ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation

TL;DR

ArchRAG introduces a hierarchical, community-based graph retrieval method that enhances accuracy and reduces token costs in retrieval-augmented generation tasks.

Contribution

It proposes a novel hierarchical clustering and indexing approach for attributed communities, improving retrieval relevance and efficiency in graph-based RAG.

Findings

ArchRAG outperforms existing methods in accuracy.

ArchRAG reduces token consumption during retrieval.

Hierarchical index structure improves retrieval relevance.

Abstract

Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs) for solving question-answer (QA) tasks. The state-of-the-art RAG approaches often use the graph data as the external data since they capture the rich semantic information and link relationships between entities. However, existing graph-based RAG approaches cannot accurately identify the relevant information from the graph and also consume large numbers of tokens in the online retrieval process. To address these issues, we introduce a novel graph-based RAG approach, called Attributed Community-based Hierarchical RAG (ArchRAG), by augmenting the question using attributed communities, and also introducing a novel LLM-based hierarchical clustering method. To retrieve the most relevant information from the graph for the question, we build a novel hierarchical index structure for the attributed communities and develop an effective online retrieval method. Experimental results demonstrate that ArchRAG outperforms existing methods in both accuracy and token cost.