Alibaba Cloud Model Studio:Deep thinking

Sample code

from openai import OpenAI
import os

# Initialize the OpenAI client
client = OpenAI(
    # If you have not configured an environment variable, replace the following with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # The following is the base_url for the Singapore region. If you use a model in the Virginia region, replace the base_url with https://dashscope-us.aliyuncs.com/compatible-mode/v1
    # If you use a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

messages = [{"role": "user", "content": "Who are you"}]

completion = client.chat.completions.create(
    model="qwen-plus",  # You can replace this with another deep thinking model as needed
    messages=messages,
    extra_body={"enable_thinking": True},
    stream=True,
    stream_options={
        "include_usage": True
    },
)

reasoning_content = ""  # Full thinking process
answer_content = ""  # Full response
is_answering = False  # Indicates whether the response phase has started
print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")

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

    delta = chunk.choices[0].delta

    # Collect only the thinking content
    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

    # When content is received, start responding
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

Response

====================Thinking process====================

Okay, the user asked "Who are you", so I need to provide an accurate and friendly response. First, I need to confirm my identity, which is Qwen, developed by the Tongyi Lab under Alibaba Group. Next, I should explain my main functions, such as answering questions, creating text, and logical reasoning. At the same time, I should maintain a friendly tone and avoid being too technical to make the user feel at ease. I also need to avoid using complex terminology to ensure the response is concise and clear. In addition, I might need to add some interactive elements, inviting the user to ask questions to encourage further communication. Finally, I will check if I have missed any important information, such as my Chinese name "Tongyi Qianwen" and English name "Qwen", along with my parent company and lab. I need to ensure the response is comprehensive and meets the user's expectations.
====================Full response====================

Hello! I am Qwen, an ultra-large language model independently developed by the Tongyi Lab under Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code, with the goal of providing users with high-quality information and services. You can call me Qwen, or just Tongyi Qianwen. How can I help you?

Sample code

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

// Initialize the OpenAI client
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // Read from environment variable
    // The following is the base_url for the Singapore region. If you use a model in the Virginia region, replace the base_url with https://dashscope-us.aliyuncs.com/compatible-mode/v1 
    // If you use a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/compatible-mode/v1
    baseURL: 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1'
});

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

async function main() {
    try {
        const messages = [{ role: 'user', content: 'Who are you' }];
        const stream = await openai.chat.completions.create({
            model: 'qwen-plus',
            messages,
            stream: true,
            enable_thinking: true
        });
        console.log('\n' + '='.repeat(20) + 'Thinking process' + '='.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;
            
            // Collect only the thinking content
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // When content is received, start responding
            if (delta.content !== undefined && delta.content) {
                if (!isAnswering) {
                    console.log('\n' + '='.repeat(20) + 'Full response' + '='.repeat(20) + '\n');
                    isAnswering = true;
                }
                process.stdout.write(delta.content);
                answerContent += delta.content;
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

Return result

====================Thinking process====================

Okay, the user asked "Who are you", so I need to answer about my identity. First, I should clearly state that I am Qwen, an ultra-large language model developed by Alibaba Cloud. Next, I can mention my main functions, such as answering questions, creating text, and logical reasoning. I should also emphasize my multilingual support, including Chinese and English, so the user knows I can handle requests in different languages. In addition, I might need to explain my application scenarios, such as helping with study, work, and daily life. However, the user's question is quite direct, so I probably do not need to provide too much detailed information. I should keep it concise and clear. At the same time, I need to ensure a friendly tone and invite the user to ask further questions. I will check if I have missed any important information, such as my version or latest updates, but the user probably does not need that much detail. Finally, I will confirm that the response is accurate and contains no errors.
====================Full response====================

I am Qwen, an ultra-large language model independently developed by the Tongyi Lab under Alibaba Group. I can perform various tasks such as answering questions, creating text, logical reasoning, and coding. I support multiple languages, including Chinese and English. If you have any questions or need help, feel free to let me know!

