route_query
Routes NVIDIA AI questions to the optimal model and reasoning approach by evaluating dependency depth in a knowledge graph.
Instructions
Route an NVIDIA AI question to the optimal model and reasoning approach via graph depth.
The CKG graph IS the router — hop depth is a deterministic complexity metric.
Deeper NVIDIA prerequisite chains (CUDA → TensorRT → TensorRT-LLM → NIM) require
more capable models. No heuristic: the graph decides.
Routing table:
hop_depth 1 → haiku · direct (simple lookup)
hop_depth 2 → sonnet · generic_cot (moderate chain)
hop_depth 3+ → opus · sparql_cot (deep dependency, structured reasoning)
Args:
question: Concept name or natural language question about NVIDIA AI.
domain: Domain from list_domains() — e.g. "nvidia-tensorrt-triton", "nvidia-nim".
Returns:
model_tier + reasoning_approach + why + context subgraph to inject before LLM call.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| domain | No | ||
| question | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |