Alibaba Cloud Model Studio:Streaming output

Python

import os
from openai import OpenAI

# 1. Persiapan: Inisialisasi client
client = OpenAI(
    # Konfigurasikan Kunci API menggunakan variabel lingkungan untuk menghindari hardcoding.
    api_key=os.environ["DASHSCOPE_API_KEY"],
    # Kunci API terikat erat pada wilayah. Pastikan base_url sesuai dengan wilayah Kunci API Anda.
    # URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

# 2. Lakukan permintaan streaming
completion = client.chat.completions.create(
    model="qwen-plus",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Please introduce yourself"}
    ],
    stream=True,
    stream_options={"include_usage": True}
)

# 3. Tangani respons streaming
# Simpan fragmen respons dalam daftar. Menggabungkannya di akhir lebih efisien daripada penggabungan string berulang.
content_parts = []
print("AI: ", end="", flush=True)

for chunk in completion:
    if chunk.choices:
        content = chunk.choices[0].delta.content or ""
        print(content, end="", flush=True)
        content_parts.append(content)
    elif chunk.usage:
        print("\n--- Request usage ---")
        print(f"Input Tokens: {chunk.usage.prompt_tokens}")
        print(f"Output Tokens: {chunk.usage.completion_tokens}")
        print(f"Total Tokens: {chunk.usage.total_tokens}")

full_response = "".join(content_parts)
# print(f"\n--- Full response ---\n{full_response}")

Respons

AI: Hello! I am Qwen, a large-scale language model independently developed by Tongyi Lab under Alibaba Group. I can answer questions, create content such as stories, official documents, emails, scripts, perform logical reasoning, programming, express opinions, play games, and more. I support multiple languages, including but not limited to Chinese, English, German, French, and Spanish. If you have any questions or need help, feel free to ask me anytime!
--- Request usage ---
Input Tokens: 26
Output Tokens: 87
Total Tokens: 113

Node.js

import OpenAI from "openai";

async function main() {
    // 1. Persiapan: Inisialisasi client
    // Konfigurasikan Kunci API menggunakan variabel lingkungan untuk menghindari hardcoding.
    if (!process.env.DASHSCOPE_API_KEY) {
        throw new Error("Set the DASHSCOPE_API_KEY environment variable");
    }
    // Kunci API terikat erat pada wilayah. Pastikan baseURL sesuai dengan wilayah Kunci API Anda.
    // URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
    const client = new OpenAI({
        apiKey: process.env.DASHSCOPE_API_KEY,
        baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
    });

    try {
        // 2. Lakukan permintaan streaming
        const stream = await client.chat.completions.create({
            model: "qwen-plus",
            messages: [
                { role: "system", content: "You are a helpful assistant." },
                { role: "user", content: "Please introduce yourself" },
            ],
            stream: true,
            // Tujuan: Mendapatkan penggunaan token pada chunk terakhir.
            stream_options: { include_usage: true },
        });

        // 3. Tangani respons streaming
        const contentParts = [];
        process.stdout.write("AI: ");

        for await (const chunk of stream) {
            // Chunk terakhir tidak berisi choices tetapi mencakup informasi penggunaan.
            if (chunk.choices && chunk.choices.length > 0) {
                const content = chunk.choices[0]?.delta?.content || "";
                process.stdout.write(content);
                contentParts.push(content);
            } else if (chunk.usage) {
                // Permintaan selesai. Cetak penggunaan token.
                console.log("\n--- Request usage ---");
                console.log(`Input Tokens: ${chunk.usage.prompt_tokens}`);
                console.log(`Output Tokens: ${chunk.usage.completion_tokens}`);
                console.log(`Total Tokens: ${chunk.usage.total_tokens}`);
            }
        }

        const fullResponse = contentParts.join("");
        // console.log(`\n--- Full response ---\n${fullResponse}`);

    } catch (error) {
        console.error("Request failed:", error);
    }
}

main();

