RAG Tutorial: How to Build a RAG System on a Knowledge Graph

The tutorial on building a retrieval-augmented generation (RAG) system using a knowledge graph provides a comprehensive guide for developers and AI engineers looking to enhance large language model (LLM) applications with structured and unstructured data retrieval. It introduces the concept of GraphRAG, which combines vector search for semantic similarity with graph search for relational queries, offering a more accurate and explainable alternative to traditional vector-only RAG systems. By integrating Neo4j for knowledge graphs and LangChain for orchestration, the tutorial walks through setting up a GraphRAG system, including environment setup, vector indexing, and Cypher query implementation, to create a scalable application capable of answering complex queries with both unstructured and structured data. The guide emphasizes overcoming common challenges in RAG systems, such as hallucinations and retrieval limitations, and highlights the benefits of using a hybrid approach to build more trustworthy and adaptable LLM applications.

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