Executive Summary
- Vector databases are brilliant at fuzzy semantic search, but terrible at finding exact relationships (e.g., 'Who is the CEO of the parent company that acquired X?').
- Knowledge Graphs (like Neo4j) map entities and their relationships explicitly.
- Graph RAG merges both, querying the vector DB for context and the Graph DB for factual relationship mapping.
Pure vector search typically fails below 40% accuracy on questions requiring linking more than three distinct facts.
1. The Vector Limitation
If you ask a Vector DB: 'List all software tools used by the marketing team in London', it will find documents mentioning 'software', 'marketing', and 'London', but it often retrieves the wrong tool because it lacks structural understanding of the org chart.
Accuracy on Topology vs Semantic Queries
Automated Graph Construction
2. Cypher Generation
Instead of just embedding the question, the orchestrator instructs an LLM to generate a Neo4j Cypher query: MATCH (u:User)-[:WORKS_IN]->(:Dept {name: 'Marketing'}). This deterministic query ensures absolute accuracy.
The Enterprise Application
Graph RAG is mandatory for highly specific structural questions—such as legal entity corporate structure auditing, cyber threat mapping, and supply chain cascading failure analysis.