Vector RAG is great for finding similar documents, but it struggles with "global" questions or understanding the relationships between disparate facts. **GraphRAG** addresses this by building a Knowledge Graph of your data—identifying entities and their connections—before the retrieval process starts.
1. Why Standard Vector RAG Fails Complex Queries
If you ask a vector database "What are the common themes across all my project post-mortems?", it will find pieces of individual documents but struggle to synthesize a high-level summary. We explain the structural limitations of vector-only retrieval.
2. Building the Knowledge Graph
GraphRAG uses LLMs to extract entities (people, places, concepts) and their relationships from your text. These are then stored in a graph database (like Neo4j or FalkorDB), allowing the AI to "walk" the graph and find non-obvious connections between data points.
3. Strategic Implementation: GraphRAG vs Vector RAG
You don't always need GraphRAG. We provide a decision matrix to help you determine when the added complexity of a knowledge graph is worth the investment, and how to combine both approaches into a "Hybrid RAG" system for maximum performance.