Build a Document Chatbot with LlamaIndex, NVIDIA NIM, and Streamlit

Interacting with custom documents using generative artificial intelligence is one of the most valuable workflows in modern software development. In this guide, you will learn how to build a complete Retrieval-Augmented Generation (RAG) system that ingests PDFs and provides accurate, context-aware answers to user queries. What you'll learn: - Understand foundational RAG concepts, embedding models, and vector retrieval pipelines - Extract, clean, and chunk text from multi-page PDF documents for optimal search accuracy - Configure LlamaIndex to manage document indexing, storage, and context retrieval - Connect NVIDIA NIM inference endpoints for high-performance language model generation - Build an intuitive, responsive web chat interface using Streamlit - Apply prompt engineering techniques to refine response accuracy and source attribution The course begins with fundamental terminology and core architectural concepts before moving into step-by-step written code implementations that connect document loaders, indexers, and user interfaces into a unified application. It is tailored for beginners with basic Python familiarity looking to create practical AI applications. Begin reading today to assemble your own document-aware conversational assistant.