4.6.7 fix (#752)

docSite/content/docs/development/docker.md

@@ -17,6 +17,11 @@ weight: 707
 | 500w 组向量 | 8c32g | 16c64g 200GB |
 {{< /table >}}
 
+## 部署架构图
+
+![](/imgs/sealos-fastgpt.webp)
+
+
 ### 1. 准备好代理环境（国外服务器可忽略）
 
 确保可以访问 OpenAI，具体方案可以参考：[代理方案](/docs/development/proxy/)。或直接在 Sealos 上 [部署 OneAPI](/docs/development/one-api)，既解决代理问题也能实现多 Key 轮询、接入其他大模型。

docSite/content/docs/development/qa.md

@@ -19,6 +19,10 @@ images: []
 
 ## 通用问题
 
+### 能否纯本地允许
+
+可以。需要准备好向量模型和LLM模型。
+
 ### insufficient_user_quota user quota is not enough 
 
 OneAPI 账号的余额不足，默认 root 用户只有 200 刀，可以手动修改。
@@ -105,7 +109,7 @@ mongo连接失败，检查
 
 ### TypeError: Cannot read properties of null (reading 'useMemo' )
 
-用 Node18 试试，可能最新的 Node 有问题。 本地开发流程：
+删除所有的`node_modules`，用 Node18 重新 install 试试，可能最新的 Node 有问题。 本地开发流程：
 
 1. 根目录: `pnpm i`
 2. 复制 `config.json` -> `config.local.json`

packages/global/common/file/icon.ts

@@ -3,10 +3,12 @@ export const fileImgs = [
   { suffix: 'csv', src: 'file/fill/csv' },
   { suffix: '(doc|docs)', src: 'file/fill/doc' },
   { suffix: 'txt', src: 'file/fill/txt' },
-  { suffix: 'md', src: 'file/fill/markdown' }
+  { suffix: 'md', src: 'file/fill/markdown' },
+  { suffix: 'html', src: 'file/fill/html' }
+
   // { suffix: '.', src: '/imgs/files/file.svg' }
 ];
 
-export function getFileIcon(name = '', defaultImg = '/imgs/files/file.svg') {
+export function getFileIcon(name = '', defaultImg = 'file/fill/file') {
   return fileImgs.find((item) => new RegExp(item.suffix, 'gi').test(name))?.src || defaultImg;
 }

packages/global/common/string/markdown.ts

@@ -51,19 +51,18 @@ export const uploadMarkdownBase64 = async ({
     // match base64, upload and replace it
     const base64Regex = /data:image\/.*;base64,([^\)]+)/g;
     const base64Arr = rawText.match(base64Regex) || [];
-    // upload base64 and replace it
-    await Promise.all(
-      base64Arr.map(async (base64Img) => {
-        try {
-          const str = await uploadImgController(base64Img);
 
-          rawText = rawText.replace(base64Img, str);
-        } catch (error) {
-          rawText = rawText.replace(base64Img, '');
-          rawText = rawText.replace(/!\[.*\]\(\)/g, '');
-        }
-      })
-    );
+    // upload base64 and replace it
+    for await (const base64Img of base64Arr) {
+      try {
+        const str = await uploadImgController(base64Img);
+
+        rawText = rawText.replace(base64Img, str);
+      } catch (error) {
+        rawText = rawText.replace(base64Img, '');
+        rawText = rawText.replace(/!\[.*\]\(\)/g, '');
+      }
+    }
   }
 
   // Remove white space on both sides of the picture

packages/global/core/dataset/api.d.ts

@@ -48,10 +48,6 @@ export type FileCreateDatasetCollectionParams = ApiCreateDatasetCollectionParams
   name: string;
   rawTextLength: number;
   hashRawText: string;
-  trainingType: `${TrainingModeEnum}`;
-  chunkSize: number;
-  chunkSplitter: string;
-  qaPrompt: string;
 
   fileMetadata?: Record<string, any>;
   collectionMetadata?: Record<string, any>;
@@ -74,3 +70,14 @@ export type PostWebsiteSyncParams = {
   datasetId: string;
   billId: string;
 };
+
+export type PushDatasetDataProps = {
+  collectionId: string;
+  data: PushDatasetDataChunkProps[];
+  trainingMode: `${TrainingModeEnum}`;
+  prompt?: string;
+  billId?: string;
+};
+export type PushDatasetDataResponse = {
+  insertLen: number;
+};

packages/global/core/dataset/controller.d.ts

@@ -21,7 +21,7 @@ export type UpdateDatasetDataProps = {
 };
 
 export type PatchIndexesProps = {
-  type: 'create' | 'update' | 'delete';
+  type: 'create' | 'update' | 'delete' | 'unChange';
   index: Omit<DatasetDataIndexItemType, 'dataId'> & {
     dataId?: string;
   };

packages/service/common/file/image/controller.ts

@@ -46,8 +46,17 @@ export async function readMongoImg({ id }: { id: string }) {
   return data?.binary;
 }
 
