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Revert "chore: use node specify llm to auto generate prompt" (#6555)
This commit is contained in:
parent
e80412df23
commit
d726473c6d
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@ -14,7 +14,6 @@ import PromptEditorHeightResizeWrap from './prompt-editor-height-resize-wrap'
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import cn from '@/utils/classnames'
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import { type PromptVariable } from '@/models/debug'
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import Tooltip from '@/app/components/base/tooltip'
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import type { CompletionParams } from '@/types/app'
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import { AppType } from '@/types/app'
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import { getNewVar, getVars } from '@/utils/var'
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import AutomaticBtn from '@/app/components/app/configuration/config/automatic/automatic-btn'
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@ -59,7 +58,6 @@ const Prompt: FC<ISimplePromptInput> = ({
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const { eventEmitter } = useEventEmitterContextContext()
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const {
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modelConfig,
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completionParams,
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dataSets,
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setModelConfig,
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setPrevPromptConfig,
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@ -249,14 +247,6 @@ const Prompt: FC<ISimplePromptInput> = ({
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{showAutomatic && (
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<GetAutomaticResModal
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mode={mode as AppType}
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model={
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{
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provider: modelConfig.provider,
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name: modelConfig.model_id,
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mode: modelConfig.mode,
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completion_params: completionParams as CompletionParams,
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}
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}
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isShow={showAutomatic}
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onClose={showAutomaticFalse}
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onFinished={handleAutomaticRes}
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@ -20,7 +20,6 @@ import Button from '@/app/components/base/button'
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import Toast from '@/app/components/base/toast'
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import { generateRule } from '@/service/debug'
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import ConfigPrompt from '@/app/components/app/configuration/config-prompt'
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import type { Model } from '@/types/app'
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import { AppType } from '@/types/app'
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import ConfigVar from '@/app/components/app/configuration/config-var'
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import OpeningStatement from '@/app/components/app/configuration/features/chat-group/opening-statement'
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@ -34,7 +33,6 @@ import { Generator } from '@/app/components/base/icons/src/vender/other'
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export type IGetAutomaticResProps = {
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mode: AppType
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model: Model
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isShow: boolean
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onClose: () => void
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onFinished: (res: AutomaticRes) => void
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@ -59,7 +57,6 @@ const TryLabel: FC<{
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const GetAutomaticRes: FC<IGetAutomaticResProps> = ({
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mode,
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model,
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isShow,
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onClose,
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isInLLMNode,
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@ -152,17 +149,10 @@ const GetAutomaticRes: FC<IGetAutomaticResProps> = ({
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return
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setLoadingTrue()
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try {
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const { error, ...res } = await generateRule({
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const res = await generateRule({
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instruction,
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model_config: model,
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})
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setRes(res)
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if (error) {
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Toast.notify({
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type: 'error',
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message: error,
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})
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}
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}
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finally {
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setLoadingFalse()
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@ -9,7 +9,6 @@ import { useTranslation } from 'react-i18next'
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import { useBoolean } from 'ahooks'
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import { BlockEnum, EditionType } from '../../../../types'
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import type {
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ModelConfig,
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Node,
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NodeOutPutVar,
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Variable,
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@ -59,7 +58,6 @@ type Props = {
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availableNodes?: Node[]
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isSupportPromptGenerator?: boolean
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onGenerated?: (prompt: string) => void
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modelConfig?: ModelConfig
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// for jinja
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isSupportJinja?: boolean
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editionType?: EditionType
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@ -92,7 +90,6 @@ const Editor: FC<Props> = ({
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varList = [],
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handleAddVariable,
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onGenerated,
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modelConfig,
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}) => {
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const { t } = useTranslation()
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const { eventEmitter } = useEventEmitterContextContext()
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@ -133,7 +130,7 @@ const Editor: FC<Props> = ({
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<div className='flex items-center'>
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<div className='leading-[18px] text-xs font-medium text-gray-500'>{value?.length || 0}</div>
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{isSupportPromptGenerator && (
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<PromptGeneratorBtn className='ml-[5px]' onGenerated={onGenerated} modelConfig={modelConfig} />
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<PromptGeneratorBtn className='ml-[5px]' onGenerated={onGenerated} />
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)}
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<div className='w-px h-3 ml-2 mr-2 bg-gray-200'></div>
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@ -4,7 +4,7 @@ import React, { useCallback, useEffect, useState } from 'react'
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import { uniqueId } from 'lodash-es'
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import { useTranslation } from 'react-i18next'
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import { RiQuestionLine } from '@remixicon/react'
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import type { ModelConfig, PromptItem, Variable } from '../../../types'
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import type { PromptItem, Variable } from '../../../types'
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import { EditionType } from '../../../types'
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import { useWorkflowStore } from '../../../store'
