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Building Data-Driven Applications with LlamaIndex


Building Data-Driven Applications with LlamaIndex: A Practical Guide to Retrieval-augmented Generation (RAG) to Enhance LLM Applications by Andrei Gheorghiu
English | May 10th, 2024 | ISBN: 183508950X | 368 pages | True PDF | 19.73 MB​

Solve real-world problems easily with artificial intelligence (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 the knowledge you gain to a hands-on project

Book Description
Generative AI, such as Large Language Models (LLMs) possess immense potential. 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'll go from preparing the environment to gradually adding features and deploying the final project. You'll gradually progress from fundamental LLM concepts to exploring the features of this framework. Practical examples will guide you through essential steps for personalizing and launching your LlamaIndex projects. Additionally, you'll overcome LLM limitations, build end-user applications, and acquire skills in ingesting, indexing, querying, and connecting dynamic knowledge bases, covering Generative AI and LLM, as well as LlamaIndex deployment. As you approach the conclusion, you'll delve into customization, gaining a holistic grasp of LlamaIndex's capabilities and applications.

By the end of the book, you'll 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
• 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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