AI 提问追问环境demo 添加
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@@ -8,6 +8,7 @@ import com.vetti.common.ai.gpt.OpenAiStreamClient;
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import com.vetti.common.ai.gpt.service.OpenAiStreamListenerService;
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import com.vetti.common.ai.whisper.WhisperClient;
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import com.vetti.common.config.RuoYiConfig;
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import com.vetti.common.core.redis.RedisCache;
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import com.vetti.common.utils.spring.SpringUtils;
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import lombok.extern.slf4j.Slf4j;
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import okhttp3.*;
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@@ -24,6 +25,7 @@ import java.io.*;
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import java.nio.ByteBuffer;
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import java.util.*;
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import java.util.concurrent.ConcurrentHashMap;
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import java.util.concurrent.CopyOnWriteArrayList;
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/**
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* 语音面试 web处理器
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@@ -60,6 +62,16 @@ public class ChatWebSocketHandler {
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*/
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private final Map<String,String> cacheReplyFlag = new ConcurrentHashMap<>();
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/**
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* 缓存客户端,面试回答信息
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*/
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private final Map<String,String> cacheMsgMapData = new ConcurrentHashMap<>();
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/**
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* 缓存客户端,AI提问的问题结果信息
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*/
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private final Map<String,String> cacheQuestionResult = new ConcurrentHashMap<>();
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// 语音文件保存目录
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private static final String VOICE_STORAGE_DIR = "/voice_files/";
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@@ -92,6 +104,10 @@ public class ChatWebSocketHandler {
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createWhisperRealtimeSocket(session.getId());
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//是初次自我介绍后的问答环节
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cacheReplyFlag.put(session.getId(),"YES");
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//初始化面试回答数据记录
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cacheMsgMapData.put(session.getId(),"");
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//初始化面试问题
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cacheQuestionResult.put(session.getId(),"");
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//发送初始化面试官语音流
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String openingPathUrl = RuoYiConfig.getProfile() + VOICE_SYSTEM_DIR + "opening.wav";
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try {
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@@ -128,10 +144,11 @@ public class ChatWebSocketHandler {
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String startFlag = cacheReplyFlag.get(session.getId());
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//语音结束,开始进行回答解析
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String cacheResultText = cacheClientTts.get(clientId);
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log.info("返回的结果为:{}", cacheResultText);
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log.info("面试者回答信息为:{}", cacheResultText);
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if (StrUtil.isEmpty(cacheResultText)) {
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cacheResultText = "Hi.";
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}
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String promptJson = "";
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if("YES".equals(startFlag)) {
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//自我介绍结束后马上返回一个Good
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//发送初始化面试官语音流
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@@ -146,7 +163,37 @@ public class ChatWebSocketHandler {
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} catch (IOException e) {
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e.printStackTrace();
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}
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cacheResultText = "你是面试官，根据Construction Labourer候选人回答生成追问。只要一个问题";
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Map<String,String> mapEntity = new HashMap<>();
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mapEntity.put("role","system");
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mapEntity.put("content","你是面试官，根据Construction Labourer候选人回答生成追问。只要一个问题,问题不要重复");
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List<Map<String,String>> list = new LinkedList();
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list.add(mapEntity);
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promptJson = JSONUtil.toJsonStr(list);
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//记录缓存中
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cacheMsgMapData.put(session.getId(),promptJson);
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}else{
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//开始根据面试者回答的问题,进行追问回答
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// {
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// role: "system",
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// content: "你是面试官，根据Construction Labourer候选人回答生成追问。"
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// },
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// {
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// role: "user",
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// content: `问题：${question}\n候选人回答：${answer}`
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// }
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//获取面试者回答信息
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//获取缓存记录
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String msgMapData = cacheMsgMapData.get(session.getId());
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if(StrUtil.isNotEmpty(msgMapData)){
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List<Map> list = JSONUtil.toList(msgMapData, Map.class);
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//获取最后一条数据记录
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Map<String,String> mapEntity = list.get(list.size()-1);
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//更新问题记录
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String content = mapEntity.get("content");
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mapEntity.put("content", StrUtil.format(content, cacheResultText));
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promptJson = JSONUtil.toJsonStr(list);
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cacheMsgMapData.put(session.getId(),promptJson);
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}
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}
