NVIDIA AI CKG

Requires Ollama running locally with a Qwen model pulled:
    ollama pull qwen2.5:14b

Override model:  NVIDIA_CKG_MODEL env var  (default: qwen2.5:14b)
Override host:   NVIDIA_CKG_OLLAMA env var (default: http://localhost:11434)

Args:
    question: Natural-language question about the NVIDIA AI stack.
    domain:   Domain from list_domains() — auto-detected from question if omitted.

Args:
    concept: Target concept — e.g. 'Speculative Decoding', 'Isaac Lab', 'NeMo Guardrails'.
    domain:  Domain name from list_domains().

Args:
    concept: Concept name (partial match supported) — e.g. 'TensorRT', 'NIM', 'Isaac Lab'.
    domain:  Domain name from list_domains() — e.g. 'nvidia-tensorrt-triton', 'nvidia-isaac'.
    depth:   Traversal depth 1–5 (default 3).

query_ckg walks outward from one concept. This intersects the reachable sets of two or
more, which is the shape of most real questions — "the component that satisfies A AND
applies to B". Neither anchor alone answers it; the answer lives in the overlap.

Every branch is an exact set of declared edges, so the intersection is exact. A concept
appears only if a declared path reaches it from each anchor. A relation missing from the
graph produces an empty result, never a guess.

Args:
    branches: Two or more branches. Either a bare anchor ("TensorRT-LLM"), which takes
        everything within `depth` hops, or an anchor plus an explicit relation path using
        '>' ("TensorRT-LLM > REQUIRES > ENABLES"), where each relation replaces the
        frontier. '*' matches any relation. Mix both forms freely.
    domain: Domain name from list_domains().
    depth: Hops for bare-anchor branches, 1-5 (default 2). Ignored for explicit paths.
    direction: 'out' follows dependencies, 'in' follows them backwards, 'both' (default).
    mode: 'AND' (default) intersects branches; 'OR' unions them.
    limit: Max concepts listed, 1-200 (default 40). The true count is always shown.

Returns:
    Markdown with the query plan and its per-step set sizes, then the answer set with
    taxonomy tags. Reports which branch was empty when the intersection is empty.

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.

Args:
    query:  Search term — e.g. 'inference', 'sandbox', 'quantization', 'guardrails'.
    domain: Domain name from list_domains() — e.g. 'nvidia-nim', 'nvidia-openshell'.

Audit chain: edge answer → graph commit → source_content_hash → source_url (fetch hint).
Verification: curl -s <source_url> | sha256sum  # compare to source_hash

Args:
    concept: Concept label (partial match supported).
    domain:  Domain from list_domains() — e.g. 'nvidia-nim', 'nvidia-tensorrt-triton'.