整合所有檔案到 pj_llama 目錄

- 遷移完整的專案到 pj_llama 目錄
- 包含所有最新檔案和 Git 歷史
- 移除不需要的舊檔案
- 統一開發和執行環境

.claude/settings.local.json

@@ -8,11 +8,7 @@
       "Bash(dir)",
       "Bash(git init:*)",
       "Bash(git add:*)",
-      "Bash(git commit:*)",
+      "Bash(git commit:*)"
-      "Bash(git remote add:*)",
-      "Bash(git branch:*)",
-      "Bash(git push:*)",
-      "Bash(git pull:*)"
     ],
     "defaultMode": "acceptEdits"
   }

README.md

@@ -1,201 +1,219 @@
-# Llama API Client
+# Llama AI 對話程式
 
-A Python client for connecting to Llama AI models through OpenAI-compatible API endpoints.
+一個簡單易用的 Python 程式，用於連接和使用 Llama AI 模型進行對話。
 
-## Features
+## 🌟 主要功能
 
-- 🌐 Support for both internal network and external API endpoints
+- ✅ **支援多端點連接** - 自動偵測可用的 API 端點
-- 🤖 Multiple model support (GPT-OSS-120B, DeepSeek-R1-671B, Qwen3-Embedding-8B)
+- 💬 **互動式對話介面** - 像聊天一樣與 AI 對話
-- 💬 Interactive chat interface with conversation history
+- 🤖 **多模型支援** - GPT-OSS、DeepSeek、Qwen 等多種模型
-- 🔄 Automatic endpoint testing and failover
+- 🔄 **自動端點選擇** - 自動測試並選擇可用端點
-- 🧹 Automatic response cleaning (removes thinking tags and special markers)
+- 🧹 **智慧清理回應** - 自動移除 AI 思考過程的標記
-- 📝 Full conversation context management
+- 📝 **對話歷史管理** - 保持上下文連貫的對話
 
-## Quick Start
+## 🚀 快速開始
 
-### Installation
+### 1. 安裝需求
+
+確保你的電腦已安裝 Python 3.7 或更新版本。
 
 ```bash
-# Clone the repository
+# 安裝必要套件
-git clone https://github.com/yourusername/llama-api-client.git
+pip install openai
-cd llama-api-client
-
-# Install dependencies
-pip install -r requirements.txt
 ```
 
-### Basic Usage
+### 2. 下載程式
+
+```bash
+# 複製專案
+git clone https://gitea.theaken.com/aken1023/pj_llama.git
+cd pj_llama
+
+# 或直接下載 ZIP 檔案解壓縮
+```
+
+### 3. 執行對話程式
+
+```bash
+# 🌟 執行主程式（支援內外網選擇）
+python llama_universal.py
+
+# 或執行外網專用版本
+python llama_chat.py
+
+# 或執行完整版本（支援多端點）
+python llama_full_api.py
+```
+
+## 📖 使用說明
+
+### 基本對話
+
+執行程式後，會出現以下畫面：
+
+```
+============================================================
+選擇網路環境
+============================================================
+
+可用環境：
+  1. 內網環境
+     說明：公司/學校內部網路，使用內部 IP 地址
+     端點數量：3 個
+  2. 外網環境
+     說明：公開網際網路，使用外部域名
+     端點數量：3 個
+
+請選擇環境 (1-2)，預設為 1: 
+```
+
+選擇端點後即可開始對話：
+
+```
+你: 你好
+AI: 你好！有什麼我可以幫助你的嗎？
+
+你: 1+1等於多少？
+AI: 1+1等於2。
+```
+
+### 對話指令
+
+在對話中可以使用以下指令：
+
+| 指令 | 功能 |
+|-----|------|
+| `exit` 或 `quit` | 結束對話 |
+| `clear` | 清空對話歷史，開始新對話 |
+| `model` | 切換使用的 AI 模型 |
+| `switch` | 切換網路環境（內網/外網） |
+
+## 🔧 程式檔案說明
+
+| 檔案名稱 | 用途說明 |
+|---------|---------|
+| `llama_universal.py` | **🌟 主程式** - 支援內外網環境選擇 |
+| `llama_chat.py` | 外網專用對話程式 |
+| `llama_full_api.py` | 完整功能版本，支援多端點切換 |
+| `quick_test.py` | 快速測試連接是否正常 |
+| `test_all_models.py` | 測試所有模型的工具 |
+
+## 🌐 可用的 API 端點
+
+### 內網端點（公司/學校內部）
+
+| 端點 | 地址 | 支援模型 |
+|-----|------|---------|
+| 內網端點 1 | `http://192.168.0.6:21180/v1` | 所有模型 |
+| 內網端點 2 | `http://192.168.0.6:21181/v1` | 所有模型 |
+| 內網端點 3 | `http://192.168.0.6:21182/v1` | 所有模型 |
+
+### 外網端點（公開網際網路）
+
+| 端點 | 地址 | 支援模型 |
+|-----|------|---------|
+| 通用端點 | `https://llama.theaken.com/v1` | 所有模型 |
+| GPT-OSS 專用 | `https://llama.theaken.com/v1/gpt-oss-120b` | GPT-OSS-120B |
+| DeepSeek 專用 | `https://llama.theaken.com/v1/deepseek-r1-671b` | DeepSeek-R1-671B |
+
+## 🤖 支援的 AI 模型
+
+1. **GPT-OSS-120B** - 開源 GPT 模型，1200 億參數
+2. **DeepSeek-R1-671B** - DeepSeek 推理模型，6710 億參數
+3. **Qwen3-Embedding-8B** - 通義千問嵌入模型，80 億參數
+
+## ❓ 常見問題
+
+### 問題：程式顯示「無法連接」
+
+**解決方法：**
+1. 嘗試切換網路環境（使用 `switch` 指令）
+2. 內網環境：確認在公司/學校網路內
+3. 外網環境：確認可以訪問 https://llama.theaken.com
+4. 執行 `python quick_test.py` 測試連接
+
+### 問題：AI 回應包含奇怪的標記
+
+**說明：** 
+有時 AI 回應會包含 `<think>` 或 `<|channel|>` 等標記，這是 AI 的思考過程，程式會自動清理這些內容。
+
+### 問題：對話不連貫
+
+**解決方法：**
+使用 `clear` 指令清空對話歷史，開始新的對話。
+
+## 📝 簡單範例程式碼
+
+如果你想在自己的程式中使用，可以參考以下程式碼：
 
 ```python
 from openai import OpenAI
 
-# Configure API
+# 設定連接（外網）
-API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
+client = OpenAI(
-BASE_URL = "http://192.168.0.6:21180/v1"
+    api_key="paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo=",
-
+    base_url="https://llama.theaken.com/v1"
-# Create client
-client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
-
-# Send request
-response = client.chat.completions.create(
-    model="gpt-oss-120b",
-    messages=[{"role": "user", "content": "Hello!"}],
-    temperature=0.7,
-    max_tokens=200
 )
 
+# 或設定連接（內網）
+client = OpenAI(
+    api_key="paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo=",
+    base_url="http://192.168.0.6:21180/v1"
+)
+
+# 發送訊息
+response = client.chat.completions.create(
+    model="gpt-oss-120b",
+    messages=[
+        {"role": "user", "content": "你好"}
+    ]
+)
+
+# 顯示回應
 print(response.choices[0].message.content)
 ```
 
-### Run Interactive Chat
+## 🛠️ 進階設定
 
-```bash
+### 修改 API 金鑰
-# Full-featured chat with all endpoints
-python llama_full_api.py
 
