Create Experiment Async

Create Dependent Variable

experiments

Get Status

tasks

Get Runs

Get Public Runs Count

Get Run

Update Config

Get Run Artifact

runs

Create Attributes And Levels

Create Orthogonal Attributes And Levels

Create Attributes

Create Levels

attributes_levels

Get Personas

personas

gets list of traits from database connection

Get Traits

traits

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
}

Check Causality

Checks whether an idea complies with our content policy and provides reasons if it doesn't.

Moderate

copilot

Get Amce

Run Async Analytics

Get Showcase for a given Human Baseline ID

Get Showcase

analytics

Root

Run Task

Trigger Error

Main

CausalityResponse

List of rephrased versions of the original idea into a causal format, if the original idea is not causal. Base your suggestions on human decisions.

DependentVariableResponse

ExperimentAsyncResponse

HTTPValidationError

LeveledAttribute

LeveledAttributeBase

Enum representing the llm model types used in the experiments.

ModelType

Enum representing the type of prompts used in experiment designs.

PromptType

TraitLevel

ValidationError

ValidationErrorLocItem

ExperimentPreCookedAttributesAndLevelsLookupItemItem

Subconscious AI | Docs

1	curl -X POST /levels \
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	"attributes": [
7	"Battery Range per Charge",
8	"Vehicle Warranty"
9	],
10	"num_levels": 4,
11	"model_type": "gpt3"
12	}'

Request

Response