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.
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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Book DescriptionLarge language models can generate impressive responses, but they often struggle with outdated knowledge, limited access to proprietary data, hallucinations, and inconsistent reasoning in real-world applications.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.
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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.
What you will learn
Understand the LlamaIndex ecosystem and core use cases
Master techniques to ingest and parse data from diverse sources
Build optimized indexes for RAG applications
Query data using retrievers, postprocessors, and response synthesizers
Design agentic workflows and multi-agent systems
Deploy AI applications with Python and Streamlit
Evaluate and tune your RAG implementation using observability tools and key metrics
Apply prompt engineering best practices to improve AI responses
Who this book is forThis book is for Python developers with a basic knowledge of LLMs who want to build interactive, generative, and agentic AI applications grounded in proprietary data. Experienced developers and AI practitioners will also benefit from the advanced techniques covered like agentic workflows, multi-agent orchestration, RAG evaluation, and enterprise tooling. A working knowledge of Python and familiarity with generative AI concepts is assumed.
The book is aimed at those with a basic knowledge of Python and working knowledge in developing applications using Generative AI models.