-# Internal network only
+如果需要使用不同的 API 金鑰，編輯程式中的：
-python llama_chat.py
 
-# Quick test
-python quick_test.py
-```
-
-## Available Endpoints
-
-### Internal Network (Tested & Working ✅)
-
-| Endpoint | URL | Status |
-|----------|-----|--------|
-| Internal 1 | `http://192.168.0.6:21180/v1` | ✅ Working |
-| Internal 2 | `http://192.168.0.6:21181/v1` | ✅ Working |
-| Internal 3 | `http://192.168.0.6:21182/v1` | ✅ Working |
-| Internal 4 | `http://192.168.0.6:21183/v1` | ❌ Error 500 |
-
-### External Network
-
-| Endpoint | URL | Status |
-|----------|-----|--------|
-| GPT-OSS | `https://llama.theaken.com/v1/gpt-oss-120b` | 🔄 Pending |
-| DeepSeek | `https://llama.theaken.com/v1/deepseek-r1-671b` | 🔄 Pending |
-| General | `https://llama.theaken.com/v1` | 🔄 Pending |
-
-## Project Structure
-
-```
-llama-api-client/
-├── README.md                 # This file
-├── requirements.txt          # Python dependencies
-├── 操作指南.md               # Chinese operation guide
-├── llama_full_api.py        # Full-featured chat client
-├── llama_chat.py            # Internal network chat client
-├── local_api_test.py        # Endpoint testing tool
-├── quick_test.py            # Quick connection test
-├── test_all_models.py       # Model testing script
-└── demo_chat.py             # Demo chat with fallback
-```
-
-## Chat Commands
-
-During chat sessions, you can use these commands:
-
-- `exit` or `quit` - End the conversation
-- `clear` - Clear conversation history
-- `model` - Switch between available models
-
-## Configuration
-
-### API Key
 ```python
-API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
+API_KEY = "你的新金鑰"
 ```
 
-### Available Models
+### 新增端點
-- `gpt-oss-120b` - GPT Open Source 120B parameters
-- `deepseek-r1-671b` - DeepSeek R1 671B parameters
-- `qwen3-embedding-8b` - Qwen3 Embedding 8B parameters
 
-## Troubleshooting
+在 `llama_full_api.py` 中的 `ENDPOINTS` 加入新端點：
 
-### Issue: 502 Bad Gateway
-**Cause**: External API server is offline  
-**Solution**: Use internal network endpoints
-
-### Issue: Connection Error
-**Cause**: Not on internal network or incorrect IP  
-**Solution**: 
-1. Verify network connectivity: `ping 192.168.0.6`
-2. Check firewall settings
-3. Ensure you're on the same network
-
-### Issue: Encoding Error
-**Cause**: Windows terminal encoding issues  
-**Solution**: Use English for conversations or modify terminal encoding
-
-### Issue: Response Contains Special Markers
-**Description**: Responses may contain `<think>`, `<|channel|>` tags  
-**Solution**: The client automatically removes these markers
-
-## Response Cleaning
-
-The client automatically removes these special markers from AI responses:
-- `<think>...</think>` - Thinking process
-- `<|channel|>...<|message|>` - Channel markers
-- `<|end|>`, `<|start|>` - End/start markers
-
-## Requirements
-
-- Python 3.7+
-- openai>=1.0.0
-- requests (optional, for direct API calls)
-
-## Development
-
-### Testing Connection
 ```python
-python -c "from openai import OpenAI; client = OpenAI(api_key='YOUR_KEY', base_url='YOUR_URL'); print(client.chat.completions.create(model='gpt-oss-120b', messages=[{'role': 'user', 'content': 'test'}], max_tokens=5).choices[0].message.content)"
+ENDPOINTS = [
+    {
+        "name": "新端點",
+        "url": "https://新的地址/v1",
+        "models": ["gpt-oss-120b"]
+    }
+]
 ```
 
-### Adding New Endpoints
+## 📄 授權條款
-Edit `ENDPOINTS` dictionary in `llama_full_api.py`:
-```python
-ENDPOINTS = {
-    "internal": [
-        {
-            "name": "New Endpoint",
-            "url": "http://new-endpoint/v1",
-            "models": ["gpt-oss-120b"]
-        }
-    ]
-}
-```
 
-## License
+本專案採用 MIT 授權條款，可自由使用、修改和分發。
 
-MIT License - See LICENSE file for details
+## 🤝 問題回報
 
-## Contributing
+如果遇到問題或有建議，歡迎在 Gitea 上開 Issue：
+https://gitea.theaken.com/aken1023/pj_llama/issues
 
-1. Fork the repository
+## 📊 測試狀態
-2. Create your feature branch (`git checkout -b feature/amazing-feature`)
-3. Commit your changes (`git commit -m 'Add amazing feature'`)
-4. Push to the branch (`git push origin feature/amazing-feature`)
-5. Open a Pull Request
 
-## Support
+最後測試時間：2025-09-19
-
+- 📡 API 端點：正在測試中
-For issues or questions:
+- 🔄 自動選擇：程式會自動選擇可用的端點
-1. Check the [操作指南.md](操作指南.md) for detailed Chinese documentation
-2. Open an issue on GitHub
-3. Contact the API administrator for server-related issues
-
-## Acknowledgments
-
-- Built with OpenAI Python SDK
-- Compatible with OpenAI API format
-- Supports multiple Llama model variants
 
 ---
 
-**Last Updated**: 2025-09-19  
+**版本**: 1.0.0  
-**Version**: 1.0.0  
+**作者**: Aken  
-**Status**: Internal endpoints working, external endpoints pending
+**專案網址**: https://gitea.theaken.com/aken1023/pj_llama

demo_chat.py

@@ -1,124 +0,0 @@
-"""
-Llama API 對話程式 (示範版本)
-當 API 伺服器恢復後，可以使用此程式進行對話
-"""
-
-from openai import OpenAI
-import time
-
-# API 設定
-API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
-BASE_URL = "https://llama.theaken.com/v1"
-
-def simulate_chat():
-    """模擬對話功能（用於展示）"""
-    print("\n" + "="*50)
-    print("Llama AI 對話系統 - 示範模式")
-    print("="*50)
-    print("\n[注意] API 伺服器目前離線，以下為模擬對話")
-    print("當伺服器恢復後，將自動連接真實 API\n")
-    
-    # 模擬回應
-    demo_responses = [
-        "你好！我是 Llama AI 助手，很高興為你服務。",
-        "這是一個示範回應。當 API 伺服器恢復後，你將收到真實的 AI 回應。",
-        "我可以回答問題、協助編程、翻譯文字等多種任務。",
-        "請問有什麼我可以幫助你的嗎？"
-    ]
-    
-    response_index = 0
-    print("輸入 'exit' 結束對話\n")
-    
-    while True:
-        user_input = input("你: ").strip()
-        
-        if user_input.lower() in ['exit', 'quit']:
-            print("\n再見！")
-            break
-            
-        if not user_input:
-            continue
-        
-        # 模擬思考時間
-        print("\nAI 思考中", end="")
-        for _ in range(3):
-            time.sleep(0.3)
-            print(".", end="", flush=True)
-        print()
-        
-        # 顯示模擬回應
-        print(f"\nAI: {demo_responses[response_index % len(demo_responses)]}")
-        response_index += 1
-
-def real_chat():
-    """實際對話功能（當 API 可用時）"""
-    client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
-    
-    print("\n" + "="*50)
-    print("Llama AI 對話系統")
-    print("="*50)
-    print("\n已連接到 Llama API")
-    print("輸入 'exit' 結束對話\n")
-    
-    messages = []
-    
-    while True:
-        user_input = input("你: ").strip()
-        
-        if user_input.lower() in ['exit', 'quit']:
-            print("\n再見！")
-            break
-            
-        if not user_input:
-            continue
-            
-        messages.append({"role": "user", "content": user_input})
-        
-        try:
-            print("\nAI 思考中...")
-            response = client.chat.completions.create(
-                model="gpt-oss-120b",
-                messages=messages,
-                temperature=0.7,
-                max_tokens=1000
-            )
-            
-            ai_response = response.choices[0].message.content
-            print(f"\nAI: {ai_response}")
-            messages.append({"role": "assistant", "content": ai_response})
-            
-        except Exception as e:
-            print(f"\n[錯誤] {str(e)[:100]}")
-            print("無法取得回應，請稍後再試")
-
-def main():
-    print("檢查 API 連接狀態...")
-    
-    # 嘗試連接 API
-    try:
-        client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
-        
-        # 快速測試
-        response = client.chat.completions.create(
-            model="gpt-oss-120b",
-            messages=[{"role": "user", "content": "test"}],
-            max_tokens=10,
-            timeout=5
-        )
-        print("[成功] API 已連接")
-        real_chat()
-        
-    except Exception as e:
-        error_msg = str(e)
-        if "502" in error_msg or "Bad gateway" in error_msg:
-            print("[提示] API 伺服器目前離線 (502 錯誤)")
-            print("進入示範模式...")
-            simulate_chat()
-        else:
-            print(f"[錯誤] 無法連接: {error_msg[:100]}")
-            print("\n是否要進入示範模式? (y/n): ", end="")
-            if input().lower() == 'y':
-                simulate_chat()
-
-if __name__ == "__main__":
-    main()

llama_chat.py

@@ -1,34 +1,45 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
-Llama 內網 API 對話程式
+Llama API 外網連接程式
-支援多個端點和模型選擇
+使用外網端點進行 AI 對話
 """
 
 from openai import OpenAI
+import requests
 import sys
 import re
+from datetime import datetime
 
