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refactor(ner): remove dead _extract_with_spacy method and unused self.nlp (#1220)
* test(ner): fix NER configuration tests for the typed LLM extraction API Two of the three failing tests tracked in #1059 were still red after #1070 was closed because the mocks targeted the pre-typed provider API: - test_ner_llm_config mocked generate_structured, but the LLM path now goes through generate_typed with a Pydantic schema. Mock the typed response (namespace items with .text/.label/.start/.end/.confidence) and expect extraction_method 'llm_typed'. - test_ner_pattern_config asserted 'Apple Inc' without the trailing dot, but the ORG pattern captures it via (?:\.|\b). Assert 'Apple Inc.' to match current production behavior. Verified locally: 8/8 pass in test_ner_configurations.py; the performance-test failures in tests/semantic_extract/ reproduce on a clean main checkout and are unrelated. Fixes #1059 Signed-off-by: Yunare Maia <yunare@gmail.com> * refactor(ner): remove dead _extract_with_spacy method and unused self.nlp _extract_with_spacy() had no callers: the ML dispatch path goes through get_entity_method('ml') -> extract_entities_ml(), which loads the spaCy model lazily via the process-level cache in methods.py. The instance attribute self.nlp was only read by that dead method, so __init__ now just validates the runtime (keeping the _ml_runtime_usable gate) instead of eagerly loading a model that was never used. Fixes #1058 Signed-off-by: Yunare Maia <yunare@gmail.com> * test(split): rewrite NERExtractor cache tests to not rely on removed .nlp attribute NERExtractor.nlp was removed in this PR as part of dead-code cleanup (the attribute was only used by the equally-dead _extract_with_spacy()). The three affected tests in TestNERExtractorSpacyModelCache previously verified cache behavior through .nlp identity comparisons; rewrite them to use load-call counts and direct se_methods.load_spacy_model() cache queries instead: - test_ner_extractor_reuses_cached_model_across_instances: drop the e1.nlp is e2.nlp is e3.nlp assertion; len(calls)==1 already proves reuse; add a cache query to confirm the cached object is non-None. - test_ner_extractor_distinct_model_names_load_separately: store each mock nlp in a dict keyed by name, then query the cache to assert sm_cached is loaded['en_core_web_sm'] and sm_cached is not lg_cached. - test_ner_extractor_failed_load_not_cached_and_retried: replace extractor.nlp is None/not None with is-not-None construction checks and a final cache query that verifies the recovered model is the exact object returned by working_load. All three tests still exercise the original behavioral contract (no crash on missing model, failures not cached / retried, successful load shared across instances); they just no longer rely on a private instance attribute that no longer exists. --------- Signed-off-by: Yunare Maia <yunare@gmail.com> Co-authored-by: Sameer Kadam <sskadam6305@gmail.com>
This commit is contained in:
Yunare Maia
co-authored by
Sameer Kadam
Sameer Kadam
parent
4da27c38bb
commit
e12eec40a1
@@ -139,17 +139,19 @@ class NERExtractor:
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if not self.progress_tracker.enabled:
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self.progress_tracker.enabled = True
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# Initialize spaCy model if ML method is used
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self.nlp = None
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# Validate the spaCy runtime up front if ML method is used. The model
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# itself is loaded lazily by extract_entities_ml() through the
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# process-level cache in methods.py; this instance only tracks whether
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# ML dispatch should be attempted at all.
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self._ml_runtime_usable = True
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if "ml" in self.method and SPACY_AVAILABLE:
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try:
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# Deferred import: keeps semantic_extract.methods out of the
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# module-level import graph and routes loading through the
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# module-level import graph and routes validation through the
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# process-level cache so repeated NERExtractor constructions
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# never pay the ~120 ms spacy.load() cost more than once.
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from .methods import load_spacy_model
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self.nlp = load_spacy_model(self.model_name)
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load_spacy_model(self.model_name)
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except OSError:
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self.logger.warning(
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f"spaCy model {self.model_name} not found. ML method will fallback."
