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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

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835089507
Length 368 pages
Edition 1st Edition
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Author (1):
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Andrei Gheorghiu Andrei Gheorghiu
Author Profile Icon Andrei Gheorghiu
Andrei Gheorghiu
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Introduction to Generative AI and LlamaIndex FREE CHAPTER
2. Chapter 1: Understanding Large Language Models 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

To get the most out of this book

You will need to have a basic understanding of Python development. General experience in using Generative AI models is also recommended. All the examples provided in the book have been specifically designed to run in a local Python environment, and because several libraries will be required along the way, it is recommended that you have a minimum of 20 GB of storage space available on your computer.

Software/hardware covered in the book

Operating system requirements

Python >= 3.11

Windows or Linux

LlamaIndex >= 0.10

Because most of the examples presented in the book rely on the OpenAI API, you’ll also need to obtain an OpenAI API key.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

As many of the code examples rely on the OpenAI API, keep in mind that running them will incur costs. Everything has been optimized for minimum cost but neither the author nor the publisher are responsible for these costs. You should also be advised of the security implications when using a public API such as the one provided by OpenAI. If you choose to use your own proprietary data to experiment with different examples, make sure you consult OpenAI’s privacy policy in advance.

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