474 lines
14 KiB
Python
474 lines
14 KiB
Python
"""
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Cognitive Conflict Detection & Resolution Service - 认知冲突检测与解决服务
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核心功能：
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1. 自动检测团队隐性认知差异
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2. 识别定义不一致（如"留存"vs"DAU"）
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3. 检测目标冲突（产品说X，开发实现Y）
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4. 主动推送对齐提醒
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5. 异步 1v1 认知对齐
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"""
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import asyncio
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import json
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from datetime import datetime, timedelta, timezone
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from typing import Any, Dict, List, Optional
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.future import select
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from sqlalchemy import text
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from ..models import (
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User, Project, TruthNode, Task,
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CognitiveConflict, ProjectDomain
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)
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from .llm import LLMService
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class CognitiveConflictService:
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"""认知冲突检测与解决服务"""
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def __init__(self, db: AsyncSession):
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self.db = db
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self.llm = LLMService()
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async def detect_charter_conflicts(
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self,
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project_id: int,
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tenant_id: int = 1
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) -> List[Dict[str, Any]]:
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"""
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检测项目宪章中的潜在冲突
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扫描：
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1. 宪章中的成功指标定义是否清晰
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2. 是否有模糊的目标描述
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3. 是否有相互冲突的约束
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"""
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# 获取项目宪章
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result = await self.db.execute(
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select(TruthNode).where(
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TruthNode.project_id == project_id,
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TruthNode.node_type == 'charter',
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TruthNode.tenant_id == tenant_id
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)
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)
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charter = result.scalar_one_or_none()
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if not charter:
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return []
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# AI 分析宪章内容
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conflicts = await self._analyze_charter_for_conflicts(
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charter.content,
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tenant_id=tenant_id,
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)
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# 保存检测到的冲突
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for conflict_data in conflicts:
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await self._create_conflict_record(
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truth_node_id=charter.id,
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conflict_type=conflict_data['type'],
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description=conflict_data['description'],
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severity=conflict_data['severity'],
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suggested_resolution=conflict_data['suggested_resolution'],
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tenant_id=tenant_id
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)
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return conflicts
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async def detect_task_charter_mismatch(
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self,
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task: Task,
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charter_content: str,
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tenant_id: int = 1
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) -> Optional[Dict[str, Any]]:
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"""
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检测任务与宪章的一致性
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检查：
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1. 任务描述是否与宪章目标一致
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2. 技术选型是否符合约束
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3. 是否偏离项目方向
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"""
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prompt = f"""检查以下任务是否符合项目宪章：
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宪章内容：
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{charter_content}
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任务信息：
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标题：{task.title}
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描述：{task.description}
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优先级：{task.priority}
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预计工时：{task.estimated_hours} 小时
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请判断：
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1. 任务是否与宪章目标一致？
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2. 是否有潜在的偏离？
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3. 是否存在隐性冲突？
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以 JSON 格式返回：
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{{
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"is_aligned": boolean,
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"conflict_type": "目标不一致/技术约束冲突/优先级错配",
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"description": "冲突描述",
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"severity": "high/medium/low",
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"suggestion": "建议调整"
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}}
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如果任务与宪章一致，is_aligned 设为 true，conflict_type 为 null。"""
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try:
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response = await self.llm.generate(
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prompt,
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db=self.db,
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tenant_id=tenant_id,
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project_id=task.project_id if getattr(task, "project_id", None) is not None else None,
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)
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response = response.strip()
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if response.startswith('```'):
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response = response.split('```')[1]
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if response.startswith('json'):
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response = response[4:]
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result = json.loads(response.strip())
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if not result.get('is_aligned', True):
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# 创建冲突记录
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await self._create_conflict_record(
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truth_node_id=task.truth_node_id,
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conflict_type=result.get('conflict_type', 'unknown'),
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description=result.get('description', ''),
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severity=result.get('severity', 'medium'),
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suggested_resolution=result.get('suggestion', ''),
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related_task_id=task.id,
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tenant_id=tenant_id
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)
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return result
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return None
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except Exception as e:
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print(f"[CognitiveConflict] Task mismatch detection failed: {e}")
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return None
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async def detect_user_definition_conflicts(
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self,
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project_id: int,
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tenant_id: int = 1
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) -> List[Dict[str, Any]]:
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"""
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检测用户定义冲突
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例如：
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- 产品在宪章中写"用户留存提升10%"
