mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-29 09:01:06 +03:00
Henry Heng
a107aa7a77
* Enhancement: Update issue templates and add new tools - Updated bug report template to include a default label of 'bug'. - Updated feature request template to include a default label of 'enhancement'. - Added new credential class for Agentflow API. - Enhanced Agent and HTTP nodes to improve tool management and error handling. - Added deprecation badges to several agent and chain classes. - Introduced new tools for handling requests (GET, POST, DELETE, PUT) with improved error handling. - Added new chatflows and agentflows for various use cases, including document QnA and translation. - Updated UI components for better handling of agent flows and marketplace interactions. - Refactored utility functions for improved functionality and clarity. * Refactor: Remove beta badge and streamline template title assignment - Removed the 'BETA' badge from the ExtractMetadataRetriever class. - Simplified the title assignment in the agentflowv2 generator by using a variable instead of inline string manipulation.
93 lines
3.1 KiB
TypeScript
93 lines
3.1 KiB
TypeScript
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
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import { VectorStore } from '@langchain/core/vectorstores'
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import { INode, INodeData, INodeParams } from '../../../src/Interface'
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import { BabyAGI } from './core'
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import { checkInputs, Moderation } from '../../moderation/Moderation'
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import { formatResponse } from '../../outputparsers/OutputParserHelpers'
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class BabyAGI_Agents implements INode {
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label: string
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name: string
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version: number
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description: string
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type: string
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icon: string
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category: string
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baseClasses: string[]
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inputs: INodeParams[]
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badge: string
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constructor() {
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this.label = 'BabyAGI'
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this.name = 'babyAGI'
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this.version = 2.0
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this.type = 'BabyAGI'
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this.category = 'Agents'
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this.icon = 'babyagi.svg'
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this.badge = 'DEPRECATING'
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this.description = 'Task Driven Autonomous Agent which creates new task and reprioritizes task list based on objective'
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this.baseClasses = ['BabyAGI']
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this.inputs = [
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{
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label: 'Chat Model',
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name: 'model',
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type: 'BaseChatModel'
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},
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{
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label: 'Vector Store',
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name: 'vectorStore',
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type: 'VectorStore'
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},
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{
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label: 'Task Loop',
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name: 'taskLoop',
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type: 'number',
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default: 3
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},
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{
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label: 'Input Moderation',
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description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
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name: 'inputModeration',
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type: 'Moderation',
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optional: true,
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list: true
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}
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]
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}
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async init(nodeData: INodeData): Promise<any> {
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const model = nodeData.inputs?.model as BaseChatModel
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const vectorStore = nodeData.inputs?.vectorStore as VectorStore
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const taskLoop = nodeData.inputs?.taskLoop as string
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const k = (vectorStore as any)?.k ?? 4
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const babyAgi = BabyAGI.fromLLM(model, vectorStore, parseInt(taskLoop, 10), k)
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return babyAgi
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}
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async run(nodeData: INodeData, input: string): Promise<string | object> {
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const executor = nodeData.instance as BabyAGI
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const moderations = nodeData.inputs?.inputModeration as Moderation[]
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if (moderations && moderations.length > 0) {
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try {
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// Use the output of the moderation chain as input for the BabyAGI agent
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input = await checkInputs(moderations, input)
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} catch (e) {
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await new Promise((resolve) => setTimeout(resolve, 500))
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// if (options.shouldStreamResponse) {
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// streamResponse(options.sseStreamer, options.chatId, e.message)
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// }
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return formatResponse(e.message)
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}
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}
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const objective = input
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const res = await executor.call({ objective })
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return res
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}
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}
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module.exports = { nodeClass: BabyAGI_Agents }
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