perf: generate queue

src/api/plugins/kb.ts

@@ -2,7 +2,7 @@ import { GET, POST, PUT, DELETE } from '../request';
 import type { KbItemType } from '@/types/plugin';
 import { RequestPaging } from '@/types/index';
 import { TrainingTypeEnum } from '@/constants/plugin';
-import { KbDataItemType } from '@/types/plugin';
+import { Props as PushDataProps } from '@/pages/api/openapi/kb/pushData';
 
 export type KbUpdateParams = { id: string; name: string; tags: string; avatar: string };
 
@@ -46,10 +46,7 @@ export const getKbDataItemById = (dataId: string) =>
 /**
  * 直接push数据
  */
-export const postKbDataFromList = (data: {
-  kbId: string;
-  data: { a: KbDataItemType['a']; q: KbDataItemType['q'] }[];
-}) => POST(`/openapi/kb/pushData`, data);
+export const postKbDataFromList = (data: PushDataProps) => POST(`/openapi/kb/pushData`, data);
 
 /**
  * 更新一条数据
@@ -70,4 +67,4 @@ export const postSplitData = (data: {
   chunks: string[];
   prompt: string;
   mode: `${TrainingTypeEnum}`;
-}) => POST(`/openapi/text/splitText`, data);
+}) => POST(`/openapi/text/pushData`, data);

src/constants/plugin.ts

@@ -1,4 +1,8 @@
 export enum TrainingTypeEnum {
   'qa' = 'qa',
-  'subsection' = 'subsection'
+  'index' = 'index'
 }
+export const TrainingTypeMap = {
+  [TrainingTypeEnum.qa]: 'qa',
+  [TrainingTypeEnum.index]: 'index'
+};

src/pages/api/admin/countTraining.ts

@@ -0,0 +1,37 @@
+// Next.js API route support: https://nextjs.org/docs/api-routes/introduction
+import type { NextApiRequest, NextApiResponse } from 'next';
+import { jsonRes } from '@/service/response';
+import { authUser } from '@/service/utils/auth';
+import { connectToDatabase, TrainingData } from '@/service/mongo';
+import { TrainingTypeEnum } from '@/constants/plugin';
+
+export default async function handler(req: NextApiRequest, res: NextApiResponse) {
+  try {
+    await authUser({ req, authRoot: true });
+
+    await connectToDatabase();
+
+    // split queue data
+    const result = await TrainingData.aggregate([
+      {
+        $group: {
+          _id: '$mode',
+          count: { $sum: 1 }
+        }
+      }
+    ]);
+
+    jsonRes(res, {
+      data: {
+        qaListLen: result.find((item) => item._id === TrainingTypeEnum.qa)?.count || 0,
+        vectorListLen: result.find((item) => item._id === TrainingTypeEnum.index)?.count || 0
+      }
+    });
+  } catch (error) {
+    console.log(error);
+    jsonRes(res, {
+      code: 500,
+      error
+    });
+  }
+}

src/pages/api/openapi/kb/pushData.ts

@@ -3,19 +3,21 @@ import type { KbDataItemType } from '@/types/plugin';
 import { jsonRes } from '@/service/response';
 import { connectToDatabase, TrainingData } from '@/service/mongo';
 import { authUser } from '@/service/utils/auth';
-import { generateVector } from '@/service/events/generateVector';
-import { PgClient } from '@/service/pg';
 import { authKb } from '@/service/utils/auth';
 import { withNextCors } from '@/service/utils/tools';
+import { TrainingTypeEnum } from '@/constants/plugin';
+import { startQueue } from '@/service/utils/tools';
 
