Enhancing RAG Systems with Knowledge Graph Integration

Retrieval-Augmented Generation (RAG) systems have demonstrated remarkable capabilities in enhancing large language models with external knowledge. However, traditional RAG implementations primarily rely on vector similarity search, which often overlooks the rich structural relationships inherent in knowledge domains. This paper presents a comparative study of three retrieval strategies for RAG systems: (1) baseline vector search, (2) graph-enhanced retrieval leveraging knowledge graph structures, and (3) Graph Neural Network (GNN)-based embeddings. We construct a synthetic knowledge graph from textual documents and evaluate each approach on question-answering tasks. Our experiments demonstrate that graph-enhanced methods achieve 15-25% improvement in retrieval accuracy compared to the baseline, with GNN-based approaches showing superior performance in capturing multi-hop relationships. We provide implementation details, comparative analysis, and insights into the trade-offs between computational complexity and retrieval quality.

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