## Set up the ontology and link the tools created above.
ontology_builder = lb.OntologyBuilder(
classifications=[ # List of Classification objects
lb.Classification(
class_type=lb.Classification.Type.RADIO,
name="radio_question",
options=[lb.Option(value="first_radio_answer")]
),
lb.Classification(
class_type=lb.Classification.Type.RADIO,
name="nested_radio_question",
options=[
lb.Option(value="first_radio_answer",
options=[
lb.Classification(
class_type=lb.Classification.Type.RADIO,
name="sub_radio_question",
options=[
lb.Option(value="first_sub_radio_answer")
]
),
]
)
],
),
lb.Classification(
class_type=lb.Classification.Type.CHECKLIST,
name="checklist_question",
options=[
lb.Option(value="first_checklist_answer"),
lb.Option(value="second_checklist_answer"),
lb.Option(value="third_checklist_answer")
]
),
lb.Classification(
class_type=lb.Classification.Type.TEXT,
name="free_text"
),
lb.Classification(
class_type=lb.Classification.Type.CHECKLIST,
name="nested_checklist_question",
options=[
lb.Option("first_checklist_answer",
options=[
lb.Classification(
class_type=lb.Classification.Type.CHECKLIST,
name="sub_checklist_question",
options=[lb.Option("first_sub_checklist_answer")]
)
]
)
]
)
],
tools=[ # List of Tool objects
lb.Tool(tool=lb.Tool.Type.NER,
name="named_entity")
]
)
ontology = client.create_ontology("Ontology Text Predictions", ontology_builder.asdict() , media_type=lb.MediaType.Text)