Vector RAG vs. GraphRAG: Which retrieval do you need?

Vector RAG retrieves information by ranking embedded text chunks according to semantic similarity, making it effective for straightforward questions answerable from a single relevant passage, but it cannot inherently connect facts stored across separate records. GraphRAG instead represents entities and their relationships in a knowledge or context graph, enabling multi-hop traversal that can link items such as support tickets, services, vendors, and outage reports while providing an auditable path to the supporting evidence. GraphRAG commonly combines vector or full-text search to identify a starting point with graph queries to retrieve connected context, rather than replacing vector search entirely. The post cites a 2026 NICD benchmark in which a vector-plus-graph approach achieved substantially higher truthfulness and answered more complex questions than vector-only retrieval, while noting that graph modeling, maintenance, and domain-specific queries add implementation complexity. It recommends evaluating retrieval architectures against realistic production-style test sets using measures such as context precision, recall, answer faithfulness, latency, and token cost, adopting GraphRAG when relationship-aware reasoning and explainability produce measurable benefits.

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