hi

app.py

@@ -8,12 +8,19 @@ from typing import List, Dict, Any
 from contextlib import asynccontextmanager
 from fastapi import FastAPI, Request, HTTPException, status
 import uvicorn
-from linebot.v3.messaging import MessagingApi, TextMessage, ReplyMessageRequest  # 更新：新增 ReplyMessageRequest
+# ----------------- LINE Bot SDK v3 修正引用方式 -----------------
+from linebot.v3.messaging import (
+    AsyncApiClient,
+    AsyncMessagingApi,
+    Configuration,
+    TextMessage,
+    ReplyMessageRequest
+)
+from linebot.v3.webhook import WebhookParser
+from linebot.v3.exceptions import InvalidSignatureError
+# --------------------------------------------------------------
 from openai import OpenAI
-import hmac
-import base64
-import hashlib
-from ddgs import DDGS  # 更新：從 ddgs 匯入 DDGS
+from ddgs import DDGS
 
 # ==== CONFIG (從環境變數載入，或使用預設值) ====
 def _require_env(var: str) -> str:
@@ -22,20 +29,25 @@ def _require_env(var: str) -> str:
         raise RuntimeError(f"FATAL: Missing required environment variable: {var}")
     return v
 
+# 從環境變數讀取 LINE Bot 的憑證
 CHANNEL_SECRET = _require_env("CHANNEL_SECRET")
 CHANNEL_ACCESS_TOKEN = _require_env("CHANNEL_ACCESS_TOKEN")
 
+# LLM API 設定
 LLM_API_CONFIG = {
-    "base_url": "https://litellm-ekkks8gsocw.dgx-coolify.apmic.ai/",
-    "api_key": "sk-eT_04m428oAPUD5kUmIhVA",
+    "base_url": os.getenv("LLM_BASE_URL", "https://litellm-ekkks8gsocw.dgx-coolify.apmic.ai/"),
+    "api_key": os.getenv("LLM_API_KEY", "sk-eT_04m428oAPUD5kUmIhVA"),
 }
+
+# LLM 模型設定
 LLM_MODEL_CONFIG = {
-    "model": "gpt-oss-120b",
-    "max_tokens": int(os.getenv("MAX_TOKENS", 127000)),
+    "model": os.getenv("LLM_MODEL", "gpt-oss-120b"),
+    "max_tokens": int(os.getenv("MAX_TOKENS", 4096)), # 調整為一個比較合理的預設值
     "temperature": float(os.getenv("TEMPERATURE", 0.3)),
     "seed": int(os.getenv("LLM_SEED", 42)),
 }
 
+# 系統提示詞
 SYSTEM_PROMPT = """你是一個友好的AI助手，請用簡單、親切的中文回覆用戶的問題。
 在回應時，不要使用 Markdown 格式，如粗體、斜體或標題。避免使用 * 號或任何特殊符號來格式化。
 使用條列式（如 - 或 1. 2. 3.）來整理內容，讓它適合在 LINE 上閱讀。
@@ -44,7 +56,7 @@ SYSTEM_PROMPT = """你是一個友好的AI助手，請用簡單、親切的中
 # ---------- 記憶體儲存對話歷史 ----------
 conversations: Dict[str, List[Dict[str, str]]] = {}  # {user_id: messages_list}
 
-# ---------- Helper to split long message for LINE API ----------
+# ---------- 將長訊息分割以符合 LINE API 限制 ----------
 def split_text_for_line(text: str, max_length: int = 4800) -> List[str]:
     if len(text) <= max_length:
         return [text]
@@ -62,7 +74,7 @@ def split_text_for_line(text: str, max_length: int = 4800) -> List[str]:
 
 # ---------- 網路搜尋函數 ----------
 def perform_web_search(query: str, max_results: int = 5) -> str:
-    """使用 DuckDuckGo 進行簡單的網路搜尋，並返回摘要結果。"""
+    """使用 DuckDuckGo 進行網路搜尋，並返回摘要結果。"""
     try:
         with DDGS() as ddgs:
             results = [r for r in ddgs.text(query, max_results=max_results)]
@@ -74,9 +86,10 @@ def perform_web_search(query: str, max_results: int = 5) -> str:
             search_summary += f"{i}. {result['title']}: {result['body'][:200]}... (來源: {result['href']})\n"
         return search_summary
     except Exception as e:
+        print(f"Web search error: {e}")
         return f"搜尋時發生錯誤：{str(e)}。請稍後再試。"
 
-# ---------- Chat Pipeline ----------
+# ---------- 聊天處理流程 ----------
 class ChatPipeline:
     def __init__(self):
         if not LLM_API_CONFIG["api_key"] or not LLM_API_CONFIG["base_url"]:
@@ -95,13 +108,14 @@ class ChatPipeline:
             content = response.choices[0].message.content or ""
             return content
         except Exception as e:
+            print(f"LLM call error: {e}")
             raise
 
     def get_conversation_history(self, user_id: str) -> List[Dict[str, str]]:
         return conversations.get(user_id, [])
 
     def update_conversation_history(self, user_id: str, messages: List[Dict[str, str]]):
-        # 保留最近10條訊息
+        # 保留最近10則訊息 (5組對話)
         if len(messages) > 10:
             messages = messages[-10:]
         conversations[user_id] = messages
@@ -111,25 +125,25 @@ class ChatPipeline:
             del conversations[user_id]
 
     def answer_question(self, user_id: str, user_text: str) -> str:
-        if user_text.strip() == "/clear":
+        if user_text.strip().lower() == "/clear":
             self.clear_conversation_history(user_id)
             return "對話紀錄已清除！現在開始新的對話。"
         
