PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths

Understand

Retrieval-augmented generation (RAG) improves the response quality of large language models (LLMs) by retrieving knowledge from external databases.

• Typical RAG approaches split the text database into chunks, organizing them in a flat structure for efficient searches.
• To better capture the inherent dependencies and structured relationships across the text database, researchers propose to organize textual information into an indexing graph, known asgraph-based RAG.
• However, we argue that the limitation of current graph-based RAG methods lies in the redundancy of the retrieved information, rather than its insufficiency.