ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation

Understand

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.