👾 DIGIMON: Deep Analysis of Graph-Based Retrieval-Augmented Generation (RAG) Systems

GraphRAG is a popular 🔥🔥🔥 and powerful 💪💪💪 RAG system! 🚀💡 Inspired by systems like Microsoft's, graph-based RAG is unlocking endless possibilities in AI.

Our project focuses on modularizing and decoupling these methods 🧩 to unveil the mystery 🕵️‍♂️🔍✨ behind them and share fun and valuable insights! 🤩💫

Representative Methods

We select the following Graph RAG methods:

Method	Description	Link	Graph Type
RAPTOR	ICLR 2024		Tree
KGP	AAAI 2024		Passage Graph
DALK	EMNLP 2024		ER Graph
HippoRAG	NIPS 2024		ER Graph
MedGraphRAG	Medical Domain		ER Graph
G-retriever	NIPS 2024		ER Graph
ToG	NIPS 2024		ER Graph
GraphCoT	ACL 2024		ER Graph
MS GraphRAG	Microsoft Project		KG
FastGraphRAG	CircleMind Project		KG
LightRAG	High Star Project		RKG

Graph Types

Based on the entity and relation, we categorize the graph into the following types:

• Chunk Tree: A tree structure formed by document content and summary.
• Passage Graph: A relational network composed of passages, tables, and other elements within documents.
• ER Graph: An Entity-Relation Graph, which contains only entities and relations, is commonly represented as triples.
• KG: A Knowledge Graph, which enriches entities with detailed descriptions and type information.
• RKG: A Rich Knowledge Graph, which further incorporates keywords associated with relations.

The criteria for the classification of graph types are as follows:

Graph Attributes	Chunk Tree	Passage Graph	ER	KG	RKG
Original Content	✅	✅	❌	❌	❌
Entity Name	❌	❌	✅	✅	✅
Entity Type	❌	❌	❌	✅	✅
Entity Description	❌	❌	❌	✅	✅
Relation Name	❌	❌	✅	❌	✅
Relation keyword	❌	❌	❌	❌	✅
Relation Description	❌	❌	❌	✅	✅
Edge Weight	❌	❌	✅	✅	✅