GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation

TL;DR

GraphER introduces a graph-based method for enhancing retrieval in RAG systems by capturing multiple proximities, improving efficiency and effectiveness without requiring knowledge graphs or significant latency.

Contribution

It presents a novel, retriever-agnostic graph enrichment and reranking approach that integrates seamlessly with existing vector stores and improves retrieval quality.

Findings

Effective across multiple benchmarks

Seamless integration with standard vector stores

Negligible latency overhead

Abstract

Semantic search in retrieval-augmented generation (RAG) systems is often insufficient for complex information needs, particularly when relevant evidence is scattered across multiple sources. Prior approaches to this problem include agentic retrieval strategies, which expand the semantic search space by generating additional queries. However, these methods do not fully leverage the organizational structure of the data and instead rely on iterative exploration, which can lead to inefficient retrieval. Another class of approaches employs knowledge graphs to model non-semantic relationships through graph edges. Although effective in capturing richer proximities, such methods incur significant maintenance costs and are often incompatible with the vector stores used in most production systems. To address these limitations, we propose GraphER, a graph-based enrichment and reranking method that captures multiple forms of proximity beyond semantic similarity. GraphER independently enriches data objects during offline indexing and performs graph-based reranking over candidate objects at query time. This design does not require a knowledge graph, allowing GraphER to integrate seamlessly with standard vector stores. In addition, GraphER is retriever-agnostic and introduces negligible latency overhead. Experiments on multiple retrieval benchmarks demonstrate the effectiveness of the proposed approach.