Cara menggunakan Code Interpreter di API Qwen - Alibaba Cloud Model Studio

Aktifkan Python Code Interpreter bawaan saat memanggil model. Model menulis dan menjalankan kode Python dalam sandbox untuk menyelesaikan masalah kompleks seperti perhitungan matematis dan analitik data.

Cara menggunakan

Code Interpreter mendukung tiga metode pemanggilan, masing-masing dengan parameter berbeda:

OpenAI-compatible - Responses API

Untuk mengaktifkan Code Interpreter, tambahkan tool code_interpreter ke parameter tools.

Untuk hasil terbaik, aktifkan tool code_interpreter, web_search, dan web_extractor secara bersamaan.

# Impor dependensi dan buat klien...
response = client.responses.create(
    model="qwen3.7-plus",
    input="Berapa 123 pangkat 21?",
    tools=[
        {"type": "code_interpreter"},
        {"type": "web_search"},
        {"type": "web_extractor"},
    ],
    extra_body={
        "enable_thinking": True
    }
)

print(response.output_text)

OpenAI-compatible - Chat Completions API

Untuk mengaktifkan Code Interpreter, sertakan enable_code_interpreter: true dalam permintaan API.

# Impor dependensi dan buat klien...
completion = client.chat.completions.create(
    # Gunakan model yang mendukung Code Interpreter
    model="qwen3.7-plus",
    messages=[{"role": "user", "content": "Berapa 123 pangkat 21?"}],
    # Karena enable_code_interpreter bukan parameter standar OpenAI, Anda harus melewatkan melalui extra_body saat menggunakan Python SDK. Saat menggunakan Node.js SDK, lewatkan sebagai parameter tingkat atas.
    extra_body={
        "enable_code_interpreter": True,
        # Fitur Code Interpreter hanya mendukung panggilan dalam mode thinking
        "enable_thinking": True,
    },
    # Hanya panggilan dengan streaming output yang didukung
    stream=True
)

Protokol kompatibel OpenAI tidak mengembalikan detail eksekusi kode.

DashScope

Untuk mengaktifkan Code Interpreter, atur enable_code_interpreter ke true dalam permintaan API.

# Impor dependensi...
response = dashscope.MultiModalConversation.call(
    # Jika variabel lingkungan belum dikonfigurasi, ganti baris berikut dengan: api_key="sk-xxx", menggunakan Kunci API Model Studio Anda.
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen3.7-plus",
    messages=[{"role": "user", "content": "Berapa 123 pangkat 21?"}],
    # Aktifkan Code Interpreter menggunakan parameter enable_code_interpreter
    enable_code_interpreter=True,
    # Fitur Code Interpreter hanya mendukung mode thinking
    enable_thinking=True,
    result_format="message",
    # Hanya panggilan dengan streaming output yang didukung
    stream=True
)

Kode yang dieksekusi dikembalikan dalam bidang tool_info.

Setelah Code Interpreter diaktifkan, model memproses permintaan dalam tahapan berikut:

Thinking: Model menganalisis permintaan pengguna dan menghasilkan ide serta langkah-langkah untuk menyelesaikan masalah.
Code execution: Model menghasilkan dan mengeksekusi kode Python.
Result integration: Model menerima hasil eksekusi kode dan merencanakan langkah selanjutnya.
Response: Model menghasilkan tanggapan dalam bahasa alami.

Langkah 2 dan 3 dapat berulang beberapa kali.

Bidang yang dikembalikan oleh API berbeda-beda:

Responses API: Konten thinking dikembalikan dalam objek dengan type="reasoning" pada output. Eksekusi kode dikembalikan dengan type="code_interpreter_call". Tanggapan dikembalikan dengan type="message".
Chat Completions API / DashScope: Konten thinking dikembalikan dalam bidang reasoning_content. Tanggapan dikembalikan dalam bidang content. DashScope juga mendukung pengembalian konten kode dalam bidang tool_info.

