DashScope4j：灵积 Java SDK

DashScope4j是一个开源的灵积非官方 Java SDK，基于 JDK17 构建。它旨在提供一个功能丰富、易于集成和使用灵积API（通义千问模型）的Java库，以便开发者能够通灵积API轻松实现多模态对话、向量嵌入和图像处理等功能。

请注意：在使用 DashScope4j 时，你需要遵守灵积API的使用条款和条件。

依赖使用

<dependency>
    <groupId>io.github.oldmanpushcart</groupId>
    <artifactId>dashscope4j</artifactId>
    <version>2.2.1</version>
</dependency>

重要更新

2.2.1： 问题修复版本，修复问题 #19 #18
2.2.0： 语音识别与合成支持

例子：如何用通义千问做一个同声传译
- 新增双工数据交互操作接口Exchange，用于支持语音、视频等多模态模型交互
- 支持实时语音识别
- 支持实时语音合成
- 支持音视频转录
- 支持远程、本地Tokenizer
- 优化部分API，存在不向下兼容可能
  - 所有对外暴露的CompletableFuture变更为CompletionStage，解决暴露接口功能过于强大的问题
  - 增加HttpApiRequest / Response和ExchangeApiRequest / Response的API分层，为了更好地支持多模态数据交互做准备
2.1.0： 不兼容API修复
- 修复模块中错误的exports，该修复会将模块中不应该暴露的内部api重新收回。本次修复不向下兼容。
- 修复ChatRequest中函数调用因为Message序列化问题丢失PluginCall、Plugin、ToolCall、Tool等信息的BUG
2.0.0： 大版本重构。核心API进行不兼容调整和实现重构（请注意），删除1.x.x版本被标记为已废弃的方法
- 重构拦截器接口和实现重构，并添加了流控、重试等拦截器实现
- 调整部分类、API的位置和命名；删除已废弃的方法
- Flow相关实现进行重构
1.x.x： 历代版本核心功能
- 1.4.0： 支持无感临时空间、请求和应答拦截器
- 1.3.0： 支持多模态向量计算
- 1.2.0： 支持多函数级联调用
- 1.1.1： 第一个稳定版本

语音合成

我们可以通过以下代码进行语音合成：

// 文本集合
final var strings = new String[]{
    "白日依山尽，",
    "黄河入海流。",
    "欲穷千里目，",
    "更上一层楼。"
};

/*
 * 语音合成请求
 * 采样率：16000
 * 编码格式：WAV(PCM)
 */
final var request = SpeechSynthesisRequest.newBuilder()
    .model(SpeechSynthesisModel.COSYVOICE_LONGXIAOCHUN_V1)
    .option(SpeechSynthesisOptions.SAMPLE_RATE, 16000)
    .option(SpeechSynthesisOptions.FORMAT, SpeechSynthesisRequest.Format.WAV)
    .build();

// 以语音合成请求为模板，对每个文本生成一个语音合成请求
final var requests = Stream.of(strings)
    .map(string -> SpeechSynthesisRequest.newBuilder(request)
        .text(string)
        .build()
    )
    .toList();

// 聚合成请求发布器
final var requestPublisher = FlowPublishers.fromIterator(requests);

// 进行语音合成
client.audio().synthesis(request)

    // 打开语音合成数据交互通道：全双工模式，输出到audio.wav文件
    .exchange(Exchange.Mode.DUPLEX, ExchangeListeners.ofPath(Path.of("./audio.wav")))
    
    // 发送语音合成请求序列
    .thenCompose(exchange -> exchange.writeDataPublisher(requestPublisher))
    
    // 语音合成结束
    .thenCompose(Exchange::finishing)
    
    // 等待通道关闭
    .thenCompose(Exchange::closeFuture)
    .toCompletableFuture()
    .join();

这样我们就可以获取到生成的audio.wav文件，可以试着播放下，符不符合你的要求。

当然你也可以通过ExchangeListeners.ofByteChannel(...)方法，将输出的字节流转入到你指定的ByteChannel中，比如音频播放设备。这样就可以实现语音播放。在语音合成结束后，为了避免对通道的占用，需要及时关闭Exchange

发送语音合成结束请求：

exchange.finishing()

服务端收到结束请求后会主动来关闭通道，你可以调用Exchange.closeFuture()方法来获取关闭通道的Future。

exchange.closeFuture()

语音识别

基于上一节 "语音合成" 的示例，我们得到了audio.wav，接下来我们可以用语音识别来识别这个音频文件。

// 构建音频文件的ByteBuffer发布器
final var byteBufPublisher = FlowPublishers.fromURI(Path.of("./audio.wav").toUri());

/*
 * 构建语音识别请求
 * 采样率：16000
 * 音频格式：WAV(PCM)
 */
final var request = RecognitionRequest.newBuilder()
    .model(RecognitionModel.PARAFORMER_REALTIME_V2)
    .option(RecognitionOptions.SAMPLE_RATE, 16000)
    .option(RecognitionOptions.FORMAT, RecognitionRequest.Format.WAV)
    .build();

// 识别文本缓存
final var stringBuf = new StringBuilder();

// 进行语音识别
client.audio().recognition(request)

    // 打开语音识别数据交互通道：全双工模式，输出到文本缓存
    .exchange(Exchange.Mode.DUPLEX, ExchangeListeners.ofConsume(response -> {
        if (response.output().sentence().isEnd()) {
            stringBuf.append(response.output().sentence().text()).append("\n");
        }
    }))
    
    // 发送音频文件字节流数据
    .thenCompose(exchange -> exchange.writeByteBufferPublisher(byteBufPublisher))
    
    // 语音识别结束
    .thenCompose(Exchange::finishing)
    
