Alibaba Cloud Model Studio:Salida de transmisión

Python

import os
from openai import OpenAI

# 1. Prepare: Initialize the client
client = OpenAI(
    # Configure the API key using an environment variable to avoid hard coding.
    api_key=os.environ["DASHSCOPE_API_KEY"],
    # The API key is tightly bound to a region. Ensure base_url matches the region of your API key.
    # Singapore region URL. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

# 2. Make a streaming request
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. Handle the streaming response
# Store response fragments in a list. Joining them at the end is more efficient than repeated string concatenation.
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}")

Respuesta

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. Prepare: Initialize the client
    // Configure the API key using an environment variable to avoid hard coding.
    if (!process.env.DASHSCOPE_API_KEY) {
        throw new Error("Set the DASHSCOPE_API_KEY environment variable");
    }
    // The API key is tightly bound to a region. Ensure baseURL matches the region of your API key.
    // Singapore region URL. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region.
    const client = new OpenAI({
        apiKey: process.env.DASHSCOPE_API_KEY,
        baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
    });

    try {
        // 2. Make a streaming request
        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,
            // Purpose: Get token usage in the last chunk.
            stream_options: { include_usage: true },
        });

        // 3. Handle the streaming response
        const contentParts = [];
        process.stdout.write("AI: ");

        for await (const chunk of stream) {
            // The last chunk contains no choices but includes usage information.
            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) {
                // Request complete. Print token usage.
                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();

Respuesta

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

Solicitud

# ======= Important notes =======
# Ensure the DASHSCOPE_API_KEY environment variable is set
# API keys differ by region. Get your API key: https://www.alibabacloud.com/help/model-studio/get-api-key

# === Delete this comment before running ===

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}
}'

Respuesta

La respuesta sigue el protocolo SSE. Cada línea que comienza con data: representa un fragmento de datos.

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:: La carga útil del mensaje, generalmente una cadena JSON.
• [DONE]: Indica el final de toda la respuesta de streaming.

Python

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

# 1. Prepare: Configure the API key and region
# Configure the API key using an environment variable to avoid hard coding.
try:
    dashscope.api_key = os.environ["DASHSCOPE_API_KEY"]
except KeyError:
    raise ValueError("Set the DASHSCOPE_API_KEY environment variable")

# The API key is tightly bound to a region. Ensure base_url matches the region of your API key.

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

# 2. Make a streaming request
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,
        # Key: Set to True for incremental output, which offers better performance.
        incremental_output=True,
    )

    # 3. Handle the streaming response
    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)

            # Check if this is the last packet
            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:
            # Handle errors
            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. Get the API key
        String apiKey = System.getenv("DASHSCOPE_API_KEY");
        if (apiKey == null || apiKey.isEmpty()) {
            System.err.println("Set the DASHSCOPE_API_KEY environment variable");
            return;
        }

        // 2. Initialize the Generation instance
        // The API key is tightly bound to a region. Ensure baseUrl matches the region of your API key.

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

        // 3. Build request parameters
        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) // Enable incremental output for streaming
                .build();
        // 4. Make a streaming call and handle the response
        try {
            Flowable<GenerationResult> result = gen.streamCall(param);
            StringBuilder fullContent = new StringBuilder();
            System.out.print("AI: ");
            result
                    .subscribeOn(Schedulers.io()) // Execute request on IO thread
                    .observeOn(Schedulers.computation()) // Process response on computation thread
                    .subscribe(
                            // onNext: Handle each response fragment
                            message -> {
                                String content = message.getOutput().getChoices().get(0).getMessage().getContent();
                                String finishReason = message.getOutput().getChoices().get(0).getFinishReason();
                                // Output content
                                System.out.print(content);
                                fullContent.append(content);
                                // When finishReason is not null, it indicates the last chunk. Output usage info.
                                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 immediately
                            },
                            // onError: Handle errors
                            error -> {
                                System.err.println("\nRequest failed: " + error.getMessage());
                                latch.countDown();
                            },
                            // onComplete: Completion callback
                            () -> {
                                System.out.println(); // New line
                                // System.out.println("Full response: " + fullContent.toString());
                                latch.countDown();
                            }
                    );
            // Main thread waits for the async task to complete
            latch.await();
            System.out.println("Program execution complete");
        } catch (Exception e) {
            System.err.println("Request exception: " + e.getMessage());
            e.printStackTrace();
        }
    }
}

