feat: 拆分文本增加滑块，增加直接分段导入方式

src/api/model.ts

@@ -85,8 +85,12 @@ export const postModelDataInput = (data: {
 /**
  * 拆分数据
  */
-export const postModelDataSplitData = (data: { modelId: string; text: string; prompt: string }) =>
-  POST(`/model/data/splitData`, data);
+export const postModelDataSplitData = (data: {
+  modelId: string;
+  chunks: string[];
+  prompt: string;
+  mode: 'qa' | 'subsection';
+}) => POST(`/model/data/splitData`, data);
 
 /**
  * json导入数据

src/components/Radio/index.tsx

@@ -0,0 +1,52 @@
+import React from 'react';
+import { Stack, Box, Flex, useTheme } from '@chakra-ui/react';
+import type { StackProps } from '@chakra-ui/react';
+
+// @ts-ignore
+interface Props extends StackProps {
+  list: { label: string; value: string | number }[];
+  value: string | number;
+  onChange: (e: string | number) => void;
+}
+
+const Radio = ({ list, value, onChange, ...props }: Props) => {
+  return (
+    <Stack {...props} spacing={5} direction={'row'}>
+      {list.map((item) => (
+        <Flex
+          key={item.value}
+          alignItems={'center'}
+          cursor={'pointer'}
+          userSelect={'none'}
+          _before={{
+            content: '""',
+            w: '16px',
+            h: '16px',
+            mr: 1,
+            borderRadius: '16px',
+            transition: '0.2s',
+            ...(value === item.value
+              ? {
+                  border: '5px solid',
+                  borderColor: 'blue.500'
+                }
+              : {
+                  border: '2px solid',
+                  borderColor: 'gray.200'
+                })
+          }}
+          _hover={{
+            _before: {
+              borderColor: 'blue.400'
+            }
+          }}
+          onClick={() => onChange(item.value)}
+        >
+          {item.label}
+        </Flex>
+      ))}
+    </Stack>
+  );
+};
+
+export default Radio;

src/pages/api/chat/vectorGpt.ts

@@ -106,16 +106,20 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
       });
     } else {
       // 有匹配情况下，添加知识库内容。
-      // 系统提示词过滤，最多 2000 tokens
-      const systemPrompt = systemPromptFilter(formatRedisPrompt, 2000);
+      // 系统提示词过滤，最多 3000 tokens
+      const systemPrompt = systemPromptFilter(formatRedisPrompt, 3000);
 
       prompts.unshift({
         obj: 'SYSTEM',
-        value: `${
-          model.systemPrompt || '根据知识库内容回答'
-        } 知识库是最新的,下面是知识库内容:当前时间为${dayjs().format(
-          'YYYY/MM/DD HH:mm:ss'
-        )}\n${systemPrompt}`
+        value: `
+${model.systemPrompt}
+${
+  model.search.mode === ModelVectorSearchModeEnum.hightSimilarity
+    ? `你只能从知识库选择内容回答.不在知识库内容拒绝回复`
+    : ''
+}
+知识库内容为: 当前时间为${dayjs().format('YYYY/MM/DD HH:mm:ss')}\n${systemPrompt}'
+`
       });
     }
 

src/pages/api/model/data/pushModelDataInput.ts

@@ -4,7 +4,7 @@ import { connectToDatabase, Model } from '@/service/mongo';
 import { authToken } from '@/service/utils/tools';
 import { ModelDataSchema } from '@/types/mongoSchema';
 import { generateVector } from '@/service/events/generateVector';
-import { connectPg, PgClient } from '@/service/pg';
+import { PgClient } from '@/service/pg';
 
 export default async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
   try {
@@ -26,7 +26,6 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse<
     const userId = await authToken(authorization);
 
     await connectToDatabase();
-    const pg = await connectPg();
 
     // 验证是否是该用户的 model
     const model = await Model.findOne({

src/pages/api/model/data/splitData.ts

@@ -2,14 +2,20 @@ import type { NextApiRequest, NextApiResponse } from 'next';
 import { jsonRes } from '@/service/response';
 import { connectToDatabase, SplitData, Model } from '@/service/mongo';
 import { authToken } from '@/service/utils/tools';
+import { generateVector } from '@/service/events/generateVector';
 import { generateQA } from '@/service/events/generateQA';
-import { encode } from 'gpt-token-utils';
+import { PgClient } from '@/service/pg';
 
