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

https://rapidgator.net/file/74452e83523544f2c154085b1f9bca92/Building_Smarter_RealWorld_Generative_AI_Systems.rar.html