import { AzureOpenAIInput, AzureOpenAI, OpenAIInput } from '@langchain/openai' import { BaseCache } from '@langchain/core/caches' import { BaseLLMParams } from '@langchain/core/language_models/llms' import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface' import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils' import { getModels, MODEL_TYPE } from '../../../src/modelLoader' const serverCredentialsExists = !!process.env.AZURE_OPENAI_API_KEY && !!process.env.AZURE_OPENAI_API_INSTANCE_NAME && !!process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME && !!process.env.AZURE_OPENAI_API_VERSION class AzureOpenAI_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 = 'Azure OpenAI' this.name = 'azureOpenAI' this.version = 4.0 this.type = 'AzureOpenAI' this.icon = 'Azure.svg' this.category = 'LLMs' this.description = 'Wrapper around Azure OpenAI large language models' this.baseClasses = [this.type, ...getBaseClasses(AzureOpenAI)] this.credential = { label: 'Connect Credential', name: 'credential', type: 'credential', credentialNames: ['azureOpenAIApi'], optional: serverCredentialsExists } this.inputs = [ { label: 'Cache', name: 'cache', type: 'BaseCache', optional: true }, { label: 'Model Name', name: 'modelName', type: 'asyncOptions', loadMethod: 'listModels', default: 'text-davinci-003' }, { label: 'Temperature', name: 'temperature', type: 'number', step: 0.1, default: 0.9, optional: true }, { label: 'Max Tokens', name: 'maxTokens', type: 'number', step: 1, optional: true, additionalParams: true }, { label: 'Top Probability', name: 'topP', type: 'number', step: 0.1, optional: true, additionalParams: true }, { label: 'Best Of', name: 'bestOf', type: 'number', step: 1, optional: true, additionalParams: true }, { label: 'Frequency Penalty', name: 'frequencyPenalty', type: 'number', step: 0.1, optional: true, additionalParams: true }, { label: 'Presence Penalty', name: 'presencePenalty', type: 'number', step: 0.1, optional: true, additionalParams: true }, { label: 'Timeout', name: 'timeout', type: 'number', step: 1, optional: true, additionalParams: true }, { label: 'BasePath', name: 'basepath', type: 'string', optional: true, additionalParams: true } ] } //@ts-ignore loadMethods = { async listModels(): Promise { return await getModels(MODEL_TYPE.LLM, 'azureOpenAI') } } async init(nodeData: INodeData, _: string, options: ICommonObject): Promise { const temperature = nodeData.inputs?.temperature as string const modelName = nodeData.inputs?.modelName as string const maxTokens = nodeData.inputs?.maxTokens as string const topP = nodeData.inputs?.topP as string const frequencyPenalty = nodeData.inputs?.frequencyPenalty as string const presencePenalty = nodeData.inputs?.presencePenalty as string const timeout = nodeData.inputs?.timeout as string const bestOf = nodeData.inputs?.bestOf as string const streaming = nodeData.inputs?.streaming as boolean const basePath = nodeData.inputs?.basepath as string const credentialData = await getCredentialData(nodeData.credential ?? '', options) const azureOpenAIApiKey = getCredentialParam('azureOpenAIApiKey', credentialData, nodeData) const azureOpenAIApiInstanceName = getCredentialParam('azureOpenAIApiInstanceName', credentialData, nodeData) const azureOpenAIApiDeploymentName = getCredentialParam('azureOpenAIApiDeploymentName', credentialData, nodeData) const azureOpenAIApiVersion = getCredentialParam('azureOpenAIApiVersion', credentialData, nodeData) const cache = nodeData.inputs?.cache as BaseCache const obj: Partial & BaseLLMParams & Partial = { temperature: parseFloat(temperature), modelName, azureOpenAIApiKey, azureOpenAIApiInstanceName, azureOpenAIApiDeploymentName, azureOpenAIApiVersion, streaming: streaming ?? true } if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10) if (topP) obj.topP = parseFloat(topP) if (frequencyPenalty) obj.frequencyPenalty = parseFloat(frequencyPenalty) if (presencePenalty) obj.presencePenalty = parseFloat(presencePenalty) if (timeout) obj.timeout = parseInt(timeout, 10) if (bestOf) obj.bestOf = parseInt(bestOf, 10) if (cache) obj.cache = cache if (basePath) obj.azureOpenAIBasePath = basePath const model = new AzureOpenAI(obj) return model } } module.exports = { nodeClass: AzureOpenAI_LLMs }