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新模型业务逻辑调整,以及衔接语更新

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wangxiangshun
2025-11-10 22:51:39 +08:00
parent d24afb1062
commit 2fa0d46d48
6 changed files with 642 additions and 118 deletions
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181
vetti-admin/src/main/java/com/vetti/socket/ChatWebSocketHandler.java
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@@ -2,6 +2,7 @@ package com.vetti.socket;
import cn.hutool.core.collection.CollectionUtil;
import cn.hutool.core.date.DateUtil;
import cn.hutool.core.util.ObjectUtil;
import cn.hutool.core.util.StrUtil;
import cn.hutool.json.JSONUtil;
import com.vetti.common.ai.elevenLabs.ElevenLabsClient;
@@ -167,77 +168,44 @@ public class ChatWebSocketHandler {
promptJson = JSONUtil.toJsonStr(list);
cacheMsgMapData.put(session.getId(), promptJson);
}
//开始返回衔接语
String openingPathUrl = RuoYiConfig.getProfile() + VOICE_SYSTEM_DIR + "good.wav";
sendVoiceBuffer(openingPathUrl, session);
//开始使用模型进行追问
//把提问的文字发送给CPT(流式处理)
OpenAiStreamClient aiStreamClient = SpringUtils.getBean(OpenAiStreamClient.class);
//把提问的文字发送给GPT
ChatGPTClient chatGPTClient = SpringUtils.getBean(ChatGPTClient.class);
log.info("AI提示词为:{}", promptJson);
//先获取回答的评分,是否符合要求
Boolean isEndFlag = getInterviewScore(clientId,promptJson, session, "");
if(isEndFlag){
log.info("面试回答符合条件规则,继续追问啦!!!!!");
final int[] resultNum = {(int) (Math.random() * 2) + 1};
aiStreamClient.streamChat(promptJson, new OpenAiStreamListenerService() {
@Override
public void onMessage(String content) {
log.info("返回AI结果：{}", content.replaceAll("\n", ""));
//获取1和2的随机数
if(resultNum[0] == 1){
content = "";
}
resultNum[0] = resultNum[0] +1;
log.info("提问的问题:{}",content);
// String contentData = content.replaceAll("\n", "");
//返回是追问的问题
//获取的是追问的问题
if (StrUtil.isNotEmpty(content)) {
//对问题进行数据缓存
cacheQuestionResult.put(session.getId(), content);
//开始进行语音输出-流式持续输出
sendTTSBuffer(clientId, content, session);
// 实时输出内容
try {
//把文本也给前端返回去
Map<String, String> dataText = new HashMap<>();
dataText.put("type", "question");
dataText.put("content", content);
log.info("提问的问题文本发送啦:{}",JSONUtil.toJsonStr(dataText));
session.getBasicRemote().sendText(JSONUtil.toJsonStr(dataText));
} catch (Exception e) {
e.printStackTrace();
}
}
}
@Override
public void onComplete() {
String resultMsg = chatGPTClient.handleAiChat(promptJson,"QA");
if(StrUtil.isNotEmpty(resultMsg)) {
//开始解析返回结果
Map mapResultData = JSONUtil.toBean(resultMsg,Map.class);
//获取评分
//验证是否触发对应的规则
Boolean isEndFlag = getInterviewScore(resultMsg, session);
if(isEndFlag){
log.info("面试回答符合条件规则,继续追问啦!!!!!");
int resultNum = (int) (Math.random() * 2);
List<String> questions = JSONUtil.toList(mapResultData.get("follow_up_questions").toString(), String.class);
String questionStr = questions.get(resultNum);
if (StrUtil.isNotEmpty(questionStr)) {
//开始进行语音输出-流式持续输出
sendTTSBuffer(clientId, questionStr, session);
// 实时输出内容
try {
//开始往缓存中记录提问的问题
String questionResult = cacheQuestionResult.get(session.getId());
if (StrUtil.isNotEmpty(questionResult)) {
//获取缓存记录
String msgMapData = cacheMsgMapData.get(session.getId());
if (StrUtil.isNotEmpty(msgMapData)) {
List<Map> list = JSONUtil.toList(msgMapData, Map.class);
Map<String, String> mapEntity = new HashMap<>();
mapEntity.put("role", "user");