-export async function delImgByRelatedId(relateIds: string[]) {
+export async function delImgByRelatedId({
+  teamId,
+  relateIds
+}: {
+  teamId: string;
+  relateIds: string[];
+}) {
+  if (relateIds.length === 0) return;
+
   return MongoImage.deleteMany({
+    teamId,
     'metadata.relatedId': { $in: relateIds.map((id) => String(id)) }
   });
 }

packages/service/common/file/image/schema.ts

@@ -34,9 +34,8 @@ const ImageSchema = new Schema({
 try {
   ImageSchema.index({ expiredTime: 1 }, { expireAfterSeconds: 60 });
   ImageSchema.index({ type: 1 });
-  ImageSchema.index({ teamId: 1 });
   ImageSchema.index({ createTime: 1 });
-  ImageSchema.index({ 'metadata.relatedId': 1 });
+  ImageSchema.index({ teamId: 1, 'metadata.relatedId': 1 });
 } catch (error) {
   console.log(error);
 }

packages/service/common/file/multer.ts

@@ -28,12 +28,16 @@ export const getUploadModel = ({ maxSize = 500 }: { maxSize?: number }) => {
         // },
         filename: async (req, file, cb) => {
           const { ext } = path.parse(decodeURIComponent(file.originalname));
-          cb(null, `${getNanoid(32)}${ext}`);
+          cb(null, `${getNanoid()}${ext}`);
         }
       })
     }).single('file');
 
-    async doUpload<T = Record<string, any>>(req: NextApiRequest, res: NextApiResponse) {
+    async doUpload<T = Record<string, any>>(
+      req: NextApiRequest,
+      res: NextApiResponse,
+      originBuckerName?: `${BucketNameEnum}`
+    ) {
       return new Promise<{
         file: FileType;
         metadata: Record<string, any>;
@@ -47,7 +51,7 @@ export const getUploadModel = ({ maxSize = 500 }: { maxSize?: number }) => {
           }
 
           // check bucket name
-          const bucketName = req.body?.bucketName as `${BucketNameEnum}`;
+          const bucketName = (req.body?.bucketName || originBuckerName) as `${BucketNameEnum}`;
           if (bucketName && !bucketNameMap[bucketName]) {
             return reject('BucketName is invalid');
           }

packages/service/common/vectorStore/pg/controller.ts

@@ -39,14 +39,15 @@ export const insertDatasetDataVector = async (
   }
 ): Promise<{ insertId: string }> => {
   const { teamId, datasetId, collectionId, vectors, retry = 3 } = props;
+
   try {
     const { rows } = await PgClient.insert(PgDatasetTableName, {
       values: [
         [
           { key: 'vector', value: `[${vectors[0]}]` },
           { key: 'team_id', value: String(teamId) },
-          { key: 'dataset_id', value: datasetId },
-          { key: 'collection_id', value: collectionId }
+          { key: 'dataset_id', value: String(datasetId) },
+          { key: 'collection_id', value: String(collectionId) }
         ]
       ]
     });
@@ -176,8 +177,8 @@ export const getVectorDataByTime = async (start: Date, end: Date) => {
   `);
 
   return rows.map((item) => ({
-    id: item.id,
-    datasetId: item.dataset_id,
-    teamId: item.team_id
+    id: String(item.id),
+    teamId: item.team_id,
+    datasetId: item.dataset_id
   }));
 };

packages/service/core/chat/chatItemSchema.ts

@@ -89,6 +89,7 @@ try {
      close custom feedback; 
   */
   ChatItemSchema.index({ appId: 1, chatId: 1, dataId: 1 }, { background: true });
+  ChatItemSchema.index({ time: -1 }, { background: true });
   ChatItemSchema.index({ userGoodFeedback: 1 }, { background: true });
   ChatItemSchema.index({ userBadFeedback: 1 }, { background: true });
   ChatItemSchema.index({ customFeedbacks: 1 }, { background: true });

packages/service/core/dataset/collection/controller.ts

@@ -25,7 +25,7 @@ export async function createOneCollection({
   type,
 
   trainingType = TrainingModeEnum.chunk,
-  chunkSize = 0,
+  chunkSize = 512,
   chunkSplitter,
   qaPrompt,
 
@@ -134,7 +134,10 @@ export async function delCollectionAndRelatedSources({
 
   // delete file and imgs
   await Promise.all([
-    delImgByRelatedId(relatedImageIds),
+    delImgByRelatedId({
+      teamId,
+      relateIds: relatedImageIds
+    }),
     delFileByFileIdList({
       bucketName: BucketNameEnum.dataset,
       fileIdList

packages/service/core/dataset/training/controller.ts

@@ -1,5 +1,15 @@
 import { delay } from '@fastgpt/global/common/system/utils';
 import { MongoDatasetTraining } from './schema';
+import type {
+  PushDatasetDataChunkProps,
+  PushDatasetDataProps,
+  PushDatasetDataResponse
+} from '@fastgpt/global/core/dataset/api.d';
+import { getCollectionWithDataset } from '../controller';
+import { TrainingModeEnum } from '@fastgpt/global/core/dataset/constants';
+import { simpleText } from '@fastgpt/global/common/string/tools';
+import { countPromptTokens } from '@fastgpt/global/common/string/tiktoken';
+import type { VectorModelItemType, LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
 
