chore: import upstream snapshot with attribution

apps/pii/engines.py

@@ -0,0 +1,267 @@
+"""Analyzer engine builders for the PII service.
+
+Two NER engines share one recognizer surface:
+
+- spacy (default): the 5 large spaCy models do NER (PERSON/LOCATION/NRP/
+  DATE_TIME) and tokenization.
+- gliner (opt-in): one multilingual GLiNER model does NER on CPU or GPU;
+  small spaCy models remain only for tokenization + lemmas.
+
+Both engines register the identical regex/checksum recognizer set (Presidio
+defaults, EXTRA_RECOGNIZERS, VIN) — only the source of the 4 NER entity types
+differs. Side-effect free: importing this module loads no models.
+"""
+
+import importlib.util
+
+import spacy.util
+from presidio_analyzer import AnalyzerEngine, Pattern, PatternRecognizer
+from presidio_analyzer.nlp_engine import NlpEngineProvider
+from presidio_analyzer.predefined_recognizers import (
+    AuAbnRecognizer,
+    AuAcnRecognizer,
+    AuMedicareRecognizer,
+    AuTfnRecognizer,
+    EsNieRecognizer,
+    EsNifRecognizer,
+    FiPersonalIdentityCodeRecognizer,
+    GLiNERRecognizer,
+    InAadhaarRecognizer,
+    InPanRecognizer,
+    InPassportRecognizer,
+    InVehicleRegistrationRecognizer,
+    InVoterRecognizer,
+    ItDriverLicenseRecognizer,
+    ItFiscalCodeRecognizer,
+    ItIdentityCardRecognizer,
+    ItPassportRecognizer,
+    ItVatCodeRecognizer,
+    PlPeselRecognizer,
+    SgFinRecognizer,
+    SgUenRecognizer,
+    UkNinoRecognizer,
+)
+
+# Languages served. Each needs its spaCy model installed in the image; the
+# es/it/pl/fi predefined recognizers (ES_NIF, IT_FISCAL_CODE, PL_PESEL, ...)
+# auto-load once their NLP engine is present.
+NLP_CONFIGURATION = {
+    "nlp_engine_name": "spacy",
+    "models": [
+        {"lang_code": "en", "model_name": "en_core_web_lg"},
+        {"lang_code": "es", "model_name": "es_core_news_lg"},
+        {"lang_code": "it", "model_name": "it_core_news_lg"},
+        {"lang_code": "pl", "model_name": "pl_core_news_lg"},
+        {"lang_code": "fi", "model_name": "fi_core_news_lg"},
+    ],
+}
+SUPPORTED_LANGUAGES = [m["lang_code"] for m in NLP_CONFIGURATION["models"]]
+
+# The gliner engine still needs a spaCy pipeline per language: the regex
+# recognizers consume NlpArtifacts and the LemmaContextAwareEnhancer boosts
+# scores from surrounding lemmas. The small models (~12-40MB each vs ~400MB
+# large) keep tokenization + lemmas intact while GLiNER owns NER. Blank
+# pipelines ("blank:xx") are not an option: Presidio's SpacyNlpEngine treats
+# unknown model names as pip packages and tries to download them.
+# labels_to_ignore strips the small models' NER output from NlpArtifacts —
+# correctness comes from removing SpacyRecognizer in build_gliner_analyzer;
+# this only silences unmapped-label noise.
+GLINER_NLP_CONFIGURATION = {
+    "nlp_engine_name": "spacy",
+    "models": [
+        {"lang_code": "en", "model_name": "en_core_web_sm"},
+        {"lang_code": "es", "model_name": "es_core_news_sm"},
+        {"lang_code": "it", "model_name": "it_core_news_sm"},
+        {"lang_code": "pl", "model_name": "pl_core_news_sm"},
+        {"lang_code": "fi", "model_name": "fi_core_news_sm"},
+    ],
+    "ner_model_configuration": {
+        "labels_to_ignore": [
+            "CARDINAL", "DATE", "EVENT", "FAC", "GPE", "LANGUAGE", "LAW",
+            "LOC", "MISC", "MONEY", "NORP", "ORDINAL", "ORG", "PER",
+            "PERCENT", "PERSON", "PRODUCT", "QUANTITY", "TIME", "WORK_OF_ART",
+        ],
+    },
+}
+
+# Zero-shot label prompts -> the 4 Presidio NER entities GLiNER owns. Multiple
+# prompts per entity trade a little inference cost for recall; tune against
+# scripts/bench_engines.py output.
