explain/_find_node cannot resolve node IDs containing punctuation (e.g. concept:domain:x)

[sean-soomgo]

Summary

_find_node (and therefore graphify explain) cannot resolve any node ID that contains punctuation. In our graph this silently disabled lookup for 2,327 of 2,327 concept/overlay nodes (concept:domain:*, concept:class:*, entity:*, commit:*), while IDs without punctuation resolved 400/400.

The docstring states the intent clearly, so this looks like an oversight rather than a design decision:

Return node IDs whose label or ID matches the search term (diacritic-insensitive).
[...] Node-ID exact matches are grouped with label exact matches.

find_node_ambiguity's user-facing hint also assumes ID lookup works:

Retry with the repo-relative path or the full node id.

Version: graphifyy 0.9.32

Reproduction

import networkx as nx
from graphify.serve import _find_node

G = nx.DiGraph()
G.add_node("concept:domain:gosu", label="Gosu")   # id contains ':'
G.add_node("plain_node_id", label="Plain")        # id has no punctuation

print("colon id  ->", _find_node(G, "concept:domain:gosu"))
print("plain id  ->", _find_node(G, "plain_node_id"))
print("by label  ->", _find_node(G, "Gosu"))

Actual:

colon id  -> []
plain id  -> ['plain_node_id']
by label  -> ['concept:domain:gosu']

Expected: the first line resolves to ['concept:domain:gosu'].

Same through the CLI:

$ graphify explain "concept:domain:gosu"
No node matching 'concept:domain:gosu' found.     # node exists in the graph

Root cause

In graphify/serve.py, _find_node_tiers builds two normalizations of the query:

term       = " ".join(_search_tokens(label))          # \w+ tokens -> punctuation becomes a space
norm_query = _strip_diacritics(str(label)).lower().strip()   # punctuation preserved

The exact-match tier compares the node ID against term only:

elif (
    term == norm_label or term == bare_label or term == label_tokens or term == nid_lower
    or norm_query == norm_label or norm_query == bare_label
):

For concept:domain:gosu:

value
term	concept domain gosu
norm_query	concept:domain:gosu
nid_lower	concept:domain:gosu

term == nid_lower is always False once the ID contains punctuation. norm_query — which already holds the correct form and is even part of the trigram prefilter (_trigram_candidates(G, [term, norm_query])) — is never compared against nid_lower.

norm_query appears to have been introduced for punctuated labels (the comment references #1704); it just wasn't extended to node IDs.

Suggested fix

One additional clause in the exact tier:

elif (
    term == norm_label or term == bare_label or term == label_tokens or term == nid_lower
    or norm_query == norm_label or norm_query == bare_label or norm_query == nid_lower
):

Why this is worse than a plain lookup failure

The natural workaround — calling explain with the node's label instead of its ID — resolves to the wrong node silently. Across our 35 concept nodes, only 4 resolved correctly; 31 landed on a same-named code node:

query	resolves to	intended
Repository	data_repository_feedrepository_ts	concept:class:Repository
API	api_apitypehelper_ts	concept:class:API
Gosu	entity:Gosu	concept:domain:gosu

So users get a plausible-looking wrong answer rather than an error, which is hard to notice in downstream reasoning.

[sean-soomgo]

Follow-up: the fix I suggested in the issue body is incomplete, and there is a second class of unresolvable IDs with the same root cause.

Non-ASCII IDs are also affected, without any punctuation

_strip_diacritics NFD-decomposes the query, but the node ID is compared in its raw form. For any ID containing Hangul (and presumably any NFD-decomposable script), the two sides can never be equal:

nid = "artifact:story:공통"
_strip_diacritics(nid).lower()   # '...공통' as NFD  -> ends U+1169 U+11BC
nid.lower()                      # '...공통' as NFC  -> ends U+ACF5 U+D1B5

They print identically but differ byte-wise.

Measured on our graph (14.9k code nodes, Korean source filenames are common):

ID class	count	resolves today
contains :	2,327	0
contains Hangul, no punctuation	353	0
ASCII, no punctuation	300 (sample)	300

So my original repro understated the scope — I first sampled only ASCII IDs and reported 400/400 passing.

Revised suggestion

Normalize both sides rather than adding norm_query == nid_lower:

elif (
    term == norm_label or term == bare_label or term == label_tokens or term == nid_lower
    or norm_query == norm_label or norm_query == bare_label
    or norm_query == _strip_diacritics(nid).lower()      # <-- covers both classes
):

Evaluated over the same graph, replicating the exact-tier predicate:

ID class	current	norm_query == nid_lower	norm_query == _strip_diacritics(nid).lower()
contains : (2,327)	0	2,326	2,327
Hangul, no punctuation (353)	0	0	353
ASCII, no punctuation (300)	300	300	300

No regression: 400 label-based queries returned identical results before and after.

(The single colon ID the narrower fix misses is artifact:story:공통 — it is in both classes.)

User-visible consequence

find_node_ambiguity tells users to disambiguate with the full node ID:

Retry with the repo-relative path or the full node id.

For a repo with non-ASCII filenames that retry cannot succeed — explain "<label>" returns Ambiguous, and every ID it prints as the remedy fails to resolve.

[sean-soomgo]

Correction to my previous comment — the non-ASCII part of that table is wrong.