Respuesta

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

Solicitud

# ======= Important notes =======
# Ensure the DASHSCOPE_API_KEY environment variable is set
# API keys differ by region. Get your API key: https://www.alibabacloud.com/help/model-studio/get-api-key
# Singapore region URL. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region.
# === Delete this comment before running ===
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
    }
}'

Respuesta

La respuesta sigue el formato Server-Sent Events (SSE). Cada mensaje incluye:

• id: Número de fragmento de datos.
• event: Tipo de evento, siempre "result".
• Información del código de estado HTTP.
• data: Datos en formato 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

Código de ejemplo

from openai import OpenAI
import os

# Initialize the OpenAI client
client = OpenAI(
    # If you haven't configured an environment variable, replace with your Alibaba Cloud Model Studio API key: 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",  # Replace with other deep-thinking models as needed
    messages=messages,
    # The enable_thinking parameter enables the thinking process. This parameter has no effect on models QwQ.
    extra_body={"enable_thinking": True},
    stream=True,
    # stream_options={
    #     "include_usage": True
    # },
)

reasoning_content = ""  # Full thought process
answer_content = ""  # Full response
is_answering = False  # Whether in the response stage
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

    # Collect only thinking content
    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
        reasoning_content += delta.reasoning_content

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

Respuesta

====================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

Código de ejemplo

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

// Initialize the openai client
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // Read from environment variable
    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({
            // Replace with other Qwen3 models or QwQ models as needed
            model: 'qwen-plus',
            messages,
            stream: true,
            // The enable_thinking parameter enables the thinking process. This parameter has no effect on models QwQ.
            enable_thinking: true
        });
        console.log('\n' + '='.repeat(20) + 'Thought process' + '='.repeat(20) + '\n');

        for await (const chunk of stream) {
            if (!chunk.choices?.length) {
                console.log('\nUsage:');
                console.log(chunk.usage);
                continue;
            }

            const delta = chunk.choices[0].delta;

            // Collect only thinking content
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

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

main();

Respuesta

====================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

Código de ejemplo

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
}'

Respuesta

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

Código de ejemplo

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(
    # If you haven't configured an environment variable, replace the next line with your Alibaba Cloud Model Studio API key: api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Replace with other deep-thinking models as needed
    model="qwen-plus",
    messages=messages,
    result_format="message", # Qwen3 open-source models only support "message"; for better experience, we recommend setting this to "message" for other models too.
    # Enable deep thinking. This parameter has no effect on models QwQ.
    enable_thinking=True,
    stream=True,
    incremental_output=True, # Qwen3 open-source models only support true; for better experience, we recommend setting this to true for other models too.
)

# Define full thought process
reasoning_content = ""
# Define full response
answer_content = ""
# Determine if finished thinking and started responding
is_answering = False

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

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

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

Respuesta

====================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

Código de ejemplo

// 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()
                // If you haven't configured an environment variable, replace the next line with your Alibaba Cloud Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen-plus")
                .enableThinking(true)
                .incrementalOutput(true)
                .resultFormat("message")
                .messages(Arrays.asList(userMsg))
                .build();
    }
    public static void streamCallWithMessage(Generation gen, Message userMsg)
            throws NoApiKeyException, ApiException, InputRequiredException {
        GenerationParam param = buildGenerationParam(userMsg);
        Flowable<GenerationResult> result = gen.streamCall(param);
        result.blockingForEach(message -> handleGenerationResult(message));
    }

    public static void main(String[] args) {
        try {
            Generation gen = new Generation();
            Message userMsg = Message.builder().role(Role.USER.getValue()).content("Who are you?").build();
            streamCallWithMessage(gen, userMsg);
//             Print final result
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================Full response====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

Respuesta

====================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

Código de ejemplo

# ======= Important notes =======
# API keys differ by region. Get your API key: https://www.alibabacloud.com/help/model-studio/get-api-key
# Singapore region URL. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region.
# === Delete this comment before running ===
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"
    }
}'

Respuesta

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"}