feat: dataset index prefix (#5061)

deploy/docker/docker-compose-milvus.yml

@@ -133,15 +133,15 @@ services:
   # fastgpt
   sandbox:
     container_name: sandbox
-    image: ghcr.io/labring/fastgpt-sandbox:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt-sandbox:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.13 # 阿里云
     networks:
       - fastgpt
     restart: always
   fastgpt-mcp-server:
     container_name: fastgpt-mcp-server
-    image: ghcr.io/labring/fastgpt-mcp_server:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt-mcp_server:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.13 # 阿里云
     ports:
       - 3005:3000
     networks:
@@ -151,8 +151,8 @@ services:
       - FASTGPT_ENDPOINT=http://fastgpt:3000
   fastgpt:
     container_name: fastgpt
-    image: ghcr.io/labring/fastgpt:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.13 # 阿里云
     ports:
       - 3000:3000
     networks:

deploy/docker/docker-compose-oceanbase/docker-compose.yml

@@ -109,15 +109,15 @@ services:
   # fastgpt
   sandbox:
     container_name: sandbox
-    image: ghcr.io/labring/fastgpt-sandbox:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt-sandbox:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.13 # 阿里云
     networks:
       - fastgpt
     restart: always
   fastgpt-mcp-server:
     container_name: fastgpt-mcp-server
-    image: ghcr.io/labring/fastgpt-mcp_server:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt-mcp_server:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.13 # 阿里云
     ports:
       - 3005:3000
     networks:
@@ -127,8 +127,8 @@ services:
       - FASTGPT_ENDPOINT=http://fastgpt:3000
   fastgpt:
     container_name: fastgpt
-    image: ghcr.io/labring/fastgpt:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.13 # 阿里云
     ports:
       - 3000:3000
     networks:

deploy/docker/docker-compose-pgvector.yml

@@ -96,15 +96,15 @@ services:
   # fastgpt
   sandbox:
     container_name: sandbox
-    image: ghcr.io/labring/fastgpt-sandbox:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt-sandbox:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.13 # 阿里云
     networks:
       - fastgpt
     restart: always
   fastgpt-mcp-server:
     container_name: fastgpt-mcp-server
-    image: ghcr.io/labring/fastgpt-mcp_server:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt-mcp_server:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.13 # 阿里云
     ports:
       - 3005:3000
     networks:
@@ -114,8 +114,8 @@ services:
       - FASTGPT_ENDPOINT=http://fastgpt:3000
   fastgpt:
     container_name: fastgpt
-    image: ghcr.io/labring/fastgpt:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.13 # 阿里云
     ports:
       - 3000:3000
     networks:

deploy/docker/docker-compose-zilliz.yml

@@ -72,15 +72,15 @@ services:
 
   sandbox:
     container_name: sandbox
-    image: ghcr.io/labring/fastgpt-sandbox:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt-sandbox:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.13 # 阿里云
     networks:
       - fastgpt
     restart: always
   fastgpt-mcp-server:
     container_name: fastgpt-mcp-server
-    image: ghcr.io/labring/fastgpt-mcp_server:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt-mcp_server:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-mcp_server:v4.9.13 # 阿里云
     ports:
       - 3005:3000
     networks:
@@ -90,8 +90,8 @@ services:
       - FASTGPT_ENDPOINT=http://fastgpt:3000
   fastgpt:
     container_name: fastgpt
-    image: ghcr.io/labring/fastgpt:v4.9.11 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.11 # 阿里云
+    image: ghcr.io/labring/fastgpt:v4.9.13 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.13 # 阿里云
     ports:
       - 3000:3000
     networks:

docSite/content/zh-cn/docs/development/upgrading/4912.md

@@ -9,7 +9,11 @@ weight: 788
 
 ## 更新指南
 
-### 1. 更新镜像：
+### 1. 更新环境变量
+
+在 `fastgpt`和`fastgpt-pro`镜像环境变量中加入: `AES256_SECRET_KEY=` 变量，用于密钥加密。
+
+### 2. 更新镜像：
 
 - 更新 FastGPT 镜像 tag: v4.9.12
 - 更新 FastGPT 商业版镜像 tag: v4.9.12

docSite/content/zh-cn/docs/development/upgrading/4913.md

 ---
-title: 'V4.9.13(进行中)'
+title: 'V4.9.13'
 description: 'FastGPT V4.9.13 更新说明'
 icon: 'upgrade'
 draft: false

docSite/content/zh-cn/docs/development/upgrading/4914.md

+---
+title: 'V4.9.14(进行中)'
+description: 'FastGPT V4.9.14 更新说明'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 787
+---
+
+
+## 🚀 新增内容
+
+1. 知识库导入，支持配置：自动将文件名加入系统索引中。
+
+## ⚙️ 优化
+
+1. 统一知识库训练队列代码逻辑。
+2. 输入框 UX。
+
+## 🐛 修复

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

@@ -10,6 +10,7 @@ export type CreateDatasetDataProps = {
   a?: string;
   imageId?: string;
   indexes?: Omit<DatasetDataIndexItemType, 'dataId'>[];
+  indexPrefix?: string;
 };
 
 export type UpdateDatasetDataProps = {
@@ -21,6 +22,7 @@ export type UpdateDatasetDataProps = {
     dataId?: string; // pg data id
   })[];
   imageId?: string;
+  indexPrefix?: string;
 };
 
 export type PatchIndexesProps =

packages/global/core/dataset/training/type.d.ts

@@ -7,9 +7,9 @@ export type PushDataToTrainingQueueProps = {
   datasetId: string;
   collectionId: string;
 
+  data: PushDatasetDataChunkProps[];
   mode?: TrainingModeEnum;
   data: PushDatasetDataChunkProps[];
-  prompt?: string;
 
   agentModel: string;
   vectorModel: string;

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

@@ -36,6 +36,7 @@ export type ChunkSettingsType = {
   // Index enhance
   imageIndex?: boolean;
   autoIndexes?: boolean;
+  indexPrefixTitle?: boolean;
 