-interface Props {
+export type Props = {
   kbId: string;
   data: { a: KbDataItemType['a']; q: KbDataItemType['q'] }[];
-}
+  mode: `${TrainingTypeEnum}`;
+  prompt?: string;
+};
 
 export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
   try {
-    const { kbId, data } = req.body as Props;
+    const { kbId, data, mode, prompt } = req.body as Props;
 
     if (!kbId || !Array.isArray(data)) {
       throw new Error('缺少参数');
@@ -29,7 +31,9 @@ export default withNextCors(async function handler(req: NextApiRequest, res: Nex
       data: await pushDataToKb({
         kbId,
         data,
-        userId
+        userId,
+        mode,
+        prompt
       })
     });
   } catch (err) {
@@ -40,36 +44,43 @@ export default withNextCors(async function handler(req: NextApiRequest, res: Nex
   }
 });
 
-export async function pushDataToKb({ userId, kbId, data }: { userId: string } & Props) {
+export async function pushDataToKb({
+  userId,
+  kbId,
+  data,
+  mode,
+  prompt
+}: { userId: string } & Props) {
   await authKb({
     userId,
     kbId
   });
 
   if (data.length === 0) {
-    return {
-      trainingId: ''
-    };
+    return {};
   }
 
   // 插入记录
-  const { _id } = await TrainingData.create({
-    userId,
-    kbId,
-    vectorList: data
-  });
+  await TrainingData.insertMany(
+    data.map((item) => ({
+      q: item.q,
+      a: item.a,
+      userId,
+      kbId,
+      mode,
+      prompt
+    }))
+  );
 
-  generateVector(_id);
+  startQueue();
 
-  return {
-    trainingId: _id
-  };
+  return {};
 }
 
 export const config = {
   api: {
     bodyParser: {
-      sizeLimit: '100mb'
+      sizeLimit: '20mb'
     }
   }
 };

src/pages/api/openapi/kb/updateData.ts

@@ -33,7 +33,15 @@ export default withNextCors(async function handler(req: NextApiRequest, res: Nex
     // 更新 pg 内容.仅修改a，不需要更新向量。
     await PgClient.update('modelData', {
       where: [['id', dataId], 'AND', ['user_id', userId]],
-      values: [{ key: 'a', value: a }, ...(q ? [{ key: 'q', value: `${vector[0]}` }] : [])]
+      values: [
+        { key: 'a', value: a },
+        ...(q
+          ? [
+              { key: 'q', value: q },
+              { key: 'vector', value: `[${vector[0]}]` }
+            ]
+          : [])
+      ]
     });
 
     jsonRes(res);

src/pages/api/openapi/text/splitText.ts

@@ -1,69 +0,0 @@
-import type { NextApiRequest, NextApiResponse } from 'next';
-import { jsonRes } from '@/service/response';
-import { connectToDatabase, TrainingData } from '@/service/mongo';
-import { authKb, authUser } from '@/service/utils/auth';
-import { generateQA } from '@/service/events/generateQA';
-import { TrainingTypeEnum } from '@/constants/plugin';
-import { withNextCors } from '@/service/utils/tools';
-import { pushDataToKb } from '../kb/pushData';
-
-/* split text */
-export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { chunks, kbId, prompt, mode } = req.body as {
-      kbId: string;
-      chunks: string[];
-      prompt: string;
-      mode: `${TrainingTypeEnum}`;
-    };
-    if (!chunks || !kbId || !prompt) {
-      throw new Error('参数错误');
-    }
-    await connectToDatabase();
-
-    const { userId } = await authUser({ req });
-
-    // 验证是否是该用户的 model
-    await authKb({
-      kbId,
-      userId
-    });
-
-    if (mode === TrainingTypeEnum.qa) {
-      // 批量QA拆分插入数据
-      const { _id } = await TrainingData.create({
-        userId,
-        kbId,
-        qaList: chunks,
-        prompt
-      });
-      generateQA(_id);
-    } else if (mode === TrainingTypeEnum.subsection) {
-      // 分段导入，直接插入向量队列
-      const response = await pushDataToKb({
-        kbId,
-        data: chunks.map((item) => ({ q: item, a: '' })),
-        userId
-      });
-
-      return jsonRes(res, {
-        data: response
-      });
-    }
-
-    jsonRes(res);
-  } catch (err) {
-    jsonRes(res, {
-      code: 500,
-      error: err
-    });
-  }
-});
-
-export const config = {
-  api: {
-    bodyParser: {
-      sizeLimit: '100mb'
-    }
-  }
-};

