509 lines
16 KiB
Python
509 lines
16 KiB
Python
"""
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AI Steward Service - 主动式 AI 管家核心服务
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核心功能：
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1. 零配置项目创建 - 从一句话生成完整 SSTC 链路
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2. 主动信号捕获 - 从多个源自动抓取并评估
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3. 认知冲突检测 - 自动识别并消解团队认知差异
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4. 阻塞自动解决 - 主动识别和解决开发阻塞
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5. 个性化进度简报 - 为每个角色生成定制化简报
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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 ..database import get_db
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from ..models import (
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User, Project, Signal, TruthNode, Task,
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ProjectDomain, CognitiveConflict, UserSkillBinding
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)
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from .llm import LLMService
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class AIStewardService:
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"""主动式 AI 管家服务"""
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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 create_project_from_natural_language(
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self,
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input_text: str,
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creator_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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零配置项目创建 - 从一句话生成完整 SSTC 链路
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输入示例：
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"我们要做一个 AI 客服工具，基于 GPT-4o mini，对标 XX 竞品，3 周上线"
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输出：完整的 SSTC 链路
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- 竞品信号分析
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- 宪章（TruthNode）
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- 任务拆解（Task）
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- 依赖关系
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- 风险预警
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"""
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# Step 0: 获取下一个可用的项目 ID（SQLite 兼容）
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from sqlalchemy import text
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next_id = (await self.db.execute(text("SELECT COALESCE(MAX(id),0)+1 FROM projects"))).scalar() or 1
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# Step 1: AI 解析用户输入，提取关键信息
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analysis = await self._analyze_project_input(input_text, tenant_id)
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# Step 2: 自动生成项目
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project = Project(
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id=next_id,
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name=analysis.get('project_name', '未命名项目')[:50],
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title=analysis.get('project_title', input_text[:50]),
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description=input_text,
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owner_id=creator_id,
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status='active',
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domain_id=analysis.get('domain_id'),
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assigned_role=analysis.get('primary_role', 'backend'),
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signal_count=0,
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tenant_id=tenant_id
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)
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self.db.add(project)
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await self.db.flush()
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# Step 3: 生成信号（竞品分析）
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signal = await self._generate_competitive_signal(
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project.id,
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analysis,
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tenant_id
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)
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# Step 4: 生成宪章（核心真理节点）
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charter = await self._generate_charter(
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project.id,
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analysis,
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creator_id,
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tenant_id
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)
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charter_id = int(charter['id']) # Convert string ID to int
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# Step 5: 生成任务链
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tasks = await self._generate_task_chain(
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project.id,
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charter_id,
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analysis,
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tenant_id
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)
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# Step 6: 生成风险预警规则
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risks = await self._generate_risk_alert_rules(
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project.id,
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charter_id,
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analysis,
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tenant_id
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)
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await self.db.commit()
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return {
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'project': {
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'id': str(project.id),
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'name': project.name,
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'title': project.title,
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'description': project.description,
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'status': project.status
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},
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'signal': signal,
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'charter': charter,
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'tasks': tasks,
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'risks': risks,
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'analysis': analysis
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}
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async def _analyze_project_input(self, input_text: str, tenant_id: int) -> Dict[str, Any]:
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"""使用 AI 分析项目输入，提取关键信息"""
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prompt = f"""分析以下项目需求，提取关键信息并以 JSON 格式返回：
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用户输入：{input_text}
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请提取以下信息：
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{{
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"project_name": "项目名称（简短）",
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"project_title": "项目标题",
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"tech_stack": ["技术栈列表"],
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"target_audience": "目标用户",
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"timeline": "时间周期",
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"primary_role": "主要角色类型（ai_engineer/backend/frontend）",
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"domain_id": 1,
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"key_features": ["核心功能列表"],
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"competitors": ["竞品列表"],
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"success_metrics": ["成功指标"]
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}}
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只返回 JSON，不要其他内容。"""
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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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try:
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# 清理可能的 markdown 代码块标记
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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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analysis = json.loads(response.strip())
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except:
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# 降级处理
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analysis = {
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'project_name': input_text[:20],
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'project_title': input_text[:50],
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'tech_stack': ['GPT-4o-mini'],
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'target_audience': '通用',
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'timeline': '3周',
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'primary_role': 'ai_engineer',
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'domain_id': 1,
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'key_features': [],
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'competitors': [],
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'success_metrics': []
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}
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return analysis
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async def _generate_competitive_signal(
