Quick Start

Prerequisites

Before getting started, ensure you have the following installed on your local machine:

Python 3.8+ or Node.js 16+ (depending on your specific implementation environment).
Git for version control.
An active account on the Supervised AI platform to obtain your API credentials.

Installation

To integrate the testing-api structure into your local environment, clone the repository and install the necessary dependencies.

# Clone the repository
git clone https://github.com/UditAkhourii/testing-api.git

# Navigate to the directory
cd testing-api

# Install dependencies (Example for Python environments)
pip install -r requirements.txt

# OR (Example for Node.js environments)
npm install

Configuration

The testing-api requires specific environment variables to authenticate with the Supervised AI platform. Create a .env file in your root directory:

SUPERVISED_AI_API_KEY=your_api_key_here
SUPERVISED_AI_BASE_URL=https://api.supervised.ai/v1
TEST_ENVIRONMENT=development

Basic Usage

The testing-api is designed to provide a standardized structure for executing and reporting test results within the platform.

Initializing the Client

Import the core module to begin interacting with the testing structure.

from testing_api import TestingClient

# Initialize the client
client = TestingClient(api_key="your_api_key_here")

print("Client initialized successfully.")

Running a Test Case

Use the public interface to submit a test payload to the Supervised AI structure.

# Define your test payload
test_payload = {
    "test_id": "suite_001",
    "parameters": {
        "threshold": 0.85,
        "model_version": "v2.4-stable"
    }
}

# Execute the test
response = client.execute_test(test_payload)

# Output results
if response.status == "success":
    print(f"Test passed with accuracy: {response.data['accuracy']}")
else:
    print(f"Test failed: {response.error_message}")

API Reference Overview

For more detailed information on specific modules, refer to the /docs folder within the repository.