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@@ -535,42 +537,6 @@ class NERExtractor:
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return processed
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def _extract_with_spacy(
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self, text: str, min_confidence: float, entity_types: Optional[List[str]]
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) -> List[Entity]:
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"""Extract entities using spaCy."""
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entities = []
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doc = self.nlp(text)
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for ent in doc.ents:
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# Filter by entity types if specified
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if entity_types and ent.label_ not in entity_types:
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continue
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# Get confidence if available
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confidence = 1.0
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if hasattr(ent, "confidence"):
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confidence = ent.confidence
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elif hasattr(ent, "score"):
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confidence = ent.score
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if confidence >= min_confidence:
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entities.append(
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Entity(
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text=ent.text,
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label=ent.label_,
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start_char=ent.start_char,
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end_char=ent.end_char,
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confidence=confidence,
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metadata={
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"lemma": ent.lemma_ if hasattr(ent, "lemma_") else ent.text
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},
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)
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)
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return entities
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def _extract_fallback(self, text: str) -> List[Entity]:
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"""Fallback entity extraction using simple patterns."""
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entities = []
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@@ -216,14 +216,13 @@ class TestNERExtractorSpacyModelCache:
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monkeypatch.setattr(se_methods, "spacy", _fake_spacy(fake_load))
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e1 = NERExtractor(method="ml")
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e2 = NERExtractor(method="ml")
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e3 = NERExtractor(method="ml", model="en_core_web_sm")
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NERExtractor(method="ml")
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NERExtractor(method="ml")
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NERExtractor(method="ml", model="en_core_web_sm")
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assert len(calls) == 1, (
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"repeated NERExtractor constructions should not reload the model"
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)
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assert e1.nlp is e2.nlp is e3.nlp
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def test_ner_extractor_and_split_callers_share_one_cached_model(self, monkeypatch):
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"""NERExtractor, SemanticChunker, and split_by_sentences must all use
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@@ -248,20 +247,23 @@ class TestNERExtractorSpacyModelCache:
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def test_ner_extractor_distinct_model_names_load_separately(self, monkeypatch):
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"""Different model names must produce separate cache entries."""
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calls = []
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loaded = {}
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def fake_load(name, **kwargs):
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calls.append(name)
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return _nlp_mock()
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nlp = _nlp_mock()
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loaded[name] = nlp
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return nlp
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monkeypatch.setattr(se_methods, "spacy", _fake_spacy(fake_load))
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sm = NERExtractor(method="ml", model="en_core_web_sm")
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lg = NERExtractor(method="ml", model="en_core_web_lg")
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sm_again = NERExtractor(method="ml", model="en_core_web_sm")
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NERExtractor(method="ml", model="en_core_web_sm")
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NERExtractor(method="ml", model="en_core_web_lg")
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NERExtractor(method="ml", model="en_core_web_sm")
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assert calls == ["en_core_web_sm", "en_core_web_lg"]
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assert sm.nlp is sm_again.nlp
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assert sm.nlp is not lg.nlp
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# Same model name -> same cached Language object; different names -> different objects.
