feat(ai): Task 5 AI健康管家补全 — health_butler_graph + 前端路由修复

变更内容:
- 新建 health_butler_graph.py (322行): Java KB集成的健康管家图
  (load_knowledge → search_knowledge → llm_answer → save_memory)
  查询 Java 健康知识库(bacteria/indicator/food)注入 LLM 上下文
- 修复 health_coach_graph.py: HEALTH_BUTLER_PROMPT → HEALTH_COACH_PROMPT
- 修复 chat.vue: aiSendHealthButlerMessage → aiSendHealthCoachMessage
- 修复 AIFloatingAvatar.vue: 从身/智/心页面自动传递 mode=health
  确保健康维度页面进入 AI 聊天时使用健康教练模式
- 同步 PROJECT-OVERVIEW.md 远程更新(Phase 4/健康7维)

cfc-frontend/components/AIFloatingAvatar.vue

@@ -43,9 +43,10 @@ export default {
       if (currentPage) {
         fromPage = currentPage.route || ''
       }
-      var url = '/pages/ai/chat'
-      if (fromPage) {
-        url += '?fromPage=' + encodeURIComponent(fromPage)
+      var url = '/pages/ai/chat?fromPage=' + encodeURIComponent(fromPage)
+      // 健康维度页面（身/智/心）自动切换为健康教练模式
+      if (fromPage === 'pages/body/index' || fromPage === 'pages/wisdom/index' || fromPage === 'pages/mind/index') {
+        url += '&mode=health'
       }
       uni.navigateTo({ url: url })
     }

cfc-frontend/pages/ai/chat.vue

@@ -319,7 +319,7 @@ export default {
           }
         }
       } else if (this.mode === 'health') {
-        res = await aiSendHealthButlerMessage(params)
+        res = await aiSendHealthCoachMessage(params)
         data = res.data || {}
         const lastMsg = this.messages[this.messages.length - 1]
         if (lastMsg) {

