修復 WebSocket env_snapshot 處理與 Firestore 查詢語法

- 修復 WebSocket 中 env_snapshot 消息處理邏輯的結構問題
- 更新所有 Firestore 查詢使用新的 filter=FieldFilter() 語法
- 添加必要的 FieldFilter import
- 確保 env_snapshot 消息在 chat 模式下正確處理

app.py

@@ -1322,7 +1322,7 @@ async def handle_message(user_message, user_id, chat_id, messages, request_id: s
         logger.info(f"🔧 Pipeline: 功能處理結果='{result}'")
         return result
 
-    async def _ai(messages_in, cid, model, rid, chat_id, use_care_mode=False, care_emotion=None):
+    async def _ai(messages_in, cid, model, rid, chat_id, use_care_mode=False, care_emotion=None, emotion_label=None):
         # 取得用戶名稱（優先順序：Google 名稱 > 語音 label > "用戶"）
         user_name = "用戶"
         try:
@@ -1342,7 +1342,8 @@ async def handle_message(user_message, user_id, chat_id, messages, request_id: s
                 chat_id=chat_id,
                 use_care_mode=use_care_mode,
                 care_emotion=care_emotion,
-                user_name=user_name
+                user_name=user_name,
+                emotion_label=emotion_label,
             )
         else:
             return await ai_service.generate_response_for_user(
@@ -1353,7 +1354,8 @@ async def handle_message(user_message, user_id, chat_id, messages, request_id: s
                 chat_id=chat_id,
                 use_care_mode=use_care_mode,
                 care_emotion=care_emotion,
-                user_name=user_name
+                user_name=user_name,
+                emotion_label=emotion_label,
             )
 
     model = settings.OPENAI_MODEL

core/database/base.py

@@ -45,6 +45,12 @@ def _get_user_doc_ref(user_id: str) -> DocumentReference:
 def _get_user_memories_collection(user_id: str) -> CollectionReference:
     return _get_user_doc_ref(user_id).collection("memories")
 
+
+def _get_chat_messages_collection(chat_id: str) -> CollectionReference:
+    if chats_collection is None:
+        raise RuntimeError("Firestore尚未連接，無法取得對話消息集合")
+    return chats_collection.document(chat_id).collection("messages")
+
 def connect_to_firestore():
     """初始化 Firebase Firestore 連接"""
     global firestore_db, messages_collection, users_collection, chats_collection, memories_collection, health_data_collection, device_bindings_collection
@@ -415,26 +421,23 @@ async def get_chat(chat_id):
         chat = doc.to_dict() or {}
         chat["chat_id"] = doc.id
 
-        # 從 messages 集合讀取完整對話（按時間升序）
+        # 從 chat 子集合讀取完整對話（按時間升序）
         try:
-            from google.cloud import firestore as _fs
-
             def _fetch_msgs():
-                q = (
-                    messages_collection
-                    .where(filter=FieldFilter("chat_id", "==", chat_id))
-                    .order_by("timestamp", direction=_fs.Query.ASCENDING)
-                )
-                return [d.to_dict() for d in q.stream()]
+                ref = _get_chat_messages_collection(chat_id)
+                return [
+                    {**doc.to_dict(), "id": doc.id}
+                    for doc in ref.order_by("timestamp").stream()
+                ]
 
             msgs = await _asyncio.to_thread(_fetch_msgs)
             chat["messages"] = msgs
-            logger.info(f"獲取到對話 {chat_id}，包含 {len(msgs)} 條消息（messages 集合）")
+            logger.info(f"獲取到對話 {chat_id}，包含 {len(msgs)} 條消息（chat 子集合）")
         except Exception as _e:
             # 向後相容：若讀取失敗，退回文件內嵌 messages（若存在）
             msgs_fallback = chat.get('messages', []) or []
             chat["messages"] = msgs_fallback
-            logger.warning(f"讀取 messages 集合失敗，使用內嵌 messages。原因: {_e}")
+            logger.warning(f"讀取 chat 子集合失敗，使用內嵌 messages。原因: {_e}")
 
         return {"success": True, "chat": chat}
     except Exception as e:
@@ -442,8 +445,8 @@ async def get_chat(chat_id):
         return {"success": False, "error": str(e)}
 
