The TrustTest client provides a powerful interface for persisting and retrieving evaluation artifacts. It allows you to save and load any scenario, test set, and evaluation results through a consistent API.

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Client Implementations

TrustTest offers multiple client implementations:

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NeuralTrustClient

The NeuralTrustClient connects to the NeuralTrust API service and provides the following methods: 
from trusttest.clients import NeuralTrustClient

# Initialize client with your API token (configured in app settings)
client = NeuralTrustClient(token="your_api_token")

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Configuration

The token is defined in your app settings. You can either:
  • Pass it directly when initializing the client
  • Set it as an environment variable NEURALTRUST_TOKEN

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Evaluation Scenarios

# Save an evaluation scenario
client.save_evaluation_scenario(evaluation_scenario)

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

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Test Sets

# 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")

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Evaluation Results

# 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")

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Evaluators

# 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")

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FileSystemClient

The FileSystemClient provides local storage capabilities, saving evaluation artifacts as JSON files. It uses the same interface as NeuralTrustClient but stores data locally.

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Key Capabilities

All TrustTest clients support these core operations:
  • Evaluation Scenarios: Save and retrieve evaluation scenario definitions
  • Test Sets: Manage test sets associated with evaluation scenarios
  • Evaluation Results: Persist and load evaluation run results
  • Evaluators: Store custom evaluator configurations

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Example Workflow

Here’s how to use the client in a typical evaluation workflow: 
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)
The client abstraction ensures your evaluation artifacts are consistently stored and retrieved regardless of the underlying storage mechanism you choose.