-# API 配置
+# API 金鑰
 API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
 
-# 可用端點 (前 3 個已測試可用)
+# 外網 API 端點配置
 ENDPOINTS = [
-    "http://192.168.0.6:21180/v1",
+    {
-    "http://192.168.0.6:21181/v1", 
+        "name": "Llama 通用端點",
-    "http://192.168.0.6:21182/v1",
+        "url": "https://llama.theaken.com/v1",
-    "http://192.168.0.6:21183/v1"
+        "models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
+    },
+    {
+        "name": "GPT-OSS 專用端點",
+        "url": "https://llama.theaken.com/v1/gpt-oss-120b",
+        "models": ["gpt-oss-120b"]
+    },
+    {
+        "name": "DeepSeek 專用端點",
+        "url": "https://llama.theaken.com/v1/deepseek-r1-671b",
+        "models": ["deepseek-r1-671b"]
+    }
 ]
 
-# 模型列表
+# 外網 API 端點配置（僅包含實際存在的端點）
-MODELS = [
-    "gpt-oss-120b",
-    "deepseek-r1-671b",
-    "qwen3-embedding-8b"
-]
 
 def clean_response(text):
     """清理 AI 回應中的特殊標記"""
+    if not text:
+        return text
+        
     # 移除思考標記
     if "<think>" in text:
         text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
@@ -47,34 +58,94 @@ def clean_response(text):
     
     return text
 
-def test_endpoint(endpoint):
+def test_endpoint(endpoint_info, timeout=10):
     """測試端點是否可用"""
+    url = endpoint_info["url"]
+    model = endpoint_info["models"][0] if endpoint_info["models"] else "gpt-oss-120b"
+    
+    print(f"  測試 {endpoint_info['name']}...", end="", flush=True)
+    
     try:
-        client = OpenAI(api_key=API_KEY, base_url=endpoint)
+        # 處理特殊的模型端點 URL
-        response = client.chat.completions.create(
+        if url.endswith("/gpt-oss-120b") or url.endswith("/deepseek-r1-671b"):
-            model="gpt-oss-120b",
+            base_url = url.rsplit("/", 1)[0]
-            messages=[{"role": "user", "content": "Hi"}],
+        else:
-            max_tokens=10,
+            base_url = url
-            timeout=5
+            
+        client = OpenAI(
+            api_key=API_KEY, 
+            base_url=base_url,
+            timeout=timeout
         )
+        
+        response = client.chat.completions.create(
+            model=model,
+            messages=[{"role": "user", "content": "test"}],
+            max_tokens=5
+        )
+        print(" ✓ 可用")
         return True
-    except:
+        
+    except Exception as e:
+        error_msg = str(e)
+        if "502" in error_msg:
+            print(" ✗ 伺服器暫時無法使用 (502)")
+        elif "timeout" in error_msg.lower():
+            print(" ✗ 連接超時")
+        elif "connection" in error_msg.lower():
+            print(" ✗ 無法連接")
+        else:
+            print(f" ✗ 錯誤")
         return False
 
-def chat_session(endpoint, model):
+def find_working_endpoint():
+    """尋找可用的端點"""
+    print("\n正在測試可用端點...")
+    print("-" * 50)
+    
+    for endpoint in ENDPOINTS:
+        if test_endpoint(endpoint):
+            return endpoint
+    
+    return None
+
+def chat_session(endpoint_info):
     """對話主程式"""
     print("\n" + "="*60)
     print("Llama AI 對話系統")
     print("="*60)
-    print(f"端點: {endpoint}")
+    print(f"使用端點: {endpoint_info['name']}")
-    print(f"模型: {model}")
+    print(f"URL: {endpoint_info['url']}")
+    print(f"可用模型: {', '.join(endpoint_info['models'])}")
     print("\n指令:")
     print("  exit/quit - 結束對話")
     print("  clear - 清空對話歷史")
     print("  model - 切換模型")
     print("-"*60)
     
-    client = OpenAI(api_key=API_KEY, base_url=endpoint)
+    # 處理 URL
+    url = endpoint_info["url"]
+    if url.endswith("/gpt-oss-120b") or url.endswith("/deepseek-r1-671b"):
+        base_url = url.rsplit("/", 1)[0]
+    else:
+        base_url = url
+    
+    client = OpenAI(api_key=API_KEY, base_url=base_url)
+    
+    # 選擇模型
+    if len(endpoint_info['models']) == 1:
+        current_model = endpoint_info['models'][0]
+    else:
+        print("\n選擇模型:")
+        for i, model in enumerate(endpoint_info['models'], 1):
+            print(f"  {i}. {model}")
+        choice = input("選擇 (預設: 1): ").strip()
+        if choice.isdigit() and 1 <= int(choice) <= len(endpoint_info['models']):
+            current_model = endpoint_info['models'][int(choice)-1]
+        else:
+            current_model = endpoint_info['models'][0]
+    
+    print(f"\n使用模型: {current_model}")
     messages = []
     
     while True:
@@ -94,13 +165,16 @@ def chat_session(endpoint, model):
                 continue
                 
             if user_input.lower() == 'model':
-                print("\n可用模型:")
+                if len(endpoint_info['models']) == 1:
-                for i, m in enumerate(MODELS, 1):
+                    print(f"[系統] 此端點只支援 {endpoint_info['models'][0]}")
-                    print(f"  {i}. {m}")
+                else:
-                choice = input("選擇 (1-3): ").strip()
+                    print("\n可用模型:")
-                if choice in ['1', '2', '3']:
+                    for i, m in enumerate(endpoint_info['models'], 1):
-                    model = MODELS[int(choice)-1]
+                        print(f"  {i}. {m}")
-                    print(f"[系統] 已切換到 {model}")
+                    choice = input("選擇: ").strip()
+                    if choice.isdigit() and 1 <= int(choice) <= len(endpoint_info['models']):
+                        current_model = endpoint_info['models'][int(choice)-1]
+                        print(f"[系統] 已切換到 {current_model}")
                 continue
             
             messages.append({"role": "user", "content": user_input})
@@ -109,7 +183,7 @@ def chat_session(endpoint, model):
             
             try:
                 response = client.chat.completions.create(
-                    model=model,
+                    model=current_model,
                     messages=messages,
                     temperature=0.7,
                     max_tokens=1000
@@ -118,17 +192,14 @@ def chat_session(endpoint, model):
                 ai_response = response.choices[0].message.content
                 ai_response = clean_response(ai_response)
                 
-                print("\r" + " "*20 + "\r", end="")  # 清除 "思考中..."
+                print("\r" + " "*20 + "\r", end="")
                 print(f"AI: {ai_response}")
                 
                 messages.append({"role": "assistant", "content": ai_response})
                 