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self,
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project_id: int,
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analysis: Dict[str, Any],
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tenant_id: int
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) -> Dict[str, Any]:
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"""生成竞品分析信号"""
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from sqlalchemy import text
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# 获取下一个可用的 signal ID
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next_signal_id = (await self.db.execute(text("SELECT COALESCE(MAX(id),0)+1 FROM signals"))).scalar() or 1
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competitors = analysis.get('competitors', [])
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if not competitors:
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competitors = ['行业同类产品']
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prompt = f"""基于以下项目信息，生成竞品分析信号：
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项目：{analysis.get('project_title')}
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技术栈：{', '.join(analysis.get('tech_stack', []))}
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竞品：{', '.join(competitors)}
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请生成：
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1. 竞品现状分析
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2. 差异化机会
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3. 技术风险评估
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以 JSON 格式返回：
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{{
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"content": "信号内容（200字以内）",
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"impact_score": 0.9,
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"priority": "high",
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"strategic_analysis": {{"is_strategic": true, "threat_level": "MEDIUM"}}
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}}"""
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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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try:
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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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signal_data = json.loads(response.strip())
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except:
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signal_data = {
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'content': f'竞品分析：{", ".join(competitors)} 的技术现状和差异化机会',
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'impact_score': 0.8,
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'priority': 'high',
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'strategic_analysis': {'is_strategic': True}
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}
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signal = Signal(
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id=next_signal_id,
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source='AI-Steward',
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content=signal_data['content'],
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impact_score=signal_data.get('impact_score', 0.8),
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priority=signal_data.get('priority', 'high'),
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processed=True,
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strategic_analysis=json.dumps(signal_data.get('strategic_analysis', {})),
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tasks_generated=json.dumps([]),
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tenant_id=tenant_id
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)
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self.db.add(signal)
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await self.db.flush()
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# 创建信号与项目的关联
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from ..models import ProjectSignalLink
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next_link_id = (await self.db.execute(text("SELECT COALESCE(MAX(id),0)+1 FROM project_signal_links"))).scalar() or 1
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link = ProjectSignalLink(
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id=next_link_id,
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project_id=project_id,
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signal_id=signal.id,
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link_type='competitive_analysis',
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relevance_score=0.9,
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tenant_id=tenant_id
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)
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self.db.add(link)
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await self.db.flush()
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return {
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'id': str(signal.id),
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'content': signal.content,
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'impact_score': signal.impact_score
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}
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async def _generate_charter(
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self,
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project_id: int,
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analysis: Dict[str, Any],
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creator_id: int,
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tenant_id: int
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) -> Dict[str, Any]:
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"""生成项目宪章（核心真理节点）"""
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prompt = f"""基于以下项目信息，生成项目宪章：
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项目：{analysis.get('project_title')}
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描述：{analysis.get('project_title')}
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技术栈：{', '.join(analysis.get('tech_stack', []))}
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核心功能：{', '.join(analysis.get('key_features', []))}
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成功指标：{', '.join(analysis.get('success_metrics', []))}
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请生成包含以下内容的宪章：
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1. 项目目标（明确、可衡量）
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2. 成功标准（具体指标）
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3. 技术约束
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4. 时间节点
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以 JSON 格式返回：
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{{
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"title": "宪章标题",
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"content": "宪章完整内容（包含上述4个方面）",
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"success_criteria": ["标准1", "标准2"],
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"constraints": ["约束1", "约束2"]
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}}"""
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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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try:
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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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charter_data = json.loads(response.strip())
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except:
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charter_data = {
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'title': f'{analysis.get("project_name")}项目宪章',
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'content': f'项目目标：{analysis.get("project_title")}\n成功标准：按时上线',
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'success_criteria': ['按时交付', '功能完整'],
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'constraints': ['时间限制']
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}
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raw_content = charter_data.get('content', '')
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if isinstance(raw_content, dict):
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charter_content = dict(raw_content)
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else:
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charter_content = {
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'rationale': str(raw_content),
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'success_criteria': charter_data.get('success_criteria', []),
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'constraints': charter_data.get('constraints', []),
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'signal_ids': [],
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}
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# 获取下一个可用的 truth_node ID
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from sqlalchemy import text
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next_charter_id = (await self.db.execute(text("SELECT COALESCE(MAX(id),0)+1 FROM truth_nodes"))).scalar() or 1
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charter = TruthNode(
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id=next_charter_id,
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node_type='charter',
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project_id=project_id,
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created_by=creator_id,
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title=charter_data.get('title', '项目宪章'),
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content=charter_content,