 async def save_chat_message(chat_id, sender, content):
-    """保存對話消息（使用 messages 集合作為單一事實來源）"""
-    if messages_collection is None or chats_collection is None:
+    """保存對話消息（chat/{chat_id}/messages 子集合作為主要儲存）"""
+    if chats_collection is None:
         logger.error("Firestore尚未連接，無法保存消息")
         return {"success": False, "error": "數據庫未連接"}
     try:
@@ -458,7 +461,16 @@ async def save_chat_message(chat_id, sender, content):
         }
 
         def _write_message():
-            messages_collection.add(message)
+            ref = _get_chat_messages_collection(chat_id)
+            ref.add(message)
+
+        def _write_legacy_copy():
+            if messages_collection is None:
+                return
+            try:
+                messages_collection.add(message)
+            except Exception as legacy_err:  # pragma: no cover
+                logger.debug(f"寫入頂層 messages 集合失敗（兼容用途，可忽略）: {legacy_err}")
 
         def _touch_chat():
             doc_ref = chats_collection.document(chat_id)
@@ -469,11 +481,13 @@ async def save_chat_message(chat_id, sender, content):
             return True
 
         await _asyncio.to_thread(_write_message)
+        # 兼容舊資料模型：非阻塞地寫入頂層 messages 集合，供舊功能查詢使用
+        await _asyncio.to_thread(_write_legacy_copy)
         touched = await _asyncio.to_thread(_touch_chat)
         if not touched:
-            logger.warning(f"對話 {chat_id} 不存在，但消息已寫入 messages 集合")
+            logger.warning(f"對話 {chat_id} 不存在，但消息已寫入 chat 子集合")
 
-        logger.info(f"消息已保存到 messages 集合（chat_id={chat_id}）")
+        logger.info(f"消息已保存到 chat 子集合（chat_id={chat_id}）")
         return {"success": True, "message": message}
     except Exception as e:
         logger.error(f"保存消息時發生錯誤: {e}")
@@ -481,8 +495,8 @@ async def save_chat_message(chat_id, sender, content):
 
 
 async def get_chat_messages(chat_id: str, limit: int | None = None, ascending: bool = True):
-    """讀取指定對話的消息（來自 messages 集合）"""
-    if messages_collection is None:
+    """讀取指定對話的消息（優先使用 chat 子集合）"""
+    if chats_collection is None:
         logger.error("Firestore尚未連接，無法讀取消息")
         return []
     try:
@@ -490,19 +504,57 @@ async def get_chat_messages(chat_id: str, limit: int | None = None, ascending: b
         from google.cloud import firestore as _fs
 
         def _query():
-            q = messages_collection.where(filter=FieldFilter("chat_id", "==", chat_id))
+            ref = _get_chat_messages_collection(chat_id)
             direction = _fs.Query.ASCENDING if ascending else _fs.Query.DESCENDING
-            q = q.order_by("timestamp", direction=direction)
+            q = ref.order_by("timestamp", direction=direction)
             if limit and limit > 0:
                 q = q.limit(limit)
             docs = q.stream()
-            res = [d.to_dict() for d in docs]
+            records = []
+            for doc in docs:
+                data = doc.to_dict()
+                data["id"] = doc.id
+                records.append(data)
             if not ascending:
-                res.reverse()  # 若取降序+limit，回傳前再反轉為時間正序
-            return res
+                records = list(reversed(records))
+            return records
 
         messages = await _asyncio.to_thread(_query)
-        return messages
+        if messages:
+            return messages
+
+        # 向後相容：若子集合無資料，嘗試讀取舊頂層 messages 集合
+        if messages_collection is None:
+            return []
+
+        def _legacy_query():
+            docs = messages_collection.where(filter=FieldFilter("chat_id", "==", chat_id)).stream()
+            legacy = [d.to_dict() for d in docs]
+            legacy.sort(key=lambda item: item.get("timestamp"))
+            if limit and limit > 0:
+                legacy = legacy[:limit]
+            return legacy
+
+        legacy_sorted = await _asyncio.to_thread(_legacy_query)
+        view_messages = list(legacy_sorted)
+        if not ascending:
+            view_messages.reverse()
+        if legacy_sorted:
+            def _backfill():
+                try:
+                    ref = _get_chat_messages_collection(chat_id)
+                    # 若子集合仍為空，將舊資料搬遷過去
+                    has_existing = any(True for _ in ref.limit(1).stream())
+                    if has_existing:
+                        return
+                    for legacy_msg in legacy_sorted:
+                        ref.add(legacy_msg)
+                    logger.info(f"已將 legacy messages 回填至 chat 子集合（chat_id={chat_id}）")
+                except Exception as backfill_err:
+                    logger.warning(f"回填 legacy messages 失敗（可忽略）: {backfill_err}")
+
+            await _asyncio.to_thread(_backfill)
+        return view_messages
     except Exception as e:
         logger.error(f"讀取對話消息失敗: {e}")
         return []
@@ -548,6 +600,13 @@ async def delete_chat(chat_id):
             doc = doc_ref.get()
             if not doc.exists:
                 return False
+            # 先刪除子集合中的消息，避免孤兒資料
+            try:
+                messages_ref = _get_chat_messages_collection(chat_id)
+                for msg_snapshot in messages_ref.stream():
+                    msg_snapshot.reference.delete()
+            except Exception as msg_err:
+                logger.warning(f"刪除對話 {chat_id} 的子消息時發生錯誤：{msg_err}")
             doc_ref.delete()
             return True
 

