GraphRAG vs Vector RAG

Entities and relationships into a knowledge graph, traversed for corpus-spanning questions

GraphRAG vs Vector RAG Entities and relationships into a knowledge graph, traversed for corpus-spanning questions 01 / Corpus 02 / Extract 03 / Knowledge Graph 04 / Retrieve 05 / Answer Documents · raw corpus · 01 / Corpus · source Documents raw corpus source Extractor · entities + relations · 02 / Extract Extractor entities + relations Knowledge Graph · nodes + edges · 03 / Knowledge Graph · linked Knowledge Graph nodes + edges linked Community Summaries · clustered themes · 03 / Knowledge Graph Community Summaries clustered themes Question · corpus-spanning · 03 / Knowledge Graph · query Question corpus-spanning query Graph Traversal · multi-hop · 04 / Retrieve · connects Graph Traversal multi-hop connects Answer · grounded synthesis · 05 / Answer · cited Answer grounded synthesis cited parse build graph summarize clusters edges themes graph query cross-doc synthesis Legend primary data policy / PII async batch data store

GraphRAG Path

  • • Entities and relations are extracted once into a reusable graph
  • • Community summaries give the model corpus-wide themes
  • • Traversal assembles evidence across many documents

Why Graph Beats Vector

  • • Vector RAG matches local chunks and misses cross-document links
  • • Corpus-spanning questions need relationships, not nearest neighbours
  • • Grounded answers cite connected evidence

Data-flow diagram • Built with Archify • Create yours ↗ • Hover to trace • R route • Click to focus • +/− zoom • M radar • T theme • E export