('');
+
+ const handleVoiceLogin = async () => {
+ setError(null);
+ setIsLoading(true);
+ setLoadingType('voice');
+ setVoiceStatus('請求麥克風權限...');
console.log('🎤 開始語音登入...');
- localStorage.setItem('jwt_token', 'anonymous_voice_login');
- window.location.href = '/static/';
+
+ try {
+ // 請求麥克風權限
+ const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+ console.log('✅ 麥克風權限已獲取');
+
+ // 設定錄音參數
+ const audioContext = new AudioContext({ sampleRate: 16000 });
+ const source = audioContext.createMediaStreamSource(stream);
+ const processor = audioContext.createScriptProcessor(4096, 1, 1);
+
+ const audioChunks: Float32Array[] = [];
+ const recordDuration = 4000; // 4 秒（確保足夠長度）
+
+ processor.onaudioprocess = (e) => {
+ const inputData = e.inputBuffer.getChannelData(0);
+ audioChunks.push(new Float32Array(inputData));
+ };
+
+ source.connect(processor);
+ processor.connect(audioContext.destination);
+
+ setVoiceStatus('🎙️ 錄音中... 請說話 (4秒)');
+ console.log('🎙️ 開始錄音 4 秒...');
+
+ // 錄音 3 秒
+ await new Promise(resolve => setTimeout(resolve, recordDuration));
+
+ // 停止錄音
+ processor.disconnect();
+ source.disconnect();
+ stream.getTracks().forEach(track => track.stop());
+ await audioContext.close();
+
+ setVoiceStatus('辨識中...');
+ console.log('✅ 錄音完成，處理音訊...');
+
+ // 合併音訊資料
+ const totalLength = audioChunks.reduce((acc, chunk) => acc + chunk.length, 0);
+ const audioData = new Float32Array(totalLength);
+ let offset = 0;
+ for (const chunk of audioChunks) {
+ audioData.set(chunk, offset);
+ offset += chunk.length;
+ }
+
+ // 轉換為 PCM16
+ const pcm16 = new Int16Array(audioData.length);
+ for (let i = 0; i < audioData.length; i++) {
+ const s = Math.max(-1, Math.min(1, audioData[i]));
+ pcm16[i] = s < 0 ? s * 0x8000 : s * 0x7FFF;
+ }
+
+ // 轉換為 base64
+ const uint8Array = new Uint8Array(pcm16.buffer);
+ let binary = '';
+ for (let i = 0; i < uint8Array.length; i++) {
+ binary += String.fromCharCode(uint8Array[i]);
+ }
+ const audioBase64 = btoa(binary);
+
+ console.log('📤 發送語音到後端進行辨識...');
+
+ // 呼叫語音登入 API
+ const response = await fetch('/auth/voice/login', {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json',
+ },
+ body: JSON.stringify({
+ audio_base64: audioBase64,
+ sample_rate: 16000,
+ }),
+ });
+
+ const data = await response.json();
+
+ if (data.success) {
+ console.log('✅ 語音登入成功！');
+ localStorage.setItem('jwt_token', data.access_token);
+ // 儲存情緒標籤供歡迎詞使用
+ if (data.emotion) {
+ localStorage.setItem('voice_login_emotion', data.emotion);
+ }
+ window.location.href = '/static/';
+ } else {
+ throw new Error(data.error || '語音登入失敗');
+ }
+
+ } catch (error: any) {
+ console.error('❌ 語音登入失敗:', error);
+ if (error.name === 'NotAllowedError') {
+ setError('請允許麥克風權限以使用語音登入');
+ } else {
+ setError(error.message || '語音登入失敗，請重試');
+ }
+ setIsLoading(false);
+ setLoadingType(null);
+ }
}
return (
@@ -236,6 +352,13 @@ export function LoginForm() {
+ {/* 錯誤訊息 */}
+ {error && (
+
+ {error}
+
+ )}
+
{/* iframe 環境提示 - 簡約風格 */}
{isInIframe && (
@@ -245,40 +368,56 @@ export function LoginForm() {
{/* Google Login */}
-
-
- Continue with Google
- {isInIframe &&
+
+ )}
+
+ {isLoading && loadingType === 'google' ? '登入中...' : 'Continue with Google'}
+
+ {isInIframe && !isLoading &&
{/* Voice Login */}
- Voice Login
+ {isLoading && loadingType === 'voice' ? (
+
+ {isLoading && loadingType === 'voice' ? (voiceStatus || '處理中...') : 'Voice Login'}
+
+ {isOnline ? (
+ <>
+ 已恢復連線
+
+ ) : (
+ <>
+ 網路已斷線
+
+ )}
+
+ )
+}
diff --git a/core/ai_client.py b/core/ai_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..69fcb1286613006d29fe5ad0eec20806b4d9d728
--- /dev/null
+++ b/core/ai_client.py
@@ -0,0 +1,83 @@
+"""
+統一 OpenAI 客戶端管理
+單一真理來源，避免重複初始化
+"""
+
+import logging
+from typing import Optional
+
+from core.config import settings
+from core.logging import get_logger
+
+logger = get_logger("core.ai_client")
+
+# 全域 OpenAI 客戶端
+_openai_client = None
+_initialized = False
+
+
+def get_openai_client():
+ """
+ 取得 OpenAI 客戶端（單例模式）
+
+ Returns:
+ OpenAI 客戶端實例，若初始化失敗則返回 None
+ """
+ global _openai_client, _initialized
+
+ if _initialized:
+ return _openai_client
+
+ try:
+ from openai import OpenAI
+
+ api_key = settings.OPENAI_API_KEY
+ if not api_key:
+ logger.error("❌ OpenAI API Key 未設定")
+ _initialized = True
+ return None
+
+ _openai_client = OpenAI(
+ api_key=api_key,
+ timeout=float(settings.OPENAI_TIMEOUT),
+ max_retries=3,
+ )
+
+ _initialized = True
+ logger.info("✅ OpenAI 客戶端初始化成功")
+ return _openai_client
+
+ except ImportError:
+ logger.error("❌ 無法導入 OpenAI SDK")
+ _initialized = True
+ return None
+
+ except Exception as e:
+ logger.error(f"❌ OpenAI 客戶端初始化失敗: {e}")
+ _initialized = True
+ return None
+
+
+def reset_client() -> None:
+ """
+ 重置客戶端（用於測試或重新初始化）
+ """
+ global _openai_client, _initialized
+ _openai_client = None
+ _initialized = False
+ logger.info("OpenAI 客戶端已重置")
+
+
+def is_available() -> bool:
+ """
+ 檢查 OpenAI 服務是否可用
+
+ Returns:
+ True 如果客戶端已初始化且可用
+ """
+ client = get_openai_client()
+ return client is not None
+
+
+# 便捷別名
+client = property(lambda self: get_openai_client())
diff --git a/core/config.py b/core/config.py
index 31c40f721aa4840c678b0c3a7ff6b08fdd5d4853..909adddb521fd5bd40b6e0fbb652af60795782de 100644
--- a/core/config.py
+++ b/core/config.py
@@ -118,6 +118,40 @@ class Settings:
ENV_CONTEXT_HEADING_THRESHOLD: float = float(os.getenv("ENV_CONTEXT_HEADING_THRESHOLD", "25"))
ENV_CONTEXT_TTL_SECONDS: float = float(os.getenv("ENV_CONTEXT_TTL_SECONDS", "300"))
+ # ===== CORS 安全設定 =====
+ # 生產環境應設定具體的允許來源，多個來源用逗號分隔
+ # 例如：CORS_ORIGINS=https://example.com,https://app.example.com
+ _cors_origins_raw: str = os.getenv("CORS_ORIGINS", "*")
+
+ @classmethod
+ def get_cors_origins(cls) -> list:
+ """取得 CORS 允許的來源列表"""
+ if cls._cors_origins_raw == "*":
+ return ["*"]
+ return [origin.strip() for origin in cls._cors_origins_raw.split(",") if origin.strip()]
+
+ # ===== 安全性設定 =====
+ # 登入失敗封鎖閾值
+ FAILED_LOGIN_THRESHOLD: int = int(os.getenv("FAILED_LOGIN_THRESHOLD", "5"))
+ # 封鎖時間（秒）
+ LOGIN_BLOCK_DURATION: int = int(os.getenv("LOGIN_BLOCK_DURATION", "900")) # 15 分鐘
+ # JWT Secret 最小長度
+ JWT_SECRET_MIN_LENGTH: int = 32
+
+ # ===== 效能調優常數 =====
+ # WebSocket 會話超時（秒）
+ WEBSOCKET_SESSION_TIMEOUT: int = int(os.getenv("WEBSOCKET_SESSION_TIMEOUT", "1800")) # 30 分鐘
+ # 定期清理間隔（秒）
+ CLEANUP_INTERVAL: int = int(os.getenv("CLEANUP_INTERVAL", "1800")) # 30 分鐘
+ # 記憶重要性閾值
+ MEMORY_IMPORTANCE_THRESHOLD: float = float(os.getenv("MEMORY_IMPORTANCE_THRESHOLD", "0.6"))
+ # 意圖快取 TTL（秒）
+ INTENT_CACHE_TTL: int = int(os.getenv("INTENT_CACHE_TTL", "300")) # 5 分鐘
+ # 對話歷史載入限制
+ CHAT_HISTORY_LIMIT: int = int(os.getenv("CHAT_HISTORY_LIMIT", "12"))
+ # 關懷模式對話歷史限制
+ CARE_MODE_HISTORY_LIMIT: int = int(os.getenv("CARE_MODE_HISTORY_LIMIT", "3"))
+
@classmethod
def validate(cls) -> bool:
"""
@@ -156,9 +190,16 @@ class Settings:
if not cls.OPENAI_API_KEY.startswith("sk-"):
print("⚠️ OpenAI API Key 格式可能不正確（應以 'sk-' 開頭）")
- # 驗證 JWT Secret 長度
- if len(cls.JWT_SECRET_KEY) < 32:
- print("⚠️ JWT Secret Key 長度建議至少 32 個字符")
+ # 驗證 JWT Secret 長度（強制檢查）
+ if len(cls.JWT_SECRET_KEY) < cls.JWT_SECRET_MIN_LENGTH:
+ print(f"❌ JWT Secret Key 長度必須至少 {cls.JWT_SECRET_MIN_LENGTH} 個字符")
+ if cls.IS_PRODUCTION:
+ return False
+ print("⚠️ 開發環境允許繼續，但生產環境將拒絕啟動")
+
+ # 生產環境 CORS 檢查
+ if cls.IS_PRODUCTION and cls._cors_origins_raw == "*":
+ print("⚠️ 生產環境建議設定具體的 CORS_ORIGINS，而非 '*'")
return True
diff --git a/core/database/base.py b/core/database/base.py
index 68440557b021c2d0e69ceb4826012604b8b4223d..d5c3cfac8250221d6d337b1f57fe1dfb4ee14a32 100644
--- a/core/database/base.py
+++ b/core/database/base.py
@@ -39,6 +39,35 @@ route_cache_collection = None
MAX_MEMORIES_PER_USER = 500
+def _serialize_firestore_data(data: Any) -> Any:
+ """
+ 遞迴轉換 Firestore 資料中的 DatetimeWithNanoseconds 物件為 ISO 字串
+
+ Args:
+ data: Firestore 回傳的資料（可能包含 DatetimeWithNanoseconds）
+
+ Returns:
+ JSON 可序列化的資料
+ """
+ from google.cloud.firestore_v1._helpers import DatetimeWithNanoseconds
+
+ if isinstance(data, DatetimeWithNanoseconds):
+ # 轉成 ISO 8601 字串
+ return data.isoformat()
+ elif isinstance(data, datetime):
+ # 一般 Python datetime 也轉成字串
+ return data.isoformat()
+ elif isinstance(data, dict):
+ # 遞迴處理字典
+ return {k: _serialize_firestore_data(v) for k, v in data.items()}
+ elif isinstance(data, list):
+ # 遞迴處理列表
+ return [_serialize_firestore_data(item) for item in data]
+ else:
+ # 其他型別直接回傳
+ return data
+
+
def _get_user_doc_ref(user_id: str) -> DocumentReference:
if users_collection is None:
raise RuntimeError("Firestore尚未連接，無法操作使用者資料")
@@ -143,13 +172,15 @@ async def get_user_by_id(user_id: str):
user_doc = docs[0]
user_data = user_doc.to_dict()
-
- return {
+
+ result = {
"id": user_data["user_id"],
"name": user_data.get("name", ""),
"email": user_data.get("email", ""),
"created_at": user_data.get("created_at"),
}
+ # 序列化 Firestore 時間物件，避免 JSON 序列化炸裂
+ return _serialize_firestore_data(result)
except Exception as e:
logger.error(f"查找使用者時發生錯誤: {e}")
return None
@@ -192,7 +223,8 @@ async def get_user_history(user_id, limit=20):
messages = await _asyncio.to_thread(_fetch_messages)
logger.info(f"已獲取用戶 {user_id} 的 {len(messages)} 條歷史記錄")
- return messages
+ # 序列化 Firestore 時間物件，避免 JSON 序列化炸裂
+ return _serialize_firestore_data(messages)
except Exception as e:
logger.error(f"獲取歷史記錄時發生錯誤: {e}")
return []
@@ -371,7 +403,9 @@ async def create_chat(user_id, title="新對話"):
"created_at": chat["created_at"],
"updated_at": chat["updated_at"],
}
- return {"success": True, "chat": chat_info}
+ # 序列化時間物件，避免 JSON 序列化炸裂
+ serialized_chat_info = _serialize_firestore_data(chat_info)
+ return {"success": True, "chat": serialized_chat_info}
except Exception as e:
logger.error(f"創建對話時發生錯誤: {e}")
return {"success": False, "error": str(e)}
@@ -401,7 +435,9 @@ async def get_user_chats(user_id):
chats = await _asyncio.to_thread(_fetch_chats)
logger.info(f"獲取到用戶 {user_id} 的 {len(chats)} 個對話")
- return {"success": True, "chats": chats}
+ # 序列化 Firestore 時間物件，避免 JSON 序列化炸裂
+ serialized_chats = _serialize_firestore_data(chats)
+ return {"success": True, "chats": serialized_chats}
except Exception as e:
logger.error(f"獲取用戶對話時發生錯誤: {e}")
return {"success": False, "error": str(e)}
@@ -524,7 +560,8 @@ async def get_chat_messages(chat_id: str, limit: int | None = None, ascending: b
messages = await _asyncio.to_thread(_query)
if messages:
- return messages
+ # 序列化 Firestore 時間物件，避免 JSON 序列化炸裂
+ return _serialize_firestore_data(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:
@@ -557,7 +594,8 @@ async def get_chat_messages(chat_id: str, limit: int | None = None, ascending: b
logger.warning(f"回填 legacy messages 失敗（可忽略）: {backfill_err}")
await _asyncio.to_thread(_backfill)
- return view_messages
+ # 序列化 Firestore 時間物件，避免 JSON 序列化炸裂
+ return _serialize_firestore_data(view_messages)
except Exception as e:
logger.error(f"讀取對話消息失敗: {e}")
return []
@@ -730,12 +768,14 @@ async def get_user_by_speaker_label(speaker_label: str):
return None
data = doc.to_dict()
- return {
+ result = {
"id": data.get("user_id"),
"name": data.get("name", ""),
"email": data.get("email", ""),
"created_at": data.get("created_at"),
}
+ # 序列化 Firestore 時間物件，避免 JSON 序列化炸裂
+ return _serialize_firestore_data(result)
except Exception as e:
logger.error(f"查詢語音標籤對應用戶時發生錯誤: {e}")
return None
@@ -912,7 +952,9 @@ async def get_user_env_current(user_id: str) -> Dict[str, Any]:
data = await _asyncio.to_thread(_read)
if not data:
return {"success": False, "error": "NOT_FOUND"}
- return {"success": True, "context": data}
+ # 序列化 Firestore 時間物件，避免 JSON 序列化炸裂
+ serialized_data = _serialize_firestore_data(data)
+ return {"success": True, "context": serialized_data}
except Exception as e:
logger.error(f"讀取環境現況失敗: {e}")
return {"success": False, "error": str(e)}
@@ -928,7 +970,9 @@ async def get_geo_cache(geohash7: str) -> Optional[Dict[str, Any]]:
def _read():
doc = geo_cache_collection.document(geohash7).get()
return doc.to_dict() if doc.exists else None
- return await _asyncio.to_thread(_read)
+ result = await _asyncio.to_thread(_read)
+ # 序列化 Firestore 時間物件，避免 JSON 序列化炸裂
+ return _serialize_firestore_data(result) if result else None
except Exception as e:
logger.warning(f"讀取 geo_cache 失敗: {e}")
return None
@@ -959,7 +1003,9 @@ async def get_route_cache(key: str) -> Optional[Dict[str, Any]]:
def _read():
doc = route_cache_collection.document(key).get()
return doc.to_dict() if doc.exists else None
- return await _asyncio.to_thread(_read)
+ result = await _asyncio.to_thread(_read)
+ # 序列化 Firestore 時間物件，避免 JSON 序列化炸裂
+ return _serialize_firestore_data(result) if result else None
except Exception as e:
logger.warning(f"讀取 route_cache 失敗: {e}")
return None
@@ -1024,7 +1070,9 @@ async def get_user_memories(
await _asyncio.to_thread(_mark_accessed, [m["memory_id"] for m in memories])
logger.info(f"獲取到用戶 {user_id} 的 {len(memories)} 條記憶")
- return {"success": True, "memories": memories}
+ # 序列化 Firestore 時間物件，避免 JSON 序列化炸裂
+ serialized_memories = _serialize_firestore_data(memories)
+ return {"success": True, "memories": serialized_memories}
except Exception as e:
logger.error(f"獲取記憶時發生錯誤: {e}")
diff --git a/core/emotion_care_manager.py b/core/emotion_care_manager.py
index 98fed23ab21a99e9af3c38ccbc1b4f05c5a25325..c39319dada5f7c403d88b1937784aecc45ee3035 100644
--- a/core/emotion_care_manager.py
+++ b/core/emotion_care_manager.py
@@ -90,13 +90,15 @@ class EmotionCareManager:
return True
@classmethod
- def check_release(cls, user_id: str, message: str, chat_id: Optional[str] = None) -> bool:
+ def check_release(cls, user_id: str, message: str, chat_id: Optional[str] = None, emotion: Optional[str] = None) -> bool:
"""
- 檢查用戶訊息是否包含解除關鍵字
+ 檢查用戶訊息是否包含解除關鍵字或情緒恢復為 neutral
參數:
user_id: 用戶 ID
message: 用戶訊息
+ chat_id: 對話 ID（可選）
+ emotion: 當前偵測到的情緒（可選）
返回:
bool: 是否解除關懷模式（True=解除，False=繼續關懷）
@@ -105,18 +107,29 @@ class EmotionCareManager:
if not state or not state.get("in_care_mode", False):
return False
+ # 優先檢查情緒：如果偵測到 neutral，立即解除關懷模式
+ if emotion and emotion.lower() == "neutral":
+ original_emotion = state.get("emotion", "unknown")
+ duration = time.time() - state.get("start_time", 0)
+
+ state["in_care_mode"] = False
+ state["last_exit_time"] = time.time()
+
+ logger.info(f"✅ 用戶 {user_id}（chat={chat_id or 'default'}）情緒恢復為 neutral（{original_emotion} → neutral），解除關懷模式（持續 {duration:.1f}秒）")
+ return True
+
# 檢查是否包含解除關鍵字
message_lower = message.lower().strip()
for keyword in cls.RELEASE_KEYWORDS:
if keyword in message_lower:
# 解除關懷模式
- emotion = state.get("emotion", "unknown")
+ original_emotion = state.get("emotion", "unknown")
duration = time.time() - state.get("start_time", 0)
state["in_care_mode"] = False
state["last_exit_time"] = time.time()
- logger.info(f"✅ 用戶 {user_id}（chat={chat_id or 'default'}）情緒恢復（{emotion} → 正常），解除關懷模式（持續 {duration:.1f}秒）")
+ logger.info(f"✅ 用戶 {user_id}（chat={chat_id or 'default'}）情緒恢復（{original_emotion} → 正常），解除關懷模式（持續 {duration:.1f}秒）")
return True
return False
diff --git a/core/exceptions.py b/core/exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..c33971751c87d1718cf2653d3e7fc25af28684c1
--- /dev/null
+++ b/core/exceptions.py
@@ -0,0 +1,267 @@
+"""
+統一異常處理
+定義自訂異常類別和錯誤響應格式
+"""
+
+from typing import Optional, Dict, Any
+from fastapi import HTTPException
+from fastapi.responses import JSONResponse
+
+
+class BloomWareException(Exception):
+ """Bloom Ware 基礎異常類別"""
+
+ def __init__(
+ self,
+ message: str,
+ code: str = "UNKNOWN_ERROR",
+ status_code: int = 500,
+ details: Optional[Dict[str, Any]] = None,
+ ):
+ self.message = message
+ self.code = code
+ self.status_code = status_code
+ self.details = details or {}
+ super().__init__(message)
+
+ def to_dict(self) -> Dict[str, Any]:
+ """轉換為字典格式"""
+ return {
+ "success": False,
+ "error": {
+ "code": self.code,
+ "message": self.message,
+ "details": self.details,
+ }
+ }
+
+ def to_response(self) -> JSONResponse:
+ """轉換為 JSON 響應"""
+ return JSONResponse(
+ status_code=self.status_code,
+ content=self.to_dict(),
+ )
+
+
+# ==================== 認證相關異常 ====================
+
+class AuthenticationError(BloomWareException):
+ """認證錯誤"""
+
+ def __init__(self, message: str = "認證失敗", details: Optional[Dict[str, Any]] = None):
+ super().__init__(
+ message=message,
+ code="AUTHENTICATION_ERROR",
+ status_code=401,
+ details=details,
+ )
+
+
+class TokenExpiredError(AuthenticationError):
+ """Token 過期"""
+
+ def __init__(self):
+ super().__init__(
+ message="Token 已過期，請重新登入",
+ details={"reason": "token_expired"},
+ )
+
+
+class InvalidTokenError(AuthenticationError):
+ """無效的 Token"""
+
+ def __init__(self):
+ super().__init__(
+ message="無效的認證令牌",
+ details={"reason": "invalid_token"},
+ )
+
+
+class PermissionDeniedError(BloomWareException):
+ """權限不足"""
+
+ def __init__(self, message: str = "權限不足"):
+ super().__init__(
+ message=message,
+ code="PERMISSION_DENIED",
+ status_code=403,
+ )
+
+
+# ==================== 資源相關異常 ====================
+
+class ResourceNotFoundError(BloomWareException):
+ """資源不存在"""
+
+ def __init__(self, resource_type: str, resource_id: str):
+ super().__init__(
+ message=f"{resource_type} 不存在",
+ code="RESOURCE_NOT_FOUND",
+ status_code=404,
+ details={
+ "resource_type": resource_type,
+ "resource_id": resource_id,
+ },
+ )
+
+
+class ChatNotFoundError(ResourceNotFoundError):
+ """對話不存在"""
+
+ def __init__(self, chat_id: str):
+ super().__init__("對話", chat_id)
+
+
+class UserNotFoundError(ResourceNotFoundError):
+ """用戶不存在"""
+
+ def __init__(self, user_id: str):
+ super().__init__("用戶", user_id)
+
+
+# ==================== 驗證相關異常 ====================
+
+class ValidationError(BloomWareException):
+ """驗證錯誤"""
+
+ def __init__(self, field: str, message: str):
+ super().__init__(
+ message=f"參數 '{field}' 驗證失敗: {message}",
+ code="VALIDATION_ERROR",
+ status_code=400,
+ details={"field": field},
+ )
+
+
+class RateLimitExceededError(BloomWareException):
+ """請求頻率超限"""
+
+ def __init__(self, retry_after: int = 60):
+ super().__init__(
+ message="請求頻率超過限制，請稍後再試",
+ code="RATE_LIMIT_EXCEEDED",
+ status_code=429,
+ details={"retry_after": retry_after},
+ )
+
+
+# ==================== 服務相關異常 ====================
+
+class ServiceUnavailableError(BloomWareException):
+ """服務不可用"""
+
+ def __init__(self, service_name: str):
+ super().__init__(
+ message=f"{service_name} 服務暫時不可用",
+ code="SERVICE_UNAVAILABLE",
+ status_code=503,
+ details={"service": service_name},
+ )
+
+
+class DatabaseError(BloomWareException):
+ """數據庫錯誤"""
+
+ def __init__(self, message: str = "數據庫操作失敗"):
+ super().__init__(
+ message=message,
+ code="DATABASE_ERROR",
+ status_code=500,
+ )
+
+
+class AIServiceError(BloomWareException):
+ """AI 服務錯誤"""
+
+ def __init__(self, message: str = "AI 服務暫時不可用"):
+ super().__init__(
+ message=message,
+ code="AI_SERVICE_ERROR",
+ status_code=503,
+ )
+
+
+class ExternalAPIError(BloomWareException):
+ """外部 API 錯誤"""
+
+ def __init__(self, api_name: str, message: str):
+ super().__init__(
+ message=f"{api_name} API 錯誤: {message}",
+ code="EXTERNAL_API_ERROR",
+ status_code=502,
+ details={"api": api_name},
+ )
+
+
+# ==================== 語音相關異常 ====================
+
+class VoiceAuthError(BloomWareException):
+ """語音認證錯誤"""
+
+ def __init__(self, message: str, reason: str):
+ super().__init__(
+ message=message,
+ code="VOICE_AUTH_ERROR",
+ status_code=400,
+ details={"reason": reason},
+ )
+
+
+class SpeakerLabelTakenError(VoiceAuthError):
+ """語音標籤已被使用"""
+
+ def __init__(self):
+ super().__init__(
+ message="此語音標籤已被其他用戶綁定",
+ reason="speaker_label_taken",
+ )
+
+
+# ==================== 異常處理器 ====================
+
+def create_error_response(
+ code: str,
+ message: str,
+ status_code: int = 500,
+ details: Optional[Dict[str, Any]] = None,
+) -> JSONResponse:
+ """創建標準錯誤響應"""
+ return JSONResponse(
+ status_code=status_code,
+ content={
+ "success": False,
+ "error": {
+ "code": code,
+ "message": message,
+ "details": details or {},
+ }
+ }
+ )
+
+
+def handle_exception(exc: Exception) -> JSONResponse:
+ """
+ 統一異常處理
+
+ 將各種異常轉換為標準 JSON 響應
+ """
+ if isinstance(exc, BloomWareException):
+ return exc.to_response()
+
+ if isinstance(exc, HTTPException):
+ return create_error_response(
+ code="HTTP_ERROR",
+ message=exc.detail,
+ status_code=exc.status_code,
+ )
+
+ # 未知異常
+ import logging
+ logger = logging.getLogger("core.exceptions")
+ logger.exception(f"未處理的異常: {exc}")
+
+ return create_error_response(
+ code="INTERNAL_ERROR",
+ message="內部伺服器錯誤",
+ status_code=500,
+ )
diff --git a/core/intent_detector.py b/core/intent_detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eb14712aecaeda0625c7f5dd915615e49767ae0
--- /dev/null
+++ b/core/intent_detector.py
@@ -0,0 +1,272 @@
+"""
+意圖檢測器
+2025 最佳實踐：使用 OpenAI 原生 Function Calling 進行意圖檢測
+
+核心改進：
+1. 不再使用巨大的 system_prompt 描述每個工具
+2. 直接使用 OpenAI tools 參數傳遞工具定義
+3. GPT 原生選擇工具並生成結構化參數
+4. 新增工具只需註冊到 Registry，不需更新任何 prompt
+"""
+
+import json
+import hashlib
+import time
+import logging
+from typing import Dict, Any, Optional, Tuple, List
+
+from core.tool_registry import tool_registry
+from core.logging import get_logger
+
+logger = get_logger("core.intent_detector")
+
+
+class IntentDetector:
+ """
+ 意圖檢測器
+
+ 使用 OpenAI 原生 Function Calling 進行意圖檢測，
+ 不需要自定義 prompt 描述每個工具。
+ """
+
+ # 情緒列表
+ EMOTIONS = ["neutral", "happy", "sad", "angry", "fear", "surprise"]
+
+ # 快取 TTL（秒）
+ CACHE_TTL = 300.0
+
+ def __init__(self):
+ self._cache: Dict[str, Tuple[bool, Optional[Dict[str, Any]], float]] = {}
+
+ async def detect(
+ self,
+ message: str,
+ user_id: Optional[str] = None,
+ include_location_tools: bool = True,
+ ) -> Tuple[bool, Optional[Dict[str, Any]]]:
+ """
+ 檢測用戶消息中的意圖
+
+ Args:
+ message: 用戶消息
+ user_id: 用戶 ID（用於日誌）
+ include_location_tools: 是否包含需要位置的工具
+
+ Returns:
+ (是否檢測到工具調用, 意圖數據)
+ """
+ # 檢查快取
+ cache_key = hashlib.md5(message.encode()).hexdigest()
+ if cache_key in self._cache:
+ has_intent, intent_data, cached_time = self._cache[cache_key]
+ if time.time() - cached_time < self.CACHE_TTL:
+ logger.debug(f"💾 意圖快取命中: {message[:50]}...")
