QwQ-32B 测评和使用教程来了！

import osfrom openai import OpenAIimport efinance as efimport json# 设置模型的 API 地址openai_api_base = "http://localhost:8000/v1"api_key = "dummy_key"  # 本地部署不需要真实 key# 实例化 OpenAI 客户端client = OpenAI(    api_key=api_key,    base_url=openai_api_base,)# 定义可用的函数def query_stock_code(stock_name):    """查询股票代码"""    try:        stock_data = ef.stock.get_realtime_quotes(stock_name)        return stock_data.to_dict('records')    except Exception as e:        return {"error": str(e)}def get_stock_history(stock_code, start_date=None, end_date=None):    """获取股票历史数据"""    try:        history_data = ef.stock.get_quote_history(stock_code, start_date, end_date)        return history_data.to_dict('records')    except Exception as e:        return {"error": str(e)}def get_stock_financial(stock_code):    """获取股票财务数据"""    try:        financial_data = ef.stock.get_financial_report(stock_code)        return financial_data.to_dict('records')    except Exception as e:        return {"error": str(e)}# 定义函数映射function_map = {    "query_stock_code": query_stock_code,    "get_stock_history": get_stock_history,    "get_stock_financial": get_stock_financial}# 定义函数描述，用于告诉模型可用的函数functions = [    {        "name": "query_stock_code",        "description": "查询股票的实时行情数据",        "parameters": {            "type": "object",            "properties": {                "stock_name": {                    "type": "string",                    "description": "股票名称或代码"                }            },            "required": ["stock_name"]        }    },    {        "name": "get_stock_history",        "description": "获取股票的历史行情数据",        "parameters": {            "type": "object",            "properties": {                "stock_code": {                    "type": "string",                    "description": "股票代码"                },                "start_date": {                    "type": "string",                    "description": "开始日期，格式为YYYY-MM-DD"                },                "end_date": {                    "type": "string",                    "description": "结束日期，格式为YYYY-MM-DD"                }            },            "required": ["stock_code"]        }    },    {        "name": "get_stock_financial",        "description": "获取股票的财务报表数据",        "parameters": {            "type": "object",            "properties": {                "stock_code": {                    "type": "string",                    "description": "股票代码"                }            },            "required": ["stock_code"]        }    }]def interactive_stock_query():    """交互式股票查询功能"""    print("欢迎使用股票查询助手！您可以询问任何有关股票的问题。输入'退出'结束对话。")        # 保存对话历史    conversation_history = []        while True:        user_input = input("\n请输入您的问题: ")        if user_input.lower() in ['退出', 'exit', 'quit']:            print("感谢使用，再见！")            break                # 添加用户输入到对话历史        conversation_history.append({"role": "user", "content": user_input})                try:            # 调用模型，允许函数调用            response = client.chat.completions.create(                model="QwQ-32B",                messages=conversation_history,                functions=functions,                function_call="auto"            )                        assistant_message = response.choices[0].message                        # 添加助手回复到对话历史            conversation_history.append(assistant_message)                        # 检查是否有函数调用            if assistant_message.function_call:                function_call = assistant_message.function_call                function_name = function_call.name                                # 解析函数参数                try:                    function_args = json.loads(function_call.arguments)                except json.JSONDecodeError:                    print("函数参数解析错误")                    continue                                print(f"正在调用函数: {function_name}")                                # 执行函数                if function_name in function_map:                    function_to_call = function_map[function_name]                    function_response = function_to_call(**function_args)                                        # 将函数执行结果添加到对话历史                    conversation_history.append({                        "role": "function",                        "name": function_name,                        "content": json.dumps(function_response, ensure_ascii=False)                    })                                        # 再次调用模型，让它解释函数执行结果                    second_response = client.chat.completions.create(                        model="QwQ-32B",                        messages=conversation_history                    )                                        # 输出模型解释                    print("\n助手:", second_response.choices[0].message.content)                                        # 添加到对话历史                    conversation_history.append(second_response.choices[0].message)                else:                    print(f"未知函数: {function_name}")            else:                # 直接输出模型回复                print("\n助手:", assistant_message.content)                        except Exception as e:            print(f"发生错误: {str(e)}")if __name__ == "__main__":    interactive_stock_query()