Cakupan

Model yang direkomendasikan

Responses API

Qwen-Max: seri Qwen3.7-Max

Qwen-Plus: seri Qwen3.7-Plus, seri Qwen3.6-Plus, seri Qwen3.5-Plus

Chat Completions API / DashScope

Qwen-Max (mode thinking): seri Qwen3-Max
Qwen-Plus: seri Qwen3.7-Plus, seri Qwen3.6-Plus, seri Qwen3.5-Plus

Model lainnya

Model-model berikut juga mendukung Code Interpreter tetapi mungkin tidak memberikan performa sebaik model yang direkomendasikan.

Qwen-Flash: seri Qwen3.6-Flash, seri Qwen3.5-Flash
Seri open-source Qwen3.6 (kecuali qwen3.6-27b)
Seri open source Qwen3.5

Mulai

Contoh-contoh berikut menunjukkan cara Code Interpreter menyelesaikan masalah matematika.

OpenAI-compatible - Responses API

Untuk hasil terbaik, aktifkan tool code_interpreter, web_search, dan web_extractor secara bersamaan.

Python

import os
from openai import OpenAI

client = OpenAI(
    # Jika variabel lingkungan belum dikonfigurasi, ganti baris berikut dengan: api_key="sk-xxx", menggunakan Kunci API Model Studio Anda.
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # URL berikut untuk wilayah Singapura. Saat memanggil, ganti WorkspaceId dengan ID ruang kerja aktual Anda. URL berbeda-beda tergantung wilayah.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
)

response = client.responses.create(
    model="qwen3.7-plus",
    input="12 pangkat 3",
    tools=[
        {
            "type": "code_interpreter"
        },
        {
            "type": "web_search"
        },
        {
            "type": "web_extractor"
        }
    ],
    extra_body = {
        "enable_thinking": True
    }
)
# Hapus komentar baris berikut untuk melihat output proses antara
# print(response.output)
print("="*20+"Konten Tanggapan"+"="*20)
print(response.output_text)
print("="*20+"Konsumsi Token dan Pemanggilan Tool"+"="*20)
print(response.usage)

Node.js

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

const openai = new OpenAI({
    // Jika variabel lingkungan belum dikonfigurasi, ganti baris berikut dengan: apiKey: "sk-xxx", menggunakan Kunci API Model Studio Anda.
    apiKey: process.env.DASHSCOPE_API_KEY,
    // URL berikut untuk wilayah Singapura. Saat memanggil, ganti WorkspaceId dengan ID ruang kerja aktual Anda. URL berbeda-beda tergantung wilayah.
    baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
});

async function main() {
    const response = await openai.responses.create({
        model: "qwen3.7-plus",
        input: "Hitung 12 pangkat 3",
        tools: [
            { type: "code_interpreter" },
            { type: "web_search" },
            { type: "web_extractor" }
        ],
        enable_thinking: true
    });

    console.log("====================Konten Tanggapan====================");
    console.log(response.output_text);

    // Cetak jumlah pemanggilan tool
    console.log("====================Konsumsi Token dan Pemanggilan Tool====================");
    if (response.usage && response.usage.x_tools) {
        console.log(`Jumlah eksekusi Code Interpreter: ${response.usage.x_tools.code_interpreter?.count || 0}`);
    }
    // Hapus komentar baris berikut untuk melihat output proses antara
    // console.log(JSON.stringify(response.output[0], null, 2));
}

main();

curl

# URL berikut untuk wilayah Singapura. Saat memanggil, ganti WorkspaceId dengan ID ruang kerja aktual Anda. URL berbeda-beda tergantung wilayah.
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3.7-plus",
    "input": "Hitung 12 pangkat 3",
    "tools": [
        {"type": "code_interpreter"},
        {"type": "web_search"},
        {"type": "web_extractor"}
    ],
    "enable_thinking": true
}'

Contoh tanggapan

====================Konten Tanggapan====================
12 pangkat 3 adalah **1728**.