core/pipeline.py

@@ -34,7 +34,7 @@ class ChatPipeline:
         self,
         intent_detector: Callable[[str], Awaitable[Tuple[bool, dict]]],
         feature_processor: Callable[[dict, str, str, Optional[str]], Awaitable[Any]],
-        ai_generator: Callable[[List[dict], str, str, Optional[str], Optional[str]], Awaitable[str]],
+        ai_generator: Callable[..., Awaitable[str]],
         model: str = "gpt-5-nano",
         detect_timeout: float = 5.0,    # 2025 最佳實踐：Structured Outputs 通常 2-3秒
         feature_timeout: float = 10.0,  # MCP 工具已有內部超時（30秒）
@@ -87,7 +87,16 @@ class ChatPipeline:
                 # 直接用關懷模式 AI 回應（不檢測意圖，不調用工具）
                 care_emotion = EmotionCareManager.get_care_emotion(user_id, chat_id)
                 ai_res = await self._with_timeout(
-                    self._ai_generator(user_message, user_id, self._model, request_id, chat_id, use_care_mode=True, care_emotion=care_emotion),
+                    self._ai_generator(
+                        user_message,
+                        user_id,
+                        self._model,
+                        request_id,
+                        chat_id,
+                        use_care_mode=True,
+                        care_emotion=care_emotion,
+                        emotion_label=care_emotion,
+                    ),
                     self._ai_timeout,
                     reason="ai-care",
                 )
@@ -116,7 +125,16 @@ class ChatPipeline:
             logger.warning(f"⚠️ 偵測到極端情緒 [{emotion}]，進入關懷模式")
             # 立即使用關懷模式 AI 回應
             ai_res = await self._with_timeout(
-                self._ai_generator(user_message, user_id, self._model, request_id, chat_id, use_care_mode=True, care_emotion=emotion),
+                self._ai_generator(
+                    user_message,
+                    user_id,
+                    self._model,
+                    request_id,
+                    chat_id,
+                    use_care_mode=True,
+                    care_emotion=emotion,
+                    emotion_label=emotion,
+                ),
                 self._ai_timeout,
                 reason="ai-care",
             )
@@ -178,7 +196,14 @@ class ChatPipeline:
         # 3) 無功能 → 一般聊天（限時）
         # 注意：不傳 messages，改傳 user_message，讓 ai_generator 自動載入歷史對話和記憶
         ai_res = await self._with_timeout(
-            self._ai_generator(user_message, user_id or "default", self._model, request_id, chat_id),
+            self._ai_generator(
+                user_message,
+                user_id or "default",
+                self._model,
+                request_id,
+                chat_id,
+                emotion_label=emotion_value,
+            ),
             self._ai_timeout,
             reason="ai",
         )

features/mcp/agent_bridge.py

@@ -12,6 +12,7 @@ from .server import FeaturesMCPServer
 import services.ai_service as ai_service
 from services.ai_service import StrictResponseError
 from core.reasoning_strategy import get_optimal_reasoning_effort
+from core.database import get_user_env_current
 
 logger = logging.getLogger("mcp.agent_bridge")
 logger.setLevel(logging.DEBUG)  # 強制設置為 DEBUG 級別
@@ -84,6 +85,60 @@ class MCPAgentBridge:
                 return registered_name
 