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import Editor from '@/app/components/workflow/nodes/_base/components/prompt/editor'
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@ -38,7 +38,6 @@ type Props = {
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availableNodes: any
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varList: Variable[]
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handleAddVariable: (payload: any) => void
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modelConfig?: ModelConfig
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}
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const roleOptions = [
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@ -78,7 +77,6 @@ const ConfigPromptItem: FC<Props> = ({
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availableNodes,
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varList,
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handleAddVariable,
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modelConfig,
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}) => {
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const { t } = useTranslation()
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const workflowStore = useWorkflowStore()
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@ -140,7 +138,6 @@ const ConfigPromptItem: FC<Props> = ({
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availableNodes={availableNodes}
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isSupportPromptGenerator={payload.role === PromptRole.system}
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onGenerated={handleGenerated}
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modelConfig={modelConfig}
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isSupportJinja
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editionType={payload.edition_type}
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onEditionTypeChange={onEditionTypeChange}
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@ -5,7 +5,7 @@ import { useTranslation } from 'react-i18next'
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import produce from 'immer'
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import { ReactSortable } from 'react-sortablejs'
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import { v4 as uuid4 } from 'uuid'
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import type { ModelConfig, PromptItem, ValueSelector, Var, Variable } from '../../../types'
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import type { PromptItem, ValueSelector, Var, Variable } from '../../../types'
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import { EditionType, PromptRole } from '../../../types'
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import useAvailableVarList from '../../_base/hooks/use-available-var-list'
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import { useWorkflowStore } from '../../../store'
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@ -33,7 +33,6 @@ type Props = {
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}
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varList?: Variable[]
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handleAddVariable: (payload: any) => void
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modelConfig: ModelConfig
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}
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const ConfigPrompt: FC<Props> = ({
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@ -48,7 +47,6 @@ const ConfigPrompt: FC<Props> = ({
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hasSetBlockStatus,
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varList = [],
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handleAddVariable,
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modelConfig,
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}) => {
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const { t } = useTranslation()
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const workflowStore = useWorkflowStore()
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@ -201,7 +199,6 @@ const ConfigPrompt: FC<Props> = ({
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availableNodes={availableNodesWithParent}
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varList={varList}
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handleAddVariable={handleAddVariable}
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modelConfig={modelConfig}
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/>
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</div>
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)
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@ -237,7 +234,6 @@ const ConfigPrompt: FC<Props> = ({
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onEditionTypeChange={handleCompletionEditionTypeChange}
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handleAddVariable={handleAddVariable}
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onGenerated={handleGenerated}
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modelConfig={modelConfig}
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/>
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</div>
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)}
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@ -7,19 +7,14 @@ import { Generator } from '@/app/components/base/icons/src/vender/other'
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import GetAutomaticResModal from '@/app/components/app/configuration/config/automatic/get-automatic-res'
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import { AppType } from '@/types/app'
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import type { AutomaticRes } from '@/service/debug'
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import type { ModelConfig } from '@/app/components/workflow/types'
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import type { Model } from '@/types/app'
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type Props = {
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className?: string
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onGenerated?: (prompt: string) => void
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modelConfig?: ModelConfig
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}
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const PromptGeneratorBtn: FC<Props> = ({
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className,
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onGenerated,
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modelConfig,
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}) => {
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const [showAutomatic, { setTrue: showAutomaticTrue, setFalse: showAutomaticFalse }] = useBoolean(false)
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const handleAutomaticRes = useCallback((res: AutomaticRes) => {
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@ -37,7 +32,6 @@ const PromptGeneratorBtn: FC<Props> = ({
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isShow={showAutomatic}
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onClose={showAutomaticFalse}
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onFinished={handleAutomaticRes}
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model={modelConfig as Model}
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isInLLMNode
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/>
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)}
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@ -178,7 +178,6 @@ const Panel: FC<NodePanelProps<LLMNodeType>> = ({
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hasSetBlockStatus={hasSetBlockStatus}
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varList={inputs.prompt_config?.jinja2_variables || []}
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handleAddVariable={handleAddVariable}
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modelConfig={model}
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/>
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)}
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@ -7,7 +7,6 @@ export type AutomaticRes = {
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prompt: string
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variables: string[]
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opening_statement: string
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error?: string
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}
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export const sendChatMessage = async (appId: string, body: Record<string, any>, { onData, onCompleted, onThought, onFile, onError, getAbortController, onMessageEnd, onMessageReplace }: {
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109
web/types/app.ts
109
web/types/app.ts
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@ -135,64 +135,9 @@ export enum AgentStrategy {
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react = 'react',
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}
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export type CompletionParams = {
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/** Maximum number of tokens in the answer message returned by Completion */
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max_tokens: number
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/**
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* A number between 0 and 2.
