Towards Practical GraphRAG: Efficient Knowledge Graph Construction and Hybrid Retrieval at Scale

TL;DR

This paper presents a scalable, cost-efficient framework for deploying GraphRAG in enterprise settings, combining an efficient knowledge graph construction method with a hybrid retrieval strategy to enable practical, large-scale retrieval-augmented generation.

Contribution

It introduces a dependency parsing-based knowledge graph construction pipeline and a hybrid retrieval method combining vector similarity with graph traversal, reducing costs and improving scalability.

Findings

Achieves 94% of LLM-based performance with dependency parsing

Improves retrieval effectiveness by up to 15% over baseline methods

Demonstrates feasibility of deploying GraphRAG in enterprise environments

Abstract

We propose a scalable and cost-efficient framework for deploying Graph-based Retrieval-Augmented Generation (GraphRAG) in enterprise environments. While GraphRAG has shown promise for multi- hop reasoning and structured retrieval, its adoption has been limited due to reliance on expensive large language model (LLM)-based extraction and complex traversal strategies. To address these challenges, we introduce two core innovations: (1) an efficient knowledge graph construction pipeline that leverages dependency parsing to achieve 94% of LLM-based performance (61.87% vs. 65.83%) while significantly reducing costs and improving scalability; and (2) a hybrid retrieval strategy that fuses vector similarity with graph traversal using Reciprocal Rank Fusion (RRF), maintaining separate embeddings for entities, chunks, and relations to enable multi-granular matching. We evaluate our framework on two enterprise datasets focused on legacy code migration and demonstrate improvements of up to 15% and 4.35% over vanilla vector retrieval baselines using LLM-as-Judge evaluation metrics. These results validate the feasibility of deploying GraphRAG in production enterprise environments, demonstrating that careful engineering of classical NLP techniques can match modern LLM-based approaches while enabling practical, cost-effective, and domain-adaptable retrieval-augmented reasoning at scale.