aken1023
e71495ece4
主要變更： - 移除所有內網 IP (192.168.x.x) - 改用外網端點 (https://llama.theaken.com) - 新增 llama_external_api.py 專門處理外網連接 - 更新所有文檔為外網版本 - 加入備用端點自動切換機制 - 優化錯誤處理和超時設定
270 lines
8.1 KiB
Python
270 lines
8.1 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Llama API 外網連接程式
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使用外網端點進行 AI 對話
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"""
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from openai import OpenAI
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import requests
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import sys
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import re
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from datetime import datetime
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# API 金鑰
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API_KEY = "paVrIT+XU1NhwCAOb0X4aYi75QKogK5YNMGvQF1dCyo="
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# 外網 API 端點配置
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ENDPOINTS = [
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{
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"name": "Llama 通用端點",
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"url": "https://llama.theaken.com/v1",
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"models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
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},
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{
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"name": "GPT-OSS 專用端點",
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"url": "https://llama.theaken.com/v1/gpt-oss-120b",
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"models": ["gpt-oss-120b"]
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},
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{
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"name": "DeepSeek 專用端點",
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"url": "https://llama.theaken.com/v1/deepseek-r1-671b",
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"models": ["deepseek-r1-671b"]
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}
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]
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# 備用外網端點（如果主要端點無法使用）
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BACKUP_ENDPOINTS = [
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{
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"name": "備用端點 1",
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"url": "https://api.llama.theaken.com/v1",
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"models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
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},
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{
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"name": "備用端點 2",
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"url": "https://llama-api.theaken.com/v1",
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"models": ["gpt-oss-120b", "deepseek-r1-671b", "qwen3-embedding-8b"]
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}
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]
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def clean_response(text):
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"""清理 AI 回應中的特殊標記"""
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if not text:
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return text
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# 移除思考標記
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if "<think>" in text:
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text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
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# 移除 channel 標記
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if "<|channel|>" in text:
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parts = text.split("<|message|>")
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if len(parts) > 1:
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text = parts[-1]
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# 移除結束標記
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text = text.replace("<|end|>", "").replace("<|start|>", "")
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# 清理多餘空白
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text = text.strip()
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return text
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def test_endpoint(endpoint_info, timeout=10):
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"""測試端點是否可用"""
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url = endpoint_info["url"]
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model = endpoint_info["models"][0] if endpoint_info["models"] else "gpt-oss-120b"
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print(f" 測試 {endpoint_info['name']}...", end="", flush=True)
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try:
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# 處理特殊的模型端點 URL
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if url.endswith("/gpt-oss-120b") or url.endswith("/deepseek-r1-671b"):
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base_url = url.rsplit("/", 1)[0]
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else:
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base_url = url
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client = OpenAI(
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api_key=API_KEY,
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base_url=base_url,
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timeout=timeout
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)
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response = client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": "test"}],
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max_tokens=5
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)
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print(" ✓ 可用")
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return True
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except Exception as e:
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error_msg = str(e)
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if "502" in error_msg:
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print(" ✗ 伺服器暫時無法使用 (502)")
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elif "timeout" in error_msg.lower():
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print(" ✗ 連接超時")
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elif "connection" in error_msg.lower():
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print(" ✗ 無法連接")
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else:
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print(f" ✗ 錯誤")
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return False
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def find_working_endpoint():
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"""尋找可用的端點"""
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print("\n正在測試外網端點...")
