深度思考

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",
)

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

completion = client.chat.completions.create(
    model="qwen-plus-2025-04-28",  # 您可以按需更换为其它深度思考模型
    messages=messages,
    # enable_thinking 参数开启思考过程，QwQ 与 DeepSeek-R1 模型总会进行思考，不支持该参数
    extra_body={"enable_thinking": True},
    stream=True,
    # stream_options={
    #     "include_usage": True
    # },
)

reasoning_content = ""  # 完整思考过程
answer_content = ""  # 完整回复
is_answering = False  # 是否进入回复阶段
print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")

for chunk in completion:
    if not chunk.choices:
        print("\nUsage:")
        print(chunk.usage)
        continue

    delta = chunk.choices[0].delta

    # 只收集思考内容
    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
        reasoning_content += delta.reasoning_content

    # 收到content，开始进行回复
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

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

好的，用户问“你是谁”，我需要给出一个准确且友好的回答。首先，我要确认自己的身份，即通义千问，由阿里巴巴集团旗下的通义实验室研发。接下来，应该说明我的主要功能，比如回答问题、创作文字、逻辑推理等。同时，要保持语气亲切，避免过于技术化，让用户感觉轻松。还要注意不要使用复杂术语，确保回答简洁明了。另外，可能需要加入一些互动元素，邀请用户提问，促进进一步交流。最后，检查是否有遗漏的重要信息，比如我的中文名称“通义千问”和英文名称“Qwen”，以及所属公司和实验室。确保回答全面且符合用户期望。
====================完整回复====================

你好！我是通义千问，是阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以回答问题、创作文字、进行逻辑推理、编程等，旨在为用户提供高质量的信息和服务。你可以叫我Qwen，或者直接叫我通义千问。有什么我可以帮你的吗？

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 messages = [{ role: 'user', content: '你是谁' }];
        const stream = await openai.chat.completions.create({
            // 您可以按需更换为其它 Qwen3 模型、QwQ模型或DeepSeek-R1 模型
            model: 'qwen-plus-2025-04-28',
            messages,
            stream: true,
            // enable_thinking 参数开启思考过程，QwQ 与 DeepSeek-R1 模型总会进行思考，不支持该参数
            enable_thinking: 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;
            
            // 只收集思考内容
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // 收到content，开始进行回复
            if (delta.content !== undefined && 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();

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

好的，用户问“你是谁”，我需要回答我的身份。首先，我应该明确说明我是通义千问，由阿里云开发的超大规模语言模型。接下来，可以提到我的主要功能，比如回答问题、创作文字、逻辑推理等。还要强调我的多语言支持，包括中文和英文，这样用户知道我可以处理不同语言的请求。另外，可能需要解释一下我的应用场景，比如学习、工作和生活中的帮助。不过用户的问题比较直接，可能不需要太详细的信息，保持简洁明了。同时，要确保语气友好，邀请用户进一步提问。检查有没有遗漏的重要信息，比如我的版本或最新更新，但可能用户不需要那么详细。最后，确认回答准确无误，没有错误信息。
====================完整回复====================

我是通义千问，是阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我能够回答问题、创作文字、逻辑推理、编程等多种任务，支持中英文等多种语言。如果你有任何问题或需要帮，欢迎随时告诉我！

data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1745485391,"system_fingerprint":null,"model":"qwen-plus-2025-04-28","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

.....

data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1745485391,"system_fingerprint":null,"model":"qwen-plus-2025-04-28","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":10,"completion_tokens":360,"total_tokens":370},"created":1745485391,"system_fingerprint":null,"model":"qwen-plus-2025-04-28","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

data: [DONE]

import os
from dashscope import Generation

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


completion = 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"),
    # 可按需更换为其它深度思考模型
    model="qwen-plus-2025-04-28",
    messages=messages,
    result_format="message", # Qwen3开源版模型只支持设定为"message"；为了更好的体验，其它模型也推荐您优先设定为"message"
    # 开启深度思考，该参数对 QwQ、DeepSeek-R1 模型无效
    enable_thinking=True,
    stream=True,
    incremental_output=True, # Qwen3开源版模型只支持 true；为了更好的体验，其它模型也推荐您优先设定为 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 completion:
    # 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}")

