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+"""AI 营养助手 LangGraph — 基于健康报告的个性化营养建议"""
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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
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+from langchain_core.messages import SystemMessage, HumanMessage
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+from app.tools.java_client import JavaClient
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+from app.tools.product_tools import (
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+ search_product_by_keyword,
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+ search_article_by_keyword,
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+)
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+from app.memory.store import MemoryManager
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+from app.config import settings
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+from app.prompt_service import get_prompt
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+from app.portrait_service import get_user_portrait_text
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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 NutritionState(TypedDict):
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+ query: str
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+ user_id: int
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+ conversation_id: str
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+ child_id: int | None
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+ report_id: int | None
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+ context: dict | None
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+ answer: str | None
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+ tasks: list[dict]
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+ sources: list[dict]
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+ messages: list | None
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+
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+
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+DEFAULT_NUTRITION_PROMPT = """你是一个儿童营养健康顾问，专注于基于检测报告提供精准的营养改善建议。
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+
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+你的能力:
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+1. 解读儿童健康报告（菌群、营养指标、体检数据）
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+2. 基于报告数据推荐合适的营养产品和膳食方案
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+3. 结合孩子体质给出个性化的饮食建议
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+4. 推荐相关的健康活动和科普文章
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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，像营养师一样亲切
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+2. 必须基于报告数据给出建议，不凭空猜测
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+3. 引用知识库中的营养素/食物信息作为证据
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+4. 需要产品推荐时使用搜索工具，说明推荐理由
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+5. 生成 [TASK: {"title": "任务名", "dimension": "身", "points": 10, "frequency": "每天"}] 标记创建营养行动任务
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+6. 严重健康问题建议咨询专业医生或营养师
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+7. 不要编造医学建议，不夸大效果
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+"""
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+
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+
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+def create_nutrition_graph():
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+ """创建营养助手 StateGraph"""
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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=settings.llm_temperature,
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+ )
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+
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+ from app.rag.embeddings import get_embeddings
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+ embeddings = get_embeddings()
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+ memory_mgr = MemoryManager(embeddings)
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+ java = JavaClient()
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+
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+ llm_with_tools = llm.bind_tools([
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+ search_product_by_keyword,
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+ search_article_by_keyword,
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+ ])
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+
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+ builder = StateGraph(NutritionState)
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+
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+ async def generate_answer(state: NutritionState) -> dict:
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+ """核心 LLM 调用 + 画像注入"""
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+ # 加载营养助手 prompt
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+ nutrition_prompt = await get_prompt("nutrition_assistant") or DEFAULT_NUTRITION_PROMPT
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+ messages = [SystemMessage(content=nutrition_prompt)]
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+
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+ # 画像注入（在人格 prompt 之后）
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+ member_id = state.get("child_id")
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+ portrait_text = await get_user_portrait_text(java, state["user_id"], member_id)
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+ if portrait_text:
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+ messages.insert(1, SystemMessage(content=portrait_text))
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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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+ ctx_parts = []
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+ if ctx.get("child_id"):
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+ ctx_parts.append(f"孩子ID: {ctx['child_id']}")
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+ if ctx.get("family_id"):
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+ ctx_parts.append(f"家庭ID: {ctx['family_id']}")
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+ if ctx.get("report_id"):
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+ ctx_parts.append(f"报告ID: {ctx['report_id']}")
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+ if ctx_parts:
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+ messages.append(SystemMessage(content="用户背景信息: " + ", ".join(ctx_parts)))
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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(content=f"该用户的历史咨询记录:\n{mem_text}"))
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+ except Exception as e:
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+ logger.warning("召回营养记忆失败: %s", e)
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+
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+ # 用户消息
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+ messages.append(HumanMessage(content=state["query"]))
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+
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+ response = await llm_with_tools.ainvoke(messages)
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+ answer = response.content
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+
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+ # 提取任务和来源
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+ tasks = []
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+ sources = []
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+ if response.response_metadata.get("tool_calls"):
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+ for tc in response.response_metadata["tool_calls"]:
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+ sources.append({
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+ "type": "tool",
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+ "name": tc.get("name", ""),
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+ "input": tc.get("args", {}),
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+ })
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+
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+ return {
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+ "answer": answer,
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+ "tasks": tasks,
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+ "sources": sources,
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+ "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: NutritionState) -> 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("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"nutrition_{state['user_id']}",
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+ state["messages"],
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+ )
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+ except Exception as e:
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+ logger.warning("保存营养记忆失败: %s", e)
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+ return {}
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+
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+ # 构建图
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+ 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