Fixing regression with annotated types

Author: leandrodamascena

aws_lambda_powertools/event_handler/openapi/compat.py

@@ -14,6 +14,7 @@
 # We use this for forward reference, as it allows us to handle forward references in type annotations.
 from pydantic._internal._typing_extra import eval_type_lenient
 from pydantic._internal._utils import lenient_issubclass
+from pydantic.fields import FieldInfo as PydanticFieldInfo
 from pydantic_core import PydanticUndefined, PydanticUndefinedType
 from typing_extensions import Annotated, Literal, get_args, get_origin
 
@@ -200,15 +201,9 @@ def extract_metadata(ann: Any) -> tuple[Any, list[Any]]:
         # If base type is also Annotated, recursively extract its metadata
         if get_origin(base_type) is Annotated:
             inner_base, inner_metadata = extract_metadata(base_type)
-            # Combine metadata from both levels, filtering out FieldInfo instances
-            from pydantic.fields import FieldInfo as PydanticFieldInfo
-
             all_metadata = [m for m in inner_metadata + metadata if not isinstance(m, PydanticFieldInfo)]
             return inner_base, all_metadata
         else:
-            # Filter out FieldInfo instances from metadata
-            from pydantic.fields import FieldInfo as PydanticFieldInfo
-
             constraint_metadata = [m for m in metadata if not isinstance(m, PydanticFieldInfo)]
             return base_type, constraint_metadata
 

aws_lambda_powertools/event_handler/openapi/params.py

@@ -1131,8 +1131,6 @@ def get_field_info_annotated_type(annotation, value, is_path_param: bool) -> tup
     # Reconstruct type_annotation with non-FieldInfo metadata if present
     # This ensures constraints like Interval are preserved
     if other_metadata and not has_discriminator_with_param:
-        from typing_extensions import Annotated
-
         type_annotation = Annotated[(type_annotation, *other_metadata)]
 
     # Process the annotation if it exists