Building GraphRAG Applications with LlamaIndex

Standard Retrieval-Augmented Generation (RAG) often struggles to connect the dots across multiple documents. By introducing knowledge graphs into your AI pipelines, you can capture complex relationships and dramatically improve the accuracy of your language models. This course teaches you how to leverage GraphRAG to enhance your data retrieval capabilities. Using LlamaIndex, you will learn how to extract structured entities from unstructured text, build interconnected graphs, and perform highly accurate, context-rich queries that outperform basic vector search. What you will learn: * Understand the foundational concepts of RAG, vector databases, and knowledge graphs. * Build and structure knowledge graphs from raw text using LlamaIndex. * Implement hybrid retrieval strategies combining semantic search with graph traversal. * Design effective prompts for reliable entity and relationship extraction. * Apply foundational agentic AI concepts to reason over interconnected data. You will begin with essential terminology and core AI concepts before diving into practical, text-based lessons. Through detailed written explanations and clear code snippets, you will progressively build a complete GraphRAG pipeline. Designed for developers, data engineers, and AI beginners, this course requires no prior experience with graph databases or advanced machine learning. Start reading today to transform how your AI applications process and retrieve complex information.