1. The Blind Spot of Pure Vector RAG
Standard vector search operates on local semantic similarity. When asked a holistic question spanning thousands of documents—such as "What are the common supply chain failure modes across all our Asian vendors over the last three years?"—vector search fails catastrophically. It can retrieve individual snippets, but cannot connect disparate entities across distant documents to synthesize global thematic answers.
2. GraphRAG: Constructing Entity-Relationship Networks
Pioneered by Microsoft Research, GraphRAG extracts structured Knowledge Graphs directly from unstructured text collections using LLMs. Documents are parsed to extract Entities (People, Organizations, Technologies, Locations) and directed Relationships (e.g., "Company X -> supplies -> Hospital Y"). These entities and edges are loaded into a graph database (such as Neo4j or Memgraph).
3. Hierarchical Community Detection (Leiden Algorithm)
GraphRAG applies community detection algorithms (such as the Leiden or Louvain algorithm) to group interconnected entities into hierarchical thematic clusters. An LLM generates pre-computed summaries for each community cluster at multiple levels of abstraction, creating a comprehensive conceptual map of the entire enterprise corpus.
4. Answering Multi-Hop and Global Queries
When a complex query arrives, GraphRAG traverses both structural graph edges (multi-hop traversal) and pre-computed community summaries, combining them with vector search. The resulting LLM response synthesizes broad, accurate insights with verified source attributions, completely outperforming traditional RAG.
