mypy

llama_stack/strong_typing/inspection.py

@@ -570,15 +570,17 @@ def get_class_properties(typ: type) -> Iterable[Tuple[str, type | str]]:
     elif hasattr(typ, "model_fields"):
         # Pydantic BaseModel - use model_fields to exclude ClassVar and other non-field attributes
         # Reconstruct Annotated type if discriminator exists to preserve metadata
-        from typing import Annotated
+        from typing import Annotated, Any, cast
         from pydantic.fields import FieldInfo
 
-        def get_field_type(name: str, field) -> type | str:
+        def get_field_type(name: str, field: Any) -> type | str:
             # If field has discriminator, wrap in Annotated to preserve it for schema generation
             if field.discriminator:
                 field_info = FieldInfo(annotation=None, discriminator=field.discriminator)
-                return Annotated[field.annotation, field_info]
+                # Annotated returns _AnnotatedAlias which isn't a type but is valid here
-            return field.annotation
+                return Annotated[field.annotation, field_info]  # type: ignore[return-value]
+            # field.annotation can be Union types, Annotated, etc. which aren't type but are valid
+            return field.annotation  # type: ignore[return-value,no-any-return]
 
         return ((name, get_field_type(name, field)) for name, field in typ.model_fields.items())
     else:

llama_stack/strong_typing/schema.py

@@ -92,7 +92,7 @@ def get_class_property_docstrings(
     :returns: A dictionary mapping property names to descriptions.
     """
 
-    result = {}
+    result: Dict[str, str] = {}
     # Only try to get MRO if data_type is actually a class
     # Special types like Literal, Union, etc. don't have MRO
     if not inspect.isclass(data_type):