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

Phrases: "vector or graph", "do we need GraphRAG", "will a bigger context window fix it", "retrieval architecture", "why does RAG fail on this query", "local vs global queries".

When NOT to Use

• Tuning an existing pipeline (chunk size, embedding model, reranker) —

this chooses the architecture, not its hyperparameters.

• Consumer FAQ / support bots where DataLocal lookups dominate — the answer

is VECTOR and you already know it.

• As a graph builder. This recommends graph; it does not construct one.

See Ch3 skills (graph-model-selector, schema-pattern-selector).

Process

| Step | Input | Action | Output | Verification | |------|-------|--------|--------|--------------| | 1 | scope / type / multi-hop / temporal / structure / scale / latency | lib.recommend(...) | VECTOR / GRAPH / HYBRID + reasons | DataLocal lookup -> VECTOR; ActivityGlobal multi-hop -> GRAPH | | 2 | agentic workload | recommend(..., agentic=True) | HYBRID | agent workloads default to the parallel hybrid | | 3 | graph signals + unstructured/latency-critical | recommend(...) | HYBRID (not pure GRAPH) | costly-graph condition down-shifts GRAPH -> HYBRID | | 4 | larger_context_window=True | read larger_context_window_rebuttal | the ~1M-token rebuttal text | rebuttal cites the 1M-token BenchmarkQED test | | 5 | dataset_scale_pages >= 100000 | read scale_note | EyeLevel 12%-vs-2% note | scale note present at 100k+ pages | | 6 | GRAPH/HYBRID result | read graphrag_costs | the four GraphRAG struggles | costs present for GRAPH/HYBRID, absent for VECTOR | | 7 | list of workloads | lib.recommend_batch(...) | per-workload + tally | tally sums to the workload count |

Rationalizations

| Agent rationalization | Documented rebuttal | |------------------------|--------------------| | "Just use a bigger context window and skip the graph." | Ch1's direct test: BenchmarkQED ran vector RAG with a ~1M-token window (the whole dataset) and it still lost on every query type except the most basic factual questions. More tokens don't create relationships, temporal evolution, or systematic patterns — they worsen "lost in the middle." | | "Vector RAG scored 90%, it's fine." | That 90% is DataLocal only. On ActivityGlobal the same system scores 20-30%. If your workload has global/multi-hop queries, the headline number does not apply. | | "Graphs are always better, use GRAPH everywhere." | Ch1 names GraphRAG's costs: upfront construction, query latency, nuance loss in triples, schema-evolution burden. For a latency-critical DataLocal lookup, vector wins. The selector down-shifts to HYBRID/VECTOR when those costs bite. | | "Our data is unstructured, so a graph is impossible." | Then the recommendation is HYBRID, not 'give up on graph': run vector first, traverse a partial graph selectively, synthesize. Ch1's hybrid is exactly this parallel path. | | "Scale doesn't change the vector-vs-graph answer." | EyeLevel.ai (Ch1): at 100k pages vector drops up to 12% while graph drops ~2%. Scale widens the gap; the selector attaches the scale note at 100k+ pages. |

Red Flags

• A multi-hop / temporal / global workload recommended VECTOR. The scope or

multi-hop flags are unset; re-check the workload description against the associativity-gap example.

• GRAPH recommended for a latency-critical DataLocal lookup. Graph

traversal is slower than an ANN lookup here; the selector should have chosen VECTOR or HYBRID — verify the flags.

• HYBRID chosen for everything. Either the workload is genuinely agentic

(legitimate — that is the book's default) or the signals are under-specified; add concrete scope/type/multi-hop values.

• A GRAPH/HYBRID recommendation adopted without reading graphrag_costs.

The upfront-construction and maintenance cost is real; budget for it before committing.

Non-Negotiable Verification

1. Run the benchmark battery. python cli.py benchmark must report 8/8:

• DataLocal lookup -> VECTOR; ActivityGlobal multi-hop -> GRAPH; agentic ->

HYBRID; graph-signals + unstructured -> HYBRID

• the larger-context-window flag attaches the 1M-token rebuttal
• 100k+ pages attaches the EyeLevel 12%-vs-2% scale note
• GRAPH/HYBRID surface GraphRAG costs; VECTOR does not
• the ActivityGlobal quadrant carries the 20-30% anchor

1. Verify CLI help. python cli.py --help exits 0 and prints the SKILL.md

description.

1. Inspect the scenario. python cli.py scenario devops should recommend

VECTOR for the 5xx lookup, GRAPH for the cascading-migration query, and HYBRID for the autonomous agent.

Security Posture

• Prompt injection. The selector consumes structured flags and booleans,

not free-text documents, so there is no injection surface in lib.py. The numeric anchors and rebuttal text are author-controlled constants, not model-generated.

• Data exfiltration. No network calls; the only file read is the workloads

JSON path the caller supplies (default: the bundled sample). --json output goes to stdout.

• Privilege escalation. No shell invocation, no dynamic import, no file

writes. The recommendation is advisory; it selects an architecture and does not touch any datastore or credential.

• Decision integrity. Treat the recommendation as a starting point, not a

mandate — the chapter's numbers are reported so a human can audit the rationale rather than trust an opaque verdict.

Source Attribution

Distilled from Agentic GraphRAG (O'Reilly, by Anthony Alcaraz and Sam Julien), Chapter 1 — Defining Agentic AI, "The Limitations of Vector-Based Retrieval" and "GraphRAG" sections. The BenchmarkQED quadrants, the 90% / 20-30% vector-RAG numbers, the LazyGraphRAG +50-60% multi-hop figure, the EyeLevel.ai 12%-vs-2%-at-100k-pages result, and the ~1-million-token larger-context-window rebuttal are all the chapter's, anchored in Microsoft's "From Local to Global: A GraphRAG Approach to Query-Focused Summarization" and BenchmarkQED research.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: AnthonyAlcaraz
• Source: AnthonyAlcaraz/agentic-graph-rag-skills
• License: MIT
• Homepage: https://www.oreilly.com/library/view/agentic-graphrag/9798341623163/

Install and usage instructions live in the source repository linked above.