"""意图分类器：识别查询领域（code/math/legal/medical/general）与难度。 - RuleClassifier：关键词/正则规则打分，纯标准库，零依赖，可离线运行。 - HuggingFaceClassifier：可选，基于 transformers 的分类模型（需安装 ML 依赖）。 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， confidence = 1 - exp(-s)，保证 s=1 -> 0.63，s=2 -> 0.86，s=3 -> 0.95。 无领域命中（或最高分领域为 general）时置信度低，触发 should_fallback。 """ from __future__ import annotations import math from typing import Dict, List, Tuple from .difficulty import estimate_difficulty from .models import Classification # --------------------------------------------------------------- # 领域关键词规则： (关键词, 权重) # --------------------------------------------------------------- DOMAIN_RULES: Dict[str, List[Tuple[str, float]]] = { "code": [ # 中文 ("python", 1.2), ("java", 1.2), ("javascript", 1.2), ("typescript", 1.2), ("代码", 1.2), ("编程", 1.2), ("函数", 0.9), ("接口", 0.8), ("报错", 0.9), ("调试", 0.9), ("部署", 0.8), ("算法", 0.8), ("数组", 0.8), ("排序", 0.9), ("正则", 0.8), ("数据库", 0.7), ("sql", 0.8), ("git", 0.7), ("api", 0.7), ("变量", 0.7), ("循环", 0.7), ("递归", 0.8), ("重构", 0.8), ("编译", 0.9), ("测试", 0.6), ("前端", 0.8), ("后端", 0.8), ("爬虫", 0.8), ("脚本", 0.7), # 英文 ("function", 0.9), ("class", 0.8), ("bug", 0.9), ("debug", 0.9), ("compile", 0.9), ("error", 0.6), ("code", 0.7), ("script", 0.7), ("algorithm", 0.8), ("sort", 0.7), ("array", 0.7), ("regex", 0.8), ("import", 0.7), ("loop", 0.7), ("recursion", 0.8), ("refactor", 0.8), ("deploy", 0.8), ("docker", 0.8), ("kubernetes", 0.8), ("async", 0.7), ("flask", 0.7), ("django", 0.7), ("api", 0.7), ("索引", 0.8), ("优化", 0.7), ("查询", 0.6), ], "math": [ ("数学", 1.2), ("方程", 1.0), ("求解", 0.8), ("导数", 1.0), ("积分", 1.0), ("矩阵", 0.9), ("概率", 0.9), ("统计", 0.8), ("证明", 0.8), ("定理", 0.9), ("微积分", 1.1), ("代数", 0.9), ("几何", 0.9), ("不等式", 0.9), ("equation", 1.0), ("derivative", 1.0), ("integral", 1.0), ("calculus", 1.1), ("matrix", 0.9), ("probability", 0.9), ("statistics", 0.8), ("proof", 0.8), ("theorem", 0.9), ("algebra", 0.9), ("geometry", 0.9), ("sqrt", 0.8), ("gcd", 0.8), ("lim", 0.8), ("polynomial", 0.9), ("summation", 0.7), ("math", 0.7), ("解", 0.6), ("计算", 0.8), ("等于", 0.6), ("求值", 0.7), ("函数", 0.6), ], "legal": [ ("法律", 1.2), ("合同", 1.0), ("法条", 1.0), ("合规", 1.0), ("诉讼", 1.0), ("知识产权", 1.1), ("版权", 0.9), ("专利", 0.9), ("违约", 0.9), ("赔偿", 0.8), ("仲裁", 0.9), ("劳动法", 1.0), ("刑法", 1.0), ("民法典", 1.0), ("法规", 0.8), ("条款", 0.7), ("律师", 0.8), ("起诉", 0.9), ("判决", 0.9), ("law", 1.0), ("legal", 1.1), ("contract", 1.0), ("compliance", 1.0), ("litigation", 1.0), ("copyright", 0.9), ("patent", 0.9), ("trademark", 0.9), ("liability", 0.9), ("regulatory", 0.8), ("jurisdiction", 0.9), ("clause", 0.8), ("agreement", 0.7), ("申请", 0.6), ], "medical": [ ("医疗", 1.2), ("药物", 1.0), ("症状", 1.0), ("诊断", 1.0), ("治疗", 0.9), ("医生", 0.9), ("血压", 0.9), ("高血压", 1.0), ("糖尿病", 1.0), ("感冒", 0.9), ("剂量", 0.9), ("副作用", 0.9), ("手术", 0.9), ("患者", 0.9), ("吃药", 0.9), ("发烧", 1.0), ("疫苗", 0.9), ("感染", 0.9), ("体检", 0.7), ("medical", 1.0), ("patient", 0.9), ("symptom", 1.0), ("disease", 0.9), ("diagnosis", 1.0), ("treatment", 0.8), ("prescription", 1.0), ("dosage", 0.9), ("side effect", 0.9), ("hypertension", 1.0), ("diabetes", 1.0), ("surgery", 0.8), ("clinic", 0.7), ("vaccine", 0.9), ("infection", 0.9), ], "general": [ ("总结", 0.4), ("翻译", 0.4), ("介绍", 0.4), ("解释", 0.3), ("summarize", 0.4), ("translate", 0.4), ("explain", 0.3), ("introduce", 0.3), ("what is", 0.3), ("tell me", 0.3), ("write an essay", 0.4), ("邮件", 0.4), ("email", 0.3), ("推荐", 0.3), ("评价", 0.3), ], } _STOPWORDS = { "的", "了", "吗", "呢", "啊", "是", "在", "有", "和", "与", "或", "及", "一个", "如何", "the", "a", "an", "is", "are", "to", "of", "in", "on", "for", "with", "and", "or", "do", "does", "can", "could", "would", "should", "please", "me", "my", } class BaseClassifier: def classify(self, query: str) -> Classification: raise NotImplementedError def should_fallback(self, classification: Classification, threshold: float) -> bool: return classification.confidence < threshold class RuleClassifier(BaseClassifier): """基于关键词规则的分类器（零依赖）。""" def __init__(self, confidence_floor: float = 0.55): self.confidence_floor = confidence_floor self.rules = DOMAIN_RULES def _score(self, query: str) -> Tuple[Dict[str, float], Dict[str, List[str]]]: q = query.lower() scores: Dict[str, float] = {} matched: Dict[str, List[str]] = {} for domain, rules in self.rules.items(): s = 0.0 hits = [] for kw, w in rules: if kw in q: s += w hits.append(kw) if s > 0: scores[domain] = s matched[domain] = hits return scores, matched def classify(self, query: str) -> Classification: raw, matched = self._score(query) if not raw: # 完全无命中 -> general，低置信度 diff, ds = estimate_difficulty(query) return Classification( domain="general", confidence=0.50, difficulty=diff, difficulty_score=ds, raw_scores={}, matched_rules=[], ) # 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 best_domain = max(sorted(raw), key=lambda d: raw[d]) best_score = raw[best_domain] confidence = 1.0 - math.exp(-best_score) # general 领域天然置信度压低 if best_domain == "general": confidence = min(confidence, self.confidence_floor + 0.05) # 与次高分的差距影响置信度（区分度） if len(raw) > 1: second = max(v for d, v in raw.items() if d != best_domain) if second > 0.7 * best_score: confidence *= 0.85 diff, ds = estimate_difficulty(query) return Classification( domain=best_domain, confidence=round(min(0.99, confidence), 4), difficulty=diff, difficulty_score=ds, raw_scores={k: round(v, 3) for k, v in raw.items()}, matched_rules=matched.get(best_domain, []), ) class HuggingFaceClassifier(BaseClassifier): """可选：基于 transformers 的序列分类模型。 仅当安装 torch+transformers 且模型可加载时可用；否则抛错提示。 """ def __init__(self, model_name: str, num_labels: int = 5, confidence_floor: float = 0.55): try: from transformers import AutoModelForSequenceClassification, AutoTokenizer except ImportError as e: raise RuntimeError( "HuggingFaceClassifier 需要安装 ML 依赖：pip install -r requirements-ml.txt" ) from e self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForSequenceClassification.from_pretrained( model_name, num_labels=num_labels ) self.labels = ["code", "math", "legal", "medical", "general"] self.confidence_floor = confidence_floor def classify(self, query: str) -> Classification: import torch # type: ignore inputs = self.tokenizer(query, return_tensors="pt", truncation=True, max_length=256) with torch.no_grad(): logits = self.model(**inputs).logits probs = torch.softmax(logits, dim=-1)[0] idx = int(probs.argmax()) diff, ds = estimate_difficulty(query) return Classification( domain=self.labels[idx], confidence=round(float(probs[idx]), 4), difficulty=diff, difficulty_score=ds, raw_scores={self.labels[i]: round(float(probs[i]), 3) for i in range(len(self.labels))}, ) def build_classifier(cfg: Dict) -> BaseClassifier: """根据配置构建分类器。cfg 为 classifier 段配置。""" ctype = cfg.get("type", "rule") floor = cfg.get("confidence_floor", 0.55) if ctype == "rule": return RuleClassifier(confidence_floor=floor) if ctype == "hf": return HuggingFaceClassifier(cfg.get("model", "Qwen/Qwen3-0.6B"), confidence_floor=floor) raise ValueError(f"未知分类器类型: {ctype}（支持 rule | hf）")