2637 lines
109 KiB
TypeScript
2637 lines
109 KiB
TypeScript
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
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import {
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ICommonObject,
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IDatabaseEntity,
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IHumanInput,
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IMessage,
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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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IUsedTool
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} from '../../../src/Interface'
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import { AIMessageChunk, BaseMessageLike, MessageContentText } from '@langchain/core/messages'
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import { AnalyticHandler } from '../../../src/handler'
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import { DEFAULT_SUMMARIZER_TEMPLATE } from '../prompt'
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import { ILLMMessage } from '../Interface.Agentflow'
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import { Tool } from '@langchain/core/tools'
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import { ARTIFACTS_PREFIX, SOURCE_DOCUMENTS_PREFIX, TOOL_ARGS_PREFIX } from '../../../src/agents'
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import { flatten } from 'lodash'
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import zodToJsonSchema from 'zod-to-json-schema'
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import { getErrorMessage } from '../../../src/error'
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import { DataSource } from 'typeorm'
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import {
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addImageArtifactsToMessages,
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extractArtifactsFromResponse,
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getPastChatHistoryImageMessages,
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getUniqueImageMessages,
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processMessagesWithImages,
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replaceBase64ImagesWithFileReferences,
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replaceInlineDataWithFileReferences,
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updateFlowState
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} from '../utils'
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import { convertMultiOptionsToStringArray, processTemplateVariables, configureStructuredOutput } from '../../../src/utils'
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interface ITool {
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agentSelectedTool: string
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agentSelectedToolConfig: ICommonObject
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agentSelectedToolRequiresHumanInput: boolean
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}
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interface IKnowledgeBase {
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documentStore: string
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docStoreDescription: string
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returnSourceDocuments: boolean
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}
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interface IKnowledgeBaseVSEmbeddings {
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vectorStore: string
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vectorStoreConfig: ICommonObject
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embeddingModel: string
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embeddingModelConfig: ICommonObject
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knowledgeName: string
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knowledgeDescription: string
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returnSourceDocuments: boolean
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}
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interface ISimpliefiedTool {
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name: string
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description: string
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schema: any
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toolNode: {
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label: string
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name: string
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}
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}
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class Agent_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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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 = 'Agent'
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this.name = 'agentAgentflow'
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this.version = 3.2
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this.type = 'Agent'
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this.category = 'Agent Flows'
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this.description = 'Dynamically choose and utilize tools during runtime, enabling multi-step reasoning'
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this.color = '#4DD0E1'
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this.baseClasses = [this.type]
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this.inputs = [
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{
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label: 'Model',
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name: 'agentModel',
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type: 'asyncOptions',
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loadMethod: 'listModels',
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loadConfig: true
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},
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{
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label: 'Messages',
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name: 'agentMessages',
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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: 'Role',
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name: 'role',
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type: 'options',
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options: [
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{
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label: 'System',
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name: 'system'
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},
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{
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label: 'Assistant',
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name: 'assistant'
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},
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{
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label: 'Developer',
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name: 'developer'
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},
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{
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label: 'User',
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name: 'user'
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}
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]
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},
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{
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label: 'Content',
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name: 'content',
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type: 'string',
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acceptVariable: true,
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generateInstruction: true,
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rows: 4
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}
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]
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},
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{
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label: 'OpenAI Built-in Tools',
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name: 'agentToolsBuiltInOpenAI',
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type: 'multiOptions',
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optional: true,
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options: [
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{
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label: 'Web Search',
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name: 'web_search_preview',
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description: 'Search the web for the latest information'
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},
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{
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label: 'Code Interpreter',
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name: 'code_interpreter',
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description: 'Write and run Python code in a sandboxed environment'
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},
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{
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label: 'Image Generation',
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name: 'image_generation',
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description: 'Generate images based on a text prompt'
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}
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],
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show: {
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agentModel: 'chatOpenAI'
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}
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},
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{
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label: 'Gemini Built-in Tools',
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name: 'agentToolsBuiltInGemini',
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type: 'multiOptions',
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optional: true,
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options: [
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{
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label: 'URL Context',
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name: 'urlContext',
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description: 'Extract content from given URLs'
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},
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{
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label: 'Google Search',
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name: 'googleSearch',
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description: 'Search real-time web content'
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},
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{
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label: 'Code Execution',
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name: 'codeExecution',
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description: 'Write and run Python code in a sandboxed environment'
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}
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],
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show: {
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agentModel: 'chatGoogleGenerativeAI'
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}
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},
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{
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label: 'Anthropic Built-in Tools',
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name: 'agentToolsBuiltInAnthropic',
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type: 'multiOptions',
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optional: true,
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options: [
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{
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label: 'Web Search',
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name: 'web_search_20250305',
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description: 'Search the web for the latest information'
