Improving Retrieval-Augmented Generation Pipelines through Knowledge Graph Integration

Retrieval-Augmented Generation (RAG) systems have shown strong potential in connecting large language models to external knowledge sources, yet conventional setups rely heavily on vector similarity search, which often misses deeper structural relationships within knowledge domains. This paper compares three retrieval approaches: standard vector search, graph-enhanced retrieval using knowledge graph architecture, and GNN-based embeddings. Results show that graph-enhanced methods achieve a 15–25% improvement in retrieval accuracy over the baseline, with GNN-driven approaches excelling at multi-hop relational queries.

Hali tarjima qilinmagan