cfc-langgraph/app/graphs/health_butler_graph.py

@@ -0,0 +1,322 @@
+from typing import TypedDict, Optional, Literal
+from langgraph.graph import StateGraph, START, END
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_core.messages import SystemMessage, HumanMessage
+from app.tools.java_client import JavaClient
+from app.tools.product_tools import (
+    search_product_by_keyword,
+    search_article_by_keyword,
+)
+from app.memory.store import MemoryManager
+from app.config import settings
+import logging
+import json
+
+logger = logging.getLogger(__name__)
+
+
+class HealthButlerState(TypedDict):
+    query: str
+    user_id: int
+    conversation_id: str
+    family_id: int | None
+    child_id: int | None
+    report_id: int | None
+    focus: str | None          # gut / nutrition / overall / chronic
+    kb_context: dict | None
+    knowledge_results: list[dict]
+    answer: str | None
+    tasks: list[dict]
+    sources: list[dict]
+    messages: list | None
+
+
+HEALTH_BUTLER_PROMPT = """你是一个家庭AI健康管家, 为用户提供专业、温暖的个性化健康建议。
+
+你的能力:
+1. 基于健康知识库（菌属功能、指标解读、营养素、食物）提供科学建议
+2. 解读健康报告数据, 结合知识库给出改善方案
+3. 根据菌群检测结果推荐精准营养方案
+4. 提供可执行的健康微行动
+
+回答原则:
+1. 用中文, 像家庭医生一样亲切但专业
+2. 引用知识库中的菌属/指标/食物信息作为证据
+3. 优先使用知识库数据, 其次使用通用健康知识
+4. 需要产品推荐时使用搜索工具
+5. 生成 [TASK: {"title": "任务名", "dimension": "身", "points": 10, "frequency": "每天"}] 标记创建健康行动任务
+6. 超出知识库范围的慢性病/急症问题, 建议咨询专业医生
+7. 不要编造医学建议, 不夸大效果
+"""
+
+
+def create_health_butler_graph():
+    """创建 AI 健康管家 StateGraph — 知识检索 + LLM 建议 + 记忆"""
+    llm = ChatOpenAI(
+        model=settings.llm_model,
+        api_key=settings.llm_api_key,
+        base_url=settings.llm_base_url,
+        temperature=settings.llm_temperature,
+    )
+
+    embeddings = OpenAIEmbeddings(
+        model=settings.embedding_model,
+        api_key=settings.effective_embedding_api_key,
+        base_url=settings.effective_embedding_base_url,
+    )
+    memory_mgr = MemoryManager(embeddings)
+
+    java = JavaClient()
+
+    llm_with_tools = llm.bind_tools([
+        search_product_by_keyword,
+        search_article_by_keyword,
+    ])
+
+    builder = StateGraph(HealthButlerState)
+
+    async def load_knowledge(state: HealthButlerState) -> dict:
+        """阶段1: 从 Java 后端加载健康知识库上下文"""
+        context = {}
+        try:
+            client = await java._get_client()
+            resp = await client.post(
+                "/api/health/knowledge/list",
+                json={},
+            )
+            data = resp.json()
+            if data.get("code") == 200:
+                items = data.get("data", [])
+                # 按 itemType 分组
+                by_type = {}
+                for item in items:
+                    t = item.get("itemType", "unknown")
+                    if t not in by_type:
+                        by_type[t] = []
+                    by_type[t].append(item)
+                context["kb_items"] = len(items)
+                context["kb_by_type"] = {k: len(v) for k, v in by_type.items()}
+                context["kb_loaded"] = True
+            else:
+                context["kb_loaded"] = False
+        except Exception as e:
+            logger.warning("加载健康知识库失败: %s", e)
+            context["kb_loaded"] = False
+
+        return {"kb_context": context}
+
+    async def search_knowledge(state: HealthButlerState) -> dict:
+        """阶段2: 根据 query 意图精准搜索知识库"""
+        query = state["query"]
+        results = []
+
+        # 提取 query 中可能的菌属名/指标名/食物名关键词
+        # 策略: 对 query 做简单的关键词匹配, 调用 Java 知识库逐个查询
+        keywords = _extract_health_keywords(query)
+        if keywords:
+            client = await java._get_client()
+            for kw in keywords[:5]:  # 最多查询5个关键词
+                # 优先查 bacteria 类型
+                for item_type in ["bacteria", "indicator", "food", "nutrient"]:
+                    try:
+                        resp = await client.post(
+                            "/api/health/knowledge/query",
+                            json={"itemType": item_type, "itemName": kw},
+                        )
+                        data = resp.json()
+                        if data.get("code") == 200 and data.get("data"):
+                            results.append({
+                                "type": item_type,
+                                "keyword": kw,
+                                "data": data["data"],
+                            })
+                            break  # 命中即跳出
+                    except Exception:
+                        continue
+
+        return {"knowledge_results": results}
+
+    async def llm_answer(state: HealthButlerState) -> dict:
+        """阶段3: LLM 生成个性化健康建议"""
+        messages = [SystemMessage(content=HEALTH_BUTLER_PROMPT)]
+
+        # 注入健康知识库上下文
+        kb_ctx = state.get("kb_context", {})
+        knowledge_results = state.get("knowledge_results", [])
+        if knowledge_results:
+            kb_text_parts = []
+            for r in knowledge_results:
+                d = r.get("data", {})
+                name = d.get("itemName", r.get("keyword", ""))
+                cat = d.get("category", "")
+                desc = d.get("description", "")
+                suggestion = d.get("suggestion", "")
+                normal_range = d.get("normalRange", "")
+
+                parts = [f"【{r['type']}】{name}"]
+                if cat:
+                    parts.append(f"(分类: {cat})")
+                if normal_range:
+                    parts.append(f"正常范围: {normal_range}")
+                if desc:
+                    parts.append(f"{desc}")
