/* eslint-disable n8n-nodes-base/node-dirname-against-convention */ import { NodeConnectionType, type IExecuteFunctions, type INodeType, type INodeTypeDescription, type SupplyData, } from 'n8n-workflow'; import { HuggingFaceInference } from 'langchain/llms/hf'; import { logWrapper } from '../../../utils/logWrapper'; import { getConnectionHintNoticeField } from '../../../utils/sharedFields'; export class LmOpenHuggingFaceInference implements INodeType { description: INodeTypeDescription = { displayName: 'Hugging Face Inference Model', // eslint-disable-next-line n8n-nodes-base/node-class-description-name-miscased name: 'lmOpenHuggingFaceInference', icon: 'file:huggingface.svg', group: ['transform'], version: 1, description: 'Language Model HuggingFaceInference', defaults: { name: 'Hugging Face Inference Model', }, codex: { categories: ['AI'], subcategories: { AI: ['Language Models'], }, resources: { primaryDocumentation: [ { url: 'https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmopenhuggingfaceinference/', }, ], }, }, // eslint-disable-next-line n8n-nodes-base/node-class-description-inputs-wrong-regular-node inputs: [], // eslint-disable-next-line n8n-nodes-base/node-class-description-outputs-wrong outputs: [NodeConnectionType.AiLanguageModel], outputNames: ['Model'], credentials: [ { name: 'huggingFaceApi', required: true, }, ], properties: [ getConnectionHintNoticeField([NodeConnectionType.AiChain, NodeConnectionType.AiAgent]), { displayName: 'Model', name: 'model', type: 'string', default: 'gpt2', }, { displayName: 'Options', name: 'options', placeholder: 'Add Option', description: 'Additional options to add', type: 'collection', default: {}, options: [ { displayName: 'Custom Inference Endpoint', name: 'endpointUrl', default: '', description: 'Custom endpoint URL', type: 'string', }, { displayName: 'Frequency Penalty', name: 'frequencyPenalty', default: 0, typeOptions: { maxValue: 2, minValue: -2, numberPrecision: 1 }, description: "Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim", type: 'number', }, { displayName: 'Maximum Number of Tokens', name: 'maxTokens', default: 128, description: 'The maximum number of tokens to generate in the completion. Most models have a context length of 2048 tokens (except for the newest models, which support 32,768).', type: 'number', typeOptions: { maxValue: 32768, }, }, { displayName: 'Presence Penalty', name: 'presencePenalty', default: 0, typeOptions: { maxValue: 2, minValue: -2, numberPrecision: 1 }, description: "Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics", type: 'number', }, { displayName: 'Sampling Temperature', name: 'temperature', default: 1, typeOptions: { maxValue: 1, minValue: 0, numberPrecision: 1 }, description: 'Controls randomness: Lowering results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive.', type: 'number', }, { displayName: 'Top K', name: 'topK', default: 1, typeOptions: { maxValue: 1, minValue: 0, numberPrecision: 1 }, description: 'Controls the top tokens to consider within the sample operation to create new text', type: 'number', }, { displayName: 'Top P', name: 'topP', default: 1, typeOptions: { maxValue: 1, minValue: 0, numberPrecision: 1 }, description: 'Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options are considered. We generally recommend altering this or temperature but not both.', type: 'number', }, ], }, ], }; async supplyData(this: IExecuteFunctions, itemIndex: number): Promise { const credentials = await this.getCredentials('huggingFaceApi'); const modelName = this.getNodeParameter('model', itemIndex) as string; const options = this.getNodeParameter('options', itemIndex, {}) as object; const model = new HuggingFaceInference({ model: modelName, apiKey: credentials.apiKey as string, ...options, }); return { response: logWrapper(model, this), }; } }