    // 等待通道关闭
    .thenCompose(Exchange::closeFuture)
    .toCompletableFuture()
    .join();

// 输出识别文本
System.out.println(stringBuf);

文本识别结果为

白日依山尽，黄河入海流。
欲穷千里目，更上一层楼。

音视频转录

音视频转录不仅能转录音频文件，而且还可以将视频文件中的音频转录为文本。这就省得我们用ffmpeg剥离视频中的音轨这样繁琐的操作了。在这个例子中，我用我最喜欢的一个动漫《钢之炼金术士》来演示如何从通过音视频转录功能识别视频音轨的文本信息。

这对用AI做字幕多少有点启发，毕竟谁没有几个没有字幕的动作片呢？

/*
 * 构建音视频转录请求
 * 语言：日文
 * 选项：过滤语气词（日片中很多以库以库的语气词，各位懂的都懂）
 */
final var request = TranscriptionRequest.newBuilder()
    .model(TranscriptionModel.PARAFORMER_V2)
    // 也可以使用本地文件，本地文件会自动上传到DashScope的临时空间
    .resources(List.of(URI.create("https://ompc-storage.oss-cn-hangzhou.aliyuncs.com/dashscope4j/video/%5Bktxp%5D%5BFullmetal%20Alchemist%5D%5Bjap_chn%5D01.rmvb")))
    .option(TranscriptionOptions.ENABLE_DISFLUENCY_REMOVAL, true)
    .option(TranscriptionOptions.LANGUAGE_HINTS, new LanguageHint[]{LanguageHint.JA})
    .build();

// 进行音视频转录
final var response = client.audio().transcription(request)

    // 等待任务完成，每隔30s进行检查任务状态
    .task(Task.WaitStrategies.perpetual(Duration.ofMillis(1000L * 30)))
    .toCompletableFuture()
    .join();

// 合并音视频转录文本（当前只有一个视频）
final var text = response.output().results().stream()
    .map(result-> {
    
        // 下载转录结果
        final var transcription = result.lazyFetchTranscription()
            .toCompletableFuture()
            .join();
    
        // 合并转录句子（每行一个句子）
        return transcription.transcripts().stream()
            .flatMap(transcript->transcript.sentences().stream())
            .map(sentence-> "%s - %s: %s".formatted(
                sentence.begin(),
                sentence.end(),
                sentence.text()
            ))
            .reduce((a, b) -> a + "\n" + b)
            .orElse("");
    
    })
    
    // 合并多个音视频转录文本，当前只有一个视频
    .reduce((a, b) -> a + b)
    .orElse("");

// 输出音视频转录文本
System.out.println(text);

输出摘录

13920 - 19960: で き た あ る 大 丈 夫. 
20060 - 24620: 完 璧 だ. 
28380 - 32940: や る ぞ. 
49480 - 58200: 錬 金 術 と は 物 質 の 構 造 を 理 解 し 分 解 し 再 構 築 す る 科 学 技 術 で あ る. 
58820 - 64900: そ れ は う ま く す れ ば 鉛 り か ら 黄 金 を 生 み 出 す こ と も 可 能 に な る. 
65500 - 71760: し か し 科 学 で あ る 以 上 そ こ に は 大 自 然 の 計 測 が 存 在 し た. 
75300 - 79860: 質 量 が 一 の も の か ら は 一 の も の し か 生 み 出 せ な い. 
80200 - 84760: 強 化 交 換 に 計 測.

例子中采用的是一个远程文件，但实际上你也可以使用一个本地文件，例如Path.of("./video.mp4").toUri()，dashscope4j会将视频文件上传到DashScope的临时空间。

Tokenizer

在实际开发过程中，为了让AI记住上下文，你需要把之前的聊天记录作为对话上下文输入。但模型的输入长度有限，所以需要将上下文进行分段，然后进行分段输入。有一种分割方案就是根据模型能支撑的最大token进行切分，这个时候你就需要一个工具来计算一段文本的token数到底是多少，以达到最大化保留记忆的分割目的。

Tokenizer工具能很好的帮助你实现这一点，他分远程和本地两种调用方式，远程性能较差但计算的token会更精准，本地性能最佳，但会存在一定的版本滞后性。不同的模型，同样的文字，计算的token可能不一样。但大差不差。

在实际生产环境中，推荐使用本地计算的方案。

远程计算

final var messages = List.of(
    Message.ofUser("北京有哪些好玩地方？"),
    Message.ofAi("故宫、颐和园、天坛等都是可以去游玩的景点哦。"),
    Message.ofUser("帮我安排一些行程")
);

// 远程调用需要明确算法模型
final var list = client.base().tokenize().remote(ChatModel.QWEN_PLUS)
    .encode(messages)
    .toCompletableFuture()
    .join();

System.out.println("total tokens: " + list.size());

本地计算

final var messages = List.of(
    Message.ofUser("北京有哪些好玩地方？"),
    Message.ofAi("故宫、颐和园、天坛等都是可以去游玩的景点哦。"),
    Message.ofUser("帮我安排一些行程")
);

final var list = client.base().tokenize().local()
    .encode(messages)
    .toCompletableFuture()
    .join();

System.out.println("total tokens: " + list.size());

