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+"""
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+健康方案生成 LangGraph - 分步骤结构化方案生成
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+
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+工作流:
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+1. 数据收集: 家庭成员信息 + 健康指标 + 知识库检索
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+2. LLM 生成: 总览概述 + 营养/饮食/运动 三个 section
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+3. 返回结构化 JSON
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+
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+每次调用 LLM 时遵循统一的数据组装方式:
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+ - 用户指标 (来自 Java context API)
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+ - 知识库参考 (RAG 检索)
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+ - 系统提示词 (带输出格式模板)
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+"""
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+from typing import TypedDict, Literal
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+from langgraph.graph import StateGraph, START, END
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+from langchain_openai import ChatOpenAI
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+from langchain_core.messages import SystemMessage, HumanMessage
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+from app.rag.retriever import RagRetriever
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+from app.config import settings
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+from app.tools.java_client import JavaClient
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+import logging
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+
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+logger = logging.getLogger(__name__)
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+
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+SECTION_KEYS = ["nutrition", "diet", "exercise"]
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+
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+DEFAULT_PROMPT = """你是一个专业的家庭健康方案规划师。根据用户的健康数据和目标，生成结构化的改善方案。
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+
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+## 输出格式（必须严格遵守 JSON）
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+
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+```json
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+{
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+ "overview": "总体概述（100字以内，说明方案目标和核心策略）",
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+ "sections": [
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+ {
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+ "key": "nutrition",
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+ "title": "营养补充建议",
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+ "content": "Markdown 格式的详细内容，包含具体产品推荐、用量、服用时间",
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+ "items": [
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+ {"name": "产品名", "dosage": "用量", "timing": "服用时间", "reason": "推荐理由"}
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+ ]
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+ },
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+ {
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+ "key": "diet",
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+ "title": "饮食建议",
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+ "content": "Markdown 格式的餐饮建议，包含早餐/午餐/晚餐建议",
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+ "items": [{"meal": "餐型", "food": "食物建议", "notes": "注意事项"}]
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+ },
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+ {
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+ "key": "exercise",
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+ "title": "运动计划",
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+ "content": "Markdown 格式的运动建议，包含频率、时长、类型",
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+ "items": [{"type": "运动类型", "duration": "时长", "frequency": "频率", "notes": "注意事项"}]
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+ }
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+ ]
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+}
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+```
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+
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+## 原则
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+1. 基于实际数据给出建议，不编造
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+2. 引用知识库时标注来源
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+3. 建议要具体可执行，避免空泛
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+4. 营养补充部分要具体到品牌/产品类型和用量
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+5. 严重健康问题建议咨询医生
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+"""
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+
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+REGENERATE_SECTION_PROMPT = """你是一个家庭健康方案规划师。根据用户反馈重新生成指定部分的内容。
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+
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+## 当前方案内容
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+{existing_section_content}
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+
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+## 用户反馈
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+{feedback}
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+
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+## 相关背景数据
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+{context_summary}
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+
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+请重新生成该部分内容，保持与原格式一致。只输出新的 content 字段值（Markdown 格式），不需要输出 JSON 结构。"""
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+
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+
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+class PlanState(TypedDict):
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+ member_ids: str
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+ dimensions: str
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+ goal: str
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+ family_id: int
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+ members_info: dict
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+ indicators: list
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+ abnormal_indicators: list
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+ kb_results: list
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+ overview: str
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+ nutrition_section: str
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+ diet_section: str
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+ exercise_section: str
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+ full_response: dict
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+ error: str
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+
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+
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+async def collect_data(state: PlanState) -> dict:
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+ """Step 1: 收集用户数据 + 知识库检索"""
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+ java = JavaClient()
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+ retriever = RagRetriever(collection_name="cfc_knowledge")
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+
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+ member_ids_str = state.get("member_ids", "")
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+ member_ids = [m.strip() for m in member_ids_str.split(",") if m.strip()]
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+ goal = state.get("goal", "")
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+ dimensions = state.get("dimensions", "") or ""
