Building Smarter Real-World Generative AI Systems

سرفصل های دوره

Building Smarter Real-World Generative AI Systems with LangGraph and LangChain

1 - Introduction

1 - Introduction

2 - Generative AI LLMs A Comprehensive Guide

2 - Generative AI

3 - LLM

3 - Introduction to LangChain

4 - LangChain FrameWork

5 - LangChain Dataflow

4 - Setting Up Environment

6 - Python Installation

7 - LangChain and LangGraph Installation

8 - Setting up OpenAI Account

9 - Setting up Jupyter notebook

10 - Setting API Key in Environment File

5 - Tools and Agents

11 - What is Tools

12 - Types of Tools

13 - Builtin Tools

14 - Components of a Tool

15 - Custom Tools

16 - Agents

17 - ReAct Agent

18 - ReAct Agent in detail

19 - Building ReAct Agent

20 - Disadvantages of Agent

Files.zip

6 - Introduction to LangGraph

21 - What is LangGraph

22 - Key Features of LangGraph

23 - LangGraph in Action A RealWorld Example

24 - Salvation by MultiAgent Approach

25 - Smarter Planning with Stateful Systems

26 - Optimizing Decisions with Cycles and Persistence

27 - Interactive and RealTime Systems

28 - LangGraphs Inspiration

7 - LangGraph Key Terminologies

1 - Who am I.html

29 - Introduction to LangGraph World

30 - Understanding State in LangGraph

31 - State Definition in Python

32 - Node Activation and Execution

33 - Understanding Edges Connecting the Dots

34 - Exploring State Graph Managing Communication and Workflow

35 - Enhancing AI Interactions with Message Graph

8 - Creating Your First Graph

36 - First Graph Preview

37 - Building a basic Chatbot

38 - Enhancing Chatbot with Tools

9 - Introducing RAG World

39 - Introduction to RAG

40 - RAG Pipeline

41 - Components of RAG

10 - Realworld applications RAG

42 - Simple RAG App Overview

43 - Building a Retriever

44 - Agentic RAG App Overview

45 - Building Agent Node

46 - Buildng Generate Node

47 - Constructing Graph

48 - Visualize the RAG Graph

49 - Experiment the RAG

11 - Conclusion

50 - Conclusion

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تولید کننده: Udemy