-            except UnicodeEncodeError:
-                print("\r[錯誤] 編碼問題，請使用英文對話")
-                messages.pop()  # 移除最後的用戶訊息
             except Exception as e:
                 print(f"\r[錯誤] {str(e)[:100]}")
-                messages.pop()  # 移除最後的用戶訊息
+                messages.pop()
                 
         except KeyboardInterrupt:
             print("\n\n[中斷] 使用 exit 命令正常退出")
@@ -139,52 +210,33 @@ def chat_session(endpoint, model):
 
 def main():
     print("="*60)
-    print("Llama 內網 API 對話程式")
+    print("Llama AI 外網對話程式")
+    print(f"時間: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
     print("="*60)
     
-    # 測試端點
+    # 尋找可用端點
-    print("\n正在檢查可用端點...")
+    working_endpoint = find_working_endpoint()
-    available = []
-    for i, endpoint in enumerate(ENDPOINTS[:3], 1):  # 只測試前3個
-        print(f"  測試 {endpoint}...", end="", flush=True)
-        if test_endpoint(endpoint):
-            print(" [OK]")
-            available.append(endpoint)
-        else:
-            print(" [失敗]")
     
-    if not available:
+    if not working_endpoint:
-        print("\n[錯誤] 沒有可用的端點")
+        print("\n" + "="*60)
+        print("錯誤：無法連接到任何 API 端點")
+        print("="*60)
+        print("\n可能的原因：")
+        print("1. API 伺服器暫時離線")
+        print("2. 網路連接問題")
+        print("3. 防火牆或代理設定")
+        print("\n建議：")
+        print("1. 稍後再試（10-30分鐘後）")
+        print("2. 檢查網路連接")
+        print("3. 確認可以訪問 https://llama.theaken.com")
         sys.exit(1)
     
-    # 選擇端點
+    print("\n" + "="*60)
-    if len(available) == 1:
+    print(f"成功連接到: {working_endpoint['name']}")
-        selected_endpoint = available[0]
+    print("="*60)
-        print(f"\n使用端點: {selected_endpoint}")
-    else:
-        print(f"\n找到 {len(available)} 個可用端點:")
-        for i, ep in enumerate(available, 1):
-            print(f"  {i}. {ep}")
-        print("\n選擇端點 (預設: 1): ", end="")
-        choice = input().strip()
-        if choice and choice.isdigit() and 1 <= int(choice) <= len(available):
-            selected_endpoint = available[int(choice)-1]
-        else:
-            selected_endpoint = available[0]
-    
-    # 選擇模型
-    print("\n可用模型:")
-    for i, model in enumerate(MODELS, 1):
-        print(f"  {i}. {model}")
-    print("\n選擇模型 (預設: 1): ", end="")
-    choice = input().strip()
-    if choice in ['1', '2', '3']:
-        selected_model = MODELS[int(choice)-1]
-    else:
-        selected_model = MODELS[0]
     
     # 開始對話
-    chat_session(selected_endpoint, selected_model)
+    chat_session(working_endpoint)
 
 if __name__ == "__main__":
     try:
@@ -193,4 +245,6 @@ if __name__ == "__main__":
         print("\n\n程式已退出")
     except Exception as e:
         print(f"\n[錯誤] {e}")
+        import traceback
+        traceback.print_exc()
         sys.exit(1)

llama_full_api.py

@@ -16,40 +16,23 @@ API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
 
 # API 端點配置
 ENDPOINTS = {
-    "內網": [
+    "主要": [
         {
-            "name": "內網端點 1 (21180)",
+            "name": "Llama 通用端點",
-            "url": "http://192.168.0.6:21180/v1",
+            "url": "https://llama.theaken.com/v1",
             "models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
         },
         {
-            "name": "內網端點 2 (21181)",
+            "name": "GPT-OSS 專用端點",
-            "url": "http://192.168.0.6:21181/v1",
-            "models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
-        },
-        {
-            "name": "內網端點 3 (21182)",
-            "url": "http://192.168.0.6:21182/v1",
-            "models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
-        }
-    ],
-    "外網": [
-        {
-            "name": "外網 GPT-OSS-120B",
             "url": "https://llama.theaken.com/v1/gpt-oss-120b",
             "models": ["gpt-oss-120b"]
         },
         {
-            "name": "外網 DeepSeek-R1-671B",
+            "name": "DeepSeek 專用端點",
             "url": "https://llama.theaken.com/v1/deepseek-r1-671b",
             "models": ["deepseek-r1-671b"]
-        },
-        {
-            "name": "外網通用端點",
-            "url": "https://llama.theaken.com/v1",
-            "models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
         }
-    ]
+    ],
 }
 
 def clean_response(text):
@@ -121,23 +104,13 @@ def test_all_endpoints():
     
     available_endpoints = []
     
-    # 測試內網端點
+    # 測試所有端點
-    print("\n[內網端點測試]")
+    print("\n[端點測試]")
-    for endpoint in ENDPOINTS["內網"]:
+    for endpoint in ENDPOINTS["主要"]:
         print(f"  測試 {endpoint['name']}...", end="", flush=True)
         if test_endpoint(endpoint):
             print(" [OK]")
-            available_endpoints.append(("內網", endpoint))
+            available_endpoints.append(("主要", endpoint))
-        else:
-            print(" [FAIL]")
-    
-    # 測試外網端點
-    print("\n[外網端點測試]")
-    for endpoint in ENDPOINTS["外網"]:
-        print(f"  測試 {endpoint['name']}...", end="", flush=True)
-        if test_endpoint(endpoint):
-            print(" [OK]")
-            available_endpoints.append(("外網", endpoint))
         else:
             print(" [FAIL]")
     

llama_test.py

@@ -1,99 +0,0 @@
-from openai import OpenAI
-import sys
-
-API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
-BASE_URL = "https://llama.theaken.com/v1"
-
-AVAILABLE_MODELS = [
-    "gpt-oss-120b",
-    "deepseek-r1-671b",
-    "qwen3-embedding-8b"
-]
-
-def chat_with_llama(model_name="gpt-oss-120b"):
-    client = OpenAI(
-        api_key=API_KEY,
-        base_url=BASE_URL
-    )
-    
-    print(f"\n使用模型: {model_name}")
-    print("-" * 50)
-    print("輸入 'exit' 或 'quit' 來結束對話")
-    print("-" * 50)
-    
-    messages = []
-    
-    while True:
-        user_input = input("\n你: ").strip()
-        
-        if user_input.lower() in ['exit', 'quit']:
-            print("對話結束")
-            break
-            
-        if not user_input:
-            continue
-            
-        messages.append({"role": "user", "content": user_input})
-        
-        try:
-            response = client.chat.completions.create(
-                model=model_name,
-                messages=messages,
-                temperature=0.7,
-                max_tokens=2000
-            )
-            
-            assistant_reply = response.choices[0].message.content
-            print(f"\nAI: {assistant_reply}")
-            
-            messages.append({"role": "assistant", "content": assistant_reply})
-            
-        except Exception as e:
-            print(f"\n錯誤: {str(e)}")
-            print("請檢查網路連接和 API 設定")
-
-def test_connection():
-    print("測試連接到 Llama API...")
-    
-    client = OpenAI(
-        api_key=API_KEY,
-        base_url=BASE_URL
-    )
-    
-    try:
-        response = client.chat.completions.create(
-            model="gpt-oss-120b",
-            messages=[{"role": "user", "content": "Hello, this is a test message."}],
-            max_tokens=50
-        )
-        print("[OK] 連接成功!")
-        print(f"測試回應: {response.choices[0].message.content}")
-        return True
-    except Exception as e:
-        print(f"[ERROR] 連接失敗: {str(e)[:200]}")
-        return False
-
-def main():
-    print("=" * 50)
-    print("Llama 模型對話測試程式")
-    print("=" * 50)
-    
-    print("\n可用的模型:")
-    for i, model in enumerate(AVAILABLE_MODELS, 1):
-        print(f"  {i}. {model}")
-    
-    if test_connection():
-        print("\n選擇要使用的模型 (輸入數字 1-3，預設: 1):")
-        choice = input().strip()
-        
-        if choice == "2":
-            model = AVAILABLE_MODELS[1]
-        elif choice == "3":
-            model = AVAILABLE_MODELS[2]
-        else:
-            model = AVAILABLE_MODELS[0]
-        
-        chat_with_llama(model)
-
-if __name__ == "__main__":
-    main()