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},
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{
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label: 'Web Fetch',
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name: 'web_fetch_20250910',
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description: 'Retrieve full content from specified web pages'
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}
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/*
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* Not supported yet as we need to get bash_code_execution_tool_result from content:
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https://docs.claude.com/en/docs/agents-and-tools/tool-use/code-execution-tool#retrieve-generated-files
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{
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label: 'Code Interpreter',
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name: 'code_execution_20250825',
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description: 'Write and run Python code in a sandboxed environment'
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}*/
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],
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show: {
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agentModel: 'chatAnthropic'
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}
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},
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{
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label: 'Tools',
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name: 'agentTools',
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type: 'array',
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optional: true,
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array: [
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{
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label: 'Tool',
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name: 'agentSelectedTool',
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type: 'asyncOptions',
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loadMethod: 'listTools',
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loadConfig: true
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},
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{
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label: 'Require Human Input',
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name: 'agentSelectedToolRequiresHumanInput',
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type: 'boolean',
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optional: true
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}
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]
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},
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{
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label: 'Knowledge (Document Stores)',
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name: 'agentKnowledgeDocumentStores',
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type: 'array',
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description: 'Give your agent context about different document sources. 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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label: 'Describe Knowledge',
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name: 'docStoreDescription',
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type: 'string',
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generateDocStoreDescription: true,
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placeholder:
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'Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information',
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rows: 4
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},
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{
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label: 'Return Source Documents',
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name: 'returnSourceDocuments',
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type: 'boolean',
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optional: true
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}
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],
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optional: true
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},
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{
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label: 'Knowledge (Vector Embeddings)',
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name: 'agentKnowledgeVSEmbeddings',
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type: 'array',
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description: 'Give your agent context about different document sources from existing vector stores and embeddings',
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array: [
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{
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label: 'Vector Store',
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name: 'vectorStore',
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type: 'asyncOptions',
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loadMethod: 'listVectorStores',
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loadConfig: true
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},
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{
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label: 'Embedding Model',
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name: 'embeddingModel',
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type: 'asyncOptions',
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loadMethod: 'listEmbeddings',
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loadConfig: true
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},
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{
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label: 'Knowledge Name',
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name: 'knowledgeName',
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type: 'string',
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placeholder:
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'A short name for the knowledge base, this is useful for the AI to know when and how to search for correct information'
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},
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{
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label: 'Describe Knowledge',
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name: 'knowledgeDescription',
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type: 'string',
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placeholder:
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'Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information',
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rows: 4
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},
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{
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label: 'Return Source Documents',
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name: 'returnSourceDocuments',
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type: 'boolean',
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optional: true
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}
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],
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optional: true
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},
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{
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label: 'Enable Memory',
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name: 'agentEnableMemory',
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type: 'boolean',
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description: 'Enable memory for the conversation thread',
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default: true,
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optional: true
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},
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{
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label: 'Memory Type',
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name: 'agentMemoryType',
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type: 'options',
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options: [
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{
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label: 'All Messages',
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name: 'allMessages',
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description: 'Retrieve all messages from the conversation'
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},
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{
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label: 'Window Size',
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name: 'windowSize',
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description: 'Uses a fixed window size to surface the last N messages'
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},
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{
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label: 'Conversation Summary',
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name: 'conversationSummary',
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description: 'Summarizes the whole conversation'
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},
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{
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label: 'Conversation Summary Buffer',
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name: 'conversationSummaryBuffer',
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description: 'Summarize conversations once token limit is reached. Default to 2000'
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}
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],
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optional: true,
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default: 'allMessages',
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show: {
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agentEnableMemory: true
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}
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},
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{
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label: 'Window Size',
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name: 'agentMemoryWindowSize',
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type: 'number',
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default: '20',
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description: 'Uses a fixed window size to surface the last N messages',
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show: {
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agentMemoryType: 'windowSize'
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}
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},
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{
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label: 'Max Token Limit',
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name: 'agentMemoryMaxTokenLimit',
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type: 'number',
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default: '2000',
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description: 'Summarize conversations once token limit is reached. Default to 2000',
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show: {
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agentMemoryType: 'conversationSummaryBuffer'
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}
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},
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{
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label: 'Input Message',
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name: 'agentUserMessage',
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type: 'string',
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description: 'Add an input message as user message at the end of the conversation',
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rows: 4,
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optional: true,
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acceptVariable: true,
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show: {