Respons

AI: Hello! I am Qwen, a large-scale language model independently developed by Tongyi Lab under Alibaba Group. I can answer questions, create content such as stories, official documents, emails, scripts, perform logical reasoning, programming, express opinions, play games, and more. I support multiple languages, including but not limited to Chinese, English, German, French, and Spanish. If you have any questions or need help, feel free to ask me anytime!
--- Request usage ---
Input Tokens: 26
Output Tokens: 89
Total Tokens: 115

curl

Permintaan

# ======= Catatan penting =======
# Pastikan variabel lingkungan DASHSCOPE_API_KEY telah disetel
# Kunci API berbeda berdasarkan wilayah. Dapatkan Kunci API Anda: https://www.alibabacloud.com/help/zh/model-studio/get-api-key

# === Hapus komentar ini sebelum menjalankan ===

curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
--no-buffer \
-d '{
    "model": "qwen-plus",
    "messages": [
        {"role": "user", "content": "Who are you?"}
    ],
    "stream": true,
    "stream_options": {"include_usage": true}
}'

Respons

Respons mengikuti protokol SSE. Setiap baris yang diawali dengan data: merepresentasikan chunk data.

data: {"choices":[{"delta":{"content":"","role":"assistant"},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"finish_reason":null,"delta":{"content":"I am"},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"delta":{"content":" from"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"delta":{"content":" Alibaba"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"delta":{"content":"'s large-scale language"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"delta":{"content":" model, my name is Qwen"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"delta":{"content":"."},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"finish_reason":"stop","delta":{"content":""},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":22,"completion_tokens":17,"total_tokens":39},"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: [DONE]

• data:: Muatan pesan, biasanya berupa string JSON.
• [DONE]: Menandakan akhir dari seluruh respons streaming.

Python

import os
from http import HTTPStatus
import dashscope
from dashscope import Generation

# 1. Persiapan: Konfigurasikan Kunci API dan wilayah
# Konfigurasikan Kunci API menggunakan variabel lingkungan untuk menghindari hardcoding.
try:
    dashscope.api_key = os.environ["DASHSCOPE_API_KEY"]
except KeyError:
    raise ValueError("Set the DASHSCOPE_API_KEY environment variable")

# Kunci API terikat erat pada wilayah. Pastikan base_url sesuai dengan wilayah Kunci API Anda.

dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1"

# 2. Lakukan permintaan streaming
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Please introduce yourself"},
]

try:
    responses = Generation.call(
        model="qwen-plus",
        messages=messages,
        result_format="message",
        stream=True,
        # Penting: Atur ke True untuk output inkremental, yang menawarkan kinerja lebih baik.
        incremental_output=True,
    )

    # 3. Tangani respons streaming
    content_parts = []
    print("AI: ", end="", flush=True)

    for resp in responses:
        if resp.status_code == HTTPStatus.OK:
            content = resp.output.choices[0].message.content
            print(content, end="", flush=True)
            content_parts.append(content)

            # Periksa apakah ini paket terakhir
            if resp.output.choices[0].finish_reason == "stop":
                usage = resp.usage
                print("\n--- Request usage ---")
                print(f"Input Tokens: {usage.input_tokens}")
                print(f"Output Tokens: {usage.output_tokens}")
                print(f"Total Tokens: {usage.total_tokens}")
        else:
            # Tangani kesalahan
            print(
                f"\nRequest failed: request_id={resp.request_id}, code={resp.code}, message={resp.message}"
            )
            break

    full_response = "".join(content_parts)
    # print(f"\n--- Full response ---\n{full_response}")

except Exception as e:
    print(f"An unknown error occurred: {e}")

AI: Hello! I am Qwen, a large-scale language model independently developed by Tongyi Lab under Alibaba Group. I can help you answer questions, create content such as stories, official documents, emails, scripts, perform logical reasoning, programming, express opinions, play games, and more. I support multiple languages, including but not limited to Chinese, English, German, French, and Spanish. If you have any questions or need help, feel free to ask me anytime!
--- Request usage ---
Input Tokens: 26
Output Tokens: 91
Total Tokens: 117

Java

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 io.reactivex.Flowable;
import io.reactivex.schedulers.Schedulers;

import java.util.Arrays;
import java.util.concurrent.CountDownLatch;
import com.alibaba.dashscope.protocol.Protocol;

public class Main {
    public static void main(String[] args) {
        // 1. Dapatkan Kunci API
        String apiKey = System.getenv("DASHSCOPE_API_KEY");
        if (apiKey == null || apiKey.isEmpty()) {
            System.err.println("Set the DASHSCOPE_API_KEY environment variable");
            return;
        }

        // 2. Inisialisasi instance Generation
        // Kunci API terikat erat pada wilayah. Pastikan baseUrl sesuai dengan wilayah Kunci API Anda.

        Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1");
        CountDownLatch latch = new CountDownLatch(1);

        // 3. Bangun parameter permintaan
        GenerationParam param = GenerationParam.builder()
                .apiKey(apiKey)
                .model("qwen-plus")
                .messages(Arrays.asList(
                        Message.builder()
                                .role(Role.USER.getValue())
                                .content("Introduce yourself")
                                .build()
                ))
                .resultFormat(GenerationParam.ResultFormat.MESSAGE)
                .incrementalOutput(true) // Aktifkan output inkremental untuk streaming
                .build();
        // 4. Lakukan panggilan streaming dan tangani respons
        try {
            Flowable<GenerationResult> result = gen.streamCall(param);
            StringBuilder fullContent = new StringBuilder();
            System.out.print("AI: ");
            result
                    .subscribeOn(Schedulers.io()) // Jalankan permintaan pada thread IO
                    .observeOn(Schedulers.computation()) // Proses respons pada thread computation
                    .subscribe(
                            // onNext: Tangani setiap fragmen respons
                            message -> {
                                String content = message.getOutput().getChoices().get(0).getMessage().getContent();
                                String finishReason = message.getOutput().getChoices().get(0).getFinishReason();
                                // Output konten
                                System.out.print(content);
                                fullContent.append(content);
                                // Ketika finishReason tidak null, itu menandakan chunk terakhir. Output info penggunaan.
                                if (finishReason != null && !"null".equals(finishReason)) {
                                    System.out.println("\n--- Request usage ---");
                                    System.out.println("Input Tokens: " + message.getUsage().getInputTokens());
                                    System.out.println("Output Tokens: " + message.getUsage().getOutputTokens());
                                    System.out.println("Total Tokens: " + message.getUsage().getTotalTokens());
                                }
                                System.out.flush(); // Flush output segera
                            },
                            // onError: Tangani kesalahan
                            error -> {
                                System.err.println("\nRequest failed: " + error.getMessage());
                                latch.countDown();
                            },
                            // onComplete: Callback penyelesaian
                            () -> {
                                System.out.println(); // Baris baru
                                // System.out.println("Full response: " + fullContent.toString());
                                latch.countDown();
                            }
                    );
            // Thread utama menunggu tugas async selesai
            latch.await();
            System.out.println("Program execution complete");
        } catch (Exception e) {
            System.err.println("Request exception: " + e.getMessage());
            e.printStackTrace();
        }
    }
}

Respons

AI: Hello! I am Qwen, a large-scale language model independently developed by Tongyi Lab under Alibaba Group. I can help you answer questions, create content such as stories, official documents, emails, scripts, perform logical reasoning, programming, express opinions, play games, and more. I support multiple languages, including but not limited to Chinese, English, German, French, and Spanish. If you have any questions or need help, feel free to ask me anytime!
--- Request usage ---
Input Tokens: 26
Output Tokens: 91
Total Tokens: 117

curl

Permintaan

# ======= Catatan penting =======
# Pastikan variabel lingkungan DASHSCOPE_API_KEY telah disetel
# Kunci API berbeda berdasarkan wilayah. Dapatkan Kunci API Anda: https://www.alibabacloud.com/help/zh/model-studio/get-api-key
# URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
# === Hapus komentar ini sebelum menjalankan ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.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": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "user",
                "content": "Who are you?"
            }
        ]
    },
    "parameters": {
        "result_format": "message",
        "incremental_output":true
    }
}'

Respons

Respons mengikuti format Server-Sent Events (SSE). Setiap pesan mencakup:

• id: Nomor chunk data.
• event: Jenis event, selalu "result".
• Informasi kode status HTTP.
• data: Data berformat JSON.

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"I am","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":27,"output_tokens":1,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

id:2
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"Qwen","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":30,"output_tokens":4,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

id:3
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":" from Alibaba","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":33,"output_tokens":7,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

...

id:13
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"or need help, feel free to","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":90,"output_tokens":64,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

id:14
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"ask me!","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":92,"output_tokens":66,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

id:15
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","role":"assistant"},"finish_reason":"stop"}]},"usage":{"total_tokens":92,"output_tokens":66,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

Python

Kode contoh

from openai import OpenAI
import os

# Inisialisasi client OpenAI
client = OpenAI(
    # Jika Anda belum mengonfigurasi variabel lingkungan, ganti dengan Kunci API Alibaba Cloud Model Studio Anda: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

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

completion = client.chat.completions.create(
    model="qwen-plus",  # Ganti dengan model berpikir mendalam lain sesuai kebutuhan
    messages=messages,
    # Parameter enable_thinking mengaktifkan proses berpikir. Parameter ini tidak berpengaruh pada model qwen3-30b-a3b-thinking-2507, qwen3-235b-a22b-thinking-2507, dan QwQ.
    extra_body={"enable_thinking": True},
    stream=True,
    # stream_options={
    #     "include_usage": True
    # },
)

reasoning_content = ""  # Proses berpikir lengkap
answer_content = ""  # Respons lengkap
is_answering = False  # Apakah sedang dalam tahap respons
print("\n" + "=" * 20 + "Thought process" + "=" * 20 + "\n")

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

    delta = chunk.choices[0].delta

    # Kumpulkan hanya konten berpikir
    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

    # Menerima konten, mulai merespons
    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

Respons

====================Thought process====================

Okay, the user asked "Who are you," so I need to give an accurate and friendly answer. First, I should confirm my identity as Qwen, developed by Tongyi Lab under Alibaba Group. Next, explain my main functions, like answering questions, creating text, logical reasoning, etc. Keep the tone approachable and avoid overly technical terms so the user feels comfortable. Also, avoid complex jargon and ensure the answer is concise. Additionally, include some interactive elements to encourage further questions. Finally, check for any missing key information, such as my Chinese name "Tongyi Qianwen" and English name "Qwen," along with my company and lab. Make sure the response is comprehensive and meets user expectations.
====================Full response====================

Hello! I am Qwen, a large-scale language model independently developed by Tongyi Lab under Alibaba Group. I can answer questions, create text, perform logical reasoning, programming, and more, aiming to provide high-quality information and services. You can call me Qwen or simply Tongyi Qianwen. How can I help you?

Node.js

Kode contoh

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

// Inisialisasi klien openai
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // Baca dari variabel lingkungan
    baseURL: 'https://{WorkspaceId}.ap-southeast-1.maas.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({
            // Ganti dengan model Qwen3 atau model QwQ lain sesuai kebutuhan
            model: 'qwen-plus',
            messages,
            stream: true,
            // Parameter enable_thinking mengaktifkan proses berpikir. Parameter ini tidak berpengaruh pada model qwen3-30b-a3b-thinking-2507, qwen3-235b-a22b-thinking-2507, dan QwQ.
            enable_thinking: true
        });
        console.log('\n' + '='.repeat(20) + 'Proses berpikir' + '='.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;

            // Kumpulkan hanya konten pemikiran
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // Konten diterima, mulai berikan tanggapan
            if (delta.content !== undefined && delta.content) {
                if (!isAnswering) {
                    console.log('\n' + '='.repeat(20) + 'Tanggapan lengkap' + '='.repeat(20) + '\n');
                    isAnswering = true;
                }
                process.stdout.write(delta.content);
                answerContent += delta.content;
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

Respons

====================Thought process====================

Okay, the user asked "Who are you," so I need to state my identity. First, I should clearly say I am Qwen, a large-scale language model developed by Alibaba Cloud. Next, mention my main functions, like answering questions, creating text, logical reasoning, etc. Also emphasize my multilingual support, including Chinese and English, so users know I can handle requests in different languages. Additionally, explain my application scenarios, such as helping with learning, work, and daily life. However, since the user's question is direct, detailed information might not be necessary—keep it concise. Also, ensure a friendly tone and invite further questions. Check for any missing key information, like my version or latest updates, but the user probably doesn't need that much detail. Finally, confirm the response is accurate and error-free.
====================Full response====================

I am Qwen, a large-scale language model independently developed by Tongyi Lab under Alibaba Group. I can handle various tasks like answering questions, creating text, logical reasoning, and programming, supporting multiple languages including Chinese and English. If you have any questions or need help, feel free to tell me anytime!

