381 lines
15 KiB
TypeScript
381 lines
15 KiB
TypeScript
import { flatten } from 'lodash'
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import { Tool } from '@langchain/core/tools'
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import { BaseMessage } from '@langchain/core/messages'
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import { ChainValues } from '@langchain/core/utils/types'
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import { RunnableSequence } from '@langchain/core/runnables'
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import { BaseChatModel } from '@langchain/core/language_models/chat_models'
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import { ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate, PromptTemplate } from '@langchain/core/prompts'
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import { formatToOpenAIToolMessages } from 'langchain/agents/format_scratchpad/openai_tools'
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import { type ToolsAgentStep } from 'langchain/agents/openai/output_parser'
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import {
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extractOutputFromArray,
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getBaseClasses,
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handleEscapeCharacters,
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removeInvalidImageMarkdown,
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transformBracesWithColon
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} from '../../../src/utils'
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import {
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FlowiseMemory,
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ICommonObject,
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INode,
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INodeData,
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INodeParams,
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IServerSideEventStreamer,
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IUsedTool,
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IVisionChatModal
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} from '../../../src/Interface'
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import { ConsoleCallbackHandler, CustomChainHandler, CustomStreamingHandler, additionalCallbacks } from '../../../src/handler'
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import { AgentExecutor, ToolCallingAgentOutputParser } from '../../../src/agents'
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import { Moderation, checkInputs, streamResponse } from '../../moderation/Moderation'
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import { formatResponse } from '../../outputparsers/OutputParserHelpers'
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import { addImagesToMessages, llmSupportsVision } from '../../../src/multiModalUtils'
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class ToolAgent_Agents 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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baseClasses: string[]
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inputs: INodeParams[]
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sessionId?: string
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constructor(fields?: { sessionId?: string }) {
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this.label = 'Tool Agent'
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this.name = 'toolAgent'
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this.version = 2.0
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this.type = 'AgentExecutor'
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this.category = 'Agents'
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this.icon = 'toolAgent.png'
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this.description = `Agent that uses Function Calling to pick the tools and args to call`
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this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
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this.inputs = [
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{
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label: 'Tools',
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name: 'tools',
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type: 'Tool',
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list: true
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},
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{
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label: 'Memory',
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name: 'memory',
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type: 'BaseChatMemory'
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},
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{
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label: 'Tool Calling Chat Model',
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name: 'model',
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type: 'BaseChatModel',
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description:
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'Only compatible with models that are capable of function calling: ChatOpenAI, ChatMistral, ChatAnthropic, ChatGoogleGenerativeAI, ChatVertexAI, GroqChat'
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},
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{
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label: 'Chat Prompt Template',
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name: 'chatPromptTemplate',
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type: 'ChatPromptTemplate',
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description: 'Override existing prompt with Chat Prompt Template. Human Message must includes {input} variable',
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optional: true
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},
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{
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label: 'System Message',
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name: 'systemMessage',
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type: 'string',
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default: `You are a helpful AI assistant.`,
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description: 'If Chat Prompt Template is provided, this will be ignored',
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rows: 4,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Input Moderation',
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description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
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name: 'inputModeration',
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type: 'Moderation',
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optional: true,
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list: true
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},
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{
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label: 'Max Iterations',
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name: 'maxIterations',
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type: 'number',
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optional: true,
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additionalParams: true
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},
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{
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label: 'Enable Detailed Streaming',
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name: 'enableDetailedStreaming',
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type: 'boolean',
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default: false,
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description: 'Stream detailed intermediate steps during agent execution',
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optional: true,
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additionalParams: true
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}
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]
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this.sessionId = fields?.sessionId
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}
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async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
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return prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input })
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
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const memory = nodeData.inputs?.memory as FlowiseMemory
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const moderations = nodeData.inputs?.inputModeration as Moderation[]
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const enableDetailedStreaming = nodeData.inputs?.enableDetailedStreaming as boolean
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const shouldStreamResponse = options.shouldStreamResponse
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const sseStreamer: IServerSideEventStreamer = options.sseStreamer as IServerSideEventStreamer
