GraphRAG: Building Knowledge Graph Applications with Neo4j and GPT-4

Standard vector-based retrieval-augmented generation often struggles with complex, interconnected data relationships, leading to incomplete or hallucinated AI responses. By combining knowledge graphs with large language models, you can provide your applications with deep, structured context. This text-only course guides you through the process of designing and implementing a Knowledge Graph-based Retrieval-Augmented Generation (GraphRAG) system. You will learn how to structure data in Neo4j, extract entities from unstructured text, and connect this rich graph network to GPT-4 for highly accurate, context-aware generation. What you'll learn: - Understand the foundational concepts of knowledge graphs, vector databases, and GraphRAG architectures. - Configure Neo4j databases to store, query, and manage highly connected data. - Apply entity extraction techniques to transform unstructured text into structured graph nodes and relationships. - Implement hybrid search strategies combining vector search with graph traversal inside Neo4j. - Connect GPT-4 to your knowledge graph to retrieve precise context and generate accurate answers. - Design robust prompts that leverage structured graph data for better LLM performance. You will start by mastering the fundamental terminology of graph databases and RAG systems before moving on to practical Cypher query construction and LLM integration patterns. Through structured readings and step-by-step code walkthroughs, you will build a complete conceptual and practical understanding of modern GraphRAG workflows. This course is designed for software developers, data enthusiasts, and AI beginners who want to move beyond basic vector search. No prior experience with graph databases or Neo4j is required, though a basic familiarity with Python is helpful. Start reading today to unlock the power of structured context for your AI applications.