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- 开发在实现"DAU提升10%"
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- 双方都没有争论，但目标不一致
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"""
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# 获取项目的所有宪章和任务
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charters_result = await self.db.execute(
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select(TruthNode).where(
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TruthNode.project_id == project_id,
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TruthNode.node_type == 'charter',
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TruthNode.tenant_id == tenant_id
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)
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)
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charters = charters_result.scalars().all()
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tasks_result = await self.db.execute(
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select(Task).where(
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Task.project_id == project_id,
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Task.tenant_id == tenant_id
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)
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)
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tasks = tasks_result.scalars().all()
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# 构建所有定义的上下文
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definitions = []
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for charter in charters:
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definitions.append({
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'source_type': 'charter',
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'source_id': charter.id,
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'content': charter.content
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})
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for task in tasks:
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definitions.append({
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'source_type': 'task',
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'source_id': task.id,
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'content': f"{task.title}: {task.description}"
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})
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if not definitions:
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return []
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# AI 检测定义冲突
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prompt = f"""分析以下项目定义中是否存在隐性认知冲突：
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{json.dumps(definitions, ensure_ascii=False, indent=2)}
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请查找：
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1. 相同概念的不同表述（如"留存"vs"DAU"）
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2. 相矛盾的目标（如"降低成本"vs"增加功能"）
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3. 不一致的指标定义
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以 JSON 格式返回：
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[
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{{
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"conflict_type": "定义不一致",
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"description": "具体描述冲突",
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"involved_sources": ["charter:1", "task:2"],
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"severity": "high/medium/low",
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"suggested_alignment": "建议的对齐方案"
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}}
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]
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如果没有发现冲突，返回空数组。"""
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try:
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response = await self.llm.generate(
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prompt,
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db=self.db,
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tenant_id=tenant_id,
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project_id=project_id,
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)
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response = response.strip()
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if response.startswith('```'):
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response = response.split('```')[1]
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if response.startswith('json'):
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response = response[4:]
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conflicts = json.loads(response.strip())
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# 保存检测到的冲突
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for conflict in conflicts:
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await self._create_conflict_record(
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truth_node_id=int(conflict['involved_sources'][0].split(':')[1]),
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conflict_type=conflict['conflict_type'],
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description=conflict['description'],
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severity=conflict['severity'],
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suggested_resolution=conflict.get('suggested_alignment', ''),
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tenant_id=tenant_id
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)
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return conflicts
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except Exception as e:
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print(f"[CognitiveConflict] Definition conflict detection failed: {e}")
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return []
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async def propose_alignment_resolution(
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self,
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conflict_id: int,
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tenant_id: int = 1
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) -> Dict[str, Any]:
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"""
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提议认知冲突解决方案
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为冲突双方生成对齐方案：
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1. 分析冲突根因
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2. 提出妥协方案
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3. 生成可执行的对齐计划
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"""
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# 获取冲突记录
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result = await self.db.execute(
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select(CognitiveConflict).where(
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CognitiveConflict.id == conflict_id,
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CognitiveConflict.tenant_id == tenant_id
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)
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)
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conflict = result.scalar_one_or_none()
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if not conflict:
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raise ValueError(f"Conflict {conflict_id} not found")
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# AI 生成解决方案
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prompt = f"""为以下认知冲突提供解决方案：
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冲突类型：{conflict.conflict_type}
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描述：{conflict.description}
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请提供：
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1. 冲突根因分析
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2. 双方都能接受的妥协方案
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3. 具体的对齐步骤
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以 JSON 格式返回：
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{{
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"root_cause": "根本原因",
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"resolution_plan": ["步骤1", "步骤2"],
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"compromise": "妥协方案",
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"expected_outcome": "预期结果"
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}}"""
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try:
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response = await self.llm.generate(
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prompt,
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db=self.db,
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tenant_id=tenant_id,
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)
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response = response.strip()
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if response.startswith('```'):
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response = response.split('```')[1]
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if response.startswith('json'):
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response = response[4:]
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resolution = json.loads(response.strip())
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# 更新冲突状态
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conflict.status = 'resolution_proposed'
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conflict.description += f"\n\nAI 建议方案：{resolution.get('compromise', '')}"
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await self.db.commit()
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return resolution
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except Exception as e:
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print(f"[CognitiveConflict] Resolution proposal failed: {e}")
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return {}
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async def trigger_async_alignment(
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self,
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conflict_id: int,
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user1_id: int,
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user2_id: int,
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tenant_id: int = 1
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) -> Dict[str, Any]:
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"""
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触发异步 1v1 认知对齐
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为冲突双方创建独立的对齐任务：
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1. 发送对齐邀请
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2. 提供背景信息
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3. 收集双方观点
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4. AI 生成共识方案
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"""
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# 获取用户信息
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result1 = await self.db.execute(
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select(User).where(User.id == user1_id)
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)
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user1 = result1.scalar_one_or_none()
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result2 = await self.db.execute(
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select(User).where(User.id == user2_id)
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)
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user2 = result2.scalar_one_or_none()
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if not user1 or not user2:
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raise ValueError("Users not found")
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# 获取冲突详情
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result = await self.db.execute(
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select(CognitiveConflict).where(
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CognitiveConflict.id == conflict_id,
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CognitiveConflict.tenant_id == tenant_id
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)
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)
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conflict = result.scalar_one_or_none()
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# TODO: 实际实现需要：
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# 1. 发送飞书消息/邮件通知双方
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# 2. 创建对齐任务
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# 3. 记录对齐过程
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return {
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'alignment_session_id': f"align_{conflict_id}",
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'user1': user1.username,
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'user2': user2.username,
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'conflict': conflict.conflict_type,
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'status': 'initiated'
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}
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async def _create_conflict_record(
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self,
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truth_node_id: int,
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conflict_type: str,
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description: str,
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severity: str,
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suggested_resolution: str = "",
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related_task_id: Optional[int] = None,
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tenant_id: int = 1
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):
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"""创建认知冲突记录"""
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from sqlalchemy import text
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next_conflict_id = (await self.db.execute(text("SELECT COALESCE(MAX(id),0)+1 FROM cognitive_conflicts"))).scalar() or 1
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conflict = CognitiveConflict(
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id=next_conflict_id,
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truth_node_id=truth_node_id,
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conflict_type=conflict_type,
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description=description,
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involved_user_ids=json.dumps([]),
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status='detected',
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tenant_id=tenant_id
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)
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self.db.add(conflict)
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await self.db.commit()
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async def _analyze_charter_for_conflicts(
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self,
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charter_content: str,
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tenant_id: int,
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) -> List[Dict[str, Any]]:
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"""AI 分析宪章内容中的冲突"""
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prompt = f"""分析以下项目宪章中是否存在潜在冲突或问题：
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{charter_content}
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请检查：
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1. 目标是否模糊或相互矛盾？
|
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2. 约束条件是否现实？
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3. 成功指标是否可衡量？
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4. 是否遗漏重要方面？
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|
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以 JSON 格式返回：
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[
|
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{{
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"type": "目标模糊/约束冲突/指标不可测量",
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"description": "具体问题描述",
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"severity": "high/medium/low",
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"suggested_resolution": "建议的改进方案"
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}}
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"""
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try:
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response = await self.llm.generate(
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prompt,
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db=self.db,
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tenant_id=tenant_id,
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)
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response = response.strip()
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if response.startswith('```'):
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response = response.split('```')[1]
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if response.startswith('json'):
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response = response[4:]
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return json.loads(response.strip())
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except:
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return []
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|
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async def get_detected_conflicts(
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self,
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tenant_id: int = 1
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) -> List[Dict[str, Any]]:
|
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"""获取所有检测到的冲突"""
|
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result = await self.db.execute(
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select(CognitiveConflict).where(
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CognitiveConflict.tenant_id == tenant_id,
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CognitiveConflict.status == 'detected'
|
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)
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)
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conflicts = result.scalars().all()
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return [
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{
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'id': str(c.id),
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'type': c.conflict_type,
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'description': c.description,
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'severity': 'medium' if 'high' not in c.description.lower() else 'high',
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'truth_node_id': str(c.truth_node_id)
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}
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for c in conflicts
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]
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# 全局服务实例
|
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def get_cognitive_conflict_service(db: AsyncSession) -> CognitiveConflictService:
|
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"""获取认知冲突服务实例"""
|
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return CognitiveConflictService(db)
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