Alibaba Cloud Model Studio：多輪對話

import OpenAI from "openai";
import { createInterface } from 'readline/promises';

// 若使用北京地區的模型，需將base_url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1
const BASE_URL = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1";
// 各地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    baseURL: BASE_URL
});

async function getResponse(messages) {
    try {
        const completion = await openai.chat.completions.create({
            // 模型列表：https://www.alibabacloud.com/help/zh/model-studio/getting-started/models
            model: "qwen-plus",
            messages: messages,
        });
        return completion.choices[0].message.content;
    } catch (error) {
        console.error("Error fetching response:", error);
        throw error;  // 重新拋出異常以便上層處理
    }
}

// 初始化 messages 數組
const messages = [
    {
        "role": "system",
        "content": `你是一名阿里雲百鍊手機商店的店員，你負責給使用者推薦手機。手機有兩個參數：螢幕尺寸（包括6.1英寸、6.5英寸、6.7英寸）、解析度（包括2K、4K）。
        你一次只能向使用者提問一個參數。如果使用者提供的資訊不全，你需要反問他，讓他提供沒有提供的參數。如果參數收集完成，你要說：我已瞭解您的購買意向，請稍等。`,
    }
];

let assistant_output = "歡迎光臨阿里雲百鍊手機商店，您需要購買什麼尺寸的手機呢？";
console.log(assistant_output);


const readline = createInterface({
    input: process.stdin,
    output: process.stdout
});

(async () => {
    while (!assistant_output.includes("我已瞭解您的購買意向")) {
        const user_input = await readline.question("請輸入：");
        messages.push({ role: "user", content: user_input});
        try {
            const response = await getResponse(messages);
            assistant_output = response;
            messages.push({ role: "assistant", content: assistant_output });
            console.log(assistant_output);
            console.log("\n");
        } catch (error) {
            console.error("擷取響應時發生錯誤:", error);
        }
    }
    readline.close();
})();

from openai import OpenAI
import os

client = OpenAI(
    # 各地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
    # 若沒有配置環境變數，請用百鍊API Key將下行替換為：api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # 若使用北京地區的模型，需將base_url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
)
messages = [
        {"role": "user",
         "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"
                },
            },
            {"type": "text", "text": "請問圖片展現了有哪些商品？"},
        ],
    }
]
completion = client.chat.completions.create(
    model="qwen3-vl-plus",  #  可按需更換為其它多模態模型，並修改相應的 messages
    messages=messages,
    )
print(f"第一輪輸出: {completion.choices[0].message.content}")

assistant_message = completion.choices[0].message
messages.append(assistant_message.model_dump())
messages.append({
        "role": "user",
        "content": [
        {
            "type": "text",
            "text": "它們屬於什麼風格？"
        }
        ]
    })
completion = client.chat.completions.create(
    model="qwen3-vl-plus",
    messages=messages,
    )
    
print(f"第二輪輸出: {completion.choices[0].message.content}")

import OpenAI from "openai";

const openai = new OpenAI(
    {
        // 各地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
        // 若沒有配置環境變數，請用百鍊API Key將下行替換為：apiKey: "sk-xxx",
        apiKey: process.env.DASHSCOPE_API_KEY,
        // 若使用北京地區的模型，需將base_url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1
        baseURL: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
    }
);

let messages = [
    {
    role: "user", content: [
        { type: "image_url", image_url: { "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png" } },
        { type: "text", text: "請問圖片展現了有哪些商品？" },
    ]
}]
async function main() {
    let response = await openai.chat.completions.create({
        model: "qwen3-vl-plus",   // 可按需更換為其它多模態模型，並修改相應的 messages
        messages: messages
    });
    console.log(`第一輪輸出： ${response.choices[0].message.content}`);
    messages.push(response.choices[0].message);
    messages.push({"role": "user", "content": "Write a poem describing this scene"});
    response = await openai.chat.completions.create({
        model: "qwen3-vl-plus",
        messages: messages
    });
    console.log(`第二輪輸出： ${response.choices[0].message.content}`);
}

main()