 /* 拆分数据成QA */
 export default async function handler(req: NextApiRequest, res: NextApiResponse) {
   try {
-    const { text, modelId, prompt } = req.body as { text: string; modelId: string; prompt: string };
-    if (!text || !modelId || !prompt) {
+    const { chunks, modelId, prompt, mode } = req.body as {
+      modelId: string;
+      chunks: string[];
+      prompt: string;
+      mode: 'qa' | 'subsection';
+    };
+    if (!chunks || !modelId || !prompt) {
       throw new Error('参数错误');
     }
     await connectToDatabase();
@@ -28,46 +34,31 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
       throw new Error('无权操作该模型');
     }
 
-    const replaceText = text.replace(/\\n/g, '\n');
+    if (mode === 'qa') {
+      // 批量QA拆分插入数据
+      await SplitData.create({
+        userId,
+        modelId,
+        textList: chunks,
+        prompt
+      });
 
-    // 文本拆分成 chunk
-    const chunks = replaceText.split('\n').filter((item) => item.trim());
+      generateQA();
+    } else if (mode === 'subsection') {
+      // 插入记录
+      await PgClient.insert('modelData', {
+        values: chunks.map((item) => [
+          { key: 'user_id', value: userId },
+          { key: 'model_id', value: modelId },
+          { key: 'q', value: item },
+          { key: 'a', value: '' },
+          { key: 'status', value: 'waiting' }
+        ])
+      });
 
-    const textList: string[] = [];
-    let splitText = '';
-
-    /* 取 2.5k ~ 3.5K tokens 内容 */
-    chunks.forEach((chunk) => {
-      const tokens = encode(splitText + chunk).length;
-      if (tokens >= 3500) {
-        // 超过 3500，不要这块内容
-        splitText && textList.push(splitText);
-        splitText = chunk;
-      } else if (tokens >= 2500) {
-        // 超过 3000，取内容
-        splitText && textList.push(splitText + chunk);
-        splitText = '';
-      } else {
-        //没超过 3000，继续添加
-        splitText += chunk;
-      }
-    });
-
-    if (splitText) {
-      textList.push(splitText);
+      generateVector();
     }
 
-    // 批量插入数据
-    await SplitData.create({
-      userId,
-      modelId,
-      rawText: text,
-      textList,
-      prompt
-    });
-
-    generateQA();
-
     jsonRes(res);
   } catch (err) {
     jsonRes(res, {

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

@@ -126,16 +126,20 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse)
       });
     } else {
       // 有匹配或者低匹配度模式情况下，添加知识库内容。
-      // 系统提示词过滤，最多 2000 tokens
-      const systemPrompt = systemPromptFilter(formatRedisPrompt, 2000);
+      // 系统提示词过滤，最多 3000 tokens
+      const systemPrompt = systemPromptFilter(formatRedisPrompt, 3000);
 
       prompts.unshift({
         obj: 'SYSTEM',
-        value: `${
-          model.systemPrompt || '根据知识库内容回答'
-        } 知识库是最新的,下面是知识库内容:当前时间为${dayjs().format(
-          'YYYY/MM/DD HH:mm:ss'
-        )}\n${systemPrompt}`
+        value: `
+${model.systemPrompt}
+${
+  model.search.mode === ModelVectorSearchModeEnum.hightSimilarity
+    ? `你只能从知识库选择内容回答.不在知识库内容拒绝回复`
+    : ''
+}
+知识库内容为: 当前时间为${dayjs().format('YYYY/MM/DD HH:mm:ss')}\n${systemPrompt}'
+`
       });
     }
 

src/pages/chat/index.tsx

@@ -133,7 +133,7 @@ const Chat = ({ modelId, chatId }: { modelId: string; chatId: string }) => {
         if (isScroll && res.history.length > 0) {
           setTimeout(() => {
             scrollToBottom('auto');
-          }, 2000);
+          }, 1200);
         }
       } catch (e: any) {
         toast({

