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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

Preserving privacy with metadata extractors, and not only

Augmenting LLMs with your proprietary data – which, by the way, may belong to your customers in many instances – can prove to be a challenging task in terms of data privacy. While a cloud based LLM solution can enrich your proprietary data and offer numerous advantages, uncontrolled data sharing with external parties can quickly turn into a legal, security, and regulatory nightmare.

Although the topic of data privacy is more stringent in the case of indexing and querying, utilizing metadata extractors can also raise potential privacy concerns to be aware of. Therefore, I believe a brief warning is required already.

Since most extractors rely on processing content via LLMs to generate metadata, this means your actual data gets transmitted to and analyzed by external cloud services.

There is a risk of exposure or mishandling of any personal or confidential information contained in this data, whether due...

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