perf: isPc check;perf: dataset max token checker (#4872)

* perf: isPc check * perf: dataset max token checker * perf: dataset max token checker
2025-07-21 03:35:36 +00:00 · 2025-05-22 18:40:29 +08:00
parent 50481f4ca8
commit 6a6719e93d
8 changed files with 91 additions and 90 deletions
--- a/docSite/content/zh-cn/docs/development/upgrading/4910.md
+++ b/docSite/content/zh-cn/docs/development/upgrading/4910.md
@@ -17,6 +17,8 @@ weight: 790
 1. LLM stream调用，默认超时调大。
 2. 部分确认交互优化。
 3. 纠正原先知识库的“表格数据集”名称，改成“备份导入”。同时支持知识库索引的导出和导入。
+4. 工作流知识库引用上限，如果工作流中没有相关 AI 节点，则交互模式改成纯手动输入，并且上限为 1000万。
+5. 语音输入，移动端判断逻辑，准确判断是否为手机，而不是小屏。

 ## 🐛 修复

--- a/packages/global/core/app/constants.ts
+++ b/packages/global/core/app/constants.ts
@@ -60,5 +60,3 @@ export enum AppTemplateTypeEnum {
  // special type
  contribute = 'contribute'
 }
-
-export const defaultDatasetMaxTokens = 16000;
--- a/packages/service/core/app/controller.ts
+++ b/packages/service/core/app/controller.ts
@@ -11,40 +11,6 @@ export const beforeUpdateAppFormat = <T extends AppSchema['modules'] | undefined
  nodes: T;
  isPlugin: boolean;
 }) => {
-  if (nodes) {
-    // Check dataset maxTokens
-    if (isPlugin) {
-      let maxTokens = 16000;
-
-      nodes.forEach((item) => {
-        if (
-          item.flowNodeType === FlowNodeTypeEnum.chatNode ||
-          item.flowNodeType === FlowNodeTypeEnum.tools
-        ) {
-          const model =
-            item.inputs.find((item) => item.key === NodeInputKeyEnum.aiModel)?.value || '';
-          const chatModel = getLLMModel(model);
-          const quoteMaxToken = chatModel.quoteMaxToken || 16000;
-
-          maxTokens = Math.max(maxTokens, quoteMaxToken);
-        }
-      });
-
-      nodes.forEach((item) => {
-        if (item.flowNodeType === FlowNodeTypeEnum.datasetSearchNode) {
-          item.inputs.forEach((input) => {
-            if (input.key === NodeInputKeyEnum.datasetMaxTokens) {
-              const val = input.value as number;
-              if (val > maxTokens) {
-                input.value = maxTokens;
-              }
-            }
-          });
-        }
-      });
-    }
-  }
-
  return {
    nodes
  };
--- a/packages/web/common/system/utils.ts
+++ b/packages/web/common/system/utils.ts
@@ -18,10 +18,10 @@ export const getWebReqUrl = (url: string = '') => {
 };

 export const isMobile = () => {
-  // 服务端渲染时返回 false
+  // SSR return false
  if (typeof window === 'undefined') return false;

-  // 1. 检查 User-Agent
+  // 1. Check User-Agent
  const userAgent = navigator.userAgent.toLowerCase();
  const mobileKeywords = [
    'android',
@@ -36,12 +36,12 @@ export const isMobile = () => {
  ];
  const isMobileUA = mobileKeywords.some((keyword) => userAgent.includes(keyword));

-  // 2. 检查屏幕宽度
+  // 2. Check screen width
  const isMobileWidth = window.innerWidth <= 900;

-  // 3. 检查是否支持触摸事件（排除触控屏PC）
+  // 3. Check if touch events are supported (exclude touch screen PCs)
  const isTouchDevice = 'ontouchstart' in window || navigator.maxTouchPoints > 0;