一、主要功能

DashScope4j支持以下API功能：

对话（Chat）
- 提供用户与灵积进行多模态(图、文)对话
- 提供用户与灵积进行多模态(图、音)对话
- 提供用户与灵积进行函数对话
向量（Embeddings）
- 将文本转换为向量表示，用于文本相似度比较、聚类等任务
- 将图音文本转换为向量表示，用于图音文相似度比较、聚类等任务
图像（Images）
- 文生图： 将文本描述转换为相应的图像
语音识别与合成
- 支持实时语音识别
- 支持实时语音合成
- 支持音视频转录
基础功能
- 支持Tokenizer计算（远程、本地）
- 支持灵积提供的临时空间
- 请求、应答拦截器
插件应用（Plugin）
- OCR插件： 图像理解识别，并对图像内容进行总结概述，输出用户可理解的句子或段落
- PDF解析插件： 对PDF文件进行解析，提取、理解文本内容
- 计算器插件： 对用户输入的数学表达式进行计算
- 文生图插件： 将文本描述转换为相应的图像

二、系统要求

JDK17或更高版本

三、跑通测试

到阿里云的模型服务-灵积中开通服务，获取AK
到API-KEY管理中创建一个API-KEY，获取其AK
声明环境变量export DASHSCOPE_AK=<YOUR APP-KEY>
运行测试用例：mvn test

四、使用示例

创建客户端

// 线程池
final var executor = Executors.newFixedThreadPool(10);

// 创建客户端
final var client = DashScopeClient.newBuilder()
    .ak("<YOUR APP-KEY>")
    .executor(executor)
    .build();

对话示例（异步）

// 创建请求
final var request = ChatRequest.newBuilder()
    .model(ChatModel.QWEN_VL_MAX)
    .messages(List.of(
        Message.ofUser(List.of(
            Content.ofImage(new File("./document/image/image-002.jpeg").toURI()),
            Content.ofText("图片中一共多少辆自行车?")
        ))
    ))
    .build();

// 异步应答
final var response = client.chat(request)
    .async()
    .toCompletableFuture()
    .join();

// 输出结果（异步）
System.out.println(response.output().best().message().text());

输出日志

2024-02-29 00:49:56 DEBUG dashscope://chat/qwen-vl-max => {"model":"qwen-vl-max","input":{"messages":[{"role":"user","content":[{"image":"https://ompc-images.oss-cn-hangzhou.aliyuncs.com/image-002.jpeg"},{"text":"图片中一共多少辆自行车?"}]}]},"parameters":{}}
2024-02-29 00:49:59 DEBUG dashscope://chat/qwen-vl-max <= {"output":{"choices":[{"finish_reason":"stop","message":{"role":"assistant","content":[{"text":"图片中有两辆自行车。"}]}}]},"usage":{"output_tokens":7,"input_tokens":1264,"image_tokens":1230},"request_id":"f11e20f0-6774-9649-a0c9-6095e6287cdc"}
图片中有两辆自行车。

对话示例（流式）

// 创建请求
final var request = ChatRequest.newBuilder()
    .model(ChatModel.QWEN_VL_MAX)
    .option(ChatOptions.ENABLE_INCREMENTAL_OUTPUT, true)
    .messages(List.of(
        Message.ofUser(List.of(
            Content.ofImage(new File("./document/image/image-002.jpeg").toURI()),
            Content.ofText("图片中一共多少辆自行车?")
        ))
    ))
    .build();

// 流式应答
final var publisher = client.chat(request)
    .flow()
    .toCompletableFuture()
    .join();

// 应答输出（流式）
final var latch = new CountDownLatch(1);
final var stringSB = new StringBuilder();
publisher.subscribe(new Flow.Subscriber<>(){

    @Override
    public void onSubscribe(Flow.Subscription subscription) {
        subscription.request(Long.MAX_VALUE);
    }
    
    @Override
    public void onNext(ChatResponse response) {
        stringSB.append(response.output().best().message().text());
    }
    
    @Override
    public void onError(Throwable ex) {
        ex.printStackTrace(System.err);
    }
    
    @Override
    public void onComplete() {
        latch.countDown();
    }

});

// 等待处理完成
latch.await();
System.out.println(stringSB);

输出日志

2024-02-29 01:21:42 DEBUG dashscope://chat/qwen-vl-max => {"model":"qwen-vl-max","input":{"messages":[{"role":"user","content":[{"image":"https://ompc-images.oss-cn-hangzhou.aliyuncs.com/image-002.jpeg"},{"text":"图片中一共多少辆自行车?"}]}]},"parameters":{"incremental_output":true}}
2024-02-29 01:21:44 DEBUG dashscope://chat/qwen-vl-max <= {"output":{"choices":[{"message":{"content":[{"text":"图片"}],"role":"assistant"},"finish_reason":"null"}]},"usage":{"input_tokens":1264,"output_tokens":1,"image_tokens":1230},"request_id":"9713405c-31b3-97a5-8e99-ac2c685798a0"}
2024-02-29 01:21:44 DEBUG dashscope://chat/qwen-vl-max <= {"output":{"choices":[{"message":{"content":[{"text":"中有"}],"role":"assistant"},"finish_reason":"null"}]},"usage":{"input_tokens":1264,"output_tokens":2,"image_tokens":1230},"request_id":"9713405c-31b3-97a5-8e99-ac2c685798a0"}
2024-02-29 01:21:45 DEBUG dashscope://chat/qwen-vl-max <= {"output":{"choices":[{"message":{"content":[{"text":"两"}],"role":"assistant"},"finish_reason":"null"}]},"usage":{"input_tokens":1264,"output_tokens":3,"image_tokens":1230},"request_id":"9713405c-31b3-97a5-8e99-ac2c685798a0"}
2024-02-29 01:21:45 DEBUG dashscope://chat/qwen-vl-max <= {"output":{"choices":[{"message":{"content":[{"text":"辆自行车。"}],"role":"assistant"},"finish_reason":"stop"}]},"usage":{"input_tokens":1264,"output_tokens":7,"image_tokens":1230},"request_id":"9713405c-31b3-97a5-8e99-ac2c685798a0"}
图片中有两辆自行车。