   // Chunk setting
   chunkSettingMode?: ChunkSettingModeEnum; // 系统参数/自定义参数
@@ -184,8 +185,6 @@ export type DatasetTrainingSchemaType = {
   expireAt: Date;
   lockTime: Date;
   mode: TrainingModeEnum;
-  model?: string;
-  prompt?: string;
   dataId?: string;
   q: string;
   a: string;

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

@@ -103,6 +103,7 @@ export const createCollectionAndInsertData = async ({
       delete formatCreateCollectionParams.chunkSize;
       delete formatCreateCollectionParams.chunkSplitter;
       delete formatCreateCollectionParams.indexSize;
+      delete formatCreateCollectionParams.indexPrefixTitle;
     }
   }
   if (trainingType !== DatasetCollectionDataProcessModeEnum.qa) {
@@ -223,7 +224,6 @@ export const createCollectionAndInsertData = async ({
           vlmModel: dataset.vlmModel,
           indexSize,
           mode: trainingMode,
-          prompt: formatCreateCollectionParams.qaPrompt,
           billId: traingBillId,
           data: chunks.map((item, index) => ({
             ...item,

packages/service/core/dataset/schema.ts

@@ -32,6 +32,7 @@ export const ChunkSettings = {
 
   imageIndex: Boolean,
   autoIndexes: Boolean,
+  indexPrefixTitle: Boolean,
 
   chunkSettingMode: {
     type: String,

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

@@ -27,23 +27,6 @@ export const lockTrainingDataByTeamId = async (teamId: string): Promise<any> =>
   } catch (error) {}
 };
 
-export const pushDataListToTrainingQueueByCollectionId = async ({
-  collectionId,
-  ...props
-}: Omit<PushDataToTrainingQueueProps, 'datasetId' | 'agentModel' | 'vectorModel' | 'vlmModel'>) => {
-  const {
-    dataset: { _id: datasetId, agentModel, vectorModel, vlmModel }
-  } = await getCollectionWithDataset(collectionId);
-  return pushDataListToTrainingQueue({
-    ...props,
-    datasetId,
-    collectionId,
-    vectorModel,
-    agentModel,
-    vlmModel
-  });
-};
-
 export async function pushDataListToTrainingQueue({
   teamId,
   tmbId,
@@ -53,7 +36,6 @@ export async function pushDataListToTrainingQueue({
   vectorModel,
   vlmModel,
   data,
-  prompt,
   billId,
   mode = TrainingModeEnum.chunk,
   indexSize,
@@ -149,8 +131,6 @@ export async function pushDataListToTrainingQueue({
           collectionId: collectionId,
           billId,
           mode: formatTrainingMode(item, mode),
-          prompt,
-          model,
           ...(item.q && { q: item.q }),
           ...(item.a && { a: item.a }),
           ...(item.imageId && { imageId: item.imageId }),

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

@@ -10,6 +10,7 @@ import {
   TeamMemberCollectionName
 } from '@fastgpt/global/support/user/team/constant';
 import { DatasetDataIndexTypeEnum } from '@fastgpt/global/core/dataset/data/constants';
+import { DatasetDataCollectionName } from '../data/schema';
 
 export const DatasetTrainingCollectionName = 'dataset_trainings';
 
@@ -54,8 +55,6 @@ const TrainingDataSchema = new Schema({
     default: 5
   },
 
-  model: String,
-  prompt: String,
   q: {
     type: String,
     default: ''
@@ -74,7 +73,10 @@ const TrainingDataSchema = new Schema({
     type: Number,
     default: 0
   },
-  dataId: Schema.Types.ObjectId,
+  dataId: {
+    type: Schema.Types.ObjectId,
+    ref: DatasetDataCollectionName
+  },
   indexes: {
     type: [
       {
@@ -105,6 +107,12 @@ TrainingDataSchema.virtual('collection', {
   foreignField: '_id',
   justOne: true
 });
+TrainingDataSchema.virtual('data', {
+  ref: DatasetDataCollectionName,
+  localField: 'dataId',
+  foreignField: '_id',
+  justOne: true
+});
 
 try {
   // lock training data(teamId); delete training data

packages/web/i18n/en/dataset.json

@@ -111,6 +111,8 @@
   "import_param_setting": "Parameter settings",
   "import_select_file": "Select a file",
   "import_select_link": "Enter link",
+  "index_prefix_title": "Index add title",
+  "index_prefix_title_tips": "Automatically add title names to all indexes",
   "index_size": "Index size",
   "index_size_tips": "When vectorized, the system will automatically further segment the blocks according to this size.",
   "input_required_field_to_select_baseurl": "Please enter the required information first",

packages/web/i18n/zh-CN/dataset.json

@@ -111,6 +111,8 @@
   "import_param_setting": "参数设置",
   "import_select_file": "选择文件",
   "import_select_link": "输入链接",
+  "index_prefix_title": "将标题加入索引",
+  "index_prefix_title_tips": "自动给索引所有索引加标题名",
   "index_size": "索引大小",
   "index_size_tips": "向量化时内容的长度，系统会自动按该大小对分块进行进一步的分割。",
   "input_required_field_to_select_baseurl": "请先输入必填信息",

packages/web/i18n/zh-Hant/dataset.json

@@ -110,6 +110,8 @@
   "import_param_setting": "參數設定",
   "import_select_file": "選擇文件",
   "import_select_link": "輸入連結",
+  "index_prefix_title": "將標題加入索引",
+  "index_prefix_title_tips": "自動給索引所有索引加標題名",
   "index_size": "索引大小",
   "index_size_tips": "向量化時內容的長度，系統會自動按該大小對分塊進行進一步的分割。",
   "input_required_field_to_select_baseurl": "請先輸入必填信息",

projects/app/src/components/core/chat/ChatContainer/ChatBox/Input/ChatInput.tsx

-import { Box, Flex, Textarea } from '@chakra-ui/react';
+import type { FlexProps} from '@chakra-ui/react';
+import { Box, Flex, Textarea, useBoolean } from '@chakra-ui/react';
 import React, { useRef, useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'next-i18next';
 import MyTooltip from '@fastgpt/web/components/common/MyTooltip';
@@ -48,6 +49,8 @@ const ChatInput = ({
   const { setValue, watch, control } = chatForm;
   const inputValue = watch('input');
 