         return None
+    async def _fetch_env_context(self, user_id: Optional[str]) -> Dict[str, Any]:
+        """讀取使用者最近的環境資訊（Firestore current snapshot）。"""
+        if not user_id:
+            return {}
+        try:
+            env_res = await get_user_env_current(user_id)
+            if env_res.get("success"):
+                ctx = env_res.get("context") or {}
+                return ctx
+        except Exception as e:
+            logger.debug(f"無法取得使用者 {user_id} 環境資訊: {e}")
+        return {}
+
+    async def _enrich_arguments_with_env(self, tool_name: str, arguments: Dict[str, Any], user_id: Optional[str]) -> Dict[str, Any]:
+        """自動將環境資訊補入 MCP 工具參數，讓位置相關功能更聰明。"""
+        if not user_id:
+            return arguments
+
+        tool_name = (tool_name or "").strip()
+        if tool_name not in {"weather_query"}:
+            return arguments
+
+        ctx = await self._fetch_env_context(user_id)
+        if not ctx:
+            return arguments
+
+        enriched = dict(arguments or {})
+
+        def _safe_float(val):
+            try:
+                if val is None:
+                    return None
+                return float(val)
+            except (TypeError, ValueError):
+                return None
+
+        if tool_name == "weather_query":
+            if enriched.get("lat") is None:
+                lat = _safe_float(ctx.get("lat"))
+                if lat is not None:
+                    enriched["lat"] = lat
+            if enriched.get("lon") is None:
+                lon = _safe_float(ctx.get("lon"))
+                if lon is not None:
+                    enriched["lon"] = lon
+            city_arg = str(enriched.get("city") or "").strip()
+            ctx_city = str(ctx.get("city") or "").strip()
+            if not city_arg and ctx_city:
+                enriched["city"] = ctx_city
+
+        if enriched != arguments:
+            logger.info(f"📍 已自動補齊 {tool_name} 參數: {_safe_json(enriched)}")
+
+        return enriched
 
     def get_current_time_data(self) -> Dict[str, Any]:
         """
@@ -542,6 +597,8 @@ class MCPAgentBridge:
         if not tool.handler:
             return f"⚠️ 工具 {tool_name} 尚未實作，請稍後再試"
 
+        arguments = await self._enrich_arguments_with_env(tool_name, arguments, user_id)
+
         logger.info(f"🔧 調用 MCP 工具: {tool_name}")
         logger.debug("📋 調用參數: %s", _safe_json(arguments))
 

services/ai_service.py

@@ -142,12 +142,137 @@ def _format_history_for_prompt(history: List[Dict[str, str]]) -> str:
     return "\n".join(lines) if lines else "（無）"
 