HTTP

Kode contoh

curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.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
}'

Respons

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]

Python

Kode contoh

import os
from dashscope import Generation
import dashscope
dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/"

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

completion = Generation.call(
    # Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Alibaba Cloud Model Studio Anda: api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Ganti dengan model berpikir mendalam lain sesuai kebutuhan
    model="qwen-plus",
    messages=messages,
    result_format="message", # Model open-source Qwen3 hanya mendukung "message"; untuk pengalaman lebih baik, kami sarankan mengatur ini ke "message" untuk model lain juga.
    # Aktifkan pemikiran mendalam. Parameter ini tidak berpengaruh pada model qwen3-30b-a3b-thinking-2507, qwen3-235b-a22b-thinking-2507, dan QwQ.
    enable_thinking=True,
    stream=True,
    incremental_output=True, # Model open-source Qwen3 hanya mendukung true; untuk pengalaman lebih baik, kami sarankan mengatur ini ke true untuk model lain juga.
)

# Definisikan proses berpikir lengkap
reasoning_content = ""
# Definisikan respons lengkap
answer_content = ""
# Tentukan apakah sudah selesai berpikir dan mulai merespons
is_answering = False

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

for chunk in completion:
    # Jika proses berpikir dan respons keduanya kosong, lewati
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        # Jika saat ini dalam tahap berpikir
        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
        # Jika saat ini dalam tahap respons
        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

# Untuk mencetak proses berpikir lengkap dan respons lengkap, hapus komentar baris berikut dan jalankan
# print("=" * 20 + "Full thought process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")

Respons

====================Thought process====================
Okay, the user asked: "Who are you?" I need to answer this question. First, clarify my identity as Qwen, a large-scale language model developed by Alibaba Cloud. Next, explain my functions and purposes, like answering questions, creating text, logical reasoning, etc. Also, emphasize my goal of being a helpful assistant.

Keep the expression conversational, avoiding professional jargon or complex sentence structures. Add friendly phrases like "Hello there～" to make the conversation natural. Also, ensure accuracy and don't omit key points like my developer, main functions, and usage scenarios.

Consider possible follow-up questions from the user, such as specific application examples or technical details, so subtly hint at further inquiries in the response. For example, mention "Whether it's everyday questions or professional issues, I'll do my best to help," which is both comprehensive and open-ended.

Finally, check for fluency, repetition, or redundant information to keep the response concise. Maintain a balance between friendliness and professionalism so users feel both approachable and reliable.
====================Full response====================
Hello there～ I'm Qwen, a large-scale language model developed by Alibaba Cloud. I can answer questions, create text, perform logical reasoning, programming, and more, aiming to provide help and support. Whether it's everyday questions or professional issues, I'll do my best to help. How can I assist you?

Java

Kode contoh

// 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 {
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.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("====================Thought 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()
                // Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Alibaba Cloud Model Studio Anda: .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);
//             Cetak hasil akhir
//            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);
    }
}

Respons

====================Thought process====================
Okay, the user asked "Who are you?", so I need to answer based on previous settings. First, my role is Qwen, a large-scale language model under Alibaba Group. Keep it conversational and simple.

The user might be new to me or confirming my identity. Start by directly stating who I am, then briefly explain my functions and purposes, like answering questions, creating text, programming, etc. Also mention multilingual support so users know I handle different languages.

Also, per guidelines, maintain human-like qualities, so use a friendly tone, maybe add emojis for warmth. Guide users to ask further questions or use my features, like asking what they need help with.

Avoid complex terms and keep it concise. Check for missing key points like multilingual support and specific capabilities. Ensure the response meets all requirements, including conversational style and simplicity.
====================Full response====================
Hello! I'm Qwen, a large-scale language model under Alibaba Group. I can answer questions, create text like stories, official documents, emails, scripts, perform logical reasoning, programming, express opinions, play games, and more. I'm proficient in multiple languages, including but not limited to Chinese, English, German, French, and Spanish. How can I help you?

HTTP

Kode contoh

# ======= Catatan penting =======
# Kunci API berbeda berdasarkan wilayah. Dapatkan Kunci API Anda: https://www.alibabacloud.com/help/zh/model-studio/get-api-key
# URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
# === Hapus komentar ini sebelum menjalankan ===
curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.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"
    }
}'

Respons

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":"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":"asked","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":"tell","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":"me","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":"null"}]},"usage":{"total_tokens":378,"input_tokens":11,"output_tokens":367},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:365
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"}