+  const [focusing, { on: onFocus, off: offFocus }] = useBoolean();
+
   // Check voice input state
   const [mobilePreSpeak, setMobilePreSpeak] = useState(false);
 
@@ -207,6 +210,8 @@ const ChatInput = ({
                 }
               }
             }}
+            onFocus={onFocus}
+            onBlur={offFocus}
           />
         </Flex>
       </Flex>
@@ -254,7 +259,8 @@ const ChatInput = ({
               borderRadius={'sm'}
               cursor={'pointer'}
               _hover={{ bg: 'rgba(0, 0, 0, 0.04)' }}
-              onClick={() => {
+              onClick={(e) => {
+                e.stopPropagation();
                 onOpenSelectFile();
               }}
             >
@@ -276,7 +282,8 @@ const ChatInput = ({
               borderRadius={'sm'}
               cursor={'pointer'}
               _hover={{ bg: 'rgba(0, 0, 0, 0.04)' }}
-              onClick={() => {
+              onClick={(e) => {
+                e.stopPropagation();
                 VoiceInputRef.current?.onSpeak?.();
               }}
             >
@@ -307,7 +314,8 @@ const ChatInput = ({
             }
             borderRadius={['md', 'lg']}
             cursor={isChatting ? 'pointer' : canSendMessage ? 'pointer' : 'not-allowed'}
-            onClick={() => {
+            onClick={(e) => {
+              e.stopPropagation();
               if (isChatting) {
                 return onStop();
               }
@@ -343,6 +351,11 @@ const ChatInput = ({
     onStop
   ]);
 
+  const activeStyles: FlexProps = {
+    boxShadow: '0px 5px 20px -4px rgba(19, 51, 107, 0.13)',
+    border: '0.5px solid rgba(0, 0, 0, 0.24)'
+  };
+
   return (
     <Box
       m={['0 auto 10px', '10px auto']}
@@ -381,12 +394,17 @@ const ChatInput = ({
         pt={fileList.length > 0 ? '0' : mobilePreSpeak ? [0, 4] : [3, 4]}
         pb={[2, 4]}
         position={'relative'}
-        boxShadow={`0px 5px 16px -4px rgba(19, 51, 107, 0.08)`}
         borderRadius={['xl', 'xxl']}
         bg={'white'}
         overflow={'display'}
-        border={'0.5px solid rgba(0, 0, 0, 0.15)'}
-        borderColor={'rgba(0,0,0,0.12)'}
+        {...(focusing
+          ? activeStyles
+          : {
+              _hover: activeStyles,
+              border: '0.5px solid rgba(0, 0, 0, 0.18)',
+              boxShadow: `0px 5px 16px -4px rgba(19, 51, 107, 0.08)`
+            })}
+        onClick={() => TextareaDom?.current?.focus()}
       >
         <Box flex={1}>
           {/* Chat input guide box */}

projects/app/src/pageComponents/dataset/detail/Form/CollectionChunkForm.tsx

@@ -13,7 +13,8 @@ import {
   Textarea,
   useDisclosure,
   Checkbox,
-  HStack
+  HStack,
+  Grid
 } from '@chakra-ui/react';
 import MyIcon from '@fastgpt/web/components/common/Icon';
 import LeftRadio from '@fastgpt/web/components/common/Radio/LeftRadio';
@@ -35,7 +36,6 @@ import { DatasetPageContext } from '@/web/core/dataset/context/datasetPageContex
 import MySelect from '@fastgpt/web/components/common/MySelect';
 import {
   chunkAutoChunkSize,
-  getAutoIndexSize,
   getIndexSizeSelectList,
   getLLMDefaultChunkSize,
   getLLMMaxChunkSize,
@@ -44,7 +44,6 @@ import {
   minChunkSize
 } from '@fastgpt/global/core/dataset/training/utils';
 import RadioGroup from '@fastgpt/web/components/common/Radio/RadioGroup';
-import type { LLMModelItemType, EmbeddingModelItemType } from '@fastgpt/global/core/ai/model.d';
 
 const PromptTextarea = ({
   defaultValue = '',
@@ -98,6 +97,7 @@ export type CollectionChunkFormType = {
   // Index enhance
   imageIndex: boolean;
   autoIndexes: boolean;
+  indexPrefixTitle: boolean;
 
   // Chunk setting
   chunkSettingMode: ChunkSettingModeEnum; // 系统参数/自定义参数
@@ -133,6 +133,7 @@ const CollectionChunkForm = ({ form }: { form: UseFormReturn<CollectionChunkForm
   const autoIndexes = watch('autoIndexes');
   const indexSize = watch('indexSize');
   const imageIndex = watch('imageIndex');
+  const indexPrefixTitle = watch('indexPrefixTitle');
   const paragraphChunkAIMode = watch('paragraphChunkAIMode');
 
   const trainingModeList = useMemo(() => {
@@ -282,48 +283,56 @@ const CollectionChunkForm = ({ form }: { form: UseFormReturn<CollectionChunkForm
         </Box>
       )}
 
