selfrelease
fad458b2a7
- UIUX 文档：填充 19 个缺口（多主体画像/健康度/AI+看板/增长域/洞察域/创始人端/OODA/助推/商密） - UIUX 文档：插入 6 个新章节（十四~十九），旧章节重编号为二十~三十一，更新目录和交叉引用 - 作业指导书 x5：导航改为 6 域分组，新增 Context Bar/工作模式/Insight Rail/决策线程/多工作区等 UI 概念 - 新建 docs/2-task-uiux.md：50 个代码落地开发任务，按 P0-P6 分优先级 + 8 Sprint 规划 - 后端/前端：大量新增模型、路由、组件（来自之前 Phase 开发）
31 lines
1.3 KiB
Python
31 lines
1.3 KiB
Python
"""AI AAR Agent — 五问复盘。"""
|
|
|
|
from app.services.llm_client import LLMClient
|
|
|
|
|
|
async def generate_aar(trigger_event: str, original_plan: str, actual_result: str) -> dict:
|
|
"""AI 生成五问复盘。"""
|
|
llm = LLMClient()
|
|
prompt = f"""请进行 AAR 五问复盘：
|
|
|
|
触发事件：{trigger_event}
|
|
原计划：{original_plan}
|
|
实际结果：{actual_result}
|
|
|
|
以 JSON 格式返回：
|
|
{{"what_happened": "发生了什么", "why_happened": "为什么发生", "what_worked": "什么做得好", "what_failed": "什么没做好", "what_to_change": "下次怎么改", "lessons": ["教训1", "教训2"], "improvements": [{{"action": "改进措施", "owner": "负责人", "deadline": "截止日期"}}]}}"""
|
|
result = await llm.chat(prompt, temperature=0.4)
|
|
return result if isinstance(result, dict) else {}
|
|
|
|
|
|
async def check_aar_triggers(company_id: str, recent_events: list[dict]) -> list[dict]:
|
|
"""检测 AAR 触发条件。"""
|
|
triggers: list[dict] = []
|
|
for event in recent_events:
|
|
if event.get("type") in ["risk_resolved", "funding_completed", "funding_failed", "talent_joined", "talent_left"]:
|
|
triggers.append({
|
|
"trigger_event": event.get("title", ""),
|
|
"trigger_type": event.get("type", ""),
|
|
})
|
|
return triggers
|