Flutter+SpringBoot实现ChatGPT流实输出-编程知识

Flutter+SpringBoot实现ChatGPT流式输出、上下文了连续对话

最终实现Flutter的流式输出+上下文连续对话。
在这里插入图片描述

这里就是提供一个简单版的工具类和使用案例，此处页面仅参考。

服务端

这里直接封装提供工具类，修改自己的apiKey即可使用，支持连续对话

工具类及使用

http依赖这里使用okHttp

    <dependency><groupId>com.squareup.okhttp3</groupId><artifactId>okhttp</artifactId><version>4.9.3</version></dependency>

import com.alibaba.fastjson2.JSON;
import com.squareup.okhttp.Call;
import com.squareup.okhttp.MediaType;
import com.squareup.okhttp.OkHttpClient;
import com.squareup.okhttp.Request;
import com.squareup.okhttp.RequestBody;
import com.squareup.okhttp.Response;
import com.squareup.okhttp.ResponseBody;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.Getter;
import lombok.NoArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import org.springframework.stereotype.Component;
import org.springframework.util.StringUtils;
import org.springframework.web.servlet.mvc.method.annotation.SseEmitter;
import vip.ailtw.common.utils.StringUtil;import javax.annotation.PostConstruct;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.io.Serializable;
import java.util.List;
import java.util.concurrent.TimeUnit;
import java.util.function.Consumer;
import java.util.regex.Matcher;
import java.util.regex.Pattern;@Slf4j
@Component
public class ChatGptStreamUtil {/*** 修改为自己的密钥*/private final String apiKey = "xxxxxxxxxxxxxx";public final String gptCompletionsUrl = "https://api.openai.com/v1/chat/completions";private static final OkHttpClient client = new OkHttpClient();private static MediaType mediaType;private static Request.Builder requestBuilder;public final static Pattern contentPattern = Pattern.compile("\"content\":\"(.*?)\"}");/*** 对话符号*/public final static String EVENT_DATA = "d";/*** 错误结束符号*/public final static String EVENT_ERROR = "e";/*** 响应结束符号*/public final static String END = "<<END>>";@PostConstructpublic void init() {client.setConnectTimeout(60, TimeUnit.SECONDS);client.setReadTimeout(60, TimeUnit.SECONDS);mediaType = MediaType.parse("application/json; charset=utf-8");requestBuilder = new Request.Builder().url(gptCompletionsUrl).header("Content-Type", "application/json").header("Authorization", "Bearer " + apiKey);}/*** 流式对话** @param talkList 上下文对话，最早的对话放在首位* @param callable 消费者，流式对话每次响应的内容*/public GptChatResultDTO chatStream(List<ChatGptDTO> talkList, Consumer<String> callable) throws Exception {long start = System.currentTimeMillis();StringBuilder resp = new StringBuilder();Response response = chatStream(talkList);//解析对话内容try (ResponseBody responseBody = response.body();InputStream inputStream = responseBody.byteStream();BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(inputStream))) {String line;while ((line = bufferedReader.readLine()) != null) {if (!StringUtils.hasLength(line)) {continue;}Matcher matcher = contentPattern.matcher(line);if (matcher.find()) {String content = matcher.group(1);resp.append(content);callable.accept(content);}}}int wordSize = 0;for (ChatGptDTO dto : talkList) {String content = dto.getContent();wordSize += content.toCharArray().length;}wordSize += resp.toString().toCharArray().length;long end = System.currentTimeMillis();return GptChatResultDTO.builder().resContent(resp.toString()).time(end - start).wordSize(wordSize).build();}/*** 流式对话** @param talkList 上下文对话* @return 接口请求响应*/private Response chatStream(List<ChatGptDTO> talkList) throws Exception {ChatStreamDTO chatStreamDTO = new ChatStreamDTO(talkList);RequestBody bodyOk = RequestBody.create(mediaType, chatStreamDTO.toString());Request requestOk = requestBuilder.post(bodyOk).build();Call call = client.newCall(requestOk);Response response;try {response = call.execute();} catch (IOException e) {throw new IOException("请求时IO异常: " + e.getMessage());}if (response.isSuccessful()) {return response;}try (ResponseBody body = response.body()) {if (429 == response.code()) {String msg = "Open Api key 已过期,msg: " + body.string();log.error(msg);}throw new RuntimeException("chat api 请求异常, code: " + response.code() + "body: " + body.string());}}private boolean sendToClient(String event, String data, SseEmitter emitter) {try {emitter.send(SseEmitter.event().name(event).data("{" + data + "}"));return true;} catch (IOException e) {log.error("向客户端发送消息时出现异常", e);}return false;}/*** 发送事件给客户端*/public boolean sendData(String data, SseEmitter emitter) {if (StringUtil.isBlank(data)) {return true;}return sendToClient(EVENT_DATA, data, emitter);}/*** 发送结束事件,会关闭emitter*/public void sendEnd(SseEmitter emitter) {try {sendToClient(EVENT_DATA, END, emitter);} finally {emitter.complete();}}/*** 发送异常事件,会关闭emitter*/public void sendError(SseEmitter emitter) {try {sendToClient(EVENT_ERROR, "我累垮了", emitter);} finally {emitter.complete();}}/*** gpt请求结果*/@Data@NoArgsConstructor@AllArgsConstructor@Builderpublic static class GptChatResultDTO implements Serializable {/*** gpt请求返回的全部内容*/private String resContent;/*** 上下文消耗的字数*/private int wordSize;/*** 耗时*/private long time;}/*** 连续对话DTO*/@Data@Builder@NoArgsConstructor@AllArgsConstructorpublic static class ChatGptDTO implements Serializable {/*** 对话内容*/private String content;/*** 角色 {@link GptRoleEnum}*/private String role;}/*** gpt连续对话角色*/@Getterpublic static enum GptRoleEnum {USER_ROLE("user", "用户"),GPT_ROLE("assistant", "ChatGPT本身"),/*** message里role为system，是为了让ChatGPT在对话过程中设定自己的行为* 可以理解为对话的设定，如你是谁，要什么语气、等级*/SYSTEM_ROLE("system", "对话设定"),;private final String value;private final String desc;GptRoleEnum(String value, String desc) {this.value = value;this.desc = desc;}}/*** gpt请求body*/@Datapublic static class ChatStreamDTO {private static final String model = "gpt-3.5-turbo";private static final boolean stream = true;private List<ChatGptDTO> messages;public ChatStreamDTO(List<ChatGptDTO> messages) {this.messages = messages;}@Overridepublic String toString() {return "{\"model\":\"" + model + "\"," +"\"messages\":" + JSON.toJSONString(messages) + "," +"\"stream\":" + stream + "}";}}}