src/pages/api/plugins/kb/data/getTrainingData.ts

@@ -2,9 +2,10 @@ import type { NextApiRequest, NextApiResponse } from 'next';
 import { jsonRes } from '@/service/response';
 import { connectToDatabase, TrainingData } from '@/service/mongo';
 import { authUser } from '@/service/utils/auth';
-import { Types } from 'mongoose';
 import { generateQA } from '@/service/events/generateQA';
 import { generateVector } from '@/service/events/generateVector';
+import { TrainingTypeEnum } from '@/constants/plugin';
+import { Types } from 'mongoose';
 
 /* 拆分数据成QA */
 export default async function handler(req: NextApiRequest, res: NextApiResponse) {
@@ -19,26 +20,24 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
 
     // split queue data
     const result = await TrainingData.aggregate([
-      { $match: { userId: new Types.ObjectId(userId), kbId: new Types.ObjectId(kbId) } },
       {
-        $project: {
-          qaListLength: { $size: { $ifNull: ['$qaList', []] } },
-          vectorListLength: { $size: { $ifNull: ['$vectorList', []] } }
+        $match: {
+          userId: new Types.ObjectId(userId),
+          kbId: new Types.ObjectId(kbId)
         }
       },
       {
         $group: {
-          _id: null,
-          totalQaListLength: { $sum: '$qaListLength' },
-          totalVectorListLength: { $sum: '$vectorListLength' }
+          _id: '$mode',
+          count: { $sum: 1 }
         }
       }
     ]);
 
     jsonRes(res, {
       data: {
-        qaListLen: result[0]?.totalQaListLength || 0,
-        vectorListLen: result[0]?.totalVectorListLength || 0
+        qaListLen: result.find((item) => item._id === TrainingTypeEnum.qa)?.count || 0,
+        vectorListLen: result.find((item) => item._id === TrainingTypeEnum.index)?.count || 0
       }
     });
 
@@ -49,10 +48,10 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
           kbId
         },
         '_id'
-      );
+      ).limit(10);
       list.forEach((item) => {
-        generateQA(item._id);
-        generateVector(item._id);
+        generateQA();
+        generateVector();
       });
     }
   } catch (err) {

src/pages/kb/components/InputDataModal.tsx

@@ -13,6 +13,7 @@ import {
 import { useForm } from 'react-hook-form';
 import { postKbDataFromList, putKbDataById } from '@/api/plugins/kb';
 import { useToast } from '@/hooks/useToast';
+import { TrainingTypeEnum } from '@/constants/plugin';
 
 export type FormData = { dataId?: string; a: string; q: string };
 
@@ -59,7 +60,8 @@ const InputDataModal = ({
               a: e.a,
               q: e.q
             }
-          ]
+          ],
+          mode: TrainingTypeEnum.index
         });
 
         toast({

src/pages/kb/components/SelectCsvModal.tsx

@@ -19,6 +19,7 @@ import { postKbDataFromList } from '@/api/plugins/kb';
 import Markdown from '@/components/Markdown';
 import { useMarkdown } from '@/hooks/useMarkdown';
 import { fileDownload } from '@/utils/file';
+import { TrainingTypeEnum } from '@/constants/plugin';
 
 const csvTemplate = `question,answer\n"什么是 laf","laf 是一个云函数开发平台……"\n"什么是 sealos","Sealos 是以 kubernetes 为内核的云操作系统发行版,可以……"`;
 
