fix:refactored pydantic extension registration

hamilton/data_quality/default_validators.py

@@ -508,23 +508,6 @@ def _append_pandera_to_default_validators():
 _append_pandera_to_default_validators()
 
 
-def _append_pydantic_to_default_validators():
-    """Utility method to append pydantic validators as needed"""
-    try:
-        import pydantic  # noqa: F401
-    except ModuleNotFoundError:
-        logger.debug(
-            "Cannot import pydantic from pydantic_validators. Run pip install sf-hamilton[pydantic] if needed."
-        )
-        return
-    from hamilton.data_quality import pydantic_validators
-
-    AVAILABLE_DEFAULT_VALIDATORS.extend(pydantic_validators.PYDANTIC_VALIDATORS)
-
-
-_append_pydantic_to_default_validators()
-
-
 def resolve_default_validators(
     output_type: Type[Type],
     importance: str,

hamilton/plugins/h_pydantic.py

@@ -17,9 +17,9 @@ def __init__(
         importance: str = dq_base.DataValidationLevel.WARN.value,
         target: fm_base.TargetType = None,
     ):
-        """Specific output-checker for pydantic models. This decorator utilizes the output type of
-        the function, which can be any subclass of pydantic.BaseModel. The function output must
-        be declared with a type hint.
+        """Specific output-checker for pydantic models (requires ``pydantic>=2.0``).
+        This decorator utilizes the output type of the function, which can be any subclass of pydantic.BaseModel.
+        The function output must be declared with a type hint.
 
         :param model: The pydantic model to use for validation. If this is not provided, then the output type of the function is used.
         :param importance: Importance level (either "warn" or "fail") -- see documentation for check_output for more details.

hamilton/data_quality/pydantic_validators.py → hamilton/plugins/pydantic_extensions.py

@@ -1,13 +1,28 @@
 from typing import Any, Type
 
-from pydantic import BaseModel, TypeAdapter, ValidationError
-
-from hamilton.data_quality import base
+from hamilton.data_quality import base, default_validators
 from hamilton.htypes import custom_subclass_check
 
+try:
+    import pydantic  # noqa: F401
+except ModuleNotFoundError as e:
+    raise NotImplementedError(
+        "Cannot import `pydantic` from `pydantic_validators`. Run pip install 'sf-hamilton[pydantic]' if needed."
+    ) from e
+
+try:
+    from pydantic import BaseModel, TypeAdapter, ValidationError
+except ImportError as e:
+    raise NotImplementedError(
+        "`pydantic>=2.0` required to use `pydantic_validators`. Run pip install 'sf-hamilton[pydantic]' if needed."
+    ) from e
+
+
+COLUMN_FRIENDLY_DF_TYPE = False
+
 
 class PydanticModelValidator(base.BaseDefaultValidator):
-    """Pydantic model compatibility validator
+    """Pydantic model compatibility validator (requires ``pydantic>=2.0``)
 
     Note that this validator uses pydantic's strict mode, which does not allow for
     coercion of data. This means that if an object does not exactly match the reference
@@ -57,4 +72,10 @@ def name(cls) -> str:
         return "pydantic_validator"
 
 
-PYDANTIC_VALIDATORS = [PydanticModelValidator]
+def register_validators():
+    """Utility method to append pydantic validators as needed"""
+    validators = [PydanticModelValidator]
+    default_validators.AVAILABLE_DEFAULT_VALIDATORS.extend(validators)
+
+
+register_validators()

hamilton/registry.py

@@ -32,6 +32,7 @@
     "kedro",
     "huggingface",
     "mlflow",
+    "pydantic",
 ]
 HAMILTON_EXTENSIONS: Tuple[ExtensionName, ...] = get_args(ExtensionName)
 HAMILTON_AUTOLOAD_ENV = "HAMILTON_AUTOLOAD_EXTENSIONS"

tests/integrations/pydantic/test_pydantic_data_quality.py → tests/plugins/test_pydantic_extensions.py

@@ -3,10 +3,10 @@
 import pytest
 from pydantic import BaseModel, ValidationError
 
-from hamilton.data_quality.pydantic_validators import PydanticModelValidator
 from hamilton.function_modifiers import check_output
 from hamilton.node import Node
 from hamilton.plugins import h_pydantic
+from hamilton.plugins.pydantic_extensions import PydanticModelValidator
 
 
 def test_basic_pydantic_validator_passes():