Sample code

# ======= Important =======
# The following is the base_url for Singapore. If you use a model in the Beijing region, replace the base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# If you use a model in the Virginia region, replace the base_url with: https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions
# === Delete this comment before execution ===
curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen-plus",
    "messages": [
        {
            "role": "user", 
            "content": "Who are you"
        }
    ],
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "enable_thinking": true
}'

Response

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","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","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","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

data: [DONE]

Sample code

import os
from dashscope import Generation
import dashscope

# The following is the base_url for the Singapore region. If you use a model in the Virginia region, replace the base_url with https://dashscope-us.aliyuncs.com/api/v1
# If you use a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = "https://dashscope-intl.aliyuncs.com/api/v1"

messages = [{"role": "user", "content": "Who are you?"}]

completion = Generation.call(
    # If you have not configured an environment variable, replace the following line with your Model Studio API key: api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen-plus",
    messages=messages,
    result_format="message",
    enable_thinking=True,
    stream=True,
    incremental_output=True,
)

# Define the full thinking process.
reasoning_content = ""
# Define the full response.
answer_content = ""
# Determine whether the thinking process is finished and the response has started.
is_answering = False

print("=" * 20 + "Thinking process" + "=" * 20)

for chunk in completion:
    # If both the thinking process and the response are empty, ignore.
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        # If it is currently the thinking process.
        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
        # If it is currently the response.
        elif chunk.output.choices[0].message.content != "":
            if not is_answering:
                print("\n" + "=" * 20 + "Full response" + "=" * 20)
                is_answering = True
            print(chunk.output.choices[0].message.content, end="", flush=True)
            answer_content += chunk.output.choices[0].message.content

# To print the full thinking process and response, uncomment and run the following code.
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")

Response

====================Thinking process====================
Okay, the user is asking, "Who are you?" I need to answer this question. First, I must clarify my identity: I am Qwen, a large-scale language model developed by Alibaba Cloud. Next, I need to explain my functions and uses, such as answering questions, creating text, and logical reasoning. I should also emphasize that my goal is to be a helpful assistant to the user, providing help and support.

When responding, I should maintain a conversational tone and avoid technical jargon or complex sentences. I can add friendly phrases, like "Hello there!~", to make the conversation more natural. Also, I must ensure the information is accurate and does not omit key points, such as my developer, main functions, and use cases.

I also need to consider potential follow-up questions from the user, such as specific application examples or technical details. So, I can subtly plant seeds in my response to encourage further questions. For example, mentioning "Whether it's a question about daily life or a professional topic, I can do my best to help" is both comprehensive and open-ended.

Finally, I will check if the response is fluent and free of repetitive or redundant information, ensuring it is concise and clear. I will also maintain a balance between being friendly and professional, so the user finds me both approachable and reliable.
====================Full response====================
Hello there!~ I am Qwen, a large-scale language model developed by Alibaba Cloud. I can answer questions, create text, perform logical reasoning, write code, and more, with the goal of providing help and support to users. Whether you have questions about daily life or professional topics, I will do my best to assist. How can I help you?

Sample code

// DashScope SDK version >= 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 com.alibaba.dashscope.utils.Constants;

public class Main {
    static {
        // The following is the base_url for the Singapore region. If you use a model in the Virginia region, replace the base_url with https://dashscope-us.aliyuncs.com/api/v1
        // If you use a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/api/v1
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    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("====================Thinking process====================");
                isFirstPrint = false;
            }
            System.out.print(reasoning);
        }

        if (!content.isEmpty()) {
            finalContent.append(content);
            if (!isFirstPrint) {
                System.out.println("\n====================Full response====================");
                isFirstPrint = true;
            }
            System.out.print(content);
        }
    }
    private static GenerationParam buildGenerationParam(Message userMsg) {
        return GenerationParam.builder()
                // If you have not configured an environment variable, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen-plus")
                .enableThinking(true)
                .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("Who are you?").build();
            streamCallWithMessage(gen, userMsg);
//             Print the final result.
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================Full response====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

Return value

====================Thinking process====================
Okay, the user is asking "Who are you?", and I need to answer based on my predefined settings. First, my role is Qwen, a large-scale language model from Alibaba Group. I should keep the tone conversational, simple, and easy to understand.

The user might be new to me or wants to confirm my identity. I should first state who I am directly, then briefly explain my functions and uses, such as answering questions, creating text, and coding. I should also mention my multilingual support so the user knows I can handle requests in different languages.

Also, according to the guidelines, I should maintain a human-like persona, so the tone should be friendly. I might use emojis to add a touch of warmth. At the same time, I might need to guide the user to ask more questions or use my features, for example, by asking what they need help with.