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agentEnableMemory: true
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}
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},
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{
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label: 'Return Response As',
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name: 'agentReturnResponseAs',
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type: 'options',
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options: [
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{
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label: 'User Message',
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name: 'userMessage'
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},
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{
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label: 'Assistant Message',
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name: 'assistantMessage'
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}
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],
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default: 'userMessage'
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},
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{
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label: 'JSON Structured Output',
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name: 'agentStructuredOutput',
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description: 'Instruct the Agent to give output in a JSON structured schema',
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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: 'string'
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},
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{
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label: 'Type',
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name: 'type',
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type: 'options',
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options: [
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{
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label: 'String',
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name: 'string'
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},
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{
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label: 'String Array',
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name: 'stringArray'
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},
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{
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label: 'Number',
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name: 'number'
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},
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{
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label: 'Boolean',
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name: 'boolean'
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},
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{
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label: 'Enum',
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name: 'enum'
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},
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{
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label: 'JSON Array',
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name: 'jsonArray'
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}
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]
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},
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{
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label: 'Enum Values',
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name: 'enumValues',
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type: 'string',
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placeholder: 'value1, value2, value3',
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description: 'Enum values. Separated by comma',
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optional: true,
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show: {
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'agentStructuredOutput[$index].type': 'enum'
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}
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},
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{
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label: 'JSON Schema',
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name: 'jsonSchema',
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type: 'code',
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placeholder: `{
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"answer": {
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"type": "string",
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"description": "Value of the answer"
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},
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"reason": {
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"type": "string",
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"description": "Reason for the answer"
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},
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"optional": {
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"type": "boolean"
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},
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"count": {
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"type": "number"
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},
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"children": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"value": {
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"type": "string",
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"description": "Value of the children's answer"
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}
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}
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}
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}
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}`,
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description: 'JSON schema for the structured output',
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optional: true,
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hideCodeExecute: true,
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show: {
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'agentStructuredOutput[$index].type': 'jsonArray'
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}
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},
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{
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label: 'Description',
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name: 'description',
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type: 'string',
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placeholder: 'Description of the key'
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}
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]
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},
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{
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label: 'Update Flow State',
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name: 'agentUpdateState',
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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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{
|
|
label: 'Key',
|
|
name: 'key',
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|
type: 'asyncOptions',
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|
loadMethod: 'listRuntimeStateKeys'
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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 listModels(_: INodeData, options: ICommonObject): Promise<INodeOptionsValue[]> {
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const componentNodes = options.componentNodes as {
|
|
[key: string]: INode
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|
}
|
|
|
|
const returnOptions: INodeOptionsValue[] = []
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|
for (const nodeName in componentNodes) {
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|
const componentNode = componentNodes[nodeName]
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|
if (componentNode.category === 'Chat Models') {
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|
if (componentNode.tags?.includes('LlamaIndex')) {
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|
continue
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|
}
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|
returnOptions.push({
|
|
label: componentNode.label,
|
|
name: nodeName,
|
|
imageSrc: componentNode.icon
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|
})
|
|
}
|
|
}
|
|
return returnOptions
|
|
},
|
|
async listEmbeddings(_: INodeData, options: ICommonObject): Promise<INodeOptionsValue[]> {
|
|
const componentNodes = options.componentNodes as {
|
|
[key: string]: INode
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|
}
|
|
|
|
const returnOptions: INodeOptionsValue[] = []
|
|
for (const nodeName in componentNodes) {
|
|
const componentNode = componentNodes[nodeName]
|
|
if (componentNode.category === 'Embeddings') {
|
|
if (componentNode.tags?.includes('LlamaIndex')) {
|
|
continue
|
|
}
|
|
returnOptions.push({
|
|
label: componentNode.label,
|
|
name: nodeName,
|
|
imageSrc: componentNode.icon
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|
})
|
|
}
|
|
}
|
|
return returnOptions
|
|
},
|
|
async listTools(_: INodeData, options: ICommonObject): Promise<INodeOptionsValue[]> {
|
|
const componentNodes = options.componentNodes as {
|
|
[key: string]: INode
|
|
}
|
|
|
|
const removeTools = ['chainTool', 'retrieverTool', 'webBrowser']
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|
|
|
const returnOptions: INodeOptionsValue[] = []
|
|
for (const nodeName in componentNodes) {
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|
const componentNode = componentNodes[nodeName]
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|
if (componentNode.category === 'Tools' || componentNode.category === 'Tools (MCP)') {
|
|
if (componentNode.tags?.includes('LlamaIndex')) {
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|
continue
|
|
}
|
|
if (removeTools.includes(nodeName)) {
|
|
continue
|
|
}
|
|
returnOptions.push({
|
|
label: componentNode.label,
|
|
name: nodeName,
|
|
imageSrc: componentNode.icon
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|
})
|
|
}
|
|
}
|
|
return returnOptions
|
|
},
|
|
async listRuntimeStateKeys(_: INodeData, options: ICommonObject): Promise<INodeOptionsValue[]> {
|
|
const previousNodes = options.previousNodes as ICommonObject[]
|
|
const startAgentflowNode = previousNodes.find((node) => node.name === 'startAgentflow')
|
|
const state = startAgentflowNode?.inputs?.startState as ICommonObject[]
|
|
return state.map((item) => ({ label: item.key, name: item.key }))
|
|
},
|
|
async listStores(_: INodeData, options: ICommonObject): Promise<INodeOptionsValue[]> {
|
|
const returnData: INodeOptionsValue[] = []
|
|
|
|
const appDataSource = options.appDataSource as DataSource
|
|
const databaseEntities = options.databaseEntities as IDatabaseEntity
|
|
|
|
if (appDataSource === undefined || !appDataSource) {
|
|
return returnData
|
|
}
|
|
|
|
const searchOptions = options.searchOptions || {}
|
|
const stores = await appDataSource.getRepository(databaseEntities['DocumentStore']).findBy(searchOptions)
|
|
for (const store of stores) {
|
|
if (store.status === 'UPSERTED') {
|
|
const obj = {
|
|
name: `${store.id}:${store.name}`,
|
|
label: store.name,
|
|
description: store.description
|
|
}
|
|
returnData.push(obj)
|
|
}
|
|
}
|
|
return returnData
|
|
},
|
|
async listVectorStores(_: INodeData, options: ICommonObject): Promise<INodeOptionsValue[]> {
|
|
const componentNodes = options.componentNodes as {
|
|
[key: string]: INode
|
|
}
|
|
|
|
const returnOptions: INodeOptionsValue[] = []
|
|
for (const nodeName in componentNodes) {
|
|
const componentNode = componentNodes[nodeName]
|
|
if (componentNode.category === 'Vector Stores') {
|
|
if (componentNode.tags?.includes('LlamaIndex')) {
|
|
continue
|
|
}
|
|
returnOptions.push({
|
|
label: componentNode.label,
|
|
name: nodeName,
|
|
imageSrc: componentNode.icon
|
|
})
|
|
}
|
|
}
|
|
return returnOptions
|
|
}
|
|
}
|
|
|
|
async run(nodeData: INodeData, input: string | Record<string, any>, options: ICommonObject): Promise<any> {
|
|
let llmIds: ICommonObject | undefined
|
|
let analyticHandlers = options.analyticHandlers as AnalyticHandler
