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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
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

An overview of LlamaHub

LlamaHub is an extensive library of integrations that augments the capabilities of the core framework. Among many other types of integrations, LlamaHub contains numerous connectors – also known as data readers or data loaders – specially built to allow seamless integration of external data with LlamaIndex. There are over 180 readily available data readers spanning a wide range of data sources and formats, and the list is constantly increasing.

These connectors act as a standard way to ingest data, extracting data from sources such as databases, APIs, files, and websites and converting it into LlamaIndex Document objects. This relieves you from the burden of writing customized parsers and connectors for every new data source. But of course, if you’re not satisfied with the existing connectors, you can always build your own and contribute to the collection.

LlamaHub empowers you to tap into diverse data sources with just a few lines...

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