# ======= 重要提示 =======
# 若使用北京地區的模型，需將base_url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# 各地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
# === 執行時請刪除該注釋 ===

curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
  "model": "qwen3-vl-plus",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "image_url",
          "image_url": {
            "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"
          }
        },
        {
          "type": "text",
          "text": "請問圖片展現了有哪些商品？"
        }
      ]
    },
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "圖片展示了三件商品：一件淺藍色背帶褲、一件藍白條紋短袖襯衫和一雙白色運動鞋。"
        }
      ]
    },
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "它們屬於什麼風格？"
        }
      ]
    }
  ]
}'

import os
from dashscope import MultiModalConversation
import dashscope
# 若使用北京地區的模型，需將base_url替換為：https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

messages = [
    {
        "role": "user",
        "content": [
            {
                "image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"
            },
            {"text": "請問圖片展現了有哪些商品？"},
        ],
    }
]
response = MultiModalConversation.call(
    # 各地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3-vl-plus',  #  可按需更換為其它多模態模型，並修改相應的 messages
    messages=messages
    )
    
print(f"模型第一輪輸出 {response.output.choices[0].message.content[0]['text']}")
messages.append(response['output']['choices'][0]['message'])
user_msg = {"role": "user", "content": [{"text": "它們屬於什麼風格？"}]}
messages.append(user_msg)
response = MultiModalConversation.call(
    # If the environment variable is not configured, please replace the following line with: api_key="sk-xxx",
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3-vl-plus',
    messages=messages
    )
    
print(f"模型第二輪輸出 {response.output.choices[0].message.content[0]['text']}")

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;

public class Main {
    static {
        // 若使用北京地區的模型，需將base_url替換為：https://dashscope.aliyuncs.com/api/v1
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    private static final String modelName = "qwen3-vl-plus";  //  可按需更換為其它多模態模型，並修改相應的 messages
    public static void MultiRoundConversationCall() throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(Collections.singletonMap("image", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"),
                        Collections.singletonMap("text", "請問圖片展現了有哪些商品？"))).build();
        List<MultiModalMessage> messages = new ArrayList<>();
        messages.add(userMessage);
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // 各地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
                // 若沒有配置環境變數，請用百鍊API Key將下行替換為：.apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))                
                .model(modelName)
                .messages(messages)
                .build();
        MultiModalConversationResult result = conv.call(param);
        System.out.println("第一輪輸出： "+result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));        // add the result to conversation
        messages.add(result.getOutput().getChoices().get(0).getMessage());
        MultiModalMessage msg = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(Collections.singletonMap("text", "它們屬於什麼風格？"))).build();
        messages.add(msg);
        param.setMessages((List)messages);
        result = conv.call(param);
        System.out.println("第二輪輸出： "+result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text"));    }

    public static void main(String[] args) {
        try {
            MultiRoundConversationCall();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

範例程式碼

from openai import OpenAI
import os

# 初始化OpenAI用戶端
client = OpenAI(
    # 各地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
    # 如果沒有配置環境變數，請用阿里雲百鍊API Key替換：api_key="sk-xxx"
    api_key = os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
)


messages = []
conversation_idx = 1
while True:
    reasoning_content = ""  # 定義完整思考過程
    answer_content = ""     # 定義完整回複
    is_answering = False   # 判斷是否結束思考過程並開始回複
    print("="*20+f"第{conversation_idx}輪對話"+"="*20)
    conversation_idx += 1
    user_msg = {"role": "user", "content": input("請輸入你的訊息：")}
    messages.append(user_msg)
    # 建立聊天完成請求
    completion = client.chat.completions.create(
        # 您可以按需更換為其它深度思考模型
        model="qwen-plus",
        messages=messages,
        extra_body={"enable_thinking": True},
        stream=True,
        # stream_options={
        #     "include_usage": True
        # }
    )
    print("\n" + "=" * 20 + "思考過程" + "=" * 20 + "\n")
    for chunk in completion:
        # 如果chunk.choices為空白，則列印usage
        if not chunk.choices:
            print("\nUsage:")
            print(chunk.usage)
        else:
            delta = chunk.choices[0].delta
            # 列印思考過程
            if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
                print(delta.reasoning_content, end='', flush=True)
                reasoning_content += delta.reasoning_content
            else:
                # 開始回複
                if delta.content != "" and is_answering is False:
                    print("\n" + "=" * 20 + "完整回複" + "=" * 20 + "\n")
                    is_answering = True
                # 列印回複過程
                print(delta.content, end='', flush=True)
                answer_content += delta.content
    # 將模型回複的content添加到上下文中
    messages.append({"role": "assistant", "content": answer_content})
    print("\n")