 export const lockTrainingDataByTeamId = async (teamId: string, retry = 3): Promise<any> => {
   try {
@@ -19,3 +29,165 @@ export const lockTrainingDataByTeamId = async (teamId: string, retry = 3): Promi
     return Promise.reject(error);
   }
 };
+
+export async function pushDataListToTrainingQueue({
+  teamId,
+  tmbId,
+  collectionId,
+  data,
+  prompt,
+  billId,
+  trainingMode = TrainingModeEnum.chunk,
+
+  vectorModelList = [],
+  qaModelList = []
+}: {
+  teamId: string;
+  tmbId: string;
+  vectorModelList: VectorModelItemType[];
+  qaModelList: LLMModelItemType[];
+} & PushDatasetDataProps): Promise<PushDatasetDataResponse> {
+  const {
+    datasetId: { _id: datasetId, vectorModel, agentModel }
+  } = await getCollectionWithDataset(collectionId);
+
+  const checkModelValid = async ({ collectionId }: { collectionId: string }) => {
+    if (!collectionId) return Promise.reject(`CollectionId is empty`);
+
+    if (trainingMode === TrainingModeEnum.chunk) {
+      const vectorModelData = vectorModelList?.find((item) => item.model === vectorModel);
+      if (!vectorModelData) {
+        return Promise.reject(`Model ${vectorModel} is inValid`);
+      }
+
+      return {
+        maxToken: vectorModelData.maxToken * 1.5,
+        model: vectorModelData.model,
+        weight: vectorModelData.weight
+      };
+    }
+
+    if (trainingMode === TrainingModeEnum.qa) {
+      const qaModelData = qaModelList?.find((item) => item.model === agentModel);
+      if (!qaModelData) {
+        return Promise.reject(`Model ${agentModel} is inValid`);
+      }
+      return {
+        maxToken: qaModelData.maxContext * 0.8,
+        model: qaModelData.model,
+        weight: 0
+      };
+    }
+    return Promise.reject(`Training mode "${trainingMode}" is inValid`);
+  };
+
+  const { model, maxToken, weight } = await checkModelValid({
+    collectionId
+  });
+
+  // format q and a, remove empty char
+  data.forEach((item) => {
+    item.q = simpleText(item.q);
+    item.a = simpleText(item.a);
+
+    item.indexes = item.indexes
+      ?.map((index) => {
+        return {
+          ...index,
+          text: simpleText(index.text)
+        };
+      })
+      .filter(Boolean);
+  });
+
+  // filter repeat or equal content
+  const set = new Set();
+  const filterResult: Record<string, PushDatasetDataChunkProps[]> = {
+    success: [],
+    overToken: [],
+    repeat: [],
+    error: []
+  };
+
+  // filter repeat content
+  data.forEach((item) => {
+    if (!item.q) {
+      filterResult.error.push(item);
+      return;
+    }
+
+    const text = item.q + item.a;
+
+    // count q token
+    const token = countPromptTokens(item.q);
+
+    if (token > maxToken) {
+      filterResult.overToken.push(item);
+      return;
+    }
+
+    if (set.has(text)) {
+      console.log('repeat', item);
+      filterResult.repeat.push(item);
+    } else {
+      filterResult.success.push(item);
+      set.add(text);
+    }
+  });
+
+  // insert data to db
+  const insertData = async (dataList: PushDatasetDataChunkProps[], retry = 3): Promise<number> => {
+    try {
+      const results = await MongoDatasetTraining.insertMany(
+        dataList.map((item, i) => ({
+          teamId,
+          tmbId,
+          datasetId,
+          collectionId,
+          billId,
+          mode: trainingMode,
+          prompt,
+          model,
+          q: item.q,
+          a: item.a,
+          chunkIndex: item.chunkIndex ?? i,
+          weight: weight ?? 0,
+          indexes: item.indexes
+        }))
+      );
+      await delay(500);
+      return results.length;
+    } catch (error) {
+      if (retry > 0) {
+        await delay(500);
+        return insertData(dataList, retry - 1);
+      }
+      return Promise.reject(error);
+    }
+  };
+
+  let insertLen = 0;
+  const chunkSize = 50;
+  const chunkList = filterResult.success.reduce(
+    (acc, cur) => {
+      const lastChunk = acc[acc.length - 1];
+      if (lastChunk.length < chunkSize) {
+        lastChunk.push(cur);
+      } else {
+        acc.push([cur]);
+      }
+      return acc;
+    },
+    [[]] as PushDatasetDataChunkProps[][]
+  );
+  for await (const chunks of chunkList) {
+    insertLen += await insertData(chunks);
+  }
+
+  delete filterResult.success;
+
+  return {
+    insertLen,
+    ...filterResult
+  };
+}

packages/service/support/wallet/bill/schema.ts

@@ -52,7 +52,7 @@ const BillSchema = new Schema({
 });
 
 try {
-  BillSchema.index({ teamId: 1, tmbId: 1, time: -1 });
+  BillSchema.index({ teamId: 1, time: -1 });
   BillSchema.index({ time: 1 }, { expireAfterSeconds: 180 * 24 * 60 * 60 });
 } catch (error) {
   console.log(error);

packages/web/common/file/read/csv.ts

@@ -0,0 +1,40 @@
+import Papa from 'papaparse';
+import { readFileRawText } from './rawText';
+
+/**
+ * read csv to json
+ * @response {
+ *  header: string[],
+ *  data: string[][]
+ * }
+ */
+export const readCsvContent = async ({ file }: { file: File }) => {
+  try {
+    const { rawText: textArr } = await readFileRawText(file);
+    const csvArr = Papa.parse(textArr).data as string[][];
+    if (csvArr.length === 0) {
+      throw new Error('csv 解析失败');
+    }
+
+    const header = csvArr.shift() as string[];
+
+    // add title to data
+    const rawText = csvArr
+      .map((item) =>
+        item.map((value, index) => {
+          if (!header[index]) return value;
+          return `${header[index]}: ${value}`;
+        })
+      )
+      .flat()
+      .join('\n');
+
+    return {
+      rawText,
+      header,
+      data: csvArr.map((item) => item)
+    };
+  } catch (error) {
+    return Promise.reject('解析 csv 文件失败');
+  }
+};

