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

Inhaltsangabe

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

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.

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

Who this book is 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.

Table of Contents

1. Understanding Large Language Models
2. LlamaIndex: The Hidden Jewel - An Introduction to the LlamaIndex Ecosystem
3. Kickstarting Your Journey with LlamaIndex
4. Ingesting Data into Our RAG Workflow
5. Indexing with LlamaIndex
6. Querying Our Data, Part 1 – Context Retrieval
7. Querying Our Data, Part 2 – Postprocessing and Response Synthesis
8. Building Chatbots and Agents with LlamaIndex
9. Customizing and Deploying Our LlamaIndex Project
10. Prompt Engineering Guidelines and Best Practices
11. Conclusions and Additional Resources

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