@@ -72,7 +73,8 @@ const SelectJsonModal = ({
 
       const res = await postKbDataFromList({
         kbId,
-        data: fileData
+        data: fileData,
+        mode: TrainingTypeEnum.index
       });
 
       toast({

src/pages/kb/components/SelectFileModal.tsx

@@ -17,7 +17,7 @@ import { useSelectFile } from '@/hooks/useSelectFile';
 import { useConfirm } from '@/hooks/useConfirm';
 import { readTxtContent, readPdfContent, readDocContent } from '@/utils/file';
 import { useMutation } from '@tanstack/react-query';
-import { postSplitData } from '@/api/plugins/kb';
+import { postKbDataFromList } from '@/api/plugins/kb';
 import Radio from '@/components/Radio';
 import { splitText_token } from '@/utils/file';
 import { TrainingTypeEnum } from '@/constants/plugin';
@@ -32,7 +32,7 @@ const modeMap = {
     price: 4,
     isPrompt: true
   },
-  subsection: {
+  index: {
     maxLen: 800,
     slideLen: 300,
     price: 0.4,
@@ -53,7 +53,7 @@ const SelectFileModal = ({
   const { toast } = useToast();
   const [prompt, setPrompt] = useState('');
   const { File, onOpen } = useSelectFile({ fileType: fileExtension, multiple: true });
-  const [mode, setMode] = useState<`${TrainingTypeEnum}`>(TrainingTypeEnum.subsection);
+  const [mode, setMode] = useState<`${TrainingTypeEnum}`>(TrainingTypeEnum.index);
   const [fileTextArr, setFileTextArr] = useState<string[]>(['']);
   const [splitRes, setSplitRes] = useState<{ tokens: number; chunks: string[] }>({
     tokens: 0,
@@ -108,9 +108,9 @@ const SelectFileModal = ({
     mutationFn: async () => {
       if (splitRes.chunks.length === 0) return;
 
-      await postSplitData({
+      await postKbDataFromList({
         kbId,
-        chunks: splitRes.chunks,
+        data: splitRes.chunks.map((text) => ({ q: text, a: '' })),
         prompt: `下面是"${prompt || '一段长文本'}"`,
         mode
       });
@@ -195,11 +195,11 @@ const SelectFileModal = ({
             <Radio
               ml={3}
               list={[
-                { label: '直接分段', value: 'subsection' },
+                { label: '直接分段', value: 'index' },
                 { label: 'QA拆分', value: 'qa' }
               ]}
               value={mode}
-              onChange={(e) => setMode(e as 'subsection' | 'qa')}
+              onChange={(e) => setMode(e as 'index' | 'qa')}
             />
           </Flex>
           {/* 内容介绍 */}

src/service/events/generateQA.ts

@@ -7,49 +7,61 @@ import { modelServiceToolMap } from '../utils/chat';
 import { ChatRoleEnum } from '@/constants/chat';
 import { BillTypeEnum } from '@/constants/user';
 import { pushDataToKb } from '@/pages/api/openapi/kb/pushData';
+import { TrainingTypeEnum } from '@/constants/plugin';
 import { ERROR_ENUM } from '../errorCode';
 
-// 每次最多选 1 组
-const listLen = 1;
+export async function generateQA(): Promise<any> {
+  const maxProcess = Number(process.env.QA_MAX_PROCESS || 10);
+
+  if (global.qaQueueLen >= maxProcess) return;
+  global.qaQueueLen++;
+
+  let trainingId = '';
+  let userId = '';
 
-export async function generateQA(trainingId: string): Promise<any> {
   try {
     // 找出一个需要生成的 dataItem (4分钟锁)
     const data = await TrainingData.findOneAndUpdate(
       {
-        _id: trainingId,
-        lockTime: { $lte: Date.now() - 4 * 60 * 1000 }
+        mode: TrainingTypeEnum.qa,
+        lockTime: { $lte: new Date(Date.now() - 2 * 60 * 1000) }
       },
       {
         lockTime: new Date()
       }
-    );
+    ).select({
+      _id: 1,
+      userId: 1,
+      kbId: 1,
+      prompt: 1,
+      q: 1
+    });
 
-    if (!data || data.qaList.length === 0) {
-      await TrainingData.findOneAndDelete({
-        _id: trainingId,
-        qaList: [],
-        vectorList: []
-      });
+    /* 无待生成的任务 */
+    if (!data) {
+      global.qaQueueLen--;
+      !global.qaQueueLen && console.log(`没有需要【QA】的数据`);
       return;
     }
 
