linkml-runtime

import os from linkml_runtime.utils.schemaview import SchemaView from linkml_runtime.utils.dictutils import dict_to_object, object_to_dict # 1. Define a simple LinkML schema (YAML string) schema_yaml = """ id: https://example.org/my_data_model name: my_data_model prefixes: ex: https://example.org/my_data_model/ default_prefix: ex classes: Person: slots: - id - name - age required: - id - name slots: id: range: string identifier: true name: range: string age: range: integer minimum_value: 0 """ # 2. Load the schema into a SchemaView object schema_view = SchemaView(schema_yaml) # 3. Define a simple data instance (Python dictionary) person_data = { "id": "P001", "name": "Alice Wonderland", "age": 30 } # 4. Convert the dictionary data to a Python object based on the schema # `dict_to_object` uses the schema definition to instantiate a Pydantic-backed object. person_object = dict_to_object(person_data, target_class=schema_view.get_class("Person"), schemaview=schema_view) print(f"Original dict: {person_data}") print(f"Converted object: {person_object}") print(f"Object ID: {person_object.id}") print(f"Object Name: {person_object.name}") # Demonstrate implicit validation (attempting to convert invalid data) invalid_person_data = { "id": "P002", "name": None, # 'name' is required and cannot be None "age": -5 # 'age' must be non-negative } try: print("\nAttempting to convert invalid data...") dict_to_object(invalid_person_data, target_class=schema_view.get_class("Person"), schemaview=schema_view) except Exception as e: print(f"Caught expected validation error: {e.__class__.__name__}: {e}") # 5. Convert the object back to a dictionary converted_back_dict = object_to_dict(person_object, schemaview=schema_view) print(f"\nObject converted back to dict: {converted_back_dict}")