Add BabyAGI node
This commit is contained in:
Henry
parent
88b60ab17a
commit
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import { INode, INodeData, INodeParams } from '../../../src/Interface'
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import { Configuration, CreateChatCompletionRequest, CreateCompletionRequest, OpenAIApi } from 'openai'
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import { PineconeClient } from '@pinecone-database/pinecone'
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import { CreateIndexRequest } from '@pinecone-database/pinecone/dist/pinecone-generated-ts-fetch'
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import { VectorOperationsApi } from '@pinecone-database/pinecone/dist/pinecone-generated-ts-fetch'
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import { v4 as uuidv4 } from 'uuid'
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interface Task {
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id: string
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name: string
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priority: number // 1 is highest priority
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}
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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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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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constructor() {
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this.label = 'BabyAGI'
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this.name = 'babyAGI'
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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.description = 'Task Driven Autonomous Agent which creates new task and reprioritizes task list based on objective'
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this.inputs = [
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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: 'OpenAI Api Key',
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name: 'openAIApiKey',
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type: 'password'
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},
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{
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label: 'Pinecone Api Key',
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name: 'pineconeApiKey',
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type: 'password'
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},
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{
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label: 'Pinecone Environment',
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name: 'pineconeEnv',
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type: 'string'
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},
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{
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label: 'Pinecone Index',
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name: 'pineconeIndex',
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type: 'string'
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},
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{
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label: 'Model Name',
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name: 'modelName',
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type: 'options',
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options: [
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{
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label: 'gpt-4',
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name: 'gpt-4'
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},
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{
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label: 'gpt-4-0314',
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name: 'gpt-4-0314'
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},
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{
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label: 'gpt-4-32k-0314',
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name: 'gpt-4-32k-0314'
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},
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{
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label: 'gpt-3.5-turbo',
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name: 'gpt-3.5-turbo'
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},
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{
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label: 'gpt-3.5-turbo-0301',
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name: 'gpt-3.5-turbo-0301'
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}
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],
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default: 'gpt-3.5-turbo',
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optional: true
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}
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]
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}
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async getBaseClasses(): Promise<string[]> {
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return ['BabyAGI']
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}
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async init(): Promise<any> {
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return null
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}
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async run(nodeData: INodeData, input: string): Promise<string> {
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const openAIApiKey = nodeData.inputs?.openAIApiKey as string
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const pineconeApiKey = nodeData.inputs?.pineconeApiKey as string
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const pineconeEnv = nodeData.inputs?.pineconeEnv as string
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const index = nodeData.inputs?.pineconeIndex as string
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const modelName = nodeData.inputs?.modelName as string
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const taskLoop = nodeData.inputs?.taskLoop as string
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const objective = input
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const configuration = new Configuration({
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apiKey: openAIApiKey
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})
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const openai = new OpenAIApi(configuration)
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const pinecone = new PineconeClient()
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await pinecone.init({
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apiKey: pineconeApiKey,
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environment: pineconeEnv
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})
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const dimension = 1536
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const metric = 'cosine'
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const podType = 'p1'
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const indexList = await pinecone.listIndexes()
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if (!indexList.includes(index)) {
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const createIndexOptions: CreateIndexRequest = {
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createRequest: {
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name: index,
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dimension,
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metric,
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podType
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}
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}
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await pinecone.createIndex(createIndexOptions)
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}
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let vectorIndex: VectorOperationsApi = pinecone.Index(index)
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let taskList: Task[] = []
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let embeddingList = new Map<string, number[]>()
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taskList = [
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{
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id: uuidv4(),
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name: 'Develop a task list',
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priority: 1
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}
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]
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return await mainLoop(openai, pinecone, index, embeddingList, vectorIndex, taskList, objective, modelName, taskLoop)
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}
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}
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export const getADAEmbedding = async (openai: OpenAIApi, text: string, embeddingList: Map<string, number[]>): Promise<number[]> => {
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//console.log('\nGetting ADA embedding for: ', text)
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if (embeddingList.has(text)) {
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//console.log('Embedding already exists for: ', text)
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const numbers = embeddingList.get(text)
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return numbers ?? []
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}
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const embedding = (
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await openai.createEmbedding({
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input: [text],
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model: 'text-embedding-ada-002'
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})
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).data?.data[0].embedding
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embeddingList.set(text, embedding)
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return embedding
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}
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export const openAICall = async (openai: OpenAIApi, prompt: string, gptVersion: string, temperature = 0.5, max_tokens = 100) => {
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if (gptVersion === 'gpt-3.5-turbo' || gptVersion === 'gpt-4' || gptVersion === 'gpt-4-32k') {
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// Chat completion
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const options: CreateChatCompletionRequest = {
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model: gptVersion,
