_score=ds,
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raw_scores={k: round(v, 3) for k, v in raw.items()},
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matched_rules=self._matched_rules(q, best_domain),
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)
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class HuggingFaceClassifier(BaseClassifier):
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"""可选：基于 transformers 的序列分类模型。
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仅当安装 torch+transformers 且模型可加载时可用；否则抛错提示。
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"""
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def __init__(self, model_name: str, num_labels: int = 5, confidence_floor: float = 0.55):
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try:
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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except ImportError as e:
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raise RuntimeError(
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"HuggingFaceClassifier 需要安装 ML 依赖：pip install -r requirements-ml.txt"
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) from e
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self.tokenizer = AutoTokenizer.from_pretrained(model_name)
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self.model = AutoModelForSequenceClassification.from_pretrained(
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model_name, num_labels=num_labels
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)
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self.labels = ["code", "math", "legal", "medical", "general"]
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self.confidence_floor = confidence_floor
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def classify(self, query: str) -> Classification:
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import torch # type: ignore
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inputs = self.tokenizer(query, return_tensors="pt", truncation=True, max_length=256)
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with torch.no_grad():
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logits = self.model(**inputs).logits
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probs = torch.softmax(logits, dim=-1)[0]
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idx = int(probs.argmax())
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diff, ds = estimate_difficulty(query)
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return Classification(
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domain=self.labels[idx],
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confidence=round(float(probs[idx]), 4),
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difficulty=diff,
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difficulty_score=ds,
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raw_scores={self.labels[i]: round(float(probs[i]), 3) for i in range(len(self.labels))},
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)
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def build_classifier(cfg: Dict) -> BaseClassifier:
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"""根据配置构建分类器。cfg 为 classifier 段配置。"""
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ctype = cfg.get("type", "rule")
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floor = cfg.get("confidence_floor", 0.55)
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if ctype == "rule":
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return RuleClassifier(confidence_floor=floor)
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if ctype == "hf":
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return HuggingFaceClassifier(cfg.get("model", "Qwen/Qwen3-0.6B"), confidence_floor=floor)
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raise ValueError(f"未知分类器类型: {ctype}（支持 rule | hf）")
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