Proses perhitungan:
12³ = 12 × 12 × 12 = 144 × 12 = 1728
====================Konsumsi Token dan Pemanggilan Tool====================
ResponseUsage(input_tokens=1160, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=195, output_tokens_details=OutputTokensDetails(reasoning_tokens=105), total_tokens=1355, x_tools={'code_interpreter': {'count': 1}})

OpenAI-compatible - Chat Completions API

Python

from openai import OpenAI
import os

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

messages = [{"role": "user", "content": "Berapa 123 pangkat 21?"}]

completion = client.chat.completions.create(
    model="qwen3.7-plus",
    messages=messages,
    extra_body={"enable_thinking": True, "enable_code_interpreter": True},
    stream=True,
    stream_options={
        "include_usage": True
    },
)

reasoning_content = ""  # Proses thinking lengkap
answer_content = ""  # Tanggapan lengkap
is_answering = False  # Penanda untuk memeriksa apakah fase tanggapan telah dimulai
print("\n" + "=" * 20 + "Proses Thinking" + "=" * 20 + "\n")

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

    delta = chunk.choices[0].delta

    # Kumpulkan hanya konten thinking
    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

    # Saat konten diterima, mulai tanggapan
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Tanggapan Lengkap" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

Contoh tanggapan

====================Proses Thinking====================
  
Pengguna menanyakan nilai 123 pangkat 21. Ini adalah masalah perhitungan matematis. Saya perlu menghitung 123^21.

Saya dapat menggunakan kalkulator kode untuk menghitung nilai ini. Saya perlu memanggil fungsi code_interpreter dan meneruskan kode Python untuk menghitung 123**21.

Mari saya susun pemanggilan fungsi ini.
Pengguna menanyakan 123 pangkat 21, dan saya menghitung hasilnya menggunakan kode Python. Perhitungan menunjukkan bahwa 123 pangkat 21 sama dengan 77269364466549865653073473388030061522211723. Ini adalah angka yang sangat besar, dan saya harus menyajikannya secara langsung.
====================Tanggapan Lengkap====================

123 pangkat 21 adalah: 77269364466549865653073473388030061522211723
Usage:
CompletionUsage(completion_tokens=245, prompt_tokens=719, total_tokens=964, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=153, rejected_prediction_tokens=None), prompt_tokens_details=None)

Node.js

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
    // Untuk menggunakan model di wilayah Singapura, ganti dengan https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1
    baseURL: 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1'
});

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

async function main() {
    try {
        const messages = [{ role: 'user', content: 'Berapa 123 pangkat 21?' }];
        const stream = await openai.chat.completions.create({
            model: 'qwen3.7-plus',
            messages,
            stream: true,
            enable_thinking: true,
            enable_code_interpreter: true
        });
        console.log('\n' + '='.repeat(20) + 'Proses Thinking' + '='.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 thinking
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // Saat konten diterima, mulai 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();

Contoh tanggapan

====================Proses Thinking====================
  
  Pengguna menanyakan nilai 123 dipangkatkan 21. Ini adalah perhitungan matematis yang dapat saya lakukan menggunakan code interpreter Python. Saya akan menggunakan operator eksponensial ** untuk menghitung ini.
  
  Mari saya tulis kode untuk menghitung 123**21.Perhitungan telah berhasil diselesaikan. Hasil dari 123 dipangkatkan 21 adalah angka yang sangat besar: 77269364466549865653073473388030061522211723.
  
  Saya harus menyajikan hasil ini dengan jelas kepada pengguna.
  
