Knowledge Share

RAG Series (16): Graph RAG — Using Knowledge Graphs to Solve Multi-Hop Reasoning

Part 16 of the RAG series. Vector retrieval finds "similar documents" but is blind to entity relationships. Graph RAG builds a knowledge graph from documents and uses graph traversal instead of similarity search. 12 documents → 176 nodes / 139 edges. Results: context_precision jumps from 0.729 to 0.948 (+0.219), with an honest tradeoff: context_recall drops 0.062.