QwQ 模型的用法_大模型服务平台百炼(Model Studio)-阿里云帮助中心

QwQ 模型具有强大的推理能力，模型会先输出思考过程，再输出回答内容。QwQ 模型当前仅支持通过流式输出方式调用。

本文介绍的 QwQ 模型使用方法不适用于 qwq-32b-preview，如果您使用 qwq-32b-preview 模型，请参考文本生成。

概述

相比于 Qwen-Max 等文本生成模型，QwQ 模型会“三思而后言”，在回复前先进行思考：

以上组件仅供您参考，并未真实发送请求。

QwQ 模型介绍

商业版模型

基于 Qwen2.5 模型训练的 QwQ 推理模型，通过强化学习大幅度提升了模型推理能力。模型数学代码等核心指标（AIME 24/25、LiveCodeBench）以及部分通用指标（IFEval、LiveBench等）达到DeepSeek-R1 满血版水平。相较于开源版，商业版具有最新的能力和改进。

模型名称	版本	上下文长度	最大输入	最大思维链长度	最大回复长度	输入成本	输出成本	免费额度（注）
模型名称	版本	（Token数）				（每千Token）		免费额度（注）

模型名称	版本	上下文长度	最大输入	最大思维链长度	最大回复长度	输入成本	输出成本	免费额度（注）
		（Token数）				（每千Token）
qwq-plus 当前等同 qwq-plus-2025-03-05	稳定版	131,072	98,304	32,768	8,192	0.0016元 Batch调用：0.0008元	0.004元 Batch调用：0.002元	各100万 Token 有效期：百炼开通后180天内
qwq-plus-latest 始终等同最新快照版	最新版					0.0016元	0.004元
qwq-plus-2025-03-05 又称qwq-plus-0305	快照版

开源版模型

基于 Qwen2.5-32B 模型训练的 QwQ 推理模型，于2025年3月6日发布。通过强化学习大幅度提升了模型推理能力。模型数学代码等核心指标（AIME 24/25、LiveCodeBench）以及部分通用指标（IFEval、LiveBench等）达到DeepSeek-R1 满血版水平，各指标均显著超过同样基于 Qwen2.5-32B 的 DeepSeek-R1-Distill-Qwen-32B。

模型名称	上下文长度	最大输入	最大思维链长度	最大回复长度	输入成本	输出成本	免费额度（注）
模型名称	（Token数）				（每千Token）		免费额度（注）

模型名称

上下文长度

最大输入

最大思维链长度

最大回复长度

输入成本

输出成本

免费额度

（注）

（Token数）

（每千Token）

qwq-32b

131,072

98,304

32,768

8,192

0.002元

0.006元

100万 Token

有效期：百炼开通后180天内

并发限流请参考限流。

快速开始

API 使用前提：已获取API Key并完成配置API Key到环境变量。如果通过SDK调用，需要安装 OpenAI 或 DashScope SDK（DashScope Java SDK 版本需要不低于2.18.2）。

重要

QwQ 模型当前仅支持通过流式输出方式调用。
如果您使用 DashScope 方式调用 QwQ 模型：
1. incremental_output 参数默认为 true，且不支持设置为 false，仅支持增量流式返回；
2. response_format参数默认为"message"。

OpenAI兼容

DashScope

Python

Node.js

HTTP

示例代码

from openai import OpenAI
import os

# 初始化OpenAI客户端
client = OpenAI(
    # 如果没有配置环境变量，请用百炼API Key替换：api_key="sk-xxx"
    api_key = os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)

reasoning_content = ""  # 定义完整思考过程
answer_content = ""     # 定义完整回复
is_answering = False   # 判断是否结束思考过程并开始回复

# 创建聊天完成请求
completion = client.chat.completions.create(
    model="qwq-32b",  # 此处以 qwq-32b 为例，可按需更换模型名称
    messages=[
        {"role": "user", "content": "9.9和9.11谁大"}
    ],
    # QwQ 模型仅支持流式输出方式调用
    stream=True,
    # 解除以下注释会在最后一个chunk返回Token使用量
    # stream_options={
    #     "include_usage": True
    # }
)

print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")

for chunk in completion:
    # 如果chunk.choices为空，则打印usage
    if not chunk.choices:
        print("\nUsage:")
        print(chunk.usage)
    else:
        delta = chunk.choices[0].delta
        # 打印思考过程
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
            print(delta.reasoning_content, end='', flush=True)
            reasoning_content += delta.reasoning_content
        else:
            # 开始回复
            if delta.content != "" and is_answering is False:
                print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
                is_answering = True
            # 打印回复过程
            print(delta.content, end='', flush=True)
            answer_content += delta.content

# print("=" * 20 + "完整思考过程" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "完整回复" + "=" * 20 + "\n")
# print(answer_content)

返回结果

====================思考过程====================

嗯，用户问的是9.9和9.11谁大。首先我要确定这两个数字的具体含义，因为有时候在不同的上下文中可能会有不同的解释。不过通常来说，这里的数字应该是指十进制的小数，也就是数学上的数值比较。

首先，我需要比较这两个小数的大小。9.9和9.11。先看整数部分，都是9，所以整数部分相同。接下来比较小数部分。小数点后的第一位，9.9的小数点后第一位是9，而9.11的小数点后第一位是1。这时候，9比1大，所以9.9的小数部分在第一位就已经更大了，那么整个数自然更大。不过，可能有人会疑惑，因为9.11的小数点后有两位，而9.9只有两位？或者说是不是应该补零来比较？

比如，9.9可以写成9.90，而9.11是9.11，这样比较的话，第二位小数的话，9.90的第二位是0，而9.11的是1，这时候第二位的话，0比1小。不过这时候第一位已经分出胜负了，第一位9比1大，所以不管后面的小数位如何，第一位已经决定了。因此，9.9比9.11大。

不过，也有可能用户在这里的写法有其他意思，比如9.9和9.11可能指的是日期，比如9月9日和9月11日，这样的话比较的话，9.11会更大，因为11日比9日晚。但通常数字比较的话，如果没有特别说明是日期的话，应该按数值来比较。不过用户的问题用的是“谁大”，也就是数值大小，而不是日期先后，所以应该还是数值比较。

再仔细检查一遍，9.9和9.11的数值比较：

9.9 = 9 + 0.9 = 9.900...

9.11 = 9 + 0.11 = 9.110...

比较的时候，先比较整数部分，都是9，相同。然后比较小数点后的第一位，0.9的十分位是9，而0.11的十分位是1，所以9.9的十分位更大，因此整个数更大。后面的百分位虽然9.11有1，但因为十分位已经分出高下了，所以不影响结果。

所以结论应该是9.9比9.11大。不过可能有人会因为小数点后的位数不同而产生混淆，比如认为9.11有两位小数，所以更精确或者数值更大，但其实不是的，数值的大小不取决于小数点后的位数，而是每一位的数值比较。

比如，比如比较1.5和1.499，虽然1.499有三位小数，但数值上还是小于1.5。同理，9.9虽然写成一位小数，但等价于9.90，所以比9.11大。

不过，也有可能用户在这里的写法有其他意思，比如货币单位？比如9.9元和9.11元，这时候同样是9.9元更多。或者其他的上下文，但如果没有特别说明的话，应该按数值比较。

所以最终结论是9.9比9.11大。不过，为了确保正确，再举个例子，比如用分数来比较：

9.9 = 99/10 = 9.9

9.11 = 911/100 = 9.11

将它们通分比较：

99/10 = 990/100

911/100 = 911/100

显然，990 > 911，所以9.9更大。

这样确认下来，答案应该是9.9更大。不过用户可能在输入时有笔误，或者有其他意图，但按照常规数学比较的话，结论应该是正确的。
====================完整回复====================

9.9比9.11大。以下是详细分析：

1. **整数部分比较**：  
   两个数的整数部分均为 **9**，因此整数部分相等。

2. **小数部分比较**：  
   - **第一位小数**：  
     9.9 的小数点后第一位是 **9**，而 9.11 的小数点后第一位是 **1**。  
     **9 > 1**，因此仅凭这一位即可判断 **9.9 > 9.11**。

   - **后续小数位**（补充说明）：  
     即使补零为相同位数（如 9.9 = 9.90），比较第二位小数时：  
     - 9.90 的第二位是 **0**，而 9.11 的第二位是 **1**。  
     但此时第一位小数已分出胜负，后续位数不影响最终结果。

3. **分数形式验证**：  
   - 9.9 = \( \frac{99}{10} = \frac{990}{100} \)  
   - 9.11 = \( \frac{911}{100} \)  
   显然，\( 990 > 911 \)，因此 **9.9 > 9.11**。

**结论**：  
在数值大小比较中，**9.9 大于 9.11**。

示例代码

import OpenAI from "openai";
import process from 'process';

// 初始化 openai 客户端
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // 从环境变量读取
    baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1'
});

let reasoningContent = '';
let answerContent = '';
let isAnswering = false;

async function main() {
    try {
        const stream = await openai.chat.completions.create({
            model: 'qwq-32b',
            messages: [{ role: 'user', content: '9.9和9.11谁大' }],
            // QwQ 模型仅支持流式输出方式调用
            stream: true
        });

        console.log('\n' + '='.repeat(20) + '思考过程' + '='.repeat(20) + '\n');

        for await (const chunk of stream) {
            if (!chunk.choices?.length) {
                console.log('\nUsage:');
                console.log(chunk.usage);
                continue;
            }

            const delta = chunk.choices[0].delta;
            
            // 处理思考过程
            if (delta.reasoning_content) {
                process.stdout.write(delta.reasoning_content);
                reasoningContent += delta.reasoning_content;
            } 
            // 处理正式回复
            else if (delta.content) {
                if (!isAnswering) {
                    console.log('\n' + '='.repeat(20) + '完整回复' + '='.repeat(20) + '\n');
                    isAnswering = true;
                }
                process.stdout.write(delta.content);
                answerContent += delta.content;
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

返回结果

====================思考过程====================

嗯，用户问的是9.9和9.11谁大。首先我要确定这两个数字的具体含义，因为有时候在不同的上下文中可能会有不同的解释。不过通常来说，这里的数字应该是指十进制的小数，也就是数学上的数值比较。

首先，我需要比较这两个小数的大小。9.9和9.11。先看整数部分，都是9，所以整数部分相同。接下来比较小数部分。小数点后的第一位，9.9的小数点后第一位是9，而9.11的小数点后第一位是1。这时候，9比1大，所以9.9的小数部分在第一位就已经更大了，那么整个数自然更大。不过，可能有人会疑惑，因为9.11的小数点后有两位，而9.9只有两位？或者说是不是应该补零来比较？

比如，9.9可以写成9.90，而9.11是9.11，这样比较的话，第二位小数的话，9.90的第二位是0，而9.11的是1，这时候第二位的话，0比1小。不过这时候第一位已经分出胜负了，第一位9比1大，所以不管后面的小数位如何，第一位已经决定了。因此，9.9比9.11大。

不过，也有可能用户在这里的写法有其他意思，比如9.9和9.11可能指的是日期，比如9月9日和9月11日，这样的话比较的话，9.11会更大，因为11日比9日晚。但通常数字比较的话，如果没有特别说明是日期的话，应该按数值来比较。不过用户的问题用的是“谁大”，也就是数值大小，而不是日期先后，所以应该还是数值比较。

再仔细检查一遍，9.9和9.11的数值比较：

9.9 = 9 + 0.9 = 9.900...

9.11 = 9 + 0.11 = 9.110...