-  // 综合判断：满足以下任一条件即视为移动端
+  // If any of the following conditions are met, it is considered a mobile device
  return isMobileUA || (isMobileWidth && isTouchDevice);
 };
--- a/projects/app/src/components/core/app/DatasetParamsModal.tsx
+++ b/projects/app/src/components/core/app/DatasetParamsModal.tsx
@@ -25,11 +25,11 @@ import SelectAiModel from '@/components/Select/AIModelSelector';
 import QuestionTip from '@fastgpt/web/components/common/MyTooltip/QuestionTip';
 import FormLabel from '@fastgpt/web/components/common/MyBox/FormLabel';
 import MyTextarea from '@/components/common/Textarea/MyTextarea';
-import { defaultDatasetMaxTokens } from '@fastgpt/global/core/app/constants';
 import InputSlider from '@fastgpt/web/components/common/MySlider/InputSlider';
 import LeftRadio from '@fastgpt/web/components/common/Radio/LeftRadio';
 import { type AppDatasetSearchParamsType } from '@fastgpt/global/core/app/type';
 import MyIcon from '@fastgpt/web/components/common/Icon';
+import MyNumberInput from '@fastgpt/web/components/common/Input/NumberInput';

 enum SearchSettingTabEnum {
  searchMode = 'searchMode',
@@ -48,7 +48,7 @@ const DatasetParamsModal = ({
  datasetSearchUsingExtensionQuery,
  datasetSearchExtensionModel,
  datasetSearchExtensionBg,
-  maxTokens = defaultDatasetMaxTokens,
+  maxTokens,
  onClose,
  onSuccess
 }: AppDatasetSearchParamsType & {
@@ -130,7 +130,7 @@ const DatasetParamsModal = ({

  // 保证只有 80 左右个刻度。
  const maxTokenStep = useMemo(() => {
-    if (maxTokens < 8000) return 80;
+    if (!maxTokens || maxTokens < 8000) return 80;
    return Math.ceil(maxTokens / 80 / 100) * 100;
  }, [maxTokens]);

@@ -301,16 +301,27 @@ const DatasetParamsModal = ({
                  <QuestionTip label={t('common:max_quote_tokens_tips')} />
                </Flex>
                <Box flex={'1 0 0'}>
-                  <InputSlider
-                    min={100}
-                    max={maxTokens}
-                    step={maxTokenStep}
-                    value={getValues(NodeInputKeyEnum.datasetMaxTokens) ?? 1000}
-                    onChange={(val) => {
-                      setValue(NodeInputKeyEnum.datasetMaxTokens, val);
-                      setRefresh(!refresh);
-                    }}
-                  />
+                  {maxTokens ? (
+                    <InputSlider
+                      min={100}
+                      max={maxTokens}
+                      step={maxTokenStep}
+                      value={getValues(NodeInputKeyEnum.datasetMaxTokens) ?? 1000}
+                      onChange={(val) => {
+                        setValue(NodeInputKeyEnum.datasetMaxTokens, val);
+                        setRefresh(!refresh);
+                      }}
+                    />
+                  ) : (
+                    <MyNumberInput
+                      size={'sm'}
+                      min={100}
+                      max={1000000}
+                      step={100}
+                      register={register}
+                      name={NodeInputKeyEnum.datasetMaxTokens}
+                    />
+                  )}
                </Box>
              </Box>
            )}
--- a/projects/app/src/components/core/chat/ChatContainer/ChatBox/Input/VoiceInput.tsx
+++ b/projects/app/src/components/core/chat/ChatContainer/ChatBox/Input/VoiceInput.tsx
@@ -214,7 +214,8 @@ const MobileVoiceInput = ({
 const VoiceInput = forwardRef<VoiceInputComponentRef, VoiceInputProps>(
  ({ onSendMessage, resetInputVal }, ref) => {
    const { t } = useTranslation();
-    const isPc = !isMobile();
+    const isMobileDevice = isMobile();
+    const { isPc } = useSystem();

    const outLinkAuthData = useContextSelector(ChatBoxContext, (v) => v.outLinkAuthData);
    const appId = useContextSelector(ChatBoxContext, (v) => v.appId);
@@ -265,10 +266,10 @@ const VoiceInput = forwardRef<VoiceInputComponentRef, VoiceInputProps>(
          return;
        }