|
|
|
|
try {
|
|
const abortController = options.abortController as AbortController
|
|
|
|
// Extract input parameters
|
|
const model = nodeData.inputs?.agentModel as string
|
|
const modelConfig = nodeData.inputs?.agentModelConfig as ICommonObject
|
|
if (!model) {
|
|
throw new Error('Model is required')
|
|
}
|
|
|
|
// Extract tools
|
|
const tools = nodeData.inputs?.agentTools as ITool[]
|
|
|
|
const toolsInstance: Tool[] = []
|
|
for (const tool of tools) {
|
|
const toolConfig = tool.agentSelectedToolConfig
|
|
const nodeInstanceFilePath = options.componentNodes[tool.agentSelectedTool].filePath as string
|
|
const nodeModule = await import(nodeInstanceFilePath)
|
|
const newToolNodeInstance = new nodeModule.nodeClass()
|
|
const newNodeData = {
|
|
...nodeData,
|
|
credential: toolConfig['FLOWISE_CREDENTIAL_ID'],
|
|
inputs: {
|
|
...nodeData.inputs,
|
|
...toolConfig
|
|
}
|
|
}
|
|
const toolInstance = await newToolNodeInstance.init(newNodeData, '', options)
|
|
|
|
// toolInstance might returns a list of tools like MCP tools
|
|
if (Array.isArray(toolInstance)) {
|
|
for (const subTool of toolInstance) {
|
|
const subToolInstance = subTool as Tool
|
|
;(subToolInstance as any).agentSelectedTool = tool.agentSelectedTool
|
|
if (tool.agentSelectedToolRequiresHumanInput) {
|
|
;(subToolInstance as any).requiresHumanInput = true
|
|
}
|
|
toolsInstance.push(subToolInstance)
|
|
}
|
|
} else {
|
|
if (tool.agentSelectedToolRequiresHumanInput) {
|
|
toolInstance.requiresHumanInput = true
|
|
}
|
|
toolsInstance.push(toolInstance as Tool)
|
|
}
|
|
}
|
|
|
|
const availableTools: ISimpliefiedTool[] = toolsInstance.map((tool, index) => {
|
|
const originalTool = tools[index]
|
|
let agentSelectedTool = (tool as any)?.agentSelectedTool
|
|
if (!agentSelectedTool) {
|
|
agentSelectedTool = originalTool?.agentSelectedTool
|
|
}
|
|
const componentNode = options.componentNodes[agentSelectedTool]
|
|
|
|
const jsonSchema = zodToJsonSchema(tool.schema as any)
|
|
if (jsonSchema.$schema) {
|
|
delete jsonSchema.$schema
|
|
}
|
|
|
|
return {
|
|
name: tool.name,
|
|
description: tool.description,
|
|
schema: jsonSchema,
|
|
toolNode: {
|
|
label: componentNode?.label || tool.name,
|
|
name: componentNode?.name || tool.name
|
|
}
|
|
}
|
|
})
|
|
|
|
// Extract knowledge
|
|
const knowledgeBases = nodeData.inputs?.agentKnowledgeDocumentStores as IKnowledgeBase[]
|
|
if (knowledgeBases && knowledgeBases.length > 0) {
|
|
for (const knowledgeBase of knowledgeBases) {
|
|
const nodeInstanceFilePath = options.componentNodes['retrieverTool'].filePath as string
|
|
const nodeModule = await import(nodeInstanceFilePath)
|
|
const newRetrieverToolNodeInstance = new nodeModule.nodeClass()
|
|
const [storeId, storeName] = knowledgeBase.documentStore.split(':')
|
|
|
|
const docStoreVectorInstanceFilePath = options.componentNodes['documentStoreVS'].filePath as string
|
|
const docStoreVectorModule = await import(docStoreVectorInstanceFilePath)
|
|
const newDocStoreVectorInstance = new docStoreVectorModule.nodeClass()
|
|
const docStoreVectorInstance = await newDocStoreVectorInstance.init(
|
|
{
|
|
...nodeData,
|
|
inputs: {
|
|
...nodeData.inputs,
|
|
selectedStore: storeId
|
|
},
|
|
outputs: {
|
|
output: 'retriever'
|
|
}
|
|
},
|
|
'',
|
|
options
|
|
)
|
|
|
|
const newRetrieverToolNodeData = {
|
|
...nodeData,
|
|
inputs: {
|
|
...nodeData.inputs,
|
|
name: storeName
|
|
.toLowerCase()
|
|
.replace(/ /g, '_')
|
|
.replace(/[^a-z0-9_-]/g, ''),
|
|
description: knowledgeBase.docStoreDescription,
|
|
retriever: docStoreVectorInstance,
|
|
returnSourceDocuments: knowledgeBase.returnSourceDocuments
|
|
}
|
|
}
|
|
const retrieverToolInstance = await newRetrieverToolNodeInstance.init(newRetrieverToolNodeData, '', options)
|
|
|
|
toolsInstance.push(retrieverToolInstance as Tool)
|
|
|
|
const jsonSchema = zodToJsonSchema(retrieverToolInstance.schema)
|
|
if (jsonSchema.$schema) {
|
|
delete jsonSchema.$schema
|
|
}
|
|
const componentNode = options.componentNodes['retrieverTool']
|
|
|
|
availableTools.push({
|
|
name: storeName
|
|
.toLowerCase()
|
|
.replace(/ /g, '_')
|
|
.replace(/[^a-z0-9_-]/g, ''),
|
|
description: knowledgeBase.docStoreDescription,
|
|
schema: jsonSchema,
|
|
toolNode: {
|
|
label: componentNode?.label || retrieverToolInstance.name,
|
|
name: componentNode?.name || retrieverToolInstance.name
|
|
}
|
|
})
|
|
}
|
|
}
|
|
|
|
const knowledgeBasesForVSEmbeddings = nodeData.inputs?.agentKnowledgeVSEmbeddings as IKnowledgeBaseVSEmbeddings[]
|
|
if (knowledgeBasesForVSEmbeddings && knowledgeBasesForVSEmbeddings.length > 0) {
|
|
for (const knowledgeBase of knowledgeBasesForVSEmbeddings) {
|
|
const nodeInstanceFilePath = options.componentNodes['retrieverTool'].filePath as string
|
|
const nodeModule = await import(nodeInstanceFilePath)
|
|
const newRetrieverToolNodeInstance = new nodeModule.nodeClass()
|
|
|
|
const selectedEmbeddingModel = knowledgeBase.embeddingModel
|
|
const selectedEmbeddingModelConfig = knowledgeBase.embeddingModelConfig
|
|
const embeddingInstanceFilePath = options.componentNodes[selectedEmbeddingModel].filePath as string
|
|
const embeddingModule = await import(embeddingInstanceFilePath)
|
|
const newEmbeddingInstance = new embeddingModule.nodeClass()
|
|
const newEmbeddingNodeData = {
|
|
...nodeData,
|
|
credential: selectedEmbeddingModelConfig['FLOWISE_CREDENTIAL_ID'],
|
|
inputs: {
|
|
...nodeData.inputs,
|
|
...selectedEmbeddingModelConfig
|
|
}
|
|
}
|
|
const embeddingInstance = await newEmbeddingInstance.init(newEmbeddingNodeData, '', options)
|
|
|
|
const selectedVectorStore = knowledgeBase.vectorStore
|
|
const selectedVectorStoreConfig = knowledgeBase.vectorStoreConfig
|
|
const vectorStoreInstanceFilePath = options.componentNodes[selectedVectorStore].filePath as string
|
|
const vectorStoreModule = await import(vectorStoreInstanceFilePath)
|
|
const newVectorStoreInstance = new vectorStoreModule.nodeClass()
|
|
const newVSNodeData = {
|
|
...nodeData,
|
|
credential: selectedVectorStoreConfig['FLOWISE_CREDENTIAL_ID'],
|
|
inputs: {
|
|
...nodeData.inputs,
|
|
...selectedVectorStoreConfig,
|
|
embeddings: embeddingInstance
|
|
},
|
|
outputs: {
|
|
output: 'retriever'
|
|
}
|
|
}
|
|
const vectorStoreInstance = await newVectorStoreInstance.init(newVSNodeData, '', options)
|
|
|
|
const knowledgeName = knowledgeBase.knowledgeName || ''
|
|
|
|
const newRetrieverToolNodeData = {
|
|
...nodeData,
|
|
inputs: {
|
|
...nodeData.inputs,
|
|
name: knowledgeName
|
|
.toLowerCase()
|
|
.replace(/ /g, '_')
|
|
.replace(/[^a-z0-9_-]/g, ''),
|
|
description: knowledgeBase.knowledgeDescription,
|
|
retriever: vectorStoreInstance,
|
|
returnSourceDocuments: knowledgeBase.returnSourceDocuments
|
|
}
|
|
}
|
|
const retrieverToolInstance = await newRetrieverToolNodeInstance.init(newRetrieverToolNodeData, '', options)
|
|
|
|
toolsInstance.push(retrieverToolInstance as Tool)
|
|
|
|
const jsonSchema = zodToJsonSchema(retrieverToolInstance.schema)
|
|
if (jsonSchema.$schema) {
|
|
delete jsonSchema.$schema
|
|
}
|
|
const componentNode = options.componentNodes['retrieverTool']
|
|
|
|
availableTools.push({
|
|
name: knowledgeName
|
|
.toLowerCase()
|
|
.replace(/ /g, '_')
|
|
.replace(/[^a-z0-9_-]/g, ''),
|
|
description: knowledgeBase.knowledgeDescription,
|
|
schema: jsonSchema,
|
|
toolNode: {
|
|
label: componentNode?.label || retrieverToolInstance.name,
|
|
name: componentNode?.name || retrieverToolInstance.name
|
|
}
|
|
})
|
|
}
|
|
}
|
|
|
|
// Extract memory and configuration options
|
|
const enableMemory = nodeData.inputs?.agentEnableMemory as boolean
|
|
const memoryType = nodeData.inputs?.agentMemoryType as string
|
|
const userMessage = nodeData.inputs?.agentUserMessage as string
|
|
const _agentUpdateState = nodeData.inputs?.agentUpdateState
|
|
const _agentStructuredOutput = nodeData.inputs?.agentStructuredOutput
|
|
const agentMessages = (nodeData.inputs?.agentMessages as unknown as ILLMMessage[]) ?? []
|
|
|
|
// Extract runtime state and history
|
|
const state = options.agentflowRuntime?.state as ICommonObject
|
|
const pastChatHistory = (options.pastChatHistory as BaseMessageLike[]) ?? []
|
|
const runtimeChatHistory = (options.agentflowRuntime?.chatHistory as BaseMessageLike[]) ?? []
|
|
const prependedChatHistory = options.prependedChatHistory as IMessage[]
|
|
const chatId = options.chatId as string
|
|
|
|
// Initialize the LLM model instance
|
|
const nodeInstanceFilePath = options.componentNodes[model].filePath as string
|
|
const nodeModule = await import(nodeInstanceFilePath)
|
|
const newLLMNodeInstance = new nodeModule.nodeClass()
|
|
const newNodeData = {
|
|
...nodeData,
|
|
credential: modelConfig['FLOWISE_CREDENTIAL_ID'],
|
|
inputs: {
|
|
...nodeData.inputs,
|
|
...modelConfig
|
|
}
|
|
}
|
|
|
|
const llmWithoutToolsBind = (await newLLMNodeInstance.init(newNodeData, '', options)) as BaseChatModel
|
|
let llmNodeInstance = llmWithoutToolsBind
|
|
|
|
const isStructuredOutput = _agentStructuredOutput && Array.isArray(_agentStructuredOutput) && _agentStructuredOutput.length > 0
|
|
|
|
const agentToolsBuiltInOpenAI = convertMultiOptionsToStringArray(nodeData.inputs?.agentToolsBuiltInOpenAI)
|
|
if (agentToolsBuiltInOpenAI && agentToolsBuiltInOpenAI.length > 0) {
|
|
for (const tool of agentToolsBuiltInOpenAI) {
|
|
const builtInTool: ICommonObject = {
|
|
type: tool
|
|
}
|
|
if (tool === 'code_interpreter') {
|
|
builtInTool.container = { type: 'auto' }
|
|
}
|
|
;(toolsInstance as any).push(builtInTool)
|
|
;(availableTools as any).push({
|
|
name: tool,
|
|
toolNode: {
|
|
label: tool,
|
|
name: tool
|
|
}
|
|
})
|
|
}
|
|
}
|
|
|
|
const agentToolsBuiltInGemini = convertMultiOptionsToStringArray(nodeData.inputs?.agentToolsBuiltInGemini)
|
|
if (agentToolsBuiltInGemini && agentToolsBuiltInGemini.length > 0) {
|
|
for (const tool of agentToolsBuiltInGemini) {
|
|
const builtInTool: ICommonObject = {
|
|
[tool]: {}
|
|
}
|
|
;(toolsInstance as any).push(builtInTool)
|
|
;(availableTools as any).push({
|
|
name: tool,
|
|
toolNode: {
|
|
label: tool,
|
|
name: tool
|
|
}
|
|
})
|
|
}
|
|
}
|
|
|
|
const agentToolsBuiltInAnthropic = convertMultiOptionsToStringArray(nodeData.inputs?.agentToolsBuiltInAnthropic)
|
|
if (agentToolsBuiltInAnthropic && agentToolsBuiltInAnthropic.length > 0) {
|
|
for (const tool of agentToolsBuiltInAnthropic) {
|
|
// split _ to get the tool name by removing the last part (date)
|
|
const toolName = tool.split('_').slice(0, -1).join('_')
|
|
|
|
if (tool === 'code_execution_20250825') {
|
|
;(llmNodeInstance as any).clientOptions = {
|
|
defaultHeaders: {
|
|
'anthropic-beta': ['code-execution-2025-08-25', 'files-api-2025-04-14']
|
|
}
|
|
}
|
|
}
|
|
|
|
if (tool === 'web_fetch_20250910') {
|
|
;(llmNodeInstance as any).clientOptions = {
|
|
defaultHeaders: {
|
|
'anthropic-beta': ['web-fetch-2025-09-10']
|
|
}
|
|
}
|
|
}
|
|
|
|
const builtInTool: ICommonObject = {
|
|
type: tool,
|
|
name: toolName
|
|
}
|
|
;(toolsInstance as any).push(builtInTool)
|
|
;(availableTools as any).push({
|
|
name: tool,
|
|
toolNode: {
|
|
label: tool,
|
|
name: tool
|
|
}
|
|
})
|
|
}
|
|
}
|
|
|
|
if (llmNodeInstance && toolsInstance.length > 0) {
|
|
if (llmNodeInstance.bindTools === undefined) {
|
|
throw new Error(`Agent needs to have a function calling capable models.`)
|
|
}
|
|
|
|
// @ts-ignore
|
|
llmNodeInstance = llmNodeInstance.bindTools(toolsInstance)
|
|
}
|
|
|
|
// Prepare messages array
|
|
const messages: BaseMessageLike[] = []
|
|
// Use to store messages with image file references as we do not want to store the base64 data into database
|
|
let runtimeImageMessagesWithFileRef: BaseMessageLike[] = []
|
|
// Use to keep track of past messages with image file references
|
|
let pastImageMessagesWithFileRef: BaseMessageLike[] = []
|
|
|
|
// Prepend history ONLY if it is the first node
|
|
if (prependedChatHistory.length > 0 && !runtimeChatHistory.length) {
|
|
for (const msg of prependedChatHistory) {
|
|
const role: string = msg.role === 'apiMessage' ? 'assistant' : 'user'
|
|
const content: string = msg.content ?? ''
|
|
messages.push({
|
|
role,
|
|
content
|
|
})
|
|
}
|
|
}
|
|
|
|
for (const msg of agentMessages) {
|
|
const role = msg.role
|
|
const content = msg.content
|
|
if (role && content) {
|
|
if (role === 'system') {
|
|
messages.unshift({ role, content })
|
|
} else {
|
|
messages.push({ role, content })
|
|
}
|
|
}
|
|
}
|
|
|
|
// Handle memory management if enabled
|
|
if (enableMemory) {
|
|
await this.handleMemory({
|
|
messages,
|
|
memoryType,
|
|
pastChatHistory,
|
|
runtimeChatHistory,
|
|
llmNodeInstance,
|
|
nodeData,
|
|
userMessage,
|
|
input,
|
|
abortController,
|
|
options,
|
|
modelConfig,
|
|
runtimeImageMessagesWithFileRef,
|
|
pastImageMessagesWithFileRef
|
|
})
|
|
} else if (!runtimeChatHistory.length) {
|
|
/*
|
|
* If this is the first node:
|
|
* - Add images to messages if exist
|
|
* - Add user message if it does not exist in the agentMessages array
|
|
*/
|
|