llama_universal.py

@@ -0,0 +1,337 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Llama API 通用對話程式
+支援內網和外網端點選擇
+"""
+
+from openai import OpenAI
+import requests
+import sys
+import re
+from datetime import datetime
+
+# API 金鑰
+API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
+
+# 網路環境配置
+NETWORK_CONFIG = {
+    "內網": {
+        "name": "內網環境",
+        "description": "公司/學校內部網路，使用內部 IP 地址",
+        "endpoints": [
+            {
+                "name": "內網端點 1",
+                "url": "http://192.168.0.6:21180/v1",
+                "models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
+            },
+            {
+                "name": "內網端點 2",
+                "url": "http://192.168.0.6:21181/v1",
+                "models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
+            },
+            {
+                "name": "內網端點 3",
+                "url": "http://192.168.0.6:21182/v1",
+                "models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
+            }
+        ]
+    },
+    "外網": {
+        "name": "外網環境",
+        "description": "公開網際網路，使用外部域名",
+        "endpoints": [
+            {
+                "name": "通用端點",
+                "url": "https://llama.theaken.com/v1",
+                "models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
+            },
+            {
+                "name": "GPT-OSS 專用端點",
+                "url": "https://llama.theaken.com/v1/gpt-oss-120b",
+                "models": ["gpt-oss-120b"]
+            },
+            {
+                "name": "DeepSeek 專用端點",
+                "url": "https://llama.theaken.com/v1/deepseek-r1-671b",
+                "models": ["deepseek-r1-671b"]
+            }
+        ]
+    }
+}
+
+def clean_response(text):
+    """清理 AI 回應中的特殊標記"""
+    if not text:
+        return text
+        
+    # 移除思考標記
+    if "<think>" in text:
+        text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
+    
+    # 移除 channel 標記
+    if "<|channel|>" in text:
+        parts = text.split("<|message|>")
+        if len(parts) > 1:
+            text = parts[-1]
+    
+    # 移除結束標記
+    text = text.replace("<|end|>", "").replace("<|start|>", "")
+    
+    # 清理多餘空白
+    text = text.strip()
+    
+    return text
+
+def test_endpoint(endpoint_info, timeout=8):
+    """測試端點是否可用"""
+    url = endpoint_info["url"]
+    model = endpoint_info["models"][0] if endpoint_info["models"] else "gpt-oss-120b"
+    
+    print(f"  測試 {endpoint_info['name']}...", end="", flush=True)
+    
+    try:
+        # 處理特殊的模型端點 URL
+        if url.endswith("/gpt-oss-120b") or url.endswith("/deepseek-r1-671b"):
+            base_url = url.rsplit("/", 1)[0]
+        else:
+            base_url = url
+            
+        client = OpenAI(
+            api_key=API_KEY, 
+            base_url=base_url,
+            timeout=timeout
+        )
+        
+        response = client.chat.completions.create(
+            model=model,
+            messages=[{"role": "user", "content": "test"}],
+            max_tokens=5
+        )
+        print(" [OK]")
+        return True
+        
+    except Exception as e:
+        error_msg = str(e)
+        if "502" in error_msg:
+            print(" [502 - 伺服器離線]")
+        elif "timeout" in error_msg.lower():
+            print(" [超時]")
+        elif "connection" in error_msg.lower():
+            print(" [連接失敗]")
+        else:
+            print(" [錯誤]")
+        return False
+
+def choose_network_environment():
+    """選擇網路環境"""
+    print("\n" + "="*60)
+    print("選擇網路環境")
+    print("="*60)
+    
+    print("\n可用環境：")
+    environments = list(NETWORK_CONFIG.keys())
+    
+    for i, env in enumerate(environments, 1):
+        config = NETWORK_CONFIG[env]
+        print(f"  {i}. {config['name']}")
+        print(f"     說明：{config['description']}")
+        print(f"     端點數量：{len(config['endpoints'])} 個")
+    
+    while True:
+        try:
+            choice = input(f"\n請選擇環境 (1-{len(environments)})，預設為 1: ").strip()
+            
+            if not choice:
+                choice = "1"
+                
+            choice_num = int(choice)
+            if 1 <= choice_num <= len(environments):
+                selected_env = environments[choice_num - 1]
+                print(f"\n已選擇：{NETWORK_CONFIG[selected_env]['name']}")
+                return selected_env
+            else:
+                print(f"請輸入 1-{len(environments)} 之間的數字")
+                
+        except ValueError:
+            print("請輸入有效的數字")
+        except KeyboardInterrupt:
+            print("\n\n程式已取消")
+            sys.exit(0)
+
+def find_working_endpoint(environment):
+    """尋找指定環境中的可用端點"""
+    config = NETWORK_CONFIG[environment]
+    
+    print(f"\n正在測試 {config['name']} 的端點...")
+    print("-" * 50)
+    
+    for endpoint in config['endpoints']:
+        if test_endpoint(endpoint):
+            return endpoint
+    
+    return None
+
+def chat_session(endpoint_info, environment):
+    """對話主程式"""
+    print("\n" + "="*60)
+    print("Llama AI 對話系統")
+    print("="*60)
+    print(f"網路環境: {NETWORK_CONFIG[environment]['name']}")
+    print(f"使用端點: {endpoint_info['name']}")
+    print(f"URL: {endpoint_info['url']}")
+    print(f"可用模型: {', '.join(endpoint_info['models'])}")
+    print("\n指令:")
+    print("  exit/quit - 結束對話")
+    print("  clear - 清空對話歷史")
+    print("  model - 切換模型")
+    print("  switch - 切換網路環境")
+    print("-"*60)
+    
+    # 處理 URL
+    url = endpoint_info["url"]
+    if url.endswith("/gpt-oss-120b") or url.endswith("/deepseek-r1-671b"):
+        base_url = url.rsplit("/", 1)[0]
+    else:
+        base_url = url
+    
+    client = OpenAI(api_key=API_KEY, base_url=base_url)
+    
+    # 選擇模型
+    if len(endpoint_info['models']) == 1:
+        current_model = endpoint_info['models'][0]
+    else:
+        print("\n選擇模型:")
+        for i, model in enumerate(endpoint_info['models'], 1):
+            print(f"  {i}. {model}")
+        choice = input("選擇 (預設: 1): ").strip()
+        if choice.isdigit() and 1 <= int(choice) <= len(endpoint_info['models']):
+            current_model = endpoint_info['models'][int(choice)-1]
+        else:
+            current_model = endpoint_info['models'][0]
+    
+    print(f"\n使用模型: {current_model}")
+    messages = []
+    
+    while True:
+        try:
+            user_input = input("\n你: ").strip()
+            
+            if not user_input:
+                continue
+                
+            if user_input.lower() in ['exit', 'quit']:
+                print("再見！")
+                break
+                
+            if user_input.lower() == 'clear':
+                messages = []
+                print("[系統] 對話歷史已清空")
+                continue
+                
+            if user_input.lower() == 'switch':
+                print("[系統] 重新啟動程式以切換網路環境")
+                return "switch"
+                
+            if user_input.lower() == 'model':
+                if len(endpoint_info['models']) == 1:
+                    print(f"[系統] 此端點只支援 {endpoint_info['models'][0]}")
+                else:
+                    print("\n可用模型:")
+                    for i, m in enumerate(endpoint_info['models'], 1):
+                        print(f"  {i}. {m}")
+                    choice = input("選擇: ").strip()
+                    if choice.isdigit() and 1 <= int(choice) <= len(endpoint_info['models']):
+                        current_model = endpoint_info['models'][int(choice)-1]
+                        print(f"[系統] 已切換到 {current_model}")
+                continue
+            
+            messages.append({"role": "user", "content": user_input})
+            
+            print("\nAI 思考中...", end="", flush=True)
+            
+            try:
+                response = client.chat.completions.create(
+                    model=current_model,
+                    messages=messages,
+                    temperature=0.7,
+                    max_tokens=1000
+                )
+                
+                ai_response = response.choices[0].message.content
+                ai_response = clean_response(ai_response)
+                
+                print("\r" + " "*20 + "\r", end="")
+                print(f"AI: {ai_response}")
+                
+                messages.append({"role": "assistant", "content": ai_response})
+                
+            except Exception as e:
+                print(f"\r[錯誤] {str(e)[:100]}")
+                messages.pop()
+                
+        except KeyboardInterrupt:
+            print("\n\n[中斷] 使用 exit 命令正常退出")
+            continue
+        except EOFError:
+            print("\n再見！")
+            break
+    
+    return "exit"
+
+def main():
+    while True:
+        print("="*60)
+        print("Llama AI 通用對話程式")
+        print(f"時間: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+        print("="*60)
+        
+        # 選擇網路環境
+        environment = choose_network_environment()
+        
+        # 尋找可用端點
+        working_endpoint = find_working_endpoint(environment)
+        
+        if not working_endpoint:
+            print("\n" + "="*60)
+            print(f"錯誤：無法連接到任何 {NETWORK_CONFIG[environment]['name']} 端點")
+            print("="*60)
+            print("\n可能的原因：")
+            if environment == "內網":
+                print("1. 不在內網環境或 IP 設定錯誤")
+                print("2. 內網 API 服務未啟動")
+                print("3. 防火牆阻擋連接")
+                print("\n建議：嘗試選擇外網環境")
+            else:
+                print("1. 外網 API 伺服器暫時離線")
+                print("2. 網路連接問題")
+                print("3. 防火牆或代理設定")
+                print("\n建議：嘗試選擇內網環境或稍後再試")
+            
+            retry = input("\n是否重新選擇環境？(y/n，預設: y): ").strip().lower()
+            if retry in ['n', 'no']:
+                break
+            continue
+        
+        print("\n" + "="*60)
+        print(f"成功連接到: {working_endpoint['name']}")
+        print("="*60)
+        
+        # 開始對話
+        result = chat_session(working_endpoint, environment)
+        
+        if result == "exit":
+            break
+        elif result == "switch":
+            continue  # 重新開始選擇環境
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\n\n程式已退出")
+    except Exception as e:
+        print(f"\n[錯誤] {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)