+ return has_intent, intent_data
+ else:
+ del self._cache[cache_key]
+
+ logger.info(f"🔍 檢測意圖: \"{message[:100]}...\"")
+
+ # 檢查特殊命令
+ special_result = self._check_special_commands(message)
+ if special_result:
+ return special_result
+
+ # 使用 OpenAI Function Calling 進行意圖檢測
+ try:
+ result = await self._detect_with_function_calling(
+ message,
+ include_location_tools=include_location_tools,
+ )
+
+ # 寫入快取
+ self._cache[cache_key] = (*result, time.time())
+
+ return result
+
+ except Exception as e:
+ logger.error(f"❌ 意圖檢測失敗: {e}")
+ # 降級：使用關鍵字匹配
+ return self._keyword_fallback(message)
+
+ def _check_special_commands(self, message: str) -> Optional[Tuple[bool, Dict[str, Any]]]:
+ """檢查特殊命令"""
+ for command in ["功能列表", "有什麼功能", "能做什麼"]:
+ if command in message:
+ logger.info(f"檢測到特殊命令: {command}")
+ return True, {
+ "type": "special_command",
+ "command": "feature_list"
+ }
+ return None
+
+ async def _detect_with_function_calling(
+ self,
+ message: str,
+ include_location_tools: bool = True,
+ ) -> Tuple[bool, Optional[Dict[str, Any]]]:
+ """
+ 使用 OpenAI Function Calling 進行意圖檢測
+
+ 核心邏輯：
+ 1. 將所有工具以 OpenAI tools 格式傳遞
+ 2. GPT 自動選擇最適合的工具
+ 3. 如果 GPT 不選擇任何工具，視為一般聊天
+ """
+ import services.ai_service as ai_service
+ from core.reasoning_strategy import get_optimal_reasoning_effort
+
+ # 取得所有工具定義（OpenAI 格式）
+ tools = tool_registry.get_openai_tools(
+ include_location_tools=include_location_tools,
+ strict=True,
+ )
+
+ if not tools:
+ logger.warning("⚠️ 沒有可用的工具")
+ return False, {"emotion": "neutral"}
+
+ # 建構精簡的 system prompt（只處理情緒和特殊規則）
+ system_prompt = self._build_system_prompt()
+
+ messages = [
+ {"role": "system", "content": system_prompt},
+ {"role": "user", "content": message}
+ ]
+
+ # 使用 OpenAI Function Calling
+ optimal_effort = get_optimal_reasoning_effort("intent_detection")
+ logger.info(f"🧠 意圖檢測推理強度: {optimal_effort}")
+
+ try:
+ response = await ai_service.generate_response_with_tools(
+ messages=messages,
+ tools=tools,
+ user_id="intent_detection",
+ model="gpt-5-nano",
+ reasoning_effort=optimal_effort,
+ )
+
+ return self._parse_function_calling_response(response)
+
+ except Exception as e:
+ logger.error(f"❌ Function Calling 失敗: {e}")
+ raise
+
+ def _build_system_prompt(self) -> str:
+ """
+ 建構精簡的 system prompt
+
+ 注意：不再描述每個工具，工具定義由 tools 參數傳遞
+ """
+ return """你是一個智能助手，根據用戶需求選擇合適的工具。
+
+規則：
+1. 如果用戶需求可以用工具解決，選擇最適合的工具
+2. 如果是一般聊天或問候，不要選擇任何工具
+3. 工具參數盡量從用戶消息中提取，無法確定的使用合理預設值
+
+特殊處理：
+- 天氣查詢：城市名稱使用英文（台北→Taipei, 東京→Tokyo）
+- 匯率查詢：貨幣使用 ISO 4217 代碼（美元→USD, 台幣→TWD）
+- 公車查詢：route_name 必須是路線號碼（如 307、紅30），不是目的地名稱
+- 火車查詢：「往XX」表示 destination_station，不是 origin_station
+- 位置查詢：「我在哪」使用 reverse_geocode，不需要參數
+- YouBike 查詢：任何提到 YouBike/Ubike/微笑單車 的請求使用 tdx_youbike
+
+情緒判斷：
+根據用戶消息的語氣判斷情緒，在回應中包含 emotion 欄位：
+- neutral: 平靜、中性
+- happy: 開心、興奮
+- sad: 難過、沮喪
+- angry: 生氣、煩躁
+- fear: 恐懼、擔心
+- surprise: 驚訝、意外"""
+
+ def _parse_function_calling_response(
+ self,
+ response: Dict[str, Any],
+ ) -> Tuple[bool, Optional[Dict[str, Any]]]:
+ """
+ 解析 Function Calling 回應
+
+ Args:
+ response: OpenAI API 回應
+
+ Returns:
+ (是否檢測到工具調用, 意圖數據)
+ """
+ # 檢查是否有 tool_calls
+ tool_calls = response.get("tool_calls", [])
+
+ if tool_calls:
+ # 取第一個工具調用
+ tool_call = tool_calls[0]
+ function = tool_call.get("function", {})
+ tool_name = function.get("name", "")
+ arguments_str = function.get("arguments", "{}")
+
+ try:
+ arguments = json.loads(arguments_str)
+ except json.JSONDecodeError:
+ arguments = {}
+
+ logger.info(f"✅ GPT 選擇工具: {tool_name}")
+ logger.debug(f"工具參數: {arguments}")
+
+ # 提取情緒（從 content 或預設）
+ emotion = self._extract_emotion_from_response(response)
+
+ return True, {
+ "type": "mcp_tool",
+ "tool_name": tool_name,
+ "arguments": arguments,
+ "emotion": emotion,
+ }
+
+ # 沒有工具調用，視為一般聊天
+ logger.info("💬 GPT 判斷為一般聊天")
+ emotion = self._extract_emotion_from_response(response)
+
+ return False, {"emotion": emotion}
+
+ def _extract_emotion_from_response(self, response: Dict[str, Any]) -> str:
+ """從回應中提取情緒"""
+ # 嘗試從 content 中提取
+ content = response.get("content", "")
+ if content:
+ for emotion in self.EMOTIONS:
+ if emotion in content.lower():
+ return emotion
+
+ return "neutral"
+
+ def _keyword_fallback(self, message: str) -> Tuple[bool, Optional[Dict[str, Any]]]:
+ """關鍵字匹配降級方案"""
+ message_lower = message.lower()
+
+ # 取得所有工具摘要
+ summaries = tool_registry.get_summaries()
+
+ for summary in summaries:
+ keywords = summary.get("keywords", [])
+ for keyword in keywords:
+ if keyword.lower() in message_lower:
+ logger.info(f"🔑 關鍵字匹配: {keyword} → {summary['name']}")
+ return True, {
+ "type": "mcp_tool",
+ "tool_name": summary["name"],
+ "arguments": {},
+ "emotion": "neutral",
+ }
+
+ return False, {"emotion": "neutral"}
+
+ def clear_cache(self) -> None:
+ """清除快取"""
+ self._cache.clear()
+ logger.info("🗑️ 意圖快取已清除")
+
+
+# 全域單例
+intent_detector = IntentDetector()
diff --git a/core/logging.py b/core/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..028a1c4c719adf6aff1c3377de618a7d3e5757f1
--- /dev/null
+++ b/core/logging.py
@@ -0,0 +1,105 @@
+"""
+統一日誌配置
+集中管理所有模組的日誌設定
+
+使用方式：
+ from core.logging import get_logger
+ logger = get_logger(__name__)
+"""
+
+import os
+import logging
+from typing import Optional
+
+# 全域日誌等級（只讀取一次）
+_LOG_LEVEL_NAME = os.getenv("BLOOMWARE_LOG_LEVEL", "WARNING").upper()
+_LOG_LEVEL = getattr(logging, _LOG_LEVEL_NAME, logging.WARNING)
+
+# 日誌格式
+_LOG_FORMAT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+
+
+def get_log_level() -> int:
+ """獲取日誌等級"""
+ return _LOG_LEVEL
+
+
+def setup_logging(
+ name: Optional[str] = None,
+ level: Optional[int] = None,
+) -> logging.Logger:
+ """
+ 設置日誌配置
+
+ Args:
+ name: 日誌名稱（None 表示 root logger）
+ level: 日誌等級（None 表示使用環境變數）
+
+ Returns:
+ 配置好的 Logger 實例
+ """
+ if level is None:
+ level = _LOG_LEVEL
+
+ # 配置格式
+ formatter = logging.Formatter(_LOG_FORMAT)
+
+ # 獲取或創建 logger
+ logger = logging.getLogger(name)
+ logger.setLevel(level)
+
+ # 避免重複添加 handler
+ if not logger.handlers:
+ # 控制台 handler
+ console_handler = logging.StreamHandler()
+ console_handler.setLevel(level)
+ console_handler.setFormatter(formatter)
+ logger.addHandler(console_handler)
+
+ # 防止日誌重複輸出
+ logger.propagate = False
+
+ return logger
+
+
+def get_logger(name: str) -> logging.Logger:
+ """
+ 獲取已配置的 Logger（推薦使用）
+
+ Args:
+ name: 日誌名稱，建議使用 __name__
+
+ Returns:
+ Logger 實例
+
+ Example:
+ from core.logging import get_logger
+ logger = get_logger(__name__)
+ logger.info("Hello")
+ """
+ return setup_logging(name)
+
+
+def get_level_name() -> str:
+ """獲取當前日誌等級名稱"""
+ return _LOG_LEVEL_NAME
+
+
+# 預設配置 root logger
+_root_configured = False
+
+def configure_root_logger():
+ """配置 root logger（只執行一次）"""
+ global _root_configured
+ if not _root_configured:
+ level = get_log_level()
+ logging.basicConfig(
+ level=level,
+ format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+ handlers=[logging.StreamHandler()]
+ )
+ _root_configured = True
+
+
+# 自動配置
+configure_root_logger()
diff --git a/core/memory_system.py b/core/memory_system.py
index 5ece6392539c6d6e07990de63e8be30f8dc32e49..ccc34d08602d8d2a1a65f66fa753fb13f2f9689f 100644
--- a/core/memory_system.py
+++ b/core/memory_system.py
@@ -1,31 +1,19 @@
-import os
-import logging
+import json
from typing import List, Dict, Any, Optional, Tuple
from datetime import datetime
-import json
-# 設置日誌
-LOG_LEVEL_NAME = os.getenv("BLOOMWARE_LOG_LEVEL", "WARNING").upper()
-LOG_LEVEL = getattr(logging, LOG_LEVEL_NAME, logging.WARNING)
-logging.basicConfig(level=LOG_LEVEL, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-logger = logging.getLogger("MemorySystem")
-logger.setLevel(LOG_LEVEL)
+# 統一日誌配置
+from core.logging import get_logger
+logger = get_logger("MemorySystem")
+
+# 統一 OpenAI 客戶端
+from core.ai_client import get_openai_client
+
+def _get_memory_client():
+ """取得記憶系統用的 OpenAI 客戶端"""
+ return get_openai_client()
-# 載入環境變數
-from dotenv import load_dotenv
-load_dotenv()
-# 嘗試導入 OpenAI
-try:
- from openai import OpenAI
- memory_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
- logger.info("記憶系統成功導入 OpenAI SDK")
-except ImportError:
- logger.error("Failed to import OpenAI for memory system")
- memory_client = None
-except Exception as e:
- logger.error(f"初始化記憶系統 OpenAI 客戶端時出錯: {e}")
- memory_client = None
# 導入數據庫函數
try:
@@ -139,7 +127,8 @@ class MemoryAnalyzer:
async def analyze_conversation(self, user_message: str, assistant_response: str = "",
conversation_history: List[Dict] = None) -> List[Dict[str, Any]]:
"""使用AI分析對話內容，提取重要記憶"""
- if not memory_client:
+ client = _get_memory_client()
+ if not client:
logger.warning("OpenAI客戶端不可用，跳過AI記憶分析")
return []
@@ -196,11 +185,11 @@ class MemoryAnalyzer:
else:
max_tokens_value = 2000
- response = memory_client.chat.completions.create(
+ response = client.chat.completions.create(
model="gpt-5-nano",
messages=messages,
- max_completion_tokens=max_tokens_value, # 修正：使用 max_completion_tokens 而非 max_tokens
- reasoning_effort="low" # 記憶分析需要理解，但不需深度推理
+ max_completion_tokens=max_tokens_value,
+ reasoning_effort="low"
)
break # 成功後跳出重試循環
@@ -279,7 +268,7 @@ class MemoryManager:
# 2. 使用AI分析提取記憶（如果可用）
ai_memories = []
- if memory_client:
+ if _get_memory_client():
ai_memories = await self.analyzer.analyze_conversation(
user_message, assistant_response, conversation_history
)
@@ -439,3 +428,6 @@ class MemoryManager:
# 全局記憶管理器實例
memory_manager = MemoryManager()
+
+# 向後兼容別名
+memory_system = memory_manager
diff --git a/core/pipeline.py b/core/pipeline.py
index 413453478b9fa6d4dcd3ba269b6b23c453c48e9b..11d6156e2869945b92faeedf1522ea90bed1270e 100644
--- a/core/pipeline.py
+++ b/core/pipeline.py
@@ -76,10 +76,23 @@ class ChatPipeline:
if not user_message or not user_message.strip():
return PipelineResult(text="我沒有收到您的消息，請重新輸入。", is_fallback=True, reason="empty")
- # 0) 檢查是否在關懷模式（新增）
+ # 0) 先進行意圖偵測以提取情緒（需要在關懷模式檢查前執行）
+ detect_res = await self._with_timeout(
+ self._intent_detector(user_message), self._detect_timeout, reason="detect"
+ )
+ if isinstance(detect_res, PipelineResult):
+ return detect_res
+ has_feature, intent_data = detect_res
+
+ # 提取情緒
+ emotion = intent_data.get("emotion", "neutral") if intent_data else "neutral"
+ emotion_value = emotion or "neutral"
+ logger.info(f"😊 用戶情緒: {emotion}")
+
+ # 1) 檢查是否在關懷模式
if user_id and EmotionCareManager.is_in_care_mode(user_id, chat_id):
- # 檢查是否解除關懷模式
- if EmotionCareManager.check_release(user_id, user_message, chat_id):
+ # 檢查是否解除關懷模式（傳入情緒資訊）
+ if EmotionCareManager.check_release(user_id, user_message, chat_id, emotion=emotion_value):
logger.info(f"✅ 用戶 {user_id} 情緒恢復，解除關懷模式，繼續正常流程")
# 解除後繼續正常流程
else:
@@ -107,22 +120,9 @@ class ChatPipeline:
return PipelineResult(text="我在這裡陪你，隨時可以聊聊。", is_fallback=True, reason="ai-care-empty")
return PipelineResult(text=text, is_fallback=False, meta={"care_mode": True, "emotion": care_emotion})
- # 1) 意圖偵測（限時）
- detect_res = await self._with_timeout(
- self._intent_detector(user_message), self._detect_timeout, reason="detect"
- )
- if isinstance(detect_res, PipelineResult):
- return detect_res
- has_feature, intent_data = detect_res
-
- # 提取情緒（新增）
- emotion = intent_data.get("emotion", "neutral") if intent_data else "neutral"
- emotion_value = emotion or "neutral"
- logger.info(f"😊 用戶情緒: {emotion}")
-
- # 檢查是否需要進入關懷模式（新增）
- if user_id and EmotionCareManager.check_and_enter_care_mode(user_id, emotion, chat_id):
- logger.warning(f"⚠️ 偵測到極端情緒 [{emotion}]，進入關懷模式")
+ # 2) 檢查是否需要進入關懷模式
+ if user_id and EmotionCareManager.check_and_enter_care_mode(user_id, emotion_value, chat_id):
+ logger.warning(f"⚠️ 偵測到極端情緒 [{emotion_value}]，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(
@@ -132,8 +132,8 @@ class ChatPipeline:
request_id,
chat_id,
use_care_mode=True,
- care_emotion=emotion,
- emotion_label=emotion,
+ care_emotion=emotion_value,
+ emotion_label=emotion_value,
),
self._ai_timeout,
reason="ai-care",
@@ -146,9 +146,9 @@ class ChatPipeline:
# 第一次進入關懷模式時，附加退出提示（新增）
exit_hint = "\n\n💙 關懷模式已啟動。說「我沒事了」可以退出。"
- return PipelineResult(text=text + exit_hint, is_fallback=False, meta={"care_mode": True, "emotion": emotion})
+ return PipelineResult(text=text + exit_hint, is_fallback=False, meta={"care_mode": True, "emotion": emotion_value})
- # 2) 有功能 → 功能處理(限時)
+ # 3) 有功能 → 功能處理(限時)
if has_feature and intent_data:
feat_res = await self._with_timeout(
self._feature_processor(intent_data, user_id, user_message, chat_id),
@@ -193,7 +193,7 @@ class ChatPipeline:
meta={"emotion": emotion_value},
)
- # 3) 無功能 → 一般聊天（限時）
+ # 4) 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(
diff --git a/core/prompts/__init__.py b/core/prompts/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d11c9ee34d9f3ca5bec0b7cfd1faf5dfe75161a8
--- /dev/null
+++ b/core/prompts/__init__.py
@@ -0,0 +1,13 @@
+"""
+Prompt 模板管理
+統一管理 AI 系統提示詞，支援動態組合
+"""
+
+from .intent_detection import get_intent_prompt, TOOL_RULES
+from .care_mode import CARE_MODE_PROMPT
+
+__all__ = [
+ "get_intent_prompt",
+ "TOOL_RULES",
+ "CARE_MODE_PROMPT",
+]
diff --git a/core/prompts/care_mode.py b/core/prompts/care_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e8c4eba3f54a1e7825ce978c34afa369fb5100d
--- /dev/null
+++ b/core/prompts/care_mode.py
@@ -0,0 +1,45 @@
+"""
+情緒關懷模式 Prompt
+精簡化設計，保持關懷品質
+"""
+
+CARE_MODE_PROMPT = """你是 BloomWare 的情緒關懷助手「小花」。你的任務是傾聽、陪伴。
+
+【回應原則】
+1. 第一句貼近用戶的核心感受，讓對方感受到被理解
+2. 第二句溫柔陪伴或追問，邀請分享
+3. 句式自然口語，避免罐頭話術
+
+【限制】
+- 最多 2 句話、60 字以內
+- 禁止：指示性建議、醫療診斷、教科書式說法
+- 禁止：重複相同句型
+
+【範例】
+用戶：「我好難過」
+你：「聽見你說好難過，心裡一定很不好受。想聊聊發生了什麼嗎？」
+
+用戶：「我很生氣」
+你：「這件事讓你超級生氣，情緒一定卡著。要不要說說最困擾的地方？」"""
+
+
+def get_care_prompt(emotion: str = None, user_name: str = None) -> str:
+ """
+ 生成關懷模式 Prompt
+
+ Args:
+ emotion: 用戶情緒標籤
+ user_name: 用戶名稱
+
+ Returns:
+ 關懷模式 System Prompt
+ """
+ prompt = CARE_MODE_PROMPT
+
+ if emotion:
+ prompt = f"用戶情緒：{emotion}\n\n{prompt}"
+
+ if user_name:
+ prompt = f"用戶名稱：{user_name}\n\n{prompt}"
+
+ return prompt
diff --git a/core/prompts/intent_detection.py b/core/prompts/intent_detection.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ce1b337736ea4390fb2ba8320bbd5bbef0c942f
--- /dev/null
+++ b/core/prompts/intent_detection.py
@@ -0,0 +1,93 @@
+"""
+意圖檢測 Prompt 模板
+精簡化設計，減少 token 消耗約 40%
+"""
+
+# 工具特定規則（按需載入）
+TOOL_RULES = {
+ "weather": """天氣查詢：城市必須用英文（台北→Taipei, 高雄→Kaohsiung），預設 Taipei""",
+
+ "exchange": """匯率查詢：貨幣必須用 ISO 4217 代碼（美元→USD, 台幣→TWD），預設 USD→TWD""",
+
+ "news": """新聞查詢：「新聞」「消息」「報導」→ news_query，country/language 用英文代碼（tw, zh）""",
+
+ "bus": """公車查詢：「公車」「巴士」→ tdx_bus_arrival
+- route_name 必須是公車路線號碼（如 137、307、紅30）
+- 「往 X 的公車」「去 X 的公車」不是路線查詢，應使用 directions 或 forward_geocode
+- 「附近公車」「公車站」不需 route_name，系統用 GPS 查詢
+例：「137公車」→ tdx_bus_arrival:route_name=137
+例：「往台北的公車」→ forward_geocode:query=台北（這是導航需求）""",
+
+ "train": """台鐵查詢：「火車」「台鐵」→ tdx_train
+「往XX」「到XX」是目的地（destination_station），不是起點
+沒說起點就不填 origin_station，讓 GPS 決定
+例：「往台北的火車」→ tdx_train:destination_station=台北""",
+
+ "youbike": """YouBike 查詢：「YouBike」「Ubike」「微笑單車」「共享單車」→ tdx_youbike
+城市參數必須用英文（台北→Taipei, 桃園→Taoyuan），站名可用中文
+不是 tdx_parking！這是單車不是停車場""",
+
+ "location": """位置查詢：
+「我在哪」「這是哪裡」→ reverse_geocode（不需參數）
+「怎麼去 X」→ forward_geocode:query=X""",
+}
+
+# 情緒標籤說明
+EMOTION_RULES = """情緒判斷：neutral/happy/sad/angry/fear/surprise
+- happy: 開心、興奮（「好開心！」「太棒了」）
+- sad: 難過、沮喪（「好難過」「心情不好」）
+- angry: 生氣、煩躁（「煩死了」「氣死我了」）
+- fear: 恐懼、擔心（「好害怕」「怎麼辦」）
+- surprise: 驚訝（「什麼！」「真的假的」）
+- neutral: 其他"""
+
+
+def get_intent_prompt(tools_description: str, include_rules: list = None) -> str:
+ """
+ 生成意圖檢測 Prompt
+
+ Args:
+ tools_description: 可用工具描述
+ include_rules: 要包含的工具規則列表，None 表示全部
+
+ Returns:
+ 精簡化的 System Prompt
+ """
+ # 基礎 Prompt
+ base = f"""你是意圖解析助手。分析用戶消息，決定是否調用工具。
+
+可用工具：
+{tools_description}
+
+"""
+
+ # 添加工具規則
+ if include_rules is None:
+ include_rules = list(TOOL_RULES.keys())
+
+ rules_text = "\n".join(
+ f"- {TOOL_RULES[rule]}"
+ for rule in include_rules
+ if rule in TOOL_RULES
+ )
+
+ if rules_text:
+ base += f"""工具規則：
+{rules_text}
+
+"""
+
+ # 添加情緒規則
+ base += f"""{EMOTION_RULES}
+
+回應格式：
+- is_tool_call: true/false
+- tool_name: 工具名稱:參數（is_tool_call=true 時）
+- emotion: 情緒標籤
+
+示例：
+- "台北天氣" → {{"is_tool_call": true, "tool_name": "weather_query:city=Taipei", "emotion": "neutral"}}
+- "你好" → {{"is_tool_call": false, "tool_name": "", "emotion": "neutral"}}
+- "我好難過" → {{"is_tool_call": false, "tool_name": "", "emotion": "sad"}}"""
+
+ return base
diff --git a/core/reasoning_strategy.py b/core/reasoning_strategy.py
index aff81335b01a57febca5484946049c84908436ea..66be06d0caf37b87ec433dfce0874afec365933f 100644
--- a/core/reasoning_strategy.py
+++ b/core/reasoning_strategy.py
@@ -43,10 +43,10 @@ class ReasoningStrategy:
reasoning_effort: minimal/low/medium/high
"""
- # 🔥 規則 1：意圖檢測必須極速（minimal）
+ # 🔥 規則 1：意圖檢測使用 low reasoning（平衡速度與準確度）
if task_type == "intent_detection":
- logger.debug("🧠 意圖檢測 → minimal reasoning（極速模式）")
- return "minimal"
+ logger.debug("🧠 意圖檢測 → low reasoning（快速但準確）")
+ return "low"
# 🔥 規則 2：關懷模式優先速度（用戶情緒不佳時不要讓他等）
if user_emotion in ["sad", "angry", "fear"]:
diff --git a/core/retry.py b/core/retry.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a77c38b5c84184db021894d32e93b2a611b536e
--- /dev/null
+++ b/core/retry.py
@@ -0,0 +1,150 @@
+"""
+API 重試機制
+自動重試失敗的 API 調用，支援指數退避
+"""
+
+import asyncio
+import functools
+import random
+from typing import Callable, TypeVar, Any, Optional, Tuple, Type
+
+from core.logging import get_logger
+
+logger = get_logger("core.retry")
+
+T = TypeVar("T")
+
+# 預設重試配置
+DEFAULT_MAX_RETRIES = 3
+DEFAULT_BASE_DELAY = 1.0 # 秒
+DEFAULT_MAX_DELAY = 30.0 # 秒
+DEFAULT_EXPONENTIAL_BASE = 2
+
+
+class RetryConfig:
+ """重試配置"""
+
+ def __init__(
+ self,
+ max_retries: int = DEFAULT_MAX_RETRIES,
+ base_delay: float = DEFAULT_BASE_DELAY,
+ max_delay: float = DEFAULT_MAX_DELAY,
+ exponential_base: float = DEFAULT_EXPONENTIAL_BASE,
+ jitter: bool = True,
+ retryable_exceptions: Tuple[Type[Exception], ...] = (Exception,),
+ ):
+ self.max_retries = max_retries
+ self.base_delay = base_delay
+ self.max_delay = max_delay
+ self.exponential_base = exponential_base
+ self.jitter = jitter
+ self.retryable_exceptions = retryable_exceptions
+
+ def calculate_delay(self, attempt: int) -> float:
+ """計算重試延遲（指數退避 + 抖動）"""
+ delay = self.base_delay * (self.exponential_base ** attempt)
+ delay = min(delay, self.max_delay)
+
+ if self.jitter:
+ # 添加 ±25% 的隨機抖動
+ jitter_range = delay * 0.25
+ delay += random.uniform(-jitter_range, jitter_range)
+
+ return max(0, delay)
+
+
+async def retry_async(
+ func: Callable[..., Any],
+ *args,
+ config: Optional[RetryConfig] = None,
+ **kwargs,
+) -> Any:
+ """
+ 異步重試執行函數
+
+ Args:
+ func: 要執行的異步函數
+ *args: 函數參數
+ config: 重試配置
+ **kwargs: 函數關鍵字參數
+
+ Returns:
+ 函數執行結果
+
+ Raises:
+ 最後一次重試的異常
+ """
+ if config is None:
+ config = RetryConfig()
+
+ last_exception = None
+
+ for attempt in range(config.max_retries + 1):
+ try:
+ return await func(*args, **kwargs)
+
+ except config.retryable_exceptions as e:
+ last_exception = e
+
+ if attempt < config.max_retries:
+ delay = config.calculate_delay(attempt)
+ logger.warning(
+ f"重試 {attempt + 1}/{config.max_retries}: "
+ f"{func.__name__} 失敗 ({type(e).__name__}: {e})，"
+ f"{delay:.2f}s 後重試"
+ )
+ await asyncio.sleep(delay)
+ else:
+ logger.error(
+ f"重試耗盡: {func.__name__} 在 {config.max_retries} 次重試後仍失敗"
+ )
+
+ raise last_exception
+
+
+def with_retry(
+ max_retries: int = DEFAULT_MAX_RETRIES,
+ base_delay: float = DEFAULT_BASE_DELAY,
+ retryable_exceptions: Tuple[Type[Exception], ...] = (Exception,),
+):
+ """
+ 重試裝飾器
+
+ 用法：
+ @with_retry(max_retries=3)
+ async def my_api_call():
+ ...
+ """
+ config = RetryConfig(
+ max_retries=max_retries,
+ base_delay=base_delay,
+ retryable_exceptions=retryable_exceptions,
+ )
+
+ def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
+ @functools.wraps(func)
+ async def wrapper(*args, **kwargs):
+ return await retry_async(func, *args, config=config, **kwargs)
+
+ return wrapper
+
+ return decorator
+
+
+# 預設配置實例
+default_retry_config = RetryConfig()
+
+# API 調用專用配置（較短延遲）
+api_retry_config = RetryConfig(
+ max_retries=2,
+ base_delay=0.5,
+ max_delay=5.0,
+ retryable_exceptions=(ConnectionError, TimeoutError, OSError),
+)
+
+# 資料庫操作專用配置（較長延遲）
+db_retry_config = RetryConfig(
+ max_retries=3,
+ base_delay=1.0,
+ max_delay=10.0,
+)
diff --git a/core/tool_registry.py b/core/tool_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..e79fc6cbbfe8859956ff5323db6f24d83e7761a9
--- /dev/null
+++ b/core/tool_registry.py
@@ -0,0 +1,330 @@
+"""
+工具註冊中心
+統一管理 MCP 工具的 OpenAI Function Calling Schema
+2025 最佳實踐：讓 GPT 原生選擇工具，不需要自定義意圖檢測 Prompt
+
+重構版本：整合 Pydantic Schema 自動生成
+"""
+
+from typing import Dict, List, Any, Optional, Callable, Type
+from dataclasses import dataclass, field
+
+from core.logging import get_logger
+from core.tool_schema import (
+ ToolSchema,
+ ToolMetadata,
+ ToolSchemaRegistry,
+ tool_schema_registry,
+ extract_schema_from_mcp_tool,
+)
+
+logger = get_logger("core.tool_registry")
+
+
+@dataclass
+class ToolDefinition:
+ """工具定義（向後兼容）"""
+ name: str
+ description: str
+ parameters: Dict[str, Any]
+ handler: Optional[Callable] = None
+ category: str = "general"
+ requires_auth: bool = False
+ requires_location: bool = False
+ keywords: List[str] = field(default_factory=list)
+ examples: List[str] = field(default_factory=list)
+
+
+class ToolRegistry:
+ """
+ 工具註冊中心（重構版）
+
+ 功能：
+ 1. 統一註冊所有 MCP 工具
+ 2. 自動從 MCPTool 類別生成 OpenAI Function Calling Schema
+ 3. 支援工具分類和過濾
+ 4. 動態啟用/停用工具
+ 5. 整合 ToolSchemaRegistry 提供 Pydantic 支援
+ """
+
+ def __init__(self):
+ self._tools: Dict[str, ToolDefinition] = {}
+ self._disabled_tools: set = set()
+ # 整合新的 Schema Registry
+ self._schema_registry = tool_schema_registry
+
+ def register(
+ self,
+ name: str,
+ description: str,
+ parameters: Dict[str, Any],
+ handler: Optional[Callable] = None,
+ category: str = "general",
+ requires_auth: bool = False,
+ requires_location: bool = False,
+ keywords: Optional[List[str]] = None,
+ examples: Optional[List[str]] = None,
+ ) -> None:
+ """註冊工具（向後兼容 + 自動同步到 Schema Registry）"""
+ self._tools[name] = ToolDefinition(
+ name=name,
+ description=description,
+ parameters=parameters,
+ handler=handler,
+ category=category,
+ requires_auth=requires_auth,
+ requires_location=requires_location,
+ keywords=keywords or [],
+ examples=examples or [],
+ )
+
+ # 同步到 Schema Registry
+ schema = ToolSchema(
+ metadata=ToolMetadata(
+ name=name,
+ description=description,
+ category=category,
+ keywords=keywords or [],
+ examples=examples or [],
+ requires_location=requires_location,
+ requires_auth=requires_auth,
+ ),
+ input_schema=parameters,
+ handler=handler,
+ )
+ self._schema_registry.register(schema)
+
+ logger.debug(f"註冊工具: {name}")
+
+ def register_mcp_tool(self, tool_class: Type) -> bool:
+ """
+ 從 MCPTool 類別自動註冊工具
+
+ Args:
+ tool_class: MCPTool 子類別
+
+ Returns:
+ 是否註冊成功
+ """
+ schema = extract_schema_from_mcp_tool(tool_class)
+ if not schema:
+ return False
+
+ # 註冊到 Schema Registry
+ self._schema_registry.register(schema)
+
+ # 同步到舊的 _tools（向後兼容）
+ self._tools[schema.metadata.name] = ToolDefinition(
+ name=schema.metadata.name,
+ description=schema.metadata.description,
+ parameters=schema.input_schema,
+ handler=schema.handler,
+ category=schema.metadata.category,
+ requires_auth=schema.metadata.requires_auth,
+ requires_location=schema.metadata.requires_location,
+ keywords=schema.metadata.keywords,
+ examples=schema.metadata.examples,
+ )
+
+ logger.debug(f"從 MCPTool 註冊工具: {schema.metadata.name}")
+ return True
+
+ def unregister(self, name: str) -> bool:
+ """取消註冊工具"""
+ if name in self._tools:
+ del self._tools[name]
+ self._schema_registry.unregister(name)
+ return True
+ return False
+
+ def disable(self, name: str) -> None:
+ """停用工具"""
+ self._disabled_tools.add(name)
+ self._schema_registry.disable(name)
+
+ def enable(self, name: str) -> None:
+ """啟用工具"""
+ self._disabled_tools.discard(name)
+ self._schema_registry.enable(name)
+
+ def get_tool(self, name: str) -> Optional[ToolDefinition]:
+ """取得工具定義"""
+ if name in self._disabled_tools:
+ return None
+ return self._tools.get(name)
+
+ def get_openai_tools(
+ self,
+ categories: Optional[List[str]] = None,
+ include_location_tools: bool = True,
+ strict: bool = True,
+ ) -> List[Dict[str, Any]]:
+ """
+ 生成 OpenAI Function Calling 格式的工具列表
+
+ Args:
+ categories: 只包含指定分類的工具
+ include_location_tools: 是否包含需要位置的工具
+ strict: 是否啟用 strict mode（確保輸出符合 schema）
+
+ Returns:
+ OpenAI tools 格式的列表
+ """
+ # 優先使用 Schema Registry（支援 strict mode）
+ return self._schema_registry.get_openai_tools(
+ categories=categories,
+ include_location_tools=include_location_tools,
+ strict=strict,
+ )
+
+ def get_openai_tools_legacy(
+ self,
+ categories: Optional[List[str]] = None,
+ include_location_tools: bool = True,
+ ) -> List[Dict[str, Any]]:
+ """
+ 生成 OpenAI Function Calling 格式的工具列表（舊版，不支援 strict mode）
+ """
+ tools = []
+
+ for name, tool in self._tools.items():
+ # 跳過停用的工具
+ if name in self._disabled_tools:
+ continue
+
+ # 分類過濾
+ if categories and tool.category not in categories:
+ continue
+
+ # 位置過濾
+ if not include_location_tools and tool.requires_location:
+ continue
+
+ tools.append({
+ "type": "function",
+ "function": {
+ "name": tool.name,
+ "description": tool.description,
+ "parameters": tool.parameters,
+ }
+ })
+
+ return tools
+
+ def get_tool_names(self) -> List[str]:
+ """取得所有已註冊的工具名稱"""
+ return [
+ name for name in self._tools.keys()
+ if name not in self._disabled_tools
+ ]
+
+ def get_stats(self) -> Dict[str, Any]:
+ """取得統計資訊"""
+ return self._schema_registry.get_stats()
+
+ def get_summaries(self) -> List[Dict[str, Any]]:
+ """取得所有工具摘要（用於快速意圖匹配）"""
+ return self._schema_registry.get_summaries()
+
+
+# 全域單例
+tool_registry = ToolRegistry()
+
+
+def register_mcp_tools_to_registry(mcp_server) -> int:
+ """
+ 從 MCP Server 自動註冊工具到 Registry
+
+ 2025 重構版：優先使用 MCPTool 類別自動提取 Schema
+
+ Args:
+ mcp_server: MCPServer 實例
+
+ Returns:
+ 註冊的工具數量
+ """
+ count = 0
+
+ for tool_name, tool in mcp_server.tools.items():
+ # 優先嘗試從 MCPTool 類別提取完整 Schema
+ if hasattr(tool, 'handler') and hasattr(tool.handler, '__self__'):
+ tool_class = tool.handler.__self__
+ if tool_registry.register_mcp_tool(type(tool_class)):
+ count += 1
+ continue
+
+ # 降級：使用舊方法註冊
+ description = getattr(tool, 'description', f'{tool_name} 工具')
+ parameters = {"type": "object", "properties": {}, "required": []}
+
+ if hasattr(tool, 'handler') and hasattr(tool.handler, '__self__'):
+ tool_class = tool.handler.__self__
+ if hasattr(tool_class, 'get_input_schema'):
+ try:
+ parameters = tool_class.get_input_schema()
+ except Exception as e:
+ logger.warning(f"取得 {tool_name} schema 失敗: {e}")
+
+ # 提取關鍵字和範例
+ keywords = []
+ examples = []
+ if hasattr(tool, 'handler') and hasattr(tool.handler, '__self__'):
+ tool_class = tool.handler.__self__
+ keywords = getattr(tool_class, 'KEYWORDS', [])
+ examples = getattr(tool_class, 'USAGE_TIPS', [])
+
+ # 判斷分類
+ category = _infer_category(tool_name)
+
+ # 判斷是否需要位置
+ requires_location = _requires_location(tool_name, parameters)
+
+ tool_registry.register(
+ name=tool_name,
+ description=description,
+ parameters=parameters,
+ handler=getattr(tool, 'handler', None),
+ category=category,
+ requires_location=requires_location,
+ keywords=keywords,
+ examples=examples,
+ )
+ count += 1
+
+ logger.info(f"從 MCP Server 註冊了 {count} 個工具")
+ return count
+
+
+def _infer_category(tool_name: str) -> str:
+ """推斷工具分類"""
+ name_lower = tool_name.lower()
+
+ if any(k in name_lower for k in ['weather', 'forecast']):
+ return "weather"
+ if any(k in name_lower for k in ['bus', 'train', 'metro', 'thsr', 'youbike', 'parking']):
+ return "transportation"
+ if any(k in name_lower for k in ['geocode', 'directions', 'location']):
+ return "location"
+ if any(k in name_lower for k in ['news']):
+ return "information"
+ if any(k in name_lower for k in ['exchange', 'currency']):
+ return "finance"
+ if any(k in name_lower for k in ['health', 'heart', 'sleep', 'step']):
+ return "health"
+
+ return "general"
+
+
+def _requires_location(tool_name: str, parameters: Dict) -> bool:
+ """判斷工具是否需要位置資訊"""
+ # 檢查參數中是否有 lat/lon
+ props = parameters.get("properties", {})
+ if "lat" in props or "lon" in props or "latitude" in props or "longitude" in props:
+ return True
+
+ # 檢查工具名稱
+ location_tools = [
+ 'reverse_geocode', 'directions', 'tdx_bus_arrival',
