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+"""
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+健康管家 LangGraph - 专为儿童健康咨询设计的对话图
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+Uses RAG retrieval from health knowledge base.
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+"""
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+from typing import TypedDict
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+from langgraph.graph import StateGraph, START, END
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+from langgraph.checkpoint.memory import MemorySaver
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+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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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.memory.store import MemoryManager
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+from app.config import settings
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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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+
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+class HealthButlerState(TypedDict):
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+ query: str
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+ user_id: int
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+ child_id: int | None
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+ conversation_id: str | None
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+ context: dict | None
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+ answer: str | None
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+ sources: list[dict]
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+ memory_messages: list | None
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+
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+
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+HEALTH_BUTLER_PROMPT = """你是一个专业、温暖的家庭健康顾问，专注于儿童健康成长。
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+
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+你的职责：
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+1. 儿童体质调理咨询（如：挑食、瘦弱、易疲劳、反复生病）
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+2. 科学喂养和营养建议（辅食搭配、食欲不振、营养补充）
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+3. 日常保健知识（科学睡眠、运动建议、季节防护）
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+4. 体质辨识基础咨询（气虚/阳虚/阴虚/痰湿等体质特征解读）
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+5. 健康问题初步判断和就医建议
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+
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+回答原则：
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+1. 用中文，语气温暖专业，像一个关心孩子的家庭医生
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+2. 涉及具体健康问题，先问清年龄、性别、症状持续时间等关键信息
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+3. 不确定时明确说"建议咨询专业医生"，绝不臆断严重疾病
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+4. 回答结构：问题分析 → 建议方案 → 日常调养tip
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+5. 涉及儿童安全（性早熟、严重外伤、急性症状等）立刻建议就医
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+
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+你可以通过检索健康知识库来获取依据。
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+"""
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+
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+
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+def create_health_butler_graph():
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+ """创建健康管家 StateGraph"""
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+ # LLM
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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=0.7,
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+ )
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+
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+ # 嵌入和检索
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+ embeddings = OpenAIEmbeddings(
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+ model=settings.embedding_model,
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+ api_key=settings.effective_embedding_api_key,
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+ base_url=settings.effective_embedding_base_url,
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+ )
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+ retriever = RagRetriever(collection_name="health_knowledge")
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+ memory_mgr = MemoryManager(embeddings)
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+
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+ # 带工具的 LLM（健康知识库检索）
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+ async def health_retrieve(query: str, user_id: int) -> list[dict]:
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+ try:
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+ results = await retriever.retrieve(
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+ query,
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+ filters={"user_id": user_id},
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+ k=3,
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+ )
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+ return results
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+ except Exception as e:
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+ logger.warning("健康知识检索失败: %s", e)
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+ return []
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+
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+ async def generate_answer(state: HealthButlerState) -> dict:
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+ """带健康知识检索的 LLM 生成"""
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+ messages = [SystemMessage(content=HEALTH_BUTLER_PROMPT)]
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+
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+ # 家庭上下文
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+ ctx = state.get("context") or {}
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+ if ctx:
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+ child_info = []
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+ if ctx.get("child_id"):
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+ child_info.append(f"孩子ID: {ctx['child_id']}")
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+ if ctx.get("family_id"):
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+ child_info.append(f"家庭ID: {ctx['family_id']}")
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+ if child_info:
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+ messages.append(SystemMessage(
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+ content="用户背景信息: " + ", ".join(child_info)
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+ ))
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+
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+ # 健康知识检索
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+ retrieved = await health_retrieve(state["query"], state["user_id"])
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+ sources = []
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+ if retrieved:
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+ context_texts = []
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+ for i, r in enumerate(retrieved[:3]):
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+ src = r.get("metadata", {})
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+ title = src.get("title", "健康知识")
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+ content = r["content"][:300]
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+ context_texts.append(f"[{i+1}] {title}:\n{content}")
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+ sources.append({
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+ "type": "knowledge",
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+ "title": title,
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+ "content": content,
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+ })
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+ knowledge_context = "\n\n---\n".join(context_texts)
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+ messages.append(SystemMessage(
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+ content=f"以下是与问题相关的健康知识参考：\n{knowledge_context}"
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+ ))
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+
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+ # 长期记忆
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+ try:
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+ memories = await memory_mgr.recall(state["user_id"], state["query"])
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+ if memories:
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+ mem_text = "\n".join([f"- {m}" for m in memories[:3]])
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+ messages.append(SystemMessage(
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+ content=f"该用户的历史健康咨询记录:\n{mem_text}"
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+ ))
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+ except Exception as e:
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+ logger.warning("召回健康记忆失败: %s", e)
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+
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+ # 用户问题
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+ messages.append(HumanMessage(content=state["query"]))
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+
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+ response = await llm.ainvoke(messages)
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+ answer = response.content
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+
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+ return {
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+ "answer": answer,
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+ "sources": sources,
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+ "memory_messages": [
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+ {"role": "user", "content": state["query"]},
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+ {"role": "assistant", "content": answer},
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+ ],
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+ }
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+
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+ async def save_memory(state: HealthButlerState) -> dict:
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+ """保存对话到长期记忆"""
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+ try:
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+ if state.get("memory_messages"):
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+ await memory_mgr.save_conversation(
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+ state["user_id"],
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+ state.get("conversation_id") or f"health_{state['user_id']}",
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+ state["memory_messages"],
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+ )
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+ except Exception as e:
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+ logger.warning("保存健康管家记忆失败: %s", e)
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+ return {}
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+
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+ # ── 构建图 ──
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+
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+ builder = StateGraph(HealthButlerState)
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+
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+ builder.add_node("generate_answer", generate_answer)
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+ builder.add_node("save_memory", save_memory)
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+
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+ builder.add_edge(START, "generate_answer")
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+ builder.add_edge("generate_answer", "save_memory")
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+ builder.add_edge("save_memory", END)
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+
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+ checkpointer = MemorySaver()
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+ graph = builder.compile(checkpointer=checkpointer)
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+
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+ return graph
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iwt