mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 15:00:57 +03:00
Feature/agentflow v2 (#4298)
* agent flow v2 * chat message background * conditon agent flow * add sticky note * update human input dynamic prompt * add HTTP node * add default tool icon * fix export duplicate agentflow v2 * add agentflow v2 marketplaces * refractor memoization, add iteration nodes * add agentflow v2 templates * add agentflow generator * add migration scripts for mysql, mariadb, posrgres and fix date filters for executions * update agentflow chat history config * fix get all flows error after deletion and rename * add previous nodes from parent node * update generator prompt * update run time state when using iteration nodes * prevent looping connection, prevent duplication of start node, add executeflow node, add nodes agentflow, chat history variable * update embed * convert form input to string * bump openai version * add react rewards * add prompt generator to prediction queue * add array schema to overrideconfig * UI touchup * update embedded chat version * fix node info dialog * update start node and loop default iteration * update UI fixes for agentflow v2 * fix async drop down * add export import to agentflowsv2, executions, fix UI bugs * add default empty object to flowlisttable * add ability to share trace link publicly, allow MCP tool use for Agent and Assistant * add runtime message length to variable, display conditions on UI * fix array validation * add ability to add knowledge from vector store and embeddings for agent * add agent tool require human input * add ephemeral memory to start node * update agent flow node to show vs and embeddings icons * feat: add import chat data functionality for AgentFlowV2 * feat: set chatMessage.executionId to null if not found in import JSON file or database * fix: MariaDB execution migration script to utf8mb4_unicode_520_ci --------- Co-authored-by: Ong Chung Yau <33013947+chungyau97@users.noreply.github.com> Co-authored-by: chungyau97 <chungyau97@gmail.com>
This commit is contained in:
Henry Heng
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import {
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ICommonObject,
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IDatabaseEntity,
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INode,
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INodeData,
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INodeOptionsValue,
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INodeParams,
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IServerSideEventStreamer
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} from '../../../src/Interface'
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import { updateFlowState } from '../utils'
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import { DataSource } from 'typeorm'
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import { BaseRetriever } from '@langchain/core/retrievers'
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import { Document } from '@langchain/core/documents'
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interface IKnowledgeBase {
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documentStore: string
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}
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class Retriever_Agentflow implements INode {
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label: string
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name: string
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version: number
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description: string
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type: string
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icon: string
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category: string
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color: string
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hideOutput: boolean
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hint: string
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baseClasses: string[]
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documentation?: string
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credential: INodeParams
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inputs: INodeParams[]
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constructor() {
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this.label = 'Retriever'
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this.name = 'retrieverAgentflow'
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this.version = 1.0
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this.type = 'Retriever'
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this.category = 'Agent Flows'
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this.description = 'Retrieve information from vector database'
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this.baseClasses = [this.type]
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this.color = '#b8bedd'
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this.inputs = [
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{
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label: 'Knowledge (Document Stores)',
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name: 'retrieverKnowledgeDocumentStores',
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type: 'array',
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description: 'Document stores to retrieve information from. Document stores must be upserted in advance.',
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array: [
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{
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label: 'Document Store',
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name: 'documentStore',
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type: 'asyncOptions',
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loadMethod: 'listStores'
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}
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]
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},
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{
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label: 'Retriever Query',
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name: 'retrieverQuery',
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type: 'string',
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placeholder: 'Enter your query here',
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rows: 4,
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acceptVariable: true
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},
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{
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label: 'Output Format',
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name: 'outputFormat',
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type: 'options',
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options: [
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{ label: 'Text', name: 'text' },
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{ label: 'Text with Metadata', name: 'textWithMetadata' }
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],
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default: 'text'
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},
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{
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label: 'Update Flow State',
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name: 'retrieverUpdateState',
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description: 'Update runtime state during the execution of the workflow',
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type: 'array',
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optional: true,
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acceptVariable: true,
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array: [
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{
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label: 'Key',
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name: 'key',
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type: 'asyncOptions',
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loadMethod: 'listRuntimeStateKeys',
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freeSolo: true
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},
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{
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label: 'Value',
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name: 'value',
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type: 'string',
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acceptVariable: true,
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acceptNodeOutputAsVariable: true
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}
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]
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}
