Large language models can generate impressive responses, but they often struggle with outdated knowledge, limited access to proprietary data, hallucinations, and inconsistent reasoning in real-world applications. LlamaIndex addresses these challenges through RAG, enabling developers to connect LLMs with external data sources and build more reliable AI applications.
This fully updated second edition reflects the latest evolution of the LlamaIndex ecosystem. You will learn how to ingest and parse data from multiple sources, build optimized indexes, and implement advanced retrieval strategies for high-quality RAG applications.

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The book introduces modern agentic AI patterns using LlamaIndex Workflows, chat engines, agents, and multi-agent orchestration. You will also explore observability and RAG evaluation, prompt engineering best practices, and deployment strategies using Streamlit.
Throughout the book, you will build a practical Contract Review Expert application that evolves chapter by chapter from a simple query engine into a fully deployed AI-powered web application. You will also learn how to use enterprise tooling such as LlamaParse alongside open source alternatives such as LiteParse.
By the end of this book, you will be able to design, build, evaluate, and deploy scalable LlamaIndex applications grounded in your own data.

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Spis treści

Building Data-Driven Applications with LlamaIndex. A practical guide to RAG pipelines, agentic workflows, and production AI deployment - Second Edition eBook -- spis treści

• 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 Faster and Smarter with Workflows
• 9. Understanding Basic Agent Architectures
• 10. Exploring Advanced Agentic Use Cases
• 11. Customizing and Deploying Our LlamaIndex Project
• 12. Tuning Our RAG Implementation – Observability and Evaluation
• 13. Prompt Engineering Guidelines and Best Practices
• 14. Going Enterprise with LlamaParse
• 15. Conclusion and Additional Resources