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https://github.com/labring/FastGPT.git
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perf: template
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@@ -31,6 +31,10 @@ export enum FlowModuleTypeEnum {
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classifyQuestion = 'classifyQuestion'
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}
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export enum SpecialInputKeyEnum {
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'answerText' = 'text'
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}
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export const initModuleType: Record<string, boolean> = {
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[FlowModuleTypeEnum.historyNode]: true,
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[FlowModuleTypeEnum.questionInput]: true
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@@ -38,8 +42,8 @@ export const initModuleType: Record<string, boolean> = {
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export const edgeOptions = {
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style: {
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strokeWidth: 1,
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strokeWidth: 1.5,
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stroke: '#5A646Es'
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}
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};
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export const connectionLineStyle = { strokeWidth: 1, stroke: '#5A646Es' };
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export const connectionLineStyle = { strokeWidth: 1.5, stroke: '#5A646Es' };
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@@ -41,15 +41,19 @@ const NodeCard = ({
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</MyTooltip>
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)}
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<Box flex={1} />
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<MyIcon
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<Flex
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className={'nodrag'}
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name="delete"
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cursor={'pointer'}
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w={'22px'}
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h={'22px'}
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alignItems={'center'}
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justifyContent={'center'}
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color={'myGray.600'}
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w={'16px'}
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_hover={{ color: 'red.600' }}
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cursor={'pointer'}
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onClick={() => onDelNode(moduleId)}
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/>
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>
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<MyIcon name="delete" w={'16px'} />
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</Flex>
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</Flex>
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{children}
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</Box>
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@@ -283,7 +283,6 @@ const AppEdit = ({ app, fullScreen, onFullScreen }: Props) => {
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useEffect(() => {
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initData(JSON.parse(JSON.stringify(app)));
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}, [app, initData]);
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console.log(flow2AppModules());
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return (
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<>
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@@ -51,7 +51,7 @@ export const dispatchClassifyQuestion = async (props: Record<string, any>): Prom
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// function body
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const agentFunction = {
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name: agentFunName,
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description: '判断用户问题的类型,并返回指定值',
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description: '判断用户问题的类型属于哪方面,返回对应的枚举字段',
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parameters: {
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type: 'object',
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properties: {
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@@ -81,13 +81,9 @@ export const dispatchClassifyQuestion = async (props: Record<string, any>): Prom
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const arg = JSON.parse(response.data.choices?.[0]?.message?.function_call?.arguments || '');
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if (!arg.type) {
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throw new Error('');
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}
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const tokens = response.data.usage?.total_tokens || 0;
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const result = agents.find((item) => item.key === arg.type) || agents[0];
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const result = agents.find((item) => item.key === arg?.type) || agents[0];
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return {
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[result.key]: 1,
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@@ -31,7 +31,7 @@ const simplifyStr = (str = '') =>
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.replace(/[^\S\r\n]+/g, ' ') // 连续空白内容
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.trim();
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/* 聊天上下文 tokens 截断 */
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/* slice chat context by tokens */
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export const ChatContextFilter = ({
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model,
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prompts,
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@@ -41,33 +41,19 @@ export const ChatContextFilter = ({
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prompts: ChatItemType[];
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maxTokens: number;
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}) => {
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const systemPrompts: ChatItemType[] = [];
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const chatPrompts: ChatItemType[] = [];
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const rawTextLen = prompts.reduce((sum, item) => sum + item.value.length, 0);
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let rawTextLen = 0;
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prompts.forEach((item) => {
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const val = simplifyStr(item.value);
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rawTextLen += val.length;
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const data = {
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_id: item._id,
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obj: item.obj,
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value: val
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};
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if (item.obj === ChatRoleEnum.System) {
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systemPrompts.push(data);
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} else {
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chatPrompts.push(data);
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}
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});
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// 长度太小时,不需要进行 token 截断
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// If the text length is less than half of the maximum token, no calculation is required
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if (rawTextLen < maxTokens * 0.5) {
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return [...systemPrompts, ...chatPrompts];
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return prompts;
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}
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// 去掉 system 的 token
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// filter startWith system prompt
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const chatStartIndex = prompts.findIndex((item) => item.obj !== ChatRoleEnum.System);
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const systemPrompts: ChatItemType[] = prompts.slice(0, chatStartIndex);
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const chatPrompts: ChatItemType[] = prompts.slice(chatStartIndex);
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// reduce token of systemPrompt
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maxTokens -= modelToolMap.countTokens({
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model,
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messages: systemPrompts
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@@ -1,7 +1,8 @@
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import type { AppModuleInputItemType, AppModuleItemType, VariableItemType } from '@/types/app';
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import { chatModelList, vectorModelList } from '@/store/static';
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import { FlowModuleTypeEnum } from '@/constants/flow';
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import { FlowModuleTypeEnum, SpecialInputKeyEnum } from '@/constants/flow';
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import { SystemInputEnum } from '@/constants/app';
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import { TaskResponseKeyEnum } from '@/constants/chat';
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import type { SelectedKbType } from '@/types/plugin';
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export type EditFormType = {
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@@ -128,7 +129,7 @@ export const appModules2Form = (modules: AppModuleItemType[]) => {
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if (emptyOutput) {
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const target = modules.find((item) => item.moduleId === emptyOutput.moduleId);
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defaultAppForm.kb.searchEmptyText =
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target?.inputs?.find((item) => item.key === 'answerText')?.value || '';
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target?.inputs?.find((item) => item.key === SpecialInputKeyEnum.answerText)?.value || '';
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}
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} else if (module.flowType === FlowModuleTypeEnum.userGuide) {
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const val =
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@@ -173,6 +174,10 @@ const chatModelInput = (formData: EditFormType): AppModuleInputItemType[] => [
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value: formData.chatModel.limitPrompt,
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connected: true
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},
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{
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key: 'switch',
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connected: formData.kb.list.length > 0
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},
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{
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key: 'quoteQA',
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connected: formData.kb.list.length > 0
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@@ -289,7 +294,7 @@ const simpleChatTemplate = (formData: EditFormType): AppModuleItemType[] => [
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inputs: chatModelInput(formData),
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outputs: [
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{
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key: 'answerText',
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key: TaskResponseKeyEnum.answerText,
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targets: []
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}
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],
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@@ -439,7 +444,7 @@ const kbTemplate = (formData: EditFormType): AppModuleItemType[] => [
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connected: true
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},
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{
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key: 'answerText',
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key: SpecialInputKeyEnum.answerText,
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value: formData.kb.searchEmptyText,
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connected: true
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}
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@@ -458,7 +463,7 @@ const kbTemplate = (formData: EditFormType): AppModuleItemType[] => [
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inputs: chatModelInput(formData),
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outputs: [
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{
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key: 'answerText',
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key: TaskResponseKeyEnum.answerText,
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targets: []
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}
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],
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