使用案例:

    public static void main(String[] args) throws Exception {ChatGptStreamUtil chatGptStreamUtil = new ChatGptStreamUtil();chatGptStreamUtil.init();//构建一个上下文对话情景List<ChatGptDTO> talkList = new ArrayList<>();//设定gpttalkList.add(ChatGptDTO.builder().content("你是chatgpt助手，能过帮助我查阅资料，编写教学报告。").role(GptRoleEnum.GPT_ROLE.getValue()).build());//开始提问talkList.add(ChatGptDTO.builder().content("请帮我写一篇小学数学加法运算教案").role(GptRoleEnum.USER_ROLE.getValue()).build());chatGptStreamUtil.chatStream(talkList, (respContent) -> {//这里是gpt每次流式返回的内容System.out.println("gpt返回：" + respContent);});}

SpringBoot接口

基于SpringBoot工程,提供接口，供Flutter端使用。

通过上面的工具类的使用，可以知道gpt返回给我们的内容是一段一段的，因此如果我们服务端也要提供类似的效果，提供两个思路和实现：

WebSocket，服务端接收gpt返回的内容时推送内容给flutter
使用Http长链接，也就是 SseEmitter，这里也是采用这种方式。

代码:

@RestController
@RequestMapping("/chat")
@Slf4j
public class ChatController {@Autowiredprivate ChatGptStreamUtil chatGptStreamUtil;@PostMapping(value = "/chatStream")@ApiOperation("流式对话")public SseEmitter chatStream() {SseEmitter emitter = new SseEmitter(80000L);//构建一个上下文对话情景List<ChatGptDTO> talkList = new ArrayList<>();//设定gpttalkList.add(ChatGptDTO.builder().content("你是chatgpt助手，能过帮助我查阅资料，编写教学报告。").role(GptRoleEnum.GPT_ROLE.getValue()).build());//开始提问talkList.add(ChatGptDTO.builder().content("请帮我写一篇小学数学加法运算教案").role(GptRoleEnum.USER_ROLE.getValue()).build());GptChatResultDTO gptChatResultDTO = chatGptStreamUtil.chatStream(talkList, (content) -> {//这里服务端接收到消息就发送给FlutterchatGptStreamUtil.sendData(content, emitter);});return emitter;}}

Flutter端

这里使用dio作为网络请求的工具

依赖

	dio: ^5.2.1+1

工具类

import 'dart:async';
import 'dart:convert';import 'package:dio/dio.dart';
import 'package:flutter/cupertino.dart';
import 'package:flutter/foundation.dart';
import 'package:get/get.dart' hide Response;///http工具类
class HttpUtil {Dio? client;static HttpUtil of() {return HttpUtil.init();}//初始化http工具HttpUtil.init() {if (client == null) {var options = BaseOptions(baseUrl: Config.baseUrl,connectTimeout: const Duration(seconds: 100),receiveTimeout: const Duration(seconds: 100));client = Dio(options);// 请求与响应拦截器/异常拦截器client?.interceptors.add(OnReqResInterceptors());}}Future<Stream<String>?> postStream(String path,[Map<String, dynamic>? params]) async {Response<ResponseBody> rs =await Dio().post<ResponseBody>(Config.baseUrl + path,options: Options(headers: {"Accept": "text/event-stream","Cache-Control": "no-cache"}, responseType: ResponseType.stream),data: params );StreamTransformer<Uint8List, List<int>> unit8Transformer =StreamTransformer.fromHandlers(handleData: (data, sink) {sink.add(List<int>.from(data));},);var resp = rs.data?.stream.transform(unit8Transformer).transform(const Utf8Decoder()).transform(const LineSplitter());return resp;}/// Dio 请求与响应拦截器
class OnReqResInterceptors extends InterceptorsWrapper {@overrideFuture<void> onRequest(RequestOptions options, RequestInterceptorHandler handler) async {//统一添加tokenvar headers = options.headers;headers['Authorization'] = '请求头token';return super.onRequest(options, handler);}@overridevoid onError(DioError err, ErrorInterceptorHandler handler) {if (err.type == DioErrorType.unknown) {// 网络不可用，请稍后再试}return super.onError(err, handler);}@overridevoid onResponse(Response<dynamic> response, ResponseInterceptorHandler handler) {Response res = response;return super.onResponse(res, handler);}
}

使用

  //构建文章、流式对话chatStream() async {final stream = await HttpUtil.of().postStream("/api/chat/chatStream");String respContent = "";stream?.listen((content) {debugPrint(content);if (content != '' && content.contains("data:")) {//解析数据var start = content.indexOf("{") + 1;var end = content.indexOf("}");var substring = content.substring(start, end);content = substring;respContent += content;print("返回的内容:$content");}});}