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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

Introducing GenAI and LLMs

Introductions are sometimes boring, but here, it is important for us to set the context and help you familiarize yourself with GenAI and LLMs before we dive deep into LlamaIndex. I will try to be as concise as possible and, if the reader is already familiar with this information, I apologize for the brief digression.

What is GenAI?

GenAI refers to systems that are capable of generating new content such as text, images, audio, or video. Unlike more specialized AI systems that are designed for specific tasks such as image classification or speech recognition, GenAI models can create completely new assets that are often very difficult – if not impossible – to distinguish from human-created content.

These systems use machine learning (ML) techniques such as neural networks (NNs) that are trained on vast amounts of data. By learning patterns and structures within the training data, generative models can model the underlying probability distribution...

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