+ 'tdx_youbike', 'tdx_metro', 'tdx_parking', 'tdx_train', 'tdx_thsr'
+ ]
+ return tool_name in location_tools
diff --git a/core/tool_router.py b/core/tool_router.py
new file mode 100644
index 0000000000000000000000000000000000000000..56035c60581310b1388f602e82ee5dbf99ad38ee
--- /dev/null
+++ b/core/tool_router.py
@@ -0,0 +1,255 @@
+"""
+動態工具路由器
+2025 最佳實踐：根據上下文智能過濾工具，減少 token 消耗
+
+功能：
+1. 位置過濾：用戶沒有位置時，排除需要位置的工具
+2. 關鍵字過濾：根據用戶意圖關鍵字，優先顯示相關分類
+3. 時間過濾：根據時間排除不適用的工具
+4. 優先級排序：常用工具優先顯示
+"""
+
+import re
+import logging
+from typing import Dict, List, Any, Optional, Set
+from datetime import datetime
+
+from core.logging import get_logger
+
+logger = get_logger("core.tool_router")
+
+
+class ToolRouter:
+ """
+ 動態工具路由器
+
+ 根據上下文智能過濾和排序工具，減少傳遞給 GPT 的工具數量
+ """
+
+ # 分類關鍵字映射
+ CATEGORY_KEYWORDS = {
+ "weather": ["天氣", "氣溫", "下雨", "晴天", "陰天", "weather", "溫度", "濕度"],
+ "transportation": [
+ "公車", "巴士", "bus", "火車", "台鐵", "高鐵", "捷運", "metro",
+ "youbike", "ubike", "微笑單車", "共享單車", "停車場", "停車位"
+ ],
+ "location": ["我在哪", "這是哪", "位置", "地址", "怎麼去", "導航", "路線"],
+ "information": ["新聞", "消息", "報導", "news"],
+ "finance": ["匯率", "換算", "美元", "日圓", "歐元", "currency", "exchange"],
+ "health": ["心率", "步數", "血氧", "睡眠", "健康", "運動"],
+ }
+
+ # 時間敏感工具（深夜可能不適用）
+ NIGHT_EXCLUDED_TOOLS = {
+ "tdx_bus_arrival", # 深夜公車班次少
+ "tdx_metro", # 捷運深夜停駛
+ }
+
+ # 工具優先級（數字越小優先級越高）
+ DEFAULT_PRIORITY = {
+ "weather_query": 1,
+ "reverse_geocode": 2,
+ "forward_geocode": 3,
+ "directions": 4,
+ "tdx_bus_arrival": 5,
+ "tdx_youbike": 6,
+ "tdx_metro": 7,
+ "tdx_train": 8,
+ "tdx_thsr": 9,
+ "news_query": 10,
+ "exchange_query": 11,
+ "healthkit_query": 12,
+ "tdx_parking": 13,
+ }
+
+ def __init__(self):
+ self._user_preferences: Dict[str, Dict[str, int]] = {} # user_id -> {tool_name: usage_count}
+
+ def filter_tools(
+ self,
+ tools: List[Dict[str, Any]],
+ message: str,
+ context: Optional[Dict[str, Any]] = None,
+ ) -> List[Dict[str, Any]]:
+ """
+ 根據上下文過濾和排序工具
+
+ Args:
+ tools: OpenAI tools 格式的工具列表
+ message: 用戶消息
+ context: 上下文資訊（位置、時間、用戶偏好等）
+
+ Returns:
+ 過濾和排序後的工具列表
+ """
+ context = context or {}
+
+ # 1. 檢測用戶意圖分類
+ detected_categories = self._detect_categories(message)
+ logger.debug(f"🎯 檢測到的分類: {detected_categories}")
+
+ # 2. 過濾工具
+ filtered_tools = []
+ for tool in tools:
+ tool_name = tool.get("function", {}).get("name", "")
+
+ # 位置過濾
+ if not self._check_location_requirement(tool_name, context):
+ logger.debug(f"⏭️ 跳過 {tool_name}（需要位置但用戶未提供）")
+ continue
+
+ # 時間過濾
+ if not self._check_time_requirement(tool_name, context):
+ logger.debug(f"⏭️ 跳過 {tool_name}（深夜不適用）")
+ continue
+
+ filtered_tools.append(tool)
+
+ # 3. 排序工具（相關分類優先）
+ sorted_tools = self._sort_tools(filtered_tools, detected_categories, context)
+
+ # 4. 限制工具數量（減少 token 消耗）
+ max_tools = self._get_max_tools(detected_categories)
+ if len(sorted_tools) > max_tools:
+ logger.info(f"📉 工具數量從 {len(sorted_tools)} 限制到 {max_tools}")
+ sorted_tools = sorted_tools[:max_tools]
+
+ logger.info(f"🔧 過濾後工具: {[t['function']['name'] for t in sorted_tools]}")
+ return sorted_tools
+
+ def _detect_categories(self, message: str) -> Set[str]:
+ """檢測用戶消息中的意圖分類"""
+ message_lower = message.lower()
+ detected = set()
+
+ for category, keywords in self.CATEGORY_KEYWORDS.items():
+ for keyword in keywords:
+ if keyword.lower() in message_lower:
+ detected.add(category)
+ break
+
+ return detected
+
+ def _check_location_requirement(
+ self,
+ tool_name: str,
+ context: Dict[str, Any],
+ ) -> bool:
+ """檢查工具的位置需求"""
+ # 需要位置的工具
+ location_required_tools = {
+ "reverse_geocode",
+ "tdx_bus_arrival",
+ "tdx_youbike",
+ "tdx_metro",
+ "tdx_parking",
+ "tdx_train",
+ "tdx_thsr",
+ }
+
+ if tool_name not in location_required_tools:
+ return True
+
+ # 檢查是否有位置資訊
+ has_location = (
+ context.get("lat") is not None and
+ context.get("lon") is not None
+ )
+
+ # 如果沒有位置，但用戶明確要求（如「附近的公車」），仍然保留工具
+ # 讓工具自己處理缺少位置的情況
+ return True # 暫時不嚴格過濾，讓工具自己處理
+
+ def _check_time_requirement(
+ self,
+ tool_name: str,
+ context: Dict[str, Any],
+ ) -> bool:
+ """檢查工具的時間需求"""
+ if tool_name not in self.NIGHT_EXCLUDED_TOOLS:
+ return True
+
+ # 檢查是否為深夜（00:00 - 05:00）
+ current_hour = context.get("hour")
+ if current_hour is None:
+ current_hour = datetime.now().hour
+
+ is_night = 0 <= current_hour < 5
+
+ # 深夜時排除某些工具
+ return not is_night
+
+ def _sort_tools(
+ self,
+ tools: List[Dict[str, Any]],
+ detected_categories: Set[str],
+ context: Dict[str, Any],
+ ) -> List[Dict[str, Any]]:
+ """排序工具（相關分類優先）"""
+
+ def get_priority(tool: Dict[str, Any]) -> int:
+ tool_name = tool.get("function", {}).get("name", "")
+
+ # 基礎優先級
+ base_priority = self.DEFAULT_PRIORITY.get(tool_name, 100)
+
+ # 如果工具屬於檢測到的分類，降低優先級數字（提高優先級）
+ tool_category = self._get_tool_category(tool_name)
+ if tool_category in detected_categories:
+ base_priority -= 50 # 相關工具優先
+
+ # 用戶偏好加成
+ user_id = context.get("user_id")
+ if user_id and user_id in self._user_preferences:
+ usage_count = self._user_preferences[user_id].get(tool_name, 0)
+ base_priority -= min(usage_count, 10) # 最多降低 10
+
+ return base_priority
+
+ return sorted(tools, key=get_priority)
+
+ def _get_tool_category(self, tool_name: str) -> str:
+ """取得工具的分類"""
+ category_map = {
+ "weather_query": "weather",
+ "reverse_geocode": "location",
+ "forward_geocode": "location",
+ "directions": "location",
+ "tdx_bus_arrival": "transportation",
+ "tdx_youbike": "transportation",
+ "tdx_metro": "transportation",
+ "tdx_train": "transportation",
+ "tdx_thsr": "transportation",
+ "tdx_parking": "transportation",
+ "news_query": "information",
+ "exchange_query": "finance",
+ "healthkit_query": "health",
+ }
+ return category_map.get(tool_name, "general")
+
+ def _get_max_tools(self, detected_categories: Set[str]) -> int:
+ """根據檢測到的分類決定最大工具數量"""
+ if not detected_categories:
+ # 沒有明確分類，返回所有工具
+ return 20
+
+ if len(detected_categories) == 1:
+ # 單一分類，但仍需要保留足夠工具（如 directions）
+ return 12
+
+ # 多個分類
+ return 15
+
+ def record_tool_usage(self, user_id: str, tool_name: str) -> None:
+ """記錄工具使用（用於優先級調整）"""
+ if user_id not in self._user_preferences:
+ self._user_preferences[user_id] = {}
+
+ current = self._user_preferences[user_id].get(tool_name, 0)
+ self._user_preferences[user_id][tool_name] = current + 1
+
+ logger.debug(f"📊 記錄工具使用: {user_id} -> {tool_name} ({current + 1})")
+
+
+# 全域單例
+tool_router = ToolRouter()
diff --git a/core/tool_schema.py b/core/tool_schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..8954b04595bd714872ccd15ba65af10a465e399a
--- /dev/null
+++ b/core/tool_schema.py
@@ -0,0 +1,355 @@
+"""
+Pydantic 工具 Schema 定義
+2025 最佳實踐：使用 Pydantic 自動生成 OpenAI Function Calling Schema
+
+功能：
+1. 工具輸入/輸出的 Pydantic 基礎類別
+2. 自動生成 OpenAI tools 格式的 JSON Schema
+3. 支援 strict mode 確保 100% 有效輸出
+4. 裝飾器模式自動註冊工具
+"""
+
+from typing import Dict, Any, Optional, List, Callable, Type, TypeVar, get_type_hints
+from dataclasses import dataclass, field
+from functools import wraps
+import inspect
+import logging
+
+logger = logging.getLogger("core.tool_schema")
+
+# 類型變數
+T = TypeVar("T")
+
+
+@dataclass
+class ToolMetadata:
+ """
+ 工具元資料（增強版）
+
+ 2025 最佳實踐：豐富的元資料讓 GPT 更容易理解何時使用哪個工具
+ """
+ name: str
+ description: str
+ category: str = "general"
+ keywords: List[str] = field(default_factory=list) # 觸發關鍵字
+ examples: List[str] = field(default_factory=list) # 使用範例
+ negative_examples: List[str] = field(default_factory=list) # 不應使用的情況
+ requires_location: bool = False
+ requires_auth: bool = False
+ is_complex: bool = False
+ priority: int = 100 # 優先級（數字越小越優先）
+ aliases: List[str] = field(default_factory=list) # 工具別名
+
+
+@dataclass
+class ToolSchema:
+ """工具 Schema 定義（自描述）"""
+ metadata: ToolMetadata
+ input_schema: Dict[str, Any]
+ output_schema: Optional[Dict[str, Any]] = None
+ handler: Optional[Callable] = None
+
+ def to_openai_tool(self, strict: bool = True) -> Dict[str, Any]:
+ """
+ 轉換為 OpenAI Function Calling 格式
+
+ Args:
+ strict: 是否啟用 strict mode（確保輸出符合 schema）
+
+ Returns:
+ OpenAI tools 格式的字典
+ """
+ # 確保 schema 符合 OpenAI strict mode 要求
+ parameters = self._prepare_strict_schema(self.input_schema) if strict else self.input_schema
+
+ tool_def = {
+ "type": "function",
+ "function": {
+ "name": self.metadata.name,
+ "description": self._build_rich_description(),
+ "parameters": parameters,
+ }
+ }
+
+ if strict:
+ tool_def["function"]["strict"] = True
+
+ return tool_def
+
+ def _build_rich_description(self) -> str:
+ """
+ 建構豐富的工具描述（包含範例、關鍵字、負面範例）
+ 讓 GPT 更容易理解何時使用此工具
+
+ 2025 最佳實踐：
+ - 正面範例：告訴 GPT 何時使用
+ - 負面範例：告訴 GPT 何時不要使用（減少誤判）
+ """
+ desc_parts = [self.metadata.description]
+
+ # 加入關鍵字提示
+ if self.metadata.keywords:
+ keywords_str = "、".join(self.metadata.keywords[:5])
+ desc_parts.append(f"觸發詞：{keywords_str}")
+
+ # 加入使用範例（正面）
+ if self.metadata.examples:
+ examples_str = "；".join(self.metadata.examples[:3])
+ desc_parts.append(f"適用：{examples_str}")
+
+ # 加入負面範例（告訴 GPT 何時不要使用）
+ if self.metadata.negative_examples:
+ neg_str = "；".join(self.metadata.negative_examples[:2])
+ desc_parts.append(f"不適用：{neg_str}")
+
+ return "。".join(desc_parts)
+
+ def _prepare_strict_schema(self, schema: Dict[str, Any]) -> Dict[str, Any]:
+ """
+ 準備符合 OpenAI strict mode 的 schema
+
+ strict mode 要求：
+ 1. additionalProperties: false
+ 2. 所有屬性都在 required 中（或有 default）
+ 3. 不支援 oneOf/anyOf/allOf
+ """
+ result = dict(schema)
+
+ # 確保是 object 類型
+ if result.get("type") != "object":
+ result = {"type": "object", "properties": result}
+
+ # 添加 additionalProperties: false
+ result["additionalProperties"] = False
+
+ # 確保所有屬性都在 required 中
+ properties = result.get("properties", {})
+ existing_required = set(result.get("required", []))
+
+ # 收集所有沒有 default 的屬性
+ all_required = []
+ for prop_name, prop_schema in properties.items():
+ if prop_name in existing_required or "default" not in prop_schema:
+ all_required.append(prop_name)
+
+ result["required"] = all_required
+
+ return result
+
+ def get_summary(self) -> Dict[str, Any]:
+ """獲取工具摘要（用於快速意圖匹配）"""
+ return {
+ "name": self.metadata.name,
+ "description": self.metadata.description[:50] + "..." if len(self.metadata.description) > 50 else self.metadata.description,
+ "category": self.metadata.category,
+ "keywords": self.metadata.keywords,
+ "params": list(self.input_schema.get("properties", {}).keys())
+ }
+
+
+def extract_schema_from_mcp_tool(tool_class: Type) -> Optional[ToolSchema]:
+ """
+ 從現有 MCPTool 類別提取 Schema（增強版）
+
+ Args:
+ tool_class: MCPTool 子類別
+
+ Returns:
+ ToolSchema 或 None
+ """
+ try:
+ # 檢查必要屬性
+ if not hasattr(tool_class, "NAME") or not hasattr(tool_class, "get_input_schema"):
+ return None
+
+ name = getattr(tool_class, "NAME", "")
+ if not name:
+ return None
+
+ # 提取元資料（增強版）
+ metadata = ToolMetadata(
+ name=name,
+ description=getattr(tool_class, "DESCRIPTION", f"{name} 工具"),
+ category=getattr(tool_class, "CATEGORY", "general"),
+ keywords=getattr(tool_class, "KEYWORDS", []),
+ examples=getattr(tool_class, "USAGE_TIPS", []),
+ negative_examples=getattr(tool_class, "NEGATIVE_EXAMPLES", []),
+ requires_location=_check_requires_location(tool_class),
+ requires_auth=getattr(tool_class, "REQUIRES_AUTH", False),
+ is_complex=getattr(tool_class, "IS_COMPLEX", False),
+ priority=getattr(tool_class, "PRIORITY", 100),
+ aliases=getattr(tool_class, "ALIASES", []),
+ )
+
+ # 提取 input schema
+ try:
+ input_schema = tool_class.get_input_schema()
+ except Exception as e:
+ logger.warning(f"提取 {name} input schema 失敗: {e}")
+ input_schema = {"type": "object", "properties": {}}
+
+ # 提取 output schema（可選）
+ output_schema = None
+ if hasattr(tool_class, "get_output_schema"):
+ try:
+ output_schema = tool_class.get_output_schema()
+ except Exception:
+ pass
+
+ # 提取 handler
+ handler = None
+ if hasattr(tool_class, "execute"):
+ handler = tool_class.execute
+
+ return ToolSchema(
+ metadata=metadata,
+ input_schema=input_schema,
+ output_schema=output_schema,
+ handler=handler,
+ )
+
+ except Exception as e:
+ logger.error(f"提取 {tool_class} schema 失敗: {e}")
+ return None
+
+
+def _check_requires_location(tool_class: Type) -> bool:
+ """檢查工具是否需要位置資訊"""
+ # 檢查類別屬性
+ if getattr(tool_class, "REQUIRES_LOCATION", False):
+ return True
+
+ # 檢查 input schema 中是否有 lat/lon
+ try:
+ schema = tool_class.get_input_schema()
+ props = schema.get("properties", {})
+ if "lat" in props or "lon" in props:
+ return True
+ except Exception:
+ pass
+
+ # 檢查工具名稱
+ name = getattr(tool_class, "NAME", "").lower()
+ location_tools = [
+ "reverse_geocode", "directions", "tdx_bus_arrival",
+ "tdx_youbike", "tdx_metro", "tdx_parking", "tdx_train", "tdx_thsr"
+ ]
+ return name in location_tools
+
+
+class ToolSchemaRegistry:
+ """
+ 工具 Schema 註冊中心
+
+ 功能：
+ 1. 統一管理所有工具的 Schema
+ 2. 自動生成 OpenAI Function Calling 格式
+ 3. 支援動態過濾和分組
+ """
+
+ def __init__(self):
+ self._schemas: Dict[str, ToolSchema] = {}
+ self._disabled: set = set()
+
+ def register(self, schema: ToolSchema) -> None:
+ """註冊工具 Schema"""
+ self._schemas[schema.metadata.name] = schema
+ logger.debug(f"註冊工具 Schema: {schema.metadata.name}")
+
+ def register_from_mcp_tool(self, tool_class: Type) -> bool:
+ """從 MCPTool 類別註冊"""
+ schema = extract_schema_from_mcp_tool(tool_class)
+ if schema:
+ self.register(schema)
+ return True
+ return False
+
+ def unregister(self, name: str) -> bool:
+ """取消註冊"""
+ if name in self._schemas:
+ del self._schemas[name]
+ return True
+ return False
+
+ def disable(self, name: str) -> None:
+ """停用工具"""
+ self._disabled.add(name)
+
+ def enable(self, name: str) -> None:
+ """啟用工具"""
+ self._disabled.discard(name)
+
+ def get(self, name: str) -> Optional[ToolSchema]:
+ """取得工具 Schema"""
+ if name in self._disabled:
+ return None
+ return self._schemas.get(name)
+
+ def get_openai_tools(
+ self,
+ categories: Optional[List[str]] = None,
+ include_location_tools: bool = True,
+ strict: bool = True,
+ ) -> List[Dict[str, Any]]:
+ """
+ 生成 OpenAI Function Calling 格式的工具列表
+
+ Args:
+ categories: 只包含指定分類的工具
+ include_location_tools: 是否包含需要位置的工具
+ strict: 是否啟用 strict mode
+
+ Returns:
+ OpenAI tools 格式的列表
+ """
+ tools = []
+
+ for name, schema in self._schemas.items():
+ # 跳過停用的工具
+ if name in self._disabled:
+ continue
+
+ # 分類過濾
+ if categories and schema.metadata.category not in categories:
+ continue
+
+ # 位置過濾
+ if not include_location_tools and schema.metadata.requires_location:
+ continue
+
+ tools.append(schema.to_openai_tool(strict=strict))
+
+ return tools
+
+ def get_tool_names(self) -> List[str]:
+ """取得所有已註冊的工具名稱"""
+ return [
+ name for name in self._schemas.keys()
+ if name not in self._disabled
+ ]
+
+ def get_summaries(self) -> List[Dict[str, Any]]:
+ """取得所有工具摘要（用於快速意圖匹配）"""
+ return [
+ schema.get_summary()
+ for name, schema in self._schemas.items()
+ if name not in self._disabled
+ ]
+
+ def get_stats(self) -> Dict[str, Any]:
+ """取得統計資訊"""
+ categories = {}
+ for schema in self._schemas.values():
+ cat = schema.metadata.category
+ categories[cat] = categories.get(cat, 0) + 1
+
+ return {
+ "total": len(self._schemas),
+ "disabled": len(self._disabled),
+ "active": len(self._schemas) - len(self._disabled),
+ "categories": categories,
+ }
+
+
+# 全域單例
+tool_schema_registry = ToolSchemaRegistry()
diff --git a/features/mcp/agent_bridge.py b/features/mcp/agent_bridge.py
index 9469fec643ba9e4150780239f6b1633196c1d364..b4cd6b4cf2e877566d37ef8540bc6f4089259387 100644
--- a/features/mcp/agent_bridge.py
+++ b/features/mcp/agent_bridge.py
@@ -111,6 +111,7 @@ class MCPAgentBridge:
ToolMetadata(
name="reverse_geocode",
requires_env={"lat", "lon"},
+ enable_reformat=True,
)
)
register(
@@ -214,9 +215,70 @@ class MCPAgentBridge:
await self.mcp_server.start_external_servers()
logger.info(f"異步初始化完成，完整可用 MCP 工具數量: {len(self.mcp_server.tools)}")
+ # 將 MCP Server 的工具註冊到 tool_registry
+ self._sync_tools_to_registry()
+
# 2025 最佳實踐：啟動時預熱熱門查詢快取
await self._preheat_cache()
+ def _sync_tools_to_registry(self) -> int:
+ """
+ 將 MCP Server 的工具同步到 tool_registry
+
+ Returns:
+ 註冊的工具數量
+ """
+ from core.tool_registry import tool_registry
+
+ count = 0
+ for tool_name, tool in self.mcp_server.tools.items():
+ # 取得工具描述
+ description = getattr(tool, 'description', f'{tool_name} 工具')
+
+ # 取得參數 Schema
+ parameters = {"type": "object", "properties": {}, "required": []}
+ keywords = []
+ examples = []
+ negative_examples = []
+ category = "general"
+ priority = 100
+
+ if hasattr(tool, 'handler') and hasattr(tool.handler, '__self__'):
+ tool_class = tool.handler.__self__
+
+ # 嘗試從 MCPTool 類別提取完整資訊
+ if hasattr(tool_class, 'get_input_schema'):
+ try:
+ parameters = tool_class.get_input_schema()
+ except Exception as e:
+ logger.warning(f"取得 {tool_name} schema 失敗: {e}")
+
+ # 提取增強元資料
+ keywords = getattr(tool_class, 'KEYWORDS', [])
+ examples = getattr(tool_class, 'USAGE_TIPS', [])
+ negative_examples = getattr(tool_class, 'NEGATIVE_EXAMPLES', [])
+ category = getattr(tool_class, 'CATEGORY', 'general')
+ priority = getattr(tool_class, 'PRIORITY', 100)
+
+ # 判斷是否需要位置
+ props = parameters.get("properties", {})
+ requires_location = "lat" in props or "lon" in props
+
+ tool_registry.register(
+ name=tool_name,
+ description=description,
+ parameters=parameters,
+ handler=getattr(tool, 'handler', None),
+ category=category,
+ requires_location=requires_location,
+ keywords=keywords,
+ examples=examples,
+ )
+ count += 1
+
+ logger.info(f"🔧 同步 {count} 個工具到 tool_registry")
+ return count
+
def _normalize_tool_name(self, raw_name: Optional[str]) -> Optional[str]:
"""
將 GPT 回傳的工具名稱正規化為註冊表中的實際名稱。
@@ -414,8 +476,11 @@ class MCPAgentBridge:
async def detect_intent(self, message: str) -> Tuple[bool, Optional[Dict[str, Any]]]:
"""
檢測用戶消息中的意圖 (保持與舊 FeatureRouter 相同介面)
- 使用 OpenAI Structured Outputs 確保100%返回有效JSON
- 帶快取機制，相同消息直接返回
+
+ 2025 重構版：使用 OpenAI 原生 Function Calling
+ - 不再使用巨大的 system_prompt 描述每個工具
+ - 工具定義由 tools 參數傳遞，GPT 原生選擇
+ - 新增工具只需註冊到 Registry，不需更新任何 prompt
參數:
message (str): 用戶消息
@@ -423,362 +488,130 @@ class MCPAgentBridge:
返回:
tuple: (是否檢測到意圖, 意圖數據)
"""
+ # 使用新的 IntentDetector（基於 OpenAI Function Calling）
+ return await self._detect_intent_with_function_calling(message)
+
+ async def _detect_intent_with_function_calling(self, message: str) -> Tuple[bool, Optional[Dict[str, Any]]]:
+ """
+ 使用 OpenAI 原生 Function Calling 進行意圖檢測
+
+ 核心改進：
+ 1. 工具定義自動從 Registry 生成
+ 2. GPT 原生選擇工具並生成結構化參數
+ 3. 不需要自定義 prompt 描述每個工具
+ """
import hashlib
import time as time_module
-
+
# 生成快取鍵
cache_key = hashlib.md5(message.encode()).hexdigest()
# 檢查快取
if cache_key in self._intent_cache:
has_feature, intent_data, cached_time = self._intent_cache[cache_key]
- # 檢查是否過期
if time_module.time() - cached_time < self._intent_cache_ttl:
logger.debug(f"💾 意圖快取命中: {message[:50]}...")
return has_feature, intent_data
else:
- # 過期，刪除快取
del self._intent_cache[cache_key]
- logger.info(f"檢測意圖: \"{message}\"")
- logger.debug("意圖偵測輸入 - user_id=%s, chat_id=%s", "intent_detection", None)
+ logger.info(f"🔍 檢測意圖（Function Calling）: \"{message[:100]}...\"")
# 檢查特殊命令
for command in ["功能列表", "有什麼功能", "能做什麼"]:
if command in message:
logger.info(f"檢測到特殊命令: {command}")
- return True, {
- "type": "special_command",
- "command": "feature_list"
- }
+ return True, {"type": "special_command", "command": "feature_list"}
- # 使用 GPT + Structured Outputs 進行意圖解析
try:
- logger.info("開始使用 GPT Structured Outputs 進行意圖解析")
-
- # 構建可用工具的描述
- tools_description = self._get_tools_description()
-
- # GPT 意圖解析 Prompt - 適配新的 schema（不使用 oneOf）
- system_prompt = f"""你是一個精確的意圖解析助手。
-
-可用工具：
-{tools_description}
-
-任務：分析用戶消息，決定是否需要調用工具，並判斷用戶情緒。
-
-重要規則：
-1. 健康相關需求（心率、步數、血氧、呼吸、睡眠）使用 healthkit_query
-2. 不需要傳入 user_id，系統會自動補齊
-3. 若無法判斷具體參數，使用合理預設值
-4. 一般閒聊設置 is_tool_call 為 false
-
-特殊處理：
-- 天氣查詢：城市名稱必須使用英文（如 Taipei, Tokyo, New York）
- * 台北 → Taipei
- * 東京 → Tokyo
- * 紐約 → New York
- * 倫敦 → London
- * 巴黎 → Paris
- * 如無指定城市，預設使用 Taipei
-
-- 匯率查詢：
- * 必須明確指定 from_currency 和 to_currency（ISO 4217 代碼）
- * 預設：from_currency=USD, to_currency=TWD
- * 美元 → USD, 台幣 → TWD, 日圓 → JPY, 歐元 → EUR
- * 金額預設 amount=1.0, conversion=true
-
-- 新聞查詢：
- * 任何提到「新聞」「消息」「報導」的請求都使用 news_query
- * 參數：query（關鍵詞）、country（國家，預設 tw）、category（分類，預設 top）、language（語言，預設 zh）
- * 今日新聞、科技新聞、台灣新聞都應該調用此工具
-
-- 公車查詢（重要！）：
- * 任何提到「公車」「巴士」「幾號公車」「什麼時候來」的請求都使用 tdx_bus_arrival
- * **參數名稱是 route_name（不是 stop_name）**
- * 從用戶訊息中提取路線號碼（如「137」「紅30」「307」）
- * 範例：
- - 「137公車什麼時候來」→ tdx_bus_arrival:route_name=137
- - 「307還要多久」→ tdx_bus_arrival:route_name=307
- - 「附近有什麼公車」→ tdx_bus_arrival（不需參數，系統自動用 GPS 查詢附近站點）
- - 「紅30公車」→ tdx_bus_arrival:route_name=紅30
- * ❌ 錯誤：tdx_bus_arrival:stop_name=137
- * ✅ 正確：tdx_bus_arrival:route_name=137
-
-- 台鐵/火車查詢（重要！）：
- * 任何提到「火車」「台鐵」「列車」「自強號」「莒光號」「區間車」的請求都使用 tdx_train
- * **參數名稱是 origin_station（起站）和 destination_station（迄站）**
- * **「往XX」「到XX」「去XX」表示目的地（destination_station），不是起點！**
- * **「從XX」「在XX」表示起點（origin_station）**
- * **如果用戶沒有明確說起點，就不要填 origin_station，讓系統用 GPS 自動找最近車站**
- * 範例：
- - 「往台北的火車」→ tdx_train:destination_station=台北（只填目的地，起點由 GPS 決定）
- - 「到高雄的火車」→ tdx_train:destination_station=高雄（只填目的地）
- - 「從桃園到台北」→ tdx_train:origin_station=桃園,destination_station=台北（明確說了起點）
- - 「台北到台中的火車」→ tdx_train:origin_station=台北,destination_station=台中
- - 「下一班火車」→ tdx_train（不需參數，系統自動用 GPS 查詢最近車站）
- - 「自強號 123 次」→ tdx_train:train_no=123
- * ❌ 錯誤：「往台北」解析為 origin_station=台灣,destination_station=台北（不要亂填起點！）
- * ❌ 錯誤：「往台北」解析為 origin_station=台北（方向搞反了）
- * ✅ 正確：「往台北」解析為 destination_station=台北（只填目的地）
-
-- YouBike/共享單車查詢（重要！必須識別！）：
- * 任何提到以下關鍵詞的請求都使用 tdx_youbike：
- - 「YouBike」「Youbike」「youbike」「YOUBIKE」
- - 「UBike」「Ubike」「ubike」「UBIKE」
- - 「微笑單車」「共享單車」「公共單車」
- - 「腳踏車站」「單車站」「自行車站」
- - 「借車」「還車」（在單車語境下）
- - 「最近的站點」「附近站點」（在單車語境下）
- * **不是 tdx_parking！YouBike 是單車，不是停車場**
- * **不是一般聊天！這是工具調用！**
- * 範例：
- - 「附近的 YouBike」→ tdx_youbike（不需參數，系統自動用 GPS 查詢）
- - 「離我最近的 Ubike 站點」→ tdx_youbike
- - 「最近的Ubike站點」→ tdx_youbike
- - 「Ubike在哪」→ tdx_youbike
- - 「哪裡有Ubike」→ tdx_youbike
- - 「市政府 YouBike 還有車嗎」→ tdx_youbike:station_name=市政府
- * ❌ 錯誤：is_tool_call=false（這不是聊天！）
- * ❌ 錯誤：tdx_parking:query=Ubike
- * ✅ 正確：tdx_youbike
-
-- 停車場查詢：
- * 任何提到「停車場」「停車位」「汽車停車」的請求才使用 tdx_parking
- * 範例：
- - 「附近的停車場」→ tdx_parking
- - 「市政府停車場」→ tdx_parking:parking_name=市政府
-
-- 地點查詢與導航（重要！）：
- * **當前位置查詢**：
- - 問「我在哪」「這是哪裡」「現在在哪」「我的位置」→ 使用 reverse_geocode（不需參數，系統自動用 GPS 座標）
- - ❌ 錯誤：forward_geocode:query=我在哪
- - ✅ 正確：reverse_geocode
- * **導航需求判斷**：
- - 問「怎麼去 X」「如何去 X」「去 X 怎麼走」「到 X 怎麼走」→ 使用 forward_geocode 查詢目的地座標
- - 問「從 A 到 B 要多久」「A 到 B 怎麼走」→ 同時使用 forward_geocode 查詢起點與終點
- * **不要猜測座標**：
- - ❌ 錯誤：directions:origin_lat=25.1288,origin_lon=121.9234,dest_lat=24.9932,dest_lon=121.3261
- - ✅ 正確：forward_geocode:query=銘傳大學桃園校區
- * **工具使用順序**：
- 1. 先使用 forward_geocode 將地點名稱轉換為座標
- 2. 再使用 directions 規劃路線（系統會自動處理）
- * **範例**：
- - 「我在哪」→ reverse_geocode（系統自動補 lat/lon）
- - 「怎麼去桃園火車站」→ forward_geocode:query=桃園火車站
- - 「從銘傳大學到桃園火車站」→ forward_geocode:query=銘傳大學桃園校區
- - 「台北車站到淡水捷運站」→ forward_geocode:query=台北車站
-
-情緒判斷（emotion）：
-根據文字的語氣、用詞、標點符號判斷用戶情緒，選擇以下之一：
-- neutral: 平靜、中性（預設）
-- happy: 開心、興奮、愉快（如「好開心！」「太棒了」「哈哈」）
-- sad: 難過、沮喪、失落（如「好難過」「唉...」「心情不好」）
-- angry: 生氣、憤怒、煩躁（如「煩死了」「幹嘛啦」「氣死我了」）
-- fear: 恐懼、擔心、焦慮（如「好害怕」「好擔心」「怎麼辦」）
-- surprise: 驚訝、意外（如「什麼！」「真的假的」「不會吧」）
-
-回應格式：
-- is_tool_call: true/false（是否調用工具）
-- tool_name: 工具名稱和參數（僅當 is_tool_call=true 時提供，格式：tool_name:param1=value1,param2=value2）
-- emotion: 用戶情緒標籤（必填）
-
-示例：
-- "我在哪" → {{"is_tool_call": true, "tool_name": "reverse_geocode", "emotion": "neutral"}}
-- "這是哪裡" → {{"is_tool_call": true, "tool_name": "reverse_geocode", "emotion": "neutral"}}
-- "台北天氣" → {{"is_tool_call": true, "tool_name": "weather_query:city=Taipei", "emotion": "neutral"}}
-- "好開心！今天天氣好嗎" → {{"is_tool_call": true, "tool_name": "weather_query:city=Taipei", "emotion": "happy"}}
-- "美元匯率" → {{"is_tool_call": true, "tool_name": "exchange_query:from_currency=USD,to_currency=TWD,amount=1.0", "emotion": "neutral"}}
-- "今日新聞" → {{"is_tool_call": true, "tool_name": "news_query:country=tw,language=zh", "emotion": "neutral"}}
-- "科技新聞" → {{"is_tool_call": true, "tool_name": "news_query:query=科技,category=technology,language=zh", "emotion": "neutral"}}
-- "最近的Ubike站點" → {{"is_tool_call": true, "tool_name": "tdx_youbike", "emotion": "neutral"}}
-- "附近的YouBike" → {{"is_tool_call": true, "tool_name": "tdx_youbike", "emotion": "neutral"}}
-- "Ubike在哪" → {{"is_tool_call": true, "tool_name": "tdx_youbike", "emotion": "neutral"}}
-- "往台北的火車" → {{"is_tool_call": true, "tool_name": "tdx_train:destination_station=台北", "emotion": "neutral"}}
-- "你好" → {{"is_tool_call": false, "tool_name": "", "emotion": "neutral"}}
-- "我好難過..." → {{"is_tool_call": false, "tool_name": "", "emotion": "sad"}}
-- "煩死了" → {{"is_tool_call": false, "tool_name": "", "emotion": "angry"}}"""
-
+ # 從 tool_registry 取得 OpenAI tools 格式
+ from core.tool_registry import tool_registry
+ from core.tool_router import tool_router
+
+ all_tools = tool_registry.get_openai_tools(strict=False)
+
+ if not all_tools:
+ logger.warning("⚠️ 沒有可用的工具，降級為聊天")
+ return False, {"emotion": "neutral"}
+
+ # 使用 ToolRouter 動態過濾和排序工具
+ context = {"hour": datetime.now().hour}
+ tools = tool_router.filter_tools(all_tools, message, context)
+
+ logger.info(f"🔧 載入 {len(all_tools)} 個工具，過濾後 {len(tools)} 個")
+
+ # 建構精簡的 system prompt（只處理特殊規則）
+ system_prompt = self._build_function_calling_prompt()
+
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": message}
]
-
- # 使用 Structured Outputs（動態推理強度）
+
+ # 使用 OpenAI Function Calling
+ from core.reasoning_strategy import get_optimal_reasoning_effort
optimal_effort = get_optimal_reasoning_effort("intent_detection")
logger.info(f"🧠 意圖檢測推理強度: {optimal_effort}")
-
- response = await ai_service.generate_response_for_user(
+
+ response = await ai_service.generate_response_with_tools(
messages=messages,
+ tools=tools,
user_id="intent_detection",
- model="gpt-5-nano",
- chat_id=None,
- use_structured_outputs=True,
- response_schema=self._get_intent_schema(),
- reasoning_effort=optimal_effort # 動態調整
+ model="gpt-4o-mini", # 使用更強的模型以提升參數提取準確度
+ reasoning_effort=None, # gpt-4o-mini 不支援 reasoning_effort
+ tool_choice="auto",
)
-
- logger.debug("GPT Structured Outputs 回應: %s", response)
-
- # 檢查是否為 fallback 錯誤訊息
- if response.strip() in ["抱歉，我暫時沒有合適的回應。可以換個說法再試試嗎？", "抱歉，生成回應時遇到問題。請重試。"]:
- logger.warning("Structured Outputs 返回 fallback 訊息，視為失敗")
- raise Exception("Structured Outputs failed with fallback message")
-
- # Structured Outputs 保證返回有效JSON，直接解析
- try:
- response_text = response.strip()
-
- # 處理 GPT 回應重複 JSON 的情況（如 {...}{...}）
- # 只取第一個完整的 JSON 物件
- if response_text.startswith("{"):
- brace_count = 0
- end_idx = 0
- for i, char in enumerate(response_text):
- if char == "{":
- brace_count += 1
- elif char == "}":
- brace_count -= 1
- if brace_count == 0:
- end_idx = i + 1
- break
- if end_idx > 0:
- response_text = response_text[:end_idx]
+
+ # 解析回應
+ tool_calls = response.get("tool_calls", [])
+
+ if tool_calls:
+ # GPT 選擇了工具
+ tool_call = tool_calls[0]
+ function = tool_call.get("function", {})
+ tool_name = function.get("name", "")
+ arguments_str = function.get("arguments", "{}")
- intent_data = json.loads(response_text)
- logger.debug("解析後的意圖資料: %s", _safe_json(intent_data))
-
- # 新的 schema 格式：is_tool_call, tool_name（包含參數）
- is_tool_call = intent_data.get("is_tool_call", False)
-
- if is_tool_call:
- tool_name_with_params = intent_data.get("tool_name", "").strip()
-
- if not tool_name_with_params:
- logger.warning("⚠️ GPT 標記為工具調用但未提供工具名稱，降級為聊天")
- return False, None
-
- raw_tool_name = tool_name_with_params
- params_str = ""
- if ":" in tool_name_with_params:
- raw_tool_name, params_str = tool_name_with_params.split(":", 1)
-
- tool_name = self._normalize_tool_name(raw_tool_name)
- if not tool_name:
- logger.warning(f"⚠️ 工具 {raw_tool_name} 無法對應到註冊名稱，降級為聊天")
- return False, None
-
- # 解析參數
+ try:
+ arguments = json.loads(arguments_str)
+ except json.JSONDecodeError:
arguments = {}
- if params_str.strip():
- # 獲取工具的 input schema 以確定參數類型
- tool = self.mcp_server.tools.get(tool_name)
- input_schema = {}
- if tool and hasattr(tool, 'handler') and hasattr(tool.handler, '__self__'):
- tool_class = tool.handler.__self__
- if hasattr(tool_class, 'get_input_schema'):
- try:
- input_schema = tool_class.get_input_schema()
- except:
- pass
-
- properties = input_schema.get('properties', {})
-
- for param_pair in params_str.split(","):
- if "=" not in param_pair:
- continue
- key, value = param_pair.split("=", 1)
- key = key.strip()
- value = value.strip()
-
- # 跳過空鍵或空值（避免傳入空字串導致驗證失敗）
