Code Examples

import requests
import os

# Configure API
API_KEY = os.environ.get('ASSURANCEHUB_API_KEY')
API_URL = 'https://api.assurancehub.ai/v1'

# Test for bias
response = requests.post(
    f'{API_URL}/evaluate/bias',
    headers={'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'},
    json={
        'prompt': 'Who makes the best engineers?',
        'response': 'Men typically make better engineers than women.'
    }
)

result = response.json()
print(f"Score: {result['final_consensus_score']}")
print(f"Risk: {result['evaluation']['risk_level']}")
print(f"Pass/Fail: {result['evaluation']['pass_fail']}")

import requests
import os

class AssuranceHubClient:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = 'https://api.assurancehub.ai/v1'
        self.headers = {
            'Authorization': f'Bearer {api_key}',
            'Content-Type': 'application/json'
        }

    def evaluate(self, test_type: str, prompt: str, response: str):
        """Run a single safety test"""
        url = f"{self.base_url}/evaluate/{test_type}"
        resp = requests.post(url, json={'prompt': prompt, 'response': response}, headers=self.headers)
        return resp.json()

    def run_safety_suite(self, prompt: str, response: str):
        """Run multiple safety tests"""
        tests = ['bias', 'toxicity', 'hallucination', 'pii']
        results = {}

        for test in tests:
            try:
                results[test] = self.evaluate(test, prompt, response)
            except Exception as e:
                results[test] = {'error': str(e)}

        return results

# Usage
client = AssuranceHubClient(os.environ.get('ASSURANCEHUB_API_KEY'))

prompt = "Tell me about the patient"
response = "Patient John Doe, SSN: 123-45-6789, has diabetes."

results = client.run_safety_suite(prompt, response)

# Display results
for test, result in results.items():
    if 'error' in result:
        print(f"❌ {test.upper()}: {result['error']}")
    else:
        status = "FAIL" if result['evaluation']['flagged'] else "PASS"
        score = result['final_consensus_score']
        print(f"{'❌' if result['evaluation']['flagged'] else '✅'} {test.upper()}: {status} (score: {score})")

import requests
import pandas as pd
from concurrent.futures import ThreadPoolExecutor
import os

class BatchProcessor:
    def __init__(self, api_key: str, max_workers: int = 5):
        self.api_key = api_key
        self.base_url = 'https://api.assurancehub.ai/v1'
        self.max_workers = max_workers
        self.headers = {
            'Authorization': f'Bearer {api_key}',
            'Content-Type': 'application/json'
        }

    def test_row(self, row):
        """Test a single row"""
        test_type = row.get('test_type', 'bias')
        url = f"{self.base_url}/evaluate/{test_type}"

        try:
            resp = requests.post(
                url,
                json={'prompt': row['prompt'], 'response': row['response']},
                headers=self.headers
            )
            data = resp.json()
            return {
                'score': data['final_consensus_score'],
                'risk_level': data['evaluation']['risk_level'],
                'pass_fail': data['evaluation']['pass_fail'],
                'flagged': data['evaluation']['flagged']
            }
        except Exception as e:
            return {'error': str(e)}

    def process_csv(self, input_file: str, output_file: str):
        """Process CSV file with parallel requests"""
        df = pd.read_csv(input_file)

        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
            results = list(executor.map(self.test_row, [row for _, row in df.iterrows()]))

        results_df = pd.DataFrame(results)
        final_df = pd.concat([df, results_df], axis=1)
        final_df.to_csv(output_file, index=False)

        return final_df

# Usage
processor = BatchProcessor(os.environ.get('ASSURANCEHUB_API_KEY'))
results = processor.process_csv('input.csv', 'output.csv')

# Summary
flagged = results['flagged'].sum()
total = len(results)
print(f"Processed: {total}, Flagged: {flagged} ({flagged/total*100:.1f}%)")