-      {trainingType === DatasetCollectionDataProcessModeEnum.chunk &&
-        feConfigs?.show_dataset_enhance !== false && (
-          <Box mt={6}>
-            <Box fontSize={'sm'} mb={2} color={'myGray.600'}>
-              {t('dataset:enhanced_indexes')}
-            </Box>
-            <HStack gap={[3, 7]}>
-              <HStack flex={'1'} spacing={1}>
-                <MyTooltip label={!feConfigs?.isPlus ? t('common:commercial_function_tip') : ''}>
-                  <Checkbox
-                    isDisabled={!feConfigs?.isPlus}
-                    isChecked={autoIndexes}
-                    {...register('autoIndexes')}
-                  >
-                    <FormLabel>{t('dataset:auto_indexes')}</FormLabel>
-                  </Checkbox>
-                </MyTooltip>
-                <QuestionTip label={t('dataset:auto_indexes_tips')} />
-              </HStack>
-              <HStack flex={'1'} spacing={1}>
-                <MyTooltip
-                  label={
-                    !feConfigs?.isPlus
-                      ? t('common:commercial_function_tip')
-                      : !datasetDetail?.vlmModel
-                        ? t('common:error_vlm_not_config')
-                        : ''
-                  }
-                >
-                  <Checkbox
-                    isDisabled={!feConfigs?.isPlus || !datasetDetail?.vlmModel}
-                    isChecked={imageIndex}
-                    {...register('imageIndex')}
+      <Box mt={6}>
+        <Box fontSize={'sm'} mb={2} color={'myGray.600'}>
+          {t('dataset:enhanced_indexes')}
+        </Box>
+        <Grid gridTemplateColumns={'1fr 1fr'} rowGap={[2, 4]} columnGap={[3, 7]}>
+          <HStack flex={'1'} spacing={1}>
+            <Checkbox isChecked={indexPrefixTitle} {...register('indexPrefixTitle')}>
+              <FormLabel>{t('dataset:index_prefix_title')}</FormLabel>
+            </Checkbox>
+            <QuestionTip label={t('dataset:index_prefix_title_tips')} />
+          </HStack>
+          {trainingType === DatasetCollectionDataProcessModeEnum.chunk &&
+            feConfigs?.show_dataset_enhance !== false && (
+              <>
+                <HStack flex={'1'} spacing={1}>
+                  <MyTooltip label={!feConfigs?.isPlus ? t('common:commercial_function_tip') : ''}>
+                    <Checkbox
+                      isDisabled={!feConfigs?.isPlus}
+                      isChecked={autoIndexes}
+                      {...register('autoIndexes')}
+                    >
+                      <FormLabel>{t('dataset:auto_indexes')}</FormLabel>
+                    </Checkbox>
+                  </MyTooltip>
+                  <QuestionTip label={t('dataset:auto_indexes_tips')} />
+                </HStack>
+                <HStack flex={'1'} spacing={1}>
+                  <MyTooltip
+                    label={
+                      !feConfigs?.isPlus
+                        ? t('common:commercial_function_tip')
+                        : !datasetDetail?.vlmModel
+                          ? t('common:error_vlm_not_config')
+                          : ''
+                    }
                   >
-                    <FormLabel>{t('dataset:image_auto_parse')}</FormLabel>
-                  </Checkbox>
-                </MyTooltip>
-                <QuestionTip label={t('dataset:image_auto_parse_tips')} />
-              </HStack>
-            </HStack>
-          </Box>
-        )}
+                    <Checkbox
+                      isDisabled={!feConfigs?.isPlus || !datasetDetail?.vlmModel}
+                      isChecked={imageIndex}
+                      {...register('imageIndex')}
+                    >
+                      <FormLabel>{t('dataset:image_auto_parse')}</FormLabel>
+                    </Checkbox>
+                  </MyTooltip>
+                  <QuestionTip label={t('dataset:image_auto_parse_tips')} />
+                </HStack>
+              </>
+            )}
+        </Grid>
+      </Box>
       <Box mt={6}>
         <Box fontSize={'sm'} mb={2} color={'myGray.600'}>
           {t('dataset:chunk_process_params')}

projects/app/src/pageComponents/dataset/detail/Import/Context.tsx

@@ -49,6 +49,7 @@ export const defaultFormData: ImportFormType = {
 
   imageIndex: false,
   autoIndexes: false,
+  indexPrefixTitle: true,
 
   chunkSettingMode: ChunkSettingModeEnum.auto,
   chunkSplitMode: DataChunkSplitModeEnum.paragraph,

projects/app/src/pageComponents/dataset/detail/Import/diffSource/ReTraining.tsx

@@ -55,8 +55,9 @@ const ReTraining = () => {
         dataEnhanceCollectionName:
           collection.dataEnhanceCollectionName || defaultFormData.dataEnhanceCollectionName,
 
-        imageIndex: collection.imageIndex || defaultFormData.imageIndex,
-        autoIndexes: collection.autoIndexes || defaultFormData.autoIndexes,
+        imageIndex: collection.imageIndex ?? defaultFormData.imageIndex,
+        autoIndexes: collection.autoIndexes ?? defaultFormData.autoIndexes,
+        indexPrefixTitle: collection.indexPrefixTitle ?? defaultFormData.indexPrefixTitle,
 
         chunkSettingMode: collection.chunkSettingMode || defaultFormData.chunkSettingMode,
         chunkSplitMode: collection.chunkSplitMode || defaultFormData.chunkSplitMode,

projects/app/src/pageComponents/dataset/detail/InputDataModal.tsx

@@ -84,15 +84,13 @@ const InputDataModal = ({
       onSuccess(res) {
         if (res.type === DatasetCollectionTypeEnum.images) {
           setCurrentTab(TabEnum.image);
-        } else {
-          setCurrentTab(TabEnum.chunk);
         }
       }
     }
   );
 
   // Get data
-  const { loading: isFetchingData } = useRequest2(
+  const { data: dataItem, loading: isFetchingData } = useRequest2(
     async () => {
       if (dataId) return getDatasetDataItemById(dataId);
       return null;
@@ -125,6 +123,11 @@ const InputDataModal = ({
     }
   );
 
