完成世界书、骰子、apiconfig页面处理

test.py

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+import sys
+from pathlib import Path
+import os
+import time
+
+# 添加项目根目录到 Python 路径
+project_root = Path(__file__).parent
+sys.path.insert(0, str(project_root))
+
+from backend.core.models.chat_history import ChatHistory
+
+# 导入 LangChain
+try:
+    from langchain_openai import ChatOpenAI
+    from langchain_core.messages import HumanMessage
+except ImportError:
+    print("⚠️  未安装 langchain 库，请先运行: pip install langchain-openai langchain-core")
+    sys.exit(1)
+
+
+def test_api_key(api_key: str, base_url: str = None, model: str = "gpt-3.5-turbo") -> dict:
+    """
+    使用 LangChain 测试 API Key
+
+    Args:
+        api_key: API密钥
+        base_url: API基础URL（可选）
+        model: 模型名称
+
+    Returns:
+        dict: 测试结果
+    """
+    try:
+        # 创建 LLM 实例
+        llm_kwargs = {
+            "model": model,
+            "api_key": api_key,
+            "temperature": 0,
+            "max_tokens": 5
+        }
+
+        if base_url:
+            llm_kwargs["base_url"] = base_url
+
+        llm = ChatOpenAI(**llm_kwargs)
+
+        # 发送测试消息
+        start_time = time.time()
+        response = llm.invoke([HumanMessage(content="Hi")])
+        end_time = time.time()
+
+        return {
+            "valid": True,
+            "message": f"✅ API Key 有效！响应时间: {end_time - start_time:.2f}秒",
+            "response_time": end_time - start_time,
+            "content": response.content
+        }
+
+    except Exception as e:
+        return {
+            "valid": False,
+            "message": f"❌ API Key 无效: {str(e)}",
+            "error": str(e)
+        }
+
+
+def check_api_status():
+    """检查 API Key 状态"""
+    print("\n" + "=" * 60)
+    print("API Key 检测 (LangChain)")
+    print("=" * 60)
+
+    # 从环境变量读取配置
+    API_KEY = os.getenv("MAIN_LLM_API_KEY", "")
+    BASE_URL = os.getenv("MAIN_LLM_BASE_URL", "")
+    MODEL = os.getenv("MAIN_LLM_MODEL", "gpt-3.5-turbo")
+
+    print(f"\n配置信息:")
+    print(f"  模型: {MODEL}")
+    if BASE_URL:
+        print(f"  API地址: {BASE_URL}")
+
+    if not API_KEY:
+        print("\n❌ 未找到 API Key!")
+        print("\n请在 .env 文件中设置 MAIN_LLM_API_KEY")
+        return False
+
+    print(f"  API密钥: {'*' * 8}{API_KEY[-4:]}")
+    print(f"\n正在测试...")
+
+    # 执行测试
+    result = test_api_key(API_KEY, BASE_URL if BASE_URL else None, MODEL)
+
+    print(f"\n{result['message']}")
+
+    if result["valid"]:
+        print(f"  响应时间: {result['response_time']:.2f}秒")
+        print(f"\n💡 API Key 可以正常使用！")
+        return True
+    else:
+        print(f"\n💡 请检查:")
+        print("  1. API Key 是否正确")
+        print("  2. API 端点地址是否正确")
+        print("  3. 网络连接是否正常")
+        return False
+
+
+def quick_test():
+    """快速测试"""
+    API_KEY = os.getenv("MAIN_LLM_API_KEY", "")
+    BASE_URL = os.getenv("MAIN_LLM_BASE_URL", "")
+    MODEL = os.getenv("MAIN_LLM_MODEL", "gpt-3.5-turbo")
+
+    if not API_KEY:
+        print("❌ 未配置 API Key")
+        return False
+
+    result = test_api_key(API_KEY, BASE_URL if BASE_URL else None, MODEL)
+    print(result['message'])
+    return result['valid']
+
+
+def test_list_all_chats():
+    """测试聊天历史"""
+    try:
+        result = ChatHistory.list_all_chats()
+        print(f"\n获取到 {len(result.get('chat', []))} 个聊天")
+        return result
+    except Exception as e:
+        print(f"错误: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return None
+
+
+# 运行测试
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        if sys.argv[1] == "--check":
+            check_api_status()
+        elif sys.argv[1] == "--quick":
+            quick_test()
+        elif sys.argv[1] == "--chat":
+            test_list_all_chats()
+        else:
+            print("用法:")
+            print("  python test.py --check   # 检查 API Key")
+            print("  python test.py --quick   # 快速验证")
+            print("  python test.py --chat    # 测试聊天历史")
+    else:
+        check_api_status()