Enterprise Multi-Hop Scenario Query
Ready
3-Hop Active
Knowledge Graph RAG (Cytoscape.js)
4 Nodes | 3 Relational Edges
Vector Embedding Space (D3 Cosine Clusters)
3 Matches | Cosine Threshold > 0.75
Knowledge Graph Graph Modifier & Edge Builder
Retrieved LLM Context Windows
Real-Time Prompt Assembly
Pure Vector Search Context
Top-3 Cosine
Loading vector matches...
Completeness: 45.0%
Incomplete Context
GraphRAG Subgraph Context
3-Hop Path
Loading graph traversal path...
Completeness: 98.0%
100% Context Precision
Entity Provenance & Relational Traversal
Verified Knowledge Graph Traversal Path:
Why Vector Search Hits an Enterprise Ceiling
Basic vector search converts chunks into semantic embeddings based on word proximity. In multi-hop queries (e.g. TechCorp → BattSource → LithiumCo → Port Alpha), vector search selects chunks with high standalone similarity scores (like general battery specs), missing explicit 2nd and 3rd hop relational links. GraphRAG uses structural subgraphs to assemble exact relational paths directly into the LLM context.