比较的时候，先比较整数部分，都是9，相同。然后比较小数点后的第一位，0.9的十分位是9，而0.11的十分位是1，所以9.9的十分位更大，因此整个数更大。后面的百分位虽然9.11有1，但因为十分位已经分出高下了，所以不影响结果。

所以结论应该是9.9比9.11大。不过可能有人会因为小数点后的位数不同而产生混淆，比如认为9.11有两位小数，所以更精确或者数值更大，但其实不是的，数值的大小不取决于小数点后的位数，而是每一位的数值比较。

比如，比如比较1.5和1.499，虽然1.499有三位小数，但数值上还是小于1.5。同理，9.9虽然写成一位小数，但等价于9.90，所以比9.11大。

不过，也有可能用户在这里的写法有其他意思，比如货币单位？比如9.9元和9.11元，这时候同样是9.9元更多。或者其他的上下文，但如果没有特别说明的话，应该按数值比较。

所以最终结论是9.9比9.11大。不过，为了确保正确，再举个例子，比如用分数来比较：

9.9 = 99/10 = 9.9

9.11 = 911/100 = 9.11

将它们通分比较：

99/10 = 990/100

911/100 = 911/100

显然，990 > 911，所以9.9更大。

这样确认下来，答案应该是9.9更大。不过用户可能在输入时有笔误，或者有其他意图，但按照常规数学比较的话，结论应该是正确的。
====================完整回复====================

9.9比9.11大。以下是详细分析：

1. **整数部分比较**：  
   两个数的整数部分均为 **9**，因此整数部分相等。

2. **小数部分比较**：  
   - **第一位小数**：  
     9.9 的小数点后第一位是 **9**，而 9.11 的小数点后第一位是 **1**。  
     **9 > 1**，因此仅凭这一位即可判断 **9.9 > 9.11**。

   - **后续小数位**（补充说明）：  
     即使补零为相同位数（如 9.9 = 9.90），比较第二位小数时：  
     - 9.90 的第二位是 **0**，而 9.11 的第二位是 **1**。  
     但此时第一位小数已分出胜负，后续位数不影响最终结果。

3. **分数形式验证**：  
   - 9.9 = \( \frac{99}{10} = \frac{990}{100} \)  
   - 9.11 = \( \frac{911}{100} \)  
   显然，\( 990 > 911 \)，因此 **9.9 > 9.11**。

**结论**：  
在数值大小比较中，**9.9 大于 9.11**。

示例代码

curl

curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwq-32b",
    "messages": [
        {
            "role": "user", 
            "content": "9.9和9.11谁大"
        }
    ],
    "stream": true,
    "stream_options": {
        "include_usage": true
    }
}'

返回结果

data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}

data: {"choices":[{"finish_reason":null,"delta":{"content":null,"reasoning_content":"嗯"},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"，"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"用户"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}
......

data: {"choices":[{"delta":{"content":"需","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}

data: {"choices":[{"delta":{"content":"进一步","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}

data: {"choices":[{"delta":{"content":"确认","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}

data: {"choices":[{"delta":{"content":"规则","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}

data: {"choices":[{"delta":{"content":"。","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}

data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}

data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":18,"completion_tokens":1015,"total_tokens":1033},"created":1741185359,"system_fingerprint":null,"model":"qwq-32b","id":"chatcmpl-6c2000e7-b744-9e61-bd03-fc4333d0e5d1"}

data: [DONE]

Python

Java

HTTP

示例代码

import os
import dashscope

messages = [
    {'role': 'user', 'content': '你是谁？'}
]

response = dashscope.Generation.call(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model="qwq-32b",  # 此处以qwq-32b为例，可按需更换模型名称
    messages=messages,
    # QwQ 模型仅支持流式输出方式调用
    stream=True,
)

# 定义完整思考过程
reasoning_content = ""
# 定义完整回复
answer_content = ""
# 判断是否结束思考过程并开始回复
is_answering = False

print("=" * 20 + "思考过程" + "=" * 20)

for chunk in response:
    # 如果思考过程与回复皆为空，则忽略
    if (chunk.output.choices[0].message.content == "" and 
        chunk.output.choices[0].message.reasoning_content == ""):
        pass
    else:
        # 如果当前为思考过程
        if (chunk.output.choices[0].message.reasoning_content != "" and 
            chunk.output.choices[0].message.content == ""):
            print(chunk.output.choices[0].message.reasoning_content, end="",flush=True)
            reasoning_content += chunk.output.choices[0].message.reasoning_content
        # 如果当前为回复
        elif chunk.output.choices[0].message.content != "":
            if not is_answering:
                print("\n" + "=" * 20 + "完整回复" + "=" * 20)
                is_answering = True
            print(chunk.output.choices[0].message.content, end="",flush=True)
            answer_content += chunk.output.choices[0].message.content

# 如果您需要打印完整思考过程与完整回复，请将以下代码解除注释后运行
# print("=" * 20 + "完整思考过程" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "完整回复" + "=" * 20 + "\n")
# print(f"{answer_content}")

返回结果

====================思考过程====================
嗯，用户问“你是谁？”，我需要先确定他们想知道什么。可能他们刚接触我，想了解我的基本信息。首先，我应该介绍自己的身份，比如我是通义千问，阿里巴巴集团旗下的超大规模语言模型。然后，说明我的功能，比如回答问题、创作文字、编程等。还要提到我的特点，比如多语言支持和持续学习的能力。

接下来，用户可能想知道如何与我互动，所以需要给出使用建议，比如直接提问或给出任务。同时，要确保回答简洁明了，避免技术术语太多，让用户容易理解。另外，可能需要考虑用户的使用场景，比如是个人使用还是商业用途，但问题比较基础，暂时不需要深入。还要检查是否有遗漏的重要信息，比如我的训练数据截止时间或者更新情况，但用户可能不需要这么详细。最后，保持友好和开放的态度，鼓励用户进一步提问，这样能促进更深入的交流。
====================完整回复====================
你好！我是通义千问，阿里巴巴集团旗下的超大规模语言模型。我能够回答问题、创作文字，比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等，还能表达观点，玩游戏等。我熟练掌握多种语言，包括但不限于中文、英文、德语、法语、西班牙语等。

你可以直接告诉我你需要什么帮助，或者有任何问题想问我，我会尽力为你提供支持！有什么我可以帮到你的吗？

示例代码

// dashscope SDK的版本 >= 2.18.2
import java.util.Arrays;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import java.lang.System;

public class Main {
    private static final Logger logger = LoggerFactory.getLogger(Main.class);
    private static StringBuilder reasoningContent = new StringBuilder();
    private static StringBuilder finalContent = new StringBuilder();
    private static boolean isFirstPrint = true;

    private static void handleGenerationResult(GenerationResult message) {
        String reasoning = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
        String content = message.getOutput().getChoices().get(0).getMessage().getContent();

        if (!reasoning.isEmpty()) {
            reasoningContent.append(reasoning);
            if (isFirstPrint) {
                System.out.println("====================思考过程====================");
                isFirstPrint = false;
            }
            System.out.print(reasoning);
        }

        if (!content.isEmpty()) {
            finalContent.append(content);
            if (!isFirstPrint) {
                System.out.println("\n====================完整回复====================");
                isFirstPrint = true;
            }
            System.out.print(content);
        }
    }
    private static GenerationParam buildGenerationParam(Message userMsg) {
        return GenerationParam.builder()
                // 若没有配置环境变量，请用百炼API Key将下行替换为：.apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                // 此处以 qwq-32b 为例，可按需更换模型名称
                .model("qwq-32b")
                .messages(Arrays.asList(userMsg))
                .build();
    }
    public static void streamCallWithMessage(Generation gen, Message userMsg)
            throws NoApiKeyException, ApiException, InputRequiredException {
        GenerationParam param = buildGenerationParam(userMsg);
        Flowable<GenerationResult> result = gen.streamCall(param);
        result.blockingForEach(message -> handleGenerationResult(message));
    }

    public static void main(String[] args) {
        try {
            Generation gen = new Generation();
            Message userMsg = Message.builder().role(Role.USER.getValue()).content("你是谁？").build();
            streamCallWithMessage(gen, userMsg);
//             打印最终结果
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================完整回复====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

返回结果

====================思考过程====================
好的，用户问“你是谁？”，我需要根据之前的设定来回答。首先，我的角色是通义千问，阿里巴巴集团旗下的超大规模语言模型。要保持口语化，简洁易懂。

用户可能刚接触我，或者想确认我的身份。应该先直接回答我是谁，然后简要说明我的功能和用途，比如回答问题、创作文字、编程等。还要提到支持多语言，这样用户知道我可以处理不同语言的需求。

另外，根据指导方针，要保持拟人性，所以语气要友好，可能用表情符号增加亲切感。同时，可能需要引导用户进一步提问或使用我的功能，比如问他们需要什么帮助。

需要注意不要使用复杂术语，避免冗长。检查是否有遗漏的关键点，比如多语言支持和具体能力。确保回答符合所有要求，包括口语化和简洁。
====================完整回复====================
你好！我是通义千问，阿里巴巴集团旗下的超大规模语言模型。我能够回答问题、创作文字，比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等，还能表达观点，玩游戏等。我熟练掌握多种语言，包括但不限于中文、英文、德语、法语、西班牙语等。有什么需要我帮忙的吗？

示例代码

curl

curl -X POST "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-DashScope-SSE: enable" \
-d '{
    "model": "qwq-32b",
    "input":{
        "messages":[      
            {
                "role": "user",
                "content": "你是谁？"
            }
        ]
    }
}'

返回结果

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"嗯","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":14,"input_tokens":11,"output_tokens":3},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:2
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"，","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":15,"input_tokens":11,"output_tokens":4},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:3
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"用户","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":16,"input_tokens":11,"output_tokens":5},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:4
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"问","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":17,"input_tokens":11,"output_tokens":6},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:5
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"“","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":18,"input_tokens":11,"output_tokens":7},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}
......

id:358
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"帮助","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":373,"input_tokens":11,"output_tokens":362},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:359
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"，","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":374,"input_tokens":11,"output_tokens":363},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:360
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"欢迎","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":375,"input_tokens":11,"output_tokens":364},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:361
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"随时","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":376,"input_tokens":11,"output_tokens":365},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:362
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"告诉我","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":377,"input_tokens":11,"output_tokens":366},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:363
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"！","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":378,"input_tokens":11,"output_tokens":367},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:364
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"stop"}]},"usage":{"total_tokens":378,"input_tokens":11,"output_tokens":367},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

多轮对话

QwQ 模型 API 默认不会记录您的历史对话信息。多轮对话功能可以让大模型“拥有记忆”，满足如追问、信息采集等需要连续交流的场景。您在使用 QwQ 模型时，会接收到reasoning_content字段（思考过程）与content（回复内容），您可以将content字段通过{'role': 'assistant', 'content':拼接后的流式输出content}添加到上下文，无需添加reasoning_content字段。

OpenAI兼容

DashScope

您可以通过 OpenAI SDK 或 OpenAI 兼容的 HTTP 方式使用多轮对话功能。

Python

Node.js

HTTP

示例代码

from openai import OpenAI
import os

# 初始化OpenAI客户端
client = OpenAI(
    # 如果没有配置环境变量，请用百炼API Key替换：api_key="sk-xxx"
    api_key = os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)

reasoning_content = ""  # 定义完整思考过程
answer_content = ""     # 定义完整回复
is_answering = False   # 判断是否结束思考过程并开始回复

messages = []
conversation_idx = 1
while True:
    print("="*20+f"第{conversation_idx}轮对话"+"="*20)
    conversation_idx += 1
    user_msg = {"role": "user", "content": input("请输入你的消息：")}
    messages.append(user_msg)
    # 创建聊天完成请求
    completion = client.chat.completions.create(
        model="qwq-32b",  # 此处以 qwq-32b 为例，可按需更换模型名称
        messages=messages,
        # QwQ 模型仅支持流式输出方式调用
        stream=True
    )
    print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")
    for chunk in completion:
        # 如果chunk.choices为空，则打印usage
        if not chunk.choices:
            print("\nUsage:")
            print(chunk.usage)
        else:
            delta = chunk.choices[0].delta
            # 打印思考过程
            if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
                print(delta.reasoning_content, end='', flush=True)
                reasoning_content += delta.reasoning_content
            else:
                # 开始回复
                if delta.content != "" and is_answering is False:
                    print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
                    is_answering = True
                # 打印回复过程
                print(delta.content, end='', flush=True)
                answer_content += delta.content
    messages.append({"role": "assistant", "content": answer_content})
    print("\n")
    # print("=" * 20 + "完整思考过程" + "=" * 20 + "\n")
    # print(reasoning_content)
    # print("=" * 20 + "完整回复" + "=" * 20 + "\n")
    # print(answer_content)