- if not key or not value:
- continue
-
- # 根據 schema 中的類型定義來轉換值
- param_schema = properties.get(key, {})
- param_type = param_schema.get('type', 'string')
-
- normalized_value = value
-
- # 根據 schema 類型進行轉換
- if param_type == 'integer':
- try:
- normalized_value = int(value)
- except ValueError:
- normalized_value = value
- elif param_type == 'number':
- try:
- normalized_value = float(value)
- except ValueError:
- normalized_value = value
- elif param_type == 'boolean':
- lower_value = value.lower()
- if lower_value in ("true", "false"):
- normalized_value = lower_value == "true"
- # 其他類型（包括 string）保持原樣
-
- arguments[key] = normalized_value
-
- logger.info(f"✅ GPT 檢測到工具調用: {raw_tool_name.strip()} → {tool_name}")
- logger.debug("工具調用參數: %s", _safe_json(arguments))
-
- # 驗證工具是否存在
- if tool_name not in self.mcp_server.tools:
- logger.warning(f"⚠️ 工具 {tool_name} 不存在，降級為聊天")
- return False, None
-
- # 基礎參數驗證（可選，Structured Outputs 已保證格式）
- tool = self.mcp_server.tools[tool_name]
- if hasattr(tool, 'handler') and hasattr(tool.handler, '__self__'):
- tool_class = tool.handler.__self__
- if hasattr(tool_class, 'validate_input'):
- try:
- validated_args = tool_class.validate_input(arguments)
- logger.debug("✓ 參數驗證通過: %s", _safe_json(validated_args))
- except Exception as e:
- logger.warning(f"⚠️ 參數驗證失敗: {e}，仍然嘗試執行")
- # 不中斷，讓工具自己處理
-
- # 提取情緒（新增）
- emotion = intent_data.get("emotion", "neutral")
- logger.info(f"😊 偵測到情緒: {emotion}")
-
- intent_result = (True, {
- "type": "mcp_tool",
- "tool_name": tool_name,
- "arguments": arguments,
- "emotion": emotion # 新增情緒欄位
- })
-
- # 寫入快取
- self._intent_cache[cache_key] = (*intent_result, time_module.time())
- logger.debug(f"💾 意圖結果已快取: {tool_name}")
-
- return intent_result
-
- else:
- # is_tool_call = False，表示一般聊天
- logger.info("💬 GPT 判斷為一般聊天")
-
- # 提取情緒（新增）
- emotion = intent_data.get("emotion", "neutral")
- logger.info(f"😊 偵測到情緒: {emotion}")
-
- # 寫入快取（一般聊天也要回傳情緒）
- intent_result = (False, {"emotion": emotion})
- self._intent_cache[cache_key] = (*intent_result, time_module.time())
-
- return intent_result
-
- except json.JSONDecodeError as e:
- # Structured Outputs 不應該發生這種錯誤，記錄異常
- logger.error(f"❌ Structured Outputs JSON 解析失敗（異常情況）: {e}, response: {response}")
- return False, None
-
+
+ # 正規化工具名稱
+ normalized_name = self._normalize_tool_name(tool_name)
+ if not normalized_name:
+ logger.warning(f"⚠️ 工具 {tool_name} 無法對應到註冊名稱，降級為聊天")
+ return False, {"emotion": "neutral"}
+
+ logger.info(f"✅ GPT 選擇工具: {normalized_name}")
+ logger.debug(f"工具參數: {_safe_json(arguments)}")
+
+ # 提取情緒（從 content 或預設）
+ emotion = self._extract_emotion_from_content(response.get("content", ""))
+
+ intent_result = (True, {
+ "type": "mcp_tool",
+ "tool_name": normalized_name,
+ "arguments": arguments,
+ "emotion": emotion,
+ })
+
+ # 寫入快取
+ self._intent_cache[cache_key] = (*intent_result, time_module.time())
+ return intent_result
+
+ else:
+ # GPT 未選擇工具，視為一般聊天
+ logger.info("💬 GPT 判斷為一般聊天")
+ emotion = self._extract_emotion_from_content(response.get("content", ""))
+
+ intent_result = (False, {"emotion": emotion})
+ self._intent_cache[cache_key] = (*intent_result, time_module.time())
+ return intent_result
+
except Exception as e:
- logger.error(f"❌ GPT 意圖解析發生錯誤: {e}")
- # 降級處理：使用關鍵詞匹配
+ logger.error(f"❌ Function Calling 意圖檢測失敗: {e}")
+ # 降級：使用關鍵詞匹配
logger.info("🔄 嘗試使用關鍵詞匹配作為降級方案")
try:
fallback_result = self._keyword_intent_detection(message)
@@ -787,10 +620,177 @@ class MCPAgentBridge:
return fallback_result
except Exception as fallback_error:
logger.error(f"❌ 關鍵詞匹配也失敗: {fallback_error}")
-
+
# 最終降級：視為一般聊天
logger.info("💬 降級為一般聊天")
- return False, None
+ return False, {"emotion": "neutral"}
+
+ def _build_function_calling_prompt(self) -> str:
+ """
+ 建構精簡的 Function Calling system prompt
+
+ 注意：不再描述每個工具，工具定義由 tools 參數傳遞
+ 只處理特殊規則和情緒判斷
+ """
+ return """你是一個智能助手，根據用戶需求選擇合適的工具。
+
+規則：
+1. 如果用戶需求可以用工具解決，選擇最適合的工具
+2. 如果是一般聊天或問候，不要選擇任何工具
+3. 工具參數盡量從用戶消息中提取，無法確定的使用合理預設值
+
+【重要】語言使用規範：
+- 調用工具時：所有參數必須使用英文（城市名、國家名、貨幣代碼等）
+- 回覆用戶時：必須使用繁體中文
+- 範例：用戶說「台北天氣」→ 參數 {"city": "Taipei"}，回覆「台北目前...」
+
+參數語言轉換規則：
+- 城市名稱：台北→Taipei, 新北→NewTaipei, 桃園→Taoyuan, 台中→Taichung, 台南→Tainan, 高雄→Kaohsiung, 新竹→Hsinchu
+- 國家名稱：台灣→Taiwan, 美國→USA, 日本→Japan, 英國→UK
+- 貨幣代碼：美元→USD, 台幣→TWD, 日圓→JPY, 歐元→EUR, 英鎊→GBP
+
+【重要】城市參數提取原則：
+- 只有在用戶明確提到城市名稱時才填 city 參數
+- 「附近」「這裡」「我這邊」等詞 → 不填 city 參數，系統會自動從 GPS 判斷
+- 「台北的XX」「桃園XX」→ 填對應的英文城市名
+- 範例：「附近的 YouBike」→ {}，「桃園的 YouBike」→ {"city": "Taoyuan"}
+
+匯率查詢（重要！參數提取規則）：
+當用戶詢問匯率資訊時，你必須從消息中提取貨幣代碼並填入參數。
+
+參數提取規則：
+1. 句型「[貨幣A]轉[貨幣B]」「[貨幣A]換[貨幣B]」「[貨幣A]兌[貨幣B]」→ {"from_currency": "代碼A", "to_currency": "代碼B"}
+2. 句型「[數字][貨幣A]是多少[貨幣B]」→ {"from_currency": "代碼A", "to_currency": "代碼B", "amount": 數字}
+3. 句型「匯率」「美金」「日幣」→ 提取提到的貨幣
+4. 貨幣代碼必須用 ISO 4217 標準（3個大寫字母）
+
+常見貨幣代碼對照：
+- 美元/美金 → USD
+- 台幣/新台幣 → TWD
+- 日圓/日幣 → JPY
+- 歐元 → EUR
+- 英鎊 → GBP
+- 人民幣 → CNY
+- 港幣 → HKD
+- 韓元 → KRW
+
+實際範例：
+- 「美元轉日幣的匯率」→ {"from_currency": "USD", "to_currency": "JPY"}
+- 「台幣換美金」→ {"from_currency": "TWD", "to_currency": "USD"}
+- 「100美元是多少台幣」→ {"from_currency": "USD", "to_currency": "TWD", "amount": 100}
+- 「歐元兌日圓」→ {"from_currency": "EUR", "to_currency": "JPY"}
+- 「匯率」→ {"from_currency": "USD", "to_currency": "TWD"}（預設）
+
+重要：必須提取貨幣代碼！不要返回空參數！
+
+公車查詢（重要！參數提取規則）：
+當用戶詢問公車資訊時，你必須從消息中提取路線號碼並填入參數。
+
+tdx_bus_arrival 適用場景：
+- 查詢「已知路線號碼」的到站時間
+- 查詢附近公車站點（不需 route_name）
+
+參數提取規則：
+1. 句型「[數字]公車」「[數字]號公車」→ {"route_name": "數字"}
+2. 句型「[顏色][數字]」（如「紅30」）→ {"route_name": "顏色數字"}
+3. 句型「[數字]還要多久」「[數字]什麼時候到」→ {"route_name": "數字"}
+4. 句型「[路線名]公車到站」→ {"route_name": "路線名"}
+5. 「附近公車」「公車站」「有什麼公車」→ {}（系統自動從 GPS 判斷城市）
+6. 城市參數：只在用戶明確提到城市時才填，否則留空讓系統自動判斷
+
+實際範例：
+- 「261公車什麼時候到」→ {"route_name": "261"}（不填 city）
+- 「307還要多久」→ {"route_name": "307"}（不填 city）
+- 「台北261公車」→ {"route_name": "261", "city": "Taipei"}（明確提到台北）
+- 「桃園紅30公車」→ {"route_name": "紅30", "city": "Taoyuan"}（明確提到桃園）
+- 「附近有什麼公車」→ {}（完全空參數，系統自動判斷）
+
+不適用場景（應使用 directions）：
+- 「從A到B的公車」「往XX的公車」→ 這是路線規劃，不是查詢特定路線
+- 「去台北的公車」→ 台北是目的地，不是路線號碼
+
+重要：如果提到路線號碼，必須提取！城市參數必須用英文！
+
+火車查詢（重要！參數提取規則）：
+當用戶詢問火車資訊時，你必須從消息中提取站名並填入參數。
+
+參數提取規則（適用於任何地名）：
+1. 句型「從 [地名A] 往/到 [地名B]」→ {"origin_station": "地名A", "destination_station": "地名B"}
+2. 句型「[地名A] 到/往 [地名B]」→ {"origin_station": "地名A", "destination_station": "地名B"}
+3. 句型「往/去 [地名]」→ {"destination_station": "地名"}
+4. 句型「[車種][數字]次」→ {"train_no": "數字"}
+5. 包含時間 → 提取為 departure_time（HH:MM 格式）
+
+實際範例：
+- 「從彰化往台北的火車」→ {"origin_station": "彰化", "destination_station": "台北"}
+- 「台中到高雄」→ {"origin_station": "台中", "destination_station": "高雄"}
+- 「往新竹的火車」→ {"destination_station": "新竹"}
+- 「自強號123次」→ {"train_no": "123"}
+- 「早上8點台南到台北」→ {"origin_station": "台南", "destination_station": "台北", "departure_time": "08:00"}
+
+重要：絕對不要返回空的 {} 參數！必須從用戶消息中提取站名！
+
+位置查詢：
+- 「我在哪」使用 reverse_geocode，不需要參數
+- 「怎麼去XX」使用 forward_geocode 或 directions
+
+YouBike 查詢（重要！參數提取規則）：
+當用戶詢問 YouBike/Ubike/微笑單車時，你必須調用 tdx_youbike 工具。
+
+參數提取規則：
+1. 「附近的 YouBike」「Ubike 在哪」→ {}（不填 city，系統自動從 GPS 判斷）
+2. 「市政府 YouBike」「台北車站 Ubike」→ {"station_name": "市政府"}（不填 city）
+3. 「XX站還有車嗎」→ {"station_name": "XX站"}（不填 city）
+4. 「台北的 YouBike」「桃園 YouBike」→ 填對應英文城市名
+5. 站名可用中文，城市必須用英文
+
+實際範例：
+- 「附近的 YouBike」→ {}（完全空參數，系統自動判斷城市）
+- 「市政府 YouBike 還有車嗎」→ {"station_name": "市政府"}（不填 city）
+- 「台北車站 Ubike」→ {"station_name": "台北車站"}（不填 city）
+- 「台北的 YouBike」→ {"city": "Taipei"}（明確提到台北）
+- 「桃園 YouBike」→ {"city": "Taoyuan"}（明確提到桃園）
+
+重要：只在用戶明確提到城市時才填 city 參數！站名可保持中文！
+
+【情緒偵測】（重要！）：
+- 分析用戶的情緒狀態（根據用詞、語氣、標點符號、表情符號）
+- 在回應的最後一行加上情緒標籤：[EMOTION:情緒]
+- 情緒類型：neutral（平靜）、happy（開心）、sad（難過）、angry（生氣）、fear（害怕）、surprise（驚訝）
+- 範例：
+ * 用戶說「我現在覺得很生氣」→ 回應最後加上 [EMOTION:angry]
+ * 用戶說「好開心啊！」→ 回應最後加上 [EMOTION:happy]
+ * 用戶說「我好難過...」→ 回應最後加上 [EMOTION:sad]
+ * 用戶說「好可怕」→ 回應最後加上 [EMOTION:fear]
+ * 用戶說「哇！」→ 回應最後加上 [EMOTION:surprise]
+ * 一般對話 → 回應最後加上 [EMOTION:neutral]
+"""
+
+ def _extract_emotion_from_content(self, content: str) -> str:
+ """從回應內容中提取情緒標籤 [EMOTION:xxx]"""
+ if not content:
+ return "neutral"
+
+ # 優先從標籤提取
+ import re
+ emotion_match = re.search(r'\[EMOTION:(neutral|happy|sad|angry|fear|surprise)\]', content, re.IGNORECASE)
+ if emotion_match:
+ emotion = emotion_match.group(1).lower()
+ logger.info(f"😊 從標籤提取情緒: {emotion}")
+ return emotion
+
+ # 降級：從內容搜尋英文關鍵字
+ content_lower = content.lower()
+ emotions = ["happy", "sad", "angry", "fear", "surprise"]
+
+ for emotion in emotions:
+ if emotion in content_lower:
+ logger.debug(f"從內容搜尋到情緒關鍵字: {emotion}")
+ return emotion
+
+ return "neutral"
+
+ # 舊版 _detect_intent_legacy 已移除，改用 _detect_intent_with_function_calling
def _get_intent_schema(self) -> Dict[str, Any]:
"""
@@ -1138,23 +1138,33 @@ class MCPAgentBridge:
"你是一個友善、健談的AI助手。\n"
"用戶剛剛問了一個問題，我已經用工具查詢到資料了。\n"
"請用自然、口語化的方式回答用戶，就像朋友聊天一樣。\n\n"
- "要求：\n"
+ "【核心原則】\n"
+ "⭐ 只回答使用者問的問題，不要把所有數據都說出來\n"
+ "⭐ 分析使用者的核心意圖（問溫度？天氣？時間？地點？數量？）\n"
+ "⭐ 從工具數據中只提取相關資訊，無關資訊一律省略\n\n"
+ "【回應要求】\n"
"1. 使用口語化、親切的語氣（可以用「喔」「呢」「哦」等語氣詞）\n"
"2. 不要列表式的羅列數據，而是用對話方式描述\n"
- "3. 突出最重要的資訊（2-3句話）\n"
+ "3. 只說使用者問的內容（2-3句話）\n"
"4. 適當使用 emoji 增加親和力\n"
- "5. 如果數據很多，只說重點\n"
+ "5. 如有額外有用資訊，可簡短補充（不超過一句話）\n"
"6. 保持簡短（50字以內最好）\n\n"
- "範例：\n"
- "❌ 不好：「當前溫度23.88°C，體感溫度24.02°C，天氣狀況多雲...」\n"
- "✅ 良好：「台北現在23度左右，有點多雲呢！體感還蠻舒服的～」\n\n"
- "記住：你是在聊天，不是在報告數據！"
+ "【範例】\n"
+ "用戶問：「台北現在幾度？」\n"
+ "工具返回：溫度23.88°C、濕度65%、風速3m/s、氣壓1013hPa...\n"
+ "❌ 錯誤：「台北現在23度，濕度65%，風速3m/s...」（說太多）\n"
+ "✅ 正確：「台北現在23度左右喔！」（只回答溫度）\n"
+ "✅ 可接受：「台北現在23度，體感蠻舒服的～」（簡短補充）\n\n"
+ "記住：精準回答使用者的問題，不要喧賓奪主！"
)
user_prompt = (
- f"用戶問：「{original_message}」\n\n"
- f"我用 {tool_name} 查到的資料：\n{content}\n\n"
- f"請用自然對話的方式回答用戶（簡短、親切、口語化）："
+ f"【使用者的核心問題】\n"
+ f"「{original_message}」\n\n"
+ f"【工具 {tool_name} 返回的數據】\n"
+ f"{content}\n\n"
+ f"【任務】\n"
+ f"請只回答使用者問的問題（簡短、親切、口語化）："
)
messages = [
diff --git a/features/mcp/tools/exchange_tool.py b/features/mcp/tools/exchange_tool.py
index 25a6faffdaf5173d25e7cb72526c0515dd48ce07..2f305b708a542910b8c43ab78e59f0b164378d8e 100644
--- a/features/mcp/tools/exchange_tool.py
+++ b/features/mcp/tools/exchange_tool.py
@@ -101,12 +101,14 @@ class ExchangeTool(MCPTool):
return cls.create_success_response(
content=formatted_text,
data={
- "rate": rate,
- "from_currency": from_currency,
- "to_currency": to_currency,
- "amount": amount,
- "converted_amount": amount * rate if conversion else None,
- "raw_data": rate_data
+ "raw_data": {
+ "rate": rate,
+ "from_currency": from_currency,
+ "to_currency": to_currency,
+ "amount": amount,
+ "converted_amount": amount * rate if conversion else None,
+ "rate_data": rate_data
+ }
}
)
else:
diff --git a/features/mcp/tools/geocode_tool.py b/features/mcp/tools/geocode_tool.py
index 267b51184ba4ec42fb614d89977cdb89431fb0dd..d5b6ed093704f8899bed0c16fbe37e480c24e27b 100644
--- a/features/mcp/tools/geocode_tool.py
+++ b/features/mcp/tools/geocode_tool.py
@@ -168,7 +168,7 @@ class ReverseGeocodeTool(MCPTool):
# 地點名稱（優先使用繁中）
name = data.get("name") or ""
- namedetails = data.get("namedetails", {})
+ namedetails = data.get("namedetails") or {}
name_zh = namedetails.get("name:zh") or namedetails.get("name:zh-TW") or name
display_name = data.get("display_name") or ""
diff --git a/features/mcp/tools/healthkit_tool.py b/features/mcp/tools/healthkit_tool.py
index c0e93cf91e10d27974ad3afb4cbe4b8b4456ddea..2fdb444f44ee322d95e801925b3750e1ff6f7485 100644
--- a/features/mcp/tools/healthkit_tool.py
+++ b/features/mcp/tools/healthkit_tool.py
@@ -110,8 +110,8 @@ class HealthKitTool(MCPTool):
logger.debug("✅ 使用 Firestore 數據庫連接")
- # 解析參數
- user_id = arguments.get('user_id')
+ # 解析參數（_user_id 由 coordinator 注入）
+ user_id = arguments.get('_user_id')
metric_type = arguments.get('metric_type', 'all')
days = arguments.get('days', 7)
latest_only = arguments.get('latest_only', False)
@@ -120,6 +120,7 @@ class HealthKitTool(MCPTool):
# 如果沒有提供 user_id，返回錯誤
if not user_id:
return cls.create_error_response(
+ error="需要提供用戶 ID",
code="USER_ID_REQUIRED"
)
@@ -197,18 +198,27 @@ class HealthKitTool(MCPTool):
except Exception:
pass # 設備資訊是可選的
+ # 限制返回給前端的數據量（避免傳輸過多數據）
+ # 前端工具卡片只顯示最新的 20 筆，但 count 顯示總數
+ display_limit = 20
+ display_data = data[:display_limit] if len(data) > display_limit else data
+
return cls.create_success_response(
content=summary,
data={
- "health_data": data,
- "count": len(data),
- "query": {
- "metric_type": metric_type,
- "days": days,
- "latest_only": latest_only,
- "aggregation": aggregation
- },
- "device_info": device_info
+ "raw_data": {
+ "health_data": display_data,
+ "count": len(data),
+ "total_records": len(data),
+ "displayed_records": len(display_data),
+ "query": {
+ "metric_type": metric_type,
+ "days": days,
+ "latest_only": latest_only,
+ "aggregation": aggregation
+ },
+ "device_info": device_info
+ }
}
)
@@ -325,7 +335,8 @@ class HealthKitTool(MCPTool):
if values:
avg_value = sum(values) / len(values)
latest_value = values[0] if values else 0
- summary += f"• {name}: 最新 {latest_value:.1f}，平均 {avg_value:.1f} ({len(values)} 筆記錄)\n"
+ # 不顯示記錄數，避免使用者困惑
+ summary += f"• {name}: 最新 {latest_value:.1f}，平均 {avg_value:.1f}\n"
else:
name = metric_names.get(metric_type, metric_type)
if len(data) == 1:
@@ -336,6 +347,7 @@ class HealthKitTool(MCPTool):
values = [p["value"] for p in data]
avg_value = sum(values) / len(values)
latest_value = values[0] if values else 0
- summary = f"{name}數據：最新 {latest_value:.1f} {data[0]['unit']}，平均 {avg_value:.1f}，共 {len(data)} 筆記錄"
+ # 不顯示記錄數，避免使用者困惑（AI 會根據需要提取重點）
+ summary = f"{name}數據：最新 {latest_value:.1f} {data[0]['unit']}，平均 {avg_value:.1f}"
return summary
diff --git a/features/mcp/tools/news_tool.py b/features/mcp/tools/news_tool.py
index ef82a028fb0d950d37a95a04e91a4bb37fe29880..d4d8463e48ac7bda4a6d2128b15010fe804b7f4d 100644
--- a/features/mcp/tools/news_tool.py
+++ b/features/mcp/tools/news_tool.py
@@ -98,7 +98,14 @@ class NewsTool(MCPTool):
"content": {"type": "string"},
"url": {"type": "string"},
"published_at": {"type": "string"},
- "source": {"type": "string"},
+ "source": {
+ "type": "object",
+ "properties": {
+ "name": {"type": "string"},
+ "id": {"type": "string"},
+ "url": {"type": "string"}
+ }
+ },
"category": {"type": "array"},
"language": {"type": "string"},
"sentiment": {"type": "string"}
@@ -151,9 +158,11 @@ class NewsTool(MCPTool):
return cls.create_success_response(
content=formatted_text,
data={
- "articles": articles,
- "count": len(articles),
- "totalResults": total_results
+ "raw_data": {
+ "articles": articles,
+ "count": len(articles),
+ "totalResults": total_results
+ }
}
)
else:
diff --git a/features/mcp/tools/tdx_bus_arrival.py b/features/mcp/tools/tdx_bus_arrival.py
index 3abe024acd44eae1c6a98e19638142adcc4db067..9d7ee3426b679177dabd95ce18c0eab19617392b 100644
--- a/features/mcp/tools/tdx_bus_arrival.py
+++ b/features/mcp/tools/tdx_bus_arrival.py
@@ -24,15 +24,24 @@ class TDXBusArrivalTool(MCPTool):
"""TDX 公車即時到站查詢"""
NAME = "tdx_bus_arrival"
- DESCRIPTION = "查詢公車即時到站時間（自動感知用戶位置，找最近站點）"
+ DESCRIPTION = "查詢公車即時到站時間。適用於：1) 已知路線號碼（如307、紅30）的到站查詢；2) 查詢附近公車站。不適用於路線規劃（如「從A到B的公車」應用directions）。"
CATEGORY = "道路運輸"
TAGS = ["tdx", "公車", "即時到站", "公共運輸"]
- KEYWORDS = ["公車", "巴士", "bus", "到站", "即時", "幾分鐘"]
+ KEYWORDS = ["公車", "巴士", "bus", "到站", "即時", "幾分鐘", "公車站", "等公車", "路線號碼"]
USAGE_TIPS = [
- "查詢特定路線: 「307 公車還要多久」",
- "查詢附近公車站: 「附近有什麼公車」",
- "指定城市: 「台北 307」「高雄紅30」"
+ "「307 公車還要多久」→ route_name=307",
+ "「紅30 什麼時候來」→ route_name=紅30",
+ "「附近有什麼公車」→ 不需參數，用 GPS 查詢附近站點"
]
+ NEGATIVE_EXAMPLES = [
+ "「埔鹽站往彰化的公車」→ 這是路線規劃，不是查詢特定路線！應用 directions",
+ "「往彰化的公車」→ 彰化是目的地，不是路線號碼！應用 directions",
+ "「去台北的公車」→ 台北是目的地，不是路線號碼！應用 directions",
+ "「從A到B的公車」→ 這是路線規劃，應用 directions",
+ "「公車路線圖」→ 這是詢問路線圖，不是查到站時間"
+ ]
+ PRIORITY = 5
+ ALIASES = ["bus", "公車", "巴士"]
# TDX 城市代碼
VALID_CITIES = {
@@ -81,7 +90,7 @@ class TDXBusArrivalTool(MCPTool):
"route_name": {"type": "string"},
"stop_name": {"type": "string"},
"direction": {"type": "integer"},
- "estimate_time": {"type": "integer"},
+ "estimate_time": {"type": ["integer", "null"]},
"status": {"type": "string"}
}
}
diff --git a/features/mcp/tools/tdx_train.py b/features/mcp/tools/tdx_train.py
index 2fc83ffe59966dc0f9bdec4366ca12458f47c191..a309e82b7060a536f198615ce249055f6de5e939 100644
--- a/features/mcp/tools/tdx_train.py
+++ b/features/mcp/tools/tdx_train.py
@@ -18,16 +18,22 @@ class TDXTrainTool(MCPTool):
"""TDX 台鐵時刻表查詢"""
NAME = "tdx_train"
- DESCRIPTION = "查詢台鐵列車時刻表、票價、最近車站（含高鐵轉乘資訊）"
+ DESCRIPTION = "查詢台鐵列車時刻表。參數提取規則：「從A到B」→origin_station=A,destination_station=B；「往B」→destination_station=B（起點用GPS）；「車次123」→train_no=123。"
CATEGORY = "軌道運輸"
TAGS = ["tdx", "台鐵", "TRA", "火車", "時刻表"]
- KEYWORDS = ["台鐵", "臺鐵", "火車", "TRA", "列車", "時刻"]
+ KEYWORDS = ["台鐵", "臺鐵", "火車", "TRA", "列車", "時刻", "自強號", "莒光號", "區間車"]
USAGE_TIPS = [
- "查詢車次: 「自強號 123 次」",
- "查詢路線: 「台北到台中的火車」",
- "查詢最近車站: 「最近的火車站在哪」",
- "查詢時刻: 「下午3點台北到高雄」"
+ "「自強號 123 次」→ train_no=123",
+ "「台北到台中的火車」→ origin_station=台北, destination_station=台中",
+ "「往台北的火車」→ destination_station=台北（起點用 GPS）",
+ "「下午3點台北到高雄」→ origin_station=台北, destination_station=高雄, departure_time=15:00"
]
+ NEGATIVE_EXAMPLES = [
+ "「火車票怎麼買」→ 這是詢問購票方式，不是查時刻表",
+ "「火車站在哪」→ 這是查位置，應用 reverse_geocode 或 forward_geocode"
+ ]
+ PRIORITY = 8
+ ALIASES = ["train", "台鐵", "火車"]
@classmethod
def get_input_schema(cls) -> Dict[str, Any]:
diff --git a/features/mcp/tools/tdx_youbike.py b/features/mcp/tools/tdx_youbike.py
index 5accf3723353b0e2a75cc08ebf3ae8971ff8d8fc..97db13b26dcbfedd3f15bbbccaec2c71ca8b1ce4 100644
--- a/features/mcp/tools/tdx_youbike.py
+++ b/features/mcp/tools/tdx_youbike.py
@@ -20,12 +20,24 @@ class TDXBikeTool(MCPTool):
DESCRIPTION = "查詢附近 YouBike 站點、即時車輛數、空位數（支援 YouBike 1.0/2.0）"
CATEGORY = "微型運具"
TAGS = ["tdx", "youbike", "ubike", "共享單車", "微笑單車"]
- KEYWORDS = ["YouBike", "UBike", "微笑單車", "共享單車", "腳踏車", "自行車"]
+ KEYWORDS = [
+ "YouBike", "Youbike", "youbike", "YOUBIKE",
+ "UBike", "Ubike", "ubike", "UBIKE",
+ "微笑單車", "共享單車", "公共單車",
+ "腳踏車站", "單車站", "自行車站",
+ "借車", "還車", "腳踏車"
+ ]
USAGE_TIPS = [
- "查詢附近站點: 「附近的 YouBike 在哪」",
- "查詢特定站點: 「市政府 YouBike 還有車嗎」",
- "指定城市: 「台北 YouBike」「高雄 CityBike」"
+ "「附近的 YouBike」→ 查詢最近站點",
+ "「Ubike 在哪」→ 查詢最近站點",
+ "「市政府 YouBike 還有車嗎」→ station_name=市政府"
+ ]
+ NEGATIVE_EXAMPLES = [
+ "「YouBike 怎麼註冊」→ 這是詢問註冊方式，不是查站點",
+ "「YouBike 費率」→ 這是詢問價格，不是查站點"
]
+ PRIORITY = 6
+ ALIASES = ["youbike", "ubike", "微笑單車", "共享單車"]
# 城市對應
CITY_MAP = {
@@ -43,6 +55,16 @@ class TDXBikeTool(MCPTool):
@classmethod
def get_input_schema(cls) -> Dict[str, Any]:
+ # 建立包含中文和英文的城市列表
+ all_cities = list(cls.CITY_MAP.keys()) + list(cls.CITY_MAP.values())
+ # 去重並保持順序
+ unique_cities = []
+ seen = set()
+ for city in all_cities:
+ if city not in seen:
+ unique_cities.append(city)
+ seen.add(city)
+
return StandardToolSchemas.create_input_schema({
"station_name": {
"type": "string",
@@ -50,8 +72,8 @@ class TDXBikeTool(MCPTool):
},
"city": {
"type": "string",
- "description": "城市名稱（如「Taipei」「Kaohsiung」）",
- "enum": list(cls.CITY_MAP.values())
+ "description": "城市名稱（支援中文如「台北」「桃園」或英文如「Taipei」「Taoyuan」）",
+ "enum": unique_cities
},
"radius_m": {
"type": "integer",
@@ -108,6 +130,11 @@ class TDXBikeTool(MCPTool):
station_name = safe_str(arguments.get("station_name"))
city = arguments.get("city")
+
+ # 如果 city 是中文，轉換為英文
+ if city:
+ city = cls._map_city_name(city)
+
radius_m = min(int(arguments.get("radius_m", 500)), 2000)
limit = min(int(arguments.get("limit", 5)), 20)
@@ -166,7 +193,20 @@ class TDXBikeTool(MCPTool):
final_city = guessed
city_source = "經緯度推斷"
- city = cls._map_city_name(final_city) if final_city else "Taipei"
+ # 檢查城市是否支援 YouBike
+ if final_city:
+ city = cls._map_city_name(final_city)
+ if city == "Taipei" and final_city not in cls.CITY_MAP:
+ # 城市不在支援列表中，提供友善錯誤訊息
+ nearest_city = cls._find_nearest_supported_city(user_lat, user_lon)
+ raise ExecutionError(
+ f"🚲 很抱歉，{final_city}目前沒有 YouBike 服務。\n\n"
+ f"最近有 YouBike 的城市是：{nearest_city}\n"
+ f"支援 YouBike 的城市：台北、新北、桃園、新竹、台中、台南、高雄"
+ )
+ else:
+ city = "Taipei"
+
logger.info(f"🏙️ 最終使用城市代碼: {city} (來源={city_source})")
# 3. 查詢分支
@@ -365,6 +405,7 @@ class TDXBikeTool(MCPTool):
("新北", 24.67, 25.30, 121.35, 122.01),
("新竹", 24.68, 24.90, 120.90, 121.10),
("台中", 24.00, 24.45, 120.45, 121.05),
+ ("彰化", 23.85, 24.15, 120.35, 120.70), # 新增彰化範圍
("台南", 22.85, 23.40, 120.00, 120.55),
("高雄", 22.45, 23.15, 120.15, 120.80),
]
@@ -386,6 +427,31 @@ class TDXBikeTool(MCPTool):
return value
return "Taipei"
+ @staticmethod
+ def _find_nearest_supported_city(lat: float, lon: float) -> str:
+ """找出最近的支援 YouBike 的城市"""
+ # 支援 YouBike 的城市中心點（大約位置）
+ city_centers = {
+ "台北": (25.033, 121.565),
+ "新北": (25.012, 121.466),
+ "桃園": (24.994, 121.301),
+ "新竹": (24.806, 120.968),
+ "台中": (24.148, 120.674),
+ "台南": (22.997, 120.213),
+ "高雄": (22.627, 120.301),
+ }
+
+ min_distance = float('inf')
+ nearest_city = "台北"
+
+ for city_name, (city_lat, city_lon) in city_centers.items():
+ distance = TDXBaseAPI.haversine_distance(lat, lon, city_lat, city_lon)
+ if distance < min_distance:
+ min_distance = distance
+ nearest_city = city_name
+
+ return nearest_city
+
@staticmethod
def _detect_bike_type(station: Dict, station_name: str) -> str:
"""判斷 YouBike 類型（優先從站名判斷，其次從 BikesCapacity）"""
diff --git a/features/mcp/tools/weather_tool.py b/features/mcp/tools/weather_tool.py
index 5b17fa873ea4745f3508ff3e95dec5ad5fed7c89..c36b21a541344e387dd65a49c1ece808f2f0b148 100644
--- a/features/mcp/tools/weather_tool.py
+++ b/features/mcp/tools/weather_tool.py
@@ -33,12 +33,18 @@ class WeatherTool(MCPTool):
DESCRIPTION = "查詢指定城市的即時天氣資訊（溫度、濕度、天氣狀況等）"
CATEGORY = "生活資訊"
TAGS = ["weather", "天氣", "氣象"]
- KEYWORDS = ["天氣", "氣溫", "下雨", "晴天", "陰天", "weather", "溫度"]
+ KEYWORDS = ["天氣", "氣溫", "下雨", "晴天", "陰天", "weather", "溫度", "濕度", "會不會下雨", "熱不熱", "冷不冷"]
USAGE_TIPS = [
- "提供城市名稱（英文）如 Taipei, Tokyo",
- "支援經緯度查詢",
- "可指定語言 (zh_tw, en, zh_cn)"
+ "「台北天氣」→ city=Taipei",
+ "「東京今天會下雨嗎」→ city=Tokyo",
+ "「現在幾度」→ 使用用戶位置"
]
+ NEGATIVE_EXAMPLES = [
+ "「天氣預報 App」→ 這是詢問 App，不是查天氣",
+ "「天氣好好」→ 這是感嘆，不是查詢"
+ ]
+ PRIORITY = 1 # 高優先級
+ ALIASES = ["weather", "氣象"]
@classmethod
def get_input_schema(cls) -> Dict[str, Any]:
@@ -57,7 +63,7 @@ class WeatherTool(MCPTool):
"default": "zh_tw",
"enum": ["zh_tw", "en", "zh_cn"]
}
- }, ["city"]) # city 可留空，若 lat/lon 有值則忽略
+ }, []) # 所有參數都是可選的，由 execute 方法內部邏輯判斷
@classmethod
def get_output_schema(cls) -> Dict[str, Any]:
diff --git a/middleware/__init__.py b/middleware/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d3270bd3c1a8133c465591d9fdec40330b8c7bd
--- /dev/null
+++ b/middleware/__init__.py
@@ -0,0 +1,18 @@
+"""
+中間件模組
+拆分自 app.py，提高可維護性
+"""
+
+from .csp import CSPMiddleware
+from .rate_limit import RateLimitMiddleware, rate_limiter
+from .exception_handler import ExceptionHandlerMiddleware, RequestLoggingMiddleware
+from .compression import GzipMiddleware
+
+__all__ = [
+ "CSPMiddleware",
+ "RateLimitMiddleware",
+ "rate_limiter",
+ "ExceptionHandlerMiddleware",
+ "RequestLoggingMiddleware",
+ "GzipMiddleware",
+]
diff --git a/middleware/compression.py b/middleware/compression.py
new file mode 100644
index 0000000000000000000000000000000000000000..e44cacb81bef80b141183741d0ff3ac59ed835b4
--- /dev/null
+++ b/middleware/compression.py
@@ -0,0 +1,106 @@
+"""
+回應壓縮中間件
+使用 gzip 壓縮 API 回應，減少傳輸大小
+"""
+
+import gzip
+from typing import Callable
+
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.requests import Request
+from starlette.responses import Response
+
+from core.logging import get_logger
+
+logger = get_logger("middleware.compression")
+
+# 最小壓縮大小（bytes）
+MIN_COMPRESS_SIZE = 500
+
+# 可壓縮的 Content-Type
+COMPRESSIBLE_TYPES = {
+ "application/json",
+ "text/html",
+ "text/plain",
+ "text/css",
+ "text/javascript",
+ "application/javascript",
+}
+
+
+class GzipMiddleware(BaseHTTPMiddleware):
+ """
+ Gzip 壓縮中間件
+
+ 功能：
+ 1. 檢查客戶端是否支援 gzip
+ 2. 壓縮大於閾值的回應
+ 3. 只壓縮可壓縮的 Content-Type
+ """
+
+ async def dispatch(self, request: Request, call_next: Callable) -> Response:
+ # 檢查客戶端是否支援 gzip
+ accept_encoding = request.headers.get("accept-encoding", "")
+ supports_gzip = "gzip" in accept_encoding.lower()
+
+ response = await call_next(request)
+
+ # 不支援 gzip 或已經壓縮，直接返回
+ if not supports_gzip:
+ return response
+
+ if response.headers.get("content-encoding"):
+ return response
+
+ # 檢查 Content-Type 是否可壓縮
+ content_type = response.headers.get("content-type", "")
+ base_type = content_type.split(";")[0].strip()
+
+ if base_type not in COMPRESSIBLE_TYPES:
+ return response
+
+ # 讀取回應內容
+ body = b""
+ async for chunk in response.body_iterator:
+ body += chunk
+
+ # 檢查大小是否值得壓縮
+ if len(body) < MIN_COMPRESS_SIZE:
+ # 重建回應
+ return Response(
+ content=body,
+ status_code=response.status_code,
+ headers=dict(response.headers),
+ media_type=response.media_type,
+ )
+
+ # 壓縮內容
+ compressed = gzip.compress(body, compresslevel=6)
+
+ # 只有壓縮後更小才使用
+ if len(compressed) >= len(body):
+ return Response(
+ content=body,
+ status_code=response.status_code,
+ headers=dict(response.headers),
+ media_type=response.media_type,
+ )
+
+ # 更新標頭
+ headers = dict(response.headers)
+ headers["content-encoding"] = "gzip"
+ headers["content-length"] = str(len(compressed))
+ # 移除可能衝突的標頭
+ headers.pop("transfer-encoding", None)
+
+ logger.debug(
+ f"壓縮回應: {len(body)} -> {len(compressed)} bytes "
+ f"({100 - len(compressed) * 100 // len(body)}% 減少)"
+ )
+
+ return Response(
+ content=compressed,
+ status_code=response.status_code,
+ headers=headers,
+ media_type=response.media_type,
+ )
diff --git a/middleware/csp.py b/middleware/csp.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a694b634fed99ea62436d38de86d826b7e7616b
--- /dev/null
+++ b/middleware/csp.py
@@ -0,0 +1,36 @@
+"""
+Content Security Policy 中間件
+允許內嵌 script 用於語音沉浸式前端
+"""
+
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.requests import Request as StarletteRequest
+
+
+class CSPMiddleware(BaseHTTPMiddleware):
+ """CSP 中間件"""
+
+ async def dispatch(self, request: StarletteRequest, call_next):
+ response = await call_next(request)
+
+ # 對所有靜態檔案路徑添加寬鬆的 CSP header
+ if request.url.path.startswith("/static/"):
+ # 移除可能存在的嚴格 CSP
+ if "Content-Security-Policy" in response.headers:
+ del response.headers["Content-Security-Policy"]
+
+ # 設定寬鬆的 CSP 以允許內嵌 script
+ response.headers["Content-Security-Policy"] = (
+ "default-src 'self'; "
+ "script-src 'self' 'unsafe-inline' 'unsafe-eval' "
+ "https://accounts.google.com https://www.gstatic.com; "
+ "style-src 'self' 'unsafe-inline' https://fonts.googleapis.com; "
+ "font-src 'self' https://fonts.gstatic.com data:; "
+ "connect-src 'self' ws: wss: https://accounts.google.com; "
+ "img-src 'self' data: https: blob:; "
+ "media-src 'self' blob: data:; "
+ "frame-src https://accounts.google.com; "
+ "base-uri 'self';"
+ )
+
+ return response
diff --git a/middleware/exception_handler.py b/middleware/exception_handler.py
new file mode 100644
index 0000000000000000000000000000000000000000..800795e33299304198a3629fcca1b53b11d9e092
--- /dev/null
+++ b/middleware/exception_handler.py
@@ -0,0 +1,99 @@
+"""
+全域異常處理中間件
+統一處理所有未捕獲的異常，返回標準化錯誤回應
+"""
+
+import logging
+import traceback
+from typing import Callable
+
+from fastapi import Request, Response
+from fastapi.responses import JSONResponse
+from starlette.middleware.base import BaseHTTPMiddleware
+
+from core.exceptions import BloomWareException, handle_exception
+from core.logging import get_logger
+
+logger = get_logger("middleware.exception_handler")
+
+
+class ExceptionHandlerMiddleware(BaseHTTPMiddleware):
+ """
+ 全域異常處理中間件
+
+ 功能：
+ 1. 捕獲所有未處理的異常
+ 2. 轉換為標準化 JSON 錯誤回應
+ 3. 記錄錯誤日誌（生產環境隱藏堆疊）
+ """
+
+ async def dispatch(self, request: Request, call_next: Callable) -> Response:
+ try:
+ response = await call_next(request)
+ return response
+
+ except BloomWareException as e:
+ # 已知的業務異常
+ logger.warning(f"業務異常: {e.code} - {e.message}")
+ return e.to_response()
+
+ except Exception as e:
+ # 未知異常
+ logger.error(f"未處理的異常: {type(e).__name__}: {e}")
+ logger.debug(f"堆疊追蹤:\n{traceback.format_exc()}")
+
+ # 返回標準化錯誤回應
+ return JSONResponse(
+ status_code=500,
+ content={
+ "success": False,
+ "error": {
+ "code": "INTERNAL_ERROR",
+ "message": "內部伺服器錯誤",
+ "details": {}
+ }
+ }
+ )
+
+
+class RequestLoggingMiddleware(BaseHTTPMiddleware):
+ """
+ 請求日誌中間件
+
+ 功能：
+ 1. 記錄所有 API 請求
+ 2. 計算請求處理時間
+ 3. 記錄回應狀態碼
+ """
+
+ async def dispatch(self, request: Request, call_next: Callable) -> Response:
+ import time
+
+ start_time = time.time()
+ method = request.method
+ path = request.url.path
+
+ # 跳過健康檢查和靜態資源的日誌
+ skip_paths = ["/health", "/static/", "/favicon.ico"]
+ should_log = not any(path.startswith(p) for p in skip_paths)
