fix(analyzer): honour config_path for LangExtract recognizers in YAML registry

CHANGELOG.md

@@ -6,7 +6,8 @@ All notable changes to this project will be documented in this file.
 
 ### Analyzer
 #### Fixed
-- `BasicLangExtractRecognizer` now honours values under `langextract.model.provider.language_model_params` (including `timeout` and `num_ctx`). Previously these were silently dropped because `langextract.extract()` ignores its `language_model_params` argument when a pre-built `ModelConfig` is passed via `config=`, causing Ollama-backed recognizers to fall back to langextract's 120s default regardless of the configured timeout. The recognizer now merges `language_model_params` into `ModelConfig.provider_kwargs`, which is the path that reaches the provider constructor. Explicit entries under `provider.kwargs:` still take precedence. Also fixed a `TypeError` when `kwargs:` or `language_model_params:` is `null` in the YAML. (#1943, Thanks @lsternlicht)
+- Language model recognizers (`BasicLangExtractRecognizer`, `AzureOpenAILangExtractRecognizer`) configured in a recognizer registry YAML now honour `config_path` (and other recognizer-specific kwargs). Previously these entries were validated by the strict `PredefinedRecognizerConfig` schema, which has no `config_path` field and does not allow extra keys, so `config_path` was silently dropped and the recognizer fell back to its bundled default model configuration. Added a `LangExtractRecognizerConfig` model (`extra="allow"`) and registered both recognizer class names in `CONFIG_MODEL_MAP`.
+- `BasicLangExtractRecognizer` now honours values under `langextract.model.provider.language_model_params` (including `timeout` and `num_ctx`). Previously these were silently dropped because `langextract.extract()` ignores its `language_model_params` argument when a pre-built `ModelConfig` is passed via `config=`, causing Ollama-backed recognizers to fall back to langextract's 120s default timeout regardless of the configured timeout. The recognizer now merges `language_model_params` into `ModelConfig.provider_kwargs`, which is the path that reaches the provider constructor. Explicit entries under `provider.kwargs:` still take precedence. Also fixed a `TypeError` when `kwargs:` or `language_model_params:` is `null` in the YAML. (#1943, Thanks @lsternlicht)
 
 ### Anonymizer
 ### General

presidio-analyzer/presidio_analyzer/input_validation/yaml_recognizer_models.py

@@ -224,6 +224,34 @@ def model_dump(self, *args, **kwargs) -> Dict[str, Any]:
         return super().model_dump(*args, **kwargs)
 
 
+class LangExtractRecognizerConfig(PredefinedRecognizerConfig):
+    """Configuration for language model (LangExtract) recognizers.
+
+    Covers ``BasicLangExtractRecognizer`` and ``AzureOpenAILangExtractRecognizer``.
+    ``extra="allow"`` lets recognizer-specific kwargs (most importantly
+    ``config_path``, pointing at the langextract model YAML) survive validation
+    and reach the recognizer constructor. Without this, the strict
+    ``PredefinedRecognizerConfig`` schema drops ``config_path`` and the recognizer
+    silently falls back to its bundled default model configuration.
+    """
+
+    model_config = ConfigDict(extra="allow")
+
+    config_path: Optional[str] = Field(
+        None, description="Path to the langextract model configuration YAML"
+    )
+
+    def model_dump(self, *args, **kwargs) -> Dict[str, Any]:
+        """Serialize the config without None values by default.
+
+        LangExtract recognizer kwargs are passed directly to the recognizer
+        constructor. Excluding None values preserves constructor defaults for
+        omitted YAML fields instead of overriding them with explicit None.
+        """
+        kwargs.setdefault("exclude_none", True)
+        return super().model_dump(*args, **kwargs)
+
+
 class CustomRecognizerConfig(BaseRecognizerConfig):
     """Configuration for custom pattern-based recognizers."""
 
@@ -534,4 +562,6 @@ def validate_language_presence(self):
 CONFIG_MODEL_MAP: Dict[str, Type[BaseModel]] = {
     "HuggingFaceNerRecognizer": HuggingFaceRecognizerConfig,
     "GLiNERRecognizer": GLiNERRecognizerConfig,
+    "BasicLangExtractRecognizer": LangExtractRecognizerConfig,
+    "AzureOpenAILangExtractRecognizer": LangExtractRecognizerConfig,
 }

presidio-analyzer/tests/test_yaml_recognizer_models.py

@@ -4,6 +4,7 @@
 from presidio_analyzer.input_validation.yaml_recognizer_models import (
     BaseRecognizerConfig,
     CustomRecognizerConfig,
+    LangExtractRecognizerConfig,
     LanguageContextConfig,
     PredefinedRecognizerConfig,
     RecognizerRegistryConfig,
@@ -306,6 +307,69 @@ def test_configuration_validator_uses_recognizer_specific_dump_rules():
     assert predefined_recognizer["supported_language"] is None
 
 
+def test_langextract_config_preserves_config_path():
+    """A BasicLangExtractRecognizer YAML entry must keep ``config_path``.
+
+    Regression: without a dedicated config model (extra="allow"), the strict
+    ``PredefinedRecognizerConfig`` schema drops ``config_path``, so the recognizer
+    silently falls back to its bundled default model config.
+    """
+    config = LangExtractRecognizerConfig(
+        name="SmLlama32_3b",
+        class_name="BasicLangExtractRecognizer",
+        supported_languages=["en"],
+        config_path="/path/to/langextract_config.yml",
+    )
+    assert config.config_path == "/path/to/langextract_config.yml"
+    assert config.model_dump()["config_path"] == "/path/to/langextract_config.yml"
+
+
+def test_langextract_config_selected_via_class_name():
+    """``class_name: BasicLangExtractRecognizer`` selects the LangExtract model
+    and preserves ``config_path`` (plus arbitrary extra kwargs) through the full
+    registry validation used by AnalyzerEngineProvider."""
+    from presidio_analyzer.input_validation.schemas import ConfigurationValidator
+
+    raw_config = {
+        "supported_languages": ["en"],
+        "recognizers": [
+            {
+                "name": "SmLlama32_3b",
+                "type": "predefined",
+                "class_name": "BasicLangExtractRecognizer",
+                "enabled": True,
+                "supported_languages": ["en"],
+                "config_path": "/path/to/langextract_config.yml",
+            }
+        ],
+    }
+
+    validated = ConfigurationValidator.validate_recognizer_registry_configuration(
+        raw_config
+    )
+    lm_recognizer = validated["recognizers"][0]
+    assert lm_recognizer["class_name"] == "BasicLangExtractRecognizer"
+    assert lm_recognizer["config_path"] == "/path/to/langextract_config.yml"
+
+
+def test_langextract_config_azure_variant_selected():
+    """AzureOpenAILangExtractRecognizer also maps to the LangExtract config model."""
+    config = RecognizerRegistryConfig(
+        supported_languages=["en"],
+        recognizers=[
+            {
+                "name": "AzureLM",
+                "type": "predefined",
+                "class_name": "AzureOpenAILangExtractRecognizer",
+                "config_path": "/path/to/azure_config.yml",
+            }
+        ],
+    )
+    recognizer = config.recognizers[0]
+    assert isinstance(recognizer, LangExtractRecognizerConfig)
+    assert recognizer.config_path == "/path/to/azure_config.yml"
+
+
 def test_custom_recognizer_config_with_deny_list():
     """Test custom recognizer with deny list only."""
     config = CustomRecognizerConfig(