What is Graph RAG?
Graph Retrieval-Augmented Generation
It is a method where artificial intelligence works by looking not only at documents but also at the networks of connections between data when generating answers.
Overview
While standard RAG methods usually perform searches on text chunks, Graph RAG stores data as a knowledge graph. This allows artificial intelligence to bridge information that may seem distant but is actually related. It enables the generation of deeper and more contextual answers.
How it works
Data is first converted into a knowledge graph in the form of nodes and relationships. When a query arrives, the system retrieves the most relevant information by looking not just at the text, but at the connection paths in this graph.
Where it is used
It is used in the analysis of complex corporate data, medical diagnostic systems, and academic research.
Commonly confused with
It can be confused with classic RAG; the classic one focuses on text similarity, while this one focuses on structural relationships.
Frequently asked questions
When should it be preferred?
If you have complex hierarchies or multiple connections between your data, Graph RAG provides much better results.
Related terms
Related tools
This explanation was written in plain language for TreScout and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →