criteria

"efficiency", "scalability", "maintainability", "cost" ] def evaluate(self, technology): # Implement the evaluation logic here scores = { "accuracy": 0…

return llm.accuracy elif criterion == "latency": return llm.latency else: return 0 # Example usage: criteria = ["accuracy", "latency", "cost"] evaluator = LLMEvaluator(criteria) llm = {"a…

def __init__(self, criteria, weights=None): self.criteria = criteria self.weights = weights if weights else [1] * len(criteria) def evaluate(self, llm): scores = [] for criterion, weight in zip(self.…

threshold = 0.10 return max(0, 1 - (cost / threshold)) # Example usage: criteria = ["accuracy", "latency", "cost"] weights = [2, 1, 1] # Example weights: accuracy is twice as important as latency and cost evaluator = LLMEv…

# Normalize latency to a 0-1 scale, assuming a threshold of 200ms threshold = 200 return max(0, 1 - (latency / threshold)) def _normalize_cost(self, cost): # Normalize cost to a 0-1 scale, assuming a thr…

2. **Mean Time Between Failures (MTBF)**: The average time between system failures. 3. **Mean Time to Recovery (MTTR)**: The average time it takes to recover from a failure. 4. **Error Rate**: The frequency of errors or failures during peak…