I need to be careful not to use complex terminology and avoid being verbose. I will check for any missed key points, such as multilingual support and specific capabilities. I must ensure the response meets all requirements, including being conversational and concise.
====================Full response====================
Hello! I am Qwen, a large-scale language model from Alibaba Group. I can answer questions and create text, such as stories, official documents, emails, and playbooks. I can also perform logical reasoning, write code, express opinions, and play games. I am proficient in multiple languages, including but not limited to Chinese, English, German, French, and Spanish. Is there anything I can help you with?

Sample code

# ======= Important =======
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation
# If you use a model in the Virginia region, replace the base_url with: https://dashscope-us.aliyuncs.com/api/v1/services/aigc/text-generation/generation
# === Delete this comment before execution ===
curl -X POST "https://dashscope-intl.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": "qwen-plus",
    "input":{
        "messages":[      
            {
                "role": "user",
                "content": "Who are you?"
            }
        ]
    },
    "parameters":{
        "enable_thinking": true,
        "incremental_output": true,
        "result_format": "message"
    }
}'

Response

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"Hmm","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":"the user","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":" asks","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":"help","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":" feel free","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":" to","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":" let me know","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"}

Sample code

from openai import OpenAI
import os

# Initialize the OpenAI client
client = OpenAI(
    # If you have not configured an environment variable, replace the following with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # The following is the base_url for the Singapore region. If you use a model in the Virginia region, replace the base_url with https://dashscope-us.aliyuncs.com/compatible-mode/v1 # If you use a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/compatible-mode/v1
    # If you use a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)

messages = [{"role": "user", "content": "Who are you"}]

completion = client.chat.completions.create(
    model="qwen-plus",
    messages=messages,
    # The enable_thinking parameter enables the thinking process, and the thinking_budget parameter sets the maximum number of tokens for the reasoning process.
    extra_body={
        "enable_thinking": True,
        "thinking_budget": 50
        },
    stream=True,
    stream_options={
        "include_usage": True
    },
)

reasoning_content = ""  # Full thinking process
answer_content = ""  # Full response
is_answering = False  # Indicates whether the response phase has started
print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")

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

    delta = chunk.choices[0].delta

    # Collect only the thinking content
    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

    # When content is received, start responding
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

Response

====================Thinking process====================

Okay, the user asked "Who are you", so I need to give a clear and friendly response. First, I should state my identity, which is Qwen, developed by the Tongyi Lab under Alibaba Group. Next, I should explain my main functions, such as answering
====================Full response====================

I am Qwen, a large-scale language model developed by the Tongyi Lab at Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code, with the goal of providing help and convenience to users. How can I help you?

Sample code

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

// Initialize the OpenAI client
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // Read from environment variable
    // The following is the base_url for the Singapore region. If you use a model in the Virginia region, replace the base_url with https://dashscope-us.aliyuncs.com/compatible-mode/v1 
    // If you use a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/compatible-mode/v1
    baseURL: 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1'
});

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


async function main() {
    try {
        const messages = [{ role: 'user', content: 'Who are you' }];
        const stream = await openai.chat.completions.create({
            model: 'qwen-plus',
            messages,
            stream: true,
            // The enable_thinking parameter enables the thinking process, and the thinking_budget parameter sets the maximum number of tokens for the reasoning process.
            enable_thinking: true,
            thinking_budget: 50
        });
        console.log('\n' + '='.repeat(20) + 'Thinking process' + '='.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;
            
            // Collect only the thinking content
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // When content is received, start responding
            if (delta.content !== undefined && delta.content) {
                if (!isAnswering) {
                    console.log('\n' + '='.repeat(20) + 'Full response' + '='.repeat(20) + '\n');
                    isAnswering = true;
                }
                process.stdout.write(delta.content);
                answerContent += delta.content;
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

Response

====================Thinking process====================

Okay, the user asked "Who are you", so I need to give a clear and accurate response. First, I should introduce my identity, which is Qwen, developed by the Tongyi Lab under Alibaba Group. Next, I should explain my main functions, such as answering questions
====================Full response====================

I am Qwen, an ultra-large language model independently developed by the Tongyi Lab under Alibaba Group. I can perform various tasks such as answering questions, creating text, logical reasoning, and coding. If you have any questions or need help, feel free to let me know!

