feat: lafgpt请求;fix: 修复发送按键

src/pages/api/chat/lafGpt.ts

+import type { NextApiRequest, NextApiResponse } from 'next';
+import { createParser, ParsedEvent, ReconnectInterval } from 'eventsource-parser';
+import { connectToDatabase } from '@/service/mongo';
+import { getOpenAIApi, authChat } from '@/service/utils/chat';
+import { httpsAgent, openaiChatFilter, systemPromptFilter } from '@/service/utils/tools';
+import { ChatCompletionRequestMessage, ChatCompletionRequestMessageRoleEnum } from 'openai';
+import { ChatItemType } from '@/types/chat';
+import { jsonRes } from '@/service/response';
+import type { ModelSchema } from '@/types/mongoSchema';
+import { PassThrough } from 'stream';
+import { modelList } from '@/constants/model';
+import { pushChatBill } from '@/service/events/pushBill';
+import { connectRedis } from '@/service/redis';
+import { VecModelDataPrefix } from '@/constants/redis';
+import { vectorToBuffer } from '@/utils/tools';
+import { openaiCreateEmbedding } from '@/service/utils/openai';
+
+/* 发送提示词 */
+export default async function handler(req: NextApiRequest, res: NextApiResponse) {
+  let step = 0; // step=1时，表示开始了流响应
+  const stream = new PassThrough();
+  stream.on('error', () => {
+    console.log('error: ', 'stream error');
+    stream.destroy();
+  });
+  res.on('close', () => {
+    stream.destroy();
+  });
+  res.on('error', () => {
+    console.log('error: ', 'request error');
+    stream.destroy();
+  });
+
+  try {
+    const { chatId, prompt } = req.body as {
+      prompt: ChatItemType;
+      chatId: string;
+    };
+
+    const { authorization } = req.headers;
+    if (!chatId || !prompt) {
+      throw new Error('缺少参数');
+    }
+
+    await connectToDatabase();
+    const redis = await connectRedis();
+    let startTime = Date.now();
+
+    const { chat, userApiKey, systemKey, userId } = await authChat(chatId, authorization);
+
+    const model: ModelSchema = chat.modelId;
+    const modelConstantsData = modelList.find((item) => item.model === model.service.modelName);
+    if (!modelConstantsData) {
+      throw new Error('模型加载异常');
+    }
+    // 获取 chatAPI
+    const chatAPI = getOpenAIApi(userApiKey || systemKey);
+
+    // 请求一次 chatgpt 拆解需求
+    const promptResponse = await chatAPI.createChatCompletion(
+      {
+        model: model.service.chatModel,
+        temperature: 0,
+        // max_tokens: modelConstantsData.maxToken,
+        messages: [
+          {
+            role: 'system',
+            content: `服务端逻辑生成器。根据用户输入的需求，拆解成代码实现的步骤，并按下面格式返回：
+          1. 
+          2. 
+          3. 
+          ....
+          
+          下面是一些例子：
+          实现一个手机号注册账号的方法
+          发送手机验证码函数： 
+          1. 从 query 中获取 phone 
+          2. 校验手机号格式是否正确，不正确返回{error: "手机号格式错误"} 
+          3. 给 phone 发送一个短信验证码，验证码长度为6位字符串，内容为：你正在注册laf, 验证码为：code 
+          4. 数据库添加数据，表为"codes"，内容为 {phone, code} 
+          注册函数 
+          1. 从 body 中获取 phone 和 code 
+          2. 校验手机号格式是否正确，不正确返回{error: "手机号格式错误"} 
+          2. 获取数据库数据，表为"codes"，查找是否有符合 phone, code 等于body参数的记录，没有的话返回 {error:"验证码不正确"} 
+          4. 添加数据库数据，表为"users" ，内容为{phone, code, createTime}
+          5. 删除数据库数据，删除 code 记录
+          ---------------
+          更新播客记录。传入blogId，blogText，tags，还需要记录更新的时间
+          1. 从 body 中获取 blogId，blogText 和 tags
+          2. 校验 blogId 是否为空，为空则返回 {error: "博客ID不能为空"}
+          3. 校验 blogText 是否为空，为空则返回 {error: "博客内容不能为空"}
+          4. 校验 tags 是否为数组，不是则返回 {error: "标签必须为数组"}
+          5. 获取当前时间，记录为 updateTime
+          6. 更新数据库数据，表为"blogs"，更新符合 blogId 的记录的内容为{blogText, tags, updateTime}
+          7. 返回结果 {message: "更新博客记录成功"}`
+          },
+          {
+            role: 'user',
+            content: prompt.value
+          }
+        ]
+      },
+      {
+        timeout: 40000,
+        httpsAgent
+      }
+    );
+
+    const promptResolve = promptResponse.data.choices?.[0]?.message?.content || '';
+    if (!promptResolve) {
+      throw new Error('gpt 异常');
+    }
+
+    prompt.value += `\n${promptResolve}`;
+    console.log('prompt resolve success, time:', `${(Date.now() - startTime) / 1000}s`);
+
+    // 获取提示词的向量
+    const { vector: promptVector } = await openaiCreateEmbedding({
+      isPay: !userApiKey,
+      apiKey: userApiKey || systemKey,
+      userId,
+      text: prompt.value
+    });
+
+    // 读取对话内容
+    const prompts = [...chat.content, prompt];
+
