renjianbo
beff3fac8d
- Fix delete agent 500: clean up FK records (agent_llm_logs, permissions, schedules, executions, team_members) and unbind goals/tasks before delete - Remove hardcoded personality templates in Android, replace with dynamic system prompt generation from name + description - Set promptSectionsEnabled=false to bypass PromptComposer for personality - Add Tencent Cloud Linux deployment guide (Docker Compose) - Accumulated backend service updates, frontend UI fixes, Android app changes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
212 lines
7.5 KiB
Python
212 lines
7.5 KiB
Python
"""
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用户反馈学习服务 — 采集反馈信号，自动调整 Agent 策略
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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 collections import Counter, defaultdict
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from sqlalchemy import func, desc
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from sqlalchemy.orm import Session
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from app.core.database import SessionLocal
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from app.models.feedback_record import FeedbackRecord
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from app.models.agent_execution_log import AgentExecutionLog
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logger = logging.getLogger(__name__)
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class FeedbackLearner:
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"""从用户反馈中学习，自动调整 Agent 策略"""
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def record_feedback(
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self,
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user_id: str,
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signal_type: str,
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*,
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execution_log_id: Optional[str] = None,
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agent_name: Optional[str] = None,
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task_id: Optional[str] = None,
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original_output: Optional[str] = None,
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user_correction: Optional[str] = None,
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feedback_context: Optional[Dict[str, Any]] = None,
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) -> Optional[str]:
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"""记录一条用户反馈。"""
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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severity = 0.5
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if signal_type == "reject_approval":
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severity = 0.9
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elif signal_type == "thumbs_down":
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severity = 0.7
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elif signal_type == "manual_edit":
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severity = 0.6
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elif signal_type == "retry_command":
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severity = 0.4
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elif signal_type == "thumbs_up":
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severity = 0.1
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entry = FeedbackRecord(
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user_id=user_id,
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signal_type=signal_type,
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severity=severity,
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execution_log_id=execution_log_id,
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agent_name=agent_name,
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task_id=task_id,
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original_output=original_output[:5000] if original_output else None,
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user_correction=user_correction[:5000] if user_correction else None,
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feedback_context=feedback_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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# 标记相关执行日志的反馈
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if execution_log_id:
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exec_log = db.query(AgentExecutionLog).filter(
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AgentExecutionLog.id == execution_log_id
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).first()
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if exec_log:
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exec_log.user_rating = 1 if signal_type in ("thumbs_down", "reject_approval") else (5 if signal_type == "thumbs_up" else 3)
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exec_log.user_feedback = signal_type
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db.commit()
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return str(entry.id)
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except Exception as e:
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logger.error("记录反馈失败: %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 analyze_feedback_patterns(self, agent_name: Optional[str] = None, days: int = 7) -> Dict[str, Any]:
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"""分析反馈模式，识别需要调整的策略。"""
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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(FeedbackRecord).filter(FeedbackRecord.created_at >= since)
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if agent_name:
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q = q.filter(FeedbackRecord.agent_name == agent_name)
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records = q.all()
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if not records:
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return {"total_feedback": 0, "message": "近期无反馈"}
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# 统计信号类型
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signal_dist = Counter(r.signal_type for r in records)
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# 按 Agent 分组
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by_agent = defaultdict(lambda: {"total": 0, "negative": 0, "patterns": []})
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for r in records:
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name = r.agent_name or "unknown"
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by_agent[name]["total"] += 1
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if r.signal_type in ("thumbs_down", "reject_approval"):
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by_agent[name]["negative"] += 1
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# 生成策略建议
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strategy_advice = []
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total = len(records)
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negative_rate = (signal_dist.get("thumbs_down", 0) + signal_dist.get("reject_approval", 0)) / total if total > 0 else 0
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if negative_rate > 0.3:
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strategy_advice.append({
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"type": "adjust_temperature",
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"reason": f"负面反馈率 {negative_rate:.1%}，建议降低 temperature",
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"action": "temperature -= 0.1",
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})
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if signal_dist.get("retry_command", 0) / total > 0.2 if total > 0 else False:
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strategy_advice.append({
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"type": "enhance_prompt",
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"reason": "用户频繁要求重试，输出可能不够精准",
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"action": "在 system prompt 中增加更具体的输出要求",
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})
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if signal_dist.get("manual_edit", 0) / total > 0.2 if total > 0 else False:
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strategy_advice.append({
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"type": "suggest_review",
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"reason": "输出频繁被手动修改，建议开启 self_review",
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"action": "开启输出质量自检",
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})
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# 推荐有问题的 Agent
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problematic_agents = [
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{"agent": name, "negative_rate": round(data["negative"] / data["total"], 2)}
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for name, data in by_agent.items()
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if data["total"] >= 3 and data["negative"] / data["total"] > 0.3
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]
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return {
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"total_feedback": total,
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"period_days": days,
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"signal_distribution": dict(signal_dist),
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"overall_negative_rate": round(negative_rate, 3),
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"problematic_agents": problematic_agents,
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"strategy_advice": strategy_advice,
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}
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except Exception as e:
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logger.error("分析反馈模式失败: %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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def generate_negative_examples(self, agent_name: str, limit: int = 5) -> List[Dict[str, Any]]:
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"""为 Agent 生成反例（用于更新 system prompt）。"""
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db: Optional[Session] = None
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try:
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db = SessionLocal()
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records = (
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db.query(FeedbackRecord)
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.filter(
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FeedbackRecord.agent_name == agent_name,
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FeedbackRecord.original_output.isnot(None),
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FeedbackRecord.user_correction.isnot(None),
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FeedbackRecord.signal_type.in_(["thumbs_down", "manual_edit"]),
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)
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.order_by(desc(FeedbackRecord.created_at))
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.limit(limit)
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.all()
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)
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examples = []
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for r in records:
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examples.append({
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"original": (r.original_output or "")[:500],
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"corrected": (r.user_correction or "")[:500],
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"signal": r.signal_type,
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})
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return examples
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except Exception as e:
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logger.error("生成反例失败: %s", e)
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return []
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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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feedback_learner = FeedbackLearner()
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