mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 19:00:59 +03:00
pass sessionId to custom tool
This commit is contained in:
Henry
@@ -1,10 +1,18 @@
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import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
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import { getBaseClasses, mapChatHistory } from '../../../src/utils'
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import { BaseLanguageModel } from 'langchain/base_language'
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import { AgentExecutor, AgentExecutorInput } from 'langchain/agents'
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import { ChainValues, AgentStep, AgentFinish, AgentAction, BaseMessage, FunctionMessage, AIMessage } from 'langchain/schema'
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import { OutputParserException } from 'langchain/schema/output_parser'
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import { CallbackManagerForChainRun } from 'langchain/callbacks'
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import { formatToOpenAIFunction } from 'langchain/tools'
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import { ToolInputParsingException, Tool } from '@langchain/core/tools'
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import { getBaseClasses } from '../../../src/utils'
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import { flatten } from 'lodash'
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import { BaseChatMemory } from 'langchain/memory'
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import { RunnableSequence } from 'langchain/schema/runnable'
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import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
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import { ChatPromptTemplate, MessagesPlaceholder } from 'langchain/prompts'
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import { ChatOpenAI } from 'langchain/chat_models/openai'
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import { OpenAIFunctionsAgentOutputParser } from 'langchain/agents/openai/output_parser'
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class OpenAIFunctionAgent_Agents implements INode {
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label: string
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@@ -16,8 +24,9 @@ class OpenAIFunctionAgent_Agents implements INode {
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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() {
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constructor(fields: { sessionId?: string }) {
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this.label = 'OpenAI Function Agent'
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this.name = 'openAIFunctionAgent'
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this.version = 3.0
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@@ -52,54 +61,323 @@ class OpenAIFunctionAgent_Agents implements INode {
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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): Promise<any> {
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const model = nodeData.inputs?.model as BaseLanguageModel
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const memory = nodeData.inputs?.memory as BaseChatMemory
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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 executor = await initializeAgentExecutorWithOptions(tools, model, {
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agentType: 'openai-functions',
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verbose: process.env.DEBUG === 'true' ? true : false,
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agentArgs: {
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prefix: systemMessage ?? `You are a helpful AI assistant.`
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}
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})
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const executor = prepareAgent(nodeData, this.sessionId)
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if (memory) executor.memory = memory
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return executor
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}
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async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
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const executor = nodeData.instance as AgentExecutor
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const memory = nodeData.inputs?.memory as BaseChatMemory
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const memory = nodeData.inputs?.memory
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if (options && options.chatHistory) {
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const chatHistoryClassName = memory.chatHistory.constructor.name
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// Only replace when its In-Memory
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if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
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memory.chatHistory = mapChatHistory(options)
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executor.memory = memory
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}
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}
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;(executor.memory as any).returnMessages = true // Return true for BaseChatModel
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const executor = prepareAgent(nodeData, this.sessionId)
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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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if (options.socketIO && options.socketIOClientId) {
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const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
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const result = await executor.run(input, [loggerHandler, handler, ...callbacks])
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return result
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res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
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} else {
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const result = await executor.run(input, [loggerHandler, ...callbacks])
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return result
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res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
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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: res?.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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return res?.output
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}
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}
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const formatAgentSteps = (steps: AgentStep[]): BaseMessage[] =>
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steps.flatMap(({ action, observation }) => {
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if ('messageLog' in action && action.messageLog !== undefined) {
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const log = action.messageLog as BaseMessage[]
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return log.concat(new FunctionMessage(observation, action.tool))
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} else {
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return [new AIMessage(action.log)]
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}
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})
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const prepareAgent = (nodeData: INodeData, sessionId?: string) => {
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const model = nodeData.inputs?.model as ChatOpenAI
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const memory = nodeData.inputs?.memory
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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 ?? 'chat_history'
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const prompt = ChatPromptTemplate.fromMessages([
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['ai', systemMessage ?? `You are a helpful AI assistant.`],
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new MessagesPlaceholder(memoryKey),
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['human', '{input}'],
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new MessagesPlaceholder('agent_scratchpad')
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])
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const modelWithFunctions = model.bind({
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functions: [...tools.map((tool: any) => formatToOpenAIFunction(tool))]
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})
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const runnableAgent = RunnableSequence.from([
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{
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input: (i: { input: string; steps: AgentStep[] }) => i.input,
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agent_scratchpad: (i: { input: string; steps: AgentStep[] }) => formatAgentSteps(i.steps),
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[memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
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const messages: BaseMessage[] = await memory.getChatMessages(sessionId, true)
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return messages ?? []
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}
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},
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prompt,
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modelWithFunctions,
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new OpenAIFunctionsAgentOutputParser()
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])
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const executor = AgentExecutorExtended.fromAgentAndTools({
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agent: runnableAgent,
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tools,
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sessionId
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})
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return executor
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}
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type AgentExecutorOutput = ChainValues
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class AgentExecutorExtended extends AgentExecutor {
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sessionId?: string
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static fromAgentAndTools(fields: AgentExecutorInput & { sessionId?: string }): AgentExecutorExtended {
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const newInstance = new AgentExecutorExtended(fields)
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if (fields.sessionId) newInstance.sessionId = fields.sessionId
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return newInstance
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}
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shouldContinueIteration(iterations: number): boolean {
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return this.maxIterations === undefined || iterations < this.maxIterations
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}
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async _call(inputs: ChainValues, runManager?: CallbackManagerForChainRun): Promise<AgentExecutorOutput> {
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const toolsByName = Object.fromEntries(this.tools.map((t) => [t.name.toLowerCase(), t]))
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const steps: AgentStep[] = []
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let iterations = 0
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const getOutput = async (finishStep: AgentFinish): Promise<AgentExecutorOutput> => {
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const { returnValues } = finishStep
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const additional = await this.agent.prepareForOutput(returnValues, steps)
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if (this.returnIntermediateSteps) {
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return { ...returnValues, intermediateSteps: steps, ...additional }
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}
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await runManager?.handleAgentEnd(finishStep)
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return { ...returnValues, ...additional }
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}
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while (this.shouldContinueIteration(iterations)) {
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let output
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try {
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output = await this.agent.plan(steps, inputs, runManager?.getChild())
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} catch (e) {
