Building Data Driven Applications with LlamaIndex: A Practical Guide on Retrieval Augmented Generation (RAG) for Enhancing LLM Applications

Building Data Driven Applications with LlamaIndex: A Practical Guide on Retrieval Augmented Generation (RAG) for Enhancing LLM Applications
Author: Andrei Gheorghiu (Author)
Publisher finelybook 出版社:‏ Packt Publishing – ebooks Account
Publication Date 出版日期:‏ 2024-undefined-Jun.
Language 语言: English
Print Length 页数: 379 pages
ISBN-10: 183508950X
ISBN-13: 9781835089507

Book Description

Elegantly solve real-world problems with AI, using the LlamaIndex data framework to enhance your LLM-based Python applications

Key Features

• 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 fresh knowledge with a useful hands-on project

Book Description

Many enthusiasts, as well as more experienced programmers, have already discovered the immense potential that Generative AI, such as Large Language Models, possess. These models simplify problems but have limitations, including contextual memory constraints, prompt size issues, real-time data gaps, and occasional “hallucinations.”

With this book you will be taken through all the necessary steps: from preparing the environment to gradually adding features and deploying the final project. Starting from fundamental LLM concepts to exploring the features of this framework. Practical examples guide you through necessary steps on personalising and launching your LlamaIndex projects. Overcome LLM limitations, build end-user applications, and acquire skills in ingesting, indexing, querying, and connecting dynamic knowledge bases. The book covers Generative AI and LLM understanding, LlamaIndex deployment, and concludes with customisation, providing a holistic grasp of LlamaIndex’s capabilities and applications.

By the end of the book, you will be able to resolve challenges in LLMs and build interactive AI-driven applications by applying best practices in prompt engineering and troubleshooting Generative AI projects.

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
• Learn to query LlamaIndex effectively and interpret responses
• Build an end-to-end interactive web application with LlamaIndex, Python and Streamlit
• Customize LlamaIndex configuration based on your project needs
• Predict costs and deal with potential privacy issues
• Deploy LlamaIndex applications for others to utilize

Who this book is for

This book is ideal for Python developers with basic knowledge of NLP and LLMs aiming to build interactive LLM applications. Experienced developers and conversational AI developers will benefit from advanced techniques to fully unleash the capabilities of the framework.

Table of Contents

1. Understanding Large Language Models
2. LlamaIndex: The Hidden Jewel
3. Kickstarting your journey with LlamaIndex
4. Data ingestion with LlamaIndex
5. Indexing with LlamaIndex
6. Querying your data Part 1 – Context Retrieval
7. Querying our data – Part 2 – Post-processing and response synthesis
8. Building chatbots and agents with LlamaIndex
9. Customizing and deploying our LlamaIndex project
10. Prompt engineering guidelines and best practice
11. Final conclusion and additional resources