mapEntity.put("content", "Question：" + questionResult + "\\nCandidate Answer：{}");
list.add(mapEntity);
cacheMsgMapData.put(session.getId(), JSONUtil.toJsonStr(list));
}
}
//清空问题
cacheQuestionResult.put(session.getId(), "");
//把文本也给前端返回去
Map<String, String> dataText = new HashMap<>();
dataText.put("type", "question");
dataText.put("content", questionStr);
log.info("提问的问题文本发送啦:{}",JSONUtil.toJsonStr(dataText));
session.getBasicRemote().sendText(JSONUtil.toJsonStr(dataText));
} catch (Exception e) {
throw new RuntimeException(e);
e.printStackTrace();
}
//开始对问题进行缓存
recordQuestion(questionStr,session);
}
@Override
public void onError(Throwable throwable) {
throwable.printStackTrace();
}
});
}
}
}
} else if ("end".equals(resultFlag)) {
@@ -347,7 +315,7 @@ public class ChatWebSocketHandler {
List<Map<String, String>> list = new LinkedList();
Map<String, String> mapEntity = new HashMap<>();
mapEntity.put("role", "system");
mapEntity.put("content", "You are an interviewer. Generate in-depth follow-up questions based on candidate responses.");
mapEntity.put("content", "You are a construction industry interview expert. Evaluate candidate responses and provide scores (1-5) and follow-up questions when needed. Always respond in JSON format.");
list.add(mapEntity);
//获取预设问题-直接TTS转换返回语音结果
IHotakeProblemBaseInfoService problemBaseInfoService = SpringUtils.getBean(IHotakeProblemBaseInfoService.class);
@@ -358,7 +326,7 @@ public class ChatWebSocketHandler {
if (CollectionUtil.isNotEmpty(baseInfoList)) {
HotakeProblemBaseInfo baseInfo = baseInfoList.get(0);
if (StrUtil.isNotEmpty(baseInfo.getContents())) {
String[] qStrs = baseInfo.getContents().split(",");
String[] qStrs = baseInfo.getContents().split("#AA#");
int random_index = (int) (Math.random() * qStrs.length);
//获取问题文本
String question = qStrs[random_index];
@@ -435,13 +403,9 @@ public class ChatWebSocketHandler {
resultEntity.put("content", resultMsg);
resultEntity.put("type", "score");
try{
//返回评分语音
// sendTTSBuffer(clientId,resultMsg,session);
//返回最终的评分结构
log.info("返回最终的评分结构:{}",JSONUtil.toJsonStr(resultEntity));
session.getBasicRemote().sendText(JSONUtil.toJsonStr(resultEntity));
}catch (Exception e){
e.printStackTrace();
}
@@ -458,12 +422,12 @@ public class ChatWebSocketHandler {
* @param content
* @param session return false 立即结束面试
*/
private Boolean handleScoreRecord(String content, Session session) {
private Boolean handleScoreRecord(Object content, Session session) {
Map<String, Integer> scoreRecordMap = cacheScoreResult.get(session.getId());
log.info("获取评分结果:{}",content);
//对评分进行处理
if (StrUtil.isNotEmpty(content)) {
String[] strs = content.split("/");
if (ObjectUtil.isNotEmpty(content)) {
String[] strs = content.toString().split("/");
//取第一个数就是对应的评分
log.info("获取的数据为:{}",strs[0]);
BigDecimal score = new BigDecimal(strs[0].trim());
@@ -504,57 +468,28 @@ public class ChatWebSocketHandler {
}
/**
* 获取面试回答评分,并且校验是否结束面试
* 校验是否结束面试,结束后直接返回评分
*
* @param promptJson 提示词数据json
* @param resultMsg 问答AI返回的结果数据
* @param session 客户端会话
* @param position 职位
*/
private Boolean getInterviewScore(String clientId,String promptJson, Session session, String position) {
private Boolean getInterviewScore(String resultMsg, Session session) {
//返回文本评分
//获取缓存记录
String msgMapData = cacheMsgMapData.get(session.getId());
if (StrUtil.isNotEmpty(msgMapData)) {
List<Map> list = JSONUtil.toList(msgMapData, Map.class);
//获取第一条数据记录
Map<String, String> mapEntity = list.get(0);
//更新问题记录
mapEntity.put("role", "system");
mapEntity.put("content", "You are a construction industry interview expert. Rate candidate responses on a 1-5 scale and analyze key signals.");
//每个回答的内容前面要加上候选人的职位
if (StrUtil.isNotEmpty(position)) {
for (Map map : list) {
if ("user".equals(map.get("role").toString())) {
map.put("content", "Position: " + position + "\\n" + map.get("content"));
}
}
}
promptJson = JSONUtil.toJsonStr(list);
}
log.info("评分AI提示词为:{}", promptJson);
ChatGPTClient gptClient = SpringUtils.getBean(ChatGPTClient.class);
String resultMsg = gptClient.handleAiChat(promptJson, "QA");
//评论格式为: Score: 3/5\nAssessment: Basically correct answer but lacks detail
String resultScore = "";
String scoreText = resultMsg;
if (StrUtil.isNotEmpty(resultMsg)) {
resultMsg = resultMsg.replaceAll("\n","#AA#");
String[] resultMsgs = resultMsg.split("#AA#");
resultScore = resultMsgs[0].replaceAll(SCORE_FLAG, "");
}
//开始解析返回结果
Map mapResultData = JSONUtil.toBean(resultMsg,Map.class);
//获取评分
Object scoreStr = mapResultData.get("score");
Object assessment = mapResultData.get("assessment");
//校验面试是否结束
Boolean flag = handleScoreRecord(resultScore, session);
Boolean flag = handleScoreRecord(scoreStr, session);
try {
if (!flag) {
//发送面试官结束语音流
String openingPathUrl = RuoYiConfig.getProfile() + VOICE_SYSTEM_DIR + "end.wav";
sendVoiceBuffer(openingPathUrl, session);
//返回评分语音
// sendTTSBuffer(clientId,scoreText,session);
Map<String, String> resultEntity = new HashMap<>();
resultEntity.put("content", scoreText);
resultEntity.put("content", scoreStr +"\n"+assessment);
resultEntity.put("type", "score");
//返回评分结果
log.info("返回最终的评分结果:{}",JSONUtil.toJsonStr(resultEntity));
@@ -567,5 +502,25 @@ public class ChatWebSocketHandler {
return flag;
}
/**
* 记录问题
* @param questionResult
* @param session
*/
private void recordQuestion(String questionResult,Session session) {
if (StrUtil.isNotEmpty(questionResult)) {
//获取缓存记录
String msgMapData = cacheMsgMapData.get(session.getId());
if (StrUtil.isNotEmpty(msgMapData)) {
List<Map> list = JSONUtil.toList(msgMapData, Map.class);
Map<String, String> mapEntity = new HashMap<>();
mapEntity.put("role", "user");
mapEntity.put("content", "Question：" + questionResult + "\\nCandidate Answer：{}");
list.add(mapEntity);
cacheMsgMapData.put(session.getId(), JSONUtil.toJsonStr(list));
}
}
}
}

571
vetti-admin/src/main/java/com/vetti/socket/ChatWebSocketHandler2.java Normal file
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@@ -0,0 +1,571 @@
package com.vetti.socket;
import cn.hutool.core.collection.CollectionUtil;
import cn.hutool.core.date.DateUtil;
import cn.hutool.core.util.StrUtil;
import cn.hutool.json.JSONUtil;
import com.vetti.common.ai.elevenLabs.ElevenLabsClient;
import com.vetti.common.ai.gpt.ChatGPTClient;
import com.vetti.common.ai.gpt.OpenAiStreamClient;
import com.vetti.common.ai.gpt.service.OpenAiStreamListenerService;
import com.vetti.common.config.RuoYiConfig;
import com.vetti.common.utils.spring.SpringUtils;
import com.vetti.hotake.domain.HotakeProblemBaseInfo;
import com.vetti.hotake.service.IHotakeProblemBaseInfoService;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.io.FileUtils;