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const chatId = options.chatId
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if (moderations && moderations.length > 0) {
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try {
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// Use the output of the moderation chain as input for the OpenAI Function Agent
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input = await checkInputs(moderations, input)
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} catch (e) {
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await new Promise((resolve) => setTimeout(resolve, 500))
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if (shouldStreamResponse) {
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streamResponse(sseStreamer, chatId, e.message)
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}
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return formatResponse(e.message)
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}
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}
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const executor = await prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input })
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const loggerHandler = new ConsoleCallbackHandler(options.logger)
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const callbacks = await additionalCallbacks(nodeData, options)
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// Add custom streaming handler if detailed streaming is enabled
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let customStreamingHandler = null
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if (enableDetailedStreaming && shouldStreamResponse) {
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customStreamingHandler = new CustomStreamingHandler(sseStreamer, chatId)
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}
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let res: ChainValues = {}
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let sourceDocuments: ICommonObject[] = []
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let usedTools: IUsedTool[] = []
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let artifacts = []
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if (shouldStreamResponse) {
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const handler = new CustomChainHandler(sseStreamer, chatId)
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const allCallbacks = [loggerHandler, handler, ...callbacks]
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// Add detailed streaming handler if enabled
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if (enableDetailedStreaming && customStreamingHandler) {
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allCallbacks.push(customStreamingHandler)
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}
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res = await executor.invoke({ input }, { callbacks: allCallbacks })
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if (res.sourceDocuments) {
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if (sseStreamer) {
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sseStreamer.streamSourceDocumentsEvent(chatId, flatten(res.sourceDocuments))
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}
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sourceDocuments = res.sourceDocuments
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}
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if (res.usedTools) {
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if (sseStreamer) {
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sseStreamer.streamUsedToolsEvent(chatId, flatten(res.usedTools))
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}
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usedTools = res.usedTools
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}
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if (res.artifacts) {
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if (sseStreamer) {
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sseStreamer.streamArtifactsEvent(chatId, flatten(res.artifacts))
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}
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artifacts = res.artifacts
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}
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// If the tool is set to returnDirect, stream the output to the client
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if (res.usedTools && res.usedTools.length) {
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let inputTools = nodeData.inputs?.tools
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inputTools = flatten(inputTools)
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for (const tool of res.usedTools) {
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const inputTool = inputTools.find((inputTool: Tool) => inputTool.name === tool.tool)
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if (inputTool && inputTool.returnDirect && shouldStreamResponse) {
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sseStreamer.streamTokenEvent(chatId, tool.toolOutput)
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}
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}
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}
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} else {
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const allCallbacks = [loggerHandler, ...callbacks]
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// Add detailed streaming handler if enabled
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if (enableDetailedStreaming && customStreamingHandler) {
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allCallbacks.push(customStreamingHandler)
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}
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res = await executor.invoke({ input }, { callbacks: allCallbacks })
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if (res.sourceDocuments) {
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sourceDocuments = res.sourceDocuments
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}
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if (res.usedTools) {
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usedTools = res.usedTools
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}
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if (res.artifacts) {
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artifacts = res.artifacts
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}
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}
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let output = res?.output
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output = extractOutputFromArray(res?.output)
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output = removeInvalidImageMarkdown(output)
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// Claude 3 Opus tends to spit out <thinking>..</thinking> as well, discard that in final output
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// https://docs.anthropic.com/en/docs/build-with-claude/tool-use#chain-of-thought
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const regexPattern: RegExp = /<thinking>[\s\S]*?<\/thinking>/
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const matches: RegExpMatchArray | null = output.match(regexPattern)
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if (matches) {
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for (const match of matches) {
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output = output.replace(match, '')
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}
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}
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await memory.addChatMessages(
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[
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{
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text: input,
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type: 'userMessage'
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},
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{
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text: output,
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type: 'apiMessage'
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}
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],
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this.sessionId
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)
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let finalRes = output
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if (sourceDocuments.length || usedTools.length || artifacts.length) {
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const finalRes: ICommonObject = { text: output }
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if (sourceDocuments.length) {
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finalRes.sourceDocuments = flatten(sourceDocuments)
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}
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if (usedTools.length) {
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finalRes.usedTools = usedTools