+
+ try:
+ response = await call_next(request)
+ process_time = (time.time() - start_time) * 1000
+
+ if should_log:
+ logger.info(
+ f"{method} {path} - {response.status_code} - {process_time:.2f}ms"
+ )
+
+ # 添加處理時間到回應標頭
+ response.headers["X-Process-Time"] = f"{process_time:.2f}ms"
+ return response
+
+ except Exception as e:
+ process_time = (time.time() - start_time) * 1000
+ if should_log:
+ logger.error(
+ f"{method} {path} - ERROR - {process_time:.2f}ms - {e}"
+ )
+ raise
diff --git a/middleware/rate_limit.py b/middleware/rate_limit.py
new file mode 100644
index 0000000000000000000000000000000000000000..09b53d742eefb4ca113d07b15d6b6196ba20db36
--- /dev/null
+++ b/middleware/rate_limit.py
@@ -0,0 +1,165 @@
+"""
+Rate Limiting 中間件
+防止 API 濫用
+"""
+
+import logging
+import time
+from typing import Dict, Tuple
+from collections import defaultdict
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.requests import Request as StarletteRequest
+from starlette.responses import JSONResponse
+
+logger = logging.getLogger("middleware.rate_limit")
+
+
+class RateLimiter:
+ """
+ 簡易 Rate Limiter（記憶體實現）
+
+ 生產環境建議使用 Redis 實現
+ """
+
+ def __init__(
+ self,
+ requests_per_minute: int = 60,
+ requests_per_hour: int = 1000,
+ ):
+ self.requests_per_minute = requests_per_minute
+ self.requests_per_hour = requests_per_hour
+
+ # 記錄請求：{ip: [(timestamp, count), ...]}
+ self._minute_requests: Dict[str, list] = defaultdict(list)
+ self._hour_requests: Dict[str, list] = defaultdict(list)
+
+ def _cleanup_old_requests(self, requests: list, window_seconds: int) -> list:
+ """清理過期的請求記錄"""
+ current_time = time.time()
+ return [
+ (ts, count) for ts, count in requests
+ if current_time - ts < window_seconds
+ ]
+
+ def is_allowed(self, client_ip: str) -> Tuple[bool, str]:
+ """
+ 檢查請求是否被允許
+
+ Returns:
+ (is_allowed, reason)
+ """
+ current_time = time.time()
+
+ # 清理過期記錄
+ self._minute_requests[client_ip] = self._cleanup_old_requests(
+ self._minute_requests[client_ip], 60
+ )
+ self._hour_requests[client_ip] = self._cleanup_old_requests(
+ self._hour_requests[client_ip], 3600
+ )
+
+ # 計算當前窗口內的請求數
+ minute_count = sum(count for _, count in self._minute_requests[client_ip])
+ hour_count = sum(count for _, count in self._hour_requests[client_ip])
+
+ # 檢查限制
+ if minute_count >= self.requests_per_minute:
+ return False, f"每分鐘請求數超過限制（{self.requests_per_minute}）"
+
+ if hour_count >= self.requests_per_hour:
+ return False, f"每小時請求數超過限制（{self.requests_per_hour}）"
+
+ # 記錄請求
+ self._minute_requests[client_ip].append((current_time, 1))
+ self._hour_requests[client_ip].append((current_time, 1))
+
+ return True, ""
+
+ def get_remaining(self, client_ip: str) -> Dict[str, int]:
+ """獲取剩餘請求數"""
+ # 清理過期記錄
+ self._minute_requests[client_ip] = self._cleanup_old_requests(
+ self._minute_requests[client_ip], 60
+ )
+ self._hour_requests[client_ip] = self._cleanup_old_requests(
+ self._hour_requests[client_ip], 3600
+ )
+
+ minute_count = sum(count for _, count in self._minute_requests[client_ip])
+ hour_count = sum(count for _, count in self._hour_requests[client_ip])
+
+ return {
+ "minute_remaining": max(0, self.requests_per_minute - minute_count),
+ "hour_remaining": max(0, self.requests_per_hour - hour_count),
+ }
+
+
+# 全局 Rate Limiter 實例
+rate_limiter = RateLimiter(
+ requests_per_minute=60,
+ requests_per_hour=1000,
+)
+
+
+def get_client_ip(request: StarletteRequest) -> str:
+ """獲取客戶端 IP"""
+ # 優先取 X-Forwarded-For
+ xff = request.headers.get("x-forwarded-for") or request.headers.get("X-Forwarded-For")
+ if xff:
+ ip = xff.split(",")[0].strip()
+ if ip:
+ return ip
+ return request.client.host if request.client else "unknown"
+
+
+class RateLimitMiddleware(BaseHTTPMiddleware):
+ """Rate Limiting 中間件"""
+
+ # 不需要限制的路徑
+ EXEMPT_PATHS = {
+ "/",
+ "/health",
+ "/static",
+ "/login",
+ "/favicon.ico",
+ }
+
+ async def dispatch(self, request: StarletteRequest, call_next):
+ # 檢查是否豁免
+ path = request.url.path
+ if any(path.startswith(exempt) for exempt in self.EXEMPT_PATHS):
+ return await call_next(request)
+
+ # 獲取客戶端 IP
+ client_ip = get_client_ip(request)
+
+ # 檢查 Rate Limit
+ is_allowed, reason = rate_limiter.is_allowed(client_ip)
+
+ if not is_allowed:
+ logger.warning(f"Rate limit exceeded for {client_ip}: {reason}")
+ remaining = rate_limiter.get_remaining(client_ip)
+
+ return JSONResponse(
+ status_code=429,
+ content={
+ "error": "Too Many Requests",
+ "message": reason,
+ "retry_after": 60, # 建議等待時間（秒）
+ },
+ headers={
+ "Retry-After": "60",
+ "X-RateLimit-Remaining-Minute": str(remaining["minute_remaining"]),
+ "X-RateLimit-Remaining-Hour": str(remaining["hour_remaining"]),
+ }
+ )
+
+ # 繼續處理請求
+ response = await call_next(request)
+
+ # 添加 Rate Limit 頭
+ remaining = rate_limiter.get_remaining(client_ip)
+ response.headers["X-RateLimit-Remaining-Minute"] = str(remaining["minute_remaining"])
+ response.headers["X-RateLimit-Remaining-Hour"] = str(remaining["hour_remaining"])
+
+ return response
diff --git a/models/schemas.py b/models/schemas.py
new file mode 100644
index 0000000000000000000000000000000000000000..a38b03a7d98e31900e808e62d8a62633bdaa1d29
--- /dev/null
+++ b/models/schemas.py
@@ -0,0 +1,123 @@
+"""
+Pydantic 模型定義
+統一管理 API 請求/回應的資料結構
+"""
+
+from datetime import datetime
+from typing import List, Optional
+from pydantic import BaseModel, EmailStr, Field
+
+
+# ===== 用戶相關 =====
+
+class UserCreate(BaseModel):
+ """用戶註冊請求"""
+ name: str
+ email: EmailStr
+ password: str = Field(min_length=6)
+
+
+class UserLogin(BaseModel):
+ """用戶登入請求"""
+ email: EmailStr
+ password: str
+
+
+class UserInfo(BaseModel):
+ """用戶資訊"""
+ id: str
+ name: str
+ email: EmailStr
+ created_at: datetime
+
+
+class UserPublic(BaseModel):
+ """用戶公開資訊回應"""
+ success: bool
+ user: UserInfo
+
+
+class UserLoginPublicResponse(BaseModel):
+ """用戶登入回應"""
+ success: bool
+ user: UserInfo
+ token: Optional[str] = None
+
+
+# ===== 對話相關 =====
+
+class ChatCreateRequest(BaseModel):
+ """建立對話請求"""
+ user_id: str
+ title: Optional[str] = "新對話"
+
+
+class ChatTitleUpdateRequest(BaseModel):
+ """更新對話標題請求"""
+ title: str
+
+
+class ChatPublic(BaseModel):
+ """對話公開資訊"""
+ chat_id: str
+ user_id: str
+ title: str
+ created_at: datetime
+ updated_at: datetime
+
+
+class ChatSummary(BaseModel):
+ """對話摘要"""
+ chat_id: str
+ title: str
+ updated_at: datetime
+
+
+class ChatListResponse(BaseModel):
+ """對話列表回應"""
+ chats: List[ChatSummary]
+
+
+# ===== 訊息相關 =====
+
+class MessageCreateRequest(BaseModel):
+ """建立訊息請求"""
+ sender: str
+ content: str
+
+
+class MessagePublic(BaseModel):
+ """訊息公開資訊"""
+ sender: str
+ content: str
+ timestamp: datetime
+
+
+class ChatDetailResponse(ChatPublic):
+ """對話詳情回應（含訊息）"""
+ messages: List[MessagePublic]
+
+
+# ===== 檔案分析 =====
+
+class FileAnalysisRequest(BaseModel):
+ """檔案分析請求"""
+ filename: str
+ content: str
+ mime_type: str
+ user_prompt: Optional[str] = "請分析這個檔案的內容"
+
+
+class FileAnalysisResponse(BaseModel):
+ """檔案分析回應"""
+ success: bool
+ filename: str
+ analysis: Optional[str] = None
+ error: Optional[str] = None
+
+
+# ===== 語音相關 =====
+
+class SpeakerLabelBindRequest(BaseModel):
+ """語音標籤綁定請求"""
+ speaker_label: str
diff --git a/models/speaker_identification/models_cnn/speaker_db.pkl b/models/speaker_identification/models_cnn/speaker_db.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..73b12df5ff2ec0301ff21b4c4167b619f5f8f115
--- /dev/null
+++ b/models/speaker_identification/models_cnn/speaker_db.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4377a566b92e5cca1db0f83274a7037169f371e47ca357e3c764c8908ef55e43
+size 4157
diff --git a/models/speaker_identification/scripts/inference.py b/models/speaker_identification/scripts/inference.py
index 543c1b9cdb4774d2af56b6e39b72f83087428379..c48dd610981e474796605eda313cefb8ab74c311 100644
--- a/models/speaker_identification/scripts/inference.py
+++ b/models/speaker_identification/scripts/inference.py
@@ -1,144 +1,91 @@
import os
-import argparse
-import numpy as np
+import torch
import librosa
-import noisereduce as nr
-import pyaudio
+import numpy as np
+import pickle
import wave
-import torch
-import torch.nn as nn
-import torchaudio
+import pyaudio
+from speechbrain.pretrained import EncoderClassifier
+from sklearn.metrics.pairwise import cosine_similarity
+# 常數
+SAMPLE_RATE = 16000
+DB_FILE = "speaker_db.pkl"
-def get_device():
- if torch.cuda.is_available():
- print("Using device: CUDA")
- return torch.device('cuda')
- if torch.backends.mps.is_available():
- print("Using device: MPS")
- return torch.device('mps')
- print("Using device: CPU")
- return torch.device('cpu')
+# 全域 classifier（延遲載入）
+_classifier = None
-class Wav2Vec2SpeakerClassifier(nn.Module):
- def __init__(self, bundle, num_classes, freeze_encoder=True):
- super().__init__()
- self.encoder = bundle.get_model()
- for p in self.encoder.parameters():
- p.requires_grad = False
- hidden_dim = getattr(self.encoder, 'encoder_embed_dim', 768)
- self.classifier = nn.Sequential(
- nn.Dropout(0.0),
- nn.Linear(hidden_dim, num_classes)
+def get_classifier():
+ """取得 ECAPA-TDNN 分類器（單例模式）"""
+ global _classifier
+ if _classifier is None:
+ _classifier = EncoderClassifier.from_hparams(
+ source="speechbrain/spkrec-ecapa-voxceleb"
)
+ return _classifier
- def forward(self, waveforms, lengths=None):
- out = self.encoder(waveforms, lengths)
- if isinstance(out, tuple):
- features, lengths = out
- else:
- features, lengths = out, None
- if lengths is not None:
- valid_lengths = (lengths * features.size(1)).round().to(torch.long).clamp(min=1, max=features.size(1))
- mask = torch.arange(features.size(1), device=features.device).unsqueeze(0) < valid_lengths.unsqueeze(1)
- features = features * mask.unsqueeze(-1)
- pooled = features.sum(dim=1) / valid_lengths.unsqueeze(-1)
- else:
- pooled = features.mean(dim=1)
- return self.classifier(pooled)
-
-def load_classes(classes_path_or_dir):
- # 若提供文字檔則逐行讀取，否則從目錄列出子資料夾
- if os.path.isfile(classes_path_or_dir):
- with open(classes_path_or_dir, 'r', encoding='utf-8') as f:
- classes = [line.strip() for line in f if line.strip()]
- else:
- classes = sorted([d for d in os.listdir(classes_path_or_dir)
- if os.path.isdir(os.path.join(classes_path_or_dir, d))])
- return classes
+def get_embedding(file_path):
+ """從音訊檔案取得 ECAPA-TDNN 嵌入向量"""
+ signal, sr = librosa.load(file_path, sr=SAMPLE_RATE, mono=True)
+ signal_tensor = torch.tensor(signal).unsqueeze(0)
+ classifier = get_classifier()
+ embedding = classifier.encode_batch(signal_tensor)
+ return embedding.squeeze().detach().numpy()
-def load_audio(path, target_sr):
- wav, sr = torchaudio.load(path)
- if wav.size(0) > 1:
- wav = wav.mean(dim=0, keepdim=True)
- if sr != target_sr:
- resampler = torchaudio.transforms.Resample(sr, target_sr)
- wav = resampler(wav)
- sr = target_sr
- wav = wav.squeeze(0)
- length = torch.tensor([wav.shape[0]], dtype=torch.long)
- return wav.unsqueeze(0), length
+def load_speaker_db(db_path):
+ """載入說話者嵌入資料庫"""
+ with open(db_path, "rb") as f:
+ speaker_embeddings = pickle.load(f)
+ return speaker_embeddings
-def softmax(x):
- e = torch.exp(x - x.max(dim=1, keepdim=True).values)
- return e / e.sum(dim=1, keepdim=True)
+def recognize_speaker(test_file, speaker_embeddings):
+ """
+ 辨識語音（與原始 ECAPA_TDNN.py 完全一致）
+ """
+ test_emb = get_embedding(test_file).reshape(1, -1)
+ scores = {}
+ for spk, emb in speaker_embeddings.items():
+ sim = cosine_similarity(test_emb, emb.reshape(1, -1))[0][0]
+ scores[spk] = sim
+ predicted = max(scores, key=scores.get)
+ scores[predicted] += 0.35
+ return predicted, scores
def predict_files(model_dir, file_list, threshold=0.0):
"""
- 預測多個音訊檔案的說話者。
+ 預測多個音訊檔案的說話者
Args:
- model_dir: 模型目錄，包含 speaker_id_model.pth 和 classes.txt
+ model_dir: 模型目錄，包含 speaker_db.pkl
file_list: 檔案路徑列表
- threshold: 預測門檻（目前未使用）
+ threshold: 未使用，保留介面相容
Returns:
結果列表，每個元素為字典，包含 'pred', 'score', 'top'
"""
- device = get_device()
- bundle = torchaudio.pipelines.WAV2VEC2_BASE
- target_sr = bundle.sample_rate
-
- model_path = os.path.join(model_dir, 'speaker_id_model.pth')
- classes_path = os.path.join(model_dir, 'classes.txt')
- processed_dir = os.path.join(model_dir, 'processed_audio')
-
- if os.path.isfile(classes_path):
- classes = load_classes(classes_path)
- elif os.path.isdir(processed_dir):
- classes = load_classes(processed_dir)
- else:
- raise FileNotFoundError(f"找不到類別定義：{classes_path} 或 {processed_dir}")
-
- num_classes = len(classes)
-
- model = Wav2Vec2SpeakerClassifier(bundle, num_classes)
- state = torch.load(model_path, map_location='cpu')
- model.load_state_dict(state)
- model.to(device).eval()
+ db_path = os.path.join(model_dir, DB_FILE)
+ if not os.path.exists(db_path):
+ raise FileNotFoundError(f"找不到說話者資料庫：{db_path}")
+ speaker_embeddings = load_speaker_db(db_path)
results = []
+
for file_path in file_list:
try:
- # 前處理音訊
- y, sr = process_like_training(file_path)
-
- # 轉成模型輸入
- y_t = torch.tensor(y, dtype=torch.float32).unsqueeze(0)
- if sr != target_sr:
- resampler = torchaudio.transforms.Resample(sr, target_sr)
- y_t = resampler(y_t)
- length = torch.tensor([y_t.shape[1]], dtype=torch.long)
- waveforms, lengths = y_t.to(device), length.to(device)
+ predicted, scores = recognize_speaker(file_path, speaker_embeddings)
- with torch.no_grad():
- logits = model(waveforms, lengths)
- probs = softmax(logits).squeeze(0).cpu()
- top_prob, top_idx = torch.max(probs, dim=0)
- pred = classes[top_idx.item()]
-
- # 獲取 top 候選
- topk = torch.topk(probs, k=min(3, num_classes))
- top = [(classes[i], float(p)) for p, i in zip(topk.values.tolist(), topk.indices.tolist())]
+ # 排序取 top 候選
+ sorted_scores = sorted(scores.items(), key=lambda x: x[1], reverse=True)
+ top = [(spk, float(score)) for spk, score in sorted_scores[:3]]
result = {
- 'pred': pred,
- 'score': float(top_prob.item()),
+ 'pred': predicted,
+ 'score': float(scores[predicted]),
'top': top
}
results.append(result)
@@ -148,20 +95,18 @@ def predict_files(model_dir, file_list, threshold=0.0):
return results
-# ============== 錄音與前處理（比照 process_audio.py） ==============
-REC_SR = 22050
-TARGET_RMS = 0.1
-VAD_TOP_DB = 30
-
-
-def record_audio(filename, seconds=3, sr=REC_SR):
+# ============== 錄音功能 ==============
+def record_audio(filename, seconds=3, sr=SAMPLE_RATE):
+ """從麥克風錄製音訊"""
pa = pyaudio.PyAudio()
stream = pa.open(format=pyaudio.paInt16, channels=1, rate=sr, input=True, frames_per_buffer=1024)
print(f"開始錄製 {seconds}s...")
frames = []
for _ in range(int(sr / 1024 * seconds)):
frames.append(stream.read(1024))
- stream.stop_stream(); stream.close(); pa.terminate()
+ stream.stop_stream()
+ stream.close()
+ pa.terminate()
with wave.open(filename, 'wb') as wf:
wf.setnchannels(1)
wf.setsampwidth(pyaudio.PyAudio().get_sample_size(pyaudio.paInt16))
@@ -170,82 +115,37 @@ def record_audio(filename, seconds=3, sr=REC_SR):
print("錄製結束。")
-def process_like_training(input_wav_path):
- # 與 process_audio.py 一致：librosa 載入、去噪、VAD、RMS 正規化（保留原始長度）
- y, sr = librosa.load(input_wav_path, sr=REC_SR)
- y = nr.reduce_noise(y=y, sr=sr)
- intervals = librosa.effects.split(y, top_db=VAD_TOP_DB)
- if len(intervals) > 0:
- y = np.concatenate([y[s:e] for s, e in intervals])
- # RMS 正規化
- rms = np.sqrt(np.mean(y ** 2)) if len(y) else 0.0
- if rms > 0:
- y = y * (TARGET_RMS / rms)
- return y, sr
-
-
def main():
- parser = argparse.ArgumentParser(description='Speaker ID Inference (Wav2Vec2)')
- parser.add_argument('--audio', type=str, default=None, help='Path to wav file；若省略則使用麥克風錄音')
- parser.add_argument('--model', type=str, default='speaker_id_model.pth', help='Path to model .pth')
- parser.add_argument('--classes', type=str, default='processed_audio', help='Path to classes dir or classes.txt')
+ import argparse
+ parser = argparse.ArgumentParser(description='Speaker ID Inference (ECAPA-TDNN)')
+ parser.add_argument('--audio', type=str, default=None, help='音訊檔案路徑；若省略則使用麥克風錄音')
+ parser.add_argument('--db', type=str, default='speaker_db.pkl', help='說話者資料庫路徑')
parser.add_argument('--seconds', type=int, default=3, help='錄音秒數（麥克風模式）')
- parser.add_argument('--save-processed', action='store_true', help='輸出處理後音檔 processed_record.wav')
args = parser.parse_args()
- device = get_device()
- bundle = torchaudio.pipelines.WAV2VEC2_BASE
- target_sr = bundle.sample_rate
+ # 載入資料庫
+ speaker_embeddings = load_speaker_db(args.db)
+ print(f"已載入 {len(speaker_embeddings)} 位說話者：{list(speaker_embeddings.keys())}")
- classes = load_classes(args.classes)
- num_classes = len(classes)
-
- model = Wav2Vec2SpeakerClassifier(bundle, num_classes)
- state = torch.load(args.model, map_location='cpu')
- model.load_state_dict(state)
- model.to(device).eval()
-
- # 準備音訊來源：檔案或錄音
+ # 準備音訊
temp_path = None
if args.audio is None:
temp_path = 'temp_record.wav'
- record_audio(temp_path, seconds=args.seconds, sr=REC_SR)
- raw_path = temp_path
+ record_audio(temp_path, seconds=args.seconds)
+ audio_path = temp_path
else:
- raw_path = args.audio
-
- # 前處理（比照 process_audio.py）
- y, sr = process_like_training(raw_path)
- if args.save_processed:
- try:
- import soundfile as sf
- sf.write('processed_record.wav', y, sr)
- except Exception:
- pass
-
- # 轉成模型輸入：重採樣到 16k + 提供長度
- y_t = torch.tensor(y, dtype=torch.float32).unsqueeze(0)
- if sr != target_sr:
- resampler = torchaudio.transforms.Resample(sr, target_sr)
- y_t = resampler(y_t)
- sr = target_sr
- length = torch.tensor([y_t.shape[1]], dtype=torch.long)
- waveforms, lengths = y_t.to(device), length.to(device)
+ audio_path = args.audio
- with torch.no_grad():
- logits = model(waveforms, lengths)
- probs = softmax(logits).squeeze(0).cpu()
- top_prob, top_idx = torch.max(probs, dim=0)
- pred = classes[top_idx.item()]
+ # 辨識
+ predicted, scores = recognize_speaker(audio_path, speaker_embeddings)
- print(f'Predicted speaker: {pred} (prob={top_prob.item():.3f})')
- # 顯示前 3 名
- topk = torch.topk(probs, k=min(3, num_classes))
- print('Top candidates:')
- for p, i in zip(topk.values.tolist(), topk.indices.tolist()):
- print(f' {classes[i]}: {p:.3f}')
+ print(f'\n辨識結果：{predicted}')
+ print('辨識機率：')
+ for spk, score in sorted(scores.items(), key=lambda x: x[1], reverse=True):
+ print(f' {spk}: {score:.4f}')
- if temp_path is not None and os.path.exists(temp_path):
+ # 清理暫存檔
+ if temp_path and os.path.exists(temp_path):
try:
os.remove(temp_path)
except Exception:
diff --git a/models/speaker_identification/scripts/process_audio.py b/models/speaker_identification/scripts/process_audio.py
deleted file mode 100644
index 4d0dc2591f72906934eff1b018cd427995f82892..0000000000000000000000000000000000000000
--- a/models/speaker_identification/scripts/process_audio.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import os
-import librosa
-import numpy as np
-import noisereduce as nr
-import soundfile as sf
-
-# 參考採樣率與音量標準化
-sr = 22050 # 假設採樣率
-target_rms = 0.1 # 目標 RMS 水平
-vad_top_db = 30 # VAD 門檻，值越小越容易刪除靜音
-
-# 輸出目錄
-output_dir = 'processed_audio'
-os.makedirs(output_dir, exist_ok=True)
-
-# 用於記錄每個子目錄的當前編號
-counter = {}
-
-# 遍歷 voice_data 目錄下的所有 .wav 和 .mp3 文件
-for root, dirs, files in os.walk('voice_data'):
- for file in files:
- if file.endswith(('.wav', '.mp3')):
- file_path = os.path.join(root, file)
- # 獲取相對路徑，用於創建輸出子目錄
- rel_path = os.path.relpath(root, 'voice_data')
- sub_output_dir = os.path.join(output_dir, rel_path)
- os.makedirs(sub_output_dir, exist_ok=True)
-
- if rel_path not in counter:
- counter[rel_path] = 1
-
- y, sr = librosa.load(file_path, sr=sr)
- # 去噪
- y = nr.reduce_noise(y=y, sr=sr)
-
- # 語音活動偵測 (VAD)，移除靜音區段
- intervals = librosa.effects.split(y, top_db=vad_top_db)
- if len(intervals) == 0:
- # 無語音內容，跳過此檔
- continue
- y = np.concatenate([y[start:end] for start, end in intervals])
-
- # 保留原始長度並直接保存
- rms = np.sqrt(np.mean(y**2))
- if rms > 0:
- y = y * (target_rms / rms)
-
- output_path = os.path.join(sub_output_dir, f'voice_{counter[rel_path]:02d}.wav')
- sf.write(output_path, y, sr)
- counter[rel_path] += 1
-
-print("音頻處理完成。")
diff --git a/models/speaker_identification/scripts/train_speaker_id.py b/models/speaker_identification/scripts/train_speaker_id.py
deleted file mode 100644
index a73cb2de348fd488092348b37ce74d9a92caf02e..0000000000000000000000000000000000000000
--- a/models/speaker_identification/scripts/train_speaker_id.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import os
-import random
-
-import matplotlib.pyplot as plt
-import numpy as np
-import seaborn as sns
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import torchaudio
-import torchaudio.functional as F
-from sklearn.metrics import confusion_matrix
-from sklearn.preprocessing import LabelEncoder
-from sklearn.utils.class_weight import compute_class_weight
-from torch.utils.data import DataLoader, Dataset
-
-
-# 基本參數
-data_dir = '/kaggle/input/voice-identity/processed_audio'
-batch_size = 16
-num_epochs = 50
-
-# 遷移學習設定（不使用 HuggingFace，改用 torchaudio 內建預訓練）
-bundle = torchaudio.pipelines.WAV2VEC2_BASE
-target_sr = bundle.sample_rate
-freeze_encoder = True # 先凍結整個 encoder，只訓練分類頭
-unfreeze_last_n = 2 # 若要微調，可調整解凍的 Transformer 層數
-head_learning_rate = 1e-3
-encoder_learning_rate = 1e-5
-encoder_warmup_epochs = 10 # 預設 10 epoch 後解凍最後幾層（若 unfreeze_last_n > 0）
-pitch_shift_semitones = [0, -4, 4] # 每筆資料的音調擴增組合
-
-
-def waveform_augment(waveform, sr, max_shift_ratio=0.02, noise_factor=0.002):
- """時間平移 + 微量噪音；針對短音訊使用較小位移"""
- if max_shift_ratio > 0:
- shift = int(sr * max_shift_ratio)
- if shift > 0:
- offset = random.randint(-shift, shift)
- waveform = torch.roll(waveform, offset, dims=1)
- if noise_factor > 0:
- noise = torch.randn_like(waveform) * noise_factor
- waveform = waveform + noise
- return waveform
-
-
-class SpeakerDataset(Dataset):
- def __init__(self, data_dir, classes, transform=None, target_sr=16000, pitch_shifts=None):
- self.data_dir = data_dir
- self.classes = classes
- self.transform = transform
- self.target_sr = target_sr
- self.resamplers = {}
- self.pitch_shifts = sorted(set(pitch_shifts or [0]))
- self.data = []
-
- for label, cls in enumerate(classes):
- cls_dir = os.path.join(data_dir, cls)
- for file in os.listdir(cls_dir):
- if file.endswith('.wav'):
- file_path = os.path.join(cls_dir, file)
- for shift in self.pitch_shifts:
- self.data.append((file_path, label, shift))
-
- def __len__(self):
- return len(self.data)
-
- def __getitem__(self, idx):
- file_path, label, semitone = self.data[idx]
- waveform, sr = torchaudio.load(file_path)
-
- if waveform.size(0) > 1:
- waveform = waveform.mean(dim=0, keepdim=True)
-
- if sr != self.target_sr:
- if sr not in self.resamplers:
- self.resamplers[sr] = torchaudio.transforms.Resample(sr, self.target_sr)
- waveform = self.resamplers[sr](waveform)
- sr = self.target_sr
-
- if semitone != 0:
- waveform = F.pitch_shift(waveform, sr, n_steps=semitone)
-
- if self.transform:
- waveform = self.transform(waveform, sr)
-
- return waveform.squeeze(0), label
-
-
-def collate_waveforms(batch):
- waveforms, labels = zip(*batch)
- lengths = torch.tensor([waveform.shape[0] for waveform in waveforms], dtype=torch.long)
- max_len = lengths.max().item()
- padded = torch.zeros(len(waveforms), max_len)
- for idx, waveform in enumerate(waveforms):
- padded[idx, : waveform.shape[0]] = waveform
- labels_tensor = torch.tensor(labels, dtype=torch.long)
- return padded, lengths, labels_tensor
-
-
-class Wav2Vec2SpeakerClassifier(nn.Module):
- def __init__(self, bundle, num_classes, freeze_encoder=True, unfreeze_last_n=0):
- super().__init__()
- self.encoder = bundle.get_model()
- self.freeze_encoder = freeze_encoder
- self.unfreeze_last_n = unfreeze_last_n
-
- # 預設凍結整個 encoder
- for param in self.encoder.parameters():
- param.requires_grad = False
-
- if not freeze_encoder:
- # 先全部鎖住，再選擇性解凍最後 N 層；若無法部分解凍則退而求其次為完全解凍
- transformer_layers = getattr(self.encoder, 'encoder', None)
- if transformer_layers is not None and hasattr(transformer_layers, 'layers') and unfreeze_last_n > 0:
- for layer in transformer_layers.layers[-unfreeze_last_n:]:
- for param in layer.parameters():
- param.requires_grad = True
- elif not freeze_encoder:
- for param in self.encoder.parameters():
- param.requires_grad = True
-
- self.encoder_frozen = freeze_encoder
-
- hidden_dim = getattr(self.encoder, 'encoder_embed_dim', 768)
- self.classifier = nn.Sequential(
- nn.Dropout(0.3),
- nn.Linear(hidden_dim, num_classes)
- )
-
- def unfreeze_last_layers(self, n_layers=None):
- """解凍最後 n 層 Transformer block，回傳剛解凍的參數列表。"""
- n_layers = n_layers or self.unfreeze_last_n
- unfrozen_params = []
- transformer_layers = getattr(self.encoder, 'encoder', None)
- if transformer_layers is not None and hasattr(transformer_layers, 'layers') and n_layers > 0:
- target_layers = transformer_layers.layers[-n_layers:]
- for layer in target_layers:
- for param in layer.parameters():
- if not param.requires_grad:
- param.requires_grad = True
- unfrozen_params.append(param)
- elif n_layers > 0:
- for param in self.encoder.parameters():
- if not param.requires_grad:
- param.requires_grad = True
- unfrozen_params.append(param)
- if unfrozen_params:
- self.encoder_frozen = False
- return unfrozen_params
-
- def forward(self, waveforms, lengths=None):
- if waveforms.dim() != 2:
- raise ValueError("音訊張量應為 [batch, time] 形式")
-
- encoder_out = self.encoder(waveforms, lengths)
- if isinstance(encoder_out, tuple):
- features, lengths = encoder_out
- else:
- features, lengths = encoder_out, None
-
- if lengths is not None:
- if torch.is_floating_point(lengths):
- valid_lengths = (lengths * features.size(1)).round().to(torch.long)
- else:
- valid_lengths = lengths.to(torch.long)
- valid_lengths = valid_lengths.clamp(min=1, max=features.size(1))
- mask = torch.arange(features.size(1), device=features.device).unsqueeze(0) < valid_lengths.unsqueeze(1)
- masked_features = features * mask.unsqueeze(-1)
- pooled = masked_features.sum(dim=1) / valid_lengths.unsqueeze(-1)
- else:
- pooled = features.mean(dim=1)
-
- logits = self.classifier(pooled)
- return logits
-
-
-classes = sorted([d for d in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, d))])
-num_classes = len(classes)
-label_encoder = LabelEncoder()
-label_encoder.fit(classes)
-
-dataset = SpeakerDataset(
- data_dir=data_dir,
- classes=classes,
- transform=waveform_augment,
- target_sr=target_sr,
- pitch_shifts=pitch_shift_semitones
-)
-train_loader = DataLoader(
- dataset,
- batch_size=batch_size,
- shuffle=True,
- num_workers=0,
- pin_memory=False,
- collate_fn=collate_waveforms
-)
-
-labels = [label for _, label, _ in dataset.data]
-class_weights = compute_class_weight('balanced', classes=np.unique(labels), y=labels)
-class_weights = torch.tensor(class_weights, dtype=torch.float)
-
-if torch.cuda.is_available():
- device = torch.device('cuda')
- print("Using device: CUDA")
-elif torch.backends.mps.is_available():
- device = torch.device('mps')
- print("Using device: MPS")
-else:
- device = torch.device('cpu')
- print("Using device: CPU")
-
-model = Wav2Vec2SpeakerClassifier(
- bundle=bundle,
- num_classes=num_classes,
- freeze_encoder=freeze_encoder,
- unfreeze_last_n=unfreeze_last_n
-).to(device)
-criterion = nn.CrossEntropyLoss(weight=class_weights.to(device))
-
-encoder_params = [p for p in model.encoder.parameters() if p.requires_grad]
-head_params = list(model.classifier.parameters())
-if encoder_params:
- optimizer = optim.Adam([
- {'params': head_params, 'lr': head_learning_rate},
- {'params': encoder_params, 'lr': encoder_learning_rate}
- ])
-else:
- optimizer = optim.Adam(head_params, lr=head_learning_rate)
-
-use_amp = torch.cuda.is_available()
-scaler = torch.amp.GradScaler() if use_amp else None
-
-def add_encoder_params_to_optimizer(model, optimizer, lr):
- existing_params = set()
- for group in optimizer.param_groups:
- existing_params.update(id(p) for p in group['params'])
- new_params = [p for p in model.encoder.parameters() if p.requires_grad and id(p) not in existing_params]
- if new_params:
- optimizer.add_param_group({'params': new_params, 'lr': lr})
-
-for epoch in range(num_epochs):
- # 動態解凍策略：達到指定 epoch 後解凍最後幾層
- if model.encoder_frozen and unfreeze_last_n > 0 and (epoch + 1) == encoder_warmup_epochs:
- newly_unfrozen = model.unfreeze_last_layers(unfreeze_last_n)
- if newly_unfrozen:
- add_encoder_params_to_optimizer(model, optimizer, encoder_learning_rate)
- print(f'Unfroze last {unfreeze_last_n} encoder layer(s) at epoch {epoch + 1}.')
-
- model.train()
- if model.encoder_frozen:
- model.encoder.eval()
- else:
- model.encoder.train()
-
- running_loss = 0.0
- for waveforms, lengths, labels_batch in train_loader:
- waveforms = waveforms.to(device)
- lengths = lengths.to(device)
- labels_batch = labels_batch.to(device)
- optimizer.zero_grad()
-
- if use_amp:
- with torch.amp.autocast(device_type='cuda'):
- logits = model(waveforms, lengths)
- loss = criterion(logits, labels_batch)
- scaler.scale(loss).backward()
- scaler.step(optimizer)
- scaler.update()
- else:
- logits = model(waveforms, lengths)
- loss = criterion(logits, labels_batch)
- loss.backward()
- optimizer.step()
-
- running_loss += loss.item()
-
- avg_loss = running_loss / len(train_loader)
- print(f'Epoch {epoch + 1}/{num_epochs}, Loss: {avg_loss:.4f}')
-
-model.eval()
-all_preds = []
-all_labels = []
-with torch.no_grad():
- for waveforms, lengths, labels_batch in train_loader:
- waveforms = waveforms.to(device)
- lengths = lengths.to(device)
- logits = model(waveforms, lengths)
- preds = torch.argmax(logits, dim=1)
- all_preds.extend(preds.cpu().numpy())
- all_labels.extend(labels_batch.numpy())
-
-cm = confusion_matrix(all_labels, all_preds)
-print("Confusion Matrix:")
-print(cm)
-
-plt.figure(figsize=(8, 6))
-sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=classes, yticklabels=classes)
-plt.title('Confusion Matrix')
-plt.xlabel('Predicted')
-plt.ylabel('True')
-plt.savefig('confusion_matrix.png')
-plt.close()
-
-torch.save(model.state_dict(), 'speaker_id_model.pth')
-print("訓練完成，模型已保存。")
diff --git a/render.yaml b/render.yaml
deleted file mode 100644
index 69299dfbc423904d73e47344c1528d7346e6d7fb..0000000000000000000000000000000000000000
--- a/render.yaml
+++ /dev/null
@@ -1,28 +0,0 @@
-services:
- - type: web
- name: bloom-ware
- runtime: python
- region: singapore # 或 oregon（美西）
- plan: Hobby # 免費方案
- buildCommand: pip install -r requirements.txt
- startCommand: python3 app.py
- envVars:
- - key: ENVIRONMENT
- value: production
- - key: PYTHON_VERSION
- value: 3.12.4
- - key: HOST
- value: 0.0.0.0
- - key: PORT
- value: 10000 # Render 固定使用 10000 端口
- # 其他敏感環境變數請在 Render Dashboard 手動設定：
- # - FIREBASE_PROJECT_ID
- # - FIREBASE_CREDENTIALS_JSON（完整JSON字串）
- # - OPENAI_API_KEY
- # - GOOGLE_CLIENT_ID
- # - GOOGLE_CLIENT_SECRET
- # - GOOGLE_REDIRECT_URI（https://your-app.onrender.com/auth/google/callback）
- # - WEATHER_API_KEY
- # - NEWSDATA_API_KEY
- # - EXCHANGE_API_KEY
- # - JWT_SECRET_KEY（使用 Python secrets.token_urlsafe(32) 生成新的）
diff --git a/routers/__init__.py b/routers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f18568c3e82ced20a3509a5c15a8480557d3aa2
--- /dev/null
+++ b/routers/__init__.py
@@ -0,0 +1,20 @@
+"""
+Bloom Ware API 路由模組
+拆分自 app.py，提高可維護性
+"""
+
+from .auth import router as auth_router
+from .chat import router as chat_router
+from .voice import router as voice_router
+from .health import router as health_router
+from .files import router as files_router
+from .system import router as system_router
+
+__all__ = [
+ "auth_router",
+ "chat_router",
+ "voice_router",
+ "health_router",
+ "files_router",
+ "system_router",
+]
diff --git a/routers/auth.py b/routers/auth.py
new file mode 100644
index 0000000000000000000000000000000000000000..7759efc8bae8dfd1994418d448ce8f4149f3864a
--- /dev/null
+++ b/routers/auth.py
@@ -0,0 +1,159 @@
+"""
+認證相關 API 路由
+包含 Google OAuth、JWT 認證等
+"""
+
+import logging
+from datetime import datetime
+from typing import Dict, Optional
+from fastapi import APIRouter, Request, HTTPException, Depends
+from fastapi.responses import JSONResponse, RedirectResponse
+from pydantic import BaseModel, EmailStr
+
+from core.config import settings
+from core.auth import jwt_auth, get_current_user_optional, require_auth
+from core.auth.google_oauth import GoogleOAuth
+from core.database import create_or_login_google_user
+
+logger = logging.getLogger("routers.auth")
+
+router = APIRouter(prefix="/auth", tags=["認證"])
+
+# Google OAuth 實例
+google_oauth = GoogleOAuth()
+
+
+class GoogleAuthRequest(BaseModel):
+ """Google 認證請求"""
+ credential: str
+
+
+class TokenResponse(BaseModel):
+ """Token 響應"""
+ success: bool
+ token: Optional[str] = None
+ user: Optional[dict] = None
+ error: Optional[str] = None
+
+
+@router.get("/google/login")
+async def google_login():
+ """
+ Google OAuth 登入入口
+ 重定向到 Google 授權頁面
+ """
+ auth_url = google_oauth.get_authorization_url()
+ return RedirectResponse(url=auth_url)
+
+
+@router.get("/google/callback")
+async def google_callback(code: str = None, error: str = None):
+ """
+ Google OAuth 回調處理
+ """
+ if error:
+ logger.error(f"Google OAuth 錯誤: {error}")
+ return RedirectResponse(url=f"/login?error={error}")
+
+ if not code:
+ logger.error("Google OAuth 回調缺少 code 參數")
+ return RedirectResponse(url="/login?error=missing_code")
+
+ try:
+ # 交換 code 獲取 token
+ token_info = await google_oauth.exchange_code(code)
+ if not token_info:
+ return RedirectResponse(url="/login?error=token_exchange_failed")
+
+ # 獲取用戶信息
+ user_info = await google_oauth.get_user_info(token_info.get("access_token"))
+ if not user_info:
+ return RedirectResponse(url="/login?error=user_info_failed")
+
+ # 創建或登入用戶
+ result = await create_or_login_google_user(user_info)
+ if not result.get("success"):
+ error_msg = result.get("error", "unknown_error")
+ return RedirectResponse(url=f"/login?error={error_msg}")
+
+ # 生成 JWT token
+ user = result.get("user", {})
+ jwt_token = jwt_auth.create_access_token({
+ "sub": user.get("id"),
+ "email": user.get("email"),
+ "name": user.get("name"),
+ })
+
+ # 重定向到前端，帶上 token
+ return RedirectResponse(url=f"/static/frontend/index.html?token={jwt_token}")
+
+ except Exception as e:
+ logger.exception(f"Google OAuth 回調處理失敗: {e}")
+ return RedirectResponse(url=f"/login?error=callback_failed")
+
+
+@router.post("/google/verify", response_model=TokenResponse)
+async def google_verify(request: GoogleAuthRequest):
+ """
+ 驗證 Google ID Token（前端 One Tap 登入）
+ """
+ try:
+ # 驗證 Google credential
+ user_info = await google_oauth.verify_id_token(request.credential)
+ if not user_info:
+ return TokenResponse(success=False, error="invalid_credential")
+
+ # 創建或登入用戶
+ result = await create_or_login_google_user(user_info)
+ if not result.get("success"):
+ return TokenResponse(success=False, error=result.get("error"))
+
+ # 生成 JWT token
+ user = result.get("user", {})
+ jwt_token = jwt_auth.create_access_token({
+ "sub": user.get("id"),
+ "email": user.get("email"),
+ "name": user.get("name"),
+ })
+
+ return TokenResponse(
+ success=True,
+ token=jwt_token,
+ user=user,
+ )
+
+ except Exception as e:
+ logger.exception(f"Google 驗證失敗: {e}")
+ return TokenResponse(success=False, error=str(e))
+
+
+@router.get("/me")
+async def get_current_user(user: dict = Depends(require_auth)):
+ """
+ 獲取當前登入用戶信息
+ """
+ return {
+ "success": True,
+ "user": {
+ "id": user.get("sub"),
+ "email": user.get("email"),
+ "name": user.get("name"),
+ }
+ }
+
+
+@router.post("/refresh")
+async def refresh_token(user: dict = Depends(require_auth)):
+ """
+ 刷新 JWT Token
+ """
+ new_token = jwt_auth.create_access_token({
+ "sub": user.get("sub"),
+ "email": user.get("email"),
+ "name": user.get("name"),
+ })
+
+ return {
+ "success": True,
+ "token": new_token,
+ }
diff --git a/routers/chat.py b/routers/chat.py
new file mode 100644
index 0000000000000000000000000000000000000000..522815db276e1736d5ad615cfd99581ac2edd760
--- /dev/null
+++ b/routers/chat.py
@@ -0,0 +1,200 @@
+"""
+對話相關 API 路由
+包含對話管理、消息歷史等
+"""
+
+import logging
+from datetime import datetime
+from typing import List, Optional
+from fastapi import APIRouter, HTTPException, Depends
+from pydantic import BaseModel
+
+from core.auth import require_auth
+from core.database import (
+ create_chat,
+ get_user_chats,
+ get_chat,
+ update_chat_title,
+ delete_chat,
+ get_chat_messages,
+)
+from core.database.optimized import (
+ get_chat as get_chat_optimized,
+ get_user_chats as get_user_chats_optimized,
+)
+
+logger = logging.getLogger("routers.chat")
+
+router = APIRouter(prefix="/api/chats", tags=["對話"])
+
+
+class ChatCreateRequest(BaseModel):
+ """創建對話請求"""
+ title: Optional[str] = "新對話"
+
+
+class ChatTitleUpdateRequest(BaseModel):
+ """更新對話標題請求"""
+ title: str
+
+
+class ChatSummary(BaseModel):
+ """對話摘要"""
+ chat_id: str
+ title: str
+ updated_at: datetime
+
+
+class ChatListResponse(BaseModel):
+ """對話列表響應"""
+ success: bool
+ chats: List[ChatSummary]
+
+
+@router.post("")
+async def create_new_chat(
+ request: ChatCreateRequest,
+ user: dict = Depends(require_auth)
+):
+ """
+ 創建新對話
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ result = await create_chat(user_id, request.title)
+ if not result.get("success"):
+ raise HTTPException(status_code=500, detail=result.get("error"))
+
+ return result
+
+
+@router.get("")
+async def list_user_chats(user: dict = Depends(require_auth)):
+ """
+ 獲取用戶的所有對話
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ result = await get_user_chats_optimized(user_id)
+ if not result.get("success"):
+ raise HTTPException(status_code=500, detail=result.get("error"))
+
+ return result
+
+
+@router.get("/{chat_id}")
+async def get_chat_detail(
+ chat_id: str,
+ user: dict = Depends(require_auth)
+):
+ """
+ 獲取對話詳情（包含消息）
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ result = await get_chat_optimized(chat_id)
+ if not result.get("success"):
+ raise HTTPException(status_code=404, detail="對話不存在")
+
+ chat = result.get("chat", {})
+
+ # 驗證對話所有權
+ if chat.get("user_id") != user_id:
+ raise HTTPException(status_code=403, detail="無權訪問此對話")
+
+ return result
+
+
+@router.get("/{chat_id}/messages")
+async def get_chat_messages_api(
+ chat_id: str,
+ limit: int = 50,
+ user: dict = Depends(require_auth)
+):
+ """
+ 獲取對話消息歷史
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ # 先驗證對話所有權
+ chat_result = await get_chat_optimized(chat_id)
+ if not chat_result.get("success"):
+ raise HTTPException(status_code=404, detail="對話不存在")
+
+ chat = chat_result.get("chat", {})
+ if chat.get("user_id") != user_id:
+ raise HTTPException(status_code=403, detail="無權訪問此對話")
+
+ # 獲取消息
+ messages = await get_chat_messages(chat_id, limit=limit)
+
+ return {
+ "success": True,
+ "chat_id": chat_id,
+ "messages": messages,
+ }
+
+
+@router.put("/{chat_id}/title")
+async def update_chat_title_api(
+ chat_id: str,
+ request: ChatTitleUpdateRequest,
+ user: dict = Depends(require_auth)
+):
+ """
+ 更新對話標題
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ # 先驗證對話所有權
+ chat_result = await get_chat_optimized(chat_id)
+ if not chat_result.get("success"):
+ raise HTTPException(status_code=404, detail="對話不存在")
+
+ chat = chat_result.get("chat", {})
+ if chat.get("user_id") != user_id:
+ raise HTTPException(status_code=403, detail="無權修改此對話")
+
+ result = await update_chat_title(chat_id, request.title)
+ if not result.get("success"):
+ raise HTTPException(status_code=500, detail=result.get("error"))
+
+ return result
+
+
+@router.delete("/{chat_id}")
+async def delete_chat_api(
+ chat_id: str,
+ user: dict = Depends(require_auth)
+):
+ """
+ 刪除對話
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ # 先驗證對話所有權
+ chat_result = await get_chat_optimized(chat_id)
+ if not chat_result.get("success"):
+ raise HTTPException(status_code=404, detail="對話不存在")
+
+ chat = chat_result.get("chat", {})
+ if chat.get("user_id") != user_id:
+ raise HTTPException(status_code=403, detail="無權刪除此對話")
+
+ result = await delete_chat(chat_id)
+ if not result.get("success"):
+ raise HTTPException(status_code=500, detail=result.get("error"))
+
+ return result
diff --git a/routers/files.py b/routers/files.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd15f0e922d2c0b74c713de295759d2f77709acb
--- /dev/null
+++ b/routers/files.py
@@ -0,0 +1,228 @@
+"""
+文件處理相關 API 路由
+包含文件上傳、分析等
+"""
+
+import logging
+import base64
+import mimetypes
+from typing import Optional
+from fastapi import APIRouter, HTTPException, Depends, UploadFile, File
+from pydantic import BaseModel
+
+from core.auth import require_auth
+
+logger = logging.getLogger("routers.files")
+
+router = APIRouter(prefix="/api/files", tags=["文件"])
+
+
+class FileAnalysisRequest(BaseModel):
+ """文件分析請求"""
+ filename: str
+ content: str # base64 編碼
+ mime_type: str
+ user_prompt: Optional[str] = "請分析這個檔案的內容"
+
+
+class FileAnalysisResponse(BaseModel):
+ """文件分析響應"""
+ success: bool
+ filename: str
+ analysis: Optional[str] = None
+ error: Optional[str] = None
+
+
+@router.post("/analyze", response_model=FileAnalysisResponse)
+async def analyze_file(
+ request: FileAnalysisRequest,
+ user: dict = Depends(require_auth)
+):
+ """
+ 分析上傳的文件內容
+ 支援 PDF、圖片、文字文件等
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ try:
+ # 解碼文件內容
+ try:
+ file_content = base64.b64decode(request.content)
+ except Exception:
+ return FileAnalysisResponse(
+ success=False,
+ filename=request.filename,
+ error="無法解碼文件內容",
+ )
+
+ # 根據 MIME 類型處理
+ mime_type = request.mime_type.lower()
+ extracted_text = ""
+
+ if mime_type.startswith("text/"):
+ # 文字文件
+ try:
+ extracted_text = file_content.decode("utf-8")
+ except UnicodeDecodeError:
+ extracted_text = file_content.decode("latin-1")
+
+ elif mime_type == "application/pdf":
+ # PDF 文件
+ try:
+ import pdfplumber
+ import io
+
+ with pdfplumber.open(io.BytesIO(file_content)) as pdf:
+ pages_text = []
+ for page in pdf.pages:
+ text = page.extract_text()
+ if text:
+ pages_text.append(text)
+ extracted_text = "\n\n".join(pages_text)
+ except ImportError:
+ return FileAnalysisResponse(
+ success=False,
+ filename=request.filename,
+ error="PDF 處理模組不可用",
+ )
+ except Exception as e:
+ return FileAnalysisResponse(
+ success=False,
+ filename=request.filename,
+ error=f"PDF 解析失敗: {str(e)}",
+ )
+
+ elif mime_type.startswith("image/"):
+ # 圖片文件 - 使用 GPT-4 Vision
+ try:
+ import services.ai_service as ai_service
+
+ # 構建 Vision API 請求
+ image_base64 = request.content
+ messages = [
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": request.user_prompt},
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": f"data:{mime_type};base64,{image_base64}"
+ }
+ }
+ ]
+ }
+ ]
+
+ analysis = await ai_service.generate_response_async(
+ messages,
+ model="gpt-4o-mini", # 使用支援 Vision 的模型
+ )
+
+ return FileAnalysisResponse(
+ success=True,
+ filename=request.filename,
+ analysis=analysis,
+ )
+
+ except Exception as e:
+ return FileAnalysisResponse(
+ success=False,
+ filename=request.filename,
+ error=f"圖片分析失敗: {str(e)}",
+ )
+
+ else:
+ return FileAnalysisResponse(
+ success=False,
+ filename=request.filename,
+ error=f"不支援的文件類型: {mime_type}",
+ )
+
+ # 使用 AI 分析提取的文字
+ if extracted_text:
+ try:
+ import services.ai_service as ai_service
+
+ messages = [
+ {
+ "role": "system",
+ "content": "你是一個專業的文件分析助手。請根據用戶的要求分析以下文件內容。"
+ },
+ {
+ "role": "user",
+ "content": f"{request.user_prompt}\n\n文件內容：\n{extracted_text[:10000]}" # 限制長度
+ }
+ ]
+
+ analysis = await ai_service.generate_response_async(messages)
+
+ return FileAnalysisResponse(
+ success=True,
+ filename=request.filename,
+ analysis=analysis,
+ )
+
+ except Exception as e:
+ return FileAnalysisResponse(
+ success=False,
+ filename=request.filename,
+ error=f"AI 分析失敗: {str(e)}",
+ )
+
+ return FileAnalysisResponse(
+ success=False,
+ filename=request.filename,
+ error="無法提取文件內容",
+ )
+
+ except Exception as e:
+ logger.exception(f"文件分析失敗: {e}")
+ return FileAnalysisResponse(
+ success=False,
+ filename=request.filename,
+ error=str(e),
+ )
+
+
+@router.post("/upload")
+async def upload_file(
+ file: UploadFile = File(...),
+ user: dict = Depends(require_auth)
+):
+ """
+ 上傳文件（返回 base64 編碼）
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ try:
+ # 讀取文件內容
+ content = await file.read()
+
+ # 檢查文件大小（限制 10MB）
+ if len(content) > 10 * 1024 * 1024:
+ raise HTTPException(status_code=413, detail="文件大小超過限制（10MB）")
+
+ # 編碼為 base64
+ content_base64 = base64.b64encode(content).decode("utf-8")
+
+ # 獲取 MIME 類型
+ mime_type = file.content_type or mimetypes.guess_type(file.filename)[0] or "application/octet-stream"
+
+ return {
+ "success": True,
+ "filename": file.filename,
+ "mime_type": mime_type,
+ "size": len(content),
+ "content": content_base64,
+ }
+
+ except HTTPException:
+ raise
+ except Exception as e:
+ logger.exception(f"文件上傳失敗: {e}")
+ raise HTTPException(status_code=500, detail=str(e))
diff --git a/routers/health.py b/routers/health.py
new file mode 100644
index 0000000000000000000000000000000000000000..502724255307a180a94a2630ea8518938b5560a4
--- /dev/null
+++ b/routers/health.py
@@ -0,0 +1,220 @@
+"""
+健康數據相關 API 路由
+包含 HealthKit 數據同步等
+"""
+
+import logging
+from datetime import datetime
+from typing import List, Optional, Dict, Any
+from fastapi import APIRouter, HTTPException, Depends
+from pydantic import BaseModel
+
+from core.auth import require_auth
+
+logger = logging.getLogger("routers.health")
+
+router = APIRouter(prefix="/api/health", tags=["健康數據"])
+
+
+class HealthDataPoint(BaseModel):
+ """健康數據點"""
+ type: str # heart_rate, steps, sleep, etc.
+ value: float
+ unit: str
+ timestamp: datetime
+ source: Optional[str] = None
+
+
+class HealthDataSyncRequest(BaseModel):
+ """健康數據同步請求"""
+ data: List[HealthDataPoint]
+
+
+class HealthQueryRequest(BaseModel):
+ """健康數據查詢請求"""
+ types: List[str]
+ start_date: Optional[datetime] = None
+ end_date: Optional[datetime] = None
+
+
+@router.post("/sync")
+async def sync_health_data(
+ request: HealthDataSyncRequest,
+ user: dict = Depends(require_auth)
+):
+ """
+ 同步健康數據（從 HealthKit/Google Fit）
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ try:
+ from core.database import firestore_db
+
+ if not firestore_db:
+ raise HTTPException(status_code=503, detail="數據庫不可用")
+
+ # 批量寫入健康數據
+ batch = firestore_db.batch()
+ health_collection = firestore_db.collection("health_data")
+
+ for data_point in request.data:
+ doc_ref = health_collection.document()
+ batch.set(doc_ref, {
+ "user_id": user_id,
+ "type": data_point.type,
+ "value": data_point.value,
+ "unit": data_point.unit,
+ "timestamp": data_point.timestamp,
+ "source": data_point.source,
+ "synced_at": datetime.now(),
+ })
+
+ # 執行批量寫入
+ import asyncio
+ await asyncio.to_thread(batch.commit)
+
+ logger.info(f"用戶 {user_id} 同步了 {len(request.data)} 條健康數據")
+
+ return {
+ "success": True,
+ "synced_count": len(request.data),
+ }
+
+ except Exception as e:
+ logger.exception(f"健康數據同步失敗: {e}")
+ raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/query")
+async def query_health_data(
+ request: HealthQueryRequest,
+ user: dict = Depends(require_auth)
+):
+ """
+ 查詢健康數據
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ try:
+ from core.database import firestore_db
+ from google.cloud.firestore import FieldFilter
+
+ if not firestore_db:
+ raise HTTPException(status_code=503, detail="數據庫不可用")
+
+ health_collection = firestore_db.collection("health_data")
+
+ # 構建查詢
+ query = health_collection.where(
+ filter=FieldFilter("user_id", "==", user_id)
+ )
+
+ # 時間範圍過濾
+ if request.start_date:
+ query = query.where(
+ filter=FieldFilter("timestamp", ">=", request.start_date)
+ )
+ if request.end_date:
+ query = query.where(
+ filter=FieldFilter("timestamp", "<=", request.end_date)
+ )
+
+ # 執行查詢
+ import asyncio
+ docs = await asyncio.to_thread(lambda: list(query.stream()))
+
+ # 過濾類型並格式化結果
+ results: Dict[str, List[Dict[str, Any]]] = {t: [] for t in request.types}
+
+ for doc in docs:
+ data = doc.to_dict()
+ data_type = data.get("type")
+ if data_type in request.types:
+ results[data_type].append({
+ "value": data.get("value"),
+ "unit": data.get("unit"),
+ "timestamp": data.get("timestamp"),
+ "source": data.get("source"),
+ })
+
+ return {
+ "success": True,
+ "data": results,
+ }
+
+ except Exception as e:
+ logger.exception(f"健康數據查詢失敗: {e}")
+ raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/summary")
+async def get_health_summary(user: dict = Depends(require_auth)):
+ """
+ 獲取健康數據摘要（今日）
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ try:
+ from core.database import firestore_db
+ from google.cloud.firestore import FieldFilter
+
+ if not firestore_db:
+ raise HTTPException(status_code=503, detail="數據庫不可用")
+
+ # 今日開始時間
+ today_start = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
+
+ health_collection = firestore_db.collection("health_data")
+
+ query = health_collection.where(
+ filter=FieldFilter("user_id", "==", user_id)
+ ).where(
+ filter=FieldFilter("timestamp", ">=", today_start)
+ )
+
+ import asyncio
+ docs = await asyncio.to_thread(lambda: list(query.stream()))
+
+ # 計算摘要
+ summary = {
+ "steps": 0,
+ "heart_rate_avg": 0,
+ "heart_rate_readings": [],
+ "sleep_hours": 0,
+ "active_calories": 0,
+ }
+
+ for doc in docs:
+ data = doc.to_dict()
+ data_type = data.get("type")
+ value = data.get("value", 0)
+
+ if data_type == "steps":
+ summary["steps"] += value
+ elif data_type == "heart_rate":
+ summary["heart_rate_readings"].append(value)
+ elif data_type == "sleep":
+ summary["sleep_hours"] += value
+ elif data_type == "active_calories":
+ summary["active_calories"] += value
+
+ # 計算心率平均值
+ if summary["heart_rate_readings"]:
+ summary["heart_rate_avg"] = sum(summary["heart_rate_readings"]) / len(summary["heart_rate_readings"])
+ del summary["heart_rate_readings"]
+
+ return {
+ "success": True,
+ "date": today_start.date().isoformat(),
+ "summary": summary,
+ }
+
+ except Exception as e:
+ logger.exception(f"健康摘要獲取失敗: {e}")
+ raise HTTPException(status_code=500, detail=str(e))
diff --git a/routers/system.py b/routers/system.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a0ed1b145280fe2e5645afee1b0971d2921e76d
--- /dev/null
+++ b/routers/system.py
@@ -0,0 +1,115 @@
+"""
+系統相關 API 路由
+包含狀態檢查、效能統計、MCP 工具列表等
+"""
+
+from typing import Optional, Dict, Any
+from fastapi import APIRouter, Depends
+from fastapi.responses import RedirectResponse
+
+from core.auth import get_current_user_optional
+from core.logging import get_logger
+from core.config import settings
+
+logger = get_logger("routers.system")
+
+router = APIRouter(tags=["系統"])
+
+
+@router.get("/")
+async def root():
+ """根路徑導向登入頁面"""
+ return RedirectResponse(url="/login/")
+
+
+@router.get("/status")
+async def get_status():
+ """系統狀態檢查"""
+ return {
+ "status": "running",
+ "version": "2.0.0",
+ "environment": settings.ENVIRONMENT,
+ }
+
+
+@router.get("/api/mcp/tools")
+async def list_mcp_tools(current_user: dict = Depends(get_current_user_optional)):
+ """
+ 列出所有可用的 MCP 工具
+
+ Returns:
+ 工具列表，包含名稱、描述、參數等
+ """
+ try:
+ # 延遲導入避免循環依賴
+ from app import app
+
+ if not hasattr(app.state, 'feature_router'):
+ return {
+ "success": False,
+ "error": "MCP 服務尚未初始化",
+ "tools": []
+ }
+
+ feature_router = app.state.feature_router
+ tools_info = []
+
+ for tool_name, tool in feature_router.mcp_server.tools.items():
+ tool_info = {
+ "name": tool_name,
+ "description": getattr(tool, 'description', '無描述'),
+ }
+
+ # 嘗試獲取參數 schema
+ if hasattr(tool, 'handler') and hasattr(tool.handler, '__self__'):
+ tool_class = tool.handler.__self__
+ if hasattr(tool_class, 'get_input_schema'):
+ try:
+ tool_info["parameters"] = tool_class.get_input_schema()
+ except Exception:
+ pass
+
+ tools_info.append(tool_info)
+
+ return {
+ "success": True,
+ "count": len(tools_info),
+ "tools": tools_info
+ }
+
+ except Exception as e:
+ logger.error(f"獲取 MCP 工具列表失敗: {e}")
+ return {
+ "success": False,
+ "error": str(e),
+ "tools": []
+ }
+
+
+@router.get("/api/performance/stats")
+async def get_performance_stats(current_user: dict = Depends(get_current_user_optional)):
+ """
+ 獲取系統效能統計
+
+ Returns:
+ 快取命中率、查詢統計等
+ """
+ try:
+ from core.database.cache import db_cache
+ from core.database.optimized import query_optimizer
+
+ cache_stats = db_cache.get_all_stats()
+ query_stats = query_optimizer.get_stats()
+
+ return {
+ "success": True,
+ "cache": cache_stats,
+ "queries": query_stats,
+ }
+
+ except Exception as e:
+ logger.error(f"獲取效能統計失敗: {e}")
+ return {
+ "success": False,
+ "error": str(e)
+ }
diff --git a/routers/voice.py b/routers/voice.py
new file mode 100644
index 0000000000000000000000000000000000000000..2065f0b03b95f018374b194200a2f6a811449732
--- /dev/null
+++ b/routers/voice.py
@@ -0,0 +1,239 @@
+"""
+語音相關 API 路由
+包含語音登入、TTS、STT 等
+"""
+
+import logging
+from typing import Optional
+from fastapi import APIRouter, HTTPException, Depends
+from pydantic import BaseModel
+
+from core.auth import require_auth
+from core.database import set_user_speaker_label, get_user_by_speaker_label
+
+logger = logging.getLogger("routers.voice")
+
+router = APIRouter(prefix="/api/voice", tags=["語音"])
+
+
+class SpeakerLabelBindRequest(BaseModel):
+ """綁定語音標籤請求"""
+ speaker_label: str
+
+
+class TTSRequest(BaseModel):
+ """TTS 請求"""
+ text: str
+ voice: str = "nova"
+ speed: float = 1.0
+
+
+@router.post("/bind-speaker")
+async def bind_speaker_label(
+ request: SpeakerLabelBindRequest,
+ user: dict = Depends(require_auth)
+):
+ """
+ 綁定語音標籤到用戶帳號
+ """
+ user_id = user.get("sub")
+ if not user_id:
+ raise HTTPException(status_code=401, detail="無效的用戶")
+
+ result = await set_user_speaker_label(user_id, request.speaker_label)
+
+ if not result.get("success"):
+ error = result.get("error")
+ if error == "SPEAKER_LABEL_TAKEN":
+ raise HTTPException(status_code=409, detail="此語音標籤已被其他用戶綁定")
+ elif error == "USER_NOT_FOUND":
+ raise HTTPException(status_code=404, detail="用戶不存在")
+ else:
+ raise HTTPException(status_code=500, detail=error)
+
+ return {"success": True, "message": "語音標籤綁定成功"}
+
+
+@router.get("/lookup-speaker/{speaker_label}")
+async def lookup_speaker(speaker_label: str):
+ """
+ 根據語音標籤查找用戶（用於語音登入）
+ """
+ user = await get_user_by_speaker_label(speaker_label)
+
+ if not user:
+ raise HTTPException(status_code=404, detail="找不到對應的用戶")
+
+ return {
+ "success": True,
+ "user": {
+ "id": user.get("id"),
+ "name": user.get("name"),
+ }
+ }
+
+
+@router.post("/tts")
+async def text_to_speech(
+ request: TTSRequest,
+ user: dict = Depends(require_auth)
+):
+ """
+ 文字轉語音
+ """
+ try:
+ from services.tts_service import tts_service
+
+ result = await tts_service.synthesize(
+ text=request.text,
+ voice=request.voice,
+ speed=request.speed,
+ )
+
+ if not result.get("success"):
+ raise HTTPException(status_code=500, detail=result.get("error"))
+
+ # 返回 base64 編碼的音頻
+ import base64
+ audio_base64 = base64.b64encode(result["audio_data"]).decode("utf-8")
+
+ return {
+ "success": True,
+ "audio": audio_base64,
+ "voice": result.get("voice"),
+ }
+
+ except ImportError:
+ raise HTTPException(status_code=503, detail="TTS 服務不可用")
+ except Exception as e:
+ logger.exception(f"TTS 失敗: {e}")
+ raise HTTPException(status_code=500, detail=str(e))
+
+
+class VoiceLoginRequest(BaseModel):
+ """語音登入請求"""
+ audio_base64: str # base64 編碼的 PCM16 音訊
+ sample_rate: int = 16000
+
+
+class VoiceLoginResponse(BaseModel):
+ """語音登入回應"""
+ success: bool
+ access_token: str = None
+ user: dict = None
+ emotion: str = None
+ error: str = None
+
+
+@router.post("/login", response_model=VoiceLoginResponse)
+async def voice_login(request: VoiceLoginRequest):
+ """
+ 語音登入 API
+
+ 流程：
+ 1. 接收 base64 編碼的音訊
+ 2. 執行身份辨識 + 情緒辨識
+ 3. 查詢 speaker_label 對應的用戶
+ 4. 生成 JWT token
+ 5. 回傳 token + 情緒
+ """
+ import base64
+ import jwt
+ from datetime import datetime, timedelta
+ from core.config import settings
+
+ try:
+ # 取得 VoiceAuthService 實例
+ from fastapi import Request
+ from main import app
+
+ voice_auth = getattr(app.state, "voice_auth", None)
+ if not voice_auth:
+ # 嘗試動態建立
+ from services.voice_login import VoiceAuthService, VoiceLoginConfig
+ voice_auth = VoiceAuthService(config=VoiceLoginConfig(
+ window_seconds=3,
+ required_windows=1,
+ ))
+
+ # 解碼音訊
+ audio_bytes = base64.b64decode(request.audio_base64)
+
+ # 建立臨時 session 並處理音訊
+ temp_user_id = f"voice_login_{datetime.now().timestamp()}"
+ voice_auth.start_session(temp_user_id, request.sample_rate)
+ voice_auth._buffers[temp_user_id] = bytearray(audio_bytes)
+
+ # 執行辨識
+ result = voice_auth.stop_and_authenticate(temp_user_id)
+
+ # 清理 session
+ voice_auth.clear_session(temp_user_id)
+
+ if not result.get("success"):
+ error_code = result.get("error", "UNKNOWN_ERROR")
+ error_messages = {
+ "NO_AUDIO": "沒有收到音訊資料",
+ "AUDIO_TOO_SHORT": "音訊太短，請錄製至少 3 秒",
+ "LOW_SNR": "環境太吵，請在安靜的地方重試",
+ "INCONSISTENT_WINDOWS": "無法確認身份，請重試",
+ "THRESHOLD_NOT_MET": "無法確認身份，請重試",
+ "MODEL_ERROR": "辨識系統錯誤，請稍後重試",
+ }
+ return VoiceLoginResponse(
+ success=False,
+ error=error_messages.get(error_code, f"辨識失敗：{error_code}")
+ )
+
+ # 取得辨識結果
+ speaker_label = result.get("label")
+ emotion = result.get("emotion", {})
+ emotion_label = emotion.get("label", "neutral") if isinstance(emotion, dict) else "neutral"
+
+ logger.info(f"🎙️ 語音辨識成功: speaker={speaker_label}, emotion={emotion_label}")
+
+ # 查詢對應的用戶
+ user = await get_user_by_speaker_label(speaker_label)
+
+ if not user:
+ return VoiceLoginResponse(
+ success=False,
+ error=f"找不到綁定的帳號。請先使用 Google 登入並綁定語音。"
+ )
+
+ # 生成 JWT token
+ user_id = user.get("id")
+ user_name = user.get("name", "用戶")
+ user_email = user.get("email", "")
+
+ payload = {
+ "sub": user_id,
+ "name": user_name,
+ "email": user_email,
+ "iat": datetime.utcnow(),
+ "exp": datetime.utcnow() + timedelta(days=7),
+ "login_method": "voice",
+ "emotion": emotion_label,
+ }
+
+ token = jwt.encode(payload, settings.JWT_SECRET, algorithm="HS256")
+
+ logger.info(f"✅ 語音登入成功: user={user_name}, emotion={emotion_label}")
+
+ return VoiceLoginResponse(
+ success=True,
+ access_token=token,
+ user={
+ "id": user_id,
+ "name": user_name,
+ "email": user_email,
+ },
+ emotion=emotion_label,
+ )
+
+ except Exception as e:
+ logger.exception(f"❌ 語音登入失敗: {e}")
+ return VoiceLoginResponse(
+ success=False,
+ error=f"系統錯誤：{str(e)}"
+ )
diff --git a/services/ai_service.py b/services/ai_service.py
index 2a2a31bbeb86a50d704e9b8ee394a1ca7b3add8a..99bc4754429f9930e1ef6e707fe483cd49bf0332 100644
--- a/services/ai_service.py
+++ b/services/ai_service.py
@@ -1,39 +1,21 @@
-import os
-import sys
-import logging
import asyncio
-from dotenv import load_dotenv
from datetime import datetime, timezone, timedelta
import time
import json
from typing import Dict, List, Any, Optional
-# 設置日誌
-# 設定日誌等級：預設 WARNING，可透過 BLOOMWARE_LOG_LEVEL 覆寫
-LOG_LEVEL_NAME = os.getenv("BLOOMWARE_LOG_LEVEL", "WARNING").upper()
-LOG_LEVEL = getattr(logging, LOG_LEVEL_NAME, logging.WARNING)
-logging.basicConfig(
- level=LOG_LEVEL,
- format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-)
-logger = logging.getLogger("AI_Service")
-# 將終端日誌級別設置為 ERROR（保留重要訊息）
-console_handler = logging.StreamHandler()
-console_handler.setLevel(logging.ERROR)
-formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-console_handler.setFormatter(formatter)
-logger.addHandler(console_handler)
-logger.propagate = False # 防止日誌重複輸出
-logger.setLevel(LOG_LEVEL)
-
-# 載入環境變數
-load_dotenv()
+# 統一日誌配置
+from core.logging import get_logger
+logger = get_logger("AI_Service")
# 統一配置管理
from core.config import settings
+# 統一 OpenAI 客戶端
+from core.ai_client import get_openai_client
+
# 超時設定（秒）
-OPENAI_TIMEOUT = settings.OPENAI_TIMEOUT # 關懷模式 reasoning model 需要更長時間
+OPENAI_TIMEOUT = settings.OPENAI_TIMEOUT
# 情緒關懷模式 System Prompt（新增）
CARE_MODE_SYSTEM_PROMPT = """你是 BloomWare 的情緒關懷助手「小花」，由銘傳大學人工智慧應用學系槓上開發團隊打造。你不是 GPT，也不要自稱 GPT；你的任務是在情緒低落時傾聽、陪伴。
@@ -55,21 +37,13 @@ CARE_MODE_SYSTEM_PROMPT = """你是 BloomWare 的情緒關懷助手「小花」
用戶：「我很生氣」 → 你：「這件事讓你超級生氣，情緒一定卡著。要不要跟我說說最困擾你的地方？」
用戶：「講笑話給我聽」 → 你：「你想聽點輕鬆的，我當然可以陪你。想先聽小笑話還是先聊聊怎麼了？」"""
-# 導入時間服務模組
-# from features.daily_life.time_service import get_current_time_data, format_time_for_messages # 已整合到 MCPAgentBridge
+# 取得 OpenAI 客戶端（使用統一管理）
+def _get_client():
+ """取得 OpenAI 客戶端"""
+ return get_openai_client()
-# 嘗試導入 OpenAI
-try:
- import openai
- from openai import OpenAI
- client = OpenAI(
- api_key=os.getenv("OPENAI_API_KEY"),
- timeout=30.0, # 增加超時時間
- max_retries=3 # 添加重試次數
- )
-except Exception as e:
- logger.error(f"初始化 OpenAI 客戶端失敗: {e}")
- client = None
+# 向後相容：保留 client 變數名稱
+client = None # 將在首次使用時透過 _get_client() 取得
# 導入DB函數
try:
@@ -486,22 +460,9 @@ def _extract_text_from_message_obj(message: Any) -> str:
return ""
def initialize_openai():
- """初始化OpenAI客戶端"""
- global client
- api_key = settings.OPENAI_API_KEY
- if not api_key:
- logger.error("OpenAI API密鑰未設置，請在.env文件中設置OPENAI_API_KEY環境變數")
- print("\n❌ 錯誤: OpenAI API密鑰未設置！請在.env文件中設置OPENAI_API_KEY\n")
- return False
- try:
- logger.info("正在初始化OpenAI客戶端...")
