Spring Ai入门

        SpringAI是一个AI工程应用框架，旨在将Spring生态系统的设计原则（如可移植性和模块化设计）应用于AI领域。它推广使用Plain Old Java Object（POJO）作为AI应用程序的构建块，从而为Java开发者提供了一种更简洁的方式与人工智能进行交互。SpringAI的推出被认为是Java开发领域的一大福音，因为它结合了Spring生态系统的设计原则和模块化的概念，降低了接入大型语言模型（LLM）的学习成本。

下面演示如何基于springBoot和OpenAi接口实现ChatGPT：

1、创建Spring Ai工程

2、添加api配置

spring:
  application:
   name: spring-ai-chatgpt
 
  ai:
    openai:
      api-key: xxxxxxxxxxxxxxxxxxxxxx(自己的api-key)
      base-url: xxxxxxxxxxxxxxxxxxxxxx(自己的中转地址)

3、在controller里调用api

（1）使用chatClient

@Autowired
    private ChatClient chatClient;
 
    //chatclient
    @GetMapping("/chat")
    public String chat(@RequestParam("message") String message) {
        return this.chatClient.prompt()
                .user(message)
                .call()
                .content();
    }
 
    //chatclient流式访问
    @GetMapping(value = "/stream", produces = "text/html;charset=UTF-8")
    public Flux<String> chatStream(@RequestParam("message") String message) {
        Flux<String> output = chatClient.prompt()
                .user(message)
                .stream()
                .content();
        return output;
    }

@Configuration
public class AIConfig {
    @Bean
    ChatClient chatClient(ChatClient.Builder builder) {
        return builder.defaultSystem("You are a friendly chat bot that answers question in the voice of a Pirate")
                .build();
    }
}

（2）使用chatModel

@Autowired
    private ChatModel chatModel;
 
    //ChatModel
    @GetMapping(value = "/chat/model", produces = "text/html;charset=UTF-8")
    public String chatModel(@RequestParam("message") String message) {
        ChatResponse response = chatModel.call(
                new Prompt(
                        message,
                        OpenAiChatOptions.builder()
                                .withModel("gpt-4-32k")
                                .withTemperature(0.8F)
                                .build()
                ));
        return response.getResult().getOutput().getContent();
    }

（3）文生图

@Autowired
    private OpenAiImageModel openAiImageModel;
    //文生图
    @GetMapping(value = "/text2Img", produces = "text/html;charset=UTF-8")
    public String text2Img(@RequestParam("message") String message) {
        ImageResponse response = openAiImageModel.call(
                new ImagePrompt(message,
                        OpenAiImageOptions.builder()
                                .withQuality("hd")
                                .withN(1)
                                .withHeight(1024)
                                .withWidth(1024).build())
 
        );
        return response.getResult().getOutput().getUrl();
    }

（4）文生语音

@Autowired
    private OpenAiAudioSpeechModel openAiAudioSpeechModel;
    //文生语音
    @GetMapping(value = "/text2audio", produces = "text/html;charset=UTF-8")
    public String text2audit(@RequestParam("message") String message) {
        OpenAiAudioSpeechOptions speechOptions = OpenAiAudioSpeechOptions.builder()
                .withModel("tts-1")
                .withVoice(OpenAiAudioApi.SpeechRequest.Voice.ALLOY)
                .withResponseFormat(OpenAiAudioApi.SpeechRequest.AudioResponseFormat.MP3)
                .withSpeed(1.0f)
                .build();
 
        SpeechPrompt speechPrompt = new SpeechPrompt("大家下午好，我叫王大锤", speechOptions);
        SpeechResponse response = openAiAudioSpeechModel.call(speechPrompt);
 
        byte[] body = response.getResult().getOutput();
 
        //将byte[]存为MP3文件
        try {
            writeByteArrayToMp3(body, System.getProperty("user.dir"));
        } catch (IOException e) {
            throw new RuntimeException(e);
        }
 
        return "ok";
    }
 
    public static void writeByteArrayToMp3(byte[] audioBytes, String outputFilePath) throws IOException {
        //创建FileOutputStream实例
        FileOutputStream fos = new FileOutputStream(outputFilePath + "/yuyin.mp3");
        //将字节数组写入文件
        fos.write(audioBytes);
        //关闭文件输入流
        fos.close();
    }

（5）语音转文本

@Autowired
    private OpenAiAudioTranscriptionModel openAiAudioTranscriptionModel;
    //语音转文本
    @GetMapping(value = "/text2audio", produces = "text/html;charset=UTF-8")
    public String audio2text(@RequestParam("message") String message) {
        OpenAiAudioApi.TranscriptResponseFormat responseFormat = OpenAiAudioApi.TranscriptResponseFormat.VTT;
 
        OpenAiAudioTranscriptionOptions transcriptionOptions = OpenAiAudioTranscriptionOptions.builder()
                .withResponseFormat(OpenAiAudioApi.TranscriptResponseFormat.TEXT)
                .withTemperature(0f)
                .withResponseFormat(responseFormat)
                .build();
 
        var audioFile = new ClassPathResource("/hello.mp3");
 
        AudioTranscriptionPrompt transcriptionRequest = new AudioTranscriptionPrompt(audioFile, transcriptionOptions);
        AudioTranscriptionResponse response = openAiAudioTranscriptionModel.call(transcriptionRequest);
 
        return response.getResult().getOutput();
    }

（6）多模态

@GetMapping("/mutil")
    public String mutilModel(@RequestParam(value = "message", defaultValue = "你从这个图片中看到了什么呢") String message) throws IOException {
 
        // 图片的二进制流
        byte[] imageData = new ClassPathResource("/test.png").getContentAsByteArray();
 
        var userMessage = new UserMessage(
                message,
                List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData))); //media
 
        OpenAiChatOptions aiChatOptions = OpenAiChatOptions.builder()
                .withModel(OpenAiApi.ChatModel.GPT_4_TURBO_PREVIEW.getValue())
                .build();
 
        ChatResponse response = chatModel.call(new Prompt(userMessage, aiChatOptions));
 
        return response.getResult().getOutput().getContent();
    }