RAG architecture that combines knowledge graphs with vector databases, enabling multi-hop reasoning, relationship traversal, and structured knowledge representation for more accurate and explainable AI responses.
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Graph RAG is an architecture that combines knowledge graphs with large language models (LLMs), providing structured memory architecture where entities and their relationships form a graph that the LLM can traverse and reason over.
Vector Databases: Best for broad similarity matching and unstructured data retrieval
Graph RAG: Excels when:
One of the biggest breakthroughs in 2026 is the rise of graph-enhanced vector retrieval, combining the strengths of both approaches for more sophisticated AI applications.