if (options.uploads) {
|
|
const imageContents = await getUniqueImageMessages(options, messages, modelConfig)
|
|
if (imageContents) {
|
|
const { imageMessageWithBase64, imageMessageWithFileRef } = imageContents
|
|
messages.push(imageMessageWithBase64)
|
|
runtimeImageMessagesWithFileRef.push(imageMessageWithFileRef)
|
|
}
|
|
}
|
|
|
|
if (input && typeof input === 'string' && !agentMessages.some((msg) => msg.role === 'user')) {
|
|
messages.push({
|
|
role: 'user',
|
|
content: input
|
|
})
|
|
}
|
|
}
|
|
delete nodeData.inputs?.agentMessages
|
|
|
|
// Initialize response and determine if streaming is possible
|
|
let response: AIMessageChunk = new AIMessageChunk('')
|
|
const isLastNode = options.isLastNode as boolean
|
|
const isStreamable = isLastNode && options.sseStreamer !== undefined && modelConfig?.streaming !== false && !isStructuredOutput
|
|
|
|
// Start analytics
|
|
if (analyticHandlers && options.parentTraceIds) {
|
|
const llmLabel = options?.componentNodes?.[model]?.label || model
|
|
llmIds = await analyticHandlers.onLLMStart(llmLabel, messages, options.parentTraceIds)
|
|
}
|
|
|
|
// Handle tool calls with support for recursion
|
|
let usedTools: IUsedTool[] = []
|
|
let sourceDocuments: Array<any> = []
|
|
let artifacts: any[] = []
|
|
let fileAnnotations: any[] = []
|
|
let additionalTokens = 0
|
|
let isWaitingForHumanInput = false
|
|
|
|
// Store the current messages length to track which messages are added during tool calls
|
|
const messagesBeforeToolCalls = [...messages]
|
|
let _toolCallMessages: BaseMessageLike[] = []
|
|
|
|
/**
|
|
* Add image artifacts from previous assistant responses as user messages
|
|
* Images are converted from FILE-STORAGE::<image_path> to base 64 image_url format
|
|
*/
|
|
await addImageArtifactsToMessages(messages, options)
|
|
|
|
// Check if this is hummanInput for tool calls
|
|
const _humanInput = nodeData.inputs?.humanInput
|
|
const humanInput: IHumanInput = typeof _humanInput === 'string' ? JSON.parse(_humanInput) : _humanInput
|
|
const humanInputAction = options.humanInputAction
|
|
const iterationContext = options.iterationContext
|
|
|
|
// Track execution time
|
|
const startTime = Date.now()
|
|
|
|
// Get initial response from LLM
|
|
const sseStreamer: IServerSideEventStreamer | undefined = options.sseStreamer
|
|
|
|
if (humanInput) {
|
|
if (humanInput.type !== 'proceed' && humanInput.type !== 'reject') {
|
|
throw new Error(`Invalid human input type. Expected 'proceed' or 'reject', but got '${humanInput.type}'`)
|
|
}
|
|
const result = await this.handleResumedToolCalls({
|
|
humanInput,
|
|
humanInputAction,
|
|
messages,
|
|
toolsInstance,
|
|
sseStreamer,
|
|
chatId,
|
|
input,
|
|
options,
|
|
abortController,
|
|
llmWithoutToolsBind,
|
|
isStreamable,
|
|
isLastNode,
|
|
iterationContext,
|
|
isStructuredOutput
|
|
})
|
|
|
|
response = result.response
|
|
usedTools = result.usedTools
|
|
sourceDocuments = result.sourceDocuments
|
|
artifacts = result.artifacts
|
|
additionalTokens = result.totalTokens
|
|
isWaitingForHumanInput = result.isWaitingForHumanInput || false
|
|
|
|
// Calculate which messages were added during tool calls
|
|
_toolCallMessages = messages.slice(messagesBeforeToolCalls.length)
|
|
|
|
// Stream additional data if this is the last node
|
|
if (isLastNode && sseStreamer) {
|
|
if (usedTools.length > 0) {
|
|
sseStreamer.streamUsedToolsEvent(chatId, flatten(usedTools))
|
|
}
|
|
|
|
if (sourceDocuments.length > 0) {
|
|
sseStreamer.streamSourceDocumentsEvent(chatId, flatten(sourceDocuments))
|
|
}
|
|
|
|
if (artifacts.length > 0) {
|
|
sseStreamer.streamArtifactsEvent(chatId, flatten(artifacts))
|
|
}
|
|
}
|
|
} else {
|
|
if (isStreamable) {
|
|
response = await this.handleStreamingResponse(
|
|
sseStreamer,
|
|
llmNodeInstance,
|
|
messages,
|
|
chatId,
|
|
abortController,
|
|
isStructuredOutput
|
|
)
|
|
} else {
|
|
response = await llmNodeInstance.invoke(messages, { signal: abortController?.signal })
|
|
}
|
|
}
|
|
|
|
// Address built in tools (after artifacts are processed)
|
|
const builtInUsedTools: IUsedTool[] = await this.extractBuiltInUsedTools(response, [])
|
|
|
|
if (!humanInput && response.tool_calls && response.tool_calls.length > 0) {
|
|
const result = await this.handleToolCalls({
|
|
response,
|
|
messages,
|
|
toolsInstance,
|
|
sseStreamer,
|
|
chatId,
|
|
input,
|
|
options,
|
|
abortController,
|
|
llmNodeInstance,
|
|
isStreamable,
|
|
isLastNode,
|
|
iterationContext,
|
|
isStructuredOutput
|
|
})
|
|
|
|
response = result.response
|
|
usedTools = result.usedTools
|
|
sourceDocuments = result.sourceDocuments
|
|
artifacts = result.artifacts
|
|
additionalTokens = result.totalTokens
|
|
isWaitingForHumanInput = result.isWaitingForHumanInput || false
|
|
|
|
// Calculate which messages were added during tool calls
|
|
_toolCallMessages = messages.slice(messagesBeforeToolCalls.length)
|
|
|
|
// Stream additional data if this is the last node
|
|
if (isLastNode && sseStreamer) {
|
|
if (usedTools.length > 0) {
|
|
sseStreamer.streamUsedToolsEvent(chatId, flatten(usedTools))
|
|
}
|
|
|
|
if (sourceDocuments.length > 0) {
|
|
sseStreamer.streamSourceDocumentsEvent(chatId, flatten(sourceDocuments))
|
|
}
|
|
|
|
if (artifacts.length > 0) {
|
|
sseStreamer.streamArtifactsEvent(chatId, flatten(artifacts))
|
|
}
|
|
}
|
|
} else if (!humanInput && !isStreamable && isLastNode && sseStreamer && !isStructuredOutput) {
|
|
// Stream whole response back to UI if not streaming and no tool calls
|
|
// Skip this if structured output is enabled - it will be streamed after conversion
|
|
let finalResponse = ''
|
|
if (response.content && Array.isArray(response.content)) {
|
|
finalResponse = response.content
|
|
.map((item: any) => {
|
|
if ((item.text && !item.type) || (item.type === 'text' && item.text)) {
|
|
return item.text
|
|
}
|
|
return ''
|
|
})
|
|
.filter((text: string) => text)
|
|
.join('\n')
|
|
} else if (response.content && typeof response.content === 'string') {
|
|
finalResponse = response.content
|
|
} else {
|
|
finalResponse = JSON.stringify(response, null, 2)
|
|
}
|
|
sseStreamer.streamTokenEvent(chatId, finalResponse)
|
|
}
|
|
|
|
// Calculate execution time
|
|
const endTime = Date.now()
|
|
const timeDelta = endTime - startTime
|
|
|
|
// Update flow state if needed
|
|
let newState = { ...state }
|
|
if (_agentUpdateState && Array.isArray(_agentUpdateState) && _agentUpdateState.length > 0) {
|
|
newState = updateFlowState(state, _agentUpdateState)
|
|
}
|
|
|
|
// Clean up empty inputs
|
|
for (const key in nodeData.inputs) {
|
|
if (nodeData.inputs[key] === '') {
|
|
delete nodeData.inputs[key]
|
|
}
|
|
}
|
|
|
|
// Prepare final response and output object
|
|
let finalResponse = ''
|
|
if (response.content && Array.isArray(response.content)) {
|
|
// Process items and concatenate consecutive text items
|
|
const processedParts: string[] = []
|
|
let currentTextBuffer = ''
|
|
|
|
for (const item of response.content) {
|
|
const itemAny = item as any
|
|
const isTextItem = (itemAny.text && !itemAny.type) || (itemAny.type === 'text' && itemAny.text)
|
|
|
|
if (isTextItem) {
|
|
// Accumulate consecutive text items
|
|
currentTextBuffer += itemAny.text
|
|
} else {
|
|
// Flush accumulated text before processing other types
|
|
if (currentTextBuffer) {
|
|
processedParts.push(currentTextBuffer)
|
|
currentTextBuffer = ''
|
|
}
|
|
|
|
// Process non-text items
|
|
if (itemAny.type === 'executableCode' && itemAny.executableCode) {
|
|
// Format executable code as a code block
|
|
const language = itemAny.executableCode.language?.toLowerCase() || 'python'
|
|
processedParts.push(`\n\`\`\`${language}\n${itemAny.executableCode.code}\n\`\`\`\n`)
|
|
} else if (itemAny.type === 'codeExecutionResult' && itemAny.codeExecutionResult) {
|
|
// Format code execution result
|
|
const outcome = itemAny.codeExecutionResult.outcome || 'OUTCOME_OK'
|
|
const output = itemAny.codeExecutionResult.output || ''
|
|
if (outcome === 'OUTCOME_OK' && output) {
|
|
processedParts.push(`**Code Output:**\n\`\`\`\n${output}\n\`\`\`\n`)
|
|
} else if (outcome !== 'OUTCOME_OK') {
|
|
processedParts.push(`**Code Execution Error:**\n\`\`\`\n${output}\n\`\`\`\n`)
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Flush any remaining text
|
|
if (currentTextBuffer) {
|
|
processedParts.push(currentTextBuffer)
|
|
}
|
|
|
|
finalResponse = processedParts.filter((text) => text).join('\n')
|
|
} else if (response.content && typeof response.content === 'string') {
|
|
finalResponse = response.content
|
|
} else if (response.content === '') {
|
|
// Empty response content, this could happen when there is only image data
|
|
finalResponse = ''
|
|
} else {
|
|
finalResponse = JSON.stringify(response, null, 2)
|
|
}
|
|
|
|
// Address built in tools
|
|
const additionalBuiltInUsedTools: IUsedTool[] = await this.extractBuiltInUsedTools(response, builtInUsedTools)
|
|
if (additionalBuiltInUsedTools.length > 0) {
|
|
usedTools = [...new Set([...usedTools, ...additionalBuiltInUsedTools])]
|
|
|
|
// Stream used tools if this is the last node
|
|
if (isLastNode && sseStreamer) {
|
|
sseStreamer.streamUsedToolsEvent(chatId, flatten(usedTools))
|
|
}
|
|
}
|
|
|
|
// Extract artifacts from annotations in response metadata and replace inline data
|
|
if (response.response_metadata) {
|
|
const {
|
|
artifacts: extractedArtifacts,
|
|
fileAnnotations: extractedFileAnnotations,
|
|
savedInlineImages
|
|
} = await extractArtifactsFromResponse(response.response_metadata, newNodeData, options)
|
|
if (extractedArtifacts.length > 0) {
|
|
artifacts = [...artifacts, ...extractedArtifacts]
|
|
|
|
// Stream artifacts if this is the last node
|
|
if (isLastNode && sseStreamer) {
|
|
sseStreamer.streamArtifactsEvent(chatId, extractedArtifacts)
|
|
}
|
|
}
|
|
|
|
if (extractedFileAnnotations.length > 0) {
|
|
fileAnnotations = [...fileAnnotations, ...extractedFileAnnotations]
|
|
|
|
// Stream file annotations if this is the last node
|
|
if (isLastNode && sseStreamer) {
|
|
sseStreamer.streamFileAnnotationsEvent(chatId, fileAnnotations)
|
|
}
|
|
}
|
|
|
|
// Replace inlineData base64 with file references in the response
|
|
if (savedInlineImages && savedInlineImages.length > 0) {
|
|
replaceInlineDataWithFileReferences(response, savedInlineImages)
|
|
}
|
|
}
|
|
|
|
// Replace sandbox links with proper download URLs. Example: [Download the script](sandbox:/mnt/data/dummy_bar_graph.py)
|
|
if (finalResponse.includes('sandbox:/')) {
|
|
finalResponse = await this.processSandboxLinks(finalResponse, options.baseURL, options.chatflowid, chatId)
|
|
}
|
|
|
|
// If is structured output, then invoke LLM again with structured output at the very end after all tool calls
|
|
if (isStructuredOutput) {
|
|
llmNodeInstance = configureStructuredOutput(llmNodeInstance, _agentStructuredOutput)
|
|
const prompt = 'Convert the following response to the structured output format: ' + finalResponse
|
|
response = await llmNodeInstance.invoke(prompt, { signal: abortController?.signal })
|
|
|
|
if (typeof response === 'object') {
|
|
finalResponse = '```json\n' + JSON.stringify(response, null, 2) + '\n```'
|
|
} else {
|
|
finalResponse = response
|
|
}
|
|
|
|
if (isLastNode && sseStreamer) {
|
|
sseStreamer.streamTokenEvent(chatId, finalResponse)
|
|
}
|
|
}
|
|
|
|
const output = this.prepareOutputObject(
|
|
response,
|
|
availableTools,
|
|
finalResponse,
|
|
startTime,
|
|
endTime,
|
|
timeDelta,
|
|
usedTools,
|
|
sourceDocuments,
|
|
artifacts,
|
|
additionalTokens,
|
|
isWaitingForHumanInput,
|
|
fileAnnotations,
|
|
isStructuredOutput
|
|
)
|
|
|
|
// End analytics tracking
|
|
if (analyticHandlers && llmIds) {
|
|
await analyticHandlers.onLLMEnd(llmIds, finalResponse)
|
|
}
|
|
|
|
// Send additional streaming events if needed
|
|
if (isStreamable) {
|
|
this.sendStreamingEvents(options, chatId, response)
|
|
}
|
|
|
|
// Stream file annotations if any were extracted
|
|
if (fileAnnotations.length > 0 && isLastNode && sseStreamer) {
|
|
sseStreamer.streamFileAnnotationsEvent(chatId, fileAnnotations)
|
|
}
|
|
|
|
// Process template variables in state
|
|
newState = processTemplateVariables(newState, finalResponse)
|
|
|
|
/**
|
|
* Remove the temporarily added image artifact messages before storing
|
|
* This is to avoid storing the actual base64 data into database
|
|
*/
|
|
const messagesToStore = messages.filter((msg: any) => !msg._isTemporaryImageMessage)
|
|
|
|
// Replace the actual messages array with one that includes the file references for images instead of base64 data
|
|
const messagesWithFileReferences = replaceBase64ImagesWithFileReferences(
|
|
messagesToStore,
|
|
runtimeImageMessagesWithFileRef,
|
|
pastImageMessagesWithFileRef
|
|
)
|
|
|
|
// Only add to runtime chat history if this is the first node
|
|
const inputMessages = []
|
|
if (!runtimeChatHistory.length) {
|
|
if (runtimeImageMessagesWithFileRef.length) {
|
|
inputMessages.push(...runtimeImageMessagesWithFileRef)
|
|
}
|
|