packages/web/common/file/read/index.ts

@@ -1,4 +1,5 @@
 import { loadFile2Buffer } from '../utils';
+import { readCsvContent } from './csv';
 import { readHtmlFile } from './html';
 import { readMdFile } from './md';
 import { readPdfFile } from './pdf';
@@ -29,6 +30,8 @@ export const readFileRawContent = async ({
         file,
         uploadImgController: uploadBase64Controller
       });
+    case 'csv':
+      return readCsvContent({ file });
     case 'pdf':
       const pdf = await loadFile2Buffer({ file });
       return readPdfFile({ pdf });

packages/web/components/common/Textarea/JsonEditor/index.tsx

@@ -74,7 +74,7 @@ const JSONEditor = ({ defaultValue, value, onChange, resize, ...props }: Props)
 
       <Box
         borderWidth={'1px'}
-        borderRadius={'base'}
+        borderRadius={'md'}
         borderColor={'myGray.200'}
         py={2}
         {...props}

packages/web/package.json

@@ -22,13 +22,15 @@
     "react-dom": "18.2.0",
     "react-i18next": "^12.3.1",
     "turndown": "^7.1.2",
-    "lexical":"0.12.6",
+    "lexical": "0.12.6",
     "@lexical/react": "0.12.6",
+    "papaparse": "^5.4.1",
     "@lexical/utils": "0.12.6",
     "@lexical/text": "0.12.6"
   },
   "devDependencies": {
     "@types/react": "18.2.0",
+    "@types/papaparse": "^5.3.7",
     "@types/react-dom": "18.2.0",
     "@types/turndown": "^5.0.4"
   }

pnpm-lock.yaml

@@ -196,6 +196,9 @@ importers:
       next-i18next:
         specifier: ^13.3.0
         version: registry.npmmirror.com/next-i18next@13.3.0(i18next@22.5.1)(next@13.5.2)(react-i18next@12.3.1)(react@18.2.0)
+      papaparse:
+        specifier: ^5.4.1
+        version: registry.npmmirror.com/papaparse@5.4.1
       pdfjs-dist:
         specifier: ^4.0.269
         version: registry.npmmirror.com/pdfjs-dist@4.0.269
@@ -212,6 +215,9 @@ importers:
         specifier: ^7.1.2
         version: registry.npmmirror.com/turndown@7.1.2
     devDependencies:
+      '@types/papaparse':
+        specifier: ^5.3.7
+        version: registry.npmmirror.com/@types/papaparse@5.3.7
       '@types/react':
         specifier: 18.2.0
         version: registry.npmmirror.com/@types/react@18.2.0
@@ -323,9 +329,6 @@ importers:
       nprogress:
         specifier: ^0.2.0
         version: registry.npmmirror.com/nprogress@0.2.0
-      papaparse:
-        specifier: ^5.4.1
-        version: registry.npmmirror.com/papaparse@5.4.1
       react:
         specifier: 18.2.0
         version: registry.npmmirror.com/react@18.2.0
@@ -390,9 +393,6 @@ importers:
       '@types/node':
         specifier: ^20.8.5
         version: registry.npmmirror.com/@types/node@20.8.5
-      '@types/papaparse':
-        specifier: ^5.3.7
-        version: registry.npmmirror.com/@types/papaparse@5.3.7
       '@types/react':
         specifier: 18.2.0
         version: registry.npmmirror.com/@types/react@18.2.0

projects/app/package.json

@@ -42,7 +42,6 @@
     "next": "13.5.2",
     "next-i18next": "^13.3.0",
     "nprogress": "^0.2.0",
-    "papaparse": "^5.4.1",
     "react": "18.2.0",
     "react-day-picker": "^8.7.1",
     "react-dom": "18.2.0",
@@ -66,7 +65,6 @@
     "@types/jsonwebtoken": "^9.0.3",
     "@types/lodash": "^4.14.191",
     "@types/node": "^20.8.5",
-    "@types/papaparse": "^5.3.7",
     "@types/react": "18.2.0",
     "@types/react-dom": "18.2.0",
     "@types/react-syntax-highlighter": "^15.5.6",

projects/app/public/locales/zh/common.json

@@ -226,7 +226,7 @@
     "Chat test": "测试对话",
     "Max Token": "单条数据上限",
     "Start chat": "立即对话",
-    "Total chars": "总字符数: {{total}}",
+    "Total chars": "总字数: {{total}}",
     "Total tokens": "总 Tokens: {{total}}",
     "ai": {
       "Model": "AI 模型",
@@ -541,8 +541,7 @@
             "success": "开始同步"
           }
         },
-        "training": {
-        }
+        "training": {}
       },
       "data": {
         "Auxiliary Data": "辅助数据",

projects/app/src/components/core/module/Flow/components/modules/ButtonEdge.tsx

@@ -17,7 +17,7 @@ const ButtonEdge = (props: EdgeProps) => {
     style = {}
   } = props;
 
-  const [labelX, labelY] = getBezierPath({
+  const [, labelX, labelY] = getBezierPath({
     sourceX,
     sourceY,
     sourcePosition,

projects/app/src/global/core/api/datasetRes.d.ts

@@ -8,6 +8,3 @@ import { DatasetCollectionSchemaType } from '@fastgpt/global/core/dataset/type';
 /* ======= collection =========== */
 
 /* ==== data ===== */
-export type PushDataResponse = {
-  insertLen: number;
-};

projects/app/src/global/core/dataset/api.d.ts

@@ -27,13 +27,7 @@ export type CreateDatasetParams = {
 export type InsertOneDatasetDataProps = PushDatasetDataChunkProps & {
   collectionId: string;
 };
-export type PushDatasetDataProps = {
-  collectionId: string;
-  data: PushDatasetDataChunkProps[];
-  trainingMode: `${TrainingModeEnum}`;
-  prompt?: string;
-  billId?: string;
-};
+
 export type UpdateDatasetDataProps = {
   id: string;
   q?: string; // embedding content

projects/app/src/pages/api/core/dataset/collection/create/text.ts

@@ -16,11 +16,15 @@ import { checkDatasetLimit } from '@fastgpt/service/support/permission/limit/dat
 import { predictDataLimitLength } from '@fastgpt/global/core/dataset/utils';
 import { pushDataToTrainingQueue } from '@/service/core/dataset/data/controller';
 import { hashStr } from '@fastgpt/global/common/string/tools';
+import { createTrainingBill } from '@fastgpt/service/support/wallet/bill/controller';
+import { BillSourceEnum } from '@fastgpt/global/support/wallet/bill/constants';
+import { getQAModel, getVectorModel } from '@/service/core/ai/model';
 
 export default async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
   try {
     await connectToDatabase();
     const {
+      name,
       text,
       trainingType = TrainingModeEnum.chunk,
       chunkSize = 512,
@@ -29,7 +33,7 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse<
       ...body
     } = req.body as TextCreateDatasetCollectionParams;
 
-    const { teamId, tmbId } = await authDataset({
+    const { teamId, tmbId, dataset } = await authDataset({
       req,
       authToken: true,
       authApiKey: true,
@@ -52,21 +56,32 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse<
       insertLen: predictDataLimitLength(trainingType, chunks)
     });
 
-    // 3. create collection
-    const collectionId = await createOneCollection({
-      ...body,
-      teamId,
-      tmbId,
-      type: DatasetCollectionTypeEnum.virtual,
+    // 3. create collection and training bill
+    const [collectionId, { billId }] = await Promise.all([
+      createOneCollection({
+        ...body,
+        teamId,
+        tmbId,
+        type: DatasetCollectionTypeEnum.virtual,
 
-      trainingType,
-      chunkSize,
-      chunkSplitter,
-      qaPrompt,
+        name,
+        trainingType,
+        chunkSize,
+        chunkSplitter,
+        qaPrompt,
 
-      hashRawText: hashStr(text),
-      rawTextLength: text.length
-    });
+        hashRawText: hashStr(text),
+        rawTextLength: text.length
+      }),
+      createTrainingBill({
+        teamId,
+        tmbId,
+        appName: name,
+        billSource: BillSourceEnum.training,
+        vectorModel: getVectorModel(dataset.vectorModel)?.name,
+        agentModel: getQAModel(dataset.agentModel)?.name
+      })
+    ]);
 
     // 4. push chunks to training queue
     const insertResults = await pushDataToTrainingQueue({
@@ -74,6 +89,8 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse<
       tmbId,
       collectionId,
       trainingMode: trainingType,
+      prompt: qaPrompt,
+      billId,
       data: chunks.map((text, index) => ({
         q: text,
         chunkIndex: index
@@ -90,3 +107,11 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse<
     });
   }
 }
+
+export const config = {
+  api: {
+    bodyParser: {
+      sizeLimit: '10mb'
+    }
+  }
+};

projects/app/src/pages/api/core/dataset/data/pushData.ts

@@ -3,8 +3,10 @@ import type { NextApiRequest, NextApiResponse } from 'next';
 import { jsonRes } from '@fastgpt/service/common/response';
 import { connectToDatabase } from '@/service/mongo';
 import { withNextCors } from '@fastgpt/service/common/middle/cors';
-import type { PushDataResponse } from '@/global/core/api/datasetRes.d';
-import type { PushDatasetDataProps } from '@/global/core/dataset/api.d';
+import type {
+  PushDatasetDataProps,
+  PushDatasetDataResponse
+} from '@fastgpt/global/core/dataset/api.d';
 import { authDatasetCollection } from '@fastgpt/service/support/permission/auth/dataset';
 import { checkDatasetLimit } from '@fastgpt/service/support/permission/limit/dataset';
 import { predictDataLimitLength } from '@fastgpt/global/core/dataset/utils';
@@ -39,7 +41,7 @@ export default withNextCors(async function handler(req: NextApiRequest, res: Nex
       insertLen: predictDataLimitLength(collection.trainingType, data)
     });
 
-    jsonRes<PushDataResponse>(res, {
+    jsonRes<PushDatasetDataResponse>(res, {
       data: await pushDataToTrainingQueue({
         ...req.body,
         teamId,

projects/app/src/pages/api/plusApi/[...path].ts

@@ -12,16 +12,13 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     const method = (req.method || 'POST') as Method;
     const { path = [], ...query } = req.query as any;
 
