GraphRAG: The Next Big Leap in Information Retrieval

Retrieval-Augmented Generation (RAG) is the default architecture for connecting LLMs to private data. However, standard vector-search RAG often fails when answering holistic questions like “What are the main themes in these 100 research papers?”

Enter GraphRAG.

What is GraphRAG?

By combining semantic vector search with structured Knowledge Graphs, GraphRAG structures raw documents into entity-relationship maps. The model can then traverse these relationships to synthesize high-level summaries and understand deep contextual links that vector distance metrics miss entirely.