src/pages/model/detail/components/InputDataModal.tsx

@@ -122,9 +122,9 @@ const InputDataModal = ({
             <Box h={'30px'}>问题</Box>
             <Textarea
               placeholder={
-                '相关问题，可以输入多个问法, 最多500字。例如：\n1. laf 是什么？\n2. laf 可以做什么？\n3. laf怎么用'
+                '相关问题，可以输入多个问法, 最多 1000 字。例如：\n1. laf 是什么？\n2. laf 可以做什么？\n3. laf怎么用'
               }
-              maxLength={500}
+              maxLength={1000}
               resize={'none'}
               h={'calc(100% - 30px)'}
               {...register(`q`, {
@@ -136,9 +136,9 @@ const InputDataModal = ({
             <Box h={'30px'}>知识点</Box>
             <Textarea
               placeholder={
-                '知识点，最多1000字。请保持主语的完整性，缺少主语会导致效果不佳。例如：\n1. laf是一个云函数开发平台。\n2. laf 什么都能做。\n3. 下面是使用 laf 的例子: ……'
+                '知识点，最多 2000 字。例如：\n1. laf是一个云函数开发平台。\n2. laf 什么都能做。\n3. 下面是使用 laf 的例子: ……'
               }
-              maxLength={1000}
+              maxLength={2000}
               resize={'none'}
               h={'calc(100% - 30px)'}
               {...register(`a`, {

src/pages/model/detail/components/ModelDataCard.tsx

@@ -18,6 +18,7 @@ import {
   MenuItem,
   Input
 } from '@chakra-ui/react';
+import type { BoxProps } from '@chakra-ui/react';
 import type { ModelSchema } from '@/types/mongoSchema';
 import type { ModelDataItemType } from '@/types/model';
 import { ModelDataStatusMap } from '@/constants/model';
@@ -114,6 +115,14 @@ const ModelDataCard = ({ model }: { model: ModelSchema }) => {
     }
   });
 
+  const tdStyles: BoxProps = {
+    fontSize: 'xs',
+    maxW: '500px',
+    whiteSpace: 'pre-wrap',
+    maxH: '250px',
+    overflowY: 'auto'
+  };
+
   return (
     <>
       <Flex>
@@ -156,8 +165,8 @@ const ModelDataCard = ({ model }: { model: ModelSchema }) => {
             >
               手动输入
             </MenuItem>
-            <MenuItem onClick={onOpenSelectFileModal}>文本/文件 QA 拆分</MenuItem>
-            <MenuItem onClick={onOpenSelectUrlModal}>网站内容 QA 拆分</MenuItem>
+            <MenuItem onClick={onOpenSelectFileModal}>文本/文件拆分</MenuItem>
+            {/* <MenuItem onClick={onOpenSelectUrlModal}>网站内容拆分</MenuItem> */}
             <MenuItem onClick={onOpenSelectCsvModal}>csv 问答对导入</MenuItem>
           </MenuList>
         </Menu>
@@ -191,33 +200,23 @@ const ModelDataCard = ({ model }: { model: ModelSchema }) => {
 
       <Box mt={4}>
         <TableContainer minH={'500px'}>
-          <Table variant={'simple'}>
+          <Table variant={'simple'} w={'100%'}>
             <Thead>
               <Tr>
-                <Th>Question</Th>
-                <Th>Text</Th>
-                <Th>Status</Th>
+                <Th>匹配内容(问题)</Th>
+                <Th>对应答案</Th>
+                <Th>状态</Th>
                 <Th>操作</Th>
               </Tr>
             </Thead>
             <Tbody>
               {modelDataList.map((item) => (
                 <Tr key={item.id}>
-                  <Td minW={'200px'}>
-                    <Box fontSize={'xs'} whiteSpace={'pre-wrap'}>
-                      {item.q}
-                    </Box>
+                  <Td>
+                    <Box {...tdStyles}>{item.q}</Box>
                   </Td>
-                  <Td minW={'200px'}>
-                    <Box
-                      w={'100%'}
-                      fontSize={'xs'}
-                      whiteSpace={'pre-wrap'}
-                      maxH={'250px'}
-                      overflowY={'auto'}
-                    >
-                      {item.a}
-                    </Box>
+                  <Td>
+                    <Box {...tdStyles}>{item.a || '-'}</Box>
                   </Td>
                   <Td>{ModelDataStatusMap[item.status]}</Td>
                   <Td>

src/pages/model/detail/components/SelectFileModal.tsx

@@ -1,4 +1,4 @@
-import React, { useState, useCallback } from 'react';
+import React, { useState, useCallback, useMemo } from 'react';
 import {
   Box,
   Flex,
@@ -20,9 +20,26 @@ import { readTxtContent, readPdfContent, readDocContent } from '@/utils/file';
 import { useMutation } from '@tanstack/react-query';
 import { postModelDataSplitData } from '@/api/model';
 import { formatPrice } from '@/utils/user';
+import Radio from '@/components/Radio';
+import { splitText } from '@/utils/file';
 
 const fileExtension = '.txt,.doc,.docx,.pdf,.md';
 