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//获取完问答数据,直接清空缓存数据
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cacheClientTts.put(clientId,"");
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@@ -154,10 +201,18 @@ public class ChatWebSocketHandler {
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log.info("1、开始进行AI回答时间:{}", System.currentTimeMillis() / 1000);
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//把提问的文字发送给CPT(流式处理)
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OpenAiStreamClient aiStreamClient = SpringUtils.getBean(OpenAiStreamClient.class);
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aiStreamClient.streamChat(cacheResultText, new OpenAiStreamListenerService() {
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log.info("AI提示词为:{}",promptJson);
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aiStreamClient.streamChat(promptJson, new OpenAiStreamListenerService() {
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@Override
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public void onMessage(String content) {
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log.info("返回AI结果：{}", content);
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String questionResult = cacheQuestionResult.get(session.getId());
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if(StrUtil.isEmpty(questionResult)){
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questionResult = content;
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}else{
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questionResult = questionResult + content;
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}
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cacheQuestionResult.put(session.getId(),questionResult);
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// 实时输出内容
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//开始进行语音输出-流式持续输出
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log.info("2、开始进行AI回答时间:{}", System.currentTimeMillis() / 1000);
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@@ -185,6 +240,21 @@ public class ChatWebSocketHandler {
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@Override
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public void onComplete() {
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try {
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//开始往缓存中记录提问的问题
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String questionResult = cacheQuestionResult.get(session.getId());
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//获取缓存记录
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String msgMapData = cacheMsgMapData.get(session.getId());
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if(StrUtil.isNotEmpty(msgMapData)){
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List<Map> list = JSONUtil.toList(msgMapData, Map.class);
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Map<String,String> mapEntity = new HashMap<>();
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mapEntity.put("role","system");
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mapEntity.put("content","问题："+questionResult+"\\n候选人回答：{}");
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list.add(mapEntity);
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cacheMsgMapData.put(session.getId(),JSONUtil.toJsonStr(list));
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}
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//清空问题
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cacheQuestionResult.put(session.getId(),"");
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Map<String, String> resultEntity = new HashMap<>();
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resultEntity.put("msg", "done");
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//发送通知告诉客户端已经回答结束了
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@@ -454,6 +524,5 @@ public class ChatWebSocketHandler {
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}
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}
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}
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@@ -10,6 +10,7 @@ import org.springframework.stereotype.Component;
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import java.io.IOException;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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import java.util.concurrent.TimeUnit;
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@@ -37,10 +38,10 @@ public class OpenAiStreamClient {
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/**
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* 发送流式请求
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*
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* @param prompt 提示词
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* @param promptJson 提示词json数据集合
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* @param listener 流式响应监听器
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*/
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public void streamChat(String prompt, OpenAiStreamListenerService listener) {
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public void streamChat(String promptJson, OpenAiStreamListenerService listener) {
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OkHttpClient client = new OkHttpClient.Builder()
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.connectTimeout(30, TimeUnit.SECONDS)
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.readTimeout(60, TimeUnit.SECONDS)
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@@ -53,13 +54,18 @@ public class OpenAiStreamClient {
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Map<String, Object> requestBody = new HashMap<>();
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requestBody.put("model", model);
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requestBody.put("stream", true);
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// 构建消息
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Map<String, String> message = new HashMap<>();
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message.put("role", role);
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message.put("content", prompt);
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requestBody.put("messages", new Object[]{message});
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if(StrUtil.isNotEmpty(promptJson)) {
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List<Map> promptList = JSONUtil.toList(promptJson, Map.class);
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Object[] objects = new Object[promptList.size()];
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for (int i = 0; i < promptList.size(); i++) {
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objects[i] = promptList.get(i);
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}
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//获取到的提示
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requestBody.put("messages", objects);
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}
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//开始给AI发送请求数据
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System.out.println("请求AI数据参数为:"+JSONUtil.toJsonStr(requestBody));
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// 创建请求
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Request request = new Request.Builder()
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.url(apiUrl)
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