Seedlabs

Relational Query Router: Send Only Multi-Hop Questions to GraphRAG

A drop-in router in front of an enterprise RAG stack that classifies each incoming question as a simple lookup or a multi-hop relational query, routing the former to cheap vector search and the latter to GraphRAG — cutting retrieval cost and latency without losing relational accuracy.

Computer ScienceTopic Modeling
Cross-Corpus Speech Emotion RecognitionEnterprise AI assistants and support knowledge bases — retrieval cost and latency optimization.

Concept

GraphRAG retrieval is expensive — graph construction, traversal, and larger contexts — but the survey's own framing shows the benefit is concentrated in relational questions [0]. The router is a thin classifier-plus-dispatcher installed at the query front door: simple factual lookups ('what is the PTO policy?') go to the existing vector RAG; questions that require reasoning across relationships among entities ('which supplier in our EMEA contracts is a subsidiary of a sanctioned parent?') are routed to the GraphRAG path. One wedge: pay the graph tax only on the queries that need it. The product ships with a tuning loop — routing decisions are logged and spot-checked against answer quality — so the classification threshold is tuned per deployment.

Why now

The survey [0] draws the exact line the router automates: standard RAG fails on 'the complex structure of relationships among different entities in databases,' while GraphRAG 'leverages structural information across entities... capturing relational knowledge' for more accurate, context-aware responses. That is a per-query-type division of labor — relational questions need the graph, the rest do not — and the survey's formalization of Graph-Guided Retrieval as a distinct, separable stage of the workflow [0] gives the router a well-defined insertion point in a now-standardized architecture. With GraphRAG surveyed as a mature, industrially deployed category [0], the marginal-cost question ('when do I actually need the graph?') becomes the natural next product.

Written by GLM · 10 Oct 2026

AI assessment

Backed by 1 paper88

A pragmatic, high-value optimization layer that solves the cost-latency trade-off of GraphRAG by routing queries based on complexity.

Evidence strength
4/5
The survey explicitly distinguishes between simple retrieval and the need for structural/relational knowledge, providing a theoretical basis for a routing mechanism.
Market pull
5/5
Enterprise RAG users face a genuine 'cost vs. accuracy' tension, and the named beneficiaries (Glean, LlamaIndex) are actively building the infrastructure where this would sit.
Novelty & moat
3/5
While it shares a theme with the 'Payoff Bench' idea, it moves from a diagnostic tool to a production runtime component, creating a distinct value proposition.
Feasibility
5/5
Building a classifier to distinguish between simple and multi-hop queries is a well-understood NLP task that can be implemented as a lightweight LLM call or a small BERT-style model.
Wedge clarity
5/5
The 'pay the graph tax only when needed' is a sharp, quantifiable value proposition that targets a specific pain point (cost/latency).
Simplicity / focus
5/5
The idea is focused on a single, clear function: a router that acts as a dispatcher between two existing retrieval paths.

Scored by Gemma 4 · 10 Oct 2026. Rated against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity), relative to the closest ideas already in the catalog. A prior estimate to compare ideas before real-world signal arrives.

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Who benefits

  • Gleancompany

    Glean answers enterprise questions at scale; per-query routing between vector and graph paths directly cuts retrieval and inference spend across its customer deployments.

  • LlamaIndexcompany

    LlamaIndex hosts both vector and graph retrievers in one framework; a router module turns a manual architecture choice into an automatic per-query decision for its users.

  • Elasticcompany

    Elastic's hybrid search stack gains differentiation by adding graph-aware dispatch on top of its existing vector and keyword retrieval.

  • Notioncompany

    Notion AI Q&A stays fast on cheap vector search for most workspace questions, reserving graph retrieval for cross-document relational asks.

Research it builds on

  1. Graph Retrieval-Augmented Generation: A Survey
    Boci Peng, Yun Zhu, Yongchao Liu et al. · 2025 · 152 citations
    All ideas from this paper →
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feasibility