示例代码

import OpenAI from "openai";
import process from 'process';
import readline from 'readline/promises';

// 初始化 readline 接口
const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout
});

// 初始化 openai 客户端
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // 从环境变量读取
    baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1'
});

let reasoningContent = '';
let answerContent = '';
let isAnswering = false;
let messages = [];
let conversationIdx = 1;

async function main() {
    while (true) {
        console.log("=".repeat(20) + `第${conversationIdx}轮对话` + "=".repeat(20));
        conversationIdx++;
        
        // 读取用户输入
        const userInput = await rl.question("请输入你的消息：");
        messages.push({ role: 'user', content: userInput });

        // 重置状态
        reasoningContent = '';
        answerContent = '';
        isAnswering = false;

        try {
            const stream = await openai.chat.completions.create({
                model: 'qwq-32b',
                messages: messages,
                // QwQ 模型仅支持流式输出方式调用
                stream: true
            });

            console.log("\n" + "=".repeat(20) + "思考过程" + "=".repeat(20) + "\n");

            for await (const chunk of stream) {
                if (!chunk.choices?.length) {
                    console.log('\nUsage:');
                    console.log(chunk.usage);
                    continue;
                }

                const delta = chunk.choices[0].delta;
                
                // 处理思考过程
                if (delta.reasoning_content) {
                    process.stdout.write(delta.reasoning_content);
                    reasoningContent += delta.reasoning_content;
                }
                
                // 处理正式回复
                if (delta.content) {
                    if (!isAnswering) {
                        console.log('\n' + "=".repeat(20) + "完整回复" + "=".repeat(20) + "\n");
                        isAnswering = true;
                    }
                    process.stdout.write(delta.content);
                    answerContent += delta.content;
                }
            }
            
            // 将完整回复加入消息历史
            messages.push({ role: 'assistant', content: answerContent });
            console.log("\n");
            
        } catch (error) {
            console.error('Error:', error);
        }
    }
}

// 启动程序
main().catch(console.error);

示例代码

curl

curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwq-32b",
    "messages": [
        {
            "role": "user", 
            "content": "你好"
        },
        {
            "role": "assistant",
            "content": "你好！很高兴见到你，有什么我可以帮忙的吗？"
        },
        {
            "role": "user",
            "content": "你是谁？"
        }
    ],
    "stream": true
}'

您可以通过 DashScope SDK 或 HTTP 方式使用多轮对话功能。

Python

Java

HTTP

示例代码

import os
import dashscope

messages = []
conversation_idx = 1
while True:
    print("=" * 20 + f"第{conversation_idx}轮对话" + "=" * 20)
    conversation_idx += 1
    user_msg = {"role": "user", "content": input("请输入你的消息：")}
    messages.append(user_msg)
    response = dashscope.Generation.call(
        # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
        api_key=os.getenv('DASHSCOPE_API_KEY'),
        model="qwq-32b",  # 此处以qwq-32b为例，可按需更换模型名称。
        messages=messages,
        # QwQ 模型仅支持流式输出方式调用
        stream=True,
    )
    # 定义完整思考过程
    reasoning_content = ""
    # 定义完整回复
    answer_content = ""
    # 判断是否结束思考过程并开始回复
    is_answering = False
    print("=" * 20 + "思考过程" + "=" * 20)
    for chunk in response:
        # 如果思考过程与回复皆为空，则忽略
        if (chunk.output.choices[0].message.content == "" and 
            chunk.output.choices[0].message.reasoning_content == ""):
            pass
        else:
            # 如果当前为思考过程
            if (chunk.output.choices[0].message.reasoning_content != "" and 
                chunk.output.choices[0].message.content == ""):
                print(chunk.output.choices[0].message.reasoning_content, end="",flush=True)
                reasoning_content += chunk.output.choices[0].message.reasoning_content
            # 如果当前为回复
            elif chunk.output.choices[0].message.content != "":
                if not is_answering:
                    print("\n" + "=" * 20 + "完整回复" + "=" * 20)
                    is_answering = True
                print(chunk.output.choices[0].message.content, end="",flush=True)
                answer_content += chunk.output.choices[0].message.content
    messages.append({"role": "assistant", "content": answer_content})
    print("\n")
    # 如果您需要打印完整思考过程与完整回复，请将以下代码解除注释后运行
    # print("=" * 20 + "完整思考过程" + "=" * 20 + "\n")
    # print(f"{reasoning_content}")
    # print("=" * 20 + "完整回复" + "=" * 20 + "\n")
    # print(f"{answer_content}")

示例代码

// dashscope SDK的版本 >= 2.18.2
import java.util.Arrays;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import java.lang.System;
import java.util.List;

public class Main {
    private static final Logger logger = LoggerFactory.getLogger(Main.class);
    private static StringBuilder reasoningContent = new StringBuilder();
    private static StringBuilder finalContent = new StringBuilder();
    private static boolean isFirstPrint = true;

    private static void handleGenerationResult(GenerationResult message) {
        String reasoning = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
        String content = message.getOutput().getChoices().get(0).getMessage().getContent();

        if (!reasoning.isEmpty()) {
            reasoningContent.append(reasoning);
            if (isFirstPrint) {
                System.out.println("====================思考过程====================");
                isFirstPrint = false;
            }
            System.out.print(reasoning);
        }

        if (!content.isEmpty()) {
            finalContent.append(content);
            if (!isFirstPrint) {
                System.out.println("\n====================完整回复====================");
                isFirstPrint = true;
            }
            System.out.print(content);
        }
    }
    private static GenerationParam buildGenerationParam(List Msg) {
        return GenerationParam.builder()
                // 若没有配置环境变量，请用百炼API Key将下行替换为：.apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                // 此处以 qwq-32b 为例，可按需更换模型名称
                .model("qwq-32b")
                .messages(Msg)
                .incrementalOutput(true)
                .build();
    }
    public static void streamCallWithMessage(Generation gen, List Msg)
            throws NoApiKeyException, ApiException, InputRequiredException {
        GenerationParam param = buildGenerationParam(Msg);
        Flowable<GenerationResult> result = gen.streamCall(param);
        result.blockingForEach(message -> handleGenerationResult(message));
    }

    public static void main(String[] args) {
        try {
            Generation gen = new Generation();
            Message userMsg1 = Message.builder()
                    .role(Role.USER.getValue())
                    .content("你好")
                    .build();
            Message AssistantMsg = Message.builder()
                    .role(Role.ASSISTANT.getValue())
                    .content("你好！很高兴见到你，有什么我可以帮忙的吗？")
                    .build();
            Message userMsg2 = Message.builder()
                    .role(Role.USER.getValue())
                    .content("你是谁")
                    .build();
            List Msg = Arrays.asList(userMsg1,AssistantMsg,userMsg2);
            streamCallWithMessage(gen, Msg);
//             打印最终结果
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================完整回复====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

示例代码

curl

curl -X POST "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-DashScope-SSE: enable" \
-d '{
    "model": "qwq-32b",
    "input":{
        "messages":[      
            {
                "role": "user",
                "content": "你好"
            },
            {
                "role": "assistant",
                "content": "你好！很高兴见到你，有什么我可以帮忙的吗？"
            },
            {
                "role": "user",
                "content": "你是谁？"
            }
        ]
    }
}'

Function Calling（工具调用）

QwQ 模型在回答需要实时信息的问题时效果不佳。您可以使用 Function Calling 功能，通过引入外部工具，使得 QwQ 模型可以与外部世界进行交互。

QwQ 模型会在思考完成后进行工具调用信息的输出，tool_choice参数只支持设置为"auto"（默认值，表示由模型自主选择工具）或"none"（强制模型不选择工具）。

OpenAI兼容

DashScope

Python

Node.js

HTTP

示例代码

import os
from openai import OpenAI

# 初始化OpenAI客户端，配置阿里云DashScope服务
client = OpenAI(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # 从环境变量读取API密钥
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

# 定义可用工具列表
tools = [
    # 工具1 获取当前时刻的时间
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "当你想知道现在的时间时非常有用。",
            "parameters": {}  # 无需参数
        }
    },  
    # 工具2 获取指定城市的天气
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "当你想查询指定城市的天气时非常有用。",
            "parameters": {  
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "城市或县区，比如北京市、杭州市、余杭区等。"
                    }
                },
                "required": ["location"]  # 必填参数
            }
        }
    }
]

# 定义问题，请不要设置 System Message
messages = [{"role": "user", "content": input("请输入问题：")}]
completion = client.chat.completions.create(
    model="qwq-plus",  # 此处以qwq-plus为例，可更换为其它qwq模型
    messages=messages,
    tools=tools,
    parallel_tool_calls=True,
    stream=True,
    # 解除注释后，可以获取到token消耗信息
    # stream_options={
    #     "include_usage": True
    # }
)

reasoning_content = ""  # 定义完整思考过程
answer_content = ""     # 定义完整回复
tool_info = []          # 存储工具调用信息
is_answering = False   # 判断是否结束思考过程并开始回复
print("="*20+"思考过程"+"="*20)
for chunk in completion:
    if not chunk.choices:
        # 处理用量统计信息
        print("\n"+"="*20+"Usage"+"="*20)
        print(chunk.usage)
    else:
        delta = chunk.choices[0].delta
        # 处理AI的思考过程（链式推理）
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content is not None:
            reasoning_content += delta.reasoning_content
            print(delta.reasoning_content,end="",flush=True)  # 实时输出思考过程
            
        # 处理最终回复内容
        else:
            if not is_answering:  # 首次进入回复阶段时打印标题
                is_answering = True
                print("\n"+"="*20+"回复内容"+"="*20)
            if delta.content is not None:
                answer_content += delta.content
                print(delta.content,end="",flush=True)  # 流式输出回复内容
            
            # 处理工具调用信息（支持并行工具调用）
            if delta.tool_calls is not None:
                for tool_call in delta.tool_calls:
                    index = tool_call.index  # 工具调用索引，用于并行调用
                    
                    # 动态扩展工具信息存储列表
                    while len(tool_info) <= index:
                        tool_info.append({})
                    
                    # 收集工具调用ID（用于后续函数调用）
                    if tool_call.id:
                        tool_info[index]['id'] = tool_info[index].get('id', '') + tool_call.id
                    
                    # 收集函数名称（用于后续路由到具体函数）
                    if tool_call.function and tool_call.function.name:
                        tool_info[index]['name'] = tool_info[index].get('name', '') + tool_call.function.name
                    
                    # 收集函数参数（JSON字符串格式，需要后续解析）
                    if tool_call.function and tool_call.function.arguments:
                        tool_info[index]['arguments'] = tool_info[index].get('arguments', '') + tool_call.function.arguments
            
print(f"\n"+"="*19+"工具调用信息"+"="*19)
if not tool_info:
    print("没有工具调用")
else:
    print(tool_info)

返回结果

输入“四个直辖市的天气”，得到以下返回结果：

请输入问题：四个直辖市的天气
====================思考过程====================
好的，用户问的是四个直辖市的天气。首先，我需要明确中国的四个直辖市分别是哪几个。北京、上海、天津和重庆，对吧？所以用户需要这四个城市的天气情况。

接下来，我需要调用获取天气的函数get_current_weather，每个城市都要单独查询。不过这个函数每次只能查询一个地点，所以得分别调用四次。首先，我应该检查函数的参数是否需要其他信息，但根据描述，参数只需要location，类型是字符串，比如城市名。所以应该没问题。

然后，我需要确保每个城市的名称正确。比如北京应该用“北京市”，上海是“上海市”，天津是“天津市”，重庆的话可能是“重庆市”或者直接“重庆”，但为了统一，可能用全称更好。用户可能希望得到每个城市的当前天气，所以需要依次调用四次函数。

不过，用户可能希望一次性得到所有四个直辖市的天气，但根据工具的限制，可能需要多次调用。这时候可能需要分四次调用，然后把结果汇总。所以在回复里，我应该生成四个tool_call，每个对应一个城市。

另外，还要注意用户是否可能有其他意图，比如是否只需要今天的天气还是未来几天的？不过根据问题描述，函数是获取当前天气，所以应该没问题。不需要额外处理。

最后，确保每个tool_call的格式正确，参数正确，名称正确。然后按顺序生成四个调用，这样用户就能得到所有四个直辖市的天气数据了。
====================回复内容====================

===================工具调用信息===================
[{'id': 'call_0bfdb95b784e46fab218c0', 'name': 'get_current_weather', 'arguments': '{"location": "北京市"}'}, {'id': 'call_9631576c3ccd419f8d8bc7', 'name': 'get_current_weather', 'arguments': '{"location": "上海市"}'}, {'id': 'call_d862b1bfe8374d2d97f508', 'name': 'get_current_weather', 'arguments': '{"location": "天津市"}'}, {'id': 'call_7222cdcc3cd04c839f2427', 'name': 'get_current_weather', 'arguments': '{"location": "重庆市"}'}]

示例代码

import OpenAI from "openai";
import readline from 'node:readline/promises';
import { stdin as input, stdout as output } from 'node:process';

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1"
});

const tools = [
    {
        type: "function",
        function: {
            name: "get_current_time",
            description: "当你想知道现在的时间时非常有用。",
            parameters: {}
        }
    },
    {
        type: "function",
        function: {
            name: "get_current_weather",
            description: "当你想查询指定城市的天气时非常有用。",
            parameters: {
                type: "object",
                properties: {
                    location: {
                        type: "string",
                        description: "城市或县区，比如北京市、杭州市、余杭区等。"
                    }
                },
                required: ["location"]
            }
        }
    }
];

async function main() {
    const rl = readline.createInterface({ input, output });
    const question = await rl.question("请输入您的问题："); 
    rl.close();
    
    const messages = [{ role: "user", content: question }];
    
    let reasoningContent = "";
    let answerContent = "";
    const toolInfo = [];
    let isAnswering = false;

    console.log("=".repeat(20) + "思考过程" + "=".repeat(20));
    
    try {
        const stream = await openai.chat.completions.create({
            model: "qwq-plus",
            messages,
            tools,
            stream: true,
            parallel_tool_calls: true
        });

        for await (const chunk of stream) {
            if (!chunk.choices?.length) {
                console.log("\n" + "=".repeat(20) + "Usage" + "=".repeat(20));
                console.log(chunk.usage);
                continue;
            }

            const delta = chunk.choices[0]?.delta;
            if (!delta) continue;

            // 处理思考过程
            if (delta.reasoning_content) {
                reasoningContent += delta.reasoning_content;
                process.stdout.write(delta.reasoning_content);
            }
            // 处理回复内容
            else {
                if (!isAnswering) {
                    isAnswering = true;
                    console.log("\n" + "=".repeat(20) + "回复内容" + "=".repeat(20));
                }
                if (delta.content) {
                    answerContent += delta.content;
                    process.stdout.write(delta.content);
                }
                // 处理工具调用
                if (delta.tool_calls) {
                    for (const toolCall of delta.tool_calls) {
                        const index = toolCall.index;
                        
                        // 确保数组长度足够
                        while (toolInfo.length <= index) {
                            toolInfo.push({});
                        }
                        
                        // 更新工具ID
                        if (toolCall.id) {
                            toolInfo[index].id = (toolInfo[index].id || "") + toolCall.id;
                        }
                        
                        // 更新函数名称
                        if (toolCall.function?.name) {
                            toolInfo[index].name = (toolInfo[index].name || "") + toolCall.function.name;
                        }
                        
                        // 更新参数
                        if (toolCall.function?.arguments) {
                            toolInfo[index].arguments = (toolInfo[index].arguments || "") + toolCall.function.arguments;
                        }
                    }
                }
            }
        }

        console.log("\n" + "=".repeat(19) + "工具调用信息" + "=".repeat(19));
        console.log(toolInfo.length ? toolInfo : "没有工具调用");

    } catch (error) {
        console.error("发生错误:", error);
    }
}

main();

返回结果

输入“四个直辖市的天气”，得到以下返回结果：

请输入您的问题：四个直辖市的天气
====================思考过程====================
好的，用户问的是四个直辖市的天气。首先，我需要明确中国的四个直辖市分别是哪几个。北京、上海、天津和重庆，对吧？接下来，我需要为每个城市调用天气查询功能。

但是用户的问题可能需要我分别获取这四个城市的天气情况。每个城市都需要调用一次get_current_weather函数，参数是各自的城市名称。我需要确保参数正确，比如直辖市的全名，比如“北京市”、“上海市”、“天津市”和“重庆市”。

然后，我需要按照顺序依次调用这四个城市的天气接口。每个调用都需要单独的tool_call。用户可能希望得到每个城市的当前天气信息，所以需要确保每个调用都正确无误。可能需要注意每个城市的正确拼写和名称，避免出现错误。例如，重庆有时可能被简称为“重庆市”，所以参数里应该用全称。

现在，我需要生成四个tool_call，每个对应一个直辖市。检查每个参数是否正确，然后按顺序排列。这样用户就能得到四个直辖市的天气数据了。
====================回复内容====================

===================工具调用信息===================
[
  {
    id: 'call_21dc802e717f491298d1b2',
    name: 'get_current_weather',
    arguments: '{"location": "北京市"}'
  },
  {
    id: 'call_2cd3be1d2f694c4eafd4e5',
    name: 'get_current_weather',
    arguments: '{"location": "上海市"}'
  },
  {
    id: 'call_48cf3f78e02940bd9085e4',
    name: 'get_current_weather',
    arguments: '{"location": "天津市"}'
  },
  {
    id: 'call_e230a2b4c64f4e658d223e',
    name: 'get_current_weather',
    arguments: '{"location": "重庆市"}'
  }
]

示例代码

curl

curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwq-plus",
    "messages": [
        {
            "role": "user", 
            "content": "杭州天气怎么样"
        }
    ],
    "tools": [
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "当你想知道现在的时间时非常有用。",
            "parameters": {}
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "当你想查询指定城市的天气时非常有用。",
            "parameters": {
                "type": "object",
                "properties": {
                    "location":{
                        "type": "string",
                        "description": "城市或县区，比如北京市、杭州市、余杭区等。"
                    }
                },
                "required": ["location"]
            }
        }
    }
  ],
  "stream": true
}'

返回结果

data: {"choices":[{"delta":{"content":null,"reasoning_content":"好的，用户问","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"的是杭州的天气怎么样"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"。我需要使用"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"get_current_weather这个"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"函数来获取信息"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"。首先，确认函数的"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"参数要求，里面"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"需要location参数，类型是"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"字符串，比如城市"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"或县区。"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"用户提到的是杭州市"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"，所以参数应该"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"填“杭州市”。然后检查"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"是否有其他可能的参数或"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"需要注意的地方，这里没有"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"其他参数。所以"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"正确的调用应该是调"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"用get_current_weather，参数"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"location设为“杭州市"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"”。不需要调用其他"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"函数，因为用户"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"只问了天气。"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"确认没有遗漏必要"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"参数，然后生成"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"对应的tool_call。"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":null,"tool_calls":[{"function":{"name":"get_current_weather","arguments":"{\"location\": \"杭州市"},"index":0,"id":"call_c7fe3e2aa1fc4b67a79995","type":"function"}]},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"delta":{"tool_calls":[{"function":{"arguments":"\"}"},"index":0,"id":"","type":"function"}]},"index":0}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: {"choices":[{"finish_reason":"tool_calls","delta":{},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742287815,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-423eb42c-57d8-90b8-a976-15d30c588792"}

data: [DONE]

Python

Java

HTTP

示例代码

import os
import dashscope


tools = [
    # 工具1 获取当前时刻的时间
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "当你想知道现在的时间时非常有用。",
            "parameters": {}  # 因为获取当前时间无需输入参数，因此parameters为空字典
        }
    },  
    # 工具2 获取指定城市的天气
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "当你想查询指定城市的天气时非常有用。",
            "parameters": {  
                "type": "object",
                "properties": {
                    # 查询天气时需要提供位置，因此参数设置为location
                    "location": {
                        "type": "string",
                        "description": "城市或县区，比如北京市、杭州市、余杭区等。"
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# 定义问题
messages = [{"role": "user", "content": input("请输入问题：")}]
completion = dashscope.Generation.call(
    model="qwq-plus",  # 此处以qwq-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
    messages=messages,
    tools=tools,
    parallel_tool_calls=True,
    stream=True
)

reasoning_content = ""
answer_content = ""
tool_info = []
is_answering = False
print("="*20+"思考过程"+"="*20)

for chunk in completion:
    if chunk.status_code == 200:
        msg = chunk.output.choices[0].message
        
        # 处理思考过程
        if 'reasoning_content' in msg and msg.reasoning_content:
            reasoning_content += msg.reasoning_content
            print(msg.reasoning_content, end="", flush=True)
        
        # 处理回复内容
        if 'content' in msg and msg.content:
            if not is_answering:
                is_answering = True
                print("\n"+"="*20+"回复内容"+"="*20)
            answer_content += msg.content
            print(msg.content, end="", flush=True)
        
        # 处理工具调用
        if 'tool_calls' in msg and msg.tool_calls:
            for tool_call in msg.tool_calls:
                index = tool_call['index']
                
                while len(tool_info) <= index:
                    tool_info.append({'id': '', 'name': '', 'arguments': ''})  # 初始化所有字段
                
                # 增量更新工具ID
                if 'id' in tool_call:
                    tool_info[index]['id'] += tool_call.get('id', '')
                
                # 增量更新函数信息
                if 'function' in tool_call:
                    func = tool_call['function']
                    # 增量更新函数名称
                    if 'name' in func:
                        tool_info[index]['name'] += func.get('name', '')
                    # 增量更新参数
                    if 'arguments' in func:
                        tool_info[index]['arguments'] += func.get('arguments', '')

print(f"\n"+"="*19+"工具调用信息"+"="*19)
if not tool_info:
    print("没有工具调用")
else:
    print(tool_info)

返回结果

输入“四个直辖市的天气”，得到以下返回结果：

请输入问题：四个直辖市的天气
====================思考过程====================
好的，用户问的是四个直辖市的天气。首先，我需要确认中国的四个直辖市分别是哪些。北京、上海、天津和重庆，对吧？接下来，用户需要每个城市的天气情况，所以我需要调用天气查询功能。

不过，问题来了，用户没有指定具体的城市名称，只是说四个直辖市。可能需要我明确每个直辖市的名称，然后分别查询。比如，北京、上海、天津、重庆这四个直辖市。我需要确保每个城市都正确无误。

然后，我要检查可用的工具，用户提供的函数是get_current_weather，参数是location。因此，我需要为每个直辖市调用这个函数，传入对应的城市名称作为参数。比如，第一次调用location是北京市，第二次是上海市，第三次是天津市，第四次是重庆市。

不过，可能需要注意，像重庆这样的直辖市，有时候可能需要更具体的区，但用户可能只需要市级的天气。所以直接使用直辖市名称应该没问题。接下来，我需要生成四个独立的函数调用，每个对应一个直辖市。这样用户就能得到四个城市的天气情况了。

