Building Data-Driven Applications with LlamaIndex : A practical guide to RAG pipelines, agentic workflows, and production AI deployment (2ND)

Build data-grounded AI applications with LlamaIndex through hands-on examples covering RAG pipelines, agentic workflows, multi-agent systems, prompt engineering, evaluation, and deployment with Python and Streamlit. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Build complete RAG pipelines from ingestion to deployment with practical working examples Design agentic workflows and multi-agent architectures for production-ready AI systems Develop a hands-on project from a simple LLM app to a deployed AI-powered web application Run everything locally with Ollama - no API keys or costs required Book DescriptionLarge language models can generate impressive responses, but they often struggle with outdated knowledge, limited access to proprietary data, hallucinations, and inconsistent reasoning in real-world applications.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. 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 ow