-    const url = `/${path?.join('/')}`;
+    const url = `/${path?.join('/')}?${new URLSearchParams(query).toString()}`;
 
     if (!url) {
       throw new Error('url is empty');
     }
 
-    const data = {
-      ...req.body,
-      ...query
-    };
+    const data = req.body || query;
 
     const repose = await request(
       url,
@@ -56,3 +53,12 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
     });
   }
 }
+
+export const config = {
+  api: {
+    bodyParser: {
+      sizeLimit: '10mb'
+    },
+    responseLimit: '10mb'
+  }
+};

projects/app/src/pages/dataset/detail/components/Import/diffSource/FileLocal.tsx

@@ -27,7 +27,7 @@ const Upload = dynamic(() => import('../commonProgress/Upload'));
 const PreviewRawText = dynamic(() => import('../components/PreviewRawText'));
 
 type FileItemType = ImportSourceItemType & { file: File };
-const fileType = '.txt, .docx, .pdf, .md, .html';
+const fileType = '.txt, .docx, .csv, .pdf, .md, .html';
 const maxSelectFileCount = 1000;
 
 const FileLocal = ({ activeStep, goToNext }: ImportDataComponentProps) => {

projects/app/src/pages/dataset/detail/components/Import/diffSource/TableLocal.tsx

@@ -14,7 +14,8 @@ import { useImportStore } from '../Provider';
 import { feConfigs } from '@/web/common/system/staticData';
 
 import dynamic from 'next/dynamic';
-import { fileDownload, readCsvContent } from '@/web/common/file/utils';
+import { fileDownload } from '@/web/common/file/utils';
+import { readCsvContent } from '@fastgpt/web/common/file/read/csv';
 
 const PreviewData = dynamic(() => import('../commonProgress/PreviewData'));
 const Upload = dynamic(() => import('../commonProgress/Upload'));
@@ -56,7 +57,7 @@ const SelectFile = React.memo(function SelectFile({ goToNext }: { goToNext: () =
       {
         for await (const selectFile of files) {
           const { file, folderPath } = selectFile;
-          const { header, data } = await readCsvContent(file);
+          const { header, data } = await readCsvContent({ file });
 
           const filterData: FileItemType['chunks'] = data
             .filter((item) => item[0])

projects/app/src/pages/dataset/detail/components/InputDataModal.tsx

@@ -193,7 +193,10 @@ const InputDataModal = ({
       // not exactly same
       await putDatasetDataById({
         id: dataId,
-        ...e
+        ...e,
+        indexes: e.indexes.map((index) =>
+          index.defaultIndex ? getDefaultIndex({ q: e.q, a: e.a }) : index
+        )
       });
 
       return {

projects/app/src/pages/dataset/detail/components/Test.tsx

@@ -35,7 +35,8 @@ import dynamic from 'next/dynamic';
 import { useForm } from 'react-hook-form';
 import MySelect from '@/components/Select';
 import { useSelectFile } from '@/web/common/file/hooks/useSelectFile';
-import { fileDownload, readCsvContent } from '@/web/common/file/utils';
+import { fileDownload } from '@/web/common/file/utils';
+import { readCsvContent } from '@fastgpt/web/common/file/read/csv';
 import { delay } from '@fastgpt/global/common/system/utils';
 import QuoteItem from '@/components/core/dataset/QuoteItem';
 
@@ -125,7 +126,7 @@ const Test = ({ datasetId }: { datasetId: string }) => {
   const { mutate: onFileTest, isLoading: fileTestIsLoading } = useRequest({
     mutationFn: async ({ searchParams }: FormType) => {
       if (!selectFile) return Promise.reject('File is not selected');
-      const { data } = await readCsvContent(selectFile);
+      const { data } = await readCsvContent({ file: selectFile });
       const testList = data.slice(0, 100);
       const results: SearchTestResponse[] = [];
 

projects/app/src/service/common/system/cron.ts

@@ -3,6 +3,11 @@ import { generateQA } from '@/service/events/generateQA';
 import { generateVector } from '@/service/events/generateVector';
 import { setCron } from '@fastgpt/service/common/system/cron';
 
+export const startCron = () => {
+  setUpdateSystemConfigCron();
+  setTrainingQueueCron();
+};
+
 export const setUpdateSystemConfigCron = () => {
   setCron('*/5 * * * *', () => {
     initSystemConfig();
@@ -11,7 +16,7 @@ export const setUpdateSystemConfigCron = () => {
 };
 
 export const setTrainingQueueCron = () => {
-  setCron('*/3 * * * *', () => {
+  setCron('*/1 * * * *', () => {
     generateVector();
     generateQA();
   });

