Connect to NeuralTrust

from trusttest.clients import NeuralTrustClient

# Initialize client with your API token and target ID
client = NeuralTrustClient(
    token="your_api_token",
    target_id="your_target_id",
)

# Save an evaluation scenario
client.save_evaluation_scenario(evaluation_scenario)

# Load an evaluation scenario by ID
scenario = client.get_evaluation_scenario("scenario_id")

# Save a test set for a specific scenario
client.save_evaluation_scenario_test_set("scenario_id", test_set)

# Update an existing test set or create if it doesn't exist
client.upsert_evaluation_scenario_test_set("scenario_id", test_set)

# Load a test set for a specific scenario
test_set = client.get_evaluation_scenario_test_set("scenario_id")

# Save evaluation run results
client.save_evaluation_scenario_run(evaluation_run)

# Load evaluation run results for a scenario
results = client.get_evaluation_scenario_run("scenario_id")

# Save a custom evaluator with optional name and description
client.save_evaluator(evaluator, name="my_evaluator", description="Custom evaluator")

# Load an evaluator by name
evaluator = client.get_evaluator("my_evaluator")

from trusttest.clients import FileSystemClient
from trusttest.evaluation_scenarios import EvaluationScenario
from trusttest.probes import TestSet

# Initialize a client
client = FileSystemClient()

# Create and save an evaluation scenario
scenario = EvaluationScenario(name="My Test", description="Testing functionality")
client.save_evaluation_scenario(scenario)

# Save a test set for the scenario
test_set = TestSet(test_cases=[...])
client.save_evaluation_scenario_test_set(scenario.id, test_set)

# Later, retrieve the scenario and its test set
loaded_scenario = client.get_evaluation_scenario(scenario.id)
loaded_test_set = client.get_evaluation_scenario_test_set(scenario.id)

# After running an evaluation, save the results
client.save_evaluation_scenario_run(evaluation_run)