if (input && typeof input === 'string') {
|
|
if (!enableMemory) {
|
|
if (!agentMessages.some((msg) => msg.role === 'user')) {
|
|
inputMessages.push({ role: 'user', content: input })
|
|
} else {
|
|
agentMessages.map((msg) => {
|
|
if (msg.role === 'user') {
|
|
inputMessages.push({ role: 'user', content: msg.content })
|
|
}
|
|
})
|
|
}
|
|
} else {
|
|
inputMessages.push({ role: 'user', content: input })
|
|
}
|
|
}
|
|
}
|
|
|
|
const returnResponseAs = nodeData.inputs?.agentReturnResponseAs as string
|
|
let returnRole = 'user'
|
|
if (returnResponseAs === 'assistantMessage') {
|
|
returnRole = 'assistant'
|
|
}
|
|
|
|
// Prepare and return the final output
|
|
return {
|
|
id: nodeData.id,
|
|
name: this.name,
|
|
input: {
|
|
messages: messagesWithFileReferences,
|
|
...nodeData.inputs
|
|
},
|
|
output,
|
|
state: newState,
|
|
chatHistory: [
|
|
...inputMessages,
|
|
|
|
// Add the messages that were specifically added during tool calls, this enable other nodes to see the full tool call history, temporaraily disabled
|
|
// ...toolCallMessages,
|
|
|
|
// End with the final assistant response
|
|
{
|
|
role: returnRole,
|
|
content: finalResponse,
|
|
name: nodeData?.label ? nodeData?.label.toLowerCase().replace(/\s/g, '_').trim() : nodeData?.id,
|
|
...(((artifacts && artifacts.length > 0) ||
|
|
(fileAnnotations && fileAnnotations.length > 0) ||
|
|
(usedTools && usedTools.length > 0)) && {
|
|
additional_kwargs: {
|
|
...(artifacts && artifacts.length > 0 && { artifacts }),
|
|
...(fileAnnotations && fileAnnotations.length > 0 && { fileAnnotations }),
|
|
...(usedTools && usedTools.length > 0 && { usedTools })
|
|
}
|
|
})
|
|
}
|
|
]
|
|
}
|
|
} catch (error) {
|
|
if (options.analyticHandlers && llmIds) {
|
|
await options.analyticHandlers.onLLMError(llmIds, error instanceof Error ? error.message : String(error))
|
|
}
|
|
|
|
if (error instanceof Error && error.message === 'Aborted') {
|
|
throw error
|
|
}
|
|
throw new Error(`Error in Agent node: ${error instanceof Error ? error.message : String(error)}`)
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Extracts built-in used tools from response metadata and processes image generation results
|
|
*/
|
|
private async extractBuiltInUsedTools(response: AIMessageChunk, builtInUsedTools: IUsedTool[] = []): Promise<IUsedTool[]> {
|
|
if (!response.response_metadata) {
|
|
return builtInUsedTools
|
|
}
|
|
|
|
const { output, tools, groundingMetadata, urlContextMetadata } = response.response_metadata
|
|
|
|
// Handle OpenAI built-in tools
|
|
if (output && Array.isArray(output) && output.length > 0 && tools && Array.isArray(tools) && tools.length > 0) {
|
|
for (const outputItem of output) {
|
|
if (outputItem.type && outputItem.type.endsWith('_call')) {
|
|
let toolInput = outputItem.action ?? outputItem.code
|
|
let toolOutput = outputItem.status === 'completed' ? 'Success' : outputItem.status
|
|
|
|
// Handle image generation calls specially
|
|
if (outputItem.type === 'image_generation_call') {
|
|
// Create input summary for image generation
|
|
toolInput = {
|
|
prompt: outputItem.revised_prompt || 'Image generation request',
|
|
size: outputItem.size || '1024x1024',
|
|
quality: outputItem.quality || 'standard',
|
|
output_format: outputItem.output_format || 'png'
|
|
}
|
|
|
|
// Check if image has been processed (base64 replaced with file path)
|
|
if (outputItem.result && !outputItem.result.startsWith('data:') && !outputItem.result.includes('base64')) {
|
|
toolOutput = `Image generated and saved`
|
|
} else {
|
|
toolOutput = `Image generated (base64)`
|
|
}
|
|
}
|
|
|
|
// Remove "_call" suffix to get the base tool name
|
|
const baseToolName = outputItem.type.replace('_call', '')
|
|
|
|
// Find matching tool that includes the base name in its type
|
|
const matchingTool = tools.find((tool) => tool.type && tool.type.includes(baseToolName))
|
|
|
|
if (matchingTool) {
|
|
// Check for duplicates
|
|
if (builtInUsedTools.find((tool) => tool.tool === matchingTool.type)) {
|
|
continue
|
|
}
|
|
|
|
builtInUsedTools.push({
|
|
tool: matchingTool.type,
|
|
toolInput,
|
|
toolOutput
|
|
})
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Handle Gemini googleSearch tool
|
|
if (groundingMetadata && groundingMetadata.webSearchQueries && Array.isArray(groundingMetadata.webSearchQueries)) {
|
|
// Check for duplicates
|
|
const isDuplicate = builtInUsedTools.find(
|
|
(tool) =>
|
|
tool.tool === 'googleSearch' &&
|
|
JSON.stringify((tool.toolInput as any)?.queries) === JSON.stringify(groundingMetadata.webSearchQueries)
|
|
)
|
|
if (!isDuplicate) {
|
|
builtInUsedTools.push({
|
|
tool: 'googleSearch',
|
|
toolInput: {
|
|
queries: groundingMetadata.webSearchQueries
|
|
},
|
|
toolOutput: `Searched for: ${groundingMetadata.webSearchQueries.join(', ')}`
|
|
})
|
|
}
|
|
}
|
|
|
|
// Handle Gemini urlContext tool
|
|
if (urlContextMetadata && urlContextMetadata.urlMetadata && Array.isArray(urlContextMetadata.urlMetadata)) {
|
|
// Check for duplicates
|
|
const isDuplicate = builtInUsedTools.find(
|
|
(tool) =>
|
|
tool.tool === 'urlContext' &&
|
|
JSON.stringify((tool.toolInput as any)?.urlMetadata) === JSON.stringify(urlContextMetadata.urlMetadata)
|
|
)
|
|
if (!isDuplicate) {
|
|
builtInUsedTools.push({
|
|
tool: 'urlContext',
|
|
toolInput: {
|
|
urlMetadata: urlContextMetadata.urlMetadata
|
|
},
|
|
toolOutput: `Processed ${urlContextMetadata.urlMetadata.length} URL(s)`
|
|
})
|
|
}
|
|
}
|
|
|
|
// Handle Gemini codeExecution tool
|
|
if (response.content && Array.isArray(response.content)) {
|
|
for (let i = 0; i < response.content.length; i++) {
|
|
const item = response.content[i]
|
|
|
|
if (item.type === 'executableCode' && item.executableCode) {
|
|
const language = item.executableCode.language || 'PYTHON'
|
|
const code = item.executableCode.code || ''
|
|
let toolOutput = ''
|
|
|
|
// Check for duplicates
|
|
const isDuplicate = builtInUsedTools.find(
|
|
(tool) =>
|
|
tool.tool === 'codeExecution' &&
|
|
(tool.toolInput as any)?.language === language &&
|
|
(tool.toolInput as any)?.code === code
|
|
)
|
|
if (isDuplicate) {
|
|
continue
|
|
}
|
|
|
|
// Check the next item for the output
|
|
const nextItem = i + 1 < response.content.length ? response.content[i + 1] : null
|
|
|
|
if (nextItem) {
|
|
if (nextItem.type === 'codeExecutionResult' && nextItem.codeExecutionResult) {
|
|
const outcome = nextItem.codeExecutionResult.outcome
|
|
const output = nextItem.codeExecutionResult.output || ''
|
|
toolOutput = outcome === 'OUTCOME_OK' ? output : `Error: ${output}`
|
|
} else if (nextItem.type === 'inlineData') {
|
|
toolOutput = 'Generated image data'
|
|
}
|
|
}
|
|
|
|
builtInUsedTools.push({
|
|
tool: 'codeExecution',
|
|
toolInput: {
|
|
language,
|
|
code
|
|
},
|
|
toolOutput
|
|
})
|
|
}
|
|
}
|
|
}
|
|
|
|
return builtInUsedTools
|
|
}
|
|
|
|
/**
|
|
* Handles memory management based on the specified memory type
|
|
*/
|
|
private async handleMemory({
|
|
messages,
|
|
memoryType,
|
|
pastChatHistory,
|
|
runtimeChatHistory,
|
|
llmNodeInstance,
|
|
nodeData,
|
|
userMessage,
|
|
input,
|
|
abortController,
|
|
options,
|
|
modelConfig,
|
|
runtimeImageMessagesWithFileRef,
|
|
pastImageMessagesWithFileRef
|
|
}: {
|
|
messages: BaseMessageLike[]
|
|
memoryType: string
|
|
pastChatHistory: BaseMessageLike[]
|
|
runtimeChatHistory: BaseMessageLike[]
|
|
llmNodeInstance: BaseChatModel
|
|
nodeData: INodeData
|
|
userMessage: string
|
|
input: string | Record<string, any>
|
|
abortController: AbortController
|
|
options: ICommonObject
|
|
modelConfig: ICommonObject
|
|
runtimeImageMessagesWithFileRef: BaseMessageLike[]
|
|
pastImageMessagesWithFileRef: BaseMessageLike[]
|
|
}): Promise<void> {
|
|
const { updatedPastMessages, transformedPastMessages } = await getPastChatHistoryImageMessages(pastChatHistory, options)
|
|
pastChatHistory = updatedPastMessages
|
|
pastImageMessagesWithFileRef.push(...transformedPastMessages)
|
|
|
|
let pastMessages = [...pastChatHistory, ...runtimeChatHistory]
|
|
if (!runtimeChatHistory.length && input && typeof input === 'string') {
|
|
/*
|
|
* If this is the first node:
|
|
* - Add images to messages if exist
|
|
* - Add user message
|
|
*/
|
|
if (options.uploads) {
|
|
const imageContents = await getUniqueImageMessages(options, messages, modelConfig)
|
|
if (imageContents) {
|
|
const { imageMessageWithBase64, imageMessageWithFileRef } = imageContents
|
|
pastMessages.push(imageMessageWithBase64)
|
|
runtimeImageMessagesWithFileRef.push(imageMessageWithFileRef)
|
|
}
|
|
}
|
|
pastMessages.push({
|
|
role: 'user',
|
|
content: input
|
|
})
|
|
}
|
|
const { updatedMessages, transformedMessages } = await processMessagesWithImages(pastMessages, options)
|
|
pastMessages = updatedMessages
|
|
pastImageMessagesWithFileRef.push(...transformedMessages)
|
|
|
|
if (pastMessages.length > 0) {
|
|
if (memoryType === 'windowSize') {
|
|
// Window memory: Keep the last N messages
|
|
const windowSize = nodeData.inputs?.agentMemoryWindowSize as number
|
|
const windowedMessages = pastMessages.slice(-windowSize * 2)
|
|
messages.push(...windowedMessages)
|
|
} else if (memoryType === 'conversationSummary') {
|
|
// Summary memory: Summarize all past messages
|
|
const summary = await llmNodeInstance.invoke(
|
|
[
|
|
{
|
|
role: 'user',
|
|
content: DEFAULT_SUMMARIZER_TEMPLATE.replace(
|
|
'{conversation}',
|
|
pastMessages.map((msg: any) => `${msg.role}: ${msg.content}`).join('\n')
|
|
)
|
|
}
|
|
],
|
|
{ signal: abortController?.signal }
|
|
)
|
|
messages.push({ role: 'assistant', content: summary.content as string })
|
|
} else if (memoryType === 'conversationSummaryBuffer') {
|
|
// Summary buffer: Summarize messages that exceed token limit
|
|
await this.handleSummaryBuffer(messages, pastMessages, llmNodeInstance, nodeData, abortController)
|
|
} else {
|
|
// Default: Use all messages
|
|
messages.push(...pastMessages)
|
|
}
|
|
}
|
|
|
|
// Add user message
|
|
if (userMessage) {
|
|
messages.push({
|
|
role: 'user',
|
|
content: userMessage
|
|
})
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Handles conversation summary buffer memory type
|
|
*/
|
|
private async handleSummaryBuffer(
|
|
messages: BaseMessageLike[],
|
|
pastMessages: BaseMessageLike[],
|
|
llmNodeInstance: BaseChatModel,
|
|
nodeData: INodeData,
|
|
abortController: AbortController
|
|
): Promise<void> {
|
|
const maxTokenLimit = (nodeData.inputs?.agentMemoryMaxTokenLimit as number) || 2000
|
|
|
|
// Convert past messages to a format suitable for token counting
|
|
const messagesString = pastMessages.map((msg: any) => `${msg.role}: ${msg.content}`).join('\n')
|
|
const tokenCount = await llmNodeInstance.getNumTokens(messagesString)
|
|
|
|
if (tokenCount > maxTokenLimit) {
|
|
// Calculate how many messages to summarize (messages that exceed the token limit)
|
|
let currBufferLength = tokenCount
|
|
const messagesToSummarize = []
|
|
const remainingMessages = [...pastMessages]
|
|
|
|
// Remove messages from the beginning until we're under the token limit
|
|
while (currBufferLength > maxTokenLimit && remainingMessages.length > 0) {
|
|
const poppedMessage = remainingMessages.shift()
|
|
if (poppedMessage) {
|
|
messagesToSummarize.push(poppedMessage)
|
|
// Recalculate token count for remaining messages
|
|
const remainingMessagesString = remainingMessages.map((msg: any) => `${msg.role}: ${msg.content}`).join('\n')
|
|
currBufferLength = await llmNodeInstance.getNumTokens(remainingMessagesString)
|
|
}
|
|
}
|
|
|
|
// Summarize the messages that were removed
|
|
const messagesToSummarizeString = messagesToSummarize.map((msg: any) => `${msg.role}: ${msg.content}`).join('\n')
|
|
|
|
const summary = await llmNodeInstance.invoke(
|
|
[
|
|
{
|
|
role: 'user',
|
|
content: DEFAULT_SUMMARIZER_TEMPLATE.replace('{conversation}', messagesToSummarizeString)
|
|
}
|
|
],
|
|
{ signal: abortController?.signal }
|
|
)
|
|
|
|
// Add summary as a system message at the beginning, then add remaining messages
|
|
messages.push({ role: 'system', content: `Previous conversation summary: ${summary.content}` })
|
|
messages.push(...remainingMessages)
|
|
} else {
|
|
// If under token limit, use all messages
|
|
messages.push(...pastMessages)
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Handles streaming response from the LLM
|
|
*/
|
|
private async handleStreamingResponse(
|
|
sseStreamer: IServerSideEventStreamer | undefined,
|
|
llmNodeInstance: BaseChatModel,
|
|
messages: BaseMessageLike[],
|
|
chatId: string,
|
|
abortController: AbortController,
|
|
isStructuredOutput: boolean = false
|
|
): Promise<AIMessageChunk> {
|
|
let response = new AIMessageChunk('')
|
|
|
|
try {
|
|
for await (const chunk of await llmNodeInstance.stream(messages, { signal: abortController?.signal })) {
|
|
if (sseStreamer && !isStructuredOutput) {
|
|
let content = ''
|
|
|
|
if (typeof chunk === 'string') {
|
|
content = chunk
|
|
} else if (Array.isArray(chunk.content) && chunk.content.length > 0) {
|
|
content = chunk.content
