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https://github.com/labring/FastGPT.git
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V4.6.6-2 (#673)
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@@ -1,6 +1,6 @@
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import React, { useCallback } from 'react';
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import React, { useCallback, useMemo } from 'react';
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import { useRouter } from 'next/router';
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import { Box, Flex, IconButton, useTheme } from '@chakra-ui/react';
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import { Box, Flex, IconButton, useTheme, Progress } from '@chakra-ui/react';
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import { useToast } from '@/web/common/hooks/useToast';
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import { useQuery } from '@tanstack/react-query';
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import { getErrText } from '@fastgpt/global/common/error/utils';
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@@ -92,9 +92,55 @@ const Detail = ({ datasetId, currentTab }: { datasetId: string; currentTab: `${T
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}
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});
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const { data: trainingQueueLen = 0 } = useQuery(['getTrainingQueueLen'], getTrainingQueueLen, {
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refetchInterval: 10000
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});
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const { data: { vectorTrainingCount = 0, agentTrainingCount = 0 } = {} } = useQuery(
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['getTrainingQueueLen'],
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() =>
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getTrainingQueueLen({
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vectorModel: datasetDetail.vectorModel.model,
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agentModel: datasetDetail.agentModel.model
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}),
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{
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refetchInterval: 10000
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}
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);
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const { vectorTrainingMap, agentTrainingMap } = useMemo(() => {
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const vectorTrainingMap = (() => {
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if (vectorTrainingCount < 1000)
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return {
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colorSchema: 'green',
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tip: t('core.dataset.training.Leisure')
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};
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if (vectorTrainingCount < 10000)
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return {
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colorSchema: 'yellow',
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tip: t('core.dataset.training.Waiting')
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};
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return {
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colorSchema: 'red',
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tip: t('core.dataset.training.Full')
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};
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})();
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const agentTrainingMap = (() => {
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if (agentTrainingCount < 100)
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return {
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colorSchema: 'green',
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tip: t('core.dataset.training.Leisure')
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};
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if (agentTrainingCount < 1000)
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return {
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colorSchema: 'yellow',
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tip: t('core.dataset.training.Waiting')
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};
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return {
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colorSchema: 'red',
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tip: t('core.dataset.training.Full')
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};
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})();
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return {
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vectorTrainingMap,
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agentTrainingMap
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};
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}, [agentTrainingCount, t, vectorTrainingCount]);
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return (
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<>
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@@ -155,19 +201,32 @@ const Detail = ({ datasetId, currentTab }: { datasetId: string; currentTab: `${T
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setCurrentTab(e);
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}}
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/>
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<Box textAlign={'center'}>
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<Flex justifyContent={'center'} alignItems={'center'}>
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<MyIcon mr={1} name="overviewLight" w={'16px'} color={'green.500'} />
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<Box>{t('dataset.System Data Queue')}</Box>
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<MyTooltip
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label={t('dataset.Queue Desc', { title: feConfigs?.systemTitle })}
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placement={'top'}
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>
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<QuestionOutlineIcon ml={1} w={'16px'} />
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</MyTooltip>
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</Flex>
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<Box mt={1} fontWeight={'bold'}>
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{trainingQueueLen}
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<Box>
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<Box mb={3}>
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<Box fontSize={'sm'}>
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{t('core.dataset.training.Agent queue')}({agentTrainingMap.tip})
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</Box>
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<Progress
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value={100}
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size={'xs'}
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colorScheme={agentTrainingMap.colorSchema}
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borderRadius={'10px'}
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isAnimated
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hasStripe
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/>
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</Box>
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<Box mb={3}>
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<Box fontSize={'sm'}>
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{t('core.dataset.training.Vector queue')}({vectorTrainingMap.tip})
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</Box>
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<Progress
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value={100}
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size={'xs'}
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colorScheme={vectorTrainingMap.colorSchema}
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borderRadius={'10px'}
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isAnimated
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hasStripe
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/>
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</Box>
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</Box>
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<Flex
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