import org.springframework.stereotype.Component;
import javax.websocket.*;
import javax.websocket.server.PathParam;
import javax.websocket.server.ServerEndpoint;
import java.io.File;
import java.io.IOException;
import java.math.BigDecimal;
import java.nio.ByteBuffer;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
/**
* 语音面试 web处理器
*/
@Slf4j
@ServerEndpoint("/voice-websocket222222/{clientId}")
@Component
public class ChatWebSocketHandler2 {
/**
* 评分标记
*/
private final String SCORE_FLAG = "Score:";
/**
* 缓存客户端流式解析的语音文本数据
*/
private final Map<String, String> cacheClientTts = new ConcurrentHashMap<>();
/**
* 缓存客户端,标记是否是自我介绍后的初次问答
*/
private final Map<String, String> cacheReplyFlag = new ConcurrentHashMap<>();
/**
* 缓存客户端,面试回答信息
*/
private final Map<String, String> cacheMsgMapData = new ConcurrentHashMap<>();
/**
* 缓存客户端,AI提问的问题结果信息
*/
private final Map<String, String> cacheQuestionResult = new ConcurrentHashMap<>();
/**
* 缓存客户端,得分结果记录
*/
private final Map<String, Map<String, Integer>> cacheScoreResult = new ConcurrentHashMap<>();
// 语音文件保存目录
private static final String VOICE_STORAGE_DIR = "/voice_files/";
// 语音结果文件保存目录
private static final String VOICE_STORAGE_RESULT_DIR = "/voice_result_files/";
// 系统语音目录
private static final String VOICE_SYSTEM_DIR = "/system_files/";
public ChatWebSocketHandler2() {
// 初始化存储目录
File dir = new File(RuoYiConfig.getProfile() + VOICE_STORAGE_DIR);
if (!dir.exists()) {
dir.mkdirs();
}
File resultDir = new File(RuoYiConfig.getProfile() + VOICE_STORAGE_RESULT_DIR);
if (!resultDir.exists()) {
resultDir.mkdirs();
}
}
// 连接建立时调用
@OnOpen
public void onOpen(Session session, @PathParam("clientId") String clientId) {
log.info("WebSocket 链接已建立:{}", clientId);
log.info("WebSocket session 链接已建立:{}", session.getId());
cacheClientTts.put(clientId, new String());
//是初次自我介绍后的问答环节
cacheReplyFlag.put(session.getId(), "YES");
//初始化面试回答数据记录
cacheMsgMapData.put(session.getId(), "");
//初始化面试问题
cacheQuestionResult.put(session.getId(), "");
//初始化得分结果记录
Map<String, Integer> scoreResultData = new HashMap<>();
scoreResultData.put("0-1", 0);
scoreResultData.put("4-5", 0);
scoreResultData.put("2-3", 0);
scoreResultData.put("2-5", 0);
cacheScoreResult.put(session.getId(), scoreResultData);
//发送初始化面试官语音流
String openingPathUrl = RuoYiConfig.getProfile() + VOICE_SYSTEM_DIR + "opening.wav";
sendVoiceBuffer(openingPathUrl, session);
}
/**
* 接收文本消息
*
* @param session 客户端会话
* @param message 消息
* 如:
* {
* "type": "start | done | end",
* "content": "内容"
* }
* @param clientId 用户ID
*/
@OnMessage
public void onTextMessage(Session session, String message, @PathParam("clientId") String clientId) {
log.info("我是接收文本消息:{}", message);
try {
//处理文本结果
if (StrUtil.isNotEmpty(message)) {
Map<String, String> mapResult = JSONUtil.toBean(JSONUtil.parseObj(message), Map.class);
String resultFlag = mapResult.get("type");
if ("done".equals(resultFlag)) {
//开始合并语音流
String startFlag = cacheReplyFlag.get(session.getId());
//语音结束,开始进行回答解析
log.info("开始文本处理,客户端ID为:{}", clientId);
String cacheResultText = mapResult.get("content");
log.info("开始文本处理,面试者回答信息为:{}", cacheResultText);
if (StrUtil.isEmpty(cacheResultText)) {
cacheResultText = "";
}
//这是初次处理的逻辑
if ("YES".equals(startFlag)) {
//初始化-不走大模型-直接对候选人进行提问
initializationQuestion(clientId, session);