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messages: [{ role: 'user', content: prompt }],
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temperature,
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max_tokens,
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n: 1
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}
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const data = (await openai.createChatCompletion(options)).data
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return data?.choices[0]?.message?.content.trim() ?? ''
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} else {
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// Prompt completion
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const options: CreateCompletionRequest = {
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model: gptVersion,
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prompt,
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temperature,
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max_tokens,
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top_p: 1,
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frequency_penalty: 0,
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presence_penalty: 0
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}
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const data = (await openai.createCompletion(options)).data
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return data?.choices[0]?.text?.trim() ?? ''
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}
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}
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export const taskCreationAgent = async (
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openai: OpenAIApi,
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taskList: Task[],
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objective: string,
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result: string,
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taskDescription: string,
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gptVersion = 'gpt-3.5-turbo'
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): Promise<Task[]> => {
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const prompt = `You are an task creation AI that uses the result of an execution agent to create new tasks with the following objective: ${objective}, The last completed task has the result: ${result}. This result was based on this task description: ${taskDescription}. These are incomplete tasks: ${taskList.join(
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', '
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)}. Based on the result, create new tasks to be completed by the AI system that do not overlap with incomplete tasks. Return the tasks as an array.`
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const response = await openAICall(openai, prompt, gptVersion)
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const newTaskNames = response.split('\n')
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return newTaskNames.map((name) => ({
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id: uuidv4(),
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name,
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priority: taskList.length + 1
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}))
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}
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export const prioritizationAgent = async (
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openai: OpenAIApi,
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taskList: Task[],
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taskPriority: number,
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objective: string,
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gptVersion = 'gpt-3.5-turbo'
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): Promise<Task[]> => {
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const taskNames = taskList.map((t) => t.name)
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const startPriority = taskPriority + 1
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const prompt = `You are an task prioritization AI tasked with cleaning the formatting of and reprioritizing the following tasks: ${taskNames}. Consider the ultimate objective of your team: ${objective}. Do not remove any tasks. Return the result as a list, like:
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#. First task
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#. Second task
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Start the task list with number ${startPriority}.`
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const response = await openAICall(openai, prompt, gptVersion)
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const newTasks = response.split('\n')
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// Parse and add new tasks
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return (
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newTasks
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.map((taskString) => {
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const taskParts = taskString.trim().split('.', 2)
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if (taskParts.length === 2) {
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const id = uuidv4()
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const name = taskParts[1].trim()
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const priority = parseInt(taskParts[0])
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return {
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id,
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name,
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priority
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} as Task
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}
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})
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// Remove lines that don't have a task
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.filter((t) => t !== undefined)
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// Sort by priority
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.sort((a, b) => a!.priority - b!.priority) as Task[]
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)
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}
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export const contextAgent = async (
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openai: OpenAIApi,
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pinecone: PineconeClient,
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indexName: string,
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embeddingList: Map<string, number[]>,
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objective: string,
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topK: number
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) => {
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const index = pinecone.Index(indexName)
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const queryEmbedding = await getADAEmbedding(openai, objective, embeddingList)
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const results = await index.query({
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queryRequest: {
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vector: queryEmbedding,
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includeMetadata: true,
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topK
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}
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})
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const sortedResults = results.matches?.sort((a, b) => (b?.score ?? 0) - (a?.score ?? 0)) ?? []
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return sortedResults.map((item) => (item.metadata as any)?.task ?? '')
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}
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export const executionAgent = async (
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openai: OpenAIApi,
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pinecone: PineconeClient,
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indexName: string,
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embeddingList: Map<string, number[]>,
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objective: string,
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task: Task,
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gptVersion = 'gpt-3.5-turbo'
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) => {
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const context = await contextAgent(openai, pinecone, indexName, embeddingList, objective, 5)
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const prompt = `You are an AI who performs one task based on the following objective: ${objective}.\nTake into account these previously completed tasks: ${context}\nYour task: ${task.name}\nResponse:`
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//console.log('\nexecution prompt: ', prompt, '\n')
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return openAICall(openai, prompt, gptVersion, 0.7, 2000)
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}
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export const mainLoop = async (
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openai: OpenAIApi,
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pinecone: PineconeClient,
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indexName: string,
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embeddingList: Map<string, number[]>,
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index: VectorOperationsApi,
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taskList: Task[],
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objective: string,
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modelName: string,
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taskLoop: string
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): Promise<string> => {
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const RUN_LIMIT = parseInt(taskLoop, 10) || 3
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let finalResult = ''
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for (let run = 0; run < RUN_LIMIT; run++) {
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let enrichedResult: any
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let task: Task | undefined
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if (taskList.length > 0) {
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// Step 1: Pull the task
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task = taskList.shift()
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if (!task) {
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//console.log('No tasks left to complete. Exiting.')
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break
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}
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console.log(`\x1b[95m\x1b[1m\n*****TASK LIST*****\n\x1b[0m\x1b[0m
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${taskList.map((t) => ` ${t?.priority}. ${t?.name}`).join('\n')}
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\x1b[92m\x1b[1m\n*****NEXT TASK*****\n\x1b[0m\x1b[0m
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${task.name}`)
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// Step 2: Execute the task
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const result = await executionAgent(openai, pinecone, indexName, embeddingList, objective, task)
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console.log('\x1b[93m\x1b[1m\n*****TASK RESULT*****\n\x1b[0m\x1b[0m')
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console.log(result)
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finalResult = result
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// Step 3: Enrich result and store in Pinecone
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enrichedResult = { data: result }
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const vector = enrichedResult.data // extract the actual result from the dictionary
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const embeddingResult = await getADAEmbedding(openai, vector, embeddingList)
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await index.upsert({
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upsertRequest: {
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vectors: [
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{
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id: task.id,
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values: embeddingResult,
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metadata: { task: task.name, result }
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}
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]
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}
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})
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}
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// Step 4: Create new tasks and reprioritize task list
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if (enrichedResult) {
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const newTasks = await taskCreationAgent(openai, taskList, objective, enrichedResult.data, task!.name)
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//console.log('newTasks', newTasks)
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taskList = [...taskList, ...newTasks]
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taskList = await prioritizationAgent(openai, taskList, task!.priority, objective, modelName)
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//console.log(`Reprioritized task list: ${taskList.map((t) => `[${t?.priority}] ${t?.id}: ${t?.name}`).join(', ')}`)
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} else {
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break
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}
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}
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return finalResult
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}
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module.exports = { nodeClass: BabyAGI_Agents }
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-robot" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
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<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
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<path d="M7 7h10a2 2 0 0 1 2 2v1l1 1v3l-1 1v3a2 2 0 0 1 -2 2h-10a2 2 0 0 1 -2 -2v-3l-1 -1v-3l1 -1v-1a2 2 0 0 1 2 -2z"></path>
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<path d="M10 16h4"></path>
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<circle cx="8.5" cy="11.5" r=".5" fill="currentColor"></circle>
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<circle cx="15.5" cy="11.5" r=".5" fill="currentColor"></circle>
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<path d="M9 7l-1 -4"></path>
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<path d="M15 7l1 -4"></path>
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</svg>
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After Width: | Height: | Size: 650 B |
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@ -29,6 +29,18 @@ class ChatOpenAI_ChatModels implements INode {
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name: 'modelName',
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type: 'options',
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options: [
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{
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label: 'gpt-4',
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name: 'gpt-4'
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},
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{
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label: 'gpt-4-0314',
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name: 'gpt-4-0314'
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},
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{
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label: 'gpt-4-32k-0314',
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name: 'gpt-4-32k-0314'
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},
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{
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label: 'gpt-3.5-turbo',
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name: 'gpt-3.5-turbo'
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@ -28,10 +28,12 @@
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"moment": "^2.29.3",
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"node-fetch": "2",
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"pdfjs-dist": "^3.5.141",
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"uuid": "^9.0.0",
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"ws": "^8.9.0"
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},
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"devDependencies": {
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"@types/gulp": "4.0.9",
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"@types/uuid": "^9.0.1",
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"@types/ws": "^8.5.3",
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"gulp": "^4.0.2",