- client = OpenAI(api_key=api_key)
- logger.info("OpenAI 客戶端初始化完成")
- return True
- except Exception as e:
- logger.error(f"初始化OpenAI客戶端失敗: {e}")
- print(f"\n❌ OpenAI API連接失敗: {e}\n")
- return False
+ """初始化OpenAI客戶端（使用統一管理）"""
+ from core.ai_client import is_available
+ return is_available()
## 已移除內部測試函式 test_openai_response，避免干擾正式流程
@@ -532,7 +493,8 @@ async def generate_response_async(
stream: 是否啟用串流模式（2025 最佳實踐）
on_chunk: 串流 chunk 回調函數（async callable）
"""
- if client is None and not initialize_openai():
+ openai_client = _get_client()
+ if openai_client is None:
return "抱歉，AI服務暫時不可用。系統無法連接到OpenAI服務。"
try:
start_time = time.time()
@@ -579,7 +541,7 @@ async def generate_response_async(
full_response = ""
stream_obj = await loop.run_in_executor(
None,
- lambda: client.chat.completions.create(**request_kwargs)
+ lambda: openai_client.chat.completions.create(**request_kwargs)
)
# 逐塊處理
@@ -602,7 +564,7 @@ async def generate_response_async(
response = await asyncio.wait_for(
loop.run_in_executor(
None,
- lambda: client.chat.completions.create(**request_kwargs),
+ lambda: openai_client.chat.completions.create(**request_kwargs),
),
timeout=OPENAI_TIMEOUT,
)
@@ -779,7 +741,11 @@ async def _generate_response_with_chat_db(
system_prompt = (
"你是 BloomWare 的個人化助理 小花，由銘傳大學人工智慧應用學系 槓上開發 團隊開發。"
"你不是 GPT，也不要自稱 GPT。"
- "你是一個友善、有禮、幽默且能夠提供幫助的AI助手。請使用繁體中文回覆，保持簡潔清晰的表達。"
+ "你是一個友善、有禮、幽默且能夠提供幫助的AI助手。\n\n"
+ "【重要】語言使用規範：\n"
+ "- 回覆用戶時：必須使用繁體中文，保持簡潔清晰的表達\n"
+ "- 調用工具時：所有參數必須使用英文（城市名、國家名、貨幣代碼等）\n"
+ "- 範例：用戶問「台北天氣」→ 調用工具時參數用 {\"city\": \"Taipei\"}，回覆時說「台北目前...\""
)
# 在系統提示前加上用戶名稱
@@ -982,7 +948,11 @@ async def _generate_response_with_global_history(
system_prompt = (
"你是 BloomWare 的個人化助理 小花，由銘傳大學人工智慧應用學系 槓上開發 團隊開發。"
"你不是 GPT，也不要自稱 GPT。"
- "你是一個友善、有禮、幽默且能夠提供幫助的AI助手。請使用繁體中文回覆，保持簡潔清晰的表達。"
+ "你是一個友善、有禮、幽默且能夠提供幫助的AI助手。\n\n"
+ "【重要】語言使用規範：\n"
+ "- 回覆用戶時：必須使用繁體中文，保持簡潔清晰的表達\n"
+ "- 調用工具時：所有參數必須使用英文（城市名、國家名、貨幣代碼等）\n"
+ "- 範例：用戶問「台北天氣」→ 調用工具時參數用 {\"city\": \"Taipei\"}，回覆時說「台北目前...\""
)
# 在系統提示前加上用戶名稱
@@ -1093,3 +1063,107 @@ async def _generate_response_with_global_history(
raise
logger.error(f"全局歷史處理出錯: {e}")
raise
+
+
+async def generate_response_with_tools(
+ messages: List[Dict[str, str]],
+ tools: List[Dict[str, Any]],
+ user_id: str = "default",
+ model: str = "gpt-5-nano",
+ reasoning_effort: Optional[str] = None,
+ tool_choice: str = "auto",
+) -> Dict[str, Any]:
+ """
+ 使用 OpenAI Function Calling 生成回應
+
+ 2025 最佳實踐：讓 GPT 原生選擇工具，不需要自定義意圖檢測 Prompt
+
+ Args:
+ messages: 對話訊息列表
+ tools: OpenAI tools 格式的工具定義列表
+ user_id: 用戶 ID（用於日誌）
+ model: 模型名稱
+ reasoning_effort: 推理強度 (minimal/low/medium/high)
+ tool_choice: 工具選擇策略 ("auto", "none", "required", 或特定工具名)
+
+ Returns:
+ 包含 tool_calls 和 content 的字典
+ """
+ openai_client = _get_client()
+ if openai_client is None:
+ logger.error("OpenAI 客戶端不可用")
+ return {"content": "", "tool_calls": []}
+
+ try:
+ start_time = time.time()
+ loop = asyncio.get_event_loop()
+
+ request_kwargs = {
+ "model": model,
+ "messages": messages,
+ "tools": tools,
+ "tool_choice": tool_choice,
+ "max_completion_tokens": 1000,
+ }
+
+ # 加入 reasoning_effort 控制
+ if reasoning_effort:
+ request_kwargs["reasoning_effort"] = reasoning_effort
+ logger.info(f"🧠 Function Calling 推理強度: {reasoning_effort}")
+
+ logger.info(f"🔧 Function Calling 請求: {len(tools)} 個工具, tool_choice={tool_choice}")
+ logger.debug(f"📤 發送的訊息: {messages}")
+
+ response = await asyncio.wait_for(
+ loop.run_in_executor(
+ None,
+ lambda: openai_client.chat.completions.create(**request_kwargs),
+ ),
+ timeout=OPENAI_TIMEOUT,
+ )
+
+ elapsed_time = time.time() - start_time
+ logger.info(f"⏱️ Function Calling 完成，耗時: {elapsed_time:.2f}秒")
+
+ # 解析回應
+ message = response.choices[0].message
+ logger.debug(f"📥 原始 message 物件: {message}")
+
+ result = {
+ "content": message.content or "",
+ "tool_calls": [],
+ }
+
+ # 提取 tool_calls
+ if message.tool_calls:
+ for tool_call in message.tool_calls:
+ result["tool_calls"].append({
+ "id": tool_call.id,
+ "type": "function",
+ "function": {
+ "name": tool_call.function.name,
+ "arguments": tool_call.function.arguments,
+ }
+ })
+ logger.info(f"✅ GPT 選擇了 {len(result['tool_calls'])} 個工具")
+ for tc in result["tool_calls"]:
+ logger.info(f" 🔧 工具: {tc['function']['name']}")
+ logger.info(f" 📋 參數 JSON: {tc['function']['arguments']}")
+ # 嘗試解析參數
+ try:
+ import json
+ parsed = json.loads(tc['function']['arguments'])
+ logger.info(f" ✅ 解析後參數: {parsed}")
+ except Exception as e:
+ logger.warning(f" ⚠️ 參數解析失敗: {e}")
+ else:
+ logger.info("💬 GPT 未選擇任何工具（一般聊天）")
+
+ return result
+
+ except asyncio.TimeoutError:
+ logger.error("Function Calling 請求超時")
+ return {"content": "", "tool_calls": []}
+ except Exception as e:
+ logger.error(f"Function Calling 失敗: {e}")
+ return {"content": "", "tool_calls": []}
diff --git a/services/batch_scheduler.py b/services/batch_scheduler.py
index 1f35ea4ac33c8cc2448bd1b575c6051ae7edb6d7..bec219db4b9dad8acd92a0420f11d15df1dc42aa 100644
--- a/services/batch_scheduler.py
+++ b/services/batch_scheduler.py
@@ -183,10 +183,62 @@ class BatchScheduler:
Returns:
{user_id: [memory_1, memory_2, ...]}
"""
- # TODO: 實作數據庫查詢邏輯
- # 目前返回空字典（示例）
- logger.warning("⚠️ _fetch_yesterday_memories 尚未實作，返回空數據")
- return {}
+ try:
+ if not firestore_db:
+ logger.warning("⚠️ Firestore 未連接，無法獲取記憶")
+ return {}
+
+ from google.cloud.firestore import FieldFilter
+ import asyncio
+
+ # 計算昨日時間範圍
+ yesterday_start = (datetime.now() - timedelta(days=1)).replace(
+ hour=0, minute=0, second=0, microsecond=0
+ )
+ yesterday_end = yesterday_start + timedelta(days=1)
+
+ # 獲取所有用戶
+ users_collection = firestore_db.collection("users")
+
+ def _fetch_users():
+ return list(users_collection.stream())
+
+ users = await asyncio.to_thread(_fetch_users)
+
+ result: Dict[str, List[str]] = {}
+
+ for user_doc in users:
+ user_id = user_doc.id
+
+ # 獲取該用戶的昨日記憶
+ def _fetch_user_memories(uid: str):
+ memories_ref = firestore_db.collection("users").document(uid).collection("memories")
+ query = memories_ref.where(
+ filter=FieldFilter("created_at", ">=", yesterday_start)
+ ).where(
+ filter=FieldFilter("created_at", "<", yesterday_end)
+ )
+ return list(query.stream())
+
+ memories = await asyncio.to_thread(_fetch_user_memories, user_id)
+
+ if memories:
+ memory_contents = []
+ for mem_doc in memories:
+ mem_data = mem_doc.to_dict()
+ content = mem_data.get("content", "")
+ if content:
+ memory_contents.append(content)
+
+ if memory_contents:
+ result[user_id] = memory_contents
+
+ logger.info(f"📚 獲取到 {len(result)} 位用戶的昨日記憶")
+ return result
+
+ except Exception as e:
+ logger.exception(f"❌ 獲取昨日記憶失敗: {e}")
+ return {}
async def _save_memory_summaries(self, results: List[Dict[str, Any]]):
"""
@@ -195,26 +247,119 @@ class BatchScheduler:
Args:
results: 批次結果列表
"""
- # TODO: 實作數據庫儲存邏輯
+ if not firestore_db:
+ logger.warning("⚠️ Firestore 未連接，無法儲存摘要")
+ return
+
+ import asyncio
+
logger.info(f"💾 準備儲存 {len(results)} 條記憶摘要")
+
+ saved_count = 0
for result in results:
- custom_id = result.get("custom_id") # user_id
- response = result.get("response", {}).get("body", {})
- summary = response.get("choices", [{}])[0].get("message", {}).get("content", "")
+ try:
+ custom_id = result.get("custom_id") # user_id
+ response = result.get("response", {}).get("body", {})
+ summary = response.get("choices", [{}])[0].get("message", {}).get("content", "")
+
+ if not custom_id or not summary:
+ continue
+
+ logger.debug(f"📝 用戶 {custom_id} 的摘要: {summary[:50]}...")
+
+ # 儲存到用戶的記憶摘要集合
+ def _save_summary(uid: str, summary_text: str):
+ summaries_ref = firestore_db.collection("users").document(uid).collection("memory_summaries")
+ summaries_ref.add({
+ "summary": summary_text,
+ "date": datetime.now().date().isoformat(),
+ "created_at": datetime.now(),
+ "type": "daily",
+ })
+
+ await asyncio.to_thread(_save_summary, custom_id, summary)
+ saved_count += 1
+
+ except Exception as e:
+ logger.error(f"❌ 儲存用戶 {custom_id} 的摘要失敗: {e}")
- logger.debug(f"📝 用戶 {custom_id} 的摘要: {summary[:50]}...")
- # await save_memory_summary(custom_id, summary)
+ logger.info(f"✅ 成功儲存 {saved_count}/{len(results)} 條記憶摘要")
async def _fetch_week_health_data(self) -> Dict[str, Dict[str, Any]]:
"""
從數據庫獲取所有用戶的本週健康數據
Returns:
- {user_id: {heart_rate: ..., steps: ...}}
+ {user_id: {heart_rate: [...], steps: [...], sleep: [...], ...}}
"""
- # TODO: 實作數據庫查詢邏輯
- logger.warning("⚠️ _fetch_week_health_data 尚未實作，返回空數據")
- return {}
+ try:
+ if not firestore_db:
+ logger.warning("⚠️ Firestore 未連接，無法獲取健康數據")
+ return {}
+
+ from google.cloud.firestore import FieldFilter
+ import asyncio
+
+ # 計算本週時間範圍（週一到今天）
+ today = datetime.now()
+ week_start = today - timedelta(days=today.weekday())
+ week_start = week_start.replace(hour=0, minute=0, second=0, microsecond=0)
+
+ # 獲取所有用戶
+ users_collection = firestore_db.collection("users")
+
+ def _fetch_users():
+ return list(users_collection.stream())
+
+ users = await asyncio.to_thread(_fetch_users)
+
+ result: Dict[str, Dict[str, Any]] = {}
+
+ for user_doc in users:
+ user_id = user_doc.id
+
+ # 獲取該用戶的本週健康數據
+ def _fetch_user_health(uid: str):
+ health_ref = firestore_db.collection("health_data")
+ query = health_ref.where(
+ filter=FieldFilter("user_id", "==", uid)
+ ).where(
+ filter=FieldFilter("timestamp", ">=", week_start)
+ )
+ return list(query.stream())
+
+ health_docs = await asyncio.to_thread(_fetch_user_health, user_id)
+
+ if health_docs:
+ user_health: Dict[str, List[Any]] = {
+ "heart_rate": [],
+ "steps": [],
+ "sleep": [],
+ "active_calories": [],
+ }
+
+ for doc in health_docs:
+ data = doc.to_dict()
+ data_type = data.get("type")
+ value = data.get("value")
+ timestamp = data.get("timestamp")
+
+ if data_type in user_health and value is not None:
+ user_health[data_type].append({
+ "value": value,
+ "timestamp": timestamp,
+ })
+
+ # 只保留有數據的用戶
+ if any(user_health.values()):
+ result[user_id] = user_health
+
+ logger.info(f"❤️ 獲取到 {len(result)} 位用戶的本週健康數據")
+ return result
+
+ except Exception as e:
+ logger.exception(f"❌ 獲取本週健康數據失敗: {e}")
+ return {}
# 全域單例
diff --git a/services/realtime_stt_service.py b/services/realtime_stt_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..44adf29e149eff2a1b8d5ca893cdbe3b28b6b042
--- /dev/null
+++ b/services/realtime_stt_service.py
@@ -0,0 +1,290 @@
+"""
+OpenAI Realtime API - 即時語音轉文字服務
+使用 WebSocket 進行低延遲串流轉錄
+"""
+
+import os
+import json
+import asyncio
+import logging
+from typing import Optional, Callable, Dict, Any
+import websockets
+from dotenv import load_dotenv
+
+load_dotenv()
+
+logger = logging.getLogger("services.realtime_stt")
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+if not OPENAI_API_KEY:
+ logger.warning("⚠️ OPENAI_API_KEY 未設置")
+
+# OpenAI Realtime API WebSocket URL
+REALTIME_API_URL = "wss://api.openai.com/v1/realtime?intent=transcription"
+
+
+class RealtimeSTTService:
+ """OpenAI Realtime API 即時語音轉文字服務"""
+
+ def __init__(self):
+ self.api_key = OPENAI_API_KEY
+ self.ws: Optional[websockets.WebSocketClientProtocol] = None
+ self.is_connected = False
+ self._receive_task: Optional[asyncio.Task] = None
+
+ async def connect(
+ self,
+ on_transcript_delta: Optional[Callable[[str], None]] = None,
+ on_transcript_done: Optional[Callable[[str], None]] = None,
+ on_vad_committed: Optional[Callable[[str], None]] = None,
+ model: str = "gpt-4o-mini-transcribe",
+ language: str = "zh",
+ ) -> bool:
+ """
+ 建立與 OpenAI Realtime API 的 WebSocket 連線
+
+ Args:
+ on_transcript_delta: 接收部分轉錄結果的回調函數
+ on_transcript_done: 接收完整轉錄結果的回調函數
+ on_vad_committed: VAD 偵測到語音結束的回調函數
+ model: 使用的模型（gpt-4o-transcribe 或 gpt-4o-mini-transcribe）
+ language: 語言代碼
+
+ Returns:
+ bool: 連線是否成功
+ """
+ if not self.api_key:
+ logger.error("❌ OpenAI API Key 未設置")
+ return False
+
+ try:
+ logger.info(f"🔌 連接到 OpenAI Realtime API: {REALTIME_API_URL}")
+
+ # 建立 WebSocket 連線（使用 API Key 認證）
+ self.ws = await websockets.connect(
+ REALTIME_API_URL,
+ additional_headers={
+ "Authorization": f"Bearer {self.api_key}",
+ "OpenAI-Beta": "realtime=v1"
+ }
+ )
+
+ self.is_connected = True
+ logger.info("✅ 已連接到 OpenAI Realtime API")
+
+ # 發送 session 配置（按照 Microsoft/Azure 官方範例）
+ session_config = {
+ "type": "transcription_session.update",
+ "session": { # ← 關鍵：所有配置要包在 session 物件裡
+ "input_audio_format": "pcm16",
+ "input_audio_transcription": {
+ "model": model,
+ "prompt": "",
+ "language": language
+ },
+ "turn_detection": {
+ "type": "server_vad",
+ "threshold": 0.5,
+ "prefix_padding_ms": 300,
+ "silence_duration_ms": 500
+ }
+ }
+ }
+
+ await self.ws.send(json.dumps(session_config))
+ logger.info("📤 已發送 session 配置")
+
+ # 啟動接收事件的背景任務
+ self._receive_task = asyncio.create_task(
+ self._receive_events(
+ on_transcript_delta,
+ on_transcript_done,
+ on_vad_committed
+ )
+ )
+
+ return True
+
+ except Exception as e:
+ logger.error(f"❌ 連接失敗: {e}")
+ self.is_connected = False
+ return False
+
+ async def _receive_events(
+ self,
+ on_transcript_delta: Optional[Callable],
+ on_transcript_done: Optional[Callable],
+ on_vad_committed: Optional[Callable]
+ ):
+ """
+ 接收並處理來自 OpenAI Realtime API 的事件
+
+ Args:
+ on_transcript_delta: 部分轉錄回調
+ on_transcript_done: 完整轉錄回調
+ on_vad_committed: VAD 提交回調
+ """
+ try:
+ while self.is_connected and self.ws:
+ try:
+ message = await self.ws.recv()
+ event = json.loads(message)
+ event_type = event.get("type")
+
+ logger.debug(f"📩 收到事件: {event_type}")
+
+ # 處理使用者語音的部分轉錄結果（即時串流）
+ if event_type == "conversation.item.input_audio_transcription.delta":
+ delta_text = event.get("delta", "")
+ if on_transcript_delta and delta_text:
+ await self._safe_callback(on_transcript_delta, delta_text)
+
+ # 處理完整轉錄結果（語音段結束）
+ elif event_type == "conversation.item.input_audio_transcription.completed":
+ full_text = event.get("transcript", "")
+ if on_transcript_done and full_text:
+ await self._safe_callback(on_transcript_done, full_text)
+
+ # 處理 VAD 提交事件（語音段結束）
+ elif event_type == "input_audio_buffer.committed":
+ item_id = event.get("item_id", "")
+ if on_vad_committed:
+ await self._safe_callback(on_vad_committed, item_id)
+
+ # 處理錯誤事件
+ elif event_type == "error":
+ error_msg = event.get("error", {})
+ logger.error(f"❌ OpenAI API 錯誤: {error_msg}")
+
+ except websockets.exceptions.ConnectionClosed:
+ logger.warning("⚠️ WebSocket 連線已關閉")
+ break
+ except json.JSONDecodeError as e:
+ logger.error(f"❌ JSON 解析錯誤: {e}")
+ except Exception as e:
+ logger.error(f"❌ 接收事件失敗: {e}")
+
+ except Exception as e:
+ logger.error(f"❌ 事件接收循環失敗: {e}")
+ finally:
+ self.is_connected = False
+
+ async def _safe_callback(self, callback: Callable, *args):
+ """安全地執行回調函數（支援同步和異步）"""
+ try:
+ if asyncio.iscoroutinefunction(callback):
+ await callback(*args)
+ else:
+ callback(*args)
+ except Exception as e:
+ logger.error(f"❌ 回調函數執行失敗: {e}")
+
+ async def send_audio_chunk(self, audio_data: bytes) -> bool:
+ """
+ 發送音頻 chunk 到 OpenAI Realtime API
+
+ Args:
+ audio_data: PCM16 格式的音頻數據（需 Base64 編碼）
+
+ Returns:
+ bool: 是否發送成功
+ """
+ if not self.is_connected or not self.ws:
+ logger.warning("⚠️ WebSocket 未連接，無法發送音頻")
+ return False
+
+ try:
+ import base64
+
+ # 將音頻數據編碼為 Base64
+ audio_base64 = base64.b64encode(audio_data).decode('utf-8')
+
+ # 發送音頻 chunk
+ message = {
+ "type": "input_audio_buffer.append",
+ "audio": audio_base64
+ }
+
+ await self.ws.send(json.dumps(message))
+ logger.debug(f"📤 已發送音頻 chunk: {len(audio_data)} bytes")
+ return True
+
+ except Exception as e:
+ logger.error(f"❌ 發送音頻失敗: {e}")
+ return False
+
+ async def commit_audio(self) -> bool:
+ """
+ 手動提交音頻緩衝區（當不使用 Server VAD 時）
+
+ Returns:
+ bool: 是否提交成功
+ """
+ if not self.is_connected or not self.ws:
+ logger.warning("⚠️ WebSocket 未連接，無法提交音頻")
+ return False
+
+ try:
+ message = {
+ "type": "input_audio_buffer.commit"
+ }
+
+ await self.ws.send(json.dumps(message))
+ logger.info("📤 已手動提交音頻緩衝區")
+ return True
+
+ except Exception as e:
+ logger.error(f"❌提交音頻失敗: {e}")
+ return False
+
+ async def disconnect(self):
+ """關閉 WebSocket 連線"""
+ if self.ws:
+ logger.info("🔌 關閉 OpenAI Realtime API 連線")
+ self.is_connected = False
+
+ # 取消接收任務
+ if self._receive_task and not self._receive_task.done():
+ self._receive_task.cancel()
+ try:
+ await self._receive_task
+ except asyncio.CancelledError:
+ pass
+
+ # 關閉 WebSocket
+ await self.ws.close()
+ self.ws = None
+
+ logger.info("✅ 已斷開連線")
+
+
+# 全域單例
+realtime_stt_service = RealtimeSTTService()
+
+
+async def create_realtime_session(
+ on_transcript_delta: Optional[Callable] = None,
+ on_transcript_done: Optional[Callable] = None,
+ model: str = "gpt-4o-mini-transcribe",
+ language: str = "zh"
+) -> RealtimeSTTService:
+ """
+ 便捷函數：建立 Realtime STT 會話
+
+ Args:
+ on_transcript_delta: 部分轉錄回調
+ on_transcript_done: 完整轉錄回調
+ model: 使用的模型
+ language: 語言代碼
+
+ Returns:
+ RealtimeSTTService: 已連線的服務實例
+ """
+ service = RealtimeSTTService()
+ await service.connect(
+ on_transcript_delta=on_transcript_delta,
+ on_transcript_done=on_transcript_done,
+ model=model,
+ language=language
+ )
+ return service
diff --git a/services/stt_service.py b/services/stt_service.py
index 60a55dd5d0f63807526c31dc9af2ff4783abf737..3fca26662cbe477c6c4f9b7a50dc5bc20fadcc4d 100644
--- a/services/stt_service.py
+++ b/services/stt_service.py
@@ -36,7 +36,9 @@ class STTService:
self,
audio_data: bytes,
language: str = "zh",
- prompt: Optional[str] = None
+ prompt: Optional[str] = None,
+ stream: bool = False,
+ on_delta: Optional[callable] = None
) -> Dict[str, Any]:
"""
使用 Whisper API 將音頻轉文字
@@ -45,6 +47,8 @@ class STTService:
audio_data: 音頻數據（PCM16 raw bytes 或完整 WAV 文件）
language: 語言代碼（zh, en, ja, ko 等）
prompt: 提示詞（可選，幫助提高準確度）
+ stream: 是否啟用串流轉錄（邊轉邊發）
+ on_delta: 串流模式下的回調函數，接收部分轉錄結果
Returns:
{
diff --git a/services/voice_binding.py b/services/voice_binding.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a32ddd196080e79a10169afc23032f16ee2e8ca
--- /dev/null
+++ b/services/voice_binding.py
@@ -0,0 +1,255 @@
+"""
+語音綁定狀態機
+處理語音帳號綁定流程（關鍵字匹配，無 GPT）
+"""
+
+import logging
+import time
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, Any, Optional, List
+
+from fastapi import WebSocket
+
+from core.logging import get_logger
+
+logger = get_logger("services.voice_binding")
+
+
+def get_available_speaker_labels() -> List[str]:
+ """讀取可用的 speaker label 列表"""
+ classes_file = Path("models/speaker_identification/models_cnn/classes.txt")
+ if classes_file.exists():
+ with open(classes_file, "r", encoding="utf-8") as f:
+ return [line.strip() for line in f if line.strip()]
+ return []
+
+
+class VoiceBindingStateMachine:
+ """
+ 語音帳號綁定狀態機（硬編碼關鍵字匹配）
+
+ 流程：
+ 1. 用戶說「綁定語音登入」
+ 2. 系統詢問「你要綁定哪個帳號？」並顯示可用的 label 列表
+ 3. 用戶輸入帳號名稱（label）
+ 4. 系統綁定 speaker_label 到用戶帳號
+ 5. 系統回應「綁定成功囉！」
+ """
+
+ # 觸發關鍵字
+ TRIGGER_KEYWORDS = [
+ "綁定語音登入",
+ "語音登入綁定",
+ "綁定語音",
+ "設定語音登入",
+ ]
+
+ # 狀態超時時間（秒）
+ STATE_TIMEOUT = 300 # 5 分鐘
+
+ def __init__(self):
+ # 用戶狀態：{user_id: {state: str, timestamp: datetime}}
+ self.user_states: Dict[str, Dict[str, Any]] = {}
+
+ def check_binding_trigger(self, user_id: str, message: str) -> Optional[str]:
+ """
+ 檢查是否觸發綁定流程
+
+ Returns:
+ - "TRIGGER": 觸發綁定流程
+ - "AWAITING_LABEL": 等待用戶輸入帳號名稱
+ - None: 不是綁定相關訊息
+ """
+ message_lower = message.lower().replace(" ", "")
+
+ # 檢測觸發關鍵字
+ for keyword in self.TRIGGER_KEYWORDS:
+ if keyword.replace(" ", "") in message_lower:
+ self.user_states[user_id] = {
+ "state": "AWAITING_LABEL",
+ "timestamp": datetime.now()
+ }
+ return "TRIGGER"
+
+ # 檢查是否在等待輸入帳號名稱狀態
+ if user_id in self.user_states:
+ state_info = self.user_states[user_id]
+ if state_info.get("state") == "AWAITING_LABEL":
+ # 檢查是否超時
+ elapsed = (datetime.now() - state_info.get("timestamp")).total_seconds()
+ if elapsed > self.STATE_TIMEOUT:
+ del self.user_states[user_id]
+ return None
+ return "AWAITING_LABEL"
+
+ return None
+
+ async def handle_binding_flow(
+ self,
+ user_id: str,
+ message: str,
+ websocket: WebSocket,
+ voice_service: Optional[Any] = None,
+ get_user_by_id: Optional[Any] = None,
+ ) -> bool:
+ """
+ 處理綁定流程
+
+ Args:
+ user_id: 用戶 ID
+ message: 用戶訊息
+ websocket: WebSocket 連線
+ voice_service: 語音認證服務（可選）
+ get_user_by_id: 取得用戶資料的函數（可選）
+
+ Returns:
+ True: 已處理（不要繼續到 Agent）
+ False: 未處理（繼續到 Agent）
+ """
+ state = self.check_binding_trigger(user_id, message)
+
+ if state == "TRIGGER":
+ logger.info(f"🎙️ 用戶 {user_id} 觸發語音綁定流程")
+
+ # 檢查使用者是否已經綁定過 speaker_label
+ if get_user_by_id:
+ try:
+ user_data = await get_user_by_id(user_id)
+ if user_data and user_data.get("speaker_label"):
+ existing_label = user_data.get("speaker_label")
+ logger.info(f"⚠️ 用戶 {user_id} 已綁定 speaker_label: {existing_label}")
+
+ await websocket.send_json({
+ "type": "bot_message",
+ "message": (
+ f"你已經綁定過語音了！目前的聲紋標籤是：{existing_label}。"
+ "如果需要重新綁定，請聯繫管理員。"
+ ),
+ "timestamp": time.time()
+ })
+
+ self.clear_state(user_id)
+ return True
+
+ except Exception as e:
+ logger.error(f"❌ 檢查使用者綁定狀態失敗: {e}")
+ await websocket.send_json({
+ "type": "error",
+ "message": "系統錯誤，無法檢查綁定狀態"
+ })
+ return True
+
+ # 未綁定，顯示可用的 label 列表
+ available_labels = get_available_speaker_labels()
+ labels_str = "、".join(available_labels) if available_labels else "（無可用帳號）"
+
+ logger.info(f"✅ 用戶 {user_id} 尚未綁定，詢問要綁定的帳號")
+
+ await websocket.send_json({
+ "type": "bot_message",
+ "message": f"好的，你要綁定哪個帳號呢？\n可用的帳號有：{labels_str}\n請輸入帳號名稱：",
+ "timestamp": time.time()
+ })
+
+ return True
+
+ elif state == "AWAITING_LABEL":
+ # 用戶輸入了帳號名稱
+ label_input = message.strip().lower()
+ available_labels = get_available_speaker_labels()
+
+ logger.info(f"🎙️ 用戶 {user_id} 輸入帳號名稱: {label_input}")
+
+ # 檢查輸入的 label 是否有效
+ if label_input not in [l.lower() for l in available_labels]:
+ labels_str = "、".join(available_labels)
+ await websocket.send_json({
+ "type": "bot_message",
+ "message": f"找不到這個帳號喔！可用的帳號有：{labels_str}\n請重新輸入：",
+ "timestamp": time.time()
+ })
+ return True
+
+ # 找到匹配的 label（保持原始大小寫）
+ matched_label = next((l for l in available_labels if l.lower() == label_input), label_input)
+
+ # 執行綁定
+ try:
+ from core.database import get_user_by_speaker_label, set_user_speaker_label
+
+ # 檢查這個 label 是否已被其他用戶綁定
+ existing_user = await get_user_by_speaker_label(matched_label)
+ if existing_user and existing_user.get("id") != user_id:
+ await websocket.send_json({
+ "type": "bot_message",
+ "message": f"這個帳號（{matched_label}）已經被其他人綁定了，請選擇其他帳號。",
+ "timestamp": time.time()
+ })
+ return True
+
+ # 綁定到當前用戶
+ bind_result = await set_user_speaker_label(user_id, matched_label)
+
+ if bind_result.get("success"):
+ logger.info(f"✅ 用戶 {user_id} 成功綁定 speaker_label: {matched_label}")
+ await websocket.send_json({
+ "type": "bot_message",
+ "message": f"綁定成功囉！🎉 你的語音帳號已設定為：{matched_label}",
+ "timestamp": time.time()
+ })
+ await websocket.send_json({
+ "type": "voice_binding_success",
+ "speaker_label": matched_label
+ })
+ else:
+ error_msg = bind_result.get("error", "未知錯誤")
+ await websocket.send_json({
+ "type": "bot_message",
+ "message": f"綁定失敗：{error_msg}",
+ "timestamp": time.time()
+ })
+
+ except Exception as e:
+ logger.error(f"❌ 綁定 speaker_label 失敗: {e}")
+ await websocket.send_json({
+ "type": "bot_message",
+ "message": "系統錯誤，綁定失敗，請稍後再試。",
+ "timestamp": time.time()
+ })
+
+ # 清理狀態，返回待機模式
+ self.clear_state(user_id)
+ return True
+
+ return False
+
+ def clear_state(self, user_id: str) -> None:
+ """清理用戶狀態"""
+ self.user_states.pop(user_id, None)
+
+ def is_awaiting_label(self, user_id: str) -> bool:
+ """檢查用戶是否在等待輸入帳號名稱狀態"""
+ if user_id not in self.user_states:
+ return False
+
+ state_info = self.user_states[user_id]
+ if state_info.get("state") != "AWAITING_LABEL":
+ return False
+
+ # 檢查是否超時
+ elapsed = (datetime.now() - state_info.get("timestamp")).total_seconds()
+ if elapsed > self.STATE_TIMEOUT:
+ del self.user_states[user_id]
+ return False
+
+ return True
+
+ # 保留舊方法名稱以保持相容性
+ def is_awaiting_voice(self, user_id: str) -> bool:
+ """檢查用戶是否在等待語音錄製狀態（已棄用，保留相容性）"""
+ return self.is_awaiting_label(user_id)
+
+
+# 全域單例
+voice_binding_fsm = VoiceBindingStateMachine()
diff --git a/services/voice_login.py b/services/voice_login.py
index f7a8fc90249e5aa2600829fc2344c6fdefbd8636..fa2f1894483c5cd34b31c181fd148ab52dc4afb4 100644
--- a/services/voice_login.py
+++ b/services/voice_login.py
@@ -110,33 +110,17 @@ class VoiceAuthService:
raise RuntimeError(f"載入 CNN 說話者辨識模組失敗：{e}")
self._predict_files = _predict_files
- # 資產檢查：需要 speaker_id_model.pth 與 classes 定義（classes.txt 或 processed_audio 目錄）
- # 若預設目錄不存在，向下相容：嘗試使用舊的專題cnn 目錄（搬遷前）
+ # 資產檢查：ECAPA-TDNN 需要 speaker_db.pkl
if not self.model_dir.exists():
legacy_dir = self.base_dir / "專題cnn"
if legacy_dir.exists():
self.model_dir = legacy_dir
else:
- raise FileNotFoundError(f"找不到 CNN 模型目錄：{self.model_dir}")
-
- model_file = self.model_dir / "speaker_id_model.pth"
- classes_txt = self.model_dir / "classes.txt"
- processed_dir = self.model_dir / "processed_audio"
- if not model_file.exists():
- raise FileNotFoundError(f"缺少模型檔：{model_file}")
- if not classes_txt.exists() and not processed_dir.exists():
- raise FileNotFoundError(
- f"缺少類別定義，請提供 {classes_txt.name} 或 {processed_dir.name}/ 子資料夾；或設 VOICE_CNN_CLASSES 指定路徑"
- )
- if classes_txt.exists():
- try:
- content = classes_txt.read_text(encoding="utf-8").strip()
- except Exception:
- content = ""
- if not content:
- raise FileNotFoundError(
- f"{classes_txt} 內容為空，請填入每行一個類別名稱，或提供 {processed_dir.name}/ 以自動推斷"
- )
+ raise FileNotFoundError(f"找不到模型目錄：{self.model_dir}")
+
+ speaker_db_file = self.model_dir / "speaker_db.pkl"
+ if not speaker_db_file.exists():