函数调用示例

灵积在 2024-03-12 放出了函数调用的功能，当前支持的模型是大语言模型：qwen-turbo、qwen-plus、qwen-max、qwen-max-longcontext，下面是一个函数调用的示例：

单个函数调用示例：回显

假设我们有一个回显函数:echo

@ChatFn(name = "echo", description = "当用户输入echo:，回显后边的文字")
public class EchoFunction implements ChatFunction<EchoFunction.Echo, EchoFunction.Echo> {

  @Override
  public CompletionStage<Echo> call(Echo echo) {
    return CompletableFuture.completedFuture(new Echo(echo.words()));
  }

  public record Echo(
          @JsonPropertyDescription("需要回显的文字")
          String words
  ) {

  }

}

我们可以通过以下代码来调用这个函数：

final var request = ChatRequest.newBuilder()
    .model(ChatModel.QWEN_MAX)
    .functions(List.of(new EchoFunction()))
    .messages(List.of(
        Message.ofUser("echo: HELLO!")
    ))
    .build();
final var response = client.chat(request)
    .async()
    .toCompletableFuture()
    .join();

输出日志

2024-03-19 21:28:38 DEBUG dashscope://chat/qwen-max => {"model":"qwen-max","input":{"messages":[{"role":"user","content":"echo: HELLO!"}]},"parameters":{"result_format":"message","tools":[{"function":{"name":"echo","description":"当用户输入echo:，回显后边的文字","parameters":{"type":"object","properties":{"words":{"type":"string","description":"需要回显的文字"}}}},"type":"function"}]}}
2024-03-19 21:28:40 DEBUG dashscope://chat/qwen-max <= {"output":{"choices":[{"finish_reason":"tool_calls","message":{"role":"assistant","tool_calls":[{"function":{"name":"echo","arguments":"{\"words\": \"HELLO!\"}"},"id":"","type":"function"}],"content":""}}]},"usage":{"total_tokens":28,"output_tokens":23,"input_tokens":5},"request_id":"8af40d7a-d43d-9d7f-9f12-8d52accfe8ac"}
2024-03-19 21:28:40 DEBUG dashscope://chat/function/echo <= {"words":"HELLO!"}
2024-03-19 21:28:40 DEBUG dashscope://chat/function/echo => {"words":"HELLO!"}
2024-03-19 21:28:40 DEBUG dashscope://chat/qwen-max => {"model":"qwen-max","input":{"messages":[{"role":"user","content":"echo: HELLO!"},{"role":"assistant","tool_calls":[{"function":{"name":"echo","arguments":"{\"words\": \"HELLO!\"}"},"type":"function"}],"content":""},{"role":"tool","name":"echo","content":"{\"words\":\"HELLO!\"}"}]},"parameters":{"result_format":"message","tools":[{"function":{"name":"echo","description":"当用户输入echo:，回显后边的文字","parameters":{"type":"object","properties":{"words":{"type":"string","description":"需要回显的文字"}}}},"type":"function"}]}}
2024-03-19 21:28:42 DEBUG dashscope://chat/qwen-max <= {"output":{"choices":[{"finish_reason":"stop","message":{"role":"assistant","content":"HELLO!"}}]},"usage":{"total_tokens":8,"output_tokens":3,"input_tokens":5},"request_id":"37ff7303-c1b2-9d7c-966d-82a7446fc52e"}
HELLO!

级联函数调用示例：成绩查询

我们有两个函数

查询成绩函数：query_score
计算平均分函数：compute_avg_score

现在需要查询某个同学的所有成绩，并计算其平均分。LLM需要先调用 query_score 函数查询成绩，然后再调用 compute_avg_score 函数计算平均分。

final var request = ChatRequest.newBuilder()
    .model(ChatModel.QWEN_PLUS)
    .functions(List.of(
        new QueryScoreFunction(),
        new ComputeAvgScoreFunction()
    ))
    .messages(List.of(
        Message.ofUser("张三的所有成绩，并计算平均分")
    ))
    .build();
final var response = client.chat(request)
    .async()
    .toCompletableFuture()
    .join();

