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Spring-AI讲解

news 2025/8/3 19:55:30

Spring-AI

langchain(python) langchain4j

官网：

https://spring.io/projects/spring-ai#learn

整合chatgpt

前置准备

open-ai-key:
https://api.xty.app/register?aff=PuZD
https://xiaoai.plus/
https://eylink.cn/
或者淘宝搜： open ai key
魔法软件，由于国家相关法律规定，建议大家自行准备。（中转可暂不准备）
jdk17
4 springboot3
示例代码：https://gitee.com/xscodeit/ai-openai-examples.git

实现
在这里插入图片描述

创建完后会发现加入了依赖：

<dependencyManagement><dependencies><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-bom</artifactId><version>1.0.0-SNAPSHOT</version><type>pom</type><scope>import</scope></dependency></dependencies>
</dependencyManagement><dependencies><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-openai-spring-boot-starter</artifactId></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-test</artifactId><scope>test</scope></dependency><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId></dependency></dependencies>

设置代理, 如果你请求的大模型的api接口不是国内的。需要将程序设置代理：

public static void main(String[] args) {// 设置代理String proxy = "127.0.0.1";  // 如果代理在你本机就127.0.0.1 ，  如果代理是其他服务器相应设置int port = 7890;   //设置科学上网代理的端口，System.setProperty("proxyType", "4");System.setProperty("proxyPort", Integer.toString(port));System.setProperty("proxyHost", proxy);System.setProperty("proxySet", "true");SpringApplication.run(Application.class, args);}

设置key:

xushu:# 官方（自行配置）openai:key: ${OPENAI_KEY}url: ${OPENAI_URL}# 中转（自行配置）aicore:key: ${OPEN_AI_KEY}url: ${OPEN_AI_URL}spring:ai:openai:api-key: ${xushu.aicore.key}base-url: ${xushu.aicore.url}

示例代码：

private final ChatClient chatClient;
private final OpenAiChatModel chatClient2;
private final OpenAiImageModel imageClient;
private final OpenAiAudioTranscriptionModel audioClient;
private final  OpenAiAudioApi openAiAudioApi;@Value("${OPEN_AI_KEY}")
private String openAiKey;@GetMapping("/ai/simple")
public Map<String, String> completion(@RequestParam(value = "message", defaultValue = "给我讲个笑话") String message) {System.out.println(openAiKey);var value=chatClient.prompt().user(message).call().content();return Map.of("generation",value );
}@GetMapping(value="/ai/stream",produces="text/sse;charset=UTF-8")
public Flux<String> stream(@RequestParam(value = "message", defaultValue = "给我讲个笑话") String message ) {System.out.println(openAiKey);return chatClient.prompt().user(message).stream().content();
}@GetMapping(value="/ai/img",produces="text/html")
public String image(@RequestParam(value = "message", defaultValue = "猫") String message ) {ImageResponse response = imageClient.call(new ImagePrompt(message,OpenAiImageOptions.builder().withQuality("hd").withN(1).withModel(OpenAiImageApi.ImageModel.DALL_E_2.getValue())// dall-e-2 256.withHeight(256).withWidth(256).build()));String url = response.getResult().getOutput().getUrl();System.out.println(url);return "<img src='"+url+"'/>";}@GetMapping(value="/ai/audit2text")
public String audit2text() {var transcriptionOptions = OpenAiAudioTranscriptionOptions.builder().withResponseFormat(OpenAiAudioApi.TranscriptResponseFormat.TEXT).withTemperature(0f).build();// flac、mp3、mp4、mpeg、mpga、m4a、ogg、wav 或 webm。var audioFile = new ClassPathResource("/hello.mp3");AudioTranscriptionPrompt transcriptionRequest = new AudioTranscriptionPrompt(audioFile, transcriptionOptions);AudioTranscriptionResponse response = audioClient.call(transcriptionRequest);//openAiAudioApi.createTranscription()return response.getResult().getOutput();
}@GetMapping(value="/ai/text2audit")
public String text2audit() {ResponseEntity<byte[]> speech = openAiAudioApi.createSpeech(OpenAiAudioApi.SpeechRequest.builder().withVoice(OpenAiAudioApi.SpeechRequest.Voice.ONYX).withInput("你好，我是徐庶").build());byte[] body = speech.getBody();// 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+"/xushu.mp3");// 将字节数组写入文件fos.write(audioBytes);// 关闭文件输出流fos.close();
}@GetMapping(value="/ai/mutil")
public String mutilModel(String message,String imgUrl) throws IOException {byte[] imageData = new ClassPathResource("/test.png").getContentAsByteArray();var userMessage = new UserMessage("这个图片你看出什么?", // contentList.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData))); // mediaChatResponse response = chatClient.call(new Prompt(userMessage,OpenAiChatOptions.builder().withModel(OpenAiApi.ChatModel.GPT_4_TURBO_PREVIEW.getValue()).build()));return  response.getResult().getOutput().getContent();
}

