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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

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781835089507

Length 368 pages

Edition 1st Edition

Languages

Python

Tools

LlamaIndex

Concepts

GPT/LLMs

Author (1):

Andrei Gheorghiu

View More author details

Table of Contents (18) Chapters

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

Why subscribe?

17. Other Books You May Enjoy

Querying Our Data, Part 1 – Context Retrieval

The focus of this chapter will be on understanding the querying capabilities of LlamaIndex in an RAG workflow. We’ll be covering the overall working of the querying system, mostly focusing on the retrieval capabilities of the framework.

Here are the main sections that will be covered in this chapter:

Learning about query mechanics – an overview
Understanding the basic retrievers
Building more advanced retrieval mechanisms
Increasing efficiency with asynchronous retrieval
Working with metadata filters, tools, and selectors
Transforming queries and generating sub-queries
Understanding the concepts of dense and sparse retrieval

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Authors (1)

Andrei Gheorghiu

Andrei Gheorghiu

Andrei Gheorghiu is an IT consultant and trainer with experience in generative AI. He developed the "Prompt Engineering for Everyone" course and has researched the LlamaIndex ecosystem, experimenting with its features and applications. His recent work includes using Generative AI to translate educational materials and testing virtual assistants for classroom use. With over 20 years in the IT industry, Andrei also holds certifications like ITIL Master and has trained more than 15,000 students.

See other products by Andrei Gheorghiu

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