Vector Institute's Knowledge Graph RAG Boosts Multi-Hop Retrieval Accuracy by Up to 54%
VectorInst · x · 2026-10-09
Vector Institute's AI Engineering team unveils an Entity-Based Knowledge Graph RAG architecture targeting RAG's multi-hop reasoning failure on complex enterprise documents.
Approach
- Standard RAG retrieves isolated, relevant-sounding chunks and fails to connect facts across pages or fiscal quarters
- Using LangChain's LLMGraphTransformer, entities and relationships are mapped into a structured graph, linking connected entities back to source text
- Entities are hyphenated with their source document titles before embedding, preserving document-level hierarchies lost during chunking
Evaluation (SEC 10-Q filings of major tech companies; 100 synthetic multi-hop Q&A pairs)
- Complex multi-hop retrieval accuracy: GPT-4o from 40% to 55%
- GPT-4o-mini from 36% to 56% with only 0.05s added latency — structural context lets smaller models match larger ones
The write-up also compares Cypher-Based KG-RAG and GraphRAG alternatives.
Related event: Vector Institute Unveils Entity-Based Knowledge Graph RAG(2 posts)→
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