Why GraphRAG Outperforms RAG and RAPTOR: The Future of Data Summarization

GraphRAG, a cutting-edge RAG method using knowledge graphs, excels in high-level summarization and synthesis across unstructured documents.

GraphRAG is a knowledge graph-based retrieval-augmented generation (RAG) method. Microsoft open-sourced the GraphRAG project in early July, and it has already gained 13k stars in just about a month.

Compared to traditional RAG, GraphRAG excels in high-level summarization and synthesis across multiple unstructured documents.

For instance, when dealing with a collection of articles on environmental issues, GraphRAG can better answer, "What are the top 5 key themes in these articles?"

Such questions don’t have directly relevant documents for traditional RAG to retrieve, making them difficult for standard RAG to handle.

There have been other methods to address similar problems before GraphRAG.

For example, the paper “RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval” discusses a method that clusters documents and performs multi-level clustering based on different abstraction layers, followed by summarization, which can then be used for subsequent RAG retrieval.

The later sections of the article provide a detailed introduction to RAPTOR.

• How does GraphRAG differ from RAPTOR?

• Besides summarization, what other problems does GraphRAG excel at?

• What are the current limitations of GraphRAG?

• What insights does GraphRAG’s design offer for building RAG systems?

This article analyzes and introduces GraphRAG by addressing these questions.

The article starts with a brief introduction to the problems GraphRAG solves and its design intent. The second section focuses on GraphRAG’s principles and concepts, and the final part offers some opinions and thoughts.

GraphRAG doesn’t introduce "original" innovations but cleverly combines existing technologies.

These include LLMs, knowledge graphs, community detection and aggregation algorithms, and some Map-Reduce concepts.

The essence of GraphRAG’s design can be summarized by a line from its corresponding paper “From Local to Global: A Graph RAG Approach to Query-Focused Summarization”: “Use the natural modularity of graphs to partition data for global summarization.”

The following sections will expand on this idea.