//发送完第一次消息后,直接删除标记,开始进行正常的面试问答流程
cacheReplyFlag.put(session.getId(), "");
} else {
//开始根据面试者回答的问题,进行追问回答
//获取面试者回答信息
//获取缓存记录
String promptJson = "";
String msgMapData = cacheMsgMapData.get(session.getId());
if (StrUtil.isNotEmpty(msgMapData)) {
List<Map> list = JSONUtil.toList(msgMapData, Map.class);
//获取最后一条数据记录
Map<String, String> mapEntity = list.get(list.size() - 1);
//更新问题记录
String content = mapEntity.get("content");
mapEntity.put("content", StrUtil.format(content, cacheResultText));
promptJson = JSONUtil.toJsonStr(list);
cacheMsgMapData.put(session.getId(), promptJson);
}
//开始使用模型进行追问
//把提问的文字发送给CPT(流式处理)
OpenAiStreamClient aiStreamClient = SpringUtils.getBean(OpenAiStreamClient.class);
log.info("AI提示词为:{}", promptJson);
//先获取回答的评分,是否符合要求
Boolean isEndFlag = getInterviewScore(clientId,promptJson, session, "");
if(isEndFlag){
log.info("面试回答符合条件规则,继续追问啦!!!!!");
final int[] resultNum = {(int) (Math.random() * 2) + 1};
aiStreamClient.streamChat(promptJson, new OpenAiStreamListenerService() {
@Override
public void onMessage(String content) {
log.info("返回AI结果：{}", content.replaceAll("\n", ""));
//获取1和2的随机数
if(resultNum[0] == 1){
content = "";
}
resultNum[0] = resultNum[0] +1;
log.info("提问的问题:{}",content);
// String contentData = content.replaceAll("\n", "");
//返回是追问的问题
//获取的是追问的问题
if (StrUtil.isNotEmpty(content)) {
//对问题进行数据缓存
cacheQuestionResult.put(session.getId(), content);
//开始进行语音输出-流式持续输出
sendTTSBuffer(clientId, content, session);
// 实时输出内容
try {
//把文本也给前端返回去
Map<String, String> dataText = new HashMap<>();
dataText.put("type", "question");
dataText.put("content", content);
log.info("提问的问题文本发送啦:{}",JSONUtil.toJsonStr(dataText));
session.getBasicRemote().sendText(JSONUtil.toJsonStr(dataText));
} catch (Exception e) {
e.printStackTrace();
}
}
}
@Override
public void onComplete() {
try {
//开始往缓存中记录提问的问题
String questionResult = cacheQuestionResult.get(session.getId());
if (StrUtil.isNotEmpty(questionResult)) {
//获取缓存记录
String msgMapData = cacheMsgMapData.get(session.getId());
if (StrUtil.isNotEmpty(msgMapData)) {
List<Map> list = JSONUtil.toList(msgMapData, Map.class);
Map<String, String> mapEntity = new HashMap<>();
mapEntity.put("role", "user");
mapEntity.put("content", "Question：" + questionResult + "\\nCandidate Answer：{}");
list.add(mapEntity);
cacheMsgMapData.put(session.getId(), JSONUtil.toJsonStr(list));
}
}
//清空问题
cacheQuestionResult.put(session.getId(), "");
} catch (Exception e) {
throw new RuntimeException(e);
}
}
@Override
public void onError(Throwable throwable) {
throwable.printStackTrace();
}
});
}
}
} else if ("end".equals(resultFlag)) {
log.info("面试结束啦!!!!!");
handleInterviewEnd(clientId,session,"");
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
// 接收二进制消息（流数据）
@OnMessage
public void onBinaryMessage(Session session, @PathParam("clientId") String clientId, ByteBuffer byteBuffer) {
log.info("我是接受二进制流的-客户端ID为:{}", clientId);
}
// 连接关闭时调用
@OnClose
public void onClose(Session session, CloseReason reason) {
System.out.println("WebSocket连接已关闭: " + session.getId() + ", 原因: " + reason.getReasonPhrase());
//链接关闭,清空内存
//是初次自我介绍后的问答环节
cacheReplyFlag.put(session.getId(), "");
//初始化面试回答数据记录
cacheMsgMapData.put(session.getId(), "");
//初始化面试问题
cacheQuestionResult.put(session.getId(), "");
cacheScoreResult.put(session.getId(), null);
}
// 发生错误时调用
