Llamaindex- Develop Llm Powered Applications With Llamaindex

Learn LlamaIndex by building FAST a real world generative ai LLM powered application LLM (Python)

What you'll learn

Become proficient in LlamaIndex

Have an end to end working LlamaIndex based generative AI application

Prompt Engineering Theory: Chain of Thought, ReAct, Few Shot prompting and understand how LlamaIndex is built under the hood

Understand how to navigate inside the LlamaIndex opensource codebase

Large Language Models theory for software engineers

Vectorestores/ Vector Databases (Pinecone, FAISS,)

Retrieval Augmentation Generation (RAG)

Requirements

This is not a beginner course. Basic software engineering concepts are needed

I assume students will be familiar software engineering subjects such as: git, python, pipenv, environment variables, classes, testing and debugging

No Machine Learning experience is needed.

Description

Welcome to first LlamaIndex Udemy course - Unleashing the Power of LLM!This comprehensive course is designed to teach you how to QUICKLY harness the power the LlamaIndex library for LLM applications. This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts.In this course, you will embark on a journey from scratch to building a real-world LLM powered application using LlamaIndex. We are going to do so by build the main application:Documentation Helper- Create chatbot over a python package documentation. (and over any other data you would like)The topics covered in this course include:LlamaIndexLLMs: Few shots prompting, Chain of Thought, ReAct promptingRetrieval Augmentation GenerationVectorstores (Pinecone)Node Parers-  TextSplittersStreamlit (for UI)Throughout the course, you will work on hands-on exercises and real-world projects to reinforce your understanding of the concepts and techniques covered. By the end of the course, you will be proficient in using LlamaIndex to create powerful, efficient, and versatile LLM applications for a wide array of usages.This is not just a course, it's  also  a community. Along with lifetime access to the course, you'll get:Dedicated 1 on 1 troubleshooting support with meGithub links with additional AI resources, FAQ, troubleshooting guidesAccess to an exclusive Discord community to connect with other learners No extra cost for continuous updates and improvements to the courseDISCLAIMERSPlease note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts.The first project of the course (Ice-Breaker) requires usage of 3rd party APIs-ProxyURL, SerpAPI, Twitter API  which are generally paid services.All of those 3rd parties have a free tier we will use to create stub responses development and testing.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 3 What is Llamaindex

Lecture 4 Course's discord server

Section 2: THE GIST of LlamaIndex Get started with your "hello world" LlamaIndex Script

Lecture 5 Project Setup (Pycharm)

Lecture 6 Your First LlamaIndex Index

Lecture 7 Quick Check In

Section 3: Documentation helper

Lecture 8 Retrieval Augmentation Generation (RAG) Theory: Vectorstores, Embeddings

Lecture 9 Downloading the LlamaIndex Documentation

Lecture 10 Setting up our development environment

Lecture 11 LlamaIndex core abstractions: Pipeline imported classes

Lecture 12 Loading and Chunkifying the LlamaIndex docs into LlamaIndex nodes

Lecture 13 Ingestion the LlamaIndex nodes in Pinecone vectorstore

Lecture 14 QueryEngine & Retrieval Augmentation with LlamaIndex

Lecture 15 streamlit frontend

Section 4: Prompt Engineering Thoery

Lecture 16 intro to llms

Lecture 17 What is a Prompt? Composition of a formal prompt

Lecture 18 Zero Shot Prompting

Lecture 19 Few Shot Prompting

Lecture 20 Chain of Thought Prompting

Lecture 21 ReAct

Lecture 22 Prompt Engineering Quick Tips

Section 5: Learn More

Software Engineers that want to learn how to build Generative AI based applications with LlamaIndex,Backend Developers that want to learn how to build Generative AI based applications with LlamaIndex,Fullstack engineers that want to learn how to build Generative AI based applications with LlamaIndex