spring整合openAI大模型之Spring AI

package com.kdx.springai.controller; import org.springframework.ai.chat.client.ChatClient; import org.springframework.ai.chat.model.ChatModel; import org.springframework.ai.chat.model.ChatResponse; import org.springframework.ai.chat.prompt.Prompt; import org.springframework.ai.image.ImagePrompt; import org.springframework.ai.image.ImageResponse; import org.springframework.ai.openai.*; import org.springframework.ai.openai.api.OpenAiApi; import org.springframework.ai.openai.api.OpenAiAudioApi; import org.springframework.ai.openai.audio.speech.SpeechPrompt; import org.springframework.ai.openai.audio.speech.SpeechResponse; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.web.bind.annotation.RequestMapping; import org.springframework.web.bind.annotation.RequestParam; import org.springframework.web.bind.annotation.RestController; import reactor.core.publisher.Flux; import java.io.FileOutputStream; import java.io.IOException; @RestController @RequestMapping("/ai") public class HelloController { @Autowired private ChatClient chatClient; @Autowired(required = false) private ChatModel chatModel; @Autowired(required = false) private OpenAiImageModel openaiImageModel; @Autowired(required = false) private OpenAiAudioSpeechModel openAiAudioSpeechModel; @RequestMapping("/hello") public String hello() { return "hello"; } //交流 @RequestMapping("/chat") public String generation(@RequestParam(value = "message", defaultValue = "讲个笑话") String message) { //prompt：提示词 return this.chatClient.prompt() //用户信息 .user(message) //请求大模型 .call() //返回文本 .content(); } //流式响应 @RequestMapping(value = "/stream", produces = "text/html;charset=UTF-8") public Flux<String> stream(@RequestParam(value = "message", defaultValue = "讲个笑话") String message) { Flux<String> output = chatClient.prompt() .user(message) //流式调用 .stream() .content(); return output; } //chatModel api @RequestMapping(value = "/ChatResponse", produces = "text/html;charset=UTF-8") public String ChatResponse(@RequestParam(value = "message") String message) { ChatResponse response = chatModel.call(new Prompt( message, OpenAiChatOptions.builder() //选择gpt版本 .withModel("gpt-4-32k") .withTemperature(0.4f) .build() )); return response.getResult().getOutput().getContent(); } //文生图 @RequestMapping(value = "/openaiImageModel", produces = "text/html;charset=UTF-8") public String openaiImageModel(@RequestParam(value = "message") String message) { ImageResponse response = openaiImageModel.call( new ImagePrompt(message, OpenAiImageOptions.builder() //图片质量 .withQuality("hd") //生成几张 .withN(1) //尺寸 .withHeight(1024) .withWidth(1024).build()) ); return response.getResult().getOutput().getUrl(); } //文生语音 @RequestMapping(value = "/writeByte", produces = "text/html;charset=UTF-8") public String writeByte(@RequestParam(value = "message") String message) { OpenAiAudioSpeechOptions speechOptions = OpenAiAudioSpeechOptions.builder() .withModel(OpenAiAudioApi.TtsModel.TTS_1.value) .withVoice(OpenAiAudioApi.SpeechRequest.Voice.ALLOY) .withResponseFormat(OpenAiAudioApi.SpeechRequest.AudioResponseFormat.MP3) .withSpeed(1.0f) .build(); SpeechPrompt speechPrompt = new SpeechPrompt(message, speechOptions); SpeechResponse response = openAiAudioSpeechModel.call(speechPrompt); byte[] body = response.getResult().getOutput(); try { writeByte(body,"D:\\Project"); } catch (Exception e) { System.out.println(e); } return "ok"; } public static void writeByte(byte[] audioBytes, String outputFilePath) throws IOException { FileOutputStream fileOutputStream = new FileOutputStream(outputFilePath + "111.mp3"); fileOutputStream.write(audioBytes); fileOutputStream.close(); } //functionCall @RequestMapping(value = "/functionCall", produces = "text/html;charset=UTF-8") public String functionCall(@RequestParam(value = "message") String message) { OpenAiChatOptions aiChatOptions = OpenAiChatOptions.builder() //设置实现了function接口的bean名称 .withFunction("LocationNameFunction") .withModel(OpenAiApi.ChatModel.GPT_3_5_TURBO) .build(); ChatResponse response = chatModel.call(new Prompt(message, aiChatOptions)); return response.getResult().getOutput().getContent(); } }