@OnError
public void onError(Session session, Throwable throwable) {
System.err.println("WebSocket错误发生: " + throwable.getMessage());
throwable.printStackTrace();
}
/**
* File 转换成 ByteBuffer
*
* @param fileUrl 文件路径
* @return
*/
private ByteBuffer convertFileToByteBuffer(String fileUrl) {
File file = new File(fileUrl);
try {
return ByteBuffer.wrap(FileUtils.readFileToByteArray(file));
} catch (Exception e) {
e.printStackTrace();
}
return null;
}
/**
* 发送语音流给前端
*
* @param pathUrl 语音文件地址
* @param session 客户端会话
*/
private void sendVoiceBuffer(String pathUrl, Session session) {
try {
//文件转换成文件流
ByteBuffer outByteBuffer = convertFileToByteBuffer(pathUrl);
//发送文件流数据
session.getBasicRemote().sendBinary(outByteBuffer);
// 发送响应确认
log.info("已经成功发送了语音流给前端:{}", DateUtil.now());
} catch (IOException e) {
e.printStackTrace();
}
}
/**
* 发送文本转语音,发送语音流给前端
*
* @param clientId 用户ID
* @param content 文本内容
* @param session 客户端会话ID
*/
private void sendTTSBuffer(String clientId, String content, Session session) {
String resultFileName = clientId + "_" + System.currentTimeMillis() + ".wav";
String resultPathUrl = RuoYiConfig.getProfile() + VOICE_STORAGE_RESULT_DIR + resultFileName;
ElevenLabsClient elevenLabsClient = SpringUtils.getBean(ElevenLabsClient.class);
elevenLabsClient.handleTextToVoice(content, resultPathUrl);
//持续返回数据流给客户端
log.info("发送语音流成功啦!!!!!!!");
sendVoiceBuffer(resultPathUrl, session);
}
/**
* 对候选者初次进行提问业务逻辑处理(初始化系统随机获取第一个问题)
*
* @param clientId 用户ID
* @param session 客户端会话
*/
private void initializationQuestion(String clientId, Session session) {
try {
log.info("开始获取到clientid :{}",clientId);
//自我介绍结束后马上返回一个Good
//发送初始化面试官语音流
String openingPathUrl = RuoYiConfig.getProfile() + VOICE_SYSTEM_DIR + "good.wav";
sendVoiceBuffer(openingPathUrl, session);
//初始化面试流程的提问
List<Map<String, String>> list = new LinkedList();
Map<String, String> mapEntity = new HashMap<>();
mapEntity.put("role", "system");
mapEntity.put("content", "You are an interviewer. Generate in-depth follow-up questions based on candidate responses.");
list.add(mapEntity);
//获取预设问题-直接TTS转换返回语音结果
IHotakeProblemBaseInfoService problemBaseInfoService = SpringUtils.getBean(IHotakeProblemBaseInfoService.class);
HotakeProblemBaseInfo queryPro = new HotakeProblemBaseInfo();
queryPro.setUserId(Long.valueOf(clientId));
List<HotakeProblemBaseInfo> baseInfoList = problemBaseInfoService.selectHotakeProblemBaseInfoList(queryPro);
log.info("准备进行第一个问题的提问:{}",JSONUtil.toJsonStr(baseInfoList));
if (CollectionUtil.isNotEmpty(baseInfoList)) {
HotakeProblemBaseInfo baseInfo = baseInfoList.get(0);
if (StrUtil.isNotEmpty(baseInfo.getContents())) {
String[] qStrs = baseInfo.getContents().split(",");
int random_index = (int) (Math.random() * qStrs.length);
//获取问题文本
String question = qStrs[random_index];
Map<String, String> mapEntityQ = new HashMap<>();
mapEntityQ.put("role", "user");
mapEntityQ.put("content", "Question：" + question + "\\nCandidate Answer：{}");
list.add(mapEntityQ);
log.info("开始提问啦:{}",JSONUtil.toJsonStr(list));
//直接对该问题进行转换处理返回语音流
log.info("第一个问题为:{}",question);
sendTTSBuffer(clientId, question, session);
//发送问题文本
try {
//把文本也给前端返回去
Map<String, String> dataText = new HashMap<>();
dataText.put("type", "question");
dataText.put("content", question);
log.info("提问的问题文本发送啦:{}",JSONUtil.toJsonStr(dataText));
