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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

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Frequently bought together

Building Data-Driven Applications with LlamaIndex

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Diego Martínez Rodríguez Sep 19, 2024

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

N/A Jun 27, 2024

A comprehensive and understandable introduction to LlamaIndex.

Om S May 31, 2024

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

Mojeed Abisiga Jun 08, 2024

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

Andrei Fajardo Aug 07, 2024

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!

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

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