first pull

2026-02-03 01:20:00 +08:00
commit 0753da86a8
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+from src.modules.text_ai_module.text_ai_core.general_text_ai_req import UnifiedLLM, ModelConfig, ModelProvider, create_llm_client
+from src.config.config import get_settings
+from src.config.convert_env import EnvConverter
+from src.config.file_config import DirectoryInitializer
+
+EnvConverter().convert(backup_existing=True)    # 若是首次启动则从env模板中生成env文件
+DirectoryInitializer(get_settings())            # 初始化必要的目录(若不存在则创建)
+
+def test1():
+    """
+    测试常规调用
+    """
+    # 配置模型
+    config = ModelConfig(
+        provider=ModelProvider.OPENAI,
+        model_name=get_settings().ai_model_name,
+        base_url=get_settings().ai_api_base_url,
+        api_key=get_settings().ai_api_key,  # 从环境中取出相关的api_key
+        temperature=0.7,
+        max_tokens=2048
+    )
+
+    # 创建客户端
+    llm = UnifiedLLM(config)
+
+    # 发送消息
+    response = llm.chat([
+        {"role": "system", "content": "你是一个DeepSeek助手"},
+        {"role": "user", "content": "请介绍一下DeepSeek模型的特点"}
+    ])
+
+    print(response.content)
+
+def base_test2():
+    """
+    测试流式响应
+    """
+    # 使用快捷函数
+    deepseek_llm = create_llm_client(
+        provider="openai",  # DeepSeek使用OpenAI兼容接口
+        model_name=get_settings().ai_model_name,
+        api_key=get_settings().ai_api_key,
+        base_url=get_settings().ai_api_base_url
+    )
+
+    # 流式聊天
+    messages = [
+        {"role": "user", "content": "用Python写一个快速排序算法"}
+    ]
+
+    print("正在生成响应...")
+    for chunk in deepseek_llm.stream_chat(messages):
+        print(chunk.content, end="", flush=True)
+
+
+def test_lm_studio():
+    """测试本地 LM Studio 模型"""
+    print("=== 测试本地 LM Studio ===")
+
+    # 使用UnifiedLLM类
+    config = ModelConfig(
+        provider=ModelProvider.LM_STUDIO,
+        model_name="qwen/qwen3-4b-2507",
+        base_url="http://192.168.1.8:1234/v1",
+        api_key="",  # LM Studio不需要API密钥，留空
+        temperature=0.7,
+        max_tokens=1024,
+        streaming=False  # 启用流式响应
+    )
+
+    llm = UnifiedLLM(config)
+
+    # 发送消息
+    messages = [
+        {"role": "system", "content": "你是一个有用的助手"},
+        {"role": "user", "content": "用中文介绍一下自己"}
+    ]
+
+    print("非流式响应:")
+    response = llm.chat(messages, streaming=False)
+    print(response.content)
+
+    print("\n流式响应:")
+    for chunk in llm.stream_chat(messages):
+        print(chunk.content, end="", flush=True)
+
+if __name__ == "__main__":
+    test_lm_studio()