session.getBasicRemote().sendText(JSONUtil.toJsonStr(dataText));
} catch (Exception e) {
e.printStackTrace();
}
}
}
//初始化记录提示词数据到-缓存中
cacheMsgMapData.put(session.getId(), JSONUtil.toJsonStr(list));
} catch (Exception e) {
e.printStackTrace();
log.error("面试流程初始化失败:{}", e.getMessage());
}
}
/**
* 处理面试结束业务逻辑
*
* @param session 客户端会话
* @param position 职位
*/
private void handleInterviewEnd(String clientId,Session session,String position) {
//暂时的业务逻辑
//发送面试官结束语音流
String openingPathUrl = RuoYiConfig.getProfile() + VOICE_SYSTEM_DIR + "end.wav";
sendVoiceBuffer(openingPathUrl, session);
//返回文本评分
//处理模型提问逻辑
//获取缓存记录
String msgMapData = cacheMsgMapData.get(session.getId());
String promptJson = "";
if (StrUtil.isNotEmpty(msgMapData)) {
List<Map> list = JSONUtil.toList(msgMapData, Map.class);
//获取第一条数据记录
Map<String, String> mapEntity = list.get(0);
//更新问题记录
mapEntity.put("role", "system");
mapEntity.put("content", "You are a construction industry interview expert. Rate candidate responses on a 1-5 scale and analyze key signals.");
//每个回答的内容前面要加上候选人的职位
if (StrUtil.isNotEmpty(position)) {
for (Map map : list) {
if ("user".equals(map.get("role").toString())) {
map.put("content", "Position: " + position + "\\n" + map.get("content"));
}
}
}
promptJson = JSONUtil.toJsonStr(list);
//结束回答要清空问答数据
cacheMsgMapData.put(session.getId(), "");
}
log.info("结束AI提示词为:{}", promptJson);
ChatGPTClient gptClient = SpringUtils.getBean(ChatGPTClient.class);
String resultMsg = gptClient.handleAiChat(promptJson, "QA");
Map<String, String> resultEntity = new HashMap<>();
resultEntity.put("content", resultMsg);
resultEntity.put("type", "score");
try{
//返回评分语音
// sendTTSBuffer(clientId,resultMsg,session);
//返回最终的评分结构
log.info("返回最终的评分结构:{}",JSONUtil.toJsonStr(resultEntity));
session.getBasicRemote().sendText(JSONUtil.toJsonStr(resultEntity));
}catch (Exception e){
e.printStackTrace();
}
}
/**
* 处理评分记录
* 触发规则:
* 1、获得 0-1 分 大于1次 立即结束面试
* 2、获取 4-5 分 大于3次 立即结束面试
* 3、获取 2-3 分 大于3次 立即结束面试
* 4、获取 2-5 分 大于4次 立即结束面试
*
* @param content
* @param session return false 立即结束面试
*/
private Boolean handleScoreRecord(String content, Session session) {
Map<String, Integer> scoreRecordMap = cacheScoreResult.get(session.getId());
log.info("获取评分结果:{}",content);
//对评分进行处理
if (StrUtil.isNotEmpty(content)) {
String[] strs = content.split("/");
//取第一个数就是对应的评分
log.info("获取的数据为:{}",strs[0]);
BigDecimal score = new BigDecimal(strs[0].trim());
//记录Key为1
if (BigDecimal.ZERO.compareTo(score) <= 0 && BigDecimal.ONE.compareTo(score) >= 0) {
Integer n1 = scoreRecordMap.get("0-1") + 1;
scoreRecordMap.put("0-1", n1);
if (n1 > 1) {
return false;
}
}
//记录Key为2
if (new BigDecimal(4).compareTo(score) <= 0 && new BigDecimal(5).compareTo(score) >= 0) {
Integer n1 = scoreRecordMap.get("4-5") + 1;
scoreRecordMap.put("4-5", n1);
if (n1 > 3) {
return false;
}
}
//记录Key为3
if (new BigDecimal(2).compareTo(score) <= 0 && new BigDecimal(3).compareTo(score) >= 0) {
Integer n1 = scoreRecordMap.get("2-3") + 1;
scoreRecordMap.put("2-3", n1);
if (n1 > 3) {
return false;
}
}
//记录Key为4
if (new BigDecimal(2).compareTo(score) <= 0 && new BigDecimal(5).compareTo(score) >= 0) {
Integer n1 = scoreRecordMap.get("2-5") + 1;
scoreRecordMap.put("2-5", n1);
if (n1 > 4) {
return false;
}
}
}
return true;
}
/**
* 获取面试回答评分,并且校验是否结束面试
*
* @param promptJson 提示词数据json
* @param session 客户端会话
* @param position 职位
*/
private Boolean getInterviewScore(String clientId,String promptJson, Session session, String position) {
//返回文本评分
//获取缓存记录
String msgMapData = cacheMsgMapData.get(session.getId());
if (StrUtil.isNotEmpty(msgMapData)) {
List<Map> list = JSONUtil.toList(msgMapData, Map.class);
//获取第一条数据记录
Map<String, String> mapEntity = list.get(0);
//更新问题记录
mapEntity.put("role", "system");
mapEntity.put("content", "You are a construction industry interview expert. Rate candidate responses on a 1-5 scale and analyze key signals.");
//每个回答的内容前面要加上候选人的职位
if (StrUtil.isNotEmpty(position)) {
for (Map map : list) {
if ("user".equals(map.get("role").toString())) {
map.put("content", "Position: " + position + "\\n" + map.get("content"));
}
}
}
promptJson = JSONUtil.toJsonStr(list);
}
log.info("评分AI提示词为:{}", promptJson);
ChatGPTClient gptClient = SpringUtils.getBean(ChatGPTClient.class);
String resultMsg = gptClient.handleAiChat(promptJson, "QA");
//评论格式为: Score: 3/5\nAssessment: Basically correct answer but lacks detail
String resultScore = "";
String scoreText = resultMsg;
if (StrUtil.isNotEmpty(resultMsg)) {
resultMsg = resultMsg.replaceAll("\n","#AA#");
String[] resultMsgs = resultMsg.split("#AA#");
resultScore = resultMsgs[0].replaceAll(SCORE_FLAG, "");
}
//校验面试是否结束
Boolean flag = handleScoreRecord(resultScore, session);
try {
if (!flag) {
//发送面试官结束语音流
String openingPathUrl = RuoYiConfig.getProfile() + VOICE_SYSTEM_DIR + "end.wav";
sendVoiceBuffer(openingPathUrl, session);
//返回评分语音
// sendTTSBuffer(clientId,scoreText,session);
Map<String, String> resultEntity = new HashMap<>();
resultEntity.put("content", scoreText);
resultEntity.put("type", "score");
//返回评分结果
log.info("返回最终的评分结果:{}",JSONUtil.toJsonStr(resultEntity));
session.getBasicRemote().sendText(JSONUtil.toJsonStr(resultEntity));
}
} catch (Exception e) {
e.printStackTrace();
}
return flag;
}
}

2
vetti-admin/src/main/java/com/vetti/web/controller/ai/AiCommonController.java
View File

@@ -53,7 +53,7 @@ public class AiCommonController extends BaseController
//你好,我是本次的面试官Vetti,请点击开始按钮后,做一段自我介绍.
//你好,我是本次的面试官Vetti,请在三秒后,开始做一段自我介绍.
//本轮面试结束,谢谢您的配合,面试结果将稍后通知
elevenLabsClient.handleTextToVoice("Hello, I am Vetti, the interviewer for this interview. Please begin a self introduction in three seconds","/Users/wangxiangshun/Desktop/临时文件/opening1.wav");
elevenLabsClient.handleTextToVoice("Ok, I have received your reply.","/Users/wangxiangshun/Desktop/临时文件/good.wav");
return success();
}

2
vetti-admin/src/main/resources/application-druid.yml
View File

@@ -169,7 +169,7 @@ whisper:
chatGpt:
apiKey: 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
apiUrl: https://api.openai.com/v1/chat/completions
model: ft:gpt-3.5-turbo-0125:vetti::CYl9OBMN
model: ft:gpt-3.5-turbo-0125:vetti:interview-unified:CaGyCXOr
modelCV: ft:gpt-3.5-turbo-0125:vetti:vetti-resume-full:CYT0C8JG
role: system

2
vetti-admin/target/classes/application-druid.yml
View File

@@ -169,7 +169,7 @@ whisper:
chatGpt:
apiKey: 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
apiUrl: https://api.openai.com/v1/chat/completions
model: ft:gpt-3.5-turbo-0125:vetti::CYl9OBMN
model: ft:gpt-3.5-turbo-0125:vetti:interview-unified:CaGyCXOr
modelCV: ft:gpt-3.5-turbo-0125:vetti:vetti-resume-full:CYT0C8JG
role: system