Building Smarter Real-World Generative Ai Systems

What you’ll learn
Explore Lang graph and Build Agentic Applications
Learn Generative AI using Langchain and Lang Graph
Explore Lang graph and Build Agentic Applications
Provides End to end tutorial for LangChain

Requirements
Basic python programming and NLP

Description
Welcome to Building a Generative AI Application with LangGraph by Learner’s Spot! This course is designed to equip you with the knowledge and skills needed to create your very own Generative AI application. Whether you’re a beginner or looking to deepen your understanding, we’ve structured this course to guide you step-by-step through essential concepts and practical applications.What You’ll Learn:Introduction to Generative AI & LLMs: Kick off your journey with a comprehensive overview of Generative AI and Large Language Models. Understand the fundamental principles behind these technologies and how they empower intelligent applications.Exploring the Langchain Framework: Dive into the components of the Langchain Framework and discover how data flows within it. We’ll prepare you for hands-on work by setting up your development environment with Python and Langchain.Utilizing Langchain’s Tools: Learn how to leverage Langchain’s built-in tools and how to create custom ones tailored to your unique needs.Understanding Agents: We’ll introduce you to the concept of Agents, with a special focus on the REACT agent, discussing its advantages and limitations.Deep Dive into LangGraph: The heart of this course is LangGraph. Explore its key features, advanced functionalities like the multi-agent approach, and smart planning through real-world examples.Mastering Key Terminologies: Get familiar with essential LangGraph terminologies, such as states, nodes, and edges, and understand their significance in building structured AI systems.Building Your First AI-Driven Chatbot: Apply what you’ve learned by constructing your first chatbot using LangGraph. This hands-on project will provide practical experience with the framework.Exploring Retrieval-Augmented Generation Applications: Discover how Retrieval-Augmented Generation (RAG) applications enhance language models by integrating external information retrieval before response generation.Hands-On RAG Application Session: Participate in a guided session to create a RAG application, solidifying your understanding of this powerful approach.

Overview
Section 1: Introduction

Lecture 1 Introduction

Section 2: Generative AI & LLMs: A Comprehensive Guide

Lecture 2 Generative AI

Lecture 3 LLM

Section 3: Introduction to LangChain

Lecture 4 LangChain FrameWork

Lecture 5 LangChain Dataflow

Section 4: Setting Up Environment

Lecture 6 Python Installation

Lecture 7 LangChain and LangGraph Installation

Lecture 8 Setting up OpenAI Account

Lecture 9 Setting up Jupyter notebook

Lecture 10 Setting API Key in Environment File

Section 5: Tools and Agents

Lecture 11 What is Tools

Lecture 12 Types of Tools

Lecture 13 Built-in Tools

Lecture 14 Components of a Tool

Lecture 15 Custom Tools

Lecture 16 Agents

Lecture 17 ReAct Agent

Lecture 18 ReAct Agent in detail

Lecture 19 Building ReAct Agent

Lecture 20 Disadvantages of Agent

Section 6: Introduction to LangGraph

Lecture 21 What is LangGraph

Lecture 22 Key Features of LangGraph

Lecture 23 LangGraph in Action: A Real-World Example

Lecture 24 Salvation by Multi-Agent Approach

Lecture 25 Smarter Planning with Stateful Systems

Lecture 26 Optimizing Decisions with Cycles and Persistence

Lecture 27 Interactive and Real-Time Systems

Lecture 28 LangGraph’s Inspiration

Section 7: LangGraph Key Terminologies

Lecture 29 Introduction to LangGraph World

Lecture 30 Understanding State in LangGraph

Lecture 31 State Definition in Python

Lecture 32 Node Activation and Execution

Lecture 33 Understanding Edges : Connecting the Dots

Lecture 34 Exploring State Graph: Managing Communication and Workflow

Lecture 35 Enhancing AI Interactions with Message Graph

Section 8: Creating Your First Graph

Lecture 36 First Graph Preview

Lecture 37 Building a basic Chatbot

Lecture 38 Enhancing Chatbot with Tools

Section 9: Introducing RAG World

Lecture 39 Introduction to RAG

Lecture 40 RAG Pipeline

Lecture 41 Components of RAG

Section 10: Real-world applications – RAG

Lecture 42 Simple RAG App Overview

Lecture 43 Building a Retriever

Lecture 44 Agentic RAG : App Overview

Lecture 45 Building Agent Node

Lecture 46 Buildng Generate Node

Lecture 47 Constructing Graph

Lecture 48 Visualize the RAG Graph

Lecture 49 Experiment the RAG

Section 11: Conclusion

Lecture 50 Conclusion

Who is interested to develop GenAI Application using LangGraph