範例程式碼

import OpenAI from "openai";
import process from 'process';
import readline from 'readline/promises';

// 初始化 readline 介面
const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout
});

// 初始化 openai 用戶端
const openai = new OpenAI({
    // 各地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
    apiKey: process.env.DASHSCOPE_API_KEY, // 從環境變數讀取
    baseURL: 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1'
});

let reasoningContent = '';
let answerContent = '';
let isAnswering = false;
let messages = [];
let conversationIdx = 1;

async function main() {
    while (true) {
        console.log("=".repeat(20) + `第${conversationIdx}輪對話` + "=".repeat(20));
        conversationIdx++;
        
        // 讀取使用者輸入
        const userInput = await rl.question("請輸入你的訊息：");
        messages.push({ role: 'user', content: userInput });

        // 重設狀態
        reasoningContent = '';
        answerContent = '';
        isAnswering = false;

        try {
            const stream = await openai.chat.completions.create({
                // 您可以按需更換為其它深度思考模型
                model: 'qwen-plus',
                messages: messages,
                enable_thinking: true,
                stream: true,
                // stream_options:{
                //     include_usage: true
                // }
            });

            console.log("\n" + "=".repeat(20) + "思考過程" + "=".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;
                
                // 處理思考過程
                if (delta.reasoning_content) {
                    process.stdout.write(delta.reasoning_content);
                    reasoningContent += delta.reasoning_content;
                }
                
                // 處理正式回複
                if (delta.content) {
                    if (!isAnswering) {
                        console.log('\n' + "=".repeat(20) + "完整回複" + "=".repeat(20) + "\n");
                        isAnswering = true;
                    }
                    process.stdout.write(delta.content);
                    answerContent += delta.content;
                }
            }
            
            // 將完整回複加入訊息歷史
            messages.push({ role: 'assistant', content: answerContent });
            console.log("\n");
            
        } catch (error) {
            console.error('Error:', error);
        }
    }
}

// 啟動程式
main().catch(console.error);

範例程式碼

# ======= 重要提示 =======
# 若使用北京地區的模型，需將base_url替換為：https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# 各地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
# === 執行時請刪除該注釋 ===

curl -X POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen-plus",
    "messages": [
        {
            "role": "user", 
            "content": "你好"
        },
        {
            "role": "assistant",
            "content": "你好！很高興見到你，有什麼我可以幫忙的嗎？"
        },
        {
            "role": "user",
            "content": "你是誰？"
        }
    ],
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "enable_thinking": true
}'

範例程式碼

import os
import dashscope
# 若使用北京地區的模型，需將base_url替換為：https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = "https://dashscope-intl.aliyuncs.com/api/v1/"

messages = []
conversation_idx = 1
while True:
    print("=" * 20 + f"第{conversation_idx}輪對話" + "=" * 20)
    conversation_idx += 1
    user_msg = {"role": "user", "content": input("請輸入你的訊息：")}
    messages.append(user_msg)
    response = dashscope.Generation.call(
        # 若沒有配置環境變數，請用阿里雲百鍊API Key將下行替換為：api_key="sk-xxx",
        api_key=os.getenv('DASHSCOPE_API_KEY'),
         # 此處以qwen-plus為例，可按需更換為其它深度思考模型
        model="qwen-plus", 
        messages=messages,
        enable_thinking=True,
        result_format="message",
        stream=True,
        incremental_output=True
    )
    # 定義完整思考過程
    reasoning_content = ""
    # 定義完整回複
    answer_content = ""
    # 判斷是否結束思考過程並開始回複
    is_answering = False
    print("=" * 20 + "思考過程" + "=" * 20)
    for chunk in response:
        # 如果思考過程與回複皆為空白，則忽略
        if (chunk.output.choices[0].message.content == "" and 
            chunk.output.choices[0].message.reasoning_content == ""):
            pass
        else:
            # 如果當前為思考過程
            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
            # 如果當前為回複
            elif chunk.output.choices[0].message.content != "":
                if not is_answering:
                    print("\n" + "=" * 20 + "完整回複" + "=" * 20)
                    is_answering = True
                print(chunk.output.choices[0].message.content, end="",flush=True)
                answer_content += chunk.output.choices[0].message.content
    # 將模型回複的content添加到上下文中
    messages.append({"role": "assistant", "content": answer_content})
    print("\n")
    # 如果您需要列印完整思考過程與完整回複，請將以下代碼解除注釋後運行
    # print("=" * 20 + "完整思考過程" + "=" * 20 + "\n")
    # print(f"{reasoning_content}")
    # print("=" * 20 + "完整回複" + "=" * 20 + "\n")
    # print(f"{answer_content}")

