import { GoogleAuthOptions } from 'google-auth-library' import { BaseCache } from '@langchain/core/caches' import { GoogleVertexAI, GoogleVertexAITextInput } from '@langchain/community/llms/googlevertexai' import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface' import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils' import { getModels, MODEL_TYPE } from '../../../src/modelLoader' class GoogleVertexAI_LLMs implements INode { label: string name: string version: number type: string icon: string category: string description: string baseClasses: string[] credential: INodeParams inputs: INodeParams[] constructor() { this.label = 'GoogleVertexAI' this.name = 'googlevertexai' this.version = 3.0 this.type = 'GoogleVertexAI' this.icon = 'GoogleVertex.svg' this.category = 'LLMs' this.description = 'Wrapper around GoogleVertexAI large language models' this.baseClasses = [this.type, ...getBaseClasses(GoogleVertexAI)] this.credential = { label: 'Connect Credential', name: 'credential', type: 'credential', credentialNames: ['googleVertexAuth'], optional: true, description: 'Google Vertex AI credential. If you are using a GCP service like Cloud Run, or if you have installed default credentials on your local machine, you do not need to set this credential.' } this.inputs = [ { label: 'Cache', name: 'cache', type: 'BaseCache', optional: true }, { label: 'Model Name', name: 'modelName', type: 'asyncOptions', loadMethod: 'listModels', default: 'text-bison' }, { label: 'Temperature', name: 'temperature', type: 'number', step: 0.1, default: 0.7, optional: true }, { label: 'max Output Tokens', name: 'maxOutputTokens', type: 'number', step: 1, optional: true, additionalParams: true }, { label: 'Top Probability', name: 'topP', type: 'number', step: 0.1, optional: true, additionalParams: true } ] } //@ts-ignore loadMethods = { async listModels(): Promise { return await getModels(MODEL_TYPE.LLM, 'googlevertexai') } } async init(nodeData: INodeData, _: string, options: ICommonObject): Promise { const credentialData = await getCredentialData(nodeData.credential ?? '', options) const googleApplicationCredentialFilePath = getCredentialParam('googleApplicationCredentialFilePath', credentialData, nodeData) const googleApplicationCredential = getCredentialParam('googleApplicationCredential', credentialData, nodeData) const projectID = getCredentialParam('projectID', credentialData, nodeData) const authOptions: GoogleAuthOptions = {} if (Object.keys(credentialData).length !== 0) { if (!googleApplicationCredentialFilePath && !googleApplicationCredential) throw new Error('Please specify your Google Application Credential') if (!googleApplicationCredentialFilePath && !googleApplicationCredential) throw new Error( 'Error: More than one component has been inputted. Please use only one of the following: Google Application Credential File Path or Google Credential JSON Object' ) if (googleApplicationCredentialFilePath && !googleApplicationCredential) authOptions.keyFile = googleApplicationCredentialFilePath else if (!googleApplicationCredentialFilePath && googleApplicationCredential) authOptions.credentials = JSON.parse(googleApplicationCredential) if (projectID) authOptions.projectId = projectID } const temperature = nodeData.inputs?.temperature as string const modelName = nodeData.inputs?.modelName as string const maxOutputTokens = nodeData.inputs?.maxOutputTokens as string const topP = nodeData.inputs?.topP as string const cache = nodeData.inputs?.cache as BaseCache const obj: Partial = { temperature: parseFloat(temperature), model: modelName } if (Object.keys(authOptions).length !== 0) obj.authOptions = authOptions if (maxOutputTokens) obj.maxOutputTokens = parseInt(maxOutputTokens, 10) if (topP) obj.topP = parseFloat(topP) if (cache) obj.cache = cache const model = new GoogleVertexAI(obj) return model } } module.exports = { nodeClass: GoogleVertexAI_LLMs }