fix: honor required fields in JSON schemas

adala/utils/pydantic_generator.py

@@ -50,9 +50,12 @@ class Person(BaseModel):
     # `description` is the model class docstring
     model_description = json_schema.get("description", "")
 
+    required_fields = set(json_schema.get("required", []))
     fields_def = {}
     for name, prop in json_schema.get("properties", {}).items():
-        fields_def[name] = json_schema_to_pydantic_field(prop)
+        fields_def[name] = json_schema_to_pydantic_field(
+            prop, required=name in required_fields
+        )
 
     # Create the BaseModel class using create_model().
     model = create_model(model_name, **fields_def)
@@ -63,13 +66,16 @@ class Person(BaseModel):
     return model
 
 
-def json_schema_to_pydantic_field(json_schema: Dict[str, Any]) -> Tuple[Any, Field]:
+def json_schema_to_pydantic_field(
+    json_schema: Dict[str, Any], required: bool = True
+) -> Tuple[Any, Field]:
     """
     Converts a JSON schema property to a Pydantic field definition.
 
     Args:
         name: The field name.
         json_schema: The JSON schema property.
+        required: Whether the containing object requires this property.
 
     Returns:
         A Pydantic field definition.
@@ -95,8 +101,10 @@ def json_schema_to_pydantic_field(json_schema: Dict[str, Any]) -> Tuple[Any, Fie
         if constraint in json_schema:
             field_params[constraint] = json_schema[constraint]
 
+    default = ... if required else json_schema.get("default", None)
+
     # Create a Field object with the type and optional parameters.
-    return type_, Field(..., **field_params)
+    return type_, Field(default, **field_params)
 
 
 def json_schema_to_pydantic_type(
@@ -195,6 +203,9 @@ class Template(BaseModel):
         "title": class_name,
         "description": description,
         "properties": field_schema,
+        # Skill response fields have historically all been required. Keep that
+        # contract while the general JSON Schema converter honors `required`.
+        "required": list(field_schema),
     }
 
     return json_schema_to_model(json_schema)

tests/test_pydantic_generator.py

@@ -21,6 +21,7 @@
             "enum": ["engineer", "doctor", "teacher"],
         },
     },
+    "required": ["name", "age", "profession"],
 }
 
 
@@ -59,6 +60,61 @@ def test_json_schema_to_model():
     assert instance.profession == expected_instance.profession
 
 
+def test_json_schema_to_model_honors_required_fields():
+    GeneratedModel = json_schema_to_model(
+        {
+            "type": "object",
+            "title": "Record",
+            "properties": {
+                "required_name": {"type": "string"},
+                "optional_note": {"type": "string"},
+                "priority": {"type": "integer", "default": 3},
+            },
+            "required": ["required_name"],
+        }
+    )
+
+    assert GeneratedModel.model_fields["required_name"].is_required()
+    assert not GeneratedModel.model_fields["optional_note"].is_required()
+    assert not GeneratedModel.model_fields["priority"].is_required()
+
+    instance = GeneratedModel(required_name="task")
+    assert instance.optional_note is None
+    assert instance.priority == 3
+    assert instance.model_dump(exclude_unset=True) == {"required_name": "task"}
+
+    from pydantic import ValidationError
+
+    with pytest.raises(ValidationError):
+        GeneratedModel(required_name="task", optional_note=None)
+
+
+def test_json_schema_to_model_honors_nested_required_fields():
+    GeneratedModel = json_schema_to_model(
+        {
+            "type": "object",
+            "properties": {
+                "profile": {
+                    "type": "object",
+                    "properties": {
+                        "name": {"type": "string"},
+                        "note": {"type": "string"},
+                    },
+                    "required": ["name"],
+                }
+            },
+        }
+    )
+
+    assert GeneratedModel().profile is None
+    assert GeneratedModel(profile={"name": "Ada"}).profile.note is None
+
+    from pydantic import ValidationError
+
+    with pytest.raises(ValidationError):
+        GeneratedModel(profile={"note": "missing name"})
+
+
 @pytest.mark.parametrize(
     "field_schema, fields_descriptions, good_params, bad_params, ",
     (