输出日志

2024-03-20 23:50:17 DEBUG dashscope://chat/qwen-plus => {"model":"qwen-plus","input":{"messages":[{"role":"user","content":"张三的所有成绩，并计算平均分"}]},"parameters":{"result_format":"message","tools":[{"function":{"name":"query_score","description":"query student's scores","parameters":{"type":"object","properties":{"name":{"type":"string","description":"the student name to query"},"subjects":{"type":"array","description":"the subjects to query","items":{"type":"string","enum":["CHINESE","MATH","ENGLISH"]}}},"required":["name","subjects"]}},"type":"function"},{"function":{"name":"compute_avg_score","description":"计算平均成绩","parameters":{"type":"object","properties":{"scores":{"type":"array","description":"分数集合","items":{"type":"number"}}}}},"type":"function"}]}}
2024-03-20 23:50:20 DEBUG dashscope://chat/qwen-plus <= {"output":{"choices":[{"finish_reason":"tool_calls","message":{"role":"assistant","tool_calls":[{"function":{"name":"query_score","arguments":"{\"name\": \"张三\", \"subjects\": [\"CHINESE\", \"MATH\", \"ENGLISH\"]}"},"id":"","type":"function"}],"content":""}}]},"usage":{"total_tokens":47,"output_tokens":39,"input_tokens":8},"request_id":"4703f631-a245-967e-ba86-8f01327a82bf"}
2024-03-20 23:50:20 DEBUG dashscope://chat/function/query_score <= {"name":"张三","subjects":["CHINESE","MATH","ENGLISH"]}
2024-03-20 23:50:20 DEBUG dashscope://chat/function/query_score => {"message":"查询成功","data":[{"name":"张三","subject":"CHINESE","value":90.0},{"name":"张三","subject":"MATH","value":80.0},{"name":"张三","subject":"ENGLISH","value":70.0}],"success":true}
2024-03-20 23:50:20 DEBUG dashscope://chat/qwen-plus => {"model":"qwen-plus","input":{"messages":[{"role":"user","content":"张三的所有成绩，并计算平均分"},{"role":"assistant","tool_calls":[{"function":{"arguments":"{\"name\": \"张三\", \"subjects\": [\"CHINESE\", \"MATH\", \"ENGLISH\"]}","name":"query_score"},"type":"function"}],"content":""},{"role":"tool","name":"query_score","content":"{\"message\":\"查询成功\",\"data\":[{\"name\":\"张三\",\"subject\":\"CHINESE\",\"value\":90.0},{\"name\":\"张三\",\"subject\":\"MATH\",\"value\":80.0},{\"name\":\"张三\",\"subject\":\"ENGLISH\",\"value\":70.0}],\"success\":true}"}]},"parameters":{"result_format":"message","tools":[{"function":{"name":"query_score","description":"query student's scores","parameters":{"type":"object","properties":{"name":{"type":"string","description":"the student name to query"},"subjects":{"type":"array","description":"the subjects to query","items":{"type":"string","enum":["CHINESE","MATH","ENGLISH"]}}},"required":["name","subjects"]}},"type":"function"},{"function":{"name":"compute_avg_score","description":"计算平均成绩","parameters":{"type":"object","properties":{"scores":{"type":"array","description":"分数集合","items":{"type":"number"}}}}},"type":"function"}]}}
2024-03-20 23:50:24 DEBUG dashscope://chat/qwen-plus <= {"output":{"choices":[{"finish_reason":"tool_calls","message":{"role":"assistant","tool_calls":[{"function":{"name":"compute_avg_score","arguments":"{\"scores\": [90.0, 80.0, 70.0]}"},"id":"","type":"function"}],"content":"张三的成绩如下：\n\n- 中文: 90.0分\n- 数学: 80.0分\n- 英语: 70.0分\n\n现在我们来计算他的平均分。"}}]},"usage":{"total_tokens":93,"output_tokens":85,"input_tokens":8},"request_id":"0f662c8b-ca5d-9512-9f92-597045977eca"}
2024-03-20 23:50:24 DEBUG dashscope://chat/function/compute_avg_score <= {"scores":[90.0,80.0,70.0]}
2024-03-20 23:50:24 DEBUG dashscope://chat/function/compute_avg_score => {"avg_score":80.0}
2024-03-20 23:50:24 DEBUG dashscope://chat/qwen-plus => {"model":"qwen-plus","input":{"messages":[{"role":"user","content":"张三的所有成绩，并计算平均分"},{"role":"assistant","tool_calls":[{"function":{"arguments":"{\"name\": \"张三\", \"subjects\": [\"CHINESE\", \"MATH\", \"ENGLISH\"]}","name":"query_score"},"type":"function"}],"content":""},{"role":"tool","name":"query_score","content":"{\"message\":\"查询成功\",\"data\":[{\"name\":\"张三\",\"subject\":\"CHINESE\",\"value\":90.0},{\"name\":\"张三\",\"subject\":\"MATH\",\"value\":80.0},{\"name\":\"张三\",\"subject\":\"ENGLISH\",\"value\":70.0}],\"success\":true}"},{"role":"assistant","tool_calls":[{"function":{"arguments":"{\"scores\": [90.0, 80.0, 70.0]}","name":"compute_avg_score"},"type":"function"}],"content":"张三的成绩如下：\n\n- 中文: 90.0分\n- 数学: 80.0分\n- 英语: 70.0分\n\n现在我们来计算他的平均分。"},{"role":"tool","name":"compute_avg_score","content":"{\"avg_score\":80.0}"}]},"parameters":{"result_format":"message","tools":[{"function":{"name":"query_score","description":"query student's scores","parameters":{"type":"object","properties":{"name":{"type":"string","description":"the student name to query"},"subjects":{"type":"array","description":"the subjects to query","items":{"type":"string","enum":["CHINESE","MATH","ENGLISH"]}}},"required":["name","subjects"]}},"type":"function"},{"function":{"name":"compute_avg_score","description":"计算平均成绩","parameters":{"type":"object","properties":{"scores":{"type":"array","description":"分数集合","items":{"type":"number"}}}}},"type":"function"}]}}
2024-03-20 23:50:25 DEBUG dashscope://chat/qwen-plus <= {"output":{"choices":[{"finish_reason":"stop","message":{"role":"assistant","content":"张三的平均分是 80.0 分。"}}]},"usage":{"total_tokens":68,"output_tokens":13,"input_tokens":55},"request_id":"c01da60a-21d7-9e2f-ae5d-17a9b622ed41"}
张三的平均分是 80.0 分。

文生图示例

// 创建请求
final var request = GenImageRequest.newBuilder()
    .model(GenImageModel.WANX_V1)
    .option(GenImageOptions.NUMBER, 1)
    .option(GenImageOptions.STYLE, GenImageRequest.Style.ANIME)
    .prompt("画古风美少女，黑发，面容白皙精致，发饰精美")
    .build();

// 任务应答
final var response = client.image().generation(request)
    .task(Task.WaitStrategies.perpetual(Duration.ofSeconds(1L)))
    .toCompletableFuture()
    .join();

