Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Building Data-Driven Applications with LlamaIndex

You're reading from   Building Data-Driven Applications with LlamaIndex A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

Arrow left icon
Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835089507
Length 368 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Author (1):
Arrow left icon
Andrei Gheorghiu Andrei Gheorghiu
Author Profile Icon Andrei Gheorghiu
Andrei Gheorghiu
Arrow right icon
View More author details
Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1:Introduction to Generative AI and LlamaIndex
2. Chapter 1: Understanding Large Language Models FREE CHAPTER 3. Chapter 2: LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem 4. Part 2: Starting Your First LlamaIndex Project
5. Chapter 3: Kickstarting Your Journey with LlamaIndex 6. Chapter 4: Ingesting Data into Our RAG Workflow 7. Chapter 5: Indexing with LlamaIndex 8. Part 3: Retrieving and Working with Indexed Data
9. Chapter 6: Querying Our Data, Part 1 – Context Retrieval 10. Chapter 7: Querying Our Data, Part 2 – Postprocessing and Response Synthesis 11. Chapter 8: Building Chatbots and Agents with LlamaIndex 12. Part 4: Customization, Prompt Engineering, and Final Words
13. Chapter 9: Customizing and Deploying Our LlamaIndex Project 14. Chapter 10: Prompt Engineering Guidelines and Best Practices 15. Chapter 11: Conclusion and Additional Resources 16. Index 17. Other Books You May Enjoy

Familiarizing ourselves with the structure of the LlamaIndex code repository

Because you’ll probably spend a lot of time browsing the official code repository of the LlamaIndex framework, it’s good to have an overall image of its general structure. You can always consult the repository here: https://github.com/run-llama/llama_index.

Starting with version 0.10, the code has been thoroughly reorganized into a more modular structure. The purpose of this new structure is to improve efficiency, by avoiding loading any unnecessary dependencies, while also improving readability and overall user experience for developers.

Figure 2.6 describes the main components of the code structure:

Figure 2.6 – The LlamaIndex GitHub repository code structure

Figure 2.6 – The LlamaIndex GitHub repository code structure

The llama-index-core folder serves as the foundational package for LlamaIndex, enabling developers to install the essential framework and then selectively add from over 300 integration packages...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image