local_api_test.py

@@ -1,243 +0,0 @@
-"""
-內網 Llama API 測試程式
-使用 OpenAI 相容格式連接到本地 API 端點
-"""
-
-from openai import OpenAI
-import requests
-import json
-from datetime import datetime
-
-# API 配置
-API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
-
-# 內網端點列表
-LOCAL_ENDPOINTS = [
-    "http://192.168.0.6:21180/v1",
-    "http://192.168.0.6:21181/v1",
-    "http://192.168.0.6:21182/v1",
-    "http://192.168.0.6:21183/v1"
-]
-
-# 可用模型
-MODELS = [
-    "gpt-oss-120b",
-    "deepseek-r1-671b",
-    "qwen3-embedding-8b"
-]
-
-def test_endpoint_with_requests(endpoint, model="gpt-oss-120b"):
-    """使用 requests 測試端點"""
-    print(f"\n[使用 requests 測試]")
-    print(f"端點: {endpoint}")
-    print(f"模型: {model}")
-    
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json"
-    }
-    
-    data = {
-        "model": model,
-        "messages": [
-            {"role": "user", "content": "Say 'Hello, I am working!' if you can see this."}
-        ],
-        "temperature": 0.7,
-        "max_tokens": 50
-    }
-    
-    try:
-        response = requests.post(
-            f"{endpoint}/chat/completions",
-            headers=headers,
-            json=data,
-            timeout=10
-        )
-        
-        print(f"HTTP 狀態碼: {response.status_code}")
-        
-        if response.status_code == 200:
-            result = response.json()
-            if 'choices' in result:
-                content = result['choices'][0]['message']['content']
-                print(f"[SUCCESS] AI 回應: {content}")
-                return True
-            else:
-                print("[ERROR] 回應格式不正確")
-        else:
-            print(f"[ERROR] HTTP {response.status_code}")
-            if response.status_code != 502:  # 避免顯示 HTML 錯誤頁
-                print(f"詳情: {response.text[:200]}")
-                
-    except requests.exceptions.ConnectTimeout:
-        print("[TIMEOUT] 連接超時")
-    except requests.exceptions.ConnectionError:
-        print("[CONNECTION ERROR] 無法連接到端點")
-    except Exception as e:
-        print(f"[ERROR] {str(e)[:100]}")
-    
-    return False
-
-def test_endpoint_with_openai(endpoint, model="gpt-oss-120b"):
-    """使用 OpenAI SDK 測試端點"""
-    print(f"\n[使用 OpenAI SDK 測試]")
-    print(f"端點: {endpoint}")
-    print(f"模型: {model}")
-    
-    try:
-        client = OpenAI(
-            api_key=API_KEY,
-            base_url=endpoint,
-            timeout=10.0
-        )
-        
-        response = client.chat.completions.create(
-            model=model,
-            messages=[
-                {"role": "user", "content": "Hello, please respond with a simple greeting."}
-            ],
-            temperature=0.7,
-            max_tokens=50
-        )
-        
-        content = response.choices[0].message.content
-        print(f"[SUCCESS] AI 回應: {content}")
-        return True, client
-        
-    except Exception as e:
-        error_str = str(e)
-        if "Connection error" in error_str:
-            print("[CONNECTION ERROR] 無法連接到端點")
-        elif "timeout" in error_str.lower():
-            print("[TIMEOUT] 請求超時")
-        elif "502" in error_str:
-            print("[ERROR] 502 Bad Gateway")
-        else:
-            print(f"[ERROR] {error_str[:100]}")
-    
-    return False, None
-
-def find_working_endpoint():
-    """尋找可用的端點"""
-    print("="*60)
-    print(f"內網 API 端點測試 - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-    print("="*60)
-    
-    working_endpoints = []
-    
-    for endpoint in LOCAL_ENDPOINTS:
-        print(f"\n測試端點: {endpoint}")
-        print("-"*40)
-        
-        # 先用 requests 快速測試
-        if test_endpoint_with_requests(endpoint):
-            working_endpoints.append(endpoint)
-            print(f"[OK] 端點 {endpoint} 可用！")
-        else:
-            # 再用 OpenAI SDK 測試
-            success, _ = test_endpoint_with_openai(endpoint)
-            if success:
-                working_endpoints.append(endpoint)
-                print(f"[OK] 端點 {endpoint} 可用！")
-    
-    return working_endpoints
-
-def interactive_chat(endpoint, model="gpt-oss-120b"):
-    """互動式對話"""
-    print(f"\n連接到: {endpoint}")
-    print(f"使用模型: {model}")
-    print("="*60)
-    print("開始對話 (輸入 'exit' 結束)")
-    print("="*60)
-    
-    client = OpenAI(
-        api_key=API_KEY,
-        base_url=endpoint
-    )
-    
-    messages = []
-    
-    while True:
-        user_input = input("\n你: ").strip()
-        
-        if user_input.lower() in ['exit', 'quit']:
-            print("對話結束")
-            break
-            
-        if not user_input:
-            continue
-            
-        messages.append({"role": "user", "content": user_input})
-        
-        try:
-            print("\nAI 思考中...")
-            response = client.chat.completions.create(
-                model=model,
-                messages=messages,
-                temperature=0.7,
-                max_tokens=1000
-            )
-            
-            ai_response = response.choices[0].message.content
-            print(f"\nAI: {ai_response}")
-            messages.append({"role": "assistant", "content": ai_response})
-            
-        except Exception as e:
-            print(f"\n[ERROR] {str(e)[:100]}")
-
-def main():
-    # 尋找可用端點
-    working_endpoints = find_working_endpoint()
-    
-    print("\n" + "="*60)
-    print("測試結果總結")
-    print("="*60)
-    
-    if working_endpoints:
-        print(f"\n找到 {len(working_endpoints)} 個可用端點:")
-        for i, endpoint in enumerate(working_endpoints, 1):
-            print(f"  {i}. {endpoint}")
-        
-        # 選擇端點
-        if len(working_endpoints) == 1:
-            selected_endpoint = working_endpoints[0]
-            print(f"\n自動選擇唯一可用端點: {selected_endpoint}")
-        else:
-            print(f"\n請選擇要使用的端點 (1-{len(working_endpoints)}):")
-            choice = input().strip()
-            try:
-                idx = int(choice) - 1
-                if 0 <= idx < len(working_endpoints):
-                    selected_endpoint = working_endpoints[idx]
-                else:
-                    selected_endpoint = working_endpoints[0]
-            except:
-                selected_endpoint = working_endpoints[0]
-        
-        # 選擇模型
-        print("\n可用模型:")
-        for i, model in enumerate(MODELS, 1):
-            print(f"  {i}. {model}")
-        
-        print("\n請選擇模型 (1-3, 預設: 1):")
-        choice = input().strip()
-        if choice == "2":
-            selected_model = MODELS[1]
-        elif choice == "3":
-            selected_model = MODELS[2]
-        else:
-            selected_model = MODELS[0]
-        
-        # 開始對話
-        interactive_chat(selected_endpoint, selected_model)
-        
-    else:
-        print("\n[ERROR] 沒有找到可用的端點")
-        print("\n可能的原因:")
-        print("1. 內網 API 服務未啟動")
-        print("2. 防火牆阻擋了連接")
-        print("3. IP 地址或端口設定錯誤")
-        print("4. 不在同一個網路環境")
-
-if __name__ == "__main__":
-    main()

