import { PromptTemplate } from '@langchain/core/prompts' import { INode, INodeData, INodeParams } from '../../../src/Interface' import { MultiQueryRetriever } from 'langchain/retrievers/multi_query' const defaultPrompt = `You are an AI language model assistant. Your task is to generate 3 different versions of the given user question to retrieve relevant documents from a vector database. By generating multiple perspectives on the user question, your goal is to help the user overcome some of the limitations of distance-based similarity search. Provide these alternative questions separated by newlines between XML tags. For example: Question 1 Question 2 Question 3 Original question: {question}` class MultiQueryRetriever_Retrievers implements INode { label: string name: string version: number description: string type: string icon: string category: string baseClasses: string[] inputs: INodeParams[] constructor() { this.label = 'Multi Query Retriever' this.name = 'multiQueryRetriever' this.version = 1.0 this.type = 'MultiQueryRetriever' this.icon = 'multiQueryRetriever.svg' this.category = 'Retrievers' this.description = 'Generate multiple queries from different perspectives for a given user input query' this.baseClasses = [this.type, 'BaseRetriever'] this.inputs = [ { label: 'Vector Store', name: 'vectorStore', type: 'VectorStore' }, { label: 'Language Model', name: 'model', type: 'BaseLanguageModel' }, { label: 'Prompt', name: 'modelPrompt', description: 'Prompt for the language model to generate alternative questions. Use {question} to refer to the original question', type: 'string', rows: 4, default: defaultPrompt } ] } async init(nodeData: INodeData, input: string): Promise { const model = nodeData.inputs?.model const vectorStore = nodeData.inputs?.vectorStore let prompt = nodeData.inputs?.modelPrompt || (defaultPrompt as string) prompt = prompt.replaceAll('{question}', input) const retriever = MultiQueryRetriever.fromLLM({ llm: model, retriever: vectorStore.asRetriever({ filter: vectorStore?.lc_kwargs?.filter ?? vectorStore?.filter }), verbose: process.env.DEBUG === 'true', // @ts-ignore prompt: PromptTemplate.fromTemplate(prompt) }) return retriever } } module.exports = { nodeClass: MultiQueryRetriever_Retrievers }