+                if suggestion:
+                    parts.append(f"建议: {suggestion}")
+                kb_text_parts.append(" ".join(parts))
+
+            kb_text = "\n---\n".join(kb_text_parts)
+            messages.append(SystemMessage(content=f"健康知识库参考:\n{kb_text}"))
+
+        # 注入家庭上下文
+        family_id = state.get("family_id")
+        if family_id:
+            try:
+                client = await java._get_client()
+                resp = await client.post(
+                    "/api/ai/context",
+                    json={
+                        "user_id": str(state["user_id"]),
+                        "params": {
+                            "familyId": family_id,
+                            **({"childId": state["child_id"]} if state.get("child_id") else {}),
+                        },
+                    },
+                )
+                data = resp.json()
+                if data.get("code") == 200:
+                    ctx = data.get("data", {})
+                    if ctx:
+                        messages.append(SystemMessage(content=f"家庭上下文:\n{json.dumps(ctx, ensure_ascii=False, indent=2)}"))
+            except Exception as e:
+                logger.debug("加载家庭上下文跳过: %s", e)
+
+        # 注入长期记忆
+        try:
+            memories = await memory_mgr.recall(state["user_id"], state["query"])
+            if memories:
+                mem_text = "\n".join([f"- {m}" for m in memories])
+                messages.append(SystemMessage(content=f"相关历史对话:\n{mem_text}"))
+        except Exception as e:
+            logger.warning("召回记忆失败: %s", e)
+
+        # 用户消息
+        focus = state.get("focus")
+        query = state["query"]
+        if focus:
+            query = f"[重点关注: {focus}] {query}"
+        messages.append(HumanMessage(content=query))
+
+        response = await llm_with_tools.ainvoke(messages)
+        answer = response.content
+
+        # 提取任务
+        import re
+        tasks = []
+        pattern = r'\[TASK:\s*\{([^}]+)\}\]'
+        for match in re.findall(pattern, answer):
+            task_info = {}
+            for kv in match.split(","):
+                if ":" in kv:
+                    k, v = kv.split(":", 1)
+                    task_info[k.strip()] = v.strip().strip('"').strip("'")
+            if task_info.get("title"):
+                tasks.append(task_info)
+
+        sources = []
+        for r in knowledge_results:
+            d = r.get("data", {})
+            sources.append({
+                "type": "health_kb",
+                "name": d.get("itemName", r.get("keyword", "")),
+                "category": d.get("category", ""),
+            })
+        if response.response_metadata.get("tool_calls"):
+            for tc in response.response_metadata["tool_calls"]:
+                sources.append({
+                    "type": "tool",
+                    "name": tc.get("name", ""),
+                })
+
+        return {
+            "answer": answer,
+            "tasks": tasks,
+            "sources": sources,
+            "messages": [
+                {"role": "user", "content": state["query"]},
+                {"role": "assistant", "content": answer},
+            ],
+        }
+
+    async def save_memory(state: HealthButlerState) -> dict:
+        try:
+            if state.get("messages"):
+                await memory_mgr.save_conversation(
+                    state["user_id"],
+                    state["conversation_id"],
+                    state["messages"],
+                )
+        except Exception as e:
+            logger.warning("保存记忆失败: %s", e)
+        return {}
+
+    # ── 路由 ──
+
+    def route_after_knowledge(state: HealthButlerState) -> Literal["search_knowledge", "llm_answer"]:
+        kb_ctx = state.get("kb_context", {})
+        if kb_ctx.get("kb_loaded"):
+            return "search_knowledge"
+        return "llm_answer"
+
+    # ── 构建图 ──
+
+    builder.add_node("load_knowledge", load_knowledge)
+    builder.add_node("search_knowledge", search_knowledge)
+    builder.add_node("llm_answer", llm_answer)
+    builder.add_node("save_memory", save_memory)
+
+    builder.add_edge(START, "load_knowledge")
+    builder.add_conditional_edges("load_knowledge", route_after_knowledge)
+    builder.add_edge("search_knowledge", "llm_answer")
+    builder.add_edge("llm_answer", "save_memory")
+    builder.add_edge("save_memory", END)
+
+    checkpointer = MemorySaver()
+    graph = builder.compile(checkpointer=checkpointer)
+
+    return graph
+
+
+def _extract_health_keywords(query: str) -> list[str]:
+    """从 query 中提取健康相关关键词（菌属、指标、食物等名词）"""
+    # 简单策略: 提取2字以上的中文词作为候选
+    import re
+    # 提取中文字符组成的连续词段
+    chinese_words = re.findall(r'[\u4e00-\u9fff]{2,}', query)
+    # 提取英文/拉丁名词组
+    english_words = re.findall(r'[A-Za-z][A-Za-z\s]{2,20}[A-Za-z]', query)
+
+    keywords = []
+    # 过滤常见通用词
+    stopwords = {"请问", "帮我", "能不能", "可不可以", "怎么样", "为什么",
+                 "是什么", "怎么办", "什么意思", "好不好", "是否", "你好",
+                 "谢谢", "请", "我想", "需要", "关于", "一个", "这个",
+                 "那个", "哪些", "什么", "怎么", "如何", "多少", "是不是"}
+    for w in chinese_words:
+        if w not in stopwords and len(w) >= 2:
+            keywords.append(w)
+
+    for w in english_words:
+        w = w.strip()
+        if len(w) > 2:
+            keywords.append(w)
+
+    # 去重 + 限数量
+    seen = set()
+    deduped = []
+    for kw in keywords:
+        if kw not in seen:
+            seen.add(kw)
+            deduped.append(kw)
+
+    return deduped[:10]

