217 lines
8.4 KiB
Python
217 lines
8.4 KiB
Python
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"""
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影子模式执行引擎 — 数字分身生成建议但不执行，对比人类决策
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"""
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from __future__ import annotations
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import logging
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from typing import Any, Dict, List, Optional
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from sqlalchemy.orm import Session
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from sqlalchemy import func, desc
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from app.core.database import SessionLocal
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from app.models.shadow_comparison import ShadowComparison
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from app.services.fingerprint_engine import fingerprint_engine
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logger = logging.getLogger(__name__)
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class ShadowExecutor:
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"""影子模式执行器 — 观察学习阶段的核心引擎"""
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def __init__(self):
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self._unlock_thresholds = {
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"code_review": 0.85,
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"email": 0.90,
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"document": 0.85,
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"decision": 0.90,
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}
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def generate_suggestion(self, user_id: str, category: str, context: Dict[str, Any]) -> Dict[str, Any]:
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"""基于用户指纹生成影子建议（不使用LLM，纯规则+指纹推断）。"""
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fp = fingerprint_engine.get_fingerprint(user_id)
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preference = (fp.get("preference_weights", {}).get(category) if fp else None) or {}
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rules = fp.get("decision_rules", []) if fp else []
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# 匹配决策规则
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matched_rules = []
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for rule in rules:
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if rule.get("action", "").startswith(category):
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matched_rules.append(rule)
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suggestion = {
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"category": category,
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"based_on": "fingerprint" if fp else "default",
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"preference_applied": preference,
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"matched_rules": matched_rules[:5],
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"suggested_actions": self._generate_actions(category, context, preference, matched_rules),
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"confidence": min(0.5 + 0.05 * len(matched_rules), 0.95),
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}
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return suggestion
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def _generate_actions(self, category: str, context: Dict[str, Any],
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preference: Dict[str, Any], rules: List[Dict]) -> List[Dict[str, Any]]:
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"""根据分类和偏好生成建议动作。"""
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actions = []
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if category == "code_review":
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if preference.get("security", 0.3) > 0.3:
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actions.append({"priority": "high", "action": "检查安全漏洞", "detail": "SQL注入/XSS/权限校验"})
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if preference.get("performance", 0.25) > 0.25:
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actions.append({"priority": "medium", "action": "检查性能", "detail": "N+1查询/内存泄漏/大循环"})
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if preference.get("readability", 0.25) > 0.25:
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actions.append({"priority": "low", "action": "检查可读性", "detail": "命名/注释/函数长度"})
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elif category == "document":
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actions.append({"priority": "medium", "action": "结构检查", "detail": "章节完整性/逻辑连贯性"})
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actions.append({"priority": "low", "action": "风格统一", "detail": "术语一致性/格式规范"})
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elif category == "decision":
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actions.append({"priority": "high", "action": "数据验证", "detail": "检查决策依据是否充分"})
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actions.append({"priority": "medium", "action": "风险评估", "detail": "识别潜在风险和副作用"})
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elif category == "email":
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actions.append({"priority": "medium", "action": "语气检查", "detail": "与收件人关系的匹配度"})
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actions.append({"priority": "low", "action": "完整性检查", "detail": "回复是否涵盖所有要点"})
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return actions
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def compare(self, user_id: str, shadow_suggestion: Dict[str, Any],
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user_decision: Dict[str, Any], user_action: str) -> Dict[str, Any]:
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"""对比影子建议与用户实际决策。"""
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matched = 0
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diverged = 0
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suggested_actions = shadow_suggestion.get("suggested_actions", [])
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if user_action == "accept":
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match_score = 1.0
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matched = len(suggested_actions)
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elif user_action == "modify":
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# 部分匹配
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if user_decision.get("modified_actions"):
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user_actions = {a.get("action", "") for a in user_decision["modified_actions"]}
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shadow_actions = {a.get("action", "") for a in suggested_actions}
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matched = len(user_actions & shadow_actions)
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diverged = len(user_actions - shadow_actions)
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match_score = 0.5 if len(suggested_actions) > 0 else 0.5
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elif user_action == "reject":
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match_score = 0.0
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diverged = len(suggested_actions)
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else: # ignore
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match_score = 0.0
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return {
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"match_score": round(match_score, 2),
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"matched_points": matched,
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"diverged_points": diverged,
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}
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def record_comparison(self, user_id: str, category: str,
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shadow_suggestion: Dict[str, Any],
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user_decision: Dict[str, Any],
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user_action: str,
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context: Optional[Dict[str, Any]] = None) -> Optional[str]:
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"""记录一次影子对比。"""
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comparison = self.compare(user_id, shadow_suggestion, user_decision, user_action)
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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entry = ShadowComparison(
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user_id=user_id,
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category=category,
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shadow_suggestion=shadow_suggestion,
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shadow_confidence=shadow_suggestion.get("confidence", 0.5),
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user_decision=user_decision,
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user_action=user_action,
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match_score=comparison["match_score"],
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match_detail=comparison,
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context=context,
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)
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db.add(entry)
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db.commit()
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db.refresh(entry)
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return str(entry.id)
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except Exception as e:
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logger.error("记录影子对比失败: %s", e)
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if db:
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try:
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db.rollback()
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except Exception:
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pass
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return None
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finally:
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if db:
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try:
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db.close()
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except Exception:
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pass
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def get_accuracy(self, user_id: str, category: Optional[str] = None, days: int = 30) -> Dict[str, Any]:
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"""获取影子模式准确率统计。"""
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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from datetime import datetime, timedelta
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since = datetime.now() - timedelta(days=days)
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q = db.query(ShadowComparison).filter(
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ShadowComparison.user_id == user_id,
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ShadowComparison.created_at >= since,
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)
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if category:
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q = q.filter(ShadowComparison.category == category)
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records = q.all()
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total = len(records)
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if total == 0:
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return {"total_comparisons": 0, "message": "暂无数据"}
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avg_score = sum(r.match_score or 0 for r in records) / total
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accepted = sum(1 for r in records if r.user_action == "accept")
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rejected = sum(1 for r in records if r.user_action == "reject")
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modified = sum(1 for r in records if r.user_action == "modify")
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by_category = {}
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for r in records:
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cat = r.category
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if cat not in by_category:
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by_category[cat] = {"total": 0, "sum_score": 0.0}
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by_category[cat]["total"] += 1
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by_category[cat]["sum_score"] += (r.match_score or 0)
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cat_accuracy = {
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cat: round(d["sum_score"] / d["total"], 3)
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for cat, d in by_category.items()
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}
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unlocked = {
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cat: acc >= self._unlock_thresholds.get(cat, 0.90)
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for cat, acc in cat_accuracy.items()
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}
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return {
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"total_comparisons": total,
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"average_accuracy": round(avg_score, 3),
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"accepted": accepted,
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"rejected": rejected,
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"modified": modified,
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"by_category": cat_accuracy,
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"unlocked_categories": unlocked,
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"period_days": days,
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}
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except Exception as e:
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logger.error("获取准确率失败: %s", e)
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return {"error": str(e)}
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finally:
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if db:
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try:
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db.close()
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except Exception:
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pass
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shadow_executor = ShadowExecutor()
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