How do GraphRAG and Knowledge Graphs enhance vector retrieval?

Short answer: GraphRAG extracts structured entities and relationships from text into a Knowledge Graph (KG), combining vector similarity search with graph traversal (multi-hop relational querying and community summarization) to answer global dataset questions that standard vector retrieval misses.

Detail

Standard vector RAG excels at localized passage retrieval (e.g. "What is Company X's Q3 revenue?"), but struggles with global, holistic dataset queries (e.g. "What are the top 5 overarching themes across all 500 customer support transcripts?").

Unstructured Text ──► [LLM Entity Extractor] ──► Knowledge Graph (Nodes & Edges)
                                                       │
[User Query] ──► Vector Search + Graph Traversal (Sub-graph extraction) ──► Global Summary

Core Architecture of GraphRAG

1. Entity & Relationship Extraction: An LLM parses text to extract entities (Nodes: Person, Product, Org) and relationships (Edges: WORKS_AT, VULNERABLE_TO).
2. Community Detection: Algorithms (e.g. Leiden algorithm) cluster graph nodes into hierarchical communities.
3. Community Summarization: LLMs generate pre-computed summaries for each community level.
4. Hybrid Graph Retrieval: At query time, GraphRAG queries graph community summaries alongside dense vector indexes.

Example

Conceptual Cypher (Neo4j) query combining graph traversal with vector scoring:

// GraphRAG hybrid retrieval example
MATCH (c:Company {name: "Acme Corp"})-[:PRODUCES]->(p:Product)-[:HAS_VULNERABILITY]->(v:Vulnerability)
RETURN c.name, p.name, v.severity
ORDER BY v.severity DESC LIMIT 5

Interview tips

• Highlight that GraphRAG significantly reduces hallucinations for multi-hop relational reasoning questions.
• Emphasize the trade-off: GraphRAG requires expensive LLM processing during indexing to construct graphs and community summaries.

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