====================思考过程====================
好的，用户问：“你是谁？”我需要回答这个问题。首先，我要明确自己的身份，即通义千问，由阿里云开发的超大规模语言模型。接下来，要说明我的功能和用途，比如回答问题、创作文字、逻辑推理等。同时，要强调我的目标是成为用户的得力助手，提供帮助和支持。

在表达时，要保持口语化，避免使用专业术语或复杂句式。可以加入一些亲切的语气词，比如“你好呀～”，让对话更自然。另外，要确保信息准确，不遗漏关键点，比如我的开发者、主要功能和使用场景。

还要考虑用户可能的后续问题，比如具体的应用例子或技术细节，所以在回答中可以适当埋下伏笔，引导用户进一步提问。例如，提到“无论是日常生活的疑问还是专业领域的问题，我都能尽力提供帮助”，这样既全面又开放。

最后，检查回答是否流畅，有没有重复或冗余的信息，确保简洁明了。同时，保持友好和专业的平衡，让用户感受到既亲切又可靠。
====================完整回复====================
你好呀～我是通义千问，是阿里云开发的一款超大规模语言模型。我能够回答问题、创作文字、进行逻辑推理、编程等等，旨在为用户提供帮助和支持。无论是日常生活的疑问还是专业领域的问题，我都能尽力提供帮助。有什么我可以帮你的吗？

// dashscope SDK的版本 >= 2.19.4
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.Constants;
import io.reactivex.Flowable;
import java.lang.System;
import java.util.Arrays;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

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"))
                // 此处使用qwen-plus-2025-04-28模型，可以按需替换为其它深度思考模型
                .model("qwen-plus-2025-04-28")
                // 开启深度思考，对 QwQ、DeepSeek-R1模型无效
                .enableThinking(true)
                .incrementalOutput(true) // Qwen3开源版模型只支持 true；为了更好的体验，其它模型也推荐您优先设定为 true
                .resultFormat("message") // Qwen3开源版模型只支持设定为"message"；为了更好的体验，其它模型也推荐您优先设定为"message"
                .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);
    }
}

====================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？

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"}

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

messages = []
conversation_idx = 1
while True:
    is_answering = False   # 判断是否结束思考过程并开始回复
    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(
        # 您可以按需更换为其它深度思考模型
        model="qwen-plus-2025-04-28",
        messages=messages,
        # enable_thinking 参数开启思考过程，QwQ 与 DeepSeek-R1 模型总会进行思考，不支持该参数
        extra_body={"enable_thinking": True},
        stream=True,
        # 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
    # 将模型回复的content添加到上下文中
    messages.append({"role": "assistant", "content": answer_content})
    print("\n")

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({
                // 您可以按需更换为其它深度思考模型
                model: 'qwen-plus-2025-04-28',
                messages: messages,
                // enable_thinking 参数开启思考过程，QwQ 与 DeepSeek-R1 模型总会进行思考，不支持该参数
                enable_thinking: true,
                stream: true,
                // stream_options:{
                //     include_usage: 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);

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'),
         # 此处以qwen-plus-2025-04-28为例，可按需更换为其它深度思考模型
        model="qwen-plus-2025-04-28", 
        messages=messages,
        # enable_thinking 参数开启思考过程，QwQ 与 DeepSeek-R1 模型总会进行思考，不支持该参数
        enable_thinking=True,
        result_format="message",
        stream=True,
        incremental_output=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
    # 将模型回复的content添加到上下文中
    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}")

// dashscope SDK的版本 >= 2.19.4
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 qwen-plus-2025-04-28 为例，可按需更换模型名称
                .model("qwen-plus-2025-04-28")
                .enableThinking(true)
                .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);
    }
}

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",
)

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

completion = client.chat.completions.create(
    model="qwen-plus-2025-04-28",  # 您可以按需更换为其它深度思考模型
    messages=messages,
    # enable_thinking 参数开启思考过程，thinking_budget 参数设置最大推理过程 Token 数，两个参数对 QwQ 与 DeepSeek-R1 模型无效
    extra_body={
        "enable_thinking": True,
        "thinking_budget": 50
        },
    stream=True,
    # stream_options={
    #     "include_usage": True
    # },
)

reasoning_content = ""  # 完整思考过程
answer_content = ""  # 完整回复
is_answering = False  # 是否进入回复阶段
print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")

for chunk in completion:
    if not chunk.choices:
        print("\nUsage:")
        print(chunk.usage)
        continue

    delta = chunk.choices[0].delta

    # 只收集思考内容
    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
        reasoning_content += delta.reasoning_content

    # 收到content，开始进行回复
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

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

好的，用户问“你是谁”，我需要给出一个清晰且友好的回答。首先，应该明确自己的身份，即通义千问，由阿里巴巴集团旗下的通义实验室研发。接下来，要说明自己的主要功能，比如回答
====================完整回复====================

我是通义千问，是阿里巴巴集团旗下的通义实验室研发的超大规模语言模型。我能够回答问题、创作文字、逻辑推理、编程等，旨在为用户提供帮助和便利。有什么我可以帮您的吗？

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 messages = [{ role: 'user', content: '你是谁' }];
        const stream = await openai.chat.completions.create({
            // 此处以qwen-plus-2025-04-28为例，可按需更换为其它深度思考模型
            model: 'qwen-plus-2025-04-28',
            messages,
            stream: true,
            // enable_thinking 参数开启思考过程，thinking_budget 参数设置最大推理过程 Token 数，两个参数对 QwQ 与 DeepSeek-R1 模型无效
            enable_thinking: true,
            thinking_budget: 50
        });
        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;
            
            // 只收集思考内容
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // 收到content，开始进行回复
            if (delta.content !== undefined && 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();

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

好的，用户问“你是谁”，我需要给出一个清晰准确的回答。首先，我应该介绍自己的身份，即通义千问，由阿里巴巴集团旗下的通义实验室研发。接下来，要说明我的主要功能，比如回答问题
====================完整回复====================

我是通义千问，是阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我能够回答问题、创作文字、逻辑推理、编程等多种任务。如果你有任何问题或需要帮助，欢迎随时告诉我！

data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1745485391,"system_fingerprint":null,"model":"qwen-plus-2025-04-28","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

.....

data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1745485391,"system_fingerprint":null,"model":"qwen-plus-2025-04-28","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":10,"completion_tokens":360,"total_tokens":370},"created":1745485391,"system_fingerprint":null,"model":"qwen-plus-2025-04-28","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

data: [DONE]

import os
from dashscope import Generation

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


completion = 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"),
    # 可按需更换为其它深度思考模型
    model="qwen-plus-2025-04-28",
    messages=messages,
    result_format="message",
    # 开启深度思考，该参数对 QwQ、DeepSeek-R1 模型无效
    enable_thinking=True,
    # 设置最大推理过程 Token 数，该参数对 QwQ、DeepSeek-R1 模型无效
    thinking_budget=50,
    stream=True,
    incremental_output=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 completion:
    # 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}")

====================思考过程====================
好的，用户问“你是谁？”，我需要给出一个清晰且友好的回答。首先，我要介绍自己的身份，也就是通义千问，由阿里巴巴集团旗下的通义实验室研发。接下来，应该说明我的主要功能，比如
====================完整回复====================
我是通义千问，是阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我能够回答问题、创作文字、逻辑推理、编程等，旨在为用户提供全面、准确和有用的信息与帮助。有什么我可以帮您的吗？

// dashscope SDK的版本 >= 2.19.4
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.Constants;
import io.reactivex.Flowable;
import java.lang.System;
import java.util.Arrays;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

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"))
                // 此处使用qwen-plus-2025-04-28模型，可以按需替换为其它深度思考模型
                .model("qwen-plus-2025-04-28")
                // 开启深度思考，对 QwQ、DeepSeek-R1模型无效
                .enableThinking(true)
                .thinkingBudget(50)
                .incrementalOutput(true)
                .resultFormat("message")
                .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);
    }
}

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"好的","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":14,"output_tokens":3,"input_tokens":11,"output_tokens_details":{"reasoning_tokens":1}},"request_id":"2ce91085-3602-9c32-9c8b-fe3d583a2c38"}

id:2
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"，","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":15,"output_tokens":4,"input_tokens":11,"output_tokens_details":{"reasoning_tokens":2}},"request_id":"2ce91085-3602-9c32-9c8b-fe3d583a2c38"}

......

id:133
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"！","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":149,"output_tokens":138,"input_tokens":11,"output_tokens_details":{"reasoning_tokens":50}},"request_id":"2ce91085-3602-9c32-9c8b-fe3d583a2c38"}

id:134
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"stop"}]},"usage":{"total_tokens":149,"output_tokens":138,"input_tokens":11,"output_tokens_details":{"reasoning_tokens":50}},"request_id":"2ce91085-3602-9c32-9c8b-fe3d583a2c38"}

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'),
    # 此处以qwen-plus-2025-04-28为例，可按需更换为支持联网搜索的模型
    model="qwen-plus-2025-04-28",  
    messages=messages,
    # 开启深度思考的参数，对 QwQ 模型无效
    enable_thinking = True,
    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
    },
    stream=True,
    incremental_output=True,
    result_format="message",
)

# 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)

====================搜索信息====================
[1]: [《哪吒2》票房破119亿元 2025累计票房破200亿!](https://www.1905.com/news/20250217/1714723.shtml)
[2]: [《哪吒2》票房将达215亿,超《阿凡达》成全球第一,5大理由支撑](https://www.360kuai.com/pc/9630dbf8c5a1770de?cota=3&kuai_so=1&sign=360_e39369d1)
[3]: [《哪吒2》票房破135亿:2025年中国电影市场的梦幻开局](https://www.sohu.com/a/862924685_122077416?scm=10001.325_13-109000.0.0.5_32)
[4]: [哪吒2票房突破120亿元](https://baijiahao.baidu.com/s?id=1824275513693903228)
[5]: [哪吒2票房突破153亿](https://m.gmw.cn/2025-03/23/content_1303998589.htm)
[6]: [哪吒2总票房破131亿](https://m.gmw.cn/2025-02/22/content_1303977481.htm)
[7]: [2025年娱乐新趋势哪吒2票房破150亿!国产动画如何征服全球市场?](https://m.sohu.com/a/874897462_122173903/?scm=10001.334_13-101000-0_922.0-0.0.a2_5X162X1746)
[8]: [哪吒2全球票房破156亿](https://m.gmw.cn/2025-04/09/content_1304011206.htm)
[9]: [《哪吒2》票房彻底疯了 ](https://m.china-xian.com/question/202503/5sy28p2jld.htm)
====================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，我要查看提供的知识库内容，找到相关的票房数据。

看到有多个引用提到《哪吒2》的票房情况。比如，ref_1提到票房破119亿元，但后面还有其他ref提到更高的数字。ref_3说票房破135亿，ref_5提到全球票房突破153亿，ref_8则提到全球票房破156亿。另外，ref_7提到票房破150亿，而ref_5和ref_8的数据似乎更具体，分别达到153亿和156亿。还有ref_1提到累计票房即将冲击120亿元，但后续的ref_3、ref_5、ref_8都有更高的数据。

需要确认这些数据的时间点。例如，ref_1的日期是2025年第6周，也就是2月10日到16日，此时票房是119亿。而ref_3提到的是截至2025年2月24日，票房破135亿。之后ref_5在某个时间点提到153亿，ref_8提到156亿。可能这些数据是逐步更新的，所以最新的应该是ref_8的156亿。不过需要检查是否有更近的引用。

另外，ref_7提到的是150亿，而ref_5和ref_8的数据更高。可能不同的ref有不同的统计时间和范围，比如是否包含海外票房。例如，ref_5提到全球票房（含预售及海外）突破153亿，而ref_8也是全球票房破156亿。因此，应该综合这些信息，指出不同时间点的票房成绩，并说明最新数据是156亿。

还需要注意是否有矛盾的地方。例如，ref_1提到累计票房突破200亿，但其他ref的数字没有达到这个数值，可能ref_1中的2025全年电影票房累计突破200亿是指整个市场的总票房，而不是《哪吒2》单独的票房。需要仔细区分。

总结一下，用户的问题是关于《哪吒2》的票房，需要列出不同时间点的票房数据，并指出最高纪录。同时要引用对应的ref编号，确保准确性。
====================完整回复====================
根据现有资料，《哪吒之魔童闹海》（《哪吒2》）的票房表现如下：

1. **国内票房**：截至2025年2月24日，该片国内票房已突破135亿元[ref_3]，随后持续增长，累计票房接近150亿元[ref_7]。

2. **全球票房**：  
   - 截至2025年4月，影片全球票房（含预售及海外）已突破**156亿元**[ref_8]。  
   - 此前，全球票房曾突破**153亿元**[ref_5]，并位居全球影史票房榜前五[ref_8]。

3. **市场地位**：  
   - 《哪吒2》成为首部进入全球票房前十的非好莱坞动画电影，且在欧洲37国同步上映，创下华语影片发行新纪录[ref_7]。  
   - 影片还以150亿元票房成绩超越《阿凡达》（2009年第一部），一度登上全球票房榜首[ref_2]。

4. **其他数据**：  
   - 2025年第6周（2月10日-16日），周票房达38.4亿元，累计票房接近120亿元[ref_1]。  
   - 影片在海外市场表现亮眼，例如在英国超级点映票房达100万美元，创造国际动画电影点映新纪录[ref_1]。

综上，《哪吒2》的票房成绩体现了其在国内及全球市场的巨大影响力，最终全球票房有望进一步攀升。

// dashscope SDK的版本 >= 2.19.4
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"))
                .model("qwen-plus-2025-04-28")
                .enableThinking(true)
                .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);
    }
}

====================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)

id:1
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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(
    # 此处以qwen-plus-2025-04-28为例，可更换为其它支持联网搜索的深度思考模型
    model="qwen-plus-2025-04-28",
    messages=[{"role": "user", "content": "哪吒2的票房"}],
    extra_body={
        # 开启深度思考的参数，对 QwQ 模型无效
        "enable_thinking": True,
        "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)

====================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，我得仔细查看提供的知识库内容，里面有很多关于这部电影的票房数据。

用户的问题是“哪吒2的票房”，所以我要找到最新的票房数字。根据知识库中的信息，最近的数据是来自不同时间点的多个记录。例如，有提到截至2025年4月17日，票房破155亿，而另一处提到全球票房破156亿。还有3月8日的数据是148亿，3月24日左右是153亿，以及另一条提到155亿和156亿。看起来最新的应该是4月17日的155亿和之后的156亿。不过需要确认时间顺序。

注意到知识库中有一条提到“哪吒2全球票房破156亿”的条目，其中包含4月16日神舟二十号的消息，可能时间接近当前系统时间2025年4月27日。因此，可能156亿是最近的数据。但还有另一条提到“破155亿”并提到4月17日联合国教科文组织的消息，可能两者都是同一时间段的数据，可能存在不同来源的统计差异。

另外，用户提供的系统时间是4月27日，而知识库中最新的数据是到4月17日左右，之后可能没有更新的数据。因此，我需要综合所有信息，给出最接近当前时间的票房数字。可能需要指出不同来源的数字，并说明当前的最新情况，比如提到156亿可能更接近，但需要确认是否有更高数据。

此外，知识库中还提到《哪吒2》在全球影史票房榜的位置，比如第5名或第6名，这可能帮助确认票房数值是否准确。例如，156亿应该位于全球前五的位置，而之前的153亿是第5名。因此，可能最新的数据是156亿，并且仍在增长，接近或超过某些里程碑，比如超过《阿凡达》的票房（假设《阿凡达》的票房约为28亿美元，但需要换算人民币）。

最后，我需要整理这些信息，给出一个清晰的回答，说明当前票房的最新数据，并引用知识库中的不同来源，同时注意可能的更新情况。
====================完整回复====================

根据提供的知识库信息，截至2025年4月17日，《哪吒之魔童闹海》（《哪吒2》）的全球票房已突破**155亿元人民币**，并随后进一步攀升至**156亿元**（部分数据来源显示）。以下是关键信息整理：

---

### **票房里程碑**
1. **156亿元（全球票房，含预售及海外）**  
   - 根据4月17日左右的最新数据，《哪吒2》全球票房突破156亿元，暂列**全球影史票房榜前5名**（部分统计显示已超越《复仇者联盟4》等影片）。  
   - 在欧洲地区，英国点映票房达100万美元，创国际动画电影点映新纪录，进一步推动海外票房增长。

2. **国内票房表现**  
   - 截至2025年2月24日，国内票房已破135亿元，刷新国产动画电影票房纪录。  
   - 2025年第6周（2月10-16日），单周票房达38.4亿元，持续蝉联周冠军，累计票房迅速向120亿元迈进。

3. **国际影响力**  
   - 在欧洲37国同步上映，柏林电影节评委称赞其为“动画界的《寄生虫》”，亚洲市场份额占比超79%（中国58%+东南亚21%）。  
   - 海外发行策略成功，英国、东南亚等地票房表现强劲，助力全球票房突破150亿元大关。

---

### **票房分析与行业影响**
- **技术与内容创新**：采用AI辅助的VRAI技术，缩短70%渲染时间，同时以东方哲学和“逆天命”主题引发全球共鸣，豆瓣评分8.5分，成人观众占比达87%。  
- **产业升级**：带动北京200多家视效公司协同发展，形成全产业链闭环，推动国产动画从“代工”向“文化输出”转型。  
- **目标与挑战**：业界预测其最终票房可能达到**215亿元**，有望超越《阿凡达》（约28亿美元，按汇率换算约合人民币190亿元），成为全球影史票房冠军。

---

### **当前进展**
- 截至知识库最新数据（4月17日），票房已达**156亿元**，并仍在增长中。  
- 若持续当前势头，或将在未来几周内冲击**160亿元**，进一步巩固其全球票房地位。

如需更实时数据，建议参考权威票房平台（如猫眼、灯塔专业版）或最新新闻报道。
====================Usage====================
CompletionUsage(completion_tokens=1088, prompt_tokens=4355, total_tokens=5443, completion_tokens_details=None, prompt_tokens_details=None)

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({
            // 此处以qwen-plus-2025-04-28为例，可更换为其它支持联网搜索的深度思考模型
            model: 'qwen-plus-2025-04-28',
            messages: [{ role: 'user', content: '哪吒2的票房' }],
            // 开启深度思考的参数，对 QwQ 模型无效
            enable_thinking: true,
            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();

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

好的，用户问的是《哪吒2》的票房情况。我需要先看看知识库里的相关信息。根据提供的资料，《哪吒之魔童闹海》的全球票房已经突破156亿，暂列全球影史票房榜前五。还有提到超过150亿，创亚洲纪录，以及日本上映后全球票房超155.16亿。这些数据可能有时间上的差异，比如不同日期的统计结果。

需要确认用户的问题是否有特定的时间点，但用户没有说明，所以应该给出最新的数据。知识库里最新的信息是“全球票房破156亿”和“全球票房超155.16亿”，可能前者是更近的统计数据。另外，要注意到不同的来源可能有不同的数字，比如有的说156亿，有的说155.16亿，这可能是因为统计时间和方式不同。

还需要注意，用户可能想知道当前的票房，而系统时间是2025年4月27日，但知识库中的信息可能截止到2023年或更早，因为《哪吒2》在2025年可能已经下映了。不过根据知识库内容，电影在国内持续上映至4月30日，所以可能在2025年4月27日还在上映中，因此票房可能还在增长。

需要综合所有信息，给出一个准确的回答，同时指出数据可能有变动，并建议参考权威渠道获取最新数据。另外，要提到票房排名情况，比如全球第五，以及亚洲纪录等信息。
====================完整回复====================

根据现有信息，《哪吒之魔童闹海》（《哪吒2》）的全球票房已突破**156亿元人民币**，暂列全球影史票房榜前五。该片在海外市场的表现也十分亮眼，截至相关报道发布时，其全球票房已超过**155.16亿元**，并创下了亚洲电影的票房新纪录。

需要注意的是：
1. **数据时间差异**：不同报道中的票房数字（如150亿、155亿、156亿）可能因统计时间或渠道不同而存在细微差异。
2. **上映周期**：该片在中国大陆的上映时间曾两次延期，最终持续至**2025年4月30日**，因此实际票房可能仍在动态变化中。
3. **票房计算方式**：电影票房通常指影片上映后的放映收益总和，并非直接等同于观众人次。

如需最新数据，建议参考权威影视票房统计平台或官方公告。

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
            }
        }
    }
]

messages = [{"role": "user", "content": input("请输入问题：")}]
completion = client.chat.completions.create(
    # 此处以qwen-plus-2025-04-28为例，可更换为其它深度思考模型
    model="qwen-plus-2025-04-28",
    messages=messages,
    extra_body={
        # 开启深度思考，该参数对 QwQ 模型无效
        "enable_thinking": True
    },
    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)

====================思考过程====================
好的，用户问的是“四个直辖市的天气”。首先，我需要明确四个直辖市是哪几个。根据中国的行政区划，直辖市包括北京、上海、天津和重庆。所以用户想知道这四个城市的天气情况。

