Semantic Text Search Using LangChain (OpenAI) and Redis

import { Document } from 'langchain/document';
import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
import { RedisVectorStore } from 'langchain/vectorstores/redis';

const convertToVectorDocuments = async (
  _products: Prisma.ProductCreateInput[],
) => {
  const vectorDocs: Document[] = [];

  if (_products?.length > 0) {
    for (let product of _products) {
      let doc = new Document({
        metadata: {
          productId: product.productId,
        },
        pageContent: ` Product details are as follows:
                productId: ${product.productId}.
    
                productDisplayName: ${product.productDisplayName}.
                
                price: ${product.price}.
    
                variantName: ${product.variantName}.
    
                brandName: ${product.brandName}.
    
                ageGroup: ${product.ageGroup}.
    
                gender: ${product.gender}.
    
                productColors: ${product.productColors}
    
                Category:  ${product.displayCategories}, ${product.masterCategory_typeName} - ${product.subCategory_typeName}
    
                productDescription:  ${product.productDescriptors_description_value}`,
      });

      vectorDocs.push(doc);
    }
  }
  return vectorDocs;
};

const seedOpenAIEmbeddings = async (
  vectorDocs: Document[],
  _redisClient: NodeRedisClientType,
  _openAIApiKey: string,
) => {
  if (vectorDocs?.length && _redisClient && _openAIApiKey) {
    console.log('openAIEmbeddings started !');

    const embeddings = new OpenAIEmbeddings({
      openAIApiKey: _openAIApiKey,
    });
    const vectorStore = await RedisVectorStore.fromDocuments(
      vectorDocs,
      embeddings,
      {
        redisClient: _redisClient,
        indexName: 'openAIProductsIdx',
        keyPrefix: 'openAIProducts:',
      },
    );
    console.log('OpenAIEmbeddings completed');
  }
};

const addEmbeddingsToRedis = async (
  _products: Prisma.ProductCreateInput[],
  _redisClient: NodeRedisClientType,
  _openAIApiKey: string,
  _huggingFaceApiKey?: string,
) => {
  if (_products?.length > 0 && _redisClient && _openAIApiKey) {
    const vectorDocs = await convertToVectorDocuments(_products);

    await seedOpenAIEmbeddings(vectorDocs, _redisClient, _openAIApiKey);
  }
};