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@@ -0,0 +1,116 @@
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+import json
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+import logging
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+from typing import TypedDict, Optional
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+from langgraph.graph import StateGraph, START, END
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+from langchain_core.messages import SystemMessage, HumanMessage
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+from app.llm.client import get_llm
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+from app.monitoring import monitor_agent
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+
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+logger = logging.getLogger(__name__)
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+
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+SYSTEM_PROMPT = """你是一位家庭健康顾问，基于五维自检结果（身·智·富·行·心，每维0-9分，满分45）给出个性化建议。
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+
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+要求：
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+1. 对每个低分维度（≤6分）给出1-2句解读和2-3个具体可执行的微行动
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+2. 如有历史数据，简要对比趋势（改善/下滑）
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+3. 语气温暖口语化，每条解读不超过80字
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+4. 最后给出1句家庭整体洞察（30字以内）
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+
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+返回 JSON（严格格式，不要额外文字）：
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+{
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+ "advice": [
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+ {
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+ "dimension": "mind",
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+ "dimensionName": "心",
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+ "interpretation": "你的情绪能量偏低，可能最近压力较大，建议...",
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+ "microActions": ["今晚睡前做10分钟深呼吸", "和伴侣约定每周一次夜谈"],
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+ "fallbackUsed": false
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+ }
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+ ],
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+ "familyInsight": "建议从行动维度入手，关系顺畅了内心才能安定"
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+}
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+"""
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+
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+
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+class GraphState(TypedDict):
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+ scores: dict
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+ question_ids: list
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+ user_id: int
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+ recent_history: list
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+ advice: Optional[dict]
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+ error: Optional[str]
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+
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+
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+class SelfCheckAnalysisAgent:
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+ def __init__(self):
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+ self.llm = get_llm()
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+
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+ @monitor_agent("self_check_analysis")
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+ async def run(self, scores: dict, question_ids: list, user_id: int, recent_history: list) -> dict:
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+ try:
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+ score_summary = "\n".join(
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+ f"{v.get('dimensionName', k)}（{k}）: {v.get('score', 0)}分"
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+ for k, v in scores.items()
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+ )
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+ history_summary = ""
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+ if recent_history:
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+ history_summary = "历史趋势：\n" + "\n".join(
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+ f"- {h.get('createdAt', '')}: 总分{h.get('totalScore', 0)}分"
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+ for h in recent_history[:3]
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+ )
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+ messages = [
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+ SystemMessage(content=SYSTEM_PROMPT),
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+ HumanMessage(content=f"用户ID: {user_id}\n当前自检得分:\n{score_summary}\n{history_summary}"),
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+ ]
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+ response = await self.llm.ainvoke(messages)
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+ text = response.content.strip()
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+ if "```json" in text:
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+ text = text.split("```json")[1].split("```")[0].strip()
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+ elif "```" in text:
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+ text = text.split("```")[1].split("```")[0].strip()
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+ data = json.loads(text)
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+ advice_list = data.get("advice", [])
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+ for item in advice_list:
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+ item.setdefault("fallbackUsed", False)
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+ if advice_list:
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+ advice_list[0]["familyInsight"] = data.get("familyInsight", "")
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+ return {"advice": {"advice_json": json.dumps(advice_list, ensure_ascii=False), "fallback_used": False}}
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+ except Exception as e:
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+ logger.warning("自检建议生成失败: %s", e)
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+ return {"advice": {"advice_json": None, "fallback_used": True}}
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+
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+
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+def build_graph():
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+ agent = SelfCheckAnalysisAgent()
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+
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+ def parse_input(state: GraphState) -> GraphState:
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+ return state
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+
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+ async def call_llm(state: GraphState) -> dict:
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+ return await agent.run(state["scores"], state["question_ids"], state["user_id"], state["recent_history"])
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+
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+ def validate(state: GraphState) -> GraphState:
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+ adv = state.get("advice")
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+ if adv is None or adv.get("advice_json") is None:
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+ return {**state, "error": "AI 建议生成失败"}
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+ return state
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+
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+ graph = StateGraph(GraphState)
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+ graph.add_node("parse", parse_input)
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+ graph.add_node("llm", call_llm)
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+ graph.add_node("validate", validate)
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+ graph.add_edge(START, "parse")
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+ graph.add_edge("parse", "llm")
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+ graph.add_edge("llm", "validate")
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+ graph.add_edge("validate", END)
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+ return graph.compile()
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+
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+
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+_graph = None
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+
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+
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+def get_graph():
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+ global _graph
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+ if _graph is None:
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+ _graph = build_graph()
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+ return _graph
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iwt