dtamind-components
Version:
DTAmindai Components
68 lines (65 loc) • 2.65 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const prompts_1 = require("@langchain/core/prompts");
const multi_query_1 = require("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:
<questions>
Question 1
Question 2
Question 3
</questions>
Original question: {question}`;
class MultiQueryRetriever_Retrievers {
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, input) {
const model = nodeData.inputs?.model;
const vectorStore = nodeData.inputs?.vectorStore;
let prompt = nodeData.inputs?.modelPrompt || defaultPrompt;
prompt = prompt.replaceAll('{question}', input);
const retriever = multi_query_1.MultiQueryRetriever.fromLLM({
llm: model,
retriever: vectorStore.asRetriever({ filter: vectorStore?.lc_kwargs?.filter ?? vectorStore?.filter }),
verbose: process.env.DEBUG === 'true',
// @ts-ignore
prompt: prompts_1.PromptTemplate.fromTemplate(prompt)
});
return retriever;
}
}
module.exports = { nodeClass: MultiQueryRetriever_Retrievers };
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