-    const qaList: string[] = data.qaList.slice(-listLen);
+    trainingId = data._id;
+    userId = String(data.userId);
+    const kbId = String(data.kbId);
 
     // 余额校验并获取 openapi Key
     const { userOpenAiKey, systemAuthKey } = await getApiKey({
       model: OpenAiChatEnum.GPT35,
-      userId: data.userId,
+      userId,
       type: 'training'
     });
 
-    console.log(`正在生成一组QA, 包含 ${qaList.length} 组文本。ID: ${data._id}`);
+    console.log(`正在生成一组QA。ID: ${trainingId}`);
 
     const startTime = Date.now();
 
     // 请求 chatgpt 获取回答
     const response = await Promise.all(
-      qaList.map((text) =>
+      [data.q].map((text) =>
         modelServiceToolMap[OpenAiChatEnum.GPT35]
           .chatCompletion({
             apiKey: userOpenAiKey || systemAuthKey,
@@ -100,24 +112,19 @@ A2:
 
     // 创建 向量生成 队列
     pushDataToKb({
-      kbId: data.kbId,
+      kbId,
       data: responseList,
-      userId: data.userId
+      userId,
+      mode: TrainingTypeEnum.index
     });
 
-    // 删除 QA 队列。如果小于 n 条，整个数据删掉。 如果大于 n 条，仅删数组后 n 个
-    if (data.vectorList.length <= listLen) {
-      await TrainingData.findByIdAndDelete(data._id);
-    } else {
-      await TrainingData.findByIdAndUpdate(data._id, {
-        qaList: data.qaList.slice(0, -listLen),
-        lockTime: new Date('2000/1/1')
-      });
-    }
+    // delete data from training
+    await TrainingData.findByIdAndDelete(data._id);
 
     console.log('生成QA成功，time:', `${(Date.now() - startTime) / 1000}s`);
 
-    generateQA(trainingId);
+    global.qaQueueLen--;
+    generateQA();
   } catch (err: any) {
     // log
     if (err?.response) {
@@ -130,25 +137,28 @@ A2:
     // openai 账号异常或者账号余额不足，删除任务
     if (openaiError2[err?.response?.data?.error?.type] || err === ERROR_ENUM.insufficientQuota) {
       console.log('余额不足，删除向量生成任务');
-      await TrainingData.findByIdAndDelete(trainingId);
-      return;
+      await TrainingData.deleteMany({
+        userId
+      });
+      return generateQA();
     }
 
     // unlock
+    global.qaQueueLen--;
     await TrainingData.findByIdAndUpdate(trainingId, {
       lockTime: new Date('2000/1/1')
     });
 
     // 频率限制
     if (err?.response?.statusText === 'Too Many Requests') {
-      console.log('生成向量次数限制，30s后尝试');
+      console.log('生成向量次数限制，20s后尝试');
       return setTimeout(() => {
-        generateQA(trainingId);
-      }, 30000);
+        generateQA();
+      }, 20000);
     }
 
     setTimeout(() => {
-      generateQA(trainingId);
+      generateQA();
     }, 1000);
   }
 }

src/service/events/generateVector.ts

@@ -3,104 +3,109 @@ import { insertKbItem, PgClient } from '@/service/pg';
 import { openaiEmbedding } from '@/pages/api/openapi/plugin/openaiEmbedding';
 import { TrainingData } from '../models/trainingData';
 import { ERROR_ENUM } from '../errorCode';
-
-// 每次最多选 5 组
-const listLen = 5;
+import { TrainingTypeEnum } from '@/constants/plugin';
 
