Definition
Graph RAG is an advanced retrieval architecture that combines structured Knowledge Graphs (KG) with unstructured vector retrieval to capture high-order relationships and global context often missed by semantic similarity alone. It enables 'multi-hop' reasoning by allowing the model to traverse explicit edges between entities, making it superior for complex queries across disparate documents, though it incurs higher indexing latency and computational cost than standard RAG.
Not to be confused with standard Vector RAG; Graph RAG uses topological traversals and nodes rather than just point-to-point cosine similarity.
"A subway map where nodes are stations and edges are lines, allowing the AI to travel across the city to find connections rather than just looking at a GPS coordinate."
- Knowledge Graph(Prerequisite)
- Entity Extraction(Component)
- Multi-hop Reasoning(Capability)
- Vector Embeddings(Complementary Component)
Conceptual Overview
Graph RAG is an advanced retrieval architecture that combines structured Knowledge Graphs (KG) with unstructured vector retrieval to capture high-order relationships and global context often missed by semantic similarity alone. It enables 'multi-hop' reasoning by allowing the model to traverse explicit edges between entities, making it superior for complex queries across disparate documents, though it incurs higher indexing latency and computational cost than standard RAG.
Disambiguation
Not to be confused with standard Vector RAG; Graph RAG uses topological traversals and nodes rather than just point-to-point cosine similarity.
Visual Analog
A subway map where nodes are stations and edges are lines, allowing the AI to travel across the city to find connections rather than just looking at a GPS coordinate.