Sample code

# ======= Important =======
# The following is the base_url for Singapore. If you use a model in the Beijing region, replace the base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# If you use a model in the Virginia region, replace the base_url with: https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions
# === Delete this comment before execution ===
curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen-plus",
    "messages": [
        {
            "role": "user", 
            "content": "Who are you"
        }
    ],
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "enable_thinking": true,
    "thinking_budget": 50
}'

Response

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","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","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","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

data: [DONE]

Sample code

import os
from dashscope import Generation
import dashscope
# The following is the base_url for the Singapore region. If you use a model in the Virginia region, replace the base_url with https://dashscope-us.aliyuncs.com/api/v1
# If you use a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = "https://dashscope-intl.aliyuncs.com/api/v1/"

messages = [{"role": "user", "content": "Who are you?"}]

completion = Generation.call(
    # If you have not configured an environment variable, replace the following line with your Model Studio API key: api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen-plus",
    messages=messages,
    result_format="message",
    enable_thinking=True,
    # Set the maximum number of tokens for the reasoning process.
    thinking_budget=50,
    stream=True,
    incremental_output=True,
)

# Define the full thinking process.
reasoning_content = ""
# Define the full response.
answer_content = ""
# Determine whether the thinking process is finished and the response has started.
is_answering = False

print("=" * 20 + "Thinking process" + "=" * 20)

for chunk in completion:
    # If both the thinking process and the response are empty, ignore.
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        # If it is currently in the thinking process.
        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
        # If it is currently in the response stage.
        elif chunk.output.choices[0].message.content != "":
            if not is_answering:
                print("\n" + "=" * 20 + "Full response" + "=" * 20)
                is_answering = True
            print(chunk.output.choices[0].message.content, end="", flush=True)
            answer_content += chunk.output.choices[0].message.content

# To print the full thinking process and the full response, uncomment and run the following code.
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")

Return result

====================Thinking process====================
Okay, the user is asking "Who are you?", so I need to give a clear and friendly response. First, I should introduce my identity, which is Qwen, developed by the Tongyi Lab under Alibaba Group. Next, I should explain my main functions, such as
====================Full response====================
I am Qwen, a large-scale language model independently developed by the Tongyi Lab at Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code, with the goal of providing users with comprehensive, accurate, and useful information and assistance. How can I help you?

Sample code

// DashScope SDK version >= 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 com.alibaba.dashscope.utils.Constants;

public class Main {
    static {
        // The following is the base_url for the Singapore region. If you use a model in the Virginia region, replace the base_url with https://dashscope-us.aliyuncs.com/api/v1
        // If you use a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/api/v1
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    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("====================Thinking process====================");
                isFirstPrint = false;
            }
            System.out.print(reasoning);
        }

        if (!content.isEmpty()) {
            finalContent.append(content);
            if (!isFirstPrint) {
                System.out.println("\n====================Full response====================");
                isFirstPrint = true;
            }
            System.out.print(content);
        }
    }
    private static GenerationParam buildGenerationParam(Message userMsg) {
        return GenerationParam.builder()
                // If you have not configured an environment variable, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen-plus")
                .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("Who are you?").build();
            streamCallWithMessage(gen, userMsg);
//             Print the final result.
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================Full response====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

====================Thinking process====================
Okay, the user is asking "Who are you?", so I need to give a clear and friendly response. First, I should introduce my identity, which is Qwen, developed by the Tongyi Lab under Alibaba Group. Next, I should explain my main functions, such as
====================Full response====================
I am Qwen, a large-scale language model independently developed by the Tongyi Lab at Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code, with the goal of providing users with comprehensive, accurate, and useful information and assistance. How can I help you?

Sample code

# ======= Important =======
# The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation
# If you use a model in the Virginia region, replace the base_url with: https://dashscope-us.aliyuncs.com/api/v1/services/aigc/text-generation/generation
# === Delete this comment before execution ===
curl -X POST "https://dashscope-intl.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": "qwen-plus",
    "input":{
        "messages":[      
            {
                "role": "user",
                "content": "Who are you?"
            }
        ]
    },
    "parameters":{
        "enable_thinking": true,
        "thinking_budget": 50,
        "incremental_output": true,
        "result_format": "message"
    }
}'

Return values

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"Okay","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"}