|
|
.map((item: any) => {
|
|
if ((item.text && !item.type) || (item.type === 'text' && item.text)) {
|
|
return item.text
|
|
} else if (item.type === 'executableCode' && item.executableCode) {
|
|
const language = item.executableCode.language?.toLowerCase() || 'python'
|
|
return `\n\`\`\`${language}\n${item.executableCode.code}\n\`\`\`\n`
|
|
} else if (item.type === 'codeExecutionResult' && item.codeExecutionResult) {
|
|
const outcome = item.codeExecutionResult.outcome || 'OUTCOME_OK'
|
|
const output = item.codeExecutionResult.output || ''
|
|
if (outcome === 'OUTCOME_OK' && output) {
|
|
return `**Code Output:**\n\`\`\`\n${output}\n\`\`\`\n`
|
|
} else if (outcome !== 'OUTCOME_OK') {
|
|
return `**Code Execution Error:**\n\`\`\`\n${output}\n\`\`\`\n`
|
|
}
|
|
}
|
|
return ''
|
|
})
|
|
.filter((text: string) => text)
|
|
.join('')
|
|
} else if (chunk.content) {
|
|
content = chunk.content.toString()
|
|
}
|
|
sseStreamer.streamTokenEvent(chatId, content)
|
|
}
|
|
|
|
const messageChunk = typeof chunk === 'string' ? new AIMessageChunk(chunk) : chunk
|
|
response = response.concat(messageChunk)
|
|
}
|
|
} catch (error) {
|
|
console.error('Error during streaming:', error)
|
|
throw error
|
|
}
|
|
|
|
// Only convert to string if all content items are text (no inlineData or other special types)
|
|
if (Array.isArray(response.content) && response.content.length > 0) {
|
|
const hasNonTextContent = response.content.some(
|
|
(item: any) => item.type === 'inlineData' || item.type === 'executableCode' || item.type === 'codeExecutionResult'
|
|
)
|
|
if (!hasNonTextContent) {
|
|
const responseContents = response.content as MessageContentText[]
|
|
response.content = responseContents.map((item) => item.text).join('')
|
|
}
|
|
}
|
|
return response
|
|
}
|
|
|
|
/**
|
|
* Prepares the output object with response and metadata
|
|
*/
|
|
private prepareOutputObject(
|
|
response: AIMessageChunk,
|
|
availableTools: ISimpliefiedTool[],
|
|
finalResponse: string,
|
|
startTime: number,
|
|
endTime: number,
|
|
timeDelta: number,
|
|
usedTools: IUsedTool[],
|
|
sourceDocuments: Array<any>,
|
|
artifacts: any[],
|
|
additionalTokens: number = 0,
|
|
isWaitingForHumanInput: boolean = false,
|
|
fileAnnotations: any[] = [],
|
|
isStructuredOutput: boolean = false
|
|
): any {
|
|
const output: any = {
|
|
content: finalResponse,
|
|
timeMetadata: {
|
|
start: startTime,
|
|
end: endTime,
|
|
delta: timeDelta
|
|
}
|
|
}
|
|
|
|
if (response.tool_calls) {
|
|
output.calledTools = response.tool_calls
|
|
}
|
|
|
|
// Include token usage metadata with accumulated tokens from tool calls
|
|
if (response.usage_metadata) {
|
|
const originalTokens = response.usage_metadata.total_tokens || 0
|
|
output.usageMetadata = {
|
|
...response.usage_metadata,
|
|
total_tokens: originalTokens + additionalTokens,
|
|
tool_call_tokens: additionalTokens
|
|
}
|
|
} else if (additionalTokens > 0) {
|
|
// If no original usage metadata but we have tool tokens
|
|
output.usageMetadata = {
|
|
total_tokens: additionalTokens,
|
|
tool_call_tokens: additionalTokens
|
|
}
|
|
}
|
|
|
|
if (response.response_metadata) {
|
|
output.responseMetadata = response.response_metadata
|
|
}
|
|
|
|
if (isStructuredOutput && typeof response === 'object') {
|
|
const structuredOutput = response as Record<string, any>
|
|
for (const key in structuredOutput) {
|
|
if (structuredOutput[key] !== undefined && structuredOutput[key] !== null) {
|
|
output[key] = structuredOutput[key]
|
|
}
|
|
}
|
|
}
|
|
|
|
// Add used tools, source documents and artifacts to output
|
|
if (usedTools && usedTools.length > 0) {
|
|
output.usedTools = flatten(usedTools)
|
|
}
|
|
|
|
if (sourceDocuments && sourceDocuments.length > 0) {
|
|
output.sourceDocuments = flatten(sourceDocuments)
|
|
}
|
|
|
|
if (artifacts && artifacts.length > 0) {
|
|
output.artifacts = flatten(artifacts)
|
|
}
|
|
|
|
if (availableTools && availableTools.length > 0) {
|
|
output.availableTools = availableTools
|
|
}
|
|
|
|
if (isWaitingForHumanInput) {
|
|
output.isWaitingForHumanInput = isWaitingForHumanInput
|
|
}
|
|
|
|
if (fileAnnotations && fileAnnotations.length > 0) {
|
|
output.fileAnnotations = fileAnnotations
|
|
}
|
|
|
|
return output
|
|
}
|
|
|
|
/**
|
|
* Sends additional streaming events for tool calls and metadata
|
|
*/
|
|
private sendStreamingEvents(options: ICommonObject, chatId: string, response: AIMessageChunk): void {
|
|
const sseStreamer: IServerSideEventStreamer = options.sseStreamer as IServerSideEventStreamer
|
|
|
|
if (response.tool_calls) {
|
|
const formattedToolCalls = response.tool_calls.map((toolCall: any) => ({
|
|
tool: toolCall.name || 'tool',
|
|
toolInput: toolCall.args,
|
|
toolOutput: ''
|
|
}))
|
|
sseStreamer.streamCalledToolsEvent(chatId, flatten(formattedToolCalls))
|
|
}
|
|
|
|
if (response.usage_metadata) {
|
|
sseStreamer.streamUsageMetadataEvent(chatId, response.usage_metadata)
|
|
}
|
|
|
|
sseStreamer.streamEndEvent(chatId)
|
|
}
|
|
|
|
/**
|
|
* Handles tool calls and their responses, with support for recursive tool calling
|
|
*/
|
|
private async handleToolCalls({
|
|
response,
|
|
messages,
|
|
toolsInstance,
|
|
sseStreamer,
|
|
chatId,
|
|
input,
|
|
options,
|
|
abortController,
|
|
llmNodeInstance,
|
|
isStreamable,
|
|
isLastNode,
|
|
iterationContext,
|
|
isStructuredOutput = false
|
|
}: {
|
|
response: AIMessageChunk
|
|
messages: BaseMessageLike[]
|
|
toolsInstance: Tool[]
|
|
sseStreamer: IServerSideEventStreamer | undefined
|
|
chatId: string
|
|
input: string | Record<string, any>
|
|
options: ICommonObject
|
|
abortController: AbortController
|
|
llmNodeInstance: BaseChatModel
|
|
isStreamable: boolean
|
|
isLastNode: boolean
|
|
iterationContext: ICommonObject
|
|
isStructuredOutput?: boolean
|
|
}): Promise<{
|
|
response: AIMessageChunk
|
|
usedTools: IUsedTool[]
|
|
sourceDocuments: Array<any>
|
|
artifacts: any[]
|
|
totalTokens: number
|
|
isWaitingForHumanInput?: boolean
|
|
}> {
|
|
// Track total tokens used throughout this process
|
|
let totalTokens = response.usage_metadata?.total_tokens || 0
|
|
const usedTools: IUsedTool[] = []
|
|
let sourceDocuments: Array<any> = []
|
|
let artifacts: any[] = []
|
|
let isWaitingForHumanInput: boolean | undefined
|
|
|
|
if (!response.tool_calls || response.tool_calls.length === 0) {
|
|
return { response, usedTools: [], sourceDocuments: [], artifacts: [], totalTokens }
|
|
}
|
|
|
|
// Stream tool calls if available
|
|
if (sseStreamer) {
|
|
const formattedToolCalls = response.tool_calls.map((toolCall: any) => ({
|
|
tool: toolCall.name || 'tool',
|
|
toolInput: toolCall.args,
|
|
toolOutput: ''
|
|
}))
|
|
sseStreamer.streamCalledToolsEvent(chatId, flatten(formattedToolCalls))
|
|
}
|
|
|
|
// Remove tool calls with no id
|
|
const toBeRemovedToolCalls = []
|
|
for (let i = 0; i < response.tool_calls.length; i++) {
|
|
const toolCall = response.tool_calls[i]
|
|
if (!toolCall.id) {
|
|
toBeRemovedToolCalls.push(toolCall)
|
|
usedTools.push({
|
|
tool: toolCall.name || 'tool',
|
|
toolInput: toolCall.args,
|
|
toolOutput: response.content
|
|
})
|
|
}
|
|
}
|
|
|
|
for (const toolCall of toBeRemovedToolCalls) {
|
|
response.tool_calls.splice(response.tool_calls.indexOf(toolCall), 1)
|
|
}
|
|
|
|
// Add LLM response with tool calls to messages
|
|
messages.push({
|
|
id: response.id,
|
|
role: 'assistant',
|
|
content: response.content,
|
|
tool_calls: response.tool_calls,
|
|
usage_metadata: response.usage_metadata
|
|
})
|
|
|
|
// Process each tool call
|
|
for (let i = 0; i < response.tool_calls.length; i++) {
|
|
const toolCall = response.tool_calls[i]
|
|
|
|
const selectedTool = toolsInstance.find((tool) => tool.name === toolCall.name)
|
|
if (selectedTool) {
|
|
let parsedDocs
|
|
let parsedArtifacts
|
|
let isToolRequireHumanInput =
|
|
(selectedTool as any).requiresHumanInput && (!iterationContext || Object.keys(iterationContext).length === 0)
|
|
|
|
const flowConfig = {
|
|
chatflowId: options.chatflowid,
|
|
sessionId: options.sessionId,
|
|
chatId: options.chatId,
|
|
input: input,
|
|
state: options.agentflowRuntime?.state
|
|
}
|
|
|
|
if (isToolRequireHumanInput) {
|
|
const toolCallDetails = '```json\n' + JSON.stringify(toolCall, null, 2) + '\n```'
|
|
const responseContent = response.content + `\nAttempting to use tool:\n${toolCallDetails}`
|
|
response.content = responseContent
|
|
if (!isStructuredOutput) {
|
|
sseStreamer?.streamTokenEvent(chatId, responseContent)
|
|
}
|
|
return { response, usedTools, sourceDocuments, artifacts, totalTokens, isWaitingForHumanInput: true }
|
|
}
|
|
|
|
let toolIds: ICommonObject | undefined
|
|
if (options.analyticHandlers) {
|
|
toolIds = await options.analyticHandlers.onToolStart(toolCall.name, toolCall.args, options.parentTraceIds)
|
|
}
|
|
|
|
try {
|
|
//@ts-ignore
|
|
let toolOutput = await selectedTool.call(toolCall.args, { signal: abortController?.signal }, undefined, flowConfig)
|
|
|
|
if (options.analyticHandlers && toolIds) {
|
|
await options.analyticHandlers.onToolEnd(toolIds, toolOutput)
|
|
}
|
|
|
|
// Extract source documents if present
|
|
if (typeof toolOutput === 'string' && toolOutput.includes(SOURCE_DOCUMENTS_PREFIX)) {
|
|
const [output, docs] = toolOutput.split(SOURCE_DOCUMENTS_PREFIX)
|
|
toolOutput = output
|
|
try {
|
|
parsedDocs = JSON.parse(docs)
|
|
sourceDocuments.push(parsedDocs)
|
|
} catch (e) {
|
|
console.error('Error parsing source documents from tool:', e)
|
|
}
|
|
}
|
|
|
|
// Extract artifacts if present
|
|
if (typeof toolOutput === 'string' && toolOutput.includes(ARTIFACTS_PREFIX)) {
|
|
const [output, artifact] = toolOutput.split(ARTIFACTS_PREFIX)
|
|
toolOutput = output
|
|
try {
|
|
parsedArtifacts = JSON.parse(artifact)
|
|
artifacts.push(parsedArtifacts)
|
|
} catch (e) {
|
|
console.error('Error parsing artifacts from tool:', e)
|
|
}
|
|
}
|
|
|
|
let toolInput
|
|
if (typeof toolOutput === 'string' && toolOutput.includes(TOOL_ARGS_PREFIX)) {
|
|
const [output, args] = toolOutput.split(TOOL_ARGS_PREFIX)
|
|
toolOutput = output
|
|
try {
|
|
toolInput = JSON.parse(args)
|
|
} catch (e) {
|
|
console.error('Error parsing tool input from tool:', e)
|
|
}
|
|
}
|
|
|
|
// Add tool message to conversation
|
|
messages.push({
|
|
role: 'tool',
|
|
content: toolOutput,
|
|
tool_call_id: toolCall.id,
|
|
name: toolCall.name,
|
|
additional_kwargs: {
|
|
artifacts: parsedArtifacts,
|
|
sourceDocuments: parsedDocs
|
|
}
|
|
})
|
|
|
|
// Track used tools
|
|
usedTools.push({
|
|
tool: toolCall.name,
|
|
toolInput: toolInput ?? toolCall.args,
|
|
toolOutput
|
|
})
|
|
} catch (e) {
|
|
if (options.analyticHandlers && toolIds) {
|
|
await options.analyticHandlers.onToolEnd(toolIds, e)
|
|
}
|
|
|
|
console.error('Error invoking tool:', e)
|
|
const errMsg = getErrorMessage(e)
|
|
let toolInput = toolCall.args
|
|
if (typeof errMsg === 'string' && errMsg.includes(TOOL_ARGS_PREFIX)) {
|
|
const [_, args] = errMsg.split(TOOL_ARGS_PREFIX)
|
|
try {
|
|
toolInput = JSON.parse(args)
|
|
} catch (e) {
|
|
console.error('Error parsing tool input from tool:', e)
|
|
}
|
|
}
|
|
|
|
usedTools.push({
|
|
tool: selectedTool.name,
|
|
toolInput,
|
|
toolOutput: '',
|
|
error: getErrorMessage(e)
|
|
})
|
|
sseStreamer?.streamUsedToolsEvent(chatId, flatten(usedTools))
|
|
throw new Error(getErrorMessage(e))
|
|
}
|
|
}
|
|
}
|
|
|
|
// Return direct tool output if there's exactly one tool with returnDirect
|
|
if (response.tool_calls.length === 1) {
|
|
const selectedTool = toolsInstance.find((tool) => tool.name === response.tool_calls?.[0]?.name)
|
|
if (selectedTool && selectedTool.returnDirect) {
|
|
const lastToolOutput = usedTools[0]?.toolOutput || ''
|
|
const lastToolOutputString = typeof lastToolOutput === 'string' ? lastToolOutput : JSON.stringify(lastToolOutput, null, 2)
|
|
|
|
if (sseStreamer && !isStructuredOutput) {
|
|
sseStreamer.streamTokenEvent(chatId, lastToolOutputString)
|
|
}
|
|
|
|
return {
|
|
response: new AIMessageChunk(lastToolOutputString),
|
|
usedTools,
|
|
sourceDocuments,
|
|
artifacts,
|
|
totalTokens
|
|
}
|
|
}
|
|
}
|
|
|
|
if (response.tool_calls.length === 0) {
|
|
const responseContent = typeof response.content === 'string' ? response.content : JSON.stringify(response.content, null, 2)
|
|
return {
|
|
response: new AIMessageChunk(responseContent),
|
|
usedTools,
|
|
sourceDocuments,
|
|
artifacts,
|
|
totalTokens
|
|
}
|
|
}
|
|
|
|
// Get LLM response after tool calls
|
|
let newResponse: AIMessageChunk
|
|
|
|
if (isStreamable) {
|
|
newResponse = await this.handleStreamingResponse(
|
|
sseStreamer,
|
|
llmNodeInstance,
|
|
messages,
|
|
chatId,
|
|
abortController,
|
|
isStructuredOutput
|
|
)
|
|
} else {
|
|
newResponse = await llmNodeInstance.invoke(messages, { signal: abortController?.signal })
|
|
|
|
// Stream non-streaming response if this is the last node
|
|
if (isLastNode && sseStreamer && !isStructuredOutput) {
|
|
let responseContent = JSON.stringify(newResponse, null, 2)
|
|
if (typeof newResponse.content === 'string') {