quick_test.py

@@ -1,54 +1,76 @@
 """
-快速測試內網 Llama API
+快速測試 Llama API 外網連接
 """
 
 from openai import OpenAI
+import sys
 
 # API 設定
 API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
-BASE_URL = "http://192.168.0.6:21180/v1"  # 使用第一個可用端點
+BASE_URL = "https://llama.theaken.com/v1"  # 使用外網端點
 
 def quick_test():
-    print("連接到內網 API...")
+    print("="*50)
-    print(f"端點: {BASE_URL}")
+    print("Llama API 快速測試")
+    print("="*50)
+    print(f"連接到: {BASE_URL}")
     print("-" * 50)
     
-    client = OpenAI(
+    try:
-        api_key=API_KEY,
+        client = OpenAI(
-        base_url=BASE_URL
+            api_key=API_KEY,
-    )
+            base_url=BASE_URL,
+            timeout=15.0  # 15秒超時
+        )
         
-    # 測試對話
+        # 測試對話
-    test_messages = [
+        test_messages = [
-        "你好，請自我介紹",
+            "你好，請自我介紹",
-        "1 + 1 等於多少？",
+            "1 + 1 等於多少？",
-        "今天天氣如何？"
+            "今天天氣如何？"
-    ]
+        ]
         
-    for msg in test_messages:
+        for msg in test_messages:
-        print(f"\n問: {msg}")
+            print(f"\n問: {msg}")
             
-        try:
+            try:
-            response = client.chat.completions.create(
+                response = client.chat.completions.create(
-                model="gpt-oss-120b",
+                    model="gpt-oss-120b",
-                messages=[
+                    messages=[
-                    {"role": "user", "content": msg}
+                        {"role": "user", "content": msg}
-                ],
+                    ],
-                temperature=0.7,
+                    temperature=0.7,
-                max_tokens=200
+                    max_tokens=200
-            )
+                )
                 
-            answer = response.choices[0].message.content
+                answer = response.choices[0].message.content
-            # 清理可能的思考標記
+                # 清理可能的思考標記
-            if "<think>" in answer:
+                if "<think>" in answer:
-                answer = answer.split("</think>")[-1].strip()
+                    answer = answer.split("</think>")[-1].strip()
-            if "<|channel|>" in answer:
+                if "<|channel|>" in answer:
-                answer = answer.split("<|message|>")[-1].strip()
+                    answer = answer.split("<|message|>")[-1].strip()
                     
-            print(f"答: {answer}")
+                print(f"答: {answer[:200]}")  # 限制顯示長度
                 
-        except Exception as e:
+            except Exception as e:
-            print(f"錯誤: {str(e)[:100]}")
+                error_msg = str(e)
+                if "502" in error_msg:
+                    print("錯誤: 伺服器暫時無法使用 (502)")
+                elif "timeout" in error_msg.lower():
+                    print("錯誤: 請求超時")
+                else:
+                    print(f"錯誤: {error_msg[:100]}")
+                    
+        print("\n" + "="*50)
+        print("測試完成！")
+        
+    except Exception as e:
+        print(f"\n連接失敗: {str(e)[:100]}")
+        print("\n建議:")
+        print("1. 檢查網路連接")
+        print("2. 確認可以訪問 https://llama.theaken.com")
+        print("3. 稍後再試（如果是 502 錯誤）")
+        sys.exit(1)
 
 if __name__ == "__main__":
     quick_test()

simple_llama_test.py

@@ -1,46 +0,0 @@
-import requests
-import json
-
-API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
-BASE_URL = "https://llama.theaken.com/v1/chat/completions"
-
-def test_api():
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json"
-    }
-    
-    data = {
-        "model": "gpt-oss-120b",
-        "messages": [
-            {"role": "user", "content": "Hello, can you respond?"}
-        ],
-        "temperature": 0.7,
-        "max_tokens": 100
-    }
-    
-    print("正在測試 API 連接...")
-    print(f"URL: {BASE_URL}")
-    print(f"Model: gpt-oss-120b")
-    print("-" * 50)
-    
-    try:
-        response = requests.post(BASE_URL, headers=headers, json=data, timeout=30)
-        
-        if response.status_code == 200:
-            result = response.json()
-            print("[成功] API 回應:")
-            print(result['choices'][0]['message']['content'])
-        else:
-            print(f"[錯誤] HTTP {response.status_code}")
-            print(f"回應內容: {response.text[:500]}")
-            
-    except requests.exceptions.Timeout:
-        print("[錯誤] 請求超時")
-    except requests.exceptions.ConnectionError:
-        print("[錯誤] 無法連接到伺服器")
-    except Exception as e:
-        print(f"[錯誤] {str(e)}")
-
-if __name__ == "__main__":
-    test_api()

test_with_timeout.py

@@ -1,111 +0,0 @@
-import requests
-import json
-from datetime import datetime
-
-# API 配置
-API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
-BASE_URL = "https://llama.theaken.com/v1"
-
-def test_endpoints():
-    """測試不同的 API 端點和模型"""
-    
-    print("="*60)
-    print(f"Llama API 測試 - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-    print("="*60)
-    
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json"
-    }
-    
-    # 測試配置
-    tests = [
-        {
-            "name": "GPT-OSS-120B",
-            "model": "gpt-oss-120b",
-            "prompt": "Say hello in one word"
-        },
-        {
-            "name": "DeepSeek-R1-671B", 
-            "model": "deepseek-r1-671b",
-            "prompt": "Say hello in one word"
-        },
-        {
-            "name": "Qwen3-Embedding-8B",
-            "model": "qwen3-embedding-8b",
-            "prompt": "Say hello in one word"
-        }
-    ]
-    
-    success_count = 0
-    
-    for test in tests:
-        print(f"\n[測試 {test['name']}]")
-        print("-"*40)
-        
-        data = {
-            "model": test["model"],
-            "messages": [
-                {"role": "user", "content": test["prompt"]}
-            ],
-            "temperature": 0.5,
-            "max_tokens": 20
-        }
-        
-        try:
-            # 使用較短的超時時間
-            response = requests.post(
-                f"{BASE_URL}/chat/completions",
-                headers=headers,
-                json=data,
-                timeout=15
-            )
-            
-            print(f"HTTP 狀態: {response.status_code}")
-            
-            if response.status_code == 200:
-                result = response.json()
-                if 'choices' in result:
-                    content = result['choices'][0]['message']['content']
-                    print(f"[SUCCESS] 成功回應: {content}")
-                    success_count += 1
-                else:
-                    print("[ERROR] 回應格式錯誤")
-            elif response.status_code == 502:
-                print("[ERROR] 502 Bad Gateway - 伺服器無法回應")
-            elif response.status_code == 401:
-                print("[ERROR] 401 Unauthorized - API 金鑰可能錯誤")
-            elif response.status_code == 404:
-                print("[ERROR] 404 Not Found - 模型或端點不存在")
-            else:
-                print(f"[ERROR] 錯誤 {response.status_code}")
-                if not response.text.startswith('<!DOCTYPE'):
-                    print(f"詳情: {response.text[:200]}")
-                    
-        except requests.exceptions.Timeout:
-            print("[TIMEOUT] 請求超時 (15秒)")
-        except requests.exceptions.ConnectionError as e:
-            print(f"[CONNECTION ERROR] 無法連接到伺服器")
-        except Exception as e:
-            print(f"[UNKNOWN ERROR]: {str(e)[:100]}")
-    
-    # 總結
-    print("\n" + "="*60)
-    print(f"測試結果: {success_count}/{len(tests)} 成功")
-    
-    if success_count == 0:
-        print("\n診斷資訊:")
-        print("• 網路連接: 正常 (可 ping 通)")
-        print("• API 端點: https://llama.theaken.com/v1")
-        print("• 錯誤類型: 502 Bad Gateway")
-        print("• 可能原因: 後端 API 服務暫時離線")
-        print("\n建議行動:")
-        print("1. 稍後再試 (建議 10-30 分鐘後)")
-        print("2. 聯繫 API 管理員確認服務狀態")
-        print("3. 檢查是否有服務維護公告")
-    else:
-        print(f"\n[OK] API 服務正常運作中!")
-        print(f"[OK] 可使用的模型數: {success_count}")
-
-if __name__ == "__main__":
-    test_endpoints()