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* The larger the number, the more random the result;
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* otherwise, the more deterministic.
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* When in use, choose either `temperature` or `top_p`.
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* Default is 1.
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*/
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temperature: number
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/**
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* Represents the proportion of probability mass samples to take,
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* e.g., 0.1 means taking the top 10% probability mass samples.
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* The determinism between the samples is basically consistent.
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* Among these results, the `top_p` probability mass results are taken.
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* When in use, choose either `temperature` or `top_p`.
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* Default is 1.
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*/
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top_p: number
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/** When enabled, the Completion Text will concatenate the Prompt content together and return it. */
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echo: boolean
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/**
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* Specify up to 4 to automatically stop generating before the text specified in `stop`.
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* Suitable for use in chat mode.
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* For example, specify "Q" and "A",
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* and provide some Q&A examples as context,
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* and the model will give out in Q&A format and stop generating before Q&A.
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*/
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stop: string[]
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/**
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* A number between -2.0 and 2.0.
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* The larger the value, the less the model will repeat topics and the more it will provide new topics.
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*/
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presence_penalty: number
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/**
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* A number between -2.0 and 2.0.
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* A lower setting will make the model appear less cultured,
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* always repeating expressions.
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* The difference between `frequency_penalty` and `presence_penalty`
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* is that `frequency_penalty` penalizes a word based on its frequency in the training data,
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* while `presence_penalty` penalizes a word based on its occurrence in the input text.
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*/
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frequency_penalty: number
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}
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/**
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* Model configuration. The backend type.
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*/
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export type Model = {
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/** LLM provider, e.g., OPENAI */
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provider: string
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/** Model name, e.g, gpt-3.5.turbo */
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name: string
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mode: ModelModeType
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/** Default Completion call parameters */
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completion_params: CompletionParams
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}
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export type ModelConfig = {
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opening_statement: string
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suggested_questions?: string[]
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@ -229,7 +174,59 @@ export type ModelConfig = {
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strategy?: AgentStrategy
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tools: ToolItem[]
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}
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model: Model
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model: {
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/** LLM provider, e.g., OPENAI */
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provider: string
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/** Model name, e.g, gpt-3.5.turbo */
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name: string
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mode: ModelModeType
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/** Default Completion call parameters */
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completion_params: {
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/** Maximum number of tokens in the answer message returned by Completion */
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max_tokens: number
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/**
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* A number between 0 and 2.
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* The larger the number, the more random the result;
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* otherwise, the more deterministic.
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* When in use, choose either `temperature` or `top_p`.
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* Default is 1.
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*/
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temperature: number
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/**
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* Represents the proportion of probability mass samples to take,
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* e.g., 0.1 means taking the top 10% probability mass samples.
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* The determinism between the samples is basically consistent.
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* Among these results, the `top_p` probability mass results are taken.
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* When in use, choose either `temperature` or `top_p`.
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* Default is 1.
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*/
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top_p: number
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/** When enabled, the Completion Text will concatenate the Prompt content together and return it. */
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echo: boolean
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/**
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* Specify up to 4 to automatically stop generating before the text specified in `stop`.
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* Suitable for use in chat mode.
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* For example, specify "Q" and "A",
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* and provide some Q&A examples as context,
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* and the model will give out in Q&A format and stop generating before Q&A.
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*/
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stop: string[]
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/**
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* A number between -2.0 and 2.0.
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* The larger the value, the less the model will repeat topics and the more it will provide new topics.
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*/
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presence_penalty: number
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/**
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* A number between -2.0 and 2.0.
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* A lower setting will make the model appear less cultured,
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* always repeating expressions.
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* The difference between `frequency_penalty` and `presence_penalty`
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* is that `frequency_penalty` penalizes a word based on its frequency in the training data,
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* while `presence_penalty` penalizes a word based on its occurrence in the input text.
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*/
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frequency_penalty: number
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}
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}
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dataset_configs: DatasetConfigs
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file_upload?: {
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image: VisionSettings
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