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"typescript": "^4.8.4"
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@ -0,0 +1,104 @@
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{
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"description": "Given an objective, tasks will be created, stored into Pinecone and reprioritized",
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"nodes": [
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{
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"width": 300,
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"height": 769,
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"id": "babyAGI_0",
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"position": {
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"x": 542.130412774738,
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"y": 154.52145148106695
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},
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"type": "customNode",
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"data": {
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"id": "babyAGI_0",
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"label": "BabyAGI",
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"name": "babyAGI",
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"type": "BabyAGI",
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"baseClasses": ["AgentExecutor"],
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"category": "Agents",
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"description": "Conversational agent for a chat model. It will utilize chat specific prompts",
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"inputParams": [
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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": "OpenAI Api Key",
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"name": "openAIApiKey",
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"type": "password"
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},
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{
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"label": "Pinecone Api Key",
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"name": "pineconeApiKey",
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"type": "password"
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},
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{
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"label": "Pinecone Environment",
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"name": "pineconeEnv",
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"type": "string"
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},
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{
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"label": "Pinecone Index",
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"name": "pineconeIndex",
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"type": "string"
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},
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{
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"label": "Model Name",
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"name": "modelName",
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"type": "options",
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"options": [
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{
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"label": "gpt-4",
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"name": "gpt-4"
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},
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{
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"label": "gpt-4-0314",
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"name": "gpt-4-0314"
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},
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{
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"label": "gpt-4-32k-0314",
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"name": "gpt-4-32k-0314"
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},
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{
|
||||
"label": "gpt-3.5-turbo",
|
||||
"name": "gpt-3.5-turbo"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-0301",
|
||||
"name": "gpt-3.5-turbo-0301"
|
||||
}
|
||||
],
|
||||
"default": "gpt-3.5-turbo",
|
||||
"optional": true
|
||||
}
|
||||
],
|
||||
"inputAnchors": [],
|
||||
"inputs": {
|
||||
"taskLoop": "3",
|
||||
"pineconeEnv": "us-west4-gcp",
|
||||
"pineconeIndex": "test",
|
||||
"modelName": "gpt-3.5-turbo"
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "babyAGI_0-output-babyAGI-AgentExecutor",
|
||||
"name": "babyAGI",
|
||||
"label": "BabyAGI",
|
||||
"type": "AgentExecutor"
|
||||
}
|
||||
],
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"dragging": false,
|
||||
"positionAbsolute": {
|
||||
"x": 542.130412774738,
|
||||
"y": 154.52145148106695
|
||||
}
|
||||
}
|
||||
],
|
||||
"edges": []
|
||||
}
|
||||
|
|
@ -107,7 +107,9 @@ export const getEndingNode = (nodeDependencies: INodeDependencies, graph: INodeD
|
|||
// Find ending node
|
||||
let endingNodeId = ''
|
||||
Object.keys(graph).forEach((nodeId) => {
|
||||
if (!graph[nodeId].length && nodeDependencies[nodeId] > 0) {
|
||||
if (Object.keys(nodeDependencies).length === 1) {
|
||||
endingNodeId = nodeId
|
||||
} else if (!graph[nodeId].length && nodeDependencies[nodeId] > 0) {
|
||||
endingNodeId = nodeId
|
||||
}
|
||||
})
|
||||
|
|
|
|||
|
|
@ -1,7 +1,3 @@
|
|||
.cloudform {
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.messagelist {
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
|
|
@ -113,13 +109,11 @@
|
|||
position: relative;
|
||||
flex-direction: column;
|
||||
padding: 10px;
|
||||
max-width: 500px;
|
||||
}
|
||||
|
||||
.cloud {
|
||||
width: '100%';
|
||||
max-width: 500px;
|
||||
height: 73vh;
|
||||
width: 400px;
|
||||
height: calc(100vh - 260px);
|
||||
border-radius: 0.5rem;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
|
|
|
|||
|
|
@ -336,13 +336,13 @@ export const ChatMessage = ({ chatflowid }) => {
|
|||
</div>
|
||||
<Divider />
|
||||
<div className='center'>
|
||||
<div className='cloudform'>
|
||||
<form onSubmit={handleSubmit}>
|
||||
<div style={{ width: '100%' }}>
|
||||
<form style={{ width: '100%' }} onSubmit={handleSubmit}>
|
||||
<OutlinedInput
|
||||
inputRef={inputRef}
|
||||
// eslint-disable-next-line
|
||||
autoFocus
|
||||
sx={{ width: '50vh' }}
|
||||
sx={{ width: '100%' }}
|
||||
disabled={loading || !chatflowid}
|
||||
onKeyDown={handleEnter}
|
||||
id='userInput'
|
||||
|
|
|
|||
Loading…
Reference in New Issue