+    // 搜索系统提示词, 按相似度从 redis 中搜出相关的 q 和 text
+    const redisData: any[] = await redis.sendCommand([
+      'FT.SEARCH',
+      `idx:${VecModelDataPrefix}:hash`,
+      `@modelId:{${String(
+        chat.modelId._id
+      )}} @vector:[VECTOR_RANGE 0.25 $blob]=>{$YIELD_DISTANCE_AS: score}`,
+      // `@modelId:{${String(chat.modelId._id)}}=>[KNN 10 @vector $blob AS score]`,
+      'RETURN',
+      '1',
+      'text',
+      'SORTBY',
+      'score',
+      'PARAMS',
+      '2',
+      'blob',
+      vectorToBuffer(promptVector),
+      'LIMIT',
+      '0',
+      '20',
+      'DIALECT',
+      '2'
+    ]);
+
+    // 格式化响应值，获取 qa
+    const formatRedisPrompt = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
+      .map((i) => {
+        if (!redisData[i]) return '';
+        const text = (redisData[i][1] as string) || '';
+
+        if (!text) return '';
+
+        return text;
+      })
+      .filter((item) => item);
+
+    if (formatRedisPrompt.length === 0) {
+      throw new Error('对不起，我没有找到你的问题');
+    }
+
+    // textArr 筛选，最多 3000 tokens
+    const systemPrompt = systemPromptFilter(formatRedisPrompt, 3400);
+
+    prompts.unshift({
+      obj: 'SYSTEM',
+      value: `${model.systemPrompt} 知识库内容是最新的，知识库内容为: "${systemPrompt}"`
+    });
+
+    // 控制在 tokens 数量，防止超出
+    const filterPrompts = openaiChatFilter(prompts, modelConstantsData.contextMaxToken);
+
+    // 格式化文本内容成 chatgpt 格式
+    const map = {
+      Human: ChatCompletionRequestMessageRoleEnum.User,
+      AI: ChatCompletionRequestMessageRoleEnum.Assistant,
+      SYSTEM: ChatCompletionRequestMessageRoleEnum.System
+    };
+    const formatPrompts: ChatCompletionRequestMessage[] = filterPrompts.map(
+      (item: ChatItemType) => ({
+        role: map[item.obj],
+        content: item.value
+      })
+    );
+    console.log(formatPrompts);
+    // 计算温度
+    const temperature = modelConstantsData.maxTemperature * (model.temperature / 10);
+
+    // 发出请求
+    const chatResponse = await chatAPI.createChatCompletion(
+      {
+        model: model.service.chatModel,
+        temperature: temperature,
+        // max_tokens: modelConstantsData.maxToken,
+        messages: formatPrompts,
+        frequency_penalty: 0.5, // 越大，重复内容越少
+        presence_penalty: -0.5, // 越大，越容易出现新内容
+        stream: true
+      },
+      {
+        timeout: 40000,
+        responseType: 'stream',
+        httpsAgent
+      }
+    );
+
+    console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
+
+    // 创建响应流
+    res.setHeader('Content-Type', 'text/event-stream;charset-utf-8');
+    res.setHeader('Access-Control-Allow-Origin', '*');
+    res.setHeader('X-Accel-Buffering', 'no');
+    res.setHeader('Cache-Control', 'no-cache, no-transform');
+    step = 1;
+
+    let responseContent = '';
+    stream.pipe(res);
+
+    const onParse = async (event: ParsedEvent | ReconnectInterval) => {
+      if (event.type !== 'event') return;
+      const data = event.data;
+      if (data === '[DONE]') return;
+      try {
+        const json = JSON.parse(data);
+        const content: string = json?.choices?.[0].delta.content || '';
+        if (!content || (responseContent === '' && content === '\n')) return;
+
+        responseContent += content;
+        // console.log('content:', content)
+        !stream.destroyed && stream.push(content.replace(/\n/g, '<br/>'));
+      } catch (error) {
+        error;
+      }
+    };
+
+    const decoder = new TextDecoder();
+    try {
+      for await (const chunk of chatResponse.data as any) {
+        if (stream.destroyed) {
+          // 流被中断了，直接忽略后面的内容
+          break;
+        }
+        const parser = createParser(onParse);
+        parser.feed(decoder.decode(chunk));
+      }
+    } catch (error) {
+      console.log('pipe error', error);
+    }
+    // close stream
+    !stream.destroyed && stream.push(null);
+    stream.destroy();
+
+    const promptsContent = formatPrompts.map((item) => item.content).join('');
+    // 只有使用平台的 key 才计费
+    pushChatBill({
+      isPay: !userApiKey,
+      modelName: model.service.modelName,
+      userId,
+      chatId,
+      text: promptsContent + responseContent
+    });
+  } catch (err: any) {
+    if (step === 1) {
+      // 直接结束流
+      console.log('error，结束');
+      stream.destroy();
+    } else {
+      res.status(500);
+      jsonRes(res, {
+        code: 500,
+        error: err
+      });
+    }
+  }
+}

