Unprocessable Entity

Check whether an idea is causal or not.
Optional parameter model_type is used to specify the model to use for the check.
Returns a JSON object with the following structure:
{
"is_causal": bool,
"suggestions": list[str, str, str] - rephrased of the original idea into a causal idea
}

analytics

attributesLevels

copilot

experiments

main

personas

runs

tasks

traits

CausalityResponse

DependentVariableResponse

ExperimentAsyncResponse

HTTPValidationError

LeveledAttribute

LeveledAttributeBase

ModelType

PromptType

TraitLevel

ValidationError

ValidationErrorLocItem

ExperimentPreCookedAttributesAndLevelsLookupItemItem

Subconscious AI | Docs

1	curl -X POST /attributes-levels/orthogonal \
2	-H "Authorization: Bearer <token>" \
3	-H "Content-Type: application/json" \
4	-d '{
5	"why_prompt": "I would like to design a new electric car for the American markets",
6	"existing_attributes_levels": [
7	{
8	"attribute": "Battery Range per Charge",
9	"levels": [
10	"Less than 150 miles",
11	"150 to 250 miles",
12	"250 to 350 miles",
13	"350 to 450 miles",
14	"More than 450 miles"
15	]
16	},
17	{
18	"attribute": "Vehicle Warranty",
19	"levels": [
20	"3 years or 36,000 miles",
21	"4 years or 50,000 miles",
22	"5 years or 60,000 miles",
23	"6 years or 70,000 miles",
24	"7 years or 100,000 miles"
25	]
26	}
27	],
28	"number_of_new_attributes": 4,
29	"number_of_levels": 3,
30	"model_type": "gpt3"
31	}'

Create Orthogonal Attributes And Levels

Request

Response

1	[
2	{
3	"attribute": "attribute",
4	"levels": [
5	"levels"
6	]
7	}
8	]