perf: token split text

src/pages/api/chat/chat.ts

@@ -1,11 +1,11 @@
 import type { NextApiRequest, NextApiResponse } from 'next';
 import { connectToDatabase } from '@/service/mongo';
 import { getOpenAIApi, authChat } from '@/service/utils/auth';
-import { axiosConfig, openaiChatFilter, systemPromptFilter } from '@/service/utils/tools';
+import { axiosConfig, openaiChatFilter } from '@/service/utils/tools';
 import { ChatItemSimpleType } from '@/types/chat';
 import { jsonRes } from '@/service/response';
 import { PassThrough } from 'stream';
-import { modelList, ModelVectorSearchModeMap, ModelVectorSearchModeEnum } from '@/constants/model';
+import { modelList, ModelVectorSearchModeMap } from '@/constants/model';
 import { pushChatBill } from '@/service/events/pushBill';
 import { gpt35StreamResponse } from '@/service/utils/openai';
 import { searchKb_openai } from '@/service/tools/searchKb';

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

@@ -118,11 +118,11 @@ const InputDataModal = ({
           px={6}
           pb={2}
         >
-          <Box flex={2} mr={[0, 4]} mb={[4, 0]} h={['230px', '100%']}>
+          <Box flex={1} mr={[0, 4]} mb={[4, 0]} h={['230px', '100%']}>
             <Box h={'30px'}>{'匹配的知识点'}</Box>
             <Textarea
-              placeholder={'匹配的知识点。这部分内容会被搜索，请把控内容的质量。最多 1000 字。'}
+              placeholder={'匹配的知识点。这部分内容会被搜索，请把控内容的质量。最多 1500 字。'}
-              maxLength={2000}
+              maxLength={1500}
               resize={'none'}
               h={'calc(100% - 30px)'}
               {...register(`q`, {
@@ -130,13 +130,13 @@ const InputDataModal = ({
               })}
             />
           </Box>
-          <Box flex={3} h={['330px', '100%']}>
+          <Box flex={1} h={['330px', '100%']}>
             <Box h={'30px'}>补充知识</Box>
             <Textarea
               placeholder={
-                '补充知识。这部分内容不会被搜索，但会作为"匹配的知识点"的内容补充，你可以讲一些细节的内容填写在这里。最多 2000 字。'
+                '补充知识。这部分内容不会被搜索，但会作为"匹配的知识点"的内容补充，你可以讲一些细节的内容填写在这里。最多 1500 字。'
               }
-              maxLength={2000}
+              maxLength={1500}
               resize={'none'}
               h={'calc(100% - 30px)'}
               {...register('a')}

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

@@ -16,8 +16,10 @@ import {
   MenuButton,
   MenuList,
   MenuItem,
-  Input
+  Input,
+  Tooltip
 } from '@chakra-ui/react';
+import { QuestionOutlineIcon } from '@chakra-ui/icons';
 import type { BoxProps } from '@chakra-ui/react';
 import type { ModelDataItemType } from '@/types/model';
 import { ModelDataStatusMap } from '@/constants/model';
@@ -208,7 +210,16 @@ const ModelDataCard = ({ modelId, isOwner }: { modelId: string; isOwner: boolean
           <Table variant={'simple'} w={'100%'}>
             <Thead>
               <Tr>
-                <Th>{'匹配的知识点'}</Th>
+                <Th>
+                  匹配的知识点
+                  <Tooltip
+                    label={
+                      '对话时，会将用户的问题和知识库的 "匹配知识点" 进行比较，找到最相似的前 n 条记录，将这些记录的 "匹配知识点"+"补充知识点" 作为 chatgpt 的系统提示词。'
+                    }
+                  >
+                    <QuestionOutlineIcon ml={1} />
+                  </Tooltip>
+                </Th>
                 <Th>补充知识</Th>
                 <Th>状态</Th>
                 {isOwner && <Th>操作</Th>}

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

@@ -20,8 +20,7 @@ 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';
+import { splitText_token } from '@/utils/file';
-import { countChatTokens } from '@/utils/tools';
 
 const fileExtension = '.txt,.doc,.docx,.pdf,.md';
 
@@ -49,7 +48,7 @@ const SelectFileModal = ({
   onSuccess: () => void;
   modelId: string;
 }) => {
-  const [selecting, setSelecting] = useState(false);
+  const [btnLoading, setBtnLoading] = useState(false);
   const { toast } = useToast();
   const [prompt, setPrompt] = useState('');
   const { File, onOpen } = useSelectFile({ fileType: fileExtension, multiple: true });
@@ -62,17 +61,21 @@ const SelectFileModal = ({
   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，未完成的任务会被直接清除。一共 ${
       splitRes.chunks.length
-    } 组，大约 ${splitRes.tokens || '数量太多,未计算'} 个tokens, 约 ${formatPrice(
+    } 组。${
-      splitRes.tokens * modeMap[mode].price
+      splitRes.tokens
-    )} 元`
+        ? `大约 ${splitRes.tokens} 个tokens, 约 ${formatPrice(
+            splitRes.tokens * modeMap[mode].price
+          )} 元`
+        : ''
+    }`
   });
 
   const onSelectFile = useCallback(
-    async (e: File[]) => {
+    async (files: File[]) => {
-      setSelecting(true);
+      setBtnLoading(true);
       try {
         let promise = Promise.resolve();
-        e.map((file) => {
+        files.forEach((file) => {
           promise = promise.then(async () => {
             const extension = file?.name?.split('.')?.pop()?.toLowerCase();
             let text = '';
@@ -101,7 +104,7 @@ const SelectFileModal = ({
           status: 'error'
         });
       }
-      setSelecting(false);
+      setBtnLoading(false);
     },
     [toast]
   );
@@ -131,31 +134,27 @@ const SelectFileModal = ({
     }
   });
 
-  const onclickImport = useCallback(() => {
+  const onclickImport = useCallback(async () => {
-    const chunks = fileTextArr
+    setBtnLoading(true);
+    let promise = Promise.resolve();
+
+    const splitRes = fileTextArr
       .filter((item) => item)
       .map((item) =>
-        splitText({
+        splitText_token({
           text: item,
           ...modeMap[mode]
         })
-      )
-      .flat();
-
-    let tokens: number[] = [];
-
-    // just count 100 sets of tokens
-    if (chunks.length < 100) {
-      tokens = chunks.map((item) =>
-        countChatTokens({ messages: [{ role: 'system', content: item }] })
       );
-    }
 
     setSplitRes({
-      tokens: tokens.reduce((sum, item) => sum + item, 0),
+      tokens: splitRes.reduce((sum, item) => sum + item.tokens, 0),
-      chunks
+      chunks: splitRes.map((item) => item.chunks).flat()
     });
 
+    setBtnLoading(false);
+
+    await promise;
     openConfirm(mutate)();
   }, [fileTextArr, mode, mutate, openConfirm]);
 
@@ -239,7 +238,7 @@ const SelectFileModal = ({
         </ModalBody>
 
         <Flex px={6} pt={2} pb={4}>
-          <Button isLoading={selecting} onClick={onOpen}>
+          <Button isLoading={btnLoading} onClick={onOpen}>
             选择文件
           </Button>
           <Box flex={1}></Box>
@@ -247,8 +246,8 @@ const SelectFileModal = ({
             取消
           </Button>
           <Button
-            isLoading={isLoading}
+            isLoading={isLoading || btnLoading}
-            isDisabled={selecting || fileTextArr[0] === ''}
+            isDisabled={isLoading || btnLoading || fileTextArr[0] === ''}
             onClick={onclickImport}
           >
             确认导入

src/service/events/generateQA.ts

@@ -106,7 +106,7 @@ A2:
           )
           .then((res) => {
             const rawContent = res?.data.choices[0].message?.content || ''; // chatgpt 原本的回复
-            const result = splitText(res?.data.choices[0].message?.content || ''); // 格式化后的QA对
+            const result = formatSplitText(res?.data.choices[0].message?.content || ''); // 格式化后的QA对
             console.log(`split result length: `, result.length);
             // 计费
             pushSplitDataBill({
@@ -190,7 +190,7 @@ A2:
 /**
  * 检查文本是否按格式返回
  */
-function splitText(text: string) {
+function formatSplitText(text: string) {
   const regex = /Q\d+:(\s*)(.*)(\s*)A\d+:(\s*)([\s\S]*?)(?=Q|$)/g; // 匹配Q和A的正则表达式
   const matches = text.matchAll(regex); // 获取所有匹配到的结果
 

src/service/utils/tools.ts

@@ -1,7 +1,7 @@
 import crypto from 'crypto';
 import jwt from 'jsonwebtoken';
 import { ChatItemSimpleType } from '@/types/chat';
-import { countChatTokens } from '@/utils/tools';
+import { countChatTokens, sliceTextByToken } from '@/utils/tools';
 import { ChatCompletionRequestMessageRoleEnum, ChatCompletionRequestMessage } from 'openai';
 import { ChatModelEnum } from '@/constants/model';
 
@@ -111,18 +111,11 @@ export const systemPromptFilter = ({
   prompts: string[];
   maxTokens: number;
 }) => {
-  let splitText = '';
+  const systemPrompt = prompts.join('\n');
 
-  // 从前往前截取
+  return sliceTextByToken({
-  for (let i = 0; i < prompts.length; i++) {
+    model,
-    const prompt = simplifyStr(prompts[i]);
+    text: systemPrompt,
-
+    length: maxTokens
-    splitText += `${prompt}\n`;
+  });
-    const tokens = countChatTokens({ model, messages: [{ role: 'system', content: splitText }] });
-    if (tokens >= maxTokens) {
-      break;
-    }
-  }
-
-  return splitText.slice(0, splitText.length - 1);
 };

src/utils/file.ts

@@ -1,6 +1,6 @@
 import mammoth from 'mammoth';
 import Papa from 'papaparse';
-import { countChatTokens } from './tools';
+import { getEncMap } from './tools';
 
 /**
  * 读取 txt 文件内容
@@ -145,7 +145,7 @@ export const fileDownload = ({
  * slideLen - The size of the before and after Text
  * maxLen > slideLen
  */
-export const splitText = ({
+export const splitText_token = ({
   text,
   maxLen,
   slideLen
@@ -154,39 +154,32 @@ export const splitText = ({
   maxLen: number;
   slideLen: number;
 }) => {
-  const textArr =
+  const enc = getEncMap()['gpt-3.5-turbo'];
-    text.split(/(?<=[。！？\.!\?\n])/g)?.filter((item) => {
+  // filter empty text. encode sentence
-      const text = item.replace(/(\\n)/g, '\n').trim();
+  const encodeText = enc.encode(text);
-      if (text && text !== '\n') return true;
-      return false;
-    }) || [];
 
-  const chunks: { sum: number; arr: string[] }[] = [{ sum: 0, arr: [] }];
+  const chunks: string[] = [];
+  let tokens = 0;
 
-  for (let i = 0; i < textArr.length; i++) {
+  let startIndex = 0;
-    const tokenLen = countChatTokens({ messages: [{ role: 'system', content: textArr[i] }] });
+  let endIndex = Math.min(startIndex + maxLen, encodeText.length);
-    chunks[chunks.length - 1].sum += tokenLen;
+  let chunkEncodeArr = encodeText.slice(startIndex, endIndex);
-    chunks[chunks.length - 1].arr.push(textArr[i]);
 
-    //  current length is over maxLen. create new chunk
+  const decoder = new TextDecoder();
-    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 = countChatTokens({ messages: [{ role: 'system', content: chunkText }] });
-        chunk.sum += tokenLen;
-        chunk.arr.unshift(chunkText);
 
-        if (chunk.sum >= slideLen) {
+  while (startIndex < encodeText.length) {
-          break;
+    tokens += chunkEncodeArr.length;
-        }
+    chunks.push(decoder.decode(enc.decode(chunkEncodeArr)));
-      }
+
-      chunks.push(chunk);
+    startIndex += maxLen - slideLen;
-    }
+    endIndex = Math.min(startIndex + maxLen, encodeText.length);
+    chunkEncodeArr = encodeText.slice(Math.min(encodeText.length - slideLen, startIndex), endIndex);
   }
-  const result = chunks.map((item) => item.arr.join(''));
+
-  return result;
+  return {
+    chunks,
+    tokens
+  };
 };
 
 export const fileToBase64 = (file: File) => {

src/utils/tools.ts

@@ -7,7 +7,7 @@ import { ChatModelEnum } from '@/constants/model';
 const textDecoder = new TextDecoder();
 const graphemer = new Graphemer();
 let encMap: Record<string, Tiktoken>;
-const getEncMap = () => {
+export const getEncMap = () => {
   if (encMap) return encMap;
   encMap = {
     'gpt-3.5-turbo': encoding_for_model('gpt-3.5-turbo', {
@@ -136,3 +136,18 @@ export const countChatTokens = ({
   const text = getChatGPTEncodingText(messages, model);
   return text2TokensLen(getEncMap()[model], text);
 };
+
+export const sliceTextByToken = ({
+  model = 'gpt-3.5-turbo',
+  text,
+  length
+}: {
+  model?: `${ChatModelEnum}`;
+  text: string;
+  length: number;
+}) => {
+  const enc = getEncMap()[model];
+  const encodeText = enc.encode(text);
+  const decoder = new TextDecoder();
+  return decoder.decode(enc.decode(encodeText.slice(0, length)));
+};