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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

LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem

Now that you’ve got a solid understanding of what large language models (LLMs) are and what they can (and cannot) do. It’s time to discover how LlamaIndex can take your interactive AI applications to the next level. We’ll explore how retrieval-augmented generation (RAG) using LlamaIndex can provide the missing link between the vast knowledge of LLMs and your proprietary data.

In this chapter, we will cover the following main topics:

  • Optimizing language models – The symbiosis of fine-tuning, RAG, and LlamaIndex
  • Discovering the advantages of progressively disclosing complexity
  • Introducing personalized intelligent tutoring system (PITS) – our hands-on LlamaIndex project
  • Preparing our coding environment
  • Familiarizing ourselves with the structure of the LlamaIndex code repository
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