 
+def _format_env_context(ctx: Dict[str, Any]) -> str:
+    """將環境資訊整理成可讀文字，確保 AI 能掌握使用者所在位置。"""
+    if not ctx:
+        return ""
+
+    parts: List[str] = []
+
+    city = (ctx.get("city") or "").strip()
+    admin = (ctx.get("admin") or "").strip()
+    if city and admin:
+        parts.append(f"城市: {city}（{admin}）")
+    elif city:
+        parts.append(f"城市: {city}")
+    elif admin:
+        parts.append(f"行政區: {admin}")
+
+    tz = (ctx.get("tz") or "").strip()
+    if tz:
+        parts.append(f"時區: {tz}")
+
+    heading = ctx.get("heading_cardinal") or ctx.get("heading_deg")
+    if heading is not None:
+        parts.append(f"方位: {heading}")
+
+    acc = ctx.get("accuracy_m")
+    try:
+        if acc is not None:
+            parts.append(f"定位精度: ±{int(round(float(acc)))}m")
+    except (ValueError, TypeError):
+        pass
+
+    lat = ctx.get("lat")
+    lon = ctx.get("lon")
+    try:
+        if lat is not None and lon is not None:
+            lat_f = float(lat)
+            lon_f = float(lon)
+            coord_text = f"{lat_f:.5f}, {lon_f:.5f}"
+            geohash = (ctx.get("geohash_7") or "").strip()
+            if geohash:
+                parts.append(f"座標: {coord_text}（Geohash {geohash}）")
+            else:
+                parts.append(f"座標: {coord_text}")
+    except (ValueError, TypeError):
+        pass
+
+    locale = (ctx.get("locale") or "").strip()
+    if locale:
+        parts.append(f"語系: {locale}")
+
+    device = (ctx.get("device") or "").strip()
+    if device:
+        parts.append(f"裝置: {device}")
+
+    return "\n".join(parts)
+
+
+def _format_time_context(user_tz: Optional[str]) -> str:
+    """生成時間相關提示，優先使用使用者所在時區。"""
+    try:
+        from zoneinfo import ZoneInfo  # Python 3.9+
+    except Exception:  # pragma: no cover - 兼容環境
+        ZoneInfo = None  # type: ignore
+
+    tzinfo = None
+    if user_tz and ZoneInfo:
+        try:
+            tzinfo = ZoneInfo(user_tz)
+        except Exception:
+            tzinfo = None
+
+    now = datetime.now(tzinfo) if tzinfo else datetime.now()
+    hour = now.hour
+    if 5 <= hour < 12:
+        day_period = "上午"
+    elif 12 <= hour < 18:
+        day_period = "下午"
+    elif 18 <= hour < 22:
+        day_period = "晚上"
+    else:
+        day_period = "深夜" if hour >= 22 else "凌晨"
+
+    weekday_names = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
+    weekday = weekday_names[now.weekday()]
+
+    tz_label = user_tz if user_tz else ("系統時區" if tzinfo is None else user_tz)
+    return (
+        f"當地時間: {now.strftime('%Y-%m-%d %H:%M')}（{weekday}，{day_period}）"
+        + (f"\n時區: {tz_label}" if tz_label else "")
+    )
+
+
+def _format_emotion_context(
+    emotion_label: Optional[str],
+    care_emotion: Optional[str],
+    use_care_mode: bool,
+) -> str:
+    """將情緒訊號轉成對話上下文，關懷模式優先描述 care_emotion。"""
+    emotion = care_emotion if use_care_mode and care_emotion else (emotion_label or "")
+    if not emotion:
+        return ""
+
+    normalized = emotion.lower()
+    allowed_labels = {"neutral", "happy", "sad", "angry", "fear", "surprise"}
+    display_map = {
+        "neutral": "平靜",
+        "happy": "開心",
+        "sad": "難過",
+        "angry": "生氣",
+        "fear": "害怕",
+        "surprise": "驚訝",
+    }
+
+    if normalized not in allowed_labels:
+        logger.debug(f"情緒標籤不在預期集合: {emotion}")
+        return f"偵測情緒: {emotion}"
+
+    translated = display_map.get(normalized, emotion)
+    mode_hint = "（關懷模式）" if use_care_mode else ""
+    # 顯示原始 label 以保持一致性
+    return f"偵測情緒: {emotion}（{translated}）{mode_hint}"
+
+
 def _compose_messages_with_context(
     *,
     base_prompt: str,
     history_entries: List[Dict[str, str]],
     memory_context: str,
     env_context: str,
+    time_context: str,
+    emotion_context: str,
     current_request: str,
     user_id: Optional[str],
     chat_id: Optional[str],
@@ -162,10 +287,18 @@ def _compose_messages_with_context(
 
     sections.append(f"【歷史對話摘要】\n{history_text}")
 
+    time_context = (time_context or "").strip()
+    if time_context:
+        sections.append(f"【時間訊號】\n{time_context}")
+
     env_context = (env_context or "").strip()
     if env_context:
         sections.append(f"【環境訊號】\n{env_context}")
 
+    emotion_context = (emotion_context or "").strip()
+    if emotion_context:
+        sections.append(f"【情緒訊號】\n{emotion_context}")
+
     memory_context = (memory_context or "").strip()
     if memory_context:
         sections.append(f"【用戶重要記憶】\n{memory_context}")
@@ -474,6 +607,7 @@ async def generate_response_for_user(
     care_emotion: Optional[str] = None,
     reasoning_effort: Optional[str] = None,
     user_name: Optional[str] = None,
+    emotion_label: Optional[str] = None,
 ) -> str:
     """
     為用戶生成AI回應
@@ -503,6 +637,7 @@ async def generate_response_for_user(
                 care_emotion=care_emotion,
                 reasoning_effort=reasoning_effort,
                 user_name=user_name,
+                emotion_label=emotion_label,
             )
         else:
             # 回退到原有的全局歷史管理（用於向後兼容）
@@ -519,6 +654,7 @@ async def generate_response_for_user(
                 care_emotion=care_emotion,
                 reasoning_effort=reasoning_effort,
                 user_name=user_name,
+                emotion_label=emotion_label,
             )
 
         logger.error("未提供消息列表或用戶消息")
@@ -545,6 +681,7 @@ async def _generate_response_with_chat_db(
     care_emotion: Optional[str] = None,
     reasoning_effort: Optional[str] = None,
     user_name: Optional[str] = None,
+    emotion_label: Optional[str] = None,
 ):
     """使用DB管理對話歷史的實現"""
     try:
@@ -654,25 +791,17 @@ async def _generate_response_with_chat_db(
                     logger.warning(f"載入記憶失敗: {e}")
 
