GraphRAG: Why Knowledge Graphs Outperform Vector-Only RAG for Complex Multi-Hop Questions
Standard RAG retrieves chunks by semantic similarity. But when a question requires connecting facts across multiple documents — "Which of our clients in the healthcare sector had contracts signed before our HIPAA certification?" — vector search alone fails.
GraphRAG extracts entities and relationships from your corpus into a knowledge graph, then traverses the graph to answer multi-hop questions.
1. GraphRAG vs. Vector RAG
| Capability | Vector RAG | GraphRAG |
|---|---|---|
| "What is X?" | ✅ Excellent | ✅ Excellent |
| "How does X relate to Y?" | ⚠ Unreliable | ✅ Traverses relationship edges |
| "Summarize all documents about topic Z" | ❌ Limited by top-K | ✅ Community summaries |
| Multi-hop reasoning | ❌ Fails | ✅ Graph traversal |
2. Knowledge Graph Construction
from graphrag import GraphRAGIndexer
indexer = GraphRAGIndexer(
llm_model="gemma-4-12b",
entity_extraction_prompt="Extract all people, organizations, dates, and technologies...",
relationship_extraction_prompt="Identify relationships between extracted entities..."
)
# Build graph from documents
graph = indexer.index(documents=[
"Acme Corp signed a $2M contract in March 2025...",
"Acme Corp is headquartered in Dubai, healthcare sector...",
"Our HIPAA certification was obtained in June 2025..."
])
# Multi-hop query
answer = graph.query("Which healthcare clients signed contracts before our HIPAA certification?")
# → "Acme Corp signed a $2M contract in March 2025, before HIPAA certification in June 2025"
GraphRAG transforms document collections into queryable knowledge structures — enabling enterprise QA that actually connects the dots across your entire corpus.