src/pages/api/chat/vectorGpt.ts

@@ -57,7 +57,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     // 读取对话内容
     const prompts = [...chat.content, prompt];
 
-    // 获取 chatAPI
+    // 获取提示词的向量
     const { vector: promptVector, chatAPI } = await openaiCreateEmbedding({
       isPay: !userApiKey,
       apiKey: userApiKey || systemKey,
@@ -71,7 +71,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
       `idx:${VecModelDataPrefix}:hash`,
       `@modelId:{${String(
         chat.modelId._id
-      )}} @vector:[VECTOR_RANGE 0.2 $blob]=>{$YIELD_DISTANCE_AS: score}`,
+      )}} @vector:[VECTOR_RANGE 0.25 $blob]=>{$YIELD_DISTANCE_AS: score}`,
       // `@modelId:{${String(chat.modelId._id)}}=>[KNN 10 @vector $blob AS score]`,
       'RETURN',
       '1',

src/pages/chat/index.tsx

@@ -120,6 +120,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
       const urlMap: Record<string, string> = {
         [ChatModelNameEnum.GPT35]: '/api/chat/chatGpt',
         [ChatModelNameEnum.VECTOR_GPT]: '/api/chat/vectorGpt',
+        // [ChatModelNameEnum.VECTOR_GPT]: '/api/chat/lafGpt',
         [ChatModelNameEnum.GPT3]: '/api/chat/gpt3'
       };
 
@@ -198,7 +199,12 @@ const Chat = ({ chatId }: { chatId: string }) => {
       .split('\n')
       .filter((val) => val)
       .join('\n');
-    if (!chatData?.modelId || !val || !ChatBox.current || isChatting) {
+
+    if (!chatData?.modelId || !val || isChatting) {
+      toast({
+        title: '内容为空',
+        status: 'warning'
+      });
       return;
     }
 
@@ -453,7 +459,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
         {/* 发送区 */}
         <Box m={media('20px auto', '0 auto')} w={'100%'} maxW={media('min(750px, 100%)', 'auto')}>
           <Flex
-            alignItems={'flex-end'}
+            alignItems={'center'}
             py={5}
             position={'relative'}
             boxShadow={`0 0 15px rgba(0,0,0,0.1)`}
@@ -501,7 +507,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
               }}
             />
             {/* 发送和等待按键 */}
-            <Box px={4} onClick={sendPrompt}>
+            <Flex px={4} h={'30px'} alignItems={'flex-end'} onClick={sendPrompt}>
               {isChatting ? (
                 <Image
                   style={{ transform: 'translateY(4px)' }}
@@ -520,7 +526,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
                   ></Icon>
                 </Box>
               )}
-            </Box>
+            </Flex>
           </Flex>
         </Box>
       </Flex>