feat: 知识库对外api

src/components/Layout/index.tsx

@@ -31,13 +31,13 @@ const navbarList = [
     icon: 'user',
     link: '/number/setting',
     activeLink: ['/number/setting']
+  },
+  {
+    label: '开发',
+    icon: 'develop',
+    link: '/openapi',
+    activeLink: ['/openapi']
   }
-  // {
-  //   label: '开发',
-  //   icon: 'develop',
-  //   link: '/openapi',
-  //   activeLink: ['/openapi']
-  // }
 ];
 
 const Layout = ({ children }: { children: JSX.Element }) => {

src/pages/api/chat/vectorGpt.ts

@@ -82,14 +82,14 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
       vectorToBuffer(promptVector),
       'LIMIT',
       '0',
-      '20',
+      '30',
       'DIALECT',
       '2'
     ]);
 
     const formatRedisPrompt: string[] = [];
     // 格式化响应值，获取 qa
-    for (let i = 2; i < 42; i += 2) {
+    for (let i = 2; i < 61; i += 2) {
       const text = redisData[i]?.[1];
       if (text) {
         formatRedisPrompt.push(text);

src/pages/api/openapi/chat/lafGpt.ts

@@ -126,7 +126,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     // 获取提示词的向量
     const { vector: promptVector } = await openaiCreateEmbedding({
       isPay: true,
-      apiKey: apiKey,
+      apiKey,
       userId,
       text: prompt.value
     });

src/pages/api/openapi/chat/vectorGpt.ts

@@ -0,0 +1,210 @@
+import type { NextApiRequest, NextApiResponse } from 'next';
+import { connectToDatabase, Model } from '@/service/mongo';
+import {
+  httpsAgent,
+  openaiChatFilter,
+  systemPromptFilter,
+  authOpenApiKey
+} from '@/service/utils/tools';
+import { ChatCompletionRequestMessage, ChatCompletionRequestMessageRoleEnum } from 'openai';
+import { ChatItemType } from '@/types/chat';
+import { jsonRes } from '@/service/response';
+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, gpt35StreamResponse } 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 {
+      prompts,
+      modelId,
+      isStream = true
+    } = req.body as {
+      prompts: ChatItemType[];
+      modelId: string;
+      isStream: boolean;
+    };
+
+    if (!prompts || !modelId) {
+      throw new Error('缺少参数');
+    }
+    if (!Array.isArray(prompts)) {
+      throw new Error('prompts is not array');
+    }
+    if (prompts.length > 30 || prompts.length === 0) {
+      throw new Error('prompts length range 1-30');
+    }
+
+    await connectToDatabase();
+    const redis = await connectRedis();
+    let startTime = Date.now();
+
+    /* 凭证校验 */
+    const { apiKey, userId } = await authOpenApiKey(req);
+
+    const model = await Model.findOne({
+      _id: modelId,
+      userId
+    });
+
+    if (!model) {
+      throw new Error('无权使用该模型');
+    }
+
+    const modelConstantsData = modelList.find((item) => item.model === model?.service?.modelName);
+    if (!modelConstantsData) {
+      throw new Error('模型初始化异常');
+    }
+
+    // 获取提示词的向量
+    const { vector: promptVector, chatAPI } = await openaiCreateEmbedding({
+      isPay: true,
+      apiKey,
+      userId,
+      text: prompts[prompts.length - 1].value // 取最后一个
+    });
+
+    // 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:${VecModelDataPrefix}:hash`,
+      `@modelId:{${modelId}} @vector:[VECTOR_RANGE 0.24 $blob]=>{$YIELD_DISTANCE_AS: score}`,
+      'RETURN',
+      '1',
+      'text',
+      'SORTBY',
+      'score',
+      'PARAMS',
+      '2',
+      'blob',
+      vectorToBuffer(promptVector),
+      'LIMIT',
+      '0',
+      '30',
+      'DIALECT',
+      '2'
+    ]);
+
+    const formatRedisPrompt: string[] = [];
+
+    // 格式化响应值，获取 qa
+    for (let i = 2; i < 61; i += 2) {
+      const text = redisData[i]?.[1];
+      if (text) {
+        formatRedisPrompt.push(text);
+      }
+    }
+
+    if (formatRedisPrompt.length === 0) {
+      throw new Error('对不起，我没有找到你的问题');
+    }
+
+    // system 合并
+    if (prompts[0].obj === 'SYSTEM') {
+      formatRedisPrompt.unshift(prompts.shift()?.value || '');
+    }
+
+    // textArr 筛选，最多 2800 tokens
+    const systemPrompt = systemPromptFilter(formatRedisPrompt, 2800);
+
+    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,
+        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: isStream
+      },
+      {
+        timeout: 120000,
+        responseType: isStream ? 'stream' : 'json',
+        httpsAgent
+      }
+    );
+
+    console.log('api response time:', `${(Date.now() - startTime) / 1000}s`);
+
+    step = 1;
+    let responseContent = '';
+
+    if (isStream) {
+      const streamResponse = await gpt35StreamResponse({
+        res,
+        stream,
+        chatResponse
+      });
+      responseContent = streamResponse.responseContent;
+    } else {
+      responseContent = chatResponse.data.choices?.[0]?.message?.content || '';
+      jsonRes(res, {
+        data: responseContent
+      });
+    }
+
+    const promptsContent = formatPrompts.map((item) => item.content).join('');
+    pushChatBill({
+      isPay: true,
+      modelName: model.service.modelName,
+      userId,
+      text: promptsContent + responseContent
+    });
+    // jsonRes(res);
+  } 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/chat/index.tsx

@@ -300,10 +300,9 @@ const Chat = ({ chatId }: { chatId: string }) => {
 
   // 复制内容
   const onclickCopy = useCallback(
-    (chatId: string) => {
+    (value: string) => {
-      const dom = document.getElementById(chatId);
+      const val = value.replace(/\n+/g, '\n');
-      const innerText = dom?.innerText;
+      copyData(val);
-      innerText && copyData(innerText);
     },
     [copyData]
   );
@@ -434,7 +433,7 @@ const Chat = ({ chatId }: { chatId: string }) => {
                     />
                   </MenuButton>
                   <MenuList fontSize={'sm'}>
-                    <MenuItem onClick={() => onclickCopy(`chat${index}`)}>复制</MenuItem>
+                    <MenuItem onClick={() => onclickCopy(item.value)}>复制</MenuItem>
                     <MenuItem onClick={() => delChatRecord(index)}>删除该行</MenuItem>
                   </MenuList>
                 </Menu>

src/service/errorCode.ts

@@ -7,7 +7,8 @@ export const openaiError: Record<string, string> = {
   'Bad Gateway': '网关异常，请重试'
 };
 export const openaiError2: Record<string, string> = {
-  insufficient_quota: 'API 余额不足'
+  insufficient_quota: 'API 余额不足',
+  invalid_request_error: '输入参数异常'
 };
 export const proxyError: Record<string, boolean> = {
   ECONNABORTED: true,

src/service/response.ts

@@ -25,8 +25,11 @@ export const jsonRes = <T = any>(
       msg = error;
     } else if (proxyError[error?.code]) {
       msg = '服务器代理出错';
-    } else if (openaiError2[error?.response?.data?.error?.type]) {
+    } else if (error?.response?.data?.error) {
-      msg = openaiError2[error?.response?.data?.error?.type];
+      msg =
+        openaiError2[error?.response?.data?.error?.type] ||
+        error?.response?.data?.error?.message ||
+        'openai 错误';
     } else if (openaiError[error?.response?.statusText]) {
       msg = openaiError[error.response.statusText];
     }