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DashScope4j:灵积 Java SDK

License JDK17+ LLM-通义千问

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 / ResponseExchangeApiRequest / 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

  1. JDK17或更高版本

三、跑通测试

  1. 到阿里云的模型服务-灵积中开通服务,获取AK
  2. API-KEY管理中创建一个API-KEY,获取其AK
  3. 声明环境变量export DASHSCOPE_AK=<YOUR APP-KEY>
  4. 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

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。

文生图-美女

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

对话多模态向量计算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:

  1. Fork本项目到你的GitHub账户。
  2. 克隆项目到你的本地环境。
  3. 创建一个新的分支用于你的修改。
  4. 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

  1. 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
  2. 个人练手习惯,反正也不花我多少时间,嗯嗯

七、相关链接