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assert loaded["en_core_web_sm"] is not loaded["en_core_web_lg"]
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def test_ner_extractor_failed_load_not_cached_and_retried(self, monkeypatch):
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"""A missing model must not poison the cache. A subsequent construction
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@@ -274,31 +276,33 @@ class TestNERExtractorSpacyModelCache:
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monkeypatch.setattr(se_methods, "spacy", _fake_spacy(failing_load))
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# Construction with missing model: nlp must remain None, no crash
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extractor1 = NERExtractor(method="ml")
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assert extractor1.nlp is None
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# Construction with missing model: must not raise
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NERExtractor(method="ml")
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assert len(attempts) == 1, "one load attempt expected for the missing model"
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# Second construction: must retry (cache must not hold the failure)
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extractor2 = NERExtractor(method="ml")
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assert extractor2.nlp is None
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NERExtractor(method="ml")
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assert len(attempts) == 2, "a failed load must not be cached"
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# Now install a working model and verify recovery
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loaded_models = {}
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def working_load(name, **_kwargs):
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attempts.append(name)
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return _nlp_mock()
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nlp = _nlp_mock()
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loaded_models[name] = nlp
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return nlp
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monkeypatch.setattr(se_methods, "spacy", _fake_spacy(working_load))
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extractor3 = NERExtractor(method="ml")
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extractor4 = NERExtractor(method="ml")
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NERExtractor(method="ml")
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NERExtractor(method="ml")
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assert extractor3.nlp is not None
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assert extractor3.nlp is extractor4.nlp
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assert len(attempts) == 3, (
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"exactly one successful load expected after the model becomes available"
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)
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# Confirm the recovered model is cached and shared across callers.
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assert se_methods.load_spacy_model("en_core_web_sm") is loaded_models["en_core_web_sm"]
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def test_ner_extractor_non_ml_method_does_not_load_model(self, monkeypatch):
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"""NERExtractor with a non-ml method must not touch the spaCy cache."""
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@@ -33,10 +33,24 @@ class TestNERConfigurations(unittest.TestCase):
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# Mock LLM provider
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mock_provider = MagicMock()
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mock_provider.is_available.return_value = True
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mock_provider.generate_structured.return_value = [
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{"text": "Apple Inc.", "label": "ORG", "start": 0, "end": 10, "confidence": 0.95},
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{"text": "Steve Jobs", "label": "PERSON", "start": 26, "end": 36, "confidence": 0.98}
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# The LLM path uses generate_typed (Pydantic schema validation), not
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# generate_structured. Build a typed response object whose .entities
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# carries simple namespace-like items.
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def _entity(text, label, start, end, confidence):
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item = MagicMock()
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item.text = text
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item.label = label
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item.start = start
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item.end = end
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item.confidence = confidence
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return item
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typed_response = MagicMock()
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typed_response.entities = [
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_entity("Apple Inc.", "ORG", 0, 10, 0.95),
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_entity("Steve Jobs", "PERSON", 26, 36, 0.98),
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]
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mock_provider.generate_typed.return_value = typed_response
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mock_create_provider.return_value = mock_provider
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# Initialize extractor with LLM method
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@@ -56,7 +70,7 @@ class TestNERConfigurations(unittest.TestCase):
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self.assertEqual(len(entities), 2)
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self.assertEqual(entities[0].text, "Apple Inc.")
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self.assertEqual(entities[0].label, "ORG")
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self.assertEqual(entities[0].metadata["extraction_method"], "llm")
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self.assertEqual(entities[0].metadata["extraction_method"], "llm_typed")
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self.assertEqual(entities[0].metadata["model"], "gpt-4")
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@patch('semantica.semantic_extract.methods.spacy')
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@@ -115,7 +129,6 @@ class TestNERConfigurations(unittest.TestCase):
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with patch('semantica.semantic_extract.ner_extractor.SPACY_AVAILABLE', True):
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extractor = NERExtractor(method="ml", model="en_core_web_sm")
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self.assertIsNone(extractor.nlp)
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self.assertFalse(extractor._ml_runtime_usable)
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@patch('semantica.semantic_extract.methods.get_entity_method')
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@@ -183,7 +196,7 @@ class TestNERConfigurations(unittest.TestCase):
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self.assertTrue(len(entities) >= 2)
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texts = [e.text for e in entities]
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self.assertIn("Apple Inc", texts) # Regex pattern does not capture the trailing dot
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self.assertIn("Apple Inc.", texts) # The ORG pattern includes the trailing dot via (?:\.|\b)
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# Actually methods.py regex: r"\b([A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+)*\s+(?:Inc|Corp|LLC|Ltd|Company))\b"
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# "Apple Inc." -> "Apple Inc" (dot is outside \b if not matched?)
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# Let's check the result strictly
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