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+
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+ # 1a. 获取家庭成员信息
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+ members_info = []
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+ for uid_str in member_ids:
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+ ctx = await java.get_family_context(int(uid_str), "child_info")
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+ children = ctx.get("children", []) if isinstance(ctx, dict) else []
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+ for child in children:
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+ cid = str(child.get("用户ID", ""))
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+ if cid == uid_str:
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+ members_info.append({
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+ "id": cid,
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+ "name": child.get("姓名", f"成员{cid}"),
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+ "age": child.get("年龄", "未知"),
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+ "energy": child.get("能量", 0),
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+ })
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+ break
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+ if not any(m["id"] == uid_str for m in members_info):
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+ members_info.append({"id": uid_str, "name": f"成员{uid_str}", "age": "未知", "energy": 0})
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+
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+ # 1b. 获取健康指标
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+ all_indicators = []
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+ abnormal_list = []
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+ for member in members_info:
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+ reports = await java.get_member_reports(int(member["id"]))
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+ if not reports:
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+ continue
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+ latest = max(reports, key=lambda r: r.get("reportDate", ""))
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+ report_id = latest.get("id")
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+ indicators = await java.get_report_indicators(report_id)
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+ for ind in indicators:
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+ ind["_member_id"] = member["id"]
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+ ind["_member_name"] = member["name"]
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+ all_indicators.append(ind)
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+
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+ # 1c. 识别异常指标
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+ known_indicators = {}
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+ for ind in all_indicators:
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+ name = ind.get("indicatorName", "").strip()
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+ if not name or name in known_indicators:
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+ continue
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+ for itype in ["indicator", "bacteria", "nutrient"]:
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+ kb = await java.query_health_knowledge(itype, name)
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+ if kb:
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+ known_indicators[name] = kb
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+ break
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+
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+ for ind in all_indicators:
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+ name = ind.get("indicatorName", "")
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+ status = ind.get("status", "")
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+ kb = known_indicators.get(name, {})
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+ entry = {
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+ "member": ind.get("_member_name", ""),
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+ "indicator": name,
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+ "value": ind.get("indicatorValue", ""),
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+ "unit": ind.get("unit", ""),
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+ "ref_range": kb.get("normalRange", ind.get("refRange", "")),
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+ "description": kb.get("description", ""),
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+ "suggestion": kb.get("suggestion", ""),
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+ }
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+ if status in ("abnormal", "high", "low", "偏高", "偏低"):
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+ abnormal_list.append(entry)
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+
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+ # 1d. 知识库检索
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+ kb_results = []
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+ queries = set()
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+ for ind in abnormal_list:
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+ queries.add(ind["indicator"])
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+ queries.add(goal)
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+ if dimensions:
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+ queries.add(dimensions)
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+ for q in list(queries)[:8]:
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+ results = await retriever.retrieve(q, k=3)
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+ kb_results.extend(results)
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+
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+ return {
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+ "members_info": members_info,
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+ "indicators": all_indicators,
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+ "abnormal_indicators": abnormal_list,
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+ "kb_results": kb_results,
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+ }
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+
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+
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+def build_prompt(state: PlanState) -> str:
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+ """组装 LLM prompt"""
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+ parts = [DEFAULT_PROMPT]
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+
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+ parts.append(f"\n## 用户目标\n{state['goal']}")
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+ if state.get("dimensions"):
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+ parts.append(f"\n## 重点关注维度\n{state['dimensions']}")
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+
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+ parts.append("\n## 家庭成员")
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+ for m in state["members_info"]:
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+ parts.append(f"- {m['name']} (年龄: {m['age']})")
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+
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+ if state["abnormal_indicators"]:
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+ parts.append("\n## 异常指标")
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+ for ind in state["abnormal_indicators"][:8]:
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+ parts.append(
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+ f"- {ind['member']} - {ind['indicator']}: {ind['value']}{ind.get('unit','')} "
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+ f"(参考: {ind['ref_range']})"
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+ )
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+ if ind.get("description"):
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+ parts.append(f" 说明: {ind['description']}")
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+
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+ if state["kb_results"]:
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+ parts.append("\n## 知识库参考")
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+ for r in state["kb_results"][:6]:
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+ title = r.get("metadata", {}).get("title", "")
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+ content = r.get("content", "")[:200]
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+ parts.append(f"---\n{title}\n{content}")
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+
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+ return "\n".join(parts)
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+
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+
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+async def generate_plan(state: PlanState) -> dict:
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+ """Step 2: 调用 LLM 生成结构化方案"""
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+ llm = ChatOpenAI(
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+ model=settings.llm_model,
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+ api_key=settings.llm_api_key,
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+ base_url=settings.llm_base_url,
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+ temperature=0.3,
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+ )
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+ prompt = build_prompt(state)
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+ messages = [SystemMessage(content=prompt)]
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+ try:
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+ response = await llm.ainvoke(messages)
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+ answer = response.content
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+ # 解析 JSON
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+ import json
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+ try:
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+ # 提取 JSON 块
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+ start = answer.find("{")
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+ end = answer.rfind("}") + 1
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+ if start >= 0 and end > start:
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+ json_str = answer[start:end]
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+ parsed = json.loads(json_str)
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+ return {"full_response": parsed, "overview": parsed.get("overview", "")}
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+ except (json.JSONDecodeError, Exception) as e:
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+ logger.warning("解析方案 JSON 失败，使用原始文本: %s", e)
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+ return {"full_response": {"raw": answer}, "overview": answer[:200]}
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+ except Exception as e:
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+ logger.error("LLM 生成方案失败: %s", e)
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+ return {"error": str(e)}
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+
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+
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+async def regenerate_section(state: PlanState) -> dict:
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+ """重新生成指定 section"""
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+ section_key = state.get("section", "nutrition")
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+ feedback = state.get("feedback", "")
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+ existing_content = state.get("existing_section_content", "")
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+ llm = ChatOpenAI(
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+ model=settings.llm_model,
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+ api_key=settings.llm_api_key,
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+ base_url=settings.llm_base_url,
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+ temperature=0.3,
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+ )
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+
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+ # 构建上下文摘要
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+ ctx_parts = []
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+ for m in state.get("members_info", []):
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+ ctx_parts.append(f"- {m['name']} (年龄: {m['age']})")
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+ if state.get("goal"):
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+ ctx_parts.append(f"目标: {state['goal']}")
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+ if state.get("abnormal_indicators"):
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+ for ind in state["abnormal_indicators"][:5]:
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+ ctx_parts.append(f"- {ind['member']}: {ind['indicator']}={ind['value']}")
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+
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+ prompt = REGENERATE_SECTION_PROMPT.format(
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+ existing_section_content=existing_content[:500],
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+ feedback=feedback,
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+ context_summary="\n".join(ctx_parts),
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+ )
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+ try:
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+ response = await llm.ainvoke([SystemMessage(content=prompt)])
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+ return {"regenerated_content": response.content}
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+ except Exception as e:
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+ logger.error("重新生成方案 section 失败: %s", e)
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+ return {"error": str(e)}
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+
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+
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+def create_health_plan_graph():
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+ builder = StateGraph(PlanState)
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+ builder.add_node("collect_data", collect_data)
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+ builder.add_node("generate_plan", generate_plan)
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+ builder.add_node("regenerate_section", regenerate_section)
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+ builder.add_edge(START, "collect_data")
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+ builder.add_edge("collect_data", "generate_plan")
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+ builder.add_edge("generate_plan", END)
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+ # regenerate_section 从外部直接调用，不走图
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+ graph = builder.compile()
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+ return graph
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Sisyphus