-        if (isPc) {
-          renderAudioGraphPc(analyser, canvas);
-        } else {
+        if (isMobileDevice) {
          renderAudioGraphMobile(analyser, canvas);
+        } else {
+          renderAudioGraphPc(analyser, canvas);
        }
        animationFrameId = window.requestAnimationFrame(renderCurve);
      };
@@ -283,7 +284,7 @@ const VoiceInput = forwardRef<VoiceInputComponentRef, VoiceInputProps>(
        source.disconnect();
        analyser.disconnect();
      };
-    }, [stream, canvasRef, renderAudioGraphPc, renderAudioGraphMobile, isPc]);
+    }, [stream, canvasRef, renderAudioGraphPc, renderAudioGraphMobile, isMobileDevice]);

    const onStartSpeak = useCallback(() => {
      const finishWhisperTranscription = (text: string) => {
@@ -301,12 +302,12 @@ const VoiceInput = forwardRef<VoiceInputComponentRef, VoiceInputProps>(
    }, [autoTTSResponse, onSendMessage, resetInputVal, startSpeak, whisperConfig?.autoSend]);

    const onSpeach = useCallback(() => {
-      if (isPc) {
-        onStartSpeak();
-      } else {
+      if (isMobileDevice) {
        setMobilePreSpeak(true);
+      } else {
+        onStartSpeak();
      }
-    }, [isPc, onStartSpeak]);
+    }, [isMobileDevice, onStartSpeak]);
    useImperativeHandle(ref, () => ({
      onSpeak: onSpeach
    }));
@@ -328,13 +329,7 @@ const VoiceInput = forwardRef<VoiceInputComponentRef, VoiceInputProps>(
        borderRadius={isPc ? 'md' : ''}
        onContextMenu={(e) => e.preventDefault()}
      >
-        {isPc ? (
-          <PCVoiceInput
-            speakingTimeString={speakingTimeString}
-            stopSpeak={stopSpeak}
-            canvasRef={canvasRef}
-          />
-        ) : (
+        {isMobileDevice ? (
          <MobileVoiceInput
            isSpeaking={isSpeaking}
            onStartSpeak={onStartSpeak}
@@ -342,6 +337,12 @@ const VoiceInput = forwardRef<VoiceInputComponentRef, VoiceInputProps>(
            stopSpeak={stopSpeak}
            canvasRef={canvasRef}
          />
+        ) : (
+          <PCVoiceInput
+            speakingTimeString={speakingTimeString}
+            stopSpeak={stopSpeak}
+            canvasRef={canvasRef}
+          />
        )}

        {isTransCription && (
--- a/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/nodes/NodeDatasetConcat.tsx
+++ b/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/nodes/NodeDatasetConcat.tsx
@@ -24,6 +24,8 @@ import FormLabel from '@fastgpt/web/components/common/MyBox/FormLabel';
 import ValueTypeLabel from './render/ValueTypeLabel';
 import MyIcon from '@fastgpt/web/components/common/Icon';
 import { getWebLLMModel } from '@/web/common/system/utils';
+import InputSlider from '@fastgpt/web/components/common/MySlider/InputSlider';
+import MyNumberInput from '@fastgpt/web/components/common/Input/NumberInput';

 const NodeDatasetConcat = ({ data, selected }: NodeProps<FlowNodeItemType>) => {
  const { t } = useTranslation();
@@ -32,35 +34,58 @@ const NodeDatasetConcat = ({ data, selected }: NodeProps<FlowNodeItemType>) => {

  const CustomComponent = useMemo(() => {
    const quoteList = inputs.filter((item) => item.canEdit);
-    const tokenLimit = (() => {
-      let maxTokens = 16000;
+    const maxTokens = (() => {
+      let maxTokens = 0;

      nodeList.forEach((item) => {
        if ([FlowNodeTypeEnum.chatNode, FlowNodeTypeEnum.tools].includes(item.flowNodeType)) {
          const model =
            item.inputs.find((item) => item.key === NodeInputKeyEnum.aiModel)?.value || '';
-          const quoteMaxToken = getWebLLMModel(model)?.quoteMaxToken || 16000;
+          const quoteMaxToken = getWebLLMModel(model)?.quoteMaxToken || 0;

          maxTokens = Math.max(maxTokens, quoteMaxToken);
        }
      });

-      return maxTokens;
+      return maxTokens ? maxTokens : undefined;
+    })();
+
+    const maxTokenStep = (() => {
+      if (!maxTokens || maxTokens < 8000) return 80;
+      return Math.ceil(maxTokens / 80 / 100) * 100;
    })();

    return {
-      [NodeInputKeyEnum.datasetMaxTokens]: (item: FlowNodeInputItemType) => (
-        <Box px={2}>
-          <MySlider
-            markList={[
-              { label: '100', value: 100 },
-              { label: tokenLimit, value: tokenLimit }
-            ]}
-            width={'100%'}
+      [NodeInputKeyEnum.datasetMaxTokens]: (item: FlowNodeInputItemType) =>
+        maxTokens ? (
+          <Box px={2} bg={'white'} py={2} border={'base'} borderRadius={'md'}>
+            <InputSlider
+              min={100}
+              max={maxTokens}
+              step={maxTokenStep}
+              value={item.value}
+              onChange={(e) => {
+                onChangeNode({
+                  nodeId,
+                  type: 'updateInput',
+                  key: item.key,
+                  value: {
+                    ...item,
+                    value: e
+                  }
+                });
+              }}
+            />
+          </Box>
+        ) : (
+          <MyNumberInput
+            size={'sm'}
            min={100}
-            max={tokenLimit}
-            step={50}
+            max={1000000}
+            step={100}
            value={item.value}
+            name={NodeInputKeyEnum.datasetMaxTokens}
+            bg={'white'}
            onChange={(e) => {
              onChangeNode({
                nodeId,
@@ -73,8 +98,7 @@ const NodeDatasetConcat = ({ data, selected }: NodeProps<FlowNodeItemType>) => {
              });
            }}
          />
-        </Box>
-      ),
+        ),
      [NodeInputKeyEnum.datasetQuoteList]: (item: FlowNodeInputItemType) => {
        return (
          <>
--- a/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/nodes/render/RenderInput/templates/SelectDatasetParams.tsx
+++ b/projects/app/src/pageComponents/app/detail/WorkflowComponents/Flow/nodes/render/RenderInput/templates/SelectDatasetParams.tsx
@@ -12,7 +12,6 @@ import SearchParamsTip from '@/components/core/dataset/SearchParamsTip';
 import { useContextSelector } from 'use-context-selector';
 import { WorkflowContext } from '@/pageComponents/app/detail/WorkflowComponents/context';
 import { getWebLLMModel } from '@/web/common/system/utils';
-import { defaultDatasetMaxTokens } from '@fastgpt/global/core/app/constants';
 import { type AppDatasetSearchParamsType } from '@fastgpt/global/core/app/type';

 const SelectDatasetParam = ({ inputs = [], nodeId }: RenderInputProps) => {
@@ -36,19 +35,19 @@ const SelectDatasetParam = ({ inputs = [], nodeId }: RenderInputProps) => {
  });

  const tokenLimit = useMemo(() => {
-    let maxTokens = defaultDatasetMaxTokens;
+    let maxTokens = 0;

    nodeList.forEach((item) => {
      if ([FlowNodeTypeEnum.chatNode, FlowNodeTypeEnum.tools].includes(item.flowNodeType)) {
        const model =
          item.inputs.find((item) => item.key === NodeInputKeyEnum.aiModel)?.value || '';
-        const quoteMaxToken = getWebLLMModel(model)?.quoteMaxToken || defaultDatasetMaxTokens;
+        const quoteMaxToken = getWebLLMModel(model)?.quoteMaxToken ?? 0;

        maxTokens = Math.max(maxTokens, quoteMaxToken);
      }
    });

-    return maxTokens;
+    return maxTokens ? maxTokens : undefined;
  }, [nodeList]);

  const { isOpen, onOpen, onClose } = useDisclosure();