  ====================Tanggapan Lengkap====================
  
  123 pangkat 21 adalah: 77269364466549865653073473388030061522211723

curl

# Untuk menggunakan model di wilayah Singapura, ganti dengan https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions
curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3.7-plus",
    "messages": [
        {
            "role": "user", 
            "content": "Berapa 123 pangkat 21?"
        }
    ],
    "enable_code_interpreter": true,
    "enable_thinking": true,
    "stream": true
}'

Contoh tanggapan

data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"}
  
data: {"choices":[{"finish_reason":null,"logprobs":null,"delta":{"content":null,"reasoning_content":"The user"},"index":0}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":" is asking"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"}

data: {"choices":[{"delta":{"content":null,"reasoning_content":" for"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"}

...

data: {"choices":[{"delta":{"content":"is a very large number, with a total","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"}

data: {"choices":[{"delta":{"content":"of 43 digits","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"}

data: {"choices":[{"delta":{"content":".","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"}

data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"}

data: [DONE]

DashScope

Java SDK tidak didukung.

Python

import os
import dashscope

# Wilayah China (Beijing). Ganti {WorkspaceId} dengan ID Ruang Kerja aktual Anda. URL berbeda-beda tergantung wilayah.
dashscope.base_http_api_url = 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1'

messages = [
    {"role": "user", "content": "Berapa 123 pangkat 21?"},
]

response = dashscope.MultiModalConversation.call(
    # Jika variabel lingkungan belum dikonfigurasi, ganti baris berikut dengan: api_key="sk-xxx", menggunakan Kunci API Model Studio Anda.
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen3.7-plus",
    messages=messages,
    enable_code_interpreter=True,
    enable_thinking=True,
    result_format="message",
    # Hanya streaming output yang didukung
    stream=True
)

for chunk in response:
    output = chunk["output"]
    print(output)

Contoh tanggapan

{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": "The"}}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " user is asking"}}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " me"}}]}
...
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " I'll write a"}}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " simple Python program to calculate"}}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": "The"}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " user"}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " asked"}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
...
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " I should present this result"}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " to the user in"}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " a clear format."}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "123 to the power of ", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "21 is:\n\n", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "772693", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "644665", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "498656", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "530734", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "733880", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "300615", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "222117", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "23", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}
{"text": null, "finish_reason": null, "choices": [{"finish_reason": "stop", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]}

curl

curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-DashScope-SSE: enable" \
-d '{
    "model": "qwen3.7-plus",
    "input":{
        "messages":[
            {
                "role": "user",
                "content": "Berapa 123 pangkat 21?"
            }
        ]
    },
    "parameters": {
        "enable_code_interpreter": true,
        "enable_thinking": true,
        "result_format": "message"
    }
}'

Contoh tanggapan

Teks `<...text content...>` adalah komentar penjelasan dan bukan bagian dari tanggapan API aktual. Komentar ini digunakan untuk mengidentifikasi tahapan pemrosesan yang berbeda.

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"The","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":290,"output_tokens":3,"input_tokens":287,"output_tokens_details":{"reasoning_tokens":1}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"}

id:2
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":" user is asking","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":293,"output_tokens":6,"input_tokens":287,"output_tokens_details":{"reasoning_tokens":4}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"}

...Tahap thinking...

id:21
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":388,"output_tokens":101,"input_tokens":287,"output_tokens_details":{"reasoning_tokens":68}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"}

...Thinking berakhir, memulai Code Interpreter...

id:22
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":388,"output_tokens":101,"input_tokens":287,"output_tokens_details":{"reasoning_tokens":68},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"}

...Thinking dimulai setelah menjalankan Code Interpreter...

id:23
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"The","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":838,"output_tokens":104,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":69},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"}

id:24
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":" user","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":839,"output_tokens":105,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":70},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"}

...Tahap thinking...

id:43
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":" a clear format.","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":942,"output_tokens":208,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":171},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"}

...Thinking berakhir, memulai tanggapan...

id:44
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"123 to the power of","reasoning_content":"","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":947,"output_tokens":213,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":171},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"}

...

id:53
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"23","reasoning_content":"","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":997,"output_tokens":263,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":171},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"}

id:54
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"stop"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":997,"output_tokens":263,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":171},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"}

