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70 lines
2.4 KiB
70 lines
2.4 KiB
1 year ago
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package cn.teammodel.service.impl;
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import cn.teammodel.ai.SparkGptClient;
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import cn.teammodel.ai.SseHelper;
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import cn.teammodel.ai.domain.SparkChatRequestParam;
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import cn.teammodel.ai.listener.SparkGptStreamListener;
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import cn.teammodel.model.dto.ai.ChatCompletionReqDto;
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import cn.teammodel.model.entity.User;
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import cn.teammodel.security.utils.SecurityUtil;
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import cn.teammodel.service.ChatMessageService;
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import com.google.common.collect.Lists;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.stereotype.Service;
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import org.springframework.web.servlet.mvc.method.annotation.SseEmitter;
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import javax.annotation.Resource;
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import java.util.List;
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/**
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* @author winter
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* @create 2023-12-18 15:20
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*/
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@Service
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@Slf4j
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public class ChatMessageServiceImpl implements ChatMessageService {
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@Resource
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private SparkGptClient sparkGptClient;
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@Override
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public SseEmitter chatCompletion(ChatCompletionReqDto chatCompletionReqDto) {
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// 目前仅使用讯飞星火大模型
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User user = SecurityUtil.getLoginUser();
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String userId = user.getId();
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String text = chatCompletionReqDto.getText();
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// String userId = "123";
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// String text = "hello, how should I call you?";
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SseEmitter sseEmitter = new SseEmitter(-1L);
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SparkGptStreamListener listener = new SparkGptStreamListener(sseEmitter);
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// open 回调
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listener.setOnOpen((s) -> {
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// 敏感词检查,计费
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log.info("callback: ws open event emmit");
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});
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// 对话完成的回调
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listener.setOnComplete((s) -> {
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log.info("callback: ws complete event emmit");
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SseHelper.send(sseEmitter, "[DONE]");
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// 处理完成后的事件: 保存消息记录
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});
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// 错误的回调
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listener.setOnError((s) -> {
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log.error("callback: ws error" );
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// 返还积分
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});
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// todo: 拉取对话上下文
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List<SparkChatRequestParam.Message> messageList = Lists.newArrayList();
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messageList.add(SparkChatRequestParam.Message.ofUser(text));
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// todo: sessionId
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SparkChatRequestParam requestParam = SparkChatRequestParam
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.builder()
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.uid(userId)
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.chatId("123")
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.messageList(messageList)
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.build();
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sparkGptClient.streamChatCompletion(requestParam, listener);
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return sseEmitter;
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}
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}
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