fix(serve): drop German and Romance question stopwords from query terms (#1900)

Full-sentence non-English queries picked wrong BFS seeds because
_QUERY_STOPWORDS was English-only: in a mostly-English code corpus,
fillers like wie/die/das/funktioniert are rare, get high IDF weight,
and out-seed the real keyword (~175x score collapse).

Extend the frozenset with a curated German set plus trimmed French/
Spanish/Portuguese/Italian question/filler words, diacritics intact.
English-collision words (war, bald, comment, come, son, sin, con,
pour, des) are deliberately omitted; die/hat are kept since the
all-stopword fallback and unfiltered find_node protect English use.
Filtering stays in _query_terms, so the per-term seed guarantee in
_pick_seeds composes unchanged.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

graphify/serve.py

@@ -107,14 +107,30 @@ def _is_searchable(term: str) -> bool:
     return True
 
 
-# English question/filler words dropped from query terms so content words drive
-# BFS seeding. Without this, "how does the frontier cache work" seeds on "how"/
+# Question/filler words dropped from query terms so content words drive BFS
+# seeding. Without this, "how does the frontier cache work" seeds on "how"/
 # "the"/"work" (which prefix-match prose labels like "Working Principles" at 100x)
 # instead of "frontier"/"cache", and lands in the wrong part of the graph. Applied
 # to query terms only — node text is never filtered, so a symbol literally named
 # `work` stays findable via explain/path. `work`/`works`/`working` are included
 # because "how does X work" / "how X works" is the most common question phrasing.
+#
+# Non-English question words are just as damaging (#1900): in a mostly-English
+# code corpus, German "wie"/"funktioniert" are rare, so they get HIGH IDF weight
+# and out-seed the actual content noun by orders of magnitude. So this also
+# carries a curated German set plus a trimmed French/Spanish/Portuguese/Italian
+# set of question/filler words. Diacritics are kept intact (the query tokenizer
+# does not NFKD-strip).
+#
+# Collision tradeoff: a few foreign stopwords are also English content words.
+# We include high-German-value ones like "die"/"hat" (the all-stopword fallback
+# in _query_terms and the unfiltered find_node path keep an English "die"/"hat"
+# query workable), but deliberately OMIT "war"/"bald" (German was/soon) so
+# English queries about "war" or "bald" are not clobbered. On the Romance side
+# we likewise omit "comment" (FR how), "come" (IT how), "son"/"sin"/"con" (ES),
+# and "pour"/"des" (FR) — all too common as English/code terms.
 _QUERY_STOPWORDS = frozenset({
+    # English
     "how", "what", "why", "when", "where", "which", "who", "whom", "whose",
     "does", "did", "is", "are", "was", "were", "be", "been", "being",
     "can", "could", "should", "would", "will", "shall", "may", "might", "must",
@@ -122,14 +138,35 @@ _QUERY_STOPWORDS = frozenset({
     "without", "into", "onto", "off", "that", "this", "these", "those", "there",
     "here", "its", "their", "them", "they", "about", "any", "all", "some",
     "work", "works", "working",
+    # German (articles/conjunctions/question words/auxiliaries/prepositions)
+    "der", "die", "das", "den", "dem", "ein", "eine", "und", "oder", "nicht",
+    "wie", "wer", "wann", "wo", "warum", "wieso",
+    "welche", "welcher", "welches",
+    "ist", "sind", "wird", "wurde", "hat", "haben",
+    "kann", "koennen", "können", "soll", "muss", "sich",
+    "bei", "mit", "von", "fuer", "für", "ueber", "über", "nach", "aus",
+    "gibt", "es",
+    "funktioniert", "geaendert", "geändert", "aendert", "ändert",
+    # French
+    "pourquoi", "quand", "quel", "quelle", "quels", "quelles", "quoi",
+    "qui", "que", "est", "sont", "fonctionne", "cette", "dans", "avec", "où",
+    # Spanish
+    "cómo", "como", "qué", "cuál", "cuáles", "cuándo", "dónde", "donde",
+    "porque", "por", "para", "funciona", "está", "están", "hay",
+    # Portuguese
+    "qual", "quais", "quando", "onde", "são", "estão", "tem", "uma", "não",
+    # Italian
+    "perché", "cosa", "quale", "quali", "dove", "funziona", "sono", "che",
+    "della",
 })
 
 
 def _query_terms(question: str) -> list[str]:
     """Split a query into searchable terms, segmenting Chinese text, then drop
-    English question/filler words (`_QUERY_STOPWORDS`) so content words drive
-    seeding. Falls back to the unfiltered terms if the query is all stopwords, so
-    a question like "how does it work" still seeds on something."""
+    question/filler words (`_QUERY_STOPWORDS`, English plus common German/
+    Romance-language fillers) so content words drive seeding. Falls back to the
+    unfiltered terms if the query is all stopwords, so a question like "how does
+    it work" or "wie funktioniert das" still seeds on something."""
     terms: list[str] = []
     for raw in question.split():
         if _has_chinese(raw):

tests/test_serve.py

@@ -364,6 +364,39 @@ def test_query_terms_all_stopwords_falls_back_to_unfiltered():
     assert _query_terms("how does it work") == ["how", "does", "work"]
 
 
+def test_query_terms_drops_german_question_stopwords():
+    # #1900: German full-sentence queries must reduce to the content noun.
+    # In a mostly-English corpus "wie"/"funktioniert" are rare, get high IDF
+    # weight, and out-seed the actual keyword unless dropped here.
+    assert _query_terms("Wie funktioniert die Authentifizierung?") == ["authentifizierung"]
+
+
+def test_query_terms_all_german_stopwords_falls_back_to_unfiltered():
+    # Existing all-stopword fallback applies to German fillers too: the query
+    # keeps its terms rather than seeding on nothing.
+    terms = _query_terms("wie funktioniert das")
+    assert terms == ["wie", "funktioniert", "das"]
+
+
+def test_pick_seeds_german_query_seeds_content_node_not_heading_noise():
+    """End-to-end for #1900: a German question over a graph with German
+    heading-noise nodes must seed on the content noun, not on nodes that
+    happen to contain 'die'/'wie'/'wird'."""
+    G = nx.DiGraph()
+    G.add_node("cfg", label="Die Konfiguration", source_file="docs/konfiguration.md")
+    G.add_node("sec", label="Wie wird gesichert", source_file="docs/sicherheit.md")
+    G.add_node("auth", label="Authentifizierung", source_file="src/auth.py")
+    G.add_node("helper", label="login_helper", source_file="src/auth.py")
+    G.add_edge("helper", "auth")
+
+    q = "Wie funktioniert die Authentifizierung?"
+    terms = _query_terms(q)
+    seeds = _pick_seeds(_score_nodes(G, terms), G=G, terms=terms)
+    assert "auth" in seeds
+    assert "cfg" not in seeds
+    assert "sec" not in seeds
+
+
 def test_query_terms_filters_only_short_english_terms(monkeypatch):
     import graphify.serve as serve_mod