LLM API Gateway for A/B Testing

# LLM A/B Testing Framework
import asyncio
from typing import Dict, List, Tuple
import numpy as np
from scipy import stats

class LLMABTest:
    def __init__(self, experiment_id: str):
        self.experiment_id = experiment_id
        self.variants = {}
        self.results = {}
        self.metrics = ["accuracy", "response_time", "cost"]
    
    async def run_test(self, variants: Dict[str, Dict], 
                      sample_size: int = 1000) -> Dict:
        """Run A/B test across multiple LLM variants"""
        self.variants = variants
        
        # Distribute traffic
        traffic_distribution = self.calculate_traffic_distribution(len(variants))
        
        async with asyncio.TaskGroup() as tg:
            for variant_name, config in variants.items():
                tg.create_task(
                    self.test_variant(
                        variant_name, config, sample_size, traffic_distribution[variant_name]
                    )
                )
        
        # Calculate statistical significance
        significance = self.calculate_significance()
        
        # Generate comprehensive report
        report = self.generate_report(significance)
        
        return report
    
    def calculate_significance(self) -> Dict:
        """Calculate statistical significance between variants"""
        significance_results = {}
        
        for metric in self.metrics:
            variant_results = []
            
            for variant_name, results in self.results.items():
                if metric in results:
                    variant_results.append(results[metric])
            
            # Perform statistical tests
            if len(variant_results) >= 2:
                # T-test for comparing means
                t_stat, p_value = stats.ttest_ind(variant_results[0], variant_results[1])
                
                significance_results[metric] = {
                    "p_value": p_value,
                    "significant": p_value < 0.05,
                    "confidence": (1 - p_value) * 100,
                    "effect_size": self.calculate_effect_size(variant_results)
                }
        
        return significance_results