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if (e instanceof OutputParserException) {
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let observation
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let text = e.message
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if (this.handleParsingErrors === true) {
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if (e.sendToLLM) {
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observation = e.observation
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text = e.llmOutput ?? ''
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} else {
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observation = 'Invalid or incomplete response'
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}
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} else if (typeof this.handleParsingErrors === 'string') {
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observation = this.handleParsingErrors
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} else if (typeof this.handleParsingErrors === 'function') {
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observation = this.handleParsingErrors(e)
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} else {
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throw e
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}
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output = {
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tool: '_Exception',
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toolInput: observation,
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log: text
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} as AgentAction
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} else {
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throw e
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}
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}
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// Check if the agent has finished
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if ('returnValues' in output) {
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return getOutput(output)
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}
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let actions: AgentAction[]
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if (Array.isArray(output)) {
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actions = output as AgentAction[]
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} else {
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actions = [output as AgentAction]
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}
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const newSteps = await Promise.all(
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actions.map(async (action) => {
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await runManager?.handleAgentAction(action)
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const tool = action.tool === '_Exception' ? new ExceptionTool() : toolsByName[action.tool?.toLowerCase()]
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let observation
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try {
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// here we need to override Tool call method to include sessionId as parameter
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observation = tool
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? // @ts-ignore
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await tool.call(action.toolInput, runManager?.getChild(), undefined, this.sessionId)
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: `${action.tool} is not a valid tool, try another one.`
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} catch (e) {
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if (e instanceof ToolInputParsingException) {
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if (this.handleParsingErrors === true) {
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observation = 'Invalid or incomplete tool input. Please try again.'
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} else if (typeof this.handleParsingErrors === 'string') {
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observation = this.handleParsingErrors
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} else if (typeof this.handleParsingErrors === 'function') {
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observation = this.handleParsingErrors(e)
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} else {
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throw e
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}
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observation = await new ExceptionTool().call(observation, runManager?.getChild())
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return { action, observation: observation ?? '' }
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}
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}
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return { action, observation: observation ?? '' }
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})
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)
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steps.push(...newSteps)
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const lastStep = steps[steps.length - 1]
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const lastTool = toolsByName[lastStep.action.tool?.toLowerCase()]
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if (lastTool?.returnDirect) {
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return getOutput({
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returnValues: { [this.agent.returnValues[0]]: lastStep.observation },
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log: ''
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})
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}
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iterations += 1
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}
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const finish = await this.agent.returnStoppedResponse(this.earlyStoppingMethod, steps, inputs)
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return getOutput(finish)
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}
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async _takeNextStep(
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nameToolMap: Record<string, Tool>,
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inputs: ChainValues,
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intermediateSteps: AgentStep[],
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runManager?: CallbackManagerForChainRun
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): Promise<AgentFinish | AgentStep[]> {
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let output
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try {
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output = await this.agent.plan(intermediateSteps, inputs, runManager?.getChild())
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} catch (e) {
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if (e instanceof OutputParserException) {
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let observation
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let text = e.message
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if (this.handleParsingErrors === true) {
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if (e.sendToLLM) {
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observation = e.observation
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text = e.llmOutput ?? ''
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} else {
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observation = 'Invalid or incomplete response'
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}
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} else if (typeof this.handleParsingErrors === 'string') {
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observation = this.handleParsingErrors
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} else if (typeof this.handleParsingErrors === 'function') {
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observation = this.handleParsingErrors(e)
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} else {
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throw e
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}
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output = {
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tool: '_Exception',
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toolInput: observation,
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log: text
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} as AgentAction
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} else {
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throw e
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}
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}
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if ('returnValues' in output) {
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return output
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}
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let actions: AgentAction[]
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if (Array.isArray(output)) {
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actions = output as AgentAction[]
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} else {
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actions = [output as AgentAction]
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}
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const result: AgentStep[] = []
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for (const agentAction of actions) {
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let observation = ''
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if (runManager) {
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await runManager?.handleAgentAction(agentAction)
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}
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if (agentAction.tool in nameToolMap) {
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const tool = nameToolMap[agentAction.tool]
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try {
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// here we need to override Tool call method to include sessionId as parameter
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// @ts-ignore
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observation = await tool.call(agentAction.toolInput, runManager?.getChild(), undefined, this.sessionId)
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} catch (e) {
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if (e instanceof ToolInputParsingException) {
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if (this.handleParsingErrors === true) {
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observation = 'Invalid or incomplete tool input. Please try again.'
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} else if (typeof this.handleParsingErrors === 'string') {
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observation = this.handleParsingErrors
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} else if (typeof this.handleParsingErrors === 'function') {
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observation = this.handleParsingErrors(e)
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} else {
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throw e
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}
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observation = await new ExceptionTool().call(observation, runManager?.getChild())
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}
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}
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} else {
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observation = `${agentAction.tool} is not a valid tool, try another available tool: ${Object.keys(nameToolMap).join(', ')}`
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}
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result.push({
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action: agentAction,
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observation
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})
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}
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return result
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}
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}
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class ExceptionTool extends Tool {
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name = '_Exception'
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description = 'Exception tool'
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async _call(query: string) {
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return query
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}
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}
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