Hybrid Retrieval for Retrieval-Augmented Generation using Vector and Graph Databases

Retrieval-Augmented Generation (RAG) supplements a large language model with evidence retrieved from an external knowledge source. Vector retrieval is effective for semantic similarity, whereas graph retrieval represents entities and explicit relationships that can support multi-hop and corpus-level questions. This paper develops a comparative framework for evaluating vector-only, graph-only, and hybrid vector-graph retrieval under a shared generation setup. The review covers dense passage retrieval, GraphRAG, HippoRAG, LightRAG, and evaluation frameworks for retrieval-augmented systems. A reproducible evaluation protocol is proposed around 50 stratified questions and metrics for answer accuracy, evidence recall, faithfulness, citation coverage, latency, and resource overhead. Because no query logs, model outputs, corpus, or annotation records were supplied for this manuscript, Section 5 uses clearly identified synthetic values solely to demonstrate how a 50-question analysis and its figures would be reported. These illustrative values suggest a plausible trade-off: hybrid retrieval may improve coverage of relational questions while increasing latency and infrastructure complexity. They are not empirical findings and cannot establish superiority. The contribution is therefore a transparent comparative design, a reusable evaluation matrix, and a set of hypotheses for a controlled experiment. A publishable empirical claim requires rerunning the protocol on a documented dataset and replacing all synthetic values with measured results.