+const modeMap = {
+  qa: {
+    maxLen: 2800,
+    slideLen: 800,
+    price: 3,
+    isPrompt: true
+  },
+  subsection: {
+    maxLen: 1000,
+    slideLen: 300,
+    price: 0.4,
+    isPrompt: false
+  }
+};
+
 const SelectFileModal = ({
   onClose,
   onSuccess,
@@ -36,38 +53,45 @@ const SelectFileModal = ({
   const { toast } = useToast();
   const [prompt, setPrompt] = useState('');
   const { File, onOpen } = useSelectFile({ fileType: fileExtension, multiple: true });
-  const [fileText, setFileText] = useState('');
+  const [mode, setMode] = useState<'qa' | 'subsection'>('qa');
+  const [fileTextArr, setFileTextArr] = useState<string[]>(['']);
   const { openConfirm, ConfirmChild } = useConfirm({
     content: '确认导入该文件，需要一定时间进行拆解，该任务无法终止！如果余额不足，任务讲被终止。'
   });
 
+  const fileText = useMemo(() => {
+    const chunks = fileTextArr.map((item) =>
+      splitText({
+        text: item,
+        ...modeMap[mode]
+      })
+    );
+    return chunks.join('');
+  }, [fileTextArr, mode]);
+
   const onSelectFile = useCallback(
     async (e: File[]) => {
       setSelecting(true);
       try {
-        const fileTexts = (
-          await Promise.all(
-            e.map((file) => {
-              // @ts-ignore
-              const extension = file?.name?.split('.').pop().toLowerCase();
-              switch (extension) {
-                case 'txt':
-                case 'md':
-                  return readTxtContent(file);
-                case 'pdf':
-                  return readPdfContent(file);
-                case 'doc':
-                case 'docx':
-                  return readDocContent(file);
-                default:
-                  return '';
-              }
-            })
-          )
-        )
-          .join(' ')
-          .replace(/(\\n|\n)+/g, '\n');
-        setFileText(fileTexts);
+        const fileTexts = await Promise.all(
+          e.map((file) => {
+            // @ts-ignore
+            const extension = file?.name?.split('.').pop().toLowerCase();
+            switch (extension) {
+              case 'txt':
+              case 'md':
+                return readTxtContent(file);
+              case 'pdf':
+                return readPdfContent(file);
+              case 'doc':
+              case 'docx':
+                return readDocContent(file);
+              default:
+                return '';
+            }
+          })
+        );
+        setFileTextArr(fileTexts);
       } catch (error: any) {
         console.log(error);
         toast({
@@ -77,16 +101,25 @@ const SelectFileModal = ({
       }
       setSelecting(false);
     },
-    [setSelecting, toast]
+    [toast]
   );
 
   const { mutate, isLoading } = useMutation({
     mutationFn: async () => {
       if (!fileText) return;
+      const chunks = fileTextArr
+        .map((item) =>
+          splitText({
+            text: item,
+            ...modeMap[mode]
+          })
+        )
+        .flat();
       await postModelDataSplitData({
         modelId,
-        text: fileText.replace(/\\n/g, '\n').replace(/\n+/g, '\n'),
-        prompt: `下面是"${prompt || '一段长文本'}"`
+        chunks,
+        prompt: `下面是"${prompt || '一段长文本'}"`,
+        mode
       });
       toast({
         title: '导入数据成功,需要一段拆解和训练',
@@ -106,58 +139,82 @@ const SelectFileModal = ({
   return (
     <Modal isOpen={true} onClose={onClose} isCentered>
       <ModalOverlay />
-      <ModalContent maxW={'min(900px, 90vw)'} m={0} position={'relative'} h={'90vh'}>
+      <ModalContent maxW={'min(1000px, 90vw)'} m={0} position={'relative'} h={'90vh'}>
         <ModalHeader>文件导入</ModalHeader>
         <ModalCloseButton />
 