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version=1,
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status='active',
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tenant_id=tenant_id
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)
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self.db.add(charter)
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await self.db.flush()
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return {
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'id': str(charter.id),
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'title': charter.title,
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'content': charter_content
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}
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async def _generate_task_chain(
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self,
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project_id: int,
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charter_id: int,
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analysis: Dict[str, Any],
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tenant_id: int
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) -> List[Dict[str, Any]]:
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"""生成任务链"""
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prompt = f"""基于以下信息，生成完整的任务链：
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项目：{analysis.get('project_title')}
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技术栈：{', '.join(analysis.get('tech_stack', []))}
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时间周期：{analysis.get('timeline', '3周')}
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请生成：
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1. 任务列表（按优先级排序）
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2. 每个任务包含：名称、类型、预估工时、依赖关系、技能要求
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以 JSON 格式返回：
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{{
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"tasks": [
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{{
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"title": "任务名称",
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"type": "backend/frontend/ai_engineer",
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"estimated_hours": 8,
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"description": "任务描述",
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"priority": "high/medium/low",
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"required_skills": ["技能1", "技能2"]
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}}
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]
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}}"""
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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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try:
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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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tasks_data = json.loads(response.strip())
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tasks_list = tasks_data.get('tasks', [])
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except:
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# 降级生成基础任务
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tasks_list = [
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{
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'title': '技术方案设计',
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'type': analysis.get('primary_role', 'backend'),
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'estimated_hours': 16,
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'description': '完成项目技术方案设计',
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'priority': 'high',
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'required_skills': ['系统设计']
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},
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{
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'title': '核心功能开发',
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'type': analysis.get('primary_role', 'backend'),
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'estimated_hours': 40,
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'description': '完成核心功能开发',
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'priority': 'high',
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'required_skills': ['开发']
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}
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]
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tasks = []
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from sqlalchemy import text
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next_task_id = (await self.db.execute(text("SELECT COALESCE(MAX(id),0)+1 FROM tasks"))).scalar() or 1
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for i, task_data in enumerate(tasks_list):
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task = Task(
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id=next_task_id,
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truth_node_id=charter_id,
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project_id=project_id,
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assignee_id=None, # AI 推荐稍后分配
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title=task_data.get('title'),
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description=task_data.get('description', ''),
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required_skill_tag_ids=task_data.get('required_skills', []), # TypeDecorator 会自动处理
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status='pending',
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priority=task_data.get('priority', 'medium').upper(),
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estimated_hours=task_data.get('estimated_hours', 8),
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actual_hours=0,
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quality_score=0,
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tenant_id=tenant_id
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)
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self.db.add(task)
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await self.db.flush()
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next_task_id += 1
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tasks.append({
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'id': str(task.id),
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'title': task.title,
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'type': task_data.get('type'),
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'estimated_hours': task.estimated_hours,
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'priority': task.priority
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})
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return tasks
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async def _generate_risk_alert_rules(
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self,
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project_id: int,
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charter_id: int,
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analysis: Dict[str, Any],
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tenant_id: int
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) -> List[Dict[str, Any]]:
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"""生成风险预警规则"""
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risks = [
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{
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'type': 'schedule_delay',
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'description': '任务超时预警',
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'threshold_hours': analysis.get('estimated_hours', 40) * 1.2,
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'severity': 'medium'
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},
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{
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'type': 'quality_drop',
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'description': '质量下降预警',
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'threshold': 0.85,
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'severity': 'high'
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},
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{
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'type': 'skill_mismatch',
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'description': '技能匹配度不足',
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'threshold': 0.6,
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'severity': 'medium'
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}
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]
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# 获取下一个可用的 cognitive_conflict ID
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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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# 创建认知冲突记录作为风险预警
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for risk in risks:
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conflict = CognitiveConflict(
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id=next_conflict_id,
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truth_node_id=charter_id, # 关联到宪章
|
||
conflict_type=risk['type'],
|
||
description=risk['description'],
|
||
involved_user_ids=json.dumps([]),
|
||
status='active',
|
||
tenant_id=tenant_id
|
||
)
|
||
self.db.add(conflict)
|
||
next_conflict_id += 1
|
||
|
||
return risks
|
||
|
||
|
||
# 全局服务实例
|
||
def get_ai_steward_service(db: AsyncSession) -> AIStewardService:
|
||
"""获取 AI Steward 服务实例"""
|
||
return AIStewardService(db)
|