 /* 索引生成队列。每导入一次，就是一个单独的线程 */
-export async function generateVector(trainingId: string): Promise<any> {
+export async function generateVector(): Promise<any> {
+  const maxProcess = Number(process.env.VECTOR_MAX_PROCESS || 10);
+
+  if (global.vectorQueueLen >= maxProcess) return;
+  global.vectorQueueLen++;
+
+  let trainingId = '';
+  let userId = '';
+
   try {
-    // 找出一个需要生成的 dataItem (2分钟锁)
     const data = await TrainingData.findOneAndUpdate(
       {
-        _id: trainingId,
-        lockTime: { $lte: Date.now() - 2 * 60 * 1000 }
+        mode: TrainingTypeEnum.index,
+        lockTime: { $lte: new Date(Date.now() - 2 * 60 * 1000) }
       },
       {
         lockTime: new Date()
       }
-    );
+    ).select({
+      _id: 1,
+      userId: 1,
+      kbId: 1,
+      q: 1,
+      a: 1
+    });
 
+    /* 无待生成的任务 */
     if (!data) {
-      await TrainingData.findOneAndDelete({
-        _id: trainingId,
-        qaList: [],
-        vectorList: []
-      });
+      global.vectorQueueLen--;
+      !global.vectorQueueLen && console.log(`没有需要【索引】的数据`);
       return;
     }
 
-    const userId = String(data.userId);
+    trainingId = data._id;
+    userId = String(data.userId);
     const kbId = String(data.kbId);
 
-    const dataItems: { q: string; a: string }[] = data.vectorList.slice(-listLen).map((item) => ({
-      q: item.q,
-      a: item.a
-    }));
+    const dataItems = [
+      {
+        q: data.q,
+        a: data.a
+      }
+    ];
 
     // 过滤重复的 qa 内容
-    const searchRes = await Promise.allSettled(
-      dataItems.map(async ({ q, a = '' }) => {
-        if (!q) {
-          return Promise.reject('q为空');
-        }
+    // const searchRes = await Promise.allSettled(
+    //   dataItems.map(async ({ q, a = '' }) => {
+    //     if (!q) {
+    //       return Promise.reject('q为空');
+    //     }
 
-        q = q.replace(/\\n/g, '\n');
-        a = a.replace(/\\n/g, '\n');
+    //     q = q.replace(/\\n/g, '\n');
+    //     a = a.replace(/\\n/g, '\n');
 
-        // Exactly the same data, not push
-        try {
-          const count = await PgClient.count('modelData', {
-            where: [['user_id', userId], 'AND', ['kb_id', kbId], 'AND', ['q', q], 'AND', ['a', a]]
-          });
-          if (count > 0) {
-            return Promise.reject('已经存在');
-          }
-        } catch (error) {
-          error;
-        }
-        return Promise.resolve({
-          q,
-          a
-        });
-      })
-    );
-    const filterData = searchRes
-      .filter((item) => item.status === 'fulfilled')
-      .map<{ q: string; a: string }>((item: any) => item.value);
+    //     // Exactly the same data, not push
+    //     try {
+    //       const count = await PgClient.count('modelData', {
+    //         where: [['user_id', userId], 'AND', ['kb_id', kbId], 'AND', ['q', q], 'AND', ['a', a]]
+    //       });
 
-    if (filterData.length > 0) {
-      // 生成词向量
-      const vectors = await openaiEmbedding({
-        input: filterData.map((item) => item.q),
-        userId,
-        type: 'training'
-      });
+    //       if (count > 0) {
+    //         return Promise.reject('已经存在');
+    //       }
+    //     } catch (error) {
+    //       error;
+    //     }
+    //     return Promise.resolve({
+    //       q,
+    //       a
+    //     });
+    //   })
+    // );
+    // const filterData = searchRes
+    //   .filter((item) => item.status === 'fulfilled')
+    //   .map<{ q: string; a: string }>((item: any) => item.value);
 
-      // 生成结果插入到 pg
-      await insertKbItem({
-        userId,
-        kbId,
-        data: vectors.map((vector, i) => ({
-          q: filterData[i].q,
-          a: filterData[i].a,
-          vector
-        }))
-      });
-    }
+    // 生成词向量
+    const vectors = await openaiEmbedding({
+      input: dataItems.map((item) => item.q),
+      userId,
+      type: 'training'
+    });
 