+  useEffect(() => {
+    if (currentTab || !dataItem) return;
+    setCurrentTab(dataItem.a ? TabEnum.qa : TabEnum.chunk);
+  }, [collection, dataItem, currentTab]);
+
   // Import new data
   const { runAsync: sureImportData, loading: isImporting } = useRequest2(
     async (e: InputDataType) => {

projects/app/src/pageComponents/dataset/detail/MetaDataCard.tsx

@@ -47,6 +47,10 @@ const MetaDataCard = ({ datasetId }: { datasetId: string }) => {
 
     return [
       {
+        label: t('common:core.dataset.collection.id'),
+        value: collection?._id
+      },
+      {
         label: t('common:core.dataset.collection.metadata.source'),
         value: t(DatasetCollectionTypeMap[collection.type]?.name as any)
       },
@@ -94,6 +98,14 @@ const MetaDataCard = ({ datasetId }: { datasetId: string }) => {
             }
           ]
         : []),
+      ...(collection.indexPrefixTitle !== undefined
+        ? [
+            {
+              label: t('dataset:index_prefix_title'),
+              value: collection.indexPrefixTitle ? 'Yes' : 'No'
+            }
+          ]
+        : []),
       ...(collection.imageIndex !== undefined
         ? [
             {
@@ -146,26 +158,22 @@ const MetaDataCard = ({ datasetId }: { datasetId: string }) => {
   }, [collection, t]);
 
   return (
-    <MyBox isLoading={isLoading} w={'100%'} h={'100%'} p={6}>
-      <Box fontSize={'md'} pb={4}>
+    <MyBox isLoading={isLoading} w={'100%'} h={'100%'} p={6} overflow={'auto'}>
+      <Box fontSize={'md'} fontWeight={'bold'} color={'myGray.900'} pb={4}>
         {t('common:core.dataset.collection.metadata.metadata')}
       </Box>
-      <Flex mb={3} wordBreak={'break-all'} fontSize={'sm'}>
-        <Box color={'myGray.500'} flex={'0 0 90px'}>
-          {t('common:core.dataset.collection.id')}:
-        </Box>
-        <Box>{collection?._id}</Box>
-      </Flex>
       {metadataList.map(
         (item, i) =>
           item.label &&
           item.value && (
-            <Flex key={i} alignItems={'center'} mb={3} wordBreak={'break-all'} fontSize={'sm'}>
-              <Box color={'myGray.500'} flex={'0 0 90px'}>
+            <Box key={i} mb={3} wordBreak={'break-all'}>
+              <Box color={'myGray.500'} fontSize={'xs'}>
                 {item.label}
               </Box>
-              <Box>{item.value}</Box>
-            </Flex>
+              <Box color={'myGray.900'} fontSize={'sm'}>
+                {item.value}
+              </Box>
+            </Box>
           )
       )}
       {collection?.sourceId && (

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

@@ -48,7 +48,9 @@ async function handler(req: NextApiRequest) {
 
   const [
     {
-      dataset: { _id: datasetId, vectorModel, agentModel }
+      dataset: { _id: datasetId, vectorModel, agentModel },
+      indexPrefixTitle,
+      name
     }
   ] = await Promise.all([getCollectionWithDataset(collectionId)]);
 
@@ -84,6 +86,7 @@ async function handler(req: NextApiRequest) {
     q: formatQ,
     a: formatA,
     chunkIndex: 0,
+    indexPrefix: indexPrefixTitle ? `# ${name}` : undefined,
     embeddingModel: vectorModelData.model,
     indexes: formatIndexes
   });

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

@@ -8,13 +8,16 @@ import { type ApiRequestProps } from '@fastgpt/service/type/next';
 import { addOperationLog } from '@fastgpt/service/support/operationLog/addOperationLog';
 import { OperationLogEventEnum } from '@fastgpt/global/support/operationLog/constants';
 import { getI18nDatasetType } from '@fastgpt/service/support/operationLog/util';
+
 async function handler(req: ApiRequestProps<UpdateDatasetDataProps>) {
   const { dataId, q, a, indexes = [] } = req.body;
 
   // auth data permission
   const {
     collection: {
-      dataset: { vectorModel }
+      dataset: { vectorModel },
+      name,
+      indexPrefixTitle
     },
     teamId,
     tmbId,
@@ -33,7 +36,8 @@ async function handler(req: ApiRequestProps<UpdateDatasetDataProps>) {
       q,
       a,
       indexes,
-      model: vectorModel
+      model: vectorModel,
+      indexPrefix: indexPrefixTitle ? `# ${name}` : undefined
     });
 
     pushGenerateVectorUsage({

projects/app/src/pages/dataset/detail/index.tsx

@@ -41,7 +41,7 @@ type Props = { datasetId: string; currentTab: TabEnum };
 const sliderStyles: FlexProps = {
   bg: 'white',
   borderRadius: 'md',
-  overflowY: 'scroll',
+  overflowY: 'auto',
   boxShadow: 2
 };
 

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

@@ -25,13 +25,15 @@ const formatIndexes = async ({
   q,
   a = '',
   indexSize,
-  maxIndexSize
+  maxIndexSize,
+  indexPrefix
 }: {
   indexes?: (Omit<DatasetDataIndexItemType, 'dataId'> & { dataId?: string })[];
   q: string;
   a?: string;
   indexSize: number;
   maxIndexSize: number;
+  indexPrefix?: string;
 }): Promise<
   {
     type: `${DatasetDataIndexTypeEnum}`;
@@ -39,6 +41,12 @@ const formatIndexes = async ({
     dataId?: string;
   }[]
 > => {
+  const formatText = (text: string) => {
+    if (indexPrefix && !text.startsWith(indexPrefix)) {
+      return `${indexPrefix}\n${text}`;
+    }
+    return text;
+  };
   /* get dataset data default index */
   const getDefaultIndex = async ({
     q = '',
@@ -62,11 +70,11 @@ const formatIndexes = async ({
 
     return [
       ...qChunks.map((text) => ({
-        text,
+        text: formatText(text),
         type: DatasetDataIndexTypeEnum.default
       })),
       ...aChunks.map((text) => ({
-        text,
+        text: formatText(text),
         type: DatasetDataIndexTypeEnum.default
       }))
     ];
@@ -130,9 +138,22 @@ const formatIndexes = async ({
         return item;
       })
     )
-  ).flat();
+  )
+    .flat()
+    .filter((item) => !!item.text.trim());
+
+  // Add prefix
+  const prefixIndexes = indexPrefix
+    ? chekcIndexes.map((index) => {
+        if (index.type === DatasetDataIndexTypeEnum.custom) return index;
+        return {
+          ...index,
+          text: formatText(index.text)
+        };
+      })
+    : chekcIndexes;
 
