vector-vs-graph-retrieval-selector

Vector-vs-Graph Retrieval Selector

Overview

Ch1 makes the vector-vs-graph choice evidence-based rather than ideological. Microsoft's BenchmarkQED classifies queries on two axes:

• Scope — local (specific facts in a small number of regions) vs global / sensemaking (reasoning over large portions of the dataset).
• Type — data (direct fact retrieval) vs activity (interpretive / strategic).

The chapter's numeric anchors:

• Vector RAG: ~90% accuracy on simple lookups (DataLocal); 20-30% on complex reasoning (ActivityGlobal). "The very mechanism that makes vector search efficient becomes its fundamental limitation."
• LazyGraphRAG outperforms vector RAG by 50-60% on multi-hop reasoning.
• EyeLevel.ai: at 100,000 pages, vector accuracy drops up to 12% while graph drops only ~2%.
• The larger-context-window rebuttal: BenchmarkQED tested vector RAG against LazyGraphRAG with a ~1-million-token window (essentially the whole dataset); vector RAG still lost on every query type except the most basic factual questions, and bigger windows worsen "lost in the middle."

Where vector RAG shines (Ch1): local, fact-based lookups — customer support, FAQ, recommendation. Where it collapses: multi-hop reasoning, temporal awareness, the associativity gap ("which services were affected by the database migration that followed the security patch we discussed last month").

Ch1's own recommendation for agents is a HYBRID architecture — parallel vector + graph (vector search -> graph traversal -> context synthesis) — because agentic behavior "requires constantly moving between local and global understanding." GraphRAG is not free: the chapter names upfront graph-construction cost, query latency that grows with graph size, contextual nuance lost in triples, and schema-evolution cost. The selector surfaces those costs whenever it recommends GRAPH or HYBRID.

When to Use

• Choosing a retrieval architecture for a new enterprise agent
• Answering "should we add a graph, or is vector RAG enough?"
• Rebutting "let's just use a bigger context window instead of a graph"
• Mapping a mixed query workload to the right per-query strategy
• Teaching the BenchmarkQED local/global x data/activity quadrants