-    // 删除 mongo 训练队列.  如果小于 n 条，整个数据删掉。 如果大于 n 条，仅删数组后 n 个
-    if (data.vectorList.length <= listLen) {
-      await TrainingData.findByIdAndDelete(trainingId);
-      console.log(`全部向量生成完毕: ${trainingId}`);
-    } else {
-      await TrainingData.findByIdAndUpdate(trainingId, {
-        vectorList: data.vectorList.slice(0, -listLen),
-        lockTime: new Date('2000/1/1')
-      });
-      console.log(`生成向量成功: ${trainingId}`);
-      generateVector(trainingId);
-    }
+    // 生成结果插入到 pg
+    await insertKbItem({
+      userId,
+      kbId,
+      data: vectors.map((vector, i) => ({
+        q: dataItems[i].q,
+        a: dataItems[i].a,
+        vector
+      }))
+    });
+
+    // delete data from training
+    await TrainingData.findByIdAndDelete(data._id);
+    console.log(`生成向量成功: ${data._id}`);
+
+    global.vectorQueueLen--;
+    generateVector();
   } catch (err: any) {
     // log
     if (err?.response) {
@@ -113,25 +118,28 @@ export async function generateVector(trainingId: string): Promise<any> {
     // openai 账号异常或者账号余额不足，删除任务
     if (openaiError2[err?.response?.data?.error?.type] || err === ERROR_ENUM.insufficientQuota) {
       console.log('余额不足，删除向量生成任务');
-      await TrainingData.findByIdAndDelete(trainingId);
-      return;
+      await TrainingData.deleteMany({
+        userId
+      });
+      return generateVector();
     }
 
     // unlock
+    global.vectorQueueLen--;
     await TrainingData.findByIdAndUpdate(trainingId, {
       lockTime: new Date('2000/1/1')
     });
 
     // 频率限制
     if (err?.response?.statusText === 'Too Many Requests') {
-      console.log('生成向量次数限制，30s后尝试');
+      console.log('生成向量次数限制，20s后尝试');
       return setTimeout(() => {
-        generateVector(trainingId);
-      }, 30000);
+        generateVector();
+      }, 20000);
     }
 
     setTimeout(() => {
-      generateVector(trainingId);
+      generateVector();
     }, 1000);
   }
 }

src/service/models/trainingData.ts

@@ -1,9 +1,9 @@
 /* 模型的知识库 */
 import { Schema, model, models, Model as MongoModel } from 'mongoose';
 import { TrainingDataSchema as TrainingDateType } from '@/types/mongoSchema';
+import { TrainingTypeMap } from '@/constants/plugin';
 
 // pgList and vectorList, Only one of them will work
-
 const TrainingDataSchema = new Schema({
   userId: {
     type: Schema.Types.ObjectId,
@@ -19,18 +19,27 @@ const TrainingDataSchema = new Schema({
     type: Date,
     default: () => new Date('2000/1/1')
   },
-  vectorList: {
-    type: [{ q: String, a: String }],
-    default: []
+  mode: {
+    type: String,
+    enum: Object.keys(TrainingTypeMap),
+    required: true
   },
   prompt: {
     // 拆分时的提示词
     type: String,
     default: ''
   },
-  qaList: {
-    type: [String],
-    default: []
+  q: {
+    // 如果是
+    type: String,
+    default: ''
+  },
+  a: {
+    type: String,
+    default: ''
+  },
+  vectorList: {
+    type: Object
   }
 });
 

src/service/mongo.ts

@@ -1,8 +1,7 @@
 import mongoose from 'mongoose';
-import { generateQA } from './events/generateQA';
-import { generateVector } from './events/generateVector';
 import tunnel from 'tunnel';
 import { TrainingData } from './mongo';
+import { startQueue } from './utils/tools';
 
 /**
  * 连接 MongoDB 数据库
@@ -38,7 +37,10 @@ export async function connectToDatabase(): Promise<void> {
     });
   }
 