cfc-langgraph/app/graphs/health_coach_graph.py

@@ -88,7 +88,7 @@ def create_health_coach_graph():
 
     async def generate_answer(state: HealthCoachState) -> dict:
         """带健康知识检索的 LLM 生成"""
-        messages = [SystemMessage(content=HEALTH_BUTLER_PROMPT)]
+        messages = [SystemMessage(content=HEALTH_COACH_PROMPT)]
 
         # 家庭上下文
         ctx = state.get("context") or {}

docs/superpowers/PROJECT-OVERVIEW.md

@@ -15,7 +15,7 @@
 | **Phase 1** | 2026-06~07 | 五维平台核心 | 五维重构、家庭成员体系、文章中心、推广体系 | 🚧 进行中 |
 | **Phase 2** | 2026-07~08 | 商业模式 & AI 落地 | LangGraph前端集成、家庭推广体系、AI健康管家 | 🚧 进行中 |
 | **Phase 3** | 2026-Q3 | 五维增强 | 富维度子维度拆分、文章发布系统 | ⏳ 待开发 |
-| **Phase 4** | 2026-Q4 | 平台扩展 | 供应商体系、健康7维模型、成长档案 | ⏳ 规划中 |
+| **Phase 4** | 2026-Q4 | 平台扩展 | 供应商体系、健康7维模型、成长档案 | 🚧 后端主体完成（健康7维/成长档案/供应商体系后端全实现，细节完善中） |
 | **Phase 5** | 2026-Q3~ | 主动健康六阶模型 | 微启动+即时反馈+智能预警+身份重塑+家庭互动+社区扩散 | 🚧 设计中 |
 
 ---
@@ -90,8 +90,8 @@
 | 功能 | 阶段 | 状态 | 关联文档 |
 |------|:----:|:----:|---------|
 | 菌群报告页面重构（200+菌属全覆盖） | Phase 1 | ✅ 已完成（4页面 1214行 + HealthKnowledgeBase后端） | `specs/2026-07-17-flora-microbiome-report-page-design.md` + `plans/2026-07-17-flora-microbiome-report-implementation.md` |
-| 身体健康7维子体系模型 | Phase 4 | 🟡 计划中 | `specs/2026-06-26-health-dimensions-design-spec.md` |
-| 成长档案重建 | Phase 4 | 🟡 计划中 | `specs/2026-06-22-growth-record-rebuild-design.md` |
+| 身体健康7维子体系模型 | Phase 4 | ✅ 后端已实现（HealthDimensionController 七维指标 + DimensionScoreService + HealthDimensionScore实体） | `specs/2026-06-26-health-dimensions-design-spec.md` |
+| 成长档案重建 | Phase 4 | ✅ 后端已实现（HealthGrowthRecord + HealthGrowthRecordController + HealthGrowthRecordService） | `specs/2026-06-22-growth-record-rebuild-design.md` |
 | 营养健康部分缺陷修复 | Phase 1 | ✅ 已实施（5项缺陷已修复） | `plans/2026-06-22-营养健康部分缺陷修复计划.md` |
 | 主动健康六阶模型改进（P0-P3） | Phase 5 | ✅ P0/P1-1/P1-2/P2-1/P2-2/P3 后端全完成（P2-2家庭互动+ P3社区扩散Controller/Service/Entity全量存在） | `specs/2026-07-22-active-health-six-stage-design.md` (设计总纲) |