+GLINER_ENTITY_MAPPING = {
+    "person": "PERSON",
+    "name": "PERSON",
+    "location": "LOCATION",
+    "address": "LOCATION",
+    "date": "DATE_TIME",
+    "time": "DATE_TIME",
+    "nationality": "NRP",
+    "religious group": "NRP",
+    "political group": "NRP",
+    "ethnic group": "NRP",
+}
+
+# Predefined recognizers Presidio ships but does NOT load into the default
+# registry — they must be added explicitly. Each carries its own
+# supported_language, so it fires under that language once its NLP model is
+# loaded. en: UK/AU/IN/SG locale ids; es/it/pl/fi: national ids.
+EXTRA_RECOGNIZERS = [
+    UkNinoRecognizer,
+    AuAbnRecognizer,
+    AuAcnRecognizer,
+    AuTfnRecognizer,
+    AuMedicareRecognizer,
+    InPanRecognizer,
+    InAadhaarRecognizer,
+    InVehicleRegistrationRecognizer,
+    InVoterRecognizer,
+    InPassportRecognizer,
+    SgFinRecognizer,
+    SgUenRecognizer,
+    EsNifRecognizer,
+    EsNieRecognizer,
+    ItFiscalCodeRecognizer,
+    ItDriverLicenseRecognizer,
+    ItVatCodeRecognizer,
+    ItPassportRecognizer,
+    ItIdentityCardRecognizer,
+    PlPeselRecognizer,
+    FiPersonalIdentityCodeRecognizer,
+]
+
+
+class VinRecognizer(PatternRecognizer):
+    """VIN (17 chars, A-Z/0-9 excluding I/O/Q) with ISO 3779 check-digit
+    validation (position 9). Validation makes accidental matches on arbitrary
+    17-char codes (request ids, SKUs, tokens) extremely unlikely. Some
+    non-North-American VINs omit the check digit and are skipped — an
+    intentional bias toward precision.
+    """
+
+    _TRANSLIT = {
+        **{str(d): d for d in range(10)},
+        "A": 1, "B": 2, "C": 3, "D": 4, "E": 5, "F": 6, "G": 7, "H": 8,
+        "J": 1, "K": 2, "L": 3, "M": 4, "N": 5, "P": 7, "R": 9,
+        "S": 2, "T": 3, "U": 4, "V": 5, "W": 6, "X": 7, "Y": 8, "Z": 9,
+    }
+    _WEIGHTS = [8, 7, 6, 5, 4, 3, 2, 10, 0, 9, 8, 7, 6, 5, 4, 3, 2]
+
+    def validate_result(self, pattern_text: str):
+        vin = pattern_text.upper()
+        if len(vin) != 17:
+            return False
+        try:
+            total = sum(self._TRANSLIT[c] * w for c, w in zip(vin, self._WEIGHTS))
+        except KeyError:
+            return False
+        check = total % 11
+        expected = "X" if check == 10 else str(check)
+        return vin[8] == expected
+
+
+class SharedModelGLiNERRecognizer(GLiNERRecognizer):
+    """Per-language GLiNER recognizer sharing ONE loaded model.
+
+    Presidio routes recognizers by supported_language, so the registry holds
+    one instance per served language — but each instance's load() would pull
+    its own ~1.2GB model copy. The first instance loads (an ImportError from
+    a missing gliner package propagates — fail fast in the lean image); the
+    rest reuse the cached model.
+    """
+
+    _shared_models: dict = {}
+
+    def load(self) -> None:
+        key = (self.model_name, self.map_location)
+        cached = self._shared_models.get(key)
+        if cached is None:
+            super().load()
+            self._shared_models[key] = self.gliner
+        else:
+            self.gliner = cached
+
+    def analyze(self, text, entities, nlp_artifacts=None):
+        """GLiNERRecognizer appends any requested entity it doesn't know as an
+        ad-hoc zero-shot label and returns its hits. The analyzer passes ALL
+        supported entities (~40) when a request doesn't narrow them, which
+        would prompt GLiNER for CREDIT_CARD/VIN/ES_NIF/... — wrong scope, and
+        inference cost scales with label count. Restrict to the NER entities
+        this recognizer owns."""
