Analyzing Knowledge Graphs in Retrieval-Augmented Generation (RAG)

The text discusses an analysis of knowledge graphs in the context of RAG (Retrieval-Augmented Generation) using Microsoft's GraphRAG paper as a starting point. The analysis compares knowledge graph methods in RAG, specifically Neo4j versus FAISS, and evaluates their impact on context retrieval and answer relevancy scores. The study finds that while knowledge graphs may not significantly impact context retrieval, using Neo4j with its own index can improve answer relevancy and faithfulness scores. The analysis methodology involved loading a document into Neo4j, creating retrievers for different variants, and evaluating the results using RAGAS. The study also explores the metrics used in the Microsoft paper and delves into the vagueness of reported lift magnitudes. The results show that effective indexing is crucial for precise and accurate content retrieval in RAG applications. The analysis raises questions about the practical applications of GraphRAG methods and the trade-offs involved in using knowledge graphs in RAG applications.

• Knowledge graphs may not significantly impact context retrieval
• Neo4j with its own index improves answer relevancy and faithfulness scores
• Vagueness of reported lift magnitudes in the Microsoft paper is explored
• Effective indexing is crucial for precise and accurate content retrieval in RAG applications