Alibaba Cloud Model Studio：流式輸出

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",
)

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=[
        {"role": "user",
        "content": [{"type": "image_url",
                    "image_url": {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"},},
                    {"type": "text", "text": "圖中描繪的是什麼景象？"}]}],
    stream=True,
  # stream_options={"include_usage": True}
)
full_content = ""
print("流式輸出內容為：")
for chunk in completion:
    # 如果stream_options.include_usage為True，則最後一個chunk的choices欄位為空白列表，需要跳過（可以通過chunk.usage擷取 Token 使用量）
    if chunk.choices and chunk.choices[0].delta.content != "":
        full_content += chunk.choices[0].delta.content
        print(chunk.choices[0].delta.content)
print(f"完整內容為：{full_content}")

# ======= 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 --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3-vl-plus",
    "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "image_url",
          "image_url": {
            "url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"
          }
        },
        {
          "type": "text",
          "text": "圖中描繪的是什麼景象?"
        }
      ]
    }
  ],
    "stream":true,
    "stream_options":{"include_usage":true}
}'

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://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"},
            {"text": "圖中描繪的是什麼景象?"}
        ]
    }
]

responses = 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
    # 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"),
    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,
    stream=True,
    incremental_output=True)
    
full_content = ""
print("流式輸出內容為：")
for response in responses:
    if response["output"]["choices"][0]["message"].content:
        print(response.output.choices[0].message.content[0]['text'])
        full_content += response.output.choices[0].message.content[0]['text']
print(f"完整內容為：{full_content}")

import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;

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 io.reactivex.Flowable;
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";
    }
    public static void streamCall()
            throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        // must create mutable map.
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(Collections.singletonMap("image", "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"),
                        Collections.singletonMap("text", "圖中描繪的是什麼景象？"))).build();
        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("qwen3-vl-plus")  //  可按需更換為其它多模態模型，並修改相應的 messages
                .messages(Arrays.asList(userMessage))
                .incrementalOutput(true)
                .build();
        Flowable<MultiModalConversationResult> result = conv.streamCall(param);
        result.blockingForEach(item -> {
            try {
                List<Map<String, Object>> content = item.getOutput().getChoices().get(0).getMessage().getContent();
                    // 判斷content是否存在且不為空白
                if (content != null &&  !content.isEmpty()) {
                    System.out.println(content.get(0).get("text"));
                    }
            } catch (Exception e){
                System.exit(0);
            }
        });
    }

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

# ======= 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/multimodal-generation/generation
# === 執行時請刪除該注釋 ===

curl -X POST https://dashscope-intl.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-vl-plus",
    "input":{
        "messages":[
            {
                "role": "user",
                "content": [
                    {"image": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"},
                    {"text": "圖中描繪的是什麼景象？"}
                ]
            }
        ]
    },
    "parameters": {
        "incremental_output": true
    }
}'

範例程式碼

from openai import OpenAI
import os

# 初始化OpenAI用戶端
client = OpenAI(
    # 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 = [{"role": "user", "content": "你是誰"}]

completion = client.chat.completions.create(
    model="qwen-plus",  # 您可以按需更換為其它深度思考模型
    messages=messages,
    # enable_thinking 參數開啟思考過程，該參數對 qwen3-30b-a3b-thinking-2507、qwen3-235b-a22b-thinking-2507、QwQ 模型無效
    extra_body={"enable_thinking": True},
    stream=True,
    # stream_options={
    #     "include_usage": True
    # },
)

reasoning_content = ""  # 完整思考過程
answer_content = ""  # 完整回複
is_answering = False  # 是否進入回複階段
print("\n" + "=" * 20 + "思考過程" + "=" * 20 + "\n")

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

    delta = chunk.choices[0].delta

    # 只收集思考內容
    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

    # 收到content，開始進行回複
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "完整回複" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

