TopoRAG: Graph-based RAG via Topology-aware Approximate Nearest Neighbor Search

Retrieval-augmented generation (RAG) has become a core technique for improving the factuality and reasoning ability of large language models. Recent efforts extend RAG with graphstructured knowledge, enhancing retrieval to capture relational context beyond isolated text chunks. However, many graph-based RAG systems rely on a two-stage pipeline: (i) classical approximate nearest neighbor (ANN) search to identify top-k entities in the embedding space, (ii) heuristic neighbor expansion which augments the retrieved set by traversing immediate neighbors. This design underutilizes graph topology during retrieval and often introduces noisy or high-degree neighbors, leading to suboptimal evidence selection. In this paper, we propose TopoRAG, a retrieval framework that directly integrates structural constraints into ANN search via a diameter-constrained formulation. By selecting entities whose induced subgraph satisfies a diameter bound, TopoRAG enables topology-aware and noise-controlled graph retrieval. Experiments show that our approach consistently improves precision and significantly reduces context redundancy compared to existing methods.