Chore(deps): Bump scikit-learn in the data-nlp-search group

Bumps the data-nlp-search group with 1 update: [scikit-learn](https://github.com/scikit-learn/scikit-learn).


Updates `scikit-learn` from 1.8.0 to 1.9.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](https://github.com/scikit-learn/scikit-learn/compare/1.8.0...1.9.0)

---
updated-dependencies:
- dependency-name: scikit-learn
  dependency-version: 1.9.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: data-nlp-search
...

Signed-off-by: dependabot[bot] <support@github.com>

pyproject.toml

@@ -69,7 +69,7 @@ dependencies = [
   "rapidfuzz~=3.14.5",
   "redis[hiredis]~=5.2.1",
   "regex>=2026.4.4",
-  "scikit-learn~=1.8.0",
+  "scikit-learn~=1.9.0",
   "sentence-transformers>=5.4.1",
   "setproctitle~=1.3.4",
   "sqlite-vec==0.1.9",

src/documents/search/_tokenizer.py

@@ -48,9 +48,6 @@ _LANGUAGE_MAP: dict[str, str] = {
 }
 
 SUPPORTED_LANGUAGES: frozenset[str] = frozenset(_LANGUAGE_MAP)
-# Document.title is max_length=128, so use 129 as the limit for
-# Tantivy's remove_long filter
-_TOKEN_REMOVE_LONG_LIMIT: Final[int] = 129
 
 
 def register_tokenizers(index: tantivy.Index, language: str | None) -> None:
@@ -80,10 +77,10 @@ def register_tokenizers(index: tantivy.Index, language: str | None) -> None:
 
 
 def _paperless_text(language: str | None) -> tantivy.TextAnalyzer:
-    """Main full-text tokenizer for content, title, etc: simple -> remove_long(129) -> lowercase -> ascii_fold [-> stemmer]"""
+    """Main full-text tokenizer for content, title, etc: simple -> remove_long(65) -> lowercase -> ascii_fold [-> stemmer]"""
     builder = (
         tantivy.TextAnalyzerBuilder(tantivy.Tokenizer.simple())
-        .filter(tantivy.Filter.remove_long(_TOKEN_REMOVE_LONG_LIMIT))
+        .filter(tantivy.Filter.remove_long(65))
         .filter(tantivy.Filter.lowercase())
         .filter(tantivy.Filter.ascii_fold())
     )
@@ -122,12 +119,12 @@ def _bigram_analyzer() -> tantivy.TextAnalyzer:
 
 
 def _simple_search_analyzer() -> tantivy.TextAnalyzer:
-    """Tokenizer for simple substring search fields: non-whitespace chunks -> remove_long(129) -> lowercase -> ascii_fold."""
+    """Tokenizer for simple substring search fields: non-whitespace chunks -> remove_long(65) -> lowercase -> ascii_fold."""
     return (
         tantivy.TextAnalyzerBuilder(
             tantivy.Tokenizer.regex(r"\S+"),
         )
-        .filter(tantivy.Filter.remove_long(_TOKEN_REMOVE_LONG_LIMIT))
+        .filter(tantivy.Filter.remove_long(65))
         .filter(tantivy.Filter.lowercase())
         .filter(tantivy.Filter.ascii_fold())
         .build()

src/documents/tests/search/test_backend.py

@@ -261,36 +261,6 @@ class TestSearch:
             == 1
         )
 
-    @pytest.mark.parametrize(
-        ("search_mode", "query"),
-        [
-            pytest.param(SearchMode.TITLE, "12345", id="title_search"),
-            pytest.param(SearchMode.TEXT, "12345", id="text_search"),
-            pytest.param(SearchMode.QUERY, None, id="query_title_exact"),
-        ],
-    )
-    def test_search_modes_match_model_limit_title_tokens(
-        self,
-        backend: TantivyBackend,
-        search_mode: SearchMode,
-        query: str | None,
-    ) -> None:
-        """Search must keep filename-like title tokens up to the model limit."""
-        long_title = "1234567890" * 12 + "12345678"
-        doc = Document.objects.create(
-            title=long_title,
-            content="ordinary content",
-            checksum="TXT12",
-            pk=18,
-        )
-        backend.add_or_update(doc)
-
-        assert backend.search_ids(
-            query or f"title:{long_title}",
-            user=None,
-            search_mode=search_mode,
-        ) == [doc.pk]
-
     @pytest.mark.parametrize(
         ("mode", "title", "content", "hits", "misses"),
         [

src/documents/tests/search/test_tokenizer.py

@@ -99,25 +99,6 @@ class TestTokenizers:
         )
         assert simple_search_index.searcher().search(q, limit=5).count == 1
 
-    def test_simple_search_analyzer_supports_model_limit_token_substrings(
-        self,
-        simple_search_index: tantivy.Index,
-    ) -> None:
-        """Simple substring search keeps tokens up to Document.title's model limit."""
-        long_token = "abcdefghij" * 12 + "abcdefgh"
-        writer = simple_search_index.writer()
-        doc = tantivy.Document()
-        doc.add_text("simple_content", long_token)
-        writer.add_document(doc)
-        writer.commit()
-        simple_search_index.reload()
-        q = tantivy.Query.regex_query(
-            simple_search_index.schema,
-            "simple_content",
-            ".*cdefg.*",
-        )
-        assert simple_search_index.searcher().search(q, limit=5).count == 1
-
     def test_unsupported_language_logs_warning(self, caplog: LogCaptureFixture) -> None:
         """Unsupported language codes should log a warning and disable stemming gracefully."""
         sb = tantivy.SchemaBuilder()

uv.lock

@@ -2692,6 +2692,15 @@ version = "2.2.8"
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+[[package]]
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+version = "2.23.0"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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+]
+
 [[package]]
 name = "nest-asyncio"
 version = "1.6.0"
@@ -3073,7 +3082,7 @@ requires-dist = [
     { name = "rapidfuzz", specifier = "~=3.14.5" },
     { name = "redis", extras = ["hiredis"], specifier = "~=5.2.1" },
     { name = "regex", specifier = ">=2026.4.4" },
-    { name = "scikit-learn", specifier = "~=1.8.0" },
+    { name = "scikit-learn", specifier = "~=1.9.0" },
     { name = "sentence-transformers", specifier = ">=5.4.1" },
     { name = "setproctitle", specifier = "~=1.3.4" },
     { name = "sqlite-vec", specifier = "==0.1.9" },
@@ -4405,40 +4414,37 @@ wheels = [
 
 [[package]]
 name = "scikit-learn"
-version = "1.8.0"
+version = "1.9.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "joblib", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "narwhals", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "numpy", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "scipy", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "threadpoolctl", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
 ]
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