fix: auto-apply safe defaults for missing fields in load_engine_object (#1104)

* fix: auto-apply safe defaults for missing fields in load_engine_object

* style: apply pre-commit formatting to modified files

Author: princyballabh

python/cocoindex/convert.py

@@ -783,21 +783,57 @@ def load_engine_object(expected_type: Any, v: Any) -> Any:
             # Drop auxiliary discriminator "kind" if present
             dc_init_kwargs: dict[str, Any] = {}
             field_types = {f.name: f.type for f in dataclasses.fields(struct_type)}
+            dataclass_fields = {f.name: f for f in dataclasses.fields(struct_type)}
+
             for name, f_type in field_types.items():
                 if name in v:
                     dc_init_kwargs[name] = load_engine_object(f_type, v[name])
+                else:
+                    # Field is missing from input, check if it has a default or can use auto-default
+                    field = dataclass_fields[name]
+                    if field.default is not dataclasses.MISSING:
+                        # Field has an explicit default value
+                        dc_init_kwargs[name] = field.default
+                    elif field.default_factory is not dataclasses.MISSING:
+                        # Field has a default factory
+                        dc_init_kwargs[name] = field.default_factory()
+                    else:
+                        # No explicit default, try to get auto-default
+                        type_info = analyze_type_info(f_type)
+                        auto_default, is_supported = _get_auto_default_for_type(
+                            type_info
+                        )
+                        if is_supported:
+                            dc_init_kwargs[name] = auto_default
+                        # If not supported, skip the field (let dataclass constructor handle the error)
             return struct_type(**dc_init_kwargs)
         elif is_namedtuple_type(struct_type):
             if not isinstance(v, Mapping):
                 raise ValueError(f"Expected dict for NamedTuple, got {type(v)}")
             # Dict format (from dump/load functions)
             annotations = getattr(struct_type, "__annotations__", {})
             field_names = list(getattr(struct_type, "_fields", ()))
+            field_defaults = getattr(struct_type, "_field_defaults", {})
             nt_init_kwargs: dict[str, Any] = {}
+
             for name in field_names:
                 f_type = annotations.get(name, Any)
                 if name in v:
                     nt_init_kwargs[name] = load_engine_object(f_type, v[name])
+                else:
+                    # Field is missing from input, check if it has a default or can use auto-default
+                    if name in field_defaults:
+                        # Field has an explicit default value
+                        nt_init_kwargs[name] = field_defaults[name]
+                    else:
+                        # No explicit default, try to get auto-default
+                        type_info = analyze_type_info(f_type)
+                        auto_default, is_supported = _get_auto_default_for_type(
+                            type_info
+                        )
+                        if is_supported:
+                            nt_init_kwargs[name] = auto_default
+                        # If not supported, skip the field (let NamedTuple constructor handle the error)
             return struct_type(**nt_init_kwargs)
         elif is_pydantic_model(struct_type):
             if not isinstance(v, Mapping):
@@ -812,9 +848,33 @@ def load_engine_object(expected_type: Any, v: Any) -> Any:
             field_types = {
                 name: field.annotation for name, field in model_fields.items()
             }
+
             for name, f_type in field_types.items():
                 if name in v:
                     pydantic_init_kwargs[name] = load_engine_object(f_type, v[name])
+                else:
+                    # Field is missing from input, check if it has a default or can use auto-default
+                    field = model_fields[name]
+                    if (
+                        hasattr(field, "default") and field.default is not ...
+                    ):  # ... is Pydantic's sentinel for no default
+                        # Field has an explicit default value
+                        pydantic_init_kwargs[name] = field.default
+                    elif (
+                        hasattr(field, "default_factory")
+                        and field.default_factory is not None
+                    ):
+                        # Field has a default factory
+                        pydantic_init_kwargs[name] = field.default_factory()
+                    else:
+                        # No explicit default, try to get auto-default
+                        type_info = analyze_type_info(f_type)
+                        auto_default, is_supported = _get_auto_default_for_type(
+                            type_info
+                        )
+                        if is_supported:
+                            pydantic_init_kwargs[name] = auto_default
+                        # If not supported, skip the field (let Pydantic constructor handle the error)
             return struct_type(**pydantic_init_kwargs)
         return v
 

python/cocoindex/tests/test_load_convert.py

@@ -116,3 +116,68 @@ def test_namedtuple_roundtrip_via_dump_load() -> None:
     loaded = load_engine_object(LocalPoint, dumped)
     assert isinstance(loaded, LocalPoint)
     assert loaded == p
+
+
+def test_dataclass_missing_fields_with_auto_defaults() -> None:
+    """Test that missing fields are automatically assigned safe default values."""
+
+    @dataclasses.dataclass
+    class TestClass:
+        required_field: str
+        optional_field: str | None  # Should get None
+        list_field: list[str]  # Should get []
+        dict_field: dict[str, int]  # Should get {}
+        explicit_default: str = "default"  # Should use explicit default
+
+    # Input missing optional_field, list_field, dict_field (but has explicit_default via class definition)
+    input_data = {"required_field": "test_value"}
+
+    loaded = load_engine_object(TestClass, input_data)
+
+    assert isinstance(loaded, TestClass)
+    assert loaded.required_field == "test_value"
+    assert loaded.optional_field is None  # Auto-default for Optional
+    assert loaded.list_field == []  # Auto-default for list
+    assert loaded.dict_field == {}  # Auto-default for dict
+    assert loaded.explicit_default == "default"  # Explicit default from class
+
+
+def test_namedtuple_missing_fields_with_auto_defaults() -> None:
+    """Test that missing fields in NamedTuple are automatically assigned safe default values."""
+    from typing import NamedTuple
+
+    class TestTuple(NamedTuple):
+        required_field: str
+        optional_field: str | None  # Should get None
+        list_field: list[str]  # Should get []
+        dict_field: dict[str, int]  # Should get {}
+
+    # Input missing optional_field, list_field, dict_field
+    input_data = {"required_field": "test_value"}
+
+    loaded = load_engine_object(TestTuple, input_data)
+
+    assert isinstance(loaded, TestTuple)
+    assert loaded.required_field == "test_value"
+    assert loaded.optional_field is None  # Auto-default for Optional
+    assert loaded.list_field == []  # Auto-default for list
+    assert loaded.dict_field == {}  # Auto-default for dict
+
+
+def test_dataclass_unsupported_type_still_fails() -> None:
+    """Test that fields with unsupported types still cause errors when missing."""
+
+    @dataclasses.dataclass
+    class TestClass:
+        required_field1: str
+        required_field2: int  # No auto-default for int
+
+    # Input missing required_field2 which has no safe auto-default
+    input_data = {"required_field1": "test_value"}
+
+    # Should still raise an error because int has no safe auto-default
+    try:
+        load_engine_object(TestClass, input_data)
+        assert False, "Expected TypeError to be raised"
+    except TypeError:
+        pass  # Expected behavior