Penguraian tanggapan

OpenAI-compatible - Responses API

Contoh berikut menggunakan OpenAI Python SDK untuk mengurai tanggapan streaming.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # URL berikut untuk wilayah Singapura. Saat memanggil, ganti WorkspaceId dengan ID ruang kerja aktual Anda. URL berbeda-beda tergantung wilayah.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
)

response = client.responses.create(
    model="qwen3.7-plus",
    input="12 pangkat 3",
    tools=[
        {"type": "code_interpreter"}
    ],
    extra_body={
        "enable_thinking": True
    },
    stream=True
)

def print_section(title):
    print(f"\n{'=' * 20}{title}{'=' * 20}")

current_section = None
final_response = None

for event in response:
    # Output inkremental proses thinking
    if event.type == "response.reasoning_summary_text.delta":
        if current_section != "reasoning":
            print_section("Proses Thinking")
            current_section = "reasoning"
        print(event.delta, end="", flush=True)

    # Pemanggilan Code Interpreter selesai
    elif event.type == "response.output_item.done" and hasattr(event.item, "code"):
        print_section("Eksekusi Kode")
        print(f"Kode:\n{event.item.code}")
        if event.item.outputs:
            print(f"Hasil: {event.item.outputs[0].logs}")
        current_section = "code"

    # Output inkremental tanggapan akhir
    elif event.type == "response.output_text.delta":
        if current_section != "answer":
            print_section("Tanggapan Lengkap")
            current_section = "answer"
        print(event.delta, end="", flush=True)

    # Tanggapan selesai, simpan hasil akhir untuk mendapatkan usage
    elif event.type == "response.completed":
        final_response = event.response

# Output konsumsi token dan jumlah pemanggilan tool
if final_response and final_response.usage:
    print_section("Konsumsi Token dan Pemanggilan Tool")
    usage = final_response.usage
    print(f"Token Input: {usage.input_tokens}")
    print(f"Token Output: {usage.output_tokens}")
    print(f"Token Thinking: {usage.output_tokens_details.reasoning_tokens}")
    print(f"Jumlah pemanggilan Code Interpreter: {usage.x_tools.get('code_interpreter', {}).get('count', 0)}")

DashScope

Contoh berikut menggunakan DashScope Python SDK untuk melakukan dua perhitungan dalam satu permintaan dan mengurai kode serta jumlah pemanggilan yang dikembalikan.

API OpenAI Chat Completions tidak mengembalikan data selama tahap code execution, sehingga tidak ada tanggapan yang dikirim antara tahap thinking dan result integration. Kedua tahap tersebut mengembalikan konten melalui bidang reasoning_content, sehingga Anda dapat memprosesnya bersama sebagai tahap thinking. Untuk contoh penguraian tanggapan, lihat kode di bagian Mulai.

import os  
from dashscope import MultiModalConversation  
# Wilayah China (Beijing). Ganti {WorkspaceId} dengan ID Ruang Kerja aktual Anda. URL berbeda-beda tergantung wilayah.
dashscope.base_http_api_url = 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1'

messages = [{"role": "user", "content": "Jalankan Code Interpreter dua kali: pertama, hitung nilai 123 pangkat 23. Kedua, bagi hasilnya dengan 5."}]  

response = MultiModalConversation.call(  
    api_key=os.getenv("DASHSCOPE_API_KEY"),  
    model="qwen3.7-plus",  
    messages=messages,  
    result_format="message",
    enable_thinking=True,
    enable_code_interpreter=True,
    stream=True,
    incremental_output=True,
)  

# Penanda status: lacak apakah info tool sudah dicetak, apakah fase menjawab telah dimulai, dan apakah dalam bagian reasoning
is_answering = False  
in_reasoning_section = False  
cur_tools = []