输出日志

2024-02-29 01:27:01 DEBUG dashscope://image/generation/wanx-v1 => {"model":"wanx-v1","input":{"prompt":"画古风美少女，黑发，面容白皙精致，发饰精美","negative_prompt":null},"parameters":{"style":"<anime>","n":1}}
2024-02-29 01:27:02 DEBUG dashscope://task/get => dd693d99-0b5e-4261-8b08-7c633919d2fc
2024-02-29 01:27:02 DEBUG dashscope://task/get <= {"request_id":"becb655f-2e7c-9a82-8d0a-c0e48f925221","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"RUNNING","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","task_metrics":{"TOTAL":1,"SUCCEEDED":0,"FAILED":0}}}
2024-02-29 01:27:03 DEBUG dashscope://task/get => dd693d99-0b5e-4261-8b08-7c633919d2fc
2024-02-29 01:27:03 DEBUG dashscope://task/get <= {"request_id":"f329109b-8d7f-9e7f-9049-faced6937cc8","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"RUNNING","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","task_metrics":{"TOTAL":1,"SUCCEEDED":0,"FAILED":0}}}
2024-02-29 01:27:04 DEBUG dashscope://task/get => dd693d99-0b5e-4261-8b08-7c633919d2fc
2024-02-29 01:27:04 DEBUG dashscope://task/get <= {"request_id":"ec190c02-33b4-91ca-8fa2-2abb0dfd979e","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"RUNNING","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","task_metrics":{"TOTAL":1,"SUCCEEDED":0,"FAILED":0}}}
2024-02-29 01:27:05 DEBUG dashscope://task/get => dd693d99-0b5e-4261-8b08-7c633919d2fc
2024-02-29 01:27:05 DEBUG dashscope://task/get <= {"request_id":"f7e2dbe9-11e8-9681-9a07-1e5076369d62","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"RUNNING","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","task_metrics":{"TOTAL":1,"SUCCEEDED":0,"FAILED":0}}}
...
2024-02-29 01:27:26 DEBUG dashscope://task/get => dd693d99-0b5e-4261-8b08-7c633919d2fc
2024-02-29 01:27:26 DEBUG dashscope://task/get <= {"request_id":"5e50bc5e-9215-9737-9ecb-dc3fd8c586d1","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"RUNNING","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","task_metrics":{"TOTAL":1,"SUCCEEDED":0,"FAILED":0}}}
2024-02-29 01:27:27 DEBUG dashscope://task/get => dd693d99-0b5e-4261-8b08-7c633919d2fc
2024-02-29 01:27:27 DEBUG dashscope://task/get <= {"request_id":"408498bc-26c9-9792-b47d-d8b4950a116c","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"RUNNING","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","task_metrics":{"TOTAL":1,"SUCCEEDED":0,"FAILED":0}}}
2024-02-29 01:27:28 DEBUG dashscope://task/get => dd693d99-0b5e-4261-8b08-7c633919d2fc
2024-02-29 01:27:29 DEBUG dashscope://task/get <= {"request_id":"cd3bcc40-3ad6-9f7c-acad-f29653f53c2d","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"RUNNING","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","task_metrics":{"TOTAL":1,"SUCCEEDED":0,"FAILED":0}}}
2024-02-29 01:27:30 DEBUG dashscope://task/get => dd693d99-0b5e-4261-8b08-7c633919d2fc
2024-02-29 01:27:30 DEBUG dashscope://task/get <= {"request_id":"34b79abd-aa21-988c-87b5-f00261a40ad9","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"RUNNING","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","task_metrics":{"TOTAL":1,"SUCCEEDED":0,"FAILED":0}}}
2024-02-29 01:27:31 DEBUG dashscope://task/get => dd693d99-0b5e-4261-8b08-7c633919d2fc
2024-02-29 01:27:31 DEBUG dashscope://task/get <= {"request_id":"682cec73-19d1-9fbd-a50c-465908c64106","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"RUNNING","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","task_metrics":{"TOTAL":1,"SUCCEEDED":0,"FAILED":0}}}
2024-02-29 01:27:32 DEBUG dashscope://task/get => dd693d99-0b5e-4261-8b08-7c633919d2fc
2024-02-29 01:27:32 DEBUG dashscope://task/get <= {"request_id":"7414bd26-8b51-9e04-abad-c223985ee809","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"SUCCEEDED","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","end_time":"2024-02-29 01:27:32.081","results":[{"url":"https://dashscope-result-bj.oss-cn-beijing.aliyuncs.com/1d/a8/20240229/c34adf05/3464efa2-8874-4aa9-86a4-8aba14b9707d-1.png?Expires=1709227651&OSSAccessKeyId=LTAI5tQZd8AEcZX6KZV4G8qL&Signature=yZ04L3oCKULBD%2By3PGSdmh0jg6M%3D"}],"task_metrics":{"TOTAL":1,"SUCCEEDED":1,"FAILED":0}},"usage":{"image_count":1}}
2024-02-29 01:27:32 DEBUG dashscope://image/generation/wanx-v1 <= {"request_id":"7414bd26-8b51-9e04-abad-c223985ee809","output":{"task_id":"dd693d99-0b5e-4261-8b08-7c633919d2fc","task_status":"SUCCEEDED","submit_time":"2024-02-29 01:27:02.856","scheduled_time":"2024-02-29 01:27:02.911","end_time":"2024-02-29 01:27:32.081","results":[{"url":"https://dashscope-result-bj.oss-cn-beijing.aliyuncs.com/1d/a8/20240229/c34adf05/3464efa2-8874-4aa9-86a4-8aba14b9707d-1.png?Expires=1709227651&OSSAccessKeyId=LTAI5tQZd8AEcZX6KZV4G8qL&Signature=yZ04L3oCKULBD%2By3PGSdmh0jg6M%3D"}],"task_metrics":{"TOTAL":1,"SUCCEEDED":1,"FAILED":0}},"usage":{"image_count":1}}

