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ar9av
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`graph-query` is the front door `wiki-query` calls first, and it built its
own graph in graphrag.py with its own page-selection rules, its own slug
helper and its own BFS. Those rules did not exclude the vault bookkeeping
files, and `index.md` links to every page — so almost any two pages looked
about two hops apart and the returned "connection" was meaningless:
owasp-agentic-quotes -> prismor-overview
before: owasp-agentic-quotes -> index -> arnav -> prismor-overview
after: owasp-agentic-quotes -> agentic-governance-maturity
-> prismor -> arnav -> prismor-overview
On a 38-page vault, 52 of 630 page pairs (8%) routed through a bookkeeping
file, and 51 of those were shorter than the real semantic path, so BFS
preferred them. The share grows with vault size.
graphrag now uses the shared page selection from graph_analysis and delegates
to its `shortest_path`, so there is one graph and one BFS in the codebase.
On top of that, `graph-query` now answers questions about the shape of the
vault, phrased in plain English, and returns them fully computed in a new
`graph` field (null for ordinary queries, so the response stays backwards
compatible):
impact "what breaks if I delete X" / "what depends on X"
bridges "which pages bridge my clusters" / "what would fragment my vault"
hubs "what's central" / "top hubs" / "my main topics"
clusters "what clusters do I have" / "how is my wiki organised"
surprising "surprising connections" / "unexpected links"
These are computed lazily: only `bridges` needs betweenness, so every other
intent stays as cheap as an ordinary lookup even on a 5,000-page vault
(<= 0.71s measured). A structural question that names an unresolvable page
falls back to ordinary retrieval rather than guessing.