Alibaba Cloud Model Studio:ディープシンキング

サンプルコード

from openai import OpenAI
import os

# OpenAI クライアントを初期化
client = OpenAI(
    # 環境変数を設定していない場合は、次の行をご利用の Model Studio API キーに置き換えてください：api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    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",  # 必要に応じて、これを別のディープシンキングモデルに置き換えることができます
    messages=messages,
    extra_body={"enable_thinking": True},
    stream=True,
    stream_options={
        "include_usage": True
    },
)

reasoning_content = ""  # 完全な思考プロセス
answer_content = ""  # 完全な返信
is_answering = False  # 応答フェーズが開始されたかどうかを示します
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

    # 思考コンテンツのみを収集
    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

    # コンテンツが受信されたら、応答を開始
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Full Reply" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

応答

====================Thinking Process====================

Okay, the user is asking "Who are you". I need to give an accurate and friendly answer. First, I must 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. I should also maintain a friendly tone and avoid being too technical to make the user feel at ease. I must also be careful not to use complex terms and ensure the answer is concise and clear. Additionally, 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 answer is comprehensive and meets the user's expectations.
====================Full Reply====================

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 code, aiming to provide users with high-quality information and services. You can call me Qwen, or just Tongyi Qianwen. How can I help you?

サンプルコード

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

// OpenAI クライアントを初期化
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // 環境変数から読み込み
    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;
            
            // 思考コンテンツのみを収集
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // コンテンツが受信されたら、応答を開始
            if (delta.content !== undefined && delta.content) {
                if (!isAnswering) {
                    console.log('\n' + '='.repeat(20) + 'Full Reply' + '='.repeat(20) + '\n');
                    isAnswering = true;
                }
                process.stdout.write(delta.content);
                answerContent += delta.content;
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

応答

====================Thinking Process====================

Okay, the user is asking "Who are you". 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. Additionally, 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 don't need 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 should check if I have missed any important information, such as my version or latest updates, but the user probably doesn't need that much detail. Finally, I will confirm that the answer is accurate and contains no errors.
====================Full Reply====================

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!

サンプルコード

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
}'

応答

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]

サンプルコード

import os
from dashscope import Generation
import dashscope
dashscope.base_http_api_url = "https://dashscope-intl.aliyuncs.com/api/v1/"

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

completion = Generation.call(
    # 環境変数を設定していない場合は、次の行をご利用の Model Studio API キーに置き換えてください：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,
)

# 完全な思考プロセスを定義します。
reasoning_content = ""
# 完全な応答を定義します。
answer_content = ""
# 思考プロセスが終了し、応答が開始されたかどうかを確認します。
is_answering = False

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

for chunk in completion:
    # 思考プロセスと応答の両方が空の場合は無視します。
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        # 現在が思考プロセスの場合。
        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
        # 現在が応答の場合。
        elif chunk.output.choices[0].message.content != "":
            if not is_answering:
                print("\n" + "=" * 20 + "Complete Response" + "=" * 20)
                is_answering = True
            print(chunk.output.choices[0].message.content, end="", flush=True)
            answer_content += chunk.output.choices[0].message.content

# 完全な思考プロセスと応答を印刷するには、次のコードのコメントを解除して実行してください。
# print("=" * 20 + "Complete Thinking Process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Complete Response" + "=" * 20 + "\n")
# print(f"{answer_content}")

応答

====================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 answer 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.
====================Complete 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. My purpose is to provide 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?

サンプルコード

// DashScope SDK バージョン >= 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 {
        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====================Complete Response====================");
                isFirstPrint = true;
            }
            System.out.print(content);
        }
    }
    private static GenerationParam buildGenerationParam(Message userMsg) {
        return GenerationParam.builder()
                // 環境変数を設定していない場合は、次の行をご利用の Model Studio API キーに置き換えてください：.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);
//             最終結果を印刷します。
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================Complete Response====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

応答

====================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.
====================Complete 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?

サンプルコード

# ======= 重要 =======
# シンガポールリージョンと北京リージョンの API キーは異なります。API キーを取得するには、https://www.alibabacloud.com/help/model-studio/get-api-key をご参照ください
# 以下の URL はシンガポールリージョン用です。北京リージョンのモデルを使用する場合は、URL を https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation に置き換えてください
# === 実行前にこのコメントを削除してください ===

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"
    }
}'

応答

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":" welcome","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":" anytime","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":" to tell me","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"}

サンプルコード

from openai import OpenAI
import os

# OpenAI クライアントを初期化
client = OpenAI(
    # 環境変数が設定されていない場合は、api_key="sk-xxx" をご利用の Model Studio API キーに置き換えてください。
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    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,
    # enable_thinking パラメーターは思考プロセスを有効にし、thinking_budget パラメーターは推論プロセスの最大トークン数を設定します。
    extra_body={
        "enable_thinking": True,
        "thinking_budget": 50
        },
    stream=True,
    stream_options={
        "include_usage": True
    },
)

reasoning_content = ""  # 完全な思考プロセス
answer_content = ""  # 完全な応答
is_answering = False  # モデルが応答段階にあるかどうかを示します
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

    # 思考コンテンツのみを収集
    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

    # コンテンツが受信されたら、応答の生成を開始
    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

応答

====================Thinking Process====================

Okay, the user is asking "Who are you?" I need to give a clear and friendly answer. First, I should state my identity, which is Qwen, developed by the Qwen team at 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 Qwen team at Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code. My purpose is to provide help and convenience to users. How can I help you?

サンプルコード

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

// OpenAI クライアントを初期化
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // 環境変数から読み込み
    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 パラメーターは思考プロセスを有効にし、thinking_budget パラメーターは推論プロセスの最大トークン数を設定します。
            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;
            
            // 思考コンテンツのみを収集
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // コンテンツが受信されたら、応答の生成を開始
            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();

応答

====================Thinking Process====================

Okay, the user is asking "Who are you?" I need to give a clear and accurate answer. First, I should introduce myself as Qwen, developed by the Qwen team at Alibaba Group. Next, I should explain my main functions, such as answering questions
====================Full Response====================

I am Qwen, a large-scale language model independently developed by the Qwen team at Alibaba Group. I can perform various tasks such as answering questions, creating text, performing logical reasoning, and writing code. If you have any questions or need help, feel free to ask me anytime!

サンプルコード

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
}'

応答

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]

サンプルコード

import os
from dashscope import Generation
import dashscope
dashscope.base_http_api_url = "https://dashscope-intl.aliyuncs.com/api/v1/"

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

completion = Generation.call(
    # 環境変数が設定されていない場合は、次の行をご利用の Model Studio API キーに置き換えてください：api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen-plus",
    messages=messages,
    result_format="message",
    enable_thinking=True,
    # 推論プロセスの最大トークン数を設定します。
    thinking_budget=50,
    stream=True,
    incremental_output=True,
)

# 完全な思考プロセスを定義します。
reasoning_content = ""
# 完全な応答を定義します。
answer_content = ""
# 思考プロセスを終了して応答を開始するかどうかを決定します。
is_answering = False

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

for chunk in completion:
    # 思考プロセスと応答の両方が空の場合は無視します。
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        # 現在が思考プロセスの場合。
        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
        # 現在が応答段階の場合。
        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

# 完全な思考プロセスと完全な応答を印刷するには、次のコードのコメントを解除して実行してください。
# print("=" * 20 + "Full Thinking Process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full Response" + "=" * 20 + "\n")
# print(f"{answer_content}")

応答

====================Thinking Process====================
Okay, the user is asking "Who are you?" I need to give a clear and friendly answer. First, I should introduce myself as Qwen, developed by the Qwen team at 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 Qwen team at Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code. My purpose is to provide users with comprehensive, accurate, and useful information and assistance. How can I help you?

サンプルコード

// DashScope SDK バージョン >= 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 {
        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()
                // 環境変数が設定されていない場合は、次の行をご利用の Model Studio API キーに置き換えてください：.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);
//             最終結果を印刷します。
//            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?" I need to give a clear and friendly answer. First, I should introduce myself as Qwen, developed by the Qwen team at 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 Qwen team at Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code. My purpose is to provide users with comprehensive, accurate, and useful information and assistance. How can I help you?

サンプルコード

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"
    }
}'

応答

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"}