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"""
Evaluation module for assessing RAG system performance.
Provides metrics like RAGAS, ROUGE, BLEU, and BERTScore.
"""
from typing import List, Dict, Any, Optional
import numpy as np
class RAGEvaluator:
"""Evaluator for RAG system performance metrics."""
def __init__(self):
"""Initialize the evaluator with optional metric libraries."""
self.metrics_available = self._check_available_metrics()
print(f"✓ RAG Evaluator initialized")
print(f" Available metrics: {', '.join(self.metrics_available)}")
def _check_available_metrics(self) -> List[str]:
"""Check which evaluation metrics are available."""
available = []
try:
from rouge_score import rouge_scorer
available.append("ROUGE")
except ImportError:
pass
try:
from bert_score import score
available.append("BERTScore")
except ImportError:
pass
try:
from ragas import evaluate
available.append("RAGAS")
except ImportError:
pass
# Basic metrics are always available
available.extend(["Length", "Similarity"])
return available
def evaluate_response(
self,
query: str,
generated_answer: str,
reference_answer: Optional[str] = None,
retrieved_contexts: Optional[List[str]] = None
) -> Dict[str, Any]:
"""
Evaluate a single response.
Args:
query: Original question
generated_answer: Generated answer
reference_answer: Optional ground truth answer
retrieved_contexts: Optional retrieved contexts
Returns:
Dictionary of evaluation metrics
"""
results = {
"query": query,
"generated_answer": generated_answer,
"metrics": {}
}
# Basic metrics
results["metrics"]["answer_length"] = len(generated_answer)
results["metrics"]["word_count"] = len(generated_answer.split())
# Reference-based metrics (if reference answer provided)
if reference_answer:
results["reference_answer"] = reference_answer
# ROUGE scores
if "ROUGE" in self.metrics_available:
rouge_scores = self._calculate_rouge(generated_answer, reference_answer)
results["metrics"]["rouge"] = rouge_scores
# BERTScore
if "BERTScore" in self.metrics_available:
bert_score = self._calculate_bertscore(generated_answer, reference_answer)
results["metrics"]["bertscore"] = bert_score
# Context-based metrics (if contexts provided)
if retrieved_contexts:
results["num_contexts"] = len(retrieved_contexts)
# Calculate answer-context relevance
if "BERTScore" in self.metrics_available:
context_relevance = self._calculate_context_relevance(
generated_answer,
retrieved_contexts
)
results["metrics"]["context_relevance"] = context_relevance
return results
def _calculate_rouge(
self,
generated: str,
reference: str
) -> Dict[str, float]:
"""Calculate ROUGE scores."""
try:
from rouge_score import rouge_scorer
scorer = rouge_scorer.RougeScorer(
['rouge1', 'rouge2', 'rougeL'],
use_stemmer=True
)
scores = scorer.score(reference, generated)
return {
"rouge1": scores['rouge1'].fmeasure,
"rouge2": scores['rouge2'].fmeasure,
"rougeL": scores['rougeL'].fmeasure
}
except Exception as e:
print(f"Error calculating ROUGE: {e}")
return {}
def _calculate_bertscore(
self,
generated: str,
reference: str
) -> Dict[str, float]:
"""Calculate BERTScore."""
try:
from bert_score import score
P, R, F1 = score(
[generated],
[reference],
lang="en",
verbose=False
)
return {
"precision": float(P[0]),
"recall": float(R[0]),
"f1": float(F1[0])
}
except Exception as e:
print(f"Error calculating BERTScore: {e}")
return {}
def _calculate_context_relevance(
self,
answer: str,
contexts: List[str]
) -> float:
"""Calculate relevance between answer and contexts using BERTScore."""
try:
from bert_score import score
# Calculate BERTScore between answer and each context
scores = []
for context in contexts:
_, _, F1 = score(
[answer],
[context],
lang="en",
verbose=False
)
scores.append(float(F1[0]))
# Return average score
return float(np.mean(scores)) if scores else 0.0
except Exception as e:
print(f"Error calculating context relevance: {e}")
return 0.0
def evaluate_batch(
self,
evaluations: List[Dict[str, Any]]
) -> Dict[str, Any]:
"""
Evaluate multiple responses and aggregate results.
Args:
evaluations: List of evaluation dictionaries with query, generated_answer, etc.
Returns:
Aggregated evaluation results with statistics
"""
print(f"\n{'='*60}")
print(f"📊 BATCH EVALUATION - {len(evaluations)} responses")
print(f"{'='*60}\n")
all_results = []
for i, eval_data in enumerate(evaluations, 1):
print(f"[{i}/{len(evaluations)}] Evaluating: {eval_data['query'][:50]}...")
result = self.evaluate_response(
query=eval_data["query"],
generated_answer=eval_data["generated_answer"],
reference_answer=eval_data.get("reference_answer"),
retrieved_contexts=eval_data.get("retrieved_contexts")
)
all_results.append(result)
# Aggregate metrics
aggregated = self._aggregate_metrics(all_results)
print(f"\n{'='*60}")
print("✅ BATCH EVALUATION COMPLETE")
print(f"{'='*60}\n")
self._print_summary(aggregated)
return aggregated
def _aggregate_metrics(
self,
results: List[Dict[str, Any]]
) -> Dict[str, Any]:
"""
Aggregate metrics from multiple evaluations.
Args:
results: List of evaluation results
Returns:
Aggregated metrics dictionary
"""
aggregated = {
"total_evaluations": len(results),
"metrics": {},
"individual_results": results
}
# Aggregate basic metrics
lengths = [r["metrics"]["answer_length"] for r in results]
word_counts = [r["metrics"]["word_count"] for r in results]
aggregated["metrics"]["avg_answer_length"] = float(np.mean(lengths))
aggregated["metrics"]["avg_word_count"] = float(np.mean(word_counts))
# Aggregate ROUGE scores if available
rouge_results = [r["metrics"].get("rouge") for r in results if "rouge" in r["metrics"]]
if rouge_results:
rouge_agg = {
"rouge1": float(np.mean([r["rouge1"] for r in rouge_results])),
"rouge2": float(np.mean([r["rouge2"] for r in rouge_results])),
"rougeL": float(np.mean([r["rougeL"] for r in rouge_results]))
}
aggregated["metrics"]["avg_rouge"] = rouge_agg
# Aggregate BERTScore if available
bert_results = [r["metrics"].get("bertscore") for r in results if "bertscore" in r["metrics"]]
if bert_results:
bert_agg = {
"precision": float(np.mean([r["precision"] for r in bert_results])),
"recall": float(np.mean([r["recall"] for r in bert_results])),
"f1": float(np.mean([r["f1"] for r in bert_results]))
}
aggregated["metrics"]["avg_bertscore"] = bert_agg
# Aggregate context relevance if available
context_scores = [r["metrics"].get("context_relevance") for r in results if "context_relevance" in r["metrics"]]
if context_scores:
aggregated["metrics"]["avg_context_relevance"] = float(np.mean(context_scores))
return aggregated
def _print_summary(self, aggregated: Dict[str, Any]) -> None:
"""
Print a formatted summary of aggregated metrics.
Args:
aggregated: Aggregated metrics dictionary
"""
print("📊 EVALUATION SUMMARY")
print("-" * 60)
print(f"Total Evaluations: {aggregated['total_evaluations']}")
print()
metrics = aggregated["metrics"]
print("Basic Metrics:")
print(f" Average Answer Length: {metrics.get('avg_answer_length', 0):.1f} characters")
print(f" Average Word Count: {metrics.get('avg_word_count', 0):.1f} words")
print()
if "avg_rouge" in metrics:
rouge = metrics["avg_rouge"]
print("ROUGE Scores:")
print(f" ROUGE-1: {rouge['rouge1']:.3f}")
print(f" ROUGE-2: {rouge['rouge2']:.3f}")
print(f" ROUGE-L: {rouge['rougeL']:.3f}")
print()
if "avg_bertscore" in metrics:
bert = metrics["avg_bertscore"]
print("BERTScore:")
print(f" Precision: {bert['precision']:.3f}")
print(f" Recall: {bert['recall']:.3f}")
print(f" F1: {bert['f1']:.3f}")
print()
if "avg_context_relevance" in metrics:
print(f"Context Relevance: {metrics['avg_context_relevance']:.3f}")
print()
def save_results(
self,
results: Dict[str, Any],
output_path: str
) -> None:
"""
Save evaluation results to a JSON file.
Args:
results: Evaluation results dictionary
output_path: Path to save the results
"""
import json
try:
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(results, f, indent=2, ensure_ascii=False)
print(f"✓ Results saved to: {output_path}")
except Exception as e:
print(f"❌ Error saving results: {e}")
# Convenience function to create evaluator
def create_evaluator() -> RAGEvaluator:
"""Create and return a RAG evaluator instance."""
return RAGEvaluator()
# Example usage
if __name__ == "__main__":
# Example batch evaluation
evaluator = create_evaluator()
sample_evaluations = [
{
"query": "What is machine learning?",
"generated_answer": "Machine learning is a subset of AI that enables systems to learn from data.",
"reference_answer": "Machine learning is a type of artificial intelligence that allows software applications to learn from data.",
"retrieved_contexts": ["ML is part of AI...", "Data-driven learning..."]
},
{
"query": "Explain Python programming",
"generated_answer": "Python is a high-level programming language known for its readability.",
"reference_answer": "Python is an interpreted, high-level programming language.",
"retrieved_contexts": ["Python was created...", "Python is versatile..."]
}
]
results = evaluator.evaluate_batch(sample_evaluations)
evaluator.save_results(results, "evaluation_results.json") |