最后，确保每个调用的参数正确，并且没有遗漏。这样用户的问题就能得到完整的回答了。
===================工具调用信息===================
[{'id': 'call_2f774ed97b0e4b24ab10ec', 'name': 'get_current_weather', 'arguments': '{"location": "北京市"}'}, {'id': 'call_dc3b05b88baa48c58bc33a', 'name': 'get_current_weather', 'arguments': '{"location": "上海市"}}'}, {'id': 'call_249b2de2f73340cdb46cbc', 'name': 'get_current_weather', 'arguments': '{"location": "天津市"}'}, {'id': 'call_833333634fda49d1b39e87', 'name': 'get_current_weather', 'arguments': '{"location": "重庆市"}}'}]

示例代码

import java.util.Arrays;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.utils.JsonUtils;
import com.alibaba.dashscope.tools.ToolFunction;
import com.alibaba.dashscope.tools.FunctionDefinition;
import io.reactivex.Flowable;
import com.fasterxml.jackson.databind.node.ObjectNode;
import java.lang.System;
import com.github.victools.jsonschema.generator.Option;
import com.github.victools.jsonschema.generator.OptionPreset;
import com.github.victools.jsonschema.generator.SchemaGenerator;
import com.github.victools.jsonschema.generator.SchemaGeneratorConfig;
import com.github.victools.jsonschema.generator.SchemaGeneratorConfigBuilder;
import com.github.victools.jsonschema.generator.SchemaVersion;
import java.time.LocalDateTime;
import java.time.format.DateTimeFormatter;

public class Main {
    private static final Logger logger = LoggerFactory.getLogger(Main.class);
    private static ObjectNode jsonSchemaWeather;
    private static ObjectNode jsonSchemaTime;

    static class TimeTool {
        public String call() {
            LocalDateTime now = LocalDateTime.now();
            DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss");
            return "当前时间：" + now.format(formatter) + "。";
        }
    }

    static class WeatherTool {
        private String location;

        public WeatherTool(String location) {
            this.location = location;
        }

        public String call() {
            return location + "今天是晴天";
        }
    }

    static {
        SchemaGeneratorConfigBuilder configBuilder = new SchemaGeneratorConfigBuilder(
                SchemaVersion.DRAFT_2020_12, OptionPreset.PLAIN_JSON);
        SchemaGeneratorConfig config = configBuilder
                .with(Option.EXTRA_OPEN_API_FORMAT_VALUES)
                .without(Option.FLATTENED_ENUMS_FROM_TOSTRING)
                .build();
        SchemaGenerator generator = new SchemaGenerator(config);
        jsonSchemaWeather = generator.generateSchema(WeatherTool.class);
        jsonSchemaTime = generator.generateSchema(TimeTool.class);
    }

    private static void handleGenerationResult(GenerationResult message) {
        System.out.println(JsonUtils.toJson(message));
    }

    public static void streamCallWithMessage(Generation gen, Message userMsg)
            throws NoApiKeyException, ApiException, InputRequiredException {
        GenerationParam param = buildGenerationParam(userMsg);
        Flowable<GenerationResult> result = gen.streamCall(param);
        result.blockingForEach(message -> handleGenerationResult(message));
    }

    private static GenerationParam buildGenerationParam(Message userMsg) {
        FunctionDefinition fdWeather = buildFunctionDefinition(
                "get_current_weather", "获取指定地区的天气", jsonSchemaWeather);
        FunctionDefinition fdTime = buildFunctionDefinition(
                "get_current_time", "获取当前时刻的时间", jsonSchemaTime);

        return GenerationParam.builder()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwq-plus")
                .messages(Arrays.asList(userMsg))
                .resultFormat(GenerationParam.ResultFormat.MESSAGE)
                .incrementalOutput(true)
                .tools(Arrays.asList(
                        ToolFunction.builder().function(fdWeather).build(),
                        ToolFunction.builder().function(fdTime).build()))
                .build();
    }

    private static FunctionDefinition buildFunctionDefinition(
            String name, String description, ObjectNode schema) {
        return FunctionDefinition.builder()
                .name(name)
                .description(description)
                .parameters(JsonUtils.parseString(schema.toString()).getAsJsonObject())
                .build();
    }

    public static void main(String[] args) {
        try {
            Generation gen = new Generation();
            Message userMsg = Message.builder()
                    .role(Role.USER.getValue())
                    .content("请告诉我杭州的天气")
                    .build();
            streamCallWithMessage(gen, userMsg);
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

返回结果

{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":6,"total_tokens":244},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"好的，用户让我"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":12,"total_tokens":250},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"告诉杭州的天气。我"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":16,"total_tokens":254},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"需要先确定是否有"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":22,"total_tokens":260},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"相关的工具可用。查看提供的"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":28,"total_tokens":266},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"工具，发现有一个get_current"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":34,"total_tokens":272},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"_weather函数，参数是location"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":38,"total_tokens":276},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"。所以应该调"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":43,"total_tokens":281},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"用这个函数，参数"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":48,"total_tokens":286},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"设为杭州。不需要"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":52,"total_tokens":290},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"其他工具，因为"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":56,"total_tokens":294},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"用户只问了"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":60,"total_tokens":298},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"天气。接下来构造"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":64,"total_tokens":302},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"tool_call，把"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":68,"total_tokens":306},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"名称和参数填"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":73,"total_tokens":311},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"进去。确保参数是"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":78,"total_tokens":316},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"JSON对象，location是"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":82,"total_tokens":320},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"字符串。检查无"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":88,"total_tokens":326},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"误后返回。"}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":106,"total_tokens":344},"output":{"choices":[{"finish_reason":"null","message":{"role":"assistant","content":"","reasoning_content":"","tool_calls":[{"type":"function","id":"call_ecc41296dccc47baa01567","function":{"name":"get_current_weather","arguments":"{\"location\": \"杭州"}}]}}]}}
{"requestId":"4edb81cd-4647-9d5d-88f9-a4f30bc6d8dd","usage":{"input_tokens":238,"output_tokens":108,"total_tokens":346},"output":{"choices":[{"finish_reason":"tool_calls","message":{"role":"assistant","content":"","reasoning_content":"","tool_calls":[{"type":"function","id":"","function":{"arguments":"\"}"}}]}}]}}

示例代码

curl

curl --location "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header "Content-Type: application/json" \
--header "X-DashScope-SSE: enable" \
--data '{
    "model": "qwq-plus",
    "input":{
        "messages":[      
            {
                "role": "user",
                "content": "杭州天气"
            }
        ]
    },
    "parameters": {
        "result_format": "message",
        "tools": [{
            "type": "function",
            "function": {
                "name": "get_current_time",
                "description": "当你想知道现在的时间时非常有用。",
                "parameters": {}
            }
        },{
            "type": "function",
            "function": {
                "name": "get_current_weather",
                "description": "当你想查询指定城市的天气时非常有用。",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "城市或县区，比如北京市、杭州市、余杭区等。"
                        }
                    },
                    "required": ["location"]
                }
            }
        }]
    }
}'

返回结果

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"好的，用户问","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":228,"input_tokens":222,"output_tokens":6},"request_id":"351adfc9-47cc-9055-83ba-7300c1a4ef56"}

id:2
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"的是杭州的天气，","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":233,"input_tokens":222,"output_tokens":11},"request_id":"351adfc9-47cc-9055-83ba-7300c1a4ef56"}

id:3
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"我需要调用天气查询","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":239,"input_tokens":222,"output_tokens":17},"request_id":"351adfc9-47cc-9055-83ba-7300c1a4ef56"}

......

id:22
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"最后确保格式正确，用","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":338,"input_tokens":222,"output_tokens":116},"request_id":"351adfc9-47cc-9055-83ba-7300c1a4ef56"}

id:23
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"XML标签包裹JSON对象。","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":344,"input_tokens":222,"output_tokens":122},"request_id":"351adfc9-47cc-9055-83ba-7300c1a4ef56"}

id:24
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"这样应该就能正确","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":348,"input_tokens":222,"output_tokens":126},"request_id":"351adfc9-47cc-9055-83ba-7300c1a4ef56"}

id:25
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"获取杭州的天气信息","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":353,"input_tokens":222,"output_tokens":131},"request_id":"351adfc9-47cc-9055-83ba-7300c1a4ef56"}

id:26
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"tool_calls":[{"index":0,"id":"call_758d97beb1224496b77293","type":"function","function":{"name":"get_current_weather","arguments":"{\""}}],"role":"assistant","content":"","reasoning_content":""},"finish_reason":"null"}]},"usage":{"total_tokens":371,"input_tokens":222,"output_tokens":149},"request_id":"351adfc9-47cc-9055-83ba-7300c1a4ef56"}

id:27
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"tool_calls":[{"index":0,"id":"","type":"function","function":{"arguments":"location\": \"杭州市\"}"}}],"role":"assistant","content":"","reasoning_content":""},"finish_reason":"null"}]},"usage":{"total_tokens":377,"input_tokens":222,"output_tokens":155},"request_id":"351adfc9-47cc-9055-83ba-7300c1a4ef56"}

id:28
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"tool_calls":[{"index":0,"id":"","type":"function","function":{}}],"role":"assistant","content":"","reasoning_content":""},"finish_reason":"tool_calls"}]},"usage":{"total_tokens":377,"input_tokens":222,"output_tokens":155},"request_id":"351adfc9-47cc-9055-83ba-7300c1a4ef56"}

在得到 Function Calling 输出的工具信息后，您可以参考运行工具函数与大模型总结工具函数输出，使大模型能够根据工具运行的结果进行回答。

联网搜索

由于训练数据的时效性，QwQ 模型无法准确回答如股票价格、今日资讯等时效性问题。您可以通过设置enable_search参数为true以启用联网检索功能，使 QwQ 模型可以基于实时检索数据进行回复。

开启enable_search后，模型会先判断是否需要使用联网搜索能力来回答您的输入问题：

需要联网搜索
当您输入的问题被模型判断需要使用联网搜索能力，模型会根据联网搜索的结果进行回复。联网搜索功能当前免费，但搜索到的信息会增加 Token 消耗。
不需要联网搜索
QwQ 模型本身已经可以回答如“你是谁”、“一年有多少天”等简单或常识性的问题。此时模型不会去联网搜索，而是直接进行回答。
如果您希望强制开启联网搜索功能，请参见以下代码中的forced_search参数。

除了快照版，其余的 QwQ 模型均支持联网搜索。

DashScope

OpenAI兼容

在设置enable_search参数为true后，您可以通过search_options参数来配置联网搜索策略，包括以下方面：

是否强制开启联网搜索
通过forced_search配置，可选值：
- true
  强制开启。
- false（默认值）
  不强制开启。
返回数据是否包含搜索来源的信息
通过enable_source配置，可选值：
- true
  返回数据中包含搜索来源的信息。搜索来源信息通过search_info参数返回。
- false（默认值）
  返回数据中不包含搜索来源的信息。
是否开启角标标注功能
通过enable_citation配置，在enable_source为true时生效。可选值：
- true
  开启角标标注。
  角标标注的样式可以通过下方的citation_format参数设置。
- false（默认值）
  不开启角标标注。
角标标注的样式
通过citation_format配置，在enable_citation为true时生效。可选值：
- "[<number>]"（默认值）
  角标形式为[i]。
- "[ref_<number>]"
  角标形式为[ref_i]。
搜索互联网信息的数量
通过search_strategy配置。可选值：
- "standard"（默认值）
  模型会联网搜索5条信息。
- "pro"
  模型会联网搜索10条信息。