返回結果

====================思考過程====================

好的，使用者問“你是誰”，我需要給出一個準確且友好的回答。首先，我要確認自己的身份，即通義千問，由阿里巴巴集團旗下的通義實驗室研發。接下來，應該說明我的主要功能，比如回答問題、創作文字、邏輯推理等。同時，要保持語氣親切，避免過於技術化，讓使用者感覺輕鬆。還要注意不要使用複雜術語，確保回答簡潔明了。另外，可能需要加入一些互動元素，邀請使用者提問，促進進一步交流。最後，檢查是否有遺漏的重要訊息，比如我的中文名稱“通義千問”和英文名稱“Qwen”，以及所屬公司和實驗室。確保回答全面且符合使用者期望。
====================完整回複====================

你好！我是通義千問，是阿里巴巴集團旗下的通義實驗室自主研發的超大規模語言模型。我可以回答問題、創作文字、進行邏輯推理、編程等，旨在為使用者提供高品質的資訊和服務。你可以叫我Qwen，或者直接叫我通義千問。有什麼我可以幫你的嗎？

範例程式碼

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

// 初始化 openai 用戶端
const openai = new OpenAI({
    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;

async function main() {
    try {
        const messages = [{ role: 'user', content: '你是誰' }];
        const stream = await openai.chat.completions.create({
            // 您可以按需更換為其它 Qwen3 模型、QwQ模型
            model: 'qwen-plus',
            messages,
            stream: true,
            // enable_thinking 參數開啟思考過程，該參數對 qwen3-30b-a3b-thinking-2507、qwen3-235b-a22b-thinking-2507、QwQ 模型無效
            enable_thinking: 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;
            
            // 只收集思考內容
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // 收到content，開始進行回複
            if (delta.content !== undefined && 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;
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

返回結果

====================思考過程====================

好的，使用者問“你是誰”，我需要回答我的身份。首先，我應該明確說明我是通義千問，由阿里雲開發的超大規模語言模型。接下來，可以提到我的主要功能，比如回答問題、創作文字、邏輯推理等。還要強調我的多語言支援，包括中文和英文，這樣使用者知道我可以處理不同語言的請求。另外，可能需要解釋一下我的應用程式情境，比如學習、工作和生活中的協助。不過使用者的問題比較直接，可能不需要太詳細的資訊，保持簡潔明了。同時，要確保語氣友好，邀請使用者進一步提問。檢查有沒有遺漏的重要訊息，比如我的版本或最新更新，但可能使用者不需要那麼詳細。最後，確認回答準確無誤，沒有錯誤資訊。
====================完整回複====================

我是通義千問，是阿里巴巴集團旗下的通義實驗室自主研發的超大規模語言模型。我能夠回答問題、創作文字、邏輯推理、編程等多種任務，支援中英文等多種語言。如果你有任何問題或需要幫，歡迎隨時告訴我！

範例程式碼

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": "你是誰"
        }
    ],
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "enable_thinking": true
}'

返回結果

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]

範例程式碼

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

messages = [{"role": "user", "content": "你是誰？"}]

completion = 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"),
    # 可按需更換為其它深度思考模型
    model="qwen-plus",
    messages=messages,
    result_format="message", # Qwen3開源版模型只支援設定為"message"；為了更好的體驗，其它模型也推薦您優先設定為"message"
    # 開啟深度思考，該參數對qwen3-30b-a3b-thinking-2507、qwen3-235b-a22b-thinking-2507、QwQ模型無效
    enable_thinking=True,
    stream=True,
    incremental_output=True, # Qwen3開源版模型只支援 true；為了更好的體驗，其它模型也推薦您優先設定為 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 completion:
    # 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

# 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}")

返回結果

====================思考過程====================
好的，使用者問：“你是誰？”我需要回答這個問題。首先，我要明確自己的身份，即通義千問，由阿里雲開發的超大規模語言模型。接下來，要說明我的功能和用途，比如回答問題、創作文字、邏輯推理等。同時，要強調我的目標是成為使用者的得力助手，提供協助和支援。