-        # 新增：進行網路搜尋
         search_results = perform_web_search(user_text)
         
         history = self.get_conversation_history(user_id)
         messages = [{"role": "system", "content": SYSTEM_PROMPT}]
         messages.extend(history)
         messages.append({"role": "user", "content": user_text})
-        # 新增：將搜尋結果加入到訊息中，讓 LLM 參考
         messages.append({"role": "system", "content": f"網路搜尋結果：{search_results}"})
         
         response = self._llm_call(messages)
-        response = response.replace('*', '')  # 移除所有 '*' 符號
-        # 更新歷史（包含搜尋結果以維持上下文）
+        response = response.replace('*', '')
+        
+        # 更新歷史紀錄
         history.append({"role": "user", "content": user_text})
-        history.append({"role": "system", "content": f"網路搜尋結果：{search_results}"})
+        # 為了節省 token，可以選擇不將搜尋結果存入歷史
+        # history.append({"role": "system", "content": f"網路搜尋結果：{search_results}"})
         history.append({"role": "assistant", "content": response})
         self.update_conversation_history(user_id, history)
         return response
@@ -144,49 +158,69 @@ async def lifespan(app: FastAPI):
 app = FastAPI(lifespan=lifespan)
 chat_pipeline = None
 
-# LINE Bot API 更新：使用 v3 MessagingApi
-line_bot_api = MessagingApi(CHANNEL_ACCESS_TOKEN)
+# ----------------- LINE Bot API v3 初始化修正 -----------------
+# 建立一個 Configuration 物件，並傳入你的 Access Token
+configuration = Configuration(access_token=CHANNEL_ACCESS_TOKEN)
+# 使用 Configuration 物件來初始化 AsyncApiClient 和 AsyncMessagingApi
+async_api_client = AsyncApiClient(configuration)
+line_bot_api = AsyncMessagingApi(async_api_client)
+# 建立 WebhookParser 來解析請求
+parser = WebhookParser(CHANNEL_SECRET)
+# --------------------------------------------------------------
 
 # ---------- LINE Webhook 處理 ----------
 @app.post("/webhook")
 async def line_webhook(request: Request):
-    signature = request.headers.get('X-Line-Signature', '')
+    # 驗證簽名
+    signature = request.headers['X-Line-Signature']
     body = await request.body()
-    hash_ = hmac.new(CHANNEL_SECRET.encode('utf-8'), body, hashlib.sha256).digest()
-    expected_signature = base64.b64encode(hash_).decode()
-    if not hmac.compare_digest(signature, expected_signature):
-        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid signature")
-    
-    body_str = body.decode('utf-8')
     try:
-        payload = json.loads(body_str)
-    except json.JSONDecodeError:
-        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid JSON")
-    
-    for event in payload.get('events', []):
-        user_id = event['source']['userId']
-        reply_token = event.get('replyToken')
-        if event['type'] == 'message' and event['message']['type'] == 'text':
-            user_text = event['message']['text'].strip()
-            if not user_text:
-                continue
-            try:
-                ai_response = chat_pipeline.answer_question(user_id, user_text)
-                chunks = split_text_for_line(ai_response)
-                messages_to_send = [TextMessage(text=chunk) for chunk in chunks]
-                reply_request = ReplyMessageRequest(
+        events = parser.parse(body.decode(), signature)
+    except InvalidSignatureError:
+        raise HTTPException(status_code=400, detail="Invalid signature")
+
+    for event in events:
+        # 只處理文字訊息事件
+        if event.type != 'message' or event.message.type != 'text':
+            continue
+
+        user_id = event.source.user_id
+        reply_token = event.reply_token
+        user_text = event.message.text.strip()
+
+        if not user_text:
+            continue
+            
+        try:
+            ai_response = chat_pipeline.answer_question(user_id, user_text)
+            chunks = split_text_for_line(ai_response)
+            messages_to_send = [TextMessage(text=chunk) for chunk in chunks]
+            
+            # 使用 await 來呼叫非同步的 reply_message
+            await line_bot_api.reply_message(
+                ReplyMessageRequest(
                     reply_token=reply_token,
-                    messages=messages_to_send[:5]  # LINE限5條
+                    messages=messages_to_send[:5]  # LINE 一次最多回覆 5 則訊息
                 )
-                line_bot_api.reply_message(reply_request)
-            except Exception:
-                error_message = "抱歉，系統發生錯誤，請稍後再試。"
-                error_reply_request = ReplyMessageRequest(
+            )
+        except Exception as e:
+            print(f"Error processing message: {e}")
+            error_message = "抱歉，系統發生錯誤，請稍後再試。"
+            # 使用 await 來呼叫非同步的 reply_message
+            await line_bot_api.reply_message(
+                 ReplyMessageRequest(
                     reply_token=reply_token,
                     messages=[TextMessage(text=error_message)]
                 )
-                line_bot_api.reply_message(error_reply_request)
+            )
+            
+    return {"status": "ok"}
+
+# 健康檢查端點
+@app.get("/health")
+async def health_check():
     return {"status": "ok"}
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    port = int(os.getenv("PORT", 7860))
+    uvicorn.run(app, host="0.0.0.0", port=port)