Fix Pydantic 2.12+ compatibility for custom FieldInfo with Annotated types

Pydantic 2.12+ converts custom FieldInfo subclasses to plain PydanticFieldInfo
for fields using Annotated types with metadata (like Coordinates). This caused
custom attributes like index, sortable, etc. to be lost.

Fix: Capture original FieldInfo objects before Pydantic processes them and
restore them when Pydantic has converted them to plain PydanticFieldInfo.

Author: abrookins

aredis_om/model/model.py

@@ -2068,6 +2068,15 @@ class ModelMeta(ModelMetaclass):
     def __new__(cls, name, bases, attrs, **kwargs):  # noqa C901
         meta = attrs.pop("Meta", None)
 
+        # Capture original FieldInfo objects from attrs before Pydantic processes them.
+        # Pydantic 2.12+ may convert custom FieldInfo subclasses to plain PydanticFieldInfo
+        # for Annotated types, losing custom attributes like index, sortable, etc.
+        original_field_infos: Dict[str, FieldInfo] = {}
+        if PYDANTIC_V2:
+            for attr_name, attr_value in attrs.items():
+                if isinstance(attr_value, FieldInfo):
+                    original_field_infos[attr_name] = attr_value
+
         # Duplicate logic from Pydantic to filter config kwargs because if they are
         # passed directly including the registry Pydantic will pass them over to the
         # superclass causing an error
@@ -2141,13 +2150,31 @@ class Config:
             model_fields = new_class.__fields__
 
         for field_name, field in model_fields.items():
+            pydantic_field = field  # Keep reference to Pydantic's processed field
             if type(field) is PydanticFieldInfo:
+                # Pydantic converted our FieldInfo to a plain PydanticFieldInfo.
+                # This happens with Annotated types in Pydantic 2.12+.
+                # Use the original FieldInfo if we captured it, otherwise create a new one.
                 if PYDANTIC_V2:
-                    field = FieldInfo(**field._attributes_set)
+                    if field_name in original_field_infos:
+                        # Use the original FieldInfo with custom attributes preserved
+                        field = original_field_infos[field_name]
+                        # Copy the annotation from Pydantic's processed field
+                        # since it wasn't set on the original FieldInfo
+                        if hasattr(pydantic_field, "annotation"):
+                            field.annotation = pydantic_field.annotation
+                        # Also copy metadata from Pydantic's field (validators, serializers, etc.)
+                        if hasattr(pydantic_field, "metadata"):
+                            field.metadata = pydantic_field.metadata
+                    else:
+                        field = FieldInfo(**field._attributes_set)
                 else:
                     # Pydantic v1 compatibility
                     field = FieldInfo()
                 setattr(new_class, field_name, field)
+                # Also update model_fields so schema generation uses our fixed field
+                if PYDANTIC_V2:
+                    model_fields[field_name] = field
 
             if is_indexed:
                 setattr(new_class, field_name, ExpressionProxy(field, []))

tests/test_json_model.py

@@ -1528,7 +1528,9 @@ class Product(JsonModel, index=True):
         name: str = Field(index=True, sortable=True)  # TAG field with sortable
         category: str = Field(index=True, sortable=True)  # TAG field with sortable
         price: int = Field(index=True, sortable=True)  # NUMERIC field with sortable
-        tags: List[str] = Field(index=True, sortable=True)  # TAG field (list) with sortable
+        tags: List[str] = Field(
+            index=True, sortable=True
+        )  # TAG field (list) with sortable
 
         class Meta:
             global_key_prefix = key_prefix
@@ -1543,9 +1545,13 @@ class Meta:
     await Migrator().run()
 
     # Create test data
-    product1 = Product(name="Zebra", category="Animals", price=100, tags=["wild", "africa"])
+    product1 = Product(
+        name="Zebra", category="Animals", price=100, tags=["wild", "africa"]
+    )
     product2 = Product(name="Apple", category="Fruits", price=50, tags=["red", "sweet"])
-    product3 = Product(name="Banana", category="Fruits", price=30, tags=["yellow", "sweet"])
+    product3 = Product(
+        name="Banana", category="Fruits", price=30, tags=["yellow", "sweet"]
+    )
 
     await product1.save()
     await product2.save()