在表達時，要保持口語化，避免使用專業術語或複雜句式。可以加入一些親切的語氣詞，比如“你好呀～”，讓對話更自然。另外，要確保資訊準確，不遺漏關鍵點，比如我的開發人員、主要功能和使用情境。

還要考慮使用者可能的後續問題，比如具體的應用例子或技術細節，所以在回答中可以適當埋下伏筆，引導使用者進一步提問。例如，提到“無論是日常生活的疑問還是專業領域的問題，我都能儘力提供協助”，這樣既全面又開放。

最後，檢查回答是否流暢，有沒有重複或冗餘的資訊，確保簡潔明了。同時，保持友好和專業的平衡，讓使用者感受到既親切又可靠。
====================完整回複====================
你好呀～我是通義千問，是阿里雲開發的一款超大規模語言模型。我能夠回答問題、創作文字、進行邏輯推理、編程等等，旨在為使用者提供協助和支援。無論是日常生活的疑問還是專業領域的問題，我都能儘力提供協助。有什麼我可以幫你的嗎？

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 com.alibaba.dashscope.utils.Constants;

public class Main {
    static {
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    private static final Logger logger = LoggerFactory.getLogger(Main.class);
    private static StringBuilder reasoningContent = new StringBuilder();
    private static StringBuilder finalContent = new StringBuilder();
    private static boolean isFirstPrint = true;

    private static void handleGenerationResult(GenerationResult message) {
        String reasoning = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
        String content = message.getOutput().getChoices().get(0).getMessage().getContent();

        if (!reasoning.isEmpty()) {
            reasoningContent.append(reasoning);
            if (isFirstPrint) {
                System.out.println("====================思考過程====================");
                isFirstPrint = false;
            }
            System.out.print(reasoning);
        }

        if (!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(Message userMsg) {
        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"))
                .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("你是誰？").build();
            streamCallWithMessage(gen, userMsg);
//             列印最終結果
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================完整回複====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

返回結果

====================思考過程====================
好的，使用者問“你是誰？”，我需要根據之前的設定來回答。首先，我的角色是通義千問，阿里巴巴集團旗下的超大規模語言模型。要保持口語化，簡潔易懂。

使用者可能剛接觸我，或者想確認我的身份。應該先直接回答我是誰，然後簡要說明我的功能和用途，比如回答問題、創作文字、編程等。還要提到支援多語言，這樣使用者知道我可以處理不同語言的需求。

另外，根據指導方針，要保持擬人性，所以語氣要友好，可能用Emoji增加親切感。同時，可能需要引導使用者進一步提問或使用我的功能，比如問他們需要什麼協助。

需要注意不要使用複雜術語，避免冗長。檢查是否有遺漏的關鍵點，比如多語言支援和具體能力。確保回答符合所有要求，包括口語化和簡潔。
====================完整回複====================
你好！我是通義千問，阿里巴巴集團旗下的超大規模語言模型。我能夠回答問題、創作文字，比如寫故事、寫公文、寫郵件、寫劇本、邏輯推理、編程等等，還能表達觀點，玩遊戲等。我熟練掌握多種語言，包括但不限於中文、英文、德語、法語、西班牙語等。有什麼需要我幫忙的嗎？

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": "你是誰？"
            }
        ]
    },
    "parameters":{
        "enable_thinking": true,
        "incremental_output": true,
        "result_format": "message"
    }
}'

返回結果

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"嗯","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":"使用者","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":"問","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":"協助","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":"歡迎","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":"隨時","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":"告訴我","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":377,"input_tokens":11,"output_tokens":366},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:363
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"！","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":378,"input_tokens":11,"output_tokens":367},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:364
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"stop"}]},"usage":{"total_tokens":378,"input_tokens":11,"output_tokens":367},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}