-  return chekcIndexes.filter((item) => !!item.text.trim());
+  return prefixIndexes;
 };
 /* insert data.
  * 1. create data id
@@ -150,6 +171,7 @@ export async function insertData2Dataset({
   chunkIndex = 0,
   indexSize = 512,
   indexes,
+  indexPrefix,
   embeddingModel,
   session
 }: CreateDatasetDataProps & {
@@ -174,7 +196,8 @@ export async function insertData2Dataset({
     q,
     a,
     indexSize,
-    maxIndexSize: embModel.maxToken
+    maxIndexSize: embModel.maxToken,
+    indexPrefix
   });
 
   // insert to vector store
@@ -255,7 +278,8 @@ export async function updateData2Dataset({
   a,
   indexes,
   model,
-  indexSize = 512
+  indexSize = 512,
+  indexPrefix
 }: UpdateDatasetDataProps & { model: string; indexSize?: number }) {
   if (!Array.isArray(indexes)) {
     return Promise.reject('indexes is required');
@@ -271,7 +295,8 @@ export async function updateData2Dataset({
     q,
     a,
     indexSize,
-    maxIndexSize: getEmbeddingModel(model).maxToken
+    maxIndexSize: getEmbeddingModel(model).maxToken,
+    indexPrefix
   });
 
   // 3. Patch indexes, create, update, delete

projects/app/src/service/core/dataset/queues/datasetParse.ts

@@ -101,21 +101,13 @@ export const datasetParseQueue = async (): Promise<any> => {
           $inc: { retryCount: -1 }
         }
       )
-        .select({
-          _id: 1,
-          teamId: 1,
-          tmbId: 1,
-          datasetId: 1,
-          collectionId: 1,
-          billId: 1,
-          q: 1
-        })
         .populate<{
           dataset: DatasetSchemaType;
           collection: DatasetCollectionSchemaType;
         }>([
           {
-            path: 'collection'
+            path: 'collection',
+            select: '-qaPrompt'
           },
           {
             path: 'dataset'
@@ -300,7 +292,6 @@ export const datasetParseQueue = async (): Promise<any> => {
         vlmModel: dataset.vlmModel,
         indexSize: collection.indexSize,
         mode: trainingMode,
-        prompt: collection.qaPrompt,
         billId: data.billId,
         data: chunks.map((item, index) => ({
           ...item,

projects/app/src/service/core/dataset/queues/generateQA.ts

@@ -14,7 +14,6 @@ import {
   countGptMessagesTokens,
   countPromptTokens
 } from '@fastgpt/service/common/string/tiktoken/index';
-import { pushDataListToTrainingQueueByCollectionId } from '@fastgpt/service/core/dataset/training/controller';
 import { loadRequestMessages } from '@fastgpt/service/core/chat/utils';
 import { llmCompletionsBodyFormat, formatLLMResponse } from '@fastgpt/service/core/ai/utils';
 import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
@@ -24,6 +23,7 @@ import {
 } from '@fastgpt/global/core/dataset/training/utils';
 import { getErrText } from '@fastgpt/global/common/error/utils';
 import { text2Chunks } from '@fastgpt/service/worker/function';
+import { pushDataListToTrainingQueue } from '@fastgpt/service/core/dataset/training/controller';
 
 const reduceQueue = () => {
   global.qaQueueLen = global.qaQueueLen > 0 ? global.qaQueueLen - 1 : 0;
@@ -41,6 +41,11 @@ const reduceQueueAndReturn = (delay = 0) => {
   }
 };
 
+type PopulateType = {
+  dataset: { vectorModel: string; agentModel: string; vlmModel: string };
+  collection: { qaPrompt?: string };
+};
+
 export async function generateQA(): Promise<any> {
   const max = global.systemEnv?.qaMaxProcess || 10;
   addLog.debug(`[QA Queue] Queue size: ${global.qaQueueLen}`);
@@ -68,18 +73,16 @@ export async function generateQA(): Promise<any> {
           $inc: { retryCount: -1 }
         }
       )
-        .select({
-          _id: 1,
-          teamId: 1,
-          tmbId: 1,
-          datasetId: 1,
-          collectionId: 1,
-          q: 1,
-          model: 1,
-          chunkIndex: 1,
-          billId: 1,
-          prompt: 1
-        })
+        .populate<PopulateType>([
+          {
+            path: 'dataset',
+            select: 'agentModel vectorModel vlmModel'
+          },
+          {
+            path: 'collection',
+            select: 'qaPrompt'
+          }
+        ])
         .lean();
 
       // task preemption
@@ -110,6 +113,13 @@ export async function generateQA(): Promise<any> {
     return reduceQueueAndReturn();
   }
 
+  if (!data.dataset || !data.collection) {
+    addLog.info(`[QA Queue] Dataset or collection not found`, data);
+    // Delete data
+    await MongoDatasetTraining.deleteOne({ _id: data._id });
+    return reduceQueueAndReturn();
+  }
+
   // auth balance
   if (!(await checkTeamAiPointsAndLock(data.teamId))) {
     return reduceQueueAndReturn();
@@ -117,8 +127,8 @@ export async function generateQA(): Promise<any> {
   addLog.info(`[QA Queue] Start`);
 
   try {
-    const modelData = getLLMModel(data.model);
-    const prompt = `${data.prompt || Prompt_AgentQA.description}
+    const modelData = getLLMModel(data.dataset.agentModel);
+    const prompt = `${data.collection.qaPrompt || Prompt_AgentQA.description}
 ${replaceVariable(Prompt_AgentQA.fixedText, { text })}`;
 
     // request LLM to get QA
@@ -147,16 +157,20 @@ ${replaceVariable(Prompt_AgentQA.fixedText, { text })}`;
     const qaArr = await formatSplitText({ answer, rawText: text, llmModel: modelData }); // 格式化后的QA对
 
     // get vector and insert
-    await pushDataListToTrainingQueueByCollectionId({
+    await pushDataListToTrainingQueue({
       teamId: data.teamId,
       tmbId: data.tmbId,
+      datasetId: data.datasetId,
       collectionId: data.collectionId,
       mode: TrainingModeEnum.chunk,
       data: qaArr.map((item) => ({
         ...item,
         chunkIndex: data.chunkIndex
       })),
-      billId: data.billId
+      billId: data.billId,
+      vectorModel: data.dataset.vectorModel,
+      agentModel: data.dataset.agentModel,
+      vlmModel: data.dataset.vlmModel
     });
 