然后你就可以通过response.output().results().get(0)拿到生成的图片地址。

插件调用示例

PDF提取插件

我从网上下载了一份《第十四个五年规划》的PDF文件，然后通过PDF提取插件来提取PDF文件的内容。

final var request = ChatRequest.newBuilder()
    .model(ChatModel.QWEN_PLUS)
    .plugins(List.of(ChatPlugin.PDF_EXTRACTER))
    .messages(List.of(
        Message.ofUser(List.of(
            Content.ofText("请总结这篇文档"),
            Content.ofFile(URI.create("https://ompc.oss-cn-hangzhou.aliyuncs.com/share/P020210313315693279320.pdf"))
        ))
    ))
    .build();
final var response = client.chat(request)
    .async()
    .toCompletableFuture()
    .join();

输出日志

2024-03-20 00:24:22 DEBUG dashscope://chat/qwen-plus => {"model":"qwen-plus","input":{"messages":[{"role":"user","content":[{"text":"请总结这篇文档"},{"file":"https://ompc.oss-cn-hangzhou.aliyuncs.com/share/P020210313315693279320.pdf"}]}]},"parameters":{"result_format":"message"}}
2024-03-20 00:24:47 DEBUG dashscope://chat/qwen-plus <= {"output":{"choices":[{"finish_reason":"stop","message":{"role":"assistant","content":"这篇文档是中国国民经济和社会发展第十四个五年规划和2035年远景目标纲要，它阐述了中国在新发展阶段的主要任务和战略目标。规划涵盖了多个领域，包括创新驱动发展、产业升级、市场体系建设、数字化发展、深化改革、乡村振兴、城镇化、区域协调发展、文化建设、绿色发展、对外开放、教育与健康、民生福祉、国家安全、国防和军队现代化、民主法治以及监督制度的完善。\n\n在创新驱动方面，强调了强化国家战略科技力量，提升企业创新能力，激发人才创新活力，并完善科技创新机制。在产业发展上，致力于制造业升级，发展战略性新兴产业，促进服务业繁荣，同时建设现代化基础设施体系。\n\n为了构建新发展格局，规划提出畅通国内大循环，促进国内外双循环，加快培育完整内需体系。数字化发展被放在重要位置，旨在打造数字经济新优势，加速数字社会建设，提升数字政府水平，并营造健康的数字环境。\n\n在深化改革方面，将激发各类市场主体活力，建设高标准市场体系，改革财税金融体制，提升政府经济治理能力。农业、农村发展和乡村振兴是关注焦点，包括提高农业质量和效益，实施乡村建设行动，以及城乡融合发展。\n\n此外，规划还强调优化区域经济布局，促进区域协调发展，发展社会主义先进文化，推动绿色发展，实行高水平对外开放，提升国民素质，增进民生福祉，加强国家安全和国防建设，以及加强民主法治和监督制度。\n\n最后，规划提出了一系列实施保障措施，包括加强党的领导，健全规划体系，完善实施机制，确保党中央重大决策的贯彻落实，激发全社会参与规划实施的积极性，并通过监测评估、政策保障和考核监督机制，确保规划目标的实现。整个规划为全面建设社会主义现代化国家描绘了蓝图，是全国各族人民共同遵循的行动纲领。"}}]},"usage":{"total_tokens":365,"output_tokens":361,"input_tokens":4},"request_id":"d1eb2274-35cc-97fb-b60a-570fab96ab0b"}

计算器插件

final var request = ChatRequest.newBuilder()
    .model(ChatModel.QWEN_PLUS)
    .plugins(List.of(ChatPlugin.CALCULATOR))
    .messages(List.of(
        Message.ofUser("1+2*3-4/5=?")
    ))
    .build();
final var response = client.chat(request)
    .async()
    .toCompletableFuture()
    .join();

输出日志

2024-03-20 00:29:11 DEBUG dashscope://chat/qwen-plus => {"model":"qwen-plus","input":{"messages":[{"role":"user","content":"1+2*3-4/5=?"}]},"parameters":{"result_format":"message"}}
2024-03-20 00:29:15 DEBUG dashscope://chat/qwen-plus <= {"output":{"choices":[{"finish_reason":"stop","messages":[{"role":"assistant","plugin_call":{"name":"calculator","arguments":"{\"payload__input__text\": \"1+2*3-4/5\"}"},"content":""},{"role":"plugin","name":"calculator","content":"{\"equations\": [\"1 + 2 * 3 - 4 / 5\"], \"results\": [6.2]}","status":{"code":200,"name":"Success","message":"success"}},{"role":"assistant","content":"The result of the expression \\(1 + 2 \\times 3 - \\frac{4}{5}\\) is approximately 6.2."}]}]},"usage":{"total_tokens":103,"output_tokens":93,"input_tokens":10},"request_id":"3659340f-7dad-9815-bd11-ce6c6eb4d1c2"}

向量计算示例

文本向量计算

// 请求
final var request = EmbeddingRequest.newBuilder()
    .model(EmbeddingModel.TEXT_EMBEDDING_V2)
    .documents(List.of(
        "我爱北京天安门", 
        "天安门上太阳升"
    ))
    .build();

// 应答
final var response = client.embedding(request)
    .async()
    .toCompletableFuture()
    .join();

输出日志

2024-03-29 00:06:04 DEBUG dashscope://embedding/text-embedding-v2 => {"model":"text-embedding-v2","input":{"texts":["我爱北京天安门","天安门上太阳升"]},"parameters":{}}
2024-03-29 00:06:05 DEBUG dashscope://embedding/text-embedding-v2 <= 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多模态向量计算