Python

Java

HTTP

示例代码

import os
import dashscope

messages = [
    {'role': 'user', 'content': '哪吒2的票房'}
]

response = dashscope.Generation.call(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx"
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model="qwq-plus",  # 此处以qwq-plus为例，可按需更换模型名称
    messages=messages,
    enable_search = True, # 开启联网搜索的参数
    search_options = {
        "forced_search": True, # 强制开启联网搜索
        "enable_source": True, # 使返回结果包含搜索来源的信息，OpenAI 兼容方式暂不支持返回
        "enable_citation": True, # 开启角标标注功能
        "citation_format": "[ref_<number>]", # 角标形式为[ref_i]
        "search_strategy": "pro" # 模型将搜索10条互联网信息
    },
    # QwQ 模型仅支持流式输出方式调用
    stream=True,
)

# 定义完整思考过程
reasoning_content = ""
# 定义完整回复
answer_content = ""
# 判断是否结束思考过程并开始回复
is_answering = False
# 判断是否为第一个chunk，便于打印搜索信息
is_first_chunk = True

print("=" * 20 + "搜索信息" + "=" * 20)

for chunk in response:
    if is_first_chunk:
        search_results = chunk.output.search_info["search_results"]
        for web in search_results:
            print(f"[{web['index']}]: [{web['title']}]({web['url']})")
        print("=" * 20 + "思考过程" + "=" * 20)
        reasoning_content += chunk.output.choices[0].message.reasoning_content
        print(chunk.output.choices[0].message.reasoning_content,end="",flush=True)
        is_first_chunk = False
    else:
        # 如果思考过程与回复皆为空，则忽略
        if (chunk.output.choices[0].message.content == "" and 
            chunk.output.choices[0].message.reasoning_content == ""):
            pass
        else:
            # 如果当前为思考过程
            if (chunk.output.choices[0].message.reasoning_content != "" and 
                chunk.output.choices[0].message.content == ""):
                print(chunk.output.choices[0].message.reasoning_content, end="",flush=True)
                reasoning_content += chunk.output.choices[0].message.reasoning_content
            # 如果当前为回复
            elif chunk.output.choices[0].message.content != "":
                if not is_answering:
                    print("\n" + "=" * 20 + "完整回复" + "=" * 20)
                    is_answering = True
                print(chunk.output.choices[0].message.content, end="",flush=True)
                answer_content += chunk.output.choices[0].message.content

# 如果您需要打印完整思考过程与完整回复，请将以下代码解除注释后运行
# print("=" * 20 + "完整思考过程" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "完整回复" + "=" * 20 + "\n")
# print(f"{answer_content}")
# 如果您需要打印本次请求的 Token 消耗，请将以下代码解除注释后运行
# print("\n"+"="*20+"Token 消耗"+"="*20)
# print(chunk.usage)

返回结果

====================搜索信息====================
[1]: [哪吒2票房超153亿 小吒儿太争气了！-中华网](https://3g.china.com/act/news/10000169/20250324/48120687.html)
[2]: [《哪吒2》全球票房破153亿, 距离超越《泰坦尼克号》还有多远?-倾城一笑红颜吖](https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=effc0af9cee743b2b3485a75eb85dfae!!wm_id=01887e0e8266475b87b8ef1e578e5239)
[3]: [《哪吒2》导演总票房200亿, 他个人能赚多少? 普通人9辈子花不完-光影新天地](https://mparticle.uc.cn/article.html?title_type=1&uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=a9a1a74021c34364972f063ee24ff428!!wm_id=62bcfb22b72b4e86b229d7e92d86200f)
[4]: [《哪吒2》全球票房破153亿, 距离超越《泰坦尼克号》还有多远?-倾城一笑红颜吖](https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=effc0af9cee743b2b3485a75eb85dfae!!wm_id=01887e0e8266475b87b8ef1e578e5239)
[5]: [152.83亿票房神话背后: 《哪吒2》的荣耀与争议-观止之影](https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=9c926b6274f448468fb5b53eaba4297e!!wm_id=1f462f41cd824ce0aac3cafcb410dca6)
[6]: [《哪吒2》密钥再延一个月: 全球票房破152亿元, 白领贡献最多票房-第一财经](https://mparticle.uc.cn/article_org.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_cid=676384453016170496!!wm_id=1709157a815b423aab98ca7338f2971a)
[7]: [《哪吒2》密钥再延一个月：全球票房破152亿元，白领贡献最多票房-一财网](http://m.yicai.com/news/102529223.html)
[8]: [20亿好莱坞大片也不是对手, 《哪吒2》票房逆跌, 重登中国香港日冠-影视原说](https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=b7e62c0d086247d5aa38c2c00caf75e9!!wm_id=3660dc355efa44509f7f22e4240a7845)
[9]: [《哪吒2》全球票房之战: 国产动画的崛起与挑战-汪逸晨说](https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=25f5e73ea4af42f195d2f86e780cdbfb!!wm_id=6acd8b1f575e437c9f9b8c9f84811e12)
[10]: [票房｜《哪吒2》登陆英国、日本院线 国内放映延长到五一-财新网](https://www.caixin.com/2025-03-24/102301462.html)
====================思考过程====================
好的，我现在需要回答用户关于《哪吒2》票房的问题。首先，我要查看用户提供的知识库内容，找到相关的票房数据。

根据知识库中的信息，多个来源提到了《哪吒2》的全球票房。例如，ref_1提到票房超过153亿人民币，而ref_2和ref_4也指出突破了153亿，并且是全球第五。ref_10更具体地指出截至3月24日9时，全球累计票房为153.09亿元，其中中国本土票房150.4亿元，海外2.69亿元。此外，ref_4提到距离《泰坦尼克号》还有约11.23亿的差距，而ref_10则说是近10亿元的差距，这里可能需要注意数据更新的时间点不同导致的差异。

用户的问题只是问票房，所以需要整合这些数据，说明当前的总票房，国内和海外的分布，以及可能的后续目标。同时要引用正确的来源。需要确认各个引用的准确性，比如不同来源提到的数字是否有冲突，但看起来主要的数据是153亿左右，并且正在接近《泰坦尼克号》。此外，ref_6和ref_7提到延长上映到4月30日，可能有助于票房进一步增长。

最后，确保回答中正确标注引用来源，比如总票房引用ref_1、ref_2、ref_4、ref_10，差距部分引用ref_4和ref_10。同时注意不要遗漏关键点，如海外票房占比相对较低，主要收入来自国内，以及可能的挑战。
====================完整回复====================
截至2025年3月24日9时，《哪吒2》全球累计票房已达到**153.09亿元人民币**，其中中国内地（含港澳台）票房为**150.4亿元**，海外票房为**2.69亿元**[ref_10]。这一成绩使其位列全球影史票房榜第五名，并成为唯一一部跻身21亿美元俱乐部的非好莱坞电影[ref_2][ref_4]。

目前，《哪吒2》距离超越全球票房第四名的《泰坦尼克号》（当前约为164.32亿元人民币）仍需约**11.23亿元**[ref_4]，或接近10亿元的差距（不同数据源统计可能存在细微差异）[ref_10]。考虑到其在海外市场（尤其是香港）的持续表现——例如曾连续近一个月夺得香港日冠，并在英国、日本等地陆续上映[ref_8][ref_10]，以及国内密钥延期至4月30日的策略[ref_6][ref_7]，仍有进一步冲高票房的空间。

值得注意的是，影片的国内票房高度集中，占比超98%，而海外票房占比不足2%[ref_10]，这与好莱坞大片的全球发行策略形成对比。此外，其国内观众群体以25岁及以上白领和三四线城市观众为主，女性观众占比58%[ref_6][ref_7]。

示例代码

// dashscope SDK的版本 >= 2.18.2
import java.util.Arrays;
import com.alibaba.dashscope.aigc.generation.SearchInfo;
import com.alibaba.dashscope.aigc.generation.SearchOptions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import java.lang.System;

public class Main {
    private static final Logger logger = LoggerFactory.getLogger(Main.class);
    private static StringBuilder reasoningContent = new StringBuilder();
    private static StringBuilder finalContent = new StringBuilder();
    private static boolean isFirstPrint = true;

    private static void handleGenerationResult(GenerationResult message) {
        String reasoning = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
        String content = message.getOutput().getChoices().get(0).getMessage().getContent();
        SearchInfo searchInfo = message.getOutput().getSearchInfo();
        if (!reasoning.isEmpty()) {
            reasoningContent.append(reasoning);
            if (isFirstPrint) {
                System.out.println("====================搜索信息====================");
                isFirstPrint = false;
                System.out.print(searchInfo.getSearchResults());

                System.out.println("\n====================思考过程====================");
            }
            System.out.print(reasoning);
        }

        if (!content.isEmpty()) {
            finalContent.append(content);
            if (!isFirstPrint) {
                System.out.println("\n====================完整回复====================");
                isFirstPrint = true;
            }
            System.out.print(content);
        }
        if (message.getOutput().getChoices().get(0).getFinishReason().equals("stop") || message.getOutput().getChoices().get(0).getFinishReason().equals("length")){
            System.out.println("\n====================Token 消耗====================");
            System.out.println(message.getUsage());
        }
    }
    private static GenerationParam buildGenerationParam(Message userMsg) {
        // 定义搜索策略
        SearchOptions searchOptions = SearchOptions.builder()
                // 使返回结果中包含搜索信息的来源
                .enableSource(true)
                // 强制开启互联网搜索
                .forcedSearch(true)
                // 开启角标标注
                .enableCitation(true)
                // 设置角标标注样式为[ref_i]
                .citationFormat("[ref_<number>]")
                // 联网搜索10条信息
                .searchStrategy("pro")
                .build();

        return GenerationParam.builder()
                // 若没有配置环境变量，请用百炼API Key将下行替换为：.apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                // 此处以 qwq-plus 为例，可按需更换模型名称
                .model("qwq-plus")
                .messages(Arrays.asList(userMsg))
                // 开启联网搜索的参数
                .enableSearch(true)
                .searchOptions(searchOptions)
                .build();
    }
    public static void streamCallWithMessage(Generation gen, Message userMsg)
            throws NoApiKeyException, ApiException, InputRequiredException {
        GenerationParam param = buildGenerationParam(userMsg);
        Flowable<GenerationResult> result = gen.streamCall(param);
        result.blockingForEach(message -> handleGenerationResult(message));
    }

    public static void main(String[] args) {
        try {
            Generation gen = new Generation();
            Message userMsg = Message.builder().role(Role.USER.getValue()).content("哪吒2的票房？").build();
            streamCallWithMessage(gen, userMsg);
//             打印最终结果
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================完整回复====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

