mirror of
https://github.com/labring/FastGPT.git
synced 2025-07-23 05:12:39 +00:00
perf: chunk filter
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@@ -118,9 +118,9 @@ const Navbar = ({ unread }: { unread: number }) => {
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}
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: {
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color: 'myGray.500',
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backgroundColor: 'transparent'
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backgroundColor: 'transparent',
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onClick: () => router.push(item.link)
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})}
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onClick={() => router.push(item.link)}
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>
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<MyIcon
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name={
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@@ -258,7 +258,9 @@ const ChunkImport = ({ kbId }: { kbId: string }) => {
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<Box>
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段落长度
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<MyTooltip
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label={'基于 Gpt3.5 的 Token 计算方法进行分段。前后段落会有 30% 的内容重叠。'}
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label={
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'按结束标点符号进行分段。前后段落会有 30% 的内容重叠。\n中文文档建议不要超过800,英文不要超过1500'
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}
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forceShow
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>
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<QuestionOutlineIcon ml={1} />
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@@ -269,7 +271,7 @@ const ChunkImport = ({ kbId }: { kbId: string }) => {
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flex={1}
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defaultValue={chunkLen}
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min={300}
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max={1000}
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max={2000}
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step={10}
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onChange={(e) => {
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setChunkLen(+e);
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@@ -294,10 +296,7 @@ const ChunkImport = ({ kbId }: { kbId: string }) => {
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<QuestionOutlineIcon ml={1} />
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</MyTooltip>
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</Box>
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<Box ml={4}>
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{}
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{price}元
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</Box>
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<Box ml={4}>{price}元</Box>
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</Flex>
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<Flex mt={3}>
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{showRePreview && (
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@@ -1,18 +1,5 @@
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import React, { useState, useCallback, useMemo } from 'react';
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import {
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Box,
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Flex,
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Button,
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useTheme,
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NumberInput,
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NumberInputField,
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NumberInputStepper,
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NumberIncrementStepper,
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NumberDecrementStepper,
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Image,
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Textarea,
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Input
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} from '@chakra-ui/react';
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import { Box, Flex, Button, useTheme, Image, Input } from '@chakra-ui/react';
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import { useToast } from '@/hooks/useToast';
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import { useConfirm } from '@/hooks/useConfirm';
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import { readTxtContent, readPdfContent, readDocContent } from '@/utils/file';
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@@ -48,7 +35,7 @@ type FileItemType = {
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const QAImport = ({ kbId }: { kbId: string }) => {
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const model = qaModelList[0]?.model;
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const unitPrice = qaModelList[0]?.price || 3;
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const chunkLen = qaModelList[0].maxToken / 2;
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const chunkLen = qaModelList[0].maxToken * 0.45;
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const theme = useTheme();
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const router = useRouter();
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const { toast } = useToast();
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@@ -129,16 +129,26 @@ export const pushGenerateVectorBill = async ({
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try {
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// 计算价格. 至少为1
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const unitPrice = global.vectorModels.find((item) => item.model === model)?.price || 0.2;
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const vectorModel =
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global.vectorModels.find((item) => item.model === model) || global.vectorModels[0];
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const unitPrice = vectorModel.price || 0.2;
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let total = unitPrice * tokenLen;
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total = total > 1 ? total : 1;
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// 插入 Bill 记录
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const res = await Bill.create({
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userId,
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model,
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model: vectorModel.model,
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appName: '索引生成',
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total
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total,
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list: [
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{
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moduleName: '索引生成',
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amount: total,
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model: vectorModel.model,
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tokenLen
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}
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]
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});
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billId = res._id;
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@@ -2,7 +2,6 @@ import mammoth from 'mammoth';
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import Papa from 'papaparse';
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import { getOpenAiEncMap } from './plugin/openai';
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import { getErrText } from './tools';
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import { OpenAiChatEnum } from '@/constants/model';
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import { uploadImg } from '@/api/system';
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/**
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@@ -145,38 +144,39 @@ export const fileDownload = ({
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/**
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* text split into chunks
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* maxLen - one chunk len. max: 3500
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* slideLen - The size of the before and after Text
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* maxLen > slideLen
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* overlapLen - The size of the before and after Text
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* maxLen > overlapLen
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*/
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export const splitText_token = ({ text, maxLen }: { text: string; maxLen: number }) => {
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const slideLen = Math.floor(maxLen * 0.3);
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const overlapLen = Math.floor(maxLen * 0.3); // Overlap length
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try {
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const enc = getOpenAiEncMap();
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// filter empty text. encode sentence
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const encodeText = enc.encode(text);
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const splitTexts = text.split(/(?<=[。!?.!?])/g);
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const chunks: string[] = [];
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let tokens = 0;
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let startIndex = 0;
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let endIndex = Math.min(startIndex + maxLen, encodeText.length);
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let chunkEncodeArr = encodeText.slice(startIndex, endIndex);
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const decoder = new TextDecoder();
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while (startIndex < encodeText.length) {
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tokens += chunkEncodeArr.length;
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chunks.push(decoder.decode(enc.decode(chunkEncodeArr)));
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startIndex += maxLen - slideLen;
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endIndex = Math.min(startIndex + maxLen, encodeText.length);
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chunkEncodeArr = encodeText.slice(
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Math.min(encodeText.length - slideLen, startIndex),
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endIndex
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);
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let preChunk = '';
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let chunk = '';
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for (let i = 0; i < splitTexts.length; i++) {
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const text = splitTexts[i];
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chunk += text;
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if (chunk.length > maxLen - overlapLen) {
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preChunk += text;
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}
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if (chunk.length >= maxLen) {
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chunks.push(chunk);
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chunk = preChunk;
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preChunk = '';
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}
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}
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if (chunk) {
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chunks.push(chunk);
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}
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const enc = getOpenAiEncMap();
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const encodeText = enc.encode(chunks.join(''));
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const tokens = encodeText.length;
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return {
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chunks,
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tokens
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