|
|
responseContent = newResponse.content
|
|
}
|
|
sseStreamer.streamTokenEvent(chatId, responseContent)
|
|
}
|
|
}
|
|
|
|
// Add tokens from this response
|
|
if (newResponse.usage_metadata?.total_tokens) {
|
|
totalTokens += newResponse.usage_metadata.total_tokens
|
|
}
|
|
|
|
// Check for recursive tool calls and handle them
|
|
if (newResponse.tool_calls && newResponse.tool_calls.length > 0) {
|
|
const {
|
|
response: recursiveResponse,
|
|
usedTools: recursiveUsedTools,
|
|
sourceDocuments: recursiveSourceDocuments,
|
|
artifacts: recursiveArtifacts,
|
|
totalTokens: recursiveTokens,
|
|
isWaitingForHumanInput: recursiveIsWaitingForHumanInput
|
|
} = await this.handleToolCalls({
|
|
response: newResponse,
|
|
messages,
|
|
toolsInstance,
|
|
sseStreamer,
|
|
chatId,
|
|
input,
|
|
options,
|
|
abortController,
|
|
llmNodeInstance,
|
|
isStreamable,
|
|
isLastNode,
|
|
iterationContext,
|
|
isStructuredOutput
|
|
})
|
|
|
|
// Merge results from recursive tool calls
|
|
newResponse = recursiveResponse
|
|
usedTools.push(...recursiveUsedTools)
|
|
sourceDocuments = [...sourceDocuments, ...recursiveSourceDocuments]
|
|
artifacts = [...artifacts, ...recursiveArtifacts]
|
|
totalTokens += recursiveTokens
|
|
isWaitingForHumanInput = recursiveIsWaitingForHumanInput
|
|
}
|
|
|
|
return { response: newResponse, usedTools, sourceDocuments, artifacts, totalTokens, isWaitingForHumanInput }
|
|
}
|
|
|
|
/**
|
|
* Handles tool calls and their responses, with support for recursive tool calling
|
|
*/
|
|
private async handleResumedToolCalls({
|
|
humanInput,
|
|
humanInputAction,
|
|
messages,
|
|
toolsInstance,
|
|
sseStreamer,
|
|
chatId,
|
|
input,
|
|
options,
|
|
abortController,
|
|
llmWithoutToolsBind,
|
|
isStreamable,
|
|
isLastNode,
|
|
iterationContext,
|
|
isStructuredOutput = false
|
|
}: {
|
|
humanInput: IHumanInput
|
|
humanInputAction: Record<string, any> | undefined
|
|
messages: BaseMessageLike[]
|
|
toolsInstance: Tool[]
|
|
sseStreamer: IServerSideEventStreamer | undefined
|
|
chatId: string
|
|
input: string | Record<string, any>
|
|
options: ICommonObject
|
|
abortController: AbortController
|
|
llmWithoutToolsBind: BaseChatModel
|
|
isStreamable: boolean
|
|
isLastNode: boolean
|
|
iterationContext: ICommonObject
|
|
isStructuredOutput?: boolean
|
|
}): Promise<{
|
|
response: AIMessageChunk
|
|
usedTools: IUsedTool[]
|
|
sourceDocuments: Array<any>
|
|
artifacts: any[]
|
|
totalTokens: number
|
|
isWaitingForHumanInput?: boolean
|
|
}> {
|
|
let llmNodeInstance = llmWithoutToolsBind
|
|
const usedTools: IUsedTool[] = []
|
|
let sourceDocuments: Array<any> = []
|
|
let artifacts: any[] = []
|
|
let isWaitingForHumanInput: boolean | undefined
|
|
|
|
const lastCheckpointMessages = humanInputAction?.data?.input?.messages ?? []
|
|
if (!lastCheckpointMessages.length) {
|
|
return { response: new AIMessageChunk(''), usedTools: [], sourceDocuments: [], artifacts: [], totalTokens: 0 }
|
|
}
|
|
|
|
// Use the last message as the response
|
|
const response = lastCheckpointMessages[lastCheckpointMessages.length - 1] as AIMessageChunk
|
|
|
|
// Replace messages array
|
|
messages.length = 0
|
|
messages.push(...lastCheckpointMessages.slice(0, lastCheckpointMessages.length - 1))
|
|
|
|
// Track total tokens used throughout this process
|
|
let totalTokens = response.usage_metadata?.total_tokens || 0
|
|
|
|
if (!response.tool_calls || response.tool_calls.length === 0) {
|
|
return { response, usedTools: [], sourceDocuments: [], artifacts: [], totalTokens }
|
|
}
|
|
|
|
// Stream tool calls if available
|
|
if (sseStreamer) {
|
|
const formattedToolCalls = response.tool_calls.map((toolCall: any) => ({
|
|
tool: toolCall.name || 'tool',
|
|
toolInput: toolCall.args,
|
|
toolOutput: ''
|
|
}))
|
|
sseStreamer.streamCalledToolsEvent(chatId, flatten(formattedToolCalls))
|
|
}
|
|
|
|
// Remove tool calls with no id
|
|
const toBeRemovedToolCalls = []
|
|
for (let i = 0; i < response.tool_calls.length; i++) {
|
|
const toolCall = response.tool_calls[i]
|
|
if (!toolCall.id) {
|
|
toBeRemovedToolCalls.push(toolCall)
|
|
usedTools.push({
|
|
tool: toolCall.name || 'tool',
|
|
toolInput: toolCall.args,
|
|
toolOutput: response.content
|
|
})
|
|
}
|
|
}
|
|
|
|
for (const toolCall of toBeRemovedToolCalls) {
|
|
response.tool_calls.splice(response.tool_calls.indexOf(toolCall), 1)
|
|
}
|
|
|
|
// Add LLM response with tool calls to messages
|
|
messages.push({
|
|
id: response.id,
|
|
role: 'assistant',
|
|
content: response.content,
|
|
tool_calls: response.tool_calls,
|
|
usage_metadata: response.usage_metadata
|
|
})
|
|
|
|
// Process each tool call
|
|
for (let i = 0; i < response.tool_calls.length; i++) {
|
|
const toolCall = response.tool_calls[i]
|
|
|
|
const selectedTool = toolsInstance.find((tool) => tool.name === toolCall.name)
|
|
if (selectedTool) {
|
|
let parsedDocs
|
|
let parsedArtifacts
|
|
|
|
const flowConfig = {
|
|
chatflowId: options.chatflowid,
|
|
sessionId: options.sessionId,
|
|
chatId: options.chatId,
|
|
input: input,
|
|
state: options.agentflowRuntime?.state
|
|
}
|
|
|
|
if (humanInput.type === 'reject') {
|
|
messages.pop()
|
|
const toBeRemovedTool = toolsInstance.find((tool) => tool.name === toolCall.name)
|
|
if (toBeRemovedTool) {
|
|
toolsInstance = toolsInstance.filter((tool) => tool.name !== toolCall.name)
|
|
// Remove other tools with the same agentSelectedTool such as MCP tools
|
|
toolsInstance = toolsInstance.filter(
|
|
(tool) => (tool as any).agentSelectedTool !== (toBeRemovedTool as any).agentSelectedTool
|
|
)
|
|
}
|
|
}
|
|
if (humanInput.type === 'proceed') {
|
|
let toolIds: ICommonObject | undefined
|
|
if (options.analyticHandlers) {
|
|
toolIds = await options.analyticHandlers.onToolStart(toolCall.name, toolCall.args, options.parentTraceIds)
|
|
}
|
|
|
|
try {
|
|
//@ts-ignore
|
|
let toolOutput = await selectedTool.call(toolCall.args, { signal: abortController?.signal }, undefined, flowConfig)
|
|
|
|
if (options.analyticHandlers && toolIds) {
|
|
await options.analyticHandlers.onToolEnd(toolIds, toolOutput)
|
|
}
|
|
|
|
// Extract source documents if present
|
|
if (typeof toolOutput === 'string' && toolOutput.includes(SOURCE_DOCUMENTS_PREFIX)) {
|
|
const [output, docs] = toolOutput.split(SOURCE_DOCUMENTS_PREFIX)
|
|
toolOutput = output
|
|
try {
|
|
parsedDocs = JSON.parse(docs)
|
|
sourceDocuments.push(parsedDocs)
|
|
} catch (e) {
|
|
console.error('Error parsing source documents from tool:', e)
|
|
}
|
|
}
|
|
|
|
// Extract artifacts if present
|
|
if (typeof toolOutput === 'string' && toolOutput.includes(ARTIFACTS_PREFIX)) {
|
|
const [output, artifact] = toolOutput.split(ARTIFACTS_PREFIX)
|
|
toolOutput = output
|
|
try {
|
|
parsedArtifacts = JSON.parse(artifact)
|
|
artifacts.push(parsedArtifacts)
|
|
} catch (e) {
|
|
console.error('Error parsing artifacts from tool:', e)
|
|
}
|
|
}
|
|
|
|
let toolInput
|
|
if (typeof toolOutput === 'string' && toolOutput.includes(TOOL_ARGS_PREFIX)) {
|
|
const [output, args] = toolOutput.split(TOOL_ARGS_PREFIX)
|
|
toolOutput = output
|
|
try {
|
|
toolInput = JSON.parse(args)
|
|
} catch (e) {
|
|
console.error('Error parsing tool input from tool:', e)
|
|
}
|
|
}
|
|
|
|
// Add tool message to conversation
|
|
messages.push({
|
|
role: 'tool',
|
|
content: toolOutput,
|
|
tool_call_id: toolCall.id,
|
|
name: toolCall.name,
|
|
additional_kwargs: {
|
|
artifacts: parsedArtifacts,
|
|
sourceDocuments: parsedDocs
|
|
}
|
|
})
|
|
|
|
// Track used tools
|
|
usedTools.push({
|
|
tool: toolCall.name,
|
|
toolInput: toolInput ?? toolCall.args,
|
|
toolOutput
|
|
})
|
|
} catch (e) {
|
|
if (options.analyticHandlers && toolIds) {
|
|
await options.analyticHandlers.onToolEnd(toolIds, e)
|
|
}
|
|
|
|
console.error('Error invoking tool:', e)
|
|
const errMsg = getErrorMessage(e)
|
|
let toolInput = toolCall.args
|
|
if (typeof errMsg === 'string' && errMsg.includes(TOOL_ARGS_PREFIX)) {
|
|
const [_, args] = errMsg.split(TOOL_ARGS_PREFIX)
|
|
try {
|
|
toolInput = JSON.parse(args)
|
|
} catch (e) {
|
|
console.error('Error parsing tool input from tool:', e)
|
|
}
|
|
}
|
|
|
|
usedTools.push({
|
|
tool: selectedTool.name,
|
|
toolInput,
|
|
toolOutput: '',
|
|
error: getErrorMessage(e)
|
|
})
|
|
sseStreamer?.streamUsedToolsEvent(chatId, flatten(usedTools))
|
|
throw new Error(getErrorMessage(e))
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Return direct tool output if there's exactly one tool with returnDirect
|
|
if (response.tool_calls.length === 1) {
|
|
const selectedTool = toolsInstance.find((tool) => tool.name === response.tool_calls?.[0]?.name)
|
|
if (selectedTool && selectedTool.returnDirect) {
|
|
const lastToolOutput = usedTools[0]?.toolOutput || ''
|
|
const lastToolOutputString = typeof lastToolOutput === 'string' ? lastToolOutput : JSON.stringify(lastToolOutput, null, 2)
|
|
|
|
if (sseStreamer && !isStructuredOutput) {
|
|
sseStreamer.streamTokenEvent(chatId, lastToolOutputString)
|
|
}
|
|
|
|
return {
|
|
response: new AIMessageChunk(lastToolOutputString),
|
|
usedTools,
|
|
sourceDocuments,
|
|
artifacts,
|
|
totalTokens
|
|
}
|
|
}
|
|
}
|
|
|
|
// Get LLM response after tool calls
|
|
let newResponse: AIMessageChunk
|
|
|
|
if (llmNodeInstance && (llmNodeInstance as any).builtInTools && (llmNodeInstance as any).builtInTools.length > 0) {
|
|
toolsInstance.push(...(llmNodeInstance as any).builtInTools)
|
|
}
|
|
|
|
if (llmNodeInstance && toolsInstance.length > 0) {
|
|
if (llmNodeInstance.bindTools === undefined) {
|
|
throw new Error(`Agent needs to have a function calling capable models.`)
|
|
}
|
|
|
|
// @ts-ignore
|
|
llmNodeInstance = llmNodeInstance.bindTools(toolsInstance)
|
|
}
|
|
|
|
if (isStreamable) {
|
|
newResponse = await this.handleStreamingResponse(
|
|
sseStreamer,
|
|
llmNodeInstance,
|
|
messages,
|
|
chatId,
|
|
abortController,
|
|
isStructuredOutput
|
|
)
|
|
} else {
|
|
newResponse = await llmNodeInstance.invoke(messages, { signal: abortController?.signal })
|
|
|
|
// Stream non-streaming response if this is the last node
|
|
if (isLastNode && sseStreamer && !isStructuredOutput) {
|
|
let responseContent = JSON.stringify(newResponse, null, 2)
|
|
if (typeof newResponse.content === 'string') {
|
|
responseContent = newResponse.content
|
|
}
|
|
sseStreamer.streamTokenEvent(chatId, responseContent)
|
|
}
|
|
}
|
|
|
|
// Add tokens from this response
|
|
if (newResponse.usage_metadata?.total_tokens) {
|
|
totalTokens += newResponse.usage_metadata.total_tokens
|
|
}
|
|
|
|
// Check for recursive tool calls and handle them
|
|
if (newResponse.tool_calls && newResponse.tool_calls.length > 0) {
|
|
const {
|
|
response: recursiveResponse,
|
|
usedTools: recursiveUsedTools,
|
|
sourceDocuments: recursiveSourceDocuments,
|
|
artifacts: recursiveArtifacts,
|
|
totalTokens: recursiveTokens,
|
|
isWaitingForHumanInput: recursiveIsWaitingForHumanInput
|
|
} = await this.handleToolCalls({
|
|
response: newResponse,
|
|
messages,
|
|
toolsInstance,
|
|
sseStreamer,
|
|
chatId,
|
|
input,
|
|
options,
|
|
abortController,
|
|
llmNodeInstance,
|
|
isStreamable,
|
|
isLastNode,
|
|
iterationContext,
|
|
isStructuredOutput
|
|
})
|
|
|
|
// Merge results from recursive tool calls
|
|
newResponse = recursiveResponse
|
|
usedTools.push(...recursiveUsedTools)
|
|
sourceDocuments = [...sourceDocuments, ...recursiveSourceDocuments]
|
|
artifacts = [...artifacts, ...recursiveArtifacts]
|
|
totalTokens += recursiveTokens
|
|
isWaitingForHumanInput = recursiveIsWaitingForHumanInput
|
|
}
|
|
|
|
return { response: newResponse, usedTools, sourceDocuments, artifacts, totalTokens, isWaitingForHumanInput }
|
|
}
|
|
|
|
/**
|
|
* Processes sandbox links in the response text and converts them to file annotations
|
|
*/
|
|
private async processSandboxLinks(text: string, baseURL: string, chatflowId: string, chatId: string): Promise<string> {
|
|
let processedResponse = text
|
|
|
|
// Regex to match sandbox links: [text](sandbox:/path/to/file)
|
|
const sandboxLinkRegex = /\[([^\]]+)\]\(sandbox:\/([^)]+)\)/g
|
|
const matches = Array.from(text.matchAll(sandboxLinkRegex))
|
|
|
|
for (const match of matches) {
|
|
const fullMatch = match[0]
|
|
const linkText = match[1]
|
|
const filePath = match[2]
|
|
|
|
try {
|
|
// Extract filename from the file path
|
|
const fileName = filePath.split('/').pop() || filePath
|
|
|
|
// Replace sandbox link with proper download URL
|
|
const downloadUrl = `${baseURL}/api/v1/get-upload-file?chatflowId=${chatflowId}&chatId=${chatId}&fileName=${fileName}&download=true`
|
|
const newLink = `[${linkText}](${downloadUrl})`
|
|
|
|
processedResponse = processedResponse.replace(fullMatch, newLink)
|
|
} catch (error) {
|
|
console.error('Error processing sandbox link:', error)
|
|
// If there's an error, remove the sandbox link as fallback
|
|
processedResponse = processedResponse.replace(fullMatch, linkText)
|
|
}
|
|
}
|
|
|
|
return processedResponse
|
|
}
|
|
}
|
|
|
|
module.exports = { nodeClass: Agent_Agentflow }
|