     // delete data from training
@@ -192,7 +206,7 @@ ${replaceVariable(Prompt_AgentQA.fixedText, { text })}`;
       }
     );
 
-    return reduceQueueAndReturn(1000);
+    return reduceQueueAndReturn(500);
   }
 }
 

projects/app/src/service/core/dataset/queues/generateVector.ts

@@ -12,10 +12,13 @@ import {
 } from '@fastgpt/service/common/vectorDB/controller';
 import { getEmbeddingModel } from '@fastgpt/service/core/ai/model';
 import { mongoSessionRun } from '@fastgpt/service/common/mongo/sessionRun';
-import { type DatasetTrainingSchemaType } from '@fastgpt/global/core/dataset/type';
-import type { Document } from '@fastgpt/service/common/mongo';
 import { getErrText } from '@fastgpt/global/common/error/utils';
 import { getMaxIndexSize } from '@fastgpt/global/core/dataset/training/utils';
+import type {
+  DatasetDataSchemaType,
+  DatasetTrainingSchemaType
+} from '@fastgpt/global/core/dataset/type';
+import { retryFn } from '@fastgpt/global/common/system/utils';
 
 const reduceQueue = () => {
   global.vectorQueueLen = global.vectorQueueLen > 0 ? global.vectorQueueLen - 1 : 0;
@@ -33,6 +36,13 @@ const reduceQueueAndReturn = (delay = 0) => {
   }
 };
 
+type PopulateType = {
+  dataset: { vectorModel: string };
+  collection: { name: string; indexPrefixTitle: boolean };
+  data: { _id: string; indexes: DatasetDataSchemaType['indexes'] };
+};
+type TrainingDataType = DatasetTrainingSchemaType & PopulateType;
+
 /* 索引生成队列。每导入一次，就是一个单独的线程 */
 export async function generateVector(): Promise<any> {
   const max = global.systemEnv?.vectorMaxProcess || 10;
@@ -59,7 +69,22 @@ export async function generateVector(): Promise<any> {
           lockTime: new Date(),
           $inc: { retryCount: -1 }
         }
-      );
+      )
+        .populate<PopulateType>([
+          {
+            path: 'dataset',
+            select: 'vectorModel'
+          },
+          {
+            path: 'collection',
+            select: 'name indexPrefixTitle'
+          },
+          {
+            path: 'data',
+            select: '_id indexes'
+          }
+        ])
+        .lean();
 
       // task preemption
       if (!data) {
@@ -89,6 +114,13 @@ export async function generateVector(): Promise<any> {
     return reduceQueueAndReturn();
   }
 
+  if (!data.dataset || !data.collection) {
+    addLog.info(`[Vector Queue] Dataset or collection not found`, data);
+    // Delete data
+    await MongoDatasetTraining.deleteOne({ _id: data._id });
+    return reduceQueueAndReturn();
+  }
+
   // auth balance
   if (!(await checkTeamAiPointsAndLock(data.teamId))) {
     return reduceQueueAndReturn();
@@ -110,7 +142,7 @@ export async function generateVector(): Promise<any> {
       teamId: data.teamId,
       tmbId: data.tmbId,
       inputTokens: tokens,
-      model: data.model,
+      model: data.dataset.vectorModel,
       billId: data.billId
     });
 
@@ -131,75 +163,62 @@ export async function generateVector(): Promise<any> {
         errorMsg: getErrText(err, 'unknown error')
       }
     );
-    return reduceQueueAndReturn(1000);
+    return reduceQueueAndReturn(500);
   }
 }
 
-const rebuildData = async ({
-  trainingData
-}: {
-  trainingData: Document<unknown, {}, DatasetTrainingSchemaType> &
-    Omit<
-      DatasetTrainingSchemaType &
-        Required<{
-          _id: string;
-        }>,
-      never
-    >;
-}) => {
-  // find data
-  const mongoData = await MongoDatasetData.findById(
-    trainingData.dataId,
-    'indexes teamId datasetId collectionId'
-  );
-
-  if (!mongoData) {
-    await trainingData.deleteOne();
+const rebuildData = async ({ trainingData }: { trainingData: TrainingDataType }) => {
+  if (!trainingData.data) {
+    await MongoDatasetTraining.deleteOne({ _id: trainingData._id });
     return Promise.reject('Not data');
   }
 
-  const deleteVectorIdList = mongoData.indexes.map((index) => index.dataId);
+  // Old vectorId
+  const deleteVectorIdList = trainingData.data.indexes.map((index) => index.dataId);
 
   // Find next rebuilding data to insert training queue
-  await mongoSessionRun(async (session) => {
-    // get new mongoData insert to training
-    const newRebuildingData = await MongoDatasetData.findOneAndUpdate(
-      {
-        rebuilding: true,
-        teamId: mongoData.teamId,
-        datasetId: mongoData.datasetId
-      },
-      {
-        $unset: {
-          rebuilding: null
-        },
-        updateTime: new Date()
-      },
-      { session }
-    ).select({
-      _id: 1,
-      collectionId: 1
-    });
-
-    if (newRebuildingData) {
-      await MongoDatasetTraining.create(
-        [
+  try {
+    await retryFn(() =>
+      mongoSessionRun(async (session) => {
+        // get new mongoData insert to training
+        const newRebuildingData = await MongoDatasetData.findOneAndUpdate(
           {
-            teamId: mongoData.teamId,
-            tmbId: trainingData.tmbId,
-            datasetId: mongoData.datasetId,
-            collectionId: newRebuildingData.collectionId,
-            billId: trainingData.billId,
-            mode: TrainingModeEnum.chunk,
-            model: trainingData.model,
-            dataId: newRebuildingData._id,
-            retryCount: 50
-          }
-        ],
-        { session, ordered: true }
-      );
-    }
-  });
+            rebuilding: true,
+            teamId: trainingData.teamId,
+            datasetId: trainingData.datasetId
+          },
+          {
+            $unset: {
+              rebuilding: null
+            },
+            updateTime: new Date()
+          },
+          { session }
+        ).select({
+          _id: 1,
+          collectionId: 1
+        });
+
+        if (newRebuildingData) {
+          await MongoDatasetTraining.create(
+            [
+              {
+                teamId: trainingData.teamId,
+                tmbId: trainingData.tmbId,
+                datasetId: trainingData.datasetId,
+                collectionId: newRebuildingData.collectionId,
+                billId: trainingData.billId,
+                mode: TrainingModeEnum.chunk,
+                dataId: newRebuildingData._id,
+                retryCount: 50
+              }
+            ],
+            { session, ordered: true }
+          );
+        }
+      })
+    );
+  } catch (error) {}
 