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]
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}
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//@ts-ignore
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loadMethods = {
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async listRuntimeStateKeys(_: INodeData, options: ICommonObject): Promise<INodeOptionsValue[]> {
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const previousNodes = options.previousNodes as ICommonObject[]
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const startAgentflowNode = previousNodes.find((node) => node.name === 'startAgentflow')
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const state = startAgentflowNode?.inputs?.startState as ICommonObject[]
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return state.map((item) => ({ label: item.key, name: item.key }))
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},
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async listStores(_: INodeData, options: ICommonObject): Promise<INodeOptionsValue[]> {
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const returnData: INodeOptionsValue[] = []
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const appDataSource = options.appDataSource as DataSource
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const databaseEntities = options.databaseEntities as IDatabaseEntity
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if (appDataSource === undefined || !appDataSource) {
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return returnData
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}
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const stores = await appDataSource.getRepository(databaseEntities['DocumentStore']).find()
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for (const store of stores) {
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if (store.status === 'UPSERTED') {
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const obj = {
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name: `${store.id}:${store.name}`,
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label: store.name,
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description: store.description
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}
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returnData.push(obj)
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}
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}
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return returnData
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}
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
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const retrieverQuery = nodeData.inputs?.retrieverQuery as string
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const outputFormat = nodeData.inputs?.outputFormat as string
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const _retrieverUpdateState = nodeData.inputs?.retrieverUpdateState
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const state = options.agentflowRuntime?.state as ICommonObject
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const chatId = options.chatId as string
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const isLastNode = options.isLastNode as boolean
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const isStreamable = isLastNode && options.sseStreamer !== undefined
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const abortController = options.abortController as AbortController
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// Extract knowledge
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let docs: Document[] = []
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const knowledgeBases = nodeData.inputs?.retrieverKnowledgeDocumentStores as IKnowledgeBase[]
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if (knowledgeBases && knowledgeBases.length > 0) {
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for (const knowledgeBase of knowledgeBases) {
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const [storeId, _] = knowledgeBase.documentStore.split(':')
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const docStoreVectorInstanceFilePath = options.componentNodes['documentStoreVS'].filePath as string
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const docStoreVectorModule = await import(docStoreVectorInstanceFilePath)
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const newDocStoreVectorInstance = new docStoreVectorModule.nodeClass()
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const docStoreVectorInstance = (await newDocStoreVectorInstance.init(
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{
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...nodeData,
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inputs: {
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...nodeData.inputs,
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selectedStore: storeId
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},
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outputs: {
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output: 'retriever'
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}
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},
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'',
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options
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)) as BaseRetriever
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docs = await docStoreVectorInstance.invoke(retrieverQuery || input, { signal: abortController?.signal })
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}
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}
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const docsText = docs.map((doc) => doc.pageContent).join('\n')
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// Update flow state if needed
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let newState = { ...state }
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if (_retrieverUpdateState && Array.isArray(_retrieverUpdateState) && _retrieverUpdateState.length > 0) {
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newState = updateFlowState(state, _retrieverUpdateState)
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}
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try {
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let finalOutput = ''
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if (outputFormat === 'text') {
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finalOutput = docsText
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} else if (outputFormat === 'textWithMetadata') {
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finalOutput = JSON.stringify(docs, null, 2)
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}
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if (isStreamable) {
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const sseStreamer: IServerSideEventStreamer = options.sseStreamer
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sseStreamer.streamTokenEvent(chatId, finalOutput)
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}
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// Process template variables in state
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if (newState && Object.keys(newState).length > 0) {
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for (const key in newState) {
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if (newState[key].toString().includes('{{ output }}')) {
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newState[key] = finalOutput
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}
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}
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}
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const returnOutput = {
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id: nodeData.id,
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name: this.name,
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input: {
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question: retrieverQuery || input
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},
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output: {
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content: finalOutput
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},
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state: newState
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}
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return returnOutput
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} catch (e) {
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throw new Error(e)
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}
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}
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}
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module.exports = { nodeClass: Retriever_Agentflow }
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