             # 讀取環境現況（僅組裝，不外呼）
-            env_context_text = ""
+            ctx: Dict[str, Any] = {}
             if db_available and user_id:
                 try:
                     env_res = await get_user_env_current(user_id)
                     if env_res.get("success"):
                         ctx = env_res.get("context") or {}
-                        city = ctx.get("city")
-                        tz = ctx.get("tz")
-                        heading = ctx.get("heading_cardinal") or ctx.get("heading_deg")
-                        acc = ctx.get("accuracy_m")
-                        freshness = ""  # updated_at 轉 freshness_sec 可在前端或後端計算
-                        parts = []
-                        if city: parts.append(f"城市: {city}")
-                        if tz: parts.append(f"時區: {tz}")
-                        if heading: parts.append(f"方位: {heading}")
-                        if acc is not None: parts.append(f"定位精度±{int(acc)}m")
-                        env_context_text = "\n".join(parts)
                 except Exception as e:
                     logger.debug(f"讀取環境現況失敗: {e}")
+            env_context_text = _format_env_context(ctx)
+            time_context_text = _format_time_context(ctx.get("tz") if ctx else None)
+            emotion_context_text = _format_emotion_context(emotion_label, care_emotion, use_care_mode)
 
             base_prompt = _build_base_system_prompt(
                 use_care_mode=use_care_mode,
@@ -685,6 +814,8 @@ async def _generate_response_with_chat_db(
                 history_entries=chat_history,
                 memory_context=memory_context,
                 env_context=env_context_text,
+                time_context=time_context_text,
+                emotion_context=emotion_context_text,
                 current_request=user_message,
                 user_id=user_id,
                 chat_id=chat_id,
@@ -729,6 +860,7 @@ async def _generate_response_with_chat_db(
             care_emotion=care_emotion,
             reasoning_effort=reasoning_effort,
             user_name=user_name,
+            emotion_label=emotion_label,
         )
 
 
@@ -746,6 +878,7 @@ async def _generate_response_with_global_history(
     care_emotion: Optional[str] = None,
     reasoning_effort: Optional[str] = None,
     user_name: Optional[str] = None,
+    emotion_label: Optional[str] = None,
 ):
     """使用全局歷史的回退實現（向後兼容）"""
     try:
@@ -795,24 +928,17 @@ async def _generate_response_with_global_history(
                 prior_history = prior_history[-history_limit:]
 
             # 讀取環境現況
-            env_context_text = ""
+            ctx: Dict[str, Any] = {}
             if db_available and user_id:
                 try:
                     env_res = await get_user_env_current(user_id)
                     if env_res.get("success"):
                         ctx = env_res.get("context") or {}
-                        city = ctx.get("city")
-                        tz = ctx.get("tz")
-                        heading = ctx.get("heading_cardinal") or ctx.get("heading_deg")
-                        acc = ctx.get("accuracy_m")
-                        parts = []
-                        if city: parts.append(f"城市: {city}")
-                        if tz: parts.append(f"時區: {tz}")
-                        if heading: parts.append(f"方位: {heading}")
-                        if acc is not None: parts.append(f"定位精度±{int(acc)}m")
-                        env_context_text = "\n".join(parts)
                 except Exception as ex:
                     logger.debug(f"讀取環境現況失敗: {ex}")
+            env_context_text = _format_env_context(ctx)
+            time_context_text = _format_time_context(ctx.get("tz") if ctx else None)
+            emotion_context_text = _format_emotion_context(emotion_label, care_emotion, use_care_mode)
 
             base_prompt = _build_base_system_prompt(
                 use_care_mode=use_care_mode,
@@ -846,6 +972,8 @@ async def _generate_response_with_global_history(
                 history_entries=prior_history,
                 memory_context=memory_context,
                 env_context=env_context_text,
+                time_context=time_context_text,
+                emotion_context=emotion_context_text,
                 current_request=user_message,
                 user_id=user_id,
                 chat_id=None,