While Retrieval-Augmented Generation (RAG) has become standard practice for enterprise AI, standard vector search suffers from a fundamental limitation: loss of global structural context. When querying a database of thousands of interlinked corporate policies, plain cosine similarity matching retrieves isolated document chunks but fails to synthesize broader relationships spanning multiple departments.
Why Graph RAG Outperforms Standard Vector RAG
Graph RAG overcomes this bottleneck by extracting entities (people, projects, APIs, microservices) and explicit relationships (e.g., `[Service A] DEPENDS_ON [Database B]`) from unstructured text, storing them in a dedicated Knowledge Graph (such as Neo4j or Memgraph).
When a complex query is initiated, the system performs dual retrieval:
- Vector Similarity Search: Locates semantically relevant text passages.
- Graph Traversal: Traverses multi-hop entity relationships to build a complete context tree.
Benchmark Result: Graph RAG achieved a 94% accuracy score on multi-hop reasoning queries, compared to only 58% for standard dense vector search.