範例程式碼

// dashscope SDK的版本 >= 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 java.util.List;
import com.alibaba.dashscope.protocol.Protocol;

public class Main {
    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) {
        if (message != null && message.getOutput() != null 
            && message.getOutput().getChoices() != null 
            && !message.getOutput().getChoices().isEmpty() 
            && message.getOutput().getChoices().get(0) != null
            && message.getOutput().getChoices().get(0).getMessage() != null) {
            
            String reasoning = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
            String content = message.getOutput().getChoices().get(0).getMessage().getContent();
            
            if (reasoning != null && !reasoning.isEmpty()) {
                reasoningContent.append(reasoning);
                if (isFirstPrint) {
                    System.out.println("====================思考過程====================");
                    isFirstPrint = false;
                }
                System.out.print(reasoning);
            }

            if (content != null && !content.isEmpty()) {
                finalContent.append(content);
                if (!isFirstPrint) {
                    System.out.println("\n====================完整回複====================");
                    isFirstPrint = true;
                }
                System.out.print(content);
            }
        }
    }
    
    private static GenerationParam buildGenerationParam(List<Message> messages) {
        return GenerationParam.builder()
                // 若沒有配置環境變數，請用阿里雲百鍊API Key將下行替換為：.apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                // 此處以 qwen-plus 為例，可按需更換模型名稱
                .model("qwen-plus")
                .enableThinking(true)
                .messages(messages)
                .incrementalOutput(true)
                .resultFormat("message")
                .build();
    }
    
    public static void streamCallWithMessage(Generation gen, List<Message> messages)
            throws NoApiKeyException, ApiException, InputRequiredException {
        GenerationParam param = buildGenerationParam(messages);
        Flowable<GenerationResult> result = gen.streamCall(param);
        result.doOnError(throwable -> logger.error("Error occurred in stream processing: {}", throwable.getMessage(), throwable))
              .blockingForEach(Main::handleGenerationResult);
    }

    public static void main(String[] args) {
        try {
            // 若使用北京地區的模型，需將base_url替換為：https://dashscope.aliyuncs.com/api/v1
            Generation gen = new Generation(Protocol.HTTP.getValue(), "https://dashscope-intl.aliyuncs.com/api/v1");
            Message userMsg1 = Message.builder()
                    .role(Role.USER.getValue())
                    .content("你好")
                    .build();
            Message assistantMsg = Message.builder()
                    .role(Role.ASSISTANT.getValue())
                    .content("你好！很高興見到你，有什麼我可以幫忙的嗎？")
                    .build();
            Message userMsg2 = Message.builder()
                    .role(Role.USER.getValue())
                    .content("你是誰")
                    .build();
            List<Message> messages = Arrays.asList(userMsg1, assistantMsg, userMsg2);
            streamCallWithMessage(gen, messages);
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage(), e);
        } catch (Exception e) {
            logger.error("Unexpected error occurred: {}", e.getMessage(), e);
        } finally {
            // 確保程式正常退出
            System.exit(0);
        }
    }
}

範例程式碼

# ======= 重要提示 =======
# 各地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
# 若使用北京地區的模型，需將base_url替換為：https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation
# === 執行時請刪除該注釋 ===
curl -X POST "https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-DashScope-SSE: enable" \
-d '{
    "model": "qwen-plus",
    "input":{
        "messages":[      
            {
                "role": "user",
                "content": "你好"
            },
            {
                "role": "assistant",
                "content": "你好！很高興見到你，有什麼我可以幫忙的嗎？"
            },
            {
                "role": "user",
                "content": "你是誰？"
            }
        ]
    },
    "parameters":{
        "enable_thinking": true,
        "incremental_output": true,
        "result_format": "message"
    }
}'