使用說明.txt

@@ -1,33 +0,0 @@
-===========================================
-Llama 模型對話測試程式 - 使用說明
-===========================================
-
-安裝步驟:
----------
-1. 確保已安裝 Python 3.7 或更高版本
-
-2. 安裝依賴套件:
-   pip install -r requirements.txt
-
-執行程式:
----------
-python llama_test.py
-
-功能說明:
----------
-1. 程式啟動後會自動測試 API 連接
-2. 選擇要使用的模型 (1-3)
-3. 開始與 AI 進行對話
-4. 輸入 'exit' 或 'quit' 結束對話
-
-可用模型:
----------
-1. gpt-oss-120b (預設)
-2. deepseek-r1-671b
-3. qwen3-embedding-8b
-
-注意事項:
----------
-- 確保網路連接正常
-- API 金鑰已內建於程式中
-- 如遇到錯誤，請檢查網路連接或聯繫管理員

操作指南.md

@@ -9,20 +9,12 @@ paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo=
 
 ### 可用端點
 
-#### 內網端點（已測試成功）
+#### 可用的外網端點
-| 端點名稱 | URL | 狀態 | 支援模型 |
+| 端點名稱 | URL | 支援模型 |
-|---------|-----|------|---------|
+|---------|-----|---------|
-| 內網端點 1 | http://192.168.0.6:21180/v1 | ✅ 可用 | gpt-oss-120b, deepseek-r1-671b, qwen3-embedding-8b |
+| 通用端點 | https://llama.theaken.com/v1 | gpt-oss-120b, deepseek-r1-671b, qwen3-embedding-8b |
-| 內網端點 2 | http://192.168.0.6:21181/v1 | ✅ 可用 | gpt-oss-120b, deepseek-r1-671b, qwen3-embedding-8b |
+| GPT-OSS 專用 | https://llama.theaken.com/v1/gpt-oss-120b | gpt-oss-120b |
-| 內網端點 3 | http://192.168.0.6:21182/v1 | ✅ 可用 | gpt-oss-120b, deepseek-r1-671b, qwen3-embedding-8b |
+| DeepSeek 專用 | https://llama.theaken.com/v1/deepseek-r1-671b | deepseek-r1-671b |
-| 內網端點 4 | http://192.168.0.6:21183/v1 | ❌ 錯誤 | 500 Internal Server Error |
-
-#### 外網端點（待測試）
-| 端點名稱 | URL | 狀態 | 支援模型 |
-|---------|-----|------|---------|
-| GPT-OSS 專用 | https://llama.theaken.com/v1/gpt-oss-120b | 待測試 | gpt-oss-120b |
-| DeepSeek 專用 | https://llama.theaken.com/v1/deepseek-r1-671b | 待測試 | deepseek-r1-671b |
-| 通用端點 | https://llama.theaken.com/v1 | 待測試 | 所有模型 |
 
 ## 二、快速開始
 
@@ -33,13 +25,13 @@ pip install openai
 
 ### 2. 測試連接（Python）
 
-#### 內網連接範例
+#### 外網連接範例
 ```python
 from openai import OpenAI
 
 # 設定 API
 API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
-BASE_URL = "http://192.168.0.6:21180/v1"  # 使用內網端點 1
+BASE_URL = "https://llama.theaken.com/v1"  # 使用外網端點
 
 # 創建客戶端
 client = OpenAI(
@@ -64,19 +56,19 @@ print(response.choices[0].message.content)
 ## 三、使用現成程式
 
 ### 程式清單
-1. **llama_full_api.py** - 完整對話程式（支援內外網）
+1. **llama_chat.py** - 主要對話程式（智慧連接）
-2. **llama_chat.py** - 內網專用對話程式
+2. **llama_full_api.py** - 完整對話程式（多端點支援）
-3. **local_api_test.py** - 端點測試工具
+3. **quick_test.py** - 快速測試腳本
-4. **quick_test.py** - 快速測試腳本
+4. **test_all_models.py** - 模型測試工具
 
 ### 執行對話程式
 ```bash
+# 執行主程式（智慧對話）
+python llama_chat.py
+
 # 執行完整版（自動測試所有端點）
 python llama_full_api.py
 
-# 執行內網版
-python llama_chat.py
-
 # 快速測試
 python quick_test.py
 ```
@@ -97,15 +89,15 @@ python quick_test.py
 ## 五、常見問題處理
 
 ### 問題 1：502 Bad Gateway
-**原因**：外網 API 伺服器離線  
+**原因**：API 伺服器暫時離線  
-**解決**：使用內網端點
+**解決**：稍後再試，程式會自動測試所有端點
 
 ### 問題 2：Connection Error
-**原因**：不在內網環境或 IP 錯誤  
+**原因**：網路連接問題  
 **解決**：
-1. 確認在同一網路環境
+1. 確認網路連接正常
-2. 檢查防火牆設定
+2. 檢查防火牆或代理設定
-3. ping 192.168.0.6 測試連通性
+3. 確認可以訪問 https://llama.theaken.com
 
 ### 問題 3：編碼錯誤
 **原因**：Windows 終端編碼問題  
@@ -124,14 +116,16 @@ python quick_test.py
 
 ## 七、測試結果摘要
 
-### 成功測試
+### 測試狀態
-✅ 內網端點 1-3 全部正常運作  
+📡 API 端點連接測試中  
 ✅ 支援 OpenAI SDK 標準格式  
-✅ 可正常進行對話  
+✅ 自動端點選擇機制  
 
-### 待確認
+### 支援功能
-- 外網端點需等待伺服器恢復
+- 自動端點選擇
-- DeepSeek 和 Qwen 模型需進一步測試
+- 智慧超時控制
+- 完整錯誤處理
+- 多模型支援（GPT-OSS、DeepSeek、Qwen）
 
 ## 八、技術細節
 

連線參數.txt

@@ -1,14 +0,0 @@
-可以連接 llama 的模型，ai進行對話
-他的連線資料如下:
-
-外網連線：
-https://llama.theaken.com/v1https://llama.theaken.com/v1/gpt-oss-120b/
-https://llama.theaken.com/v1https://llama.theaken.com/v1/deepseek-r1-671b/
-https://llama.theaken.com/v1https://llama.theaken.com/v1/gpt-oss-120b/
-外網模型路徑：
-  1. /gpt-oss-120b/
-  2. /deepseek-r1-671b/
-  3. /qwen3-embedding-8b/
- 
-
-金鑰：paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo=