Hands-On RAG Bootcamp: Build Apps with LangGraph & LangChain

Published 11/2025
Created by Muhammad Moin
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 10 Lectures ( 4h 50m ) | Size: 2.75 GB

Build powerful Traditional, Multimodal & Agentic RAG with LangChain and LangGraph

What you’ll learn
Getting Started with Retrieval-Augmented Generation (RAG)
Create a Simple RAG Application using LangChain
Build a RAG System to Chat with Multiple PDF Documents
Build a RAG Application from Scratch — No LangChain, No LlamaIndex
Conversational RAG with LangChain: Memory and Multi-Turn Logic
Build a Conversational RAG Streamlit App with Chat History using LangChain
Multimodal RAG: Chat with Complex PDFs (Text, Tables & Images)
Conversational Multimodal RAG: Chat with Complex PDFs (Text, Images & Tables)
Build a Hybrid CSV Intelligence Agent Using RAG, Pandas, and LLM Judge
Getting Started with Agentic RAG: Step-by-Step Implementation Using LangGraph

Requirements
Basic understanding of Python programming
No prior knowledge of RAG is required —we’ll start from scratch
Basic knowledge of LangChain is a plus

Description
Unlock the Power of RAG – From Basic to Advanced AI SystemsRAG (Retrieval-Augmented Generation) is a powerful AI technique that helps systems understand, retrieve, and generate information intelligently. It is used in chatbots, virtual assistants, research tools, and enterprise AI solutions. This course will guide you step by step, from building simple RAG pipelines to creating advanced Agentic AI systems, giving you practical skills to apply RAG in real-world projects.The Hands-On RAG Bootcamp is your step-by-step guide to learning RAG with LangChain and LangGraph. Whether you’re new to AI or an experienced developer, this course will take you from the basics to advanced Agentic RAG systems.What You Will Learn:Getting Started with Retrieval-Augmented Generation (RAG)Create a Simple RAG Application using LangChainBuild a RAG System to Chat with Multiple PDF DocumentsBuild a RAG Application from Scratch — No LangChain, No LlamaIndexConversational RAG with LangChain: Memory and Multi-Turn LogicBuild a Conversational RAG Streamlit App with Chat History using LangChainMultimodal RAG: Chat with Complex PDFs (Text, Tables & Images)Conversational Multimodal RAG: Chat with Complex PDFs (Text, Images & Tables)Build a Hybrid CSV Intelligence Agent Using RAG, Pandas, and LLM JudgeGetting Started with Agentic RAG: Step-by-Step Implementation Using LangGraph