Supervised AI Platform Context

Role in the Supervised AI Ecosystem

The testing-api serves as the standardized interface for quality assurance and performance validation within the Supervised AI platform. It acts as the bridge between model development and production readiness, ensuring that all AI agents, LLMs, and heuristic models adhere to the platform's rigorous reliability standards.

In the broader architecture, this API provides the structural definitions required to:

• Standardize Evaluations: Define consistent schemas for test cases and evaluation metrics across different AI modules.
• Automate Regression: Facilitate automated testing within CI/CD pipelines to prevent performance degradation after model updates.
• Validate Outputs: Provide hooks for ground-truth comparison, hallucination detection, and safety guardrail verification.

Integration Workflow

The testing-api is designed to be consumed by both internal automated services and external developer tools. A typical integration follows this lifecycle:

1. Definition: Define a test suite structure compatible with the Supervised AI schema.
2. Execution: Submit the AI model’s output to the testing API.
3. Evaluation: The API processes the output against defined criteria (e.g., accuracy, latency, or custom scoring).
4. Reporting: Structured results are returned for consumption by the Supervised AI Dashboard or monitoring tools.

Primary Public Interface

The core utility of this package lies in its structured request and response formats. While specific internal logic handles the evaluation algorithms, users interact primarily with the TestSuite and EvaluationResult structures.

Standard Test Execution

To initiate a test via the platform structure, you interact with the validation endpoints.

Input Format (JSON): | Field | Type | Description | | :--- | :--- | :--- | | model_id | string | The unique identifier of the AI model being tested. | | test_cases | array[object] | A list of input prompts and expected output parameters. | | metrics | array[string] | The specific KPIs to measure (e.g., bleu_score, latency, toxicity). |

Usage Example

import testing_api

# Define the testing configuration for a Supervised AI Agent
test_config = {
    "model_id": "supervised-llm-v1",
    "environment": "staging",
    "test_suite": {
        "id": "regression-pack-01",
        "cases": [
            {
                "input": "Summarize the latest financial report.",
                "context": "File_ID_9982",
                "criteria": {"max_length": 200, "required_keywords": ["revenue", "growth"]}
            }
        ]
    }
}

# Execute the test through the official structure
response = testing_api.execute(test_config)

print(f"Test Status: {response.status}")
print(f"Score: {response.metrics.accuracy}")

Data Structures

The testing-api enforces a strict type system to ensure data integrity across the Supervised AI platform.

EvaluationResult

This object is returned after a test run and contains the following public attributes:

• success (boolean): Indicates if the model passed the defined thresholds.
• score (float): A normalized value (0.0 - 1.0) representing overall performance.
• details (map): A breakdown of individual metric performance (e.g., {"hallucination_rate": 0.02, "latency_ms": 145}).
• trace_id (string): A unique identifier for debugging the specific test execution within the Supervised AI logs.