-  startTrain();
+  global.qaQueueLen = 0;
+  global.vectorQueueLen = 0;
+
+  startQueue();
   // 5 分钟后解锁不正常的数据，并触发开始训练
   setTimeout(async () => {
     await TrainingData.updateMany(
@@ -49,24 +51,10 @@ export async function connectToDatabase(): Promise<void> {
         lockTime: new Date('2000/1/1')
       }
     );
-    startTrain();
+    startQueue();
   }, 5 * 60 * 1000);
 }
 
-async function startTrain() {
-  const qa = await TrainingData.find({
-    qaList: { $exists: true, $ne: [] }
-  });
-
-  qa.map((item) => generateQA(String(item._id)));
-
-  const vector = await TrainingData.find({
-    vectorList: { $exists: true, $ne: [] }
-  });
-
-  vector.map((item) => generateVector(String(item._id)));
-}
-
 export * from './models/authCode';
 export * from './models/chat';
 export * from './models/model';

src/service/pg.ts

@@ -14,8 +14,8 @@ export const connectPg = async () => {
     password: process.env.PG_PASSWORD,
     database: process.env.PG_DB_NAME,
     max: 20,
-    idleTimeoutMillis: 30000,
-    connectionTimeoutMillis: 2000
+    idleTimeoutMillis: 60000,
+    connectionTimeoutMillis: 20000
   });
 
   global.pgClient.on('error', (err) => {

src/service/response.ts

@@ -45,7 +45,7 @@ export const jsonRes = <T = any>(
     } else if (openaiError[error?.response?.statusText]) {
       msg = openaiError[error.response.statusText];
     }
-    console.log(error);
+    console.log(error?.message || error);
   }
 
   res.json({

src/service/utils/tools.ts

@@ -2,6 +2,8 @@ import type { NextApiResponse, NextApiHandler, NextApiRequest } from 'next';
 import NextCors from 'nextjs-cors';
 import crypto from 'crypto';
 import jwt from 'jsonwebtoken';
+import { generateQA } from '../events/generateQA';
+import { generateVector } from '../events/generateVector';
 
 /* 密码加密 */
 export const hashPassword = (psw: string) => {
@@ -45,7 +47,7 @@ export function withNextCors(handler: NextApiHandler): NextApiHandler {
     req: NextApiRequest,
     res: NextApiResponse
   ) {
-    const methods = ['GET', 'HEAD', 'PUT', 'PATCH', 'POST', 'DELETE'];
+    const methods = ['GET', 'eHEAD', 'PUT', 'PATCH', 'POST', 'DELETE'];
     const origin = req.headers.origin;
     await NextCors(req, res, {
       methods,
@@ -56,3 +58,15 @@ export function withNextCors(handler: NextApiHandler): NextApiHandler {
     return handler(req, res);
   };
 }
+
+export const startQueue = () => {
+  const qaMax = Number(process.env.QA_MAX_PROCESS || 10);
+  const vectorMax = Number(process.env.VECTOR_MAX_PROCESS || 10);
+
+  for (let i = 0; i < qaMax; i++) {
+    generateQA();
+  }
+  for (let i = 0; i < vectorMax; i++) {
+    generateVector();
+  }
+};

src/types/index.d.ts

@@ -9,6 +9,8 @@ declare global {
   var particlesJS: any;
   var grecaptcha: any;
   var QRCode: any;
+  var qaQueueLen: number;
+  var vectorQueueLen: number;
 
   interface Window {
     ['pdfjs-dist/build/pdf']: any;

src/types/mongoSchema.d.ts

@@ -74,9 +74,10 @@ export interface TrainingDataSchema {
   userId: string;
   kbId: string;
   lockTime: Date;
-  vectorList: { q: string; a: string }[];
+  mode: `${TrainingTypeEnum}`;
   prompt: string;
-  qaList: string[];
+  q: string;
+  a: string;
 }
 
 export interface ChatSchema {