         <ModalBody
           display={'flex'}
           flexDirection={'column'}
-          p={4}
+          p={0}
           h={'100%'}
           alignItems={'center'}
           justifyContent={'center'}
           fontSize={'sm'}
         >
-          <Button isLoading={selecting} onClick={onOpen}>
-            选择文件
-          </Button>
-          <Box mt={2} maxW={['100%', '70%']}>
+          <Box mt={2} px={4} maxW={['100%']} textAlign={'justify'} color={'blackAlpha.600'}>
             支持 {fileExtension} 文件。模型会自动对文本进行 QA 拆分，需要较长训练时间，拆分需要消耗
-            tokens，账号余额不足时，未拆分的数据会被删除。
+            tokens，账号余额不足时，未拆分的数据会被删除。当前一共 {encode(fileText).length}{' '}
+            个tokens，大约 {formatPrice(encode(fileText).length * modeMap[mode].price)}元
           </Box>
-          <Box mt={2}>
-            一共 {encode(fileText).length} 个tokens，大约 {formatPrice(encode(fileText).length * 3)}
-            元
-          </Box>
-          <Flex w={'100%'} alignItems={'center'} my={4}>
-            <Box flex={'0 0 auto'} mr={2}>
-              下面是
-            </Box>
-            <Input
-              placeholder="提示词，例如: Laf的介绍/关于gpt4的论文/一段长文本"
-              value={prompt}
-              onChange={(e) => setPrompt(e.target.value)}
-              size={'sm'}
+          {/* 拆分模式 */}
+          <Flex w={'100%'} px={5} alignItems={'center'} mt={4}>
+            <Box flex={'0 0 70px'}>分段模式:</Box>
+            <Radio
+              ml={3}
+              list={[
+                { label: 'QA拆分', value: 'qa' },
+                { label: '直接分段', value: 'subsection' }
+              ]}
+              value={mode}
+              onChange={(e) => setMode(e as 'subsection' | 'qa')}
             />
           </Flex>
-          <Textarea
-            flex={'1 0 0'}
-            h={0}
-            w={'100%'}
-            placeholder="文件内容"
-            maxLength={-1}
-            resize={'none'}
-            fontSize={'xs'}
-            whiteSpace={'pre-wrap'}
-            value={fileText}
-            onChange={(e) => setFileText(e.target.value)}
-          />
+          {/* 内容介绍 */}
+          {modeMap[mode].isPrompt && (
+            <Flex w={'100%'} px={5} alignItems={'center'} mt={4}>
+              <Box flex={'0 0 70px'} mr={2}>
+                下面是
+              </Box>
+              <Input
+                placeholder="提示词，例如: Laf的介绍/关于gpt4的论文/一段长文本"
+                value={prompt}
+                onChange={(e) => setPrompt(e.target.value)}
+                size={'sm'}
+              />
+            </Flex>
+          )}
+          {/* 文本内容 */}
+          <Box flex={'1 0 0'} px={5} h={0} w={'100%'} overflowY={'auto'} mt={4}>
+            {fileTextArr.map((item, i) => (
+              <Box key={i} mb={5}>
+                <Box mb={1}>文本{i + 1}</Box>
+                <Textarea
+                  placeholder="文件内容"
+                  maxLength={-1}
+                  rows={10}
+                  fontSize={'xs'}
+                  whiteSpace={'pre-wrap'}
+                  value={item}
+                  onChange={(e) => {
+                    setFileTextArr([
+                      ...fileTextArr.slice(0, i),
+                      e.target.value,
+                      ...fileTextArr.slice(i + 1)
+                    ]);
+                  }}
+                />
+              </Box>
+            ))}
+          </Box>
         </ModalBody>
 
         <Flex px={6} pt={2} pb={4}>
+          <Button isLoading={selecting} onClick={onOpen}>
+            选择文件
+          </Button>
           <Box flex={1}></Box>
-          <Button variant={'outline'} mr={3} onClick={onClose}>
+          <Button variant={'outline'} colorScheme={'gray'} mr={3} onClick={onClose}>
             取消
           </Button>
           <Button isLoading={isLoading} isDisabled={fileText === ''} onClick={openConfirm(mutate)}>

src/pages/model/detail/components/SelectUrlModal.tsx

@@ -44,8 +44,9 @@ const SelectUrlModal = ({
       if (!webText) return;
       await postModelDataSplitData({
         modelId,
-        text: webText,
-        prompt: `下面是"${prompt || '一段长文本'}"`
+        chunks: [],
+        prompt: `下面是"${prompt || '一段长文本'}"`,
+        mode: 'qa'
       });
       toast({
         title: '导入数据成功,需要一段拆解和训练',
@@ -89,7 +90,7 @@ const SelectUrlModal = ({
     <Modal isOpen={true} onClose={onClose} isCentered>
       <ModalOverlay />
       <ModalContent maxW={'min(900px, 90vw)'} m={0} position={'relative'} h={'90vh'}>
-        <ModalHeader>网站地址导入</ModalHeader>
+        <ModalHeader>静态网站内容导入</ModalHeader>
         <ModalCloseButton />
 
         <ModalBody
@@ -102,7 +103,7 @@ const SelectUrlModal = ({
           fontSize={'sm'}
         >
           <Box mt={2} maxW={['100%', '70%']}>
-            根据网站地址，获取网站文本内容（请注意获取后的内容，不是每个网站内容都能获取到的）。模型会对文本进行
+            根据网站地址，获取网站文本内容（请注意仅能获取静态网站文本，注意看下获取后的内容是否正确）。模型会对文本进行
             QA 拆分，需要较长训练时间，拆分需要消耗 tokens，账号余额不足时，未拆分的数据会被删除。
           </Box>
           <Box mt={2}>

src/service/events/generateQA.ts

@@ -69,9 +69,13 @@ export async function generateQA(next = false): Promise<any> {
     const chatAPI = getOpenAIApi(userApiKey || systemKey);
     const systemPrompt: ChatCompletionRequestMessage = {
       role: 'system',
-      content: `你是出题官.${
-        dataItem.prompt || '下面是"一段长文本"'
-      },从中选出5至20个题目和答案,题目包含问答题,计算题,代码题等.答案要详细.按格式返回: Q1:\nA1:\nQ2:\nA2:\n`
+      content: `你是出题人
+${dataItem.prompt || '下面是"一段长文本"'}
+从中选出5至20个题目和答案,题目包含问答题,计算题,代码题等.答案要详细.按格式返回: Q1:
+A1:
+Q2:
+A2:
+...`
     };
 
     // 请求 chatgpt 获取回答

src/service/models/splitData.ts

@@ -18,10 +18,6 @@ const SplitDataSchema = new Schema({
     ref: 'model',
     required: true
   },
-  rawText: {
-    type: String,
-    required: true
-  },
   textList: {
     type: [String],
     default: []

src/types/mongoSchema.d.ts

@@ -75,7 +75,6 @@ export interface ModelSplitDataSchema {
   _id: string;
   userId: string;
   modelId: string;
-  rawText: string;
   prompt: string;
   errorText: string;
   textList: string[];

src/utils/file.ts

@@ -1,5 +1,6 @@
 import mammoth from 'mammoth';
 import Papa from 'papaparse';
+import { encode } from 'gpt-token-utils';
 
 /**
  * 读取 txt 文件内容
@@ -137,3 +138,54 @@ export const fileDownload = ({
   downloadLink.click();
   document.body.removeChild(downloadLink);
 };
+
+/**
+ * text split into chunks
+ * maxLen - one chunk len. max: 3500
+ * slideLen - The size of the before and after Text
+ * maxLen > slideLen
+ */
+export const splitText = ({
+  text,
+  maxLen,
+  slideLen
+}: {
+  text: string;
+  maxLen: number;
+  slideLen: number;
+}) => {
+  const textArr =
+    text.match(/[！？。\n.]+|[^\s]+/g)?.filter((item) => {
+      const text = item.replace(/(\\n)/g, '\n').trim();
+      if (text && text !== '\n') return true;
+      return false;
+    }) || [];
+
+  const chunks: { sum: number; arr: string[] }[] = [{ sum: 0, arr: [] }];
+
+  for (let i = 0; i < textArr.length; i++) {
+    const tokenLen = encode(textArr[i]).length;
+    chunks[chunks.length - 1].sum += tokenLen;
+    chunks[chunks.length - 1].arr.push(textArr[i]);
+
+    //  current length is over maxLen. create new chunk
+    if (chunks[chunks.length - 1].sum + tokenLen >= maxLen) {
+      // get slide len text as the initial value
+      const chunk: { sum: number; arr: string[] } = { sum: 0, arr: [] };
+      for (let j = chunks[chunks.length - 1].arr.length - 1; j >= 0; j--) {
+        const chunkText = chunks[chunks.length - 1].arr[j];
+        const tokenLen = encode(chunkText).length;
+        chunk.sum += tokenLen;
+        chunk.arr.unshift(chunkText);
+
+        if (chunk.sum >= slideLen) {
+          break;
+        }
+      }
+      chunks.push(chunk);
+    }
+  }
+
+  const result = chunks.map((item) => item.arr.join(''));
+  return result;
+};