返回结果

====================搜索信息====================
[SearchInfo.SearchResult(siteName=, icon=, index=1, title=哪吒2海外票房破3亿了, 中国动画的全球崛起-行知探索圈, url=https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=6f339f9b001142ef81f9e5dd04f40777!!wm_id=217a3dfad10e46d095fa9af00282c896), SearchInfo.SearchResult(siteName=, icon=, index=2, title=《哪吒2》153亿票房背后: 我们追捧的到底是国漫, 还是特效鸦片?-逍遥扒扒影, url=https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=1331f1eb7f174d0d8c5ba11efa65daad!!wm_id=a1eb50c1972d4eb2b0dc95d47c886282), SearchInfo.SearchResult(siteName=, icon=, index=3, title=《哪吒2》全球票房破153亿, 距离超越《泰坦尼克号》还有多远?-倾城一笑红颜吖, url=https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=effc0af9cee743b2b3485a75eb85dfae!!wm_id=01887e0e8266475b87b8ef1e578e5239), SearchInfo.SearchResult(siteName=, icon=, index=4, title=《哪吒2》153亿票房背后: 我们追捧的到底是国漫, 还是特效鸦片?-逍遥扒扒影, url=https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=1331f1eb7f174d0d8c5ba11efa65daad!!wm_id=a1eb50c1972d4eb2b0dc95d47c886282), SearchInfo.SearchResult(siteName=, icon=, index=5, title=哪吒2海外票房破3亿了, 中国动画的全球崛起-行知探索圈, url=https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=6f339f9b001142ef81f9e5dd04f40777!!wm_id=217a3dfad10e46d095fa9af00282c896), SearchInfo.SearchResult(siteName=新浪网, icon=https://img.alicdn.com/imgextra/i2/O1CN01mvEkNg1mbgbrppM5C_!!6000000004973-55-tps-32-32.svg, index=6, title=《哪吒2》海外票房破3亿 暂列全球票房榜第5名-新浪财经, url=https://finance.sina.com.cn/tech/roll/2025-03-25/doc-ineqvkuu7132514.shtml), SearchInfo.SearchResult(siteName=, icon=, index=7, title=【#哪吒2海外票房破3亿#】3月24日，据专业平台数据，《哪吒之魔童闹海》海外票-观察者网, url=https://mparticle.uc.cn/article.html?biz_id=1034&uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_cid=677014919905815552!!wm_id=21b429951d2447878286e0640a469ef6), SearchInfo.SearchResult(siteName=第一财经, icon=https://img.alicdn.com/imgextra/i2/O1CN01IBw5aS1M3d9GmYvnx_!!6000000001379-55-tps-32-32.svg, index=8, title=《哪吒2》海外票房破3亿-一财网, url=http://m.yicai.com/news/102532036.html), SearchInfo.SearchResult(siteName=163网易免费邮, icon=https://img.alicdn.com/imgextra/i2/O1CN01rbsKN11MIkDguCxUW_!!6000000001412-55-tps-32-32.svg, index=9, title=《哪吒2》153亿票房遭"冷嘲热讽"，我们为什么对国产动画如此苛刻-网易订阅, url=https://m.163.com/dy/article/JREIHES40556B7G8.html), SearchInfo.SearchResult(siteName=, icon=, index=10, title=《哪吒2》抄袭被实锤? 全球票房超153亿, 让太多人眼红-影视原说, url=https://mparticle.uc.cn/article.html?uc_param_str=frdnsnpfvecpntnwprdssskt#!wm_aid=fd1f933252da43d79c09fc43ea53b972!!wm_id=3660dc355efa44509f7f22e4240a7845)]
====================思考过程====================
好的，我现在需要回答用户关于《哪吒2》票房的问题。首先，我得仔细看一下用户提供的知识库内容，里面有很多相关的引用资料。

用户的问题很简单，就是询问《哪吒2》的票房情况。根据知识库里的信息，我需要找到最新的票房数据以及相关的排名信息。首先，我注意到多个引用提到了全球累计票房达到153亿人民币，比如ref_1、ref_3、ref_5、ref_6、ref_7、ref_8、ref_10都有提到这一点。特别是ref_6和ref_8提到全球票房超过153.47亿元，而ref_5和ref_7指出这个成绩让该片成为全球票房第五的电影，同时也是亚洲电影中的首例。

另外，用户可能还关心海外票房的情况。根据ref_5、ref_6、ref_7、ref_8，海外票房已经突破3亿元人民币，这也是一个重要的数据点，说明影片在国际市场上的表现。同时，ref_6提到密钥延期到4月30日，这可能意味着票房还有进一步增长的空间。

需要注意的是，不同的引用可能有细微的数据差异，比如有的提到153亿，有的则是153.47亿，这可能是因为统计时间不同导致的。根据系统时间2025年3月25日，而部分引用如ref_6和ref_8的数据是在3月24日或25日发布的，所以应该以最新的数据为准，即超过153.47亿元。

另外，用户可能还想了解该票房在全球的排名情况，根据多个引用，目前《哪吒2》暂列全球票房榜第五名，仅次于像《阿凡达》《泰坦尼克号》这样的大片。例如，ref_3提到它进入前五，并且是唯一非好莱坞电影进入21亿美元俱乐部，这显示了其国际影响力。

不过，也要注意有些引用如ref_9和ref_10提到了一些争议，比如票房的争议或抄袭问题，但用户的问题主要是询问票房数字，所以这部分可能不需要深入，除非用户后续追问。但根据要求，回答需要优先引用提供的资料，所以应集中在票房数据上。

总结一下，我需要将全球票房和海外票房分开说明，并引用对应的来源。同时指出其排名和里程碑意义，比如成为亚洲电影中的首部进入全球前五，以及非好莱坞电影的突破。需要确保引用正确的ref编号，避免错误。
====================完整回复====================
截至2025年3月25日，《哪吒2》（《哪吒之魔童闹海》）的全球累计票房已突破**153.47亿元人民币**（含预售及海外票房），暂列全球影史票房榜第5名[ref_6][ref_7][ref_8]。其中，其海外票房已超过3亿元人民币，刷新了中国动画电影海外发行的多项纪录[ref_5][ref_6][ref_8]。

该片不仅是首部跻身全球票房前五的亚洲电影，也是唯一一部进入全球票房21亿美元俱乐部的非好莱坞电影，标志着中国动画电影在国际市场的里程碑式突破[ref_1][ref_3][ref_5]。目前其票房已超越《蜘蛛侠：跨纬度》等好莱坞大片，与排名第四的《泰坦尼克号》（约17.亿美元，约合115亿人民币）仅差约11亿元人民币[ref_3][ref_10]。

值得注意的是，《哪吒2》自上映以来持续延长上映周期，密钥已延期至2025年4月30日，票房仍有进一步增长的空间[ref_6]。
====================Token 消耗====================
GenerationUsage(inputTokens=1272, outputTokens=794, totalTokens=2066)

示例代码

curl

curl -X POST "https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-DashScope-SSE: enable" \
-d '{
    "model": "qwq-plus",
    "input":{
        "messages":[      
            {
                "role": "user",
                "content": "哪吒2的票房"
            }
        ]
    },
    "parameters":{
        "enable_search": true,
        "search_options": {
            "forced_search": true,
            "enable_source": true,
            "enable_citation": true,
            "citation_format": "[ref_<number>]",
            "search_strategy": "pro"
        }
    }
}'

返回结果

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...

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id:129
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在设置enable_search参数为true后，您可以通过search_options参数来配置联网搜索策略，包括以下方面：

是否强制开启联网搜索
通过forced_search配置，可选值：
- true
  强制开启。
- false（默认值）
  不强制开启。
搜索互联网信息的数量
通过search_strategy配置。可选值：
- "standard"（默认值）
  模型会联网搜索5条信息。
- "pro"
  模型会联网搜索10条信息。

OpenAI 兼容方式暂不支持返回搜索来源的信息。

Python

Node.js

HTTP

示例代码

from openai import OpenAI
import os

# 初始化OpenAI客户端
client = OpenAI(
    # 如果没有配置环境变量，请用百炼API Key替换：api_key="sk-xxx"
    api_key = os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)

reasoning_content = ""  # 定义完整思考过程
answer_content = ""     # 定义完整回复
is_answering = False   # 判断是否结束思考过程并开始回复

# 创建聊天完成请求
completion = client.chat.completions.create(
    model="qwq-plus",  # 此处以 qwq-plus 为例，可按需更换模型名称
    messages=[
        {"role": "user", "content": "哪吒2的票房"}
    ],
    extra_body={
        "enable_search": True, # 开启联网搜索的参数
        "search_options": {
            "forced_search": True, # 强制联网搜索的参数
            "search_strategy": "pro" # 模型将搜索10条互联网信息
        }
    },
    # QwQ 模型仅支持流式输出方式调用
    stream=True,
    # 解除以下注释会在最后一个chunk返回Token使用量
    stream_options={
        "include_usage": True
    }
)

print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")

for chunk in completion:
    # 如果chunk.choices为空，则打印usage
    if not chunk.choices:
        print("\n" + "=" * 20 + "Usage" + "=" * 20)
        print(chunk.usage)
    else:
        delta = chunk.choices[0].delta
        # 打印思考过程
        if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
            print(delta.reasoning_content, end='', flush=True)
            reasoning_content += delta.reasoning_content
        else:
            # 开始回复
            if delta.content != "" and is_answering is False:
                print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
                is_answering = True
            # 打印回复过程
            print(delta.content, end='', flush=True)
            answer_content += delta.content

# print("=" * 20 + "完整思考过程" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "完整回复" + "=" * 20 + "\n")
# print(answer_content)

返回结果

====================思考过程====================

嗯，用户问的是《哪吒2》的票房情况，我需要先整理一下知识库里的信息。首先，知识库里的多篇文章都提到了《哪吒2》的全球票房已经突破153亿人民币，其中海外票房部分有不同的数据，比如有的说超过3亿，有的提到153.47亿。可能要注意到不同来源的时间差异，比如观察者网和新浪网的数据是到3月24日或25日的，而用户提供的当前时间是2025年3月25日，所以这些数据应该是最新的。

然后，用户可能想知道具体的数据，比如国内和海外的分别，以及在全球票房榜的位置。根据知识库内容，全球票房排在第五，暂时位于《泰坦尼克号》之后，还差约11亿才能超过。需要指出这个差距，以及海外票房还在增长，因为有些国家还没上映，比如日本、韩国等，可能后续还有增长空间。

另外，用户可能还关心票房背后的意义，比如是否代表国漫崛起，或者存在争议。但问题直接问票房，所以可能需要简洁回答数据，再补充一些相关情况。要注意不同来源的数据可能略有差异，比如有的说153亿，有的153.47亿，可能需要说明是综合数据，或者以最近的为准。

最后，确保回答清晰，分点列出国内、海外、全球总票房，排名情况，以及可能的后续发展。同时引用多个来源的信息，确保准确性。可能还要提到上映延期到4月30日，有助于票房继续增长。这样用户的问题就能得到全面解答了。
====================完整回复====================

根据最新的权威信息，《哪吒2》（《哪吒之魔童闹海》）的票房情况如下：

### **全球票房**
- **总票房**：已突破 **153.47亿元人民币**（含中国内地及海外票房），目前暂列全球影史票房榜第5位。

---

### **分地区票房**
1. **中国内地票房**：
   - 截至3月21日，内地票房已达 **149亿元人民币**，且仍在延长上映中（密钥延期至4月30日），预计将进一步增长。

2. **海外票房**：
   - **已突破3亿元人民币**（截至3月24日数据）。
   - **主要市场表现**：
     - **北美**：票房约2048万美元（约合1.4亿元人民币）。
     - **澳大利亚、新西兰**：票房约559万美元（约合3900万元人民币）。
   - **待上映地区**：日本、韩国、俄罗斯、南非、印度尼西亚等市场已确认引进，但具体档期尚未公布，后续票房仍有较大增长空间。

---

### **与《泰坦尼克号》的差距**
- 当前全球票房排名第4的是《泰坦尼克号》（重映版），票房约 **154.6亿元人民币**。
- **《哪吒2》需再增加约11亿元人民币**方能超越，登顶全球影史前四。

---

### **关键背景与意义**
1. **历史地位**：
   - 已成为中国影史票房冠军，并是全球动画电影票房冠军。
   - 近20年来国产电影在海外市场的最佳表现（非内地票房占比虽小，但创纪录）。

2. **延长上映与潜力**：
   - 因持续热度，影片密钥延期至4月30日，国内票房可能进一步攀升。
   - 海外多国待上映，若后续开画表现良好，全球票房有望继续突破。

---

### **争议与讨论**
- 部分观众认为影片成功依赖“视觉特效堆砌”，而非叙事深度，引发“国漫崛起”与“特效鸦片”之争。
- 尽管票房亮眼，仍存在对国产动画内容深度和可持续性的质疑。

---

### **总结**
《哪吒2》目前以 **153.47亿** 稳居全球票房第五，正逼近《泰坦尼克号》的纪录。其国内票房表现强势，海外虽起步较慢但潜力巨大，未来能否更进一步值得持续关注。
====================Usage====================
CompletionUsage(completion_tokens=903, prompt_tokens=1198, total_tokens=2101, completion_tokens_details=None, prompt_tokens_details=None)