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print("-" * 50)
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# 先測試主要端點
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print("主要端點：")
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for endpoint in ENDPOINTS:
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if test_endpoint(endpoint):
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return endpoint
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# 如果主要端點都不可用，測試備用端點
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print("\n備用端點：")
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for endpoint in BACKUP_ENDPOINTS:
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if test_endpoint(endpoint):
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return endpoint
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return None
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def chat_session(endpoint_info):
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"""對話主程式"""
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print("\n" + "="*60)
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print("Llama AI 對話系統")
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print("="*60)
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print(f"使用端點: {endpoint_info['name']}")
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print(f"URL: {endpoint_info['url']}")
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print(f"可用模型: {', '.join(endpoint_info['models'])}")
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print("\n指令:")
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print(" exit/quit - 結束對話")
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print(" clear - 清空對話歷史")
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print(" model - 切換模型")
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print("-"*60)
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# 處理 URL
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url = endpoint_info["url"]
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if url.endswith("/gpt-oss-120b") or url.endswith("/deepseek-r1-671b"):
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base_url = url.rsplit("/", 1)[0]
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else:
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base_url = url
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client = OpenAI(api_key=API_KEY, base_url=base_url)
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# 選擇模型
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if len(endpoint_info['models']) == 1:
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current_model = endpoint_info['models'][0]
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else:
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print("\n選擇模型:")
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for i, model in enumerate(endpoint_info['models'], 1):
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print(f" {i}. {model}")
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choice = input("選擇 (預設: 1): ").strip()
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if choice.isdigit() and 1 <= int(choice) <= len(endpoint_info['models']):
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current_model = endpoint_info['models'][int(choice)-1]
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else:
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current_model = endpoint_info['models'][0]
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print(f"\n使用模型: {current_model}")
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messages = []
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while True:
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try:
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user_input = input("\n你: ").strip()
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if not user_input:
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continue
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if user_input.lower() in ['exit', 'quit']:
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print("再見！")
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break
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if user_input.lower() == 'clear':
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messages = []
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print("[系統] 對話歷史已清空")
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continue
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if user_input.lower() == 'model':
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if len(endpoint_info['models']) == 1:
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print(f"[系統] 此端點只支援 {endpoint_info['models'][0]}")
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else:
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print("\n可用模型:")
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for i, m in enumerate(endpoint_info['models'], 1):
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print(f" {i}. {m}")
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choice = input("選擇: ").strip()
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if choice.isdigit() and 1 <= int(choice) <= len(endpoint_info['models']):
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current_model = endpoint_info['models'][int(choice)-1]
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print(f"[系統] 已切換到 {current_model}")
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continue
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messages.append({"role": "user", "content": user_input})
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print("\nAI 思考中...", end="", flush=True)
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try:
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response = client.chat.completions.create(
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model=current_model,
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messages=messages,
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temperature=0.7,
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max_tokens=1000
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)
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ai_response = response.choices[0].message.content
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ai_response = clean_response(ai_response)
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print("\r" + " "*20 + "\r", end="")
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print(f"AI: {ai_response}")
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messages.append({"role": "assistant", "content": ai_response})
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except Exception as e:
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print(f"\r[錯誤] {str(e)[:100]}")
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messages.pop()
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except KeyboardInterrupt:
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print("\n\n[中斷] 使用 exit 命令正常退出")
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continue
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except EOFError:
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print("\n再見！")
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break
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def main():
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print("="*60)
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print("Llama AI 外網對話程式")
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print(f"時間: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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print("="*60)
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# 尋找可用端點
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working_endpoint = find_working_endpoint()
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if not working_endpoint:
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print("\n" + "="*60)
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print("錯誤：無法連接到任何外網端點")
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print("="*60)
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print("\n可能的原因：")
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print("1. 外網 API 伺服器暫時離線")
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print("2. 網路連接問題")
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print("3. 防火牆或代理設定")
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print("\n建議：")
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print("1. 稍後再試（10-30分鐘後）")
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print("2. 檢查網路連接")
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print("3. 聯繫 API 管理員")
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sys.exit(1)
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print("\n" + "="*60)
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print(f"成功連接到: {working_endpoint['name']}")
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print("="*60)
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# 開始對話
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chat_session(working_endpoint)
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if __name__ == "__main__":
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try:
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main()
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except KeyboardInterrupt:
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print("\n\n程式已退出")
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except Exception as e:
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print(f"\n[錯誤] {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1) |