function-call

AI本身是不具备实时消息能力的，比如问“现在北京的天气是什么”， AI是不知道的，这个时候我们需要通过接口来帮助AI完成,大致流程：
在这里插入图片描述

function-call实现的代码：

在发送文本时，同时设置Funcation ；关键代码：.withFunction(“getWaitTime”)

ChatResponse response = chatClient.call(new Prompt(List.of(userMessage),OpenAiChatOptions.builder().withFunction("getWaitTime").build()));

当代码执行完call时（AI响应之后），会再调用getWaitTime对应的bean的apply方法。

@Bean
public Function<WaitTimeService.Request, WaitTimeService.Response> getWaitTime() {return new WaitTimeService();
}

并且会把 getWaitTime该bean的Request作为funcation-call的返回参数，即可在apply方法中获取Request对应的参数

@Override
public Response apply(Request request) {String name =  request.name();String location =  request.location();// todo...return new Response(location+"有10个！");
}
// jdk17新特性 密封类
public record Request(String name,String location) {}
public record Response(String weather) {}

随后Response会再次丢给AI组织语言，进行响应，最终

源码：

看源码之前，了解下该接口需要哪些参数：https://platform.openai.com/docs/api-reference/chat/create
请求： userMessage=“长沙有多少叫徐庶的”
调用call方法，执行openai的远程api请求

@Override
public ChatResponse call(Prompt prompt) {ChatCompletionRequest request = createRequest(prompt, false);return this.retryTemplate.execute(ctx -> {ResponseEntity<ChatCompletion> completionEntity = this.callWithFunctionSupport(request);var chatCompletion = completionEntity.getBody();if (chatCompletion == null) {logger.warn("No chat completion returned for prompt: {}", prompt);return new ChatResponse(List.of());}RateLimit rateLimits = OpenAiResponseHeaderExtractor.extractAiResponseHeaders(completionEntity);List<Generation> generations = chatCompletion.choices().stream().map(choice -> {return new Generation(choice.message().content(), toMap(chatCompletion.id(), choice)).withGenerationMetadata(ChatGenerationMetadata.from(choice.finishReason().name(), null));}).toList();return new ChatResponse(generations,OpenAiChatResponseMetadata.from(completionEntity.getBody()).withRateLimit(rateLimits));});

通过createRequest将withFunction参数解析到request.tools

ChatCompletionRequest request = createRequest(prompt, false);

tools属性：包含了
在这里插入图片描述

关于open-ai对tools参数的说明：https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools
在这里插入图片描述

来到callWithFunctionSupport 真正调用远程api接口
protected Resp callWithFunctionSupport(Req request) { Resp response = this.doChatCompletion(request); return this.handleFunctionCallOrReturn(request, response); }
这里2句关键代码：
this.doChatCompletion(request); 方法会正常请求chat completion 接口并且会带上funcation-call参数并携带tools属性，并且返回对话中的funcation-call所需参数（即WaitTimeService.Request的参数）
handleFunctionCallOrReturn 执行Function-callback方法, 此时会调用WaitTimeService.apply方法
a. 拿到之前解析的functionCallback即
b. 将arguments从Json转换为对象调用WaitTimeService.apply
c. 将返回的数据再次请求大模型