示例代码

import OpenAI from "openai";
import process from 'process';

// 初始化 openai 客户端
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // 从环境变量读取
    baseURL: 'https://dashscope.aliyuncs.com/compatible-mode/v1'
});

let reasoningContent = '';
let answerContent = '';
let isAnswering = false;

async function main() {
    try {
        const stream = await openai.chat.completions.create({
            model: 'qwq-plus',
            messages: [{ role: 'user', content: '哪吒2的票房' }],
            enable_search: true, // 开启联网搜索的参数
            search_options: {
                forced_search: true // 强制联网搜索的参数
            },
            // QwQ 模型仅支持流式输出方式调用
            stream: true
        });

        console.log('\n' + '='.repeat(20) + '思考过程' + '='.repeat(20) + '\n');

        for await (const chunk of stream) {
            if (!chunk.choices?.length) {
                console.log('\nUsage:');
                console.log(chunk.usage);
                continue;
            }

            const delta = chunk.choices[0].delta;
            
            // 处理思考过程
            if (delta.reasoning_content) {
                process.stdout.write(delta.reasoning_content);
                reasoningContent += delta.reasoning_content;
            } 
            // 处理正式回复
            else if (delta.content) {
                if (!isAnswering) {
                    console.log('\n' + '='.repeat(20) + '完整回复' + '='.repeat(20) + '\n');
                    isAnswering = true;
                }
                process.stdout.write(delta.content);
                answerContent += delta.content;
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

返回结果

====================思考过程====================

好的，用户问的是《哪吒2》的票房，我需要从知识库里的信息中整理出最新的数据。首先看一下知识库里的几条信息：

第一条是新华网的报道，提到票房冲上全球第7，突破141.6亿，超过《蜘蛛侠：英雄无归》。时间应该是较早的，因为后面还有更高的数据。

第二条科技日报的再刷纪录，提到超过130亿，暂时排第8位，但可能已经被后续数据更新了。

第三条中国网的新闻在3月14日，指出全球票房突破150亿，接近《星球大战：原力觉醒》的第五名。这里的时间是3月14日，而当前系统时间是3月21日，可能之后还有更新。

第四条科技日报的海外票房破2亿，全球票房超过149.79亿，冲击第五位。这里提到的全球票房是149.79亿，接近150亿，但可能还没到。

第五条新华每日电讯提到海外票房突破2亿，全球票房超过149.79亿，并且评分不错，但同样可能是在3月14日左右的数据。

现在需要确定最新的数据。第三条中国网的新闻明确提到3月14日突破150亿，而第四条可能是在同一天或之后的数据，但第四条的全球票房是149.79亿，接近150亿，可能稍早一点。但第三条已经明确说突破150亿，所以应该以第三条为准。另外，系统时间是3月21日，可能之后票房还在增长，但知识库里的最新数据是到3月14日的150亿，所以应该以这个为准。不过第四条提到的149.79亿可能是在3月14日当天的数据，而第三条突破150亿也是同一天。可能中国网的报道是当天的，所以综合来看，全球票房已经突破150亿，暂列第五位，正在冲击前五。需要确认是否已经进入前五，但根据第三条，它接近第五名的《星球大战：原力觉醒》，可能已经超越，但可能还未正式确认。需要看是否有明确的排名信息。第三条提到“暂列亚洲影史新纪录”，但全球排名可能还在接近第五的位置。所以最终回答应该综合这些信息，给出最新的150亿，并说明排名情况，以及海外票房的数据。需要确保数据准确，优先使用最新的日期，即中国网的3月14日突破150亿，并且海外票房2亿，冲击前五。可能用户想知道当前的最新数据，但根据知识库，最新的就是到3月14日的150亿，之后可能还在增长，但知识库没有更新到21日的数据，所以只能根据现有信息回答。
====================完整回复====================

截至2025年3月14日，《哪吒之魔童闹海》（《哪吒2》）的全球票房（含预售及海外）已突破**150亿元人民币**，创下亚洲影史新纪录，并成功跻身全球影史票房榜前五，目前暂列第五位，正逼近《星球大战：原力觉醒》的全球票房成绩。

### 具体数据：
1. **全球总票房**：超过150亿元人民币（含海外票房约2亿元人民币）。
2. **海外表现**：海外票房突破2亿元，且在烂番茄获得100%新鲜度，爆米花指数达99%，热度持续攀升。
3. **排名**：超越《蜘蛛侠：英雄无归》等影片，目前位列全球影史票房榜第5名，仅次于《阿凡达》《泰坦尼克号》《星球大战：原力觉醒》和《复仇者联盟4》。

### 说明：
- 知识库中最新数据为3月14日的150亿元，当前时间（3月21日）票房可能仍有小幅增长，但尚未有更新的官方数据公布。
- 该片已连续打破多项纪录，成为全球动画电影票房冠军，并持续推动中国电影在国际市场的影响力。

如需更实时的数据，建议关注权威票房平台或官方公告。

示例代码

curl

curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwq-plus",
    "messages": [
        {
            "role": "user", 
            "content": "哪吒2的票房"
        }
    ],
    "enable_search": true,
    "search_options": {
        "forced_search": true
    },
    "stream": true,
    "stream_options": {
        "include_usage": true
    }
}'

返回结果

data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"finish_reason":null,"delta":{"content":null,"reasoning_content":"好的，我现在"},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"需要回答用户关于《哪"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"吒2》票房的问题。"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"首先，我要查看用户提供的"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":"知识库内容，里面有几个"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

......

data: {"choices":[{"delta":{"content":"票房冠军，并跻身","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":"全球前五，持续","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":"刷新纪录。具体","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":"排名可能因不同榜单","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":"的统计方式略有差异，","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":"但其市场表现和","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":"影响力已得到广泛","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"delta":{"content":"认可。","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":511,"completion_tokens":1057,"total_tokens":1568},"created":1742537753,"system_fingerprint":null,"model":"qwq-plus","id":"chatcmpl-32c5e654-b497-9fc8-bba0-27e2f34026b2"}

data: [DONE]

注意事项

不支持的功能与参数
- 不支持的功能
  结构化输出、前缀续写、上下文缓存
- 不支持的参数
  temperature、top_p、presence_penalty、frequency_penalty、logprobs、top_logprobs
  设置这些参数都不会生效，即使没有输出错误提示。
- 为了达到模型的最佳推理效果，不建议设置 System Message。
我的应用：工作流应用已支持QwQ模型；智能体应用已支持QwQ模型（不包括插件、流程、音视频交互能力）。
模型在线体验：支持体验 QwQ 模型。

常见问题

Q：怎么关闭思考过程？

A：无法关闭，QwQ 模型的推理模式是先输出思考内容，再输出回复内容。

Q：免费额度用完后如何购买 Token？

A：您可以访问费用与成本中心进行充值，确保您的账户没有欠费即可调用 QwQ 模型。

超出免费额度后，调用 QwQ 模型会自动扣费，出账周期为一小时，消费明细请前往账单详情进行查看。

Q：如何接入Chatbox或Cherry Studio？

A：请根据您的使用情况参考以下步骤：

此处以使用较多的工具为例，其它大模型工具接入的方法较为类似。

Dify 云服务暂时不支持接入 QwQ 模型。

Chatbox

Cherry Studio

在设置界面的模型提供方选择添加自定义提供方。
进行 API 设置
1. 名称输入“阿里云”（可自定义）；
2. API 域名输入https://dashscope.aliyuncs.com/compatible-mode/v1；
3. API 路径输入/chat/completions；
4. API 密钥输入您的 API Key，获取方法请参见：获取API Key；
5. 模型输入您需要使用的 QwQ模型，此处以 qwq-plus 为例（请注意字母的大小写）；
6. 单击保存，完成设置。
进行对话测试
在输入框输入“你是谁？”进行测试：

单击左下角的设置按钮，在模型服务栏中找到阿里云百炼，API 密钥输入您的 API Key，获取方法请参见：获取API Key；API 地址填入https://dashscope.aliyuncs.com/compatible-mode/v1/；单击添加。
在模型 ID填入您需要使用的 QwQ 模型，此处以 qwq-plus 为例（请注意模型 ID的大小写）； 模型名称与分组名称会自动生成。
如果您点击 API 密钥位置的检查后报错：“连接失败 400 This model only support stream mode, please enable the stream parameter to access the model”，请忽略该信息，您仍可以在对话框中与 QwQ 模型正常对话。
在界面上方选中添加的模型。输入“你是谁”进行测试：

Q：可以上传图片或文档进行提问吗？

A：QwQ 模型仅支持文本输入，不支持输入图片或文档。通义千问VL模型支持图片输入，Qwen- Long模型支持文档输入。

Q：如何在使用 LangChain 时输出思考过程？

A：请参考以下步骤：

更新依赖库
确保 langchain_community 和 dashscope 为最新版本：
```
pip install -U langchain_community dashscope
```

调用 QwQ 模型

QwQ 模型目前仅支持流式输出，通过以下代码来分开打印“思考过程”与“回复内容”：

from langchain_community.chat_models.tongyi import ChatTongyi
from langchain_core.messages import HumanMessage

chatLLM = ChatTongyi(
    model="qwq-plus",   # 此处以qwq-plus为例，您可按需更换模型名称。
)
completion = chatLLM.stream([HumanMessage(content="你是谁")])
is_answering = False
print("="*20+"思考过程 "+"="*20)
for chunk in completion:
    if chunk.additional_kwargs.get("reasoning_content"):
        print(chunk.additional_kwargs.get("reasoning_content"),end="",flush=True)
    else:   
        if not is_answering:
            print("\n"+"="*20+"回复内容"+"="*20)
            is_answering = True
        print(chunk.content,end="",flush=True)

可以获得如下输出：

====================思考过程 ====================
嗯，用户问我“你是谁”，我需要先确定他们想了解什么。可能他们刚接触我的时候，或者想确认我的身份。首先，我应该简要介绍自己是通义千问，阿里巴巴集团旗下的超大规模语言模型。然后说明我的功能，比如回答问题、创作文字、编程等，这样用户知道我能做什么。还要提到我支持多语言，这样用户知道语言不是障碍。接下来，可能需要解释我的应用场景，比如写故事、写公文、写邮件、写剧本等，这样用户能具体了解我的用途。还要提到我的训练数据截止时间，这样用户知道我的知识更新到什么时候。最后，邀请用户提问，保持友好和开放的态度。需要确保回答简洁明了，信息全面，同时口语化，避免使用专业术语。可能用户是普通用户，不需要太技术性的解释。还要注意不要遗漏关键点，比如我的研发公司、功能、多语言支持、应用场景和训练时间。检查有没有重复或冗余的信息，确保流畅自然。可能用户还想知道如何与我互动，所以最后的邀请提问很重要。总之，结构应该是先介绍身份，再功能，应用场景，训练数据，最后邀请提问。这样用户能快速获取所需信息，并知道下一步该怎么做。
====================回复内容====================
你好！我是通义千问，阿里巴巴集团旗下的超大规模语言模型。我能够回答问题、创作文字，比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等，还能表达观点，玩游戏等。我支持多种语言，包括但不限于中文、英文、德语、法语、西班牙语等。

我的训练数据截止时间是2024年12月，这意味着我对2024年之前的信息有较好的了解，但对之后发生的事情可能不了解。如果你有任何问题或需要帮助，欢迎随时告诉我！

Q：如何查看Token消耗量及调用次数？

A：模型调用完一小时后，在模型观测页面的模型列表区域，找到目标模型，然后单击监控，即可查看调用统计。具体请参见模型观测文档。

数据按小时更新，高峰期可能有小时级延迟，请您耐心等待。

错误码

如果执行报错，请参见错误信息进行解决。