LangChain Data Handling: RAG & Document Processing

Overview

Build Retrieval-Augmented Generation (RAG) pipelines: load documents, split into chunks, embed with OpenAI/Cohere, store in vector databases (FAISS, Chroma, Pinecone), and query with retrieval chains.

Prerequisites

• @langchain/core, @langchain/openai installed
• For vector stores: npm install @langchain/community (FAISS) or npm install @langchain/pinecone @pinecone-database/pinecone

Step 1: Document Loaders

import { TextLoader } from "langchain/document_loaders/fs/text";
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
import { DirectoryLoader } from "langchain/document_loaders/fs/directory";
import { CSVLoader } from "@langchain/community/document_loaders/fs/csv";

// Load a single file