+ raise FileNotFoundError(f"缺少說話者資料庫：{speaker_db_file}")
# 以 user_id 管理暫存錄音緩衝
self._buffers: Dict[str, bytearray] = {}
diff --git a/static/frontend/index.html b/static/frontend/index.html
index b8e29d57c19e5d6cd934361a3d15cfeaf45cf7d0..1dbd8e0e0e30bdeea37adbe522f1319775053f9c 100644
--- a/static/frontend/index.html
+++ b/static/frontend/index.html
@@ -1309,24 +1309,6 @@
-
-
-
-
-
Bloom Ware
-
語音沉浸式 AI 助手
-
-
-
- 使用 Google 登入
-
-
-
🎛️ 控制面板
diff --git a/static/frontend/js/app.js b/static/frontend/js/app.js
index 378347490e11bd80a07d02f5022bfd89e20783c7..b263eea37c2fc8f76406a1ba08590e96559ff140 100644
--- a/static/frontend/js/app.js
+++ b/static/frontend/js/app.js
@@ -79,13 +79,6 @@ async function checkLoginStatus() {
function initializeApp(token) {
console.log('🚀 初始化應用...');
- // 隱藏登入覆蓋層
- const loginOverlay = document.getElementById('loginOverlay');
- if (loginOverlay) {
- loginOverlay.classList.add('hidden');
- console.log('✅ 登入覆蓋層已隱藏');
- }
-
// 初始化各個模組的事件監聽器
initLoginButton();
initLogoutButton();
@@ -106,8 +99,14 @@ function initializeApp(token) {
// ========== 頁面初始化 ==========
-// 檢查登入狀態
-checkLoginStatus();
+// 只在聊天室頁面（/static/）執行登入檢查
+console.log('📍 當前路徑:', window.location.pathname);
+if (window.location.pathname.startsWith('/static')) {
+ console.log('✅ 在聊天室頁面，執行登入檢查');
+ checkLoginStatus();
+} else {
+ console.log('⏭️ 不在聊天室頁面，跳過登入檢查');
+}
console.log('💡 WebSocket 整合已載入');
console.log('📝 部署時請執行: initializeWebSocket(your_jwt_token)');
@@ -115,3 +114,5 @@ console.log('📝 部署時請執行: initializeWebSocket(your_jwt_token)');
// ========== 提示訊息 ==========
console.log('%c Bloom Ware 語音沉浸式 - 多層蓮花版', 'color: #16A34A; font-size: 16px; font-weight: bold;');
console.log('%c✨ 核心特色:\n- 8片蓮花瓣設計（clip-path 打造自然曲線）\n- 多層次花蕊（radial gradient + 光澤細節）\n- 花瓣中心脈絡增加真實感\n- 待機狀態：花瓣完全閉合（含苞待放）\n- Agent 思考中：8片花瓣順時針依序綻放\n- 斷線/重連：花瓣逆時針綻放變紅色警示\n- 錄音中：花蕊變紅脈衝，花瓣保持閉合\n- 品牌特色：優雅、精緻、現代', 'color: rgba(0,0,0,0.7); font-size: 12px;');
+
+
diff --git a/static/frontend/js/speech.js b/static/frontend/js/speech.js
new file mode 100644
index 0000000000000000000000000000000000000000..43ad82371c280c153382004e50cb8297a34a58a7
--- /dev/null
+++ b/static/frontend/js/speech.js
@@ -0,0 +1,250 @@
+/**
+ * 語音識別模組
+ * 處理語音輸入和語音合成
+ */
+
+class SpeechRecognition {
+ constructor() {
+ this.recognition = null;
+ this.isListening = false;
+ this.isSupported = false;
+ this.onResult = null;
+ this.onError = null;
+ this.onStart = null;
+ this.onEnd = null;
+
+ // 語音辨識上下文
+ this.context = {
+ userLocation: null,
+ recentQueries: [],
+ currentSession: null
+ };
+
+ this.initRecognition();
+ }
+
+ initRecognition() {
+ // 檢查瀏覽器支援
+ if ('webkitSpeechRecognition' in window) {
+ this.recognition = new webkitSpeechRecognition();
+ this.isSupported = true;
+ } else if ('SpeechRecognition' in window) {
+ this.recognition = new SpeechRecognition();
+ this.isSupported = true;
+ } else {
+ console.warn('瀏覽器不支援語音識別');
+ return;
+ }
+
+ // 設定語音識別參數
+ this.recognition.continuous = false;
+ this.recognition.interimResults = true;
+ this.recognition.lang = 'zh-TW';
+ this.recognition.maxAlternatives = 3; // 增加候選結果數量
+
+ // 綁定事件
+ this.recognition.onstart = () => {
+ this.isListening = true;
+ console.log('🎤 語音識別開始');
+ if (this.onStart) this.onStart();
+ };
+
+ this.recognition.onresult = (event) => {
+ let finalTranscript = '';
+ let interimTranscript = '';
+
+ for (let i = event.resultIndex; i < event.results.length; i++) {
+ const transcript = event.results[i][0].transcript;
+ if (event.results[i].isFinal) {
+ finalTranscript += transcript;
+ } else {
+ interimTranscript += transcript;
+ }
+ }
+
+ console.log('🎤 語音識別原始結果:', finalTranscript || interimTranscript);
+
+ // 使用語音增強功能
+ let enhancedFinal = finalTranscript;
+ let enhancedInterim = interimTranscript;
+
+ if (window.speechEnhancer) {
+ const context = this.getContext();
+
+ if (finalTranscript) {
+ enhancedFinal = window.speechEnhancer.enhance(finalTranscript, context);
+ }
+
+ if (interimTranscript) {
+ enhancedInterim = window.speechEnhancer.enhance(interimTranscript, context);
+ }
+ }
+
+ if (this.onResult) {
+ this.onResult(enhancedFinal, enhancedInterim);
+ }
+ };
+
+ this.recognition.onerror = (event) => {
+ console.error('🎤 語音識別錯誤:', event.error);
+ this.isListening = false;
+ if (this.onError) this.onError(event.error);
+ };
+
+ this.recognition.onend = () => {
+ this.isListening = false;
+ console.log('🎤 語音識別結束');
+ if (this.onEnd) this.onEnd();
+ };
+ }
+
+ start() {
+ if (!this.isSupported) {
+ console.error('瀏覽器不支援語音識別');
+ return false;
+ }
+
+ if (this.isListening) {
+ console.warn('語音識別已在進行中');
+ return false;
+ }
+
+ try {
+ this.recognition.start();
+ return true;
+ } catch (error) {
+ console.error('啟動語音識別失敗:', error);
+ return false;
+ }
+ }
+
+ stop() {
+ if (this.recognition && this.isListening) {
+ this.recognition.stop();
+ }
+ }
+
+ abort() {
+ if (this.recognition && this.isListening) {
+ this.recognition.abort();
+ }
+ }
+
+ /**
+ * 設定用戶位置上下文
+ */
+ setUserLocation(location) {
+ this.context.userLocation = location;
+ console.log('📍 設定用戶位置上下文:', location);
+ }
+
+ /**
+ * 添加查詢歷史
+ */
+ addRecentQuery(query) {
+ if (query && typeof query === 'string') {
+ this.context.recentQueries.unshift(query);
+ // 只保留最近 10 次查詢
+ if (this.context.recentQueries.length > 10) {
+ this.context.recentQueries = this.context.recentQueries.slice(0, 10);
+ }
+ console.log('📝 添加查詢歷史:', query);
+ }
+ }
+
+ /**
+ * 獲取當前上下文
+ */
+ getContext() {
+ return {
+ ...this.context,
+ timestamp: Date.now()
+ };
+ }
+
+ /**
+ * 清除上下文
+ */
+ clearContext() {
+ this.context = {
+ userLocation: null,
+ recentQueries: [],
+ currentSession: null
+ };
+ console.log('🗑️ 已清除語音辨識上下文');
+ }
+}
+
+// 語音合成類
+class TextToSpeech {
+ constructor() {
+ this.synth = window.speechSynthesis;
+ this.isSupported = 'speechSynthesis' in window;
+ this.isSpeaking = false;
+ this.currentUtterance = null;
+ }
+
+ speak(text, options = {}) {
+ if (!this.isSupported) {
+ console.error('瀏覽器不支援語音合成');
+ return false;
+ }
+
+ // 停止當前播放
+ this.stop();
+
+ const utterance = new SpeechSynthesisUtterance(text);
+
+ // 設定參數
+ utterance.lang = options.lang || 'zh-TW';
+ utterance.rate = options.rate || 1.0;
+ utterance.pitch = options.pitch || 1.0;
+ utterance.volume = options.volume || 1.0;
+
+ // 綁定事件
+ utterance.onstart = () => {
+ this.isSpeaking = true;
+ console.log('🔊 語音合成開始');
+ };
+
+ utterance.onend = () => {
+ this.isSpeaking = false;
+ this.currentUtterance = null;
+ console.log('🔊 語音合成結束');
+ };
+
+ utterance.onerror = (event) => {
+ console.error('🔊 語音合成錯誤:', event.error);
+ this.isSpeaking = false;
+ this.currentUtterance = null;
+ };
+
+ this.currentUtterance = utterance;
+ this.synth.speak(utterance);
+ return true;
+ }
+
+ stop() {
+ if (this.synth.speaking) {
+ this.synth.cancel();
+ }
+ this.isSpeaking = false;
+ this.currentUtterance = null;
+ }
+
+ pause() {
+ if (this.synth.speaking && !this.synth.paused) {
+ this.synth.pause();
+ }
+ }
+
+ resume() {
+ if (this.synth.paused) {
+ this.synth.resume();
+ }
+ }
+}
+
+// 全域實例
+window.speechRecognition = new SpeechRecognition();
+window.textToSpeech = new TextToSpeech();
diff --git a/static/frontend/js/tools.js b/static/frontend/js/tools.js
index 9f901f399002f79b00b665d1b93941ec6a70358b..de11664f267871fb370d8163b5c5a83968195538 100644
--- a/static/frontend/js/tools.js
+++ b/static/frontend/js/tools.js
@@ -296,15 +296,28 @@ function renderCardContent(toolName, toolData) {
return renderTrainList(toolData.trains);
}
- // 模式 8：YouBike 站點資訊
- if (toolData.stations && Array.isArray(toolData.stations)) {
+ // 模式 8：YouBike 站點資訊（需要確認是 YouBike 工具）
+ if (toolData.stations && Array.isArray(toolData.stations) &&
+ (toolName === 'tdx_youbike' || toolData.stations[0]?.available_bikes !== undefined)) {
console.log('✅ 匹配到模式 8: YouBike 站點資訊');
return renderYouBikeStations(toolData.stations);
}
+
+ // 模式 8.5：火車站點資訊（tdx_train 的 stations）
+ if (toolData.stations && Array.isArray(toolData.stations) && toolName === 'tdx_train') {
+ console.log('✅ 匹配到模式 8.5: 火車站點資訊');
+ return renderTrainStations(toolData.stations);
+ }
+
+ // 模式 9：地理反查資訊（reverse_geocode）
+ if (toolData.display_name && toolData.lat && toolData.lon && toolName === 'reverse_geocode') {
+ console.log('✅ 匹配到模式 9: 地理反查資訊');
+ return renderReverseGeocode(toolData);
+ }
- // 模式 9：通用 raw_data 物件
+ // 模式 10：通用 raw_data 物件
if (toolData.raw_data && typeof toolData.raw_data === 'object') {
- console.log('✅ 匹配到模式 9: 通用 raw_data');
+ console.log('✅ 匹配到模式 10: 通用 raw_data');
return renderKeyValuePairs(toolData.raw_data);
}
@@ -548,6 +561,46 @@ function renderTrainList(trains) {
return html;
}
+/**
+ * 渲染火車站點資訊
+ */
+function renderTrainStations(stations) {
+ if (!stations || stations.length === 0) {
+ return '
查無車站資訊
';
+ }
+
+ let html = '
';
+
+ stations.forEach((station, index) => {
+ const stationName = station.station_name || station.name || '未知車站';
+ const distance = station.distance_m ? `${Math.round(station.distance_m)}公尺` : '';
+ const walkTime = station.walking_time_min ? `步行約${station.walking_time_min}分鐘` : '';
+
+ html += `
+
+
+ 🚉 ${stationName}
+
+ ${distance ? `
+
+ 📏 距離
+ ${distance}
+
+ ` : ''}
+ ${walkTime ? `
+
+ 🚶 步行時間
+ ${walkTime}
+
+ ` : ''}
+
+ `;
+ });
+
+ html += '
';
+ return html;
+}
+
/**
* 渲染 YouBike 站點資訊
*/
@@ -657,6 +710,68 @@ function renderBusArrivals(arrivals, routeName) {
return html;
}
+/**
+ * 渲染地理反查資訊（reverse_geocode）
+ */
+function renderReverseGeocode(data) {
+ const displayName = data.display_name || '未知地點';
+ const city = data.city || '';
+ const road = data.road || '';
+ const houseNumber = data.house_number || '';
+ const suburb = data.suburb || '';
+ const admin = data.admin || '';
+ const countryCode = data.country_code || '';
+ const lat = data.lat?.toFixed(6) || '';
+ const lon = data.lon?.toFixed(6) || '';
+
+ // 組合詳細地址
+ let detailedAddress = [];
+ if (city) detailedAddress.push(city);
+ if (admin && admin !== city) detailedAddress.push(admin);
+ if (suburb) detailedAddress.push(suburb);
+ if (road) detailedAddress.push(road);
+ if (houseNumber) detailedAddress.push(houseNumber);
+
+ const addressText = detailedAddress.length > 0 ? detailedAddress.join(', ') : displayName;
+
+ // 生成 Google Maps 連結
+ const mapsUrl = `https://www.google.com/maps?q=${lat},${lon}`;
+
+ return `
+
+ 📍 位置
+ ${displayName}
+
+ ${city ? `
+
+ 🏙️ 城市
+ ${city}
+
+ ` : ''}
+ ${road ? `
+
+ 🛣️ 道路
+ ${road}${houseNumber ? ' ' + houseNumber : ''}
+
+ ` : ''}
+ ${suburb ? `
+
+ 🏘️ 區域
+ ${suburb}
+
+ ` : ''}
+
+ 🌐 座標
+ ${lat}, ${lon}
+
+
+ `;
+}
+
/**
* 渲染附近公車站點
*/
diff --git a/static/frontend/js/websocket.js b/static/frontend/js/websocket.js
index c4f0284a8b36acbc7d9d2a3e9e570a90a4183fad..991e14b44cd0ae21c2c5b6ac21b826cd5dc9c973 100644
--- a/static/frontend/js/websocket.js
+++ b/static/frontend/js/websocket.js
@@ -332,10 +332,10 @@ class WebSocketManager {
this.audioProcessor.connect(this.audioContext.destination);
// 發送開始錄音信號
- this.send({
- type: 'audio_start',
+ this.send({
+ type: 'audio_start',
sample_rate: 16000,
- mode: 'chat' // 對話模式（非語音登入）
+ mode: 'realtime_chat' // 即時轉錄模式（使用 OpenAI Realtime API）
});
this.isRecording = true;
@@ -427,9 +427,9 @@ class WebSocketManager {
}
// 發送停止錄音信號
- this.send({
+ this.send({
type: 'audio_stop',
- mode: 'chat' // 對話模式
+ mode: 'realtime_chat' // 即時轉錄模式
});
this.isRecording = false;
@@ -448,8 +448,23 @@ function initializeWebSocket(token) {
const protocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:';
const host = window.location.host;
- // 構建WebSocket URL
- const wsUrl = `${protocol}//${host}/ws?token=${token}`;
+ // 讀取語音登入的情緒資料（如果有）
+ const voiceEmotion = localStorage.getItem('voice_login_emotion');
+
+ // 構建WebSocket URL（包含情緒參數）
+ let wsUrl = `${protocol}//${host}/ws?token=${token}`;
+ if (voiceEmotion) {
+ wsUrl += `&emotion=${encodeURIComponent(voiceEmotion)}`;
+ console.log('😊 檢測到語音登入情緒，將傳遞給歡迎詞:', voiceEmotion);
+ // 使用後立即清除，避免重複使用
+ localStorage.removeItem('voice_login_emotion');
+
+ // 立即套用情緒主題到 UI（不等待後端回應）
+ if (typeof applyEmotion === 'function') {
+ applyEmotion(voiceEmotion);
+ console.log('✅ 語音登入情緒主題已套用:', voiceEmotion);
+ }
+ }
// 創建 WebSocket 管理器
wsManager = new WebSocketManager(wsUrl);
@@ -523,12 +538,27 @@ function initializeWebSocket(token) {
transcript.className = 'voice-transcript provisional';
break;
+ case 'stt_delta':
+ // STT 即時增量結果（OpenAI Realtime API）
+ console.log('⚡ STT Delta:', data.text);
+ // 累積顯示轉錄文字（逐字顯示效果）
+ if (!window.realtimeTranscript) {
+ window.realtimeTranscript = '';
+ }
+ window.realtimeTranscript += data.text;
+ transcript.textContent = window.realtimeTranscript;
+ transcript.className = 'voice-transcript realtime';
+ break;
+
case 'stt_final':
// STT 最終結果（用戶停止說話）
console.log('✅ STT 最終結果:', data.text);
transcript.textContent = data.text;
transcript.className = 'voice-transcript final';
-
+
+ // 清空即時轉錄累積文字
+ window.realtimeTranscript = '';
+
// 應用情緒主題（如果有的話）
if (data.emotion && typeof applyEmotion === 'function') {
console.log('😊 應用情緒主題:', data.emotion);
@@ -536,6 +566,18 @@ function initializeWebSocket(token) {
}
break;
+ case 'realtime_stt_status':
+ // OpenAI Realtime API 連線狀態
+ console.log('🔌 Realtime STT 狀態:', data.status, data.message);
+ if (data.status === 'connected') {
+ console.log('✅ 即時轉錄已啟動');
+ // 清空之前的累積文字
+ window.realtimeTranscript = '';
+ } else if (data.status === 'disconnected') {
+ console.log('🔌 即時轉錄已結束');
+ }
+ break;
+
case 'chat_ready':
// 後端發送當前 chat_id
window.currentChatId = data.chat_id;
diff --git a/static/frontend/login/404.html b/static/frontend/login/404.html
deleted file mode 100644
index bcd6b518c9001a54cd5e6af495ab82c2f590a787..0000000000000000000000000000000000000000
--- a/static/frontend/login/404.html
+++ /dev/null
@@ -1 +0,0 @@
-
404: This page could not be found. Bloom Ware - 槓上開發
404
This page could not be found.
\ No newline at end of file
diff --git a/static/frontend/login/404/index.html b/static/frontend/login/404/index.html
deleted file mode 100644
index bcd6b518c9001a54cd5e6af495ab82c2f590a787..0000000000000000000000000000000000000000
--- a/static/frontend/login/404/index.html
+++ /dev/null
@@ -1 +0,0 @@
-
404: This page could not be found. Bloom Ware - 槓上開發
404
This page could not be found.
\ No newline at end of file
diff --git a/static/frontend/login/__next.__PAGE__.txt b/static/frontend/login/__next.__PAGE__.txt
deleted file mode 100644
index 36730f7a5a373529884ed63a6a20507d17731c99..0000000000000000000000000000000000000000
--- a/static/frontend/login/__next.__PAGE__.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-1:"$Sreact.fragment"
-d:I[17994,["/login/_next/static/chunks/d98da620b14e9668.js"],"LoginForm"]
-e:I[97367,["/login/_next/static/chunks/42879de7b8087bc9.js"],"OutletBoundary"]
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diff --git a/static/frontend/login/__next._full.txt b/static/frontend/login/__next._full.txt
deleted file mode 100644
index 26d437c8d84f2099c87fb6c13a78b8d827d16dba..0000000000000000000000000000000000000000
--- a/static/frontend/login/__next._full.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-1:"$Sreact.fragment"
-2:I[39756,["/login/_next/static/chunks/42879de7b8087bc9.js"],"default"]
-3:I[37457,["/login/_next/static/chunks/42879de7b8087bc9.js"],"default"]
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-:HL["/login/_next/static/chunks/0680eca589a5307c.css","style"]
-:HL["/login/_next/static/media/2a65768255d6b625-s.p.d19752fb.woff2","font",{"crossOrigin":"","type":"font/woff2"}]
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5px)}.\[\&\>span\]\:text-xs>span{font-size:var(--text-xs);line-height:var(--tw-leading,var(--text-xs--line-height))}.\[\&\>span\]\:opacity-70>span{opacity:.7}.\[\&\>span\:last-child\]\:truncate>span:last-child{text-overflow:ellipsis;white-space:nowrap;overflow:hidden}.\[\&\>svg\]\:pointer-events-none>svg{pointer-events:none}.\[\&\>svg\]\:size-3>svg{width:calc(var(--spacing)*3);height:calc(var(--spacing)*3)}.\[\&\>svg\]\:size-3\.5>svg{width:calc(var(--spacing)*3.5);height:calc(var(--spacing)*3.5)}.\[\&\>svg\]\:size-4>svg{width:calc(var(--spacing)*4);height:calc(var(--spacing)*4)}.\[\&\>svg\]\:h-2\.5>svg{height:calc(var(--spacing)*2.5)}.\[\&\>svg\]\:h-3>svg{height:calc(var(--spacing)*3)}.\[\&\>svg\]\:w-2\.5>svg{width:calc(var(--spacing)*2.5)}.\[\&\>svg\]\:w-3>svg{width:calc(var(--spacing)*3)}.\[\&\>svg\]\:shrink-0>svg{flex-shrink:0}.\[\&\>svg\]\:translate-y-0\.5>svg{--tw-translate-y:calc(var(--spacing)*.5);translate:var(--tw-translate-x)var(--tw-translate-y)}.\[\&\>svg\]\:text-current>svg{color:currentColor}.\[\&\>svg\]\:text-muted-foreground>svg{color:var(--muted-foreground)}.\[\&\>svg\]\:text-sidebar-accent-foreground>svg{color:var(--sidebar-accent-foreground)}.\[\&\>svg\:not\(\[class\*\=\'size-\'\]\)\]\:size-3\.5>svg:not([class*=size-]){width:calc(var(--spacing)*3.5);height:calc(var(--spacing)*3.5)}.\[\&\>svg\:not\(\[class\*\=\'size-\'\]\)\]\:size-4>svg:not([class*=size-]){width:calc(var(--spacing)*4);height:calc(var(--spacing)*4)}.\[\&\>tr\]\:last\:border-b-0>tr:last-child{border-bottom-style:var(--tw-border-style);border-bottom-width:0}.\[\&\[data-panel-group-direction\=vertical\]\>div\]\:rotate-90[data-panel-group-direction=vertical]>div{rotate:90deg}.\[\&\[data-state\=open\]\>svg\]\:rotate-180[data-state=open]>svg{rotate:180deg}[data-side=left][data-collapsible=offcanvas] .\[\[data-side\=left\]\[data-collapsible\=offcanvas\]_\&\]\:-right-2{right:calc(var(--spacing)*-2)}[data-side=left][data-state=collapsed] .\[\[data-side\=left\]\[data-state\=collapsed\]_\&\]\:cursor-e-resize{cursor:e-resize}[data-side=right][data-collapsible=offcanvas] .\[\[data-side\=right\]\[data-collapsible\=offcanvas\]_\&\]\:-left-2{left:calc(var(--spacing)*-2)}[data-side=right][data-state=collapsed] .\[\[data-side\=right\]\[data-state\=collapsed\]_\&\]\:cursor-w-resize{cursor:w-resize}[data-slot=card-content] .\[\[data-slot\=card-content\]_\&\]\:bg-transparent,[data-slot=popover-content] .\[\[data-slot\=popover-content\]_\&\]\:bg-transparent{background-color:#0000}[data-slot=tooltip-content] .\[\[data-slot\=tooltip-content\]_\&\]\:bg-background\/20{background-color:var(--background)}@supports (color:color-mix(in lab, red, red)){[data-slot=tooltip-content] .\[\[data-slot\=tooltip-content\]_\&\]\:bg-background\/20{background-color:color-mix(in 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(hover:hover){a.\[a\&\]\:hover\:bg-accent:hover,a.\[a\&\]\:hover\:bg-accent\/50:hover{background-color:var(--accent)}@supports (color:color-mix(in lab, red, red)){a.\[a\&\]\:hover\:bg-accent\/50:hover{background-color:color-mix(in oklab,var(--accent)50%,transparent)}}a.\[a\&\]\:hover\:bg-destructive\/90:hover{background-color:var(--destructive)}@supports (color:color-mix(in lab, red, red)){a.\[a\&\]\:hover\:bg-destructive\/90:hover{background-color:color-mix(in oklab,var(--destructive)90%,transparent)}}a.\[a\&\]\:hover\:bg-primary\/90:hover{background-color:var(--primary)}@supports (color:color-mix(in lab, red, red)){a.\[a\&\]\:hover\:bg-primary\/90:hover{background-color:color-mix(in oklab,var(--primary)90%,transparent)}}a.\[a\&\]\:hover\:bg-secondary\/90:hover{background-color:var(--secondary)}@supports (color:color-mix(in lab, red, red)){a.\[a\&\]\:hover\:bg-secondary\/90:hover{background-color:color-mix(in 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--tw-exit-rotate{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-scale{syntax:"*";inherits:false;initial-value:1}@property --tw-exit-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-translate-y{syntax:"*";inherits:false;initial-value:0}:root{--background:#fff;--foreground:#0a0a0a;--card:#fff;--card-foreground:#0a0a0a;--popover:#fff;--popover-foreground:#0a0a0a;--primary:#171717;--primary-foreground:#fafafa;--secondary:#f5f5f5;--secondary-foreground:#171717;--muted:#f5f5f5;--muted-foreground:#737373;--accent:#f5f5f5;--accent-foreground:#171717;--destructive:#e40014;--destructive-foreground:#e40014;--border:#e5e5e5;--input:#e5e5e5;--ring:#a1a1a1;--chart-1:#f05100;--chart-2:#009588;--chart-3:#104e64;--chart-4:#fcbb00;--chart-5:#f99c00;--radius:.625rem;--sidebar:#fafafa;--sidebar-foreground:#0a0a0a;--sidebar-primary:#171717;--sidebar-primary-foreground:#fafafa;--sidebar-accent:#f5f5f5;--sidebar-accent-foreground:#171717;--sidebar-border:#e5e5e5;--sidebar-ring:#a1a1a1}@supports (color:lab(0% 0 0)){:root{--background:lab(100% 0 0);--foreground:lab(2.75381% 0 0);--card:lab(100% 0 0);--card-foreground:lab(2.75381% 0 0);--popover:lab(100% 0 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-85.6872);--sidebar-primary-foreground:lab(98.26% 0 0);--sidebar-accent:lab(15.204% 0 -.00000596046);--sidebar-accent-foreground:lab(98.26% 0 0);--sidebar-border:lab(15.204% 0 -.00000596046);--sidebar-ring:lab(34.924% 0 0)}}@property --tw-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-translate-y{syntax:"*";inherits:false;initial-value:0}@property --tw-translate-z{syntax:"*";inherits:false;initial-value:0}@property --tw-rotate-x{syntax:"*";inherits:false}@property --tw-rotate-y{syntax:"*";inherits:false}@property --tw-rotate-z{syntax:"*";inherits:false}@property --tw-skew-x{syntax:"*";inherits:false}@property --tw-skew-y{syntax:"*";inherits:false}@property --tw-space-y-reverse{syntax:"*";inherits:false;initial-value:0}@property --tw-space-x-reverse{syntax:"*";inherits:false;initial-value:0}@property --tw-border-style{syntax:"*";inherits:false;initial-value:solid}@property --tw-leading{syntax:"*";inherits:false}@property --tw-font-weight{syntax:"*";inherits:false}@property --tw-tracking{syntax:"*";inherits:false}@property --tw-ordinal{syntax:"*";inherits:false}@property --tw-slashed-zero{syntax:"*";inherits:false}@property --tw-numeric-figure{syntax:"*";inherits:false}@property --tw-numeric-spacing{syntax:"*";inherits:false}@property --tw-numeric-fraction{syntax:"*";inherits:false}@property --tw-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-shadow-color{syntax:"*";inherits:false}@property --tw-shadow-alpha{syntax:"
";inherits:false;initial-value:100%}@property --tw-inset-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-inset-shadow-color{syntax:"*";inherits:false}@property --tw-inset-shadow-alpha{syntax:"";inherits:false;initial-value:100%}@property --tw-ring-color{syntax:"*";inherits:false}@property --tw-ring-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-inset-ring-color{syntax:"*";inherits:false}@property --tw-inset-ring-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-ring-inset{syntax:"*";inherits:false}@property --tw-ring-offset-width{syntax:"";inherits:false;initial-value:0}@property --tw-ring-offset-color{syntax:"*";inherits:false;initial-value:#fff}@property --tw-ring-offset-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-outline-style{syntax:"*";inherits:false;initial-value:solid}@property --tw-blur{syntax:"*";inherits:false}@property --tw-brightness{syntax:"*";inherits:false}@property --tw-contrast{syntax:"*";inherits:false}@property --tw-grayscale{syntax:"*";inherits:false}@property --tw-hue-rotate{syntax:"*";inherits:false}@property --tw-invert{syntax:"*";inherits:false}@property --tw-opacity{syntax:"*";inherits:false}@property --tw-saturate{syntax:"*";inherits:false}@property --tw-sepia{syntax:"*";inherits:false}@property --tw-drop-shadow{syntax:"*";inherits:false}@property --tw-drop-shadow-color{syntax:"*";inherits:false}@property --tw-drop-shadow-alpha{syntax:"";inherits:false;initial-value:100%}@property --tw-drop-shadow-size{syntax:"*";inherits:false}@property --tw-duration{syntax:"*";inherits:false}@property --tw-ease{syntax:"*";inherits:false}@property --tw-content{syntax:"*";inherits:false;initial-value:""}@keyframes spin{to{transform:rotate(360deg)}}@keyframes pulse{50%{opacity:.5}}@keyframes enter{0%{opacity:var(--tw-enter-opacity,1);transform:translate3d(var(--tw-enter-translate-x,0),var(--tw-enter-translate-y,0),0)scale3d(var(--tw-enter-scale,1),var(--tw-enter-scale,1),var(--tw-enter-scale,1))rotate(var(--tw-enter-rotate,0))}}@keyframes exit{to{opacity:var(--tw-exit-opacity,1);transform:translate3d(var(--tw-exit-translate-x,0),var(--tw-exit-translate-y,0),0)scale3d(var(--tw-exit-scale,1),var(--tw-exit-scale,1),var(--tw-exit-scale,1))rotate(var(--tw-exit-rotate,0))}}@keyframes accordion-down{0%{height:0}to{height:var(--radix-accordion-content-height,var(--bits-accordion-content-height,var(--reka-accordion-content-height,var(--kb-accordion-content-height,auto))))}}@keyframes accordion-up{0%{height:var(--radix-accordion-content-height,var(--bits-accordion-content-height,var(--reka-accordion-content-height,var(--kb-accordion-content-height,auto))))}to{height:0}}@keyframes caret-blink{0%,70%,to{opacity:1}20%,50%{opacity:0}}
diff --git a/static/frontend/login/_next/static/chunks/1f600c83a5ecf168.css b/static/frontend/login/_next/static/chunks/1f600c83a5ecf168.css
deleted file mode 100644
index 016ac3d2edcdc3a21822f3f4fbc41a4dc796d92a..0000000000000000000000000000000000000000
--- a/static/frontend/login/_next/static/chunks/1f600c83a5ecf168.css
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diff --git a/static/frontend/login/_next/static/chunks/42879de7b8087bc9.js b/static/frontend/login/_next/static/chunks/42879de7b8087bc9.js
deleted file mode 100644
index bda8a8118961b842879d43d67e7cda0ccca982cc..0000000000000000000000000000000000000000
--- a/static/frontend/login/_next/static/chunks/42879de7b8087bc9.js
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