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}
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if (artifacts.length) {
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finalRes.artifacts = artifacts
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}
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return finalRes
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}
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return finalRes
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}
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}
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const prepareAgent = async (
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nodeData: INodeData,
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options: ICommonObject,
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flowObj: { sessionId?: string; chatId?: string; input?: string }
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) => {
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const model = nodeData.inputs?.model as BaseChatModel
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const maxIterations = nodeData.inputs?.maxIterations as string
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const memory = nodeData.inputs?.memory as FlowiseMemory
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let systemMessage = nodeData.inputs?.systemMessage as string
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let tools = nodeData.inputs?.tools
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tools = flatten(tools)
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const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
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const inputKey = memory.inputKey ? memory.inputKey : 'input'
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const prependMessages = options?.prependMessages
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systemMessage = transformBracesWithColon(systemMessage)
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let prompt = ChatPromptTemplate.fromMessages([
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['system', systemMessage],
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new MessagesPlaceholder(memoryKey),
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['human', `{${inputKey}}`],
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new MessagesPlaceholder('agent_scratchpad')
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])
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let promptVariables = {}
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const chatPromptTemplate = nodeData.inputs?.chatPromptTemplate as ChatPromptTemplate
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if (chatPromptTemplate && chatPromptTemplate.promptMessages.length) {
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const humanPrompt = chatPromptTemplate.promptMessages[chatPromptTemplate.promptMessages.length - 1]
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const messages = [
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...chatPromptTemplate.promptMessages.slice(0, -1),
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new MessagesPlaceholder(memoryKey),
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humanPrompt,
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new MessagesPlaceholder('agent_scratchpad')
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]
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prompt = ChatPromptTemplate.fromMessages(messages)
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if ((chatPromptTemplate as any).promptValues) {
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const promptValuesRaw = (chatPromptTemplate as any).promptValues
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const promptValues = handleEscapeCharacters(promptValuesRaw, true)
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for (const val in promptValues) {
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promptVariables = {
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...promptVariables,
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[val]: () => {
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return promptValues[val]
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}
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}
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}
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}
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}
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if (llmSupportsVision(model)) {
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const visionChatModel = model as IVisionChatModal
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const messageContent = await addImagesToMessages(nodeData, options, model.multiModalOption)
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if (messageContent?.length) {
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visionChatModel.setVisionModel()
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// Pop the `agent_scratchpad` MessagePlaceHolder
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let messagePlaceholder = prompt.promptMessages.pop() as MessagesPlaceholder
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if (prompt.promptMessages.at(-1) instanceof HumanMessagePromptTemplate) {
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const lastMessage = prompt.promptMessages.pop() as HumanMessagePromptTemplate
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const template = (lastMessage.prompt as PromptTemplate).template as string
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const msg = HumanMessagePromptTemplate.fromTemplate([
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...messageContent,
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{
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text: template
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}
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])
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msg.inputVariables = lastMessage.inputVariables
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prompt.promptMessages.push(msg)
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}
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// Add the `agent_scratchpad` MessagePlaceHolder back
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prompt.promptMessages.push(messagePlaceholder)
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} else {
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visionChatModel.revertToOriginalModel()
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}
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}
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if (model.bindTools === undefined) {
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throw new Error(`This agent requires that the "bindTools()" method be implemented on the input model.`)
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}
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const modelWithTools = model.bindTools(tools)
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const runnableAgent = RunnableSequence.from([
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{
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[inputKey]: (i: { input: string; steps: ToolsAgentStep[] }) => i.input,
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agent_scratchpad: (i: { input: string; steps: ToolsAgentStep[] }) => formatToOpenAIToolMessages(i.steps),
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[memoryKey]: async (_: { input: string; steps: ToolsAgentStep[] }) => {
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const messages = (await memory.getChatMessages(flowObj?.sessionId, true, prependMessages)) as BaseMessage[]
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return messages ?? []
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},
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...promptVariables
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},
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prompt,
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modelWithTools,
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new ToolCallingAgentOutputParser()
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])
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const executor = AgentExecutor.fromAgentAndTools({
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agent: runnableAgent,
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tools,
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sessionId: flowObj?.sessionId,
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chatId: flowObj?.chatId,
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input: flowObj?.input,
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verbose: process.env.DEBUG === 'true',
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maxIterations: maxIterations ? parseFloat(maxIterations) : undefined
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})
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return executor
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}
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module.exports = { nodeClass: ToolAgent_Agents }
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