dtamind-components
Version:
DTAmindai Components
126 lines • 4.94 kB
JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const lodash_1 = require("lodash");
const documents_1 = require("@langchain/core/documents");
const vectorstores_1 = require("@langchain/core/vectorstores");
const src_1 = require("../../../src");
const defaultReturnFormat = '{{context}}\nSource: {{metadata.source}}';
class CustomRetriever_Retrievers {
constructor() {
this.label = 'Custom Retriever';
this.name = 'customRetriever';
this.version = 1.0;
this.type = 'CustomRetriever';
this.icon = 'customRetriever.svg';
this.category = 'Retrievers';
this.description = 'Return results based on predefined format';
this.baseClasses = [this.type, 'BaseRetriever'];
this.inputs = [
{
label: 'Vector Store',
name: 'vectorStore',
type: 'VectorStore'
},
{
label: 'Query',
name: 'query',
type: 'string',
description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
optional: true,
acceptVariable: true
},
{
label: 'Result Format',
name: 'resultFormat',
type: 'string',
rows: 4,
description: 'Format to return the results in. Use {{context}} to insert the pageContent of the document and {{metadata.key}} to insert metadata values.',
default: defaultReturnFormat
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Default to vector store topK',
placeholder: '4',
type: 'number',
additionalParams: true,
optional: true
}
];
this.outputs = [
{
label: 'Custom Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Document',
name: 'document',
description: 'Array of document objects containing metadata and pageContent',
baseClasses: ['Document', 'json']
},
{
label: 'Text',
name: 'text',
description: 'Concatenated string from pageContent of documents',
baseClasses: ['string', 'json']
}
];
}
async init(nodeData, input) {
const vectorStore = nodeData.inputs?.vectorStore;
const query = nodeData.inputs?.query;
const topK = nodeData.inputs?.topK;
const resultFormat = nodeData.inputs?.resultFormat;
const output = nodeData.outputs?.output;
const retriever = CustomRetriever.fromVectorStore(vectorStore, {
resultFormat,
topK: topK ? parseInt(topK, 10) : vectorStore?.k ?? 4
});
if (output === 'retriever')
return retriever;
else if (output === 'document')
return await retriever.getRelevantDocuments(query ? query : input);
else if (output === 'text') {
let finaltext = '';
const docs = await retriever.getRelevantDocuments(query ? query : input);
for (const doc of docs)
finaltext += `${doc.pageContent}\n`;
return (0, src_1.handleEscapeCharacters)(finaltext, false);
}
return retriever;
}
}
class CustomRetriever extends vectorstores_1.VectorStoreRetriever {
constructor(input) {
super(input);
this.topK = 4;
this.topK = input.topK ?? this.topK;
this.resultFormat = input.resultFormat ?? this.resultFormat;
}
async getRelevantDocuments(query) {
const results = await this.vectorStore.similaritySearchWithScore(query, this.topK, this.filter);
const finalDocs = [];
for (const result of results) {
let res = this.resultFormat.replace(/{{context}}/g, result[0].pageContent);
res = replaceMetadata(res, result[0].metadata);
finalDocs.push(new documents_1.Document({
pageContent: res,
metadata: result[0].metadata
}));
}
return finalDocs;
}
static fromVectorStore(vectorStore, options) {
return new this({ ...options, vectorStore });
}
}
function replaceMetadata(template, metadata) {
const metadataRegex = /{{metadata\.([\w.]+)}}/g;
return template.replace(metadataRegex, (match, path) => {
const value = (0, lodash_1.get)(metadata, path);
return value !== undefined ? String(value) : match;
});
}
module.exports = { nodeClass: CustomRetriever_Retrievers };
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