LlamaIndex- Develop LLM powered applications with LlamaIndex

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Eden Marco

2:43:07

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1. Introduction

1. Introduction.mp4

02:34

2.1 LlamaIndex Docs.html

2.2 LlamaIndex Github Repo.html

2. What is Llamaindex.mp4

08:57

3.1 Course Discord Server.html

3. Courses discord server.mp4

01:51

2. THE GIST of LlamaIndex Get started with your hello world LlamaIndex Script

1.1 Pipenv crash course.html

1. Project Setup (Pycharm).mp4

07:03

2.1 LlamaIndex Docs.html

2.2 LlamaIndex Github Repo.html

2.3 LlamaIndex Hello-world Official Docs.html

2.4 python-dotenv.html

2.5 python-dotenv crash course.html

2. Your First LlamaIndex Index.mp4

06:42

3. Quick Check In.mp4

00:38

3. Documentation helper

1. Retrieval Augmentation Generation (RAG) Theory Vectorstores, Embeddings.mp4

13:51

2.1 download docs.zip

2. Downloading the LlamaIndex Documentation.mp4

04:48

3.1 documentation-helper-llamaindex.zip

3.2 LlamaIndex Documentation Helper Github Repository.html

3. Setting up our development environment.mp4

10:13

4. LlamaIndex core abstractions Pipeline imported classes.mp4

07:32

5.1 documentation-helper-llamaindex.zip

5.2 LlamaHub.html

5. Loading and Chunkifying the LlamaIndex docs into LlamaIndex nodes.mp4

13:10

6.1 documentation-helper-llamaindex.zip

6.2 Pinecone Website.html

6. Ingestion the LlamaIndex nodes in Pinecone vectorstore.mp4

11:11

7.1 documentation-helper-llamaindex.zip

7.2 LlamaIndex Query Engine.html

7. QueryEngine & Retrieval Augmentation with LlamaIndex.mp4

14:22

8.1 documentation-helper-llamaindex.zip

8.2 LamaIndex Chat Engine docs.html

8.3 Streamlit Docs.html

8.4 Streamlit Repo.html

8. streamlit frontend.mp4

17:30

4. Prompt Engineering Thoery

1. intro to llms.mp4

03:52

2. What is a Prompt Composition of a formal prompt.mp4

02:55

3. Zero Shot Prompting.mp4

02:42

4. Few Shot Prompting.mp4

08:26

5. Chain of Thought Prompting.mp4

08:33

6. ReAct.mp4

07:18

7. Prompt Engineering Quick Tips.mp4

08:59

Description

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)

Who is this for?

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

What You Need to Know?

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.

More details

Description
Welcome to first LlamaIndex Udemy course - Unleashing the Power of LLM!
This comprehensive course is designed to teach you how to QUICKLYÂ 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:
LlamaIndex
Retrieval Augmentation Generation
Vectorstores (Pinecone)
Node Parers-Â TextSplitters
QueryEngines, ChatEngines
Streamlit (for UI)
Agents, LLM Reasoning
ReAct
Output Parsers
LLMs: Few shots prompting, Chain of Thought, ReAct prompting

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'sÂ alsoÂ a community. Along with lifetime access to the course, you'll get:
Dedicated 1 on 1 troubleshooting support with me
Github links with additional AI resources, FAQ, troubleshooting guides
Access to an exclusive Discord community to connect with other learners
No extra cost for continuous updates and improvements to the course

DISCLAIMERS
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/ VSCode IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts.
Who this course is for:
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

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I am a passionate Software Engineer with years of experience in back-end development, one of the first engineers at Software Engineer at Orca Security, and now I am working as a customer engineer at Google Cloud.BSC in Computer Science from the Technion - Israel Institute of TechnologyI have always been a fan of teaching and mentorship and I am teaching CS courses(Functional Programming and Introduction to CS) at Reichman University, Israel.

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