   // update vector, update dataset_data rebuilding status, delete data from training
   // 1. Insert new vector to dataset_data
@@ -208,28 +227,36 @@ const rebuildData = async ({
     insertId: string;
   }[] = [];
   let i = 0;
-  for await (const index of mongoData.indexes) {
+  for await (const index of trainingData.data.indexes) {
     const result = await insertDatasetDataVector({
       query: index.text,
-      model: getEmbeddingModel(trainingData.model),
-      teamId: mongoData.teamId,
-      datasetId: mongoData.datasetId,
-      collectionId: mongoData.collectionId
+      model: getEmbeddingModel(trainingData.dataset.vectorModel),
+      teamId: trainingData.teamId,
+      datasetId: trainingData.datasetId,
+      collectionId: trainingData.collectionId
     });
-    mongoData.indexes[i].dataId = result.insertId;
+    trainingData.data.indexes[i].dataId = result.insertId;
     updateResult.push(result);
     i++;
   }
 
   const { tokens } = await mongoSessionRun(async (session) => {
     // 2. Ensure that the training data is deleted after the Mongo update is successful
-    await mongoData.save({ session });
+    await MongoDatasetData.updateOne(
+      { _id: trainingData.data._id },
+      {
+        $set: {
+          indexes: trainingData.data.indexes
+        }
+      },
+      { session }
+    );
     // 3. Delete the training data
-    await trainingData.deleteOne({ session });
+    await MongoDatasetTraining.deleteOne({ _id: trainingData._id }, { session });
 
     // 4. Delete old vector
     await deleteDatasetDataVector({
-      teamId: mongoData.teamId,
+      teamId: trainingData.teamId,
       idList: deleteVectorIdList
     });
 
@@ -241,19 +268,8 @@ const rebuildData = async ({
   return { tokens };
 };
 
-const insertData = async ({
-  trainingData
-}: {
-  trainingData: Document<unknown, {}, DatasetTrainingSchemaType> &
-    Omit<
-      DatasetTrainingSchemaType &
-        Required<{
-          _id: string;
-        }>,
-      never
-    >;
-}) => {
-  const { tokens } = await mongoSessionRun(async (session) => {
+const insertData = async ({ trainingData }: { trainingData: TrainingDataType }) => {
+  return mongoSessionRun(async (session) => {
     // insert new data to dataset
     const { tokens } = await insertData2Dataset({
       teamId: trainingData.teamId,
@@ -264,18 +280,21 @@ const insertData = async ({
       a: trainingData.a,
       imageId: trainingData.imageId,
       chunkIndex: trainingData.chunkIndex,
-      indexSize: trainingData.indexSize || getMaxIndexSize(getEmbeddingModel(trainingData.model)),
+      indexSize:
+        trainingData.indexSize ||
+        getMaxIndexSize(getEmbeddingModel(trainingData.dataset.vectorModel)),
       indexes: trainingData.indexes,
-      embeddingModel: trainingData.model,
+      indexPrefix: trainingData.collection.indexPrefixTitle
+        ? `# ${trainingData.collection.name}`
+        : undefined,
+      embeddingModel: trainingData.dataset.vectorModel,
       session
     });
     // delete data from training
-    await trainingData.deleteOne({ session });
+    await MongoDatasetTraining.deleteOne({ _id: trainingData._id }, { session });
 
     return {
       tokens
     };
   });
-
-  return { tokens };
 };

test/cases/api/core/dataset/training/deleteTrainingData.test.ts → projects/app/test/api/core/dataset/training/deleteTrainingData.test.ts

@@ -33,8 +33,7 @@ describe('delete training data test', () => {
       tmbId: root.tmbId,
       datasetId: dataset._id,
       collectionId: collection._id,
-      mode: TrainingModeEnum.chunk,
-      model: 'test'
+      mode: TrainingModeEnum.chunk
     });
 
     const res = await Call<deleteTrainingDataBody, {}, deleteTrainingDataResponse>(handler, {

test/cases/api/core/dataset/training/getTrainingDataDetail.test.ts → projects/app/test/api/core/dataset/training/getTrainingDataDetail.test.ts

@@ -33,7 +33,6 @@ describe('get training data detail test', () => {
       tmbId: root.tmbId,
       datasetId: dataset._id,
       collectionId: collection._id,
-      model: 'test',
       mode: TrainingModeEnum.chunk,
       q: 'test',
       a: 'test'

test/cases/api/core/dataset/training/getTrainingError.test.ts → projects/app/test/api/core/dataset/training/getTrainingError.test.ts

@@ -35,7 +35,6 @@ describe('training error list test', () => {
         datasetId: dataset._id,
         collectionId: collection._id,
         mode: TrainingModeEnum.chunk,
-        model: 'test',
         errorMsg: 'test'
       }))
     );

test/cases/api/core/dataset/training/updateTrainingData.test.ts → projects/app/test/api/core/dataset/training/updateTrainingData.test.ts

@@ -33,8 +33,7 @@ describe('update training data test', () => {
       tmbId: root.tmbId,
       datasetId: dataset._id,
       collectionId: collection._id,
-      mode: TrainingModeEnum.chunk,
-      model: 'test'
+      mode: TrainingModeEnum.chunk
     });
 
     const res = await Call<updateTrainingDataBody, {}, updateTrainingDataResponse>(handler, {