I measured the strengthened clause by replicating the exact-tier predicate over every node. That skipped _trigram_candidates, which runs before the predicate. After actually applying the change to my local install and re-testing through _find_node:

ID class	before	after norm_query == _strip_diacritics(nid).lower()
contains : (300 sample)	0	299
contains Hangul (300 sample)	0	0 ← my earlier comment claimed 353/353

So the predicate change fixes the punctuation class, and does not fix the non-ASCII class. Sorry for the noise.

Why non-ASCII IDs fail earlier in the pipeline

_find_node_tiers prefilters with _trigram_candidates(G, [term, norm_query]). For a Hangul node ID the candidate set comes back empty, so the node is never scored by any predicate:

nid  = "artifact:story:공통"          # present in the graph
term = " ".join(_search_tokens(nid))
nq   = _strip_diacritics(nid).lower().strip()

_trigram_candidates(G, [term, nq])    # -> []  (0 candidates)

The cause looks like a normalization mismatch between index build and query. The postings table holds both forms:

idx = _get_trigram_index(G)["postings"]
[g for g in idx if re.search(r"[가-힣]", g)][:3]   # ['_것이', '_없으', '시_선']   NFC
[g for g in idx if re.search(r"[ᄀ-ᇿ]", g)][:3]   # ['ᅥ_ᄇ', 'ᅥᆹᄋ', 'ᅵ_ᄉ']      NFD jamo

17 of the query's 19 trigrams exist in the index individually, but the per-trigram postings intersect to the empty set — consistent with the node's own text having been indexed under one normalization while the query is trigrammed under the other (_strip_diacritics NFD-decomposes, so norm_query is NFD; term goes through the same path).

Suggested split

These look like two independent defects with one shared theme (query and stored text normalized asymmetrically):

1. Punctuation in node IDs — predicate only. The clause in my previous comment fixes it; measured 299/300 on a real graph, no regression across 400 label queries.
2. Non-ASCII in node IDs — prefilter. Needs the trigram index and the needles to agree on a normalization form (e.g. normalize both to NFC before trigramming, or apply _strip_diacritics consistently on both sides at index build time).

Happy to test a candidate patch for (2) against a graph with ~350 Hangul-named nodes if that helps.

[sean-soomgo]

Found the prefilter half. Both classes are now fixed locally; measurements below.

Root cause of the non-ASCII class

_node_search_text indexes the node ID raw, while every query path folds through _strip_diacritics (which NFD-decomposes):

# serve.py
return "\x00".join((norm_label, label_tokens, str(nid).lower(), source, source_tokens))
#                                             ^^^^^^^^^^^^^^^^ NFC, but queries arrive NFD

So for a Hangul node ID the query's trigrams never match the indexed ones and _trigram_candidates returns an empty candidate list — the node is dropped before any predicate runs.

This is why the two classes need different fixes, and why the non-ASCII class needs both: the full-scan path is gated by the predicate, the index path by the prefilter.

Suggested fix (prefilter)

Index the folded form alongside the raw one:

return "\x00".join((
    norm_label, label_tokens,
    str(nid).lower(), _strip_diacritics(str(nid)).lower(),
    source, source_tokens,
))

Additive only — candidates stay a superset and the exact predicates still decide, so no new false positives. _trigrams builds a set over the whole concatenated text, so ASCII IDs (where both forms are equal) add no duplicate postings.

Measurements

Applied to a real graph (14.9k code nodes, Korean source filenames common), full population, not a sample:

ID class	total	unpatched	predicate only	predicate + prefilter
contains :	2,327	0	2,326	2,327
contains Hangul	353	0	0	353
ASCII, no punctuation	300 (sample)	300	300	300

Regression check: 600 label-based queries compared before/after in the same process — all 600 identical. (My earlier comment compared hash() across two processes, which is salted per process and therefore meaningless; that was my error, not a real difference.)

End-to-end through the CLI:

$ graphify explain "artifact:story:공통"
Node: 공통 story
  ID:        artifact:story:공통

One gotcha for whoever writes the regression test

A synthetic graph that is too small will pass even when the bug is present: _trigram_candidates's guard (min(present) > int(n * 0.10)) bails out to a full scan, which the predicate fix alone can satisfy. I first wrote a 2-node probe and it reported success while the real graph still returned 0/300. A test for the non-ASCII class needs enough nodes (~10+) to actually exercise the index path.

[xg-gh-25]

The root cause is colon-splitting during node ID resolution — concept:domain:x gets parsed as ["concept", "domain", "x"] instead of being treated as an opaque identifier.

Why this breaks:

# Current (broken)
node_id = "concept:domain:x"
parts = node_id.split(":")  # → ["concept", "domain", "x"]
lookup_key = parts[0]       # → "concept" (wrong namespace)

# Expected
lookup_key = node_id        # → "concept:domain:x" (full ID)

The fix: Node IDs should be content-addressed, not parsed. Colons are valid in URIs (urn:isbn:, did:key:), and graphify's ID scheme shouldn't restrict that.

Recommended approach:

1. Short-term: Escape colons in node IDs during indexing: concept\:domain\:x
2. Long-term: Use a different delimiter for internal partitioning (e.g., | or #), or store IDs as opaque strings with a separate namespace field

Domain-Driven Design lens: This is a bounded context leak. The ID format (colons as structure) is bleeding into the ID semantics (should be opaque). The explain/ API shouldn't care how IDs are constructed — it should just look them up.

Test case to add:

assert explain._find_node("concept:domain:x") == expected_node
assert explain._find_node("urn:isbn:978-3-16-148410-0") == expected_node

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