// 请求
final var request = MmEmbeddingRequest.newBuilder()
    .model(MmEmbeddingModel.MM_EMBEDDING_ONE_PEACE_V1)
    .option(MmEmbeddingOptions.AUTO_TRUNCATION, true)
    .contents(List.of(
        Content.ofAudio(URI.create("https://dashscope.oss-cn-beijing.aliyuncs.com/audios/2channel_16K.wav")),
        Content.ofImage(URI.create("https://ompc-images.oss-cn-hangzhou.aliyuncs.com/image-002.jpeg")),
        Content.ofText("一个帅哥在骑自行车念经"),
        Content.ofText("有两个自行车")
    ))
    .build();

// 应答
final var response = client.mmEmbedding(request)
    .async()
    .toCompletableFuture()
    .join();

输出日志

2024-03-29 00:07:26 DEBUG dashscope://embeddingx/multimodal-embedding-one-peace-v1 => {"model":"multimodal-embedding-one-peace-v1","input":{"contents":[{"factor":1.0,"audio":"https://dashscope.oss-cn-beijing.aliyuncs.com/audios/2channel_16K.wav"},{"image":"https://ompc-images.oss-cn-hangzhou.aliyuncs.com/image-002.jpeg","factor":1.0},{"factor":1.0,"text":"一个帅哥在骑自行车念经"},{"factor":1.0,"text":"有两个自行车"}]},"parameters":{"auto_truncation":true}}
2024-03-29 00:07:27 DEBUG dashscope://embeddingx/multimodal-embedding-one-peace-v1 <= 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支持拦截API调用

通过DashScopeClient.Builder设置，可实现AOP的功能，限流控制、失败重试等功能均基于此实现。

示例代码

DashScopeClient client = DashScopeClient.newBuilder()
    .ak(AK)
    .executor(executor)
    .appendInterceptors(List.of(
        new Interceptor() {

            @Override
            public CompletableFuture<?> handle(InvocationContext context, ApiRequest<?> request, OpHandler opHandler) {
                return opHandler.handle(request);
            }

        }
    ))
    .build();

支持无感使用临时空间

在对话、多模态向量计算和文档分析插件等请求中如果需要解析图片、音频、文档等内容，不再需要提前上传到OSS转换为外网可访问的URL连接。这样极不方便也不安全。

通过灵积平台提供的临时空间可以很好解决这个问题，但操作起来需要调用额外的api且需要对url进行拼接和替换，略为繁琐。

dashscope4j帮你封装了这个繁琐的操作，你只需要设置内容的时候将本地文件直接传入Content，框架会自动识别并帮你完成临时空间上传和转换连接操作。并自带一个缓存避免重复上传。

final var request = ChatRequest.newBuilder()
    .model(ChatModel.QWEN_VL_MAX)
    .option(ChatOptions.ENABLE_INCREMENTAL_OUTPUT, true)
    .messages(List.of(
        Message.ofUser(List.of(
            Content.ofImage(new File("./document/image/image-002.jpeg").toURI()),
            Content.ofText("图片中一共多少辆自行车?")
        ))
    ))
    .build();

五、参与贡献

如果你对DashScope4j感兴趣并希望为其做出贡献，请遵循以下步骤：

Fork本项目到你的GitHub账户。
克隆项目到你的本地环境。
创建一个新的分支用于你的修改。
提交你的更改并通过Pull Request请求合并到主分支。

在提交Pull Request之前，请确保你的代码符合项目的编码规范和最佳实践，并且已经通过了相关的测试。

六、关于软件

版本号声明

软件版本号采用：大版本.小版本.漏洞修复的格式

大版本： 程序的架构设计进行重大升级或重大改造
小版本：
1. 增加新的API功能
2. 在现有架构下完成局部架构的微调
漏洞修复： 在不改变现有架构和API情况下，对漏洞修复和增强

写在最后

灵积是有官方的Java客户端的，我之所以还需要开发这个DashScope4j主要是基于以下几点考虑

官方的SDK并不开源，你无法查看其源码，也无法自行修改和定制
个人练手习惯，反正也不花我多少时间，嗯嗯

Name		Name	Last commit message	Last commit date
Latest commit History 176 Commits
document		document
shell		shell
src		src
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
pom.xml		pom.xml

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DashScope4j：灵积 Java SDK

依赖使用

重要更新

语音合成

语音识别

音视频转录

Tokenizer

远程计算

本地计算

一、主要功能

二、系统要求

三、跑通测试

四、使用示例

创建客户端

对话示例（异步）

对话示例（流式）

函数调用示例

单个函数调用示例：回显

级联函数调用示例：成绩查询

文生图示例

插件调用示例

PDF提取插件

计算器插件

向量计算示例

文本向量计算

多模态向量计算

支持拦截API调用

支持无感使用临时空间

五、参与贡献

六、关于软件

版本号声明

写在最后

七、相关链接

About

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oldmanpushcart/dashscope4j

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Repository files navigation

DashScope4j：灵积 Java SDK

依赖使用

重要更新

语音合成

语音识别

音视频转录

Tokenizer

远程计算

本地计算

一、主要功能

二、系统要求

三、跑通测试

四、使用示例

创建客户端

对话示例（异步）

对话示例（流式）

函数调用示例

单个函数调用示例：回显

级联函数调用示例：成绩查询

文生图示例

插件调用示例

PDF提取插件

计算器插件

向量计算示例

文本向量计算

多模态向量计算

支持拦截API调用

支持无感使用临时空间

五、参与贡献

六、关于软件

版本号声明

写在最后

七、相关链接

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