feature/introducting-conversational-retrieval-tool-agent (#2430)
* introducting openai-conversational-retriever-agent * fix lint * fix build * rename + update description * changing agent base from openai to tool agent * adding author for community agent
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falkor
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import { flatten } from 'lodash'
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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 { getBaseClasses } from '../../../src/utils'
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import { type ToolsAgentStep } from 'langchain/agents/openai/output_parser'
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import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams, IUsedTool, IVisionChatModal } from '../../../src/Interface'
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import { ConsoleCallbackHandler, CustomChainHandler, 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 type { Document } from '@langchain/core/documents'
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import { BaseRetriever } from '@langchain/core/retrievers'
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import { RESPONSE_TEMPLATE } from '../../chains/ConversationalRetrievalQAChain/prompts'
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import { addImagesToMessages, llmSupportsVision } from '../../../src/multiModalUtils'
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class ConversationalRetrievalToolAgent_Agents implements INode {
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label: string
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name: string
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author: 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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badge?: string
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constructor(fields?: { sessionId?: string }) {
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this.label = 'Conversational Retrieval Tool Agent'
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this.name = 'conversationalRetrievalToolAgent'
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this.author = 'niztal(falkor)'
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this.version = 1.0
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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 calls a vector store retrieval and 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.badge = 'NEW'
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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, ChatVertexAI'
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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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description: 'Taking the rephrased question, search for answer from the provided context',
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warning: 'Prompt must include input variable: {context}',
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rows: 4,
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additionalParams: true,
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optional: true,
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default: RESPONSE_TEMPLATE
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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: 'Vector Store Retriever',
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name: 'vectorStoreRetriever',
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type: 'BaseRetriever'
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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 isStreamable = options.socketIO && options.socketIOClientId
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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 (isStreamable)
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streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
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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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let res: ChainValues = {}
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let sourceDocuments: ICommonObject[] = []
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let usedTools: IUsedTool[] = []
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if (isStreamable) {
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const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
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res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
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if (res.sourceDocuments) {
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options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', flatten(res.sourceDocuments))
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sourceDocuments = res.sourceDocuments
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}
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if (res.usedTools) {
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options.socketIO.to(options.socketIOClientId).emit('usedTools', res.usedTools)
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usedTools = res.usedTools
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}
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} else {
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res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
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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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}
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let output = res?.output as string
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// Claude 3 Opus tends to spit out <thinking>..</thinking> as well, discard that in final output
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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 = res?.output
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if (sourceDocuments.length || usedTools.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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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 formatDocs = (docs: Document[]) => {
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return docs.map((doc, i) => `<doc id='${i}'>${doc.pageContent}</doc>`).join('\n')
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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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const 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 vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
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const prompt = ChatPromptTemplate.fromMessages([
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['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
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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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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)) as BaseMessage[]
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return messages ?? []
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},
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context: async (i: { input: string; chatHistory?: string }) => {
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const relevantDocs = await vectorStoreRetriever.invoke(i.input)
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const formattedDocs = formatDocs(relevantDocs)
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return formattedDocs
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}
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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' ? true : false,
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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: ConversationalRetrievalToolAgent_Agents }
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@ -1228,6 +1228,7 @@ export const isFlowValidForStream = (reactFlowNodes: IReactFlowNode[], endingNod
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'conversationalRetrievalAgent',
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'openAIToolAgent',
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'toolAgent',
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'conversationalRetrievalToolAgent',
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'openAIToolAgentLlamaIndex'
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]
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isValidChainOrAgent = whitelistAgents.includes(endingNodeData.name)
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