projects/app/src/service/core/dataset/data/controller.ts

@@ -9,13 +9,11 @@ import {
   recallFromVectorStore,
   updateDatasetDataVector
 } from '@fastgpt/service/common/vectorStore/controller';
-import { Types } from 'mongoose';
 import {
   DatasetDataIndexTypeEnum,
   DatasetSearchModeEnum,
   DatasetSearchModeMap,
-  SearchScoreTypeEnum,
-  TrainingModeEnum
+  SearchScoreTypeEnum
 } from '@fastgpt/global/core/dataset/constants';
 import { getDefaultIndex } from '@fastgpt/global/core/dataset/utils';
 import { jiebaSplit } from '@/service/common/string/jieba';
@@ -29,172 +27,26 @@ import {
 } from '@fastgpt/global/core/dataset/type';
 import { reRankRecall } from '../../ai/rerank';
 import { countPromptTokens } from '@fastgpt/global/common/string/tiktoken';
-import { hashStr, simpleText } from '@fastgpt/global/common/string/tools';
-import type { PushDatasetDataProps } from '@/global/core/dataset/api.d';
-import type { PushDataResponse } from '@/global/core/api/datasetRes';
-import { PushDatasetDataChunkProps } from '@fastgpt/global/core/dataset/api';
-import { MongoDatasetTraining } from '@fastgpt/service/core/dataset/training/schema';
-import { startQueue } from '@/service/utils/tools';
-import { getCollectionWithDataset } from '@fastgpt/service/core/dataset/controller';
-import { getQAModel, getVectorModel } from '../../ai/model';
-import { delay } from '@fastgpt/global/common/system/utils';
+import { hashStr } from '@fastgpt/global/common/string/tools';
+import type {
+  PushDatasetDataProps,
+  PushDatasetDataResponse
+} from '@fastgpt/global/core/dataset/api.d';
+import { pushDataListToTrainingQueue } from '@fastgpt/service/core/dataset/training/controller';
 
-export async function pushDataToTrainingQueue({
-  teamId,
-  tmbId,
-  collectionId,
-  data,
-  prompt,
-  billId,
-  trainingMode
-}: {
-  teamId: string;
-  tmbId: string;
-} & PushDatasetDataProps): Promise<PushDataResponse> {
-  const checkModelValid = async ({ collectionId }: { collectionId: string }) => {
-    const {
-      datasetId: { _id: datasetId, vectorModel, agentModel }
-    } = await getCollectionWithDataset(collectionId);
-
-    if (trainingMode === TrainingModeEnum.chunk) {
-      if (!collectionId) return Promise.reject(`CollectionId is empty`);
-      const vectorModelData = getVectorModel(vectorModel);
-      if (!vectorModelData) {
-        return Promise.reject(`Model ${vectorModel} is inValid`);
-      }
-
-      return {
-        datasetId,
-        maxToken: vectorModelData.maxToken * 1.5,
-        model: vectorModelData.model,
-        weight: vectorModelData.weight
-      };
-    }
-
-    if (trainingMode === TrainingModeEnum.qa) {
-      const qaModelData = getQAModel(agentModel);
-      if (!qaModelData) {
-        return Promise.reject(`Model ${agentModel} is inValid`);
-      }
-      return {
-        datasetId,
-        maxToken: qaModelData.maxContext * 0.8,
-        model: qaModelData.model,
-        weight: 0
-      };
-    }
-    return Promise.reject(`Mode ${trainingMode} is inValid`);
-  };
-
-  const { datasetId, model, maxToken, weight } = await checkModelValid({
-    collectionId
+export async function pushDataToTrainingQueue(
+  props: {
+    teamId: string;
+    tmbId: string;
+  } & PushDatasetDataProps
+): Promise<PushDatasetDataResponse> {
+  const result = await pushDataListToTrainingQueue({
+    ...props,
+    vectorModelList: global.vectorModels,
+    qaModelList: global.qaModels
   });
 
-  // format q and a, remove empty char
-  data.forEach((item) => {
-    item.q = simpleText(item.q);
-    item.a = simpleText(item.a);
-
-    item.indexes = item.indexes
-      ?.map((index) => {
-        return {
-          ...index,
-          text: simpleText(index.text)
-        };
-      })
-      .filter(Boolean);
-  });
-
-  // filter repeat or equal content
-  const set = new Set();
-  const filterResult: Record<string, PushDatasetDataChunkProps[]> = {
-    success: [],
-    overToken: [],
-    repeat: [],
-    error: []
-  };
-
-  data.forEach((item) => {
-    if (!item.q) {
-      filterResult.error.push(item);
-      return;
-    }
-
-    const text = item.q + item.a;
-
-    // count q token
-    const token = countPromptTokens(item.q);
-
-    if (token > maxToken) {
-      filterResult.overToken.push(item);
-      return;
-    }
-
-    if (set.has(text)) {
-      console.log('repeat', item);
-      filterResult.repeat.push(item);
-    } else {
-      filterResult.success.push(item);
-      set.add(text);
-    }
-  });
-
-  // 插入记录
-  const insertData = async (dataList: PushDatasetDataChunkProps[], retry = 3): Promise<number> => {
-    try {
-      const results = await MongoDatasetTraining.insertMany(
-        dataList.map((item, i) => ({
-          teamId,
-          tmbId,
-          datasetId,
-          collectionId,
-          billId,
-          mode: trainingMode,
-          prompt,
-          model,
-          q: item.q,
-          a: item.a,
-          chunkIndex: item.chunkIndex ?? i,
-          weight: weight ?? 0,
-          indexes: item.indexes
-        }))
-      );
-      await delay(500);
-      return results.length;
-    } catch (error) {
-      if (retry > 0) {
-        await delay(1000);
-        return insertData(dataList, retry - 1);
-      }
-      return Promise.reject(error);
-    }
-  };
-
-  let insertLen = 0;
-  const chunkSize = 50;
-  const chunkList = filterResult.success.reduce(
-    (acc, cur) => {
-      const lastChunk = acc[acc.length - 1];
-      if (lastChunk.length < chunkSize) {
-        lastChunk.push(cur);
-      } else {
-        acc.push([cur]);
-      }
-      return acc;
-    },
-    [[]] as PushDatasetDataChunkProps[][]
-  );
-  for await (const chunks of chunkList) {
-    insertLen += await insertData(chunks);
-  }
-
-  startQueue();
-  delete filterResult.success;
-
-  return {
-    insertLen,
-    ...filterResult
-  };
+  return result;
 }
 
