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Building Data-Driven Applications with LlamaIndex
Building Data-Driven Applications with LlamaIndex

Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications

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Profile Icon Andrei Gheorghiu
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Full star icon Full star icon Full star icon Full star icon Full star icon 5 (10 Ratings)
Paperback May 2024 368 pages 1st Edition
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Arrow left icon
Profile Icon Andrei Gheorghiu
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€18.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (10 Ratings)
Paperback May 2024 368 pages 1st Edition
eBook
€26.99
Paperback
€33.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€26.99
Paperback
€33.99
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Renews at €18.99p/m

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Building Data-Driven Applications with LlamaIndex

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Key benefits

  • Examine text chunking effects on RAG workflows and understand security in RAG app development
  • Discover chatbots and agents and learn how to build complex conversation engines
  • Build as you learn by applying the knowledge you gain to a hands-on project

Description

Discover the immense potential of Generative AI and Large Language Models (LLMs) with this comprehensive guide. Learn to overcome LLM limitations, such as contextual memory constraints, prompt size issues, real-time data gaps, and occasional ‘hallucinations’. Follow practical examples to personalize and launch your LlamaIndex projects, mastering skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. From fundamental LLM concepts to LlamaIndex deployment and customization, this book provides a holistic grasp of LlamaIndex's capabilities and applications. By the end, you'll be able to resolve LLM challenges and build interactive AI-driven applications using best practices in prompt engineering and troubleshooting Generative AI projects.

Who is this book for?

This book is for Python developers with basic knowledge of natural language processing (NLP) and LLMs looking to build interactive LLM applications. Experienced developers and conversational AI developers will also benefit from the advanced techniques covered in the book to fully unleash the capabilities of the framework.

What you will learn

  • Understand the LlamaIndex ecosystem and common use cases
  • Master techniques to ingest and parse data from various sources into LlamaIndex
  • Discover how to create optimized indexes tailored to your use cases
  • Understand how to query LlamaIndex effectively and interpret responses
  • Build an end-to-end interactive web application with LlamaIndex, Python, and Streamlit
  • Customize a LlamaIndex configuration based on your project needs
  • Predict costs and deal with potential privacy issues
  • Deploy LlamaIndex applications that others can use

Product Details

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Publication date : May 10, 2024
Length: 368 pages
Edition : 1st
Language : English
ISBN-13 : 9781835089507
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Product Details

Publication date : May 10, 2024
Length: 368 pages
Edition : 1st
Language : English
ISBN-13 : 9781835089507
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Building Data-Driven Applications with LlamaIndex
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Table of Contents

17 Chapters
Part 1:Introduction to Generative AI and LlamaIndex Chevron down icon Chevron up icon
Chapter 1: Understanding Large Language Models Chevron down icon Chevron up icon
Chapter 2: LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem Chevron down icon Chevron up icon
Part 2: Starting Your First LlamaIndex Project Chevron down icon Chevron up icon
Chapter 3: Kickstarting Your Journey with LlamaIndex Chevron down icon Chevron up icon
Chapter 4: Ingesting Data into Our RAG Workflow Chevron down icon Chevron up icon
Chapter 5: Indexing with LlamaIndex Chevron down icon Chevron up icon
Part 3: Retrieving and Working with Indexed Data Chevron down icon Chevron up icon
Chapter 6: Querying Our Data, Part 1 – Context Retrieval Chevron down icon Chevron up icon
Chapter 7: Querying Our Data, Part 2 – Postprocessing and Response Synthesis Chevron down icon Chevron up icon
Chapter 8: Building Chatbots and Agents with LlamaIndex Chevron down icon Chevron up icon
Part 4: Customization, Prompt Engineering, and Final Words Chevron down icon Chevron up icon
Chapter 9: Customizing and Deploying Our LlamaIndex Project Chevron down icon Chevron up icon
Chapter 10: Prompt Engineering Guidelines and Best Practices Chevron down icon Chevron up icon
Chapter 11: Conclusion and Additional Resources Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Diego Martínez Rodríguez Sep 19, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It's a book written with love. The author spent a lot of time organizing a large amount of information that, although it is publicly available, is not well organized on the Llama Index site. It was a great tool to advance my understanding of that RAG framework.
Feefo Verified review Feefo
N/A Jun 27, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A comprehensive and understandable introduction to LlamaIndex.
Feefo Verified review Feefo
Om S May 31, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I mean, if you're into building AI-driven apps, "Building Data-Driven Applications with LlamaIndex" by Andrei Gheorghiu is the guide you've been waiting for. This book is a practical roadmap to enhancing your Python apps using the LlamaIndex for retrieval-augmented generation (RAG). Starting with basic LLM (Large Language Model) concepts, it swiftly moves into more complex applications like chatbots and dynamic knowledge bases.The guide excels in explaining how to set up the LlamaIndex ecosystem, ingest and index diverse data sources, and tailor queries for optimal results. It's pretty cool how it lays out step-by-step instructions for building an interactive web app using LlamaIndex, Python, and Streamlit. Plus, it doesn't just leave you hanging with the build; it goes into deploying your application effectively, considering cost prediction and privacy concerns.What's truly useful are the sections on prompt engineering and troubleshooting, which are gold for developers looking to polish their skills in LLM applications. Whether you're a newbie to NLP and LLMs or an experienced developer, this book has nuggets of wisdom for everyone.
Amazon Verified review Amazon
Mojeed Abisiga Jun 08, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you want to learn how to connect a powerful LLM, like GPT-4, Claude3, Gemini or LLaMA3 to your existing proprietary data using LlamaIndex, in a clever way, with basic coding effort, I strongly recommend “Building Data-Driven Applications with LlamaIndex” by Andrei Gheorghiu
Amazon Verified review Amazon
Andrei Fajardo Aug 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
In my opinion, this book by Andrei Gehorghiu should be viewed as an essential guide for anyone looking to master the LlamaIndex Python framework.For one, it teaches off of version 0.10.x (i.e., post our big migration from a monolithic library), and begins with a nice introduction to LLMs and motivation for RAG or context augmentation. Second, the author adopts a hands-on approach building a project called PITS throughout the book — this allows the readers to gradually learn the concepts of both RAG and LlamaIndex chapter by chapter, making it an ideal starting point for beginners.The book also covers, in a sensible ordering, the core classes of LlamaIndex including our various Index, Storage, Retrieval, Post-Processors, Response-Synthesizer classes, and others. In addition to RAG, it also covers our Agent abstractions, which by the way can be used in our multi-agent system with our latest library called llama-agents (this came after the publishing of this book). Finally, the book explores various aspects of customization, the LlamaIndex CLI tool, as well as LlamaPacks!In future editions, I would hope to see our latest Workflows classes get added to this book, and some coverage on building multi-agent systems with LlamaAgents. Overall, Building Data-Driven Applications With LlamaIndex is a valuable resource for anyone interested in developing data-driven applications using the LlamaIndex Python framework, and I highly recommend it!
Amazon Verified review Amazon
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