接下来，我需要检查可用的工具。提供的工具中有get_current_weather函数，参数是location，类型字符串。每个城市需要单独查询，因为函数一次只能查一个地点。因此，我需要为每个直辖市调用一次这个函数。

然后，我需要考虑如何生成正确的工具调用。每个调用应该包含城市名称作为参数。比如，第一个调用是北京，第二个是上海，依此类推。确保参数名称是location，值是正确的城市名。

另外，用户可能希望得到每个城市的天气信息，所以需要确保每个函数调用都正确无误。可能需要连续调用四次，每次对应一个城市。不过，根据工具的使用规则，可能需要分多次处理，或者一次生成多个调用。但根据示例，可能每次只调用一个函数，所以可能需要逐步进行。

最后，确认是否有其他需要考虑的因素，比如参数是否正确，城市名称是否准确，以及是否需要处理可能的错误情况，比如城市不存在或API不可用。但目前看来，四个直辖市都是明确的，应该没问题。
====================回复内容====================

===================工具调用信息===================
[{'id': 'call_767af2834c12488a8fe6e3', 'name': 'get_current_weather', 'arguments': '{"location": "北京市"}'}, {'id': 'call_2cb05a349c89437a947ada', 'name': 'get_current_weather', 'arguments': '{"location": "上海市"}'}, {'id': 'call_988dd180b2ca4b0a864ea7', 'name': 'get_current_weather', 'arguments': '{"location": "天津市"}'}, {'id': 'call_4e98c57ea96a40dba26d12', 'name': 'get_current_weather', 'arguments': '{"location": "重庆市"}'}]

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({
            // 此处以qwen-plus-2025-04-28为例，可更换为其它深度思考模型
            model: "qwen-plus-2025-04-28",
            messages,
            // 开启深度思考，该参数对 QwQ 模型无效
            enable_thinking: true,
            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(); 

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"}'
  }
]

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(
    # 此处以qwen-plus-2025-04-28为例，可更换为其它深度思考模型
    model="qwen-plus-2025-04-28", 
    messages=messages,
    enable_thinking=True,
    tools=tools,
    parallel_tool_calls=True,
    stream=True,
    incremental_output=True,
    result_format="message"
)

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)

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"}}'}]

// dashscope SDK的版本 >= 2.19.4
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("qwen-plus-2025-04-28")
                .enableThinking(true)
                .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);
    }
}

{"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"}}]}}
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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",
)

# 在 prompt 加上/no_think，即使enable_thinking设为true也会关闭思考模式。
messages = [{"role": "user", "content": "你是谁/no_think"}]
completion = client.chat.completions.create(
    model="qwen-plus-2025-04-28",  # 您可以按需更换为其它Qwen3 模型
    messages=messages,
    # enable_thinking 参数开启思考过程，QwQ 与 DeepSeek-R1 模型总会进行思考，不支持该参数
    extra_body={"enable_thinking": True},
    stream=True,
    # stream_options={
    #     "include_usage": True
    # },
)

reasoning_content = ""  # 完整思考过程
answer_content = ""  # 完整回复
is_answering = False  # 是否进入回复阶段
print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")

for chunk in completion:
    if not chunk.choices:
        print("\nUsage:")
        print(chunk.usage)
        continue

    delta = chunk.choices[0].delta

    # 只收集思考内容
    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
        reasoning_content += delta.reasoning_content

    # 收到content，开始进行回复
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

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


====================完整回复====================

我是通义千问，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以帮助你回答问题、创作文字、进行逻辑推理、编程等任务。如果你有任何问题或需要帮助，欢迎随时向我提问！

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 {
        // 在 prompt 加上/no_think，即使enable_thinking设为true也会关闭思考模式。
        const messages = [{ role: 'user', content: '你是谁/no_think' }];
        const stream = await openai.chat.completions.create({
            // 您可以按需更换为其它Qwen3 模型
            model: 'qwen-plus-2025-04-28',
            messages,
            stream: true,
            // enable_thinking 参数开启思考过程，QwQ 与 DeepSeek-R1 模型总会进行思考，不支持该参数
            enable_thinking: 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;
            
            // 只收集思考内容
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // 收到content，开始进行回复
            if (delta.content !== undefined && 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();

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


====================完整回复====================

我是通义千问，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我可以帮助你回答问题、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。如果你有任何问题或需要帮助，欢迎随时向我提问！

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data: [DONE]