+        requested = [e for e in (entities or self.supported_entities) if e in self.supported_entities]
+        if not requested:
+            return []
+        return super().analyze(text, requested, nlp_artifacts)
+
+
+def _register_common_recognizers(analyzer: AnalyzerEngine) -> None:
+    """Regex/checksum recognizers shared by both engines."""
+    # VIN is language-agnostic, so register it under every served language —
+    # a recognizer only fires for the language the caller routes to.
+    vin_pattern = Pattern(name="vin", regex=r"\b[A-HJ-NPR-Z0-9]{17}\b", score=0.7)
+    for language in SUPPORTED_LANGUAGES:
+        analyzer.registry.add_recognizer(
+            VinRecognizer(
+                supported_entity="VIN",
+                patterns=[vin_pattern],
+                context=["vin", "vehicle", "chassis"],
+                supported_language=language,
+            )
+        )
+    for recognizer_cls in EXTRA_RECOGNIZERS:
+        analyzer.registry.add_recognizer(recognizer_cls())
+
+
+def build_spacy_analyzer() -> AnalyzerEngine:
+    nlp_engine = NlpEngineProvider(nlp_configuration=NLP_CONFIGURATION).create_engine()
+    analyzer = AnalyzerEngine(nlp_engine=nlp_engine, supported_languages=SUPPORTED_LANGUAGES)
+    _register_common_recognizers(analyzer)
+    return analyzer
+
+
+def build_gliner_analyzer(model_name: str, device: str | None) -> AnalyzerEngine:
+    """GLiNER engine: one multilingual zero-shot model replaces spaCy NER for
+    PERSON/LOCATION/NRP/DATE_TIME; everything else is unchanged.
+
+    :param model_name: HuggingFace id of the GLiNER model.
+    :param device: torch device ("cpu", "cuda", "cuda:0"); None auto-detects
+        via Presidio's device_detector (cuda when available, else cpu).
+    """
+    # Fail fast with an actionable message when gliner deps are missing (e.g.
+    # a custom-built image without them). Without these checks Presidio would
+    # try to pip-download the missing spaCy models at startup (a silent
+    # network fallback that dies with an unrelated pip permission error), and
+    # the gliner ImportError would surface only later.
+    if importlib.util.find_spec("gliner") is None:
+        raise RuntimeError(
+            "PII_ENGINE=gliner but the gliner package is not installed; "
+            "use the stock pii image (docker/pii.Dockerfile ships torch + gliner)"
+        )
+    missing = [
+        m["model_name"]
+        for m in GLINER_NLP_CONFIGURATION["models"]
+        if not spacy.util.is_package(m["model_name"])
+    ]
+    if missing:
+        raise RuntimeError(
+            f"PII_ENGINE=gliner needs spaCy models {missing}; "
+            "use the stock pii image (docker/pii.Dockerfile ships them)"
+        )
+    nlp_engine = NlpEngineProvider(nlp_configuration=GLINER_NLP_CONFIGURATION).create_engine()
+    analyzer = AnalyzerEngine(nlp_engine=nlp_engine, supported_languages=SUPPORTED_LANGUAGES)
+    # The default registry wires SpacyRecognizer per language; with GLiNER
+    # owning the NER entities it would emit duplicate/competing spans from the
+    # small models' ner pipe. remove_recognizer only logs when nothing matched,
+    # so assert the removal actually happened.
+    analyzer.registry.remove_recognizer("SpacyRecognizer")
+    if any(r.name == "SpacyRecognizer" for r in analyzer.registry.recognizers):
+        raise RuntimeError("SpacyRecognizer removal failed; Presidio registry layout changed")
+    for language in SUPPORTED_LANGUAGES:
+        analyzer.registry.add_recognizer(
+            SharedModelGLiNERRecognizer(
+                entity_mapping=GLINER_ENTITY_MAPPING,
+                model_name=model_name,
+                map_location=device,
+                supported_language=language,
+            )
+        )
+    _register_common_recognizers(analyzer)
+    return analyzer