# Cetak bagian dengan judul
def print_section(title):  
    print(f"\n{'=' * 20}{title}{'=' * 20}")  

# Awalnya cetak judul "Proses Thinking"
print_section("Proses Thinking")  
in_reasoning_section = True  

# Proses setiap blok yang dikembalikan oleh model dalam aliran
for chunk in response:  
    try:  
        # Ekstrak bidang utama dari tanggapan: konten, teks reasoning, info pemanggilan tool
        choice = chunk.output.choices[0]  
        msg = choice.message  
        content = msg.get("content", "")            # Konten jawaban akhir
        reasoning = msg.get("reasoning_content", "") # Teks proses reasoning
        tools = chunk.output.get("tool_info", None)  # Informasi pemanggilan tool
    except (IndexError, AttributeError, KeyError):
        # Lewati blok dengan struktur abnormal
        continue  
    # Jika tidak ada konten valid, lewati blok saat ini
    if not content and not reasoning and tools is None:  
        continue  
    # Output proses reasoning
    if reasoning and not is_answering:  
        if not in_reasoning_section:  
            print_section("Proses Thinking")  
            in_reasoning_section = True  
        print(reasoning, end="", flush=True)  
    if tools is not None and tools != cur_tools:  
        print_section("Informasi Tool")  
        print(tools)  
        in_reasoning_section = False  
        cur_tools = tools
    # Output konten jawaban akhir
    if content:  
        if not is_answering:  
            print_section("Tanggapan Lengkap")  
            is_answering = True  
            in_reasoning_section = False  
        print(content, end="", flush=True)  
# Cetak jumlah eksekusi Code Interpreter
print_section("Jumlah Eksekusi Code Interpreter")  
print(chunk.usage.plugins)

Contoh tanggapan

====================Proses Thinking====================
Pengguna ingin menjalankan Code Interpreter dua kali:
1. Eksekusi pertama: Hitung nilai 123 pangkat 23.
2. Eksekusi kedua: Bagi hasil eksekusi pertama dengan 5.

Saya perlu memanggil Code Interpreter terlebih dahulu untuk menghitung 123**23, lalu menggunakan hasil ini untuk memanggil Code Interpreter lagi guna membaginya dengan 5.

Mari saya lakukan perhitungan pertama.

====================Informasi Tool====================
[{'code_interpreter': {'code': '123**23'}, 'type': 'code_interpreter'}]

====================Proses Thinking====================
Perhitungan pertama menghasilkan nilai 123 pangkat 23: 1169008215014432917465348578887506800769541157267

Sekarang untuk eksekusi kedua, saya perlu membagi hasil ini dengan 5. Saya harus menggunakan nilai pasti ini untuk pembagian.
====================Informasi Tool====================
[{'code_interpreter': {'code': '123**23'}, 'type': 'code_interpreter'}, {'code_interpreter': {'code': ''}, 'type': 'code_interpreter'}]

====================Informasi Tool====================
[{'code_interpreter': {'code': '123**23'}, 'type': 'code_interpreter'}, {'code_interpreter': {'code': '1169008215014432917465348578887506800769541157267 / 5'}, 'type': 'code_interpreter'}]

====================Proses Thinking====================
Pengguna meminta untuk menjalankan Code Interpreter dua kali:
1. Pertama, hitung 123 pangkat 23. Hasilnya adalah: 1169008215014432917465348578887506800769541157267
2. Kedua, bagi hasil tersebut dengan 5. Hasilnya adalah: 2.338016430028866e+47

Sekarang saya perlu melaporkan kedua hasil ini kepada pengguna.
====================Tanggapan Lengkap====================
Hasil eksekusi pertama: 123 pangkat 23 = 1169008215014432917465348578887506800769541157267

Hasil eksekusi kedua: Hasil di atas dibagi 5 = 2.338016430028866e+47
====================Jumlah Eksekusi Code Interpreter====================
{'code_interpreter': {'count': 2}}

Catatan

Code Interpreter dan Function calling saling eksklusif.

Mengaktifkan keduanya dalam satu permintaan akan menyebabkan error.
Saat Code Interpreter diaktifkan, satu permintaan dapat memicu beberapa inferensi model. Bidang usage merangkum total konsumsi token untuk semua pemanggilan dalam permintaan tersebut.

Penagihan

Code Interpreter gratis untuk waktu terbatas tetapi meningkatkan konsumsi token.