 /* insert data.
@@ -341,6 +193,11 @@ export async function updateData2Dataset({
             text: qaStr
           }
         });
+      } else {
+        patchResult.push({
+          type: 'unChange',
+          index: item
+        });
       }
     } else {
       // not in database, create
@@ -379,6 +236,7 @@ export async function updateData2Dataset({
           model
         });
         item.index.dataId = result.insertId;
+
         return result;
       }
       if (item.type === 'delete' && item.index.dataId) {
@@ -397,13 +255,14 @@ export async function updateData2Dataset({
   );
 
   const charsLength = result.reduce((acc, cur) => acc + cur.charsLength, 0);
+  const newIndexes = patchResult.filter((item) => item.type !== 'delete').map((item) => item.index);
 
   // update mongo other data
   mongoData.q = q || mongoData.q;
   mongoData.a = a ?? mongoData.a;
   mongoData.fullTextToken = jiebaSplit({ text: mongoData.q + mongoData.a });
   // @ts-ignore
-  mongoData.indexes = indexes;
+  mongoData.indexes = newIndexes;
   await mongoData.save();
 
   return {

projects/app/src/service/mongo.ts

@@ -7,7 +7,7 @@ import { createDefaultTeam } from '@fastgpt/service/support/user/team/controller
 import { exit } from 'process';
 import { initVectorStore } from '@fastgpt/service/common/vectorStore/controller';
 import { getInitConfig } from '@/pages/api/common/system/getInitData';
-import { setUpdateSystemConfigCron, setTrainingQueueCron } from './common/system/cron';
+import { startCron } from './common/system/cron';
 
 /**
  * connect MongoDB and init data
@@ -23,8 +23,7 @@ export function connectToDatabase(): Promise<void> {
       getInitConfig();
 
       // cron
-      setUpdateSystemConfigCron();
-      setTrainingQueueCron();
+      startCron();
 
       initRootUser();
     }

projects/app/src/web/common/file/controller.ts

@@ -32,13 +32,24 @@ export const uploadFiles = ({
   });
 };
 
-export const getUploadBase64ImgController = (props: CompressImgProps & UploadImgProps) =>
-  compressBase64ImgAndUpload({
-    maxW: 4000,
-    maxH: 4000,
-    maxSize: 1024 * 1024 * 5,
-    ...props
-  });
+export const getUploadBase64ImgController = (
+  props: CompressImgProps & UploadImgProps,
+  retry = 3
+): Promise<string> => {
+  try {
+    return compressBase64ImgAndUpload({
+      maxW: 4000,
+      maxH: 4000,
+      maxSize: 1024 * 1024 * 5,
+      ...props
+    });
+  } catch (error) {
+    if (retry > 0) {
+      return getUploadBase64ImgController(props, retry - 1);
+    }
+    return Promise.reject(error);
+  }
+};
 
 /**
  * compress image. response base64

projects/app/src/web/common/file/utils.ts

@@ -1,29 +1,3 @@
-import Papa from 'papaparse';
-import { readFileRawText } from '@fastgpt/web/common/file/read/rawText';
-
-/**
- * read csv to json
- * @response {
- *  header: string[],
- *  data: string[][]
- * }
- */
-export const readCsvContent = async (file: File) => {
-  try {
-    const { rawText: textArr } = await readFileRawText(file);
-    const csvArr = Papa.parse(textArr).data as string[][];
-    if (csvArr.length === 0) {
-      throw new Error('csv 解析失败');
-    }
-    return {
-      header: csvArr.shift() as string[],
-      data: csvArr.map((item) => item)
-    };
-  } catch (error) {
-    return Promise.reject('解析 csv 文件失败');
-  }
-};
-
 /**
  * file download by text
  */

projects/app/src/web/core/dataset/api.ts

@@ -19,12 +19,14 @@ import type {
   SearchTestResponse
 } from '@/global/core/dataset/api.d';
 import type {
-  PushDatasetDataProps,
   UpdateDatasetDataProps,
   CreateDatasetParams,
   InsertOneDatasetDataProps
 } from '@/global/core/dataset/api.d';
-import type { PushDataResponse } from '@/global/core/api/datasetRes.d';
+import type {
+  PushDatasetDataProps,
+  PushDatasetDataResponse
+} from '@fastgpt/global/core/dataset/api.d';
 import type { DatasetCollectionItemType } from '@fastgpt/global/core/dataset/type';
 import {
   DatasetCollectionSyncResultEnum,
@@ -97,7 +99,7 @@ export const getDatasetDataItemById = (id: string) =>
  * push data to training queue
  */
 export const postChunks2Dataset = (data: PushDatasetDataProps) =>
-  POST<PushDataResponse>(`/core/dataset/data/pushData`, data);
+  POST<PushDatasetDataResponse>(`/core/dataset/data/pushData`, data);
 
 /**
  * insert one data to dataset (immediately insert)